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wy36101299/DATA-MINING-SOCIAL-NETWORK-ANALYSIS-HW | final-project/community-based recommendation system-PPT.ipynb | 1 | 1146611 | null | mit |
Naereen/notebooks | Demo_of_RISE_for_slides_with_Jupyter_notebooks__OCaml.ipynb | 1 | 11619 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"toc": "true"
},
"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Demo-of-RISE-for-slides-with-Jupyter-notebooks-(Python)\" data-toc-modified-id=\"Demo-of-RISE-for-slides-with-Jupyter-notebooks-(Python)-1\"><span class=\"toc-item-num\">1 </span>Demo of RISE for slides with Jupyter notebooks (Python)</a></span><ul class=\"toc-item\"><li><span><a href=\"#Title-2\" data-toc-modified-id=\"Title-2-1.1\"><span class=\"toc-item-num\">1.1 </span>Title 2</a></span><ul class=\"toc-item\"><li><span><a href=\"#Title-3\" data-toc-modified-id=\"Title-3-1.1.1\"><span class=\"toc-item-num\">1.1.1 </span>Title 3</a></span><ul class=\"toc-item\"><li><span><a href=\"#Title-4\" data-toc-modified-id=\"Title-4-1.1.1.1\"><span class=\"toc-item-num\">1.1.1.1 </span>Title 4</a></span></li></ul></li></ul></li><li><span><a href=\"#Text\" data-toc-modified-id=\"Text-1.2\"><span class=\"toc-item-num\">1.2 </span>Text</a></span></li><li><span><a href=\"#Maths\" data-toc-modified-id=\"Maths-1.3\"><span class=\"toc-item-num\">1.3 </span>Maths</a></span></li><li><span><a href=\"#And-code\" data-toc-modified-id=\"And-code-1.4\"><span class=\"toc-item-num\">1.4 </span>And code</a></span></li></ul></li><li><span><a href=\"#More-demo-of-Markdown-code\" data-toc-modified-id=\"More-demo-of-Markdown-code-2\"><span class=\"toc-item-num\">2 </span>More demo of Markdown code</a></span><ul class=\"toc-item\"><li><span><a href=\"#Lists\" data-toc-modified-id=\"Lists-2.1\"><span class=\"toc-item-num\">2.1 </span>Lists</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Images\" data-toc-modified-id=\"Images-2.1.0.1\"><span class=\"toc-item-num\">2.1.0.1 </span>Images</a></span></li><li><span><a href=\"#And-Markdown-can-include-raw-HTML\" data-toc-modified-id=\"And-Markdown-can-include-raw-HTML-2.1.0.2\"><span class=\"toc-item-num\">2.1.0.2 </span>And Markdown can include raw HTML</a></span></li></ul></li></ul></li></ul></li><li><span><a href=\"#End-of-this-demo\" data-toc-modified-id=\"End-of-this-demo-3\"><span class=\"toc-item-num\">3 </span>End of this demo</a></span></li></ul></div>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Demo of RISE for slides with Jupyter notebooks (Python)\n",
"\n",
"- This document is an example of a slideshow, written in a [Jupyter notebook](https://www.jupyter.org/) with the [RISE extension](https://github.com/damianavila/RISE).\n",
"\n",
"> By [Lilian Besson](http://perso.crans.org/besson/), Sept.2017."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"---\n",
"## Title 2\n",
"### Title 3\n",
"#### Title 4\n",
"##### Title 5\n",
"##### Title 6"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Text\n",
"With text, *emphasis*, **bold**, ~~striked~~, `inline code` and\n",
"\n",
"> *Quote.*\n",
">\n",
"> -- By a guy."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Maths\n",
"With inline math $\\sin(x)^2 + \\cos(x)^2 = 1$ and equations:\n",
"$$\\sin(x)^2 + \\cos(x)^2 = \\left(\\frac{\\mathrm{e}^{ix} - \\mathrm{e}^{-ix}}{2i}\\right)^2 + \\left(\\frac{\\mathrm{e}^{ix} + \\mathrm{e}^{-ix}}{2}\\right)^2 = \\frac{-\\mathrm{e}^{2ix}-\\mathrm{e}^{-2ix}+2 \\; ++\\mathrm{e}^{2ix}+\\mathrm{e}^{-2ix}+2}{4} = 1.$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## And code\n",
"In Markdown:\n",
"```python\n",
"Sys.command \"ocaml -version\";;\n",
"```\n",
"\n",
"And in a executable cell (with OCaml 4.04.2 kernel) :"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2021-02-16T05:08:18.070800Z",
"start_time": "2021-02-16T05:08:18.055Z"
},
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The OCaml toplevel, version 4.05.0\n"
]
},
{
"data": {
"text/plain": [
"- : int = 0\n"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Sys.command \"ocaml -version\";;"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# More demo of Markdown code"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Lists\n",
"\n",
"- Unordered\n",
"- lists\n",
"- are easy.\n",
"\n",
"And\n",
"\n",
"1. and ordered also ! Just\n",
"2. start lines by `1.`, `2.` etc\n",
"3. or simply `1.`, `1.`, ..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Images\n",
"With a HTML `<img/>` tag or the `` Markdown code:\n",
"<img width=\"100\" src=\"agreg/images/dooku.jpg\"/>\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Avec [l'ancienne API de ocaml-jupyter](https://akabe.github.io/ocaml-jupyter/notebook/JupyterNotebook.html) (pré 2020) :"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2021-02-16T05:10:31.766300Z",
"start_time": "2021-02-16T05:10:31.744Z"
}
},
"outputs": [
{
"ename": "error",
"evalue": "compile_error",
"output_type": "error",
"traceback": [
"\u001b[32mFile \"[8]\", line 5, characters 24-47:\n\u001b[31mError: Unbound module JupyterNotebook\nHint: Did you mean Jupyter_notebook?\n\u001b[36m 4: \u001b[30m\n\u001b[36m 5: \u001b[30mlet youtube_video url = \u001b[4mJupyterNotebook.display\u001b[0m\u001b[30m \"text/html\"\n\u001b[36m 6: \u001b[30m (Printf.sprintf \"<iframe width=560 height=315 src='%s'></iframe>\" url)\u001b[0m\n"
]
}
],
"source": [
"#thread ;;\n",
"#require \"jupyter.notebook\" ;;\n",
"(* https://akabe.github.io/ocaml-jupyter/notebook/JupyterNotebook.html *)\n",
"\n",
"let youtube_video url = JupyterNotebook.display \"text/html\"\n",
" (Printf.sprintf \"<iframe width=560 height=315 src='%s'></iframe>\" url)\n",
";;"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Avec [la nouvelle API de ocaml-jupyter](https://akabe.github.io/ocaml-jupyter/api/jupyter/Jupyter_notebook/) (après 2020) :"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2021-02-16T05:10:35.842500Z",
"start_time": "2021-02-16T05:10:35.832Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"val youtube_video : string -> Jupyter_notebook.display_id = <fun>\n"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#thread ;;\n",
"#require \"jupyter\" ;;\n",
"#require \"jupyter.notebook\" ;;\n",
"(* https://akabe.github.io/ocaml-jupyter/api/jupyter/Jupyter_notebook/ *)\n",
"\n",
"let youtube_video url = Jupyter_notebook.display \"text/html\"\n",
" (Printf.sprintf \"<iframe width=560 height=315 src='%s'></iframe>\" url)\n",
";;"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### And Markdown can include raw HTML\n",
"\n",
"<center><span style=\"color: green;\">This is a centered span, colored in green.</span></center>\n",
"\n",
"Iframes are disabled by default, but by using the IPython internals we can include let say a YouTube video:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2021-02-16T05:10:37.923400Z",
"start_time": "2021-02-16T05:10:37.915Z"
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe width=560 height=315 src='https://www.youtube.com/embed/FNg5_2UUCNU'></iframe>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"- : Jupyter_notebook.display_id = <abstr>\n"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"youtube_video \"https://www.youtube.com/embed/FNg5_2UUCNU\";;"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Et depuis Markdown :\n",
"<iframe width=\"557\" height=\"312\" src=\"https://www.youtube.com/embed/FNg5_2UUCNU\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# End of this demo\n",
"\n",
"- See [here for more notebooks](https://github.com/Naereen/notebooks/)!\n",
"- This document, like my other notebooks, is distributed [under the MIT License](https://lbesson.mit-license.org/)."
]
}
],
"metadata": {
"celltoolbar": "Diaporama",
"kernelspec": {
"display_name": "OCaml 4.05.0",
"language": "OCaml",
"name": "ocaml-jupyter"
},
"language_info": {
"codemirror_mode": "text/x-ocaml",
"file_extension": ".ml",
"mimetype": "text/x-ocaml",
"name": "OCaml",
"nbconverter_exporter": null,
"pygments_lexer": "OCaml",
"version": "4.05.0"
},
"toc": {
"base_numbering": 1,
"nav_menu": {
"height": "129px",
"width": "251px"
},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": true,
"toc_position": {},
"toc_section_display": "block",
"toc_window_display": false
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
the-deep-learners/TensorFlow-LiveLessons | notebooks/live_training/first_tensorflow_graphs_LT.ipynb | 1 | 3012 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# First TensorFlow Graphs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we execute elementary TensorFlow computational graphs."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Simple arithmetic"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x1 = tf.placeholder(tf.float32)\n",
"x2 = tf.placeholder(tf.float32)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sum_op = tf.add(x1, x2)\n",
"product_op = tf.multiply(x1, x2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with tf.Session() as session:\n",
" sum_result = session.run(sum_op, feed_dict={x1: , x2: }) \n",
" product_result = session.run(product_op, feed_dict={x1: , x2: }) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sum_result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"product_result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Simple array arithmetic"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with tf.Session() as session:\n",
" sum_result = session.run(sum_op, feed_dict={x1: , x2: })\n",
" product_result = session.run(product_op, feed_dict={x1: , x2: })"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sum_result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"product_result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
craffel/librosa | examples/LibROSA demo.ipynb | 1 | 1205785 | null | isc |
shikhar413/openmc | examples/jupyter/capi.ipynb | 1 | 16493 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using the C/C++ API\n",
"This notebook shows how to use the OpenMC C/C++ API through the openmc.lib module. This module is particularly useful for multiphysics coupling because it allows you to update the density of materials and the temperatures of cells in memory, without stopping the simulation.\n",
"\n",
"Warning: these bindings are still somewhat experimental and may be subject to change in future versions of OpenMC."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import openmc\n",
"import openmc.lib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>Generate Input Files</b>\n",
"\n",
"Let's start by creating a fuel rod geometry. We will make 10 zones in the z-direction which will allow us to make changes to each zone. Changes in temperature have to be made on the cell, so will make 10 cells in the axial direction. Changes in density have to be made on the material, so we will make 10 water materials. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Materials: we will make a fuel, helium, zircaloy, and 10 water materials. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"material_list = []"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"uo2 = openmc.Material(material_id=1, name='UO2 fuel at 2.4% wt enrichment')\n",
"uo2.set_density('g/cm3', 10.29769)\n",
"uo2.add_element('U', 1., enrichment=2.4)\n",
"uo2.add_element('O', 2.)\n",
"material_list.append(uo2)\n",
"\n",
"helium = openmc.Material(material_id=2, name='Helium for gap')\n",
"helium.set_density('g/cm3', 0.001598)\n",
"helium.add_element('He', 2.4044e-4)\n",
"material_list.append(helium)\n",
"\n",
"zircaloy = openmc.Material(material_id=3, name='Zircaloy 4')\n",
"zircaloy.set_density('g/cm3', 6.55)\n",
"zircaloy.add_element('Sn', 0.014, 'wo')\n",
"zircaloy.add_element('Fe', 0.00165, 'wo')\n",
"zircaloy.add_element('Cr', 0.001, 'wo')\n",
"zircaloy.add_element('Zr', 0.98335, 'wo')\n",
"material_list.append(zircaloy)\n",
"\n",
"for i in range(4, 14):\n",
" water = openmc.Material(material_id=i)\n",
" water.set_density('g/cm3', 0.7)\n",
" water.add_element('H', 2.0)\n",
" water.add_element('O', 1.0)\n",
" water.add_s_alpha_beta('c_H_in_H2O')\n",
" material_list.append(water)\n",
" \n",
"materials_file = openmc.Materials(material_list)\n",
"materials_file.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cells: we will make a fuel cylinder, a gap cylinder, a cladding cylinder, and a water exterior. Each one will be broken into 10 cells which are the 10 axial zones. The z_list is the list of axial positions that delimit those 10 zones. To keep track of all the cells, we will create lists: fuel_list, gap_list, clad_list, and water_list. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"pitch = 1.25984\n",
"fuel_or = openmc.ZCylinder(r=0.39218)\n",
"clad_ir = openmc.ZCylinder(r=0.40005)\n",
"clad_or = openmc.ZCylinder(r=0.4572)\n",
"left = openmc.XPlane(x0=-pitch/2)\n",
"right = openmc.XPlane(x0=pitch/2)\n",
"back = openmc.YPlane(y0=-pitch/2)\n",
"front = openmc.YPlane(y0=pitch/2)\n",
"z = [0., 30., 60., 90., 120., 150., 180., 210., 240., 270., 300.]\n",
"z_list = [openmc.ZPlane(z0=z_i) for z_i in z]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"left.boundary_type = 'reflective'\n",
"right.boundary_type = 'reflective'\n",
"front.boundary_type = 'reflective'\n",
"back.boundary_type = 'reflective'\n",
"z_list[0].boundary_type = 'vacuum'\n",
"z_list[-1].boundary_type = 'vacuum'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"fuel_list = []\n",
"gap_list = []\n",
"clad_list = []\n",
"water_list = []\n",
"for i in range(1, 11):\n",
" fuel_list.append(openmc.Cell(cell_id=i))\n",
" gap_list.append(openmc.Cell(cell_id=i+10))\n",
" clad_list.append(openmc.Cell(cell_id=i+20))\n",
" water_list.append(openmc.Cell(cell_id=i+30))\n",
" \n",
"for j, fuels in enumerate(fuel_list):\n",
" fuels.region = -fuel_or & +z_list[j] & -z_list[j+1]\n",
" fuels.fill = uo2\n",
" fuels.temperature = 800.\n",
"\n",
"for j, gaps in enumerate(gap_list):\n",
" gaps.region = +fuel_or & -clad_ir & +z_list[j] & -z_list[j+1]\n",
" gaps.fill = helium\n",
" gaps.temperature = 700.\n",
"\n",
"for j, clads in enumerate(clad_list):\n",
" clads.region = +clad_ir & -clad_or & +z_list[j] & -z_list[j+1]\n",
" clads.fill = zircaloy\n",
" clads.temperature = 600.\n",
"\n",
"for j, waters in enumerate(water_list):\n",
" waters.region = +clad_or & +left & -right & +back & -front & +z_list[j] & -z_list[j+1]\n",
" waters.fill = material_list[j+3]\n",
" waters.temperature = 500."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"root = openmc.Universe(name='root universe')\n",
"root.add_cells(fuel_list)\n",
"root.add_cells(gap_list)\n",
"root.add_cells(clad_list)\n",
"root.add_cells(water_list)\n",
"geometry_file = openmc.Geometry(root)\n",
"geometry_file.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are coupling this externally to a heat transfer solver, you will want to know the heat deposited by each fuel cell. So let's create a cell filter for the recoverable fission heat. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"cell_filter = openmc.CellFilter(fuel_list)\n",
"t = openmc.Tally(tally_id=1)\n",
"t.filters.append(cell_filter)\n",
"t.scores = ['fission-q-recoverable']\n",
"tallies = openmc.Tallies([t])\n",
"tallies.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot our geometry to make sure it looks like we expect. Since we made new water materials in each axial cell, and we have centered the plot at 150, we should see one color for the water material in the bottom half and a different color for the water material in the top half. "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x126d642e0>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"root.plot(basis='yz', width=[2, 10], color_by='material', origin=[0., 0., 150.], pixels=[400, 400])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Settings: everything will be standard except for the temperature settings. Since we will be working with specified temperatures, you will need temperature dependent data. I typically use the endf data found here: https://openmc.org/official-data-libraries/\n",
"Make sure your cross sections environment variable is pointing to temperature-dependent data before using the following settings."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"lower_left = [-0.62992, -pitch/2, 0]\n",
"upper_right = [+0.62992, +pitch/2, +300]\n",
"uniform_dist = openmc.stats.Box(lower_left, upper_right, only_fissionable=True)\n",
"\n",
"settings_file = openmc.Settings()\n",
"settings_file.batches = 100\n",
"settings_file.inactive = 10\n",
"settings_file.particles = 10000\n",
"settings_file.temperature = {'multipole': True, 'method': 'interpolation', 'range': [290, 2500]}\n",
"settings_file.source = openmc.source.Source(space=uniform_dist)\n",
"settings_file.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To run a regular simulation, just use openmc.run(). \n",
"However, we want to run a simulation that we can stop in the middle and update the material and cell properties. So we will use openmc.lib."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"openmc.lib.init()\n",
"openmc.lib.simulation_init()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 10 inactive batches, so we need to run next_batch() at least 10 times before the tally is activated. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"for _ in range(14):\n",
" openmc.lib.next_batch()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at the tally. There are 10 entries, one for each cell in the fuel."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 4178272.4202991 ]\n",
" [ 9595363.82759911]\n",
" [12307462.30060902]\n",
" [11772927.66594472]\n",
" [11892601.29001472]\n",
" [12203397.88895767]\n",
" [12851791.20965905]\n",
" [11760027.45873386]\n",
" [ 9293110.94735569]\n",
" [ 4511597.61592287]]\n"
]
}
],
"source": [
"t = openmc.lib.tallies[1]\n",
"print(t.mean)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's make some changes to the temperatures. For this, we need to identify each cell by its id. We can use get_temperature() to compare the temperatures of the cells before and after the change. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fuel temperature is: \n",
"800.0\n",
"gap temperature is: \n",
"700.0\n",
"clad temperature is: \n",
"600.0\n",
"water temperature is: \n",
"500.00000000000006\n"
]
}
],
"source": [
"print(\"fuel temperature is: \")\n",
"print(openmc.lib.cells[5].get_temperature())\n",
"print(\"gap temperature is: \")\n",
"print(openmc.lib.cells[15].get_temperature())\n",
"print(\"clad temperature is: \")\n",
"print(openmc.lib.cells[25].get_temperature())\n",
"print(\"water temperature is: \")\n",
"print(openmc.lib.cells[35].get_temperature())"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"for i in range(1, 11):\n",
" temp = 900.0\n",
" openmc.lib.cells[i].set_temperature(temp)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fuel temperature is: \n",
"899.9999999999999\n"
]
}
],
"source": [
"print(\"fuel temperature is: \")\n",
"print(openmc.lib.cells[5].get_temperature())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's make a similar change for the water density. Again, we need to identify each material by its id."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"for i in range(4, 14):\n",
" density = 0.65\n",
" openmc.lib.materials[i].set_density(density, units='g/cm3')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The new batches we run will use the new material and cell properties."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"for _ in range(14):\n",
" openmc.lib.next_batch()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When you're ready to end the simulation, use the following:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"openmc.lib.simulation_finalize()\n",
"openmc.lib.finalize()"
]
}
],
"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": 4
}
| mit |
nkmk/python-snippets | notebook/ast_literal_eval_example.ipynb | 1 | 9539 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import ast"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"s = '[\"a\", \"b\", \"c\"]'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['a', 'b', 'c']\n"
]
}
],
"source": [
"l = ast.literal_eval(s)\n",
"print(l)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n"
]
}
],
"source": [
"print(type(l))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"s = '[\"x\", 1, True]'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['x', 1, True]\n"
]
}
],
"source": [
"l = ast.literal_eval(s)\n",
"print(l)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n"
]
}
],
"source": [
"print(type(l[0]))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'int'>\n"
]
}
],
"source": [
"print(type(l[1]))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'bool'>\n"
]
}
],
"source": [
"print(type(l[2]))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"s = '{\"key1\": 1, \"key2\": 2}'"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'key1': 1, 'key2': 2}\n"
]
}
],
"source": [
"d = ast.literal_eval(s)\n",
"print(d)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'dict'>\n"
]
}
],
"source": [
"print(type(d))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"s = '{1, 2, 3}'"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{1, 2, 3}\n"
]
}
],
"source": [
"se = ast.literal_eval(s)\n",
"print(se)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'set'>\n"
]
}
],
"source": [
"print(type(se))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"s = '[\"x\", 1 + 10]'"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['x', 11]\n"
]
}
],
"source": [
"print(eval(s))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# print(ast.literal_eval(s))\n",
"# ValueError: malformed node or string"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 100]\n"
]
}
],
"source": [
"a = 100\n",
"print(eval('[1, a]'))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# a = 100\n",
"# print(ast.literal_eval('[1, a]'))\n",
"# ValueError: malformed node or string"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"import json"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"s = '{\"key1\": [1, 2, 3], \"key2\": \"abc\"}'"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'key1': [1, 2, 3], 'key2': 'abc'}\n"
]
}
],
"source": [
"print(json.loads(s))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'key1': [1, 2, 3], 'key2': 'abc'}\n"
]
}
],
"source": [
"print(ast.literal_eval(s))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"s = '[True, False, None]'"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# print(json.loads(s))\n",
"# JSONDecodeError: Expecting value:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[True, False, None]\n"
]
}
],
"source": [
"print(ast.literal_eval(s))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"s = '[true, false, null]'"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[True, False, None]\n"
]
}
],
"source": [
"print(json.loads(s))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"# print(ast.literal_eval(s))\n",
"# ValueError: malformed node or string"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"s = \"{'key1': 'abc', 'key2': '''xyz'''}\""
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# print(json.loads(s))\n",
"# JSONDecodeError: Expecting property name enclosed in double quotes"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'key1': 'abc', 'key2': 'xyz'}\n"
]
}
],
"source": [
"print(ast.literal_eval(s))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"s = 'a, b, c'"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['a', 'b', 'c']\n"
]
}
],
"source": [
"l = s.split(', ')\n",
"print(l)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n"
]
}
],
"source": [
"print(type(l))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"s = '1-2-3'"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['1', '2', '3']\n"
]
}
],
"source": [
"l = s.split('-')\n",
"print(l)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n"
]
}
],
"source": [
"print(type(l[0]))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 3]\n"
]
}
],
"source": [
"l = [int(c) for c in s.split('-')]\n",
"print(l)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'int'>\n"
]
}
],
"source": [
"print(type(l[0]))"
]
}
],
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
nenetto/OpenIGTLink | Notebooks/Example of use.ipynb | 4 | 21116 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import os\n",
"import sys\n",
"# Get current Directory\n",
"wd = os.getcwd()\n",
"# Add package to path\n",
"#sys.path.append(os.path.join(os.path.abspath(os.path.join(wd, os.pardir)), 'openigtlink'))\n",
"sys.path.append(os.path.abspath(os.path.join(wd, os.pardir)))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"from openigtlink import OpenIGTLink\n",
"import numpy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Header for IGTLink Message"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#### Header \n",
"\n",
"#### Version :1\n",
"#### Type :STATUS\n",
"#### Device Name :Device Name Example\n",
"#### Time Stamp :123.456 sec.\n",
"#### Body Size :34 B\n",
"#### CRC64 :5019106130826548958\n",
"#### Byte Representation :'\\x00\\x01STATUS\\x00\\x00\\x00\\x00\\x00\\x00Device Name Example\\x00\\x00\\x00\\x00{t\\xbcj~\\x00\\x00\\x00\\x00\\x00\\x00\\x00\"E\\xa7ro\\x01\\xe6\\xd6\\xde'\n",
"\n",
"\n",
"#### Header \n",
"\n",
"#### Version :1\n",
"#### Type :STATUS\n",
"#### Device Name :Device Name Example\n",
"#### Time Stamp :123.456 sec.\n",
"#### Body Size :34 B\n",
"#### CRC64 :5019106130826548958\n",
"#### Byte Representation :'\\x00\\x01STATUS\\x00\\x00\\x00\\x00\\x00\\x00Device Name Example\\x00\\x00\\x00\\x00{t\\xbcj~\\x00\\x00\\x00\\x00\\x00\\x00\\x00\"E\\xa7ro\\x01\\xe6\\xd6\\xde'\n",
"\n",
"\n"
]
}
],
"source": [
"header = OpenIGTLink.OpenIGTLinkHeader()\n",
"header.setTYPE('STATUS')\n",
"header.setDEVICE_NAME('Device Name Example')\n",
"header.setTIME_STAMP(123,0.456)\n",
"\n",
"bodyExample = 'This is an example of body message'\n",
"header.setCRC64(bodyExample) # Need the body to calculate the CRC\n",
"header.setBODY_SIZE(len(bodyExample))\n",
"\n",
"print header\n",
"\n",
"messageHeader = header.getHeaderMessage()\n",
"headerTest = OpenIGTLink.OpenIGTLinkHeader()\n",
"headerTest.unpack(messageHeader)\n",
"headerTest.setCRC64(bodyExample)\n",
"print headerTest"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Body for IGTLink Message"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#### Body \n",
"\n",
"#### Byte Representation :'This is an example of body message'\n",
"\n",
"\n"
]
}
],
"source": [
"body = OpenIGTLink.OpenIGTLinkBody()\n",
"bodyExample = 'This is an example of body message'\n",
"body.setBodyMessage(bodyExample) # Need the body to calculate the CRC\n",
"print body"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Create IGTLink Message"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"########################\n",
"\n",
"#### IGTLink Message\n",
"\n",
"#### Header \n",
"\n",
"#### Version :1\n",
"#### Type :STATUS\n",
"#### Device Name :Device Name Example\n",
"#### Time Stamp :123.456 sec.\n",
"#### Body Size :34 B\n",
"#### CRC64 :5019106130826548958\n",
"#### Byte Representation :'\\x00\\x01STATUS\\x00\\x00\\x00\\x00\\x00\\x00Device Name Example\\x00\\x00\\x00\\x00{t\\xbcj~\\x00\\x00\\x00\\x00\\x00\\x00\\x00\"E\\xa7ro\\x01\\xe6\\xd6\\xde'\n",
"\n",
"#### Body \n",
"\n",
"#### Byte Representation :'This is an example of body message'\n",
"\n",
"########################\n",
"\n"
]
}
],
"source": [
"igtmessage = OpenIGTLink.OpenIGTLinkMessage()\n",
"igtmessage.header = OpenIGTLink.OpenIGTLinkHeader()\n",
"igtmessage.header.setTYPE('STATUS')\n",
"igtmessage.header.setDEVICE_NAME('Device Name Example')\n",
"igtmessage.header.setTIME_STAMP(123,0.456)\n",
"bodyExample = 'This is an example of body message'\n",
"igtmessage.body.setBodyMessage(bodyExample)\n",
"bodyCoding = '>' + 'c' * len(bodyExample)\n",
"igtmessage.__updateMessage__(bodyCoding)\n",
"print igtmessage"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create IGTLink TRANSFORM"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"########################\n",
"\n",
"#### Transform Message\n",
"\n",
"#### Transform :\n",
"[[ 2. 0. 0. 0.]\n",
" [ 0. 3. 0. 0.]\n",
" [ 0. 0. 1. 0.]\n",
" [ 0. 15. 0. 1.]]\n",
"#### Byte Representation :'\\x00\\x01TRANSFORM\\x00\\x00\\x00RigidToTracker Test\\x00\\x00\\x00\\x00{t\\xbcj~\\x00\\x00\\x00\\x00\\x00\\x00\\x000\\xf4uW:\\xce\\xf9\\xb0>@\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00@@\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00?\\x80\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00'\n",
"\n",
"########################\n",
"\n",
"########################\n",
"\n",
"#### Transform Message\n",
"\n",
"#### Transform :\n",
"[[ 2. 0. 0. 0.]\n",
" [ 0. 3. 0. 0.]\n",
" [ 0. 0. 1. 0.]\n",
" [ 0. 0. 0. 1.]]\n",
"#### Byte Representation :'\\x00\\x01TRANSFORM\\x00\\x00\\x00RigidToTracker Test\\x00\\x00\\x00\\x00{t\\xbcj~\\x00\\x00\\x00\\x00\\x00\\x00\\x000\\xf4uW:\\xce\\xf9\\xb0>@\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00@@\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00?\\x80\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00'\n",
"\n",
"########################\n",
"\n"
]
}
],
"source": [
"transform = OpenIGTLink.OpenIGTLinkTransform()\n",
"\n",
"npTransform = numpy.eye(4)\n",
"npTransform[0,0] = 2\n",
"npTransform[1,1] = 3\n",
"npTransform[3,1] = 15\n",
"\n",
"message = transform.setOpenIGTLinkTransform( npTransform = npTransform,\\\n",
" floatTimeStamp = 123.456,\\\n",
" transformName = 'RigidToTracker Test')\n",
"print transform\n",
"\n",
"transformTest = OpenIGTLink.OpenIGTLinkTransform()\n",
"transformTest.header.unpack(message[0:transformTest.header.IGTLinkHeaderSize])\n",
"transformTest.unpackTransform(message[transformTest.header.IGTLinkHeaderSize::])\n",
"print transformTest"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create IGTLink STATUS"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"########################\n",
"\n",
"#### Status Message\n",
"\n",
"#### Status Code :9\n",
"#### Status Subcode :254\n",
"#### Error Name :Example Error Name\n",
"#### Status Message :Checksum error\n",
"#### Byte Representation :'\\x00\\x01STATUS\\x00\\x00\\x00\\x00\\x00\\x00Tracker\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00{t\\xbcj~\\x00\\x00\\x00\\x00\\x00\\x00\\x00,\\xc2\\xb8r\\xad\\xf2\\x07\\x0eA\\x00\\t\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\xfeExample Error Name\\x00\\x00Checksum error'\n",
"\n",
"########################\n",
"\n",
"########################\n",
"\n",
"#### Status Message\n",
"\n",
"#### Status Code :9\n",
"#### Status Subcode :254\n",
"#### Error Name :Example Error Name\n",
"#### Status Message :Checksum error\n",
"#### Byte Representation :'\\x00\\x01STATUS\\x00\\x00\\x00\\x00\\x00\\x00Tracker\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00{t\\xbcj~\\x00\\x00\\x00\\x00\\x00\\x00\\x00,\\xc2\\xb8r\\xad\\xf2\\x07\\x0eA\\x00\\t\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\xfeExample Error Name\\x00\\x00Checksum error'\n",
"\n",
"########################\n",
"\n"
]
}
],
"source": [
"status = OpenIGTLink.OpenIGTLinkStatus()\n",
"message = status.setOpenIGTLinkStatus(statusCode = 9,\\\n",
" statusSubCode = 254,\\\n",
" errorName = 'Example Error Name',\\\n",
" statusMessage = None,\\\n",
" deviceName = 'Tracker',\\\n",
" floatTimeStamp = 123.456)\n",
"print status\n",
"\n",
"statusTest = OpenIGTLink.OpenIGTLinkStatus()\n",
"statusTest.header.unpack(message[0:transformTest.header.IGTLinkHeaderSize])\n",
"statusTest.unpackStatus(message[transformTest.header.IGTLinkHeaderSize::])\n",
"print statusTest"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read Real Messages and decode"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Byte Representation equal: True\n"
]
}
],
"source": [
"import struct\n",
"messagefile = os.path.join(os.path.join(os.path.join(os.path.abspath(os.path.join(wd, os.pardir)),\\\n",
" 'OpenIGTLink'),\\\n",
" 'ExampleFiles'),\\\n",
" 'ExampleMessages.txt')\n",
"\n",
"text_file = open(messagefile, \"r\")\n",
"messages = text_file.read()\n",
"text_file.close()\n",
"\n",
"firstMessage = messages[0:106]\n",
"a = repr(firstMessage)\n",
"\n",
"transform = OpenIGTLink.OpenIGTLinkTransform()\n",
"transform.header.unpack(firstMessage[0:transform.header.IGTLinkHeaderSize])\n",
"transform.unpackTransform(firstMessage[transform.header.IGTLinkHeaderSize::])\n",
"print 'Byte Representation equal: ', repr(transform.getMessageToSend()) == repr(firstMessage) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read Message File"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>TYPE</th>\n",
" <th>DEVICE_NAME</th>\n",
" <th>TIME_STAMP</th>\n",
" <th>T00</th>\n",
" <th>T01</th>\n",
" <th>T02</th>\n",
" <th>T03</th>\n",
" <th>T10</th>\n",
" <th>T11</th>\n",
" <th>T12</th>\n",
" <th>T13</th>\n",
" <th>T20</th>\n",
" <th>T21</th>\n",
" <th>T22</th>\n",
" <th>T23</th>\n",
" <th>CODE</th>\n",
" <th>SUBCODE</th>\n",
" <th>ERROR_NAME</th>\n",
" <th>STATUS_MESSAGE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>TRANSFORM</td>\n",
" <td>RigidToTracker</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" <td>12</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>STATUS</td>\n",
" <td>My Device</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>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</td>\n",
" <td>0</td>\n",
" <td>OK</td>\n",
" <td>This is a status message</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TYPE DEVICE_NAME TIME_STAMP T00 T01 T02 T03 T10 T11 T12 \\\n",
"0 TRANSFORM RigidToTracker 0 1 2 3 4 5 6 7 \n",
"1 STATUS My Device 0 NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" T13 T20 T21 T22 T23 CODE SUBCODE ERROR_NAME STATUS_MESSAGE \n",
"0 8 9 10 11 12 NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN 1 0 OK This is a status message "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas\n",
"messagefile = os.path.join(os.path.join(os.path.join(os.path.abspath(os.path.join(wd, os.pardir)),\\\n",
" 'OpenIGTLink'),\\\n",
" 'ExampleFiles'),\\\n",
" 'Model.csv')\n",
"df = pandas.read_csv(messagefile)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0] \u0000\u0001TRANSFORM\u0000\u0000\u0000RigidToTracker\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u00000\u0004\u000eK�\u0010���?�\u0000\u0000@�\u0000\u0000A\u0010\u0000\u0000@\u0000\u0000\u0000@�\u0000\u0000A \u0000\u0000@@\u0000\u0000@�\u0000\u0000A0\u0000\u0000@�\u0000\u0000A\u0000\u0000\u0000A@\u0000\u0000\n",
"[1] \u0000\u0001STATUS\u0000\u0000\u0000\u0000\u0000\u0000My Device\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u00006�GJ��_\u001d",
"x\u0000\u0001\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000OK\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000This is a status message\n"
]
}
],
"source": [
"messageList = list()\n",
"for i in range(len(df.index)):\n",
" message = df.loc[i]\n",
" \n",
" m = None\n",
" if message['TYPE'] == 'TRANSFORM':\n",
" \n",
" npTransform = numpy.eye(4)\n",
" for ii in range(3):\n",
" for jj in range(4):\n",
" npTransform[ii,jj] = float(message['T' + str(ii) + str(jj)])\n",
" \n",
" \n",
" t = OpenIGTLink.OpenIGTLinkTransform()\n",
" m = t.setOpenIGTLinkTransform(npTransform = npTransform,\\\n",
" floatTimeStamp = float(message['TIME_STAMP']),\\\n",
" transformName = message['DEVICE_NAME'])\n",
" messageList.append(m)\n",
" \n",
" elif message['TYPE'] == 'STATUS':\n",
" s = OpenIGTLink.OpenIGTLinkStatus()\n",
" m = s.setOpenIGTLinkStatus(statusCode = int(message['CODE']),\\\n",
" statusSubCode = int(message['SUBCODE']),\\\n",
" errorName = message['ERROR_NAME'],\\\n",
" statusMessage = message['STATUS_MESSAGE'],\\\n",
" deviceName = message['DEVICE_NAME'],\\\n",
" floatTimeStamp = float(message['TIME_STAMP']))\n",
" messageList.append(m)\n",
" else:\n",
" print \"Problem realind row: \", i\n",
" \n",
"for i, m in enumerate(messageList):\n",
" print '[{0:d}]'.format(i), m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create and launch server\n",
"\n",
"1. Run this cell in a different ipython session\n",
"2. Run the second cell for receive the data in other ipython session\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import os\n",
"import sys\n",
"import time\n",
"# Get current Directory\n",
"wd = os.getcwd()\n",
"# Add package to path\n",
"sys.path.append(os.path.join(os.path.abspath(os.path.join(wd, os.pardir)), 'OpenIGTLink'))\n",
"\n",
"import OpenIGTLink\n",
"\n",
"messagefile = os.path.join(os.path.join(os.path.join(os.path.abspath(os.path.join(wd, os.pardir)),\\\n",
" 'OpenIGTLink'),\\\n",
" 'ExampleFiles'),\\\n",
" 'Model.csv')\n",
"server = OpenIGTLink.OpenIGTLinkServer()\n",
"server.addMessageFile(messagefile, 'File #1')\n",
"server.connect()\n",
"time.sleep(3)\n",
"server.sendData(rate = 1.0)\n",
"\n",
"server.disconnect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import os\n",
"import sys\n",
"import time\n",
"# Get current Directory\n",
"wd = os.getcwd()\n",
"# Add package to path\n",
"sys.path.append(os.path.join(os.path.abspath(os.path.join(wd, os.pardir)), 'OpenIGTLink'))\n",
"import OpenIGTLink\n",
"\n",
"messagefileReceived = os.path.join(os.path.join(os.path.join(os.path.abspath(os.path.join(wd, os.pardir)),\\\n",
" 'OpenIGTLink'),\\\n",
" 'ExampleFiles'),\\\n",
" 'ModelReceived.csv')\n",
"\n",
"client = OpenIGTLink.OpenIGTLinkClient()\n",
"client.connect()\n",
"time.sleep(2)\n",
"client.listenData()\n",
"time.sleep(5)\n",
"client.disconnect()\n",
"client.writeFile(messagefileReceived)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
qutip/qutip-notebooks | examples/landau-zener.ipynb | 2 | 108720 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# QuTiP example: Landau-Zener transitions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"J.R. Johansson and P.D. Nation\n",
"\n",
"For more information about QuTiP see [http://qutip.org](http://qutip.org)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from qutip import *"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import time"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def hamiltonian_t(t, args):\n",
" \"\"\" evaluate the hamiltonian at time t. \"\"\"\n",
" H0 = args[0]\n",
" H1 = args[1]\n",
"\n",
" return H0 + t * H1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def qubit_integrate(delta, eps0, A, gamma1, gamma2, psi0, tlist):\n",
"\n",
" # Hamiltonian\n",
" sx = sigmax()\n",
" sz = sigmaz()\n",
" sm = destroy(2)\n",
"\n",
" H0 = - delta/2.0 * sx - eps0/2.0 * sz\n",
" H1 = - A/2.0 * sz \n",
"\n",
" # collapse operators\n",
" c_op_list = []\n",
"\n",
" n_th = 0.0 # zero temperature\n",
"\n",
" # relaxation\n",
" rate = gamma1 * (1 + n_th)\n",
" if rate > 0.0:\n",
" c_op_list.append(sqrt(rate) * sm)\n",
"\n",
" # excitation\n",
" rate = gamma1 * n_th\n",
" if rate > 0.0:\n",
" c_op_list.append(sqrt(rate) * sm.dag())\n",
"\n",
" # dephasing \n",
" rate = gamma2\n",
" if rate > 0.0:\n",
" c_op_list.append(sqrt(rate) * sz)\n",
"\n",
" # evolve and calculate expectation values\n",
"\n",
" # method 1: function callback which returns the time-depdent qobj\n",
" #H_args = (H0, H1)\n",
" #output = mesolve(hamiltonian_t, psi0, tlist, c_op_list, [sm.dag() * sm], H_args) \n",
"\n",
" # method 2: a function callback that returns the coefficient for a qobj\n",
" #H = [H0, [H1, lambda x,y: x]]\n",
" #output = mesolve(H, psi0, tlist, c_op_list, [sm.dag() * sm], {}) \n",
"\n",
" # method 3: a string that defines the coefficient. The solver generates\n",
" # and compiles C code using cython. This method is usually the fastest\n",
" # for large systems or long time evolutions, but there is fixed-time\n",
" # overhead that makes it inefficient for small and short-time evolutions.\n",
" H = [H0, [H1, 't']]\n",
" output = mesolve(H, psi0, tlist, c_op_list, [sm.dag() * sm], {}) \n",
"\n",
" return output.expect[0]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#\n",
"# set up the calculation\n",
"#\n",
"delta = 0.5 * 2 * np.pi # qubit sigma_x coefficient\n",
"eps0 = 0.0 * 2 * np.pi # qubit sigma_z coefficient\n",
"A = 2.0 * 2 * np.pi # sweep rate\n",
"gamma1 = 0.0 # relaxation rate\n",
"gamma2 = 0.0 # dephasing rate\n",
"psi0 = basis(2,0) # initial state\n",
"\n",
"tlist = np.linspace(-20.0, 20.0, 5000)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"time elapsed = 11.346536874771118\n"
]
}
],
"source": [
"start_time = time.time()\n",
"p_ex = qubit_integrate(delta, eps0, A, gamma1, gamma2, psi0, tlist)\n",
"print('time elapsed = ' + str(time.time() - start_time))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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zzTczYMCAhhMiW+uEE07gD3/4A2PGjGHy5MmMHDlyu31OP/10jjnmGIqLi5kw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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10dda0908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12,8))\n",
"ax.plot(tlist, np.real(p_ex), 'b', tlist, np.real(1-p_ex), 'r')\n",
"ax.plot(tlist, 1 - np.exp(-np.pi * delta **2 / (2 * A)) * np.ones(shape(tlist)), 'k')\n",
"ax.set_xlabel('Time')\n",
"ax.set_ylabel('Occupation probability')\n",
"ax.set_title('Landau-Zener transition')\n",
"ax.legend((\"Excited state\", \"Ground state\", \"Landau-Zener formula\"), loc=0);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Steady state of strongly driven two-level system (repeated LZ transitions)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def qubit_integrate(delta, eps0, A, omega, gamma1, gamma2, psi0, tlist, option):\n",
"\n",
" # Hamiltonian\n",
" sx = sigmax()\n",
" sz = sigmaz()\n",
" sm = destroy(2)\n",
"\n",
" H0 = - delta/2.0 * sx - eps0/2.0 * sz\n",
" H1 = - A/2.0 * sz\n",
" \n",
" H = [H0, [H1, 'cos(w*t)']]\n",
" H_args = {'w' : omega}\n",
" # collapse operators\n",
" c_op_list = []\n",
"\n",
" n_th = 0.0 # zero temperature\n",
"\n",
" # relaxation\n",
" rate = gamma1 * (1 + n_th)\n",
" if rate > 0.0:\n",
" c_op_list.append(np.sqrt(rate) * sm)\n",
"\n",
" # excitation\n",
" rate = gamma1 * n_th\n",
" if rate > 0.0:\n",
" c_op_list.append(np.sqrt(rate) * sm.dag())\n",
"\n",
" # dephasing \n",
" rate = gamma2\n",
" if rate > 0.0:\n",
" c_op_list.append(np.sqrt(rate) * sz)\n",
"\n",
" if option == \"dynamics\":\n",
" # evolve and calculate expectation values\n",
" output = mesolve(H, psi0, tlist, c_op_list, [sm.dag() * sm], H_args) \n",
"\n",
" return output.expect[0]\n",
"\n",
" else: # option = steadystate\n",
"\n",
" # find the propagator for one driving period\n",
" T = 2*np.pi / omega\n",
" U = propagator(H, T, c_op_list, H_args, options=Odeoptions(nsteps=5000))\n",
"\n",
" # find the steady state of successive application of the propagator\n",
" rho_ss = propagator_steadystate(U)\n",
"\n",
" return np.real(expect(sm.dag() * sm, rho_ss))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#\n",
"# set up the calculation: a strongly driven two-level system\n",
"# (repeated LZ transitions)\n",
"#\n",
"delta = 0.05 * 2 * np.pi # qubit sigma_x coefficient\n",
"eps0 = 0.0 * 2 * np.pi # qubit sigma_z coefficient\n",
"A = 2.0 * 2 * np.pi # sweep rate\n",
"gamma1 = 0.0001 # relaxation rate\n",
"gamma2 = 0.005 # dephasing rate\n",
"psi0 = basis(2,0) # initial state\n",
"omega = 0.05 * 2 * np.pi # driving frequency\n",
"T = (2*np.pi)/omega # driving period\n",
"\n",
"tlist = np.linspace(0.0, 3 * T, 1500)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Steady state and dynamics for a fixed driving amplitude"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dynamics: time elapsed = 4.77644419670105\n"
]
}
],
"source": [
"start_time = time.time()\n",
"p_ex = qubit_integrate(delta, eps0, A, omega, gamma1, gamma2, psi0, tlist, \"dynamics\")\n",
"print('dynamics: time elapsed = ' + str(time.time() - start_time))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"steady state: time elapsed = 24.038254261016846\n"
]
}
],
"source": [
"start_time = time.time()\n",
"p_ex_ss = qubit_integrate(delta, eps0, A, omega, gamma1, gamma2, psi0, tlist, \"steadystate\")\n",
"print('steady state: time elapsed = ' + str(time.time() - start_time))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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t8F692s5NrmWkpeXcfnFxdpHg2naq9tm1vD173NuPvMKAms6dzEx3C/L116v+\n5z/WciuiOm5c/tN8/bWd6Fu0UF28OOe49HQ7eY8caS0Bnn7/3QLyo0dz5v/2W7ui97RqlR1UPG3d\nqvrHHzlbPXbutFZkz4PL/v12YD12LOd6fvWV6vz5Oac/edJayN94I+fBS9UObH362HqOH5//ASw+\n3gLxUqVUb7vN3XrYqJEd/OjikZFhgaKzBUx79bKW3VKl7OIuP/PnW0tct25563N6urUEv/yy1TVP\ny5apTp2as06mpal+/72d8D2tXp23rm3fbidkTzt2WGDrWf/37bN5njyZcz2/+cb2K08nTthFwoQJ\nOfOrWiA+aJCt5xdf5G2ZdC2/QQPbfs2aue88NW6sum5d3vxUcmVlqb74op1Hate2Y2NoaMF3eVTt\nQqtJE7tAXbgw57ijR1WfftruuuzcmXM5U6aovvmmBasuBw7oxj/+yLn/OBzWmnzsmLt+Ohw2b880\nVQuYjxzJmZaSYvup54ViVpYtN3fe9HQ7NyQkuC++PeezbZsF1Pv25X9eSUlxB+UbN7oD6dWr7UKC\nzhlvAmqf/mMXX+A/djkP1PkWitdes9dF1a9vrw3r3r24S1YyHD5sb4WYN8/6lN55p/Wx5lsJLj6q\n9oDiu+9af8mYGPvto6OLu2Qlw4kT1td74UJ7ov+mm4D777/gn+ans7RokfWt3rPH/rHYyJH2cPx5\ncCH8YxevZGba6xiPH7dnCSpUsAcLS8D7mUsSb/6xCwNqKpiqPazheo8qERFRCVTiA2o6L7wJqPmW\nDyqYCINpIiIiokIwoCYiIiIi8gIDaiIiIiIiLzCgJiIiIvKxAwcOoE+fPqhXrx6aN2+ONm3a4Kuv\nvjrv5ahTpw4OHTpUpLyTJ09GQkLCOct3MWNATURERORDqopbbrkF7dq1w86dO7Fy5UpMmzYN8bn/\nqyiAzMzMYihh/hhQFx0DaiIiIiIfWrRoEUqXLo3Bgwdnp9WuXRuPPPIIAAtIb7rpJnTq1AmdO3eG\nqmLYsGGIjIxEVFQUpjv/HfvPP/+MHj16ZM9j6NChmDx5MgBreX722WfRrFkzREVFYfPmzQCAw4cP\n47rrrkNERAQGDBiA/N7ulpWVhX79+mUv76233sKsWbMQFxeHu+++GzExMTh16hReeOEFtGjRApGR\nkRg0aBBUNd98K1euRPv27dG8eXN07doV+/bt89WmvWAEFHcBiIiIiM6bxx8H1qw5t/OMiQHGjClw\n9IYNG9BAWoxHAAAgAElEQVSsWbPTzmLVqlVYt24dKleujNmzZ2PNmjVYu3YtDh06hBYtWqBdu3aF\nFiM0NBSrVq3C+PHj8cYbb2DixIl4/vnnce2112LUqFH47rvv8NFHH+WZbs2aNdi7dy/Wr18PADh6\n9ChCQkIwduxYvPHGG4iNtbfGDR06FKNGjQIA3HPPPfj2229x++2358iXkZGBRx55BHPmzEFYWBim\nT5+Op556Ch9//HGh5S/J2EJNREREdB4NGTIE0dHRaNGiRXZaly5dULlyZQDAr7/+it69e8Pf3x/V\nqlVD+/btsWLFikLn27NnTwBA8+bNsXv3bgDA0qVL0bdvXwBA9+7dUalSpTzT1atXDzt37sQjjzyC\n77//HhUqVMh3/osXL0arVq0QFRWFRYsWYcOGDXnybNmyBevXr0eXLl0QExOD0aNH59u15WLDFmoi\nIiK6dJymJdlXIiIiMHv27Ozv48aNw6FDh7JbfgGgXBH+m25AQAAcDkf299TU1Bzjy5QpAwDw9/c/\no77YlSpVwtq1a7FgwQJMmDABM2bMyNOinJqaiocffhhxcXG4/PLL8dxzz+VZPmD9xSMiIrBs2bIi\nL/9iwBZqIiIiIh/q1KkTUlNT8d5772WnnTx5ssD8bdu2xfTp05GVlYXExEQsXboULVu2RO3atbFx\n40akpaXh6NGj+Omnnwpddrt27TBlyhQAwPz585GUlJQnz6FDh+BwOHDbbbdh9OjRWLVqFQAgODgY\nycnJANzBe2hoKFJSUjBr1qzs6T3zNWjQAImJidkBdUZGRr4t2RcbtlATERER+ZCI4Ouvv8Y///lP\nvPbaawgLC0O5cuXw6quv5pv/1ltvxbJlyxAdHQ0RwWuvvYbq1asDAO68805ERkaibt26aNq0aaHL\nfvbZZ9G7d29ERETg6quvRq1atfLk2bt3L/r375/d+v3yyy8DAPr164fBgwejbNmyWLZsGQYOHIjI\nyEhUr149R3eV3PlmzZqFRx99FMeOHUNmZiYef/xxREREnPF2K0kkv6c9L2SxsbEaFxdX3MUgIiKi\nEmLTpk1o1KhRcReDLnD51RMRWamqsQVMko1dPoiIiIiIvMCAmoiIiIjICwyoiYiIiIi8wICaiIiI\niMgLDKiJiIiIiLzAgJqIiIiIyAsMqImIiIh8zN/fHzExMdnDK6+8csbzmDt3bvZ0X3/9NTZu3HjG\n8yhfvnyR844ZM+a0/4DmTOzevRuRkZFezePo0aMYP378Oct3LjGgJiIiIvKxsmXLYs2aNdnDiBEj\nzngeN910U/Z0ZxtQn4lzGVCfC5dsQC0i3URki4hsF5E8NUdEKorINyKyVkQ2iEh/X5aHiIiI6EJx\n7NgxNGjQAFu2bAEA9O7dGx9++CEA4Pvvv0ezZs0QHR2Nzp07AwAmT56MoUOH4vfff8fcuXMxbNgw\nxMTEYMeOHdixYwe6deuG5s2bo23btti8eTMAYNeuXWjTpg2ioqLw9NNP51uOEydOoHv37oiOjkZk\nZCSmT5+Od955BwkJCejYsSM6duwIAFi4cCHatGmDZs2a4Y477kBKSgoA4IUXXkCLFi0QGRmJQYMG\nwfVPA1euXIno6GhER0dj3Lhx2ctr164d1qxZk/392muvxdq1a3OUacOGDWjZsiViYmLQpEkTbNu2\nDSNGjMCOHTsQExODYcOGISUlBZ07d0azZs0QFRWFOXPmAECefADw+uuvo0WLFmjSpAmeffZZL361\nAqiqTwYA/gB2AKgHoDSAtQAa58rzJIBXnZ/DABwBUPp0823evLkSERERFdXGjRuzPz/22GPavn37\nczo89thjhZbBz89Po6Ojs4dp06apqurChQu1devWOnXqVO3atauqqh48eFDDw8N1586dqqp6+PBh\nVVWdNGmSDhkyRFVV77vvPp05c2b2/Dt16qRbt25VVdXly5drx44dVVX1xhtv1E8++URVVceOHavl\nypXLU7ZZs2bpgAEDsr8fPXpUVVVr166tiYmJqqqamJiobdu21ZSUFFVVfeWVV/T555/PUT5V1b59\n++rcuXNVVTUqKkqXLFmiqqpPPPGERkREqKrq5MmTs7fZli1bNL/YbujQofr555+rqmpaWpqePHlS\nd+3alT0PVdWMjAw9duxYdvmuuOIKdTgcefItWLBABw4cqA6HQ7OysrR79+7Z5fLkWU9cAMRpEeLe\ngHMfomdrCWC7qu4EABGZBuBmAJ73JxRAsIgIgPLOgDrTh2UiIiIiOu9cXT5y69KlC2bOnIkhQ4Zk\nt9IuX74c7dq1Q926dQEAlStXPu28U1JS8Pvvv+OOO+7ITktLSwMA/Pbbb5g9ezYA4J577sHw4cPz\nTB8VFYV//etfGD58OHr06IG2bdvmybN8+XJs3LgR11xzDQAgPT0dbdq0AQAsXrwYr732Gk6ePIkj\nR44gIiICbdu2xdGjR9GuXbvsZc+fPx8AcMcdd+DFF1/E66+/jo8//hj9+vXLs7w2bdrgpZdeQnx8\nPHr27ImrrroqTx5VxZNPPomlS5fCz88Pe/fuxYEDB/LkW7hwIRYuXIimTZtmb69t27Zll+1c8GVA\nXRPA3x7f4wG0ypVnLIC5ABIABAPopaoOH5aJiIiILmFjxowp7iLk4HA4sGnTJgQFBSEpKQnh4eFn\nNY+QkJB8A3YAsHbLgtWvXx+rVq3CvHnz8PTTT6Nz584YNWpUjjyqii5dumDq1Kk50lNTU/Hwww8j\nLi4Ol19+OZ577jmkpqaednlBQUHo0qUL5syZgxkzZmDlypV58vTp0wetWrXCd999hxtuuAHvv/8+\n6tWrlyPPF198gcTERKxcuRKlSpVCnTp18l22qmLkyJF48MEHT1subxT3Q4ldAawBcBmAGABjRaRC\n7kwiMkhE4kQkLjEx8XyXkYiIiMgn3nrrLTRq1AhTpkxB//79kZGRgdatW2Pp0qXYtWsXAODIkSN5\npgsODkZycjIAoEKFCqhbty5mzpwJwAJIV2v3Nddcg2nTpgGwADQ/CQkJCAoKQt++fTFs2DCsWrUq\nzzJat26N3377Ddu3bwdg/a63bt2aHcCGhoYiJSUFs2bNAgCEhIQgJCQEv/76a77LHjBgAB599FG0\naNEClSpVylOmnTt3ol69enj00Udx8803Y926dTnKA1gf9KpVq6JUqVJYvHgx9uzZk6fcANC1a1d8\n/PHH2X2+9+7di4MHD+a7Lc6WLwPqvQAu9/ge7kzz1B/Al85uKtsB7ALQMPeMVPUDVY1V1diwsDCf\nFZiIiIjIF06dOpXjtXkjRozAli1bMHHiRLz55pto27Yt2rVrh9GjRyMsLAwffPABevbsiejoaPTq\n1SvP/O666y68/vrraNq0KXbs2IEvvvgCH330EaKjoxEREZH9gN7bb7+NcePGISoqCnv35g7DzJ9/\n/pn9AODzzz+f/fDioEGD0K1bN3Ts2BFhYWGYPHkyevfujSZNmqBNmzbYvHkzQkJCMHDgQERGRqJr\n165o0aJF9nwnTZqEIUOGICYmJvtBRZfmzZujQoUK6N8///dRzJgxA5GRkYiJicH69etx7733okqV\nKrjmmmsQGRmJYcOG4e6770ZcXByioqLw6aefomFDCyFz57vuuuvQp0+f7Iczb7/99hwB97kguVfw\nnM1YJADAVgCdYYH0CgB9VHWDR573ABxQ1edEpBqAVQCiVfVQQfONjY3VuLg4n5SZiIiILj6bNm1C\no0aNirsY5CEhIQEdOnTA5s2b4edX3B0mTH71RERWqmpsYdP6bA1UNRPAUAALAGwCMENVN4jIYBEZ\n7Mz2IoCrReRPAD8BGH66YJqIiIiISrZPP/0UrVq1wksvvXTBBNPe8lkLta+whZqIiIjOBFuoqSgu\nyBZqIiIiIqJLAQNqIiIiuuiVtDvydH55Wz8YUBMREdFFLTAwEIcPH2ZQTflSVRw+fBiBgYFnPQ9f\n/mMXIiIiomIXHh6O+Ph48H9ZUEECAwPP6p/quDCgJiIiootaqVKlsv+NN5EvsMsHEREREZEXGFAT\nEREREXmBATURERERkRcYUBMREREReYEBNRERERGRFxhQExERERF5gQE1EREREZEXGFATEREREXmB\nATURERERkRcYUBMREREReYEBNRERERGRFxhQExERERF5gQE1EREREZEXGFATEREREXmBATURERER\nkRcYUBMREREReYEBNRERERGRFxhQExERERF5gQE1EREREZEXGFATEREREXmhSAG1iHwpIt1FhAE4\nEREREZGHogbI4wH0AbBNRF4RkQY+LBMRERERUYlRpIBaVX9U1bsBNAOwG8CPIvK7iPQXkVIFTSci\n3URki4hsF5ERBeTpICJrRGSDiCw5m5UgIiIiIiouRe7CISJVAPQDMADAagBvwwLsHwrI7w9gHIDr\nATQG0FtEGufKEwJr/b5JVSMA3HHmq0BEREREVHwCipJJRL4C0ADAZwBuVNV9zlHTRSSugMlaAtiu\nqjud85gG4GYAGz3y9AHwpar+BQCqevDMV4GIiIiIqPgUKaAG8KGqzvNMEJEyqpqmqrEFTFMTwN8e\n3+MBtMqVpz6AUiLyM4BgAG+r6qdFLBMRERERUbErapeP0fmkLTsHyw8A0BxAdwBdATwjIvVzZxKR\nQSISJyJxiYmJ52CxRERERETnxmlbqEWkOqyluayINAUgzlEVAAQVMu+9AC73+B7uTPMUD+Cwqp4A\ncEJElgKIBrDVM5OqfgDgAwCIjY3VQpZLRERERHTeFNbloyvsQcRwAP/1SE8G8GQh064AcJWI1IUF\n0nfB+kx7mgNgrIgEACgN6xLyVpFKTkRERER0AThtQK2qnwD4RERuU9XZZzJjVc0UkaEAFgDwB/Cx\nqm4QkcHO8RNUdZOIfA9gHQAHgImquv6s1oSIiIiIqBiIasE9KESkr6p+LiL/ApAno6r+N5/JfCo2\nNlbj4gp6sQgRERER0bkhIitP8wKObIV1+Sjn/Fve+yIREREREV18Cuvy8b7z7/PnpzhERERERCVL\nYW/5eOd041X10XNbHCIiIiKikqWwLh8rz0spiIiIiIhKqKK85YOIiIiIiApQWJePMar6uIh8g/zf\n8nGTz0pGRERERFQCFNbl4zPn3zd8XRAiIiIiopKosC4fK51/l4hIaQANYS3VW1Q1/TyUj4iIiIjo\nglZYCzUAQES6A5gAYAcAAVBXRB5U1fm+LBwRERER0YWuSAE1gDcBdFTV7QAgIlcA+A4AA2oiIiIi\nuqT5FTFfsiuYdtoJINkH5SEiIiIiKlEKe8tHT+fHOBGZB2AGrA/1HQBW+LhsREREREQXvMK6fNzo\n8fkAgPbOz4kAyvqkREREREREJUhhb/nof74KQkRERERUEhX1LR+BAB4AEAEg0JWuqvf7qFxERERE\nRCVCUR9K/AxAdQBdASwBEA4+lEhEREREVOSA+kpVfQbACVX9BEB3AK18VywiIiIiopKhqAF1hvPv\nURGJBFARQFXfFImIiIiIqOQo6j92+UBEKgF4BsBcAOWdn4mIiIiILmlFCqhVdaLz4xIA9XxXHCIi\nIiKikqVIXT5EpIqIvCsiq0RkpYiMEZEqvi4cEREREdGFrqh9qKcBOAjgNgC3AzgEYLqvCkVERERE\nVFIUtQ91DVV90eP7aBHp5YsCERERERGVJEVtoV4oIneJiJ9zuBPAAl8WjIiIiIioJDhtC7WIJANQ\nAALgcQCfO0f5AUgB8IRPS0dEREREdIE7bUCtqsHnqyBERERERCVRUftQQ0RuAtDO+fVnVf3WN0Ui\nIiIiIio5ihRQi8grAFoA+MKZ9JiIXKOqIwuZrhuAtwH4A5ioqq8UkK8FgGUA7lLVWUUt/Ply4MAB\n3HTTTcVdDCIiIqJL0ty5c1GtWrXiLkaBitpCfQOAGFV1AICIfAJgNYACA2oR8QcwDkAXAPEAVojI\nXFXdmE++VwEsPPPinx9+fn6oXLlycReDiIiI6JLk51fU92gUjyJ3+QAQAuCI83PFIuRvCWC7qu4E\nABGZBuBmABtz5XsEwGxYC/gFKSwsDPPnzy/uYhARERHRBaioAfXLAFaLyGLYGz/aARhRyDQ1Afzt\n8T0eQCvPDCJSE8CtADriAg6oiYiIiIgKUmhALSIC4FcAreEOeoer6v5zsPwxznk5bDEFlmEQgEEA\nUKtWrXOwWCIiIiKic6PQgFpVVUTmqWoUgLlnMO+9AC73+B7uTPMUC2CaM5gOBXCDiGSq6te5yvAB\ngA8AIDY2Vs+gDEREREREPlXUHt6rnG/iOBMrAFwlInVFpDSAu5ArIFfVuqpaR1XrAJgF4OHcwTQR\nERER0YWsqH2oWwHoKyK7AZyA9aNWVW1S0ASqmikiQ2H/otwfwMequkFEBjvHT/Cq5EREREREF4Ci\nBtRdz2bmqjoPwLxcafkG0qra72yWQURERERUnE4bUItIIIDBAK4E8CeAj1Q183wUjIiIiIioJCis\nD/UnsAcH/wRwPYA3fV4iIiIiIqISpLAuH42db/eAiHwE4H++LxIRERERUclRWAt1husDu3oQERER\nEeVVWAt1tIgcd34WAGWd311v+ajg09IREREREV3gThtQq6r/+SoIEREREVFJVNR/7EJERERERPlg\nQE1ERERE5AUG1EREREREXmBATURERETkBQbUREREREReYEBNREREROQFBtRERERERF5gQE1ERERE\n5AUG1EREREREXmBATURERETkBQbUREREREReYEBNREREROQFBtRERERERF5gQE1ERERE5AUG1ERE\nREREXmBATURERETkBQbUREREREReYEBNREREROQFBtRERERERF5gQE1ERERE5AUG1EREREREXvBp\nQC0i3URki4hsF5ER+Yy/W0TWicifIvK7iET7sjxEREREROeazwJqEfEHMA7A9QAaA+gtIo1zZdsF\noL2qRgF4EcAHvioPEREREZEv+LKFuiWA7aq6U1XTAUwDcLNnBlX9XVWTnF+XAwj3YXmIiIiIiM45\nXwbUNQH87fE93plWkAcAzPdheYiIiIiIzrmA4i4AAIhIR1hAfW0B4wcBGAQAtWrVOo8lIyIiIiI6\nPV+2UO8FcLnH93BnWg4i0gTARAA3q+rh/Gakqh+oaqyqxoaFhfmksEREREREZ8OXAfUKAFeJSF0R\nKQ3gLgBzPTOISC0AXwK4R1W3+rAsREREREQ+4bMuH6qaKSJDASwA4A/gY1XdICKDneMnABgFoAqA\n8SICAJmqGuurMhERERERnWuiqsVdhjMSGxurcXFxxV0MIiIiIrrIicjKojT28j8lEhERERF5gQE1\nEREREZEXGFATEREREXmBATURERERkRcYUBMREREReYEBNRERERGRFxhQExERERF5gQE1EREREZEX\nGFATEREREXmBATURERERkRcYUBMREREReYEBNRERERGRFxhQExERERF5gQE1EREREZEXGFATERER\nEXmBATURERERkRcYUBMREREReYEBNRERERGRFxhQExERERF5IaC4C0AXpuRk4IsvgB07gBYtgNtu\nA/z9i7tUJce+fcBPPwFlywJdugAVKhR3ichXHA7gjz+AnTuBpk2Bxo2Lu0QlS2oqsHo1ULo0EBPD\n48zFTBVYsQLYsweIigIaNizuEpUsKSnAypVAqVJA8+ZAmTLFXSLyxBZqymP9ejvQPfQQ8PbbQK9e\nQPv2wKFD+efPzATmzAEWLLADZm5ZWcCpU74t87mkakFSfrZts/U8ebLg6d9/H6hTB7jnHuD224Ha\ntYHPPvNJUamYbd4MtGoFXH010LcvEBEB3H23nfjyk5VlwffKlfmPP13dK2ni44G4uNOvzyefAOHh\ntv1iY4F69YDp0/M/jlDJdvAgcN11tr/ceSfQqJE11CQl5Z/f4QDmzgWWLMm/Dp08CRw75tsyn0un\nTgHp6fmP27DBGmBOnMh/vCrw5pvAZZcBHToA11xjn998E8jI8FmR6UypaokamjdvruQ7cXGqlSur\n1qih+ttvqllZqpMnqwYGqkZFqR4+nDP/oUOqrVur2i6v2q2banKye/yvv6pedplqQIDq88+rOhyW\nfuCA6g03qNaurfrxx+78Cxaotmql2qeP6pEjlvbXX6o9e6refLPq1q2Wdvy46v33q/boobppk6Ul\nJ6s++KBqly6qv/xiaVlZqs8+q9qiherYse7lL12q2qyZ6jXXqK5f717+nDmqVauqhoSofvJJznUd\nP15VxNazXj3bVrm98oqNv/561bVrbTlt21ra4MGqGRlF/SXoQvfLL6qVKll9+egjq0fPPKPq56d6\n9dWqSUk58yclqXbo4N5X7rgj576yaJFqeLhquXKqb7zhrqvx8bavNG6sOmWKO/8331j9ffhh2x9U\nVbdvt32lTx/VhAT3cgcMUL39dtUtW9xpAweq3nij1VNVq5tPP6167bWqX3zhXs4PP6i2bGn71ebN\n7vRZs6y8NWuqTpvmTnc4bF8LCLD1bNzYvY+6ZGWpjhhh49u2Vf3qK1tmTIyl9e+veurUmf0edOH6\n5Rc7DwQGqr79tuqaNaovvKBaurTVj7/+ypl/3z7Vf/zDva907KiamOgeP3Giatmytq/93/+ppqdb\n+h9/qDZsaMfvF1+0Op2VZcsKD1e95Rb3frFkiWrTpqrt26tu2OCevk0b1XbtVFessLTNm1U7dbLz\nxQ8/WFpCgp2P6tSxY35WlqXPnq1aq5bNd8kSS8vKUh092ta1YkXV115z5z92TPXOO93rWbOmnQM9\nJSfbvgvY+W7ePNtfunWztDZtVHft8vYXotMBEKdFiE+LPUA+04EBte/89ptqhQoW5G7fnnPcwoWq\nZcqoxsaqHj1qaUlJ9r1MGdVPP1V95x1Vf3/bwY8etQNK+fKqV12letttVtseesgChIgIOyA2b27p\nL7+sOmOGaqlSdpAqXVo1MlJ1/nwrT7lydpCsUkV1+nQ7uPn52fwrV7YDWZs2lla9uk0/caLqXXfZ\n/K+80v4OHar65Ze27Lp1VcPCbJ2XLbN18Pe3eV97reV/9VU7+D33nPuA9uWXdtAsV84ObqoWRDz5\npOXp3dt9gFe1g/q//23juna1gyiVbLNmWb2vX191586840qVsnp06JCl/fWX1flSpWw/eeEFq6tN\nm6ru3av6+edWZxs2tIsxQPW+++yCNDxcNThYNTra0p9+WvU//7GLu1q1bD5Nmlj9rVTJ8pYta/vB\n5MlW9/39rb5WrGjBbv36FvCGhFiQ8/777gDm8svt7+OPq775pk175ZW271WsqDp3rl0cA7b/tmhh\nn596yur2gAH2vXt3u1gOC7PluAKFU6dsHwHsAtjzIjMz0y5KAJvv33+fl5+TfMThsItDVx1asybn\n+EWL7PjrasBRVf3uO6szZcuqvvuu6rhxtq/Vrav67bfu+tW5szWqAFZ3X3jB8tWubcdpVx1y1etO\nnWyeNWrYRaifn51rwsJUg4JsXqVL2/5Wo4Z9HjzYfY654grb5+6/38YHBVng7Tqu9+plnyMirKz+\n/laXXcvv2dNdrptussabhg0t33PP2QVyRIQt49lnbV/YuNHOg35+qq+/7r7Idpk2zbZfxYp2/iTf\nYEBNZ2TRIjvhXnVV3tYCl2++sZNwbKwFAE2aWIDwzTfuPK5golYt+9uwoV3NOxyqw4dbjROxg9Si\nRRZ4uoJewFrckpJUf/zR8gDWAhgXp7ptmzswDgqyA+/27XagAywwmD3bWtFdrcKeQfG//uVOa9bM\nWsn37LHWZld6hw7W2peR4T7pX3aZ/e3Xz33yT0iwYMjfX3XkSAseAGv1y8zMf/tNnGjbLyrKlksl\nj8OhOmaM1eE2bXK2mnn67js7uV9+ueojj1gwWqGC1WvPPOXLu1tyr73W6q7D4b6Ac7VarVmjmpZm\nddCzhfvkSQtUy5WztEaNbJ/480/bBwHVatXsTsnu3e6gPCzM0g4ccN9hKl3aWtozMuzC07WcHj0s\nUN6921oTXel9+1pwnJ7uDnL8/OzvyJHu/WDXLqvzfn4WVNSv776Izh0guHz5pW2bqlXdd5uoZDly\nxFqEAWtQcTXE5Pbnn+5jeO3a9jcy0t1qrGotxzVquOvY8OHuY/HHH1ug7Kqrrn1yyhS7kCtXzgJz\nh0N13To7JwGq99xjx/qEBAvOXUHvoUNWdlfw27mzXdilpNidExG7k7Jmjc1z3DhbfpkyFginp9v+\n0rOn+1z1/vuW1+GwFnrXPh8WZudBlxMnVO+918YFB9uyKlWyBq2C7Nhhd5AA1QcesHLSucWAmors\nm28sGI2IsFttp/PVVxYcuE7U8+fnzbNwobUGPPCAu9uGqh1MPv1U9dFH7cDmkplp3SvGjrUAwWXT\nJkvzbKU6dEj1ww/dXT9ULQj56KOcB+BTp+xA67rt5ln+8ePtwOWSmGgtbmPH5m0te+01C5YnTcp7\n8j9+3G6ZAxYsed6mL8gPP1je6tXdtxSpZEhPVx0yxH7vW27JWYfy88cfFqyWLm37g2d3CZd162ye\n48blvKuhqrp4sdXVgwfdaQ6HnYAXLnTfNla1oGD+/JxlOn7c6ptnIHPqlAX1rpZz13r9+KMFzJ6W\nL7fpPZeTkqL62Wd5T/AOh7WQPfGE3e3JLTnZLixq17Zt4rqzczobNtgFdECAtZQXdKFKF54FC+xC\nMCBA9a23Cj8uJiVZC3OvXnbBml93n5QUa6HObz86csS6M+VezokTeQNMhyP/oDM1NW9aWlretJMn\n8y7n5Mn857l7d/53JPfssX0uv2OIw2Gt1w89ZF1FDhzImye39HS7iHXdtZo6Ned+S94pakAtlrfk\niI2N1bi4uOIuxkVBFXjvPeCRR+ztBN9/D4SGFj5dcjKwaRMQGQkEBfm+nBe6pCSgXDl7S0FRbNgA\ndO9uD+lMngzccQcg4tMikpfWrwfuuw9YtQp44gng1VcBPz7S7XNHjwL33gt884291eCll+zBNu4v\nF+AW9usAACAASURBVJ6sLODnn4HXX7cHtxs2BD7/3H43Oj9++QV49FFgzRrgiiuAAQOAHj3sYen8\n9pnUVHvjyq5dwO7d9rlMGaB6daBBA3tjUdWq7mlVgf37ge3b7U0j4eH2cKTrWJiWZvPw9wdq1gQC\nA93T7dtnn6tVc7/J59QpOw9WqpTzTVgZGTbOleZw2FuUrriiePZ9EVmpqrGF5mNAfWlKTLS3eMye\nDdx4IzB1qgWFdH4cOADcfLO98aFLF+DZZ+1NBwwULhzqfMXXu+8CU6YAVaoAEyYAPXsWd8kuLarA\nzJnA//0fsHevneR79QI6drTX7AUH553G4bA3QCQlAUeO2PewMDuZF9QI4DoVXgr7YFbW6V9PqGpv\nddq714Z9+yz4ysiwtzplZLiH9HR7+9Hvv1twVKUKMHw4MHSovTaUzq+sLGDWLGDsWODXXy2tXDng\nqqtsXwkIsH3iwAELjj0FBNj0nmFhpUr2RhaHw37nw4dzThMYaIFuRoYF2q43sogAdesC5ctbwJ6c\nbOllylj+U6cs+Hblr1wZqFHD6t3Bg1aG0FDbb3fvtvx791oAf75dEAG1iHQD8DYAfwATVfWVXOPF\nOf4GACcB9FPVVaebJwNq7yQkWFAwZoxdTb7wgrW48d2v519GBjB+PPD883bij4iwluvOna31v0aN\nS+PkXpwyM4Hjx237799vB+y//rKW6N9+s89BQcDDDwP//rcd3Kl4pKXZu/EnT7ZAwXXqqlzZBsCC\nvpQUC6YLOrWFhgKXX26DK3BMSLCgUdXmVb26nbizsqxeHDxoy69e3Vrsype39P37rQ4FB9t0FSta\nq/rBg+704GDbj/fvd7f+lS9vr31LSrJAIzjYAtGsLCvP8ePWOhcSYtMcPmxBUNmylq9CBctz+LC9\naq1SJaubZcva8l1DYKCVNzTU8u/bZ+VISXHPq3p1u9AAbJqEBBvS0v6fvfsOj7LM2gB+n4TQmxQV\nQYqF3oRQ7AgKFhCxISWhg2ABxbKu7lrWzy26upZdFBSBUAVEAQEpgoKANGnSRXoH6QRSzvfHmTER\nAxmY8ky5f9c1V5LJZOYkb+adM085J/djImIjlRUqAA0b2iDBPfdw5jJc7NgBTJ8OrFhhPSVOnLDX\nnRIl7P+iQgUr8Vqpkn0sU8aeA7t2WUnQtWvtsm6dJdtXXWX1wytXtv/Vbdssid640b5fo4Yl7pmZ\nlgSvXWv/5xUq2Ih3fLyNNG/caEn+NdcA5cvb//wvv1iSX7q0PfcKFrSY9+2z+GrWtDKLxYqF/u/o\nPKEWkXgAGwDcAWAHgMUA2qnqmmy3uRvAE7CEuhGAd1W10fnulwn1hUlLs6YJ331nSzq++caeMA88\nYMk0m1C4d+KEjYCOGGFJXHq6XV+4sL0Qlihhy0m828EAm2KLi7MTVJ489uLoveTPnzUydOZM1qhS\nnjx2PwkJdvGONGVk/P57GRn2Pe8oVkKCPZZ3RCouzm7vHc3wxps3b9Z13kt8vF0yMuxnVe023jdw\n3sfP6XEyM7N+v7g4+11On876Xbz3I2LXnz5tCRWQ9T3v7U6e/H2i4b2cq150uXKWILRqBdx3nyU2\nFD727bPZgx9/tCTx0CG7vkABeyEuUcKSTO/H+Hibldu1C9i+3RKB7dvt+hIl7AW8bFn7XzpwwJLO\n3bvt/65MGUs+8ua1670J7+WX2/cSEiwpPnjQEvnixS0p8F5//Lj9L192mf0PexPaggUttiJFsn4+\nLs5+tmhRu+7XX+3/umRJizM1Netxiha16wsVstsdOGD/58WLZ11SU+1vtX+/JSJlyljcxYtbDN7f\ndc8ee+yiRbP+FuXK2ceyZe3nChTIOnd4L1z2RLEgHBLq6wG8oqotPF+/AACq+vdst/kIwBxVHeX5\nej2AJqq6+1z36yKhTkuzd1vqabrgveT0dZ48dqJJS8uaIktIsFGG+Hi7LjXVkoh8+eySJ09WspCa\naveTL5+dwPPmte+dOmUny5Mn7XNvApL9cvSovcPbvRvYsAFYs8beCXoLv1epYlOlHTrYO0wKP8eO\nAYsW2Tv7TZssUTh0KOsYZl/LlpFh/3dnztj/zalTdvF+7h098ia66el2W2+S7U2i4+KyvpeWlvW/\nFR9v13uTa+/9qGYl6d7beq/z/rw3Cc6eWCckWEzZk/CEhKzYvI/jjdl7W+8lb96s51H2pN/7fPFe\nvL+P9zZpaZa8ZE80sl+KFbOPl12WlUB4RzyJiCi2+ZpQB7P1eFkA27N9vQM2Cp3bbcoCOGdC7cLu\n3ZGVgMbH29RMtWo2wlavHnDzzTbKQOGtSBFb8tGsmetIiIiIyFfBTKgDRkR6AugJAOXLlw/545cs\naa2jvdPscXE2epb9a+/Ul3dEzDua5h2tPn3aRsy8U/JxcVnT1Glpdtv8+e2jSNaI9Zkzdl8FC2ZN\nZ+bPnzWFnn2DSJEiNspWsiTXRBMRERGFSjAT6p0Arsz2dTnPdRd6G6jqQAADAVvyEdgwc1eoENCx\nY6gflYiIiIgiQTC3FCwGcK2IVBKRvAAeATDxrNtMBJAspjGAI+dbP01EREREFG6CNkKtquki8jiA\nr2Fl8war6k8i8qjn+x8CmAKr8LEJVjavS7DiISIiIiIKhqCuoVbVKbCkOft1H2b7XAE8FswYiIiI\niIiCiVUkiYiIiIj8wISaiIiIiMgPTKiJiIiIiPzAhJqIiIiIyA9Baz0eLCKyH8BWRw9fCsABR49N\nOeMxCU88LuGHxyT88JiEJx6X8OPymFRQ1dK53SjiEmqXRGSJL/3cKXR4TMITj0v44TEJPzwm4YnH\nJfxEwjHhkg8iIiIiIj8woSYiIiIi8gMT6gsz0HUA9Ac8JuGJxyX88JiEHx6T8MTjEn7C/phwDTUR\nERERkR84Qk1ERERE5Acm1D4QkTtFZL2IbBKRP7mOJ1aJyGAR2Sciq7NdV0JEZojIRs/HS1zGGGtE\n5EoRmS0ia0TkJxHp67mex8UREckvIotEZIXnmLzquZ7HJAyISLyI/Cgikz1f87g4JCJbRGSViCwX\nkSWe63hMHBOR4iIyTkTWichaEbk+3I8LE+pciEg8gP8CuAtAdQDtRKS626hi1hAAd5513Z8AzFLV\nawHM8nxNoZMOoL+qVgfQGMBjnucHj4s7pwE0VdU6AOoCuFNEGoPHJFz0BbA229c8Lu7dpqp1s5Vl\n4zFx710A01S1KoA6sOdMWB8XJtS5awhgk6puVtUzAEYDaO04ppikqt8BOHTW1a0BDPV8PhTAfSEN\nKsap6m5VXeb5/BjspFcWPC7OqDnu+TLBc1HwmDgnIuUA3APg42xX87iEHx4Th0SkGIBbAHwCAKp6\nRlUPI8yPCxPq3JUFsD3b1zs811F4uExVd3s+3wPgMpfBxDIRqQjgOgA/gMfFKc+yguUA9gGYoao8\nJuHhPwCeA5CZ7ToeF7cUwEwRWSoiPT3X8Zi4VQnAfgCfepZHfSwihRDmx4UJNUUNtZI1LFvjgIgU\nBjAeQD9VPZr9ezwuoaeqGapaF0A5AA1FpOZZ3+cxCTERaQlgn6ouPddteFycuMnzXLkLtmTtluzf\n5DFxIg+AegAGqOp1AE7grOUd4XhcmFDnbieAK7N9Xc5zHYWHvSJSBgA8H/c5jifmiEgCLJkeoaqf\ne67mcQkDnmnS2bC9Bzwmbt0I4F4R2QJbOthURIaDx8UpVd3p+bgPwATYMk8eE7d2ANjhmVkDgHGw\nBDusjwsT6twtBnCtiFQSkbwAHgEw0XFMlGUigE6ezzsB+NJhLDFHRAS2zm2tqr6d7Vs8Lo6ISGkR\nKe75vACAOwCsA4+JU6r6gqqWU9WKsNeRb1S1I3hcnBGRQiJSxPs5gOYAVoPHxClV3QNgu4hU8VzV\nDMAahPlxYWMXH4jI3bC1b/EABqvq/zkOKSaJyCgATQCUArAXwMsAvgDwGYDyALYCeFhVz964SEEi\nIjcBmAtgFbLWhf4Zto6ax8UBEakN27ATDxs0+UxVXxORkuAxCQsi0gTAM6raksfFHRG5CjYqDdgy\ng5Gq+n88Ju6JSF3Y5t28ADYD6ALP+QxhelyYUBMRERER+YFLPoiIiIiI/MCEmoiIiIjID0yoiYiI\niIj8wISaiIiIiMgPTKiJiIiIiPyQx3UARER04TylvWZ5vrwcQAasXS8AnFTVG5wERkQUg1g2j4go\nwonIKwCOq+pbrmMhIopFXPJBRBRlROS452MTEflWRL4Ukc0i8g8R6SAii0RklYhc7bldaREZLyKL\nPZcb3f4GRESRhQk1EVF0qwPgUQDVACQBqKyqDWFdyJ7w3OZdAO+oagMAD3i+R0REPuIaaiKi6LZY\nVXcDgIj8DGC65/pVAG7zfH47gOoi4v2ZoiJSWFWPhzRSIqIIxYSaiCi6nc72eWa2rzOR9RoQB6Cx\nqqaGMjAiomjBJR9ERDQdWcs/ICJ1HcZCRBRxmFATEdGTABJFZKWIrIGtuSYiIh+xbB4RERERkR84\nQk1ERERE5Acm1EREREREfmBCTURERETkBybURERERER+YEJNREREROQHJtRERERERH5gQk1ERERE\n5Acm1EREREREfmBCTURERETkhzyuA7hQpUqV0ooVK7oOg4iIiIii3NKlSw+oauncbhdxCXXFihWx\nZMkS12EQERERUZQTka2+3I5LPoiIiIiI/MCEmoiIiIjID0yoiYiIiIj8EHFrqImIiIjIN2lpadix\nYwdSU1NdhxLW8ufPj3LlyiEhIeGifp4JNREREVGU2rFjB4oUKYKKFStCRFyHE5ZUFQcPHsSOHTtQ\nqVKli7oPLvkgIiIiilKpqakoWbIkk+nzEBGULFnSr1F8jlBTRNm1C/j2W2D1amD/fiA9HShdGrj2\nWuDWW4FrrgF4ziACtm6158ratcCBA0BmJnDZZUCVKkCTJkCFCq4jJAoP69cD8+cDP/0EHDkCqAKX\nXw7UqAHccgtQtqzrCP3HZDp3/v6NwiKhFpHiAD4GUBOAAuiqqgvcRkXhIi0NGDcO+OADO+kBQHw8\nUKoUkCcPsG+f3QYAqlcHuncHevYEChVyFzORC6mpwIgRwH//C/z4o12XkGDPFRFg714gI8Our1cP\n6NUL6NQJyJfPXcxELhw/DgweDHz4ob3pBID8+YESJSyh3rcv67ly8832mvLII/aaQ5Hn2WefxZQp\nU3D33XfjueeeQ8uWLXHmzBm89957uPnmmwPyGOGy5ONdANNUtSqAOgDWOo6HwsTUqUDNmkD79jbK\n9vrrwLJlwMmTwJ49wI4dlkSsXWsJd5EiwNNPA1dfDQwcaKNyRNFOFRg9Gqhc2d5QZmQAb70FrFoF\nnDhhMzs7d9pzZeVK4N//tjehvXrZiPWoUXYfRNEuMxP4+GOgUiWgb1+geHHg/feBdessyd65054v\nqanAkiXAG29Ycp2UZK9FU6a4/g3oYgwcOBArV67Em2++iVmzZqFWrVr48ccfA5ZMA7CF2C4vAIoB\n+AWA+HL7+vXrK0W/Y8dUO3dWBVQrV1adMEE1I8O3n/3+e9VbbrGfbdJEdevW4MZK5NLBg6r33Wf/\n73Xrqs6YoZqZmfvPZWaqTp+uWq+e/Wzr1qp79wY/XiJXdu5UvfVW+3+/+WbV+fN9+7nMTNXPP1et\nWtV+NjlZ9ejRoIYaUGvWrHH6+MePH9e7775ba9eurTVq1NDRo0fr4cOHtXLlyrpu3TpVVX3kkUd0\n4MCBf/jZRYsW6fXXX6+1a9fWBg0a6NGjR/XUqVPauXNnrVmzptatW1e/+eYbVVVNT0/XZ555RhMT\nE7VWrVr64Ycfqqpqq1atNC4uTuvUqaP/+Mc/9Morr9RSpUppnTp19OTJk797vJz+VgCWqC/5rC83\nCuYFQF0AiwAMAfAjbOlHoXPdngl19Fu3zk5cIqovvaR6+vSF30dmpurHH6sWKaJaurTqd98FPk4i\n15YsUS1XTjUhQfXNN1XT0y/8PtLTVd96SzVfPtXy5VWXLw98nESuzZplrwUFC6p+8olvbzrPlpqq\n+pe/qMbHq9aoobpxY+DjDAbXCfW4ceO0e/fuv319+PBhVVWdPn26Nm7cWEeNGqUtWrT4w8+dPn1a\nK1WqpIsWLVJV1SNHjmhaWpq+9dZb2qVLF1VVXbt2rV555ZV66tQp/eijj/Rvf/ubqqqmpqZq/fr1\ndfPmzaqqWqhQod/u99NPP9XHHnssx1j9SajDYTVQHgD1ADyhqj+IyLsA/gTgL94biEhPAD0BoHz5\n8k6CpND44QfgnnuAuDhg5kygadOLux8RoFs34MYbgdatgWbNgE8/BTp0CGy8RK58/TXwwAO2PnrB\nAqB+/Yu7n/h4oH9/26jYurU9Zz77DLj77oCGS+TMmDG2ZOPaa22jbrVqF3c/+fIBr71mG+Affhho\n0MCWJTZuHNh4g6pfP2D58sDeZ926wH/+c85v16pVC/3798fzzz+Pli1b/rbM4o477sDYsWPx2GOP\nYcWKFX/4ufXr16NMmTJo0KABAKBo0aIAgHnz5uGJJ54AAFStWhUVKlTAhg0bMH36dKxcuRLjxo0D\nABw5cgQbN2686DJ4Fyoc1lDvALBDVX/wfD0OlmD/RlUHqmqiqiaWLl065AFSaMyebQl0sWKWIFxs\nMp1d1aqWpN90k51Qhwzx/z6JXPv8c6BlS6tqM3/+xSfT2dWvDyxebM+Z++4DJk3y/z6JXPvoI6Bd\nO0t6v//+4pPp7Jo1s+dKyZLAHXcAc+f6f5/RrHLlyli2bBlq1aqFl156Ca+99hoAIDMzE2vXrkXB\nggXx66+/+v04qor3338fy5cvx/Lly/HLL7+gefPmft+vr5yPUKvqHhHZLiJVVHU9gGYA1riOi0Lr\nhx+AVq1so8isWVbeK1CKFwcmT7YkoWtXG/1OTg7c/ROF0tdfW7WBBg2AadMAz6BNQJQpYzNDzZvb\n6PeECTZjRBSJRo4EHn3UZlvGjQMKFAjcfV91FfDdd5Zc33mnPW+uvz5w9x805xlJDpZdu3ahRIkS\n6NixI4oXL46PP/4YAPDOO++gWrVqeOONN9ClSxcsWLDgd10Kq1Spgt27d2Px4sVo0KABjh07hgIF\nCuDmm2/GiBEj0LRpU2zYsAHbtm1DlSpV0KJFCwwYMABNmzZFQkICNmzYgLJly6JQiEp+OU+oPZ4A\nMEJE8gLYDKCL43gohNasAe66y5LoGTMCm0x7FSwITJxoSXu3bpY43HFH4B+HKJgWLADatLH6uFOm\nBDaZ9ipe3J6HzZrZtPZ33wVmBJwolKZMsZKQTZoA48dbSbxAu+IKYM4cmwFt1cpmiypXDvzjRLpV\nq1bh2WefRVxcHBISEjBgwACsX78eH3/8MRYtWoQiRYrglltuweuvv45XX331t5/LmzcvxowZgyee\neAKnTp1CgQIFMHPmTPTp0we9e/dGrVq1kCdPHgwZMgT58uVD9+7dsWXLFtSrVw+qitKlS+OLL74I\n2e8pGmG1khITE3XJkiWuw6AAOXQIaNjQyhXNn2/v+oPp6FGrKfrLL8C8eUDt2sF9PKJA2b7dRqUL\nF7bnyqWXBvfx9uyxafLTp4GFC9kIhiLHTz/Z/27lyraUMBhvPLPbtMlGp4sWtdnWUqWC+3gXau3a\ntagWiLUuMSCnv5WILFXVxNx+NhzWUFOMSk8H2ra1ROHzz4OfTAN2wvvqK6tXff/9wOHDwX9MIn+d\nPGlLlk6etJmWYCfTgHWKmzoVOHXKRsX96MhLFDK//mrPlUKFgC+/DH4yDdhehsmTrYZ1+/ZZDWEo\ntjChJmdeftnWnQ0YANxwQ+get1w5YOxYa83cpQsbWlD4e/JJ63w4cqR1Aw2VatWA4cPtsfv1C93j\nEl0MVdt8vnWrDdKUKxe6x27UyJqLzZgBZFu1QDGECTU5MWcO8Pe/2ybBrl1D//g33AC8+SbwxRfA\nO++E/vGJfDV2LPDJJ8Cf/mSVPUKtZUvg+eetWsKIEaF/fCJf/fe/NgP573+HdpDGq3t3ez37298s\nsabYwjXUFHKHDtna5YIFrY144cJu4lC1ZR9TpwJLl9pGL6Jwsm0bUKeOrQWdNw/ItgE+pNLTbXPX\n6tXWzvzKK93EQXQuP/1km2ebNbPlFyJu4jh1yuI4etSeK5dc4iaO7LiG2ndcQ00R5dFHgb17bfra\nVTIN2An3o49sPXVyMpCW5i4WorNlZtr/ZUaGPVdcJdMAkCcPMHSoPUe6d+cyKQovp0/b2uVixYDB\ng90l04CV5hs2zDb1Pvmkuzgo9JhQU0hNmGBT2K++CiTm+n4v+C691JLqZcuA//s/19EQZRk0yLq6\nvfMOcPXVrqOxGN58E5g+HRg40HU0RFn+/ndg5UpbGhWMsqsXKjEReOkl238wfrzraChUfEqoReRG\nEZkhIhtEZLOI/CIim4MdHEWXI0eAxx+3Kexnn3UdTZb77wc6drSEevVq19EQAbt2Ac89Z91CXewx\nOJdHHwVuv91alW/f7joaImDtWkuo27d3s8fgXF58EahXD3jsMXvtI3fGjh2LatWq4bbbbgMAtGvX\nDrVr18Y7Ad5A5esI9ScA3gZwE4AGABI9H4l89sILNg02aJDb6eucvPOOlVd67DFOZ5N7jz8OnDlj\nsycup6/PFhdnz9+MDODpp11HQ7EuMxPo1ctK5IXb5vKEBHv+7ttno9XkzieffIJBgwZh9uzZ2LNn\nDxYvXoyVK1fiqaeeCujj+JpQH1HVqaq6T1UPei8BjYSi2sKFVh7vySetOUW4KVUK+Oc/rSvc8OGu\no6FYNmmSLY169VWrbxtuKla0BGHcOGuDTuTK4MHA3LnAW2+Fpjb7hUpMBPr0Af73P9v4HqtOnDiB\ne+65B3Xq1EHNmjUxZswYLFmyBHXr1kXdunVRq1YtSA4jB3v37kWbNm1Qp04d1KlTB/PnzwcAvP32\n26hZsyZq1qyJ/2RrpT58+HA0bNgQdevWRa9evZCRkYHXXnsN8+bNQ7du3fDss8+iefPm2LlzJ+rW\nrYu5c+cG9Pf0qcqHiPwDQDyAzwGc9l6vqssCGo0PWOUj8mRmWhep7duBDRvcbkQ8n8xM4MYbgc2b\ngXXrwmN3NsWWM2eAmjWB+HhbExpuMzlep09bpZ7MTKtkEIy2zkTnc/gwcO21Viv922/DayYnu8OH\ngapVrTLOwoX23A4111U+xo8fj2nTpmHQoEEAgCNHjqBYsWK/ff9ZzxrQN99883c/17ZtW1x//fXo\n168fMjIycPz4cWzatAmdO3fGwoULoapo1KgRhg8fjvz58+O5557D559/joSEBPTp0weNGzdGcnIy\nmjRpgrfeeguJiYnYsmULWrZsidXnWN/pT5WPPD7+PRp5Pma/QwXQ1Mefpxg2ahSwaBEwZEj4JtOA\nTWcPGGAlj159Fcj2xpcoJD74ANi40Uo5hmsyDQD58lmszZsDb78N/PnPriOiWPP668DBg8C774Zv\nMg0AxYtbXeyOHYGUFKBzZ7fx9OsHLF8e2PusW/f8r5e1atVC//798fzzz6Nly5a4+eabf/vemDFj\nsGzZMkyfPv0PP/fNN99g2LBhAID4+HgUK1YM8+bNQ5s2bVCoUCEAwP3334+5c+ciLi4OS5cuRQPP\nFPipU6dwaYinLXxa8qGqt+VwYTJNuTpxwppC1K9vHazCXd26QLduNkW3aZPraCiW7N8PvPYacNdd\nwJ13uo4md3fcAbRuDfzjH7ZOlChUNm4E3nvPNuxed53raHLXvj3QsKFtVDxxwnU0oVe5cmUsW7YM\ntWrVwksvvYTXXnsNALB69Wq88sorGD16NOL9HLpXVXTq1AnLly/H8uXLsX79erzyyisBiP4Cg/Dl\nAuAeAM8B+Kv34uvPBvJSv359pcjxyiuqgOrcua4j8d2uXaqFCqk++KDrSCiWPPqoany86tq1riPx\n3bp1FvNjj7mOhGLJvfeqFimiunu360h8N3euvRa+9lroH3vNmjWhf9Bsdu7cqadOnVJV1UmTJmnr\n1q31119/1Zo1a+qiRYvO+XNt27bVd955R1VV09PT9fDhw7p06VKtVauWnjhxQo8fP641atTQZcuW\n6U8//aTXXHON7t27V1VVDx48qFu2bFFV1VtvvVUXL16sqqq//PKL1qhR45yPmdPfCsAS9SE/9bVs\n3ocA2gJ4AoAAeAhAhWAk+BQ9du2yjX4PPQTcdJPraHxXpoyVLBs3DvDsgSAKqrVrrbZznz623jJS\nVKkC9Oxp1Qw2bHAdDcWCuXOBiRNtmdHll7uOxnc33WQlWv/5T6t2FUtWrVr122bBV199FS+99BK+\n/PJLbN26FT169Phtc+LZ3n33XcyePRu1atVC/fr1sWbNGtSrVw+dO3dGw4YN0ahRI3Tv3h3XXXcd\nqlevjtdffx3NmzdH7dq1cccdd2D37t0h/T193ZS4UlVrZ/tYGMBUVb051x8OMG5KjByPPWZJwvr1\nwFVXuY7mwpw4YRteKlYEvv8+vNfoUeR76CFg2jTgl1+s4kwk2bvXqpHccQfw+eeuo6Fopgrceqst\n+fj5Z6BgQdcRXZhNm4Dq1W1Z4YABoXtc15sSI0koWo+f8nw8KSJXAEgDUOaCoqSYsnWr1avt1i3y\nkmnA6pq+8gqwYIElOkTB8uOPNhvy1FORl0wD1pnuuees1F8slwaj4Js+3UaoX3op8pJpwN54du9u\nHR23bHEdDQWarwn1ZBEpDuBNAMsAbAEwKlhBUeT729+sakYkF7Tv3NlGqP/6VzZ7oeD5y1+sRGMk\nN0rp2xcoUQJ4+WXXkVC0UrXXkwoVgB49XEdz8f78Z5vxfP1115FQoPla5eNvqnpYVcfD1k5XVdW/\nBDc0ilQbN1qJvEcfBcqVcx3Nxcub15KdJUuAr75yHQ1FowUL7H/r2WetvFakKlrU2pF/9ZWVyCQK\ntC++sHPxyy/buTlSlStn3R2HDLFlKxQ9zruGWkSaquo3InJ/Tt9X1ZCvmOMa6vDXsaNN/27eK8xs\nmAAAIABJREFUbNPBkSwtzTaJFStm09lcS02B1KwZsHq1PVc8ZVUj1rFjQKVK1gl16lTX0VA0ycy0\nRkLp6fZ8yeNrB40wtXu3LYVs29YS62Bbu3YtqlatmmM3Qsqiqli3bl3Q1lDf6vnYKodLywsPl6Ld\nhg3AyJHAE09EfjINWHONv/7V1rl++aXraCiazJ8PfPON1WmP9GQaAIoUsZH2adNs5J0oUCZOBH76\nyWYMIz2ZBqySVJ8+1uhl/frgP17+/Plx8OBB+FKEIlapKg4ePIj8frR99anKR7CJSDyAJQB2qup5\nE3WOUIe37t2BESNsU2KImxQFTXq67cwuXJij1BQ4rVpZ4rl1a3Qk1ABw/LiNUtevz828FBiqQOPG\nwIEDlnxGQ0INWDOkihVtlPrTT4P7WGlpadixYwdSU1OD+0ARLn/+/ChXrhwSzmpTG9DW4yKSD8AD\nACpm/xlVfe2Coj23vgDWAigaoPsjB3bsAIYNs7q00ZJMA3YC/9OfrGLJjBnWbpnIH6tWAZMnW4v7\naEmmAXvT+fTTtvFq+XLrPErkj2++sXX5H34YPck0YK+R3btb+bzXXgOuvDJ4j5WQkIBKlSoF7wEI\ngO9VPr4E0BpAOoAT2S5+E5FysC6MHwfi/sidt9+2tW7PPOM6ksDr0AEoW9aK8hP56x//sOTz8cdd\nRxJ4vXvb7/avf7mOhKLBG2/YEolOnVxHEnj9+9vHt992GwcFhq8JdTlVbauq/1LVf3svAYrhP7CW\n5pkBuj9y4OBBa+LSvr1NY0WbfPls5M07WkJ0sX7+GRg92qrglCjhOprAK17cqhiMGWONaogu1g8/\n2Dm3f3/Aj6WtYatCBXvNHDjQXkMpsvmaUM8XkVqBfnARaQlgn6qetx2AiPQUkSUismT//v2BDoMC\n4P33rbvg88+7jiR4evSwZIGj1OSPN9+0qeunnnIdSfA89RQQHw/8O1DDLhST/v53q9Heq5frSILn\nueeAkyeBDz5wHQn5y9eE+iYAS0VkvYisFJFVIrIyAI9/I4B7RWQLgNEAmorI8LNvpKoDVTVRVRNL\nly4dgIelQDp+3BLq1q2BGjVcRxM8RYrYFP2ECcC6da6joUi0e7dtQOrSBbjiCtfRBE/ZslY+c/Bg\ngGMgdDHWrrXKSk8+aUuIolWNGsC99wLvvWevpRS5fE2o7wJwLYDmyCqZ18rfB1fVF1S1nKpWBPAI\ngG9UtaO/90uhNWQIcOiQbdyLdk8+aVOPb73lOhKKRAMGWG3zaNxncLZnnwVOneLIG12c//zHzrXR\nuM/gbM8/b6+hQ4e6joT84WunxK0AiiOrBnVxz3UU4zIzgXfftbJGjRu7jib4SpcGkpKsNOCBA66j\noUiSmmoJdatWwDXXuI4m+KpVs1mrDz6wKW0iXx08aBWjkpKAUqVcRxN8N9wANGpkr6WZ3E0WsXxK\nqEWkL4ARAC71XIaLyBOBDERV5+RWg5rCz5QpwKZNQL9+riMJnb59LTkaONB1JBRJRo60N2F9+7qO\nJHT697eRtxEjXEdCkeSjj+wcG0vPlb59gY0b2WU0kvnU2MWzXvp6VT3h+boQgAWqWjvI8f0BG7uE\nl9tvt2L7mzdbV8FY0by5de7asiW2fm+6OKpAnTr2+YoVsdMcSNWavJw5Y7W3Y+X3pot35oxViqpV\nC/j6a9fRhE5amv3e1atbvwMKH4FqPf7b/QHIyPZ1huc6imGrVgGzZtkat1hLKvv1A3btAsaNcx0J\nRYI5c+z50q9fbCWVIrbv4KefgNmzXUdDkWDsWNu8G81VcHKSkGCvpTNnAqtXu46GLoavI9RPA+gE\nYILnqvsADFXVd4IYW444Qh0+une3aewdO6Kznu75ZGbaGtHixa1WKtH5tG4NzJ8PbN8enfV0zyc1\n1brA3XCDVW0gOhdVoEEDK8H6009AnK9DflHi4EGgXDmrkDNokOtoyCugI9Sq+jaALgAOeS5dXCTT\nFD727weGDweSk2MvmQbsRP/kk9bkZeFC19FQONu0CZg0yRq5xFoyDdjv3KuX/Q02b3YdDYWz778H\nli61mZxYS6YBoGRJe01NSWG5yUjk66bEFFVdpqrveS4/ikhKsIOj8DVoEHD6tCWVsapTJ6BYMSvv\nRHQu779vjVx693YdiTu9e1ujl//+13UkFM7++1+b9UtKch2JO3372msrR6gjj6/vAX/XrkNE4gHU\nD3w4FAkyMmwXdrNmtoEiVhUuDHTtCowfD+zZ4zoaCkfHj1sjl4cfju5GLrkpWxZ48EHgk0/YvIJy\ntnevnUs7dwYKFnQdjTvVqwNNm9prbEZG7ren8HHehFpEXhCRYwBqi8hRETnm+XofAK6Gi1FTpwLb\nttkUdqzr1QtIT7eOcERnGz0aOHYM6NPHdSTuPfEEcOQIS+hRzgYPtkoXfF2xv8G2bcC0aa4joQvh\n66bEv6vqCyGIJ1fclOhey5a2zm3bttir7pGTZs1snezmzTatTeSVmGhlwGKpVN65qAJ169pzZOlS\n/j0oS0YGcPXVwFVXAd984zoa99LSgPLlreTk5Mmuo6GAbEoUkXoiUg/AWO/n2S8Bi5Yixtat1syl\ne3cm0169e3M0gf5oyRJLHB99lMkjYH+DRx8FfvwRWLzYdTQUTqZNs9eWWN5nkF1Cgr3GTplivQ4o\nMpx3hFpEzlc5VFW1aeBDOj+OULv14ovAP/4B/PKLvYMmjiZQzrp3tyUfu3YBRYu6jiY8HD1qa8nb\ntrX11EQAZz1zsm0bUKkS8PzzwBtvuI4mtgVkhFpVbzvPJeTJNLl15oy9CN5zD5Pp7LKPJmzd6joa\nCgeHDwOjRgHt2zOZzq5oUfubjBplfyOiLVs465mT8uXttfaTT+y1l8Jfbks+mno+3p/TJTQhUrj4\n8kvbic1NI3/Uo4dNaQ8c6DoSCgfDhwMnT9qmVfq9Xr2AU6es1i7RoEF27uzRw3Uk4ad3b2DfPmDC\nhNxvS+7ltuTjVVV9WUQ+zeHbqqpdgxdazrjkw52mTW2px6ZN3HyXk3vvta6J27cDefO6joZcUQVq\n1bLSX4sWuY4mPDVsaG84Vq3i+vJYduaMddFs1AiYONF1NOEnMxO45hobrZ4zx3U0sStQSz5e9nzs\nksMl5Mk0ubNuHTB7NtCzJ5Ppc3n0URtN+OIL15GQS99/b22TOZNzbr162d/o++9dR0IuTZhg50xu\nRsxZXJw9V779Fli71nU0lBtfOyUWF5EnReRtEXnPewl2cBQ+Bg2y9W1d+TbqnFq0sJGEjz92HQm5\n9OGH1kGzbVvXkYSvRx6x9dQffeQ6EnLp44+BChXs3Ek569zZOq2y10H487VT4hQAFQGsArA024Vi\nwJkzwLBhtqThsstcRxO+4uOBLl2AmTO5OTFWHToEjBtnrZMLFXIdTfgqVMj+RmPHAr/+6joacmHL\nFmDWLDtnxvmaicSgyy6zKijDhllFKQpfvv4b51fVp1X1U1Ud6r0ENTIKG5MnAwcOAN26uY4k/HXp\nYh+HDHEaBjkyahRw+jSfK77o1s3+VqNGuY6EXPCeI73nTDq3bt1sacxXX7mOhM7H106JTwE4DmAy\ngNPe61X1UPBCyxk3JYbePfdYp7etW7l+2hfNmwMbNljnRI68xJb69W1T4rJlriOJDNddZ+cUntJj\nS0aGdUWsWhX4+mvX0YS/9PSsXgeTJrmOJvYEZFNiNmcAvAlgAbKWe/AUGAN27rQuVp06MZn2Vdeu\n9uZj1izXkVAoLV9uiTT3Gfiua1dr6LFihetIKJRmzbLGJXyu+CZPHltLPWWKNYqi8ORrQt0fwDWq\nWlFVK3kuVwUzMAoPQ4da6R5Oy/nuvvuASy7hJpJY8+mnVi6xfXvXkUSO9u3tb8bnSmwZPBgoUcLO\nleSbLl3stXgoF9uGLV8T6k0ATgYjABG5UkRmi8gaEflJRPoG43Howqnaie/WW60WJvkmf36gY0cr\nCXUo5IuiyIXTp4ERIyxBKFHCdTSRo2RJ+5sNH25/Q4p+Bw/aubFjRyBfPtfRRI5rrwVuucVek31Y\nqUsO+JpQnwCwXEQ+CkLZvHQA/VW1OoDGAB4TkeoBum/yw9y5wM8/c4PVxfBuuBoxwnUkFAqTJlmi\nwCnsC9e1q73xZGOP2DBihFWO4nPlwnXrZo3VvvvOdSSUE183JXbK6fpgVPoQkS8BfKCqM3L6Pjcl\nhk6nTjaSsGePdX2jC1O/vk3R/fij60go2O6+27r+bdnCvQYXKiMDqFQJqFEDmDrVdTQUTKpA3brW\n04Av4xfu5EmgTBmb1eHSj9AJ6KbE7KXyglk2T0QqArgOwA9nXd9TRJaIyJL9+/cH+mEpB0ePWo3Y\ndu2YTF+sbt2yNqpR9NqxwyoVdO7MZPpixMfb3+7rr4Ht211HQ8G0bBmwciVnPS9WwYL2mjx2LHDk\niOto6Gy+dkq8VkTGedY5b/ZeAhmIiBQGMB5AP1U9mv17qjpQVRNVNbF06dKBfFg6h9GjgVOneOLz\nR/v2tp76k09cR0LBNGyYzUR07uw6ksjVubONXg4b5joSCqZPPrFzYrt2riOJXN262Wvz6NGuI6Gz\n+bqG+lMAA2DrnW8DMAzA8EAFISIJsGR6hKp+Hqj7pYs3eLBNwTZo4DqSyFW8OPDAA8DIkUBqquto\nKBi8G3ebNAGuvtp1NJHrqquA226zv2VmputoKBhSU62JzwMP2LmRLk5iIlCzJpuHhSNfE+oCqjoL\ntuZ6q6q+AuCeQAQgIgLgEwBrVfXtQNwn+WftWuCHH6xMj4jraCJbp07A4cPWbZKij3fjLstK+q9r\nV2uGxA1X0WnyZDsXdspxRxb5SsT+hgsXWgMxCh++JtSnRSQOwEYReVxE2gAoHKAYbgSQBKCpiCz3\nXO4O0H3TRUhJsQ5/HTq4jiTyNW0KlC3LqexoNXgwUKSIjbqRf+6/3/6WfK5Ep5QU4Ior7JxI/unQ\nwV6j+VwJL74m1H0BFATwJID6sAQ4IO8zVXWeqoqq1lbVup7LlEDcN124zEyrCduiBXD55a6jiXzx\n8VZvdepUYN8+19FQIJ04AYwbBzz8MFCokOtoIl/BgsCDD9qGq5NB6XpArhw4YF3+2rfnxt1AKFMG\naN7c3qRwiVT48LXKx2JVPa6qO1S1i6rer6oLgx0chd6339pO+6Qk15FEj6QkID3d1g9S9JgwwZLq\n5GTXkUSP5GTg+HHgiy9cR0KBNHq0nQP5uhI4ycnWvp1LpMLHeetQi8gkAOe8gareG4ygzod1qIOr\nSxdg/HjWng60xETbwLZ0qetIKFBatADWr7d1v3G+zvXReWVmWk3qatWAadNcR0OB0qiRNbpavtx1\nJNHj5EmbRX7wQVt6RsETqDrUbwH4N4C3AVTzfJ79QlHk5Embwn7oISbTgZacbDVYV692HQkFwu7d\nwMyZtpyHyXTgxMXZKOaMGfY3psi3fj2waBFHpwOtYEF7rR471mbKyL3zvhSo6reeyxwAx7N9/a2q\nfhuaEClUvvjCplt54gu8du2APHlszRtFvpEjbTSVz5XAS0qyv+3Ika4joUAYPtzeKLVv7zqS6NOp\nE5dIhROfWo8DgIgsU9V6QY4nV1zyETx33mkl8375haNuwdC6tbXb3baNG3MiXd26QN68NvJGgde4\nsTWvWLHCdSTkj8xMq89eubJ1wqTA4t83NAKy5ENESngvAOJF5JKzrqMosXu3TbMmJTGZDpbkZGDX\nLmDWLNeRkD9WrbJEj5sRgyc52VpUM6GObN9/D2zZwpmcYPEukZo5E9i503U0lFvqtBTAEs/HogCW\neT73Xk9RglPYwdeypXUIY+3QyJaSYst3HnnEdSTRq21bICGBz5VIl5JiJSXbtHEdSfTyLpEaMcJ1\nJOTzko9wwSUfwVGnDpA/v3VIpODp3RsYOhTYu9eaWFBkycgAypcH6tcHJk50HU10a9MGWLAA2LHD\n3sBQZElNtSoU997LN0bBduON1oVy9Wp2Nw6GQFX5oBiwYoVNr3IKO/g6dbK1oePHu46ELsY339iy\nHc7kBF9ysr3xnDHDdSR0MSZPBo4c4XMlFJKTgTVrrJIUucOEmpCSYtOrbdu6jiT6NWoEXHutjVJT\n5ElJAYoVA1q1ch1J9Lv7bqBECY5uRqphw9hqPFQeftg2SfO54hYT6hiXnm5rr+6+GyhVynU00U/E\nRhPmzAG2bnUdDV2IEyeAzz+32q/587uOJvrly2fr1L/4wkY6KXLs3w9MncpW46FyySW2tGbkSCAt\nzXU0scvnhFpEbhKRLp7PS4tIpeCFRaEya5Z1ReRyj9Dp2NE+siZ1ZPG2GucUdugkJ9ta3HHjXEdC\nF2LMGLYaD7VOnYADB+yNDLnh06ZEEXkZQCKAKqpaWUSuADBWVW8MdoBn46bEwOrQwZ6Au3fbiBCF\nRpMm9jdft46bSCIFW42HnipQtSpQpozN6lBkYKvx0EtLA8qWBW691bonUuAEelNiGwD3AjgBAKq6\nCwBrFES4Y8ds1K1tWybToZaUBGzYACxe7DoS8oW31TjrtIeWd4nUt99aPWMKf2w17kZCgi2RmjQJ\n+PVX19HEJl9fGs6oDWUrAIhIoeCFRKEyfrxVnOByj9B78EFbh8tlH5GBddrd6dDBPg4f7jYO8g1b\njbuTnGwzAxyhdsPXhPozEfkIQHER6QFgJoBBwQuLQmHYMOCaa6zNL4VWsWLWinzUKODMGdfRUG5S\nUoCGDa3FL4VWxYrALbfYMYiwtgkxJzPTEurbb7dlOhRa9evbEikO1LjhU0Ktqm8BGAdgPIAqAP6q\nqu8HMzAKrm3bgNmzbcSNa3jdSEoCDh4Epk1zHQmdj7cFNken3eESqcjAVuNueZdIzZtnez0otHxe\nDaiqM1T1WVV9RlVZaj/CeduUeitOUOg1bw6ULs3RhHDHVuPuPfSQ7fPgcyW8sdW4e1wi5Y5PCbWI\n3C8iG0XkiIgcFZFjInI02MFRcKjaie+mm4CrrnIdTexKSLB1hhMnchNJuMrIsPXTd93FOu0ucYlU\n+EtNBT77DLj/fkuqyY3y5a2KFJdIhZ6vI9T/AnCvqhZT1aKqWkRViwYiABG5U0TWi8gmEflTIO6T\nzu/HH4G1azk6HQ6SkixB4CaS8MRW4+GDS6TC26RJbDUeLpKTgU2bgB9+cB1JbPE1od6rqmsD/eAi\nEg/gvwDuAlAdQDsRqR7ox6HfGz7c2pQ+/LDrSKhePaBaNU5lhyu2Gg8fLVpwiVQ4S0lhq/Fw8cAD\nVkWKrchD67wJtWepx/0AlojIGBFp573Oc72/GgLYpKqbVfUMgNEAWgfgfukc0tNtCvuee6xdKbnF\nTSThy9tq/OGH2Wo8HCQkAO3asc5uOGKr8fBStKitYx8zxsroUWjkNkLdynMpCuAkgObZrmsZgMcv\nC2B7tq93eK6jIJk1C9i7l8s9wkmHDpZYcxNJeGGr8fCTlMQ6u+GIrcbDT1IScOgQMGWK60hih6+t\nx29U1e9zu+6CH1zkQQB3qmp3z9dJABqp6uNn3a4ngJ4AUL58+fpbt27152FjWseOwFdfAXv2sDti\nOGnaFNi+3UqDsYxheGjRwo7Hzz+zO2K4UAWqV7cNonPnuo6GvBo1sk2JK1a4joS80tOBcuWAG26w\nmTa6eL62Hs/j4/29D6CeD9ddqJ0Arsz2dTnPdb+jqgMBDASAxMTE0O9bPXwYeOONkD9soB0/kxcT\nPvsrOlZbhnx/4TMsnCTnr48um9piYccPcH3Zba7DiXm7jxfBzBkv4oVGsxH3p69dh0MeAiCp9G14\nce5d+KXn31GpONd+uLb+UGksWvQs3moyGXjuO9fhkEceAO3LtcQHX96AQ0/8DSUKnHIdkv/+/Geg\neHHXUZzTeRNqEbkewA0ASovI09m+VRRAIFZKLQZwrYhUgiXSjwAIv4alx48DH3zgOgq/TUhvh5Np\neZG07kVg4wLX4VA2D2hh9EErpIzJi+vzRv7/WqQblfYEMjUOST8+DazY6DocyqZj5hd4EXdh+Kdp\n+EsCnyuuDU/7K+KQgfYLnwR+2OM6HMomKXMu3slcgDEfHUbvPFHQ3PrJJ8M6oT7vkg8RuRVAEwCP\nAvgw27eOAZikqn6/0ojI3QD+A0vQB6vq/53v9omJibpkyRJ/HzYmcQo7vLVvD3z9NbB7t1VhIXeu\nu842wS1a5DoSyslttwE7dwLr13OJlEuZmcDVVwOVK9u5i8KLKlCrlm1SnD/fdTSRy9clH+dNq1T1\nW1V9FUBjVX012+XtQCTTnseYoqqVVfXq3JJpuni7dwMzZ9oaaibT4YmbSMLD6tXA8uXcYBXOkpKA\njRtZZ9e1efPYajyceatILVhgdakpuHxKrVSVuwAj3KhRNprA6h7h6447gMsuY+1Q11JSrPQXW42H\nrwcftFKGrEntFluNh7/27S2x5nMl+DhWGSNSUoAGDYAqVVxHQueSJ4+d/CZPtpFqCr3MTGDECODO\nO62JCIWnokWB++4DRo9mK3JXUlOtfCFbjYe3cuWAZs3YijwUmFDHAO8UNkenw19SEpCWBnz2metI\nYtOcObY2l1PY4c+7RGrqVNeRxCa2Go8cSUnAL78A3/tV6Jhy41NCLSKlReTPIjJQRAZ7L8EOjgJj\n+HBOYUeKunWBmjW57MOVlBQb/bz3XteRUG6aNwcuvZTPFVfYajxy3H8/ULAgl30Em68j1F8CKAZg\nJoCvsl0ozHmnsFu0sBcfCm8iNprATSShd/IkMG6crc8tUMB1NJSb7Euk2Io8tPbts5mBDh3YajwS\nFC5sSfVnn9lSHQoOXxPqgqr6vKp+pqrjvZegRkYB8e23wI4dnJaLJN5NJGxFHloTJ1rJeS6NihxJ\nSbaGmkukQmv0aOvEl5zsOhLyVVKS9aibPNl1JNHL14R6sqdeNEWY4cOBIkU4hR1JuInEjZQU4Mor\ngVtvdR0J+eq666wVOaeyQyslxf72NWu6joR81awZUKYMnyvB5GtC3ReWVJ8SkaMickxEjgYzMPLf\nqVM2hf3AA7Z+iiJHUhKweTOL8YfK3r3WmKJDB9ZpjyTeJVLff28Nqyj41qwBlizh6HSkiY+389uU\nKcCBA66jiU6+1qEuoqpxqlpAVYt6vi4a7ODIP5MmAUePcgo7EnETSWiNHg1kZHBpVCTq0IFLpELJ\nW6e9XTvXkdCFSkqypTqjR7uOJDrl1nq8qqquE5F6OX1fVZcFLbJzYOtx37VqBfz4I7B1KzeORKKO\nHYGvvrIul/nzu44mujVoYAn1spCf0SgQmjWz89zGjWxFHkyZmUCFCkDt2nZuoshTty6QLx+7jF6I\ngLQeB/C05+O/c7i85VeEFFT79wPTptkGNybTkSk52TaR8IUruNatsylsjk5HrqQkW/KxcKHrSKLb\nnDm2yZ3LPSJXUhKwaBGwfr3rSKLPeRNqVe3p+XhbDhdWnwxjY8bY1A6ThMjFTSShkZJi66Y5hR25\nHnjASh2yJnVwDRvGOu2Rrn17O9/xdSXwuP0mSqWk2LRcrVquI6GLFR9vJ7+vvuImkmDJzLS1t82b\nA5df7joaulhFigBt2thAwunTrqOJTidO2Cb3hx5infZIVqYMcMcddt7LzHQdTXRhQh2F1q2zKR2O\nTke+5GSbaRgzxnUk0WnePGDbNm7cjQZJSdbgZcoU15FEpy++sKSayz0iX1KS7TmYO9d1JNGFCXUU\nGjrURjeZJES+2rXtwum54Bg2DChUCLjvPteRkL9uvx247DI+V4Jl2DCgYkXgpptcR0L+uu8+657I\n50pg+ZRQi8iNIlLI83lHEXlbRCoENzS6GBkZduK76y5OYUeLpCTbkb1hg+tIosuJE9Zh76GHLKmm\nyJa9FfmhQ66jiS67dgEzZ9q5iHXaI1+hQrbvYOxY61dBgeHrU2MAgJMiUgdAfwA/A+D2jzA0c6ad\n/Dp1ch0JBQo3kQTHhAnAsWNAly6uI6FASU4G0tK4RCrQRo609bZcRhg9kpOtT8XEia4jiR6+JtTp\nagWrWwP4QFX/C6BI8MKiizV0KHDJJVaDmqLDFVfYdDY3kQTWkCHAVVdxCjua1Klj7bD55jNwVO11\npXFj4NprXUdDgdKkCVCuHJ8rgeRrQn1MRF4A0BHAVyISByAheGHRxTh82Ebd2re3wu0UPZKSgC1b\nrMUy+W/rVuCbb2wmh1PY0cPbinzBAmDTJtfRRIcVK4DVq7kZMdrExVmX0WnTgL17XUcTHXx9KWkL\n4DSAbqq6B0A5AG8GLSq6KJ99BqSmAp07u46EAq1NG1v3xjq7gTFsmI28cWlU9Gnfnq3IA2nYMCAh\nAWjb1nUkFGhJSbbviq3IA8PXhPopVX1bVecCgKpuA1DD3wcXkTdFZJ2IrBSRCSJS3N/7jGVDhwLV\nqwP167uOhAIt+yaS1FTX0UQ2VVvu0bSptVGm6FKunDVFSkmxY00X78wZe2Ny771AiRKuo6FAq1ED\nqFePyz4CxdeE+o4crrsrAI8/A0BNVa0NYAOAFwJwnzFpwwZg/nwbnRZxHQ0FQ1IScOQIMGmS60gi\n29y5wObNnMmJZklJdoznz3cdSWT76itg/36gWzfXkVCwJCUBS5cCa9a4jiTynTehFpHeIrIKQBXP\nKLL38guAVf4+uKpOV9V0z5cLYUtJ6CIMHWprolh7OnrddpttUOSyD/8MGWKd9e6/33UkFCz33w8U\nLMiRN38NHmznnObNXUdCwdKunfWt4HPFf7mNUI8E0ArARM9H76W+qnYIcCxdAUwN8H3GBG/t6Tvv\ntLaiFJ28zXqmTQP27XMdTWQ6ftz2Gjz8MGtPR7PChS2pHj2adXYv1q5d1nWyUyc791B0uuwyoEUL\nVpEKhPMm1Kp6RFW3qGo7ADsApAFQAIVFpLwvDyAiM0VkdQ6X1tlu8yKAdAAjznEfPUVkiYgs2b9/\nv6+/W8yYPRvYsYMbrGKBtxX5iByfKZSb8eOtoQuXe0S/rl1tidTnn7uOJDKlpFiCxTrE94MbAAAe\nAUlEQVTt0S852XKI2bNdRxLZRH3YtSEijwN4BcBeAN73MOpZ++xfACKdAfQC0ExVT+Z2+8TERF2y\nZIm/DxtVOna0tW67dwP587uOhoLt+uutIP/q1Vwvf6Fuu81eODZs4N8u2mVmApUrA1deyUThQqkC\nVava6OV337mOhoLt1CmgbFmb5R450nU04UdElqpqYm6383VTYj8AVVS1hqrW8lwCkUzfCeA5APf6\nkkzTH/36q426tW/PZDpWdO9uG0gWLnQdSWTZvBmYM4cbd2NFXJyNUs+Zw5rUF+r77+1NZ9euriOh\nUChQwAbmxo8HDh50HU3k8jWh3g7gSBAe/wNYx8UZIrJcRD4MwmNEteHDrYxajx6uI6FQadvW1oh+\n/LHrSCLL0KFZjT8oNnTubIn14MGuI4ksgwfbOebBB11HQqHSvbuVSeRywovn65KPTwBUAfAVrMEL\nAEBV3w5eaDnjko8sqtZqN18+YPFi19FQKPXoYVNzu3cDRYu6jib8pacDlSpZW+qp3PocU+69186P\n27cDefK4jib8HTtmm9sfeYRv2mNNw4a2/GPlSs7iZRfoJR/bYDWj88JGlL0XcmjxYmDVKntnSbGl\ne3fg5ElgzBjXkUSGadNs7TRncmJPt27Anj1WsYJyN3asbdzlco/Y07277c1ZtMh1JJHJpxHq324s\nUtD1WmeOUGfhKGXsUgVq17Zauz/84Dqa8Ne6tf2dtm+3NsoUO9LSgPLlbfTtyy9dRxP+broJOHAA\nWLuWo5Sxxjs70a4dMGiQ62jCR0BHqEXkehFZA2Cd5+s6IvI/P2MkPxw7BowaZetpmUzHHhEbTVi0\nyKbn6Nx27gQmT7byX0ymY09CgpUU/eorq61M57ZunW1I7NKFyXQsKlLEcopRoyzHoAvj65KP/wBo\nAeAgAKjqCgC3BCsoyt2YMTYtxyns2NWxI5A3L/DJJ64jCW+ffmol1Lg0KnZ162YNsIYOdR1JePvo\nI3sDwjrtsat7d8stuJzwwvmaUENVt591VUaAY6EL8PHHQPXqQOPGriMhV0qWtG5wKSlW6YX+KDPT\nnivNmgFXX+06GnLl2muBW2+1N5/sBpezU6eAIUPsnHLZZa6jIVcaN7bcghtSL5zPZfNE5AYAKiIJ\nIvIMgLVBjIvOY9UqWw/aowen5WJd9+5Wi3zCBNeRhKcZM4CtW4GePV1HQq516wb8/DPw7beuIwlP\nY8cChw8DvXq5joRcErHc4ocfLNcg3/maUD8K4DEAZQHsBFDX8zU5MGiQTfWzni7ddpuVgxs40HUk\n4WngQKBUKduUSLHtwQeBSy4BBgxwHUl4+vBD6yzZpInrSMg173JCjlJfGJ8SalU9oKodVPUyVb1U\nVTuqKvvpOHDiBDBsGPDAAzblT7EtLs5GlObMse6JlGXPHmDiRFsPmi+f62jItQIFbLPdhAlWGYmy\nrFwJLFhg5xLOelKpUrb0Z9gwK89KvjlvQi0iz3k+vi8i7519CU2IlN3IkcCRI0CfPq4joXDRtauN\nJnDk7feGDLGGLtyMSF6PPmr/Exx5+72PPrI3nZ06uY6EwkWfPrYEaORI15FEjvPWoRaRVqo6SURy\nfJqpasj3TMdyHWpV4Lrr7OPy5RxJoCzJycAXX1iJuCJsuYSMDOCqq2wj4jffuI6GwkmLFsBPPwFb\ntrBzIgAcPw5ccQVw3302IkkEZHVijo8Hli2L7XwjIHWoVXWS5+PQnC6BCpZ8M38+sGIF8Nhjsf3P\nTX/Up4/VDR0xwnUk4WHSJGDbNuCJJ1xHQuGmd++s2uQEjB5t5w5uRqTsRCzXWL7clgNR7nzqlCgi\nMwA8pKqHPV9fAmC0qrYIcnx/EMsj1O3bZzUnKFTIdTQUTlSB+vVtOnvFCr7huv12YMMGYPNmjkLS\n76Wn20beatWA6dNdR+OWKpCYCJw5Y+uoY/28Qb93/DhQtizQsmVsD9YEtFMigNLeZBoAVPVXAJde\nbHB04fbuBcaNsw1WTKbpbCI2Sr1qFTBvnuto3Fq7Fpg1y0YimUzT2fLksdHYGTPsTVcsmz/fpvMf\nf5zJNP1R4cK2rn7sWMtB6Px8TagzRKS89wsRqQAg96FtCphBg4C0NG5GpHNr3x4oVgz43/9cR+LW\nBx/YBituRqRz6d7dEusPP3QdiVvvvWelBDt2dB0Jhas+fSz34Ebe3PmaUL8IYJ6IpIjIcADfAXgh\neGFRdunptgv79tuBKlVcR0PhqmBBKws2fryVjItFR45Ye+lHHgFKl3YdDYWryy+30qOffmqlSGPR\n9u12rujenbOedG5Vq1qn2Q8/tFyEzs3XOtTTANQDMAbAaAD1VfXrYAZGWSZOBHbssA0CROfTu7eN\nJsTqyNuwYZYgPf6460go3D3xhJUFGxqj2+sHDLA11Jz1pNw89pjlIJMmuY4kvOVWNq+qqq4TkXo5\nfV9VlwUtsnOIxU2JN99sowmbNnFNKOWuVStrG7ttG5A/v+toQicz0zaaXXIJsHCh62go3KkCjRsD\nv/4KrFtnTZJixalTwJVXArfcAnz+uetoKNylpwPXXAOULw98953raEIvUJsSn/Z8/HcOl7f8ipB8\nsmiRbTLr14/JNPnm6aeB/ftjb1f29Om2yYyj0+QLEeCpp4CNG616UiwZNQo4eBB48knXkVAkyJMH\n6NsXmDsXWLzYdTThK7cR6odUdayIXKWqm0MY1znF2gh127bAtGk2Ql20qOtoKBKoAvXqWSms1atj\nZ/d+s2bA+vVWKi9vXtfRUCRIS7PmP9dcEzsNgLwNwjIzWWKTfHf0qM1q3HWX1S6PJYEaofZuPBzn\nf0jnJiL9RURFpFQwHyfSbNlipfJ69mQyTb4TsVHqNWuAr2Nkp8OyZZYQ9e3LZJp8l5Bga6lnz7YG\nFrFg5kxLpPv2ZTJNvitaFOjRw3KSrVtdRxOeckuoD4nIdABXicjEsy+BCEBErgTQHMC2QNxfNHnv\nPVvXx2k5ulBt2wJlygBvv+06ktB46y1rud6zp+tIKNL06GFVLt55x3UkofHPf9q5gaXy6EJ5c5H3\n3nMbR7jKLaG+G8BfAexHzuuoA+EdAM+Bda1/58gRq/v48MM2zUJ0IfLmtZG3GTOs2Us027oV+Owz\nS6aLFXMdDUWa4sWBrl1tXfGuXa6jCa6lS63pUb9+Vqud6EKUL285yaBBlqPQ7+WWUH+iqgsBDFLV\nb8+++PvgItIawE5VXeHvfUWbQYOAY8eA/v1dR0KRqlcvq039VpRvH/7Pf2zqum9f15FQpOrXD8jI\niP4ZnX/9y6bue/VyHQlFqv79LTdho5c/ym1T4hoAtwOYCqAJgN+tuFLVQ7k+gMhMAJfn8K0XAfwZ\nQHNVPSIiWwAkquqBHO6jJ4CeAFC+fPn6W6N8Ac/p08BVV1kTl1jZKEPB8dRTwPvvWyWDSpVcRxN4\nv/5qMzht2gApKa6joUjWsSPwxRc241GypOtoAu/nn4HKlYFnnrFlH0QX67bbrKLS5s2xMdMRqE2J\nHwKYBaAqgKVnXXwqtaGqt6tqzbMvADYDqARghSeZLgdgmYj8IflW1YGqmqiqiaVjoP3ZkCE29fji\ni64joUj3zDNAfLyNTEWj//3PGrk884zrSCjSvfCC/S+9+67rSILj3//OKn9G5I8XX7QcJVabIp3L\neUeof7uRyABV7R3UQM4zQp1dtJfNS0uzUYTLLwfmz+cubPJf797A4ME2mlC2rOtoAufYMaBiReD6\n64HJk11HQ9GgTRtgzhwbpY6mykr79gEVKgAdOnCqnvynaufdvXttpDohwXVEwRWoEWoAgKr2FpGb\nRKSL585LiUgUTiC7N3Kklct76SUm0xQYzz9v60OjbS31//4HHDoE/PWvriOhaPHii9aOfMAA15EE\n1ptvWl36Z591HQlFAxHLUbZssc28ZHwdoX4ZQCKAKqpaWUSuADBWVW8MdoBni+YR6owMoHp120i2\nbBkTagqczp2tEsaWLcCll7qOxn/Hj9ua8MREYOpU19FQNGnRwmpSb9kCFCjgOhr/7d1rz5UHHuA+\nAwocb4Og1FTgp59saWG0CugINYA2AO4FcAIAVHUXgCIXHx7lZOxYmz7h6DQF2gsv2IkvWmrtfvgh\ncOAAR6cp8F580ZZIDBzoOpLA+Ne/bKP7X/7iOhKKJt5R6vXrrdkL+T5CvUhVG4rIMlWtJyKFACxQ\n1drBD/H3onWEOj0dqFHD1iKtXGkNXYgCqV07YNIkW0sdyaPUJ0/aiFudOsD06a6joWjUtKmNuv38\nM1C4sOtoLt7u3VYx6uGHuYGMAi8zE6hVy2bXV6+2Ta/RKNAj1J+JyEcAiotIDwAzAQzyJ0D6vSFD\nbHT6//6PyTQFxyuv2Cj1G2+4jsQ/H3xgI4gcnaZgeeMN+x+L9Iof//ynbXTn6DQFQ1yc5Szr1/MN\nG+DjCDUAiMgdsBbhAuBrVZ0RzMDOJRpHqFNTgWuvBcqVY2UPCq6ePe3Et369VciINIcOAVdfDdx4\nIyt7UHD9f3v3HiRleeVx/HuGYQQVRJBFZbygwRWiMCACayQleNdETLzhlSQqgRgEFQ0iERQDGG+o\nwQS8INQqlgqKSmKhqGiUEnEFRoFF5JaZIKAry7AKE5hn/zhNMSLCQM/083b371PVRfc7lz7U091z\n3uc9z3l69oRZs/yKTtOmsaPZc2Vl/nelVy+YMCF2NJKrtnX8KC/3/Q4aNIgdUe2r7RlqgAXALOAt\nQDsb1qJHHvEPv5EjlUxL3br9dp9VGD48diR7Z9Qo3/J29OjYkUiuu+su2LAhe3u4//73fkl+2LDY\nkUguM/Pcpaws97rj7KkaJdRmdjEwB7gIuBh438wurMvA8sWGDf5iPOMM331IpC4VF0P//jBpkte8\nZZNVq3zXx9694bjjYkcjue744+Gyy+Chh3wTi2wyf75fibr++uy8EiXZpUcPOP10L//YsCF2NPHU\ndIb6NuDEEELvEMJVQGdAVVm1YNQo+PLL7K9rlewxeLBvWjF4cOxI9sy2muk77ogbh+SPO+/0BeND\nh8aOZM/ccgs0aQJDhsSORPLFyJGey+Tz1cOaJtQFIYS11R5/uQc/K99j6VK4/3646io44YTY0Ui+\naNrUE4Tp07Onh/NHH/msev/+cPjhsaORfHHUUTBwoNcgf/BB7GhqZsYMvw0dCgceGDsayRedOsGV\nV/oW9599FjuaOGraNu8eoB2wbU+cS4DSEMItdRjbTuXSosSePWHmTO/uceihsaORfFJZ6Ze0AUpL\noagobjy7UlUFJ5/sJ6BLlvjMm0imbNgAxxzjyfW77yZ7nUtlpbeTrKyEhQthn31iRyT55J//9PfK\naafBiy/Gjqb21PbW4zcD4/Ckuh0wPkYynUtmzICXXvJZBCXTkmlFRTBmjCeoDz0UO5pdmzQJZs/2\nFmBKpiXTGjf20rzZs+Hpp2NHs2sPPACLF/t7Wsm0ZNqhh/rGSNOmwWtR+sDFtcsZajP7AdAihPDu\nDsdPBlaHEDI+sZ8LM9SbNkFJidfmffKJPvgknp/+1FuDLVoELVvGjua71q/3GY+jj/bZQfVolxiq\nqqBLF+9ksGhRMk/sVq2CNm18cVguzQ5Kdtm0yTepKyryUr1caKNXWzPUY4Cdrdn839TXZC/cdZf3\nAR47Vsm0xDVmjJ/Y/eY33k80aYYM8S3Gx45VMi3xFBTAuHGwbp0v+EuiG27w93C2b0Yj2a1BA28F\nvHixd/3IJ7v7E9UihFC648HUsSPrJKIcN3++X7q+6io488zY0Ui+O/poGDHCy4+eey52NN/25pve\n13TAAOjYMXY0ku86doQbb4RHH4W33oodzbc99xxMneq9p484InY0ku/OPNMXKI4eDQsWxI4mc3ZX\n8vFpCKH193xtaQjhB3UW2ffI5pKPLVuga1e/NLdoETRrFjsiEX9dnnQSrFjhC5kOOih2RLBxoy+a\nLCz0k9B9940dkQh8/TW0a+cz1vPnQ8OGsSOCNWv8EnurVl7nXVgYOyIRb6HXpo33QX/vvex+XdZW\nycdcM7t2J7/8GuDDvQ0uX40cCR9+6JtTKJmWpCgshMcfh6++gn79klH6MXgwrFzp7cqUTEtS7Lsv\njB/vWyzfdFPsaPy92revn4BOnJjdSYvklmbN4E9/8naTo0bFjiYzdpdQDwR+aWZvmdl9qdss4Gpg\nQN2Hlzvefdc3pLj8crjkktjRiHzb8cd7vdvzz/sl7ZheecVrpgcM8HZ5IknSowcMGuTlSLEX/02c\n6DGMGAFt28aNRWRHF18MV1wBw4fD3/8eO5q6V9M+1N2BbZv9fhJCeKNOo9qFbCz5WL/eu3oUFMC8\ned6GSSRpqqrg7LPh7bd9ViHG9t6rVkGHDr55y+zZubFCXHJPZaWXSS1f7qUfxcWZj6G01DuPdOkC\nr78O9eplPgaR3amo8M/0ykrPf5o2jR3RnqvtPtRvhhAeTt2iJdPZaOtWn5UuL/cepkqmJakKCrzn\n8wEH+MxCRUVmn7+yEnr1gn/9C559Vsm0JFdREUyeDJs3+3tl8+bMPn9FBVx0kb9XJ09WMi3J1agR\nPPMMrF4Nv/iFT9zkKjWiqmNDh8Jf/+q1RF27xo5GZNdatPATvyVL4NJL/YQwE0KAPn18Vvqxx6D1\nTpdCiyRH69Ze4z97ttcxZ2rtwbZJmk8/9WT64IMz87wie6tTJ9906OWXvRVqrlJCXYcmTPC2MX37\nwq9/HTsakZrp0cMXzk6f7rWimTB6tNeDDhvmM34i2eCii+D22+HJJ+GPf8zMc95wgycmDz8Mp5yS\nmecUSdd113kedPfd/n7JRdHXBJtZf+A6YCswPVe2NJ86Fa65Bs44Q432Jfv06+eN+ceM8TZ6t91W\nd8/16KM+a3HppZ5Qi2STYcN8o67Bg+HAA/1KS10ZPdoT6Rtv9M2YRLKFmb92ly713KhxY/j5z2NH\nVbuiJtSpxY49gfYhhM1m9m8x46ktL7/syUGXLp5YFxXFjkhkz91/v7fSGzrUd/Ssi9nqCRN81uKc\nc/y+We0/h0hd2rb2oKLCr0Y2aOAbd9W2UaP8xPOyyzI3Gy5Sm+rXhxde8I1fevXy++eeGzuq2hO7\n5KMfMDqEsBkghLA2cjxpmzABfvYzb/4/fTrst1/siET2Tr168MQTXoJx880+A1dbC0pCgPvug6uv\nhtNPhylTPGkXyUZFRd5ysnt36N0b7r239mqqq6o8kd6WTE+cqEWIkr0aNfJ1Ze3awfnne86UK2In\n1McA3czsfTObZWYnRo5nr23Z4h94v/qV16C+8YZf/hPJZoWF8NRTPvN2990+q5Bu94/Nm72kZNAg\nuPBC76Orjh6S7Ro29ERh2wlov37wzTfp/c6NG/09MmoUXHutz4Rr8xbJdk2aeI7UvbvnTEOGeA6V\n7eo8oTaz183s453ceuIlJ02BrsDNwLNm373oa2Z9zGyumc1dt25dXYe8x5YsgVNP9Q+9a67xjSka\nNYodlUjtKCyERx7xy8xTpkD79r5R0d4oLYXOnWHcOLj1Vm+nlITtm0Vqwz77eOeNW27x13jnzrBg\nwd79rvfe8/6906Z5h4Rx4zQzLbmjcWO/in/11Z47desGn30WO6r01HlCHUI4LYRw3E5u04AyYGpw\nc4Aq4KCd/I7xIYROIYROzZs3r+uQa2zdOr8M3q6dN/efONEXWKlmWnKNmc+6vf22P+7WzetEly2r\n2c+Xl/sq7w4d4PPPfZ3ByJFefyqSSwoK/GrO3/4Ga9dCx47+2i8vr9nPL1sGV17pu4Ru2eIzeQMH\nan2B5J769b1N6uTJsGgR/PCH8LvfwRdfxI5s79Rop8Q6e3KzvsChIYTbzewYYCZweNhFUDF3Sty6\nFcrK4P33vZh+2jTYtMnr2u65Bw45JEpYIhlVUeHblD/4oJdvnHWWr9bu1g1atfITyq1bYcUKn2Wb\nNs1vIXjpyPDh3jlEJNd98YW/3v/yF0+Ie/b020knwZFH+oxzZaXvuPjOO76I/dVXfaZ7wADvrqOr\nnZIPysr89T5pkr/+L7gAzjvP9+8oLo57daamOyXGTqiLgCeAEqASGLS7nRhjJNQrV/rM2vr12xea\nNGvmPUgHDIBjj81oOCKJUF4O48f7wsWyMj9m5h+GlZXbFzC2aOEbUfz2t55wi+Sb5ct9c6+nnoI1\na/xYQYGffG7evP3vSnGx15T26QMtW8aLVySWhQth7Fiftf7qKz9WWAj77+/lU4cdlvmYsiKh3hsx\nEuqKCi+ab9rUZ6FPOAFKSvxyhUi+C8Ev182Z44nDN994onDUUf4+KSlRaYcI+EnmvHl+W7bMTzwb\nNvQTzc6doU0blXaIgF/lnDvXk+jly32B7ogRcMABmY9FCbWIiIiISBpqmlBr3khEREREJA1KqEVE\nRERE0qCEWkREREQkDUqoRURERETSoIRaRERERCQNSqhFRERERNKghFpEREREJA1Z14fazNYBKyM9\n/UFAlu4yn7M0JsmkcUkejUnyaEySSeOSPDHH5IgQQvPdfVPWJdQxmdncmjT3lszRmCSTxiV5NCbJ\nozFJJo1L8mTDmKjkQ0REREQkDUqoRURERETSoIR6z4yPHYB8h8YkmTQuyaMxSR6NSTJpXJIn8WOi\nGmoRERERkTRohlpEREREJA1KqGvAzM4ys/82s6VmNjh2PPnKzJ4ws7Vm9nG1Y03N7DUz+zT174Ex\nY8w3ZnaYmb1pZgvN7BMzG5A6rnGJxMwamNkcM5ufGpM7Usc1JglgZvXM7CMzeyX1WOMSkZmtMLNS\nM5tnZnNTxzQmkZlZEzN73swWm9kiM/uPpI+LEurdMLN6wFjgbKAtcKmZtY0bVd56Ejhrh2ODgZkh\nhNbAzNRjyZwtwE0hhLZAV+C61PtD4xLPZqBHCKE9UAKcZWZd0ZgkxQBgUbXHGpf4uocQSqq1ZdOY\nxPcg8GoI4VigPf6eSfS4KKHevc7A0hDCshBCJfAM0DNyTHkphPA28D87HO4JTEzdnwicn9Gg8lwI\nYXUI4b9S9yvwD72WaFyiCW5j6mH91C2gMYnOzIqBc4HHqh3WuCSPxiQiMzsA+DHwOEAIoTKEsJ6E\nj4sS6t1rCfyj2uOy1DFJhhYhhNWp+58DLWIGk8/M7EigA/A+GpeoUmUF84C1wGshBI1JMowBbgGq\nqh3TuMQVgNfN7EMz65M6pjGJqxWwDpiQKo96zMz2I+HjooRackbwljVqWxOBme0PTAEGhhA2VP+a\nxiXzQghbQwglQDHQ2cyO2+HrGpMMM7OfAGtDCB9+3/doXKI4OfVeORsvWftx9S9qTKIoBDoCfw4h\ndAD+jx3KO5I4Lkqod68cOKza4+LUMUmGNWZ2CEDq37WR48k7ZlYfT6afCiFMTR3WuCRA6jLpm/ja\nA41JXD8CzjOzFXjpYA8z+080LlGFEMpT/64FXsDLPDUmcZUBZakrawDP4wl2osdFCfXufQC0NrNW\nZlYE9AJeihyTbPcS0Dt1vzcwLWIsecfMDK9zWxRCuL/alzQukZhZczNrkrrfEDgdWIzGJKoQwq0h\nhOIQwpH435E3QghXoHGJxsz2M7NG2+4DZwAfozGJKoTwOfAPM/v31KFTgYUkfFy0sUsNmNk5eO1b\nPeCJEMIfIoeUl8xsMnAKcBCwBhgGvAg8CxwOrAQuDiHsuHBR6oiZnQy8A5SyvS50CF5HrXGJwMza\n4Qt26uGTJs+GEO40s2ZoTBLBzE4BBoUQfqJxicfMjsJnpcHLDJ4OIfxBYxKfmZXgi3eLgGXAL0l9\nnpHQcVFCLSIiIiKSBpV8iIiIiIikQQm1iIiIiEgalFCLiIiIiKRBCbWIiIiISBqUUIuIiIiIpKEw\ndgAiIrLnUq29ZqYeHgxsxbfrBfg6hHBSlMBERPKQ2uaJiGQ5MxsObAwh3Bs7FhGRfKSSDxGRHGNm\nG1P/nmJms8xsmpktM7PRZna5mc0xs1IzOzr1fc3NbIqZfZC6/Sju/0BEJLsooRYRyW3tgb5AG+BK\n4JgQQmd8F7L+qe95EHgghHAicEHqayIiUkOqoRYRyW0fhBBWA5jZZ8CM1PFSoHvq/mlAWzPb9jON\nzWz/EMLGjEYqIpKllFCLiOS2zdXuV1V7XMX2vwEFQNcQwqZMBiYikitU8iEiIjPYXv6BmZVEjEVE\nJOsooRYRkeuBTma2wMwW4jXXIiJSQ2qbJyIiIiKSBs1Qi4iIiIikQQm1iIiIiEgalFCLiIiIiKRB\nCbWIiIiISBqUUIuIiIiIpEEJtYiIiIhIGpRQi4iIiIikQQm1iIiIiEga/h+JwC3oARPzvwAAAABJ\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1117ca550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))\n",
"\n",
"ax1.plot(tlist, np.real(p_ex), 'b', tlist, np.real(1-p_ex), 'r', \n",
" tlist, np.ones(np.shape(tlist)) * p_ex_ss, 'k')\n",
"ax1.set_xlabel('Time')\n",
"ax1.set_ylabel('Probability')\n",
"ax1.set_title('Repeated Landau-Zener-like transitions')\n",
"ax1.legend((\"Excited state\", \"Ground state\", \"Excited steady state\"), loc=0)\n",
"\n",
"ax2.plot(tlist, -delta/2.0 * np.ones(np.shape(tlist)), 'r')\n",
"ax2.plot(tlist, -(eps0/2.0 + A/2.0 * np.cos(omega * tlist)), 'b')\n",
"ax2.legend((\"sx coeff\", \"sz coeff\"))\n",
"ax2.set_xlabel('Time')\n",
"ax2.set_ylabel('Coefficients in the Hamiltonian');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Steady state as a function of driving amplitude"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"start_time = time.time()\n",
"\n",
"A_vec = 2 * np.pi * np.linspace(0.0, 5.0, 100)\n",
"\n",
"p_ex_ss_vec = np.zeros(len(A_vec))\n",
"idx = 0\n",
"start_time = time.time()\n",
"for A in A_vec:\n",
" \n",
" p_ex_ss_vec[idx] = qubit_integrate(delta, eps0, A, omega, gamma1, gamma2, psi0, tlist, \"steadystate\")\n",
" idx += 1\n",
"\n",
"print('time elapsed = ' + str(time.time() - start_time))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fig, ax = plt.subplots()\n",
"\n",
"ax.plot(A_vec/(2*np.pi), p_ex_ss_vec, 'b.-')\n",
"ax.set_title(\"Steady state of repeated LZ transitions\")\n",
"ax.set_xlabel(\"driving amplitude\")\n",
"ax.set_ylabel(\"Occupation probability\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Steadystate of a strongly driven two-level system as a function of driving amplitude and qubit bias"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Find the steady state of a strongly driven qubit as a function of driving amplitude and qubit bias. \n",
"\n",
"Note: This calculation can takes a long time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def hamiltonian_t(t, args):\n",
" #\n",
" # evaluate the hamiltonian at time t. \n",
" #\n",
" H0 = args[0]\n",
" H1 = args[1]\n",
" w = args[2]\n",
"\n",
" return H0 + H1 * np.sin(w * t)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def sd_qubit_integrate(delta, eps0_vec, A_vec, w, gamma1, gamma2):\n",
"\n",
" # Hamiltonian\n",
" sx = sigmax()\n",
" sz = sigmaz()\n",
" sm = destroy(2)\n",
"\n",
" # collapse operators\n",
" c_op_list = []\n",
"\n",
" n_th = 0.0 # zero temperature\n",
"\n",
" # relaxation\n",
" rate = gamma1 * (1 + n_th)\n",
" if rate > 0.0:\n",
" c_op_list.append(np.sqrt(rate) * sm)\n",
"\n",
" # excitation\n",
" rate = gamma1 * n_th\n",
" if rate > 0.0:\n",
" c_op_list.append(np.sqrt(rate) * sm.dag())\n",
"\n",
" # dephasing \n",
" rate = gamma2\n",
" if rate > 0.0:\n",
" c_op_list.append(np.sqrt(rate) * sz)\n",
"\n",
"\n",
" N = len(A_vec)\n",
" M = len(eps0_vec)\n",
" p_ex = np.zeros([N, M]) #, dtype=complex)\n",
"\n",
" T = 2 * np.pi / w\n",
"\n",
" sn = sm.dag() * sm\n",
"\n",
" # sweep over the driving amplitude and bias point, find the steady state \n",
" # for each point and store in a matrix\n",
" for n in range(0, N):\n",
" for m in range(0, M):\n",
"\n",
" H0 = - delta/2.0 * sx - eps0_vec[m]/2.0 * sz\n",
" H1 = - A_vec[n] * sx\n",
" \n",
" H = [H0, [H1, 'sin(w * t)']]\n",
" \n",
" H_args = {'w': omega}\n",
" \n",
" # find the propagator for one period of the time-dependent\n",
" # hamiltonian\n",
" U = propagator(H, T, c_op_list, H_args)\n",
"\n",
" # find the steady state of the driven system \n",
" rho_ss = propagator_steadystate(U)\n",
" \n",
" p_ex[n, m] = np.real(expect(sn, rho_ss))\n",
"\n",
" return p_ex"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#\n",
"# set up the parameters\n",
"#\n",
"delta = 0.2 * 2 * np.pi # qubit sigma_x coefficient\n",
"w = 1.0 * 2 * np.pi # qubit sigma_z coefficient\n",
"\n",
"A_vec = np.linspace(0.0, 4.0, 100) * 2 * np.pi # driving amplitude\n",
"eps0_vec = np.linspace(0.0, 4.0, 100) * 2 * np.pi # qubit sigma-z bias point\n",
"\n",
"gamma1 = 0.05 # relaxation rate\n",
"gamma2 = 0.0 # dephasing rate"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"start_time = time.time()\n",
"p_ex = sd_qubit_integrate(delta, eps0_vec, A_vec, w, gamma1, gamma2)\n",
"print('time elapsed = ' + str(time.time() - start_time))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fig, ax = plt.subplots(figsize=(10,10))\n",
"p = ax.pcolor(A_vec, eps0_vec, np.real(p_ex), edgecolors='none')\n",
"p.set_cmap('RdYlBu_r')\n",
"ax.set_ylabel(r'$A/\\omega$', fontsize=20)\n",
"ax.set_xlabel(r'$\\epsilon_0/\\omega$', fontsize=20)\n",
"ax.axis('tight')\n",
"ax.set_title('Excitation probabilty of qubit, in steady state', fontsize=16);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Versions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from qutip.ipynbtools import version_table\n",
"\n",
"version_table()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| lgpl-3.0 |
AssembleSoftware/IoTPy | examples/ExamplesOfTwitter.ipynb | 1 | 37824 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pprint\n",
"import json\n",
"import threading\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Download Tweepy, an open source, free Python library for Twitter.\n",
"# http://www.tweepy.org/\n",
"import tweepy\n",
"# nltk: Natural Language Toolkit. open source, free\n",
"# https://www.nltk.org/\n",
"import nltk"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#nltk.download('punkt')\n",
"from nltk.tokenize import word_tokenize\n",
"from nltk.corpus import stopwords"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append(\"../\")\n",
"from IoTPy.core.stream import Stream, run\n",
"from IoTPy.agent_types.sink import sink_element\n",
"from IoTPy.agent_types.op import map_window\n",
"from IoTPy.helper_functions.recent_values import recent_values\n",
"from IoTPy.concurrency.multicore import get_processes"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import threading\n",
"from IoTPy.concurrency.multicore import get_processes_and_procs\n",
"from IoTPy.concurrency.multicore import extend_stream, terminate_stream\n",
"import ctypes"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"access_token = \"Enter your access_token here\"\n",
"access_token_secret = \"Enter your access_token_secret here\"\n",
"consumer_key = \"Enter your consumer_key here\"\n",
"consumer_secret = \"Enter your consumer_secret here\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Authentication OK\n",
"58075024\n",
"Blackbirds\n"
]
}
],
"source": [
"auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
"auth.set_access_token(access_token, access_token_secret)\n",
"\n",
"api = tweepy.API(auth)\n",
"\n",
"try:\n",
" api.verify_credentials()\n",
" print(\"Authentication OK\")\n",
"except:\n",
" print(\"Error during authentication\")\n",
"\n",
"# Get the User object for twitter...\n",
"user = api.get_user('twitter')\n",
"print (user.followers_count)\n",
"for friend in user.friends():\n",
" print (friend.screen_name)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"class TwitterTrackwordsToStream(tweepy.streaming.StreamListener):\n",
" \"\"\"\n",
" Tweets are converted to dictionary objects and placed on a stream.\n",
"\n",
" Parameters\n",
" ----------\n",
" out_stream: Stream\n",
" The stream on which Tweet dicts are placed.\n",
" trackwords: list of Str\n",
" The list of words in Twitter that are tracked to create this\n",
" stream.\n",
" num_steps: int, optional\n",
" If num_steps is non-zero, then num_steps is the number of\n",
" Tweet dicts placed on out_stream, after which the function\n",
" closes. If num_steps is zero, the class is persistent until\n",
" an error occurs.\n",
" proc: \n",
"\n",
" Attributes\n",
" ----------\n",
" ready: threading.Event\n",
" Signals that the setup for Twitter is complete. This is\n",
" helpful to ensure that all source streams start getting\n",
" values at about the same time.\n",
" n: int\n",
" The number of Tweets placed on out_stream so far.\n",
"\n",
" \"\"\"\n",
"\n",
" def __init__(\n",
" self, consumer_key, consumer_secret,\n",
" access_token, access_token_secret,\n",
" trackwords, stream_name, procs, num_steps=0):\n",
" self.consumer_key = consumer_key\n",
" self.consumer_secret = consumer_secret\n",
" self.access_token = access_token\n",
" self.access_token_secret = access_token_secret\n",
" self.trackwords = trackwords\n",
" self.stream_name = stream_name\n",
" self.num_steps = num_steps\n",
" self.procs = procs\n",
" self.ready = threading.Event()\n",
" self.n = 0\n",
"\n",
" def on_error(self, status):\n",
" \"\"\"\n",
" Call back by Twitter\n",
"\n",
" \"\"\"\n",
" print(status)\n",
"\n",
" def on_data(self, data):\n",
" \"\"\"\n",
" Call back by Twitter.\n",
" Appends a dict object containing the Tweet to\n",
" the out_stream. Runs forever if num_steps is\n",
" 0. Halts after num_steps if it is non-zero.\n",
"\n",
" \"\"\"\n",
" try:\n",
" if data is None: return True\n",
" #Stream.scheduler.input_queue.put((self.stream_name, json.loads(data)))\n",
" print ('data is:')\n",
" print (data)\n",
" #data = bytes(data, 'utf-8')\n",
" #extend_stream(self.procs, [data], self.stream_name)\n",
" extend_stream(self.procs, data, self.stream_name)\n",
" # Increment the number of times data is copied into the stream\n",
" self.n += 1\n",
" # Exit if enough steps have completed. \n",
" # Don't stop if self.num_steps is None.\n",
" if self.num_steps and (self.n >= self.num_steps):\n",
" print ('FINISHED')\n",
" print ('----------------------------')\n",
" terminate_stream(self.procs, self.stream_name)\n",
" sys.exit()\n",
" # Yield the thread\n",
" time.sleep(0.0001)\n",
" return True\n",
" except BaseException as e:\n",
" print (' ')\n",
" if not e or str(e) == '':\n",
" print ('No data from Twitter')\n",
" else:\n",
" print(\"Error on_data from Twitter: %s\" % str(e))\n",
" print (\"See TwitterTrackwordsToStream.on_data()\")\n",
" print (' ')\n",
" sys.exit()\n",
"\n",
" def setup(self):\n",
" \"\"\"\n",
" Sets up the connection to Twitter. You must get\n",
" consumer_key, consumer_secret, access_token, and\n",
" access_token_secret from Twitter. See tweepy.\n",
"\n",
" \"\"\"\n",
" self.auth = tweepy.OAuthHandler(self.consumer_key, self.consumer_secret)\n",
" self.auth.set_access_token(self.access_token, self.access_token_secret)\n",
" self.twitter_stream = tweepy.Stream(self.auth, self)\n",
" self.ready.set()\n",
"\n",
" def start(self):\n",
" \"\"\"\n",
" This is the thread target. This thread will put Tweets\n",
" on out_stream.\n",
"\n",
" \"\"\"\n",
" print ('in class. in start()')\n",
" self.twitter_stream.filter(track=self.trackwords)\n",
" \n",
"\n",
" def get_thread_object(self):\n",
" self.setup()\n",
" return threading.Thread(target=self.start)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def twitter_to_stream(\n",
" consumer_key, consumer_secret, access_token, access_token_secret,\n",
" trackwords, stream_name, procs, num_steps):\n",
" \"\"\"\n",
" \n",
" Get Tweets from Twitter and put them on out_stream.\n",
"\n",
" Parameters\n",
" ----------\n",
" consumer_key, consumer_secret: str\n",
" Credentials that you must establish on Twitter\n",
" access_token, access_token_secret: str\n",
" Credentials that you must establish on Twitter\n",
" trackwords: list of str\n",
" The list of words that you want to track on Twitter.\n",
" stream_name: str\n",
" Tweets are placed as dicts on this output stream.\n",
" procs: dict\n",
" Generated by get_procs\n",
" num_steps: int, optional\n",
" The number of Tweets that are obtained.\n",
" If left unspecified then the agent does not stop\n",
" execution.\n",
"\n",
" \"\"\"\n",
" print ('twitter to stream')\n",
" obj = TwitterTrackwordsToStream(\n",
" consumer_key, consumer_secret, access_token, access_token_secret,\n",
" trackwords, stream_name, procs, num_steps)\n",
" return obj.get_thread_object()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def print_tweets(tweet):\n",
" print ('in print_tweets 1')\n",
" print ('tweet is ')\n",
" print (tweet)\n",
" tweet = json.loads(tweet)\n",
" print ('in print_tweets 2')\n",
" print ('tweet is ')\n",
" print (tweet)\n",
" if 'extended_tweet' in tweet:\n",
" text = tweet['extended_tweet']['full_text']\n",
" elif 'text' in tweet:\n",
" text = tweet['text']\n",
" else:\n",
" text = str()\n",
" followers_count = 0\n",
" retweet_count = 0\n",
" if 'user' in tweet:\n",
" tweet_user = tweet['user']\n",
" if 'followers_count' in tweet_user:\n",
" followers_count = tweet_user['followers_count']\n",
" if 'friends_count' in tweet_user:\n",
" friends_count = tweet_user['friends_count']\n",
" if 'retweet_count' in tweet:\n",
" retweet_count = tweet['retweet_count']\n",
"\n",
" # print output\n",
" print ('Text is: ', text)\n",
" print (' ')\n",
" print ('followers_count is: ', followers_count)\n",
" print ('retweet_count is: ', retweet_count)\n",
" print ('friends_count is: ', friends_count)\n",
" print ('--------------------------------------')\n",
" print (' ')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"twitter_analysis\n",
"source_thread\n",
"twitter to stream\n",
"[]\n",
"in class. in start()\n",
"data is:\n",
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"return_value is .com\\\" rel=\\\"nofollow\\\"\\u003eTwitter Web App\\u003c\\/a\\u003e\",\"truncated\":true,\"in_reply_to_status_id\":null,\"in_reply_to_status_id_str\":null,\"in_reply_to_user_id\":null,\"in_reply_to_user_id_str\":null,\"in_reply_to_screen_name\":null,\"user\":{\"id\":16172747,\"id_str\":\"16172747\",\"name\":\"Charlie Savage\",\"screen_name\":\"charlie_savage\",\"location\":\"Washington, DC\",\"url\":\"http:\\/\\/www.charliesavage.com\",\"description\":\"New York Times national security and legal reporter; MSNBC contributor; \\nauthor of the books \\\"Power Wars\\\" and \\\"Takeover\\\"\",\"translator_type\":\"none\",\"protected\":false,\"verified\":true,\"followers_count\":75515,\"friends_count\":2909,\"listed_count\":1944,\"favourites_count\":485,\"statuses_count\":12992,\"created_at\":\"Sun Sep 07 19:34:16 +0000 2008\",\"utc_offset\":null,\"time_zone\":null,\"geo_enabled\":true,\"lang\":null,\"contributors_enabled\":false,\"is_translator\":false,\"profile_background_color\":\"1A1B1F\",\"profile_background_image_url\":\"http:\\/\\/abs.twimg.com\\/images\\/themes\\/theme9\\/bg.gif\",\"profile_background_image_url_https\":\"https:\\/\\/abs.twimg.com\\/images\\/themes\\/theme9\\/bg.gif\",\"profile_background_tile\":false,\"profile_link_color\":\"2FC2EF\",\"profile_sidebar_border_color\":\"000000\",\"profile_sidebar_fill_color\":\"252429\",\"profile_text_color\":\"666666\",\"profile_use_background_image\":true,\"profile_image_url\":\"http:\\/\\/pbs.twimg.com\\/profile_images\\/1270379645314183169\\/W1gmeEdG_normal.jpg\",\"profile_image_url_https\":\"https:\\/\\/pbs.twimg.com\\/profile_images\\/1270379645314183169\\/W1gmeEdG_normal.jpg\",\"profile_banner_url\":\"https:\\/\\/pbs.twimg.com\\/profile_banners\\/16172747\\/1498492259\",\"default_profile\":false,\"default_profile_image\":false,\"following\":null,\"follow_request_sent\":null,\"notifications\":null},\"geo\":null,\"coordinates\":null,\"place\":null,\"contributors\":null,\"is_quote_status\":false,\"extended_tweet\":{\"full_text\":\"EXCLUSIVE: Data intercepts showed GRU-to-Taliban $ transfers & a key intermediary fled to Russia, bolstering earlier detainee accounts about a Russian bounty op. Tru\n",
"No data from Twitter\n",
" \n"
]
}
],
"source": [
"import threading\n",
"from IoTPy.concurrency.multicore import get_processes_and_procs\n",
"from IoTPy.concurrency.multicore import extend_stream, terminate_stream\n",
"import ctypes\n",
"\n",
"def twitter_analysis(\n",
" consumer_key, consumer_secret, access_token, access_token_secret,\n",
" trackwords, tweet_analyzer, stream_name, num_tweets):\n",
" print ('twitter_analysis')\n",
" # Agent function for process named 'p0'\n",
" def f(in_streams, out_streams):\n",
" #sink_element(func=tweet_analyzer, in_stream=in_streams[0])\n",
" s = Stream('s')\n",
" def convert_bytes_to_string(window):\n",
" return_value = ''.join(window)\n",
" print ('return_value is', return_value)\n",
" return str(return_value)\n",
" map_window(\n",
" func=convert_bytes_to_string, in_stream=in_streams[0], out_stream=s,\n",
" window_size=2000, step_size=2000)\n",
" print(recent_values(s))\n",
"\n",
" multicore_specification = [\n",
" # Streams\n",
" [('x', ctypes.c_wchar)],\n",
" # Processes\n",
" [{'name': 'p0', 'agent': f, 'inputs':['x'], 'sources':['x']}]]\n",
"\n",
" # PROCESSES\n",
" processes, procs = get_processes_and_procs(multicore_specification)\n",
" print ('source_thread')\n",
" source_thread = twitter_to_stream(\n",
" consumer_key, consumer_secret, access_token, access_token_secret,\n",
" trackwords, stream_name, procs, num_tweets)\n",
"\n",
" procs['p0'].threads = [source_thread]\n",
"\n",
" for process in processes: process.start()\n",
" for process in processes: process.join()\n",
" for process in processes: process.terminate()\n",
"\n",
"\n",
"twitter_analysis(\n",
" consumer_key, consumer_secret, access_token, access_token_secret,\n",
" trackwords=['Trump'], tweet_analyzer=print_tweets, stream_name='x', num_tweets=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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{
"cell_type": "code",
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"file_extension": ".py",
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| bsd-3-clause |
jasontlam/snorkel | tutorials/workshop/Workshop_2_Writing_Labeling_Functions.ipynb | 1 | 117968 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"<img align=\"left\" src=\"imgs/logo.jpg\" width=\"50px\" style=\"margin-right:10px\">\n",
"# Snorkel Workshop: Extracting Spouse Relations <br> from the News\n",
"## Part 2: Writing Labeling Functions\n",
"\n",
"In Snorkel, our primary interface through which we provide training signal to the end extraction model we are training is by writing **labeling functions (LFs)** (as opposed to hand-labeling massive training sets). We'll go through some examples for our spouse extraction task below.\n",
"\n",
"A labeling function isn't anything special. It's just a Python function that accepts a `Candidate` as the input argument and returns `1` if it says the `Candidate` should be marked as true, `-1` if it says the `Candidate` should be marked as false, and `0` if it doesn't know how to vote and abstains. In practice, many labeling functions are unipolar: it labels only `1`s and `0`s, or it labels only `-1`s and `0`s.\n",
"\n",
"Recall that our goal is to ultimately train a high-performance classification model that predicts which of our `Candidate`s are true mentions of spouse relations. It turns out that we can do this by writing potentially low-quality labeling functions!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline\n",
"\n",
"import re\n",
"import sys\n",
"import numpy as np\n",
"\n",
"# Connect to the database backend and initalize a Snorkel session\n",
"from lib.init import *\n",
"from lib.scoring import *\n",
"from lib.lf_factories import *\n",
"\n",
"from snorkel.lf_helpers import test_LF\n",
"from snorkel.annotations import load_gold_labels\n",
"from snorkel.lf_helpers import (\n",
" get_left_tokens, get_right_tokens, get_between_tokens,\n",
" get_text_between, get_tagged_text,\n",
")\n",
"\n",
"# initialize our candidate type definition\n",
"Spouse = candidate_subclass('Spouse', ['person1', 'person2'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# I. Background\n",
"\n",
"## A. Preprocessing the Database\n",
"\n",
"In a real application, there is a lot of data preparation, parsing, and database loading that needs to be completed before we dive into writing labeling functions. Here we've pre-generated a database instance for you. All _candidates_ and _gold labels_ (i.e., human-generated labels) are queried from this database for use in the the tutorial. \n",
"\n",
"See our preprocessing tutorial <a href=\"Workshop_5_Advanced_Preprocessing.ipynb\">Workshop 5 Advanced Preprocessing</a> for more details on how this database is built.\n",
"\n",
"## B. Using a _Development Set_ of Human-labeled Data\n",
"\n",
"In our setting, we will use the phrase _development set_ to refer to a set of examples (here, a subset of our training set) which we label by hand and use to help us develop and refine labeling functions. Unlike the _test set_, which we do not look at and use for final evaluation, we can inspect the development set while writing labeling functions. This is a list of `{-1,1}` labels."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"L_gold_dev = load_gold_labels(session, annotator_name='gold', split=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## C. Data Exploration\n",
"\n",
"How do we come up with good keywords and patterns to encode as labeling functions? One way is to manually explore our training data. Here we load a subset of our training candidates into a `SentenceNgramViewer` object to examine candidates in their parent context. Our goal is to build an intuition for patterns and keywords that are predictive of a candidate's true label. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"application/javascript": [
"require.undef('viewer');\n",
"\n",
"// NOTE: all elements should be selected using this.$el.find to avoid collisions with other Viewers\n",
"\n",
"define('viewer', [\"jupyter-js-widgets\"], function(widgets) {\n",
" var ViewerView = widgets.DOMWidgetView.extend({\n",
" render: function() {\n",
" this.cids = this.model.get('cids');\n",
" this.nPages = this.cids.length;\n",
" this.pid = 0;\n",
" this.cxid = 0;\n",
" this.cid = 0;\n",
"\n",
" // Insert the html payload\n",
" this.$el.append(this.model.get('html'));\n",
"\n",
" // Initialize all labels from previous sessions\n",
" this.labels = this.deserializeDict(this.model.get('_labels_serialized'));\n",
" for (var i=0; i < this.nPages; i++) {\n",
" this.pid = i;\n",
" for (var j=0; j < this.cids[i].length; j++) {\n",
" this.cxid = j;\n",
" for (var k=0; k < this.cids[i][j].length; k++) {\n",
" this.cid = k;\n",
" if (this.cids[i][j][k] in this.labels) {\n",
" this.markCurrentCandidate(false);\n",
" }\n",
" }\n",
" }\n",
" }\n",
" this.pid = 0;\n",
" this.cxid = 0;\n",
" this.cid = 0;\n",
"\n",
" // Enable button functionality for navigation\n",
" var that = this;\n",
" this.$el.find(\"#next-cand\").click(function() {\n",
" that.switchCandidate(1);\n",
" });\n",
" this.$el.find(\"#prev-cand\").click(function() {\n",
" that.switchCandidate(-1);\n",
" });\n",
" this.$el.find(\"#next-context\").click(function() {\n",
" that.switchContext(1);\n",
" });\n",
" this.$el.find(\"#prev-context\").click(function() {\n",
" that.switchContext(-1);\n",
" });\n",
" this.$el.find(\"#next-page\").click(function() {\n",
" that.switchPage(1);\n",
" });\n",
" this.$el.find(\"#prev-page\").click(function() {\n",
" that.switchPage(-1);\n",
" });\n",
" this.$el.find(\"#label-true\").click(function() {\n",
" that.labelCandidate(true, true);\n",
" });\n",
" this.$el.find(\"#label-false\").click(function() {\n",
" that.labelCandidate(false, true);\n",
" });\n",
"\n",
" // Arrow key functionality\n",
" this.$el.keydown(function(e) {\n",
" switch(e.which) {\n",
" case 74: // j\n",
" that.switchCandidate(-1);\n",
" break;\n",
"\n",
" case 73: // i\n",
" that.switchPage(-1);\n",
" break;\n",
"\n",
" case 76: // l\n",
" that.switchCandidate(1);\n",
" break;\n",
"\n",
" case 75: // k\n",
" that.switchPage(1);\n",
" break;\n",
"\n",
" case 84: // t\n",
" that.labelCandidate(true, true);\n",
" break;\n",
"\n",
" case 70: // f\n",
" that.labelCandidate(false, true);\n",
" break;\n",
" }\n",
" });\n",
"\n",
" // Show the first page and highlight the first candidate\n",
" this.$el.find(\"#viewer-page-0\").show();\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Get candidate selector for currently selected candidate, escaping id properly\n",
" getCandidate: function() {\n",
" return this.$el.find(\".\"+this.cids[this.pid][this.cxid][this.cid]);\n",
" }, \n",
"\n",
" // Color the candidate correctly according to registered label, as well as set highlighting\n",
" markCurrentCandidate: function(highlight) {\n",
" var cid = this.cids[this.pid][this.cxid][this.cid];\n",
" var tags = this.$el.find(\".\"+cid);\n",
"\n",
" // Clear color classes\n",
" tags.removeClass(\"candidate-h\");\n",
" tags.removeClass(\"true-candidate\");\n",
" tags.removeClass(\"true-candidate-h\");\n",
" tags.removeClass(\"false-candidate\");\n",
" tags.removeClass(\"false-candidate-h\");\n",
" tags.removeClass(\"highlighted\");\n",
"\n",
" if (highlight) {\n",
" if (cid in this.labels) {\n",
" tags.addClass(String(this.labels[cid]) + \"-candidate-h\");\n",
" } else {\n",
" tags.addClass(\"candidate-h\");\n",
" }\n",
" \n",
" // If un-highlighting, leave with first non-null coloring\n",
" } else {\n",
" var that = this;\n",
" tags.each(function() {\n",
" var cids = $(this).attr('class').split(/\\s+/).map(function(item) {\n",
" return parseInt(item);\n",
" });\n",
" cids.sort();\n",
" for (var i in cids) {\n",
" if (cids[i] in that.labels) {\n",
" var label = that.labels[cids[i]];\n",
" $(this).addClass(String(label) + \"-candidate\");\n",
" $(this).removeClass(String(!label) + \"-candidate\");\n",
" break;\n",
" }\n",
" }\n",
" });\n",
" }\n",
"\n",
" // Extra highlighting css\n",
" if (highlight) {\n",
" tags.addClass(\"highlighted\");\n",
" }\n",
"\n",
" // Classes for showing direction of relation\n",
" if (highlight) {\n",
" this.$el.find(\".\"+cid+\"-0\").addClass(\"left-candidate\");\n",
" this.$el.find(\".\"+cid+\"-1\").addClass(\"right-candidate\");\n",
" } else {\n",
" this.$el.find(\".\"+cid+\"-0\").removeClass(\"left-candidate\");\n",
" this.$el.find(\".\"+cid+\"-1\").removeClass(\"right-candidate\");\n",
" }\n",
" },\n",
"\n",
" // Cycle through candidates and highlight, by increment inc\n",
" switchCandidate: function(inc) {\n",
" var N = this.cids[this.pid].length\n",
" var M = this.cids[this.pid][this.cxid].length;\n",
" if (N == 0 || M == 0) { return false; }\n",
"\n",
" // Clear highlighting from previous candidate\n",
" if (inc != 0) {\n",
" this.markCurrentCandidate(false);\n",
"\n",
" // Increment the cid counter\n",
"\n",
" // Move to next context\n",
" if (this.cid + inc >= M) {\n",
" while (this.cid + inc >= M) {\n",
" \n",
" // At last context on page, halt\n",
" if (this.cxid == N - 1) {\n",
" this.cid = M - 1;\n",
" inc = 0;\n",
" break;\n",
" \n",
" // Increment to next context\n",
" } else {\n",
" inc -= M - this.cid;\n",
" this.cxid += 1;\n",
" M = this.cids[this.pid][this.cxid].length;\n",
" this.cid = 0;\n",
" }\n",
" }\n",
"\n",
" // Move to previous context\n",
" } else if (this.cid + inc < 0) {\n",
" while (this.cid + inc < 0) {\n",
" \n",
" // At first context on page, halt\n",
" if (this.cxid == 0) {\n",
" this.cid = 0;\n",
" inc = 0;\n",
" break;\n",
" \n",
" // Increment to previous context\n",
" } else {\n",
" inc += this.cid + 1;\n",
" this.cxid -= 1;\n",
" M = this.cids[this.pid][this.cxid].length;\n",
" this.cid = M - 1;\n",
" }\n",
" }\n",
" }\n",
"\n",
" // Move within current context\n",
" this.cid += inc;\n",
" }\n",
" this.markCurrentCandidate(true);\n",
"\n",
" // Push this new cid to the model\n",
" this.model.set('_selected_cid', this.cids[this.pid][this.cxid][this.cid]);\n",
" this.touch();\n",
" },\n",
"\n",
" // Switch through contexts\n",
" switchContext: function(inc) {\n",
" this.markCurrentCandidate(false);\n",
"\n",
" // Iterate context on this page\n",
" var M = this.cids[this.pid].length;\n",
" if (this.cxid + inc < 0) {\n",
" this.cxid = 0;\n",
" } else if (this.cxid + inc >= M) {\n",
" this.cxid = M - 1;\n",
" } else {\n",
" this.cxid += inc;\n",
" }\n",
"\n",
" // Reset cid and set to first candidate\n",
" this.cid = 0;\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Switch through pages\n",
" switchPage: function(inc) {\n",
" this.markCurrentCandidate(false);\n",
" this.$el.find(\".viewer-page\").hide();\n",
" if (this.pid + inc < 0) {\n",
" this.pid = 0;\n",
" } else if (this.pid + inc > this.nPages - 1) {\n",
" this.pid = this.nPages - 1;\n",
" } else {\n",
" this.pid += inc;\n",
" }\n",
" this.$el.find(\"#viewer-page-\"+this.pid).show();\n",
"\n",
" // Show pagination\n",
" this.$el.find(\"#page\").html(this.pid);\n",
"\n",
" // Reset cid and set to first candidate\n",
" this.cid = 0;\n",
" this.cxid = 0;\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Label currently-selected candidate\n",
" labelCandidate: function(label, highlighted) {\n",
" var c = this.getCandidate();\n",
" var cid = this.cids[this.pid][this.cxid][this.cid];\n",
" var cl = String(label) + \"-candidate\";\n",
" var clh = String(label) + \"-candidate-h\";\n",
" var cln = String(!label) + \"-candidate\";\n",
" var clnh = String(!label) + \"-candidate-h\";\n",
"\n",
" // Toggle label highlighting\n",
" if (c.hasClass(cl) || c.hasClass(clh)) {\n",
" c.removeClass(cl);\n",
" c.removeClass(clh);\n",
" if (highlighted) {\n",
" c.addClass(\"candidate-h\");\n",
" }\n",
" this.labels[cid] = null;\n",
" this.send({event: 'delete_label', cid: cid});\n",
" } else {\n",
" c.removeClass(cln);\n",
" c.removeClass(clnh);\n",
" if (highlighted) {\n",
" c.addClass(clh);\n",
" } else {\n",
" c.addClass(cl);\n",
" }\n",
" this.labels[cid] = label;\n",
" this.send({event: 'set_label', cid: cid, value: label});\n",
" }\n",
"\n",
" // Set the label and pass back to the model\n",
" this.model.set('_labels_serialized', this.serializeDict(this.labels));\n",
" this.touch();\n",
" },\n",
"\n",
" // Serialization of hash maps, because traitlets Dict doesn't seem to work...\n",
" serializeDict: function(d) {\n",
" var s = [];\n",
" for (var key in d) {\n",
" s.push(key+\"~~\"+d[key]);\n",
" }\n",
" return s.join();\n",
" },\n",
"\n",
" // Deserialization of hash maps\n",
" deserializeDict: function(s) {\n",
" var d = {};\n",
" var entries = s.split(/,/);\n",
" var kv;\n",
" for (var i in entries) {\n",
" kv = entries[i].split(/~~/);\n",
" if (kv[1] == \"true\") {\n",
" d[kv[0]] = true;\n",
" } else if (kv[1] == \"false\") {\n",
" d[kv[0]] = false;\n",
" }\n",
" }\n",
" return d;\n",
" },\n",
" });\n",
"\n",
" return {\n",
" ViewerView: ViewerView\n",
" };\n",
"});\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ee73069b2dc94a2d930eb74986f8aacd"
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from snorkel.viewer import SentenceNgramViewer\n",
"\n",
"# load our list of training & development candidates\n",
"train_cands = session.query(Candidate).filter(Candidate.split == 0).all()\n",
"dev_cands = session.query(Candidate).filter(Candidate.split == 1).all()\n",
"\n",
"SentenceNgramViewer(train_cands[0:500], session, n_per_page=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## D. Labeling Function Metrics\n",
"\n",
"### 1. Coverage\n",
"One simple metric we can compute quickly is our _coverage_, the number of candidates labeled by our LF, on our training set (or any other set).\n",
"\n",
"### 2. Precision / Recall / F1\n",
"If we have gold labeled data, we can also compute standard precision, recall, and F1 metrics for the output of a single labeling function. These metrics are computed over 4 _error buckets_: _True Positives_ (tp), _False Positives_ (fp), _True Negatives_ (tn), and _False Negatives_ (fn).\n",
"\n",
"\\begin{equation*}\n",
"precision = \\frac{tp}{(tp + fp)}\n",
"\\end{equation*}\n",
"\n",
"\\begin{equation*}\n",
"recall = \\frac{tp}{(tp + fn)}\n",
"\\end{equation*}\n",
"\n",
"\\begin{equation*}\n",
"F1 = 2 \\cdot \\frac{ (precision \\cdot recall)}{(precision + recall)}\n",
"\\end{equation*}"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# II. Labeling Functions\n",
"\n",
"## A. Pattern Matching Labeling Functions\n",
"\n",
"One powerful form of labeling function design is defining sets of keywords or regular expressions that, as a human labeler, you know are correlated with the true label. In the terminology of [Bayesian inference](https://en.wikipedia.org/wiki/Statistical_inference#Bayesian_inference), this can be thought of as defining a [_prior_](https://en.wikipedia.org/wiki/Prior_probability) over your word features. \n",
"\n",
"For example, we could define a dictionary of terms that occur between person names in a candidate. One simple dictionary of terms indicating a true relation could be:\n",
" \n",
" marriage = {'husband', 'wife'}\n",
" \n",
"We can then write a labeling function that checks for a match with these terms in the text that occurs between person names.\n",
"\n",
" def LF_marriage_terms_between(c):\n",
" return 1 if len(marriage.intersection(get_between_tokens(c))) > 0 else 0\n",
" \n",
"The idea is that we can easily create dictionaries that encode themes or categories descibing all kinds of relationships between 2 people and then use these objects to _weakly supervise_ our classification task.\n",
"\n",
" other_relationship = {'boyfriend', 'girlfriend'}\n",
" \n",
"**IMPORTANT** Good labeling functions manage a trade-off between high coverage and high precision. When constructing your dictionaries, think about building larger, noiser sets of terms instead of relying on 1 or 2 keywords. Sometimes a single word can be very predictive (e.g., `ex-wife`) but it's almost always better to define something more general, such as a regular expression pattern capturing _any_ string with the `ex-` prefix. \n",
" \n",
"\n",
"### 1. Labeling Function Factories\n",
"The above is a reasonable way to write labeling functions. However, this type of design pattern is so common that we rely on another abstraction to help us build LFs more quickly: _labeling function factories_. Factories accept simple inputs, like dictionaries or a set of regular expressions, and automatically builds labeling functions for you.\n",
"\n",
"The `MatchTerms` and `MatchRegex` factories require a few parameter definitions to setup:\n",
" \n",
" name: a string that describes the category of terms/regular expressons\n",
" label: patterns correlate with a True or False label (1 or -1) \n",
" search: search a specific part of the sentence ('left'|'right'|'between'|'sentence')\n",
" window: the length of tokens to match against for ('left'|'right') search spaces \n",
"\n",
"### 2. Term Matching Factory\n",
"We illustrate below how you can use the `MatchTerms` factory to create and test an LF on training candidates. When examining candidates in the `SentenceNgramViewer`, notice that husband or wife always occurs between person names. That is the supervision signal encoded by this LF!"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coverage: 7.67% (1703/22195)\n"
]
},
{
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"require.undef('viewer');\n",
"\n",
"// NOTE: all elements should be selected using this.$el.find to avoid collisions with other Viewers\n",
"\n",
"define('viewer', [\"jupyter-js-widgets\"], function(widgets) {\n",
" var ViewerView = widgets.DOMWidgetView.extend({\n",
" render: function() {\n",
" this.cids = this.model.get('cids');\n",
" this.nPages = this.cids.length;\n",
" this.pid = 0;\n",
" this.cxid = 0;\n",
" this.cid = 0;\n",
"\n",
" // Insert the html payload\n",
" this.$el.append(this.model.get('html'));\n",
"\n",
" // Initialize all labels from previous sessions\n",
" this.labels = this.deserializeDict(this.model.get('_labels_serialized'));\n",
" for (var i=0; i < this.nPages; i++) {\n",
" this.pid = i;\n",
" for (var j=0; j < this.cids[i].length; j++) {\n",
" this.cxid = j;\n",
" for (var k=0; k < this.cids[i][j].length; k++) {\n",
" this.cid = k;\n",
" if (this.cids[i][j][k] in this.labels) {\n",
" this.markCurrentCandidate(false);\n",
" }\n",
" }\n",
" }\n",
" }\n",
" this.pid = 0;\n",
" this.cxid = 0;\n",
" this.cid = 0;\n",
"\n",
" // Enable button functionality for navigation\n",
" var that = this;\n",
" this.$el.find(\"#next-cand\").click(function() {\n",
" that.switchCandidate(1);\n",
" });\n",
" this.$el.find(\"#prev-cand\").click(function() {\n",
" that.switchCandidate(-1);\n",
" });\n",
" this.$el.find(\"#next-context\").click(function() {\n",
" that.switchContext(1);\n",
" });\n",
" this.$el.find(\"#prev-context\").click(function() {\n",
" that.switchContext(-1);\n",
" });\n",
" this.$el.find(\"#next-page\").click(function() {\n",
" that.switchPage(1);\n",
" });\n",
" this.$el.find(\"#prev-page\").click(function() {\n",
" that.switchPage(-1);\n",
" });\n",
" this.$el.find(\"#label-true\").click(function() {\n",
" that.labelCandidate(true, true);\n",
" });\n",
" this.$el.find(\"#label-false\").click(function() {\n",
" that.labelCandidate(false, true);\n",
" });\n",
"\n",
" // Arrow key functionality\n",
" this.$el.keydown(function(e) {\n",
" switch(e.which) {\n",
" case 74: // j\n",
" that.switchCandidate(-1);\n",
" break;\n",
"\n",
" case 73: // i\n",
" that.switchPage(-1);\n",
" break;\n",
"\n",
" case 76: // l\n",
" that.switchCandidate(1);\n",
" break;\n",
"\n",
" case 75: // k\n",
" that.switchPage(1);\n",
" break;\n",
"\n",
" case 84: // t\n",
" that.labelCandidate(true, true);\n",
" break;\n",
"\n",
" case 70: // f\n",
" that.labelCandidate(false, true);\n",
" break;\n",
" }\n",
" });\n",
"\n",
" // Show the first page and highlight the first candidate\n",
" this.$el.find(\"#viewer-page-0\").show();\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Get candidate selector for currently selected candidate, escaping id properly\n",
" getCandidate: function() {\n",
" return this.$el.find(\".\"+this.cids[this.pid][this.cxid][this.cid]);\n",
" }, \n",
"\n",
" // Color the candidate correctly according to registered label, as well as set highlighting\n",
" markCurrentCandidate: function(highlight) {\n",
" var cid = this.cids[this.pid][this.cxid][this.cid];\n",
" var tags = this.$el.find(\".\"+cid);\n",
"\n",
" // Clear color classes\n",
" tags.removeClass(\"candidate-h\");\n",
" tags.removeClass(\"true-candidate\");\n",
" tags.removeClass(\"true-candidate-h\");\n",
" tags.removeClass(\"false-candidate\");\n",
" tags.removeClass(\"false-candidate-h\");\n",
" tags.removeClass(\"highlighted\");\n",
"\n",
" if (highlight) {\n",
" if (cid in this.labels) {\n",
" tags.addClass(String(this.labels[cid]) + \"-candidate-h\");\n",
" } else {\n",
" tags.addClass(\"candidate-h\");\n",
" }\n",
" \n",
" // If un-highlighting, leave with first non-null coloring\n",
" } else {\n",
" var that = this;\n",
" tags.each(function() {\n",
" var cids = $(this).attr('class').split(/\\s+/).map(function(item) {\n",
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" });\n",
" cids.sort();\n",
" for (var i in cids) {\n",
" if (cids[i] in that.labels) {\n",
" var label = that.labels[cids[i]];\n",
" $(this).addClass(String(label) + \"-candidate\");\n",
" $(this).removeClass(String(!label) + \"-candidate\");\n",
" break;\n",
" }\n",
" }\n",
" });\n",
" }\n",
"\n",
" // Extra highlighting css\n",
" if (highlight) {\n",
" tags.addClass(\"highlighted\");\n",
" }\n",
"\n",
" // Classes for showing direction of relation\n",
" if (highlight) {\n",
" this.$el.find(\".\"+cid+\"-0\").addClass(\"left-candidate\");\n",
" this.$el.find(\".\"+cid+\"-1\").addClass(\"right-candidate\");\n",
" } else {\n",
" this.$el.find(\".\"+cid+\"-0\").removeClass(\"left-candidate\");\n",
" this.$el.find(\".\"+cid+\"-1\").removeClass(\"right-candidate\");\n",
" }\n",
" },\n",
"\n",
" // Cycle through candidates and highlight, by increment inc\n",
" switchCandidate: function(inc) {\n",
" var N = this.cids[this.pid].length\n",
" var M = this.cids[this.pid][this.cxid].length;\n",
" if (N == 0 || M == 0) { return false; }\n",
"\n",
" // Clear highlighting from previous candidate\n",
" if (inc != 0) {\n",
" this.markCurrentCandidate(false);\n",
"\n",
" // Increment the cid counter\n",
"\n",
" // Move to next context\n",
" if (this.cid + inc >= M) {\n",
" while (this.cid + inc >= M) {\n",
" \n",
" // At last context on page, halt\n",
" if (this.cxid == N - 1) {\n",
" this.cid = M - 1;\n",
" inc = 0;\n",
" break;\n",
" \n",
" // Increment to next context\n",
" } else {\n",
" inc -= M - this.cid;\n",
" this.cxid += 1;\n",
" M = this.cids[this.pid][this.cxid].length;\n",
" this.cid = 0;\n",
" }\n",
" }\n",
"\n",
" // Move to previous context\n",
" } else if (this.cid + inc < 0) {\n",
" while (this.cid + inc < 0) {\n",
" \n",
" // At first context on page, halt\n",
" if (this.cxid == 0) {\n",
" this.cid = 0;\n",
" inc = 0;\n",
" break;\n",
" \n",
" // Increment to previous context\n",
" } else {\n",
" inc += this.cid + 1;\n",
" this.cxid -= 1;\n",
" M = this.cids[this.pid][this.cxid].length;\n",
" this.cid = M - 1;\n",
" }\n",
" }\n",
" }\n",
"\n",
" // Move within current context\n",
" this.cid += inc;\n",
" }\n",
" this.markCurrentCandidate(true);\n",
"\n",
" // Push this new cid to the model\n",
" this.model.set('_selected_cid', this.cids[this.pid][this.cxid][this.cid]);\n",
" this.touch();\n",
" },\n",
"\n",
" // Switch through contexts\n",
" switchContext: function(inc) {\n",
" this.markCurrentCandidate(false);\n",
"\n",
" // Iterate context on this page\n",
" var M = this.cids[this.pid].length;\n",
" if (this.cxid + inc < 0) {\n",
" this.cxid = 0;\n",
" } else if (this.cxid + inc >= M) {\n",
" this.cxid = M - 1;\n",
" } else {\n",
" this.cxid += inc;\n",
" }\n",
"\n",
" // Reset cid and set to first candidate\n",
" this.cid = 0;\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Switch through pages\n",
" switchPage: function(inc) {\n",
" this.markCurrentCandidate(false);\n",
" this.$el.find(\".viewer-page\").hide();\n",
" if (this.pid + inc < 0) {\n",
" this.pid = 0;\n",
" } else if (this.pid + inc > this.nPages - 1) {\n",
" this.pid = this.nPages - 1;\n",
" } else {\n",
" this.pid += inc;\n",
" }\n",
" this.$el.find(\"#viewer-page-\"+this.pid).show();\n",
"\n",
" // Show pagination\n",
" this.$el.find(\"#page\").html(this.pid);\n",
"\n",
" // Reset cid and set to first candidate\n",
" this.cid = 0;\n",
" this.cxid = 0;\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Label currently-selected candidate\n",
" labelCandidate: function(label, highlighted) {\n",
" var c = this.getCandidate();\n",
" var cid = this.cids[this.pid][this.cxid][this.cid];\n",
" var cl = String(label) + \"-candidate\";\n",
" var clh = String(label) + \"-candidate-h\";\n",
" var cln = String(!label) + \"-candidate\";\n",
" var clnh = String(!label) + \"-candidate-h\";\n",
"\n",
" // Toggle label highlighting\n",
" if (c.hasClass(cl) || c.hasClass(clh)) {\n",
" c.removeClass(cl);\n",
" c.removeClass(clh);\n",
" if (highlighted) {\n",
" c.addClass(\"candidate-h\");\n",
" }\n",
" this.labels[cid] = null;\n",
" this.send({event: 'delete_label', cid: cid});\n",
" } else {\n",
" c.removeClass(cln);\n",
" c.removeClass(clnh);\n",
" if (highlighted) {\n",
" c.addClass(clh);\n",
" } else {\n",
" c.addClass(cl);\n",
" }\n",
" this.labels[cid] = label;\n",
" this.send({event: 'set_label', cid: cid, value: label});\n",
" }\n",
"\n",
" // Set the label and pass back to the model\n",
" this.model.set('_labels_serialized', this.serializeDict(this.labels));\n",
" this.touch();\n",
" },\n",
"\n",
" // Serialization of hash maps, because traitlets Dict doesn't seem to work...\n",
" serializeDict: function(d) {\n",
" var s = [];\n",
" for (var key in d) {\n",
" s.push(key+\"~~\"+d[key]);\n",
" }\n",
" return s.join();\n",
" },\n",
"\n",
" // Deserialization of hash maps\n",
" deserializeDict: function(s) {\n",
" var d = {};\n",
" var entries = s.split(/,/);\n",
" var kv;\n",
" for (var i in entries) {\n",
" kv = entries[i].split(/~~/);\n",
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" d[kv[0]] = true;\n",
" } else if (kv[1] == \"false\") {\n",
" d[kv[0]] = false;\n",
" }\n",
" }\n",
" return d;\n",
" },\n",
" });\n",
"\n",
" return {\n",
" ViewerView: ViewerView\n",
" };\n",
"});\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "087e301bb7da4cb8ba9178463d67f392"
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"marriage = {'husband', 'wife'}\n",
"\n",
"# we'll initialize our LFG and test its coverage on training candidates\n",
"LF_marriage = MatchTerms(name='marriage', terms=marriage, label=1, search='between').lf()\n",
"\n",
"# what candidates are covered by this LF?\n",
"labeled = coverage(session, LF_marriage, split=0)\n",
"\n",
"# now let's view what this LF labeled\n",
"SentenceNgramViewer(labeled, session, n_per_page=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"#### Viewing Error Buckets\n",
"If we have gold labeled data, we can evaluate formal metrics. It's useful to view specific errors for a given LF input in the `SentenceNgramViewer`.\n",
"\n",
"Below, we'll compute our empirical scores using human-labeled development set data and then look at any false positive matches by our `LF_marriage` LF. We can see below from our scores that this LF isn't very accurate -- only 36% precision!"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"========================================\n",
"LF Score\n",
"========================================\n",
"Pos. class accuracy: 1.0\n",
"Neg. class accuracy: 0.0\n",
"Precision 0.356\n",
"Recall 1.0\n",
"F1 0.525\n",
"----------------------------------------\n",
"TP: 63 | FP: 114 | TN: 0 | FN: 0\n",
"========================================\n",
"\n"
]
},
{
"data": {
"application/javascript": [
"require.undef('viewer');\n",
"\n",
"// NOTE: all elements should be selected using this.$el.find to avoid collisions with other Viewers\n",
"\n",
"define('viewer', [\"jupyter-js-widgets\"], function(widgets) {\n",
" var ViewerView = widgets.DOMWidgetView.extend({\n",
" render: function() {\n",
" this.cids = this.model.get('cids');\n",
" this.nPages = this.cids.length;\n",
" this.pid = 0;\n",
" this.cxid = 0;\n",
" this.cid = 0;\n",
"\n",
" // Insert the html payload\n",
" this.$el.append(this.model.get('html'));\n",
"\n",
" // Initialize all labels from previous sessions\n",
" this.labels = this.deserializeDict(this.model.get('_labels_serialized'));\n",
" for (var i=0; i < this.nPages; i++) {\n",
" this.pid = i;\n",
" for (var j=0; j < this.cids[i].length; j++) {\n",
" this.cxid = j;\n",
" for (var k=0; k < this.cids[i][j].length; k++) {\n",
" this.cid = k;\n",
" if (this.cids[i][j][k] in this.labels) {\n",
" this.markCurrentCandidate(false);\n",
" }\n",
" }\n",
" }\n",
" }\n",
" this.pid = 0;\n",
" this.cxid = 0;\n",
" this.cid = 0;\n",
"\n",
" // Enable button functionality for navigation\n",
" var that = this;\n",
" this.$el.find(\"#next-cand\").click(function() {\n",
" that.switchCandidate(1);\n",
" });\n",
" this.$el.find(\"#prev-cand\").click(function() {\n",
" that.switchCandidate(-1);\n",
" });\n",
" this.$el.find(\"#next-context\").click(function() {\n",
" that.switchContext(1);\n",
" });\n",
" this.$el.find(\"#prev-context\").click(function() {\n",
" that.switchContext(-1);\n",
" });\n",
" this.$el.find(\"#next-page\").click(function() {\n",
" that.switchPage(1);\n",
" });\n",
" this.$el.find(\"#prev-page\").click(function() {\n",
" that.switchPage(-1);\n",
" });\n",
" this.$el.find(\"#label-true\").click(function() {\n",
" that.labelCandidate(true, true);\n",
" });\n",
" this.$el.find(\"#label-false\").click(function() {\n",
" that.labelCandidate(false, true);\n",
" });\n",
"\n",
" // Arrow key functionality\n",
" this.$el.keydown(function(e) {\n",
" switch(e.which) {\n",
" case 74: // j\n",
" that.switchCandidate(-1);\n",
" break;\n",
"\n",
" case 73: // i\n",
" that.switchPage(-1);\n",
" break;\n",
"\n",
" case 76: // l\n",
" that.switchCandidate(1);\n",
" break;\n",
"\n",
" case 75: // k\n",
" that.switchPage(1);\n",
" break;\n",
"\n",
" case 84: // t\n",
" that.labelCandidate(true, true);\n",
" break;\n",
"\n",
" case 70: // f\n",
" that.labelCandidate(false, true);\n",
" break;\n",
" }\n",
" });\n",
"\n",
" // Show the first page and highlight the first candidate\n",
" this.$el.find(\"#viewer-page-0\").show();\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Get candidate selector for currently selected candidate, escaping id properly\n",
" getCandidate: function() {\n",
" return this.$el.find(\".\"+this.cids[this.pid][this.cxid][this.cid]);\n",
" }, \n",
"\n",
" // Color the candidate correctly according to registered label, as well as set highlighting\n",
" markCurrentCandidate: function(highlight) {\n",
" var cid = this.cids[this.pid][this.cxid][this.cid];\n",
" var tags = this.$el.find(\".\"+cid);\n",
"\n",
" // Clear color classes\n",
" tags.removeClass(\"candidate-h\");\n",
" tags.removeClass(\"true-candidate\");\n",
" tags.removeClass(\"true-candidate-h\");\n",
" tags.removeClass(\"false-candidate\");\n",
" tags.removeClass(\"false-candidate-h\");\n",
" tags.removeClass(\"highlighted\");\n",
"\n",
" if (highlight) {\n",
" if (cid in this.labels) {\n",
" tags.addClass(String(this.labels[cid]) + \"-candidate-h\");\n",
" } else {\n",
" tags.addClass(\"candidate-h\");\n",
" }\n",
" \n",
" // If un-highlighting, leave with first non-null coloring\n",
" } else {\n",
" var that = this;\n",
" tags.each(function() {\n",
" var cids = $(this).attr('class').split(/\\s+/).map(function(item) {\n",
" return parseInt(item);\n",
" });\n",
" cids.sort();\n",
" for (var i in cids) {\n",
" if (cids[i] in that.labels) {\n",
" var label = that.labels[cids[i]];\n",
" $(this).addClass(String(label) + \"-candidate\");\n",
" $(this).removeClass(String(!label) + \"-candidate\");\n",
" break;\n",
" }\n",
" }\n",
" });\n",
" }\n",
"\n",
" // Extra highlighting css\n",
" if (highlight) {\n",
" tags.addClass(\"highlighted\");\n",
" }\n",
"\n",
" // Classes for showing direction of relation\n",
" if (highlight) {\n",
" this.$el.find(\".\"+cid+\"-0\").addClass(\"left-candidate\");\n",
" this.$el.find(\".\"+cid+\"-1\").addClass(\"right-candidate\");\n",
" } else {\n",
" this.$el.find(\".\"+cid+\"-0\").removeClass(\"left-candidate\");\n",
" this.$el.find(\".\"+cid+\"-1\").removeClass(\"right-candidate\");\n",
" }\n",
" },\n",
"\n",
" // Cycle through candidates and highlight, by increment inc\n",
" switchCandidate: function(inc) {\n",
" var N = this.cids[this.pid].length\n",
" var M = this.cids[this.pid][this.cxid].length;\n",
" if (N == 0 || M == 0) { return false; }\n",
"\n",
" // Clear highlighting from previous candidate\n",
" if (inc != 0) {\n",
" this.markCurrentCandidate(false);\n",
"\n",
" // Increment the cid counter\n",
"\n",
" // Move to next context\n",
" if (this.cid + inc >= M) {\n",
" while (this.cid + inc >= M) {\n",
" \n",
" // At last context on page, halt\n",
" if (this.cxid == N - 1) {\n",
" this.cid = M - 1;\n",
" inc = 0;\n",
" break;\n",
" \n",
" // Increment to next context\n",
" } else {\n",
" inc -= M - this.cid;\n",
" this.cxid += 1;\n",
" M = this.cids[this.pid][this.cxid].length;\n",
" this.cid = 0;\n",
" }\n",
" }\n",
"\n",
" // Move to previous context\n",
" } else if (this.cid + inc < 0) {\n",
" while (this.cid + inc < 0) {\n",
" \n",
" // At first context on page, halt\n",
" if (this.cxid == 0) {\n",
" this.cid = 0;\n",
" inc = 0;\n",
" break;\n",
" \n",
" // Increment to previous context\n",
" } else {\n",
" inc += this.cid + 1;\n",
" this.cxid -= 1;\n",
" M = this.cids[this.pid][this.cxid].length;\n",
" this.cid = M - 1;\n",
" }\n",
" }\n",
" }\n",
"\n",
" // Move within current context\n",
" this.cid += inc;\n",
" }\n",
" this.markCurrentCandidate(true);\n",
"\n",
" // Push this new cid to the model\n",
" this.model.set('_selected_cid', this.cids[this.pid][this.cxid][this.cid]);\n",
" this.touch();\n",
" },\n",
"\n",
" // Switch through contexts\n",
" switchContext: function(inc) {\n",
" this.markCurrentCandidate(false);\n",
"\n",
" // Iterate context on this page\n",
" var M = this.cids[this.pid].length;\n",
" if (this.cxid + inc < 0) {\n",
" this.cxid = 0;\n",
" } else if (this.cxid + inc >= M) {\n",
" this.cxid = M - 1;\n",
" } else {\n",
" this.cxid += inc;\n",
" }\n",
"\n",
" // Reset cid and set to first candidate\n",
" this.cid = 0;\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Switch through pages\n",
" switchPage: function(inc) {\n",
" this.markCurrentCandidate(false);\n",
" this.$el.find(\".viewer-page\").hide();\n",
" if (this.pid + inc < 0) {\n",
" this.pid = 0;\n",
" } else if (this.pid + inc > this.nPages - 1) {\n",
" this.pid = this.nPages - 1;\n",
" } else {\n",
" this.pid += inc;\n",
" }\n",
" this.$el.find(\"#viewer-page-\"+this.pid).show();\n",
"\n",
" // Show pagination\n",
" this.$el.find(\"#page\").html(this.pid);\n",
"\n",
" // Reset cid and set to first candidate\n",
" this.cid = 0;\n",
" this.cxid = 0;\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Label currently-selected candidate\n",
" labelCandidate: function(label, highlighted) {\n",
" var c = this.getCandidate();\n",
" var cid = this.cids[this.pid][this.cxid][this.cid];\n",
" var cl = String(label) + \"-candidate\";\n",
" var clh = String(label) + \"-candidate-h\";\n",
" var cln = String(!label) + \"-candidate\";\n",
" var clnh = String(!label) + \"-candidate-h\";\n",
"\n",
" // Toggle label highlighting\n",
" if (c.hasClass(cl) || c.hasClass(clh)) {\n",
" c.removeClass(cl);\n",
" c.removeClass(clh);\n",
" if (highlighted) {\n",
" c.addClass(\"candidate-h\");\n",
" }\n",
" this.labels[cid] = null;\n",
" this.send({event: 'delete_label', cid: cid});\n",
" } else {\n",
" c.removeClass(cln);\n",
" c.removeClass(clnh);\n",
" if (highlighted) {\n",
" c.addClass(clh);\n",
" } else {\n",
" c.addClass(cl);\n",
" }\n",
" this.labels[cid] = label;\n",
" this.send({event: 'set_label', cid: cid, value: label});\n",
" }\n",
"\n",
" // Set the label and pass back to the model\n",
" this.model.set('_labels_serialized', this.serializeDict(this.labels));\n",
" this.touch();\n",
" },\n",
"\n",
" // Serialization of hash maps, because traitlets Dict doesn't seem to work...\n",
" serializeDict: function(d) {\n",
" var s = [];\n",
" for (var key in d) {\n",
" s.push(key+\"~~\"+d[key]);\n",
" }\n",
" return s.join();\n",
" },\n",
"\n",
" // Deserialization of hash maps\n",
" deserializeDict: function(s) {\n",
" var d = {};\n",
" var entries = s.split(/,/);\n",
" var kv;\n",
" for (var i in entries) {\n",
" kv = entries[i].split(/~~/);\n",
" if (kv[1] == \"true\") {\n",
" d[kv[0]] = true;\n",
" } else if (kv[1] == \"false\") {\n",
" d[kv[0]] = false;\n",
" }\n",
" }\n",
" return d;\n",
" },\n",
" });\n",
"\n",
" return {\n",
" ViewerView: ViewerView\n",
" };\n",
"});\n"
],
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"source": [
"tp, fp, tn, fn = error_analysis(session, LF_marriage, split=1, gold=L_gold_dev)\n",
"\n",
"# now let's view what this LF labeled\n",
"SentenceNgramViewer(fp, session, n_per_page=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
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"source": [
"#### Other Search Contexts\n",
"We can also search other sentence contexts, such as a window of text to the left or right of our candidate spans. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coverage: 0.39% (11/2796)\n"
]
},
{
"data": {
"application/javascript": [
"require.undef('viewer');\n",
"\n",
"// NOTE: all elements should be selected using this.$el.find to avoid collisions with other Viewers\n",
"\n",
"define('viewer', [\"jupyter-js-widgets\"], function(widgets) {\n",
" var ViewerView = widgets.DOMWidgetView.extend({\n",
" render: function() {\n",
" this.cids = this.model.get('cids');\n",
" this.nPages = this.cids.length;\n",
" this.pid = 0;\n",
" this.cxid = 0;\n",
" this.cid = 0;\n",
"\n",
" // Insert the html payload\n",
" this.$el.append(this.model.get('html'));\n",
"\n",
" // Initialize all labels from previous sessions\n",
" this.labels = this.deserializeDict(this.model.get('_labels_serialized'));\n",
" for (var i=0; i < this.nPages; i++) {\n",
" this.pid = i;\n",
" for (var j=0; j < this.cids[i].length; j++) {\n",
" this.cxid = j;\n",
" for (var k=0; k < this.cids[i][j].length; k++) {\n",
" this.cid = k;\n",
" if (this.cids[i][j][k] in this.labels) {\n",
" this.markCurrentCandidate(false);\n",
" }\n",
" }\n",
" }\n",
" }\n",
" this.pid = 0;\n",
" this.cxid = 0;\n",
" this.cid = 0;\n",
"\n",
" // Enable button functionality for navigation\n",
" var that = this;\n",
" this.$el.find(\"#next-cand\").click(function() {\n",
" that.switchCandidate(1);\n",
" });\n",
" this.$el.find(\"#prev-cand\").click(function() {\n",
" that.switchCandidate(-1);\n",
" });\n",
" this.$el.find(\"#next-context\").click(function() {\n",
" that.switchContext(1);\n",
" });\n",
" this.$el.find(\"#prev-context\").click(function() {\n",
" that.switchContext(-1);\n",
" });\n",
" this.$el.find(\"#next-page\").click(function() {\n",
" that.switchPage(1);\n",
" });\n",
" this.$el.find(\"#prev-page\").click(function() {\n",
" that.switchPage(-1);\n",
" });\n",
" this.$el.find(\"#label-true\").click(function() {\n",
" that.labelCandidate(true, true);\n",
" });\n",
" this.$el.find(\"#label-false\").click(function() {\n",
" that.labelCandidate(false, true);\n",
" });\n",
"\n",
" // Arrow key functionality\n",
" this.$el.keydown(function(e) {\n",
" switch(e.which) {\n",
" case 74: // j\n",
" that.switchCandidate(-1);\n",
" break;\n",
"\n",
" case 73: // i\n",
" that.switchPage(-1);\n",
" break;\n",
"\n",
" case 76: // l\n",
" that.switchCandidate(1);\n",
" break;\n",
"\n",
" case 75: // k\n",
" that.switchPage(1);\n",
" break;\n",
"\n",
" case 84: // t\n",
" that.labelCandidate(true, true);\n",
" break;\n",
"\n",
" case 70: // f\n",
" that.labelCandidate(false, true);\n",
" break;\n",
" }\n",
" });\n",
"\n",
" // Show the first page and highlight the first candidate\n",
" this.$el.find(\"#viewer-page-0\").show();\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Get candidate selector for currently selected candidate, escaping id properly\n",
" getCandidate: function() {\n",
" return this.$el.find(\".\"+this.cids[this.pid][this.cxid][this.cid]);\n",
" }, \n",
"\n",
" // Color the candidate correctly according to registered label, as well as set highlighting\n",
" markCurrentCandidate: function(highlight) {\n",
" var cid = this.cids[this.pid][this.cxid][this.cid];\n",
" var tags = this.$el.find(\".\"+cid);\n",
"\n",
" // Clear color classes\n",
" tags.removeClass(\"candidate-h\");\n",
" tags.removeClass(\"true-candidate\");\n",
" tags.removeClass(\"true-candidate-h\");\n",
" tags.removeClass(\"false-candidate\");\n",
" tags.removeClass(\"false-candidate-h\");\n",
" tags.removeClass(\"highlighted\");\n",
"\n",
" if (highlight) {\n",
" if (cid in this.labels) {\n",
" tags.addClass(String(this.labels[cid]) + \"-candidate-h\");\n",
" } else {\n",
" tags.addClass(\"candidate-h\");\n",
" }\n",
" \n",
" // If un-highlighting, leave with first non-null coloring\n",
" } else {\n",
" var that = this;\n",
" tags.each(function() {\n",
" var cids = $(this).attr('class').split(/\\s+/).map(function(item) {\n",
" return parseInt(item);\n",
" });\n",
" cids.sort();\n",
" for (var i in cids) {\n",
" if (cids[i] in that.labels) {\n",
" var label = that.labels[cids[i]];\n",
" $(this).addClass(String(label) + \"-candidate\");\n",
" $(this).removeClass(String(!label) + \"-candidate\");\n",
" break;\n",
" }\n",
" }\n",
" });\n",
" }\n",
"\n",
" // Extra highlighting css\n",
" if (highlight) {\n",
" tags.addClass(\"highlighted\");\n",
" }\n",
"\n",
" // Classes for showing direction of relation\n",
" if (highlight) {\n",
" this.$el.find(\".\"+cid+\"-0\").addClass(\"left-candidate\");\n",
" this.$el.find(\".\"+cid+\"-1\").addClass(\"right-candidate\");\n",
" } else {\n",
" this.$el.find(\".\"+cid+\"-0\").removeClass(\"left-candidate\");\n",
" this.$el.find(\".\"+cid+\"-1\").removeClass(\"right-candidate\");\n",
" }\n",
" },\n",
"\n",
" // Cycle through candidates and highlight, by increment inc\n",
" switchCandidate: function(inc) {\n",
" var N = this.cids[this.pid].length\n",
" var M = this.cids[this.pid][this.cxid].length;\n",
" if (N == 0 || M == 0) { return false; }\n",
"\n",
" // Clear highlighting from previous candidate\n",
" if (inc != 0) {\n",
" this.markCurrentCandidate(false);\n",
"\n",
" // Increment the cid counter\n",
"\n",
" // Move to next context\n",
" if (this.cid + inc >= M) {\n",
" while (this.cid + inc >= M) {\n",
" \n",
" // At last context on page, halt\n",
" if (this.cxid == N - 1) {\n",
" this.cid = M - 1;\n",
" inc = 0;\n",
" break;\n",
" \n",
" // Increment to next context\n",
" } else {\n",
" inc -= M - this.cid;\n",
" this.cxid += 1;\n",
" M = this.cids[this.pid][this.cxid].length;\n",
" this.cid = 0;\n",
" }\n",
" }\n",
"\n",
" // Move to previous context\n",
" } else if (this.cid + inc < 0) {\n",
" while (this.cid + inc < 0) {\n",
" \n",
" // At first context on page, halt\n",
" if (this.cxid == 0) {\n",
" this.cid = 0;\n",
" inc = 0;\n",
" break;\n",
" \n",
" // Increment to previous context\n",
" } else {\n",
" inc += this.cid + 1;\n",
" this.cxid -= 1;\n",
" M = this.cids[this.pid][this.cxid].length;\n",
" this.cid = M - 1;\n",
" }\n",
" }\n",
" }\n",
"\n",
" // Move within current context\n",
" this.cid += inc;\n",
" }\n",
" this.markCurrentCandidate(true);\n",
"\n",
" // Push this new cid to the model\n",
" this.model.set('_selected_cid', this.cids[this.pid][this.cxid][this.cid]);\n",
" this.touch();\n",
" },\n",
"\n",
" // Switch through contexts\n",
" switchContext: function(inc) {\n",
" this.markCurrentCandidate(false);\n",
"\n",
" // Iterate context on this page\n",
" var M = this.cids[this.pid].length;\n",
" if (this.cxid + inc < 0) {\n",
" this.cxid = 0;\n",
" } else if (this.cxid + inc >= M) {\n",
" this.cxid = M - 1;\n",
" } else {\n",
" this.cxid += inc;\n",
" }\n",
"\n",
" // Reset cid and set to first candidate\n",
" this.cid = 0;\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Switch through pages\n",
" switchPage: function(inc) {\n",
" this.markCurrentCandidate(false);\n",
" this.$el.find(\".viewer-page\").hide();\n",
" if (this.pid + inc < 0) {\n",
" this.pid = 0;\n",
" } else if (this.pid + inc > this.nPages - 1) {\n",
" this.pid = this.nPages - 1;\n",
" } else {\n",
" this.pid += inc;\n",
" }\n",
" this.$el.find(\"#viewer-page-\"+this.pid).show();\n",
"\n",
" // Show pagination\n",
" this.$el.find(\"#page\").html(this.pid);\n",
"\n",
" // Reset cid and set to first candidate\n",
" this.cid = 0;\n",
" this.cxid = 0;\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Label currently-selected candidate\n",
" labelCandidate: function(label, highlighted) {\n",
" var c = this.getCandidate();\n",
" var cid = this.cids[this.pid][this.cxid][this.cid];\n",
" var cl = String(label) + \"-candidate\";\n",
" var clh = String(label) + \"-candidate-h\";\n",
" var cln = String(!label) + \"-candidate\";\n",
" var clnh = String(!label) + \"-candidate-h\";\n",
"\n",
" // Toggle label highlighting\n",
" if (c.hasClass(cl) || c.hasClass(clh)) {\n",
" c.removeClass(cl);\n",
" c.removeClass(clh);\n",
" if (highlighted) {\n",
" c.addClass(\"candidate-h\");\n",
" }\n",
" this.labels[cid] = null;\n",
" this.send({event: 'delete_label', cid: cid});\n",
" } else {\n",
" c.removeClass(cln);\n",
" c.removeClass(clnh);\n",
" if (highlighted) {\n",
" c.addClass(clh);\n",
" } else {\n",
" c.addClass(cl);\n",
" }\n",
" this.labels[cid] = label;\n",
" this.send({event: 'set_label', cid: cid, value: label});\n",
" }\n",
"\n",
" // Set the label and pass back to the model\n",
" this.model.set('_labels_serialized', this.serializeDict(this.labels));\n",
" this.touch();\n",
" },\n",
"\n",
" // Serialization of hash maps, because traitlets Dict doesn't seem to work...\n",
" serializeDict: function(d) {\n",
" var s = [];\n",
" for (var key in d) {\n",
" s.push(key+\"~~\"+d[key]);\n",
" }\n",
" return s.join();\n",
" },\n",
"\n",
" // Deserialization of hash maps\n",
" deserializeDict: function(s) {\n",
" var d = {};\n",
" var entries = s.split(/,/);\n",
" var kv;\n",
" for (var i in entries) {\n",
" kv = entries[i].split(/~~/);\n",
" if (kv[1] == \"true\") {\n",
" d[kv[0]] = true;\n",
" } else if (kv[1] == \"false\") {\n",
" d[kv[0]] = false;\n",
" }\n",
" }\n",
" return d;\n",
" },\n",
" });\n",
"\n",
" return {\n",
" ViewerView: ViewerView\n",
" };\n",
"});\n"
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"source": [
"other_relationship = {'boyfriend', 'girlfriend'}\n",
"\n",
"LF_other_relationship = MatchTerms(name='other_relationship', terms=other_relationship, \n",
" label=-1, search='left', window=1).lf()\n",
"labeled = coverage(session, LF_other_relationship, split=1)\n",
"\n",
"# now let's view what this LF labeled\n",
"SentenceNgramViewer(labeled, session, n_per_page=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
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"source": [
"### 4. Regular Expression Factory\n",
"\n",
"Sometimes we want to express more generic textual patterns to match against candidates. Perhaps we want to match a specific phrase like 'power couple' or look for modifier prefixes like 'ex' wife, husband, etc. \n",
"\n",
"We can generate this supervision in the same way as above using sets of [regular expressions](https://en.wikipedia.org/wiki/Regular_expression) -- a formal language for string matching."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coverage: 0.43% (12/2796)\n"
]
},
{
"data": {
"application/javascript": [
"require.undef('viewer');\n",
"\n",
"// NOTE: all elements should be selected using this.$el.find to avoid collisions with other Viewers\n",
"\n",
"define('viewer', [\"jupyter-js-widgets\"], function(widgets) {\n",
" var ViewerView = widgets.DOMWidgetView.extend({\n",
" render: function() {\n",
" this.cids = this.model.get('cids');\n",
" this.nPages = this.cids.length;\n",
" this.pid = 0;\n",
" this.cxid = 0;\n",
" this.cid = 0;\n",
"\n",
" // Insert the html payload\n",
" this.$el.append(this.model.get('html'));\n",
"\n",
" // Initialize all labels from previous sessions\n",
" this.labels = this.deserializeDict(this.model.get('_labels_serialized'));\n",
" for (var i=0; i < this.nPages; i++) {\n",
" this.pid = i;\n",
" for (var j=0; j < this.cids[i].length; j++) {\n",
" this.cxid = j;\n",
" for (var k=0; k < this.cids[i][j].length; k++) {\n",
" this.cid = k;\n",
" if (this.cids[i][j][k] in this.labels) {\n",
" this.markCurrentCandidate(false);\n",
" }\n",
" }\n",
" }\n",
" }\n",
" this.pid = 0;\n",
" this.cxid = 0;\n",
" this.cid = 0;\n",
"\n",
" // Enable button functionality for navigation\n",
" var that = this;\n",
" this.$el.find(\"#next-cand\").click(function() {\n",
" that.switchCandidate(1);\n",
" });\n",
" this.$el.find(\"#prev-cand\").click(function() {\n",
" that.switchCandidate(-1);\n",
" });\n",
" this.$el.find(\"#next-context\").click(function() {\n",
" that.switchContext(1);\n",
" });\n",
" this.$el.find(\"#prev-context\").click(function() {\n",
" that.switchContext(-1);\n",
" });\n",
" this.$el.find(\"#next-page\").click(function() {\n",
" that.switchPage(1);\n",
" });\n",
" this.$el.find(\"#prev-page\").click(function() {\n",
" that.switchPage(-1);\n",
" });\n",
" this.$el.find(\"#label-true\").click(function() {\n",
" that.labelCandidate(true, true);\n",
" });\n",
" this.$el.find(\"#label-false\").click(function() {\n",
" that.labelCandidate(false, true);\n",
" });\n",
"\n",
" // Arrow key functionality\n",
" this.$el.keydown(function(e) {\n",
" switch(e.which) {\n",
" case 74: // j\n",
" that.switchCandidate(-1);\n",
" break;\n",
"\n",
" case 73: // i\n",
" that.switchPage(-1);\n",
" break;\n",
"\n",
" case 76: // l\n",
" that.switchCandidate(1);\n",
" break;\n",
"\n",
" case 75: // k\n",
" that.switchPage(1);\n",
" break;\n",
"\n",
" case 84: // t\n",
" that.labelCandidate(true, true);\n",
" break;\n",
"\n",
" case 70: // f\n",
" that.labelCandidate(false, true);\n",
" break;\n",
" }\n",
" });\n",
"\n",
" // Show the first page and highlight the first candidate\n",
" this.$el.find(\"#viewer-page-0\").show();\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Get candidate selector for currently selected candidate, escaping id properly\n",
" getCandidate: function() {\n",
" return this.$el.find(\".\"+this.cids[this.pid][this.cxid][this.cid]);\n",
" }, \n",
"\n",
" // Color the candidate correctly according to registered label, as well as set highlighting\n",
" markCurrentCandidate: function(highlight) {\n",
" var cid = this.cids[this.pid][this.cxid][this.cid];\n",
" var tags = this.$el.find(\".\"+cid);\n",
"\n",
" // Clear color classes\n",
" tags.removeClass(\"candidate-h\");\n",
" tags.removeClass(\"true-candidate\");\n",
" tags.removeClass(\"true-candidate-h\");\n",
" tags.removeClass(\"false-candidate\");\n",
" tags.removeClass(\"false-candidate-h\");\n",
" tags.removeClass(\"highlighted\");\n",
"\n",
" if (highlight) {\n",
" if (cid in this.labels) {\n",
" tags.addClass(String(this.labels[cid]) + \"-candidate-h\");\n",
" } else {\n",
" tags.addClass(\"candidate-h\");\n",
" }\n",
" \n",
" // If un-highlighting, leave with first non-null coloring\n",
" } else {\n",
" var that = this;\n",
" tags.each(function() {\n",
" var cids = $(this).attr('class').split(/\\s+/).map(function(item) {\n",
" return parseInt(item);\n",
" });\n",
" cids.sort();\n",
" for (var i in cids) {\n",
" if (cids[i] in that.labels) {\n",
" var label = that.labels[cids[i]];\n",
" $(this).addClass(String(label) + \"-candidate\");\n",
" $(this).removeClass(String(!label) + \"-candidate\");\n",
" break;\n",
" }\n",
" }\n",
" });\n",
" }\n",
"\n",
" // Extra highlighting css\n",
" if (highlight) {\n",
" tags.addClass(\"highlighted\");\n",
" }\n",
"\n",
" // Classes for showing direction of relation\n",
" if (highlight) {\n",
" this.$el.find(\".\"+cid+\"-0\").addClass(\"left-candidate\");\n",
" this.$el.find(\".\"+cid+\"-1\").addClass(\"right-candidate\");\n",
" } else {\n",
" this.$el.find(\".\"+cid+\"-0\").removeClass(\"left-candidate\");\n",
" this.$el.find(\".\"+cid+\"-1\").removeClass(\"right-candidate\");\n",
" }\n",
" },\n",
"\n",
" // Cycle through candidates and highlight, by increment inc\n",
" switchCandidate: function(inc) {\n",
" var N = this.cids[this.pid].length\n",
" var M = this.cids[this.pid][this.cxid].length;\n",
" if (N == 0 || M == 0) { return false; }\n",
"\n",
" // Clear highlighting from previous candidate\n",
" if (inc != 0) {\n",
" this.markCurrentCandidate(false);\n",
"\n",
" // Increment the cid counter\n",
"\n",
" // Move to next context\n",
" if (this.cid + inc >= M) {\n",
" while (this.cid + inc >= M) {\n",
" \n",
" // At last context on page, halt\n",
" if (this.cxid == N - 1) {\n",
" this.cid = M - 1;\n",
" inc = 0;\n",
" break;\n",
" \n",
" // Increment to next context\n",
" } else {\n",
" inc -= M - this.cid;\n",
" this.cxid += 1;\n",
" M = this.cids[this.pid][this.cxid].length;\n",
" this.cid = 0;\n",
" }\n",
" }\n",
"\n",
" // Move to previous context\n",
" } else if (this.cid + inc < 0) {\n",
" while (this.cid + inc < 0) {\n",
" \n",
" // At first context on page, halt\n",
" if (this.cxid == 0) {\n",
" this.cid = 0;\n",
" inc = 0;\n",
" break;\n",
" \n",
" // Increment to previous context\n",
" } else {\n",
" inc += this.cid + 1;\n",
" this.cxid -= 1;\n",
" M = this.cids[this.pid][this.cxid].length;\n",
" this.cid = M - 1;\n",
" }\n",
" }\n",
" }\n",
"\n",
" // Move within current context\n",
" this.cid += inc;\n",
" }\n",
" this.markCurrentCandidate(true);\n",
"\n",
" // Push this new cid to the model\n",
" this.model.set('_selected_cid', this.cids[this.pid][this.cxid][this.cid]);\n",
" this.touch();\n",
" },\n",
"\n",
" // Switch through contexts\n",
" switchContext: function(inc) {\n",
" this.markCurrentCandidate(false);\n",
"\n",
" // Iterate context on this page\n",
" var M = this.cids[this.pid].length;\n",
" if (this.cxid + inc < 0) {\n",
" this.cxid = 0;\n",
" } else if (this.cxid + inc >= M) {\n",
" this.cxid = M - 1;\n",
" } else {\n",
" this.cxid += inc;\n",
" }\n",
"\n",
" // Reset cid and set to first candidate\n",
" this.cid = 0;\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Switch through pages\n",
" switchPage: function(inc) {\n",
" this.markCurrentCandidate(false);\n",
" this.$el.find(\".viewer-page\").hide();\n",
" if (this.pid + inc < 0) {\n",
" this.pid = 0;\n",
" } else if (this.pid + inc > this.nPages - 1) {\n",
" this.pid = this.nPages - 1;\n",
" } else {\n",
" this.pid += inc;\n",
" }\n",
" this.$el.find(\"#viewer-page-\"+this.pid).show();\n",
"\n",
" // Show pagination\n",
" this.$el.find(\"#page\").html(this.pid);\n",
"\n",
" // Reset cid and set to first candidate\n",
" this.cid = 0;\n",
" this.cxid = 0;\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Label currently-selected candidate\n",
" labelCandidate: function(label, highlighted) {\n",
" var c = this.getCandidate();\n",
" var cid = this.cids[this.pid][this.cxid][this.cid];\n",
" var cl = String(label) + \"-candidate\";\n",
" var clh = String(label) + \"-candidate-h\";\n",
" var cln = String(!label) + \"-candidate\";\n",
" var clnh = String(!label) + \"-candidate-h\";\n",
"\n",
" // Toggle label highlighting\n",
" if (c.hasClass(cl) || c.hasClass(clh)) {\n",
" c.removeClass(cl);\n",
" c.removeClass(clh);\n",
" if (highlighted) {\n",
" c.addClass(\"candidate-h\");\n",
" }\n",
" this.labels[cid] = null;\n",
" this.send({event: 'delete_label', cid: cid});\n",
" } else {\n",
" c.removeClass(cln);\n",
" c.removeClass(clnh);\n",
" if (highlighted) {\n",
" c.addClass(clh);\n",
" } else {\n",
" c.addClass(cl);\n",
" }\n",
" this.labels[cid] = label;\n",
" this.send({event: 'set_label', cid: cid, value: label});\n",
" }\n",
"\n",
" // Set the label and pass back to the model\n",
" this.model.set('_labels_serialized', this.serializeDict(this.labels));\n",
" this.touch();\n",
" },\n",
"\n",
" // Serialization of hash maps, because traitlets Dict doesn't seem to work...\n",
" serializeDict: function(d) {\n",
" var s = [];\n",
" for (var key in d) {\n",
" s.push(key+\"~~\"+d[key]);\n",
" }\n",
" return s.join();\n",
" },\n",
"\n",
" // Deserialization of hash maps\n",
" deserializeDict: function(s) {\n",
" var d = {};\n",
" var entries = s.split(/,/);\n",
" var kv;\n",
" for (var i in entries) {\n",
" kv = entries[i].split(/~~/);\n",
" if (kv[1] == \"true\") {\n",
" d[kv[0]] = true;\n",
" } else if (kv[1] == \"false\") {\n",
" d[kv[0]] = false;\n",
" }\n",
" }\n",
" return d;\n",
" },\n",
" });\n",
"\n",
" return {\n",
" ViewerView: ViewerView\n",
" };\n",
"});\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8ec5c05814f744209dc1a14ca21d71c7"
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"source": [
"exes_rgxs = {' ex[- ](husband|wife)'}\n",
"\n",
"LF_exes = MatchRegex(name='exes', rgxs=exes_rgxs, label=-1, search='between').lf()\n",
"labeled = coverage(session, LF_exes, split=1)\n",
"\n",
"# now let's view what this LF labeled\n",
"SentenceNgramViewer(labeled, session, n_per_page=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## B. Distant Supervision Labeling Functions\n",
"\n",
"In addition to using factories that encode pattern matching heuristics, we can also write labeling functions that _distantly supervise_ examples. Here, we'll load in a list of known spouse pairs and check to see if the candidate pair matches one of these.\n",
"\n",
"**DBpedia**\n",
"http://wiki.dbpedia.org/\n",
"Out database of known spouses comes from DBpedia, which is a community-driven resource similar to Wikipedia but for curating structured data. We'll use a preprocessed snapshot as our knowledge base for all labeling function development.\n",
"\n",
"We can look at some of the example entries from DBPedia and use them in a simple distant supervision labeling function."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"[('Eleanor Powell', 'Glenn Ford'),\n",
" ('Andronikos Doukas', 'Maria of Bulgaria'),\n",
" ('Marjorie Rambeau', 'Willard Mack'),\n",
" ('Margo St. James', 'Paul Avery'),\n",
" ('Joan of England', 'William II the Good')]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from lib.dbpedia import known_spouses \n",
"\n",
"list(known_spouses)[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coverage: 0.11% (3/2796)\n",
"========================================\n",
"LF Score\n",
"========================================\n",
"Pos. class accuracy: 1.0\n",
"Neg. class accuracy: 0.0\n",
"Precision 0.667\n",
"Recall 1.0\n",
"F1 0.8\n",
"----------------------------------------\n",
"TP: 2 | FP: 1 | TN: 0 | FN: 0\n",
"========================================\n",
"\n"
]
},
{
"data": {
"application/javascript": [
"require.undef('viewer');\n",
"\n",
"// NOTE: all elements should be selected using this.$el.find to avoid collisions with other Viewers\n",
"\n",
"define('viewer', [\"jupyter-js-widgets\"], function(widgets) {\n",
" var ViewerView = widgets.DOMWidgetView.extend({\n",
" render: function() {\n",
" this.cids = this.model.get('cids');\n",
" this.nPages = this.cids.length;\n",
" this.pid = 0;\n",
" this.cxid = 0;\n",
" this.cid = 0;\n",
"\n",
" // Insert the html payload\n",
" this.$el.append(this.model.get('html'));\n",
"\n",
" // Initialize all labels from previous sessions\n",
" this.labels = this.deserializeDict(this.model.get('_labels_serialized'));\n",
" for (var i=0; i < this.nPages; i++) {\n",
" this.pid = i;\n",
" for (var j=0; j < this.cids[i].length; j++) {\n",
" this.cxid = j;\n",
" for (var k=0; k < this.cids[i][j].length; k++) {\n",
" this.cid = k;\n",
" if (this.cids[i][j][k] in this.labels) {\n",
" this.markCurrentCandidate(false);\n",
" }\n",
" }\n",
" }\n",
" }\n",
" this.pid = 0;\n",
" this.cxid = 0;\n",
" this.cid = 0;\n",
"\n",
" // Enable button functionality for navigation\n",
" var that = this;\n",
" this.$el.find(\"#next-cand\").click(function() {\n",
" that.switchCandidate(1);\n",
" });\n",
" this.$el.find(\"#prev-cand\").click(function() {\n",
" that.switchCandidate(-1);\n",
" });\n",
" this.$el.find(\"#next-context\").click(function() {\n",
" that.switchContext(1);\n",
" });\n",
" this.$el.find(\"#prev-context\").click(function() {\n",
" that.switchContext(-1);\n",
" });\n",
" this.$el.find(\"#next-page\").click(function() {\n",
" that.switchPage(1);\n",
" });\n",
" this.$el.find(\"#prev-page\").click(function() {\n",
" that.switchPage(-1);\n",
" });\n",
" this.$el.find(\"#label-true\").click(function() {\n",
" that.labelCandidate(true, true);\n",
" });\n",
" this.$el.find(\"#label-false\").click(function() {\n",
" that.labelCandidate(false, true);\n",
" });\n",
"\n",
" // Arrow key functionality\n",
" this.$el.keydown(function(e) {\n",
" switch(e.which) {\n",
" case 74: // j\n",
" that.switchCandidate(-1);\n",
" break;\n",
"\n",
" case 73: // i\n",
" that.switchPage(-1);\n",
" break;\n",
"\n",
" case 76: // l\n",
" that.switchCandidate(1);\n",
" break;\n",
"\n",
" case 75: // k\n",
" that.switchPage(1);\n",
" break;\n",
"\n",
" case 84: // t\n",
" that.labelCandidate(true, true);\n",
" break;\n",
"\n",
" case 70: // f\n",
" that.labelCandidate(false, true);\n",
" break;\n",
" }\n",
" });\n",
"\n",
" // Show the first page and highlight the first candidate\n",
" this.$el.find(\"#viewer-page-0\").show();\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Get candidate selector for currently selected candidate, escaping id properly\n",
" getCandidate: function() {\n",
" return this.$el.find(\".\"+this.cids[this.pid][this.cxid][this.cid]);\n",
" }, \n",
"\n",
" // Color the candidate correctly according to registered label, as well as set highlighting\n",
" markCurrentCandidate: function(highlight) {\n",
" var cid = this.cids[this.pid][this.cxid][this.cid];\n",
" var tags = this.$el.find(\".\"+cid);\n",
"\n",
" // Clear color classes\n",
" tags.removeClass(\"candidate-h\");\n",
" tags.removeClass(\"true-candidate\");\n",
" tags.removeClass(\"true-candidate-h\");\n",
" tags.removeClass(\"false-candidate\");\n",
" tags.removeClass(\"false-candidate-h\");\n",
" tags.removeClass(\"highlighted\");\n",
"\n",
" if (highlight) {\n",
" if (cid in this.labels) {\n",
" tags.addClass(String(this.labels[cid]) + \"-candidate-h\");\n",
" } else {\n",
" tags.addClass(\"candidate-h\");\n",
" }\n",
" \n",
" // If un-highlighting, leave with first non-null coloring\n",
" } else {\n",
" var that = this;\n",
" tags.each(function() {\n",
" var cids = $(this).attr('class').split(/\\s+/).map(function(item) {\n",
" return parseInt(item);\n",
" });\n",
" cids.sort();\n",
" for (var i in cids) {\n",
" if (cids[i] in that.labels) {\n",
" var label = that.labels[cids[i]];\n",
" $(this).addClass(String(label) + \"-candidate\");\n",
" $(this).removeClass(String(!label) + \"-candidate\");\n",
" break;\n",
" }\n",
" }\n",
" });\n",
" }\n",
"\n",
" // Extra highlighting css\n",
" if (highlight) {\n",
" tags.addClass(\"highlighted\");\n",
" }\n",
"\n",
" // Classes for showing direction of relation\n",
" if (highlight) {\n",
" this.$el.find(\".\"+cid+\"-0\").addClass(\"left-candidate\");\n",
" this.$el.find(\".\"+cid+\"-1\").addClass(\"right-candidate\");\n",
" } else {\n",
" this.$el.find(\".\"+cid+\"-0\").removeClass(\"left-candidate\");\n",
" this.$el.find(\".\"+cid+\"-1\").removeClass(\"right-candidate\");\n",
" }\n",
" },\n",
"\n",
" // Cycle through candidates and highlight, by increment inc\n",
" switchCandidate: function(inc) {\n",
" var N = this.cids[this.pid].length\n",
" var M = this.cids[this.pid][this.cxid].length;\n",
" if (N == 0 || M == 0) { return false; }\n",
"\n",
" // Clear highlighting from previous candidate\n",
" if (inc != 0) {\n",
" this.markCurrentCandidate(false);\n",
"\n",
" // Increment the cid counter\n",
"\n",
" // Move to next context\n",
" if (this.cid + inc >= M) {\n",
" while (this.cid + inc >= M) {\n",
" \n",
" // At last context on page, halt\n",
" if (this.cxid == N - 1) {\n",
" this.cid = M - 1;\n",
" inc = 0;\n",
" break;\n",
" \n",
" // Increment to next context\n",
" } else {\n",
" inc -= M - this.cid;\n",
" this.cxid += 1;\n",
" M = this.cids[this.pid][this.cxid].length;\n",
" this.cid = 0;\n",
" }\n",
" }\n",
"\n",
" // Move to previous context\n",
" } else if (this.cid + inc < 0) {\n",
" while (this.cid + inc < 0) {\n",
" \n",
" // At first context on page, halt\n",
" if (this.cxid == 0) {\n",
" this.cid = 0;\n",
" inc = 0;\n",
" break;\n",
" \n",
" // Increment to previous context\n",
" } else {\n",
" inc += this.cid + 1;\n",
" this.cxid -= 1;\n",
" M = this.cids[this.pid][this.cxid].length;\n",
" this.cid = M - 1;\n",
" }\n",
" }\n",
" }\n",
"\n",
" // Move within current context\n",
" this.cid += inc;\n",
" }\n",
" this.markCurrentCandidate(true);\n",
"\n",
" // Push this new cid to the model\n",
" this.model.set('_selected_cid', this.cids[this.pid][this.cxid][this.cid]);\n",
" this.touch();\n",
" },\n",
"\n",
" // Switch through contexts\n",
" switchContext: function(inc) {\n",
" this.markCurrentCandidate(false);\n",
"\n",
" // Iterate context on this page\n",
" var M = this.cids[this.pid].length;\n",
" if (this.cxid + inc < 0) {\n",
" this.cxid = 0;\n",
" } else if (this.cxid + inc >= M) {\n",
" this.cxid = M - 1;\n",
" } else {\n",
" this.cxid += inc;\n",
" }\n",
"\n",
" // Reset cid and set to first candidate\n",
" this.cid = 0;\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Switch through pages\n",
" switchPage: function(inc) {\n",
" this.markCurrentCandidate(false);\n",
" this.$el.find(\".viewer-page\").hide();\n",
" if (this.pid + inc < 0) {\n",
" this.pid = 0;\n",
" } else if (this.pid + inc > this.nPages - 1) {\n",
" this.pid = this.nPages - 1;\n",
" } else {\n",
" this.pid += inc;\n",
" }\n",
" this.$el.find(\"#viewer-page-\"+this.pid).show();\n",
"\n",
" // Show pagination\n",
" this.$el.find(\"#page\").html(this.pid);\n",
"\n",
" // Reset cid and set to first candidate\n",
" this.cid = 0;\n",
" this.cxid = 0;\n",
" this.switchCandidate(0);\n",
" },\n",
"\n",
" // Label currently-selected candidate\n",
" labelCandidate: function(label, highlighted) {\n",
" var c = this.getCandidate();\n",
" var cid = this.cids[this.pid][this.cxid][this.cid];\n",
" var cl = String(label) + \"-candidate\";\n",
" var clh = String(label) + \"-candidate-h\";\n",
" var cln = String(!label) + \"-candidate\";\n",
" var clnh = String(!label) + \"-candidate-h\";\n",
"\n",
" // Toggle label highlighting\n",
" if (c.hasClass(cl) || c.hasClass(clh)) {\n",
" c.removeClass(cl);\n",
" c.removeClass(clh);\n",
" if (highlighted) {\n",
" c.addClass(\"candidate-h\");\n",
" }\n",
" this.labels[cid] = null;\n",
" this.send({event: 'delete_label', cid: cid});\n",
" } else {\n",
" c.removeClass(cln);\n",
" c.removeClass(clnh);\n",
" if (highlighted) {\n",
" c.addClass(clh);\n",
" } else {\n",
" c.addClass(cl);\n",
" }\n",
" this.labels[cid] = label;\n",
" this.send({event: 'set_label', cid: cid, value: label});\n",
" }\n",
"\n",
" // Set the label and pass back to the model\n",
" this.model.set('_labels_serialized', this.serializeDict(this.labels));\n",
" this.touch();\n",
" },\n",
"\n",
" // Serialization of hash maps, because traitlets Dict doesn't seem to work...\n",
" serializeDict: function(d) {\n",
" var s = [];\n",
" for (var key in d) {\n",
" s.push(key+\"~~\"+d[key]);\n",
" }\n",
" return s.join();\n",
" },\n",
"\n",
" // Deserialization of hash maps\n",
" deserializeDict: function(s) {\n",
" var d = {};\n",
" var entries = s.split(/,/);\n",
" var kv;\n",
" for (var i in entries) {\n",
" kv = entries[i].split(/~~/);\n",
" if (kv[1] == \"true\") {\n",
" d[kv[0]] = true;\n",
" } else if (kv[1] == \"false\") {\n",
" d[kv[0]] = false;\n",
" }\n",
" }\n",
" return d;\n",
" },\n",
" });\n",
"\n",
" return {\n",
" ViewerView: ViewerView\n",
" };\n",
"});\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
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"source": [
"LF_distant_supervision = DistantSupervision(\"dbpedia\", kb=known_spouses).lf()\n",
"labeled = coverage(session, LF_distant_supervision, split=1)\n",
"\n",
"# score out LF against dev set labels\n",
"score(session, LF_distant_supervision, split=1, gold=L_gold_dev)\n",
"\n",
"SentenceNgramViewer(labeled, session, n_per_page=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## C. Writing Custom Labeling Functions\n",
"\n",
"The strength of LFs is that you can write any arbitrary function and use it to supervise a classification task. This approach can combine many of the same strategies discussed above or encode other information. \n",
"\n",
"For example, we observe that when mentions of person names occur far apart in a sentence, this is a good indicator that the candidate's label is False."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def LF_too_far_apart(c):\n",
" \"\"\"Person mentions occur at a distance > 50 words\"\"\"\n",
" return -1 if len(list(get_between_tokens(c))) > 50 else 0"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"deletable": true,
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"source": [
"labeled = coverage(session, LF_too_far_apart, split=1)\n",
"score(session, LF_too_far_apart, split=1, gold=L_gold_dev)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## D. Composing Labeling Functions\n",
"\n",
"Another useful technique for writing LFs is composing multiple, weaker LFs together. For example, our `LF_marriage` example above has low precision. Instead of modifying `LF_marriage`, we'll compose it with our `LF_too_far_apart` from above.\n",
"\n",
" LF_marriage TP: 63 | FP: 114\n",
" LF_marriage AND NOT LF_too_far_apart TP: 60 | FP: 86\n",
"\n",
"We missed 3 true candidates, but we cut our false positive rate by 28 candidates!"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"========================================\n",
"LF Score\n",
"========================================\n",
"Pos. class accuracy: 1.0\n",
"Neg. class accuracy: 0.0\n",
"Precision 0.411\n",
"Recall 1.0\n",
"F1 0.583\n",
"----------------------------------------\n",
"TP: 60 | FP: 86 | TN: 0 | FN: 0\n",
"========================================\n",
"\n"
]
}
],
"source": [
"def LF_marriage_and_too_far_apart(c):\n",
" return 1 if LF_too_far_apart(c) != -1 and LF_marriage(c) == 1 else 0\n",
"\n",
"LF_marriage_and_not_same_person = lambda c: LF_too_far_apart(c) != -1 and LF_marriage(c)\n",
"\n",
"score(session, LF_marriage_and_too_far_apart, split=1, gold=L_gold_dev)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# VI. Development Sandbox\n",
"----\n",
"\n",
"## A. Writing Your Own Labeling Functions\n",
"\n",
"Using the information above, write your own labeling functions for this task. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#\n",
"# PLACE YOUR LFs HERE\n",
"#"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## B. Applying Labeling Functions\n",
"---\n",
"\n",
"Next, we need to actually run the LFs over **all** of our training candidates, producing a set of `Labels` and `LabelKeys` (just the names of the LFs) in the database. We'll do this using the `LabelAnnotator` class, a UDF which we will again run with `UDFRunner`.\n",
"\n",
"### 1. Preparing your Labeling Functions\n",
"\n",
"First we put all our labeling functions into list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"LFs = [\n",
" \n",
" # place your lf function variable names here\n",
"]\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Then we setup the label annotator class:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from snorkel.annotations import LabelAnnotator\n",
"labeler = LabelAnnotator()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 2. Generating the Label Matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"np.random.seed(1701)\n",
"\n",
"%time L_train = labeler.apply(split=0, lfs=LFs, parallelism=1)\n",
"print L_train.shape\n",
"\n",
"%time L_dev = labeler.apply_existing(split=1, lfs=LFs, parallelism=1)\n",
"print L_dev.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"L_train.lf_stats(session)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 3. Label Matrix Empirical Accuracies\n",
"\n",
"If we have a small set of human-labeled data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"L_dev.lf_stats(session, labels=L_gold_dev.toarray().ravel())"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"## 3. Iterating on Labeling Function Design\n",
"\n",
"When writing labeling functions, you will want to iterate on the process outlined above several times. You should focus on tuning individual LFs, based on emprical accuracy metrics, and adding new LFs to improve coverage. "
]
}
],
"metadata": {
"anaconda-butt": {},
"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
}
| apache-2.0 |
poldrack/fmri-analysis-vm | analysis/machinelearning/Classification.ipynb | 1 | 306723 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook we explore different approaches to classification. First let's make a function to generate some data for classification."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import scipy.stats\n",
"import matplotlib\n",
"from matplotlib.colors import ListedColormap\n",
"import sklearn.neighbors\n",
"import sklearn.cross_validation\n",
"import sklearn.metrics\n",
"import sklearn.lda\n",
"import sklearn.svm\n",
"import sklearn.linear_model\n",
"from sklearn.model_selection import GridSearchCV,KFold,StratifiedKFold,LeaveOneOut\n",
"from sklearn.metrics import classification_report\n",
"\n",
"n=100\n",
"\n",
"def make_class_data(mean=[50,110],multiplier=1.5,var=[[10,10],[10,10]],cor=-0.4,N=100):\n",
" \"\"\"\n",
" generate a synthetic classification data set with two variables\n",
" \"\"\"\n",
" cor=numpy.array([[1.,cor],[cor,1.]])\n",
" var1=numpy.array([[var[0][0],0],[0,var[0][1]]])\n",
" cov1=var1.dot(cor).dot(var1)\n",
" d1=numpy.random.multivariate_normal(mean,cov1,int(N/2))\n",
" var2=numpy.array([[var[1][0],0],[0,var[1][1]]])\n",
" cov2=var2.dot(cor).dot(var2)\n",
" d2=numpy.random.multivariate_normal(numpy.array(mean)*multiplier,cov2,int(N/2))\n",
" d=numpy.vstack((d1,d2))\n",
" cl=numpy.zeros(N)\n",
" cl[:(N/2)]=1\n",
" return cl,d"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's make some data and plot them (with different markers for the two classes)."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 50.69521155 109.76719266]\n",
"[ 56.15732766 118.61004032]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/poldrack/anaconda/envs/py34/lib/python3.4/site-packages/ipykernel/__main__.py:31: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x10bdd0da0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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UnTt35nn8gQMHpG/fvtKhQwcZP368WK3WAl1vxowZYgkMFH8vLwkODJTatWtLampqoWLP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\n7g5NeQgdGatUKbBv3z769+/Phg0biIm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"text/plain": [
"<matplotlib.figure.Figure at 0x1086e6c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cl,d=make_class_data(multiplier=[1.1,1.1],N=n)\n",
"print(numpy.mean(d[:50,:],0))\n",
"print(numpy.mean(d[50:,:],0))\n",
"\n",
"plt.scatter(d[:,0],d[:,1],c=cl,cmap=matplotlib.cm.hot)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's look at some different classification methods. \n",
"\n",
"First let's create a function that can take a dataset and a classifier and show us the \"decision surface\" - that is, which category is predicted for each value of the variables."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_cls_with_decision_surface(d,cl,clf,h = .25 ):\n",
" \"\"\"Plot the decision boundary. For that, we will assign a color to each\n",
" point in the mesh [x_min, m_max]x[y_min, y_max].\n",
" h= step size in the grid\n",
" \"\"\"\n",
" fig=plt.figure()\n",
" x_min, x_max = d[:, 0].min() - 1, d[:, 0].max() + 1\n",
" y_min, y_max = d[:, 1].min() - 1, d[:, 1].max() + 1\n",
" xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, h),\n",
" numpy.arange(y_min, y_max, h))\n",
" Z = clf.predict(numpy.c_[xx.ravel(), yy.ravel()])\n",
"\n",
" # Put the result into a color plot\n",
" Z = Z.reshape(xx.shape)\n",
" plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
"\n",
" # Plot also the training points\n",
" plt.scatter(d[:, 0], d[:, 1], c=cl, cmap=cmap_bold)\n",
" plt.xlim(xx.min(), xx.max())\n",
" plt.ylim(yy.min(), yy.max())\n",
"\n",
" return fig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Nearest neighbor classifier\n",
"\n",
"In the nearest neighbor classifier, we classify new datapoints by looking at which points are nearest in the training data. In the simplest case we could look at a single neighbor; try setting n_neighbors to 1 in the following cell and look at the results. Then try increasing the value (e.g. try 10, 20, and 40). What do you see as the number of neighbors increases?\n",
"\n",
"We call the nearest neighbor classifier a *nonparametric* method. This doesn't mean that it has no parameters; to the contrary, it means that the number of parameters is not fixed, but grows with the amount of data."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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pgWPcXFy4u2x3LBAqBJXLlOGzGzcIUBQKA28bjbzYxP4PblUoVoyjc+eyLygI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\nBZIvQe4LhKR2cLeJ3ezcvH3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/mPbvtH/goSS9Qc+4DeMo+FFB9Pn0uHZw5d0l76Z7kWed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\nntCdcy4S/wcRp9ZiM8fTywAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10883fc50>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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pgWPcXFy4u2x3LBAqBJXLlOGzGzcIUBQKA28bjbzYxP4PblUoVoyjc+eyLygI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\nBZIvQe4LhKR2cLeJ3ezcvH3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/mPbvtH/goSS9Qc+4DeMo+FFB9Pn0uHZw5d0l76Z7kWed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\nntCdcy4S/wcRp9ZiM8fTywAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10883fc50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# adapted from http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html#example-neighbors-plot-classification-py\n",
"\n",
"n_neighbors = 40\n",
"\n",
" # step size in the mesh\n",
"\n",
"# Create color maps\n",
"cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA'])\n",
"cmap_bold = ListedColormap(['#FF0000', '#00FF00'])\n",
"clf = sklearn.neighbors.KNeighborsClassifier(n_neighbors, weights='uniform')\n",
"clf.fit(d, cl)\n",
"\n",
"plot_cls_with_decision_surface(d,cl,clf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Now let's write a function to perform cross-validation and compute prediction accuracy.\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def classify(d,cl,clf,cv):\n",
" pred=numpy.zeros(n)\n",
" for train,test in cv.split(d,cl):\n",
" clf.fit(d[train,:],cl[train])\n",
" pred[test]=clf.predict(d[test,:])\n",
" return sklearn.metrics.accuracy_score(cl,pred),sklearn.metrics.confusion_matrix(cl,pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Apply that function to the nearest neighbors problem. We can look at accuracy (how often did it get the label right) and also look at the *confusion matrix* which shows each type of outcome."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"accuracy = 0.710000\n",
"confusion matrix:\n",
"[[35 15]\n",
" [14 36]]\n"
]
}
],
"source": [
"n_neighbors=40\n",
"clf=sklearn.neighbors.KNeighborsClassifier(n_neighbors, weights='uniform')\n",
"# use stratified k-fold crossvalidation, which keeps the proportion of classes roughly\n",
"# equal across folds\n",
"cv=StratifiedKFold(8)\n",
"acc,confusion=classify(d,cl,clf,cv)\n",
"print('accuracy = %f'%acc)\n",
"print('confusion matrix:')\n",
"print(confusion)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Exercise:__ Loop through different levels of n_neighbors (from 1 to 30) and compute the accuracy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now write a loop that does this using 100 different randomly generated datasets, and plot the mean across datasets. This will take a couple of minutes to run."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/poldrack/anaconda/envs/py34/lib/python3.4/site-packages/ipykernel/__main__.py:31: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1048a6048>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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pnZzXTs/Omynpa61dNndJmZkVruD9MEq6uNQJmEXa03seMBYYmC1e2FT+s4ED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\nwoIbZfkuBn6UPd8s5/wbgeN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"text/plain": [
"<matplotlib.figure.Figure at 0x10bdd7710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nn_range = range(1,61)\n",
"\n",
"accuracy_knn=numpy.zeros((100,len(nn_range)))\n",
"for x in range(100):\n",
" ds_cl,ds_x=make_class_data(multiplier=[1.1,1.1],N=n)\n",
" ds_cv=StratifiedKFold(8)\n",
" for i in nn_range:\n",
" clf=sklearn.neighbors.KNeighborsClassifier(i, weights='uniform')\n",
" accuracy_knn[x,i-1],_=classify(ds_x,ds_cl,clf,ds_cv)\n",
"plt.plot(nn_range,numpy.mean(accuracy_knn,0))\n",
"plt.xlabel('number of nearest neighbors')\n",
"plt.ylabel('accuracy')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Linear discriminant analysis\n",
"\n",
"Linear discriminant analysis is an example of a *parametric* classification method. For each class it fits a Gaussian distribution, and then makes its classification decision on the basis of which Gaussian has the highest density at each point."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"accuracy = 0.750000\n",
"confusion matrix:\n",
"[[35 15]\n",
" [10 40]]\n"
]
},
{
"data": {
"image/png": 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w1q1TvlByCNkTlM0ZvYwENAjg8ujL2MfaYR9o2zUqzXT+xI/mrzSn+SvNgYTm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\nG6JcOSotmIMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bde9fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
"clf=sklearn.lda.LDA(store_covariance=True) #QuadraticDiscriminantAnalysis()\n",
"cv=LeaveOneOut()\n",
"acc,confusion=classify(d,cl,clf,cv)\n",
"print('accuracy = %f'%acc)\n",
"print('confusion matrix:')\n",
"print(confusion)\n",
"fig=plot_cls_with_decision_surface(d,cl,clf)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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9D2tyPvrxqSR204YSzi+/HErVugXmbCX0ByYI3Xz7qn9QpSd2E6E67W2JwraS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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108a35d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plotting functions borrowed from http://scikit-learn.org/stable/auto_examples/classification/plot_lda_qda.html#sphx-glr-auto-examples-classification-plot-lda-qda-py\n",
"\n",
"from matplotlib import colors\n",
"from scipy import linalg\n",
"import matplotlib as mpl\n",
"\n",
"fig=plt.figure()\n",
"cmap = colors.LinearSegmentedColormap(\n",
" 'red_blue_classes',\n",
" {'red': [(0, 1, 1), (1, 0.7, 0.7)],\n",
" 'green': [(0, 0.7, 0.7), (1, 0.7, 0.7)],\n",
" 'blue': [(0, 0.7, 0.7), (1, 1, 1)]})\n",
"plt.cm.register_cmap(cmap=cmap)\n",
"# class 0 and 1 : areas\n",
"nx, ny = 200, 200\n",
"x_min, x_max = numpy.min(d[:,0]),numpy.max(d[:,0])\n",
"y_min, y_max = numpy.min(d[:,1]),numpy.max(d[:,1])\n",
"xx, yy = numpy.meshgrid(numpy.linspace(x_min, x_max, nx),\n",
" numpy.linspace(y_min, y_max, ny))\n",
"Z = clf.predict_proba(numpy.c_[xx.ravel(), yy.ravel()])\n",
"Z = Z[:, 1].reshape(xx.shape)\n",
"plt.pcolormesh(xx, yy, Z, cmap='red_blue_classes',\n",
" norm=colors.Normalize(0., 1.))\n",
"plt.contour(xx, yy, Z, [0.5], linewidths=2., colors='k')\n",
"\n",
"# means\n",
"plt.plot(clf.means_[0][0], clf.means_[0][1],\n",
" 'o', color='black', markersize=10)\n",
"plt.plot(clf.means_[1][0], clf.means_[1][1],\n",
"'o', color='black', markersize=10)\n",
"\n",
"def plot_ellipse(splot, mean, cov, color):\n",
" v, w = linalg.eigh(cov)\n",
" u = w[0] / linalg.norm(w[0])\n",
" angle = numpy.arctan(u[1] / u[0])\n",
" angle = 180 * angle / numpy.pi # convert to degrees\n",
" # filled Gaussian at 2 standard deviation\n",
" ell = mpl.patches.Ellipse(mean, 2 * v[0] ** 0.5, 2 * v[1] ** 0.5,\n",
" 180 + angle, facecolor=color, edgecolor='yellow',\n",
" linewidth=2, zorder=2)\n",
" ell.set_clip_box(splot.bbox)\n",
" ell.set_alpha(0.5)\n",
" ax=splot.gca()\n",
" ax.add_artist(ell)\n",
" splot.canvas.draw()\n",
"\n",
"\n",
"def plot_lda_cov(lda, splot):\n",
" plot_ellipse(splot, lda.means_[0], lda.covariance_, 'red')\n",
" plot_ellipse(splot, lda.means_[1], lda.covariance_, 'blue')\n",
"\n",
"plot_lda_cov(clf,fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Support vector machines\n",
"\n",
"A commonly used classifier for fMRI data is the *support vector machine* (SVM). The SVM uses a kernel to represent the distances between observations; this can be either linear or nonlinear. The SVM then optimizes the boundary so as to minimize the distance between observations in each class."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/poldrack/anaconda/envs/py34/lib/python3.4/site-packages/ipykernel/__main__.py:31: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"accuracy = 0.730000\n",
"confusion matrix:\n",
"[[41 9]\n",
" [18 32]]\n"
]
},
{
"data": {
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BcAXipf7z/1Mr1fi09KHZgGa4DXPj4p8XSX6VTMuhLWk3oR1TG08lf5H81OlWJ8uOKc23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"text/plain": [
"<matplotlib.figure.Figure at 0x10bde9d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cl,d=make_class_data(multiplier=[1.1,1.1],N=n)\n",
"use_linear=False\n",
"if use_linear:\n",
" clf=sklearn.svm.SVC(kernel='linear')\n",
"else:\n",
" clf=sklearn.svm.SVC(kernel='rbf',gamma=0.01)\n",
"\n",
"acc,confusion=classify(d,cl,clf,cv)\n",
"print('accuracy = %f'%acc)\n",
"print('confusion matrix:')\n",
"print(confusion)\n",
"fig=plot_cls_with_decision_surface(d,cl,clf)\n",
"\n",
"# put wide borders around the support vectors\n",
"for sv in clf.support_vectors_:\n",
" plt.scatter(sv[0],sv[1],s=80, facecolors='none', zorder=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we want to figure out what the right value of the gamma parameter is for the nonlinear SVM. We can't do this by trying a bunch of gamma values and seeing which works best, because this overfit the data (i.e. cheating). Instead, what we need to do is use a *nested crossvalidation* in wich we use crossvalidation on the training data to find the best gamma parameter, and then apply that to the test data. We can do this using the GridSearchCV() function in sklearn. See http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html#sphx-glr-auto-examples-model-selection-grid-search-digits-py for an example."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best parameters:\n",
"Mean CV training accuracy: 0.847673474113\n",
"Mean CV test accuracy: 0.754666666667\n",
"gamma 0.0001\n",
"kernel rbf\n",
"C 1\n",
"Best parameters:\n",
"Mean CV training accuracy: 0.810047929981\n",
"Mean CV test accuracy: 0.7\n",
"gamma 0.01\n",
"kernel rbf\n",
"C 100\n",
"Best parameters:\n",
"Mean CV training accuracy: 0.831904001111\n",
"Mean CV test accuracy: 0.72\n",
"gamma 0.01\n",
"kernel rbf\n",
"C 10\n",
"Best parameters:\n",
"Mean CV training accuracy: 0.830306057238\n",
"Mean CV test accuracy: 0.728666666667\n",
"kernel linear\n",
"C 1000\n",
"Performance on out-of-sample test:\n",
" precision recall f1-score support\n",
"\n",
" 0.0 0.65 0.82 0.73 50\n",
" 1.0 0.76 0.56 0.64 50\n",
"\n",
"avg / total 0.70 0.69 0.68 100\n",
"\n"
]
},
{
"data": {
"image/png": 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PvujaScX6TdGM1AxGXzOaPT33kLYqjZ1X7mTUNaNwOpxmh1asJB1N4rXuE3jv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\n/PsZZxcv+Onz1VpvP/O7BTQDUoEvKKJ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"text/plain": [
"<matplotlib.figure.Figure at 0x10c1303c8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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74uIyfLyUXIJI2mQg5ZjMiypenFkWCykuF2HAIiDMAcmFTXRs3pDhD3S/5pgf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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a853ba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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cMYJrxoyh8UMPMfbjj4vUyC4JPQg47A7G9xnPvm77SF+fTvwt8YzvPZ7Mc5m+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\nSPw/T7QtbShx66QAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c1c0898>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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WYzvNu8+XqpQty6RnnuGmsWMxeTyE22zMfOEFbD6+T0mS5B6EnE4n5PxLMlIW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\n1vo+5HnlSikVqZSK9n4cBXQE/gTmAH2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"text/plain": [
"<matplotlib.figure.Figure at 0x10c1d76d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# do 4-fold cross validation\n",
"cv=KFold(4,shuffle=True)\n",
"# let's test both linear and rbf SVMs with a range of parameteter values\n",
"param_grid = [{'C': [1, 10, 100, 1000], 'kernel': ['linear']},\n",
" {'C': [1, 10, 100, 1000], 'gamma': [0.1,0.01,0.001, 0.0001], 'kernel': ['rbf']}]\n",
"\n",
"pred=numpy.zeros(cl.shape)\n",
"for train,test in cv.split(d):\n",
" X_train=d[train,:]\n",
" X_test=d[test,:]\n",
" y_train=cl[train]\n",
" y_test=cl[test]\n",
" clf=GridSearchCV(sklearn.svm.SVC(C=1), param_grid, cv=5, scoring='accuracy')\n",
" clf.fit(X_train,y_train)\n",
" pred[test]=clf.predict(X_test)\n",
" _=plot_cls_with_decision_surface(X_train,y_train,clf.best_estimator_)\n",
" print('Best parameters:')\n",
" print('Mean CV training accuracy:',numpy.mean(clf.cv_results_['mean_train_score']))\n",
" print('Mean CV test accuracy:',numpy.mean(clf.cv_results_['mean_test_score']))\n",
" for k in clf.best_params_:\n",
" print(k,clf.best_params_[k])\n",
"\n",
"print('Performance on out-of-sample test:')\n",
"print(classification_report(cl,pred))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"git": {
"suppress_outputs": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
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| mit |
DlangScience/PydMagic | examples/test.ipynb | 2 | 4137 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Installed pyd_magic.py. To use it, type:\n",
" %load_ext pyd_magic\n"
]
}
],
"source": [
"#Run this once to install\n",
"%install_ext https://raw.githubusercontent.com/DlangScience/PydMagic/master/pyd_magic.py"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%load_ext pyd_magic"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"%%pyd\n",
"\n",
"@pdef!() string hello() {\n",
" return \"Hello World!\";\n",
"}\n",
"\n",
"@pdef!(Docstring!\"takes a single int, returns that int converted to a string\")\n",
"string intToStr(int b)\n",
"{\n",
" import std.conv;\n",
" return b.to!string;\n",
"}\n",
"\n",
"@pdef!(PyName!\"binary_zebra\") int zebra()\n",
"{\n",
" return 101010101;\n",
"}\n",
"\n",
"long[] whereExactlyIntegral(float[] data)\n",
"{\n",
" import std.algorithm, std.array;\n",
" return data.filter!(x => x == cast(long)x).map!(x => cast(long)x).array;\n",
"}\n",
"\n",
"@pdef!(PyName!\"whereExactlyIntegral\")\n",
"auto whereExactlyIntegral_numpy(float[] data)\n",
"{\n",
" import pyd.extra;\n",
" return data.whereExactlyIntegral().d_to_python_numpy_ndarray();\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Hello World!'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hello()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"101010101"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"binary_zebra()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'665543'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"intToStr(665543)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 6], dtype=int64)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"whereExactlyIntegral([0.23, 0.53, 1.0, 6.0, 3.51])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"139543"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"data = np.random.random_integers(0,1000,10000000).astype(np.float32) / 73\n",
"whereExactlyIntegral(data).size"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
dataminingapp/dataminingapp-lectures | Lecture-6/Clustering.ipynb | 2 | 21636 | {
"metadata": {
"celltoolbar": "Raw Cell Format",
"name": "",
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"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Clustering data with k-means"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import scipy as sp\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import sklearn.datasets as sk_data\n",
"import sklearn.metrics as metrics\n",
"from sklearn.cluster import KMeans\n",
"\n",
"\n",
"#import matplotlib as mpl\n",
"import seaborn as sns\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Basics of k-means clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generating our data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X, y = sk_data.make_blobs(n_samples=100, centers=3, n_features=30,center_box=(-10.0, 10.0),random_state=0)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.heatmap(X, xticklabels=False, yticklabels=False, linewidths=0,cbar=False)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Computing the pairwise distances for visualization purposes\n",
"\n",
"\n",
"We can compute pairwise distances using the **sklearn.metrics** functions summarized here:\n",
"http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"euclidean_dists = metrics.euclidean_distances(X)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualizing the data using the heatmap of pairwise distances"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.heatmap(euclidean_dists, xticklabels=False, yticklabels=False, linewidths=0, square=True,cbar=False)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Clustering data using \n",
"k-means clustering in sklearn.cluster\n",
"\n",
"http://scikit-learn.org/stable/modules/clustering.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kmeans = KMeans(init='k-means++', n_clusters=3, n_init=10)\n",
"kmeans.fit_predict(X)\n",
"centroids = kmeans.cluster_centers_\n",
"labels = kmeans.labels_\n",
"error = kmeans.inertia_\n",
"\n",
"print \"The total error of the clustering is: \", error\n",
"print '\\nCluster labels'\n",
"print labels\n",
"print '\\n Cluster Centroids'\n",
"print centroids\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 3 functions in all the clustering classes, **fit()** **predict()** and **fit_predict()**.\u00a0\n",
"\n",
"**fit()** is building the model from the training data (e.g. finding the\n",
" centroids), \n",
" \n",
"**predict()** is assigning labels to test data after building\n",
" the model, \n",
" \n",
"**fit_predict()** is doing both in the same data (e.g in\n",
" kmeans, it finds the centroids and assigns the labels to the dataset)"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Visualizing the results of clustering"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#print original and cluster data\n",
"idx = np.argsort(labels)\n",
"rX = X[idx,:]\n",
"sns.heatmap( rX,xticklabels=False, yticklabels=False, linewidths=0,cbar=False)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Rearrange so that all same labels are consecutive\n",
"\n",
"#print labels\n",
"#print labels[idx]\n",
"rearranged_dists = euclidean_dists[idx,:][:,idx]\n",
"sns.heatmap(rearranged_dists, xticklabels=False, yticklabels=False, linewidths=0, square=True,cbar=False)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Deciding the number of clusters"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Using the error function"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"error = np.zeros(11)\n",
"error[0] = 0;\n",
"for k in range(1,11):\n",
" kmeans = KMeans(init='k-means++', n_clusters=k, n_init=10)\n",
" kmeans.fit_predict(X)\n",
" error[k] = kmeans.inertia_\n",
"\n",
"plt.plot(range(1,len(error)),error[1:])\n",
"plt.xlabel('Number of clusters')\n",
"plt.ylabel('Error')"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Making this a function"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def evaluate_clusters(X,max_clusters):\n",
" error = np.zeros(max_clusters+1)\n",
" error[0] = 0;\n",
" for k in range(1,max_clusters+1):\n",
" kmeans = KMeans(init='k-means++', n_clusters=k, n_init=10)\n",
" kmeans.fit_predict(X)\n",
" error[k] = kmeans.inertia_\n",
"\n",
" plt.plot(range(1,len(error)),error[1:])\n",
" plt.xlabel('Number of clusters')\n",
" plt.ylabel('Error')\n",
"\n",
"evaluate_clusters(X,10)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Adjusted Rand Index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If $T$ is a ground truth label assignment and $C$ the clustering. Let $a$ be: the number of pairs of elements that have the same label in $T$ and the same label in $C$. Also let $b$ be: the number of pairs of elements that have different labels in $T$ and different labels in $C$. Then the Rand Index is: $$\\text{RI}(T,C) = \\frac{a+b}{n\\choose 2} $$\n",
"\n",
"\n",
"\n",
"However the RI score does not guarantee that random label assignments will get a value close to zero (esp. if the number of clusters is in the same order of magnitude as the number of samples). To counter this effect we can discount the expected RI $E[\\text{RI}]$ of random labelings by defining the adjusted Rand index as follows:\n",
"$$\\text{ARI} = \\frac{\\text{RI} - E[\\text{RI}]}{\\max(\\text{RI}) - E[\\text{RI}]}$$\n",
"\n",
"\n",
"Range?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ri = metrics.adjusted_rand_score(labels,y)\n",
"print ri"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def ri_evaluate_clusters(X,max_clusters,ground_truth):\n",
" ri = np.zeros(max_clusters+1)\n",
" ri[0] = 0;\n",
" for k in range(1,max_clusters+1):\n",
" kmeans = KMeans(init='k-means++', n_clusters=k, n_init=10)\n",
" kmeans.fit_predict(X)\n",
" ri[k] = metrics.adjusted_rand_score(kmeans.labels_,ground_truth)\n",
" plt.plot(range(1,len(ri)),ri[1:])\n",
" plt.xlabel('Number of clusters')\n",
" plt.ylabel('Adjusted Rand Index')\n",
" \n",
"ri_evaluate_clusters(X,10,y)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Advantages/Disadvantages?"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Silhouette Coefficient"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the ground truth labels are not known, evaluation must be performed using the model itself. The Silhouette Coefficient (**sklearn.metrics.silhouette_score**) is an example of such an evaluation, where a higher Silhouette Coefficient score relates to a model with better defined clusters. Let $a$ be the mean distance between a sample and all other points in the same class and $b$ be the mean distance between a sample and all other points in the next nearest cluster. Then the \n",
"**Silhoeutte Coefficient** for a clustering is:\n",
"$$s = \\frac{b - a}{\\max(a, b)}$$\n",
"\n",
"\n",
"Range?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sc = metrics.silhouette_score(X, labels, metric='euclidean')\n",
"print sc"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def sc_evaluate_clusters(X,max_clusters):\n",
" s = np.zeros(max_clusters+1)\n",
" s[0] = 0;\n",
" s[1] = 0;\n",
" for k in range(2,max_clusters+1):\n",
" kmeans = KMeans(init='k-means++', n_clusters=k, n_init=10)\n",
" kmeans.fit_predict(X)\n",
" s[k] = metrics.silhouette_score(X,kmeans.labels_,metric='cosine')\n",
" plt.plot(range(2,len(s)),s[2:])\n",
" plt.xlabel('Number of clusters')\n",
" plt.ylabel('Adjusted Rand Index')\n",
" \n",
"#sc_evaluate_clusters(X,10)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Practicing with real data"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"20 Newsgroup data\n",
"\n",
"http://scikit-learn.org/stable/datasets/twenty_newsgroups.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.datasets import fetch_20newsgroups\n",
"\n",
"\"\"\"\n",
"categories = [\n",
" 'alt.atheism',\n",
" 'talk.religion.misc',\n",
" 'comp.graphics',\n",
" 'sci.space',\n",
" 'rec.autos',\n",
" 'rec.sport.baseball'\n",
"]\"\"\"\n",
"categories = ['alt.atheism', 'sci.space','rec.sport.baseball']\n",
"news_data = fetch_20newsgroups(subset='train', categories=categories)\n",
"print news_data.target, len(news_data.target)\n",
"print news_data.target_names"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Data preprocessing"
]
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"TF-IDF for text documents : http://scikit-learn.org/stable/modules/feature_extraction.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"vectorizer = TfidfVectorizer(stop_words='english', min_df=4, max_df=0.8)\n",
"data = vectorizer.fit_transform(news_data.data)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In a large text corpus, some words will be very present (e.g. \u201cthe\u201d, \u201ca\u201d, \u201cis\u201d in English) hence carrying very little meaningful information about the actual contents of the document. If we were to feed the direct count data directly to a classifier those very frequent terms would shadow the frequencies of rarer yet more interesting terms.\n",
"\n",
"In order to re-weight the count features into floating point values suitable for usage by a classifier it is very common to use the tf\u2013idf transform.\n",
"\n",
"Tf means term-frequency while tf\u2013idf means term-frequency times inverse document-frequency. This is a originally a term weighting scheme developed for information retrieval (as a ranking function for search engines results), that has also found good use in document classification and clustering."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\\text{tf}(t,d) = \\text{Number of times term }t \\text{ occurs in document } d$$\n",
"\n",
"If $N$ is the total number of documents in the corpus $D$ then\n",
"\n",
"$$\\text{idf}(t,D)=\\frac{N}{|\\{d\\in D\\mid t\\in d \\}|}$$\n",
"\n",
"$$\\text{tf-idf}(t,d)=\\text{tf}(t,d)\\times \\text{idf}(t,D)$$"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Getting to know your data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print type(data), data.shape"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"fig, ax1 = plt.subplots(1,1,figsize=(15,10))\n",
"sns.heatmap(data[1:100,1:200].todense(), xticklabels=False, yticklabels=False, \n",
" linewidths=0, cbar=False, ax=ax1)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print news_data.target\n",
"print news_data.target_names"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Number of clusters"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"evaluate_clusters(data, 10)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ri_evaluate_clusters(data,10,news_data.target)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sc_evaluate_clusters(data,10)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Looking into our clusters"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"k=4\n",
"kmeans = KMeans(n_clusters=k, init='k-means++', max_iter=100, n_init=1)\n",
"kmeans.fit_predict(data)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"Top terms per cluster:\")\n",
"asc_order_centroids = kmeans.cluster_centers_.argsort()#[:, ::-1]\n",
"order_centroids = asc_order_centroids[:,::-1]\n",
"terms = vectorizer.get_feature_names()\n",
"for i in range(k):\n",
" print \"Cluster %d:\" % i\n",
" for ind in order_centroids[i, :10]:\n",
" print ' %s' % terms[ind]\n",
" print"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Code for setting the style of the notebook\n",
"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"../theme/custom.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<link href='http://fonts.googleapis.com/css?family=EB+Garamond' rel='stylesheet' type='text/css'>\n",
"<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n",
"<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' type='text/css'>\n",
"<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",
" .code_cell {\n",
" width: 105ex !important ;\n",
" margin-bottom: 15px !important;\n",
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" display: block;\n",
" }\n",
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" font-weight: 300;\n",
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" color: #CD2305;\n",
" font-style: italic;\n",
" margin-bottom: .5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
" }\n",
" .warning {\n",
" color: rgb( 240, 20, 20 )\n",
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"</style>\n",
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} | mit |
zzsza/TIL | pytorch/week3.ipynb | 1 | 11945 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2018-02-10T00:17:59.650082Z",
"start_time": "2018-02-10T00:17:58.878766Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"from torch.autograd import Variable\n",
"import torch.optim as optim\n",
"import torch.nn.functional as F\n",
"import numpy as np\n",
"import torchvision.transforms as transforms\n",
"import torchvision.datasets as vdatasets\n",
"import torchvision.utils as vutils\n",
"import pickle\n",
"import os, shutil\n",
"torch.manual_seed(1)\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2018-02-10T00:21:01.223155Z",
"start_time": "2018-02-10T00:21:01.219795Z"
}
},
"outputs": [],
"source": [
"port = pickle.load(open(\"port.info\", \"rb\"))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2018-02-10T00:23:11.149322Z",
"start_time": "2018-02-10T00:23:11.146493Z"
}
},
"outputs": [],
"source": [
"port = port.get('FAST_CAMPUS')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2018-02-10T00:21:05.408159Z",
"start_time": "2018-02-10T00:21:05.404414Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'TIL'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.getcwd().split(\"/\")[-2]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2018-02-10T00:22:31.705712Z",
"start_time": "2018-02-10T00:22:31.702534Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"try:\n",
" shutil.rmtree('runs/')\n",
"except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2018-02-10T00:22:50.195210Z",
"start_time": "2018-02-10T00:22:50.109812Z"
}
},
"outputs": [],
"source": [
"from tensorboardX import SummaryWriter\n",
"\n",
"writer = SummaryWriter(comment=\"-tensorboard-basic\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2018-02-10T00:35:24.340431Z",
"start_time": "2018-02-10T00:35:24.229006Z"
}
},
"outputs": [],
"source": [
"for n_iter in range(100):\n",
" s1 = torch.rand(1)\n",
" s2 = torch.rand(1)\n",
" writer.add_scalar('data/scalar1', s1[0], n_iter)\n",
" writer.add_scalars('data/scalar_group', {\"xsinx\":n_iter*np.sin(n_iter),\n",
" \"xcosx\":n_iter*np.cos(n_iter),\n",
" \"arctanx\": np.arctan(n_iter)}, n_iter)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2018-02-10T00:35:31.401854Z",
"start_time": "2018-02-10T00:35:31.383766Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You are using PyTorch==0.3, use add_graph_onnx()\n"
]
}
],
"source": [
"model = nn.Sequential(nn.Linear(2,10),nn.Sigmoid(),nn.Linear(10,1))\n",
"test_inputs = Variable(torch.randn(10,2))\n",
"outputs = model(test_inputs)\n",
"\n",
"writer.add_graph(model,outputs)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2018-02-10T00:36:00.951894Z",
"start_time": "2018-02-10T00:36:00.827084Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"for name, param in model.named_parameters():\n",
" writer.add_histogram(name, param.clone().data.numpy(), 0)\n",
" writer.add_histogram(name, param.clone().data.numpy(), 1)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2018-02-10T00:36:07.182160Z",
"start_time": "2018-02-10T00:36:05.596680Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"dummy_img = torch.rand(32, 3, 64, 64) # output from network\n",
"for n_iter in range(10):\n",
" x = vutils.make_grid(dummy_img, normalize=True, scale_each=True)\n",
" writer.add_image('Image', x, n_iter)\n",
"\n",
" writer.add_text('Text', 'text logged at step:' + str(n_iter), n_iter)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2018-02-10T00:36:46.648399Z",
"start_time": "2018-02-10T00:36:11.705133Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
"Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
"Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
"Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
"Processing...\n",
"Done!\n"
]
}
],
"source": [
"dataset = vdatasets.MNIST('../data/', train=False, download=True)\n",
"images = dataset.test_data[:100].float()\n",
"label = dataset.test_labels[:100]\n",
"\n",
"features = images.view(100, 784)\n",
"writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2018-02-10T00:36:46.883991Z",
"start_time": "2018-02-10T00:36:46.881616Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"writer.close()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2018-02-10T00:36:47.105448Z",
"start_time": "2018-02-10T00:36:47.101353Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'6006'"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"port"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"start_time": "2018-02-10T00:36:47.984Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting TensorBoard 54 at http://kyle_Macbook:6006\n",
"(Press CTRL+C to quit)\n",
"WARNING:tensorflow:Detected out of order event.step likely caused by a TensorFlow restart. Purging expired events from Tensorboard display between the previous step: 99 (timestamp: 1518222924.34) and current step: 0 (timestamp: 1518222965.68). Removing 100 scalars, 8 histograms, 8 compressed histograms, 1 images, and 0 audio.\n",
"WARNING:tensorflow:Detected out of order event.step likely caused by a TensorFlow restart. Purging expired events from Tensorboard display between the previous step: 99 (timestamp: 1518222924.34) and current step: 1 (timestamp: 1518222965.84). Removing 0 scalars, 0 histograms, 0 compressed histograms, 1 images, and 0 audio.\n",
"WARNING:tensorflow:Detected out of order event.step likely caused by a TensorFlow restart. Purging expired events from Tensorboard display between the previous step: 99 (timestamp: 1518222924.34) and current step: 2 (timestamp: 1518222966.0). Removing 0 scalars, 0 histograms, 0 compressed histograms, 1 images, and 0 audio.\n",
"WARNING:tensorflow:Detected out of order event.step likely caused by a TensorFlow restart. Purging expired events from Tensorboard display between the previous step: 99 (timestamp: 1518222924.34) and current step: 3 (timestamp: 1518222966.16). Removing 0 scalars, 0 histograms, 0 compressed histograms, 1 images, and 0 audio.\n",
"WARNING:tensorflow:Detected out of order event.step likely caused by a TensorFlow restart. Purging expired events from Tensorboard display between the previous step: 99 (timestamp: 1518222924.34) and current step: 4 (timestamp: 1518222966.32). Removing 0 scalars, 0 histograms, 0 compressed histograms, 1 images, and 0 audio.\n",
"WARNING:tensorflow:Detected out of order event.step likely caused by a TensorFlow restart. Purging expired events from Tensorboard display between the previous step: 99 (timestamp: 1518222924.34) and current step: 5 (timestamp: 1518222966.48). Removing 0 scalars, 0 histograms, 0 compressed histograms, 1 images, and 0 audio.\n",
"WARNING:tensorflow:Detected out of order event.step likely caused by a TensorFlow restart. Purging expired events from Tensorboard display between the previous step: 99 (timestamp: 1518222924.34) and current step: 6 (timestamp: 1518222966.64). Removing 0 scalars, 0 histograms, 0 compressed histograms, 1 images, and 0 audio.\n",
"WARNING:tensorflow:Detected out of order event.step likely caused by a TensorFlow restart. Purging expired events from Tensorboard display between the previous step: 99 (timestamp: 1518222924.34) and current step: 7 (timestamp: 1518222966.8). Removing 0 scalars, 0 histograms, 0 compressed histograms, 1 images, and 0 audio.\n",
"WARNING:tensorflow:Detected out of order event.step likely caused by a TensorFlow restart. Purging expired events from Tensorboard display between the previous step: 99 (timestamp: 1518222924.34) and current step: 8 (timestamp: 1518222966.97). Removing 0 scalars, 0 histograms, 0 compressed histograms, 1 images, and 0 audio.\n",
"WARNING:tensorflow:Detected out of order event.step likely caused by a TensorFlow restart. Purging expired events from Tensorboard display between the previous step: 99 (timestamp: 1518222924.34) and current step: 9 (timestamp: 1518222967.13). Removing 0 scalars, 0 histograms, 0 compressed histograms, 1 images, and 0 audio.\n"
]
}
],
"source": [
"!tensorboard --logdir runs --port 6006"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
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| mit |
mdeff/ntds_2016 | project/reports/emotion_recognition/emotion_recognition_Oleniuk_Galotta_v2.ipynb | 2 | 466305 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Network Tour of Data Science, EPFL 2016\n",
"# Project: Facial Emotion Recognition\n",
"Dataset taken from: kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge\n",
"<br>\n",
"<br> Ref: \"Challenges in Representation Learning: A report on three machine learning\n",
"contests.\" I Goodfellow, et al.\n",
"<br>\n",
"<br>\n",
"students: Patryk Oleniuk, Carmen Galotta\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The project presented here is an algorithm to recognize and detect emotions from a face picture. \n",
"\n",
"Of course, the task of recognize face emotions is very easy for humans to do even if somethimes is really hard to understand how a person feels, but what can be easily understood thanks to human brain, is difficult to emulate by a machine.\n",
"\n",
"The aim of this project is to classify faces in discrete human emotions. Due to the success of Convolutional Neural Network in images classification tasks it has been tought that employing it could be a good idea in face emotion as well.\n",
"\n",
"The dataset has been taken from the kaggle competition and consists of 35k grey images with size 48x48 pixels already labeled with a number coding for classes of emotions, namely: \n",
"\n",
"0-Angry<br>\n",
"1-Disgust<br>\n",
"2-Fear<br>\n",
"3-Happy<br>\n",
"4-Sad<br>\n",
"5-Surprise<br>\n",
"6-Neutral<br>\n",
"\n",
"The faces are mostly centered in the image."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Configuration, dataset file"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of data in the dataset: 35887\n"
]
}
],
"source": [
"import random\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"import csv\n",
"import scipy.misc\n",
"import time\n",
"import collections\n",
"import os\n",
"import utils as ut\n",
"import importlib\n",
"import copy\n",
"\n",
"importlib.reload(ut)\n",
"\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (20.0, 20.0) # set default size of plots\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"plt.rcParams['image.cmap'] = 'gray'\n",
"\n",
"# The CSV data (with removed first line ! (names))\n",
"emotions_dataset_dir = 'fer2013_full.csv'\n",
"\n",
"#obtaining the number of line of the csv file\n",
"file = open(emotions_dataset_dir)\n",
"numline = len(file.readlines())\n",
"print ('Number of data in the dataset:',numline)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load the data from *.csv file \n",
"The first step is to load the data from the .csv file. <br> The format of the csv line is<br>\n",
"class{0,1,2,3,4,5,6},pix0 pix2304,DataUsage(not used)<br>\n",
"e.g.<br>\n",
"2,234 1 34 23 ..... 234 256 0,Training<br>\n",
"The picture is always 48x48 pixels, 0-255 greyscale.\n",
"\n",
"# Data cleaning:\n",
"## 1.Remove strange data\n",
"In the database there are some images thar are not good (e.g. some images are pixelated, unrelevant, from animations).\n",
"It has been tried to filter them by looking at the maximum of the histogram. If the image is very homogenous, the maximum value of the histogram will be very high (that is to say above a certain threshold) then this image is filtered out. Of course in this way are also removed some relevant information, but it's better for the CNN not to consider these images.\n",
"## 2.Merge class 0 and 1\n",
"We discovered that class 1 has a very small amount of occurance in the test data et. This class, (disgust) is very similar to anger and that is why we merger class 0 and 1 together.\n",
"Therefore, the recognized emotions and labels are reduced to 6:<br>\n",
"0-(Angry + Disgust)<br>\n",
"1-Fear<br>\n",
"2-Happy<br>\n",
"3-Sad<br>\n",
"4-Surprise<br>\n",
"5-Neutral<br>"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading Images. It may take a while, depending on the database size.\n",
"loaded 1% of dataset (529/35858). Filtered images: 29\n",
"loaded 3% of dataset (1051/35836). Filtered images: 51\n",
"loaded 4% of dataset (1572/35815). Filtered images: 72\n",
"loaded 6% of dataset (2097/35790). Filtered images: 97\n",
"loaded 7% of dataset (2627/35760). Filtered images: 127\n",
"loaded 8% of dataset (3157/35730). Filtered images: 157\n",
"loaded 10% of dataset (3674/35713). Filtered images: 174\n",
"loaded 11% of dataset (4189/35698). Filtered images: 189\n",
"loaded 13% of dataset (4720/35667). Filtered images: 220\n",
"loaded 14% of dataset (5250/35637). Filtered images: 250\n",
"loaded 15% of dataset (5783/35604). Filtered images: 283\n",
"loaded 17% of dataset (6315/35572). Filtered images: 315\n",
"loaded 18% of dataset (6834/35553). Filtered images: 334\n",
"loaded 20% of dataset (7367/35520). Filtered images: 367\n",
"loaded 21% of dataset (7896/35491). Filtered images: 396\n",
"loaded 23% of dataset (8422/35465). Filtered images: 422\n",
"loaded 24% of dataset (8955/35432). Filtered images: 455\n",
"loaded 25% of dataset (9483/35404). Filtered images: 483\n",
"loaded 27% of dataset (10016/35371). Filtered images: 516\n",
"loaded 28% of dataset (10548/35339). Filtered images: 548\n",
"loaded 30% of dataset (11081/35306). Filtered images: 581\n",
"loaded 31% of dataset (11604/35283). Filtered images: 604\n",
"loaded 33% of dataset (12130/35257). Filtered images: 630\n",
"loaded 34% of dataset (12655/35232). Filtered images: 655\n",
"loaded 36% of dataset (13180/35207). Filtered images: 680\n",
"loaded 37% of dataset (13695/35192). Filtered images: 695\n",
"loaded 38% of dataset (14217/35170). Filtered images: 717\n",
"loaded 38% of dataset (14218/35169). Filtered images: 718\n",
"loaded 40% of dataset (14758/35129). Filtered images: 758\n",
"loaded 41% of dataset (15290/35097). Filtered images: 790\n",
"loaded 41% of dataset (15291/35096). Filtered images: 791\n",
"loaded 43% of dataset (15818/35069). Filtered images: 818\n",
"loaded 44% of dataset (16354/35033). Filtered images: 854\n",
"loaded 46% of dataset (16886/35001). Filtered images: 886\n",
"loaded 47% of dataset (17414/34973). Filtered images: 914\n",
"loaded 49% of dataset (17934/34953). Filtered images: 934\n",
"loaded 50% of dataset (18459/34928). Filtered images: 959\n",
"loaded 52% of dataset (18984/34903). Filtered images: 984\n",
"loaded 53% of dataset (19515/34872). Filtered images: 1015\n",
"loaded 53% of dataset (19516/34871). Filtered images: 1016\n",
"loaded 55% of dataset (20046/34841). Filtered images: 1046\n",
"loaded 56% of dataset (20583/34804). Filtered images: 1083\n",
"loaded 58% of dataset (21109/34778). Filtered images: 1109\n",
"loaded 59% of dataset (21639/34748). Filtered images: 1139\n",
"loaded 60% of dataset (22174/34713). Filtered images: 1174\n",
"loaded 62% of dataset (22703/34684). Filtered images: 1203\n",
"loaded 63% of dataset (23226/34661). Filtered images: 1226\n",
"loaded 65% of dataset (23747/34640). Filtered images: 1247\n",
"loaded 66% of dataset (24265/34622). Filtered images: 1265\n",
"loaded 68% of dataset (24794/34593). Filtered images: 1294\n",
"loaded 69% of dataset (25317/34570). Filtered images: 1317\n",
"loaded 71% of dataset (25845/34542). Filtered images: 1345\n",
"loaded 72% of dataset (26376/34511). Filtered images: 1376\n",
"loaded 74% of dataset (26901/34486). Filtered images: 1401\n",
"loaded 75% of dataset (27425/34462). Filtered images: 1425\n",
"loaded 77% of dataset (27945/34442). Filtered images: 1445\n",
"loaded 78% of dataset (28468/34419). Filtered images: 1468\n",
"loaded 80% of dataset (28998/34389). Filtered images: 1498\n",
"loaded 81% of dataset (29520/34367). Filtered images: 1520\n",
"loaded 83% of dataset (30063/34324). Filtered images: 1563\n",
"loaded 85% of dataset (30592/34295). Filtered images: 1592\n",
"loaded 86% of dataset (31113/34274). Filtered images: 1613\n",
"loaded 88% of dataset (31639/34248). Filtered images: 1639\n",
"loaded 89% of dataset (32168/34219). Filtered images: 1668\n",
"loaded 89% of dataset (32169/34218). Filtered images: 1669\n",
"loaded 91% of dataset (32696/34191). Filtered images: 1696\n",
"Skipped 2984 happy class images.\n",
"31196 are left after 'strange images' removal.\n",
"Deleted 1707 strange images. Images are shown below\n"
]
}
],
"source": [
"#Load the file in csv\n",
"ifile = open(emotions_dataset_dir, \"rt\")\n",
"reader = csv.reader(ifile)\n",
"\n",
"hist_threshold = 350 # images above this threshold will be removed\n",
"hist_div = 100 #parameter of the histogram\n",
"\n",
"print('Loading Images. It may take a while, depending on the database size.')\n",
"images, emotions, strange_im, num_strange, num_skipped = ut.load_dataset(reader, numline, hist_div, hist_threshold)\n",
"\n",
"ifile.close()\n",
"\n",
"print('Skipped', num_skipped, 'happy class images.')\n",
"print(str( len(images) ) + ' are left after \\'strange images\\' removal.')\n",
"print('Deleted ' + str( num_strange ) + ' strange images. Images are shown below')\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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UCsbyKNY5OGLJk215OQ7aqiHb1rLlXv2+RCJBTU2N2doHbbETV4PpwGuN2L9/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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1234cab38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# showing strange images\n",
"plt.rcParams['figure.figsize'] = (5.0, 5.0) # set default size of plots\n",
"idxs = np.random.choice(range(1,num_strange ), 6, replace=False)\n",
"for i, idx in enumerate(idxs):\n",
" plt_idx = i\n",
" plt.subplot(1, 6, plt_idx+1)\n",
" plt.imshow(strange_im[idx])\n",
" plt.axis('off')\n",
" if(i == 0):\n",
" plt.title('Some of the images removed from dataset (max(histogram) thresholded)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore the correct data\n",
"Here random pictures from each class are shown. As we can see some of them are with glasses or covered with long hair as well as some pictures taken from sideways. This is, of course, an additional challenge when training the neural network. "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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LWZO0j7xRxlmS9t57b0kDP1y/EJ5GkRbn+1ghgPsSFilmkJ0TdVcWGsoce/Wr\nXz1t47xMz8TSxllifBamio5j7eEcXwORDY7dfffd0zb0PWFjHibJc+i3jxN9itw4nfibgjPoGJ/f\n8d0jC1vl+VlY/LLg8wDQt6yvjAP9jv5lrkrz5YydDqyfMURQmn+PzNbKuOO9n0cbfM/C8jK6R1nM\naDJWmCor171SmJy/lzIvoNNYWeVsfiyC8sgUCoVCoVAoFAqFDYd14ZHJNmTiy5CvV/8SxloRSx9L\nQ5I/1mn3PMSEO7c28Jxs08uYsJclV8X7SPNJ95lnBUtLVpIufnlLw5f2mMUk8xzFL2e/J1ZCaJd5\nHriXJ6cvssnU9kL86scLIw1WLawrnmh9zz33SBo8eT4+ZCdaoqXBqsk5blHCQoo1Bkuo/01/vRzy\nQQcdJGnwNLrXJZbBdSs6VjesIJ70HYtYuNWOZG+ud08OdOF6T0S+9dZbJQ0W/WyzzGUgbsQmDePC\nYuqlc/GQwJtPfOITc/fEuugWKO4FPfFqSQMfoItvkIaOyjYV43x443MMHZKVOo5FIVzfRBl02XVP\nkTSbtE9fkFOXwWiVdmshz0FuMl4jSy7z7kHb0YCembc7btjmHi5oHQtySEM59/3333/mPtKgI9Ev\nLkv0hXmX6RCu94Rq5JjCA8xVvwcFQNyTg4eZ+QofIx3uK1h0TCtthOkeanQBJZZ33XXXaVtMcHZd\nwj0zq3t8blauPZ7r8wrEMszSID9Z6dy4IaZHIaBD0Am+lsSiKcg0BWGk+c0WXQdGneLjXbb8ZXp+\nLHogvk/wjuTn4r3C25VFSWRFWqK3xHkd+e7vWNGDlsktvMm8NPE65018l/a2WPzGEYsL8P9s64+s\ntHP2ProtuO9pr0KhUCgUCoVCoXCfx7rwyIx9lWERcGsj1im+7NwSQZwx1lj/Eo6x3R5bHD0xmfck\nlqjzNo5lX63ZJloAS89Y6UTvZ4wlzb5us+eNWX2jJ8etMFu2bJE05BS4RT9afbcXMssXx2644QZJ\nszkyceM/jxeGHliX3PPnMifNbjQZY9+d79wfi6dbdukDlmvPv8Dqg4WNksuStOeee848x0uuxnKx\n7pXAgsevexCQPaz1Pv/wzn3yk5+UJO27777TNuYf/cT6Ky031hkLmM/NmDfj8bfQKnrI/BjzzmUe\nayTP8baoc9zTAT14rvcF2mL9dL7HHCWnYbYpZ+wLspjdE/r4RoxxM8/MMg+tXfcwV8Y2yeR6lzOX\n//UC+unnxOnuAAAgAElEQVR6N3rXne/M/Rg7Lw08PeKIIyTN6l23bEq514x57/Mv5nH68+gzVmb3\nlnIe1/vGnZR5Rm68DXm+L3hmsjFsy7h8fWH+HHPMMZJm52O0OPtawD3GSkFnebm0IRvw3N89otWf\nnBVpWP/i5r7SMH/jxoo+hmyOx1wSvJTZexT9d53LddErKi1f7pjLWX5H9L5I8xtSMnanFeezHvq8\nj96SsRLLWURS7If/Df1i3rM0v+Zlz822d4g529k6yvj8eTFPKHuHj/nufj3ywTnZxvCLYONrrUKh\nUCgUCoVCoXC/Q33IFAqFQqFQKBQKhQ2HdRFatgh891ncXlki8u677y5pNlEP4NKKZYqledehu7Vo\nyxKZokvOEUszZ+5ojnl/x5IA41g8OWysfCTAjepjx11NyVQP1SPkitCKa6+9dtrmiaQ7Ak4XQmFu\nv/12SbO0xvV+2223zbXF5EkPJ8GtD33cTc558NZDP2JJZk92JoyHcBQvhYoc8xwv1YrM77bbbpJm\nw3UIMyPp0mUCGSL8y5ONCRuDxx5+QP88yRggH9DVXeoeprTWoO9Oa+QSmfdQr1gWlXA5aQiVhK7O\nd8poErLlbTGEFNpJQ2gZyMpZI3vOP2SIOekyEcO5POQDOsBvvye6ijA57yfPg3/Od8ITud7HDj3o\ng48vhpl6AYHtHVo2FqrBfIjhg9JAB+adh2gSqoMMeegcNGKOOT0J8chKj3IMunq/0QVZ2VfWQnQd\nSfx+D/hB+Jmf/5GPfESS9OIXv3jaxlxB9rZn2M+ykBXCycLYY9hXtqYTso4s+/WLJF8Dvy6GkI+F\n0iBjHsaFfGZh7fCR9xlf75FB+uY6JZaE9ner+H5Bn7zUeiwT7+8SMaQ1K/+7LGQFSuhHtqUBx2IY\nu+s1xhj1sDTwMoYPSvOFRjy8NW5vkZUjjukIWfgvujkL+8v4jgxl75UxbCwr1x/fqX0cHIOmY/ok\nK76yCDamhioUCoVCoVAoFAr3a6wrj4xbFGI5OP8S5msTa5NvJBaT/P1LmK9O2rINkLIELPqFlTrb\nADDb2DJ+UWYbVLqlDfCln21sCeif04zx8dyshHQs3ywNX9hZMQP+xqLhFn231mwPRMuZ84giBJ7I\nD7Ce4rnwJFcvOSzN8iNuYnfXXXdN27BGYWXwzbBiiV23SnF/aO0eDCwk8N3lGo8TVn8veczzkClP\nSMRLg+w6//CkcL4ni0dPhctgpJlblJbpkbnzzjslzW44iF5Anp3WeODon1uu0R1ZKUr4hnfHrVrR\n0+sei2hF9+s4Bm+95Gm08npfooXc6Qs9Yr99fHgSfK5Er4LrKZLRkQn3CsZS+JllLbO2epGMHQGn\nS5ZoDGLRk49//OPTNnjERpPMR2ngLbI35s3ISp7GDZC9n3hf3HrK8/Co+nyEN4zBIxn22muvmXG9\n5z3vmbadfPLJkvLSzFmp6vWMsY0Gs7LiMeKBOer6kHLL2T3RCbEErTSe0B09Mo6VNrDO1mb65P1F\nRhhb9u6BJ8HXi1jSPbOKx3XKvb14ifj1tWEsUiQrZrKWQN9mJa7po4+V94i48XXmUc02GYcX2ebB\ngDE7T6N+GivlHTfIzODrFedlXhcQPbPZedk7J96sbFN13pGyTdVjMZQskmYRbAzNVCgUCoVCoVAo\nFAqGdeWRyeBxygDLZ9ygTZr/anUrUjw2tvGjWw3GSjNnFpqV7ulftnwp80Xrz4vWCf+q5osXa4p7\nAmKMvd+TtpjfIM17r8ZKvbr1YI899pg7b5mIMbo+hltuuUXS8IXvY7/pppskDXLiVnvAvTI+YKX2\n0qlYaDnmniD6ifXGyxPTB/jnFh6QlYqkfxlv44ZobqHlPCzz7iXAas/1bimJm6q6tYm/s7KVywS6\n4Jprrpkeoyw088JLVkdrGDIiDblRlFV1PcP4sKplsc+Z1Tda7R14wvCQuaWNvsS8OWmwZsE3t5Qh\nQ7S5tREZQr7c6gpv6a/zD+sq93bdE8uRe18A88D1xPZGzE30ecSxrKwx6wobTvocY/7BP19z4FG2\niWDMj/TIgmh99fwuPD70zz148JTfO+64Y9pGDh18y7wolIk+7bTTpsfOOussSdLRRx8tSdpnn32m\nbRmfNzrGNuCjzXUe8yLLY4sbZrtOgP7IUuYRyPJ20K2cA/9dXpGR7P2CPiCbWYllZNjXILz0jN09\nCbE0M7TwHDjasneIWEo+yzVeFuJGnf539j5BTiNrKrrP9XZ8b8rKIEOPzIsBPVxXxrntfIuRRMiW\n5/ZAU+QtK38dN2r3sWflupGJLEccGULnZe9R0CfLz0amsvLNq5GJ8sgUCoVCoVAoFAqFDYf6kCkU\nCoVCoVAoFAobDus2tCy6ZN39hgse95+7sWIpREdM6Hd3Pa5irssSocbC1fjNQiqyEnyxPJ+XsI1u\nUMIZHISvZLuHE1bgbjr6TLhMRjPO99CrGKrndHEX4fZADC3zRFZ2NSbMZsuWLdM2aMu4nA+4O6Gj\n8w+3PC5VT3yOCYtOs5jg6W5lZI6k3Cwsi3t52eZY8tJDG/ibsJ6sZC7nUPBAGhJ7eV4W5pa5rwkl\nIIl7NUl59wa4xn2uEKpD/3wM0Dor7hFDED00MO7e7mWbx8qt42YnRMl32kZn3XPPPTP3lgYZgG8e\nzkVCd7bjNkUrYoiZj4vneogC9COkzcOdCKHYc889JQ1lqqVBd0Br7wsyEHcNl/JQzmUihkn4XKEN\nXeLhcoTqEIq63377TdsuuugiSQNdndbwm4IjWagzcul04VgsLiHN0l2a1bXMafSfy27kjespnk1/\nPTSYAgDZ+gkddmS44L0F41lkF3V0rRdGgd9ZGFdcC1zPxCItTkPWAO7luov5Gsv/ImPSIMs8w4tq\nxGR7l58Y2uWhyMyHuMWFNNAuhp35ehrL9vu6QRv6JivRuyxANx8PPM2KNADGivxkIXrol6wIVLam\njr2nwRue48+LMhy3F/Dz4btfD9055vJCigJy4+8srC88z9cnxs4aSQjmWClxb4vFbFwOxu4RUR6Z\nQqFQKBQKhUKhsOGwbj0y8YvWrcx8tdHmFtpYttC/LPlizhJnY0KjW05iYmCWRB83G5KGr0wsD275\n5IuZX2+jz5RHdSsCdKBPvqFj3OzNv26x7mMpcYsilsCYjCcNVj+sy27RP+eccyRJr3rVq7Qj4JvS\n8Tdj8QRY5AT6+AZeN9xwg6TBkuBeEKwUeCAyS1tWHALe8Jy4WaDf0y028AvrnVuLseRjDfG+cIy+\n+8aMPAf5dusN1rIsWRHZxYpGsQBpKGOMDLnsMheXkSAcNwV1YAV370ks++s6hBLOmSWINnjsBTW4\n/5iXNZZh9vPxdNx8883Ttpgg77oHr8uNN944N2YsuvDUPR8cg0fO97gxcOZNZJy+ISrWScblMk+f\nsfa5XK/Gsrat8PmAvs02d4ylQL2E/Nlnny0p3xCTzSTxYrieYJ6iy10vcS+s3t4WLcDZhnyMy8fH\nvZAbLwWNvmbeu1yjl6APJYX9XhTLyDwHXg4erMeSzGMFeMY8MxxjPrhHJnqj/HrWRHjm+p55nnkH\nkSXmms/RGAkBX30+wkeeQZEKfx6y4f1lLKwb2ftFLOwhzb9bxXK+Pj6ud1rEJPPt6eG79dZbZ/ol\nDXOfcbjcoDdZV/h1DwfXM9d8LvC+xNqRbb4evV/SIEPIgj8vlvmOif0+BraK8GgeZAhZcL0Ro5V8\nLIw9K12OzuI9iue6txf6osN8zYwRDpkHeRGsPy1UKBQKhUKhUCgUClvBuvDIZKUQORath9JgacWK\n5DF7nIflghKz0vBlyJdwthlhhpjr4M/jGF/O/iUb4yqzL28s2FmZQyzDbjHhC5hxZXHxnvsTkW3C\nxZc6VnePw6V/WR6El+hca0BXl414zDcExPKDvLglAtpibXJeYzHBs+XeDN8AU5qNRYfW5Jm4pQ16\n0l/fMIx+YQ31vmCh4jk+dko4w3+3FiMDXO+eCKypWF+8FDS85Xq3MsfSxS67zC0sw25Jhp7L8MhA\nM58PWII4tv/++89dx7jcWxNjsrOy4tDfvZBY2OGb8wg6Imfu+SOnjeu9L1jvmJMuZwceeKAkaZdd\ndpE0a9Gnz3ghfUwx98tBn2PMuzToT/JhPC8snu/Poy/oHh/Dsje7i8+I1j2fK9Ajy2OCz4cddpgk\n6YILLpi2of+yMsoxVj3bvJn54B4gvKx4ADznL47LrafMaeTN27gnPPL1YSwPAc8fa4B7DNGNWJ7d\ns7nRkOW5xg0/s3y0mAfhHuqrrrpK0jA3Xaa4N94dzwthfYHHnqvCPEK3oqtd9+GlxVvouoHz4bnr\ndvrAM7L5yTrhHgzel2IOr79L8K7DnMhyjVdjaV8rsCY6/ZlHmWcKfnMOetDnNnoX3e4ywTsU5/v7\nXbYJc+wT89a9H+igGEGRRVLwboQ+kAaZiLlg0sAvZNBLascNkLPSygD+e5RUzNdzeeP6bFPg8sgU\nCoVCoVAoFAqF+zTqQ6ZQKBQKhUKhUChsOKyL0LJF4K6qWJrOQ8Rwl+ESdRcyLnF3twPcg4QoeWgM\n9892S46782a7lmYJt57E5f2V5pPg/DrGkCXaEULBMb8Pz8NN6C5SEuEYn4cf0S/GkO0av0xkJaRx\n3XvoVyzpmCVOZiVJGTP89zbCUHDZO8+uvfZaSQPNPTGUUBHc2R6KgSzRd+c7blZcyJdffvm07RnP\neIakIbzq9NNPn7bBE57rvMXdzbzwsDPCleC3h0RAlyj70hA2RPjR9goVIDzCxxBLh8MXaaAVYQGe\nDEvIFvDQSZ5DSI274Ak/oi0La81CPGMSpSdDemEKaZij0lCGl1A0nw+EFGVJo8g6/PfxwdNYWlQa\n5JI+uG4lbBC9kpV0zXbJXmZoWXZvxoMu8H5yjHOuvvrqaRvzlPlDGWZpoDE089C5GIbpCc6xyIbr\nT/SKh4YB9G1MlPUxcG/XS8gAISUeWh3Ltmb6mxAz1wXQiDDaAw44YG7s6wkxUT5DprNiIQBfQ5B5\nQoZ8zsZEcH93QAcwb30+oa9j8r3fK/7fw77hC2uXvyNxL+7tfGLNQs5dtvgbnekhRowFuvC8rKx9\nLG/vx7KyzavZxX1bkIXaxmR5Dy2DbrHEsvP24osvliRdc801kmZDQ2MZZKcRz81CGNEPhCW7TECj\nuBWGrwmxiJTfO853pzmyyJrOr4850sSBjvXrQNz+ICtTjU5zOlX55UKhUCgUCoVCoXCfxrr3yPCl\n5xaKmFDmFszozfBETxLnsqTauGmi35OvzJhULc2XpvOvTb6KeZ6PgS9Pvm7dgkk/sTx7Etkll1wy\n8zwvDXvCCSdIGizsfk++9Nlgz603nE8iqo8duJUfZGWF1wpZQhp0pO9uMYMO9NMtGQB58bHEZHa3\njmKBgsdu+SCZDg+A04LywDzPixLAByz6Xk4TaxveHfeM4U3AUrPvvvtO2+gzfPO+xAIJJIZKA/2w\nuLi8QA9o4JY5rCfRGxL/XmtgeXTExEfnLfObNp8rgDH7vc844wxJgxdkr732mrYxZqzpFOSQ5st4\n+gaHIEv+xGpOfzOLF0Uo3KIfN81za2HcZPG2226btiFnyJeXX+Y6eJxtiId1N9u8DB3pljWX8bVG\nttkpgD4+H/C2sS74fDj88MMlDZ4YlyXkPysriwwxp12WkBN0q+sXrN543XyOxeIzHkXAGLLiAIA2\nlwnugf7MrLXQ0Usz46VDB3mhCsa1IxK4V8KYl2isn7G8bFa4h/XTC7ggE/DT9SjzCJ75OwBzkznt\n3nmuQ58ibxQWkIaCDJlOgdcxGkEavMHof5dX5OaWW26RNOtBYl2Kc87XYf5Gb2Qbb7M2+xzK1uu1\nRNzE0/sTPR3eBv3gDcVlJGnTpk0zzxjz8jngaSy6Is2/Zzn9oWnmpQXIDfz39TgWB8gihFj33XPF\nGJBrL5jAPXj34N3Y+R4LFzid4Tv88ff11aA8MoVCoVAoFAqFQmHDYd17ZLLcE77oso0tsXxgVfMv\nYv+SlGYtIFhYOObnYjnISsTxlYpVxS0LfDkTr+z9xArqZYIB8cl8nT/zmc+ctl166aWSBiuKb1J2\n1FFHSRro4p4A6JBZPvia5qt4zLKQeWaWCf96RwbiRofSELOMddNzVrgHffecFawxsTS3NFjYoI9b\nGTgPS7lbErDC8hy3wtJPLBdeojduxMfGk/5s5NKvQy4Zg8s8z0E+3cIDn5Fvt1Yhq1nJVp7D9S4v\ny8yHwAvlFkRohWVo5513nrY5jaTZjTTpJ9ZpLJDSvJfHLeXwlDanGXOL+ecWR+Y7x7wcKjzJche4\njnG6tTdufOu8pS8xL0IacuKwCO+3337Ttlju173PsWxoFv9OH/y6bbWyLYJsw8hYRtfL0kL3mF/k\nbXgeXF/H/CD3kPBseOMebZ4DDbyf8BaPnLfhCc1yB+hzVpIX/QDffeyxjGpWghhLrMv8IYccImmQ\nN/fuRUus64L1mD+T5c3ETY1jnq000JpjXkYbvjPn3ArOfI/nSEOkBeuu6y7uET3G5GNI4/MqWsrd\ni4Zs8VxfDynpzPM8T4wcKcZA5IjPBTw6PMN1YKSz64ixfKa1QMw58/5kHjiOxZwfpzn0y+ZqlH2P\ngIFuWflldLmfD5C9ONcyL1P03vj5yJR7B2lDz/kai16KeYLexi+y5LkyMTc9o3O2Zsb39TGsP01T\nKBQKhUKhUCgUCltBfcgUCoVCoVAoFAqFDYd1H1qGy8vdtbjkcGd5mVNcYri/vcwpibm4WUnOlAZ3\naSxhLA2uO9xf7nbDFUc/3XXMc7KQrVgi0hM3eQ7ufXfzn3rqqZKkCy+8UJK05557Tttw/ZI0THld\naaAftPOQg1g+NEPcuVuaDXPZHuB5hGd5XwgLwTXp/PPz4v9jSWZ3k0MrQtmcPlEG3U1MmBKhUB7S\nFEMGnIaEAVDil8RSfw4uZ0/GjcUPXK65F/f2cAlcvshlVk6Xc7wcayz76qE0y0zaJOnY6Umf4WNW\nIAG6eNI+MkB4lSftwxNCrlyHECaahV8yR2IZbWkIJ9i8ebOkWR1C6Cilb7N5SJicX8f9kROXXWSA\nUCGe74ilnaUhfCgmwkpDmEIsQS3NJ5B6UYKsyMJaw8NUYnEVpxm6FD56mMT1118/c52HZTDHssIK\nhI9B60zPx5K1DkLLsl2tsxLZ8J1QVA/xg6cUdMhCezL5Iowj49X+++8vaQh98pAU9AJt6ynp38cZ\ni56MJXZzXbatAzR3IAu0eegdocesCU4fwp+ZO67TuVcs+OPrdkwa93ck5ubRRx8taTZ8jHcNQkw9\nzBUdwjHfGZ4+0E9k2mnCc5C7rDw9/fS2ZYchZuFssbyv84Z5yjjQn76+oDfh36KyH8M/fZ1A9mJB\nHWmgHzzKihBxDvzLSjPzXA/dgoe0UUhCGviEfkMf+HnMjyzZP747Ot/pQ1w7/Z6LoDwyhUKhUCgU\nCoVCYcNh3Xpk+JLMSn1iRT3mmGPmruOr87DDDpOUf0Fj5XLrGNYJLJJuUaQPWQI0FpKsHCvWGL7q\n3cLHPd2qGcdAP33zLY6xQaInhLuVUJq1wvLFi8fJrQd8KUMP/4rn65kvZU9IXWaCXmY5wxKI1dE9\nK3HjMbcykVTL1797uOAlMuWWoS1btkgaPA5u0ceqBd/9OqxoWDm8WAPyAR/cE4d1A8uwb+AI3ZEF\n5wP3hC5eGhTLCJY1nw/Qg/PdGoLsQju/DjmBH35d3MhtLYEsutX44IMPljTIiZcqx9JNW1ZSEvr4\nJn9YSrEuOq05Rl9cF0Svx1gSphf5gGboLE/6RY7pr8susk7/kBtpKHmM7LrHEB0HrTLvCWNxCybP\n4xzXN8g6us69dMv0yGSW3Jh873o+bh7olmo8W+61BMwDeOp8QAdkhQfoA15yvObS4GEkMdb5QP+y\nDfWQ48hHadAPeImcPjFZN5u3Wcl49MSRRx4paVa/MC7o4R6uaKHOigssA1k5XZBtkxCReSOZB5mH\nDG/Lxz72MUmz1myS55EznxfoZN4BWNOl2UgLKd+UGUBL94xQSpx57GWb8ahmxVriRoxeWAfZYr3J\nPBF4y9FFriOyMtFg2R4Z+O6eAvoRN2WUhnkXvV3upeOdAV3razljzUoWR4+It6FnsjL29AEZwjPm\n+o33ili+3e9F31y3H3TQQZKG98l3vetd0zZ0Ou/b7jlELnke/Xa9yt/oH/dAwZe4UbE0/z47hvLI\nFAqFQqFQKBQKhQ2HdeWRcU8AX2bZZpLEfWJ18q9PvpKz8nNYR9jU6Kyzzpq28Zy9995bkvS6171u\n2sbXIrGv/iWLFQ2Pjn+J4iXAKoKFWBq+TrHMej/jJqA+vrhZHpZzabAwZPk6sYyuf/m69yICnsR4\n0niP7QEskYzZcwKwatInHxNWiiwPJm6g6lZtrFMnnniipNmNA7Hgk6tE7oM08BRLjZcixCKL98Qt\nXrFUp+dmwEtk8brrrpu2YWHJLK1YT2PMrzTICRY6txZjKcEK5+WssUoh117meEyW7i2e+9znSpq1\nAEarq1vKx+ST+QBdn/70p0/bkA/kxb0Z0BYrqs9H5DOz+kIzeOp8h37w9rLLLpu2Yf1kHnr5c3QI\nesZzXZAvZMn7EvNmfA5EK6XLPLyFHu5poZ9Y8n0z0DHr970FPMryS4BvfEwbVk2nJ/oWfvh6hJcM\nHe5WReifbVDpXsDYF2QVT5Dr67iZp+dG4KlFz3gOEHLNfHXvXtThPj/QAbG8uDTkwVx88cWSBn0o\nDXRkXF6WGAvweijDHN8nxsD8zTYjxEqM3pek8847T9KwFYLznHme5Q5wr7gFgPcBGeRdJ9twmLF5\n27nnnjvTJ5dXeI3ceht9wAPv6wUyiN7gOh8v16Gb/L2N8TLn3BOxTB3hffS1HyDr7v2ADpzP3PQ5\nylyhRLXPUdZb1l/Pd42eLF+zAHo36xNzGy+KPxedAI9cphhLJm/k1CFDBx544LQNftFf3xR0n332\nScfgch5p57zmnswPp2/2zrkSdryGKRQKhUKhUCgUCoVVoj5kCoVCoVAoFAqFwobDugoty5IBcc25\nO5uyqKeffrqkIYxCGlzbuMg8Uf6DH/ygJOm4446TNJTHlYaQiCOOOELSbBI99/BwCYC7lDZ3hxGK\ngQvPXbi4F3EZ+71xsxHC4UlPMTnKXb+45XD3e3gA4VXQxd17cYfebDdYnufu69XsvLpaZIma9I/x\nOT1xaUMDDwfCvY+b3UNiYlKdh1ARbrRp0yZJ0qWXXjpte9/73idJOuGEEyTNJnjGxG4Pz6HPuN49\ncRZ5oTiAu2vPPPNMSdJRRx0laTac5JxzzpE0hLt50jB8515OV0LeCIHzBHQS+uC3u5NxOxOa4OEy\nnsy61ohhgNJ4QvEYYgGPbJd6XOpZYQXCj1zOYvJstnMz8nXyySdPj/n94xi4BzLkZbChO+EbHjZB\nmCFhBz6nkT3mgd8TGmfFNpAlzvFQKuSa/rruycI51hpOQ/pA/1xGmDfMBx87gFbOP0JFLr/8ckmD\n3pYGenJvf97tt98uaVhPPDGXtSoLh2UeoWN9vlMkgHUphq9Jg073ndkPPfRQSbNJ5QAdAN+df8gA\nfUcfStJznvMcSUOYjevk1YSGLBurKUwDDVyPMhbkzEOmXvCCF0gaku2vvvrqaRv0Qc9noVbHHnus\npFl9H0vlsl67bohhZi5brF2Esrr8oIOYxx6ahCzxTuWFL5gPhHZDE9/tHrlBfglZcmRFApZZOGil\nZ8a1w/uA/ozbFnjqQAz782Iv0Pikk06SNBSCkIbCC7zzeTljaBvLtktDODOFIAgFd5ng+mxrjBge\n6fJC33kPdlpwPvTyFAnoAb14p8iKxKDLfEwxhHJbdUZ5ZAqFQqFQKBQKhcKGw7owmfBV5l+BfKHx\nZeeeB74Q+fojeU2aTdCThiR8afiSZaNIT/DFEoGV2a0q9CuzapN4yZepW3GiJdItAFjjsAh60mn0\nKrmlDosQdHHLGVZbvsqzTfCy0nvQMfsapg9YktyitD02unNgFcFK4kUQGHNMincwPrdOAcblfGDs\nJFG7ZwyZo0+e8B7l0y0f8BJeeYI9vMTCcskll0zb3v/+98/077Wvfe20DStKTN6XBpnl3r6ZJLLE\nGHyOYZGDZk5PPDfcy639y9wkNRaskMYtedAjK+GO7GBFzXQPY3FvaUzMd2S0isi8pdAvloSW5pOE\n2TRTGqztyLPTPpbDzOSa53lb7IOXOMeSxr3dssazkX230q6mjOZqEXkszXurnS7ILqXU3RrNWLmX\nj4FSudDfPUB4xqCLl01nbuIdwqMqDcVjmEc+BvgO7XwNQMdhofW+cB1rmxe0wZODR8afxz3wILl3\ngLURmfUtAyiOwtjj5sPS9t8kk7mceWezpP94HnPGCxfAB+jrWz+gb/E+PP/5z5+2ITfIiM8FvCVY\n5P19IeqQLFoilvv29ZA5zrroJXPpC+8e/s5E1EI2BzjGPXmPyjZdpL/+nhFLfDvdl+2RyTyNMek9\nK45EG7T1UufcE29btpZTlMdlidLY8M11UIwQcLqw1QBRAfTXy27zzoH8uLc2ek1dL8YIA9ft0IB3\nW5dhZIe+8C7h5/AcvL5jXjGX4dVs5VAemUKhUCgUCoVCobDhsC48MnHTIWn4WseiQU6CNL+ZmW9U\nyDEsWG49Io+CL2G3QGMhzzb54+uS2EDPuyE+Equal9rlq5xft05gRaV/boGhf3wBe5wqY8/iDbEI\ncY4/D6sy1oPMOs2Xs9MlemL8C3qZllbgMoFlAD54Tgd9gY8Zb7ESZdZifp2eH//4xyUNOSHPetaz\npm3QgZLeWDsdWKzcwg7dY6leabCU0T/37v3SL/2SpME75JYWYqy5zi3CyBlzxPPJsOQgwy67yA6/\nbh2J5a89z8etWmuNrPRoPJadA69c5rEkZZZD7sF1rkNizLpfF3O3nEfIVcyH8fOzHCBkhz65xQrL\nHkw9zg8AACAASURBVLrO49HxKCNTzheexzHP90Aes02DkYHM6o7sIUNebtRzxJYFty4y9zOrJPSH\nZl4Ge/fdd5c0eCM89ytakX2DQfTEi1/8YkmzPMLSyTrmOSvMG/rnXpDoSc3KiqMHr7zyymkbfOCY\nW4LJ5/M8QACtyPXDeipJhxxyiKSBxj4GckqxVPtahX5Brn3t2FH5M2ObZDIP2IIhy4kFnivBusT4\n3Gofcwecj2wjgYXdvW5xLaaP/lzkE3nwCAl4hO5yb0/cbNU9sugQcroyz1zc5NH7FN95snw/rss8\nz8sCPPLn8Pz4K83LCfreox3Qa2xk7HoGOuJtcT0aN8J0Tw70RhbcA88azrzKPJ2sa7Rlm6IzTu8T\nazp9ci9dfEdyfc4x1gTo7Osc96RPvkbHddHlJVtnVkJ5ZAqFQqFQKBQKhcKGQ33IFAqFQqFQKBQK\nhQ2HdRFalgE3VCzPJg1hEy95yUskDUmT0pBMyfme3IyrGBdg5prDheihGLjScJF5eA4uW9yr7hqL\nycnZDuEeAgVw6+EKdNcv98gSg7knfXL3IOPhuszVSehItls5bmx3NWdhPGuNLNwQEK4hDa57+u4h\nDvAdPnrYBPJBCIbLSwyzcXmBLiTxel9wk0IrT8aLieTuPuXZhKu5q5qS4/TT70moCHLpckcYZBYm\nFeXTiyCMhbaQEIxb2cPOoD/hKGsJ+pIl5mchI3HHa5cfeBqTOaXBHU/oT1b+nF+nIffPSoDH/rrr\nPSaW+pzmnlniMvdArkkeloYwWuSLXdn9/rR5oQrCYuiThzeiX2hzmhHWgnz6XHH9tdbIwsfgHzrO\nacfY4zl+HiGCvgYQIkLoFeFkfj7hXC77MRTR9RL6Gb77HItlzL0NuhNGRrl2aVhPuKeHXe+8886S\nhvXB5y2hLNDDQ+4IVSWE1UN6aYshMT6GLIxsexcAiHCZQBcTqvnyl79c0mwfY2J4VlyCEDEP0UVv\nZ+X9OT9LakZ3oJM5x8OQoDXP93Wb6wmXy7Z+yBLKmReEGvk7BHLD2KGJX8/fWUEe9BQy5mFPy5aH\nrHx1LAoxVnyA/vl6zXsFvx5SiD7M3tOgA2u4hzDGMDffBoR3TNYj1hLXfchAFpYVUxxcXjjGu5GH\nKSI7WVEJ6BILT3iqBHLKOuDyHosL+HpaoWWFQqFQKBQKhULhPo1165EBWXlNEkmxjvkGX3EDObcG\n8pVIW5Z8mFk+sXxhffdkJyzcfCV7kYC4sZp/pfJsrH7+lYpVLZZ8lIYvXu7tX9XcC2tClkAXv66l\necu8W2+4B1bbZZdJjMieF7/wpcHSCf3dio4liVKobmnFykESvfMfazT3dO9Q9MT4xnrQE2uWy0Qs\nA+lWe6yalFl0qwh0YJxuFeFvnpOVMOQYlnrvO/RwrxL0ow8+VxgDFii/Z+aFWCv8zd/8jaRxq1rm\nzaBPP/3TPz1tw9IFzdyqxT3gh1vMmJuZpROZYE4632NxAJfrWKbdr4vy7/+HJ/A48w4df/zxM2OR\npAsvvFDSYHV1LwHzAB2blb1nzH5PdGRM9pXyAgdrBXjlJUzhDfRxmYhFUlwnR8u4JzFzf5JvXfdA\nIyzMvjkutMVThVdEGiyy6Jms/DKWbQqHSMM8RS7dwxw9jO5R42/una1H/LpMoNvw+LqXh+TnrJRv\njFLw9WiZFvixTXGZPy6T0OX1r3+9pGHsrhOy9R0wf/l1fYFMoWP93SFuNu1rOUDH0l+30COD2SaG\nceNsB33h+d4ndDteE2/DQo7cZrRgbYbXLkc8j8I46Jrticw7m3mWYsEX6O+8ZY4h505reMM5ri/i\nhtk+Z3gux5xGRAigE9A32XrBODOZyjZs5rl4DN0jHLfx8OvgN7/Ray8N+hGZcj3LHMvWiGy7hZVQ\nHplCoVAoFAqFQqGw4bDuPTJjpZk3b94sSdp3332nbVgQ+MLzr2yshpmljvtjQXBLK1+SfCW7pS7L\njYnIvoq5f7Z5F1/A/PoXe8yt8L5wryzuHwsJx5wu0UvjHgu+vuPGmPH+y4L3M5aC9ufDI6xiblHA\nWoDV0OOMGTuy4BYkrAvIi5epxQpKDLPHlGP5x4Li1oa4OWMWPx5LGUrzfMhA330MjJ28FgfW4U2b\nNs21MS7ivd1bQ9+hh1uE3QK81rjsssskzc6VOO8ybw20c88RXq8Y2+vg+qwcOfLlFjqsS+gZlxfk\nDJnIylQjb1neG3D+Z96PldqI1ZcGLzJ5M2xqKA38Q/c4b7HWYXn0MfA3fXeP6DI3zqUv7hGF1plV\nMnrCnUfRMu6yzFzBGu2WUuYGc80tmHitON89MqxV2foQveqUWvZ+si54iWXGw3XuNUM3Zt6oWP7U\n49PRs1jSfb1FJrinl92GVtk6sb3XjujZ9PkOjZgP8MM3tow5cVnJcq5zTw66OIuOiDrHPbPcP262\n6jmceFTxBLueipEsrlPi+4GXz4fv8NP7HWUq21w0euRdH+O9Q+58TfEyw8tA9p7F31nUR/TIwHef\nF4yD+e50iHkw2eaz2QaTrC/kT/lGptyLPvA8z8lC92XvzTEf2+dgzHvyd85Yrtl5HOnDb5YHQ988\nLzF6ZHy9W02J9vLIFAqFQqFQKBQKhQ2H+pApFAqFQqFQKBQKGw7rPrQsA24w3Fd/+Id/OG3D/UYp\n0ixkBFeguxSj+8zDNXDdEcblOz4TooDbNUu8og9+T87DBenXcQyXsYct4dbFreyI5Vg97AwXN+5F\ndzlGN6ojhkKNhdCtJbKyuoTs4I70csEcI9TH3Z9cB489bJBwEEJo3J0JbzknK2FJAqy7h9mxGZp7\nCEcMqXA3a0zw9efxd1amMIb6+DNIzIU+hIdIg1zjqkbepCFUJxYS8PPoA+WipVmZW2scd9xxksZD\nATJkYXzMKQ+9AdCTsu5OT+6RlbDkOcwjD/lBV6E7PGwp3tPDF2KobFaKfazMM78ug/CIcroeHkei\nOuNy3iKDWSGVGJLrcuBhOGsN5o+PD3oi815ynLHCDw8Di6EPLi/cgxCYffbZZ9oGb5gzPo/gKaFl\nXjY9hvR6aFFMmvXroCd6yeWMfmahTFwHrXxtXCT5Hnr42kPIItd7SG/UWb527Kjyy1m4IWtiDOPL\n1muOub6JIazOxxh644B/6G0PSeNv5pWXbQbwgTH5ehHDj1x/M0766Qn9sWCQ69pYUhkaehgR9+bX\nQ5rZ7R56e6gTa/Sy4eMZCy2L8gm/nY6Mm0IgPp/gBee7vod+0Nr5xnsEutnndtxCI3t3ZCyEeLqc\nEi5GAQlfZ5CFuO2JNF/S2ceJnEKLrPwyxxinywv9y0qBrwblkSkUCoVCoVAoFAobDuvCI5NZU2Ny\nVlZ2NPuKu+WWWyTNW4qkwaLAl6knq8ZEck+YxnLNc7JyurFsnjR8TWN58BKhICvty9csVo2sVDKW\nRU+YwxrHF7R7jqAVz3GLFM/jnGyTTtrc+usWpO0BeIs13a04jAuaU5JSmh+7W4ixomLx8sQ5LM5Y\nbd1yAt1JYHSZiAmzzj8SK0mAdosw46GfboWNJTOzMsP0z60hWFqwCN9www3TNjb1o3/uHULW8Uq4\nhT0WqnCZzzyFa4WXvexlc8fGLDhRr7hVFBmKiZfSfDKqj52CAVGm/Bi6wy2dzBue53zn/mPW20US\nUv2ceA+3hscyw568jYcKq53L2ZYtWyQN/PfND9En6A4vrOAbKK41ohfM/87Ky8bSpZkVnDb3xHEd\nFlP3PEAjZMjvGb1srguQM/qZJd9CV9c98IjrXV/7uhWvi/d2XR6TwjOPDvB1jOsYu9M6ep+dR8ss\nyZ1t2RDnT7YhJp4YCjJklufMaxqjMfy6OEdZL6T5kszOq+gR4Z5+PWsI7wKue6OX3uc8awKylZXK\nZY30zXLRU4wXGno0Qixi4kU4mDOsq1kxm2UjS/Yf23Q4riGuD6MnNotyybZEiGu5zwuiOZAv52ks\nY54VpuI5rPeuo/EU01+PzuD9B15m48y8Uowlev59DsTIgYzXyILTMCvvvRLKI1MoFAqFQqFQKBQ2\nHNaFR2bMqprFasZNivwrDqt9Vn6Zr1MsyP4FjlUEa5dbFvhaxIrrX6tYOrDYuLWKY3ylekw5Y8AC\n4l/O0Qvl1lusL1g83TKEBZkv3iwWkfN9fFji6VPmIctyVpYJaOBegltvvVXSkJfiwBuVeSXgLbzJ\nNq6KsZ7+bMbs1pHzzz9/5ny3Mpx55pmShnh6t8Zg6eZeHr+PnGHB9rFTdjMrUwhvGHu2MSIWU7eU\nkBeEl8ZzJXgefXcvVswn8/G5dW6twbiyjctA1sav95P5Bj28zCUWKyyI7q2Jc8Tj4aMF2i1l8AGr\nplvtsazigXP+IVeMK5ubmSUxK40a2zL9if7LrLX0Gc+MW+3xNDKfvM29F2sN5rLP21hi2XUrPMli\n8uE757gsw1tkP/MucE42Bzjf+Y7HHL47zWLehF8XNznMNvXMZCJ6DFw+Y56Oj4/xxPLN0mDlxZvh\n82Esv3KZOTLRSuzPy54btzTISkNHa3iWzwSPMv2LbPm7A54f1nfmlSTdcccdkoY8UHS0e0h4Duvb\n7rvvPm0jt42xuIzE94uxzQu9LdIu83jGUtS+3rCGPO95z5M0lLuWZiNLloHs/WVb3mWcBrHctr/f\nIUt4TZzvzB+u23vvvefuf+WVV85c72NA3vj19wT6wJrlaxdrD9tPuHePnBo8Of5eCZCFLLcuemIW\n3SYgrlN+zlje69x9Fj6zUCgUCoVCoVAoFNYJ6kOmUCgUCoVCoVAobDisi9CybUUWdoZ7ljCwbAdt\nMLaLaOYGw+2alW7MkugJH8L15ztcE96G69BdeYRC4FZ29yB/x92dvQ+EUHn4RExoy0L1gI8BV2GW\n/JnRaK3hpaehJyFRvssxSfq44H18jAEeZ6EmuGSdR9CB36uvvnraRngbdPHnXXHFFZKG0AEv1XrB\nBRdIkj70oQ9Jkk444YRp2ymnnCJpkInLL7982hZ3Yfbwjpg8lxWOYOw+H5DdbAdd6ICr2cMtoFWW\njBeTjdcSWYJ9hMty1A8e2oIM4FJ/yUteMvccwge9AAT3oA+Zmz0rzRzDGLyAAGEVhBr5nGaOjSU+\njoWW0b8sGT7rWwwR8PLUhK4gbz5Xom50mVxmaFksOe/9IvzH6UNf4LvLPmFShFx4GzyJYV3+nCx8\nib8J+fLwk1iS+xOf+MSK4/TwEZJ04c2hhx4697ysL1FPZIUAoGNWTIQQM18LYuEIvy6Gi2TFDJaB\nbF2KBQA8/A+esL4g575OxHCerPw9tHCa0wbPfJ0nBHnPPfeUNKwbknT99dfP/DK/CDnz5/HrcyBu\nNeD6gz5lSddxZ3jXbyvtDJ+Vp487tkvDmsl9fK1YZvGHlTBWfnkRoOuybR2cF9LsPGGsWQl4dEAs\n6iTNr61c73yJdPf5SFgiMuhrEO+M3Mvlhb7EIgXSfBGbKD9+TiZvPCcL+fR3v62hPDKFQqFQKBQK\nhUJhw2HDeGSyDajiZjrSYN265JJLJEkvfOELp23RguEJe9yLr86sNDNfpr4RI1+SfNH6ZmhYcrFY\n+BjiV7mPAYvXU57ylLm+xD651ZcvbY55cjpWSu6dJTRyXZbImlnDl5n4z9c7yfHSwNu4IZc0n8jv\nfeM6+O1lqaERVnG3RJCID+0oRezILElY8i699FJJsxb9ZzzjGZIGK5yXicbTRJuPb/PmzZKkY445\nRtKsVTTOB7cWjnnNsOjCW+8L1hva3KIUvTyegMoYloGxpOGxEpogm3/IgvP24IMPliSdc845kqSz\nzjpr2rb//vtLGiz07j2JG9E635nDsUSzn4fu8DFgOc68z4uMOdu0LiLz4EVLmzSUNofmrgej18ut\njE6jtQYWRNeRPBvvi3uESKyHHu7pYD6gC/yejB2Lo8+/WHzG+cGxTE9Q8hRLNeXQHfTXdTL3p7/u\nXYjJt5mlMxaacbCG+BigQ5YMHzf1c1pn3sA4hmUgozXzjjnqnjVk/bTTTpMkHXXUUTPXSPMeCvfW\njHkhmCMk+7uu/Iu/+AtJ0lOf+lRJwybe0hBZEhPJPcoiljx3WY56MbOQc89sI0fgRSYYM3IQoxmk\n+aIYTuf4XuJjyd5H1hLZ1hlRBhd5n/FroCky72sfvMiK87COcY574ihWBf/Y0Fqan5v8uufiqquu\nkpQXl6DMPpEsRHdIQ7EOZMn5FnWKIyt25GOU5iMoshLY8Vl+70VQHplCoVAoFAqFQqGw4bBhPDJj\nyLwEmzZtkjRYV6Thi5YvS7c2YDUcK9OIFdW/Nvmq5kvW4w65J1Y1/yrn2fTXv5w5b6z0dGZ9j5uo\n+YaKcRM7t/aPbQwVc2PGNhlbS9x0001zz8diTR6Me1awimCJcGsDFgvOyXIlsJx5bCv0w9LiFoKx\n2M4sBhUwHqwinj8DeI6XZSSeGg+VbzgYrRrZhmxxk1Vp8ARg/cusZ1hcvaw4co2nyq0xMTZ42djW\nOGcsgIzPNwdjM13mtlspoRH0cJnA0shcdo8vx5Abt/YzBnSCPw9PAHzLchDiZobSIAOZFyvKSxa/\nnZXopX/oQbfoU1oV66zLkufZrDWit1Ua5JG57XoQ+cTb6l5I6MJYvDyp5+NJs+ODl5mXgb7AK893\npC/Qk3w0aaDj7bffPnO9j4dfn+9RFrKy1FkpaMaTzV/mCvPe8zyge+aNin3aXuWXs02m4VEm14zh\n2muvlTSsLwcccMD0nFhWNtvwD1n0cWIhZw64hf3UU0+VNHhkvL/olzPOOEPSoIsy+sboDr8XfHWP\nB23w35/LnIGGTqfoyck2e8TLwrHsenjh60ZW7ncZyMovL7J2ZOegW9HNrtMZN2P0d7+YM+I85Z7k\n8HkbOou5TZtvOMz1nOvPhTe8D/k7Ep5C5oJ790DmXY55cNn7c1yLHPF8P2c17xLlkSkUCoVCoVAo\nFAobDvUhUygUCoVCoVAoFDYc1kVoWdwBWNr2neRxu5FAScK1JL3oRS+SNLhP3X0W+5CFaeCac3cd\n4UOELbm7HlcjyZg+PsIJeK6HRoDM9Rld21lSFv3LElLpk4eFxOT5LDQpcyMvMzwAt6LThbAq6OiJ\npYRHEQbmYRqEYRGu5vxjrCQIO//gDS57d8FDW/hIgr40hDOS2O8uUkJLCNPwRMm4A64nSZOMxxg8\nRIW+ZPOIv2PIgDSE0MQkTn829PTkVsIk4I23LTNpMytBDE/G9EVWwhhZZ5zOd5IvkSlP2qcQAPPe\nk3cJJ0BenJ633nqrpIE+hEBKs4nxUh4+Enm8NURZyEoCZ0mc9J2xOF2yMFiAHBNe4/MvK92+Voih\nW9LAW/rkfODvnXbaSdKwA7o0H3bkOpKwVuZdtlN33KXa/4YGWbgEbR6CR9gY5zvfYyK16+tFkvw5\nx9c/5k82f2Ppdtc9zJ9s7FFOlpngvzVEveD/Ry8g66wzFPaQhnmUlS6G/q7LAaGLlMh2HpPcv8su\nu0iaDREi5BjdAF8IMZYG+aRvWSGQTPfxPpKVyo3j9Tbow724j8tRLBZAiJM0yEbc9d7Htyxk68O2\nyGMWmoZM+LtKlJMsxBMQPiwN5ZaZa742UIQEZCGUT37ykyUNvMnSBHieh75zPmPwQlHwPSsvHufF\nIu+lrq/iO4+PZTUyUR6ZQqFQKBQKhUKhsOGwLjwyYxvcbSv4Ej399NOnx4488khJ8+UDpeFrEWue\nf1FiZeR8T+qi73x5uxUqlk3OykJyjtMgbrTkFs1o5XJrCP3kWFail69q7xvnZQmbMbFsbGO+tQRW\nPzYEk4aEbDaa9LHjXeCYW7dIsCRx1q3MAK+CWxti4ptbVegfljbflI7kTWjslhosH7FUtjRY2LKS\nhmzSduGFF0qaLXSAFSdLtEOOYyEIfw594Rl+HkmnmScus9D7Bl7LQqYvMk9MPM89VXEzN5cJNiXD\nau8W/bPPPlvSQCsvuhDLsPr8I8ESK5hb+7GQRT5KeZGGiDHPaLYZKIjWMGmQCeaD9xPrKudTPlga\nrNFZsn/mwVkr0Be3IHo5Yml2rmARZ1xeUIMEW9pc5tEv/LpMRL2ZlUqOyebelm2gynPQHS6fRBvQ\nh2wjRDxI2fPGPAhZ4QL6wL3dIxO9+ZlHJlv3lunNzyIZWLuzDT+RIdrQ376VAt5/+p2VXwa+ZQDP\nZc3yccN3dKwXVIH/xx9/vKSBvr6u4e3hOqdzXKddfuAZPM4Su7ne1zz6RFu2nUHcMNSfe9BBB0ka\ndENWDGdZyDwyq0nyHytikb37RS9f5qXFm+8eGZ7HOwtzXRoiguAzMuH6Dt4iy97G33hRvS1G3nhb\nLFjgMgGfoyfG15Q4/31d5u9MH5RHplAoFAqFQqFQKNynsa48MmtRzjd+WfqmbWw4xiaZ2QY/wK3h\nMTY0y7EAblWLZQ79CxOrVvbFTllbrD7ez7ixk/czbkqUWcexELk1lX5lMddx482sVOAyAD3OP//8\n6TGsDNDOLYpYJznmVjGsjNDDrRwrPVca6I+1N4v7xEpy0UUXTdvOO+88SQNdsfBJg0UYa5zzgbwC\nLF3eF6woWG987MTTZ1aj6DVxGUGGsAz59cglVrPMIwP/vYxjtIavJVarJ+JccbkmPhtrpG/ORk4M\n8cY+p6Ef1lDXL8gnsui0iJsJehlWzuM5bu0dKy89RofYNla2NdN1lB52HcIGvVzn3hrkEa9Nlluz\nDEC7bL4ju3hapEF3YGF/2tOeNm1DBrAw+xzDo4YHLrM4Zh6IWJ7YrZnIIPlSvvHqzTffLCn3AHA+\n8fO+kSKeQvSKe2t4Hn3wPK2Yo+nW5Sg7nrsXIwrGPIdOl2V66bI+sO5l0Qr0HT2KHmYdlvJNmAHy\nwvrgGyMibzzPvTzwFD76fGL+IHfQy+fj3XffLWk8l5ZjPh9j7pjr9pg/7G3cg3mCPPk6ha6FBr7W\ncm/yDPHwxP7tKIx5+jMa83fcEFfK5QRAU3SQz9FYpt3XpfgcaO3zM+bg+TtLzMt2miOD9HvRvKmx\n0sorIcuFj1Ee3pdFUB6ZQqFQKBQKhUKhsOFQHzKFQqFQKBQKhUJhw2FdhJYtgtUm+GauwHPPPVeS\n9KxnPUvSrEsuS7AGuERj2Jo0uO5wE3roAO66WMZXGkJSOMcT7nD9Zon5uOAI/XD3G33A9e/Pw7VN\nKIaPYawMYaSxuwXHQvPuLSg16K5Vxgd9vPwy48tk4pZbbpE0uFazUCHo6vyHR56ECXDLcm9P2CM0\niZAt5x/0x61+2mmnzfXl2GOPlTQbfkQYAOEyYzsUj80LD1/ALUzonMsEskMJab8n4REx/ExabonV\ntQxRiuFcHkKBXO+6665zbfAyS9DGrY9cevhKDEvNkqLhUZY0ytizRMksyTzSKttpGR57iAGyTh+g\ngTSEHzEnPYQm6kbXZ66H1hqxLLI0zFvk0sfOvCPE00O29ttvP0nDOuFthPhAHw/xiTx1vsfd0Ckk\nIQ2hQVdffbWkoRiJNMx9+um6jnA4wqVcXtBx2fj4G/p44ngs15ol6GeIRXKy+Z8l8i5TTyB72RpH\nX1w+45oaZUQawsyyuQ3gsZfJRW9nIazwA53i8zC+70DfTLayd6N4zHmADGdhwPCd870gEvqNdwDW\nEn8W53O9l1iGBvQ72yF+vSCG9C5STMb1XJaGAJCJGHYqDe+DPM/DRlmXCNeD/r49AGHe0Njf12KI\ntRdpgDc819e8uD6NJfLTloWwZ23wPa5lUoWWFQqFQqFQKBQKhfs41oVHZpHyy9kX8ZhVJ27CIw0W\nr2uuuUbSUI5ZGqwhWFXckhCf533BQhKtXX4Mq717MLDW8zXtX598hWN5c8sJ1p4PfehDkmatRvSZ\nJGUvSeqWeGnWehBLg44leo0VF1gG3CIM/dkU0vsJHaF/tikkVjUvnRot3Z7gS7lnLBfZJmrQ0XlE\nycxjjjlmpt/SYNXi3m5ppbDBO9/5TknSL/7iL07bDjnkEEnzpTO972NWraxEJPLJMbfQRstOtukl\nbb5pl1vG1xqLemVXgp8LrRh7VqKXOenWd6xozBmft7FcelY2HVnMLLqxrKY0blmN52TemriBrjS/\ncafLPJY5Sk+7fokbKmabx2YbcG4Pzy3eFGnw0mD99s05sUYjpyR4S8OcZp3wxGzuhX7xjf6ixdHn\nJmPnXv48dMaBBx44d8+46aXr7+jlcd5Gj4FfB98pae8eNYoEwFP3IkcLuvMzbrjo+iVusOw6eZke\nmejFdIyV0YU+b3zjGyUNnnFp2LSSaIfMIw6dfF2EH9DACwjEMsb+zhF1EGu5r+nQM0vQjnrD+xsT\n+p1ncS1xXsf3g0zeARsAu0eHNQWPV+bBWBaydTMi4+lq+pUV8si2KKAv0BHPrDS8x1AgyOcocoUs\noj+yYlC844yV3Xc5jd5551ssM+2IhX+y9SluNJy9m9GWvXcvgvLIFAqFQqFQKBQKhQ2HdeuRidaU\nRcuPjllo+bL82Mc+JmmI/5fmS9tlm+dlG0fxNXzDDTdImrWAEVe9zz77SJq18DzhCU+QNIzdLdlY\nuInZve2226ZtWGSwbvh1fM1jNXJrAv3iy9dLH8YYSP/yZnxZDOMyQWlQaCdJxx13nCTp1FNPlTRr\n3Yz9HJMDt4ZHi8LYZk9uKef+0PHSSy+dtl177bWSpDe96U2SZunJPdzaC+Al/P/IRz4ybTvggANm\n+u5zBp5k1vdIDx8Dch2tzZK02267SRqsRV5iGa8S48pyiNYjshwgrNpeihbLeGZljse8bC1WbNr8\numiZy8pMZvOPfo5tKpjxPZZWdgtbLPvqY8eCTKllH0PsS7YpIM9zunhewFqDee95CXik8TB6vHfM\nHfHcNqyYlIc988wzp22MC0uz6134gP7NytdDHzZGlAYaoQvw0koDzdAvTmvuz/Weo+Z8jmA9uuCC\nC2au93uyxrnXmntmuVhjiOWo3XLMPIrlYtcC2xq9wfxlLb/qqqum57zmNa+RNHj3sg0OsWb7mspX\n1wAAIABJREFUOLGaZxvMYn1m7fAy4XFuklsFD6V5j4jP1THdEHWf8yBGKIzlPGXrYszD8Dbkjedm\nUQXLQtx0WJr3kjvGcmPiOcDHEDdcdp3OuOGtv49wPuXXXV74G/rznufyBi+zcuExKsPnOPcei7KJ\nHlYfc3xXcnAse7eOtF9NXoyjPDKFQqFQKBQKhUJhw6E+ZAqFQqFQKBQKhcKGw7oILVskHCzDWEha\ndl10HfMrSQcddJCkwU3nIUYx9MOTXHH946Zz1yX3IgzB3XZbtmyRNITl+PNigqjv/s79cVm7W5K2\njC6EH2ShKoyHcfr4YmiEuyOXWVaVELpDDz10eozQJ8KcfJxxd/psl2NCJDyRlfOzMtiE/+GW9qTo\neG+XN54zFuaRwRPOJemiiy6a/k3YCTx2WYrhAFmpXeCuamSP53poQkxK97CsmHjqoQlO27XGmJ4Y\nCynM2uIu3M6/GBqWlaLkfL+O86BZFrKQhQZGl73Pq8jTsV2RvY17IM8ui+gsyni6vBA6g37xtlj4\nwUMa4DvHPGnTQ9fWGiTFksAuDcmwhKVSHESaL2fs84NQDZLv/TrmHzr54x//+FxfoLXPB+SF52Uh\npeiVLLGa8FkvxY4MwY9sOwB47Hz/8Ic/PNMHL5CAzs/kOoYSZWFuyL7Tc6wAxPYKUV4JWVgT+oEQ\nKD+HkEJ4nIUZZ+VpkQXmo8/7TZs2SRqKD/m8RybQsfDMZSTqqazIRKaDor7IeJYlqfvffo5fz3gJ\n2ydESpovipGt0ctCVoAFHsZ3HCnfpkJabA2S5ktVe/h1LJLjfaLgD/PXnx956aHgEVyXzbMsfCyG\ngXrYPrqLdz+/PhYOyOb6WBGpGO7m7w+rKbRQHplCoVAoFAqFQqGw4bAuPDIZVrMR0WqBtYKkR0k6\n7LDDJOVWCr7O+SJ1yzlfjXxJejldSlpyz+uuu27aFq0bbvUlWXX//feXNEsDCgDwXN8MiWNY4fyL\nludlpRpBtOJK84mQbo1bplWN0thuUcR6ThKvW7BI0M6s4TE51ccQE+x97PBk3333lTRY5aSBD9kz\nkBcsw25lYDx4kNzCRkIn1hj3AGG1e/azny1pln/IR2aFi4moXr4TKxHnuAzSlm3gSL+go8+HZVvW\nVsKYnsg2KoTP0MWTt6EDltnMKplZruN8cKtt9FhkFqssWThu2Or0jUmUPlfGeETCMNfh1ZAGfcJY\nMi8B9/T554njsZ+x9Pta4olPfKKkocCGJJ1zzjmShjLoRx999LQNL1QGxsDvKaecMm37wAc+IGmY\nF+5lonjM05/+dEmzZZRZM7INI/kb62dWCABZdPlGJmIBFz/Gvd///vdP20gqZ31xRNnFOyUNc4Vf\nl4m4Ke6iG2kuE5kHNo4vK6jBuCi04HRFpl70ohdJGrz1/hx47O8AzHv65HJzxhlnSJLuuOMOSbP6\nfqx8ckT0JEmD3DFXnS/xncX1YtzYNtvUNxYs8XtzT/TN2HqwlhscLwrvT6ZvAfLvESjS+Oau2bYc\nFE3xgifIFV62+C4h5d7P2Af4nZ3DO4h7h2PhB3/XGSscEcuLZ+W6o9cl89rE/vt50HlbIzrKI1Mo\nFAqFQqFQKBQ2HNaFR2bRuMNFEONys+dgOfHyimxaiRcls0RmMYJYwPjydetE9Lr41z2WRK7LYpKx\nGGQlG2NMozTviXGLcNwA0C0tsUSrf6nzheybHoJlWt+xGvpzsSTDR4+/ZaMxrFr+1R9lwemJNZRx\n+tiRASyl3heeB82c12xkipcP74ufR96Nb6SJx+etb32rpNnNsGJuU1bCMGuLeQ0+1+LmV+6RgY7Q\nyvOmuBc0dnpGmV8PyGJt4S1WLbdcQcfM4xg9Mk5raARP3boUN5HMrFKxZLIfy+Lv4Rv39JLqWESR\nIZdr+odc+6aXMTbc+8lzMu8legm94tdlumOtQJ88pptSzO94xzskSZdddtm07Wd+5mckzeeuSPPe\nMr/nSSedJGnwvri84OWhzXNP8MoiLz7feXbcxNLPjzz2fsLjzAtyySWXSBo2TpYGrwly6RZ89At9\n8LUD3mb6nr5wvs9/+sz12Rq3DEQLsjRYxKNe874wtzKPDmsPOQwvfelLp22x1HCWI8X1Z5111rTt\nxhtvnOlntiFj/B1bc90yHzdG9OuQt8yLNmaZpy16mVy/xnLTWen6RTZwXmvE8vDSoJeyTSAZR9T3\nTsdFSgezsbBvhHrnnXfO9MXnPXTj3p4jTP4issTcc7nBG8xa7msQc5x1wmnB2OmLe/DjupTl2MS5\nk5Unj89yoIuybQkWQXlkCoVCoVAoFAqFwoZDfcgUCoVCoVAoFAqFDYd1EQeShXCMJbuNJZmPuWJj\nUpa760m823XXXSXNuluj+9xdx7RlSZkkjOHGdtcvYVGc7+7ZGLrjoUncM3O7RddtlqTsbkyAWxC3\nuyfx0r9YglNabrI/Y3Ba77333pKG8L8rrrhi2vapT31qpp9ZMnwWDgStccH7zsmEzuCCd3rSB8JW\nPGxwt912k5SH7tAHjjkfCWUhIfQ973nPtA0XcxY+BrLQJHgZS2z7scxtDh2hne86Hu/tWE3JxG1F\nxtsMsc3/D7+gmRf+oMgGJW89BCcWzXC3eSyx6vqF8zgnC33k18Na47zzcC70CuFjnlAay+ISliAN\n8gwNfHxRFrLQVQ+TAPCde3lfPORtrUHZZd8VnfCKE088UdKszoshFD7fCfdlDN5vQkEPP/xwSbPh\nahSKuPLKKyVJmzdvnrYRdkaIiZdfpsgC5eQ97Ay+Z6FlyAD9cx5RMpriBK6jYxnVLAyMsbsO8S0F\npDwEDrl2fU3fGYsX1CB0Jd57LUBBBqf1akvhS7O0I0SHcPQXvOAF07YY/umFAKArYWQXXnjhtA0a\nZ/oXxKT7rGx0VuwDWSKsz9cL+ECbz+cYruQ6M87xuL2E94ntJXyNoJAI1/tasewwM8K9XX/CG+Qk\nowP6k3H5HI3hov6exnWMizXFn8P88xBWzmPLDS/EAp2hKfz2MEl0O7JFmK00vOtwjgN9zbzMipJk\n4c0ci7KbvZ/QX38Ppi9ZMZTVFIMoj0yhUCgUCoVCoVDYcFgXHpkxxFJ/q70u+9Lna9Gt02w+eMAB\nB0iSjjrqqGlbLB/qloSYFJslK/HF7l/zMbHeSy/GJDC3uNGWeWb4mo3JeLFfsS2W1/TnxQ3Ass2+\nthfwYmAp9zKibA6FtcctQZmXBpDwh8XV6RQ3uPMyiWyaR8lkt0phmcEqk1nR6KfzDysQFuUjjjhi\n2rbnnntKku6+++65e0bv41gBiCx5O7OqIKtYdDxhm3Flc2yZid0g20htEf3gVmZkCVp5WVQ8fcgX\nVnhpfpNMt07HEuVu/UMeM0tVHIv3Mybyuy5yK700WzIXSxf9dJ0VE7O9DbnKPLDRc+uyO1baM0sO\nXStg/XYrOOXSoYHzlr8prOGeHMZMGWXXL1iveY5vUkzyLiXjL7744mkbz2HeUmrX78E93TKLXKET\nnA/IADT2jZ0pWhMLT0h5YQuAlwb+O2/xLNOnzENJX3wjVNoYg3savbDIWgMv29hcy8C4oFm2wSHy\nc9ttt03bkBNo6POSucL65J4czjv77LMlzXoEWI9icR/vE/MKD5TrIqIXfMsAgJ5gnL5ZIzSDFk7D\nWH6Z5zvPAfLgSeN4Puiny2F2j7XEHnvsMdMHaRg3OsC9yPwdix74OgNtkGWXadYXdIqPlbWcKAfX\nlcgu88+9wugzaEVf/F2HMWUFWZC3TEfHiI3M65oVpYhrVlw/vH/QNCvpnW1nsBqUR6ZQKBQKhUKh\nUChsOKwLj0xmVY1fwFlpwrF7ZffkWJYrwRfku971LkmzeQ3kzfAFm5WY5TmZBSMbA9YQvk59E7Vo\nKfUvfawpmdclftVmuS4cc8sEX+9Yj/yeMYbR+7JMq9rYBoeUvvRz3v72t0saLEBZTHQmE5wXLVHe\nlpUkxCqK14QYaGmQE+7pMaHQEQsdcfnSYNUgX+PYY4+dtsUY9sy7lMkZ1hfkxukSy7Y6P7GeIBt+\nzxg/7N7E6CVYNlbjqc3owgahXqaSMZCjhNdNGqz95Dy4pS2WcXX6Rk9clkvEdU7DKJ9+HTHn9MFl\nd6x0ZcyXcysYsgpPvTwxHsloNZTm9d/2koPnPve5kma9UdGL4WOgHDKed+ahJJ177rmS8vwSnkNp\nZa6XBisoutXnLWWQ0bdu9aWfWPmzsr2ZhZNjWPDxCPl1WRnlWJLV1zHuz7rkHi7yC+CxW3KhcebB\nc2+QNKsHl1mmfcwTg9dkzPsVcxCkYSzMi9tvv33ahmcN/Z1tukiOI54BaZAF3jXe9ra3zfXpf7J3\n5rHXXVX5f1BQZBBFUUSlVNpC57mFAi2llFEERKhhrpBIIholUeE/TSTRxL8UxYSCgRTQhLFCEWip\nnSc604nSgrZQoaAyiIJAf3+Yz9nPXfd5D/d9e2/5Xn/r+ef9vnefYQ9r73POetZ6dmV7vA8rCwYj\nKA3GCLtzO6hrireTccTGUk4sdoA9eX4o16RP/JkJ6F9/P/H3kU2A+vgc42+iDnzN4vnOePMc9DWB\nfuNf72Pahp17+3jXYy3yjdLpb3Lr/JkMq1uZQ38mVNtNmyvXevt9GP+Upwnm8lhqbp807HzunZzz\n0jq3CpqRaTQajUaj0Wg0GluH/pBpNBqNRqPRaDQaW4cdEVpWE36kXe9sm45PsrE1zMOPS8nfULkk\nV/31X//1VPZbv/Vbkgbd7rQZ5/FvCsGBAkxJlikZDHAtD3up8slO0XMcbfC2192gPcEP+rRKA0vL\nlKPf3+nHTSHRkFCrL3rRi6YyQjig/H2MEuVfr8nxnnRI+yjzcSAh/NWvfrWkxZABwhZqcp00+o8Q\nAU+OJ1SEMIS99957qS5JAIKxwRa8rIaaeB8QEpAkXqtkddrRmNAGDyGZ23l6E5hL9q9ykT7HkOtG\nMtcp7SpZ+o//+I9TGeFH9J2HR7AuIfbgIU2EEzC3XHa2JtF6PbkPYhIptJOQhtQHiZ5PwiSAuXLh\nhRcunU+fsb54MnwNKdsTuds9QU02d9SwJwd97qGdrCes829+85unsje84Q2SpN/8zd+UJJ100klT\nGQnchIT6ukjoKeGJnrxbQ0R8ba2hgWmsCH/xdYk2M24edkL4XRXr8OOxz7lr+nyv4dY+Hzie54uv\nL9gSQgLrxNxaQB+7HDmhL4QY0d4UhsS6fdttt01lJ598sqQx7v7cpT+R60/hascdd5ykxRAjnmfM\nf/rJ6839EJ5xKXFkuGmL9wnhtEnUoArHuFBHFa/BRnyN4BnGWM+9L6Rn+6aQwuD4jTDROTly5pHb\nMKG2tNnfHei3JCvMNQnZdIlk3geqdLnXl2MIV/O+ow3YYBKOwSa8vRyXyupaMhcWzZqUtgKYE6yo\nYe67i2ZkGo1Go9FoNBqNxtZhRzAyq2xqlyRC54QA6kaO3+++9avRv5LxzJ166qmSFhkLvAx4u9JG\nQolZqZ5k9+LwW/paxeOFZ8G/bvEM4DVyDwi/8RXvkqR4CPASu9e3Jk56Yrf/vW4kD01NRHRpSTx8\nc964uWtXGWZpeEUo837BG4Pn6znPec5Uhvcdb6V7paoUoXswjz/+eEnDc+JeP7w9dXNWaTnR1q9Z\nk0S9DVUu2L2F3C9t4FiZhyRJeW9hbu3A41g9w5J0xhlnLBzrSbT0I+f7BmJcg3/T3GRs3XONvSCL\nmoQVYOB8HBijmqgtzXvIKlISJfPA1wk8z0ceeaSkxY1C8bZhU16Xyuq5Ry9tnLpu+LpbvXteVr3m\n7rGkj44++mhJi2vBH/3RH0mS3vGOdyzdG9YFQQBPBGcO41F3O6MOVeLV/07y2bDpNUHfUddKaXlD\nTJ87jCXMn1+TPuL8tKky9ZtLEvayTa4Tc5th0xZPvq7CCvSFi2dU0ROX4q/RGN7nCHKkdxXOgxV8\n2cteNpWx/lIHNkp0Jpd5hXzwRz7ykakM1iX1BTZIG3xc6maX/pypAg2UuW3yDoLds9mr12luO4RN\ngXH3utbNgl3avkZA8H+XTye6BcEQl3KvLI3PGeYWkRvOSiIaVCOE/G/akt7z6vPQx2xOdp81M7H7\nHJfGuzIxbM6cGJYkXDIXJbU7m6Q2I9NoNBqNRqPRaDS2DjuCkZnzLNb4cWlelo2vuLR5T/KY1POA\nx1LCzrzlLW+RJJ1yyilTGd53PLQeV4sHi6/W5AVyr2Yt44vbPQV1k03vFzy7fA271wuPKV/MXk88\nAynOmWslj9smPa3J0169Wh6vCvuBR8n7LG34WEFb3GtfZU6TJ/K8886TtOgpY8NO+jgxMtUDKg0P\nD54d99RUD6vbTWVU3L45j/7wOF5siPaljTSBl9FW+tqPffKTn6x7E4xpnTPSYMv4zXNd8KzVvAFp\nefPQtLlq2jiwMpQ+RswxjkFKU5KOOeYYSUPa1+OLsQl+87wb9xjXulTv19yalzxs2Am27HXHlnzt\noYy+9rUnrXGbRJLyBNRrzgPIvy7R+/u///uSpDe+8Y2SpA984ANTGfZ/wgknSJIOOuigqYw4eNhj\nZ5Hx5DIPvZ+wuRRjTxk2m1iQlC9XN7R0Ngq7Ytx9XeL4OSaG+7lN0C9EMKTcr02gbrMgjWdGygvh\nmcjx1NPrS3/Ahvn8Zf1kPGCqpdGvzBl/ZtGvXMvXINYC7geb4WsR7yXk07iUeH1ep02ZuaZLl2OD\nMFAwStLyptjpfc3XrnoM7fU1+t4C9fI+ru8FzsDWzVyZKy4nTd9wjOe7zuWj8Bvzwxl/bA6b9PN4\nx6m5RmltT1t3MOdSPjegLDE5id3D9snPSpsf1zXMz6/vqikXdxU0I9NoNBqNRqPRaDS2Dv0h02g0\nGo1Go9FoNLYOOyK0rCbbpd9SiBhIIQRJJAAkyorjavKRNOhWqNjTTz99KkN6EzoZqk0aVCUUpNNu\nNcHTkyt3tdOwtLzzttezhkt4WAFUJzSm05nUpSZE+jVrSIa0HNqyadTQMCh1adDqtNP7bHeSCj0k\nDcqdMfU+A9DJnmjJNfbdd19JeQdtbColpLLjb9rpGzt1G2a8UhhRLfMQOELXCFHwscXOmEceagJd\nDTXOrsmS9PrXv173Jqos+Hvf+96pjPCflGRYZaxTsjh9Pidr7HOlCnh4CAWJmiTtIrkqjXHgmh7i\nwNrB2uPXTCIitX5gThAgtY+2e6hIDYHxULoaSuH26aErm0JKLp0LMavHOFKoBgnZL3nJSyRJ73zn\nO6eySy+9VNIIOyHETJL22WcfSdIRRxwhaYQMSdJNN90kSbr22muX2sB8SzKstc4e8lWFSfyahHjw\nr9su1yCcbm7t8fPqXEkhY0kGm5Apl5hfF5I913AeD8Oq0syE13nf1XBTP59xRCAjSVBjG568zTsD\n89flk+tznnnIOiANuWaS9tNzBqTkbcbR3z3oH9rgtkVb6ruAh8t5G/xe0ryk+KYT/5kXaYuBtLUE\n84fQQOrnbSCtgJQB39KCdwbG28PB6jPcQ7NZZ7AXn2uMb33Op3GvYZLSeH9JW31QlxTiVW3f133m\nAW1I/VTPd9S+SO1dBc3INBqNRqPRaDQaja3Dfe7tDewajUaj0Wg0Go1G456iGZlGo9FoNBqNRqOx\ndegPmUaj0Wg0Go1Go7F16A+ZRqPRaDQajUajsXXoD5lGo9FoNBqNRqOxdegPmUaj0Wg0Go1Go7F1\n6A+ZRqPRaDQajUajsXXoD5lGo9FoNBqNRqOxdegPmUaj0Wg0Go1Go7F16A+ZRqPRaDQajUajsXXo\nD5lGo9FoNBqNRqOxdegPmUaj0Wg0Go1Go7F16A+ZRqPRaDQajUajsXXoD5lGo9FoNBqNRqOxdegP\nmUaj0Wg0Go1Go7F16A+ZRqPRaDQajUajsXXoD5lGo9FoNBqNRqOxdegPmUaj0Wg0Go1Go7F16A+Z\nRqPRaDQajUajsXXoD5lGo9FoNBqNRqOxdegPmUaj0Wg0Go1Go7F16A+ZRqPRaDQajUajsXW47w+6\nApL04he/+G5JuuWWW6bffvqnf1qStPfee0uSvvnNb05l//zP/yxJ+sIXviBJ+t73vjeVPfShD5Uk\nPepRj5Ik/dRP/dRU9sAHPlCSdL/73W/hHv73gx/8YEnSj/7oj05l97///SVJP/RD//vdd5/73Gcq\n4zfq4HW5++67JUnf+ta3JEnf/e53p7Kvfe1rkqT/+q//kiR94xvfmMr+9V//VZL0P//zPwvtlKSf\n+ImfkCS94AUvWDqPdn39619fagNt/5mf+RlJ0kc+8pGp7L3vfa8k6YADDpAkHXfccVPZz//8z0sa\n/figBz1oKvv2t78tSTryyCNHh6wJ3/72t++WpAsuuGD67W1ve5sk6eabb5Y0xkUa7bvvff/XpD//\n+c9PZYzRIx/5SEnSK1/5yqnsyCOPlDTa98M//MNTGX9zfgJj7HD7+H5we6nXTNdJ96t29p//+Z9T\n2TXXXCNJuuiiiyRJV1999VR23XXXSRr24vaJ7fDvW97ylqnsSU960sL9Uv886EEPWrtNXHnllXdL\n0o/92I9NvzGPmCvf+c53pjLq9eM//uOSho1IYz2h7bRFGv3Hb1xbWuwjaXE8uCZ18LGlzvzrtss8\nAl5P6sK1/f7cm/vVukljvXC7nmsf6xH187p85StfkSR98YtfXDjfr/8jP/IjS+37yZ/8SUnS61//\n+rXbxOte97qlCXHwwQdLkk488URJY02Qxlp6+umnS5LOO++8qWzfffeVJD3iEY+QtNh22sW1WBf9\nONrp85bj6Ssfa36jrx7ykIdMZVyDsfVrch5z0+cf16Dsv//7v6cyxp2x8rp89atflTTmA/86uG+y\nedrp52FLnHf99ddPZV/+8pclSVdfffXabeI73/nO3ZL0xje+cfrt3HPPlTSeX9iwNOYN7aJffM4w\nb3l+8p4gSQ94wAMkLdoZwG44hvcTaaxLzFGOlcaY1rXV+57+pb4+HznOxx+wLtX1Kl3Lz2ed4Lf6\nrx/j10x1T22TpDPPPHPt9iBJp5122tI6kZ6lID2XdwdcOz3L+Q178XX7s5/9rCTp1ltvlbTYj8zt\n+gzxfqzPCX8XoIz1jfc9rwvj7rZf+2nuHWSuT+fOA+md5/d+7/e+r03siA+ZL33pS0u/sWDwouIv\npv/xH/8haRibv7CzYLDQsEj4bxiAl9WPHL8mhsLg+iBTxoCkl4n6seO/UV9/OWMw+UjxweWBU1/g\nvA7pQcWEoA9+4Rd+YSqrL/G+IPI3Dx5eZrwNm8Cdd94paTyAHExEXlik0Vf//u//LmnUVxqT9PnP\nf74k6aSTTprKeHjQx96mVT5I7mkf+PnYx9wHDMf4AsDf2K6/EPFRetttt0ka/SqNtnOe35f5wOL3\nrne9ayp7whOesMt6bhLYsC/u2H96gDLutMHtmuOZr7wUSMsPH5/T/JYWbuYy9/H7VUeHz1vqmR6e\nvBzTLn8w1ZektBYwtt4vvGxyP38BY21MD0LAy2B60edfXyPrh9o68bM/+7OSpH322Wfpt9tvv13S\n6AP/++EPf7ikxXWQfrnyyislLTrPOI8X0b322msqw2nGC533iz9HpMV+8RdXadEpxXGMkdsS/cn5\n/uyoH7X+YVFfjt3m6wus160+Z92W6BfszW2wfnDhSJKyXa0LPCP9Y4VnPc9NX3f5m/6n730ceW5W\nh4QfzzzydZH+YV65PdT+9LWEa9UxS+8Q9V7S8gup12nuOQPmXkznPq6wu3RtypKDML03rROrXH93\nX8JXuVZ1SEhjbvGu4g585gUfub42s85jN/zf1xTW5vpR7vfj3eiss86ayo444ghJY+30taGO5Sof\nJHNlc87f9F6zCjq0rNFoNBqNRqPRaGwd+kOm0Wg0Go1Go9FobB12RGgZFOPP/dzPTb9BNRHTTN6I\nNKhR6NoUjw01B/0mLVNyTsWuUr9EiVbq12k+wG9OlXE8dUjnpdABKL9/+7d/k7SYAwRtDjyfpYaI\neYwvYRL85mFZ9GPtO2kxhGndIITNQyOoy+GHHy5pkS4mtOQzn/mMpJFHI41Y+ec973mSFunaGl7j\nY1Tpzt0NpUr0fL3Gqtes1/K2z8XzYmennHLK0v1OO+00ScOmPCyEMAvud+mll05lxO/ut99+khZj\npDcZbogNesgH/YDN+tjO5a+B9FsNuZtrn9+vhif6+sJx/OtrFm2osfr+W6LeCRvgtxQrzZrgdaHN\naaxqCEYKV6vhZ96uFAqV4vTXhcc85jGSFkPLsA/qfsUVV0xlhHjwnHjYwx42lfGM4TfvM6514403\nShpzIF3rF3/xF6cywqkIYXN7oY9Y3/1+NczXQ7GYB6yN3r+EXRNC6uNXQ/xSDlcNFZOWw7Rpr5+H\nvblN1XBYf+awlm8Cd911l6TF52ENU/E1xJ930rBvD6/juUdZGquU18J4c7yfV9cnX5uZW4zf3PvF\n3Loxl6tS7+F1mHvm1TUzHcN9vb71PH8/8VCmTWAu5C2h2sue5sxwHQ8pZJ0hR9XzpphjnOdzhnnH\nexc27LZMWCXvT96vHEfeOfNEGrnIrKMetp9y474f5kLEVg1N69CyRqPRaDQajUaj8X8aO4KRIcHI\ngWeJL0v/IuZrH2+Qf7Xy1Zm8jRyf1F64Zkq85Ss+eUOqFzadhxcmfdUnL04VFUiM0x133CFpse+4\nH0ldCXjxnE0hef7888+XtKjmRt25z/777z+V4ZHfBM4880xJix6FqihHArs0GATKTj755KnsV3/1\nVyUNr5p7JpPSDKhenGRTc14DzkuKJWBOtSwp4CVUAYAkIMA4vvzlL5/K8Bb/yZ/8iaRFdbwKF9v4\n6Ec/KmnYwu4mQu4p8JA6O4t9MH/dO0U/4FHysaY/0vzjeMYN4RG/D15MZx6oX/J0Ui/mtHuk8Rzj\n1Wbt89+qYpCDdjlzy5hw/pwn15kV5kZasziO3/y8qtSWrrkJwEh72/E0pufD2WefLWnPBef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Di9A1S5fmk5iscZ47olSXp3rGPqdkqdeIb5uNU+SP2FfbrkMu+a5CGyNsxtHZHQG2I2Go1Go9Fo\nNBqN/+/QHzKNRqPRaDQajUZj67AjQsugPZ22q8nGnngJJQZ17cnpdQfdJLFcd9t1QKk6TVh3i/dk\nJ/5OsqMVTrHNiQTUcKB0Xt2V3ctos9PJ0M+33367pMUQAKhmaHfocGnIadJ3Tkd6OMe6QR0SFX/X\nXXdJWqRNqTsJd0nCNo13pX5T2FIKG1wlMT9JGVbJSj+vXsPDiBhT7peSRufCNZIMJ5Q4fYawgzTC\nbOoO437vJL+5yRABQlO8TjVZ322iJqW6vXIeoRRJzprxSJKpzHsPC+AarAE+//gthYQyNknatSYp\nexnjkMpqIq8nIGPjNdzNf0tju6t2OtI6uMnQshrqJ41xpp0k3EvSlVdeKWm02UOMCKEiXM0T5ekP\nkv733nvvqeyoo46SJB155JGSFpPoa2gQ9/fjEJDw9YlxSGIbdS3wMBDCdo455hhJ0uMf//ip7MQT\nT5Q0ws/e8pa3TGXYKuG6HsJ02WWXyeH9yX2YTx4e5SFofo/697pBH7jNV2lsf4ZjC1Ue2Mef5xHj\n4mNVxwMpaz+OuvjaVe/nfYe91PcZr1N9viRxAt6b/P2J/uEYX9/oJ+bFXDh8eobVuvh6jE1xbd7x\npBGi9INACteua1YVcpHGOFcJcml5bfQ+qs9wvxc2kEKskmBDPbaGInud6no/J8nvqM90t1OOJ5SV\nNW1u/d/d7SFWQTMyjUaj0Wg0Go1GY+uwIxiZtPkP3lf+TZ7rY489VtLilz1foFwzbfLHv/5lyTX5\n+nTGguP4ok1fuWmjyZqo6V+2tHUuYbfWNyEl0yHx6V48Ejzpj8997nNTGR4Sktce85jHTGX0LV5D\n9whVyb11Im2ahbcwSRriTcUj4P1SPV7unaosiPdZ3WjM7bN6w+bkROc2vkqSmympHelTbME9ijCG\nPjaA45EkJZE11flpT3va9PcnPvEJSaN9LhuLl5j7+sarPm/WDe7r/YnXF2+VJ0cC2unsSZWzTcm7\nSb4Xj2qVlpWWvUtJwCOJElSPoF+zesjSWpA2WMOW0ppVr+nn1UR+t88qFzoncJHqtwngCXRmhoR6\n2unJqdgLyameDM+Gb6yR6fmAZLI/V1gbUztZPz/1qU9JWuxr6lkT5qXldSVJ/rOGuwAL4iyw1ogM\nSGNdoc2/8iu/MpXRLjbyg2WSpEsuuWShzW7nldX19aVuhZCkfDcJZ2CpC886l7XFQw2rj+iJi8nQ\nxxzj20FUFtKfIayN2JvbCPZFnZzB4vocz9ru7Cn3ZS3z9Z8+9+gKQP2S1571O4mL1DmeNs1N8r2A\n/oTN3H///acymM5NIdlbFdBJrEmFPzfo2zm57oTK0nqfMTZJhAabrUn+SbQqSd7X4/28GpmS2P26\nyao05jRzJm0vUPsiXTthd9aIZmQajUaj0Wg0Go3G1mFHMDLIXX7xi1+cfqvx/v6FiQcMRiB5KTne\ny/h6TJKkFc6s1JhbL+MLOkmS8jde4uSt5pgk21xZm1Tn9HXMNd3byDWuv/56SYtedI7Hu+neRq6B\nHOfll1++dM1NIG3MBpNCH7hXjFhwjk9j+/GPf1ySdNttt02/YSfE6CaZSjxJzkrAAGEb3tfkbKXc\nBbwMyXNR54HHDSPxWjen8jp8/vOfl7QYo19zR3wevfa1r5U0PMMuyY3nkTq47dIu5p/XZZOgrxLr\nQrvcG1o3k0tsAcyWzyPOY6w8rr0en3JdUq4SnrzK/Pr9koeu5iMlWeNkSzWefY4pSXLdnDfHpiQG\nIcl+Onu4biDTfuihhy6V0Qa3XbzfrHXO5mPHSTYd7z7nO5vJmlP/lQYjgpSvs9iVsUgeUmzX2QX6\nmGNg26WRs4LN3njjjVMZm3lSP69L3TiRfpWkI444QtJY43w+sBZwvrc95RaCVTf73RO85z3vkTTa\nKw0bIOfTUfMSWO99G4K5NYRxYzx9PeQarFkwZdJYW8m18DGG4arS8SlShGunTVNhafza1IGxdsaZ\nv3kOOqNT5eyTFHWtm5fBusD2ucS3Pz83gTn55bkNyytL7swv/ZFk86t0f2K2WUN8flQ22Nd75lZ9\nvnjbeP5V+e56n1pW857SNVPeE/Xl3azaiF9rbjuKVTYWn0MzMo1Go9FoNBqNRmPr0B8yjUaj0Wg0\nGo1GY+uwI0LLoOs8yRz6DXrYwxNIKITiTpJxUFQeagL9lXbZrXKHfs26c6+HFXiipZST/aF+/X6E\nDyWRAOi2FB5X6T2/Jm1OIRUk2BFW5eE5NcTBkw6hlqGtnYb2UMB1g/olOV369aUvfelUBl0NBe/h\nY0ieknDrNkE4l4sfAO5NXXwcsBdCBwg1k6Tf+Z3fkTR23k6hgUnaF8ljEvvPPvvsqYy+TuEAjDNz\n5Oabb57KqB8hFS4Nynx42cteJkl60pOeNJU9//nPlzQkWr1/mK8kR3uIX0ouXReSzChjkyhp+qiG\nmEkj1CLR+yAJONBnNSlTGvaRdsyukpc+fjU81RNsKUuy8Ls6xv9OISLYI3VIYRcpkbe20+cD40D7\nvO9c4njd4Noe8lGTaX2NZL0mvMbXsBrO5WNbw8Z8jfQEd2kx1IuQMuamz5UazpHkUOljD+fiOIRw\nfA1h7iO17M9NwoqR4Od8rwNt8ZA7nsuEdHv4WAoXqdekX1OY9yZw9dVXS1ocd+rOvz7HOK5K/icp\n/iqVLo120Qc+t+uWDy48wfUJufJwb/6mr1m/fZ2r4UuO+pxwIYAaBpTEi5KACHWpofYpeZtru23S\nJvrEQ/Dc3u4tzCWT0w76gTr7e1MK961l9JHPe+ZyTdr3a9Gn3kdcK4V4AY6va7y0vD2HvwfV9xJv\nZwpdBlX4gbVpbiuIOTn+PRUBaUam0Wg0Go1Go9FobB12BCNTJU2lIc2HrJt7N/m6xUPgX3h8GeJB\nSZK0eBScTeFrFa/BtddeO5XhueJ+/iXMb9TXPTW0h69W92ThHaPMPYp4UWkXiX9+fbzwfr8q4+qM\nDJ55EjxhJ6Tx1Y70pvf1Oeecs/CbJ0u6l2HdYGx8/KgnyaevetWrpjL6Gk/rxRdfPJXBzsBGJDbq\nyU9+sqRFLyweCMbfvbckhOJx8o3uPvjBD0oaXtu0GSHtSxur1aRIacwRPKw+DnizqodPGn1GfT2J\nl7/POOMMSYvJ/qeeeqqk4V0mgVYadsnGge712eTmh3PSkHiC3HNdN37zssqE+jWrV8xlWPFiYfvu\nRWddSCzInKeqMgHen3WjVvfezcm7s54wb/3Y6i1NDHNll/zeSdq+bmoMWycNL/gmwH19neDvtLkc\nY0ofePI1x9EHiW3DblLbGSMfh3qe21n1/Ps1sTPq5GX0LfV0zyo2d91110karLA02CuOcRl6+iN5\nl2t/zEmO+/+rTPecl3aduOKKKyQtJuuTTE4bfG5S5ySRDCpDmcRyEouBbTBG/g5Av8LS+PrEvJ1j\n7eZk1Cvr4msK8yOVYWdpw9/0zJLyppcp4oA+S/Lmm3xuSDmhv5Z5/1VGJglE8RtzPNlNYnLquuvj\nSL/TN35Nn+cOHw/qVCOMpDFOKaGfdZ7x83fAue0BKqvE+c4kVYZ8TmRrT+2gGZlGo9FoNBqNRqOx\nddgRjEzyvvP1jufZ447xQCd54spw+Bce8b98NZIfIQ05TrzM5ClIw4PAfdMXJV57pHelsWEnHskk\nW3nhhRdKWvRc8OUNg5A2fnzCE56w8H+vJ33n3kYkdpN8KJ4r2uCylfQt0sPOBGxyQ0w83sRzS2Ms\n3/CGN0iS9tlnn6mMMf2nf/onSYuMGnLEz3nOcyQtxo0zDrTPvSN44rENH3c8LNzX64nt4T1Lkp81\nrloaY8nY+P3w0MBQEnvv96FdzsRVSdCDDz54Kjv//PMljXng9oI0JrK273jHO6ayylS4R2+Tse8p\nHr/Kk3ocvucxSDnnhXF07y1gTqb2MTY+Dnh0uaZ7bVPeWr1mYjhpa9qgDm8vc9S9zKylrCVeF+yT\nunjbax6gewRrHXwe0VfYKeuNt28ToM98LWdMKEub+lFfZyU4nnWaee9Ins6aU+PXBBzvUQD0S9o+\noNpqkrqmvu4hpe7Yhku4M6d5DnpuD/aMLSWPbJ1rfu/qCZaWPdRJXnoTgOFI62BiNpiv9Fli8jgG\niXvWYWnMMSIn0vYMMBuea8i8473CbaNKgdPPae0Dvk5ViW63Mfoi5ftUtsRZk8pU0063h7kcXmwz\nbQDrddgE5jalTmxN3SAySdXPbVQOWId9TWDtqXNOGrbL+4jPUeZ7XRu8/+lTnuWeB0ddqp1LY0xq\nvl+9/q5Qc2QctX/nJJbn2Jo5NCPTaDQajUaj0Wg0tg79IdNoNBqNRqPRaDS2DjsitAya1kNwoM/S\njryECkHzOVVVE+6c6mJ39FtuuWWpDuzADLXnUrRQgYSbuUw04QeElXiYRt1VN+2kmuhF6EBCd3y3\necImrrrqKkmLO+KecMIJkkZImydcQesRDuICAkceeaSkkSTpIX5cn5A0T+JF4nMToM+dGn31q18t\nSTr55JMXjpEGdQt1jyCANGwHgQUfB8Yb+tT7GrvCNjy8cb/99pM0KFkPY+LvlOBZE948gY9702a3\nawQLqMNTnvKUpboQTuD3w/awJQ+5e/rTny5pzAsXgED44alPferCPSTphhtukDRswsMHErW8LlSJ\nSL93TbyURj84hQ6wcdYLp+uxBa7pIVRV1thDtgg7ocwpecIy6B+vJ2tGCsegfpznSaP8xr8+bwkf\nw768LthslaCWltcjDwGqyew+j+gjRC+YM5J09NFHa1Oo0vHSmCPMbV8Ha/IuocTSCFUl1MfnA/1R\nd7KXxnxPIRiMLXbj63yVqvYQvCrykBJ9U0gidWBu+nk8o6ivh17RLurpYdBVeMD7up6XdhIn5Mn7\nJ0nHrgu0z22XcUjzqIYC006fT8xtBF88FOolL3mJpBEK7kIzXJOQ8+OOO24qu+CCCxbu55gL2QNV\nXtz7tNqLrzeMbRIu8PA0v78fX5O+k8BDCh1kfhFq76GwaY3eBNw+a8hTqnMNP/N+rOHhc89Bf29i\nvjJG/o6DXSbhCfqW9SzZBO9BhJZ5eCznp2096vYabt9VRj2FhlUhCK93XTf8/Lkws7myimZkGo1G\no9FoNBqNxtZhRzAywL/K+SKEOfBN1UhISrKO1UsAyyBJl19+uaTBMjz+8Y+fyvDC4aE75JBDpjKS\nsN/0pjdJWvRgkEDJF236kk3SqbV9fIH7cbAKeG6kwV4hlesbSf3lX/6lJOnMM8+UJL3uda+byvCY\n8jXvXmY87JdddpmkRY8+bAbJZ8hMS4vCBusGXgpnOn77t39bUt4QCoYK23AvM3LI2ISzWNjHhz70\nIUmLScp4J5BBZqylwc5hs7CE0vCq4NFLEogp8RwvCu1zryHeTJL1fcPP0047TdLwtLlQxfHHHy9p\nMDjOJmJL2Jl7UbBH+uOZz3zmVAazhe35vHVGa1NInhq8Sz43sXU8YO5trJLqfk3aQ58788f44aH1\nJHOuWWWKpWWvaRp/4ONQWZe04S5rj7cdb1+Sl6bNKcmV8xjHtGbxm3vYaQ82xaaE0tjo9Y//+I93\n2eY9BZ5ObzusNfMpMWqsE85QMr9Z85zpoI8ZP2dkGC88zL6G0P+UJTYDuBe9bj7ocww749r+zKkb\nMPpajneWf31zRq5RE7odiUWp3uiUMM21fIsBX9vWDWzRGRXYJ+rn61SVT2Ze+TH0D8+jF73oRVMZ\na/IrXvEKSYusC3Xg+fKnf/qnS/fl2eV2k7z8FVWaObF9jKfbQWVikshIihjw8fM6OpNYhWqcDa3s\nZFpzN4U5tqWyLv53ZcsS65rmSu13vz9rCW12eXrGom6m7vehDtwjbWhKmb8fVgbX+xx7weZ9rLlv\nsjOeqVwrMbP1eZ36mWOSqMkqaEam0Wg0Go1Go9FobB12BCOTZED5kscj4J55/8qXFr+Iq7Sge52e\n+9znShpfxy6JB7tw+OGHL5XhtTvssMMkLX5tUhc8tP51zdcwsejuOaOMtrtnl8czoyMAACAASURB\nVDwPvIa+MSIxuvzmuTx8qcMy+EZ+eC65n/dh9TY600JuBL+53PPuxDDuLuh/cjT83ni5fBycEZEW\n86DwMuCpJf9DGt6z008/XZL0vOc9bypDnvjRj360pEWP2dlnn71wX+xGWs6NSZKZybvB8YyV2zWb\nV1Jf9+i/9a1vlTRikP1+L37xiyUN+3z7298+lR1zzDGSRo6Ue5nr2DpDyfXx7DpLsEkZzTmvL7bv\nnke84Hg83bvFuMFiuaeN8aMtLrXKnGK+upxq3bDTPZV1QzX3FjP/Uk4H51En9+zRvjQOVT7UgRet\nbsgojf5LGwmzPlfPtTTmGP3qmxtvEqxL3naYd9ZK90oypjDMsNDS6BeYGc8hoY9pp7N7dd10LzYM\nP2u5e7+rFL7bIGNbNy+UxtqIPfu8pV2wPe515X7YsNcz5ZhVJPnz6l339axuspfm2CZQ5ZSlMaf4\nze2l5pqAtJEx4+f5Hayt9N155523dB5j7JEXbKEwJwVemRlf951p3BW4v/cF48BzzduJzWMjl1xy\nyVTGbzwH8do7G8oawfxPWzSw3rk9/CA2xKxSv6msHuNrJTaV2LPK0qZ8QuzFxwbbrfbqx1fW1I9h\nLFJUAMcz9/x5yN8pyqlubOtgHnm0SkXtex/r3WFd5tCMTKPRaDQajUaj0dg69IdMo9FoNBqNRqPR\n2DrsiNAyaDhP6IeSu/nmmyVJT37yk6cyqLWa9OSAyvfQsrpTdwrFSJKWhFtAiTkdhuQioW9O90LT\nQu+lXW6TRCz3JjzKQ+5ILOSahM1II+zo137t1yQtUsbQgrTvoIMOmspIiiUR3PuTMCz6sYZwbQr0\njwsy1PAHpzr33nvvhTIPxeA3+o5QKmkk9r7qVa+SJH384x+fyrj3a17zGknSHXfcMZXV3Y0JE5AG\nTct9k+w28L6Gsic80SnjZz/72ZLGeLic4/vf/35J0t/93d9Jkg499NCpDCnuG2+8UdJiyBA2RxiA\nS67WJEUPLcMWCDUghEeSPvCBD0iS/uAP/kDrRkokZC5Cf3toGesJIRsun80YEfrg4SCMKX3nu5wT\nRsKO6T5vWbOoi4cm1WRTD8dgnGkLdioNGyIcwMPcOI9xcxlPwj6ok7eBfqGvPMSAa6Yd7GkD88DD\ng6gD/enyy97WdYP6+Y7UtIu+8nlEGBZzzEPg6Cvmj6/lt99+u6Rh8z4OzH2u6fOBvuY+Hp6K6Aj1\n87Aj1q+0Mzh2xfrucxobIjTFx4/zWIM8tIfzapK4X4Pnp9syazDnpURg+tVDETcZWkZb0jMcW09J\n9KwrKcyJNtPX11xzzVRGXxG67vLLzDvCeAn9k8a88PceQKgOoYnUN4UmplDrGmLvc5yQshqGKI35\n+1d/9VeShkS/NMKvGWPCsN/3vvdNx9Rnn/cl84J3Jg+JnBM1WAeSdHgtS0noVQjA13vm2FxYJmPs\n75qsIYybrzPYKfPD78dvPH8ZYxf+qed5CCT14x5eX47nGeJl1ZZ8/mOPVWo9bf0B0hjcUzQj02g0\nGo1Go9FoNLYOO4KR4WvQGQQ8yMgT+5dp/UpOSUh8Ifp5eE6qWIA0vkDT5mT1fu5Z4lp4F9yrTb2o\ng9ezbiDk18TTwvHuaa3nu7gAX/bUYU7Kzr0AfGnDariwAtfi/DnZ2HWCPqdO0nJSpteFvxlH30CV\n/sdbhFddGn30h3/4h5KGxLM0PE94Y70P8cKmRDjuN7dxVfLw0Nd4VdxjhWesbvwpDUGG3/3d3104\nRhreGq7pzBHjjIcmbZDHOLi94F2GlXj3u989lSVbXRfShlxVttGlpxkbvOBuE4wp7XLvNMnizC1n\nearH0RlR2s41Xeqa8xhvZymwce7n41CTNn1s8fLh9XWPPjYOM+O2iyeW+zi7ALBdbzv9zzXdO4zt\n0i73BNZN9taJ5CXkOZKS05lT1N0ZEhhwronnWhoME4IhzmjTV9zX+4znCPd15oj5h4fWmXfGG1ty\nyXiAbbi3nTmJJ99tiTUyiZBg/6k/q5S3zz9sLwlxUC+Oubfkl5nnvu5S5yoJLY1+ZPw4xlkC+odx\n8fojNU60gm9WzHxlHFyWnHnBvwcccMBUxjrBv9TFbYu/sR9/HtZ3Dn/OcB/sHUEYaUTAUObrDddA\n+IJ6I4IkLa6H0iI7if2w9nqC+KaT/RMLUN/r/JjKoKdE9bqpuZfV9yR/n2S9R2jEo1x4361MnDTW\nDt4P65orLYuRJLno97znPZIWRVCYM8xRj/hImw7XsnqfOVGFTYx1MzKNRqPRaDQajUZj67AjGBm8\nFf7lRvwgHjOPvd2VNJ40voTxRDjLU3NV3LNUf0ssD/dLcrMpD6bm7vhXaj3ev475KsbT51/sMDdJ\nXrPeL3mdqIN7lCo74NfBE4m3eBPxjQk1ntPvnTwo1Jm+cg8BHkE8Gs500Nd4mRx4X+lH9yAxboyR\nj3uSPgTVrtyW6oZhbvN4Wrime8NhAtKmXxxHv7iniGulNgDq4m2h//AaOQvC5pr3FmrejM8xPFXJ\no0RfMw+czWCM6loiDS962lQMZgNPmTOb2BLz1z3z2BU26zHP1RPvbSBXBe+by60DWCGvJ2tq2qyt\nbqjmNoEN4qF3BqFuAOc5VbR9E2C83SvJs4P54zkyMBzUFw+oNPqd852RYf586lOfkrSYbwVDmRh/\n1hds0D3UgHr6WgB4Lrgt1XHw87A95No9N4o1nPN8TtdNhn0ecT9sIzE5HOPrC2V49Z2Z2yRLR/s8\n/2kVCdhqw35Mnbf+jGQNgSX39YLjmAN+HmPFGPuzDrth3jJW/j5Tn4Mpr4lx9cgN7IXzPZ+Nteik\nk06SNDY/lsbcYVNs7udrC2sCdudrEmMO6+PvNZveEBMkud9dbX6ZfkvP2Dm5fcbW5yFjyxrp446d\ncZ7bIKwJ78uc5zlWlQX19ZvcGuzF+5/jUwQG7WL8/BlU50XKC66RU16W8s7B7mzv0YxMo9FoNBqN\nRqPR2Dr0h0yj0Wg0Go1Go9HYOuyI0DIoMqdNSUgl5Mdp/krBexgRYS9J/q+GczntVpMcU9gZ9HmS\nOUwJgjUEzsug0jjPaX7CCKBbvb0clyhOKMAkT1zr5FRe3Y39hhtumP6m32m7J2x6uMOm4ONXx8b7\nk9/mRB6gTz30o+6O7GNLwht95WMEvZskgT0krKImnboMaQ2n82tyP473JF4PU/I6+TWYI15GPyR7\nqbLG3k/0C7vbe12SkMa6kKjpGuro96eetNnDerAJaHkXlaj382R45gq0PGEW0uhjQv1SQjMhUOwC\nL42wD5I/PYTRw7ekRQnwKvPs40fYCvM3SZ0yHzxMkb/TesY1q3yzH08/egLwJhO7q51KYxwIfXRx\nD9Ysxt/7mtALzvOEfvqRsBhCzKQx3oytC76QWF3noTTsMYX4Mc8p89BHxrLu8C0Ne2HcXJqZOTy3\nPrnoDOD5Q/38ecTx2FJ6VhGS4iF+KYxuXWAcfM2qzz0PgattZg647dewa39mMt410drvxzh6iBeh\nbym0HjupzzW/L21IyebUPclpY6/YiodJsqYjw+z2Sh/QP4QvelinryVef68noYa7Ezp0TzEnsTy3\nu3w9xudoDelL75OMlz+XsLeUIlEl0lO4GmtDEl7iflX8SBrPDuaFC7Jwv3322UfS4vsTNjQnmFDt\nda5P0/lzIWaroBmZRqPRaDQajUajsXXYEYwMHiKXlH3c4x4nacgVerJUTd51rzZfsMl7WxP9/GuV\n3+q/Xj+QvDl8tbp3mq/Uutlmuo97qOrmV9726lFK7UsSk7Ut7lnAk4SnxjdUxNtEolnyXG8CeA3S\nxmn0j49fTShL3in608ezJrkmb2WSB+fec16dZEv1vt6GygomD0/yQM/JkVePkNezylknycS64Z0j\n1cVlG9eNxELOefU4Hrv2elZhA7d5POV4wf0e2BBz2b25zPck9lDv555OGDVs3lkCkj7pf/fo0j7+\ndYakMmPed3XTxCR+AYuSJOOpb5LBpl1pk8ZNgPXB+wVvMh7IJFWe5JAr6+L1RjQBaW6fD6z5XMsZ\nPJgb+tPHpXrk08arJEv7Oggjdvjhh0taTNpNbC6gzjXCwNuahADq2uh1qWyGnwczwTE+/5IQyroA\n05CY2yRHmxKUpcV5Ua/j40h/0j6/B2XpWVC3enAmp25gzbG+jlSmLM2ztFl4lfd3RoW/k/3ApGCb\nSaSCtmOH6RlW2yZt1h6keUZmlfPS/xnLuXeOJBxBv9N/3nb+ru+O0lgf6qbFzqzUd4EUNcP4p02E\nub/P1doHXoYYDfdN7571/LlIoT2VZm5GptFoNBqNRqPRaGwddgQjwxciGyVJI7YYD5p/xVXvsH8h\nVllH/8KrX9CJIUkxfnwtch/PE+FrGG+qe2pqDGPycqQNFatH1z2t9FX6gp2L5wR4x7ye55577sJ5\neDQl6ayzzpI0+tWl/vB0PuMZz1i6zz3FXXfdJUm65pprpt+e9rSnSRreCh/3GmOZvvqTRHbtq5RL\nkGwi5VLVuiQ543pfZ3kYU/raz68xrykXK6HataNKjfu8qt5+vwfH0VfulSXPYBOYYxqTbDqeQ2K5\nPeeBfsS75V7tZz3rWZKG7bmHNeUJgMp2+jHchzKXhoW9wHvm7FBtq9sn6wJt9msCxs1jnrG5uqGb\nozJPDry2vtZyjYsuukjSok04S71uzEmm0k7WEmmMA33n7UOKmZwB39yVnE0YRzaelMaaiA05o8Y1\nHvvYx0pa7DPWz1onaTz3aIvbIHZN/qivg/zNPPRnELYEO5SYaWw2yfNTz+SVTkxH3fAxRR1sAtTP\nc5XqMyO1vcreeztrHlOa/9ibr+n1vomRSWssqKxQYla5h49BfR4mBp81Adv235Kkf61vmnscU3M1\nHGlNmcvbWgdWyYNJ0S0g5TmDFOWSNpYF9Vnu/VEjRFI+NufPbeTJv97/PB+Yx4ktq+2VlqOb0rYQ\nvP+md6W5vq/vJX7f3VkjmpFpNBqNRqPRaDQaW4f+kGk0Go1Go9FoNBpbhx0RWlZlRKURZkayq9Nv\nlVLz5MNEf1VwfqKukjQzNC00mst5Ui9CDpxWrtdPFFtKqiVZMe1CzP1oc6Iu5yjHtFs57TnmmGMk\njWRXadCfiC5ccMEFUxmhGJsILaPvCJ+Q5hPvqpSh91mVa/a21zCwJEEMEq0/tyPtnkoRpnDDet8k\nD57Kaj1TgvYckvQ0wg9XXnnl0vFVynudSCGTVXTBUUMzPeSn7kju1yQs5+CDD5Y0JEn9WpXel0Yy\nZtoBnVAtwsd8zaIuxx57rKSx5vlxdYdvacy/udDCtJ7V49P6SZiUhzv5Lt/SYngqoTZ33nmnJOm2\n226bylJ4ybqAvXkIDusnbfAQMUKBKfNxr9diN3Zp2ATypN6+GmbkIU2IEOy1116SxphJw15Y4wg/\n82vceuutkhZlt+va7/XExvk37ZjOeHiSN+Ff2FdK4J4Lq6rHSmNuMR/cDtJza12osvJehyrOIy2H\nfSVZXOYP13Zhhrotg881ro1t+XjUvvb1ooYmpWd6lfL3sMUaopTm4Nw6RV94iCjX552FevuY835C\nPyVJce7hzwq3xU1gLgQ1bUlRn5vpfNZGUg38nbMKACRhg7TtAesTfevy+/Qp1+Jfl7ymj1Od5sam\nvqu4ndXQULcJ1kr6Im3zsMr7z9y7zipoRqbRaDQajUaj0WhsHXYEI4OXwj0ZyFbytYkXWFpOxktJ\npykJkfvw9TcnB5kkJtkozb9k8Z6m5O/6VZ485dzXhQ6qZ9DPqx7otMlY8tDWhMJbbrllKoNNwhvg\ncn60ma95Tyj2MVk32DAQL7W0zLLNsSeOlPAIqjzm7iSm+TXnEufSNZKnpiZ2J0YtMU7Vdr0N1Wvn\nqEmuq3itpOEtxt7cJjbJyIA07mn+IVqB990ZXySWmdtpE1HahfSuNDyxSFcm7zTzNiX2Jns58MAD\nF67p41AlTr2M+nFtlzx2QRI/Rho2l2S+6T+8/G43lb1MQirUwe0ANmIToK/dBvHukoTv6xmMEetL\nWq8ZB9Y+aTxjsClnXbAh2pyS2vGCJs94nU/S4uat0iKbiEx0kqavz1IfPzzpnOfn18Rxf6ZyrZTQ\nXxPkE9MINsnCOJ7ylKdIGlEE0jJ7kPqOPmDu+LpaN3r0MmwwrZ/Vi542eK7/ej3rupaOSWtQlcOd\nK3MPO2OdIjcqw13FHPwY7NCvzf2q2Ii0yEJvEnNJ6HOMTHru1oigJAbE2MxFUCSZcNYzF6Gpz4Bk\nNxyfNqF1ifqKukF3sjOumZ4JldFN76BJkGcuKmQu0qCiGZlGo9FoNBqNRqOxddgRjAxf7R4/ylcg\nXjUvwyuavua4VpJYTp4AUL3a7k3CO4bkJtLQ0vg65hi/Np4LvFXeBjweeGyQ/JRG/DXx2F63+pU6\nJ0WbvE7U0+P+uT595xu64QVIHqVNxr5zP2T9pOW2z7EnKZYY7O6mSylOtnouEhORNsqq+TrpeO7j\n+UF4BLGhuY25VkWNZ005R7TPPfzkVOHhce+0y7auGynnqOYTJalkvFTu5cauGUcfI/q45sZJw/vK\nGuTeSOZDkt+mjP7xGHtyjWAJnAGqnvI0RtiJ3w+PHm2Z2+AsxeZzvyQLX9cEafQRm+b5xqgveMEL\ntCnUeHNprKmMu88jpJhZ6zwHiLam5wRt5nnkbDRjSjy7e6GZG4yHezNrvoaPQy3zcYBxSvlraTNW\nwBxOm0VXttq9/HWOpE36YL2SzG9icjYpv1w3ipSWWagk3Q9qLogfU6VzpTHHUq5FvbafV/NY/HnN\nfGKsWHdS/k2SpacPkjRwrWfyfM/lCXGftFF0ZQK9DzkOZtz7ybd12ATSM7z2wxwjU8+Rlsfd7bxu\n2p76P70f1PwXn4esCVU+2W2ZOrHe+NpX14a03ULaiLOymUnSee7doz63Uz/PbQ+xCpqRaTQajUaj\n0Wg0GluH/pBpNBqNRqPRaDQaW4cdEVoGPe27gkOtve1tb5MkHX744VMZCeBQbU6t1ZAIl+WsFHkK\nP+K+HjJCqBW/eVjWhz/8YUkjBOqUU05ZagPhBS5XetVVV0kaFCSyrNKQ3IRu9UTDmryXdqNNdCY0\n5L777itJuvHGG6cywi1on1N6dTdYTyje5G687MLuO2jXMIskHziXuD4XglXp3nQNtwnanij/FPJR\ny1KYRq2Djzt2nYQxVhEqSKi2lPqFYzwUitAy5Ik9/G+ToWVgTpTA8aUvfUnSWFecwmf8GCOX/2RO\nUubztoYdeWhSTYb0OtEvJJC7XbO2cT8fF9YQypzWxxYI6/EQOJLSCYFKO5mzHq4qYgHoR7dvQsoI\n51p1Z+d7iiSbzhix7nqSK6EWX/nKVyQtji19TeiL2wt9yzrt4X9nn322pLFu+07pLrcsLYYQ0/+E\nFPrYYi9JWAEkOdS6a3oKpWEtSesTSAm5nO/rIG1I2x7QhhQ+4tdYN5jLHg5LP6Z61ucCx/h8Aqz3\n3nc1rNjHgGulMGz6gPHzNZax5T7cI21DkcLd6rjMPS/SWCdZ+7pOJEEAjk+ht7xL8Fzz+s5Jeq8D\nc8+M3ZFm9mMZC9qfwoxTu2q/+zMEfO5zn1u6JmtPvaa/kzHnuKbbZhUM8lA47sO88DJsid+SlH8N\nmZ/blmKubE/RjEyj0Wg0Go1Go9HYOuwIRoavxuQ1BNdcc830N/KTeNz865FrJGnAujmYszWVXXBv\nDJ4E6nnTTTdNZTVxymWU+a165STpkksukTS8d/vvv/9Uxtc4Eqh4V6XlBMH0dZw2JcI7fcYZZ0ha\n9AjTVjyD7sHkfnh9XQjgzDPPlCS9+tWvXmrfPQV1QYZbGkwA/TmXIJakEJPs9iqJ/ynxEa8tXhkv\nm5PBrnVOjAx26t43PMhpk9Tq4ZjbEDMlds/JHNIWtxdsKXkuPblw3ZiTzwburapCHC6nWxnblISN\nVyt5kJJkLqxEFc+QRr8wt48//vip7NBDD5WUx2HOU0ndWQ/9WARDmMs+pysbmDzPVVbz+9WFPmaM\nfKy8H9YN1uQks0//uBgFNsAajtSyNPqD35ytgW1h3nuyP3174YUXSpIe//jHL13zuOOOkzQYcQfn\nO5tZ5X59jKpHd25zxyT3O7dpXZpj9bnizEFNVE8bG6bk4iREsy4cffTRkhY3QuW5nOZyZS3Stg5p\n3QVVxtzXksrEeP9yffrTx5+1BxtO7Af9SZ3c+84cYE3yvp+LTKhtcdaktiU957CRFC1DnbiOr90+\n1zaJtMbOJaHX//v5VQDCbboy/kmgpkZZSGNMeM9KG5LyLOF8txuuzXulb7I7J6Ne25einGifr5m1\nfal/596x6vvanjIzzcg0Go1Go9FoNBqNrcOOYGTwcvnXHF/ofEU6CwJTQczgXOy7fwnXL1H3eFdP\nol8TRma//faTtOiZQCKZ8zwulzZQB5ckPfXUUyUNz4vnB8HAwEC4969uOJQ8THhKfANAPMEf+9jH\nJEl77733VEYdaJd7ojgOyWnqJOUv+3UBz6nHlPM3m8Ml7+Hcl/2e5pCk/JnrrrtOkvS+971P0vC4\nSmNDtiQrXjeQc68Ubf7gBz+4dM1HPepRC3VIG50laefEJu2qLF0TJu7yyy9fOu/EE0+UtOg9dvvY\nFJJXLKG2x+cDf+PNStLv2It7M/GQMs+T3Czsmccuk1eHV8v77KKLLpI0PKqef4Ennn+T12/OC854\nuGe15nD5OjiXR1gZGZ8P9Ac27Bvc+YaUm4LXpbIRSRKf+qbN/IB7HmGGYSY9rh02HU+ps5LY2Sc/\n+UlJI/fP733zzTdLyh552GevC/Y5Z4NprWN9T2vknEe2MgYpfr7G2EvLm1K7LSUWcF3geXnUUUdN\nv8GozXnWV5Hzp31zWw/4usPxzDGfv/QZ/ePzkPWo5jq4jdbNfJMEOey5e/ZrLlDazHkVVqpu1uvH\nYyvezzV/ay7PaN2YY2LS/3eVG5NYG9rl87cyjml9Snli3Jd3W3+GVGY7jTsS8JzvY1s3tkzrBvCy\nyrwl9mwuKmRXjKcf14xMo9FoNBqNRqPR+P8O/SHTaDQajUaj0Wg0tg47IrQsJdwSegFd5/QloRsJ\nlTpOlCpwag96EPrLqU6uBW3nZZVad4qONpB4hTSptCyn6m0n0Z1kRacsPTxGWqToaxKv04qEnxAG\n4dQ4YSDI6HooCOFcT3/60yWNxGIpywauC0hPe39edtllkhaTacGclGgNP0pCAHNIyaqEN1K/v/iL\nv1i6JqFXnhxbw148LOitb32rpBFG6WNdpbgTVZ6STWv43ZwAQUpIJHzFk/0pI9wFOXRps/LLc2Fk\ntMuPYe6ncK467inMFPhcYZ6nUC/KkDP3ZGNCvJhjHqqHFDrXfNaznjWVUWfClXw9I2yEcC6XwWZu\nck3sVRq2lMQvarhIComo7ZVG/3FfXxs2mdidktoB9uyiLtgs/YFIgZcRguFtYA2gr/faa6+pjDCO\nQw45RJJ08cUXT2U1ZJkwZWnMb0I1PGSDZxwy/V4X1mBsw9uHfdSwI/+bsCUfP8aZtvtzpYaUeRm/\npR3kOS6F0mxSup9x9z6rz/45gZO58BbmhbcT+67vENLy+Hsf8HedO14HfuPdw58J1KWGbEnDJur2\nCX5t6pkk1lM4dQ25TeFnVQre61bFKdw2qxDTprC7MsyrXCuFTFX5a0d9T0siGLwz+DypAjPYhj9z\n61zzZxD15HwPEa1h5l5GPZOoxK5CWefm0KoiTbsTZtaMTKPRaDQajUaj0dg67AhGhi8+lwjlCx3v\ntHvcPaFeyhuJpQQ6vAR8mboXoCb5+9dgTcx2DxhfyXwdu1eteiXSplRp87y6SWaSlE1f+tXL7Nck\naYzznCXAy0iS5EknnTSV4Rkk2dU9kUceeeRSHdYFWCIfdxLskSZ0gQQw9xU/l+g5h5TgidedMX7G\nM54xlb33ve+VNDymLr9d2b1zzjlnKqPNiErMJeMlz0UVJ5CWE7TTJqkpKZrj8Fh7Ej+2jtfH7+GC\nFuvGHHOUGDnGBvv28YPNoM/ThqZJnpj7Jc8V3kgkT++8886pjL5CPMMFPGBImKPO0t1xxx2ShmfN\n287cZL1IDCl95l472ooNJvne5KXETriW1xNPcdqIbZMbYlZxAkdak1m7GT+Xd4c5T/0IW4NoitsS\nY4kgCuMvLTMd11577VTGs4N1Pm28SvuwV2mwoxyD4Iw01sS0SV9tl9s8Np62LaiedLcX/qaevr5U\nJtuvucnk7rShMOPOPPK1dW4TP1CFC+bkjL2syvP7esHxde74NWDWktRuFUxIzArvVP7OxDjA1nh9\n57YHqOIn/N/rMSfiwH3pLx+fOfGEdSIl68+9U80JAvA3tpyeu+mZvMq8SJuc8r5KZE/aoJK/eb9z\nsQDWmyrW4G1J8vnUM23GnZg7v56XrfIcmNuQfA7NyDQajUaj0Wg0Go2tw45gZPiKR7ZWGl6nulmj\nNDxXKQ6TL9qUJ8JXZpLFrUhfpFzLN1jj3lzLWSVnbmo9uVbyZFGGx8K/qqtUbpK5ow9S7CVf+Cle\nOX1xkxvDF75Li+JddCnndeGWW26RtOjdZNxvuOEGSYs5AbQ5eaVSX4FV4jArcyFJT3rSkySNvkMe\nWZIOO+wwSWPTUxgWaXjBkAN1LyyeOeRYnf2qHhO3pepR8jbh2Umx3VViN8VD0/+em8Hx5E953PYT\nn/hEbQq0PXltKpMgjfZhs+7NxAue5GY5LsWAVylK7xe8vTAl7gVlXWDt8FwJPLF49r2esElc2z2e\n1YuZ1pDEPrMWwCC5Lc3lPIC5eP+6EZ808jw2gSolKy17M719/M28876GfUxjhL3AxDobxfhxjD+r\nWLuf+tSnSlpkItjkmXXUx7Z68hNrRh8j7SyN/Bkk/NOmz4yRswN1DfFxZ+3n37QBdfI88zd2lsZo\nE0i5UZWJS3a9CiPDeUk6t+ZMSMv5IN7uKmvtrFvdUJI1zFmMuvmk22SVbfJ1vgAAIABJREFUvPU6\nMR7Y7dxWBd5PdTNu2uZrZ72W37cyZf7su7cYGcfuePzTsfQHc9XXmRpp4/ZSN6Z1O2VOJ2YrRQTV\na9ecQV9vGC9+Sxvipk1O+Y1nmJ9X3x12V0Z5XdtlNCPTaDQajUaj0Wg0tg79IdNoNBqNRqPRaDS2\nDjsitIwQAA9/gGZlF/fDDz98KqsSqCmZE9ouJYEmernuUp+SFvnNd5snCRp6mPp6XUhO91AhEkMJ\nQ/D71eQq75cqd+eUHPWs9/VrQEN7AjrhElwrha9Qd2/DJqV2CclwapS6sxO6h35gH6lfavjJnFSr\nn1fD1FxGlv488MADJUnXX3/9VIa0KxQ6ISTSMg3tssbYIGOTdtcmnM5tooaIOS1cJTO9P6v8rlO5\n7ERPGJ+HNxI6R12c2k67/q4LKQSuCh342FaZSu8z/p7b1bsmMkvLybcePlYTLN1+CFciDC+FnyTh\nD86jzMeBUJIUcgeSxCdzmmt5SESdI6mvudall146lblke21DkoddFxjHJJZCOz1shTnJvx4ayHpG\nO1PIMoIALn6B/dOfhJhJYx7RV694xSumMmTLedYh7CCNceDaLhzBus7x3EMaIauEN/o6X2X2k6AN\nY5t2Ka87u/s1ku0yH+hjD4vaZLI/dfHkecJiVhHJqeuptPx8SDLTNbTbz0thjowjoYGEU0tjPjF+\nhCamEF/WIGTcpWHzNcFbGmsX9fTnaA0pS9Ls/Mt1Uqgh8HWuhtB6iNQqojv3BHNhSquUzQkCAG8P\nocD85v1CfyXpYn7jOZoEeKrAjdtb7X//P7bDOHhIeH1PcPumTrRlTsJ6lS0SUvh/vY7U8suNRqPR\naDQajUbj/zh2BCPD1+eVV145/YaXAy+SJ1PXZHj3VlUPncvXVY+1e5I5LnkZKMM7jQSnNL54kZ0l\nUdvrwL9XX331VPbxj39c0hA4cKGDmjiVPJq0wdtXN9m8/fbbp7KaTIf0qjS+vjnfN1EiybB6hqVF\nT866gefTNz9FKhUGyb2iSJC6168iJQZXT5uX4ZVMCeF1E7SDDz54KsNDQv97n+Fpo32PecxjlupS\nj5UG48O88POqaMUcq5Q8HkkGEtlz2CRv+3Oe8xxJw04OOuigqWyTNpFYl+ohS2Ob5M+5FuOYklJT\nUrszItJigi0eVq6VNj9kjvk41I3K3F7wgiGrmzYjSzLY/M3a6m3AU06/+NhWmVm3LepMu3yDX+8H\naXG9dnngdSNtCkgf0XfumWd9YP3zfqlrv7MS9T4u+MKYVmluvx/PNhhcaawBeKhdzp5xw95cZAXP\nKvVDlt5BXZjHfq0qg+ttwLvvba8eeGcFahJ82kiz3kPa7AaIyTvszJQfIy17xtNzt27P4KgRG/7u\nwDXoc5+/HIcdwHRLyyJAjJ0/8xgrfnNGj+c9708+V5n/SQa5jmdiW+YYmcpu+brKuoGUub+TbJqR\nSVhFdnlus8z6WxJNwc69jIR+5ofbBMwhfeOCRqxZnFfXHWmsw4yDRwywhlBvn4NVJt6fJfTP3PNi\nLtm//jYnzbw7LIyjGZlGo9FoNBqNRqOxddgRjEyK2eULDW/BYx/72KmML1K89Slmt7IT0rJkqnuW\naly0e5NgAvha9RyZKi2ZYgv5goXR8fpR5gwJ3n3u5/1SZRwTW8M13aPPebTZvRD0P15+bwN9jBfH\nvTdzOSf3FM9+9rMX7isNjwLtIhdFGuwAnu/k8U6occbuNaxy3d5OvLx4ID3elLwnPGRnnXXWVEb/\n7b///pIWPdfchzF1OU68bnhY3a45njrg8ZKWJbWT94y+cslcpGh9jgDs+GUve5mksaGjtByju06k\ncaybe6WcMe8PgMeTf907xX0433NkqmSps56ch/fTvfZ141z3lNH/1dMujbHlPB8/jsMG0+acaR2E\nqaC+SZ44zW28yngNPV8OVoD7+Nz0XL11I23mytgkydsaH+6sWfIwg5ov5fHw1V58bLG9888/X5J0\nxhlnTGUvfOELJQ3bczajbqBYGS9vp2+uyvGsPe7t5Rrcx8uoOzblzwDmVtp8lHpS5m3AXljzkmT8\nJpCeR8hSM+7+bNwVK588zymfEKRncc0v8vWpskS+jjK3GE/mk+diEpmA/fg7Eowe4+qRDaCOnTRs\nOcmv1/mR5JcB7fS5B/OEPaR3s00xM3PMSkJl6VbZLDPJGdd1WBrjxlzzvFLed1IOWZVITqwZ5zP3\nvI71medMdV1vvL0ct8rYpH7eU5alc2QajUaj0Wg0Go3G/2n0h0yj0Wg0Go1Go9HYOuyI0LIkywZF\nRhJ92gX23HPPlbQYHgBtCZXn1+RvKLkkeQwlBv0nDYncmigqDbq07urt90vy0hwHZXzhhRdOZYQK\npDCpGgLnlCXtgbJ0ypH61URmv0ZKsAeEGvk4+L3XjSrVKI1xRqbSQ4ZIlCXsxccdmpa2O2XJuLOD\nuifXcT+oWA8f45okMjvtSngFu2u/6lWvmspqkrmHYtQdxT3sDEELJDZdkIEwF66d5kpK6K9JsR5u\nQfgk4WbMQ0k67rjjJI0EdJ9H94asagptSbZYk/U90ZowLgQ4nGavCe+OGhrmoZb8TYKth4gRGsg4\nemgSNlDlm70NKWyJ8aLPPbSFkDfKCK2Rhp2l8A+On0vsxjZc9GTvvfeWNOaPJxd7yMy6kSRM6Qfa\n5fZSE5w9rIbxog0eXlHDq9w2uFaVuPc6MFc+9rGPTWU8YxB68TGqO3T7es01sWtvA3ZGHVJyOWU+\nb2tIUQojqQnB0lhXsGGvC/dm3fR1aS7cd13w9vEcIZzLQx+r5GwSC6Cshi16WdoVvc5fX3+xG+aO\nr2HYImPFenHUUUdNxxxzzDGSxrM5CQFQJxfc4Fo1nEgaNsx5XlZDk5JUL/3CHPRnNGOAXSQhpk0h\nhTzV0LAkFFOPmTvfgX2nOVrnIeMnDel2jvHQeq45V2/AOPj5vDMwD/0dguPTe3MVikr3Tu9Wu4M5\nkYBV0IxMo9FoNBqNRqPR2DrsCEYmeUDxZCBn/Gd/9mdTGV+wMAd4JvzvlDxWJZm9rEpneiLeIYcc\nImkwMZ4UzbXwNnjyL1/ceNZ9Y0S8mU984hMX7i8NjyyerLQp3VziFV5f/9Kvm3a5txgPcvUoS8Nz\nhdfFv+LxtO7pV/gc0qZ0J598siTpne98p6TF/jz++OMljSRJ9/ZUicDE/OE5ce/DbbfdJml4sN2j\nSL/A1vnGltgl13JmBRsiad9lVaknXiwYHWl47Q499FBJi5LjeM9oXxJ5oM0+VrXNLt+JfTLe3Fca\nUrIf+tCHFvpCGvZF2Tox53lKqAmXvuFcnQ9JJKCytP4bdrnvvvsu3Y9ruU0wprBtzuRUBsFFHuh/\n5quzz5yXkm65H97XJIudZGarxGbqX9YH3/x3DptM7GZd8jldGXBvX10DnGWtScvOmuFRZc74HKsb\nbzrzzrjBaHrSPs+Kd7/73ZIWJdVhZxhHbwPjzfPFr0n9WLNcRrXCn7c1WiFtqps2SWQepc2R6b/K\niNe/1420aSVjg8jK5ZdfvnRelQ72/9fnbkpwT2W1nUnev9537jy3Se7Dby4uwvn0vdtkZdG8vszV\nau/SsIM5QZf6jgUT6XWB8ZyT0V83UtL+rmS3V7nO9wNtg8H39aJK8DsDzxjyb2L3av8768o4c4y/\nr6VnCGBskwBMZVtWSeT3Y1Z5Rt/T9aAZmUaj0Wg0Go1Go7F12BGMDF+BHs9XpU9TrB9fnx4bWuPo\n/cuwSiemeDzu4x59vOiwNB5vytc0Xob99ttvKoO5wQPi3gk87L/8y78sKXta8LR5/H7d3M89kfTL\npz/9aUmLeT70Fe3zL3VYDPrOPa01tte9eDAPxHWuE9TX85HIDyH+3jdQJdcBFsP7mj7CK+d9Rnto\nHyyMNKQuq/SqNLztSd62yhXeeuutUxkeluRxwWMCG3LBBRdMZXhd8Ka4TdBWckDcA5KkTwHjjTyu\nM1zMKer30Y9+dCp7//vfLyl77TfpfU8x2dXT5v1ZmV5nKPHE17kmjbFJXmaO53z3gmIfxKcfcMAB\nUxlrBp4576fqkXU2GI889un15D785nZNvbDP5PFKnrLq7fO6Vfldt/maY+HX2aS3lfYlz2X1Svvx\nwNtQNw/1/vTcOT9GGs+AtEFd3UbA7aVuROw5R7Cj3NfbUBlmZ4q5D88ef25yXmJI6rxN2wjAAHle\nH/ZMm719rOH39maHc15eNp384Ac/OP1GXyU2qiK9V1SPvtsYNpTyUWpOjd+3Pt/pZ+9L+jflsyX5\n3V3V159r/JY2Rqz1rXYojfmEHXh9ecdJub8/SMzlz8zZQj0vyXUnlo6xod98LcEGE7tLXWpUgK8N\n1IVnuttbnYcpNyrN2bpJqpetkus2t+n47lxnDs3INBqNRqPRaDQaja1Df8g0Go1Go9FoNBqNrcOO\nCC2DInNqD/oJ2s1DY6oUqZcRMkUiepLU45pOkdUwBKdUoekI63E5UcI6qIuHiBH+c/HFF0taDJM6\n6KCDJA1a10McCA8g6depX46D5vO2k3RNEqnTdSToEnbmlCV15jcfB8KW0u7cKVxpXaDvPJmacItj\njz1W0mJS9FVXXSVpJNUeffTRUxnjRbsSXcu/nrhO6BxCDr5z8vOf/3xJI6zOx7YmM3qiH/Q/9Pqc\nbLDbIPZF+Ak2JY0wN2zDx4W6J/llQqc++9nPShrhedIISamhEdLoP+xmk4m7jpTwWvvP53RNdHUp\nUMIuCVd85CMfuXSNlDQMHc86k0JCCfXxEDHqyfE+DjVp30N3aqiWjwPnITmeknDnJNLT7t0gJfvW\ncJW0LqU6bFJqlzolKdcU9kmfzYXzzEkeM7c9LIPxIkQp7ZjNff3ZwZxmLUh2lnZfr/PP5zt19pAy\nUEPf0rjMJfTzrPNrUz/a7Gtrvf69IbksZSlY2sVz8IgjjpjKPvKRjyzUL4XQ1PCauaRxH8ca/pUE\njRg/t5saDl1DBqVlqXS3EerA88b7Ym4birn1ZlchZUmqF9v09ZHw2pQ0vmmkULG5+1cJ7iq/Li2L\nivhaWWWoHVX23utB+DPX9sT8urbyvPD3C+rHdbxO1RZd6GQuDJR6cryHXFfBINq76jtBvd+e2kQz\nMo1Go9FoNBqNRmPrsCMYGTyXnlAJq5C8hXgA+Nr3rzhkafGKJu8YX6TpqzF9mfIlTCK4J7eTKM81\nk+wvCYbuGeQ+eFjcW8x56Uu/SrV6GV/oSPo+4xnPmMr4ioZZcYnlKr3n/VKT7pNnfhOgLb6xHv2O\nJLezIHgN2djSyyrLlpIbwcEHHzz9/axnPUvS2Cg02UTyXIGa3Cgtb0jq3o0qo+zjQNthWJAEl6Tr\nrrtO0mCsvE14TxljLyOhH1EBFzqoHiy3Xeap9yNIm0iuCzBiycs1JwnK8Z4U/eEPf1jSqG8aB8bP\nE+zreuSbx9JnsIhpM8JkL1wzeVaTzYLqYU9CB8n7XhmgdG3mkwuGgLk1snp763HrRlqzKsvtXmE8\nnCTo+7hXJtTrXX/zdZ7EZlg6t8+atO11YbwQV/F1oibYpw0Ya928Dcnm51Dt01kXmCP+9blWJZb9\nmUAfpe0ONi23W1HXeTYhlaTzzz9f0nJfJ/nlxELMrZV1jvlY1YRw96wz/3j2sBEmzzdpSEinzUjr\neCZ2kXp7nSq7lGTpq+BBSvbnX2e+6maPqQ83hVWun1iruja6mBP2wvtWmof0WXpXYe3xucD7HO+/\nvj1DFZOoglh+rSok4n8nARHKkhAIdSICxucO6wRRHbTJ77un74m9IWaj0Wg0Go1Go9H4P40dwcjw\nhehx/1WiLm14lrxxfCXz9ehSwtUL5F+yKR6+3g8v3ic/+cmpDM84XgpnlWCDiG/1PBOYI77wkyc7\nSSzXMm8Dnnk2BX3pS186lf3t3/7twn28L2CF8OjDMknLnj2/3yZjnrmfe5JgZ/ASk2ckSddee62k\nwdaQNyANryGsl3vMuD7eMDaik5a9t0lWNUk3wmwgu+wbTXIc3lf3UhI/jxfOpUyrzKy3gX6gnUmq\nnHHzuUJ+CJ69tMlflY+Vlj1r7n3xa6wbbLKJZLk0bLDKaEvLuRJpg1g2K3V7wfOcWEjmA/PcJU/5\ne46pmmOOaIt7ykDKm6q5hc4EsI4laVD+rrkPDtrCfHKwcaN72mrunmNuU8Z7ipTDVT3NPleYU8w1\nt9fqIfVxrJsPOrNCm+lHX8trTo6PLXOzboAsjbUuyUszNrQv3S89N6ln8uBz/ZrvI431hH5NeaC0\nK3mAqcOqm+RtAtyPPvPNhomYOOussySNue1zJuXN7Aqpz1NOK+MIq+vvDhx30UUXSZIuueSSpTqx\nTtHPKdeF+qaxTtLsjFHNF/Pf6ruSt5c1ga0APFc1MZ33NhLLyr/ef/zGsw4p/QsvvHA65jOf+czC\nNVN+JmPs484zh3dUZ70rG+jPl8q2prxExi+x0awv1M3Xorqth4PxJoLjnHPOmcpe97rXSRosDZtO\n+5YTsIhpDaxRUXu6RjQj02g0Go1Go9FoNLYO/SHTaDQajUaj0Wg0tg47IrTsiiuukLS4szGUU6KX\noL+htT2RCpoNutZpLKjbVXYWdYoLSg4K3xPuCA8gZMGvw73rTu/SoCoJ2fHwAGhB+iDtFAwd6bu/\nkwj+8pe/fOk8wpuoL/SmNEIqrrzySkmLYgaHH364pEw9zoXJ3FMQvuchDoRAIRPs8stQqFCySBJL\nQ5IZajQJQNDXTsEjxcy4edvpT8JCSHaTRmgZoX4e7sT1GXe/JuFNJ554oqQRwiMNG0/jQOgH88Lt\njGRB2gwdLknnnnvuQn39mjVszOcD446d+hxzKnvdePOb3yxpcYye+9znShptT3LBwEPuuAb96SGo\n0PmEbniIH/1ZdzSXhr3QHx7SxP1SiAl/z8n+cs2UnJ6SbmsYh/cZ7SMUyq9JaAjH+9rKXMHm0y7j\nabfvTYaWJfnlGvqSQgMJM/XQMuyDcfd602c1vMOvTx94f9ZnTUooZl3ycacMu/S6cB/mdHqeJWnX\nmmRMO6VhCzXczevM3E7hY9QpyWAnSec5WfB7ilVkdR2EYn/iE5+QlIVRqmSwz22A7fu6U5/h3ud1\nHnloE/OP81KodQ0HdDvArrERwpWl8dxMIaxz70GgzjnvC7ZreOYznylpMfy0itncm6A/vT30KWGm\n/t5TxXUIzWebB0nae++9JY1+97FlnGh/ei/kXw8pZC1gPnrfVpvjHt6f/M365OGqhMqn59NcmgD3\noS88VJ6Q/he+8IWSRmi+r49s+YHAjp9fw832NOywGZlGo9FoNBqNRqOxddgRjMyll14qadFTjteI\nL8yUDM9Xq39JA74i3WtfE6WT55N/0xd03RBKGl48PFlJ0g7vu2+oyNcxXpi0advcBnd4x3791399\nKkP2laTo97znPVMZX/p4It3bT/1o1wc+8IGpDKYBr5UnReOZSf1/T4FQgSeZYwvc17/skaekXTfc\ncMNUhueEBFr3XDF+eN09uY6xwbuVZHEZB5ezvv766yUNm00JezBNScq0yltKw9uT5F9rQnfyrjBX\nSMaTRiJ3ldyUlr1FafMsbDbVZROgTu9617um3+izJzzhCZKyDC/srK8hjAmMA+yZtMwuuIQtvzEe\n3tf0EX3tzC39mNaeyj6nhHnKnPFijZyziZS4znl4cr3PmA8Ik/hax/pCmW/cyTylfs4gbNIDS/18\n/awbE/qaXDcadNTng7ePMtrla0iVNXWbmJMZdmn5eh73wU593Dmuymh73VMZY5ukgFlTscskX8+/\nPh+qd9dtsD7HkgT4JpAYyjnhHMaBNjN/0zimtbkKTvj8pZ2wL850AezMGeO6ETj97BsksgbRTmc/\nqCf386R76s56mGSxk0RyFRBIjHzdssDX3B9kkj/iDrwnSOO9ILWDPuJZToSHb8+QpPvr+XWuSsvr\nuz9bYWCZR2mTzbrZps9j1gnW4xQtMSfyMLdWc4wzSEQh1HcInx/777+/pDEGN91001SGiAXiAP6u\ntDvRHc3INBqNRqPRaDQaja3DjmBk+KL1r7H69ZmkMPnq803p8FgkuVLySvgC9q/GynSkzStTvGn1\nTjiDQLwo3nr3btAe2pxii6sEtSSddtppkgZD8prXvGapfcTx+5cz1yce9LWvfe1UhoeAHCViGqUR\nA0kZm5dK0iMf+UhJ0hOf+EStG3jJPD+hesN93Bk3PC6+kdR5550naeQlufQmY0n/OCvIuGMv7qGF\n4WJMnW2DwSH3xL1wVRrWPfPYDvVzW+I4fvN+qZ5kzzWDkWSDLfpCWs6DcY8Z/Ul/eL/U+eObuW4S\naQO3008/faEOsAbSWAuYa543xZzCY+XxzfRZZVGkwT5yLR+/ytx6PhLjRW6ae09pV/Lscf2UZ1A3\nwvT1kzanDQ5hA5OHFBYJm3W2FU91sl3mBt76JEG7CaS8R2y1SlD7cdSPuG9prNepX+hPxjF5b+eQ\nPJWVNUux7inniPMo87bXjRtT29N41DrMec9XZdhSHXb3GnuCJFnN/GZuuxQ7LDVjiu3P9VPq88S+\nMe94VvkzhOc1zwufv/U+dTNEafkdyddo1hmOd4lf3kfwgqexqLbpv1XW74QTTpiOOf744yWNNWKT\nWzTsDnjfSfLXtNHXM56NPOvoT7eJyvIllpF+SDl8aeNrnru8Z/mzLm1yWVGZ0Tk55e/3W0VqJzZX\nN29PMtfcw7fN4G/sk7xdaeTOr4JmZBqNRqPRaDQajcbWoT9kGo1Go9FoNBqNxtZhR4SWQTk5jVbp\nYU8ag2KrkqbSCDGpyfR+H2jeFM5VE5mlQZ9BM3pYCDQblKCHcxEGxPlO89VkU6fioJqh284888yp\n7O///u8lSRdffLEk6dBDD53Kjj32WEnSqaeeKmmENknS+eefL0k65ZRTJC3u5k0YF9dygQSOI6TC\nE9Ocyl43KlUpDWq7hrFIgyamnoSRSaMf+NfrTVgB4+dhcoTOYVNOv+61116SsrTs8573PElDztND\nmir1620gPG2//fZbqJP/TR3cfpgbhAwhniGNMDOSFgmJk0aYITbsoU30cdptvoYWeehcEuVYF5ij\nTm3T5n/4h3+QJP3Gb/zGVMb8I4HSz6NfGBsSP6UhcEH4iYdXEIqInXnIRhUJ8H658847JQ179hDN\nGj7raxb9SZ8nSViO97nC8Yyj14VxYx54SAvjzfpJvaUxDzjPr8maij2n5PtNgDZ7n/F3FQfxMuo0\nJ8WeQkVo3yZlxhv3DDzr3HZJSibUKwn2AMp8LWNe1IT3+re0aItcmznm6y9rB9dO16zy9ylUiDoh\nOCQNGyYkysONVxGuSKGQdRsCrs07xU5GWgtoG/M8CcUgAHHggQdKGltUSKOvOD8JG6QQrzkBkJqq\nkN5R6/nJblJo6CpiC0lwZlciA9J4z6piAUlMhWt6PegX3qde+cpXTmX0+SpoRqbRaDQajUaj0Whs\nHe6zSW9Zo9FoNBqNRqPRaGwCzcg0Go1Go9FoNBqNrUN/yDQajUaj0Wg0Go2tQ3/INBqNRqPRaDQa\nja1Df8g0Go1Go9FoNBqNrUN/yDQajUaj0Wg0Go2tQ3/INBqNRqPRaDQaja1Df8g0Go1Go9FoNBqN\nrUN/yDQajUaj0Wg0Go2tQ3/INBqNRqPRaDQaja1Df8g0Go1Go9FoNBqNrUN/yDQajUaj0Wg0Go2t\nQ3/INBqNRqPRaDQaja1Df8g0Go1Go9FoNBqNrUN/yDQajUaj0Wg0Go2tQ3/INBqNRqPRaDQaja1D\nf8g0Go1Go9FoNBqNrUN/yDQajUaj0Wg0Go2tQ3/INBqNRqPRaDQaja1Df8g0Go1Go9FoNBqNrUN/\nyDQajUaj0Wg0Go2tQ3/INBqNRqPRaDQaja3DfX/QFZCkG2644W5Jus997jP99qUvfUmS9K1vfUuS\n9NCHPnQqu+uuuyRJX/nKVxaOkaQHPOABkqQHP/jBkqRHPOIRU9kDH/jAhbIf+qHxHcff3/ve95bK\n7nvf/+2mu+++W5J0v/vdb+m8b37zmwv19r+/8IUvSJI++9nPTmVf/vKXJUkPechDJEm33XbbVMa1\nHvSgBy3USZK+8Y1vLNTB6/nd735XknT/+99/qZ6Ucc199tlnKvvxH/9xSdJXv/rVhTpJ0r777itJ\n+trXviZJOuecc6Yy+v/tb3/7GLg14bvf/e7d0mL7brnlFknS3/zN30iSvv71r09lD3/4wyVJX/zi\nF6X/x96bxm53VeX/F6AgToDMMrTQeZ6APqUFWkZpKxQkGIlAUUkMmvBG0YA4xOgrIokSjbEOEYxA\npEipEoa2tKVzS2lLB0pbaAGpqIADgoDye/P/nH2ddV/P6dN/7/vhe5N1vbm/37PPOXvvtdde++w1\nbQ0+kAY9HvnIR0oa4ylJD3zgAyUNHvrmN785lX3ta1+bXWP8HZQ94AEPmK59/etflyR94xvfkCR9\n+9vfnsoe/OAHSxrjQB1+H8/7fOB+2u7v5NrnPvc5SWMcvX2MFf2VBl/BU494xCOmsne9612SpCc9\n6UmSBv9I8zHZHe5///uvnSce97jHfUeSHvrQh07X4F3a6WMLjaCBz6M6R3zcH/SgB0kaPOTPMX7g\nU5/61PQ3/Agd4Tdp8NcTn/jE2bsl6Qtf+MLsGmMtjfFCjvnYUkZ777rrrqkMOtBe57PHPvaxkqQ7\n77xT0pBP0qAn9HHQv69+9asr93zlK1+Z9dNlCHz89re/fe088cIXvnBl7WBMoYvPFeQD8sznLesK\n/YIW/g74xGUPtKXvLncf9rCHzerx+cf74Wdf42gDa5a3hXfCX9/61remsiqP/Ll3vOMdkqSXvOQl\ns/5K0llnnSVp8JA/9/CHP1zSGGOfK/R1n332Wen7tddeO+vDrl27pjJo9dGPfnTtPHHQQQd9R5rz\n56Me9ajZPV4G7yA/WX/9HmQ5cHnI84yjz3tkOfPQxx85Cp86zR/peVTBAAAgAElEQVT96EfP2s38\n934wLtTr7a3fBy7D4GXGwHmZ8Wfsbrvttqns85///OyXNcXXA76tkD/Om9CMMp97tOXmm29eOz9I\n0r777rsiJ2g3/OxyAhmMLOdbzuU23wPMR/oujbkC3zz+8Y+fyuAz6nN+gT8YW18LatuQsX5P+rat\ngDecJ/gbfvUyxoZ54d/b8ALfttDJ1ynkI/2F36WxFgH/Lv2TP/kTSdLjH//4e+SJHbGRoaMQURrC\nj0674GDCpkGG0RAK/mEDEzO50oYEYqcPItrgz9V3u6DiHbQXhpdWP668jD5QrwvR+rHjDEe/oJkL\nkdo+718V0j6heY72+SLmgmjdQEi44PnoRz8qaQhbF+rwDtf8Y5c+LwkF6vE+wROpn06/Ct6Vxo+P\nQCa6b3yZB4yxL0wIEYSXf/TwfjYk/tGK0EEwUr/3gbni/PL+979fkvT6179eUt7E7W1AD6cLcoL2\nuQxh/ODdu+++e+Wd8In3D9q6zAHMfT4CGRdvCzT3dz7hCU+YtQVFhr8Toe718kFDO12+MO5ccxnC\nxwm85POWuo866qhZf6X5xkya8z7to73OLyyg/Pr82yTv8IHo85Fxh0+S4oJNnNMT3mEe+XP0mTmd\nZCRz0z92oDttcnkG/ZyHAPSjfz5+8DGKHW/nQQcdNHvOPxr4GOYD1MeIDwjo4u2Ez5JMpq9f/OIX\nJUnHHHPMVMbGhY866CqNjeQmAK19YwDqhlQashjZCD1djtJnxsrnBTRg/rsMqgoof67OGWS0/83c\nZKx8DWNs07v5m1+fg7XMP3rZkPJdkZQAPAdNfA2DhtDeFRq8i3e7UmhpPV0HoJWvxbQD2ngbqhKL\nvvraAz8zr3yuUU/qF/Me/nI6VGWry6f6HVP5ztuQFN30gf6mjQxI8o1rzmcokJn/1Of1Uh/PO5/D\n30k+fvazn5U03wTuDu1a1mg0Go1Go9FoNLYOvZFpNBqNRqPRaDQaW4cd4VrmPpoA9wrMWG7qwnUD\nkxruF/4cJs3kKoTZz339MIVWM5g0zHxu9gLV7chNgZgAMSW6KwbAPOnuJLTBTfEVtN3fidmb9rrr\nwIEHHihpmLHdRI1pE5cD7xMuAPvtt5+kYUqUpFtuuWW37buvoA3udkF9jKPzDXTHVOnuMtCDPvtz\n0IP63G2Qv5OZtprs3TRbzbvON9XfFJpLw3RPzMKXv/zlqYzxYzzcRQzePfzww1WB7yq85Gbs6irA\nGEvDRxp/Xjfvwqt7EiuzTjDGbt5nbKG/zwfM9NVHVxqyAxcTnyvQA5cYd9li3Cjz+qo7kLuPQT93\naQG4Q8IvPv/g2cqL3mf4zceWPmPy9/gneOfmm29e6TvuY/Ceywnqg+buNlF9s32MqtvCOsE89zlW\n5XSKoYQe7mqCW1Z1k5AG76Q4yRoPkOIr0/+0k2veFtyTGAdfC6gHF1vnT8aLWCzcM6QxzgcffLAk\n6aabbprKcAO7+OKLVVHXr+TmzbyApyTpuOOOm7UJtzW/fxOgPp8PyM0UE4e8hf7IhhRXwq/zN7zP\n8z6fqku3u1ox7+FFd4XjPn6pw90WeWdyEatzIMWGpPWJsic/+cmqgGbID2SMywjozK/zLd9p8JHP\nS/9u2gQYSx9T6MY1d9GqfFLd+Pwa64zzFHTknf7NAe8zj/y56lbuPAwPUC//O9/U0AF3Kazfsb4e\nVtnuYwMYZy+DhrjXMQf8+6SOrccSVfntcoG48ZNOOmmlLRVtkWk0Go1Go9FoNBpbhx1hkamaBWk1\nAMt3/TUDl2uy0IawI/YdLbs+nvfgLN+d+vMJHgRYtXG+AyaoCa2Ka/HoK7tb3+UuaatoF/10DQO7\nW+jpGSHQGkAP1+LRrhQsjmYFrVXSXG8CjBUBrdIY55QpjD4n6wl8wtg4v6BZRyvm2hH+RkvimoWa\nHCBpaFPgG5oWeM8tR/vuu6+kQX/Xbt56662Sxji6VoPMIfSLgE1vF5pZt/KgyeE5zzQCn5x33nmS\npDPPPFPfbdS5LY2xTTKEv+mzzzF4gXHwvlPm2k+A7GDepSxEaOG8DD5D6+8BwS6HpKyFqxl/pCFz\neFeynpARzQNyaSfWVc9axnPMB7cYkhkOeiarZ7IqeV/XDWjlVgL6wDWnZ00O4NkikdPQxa1t3IdM\nWLKWpyDxtJ7U5CP+f7XUu9aVRBPVki6NZA3wzfXXXz+VnXHGGZJy9iB4ljH2d9YAeac19zHuPtbI\nbjJfuly67LLLtCnQF19H6QPt9P6hLYceybpL35nTPrcp493+DUBbUiZD/obvfIzhhfrrliDakHir\nBqkvWSzTOgr/uGWGfiFLkANef7W+Ivf8ebcO7+65dSPRaCkJCTwODzA2brWpNPbvCr79eN7pUJMr\n+bjXpA4uu6olra4b3pZqbfIykBJaUYfzd/02dlAP35r0DU8Ob0PyfoDO1Odt9HXpntAWmUaj0Wg0\nGo1Go7F12BEWmaRtZIdGmadOZUefnqua0uSTvCdtSbE17ExdU4OWl3e7Zr5qvvBblsZOm3pck8Wu\ntlptvD9cc+0GVhY0pb5j57kaE+J/0ybXTqOtqpphae+k5PV0sPhdQh9Pv1xTTqeU1fCGa8W4D+2S\na7zQlKT4hJqaeck6lc4rSrE19eyHE088cSojVe6NN94oae6DCh0YN2JfvD+k/01jBu1cy8x8+8Qn\nPrFSVn3I92RerQOMm88V+sc4phSY8Ky3s2pRvX+U1bM8pNXzQFy+VF/1xz3ucVMZ86jW688xp/05\n3lXPiJJWU6Q6XapV1s+DYJ7DS4cccshUxn1oBF1bCE/QBvf3h37Q2C2GHqexbjC3Ew+m9KZ1/Oo5\nBtJqzJI/hybfZSSxHyk96VIqaPgTzaNbALEKpbTwtD1Z0JELH/rQhySNOBVprA9Y2d2iBl+Shtkt\nFik2BtAG2udzBZmKPErzbxNAxvm6Wee5a8hrGl7altoI7VPqWupwuiJ3Wae9rJ4V4nxaryHXXFNe\n1xyX7TUmxHm5WmlSTBf86mOGx8AznvEMScPa62ttte778/Am7XbN/KYtMikdcvXMSamSoWk6V6vG\nLzm/wWcp3hk5Qf9d3iMDaFM6H8vnmL9PWuUJ/7/ys/NE9YDyuV7PnUltghbwq/MyvJDOuKEerKfO\nw3ih7AnaItNoNBqNRqPRaDS2Dr2RaTQajUaj0Wg0GluHHeFahlnJTXOYwTHJufkSc1kNyJKGCZd3\npbS4mK/cfIZ5rqZz9fYtnf6e2uKmO39eGqY4giu9jDbgyuGB+bST5/2UVDfVSjktX0Ltg7eFa9DT\nU8qmFH3rAjT/5Cc/OV0jLSpmd+8vdIdf0gno0MP7B22huZtEa3rK5CKWUN3H3Fy6VFbTIjov0S7S\npBLwKw0TLDzrcwVzPu4yzhOkX8XtyWmG+wlpmEnjLElHHHHErO17y7UsBdHXAEgPmK7BqMmkXRM6\n+N9Lp02TbtLdengOGrubG25xyYUCNxdkQXL5SW60NX2vg7kJL7k7Au3DFc1N+Lg50C9Pt878qy57\n3gb40l13/O91A3cFl7U1GYyPQ3XZcNkKPVhD3J2E8UsBzswNUhenQHngRwVQD/Pcn8PFj/HwOcZY\npkBi3NSox3mDMYWn3BUR9786xt7XJAeZi9yP3JAGD7KOuRuf89W6QRtS8iDGbykhQwpuXpIF1U3R\nkxowfvCZr9fVfczlWnUlqwlkvE9La0ldb7ws/c/96TuI9uJ+eMwxx0iaB3az3rB++/PVjcjHx93E\nNwHmypI7VQp+p/18a3jSFMY0Bd1TX0p1TBtIguEyqB4x4OtSdferbrLep/StSjtrKvH0nKOusck9\nvSYgcJrU55PbGjzhffFvjntCW2QajUaj0Wg0Go3G1mFHWGTQRBFMJg1tDtoV12TVA+5cg8kOlN2r\n70hr4JuXsYPmmu9M2SWiwUoHMaZDqUA6ZLOmnXNtFe1DY+eBs1hi6qGL/s6kUaI/S0GWaGrRyvn7\n0fpxCNumgRbP28IYpUA4+KUmiZBWUwv6GKFJhF/SIWg12M3fsZTeMqXxXNJq1GvpQL4aiC4NCwlB\ndW41I9AWjatrC9GCkQrVaQbvMf88YJv69jaSlqkGUbuWuQYQOj3r4WJuiauHHqaxQjvp87YGwacD\n1qrGUhoBwWhvU2B3TSns9SXLKPSo1mu/RvsI+vd3ooH0dnIoK2l1XTuJhQpZ7uPg2u9NwceWsUyp\n2KuV3GUrNGZsXY7WeZcOU7799ttXypiLvIsAaf+7pmGWBv34dQsGGlzeuXQgpq9VyFLGyOc08j0l\nkanJJJLWFjo6zbCOU58nu0nB0+sCc9Kt1sw75JnLcuZYnec+Z2p6WOcp6E///PBg2sJ8cn6r1pZ0\nGHOl9ZJlJWFP0i/7/zWg22URbYLfjzzySEnSNddcM91TEzt4e2tiI5edKSXzOlFTAEur8sGtH1ju\naFeyUFUrhL+7JmVyXkKOetKh+s40tjX1f0oYxN9LlrjEw3V9SfWng+GrXOQ7PfFU+m6rHgouF1x2\n3RPaItNoNBqNRqPRaDS2DjvCIsMuzFPK1rgN1/TwN8+lgwqTFSRZW2pZSqHJ30u+kNUi4H1Ivoz8\nTX2uqan+914v72K3mrTFvNM1g0v3g6RZqDEkrqFdihO5r0Db61pfxg0LnPcFWtUUxtLgkzoe0mq6\nQecX7k9pB6vmImm8qmXGnwPehz05oAt4O+FB2ut+2NAKa6dryuuBimiUpdV03UuHYu0tVOuZNNoF\nrb1/9cBP9/unf1glnJehGVo5n5vcz3iglZSGRQzNnltk0HhTr1uR0fqlVKnVYuR8gMY7WZjpK3LG\ntY20pfpxSyOVMPe7NYV60KRed911Uxm8iwxP6UY3AdqUYtugZ5IF1TIjLfuJ0y9+XSY/5znPkTTm\nj1sC4A9iVzwNNtYB5qbPP2QwY8Sht9KQyfQlHXLLuz3GEAtQ9UuXhnb46KOPliT9wz/8w1TGnEKu\nuKYUPuOeRFfud7psEswR1/LjSVAPI5RWD7VOWuma1tZlEONHjBvrt7ehplH2epOnQI17qemU/e+0\nzlTc2wMh03OMLX2nn4ceeuh0D8cl1OMQvH1Yp5yG9ybV7n2Bx8FUS71/99RYZORhSpFdrX3+7hp3\nJ81lfwX1ppjr3cXQpvib5LlRv4mdv2tZ8j6hTYknuEY/U9zOEk/Ue2o994S2yDQajUaj0Wg0Go2t\nQ29kGo1Go9FoNBqNxtZhR7iWYe5z0x5mO0xj7jKCaZLflAYUuPmU96fAfMxdKai2pjlNp+vWU4G9\nzdzj7h20nXvcDYy6cU1xszZmzJo60NueUh/WoN9kok7txJSKe40Hpi6lpLyvIPUeLhnSMPNWk640\n3Hqgi5v3a+ppH/fqNpgC4FJQXXVT3NNgyhQEWZFcXKivjpU0xjS5TsKPya0OnqtuZNII0OU5Nwd/\nt0BbfDyrKd1dW5AdyZ0IdxfmofN15ZeUsjOd4l0D832MmUe0xelZ3QadB2tQ9BIPutsM7aOf7hJR\nA7t93JFHuDJ5inPcP0gS4fOfoHbq87Zs0rWsBkxLqylAve81yUNyNeHXXUXAwQcfLGk+ftAqBcoz\nRjxHEhNpyK8bbrhhdq+0mkzE68P9DxczbycB/dDF3RvhpSc/+cmSRip3achLUul6fYwlwezuNkji\nh5qMRBpzqiaMkebufusGa4aPLTwLn/q8gsbMw7S21rTEJOiQhmsZdbBOSasuZSmF/54Edi+B9qZ0\n0yC9J9Vbn0trV3XL3W+//aZ7SFhC0L+/r363uTtZPTpi3aAdztfVtc/5uq6J0M/5hu8kXDZdznE/\n11wGwQs1UY20Goif3NmXkgLV9qewhOQ6Wb9L0ncQSAkEapKt9I2cklpUtz6vKyVD2B3aItNoNBqN\nRqPRaDS2DjvCIoMWL2mg2cm6xrQeDJSsLulQwbpzda0QO+8UZIWmjV/XHmD94Dm3ZgDa50GS9aBC\n73vVYLomaynRQT0ILFldaEtKPJBAO9lFe6pP1/atGxzWmNIloiHECiOtHnbpWo6lFNnJUlHr25NA\nyapV97aklM5LmralQ6mStaambEwpttOBarQBTZRrpHgX2ia3cH23gLYvBQsupQuFXzz1NLxDnznk\nTRrziLHyYGrmN3R0DTvvSoG9PMdvSiHNc259rikvE29gEUBTLw1ZRTvd6gKv0k/XFjLfqcdlHW2n\nPuezajnyPmwStNO1fKwVyKelVKJJMw/NkhcAZS6Tb775ZkmDxk4z6IkM8tTl0A/r55LlKh3QXIP+\n/Z28y+c0PEHgu8vyGgTvPMG6Rd99HkEH5oHLRdqM1TIlAtgEkA9uzadurFFYDKTdJ2BJawJj6/IQ\nzTFlycpXD9yWVmVX0nRXS0JKHJMOdKxyI30HJa3/kkWmXqMO15xDF+aEvw8+uOOOOyTloyM2hbTe\n1nTALs9oa0p6AurBwO5VU71pkhdB+p/7+PWyyhNpTQcpOcTSGsI70jEUIB0yX71VXG7Ud3NvSrFc\n00ZL0qmnnrryrt2hLTKNRqPRaDQajUZj67AjLDLsWn1HiwYhpb2raYJdc1LT1/k7q4+97/64H82S\nazDR9rN79AMH0f6lA4hqva4Bo11o49LBUen/pEkE7N6TvzLvgK5eVv13PS1nba/vxpcO17yvQGPu\nY8ShcGhCXMtU02e7Zaxqp/ydVauRfDuXNFfJl7lqPJZSOjvSoVugaliSVYmypBFkrJLfN7RyLTOH\nupH+2uORvltIsSfQNh2ECn8wL9yCyP30zzXS0CMduAtd0Og6XbBw8OvPIb9ou6fIph7akHyt4Snn\naywjtMFlFtYrZFeyxJEO1+c7/JGsA9AMunrfq6XY27JJ3lnyJUdeu+Ud+iWNJbIcK3mKK0Kb7Gnh\n4QlkucsQNLnQwA92Jm3tLbfcMmuvtJq+1VEt/ClGtProexmywA8yxOpI+7zv3F+9CKTBZ8Tm+BpH\nmm7WS9fcb1IDn2jGfIUuHtfBwaDVEpOsu8Q/eRwMdE1xGCBp5qtlY0/iZ/yeaoVe8jRw63D1NEgx\nowm7s9a4XKUe4ql87pOauaYRlvYsFui+oK4T0urhtim+i2vIZr+Hv/lW8TUE+Y68cX7hWopjWfIC\noawe4JrGPX3jVqugz0HeneKeqyUopanmGr8uy2qbvA7maj0iQ+oYmUaj0Wg0Go1Go/E9jt7INBqN\nRqPRaDQaja3DjnAtw9TkZulqxnTTHGYwTIJuSsY0hcnLzZfVHcvT/xEUiXnQzaWYkz2tJsAknwJ8\nq1k4pWPFhOlBmfWUen8n5jne7aZS6MCvmw6X3MBq+l4HLiK43Lnpz1NQrhv0z91C4A9cAZKJknHz\ngNvKS6mf0MDNwzWwPgXlJfN8dS1IJzYvJQJI6Z5TPfWdNamBl6UxrqeiOy8x3gcddNDseUcKGtwk\nMFe7exX9oS8+Rri0pHST9Bke8pPTcRVCXvi8Z77iNnb55ZdPZYwXJnFPUEI70zzkOfjaaY2LAryf\n3I9or5vi4ZOaYlsaPI788/pqWlyXrbjMLbk3cr+7Jm3SjSgFM/M3/fR24u7EPd5f/mYe+Ljffvvt\nkoZs9rEF9Nldr3Df4zk/3R65zvg56knnKTUzfXD5Vt1PfF2p7mbeB5J6wC8uB+s6m1wFDzzwQEnD\n/VcaMgQ6ukz2YPt1I7kew//wsK/vpE+uQeguD5E5zEN/HnoyN/07o6ZtTuNY7/E2VLfohJQsgHem\nvlQXn+ROBO28rLqnJ1dmXA3hc+ZN6kNKXLAp1LT5Xj/84u2DV7nGPPKxrePu6xLv5H7/fmXeJZ6g\nfTUVvLe3uoimMVpKLsH4uVysSQY8pTtrIzRIiSOQqzzvyRGW3Bxxa67JmqThqrsnaItMo9FoNBqN\nRqPR2DrsCIsMO1nfqbETRfPhO+mquU5B+ykRQD3AyHed7JjZGXoAbNXsehAv7eN+362y8+a5pUOG\nUjKDetimX6OepMmo9XpfubYUaJc0NNDTtb6bTKeZUpnSB7Qdrk2F/mgGXHNSLRZOT4J2ebdroKsV\nIx0uxfg7T0CXqnmRxrglDVtNzextqckWnD8r77lmiDZXjYu3M5URwEiqVk9lunSY5yaRDqqrh205\nzaAHGuGUFATNkY8t1+qhoNIYhyc96UmSpFe84hVT2Qc/+EFJQ9vsFuY6V3z84SF+0Qx73bzLaU+q\nU8bbtegEXyfrLO9Ae8qhhv4O6kvpe2mT14d8YN6lIPNNgP4lzWM9ENXvZ776GGH9gm+wkEljPmBt\nccsa8y9ZoxgbUmP7vIUvqdfHAbqntOJLKblr4LnzNf2inUkjC984SABAn2+77bapjL7SPk8BjqWJ\npAYc/CnN5cm6Ae+lRD+MhyfsYW6QmhkaOO9Xzwm3rEFr5EYK7E5HIlStufNpTQCQLPEgWWToO21K\ncpFf7ydjzdqa0sTzDZCsDTxPYL/LgZqUwMdnb60pyVpGG92SilwgyUdaezzhhzTnKSyq0MYt6dAP\nGZQOh00eG7s7tN1lis93KSfQAikpCTLP+wKqlUoaHkmseYz/0qGZLh9ZQ5hf3t+2yDQajUaj0Wg0\nGo3vaewIiwxI6e/Y0frOnl1mSn0LUho7dtA875oEdsUphSK7cnapnjqV3XVKv8wunF18Sr1YtZze\nduixlI43pUmlLGmk+E2WnGTFQvsGPVyjkQ6JWhfQ2HlboGfSuHENLWfS2rP7d00rmqqUfjBZtmp9\nPO/WPaxJ0GxPY1ZqylXX4qABREvhWrT6Tke1QpEWU1rVrLlmB00LZe4XD5/w7qXU0+tE0vbXuDfn\nCdqH5svHdikmDp6HZk5r3omGHi2uJB133HGSpPPOO0/SXGNX0yd7u4k1Y2y8nWi6Unpb5BL1uAaL\nMnjJ5RnjnOREPdg3+YRDD7f8VR5yXkzxJOtCPQxWGnIpHdhbZYfLMDSF3OPWCfpKX1KcCLyR4m5S\nylPakDSzXEMGuEWvWvdchlQrja85VevtWvqaeth5grZDz6Tl5R63oENPfOzdkpNSFK8LKQ6tWkR8\njrGucw8xM1gVpMEnaNrdooS8Zw7490E9WNE15vASz/m41riC5E1QrdAcQuntI04pxZBRv6ei5puI\ntSHNK+iLRTfJAd7ta22KuwGbTr9cYykd6WBP1nN4A15OR1Pwbk+tjsW+poKXpM997nOSxpruqeqx\nsiMLvL3VolWPzZBW4368jD7VdUMaXko8f/DBB09lxx9/vKQR/+byhnUQ3sCDIx1knrwKaB+yJXkV\n7AnaItNoNBqNRqPRaDS2Dr2RaTQajUaj0Wg0GluHHeFahmnPXRAwcabg9Bo85KYuzN4pNTPmL+pz\ncysmMky4btLDhIeZFjOcNMyRuDR5H6pJPgXAphN/q8uH94+/kxtRDQLz/vEu+uXmTNqCGdUDvTCJ\n4jKQgkc3gTQONS2x0wD6Y8pP5k/cApzWmIN5PgX019N9pcFXySReE04kd6vkGljrScHUtB2zvjTM\n3sk8iwsMrmKcIu5IJ+7yN/Q/4YQTpjJolNIabxLwcHItSyc3Qxd4wvmVuYy5HHO7NEza8IS7H0Fb\n+v72t799KoPG3O9pdZlTBPK7GxjmeNoCv0rDtQxXEZ8PuHjRdm9nTRyRUlbTB3eLhK9xe8BFwp+D\n5k7r/ffff/Y8tPB6NoHaJgduC+7mgDxDTrubw4UXXihpuAg6X9PXtB4xJrTFXZZx22GMPvOZz6y0\nhTXE5x+8C7/VIF5ve0rTDvydtDm52dSA9ZRAh/nn415dXp1mNWGB0yz1Z11Y4rfkOgfv1CQfu3bt\nmu6hLynZCnMbGvoY03fo6YHOuJIedthhkuZzlLGpfJ1cEz/+8Y9Lkj7xiU+s9Jfx93cjlwhu92MU\nanKllEKeNiVawMv8pqMOkjv8poP9k+s4c4tvG+8r87XKUXcRw1WKsSWphTS+K6jXv0OR96xBLrdx\nxeM5d9Wsrln8+rdc/XbwbwHWEhJyeBlrHfIeFzdp8Bx87bIBmuFOWb+/pcEvKd10dZl0uZD4ZHdo\ni0yj0Wg0Go1Go9HYOuwIiwy7OtecoT1Fg+E72nrwXwruS4f31MOC3PLArpiyFJRHW/wwLzRS7GhJ\n/ysNTQdaGdf6ovmgHtcUcF8NvPX20WfXfNbgZN/RQjP64v2jX2mnjkagauy8DZsAFoArrrhiugat\n2e17cOwBBxwgaWgtXHMFjdBAeTBmDdR0bQHvgGYePAyNU3Az15KloyaFcBrSL6wortVgHGiva4+W\nDtHCckOb0AZJQ/PIPc4T/E19zoOgHu65t5BSkaY0tWjGud8DdOvBXR7YizYRmePzgbmC1cYDmBlL\ntHEpqQTtc9lDW7jmWr96uJ/LwaoRTc8xZzyQtVo7kzUjpQmvaY1dE0u70DI7zQhu3QRS8G1NLuB0\noQ/pQEbazNi6pQqZwRj7XKHvSYPIvMVi5fSkDQTdulxCZtSkIo6UmrcG7bu1plpBfG2s8ycdesf8\ncTkIraGVrw8E+VOvy7qkqd8kqhU5HeZaLTNOu2OOOUbSWEucv+sYeT8rT/j/rPP8LgXNww/O7/A1\nY+XWU/gupTqGBsj/5LnBNT/glL+RgTXpkjTWi3qvNGi2lLxoU0jJn2qCCh+3egAmVnKXLdUjxeU9\ncpc5nQ72Zg64TGANqBYhaZXeS7xcjyjxerC2+Dzm3cgi/06oh4y7HGFMOTgbOeD8Rt/hb7fo1gRT\n/u57Y8lvi0yj0Wg0Go1Go9HYOuwIi0w91E7KqUEB19i9+S6u7rJdq8Yul4PH/FArdtrslr3eK6+8\nUtKIjXGfxBqXsKSZ9+ew3HC/W2voO1oE1/5wP9dcQ1O1Dq7hQTvNbzowEkuA94F66oGa0mY18Vhk\nbrrppula1fC5tgjrV9Iuo42GPu4bDr/Ug6+kQcd6AJ3fl8rgHcbG+RpeSIfZVe27a0wYy5TGtcbb\nOL9UDY1rfWuKR/fHBdzvmjWwty0x1Od0qVbWFLvAOLjGjRQ19IcAACAASURBVPGG513TVv2iUwpM\n2uLPVRp5LB1thj99jND2cY/LCWQUfXBZR/uIAXKrC32oFjlpNSVzOnSNX7dm7LPPP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SCv7qOpzWyLTGMabUR4IaaSQmqePo78Al5fbbb5/KoAcJNXDTkkawP24jF1544VTmMnHdID0/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/M/V+oHXGbU31FGff1a1sr3fd/3SZKe8pSnLMoOP/xwSWOt+BpjHTGXnvCEJyzKDjnkEEnS\npz/9aUljTkljbP/+7/9e0vLY0i5kjs9B5g5lPg6+bqT5nEr9yT1rH/pzjj766MV3xxxzzFL9vJ58\n/ou/+IttnxMvfelLvyIty1bkF/LJx+hzn/ucJOnbvu3bJA1ZIknf+73fK0n6lm/5FkljzCTpSU96\n0tK9WFfSqsxBNvj17AE+T+lr/qY1zfWsQ2msYdY7f6UxXvQ59/b7pzKew1zwPY6yeo1fR3/Sd476\ne2n0+8c+9rFtnxNfqZuypAceeECSdPHFF0uSnvGMZyzKvvEbv1HSWCPf/d3fvfS/tConfK3N5Ayf\nWWv+O0C/+Jrld9y7/u/f1b9+r/p8aYxZmpN1f/L3BH5X6+t1Yo6xPp7//OcvytLcCNj2+SBJF198\n8Vck6ZJLLll897a3vU3SaI/LLOrKGmGO+LpA5vB7X0/sHax/3+cB3yFbJOkHf/AHJY0+ZYyk0bc8\nh3dHl32nn366pDGOrEt/HuPm78bc8/rrr19qkzTeOfnrMpP+QPbRhy4DkU+0xd9PqDv9yzO8nl/6\n0pcec07sioMMnUFjpNEgOt/LEDQcSFw41I3DkV5aah3S7yijY32Q63e+aTJRGGQXJrSH3/lLJIuA\nev7d3/3dooyJQp38IMNB8POf/7yk8WLl7WISpg2VFwBvA3XgOxeW6eVxu8Bzk+Dmhc+FA3PBXwYA\nfc2LiW9Qt9xyi6QhQHwuMQfrC4C0KsR93tAvfOfjTj/ynb8QMQ6008ehbkg+DjyHe3mfVQHs82y2\nIdEP6XmU8deF+zqRNl6+800E0Pb6MuCgP1zgc2DmAHPooYcuyn7gB35g6XrvT/qDaz7+8Y8vyngh\nOPHEEyVJt99++6LsC1/4gqQhL5jLDuZimkt1rFJbfWxTP+4Pqc9oC4cyaawt5MusLtsJlBInn3zy\n4rt3vOMdS9ckOQh8/VV57Rs939Fn3p9cx+a91QMz1zFP/XccVpB1LnuoQ1KiUM/0wsz96YN0TQLX\np0MO48zLirfP5Vf931+Q1wWvC59RZvnLI2DdpYNB7bPZgcL7dTb36956oPtp3ROS/J4pm7jen8ve\nmOR+bR+/97WOXGRu3nbbbYuyU089db/1Te9k2wnu70rhuv+lwyljk/aQKnf9QIFCneu9DHl/7LHH\nSpKOO+64RRn9h8zyF3tkQlWYcB9JOuecc5bamfZ7nu9l3PupT32qpPEO6tdfd911kpbfu/anSHQ5\nyzsq/eTzhT7knT4pV7aCdi1rNBqNRqPRaDQaG4c+yDQajUaj0Wg0Go2Nw65wLUumvRpD4GX4L2Ia\nS76hIJlbq7nQP2M2T+bzZHLmd9TJXeCSXzqoJmf/Ha4puDRhdpOGqRH3MUyY0jCX45vvZlTcxvAD\n9/bxHO7tZlD6jGt8HNzdYbtRY0Kk0We03Z/PmOJL7n3PGOH+4r+78847JUmvfOUrJUmPPPLIoqy6\niiRXAx83UF2u6pz0a7bqYlHjILbqRsQawZ/Wzck8e+YSkdowc0lZpxtRekZ1D0iuXjN3EOaNx7Mc\nf/zxkobJ3s3srHPml5vQq7unuy3deuutkoY8O+GEExZluDeyRr19PA/3oXvuuWdRxvil+VVlqq93\n2pxcWrbi4kGbPd4KFxLalVx014HPfOYzkoYLlrTq9ucxfzWexduLXOD31TXKy1y+uPuHtOwaSP9z\nfXKXYJ74Peu+R2yPNMZ25gZd4zq9PfzeZRfX0QfeL9V92ttQ11+aZ6k/d8ItOcXyPPnJT5aU9+Ya\nh+J9t5X5PFs7jOPMNWmrLmn1ebP3klRvytKeXudIereaxd/wHWP+0EMPLcrYf5/2tKet/G7drmXV\nBdK/q22WxjqoY5TkBW12V1TuyXceE4kb7Mtf/nJJy+EBrHvif13G8n7HXo7L1xlnnLG4Bjdo+jq9\nr1EnX4O8NxH7ScyjX48rMTLX78FexPulg+fidpzkKu32fTjFFe0PbZFpNBqNRqPRaDQaG4ddYZGZ\nMTGlQGIPzK3XpJNzLeN6PxlWzUXSGHG9a3Oq5sufW+ueNEQJ3J+TujNA8BnLwcMPP7woI2CdU7HX\nkxM3p3lvLydfxsHrhtaO8fDfJU3wdoF6JoaMqgGVRr8wh1zbiPaV/vQARMaZ53zqU59aeR5aDbfk\n8B39kQK0q8bGkTTlsyD6pCkFlanIwZjWe0tDE5x+V4kHkjUjYZ0WGeazBzfPSDq4PlnNmLuM+0kn\nnbQoIygV8gwf2xSYXUGfY9mRhlXn6quvXmkDz2YtuxazshO6xoq5vhWyBZdnXI+1J43tLAA2MZqh\nOcT64fVcJxg/WKn82fSPzw3GL63XisTGlKx71arqc6PuVTONvNfJtbvSMuMTfZ3YlviO+eZ7QAq2\nBTPmrcqc56hB5b6HI1/SWkla2e2GP7eyKnlb6t6dmMlm+GqtCbPnzd4d9nef2T7j90a2z/aSGvTv\ndarsrUlG8Huf07CpYhXbav9uB5ifbhmh/tTR21qtNamP+ZzkKHsHfz0g/7zzztvv76gLdYN4Rhrv\ndZdffrkk6elPf7qkwaTp98KLx+vLexPy0a1TPBerj3sTMF5nnXWWJOlP//RPF2WV8TSxpfHumUhG\nqCfkBi4X3NvosdAWmUaj0Wg0Go1Go7Fx2BUWGU6/rmmv/riuVePUWbXGXpb8RkGikawa9pm1Jlld\naINrwCo9asqtwF8sEF4/tLDu/42mjhOvaxiwNODf6CdatHhQObtfJnXHMuN9thVLwDpAHTw+CKsL\n/eGn/tqfriWtdN133333ogyNSYrJQWOS+qDGlST/Zq73e9brXWNVrYDJKli5/L3uyQ+fNZIsFmhR\n0MyktZIsnFVbv07LXEKiuk4aQL5Lmj/WJvmHyEkljbVBrFiiIwczraLLF7Rg+EcTM+PtgZrZLUhu\nIZSGf7k0xtbjJ+o9a84fb0OyrFR/8a3k55GG/MJS7PJlnfEy1157raRhjZaGjCSWbia7Znk93GpG\nvySL9mwdVDp4t6xUGmyfu9SPPdEppJHh3Bu/dmmMqVuy6/PYC2a5UGb5gxwzSwH9R7+4LN+JGKok\nI5OFGcyossFWrAgp9iPFnOzvufUe+0O9V9qDEtX6gVh3UvzMzAJcrYSu2Wff5p3F8zTNxmU7wB7s\n75jVEpfalVKDgOrN4TIICwz5ijzOkn2XOnlMMnsxct5/hzyjDGtNiolNFjWuq6k/pNEXvDN6e2kf\n+9ONN964KIOSmd8jk5IXA2UpvpC6ucx1ufZYaItMo9FoNBqNRqPR2Dj0QabRaDQajUaj0WhsHHaF\naxkuEh58NKNnrMHJyWVrFpiYXMtm2Y5nAcUzSmeAec+zlmJWpM1ONYdrBNd74CymOEyOBFJJw2R7\n7733Slo25eN+gmuEmxx5DmWJYrCaJf36dYA2uxkS02oN1JbGGCU6Y9pHXxF0KI3AbvrVxwg3kOQ6\nMgucBcyz5Fo2y3LLdz6vZ1TOlGG69XHB1FzdHOtnaWczLT8eeL2ryd/dwCrFqvcda+Poo4+WtOwK\nhal/5pqWyD0Y70Ttyjjgyuj0y3fccYek4YbhgaHck/XrJnuCPZm7TgpSCSNc1tV56e5O1JPrZ267\nCcggl2cHErR5oEBWuusVsnHmxgNSMDPX+DqqVMJOi1rdkX1Oso+lPQfZn2iwkeHIPHeJYZ7geuHP\nox98LgDuwV+XS4ncobavXlvbI+X1wHP8eTsR7O99VufzzK1uhlk/gUTJnp5Vg/1nNOpg5sKcUk0k\nV+SKRDKQ3N2q+/2MNGJGPICMIM1ErftOYX8kD9LqfpvmDbKAAPnjjjtuUYZsZl9JJEnIXV8LPOep\nT32qpOyaBzlMknOV0MrHiH0pkV2xHyJLnGykjuVpp522+AzJCu9WPM8p77kX7ye+N/CZdnqZt/2x\n0BaZRqPRaDQajUajsXHYFRYZTmo1MFLK1I/11Jm0QGizXcPOiZSTqJ+Sq+UhaQiSloF7zq6vSYO8\nDK1fssikZH2cdKHE8wSOaFM/+9nPShraXy9L1MW1z5KmrlIu1s/bDbQdXl+CXOkXP7Ezzol2EKA9\n8HmGVgNtRwrsRgvuz6Ov0KqmvqhJF72eKYFc1cwlzWeiSq7axpSwDk1L0rClem4l6WXSvq1Ts0Zb\nZsQDCbTLAwmxEuzZs0fSsnYwBfnvD64Nr3MgaTq5pwdxQs5Bsktft/v27Vu6Z1q/WJWcGAC5shXa\ndP+/0oknkpWUGBE5yPx2Wninod5uoNX0wFCCYNH8pjWdyGCqBcGTq9YgZrdi0X9YQXx+IssZByws\n0ugr5qJ7JJDsLiVFBtzLyWCoH21OXgC008tAstxWq97MCp2sEUmerTMhZiLQmREy7I/oYCZT0poB\nM0vgjHwoJZasloDZ75PHSJKP9b0pBYSn96BZGaj19DnG9czXWR9uN7Cgsq6kIbsr1bI05lCdLy4T\nsMRgoSCw3++dEpPWNB6JJhy54db9I444QtJ4D8Hak6x0Sb553aUsU5D7/txKCgRhjSTdf//9kqQr\nr7xS0pBzySuEPnEvomrxco8D3ve2grbINBqNRqPRaDQajY3DrrDIcCpzTXnVhibNWf29NDQAnAK9\njFN29fWUVrUTSRuTKBSrNSNZh5IGc5bcsfrau/aPpEicePGllAatMLEjWGYcMwtX0hrNtHHr1KKg\nPfSTPX2MxtxjZLBUkRzK24D1Cspbj4dAK0FfuZaCuYR1KCWuSnEwszitOheSNaMmsZRW516ifwVe\nxpqqtIx+Hc/1NUf9Zok7k7ZpnVTMM0r12fOTJQcKS+aGa6uqpjFpmRMdZ4qb2d89XRYwj1nLrjVk\nrlLmvsdch/YODaG0qvVMsWMg+VGnxJ8zCtn6e7c8eFK37Qay0fsFa1Ci0a2WgKqllHIi29ovbjXj\nOjSIXhc0wcgSj63hemRcouBnbP2eWH4Yf79nSmoM9uf37+2rXgvSnHJ8K7Eis+TS64TXjX5Je9ZW\nLA312nSfZFmZocqzrcRbpnePNAbV+raVe0tbs6hvxWslWeH4zLxNcW3rwhVXXCFp2epZ0ys46j7L\nfHXrMpYY3jnSnjCzWvHX92Qsy2l+IXP4HbLLr63xmSkBL+3eCg23X1+TnUrSi170IklDdl1//fWS\nltc8Vhroqd3SUj2n3OvlQDx+2iLTaDQajUaj0Wg0Ng59kGk0Go1Go9FoNBobh13hWoaJa0a95qYu\nTHiJWrCaNhPlY8rGPqOYBMnNptYvuQNRB68nLj+UebAxdaEP3AxP4D/mOnfbwMXgb/7mb1baQN/O\nMn1XNzn/LgXEbSXL8eOFjx8mTtzrHLV+TpCAKfSuu+6SJJ1zzjkrv8NdzV1NaDvjkMylKdtxJUiY\nBWEmzFyouJf3S3UXTK40Nejf65ACSWeZoQ8WZpTqIPUZa83nxN69eyUNty7vlxrw7qhuFVsNCK79\nmSh6ca/wscX1lDmYKLkTEUQN9vag20oOkdw/anv9+llb6Q+vy6c//en9/u7xAip13D+9Lsndt7bP\n+4Uxoe+S/KyuYn5/iFc+85nPLMqQJ7iMeB9yD1wtnIyC51EndxOmDgTNurtMHVufw5AKJBnCPSo1\nrDSnIa+uMymlAf3pbZ+5Oj5eVDr6hBTEvpX9LGWgr3LJ21ldiFPQ/cxFrLoBzlx8Z2s8tTe9P81c\nyyrtckpLUeubZCD1vO+++xZl63YtY/2k1BIzSm6+w63ruc997uKapzzlKUu/d5er2rcu76sc9eey\nXtNcqG5jdX3575Jbbb2nv2NzD5clFbwjeVugnGZMaa8TzyAPeYa77yPLqKev2eT2uz+0RabRaDQa\njUaj0WhsHHaFRYaTomtpKjWdnzo5EdYARWnVupDumSwy6bv94UDpFYFrUwjG5QTuVoaaJMiBpgwN\ngZ+goWHlVItlRlpNupasWEkzP2vXOgM20USn4DzqmawEnPZ9TkDDCgjwlkZQMkHVHjjLPXhO0lyn\n4M9kGQH7o/qUVoMOvYy6MLaJVnxGAU2fuTaFe22FonOW6HWnkDTszMEU5FzHzbV+JCxLVpfaj7NE\nuImOPFlI6nzx+VmtGK6xqglsfc1hhUAz6OB6LAG+jtD8p6SXlb43aZfTuFdrrrfvQGg0DxQE2vo4\n0n8PPfSQpGyFTGOUaHtBtUzedtttizLGlD72wPz6XNcyVo22z7NHH31U0gjsd00lz6Ge3ga0pqec\ncoqkYXmUxrhzb28nci9Zh2o9k5UuyZ6aZDpZDNYBLFUz7e5XSxOfiBKq1WVGTDR7d9iKlcivqZa1\nVDbbE2qbpFUrRXoXmO0Jtd4pvQBgfUojiP5AkiAeCJL1qc5P74cq/w477DBJWdbyXuJyDgsQ7xqe\nFBgvgJS6o75vpXeAem2yuiQCn7oHucys6RncO4p+SpYnrmf8eG9zIof3vve9ksY7ryfbRO5Ql5S4\neStoi0yj0Wg0Go1Go9HYOOwKiwzwE3GipgOc1DgJ+6mzWl1cG1c1Aom2GSQqvaSJrHE3KYkhGhO0\na/48KFNTO9EiuWYJjUXVJqTv7rzzzkWZ+6NupV2gaqz9lLxOiwyaIdeg0B+u3QBYqJKPKfdAq+Ka\nIPoTn07XpjIHuMb98PmuUiY7klakUif62M58ret3bsGrsVjJ6pIsATMf8qQR2i1ICcCSRbVqi71f\nmEP0R6IVZ416vEDVSvu4c321zNT7++/9M3V3WnHGGcrSNM9S3A33Ys76uFca+lnM38zHehaz5L9b\nZzwE9/bEoazhZOlPCR8B/Zd8welj+s6tJ9VqluIn0HAmbSbrMM0XLCOe9JIy5JrHfp1++umShnXe\n71mtO94v1IX55jKL3yXLbV1jKUa0anR3CqkuW/GcqL/x61MagjTutQ4zquKZRabW0+ddtZCmebcV\nK5Fbq/jM3PA9ItHY7w9p76vPd0siVsZ1WWQ+97nPScpeB2mMqpw49thjJWWLKuvjlltuWZSxXonL\nTXsPKQA8hQbvg8ig9F5YvWuSLEvJzetcSOkkkofCzHOjUjqfeuqpK79nP3v729++9L80rDPIx5Rk\neStoi0yj0Wg0GhEErf0AACAASURBVI1Go9HYOPRBptFoNBqNRqPRaGwcdoVrWTJ1YWLC1OUm1UqT\n6eYwXCn4zk161S0rma54bjKJJleMWk8PksItgCAwbwNZTglMTQHFCZjl+J27GB1yyCGShunSXWLo\nB8yfMxcxN+8SrEbwmrvgJTPkdoF+TEG8jANBnZJ0wgknLH23Z8+eRdndd98tabifYLaVRp/de++9\nkpbbjnmY4FifL1yXKLK5f5pntIf+T+5dlU5ZGqbi6u4mDbcjfueBupUcYBYMP6NYnrmM7DSSCypI\n7hmY1xPNJWVpLtOPThYxc/Gqrgo+RpjQ6/g7GFt3uYCEgnntQZT0Q8ooT919jYDqSjFzk0luBAdK\nxb1Ol6IzzzxT0nI9cROdyXnK3E0V+cJa9r5D/uFG4oHyNYt5kq2Mqfd1db/xeUYQLM9LGboPPfRQ\nScNlVhoy66abbpK0HFjL/GAOexuqLPD+xJ0mlVWXSZcJM7ezdaK6wnhd0hjtr15+Tf1dculOlMPV\nDSsFzyfXpkqRnFzXK/mJr8tKapEwIzhizH1fq3tJ6suK1M98R2C4NNYaLuLbDZ6VyFlSGAF9S1oM\n1pqPEf2A+5jLGVzEkMO+z/O7Bx54QNIyXTvvI5AKQGYijbU8I45hX6qEIF4X5ICXVZKHRNvOc31f\no56sAd6/vZ+R0ddcc40k6Y477liU8Y5Kvf3eB+LW3haZRqPRaDQajUajsXHYFRaZpLGpCeQ8yIoT\nNH9d64TFImmLqwbEn8epFktAop8DlSbX6+ene7RqtM8tJJx4qZNr0bk/1/gpNQX2AvqB063TL5M4\nk1P17bffviirfQ1lqyQ9+9nPliRdddVVK/dMddguoMFw6wnjRb+irZakhx9+WNIIdiVRnjS0BmgL\nTjvttEUZGk8C79zixPVoLmZUlCkosloH/R4zTVmy1lQto2vfmatVUyaNMaJOroWpltBETTlLFncg\ngbPbgVSXZFUA1YKa1i3tdO0gcwlqbixy0tB00ef+u6qtdRplNGvQ4ro8myVO5Tm0z60EzAHWpJOJ\nIE9cjoFKcJC06CBRus6oVVNw8TpputEYO80wY0SAr7eP6+kDt6wx3qwRD7DHEkZ/JmrtRE+MTEar\nmdY9a9TrwvglDSn3RHvr85PAf5KQOiUsbUjB5TXJtI9ftSbOvBV8T5iRPKxTZvheuhXsj444zf1K\nyZ/u4/erNPZJdqXxqFS3lTjIkaiFZ4H5syTC1criZcis9LytoO59voYq2dJ2I6WdqAQePq8rQRCe\nL96fyFusL066QV+xdvy5lfzJ9xDqwn7vFnjqV0muZklSXfZW4h8nL2JfQW54fWkf79a+ByETaHuS\n9dT3qKOOkiRde+3/Y+/MYq09y/J/oeI8MyptlZa2QMevI50otUBJgyAUxYQSBDGxiYAxHBiiJ8YD\nPIATxRODRpEQEgUEAzUtaaHzBJ3oPAAWEOd5Vv4n/996rnWvaz+s3e613Yvc18m3v/VOz3A/9/O+\n93UPNy2OoS9S8qrkTbATmpFpNBqNRqPRaDQaW4cDwcgkS+TMYl3Tm/pXK9YYLGDOZvAlnCygWD74\nQnQLAV+y3NstCXzd1iKd0vga517+lVutWv51DGoRL2mwEVgL/asV61sqJon/Nfd030u+whkfZ0FI\np4eV0p/nFuC9BuORUt/W1KSSdNxxx0ka44p1VZIuvPBCSdmnFOYmWfGYZ+bPGRLugdXdx4xjKb6L\n31LxPeYr+bfybP5NMshYOUNZi9HN0kQnJPZyZn3fJGaFbFNR0Gopdwt7TYeLn7M0LPkwfm7tr4V2\nOVcaMpfkhTXGusPn2pHSkla2zeX0oYceWuqX65eqj5KORU+4Fb3qKr+uWo7TsU0WOkxgjF2fYR28\n4oorJC3rVmSCvrs1tPqzO9OB3kuFBmvacx+XqlNdf1KglXNc12EFRaengqb8S3FXaTDttM/7Tr9q\nCnj/LcXkVKY3lTuoTJe0yvSm+IRNgPam+IwZw1zZiMTI1HP9/BSXUlPkJi+QpEdre1ORZZ6XUu3W\nNZqu4/zZc/1Y9QZJHgCzgo5Vf3hM2Kb3EGJvfA8ANcZNGn0lrhY2wtche3+N2ZbGeue65JXB+Dtr\nXt8BfC9g/dZCmmn++DcxubTJZRHm6dRTT125Dj1De13O6jsgz/A1gJ5hLH2cKgPs+9tu3i+bkWk0\nGo1Go9FoNBpbh/6QaTQajUaj0Wg0GluHA+FaBn3mLjFQ/ilYFXcgqH+/rlaydrcJKC4oQQ9q5zoo\nwRRkXl3FvJ3QZZ7Ok+fh3uZUWaX5U8Xe+++/X9Ky6xyuBvQ93ZN7OYVH9diaZEAa6f8+9alPSZJO\nOumkxTHSL1MpmqB/abhGbALMm1OcUJrQj6RGlKTzzjtPkvQHf/AHkkaSAmmkTsWdxylt7gHF+dhj\njy2OMda4DHmaRNqHDLmrEEG4uIe4C0etAo6roDTmHfcTp7FTsCKo6T+d+q1pX5N7Y0pAwG+1irDf\na78rdYPk0pRcN6rLhveBcWGMvdowsoSrj88RgfXcy/ULSSVqsod6nrQ8R9VVwNct6xwd4EkekOPk\nxsE9kD0fs+peldzxkuvNOqmuUyrZTabpZuzcZYR5QD+5u0INWHb9yRgzN56sgTVN/3xtIkvcMyWm\n4Xx3H8MFjva6KxuujsieP+/EE0+UJJ199tmSRqplacgeMuyyxH7CnuryyXPYq9xFrAYV+7pHP6d9\njP3I3ffAJhNAJPdZ2pkC+WtgdHKdrH2fBci7nqnuP95v3iMYax9z/q4plh2zJCbVbc3XB3+zF7kL\nLM/hPcbfkZCNWTrtmvAk7RG0zZ/rSSk2Adrua5r+p8B43v9wWU/uY7X/KalIcrms7r5+XU0a5e+/\nNUU69/br67uf7zvVFdH1FH+jE7yfNe2zyxt7UHUD8/4yTrivpff8x5tAYvGMx3VVo9FoNBqNRqPR\naPwf4kAwMnwte3FHgrP40vOvuGpR9C/pmq40WTKwzri1v6bLc2alBp6nYGOu93tyPvdyyy4sAdYj\nt1rxN1+7bqnjN67zr2qKbDJ2sCnSsPox1s4qHXvssZKk0047belcaXxZk57YLRqbLHCGRcSTNZAW\nF3bJg7A/8pGPSFpNUSgNSyttd0aG4kzIjc/Rddddt/SbyyfPQT7dulSLV2LVkYYs1cA9aViOuWdK\nQIDFwp9HWxgrXw81XbCPC2M8CyQFPtfVAvh/xcxIo++pYFz9zdvJ+KUU54w/hRV9/TGOWPPc+luD\nvp0xRK9UFszbwPi7LkD2COLGqi4N+WBt+j3pQ2JDaENi6RK7A6pMpHTkm7S0J9TAZWnoPYLgb7vt\ntsUx2pmSbTBvpGd3lhUdgt52NqNa1p3hYv2hp11f0xbWoae2r0lk3EMAWUK+mX9psDU815MLcB0y\nkbwd2Dtc1zFGKUVuXWOJjUhrc5Nywnr1PRzGdR25Tiz0jN1lXCrbLq0WCvT3A2Sw7hd+j9rexJ5W\n9lwaY47cuk558MEHJY1kIb4HMe/IistI9eqoKaK9L7MEBrXcQz1vE3j5y18uSXrve9+7+A39WdMw\nS4OJ8nchaXn9IgMpFT/jzlg5448sMMa+JyNLnONtqrqZdrtM1f1lJsM+5tUrIDH4KZkFCXHoQyoy\nynXIlu+LNbnI432nbEam0Wg0Go1Go9FobB0OBCNDkTiPBTnllFMkDYuSW8BqUSr/eqxWo5SC+OGH\nH5a0bPHGQssXN5YpaXx1JqsKLEjyb6/pON2qRtwF/XPLGV/4fK26FRZWAkuBf+nffPPNS7/5mJFW\n75hjjlnqkzSs0fzm92SsuZfHxbileq9RrR3SavrHa6+9dnEM68ZrX/taScuWLNp86NAhScMSJUm3\n3HKLpCGDzvx99KMflTSs4ieccMLiGP7tX/3qVyUtW1N5HrLnVgasEslijkUvFTPE0pFSRQJkyv2h\nmUvGzK+rzEoqcDjz0QbJMr8JsLZ8/dVigrP+uW6o48k8StIDDzwgacwflkhpWJOYb9dZtAF58fXn\ncWcV1Ufe9QT3JC7Q55Y1gjUMH2RJuvXWWyWNMXBdVy3xKZ4lWUhTKtedkIoCbgKpQPBv/MZvSBqF\n19wyjwykuA0YXvYALNfSmG9ilJwB4tkwvr7nsN6RBR+Xmmbd5RqdXP3TvS3ci7Sm0ljvySLLPFSd\nLq2mMXemChmapTiv/vv+d7KybjJuirFLsR+pCCz9maVYpn/Ij+t7xiqx89yT/Zr3BWk1dXXSvzul\nYU7tTu9BrHsvR0CcK891fUAf8M7wvnBejTdKKZoTy1/b7UzE442NWBcUY3zzm9+8+O2uu+6SJF15\n5ZWSlnUk8a3ojtQ+xor4Ho9fRD/UosV+L+bf43lJI8/8JQ+fWiTevQlqnKzva/63tPzOiQyzv1FA\n3e+f9l9kgHPQFS6L/MY5vp/ec889S+enQsProBmZRqPRaDQajUajsXXoD5lGo9FoNBqNRqOxdTgQ\nrmXQeE7t4doF1ZbcwKAtU7pZgrid5uc6aLTjjz9+cQwKFzcuTztI6s1UOb1S8U7TVvcMd3GATqTt\n7qbFddzTXUY++MEPShq0Zko1+Za3vEWSdN999y1+u+GGGyQNWpcAf+8r7lJOVXL/lCLSA9/2Gsy/\n9wFA03oQ/U//9E9LGhTyVVddtTiGSxhz7FXAX/e610kadPvtt9++OHbuuecuPdddJHApomK7u7bg\n3oGribsbIkO4DzlljbsLcuo0K+POvHtbalpMn7+UhhHUwDxfY7PUktXFZL+queP6mNIM094UfE/7\nfG3WoFSCuKXhhsHcuKslrp3oF0/XjV4gSNyDzKHjcW9MqZlTYDdzgrtaCjLmN9dZ9B132lSpeUbd\npxTuvvalZfesGnj6eF0Edosvf/nLkpaTdBD8Tsp4kjZIQ6fiVoHO82PoHHe9YB9BXlJ6fuaBJAPS\nmGdkL6X9ZazSWNc0rtKQE9rrOgv9x3M9aB99xHwkd0PcZHzOuBf9SwHIyeW1uhX5Me/PXoN14Gtz\nN8HkSZ8xVrjnuOsdssc5uK77vdApuHhLw32HtZNS41f3reTGm0pN8DzcnP0YbtTvete7JC3vc7yj\nMMeu+2hfdVH0ea5jl1xM2d/ctWyTpRyk4VLv71k//uM/Lmn0+ZOf/OTiGGuluvj5mqmlDdx1qyYd\n8jWK7LAX3HjjjYtjJOtg3N2VGCBvydWXOUIv+jHeYz/zmc9Ikv7kT/5kcazKi78jkeYdver7DHse\nc8t7m78/VJlgLiTp8ssvX2rnTH/M0IxMo9FoNBqNRqPR2DocCEYGq6F/xfG1j9XfLTh8taU0tVjI\nwSWXXLL4my/ZP/3TP5U0UppK4ysZpuSXfumXFscI+kxfx3zlYqFzSyvWSY6lNHnJAoY1AIsnFhRp\nWAk/9KEPSVq24lx22WWSpBe/+MWSlgMLsTzype9fxTwvBbbVoD2fI0/vvNfAopCC3QhE/Nmf/dnF\nMYLUsKa6BQorg1ttAfOF9dat9oxLtbhJw6rEGKcieDVdpTTG0a1RoFrYXSawbMG2JBZyVnwrFU+r\nlrxU9JJ+piDT/U61W4NMpdXCb8mig+XKA1exsGNd8vljjRD07+seeaFgYWJ8jzzySEnLY1YTALhM\n0Hbm3+eopixN1mLu7e1EvrC0ed9nxVWrFczXUQ3onMHnYZOMHayXM7ewElhfUz/Ryb5WKHjLmKVE\nI8iJB8OiXxgfZ3l4dmW2pTEu6HRnUmvRvBl8b8QqjO7y59UizGmt0HafY/ReSqjBb/TT94cqZ5tM\n1+9gH3MmjjYwVrOCrWmtVUbO9S8eJVi4nd3lufW9RBoJANAFsyQK6AZ/btW/Lj+8O8AOe9mET3/6\n05KWPUsAbYHJ8+sqO5j2G2RsVv6ipiSXlgPANwHGz+eBvr3iFa+QtJy4hXcb3hEZT2eYeC9jzTgT\nC3iv9ORKzClMmM8jew9zyR4kjSRFPJe2OMt09dVXSxpeKC996UsXx/BkYY7cQ4jEBySV8b5wf7x6\nnMGjLSRNYr9Iybl4V05rL6XD342+aEam0Wg0Go1Go9FobB0OBCMzszpxzH01sSSQ2i75LfKF78wD\nv3EOlglpWFHf+ta3ShpWPb8/1lvYAmm16OUsbbN/iVYLnVtTsFRgoXFf9J/6qZ+SNFKF+nWk4cQ6\n6V+3+DnSFh9Ptw7XdlaLsB9zy+Neg3H1eaDtL3zhCyUt9wErA1YLL5aJZbWmhpSGpYYx9yJ4HPv8\n5z8vaTCA9R7SslUKa04tQOd/Vx9oaW7pRl6YD593wDFvW2UqUvGymuLT75FYl+q3nfz+N4GU7rPG\nZCRWl3/dmkmMy8knnyxpmfWsTJqvI6xKjD8pz6Wx3pElT4tLkUWsgN6Ham11fca9kP1UlBD5dosi\n6wem0hmZyvz5nHEspaRlbOu/0mrMw34Vu6Odvjbf9773LbXBY+lqXJ+3Dcsjv7EnOEij7UXd6tpy\neakxjKmwLOf72sSyiZz5HlD3Dr8nOgd/do+vZIyI4Uj7EW2apSpPTHHSrTNsUibYF5KuS4xMYqj8\nGmnoAvZ+35eqvMDuS9Lb3vY2SdIb3/hGScvFmGH+mDN/Xk23zL+pqG+Kn+G9x9c9YD38+q//+sp1\n6DDalApa1tjDpPPT/HI+720+P3/2Z38mSbr00ktXrtsLpIK0db91HcK7HjocttZTLNd7ep+RM/YL\nfx+pMWo+DujyD3/4w5IG0yFJF110kaQRv8J7hjPxMMV/9Ed/JGl5HZ9//vmSpJe85CWSlksC8E7F\nHHksF7JAP92bhGOkUWZ8fC9izbAuYX2k1fd7H6cLLrhA66IZmUaj0Wg0Go1Go7F16A+ZRqPRaDQa\njUajsXU4EK5lBF56ustaTTgFfdegeGlQglxPML00aDZoNKfWCNomyM2vg56Hknd6Euqf35J7ANSr\nu1/g1kG/PCC1UrdOgdIHKM9UQZUgwkSj8pu7I1SXpFmldj+WUvruNdw1olbJ9rEmpSDjCaUrjT4w\nxk4h1+raPg88B/rUXX6qW4e7r0DvpsDu6sa1Li1fA1BTKtOUanl2HbKUXLb4m3v6uFR3tSRLm4T3\nD12QArqrO56PNUHR9C+llEQneDAl9yTAl3+lMc+4Z+DeIw2XRc7xdVsDY90dqbpxpUBJ5tbbiU7F\n/TO5G5IkwN0AatV4l13kmfa5vq7uOa7rNuluiCuD9w/9hw53mUR20IPuJsx5zJsnM8H1oboPSqsu\nqD5m1XU4pQ5Pe0d143BX0ppCOu0BzL+7wKFLOeYuHtwzJfeo+sjHk2cnXVATxOxXchDmyl2fkVkP\nQga1nTW9sDTajp7wIHiuZx35uwOB0QRPpwQ8vq+Amh4+ufXVdMhp30beXUZwp63pg9PzU5IXjqXk\nDyDtA7hNIcvuxp0SKe0l0Onu+l9T/noCD+YXXYCe/9jHPrY4h3VPf3wcqr5O+ydr29/veDdhrbqb\nKnKNDHO9yxTJCXBv9LTW9Z3FExC87GUvW+rLrNyCH0MGeN/i3dr3FMYFeX//+9+/OPbHf/zHkqRX\nvvKVkqSf//mfXxxLySh2QjMyjUaj0Wg0Go1GY+twIBgZvgL9K54vPb5ek6WHL1S3jvG1WgsSScMi\nwBeif61yT6xcXvCML2/a4taYmlo5BcemwkX1q9xRg+hScbJqzZPGeNTn+nNSYgWsBSntZC1ilYo7\nbgIkT8C6Ko1gSubxuuuuWxyDWfmxH/sxSctW0VnAJNdhRfN0ulVevO/V4umB3TBHtCExMik95cxy\nXa2G6dyU5pT5nqUnTtZULHk1+Du13cdzk1bXlJK2pi51NqMyVG7dgpFBrj1xBEjWcCxm3MuZHJ6D\nJcmvq/ohBRvzPA/QZX0n1qymnnZLMNZF5NL1BPeHzaDIp7Rc1M375H2Y6etU6HCTMkHgq7OszA0J\nFnweYK1or6dYxqoIE+PFJCu76kHbMFvsJy6f1YrtllnGjzakNO2VQfLz6LP3HTlORXXZt7DgOqPG\nHOHdkNJEpwK4dW5nyUGSXtoEkEEv6nfnnXdKGvvKzOsg6Rnajoz4mHMvEur4vd/85jcvPWOWIMNR\n9+LE0iNLNejf274OQz5jcpK8AubVA8pr4oGUmAXd5PcjSc+mUN8ZpTHPsEHOItBG2s8a9fcR5obr\nne1J6xbU962Udr3qfWm8/9T3LmcZSULBewnsm9+bdqaEHjVNubSa5MFlohbgZD90/QgTQxIdby9/\nk1zAx5BU2e6VsxOakWk0Go1Go9FoNBpbhwPByCQfYaxqtTCUA6tDSgeXfEo5LzEWnMdvbh3Dh5Xr\n3HevpsFNvpDJco2ljC/3xAAlX92aZjZZ6mrMhGNWWC/FUdRUgT5mm4yR4Yv+iiuuWPx2zjnnSBpf\n7T72pOpLBSMrg5DSjmJhc6s27Axz6nPE31g3PSYHqx/ylqwbqS0zCx2YxdYk69s66VATW1OL2a1T\nBLHeY6+R2IwaQ7eupbcWHXVLXbVOORhjrNupLSk9ccUs/WxiUhOqHkuWMqxhLrv0j9/cR7/eK8nE\nrNhdiofYpEz82q/9mqTlPvzhH/7hjufDnrC/eFG4OtbO4GH15nrfH/CjZ05dX6PLsej6OmJcUhE5\n/ua53hb2H9rrehiW5Y477li6XhpME1Zl11noQZ7j7eTYzMqf2Jq6fvZLJrAO+zMYh5puX8peH1LW\nnSlml+th8Pw+jCfz7zqeNszeD0DSKXWtuoWd5yQGv/7m95y9b9W9Zx0PgsQg87zHW/zw8YB+uR6E\ngbn99tslLa//M844Q9JgqPn30KFDK9dT7sLnFllI3hyAMfZC3bDkxNs5A8S6R/YYT58rWBvWAGmj\n/bqUnpxjM48P4F4rpBCvbI/HP8HyoqM9xXItKupMpzOqXw/NyDQajUaj0Wg0Go2tQ3/INBqNRqPR\naDQaja3DgXIt83SJ0FFQrImSh7Z0ap17rVNpOAUpQ63586o7iYP7Q+s6XQcVyG9OddfUhe4mRR9S\n+uWaDje5qKRgRZAoY0BbfMxq9Wl3f3CKcVMgrbIk3X///ZKkd7/73ZKWKUqAC0Giy9dJL53cH6Cc\nnZauKcDdVaFS6CnJQ0rJPXOLWofynSUQWMcNIFXsTvK1zr02AV9bFYydryPcOWiTu4PUKufHHnvs\n4hjnzdKL1tSnfl4az+pyleQT+Bji+oSrglPvVbelezJ/yZUGzGTQ9QWuAdXV1q/bZBB3AjJxzTXX\nLH5DBnCvcD1F31MaXu6V0mCjy1nvvlcx1jVduzRcVUkykKq3M7ekIPV2fvSjH5UkPfbYYyvPY07d\nRQc3FdJou9sKKeL5zd3qmEtkISV8memSWSKAmkrYn7cJMNbuLkqgcWp7dYOaJRhKY1DXU3JrTum3\nAWsmveNU91F/10HeeFfyxBXsVTNXT46l96DZXlLfQVx/pLkG9X3Gx3fmarsXoO0eTE6SE9YDiYKk\nsQfcfPPNS/fBdUsa6YsZf5c3npfc9el3ct8FpE92VBlIewlzgX5y3cfzcAdz3V5DKxzcE/3o70Ek\nFag6MOkP9NQJJ5ywOPaGN7xBknTTTTdJGvuxJF1yySUrbdkJzcg0Go1Go9FoNBqNrcOBYGT4InZL\nFkGLMB1uQUvFmgBfjbNA9JpSOCEVJ+M6L8BJ27HCeMBYLTiVgnFTWk76ihXHLck1EcCsmFUK8EuW\nsGoN8f/TL+7pc7TJIlawLgTSSaOQEoF3nvK4pn9NxR0TG1VTyiZLLWOWip2mANhqKfFjtAHrWUqZ\nWZ8rrcd0JFlI41HBvf35tZDXJpmWdZFYKfpXC776sZqC2u+R0m5jdU9phpEB5s+DzDnP1yvgvFpo\n1P+epU0nraUHb84YGXQUa8WLn9Uihj4uNVGIrxnGLCV+qIkYUuHOTeDqq6+WNFJ1SquFXr0oJPsK\n+sL3CeYWHeCsC8cSK4iFMl1HG7jOx67q/htvvHFxjP4Q9Oupktlz+NeP1WB2goe9zzDZLi+MUQoA\nB+sk/kiFnZOc7Uewv6dtZf2w5ycL+azwJ0hB7CDtM3VdpLXGOc6oVHmrFn6/jrXt7Bt6jUKOMzZ7\nxo7NkjfUfvj5s/eTlFp408H+Kf0y/UE2jjnmmMUx1h3rh9TavrYJ9mfcXd7oYypIWlMz+9xwrwcf\nfFDSsocB+oWxSu+4sB48z1PIs24ZA1/HlV3zPYzn8Fx/R2Kc6r7hLDHPSV5SeEKwX+F1I0lXXnml\npJHCfIZmZBqNRqPRaDQajcbW4UAwMnzV+RcxliX+TX6nyY8/xR6AyuSkr+RqJZHGlyhWW7fC8gWd\nCk5WC3JiCVKRIb7QU3rNak2dpaV2zCzqs7STWIkSu/Doo4/ueM8nClLvuZUD60iy8FWksQY+Plg8\nsFwkhiTFQ/B3StvMvbB8JH9qxjVZ2GZ+3DPLVRqXGks1s4p6WypLk3z79xspLXJNDet9mFnRGUfm\nzYuhYWHHYuY+wXX9Oatb04r6+iD1LWs6saVYwTyNKmsfmZqlTXfQLmIr3BKJBTIVO63slY/L7Hlc\nl/TnJv3fKaTnY0ZKdMb6rrvuWhyrhf58DXAPdIL3AVlIhYhrcVwvBgezwr38OmQXOfnSl760OIZ+\nqOl7pcGyoQ+TlT/pAu7FnuUsZO276xnkknt6W2aeBUlvgnXSwj9ewKi6TLiFWMo6eR3WepZ6nP6m\nsg4pngGkYtOMeWXwXd/QP/Ygly3GFwbX3614zix9ekrJX/VvkjGu51zvE7qI9pK2XFouLLwJpP2T\nZ6IbXc+zJvFA4T0keQ3BiLmurOxs8hTgX3+3Jb4LXeJMemW7E5NY17/3l7WdvBfALNaR6/z9l7ms\nbUklJ5K8ME7EB/qe+bnPfW6lDTuhGZlGo9FoNBqNRqOxdegPmUaj0Wg0Go1Go7F1OBCuZdBJXg0U\ndxDoO099XGnplNa4uor5PRN9NgsM5p7VnciPQQEmui6lf6UtKeEAFF516/L2JVcv2pxclCp97n2v\ngV4+vjU4VPhqoAAAIABJREFU0t0KCKrcBB555BFJ0lOf+tTFbyeddJKk4U6Sgk5nKUJTsHitqu20\nKX1lvt1VgXEhSM3nlr8Tvc6zoaF9PGuAoMtGpWxdPut6SH1PrgJg5qq36SDM3YB+OhXPfDEus+rW\nLvOchxuQuwMhcymRAzLH3PgccT6pgF1nnXLKKUvt9cBcXBr4N+ms5ELKfCU3Hc7HdQ73Xb+Oea9u\nN37PmQtQSg6R1pjP116D9nmVetYRyUg8AUvtzywpiMsS80DKYl/vPA/ZcBcx1jlj5W6OjAv6xY/x\nbOTFE8xwflrT1SWUYG9puMcktzrc1XBhTOnk035UXYlcTmu6+/1Kt8u4eqB0TVTha6zujTP9mZCS\n6+wEH/NaKsCT55D4AV1Egga/nn3wN3/zNyVJl1566eLYmWeeKWmUL/B3K8YludSts09Ut/Z1U/Sj\nZ0h77Om/jz766JXz9xLJ5YlxYF24+y19pI3VzVwauvW1r32tpOXUwfQRl+IUcsBv/h7Fb0cddZSk\n7MZVXTZdF7Hn0H5/V61y6vqm6u2073Mv7wt7Funekx6o779+77o3EFKQjs1wcN5SGo1Go9FoNBqN\nRmNNHAhGhi89T6F52GGHSRpf8amgU/p/DTpKiQD4MvQvy8pipCA3rKhuwazWJr9nDTJ2KxcMAH1P\ngeRYwGZpf1NQVrKQzIIUZ1/jWKqxDLmV2dmEvQaBrB78VQuLOiNTU2SnYOrU9zqePtZVFry/laXD\n8iIN+UpJHmobkuUjBeph4ZgxJLN5T4XRZpbIx5syd5OJABILyd91PqRVRiYxmzC+pLuUhqzXIoHS\nkAEs5G7xuueeeyQNi/yLX/zixbHKFLq1CUsswddumb377rslDd2YWMhZenHa6YxTLVrm1v7E2O70\nvBlmyTb2EvTLdSs6A5bBi51i4WQdpGKgtWCdtJogxplidAiB1R5ED1N/7733ShqWWm8f93YLNSwS\nx1wmkEEsyF7kj0QHMCueKrWy685UwQBhWfXraoKLhLrWEjZd9BCkdtb9eZZkZZZiebfJT+p4+Lrn\nb2TX1yFMyoknnihJOv744yUtp9pmn7n44oslLRdR5Dzk1OeaPW7Gus5S3c+SLYF0jP4mfbxfjExK\n0oGsJ0YU3cG5/u7H+oVt93WIDkqyRL95x2HfkKTTTjtNknT55ZdLkt7znvcsjsH8IBO0+5Zbblmc\nc+edd0qS3vrWt0oazI60Os+u3+r6Te8s6Fp0jDQSNlR20ceypoRO8lIZaGmZUf16aEam0Wg0Go1G\no9FobB0OBCPDl6Jb3/FbxLqV0pwmBgFrAfdMxexqqlD/rfqBSsOSQFvcdxrLV/ry5rxk0cKiQ3o+\n/xLlbyxuyYpex2KnNtRjqcBhtbC4tammCvR27lQgay+Av+lXvvKVxW/VYuZxBviCM+Yp/Tbz4Oms\nq2U2yQvw/tZU3j4us/SKWFM9jSNIaThr2xPWsQgmlqf6sCerUYo9mWGThe5mltZZiuwkp4xZikHA\n+s0c+XxwPuPh8kma35e85CWSllNnVl3g1neO4SuN9V4aKSiRDbeU13Wb9Av39jWNHqPPLrsVSV7S\nsQqXyU2ydKRQdctjtSqS9lUaa5916ClXOVYLwPm9ksUSy6rHxgBid2BiXGfV9PqpkGqa95NPPlmS\ndPjhhy/dRxq6huekOLu0T9Q+ezr5GkPp4HkpDgb9uckYqQTGw9tLG1jbPp616CRIVukUF1JlYpY+\nP/3GunV98ba3vU3S0BOc4/sA3hGwNc660t/nPOc5kpaZiBnrOotr2um9y/VrjdtLpQCYF197HuO2\nCSSPFJ6J7DvbQvt5r6Ctru/xGqqeItIqy5fisZkvUi5L4x0YNuKcc85ZaRN6hvXvug9G9aqrrlq6\njzTeNVOx0lpwNelv4v18rugLv6XyJ1X2/bm0Dz3l8uLeP18Pzcg0Go1Go9FoNBqNrUN/yDQajUaj\n0Wg0Go2tw4FwLYN2gxaTBsUEtZfcH5KrySwArQaZJforBUXTPujaRE/iXuAJC6DyoM/cVQlaEerY\nXY3WCeyeUYC7TR9Zg4WTGx+BrO7+t8ngTVwLPciV9vGbu4xAs87cWFJa6uqKmJI1zJIF8JvT0szt\nbbfdJkm6//77F8de/vKXSxqykeY4VQ/HFSa1MwWwViS5BsmlsCZBmI3rJl2HHKyjlC44IaWZrUCv\nOL2P+wDj72sTHQC974GWuJmSJtzXUXXn8bGmDYy1u5gQQEpfXIcgE75GAOfjrkTbvC210ru3JclS\nHceUhCS5pLgr516jpiyXxtpijtzl5vnPf76k4SriwdMguV5VF1IP6McNy8cRcH8qq3s7eQ6/+Xqv\nbo2+xnA5pn3uBlZ1ls8jMkE7U8A6blmezIB9GVfElFo19aG6Me9XAgjeGXxuZzq8vjOsE7yeShsk\nt8N6floXjHlKNFPXjr+zsB5xH3UXQ+S7BvZLOyc3SP1NiZTq+f5/ZIpz/b0NOcUd1N1r/e9NIKWq\nRz+z1jxFPWuspjP2ZB0k+OGY97W67SUXO4Lmzz///MUxdDLP/bmf+7nFsfpegGy4i94FF1wgaZSv\ncHfV6lbrqO7X3l76x/ylhDrszcmFr66nlECCf31v342bejMyjUaj0Wg0Go1GY+twIBiZFKh76623\nSpJOP/10Sctf7Hz1p5SrNQA2BcPPLBH1XP+bL1q3ZBGge+2110patoqQuo+vaw8wvOGGGyQN6xEp\n9fx8vrwTg7BbK1cdDx+zam3y/n384x+XNKya/jWfglP3CoyBB3zBej3jGc9Y+r+0GlA6SzM8G7Mk\nLymlYLVOuSWStIzIgqeWrIGlKT3lrEhqSrtdA+xS6vBkKanrIAVtpgDfiv1Kq1qL1ta/peWkBDXJ\nRgpcxwLkskRQI8/zQmnckzSXFKWTRspLxtPbUpOI+POwkHO+r02sbchXCtqtffK/6YOnB+d5WBBd\n3mjDOtawFAic5GyTjAz6wdkQ1gptga2VhuUwpbNGn7EOXfYrm+FAJ3IvD7CFLWMMPEVrXdOuw2ph\n5ZSKHTlNbeIctxLXZDcuLzB2tNefDzuOldetpngkMFY+npVFTvvYJsCe6m1h/NiDky6YBfuDWfmC\ndM6sEHUNBJ8ldEnFgNc5Hznwc9dhoOq5fq+6p8yud1mB5UOe/H0oFRLfSyCfzl6i92BX8TqRRgHR\nmuSIvV1aTnct5QLh6Z2K8UMnkMJYGrJE23wPYR+qAfL+TkZ7jzzyyKXn+988NyXhmJWDwBvn0KFD\nK+fPGM/6XpLePbkuJeFYB83INBqNRqPRaDQaja3DgWJk3AKGpZMvaPzOpZFujuv8q3WdL/vk/wmS\npZx28eXs1g38hmFU3BcdX3e+MvHdloaVni9998PHCjuzsK8TF5G+jtNXcT3f4wWuv/56ScPy6f6Y\nu/li3i0YV9JHSkMWsJy6VZvChLTP+1d9l1M6XsbTWSbGn366Zb4WV01xFMyxW2FhGvHf95St+HTT\nPm9nZReSn+o6SL6vPMetRvwGq5RS0W7SqprAepqtW0+jjCyklOqA39w6haWr/isNqxvrgmJo0mAK\nH3jgAUnL8smYcY5b0aql06/DeoYMun7D2pkY5mrpctlljOhzigecze3M+pawSTlhHJ2xP+ussyQN\n3/Nf/uVfXhwj/ildB5gHn3fWRlorjEeKe8PamyzjzGnSS+izZCGtc+QyQZuZW98b2Wtq6nhpMO6p\nAHVdR953L44nLctuTT+fyh1sAoyr77ewVylWsKZBTrEu9dyZl4Sj6p7EiK9TkJJ5dYaNPWdW5DPp\n6iqLiXVJbMtOzEta37TFZfPZz3720nNhZryfm0IqhVDT0HsbkB36DBPjMsXf6Ghfa6wj9Exi4tK6\nr+93vmYSu7MTavFbadUbJDHOqVAwa4fxSqUjKiObimundxf+TimsYYdJcz1DMzKNRqPRaDQajUZj\n69AfMo1Go9FoNBqNRmPrcCBcy0CiOKG8P/OZzyyOvfCFL5Q03AKSS0yiVGsArGMW6Mc9UvA2dBtU\nnlOpBNhWFwJvJ32YuWnNgiTXCT70Zyd6uLpaPfjgg4u/oUYJ6kwBzJsAgWXQ0dJwMyPo1IGL1nHH\nHSdpuU+V3k8B/ciEH8NtLLnVVXo4JT6gDR5giMsIVeD9WHW3mKXfdtQ5nc1LOlaTZ0irrnPeP2Ri\nv9IuA8afMZTGmNW0n9Jq0L2Pb61KDX0ujXSzUOi+pkm/iTsC6XylkQDg5ptvliSdeuqpi2PILm6m\nLkv8RhvcDYxjtMHdnTg/uSggz0k+6Xt1e5JW5XqWRjPJ57oyu1cgaN8DUF/84hdLkq655hpJ2fWK\n37xKdU0ekwLCaxC1NHS3J3oBNQWxt4U5IVjfn4fsIQue9pW55RzXh7jAJLdBwLy7m1J1KfEEArh0\nIrMe8FzlMyUeoF9JBjcJd4FBZzBXye2TIOqZ7PObJ3TAjWidZDIzneljUl1tqluQH0vvNTVtd3oP\nSv/fTSrqWcKh2XsGyTc8yD2N+V6CsXW9i+sS/fdA85qimFIP1ZVSyuPA/sDek94na7IPaawRrvNk\nCbie04eUJAQ3N/SGzzvrPaX2nrkUUz6C5/j+VJNIMccpKQXn+Dtkvd6PpaRHO6EZmUaj0Wg0Go1G\no7F1OBCMzOyrH0uPF32rX8duHavBmG4Fql+bKUUvSCmd+UJc1xJZg7KcdUkB5yB94dd2zpISpKKJ\ntU3e95r211PKVobLrT7rBJ09XmCJ8AKcpLPG2uFWSopZcY6jWkWT1R64lQJLJDLk/a1WOJ9bZJZx\nddklkQJj7Za9ysrNrFOpWN9uMUv3zD2TRR9rVZJPt2rtNRhXt3xXC9BuWcIU7E//UkA48kF6Tp+z\n9773vZKG5ey8885bHIM9ScH3yAAWM7dUMg8kjPC+0+ZkbWRc+I0kA5J09913SxrzPgv2nSEFF9fg\nTW/fJnHVVVct/qZIKfuEF69Ehmqgrx9jHhJLXoOv/bpZ32vKWmnMH+cnKz/tS3sHiUNIYOD3T+wQ\nbebePi41ONjXMecnpqqOkVuyuX9iuzfJ5qZUx8xRLTgoraY9ryllpTEuKUC+PtfXxczTo+7h3l7m\nDyaJsXT5IfEIv7n81CQhjqrbE1tQGcj0W9IVM9YWXUTiphnbu9eozJY09jTa4fs7487ejf4mrbG0\n+m7kSZJqinsfx1pY3ecNhpk17WPEealQM6AtyHdKPU+bvGBslRff79Ez6AiX05323STnKblFTW/t\nybKcLf96aEam0Wg0Go1Go9FobB0OFCOTLBkp5dx9990naXzZuzW1pjJNKWxnbEjy8aspLR18iWIx\ncaso5yfLRbUC+VcuX8epqGe1onhf+IpO6VHr+W5R5G+sfx4jU4ty+vM2ycgQG0MqW2nEwWAVcYvi\nww8/LGkUtfKUfTMrE8ASkfyMU5rvauFxCyjXJbnBiobFxS2DFAzDmjOLf0rHZlbCVHQWK0iyMiKD\nyTJfY8zcepOsRHuF6o8vrVq8/FhiHwFzQt99PVR5SalosdC5NRxZ5V4f+MAHFsconsa/HpOD5Yk5\ncpaAe6HzYBm878wH8uN9J57P54x1hDy7fLqMfz3MWJtkkdsEsBZ6H5g31qQ/nzHjX+8DLBtj4OxC\nTYfqjMVOqXL9HoyH+7zDynFPj9tgjuiL7yuwa8i6t4XzGA+Pg6Ffaf+jfSnlOH3HoutyVj0gUipv\ndGRiODaBJG/oWebDdVZNGVsLVUpjHNOeXFPUu9wwD4khqXEBibkkXo+C28cff/ziGPtESplLUdCU\nPn+dFOvr7C8zFgW5dX1C2Qzmwp+/adYWjw1/Z2EuEpPKOkLfpsLJlRlxnU7sa/Liod/33nuvpGWv\nkxrL5e8VeCDx3JQOvcY9EosrjX2Gdyt/96jvqM7WcI8TTjhB0rKc1WLAyLmvr8oAplTU6EL3ftiN\nTDQj02g0Go1Go9FoNLYO/SHTaDQajUaj0Wg0tg4HwrWsUk+OROWSDo7UzE5jVXcJp+YqFef0aXUf\ncheAGuzodGl153J3J+hJ2pBSy62TDtkD0KH1anpAB9RjSkFM3/162o5LGeMrDfqR5/q4bDJADzcG\nD/6CZicI3wP7b7jhhqV/X/GKVyyOpWrMgL6n6sh1vt19hXmHEnWXJtrOGLubRg3wdSrVaeudsFs3\nnVqh2V1NKtXrfZgFtdLmlFJ2P4J4HZUS9/VeEzIk0M+03pOLEbKHC6MH7UPZ4wbGudII/EemPIV0\nqsIMkBPWAUH/fi/+xW1NGq6W55xzztIzpNUq5zNX28eL/QrkZT2l1L646KWkBIyrjwtzgo50uUYW\neE5yb0zzR1t4jutygoNT9W/0bkpQUgPP3e0MlxZkOK3p5GKNfue57q7CmqISuwcnV3drn3fva33e\nfsDljnnDPcddNBlPxrEmKZDGGKR9t7oPuQ6qqZ39/aDqT9cJuPZcf/31koZLMokspJHu/fWvf72k\nZXdqylUcc8wxkpZ1UUoEAehD3TekVffkWbIAxo5yCNLQT5t0K9wJrFGXQcY9JXegjwT71xIO/jdy\nTop9aSRMSqmxkTPKc7gbF+5fp512mqTlJC31HROZ8vXI89ADLue41yEL7pJKH5DJe+65Z3GMe9Em\n70stLcIx1x9V/7ueq8mS/NyUsGsnNCPTaDQajUaj0Wg0tg4HgpFJxZD4cuYLzwOxCIQkjejTnva0\nxTG+bvmKS0HYKdgNyxKWLLeO1DSnbuWqKRvd+kd6YIIj/WuzWgH8Or6w09dthVvE6GtKBEBfE7vA\nuF955ZWSlouakQo2tWWTlhWYFRI6SIOBwxqOtUmSnvvc50qSbr/99qXrJenss89eaq/LUk0J6POA\n9Y3nkhpRGuNHMLVbH5Ed5t/n4SlPeYqkYVXxVNfIdQq0rxaylFo79YFn13/9HshgsrTQFmdyauBz\nWrebQJK3ag1Pqdhn6cjrfaRhheM3Z2tgRgjwZD4l6aKLLpI05hYLljR0FNZbbyfMAevXLWXoMSyb\nrs+wtmFh83SVtJnEGB/5yEcWx5Bn4OM6S/0OkJfZOZtOpwqYW9dLNZ1q6h/r13V5DZR1NtgL10o5\nZXm18EpjbtEJvlbQxcy3W/uxwDP/iT1JfYBRwTrs+xj3rMkQpCHPKfAcJoZ2ch9p6InEbAKOuUfC\nrAD0JlDTyHugM+8MyEtNxCOtJixweavvB74XcB3npz2Z+XQZow1Y7U888URJy/sagf94JnifYPv4\nNyUM4j3DZXJW+HWnJCjJio78eXKC3RQH3WvAlrt81mQJvj8w/pV1cQa+Fi0lwYI0xoj172uU89HX\n/v6KDuCclFyAuaQvqSAm65kAfWms91R2oeqEz33uc4tjnEf/UpKG+g6fAvWTZ8xMJjrYv9FoNBqN\nRqPRaHxD40AwMskKWH01HXw9XnPNNZKGtcKv42vVrU58rfKvf4HzN1+rbh2rKaDdGoPFgq9ltzRh\nYcGC5b6MfIXz5ezWcHzYaYN/pWIl5HrvX7UQ+NjV2BjvA76T11133dK9pTFWyRI1s3A/UZCa0H1E\nYba++tWvSsrFk/ATJi2rNMYd64RbIioj5qlv6/yl2ChYQfdB5h4cc2t/9c13lgeLcEpHzlwmVhDZ\n4bq0ZpL1jLZUq6qDYy6D1bLi90wpyvcKyUpYC3K5fFYmxtfmLMUyTBrpeJMVjt98jk455RRJg2Xz\ndJyVxfI+IAs13k4a+oFzPL01sUr4/bv1nfbRL5hK/62mh6/9qag6ed2CrZtELVYsjT7UwnPSKhvv\n6525qetJGuOexqwy/S6DtQBuug4GEOZDGuwe8YrMsSQdOnRI0tBHzpBwPnolpdmHWfE9p8qg67Na\n8M/3xso0JO+BVH5gP+IkZimEnb2oVvCZHk1yXfVg6mfSozUGM72rsObYk93Czn6R4jRhIFI8FP1N\nJQd2UxCTf9P6Yt+ejdd+Inky1P0ueRYwD4lRA5U9k8ac8A7he0iNt3LWZVZYknfamurYn8s7EvrK\n40tqLKz3hb7DUn3qU59aHMMrhzmdsXSzd/nUpxoj4/KGfLvXw05oRqbRaDQajUaj0WhsHfpDptFo\nNBqNRqPRaGwdDoRrWaIfKyWbKPlHHnlE0khRKEkXXnjh0jlOt1ZXGqfdoOn8fICrCS5CqVotrk0e\n6Afll4LJ6DPP8yA0KEcoSHdJ49m4IzjtVilfp/lxHUgBYh/60IckDdcUTwlcXZpSUoJNgOe5S8y5\n5567dA7B8dIYR4KbfTyvvvpqSaPtJAaQVl0JXc6YS9zVnIJ31wRppIaWll3zJOmOO+5Y/E1a3JQe\ntaZq9XVRXZOcgq7rJx1LVb2rO+Q6qcC9Del5s8QUTxQpnXVNz75bNyng64H7k1TC3WyQCWTfdQHz\nTspjXDb9nlyXgp2rS5S3M1UbJ40n5yDn0tBtn/3sZyUNXeltTi6MM5ewdVzKgMvBJqt2J1eG6vro\nz6fvyLAnNkHv0U8/hstGcnOo68hdRZBZAso9VTL7A9efddZZi2O4cSBTLoOk98ZtxfUgegU5dXcl\nnk2/XEfWKuUO2p50AbKaXLIZj+RuusmkICl5UIXLBHs3eyrj4kk3ajIR3+erC2sKGk8p7uv6c71a\nXUoZQ3c/ZB6RW5e7mj4/peFOc8CYpeQwO+kGn1/cvd3tu977/wIzl6eURromcUKfprACZN5dvF70\nohdJkj796U9LWn4nqMl1fG5Ya8iXr3vuTxtoo7utIbPp3bG6zPq8IRO8q7jue8c73rHUzhR2Uddc\nSgiQ3NvruvD05LvREc3INBqNRqPRaDQaja3DgWBkqoVXWg3CdQtKtbR98pOfXBzDeo7V3S0n1art\n4F58pXqgLtYRAi4JNpdGoCVsTSo8lQLlU4pkgPUvsTx8DfPF7EwAX+bVqiKNoFYsPB4Mf9NNN0ka\nFrtkeaENbklO7NVegXsTYCqNsaafbjWATcLyATMjjbkkOYQH+FKkKwUDYoVBFt2CCUtXA6f9PObB\nLXv8zb1TwG2yvs+QguAruLfPH/I5s5RhUXRLbU1rnJISbAKpaGlN0+4MSW1LYgZSambuSXpjT6vJ\nOPicAtqA1dRlohbhTXKWUPvgc0UbKGRLggxppD0lXavrM0D7ZszV48UmWRhHSp5Qi7Ol9MtprTBG\nWLjdOshagSlxnczzEtOBrkkBwTV1uKfWBbQzFVqmnW6l5zlY573YHu3jud4HLLep6HNNkODJSzjP\nz9+pD2kP3wRqSv10zFHT0DNnritr4oiUfhs58GOs+5qi2X9Le08twAr75mPP/CEbiQVFVpI3SGUd\n/LdUpLwmEUoJVpDFVOLiIDAy3tfKyPhezLgzR4nFqsmffN4vuOACSaMw6QMPPLA4xhixlzhbQ4IZ\n3n9SAogq3z5HNUlESrudkjQgVzBIr3nNaxbHTj/99KUxcFlC9moq+STnKW1/HVdfs2mP3QnNyDQa\njUaj0Wg0Go2tw4FgZNKXek1pmawrfH162tFPfOITklaLQ9a/pVw8D39jt3xg1aANbp2GISH2IVlM\nQbLC1oJL0viyx5rmMSuwEclH361v0rJFGP9f2KTLL798cYw210Jr3s4UV5SYrb1CTTEoSXfddZek\n4Uvu80eRP+bGfdGxeMCiOBvFeLzgBS9Yul4aVhHu5VZRrHbOClXUdKfS8LHH+uvsULWief9qga4Z\n8+FrpVoO3cLD35zjDFtNx+rFFmHJWCMuL5tkZJLlDDBWe2H1qyncPb0tjC9zlNrCMYql+r1Yh+u2\nEzlhPlJ6afyaXb8gl1hy/VhNJZriS55o+uTkI70JIMOJpatFgP3vZGFljJk/Z1bQu1zvfumch470\ndcR4EjPg+qxa610XoDO47vnPf/7iGO1j/RHL5X8jN66z6tp0XV7jNFxPIAvedoAu4N5Pf/rTF8fY\nl1OM6Cb1BOMyi+F0ncUYMW94VXzlK19ZnOOeFlJ+H0lFeUGKRwNpXCuLxfX+XlILonp/676dWPMZ\nE5uYuaovOOaMNWmfdxNPtx9IqbFryQ3XU4xpTQ/sa6aW5fCxYk4vueQSSdLv/M7vLI7ByrLG/F2l\npk1OXhmztVNjVBOjVt/3pOGtwj7/qle9anGsei34ONV346oLvQ2Jdanx3K47d+Px04xMo9FoNBqN\nRqPR2Dr0h0yj0Wg0Go1Go9HYOhwI17IUBFTTuqUAoRQ4ixsIrlMvfelLF8dwA4NK9BSB3KO68EjD\nbalWXpeGCwCJAJKrEG4IKeUqgf3uqsBzuJfTmdDHKXiNduFiltKA0j+nqLlXrWLrbWZ8vC2bdA9I\ngYj0B3cGT4FcXbV8PGvKXHe3IkCagOnXv/71i2O4ETF27iLGs3EVc1oZlwTkzF3+ahAfsiWtBv/O\ngtOdsq/uQKlib3J7qVSxU9zV3cxlt6ZxXCdF517Cqeka0D1LF53GM1Ukr/fyhBq4nrJuU9B3SquJ\nzOLyNXMZ8GPINbLh7gCkdya1so8Lld2T6yN9faLrd1a1e1ZBexNIQcXJhYK1zNz4OqrBuymVOOvC\ndQj3QCe4SxpJZ3BNcpcmdFUtNSANXUXbcYv16/jXn4c8Irv+PI4lNyeeg35PaZiRpaRfUpBvTXWd\nKqdvAu9+97slLe/vpMPFDSolM2Bc6LsnX+AY95wFsc/cqVwWGasUGF+DtbmO9wxpzBXnuC6q7mM+\n17XUgD+3BvD7sTpnjInLJrLIXnJQgv1nuh9ZmO2pzJG7XNYkTr4Oufexxx4rSXrTm960OHbttdcu\nXZfcYrn3zC02JQyqY5zWGe30lPzI1Xnnnbdyz1p6w8eyjkvSOzPXsrpnug7cjUtyMzKNRqPRaDQa\njUZj63AgGJmUfrCyF36MLzrO8a9+vlKxon784x9fHKOgItZNt6rVYE63TmN9pZ0p+J4gXv+S5Ysb\nC48H42Hd4jq/5zHHHCNpWPKdPakBrH4MCx1993EhwOzRRx+VtGw9qFbGxPLUwHdvwyaQgvOwOD30\n0EO6IBWMAAAgAElEQVSSpDPOOGNxDCsTDIezJ5Xxc0s59ydI9p3vfOfi2Fvf+lZJg5lJgcGMOUHV\n0mAFSSTgySiQYyx7LoMwOfQlBWgmi3ctNJWsKamQW7XeeFsYv2SFQY7p836wMLUNoDKTKYEHffa+\n83dlKv2eKVgcZqWmR3UkRgYdkAqv1sDaZO3nmBcjo1gsDK73j99SkgCQitzO5rJa8tO5lS3fNBhr\nD2pHtzF/zs6y7ui7zxE6gDkmcFkaCUO4p+vPmpjG2YzKtvhY8+xkxUSPMW8u+7U4n++V/FaDlKUh\nO7TTj2GRRb+7DNbU/akwMM91BrAyY6mY4CbAvukFabGCsw+ef/75i2MUsGWNMS6u0++++25J0mtf\n+1pJeY+cpbxNXifrWJxZ0zUIPD3P71cLWs6KGCbr+SzYH/mBnfa5xMPh1FNPXWlTfe5+pWiXcpIc\n2uFrGbA2GZvKdPr1vIP5emK+eB7j4fe45ZZbJC3rJxh41lhKWFHfD5KXRXpPqJ46vGdKI0kH9163\noHFNeAAz4/LG34yP6wj0TtpHd/Ne0YxMo9FoNBqNRqPR2DocKEbGvwJremL/EuZrtVorpFWrk1vq\n7rvvPknja9XjWYh1qGn3pMGW8LXpX49YqXiuf/HzNV2tI94+vsDdKlLjILwtlclxyxl/My4UV/J7\n8BxncmqKXu9D9c135qime95LJAsKX/RYK91ixlhzzP2jmTf67lYO5ps5xcokSR/5yEckSZdddpmk\nZYs3lirmNBWJQ/bcAlGL0aWCrZURkFYtrMnSleRl5k9b40v8efiSwy65hR0L0jppPPcSyF4qIsrc\n+hqbxYCsk2aYc3xtwrIRg+JpqWlfsoYjczV1rrQau5es1YwxjKo0GATu6e2sfZhZ1Xxu17GCJdYl\nWYXT/fcarPc016l4IXsM8+GWQ8BacUtp3aMSa8axtMbS/xnHVKy23jOx35zvfagsvssZ+yT60Atp\nHnHEEZJGGQHfi0n9nAo7sx5gkykiKw3dtk7B3r0Ec+uyyDhgfadQoTQYd4oo017f62B0SKP7hje8\nYXGMOZ3pwbQ+6rrwtVfjilIxwlksZUVigNEXiY1OsYM8uxbjPvPMMxfn4H1w2223SVqWMTxbEju1\naQY3MdKVSfW9v7arlhzweyInHr/E3o8O8TWKJwnjd8UVVyyOMZYwnYlJq/rCdR/vM4yxx0kj+8yJ\nvyNxPmvH343RsbVIqI8HMYDJMwIdwT19nDzO+YmgGZlGo9FoNBqNRqOxdegPmUaj0Wg0Go1Go7F1\nOBCuZYmyrimBnbatKWWdloQmhT53uh7aE2rM7wmtCDXnblO0C2rU6TBc0nieV1mu1endrYDnQD06\nzQrlyD39GBQn1KXTygRs4Sbg9B4ubDzvuOOOWxy78cYbl8YjpcnkX3erS+mk9wo1gN3bBRVMymRJ\nOnTokKQxPu52xrwBr9R+1FFHSZJe/vKXS5Ke97znLY459Sotjyd9JyieIFJpUKhQ8P485IM5dleM\nGuw9G+tUoTi5iNUxS+5q9Mtd7pCTlJIb+ad97r65SbB+vC2sacbc18NepRf2eec5uJbhliKNgPCU\nbpjfGGPvQ02xnFKlQs/fddddi2Ocj3w75V+Dvh3VFSVVf565iNGX5CaVnrHJoF7mJrnH0RdvJ3Kd\n5gjXK+TZdQiyznPc7YjfmA8Pvq2uZZ6EBBeL6i4sDR2Q3JKrm1lyWUZ3od+8XfTFq9WzzrkuJTpA\nFpFXvwf6LI1rcrnapEzUdMrSavC26yzSLNN3XDXd7Re3s+uuu07ScqX2Sy+9dOV8UF3KfHxqcpZ1\n1pzLeU2tnNzOkCOXGXQDY+FukshSCshnDJlr9gGXaa5HX/Gvn8+7x165Fe0GKXUw7Xd5wcWSdc6Y\n+Xshew/9cDlHlrin7+XoiYsvvljS8vq96qqrJA13Tl+jtGWW5IE2oFM8OZMH90vL814TbrmcIUu8\nx7i7G++oXIfOdBc83nnQRawlaVlX1ud2sH+j0Wg0Go1Go9H4hsaBYGSSFad+bfqXWw0a9K/kmkI4\npdrla/Hwww9feR5f3v5FSYAtX6L+pYilPAXD1+Ast8JimeV8t5jwFV0t+34e5ySLcLW4OPiCPvHE\nExe/YWG74447VtpZg6l9PFNqwL0CX+bJmod1wy2DjBFWMQ9yo53M1dlnn704RhpOrLH+PPqcClfV\nJA9upWCOuN7ZFKznyIsX2KLtWPtdBlkjKWC2Wi5SIgDGLF2fUjVWFtGfwXhi3fax3mSRVKw9MwYo\nMXjJujgLzK2W0mRpZW3CAEpDJlKAPeumptGWVufE5x2rH/d2eUF3YLVzi369Zwr2TaDNs+DbWkjV\n/07FDzeJmnhCWk2p7RZjxqwmWJDmlurKkqbge673sa7j6dehFyoTIA3dxt6W1i398rGmf9zL1wP9\n4vxnPOMZi2PIM2linbEn3T2FD9O+Anx/YMySvG1STzCuvm6xmqcSA+gVPBPwbHB9yPhfcMEFS+dK\nI1ibopupZEANLPd21nOlXLS0onotpCQ9BHi7TqnpvlOKZcbH32dgYtC/FHt11rHKhveXPRpZc7Zh\nN4kLngh8PFmvqdglMsE7VUrJXRPp+DH+JgmGv6eRIIaxeuUrX7nSPljCdF0t5uvvHrzHMDe+xqvH\njb+/IR/sIUmf1nOk1SQKrC9n20477TRJQydVFsbb5GhGptFoNBqNRqPRaHxD40AwMnwJ+1dgLUSU\nin7x9Z7iBbBEJL//WgBSGl+QtZCR3wvrqzMBWG+qpd3bVwttebtmvpfJasUXOulx/euW56RYF56N\n9cDZqNNPP33p/DvvvHNxDP9GrIaebnaTVrWEas3259POo48+WtKydYQ4pte85jWSRgyRtJo+2e/J\n34nhqvEsXlSLe2GBcmtYLVTollZksBbplIZMVJ/7hORHXdMjSstMSr2ushHed36jvcnvexNg3c3S\nuCYf2+SfTn+SDDNmKb0717FesVZLwweZeKkU48S6d73E34yn6wIsorTBdQjp1WHwEjOWUmRXK29i\nxGdIDF6NadzLWKUZnK0GNR25pxJlHVXLszTWeboO2WP8fazZV2BI3EpZ2TzX1/xd0+FKYz/BIuzH\n6lgnFhL94imk0UPImccOcn/6APsiSffcc89SH1zXEeOHvvW+V+v8LPXwXoJ7+/qrMpgKG9IXZMr3\nOvQncZk+j4888oikMZ4eA1CL1bo8VFY+xTamYuGgxsj42DNH7FPOutTiun6MNmE993cB5pj4KeQo\nzSXt9f4y/xQXdbZh00xMYskro5Vi2+gH70u+noijQV5SQVL0jetczmN9+HWvfvWrJQ1m9GMf+9ji\n2PXXX790T9rrhcHxtKnMs/dpxvah39zTAFlK783ElsPSwAS99KUvXbk+rYG9QjMyjUaj0Wg0Go1G\nY+vQHzKNRqPRaDQajUZj63AgXMtw/fEgXmg/KM5UzTsFluKWUV3MpOEGlqqPglrh1NvAc931g/SC\nUGseWMrf0JH+POhBaN3kHged7QFb7uYi5Yq1jIePGb9BY3ugHS4xUJ3uCnXvvfdKGlSjB6T62O41\nkovSLN0k1WcZK6cvzzrrLEmDHvb+VXc1f+6Mumfe+M0pXFxS0rFKz6YUttC77v4HrY+8zdLqJheF\nlHbW6eN6XQ2AdBcexiOlKZ0FAj9R0HfXE8mNsiJV3k6pqiuYI0+tzT1Yvx74SFIQ3Bs9eLSmtXT9\ngl5gbad0wcibp9NFTnD9mQUG7yXWcR/cL9eytAfUAHt3MUrrrl6X3A7R+eh0d8fB7QyXVdeRzAlz\ne9JJJy2O4YpUXS+87egSP4b8o1d8P8LlmDZ46nfcQKr7kDTGJbnD1jIHrs/QS8hwSoKQ9NJ+p1/2\ntSjlxC3MbUr2wJqmYr2P67HHHrt0Tx+Dek9vE/sD57trU3W/YbxS8oZ6Hz9W9yn/OyWjYa3gUuZJ\nDZBddFJKKFET6/jaQ2deeeWVkpb1OK5Im0rJTHuSDCYXL2SIYH3WL+vL7/nAAw+sXF/LhvB+Ig25\nYn93V3fGiD3nda973eLY5ZdfLkl697vfLUl61ateJWm5ZATPQyel5BKsfx+LKid+jD2Ofz20gr6c\neeaZkoZrnMsb17mrJqi62mWpg/0bjUaj0Wg0Go3GNzSetMng3Eaj0Wg0Go1Go9HYBJqRaTQajUaj\n0Wg0GluH/pBpNBqNRqPRaDQaW4f+kGk0Go1Go9FoNBpbh/6QaTQajUaj0Wg0GluH/pBpNBqNRqPR\naDQaW4f+kGk0Go1Go9FoNBpbh/6QaTQajUaj0Wg0GluH/pBpNBqNRqPRaDQaW4f+kGk0Go1Go9Fo\nNBpbh/6QaTQajUaj0Wg0GluH/pBpNBqNRqPRaDQaW4f+kGk0Go1Go9FoNBpbh/6QaTQajUaj0Wg0\nGluH/pBpNBqNRqPRaDQaW4f+kGk0Go1Go9FoNBpbh/6QaTQajUaj0Wg0GluH/pBpNBqNRqPRaDQa\nW4f+kGk0Go1Go9FoNBpbh/6QaTQajUaj0Wg0GluH/pBpNBqNRqPRaDQaW4f+kGk0Go1Go9FoNBpb\nh2/5v26AJL397W//miR967d+6+K37//+75ck/cd//Ick6Wtf+9ri2Hd+53dKkr79279dkvTDP/zD\ni2Pf8z3fI0n65m/+ZknSf//3fy+OPfnJT5YkfdM3fdPKPfn7f//3f1fax71o33/+538ujnE+5/zP\n//zP4tg///M/S5L+4R/+YeWY30OS/u3f/m3x9z/90z9Jkv71X/9VkvRt3/ZtK/3juf/1X/+1OPak\nJz1J0hif7/7u714c82dXcN1O//ff/Bh//8zP/MzqBU8Qv/Vbv/U1aXn+wHd8x3estIXx+IEf+AFJ\n0rd8y7esnM94/uVf/uXi2FOf+lRJ0vHHH7/yHOSLMf6u7/quxTG/f20n16VxRM74F7nx39JY12MO\n1kiSCfrMby7fPJvr/d7IIzLs/eP8eo6f9wu/8At7LhPHHXfc16Tlsac/rjsA65y+s/6lMS5///d/\nL0n63u/93sUx7v+DP/iDkqQf+qEfWhz7vu/7Pkljjn3++I1/vZ20jzakeedfX6v0obZbkv793/99\nqX8O5oQ2eN+Z56QH6d9hhx221Bd/dpLFv/3bv13q51//9V+vXPfOd75zz2XipJNO+pq0PE78nXQ5\nx9Cj6EpJOuKIIySNvh955JGLY89+9rMlSU95ylMkLcsbehb9kHStz3dtS5o/dD5jzFz7dfzra5P5\nrjLl9wLezrR/AWSHY+l5Sb9wHvOP3pDGHnfqqafuuUw86UlP+lr9jbFiXFPfZ/q3vgP4/NPPf/zH\nf1w6Vxrjw28+j+BZz3qWJOmEE05Y/IbsoZfuvfdeSdLf/M3fLM5BLzH2rEHv32wvR/f5nNEH9kzm\nSZJOO+20pd9YO//yL/+ycm+OuV4FVfdKY9/+wAc+sOfy8P+xIhN7jaS36eN73vOexbF77rln6fxH\nHnlkcezRRx9duqevX/Zbxhh95e+S/h5Z24ScJtmocurPrfuZyzC/IS/09/DDD1+c89u//duShpz9\n6I/+6OIY8lHfpxxPfvKTv65MHIgPGQbKNxUWB4PmioMPA65zBcmmkj5k6saWNj8mN22C/ObPq+f7\nSx2/sXBdKfh50rKC4jw+5vxDhv4wHt4/+kxf/OUlbWz1WO3TQYBvCixAxt9fshij9FLAGKEknvnM\nZy6O8YJCn9MHEPBj9YU0jetMltIY15fWtJGml0/OY3z8ebSTf10JcR7X+z3pO8/zzQrZ43pXnv6x\nt9eYvaCmlw/OY/59PdSxTi9nKNl03ewjFbjs8jfXJbkG/jz6MFvTyLzPLfNQXzT9nqwjl3N0K+uJ\nF3d/NpuVX8f964u0t30TYBN3ow0vRRi4+DDx39hM/SOVvvKR42MN6keENPqa+l4xM575PZHHpJf4\nu76cO9I9kY9ktKtz5LLEXoUO8LVSjW4uyxxDznzfZN5OPfXUlbZvAvVFKenPNGag6l2/n7+Q+32k\n+csjsnj00UdLGh800tCxjD1j6WuOeefjw9tddaTrG9qUjDG1vz6fGADRLciDr72/+Iu/WLqP652n\nPe1pS9dhNJGWDTTbhmTYYdyuuOIKSdKDDz64OMbY/t3f/d3Sv9IYW+7pRlfmiY/cY489VtLyWv3C\nF74gaezJvuZ4LmPN+6U/D7mbvQv4uziyVw1ZDzzwwOKcd73rXZKkt7/97ZKkL33pS4tj/g4n5b1v\nHbRrWaPRaDQajUaj0dg69IdMo9FoNBqNRqPR2DocCNcyKCt3UeHv5BrBb1CrTo1CR0GNJdodpBgE\nKEFvC8dwfXNamfOga53Kg3KGOnTaFR/y6nfu5xO/4ZQxtCxtcHc8+gO9l1xb6F/yhU5uM8llpx7b\nBJIbUXWpcN/Q6lblY8a4Q5HiTiatuiI6bTqjW6uLUHJpqrLo59XYKj82cztLNDb3SPes68dl12XV\nr5dWXSFSfAnj8vSnP31xzN0n9xqsMZ/bmctIlXGXF/7mHL8Oyj3FOtX4kuRuOHNNSa6BybWjXldl\nXxrjP3NzSzLFONZ//bq/+qu/krSsX3g27cUNza9LMUdf/vKXd+zfE8WZZ54pSTrjjDMWv5144omS\nhp+296G6Pvp41rWZ1m1yy0qumRXImcsLz05yVnWIu97wN25H7pbMvPFbcllG9n39V7cz3285xny7\nKwv3qG6/3j/64NchQ29605u0KSQdSf9cn+3kHpzWJdd5Pxmr6nJb/5aW43mJiTnqqKMkjZg8aexV\nDz300NLz3NUTtx6OzdxyfCyq3PmeV+Oh/D7ID+enPYX1xdj5foBLd3V3lubvaQcdyI3rkmuvvVaS\ndNVVV0la7itjgjuWu7qzxj7/+c9LWna9OvfccyVJP/ETPyFJes5zniNpeV+78847JUm33nqrpCE/\n0pATzp/tJT6nVbf7flH3T/SN69lPfOITkkYc8qtf/erFMd7NcKuc7YUzNCPTaDQajUaj0Wg0tg4H\ngpHhK9Az3fCFzpdsCmBOTE7KkgGqlSFlDuKcZJnHApEC7h577LGVPvAFm4IksfrSXqwr0rC48S8Z\nS7zNBKT+yI/8yOIY1p6UwakGovp41kQAM6YlWac3gRQUPbOY1eQHbi3GAkKQ3Cz4eNa/ZE1N1tva\n3hQYnNows+wil+u2vbZzZmlJVnvOm1lvkiVzNwF6u0ViDOvzvC01YDol/uCeHrDK2gIp0HrGjKWA\n8NqH2W/eh5p1bMaouV6qOivNLePhiUewpKFTnU15xjOeIWkwHB6ISh9Yd96nx2tlWweXXXaZpBHg\n7+1LmbjQxWk8Z+C8lMCDPSAFwwLG01kJ/qZNPp4PP/ywpBEYSxCvNFgX9Jrvf6xX7p0y/D1eBi/J\nfNWDieFKAfKblInaNm9Dyha5kw5Pwdv85slPKiPu4De8K2ALpZFwAibGx6cmUUh94hza5qxGTX6T\nkj6k9UH/0CXOzPE+w3W0zceprgUHDERdn/XvbUHdy32NXn755ZKyFwHjwG8edP/nf/7nksb4X3TR\nRYtjr3vd6ySN5BDIi8sdyUtga2BmpKFTatZRacgE/7rcVbbVZZ/zaAv/+p7Cbx/84AclSeedd97i\nGAkgkC32GGnOcFc0I9NoNBqNRqPRaDS2DgeCkcGykGoyzKzMWJ/8y5IvvIRqIUopQqu1Uhpfoim9\nZk1B61+rHIN1catv8hsGWEGwbsL2SMOSyJd38m/na9gtyzWWY13Wpf6234xM8lOvcQrSqiXSwTik\nOg+cj7ylujUpXqdaY9Kxnf5f2w7qsxMblSyfNe7J+1dlPdWRmcXPpOu4P9e51W6TlrV12Cgfw8qg\numx43IS0vFawqM9Ys5p338/nNx/PKruzOLRZ2uYZAzSrpeN+1HWNpFoDzHdiXWB+3Q8aHc46couc\n6729BnPsvvi0k/b5PKI/a10faXWMU7xVeh6pVVNcCgw9//p4EgtZLevSsHQmFqXGbLpFl35VBsmP\nVdmXxv6Fz7qnFGZOa9xO/bv2obIYKQXwJpCeV4+lPtR9JdWg49wUY5qs7+iV4447TtKIh5FGvEuK\nras1O/g3pXamny7LHEuMeoWPUy1f4DEa6NEaZ+S6hbXOMZc/5P2YY46RtOyFknTXtgBZuPLKKxe/\nwbowDh6rgn6ApfNjvD9ecsklkqQ3vOENi2O1VERaj4z/8573PEnL6eVhjHiGyzDvmJxzyy23rLQp\n1SRDrpDPlLIe+SAl84c+9KHFMRh15NXf5ZOe2gnNyDQajUaj0Wg0Go2tQ3/INBqNRqPRaDQaja3D\ngXAtg0b3QP2a1s0pTyrCcp2nJCTYLAXe1YB+R6WMU0BjraAuDZqQYFOqrUqDOq30sDToulSlHjqa\nczyQlf4xBu5WAC1HOz2dI+M3q4qeUgmvkwBgk0juf2keqrw4VY2LSRqDGuiZ3LIY8xRIPkNyY+C6\nmpzAkaoqV9eA5A5U3QL8uhTYCeU7c1uq7gv+dwokXTd4+vGA53qQe31eqmCf3OO4R00X6kgplqtr\nmQfY8ltyW6rBkKkKdzq2jitoci2rsu59qOnE/Xk1tbY/9ytf+crSMVwjpOEGi2uDJz3ZZNXuJJ/0\nC/n2Y7h2VdcbabjD0Rd3AyPAvrqKScMNCxexlJ40zV91H3JXPRKTHHbYYSvX1z3OE5uwL/Bc73tN\nLuDriD2UfiVdl1yyeU5KylP3GpezTbqW8Rxff9V9NgW41/3F+8m9Usry+u7g7zG4lJ166qmSlue4\nBmundVJ/Wzd5A0hpv2uSlrQn0D93ua3lLjiW0mqjI7y/X/3qV5fa5u887lJ0kOEyjbzcfvvtkqT7\n779/cYyxInjfK91zDJdUD0fApewtb3mLpLH+Hcwbc5TeZ/nN548EAKwFX/8854gjjlhqoyTddttt\nkoZu8Ocx3/X9IrlO0+6bbrppcewFL3iBJOmss86StKyPacM67xTNyDQajUaj0Wg0Go2tw4FgZGAu\n3JLBVyNfeilVHMFnKaVsSqlYgxYT68IXZbK48AWbgpsJTPIApVos0/uApYW+pBSaPNcDtrB4Yg11\niwd95Te3SNXg7dT3lE63WntmQYN7iVmgZgqwB8jELBgzze0skJVx9HGpqW/TuHB9YkrS82apmes5\nqX+ztKrVcuJ9SClJawCpP491xL/7FewPUnpi2uAWnWq5SszYLJlIClhdJzA/WYRrAoDEutQ+1fvX\nPtTz3YqWWKjav8Q0VgtwYigJCE2FJknX7NZWtzjuNQhKdf2J5RCGxAPzYS8Yc+8v66EyEH5+Zdak\n1XTbPg/VQjnbO1KQONd5HygsR3uvv/76letgdDwlN8HWtOEzn/nM4lhd096/muggpSOvaVhrf6Qc\nPL8JzNIKr5N4ZVas2PULqKzUKaecsjh28sknSxoy6fd0Jk1aLqUAO1iD9b1PrLkkr/U3l7vZfNIH\n3smcdUV+YFt4L/F7V2bOWWLWBeymv9cQHH9QkfZmvHEo+OjyDUsLE+OsFfPO3F544YWLY7/4i78o\naaTmTjJY5zYl8EmF5GtSiSRLFLc+/fTTF8fQKTfccMNKP+kXc5veser7j6eE/vSnPy1pMEInnXTS\n4thuSjk0I9NoNBqNRqPRaDS2DgeCkeHr1eM9+Irjyy758xI74l9/fOUmK3q1gie/6pl1mus8vSZ/\n04bPfvazi2N8eZPSkuJm0rDQpJR2AIuFt6X64bs1F/9ojiVLaEpdnAoN1vNnlutNYB2/yGT9T8XX\nKouRrAW1gJgjFZMEyGVKSTiL00ppMWtMTYrNqM/we838zmsxWGk1DibF5CQmp8ZSeTs36fs+YxOT\npbTqgmRJriyK/51iArgXlm63OFaLl7eTYylVatU5qS2zwqvVd92vq+mGpVXG1+/JsZm8IPNeAA4G\ngLg8ZxA2mVr193//9yUtr9uaejjpLmeM6nUz1iUxofWclKo86Y4a1+ftZO9g7Pye7B08j3S20thj\nGH+/Z2VQfV6wrM76meaxxvw5qv5L1uVNIMUmzgrZ1rbMUl4zdmkdEh/r7zGsFebFx/WLX/yipPE+\nQ1pzP8b48q7jbZrFOFYZTtfVvkn5vQkgG7AnpJL2tcTYoZOSHKXih85iHiTUPcDH7mMf+5ikEfvj\nMXUwMfTVGRnY4xNOOEGS9Ku/+quLY8SxJI+B6gWQ5qoWzp6VAPA1WxlV946CKUQn+Xslv7m+r6hs\ny6FDhxbHKPgJy+weRs3INBqNRqPRaDQajW9o9IdMo9FoNBqNRqPR2DocCNcyaDcCRaVBpdYATGlQ\nalB0yW0i0a1ebVpaprqrq4/fE7oOCtiPcc9HHnlE0giI83YSuOXpkHEPgJrzKrcEf0KzOc0HbQ2d\n61Qc/amuI34sua8kd7N1sB/uATO3pVkQtmOWhjcF3YNKJzs9XFNnpgD0WerqVIm+ugilYOP0vOpi\nl9JSp+uq25G7b9a2u8zXeyU3hE0gBeyuM3/J3a2mTPVgcdwBoNDTGkuuQowR85jmnXN8PJM7a0V1\nd5NW58/bhAtMSkrA+TWhijT0SgreZow4xwN06TMuZum6TYD+kQRFymMF6ljPEmSk5BC1qrmfx78u\nb7SFtZUSOaT0y1UXP/OZz1z8jQsxew4B5dJwb3E3F8Czkes078i+P3+WQhqktVZdsmdueXuJtI/x\nW0oZD2r73N2pzr+fi5sprkLuasX7APs2KXf9nqxHgqml1VS3vA95Ol3g7w7roOpRl3P6zL++5yGf\nyAhtRB6l1RIQye2Jdx6XleTqeZCAvNx4442L3wgjwG0Qd0BpzAl99nHk/e4d73iHpOUAd/TEzG2+\n6rWUyj+5llWZ9//XvSS5TKNjfV3gHnjGGWdIkl70ohdJks4///zFObiNeRmRnfB43yWakWk0Go1G\no9FoNBpbhwPByPAV5sHwNZWlp5HkK5Ovf9L5Ofj6c8tZDZBPAeEpMBhgScAiIQ0rxWmnnbZyHZ3u\nwJ0AACAASURBVJYvvsqdceLrFsuZ37MGFLvFg3SIKTiuWs5ScDNYN9gfpC/l/WBkHJXpmH29J4ak\npkl1pKD2WrDOrSo1eDulVU0BsLXt3pbKliVrVrL6VvYkMVU1EN2RGIsabJiKZdZn1DbvNVKRv3rM\nn7+O5Sqli6wFRlPqzMqaSiN1JdZzD/qtTO/jLRyagtrRBd4/zoMxdqtrLe7nOpK+JsYQfYRecx2E\n5Rkd5+zzJlPtghnLmhJxpIDwWWHSGgCejqU1PQtYrWvMGVGu830PMP4c832TvYJ5JNGMNGQh6Sye\nnRKN1D6ktZIw6/t+sbj1eTOWru6bqehwYhqe9axnSRrB+qkQNWPv3hWsFebP2RrugU4Bzr7QTlia\nWXKDVLi3lodIY+G6z99DpFEg1wuSox/Tmod1od/OGvo9DiIY97vuumvxG8UuYUYdtTyD//9Nb3qT\nJOniiy+WtBw8X8dttk5miZsqG+rgGc6CVTYyeXwQ9H/uuecujpGm+fDDD9+xnSB5T1R9PEvCMUMz\nMo1Go9FoNBqNRmPrcCAYGdgJj2GpX/tu3eRL/tnPfvbKvapVzNPl1QKayUpZfWml8cWM5cLbwpcs\nX+z+5V3T/XqKOlL2YcFwCxj3r/9Kq9YX/5JOlsSKWYxM+iqulqxZscz9RmIJZnEwNeWutJoy1VkU\n5KWmKfa/a5pwaVg6EiNTWQW3lNV5S2lx+dePzWICqpUxpZ5OxSRnFugad7HJlMsOnpOYVJDYmsTE\nMTcwnJ4KFCso8+hyhhUTBtWL2j300EOShhXc7/m0pz1N0khF6f7Cs7ibyhx4W7DuptSgldV1qx/3\noi8ew0J/ahFgbzNslF+HnuY3H5dNpladMYzVOimtMpvJmglm1tBUVHdWiDE9g/lKa7OOv+szLPh1\nX6p/S8v7GPsrHgx+LI1HvWeKt6rXpb6n1OiPl5FcBykFf/VWmKW9T8xvXX9+DAaWtZZiU1mrzsig\ne0hjzDuBNKz86BRKMTjbg2ykAthVzr29tLMW3fT+cS8/VmWL9nohT1DTuKdj7oWym1S7+4G6/m6+\n+WZJ0h133LE4h/6nWG36zTsfsYOS9MY3vlHSGIekH9O7WB3/Gcs4ixOblRhJMWCUGDn77LMlSeed\nd97KvWep5Ov+u24sdjMyjUaj0Wg0Go1G4xsaB4KR4Yve/THJiMMXMRkypPElzzEvJAUzgoXQs3zU\noofpqzH9v8bp+BcwVrGU3Yl74BOaClTyJYyfrTSstzAxnpGHNlRGwJ+X/CMrOzD7YnbMGJn99nPe\nzXNnxddmftLJqp0yeTC2yGxi8JI1rDIx3jbugVw7Q1ljOlKhK9rrcRvV/z5Zb2fxOsl6U+Mn3HqT\nrK97jVkmu8TE0T6fP9YUft+uJziGxdTXX80s58+HReZftziil9BjZDKUhv5LmXtqRjm33nGsxrVI\nqyybW3tpA9ZCXw+wLbQlWXmRLx9rZJXxdF2326xKu0FiqmrGHj9GH2qckJ+frMOM54xJSJb8yvDP\n4sl8HcHqUXTU28l4cm8/hkww7y5T7B303VlBfmMd+Hqo2fhSAUT+9aydtIt14LGsMA2bwCyDZWJU\ndtpPXB9Wpp91Io13DebYZZ/fUoFZwLx4Zjr2frxPYOF8HfOeAHzOKiOSMtQxV/7eVQta+h5Ev7g3\nrKtnL+TesBQpbg+5cFk5CAUxfW7I2oiuvOWWWyQN3S6N/tPn5ClAny+77LLFMeb2zjvvlDT2GWl4\nGSWGszK+KeakMmBpP0zvctWDyb2HeL8mhss9oTiW1tV+ohmZRqPRaDQajUajsXXoD5lGo9FoNBqN\nRqOxdTgQrmXPfe5zJS1T1vfcc4+kQX+5uwxULPSlU7kEw/Gbu4VU+isF/CXwPChYD3LluuRuMSsE\nB10Pney0MPeqLmbSGCMowBQgSntT4OUs2DEdm7mWbTJALwWwVxeA3bq2zQLokksU/WP8kztCStcN\nLcs9XQaZd+YmBdziOuJuBIwHspFS+86K0tVCsd6WGhztbZnNMfdO6Xs3gVTUs7rspGQU/OvuC4wj\n8+Z94DzGzNPbAlyoPPj2+c9/viTpOc95zsp1uCRwT3erIClAKhhW06j6HNVjuENIwyUE1xTXRegH\nnuepTxmHmjbWf0N2ve/cn/l3fbbJwG7u7c+objUpyUNKM1zXj+9H67hM1OKX0hjrmo7Vf6tJYfx5\ntRidNFwRkUFP0VwTQPh1VdZTaQLgeqLuNSkAnHn3e1b3IpfBc845R5vCzA06ucrWAOdUkLie66mI\n+bsm/pHGewiy5DqIe+GalXQX6wh3HndXBTUpjV/HmnXdQHt59/C0z9WFMqXorm6ZuD9K0rHHHitp\nrqs55uECNc30fiIVRaavt912m6SRdtndIzkfPehjzDFcry655JLFMRI34FKGi5k0xpIime52VhP9\npFIMIL3P1oB8v66+2/qeQEFL9hJPN01fdltMfa/RjEyj0Wg0Go1Go9HYOhwIRgbrwdFHH734jS89\nig55cCzWIywZzjzUgMRkVamFMR3JEl0tNn7PmgAgWfb5KndrCl/AWMzcMsgXPoyMBxbyHPrgVoBq\nnU7WxllA/07/3wn7/RVeGaPdsjWMWWKVmDe3QCFfWNg8dWYtNOaWNuaPeyWGC1lyy3ydW5f5Gnzt\nfcCyMmNP3EIOsDqllIsAK19isRLDtUmZSIUDaQvjn4qIVvZMWi0K6MdgTz73uc9JWmaZSMrx8MMP\nrxyjKBgMs+szLKo15a405pZ7paKs1ULrv9F3tygiq6lYZmUXUkBvsqiCmjTFn8d8eFucPdxrJOti\ntRjPile6vq4JHJJurQyntJr23mWJ59SU3tLQ79zTk9bUlLp+XU3qMmPQfU1XxiGlgqafSUcmPVPZ\nJL9nHUe/zpmivUZiz2pK5pQAAqSkD3UdOIOA7DOGvp5YK7CuiUnHyu8MbvXmoE+JYUulGGoJgFQW\ngjZ5oW7WP+30fY3g9po8xYP9YRRSwWCATHubfMz2C5VNdB1CcP8nP/lJSeMdwAvM1mK1aV9ijlJC\nBMb2xBNPXBy79dZbJUk33HCDpFFoXRoyV/f5xOSlBE41OY/vXchuerfiGPPtqfWR2SOOOGLpuv0u\nzdGMTKPRaDQajUaj0dg6HAhGBh88t05jgTjmmGMkLX89YpXEf9CtmzW9rVuy+C1Zrqvf4cy3060U\nM99pruNL1q0x9Icv4FSIMfky83WcCipWK07yx13nS3mWIvQgIPVhxshUhsrHBetE8qdnrLFSuQxy\nT+QrpcjmWIqDSewHlkvk22Mmqh+vW3+qP32SyWR1quzjLF1pKhyY+rLJ9Is1JiS1c11/YeYWy6Gz\npbAtxCi5VQzLE+f4umVumG/3PYdhxqrmfaDgGJZdZ2DrnPrzZjJIf4jX8nSaWD8fe+wxScsWWfzW\na+yf/4be9XlHvmi7t2WTOiSlRa7y7DGUNXVpSpefYg7QGZzvzBgMbIqbYv4Yl8TEVZ3u7WNcnR2a\npReuKVpT6nfuOWNwZ2y3t4W/6YPrpRqX57I0ix99opjFtiRWHp2R2GdQ0y6nFLSMq6foxaOE6zzm\ngXuyVjydMmsSJiC9e3Ad4+wMAfPP+a4Xq/x4PATPq+88/py6dnx9YaHnni4rFb6nbDK20uE6i37Q\n16uvvnpx7JprrpE09Cb9mnnj+D4PYC9uuummxW9nnXWWpDF/3ibkCgbH05Qz3qyjWdxkKo1Q4axy\nfVd1Bol9DPl0nfnFL35R0tgX/6+KpDcj02g0Go1Go9FoNLYO/SHTaDQajUaj0Wg0tg4HwrUM+std\naaCooN9S+kqoLqf0KrXmVCwUXKLWa5DkzL0qBdFzL6f5amV4T9lY3QncBY7z3aUMVFrY6frqVpMq\nMO/UJ/8t9X0W/LsJzNwnUnrNel4K8E3JGmpwuo857kDJHQHK3F12AM+p6SqlMe9c75Q6LkYEf/p6\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16+Md8bbgDcGL6sUImQfOd1vCFpj/d7/73btjM2E9FZwCScQiFa+cXso0tzMh0q+VEnuT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\nBT7X9IEcm7/8y7/cHVuxbxchnZMKOSb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"text/plain": [
"<matplotlib.figure.Figure at 0x1234cad30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"classes = [0,1,2,3,4,5]\n",
"str_emotions = ['angry','scared','happy','sad','surprised','normal']\n",
"num_classes = len(classes)\n",
"samples_per_class = 6\n",
"plt.rcParams['figure.figsize'] = (10.0, 10.0) # set default size of plots\n",
"for y, cls in enumerate(classes):\n",
" idxs = np.flatnonzero(emotions == 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(images[idx])\n",
" y_h, x_h = np.histogram( images[idx], hist_div );\n",
" plt.axis('off')\n",
" if(i == 0):\n",
" plt.title(str_emotions[y] )\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prepare the Data for CNN\n",
"Here the initial data have been divided to create train and test data. <br>\n",
"This two subsets have both an associated label to train the neural network and to test its accuracy with the test data.\n",
"The number of images used for each category of emotions is shown both for the train as for the test data.<br>\n",
"The size of each batch it has been chosen to 64 because, after analyzing the performances, we discovered that decreasing the batch size from 100 to 64 actually improved the accuracy."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of clean data:31196 48x48 pix , 0-255 greyscale images\n",
"orig train data (31132, 48, 48)\n",
"orig train labels (31132,)from 0.0 to 5.0\n",
"orig test data (64, 48, 48)\n",
"orig test labels (64,)from 0.0 to 5.0\n",
"TRAIN: number of 0 labels 5134\n",
"TRAIN: number of 1 labels 4829\n",
"TRAIN: number of 2 labels 5789\n",
"TRAIN: number of 3 labels 5765\n",
"TRAIN: number of 4 labels 3739\n",
"TRAIN: number of 5 labels 5876\n",
"TEST: number of 0 labels 13\n",
"TEST: number of 1 labels 12\n",
"TEST: number of 2 labels 10\n",
"TEST: number of 3 labels 7\n",
"TEST: number of 4 labels 13\n",
"TEST: number of 5 labels 9\n"
]
}
],
"source": [
"print('number of clean data:' + str(images.shape[0]) + ' 48x48 pix , 0-255 greyscale images')\n",
"n_all = images.shape[0];\n",
"n_train = 64; # number of data for training and for batch\n",
"\n",
"# dividing the input data\n",
"train_data_orig = images[0:n_all-n_train,:,:]\n",
"train_labels = emotions[0:n_all-n_train]\n",
"test_data_orig = images[n_all-n_train:n_all,:,:]\n",
"test_labels = emotions[n_all-n_train:n_all]\n",
"\n",
"# Convert to float\n",
"train_data_orig = train_data_orig.astype('float32')\n",
"y_train = train_labels.astype('float32')\n",
"test_data_orig = test_data_orig.astype('float32')\n",
"y_test = test_labels.astype('float32')\n",
"\n",
"print('orig train data ' + str(train_data_orig.shape))\n",
"print('orig train labels ' + str(train_labels.shape) + 'from ' + str(train_labels.min()) + ' to ' + str(train_labels.max()) )\n",
"print('orig test data ' + str(test_data_orig.shape))\n",
"print('orig test labels ' + str(test_labels.shape)+ 'from ' + str(test_labels.min()) + ' to ' + str(test_labels.max()) )\n",
"\n",
"for i in range (0, 6): \n",
" print('TRAIN: number of' , i, 'labels',len(train_labels[train_labels == i]))\n",
"\n",
"for i in range (0, 6): \n",
" print('TEST: number of', i, 'labels',len(test_labels[test_labels == i]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Data Amount per class\n",
"As we can see, the number of training images for each class is different. For the initial database, the amount of \"happy\" images was even bigger(8k), that is why we decided to skip 3k random \"happy\" class images. This also helps with the speed of execution, since our database it's a bit smaller.<br>\n",
"In fact, the non homogeneous number of images per class could lead the network to be excessively accurate on one single class rather than on each one.\n",
"\n",
"# Prepare the data for CNN\n",
"Here the data is modified a little bit to be correctly fed into the CNN. <br>\n",
"What has been done is convert, normalize and subtract the const mean value from the data images.<br>\n",
"Finally the label values of the classes are converted to a binary one_hot vector.\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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C4HhMaHIWi0WeFKurq+z2DAaDXJxgeXnZdT/F40hTmsSxWIwn8fLyMv+enJzE/Lxz3svV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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1252b26a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Data pre-processing\n",
"n = train_data_orig.shape[0];\n",
"train_data = np.zeros([n,48**2])\n",
"for i in range(n):\n",
" xx = train_data_orig[i,:,:]\n",
" xx -= np.mean(xx)\n",
" xx /= np.linalg.norm(xx)\n",
" train_data[i,:] = xx.reshape(2304); #np.reshape(xx,[-1])\n",
"\n",
"n = test_data_orig.shape[0]\n",
"test_data = np.zeros([n,48**2])\n",
"for i in range(n):\n",
" xx = test_data_orig[i,:,:]\n",
" xx -= np.mean(xx)\n",
" xx /= np.linalg.norm(xx)\n",
" test_data[i] = np.reshape(xx,[-1])\n",
"\n",
"#print(train_data.shape)\n",
"#print(test_data.shape)\n",
"#print(train_data_orig[0][2][2])\n",
"#print(test_data[0][2])\n",
"plt.rcParams['figure.figsize'] = (2.0, 2.0) # set default size of plots\n",
"plt.subplot(121);\n",
"plt.imshow(train_data[9].reshape([48,48]));\n",
"plt.title(' after ');\n",
"plt.axis('off')\n",
"plt.subplot(122);\n",
"plt.imshow(train_data_orig[9]);\n",
"plt.title(' before ');\n",
"plt.axis('off');"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train labels shape (31132, 6)\n",
"test labels shape (64, 6)\n"
]
}
],
"source": [
"# Convert label values to one_hot vector\n",
"\n",
"train_labels = ut.convert_to_one_hot(train_labels,num_classes)\n",
"test_labels = ut.convert_to_one_hot(test_labels,num_classes)\n",
"\n",
"print('train labels shape',train_labels.shape)\n",
"print('test labels shape',test_labels.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model 1 \n",
"In the first model it has been implemented a baseline softmax classifier using a single convolutional layer and a one fully connected layer. For the initial baseline\n",
"it has not be used any regularization, dropout, or batch normalization.\n",
"\n",
"The equation of the classifier is simply:\n",
"\n",
"\n",
"$$\n",
"y=\\textrm{softmax}(ReLU( x \\ast W_1+b_1)W_2+b_2) \n",
"$$\n",
"\n",
"For this first attempt have been applied 64 filters with size 8x8. \n",
"The optimization scheme chosen if the AdamOptimizer with a learning rate of 0.004 s."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wcl= (8, 8, 1, 64)\n",
"bcl0= (64,)\n",
"x_2d= (64, 48, 48, 1)\n",
"x2= (64, 48, 48, 64)\n",
"x3= (64, 147456)\n",
"Wfc= (147456, 6)\n",
"bfc= (6,)\n",
"y1= (64, 6)\n",
"y2= (64, 6)\n",
"y3(SOFTMAX)= (64, 6)\n"
]
}
],
"source": [
"# Define computational graph (CG)\n",
"batch_size = n_train # batch size\n",
"d = train_data.shape[1] # data dimensionality\n",
"nc = 6 # number of classes\n",
"\n",
"# Inputs\n",
"xin = tf.placeholder(tf.float32,[batch_size,d]); \n",
"y_label = tf.placeholder(tf.float32,[batch_size,nc]); \n",
"\n",
"#Size and number of filters\n",
"K0 = 8 # size of the patch\n",
"F0 = 64 # number of filters\n",
"ncl0 = K0*K0*F0\n",
"\n",
"Wcl0 = tf.Variable(tf.truncated_normal([K0,K0,1,F0], stddev=tf.sqrt(2./tf.to_float(ncl0)) )); print('Wcl=',Wcl0.get_shape())\n",
"bcl0 = bias_variable([F0]); print('bcl0=',bcl0.get_shape()) #in ReLu case, small positive bias added to prevent killing of gradient when input is negative.\n",
"\n",
"#Reshaping the input to size 48x48 \n",
"x_2d0 = tf.reshape(xin, [-1,48,48,1]); print('x_2d=',x_2d0.get_shape())\n",
"\n",
"# Convolutional layer\n",
"x = tf.nn.conv2d(x_2d0, Wcl0, strides=[1, 1, 1, 1], padding='SAME')\n",
"x += bcl0; print('x2=',x.get_shape())\n",
"\n",
"# ReLU activation\n",
"x = tf.nn.relu(x)\n",
"\n",
"# Fully Connected layer\n",
"nfc = 48*48*F0\n",
"x = tf.reshape(x, [batch_size,-1]); print('x3=',x.get_shape())\n",
"Wfc = tf.Variable(tf.truncated_normal([nfc,nc], stddev=tf.sqrt(2./tf.to_float(nfc+nc)) )); print('Wfc=',Wfc.get_shape())\n",
"bfc = tf.Variable(tf.zeros([nc])); print('bfc=',bfc.get_shape())\n",
"y = tf.matmul(x, Wfc); print('y1=',y.get_shape())\n",
"y += bfc; print('y2=',y.get_shape())\n",
"\n",
"# Softmax\n",
"y = tf.nn.softmax(y); print('y3(SOFTMAX)=',y.get_shape())\n",
"\n",
"# Loss\n",
"cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(y), 1))\n",
"total_loss = cross_entropy\n",
"\n",
"# Optimization scheme\n",
"train_step = tf.train.AdamOptimizer(0.004).minimize(total_loss)\n",
"\n",
"# Accuracy\n",
"correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_label,1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Iteration i= 0 , train accuracy= 0.109375 , loss= 1.7942\n",
"test accuracy= 0.140625\n",
"\n",
"Iteration i= 100 , train accuracy= 0.234375 , loss= 1.71757\n",
"test accuracy= 0.203125\n",
"\n",
"Iteration i= 200 , train accuracy= 0.359375 , loss= 1.66261\n",
"test accuracy= 0.3125\n",
"\n",
"Iteration i= 300 , train accuracy= 0.40625 , loss= 1.55144\n",
"test accuracy= 0.25\n",
"\n",
"Iteration i= 400 , train accuracy= 0.4375 , loss= 1.44691\n",
"test accuracy= 0.234375\n",
"\n",
"Iteration i= 500 , train accuracy= 0.265625 , loss= 1.59834\n",
"test accuracy= 0.296875\n",
"\n",
"Iteration i= 600 , train accuracy= 0.28125 , loss= 1.72735\n",
"test accuracy= 0.265625\n",
"\n",
"Iteration i= 700 , train accuracy= 0.328125 , loss= 1.61648\n",
"test accuracy= 0.265625\n",
"\n",
"Iteration i= 800 , train accuracy= 0.40625 , loss= 1.49183\n",
"test accuracy= 0.25\n",
"\n",
"Iteration i= 900 , train accuracy= 0.421875 , loss= 1.48842\n",
"test accuracy= 0.28125\n",
"\n",
"Iteration i= 1000 , train accuracy= 0.4375 , loss= 1.36199\n",
"test accuracy= 0.203125\n"
]
}
],
"source": [
"# Run Computational Graph\n",
"n = train_data.shape[0]\n",
"indices = collections.deque()\n",
"init = tf.initialize_all_variables()\n",
"sess = tf.Session()\n",
"sess.run(init)\n",
"for i in range(1001):\n",
" \n",
" # Batch extraction\n",
" if len(indices) < batch_size:\n",
" indices.extend(np.random.permutation(n)) \n",
" idx = [indices.popleft() for i in range(batch_size)]\n",
" batch_x, batch_y = train_data[idx,:], train_labels[idx]\n",
" #print(batch_x.shape,batch_y.shape)\n",
" \n",
" # Run CG for vao to increase the test acriable training\n",
" _,acc_train,total_loss_o = sess.run([train_step,accuracy,total_loss], feed_dict={xin: batch_x, y_label: batch_y})\n",
" \n",
" # Run CG for test set\n",
" if not i%100:\n",
" print('\\nIteration i=',i,', train accuracy=',acc_train,', loss=',total_loss_o)\n",
" acc_test = sess.run(accuracy, feed_dict={xin: test_data, y_label: test_labels})\n",
" print('test accuracy=',acc_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comments\n",
"The model was overfitting the shortened (1200) data, while getting a test accuracy of only 28%. For the full dataset of 35k images the dataset was not diverging (shown above) and the accuracy was also around 25%. It could not find any features with this architecture. That is why we discarded Model 1 and started to implement more advanced architectures.\n",
"\n",
"In order to prevent overfitting in the following models have been applied different techniques such as dropout and pool, as well as tried to implement a neural network of more layers. \n",
"\n",
"This should help and improve the model since the first convolutional layer will just extract some simplest characteristics of the image such as edges, lines and curves. Adding layers will improve the performances because they will detect some high level feature which in this case could be really relevant since it's about face expressions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Techniques \n",
"\n",
"To prevent the overfitting problem observed with the first model as well as extract as many features as possible from the images, the following techniques have been studied and used.\n",
"\n",
"## 1.Choosing Hyperparameters\n",
"\n",
"One of the most challenging choise to be done while constructing a convolutional neural network is the choice of the number and dimension of the filter to be used as well as the number of layers to employ.<br>\n",
"Of course there is not a standard design, because it depends on the dataset and the features of the different images and on the complexity of the task.\n",
"For our purpose we decided to start with a higher size of filter and then decrease it.\n",
"\n",
"Besides, the activation layer that has been employed is the ReLU one (Rectified Linear Units) which is the most used and most efficient since it helps to alleviate the vanishing gradient problem.\n",
"The ReLU layer applies the non linear function f(x) = max(0, x) to the input basically just eliminating all the negative activations. \n",
"This design choice also explain why we initialized the bias vector b to a small positive value that is to prevent killing of gradient when input is negative.\n",
"\n",
"## 2.Pooling Layers\n",
"\n",
"Pool layer could be added after the ReLu ones. This layer is also known as a downsampling layer.\n",
"The intuitive reasoning behind this layer is that once we know that a specific feature is in the original input volume, its exact location is not as important as its relative location to the other features.\n",
"This layer drastically reduces the spatial dimensions of the input volume and is used to reduce the computation cost and to control overfitting.\n",
"\n",
"## 3.Dropout Layers\n",
"Finally, dropout layers can be used to deactivate with a defined probability a random set of activations in that specific layer which in the forward pass is then considered as set to zero.\n",
"Also this technique can help to prevent the overfitting problem. \n",
"\n",
"Ref: https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks-Part-2/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Advanced computational graphs - functions"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Definition of function that have been used in the CNN\n",
"\n",
"d = train_data.shape[1]\n",
"\n",
"def weight_variable2(shape, nc10):\n",
" initial2 = tf.random_normal(shape, stddev=tf.sqrt(2./tf.to_float(ncl0)) )\n",
" return tf.Variable(initial2)\n",
" \n",
"def conv2dstride2(x,W):\n",
" return tf.nn.conv2d(x,W,strides=[1, 2, 2, 1], padding='SAME')\n",
"\n",
"def conv2d(x,W):\n",
" return tf.nn.conv2d(x,W,strides=[1, 1, 1, 1], padding='SAME')\n",
" \n",
"def max_pool_2x2(x):\n",
" return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
"\n",
"def weight_variable(shape):\n",
" initial = tf.truncated_normal(shape, stddev=1/np.sqrt(d/2) )\n",
" return tf.Variable(initial)\n",
" \n",
"def bias_variable(shape):\n",
" initial = tf.constant(0.01,shape=shape)\n",
" return tf.Variable(initial)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Different architectures\n",
"During our research we tried different architectures and tested numerous techniqies to achieve the best possible results. Among others, we experimented with:\n",
"<br> - 2 to 10 convolutional layers \n",
"<br> - 1 to 4 fully connected layers in different places in the graph (middle, end, beggining)\n",
"<br> - different size of patches (from 2 to 16) and filters(from 10 to 64)\n",
"<br> - max pooling\n",
"<br> - droupout\n",
"<br> - L2 regularization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model 2: 7 Layers, Conv-Relu-Maxpool, 1 Fully Connected \n",
"\n",
"The third model consists in a 6 layer convolutional neural network with a final fully connected layer.\n",
"\n",
"$$\n",
"x= maxpool2x2( ReLU( ReLU( x* W_1+b_1) * W_2+b_2))$$ 3 times (also for $W_3,b_3 W_4,b_4 W_4,b_4 W_5,b_5 W_6,b_6$) \n",
"\n",
"for each layer it has been added a pool layer after the ReLU and this result in a decreasing dimensionality (from 48 to 6)\n",
"\n",
"$$\n",
"y=\\textrm{softmax} {( x W_{norm6}+b_{norm6})}$$\n",
"\n",
"For the 1,2,3 layers the filter used are 22 with a dimension of 8x8 while for the 4,5,6 a dimension of 4x4.\n",
"A dropout layer is also applied.\n",
"The optimization scheme used it AdamOptimizer with a learning rate of 0.001 s. The dropout probability has been set to 0.5."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"W_conv1= (8, 8, 1, 22)\n",
"b_conv1= (22,)\n",
"x_2d1= (64, 48, 48, 1)\n",
"h_conv1= (64, 48, 48, 22)\n",
"W_conv2= (8, 8, 22, 22)\n",
"b_conv2= (22,)\n",
"h_conv2= (64, 48, 48, 22)\n",
"h_conv2_pooled= (64, 24, 24, 22)\n",
"W_conv3= (8, 8, 22, 22)\n",
"b_conv3= (22,)\n",
"x_2d3= (64, 24, 24, 22)\n",
"h_conv3= (64, 24, 24, 22)\n",
"W_conv4= (4, 4, 22, 22)\n",
"b_conv4= (22,)\n",
"h_conv4= (64, 24, 24, 22)\n",
"h_conv4_pooled= (64, 12, 12, 22)\n",
"h_conv4_pooled= (64, 6, 6, 22)\n",
"W_conv5= (4, 4, 22, 22)\n",
"b_conv5= (22,)\n",
"x_2d5= (64, 6, 6, 22)\n",
"h_conv5= (64, 6, 6, 22)\n",
"W_con6= (4, 4, 22, 22)\n",
"b_conv6= (22,)\n",
"h_conv6= (64, 6, 6, 22)\n",
"h_conv6_pooled= (64, 3, 3, 22)\n",
"x2_rs (64, 792)\n",
"W_norm6= (792, 6)\n",
"b_conv6= (6,)\n",
"h_full6= (64, 6)\n",
"h_full6= (64, 6)\n",
"y3(SOFTMAX)= (64, 6)\n"
]
}
],
"source": [
"tf.reset_default_graph()\n",
"\n",
"# implementation of Conv-Relu-COVN-RELU - pool\n",
"# based on : http://cs231n.github.io/convolutional-networks/\n",
"\n",
"# Define computational graph (CG)\n",
"batch_size = n_train # batch size\n",
"d = train_data.shape[1] # data dimensionality\n",
"nc = 6 # number of classes\n",
"\n",
"# Inputs\n",
"xin = tf.placeholder(tf.float32,[batch_size,d]); #print('xin=',xin,xin.get_shape())\n",
"y_label = tf.placeholder(tf.float32,[batch_size,nc]); #print('y_label=',y_label,y_label.get_shape())\n",
"\n",
"\n",
"#for the first conc-conv\n",
"# Convolutional layer\n",
"K0 = 8 # size of the patch\n",
"F0 = 22 # number of filters\n",
"ncl0 = K0*K0*F0\n",
"\n",
"#for the second conc-conv\n",
"K1 = 4 # size of the patch\n",
"F1 = F0 # number of filters\n",
"ncl1 = K1*K1*F1\n",
"\n",
"#drouput probability\n",
"keep_prob_input=tf.placeholder(tf.float32)\n",
"\n",
"#1st set of conv followed by conv2d operation and dropout 0.5\n",
"W_conv1=weight_variable([K0,K0,1,F0]); print('W_conv1=',W_conv1.get_shape())\n",
"b_conv1=bias_variable([F0]); print('b_conv1=',b_conv1.get_shape())\n",
"x_2d1 = tf.reshape(xin, [-1,48,48,1]); print('x_2d1=',x_2d1.get_shape())\n",
"\n",
"#conv2d \n",
"h_conv1=tf.nn.relu(conv2d(x_2d1, W_conv1) + b_conv1); print('h_conv1=',h_conv1.get_shape())\n",
"#h_conv1= tf.nn.dropout(h_conv1,keep_prob_input);\n",
"\n",
"# 2nd convolutional layer + max pooling\n",
"W_conv2=weight_variable([K0,K0,F0,F0]); print('W_conv2=',W_conv2.get_shape())\n",
"b_conv2=bias_variable([F0]); print('b_conv2=',b_conv2.get_shape())\n",
"\n",
"# conv2d + max pool\n",
"h_conv2 = tf.nn.relu(conv2d(h_conv1,W_conv2)+b_conv2); print('h_conv2=',h_conv2.get_shape())\n",
"h_conv2_pooled = max_pool_2x2(h_conv2); print('h_conv2_pooled=',h_conv2_pooled.get_shape())\n",
"\n",
"#3rd set of conv \n",
"W_conv3=weight_variable([K0,K0,F0,F0]); print('W_conv3=',W_conv3.get_shape())\n",
"b_conv3=bias_variable([F1]); print('b_conv3=',b_conv3.get_shape())\n",
"x_2d3 = tf.reshape(h_conv2_pooled, [-1,24,24,F0]); print('x_2d3=',x_2d3.get_shape())\n",
"\n",
"#conv2d\n",
"h_conv3=tf.nn.relu(conv2d(x_2d3, W_conv3) + b_conv3); print('h_conv3=',h_conv3.get_shape())\n",
"\n",
"# 4th convolutional layer \n",
"W_conv4=weight_variable([K1,K1,F1,F1]); print('W_conv4=',W_conv4.get_shape())\n",
"b_conv4=bias_variable([F1]); print('b_conv4=',b_conv4.get_shape())\n",
"\n",
"#conv2d + max pool 4x4\n",
"h_conv4 = tf.nn.relu(conv2d(h_conv3,W_conv4)+b_conv4); print('h_conv4=',h_conv4.get_shape())\n",
"h_conv4_pooled = max_pool_2x2(h_conv4); print('h_conv4_pooled=',h_conv4_pooled.get_shape())\n",
"h_conv4_pooled = max_pool_2x2(h_conv4_pooled); print('h_conv4_pooled=',h_conv4_pooled.get_shape())\n",
"\n",
"#5th set of conv \n",
"W_conv5=weight_variable([K1,K1,F1,F1]); print('W_conv5=',W_conv5.get_shape())\n",
"b_conv5=bias_variable([F1]); print('b_conv5=',b_conv5.get_shape())\n",
"x_2d5 = tf.reshape(h_conv4_pooled, [-1,6,6,F1]); print('x_2d5=',x_2d5.get_shape())\n",
"\n",
"#conv2d\n",
"h_conv5=tf.nn.relu(conv2d(x_2d5, W_conv5) + b_conv5); print('h_conv5=',h_conv5.get_shape())\n",
"\n",
"# 6th convolutional layer \n",
"W_conv6=weight_variable([K1,K1,F1,F1]); print('W_con6=',W_conv6.get_shape())\n",
"b_conv6=bias_variable([F1]); print('b_conv6=',b_conv6.get_shape())\n",
"b_conv6= tf.nn.dropout(b_conv6,keep_prob_input);\n",
"\n",
"#conv2d + max pool 4x4\n",
"h_conv6 = tf.nn.relu(conv2d(h_conv5,W_conv6)+b_conv6); print('h_conv6=',h_conv6.get_shape())\n",
"h_conv6_pooled = max_pool_2x2(h_conv6); print('h_conv6_pooled=',h_conv6_pooled.get_shape())\n",
"\n",
"# reshaping for fully connected\n",
"h_conv6_pooled_rs = tf.reshape(h_conv6, [batch_size,-1]); print('x2_rs',h_conv6_pooled_rs.get_shape());\n",
"W_norm6 = weight_variable([ 6*6*F1, nc]); print('W_norm6=',W_norm6.get_shape())\n",
"b_norm6 = bias_variable([nc]); print('b_conv6=',b_norm6.get_shape())\n",
"\n",
"# fully connected layer\n",
"h_full6 = tf.matmul( h_conv6_pooled_rs, W_norm6 ); print('h_full6=',h_full6.get_shape())\n",
"h_full6 += b_norm6; print('h_full6=',h_full6.get_shape())\n",
"\n",
"y = h_full6; \n",
"\n",
"## Softmax\n",
"y = tf.nn.softmax(y); print('y3(SOFTMAX)=',y.get_shape())\n",
"\n",
"# Loss\n",
"cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(y), 1))\n",
"total_loss = cross_entropy\n",
"\n",
"# Optimization scheme\n",
"train_step = tf.train.AdamOptimizer(0.001).minimize(total_loss)\n",
"\n",
"# Accuracy\n",
"correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_label,1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "code",
"execution_count": 239,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Iteration i= 0 , train accuracy= 0.140625 , loss= 1.79194\n",
"test accuracy= 0.125\n",
"\n",
"Iteration i= 100 , train accuracy= 0.15625 , loss= 1.78374\n",
"test accuracy= 0.140625\n",
"\n",
"Iteration i= 200 , train accuracy= 0.21875 , loss= 1.7552\n",
"test accuracy= 0.140625\n",
"\n",
"Iteration i= 300 , train accuracy= 0.1875 , loss= 1.7827\n",
"test accuracy= 0.109375\n",
"\n",
"Iteration i= 400 , train accuracy= 0.265625 , loss= 1.74103\n",
"test accuracy= 0.109375\n",
"\n",
"Iteration i= 500 , train accuracy= 0.28125 , loss= 1.56178\n",
"test accuracy= 0.25\n",
"\n",
"Iteration i= 600 , train accuracy= 0.46875 , loss= 1.48862\n",
"test accuracy= 0.203125\n",
"\n",
"Iteration i= 700 , train accuracy= 0.265625 , loss= 1.61876\n",
"test accuracy= 0.171875\n",
"\n",
"Iteration i= 800 , train accuracy= 0.328125 , loss= 1.54113\n",
"test accuracy= 0.234375\n",
"\n",
"Iteration i= 900 , train accuracy= 0.421875 , loss= 1.46915\n",
"test accuracy= 0.328125\n",
"\n",
"Iteration i= 1000 , train accuracy= 0.5 , loss= 1.41078\n",
"test accuracy= 0.25\n",
"\n",
"Iteration i= 1100 , train accuracy= 0.53125 , loss= 1.39398\n",
"test accuracy= 0.265625\n",
"\n",
"Iteration i= 1200 , train accuracy= 0.421875 , loss= 1.42175\n",
"test accuracy= 0.25\n",
"\n",
"Iteration i= 1300 , train accuracy= 0.484375 , loss= 1.28492\n",
"test accuracy= 0.265625\n",
"\n",
"Iteration i= 1400 , train accuracy= 0.359375 , loss= 1.59055\n",
"test accuracy= 0.28125\n",
"\n",
"Iteration i= 1500 , train accuracy= 0.3125 , loss= 1.5398\n",
"test accuracy= 0.296875\n",
"\n",
"Iteration i= 1600 , train accuracy= 0.5 , loss= 1.27093\n",
"test accuracy= 0.25\n",
"\n",
"Iteration i= 1700 , train accuracy= 0.453125 , loss= 1.22606\n",
"test accuracy= 0.25\n",
"\n",
"Iteration i= 1800 , train accuracy= 0.421875 , loss= 1.47522\n",
"test accuracy= 0.296875\n",
"\n",
"Iteration i= 1900 , train accuracy= 0.40625 , loss= 1.42197\n",
"test accuracy= 0.28125\n",
"\n",
"Iteration i= 2000 , train accuracy= 0.578125 , loss= 1.11563\n",
"test accuracy= 0.28125\n",
"\n",
"Iteration i= 2100 , train accuracy= 0.5 , loss= 1.2982\n",
"test accuracy= 0.296875\n",
"\n",
"Iteration i= 2200 , train accuracy= 0.453125 , loss= 1.3712\n",
"test accuracy= 0.28125\n",
"\n",
"Iteration i= 2300 , train accuracy= 0.484375 , loss= 1.35187\n",
"test accuracy= 0.328125\n",
"\n",
"Iteration i= 2400 , train accuracy= 0.46875 , loss= 1.19431\n",
"test accuracy= 0.296875\n",
"\n",
"Iteration i= 2500 , train accuracy= 0.515625 , loss= 1.27885\n",
"test accuracy= 0.296875\n",
"\n",
"Iteration i= 2600 , train accuracy= 0.515625 , loss= 1.41198\n",
"test accuracy= 0.328125\n",
"\n",
"Iteration i= 2700 , train accuracy= 0.390625 , loss= 1.43115\n",
"test accuracy= 0.328125\n",
"\n",
"Iteration i= 2800 , train accuracy= 0.59375 , loss= 1.20443\n",
"test accuracy= 0.3125\n",
"\n",
"Iteration i= 2900 , train accuracy= 0.65625 , loss= 0.975287\n",
"test accuracy= 0.375\n",
"\n",
"Iteration i= 3000 , train accuracy= 0.625 , loss= 0.936872\n",
"test accuracy= 0.296875\n",
"\n",
"Iteration i= 3100 , train accuracy= 0.578125 , loss= 1.22948\n",
"test accuracy= 0.375\n",
"\n",
"Iteration i= 3200 , train accuracy= 0.5625 , loss= 1.10099\n",
"test accuracy= 0.421875\n",
"\n",
"Iteration i= 3300 , train accuracy= 0.59375 , loss= 1.19427\n",
"test accuracy= 0.375\n",
"\n",
"Iteration i= 3400 , train accuracy= 0.484375 , loss= 1.16296\n",
"test accuracy= 0.40625\n",
"\n",
"Iteration i= 3500 , train accuracy= 0.515625 , loss= 1.04793\n",
"test accuracy= 0.390625\n",
"\n",
"Iteration i= 3600 , train accuracy= 0.5625 , loss= 1.04873\n",
"test accuracy= 0.359375\n",
"\n",
"Iteration i= 3700 , train accuracy= 0.390625 , loss= 1.35736\n",
"test accuracy= 0.40625\n",
"\n",
"Iteration i= 3800 , train accuracy= 0.609375 , loss= 1.05242\n",
"test accuracy= 0.3125\n",
"\n",
"Iteration i= 3900 , train accuracy= 0.5625 , loss= 1.14358\n",
"test accuracy= 0.390625\n",
"\n",
"Iteration i= 4000 , train accuracy= 0.5625 , loss= 1.108\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 4100 , train accuracy= 0.546875 , loss= 1.11705\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 4200 , train accuracy= 0.578125 , loss= 1.13918\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 4300 , train accuracy= 0.484375 , loss= 1.33966\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 4400 , train accuracy= 0.625 , loss= 0.959784\n",
"test accuracy= 0.421875\n",
"\n",
"Iteration i= 4500 , train accuracy= 0.578125 , loss= 1.00547\n",
"test accuracy= 0.375\n",
"\n",
"Iteration i= 4600 , train accuracy= 0.625 , loss= 0.873088\n",
"test accuracy= 0.375\n",
"\n",
"Iteration i= 4700 , train accuracy= 0.59375 , loss= 1.17829\n",
"test accuracy= 0.421875\n",
"\n",
"Iteration i= 4800 , train accuracy= 0.578125 , loss= 1.10707\n",
"test accuracy= 0.375\n",
"\n",
"Iteration i= 4900 , train accuracy= 0.703125 , loss= 0.877062\n",
"test accuracy= 0.40625\n",
"\n",
"Iteration i= 5000 , train accuracy= 0.703125 , loss= 0.9895\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 5100 , train accuracy= 0.625 , loss= 0.938752\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 5200 , train accuracy= 0.5625 , loss= 1.05793\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 5300 , train accuracy= 0.578125 , loss= 0.984425\n",
"test accuracy= 0.390625\n",
"\n",
"Iteration i= 5400 , train accuracy= 0.71875 , loss= 0.92945\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 5500 , train accuracy= 0.578125 , loss= 1.01687\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 5600 , train accuracy= 0.6875 , loss= 0.677204\n",
"test accuracy= 0.40625\n",
"\n",
"Iteration i= 5700 , train accuracy= 0.609375 , loss= 0.975263\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 5800 , train accuracy= 0.578125 , loss= 0.984679\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 5900 , train accuracy= 0.625 , loss= 0.960162\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 6000 , train accuracy= 0.75 , loss= 0.898046\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 6100 , train accuracy= 0.453125 , loss= 1.33846\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 6200 , train accuracy= 0.59375 , loss= 1.09784\n",
"test accuracy= 0.375\n",
"\n",
"Iteration i= 6300 , train accuracy= 0.609375 , loss= 0.985122\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 6400 , train accuracy= 0.671875 , loss= 0.878923\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 6500 , train accuracy= 0.546875 , loss= 1.15876\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 6600 , train accuracy= 0.6875 , loss= 0.843817\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 6700 , train accuracy= 0.671875 , loss= 0.829748\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 6800 , train accuracy= 0.78125 , loss= 0.752732\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 6900 , train accuracy= 0.671875 , loss= 0.901663\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 7000 , train accuracy= 0.65625 , loss= 0.928061\n",
"test accuracy= 0.40625\n",
"\n",
"Iteration i= 7100 , train accuracy= 0.625 , loss= 0.9133\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 7200 , train accuracy= 0.578125 , loss= 1.06565\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 7300 , train accuracy= 0.75 , loss= 0.683552\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 7400 , train accuracy= 0.625 , loss= 0.886149\n",
"test accuracy= 0.390625\n",
"\n",
"Iteration i= 7500 , train accuracy= 0.6875 , loss= 0.929639\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 7600 , train accuracy= 0.65625 , loss= 0.884104\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 7700 , train accuracy= 0.5 , loss= 1.22974\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 7800 , train accuracy= 0.671875 , loss= 0.746289\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 7900 , train accuracy= 0.640625 , loss= 0.964105\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 8000 , train accuracy= 0.53125 , loss= 1.10334\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 8100 , train accuracy= 0.671875 , loss= 0.798964\n",
"test accuracy= 0.40625\n",
"\n",
"Iteration i= 8200 , train accuracy= 0.671875 , loss= 0.913957\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 8300 , train accuracy= 0.75 , loss= 0.729876\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 8400 , train accuracy= 0.78125 , loss= 0.595767\n",
"test accuracy= 0.53125\n",
"\n",
"Iteration i= 8500 , train accuracy= 0.796875 , loss= 0.735916\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 8600 , train accuracy= 0.671875 , loss= 0.778324\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 8700 , train accuracy= 0.640625 , loss= 0.958758\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 8800 , train accuracy= 0.734375 , loss= 0.628525\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 8900 , train accuracy= 0.59375 , loss= 1.04702\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 9000 , train accuracy= 0.609375 , loss= 1.02124\n",
"test accuracy= 0.421875\n",
"\n",
"Iteration i= 9100 , train accuracy= 0.65625 , loss= 0.923155\n",
"test accuracy= 0.40625\n",
"\n",
"Iteration i= 9200 , train accuracy= 0.640625 , loss= 0.870108\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 9300 , train accuracy= 0.71875 , loss= 0.674644\n",
"test accuracy= 0.421875\n",
"\n",
"Iteration i= 9400 , train accuracy= 0.6875 , loss= 0.858173\n",
"test accuracy= 0.40625\n",
"\n",
"Iteration i= 9500 , train accuracy= 0.625 , loss= 0.945199\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 9600 , train accuracy= 0.75 , loss= 0.598063\n",
"test accuracy= 0.390625\n",
"\n",
"Iteration i= 9700 , train accuracy= 0.8125 , loss= 0.604092\n",
"test accuracy= 0.421875\n",
"\n",
"Iteration i= 9800 , train accuracy= 0.65625 , loss= 0.707822\n",
"test accuracy= 0.421875\n",
"\n",
"Iteration i= 9900 , train accuracy= 0.8125 , loss= 0.639305\n",
"test accuracy= 0.390625\n",
"\n",
"Iteration i= 10000 , train accuracy= 0.765625 , loss= 0.549462\n",
"test accuracy= 0.390625\n",
"\n",
"Iteration i= 10100 , train accuracy= 0.734375 , loss= 0.769658\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 10200 , train accuracy= 0.734375 , loss= 0.746855\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 10300 , train accuracy= 0.671875 , loss= 0.840991\n",
"test accuracy= 0.390625\n",
"\n",
"Iteration i= 10400 , train accuracy= 0.765625 , loss= 0.633859\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 10500 , train accuracy= 0.75 , loss= 0.598237\n",
"test accuracy= 0.421875\n",
"\n",
"Iteration i= 10600 , train accuracy= 0.8125 , loss= 0.663027\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 10700 , train accuracy= 0.75 , loss= 0.552958\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 10800 , train accuracy= 0.734375 , loss= 0.715571\n",
"test accuracy= 0.515625\n",
"\n",
"Iteration i= 10900 , train accuracy= 0.71875 , loss= 0.713517\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 11000 , train accuracy= 0.6875 , loss= 0.968141\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 11100 , train accuracy= 0.75 , loss= 0.690156\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 11200 , train accuracy= 0.703125 , loss= 0.726283\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 11300 , train accuracy= 0.78125 , loss= 0.705732\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 11400 , train accuracy= 0.6875 , loss= 0.878256\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 11500 , train accuracy= 0.6875 , loss= 1.00681\n",
"test accuracy= 0.40625\n",
"\n",
"Iteration i= 11600 , train accuracy= 0.6875 , loss= 0.79699\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 11700 , train accuracy= 0.765625 , loss= 0.561315\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 11800 , train accuracy= 0.671875 , loss= 0.919506\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 11900 , train accuracy= 0.84375 , loss= 0.542903\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 12000 , train accuracy= 0.765625 , loss= 0.585808\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 12100 , train accuracy= 0.6875 , loss= 0.861125\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 12200 , train accuracy= 0.65625 , loss= 0.835672\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 12300 , train accuracy= 0.828125 , loss= 0.59488\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 12400 , train accuracy= 0.8125 , loss= 0.55018\n",
"test accuracy= 0.40625\n",
"\n",
"Iteration i= 12500 , train accuracy= 0.75 , loss= 0.707877\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 12600 , train accuracy= 0.765625 , loss= 0.679853\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 12700 , train accuracy= 0.78125 , loss= 0.668407\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 12800 , train accuracy= 0.734375 , loss= 0.704842\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 12900 , train accuracy= 0.703125 , loss= 0.661948\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 13000 , train accuracy= 0.84375 , loss= 0.434618\n",
"test accuracy= 0.40625\n",
"\n",
"Iteration i= 13100 , train accuracy= 0.859375 , loss= 0.425481\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 13200 , train accuracy= 0.765625 , loss= 0.469879\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 13300 , train accuracy= 0.71875 , loss= 0.648009\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 13400 , train accuracy= 0.796875 , loss= 0.467417\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 13500 , train accuracy= 0.71875 , loss= 0.618275\n",
"test accuracy= 0.421875\n",
"\n",
"Iteration i= 13600 , train accuracy= 0.828125 , loss= 0.415127\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 13700 , train accuracy= 0.734375 , loss= 0.627953\n",
"test accuracy= 0.53125\n",
"\n",
"Iteration i= 13800 , train accuracy= 0.75 , loss= 0.670546\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 13900 , train accuracy= 0.671875 , loss= 0.719572\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 14000 , train accuracy= 0.796875 , loss= 0.539482\n",
"test accuracy= 0.515625\n",
"\n",
"Iteration i= 14100 , train accuracy= 0.921875 , loss= 0.321256\n",
"test accuracy= 0.40625\n",
"\n",
"Iteration i= 14200 , train accuracy= 0.859375 , loss= 0.473588\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 14300 , train accuracy= 0.75 , loss= 0.666359\n",
"test accuracy= 0.421875\n",
"\n",
"Iteration i= 14400 , train accuracy= 0.765625 , loss= 0.743096\n",
"test accuracy= 0.40625\n",
"\n",
"Iteration i= 14500 , train accuracy= 0.71875 , loss= 0.83011\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 14600 , train accuracy= 0.8125 , loss= 0.559244\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 14700 , train accuracy= 0.78125 , loss= 0.659799\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 14800 , train accuracy= 0.78125 , loss= 0.565591\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 14900 , train accuracy= 0.8125 , loss= 0.462911\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 15000 , train accuracy= 0.765625 , loss= 0.649306\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 15100 , train accuracy= 0.84375 , loss= 0.430201\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 15200 , train accuracy= 0.765625 , loss= 0.544656\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 15300 , train accuracy= 0.8125 , loss= 0.476049\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 15400 , train accuracy= 0.84375 , loss= 0.568518\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 15500 , train accuracy= 0.78125 , loss= 0.640078\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 15600 , train accuracy= 0.796875 , loss= 0.502844\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 15700 , train accuracy= 0.84375 , loss= 0.413574\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 15800 , train accuracy= 0.765625 , loss= 0.631717\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 15900 , train accuracy= 0.78125 , loss= 0.613366\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 16000 , train accuracy= 0.671875 , loss= 0.634249\n",
"test accuracy= 0.390625\n",
"\n",
"Iteration i= 16100 , train accuracy= 0.84375 , loss= 0.506904\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 16200 , train accuracy= 0.859375 , loss= 0.448911\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 16300 , train accuracy= 0.859375 , loss= 0.395126\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 16400 , train accuracy= 0.8125 , loss= 0.488732\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 16500 , train accuracy= 0.84375 , loss= 0.461827\n",
"test accuracy= 0.421875\n",
"\n",
"Iteration i= 16600 , train accuracy= 0.84375 , loss= 0.397317\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 16700 , train accuracy= 0.828125 , loss= 0.520891\n",
"test accuracy= 0.421875\n",
"\n",
"Iteration i= 16800 , train accuracy= 0.796875 , loss= 0.5105\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 16900 , train accuracy= 0.796875 , loss= 0.427761\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 17000 , train accuracy= 0.859375 , loss= 0.437656\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 17100 , train accuracy= 0.765625 , loss= 0.457886\n",
"test accuracy= 0.515625\n",
"\n",
"Iteration i= 17200 , train accuracy= 0.84375 , loss= 0.403451\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 17300 , train accuracy= 0.84375 , loss= 0.39625\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 17400 , train accuracy= 0.796875 , loss= 0.683927\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 17500 , train accuracy= 0.875 , loss= 0.357479\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 17600 , train accuracy= 0.84375 , loss= 0.343771\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 17700 , train accuracy= 0.890625 , loss= 0.268168\n",
"test accuracy= 0.515625\n",
"\n",
"Iteration i= 17800 , train accuracy= 0.8125 , loss= 0.348731\n",
"test accuracy= 0.53125\n",
"\n",
"Iteration i= 17900 , train accuracy= 0.765625 , loss= 0.677665\n",
"test accuracy= 0.515625\n",
"\n",
"Iteration i= 18000 , train accuracy= 0.84375 , loss= 0.336307\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 18100 , train accuracy= 0.890625 , loss= 0.29896\n",
"test accuracy= 0.53125\n",
"\n",
"Iteration i= 18200 , train accuracy= 0.8125 , loss= 0.458075\n",
"test accuracy= 0.515625\n",
"\n",
"Iteration i= 18300 , train accuracy= 0.84375 , loss= 0.459532\n",
"test accuracy= 0.46875\n",
"\n",
"Iteration i= 18400 , train accuracy= 0.71875 , loss= 0.640311\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 18500 , train accuracy= 0.8125 , loss= 0.498854\n",
"test accuracy= 0.53125\n",
"\n",
"Iteration i= 18600 , train accuracy= 0.90625 , loss= 0.322264\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 18700 , train accuracy= 0.78125 , loss= 0.565783\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 18800 , train accuracy= 0.84375 , loss= 0.39525\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 18900 , train accuracy= 0.8125 , loss= 0.427192\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 19000 , train accuracy= 0.921875 , loss= 0.299445\n",
"test accuracy= 0.515625\n",
"\n",
"Iteration i= 19100 , train accuracy= 0.84375 , loss= 0.36012\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 19200 , train accuracy= 0.828125 , loss= 0.46301\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 19300 , train accuracy= 0.765625 , loss= 0.549023\n",
"test accuracy= 0.5\n",
"\n",
"Iteration i= 19400 , train accuracy= 0.875 , loss= 0.398623\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 19500 , train accuracy= 0.875 , loss= 0.293031\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 19600 , train accuracy= 0.875 , loss= 0.39247\n",
"test accuracy= 0.484375\n",
"\n",
"Iteration i= 19700 , train accuracy= 0.875 , loss= 0.430336\n",
"test accuracy= 0.453125\n",
"\n",
"Iteration i= 19800 , train accuracy= 0.8125 , loss= 0.450704\n",
"test accuracy= 0.53125\n",
"\n",
"Iteration i= 19900 , train accuracy= 0.84375 , loss= 0.373512\n",
"test accuracy= 0.4375\n",
"\n",
"Iteration i= 20000 , train accuracy= 0.84375 , loss= 0.388213\n",
"test accuracy= 0.5625\n"
]
}
],
"source": [
"# Run Computational Graph\n",
"n = train_data.shape[0]\n",
"indices = collections.deque()\n",
"init = tf.initialize_all_variables()\n",
"sess = tf.Session()\n",
"sess.run(init)\n",
"for i in range(20001):\n",
" \n",
" # Batch extraction\n",
" if len(indices) < batch_size:\n",
" indices.extend(np.random.permutation(n)) \n",
" idx = [indices.popleft() for i in range(batch_size)]\n",
" batch_x, batch_y = train_data[idx,:], train_labels[idx]\n",
" #print(batch_x.shape,batch_y.shape)\n",
" \n",
" # Run CG for vao to increase the test acriable training\n",
" _,acc_train,total_loss_o = sess.run([train_step,accuracy,total_loss], feed_dict={xin: batch_x, y_label: batch_y, keep_prob_input: 0.5})\n",
" \n",
" # Run CG for test set\n",
" if not i%100:\n",
" print('\\nIteration i=',i,', train accuracy=',acc_train,', loss=',total_loss_o)\n",
" acc_test = sess.run(accuracy, feed_dict = {xin: test_data, y_label: test_labels, keep_prob_input: 1.0})\n",
" print('test accuracy=',acc_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model 2- Results\n",
"\n",
"This second model reaches a test accuracy up to 56% which is the best result we could get.\n",
"We can also see that we have prevented the overfitting problem observed with the previous models."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Saving the trained graph in TF file\n",
"Ref:https://www.tensorflow.org/how_tos/variables/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Add ops to save and restore all the variables.\n",
"saver = tf.train.Saver()\n",
"\n",
"# Save the variables to disk.\n",
"save_path = saver.save(sess, \"model_6layers.ckpt\")\n",
"print(\"Model saved in file: %s\" % save_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reading Model from TF File\n",
"Ref:https://www.tensorflow.org/how_tos/variables/"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sess = tf.Session()\n",
"# Add ops to save and restore all the variables.\n",
"saver = tf.train.Saver()\n",
"\n",
"saver.restore(sess, \"model_6layers.ckpt\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Class Accuracy\n",
"\n",
"To see how our network behaves for every classes, it is calculated separately the accuracy for each of them.<br>\n",
"As we can see it is slightly different for each emotion, with a highest accuracy for the surprised one (72%) and a lowest for the sad one (31%)."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"accuracy angry \t 0.6956521739130435\n",
"accuracy scared \t 0.5714285714285714\n",
"accuracy happy \t 0.5714285714285714\n",
"accuracy sad \t 0.3157894736842105\n",
"accuracy surprised \t 0.7272727272727273\n",
"accuracy normal \t 0.45454545454545453\n"
]
}
],
"source": [
"# calculating accuracy for each class separately for the test set\n",
"result_cnn = sess.run([y], feed_dict = {xin: test_data, keep_prob_input: 1.0})\n",
"#result = sess.run(y, feed_dict={xin: test_data, keep_prob_input: 1.0})\n",
"\n",
"tset = test_labels.argmax(1);\n",
"result = np.asarray(result_cnn[:][0]).argmax(1);\n",
"\n",
"for i in range (0,nc):\n",
" print('accuracy',str_emotions[i]+str(' '), '\\t',ut.calc_partial_accuracy(tset, result, i))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring CNN Filters\n",
"It is possible to explore what did our CNN learn by extracting the W matrixes from the model. We could observe, that each 1st layer contains some general filters recognizing different parts of the face and the next layers using this data to extract different emotions. We could also observe, that some filters were not well developed (noise). "
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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msr5ETf850qBBA6ddr149UVu3bp3IBw4cEHnfvn0iN2vWrEB9BQCgMHFnHAAA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\nAQAAAEP+D2zr0OcM9/v9AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x149aee240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# calculating accuracy for each class separately for the test set\n",
"result_cnn, Wc1, Wc2, Wc3, Wc4, Wc5, Wc6 = sess.run([y, W_conv1, W_conv2, W_conv3, W_conv4, W_conv5, W_conv6], feed_dict = {xin: test_data, keep_prob_input: 1.0})\n",
"\n",
"#plotting extracted features (W)\n",
"\n",
"filt13 = np.zeros([3,22, 8, 8]);\n",
"filt46 = np.zeros([3,22, 4, 4]);\n",
"\n",
"for i in range(22):\n",
" filt13[0,i,:,:] = Wc1[:,:,0,i];\n",
" filt13[1,i,:,:] = Wc2[:,:,0,i];\n",
" filt13[2,i,:,:] = Wc3[:,:,0,i];\n",
" filt46[0,i,:,:] = Wc4[:,:,i,i];\n",
" filt46[1,i,:,:] = Wc5[:,:,i,i];\n",
" filt46[2,i,:,:] = Wc6[:,:,i,i];\n",
"\n",
"plt.rcParams['figure.figsize'] = (10.0, 10.0) # set default size of plots\n",
"for i in range(0, 22):\n",
" for j in range(0,3): \n",
" plt.subplot( 22, 6, 6*i+j+1)\n",
" plt.imshow(filt13[j,i,:,:])\n",
" plt.axis('off')\n",
" if(i==0):\n",
" plt.title('layer'+str(j+1))\n",
" \n",
" plt.subplot( 22, 6, 6*i+j+4)\n",
" plt.imshow(filt46[j,i,:,:])\n",
" plt.axis('off')\n",
" if(i==0):\n",
" plt.title('layer'+str(j+4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feeding the CNN with some data (camera/file)\n",
"\n",
"Finally to test if the model really works it's needed to feed some new row and unlabeled data into the neural network.\n",
"To do so some images are taken from the internet or could be taken directly from the camera."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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GMT0hLmaL7ysvfosL7fJ2zp3PjX3KKSsxpSK+z7htdBTfV/6OYjrHPvvsU/k5ERERka6g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\nriEGxCIiItKYpk2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Xm0gVYD7e6Ze4Q0mD/seAk4BXNatRTQNiEZEu0BMGw52Z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\ni4iIiEhL+/95Y/H1djKflgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1278c2278>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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GPM4ax/7E9zYuWIzvf/6s4nsbZ7VFREREOptmiEVERESkqWlALCIiIiJNrS4iE/ExfIwG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\nuDdpkVIjlhirYGnjiauAp4Cj4wC/mC3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"text/plain": [
"<matplotlib.figure.Figure at 0x127361160>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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tG97HNttsU7evu+66htco1RvKtbr3X9MrtPpEue9mqQr6Pn1eJTVDP0O/k2ab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\nIrYHro2IjwGklC4Dvg48Avw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IrozyGHlG61HgwpTSPb3U3ZblAbGZ2UqqSm1dQ44o9SdX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\n0f5/vA5LESkHTMUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1236c33c8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Xz5g2bdpC1we1VStyRYwbb7yxct8DDzywbMfKEfla4/2OsYV4zjEeke9B9+7d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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1278b4cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"faces, marked_img = ut.get_faces_from_img('diff_emotions.jpg');\n",
"#faces, marked_img = ut.get_faces_from_img('big_bang.png');\n",
"#faces, marked_img = ut.get_faces_from_img('camera');\n",
"\n",
"# if some face was found in the image\n",
"if(len(faces)): \n",
" #creating the blank test vector\n",
" data_orig = np.zeros([n_train, 48,48])\n",
"\n",
" #putting face data into the vector (only first few)\n",
" for i in range(0, len(faces)):\n",
" data_orig[i,:,:] = ut.contrast_stretch(faces[i,:,:]);\n",
"\n",
" #preparing image and putting it into the batch \n",
" \n",
" n = data_orig.shape[0];\n",
" data = np.zeros([n,48**2])\n",
" for i in range(n):\n",
" xx = data_orig[i,:,:]\n",
" xx -= np.mean(xx)\n",
" xx /= np.linalg.norm(xx)\n",
" data[i,:] = xx.reshape(2304); #np.reshape(xx,[-1])\n",
"\n",
" result = sess.run([y], feed_dict={xin: data, keep_prob_input: 1.0})\n",
" \n",
" plt.rcParams['figure.figsize'] = (10.0, 10.0) # set default size of plots\n",
" for i in range(0, len(faces)):\n",
" emotion_nr = np.argmax(result[0][i]);\n",
" plt_idx = (2*i)+1;\n",
" plt.subplot( 5, 2*len(faces)/5+1, plt_idx)\n",
" plt.imshow(np.reshape(data[i,:], (48,48)))\n",
" plt.axis('off')\n",
" plt.title(str_emotions[emotion_nr])\n",
" ax = plt.subplot(5, 2*len(faces)/5+1, plt_idx +1)\n",
" ax.bar(np.arange(nc) , result[0][i])\n",
" ax.set_xticklabels(str_emotions, rotation=45, rotation_mode=\"anchor\")\n",
" ax.set_yticks([])\n",
" plt.show()\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Conclusions and comments\n",
"- The data contains a lot of noisy data, i.e. faces are rotated and of different size. \n",
"- A lot of emotions in the dataset were labeled wrong. (e.g. happy images in sad images).\n",
"- (we think) That is why we couldn't achieve very good accuracy.\n",
"- The accuracy is very good for \"Happy\" and \"Surprised\" class. These images seems to be the most \"clean\" as data. \n",
"- The computational power to train CNN is very high, therefore it was very time consuming to try different computational graphs.\n",
"- The facial emotion recognition is has very complicated features that were hard to explore for our computational graphs.\n",
"\n",
"# Possible improvements in the future\n",
"- For sure the CNN would perfom better if the faces were always the same size and aligned straight. \n",
"- We could try another, deeper CNN architectures to extract more features.\n",
"\n",
"\n"
]
}
],
"metadata": {
"anaconda-cloud": {},
"celltoolbar": "Edit 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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
particle-physics-playground/playground | activities/physicsbkg_SpecialRelativity.ipynb | 2 | 15205 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Relativistic Kinematics Tutorial"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Brief Intro to Special Relativity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before talking about relativisic kinematics, let's briefly run through Einstein's theories of Special and General Relavitity. Einstein's theory of special relativity was derived from the following two postulates:\n",
"\n",
"* Postulate 1: The laws of physics are the same in all inertial reference frames.\n",
"* Postulate 2: The speed of light in a vacuum is equal to the value $c$, independent of the motion of the source\n",
"\n",
"To summarize, the laws of physics do not change regardless of what your perspective is. Nothing can go faster than the speed of light in the universe. Everyone *always* measures the speed of light to be $c$ ($\\approx 3\\times 10^8$ m/s) regardless of how fast or slow one is moving. \n",
"\n",
"One consequence of this is that we find that space and time are interwoven into a 4-dimensional fabric known as space-time. This fabric can be bent and stretched which causes changes in how we perceive time.\n",
"\n",
"***Keep these ideas in mind***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is Relativistic Kinematics?"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Relativistic kinematics sounds like a complicated term that the average person would be intimidated by. However, it basically it is just a precise way of calculating things like velocity, momentum, and energy. Usually, we only need to worry about this level of precision when we are dealing with objects moving at incredibly fast speeds. \n",
"\n",
"But what is considered a fast speed? \n",
"\n",
"You might have heard before that the speed of light is the speed limit of the universe. This means that no object that has mass can reach or exceed that speed (**Sidenote**: This law only says that anything *inside* space cannot exceed this speed. Space itself is actually expanding at an accelerated rate that can be faster than the speed of light!). What can reach the speed of light? Only particles that have a mass of zero can reach the speed of light, therefore only photons, the particles of light, are able to reach this speed. \n",
"\n",
"Before we go any further, we need to define a term known as the Lorentz factor which plays a key role in understanding relativistic kinematics. The Lorentz factor tells us how time, length, and relativistic mass change for an object in motion. \n",
"\n",
"$$\\gamma = \\frac{1}{\\sqrt{1-\\frac{v^2}{c^2}}} = \\frac{1}{\\sqrt{1-\\beta^2}}$$ $\\beta = \\frac{v}{c}$ tells you the fraction of light speed that the object of interest is moving at. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time Dilation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is time dilation? To start off, time isn't the same for everyone as you might have thought. Time changes depending on how fast you are travelling as well as the gravitational bodies you are near. The formula to calculate time dilation (considering only speed and not gravitational effects) is: $$\\Delta t = \\frac{\\Delta t'}{\\sqrt{1-\\frac{v^2}{c^2}}} = \\gamma \\Delta t' = \\gamma \\tau$$ $\\Delta t' = \\tau$ is known as the proper time which is the time measured in the reference frame in which the clock is at rest. $\\Delta t$ is defined as the time dilation. \n",
"\n",
"From this formula, we can tell that the time that an observer measures in a moving reference frame will always be longer unless that particle is moving at the speed of light. This is because we are multiplying $\\tau$ by a number larger than one! As you travel at higher speeds, the Lorentz factor decreases, which makes sense because v/c is getting larger. The smallest the Lorentz factor can be is 1, which occurs when the velocity is 0. The largest this factor can be is undefined, or approaching infinity, which occurs when the velocity is equal to the speed of light. Here is a video below that might help you understand this mind-boggling concept better!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://www.youtube.com/watch?v=n2s1-RHuljo\" target=\"_blank\">Time Dilation and the Twin \"Paradox\"</a>\n",
"\n",
"<a href=\"https://www.youtube.com/watch?v=n2s1-RHuljo\" target=\"_blank\"><img src=\"http://i3.ytimg.com/vi/n2s1-RHuljo/hqdefault.jpg\" \n",
"alt=\"IMAGE ALT TEXT HERE\" width=\"480\" border=\"10\" /></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A brief aside on General Relativity\n",
"\n",
"General relativity is the theory that Einstein developed during the years 1905-1915 and explains the motion of objects due to gravitational effects. It can be summarized as such:\n",
"\n",
"*\"Matter tells space how to curve and space tells matter how to move\"* - John Archibald Wheeler\n",
"\n",
"This theory tells us that time is also dilated by massive objects. If you have seen the movie *Interstellar* then you have witnessed the effects of general relativity. Being close to a massive object like a stellar black hole (which can be anywhere from a few times to a few millions times the mass of the sun) causes space-time to be stretched massively! Because of this, time happens a lot slower than it would if you were on Earth. The more massive the object, the more space-time is stretched. The more space-time is stretched, the longer it is going to take for time to occur for you.\n",
"\n",
"Fun Fact: Since sea level is closer to the center of the Earth where gravity is the strongest, time is technically happening slower than it is on a mountatin. The time difference is infinitesimal, but still not the same!\n",
"\n",
"Check out this great visual below of the effect mass has on space-time!\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"http://sci.esa.int/science-e-media/img/72/ESA_LISA-Pathfinder_spacetime_curvature_above_orig.jpg\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"from IPython.core.display import HTML \n",
"Image(url= \"http://sci.esa.int/science-e-media/img/72/ESA_LISA-Pathfinder_spacetime_curvature_above_orig.jpg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Length Contraction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As objects approach the speed of light, they contract in the direction of motion! In other words, when a stationary observer measures the length of a rod moving at near the speed of light, they see it as smaller than it is in its actual reference frame. In the reference frame of the rod, the rod is its actual length and time runs normally. It's only when we observe moving reference frames that we notice these differences. The equation to calculate length contraction is:\n",
"\n",
"$$L=\\frac{1}{\\gamma}L_p = \\sqrt{1-\\frac{v^2}{c^2}} L_p$$\n",
"\n",
"where $L_p$ is the proper length, or the length of the object at rest in a reference frame. It makes sense mathematically that the length is smaller in a moving reference frame because we are dividing by gamma, which is always greater than 1 unless v=c. The effect speed has on length contraction is shown with the baseball figure below."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"http://www.patana.ac.th/secondary/science/anrophysics/relativity_option/images/length_cont1.JPG\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"from IPython.core.display import HTML \n",
"Image(url= \"http://www.patana.ac.th/secondary/science/anrophysics/relativity_option/images/length_cont1.JPG\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Relativistic Energy and Momentum"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Many of you have probably heard of Albert Einstein's famous equation: $E=mc^2$ It turns out that this isn't always true. $E=mc^2$ is only true for objects at rest! The equation for objects in motion is $E=\\gamma m c^2$ where the Lorentz factor comes into play again. When we multiply $mc^2$ by the Lorentz factor, the mass increases. Therefore, as objects approach the speed of light, they become more massive.\n",
"\n",
"Our Newtonian understanding of momentum is also incorrect. In general physics classes, we learn that $p=mv$, however the relativistic momentum changes at near light speeds. Therefore our new formula for momentum is: $$p =\\gamma mv$$ \n",
"\n",
"In particle physics, we often don't know the masses of particles, but know the energies and momenta in the x, y, and z directions. CERN can reach energies of up to 13 TeV! At particle accelerators like CERN, we are able to use machines that can accelerate particles to near the speed of light and then collide them. We then study the byproducts of these collisions. Since some particles have shorter lifetimes than others, it is possible that some decay before they reach our detectors. We then have to trace our decay products back to something called the displaced vertex (where the original particle was in space right before it decayed). We are able to calculate the invariant mass (mass of particles at rest in their reference frame) using the following formula:\n",
"\n",
"$$E^2=m^2c^4+p^2c^2$$\n",
"\n",
"This equation allows us to calculate the masses of particles that arise from these collisions. \n",
"\n",
"This equation also proves that although photons have no mass, they still have a momentum! The energy for photons is defined as: $$E = pc$$ We can manipulate the formula and solve for the rest mass as follows:\n",
"\n",
"$$m = \\sqrt{E^2 - (p^2_x + p^2_y + p_z^2)}$$\n",
"\n",
"Now we can apply our knowledge of time dilation! Since these particles are moving at near the speed of light, time is happening slower for them relative to us. Essentially, particles will have a longer lifetime when they travel at higher speeds. Because of this, they are able to travel greater distances! We can utilize time dilation to observe some particles before they decay and therefore learn more about them.\n",
"\n",
"##### Fun Fact: There are other ways of measuring masses of particles\n",
"\n",
"In some cases, particles can move faster than the speed of light in different materials! When this happens, a cone of light is given off in the form of Cherenkov radiation. The angle at which this light is given off is directly related to the velocity. Knowing the velocity combined with the momentum, we can measure the mass. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://cds.cern.ch/record/1125472\" target=\"_blank\">Here is a great video explaining how the Large Hadron Collider works at CERN in Geneva, Switzerland</a>\n",
"\n",
"<a href=\"https://cds.cern.ch/record/1125472\" target=\"_blank\"><img src=\"https://c1.staticflickr.com/3/2326/2046228644_05507000b3_z.jpg?zz=1\" \n",
"alt=\"Photo credit: CERN (https://www.flickr.com/photos/11304375@N07/2046228644)\" width=\"480\" border=\"10\" /></a>\n",
"Photo credit: CERN (https://www.flickr.com/photos/11304375@N07/2046228644)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Some things to keep in mind\n",
"* When we calculate masses of particles, we write them in terms of $MeV/c^2$ instead of multiplying by $c^2$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Summary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The speed of light is the same for all observers.\n",
"* Massive objects bend the fabric of space-time.\n",
"* Crazy things begin to happen when you approach the speed of light! \n",
" * Time happens slower\n",
" * Objects contract in length \n",
" * Mass increases \n",
"* We can measure masses of particles given energies and momenta"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a lot to take in all at once, which is why we have provided some great resources below that you can check out to make more sense of these mind-blowing laws of physics! To learn more about the math behind some of these formulas check out Paul Avery's University of Florida lecture notes below."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://www.youtube.com/watch?v=NnMIhxWRGNw\" target=\"_blank\">Here's a cool link to a video about Special Relativity. The image is also a link.</a>\n",
"\n",
"<a href=\"https://www.youtube.com/watch?v=NnMIhxWRGNw\" target=\"_blank\"><img src=\"https://i.ytimg.com/vi/NnMIhxWRGNw/hqdefault.jpg?custom=true&w=196&h=110&stc=true&jpg444=true&jpgq=90&sp=68&sigh=_LCQE3ecxvENaJHs45OT55-b8dI\" \n",
"alt=\"IMAGE ALT TEXT HERE\" width=\"480\" border=\"10\" /></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Other resources\n",
"\n",
"* [Great source explaining Einstein's theories of special and general relativity as well as E=mc^2](http://www.emc2-explained.info/)\n",
"* [Relativisitc Kinematics I (Paul Avery - University of Florida)](http://www.phys.ufl.edu/~avery/course/4390/f2015/lectures/relativistic_kinematics_1.pdf)\n",
"* [Relativisitc Kinematics II (Paul Avery - University of Florida)](http://www.phys.ufl.edu/~avery/course/4390/f2015/lectures/relativistic_kinematics_2.pdf)\n",
"* [Relativistic Mechanics (Wikipedia)](https://en.wikipedia.org/wiki/Relativistic_mechanics)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"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": 1
}
| mit |
martinggww/lucasenlights | MachineLearning/DataScience-Python3/KMeans.ipynb | 1 | 28313 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# K-Means Clustering Example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's make some fake data that includes people clustered by income and age, randomly:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from numpy import random, array\n",
"\n",
"#Create fake income/age clusters for N people in k clusters\n",
"def createClusteredData(N, k):\n",
" random.seed(10)\n",
" pointsPerCluster = float(N)/k\n",
" X = []\n",
" for i in range (k):\n",
" incomeCentroid = random.uniform(20000.0, 200000.0)\n",
" ageCentroid = random.uniform(20.0, 70.0)\n",
" for j in range(int(pointsPerCluster)):\n",
" X.append([random.normal(incomeCentroid, 10000.0), random.normal(ageCentroid, 2.0)])\n",
" X = array(X)\n",
" return X"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll use k-means to rediscover these clusters in unsupervised learning:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
" 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n"
]
},
{
"data": {
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Id5ckIteZx45Cd6XRX4/kgbqJfHR3xnLNEtDnO0PHjh0pWrSoe4uTLEtPT+fdd98h7d/7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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x94f8208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from sklearn.cluster import KMeans\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.preprocessing import scale\n",
"from numpy import random, float\n",
"\n",
"data = createClusteredData(100, 5)\n",
"\n",
"model = KMeans(n_clusters=5)\n",
"\n",
"# Note I'm scaling the data to normalize it! Important for good results.\n",
"model = model.fit(scale(data))\n",
"\n",
"# We can look at the clusters each data point was assigned to\n",
"print(model.labels_)\n",
"\n",
"# And we'll visualize it:\n",
"plt.figure(figsize=(8, 6))\n",
"plt.scatter(data[:,0], data[:,1], c=model.labels_.astype(float))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Activity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Things to play with: what happens if you don't scale the data? What happens if you choose different values of K? In the real world, you won't know the \"right\" value of K to start with - you'll need to converge on it yourself."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"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
}
| cc0-1.0 |
ledeprogram/algorithms | class1/homework/Rivas_Paolo_1_3.ipynb | 1 | 2872 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assigment 3"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n",
"5\n",
"7\n",
"11\n",
"13\n",
"17\n",
"19\n",
"23\n",
"29\n",
"31\n",
"37\n",
"41\n",
"43\n",
"47\n",
"53\n",
"59\n",
"61\n",
"67\n",
"71\n",
"73\n",
"79\n",
"83\n",
"89\n",
"97\n"
]
}
],
"source": [
"for num in range(1,101):\n",
" prime = True\n",
" for i in range(2,num):\n",
" if (num%i==0):\n",
" prime = False\n",
" if prime:\n",
" print(num)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"3\n",
"7\n",
"13\n",
"19\n",
"29\n",
"37\n",
"43\n",
"53\n",
"61\n",
"71\n",
"79\n",
"89\n"
]
}
],
"source": [
"a = range(1,101)\n",
"doWePrint = True\n",
"for num in a:\n",
" prime = True\n",
" for i in range(2,num):\n",
" if (num%i==0):\n",
" prime = False \n",
" if prime:\n",
" if doWePrint:\n",
" print(num)\n",
" doWePrint = False\n",
" else:\n",
" doWePrint = True\n"
]
},
{
"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": []
},
{
"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 |
idekerlab/deep-cell | data-builder/elasticsearch_idex_generator.ipynb | 1 | 27337 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!python --version\n",
"\n",
"!wget http://geneontology.org/gene-associations/gene_association.sgd.gz -O ./data/gene_association.sgd.gz\n",
"!wget http://purl.obolibrary.org/obo/go.obo -O ./data/go.obo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Term Property Generator\n",
"\n",
"## Introduction\n",
"\n",
"This is a script to convert public data sets into a searchable, local Elasticsearch DB.\n",
"\n",
"## Requirments\n",
"* Python 3.x\n",
"* Elasticsearch 5.x\n",
"* "
]
},
{
"cell_type": "code",
"execution_count": 1,
"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>parent</th>\n",
" <th>child</th>\n",
" <th>type</th>\n",
" <th>in_tree</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>441932</th>\n",
" <td>GO:0015031</td>\n",
" <td>YHR083W</td>\n",
" <td>gene</td>\n",
" <td>NOT_TREE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>441933</th>\n",
" <td>GO:1902582</td>\n",
" <td>YHR083W</td>\n",
" <td>gene</td>\n",
" <td>NOT_TREE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>441934</th>\n",
" <td>GO:1902580</td>\n",
" <td>YHR083W</td>\n",
" <td>gene</td>\n",
" <td>NOT_TREE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>441935</th>\n",
" <td>GO:0098799</td>\n",
" <td>YHR083W</td>\n",
" <td>gene</td>\n",
" <td>NOT_TREE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>441936</th>\n",
" <td>GO:0098798</td>\n",
" <td>YHR083W</td>\n",
" <td>gene</td>\n",
" <td>NOT_TREE</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" parent child type in_tree\n",
"441932 GO:0015031 YHR083W gene NOT_TREE\n",
"441933 GO:1902582 YHR083W gene NOT_TREE\n",
"441934 GO:1902580 YHR083W gene NOT_TREE\n",
"441935 GO:0098799 YHR083W gene NOT_TREE\n",
"441936 GO:0098798 YHR083W gene NOT_TREE"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create list of all terms from the GO Tree file\n",
"import pandas as pd\n",
"treeSourceUrl = 'http://chianti.ucsd.edu/~kono/ci/data/collapsed_go.no_IGI.propagated.small_parent_tree'\n",
"\n",
"# Load the tree data\n",
"treeColNames = ['parent', 'child', 'type', 'in_tree']\n",
"tree = pd.read_csv(treeSourceUrl, delimiter='\\t', names=treeColNames)\n",
"tree.tail()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(441937,)\n",
"(441937,)\n",
"(883874,)\n",
"(13037,)\n",
"6618\n"
]
}
],
"source": [
"# Extract GO terms in the tree\n",
"\n",
"p_list = tree['parent']\n",
"c_list = tree['child']\n",
"print(p_list.shape)\n",
"print(c_list.shape)\n",
"\n",
"all_list = pd.concat([p_list, c_list])\n",
"print(all_list.shape)\n",
"all_set = all_list.unique()\n",
"\n",
"print(all_set.shape)\n",
"\n",
"go_set = set()\n",
"\n",
"for t in all_set:\n",
" if t.startswith('GO:'):\n",
" go_set.add(t)\n",
"\n",
"print(len(go_set))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from elasticsearch import Elasticsearch\n",
"from datetime import datetime\n",
"from elasticsearch_dsl import DocType, Date, Integer, Keyword, Text, Object, Nested, Index\n",
"from elasticsearch_dsl.connections import connections\n",
"from elasticsearch import Elasticsearch\n",
"from elasticsearch import helpers\n",
"from elasticsearch_dsl import Search\n",
"import pandas as pd\n",
"\n",
"from elasticsearch_dsl.query import MultiMatch, Match, Q\n",
"\n",
"\n",
"# Define a default Elasticsearch client\n",
"connections.create_connection(hosts=['localhost:9200'])\n",
"\n",
"treeSourceUrl = 'http://chianti.ucsd.edu/~kono/ci/data/collapsed_go.no_IGI.propagated.small_parent_tree'\n",
"oboUrl = './data/go.obo'\n",
"yeastAnnotationUrl = './data/gene_association.sgd.gz'\n",
"kegg2goUrl = 'http://geneontology.org/external2go/kegg2go'\n",
"reactome2go = 'http://geneontology.org/external2go/reactome2go'\n",
"\n",
"phenotypeUrl='http://downloads.yeastgenome.org/curation/literature/phenotype_data.tab'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load gene associations"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['DB', 'DB_Object_ID', 'DB_Object_Symbol', 'Qualifier', 'GO_ID', 'DB:Reference', 'Evidence', 'With_or_From', 'Aspect', 'DB_Object_Name', 'DB_Object_Synonym', 'DB_Object_Type', 'taxon', 'Date', 'Assigned_by', 'Annotation_Extension', 'Gene_Product_Form_ID']\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DB</th>\n",
" <th>DB_Object_ID</th>\n",
" <th>DB_Object_Symbol</th>\n",
" <th>Qualifier</th>\n",
" <th>GO_ID</th>\n",
" <th>DB:Reference</th>\n",
" <th>Evidence</th>\n",
" <th>With_or_From</th>\n",
" <th>Aspect</th>\n",
" <th>DB_Object_Name</th>\n",
" <th>DB_Object_Synonym</th>\n",
" <th>DB_Object_Type</th>\n",
" <th>taxon</th>\n",
" <th>Date</th>\n",
" <th>Assigned_by</th>\n",
" <th>Annotation_Extension</th>\n",
" <th>Gene_Product_Form_ID</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>111384</th>\n",
" <td>SGD</td>\n",
" <td>S000006732</td>\n",
" <td>tX(XXX)L</td>\n",
" <td>NaN</td>\n",
" <td>GO:0030533</td>\n",
" <td>SGD_REF:S000181097|PMID:9023104</td>\n",
" <td>ISM</td>\n",
" <td>NaN</td>\n",
" <td>F</td>\n",
" <td>tRNA of undetermined specificity, predicted by...</td>\n",
" <td>tX(XXX)L|tS(GCU)L</td>\n",
" <td>gene</td>\n",
" <td>taxon:559292</td>\n",
" <td>20030507</td>\n",
" <td>SGD</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111385</th>\n",
" <td>SGD</td>\n",
" <td>S000006732</td>\n",
" <td>tX(XXX)L</td>\n",
" <td>NaN</td>\n",
" <td>GO:0005829</td>\n",
" <td>SGD_REF:S000181097|PMID:9023104</td>\n",
" <td>IC</td>\n",
" <td>GO:0030533</td>\n",
" <td>C</td>\n",
" <td>tRNA of undetermined specificity, predicted by...</td>\n",
" <td>tX(XXX)L|tS(GCU)L</td>\n",
" <td>gene</td>\n",
" <td>taxon:559292</td>\n",
" <td>20030507</td>\n",
" <td>SGD</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111386</th>\n",
" <td>SGD</td>\n",
" <td>S000007338</td>\n",
" <td>tY(GUA)Q</td>\n",
" <td>NaN</td>\n",
" <td>GO:0070125</td>\n",
" <td>SGD_REF:S000181097|PMID:9023104</td>\n",
" <td>IC</td>\n",
" <td>GO:0030533</td>\n",
" <td>P</td>\n",
" <td>Mitochondrial tyrosine tRNA (tRNA-Tyr)</td>\n",
" <td>tY(GUA)Q</td>\n",
" <td>gene</td>\n",
" <td>taxon:559292</td>\n",
" <td>20150730</td>\n",
" <td>SGD</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111387</th>\n",
" <td>SGD</td>\n",
" <td>S000007338</td>\n",
" <td>tY(GUA)Q</td>\n",
" <td>NaN</td>\n",
" <td>GO:0005739</td>\n",
" <td>SGD_REF:S000181097|PMID:9023104</td>\n",
" <td>IC</td>\n",
" <td>GO:0030533</td>\n",
" <td>C</td>\n",
" <td>Mitochondrial tyrosine tRNA (tRNA-Tyr)</td>\n",
" <td>tY(GUA)Q</td>\n",
" <td>gene</td>\n",
" <td>taxon:559292</td>\n",
" <td>20030507</td>\n",
" <td>SGD</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111388</th>\n",
" <td>SGD</td>\n",
" <td>S000007338</td>\n",
" <td>tY(GUA)Q</td>\n",
" <td>NaN</td>\n",
" <td>GO:0030533</td>\n",
" <td>SGD_REF:S000181097|PMID:9023104</td>\n",
" <td>ISM</td>\n",
" <td>NaN</td>\n",
" <td>F</td>\n",
" <td>Mitochondrial tyrosine tRNA (tRNA-Tyr)</td>\n",
" <td>tY(GUA)Q</td>\n",
" <td>gene</td>\n",
" <td>taxon:559292</td>\n",
" <td>20060721</td>\n",
" <td>SGD</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DB DB_Object_ID DB_Object_Symbol Qualifier GO_ID \\\n",
"111384 SGD S000006732 tX(XXX)L NaN GO:0030533 \n",
"111385 SGD S000006732 tX(XXX)L NaN GO:0005829 \n",
"111386 SGD S000007338 tY(GUA)Q NaN GO:0070125 \n",
"111387 SGD S000007338 tY(GUA)Q NaN GO:0005739 \n",
"111388 SGD S000007338 tY(GUA)Q NaN GO:0030533 \n",
"\n",
" DB:Reference Evidence With_or_From Aspect \\\n",
"111384 SGD_REF:S000181097|PMID:9023104 ISM NaN F \n",
"111385 SGD_REF:S000181097|PMID:9023104 IC GO:0030533 C \n",
"111386 SGD_REF:S000181097|PMID:9023104 IC GO:0030533 P \n",
"111387 SGD_REF:S000181097|PMID:9023104 IC GO:0030533 C \n",
"111388 SGD_REF:S000181097|PMID:9023104 ISM NaN F \n",
"\n",
" DB_Object_Name DB_Object_Synonym \\\n",
"111384 tRNA of undetermined specificity, predicted by... tX(XXX)L|tS(GCU)L \n",
"111385 tRNA of undetermined specificity, predicted by... tX(XXX)L|tS(GCU)L \n",
"111386 Mitochondrial tyrosine tRNA (tRNA-Tyr) tY(GUA)Q \n",
"111387 Mitochondrial tyrosine tRNA (tRNA-Tyr) tY(GUA)Q \n",
"111388 Mitochondrial tyrosine tRNA (tRNA-Tyr) tY(GUA)Q \n",
"\n",
" DB_Object_Type taxon Date Assigned_by \\\n",
"111384 gene taxon:559292 20030507 SGD \n",
"111385 gene taxon:559292 20030507 SGD \n",
"111386 gene taxon:559292 20150730 SGD \n",
"111387 gene taxon:559292 20030507 SGD \n",
"111388 gene taxon:559292 20060721 SGD \n",
"\n",
" Annotation_Extension Gene_Product_Form_ID \n",
"111384 NaN NaN \n",
"111385 NaN NaN \n",
"111386 NaN NaN \n",
"111387 NaN NaN \n",
"111388 NaN NaN "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yeastAnnotationUrl = './data/gene_association.sgd.gz'\n",
"cols = pd.read_csv('./annotation_columns.txt', names=['col_names'])\n",
"col_names = cols['col_names'].tolist()\n",
"print(col_names)\n",
"\n",
"yeastAnnotation = pd.read_csv(yeastAnnotationUrl, delimiter='\\t', comment='!', compression='gzip', names=col_names)\n",
"yeastAnnotation.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Phenotype"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Feature_Name', 'Feature_Type', 'Gene_Name', 'SGDID', 'Reference', 'Experiment_Type', 'Mutant_Type', 'Allele', 'Strain_Background', 'Phenotype', 'Chemical', 'Condition', 'Details', 'Reporter']\n"
]
}
],
"source": [
"pUrl = 'http://downloads.yeastgenome.org/curation/literature/phenotype_data.tab'\n",
"\n",
"p_cols = pd.read_csv('./p_cols.txt', names=['col_names'])\n",
"p_col_names = p_cols['col_names'].tolist()\n",
"print(p_col_names)\n",
"\n",
"phenotype = pd.read_csv(pUrl, delimiter='\\t', names=p_col_names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ID Mapping Table"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>symbol</th>\n",
" <th>locus_name</th>\n",
" <th>acc_number</th>\n",
" <th>swiss-prot</th>\n",
" <th>sgd</th>\n",
" <th>sequence_length</th>\n",
" <th>3d</th>\n",
" <th>chromosome</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AAC1</td>\n",
" <td>YMR056C</td>\n",
" <td>P04710</td>\n",
" <td>ADT1_YEAST</td>\n",
" <td>S000004660</td>\n",
" <td>309</td>\n",
" <td>13</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AAC3</td>\n",
" <td>YBR085W</td>\n",
" <td>P18238</td>\n",
" <td>ADT3_YEAST</td>\n",
" <td>S000000289</td>\n",
" <td>307</td>\n",
" <td>(3)</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AAD10</td>\n",
" <td>YJR155W</td>\n",
" <td>P47182</td>\n",
" <td>AAD10_YEAST</td>\n",
" <td>S000003916</td>\n",
" <td>288</td>\n",
" <td>10</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AAD14</td>\n",
" <td>YNL331C</td>\n",
" <td>P42884</td>\n",
" <td>AAD14_YEAST</td>\n",
" <td>S000005275</td>\n",
" <td>376</td>\n",
" <td>14</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AAD15</td>\n",
" <td>YOL165C</td>\n",
" <td>Q08361</td>\n",
" <td>AAD15_YEAST</td>\n",
" <td>S000005525</td>\n",
" <td>143</td>\n",
" <td>15</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" symbol locus_name acc_number swiss-prot sgd sequence_length 3d \\\n",
"0 AAC1 YMR056C P04710 ADT1_YEAST S000004660 309 13 \n",
"1 AAC3 YBR085W P18238 ADT3_YEAST S000000289 307 (3) \n",
"2 AAD10 YJR155W P47182 AAD10_YEAST S000003916 288 10 \n",
"3 AAD14 YNL331C P42884 AAD14_YEAST S000005275 376 14 \n",
"4 AAD15 YOL165C Q08361 AAD15_YEAST S000005525 143 15 \n",
"\n",
" chromosome \n",
"0 NaN \n",
"1 2 \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idmap = pd.read_csv('./yeast_clean4.txt', delimiter='\\t')\n",
"idmap.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create usuful map for ID mapping\n",
"sgd2info = {}\n",
"\n",
"for idx, row in idmap.iterrows():\n",
" entry = {}\n",
" entry['locus'] = row['locus_name']\n",
" entry['acc'] = row['acc_number']\n",
" entry['swiss'] = row['swiss-prot']\n",
" entry['length'] = row['sequence_length']\n",
" \n",
" symbols = row['symbol'].split(';')\n",
" entry['symbol'] = symbols[0]\n",
" \n",
" if len(symbols) == 1:\n",
" entry['alt_symbols'] = []\n",
" else:\n",
" entry['alt_symbols'] = symbols[1:]\n",
" \n",
" if row['3d'] == '(3)':\n",
" entry['3d_struct_available'] = True\n",
" entry['chromosome'] = row['chromosome']\n",
" else:\n",
" entry['3d_struct_available'] = False\n",
" entry['chromosome'] = row['3d']\n",
" \n",
" sgd2info[row['sgd']] = entry"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'3d_struct_available': True,\n",
" 'acc': 'Q00955',\n",
" 'alt_symbols': ['ABP2', 'FAS3', 'MTR7'],\n",
" 'chromosome': '14',\n",
" 'length': '2233',\n",
" 'locus': 'YNR016C',\n",
" 'swiss': 'ACAC_YEAST',\n",
" 'symbol': 'ACC1'}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sgd2info['S000005299']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define GO Term Entry"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Map from GO Term to genes\n",
"go2gene = {}\n",
"\n",
"go2idset = {}\n",
"\n",
"for idx, row in yeastAnnotation.iterrows():\n",
" goterm = row['GO_ID']\n",
" gene_id = row['DB_Object_ID']\n",
" symbol = row['DB_Object_Symbol']\n",
" full_name = str(row['DB_Object_Name']).replace('\\r\\n', '')\n",
" \n",
" \n",
" # for gene info\n",
" if gene_id in sgd2info:\n",
" entry = sgd2info[gene_id]\n",
" entry['name'] = full_name\n",
" \n",
" cur_entry = []\n",
" \n",
" if goterm in go2gene:\n",
" cur_entry = go2gene[goterm]\n",
" gene_set = go2idset[goterm]\n",
" else:\n",
" gene_set = set()\n",
" go2idset[goterm] = gene_set\n",
" \n",
" ids = go2idset[goterm]\n",
" \n",
" if gene_id not in ids:\n",
" gene = {\n",
" 'sgdid': gene_id,\n",
" 'symbol': symbol,\n",
" 'name': full_name\n",
" }\n",
" \n",
" ids.add(gene_id)\n",
" go2idset[goterm] = ids\n",
" \n",
" cur_entry.append(gene)\n",
" go2gene[goterm] = cur_entry"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sgd2info['S000005299']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class GoTerm(DocType):\n",
" termid = Text(index='not_analyzed')\n",
" name = Text(analyzer='standard')\n",
" namespace = Text(analyzer='standard')\n",
" definition = Text(analyzer='standard')\n",
" parents = Object(multi=True)\n",
" children = Object(multi=True)\n",
"\n",
" genes = Object(multi=True)\n",
" \n",
" class Meta:\n",
" index = 'terms'\n",
"\n",
"class Gene(DocType):\n",
" id = Text(index='not_analyzed')\n",
" symbol = Text(analyzer='standard')\n",
" name = Text(analyzer='standard')\n",
" synonyms = Text(analyzer='standard', multi=True)\n",
" locus = Text(analyzer='standard')\n",
" \n",
" class Meta:\n",
" index = 'genes'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"GoTerm.init()\n",
"Gene.init()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from goatools import obo_parser\n",
"oboUrl = './data/go.obo'\n",
"obo = obo_parser.GODag(oboUrl, optional_attrs=['def'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_go_term(term):\n",
" g = {}\n",
" if term.id in go2gene:\n",
" g = go2gene[term.id]\n",
" \n",
" parents = []\n",
" children = []\n",
" \n",
" for p in term.parents:\n",
" parents.append({'id': p.id, 'name': p.name})\n",
" for c in term.children:\n",
" children.append({'id': c.id, 'name': c.name})\n",
" \n",
" definition = term.defn.split('\"')[1]\n",
" \n",
" return GoTerm(\n",
" meta={'id': term.id},\n",
" termid=term.id,\n",
" name=term.name,\n",
" namespace=term.namespace,\n",
" definition=definition,\n",
" parents=parents,\n",
" children=children,\n",
" genes=g\n",
")\n",
"\n",
"print(connections.get_connection().cluster.health())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_gene(gene, id):\n",
" name = ''\n",
" if 'name' in gene:\n",
" name = gene['name']\n",
" \n",
" return Gene(\n",
" meta={'id': id},\n",
" id = id,\n",
" symbol = gene['symbol'],\n",
" name = name,\n",
" synonyms = gene['alt_symbols'],\n",
" locus = gene['locus']\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"es = Elasticsearch(host='localhost', port=9200)\n",
"pool = []"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"term_ids = obo.keys()\n",
"print(len(term_ids))\n",
"\n",
"for id in term_ids: \n",
" if id not in go_set:\n",
" continue\n",
" \n",
" d = get_go_term(obo[id])\n",
" term = {'_index': getattr(d.meta, 'index', d._doc_type.index), '_type': d._doc_type.name, '_id': d.termid, '_source': d.to_dict()}\n",
" pool.append(term)\n",
" if len(pool) > 5000:\n",
" print('Bulk add start:')\n",
" helpers.bulk(es, pool)\n",
" print('Bulk add success!')\n",
"\n",
" pool = []\n",
"\n",
"if len(pool) > 0:\n",
" print('Last: ' + str(len(pool)))\n",
" helpers.bulk(es, pool)\n",
" print('---------------success!')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ids = sgd2info.keys()\n",
"\n",
"print(len(ids))\n",
"\n",
"for id in ids: \n",
" d = get_gene(sgd2info[id], id)\n",
" term = {'_index': getattr(d.meta, 'index', d._doc_type.index), '_type': d._doc_type.name, '_id': d.id, '_source': d.to_dict()}\n",
" pool.append(term)\n",
" if len(pool) > 5000:\n",
" print('Bulk add start:')\n",
" helpers.bulk(es, pool)\n",
" print('Bulk add success!')\n",
"\n",
" pool = []\n",
"\n",
"if len(pool) > 0:\n",
" print('Last: ' + str(len(pool)))\n",
" helpers.bulk(es, pool)\n",
" print('---------------success!')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"s = Search(using=es, index=\"_all\").query(\"match\", name='proteasome')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"response = s.execute()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"import json\n",
"\n",
"for hit in response:\n",
" print(json.dumps(hit.to_dict(), indent=4))\n"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
kdestasio/online_brain_intensive | nipype_tutorial/notebooks/z_development_github.ipynb | 1 | 647 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Github\n",
"\n",
"step by step guide on how to submit PR's etc."
]
}
],
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-2.0 |
lamastex/scalable-data-science | _in/2019/jp/12.ipynb | 1 | 133602 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"# [Introduction to Data Science: A Comp-Math-Stat Approach](https://lamastex.github.io/scalable-data-science/as/2019/)\n",
"## YOIYUI001, Summer 2019 \n",
"©2019 Raazesh Sainudiin. [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 12. Linear Regression\n",
"\n",
"- Regression \n",
" - linear models and their least-squares estimators\n",
" - assessing fit using diagnostic plots (residual analysis)\n",
" - multiple linear regression - not covered in detail and won't be on exam\n",
" - prediction; not covered - not covered and won't be on exam\n",
" - prelude to statistical ML - not covered and won't be on exam\n",
"- Introduction to R in SageMath Jupyter IPython Notebook - SageMath/R\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"**Regression** is a method for studying the relationship between a **response variable** $Y$ and a **covariate** $X$. \n",
"The covariate is also called a **feature** or a **predictor** variable.\n",
"\n",
"A simple way to summarise the relationship between $X$ and $Y$ is through the regression function $r(x)$:\n",
"\n",
"$$\n",
"r(x) = E(Y | X=x) = \\int y \\, f(y|x) dy\n",
"$$\n",
"\n",
"Our objective is to estimate the regression function $r(x)$ from data of the form:\n",
"\n",
"$$\n",
"(Y_1,X_1),(Y_2,X_2),\\ldots,(Y_n,X_n) \\overset{IID}{\\sim} F_{X,Y}\n",
"$$\n",
"\n",
"We assume that $F_{X,Y}$, the joint distribution of $X$ and $Y$, is parametric and $r$ is linear."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simple Linear Regression\n",
"\n",
"The **simple linear regression model** is when $X_i$ is real-valued (one-dimensional) and $r(x)$ is assumed to be linear:\n",
"\n",
"$$\n",
"r(x) = \\beta_0 + \\beta_1 x, \\qquad \\text{and } \\quad V(Y | X=x)=\\sigma^2 \\, \\text{ is independent of } x\n",
"$$\n",
"\n",
"Thus simple linear regression model is the following:\n",
"\n",
"$$\n",
"\\boxed{\n",
"Y_i = \\beta_0 + \\beta_1 X_i + \\epsilon_i, \\qquad \\text{ where, } \\quad E(\\epsilon_i | X_i)=0 \\text{ and } V(\\epsilon_i | X_i)=\\sigma^2\n",
"}\n",
"$$\n",
"\n",
"The unknown parameters and their estimates in the model are:\n",
"\n",
"- the intercept $\\beta_0$ and its estimate $\\widehat{\\beta}_0$,\n",
"- the slope $\\beta_1$ and its estimate $\\widehat{\\beta}_1$ and\n",
"- the variance $\\sigma^2$ and its estimate $\\widehat{\\sigma}^2$\n",
"\n",
"The **fitted line** is:\n",
"$$\n",
"\\widehat{r}(x) = \\widehat{\\beta}_0 + \\widehat{\\beta}_1 x\n",
"$$\n",
"\n",
"The **fitted or predicted values** are:\n",
"$$\n",
"\\widehat{Y}_i = \\widehat{r}(X_i) \n",
"$$\n",
"\n",
"The **residuals** are:\n",
"$$\n",
"\\widehat{\\epsilon}_i = Y_i-\\widehat{Y}_i=Y_i-\\left(\\widehat{\\beta}_0 + \\widehat{\\beta}_1 X_i\\right)\n",
"$$\n",
"\n",
"The **residual sum of squares** or **RSS**, that measures how well the line fits the data, is defined by\n",
"$$\n",
"RSS = \\sum_{i=1}^n \\widehat{\\epsilon}_i^2\n",
"$$\n",
"\n",
"The **least squares estimates** are the values $\\widehat{\\beta}_0$ and $\\widehat{\\beta}_1$ that minimise $RSS$ and they are given by:\n",
"\n",
"$$\n",
"\\boxed{\n",
"\\widehat{\\beta}_1 = \\displaystyle{\\frac{\\sum_{i=1}^n(X_i-\\overline{X}_n)(Y_i-\\overline{Y}_n)}{\\sum_{i=1}^n(X_i-\\overline{X}_n)^2}}\n",
"\\, , \\qquad\n",
"\\widehat{\\beta}_0 = \\displaystyle{\\overline{Y}_n - \\widehat{\\beta}_1 \\overline{X}_n}\n",
"\\, , \\qquad \n",
"\\widehat{\\sigma}^2 = \\displaystyle{\\left(\\frac{1}{n-2}\\right) \\sum_{i=1}^n \\widehat{\\epsilon}_i^2}\n",
"}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interactive Animations for Regression\n",
"\n",
"Check out:\n",
"- [http://setosa.io/ev/ordinary-least-squares-regression/](http://setosa.io/ev/ordinary-least-squares-regression/)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Least Squares and Maximum Likelihood\n",
"\n",
"Suppose we add the assumption about the model's noise that \n",
"\n",
"$$\\boxed{\\displaystyle{\\epsilon_i | X_i \\sim Normal(0,\\sigma^2) \\quad \\text{ i.e., }\\quad Y_i|X_i \\sim Normal(\\mu_i,\\sigma^2), \\quad \\text{ where } \\quad \\mu_i = \\beta_0+\\beta_1 X_i }}$$\n",
"\n",
"Then, the likelihood function is:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"\\displaystyle{\\prod_{i=1}^n f(X_i,Y_i)} \\,\n",
"&= \\displaystyle{\\prod_{i=1}^n f_X(X_i) \\, f_{Y|X}(Y_i|X_i)}\\\\ \n",
"&= \\displaystyle{\\prod_{i=1}^n f_X(X_i) \\, \\prod_{i=1}^n f_{Y|X}(Y_i|X_i)}\\\\\n",
"&=: L_{n,X} \\, L_{n,Y|X}\n",
"\\end{align}\n",
"$$\n",
"\n",
"where, $L_{n,X}:=\\prod_{i=1}^n f_X(X_i)$ is the marginal likelihood of $X_1,\\ldots,X_n$ that does not depend on the parameters $(\\beta_0,\\beta_1,\\sigma)$, and $L_{n,Y|X}:=\\prod_{i=1}^n f_{Y|X}(Y_i|X_i)$ is the *conditional likelihood* that does depend on the parameters. Therefore the likelihood function is given by the conditional likelihood:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"L(\\beta_0,\\beta_1,\\sigma) \\quad\n",
"&\\propto \\quad \\displaystyle{\\prod_{i=1}^n f(X_i,Y_i)} \\\\\n",
"&\\propto \\quad L_{n,Y|X} = \\displaystyle{\\prod_{i=1}^n f_{Y|X}(Y_i|X_i)}\\\\\n",
"&\\propto \\quad \\displaystyle{\\sigma^{-n} \\exp\\left(-\\frac{1}{2 \\sigma^2}\\sum_{i=1}^n\\left(Y_i-\\mu_i\\right)^2 \\right)}\\\\\n",
"\\end{align}\n",
"$$\n",
"\n",
"and the conditional log-likelihood is:\n",
"\n",
"$$\n",
"\\boxed{\n",
"l(\\beta_0,\\beta_1,\\sigma) \\quad =\\quad \\displaystyle{-n \\log(\\sigma) -\\frac{1}{2 \\sigma^2} \\sum_{i=1}^n\\left(Y_i-\\mu_i\\right)^2 }\n",
"}\n",
"$$\n",
"\n",
"To find the MLE of $(\\beta_0,\\beta_1)$ we need to maximise $\\ell(\\beta_0,\\beta_1,\\sigma)$ for a given $\\sigma$. From the above expresion it is clear that maximising the log-likelihood is equivalent to minimising the *residual sum of squares* or *RSS* given by\n",
"\n",
"$$\n",
"\\boxed{\n",
"\\sum_{i=1}^n\\left(Y_i-\\mu_i\\right)^2\n",
"}\n",
"$$\n",
"\n",
"Therefore, we have shown the following Theorem.\n",
"\n",
"### Theorem [MLE is LSE] \n",
"\n",
"> Under the assumption of normally distributed noise, the maximum likelihood estimator (MLE) is the least squares estimator (LSE).\n",
"\n",
"We can maximise $l(\\beta_0,\\beta_1,\\sigma)$ over $\\sigma$ and obtain the MLE for $\\sigma$ as follows:\n",
"\n",
"$$\n",
"\\widehat{\\sigma}^2 = \\frac{1}{n} \\sum_{i=1}^n \\ \\widehat{\\epsilon}^2 \\, .\n",
"$$\n",
"\n",
"But it is more common in practise to use the unbiased estimator, with $E(\\widehat{\\sigma}^2)=\\sigma^2$, that we saw earlier for sample size $n>2$:\n",
"\n",
"$$\n",
"\\widehat{\\sigma}^2 = \\displaystyle{\\left(\\frac{1}{n-2}\\right) \\sum_{i=1}^n \\widehat{\\epsilon}_i^2} \\, .\n",
"$$\n",
"\n",
"## Properties of the Least Squares Estimator (LSE)\n",
"\n",
"It's finally time to obtain the standard errors and limititng distribution of the least quares estimator (also the MLE).\n",
"\n",
"In regression we are interested in the properties of the estimators conditional on the covariates \n",
"\n",
"$$X_{1:n}:= (X_1,X_2,\\ldots,X_n)$$\n",
"\n",
"### Conditional Mean and Variance of LSE\n",
"\n",
"Let $\\widehat{\\beta}^T=(\\widehat{\\beta}_0,\\widehat{\\beta}_1)^T$ denote the least squares estimators (which is also the MLE). Then\n",
"\n",
"$$\n",
"\\begin{align}\n",
"E \\left(\\widehat{\\beta} \\, | \\, X_{1:n} \\right) \n",
"&= \\displaystyle{\\left( {\\begin{array}{c}\n",
" \\beta_0 \\\\\n",
" \\beta_1 \\\\\n",
" \\end{array} } \\right)}\\\\\n",
"V \\left(\\widehat{\\beta} \\, | \\, X_{1:n} \\right) \n",
"&= \\displaystyle{\\frac{\\sigma^2}{n s_X^2} \n",
"\\left( {\\begin{array}{cc}\n",
" \\frac{1}{n}\\sum_{i=1}^n X_i^2 & -\\overline{X}_n \\\\\n",
" -\\overline{X}_n & 1\\\\\n",
" \\end{array} } \\right)}\n",
"\\end{align}\n",
"$$\n",
"\n",
"where, \n",
"\n",
"$$\n",
"s_X^2 = \\frac{1}{n} \\sum_{i=1}^n \\left(X_i -\\overline{X}_n\\right)^2\n",
"$$\n",
"\n",
"### Estimated Standard Errors\n",
"\n",
"The estimated standard errors for $\\widehat{\\beta}_0$ and $\\widehat{\\beta}_1$, or more precisely, the estimated standard errors conditional on the covariates, are given by the square-root of the diagonal terms of the variance-covariance matrix $V \\left(\\widehat{\\beta} \\, | \\, X_{1:n} \\right) $ and substituting the estimate $\\widehat{\\sigma}$ for $\\sigma$, as follows:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"\\widehat{se}\\left(\\widehat{\\beta}_0\\right) := \\widehat{se}\\left(\\widehat{\\beta}_0 \\, | \\, X_{1:n} \\right) \\, \n",
"&= \\, \\frac{\\widehat{\\sigma}}{s_X \\sqrt{n}} \\sqrt{\\frac{\\sum_{i=1}^nX_i^2}{n}}\\\\\n",
"\\widehat{se}\\left(\\widehat{\\beta}_1\\right) := \\widehat{se}\\left(\\widehat{\\beta}_0 \\, | \\, X_{1:n}\\right) \\, \n",
"&= \\, \\frac{\\widehat{\\sigma}}{s_X \\sqrt{n}}\n",
"\\end{align}\n",
"$$\n",
"\n",
"Thus under appropriate modeling assumptions in simple leinear regression we have the following four properties.\n",
"\n",
"### Four Asymptotic Properties of the LSE\n",
"\n",
"#### 1. Asymptotic Consistency\n",
"\n",
"As $n \\to \\infty$, the LSE, i.e. $\\widehat{\\beta}_0$ and $\\widehat{\\beta}_1$, converges in probability to the parameters, i.e., $\\beta_0,\\beta_1$, generating the data $(Y_1,X_1),(Y_2,X_2),\\ldots,(Y_n,X_n)$ as summarised below.\n",
"\n",
"$$\n",
"\\boxed{\n",
"\\widehat{\\beta}_0 \\overset{P}{\\to} \\beta_0 \\quad \\text{ and } \\quad \\widehat{\\beta}_1 \\overset{P}{\\to} \\beta_1\n",
"}\n",
"$$\n",
"\n",
"#### 2. Asymptotic Normality\n",
"\n",
"As $n \\to \\infty$, the LSE, i.e. $\\widehat{\\beta}_0$ and $\\widehat{\\beta}_1$, converges in distribution to the parameters, i.e., $\\beta_0,\\beta_1$, generating the data $(Y_1,X_1),(Y_2,X_2),\\ldots,(Y_n,X_n)$ as summarised below.\n",
"\n",
"$$\n",
"\\boxed{\n",
"\\frac{\\widehat{\\beta}_0 - \\beta_0}{\\widehat{se}\\left(\\widehat{\\beta}_0\\right)} \\overset{d}{\\to} Normal(0,1) \\quad \\text{ and } \\quad \\frac{\\widehat{\\beta}_1 - \\beta_1}{\\widehat{se}\\left(\\widehat{\\beta}_1\\right)} \\overset{d}{\\to} Normal(0,1) \n",
"}\n",
"$$\n",
"\n",
"#### 3. Approximate $1-\\alpha$ Confidence Interval\n",
"\n",
"The $1-\\alpha$ confidence interval for $\\beta_0$ and $\\beta_1$ that is obtained from the approximately normal distribution as $n$ gets large is:\n",
"\n",
"$$\n",
"\\boxed{\n",
"\\widehat{\\beta}_0 \\, \\pm \\, z_{\\alpha/2} \\, \\widehat{se}\\left(\\widehat{\\beta}_0\\right) \\quad \\text{ and } \\quad\n",
"\\widehat{\\beta}_1 \\, \\pm \\, z_{\\alpha/2} \\, \\widehat{se}\\left(\\widehat{\\beta}_1\\right) \n",
"}\n",
"$$\n",
"\n",
"#### 4. The Wald Test\n",
"\n",
"Recall Wald test statistic for testing the null hypothesis with the null value $\\beta^{(0)}$:\n",
"\n",
"$$\n",
"H_0: \\beta = \\beta^{(0)} \\quad \\text{ versus } \\quad H_1: \\beta \\neq \\beta^{(0)} \\quad { is } \\quad W = \\frac{\\left(\\widehat{\\beta}-\\beta^{(0)}\\right)}{\\widehat{se}\\left(\\widehat{\\beta}\\right)}\n",
"$$\n",
"\n",
"Thus the Wald test for testing $H_0: \\beta_1=0$ versus $H_1: \\beta_1 \\neq 0$ is to \n",
"reject $H_0$ if $|W| > z_{\\alpha/2}$ where $W=\\frac{\\widehat{\\beta}_1}{\\widehat{se}\\left(\\widehat{\\beta}_1\\right)}$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Implementing Simple Linear Regression from Scratch\n",
"\n",
"Using the above formulas we can implement Python functions to calculate the **least squares estimates**,\n",
"$\\widehat{\\beta}_0$ and $\\widehat{\\beta}_1$, that minimise $RSS$."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np \n",
"import matplotlib.pyplot as plt\n",
"\n",
"def estimate_coefficients(x, y): \n",
" # size of the dataset \n",
" n = np.size(x) \n",
" # mean of x and y\n",
" mean_x, mean_y = np.mean(x), np.mean(y) \n",
" # xy cross-deviation and xx deviation\n",
" SS_xy = np.sum(y*x - n*mean_y*mean_x) \n",
" SS_xx = np.sum(x*x - n*mean_x*mean_x) \n",
" # calculating LSE of regression coefficients \n",
" b1_hat = SS_xy / SS_xx \n",
" b0_hat = mean_y - b1_hat*mean_x \n",
" sigma_hat2 = np.mean((y - (b0_hat + b1_hat * x))^2)\n",
" if n>2:\n",
" sigma_hat2 = sigma_hat2*n/(n-2)\n",
" sigma_hat=np.sqrt(sigma_hat2)\n",
" return(b0_hat, b1_hat, sigma_hat)\n",
"\n",
"def standard_errors(x,y):\n",
" n = np.size(x) \n",
" b0_hat,b1_hat,s_hat = estimate_coefficients(x,y)\n",
" mean_x = np.mean(x)\n",
" s2X = np.mean( (x-mean_x)^2 )\n",
" se_b1 = s_hat/np.sqrt(s2X*n)\n",
" se_b0 = se_b1*np.sqrt(np.mean(x^2))\n",
" return (se_b0, se_b1)\n",
"\n",
"def plot_regression_line(x, y, b): \n",
" # plotting the data points on a graph\n",
" plt.scatter(x, y, color = \"m\",marker = \"o\", s = 10) \n",
" # predicted response vector \n",
" y_pred = b[0] + b[1]*x \n",
" # plotting the fitted regression line\n",
" plt.plot(x, y_pred, color = \"b\")\n",
" # putting generic labels for x and y axis\n",
" plt.xlabel('x') \n",
" plt.ylabel('y') \n",
" # function to show plotted graph\n",
" plt.show()\n",
"\n",
"def SimpleLinearRegression(x,y): \n",
" # estimating coefficients \n",
" b = estimate_coefficients(x, y) \n",
" print(\"Estimated coefficients:\\nb0_hat = {} \\nb1_hat = {}\\nsigma_hat = {}\".format(b[0], b[1],b[2])) \n",
" # plotting fitted regression line with data\n",
" plot_regression_line(x, y, b)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Estimated coefficients:\n",
"b0_hat = -0.000193932215061 \n",
"b1_hat = 0.00526687127757\n",
"sigma_hat = 0.287336087556\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Datasets for x and y \n",
"LSAT=np.array([576, 635, 558, 578, 666, 580, 555, 661, 651, 605, 653, 575, 545, 572, 594]) # LSAT data\n",
"GPA=np.array([3.39, 3.30, 2.81, 3.03, 3.44, 3.07, 3.00, 3.43, 3.36, 3.13, 3.12, 2.74, 2.76, 2.88, 3.96]) # GPA data\n",
"\n",
"SimpleLinearRegression(LSAT,GPA)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can look at the residuals of the fitted line as follows."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"predictedGPA = -0.000193932215061 + 0.00526687127757*LSAT\n",
"residuals = GPA - predictedGPA\n",
"plt.scatter(LSAT, residuals, color = \"k\",marker = \"o\", s = 10) \n",
"plt.axhline()\n",
"# putting generic labels for x and y axis\n",
"plt.ylabel('$\\epsilon_i$') \n",
"plt.xlabel('LSAT') # draw a y=0 line\n",
"plt.show()\n",
"# in general we want residuals to be Normally distributes about 0 with the same variance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Residual Analysis\n",
"\n",
"Looking at the residuals $\\epsilon_i$'s in the above plot we can notice how just $4$ of the $15$ datapoints are abov $0$. If $\\epsilon_i$ were truly IID $Normal(0,\\sigma^2)$, we would expect roughly the same number of points to be spread above and below zero, i.e., the $x$-axis, in an equally likely manner. Also, we would expect more points to be closer to zero and fewer points to be further away.\n",
"\n",
"In conclusion, the residuals of the linear regression of LSAT and GPA do not look like they are normall distributed.\n",
"\n",
"We could try different approaches to improve the model. For example, we could try to increase the sample size or standardise the scales by subtracting the sample mean and dividing by the the sample standard deviation for the $x$ and $y$ values separately and doing regression with the standardised data, etc.\n",
"\n",
"The [real wiki](http://reliawiki.org/index.php/Simple_Linear_Regression_Analysis) page has some simple examples of residual plots and they are useful for insights:\n",
"\n",
"> Examples of residual plots are shown in the following figure. (a) is a satisfactory plot with the residuals falling in a horizontal band with no systematic pattern. Such a plot indicates an appropriate regression model. (b) shows residuals falling in a funnel shape. Such a plot indicates increase in variance of residuals and the assumption of constant variance is violated here. Transformation on may be helpful in this case (see Transformations). If the residuals follow the pattern of (c) or (d), then this is an indication that the linear regression model is not adequate. Addition of higher order terms to the regression model or transformation on or may be required in such cases. A plot of residuals may also show a pattern as seen in (e), indicating that the residuals increase (or decrease) as the run order sequence or time progresses. This may be due to factors such as operator-learning or instrument-creep and should be investigated further.)\n",
"\n",
"<img src=\"http://reliawiki.org/images/e/ee/Doe4.13.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can finally obtain 95% confidence intervals for the fitted parameters in the simple linear regression model and do a Wald test as follows."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Estimated standard errors for beta_0_hat and beta_1_hat are:\n",
"1.1054305503607234 0.0018374136245919459\n",
"and the approximate 95% confidence intervals for beta_0_hat is:\n",
" [ -2.2110550329365077 , 2.2106671685063857 ]\n",
"and the approximate 95% confidence intervals for beta_1_hat is:\n",
" [ 0.0015920440283845134 , 0.008941698526752296 ]\n",
"The Wald test for the null hypothesis H0 that beta_1 = 0 is:\n",
"Reject H0 that beta_1=0 at alpha=0.05, since W = 2.8664592485200906\n"
]
}
],
"source": [
"b0_hat, b1_hat, s_hat = estimate_coefficients(LSAT,GPA)\n",
"se_b0,se_b1 = standard_errors(LSAT,GPA)\n",
"print \"Estimated standard errors for beta_0_hat and beta_1_hat are:\"\n",
"print se_b0,se_b1 \n",
"print \"and the approximate 95% confidence intervals for beta_0_hat is:\"\n",
"print \" [ \", b0_hat-2*se_b0,\" , \", b0_hat+2*se_b0, \" ]\"\n",
"print \"and the approximate 95% confidence intervals for beta_1_hat is:\"\n",
"print \" [ \", b1_hat-2*se_b1,\" , \", b1_hat+2*se_b1, \" ]\"\n",
"print \"The Wald test for the null hypothesis H0 that beta_1 = 0 is:\"\n",
"W = (b1_hat-0)/se_b1\n",
"if abs(W > 2):\n",
" print \"Reject H0 that beta_1=0 at alpha=0.05, since W = \",W\n",
"else:\n",
" print \"fail to reject H0 that beta_1=0 at alpha=0.05, since W = \",W"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multiple Regression\n",
"### This is just as simple, except we have more than one covariate\n",
"\n",
"Now, let's suppose that the covariate, feature, predictor or dependent variable is a vector of length $k$. So our data for regression is of the following form:\n",
"\n",
"$$\n",
"(Y_1,X_1), (Y_2,X_2), \\ldots, (Y_i,X_i), \\ldots, (Y_n,X_n)\n",
"$$\n",
"\n",
"where, $X_i$ is a vector of length $k$ for the $i$-th observation or datapoint $(Y_i,X_i)$. \n",
"\n",
"$$\n",
"X_i = (X_{i,1},X_{i,2},\\ldots,X_{i,k}) \\, .\n",
"$$\n",
"\n",
"Then the linear regression model is:\n",
"\n",
"$$\n",
"Y_i = \\displaystyle{\\sum_{j=0}^k \\beta_j X_{i,j} + \\epsilon_i, \\quad \\text{ for } i \\in \\{1,2,\\ldots,n\\} }\n",
"$$\n",
"\n",
"where $\\beta_0$ is the intercept term with $X_{i,0}=1$ for each $i \\in \\{1,2,\\ldots,n\\}$ and\n",
"\n",
"$$\n",
"E \\left( \\epsilon_i | X_{1,i}, X_{2,i}, \\ldots, X_{k,i} \\right) = 0.\n",
"$$\n",
"\n",
"We can denote the model using matrices and vectors more conveniently as follows:\n",
"\n",
"$$\n",
"Y \n",
"= \\displaystyle{\\left( {\\begin{array}{c}\n",
" Y_1 \\\\\n",
" Y_2 \\\\\n",
" \\vdots \\\\\n",
" Y_n\n",
" \\end{array} } \\right)} \\, ,\n",
"\\qquad \n",
"X \n",
"= \\displaystyle{\\left( {\\begin{array}{cccc}\n",
" 1& X_{1,1}& \\ldots& X_{1,k} \\\\\n",
" 1& X_{2,1}& \\ldots& X_{2,k} \\\\\n",
" \\vdots & \\vdots & \\vdots & \\vdots\\\\\n",
" 1& X_{n,1}& \\ldots& X_{n,k} \n",
" \\end{array} } \\right)} \\, ,\n",
"\\qquad\n",
"\\beta \n",
"= \\displaystyle{\\left( {\\begin{array}{c}\n",
" \\beta_0 \\\\\n",
" \\beta_1 \\\\\n",
" \\vdots \\\\\n",
" \\beta_k\n",
" \\end{array} } \\right)} \\, ,\n",
"\\qquad \n",
"\\epsilon \n",
"= \\displaystyle{\\left( {\\begin{array}{c}\n",
" \\epsilon_1 \\\\\n",
" \\epsilon_2 \\\\\n",
" \\vdots \\\\\n",
" \\epsilon_n\n",
" \\end{array} } \\right)} \\, .\n",
"$$\n",
"\n",
"With $X \\in \\mathbb{R}^{n \\times (k+1)}$, i.e., $X$ being a $n \\times (k+1)$ matrix, $\\beta \\in \\mathbb{R}^{(k+1) \\times 1}$, i.e., $\\beta$ being a a column vector with $k+1$ rows, and $\\epsilon \\in \\mathbb{R}^{n \\times 1}$, i.e., $\\epsilon$ being a column vector with $n$ rows, we obtain the multiple regression model:\n",
"\n",
"$$\n",
"\\boxed{\n",
"Y = X \\beta + \\epsilon \\, .\n",
"}\n",
"$$\n",
"\n",
"Just as in the 1D case with $k=1$, the least sqaures estimate is as follows, under the assumption that $X^T X$ is invertible:\n",
"\n",
"$$\n",
"\\boxed{\n",
"\\begin{align}\n",
"\\widehat{\\beta} &= \\left( X^T X\\right)^{-1} X^T Y\\\\\n",
"V\\left(\\widehat{\\beta} | X_{1:n} \\right) &= \\sigma^2 \\left( X^T X \\right)^{-1} \\\\\n",
"\\widehat{\\beta} &\\approx Normal \\left(\\beta, \\sigma^2 \\left( X^T X\\right)^{-1} \\right)\n",
"\\end{align} \\, .\n",
"}\n",
"$$\n",
"\n",
"The estimate of the regression function is:\n",
"\n",
"$$\n",
"\\boxed{\n",
"\\widehat{r}(x) = \\sum_{j=0}^k \\widehat{\\beta}_j \\, x_j \\, .\n",
"}\n",
"$$\n",
"\n",
"An unbiased estimate of $\\sigma^2$ is:\n",
"\n",
"$$\n",
"\\widehat{\\sigma}^2 = \\left( \\frac{1}{n-k} \\right) \\sum_{i=1}^n \\widehat{\\epsilon}_i^2 \\, \n",
"$$\n",
"\n",
"where $\\widehat{\\epsilon}$ is the vector of residuals:\n",
"\n",
"$$\n",
"\\boxed{\n",
"\\widehat{\\epsilon}=X \\widehat{\\beta} - Y\n",
"} \\ , \\text{ i.e.,} \\quad\n",
"\\widehat{\\epsilon} = \\displaystyle{\\left( {\\begin{array}{c}\n",
" \\widehat{\\epsilon}_1 \\\\\n",
" \\widehat{\\epsilon}_2 \\\\\n",
" \\vdots \\\\\n",
" \\widehat{\\epsilon}_n\n",
" \\end{array} } \\right)} = \\displaystyle{\\left( {\\begin{array}{cccc}\n",
" 1& X_{1,1}& \\ldots& X_{1,k} \\\\\n",
" 1& X_{2,1}& \\ldots& X_{2,k} \\\\\n",
" \\vdots & \\vdots & \\vdots & \\vdots\\\\\n",
" 1& X_{n,1}& \\ldots& X_{n,k} \n",
" \\end{array} } \\right)} \\ \n",
" \\displaystyle{\\left( {\\begin{array}{c}\n",
" \\widehat{\\beta}_0 \\\\\n",
" \\widehat{\\beta}_1 \\\\\n",
" \\vdots \\\\\n",
" \\widehat{\\beta}_k\n",
" \\end{array} } \\right)} \\ - \\displaystyle{\\left( {\\begin{array}{c}\n",
" Y_1 \\\\\n",
" Y_2 \\\\\n",
" \\vdots \\\\\n",
" Y_n\n",
" \\end{array} } \\right)}\n",
"$$\n",
"\n",
"An approximate $1-\\alpha$ confidence interval for $\\beta_j$ is\n",
"\n",
"$$\n",
"\\boxed{\n",
"\\widehat{\\beta}_j \\pm z_{\\alpha/2} \\widehat{se}(\\widehat{\\beta}_j)\n",
"}\n",
"$$\n",
"where $\\left(\\widehat{se}(\\widehat{\\beta}_j)\\right)^2$ is the $j$-th diagonal entry of the matrix $\\widehat{\\sigma}^2 (X^T X)^{-1}$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solving Least Squares Using Numerical Linear Algebra Routine in scipy\n",
"\n",
"We can use [scipy.linalg.lstsq](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.lstsq.html#scipy.linalg.lstsq) to get the least squares solution to our regression problems quite easily, including generalisation to multiple linear regression when the covariates are in more than 1 dimension.\n",
"\n",
"Let us try to understand the code in the previous cell by learning how to do a least squares fit by setting up the right design matrix."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example 1: Fitting a Line is Simple Linear Regression"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1. , 1. ],\n",
" [1. , 2.5],\n",
" [1. , 3.5],\n",
" [1. , 4. ],\n",
" [1. , 5. ],\n",
" [1. , 7. ],\n",
" [1. , 8.5]])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from scipy.linalg import lstsq\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# suppose we have the following data\n",
"x = np.array([1, 2.5, 3.5, 4, 5, 7, 8.5])\n",
"y = np.array([0.3, 1.1, 1.5, 2.0, 3.2, 6.6, 8.6])\n",
"\n",
"#We want to fit a line of the form y = a + b*x to this data. We first form the \n",
"#“design matrix” M, with a constant column of 1s and a column containing x\n",
"M1 = x[:, np.newaxis]^[0, 1]\n",
"M1"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-1.93080357, 1.16875 ])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#We want to find the least-squares solution to \n",
"#M1.dot(p) = y, where p is a vector with length 2 that holds the parameters a and b.\n",
"p, res, rnk, s = lstsq(M1, y)\n",
"p"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x, y, 'o', label='data')\n",
"xx = np.linspace(0, 9, 101)\n",
"yy = p[0] + p[1]*xx\n",
"plt.plot(xx, yy, label='least squares fit, $y = a + bx$')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.legend(framealpha=1, shadow=True)\n",
"plt.grid(alpha=0.25)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example 2: Fitting a Quadratic is also Simple Linear Regresssion\n",
"Suppose we want to fit a quadratic polynomial of the form $y = a + b*x^2$ to the same data. \n",
"Then we first form the design matrix `M2`, with a constant column of `1`s and a column containing `x^2` as follows:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , 1. ],\n",
" [ 1. , 6.25],\n",
" [ 1. , 12.25],\n",
" [ 1. , 16. ],\n",
" [ 1. , 25. ],\n",
" [ 1. , 49. ],\n",
" [ 1. , 72.25]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"M2 = x[:, np.newaxis]^[0, 2]\n",
"M2"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# least square solution with M2\n",
"p, res, rnk, s = lstsq(M2, y)\n",
"plt.plot(x, y, 'o', label='data')\n",
"xx = np.linspace(0, 9, 101)\n",
"yy = p[0] + p[1]*xx^2\n",
"plt.plot(xx, yy, label='least squares fit, $y = a + bx$')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.legend(framealpha=1, shadow=True)\n",
"plt.grid(alpha=0.25)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example 3: Fitting a 3rd Order Polynomial is Multiple Linear Regresssion\n",
"Suppose we want to fit a degree-3 polynomial of the form $y = \\beta_0 + \\beta_1 x + \\beta_2 x^2+ \\beta_3 x^3$ to the same data. \n",
"Then we first form the design matrix `M3`, with a constant column of `1`s with `x^0` and three additional columns containing `x^1`, `x^2` and `x^3` as follows:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , 1. , 1. , 1. ],\n",
" [ 1. , 2.5 , 6.25 , 15.625],\n",
" [ 1. , 3.5 , 12.25 , 42.875],\n",
" [ 1. , 4. , 16. , 64. ],\n",
" [ 1. , 5. , 25. , 125. ],\n",
" [ 1. , 7. , 49. , 343. ],\n",
" [ 1. , 8.5 , 72.25 , 614.125]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fitting a cubic polynolial is the same idea\n",
"M3 = x[:, np.newaxis]^[0, 1, 2, 3]\n",
"M3"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"p, res, rnk, s = lstsq(M3, y)\n",
"plt.plot(x, y, 'o', label='data')\n",
"xx = np.linspace(0, 9, 101)\n",
"yy = p[0] + p[1]*xx + p[2]*xx^2 + p[3]*xx^3\n",
"plt.plot(xx, yy, label='least squares fit, $y = a + bx$')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.legend(framealpha=1, shadow=True)\n",
"plt.grid(alpha=0.25)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sample Exam Problem 8\n",
"\n",
"Using the `lstsq` method shown above, and data arrays `x` and `y` in the next cell that contain log light intensity and log surface temperature in a give range of measurements from nearby stars, compute the least squares estimates of $\\beta_0$ and $\\beta_1$ under the simple linear regression model with an intercept and a slope term. Make a plot similar to the one above with the data points and the fitted regression line."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Sample Exam Problem 8 \n",
"# do not change this import and data block ########################\n",
"from scipy.linalg import lstsq\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"logLightIntens_logSurfTemp=[(4.37,5.23),(4.56,5.74),\n",
"(4.26,4.93),(4.56,5.74),(4.30,5.19),(4.46,5.46),(3.84,4.65),(4.57,5.27),(4.26,5.57),(4.37,5.12),(3.49,5.73),\n",
"(4.43,5.45),(4.48,5.42),(4.01,4.05),(4.29,4.26),(4.42,4.58),(4.23,3.94),(4.42,4.18),(4.23,4.18),(3.49,5.89),\n",
"(4.29,4.38),(4.29,4.22),(4.42,4.42),(4.49,4.85),(4.38,5.02),(4.42,4.66),(4.29,4.66),(4.38,4.90),(4.22,4.39),\n",
"(3.48,6.05),(4.38,4.42),(4.56,5.10),(4.45,5.22),(3.49,6.29),(4.23,4.34),(4.62,5.62),(4.53,5.10),(4.45,5.22),\n",
"(4.53,5.18),(4.43,5.57),(4.38,4.62),(4.45,5.06),(4.50,5.34),(4.45,5.34),(4.55,5.54),(4.45,4.98),(4.42,4.50)]\n",
"CleanedlogLightIntens_logSurfTemp=\\\n",
"np.array([yx for yx in logLightIntens_logSurfTemp if yx[1]<5.9 and yx[0]>4]) # data range constraint\n",
"x=CleanedlogLightIntens_logSurfTemp[:,1]\n",
"y=CleanedlogLightIntens_logSurfTemp[:,0]\n",
"########### end of import and data block ##########################\n",
"\n",
"# Replace only ZZZ by the right values\n",
"M1 = ZZZ # design matrix M1\n",
"b, res, rnk, s = lstsq(ZZZ,ZZZ)\n",
"plt.plot(x, y, 'o', label='data')\n",
"xx = np.linspace(ZZZ, ZZZ, 101)\n",
"yy = ZZZ *xx\n",
"plt.plot(xx, yy, label='least squares fit')\n",
"plt.xlabel('log light intensity (X)')\n",
"plt.ylabel('log surface temperature (Y)')\n",
"plt.legend(framealpha=1, shadow=True)\n",
"plt.grid(alpha=0.25)\n",
"plt.text(4, 4.7, r'$\\widehat{r}(x) = \\widehat{\\beta}_0 + \\widehat{\\beta}_1 x, \\quad \\\n",
"\\widehat{\\beta}_0 = $ %(b0)0.3f , $\\widehat{\\beta}_1 = $ %(b1)0.3f' % {'b0': b[0], 'b1': b[1]} )\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Sample Exam Problem 8 Solution\n",
"logLightIntens_logSurfTemp=[(4.37,5.23),(4.56,5.74),\n",
"(4.26,4.93),(4.56,5.74),(4.30,5.19),(4.46,5.46),(3.84,4.65),(4.57,5.27),(4.26,5.57),(4.37,5.12),(3.49,5.73),\n",
"(4.43,5.45),(4.48,5.42),(4.01,4.05),(4.29,4.26),(4.42,4.58),(4.23,3.94),(4.42,4.18),(4.23,4.18),(3.49,5.89),\n",
"(4.29,4.38),(4.29,4.22),(4.42,4.42),(4.49,4.85),(4.38,5.02),(4.42,4.66),(4.29,4.66),(4.38,4.90),(4.22,4.39),\n",
"(3.48,6.05),(4.38,4.42),(4.56,5.10),(4.45,5.22),(3.49,6.29),(4.23,4.34),(4.62,5.62),(4.53,5.10),(4.45,5.22),\n",
"(4.53,5.18),(4.43,5.57),(4.38,4.62),(4.45,5.06),(4.50,5.34),(4.45,5.34),(4.55,5.54),(4.45,4.98),(4.42,4.50)]\n",
"CleanedlogLightIntens_logSurfTemp=\\\n",
"np.array([yx for yx in logLightIntens_logSurfTemp if yx[1]<5.9 and yx[0]>4]) # data range constraint\n",
"x=CleanedlogLightIntens_logSurfTemp[:,1]\n",
"y=CleanedlogLightIntens_logSurfTemp[:,0]\n",
"\n",
"from scipy.linalg import lstsq\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"M1 = x[:, np.newaxis]^[0, 1]\n",
"b, res, rnk, s = lstsq(M1, y)\n",
"plt.plot(x, y, 'o', label='data')\n",
"xx = np.linspace(3.9, 5.8, 101)\n",
"yy = b[0] + b[1]*xx\n",
"plt.plot(xx, yy, label='least squares fit')\n",
"plt.xlabel('log light intensity (X)')\n",
"plt.ylabel('log surface temperature (Y)')\n",
"plt.legend(framealpha=1, shadow=True)\n",
"plt.grid(alpha=0.25)\n",
"plt.text(4, 4.7, r'$\\widehat{r}(x) = \\widehat{\\beta}_0 + \\widehat{\\beta}_1 x, \\quad \\\n",
"\\widehat{\\beta}_0 = $ %(b0)0.3f , $\\widehat{\\beta}_1 = $ %(b1)0.3f' % {'b0': b[0], 'b1': b[1]} )\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"lx_assignment_number": "3",
"lx_problem_cell_type": "PROBLEM"
},
"source": [
"---\n",
"## Assignment 3, PROBLEM 8\n",
"Maximum Points = 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false,
"lx_assignment_number": "3",
"lx_assignment_type": "ASSIGNMENT",
"lx_assignment_type2print": "Assignment",
"lx_problem_cell_type": "PROBLEM",
"lx_problem_number": "8",
"lx_problem_points": "2"
},
"source": [
"\n",
"For the fitted regression model in the next cell get the residuals and plot them against the covariate [see **Residual analysis** section in latest `12.ipynb` for the basic ideas conveyed in the last lecture]. \n",
"How do the residuals compare to a Normal random variable centred at $0$ with a constant variance (summarise in a sentence or two by double-clicking this cell and writing in between the two lines `---` below)?\n",
"\n",
"---\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"deletable": false,
"lx_assignment_number": "3",
"lx_assignment_type": "ASSIGNMENT",
"lx_assignment_type2print": "Assignment",
"lx_problem_cell_type": "PROBLEM",
"lx_problem_number": "8",
"lx_problem_points": "2"
},
"outputs": [],
"source": [
"logLightIntens_logSurfTemp=[(4.37,5.23),(4.56,5.74),\n",
"(4.26,4.93),(4.56,5.74),(4.30,5.19),(4.46,5.46),(3.84,4.65),(4.57,5.27),(4.26,5.57),(4.37,5.12),(3.49,5.73),\n",
"(4.43,5.45),(4.48,5.42),(4.01,4.05),(4.29,4.26),(4.42,4.58),(4.23,3.94),(4.42,4.18),(4.23,4.18),(3.49,5.89),\n",
"(4.29,4.38),(4.29,4.22),(4.42,4.42),(4.49,4.85),(4.38,5.02),(4.42,4.66),(4.29,4.66),(4.38,4.90),(4.22,4.39),\n",
"(3.48,6.05),(4.38,4.42),(4.56,5.10),(4.45,5.22),(3.49,6.29),(4.23,4.34),(4.62,5.62),(4.53,5.10),(4.45,5.22),\n",
"(4.53,5.18),(4.43,5.57),(4.38,4.62),(4.45,5.06),(4.50,5.34),(4.45,5.34),(4.55,5.54),(4.45,4.98),(4.42,4.50)]\n",
"CleanedlogLightIntens_logSurfTemp=\\\n",
"np.array([yx for yx in logLightIntens_logSurfTemp if yx[1]<5.9 and yx[0]>4]) # data range constraint\n",
"x=CleanedlogLightIntens_logSurfTemp[:,1]\n",
"y=CleanedlogLightIntens_logSurfTemp[:,0]\n",
"\n",
"from scipy.linalg import lstsq\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"M1 = x[:, np.newaxis]^[0, 1]\n",
"b, res, rnk, s = lstsq(M1, y)\n",
"plt.plot(x, y, 'o', label='data')\n",
"xx = np.linspace(3.9, 5.8, 101)\n",
"yy = b[0] + b[1]*xx\n",
"plt.plot(xx, yy, label='least squares fit')\n",
"plt.xlabel('log light intensity (X)')\n",
"plt.ylabel('log surface temperature (Y)')\n",
"plt.legend(framealpha=1, shadow=True)\n",
"plt.grid(alpha=0.25)\n",
"plt.text(4, 4.7, r'$\\widehat{r}(x) = \\widehat{\\beta}_0 + \\widehat{\\beta}_1 x, \\quad \\\n",
"\\widehat{\\beta}_0 = $ %(b0)0.3f , $\\widehat{\\beta}_1 = $ %(b1)0.3f' % {'b0': b[0], 'b1': b[1]} )\n",
"plt.show()\n",
"\n",
"# Obtain the residuals and plot them (summarise in the markdown cell above)\n",
"XXX\n",
"XXX\n",
"XXX"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prediction\n",
"\n",
"Let's consider the 1D setting for simplicity of notation. \n",
"Suppose we have estimated a regression model:\n",
"$$\\widehat{r}(x) = \\widehat{\\beta}_0 + \\widehat{\\beta}_1 x $$\n",
"from data $(X_1,Y_1), (X_2,Y_2), \\ldots, (X_n,Y_n)$.\n",
"\n",
"Now suppose we observe the value $X=x_*$ of the covariate of a new observarion but *do not* observe the response $Y_*$ and want to **predict** it. An estimate of $Y_*$ is\n",
"\n",
"$$\n",
"\\boxed{\n",
"\\widehat{Y}_* = \\widehat{\\beta}_0 + \\widehat{\\beta}_1 x_* \\, .\n",
"}\n",
"$$\n",
"\n",
"By the formula for the variance of the sum of two random variables:\n",
"\n",
"$$\n",
"V(\\widehat{Y}_*) = V(\\widehat{\\beta}_0 + \\widehat{\\beta}_1 x_*) \n",
"= V(\\widehat{\\beta}_0) + x_*^2 V(\\widehat{\\beta}_1 ) + 2 x_* Cov (\\widehat{\\beta}_0,\\widehat{\\beta}_1)\n",
"$$\n",
"\n",
"We have all the needed terms to compute $V(\\widehat{Y}_*)$ from the earlier result on the conditional variance of the least squares estimate:\n",
"\n",
"$$\n",
"V \\left( \\widehat{\\beta} \\, | \\, X_{1:n} \\right) \n",
"= \\frac{\\sigma^2}{n s_X^2} \n",
"\\left( \n",
"{\\begin{array}{cc}\n",
" \\frac{1}{n}\\sum_{i=1}^n X_i^2 & -\\overline{X}_n \\\\\n",
" -\\overline{X}_n & 1\\\\\n",
" \\end{array}} \n",
" \\right)\n",
"$$\n",
"\n",
"The estimated standard error $\\widehat{se}(\\widehat{Y}_*)$ is just $\\sqrt{V(\\widehat{Y}_*)}$ with $\\widehat{\\sigma}^2$ substituted in for $\\sigma^2$. An approximate $1-alpha$ confidence interval for $Y^*$ is called an **approximate $1-\\alpha$ prediction interval for $Y_*$** and is given by\n",
"\n",
"$$\n",
"\\boxed{\n",
"\\widehat{Y}_* \\pm z_{\\alpha/2} \\widehat{\\xi}_n \\, , \\quad \\text{ where } \\quad \n",
"\\widehat{\\xi}^2_n = \\widehat{\\sigma}^2 \\left( \\frac{\\sum_{i=1}^n (X_i-X_*)^2}{n \\sum_{i=1}^n (X_i-\\overline{X})^2} + 1 \\right)\n",
"}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multiple Regression on 2018 Swedish Election Data\n",
"\n",
"If you are interested, you already have the basic skills to look at the data from Swedish election using these ideas.\n",
"\n",
"Try to model, say the $\\log$ of the number of district-level votes for the top two most voted parties.\n",
"\n",
"You can introduce latitude of the district centres (if you have such information from geospatial database you could join), distance of the district to one of the four largest cities in Sweden, or the socio-economic indicators of the district for Swedish Central Statistical Bureau, etc., as covariates.\n",
"\n",
"But this is a good project and beyond current scope (mainly due to time limitations)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prelude to Statistical Machine Learning\n",
"\n",
"Here, we just start you off on the path to more statistical modeling for purposes of prediction.\n",
"Now statistical learning from the 1970s is needed to mathematically justify the methods.\n",
"\n",
"The following is a teaser of what you will see in the first couple weeks of you course in 'statistical machine learning'.\n",
"\n",
"## Loss functions and gradient descent\n",
"\n",
"[this header was adapted from some notes by Benny Avelin]\n",
"\n",
"In the above example with linear regression we wanted to minimize the vertical distance between the fitted line and the data, this vertical distance is a prime example of a **loss function**. In general when we are faced with a regression problem we want a way of measure how good our model is, this quantity that we want to *minimise* is called the loss function and its expectation (over different sample data-points is called the **risk**). The mathematical statistical justification for this approach towards minimising expected loss or risk is called [empirical risk minimisation](https://en.wikipedia.org/wiki/Empirical_risk_minimization), as we will see in the sequel in more detail.\n",
"\n",
"Let us circle back to linear regression once again. The way the `np.argmin` method searched for the minimum of:\n",
"\n",
"$$L(a,b) = \\sum_{i=1}^N (y_i - f_{a,b}(x_i))^2$$\n",
"\n",
"was by simply evaluating $L(a,b)$ for each value of $a$ in the array `prop_a` with our guessed values for $a$ and picking the $a$ that minimised $L(a,b)$. Recall we fixed $b$ in the search.\n",
"\n",
"> np.argmin? # see the docstring for np.argmin and other functions/methods we are using throughout if you need to know right away.\n",
"\n",
"This approaching of evaluating the loss at a set of parameter values quickly becomes infeasible when the dimension of the problem is larger than $1$. \n",
"\n",
"Even if we just have two guess for each dimension of the parameter space with $d$ dimensions, then we will need to evaluate the loss at $2^d$ parameter values. When $d=10,100,1000$ the number of evaluation points become $1024$, $1.268e30, 1.072e301$, respectively.\n",
"\n",
"Often in big-data settings, the number of dimensions for the regression problem can easily extend over a few thousands. \n",
"Thus, we need a systematic way to find the optimal parameters, i.e., the parameters that minimise the loss function.\n",
"\n",
"The iterative solution is called **gradient descent** and it goes like this: \n",
"\n",
"- *Initialise:* Let us start with some initial parameters, say in our linear regression example $(a,b) = (0,0)$, say at iteration $i=0$.\n",
"- *Update:* then we construct an update rule like the following to update the parameter values at $i+1$ from those at iteration $i$:\n",
" - $a_i = a_{i-1}-l \\frac{dL}{da}(a_{i-1},b_{i-1})$\n",
" - $b_i = b_{i-1}-l \\frac{dL}{db}(a_{i-1},b_{i-1}) $\n",
" - where $l > 0$ is called the learning rate.\n",
"- *Stop:* Finally we stop when a stopping rule like the following is satisfied:\n",
"$$\\sqrt( ( L(a_{i+1},b_{i+1})- L(a_{i},b_{i}))^2) < \\tau, \\qquad \\text{ where, $\\tau$ is some tolerance threshold that says we are close enough to the minimum value found by our iteration}.$$\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to R in SageMath Jupyter IPython Notebook\n",
"\n",
"1. How to run R commands in SageMath\n",
" * doing linear regression regression using R's builtin `lm` (linear model) package in SageMath/R\n",
" * installing non-builtin packages, loading libraries and data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running R in SageMath is \"easy as\":\n",
"\n",
"- Use `%%r` to denote that the `Code` cell is of language `R`\n",
"\n",
"First note that SageMath/Python and R kernels will be available in the SageMath Jupyter notebook."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5.23 5.74 4.93 5.74 5.19 5.46 5.27 5.57 5.12 5.45 5.42 4.05 4.26 4.58\n",
" 3.94 4.18 4.18 4.38 4.22 4.42 4.85 5.02 4.66 4.66 4.9 4.39 4.42 5.1\n",
" 5.22 4.34 5.62 5.1 5.22 5.18 5.57 4.62 5.06 5.34 5.34 5.54 4.98 4.5 ]\n",
"[4.37 4.56 4.26 4.56 4.3 4.46 4.57 4.26 4.37 4.43 4.48 4.01 4.29 4.42\n",
" 4.23 4.42 4.23 4.29 4.29 4.42 4.49 4.38 4.42 4.29 4.38 4.22 4.38 4.56\n",
" 4.45 4.23 4.62 4.53 4.45 4.53 4.43 4.38 4.45 4.5 4.45 4.55 4.45 4.42]\n"
]
}
],
"source": [
"# this is x and y available as numpy arrays in SageMath/Python\n",
"logLightIntens_logSurfTemp=[(4.37,5.23),(4.56,5.74),\n",
"(4.26,4.93),(4.56,5.74),(4.30,5.19),(4.46,5.46),(3.84,4.65),(4.57,5.27),(4.26,5.57),(4.37,5.12),(3.49,5.73),\n",
"(4.43,5.45),(4.48,5.42),(4.01,4.05),(4.29,4.26),(4.42,4.58),(4.23,3.94),(4.42,4.18),(4.23,4.18),(3.49,5.89),\n",
"(4.29,4.38),(4.29,4.22),(4.42,4.42),(4.49,4.85),(4.38,5.02),(4.42,4.66),(4.29,4.66),(4.38,4.90),(4.22,4.39),\n",
"(3.48,6.05),(4.38,4.42),(4.56,5.10),(4.45,5.22),(3.49,6.29),(4.23,4.34),(4.62,5.62),(4.53,5.10),(4.45,5.22),\n",
"(4.53,5.18),(4.43,5.57),(4.38,4.62),(4.45,5.06),(4.50,5.34),(4.45,5.34),(4.55,5.54),(4.45,4.98),(4.42,4.50)]\n",
"CleanedlogLightIntens_logSurfTemp=\\\n",
"np.array([yx for yx in logLightIntens_logSurfTemp if yx[1]<5.9 and yx[0]>4]) # data range constraint\n",
"x=CleanedlogLightIntens_logSurfTemp[:,1]\n",
"y=CleanedlogLightIntens_logSurfTemp[:,0]\n",
"print x\n",
"print y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assigning to `x` and `y` in SageMath/R\n",
"\n",
"We use the assignment operator, `<-`, in R, as follows:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [1] 4.37 4.56 4.26 4.56 4.30 4.46 4.57 4.26 4.37 4.43 4.48 4.01 4.29 4.42 4.23\n",
"[16] 4.42 4.23 4.29 4.29 4.42 4.49 4.38 4.42 4.29 4.38 4.22 4.38 4.56 4.45 4.23\n",
"[31] 4.62 4.53 4.45 4.53 4.43 4.38 4.45 4.50 4.45 4.55 4.45 4.42\n"
]
}
],
"source": [
"%%r\n",
"x <- c(5.23, 5.74, 4.93, 5.74, 5.19, 5.46, 5.27, 5.57, 5.12,\n",
" 5.45, 5.42, 4.05, 4.26, 4.58, 3.94, 4.18, 4.18, 4.38,\n",
" 4.22, 4.42, 4.85, 5.02, 4.66, 4.66, 4.9 , 4.39, 4.42,\n",
" 5.1 , 5.22, 4.34, 5.62, 5.1 , 5.22, 5.18, 5.57, 4.62,\n",
" 5.06, 5.34, 5.34, 5.54, 4.98, 4.5)\n",
"y <- c(4.37, 4.56, 4.26, 4.56, 4.3 , 4.46, 4.57, 4.26, 4.37,\n",
" 4.43, 4.48, 4.01, 4.29, 4.42, 4.23, 4.42, 4.23, 4.29,\n",
" 4.29, 4.42, 4.49, 4.38, 4.42, 4.29, 4.38, 4.22, 4.38,\n",
" 4.56, 4.45, 4.23, 4.62, 4.53, 4.45, 4.53, 4.43, 4.38,\n",
" 4.45, 4.5 , 4.45, 4.55, 4.45, 4.42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Doing Linear Regression in SameMath/R"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Call:\n",
"lm(formula = y ~ x + I(x^2))\n",
"\n",
"Residuals:\n",
" Min 1Q Median 3Q Max \n",
"-0.22916 -0.05145 0.01121 0.06263 0.16072 \n",
"\n",
"Coefficients:\n",
" Estimate Std. Error t value Pr(>|t|)\n",
"(Intercept) 1.87480 1.44471 1.298 0.202\n",
"x 0.87443 0.59759 1.463 0.151\n",
"I(x^2) -0.07272 0.06128 -1.187 0.243\n",
"\n",
"Residual standard error: 0.09108 on 39 degrees of freedom\n",
"Multiple R-squared: 0.484,\tAdjusted R-squared: 0.4576 \n",
"F-statistic: 18.29 on 2 and 39 DF, p-value: 2.491e-06\n"
]
}
],
"source": [
"%%r\n",
"linearRegressionModel <- lm(formula = y ~ x + I(x^2))\n",
"\n",
"summary(linearRegressionModel)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running R in SageMath is \"easy as\":\n",
"\n",
"Sometimes you need additional `R` packages.\n",
"\n",
"- Installing R packages with `install.packages(...)`\n",
"\n",
"**Note:** Once a package is installed on a particular machine using `install.packages(\"wantedpackage\")` then you only need to load that library using `library(wantedpackage)` when you are using the same machine.\n",
"\n",
"### Additional Packages\n",
"\n",
"One often needs several additional packages to run certain desired `R` commands. Let's get some such packages.\n",
"\n",
"\n",
"In other words, you don't have to install packages that are already installed and thus can be automatically found by `R` in the default location it will be installed at. In the case below, you can see where the package was installed from the following line:\n",
"\n",
"- `Installing package into ‘/some_path_to_where_the_package_is_installed’`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%r\n",
"# there will be further dependencies, you may need to recursively install...\n",
"#install.packages(\"Flury\")\n",
"#library(Flury)\n",
"#data(dead.beetles)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SageMath/R docs\n",
"\n",
"For example, you can find in the docs more systematic/programmatic way to assign SageMath/Python objects to SageMath/R objects.\n",
"\n",
"- R:\n",
" - [https://cran.r-project.org/doc/manuals/R-intro.html](https://cran.r-project.org/doc/manuals/R-intro.html)\n",
"- SageMath/R:\n",
" - [http://doc.sagemath.org/html/en/reference/interfaces/sage/interfaces/r.html](http://doc.sagemath.org/html/en/reference/interfaces/sage/interfaces/r.html)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "SageMath 8.9",
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"name": "sagemath"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.15"
},
"lx_course_instance": "Summer 2019",
"lx_course_name": "Introduction to Data Science: A Comp-Math-Stat Approach",
"lx_course_number": "YOIYUI001"
},
"nbformat": 4,
"nbformat_minor": 2
}
| unlicense |
mne-tools/mne-tools.github.io | stable/_downloads/d7719f60a0c257a5313f06f110154ff3/20_dipole_fit.ipynb | 1 | 7019 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Source localization with equivalent current dipole (ECD) fit\n\nThis shows how to fit a dipole :footcite:`Sarvas1987` using mne-python.\n\nFor a comparison of fits between MNE-C and MNE-Python, see\n[this gist](https://gist.github.com/larsoner/ca55f791200fe1dc3dd2)_.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os.path as op\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.forward import make_forward_dipole\nfrom mne.evoked import combine_evoked\nfrom mne.simulation import simulate_evoked\n\nfrom nilearn.plotting import plot_anat\nfrom nilearn.datasets import load_mni152_template\n\ndata_path = mne.datasets.sample.data_path()\nsubjects_dir = op.join(data_path, 'subjects')\nfname_ave = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')\nfname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')\nfname_bem = op.join(subjects_dir, 'sample', 'bem', 'sample-5120-bem-sol.fif')\nfname_trans = op.join(data_path, 'MEG', 'sample',\n 'sample_audvis_raw-trans.fif')\nfname_surf_lh = op.join(subjects_dir, 'sample', 'surf', 'lh.white')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's localize the N100m (using MEG only)\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"evoked = mne.read_evokeds(fname_ave, condition='Right Auditory',\n baseline=(None, 0))\nevoked.pick_types(meg=True, eeg=False)\nevoked_full = evoked.copy()\nevoked.crop(0.07, 0.08)\n\n# Fit a dipole\ndip = mne.fit_dipole(evoked, fname_cov, fname_bem, fname_trans)[0]\n\n# Plot the result in 3D brain with the MRI image.\ndip.plot_locations(fname_trans, 'sample', subjects_dir, mode='orthoview')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the result in 3D brain with the MRI image using Nilearn\nIn MRI coordinates and in MNI coordinates (template brain)\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"subject = 'sample'\nmni_pos = dip.to_mni(subject=subject, trans=fname_trans,\n subjects_dir=subjects_dir)\n\nmri_pos = dip.to_mri(subject=subject, trans=fname_trans,\n subjects_dir=subjects_dir)\n\n# Find an anatomical label for the best fitted dipole\nbest_dip_idx = dip.gof.argmax()\nlabel = dip.to_volume_labels(fname_trans, subject=subject,\n subjects_dir=subjects_dir,\n aseg='aparc.a2009s+aseg')[best_dip_idx]\n\n# Draw dipole position on MRI scan and add anatomical label from parcellation\nt1_fname = op.join(subjects_dir, subject, 'mri', 'T1.mgz')\nfig_T1 = plot_anat(t1_fname, cut_coords=mri_pos[0],\n title=f'Dipole location: {label}')\n\ntry:\n template = load_mni152_template(resolution=1)\nexcept TypeError: # in nilearn < 0.8.1 this did not exist\n template = load_mni152_template()\nfig_template = plot_anat(template, cut_coords=mni_pos[0],\n title='Dipole loc. (MNI Space)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate and visualise magnetic field predicted by dipole with maximum GOF\nand compare to the measured data, highlighting the ipsilateral (right) source\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fwd, stc = make_forward_dipole(dip, fname_bem, evoked.info, fname_trans)\npred_evoked = simulate_evoked(fwd, stc, evoked.info, cov=None, nave=np.inf)\n\n# find time point with highest GOF to plot\nbest_idx = np.argmax(dip.gof)\nbest_time = dip.times[best_idx]\nprint('Highest GOF %0.1f%% at t=%0.1f ms with confidence volume %0.1f cm^3'\n % (dip.gof[best_idx], best_time * 1000,\n dip.conf['vol'][best_idx] * 100 ** 3))\n# remember to create a subplot for the colorbar\nfig, axes = plt.subplots(nrows=1, ncols=4, figsize=[10., 3.4],\n gridspec_kw=dict(width_ratios=[1, 1, 1, 0.1],\n top=0.85))\nvmin, vmax = -400, 400 # make sure each plot has same colour range\n\n# first plot the topography at the time of the best fitting (single) dipole\nplot_params = dict(times=best_time, ch_type='mag', outlines='skirt',\n colorbar=False, time_unit='s')\nevoked.plot_topomap(time_format='Measured field', axes=axes[0], **plot_params)\n\n# compare this to the predicted field\npred_evoked.plot_topomap(time_format='Predicted field', axes=axes[1],\n **plot_params)\n\n# Subtract predicted from measured data (apply equal weights)\ndiff = combine_evoked([evoked, pred_evoked], weights=[1, -1])\nplot_params['colorbar'] = True\ndiff.plot_topomap(time_format='Difference', axes=axes[2:], **plot_params)\nfig.suptitle('Comparison of measured and predicted fields '\n 'at {:.0f} ms'.format(best_time * 1000.), fontsize=16)\nfig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Estimate the time course of a single dipole with fixed position and\norientation (the one that maximized GOF) over the entire interval\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dip_fixed = mne.fit_dipole(evoked_full, fname_cov, fname_bem, fname_trans,\n pos=dip.pos[best_idx], ori=dip.ori[best_idx])[0]\ndip_fixed.plot(time_unit='s')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n.. footbibliography::\n\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": "3.8.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
scikit-rf/examples | metrology/three_receiver_cal/.ipynb_checkpoints/Calibration With Three Receivers-checkpoint.ipynb | 1 | 782191 | {
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"It has long been known that full error correction is possible given a VNA with only three recievers and no internal coaxial switch. However, since no modern VNA employs such an architecture, the software required to make fully corrected measurements is not available on today's modern VNA's. \n",
"\n",
"Recently, the application of Frequency Extender units containing only three receivers has become more common. Thus, there is a need for full error correction capability on systems with three receivers and no internal coaxial switch. This document describes how to use [scikit-rf](http://www.scikit-rf.org) to fully correct two-port measurements made on such a system."
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"A circuit model for a switch-less three receiver system is shown below."
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" <path d=\"m 367,117.36218 6,0\" id=\"path3865\" inkscape:connector-curvature=\"0\" style=\"fill:none;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
" <path d=\"m 368,119.36218 4,0\" id=\"path3869\" inkscape:connector-curvature=\"0\" style=\"fill:none;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
" <path d=\"m 369,121.36218 2,0\" id=\"path3871\" inkscape:connector-curvature=\"0\" style=\"fill:none;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
" </g>\n",
" </g>\n",
" <path d=\"m 465.5,95 0,10\" id=\"path3844\" inkscape:connector-curvature=\"0\" style=\"fill:none;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\" transform=\"translate(164.5,-27.637817)\"/>\n",
" </g>\n",
"</svg>"
],
"text": [
"<IPython.core.display.SVG at 0x7f49d69a7c10>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To fully correct an arbitrary two-port, the device must be measured in two orientations, call these forward and reverse. Because there is no switch present, this requires the operator to physically flip the device, and save the pair of measurements. In on-wafer scenarios, one could fabricate two identical devices, one in each orientation. In either case, a pair of measurements are required for each DUT before correction can occur. \n",
"\n",
"While in reality the device is being flipped, one can imaging that the device is static, and the entire VNA circuitry is flipped. This interpretation lends itself to implementation, as the existing 12-term correction can be re-used by simply copying the forward error coefficients into the corresponding reverse error coefficients. This is what `scikit-rf` does internally.\n",
"\n"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Worked Example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example demonstrates how to create a [TwoPortOnePath](http://scikit-rf.readthedocs.org/en/latest/reference/calibration/generated/skrf.calibration.calibration.TwoPortOnePath.html#skrf.calibration.calibration.TwoPortOnePath) and [EnhancedResponse](http://scikit-rf.readthedocs.org/en/latest/reference/calibration/generated/skrf.calibration.calibration.EnhancedResponse.html#skrf.calibration.calibration.EnhancedResponse) calibration from measurements taken on a Agilent PNAX with a set of VDI WR-12 TXRX-RX Frequency Extender heads. Comparisons between the two algorithms are made by correcting an asymmetric DUT."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Read in the Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The measurements of the calibration standards and DUT's were downloaded from the VNA by saving touchstone files of the raw s-parameter data to disk. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ls data/"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"attenuator (forward).s2p simulation.s2p\r\n",
"attenuator (reverse).s2p thru.s2p\r\n",
"load.s2p wr15 shim and swg (forward).s2p\r\n",
"quarter wave delay short.s2p wr15 shim and swg (reverse).s2p\r\n",
"short.s2p\r\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These files can be read by scikit-rf into `Network`s with the following. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import skrf as rf \n",
"raw = rf.read_all_networks('data/')\n",
"# list the raw measurments \n",
"raw.keys()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"['load',\n",
" 'attenuator (reverse)',\n",
" 'short',\n",
" 'attenuator (forward)',\n",
" 'wr15 shim and swg (reverse)',\n",
" 'wr15 shim and swg (forward)',\n",
" 'thru',\n",
" 'quarter wave delay short',\n",
" 'simulation']"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each `Network` can be accessed through the dictionary `raw`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"thru = raw['thru']\n",
"thru"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"2-Port Network: 'thru', 60-90 GHz, 721 pts, z0=[ 50.+0.j 50.+0.j]"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we look at the *raw* measurement of the flush thru, it can be seen that only $S_{11}$ and $S_{21}$ contain meaningful data. The other s-parameters are noise. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"thru.plot_s_db()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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c/b1/QUFhZeUn2dn1uwkHm/gjA+P6u80rvBGrMZ/qvJWp/SVr1HrFyILiVVm3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z4I8KdkKIYGkDKJRv6urO0b9xo25Qvqu8kMG0qioTbr6DKQQDG4TL+3pEgPZBlI3w5pyCfRABkG\n0vfXARlZt3p6/qixF2uLimnMlCbS79vWF/Y4BBM4+iVZ5HrtjGV8Ws8g2xZSZLxL9rP0mmMbYttW\nN/0c8GNvUeFV2pCwDlBt0mzGkkW8EOjwhDsZ1hAy9rc+VbFqhzLHFc36XSwcWkMnmoLwrKok9nw2\nOs9+dh6yAJI6ANJ8Y+6D0I0xkIKJXEtSVzwsb30p2lPPMqX4NJO6WHsGjnyxuFi5FGoqHaSDTxMY\nhzOQLK2OwEXx/NffoSSE6PssjwHIDQtRL8zaKJ2uNB68upxMaXwaYJfSIb5vFEky4Khs3YFL3/BW\n5zcATC/ibBKN87o0gGQi3bQETDZ4maQtziwlqlab58uJmvRY2uZP3wso3M471mQUHF6jaQAC88fB\nrUKr2rW7nvCESxd2UbwYzWZTDlcERR1a1InkKTw/vUfFQNrlgMQGAKSMgbSACJdxOYT1QNk6DGRB\npotfEAOpN9Rs0kxl8xYAQKjaswHIxR85kt18s8PRQFUByOa+a3H5X/4d/Jkd7oHEw8rcL+Hc2C+n\nH1EPKHBh51yOig0y7BvxUdm8FZuf94t46Gs+Hvq0WxJn6Hid+Fz9vDKZ4ACis/OZLGzIdeJ5ZsMs\naqeWi6FCynDkGCyb+fWzrKub1WzOT8sZyKIAeF4IIOW5a7v2YMuLX24+z4HNAotMABi94WkAUrBo\nu7DLhAhmjBAhJOOU1mS13I5liX1w2VdkAKTjws6Ctcx8IgUbYvadCT+NTRQbcmEXZ2Gbc2QZyGxB\naT3+LIDULmwbYNkl4bjA0S+dx+f/6H4nicUBhZWMTsrpqHRsXnN8Ywyk9bz1vM4nYOXrVJYxkK4L\nW3qTnvyyPdh0uSrc33cZSDCGe//hOOb7P1w6RGP02eB7vSSagvg/GZ7gfto6W6CnvWD4HCAe4tpO\ndCeeCpikFgZ4Hqq79uGRL58v/a3twrbPp59JkfFS3X2VOlDIlqpmnRWvMbvEW+bi5eP6LuUxALlR\n0fFbToyOmxns1VUhz8xmrxWWLOOjAuLVQqrtUFnV1rvWfY9l4fFia10rDznh7ImReaUZhepkeOtD\n1Hz0PaucgHZh67lKPFfZZ7IRvUbTXJt7TWeT4EOKdNvCE9n1obDoss3Y0PXPV1ac2tQr9H3DsgqW\nOC5t+zgthd1+qJdjFITXgBcvGgayKOsdKIa/tFIBrdVzWfxyjOpvHVCuAv9JaLnfgix4sa5X4qJO\nf6zDIJg5VzA57bAC8jwUUeMyJMFM+lNK3XmaAZCcCRz+pkBcU3NdgScnhnjd2DNXOBOmFI9dSFwN\nyBw3OHsatG5nXFuMbaDDA0oYyIzoGDSSYX6czO+s214wNKYr2PVDrkvYXh+5GMgyBjLrwiZpOAqx\nXOeyDqR1Lc382npCCFNHTxfKt13YtsRhWltTeiA0gFS6Tb17l1mhpdUA1uZ+wRqbrdNcNlNkgW92\nDhcRkNlrhlYb1YIyPgCcWESUzEOztrKsaAZAcqUDKXdrAuq57lvJW3abwnDzdsw+5+fyz9+65ywD\naScWZsfmj45tCEA6iSiGgXQBZDZumkUugNTvkCcZF7Z17m1PkBnz3aUBaJg3Zr2qnIsLvStM73Fz\nHj0eJwYyGy+bYSAz6loQmnNhn/zmIu7/x5O5sZCwhmBqSEKlzbh7Urf0liP4zVEZgz3EQwFKc0aa\nIFbYjZcngEYer+OtuemIJcdRAiA1/niMgfwPJGYjtRRcLgZSAshspqxWWFLZ6jI+vJAFlP9Wp/QD\nwIqBcSaMTthhIk3soTSvdDMaVTOQ7kJXd+YlEOoYw86ZWJIsA+lex2s00zHRwGFY7G4QFsmHJJzF\nefpM87d2i/gF/V4dADSs2Ku6Gccd7XydlvKwWxlmAWS2rI/dLUcLoZksd8/DoLEXQXQWQV8mBcW9\nEkanQLfTIITXcOsICngYufYJCKZkaAIvUozm9+UsY1nyjH0d+wI0VCAv17JLZ7Jm2PCCftN6g4u7\nCRaO2fFgapw2SH+UAFJwgWBClXHRc1Qzj5aBwTpt+BNpWIdgwhyvQQEvqGJQFAOpi6STTAFvEmZq\nw1oMKBEJbvhVt8SL/CIbz5VKYRcqQZF9PNqFLeAadyxxk2jSa9kAEsb9uh6AZJVNWN30s+p3SfqC\nVU3NIgYSmcQqLfs/cgIssMrvaJ1BgpyH5/9n78sD7KiqvH+1vH3p9fWWTqcTIIQkICEmSBBFGFEU\nRBEGRlF01M/BDWHUcUQHdEZx/XTcdQYZ5nNUHEYddBQYBdQJy8gmhC17utNZeu9+/fZavj+qbtW9\nt27Vq/e6O+lgzh/J66pb996qunXvub9zzu94FkjOZCey4EVaOBoWWoEUEIkDLJIpKeIobGeBp/qk\nRTqgxZdzBe35Bdx8YY95kkLT+sN975GOLiSWr/S0S2/MPAqkx8RPK5Ct4b4nxi3KfhfMZQbAUf5Y\nJNaUj6I9Rxg6F4VN1ZPqtFPRTlY94x1wN8Qz2sl47pcsk4ThrKO8+Z9GIE0PjQ/XgrXRpebW0Wdn\nhFRl9Tbb9Pm5xGZs/++DeP6XB5x7CHIlgqSwtHUAQFH9iYjb6TlYVijFkOZDNrzKs2geWWgJZ1v0\nkeHhYWzbtg3Dw8OYmZmBJEnIZrMYGBjA+vXr0d/fv1D9PPoSbQVQYn295IiL7ksKZGLCdihXSEl7\n4YlEHAXGQiBpklhQv22UiUcgGX3f3S1GO3sATNuE5cF7AuLAK0W8yqui1JyIaQedMxxtllUgwSuQ\nGZiYhQQbXaFTNlLzj2FIUOyZpdD2CpRnWqlydj8ECiSrPLMLIQA898sDWH/pckzsyjP36a2HLCIR\ni6rItKK4Zc6EzSOQ0/sKSOe43N+yzKAZkqKinD4didlHECs+DxMSNF2s1MV6lqHjvFMxce8v3evV\niOUKYFBZPGQFK6+/EcmJe4Cp+xDtXoZTvvZxYPbf7Efhr4gAgCHHrYjwgJR21sV2PSRvbYSghLyJ\nzovCSQofREMUOi5tITlPIstpM66iBvJXAkAleTJixeedNiKtbSgP0Que14QNAGpbDsAU1Tf3+9Lh\n3QwB1gIkcf1xEEhBLmX3Prh3YOri8UwvfnV4IAFA7eiFaexjjsnQSKNsitQaj1aSqGeqW3ADohwF\nUhebsE0ojv9kpDwMQ7VQFwuBdP0WmVzusmwrkLyvN2diJq4TZEwEmLB5EbpA8kpnpH4QDb0hlXy4\nAmU1Ygf2yiD3NLniQ4IOEAWS/d5MR4F05zs6qxkkhdn4UC27v/iNi6c8Ze7OhEMgmXEoCKIBTCEC\nyVhJ5ChgFGFqLEWPKH92qY4CySv3gDUu5USSuV9J5XwgEYxAQpI8Vg4r85J3vOtqq+cY21m3j3I8\niaEHrWQCERtFpWmhPCLJnjbpJA5SLOU55661tgmbMHLQ7nSmOy79fCDNJgMWg6RhBbJareL+++/H\nPffcg+FhL/xLS39/Py644AKcd955iAT5Zx1LwrDf09Qysrvo0j6Q1i/7UsrcU2F9QVjSbmL65nwg\naeWVisI1aVOGj+/ew7fsQ+fGk8UIpN20ImswJGvHzphzQSZfP2XWQiAlWI7Hphxl+koPZItzjyil\nCktlQfixhAsuNXkIoPtD26ZxaJvr+GxoftHGLgJp1GqAjVRKSoQhzS2nT0Os8AzUmpVxgc+xTfpB\nT3iSLKOWGHSUNi3aA1075N4vJYnBEyDtYXndJDUCNZWGqbv8g07wCxlMSsTiiyQ6JjWpylGvCdtQ\nUpCNMkMcbEKGBO8kxvTFF4G0hUEDxDQ+Tt9lMLBGtGTRrlSSJ7t1BHC0EakmV7sKJEesbtdi/8e5\nVyTSIAqkQQXHOJGPQgTShMkpvnrVa0rjOuAhr5REhJ8AdFshqyROgsT5p4p8IBFJouP8iwFscw4R\nE7YpKcxGk/eXdBcOzoStJAEdiCQJAgnoIqVDUtyUhvlHqSoIAkkUSJoAW4KGFBRUHd9m0gYrttIi\nC1BpHxOde979ufzd16Ny+CAkaYQpwrhgUJtHphrGDzAMAumP5pNn4bgXOMetNhgEkvahhx99mXeJ\nNgz7s+cVAh6BDKVAChBI+h2ZBsArkLwPn70RtBBIOrKc7Z9W1dFx8VuE85SjQAo2Z92XvQ3dy16M\nyiH33VobNdqqwfkZU8wM1gcvAzKL/pmG2NVJpwJARUK/E1pZJNH8QQikJMleBFJSHGuklEwDcP1n\nLeuCvXE1TQuBFJiwDUNypuT2V12Cwz/7yRHxgWxIgdy6dSt+8IMfYHx8HKtXr8YVV1yB1atXo7e3\nF5lMBqZpIp/P49ChQ9i+fTseffRRfO9738Odd96JN7/5zdiyZcuC38CRF28gCwBIkuKaxARZQgBr\nEtIjnTA0E7OHypBaaZOwW458wBIfhc2YhCh/FULjA8mzA89uOBNKJov2y96PyQfud69mTA028idr\nqHJZYxw0h0MgeXRASaUdJYXJGgM4gRkAUSDt42ARPLIz4xGbfOdFDuphdwq8ZM94CWYfe8htp+Y3\nyVMIZKnooI5SJIKeS98EDH/Sul5NY3LF9eja+bdW32oG8ocryHRTk4MHgbRMiYbaArlahhbrgWke\n8umHFymTIhEo6Qw3bnj+RjIGvJOIaGfvRE3TCqQc89IMcePG2Qz5KJAM+iHJ7OaGBOn6IJDFli1I\nTf8WpVZ3PpBURaRns/dCo2yGCaliR7o7KDlBR3k/NZZiyBESRCMwtZJgG1oeuW2XVT+HYNHj27sA\nipFfQ8lgfMVHYChpyBOsz5cIOTKVmEdxlVAjjTKKl+6h/BGbsCU1Cr2mIJomFhMfBJJDOJ3jpq1w\nOSZszl3GTCMmTQWaFsl8RcYZg6o0gECm152OtrPPg7nvi2whmVXSABECySJbIm8P970qAIJS1ll1\nKTJXiaNAUugWHXBl++gJGvYcMnTZSpXK82RSz0vNhKPxMRnl2YtASjC8+dS5aok/L+8DySssejSH\n3GveCL1YAC9ynCiQ3vtNrl4HLZ5DdcydSyVVBagUpN4gGr7LNgJZYRFIIZ+l2iqGt53OUgokpTCT\nYCw5FsPE7jxaV3VBATfPyooAgXSDaOQEq0Cyfr9WbnGTMKnIrAJJpPv1V1kKpF8cwAJKQwrkN77x\nDZx33nm46KKL0NPTIywTi8XQ2dmJ9evX49JLL8XBgwfxi1/8Al//+tdfGAokQ17KBrg4SeTJAkre\nH/G5UlTo0U78/tuHUJssINsp9oGkTdgw6TboRdsNrmAXLXYSUrMtWPeNHwAAph/8nXu5AIEEwCSo\ntwq690cPZj5KWU2lqXOsAlka2gPYrkIGw5Epsx8BMWFz0Yal1rPZPskyyunTES3txpjNRThwzYew\n7V2XOUX0kphGhzxDORqFUanA0FzSdtb0wD5Ho2bijz8Zw4pXnYblqybt7nMIpP0eSpkzkJn4FarJ\nNQD+CMCK6GUnfMmrtKmqZcLO0+YhUr+LNtHCmHUEky9JI8kgkCJlgFOi6iGQPKWGKIjG4b6TJea7\nKXRcgGL7uUyKSz4wRdwo1QbFOUfGr2My8/jOCfpGtWkaYgSS/jAOPDGFwmjFvo6rn0aseHTL8Fc2\nCNUVr3SKFcgUILPHiQmb90/WazzRs1eBtJQ/BbqhIqq6pi/hoiPKYe5UAuf50kE0kiShZiTqxfQ5\n1zr8ogI/b1rGBz+G7OHbES3tAq3JuOOHd5cIzoVtd5bqjg+ND3nn9QL47POyYnJ9sRVLKoWmpFLf\nliR7LCv977pOyAZhBX0Z3r7Q/tDxBJBiKbjEUieIxkr1BQCoJNdg/HAKwFNMDWSj6InCdtA/S2aj\nm612GjRhO5tonkicy2HP8EDy71Dy+kCSMrpmMCki65mwaW5Ri5pPAXTdYWOQY3E8/v3nsO6z16C3\n+H32WkmBXjFw8Mkp9J5mf/9mzfWBTGQBTDDl3XdkQpaBmsCEzWTTcuIjPHb8wPtqRup8Dax85Stf\nwTvf+U5f5VEkvb29eNe73oWvfOUrDXduyQu96CnuLsLJGuKHIAv46kQ+kDRFhgV23dkAACAASURB\nVHWA3p3bJmwKgeTLeISJYxA7O9O8ZlZXKQSS4cDkIlHTNELIThAjt37d+c2mjVOYCcPwQSB5kWQZ\n5eyLMTZ4AyYImTXnIqGVfGh07AlZSWegF/Iwq7ZSwC0qvJKlawY0TcLUBBVFafvVuLdj/S61noPJ\n/vehkj7VfU8iQJRD5qRIBNnTN9s+e3aVWTLJkIhbnspDhFpIDj+ZFrUCSJiMHKKJhKvX2Qz5mbA9\niitlQq3jAwlJ9uRHFym/vNA8gG3nXoj+d16LaK6boq4iQTTcc6XHre7S85A2xQikyQIt9MfM9TXa\nTUUpc+NI0Xz45GilJYQPpKkkODQCDier9e6o51/jg2gEJmxY928Y1BykixVIy4wm2HTYKK0wiAZA\nTRcQ2wuCGwB6nAX7QBpqxk0fyEwlivAaVvH1CaJhFEifKGzCwuGX9YuUk1UrJ7QiRlqZjRJjwmZ9\nIHuueBvazzlfjEAS1gAPSka7+Uho2fTSwL7aHfL8ZtE7w5l7avEVKBr+cQ0WD6QYgXzwm9tRSVgu\nKyK6MWctFG2CiesBz3jB+zpTwIM3iEayTNg0J6yPr6ChePN/M/1R2fMkCEvhg2h4f2jA0Rme/tl+\n51OQjLIzhylpTunnFEjJz4StezcrSy4Ku6urfqq3xbh2qYrE5GdWHNSmLts954O1+jNfR3r9Ge5p\n53yEG8yij92k/AilwAnOL+CC8ZfiaUjIJbLKEflyCmSSQiBlr48LEZ02YUssDQ6fm9dXrMSqMNU0\nei5/K7ou/nMPgqUVfLJTEFNSpgUwDDx77dXWYX7nyylmes2w/PRo6hkPAqk4/dPiy6x+mn6+X/Ag\nDpIaQXbDZuRee7lzLLna4gAjtDm1mD2Bk0HCoRAnf+G7WPu1/4fJgWsxtexdmOt4DQqtL8Ns9xt9\n7w0QIJu2L5/fu+RN2EIaH/v74Em0hfUFBKaI+th96VWIduSw5ku3INppU27YC0rXRZezF8q8csv6\nMpoCQwwfhc1kCuH62vcX76BPMueU2qTP3VBKiycXtsCsJic9xNGSaUX68j6Qke5+FmmBV+EwTdOy\niJjsJlbIAynJQtTaQqdMF0XknfZ7NwEAyhWKINqPlEAmHKE+FhemP2RTS3dRrFTRCKRIEQHAcOdK\nakTsiyuI8BeKLMPURX7cdczTnDWGzA2i+ZwokJKsoPPCN2Dwuk9YZT3PK4zCwCrPVkUUsmsaYKwf\nQtOu9VINzWTmYTo4rTBRceYUSZJw+qe/SnVBcu5dFiGQ5DnxCCR3fyS3NECPM9cqYaUydMun12+0\nynLfmxFpQ2rNqYL7tOuOpNm/SVAehUBafRTMndQ73vFUJ2qxflRTp6DljDPRfdlbkD5tE1u3pLrP\n3DQhy7Z7iiSBvn8WoLBBCw8llO8tNS0NKZDHhRWTXkhoE7YzILkdv2MPZgNZ4v2DSK5yAwocBDLC\nKZB+EYpEkZX4MpxQyIzsY8LmF0Bn5y2pjDLhCaKhEEiy698x9gr87kvPMuV4qF0SBNEoPF0FJ7Ti\n1XXxn6Pn8rd6ymhFN0CFdvomE7KaYXd6nlSG3HMwaqY1ySkq9j4whqd/NgyLB5JWKAW7Z8LBJ9pT\nCHwgrc65C5qSthDIUuvZmOl5Mwodr+SqYJ9VrLsParYVhpq1eBflCAqdF7o5ewGIFzPeB9I2S/FR\n2KLbUBTm+hghN6/HPUlLCASSRUvogC7yHK3z7a94Nfquerd7nkYg6SAahZjGRAoka8JmFUgOrU2m\nMNn/HhTazoUe7Wa9XDQrIKpW4n0hAxBIkR+ikgDP+0cUSHAIYdvLX4vBD/09VVDwvm0TtmHSfK2m\nECkP8oEETHEQDQC0n4CxlTegPHgFdQ3vQGdnvCKIdIgo7HJqPQCgYK5wjjn+e/wqKUDYaIW9+9xX\noedSl5fSN1+y81LrIZA+6fKElgLWBYoZOI6SHIBAwtq8ZDecaf/VuALJKJ3EJ9gThe1G+4smMhIo\nljr1xf7tm2yQX8cZZzo8riwqKxhnjuJPAyAq+PutFjXMHrDmfY+vLWSo6SyzOUissjJlzY1Z7k6l\nqSqe32pAi/Vh2Vuvccrpahume69261JZBdJhrrB9IKWYPwIpUe+zMJvA1PL3wpTjkGNxdL/uCshx\nPgpboca0aWESuu0ewSCQTCNY8f6PIelRgpdAFDYAlMtlPPXUU4hEIli/fj1UVUW5XMadd96JJ598\nEqVSCStXrsRll13WkLn7WBNm4MuKsxB4drD8YCYDjk5lRaMDxAdSjcDQuMHqlHcnQyWZsv1uvUE0\nXI/dXz4mbH6ic7rFIZD8JKkk0yD+wk6uYyXpiV5m+KrsBPfOuZAm7EAHZ1v0uQIAO81U1XDNG/b9\nKR4Fkv/YOQVSM6x3LCvY+WvLmbtNllgEUhUtLrYC6dEJJPC+dI4yKiCMhySjkl5PlRYjkP5CuT9I\nEjSlHapOo2ON+UAyZWWFqd8h+G5AgQxjwqbHL4vMsCZaSWLfCzgTtnMV5dd2aHQZDt+7FS+6ctAq\nZ5jsZ2sKrnOqV6HFe7ycgIBFoQTgmTtHMPjSHFqWERcR1u+OFtE3bMhJSPIMc0wiZNUcAoxoEulT\n1gK7bmfaMrkFXVIUDoH0yUTD+T/T5a2Uat4gGoc/Vkmz44BPz0tQVFuBZInFxWO7ml6HscEbUNg9\nRBVVAq+x6vYG0az/m09hbGzMrccvCttBOOuMU87Pjm8bAE7+/HegF+Y8tGwMAkn8Ebn2rHHpM/8F\n8EIGdJi6nPgEMw1C1izKBwsYEPniWBdEO3sZ9ktemecTHTiuLT7pHamDTP8AW4Hk1gG9Yrj+0IL5\ndtnV12Dyv1IAhu06rLG/d+sY2gfTmB4uYPJAO9rAKrvTvVdDj7nE4obMmbjJem4jkLFcD5RkClLM\ny+nIuL0Jou6lCE+bxiKtskzzQFJ+m9xradm0BZHDI0B+mK7M2595SsMK5NTUFD7+8Y9jfNziPurv\n78enPvUpfP7zn8dzzz3nlNu/fz8ef/xxfPazn0Uul/Or7pgWfufkpCPyi36joGiARx64CDJJssiV\nff0xrPKJgVUoqeJ6vM3TZjM6Atxr4nX/JB95hDUHcblulRSlQNpIpZCXkEEgFWbycGl86k3S9T8E\nbc4HgbQnIzXLJr0PZ8JmFV4eQW0IgZTgjAe1tR0n3vglCu3lkAmBOCpTWAWSQ3bGev4K0t1/jZ71\ntsM4j4aSxSSEAgnFQk9MKHbOXLL4hafualSBZJElif2fq48xq9ELC1VmYqYPY9vzzt803Q/AvT/e\nBBqQAcjJHc/n1qb76nn2AmVNiTMRxQCcfMsmF4UNm5TbeR8CInETABQVBth0eoYgaNzygfSOs2hP\nP9LrXgRzejcAH/M31y54JcIOMnICExg/7wBlUE2zmyeZvUdDTmGC52n0SWXIFlEgnL0lgRIuKibL\nPiZwCqHvWWb9qI5R57lnLIkVVkM3GUSKlqZM2JJXaQWHQKam7oMpRVBNngzgaUElxOeZ/4Z5Fx3u\nucvEFM/7U/O12+cDiMQBjl5IEESjpNLovvQqYO/NdvtWfyZ3zeGR23Zj7nAZidWCDDT8O1A4xdBB\nIK1NUNclV6LjgoshRwV8kPVoqlQ+85frAymZBiTZtAjCJfb70Its6ky7gTp/z18aNmH/4he/wPj4\nODZv3owLLrgAo6Oj+PrXv449e/bgfe97H2655Rb80z/9E97xjnegWCziP/7jPxa800tF6FzYlgmb\n5YH0pyUhQTI+ATJ2hgdJkjjTI7zlA7jAPMLQVQQgkKLFnMsuw+/wFEEUtsifhVcgWRN2yCCaEDtr\nbc6limDTENoKZJpTID0oEI9AmhZtD70L9mSiES1M/j6Q5N3JsRiiHdQmi5lk6uzxQqEM3MInyVAS\nScRJGk3BxOKYBDkFUljWQWeI4kj+bwSBDFGWGeteUyejcDCLEkv/47Ii2M9Z1z2LhBUgQn0Yslgh\nBfiNoFgMKnhH9AzXfuPf0HKKbXISKJCGnARP4+Oky+NMzC6nouC5OIVMYfS8cPPrs4npf8e1Vuo2\nEoXtybBBrnfbH7jmb9hTNgLpsj8INgY+IkLsyCJpylFPGk7fKGy6iK9ySRTI+j6QYgAh2O/YhMwp\nxN7NJGCvDb7PhWsjxNzAjEUBjY9slKBWR1FJr4OhZl26mVgcEwMfxMTyD7iTmwcBdete950fezdK\nTmCR+Fvl74OZeyXJ8x3R87wwiAbs86R9FKf3Fbx5up374NYGXoG0xc1EE7W4eoXP33/jyPfJPkAB\nTzVIkn2fdhwAkco4SwUmkjqRGU1JwwjkY489hjPPPBPXX389AGDlypX4zne+g0suuQTnnOMmRL/g\ngguwc+dOPPXUU35VHbMy3Xs1TCnKIgWKm8/SrKM/8ikOLWFN2OScnwJpOosmPQiDg2joAcf6QDIa\npBUswmPicoQ1YVOnpWiMpW8IQiDpaHNJZj4iTZRvWiRhcmFT/Wd2psSEnWVN2N40gAIEMi4DCquk\nsJGBASZsoVuVXRd/kiGM93ufDZqwBchObNkgkJ8ERDtTjp8vuGrX3GYhbsSc2MD0EuI+WCWYHffs\n/5ySJ7NKkrOBIyiraXiQ3EATNrfIyQEIJNOuU4X3eauZFtdsJtp4KUnwWW4k00YguVSGblYX8j68\nCK1p2vdPK566KU6j6DcGHVTOnqt8LC8m496QZZgxHQSS8EAybdVX1gSdsuvxPmPTJwqbER8k3GUS\nqKPUSmIEUmhO5oNoaFSaU4ideoICNJsKovEq4SwAaVNF2WMqc+oZyL32jWh76fnQo912K2Kff8at\nJSFQusj9hpxDvefY9rSq4aR7FObCthpzDwrWJ7ECyYEJso8CGRehh379gHiDJMkYG7wBbQdugVo9\nxCCQxB1Gq9gcoJLszCk8ubtdWZ2/5y8NI5CTk5NYt26d8/fatWsBACeddJKn7IknnojJSb8oxGNX\nqqk1qCVXedAorxJClAf+o/easE1GgaQWZb8FnKLmkGhVNZDGJ4QPJCR/MwJNykuN10gry9xPEEh6\non70tt0YekZlorB5vztRtheR+O/AmQ47QrLvWH2znk+9IBpejJoVlutBIKlFnfjAMN0QUmNY7zu5\n2vp2Wl/ycu4c/VzEC1qxxeLGLEkrhOe9InLQFy9SADVZ1/P5ApwFgDjbGwqJqG3AhF3n+dulhEed\nSZmJXhGbxRgfSIK46LpHGfGYnBmfRd53lX1Gswe8FFKZDWdRgWY+92Gv3LWydX7o4XH3nJxgM04B\nkHS7HT4Kns817vOeJVWBQWEIVuS5aFHzGwOsn56vCVuo7JO/DK7PAjOuj4jcN8yAMR0KgfQ552ZX\nqq/UijPACPrK+CXLLMLsbOD4pAygnqc3UISVMCZswTdDc6U6Ycu2EifL6L3i7YgvG+A6BTQaxCM5\nJmzOt9GWQtt5MOSEy2Xr4b1k66fT14pyYQPs+5N4xhGufeog244PAulVPoMRSE9GK1tMNQ2DrPuU\nD6TkKJC6de+0D6RIgfToj0vAB7JcLiOVcv3yknb+50TCu3jG43EYRkhU6RgUZkKXVccXqu57cnge\n/U3Yzq7MrzLfxMFhfSDFUdgmRyfhiBplfCAN+6L48kH0v+MDbNdsRZNuY2pfAXpnDH3rEwAs/0T+\nQ6wV/VOEsTfS2L6nPEOTOdsmbF6BrGNC1TUT0DSvDyQVOKMkRRML7fdKmRzSpyHdvQZrvnQLIh2d\n3CV0G+L3X86+GOXsBtSGhgL7TffVFZn735UTbvgsyk/8AfHlg+HqBbUQOAqknQ4zjPLp1NGgD6Tw\nuAjB4RRIOrraIdzVBAgkuzyzRAVWfYPX/R1KQ7s9vdn202G0r0rj1MtWQpat+a/zwjciNv4roLzH\n952aNqxvIoJf//1TgAkMnEl4PGMeFwlX+VLApBmVCQJp/++XiUZWgIDAOFf8vjeyESEKJKV41HE3\n8DYRCT4vaj0AgQw0GQeg3b6bU8KcUS87TpNR2IDCtk3x746t/ATad38eilSxUF7f1+FvQvYXGoEk\naw5diT3GApVBMg659uu9yzom7ELHK1nWCQ+wwdavVQ1E/YJoRFHtIgRS4I7imcd8NsaesePD0eue\nDnqmrsmdPHvZzh6mV7wuA570iFQdiykNI5DHxRXWTKYKEEg/ISZsMemqaZghF1RyHRX92AQCyZuw\nRWYcJZVlSHkJs0N8+SCSq1YzZUUIJGBNDhmK54r3JVn23k/UdVIHECqIhpZK3lUgnVy10Si6XufS\ni4hMF7SYumnlB+ei6OgJT0l4A54IokNPaKOrPoVqyqKQiOa6Pe86VPSkJAGSGjL4hJvMOV9Felyk\nTl6PNe/7CDOpz+beEPhe+P471FONKJDcfYijgRtRIMVmMVoxdH0gvVk9eBofRoO0685u2IzuS670\n9EavGhh7bpZ75gpoq4FQ6OA6u73CjIsWi4Jr6Lod5Joojg6K4W0vYX+zPMWRJOibry8r5+vqj0DW\nRxUd5oYGxoxwfDljWtSOrbA0g8QE+K+yzbNcg/mOCwEA5czpgsIc4sjwQFLrgZJ0yO5Nw0S1ZoE1\nhtrOVShGAIPM3gwiJ+DSlEjUdcAzk5zvpEEfTNIebcIWMlmQ6oJN2IwPpOeevZtmEU8j7dpF8tWT\n91TKbkYttjzEPBQkdUzYjpBNrjtvEIuDVtHttuogkExtUsj+NSZN0fjMzc05pum5uTkAwOzsrMdc\nXaS4+BZbHnjgATzwwAPYvn07ZmZmcM011+Dcc8/1lJucnMQtt9yCbdu2QVVVnH322bjqqqsQFSR4\nryv0AFAUSI4Zhl0oiG8TUagcGh9a4WRofNhFb2LFhyBM0Equo32rQiqQckAQjQiNs7jgaM44qk2+\nrOyjQMqKmNrAOR+BKScgGcHjJkwQDS2VWQqBpK7tuewtGL3Tojrx86+Z67gQ6sivUSvrUOM1LrqX\nDaIRI5CWMDq6KC0cIw0soqFpfKjx5ZlMgyeWcstmREs7EZ/z8Wfmc0M7CmQDaBKvQOqmIJbEp59O\n9Hp9EzaN4BBTtGnoYnOoX0xIWKWdIlK2lDviM+iDQNp+u/Rm5pnfx7HiA1Y+dsgRIYsKURwtn0ed\nQiDJ9yp4z8T/k/OBFHbNT+njEUifIBo2vzXbQC22DJHKCLVYLxQC6W/CrifFCSuwp5x+EUjavqAA\nKEa4KOxKej1KbS8TFmVN2AqLSPkoS6ZhYnSyH5HVW1DObGDr84mCNn1eK10vANcVhUEgyYALenYE\nEfP3gRS2TBSjgAA1pjw/zwSYsN1dorU+umOVvl+Bexh1enL5tZD1gjO+811vEPZLyWSh52cdHsgg\nod950DpG3NKYxBWUDyQfRCPevNGIdlOqXl1pqtZbb70Vt956K3PsH//xHxekQ83KQw89hPHxcWzc\nuBH33nuvcJepaRo+/elPIxKJ4IMf/CAKhQJuu+02FAoFvP/972+iVWoAyBHHp4Jvefm7/xqHfvwv\nLuIlyCLCTCamyUwgeqTD0zLj98gcDfgA6d1mhKbxYesQfsSSyiKQTr5jb1G/IBpJURxaExFBtaTI\nMJQ05DoKZHilyZLKHO1bKdglmwZM3tXCvq9i28tw6L5hwHwMpsaZOjllWxYgkER8PQ5EZRtCYULu\nKoUUIfYC09DOVOAvyb0Px4TdiDmSzwetmYBniPj1U4Au0IsNH0RDitltmoY3iMY6Qf2kfAPDWge8\nqG/wcyZjkA1IU50Nh6SqYho+x9dRsRZLDwIp8BF1rvUyK2iRdphyCpHKsHW8XhCNkwvbxweNMc2y\nz3m67x1QahNOXvBGxkxgEI3wWbMfoZoV5zyuzNbwuy89izXf/DSA71sHyfxQ59v0mLCDyvNuJXSE\nsMfK4iqQBiIot2wWVcj9KXaf8euD44pCI5CO6aQ+Wsa/uzD+ooB4Qxbp8FL/1Qui0X1ppCDcPAgt\nhkxWtgR0RZCOk5MTP/4FTD14P9LrN9Qty64fAc/U2bB4g2j0ij0J0DqCHwMCOa+2NAJLhJaGFciX\nvUy8m/KTpswFTch1110HSZJQLpdx7733Css89NBDGBkZwde+9jWHm1JRFHzlK1/B5Zdf3gTpOfWh\nKQpkgVMuAEQ7chi45sPuAaE2wfpASvXS+TGRb00E0TAIJBeFrXjrsAayOwQTJ5wC/M9jSJ3sklsX\nW16CeP5xp5yHxkdRINcsMmQjIpi8ZZdWxNBNyIqXqsG6jcbGFB2cwy+GcjQKo1KGWavyl7ll7J2l\nyflA8oTVSlKgQDYz/hsx/YZU/vhofevYwniwLIQJm1cGhPmgIaHQdp4HoXbvrTEfSKeMrjvK5OzB\nMrK9cehVQ+CIb0sTbgOmpIpREFpsJYXmlWQ4LP2Qa8qUbFKKCHHEZwL0nKA7yVMnUXwmV1hzVddO\nG/n0XXqIYuolEheWswozZ0wlAU2h8ivPE4EUBlQ559z7WPvNH/q6HOUuvtxKj8n4pYv9/DzCBdEE\nU1mxCCTjmuPpv+sK48v96umbi0D6i/ebESKQgcqOHwJZX9m2usn2e+3Xv8+kl3Skjgnb1E0hrCIq\nC0Bowjb9cm0GSKx3GZPNKEiYeTgQCCEAjZtxx0EgbVO9HI3ZCUR4qjq2rVpsGaZ734ZOT4n5S8MK\n5Hvf+95F6Mb8JYxS8cQTT+DEE09kiM03bdoEVVXxxBNP4NWvfnWjjbq/ZZfGx4mb8LuOX8C4uiwT\ndr1XI6BOkLi/A/rrH0QjiaPDuFRbree8CpFVm5AYPME5Npe7BHO5S4RtWFUoqCUGgenfOlHEzHlZ\ntkwGAA5vm0bvi9qA3vMD7yOMMNHdfKrGaBSolGFUrS+xnFqPeGEbg/rKEVeB5J8Ng0DGBMSxTZBv\nNYRAhhURQXMQMtWI8OZnG4HUI52Yi74Ie//rD+i7Khjh96CYPj6QfCpHcpz5H2BN2LQSppuQVI7G\nx9DRsmkL8n88F1P9F6GYjUKvfoBDIOnmQirtJv19y9Sn6WPCdlKc0vnVaUWYciHRTMgqiwBafJDU\nN0eQS2ZRJBtOcq3rb2lllQlWvFhh2w9FJF5PAZvHpoNtyz03MfBBREpDMClKNJVKvcpL7+VXew+a\ngvlW1DpPJB6kQHKBRmJeS7t5h83B9PUB9yCAdRciTiEm3wlVP/GBDPSBdtIL8dYdFzUVX0hM2Ox1\nfsiwB9jgTdi6uzmM9Q+gZfOJAObsst6xIgyYWeygX4kFIPyFmLDFPpA8D2b/uz8MTP9YXJMc86Zf\nXCBZHMP4EpWRkREsX86mG1NVFT09PThw4MC86paUiGBA+i0UwRFTpmFCrody+E4KAR96aATS2za/\niEiyiuTKEwO76A2iUVBNrcH44MdgqIIJXFEga5YCOTVUgHHOx9C34gxgfJwt16AJu1akEEhukot1\n96GYn3VQn9meKzGnzcCIuA7qfgikdY+sT+TCSPh6Ip1dUFIZ5C66LHydzmQazgeynvgjkBKKA1ei\n6xpvoIm3EsFi4C3kdzEAzr/IL4iG9gejaHxkNYKBv7Iyl9Qmx52yYfrqPW+7RfAIU5B/HoBoi2XG\npRdQP4oTXZMoH1HXB5Kh2iLBbKYAXSf3QPlVOjl2/cpy4qJ9rtVAXI56/vXM+POOwvaOaT3a7XAW\nNi2OEl5nXpZlGFUagQypEEsyq+T4cDpaAZZ+CKTYhO2LpFsdppq0lVT6PXI0PkLxQSDJvfv6EgeY\nsH0u4A5wCjPV78y607Hi3IuBXZ+wzgnGFZsEQrEsEUeSNSZovRC4Dsj2d6yVDfubc6+XU63ANFtF\nueVMJGcfRqHtFQvUYa8s1Ip3TEixWGQoiIikUiknGKhpkd1MND40Xa6IfCB5Gp+6flZkhyKhkj4V\nupLBbFcdJYIOovH1gZTFHzSfESHExOhBIO17EiqP9nmCME3ungPUpHDx8tu5rf7ct9G6xfuxMKY1\nrt8rrr0B3W94E9rP+TPnPK08AnBNiqbhUZZ4cmdBZ4PPC68Jj8LIkQjWfeuH6HrtGxvoBzF5Bis0\nYcWzoNUNEhJVwi0+QnNoPdOdj3nIlxzaLsMtGmpLG2LLBpC7kHKaZ3wg6wQHOME59D3VVyBPue7j\nyL3mUnRecLGw7/R3SXjyLZM1QWBV0BQjjvnUZKi7mTY9fs2i8Uoiwj0LkW0ii/ZB1xUUxkTp1Pg2\nFxmBdPwyF2ZpW/3Zb+GkT/2jq4TV+1RIfnBHQro78PRpPiZsw0B9FJfUKVv+eyyNGS80Amn9Ht8x\ni7xiZ0VqyAeSu9e6CCQxYYd7Rvy8z29GfFMIU31hDlHrk+T4Q4ekkmtSmEw4ge+RjDevG5dW8boV\niDh3tVgvRk+8GbVkMNAzH2kIgbziiivqFxLI7bff3vA1xWIRU1NTdcstW7asmS4xErxDCyeSGvHk\nefVv0DZV+RDsWibs8D6QhprBxMqPAQDUSgCS6pPKkLVwiU3YXi6s+kPHQ8lQxywvKQqKrS/Dw9d9\nMViJ9vnw4r39yJx2BqYfuA8AUKsqiES5CYG7NtLShu43vCmwX0x0XQACuVCyUIsfU6eQPoL0PYQC\nGfSN2M9kuvdqyHrev1yA8IuDOKVeMALJcqyJfSBTp5zm+Ct1nP8aTN53F7pe9+dsbYqCk2/+pvUt\nDdtRuKH6YYusANBg0FHYNGLgc308143eK/8S1bFDwr6z0eS2KZoaK4W2cx0uTqtN24RtUAikw9pA\nEEj/9KSuWHdf6LgASm0S8bk/ksoAAFpiBZ7bfQZKU0+IL6+XfYORRhBIwTxVR0lvVOJ9tsWKcPfW\n6b+p1xwkzMoQFLYfbFmPcmFnHQkyYfNSzpwOpTaBP37t2zj7/SeLCzFtun6WU/GXIV14mqLx8X8v\n5ewmJKd/Dy3ezxx3vsF6CGSzlptQVgsiwQikHIlAr1YWH4EUcX2KijkPzRt8p1cMIELOWeLrH73I\n0pACKQqg2bt3L4aGhtDT0+MocyMjIzh06BAGBgawcuXKpjr24IMP4rvf3eGUFwAAIABJREFU/W7d\nco0op6lUSkgtVCgUQvWT9p10ZKf1X1tHF9T2Xvug7bMgSeJrbMlks+55qRU4bP00DRPRWDzwWszE\ngDwQjcbYcoUSMCzu7zRFM9PamcM+8gelHKRSGURjMZS55traOoB0zrnfzlx3KKRp+euvxPDPfmTV\nnUkH3lN7ZydSXV3OjrUzl4Oqqp5r0pmMbz1Gaxu5fQyPbMTeH32PbaMjB8S8Ue1BIue6nGfV2tbm\n/M7lcihL7rMT9WnKVtQlCTA33AzIUeSiLZ5yjGhpYI9/nU2JUQNszutoLGHVW80Ak9ZCTLcjeuaY\njAEFIBaLec515nKI53KAfdzfu8xftCybm1zkT9fW1g6kBM9jImr3LeH0LTrhvuO2jk7Ait3CS/7v\nP7nX5XIY+NXD/p0qlJ1viQ5WynV1B3K+yooCHYBCbZhyuW5gKgYUvc+bCHnuZbj3nkilnLK1eAwY\ntY6bpqWkQnXPI2eh987zL1tmLVU23TK7LUUkEo0il8uhVOwC7D0nybPslLW/9dbWFqDFPjaddNzK\nOjpzQLTVbqodB6l7iUQibj21GLDXvqYjB9Qb/7vIMwse+7WE63PslJ1KAAX3/uqJcKwL5Hn79Sco\nC5boukI0BoNMnpJcv277GWdbWhGDW7alrZW51tgXAWrWO8q2tYjrzc8C+7m+db0Zpamv+jbf2dkF\nCII/Ozo7gaLsZG5NJlNI+t1L51tg1i5BW5T1XZywU/uZpvdZqaqKiE1vF43XWes4iff0WeXnks53\nDbAm7EQijkQuB+yyXlwmk0GGG9ddvX142i6vRGPQC3OI0uN2IWQn+2eaSmARtI5hxNocJVMpJNs7\nnHnINCUYugklJiOeSACz1vH2jh5gyPrtV6caKttXY9JQjXwAzbZt2/DQQw/h2muvxZYtW5hzW7du\nxbe//W28/e1vb6pj559/Ps4/XxBAMQ/p6+vDyMgIc0zTNIyOjqKvr6/u9WNjY55jXfb/U7OzkAzL\nd2pu1HJ2nR03URJcQxDPfH4Oqn0+NpcHGVqmYUIzDGF7RLLlEuIAqtUqZqhyamUKxADLX18qu2ph\nnlKkaXBprlCEJtiFTU3PQCuNOfc7Nj4ZypTSdulV2P/Ln8KsVlCqVAPvaWp6BsWYe35iagrR1jbP\nNYVi0beefKHg/C5D9XDTTUxOw1Ab22XOzbl1zlKuDmNjY9BmZ5i/PaK5iNBYHgCqAPyfAQDAqLnP\nOeB5NSSm7tRZqdYwOzaGRKGEDKzxOE61k8vlPO3GomvQgkcxG12DCnducmoKkXkG/uQ5F5JayWtK\nmpyahl70Biq1VKuIAahUKpi1+1acmXXOz8zOgiTbbOR5KpVpEDWU/kbGJyYC/baIa0CtZiBuz7Bj\n45PIVmuIw4p2nhD0gzz32rQ7pspV95vRS0WQLapuDytNTmPK556SpSrSAAyt7LSXs2+kVq1hemwM\ntdkiyLbS0E2YMJ32yHiZnp5CrWody1SqIMQmExOTMFTLPFoos1tOTdOceiS94KhG45OTMBV/xgO6\n3XrvSi+5cxgpm61UEafur56IxrpITNu6VKm65mDRdfmZacSotIfjdeom9zqTL6BQcy1us/k8QF3b\noutQYK0N+bmCsG21PO079/vJ2MSE0Jo0OTWNJCQYWhUKgGKxhELdOtnzms1sYZqmpz+5XA412w+j\npgevdbSs+9aPIMViGBsbQ7pUBs28q1XdLAGlUglzY2PIgay1eZQldlyP07zVtjJbrdYWbs6l2iJC\nryVB61i7VoMKoFAsoSJPOfOQbth+pQBK5Ypz/+NTs8435ldnLpdDJHSyk3AyL1vZ7bffjle84hUe\n5REAzj77bJx77rn40Y9+NJ8mFlQ2bNiAXbt2YZwKynjkkUdQq9Vw+umCTAENCO0LN/LYFB77tz0Y\n2uZTWOBPwzjcG6jvF+IsaLzjcsAAoc0VtDnZZMu4eZAps7qHY6xxZ/d6ztKeAJXQdBWUUM9NSXmx\nsGYinNmo2MVg0+IbXIw2BO4SxGQV4upKej1GV30SlfR678kFeSbs+Nr34DgKLWc7mTwaro3uk6Kg\nFutvkO+S7VMjJmzJiZilgnskiaqvzvX0eGOCgdzjhu2TaagsckuLY8I2qUxMTuS03QeFNmGbjIl2\ntusyGEoKWpwKPGT8riifai4RA+sWJPv8np8EE4kvrAxeewPazj4PmRdtDCxnajXHgqJ7MsUECe8D\n6UPJo0PAETkfEb8PSZatsWIH0YRLi8jVoZAgHp/zUrh1gRYllaaSYLh9msNKjD47I7giOKiUBL4u\nu/o9yG58CXrf/M7QfWlKQrtz0FH/1Ddnu8VI1L/WzyOwLglkXpjm3r17A3khly9fjvvvv38+TYSW\n/fv3Y//+/ahWrV3Prl27EIvFkM1msXbtWgDAS17yEvzkJz/BF7/4RVxxxRUoFAr413/9V5xzzjlN\ncEBywgWMTO6aQ+oUtsjhuSo+9/sRvO7170Hu7n9B5tQz3JMMjY8Z4qNynWxp0aM55DsuRDW52nsJ\nrUAGpTJ06By8E//4io9A1psLOKqrQHqCMfwmt4BJgfJJFFN1NL6AMdF6nGKvZFqQu+gy9l1S0pR3\nrT3JVOMrmrk6sE6A9rFs0F9MFvv4hk+76S/8O9VrJgq5ixDLU351jSxiXH7dqf73AGjQv4luz5TE\nx0WX2ePccK5pbBQoCRdX8fOBJD8NxZ+ew/GLMuggCu6dc0E0kureWzm7EeUsrzDR3w81nwSlAhXm\nYV8AEdKNef1hF0Liywaw/N3XQyrtCyxn1mqItlnzhR4Nz7xnSjI7P3vmPiogxTcdpPUODIn9Tvve\n8m4AD/i07POcZCsqPByReJ2qfelBbTWoaR9I97pJ+SzAvDPUZaXMRmcNkxQVZq2GSFsHBq/9eHP9\naERCpPW0xM8H0i8gTcZs7g3Qowtofg8h81IgI5EIdu3ahVe+UsDNBmD37t0LDpn6yYMPPog77rjD\n+fvuu+/G3XffjbVr1+LGG28EYJGG33DDDbjlllvw5S9/GZFIxEllOG+pswOYKNbwd78ZxqG5Gu7p\nOgWf+RaPzLKDKbQCKfiw/VJnMZlo/FIZUlHYoqhAI9LmZI0ILQ5hbD0KjLAIZIACySCQ3sW1OQSS\njtbj+ihJ6P3ztwVdTVpuqM3RVZ9qCOVtTEi94bjt6snC0BdxfXDeo9/kKRL3GbOceiRAodF3L3l/\nSlLdQIr+d7wfw9/9MiIdPUBlzHWID6ncsOkyqT5T96RE7e9R9s+UIUIg3bwDdh+4YLr6T1i8gAUp\nkOIArvmLkEh8kYlFtFgfatEeFNteLjxv6hqS7ZYCp0caoW5WgqOwKR5Ivw20Hu1BvvO1qCbZgJnO\nV14M7PRRIH3eh9UXGW7qoyYQSPt/vyhsZz1qcv6gx6LD6Rriuny3y1YiRSJAubQgm+BQEphtiCpm\nUnMGYwmE8DgkySc70eLKvBTIM844A/fddx8GBgbwqle9Coo98WmahnvuuQf33XcfzjnnnAXpaD25\n/PLLcfnll9ct197ejg9/+MN1yzUqkqTABCDHEzDKJc/5Hz45jkNzNWRjCnZPVmCYJmSfKGwgPKrT\nkFkuDAIpSa6pjEGtmh8qTs7TulHY4RDIIMWKXnyFxN5NKGX0s2pUWWp6uVzMqDryDEwxit2wLIQJ\n24M+N6NAUiUZ02+zSgVtOiL9ql9XdsOZlq/W2H86mSL4+kILl/mICNHXzDAKJDgLA/1Xowsns2hR\nPHRBQEGQK8x85AiasN02I5gauNb3tFmrIdFusTaIUtD6Sj0eSJoSx5edQkKp9aXh2wzsjo1AEvS6\nqTmijjrXKA+kR6g+eeoIt2F3rEtHwjUJnCtA4FziIr/MN+NYNaTAHPNHSualQF511VXYsWMHbrvt\nNtxxxx2OGfjgwYMoFovo7u5eGHTvWBB7YKz79u0Yue2bmLzvLub09okyulIqtgxk8bNnJ3EgX0V/\nNua53pHQCGTjfQRA+ZGwPiomZMafabL/fYgWt3u4ERts2Pq33kIeEoEMUuLYHMiico0rE7GeZej4\ns4vQ8uItDU80R+vDDhJzSSKQXJ0CP9yG+ulD4zOPHtndaeRZ8e3a14ahDbOJjf2erWIHghkBuXoJ\n36rGKDKk/z4UKg3Q7DAIUKClKazZzpLZ3KUwlGTdcsJ3wW+OjrC0bDobT97+Ray5/DRUUutCX2dK\nMmsh8nnvhtHoGGxSJMsH0jVhN7HxJvyQvgmKiAm7ye+TUcbsOvzTvwmPknVwMeYwkUg0ah/ChG3y\nPpDuxczxxbNWBcu8FMjW1lbcfPPN+PnPf46HH34Y+/ZZ/iHd3d149atfjYsvvhjJZP2J4IUgJjUh\n8wOjohkYnqlgc38aJ7RbqNjuyQqjQPKmF/qjmqvo0E0TLXHKiV5tZf4PI2wmGp8gGkhWjk0A0HVo\n8WXQ4vPk2nRMd8GD3DMt+pUPmkBp5ImaFKZ7rkKkPNwcAilJWPbWvwIAlIb2NHz9khMycS9UfQsw\n+XomUwetpoM2GgjcYoJompvmGHJ/R9cO3wcPat/Awq/Ek9ALeV/ld6pyAjoTz6OWWOVbhxZfjpnu\nK7gyEvOfZ0NUt49ihT7QB7JBFLncsqlumXBtHXnJnLYRqZNvxazI+hEoCju2POZNooz5kL3XEcOU\nIEvhlWoHgQyRytBPimVrA3Pw6QJSYhdxS5qeP6jx1+Q3TsbtEQmOBDiUOeCZMtYhAQIp8dcfgwgk\nACSTSVxxxRVNk4y/YCRgMOydrsAwgRPa4uhJWwN2vMBnB+B9IN2//+aefRiZreLfrzwZETuyrdB+\nPnQl05jfA2PCpqIveRM2Sd2n0xks5iFOs+IJTG1phTYz7U19GOif49MUg0C6v6vpdaimwyMCoepv\n5LolBUQuDAK5+jNfR2loD4NmN98l3rRqZ4Zo0oTtlwu7MRG03ciL5BTIRhZhJWkpkLxs/frzkAD0\nvPtKGKve5BvYRKSS4dklJOb/RpEX02cBDEQgBVmQFkqWve29iPW5UeJLAfEXus7UEVOS2RzU3Dhj\n/AmbGM/PD29BrnoXOk8MydIqc8EbTUxgpXIav/3CMzCjWax9i6CAve4shAlbkptVINV59qExYXzw\nw0Zh10ug4Pl95ORPKhf24or/xLhv2nKEWtkWR1vCeuSTZU454ycMe0c1Uaxh/6wVWb51aBbnrrTY\nIk05hlJbg/6l1GLB+CzxCGTYjDohxUGXfJypV3/mG6iOHQ4/8YYMolkU02rDPmML3oWmxVBSkPUC\neFNqMxQdABDvH0S8f3BB+pY59Qyk154GMhjVdptBrcldNpuJJvw4qOkmSpqBbIxOPSiut554/YbD\nXyvHLdO0wfErliatuUDNtNRVHoXiUDc1az4UR1SH3kQs8E6q4zyO5mlp7dTCiyeVIe9aYP3HUy2F\nr19lc1zX7Q4JouE60ICYpsXnqtYZpguxwTMdBbAx1wUHOT9CQTRscJ//fDDTexXSE3ejnDmDyWUv\n2dbBtrPPQ6OuIYshDbW6Y8eOphuaz7XHhvh/YGM22tiVjqA1bqd9K/HontgH8tEDLvHo1qHm0sS5\nLYidjhkfSEluagddp2FSu/C0mmlBcpWAdsi3voBhq4Td4TUpR8rUsQhSi1mpxmSN8KUtjA/kQogc\njWHVRz+DieUfxGzHa7HsPZ+0zzSHgvjxJ9aTL20dwVvu2IFiTWfaMx3TUfg+NBPxT0S2qXxosmxa\nlHSdbC5+fXJ+2ffBLWJ1785HoZcWeNM5bznmFEnehM3PcfZmTxedqy+SLDeWsldWuI3lPIJo/N4F\n6U+TcyrdPw8CGfJej7QJOywjgRYfwPSyd8FUEmC+s2gca758K3qv/MumN/4LKQ0hkB//+MexceNG\nvO51r8OaNWtCXfPMM8/g5z//OR577LGmcmIfMxLwMidtZbEjoSKiyMhEZUyV2UwbvH8X2Z08dcha\nQNoTqoNkLkQfGZ8zjgdSii7wYrDAju1BOzezVgtVbjHaFkmsux/AOJSMP+HzkRIt1o9Y8XlEKna+\ns+ZYKhdV9Fg39Fg3PWVSZxc/iObBYYsfbv9MFWsE7sUNvf/5+EDaCqThq0D68z/W6RT7lyzjwW9t\nhxK176uRXNVhTdjHpa5YQTT+JmynXLMIpKw4n7sJxfFt9BOrjfkhkCR6xre/5HzT87TATYWb0kwW\nNvHIkTZhIwhl9hHejSfaQbgejw7qSEtDCuRNN92Ef/mXf8GNN96I7u5ubNiwASeddBJ6enqQyWRg\nmibm5uZw6NAhbN++HY8//jhGR0cxODiIm266aZFuYenLRFFDVJGQsifp1oRaF4EkA3r7RAl9mShO\naI/h9/vyKNUMJCJNDhzaz4xBV5hCbhDNQglrMV2A+vynBLNGKdmLGR0cUiJt7cAUoAo4KY+0lDMv\nQmrqNyi1vMQ+QpPVLlWZvwk7rHlKp1wshmYqWNMqWEAbCaLx+GWF77+DQJbFCuS8/U6pb6gwRm9M\nGwmi8e9P55+9tsmO/akKb7LmkWEbgTTNJhFIOhhNgWQGK5AzVQMdwuQDDYhj4PBRhudNI0Zb1Jrz\nxiOuXEeKBzJMpL3gIvHvJWA5auipn3LKKbj55pvxyCOP4O6778Zdd92Fu+66y7f8+vXr8Za3vAWb\nNm06MtQDS1Qmixo6kqrzDNriKnZNlblS3ijsfEXHobkaXj6YRX9LFNiXx/BMBas7xdQdQ9MV7J+t\nYMuAH9rFDsQV196A/f/0FU8QzUIrkM5H48vnwEr/u65D5dB+3/NBBKzJE09B61kvR8d5Fy4uvUxI\n0e1Uc1rkyGYIEIkezdkE5eSzXzombF8JwSEoOs5mcAk3DkZmXV+j4ZkqTLjfGUufEU68PpDhr412\nWj6gSiIV+prGpLl37mc2oxHI9d/76cIEVjUjZC47SjQ+TQvvA+8zd1lE4k3Ma5JMMQkodY0PX334\nML58wjwRyHrXkiCaZutmTNhNBjcSxfMI0fj4BaEFi4/SuAR0qobVdlmWsXnzZmzevBmzs7N4+umn\nMTw8jNnZWQBANpvFwMAA1q5di2z26JvtloJMlGoYbHWVstaEisJhA1XdQFTxMR0pCnZOWkrmSR1x\ndKWsCXnftFiBPDxXxfv/y6KY+cHlKaSi3g9K4qL8WjaehYn//gVQ3EmX8uS1nbc49xZuUm8/5/x6\nFfqfURQMXGMRxWuF5lIuBkqDE1U5+2JIMFFOn7bwfWlGKIJy6RjQH5vOYiIJzFt1ZA+1qRueqQAS\nTUFGgk6OTBR29xveDDkSReerLmGOL3/39UdZOfLhZqUUyKOmPL6QxJfGB00pDowPZAjf3G2HizBP\nmB/a5bbnV8D+v+nc3tR6Zpuix3fNoW0wjVpiBTkBUEEovGRO2wgT4ZVy3TDxH89MYN90Bdds6kE6\n1mhQJT0vNaNA0jysR99yNK8o7Gw2i7POOgtnnXXWQvXnhSHUB17RDMxVDbQn3Um1zQmk0dGVtgYB\nPxhqpoQdE1ZGmxM74uhIWNfvmixDlDjy/j2zzu+hmQpOyQn4N0VmCFnmiMQlJ9JLJGRSaAhRprMo\nHEFZCggkJIUyGS8tqaTXIjX1GxRb/fPZH31p0oTdhE/T4TnXf3ZouiJuuyEeyEY5Fl2RIxF0v+FN\nnuNW9OVCiE9f6vXR5/6b5eFbcFkCqEwjYvnomQLOUJ8obFNwLozIMuUDKb4+X3HN2hXdRFkHHCih\nmeda10Q9vx2sKJXhvq1jaH/bzdCj3QCA6b6/RHLqft8NfMf5r0HH+a8J3ebz4yX82x/HAQCn96Tw\nyhPD8zDbHRX/Dr6I+rm0EMijr8K+EIXSyOgAGiKt9u8pmsqHGwzDeQ07J8qQJWBVWxy5lIqWuIId\nE7zp2y4/4/ox+QXbiJQ+SZZZREOSIMf8FcjvPnIY7/n5bmgNKINSgwjkQslSCKJZyqLF+jB6wqdR\nzm482l3xlwXIRBNWxovW93h6TxJjRQ2lmtflorH337wJe/HFT4EMvsoPRf1TdlGaj0wOXI+Z7ith\nKqyrAj/OnHSUZnMmbIkGCnwUl6EZdt0o1vxTYIaR1rNeDiWVwbKr3yM87wAKzY4dn7SAeqzHOafF\nl2G2983NUV4JZM+U+4yeOiz2Tw4SqRkTtq8bz9H/5l44q+ESFRIs00YpkG12RpkpJpCGHQxD+Rp2\nTJSxojWGmGpF6J3UHsfe6TKqundh20/5bw35RWv7pP7yBNFExAqkZpj45fZpHMjX8MeDBWEZkRAz\nXHotT2q8yLIY3FjHMI2PUI4Sf1h4aRKBbGJRGi/WEFUkrOuy0PuRWff7dCbuedH4HP0Jn4g/BUhz\nCOSSk2NEodWjnahkXuQ9wfefcLaagnNhhHLj8AuI4deNmjE/ZSXakcO6b/0Q2Re92KdEE9YsRmgE\n8siMS+Lm0hJT8NThYl1qJJNPZ0qbsJtBII8rkC8smVz2V5ju8c/3PW2bBVri7kAiyiSrQLKvYrSg\nYbKk4cR2l5PxpI4ENMOLMOqGif0zVWzoTSGhytg34+PzIfpQZclLJO6DQD4z6u64fr9vVlhGJJ0X\nvA7r//k/kFx5YuhrFkQWYRE5UtF6x4XIkTPZjBVq6EyqGLD9lYdn6e+ocRO2d4I/+hP+/OWFcA9L\nX7zKhWN/FipLpmniQ3ftxTcfPuRTH/3exHMYj0BWqUDtReEcXMgo7CMke6craI0r2DKQwWRJc6wW\nfjK17F3QbHM6ANbfM6QPJIM6NmUCXzw5+j04xkVLrEA1vQ57p8r450cPO6ThRGZsM3UrlcfaIRMv\nCxAO7u+LTm5zjvVmLD/I0Tm2jdFCDTXDxEBLFD2ZiOe8I4IB5yGYlfx9IB+nUMe9DXJSLjg1UAiR\nVBWZ0zZi2dvEJpSm6nwBmbCPBaG/i/pBKPNzkRgvauhMRjDQYo3V/bPUd0TWuoaCaKTgv4+qiMdx\nz+VvrXPdUrqHF7D4BNHYGqSn+B8PFbFjooy7d0771Ee5KgUgkN9+xd9i1Se+gExUBuvBsYgKZLNy\nhL8n3TCxb7qCwdYYTuywgB0S6OonWmIFpnuvdv6W5usD2dB8uPiyRDyfjy35/hNjKGsG3rohh6gi\nY6qk4aP3DKGkGfifvbO4ifIPnCkHIZDuFm+0qKOTauPC1W248qJV6M24vhtddh7tw5ySSpz/ezJR\nHMhXMTxThGGakHlqCD8TNjVRmJB9o7CfGyshocpY2Rbj0JkjJJJkTzohswxIElZ+6JOL26fjsrhy\nhHjPClUdxZqBzpSK7nQEEVmyxrhNJNGMCZuXpTDhB8mpt/28vjlxCaAegXKs0ff4iHejSpmwBZuY\n+/bMOL91w4TiyS0fHL1rmib2zVTR19eP9Ekr0PLcblR1cfTvQokDXDS5KT/SUcgH56qo6iYG2+I4\nwbYM7poo46zl9fKLS+KfzfBA+kRkHy05+j04xmSmrOHfn57Az5+fwvefGAMA/PFQASXNwAntcUyV\ndeyidiUEgWyhEMhMTIEisQjkEGd2bktFGeURgEPlw6OcdKBORzICzTAxW/ESxWqCudUTRAMxD2RN\nN7FzsoyTO+PoTEWQr+ioaOF4HY/LcWlejowJe9T+pjqTESiyhP6WKIanvZukxlwYlrAJWxRQF+L5\nBi3amdM3IbNh87y6dVxs4YnEKR9IkeJxMO+uCUxwJhFZdt+5gMZnpqwjX9GxotVac1oTKsr09L4o\n3958ecS810U6Fo9vd68dQLOyLYblLTFEZIlZ632FiZxukpbMFvMIuvSEkeMIZINC56a+f+8srt7Q\nhWfHLLqdvzmnDzfdux8jz1dBhvG0jUBmKb4oWZLQGlcZH8h90zWACsQTTeZtCRWqLFAgbT+M9qTq\nRHtPFDXGbA4Az09UQEgHDsxW0ZeNApLE6Y9iE7YVvGNiTS6Bqm5dMFnSPEruosoSQRfaX34BEkfa\nn/NPVhaKzDhYSHTlYJs19pe3xPDgPjrKMjwCaZgmxgo19Masr62SJHnej/6E78oCRL5ysvL6G33P\nzXZdBpjB/mILIqR/S2SuaFo8z5kiSBegwONFd00Ym6uhM8lycTJKp+B64tNO3Dda4wpquh/ytUDS\njFsILdwzWvedH0NexJzszhzRGoMqSxhsi2HXZBmmWS+9JB04Iwl/B4uf0nj055N5I5DFYhF33HEH\nPvGJT+ADH/gAtm/fDgCYnZ3FHXfcgZGRkXl3cinJ/+7PQwJw4UmtmCnr+Nb/HsKzoyV0JFR0pSI4\nrSeJik6ZsCs6MlEZKveRtCZYBXKvB+3wDg5ZktCZjGB0jp2IJ0rW5NGeUNGRtAlVi14/yGdsRRcA\nHhzO283wQTTiVIZkh9ufjaGdUlL/FKX/HR9Ax3nhucOOyzzkCE2YBElY1WaZppa3RGEIeSDr9+GH\nT47j//znbjw3E8PEig9hhgTZLQHEYP7S3JJRzm5EueXMBe7LC1d8zZsCBFI3TGYt4V2cnPoIism9\nw9myhu89NorudATnDFo+G61xFfpi+9s5vlMLg0AqiaRDKL4Ysm+6DFUGlmWt9fHE9jhmKnrdQBrf\nIJjQpvsXaBR2Pp/Hxz72Mdxxxx3I5/M4fPgwqlVLEcpkMrjvvvvw61//ekE6uhSkqht4/GABa3IJ\nXHlqJ3ozEfz3rhnsm6lgXVcSkiTh5M4EaI1stqwx5msibXEFU2Xd8QPZM8199D67sq5UBKOFGhP4\nMlnSIEvWR99h7zx55U4zTIxRxxznX9Nk6jIhCT9CopB2plRndytSUhdVXhAL8HFpTI7MhLl7soxU\nREaP7Wc80BLzEOwDwX5LTx8u4stbD+DH2yYAAA8N56FHOqjsP0tp/B47ka9NybE+V/hEYYtofCZL\nGgwTOCVnZSjjLVSe+ri6d0yUYZjA609pd6xWrXEFhrm43545Xwv2ER6LwzNV9GWiiChWu44fZD0z\nNvW+/mcoLzxepwLqd+P5ye/aMYVbHxsN2VZjMi8F8vbbb8fU1BQDTsULAAAgAElEQVT+4R/+AZ/6\n1KeYc5IkYdOmTdi2bdu8OriU5KlDRZQ1E5uXpdGaUPGJc5c75166wnKkXcOlGZwu60wADZHWhArN\nMDFXNTBd0piAGkvEgyuXiqCkGShUXQeViaKGtrgKRZbQmRSjgwfzVWZHuZMQkvMxKZIkhNbJLqsz\nGXFQzsk6O6+KZuCbDx/C7/aGp/wxTRNzAv/N4/KnKX4+P4Wqjt/smq7LwxZW9s1UMNgWc8b+QEuM\nzXQh8T9YKWsGPvO7/bifGutPHOKJhpeOUtMsorQodC7HxSMesy6V45un+CFz/VpbgTwkYuFggmjY\n9WiHrQDRlHHtCZVVIP/EaXxquoHRQg3Lsq6JPLQCSalZD+530+uG5oH0zT5T//6nShq+9b+H8bNn\nJ1HTF96tY14K5KOPPopXvvKVOPFEsT9YV1cXxsfH59PEkpIHbLPv5uVpAMCybNTxbTyjz3Jg7MlE\nHPDQNIHZis6QiBNpp7LR7J4qM8od4O8XQiKxR6ld5mRRQ7ut1JEsN9OcI/XQTIVZNEYLNTvAx+Tc\nhawyXZdcif53XeccnSjWIEtWv0lbE6VgBfIbDx/C3Tun8aWtB3AoHy5q+3uPjeLNd+zARAC6eay7\nNx2XRkT8Hdzx9AS++tAhPDNaEp63LpWgtrXXbUE3TBSqBuMz3J2OMG4njtnPZ9K/d/cM5qoG3vyi\nTvzVpm5s7Eth12Q5MFkAL5OFKu58blKYAWfBpelF+3jc5RERPwQS8Jg+iSVosC2ObEzBfgEPcJAP\n5M6JMhTJ9f8FLDOtvtjov23CDk+ozV2+gErtdEmDHpBd7eBcDYbpmq8BoL/FUiYP1lnbSlT0ajJK\nKe8N+H6StdvXHO4j/03ROs1VFx6YmZfDwMzMDPr6+nzPK4rimLSPdalqBn6zawar2mLopwbRV1+7\nEhXNQESxXqYsSYir1m8SbEKip2mhycR3T1UEn6e/CRuwFMBV7XEYpompsubwUqUiMmSJzWsKAMPT\nVc8H9/jBAlZyJmyyQPS8kSVHHy9oaEtYKGerMJMOK/umK/gthca8+87d+MprBrGyLe57jW6YuPO5\nKQDA7smKY44/Ln/C4rNIPGzv5IdmKjjLh0Vj/S0/CbU4FW2FLU1N7lYkttcX2M+E/ejIHFQZeN2a\ndsRVGYmIjEcPFPDYgTmcf4IVTON+f96FSjdMfOQ/n8bzo3N4dqyEvzlnWd1+L5T0/sU7UJuaCFd4\nqdP4vFDEh8bH8oFkv4kJxzqkoj8btcACPrCDqY+9fs9UGctbYogqbpn+lih21DFh/+TpCfxhZA4x\nVcZfntHlEPAfOVkYBXKypOHtP9mJ81e14ANn9QrLjNi0dTQCGVVkZGJK3ViA3VMVDNi/p6jQ9sYU\nZwmAifGijgNTZQy2xRHm/ndNWemQOxIqSovAmjKv2SCTyWBsbMz3/PDwMNrb6yMAx4IcmC3DBDzJ\n09sSKnq4SOR4xFqIanbKwVwdBXJougLN5AaDz+DKpazriJ9LsWrAMN0ob0mSkI4qHgVyJF9lTXKw\nFmHTND0mbJGMF2uOeVyVJWRiigflpOXO5yYBAJ+9YABbBqwV/qHhvG95ALhrh7tbOjT3wth4HBex\n/G7vLH4fyrXBOx5HZqvOhG6l8BSPWVmNQLJTTxZruq8Jp2DvzJMR9ptbTm0Ug3ggTdPE8xNlrGyL\nO5vHDb0pSGBZG4Im/O3jJTw/ainFDwzlsX+2MaL+xsXtS+7CN6DvTe9s+LqxQg17p+rTmJimiW8+\nfAif+e3+QMvCcXGFdyOSGB9ILgjGnuuzcQX9LVHMVQ0PjRurrLh1VzQD40WNUYwAazOlMqild+z+\n/PkpPDNWwuMHC7h9WxOWxnmYsOeqOrYOzdUvGEKePGR9o7/ZPYNiTYzSiRRIwFLMJutY4rZPuN/y\nZJmqvwn+y+fGy/jM76zA5DBuKPumK+jLRPHiZelAhLVZmZcCedppp+G+++5DoeDNizwyMoL7778f\nGzZsmE8TS0ZM08TLB7O48KTWumUT9iJS1gIQSArFO5CvIhGNY7rnLXjoewcABJiwUyyZeN5e/DIU\nTVAmpnjg6olizVFsAWB1Z9zKZ+0JovEOiZpuYrqsM9QQbXFF4LcJ+74NbN2XxwntcZySS+JDZ/ch\nFZXxyEjB12ft8FwV//zoYURtB+UDAWaB425Yx7ZUdQNf2noAX9x6IERp78v+w4i7EdkfgtD+17um\n8Rc/3oEP371XeL5gI5CpKKdAtrqLhUSCaASD79BcDfmKbgfQWdISVzHQEsOOCVrB8h+4JLPTa1db\n88v/7l+YxdFfmvSBpOaHd/5sF6795V5odRam+/bM4u6d03h4/xw+/uthz+Z2QYXyFTya8sMnx3Dz\n7/Y3v2j75MKGaXoUDzLXZ6KKYx3zmLGZ6kxM9b0DEwPXO0koRHRs8UiEuoJts1QzMFnS8LIVWZzW\nk8SDQ3m87xe78bnfj4S+Z5NSIHdOlIXcxSKp6gY+ee8w/meeCuR4sYabf7cfX37goHPssQNeXQZw\nzdS9aXYtb7cVyCBf7OfH3TlgjnZPaWIhM0wJh+dqqOpG3esrmoFD+RoGWmM4tSfZcFthZF4K5Bvf\n+EaUy2V89KMfxS9/+UsAwCOPPIJbb70VH/3oRxGLxfD6179+QTq6FOTCk1pDcTcRBbJg72bECKSl\nzBEFsjcTRTW9FqUZezfj005H0vKxJAgk+ehoBVKEQE4UNaSoMv3ZGAo1A5pueHJh80KQRtqXszWh\n+iKQjx+0iNXPW2VRQiiyhE3L0tg5WcZ3/nBYeM2vd83AMIGPnrMMMUXCgfxxH8gXqtApMT//+5FA\n/1hRpOEf9s8hqkjoz0axf6Y+UvfQsLXQ7JmqCFFIF4FkgwsGWgQIpAA1IEri6g7WPWN5axRjhRrK\nWn26kmH7Pi46uR1xVcIflqgCKZqX6gURPDCUhyIBV57agQP5qmOdeKGKaZr40VMTeGh4Dv/44EH8\n7T37GkZegzLR8GsQmevTUcUJhPnfkeDxU0ueCD2acxWjjHeNyjLsIWyb5Lq+bASvOakNumlFKT8w\nlMc9fukUeTEJn7COv75rL95z565Q3/Pv9s5i+0SZixJvXL7/xJgzNxDx82ccnashocrMOgtY3MtV\n3QqGFYlhmnhmzL0n04cTMqwQarHt42XUU9+suAdgRWsMp3anPNmJFkLmpUD29PTgpptuQiKRwE9/\n+lMAwK9+9Svcdddd6OnpwY033viCMWFLkBw/w3oSt01hxSoxYQtofGxlbN9MFYWqYZF6A3VhfVWW\n0JFQnZ0jmTxoovJsTEa+aji7ItM0MVnSkI66/SBIZlXTPUTivDgKJDWhtMVVFGuGMBvNkI2mrOl0\ndz3/58XdWNESw2/3zsLgNEDdMPHrXTPIJVWc3ptCbyaKA0Jk6Tj0uBSkppu4f89M09Hy91CuCluH\n8vjYr4cCSnspS54ZK2FDbwqDbTGMFzV3PAl2FqZpYvuEG2gjWsh9EUjaB5IsVkKGAjeVKH+9Cdf8\nFYQY7JupIhlV0JuJYENvCs+Nl5wsVktLvPew7TAfbc7KXtvH7s/Xd6I9oeL+Pd45YOG65/+ejpTQ\naV5/u3cWz4yV8O/bQvqY2lLxTKuuD6QIgUxGZCiyhLVdCfRlovjNrmn84MkxPHZApEi6z+ag7SrU\nJ0AgV7bTqJWPApmJYnN/mjm3O4Rbg3Uv1j09NWqNn3zVwN/fv99C1wJkn72+bOhNB5YLEssEnsfJ\nnQlcv6UXHznHiuU4LIpgh2Xx605HPEpfh8N6Ir5u/0yVQVaZOIQGfCDJWk7mpGfHiqi3HtLE59mY\nInzH85V5e0QPDg7i85//PL7whS/ggx/8IK699lp87nOfwxe+8AUMDAzUr+AYkUxcdQJl6kkqYr1Y\nzTCRjSkeZAOwHHCzMcUyI4P6gO2BEuRg25eN4mC+CtN0UxZmoiwCqRmmY0Kfqxqo6iZSlAJIlNqK\nZnhSGfIybZuqWxPefN4iFFLkL5KKKtjQl0KxZrgLqi2PHpjDZEnDn53QCkWWkEvVNwscl6MnN907\nhC8/cBD/tX2q4Wv/eKiARw4UEFfdcTZR1DDkizy45Z4dK+LtP9kJwwResbLF2QRVAhacsYLm5KMH\nWPYCIgSBpINoADickAClPwoQSOID1c6xLSy3ozSHnXsTT/gVzcC+qTJWtltcspv7MzBM4JE6KNJ8\n5PdDc/Yi1Ki4939aTxKqDDwz6l9PvqJjrKhhZVsMiizh5YNZjBZqeG4sIHr+GJenbYU6TW1Ing54\nRiL56D1D3PznggH82jBX1Z2xK0kSzl/VgnzVwO1PTeCT9+0X1O7We2DW34QtU2k7S1we3BEHgYxC\nkSX83bn9uPr0nJ2it7GN5cE5DRv7Urh8XQcOzdVcijkfGZmtQpUlnNCRCCwXJHdsm0BVN3HhSa14\n+coWbFmeQUKVhQqkbliZpbrTXpSWfPN+fpBPjxaZhASMAtkEItjfEocEWzmss0kiz/GkkMBXM7Jg\nIXUDAwM466yzsGXLFgwODi5UtUtGOlLhtfdszF1IaG4tXgZbY86n7CKQ9mIYMDb6MlGUNQtVzAtM\n2OQ38Y0huyO6TM4HgSzWvEobURJpmpPWODHBeyeL/bNVdCRUJLigBGLi2z7OLh5bbXLV809ocfpJ\nK8DOfZ22EQAQ7ezytHkkxTRN7JgovWByge+eLOMHT45hd4i8rjNlDdts6pydYfLAcvKA/a4//FI2\nypjkmfUINUl+9UHLV6kjoWJTf9pVIEVJ3m3ZY6MhBCURZY0gUdh8EA1t8iE+kCLUgPCh8nRdBC0g\niImf0/sPnhxHvmrgFSd1AgBebFOCPVUH2WtGyJgdL2i4+XcjlHk9nNAuBS/uS2N5Swy7/d4d3OdP\n2BfOXWm5tdy/ZxZPHCzgu48cXhR6kaMpz9vz2+tPca1vQxwSVU/25TWGP1eLWtHB1YLmURzyFYOZ\n20/j/N2sDYx47B2cqyKuys58zor7rg8X2O+GWIgI8LFxWRqXruvwpOgNEtOwxp5hWkFn67qtfu8J\nGE8AbJevCHINrMm01HQDv3h+CivbYniZnXlHkiT0ZCLCLD7jRYvCpxkF0qIZEz/7xqKwrTkuqijo\ny0axZ6ocmJcesObntrji2dgupBznZFgEiVALz+pOfwVyFaVcEhJY17E4GIEErA9JqEDau1Fyjgzu\ndExkwmajsH/yrBdVmhL4QLZRPJa0mKaJkdkKlrV4P+6T7B0j7zM1NF1BR1J1lFq+/0QG3vsRnHDD\nZ5E66RRP3UdSbnl0FB+6ax9+/nzjCNxSlB88OY7bn5rAdb/aiw/ftRdjc/4TOI0UEleFsGKaJh4/\nWEBPOoIz+lI4b1UL3nRaZ5263G/pQL6Gcwez+NyrVkCVJQqB9FcgydgnWTqCEMhUVLSIWkJAFZHf\n0mRJQ0tc8aQrXZaJIhmR8SxB23wQgweH8+hJR3DFBkupzsZVtCXUUAFCjQqhFlvXnfz/7L15mBxn\nfS761t7rdM/Ss49Gu2TLlmTLC16QZWzn2BwMxDE2XJsLODnJDTjADeQJCblO4IkxnJOAwxbiXIfA\nzT2xAyeB3MSG4NgYr9jGFt4ktI+k0Sw9S/f03rXdP6q+6qrqr6qrp2dGo3G/z8ODNd3VVV391fe9\n3/v7/d4fsmUVPzrc7BiufYfN3SFs6JQwV1Kw4BFuJ8VBG0yPwfWdIaxPSvjxkQz+9PFT+PdfzePp\nseCNBs4FHJ0rozfK413burB3fQeuMH2Dpz3CozToYPCVZyfwYzPdI9t/B97819OYOZSrU8FzZrtc\ngk0u0cLIOaY/IxMLBhmjjWv7ZmHS1T53fKGKZIire2aSYa6J1AtTVWUY7OyPYoNpA+QXApdVHVN5\n2fRgXhwxmsjLkDUdewZjjk1ib1RAuiDXFQERVZJGIMlaVaDkQOq6jjemixi1pcKE7dHIgGkW+apa\nEysZBhs6JUzkZN95T1Y1jGXK2NwdWlSuZVA09QvcfvvtizrJww8/vKjjzlnYfjA/b6wNNuNWq91h\nAGsDsus7syB7VmEDtQptsojGQ7UHoDsigIGhSOhafUjDDhKSsO9SSc6lm+TNlRSUFR1DlJBIT5QH\nzzKORVzTdZxeqFoEmnb9BFwojOi2C+o+dyWh6bpFHIModrKq45FD87h2Y8KRp7qaULDd50OzZTx2\nKI3rR+jj9qTZsz0ucZjMyyjKKjVFg4bpgoypvIz/sjkJlmHwiSsGIKsaHnptJlAIuzvM43cu67PO\nlzIn9JOVFEalX0GR6j1piXpOwji0Nm9k8ncrkHZYYXBKCHu2qKCbssvnWAbnpcL45WQBFUUDLeCW\nq6iYysvYt6HDsZgNd4g4Oleu9/NrAT8+nMFN5rO+qSuMqMDi6bEc3nted/APsZGK0YRkKosLOJGp\nYGd//T0gCuR6m//rXXt68fknTlvV20dmy8CWYKfXdB2PHsrggr4IRlfcd7AxyoqG0wtVXD4cR1hg\n8amrBvHT41k8dyqP6UI1cB79Z/cN48FfTONbL05iZ38EA/EYzuw3yb5tDCqajpKiIWabWziWwft2\ndOPNdBFvTJcMn0JKEW5VNSx8tvZ4hYJr55mwkV9d1zGeq2I95f4nQzxOZqq+4zZTUvC577+Ka/My\nkjDmknUJEQzDoDPE+SqQU/kqNN1YA5s1INd0HSfmK5bK6Lbk6Y0J0HRDceyL1V4jc0aK4ktMCHSB\nYv8zlZcxW1IcOaJDidrv79cW1Y7j82VsrB2FDZ0hPD2Ww8SCgn6PY95Ml6BowPbU8lRfEzT1C+zd\nu7fufyTPsb+/H3v27MGePXvQ3298rXXr1uGaa65Z+qs+R9Ahcdgz6J3oS2w/7NZA/bd9CAAQ37HL\n87ghmgLpyoEEauSOGJ3GbTs2gWMw2CGiVFUcIeypQv2DkDEJqL2nNyF57rAMIZukW40dLGPkN07b\ndrPpgoyqqjtMm93X74WfnVjA5x4/hYpiFPO8NJ5fFq8rO/KuFpKN8Mihefzdy9NW+HU1oihrGIwL\n+Kt3rgcAvHbGWxEiRI+EfsaaUCHJRsY+cQsci8G4aMsTdMIe9v2NHd0OskoUyB9ldiIz8BEUO/fW\nHU/G40iHBJFjqOE1ryIaO6zQt2tRJAVqXmGiC/oiULRaWNONo5Q2coBxj4qyhvkm88m8MFuU8c0X\nJq27yTIMLh6M4vBsGT89nm3ik2rfPyZxFonwWvSPzxvRBfvmaVd/FF9553o8+Oub0B3mm0qFeGYs\nhwdemsLH//34ihUZ/evBOSuvsREOz5ag6c7fsy9a3z3MjclcFR//t+PWv/cMxvCxy/uh6cCj7lxj\n2xi0W/jYcefulNVqN1tWwPD15GcyL0MHPf/ROE/teViwVfVkK6pR+Ek5rjPMQ9Z065myoyRr+MGB\nWdz978fx8uksXhy8FACw6ZKLLbI5kpTqcuTtIM9hb1Rw5BP6eRIT/D/70/g/Hz2Bb/58EkB94VCX\nlddf72ACAD0UNxUyZ9AUSBJBsCvCG+0biIAE+NicvYqbwYVmqP/QnPfc+4ppR3TxQDTQORaLphTI\nj33sY45/v/7663j++efxiU98AldeeaXjtWeeeQbf+ta38JGPfKT1qzxHMdopWcbCNAzERTz465sc\n1c09178L3e94p+/upDcqgGOMMEJF0RAVWYd6QXajZFBbBDLEwT5Vn58KGxVvNgY5V1Ihq5qjYChT\nVhCXnCE6LwUyS8mXtCMVFXB4pqasEL+yYRup6PAgp278pekj+MiheYwvVPGTo1m8fTSOT1+9fF08\n7BPVVACz8yfMxfnF8TzyFdWhFKwWFGUjh2p9ZwgbOyW8emYBut5DVRBOzFcQFVlc0BvGv/9qHpM5\nGeelgp0nby5CbiuM0aSEZ0/m6Gqm7Rp2uybDEM8iEeJwYLaKfGgzRMqEnCkrYBnjnDGx3h8VMBRY\nlqnZb9FQ9Qhh5yoqFE337Jq00VTexheqAEV8IonubgJJnofT2cqS5DARQ/MIIckMgyvXxfHUWA5f\neXYCvxgvoCfK413bOn07QLkbHpDNAO1ZkFUdp7IV7O6vX8SIRdLm7hBeHM+jrGi+cyXB99+oVTO/\ncDpf19hBZ41NucYtzcJ5MlvBg7+YBgD88we2+Vqh6LqO//nLGTAA9gzVzm+1n/UJYf/gwBzGbJso\njjWIwkBcwDMnc7hrT5/1mn1tyNssfNwICywkjkGmrEIaGIa0biOAGhn1s/ABnCHsfLW2RniZagO1\neT9TVuqu6dsvT+PHpsXPhy4bwdXv+W384ui7cf0Fw9Z7usI8yoqGkqzV5dAD7oK12m9xMlNBkqKA\nE6QLMv75TcM+iqwr7uvv9OiwRkhrN0UUIQqk17wCmL+N+XjcvL0bpKzpzZkSLg6wVB2bKwPk52cY\nbO4KISayhr/kCP2YV6cKSIQ4R3vK5UBLOZAPP/wwrr322jryCABXXXUV9u3bh4ceeqiVU6x59ESE\nukmpkbTNsQz640Yl9kxRcRBQwGhnCNQG8FxJBs8CEdH5vvN7I2BcvbAVncG0K2E6U1bR6UqyJvkn\nCxXne0moL+FBlHqjAkqKZil5xDDc/jDHPcipHXbvwL9/JY2fHDWI2lNjOc9uAgS6ruMHB2bxz280\nZ60B1NRYAJgvq75FCLNF2aHMHM80X3SyEijJqhW+3dYTxlxRpiaFl2QNh2dLOK8njH4zxNNMx6Cc\nNaE6x/fm7hB0OHfaNdSejUHKQveurZ2YyMn4N4981ExJRVziwLEMYiJL9WsryBoiAusbKtY8EuHJ\nfeoM08d7qoH6dGTOaDW2gaJAAv6G+m7MFmXPXMSXxvNgGXt+NoMrRuL48+tHsLFTws/GFvDPb85Z\ni6wXZlxzQzLEg2OANEWNn8xXoWjwDTVf2BeBpjfuUgUAC2UFJzIVbDQXRVrFbLHzGhSS1yDX8+6G\nnxcEz47VrqvRNR6aLePNdAk3bE44WrZ2hnnwrPcYWCgrePxYFpu6nPeJYRhs7Q5jpqg40kycymB9\nCpMdiRBvKJAMg9DIqOO1yZx3BbaB2mfaFTZ/Amkck3EVV2ZKCn58JAOWAf7bJb34rbeNojcm4KZd\nIxC42rPVFaaTOAJSsGZEuGrHnaL0/7aDFO/9122d1t/c96zT49zG+slQU5AkjgHH0BVIUpxnJ8KJ\ncG0O+6fX5wLZWR2ddzYj4FgGuweiGMt6b0im8jJGEhLYZbazaolAnjhxAiMjHhQYwMjICI4fP+75\nehuLx2BcxJlcFZP5at0OspaXUVMgu8I8WBcxvWggCoGBI79aB1OnJmRKSp2iGOIZCCyDBZfcn63U\nh7vtIGFHkldCFmB7lxuvHEg7iFHuzds6wTJG/tql5q6flsdJoGg6vvLsBL79chr/89WZOmNpTdd9\niSsJbwxbyov3uQghIspZkJD3SkPXdRRNAgXU1JJ0of5a35guQtWBXQNR9JtjbtL8/oWqiv0T9C4O\nBF7hNpKfeHi2PsxrD+HSCN5NW40FwcsAOFOujd2ohwJZrKq+BTTm2clFOP5aI5D08U7afxrjnWbC\nXcJwh1invpFwmZuw2XE6W8EvJwt49NA8PvBPh3DXvxzFn/znKep7j8+XMZqUbPeQAcMwuLAvir+8\naT3+7tc3oS8m4PlTOV/7rHFXMQXHMuiOCNTcUuvZpoT+CK7ZkADPMvjPo7Uw+jNjC1QLI9LRg6RP\n0CpmdVZCoedG6PziPQKfPbmAX5rt7V6y+SgebmAvQ4qBfs2lirIMg56I4EjdseN/vTmHiqrjlvPr\nc1FJDr2dINkFhhlbH2wakiHO01bnjKVANg5h56r2PHlCIOk5kEB9SJlc5/+2swfv2tblqeR6kTiC\n2ZLxm3dHBMez2CiV5tmTOYR4Bh/ancKfXDOMz+ytl/68CkNnyPpJmX9I6+ACbV6hpcbYPmOyqOC5\nk/6bkoritr0zjr9oIOrp6iCrhkDjFn2WAy3FRgRBwNGjR3HDDTdQXz927BgEwXvyWCqUSiX88Ic/\nxCuvvILJyUlIkoStW7fijjvuwMCAszn63NwcHnzwQbz++uvgeR5XXXUV7rzzTojiEppsmg94s0m+\nzWAwLuBF85nujzmvvZaXYeZAlhQMxOj5KuenwihlapMDaZVEUFU1FGQNSdcCyTBGP2x3mNlSID0G\nL1FkpvIyNnaFrInCvgAHUSD3TxTAs8Adu1K4blMCnSEeb0wX8eJ4AacXKp7J6o8fy+LJEwuIiiwK\nVQ2nshVs7Aqhomj49svTODpXxqHZMv7v926idhAiIfrtqTBOL1Qxmat6KiykmvCyoRj2TxSoFjJn\nG1VVh6rXdsmEyBvm2M7keuJlt7MvgojAoUPiMJWXoes6PvZvxzFfUvDXN2+sWVK5QH7PqGsnv6nL\n8DajLdBvTJWw1ef6I5baTleCs2UVW3uM7xQTWRSqal2Cf0HWLNXeC7xHVIAszl4EUjLD7G5VHzCU\np+mCgmtNaxs7LOLpYVB8YLqIex4/ZVVVkyrcsUwFs0XZEYaWzWKJzR6+eSxjkMC3Dcfww4PzODJX\nthwT3DiTqy/ISEV5ahW99Wx7bCYBI13lgt4w3kyXoOnGkvjfnzZSU7b3hPHB3SlcYOZ8HTTzSHcP\nRJE4MNdUVXNQyKqGrzw7AV0HvvhroziVrWBHbxgH0qWGavBL43mkIjzVuq07wuMkRSUzmihksC4h\n4sp1cSzc/RmUT49Zr68z3SxOZiu1ggnb2CWb/V5KhTBgbORJMZYbE7kqQjzjSTTsNjF25X48VwXH\n0KuSvQggzQqOBjJWvGxx5kpGSkpC4oCKXYH0JpCqpuPIXAm7+qOQeBaXDtM3F50e6ulsUXGkWLkR\nFVlqzmfNHoxDvusGqEK3g/xHRQ5f//kkwgKLiz1qJc7kjKKhGmoE8sevehQpNZiTlhItMZyLL74Y\nTzzxBB555BGoau2mK4qCRx55BE888QQuvvjili+yEdLpNIMaLmEAACAASURBVJ544gns2bMHn/70\np/Hbv/3bmJ+fxx//8R9jdrYWplQUBffeey9mZ2fxyU9+Eh/5yEfw3HPP4W/+5m+W9Hp6b74N8d2X\nYujDH13Sz7XDvki7H2QrhC1rUDQd2bJq5G9QF0GnjY8GxkF0LBNxyiTTIXF1KmGmAYEkvUSJYjRX\nUhDmWYfM72XjQyCrGl6fKmJ7TxhhgcWGzhCSYd4KqfglYT89tgCeZfBbZk7R3740hR8emMOXnz2D\nRw9ncMgkMUSBcIN8P1IANemziB01w5MXDxIFcukXvFZRcnkgpkziMkO5VhKCI2OvPyZgMlfFa1NF\na8E47mPB4aVARgQOfTGBugj8ooGqybEMQjxjTdZ2VBQNJUVzKJCKVm/7U6yqiDRQIDkrJ5iuQPrl\nKfZGBcxQ1DJCmN22K4BxT2IiSz0OAB45lEFV1XFBbxg3b+vE19+1Eb/3NoNivDntVHKnCoaPndMY\nvX7xuWIkDgB17d3sGKeo+6mIgFxVs8YSQSN1lmAkKaGq6pjOyw7F+eBMCX/2+Cm8fCaPTEnBwXQR\nIZ7FuoSE3ijds69VHJkto6rqkDUdf/gfJ1BWdOt8Xio3YJCU6YKM9Z1025SuMI9cxcgvt+NXMyXk\nqxquWBcHyzBIXnY1+m+5w3qdbE4dBN32+eSZ7PVQeZMhDqpO32BNmC10PVM3bAJIRat5iI4vVNEX\nE+tsq8j5gPpCFLLx9loXCLo8VECCWTNli2MZB8E9ma14KufpggxFgy8JBIzaAY5xkteqqmGholLz\nHwmiHgpkrUUqi2LXO1CJ73L8dr9z+QAYBrjvZ+Oea517zJG81O6IgC6PXOW5ABu3pUJLZ7jzzjtx\n+PBhfOc738H3v/99q/p6YmICxWIRfX19uPPOO5fkQv3Q19eHr3/96w61c/v27fjoRz+KJ554Arfe\neisA4Pnnn8f4+Di+9rWvIZUyMv85jsP999+P973vfdb1twoh2YkNv/+nS/JZXrigtyYDuEMQAsdC\n5BgUqqq1sHdFeLo1kK47HjxNZzBXqk3MtDaGBB0Sh6Pzzkl8oaxAYBnPggSL5JkPxnxJqVtgJN64\nfq8imlPZKiqqjvN7nVLIQFwEA28Cma+oeG2qiD2DMewyzXbfTJfwJqUrxhvTJVy/KVn394xNgQT8\ncwBPZasYjIvojRr9y1djCNu+Swb8Q6dzRQUdEgfRJFP9MdHI+7IRlhOZCq4arTsUQK2Ixp0DCRjj\n4peTRaiaboW3fjlZwNMnC7UEcg9EBI6a9+omMDFbwjsJGeu6HkiB5Mhi6nqEaAq6G6mogCOz5TqH\ngFfMTQqpqqQdR1OtNV3H/skCNnZKuPeG2s3eYT4Pb0wX8fb1NVWTnutWPxdsS4WRDHF4/lQOH9xd\nXxklqxqOzlfh9g6xxkxRdrR/zDTIDyUgBTWnslWEzC5et13QjcG4iPufm8DnnjiN0aSEiVwV56XC\n4EwP0MOzZVRVzRqPS4E3zLF89WgcT5v5j0MdIqbyMl6bKkLTdWooM1NWoGhAL6VtLWBX5lT0xmrX\nS4qbLvFQoFLm3GHf0NkJ33RehsTR8/OAWipRpqzAbnIlqxrSBW9V2kDtOnXdmI87WQaTuaqnYpYM\n00PYtchUAwUyQA6k5fDhqEY3HAtoG7kztq45fmAZBskQ77h2QtBp0SiCqMDiVJZedQ647cFq17y9\nN4bf2iPgr56bwFNjC3jn1k64QZ5d2vFeaTfkuXNHDZcDLT15yWQS9913H2655RYkk0mMjY1hbGwM\nnZ2duOWWW/DFL34RyWT9IrzUkCSpLlQei8WQSqUwP19Lrt+/fz82b95skUcAuPTSS8HzPPbv37/s\n17mUsNve9FNCCcauSLMUjJ4ITw+p685OhhLPOhYtshOkDca4ZJxDsS2M2YqKRIjz3NV2hHjERdbK\no5krKVTLH/eDbAdRHtzEWeJZpKK8Z6jpRKYCTTcW7K4wj5u3deK2C7rxwV0p3LgliS/ftN7y5vTq\n75spKeBZBsMdIkSO8cyB1HXd9BMziqS6wvyqDGG7E72NXB+6AjlTVBw7caJ8/8KWJzaWqSBvhond\nyFVVI3eWsuAPd4hQTBWnJGsoyip+dmIBuu6hjtgQEViqwpJ2qTOEuNp7eJcUDZrub+ED1IpP3N69\n8yUFDPxDc31RATrqKzV/MV5Ad5j3TIHoifBmFwznSY/MlrFQUXGRqyq9PyagM8zXbYjo1bb195Vl\nGFwyFMPphSpVLf/56byjGpeAtEV150GSLlUNFUhby0ey+dveE8blIzWSMpapoKrq1saN/KZLvSk7\nNFsCywD/x6U1ljyckDDQIULWdM+cVJIz7JXv6dWxZHyhCgZ0FRowfpO4yDnacdoxXZDRS+nRTEAU\nQefxhhm3jlpEiAZ7FbYGIyI0nZeh6vQCGsAgUwJbb5eVrXhHsuzwI5CqpmO+bLfMcn7n0x5h7HFX\n1xw/JMO84zdyd9yhISpyKCta3QaxIGvgWUC0FQnZ2xcyphNCiGesbmxuTNQJFDa3FY85i6zZy9mB\nhqDlM0QiEdx+++2LNhlfLiwsLGBychLXXnut9bfx8fG6oh+e59Hf348zZ86s9CW2jG+9eyN+cSZP\nfZijAouCrFoh1r6oCFCigbquOULYybDgmJSzFBNxArLrzVdUi2Bmy0rDMMVgh4jxhSqqZrJvF2Xx\n7QzT88aAmh0GLQenPybiiIcBM0m0JsUEv3VJvbR1/zs34Js/n8SPj2Qwla86DGUBo+oxIXFgGcYM\na9EJZLasoqrq1s61OyIEsv1ZaRDljuySOZZBd1SsI7uarmOuJGM0WSMtpJDm0GwZHZKRE/nz03nc\n8b3DuPvy/jqLFXvPXjdIQv7pbBX//alxs3+75luAQRAVWWrRDyE0PVGnAmknm1aiewMzdCKoV10M\ncr5kqLK0cB4BIUjzJQXEsGS2KONMrorrNyU8F/+eiABFM8KB9sXgZ2axxuVmyJmAYRjs6A3j6bGc\n0Z3EfD7JAjoQFwFrX0Q/57aeMB47msWR2XKdnc8zJ3PUIEbKypt1Va+WFURFtqFCOGL+9qcWKpaq\nMtQhIiJwuH5TAo/ZCmzOM42RiR3WUrdBnCsZIdK4xKE7zGO2ZOS/nc6SorEqNd+wUSi50yKQztzi\nebOLkZ89EMk1H/mdT6F45KD1d03XMV1QrGgKDXYFUg/ZuzoFUeWcCmSuqlpRIZpoARhjkFa4Q/MS\npoFYD9EIc7aiQtPhSSAncjJ2UoKINKcPLyRDHE6Z4XCGYaz53e8+xWxFq3YluCirCAtOMcUh4jAs\nQjyLjZ0hnJinr1mTOdlZYGd73auBAyHfjcj6UmDNtjL87ne/i1AohH379ll/KxaLiEbr/cGi0Sjy\nee+8n9WKgbiId23roi5ARIEkal1fTKAbl7oEha6wgNmibClItcFIVyABp1+j0ZfVf5IY6hCRrahW\nZSEtxJU07SdoNgd+k3VfTEBR1qg5JYRA+nUHAmAl7dNUyFy1tjAPxAVMU1pf2a+REMieCI9MWa2r\n+j7boIVZUlGpTtlZKKtQNDhIhb14a7hDxK+fX+v9625XCcBBatwgk/s/vzmLY/MVTBdkZMoqtvY0\n7qQQFeg5SMRahhAcmmcbIZMNFUjzGXO7Ns2VlIahotFkyHxv7bzEZHiDj09byuVYABgqzM9OLGAw\nLli95e0gYewD6drYPTJXRsIkRI1ACkBoBU2HZ0p1BXte1wkYpCFIHlZM4tAZ5nEqW8WpbBUix1if\n+XtvG8A3b96IEM/g2g0dFlmK+Rg4t4KM7ff88k3rcc++YaSigi1MT9/UzjQIdXbZQth2zJfrU3jc\n6JA45CoqOq+6FkMf+l3r73nLg9T7eK+cREKMaMWVFmzrhQrGUCAbEGXAUPEyJQVHZsvW+pGpGNGH\nIF6fXn6tc3V+jM51zyvyNJWXIXJMIEWuQ+JQVXWUFWOetiyLfBVIZ9EqQZGWGmMnk+amYV1SQr6q\nUQuHxhfcLiu1z/PajAdV/pcCLZ3hpz/9aaA2W4vpRlMsFh3hZy8MDdWX4//Hf/wHnnrqKXz6059G\nLNbYzsHPtuJcRVRgMSarzj6eaVoOpHMCTkZElJWKMfhFrlY9RxmMbsNvq61Wg8WYTLKk4IL22ckQ\nD00HsqV6hW8qL4Nj6BJ9f5z4E8rocC1eJ7MVxEW2ob2BRSCnS7jOlQeZq6jo7iSkUICiGbY/7u/g\nDp+Sqtq5klynai4HJnJVHJ0r4+rR+gpfO2heZcmIgMPpvGNHPGtObnYSYleARxISrt+UxEUDUdz1\nL0ep+av5quq5wK5PSmAZIyeVY4C3jcTxzMkcLhuKAw2ilBGRRUXVoWi6Qwl0/wZkoncSSKLANlAg\nGQYqANm2WdB1HfMlBef1+pPckYSRmztfUgDzpycG+jQrFIIeW0HTNlO1Gs9VkS2reMcGunJJWoK+\nMV3CZcNxyKqG4/MV7OqPON/vMW+vSxode9yWSpmSgnRRwbVD9ddLFF532sN8SfEMzboxkhBxaKYE\nkWMxmpQcitxQh4h/uHWrwy+QKMa0jcNioes65suqtcFMhnnsGTLWjx6f4jKgca5cl/X81wYzGT9D\ncf/xkwhxODij1uVf1lrM+hHImgLJWGoBU0tr8FHW7CFsXTdUQXL9KZ/QdzLE4/BsGZ/60Qls7JTw\nlXduQLZcbwXnBYNA1m8MiIWPNe/b7gVprEFDpqwiGeIDcZXamqYgLBhWeWHTScELtbHovOZGBJL8\nN8kBHstUHBv0abMVot2U3pkDSV9nFypGWo0XwVxKtEQg//qv/zrQ+xZDIJ977jk88MADDd/n7rP9\n0ksv4dvf/jbuvPNOXHrppY7XotEoisV6ValQKGDDhg0Nz2XPnVzt6IrPojxRQLqkIy7xWD/Uj0xm\nynqdfJcTvHMIjPQkgGM5qFIcqZ4o8moaHANsHu6vC7MMz+oApsGEokilejBXNB7gVCLme6/6u6oA\nZpFVjYdlqCdZ9/6h7iJwJINsRcN612tzlZPo6wihv6+37rO3DgLYn0aRDdV95mThKNZ3x9DbW3+c\nHSkAidAJzFecv7mq6ShUD6InHkYqlcJAVxFABly0A6lup7JdPGmonVuHUkilOjDaWwUOzkMVY0il\nEr7nXwq85/99CgBw5bZh9MW9SQp3xgzR9HQhlTLIcmckA1nTEUt2W9XJBxcMN4PRvk7rnnT36ACO\nAgBuuXgUqVQHUgCS4THkVYZ677rNe+dGCsCG7nEcnSniwsEO/I9bdiFfURCTeOA58z0eY6o7ngGQ\nQ6Sj02HUm5UnERE4rB/qA8MwGK6KAMbBiBHrs/i8YZzd352gf/4R4//CIQF5AGA5632FioKKqmMw\nGW04NwwnTyFjkmqWZTCnGPd15/p+pDoMksXzvONztlRFABMoMZL191dmja4ou9enqOfs7tERl07j\n0JyMVCqFNydzUDQdu9d1G+8fYwEViEZjiHpc86aecZxaqDg+/9Ax4/c/f7ATpJ2V/fW4dByZau03\nrygqCrKG/gD3BgC29i/g1ckiyoqK67bSv5sdgwUewBkwUqSledl+z3NlBYqmY6Czfv7iIlUAYyhq\nPPV8C8o0BI7BlpF+apFNJKEAOI6cWhs/+YqCqko/nx29iQy0U3mE4s7xPVY2uroMU+ZP6/tFqwCO\no8oIkKQQkANEUcRMxdg0bh3p96nCTgLmkqGBQZkRsWBK8OePDiDsseka7MwApo/nsfkKurp7MFM8\ngg3dtueOp99HAEhGz2BsvlT3etWcqzYO9CCV6gQKFcC0PR1KhjFdVKmfmZePoydWvx7QMNBdBjAP\nNtyBnp4YTi0cxWh3xHfN6OuSAaQhRGLGdZkoKUcwmPA+b08qBSGewK6KCLw0hVlFcLz3pbRx86/Y\n3G81EIpEY4iY70klTgHmPs9+XFmbQDzE162PPL/0imRLn3jPPffU/U1VVUxPT+PRRx9FOBzG+9//\n/kV99nXXXYfrrruuqWMOHjyI+++/HzfccANuvvnmutcHBwcxPj7u+JuiKJiensbg4GDd+91Ip9NN\nXc/ZBK8bD9uvpnMYSYhIp9MoZGu5ROS7yFXnrq2TNxa518Ym0aF34Mx8wUgsnp2pP0nVIOOnp+eR\nTug4vWCQJl6t+t+rqjHqj04ZE6BeKda9X9SMz5peKCGq1BKMdV3HmUwZW7pD1HOENeOzD5+Zxe6u\n2sSo6TqyJRnbuqVAv2NcZJHOlRzvXaio0AFIUJFOp8GrxjWemEgjrjk3Jsenje/Gy3mk0xWENGPV\nPXJmBoOify7kUycMI+PrNyWtooFmcMJmpfPcodO4ap23Cjk9Z+TTVYs5pNPGmCG78GPjk5ZaemLK\nmMFEtey4J3sGo1B1oF+oWH9PSBymF5z3LmfeOxGK5/0fifM4OgNsTPDWe0oAyDTodRyrGvfz5MS0\npUADwPh8EV1hDjMzxtiVC8Z9mZ5fQDptdnpJG8+EVq4fg7CdW1MM5aVUrV0/Ge9hxvs7EWxMClYI\nTtM0HJnMQOQYMOUFpCvG+E6lUo7PEWTj9zgxlUE6bWwC9o8Zr/dw3s/Y9p4QXj6Tw8kzU/jprwyC\nPBLRkU6n0a1p4AAUCkUUPY4fiHI4MCXj2OlJK+XgpePmeUXVIpD283eHOZzJ1O4hyfeNsGqg561H\nqCmJg5HGc61SMp7zybks0unFL2P2e06KMEKUMarphrp9ajZHvbbxeaMganaGMk+a6JA4nLQdT87X\naPyI5lx+7MwUhm2K9dik8ezySsnzeEO1BCYzeZT7KggDqFYrGJstoD8mWM8GDVI+D7LV1XXgZDqL\nqYKMhMQhn5mDV9LXvpEQfnSAseyyHnvtBHIVBdu7BOs63WPd8X0ZDfmKjOnpaQe5PZk25lROLiCd\nVsBVMiDW630RDq9M5DE5Ne0QOnRdx1yhinUdfKBxyCvG4B6bnEEpl0WmJGPvaMz3WN1cz06n5zAa\nVqzzFioKBMZ7/M/OzoErV9GhG8ccPDOH9Lra7/vcUYNArgsrFoEsFEvWc8sotaIh+znmCmXEBKbu\nvKlUasl9uVvKgdyxY0fd/3bu3Inrr78e9913H4rFIk6cOLFEl+qPU6dO4Utf+hIuuugi3HXXXdT3\nXHTRRTh69KjjoXnppZcgyzJ27969Ite5UiAkoKrqVrUyrUVi6p23OP5NJiiSnzhbUjy7HBA/P9LO\n0LJpkfyHFTmOhFHcvoBALaw95woZ5auGt5+Xce5ArBbCdh+n6UCiQX6m/fxZVxV4re+s8f1oOaAE\npDqX5IB1Owy6vTGRq+KvnpvAT45m8dXnJwK1unLjZZt34qEZ/+4Z7iIaoNZuy/69iB+oO/x/z7Uj\n+LNrhx1/6wrzmCsqjtQQEjb2C6vcdXEvbjm/C++7oL4jhx9If2e3F2SuqjoSySUz/6pi8+ILmgPJ\nWzmQte8UxMKHYPdA1BZCNHIghzpE31ZjtIr4Y/NlSBzj034O2NUfgWq2B3zyxALiEldnFeQ3qkjR\nj72q9chsGTzLeIbcU1HDZYCM10YtHt2wh7q39zTeNEWXIQfScpyghCsNs3VvJ4V0Qfa1egGMlA/7\nvETO1+gedZjX4+76FcScm2UMix+7ObauG2OKls9qh91nMSxwmCkpmMrLnnMvwcauEB789c1473lG\nTvT3XjfU60uGgnUHioks1a+V5GXTQtiDcaPgzN0usqRokDW9YfEOgT0t69UpYx7d2effVz1KaWZA\nGjRE/OzB2No6ErM5kxCMZSroDHGOLm1BQti5inex4lJj2YpoRFHE3r178ZOf/GS5TmEhm83iC1/4\nAsLhMG666SYcOnTI+t/p06et973tbW/D4OAg/uIv/gKvvPIKnn76afzd3/0d3v72ty+ZB+RqwUZb\nL1a/PKTkZVfjwr//V+vfwzZLDUXTkSkp6ArTJwx3DmQQkgDUCCZJ5I5RCivIJD5bcD5UZILo85is\nYxKHqMjWEUjSI9jLL82NhMQh57IoIqbphDgmPCZ2wMgZ65Bq1ZUk8buR7cijh+YhazpGkxLGF6oN\n2wPSYG97dmim3uPSDmoOpPl72wuR/BYrdwisM8yjohr5sATks2ibBYJEiMeHLuptmI/ohpWDJNsX\nSR0FV4tCYqdhX5gIgW7UypAQSNlBIIMnq9srZY0FXPHtEQ0YFfGdYd6qMJdVHYdmytjUFfKt2t23\nIQGJY3D/cxM4la3i6nVxSpW49/GWL6O5oOm6jsOzZWzolMDzxvygw3m/SD4wqZ71K76jYWtPGF+8\nYR2+cfOGhkVugL2i3jsHsiRruPfJ03jxdLACyUyD35PYKrlRqBrh+kYEsj8mYL6kWIbc1vhpcI/c\n82ztes3K5gYENBHirRazgLGB0nS3rRMFjHNOGMtUMFdSfAtKCOISZ9lMvZkuISKw1A49NMQoxW6A\nsSkROcaWV2gjkGYup9t4m/ymiYDzvkXWKwpenSyCZYAdff4bGrJ+2cdikeoB6QRjdaxjMNQhOoqA\ndF3HqWwFI+5ngbETyPpxo5uteL2KFZcay1qmEwrRw4xLjdOnT2NuzgjVfO5zn3O8dv755+NP/9Qw\n9eY4Dp/97Gfx4IMP4itf+QoEQbBaGa41bO2pPayk1zC1ChtOZTLEs+iNCjiVrWK+pECHd5/V2sPm\nJJB+JMH+OiEYtKIb4rt1eCaPXxutPUTEwsdvF9wfEzDlmkiID1lHQGuDmn+aYqmHFgkyH06viR1w\n9mAGjEXJMBP3VyBPL1QhsAx+/8oBfOKRE3hjuuRp2uv5GdkKkubOldaj2A5aFTbZrdu/13zZaCEW\nZGKye94RMmhtLpZhYqO1Mywrep2/I6kArdiILfn+Xsb3BIzJGxerQHZHBEtVV02Vzq8CmyAVqXVA\nOTRTQlnR6vwf3YhLHN5/YQ++uz+N/riAD+zsoXyhxrZDjx6aR66iGi32KiquHo1D4xPI9t0GJbTO\ncYy9BWZnmLfIUTNedI2KkeygqT5u7J8o4IXTebxwOu/bYpOgkaLXHRbwxnQJFUWz1GygVqyV8jAR\nJyDpINMFw3D9VdNIvhFhJvOM29omSBGN8TqHQzO1eYBUGDf2Rax9x86wgFdnjfNtDEgE7e1kNzfY\n9NhhEciK6lDfFioKkg6PYbsCWetCdrEtG63WASfYOCQRqkxJxevTRWzuCjXc0NLGYsGK7Pgcyziv\n/1czZWvTO1NUUFZ0hzG/eZD1X0K4H//frzZjPrQTN5p/I12UznkCWa1W8bOf/Qzd3c2FoxaDHTt2\n1BXTeKGrqwt/8Ad/sMxXdPZhr+ayFMiAD/Bwh4jXporWbptm9A0AEsdA5BiLWOUqQRVI5+u09ydC\nPEYSIp4/MY/3beuwCGMQG4m+mIhjcznIqm5VbhKVMKgCSSZlow2ki0CKTgKZpRJIFVu6a9fIs0aX\ng0Zm4mdyVfTHBQx2GJW7fu3TaDB2rlVs7g5BYBnfHrEAUFQMs1vBNjYSFAVyvmT4XwZZBAhpmCko\nVkpEEAVysQgJ9cSQTOB2f0eJQiCJGinx/t+L0EZ7lNzq8hSQJBHlhlg5rU82Xoj7YwIOzpRQlFW8\nYqrRuxsQSAC4ZUc3rtnQgRDPeqir3t+3Nypge08YB2dKODafxol5YwxtMKMalfhFdcf02MzEt3SH\nmyLXi4HVbYvSgQgwet7/1XMT1r8PpIsNCSTZMHl2dQnXNowpG4Ekz7TfnATUXAsmczL6YwKePZXD\nhk6JQhKcIBZC7vBspmw0NWjURSkR4g3DfHPzQ8a/XxoEAOhM7T4YUQkyDhpvfABjXmcZQNOdZLIR\nrPQEd0pKRXVaxDH1CqTbyocUrjXyJiYgAsP+yQIKVc2zS5TzeusjIEWSGuP329gEHfv1b+kOW/P2\niGvM6rbnNizy+MLxq7BvfYdFIBeWcZ6loaWn+5vf/Cb17/l8HocOHUIul8OHPvShVk7RRgv44O4U\npvJVaxdE7URDQV9MwMsTOo6YXnBe/nEMw1gmt0BwlSnmCitKHurPjt4IfnQ4g//2w6P4yk3rsbEr\nZCXnN1IgdRgTLvEXJCGcwDtRQiDtHpdVugKZcykDFUVDUdbqlIHuCO8bwlY0o0PEpUMxiByL7gjf\nNIGcLSkoKRqGO0SUZA0VVa9TTOwoylqd2W2SlgNZbux3SLDeVFSOzZUtspMPmGu4GEjmJsFu8k3L\nbeRZBhzjDGGTxVRq1A7PPIRGIIOSpKGEBMi1c64PsBAPWKE5GQdmSgjxbGBrHLcReFAwDIO79vTi\nnv88hbKiWcblfr2Ee1xm4rX8vuULckU97F4AOMgjAJzxMPy3gxAWrzFqt8Sxh6stw/oG95ukLByc\nKSFsdk+68ry47zGAMZ/RNpPZsn/Xr9p1G/MUSb8oWwSy0fio3YeBuIQagQxOBnsihlduI3JtB82v\nFTDmI3sOrj1HsyvMQ+KYujzCbIA8Uce5BRZhnsVxc9O0s7/xZo2Wj0tLDXLD/ruR3P2JnLEBI5u2\nde7Nhe0YkWPAMqCnCp0LCuSTTz5J/XssFsPg4CDuuusuXHnlla2coo0WcOsOl/obTIC0yBlRPPp9\ndqodDgLpHZK2g2cNQ9myovnulPZt6MCPDhuVd3/70hTu+7VRTBdk8Ky/6kOSw6fyVYtAEgUyaC4M\n2bFmbL5tlsJqfobAsYgIrCO/CLCHwtx5YjyOzJbr/AoJpvOymZtkXPNAXMRRj646XiD5jyMJyWqD\nlauqngSyJKt1eTruEHZQvzqCDV0SGBgG1gT56vJNbAKVQNJzGyWeXZwCaYadZU23fo/ZkoKIwAYy\nRwZMAmbUE+Dy4VigRY2E5k5nKzgyW8KW7nDgUGAr2NYTxkO3bcFXn5/A48eCEEiiOhtkar6kQAig\njrWCqMB65kB2mS3pbrugG997fdbTZNqOQoMcbsuU22UGHrRN35bukNVrnDxzO1KNnymymZx0dbLK\nV9VAERXyPJPno6JokIIYa9sEh+s2JTFZ4qE0GR79oEvydwAAIABJREFUo71D+MfXZrB3vb8frR00\nk/iKoqGs6K7vW3sOWIbBYIdYt1FoNvLEMAwuG47hyRPGmL8ggAIpckb7xrwjBzJAbrXtOSZrLnl+\niB/upjrl1tkKMSKwjuLBoHUIS4WWCGTQsHEbqwRBFcgo6XFcAAP4Jvt3SJxVDJMPGMIGDJucsqL5\nqpXnpSJ49pNvx6f+1348fypn9mI1dv9+1au0gpXmcyCJAllPIDts389OoAms3KSwW4E0lNH5kkJN\nuLfai1kEUsBrU0WjfWLAHTSpnB1JiFZIZaGseqojRVmrI5DxkKF4kO9VUjRUVT2wAhkROAx2iI5u\nNLllnNhIq7wqrbra9d0kjkFFcSqQLAPfVoQArIbxmm4oVTGRMypvm1D5yClCPIuPXzEQ6BiiEv38\ndB5lRce2ABXKSwWGYbCzL2oRSLcxvx3dltG2qUCWjA4rQTc+i0FM5Dzbg5YVDbv6I7hjVwo/Pb4Q\nSMkvVCm9i22gzQlAcNWHZRhcPhzHj49k8N39abBUgkDHQFzEMddmMl9VA7X6JMSWEMiirFntXP1g\nD5dKPIePXOzvn0vDxq4QPnvNcOM32kAroiHzhx8RHIiLeO5kDlVVs+YEd9QoCK4YiePJEwu4epRW\nfEZHVGSpCqRvFbZtPSYbsHRRhqbrOJAuYnN3iLI5dV5PRGCtPG5g5RXINdvKsI16BJ3M7eHhgbjg\nq7AkQjzKioaKoqEgq+BZeKpddhDiGGSg7x6IQAfw2lQBUwHCIVbbMJsNDyFDzSuQ9ZOYnfQaLSPp\n1ZG0EDbgXYltdYcwSYM9rBEURIFcl5BsNkvelapFWasrIOFZBjGRtY6b97Dw8cOmrhAm87K1qcgv\n48RGFnx7m8iChwIg8azDxqei6hA5tvGzYXMUeWk8D03XMVtUrNy/ZhAS2MBEmmwmnjlpeEXai+NW\nAjt9+izbIXAskiHOypsmBHI5ERZYR/iOoKoaKSRk0zUYF3Bmodqw41ihqiIqeIeEaXMC0Nyi/Rs7\nuhA3FbbRpBRYve6PCSjYWrRquo5CtXHXL6A2D8nmuFc0PVhOor0TTdDw1RKAFsKmKomu32kwLkKH\nkWNKsBhC9baRGD6zdwh3Xx5skwcYpNeRAxmkCtt2/Z1hHjwLpAsKTmWryFc1nE9Tp10iUJjnHArk\nwrlEIG+//XY8/fTTnq8/88wzuP3221s5RRtLiaAE0kbQRhsk+idsldiGmhVs4BJVM4i1wy4zD+Xp\nsRzKitaQQBKlbN4Wfl4oKwjzLIRGuW4myPdyK5BhnnXsSqMiW5fsXauOrLc6AbwrsS0F0gwV9ptE\nspk8yFPZCmKi0XrLXSVPQ4miQAJAXOKt3M7MIgoiyO961DQ1z1dV8Cxj5SsuJeghbHo+mxHCrr2v\nqmgNw9eAoTwCxiP00+MLWCirkDW9oXVLq4iKnBU6ZgBcHKCAZinRHRHwv+9O4f/a11hF6o7wmC8p\nUDUd2Yoa2ANysQgLrFlt7ySGpFqZPMODHSIqqk7tNWxHQdZ8c3StHEiKAmkUFDaeW/piIu69YRQ3\nb+vEB3cF76BjpeWYIc6irEFHMEU/4VIgASZYHi1j/+yVI5C0EHYQYjRozpf2dIXFzDsMw+CKkbhv\n/qIbdQpkNYACaQPLMJZrBmlQQCtWchP5sMCiZCOuzQolrWJZFci12GP6nEZAAtkhcZYqdfGg/4JF\nBmq2rFLDoV74xBUD+Idbt+DDFzWeRPtjAnqjvKXCNDKyTUhG9Z+dQGYrauBKPMCwdRE5xmGdka/W\n+2tFBRbFquZYxDIeBQQ9rjCfG2dyMkRbbtKA1dc7GIEsyipOZCoYSRjhKavIx4NAyqqOqqpTJ0oj\nNK84vk/QRHTARiDNQqyZooLOAAn/NFQi21BMXu35usjSQtj1VdiAGcJ2KJBa4wIawFIgYxKPw7Ml\npM1NQDMh7MXi41cMQOSMwpagG6ClxG/s6A5kAp0M8ciUVWQrKjS9sb9hqyBzVNmlQpJnNikRBbJm\n8eIHt2+oG9am0q1AUuYFP4wmJfzWJX1Wn+0gIJtB8iw3yte0I+nKgQT0QBt3e5FK0PSnpQAthL0Q\nIAWJbLztv3OuoiEuBogwtIiYyBndtsx1wCsC4oeeqIB0UcaUqaDSaw8oBNI2/mu2RedADmQjzM7O\nIhRa2ZBLG94IWoXNMAw+d90IKkpjGwOrWrmsoCRrgXdtrFnBHfR6dvZH8dhRo+2cl4k4AccySEic\nFXoFjBBIt4cdkdc5kyHOIk8AqAatEYGDDmMRI+qrl4lyp80fkYaJXBUD8Vp3kv4mQtiaruOBF6dQ\nlDW8Y6PRgKxDIsUw9PORiYemGneEOByeNSbExVTUugtppvMyNgSsHnYjO/hh39dFnqJAyqSgq0ER\njaIHUiDJwhASjMpfUiW5mBB2s9jWE8Y/3LrFMzdvtSAR4lBVdUyYC3gzHpCLAZlrSq7Ih3sRJRux\niZyMnT79IgpVDcMd3vOXaBbNZcr1CuRyhwzdnrO5SrCCRYCmQDa28AHgIo0rN/ZEjgHPMo7UICv/\n3Oc+jybpxXvL4T3rRiLEQ9Z0FGUNUZELVIXtRirC4/UpzaoAp65zTH0OZFnRoWo6OJaxol9Bc+Zb\nRdNnefHFF/Hiiy9a/37sscfw6quv1r0vn8/jtddew/bt21u7wjaWDk3MAUGT9S0F0gxhN0PSmsEu\nG4EMYgmRDPMW8dF1I6S2sSuYfxlBIsQ7yN5CRasz37VbOJBFLFNWwaB+sovaFjw3ZFVHuiDj8uGa\nKhEWjLyyICHs/ziSwRPHF3DRQBTXbyIE0j+EXTQnaC8FUtWNUBkh4skmQpIRgcNwQsSb6RJyFaNT\nRyPiv1hYOZA2k++8VYXt/G4h3llEU1U1i2j7wiSQRPV6bcrofd7IuoWGxSzFQfKKzzbIhumYmbYQ\ntOhqsSC/RVHWYPebIAVzhDgNeXgE2qFqRuekRopRZ5iv2wDmKmrDqEirWGzXL6BGfIlCL/hYpzlx\ndggkwxg52HlHCNu4536uHRGBw/pOCQemi1axUa6qBuqc0ypqjSdUJ4Fs4rklnrkvjOcgcYyHiujK\ngRRqKnxU5JAtK4iLbODin1bR9BN+/Phxh33PgQMHcODAgbr3hUIhbNu2Db/5m7/Z2hW2sXRYBhnf\nrkA2E8JuFleMxPCBnT2YLynY3N2Y3HaFeYwvGBNJSTFaEgYiCjYkQxyOz1eg6zoUzXhI60PYpols\nVbXy4TJlBR2hetNtYnhNS/yfylcdFj4EA3GxYegNgOXZ+ckrBywF069XN+Cf6G1fsBYTwgaASwZj\n+JcDc3jWTD3oW6ZFViAhbBsxzFVUsEw9ORY5FrJW27FXFB1SNPhzQX7DF8eN9nhDDcypnVjdCmKr\nIOPjRMZQUFZSgbTD3X2kNyqAY/wJZCMPSILuMO9QuFRNR0H2tyNbCsRd6SheNlVe6AzzqAb1PDWh\n2xXIZQ4Bu+EuTswG7LqzvSeMRw9nMJWX0RcTkK+oiPUsvwJp9wgd7BBRrBr58s1YbpHCprKiYzTh\nVSVfH8IGYCmfmXJwx46lQNNnuu2223DbbbcBMIpo7r77brz97W9f8gtr49wA2SXNFBUomo5wk32M\ng0LgWLz/QkpLNg90hnlUVd1RudhsXkgiZPieFWTNqvB17/gjYu0BJnC3MbS+g2lkTVMg3RY+BANx\nAQfSJSMU47NYzBSNPrH25GnSpWIxBJIsiLmKumhPv8uGDQL5rweNNqPLRSB51rDIqWr230A1c2Gd\nEy5RXqqqjjDLNJEDaXxOyKZ6JULcslcan0sgzxexb1ruexPx2JC5O8pwLIPemIBJPwLpkTPrRleY\nR1HWrHSdIPYyS4F6BTJ4CBsABmKC9bwHT4U4e6p3TGStHvCAMae6W6lqrJFeVYrvsf62ozeCRw9n\n8PJEAfs2dEDVV6YrS8KmQAJ0e7RGsOel9nmYvOuu+YwonOQZyFZUrEssv+JK0NIIueeee7Bz586l\nupY2zkGQB4eEWZdLgWwW1o6wpDRsUeYFe4GQl59YVKAQyJJKNRVmGMbTeoTkOdYpkGYe5GSDPMiZ\nooyeiFC3a41LnGcRDakGpy309gruTFlBZ7j5AphtPWFERRanTQV1uQgkwzAQWMZh45P16JxDqjEr\nqgZd1wPnQBLYN0jrG/QwrsfaLiokz9zx+QoYBOm13Bq8FEhaeLdD8u5aA3hX7btB2rqSMPZK+e7F\nRA6M7XzNGvMPdIhIywbhqrIBTb1XsHDGjZjIOYpoMmUjz9Sh6LECpjf9OXJ9t1p/umQoBpFj8NSJ\nhRX1RLQrkIBJIJvsumXP1bxpS9LjXfU5kIDxDCiajlwTnsFLgZZGyI4dO5BIJJbqWtpYbixDVXyY\nZyFxjBVmXS0EsstWsEJCWs0SSLsdkNdkFHFVDFYUDSVF86xADfMsyj4KpLu9WH+ASmxd15Eu0D0J\nOyTO8lBzg+zwaTml9vD3XEltOnwNGMrPTlsRlp8hfasQOcbRonC+RFeB7f2wZU2HjoAhPfPZETjG\nul/LrTqda7Ar/P1xoakCgsXAUl/qCGS9sh4V6v1a7SBVs41yCmtersbma6VICscyiNq8WZvtODIY\nF/H34zvxjZMX403h2kDH6GeRQEZFoyCLeFd6RXWcVkPGpuLSoRjeTJesTXlQlbYVJF0KJPEUbRZ/\nfv0I/mjvEC4e9KrQp+dAlmTNGhuNOiItJZpaFb73ve+BYRjccsstYFnW+ncj3HrrrQ3f08bZxULv\n4n4jhmHQHREsArTci0ZQEA+6+ZJiFVc0uzPrj9V8xYgy4VUYQxRIK1/QI3znpUCeyVUR4uvbiw0E\n8IIsyBrKika1lOmQOJzIVKjtEKdNTzkagSST9XRBxnxJwfmpxXVA2dIdxnOn8tjZFwnsEboYiBxr\nLTakBzhtIg3xRIHUrWKaIAUFOmpGkH+0dwif/+lp3LDZSyXwwlsjBxJYjDrbPMIeIex8RUVUcOaf\nRUUWFVX3bCMaNKew2+wTX6dArkCY1N71ipw3aA7kYFxESRPwDxMX4luXdjc+AABw9jZIZF7NVzV0\nhllkSiq29gSLYGzrCeOZkzm8Omm04m02930xcCuQ+aqGTYsgrhf2NfB5ravCNn6joqxafr2rNgfy\n+9//PgDgve99L1iWtf7dCG0CufpR7tjT+E0e6InwFoFcLQpkp/VAqzYC2dyESBrZn8xWLA9HN9my\nHmBT9cg26LkdFpy5PQSTORn9MbGO5AXpRkP6p9IUyLhk7OQrqm6RJ4J0wfCdpN0XEkonk3CQynca\nbtqaxEJFxXvP61rU8UEhcIxlU+LlwwnU1MaKoll+kGKQELZNvd/YFcLf37K56WusRrYAAApd72j6\n2HMByRBnGSoHsolpEX4hbLd1S+05VaktGb1aX7rR5eomtZhWeYtFXOKRNp/1BbNILKi6NmzmxUkc\nE/y3OcshbMAg9hFz0x00CrLO3Lz84owxd6VWwGorLhkpBpmygqpqRDea8YAMDnoIu1DVrLGRWiYn\nFBqaOtPXvvY14yCed/y7jbc2umwDdjlVpmZg91wkylSzVaG9MQEix+BUpmJ1InHbdVg2PqTvdAPD\n2zDPUotoClXV6jxjR0ziEBdZ3wpSspjRLGWs5PuyilDMuSBMF2SkovV5k+S4uMThjekSgMbm7V6I\nCIvrodssRDuB9PDhBOwhbJsC6RPCroZGIZbHbH9ZvIqoiilMb7r3rC7MywmGYfDlG9fjwZenLS/S\n5YRfCDsuOe9x7TnV0EGxIw1q/Fzr+V0jcsDKEMgOicXROcObNVdRERfri8S80BMR8Ed7h7C1iV7q\n+tksopFqCqTINReaJUUkxA1gsZvfZsCxhm/wXEkNnE+7GLg70dir84mh/nJbStnR1Ira29vr++82\n3pqwE5fVE8Ku5S9WVA0s07wNDcswGEmIOJWtQuBY8Gx9d42obQcINF5QjPZrmiOkTKyGvDzDtvaE\n8cvJAoqySiXofhYkNTNxp1edruuYLsg4v9fbKH4oLuJgxSSQKzAJtwKRY22dc7yr7gWbZyTxxfMr\noskM/Q4AFdDvXZoLXaPkkaA/LuKz1zRue7gU8Aph0zZjtP7KzmMCFtGEeYgcgzM5Zw7kSuTDRkUO\nimZ0j1pYhHn520bizZ1wha177EhItZAwyTgIGprtCvOICrUWs8vdbpSgOyJgtigHruhfFFy/idVt\nrKpaRYR90XOkCruNNgA4zMNXSwhb4g3z3Pmygtmigs4Q35QnF8H6ZAizJQUvjefRExHqPoNU5RIV\npNGCEhZYs3NNLSRaUXVouve9u2QoBkUD9k8UqK+XLDseekcZoL4bTbasoqrqvubegzaPw5Xc1S4G\n9iIav845JP9NUYMpkGAYgOFhVVCv7TTGcwq0EDbxZXQXl7g3em4EzYFkGSMETHKSVzIH0t7ib6Gi\nrgBpPXtzuV3pncobZD1oBIlhar2+Qzy7Yib83RHDZJ6ICDFpOc5LVyAXKiqmCzJYBsvWzIOGls9U\nLpfx9NNPY3JyErlcjtr/+qMf/Wirp2ljKbBMO0r7gF1NSlVnmMd8SUGxqjnC7M3gpq1J/OexLHR4\nK1o8y6CkuELYHonbdt8u9wLopd7uMfuRvzpZxJXr6i04imb4jern6GEmPuFR9W2H3U9sNf2uNAhc\nzcbHz/icEEhZ01BRjf8O5ItnzmtMm0GuGoT4egWSFLO5cwMJMSTPihtBcyABoyDl56dzkFUNuaqR\ni9isZctiQNTRXEVFvqr69oVeEpxFtZxEtWaLCk5njblqexOFfJcOx/DqVLGuT/pyoifCQ9NrvbiX\nRYF0zT9RgQXLGGNiKm+kJC1GKFksWiKQx44dwxe+8AXkcjnf97UJ5OqA2DuAzqvegeQV1yzp527p\nDmOoQ8R7z+takeT5oOiO8DiYLkHRdGzpWVwf5i3dYbx9NI6nxnJWUY0bEYG1Fi6i9HkrkMbfy7IG\nmPNho7ZXvVEjF5PsxN0gCyjteJpPJQBM5Om+k3bcuKUTiqZD5FhrsV6tEDnWlgNpkIROGuEnCqTZ\nWQhosk3gWQzrteEEyzCQOMZhi+Vlb9NQgZRV8CwTaDMx1CFC04HJvIxcxTD4D5qL2ArId5oqyND0\ntW0jZSmQBQXH5svoifCWK0YQXD4cw4O/mEb3Chr9d5ukdyxr5F4uRw6kWxVmGAZx0fD6nS7IlvK6\nUmjp7n73u9+FLMv4xCc+gQsvvBDxeJM5Fm2sKBiGwcjv/P6Sf25XmMc3b9645J/bKvqiAl6dNHoW\ntzKR/P5Vg7hyXQ5bPFoohoVaYUy2bCgSXpOHu3MA4B+CBozfrTcqYKpAJ5BFHwWz1nnFRSAtBdKb\nQIYFFu+7IHj3n7MJkWOgmC0KSd6UuxIXqOVAKpoOmFH9ZvrVtrG6EOJZq5oesPdAdxFIUtXro0BG\nRTaQLR1R7ccXqkYxywoROUKCybO7EvY0ZwsSzyIucTg4U8J0Qca1GzqaamTQFxPxR3uHltV71g1C\nek+axTtBPTqbgbsTDWBEmSZyVRRlbdmaNXihpRF45MgRvPvd78aVV165VNfTRhtLhv5YjRx1USqU\ng4JlGGromCDM17wdG1VH0vK2SPjbrwCpNyrg9eki1c+xRkApBJJ0XlGcqSVkEWpmV7+aYSeGmbKC\nRIin/gZWCNv0BARq/a3bOPcg8YxjbHu1+LOqsD1zILXAIUdCSk5kKshVVPTFVibqQkgwsfRaywok\nYISEj88bZOzy4ebFqaaLhloECbuPZYgCufwhbMAgkKTb10qnGrVEICVJQjLZrJluG22sDOyFHxuW\ncSdKinUANKyOpBFIPwWRoC8m4OUJHfNltS6ZnHwWLcxs77xix2ReRleYX7EE8+UGKYSpqjrmSyo1\nfA3Yimg0G4EM3BsY7RD2KoPEs448t6JHBWzNL4+uQObl+ufKC6NJCSwDHEyXkKuq2LxCRI4oWsTS\na6WUz7OFnoiA4/MVSByDiwcbGGyvAhC/SeIC4ZVPe8Hffp9aK7JY2MfBSiuQLa0eu3fvxoEDB5bq\nWtpoY0lhf5iaScBuFvYQdqPqSCsR3raQNSqiAWpkeJqSB1mUNYR4hpo8LVq9n50T1lxJWdFqveUG\nUSCrqubd9gxOpZKQ6iAK5FJO+KsDa4MISxzrSM8oeqjxhHzlfKqwgypGIsdipEPCKxMFKBpw0cDK\nkBsyd0yaBNKrWcFawdWjcWzuCuFDF/WeExvdnojg6HLkNZ5YKQQutNj1iO7ZS7DSCmRLv8oHP/hB\nHDt2DD/4wQ+gKPXdNdpo42zCTiCXJ5xgIMQbBRyqpqNA6YJhByE2pD83YCOQPpMkmRimKXmQJVmz\ninPcqClztYVT13Vky+qK9kxdbhCinDHtibxaSdpD2CTtIFCBkF5rZdjG6oHEMw5LLC8CKfEsJI6x\nbHfsUDUdZUUPVIFNsL7TiGiEeBbXbVp+03SgRoKnzU5Wy16FfZaxb0MCf3nTevzXbZ1n+1ICgWMZ\nKyUoEeJWpAc34HSbOKdyID/72c+iUqngH//xH/Hwww+js7MTLFu7aSRf6+tf/3rLF9pGG80iIXH4\ntc2Jxv1FWwRZrDJlBaqPnyNg6wxTaU6BJMfRjJBLsuZ5TmKSbc8TK8gaFE1f0Z6pyw1CAifzhjrj\nRY7tIexyMwSyjVWJEM860jP8nqUOiaMSSFKE08w4ePf2LkQEFjduSa7Y+HEX5q31HMhzET1RAacX\nqtjYGWqq6KcVXLuxA99/YxYA3ft2OdHS2VKpFBiG8Q3vrNRNbKMNNxiGwccuH1j285DFirQU9FMy\niJck6ZkN1Cqy/Yhn1NYb1o2irHqSQZ41nAvtlark3M125lnNIIv4lFlg4BnCtnwgdUu5aooArJn5\nbG2E5EWONY34dbAMY/k80iIOHSGuzg8VgGX/FMgP1MTm7hA2d/cv8qoXhzBveP6RtqprPQfyXIRi\nzrM0z+DlwnCHhN++pM90n1jZ+amlFeTP/uzPlugy2mjj3AUhkKQ/rl8/8DDPQmAZegjbh0DGfKpI\nS4qGAY9jGYaBxDOo2hRIYrS9kpPccoMorSTE76XO0BRIv1aGFiwj8TZWE0J8LSVB4hnfgrS4xGN8\noVT3d3kRBPJsgGEYRE3PP55l2vZTqxDbUxG8Pl3CDp8Wsa2BPkbPVph/7UgQbbRxlhBxKZB+XSkY\nhkFHiEPWpoQ0MhIHalWlbh87XddRlDVf8ilxrEuB9O7Ucq6CqIiExHupM7ytF3ZF0SBxTLBdezsH\nclWCFFeUFQ0Sz/o+Sx0ih7KioSRr0KFbGz2iQAp+LS1XCXqjPHJmoV47urf68P4Le3BeKmx1D1vr\nWDsrSBttnCWEeWMhmrFC2P7KXkJyhtKCFHMQgph3KZBVs4+2H4EUOadXHrGZWFsKpPH9rQIDDwIp\n2Hphl2StCQ/ItRHyXWsIWTZVxu9T8nEkiJvj/d4nT+NXMyV8410bkUoBsrm5Wu0KJGAY/x+dqwRT\nzZcAmYGPoD32g0PgGFwyFDvbl7FiaIlA3n777Q3fI4oiUqkUdu3ahfe85z1t38g21hzqQ9j+pKQj\nxONMrmj9W1Y18Cx90SPgWAYRga3LgfRrY0gg8XQFci3ZgFgKZMAQtmyGsJsOA64Z1WdtfA9C+srm\n+C76OBKQMfHalPHs/f0r0/gfGwZtCuTqvyeDZueooocd0VKjGt26IudpIyBW2RBtSbPfu3cvRkdH\nAQADAwPYs2cP9uzZg4EBo3BhdHQUu3btAgA88sgj+MM//EPMzs62eMlttLG6QAjjjKl+NeqBmpQ4\nlJWaD2FV1QOpH1GBrcuBDJK/JfGMtUgCwLzZK9rL6uZcBDEDL5ghTM8QtiMHUj8n/OXa8EbIZZTv\n50jg3lQQQ+5zJQcSqNm00NwY2ngrYHWN0ZZWkHe84x344he/iE996lO47LLLHK/9/Oc/xze+8Q18\n+MMfxvnnn48XXngBX/7yl/FP//RP+N3f/d2WLrqNNlYTSNV1utC4iAaoEbf5koL+uAhZ1a3Qqh9i\nEleXAxkkf0viWMzafFrP5KoI8eyaUiDtRJBjvFVg8jZSRBOXgvmmrTkf8TUCt01VQVY9rUzcBHLB\nTOWoaoRArv7NBPGCVNvjsY1VgJaemIceegj79u2rI48AcPnll2Pfvn146KGHAACXXXYZ9u7di1df\nfbWVU7bRxqoDUbtmS2YRTYMQdo/ZASZthrxlTbeKO/xAVyAb52+5cyDHF6oY7hDXVBK+PX807lNg\nwDAMeJaBrJIQdrB7wAoG0WS4taPargUQo3y7AumVD0zCvwDQFeaRrajQdd0y2Q+yiTvbuGggil39\nEdyzb/hsX0obbbRGII8dO4bhYe+BPDw8jOPHj1v/3rhxIzKZTCunbKONVQd3uNSvChsAUmZXmbQZ\n8paDhrBFDoWq6vBdrQQKYddyIIuyirmSgqEO0fP95yLsRQWNDJZ5lrFyIIOGsIc+9LtIXHY1+t77\n/paus42lhVWFrWqWI4HXBm5jVwjfeNcG/Mk1w9g9EIWi6ShWVcvi6lwIYUs8i89ftw573kKFGm2s\nXrS0nRYEAceOHfN8/fjx4+D52ikURUEoFGrllJ544IEHcODAAczOzoLjOKxbtw633norLrzwQsf7\n5ubm8OCDD+L1118Hz/O46qqrcOedd0IU19aC2sbKIeYyLW6kQPZaBLKmQAZRP6IiC1U3SKPd/w7w\nD79JHAtNN8K2ZxaMc641AmlXIBsRSIFjUJI1o3o9IIEUU/0YvfszLV1jG0sPewi7rOjQ4Z9CMpyQ\nMJyQ8Ma0UUgzX5Iha+dOEU0bb3WsrjHaEoG86KKL8Pjjj2PDhg24/vrrrTaGmqbhsccewxNPPIGr\nrrrKev+xY8fQ29vb2hV7QJZl3HjjjRgcHIQsy3j88cfxhS98AZ///OexZcsWAAaBvffeeyEIAj75\nyU+iUCjgO9/5DgqFAn7v935vWa6rjbUPjmUr84UeAAAgAElEQVQQFY3wMss0JiU9bgKp6ggFyEck\n3TXyVdUiTFb4zS+EbS2ymlU4YA/nrQU4Q9j+0xrPMlYRQruI5tyGvYim5kjQeJElfaQzJdl6hs6F\nHMg22lhNaIlA3nnnnTh06BAefPBBPPzww+jr6wMATE1NIZ/Po7e3F3fccQcAoFKpIJPJ4Jprrmn9\nqin42Mc+5vj37t27cffdd+Ppp5+2COTzzz+P8fFxfO1rX0MqlQIAcByH+++/H+973/vQ37+yrana\nWDuIixwKVQ2JAAa/cZGFxDE2AqlBYBs/inFCICsqeiIGCQ3Shk0yX6uoukWc1pIHJACHBVJ/zL8w\nRmBr5u1t1enchpUDqWooy8F7WpMCskxJtlT89lhoo43m0BKB7OzsxJe+9CX88Ic/xAsvvICxsTEA\nQG9vL66//nq85z3vQSRitPSRJAn33HNP61ccECzLIhKJQFVrVav79+/H5s2bLfIIAJdeeil4nsf+\n/ftx4403rtj1tbG2EJc4TOZlDARQ9hiGQSoqIG0aj8uaHmjxIrmWdhPyQASSqJVmFw7A33j8XEd/\n3J9A8iyLhYpx78+Fwok2vOEMYZsEMsDYJr3jDQXy3MmBbOOtifnB30Q4+zzk0MjZvhQHWi4pjEQi\n+MAHPoAPfOADS3E9LUNVVRSLRTz55JOYnJx0WAaNj49jZMT5A/A8j/7+fpw5c2alL7WNNYjBgLmF\nHRKH8QUjnBzUB7KDQiDlAOE30aZAlgN0vTnX0R/z/w0ElrHuw1tNdarEdiCSfR6KNHC2L2VJQH6/\nqm1sB8lrJc/SfNGmQLY3E22sUsiRzZAjm8/2ZdRhTXlSPPPMM/jqV78KwCjw+fjHP45NmzZZrxeL\nRUSj9T0qo9Eo8vn8il1nG2sPJDRMLHoaISzU+vYqmm4ZXPuBRiCDdNEQzNxkRdPfEgrkQCMF0ha9\nf6sRyHzPu1BKXAFVXJ5c9JUGCWFXVa2pzVHMSgdR2jmQbbSxSCwJgcxkMjh27Bjy+bzDYoRgMXmP\nxWIR8/PzDd83NDRk/ffu3btx3333IZfL4amnnsJXv/pVfOYzn8EFF1zg+xm0a26jjWaQN0mdVwcU\nNyICC1kzutFoerDwGfnsHC2E7UNACWFSND1Q68NzHSQ/1As8W/vubznVieHWDHkEahsAWdUD9ZR3\nH1dVdctI/K22mWijjVbREoHUdR3f/va38ZOf/ASa5t2bczEE8rnnnsMDDzzQ8H0PP/yw9d/RaBQb\nN24EAOzatQvz8/P43ve+ZxHIaDSKYrFY9xmFQgEbNmxoeC577mQbKwOe58+J+3799iz+5dUJvG3L\nIFKpeMP3d8UzAHLgogkAQDwSbvw9wxUAJyCzovVe8XgZANCX6kYqRfeGS45XAaQRjSegswUAwPBA\nn6fqea7cczdu3ZXFibkS+vv8CdL/396dB0RV7/0Df8/OJgiCTDAqCioJLril1+WamD5a2bUgbMzU\nyrpXH03xh167FTxp3nLtaj091FVRcy3T0q65EaKEdjVxQfMKZCougAvLDDvz+wPmyDgjzIFhG96v\nf5JzvnPOd76caT58vpuT6iaAQgCAu1ubZvNeW2q7NyWnkjIAaZAoVFA4VD7/3p7u8PLyqPF1MucS\nAOkoNwByhQoA8Fh7T7g72dfqBM0Vn/XGV31JRZtdsz4v/v7777F//34MHToUvXv3xqeffgqtVgtH\nR0fs3bsXLi4u0Gq1dbp2aGgoQkND61M9dOrUCcnJycLPPj4+yMzMNClTVlaGrKws+Pj41Hq97Ozs\netWHxPPy8moR7T6pRxuM6uQAT2kRsrOLai0vKa8c//jbjSwAQHlpSa3v09jVdvt+gVD2Xn4+AKAg\n7z6yJYUWX1ekrwwac+7ew31dIZQyCe7dyXnkfVpKmz9scrAbALda624oLxX+XazXN5v32lLbvSmV\nVWUP83V6ZFd1WBXr8pCdXfNe0bqqISdFpWXQF1Z+XvPv30OZzn4z880Jn/XG5+XlBYXCuq1brVWv\nT0tCQgJ69uyJWbNmoU+fPgAAf39/jB49Gh999BFyc3NNdqJpTAaDAZcvXzZZdzIkJATp6enIyXnw\n5Xny5EmUlpYK9SeqC4VMCo2ryuryxsXGjfvxWtN9ppRJ4SCXmo6BLLOiC7vqnHEMpD13X1ujeluz\n27Jlk0slkEpMJ9GI68KusGo7UCIyV69vklu3bqFv376VF6oaV2RcNsfR0RFPPvkk4uPj61nF2l28\neBHLli1DYmIiUlNTcfz4cSxduhRpaWmYMGGCUG7QoEHw8fHB8uXLcfr0aRw7dgzr1q3DsGHDuAYk\nNSrjJJbcqmDQ2rF4riop8quWoAFg1fit6gFkUdmj9wpuLap33VszeYmaN6VMInoWtvHzVlpuQEm5\nAVKJ6VqiRFS7enVhy+VyISWqUlVmX/KrutQAoG3btsjKyqrPLazi6ekJmUyGrVu3Ii8vD66urvDz\n8zPZhQaoXDT8b3/7G9auXYtVq1ZBoVAIWxkSNSYhA2lcj9DK7EcblRx5RQ8CSGP2RFXDDFJF9Qxk\nWYVdL+FjjepBIzOQLZ9SJkVJ+YOdaKxZB1IikVTuiV5eYfVe9ERkql4BZLt27YQAUaFQwNPTExcu\nXMDQoUMBAGlpaXB1da1/LWvh5eWFyMhIq8p6eHggKiqqgWtEVDMhAymiCxsA2jrI8Pv9YlQYDJBK\nJFYt42OSgSytQFsHu1q9SzRFtWC7pq5/ahkUxgykiJ1ogMo/rIrLKlBSYTB5JojIOvX6Jnn88cdx\n6tQpYbvCIUOG4Ntvv0VpaSkMBgOOHj2KUaNG2aSiRPbESWG6pqO1XdieTgqUVRiQV1SOto5y0QEk\nM5CAg5wZSHuieqgL2+oAUmbMQFbwDwmiOqhXADl27Fh06tQJxcXFUKlUeOGFF5CZmYnExEQAlesy\nNpcdaoiaE6eHMpDWLmLs6Vz5kc3Wl6Kto7xqH20JpDXsvy032a3D0OrHQFYPMFrdOpB2SCGTorRq\nIXG5VGL1uNbKALJyDCT/kCASr14BpK+vr8lC3iqVClFRUdDpdJDJZHBwcKh3BYnskXGgf26RuDGQ\nxkWyc/Rl6NrOum0QjV+oxt1yWvss7OoBpJyBQ4unlElQUGJAYZkBjnLrf58KqQQlZRUorzBwAg1R\nHTTIYChL2wUS0QOOwiQasV3YlR/ZHF3lWobWZE+EALLqXtZMMrBnzEDaF2VVJrFY5PCMyrGTlTtB\n8e8IIvFa9zcJURN5uAvb2gykl/ODDCRgXQbSGCTll9j/NobWqN6Fz8kTLV/lLOzKNU5VIp5tY+BZ\nXjUhjYjEEZ2BrMuYxq1bt4p+DZE9M2ZKjEuPWJsJa2fMQOorM5Cl5RW1jp80ZiD1pcbxlq37y1JV\nfSFxZiBbPGVVJlFXWoH2ztbvtCGXSlFSXg6ZBODfEUTiiQ4gKyoqoFAo4O/vD4kVf7VZU4aotZFJ\nJXCQS1BUVvss6uqUMimcFQ92oymtMNS4BiTwIIAsLBUXrNqr6l34rT2YtgdKmQQVBiC/uAxd3K3f\nDcrYha2UghlIojoQHUB6enoiJycHOTk5ePLJJ/Hkk0+iXbt2DVE3IrvmqJChqEzcJBoAcFbKhL18\nyysAmcK6MZBCANnKgyZHTqKxK8ZhCGUVlZ8Nq19XtZC4TCJlAElUB6IDyE8++QRnz57F4cOHsWvX\nLuzcuRM9e/bEyJEjMWDAAMhk1n+AiVozJ4UU9wor/23tMj4A4KyUoqBqPGO5wVDrBABjkCR0l7fy\noEnFSTR2pfqQBBeluDGQlWOIa/8MEZE50QGkRCJB79690bt3b+Tl5SExMRHx8fFYtWoVXF1dMWzY\nMIwcORIajaYh6ktkN5yqdaU6i5gZ7aKUIatqFnaFFUuQPBgDWRlAiglW7VH1DGRrD6btQfXfobPC\n+gSGXCpBeYUBZeUGSPmHBJFo9VrGx9XVFc888wyeeeYZXLp0CYcPH8ahQ4fw/fffY8qUKRg3bpyt\n6klkd6rPBnZRWf/F56KUQl9SgQqDAeVWLEFijJeKOAYSgOkyPuy6bPmq/0HkohK3jA8AFJdXMANJ\nVAc2S0X4+/sjJCQEfn5+AACdTmerSxPZpeqZMBcRY7eclTIYUJlRLDfUnj15OAPZ2rNuDiIWm6bm\nT1nHDKRC2OKTf0gQ1UW9FxK/evUq4uPjcfToURQUFKBDhw6YMmUKhg8fbov6EdktYxe2VAKrt18D\nHgSbupLyykk0tXz5Gc8bqn5u7TOPW/tC6vZGaTIG0voAsvrrWvlHgqhO6hRAFhYWIikpCfHx8UhP\nT4eDgwOGDBmCkSNHIiAgwNZ1JLJLDwJIcd9ezlUTBQqqurFr+/KTSSWQSoCKqghSTLBqj1p7F769\nqd6F7SxiEk31ReS5lSGReKIDyE8//RTHjx9HSUkJunXrhr/85S8YPHgwVCrr198iosplfADx2Q9j\nlqWgpNzqfXzl0soZpwAn0XBtWvvSsa1S+LeYDGT1P6QYPxKJJzqATExMhEKhwJAhQ6DRaHD37l18\n//33Nb7m+eefr3MFieyVMQMpNvthnLFdUFKOcoN1GUxFtQCytY+BJPvS3dNR+Le4DGT1AJKfCSKx\n6tSFXVpaiqSkJKvLM4AkMqeqmsxR1wxkftVuNNYkFKtnWxhAVmIz2AeTWdhixkBKq4+B5MNAJJbo\nAPK9995riHoQtTqGqjGJYtegMy75k1dUFUBa8eVnEkCyvw7bXuwGxgz248PRHXExu1DcTjTVM5Ct\ne1QHUZ2IDiCDgoIaoh5ErU55VQQpFxnJqB7aWcaqDGS1L8vWPgsbMF2Dk1q+x72c8LiXk6jXmI6B\n5GeCSCz+X5SoiThVTaLxdlGIep0xGCyuCiCt+fJjBpLIlILL+BDVS73XgSSiugnt4oa7+jKM6dpW\n1OuMAWBx1aQYa5blMR0Dyb8biap/JriMD5F4DCCJmohMKsHEXp6iX2f84isydmFb8d3HSTREpriM\nD1H9MBVB1MIYv/iKyyozkNZMwjG+Riphdx0RYJp15CxsIvEYQBK1MEIAWW7MQFqzDqTxvxIupE0E\n07HAzEASiccAkqiFUchMM5BiurDZfU1UScYxkET1wgCSqIUxZhxLjBlIK778jOvj8WuSqFL1v6W4\njA+ReAwgiVoYmVQCqaT6JJrav/yMSwXll1Q0aN2IWgp2YRPVDwNIohZILpVUm0RTe/n2zuLWmiSy\nd+zCJqofBpBELZBCJhE1iUbsYuVE9o7L+BDVDwNIohaoegbSmnXB2zOAJDLBZXyI6ocBJFELJJdK\nUFZhnIVd+5cfu7CJTDEDSVQ/3ImGqAUynQBQ+7efUiZFUHtHBHo6NmS1iFoMebX0CcdAEonHANKG\nlEolXF1duVCzDUmlUnh5eTVpHQwGA/Ly8lBSUtKk9ajOdB9f616z5KlODVQbopaHXdhE9cMA0kaU\nSiXc3Nxw9+5dlJeXN3V1yIZkMhk8PDyQm5vbbILI6guC88uPSDy5hF3YRPVhlwHkv/71L2zYsAFP\nPPEEIiMjTc7dvXsXa9euxfnz5yGXyzFkyBC8/PLLUCqV9bqnq6srg0c7VV5ejrt378Ld3R05OTlN\nXR0AD2cg+e1HJBY/Q0T1Y3cBZG5uLr766iu4urqanSsrK8MHH3wAhUKBOXPmQKfTYcOGDdDpdJg1\na1a97iuRSBg82rHy8vJmNTSBEwCI6kfGzxBRvdhdALllyxb069cPd+7cMTt3/PhxZGZmYs2aNcK4\nOplMho8//hjh4eFQq9WNXV2iOpFz/BZRvVSfRMOtDInEs6tlfNLS0nD8+HFMmjQJBoPB7HxKSgoC\nAgJMJmUMGDAAcrkcKSkpjVlVonpR1GESDRE9wAwkUf3YzVePwWDAunXr8Nxzz8Hd3d1imczMTPj4\n+Jgck8vlUKvVuHHjRmNUk8gm5JxEQ1Qv1bOO/AwRiWc3AeSPP/6IvLw8PPvss48so9fr4ezsbHbc\n2dkZBQUFDVm9FuG7777Djh07zI6HhYXhjTfeaIIamdPr9Vi6dCmGDRsGf39/9OnTB2FhYdi2bZtQ\nprS0FIsWLcKECRPg7+8PjUZj8VqJiYmYMWMGnnjiCWg0GqxcubKx3ka9cQIAke0wi08kXrMdA6nX\n63Hv3r1ay/n6+kKv12Pr1q147bXXoFBU7rghZsKDpe5uS2paj1Aqbfn/B9qzZw/u3buHF1980exc\nc5lAMn36dKSmpmLOnDno3r07srOzceLECcTHx2PixIkAKp+dbdu2ISQkBP3798dPP/1k8VoJCQm4\ndOkShg8fjt27d9f6HpvDmpRGLo53AOQDANzbtoWXl+Wse13I5fJm8z5bE7Z7U/gVANDWzQ1eXp5N\nXJfWg89645PLbR/uNdsAMjk5GZ9//nmt5bZv345vvvkGnp6e6NWrF3Q6HYDKGddlZWXQ6/VwcHCA\nVCqFs7Mz9Hq92TV0Oh06d+5c672ys7MfeY4fBnOFhYVwdLTdzicZGRk4cuQIYmNj8fTTTwvHx48f\nb1LOzc0NqampAID169cjKSnJ4vXeffddvPfeewCAH374odb7V1RU1PgMNKby0gfrURbk5yI7u8xm\n1/by8mo277M1Ybs3HV1+PrKzrUskUP3xWW98Xl5eQoLNVpptABkaGorQ0FCryt68eRMZGRmYNm2a\n2blp06bh/fffR/fu3eHj44PMzEyT82VlZcjKyjIbG9nazJkzB/v27QMAoct33rx5mDt3LoDKLO2u\nXbuwfPly3LlzBwMGDMDSpUvx2GOPAQCuXbuGwYMHY82aNfjxxx9x6NAh9O7dG1u3boVGo8HixYsx\ndepU4X4rVqxAXFwczp07Z3Ud8/LyANguWG8uWdW64CxsItvhKBAi8ZptACnGxIkTTTJSABAXFwdn\nZ2eEh4ejQ4cOAICQkBB88sknyMnJgadnZXfFyZMnUVpaij59+jR6vZuTuXPn4saNG8jPz8eSJUsA\nQAgOAeD06dPIyspCdHQ0CgsLER0djfnz52PTpk0m11m0aBHGjRuH2NhYyGSyGu8pNoALCAiAk5MT\noqOjsXDhQgwcOBAODg6irmEvTCfRNGFFiOwAxxETiWcXAaQxQKzOyckJbdq0QY8ePYRjgwYNwjff\nfIPly5cjIiICOp0OGzduxLBhwxplDcjFCddxq8D2W+GpXZR4Z4TliSLW6tSpE9zc3GAwGBASEmJ2\n3thWxgXas7OzERMTg+LiYqhUKqFcv379sHjxYqvuae3YUyMXFxcsW7YMUVFR0Gq1UCgU6Nu3L154\n4QVotVpR12rpqi/jI+WXH1G98CNEJF7Ln/nxCJayWzKZDH/729/Qrl07rFq1CuvXr8egQYOazQzj\n5qx3794mu/t07doVAHDr1i2TctYOO6ir5557DidOnMCKFSswfvx4ZGRkYP78+Zg5c2aD3re5YRc2\nke3wM0Qknl1kIC2Jjo62eNzDwwNRUVGNXJtK9c0SNqWHt4Y0DsYtLi42OW4cGtCQ3N3dERERgYiI\nCJSVlWHBggXYvn07Zs6caZJxtmdcSJzIduxgEQ2iRsePDdmUpcyvSqVCaWmpybHc3Fyb3E8ul2P6\n9OkAgPT0dJtcsyVgBpLIdriVIZF4DCBJoFQqUVRUZPPrqtVqXL58Wfi5oqICx44dEz2JRqfTobCw\n0Ox4RkYGgNa1lJK82vwkZk+I6od/hBGJZ7dd2CReQEAADhw4gP3790OtVkOtVsPb2xuA+Akv1Y0d\nOxZxcXEICgpCx44dsWXLFhQUFJhdU6PRIDIyEpGRkRavk5aWhmnTpmHixIno168fHB0dkZqaitWr\nVyM4OBgDBw4UysbHx0Ov1wvrQX7//ffCBCFfX18AwPXr14U90EtLS3Hp0iXs3bsXTk5OGDlyZJ3f\nb2NgBpLIdjiJhkg8BpAkmDJlClJTUxEZGYnc3FyTdSAtZQsfPvaojGJkZCRycnKwdOlSqFQqTJ06\nFYGBgdiwYYNQxphZrGkMpZ+fH7RaLRISErBx40YUFRVBo9FAq9VixowZJrsBvf3227h+/bpQrzff\nfBMSiQQrV65EeHg4ACApKQnz5s0Tyuzduxd79+5Fhw4dkJycXGt7NSVX1YOPLgNIovrhMj5E4kkM\n9UkttSIGgwE3b9585HmurF8/SUlJeP3113Hy5EmL+5U3B83pd/xrdiEWHPgdALBugj/aOdluh4Hm\n9D5bE7Z743tuc+VWhp8+0xkaN1UtpclW+Kw3vobYiYajp6hZOHXqFLRabbMNHpsbb5cH/yNgBpKo\nfjiJhkg8dmFTszB79uymrkKL0tbhwSwaLiROVD9cCotIPH5siFqg6uNNuZUhERE1NgaQRC0cu9+I\n6oczAYjEYxc2UQsVNdQHJzML4CBnAElUH4wficRjAEnUQg3t5IqhnVxrL0hEFrV3USKroASOcnbG\nEYnFAJKIiFqldS+F4JeMG2jryK9CIrH4ZxcREbVKHs5K9FZz6TCiumAASURERESiMIAkwXfffYcd\nO3aYHQ8LC8Mbb7zRBDUyp9frsXTpUgwbNgz+/v7o06cPwsLCsG3bNqFMaWkpFi1ahAkTJsDf3x8a\njcbsOhUVFfjkk08wfvx4BAUFITg4GFqtFmfOnGnMt0NERNQiMYAkwZ49eywGkMCj97lubNOnT8eW\nLVvw2muv4csvv8T777+P7t27Iz4+Xiij1+uxbds2ODs7o3///hbrXlhYiM8++wz9+/fHJ598gjVr\n1kAul2PChAk4d+5cY74lIiKiFocjh6nBFBYWwtHR0WbXy8jIwJEjRxAbG4unn35aOD5+/HiTcm5u\nbkhNTQUArF+/HklJSWbXcnR0RHJyMlxdH8xiHjp0KIYNG4b169dj5cqVNqs3ERGRvWEGkgAAc+bM\nwb59+3D8+HFoNBpoNBqsWrVKOG8wGLBr1y4MGTIEgYGBmDx5Mm7evCmcv3btGjQaDXbt2oXZs2ej\nR48eePXVVwEAGo0GcXFxJvdbsWIFevbsKaqOeXl5ACo3ha8vqVRqEjwCgEKhQNeuXZGVlVXv6xMR\nEdkzZiAJADB37lzcuHED+fn5WLJkCQDgscceE86fPn0aWVlZiI6ORmFhIaKjozF//nxs2rTJ5DqL\nFi3CuHHjEBsbC5lMhpqI7RYPCAiAk5MToqOjsXDhQgwcOBAODg6irlGT4uJinD9/Hs8++6zNrklE\nRGSPGEA2ot9WvY+SrFs2v66yvRqd575Xr2t06tQJbm5uMBgMCAkJMTuv0+mwceNGIWuXnZ2NmJgY\nFBcXQ6VSCeX69euHxYsXW3VPg8j9w1xcXLBs2TJERUVBq9VCoVCgb9++eOGFF6DVakVdy5LVq1cj\nLy8PU6dOrfe1iIiI7Bm7sMkqvXv3Nuny7dq1KwDg1i3TgDg0NLRB6/Hcc8/hxIkTWLFiBcaPH4+M\njAzMnz8fM2fOrNd1Dx06hDVr1uDtt99Gly5dbFRbIiIi+8QMZCOqb5awKVkaLwhUdvtW5+np2eB1\ncXd3R0REBCIiIlBWVoYFCxZg+/btmDlzJnr06CH6eikpKfjLX/6CV155Ba+99loD1JiIiMi+MANJ\nNmVpXKNKpUJpaanJsdzcXJvcTy6XY/r06QCA9PR00a9PT0/HK6+8guHDh2PRokU2qRMREZG9YwBJ\nAqVSiaKiIptfV61W4/Lly8LPFRUVOHbsmOhJNDqdDoWFhWbHMzIyAIifnX379m1MmjQJnTt3xqef\nftps1rokIiJq7tiFTYKAgAAcOHAA+/fvh1qthlqthre3NwDxE16qGzt2LOLi4hAUFISOHTtiy5Yt\nKCgoMLumRqNBZGQkIiMjLV4nLS0N06ZNw8SJE9GvXz84OjoiNTUVq1evRnBwMAYOHCiUjY+Ph16v\nF9aD/P7774UJQr6+vigsLMTLL7+MvLw8fPDBB0I5oDJjGhwcXOf3S0REZO8YQJJgypQpSE1NRWRk\nJHJzczFv3jzMnTsXgOWu6YePPSqDFxkZiZycHCxduhQqlQpTp05FYGAgNmzYIJQxZhZrGkPp5+cH\nrVaLhIQEbNy4EUVFRdBoNNBqtZgxYwak0gcJ9bfffhvXr18X6vXmm29CIpFg5cqVCA8PR05ODi5e\nvAiJRIIpU6aY3KdDhw5ITk6uqamIiIhaNYmhPqmlVsRgMJgsnP0wLy8vZGdnN2KN7EtSUhJef/11\nnDx5Es7Ozk1dHYtay++4tbzP5obt3vjY5k2D7d74vLy8hMmvtsIxkNQsnDp1ClqtttkGj0RERPQA\nu7CpWZg9e3ZTV4GIiIisxAwkEREREYnCAJKIiIiIRGEASURERESiMIAkIiIiIlEYQBIRERGRKHYz\nCzsmJgYXL140O75582bI5Q/e5t27d7F27VqcP38ecrkcQ4YMwcsvvwylUtmY1SUiIiJqsewmgJRI\nJAgODsZLL71kcrx68FhWVoYPPvgACoUCc+bMgU6nw4YNG6DT6TBr1qzGrjIRERFRi2Q3AaTBYICz\nszMCAgIeWeb48ePIzMzEmjVr4OXlBQCQyWT4+OOPER4eDrVa3VjVJSIiImqxWtUYyJSUFAQEBAjB\nIwAMGDAAcrkcKSkpTViz5uG7777Djh07zI6HhYXhjTfeaIIamdPr9Vi6dCmGDRsGf39/9OnTB2Fh\nYdi2bZtQJiUlBW+99RYGDx4Mf39/DB8+HKtWrUJxcbHJtRITEzFjxgw88cQT0Gg0WLlyZWO/HSIi\nohbJbjKQAHDmzBlMnjwZABAYGIjJkyejY8eOwvnMzEx06NDB5DVyuRxqtRo3btxo1Lo2R3v27MG9\ne/fw4osvmp2TSCRNUCNz06dPR2pqKubMmYPu3bsjOzsbJ06cQHx8PCZOnAig8n1kZmZi9uzZ6Ny5\nMy5cuIBly5bhwoUL+OKLL4RrJSQk4NKlSxg+fDh2797dbN4jERFRc2c3AWSPHj0wYsQIqNVqZGdn\n45tvvsF7772HZcuWCRlHvV5vca9lZ3e9E0kAACAASURBVGdnFBQUNHaV7V5hYSEcHR1tdr2MjAwc\nOXIEsbGxePrpp4Xj48ePNyk3c+ZMeHh4CD8PGjQIKpUKCxYsQGZmJnx9fQEA7777Lt577z0AwA8/\n/GCzehIREdm7ZhtA6vV63Lt3r9ZyxmCgetYsMDAQPXv2xNy5c/Gvf/0LU6ZMqfEaBoPBqjpV7/p+\nmFTaskcDzJkzB/v27QMAaDQaAMC8efMwd+5cAJVttGvXLixfvhx37tzBgAEDsHTpUjz22GMAgGvX\nrmHw4MFYs2YNfvzxRxw6dAi9e/fG1q1bodFosHjxYkydOlW434oVKxAXF4dz585ZXce8vDwANf8e\nAJgEj0ZBQUEAgNu3bwvPjNiMo1QqrfXe9kAul7eK99ncsN0bH9u8abDdG1/1CcU2u6bNr2gjycnJ\n+Pzzz2stt337dovH27Zti+7du+O3334Tjjk7O0Ov15uV1el06Ny5c633ys7OfuS5lv5hmDt3Lm7c\nuIH8/HwsWbIEAITgEABOnz6NrKwsREdHo7CwENHR0Zg/fz42bdpkcp1FixZh3LhxiI2NhUwmq/Ge\nYgO4gIAAODk5ITo6GgsXLsTAgQPh4OBg1WtPnToFqVQKPz8/UfesrqKiosZnwF54eXm1ivfZ3LDd\nGx/bvGmw3Rufl5cXFAqFTa/ZbAPI0NBQhIaG1vs61YMUHx8fZGZmmpwvKytDVlYWfHx86n2v2hy9\nuhIFJVk2v66Lsj2GdYys1zU6deoENzc3GAwGhISEmJ3X6XTYuHEjXF1dAVQG0zExMSguLoZKpRLK\n9evXD4sXL7bqntZmfo1cXFywbNkyREVFQavVQqFQoG/fvnjhhReg1Wof+bqsrCz84x//QFhYmMXs\nJBEREYnTsvtda3D//n38+uuvJpnFkJAQpKenIycnRzh28uRJlJaWok+fPk1RzRajd+/eQvAIAF27\ndgUA3Lp1y6ScLYL+mjz33HM4ceIEVqxYgfHjxyMjIwPz58/HzJkzLZYvKSnBn//8Z7Rp0wYxMTEN\nWjciIqLWotlmIMX4/fffsXXrVvzhD3+Ah4cHcnJysHv3bshkMpPJFoMGDcI333yD5cuXIyIiQsiq\nDRs2rFHWgKxvlrApVQ8eAQip8IeXxvH09Gzwuri7uyMiIgIREREoKyvDggULsH37dsycORM9evQQ\nyhkMBrz11lu4fPkydu/ebfYeiIiIqG7sIoBs06YNDAYDNm/ejPz8fDg6OiIoKAgTJ05Eu3bthHIy\nmQx/+9vfsHbtWqxatQoKhULYypBsw9K4RpVKhdLSUpNjubm5NrmfXC7H9OnTsX37dqSnp5sEkNHR\n0Th48CC2bt0Kf39/m9yPiIiI7CSA9PDwwMKFC60uGxUV1cA1apmUSiWKiopsfl21Wo3Lly8LP1dU\nVODYsWOiJ9HodDpIpVKzpYEyMjIAmE5kWrNmDeLi4hAbG4sBAwbUo/ZERET0MLsIIMk2AgICcODA\nAezfvx9qtRpqtRre3t4AxE94qW7s2LGIi4tDUFAQOnbsiC1btqCgoMDsmhqNBpGRkYiMtNzVn5aW\nhmnTpmHixIno168fHB0dkZqaitWrVyM4OBgDBw4EAOzatQsfffQRXnzxRXh7e+PUqVPCNTp37ixM\npLl+/bqwA1FpaSkuXbqEvXv3wsnJCSNHjqzz+yUiIrJ3DCBJMGXKFKSmpiIyMhK5ubkm60BayhY+\nfOxRGcXIyEjk5ORg6dKlUKlUmDp1KgIDA7FhwwahTGFhIYCax1D6+flBq9UiISEBGzduRFFRETQa\nDbRaLWbMmCGsxZmYmAiJRIIdO3aYbM0okUiwcuVKhIeHAwCSkpIwb9484dzevXuxd+9edOjQAcnJ\nybW2FxERUWslMdQntdSKGAwG3Lx585Hnua5V/SQlJeH111/HyZMnLe4W1By0lt9xa3mfzQ3bvfGx\nzZsG273xNcQ6kHa7jA+1LKdOnYJWq222wSMRERE9wC5sahZmz57d1FUgIiIiKzEDSURERESiMIAk\nIiIiIlEYQBIRERGRKAwgiYiIiEgUBpBEREREJAoDSCIiIiIShQEkEREREYnCAJIE3333ncnWf0Zh\nYWF44403mqBG5vR6PZYuXYphw4bB398fffr0QVhYGLZt2yaUSUlJwVtvvYXBgwfD398fw4cPx6pV\nq1BcXCyUqaiowCeffILx48cjKCgIwcHB0Gq1OHPmTFO8LSIiohaFC4mTYM+ePbh37x5efPFFs3OP\n2ue6sU2fPh2pqamYM2cOunfvjuzsbJw4cQLx8fGYOHEigMr3kZmZidmzZ6Nz5864cOECli1bhgsX\nLuCLL74AULn39meffYaIiAhhv+/169djwoQJ+Pbbb9GzZ88me49ERETNHQNIajCFhYVwdHS02fUy\nMjJw5MgRxMbG4umnnxaOjx8/3qTczJkz4eHhIfw8aNAgqFQqLFiwAJmZmfD19YWjoyOSk5Ph6uoq\nlBs6dCiGDRuG9evXY+XKlTarNxERkb1hFzYBAObMmYN9+/bh+PHj0Gg00Gg0WLVqlXDeYDBg165d\nGDJkCAIDAzF58mTcvHlTOH/t2jVoNBrs2rULs2fPRo8ePfDqq68CADQaDeLi4kzut2LFCtFZvry8\nPACVm8LXpHrwaBQUFAQAuH37NgBAKpWaBI8AoFAo0LVrV2RlZYmqFxERUWvDDCQBAObOnYsbN24g\nPz8fS5YsAQA89thjwvnTp08jKysL0dHRKCwsRHR0NObPn49NmzaZXGfRokUYN24cYmNjIZPJaryn\n2G7xgIAAODk5ITo6GgsXLsTAgQPh4OBg1WtPnToFqVQKPz+/R5YpLi7G+fPn8eyzz4qqFxERUWvD\nALIRud3YCFnZHZtft1zeDrk+r9TrGp06dYKbmxsMBgNCQkLMzut0OmzcuFHI2mVnZyMmJgbFxcVQ\nqVRCuX79+mHx4sVW3dNgMIiqo4uLC5YtW4aoqChotVooFAr07dsXL7zwArRa7SNfl5WVhX/84x8I\nCwuzmJ00Wr16NfLy8jB16lRR9SIiImpt2IVNVundu7dJl2/Xrl0BALdu3TIpFxoa2qD1eO6553Di\nxAmsWLEC48ePR0ZGBubPn4+ZM2daLF9SUoI///nPaNOmDWJiYh553UOHDmHNmjV4++230aVLlwaq\nPRERkX1gBrIR1TdL2JQsjRcEYLI0DgB4eno2eF3c3d0RERGBiIgIlJWVYcGCBdi+fTtmzpyJHj16\nCOUMBgPeeustXL58Gbt37zZ7D0YpKSn4y1/+gldeeQWvvfZag9efiIiopWMGkmzK0rhGlUqF0tJS\nk2O5ubk2uZ9cLsf06dMBAOnp6SbnoqOjcfDgQaxbtw7+/v4WX5+eno5XXnkFw4cPx6JFi2xSJyIi\nInvHDCQJlEolioqKbH5dtVqNy5cvCz9XVFTg2LFjoifR6HQ6SKVSs6WBMjIyAJjOzl6zZg3i4uIQ\nGxuLAQMGWLze7du3MWnSJHTu3Bmffvpps1nrkoiIqLljAEmCgIAAHDhwAPv374darYZarYa3tzcA\n8RNeqhs7dizi4uIQFBSEjh07YsuWLSgoKDC7pkajQWRkJCIjIy1eJy0tDdOmTcPEiRPRr18/ODo6\nIjU1FatXr0ZwcDAGDhwIANi1axc++ugjvPjii/D29sapU6eEa3Tu3BkeHh4oLCzEyy+/jLy8PHzw\nwQdITU0VyqhUKgQHB9f5/RIREdk7BpAkmDJlClJTUxEZGYnc3FzMmzdP2KXFUnbu4WOPyuBFRkYi\nJycHS5cuhUqlwtSpUxEYGIgNGzYIZQoLCwHUPIbSz88PWq0WCQkJ2LhxI4qKiqDRaKDVajFjxgxI\npZUjMhITEyGRSLBjxw6TrRklEglWrlyJ8PBw5OTk4OLFi5BIJJgyZYrJfTp06IDk5OSamoqIiKhV\nkxjqk1pqRQwGg8nC2Q/z8vJCdnZ2I9bIviQlJeH111/HyZMn4ezs3NTVsai1/I5by/tsbtjujY9t\n3jTY7o3Py8tLmPxqK5xEQ83CqVOnoNVqm23wSERERA+wC5uahdmzZzd1FYiIiMhKzEASERERkSgM\nIImIiIhIFAaQRERERCQKA0giIiIiEoUBpI0YDAbIZLKmrgY1EJlMVq/F1ImIiOwJA0gbycvLg4eH\nB4NIOySTyeDh4YG8vLymrgoREVGzYFfL+GRnZ2Pz5s04e/YsSktLoVarMWnSJPTp00coc/fuXaxd\nuxbnz5+HXC7HkCFD8PLLL0OpVNbr3iUlJcjNzYW7uzv3VLYhqVSKioqKJq2DwWBAbm4uSkpKmrQe\nREREzYXdBJA5OTl455134OfnhxkzZsDBwQFXrlxBaWmpUKasrAwffPABFAoF5syZA51Ohw0bNkCn\n02HWrFn1rkNJSQlycnLqfR16gDsWEBERNT92E0B++eWXUKvVWLhwoXAsODjYpMzx48eRmZmJNWvW\nwMvLC0Bl9+THH3+M8PBwqNXqRq0zERERUUtkF2Mg9Xo9fv75Z4wZM6bGcikpKQgICBCCRwAYMGAA\n5HI5UlJSGrqaRERERHbBLjKQGRkZKC8vBwC8++67SEtLQ9u2bTFmzBj86U9/EsplZmaiQ4cOJq+V\ny+VQq9W4ceNGo9aZiIiIqKWyiwDy/v37AIDPP/8co0ePxksvvYTz589j27ZtcHJywujRowFUZiqd\nnZ3NXu/s7IyCgoJGrTMRERFRS9VsA0i9Xo979+7VWs7X11f4d0hICLRaLQCgR48euHPnDnbv3i0E\nkI9i7fp+1bu+qXHI5XK2eyNjmzcNtnvjY5s3DbZ745PLbR/uNdsAMjk5GZ9//nmt5bZv3y5kFYOC\ngkzOBQUFISEhAUVFRXBwcICzszP0er3ZNXQ6HTp37lzjfSQSCRQKhYh3QLbCdm98bPOmwXZvfGzz\npsF2b/mabQAZGhqK0NBQq8pWz0JaYlyX0cfHB5mZmSbnysrKkJWVBR8fn7pVlIiIiKiVsYtZ2O3b\nt4dGo8G5c+dMjp87dw5qtRoqlQpAZRd3enq6yVqNJ0+eRGlpqcli40RERET0aLKYmJiYpq6ELbi7\nu+Orr75CcXExACA+Ph4HDhzAtGnT0LFjRwCVmcrjx4/jxIkTaNeuHdLT0xEXF4eBAwdi5MiRTVl9\nIiIiohZDYrB2BkkLcPToUXzzzTe4ffs2vLy88Oyzz2LUqFEmZYxbGZ47dw4KhcJmWxkSERERtRZ2\nFUASERERUcOzizGQRERERNR4GEASERERkSjNdhmfplBeXo49e/YgPj4ed+7cgaurKwYNGoQpU6aY\nlPvmm29w8OBB5Ofnw9/fH9OmTYOfn1/TVLqFs6bNZ86caTJzHgDatm2L2NjYxq6uXYiJicHFixct\nnlu8eDG6du0KgM+5rVnT7nzWbe/o0aPYs2cPbt26BScnJwQHB2PSpElwd3c3Kcfn3basaXc+77b1\n888/Y8eOHbh58ybc3d3xX//1X3jmmWfMytnqWecYyGpWr16N1NRUhIeHw9fXFzk5OcjMzMTEiROF\nMrt27cLOnTsxefJk+Pr6Ys+ePUhLS8OKFSvQtm3bJqx9y2RNm8+cOROBgYEYO3ascEwul/N/7nV0\n/fp1FBUVCT8bDAbs2LEDV65cQWxsLKRSKZ/zBmBNu/NZt60TJ05g5cqVGDNmDAYOHIh79+5h27Zt\ncHFxwYcffiisEczn3basbXc+77bz66+/Ijo6GiNHjsTgwYNx+fJlfP3115g8eTLGjRsnlLPls84M\nZJWUlBQkJydj+fLlj1yYvKSkBLt378aECRMwZswYABCyBj/88INJ0EO1s6bNjdzd3REQENBINbNv\nGo3G5OeysjKkpaVhyJAhkEqlfM4bSG3tbsRn3XaSkpLQpUsXvPrqq8IxR0dHLFu2DDdv3oSPjw+f\n9wZgTbsb8Xm3jZ07dyIwMBBvvvkmAKBXr17Q6XT4+uuvMXr0aMjlcps/6xwDWSU+Ph49e/asMZD5\nz3/+g6KiIgwePFg4plKp0K9fP6SkpDRGNe2KNW1uxER5w0lJSYFer8fQoUMB8DlvLA+3uxGfddty\ndHQ0+dnJyQnAg3bm894wamt3Iz7vtnHlyhX06tXL5JgxiLx8+TIA2z/rDCCrpKenQ61WY+3atZgy\nZQomT56M5cuX4969e0KZzMxMSKVSPPbYYyav9fX1NdsikWpnTZsbxcfHQ6vVYurUqVi5cqXZuBmq\nu6SkJLRr1w6BgYEA+Jw3lofb3YjPuu2MGjUKly5dQmJiIvR6PW7cuIHt27cjODhY+MOVz7vtWdPu\nRnzebaO0tBRyuWmnsvFn43Ns62edXdhV7t27hyNHjsDPzw9z586FXq/H5s2bsXz5cnzwwQcAAJ1O\nBwcHB2H8hpGzszNKSkpQXl4OmUzWFNVvkaxpcwAYMGAAunXrBg8PD1y/fh1ff/013nvvPSxfvlz4\nq5bqpri4GCdPnsTo0aOFY3zOG56ldgf4rNtar1698Oabb+L//u//UF5eDgDo1q0b5s2bJ5Th8257\n1rQ7wOfdltRqNdLT002OpaWlAQAKCgoA2P5ZZwBZxZhGj4qKgouLC4DKsRkxMTFITU1FUFBQU1bP\nLtXU5ufPn0dwcDAAYOrUqcJrAgMD0b17d8yfPx8JCQkmg4NJvFOnTqGkpARDhgxp6qq0Ko9qdz7r\ntvXLL78gNjYWzzzzDPr06YP79+/jq6++wrJly/Duu++ajD0l27G23fm8285TTz2FL774AocPH8YT\nTzyBtLQ0fP/99wBgFjDaCgPIKi4uLlCr1UIgAwDdu3eHXC7HtWvXEBQUBGdnZxQVFcFgMJj8QnQ6\nHZRKJf9KFammNr9+/boQQD6sQ4cO8PHxwZUrVxqppvYrKSkJarUaXbp0EY7xOW94ltrdEj7r9bN5\n82YMGjQIWq1WOGbs8Th58iQGDhzI570BWNPulvB5r7snn3wSv//+O/75z3/i888/h0qlglarxfr1\n64XZ1bZ+1vnnVxWNRoOKigqz49Ub2tfXFxUVFbh165ZJmczMTLMZllQ7a9qcGo5er0dKSopZFozP\necN6VLuT7WVlZaFTp04mx3x8fKBUKnH79m0AfN4bgjXtTrYllUrx6quvYu3atVi+fDm++OILYU1f\n439t/awzgKzSt29fXL16Ffn5+cKxixcvory8XFiTqlu3bnB0dERycrJQpri4GKdOnUKfPn0au8ot\nnjVtbsnVq1dx48aNWrM3VLOff/4ZZWVlZrOA+Zw3rEe1uyV81uvHy8sLv/32m8mx69evo6SkBF5e\nXgD4vDcEa9rdEj7v9efk5IQOHTpApVJh//796N69u7Bskq2fdXZhVxk1ahT27duHjz76CBMmTEBh\nYSE2b96MXr16oXv37gAApVKJP/3pT9i5cyecnZ3h4+ODvXv3AoDJQqhkHWva/JdffsGxY8fQv39/\nuLm5ITMzEzt37oSnpydGjBjRtG+ghUtKSoKfn5/JmmwAn/OG9qh257Nue2PGjMG6devg7u6OPn36\nIDc3F19//TXat2+Pvn37AuDz3hCsaXc+77Z1+fJlXLx4EX5+figsLERSUhLOnj2L999/Xyhj62ed\nO9FUc+vWLaxfvx4XLlyAXC7HgAEDMHXqVLPZYNzyynZqa/OrV69iw4YN+P3336HT6dCmTRv06dMH\nWq2WO0TUQ15eHt58801MnDgRzz33nMUyfM5tr6Z257PeMA4dOoT9+/fj9u3bcHJyQmBgILRaLdq3\nb29Sjs+7bdXW7nzebSsjIwNffPEFMjMzIZFI8Pjjj2PSpEno0KGDWVluZUhERERETYJjIImIiIhI\nFAaQRERERCQKA0giIiIiEoUBJBERERGJwgCSiIiIiERhAElEREREojCAJCIiIiJRGEASERERkSgM\nIImIiIhIFAaQRERERCQKA0giolbq/fffx5IlS5q6GrU6duwYJk2ahOzs7KauChFVkTd1BYjIvqWm\npuL9999/5PlZs2Zh6NChjVgjAoCff/4Zqamp+PDDDy2ev379On744Qekpqbi7t27KC0tRZs2beDn\n54e+ffvij3/8IxwcHITyn376KRITE/HZZ5/Bw8PD7HrG5yAiIgLPP/+8qLoOGTIEu3fvxqZNmxAZ\nGSnujRJRg2AASUSNYtCgQRgwYIDZ8W7dujVBbeirr75CcHAwOnfubHZu7969+PLLL+Hk5ITBgwej\nU6dOUCqVuH//Pn799VfExcUhOTkZMTExjVJXiUSCcePGITY2FtevX4dGo2mU+xLRozGAJKJG0alT\nJ1GZRoPBgNLSUiiVygasVev066+/4urVq5g5c6bZuaNHj2LTpk14/PHHERUVBWdnZ7MyOTk5SExM\nbIyqCgYPHoz169fjwIEDePXVVxv13kRkjgEkETW5hIQEfPbZZ3jnnXeQlpaGhIQEZGdnY8KECQgP\nDwcAHD9+HPv27cOVK1dQXl4OX19fjBkzBiNHjjS73rFjx/Dtt9/ixo0baNOmDf7whz9g5MiRmDdv\nHsLCwoRrGu8bHR2NHj16mFzD2CW7fft2k+O3bt3Czp07cfbsWeTn58PNzQ39+vVDREQE2rRpI5Tb\nsWMHdu7ciY8//hiJiYk4cuQIcnNz0b59e7zwwgsWg+mLFy9iz549uHTpEoqKiuDm5obAwEBERETA\n29sbH374Ic6fP4/Y2FizwO7u3buYMWMGBg0ahDlz5tTY3klJSQCAvn37mhwvKyvD5s2b4eDggHnz\n5lkMHgHA09NTdDe0JTExMbh48eIjz3/66afw9PQEADg6OqJ79+746aefGEASNQMMIImoURQXFyMv\nL8/kmFwuh5OTk/Dzl19+iZKSEowYMQKurq5o164dgAfBWHBwMMLDw6FUKpGSkoLY2FjcunULWq1W\nuMaBAwewdu1a+Pj4IDw8HDKZDMeOHcOFCxcAVHaH1tWVK1cQExMDBwcHjBw5El5eXrhx4wYOHjyI\n8+fPY8mSJSbvB6gMgoxdsBKJBAcOHMCaNWvg7e2Nrl27CuUOHz6Mzz//HG3btsWoUaPg7e2Nu3fv\n4syZM7h27Rq8vb3x1FNP4fTp00hMTMTYsWNN7pOQkACDwYBRo0bV+j5SU1OhVqvh4uJicvzSpUu4\nd+8ehg8fbhIMi1FQUAC53PyrRa/Xmx17/vnnzZ4JvV6PTZs2QSKRmIyxBCqHO5w7dw5Xr15Fx44d\n61Q/IrINBpBE1Ch2796N3bt3mxzr2rUrFi9eLPxcXFyMpUuXmnRb//bbb9i5cyfGjh2LqVOnCsdH\njx6NdevW4bvvvsOoUaPQvn176PV6fPnll/D09MSSJUvg6OgIABgzZgzefffder+H//3f/0WbNm3w\n4YcfmmTnBg0ahHfeeQf/+te/EBYWZvIaFxcX/PWvfzUpO2vWLOzbt08IIO/evYt169ahffv2+Pvf\n/24S2IWFhcFgMAAAQkJC0K5dO8THx5sEkAaDAfHx8VCr1QgODq7xPVRUVCAzMxN9+vQxO3ft2jUA\nsDgusri4GMXFxSbH2rRpYxaQR0VF1Xj/6nr16mXyc3l5OT788EOUlZUhKirKLMBVq9UAwACSqBlg\nAElEjWLEiBEYNmyYybGHs3WjR482G/N49OhRAMDIkSPNslX9+/fH/v37cfbsWYwaNQpnzpxBcXEx\nwsPDheARAJRKJZ599lmsWbOmzvW/evUqfv/9d0yYMAHl5eUmdWnfvj3UajXOnDljFkA+/fTTJj+3\na9cOPj4+uHnzpnAsOTkZZWVlCAsLMwuagAdZU6lUitDQUOzYsQNpaWkICAgAAJw7dw7Z2dmYNGlS\nre+joKAAACxmGI1ZwuptZ/Tll1/iwIEDJseqdzEbvfXWW3B1dTV7/ZUrV7Bp06Ya6xYbG4uzZ89i\n+vTpZt3r1ev88HNARI2PASQRNQpvb+9as2M+Pj5mxzIzMwHUnNkyBhS3b98GAIuzdOs7c9dYj127\ndmHXrl0Wy1RUVJgd8/b2Njvm4uKCO3fuCD8bg0lLmb+HhYaG4uuvv8bhw4eFAPLQoUOQy+UYMWJE\nra83MmY1qzMG9IWFhWbnxo4diyeeeAIAsGfPHqSkpFi8bmBgoMVlfGobOrBt2zYcOXIEf/rTnx7Z\nDW+pzkTUNBhAElGzYWnGtTFo+Otf/wqFQmHxde3bt7d5XcrLyy3WY+zYsejfv7/F11iqv1Rqeb+G\nugZDbdu2Rf/+/fHTTz9h6tSpKC4uxsmTJzFgwACLmb+Hubi4QCKRCJnI6ozdwhkZGWbnfHx8hAD/\n2LFjdar7oxw6dAi7du3C0KFD8dJLLz2ynLHObm5uNr0/EYnHAJKImjUfHx+cOXMGbdu2rTVDZxwj\nd+3aNYSEhJicM47vq87YXWwpmMrKyjKrB1AZ+NWWSRXLeO3ffvvNqrF9Tz31FH7++WckJSWhoKAA\n5eXlCA0NtepeUqkUvr6+Jl3oRt26dYO7uzv+/e9/Iy8vz6qAtL5++eUX/POf/0RQUBBmzJhRY1lj\nnTn+kajpcStDImrWhg8fDgDYsmWLWVYQqBy3V1ZWBqByUoZKpcL+/ftNZv2WlJRgz549Zq81Bm5n\nz541OX7x4kVcvnzZ5Jifnx86duyI+Ph4oTu7OoPBUOexeYMGDYJcLsfOnTstBrMPZyt79eoFb29v\nHD58GD/++CPat29vNiGlJkFBQbh9+zby8/NNjsvlcmi1WhQVFWHFihUW62KpPnWVnp6OVatWQaPR\n4P/9v/8HmUxWY/n//Oc/cHV1RYcOHWxyfyKqO2YgiahZ69KlCyIiIrB9+3bMmzcPQ4YMgYeHB3Jz\nc3H16lWcOnUKq1atgqenJ5ycnDBp0iSsW7cOb7/9NkaMGAGZTIajR49aDE58fHzQu3dvHDx4EBUV\nFejSpQuuX7+OxMREdOrUCb///rtJ+VmzZuF//ud/sGDBAowYMQIdOnRAeXk5srKy8O9//xtPPvmk\n2SSaR6kehHl4eGDatGn44osv9psLGAAAAqVJREFUMG/ePIwYMQLt27dHbm4uzpw5g2effdas23zU\nqFHYvHkzANTY7WvJH/7wB+zfvx+//PIL/vjHP5qcGz58OO7fv48tW7Zg1qxZwk40CoUC9+/fx6VL\nl3DmzBm4urpCpVKJuu/DPvzwQ5SUlGDIkCE4efKk2fknnnhCuIder8evv/5qcd1PImp8DCCJqNl7\n/vnn4e/vj3379uGHH35AYWEhXF1d4evri4kTJ5qMiRszZgycnZ3x7bff4quvvjJbSPxh//3f/431\n69cjOTkZR48eRUBAABYuXIiDBw+aBZAdO3bEsmXLsHv3bpw+fRo//vgjlEolPD09MXDgQAwePFgo\nW9OkEYlEYnZ+1KhRUKvV2LNnDw4ePIji4mK0bdsWjz/+uMUu2xEjRmDbtm0AgCeffNK6hqwSGBiI\njh07IiEhwSyABIDx48cjJCQEP/zwAy5cuICjR4+ivLwcrq6u6NSpE1599VUMHz7cZJ3GuqyvaczY\nGt/Hw3r06CEEkMaZ6mPGjBF9HyKyPYmB09qIqBXIysrCrFmzEB4ebnWWsDkrKCjAm2++ib59+1oM\njGvz888/Y8WKFfj73/+OLl26NEANbaeiogJRUVHQaDSYO3duU1eHiMAxkERELdLhw4dRVlaG0aNH\n1+n1AwcORHBw8COzf83JTz/9hNu3b2Py5MlNXRUiqsIubCKiFiQpKQl37tzB119/jW7duqFnz551\nvpYtdudpDEOHDrW4dzgRNR0GkERELcjq1auhVCrRrVs3/PnPf27q6hBRK8UxkEREREQkCsdAEhER\nEZEoDCCJiIiISBQGkEREREQkCgNIIiIiIhKFASQRERERicIAkoiIiIhEYQBJRERERKIwgCQiIiIi\nURhAEhEREZEoDCCJiIiISJT/D1mtX+ymV+eBAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f49d6b77bd0>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Create Calibration "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the code that follows a TwoPortOnePath calibration is created from corresponding measured and ideal responses of the calibration standards. The measured networks are read from disk, while their corresponding ideal responses are generated using scikit-rf. More information about using scikit-rf to do offline calibrations can be found [here](http://scikit-rf.readthedocs.org/en/latest/tutorials/calibration.html). "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from skrf.calibration import TwoPortOnePath\n",
"from skrf.media import RectangularWaveguide\n",
"from skrf import two_port_reflect as tpr\n",
"from skrf import mil\n",
"\n",
"# pull frequency information from measurements\n",
"frequency = raw['short'].frequency\n",
"\n",
"# the media object \n",
"wg = RectangularWaveguide(frequency=frequency, a=120*mil, z0=50)\n",
"\n",
"# list of 'ideal' responses of the calibration standards\n",
"ideals = [wg.short(nports=2),\n",
" tpr(wg.delay_short( 90,'deg'), wg.match()),\n",
" wg.match(nports=2),\n",
" wg.thru()]\n",
"\n",
"# corresponding measurments to the 'ideals'\n",
"measured = [raw['short'],\n",
" raw['quarter wave delay short'],\n",
" raw['load'],\n",
" raw['thru']]\n",
"\n",
"# the Calibration object\n",
"cal = TwoPortOnePath(measured = measured, ideals = ideals )"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/home/alex/code/path/skrf/calibration/calibration.py:1440: UserWarning:\n",
"\n",
"n_thrus is None, guessing which stds are transmissive\n",
"\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Apply Correction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are two types of correction possible with a 3-receiver system. \n",
"\n",
"1. Full (TwoPortOnePath)\n",
"2. Partial (EnhancedResponse)\n",
"\n",
"`scikit-rf` uses the same `Calibration` object for both, but employs different correction algorithms depending on the `type` of the DUT. The DUT used in this example is a WR-15 shim cascaded with a WR-12 1\" straight waveguide, as shown in the picture below. Measurements of this DUT are corrected with both *full* and *partial* correction and the results are compared below. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image('pics/asymmetic DUT.jpg', width='75%')"
],
"language": "python",
"metadata": {},
"outputs": [
{
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ONkl/YuivY+SQ00rSNIJMY4AA4zg+vWkMzobC9nlkihtJ5JYzh0SMkqenIxxT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ZjJgjGdz2yDwMkDCtj2ztnYyGcAzyDPsYIGeBnv5GQZ9zMfOxmDwkcyURnkzWDB+e5H4IgQIN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\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RjqwXm07dKGrrgXL8YmW8EvF2EDNGH4MBHVEOoDWV5Wyv0zQtuLVgFZiqMqo7ADgjeIzy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55UF6mLFbL84tiaP4x0UQo+TbEM2NeLe/nD7gRB/H4wPdVr/R3PkyUe5/hiLiNA0k/3hZJp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\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nj0ExCatOi3HoAHp9YG2CEI5GgK8O3GSnkn+c1zZxfOKNiPXjFEVf4xb2xL1v/OTsJK2S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\nfLRiHMoEjJnFp8XAIK8b9zBqQqjvMARKA1td5//Z\n",
"metadata": {
"jpeg": {
"width": "75%"
}
},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"<IPython.core.display.Image at 0x7f49d5f5b410>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Full Correction ( TwoPortOnePath)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Full correction is achieved by measuring each device in both orientations, **forward** and **reverse**. To be clear, this means that the DUT must be physically removed, flipped, and re-inserted. The resulting pair of measurements are then passed to the `apply_cal()` function as a tuple. This returns a single corrected response. \n",
"\n"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Partial Correction (Enhanced Response)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you pass a single measurement to the `apply_cal()` function, then the calibration will employ partial correction. This type of correction is known as `EnhancedResponse`. Depending on the measurment application, this type of correction may be *good enough*, and perhaps the only choice."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Comparison\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below are direct comparisons of the DUT shown above corrected with *full* and *partial* algorithms. It shows that the partial calibration produces a large ripple on the reflect measurements, and slightly larger ripple on the transmissive measurments. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"simulation = raw['simulation']\n",
"\n",
"dutf = raw['wr15 shim and swg (forward)']\n",
"dutr = raw['wr15 shim and swg (reverse)']\n",
"\n",
"corrected_full = cal.apply_cal((dutf, dutr)) \n",
"corrected_partial = cal.apply_cal(dutf) \n",
"\n",
"\n",
"\n",
"# plot results\n",
"\n",
"f, ax = subplots(1,2, figsize=(8,4))\n",
"\n",
"ax[0].set_title ('$S_{11}$')\n",
"ax[1].set_title ('$S_{21}$')\n",
"\n",
"corrected_partial.plot_s_db(0,0, label='Partial Correction',ax=ax[0])\n",
"corrected_partial.plot_s_db(1,0, label='Partial Correction',ax=ax[1])\n",
"\n",
"corrected_full.plot_s_db(0,0, label='Full Correction', ax = ax[0])\n",
"corrected_full.plot_s_db(1,0, label='Full Correction', ax = ax[1])\n",
"\n",
"simulation.plot_s_db(0,0,label='Simulation', ax=ax[0], color='k')\n",
"simulation.plot_s_db(1,0,label='Simulation', ax=ax[1], color='k')\n",
"\n",
"tight_layout()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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p/aL2E899LBDf/Uw8Hzv7WIpV7dnHcswsERERERERRR0Gs0RERERERBR1GMwS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\nkksuedE41Bczewb0UFY7cwzw4IMP8uSTT/LZz36WcDjcYK9qMw9zRmk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vY72mUpWxXDJd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u3aYceOHejTpw8AwOl0oqCgAP369ZN8XmZmJjp27OhzbPPmzdi1axf+/Oc/IyEhoSWrTURE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\nhq21534AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f49d68b0cd0>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"What if my DUT is Symmetric??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the DUT is known to be **reciprocal** ( $S_{21}=S_{12}$ ) and **symmetric** ( $S_{11}=S_{22}$ ), then its response should be the identical for both forward anad reverse orientations. In this case, measuring the device twice is unnecessary, and can be circumvented. This is explored in the example: [TwoPortOnePath, EnhancedResponse, and FakeFlip](TwoPortOnePath, EnhancedResponse, and FakeFlip.ipynb)"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Formating"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML\n",
"\n",
"\n",
"def css_styling():\n",
" styles = open(\"../styles/plotly.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<style>\n",
" /*body {\n",
" background-color: #F5F5F5;\n",
" }*/\n",
" div.cell{\n",
" width: 850px;\n",
" margin-left: 10% !important;\n",
" margin-right: auto;\n",
" }\n",
" h1 {\n",
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yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/Milestone Project 1- Assignment.ipynb | 4 | 2504 | {
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"# Milestone Project 1\n",
"## Congratulations on making it to your first milestone!\n",
"You've already learned a ton and are ready to work on a real project.\n",
"\n",
"Your assignment: Create a Tic Tac Toe game. You are free to use any IDE you like.\n",
"\n",
"Here are the requirements:\n",
"\n",
"* 2 players should be able to play the game (both sitting at the same computer)\n",
"* The board should be printed out every time a player makes a move\n",
"* You should be able to accept input of the player position and then place a symbol on the board\n",
"\n",
"Feel free to use Google to help you figure anything out (but don't just Google \"Tic Tac Toe in Python\" otherwise you won't learn anything!) Keep in mind that this project can take anywhere between several hours to several days.\n",
"\n",
"There are 4 Jupyter Notebooks related to this assignment:\n",
"\n",
"* This Assignment Notebook\n",
"* A \"Walk-through Steps Workbook\" Notebook\n",
"* A \"Walk-through Solution\" Notebook\n",
"* An \"Advanced Solution\" Notebook\n",
"\n",
"I encourage you to just try to start the project on your own without referencing any of the notebooks. If you get stuck, check out the next lecture which is a text lecture with helpful hints and steps. If you're still stuck after that, then check out the walk-through Steps Workbook, which breaks up the project in steps for you to solve. Still stuck? Then check out the walk-through solution video for more help on approaching the project!"
]
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"source": [
"There are parts of this that will be a struggle...and that is good! I have complete faith that if you have made it this far through the course you have all the tools and knowledge to tackle this project. Remember, its totally open book, so take your time, do a little research, and remember:\n",
"\n",
"## HAVE FUN!"
]
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| apache-2.0 |
afeiguin/comp-phys | 02_01_central_potential.ipynb | 1 | 115027 | {
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"Motion in a central potential\n",
"=============================\n",
"\n",
"\n",
"#### An object of mass $m$ under the effects of a central force $F$.\n",
"\n",
"Kepler’s problem\n",
"----------------\n",
"\n",
"The motion of the sun and the earth is an example of a “two-body\n",
"problem”. This is a relatively simple problem that can be solved\n",
"analytically (it interesting to notice here, however, that adding a new\n",
"object to the problem, the moon for instance, make it completely\n",
"intractable. This is the famous “three body problem”). We can assume\n",
"that, to a good approximation, the sun is stationary and is a convenient\n",
"origin of our coordinate system. This is equivalent to changing to a\n",
"“center of mass” coordinate system, where most of the mass is\n",
"concentrated in the sun. The problem can be reduced to an equivalent one\n",
"body problem involving an object of reduced mass $\\mu$ given by\n",
"$$\\mu=\\frac{mM}{m+M}$$ Since the mass of the earth is\n",
"$m=5.99\\times 10^{24}$ kg and the mass of the sun is\n",
"$M=1.99\\times 10^{30}$ kg we find that for most practical purposes, the\n",
"reduced mass of the earth-sun system is that of the earth. Hence, in the\n",
"following we are going to consider the problem of a single particle of\n",
"mass $m$ moving about a fixed center of force, which we take as the\n",
"origin of the coordinate system. The gravitational force on the particle\n",
"m is given by $${\\mathbf F}=-\\frac{GMm}{r^3}{\\mathbf r},$$ where the\n",
"vector ${\\mathbf r}$ is directed from $M$ to $m$, and $G$ is the\n",
"gravitation constant $$G=6.67\\times 10^{-11} \\frac{m^3}{kg.s^2}$$ The\n",
"negative sign implies that the gravitational force is attractive, and\n",
"decreases with the separation $r$. The gravitational force is a “central\n",
"force”: its magnitude depends on the separation between the particles\n",
"and its direction is along the line that connects them. The assumption\n",
"is that the motion is confined to the $xy$ plane. The angular momentum\n",
"${\\mathbf L}$ lies on the third direction $z$ and is a constant of\n",
"motion, <span>*i.e.*</span> it is conserved:\n",
"$$L_z=({\\mathbf r}\\times m{\\mathbf v})_z=m(xv_y-yv_x)=\\mathrm{const.}$$\n",
"An additional constant of motion ins the total energy $E$ given by\n",
"$$E=\\frac{1}{2}mv^2-\\frac{GmM}{r}$$ If we fix the coordinate system in\n",
"the sun, the equation of motion is\n",
"$$m\\frac{d^2{\\mathbf r}}{dt^2}=-\\frac{mMG}{r^3}{\\mathbf r}$$ For\n",
"computational purposes it is convenient to write it down in cartesian\n",
"components: $$\\begin{aligned}\n",
"&& F_x=-\\frac{GMm}{r^2}\\cos{\\theta}=-\\frac{GMm}{r^3}x, \\\\\n",
"&& F_y=-\\frac{GMm}{r^2}\\sin{\\theta}=-\\frac{GMm}{r^3}y.\\end{aligned}$$\n",
"Hence, the equations of motions in cartesian coordinates are:\n",
"$$\\begin{aligned}\n",
"&& \\frac{d^2x}{dt}=-\\frac{GM}{r^3}x, \\\\\n",
"&& \\frac{d^2y}{dt}=-\\frac{GM}{r^3}y, \\end{aligned}$$ where\n",
"$r^2=x^2+y^2$. These are coupled differential equations, since each\n",
"differential equation contains both $x$ and $y$.\n",
"\n",
"### Circular motion \n",
"\n",
"Since many planetary orbits are nearly circular, it is useful to obtain\n",
"the condition for a circular orbit. In this case, the magnitude of the\n",
"acceleration ${\\mathbf a}$ is related to the radius by\n",
"$$a=\\frac{v^2}{r}$$ where $v$ is the speed of the object. The\n",
"acceleration is always directed toward the center. Hence\n",
"$$\\frac{mv^2}{r}=\\frac{mMG}{r^2}$$ or $$v=(\\frac{MG}{r})^{1/2}.\n",
"$$ This is a general condition for the circular orbit.\n",
"We can also find the dependence of the period $T$ on the radius of a\n",
"circular orbit. Using the relation $$T=\\frac{2\\pi r}{v},$$ we obtain\n",
"$$T^2=\\frac{4\\pi^2r^3}{GM}$$\n",
"\n",
"### Elliptical orbits \n",
"\n",
"An ellipse has two foci $F_1$ and $F_2$, and has the property that for\n",
"any point the distance $F_1P+F_2P$ is a constant. It also has a\n",
"horizontal semi-axis $a$ and a vertical $b$. It is common in astronomy\n",
"to characterize an orbit by its “eccentricity” $e$, given by the ratio\n",
"of the distance between the foci, and the length of the major axis $2a$.\n",
"Since $F_1P+F_2P=2a$, it is easy to show that (consider a point $P$ at\n",
"$x=0$,$y=b$) $$e=\\sqrt{1-\\frac{b^2}{a^2}},$$ with $0<e<1$. A special\n",
"case is $a=b$ for which the ellipse reduces to a circle and $e=0$. The\n",
"earth orbit has eccentricity $e=0.0167$.\n",
"\n",
"### Astronomical units \n",
"It is useful to choose a system of units where the product $GM$ is of\n",
"the order of unity. To describe the earth’s motion, the convention is to\n",
"choose the earth’s semi-major axis as the unit of length, called\n",
"“astronomical unit” (AU) and is $$1AU=1.496 \\times 10^{11}m.$$ The unit\n",
"of time is taken to be “one year”, or $3.15 \\times 10^7$s. In these\n",
"units, $T=1$yr, $a=1AU$, and we can write\n",
"$$GM=\\frac{4\\pi ^2a^3}{T^2}=4\\pi ^2 AU^3/yr^2.$$\n",
"\n",
"### Exercise 2.1: Simulation of the orbit \n",
"\n",
"1. Write a program to simulate motion in a central force field. Verify\n",
" the case of circular orbit using (in astronomical units) ($x_0=1$,\n",
" $y_0=0$ and $v_x(t=0)=0$. Use the condition (\\[circular\\] to\n",
" calculate $v_y(t=0)$ for a circular orbit. Choose a value of\n",
" $\\Delta \n",
" t$ such that to a good approximation the total energy $E$\n",
" is conserved. Is your value of $\\Delta t$ small enough to reproduce\n",
" the orbit over several periods?\n",
"\n",
"2. Run the program for different sets of initial conditions $x_0$ and\n",
" $v_y(t=0)$ consistent with the condition for a circular orbit. Set\n",
" $y_0=0$ and $v_x(t=0)=0$. For each orbit, measure the radius and the\n",
" period to verify Kepler’s third law ($T^2/a^3=\\mathrm{const.}$).\n",
" Think of a simple condition which allows you to find the numerical\n",
" value of the period.\n",
"\n",
"3. Show that Euler’s method does not shield stable orbits for the same\n",
" choice of $\\Delta t$ used in the previous items. Is it sufficient to\n",
" simply choose a smaller $\\Delta t$ or Euler’s method is no stable\n",
" for this dynamical system? Use the average velocity\n",
" $1/2(v_n+v_{n+1})$ to obtain $x_{n+1}$. Are the results any better?\n",
"\n",
"4. Set $y_0=0$ and $v_x(t=0)=0$. By trial and error find several\n",
" choices of $x_0$ and $v_y(t=0)$ which yield coonvinient\n",
" elliptical orbits. Determine total energy, angular momentum,\n",
" semi-major and semi-nimor axes, eccentricity, and period for\n",
" each orbit.\n",
"\n",
"5. You probably noticed that Euler’s algorithm with a fixed $\\Delta t$\n",
" breaks down if you get to close to the sun. How are you able to\n",
" visually confirm this? What is the cause of the failure of the\n",
" method? Think of a simple modification of your program that can\n",
" improve your results.\n",
"\n"
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"class particle2(object):\n",
" \n",
" def __init__(self, mass=1., x=0., y=0., vx=0., vy=0.):\n",
" self.mass = mass\n",
" self.x = x\n",
" self.y = y\n",
" self.vx = vx\n",
" self.vy = vy\n",
" \n",
" def euler(self, fx, fy, dt):\n",
" self.vx = self.vx + fx/self.mass*dt\n",
" self.vy = self.vy + fy/self.mass*dt\n",
" self.x = self.x + self.vx*dt\n",
" self.y = self.y + self.vy*dt"
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"from matplotlib import pyplot\n",
"from matplotlib.colors import ColorConverter as cc\n",
"import math\n",
"\n",
"GM = 4*math.pi**2\n",
"r = 1 # radius of the orbit\n",
"v0 = math.sqrt(GM/r) # This is the condition for circular orbits \n",
"\n",
"x0 = r # initial position\n",
"y0 = 0. # we asume we start from the x axis\n",
"v0x = 0. # and the initial velocity\n",
"v0y = v0 # is perpendicular to the vector position. \n",
"\n",
"dt = 0.001 # time step\n",
"tmax = 4.\n",
"nsteps = int(tmax/dt)\n",
"\n",
"x = np.zeros(nsteps)\n",
"y = np.zeros(nsteps)\n",
"vx = np.zeros(nsteps) \n",
"vy = np.zeros(nsteps)\n",
"energy = np.zeros(nsteps)\n",
"\n",
"x[0] = x0\n",
"y[0] = y0\n",
"vx[0] = v0x\n",
"vy[0] = v0y\n",
"energy[0] = 0.5*(v0y**2+v0x**2) - GM/r\n",
"\n",
"p = particle2(1., x0, y0, v0x, v0y)\n",
"\n",
"for i in range(0,nsteps):\n",
" r = math.sqrt(p.x*p.x+p.y*p.y)\n",
" r3 = r * r * r\n",
" fx = -GM*p.x/r3\n",
" fy = -GM*p.y/r3\n",
" p.euler(fx, fy, dt)\n",
"\n",
" x[i] = p.x\n",
" y[i] = p.y\n",
" vx[i] = p.vx\n",
" vy[i] = p.vy\n",
" energy[i] = 0.5*(p.vx**2+p.vy**2) - GM/r\n",
"\n",
"t = np.linspace(0.,tmax,nsteps) \n",
"\n",
"pyplot.plot(t, energy, color='green', ls='-', lw=3)\n",
"\n",
"pyplot.xlabel('time')\n",
"pyplot.ylabel('Energy');"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pyplot.plot(t, x, color='red', ls='-', lw=3)\n",
"\n",
"pyplot.xlabel('time')\n",
"pyplot.ylabel('position x');\n",
"pyplot.xlim(0,4);"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pyplot.plot(x, y, color='blue', ls='-', lw=3)\n",
"\n",
"pyplot.xlabel('position x')\n",
"pyplot.ylabel('position y');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Challenge 2.1:\n",
"\n",
"Modify the previous code to:\n",
"\n",
"- Write the axis labels in the right units according to the previous discussion\n",
"- Answer Exercise 2.1, parts 2-5\n",
"- Modifly class particle2 to add a higher order method such as Runge-Kutta \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A mini solar system\n",
"-------------------\n",
"\n",
"The presence of other planets implies that the total force on a planet\n",
"is no longer a central force. Furthermore, since the orbits are not\n",
"exactly on the same plane, the analysis must be extended to 3D. However,\n",
"for simplicity, we are going to consider a two-dimensional solar system,\n",
"with two planets in orbit around the sun.\n",
"\n",
"The equations of motion of the two planets of mass $m_1$ and $m_2$ can\n",
"be written in vector form as $$\\begin{aligned}\n",
"&& m_1\\frac{d^2 {\\mathbf r}_1}{dt^2}=-\\frac{m_1MG}{r_1^3}{\\mathbf \n",
"r}_1+\\frac{m_1m_2G}{r_{21}^3}{\\mathbf r}_{21}, \\\\\n",
"&& m_2\\frac{d^2 {\\mathbf r}_2}{dt^2}=-\\frac{m_2MG}{r_2^3}{\\mathbf \n",
"r}_2+\\frac{m_1m_2G}{r_{21}^3}{\\mathbf r}_{21},\\end{aligned}$$ where\n",
"${\\mathbf r}_1$ and ${\\mathbf r}_2$ are directed form the sun to the\n",
"planets, and ${\\mathbf r}_{21}={\\mathbf r}_2-{\\mathbf r}_1$ is the\n",
"vector from planet 1 to planet 2. This is a problem with no analytical\n",
"solution, but its numerical solution can be obtained extending our\n",
"previous analysis for the two-body problem.\n",
"\n",
"### Exercise 2.2: A three body problem \n",
"\n",
"Let us consider astronomical units, and values for the masses\n",
"$m_1/M=0.001$ and $m_2/M=0.01$. Consider initial positions $r_1=1$ and\n",
"$r_2=4/3$ and velocities ${\\mathbf v}_{1,2}=(0,\\sqrt{GM/r_{1,2}})$.\n",
"\n",
"1. Write a program to calculate the trajectories of the two planets,\n",
" and plot them.\n",
"\n",
"2. What would the shape and the periods of the orbits be if the don’t\n",
" interact? What is the qualitative effect of the interaction. Why is\n",
" one planet affected more by the interaction that the other? Are the\n",
" angular momentum and energy of planet 1 conserved? Is the total\n",
" momentum an energy of the two planets conserved?\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"NPLANETS = 2\n",
"\n",
"m1 = 0.1\n",
"r1 = 1. # radius of the orbit\n",
"v1 = math.sqrt(GM/r1) # This is the condition for circular orbits \n",
"\n",
"m2 = 0.01\n",
"r2 = 4./3. # radius of the orbit\n",
"v2 = math.sqrt(GM/r2) # This is the condition for circular orbits \n",
"\n",
"dt = 0.001 # time step\n",
"tmax = 2.\n",
"nsteps = int(tmax/dt)\n",
"\n",
"x = np.zeros(shape=(nsteps,NPLANETS))\n",
"y = np.zeros(shape=(nsteps,NPLANETS))\n",
"vx = np.zeros(shape=(nsteps,NPLANETS)) \n",
"vy = np.zeros(shape=(nsteps,NPLANETS))\n",
"energy = np.zeros(shape=(nsteps,NPLANETS))\n",
"\n",
"x[0,0] = r1\n",
"y[0,0] = 0.\n",
"vx[0,0] = 0.\n",
"vy[0,0] = v1\n",
"energy[0][0] = 0.5*v1**2 - GM/r1\n",
"x[0,1] = r2\n",
"y[0,1] = 0.\n",
"vx[0,1] = 0.\n",
"vy[0,1] = v2\n",
"energy[0][1] = 0.5*v2**2 - GM/r2\n",
"\n",
"planets = [particle2]\n",
"for i in range(1,NPLANETS): # create a list of NPLANETS particle2's\n",
" planets.append([particle2])\n",
"\n",
"m = [m1, m2]\n",
"for i in range(NPLANETS):\n",
" planets[i] = particle2(m[i], x[0,i], y[0,i], vx[0,i], vy[0,i])\n",
"\n",
"for i in range(1,nsteps):\n",
" for n in range(0,NPLANETS):\n",
" r = math.sqrt(planets[n].x*planets[n].x+planets[n].y*planets[n].y);\n",
" r3 = r * r * r;\n",
" fx = -GM*planets[n].mass*planets[n].x/r3;\n",
" fy = -GM*planets[n].mass*planets[n].y/r3;\n",
" planets[n].euler(fx, fy, dt)\n",
"\n",
" x[i,n] = planets[n].x\n",
" y[i,n] = planets[n].y\n",
" vx[i,n] = planets[n].vx\n",
" vy[i,n] = planets[n].vy\n",
" energy[i,n] = 0.5*(planets[n].vx**2+planets[n].vy**2) - GM/r\n",
"\n",
"t = np.linspace(0.,tmax,nsteps) \n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pyplot.plot(x[:,0], y[:,0], color='blue', ls='-', lw=3)\n",
"pyplot.plot(x[:,1], y[:,1], color='red', ls='-', lw=3)\n",
"\n",
"pyplot.xlabel('position x')\n",
"pyplot.ylabel('position y');"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pyplot.plot(t, energy[:,0], color='blue', ls='-', lw=3, label='Planet 1')\n",
"pyplot.plot(t, energy[:,1], color='red', ls='-', lw=3, label = 'Planet 2')\n",
"pyplot.plot(t, energy[:,0]+energy[:,1], color='green', ls='-', lw=3, label = 'Both planets')\n",
"\n",
"pyplot.xlabel('Energy')\n",
"pyplot.ylabel('time');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this particular case, since both planets do not interact, the individual energies are conserved."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Challenge 2:2:\n",
"\n",
"Modify the previous code to introduce the gravitational interaction between planets, and repeat the calculations. Consider masses $m_1=0.1$ and $m_2=0.01$, and the initial positions ${\\bf r}_1=(1,0)$ and ${\\bf r}_2=(-3,0)$, with velocities corresponding to circular orbits. Plot the orbits for 5 periods, and the individual and total energies.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
bryanfry/nyc-schools | nyc-schools_C.ipynb | 1 | 33676 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# nyc-schools_C\n",
"\n",
"This script averages the ACS variables for the N census tracts closest to each school, and combines these averaged variables with the school outcomes in a single dataframe (saved as a *.csv)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"\n",
"bp_data = '/Users/bryanfry/projects/proj_nyc-schools/data_files'\n",
"n_tracts = 10 # Average ACS variable from 20 closest tracts to each school."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Function to compute the average value of an ACS variable across several census tracts"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Compute average value for ACS var, given a list of geoid. Ideally perhaps the tracts should\n",
"# be weighted by population rather than using a simple mean, but probably results won't be\n",
"# much different since the census tracts are intended to have roughly equal populations.\n",
"\n",
"def calc_multitract_var (df_acs, var, geoid_list, mode = 'sum'):\n",
" t = 0 # Total value\n",
" #print geoid_list.tolist()\n",
" for g in geoid_list:\n",
" #print g\n",
" try:\n",
" t = t + float (df_acs[df_acs.GEOID == g][var])\n",
" except: pass\n",
" if mode == 'avg':\n",
" t = t / len (geoid_list)\n",
" return t"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MAIN"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Load school data (with 50 closest census tracts), and ACS variables for each tract\n",
"df_sch = pd.read_csv (os.path.join (bp_data, 'df_A_school_info.csv'))\n",
"df_acs = pd.read_csv (os.path.join (bp_data, 'df_B_acs_geoid.csv'))\n",
"\n",
"# Drop first column of each imported dataframe (these are just redundent indices)\n",
"df_sch = df_sch.drop (df_sch.columns[0], axis = 1)\n",
"df_acs = df_acs.drop (df_acs.columns[0], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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>GEOID</th>\n",
" <th>BOR</th>\n",
" <th>2+_RACES</th>\n",
" <th>ASIAN</th>\n",
" <th>BLACK</th>\n",
" <th>DIFFERENT_HOUSE</th>\n",
" <th>DIFFERENT_HOUSE_ABROAD</th>\n",
" <th>DIFFERENT_HOUSE_DIFFERENT_CITY_SAME_STATE</th>\n",
" <th>DIFFERENT_HOUSE_SAME_CITY</th>\n",
" <th>DIFFERENT_HOUSE_US_DIFFERENT_STATE</th>\n",
" <th>...</th>\n",
" <th>NATIVE_CITIZEN</th>\n",
" <th>NON_CITIZEN</th>\n",
" <th>SAME_HOUSE</th>\n",
" <th>TOTAL_POP?</th>\n",
" <th>WHITE</th>\n",
" <th>FRAC_MINORITY</th>\n",
" <th>RENT_INCOME_RATIO</th>\n",
" <th>FRAC_MOVED</th>\n",
" <th>FRAC_NONCITIZEN</th>\n",
" <th>FRAC_FOREIN_BORN</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>36005000100</td>\n",
" <td>BNX</td>\n",
" <td>71.0</td>\n",
" <td>167.0</td>\n",
" <td>4851.0</td>\n",
" <td>6710.0</td>\n",
" <td>0.0</td>\n",
" <td>194.0</td>\n",
" <td>6238.0</td>\n",
" <td>278.0</td>\n",
" <td>...</td>\n",
" <td>6980.0</td>\n",
" <td>1081.0</td>\n",
" <td>1720.0</td>\n",
" <td>8430.0</td>\n",
" <td>1184.0</td>\n",
" <td>0.604152</td>\n",
" <td>1.862186</td>\n",
" <td>0.795967</td>\n",
" <td>0.128233</td>\n",
" <td>0.172005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>36005000200</td>\n",
" <td>BNX</td>\n",
" <td>183.0</td>\n",
" <td>331.0</td>\n",
" <td>1423.0</td>\n",
" <td>304.0</td>\n",
" <td>1.0</td>\n",
" <td>55.0</td>\n",
" <td>232.0</td>\n",
" <td>17.0</td>\n",
" <td>...</td>\n",
" <td>3536.0</td>\n",
" <td>558.0</td>\n",
" <td>4755.0</td>\n",
" <td>5095.0</td>\n",
" <td>1606.0</td>\n",
" <td>0.381551</td>\n",
" <td>0.624364</td>\n",
" <td>0.059666</td>\n",
" <td>0.109519</td>\n",
" <td>0.305986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>36005000400</td>\n",
" <td>BNX</td>\n",
" <td>50.0</td>\n",
" <td>36.0</td>\n",
" <td>1996.0</td>\n",
" <td>450.0</td>\n",
" <td>0.0</td>\n",
" <td>32.0</td>\n",
" <td>409.0</td>\n",
" <td>9.0</td>\n",
" <td>...</td>\n",
" <td>4385.0</td>\n",
" <td>383.0</td>\n",
" <td>5089.0</td>\n",
" <td>5572.0</td>\n",
" <td>1674.0</td>\n",
" <td>0.376525</td>\n",
" <td>0.573522</td>\n",
" <td>0.080761</td>\n",
" <td>0.068737</td>\n",
" <td>0.213029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>36005001600</td>\n",
" <td>BNX</td>\n",
" <td>38.0</td>\n",
" <td>286.0</td>\n",
" <td>1806.0</td>\n",
" <td>275.0</td>\n",
" <td>69.0</td>\n",
" <td>8.0</td>\n",
" <td>258.0</td>\n",
" <td>9.0</td>\n",
" <td>...</td>\n",
" <td>3733.0</td>\n",
" <td>744.0</td>\n",
" <td>4989.0</td>\n",
" <td>5412.0</td>\n",
" <td>1855.0</td>\n",
" <td>0.393570</td>\n",
" <td>0.707188</td>\n",
" <td>0.050813</td>\n",
" <td>0.137472</td>\n",
" <td>0.310237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>36005001900</td>\n",
" <td>BNX</td>\n",
" <td>47.0</td>\n",
" <td>39.0</td>\n",
" <td>814.0</td>\n",
" <td>539.0</td>\n",
" <td>5.0</td>\n",
" <td>14.0</td>\n",
" <td>456.0</td>\n",
" <td>69.0</td>\n",
" <td>...</td>\n",
" <td>1801.0</td>\n",
" <td>614.0</td>\n",
" <td>1999.0</td>\n",
" <td>2569.0</td>\n",
" <td>828.0</td>\n",
" <td>0.350331</td>\n",
" <td>0.894045</td>\n",
" <td>0.209809</td>\n",
" <td>0.239004</td>\n",
" <td>0.298949</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" GEOID BOR 2+_RACES ASIAN BLACK DIFFERENT_HOUSE \\\n",
"0 36005000100 BNX 71.0 167.0 4851.0 6710.0 \n",
"1 36005000200 BNX 183.0 331.0 1423.0 304.0 \n",
"2 36005000400 BNX 50.0 36.0 1996.0 450.0 \n",
"3 36005001600 BNX 38.0 286.0 1806.0 275.0 \n",
"4 36005001900 BNX 47.0 39.0 814.0 539.0 \n",
"\n",
" DIFFERENT_HOUSE_ABROAD DIFFERENT_HOUSE_DIFFERENT_CITY_SAME_STATE \\\n",
"0 0.0 194.0 \n",
"1 1.0 55.0 \n",
"2 0.0 32.0 \n",
"3 69.0 8.0 \n",
"4 5.0 14.0 \n",
"\n",
" DIFFERENT_HOUSE_SAME_CITY DIFFERENT_HOUSE_US_DIFFERENT_STATE \\\n",
"0 6238.0 278.0 \n",
"1 232.0 17.0 \n",
"2 409.0 9.0 \n",
"3 258.0 9.0 \n",
"4 456.0 69.0 \n",
"\n",
" ... NATIVE_CITIZEN NON_CITIZEN SAME_HOUSE TOTAL_POP? \\\n",
"0 ... 6980.0 1081.0 1720.0 8430.0 \n",
"1 ... 3536.0 558.0 4755.0 5095.0 \n",
"2 ... 4385.0 383.0 5089.0 5572.0 \n",
"3 ... 3733.0 744.0 4989.0 5412.0 \n",
"4 ... 1801.0 614.0 1999.0 2569.0 \n",
"\n",
" WHITE FRAC_MINORITY RENT_INCOME_RATIO FRAC_MOVED FRAC_NONCITIZEN \\\n",
"0 1184.0 0.604152 1.862186 0.795967 0.128233 \n",
"1 1606.0 0.381551 0.624364 0.059666 0.109519 \n",
"2 1674.0 0.376525 0.573522 0.080761 0.068737 \n",
"3 1855.0 0.393570 0.707188 0.050813 0.137472 \n",
"4 828.0 0.350331 0.894045 0.209809 0.239004 \n",
"\n",
" FRAC_FOREIN_BORN \n",
"0 0.172005 \n",
"1 0.305986 \n",
"2 0.213029 \n",
"3 0.310237 \n",
"4 0.298949 \n",
"\n",
"[5 rows x 25 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_acs.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Now loop on the schools, and average ACS variables across census tracts"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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>2+_RACES</th>\n",
" <th>ASIAN</th>\n",
" <th>BLACK</th>\n",
" <th>DIFFERENT_HOUSE</th>\n",
" <th>DIFFERENT_HOUSE_ABROAD</th>\n",
" <th>DIFFERENT_HOUSE_DIFFERENT_CITY_SAME_STATE</th>\n",
" <th>DIFFERENT_HOUSE_SAME_CITY</th>\n",
" <th>DIFFERENT_HOUSE_US_DIFFERENT_STATE</th>\n",
" <th>FOREIGN_BORN_INCLUDING_NATURALIZED</th>\n",
" <th>FRAC_FOREIN_BORN</th>\n",
" <th>...</th>\n",
" <th>MEDIAN_AGE</th>\n",
" <th>MEDIAN_INCOME</th>\n",
" <th>MEDIAN_MONTHLY_HOUSING_COSTS</th>\n",
" <th>NATIVE_AMERICAN</th>\n",
" <th>NATIVE_CITIZEN</th>\n",
" <th>NON_CITIZEN</th>\n",
" <th>RENT_INCOME_RATIO</th>\n",
" <th>SAME_HOUSE</th>\n",
" <th>TOTAL_POP?</th>\n",
" <th>WHITE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>344.0</td>\n",
" <td>893.0</td>\n",
" <td>12406.0</td>\n",
" <td>1252.0</td>\n",
" <td>185.0</td>\n",
" <td>50.0</td>\n",
" <td>1180.0</td>\n",
" <td>22.0</td>\n",
" <td>6941.0</td>\n",
" <td>0.426406</td>\n",
" <td>...</td>\n",
" <td>36.44</td>\n",
" <td>25765.4</td>\n",
" <td>1343.6</td>\n",
" <td>100.0</td>\n",
" <td>9036.0</td>\n",
" <td>2888.0</td>\n",
" <td>0.641336</td>\n",
" <td>14214.0</td>\n",
" <td>16193.0</td>\n",
" <td>1452.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>218.0</td>\n",
" <td>1358.0</td>\n",
" <td>3818.0</td>\n",
" <td>1853.0</td>\n",
" <td>181.0</td>\n",
" <td>49.0</td>\n",
" <td>1671.0</td>\n",
" <td>133.0</td>\n",
" <td>7770.0</td>\n",
" <td>0.366035</td>\n",
" <td>...</td>\n",
" <td>36.36</td>\n",
" <td>25270.2</td>\n",
" <td>1155.8</td>\n",
" <td>55.0</td>\n",
" <td>12243.0</td>\n",
" <td>4581.0</td>\n",
" <td>0.556566</td>\n",
" <td>17749.0</td>\n",
" <td>20013.0</td>\n",
" <td>8233.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>464.0</td>\n",
" <td>547.0</td>\n",
" <td>7315.0</td>\n",
" <td>3422.0</td>\n",
" <td>174.0</td>\n",
" <td>63.0</td>\n",
" <td>3233.0</td>\n",
" <td>126.0</td>\n",
" <td>8331.0</td>\n",
" <td>0.307253</td>\n",
" <td>...</td>\n",
" <td>29.28</td>\n",
" <td>14537.0</td>\n",
" <td>898.0</td>\n",
" <td>30.0</td>\n",
" <td>17741.0</td>\n",
" <td>5358.0</td>\n",
" <td>0.765690</td>\n",
" <td>22121.0</td>\n",
" <td>26072.0</td>\n",
" <td>3313.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>477.0</td>\n",
" <td>774.0</td>\n",
" <td>4355.0</td>\n",
" <td>4425.0</td>\n",
" <td>377.0</td>\n",
" <td>477.0</td>\n",
" <td>3175.0</td>\n",
" <td>773.0</td>\n",
" <td>7687.0</td>\n",
" <td>0.336043</td>\n",
" <td>...</td>\n",
" <td>26.14</td>\n",
" <td>11822.4</td>\n",
" <td>1063.6</td>\n",
" <td>110.0</td>\n",
" <td>14542.0</td>\n",
" <td>5529.0</td>\n",
" <td>1.334316</td>\n",
" <td>17105.0</td>\n",
" <td>22229.0</td>\n",
" <td>6731.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>191.0</td>\n",
" <td>495.0</td>\n",
" <td>10548.0</td>\n",
" <td>1188.0</td>\n",
" <td>182.0</td>\n",
" <td>29.0</td>\n",
" <td>1022.0</td>\n",
" <td>137.0</td>\n",
" <td>4981.0</td>\n",
" <td>0.342813</td>\n",
" <td>...</td>\n",
" <td>34.52</td>\n",
" <td>24536.6</td>\n",
" <td>1281.8</td>\n",
" <td>154.0</td>\n",
" <td>10037.0</td>\n",
" <td>2031.0</td>\n",
" <td>0.624219</td>\n",
" <td>13414.0</td>\n",
" <td>15018.0</td>\n",
" <td>1054.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" 2+_RACES ASIAN BLACK DIFFERENT_HOUSE DIFFERENT_HOUSE_ABROAD \\\n",
"0 344.0 893.0 12406.0 1252.0 185.0 \n",
"1 218.0 1358.0 3818.0 1853.0 181.0 \n",
"2 464.0 547.0 7315.0 3422.0 174.0 \n",
"3 477.0 774.0 4355.0 4425.0 377.0 \n",
"4 191.0 495.0 10548.0 1188.0 182.0 \n",
"\n",
" DIFFERENT_HOUSE_DIFFERENT_CITY_SAME_STATE DIFFERENT_HOUSE_SAME_CITY \\\n",
"0 50.0 1180.0 \n",
"1 49.0 1671.0 \n",
"2 63.0 3233.0 \n",
"3 477.0 3175.0 \n",
"4 29.0 1022.0 \n",
"\n",
" DIFFERENT_HOUSE_US_DIFFERENT_STATE FOREIGN_BORN_INCLUDING_NATURALIZED \\\n",
"0 22.0 6941.0 \n",
"1 133.0 7770.0 \n",
"2 126.0 8331.0 \n",
"3 773.0 7687.0 \n",
"4 137.0 4981.0 \n",
"\n",
" FRAC_FOREIN_BORN ... MEDIAN_AGE MEDIAN_INCOME \\\n",
"0 0.426406 ... 36.44 25765.4 \n",
"1 0.366035 ... 36.36 25270.2 \n",
"2 0.307253 ... 29.28 14537.0 \n",
"3 0.336043 ... 26.14 11822.4 \n",
"4 0.342813 ... 34.52 24536.6 \n",
"\n",
" MEDIAN_MONTHLY_HOUSING_COSTS NATIVE_AMERICAN NATIVE_CITIZEN NON_CITIZEN \\\n",
"0 1343.6 100.0 9036.0 2888.0 \n",
"1 1155.8 55.0 12243.0 4581.0 \n",
"2 898.0 30.0 17741.0 5358.0 \n",
"3 1063.6 110.0 14542.0 5529.0 \n",
"4 1281.8 154.0 10037.0 2031.0 \n",
"\n",
" RENT_INCOME_RATIO SAME_HOUSE TOTAL_POP? WHITE \n",
"0 0.641336 14214.0 16193.0 1452.0 \n",
"1 0.556566 17749.0 20013.0 8233.0 \n",
"2 0.765690 22121.0 26072.0 3313.0 \n",
"3 1.334316 17105.0 22229.0 6731.0 \n",
"4 0.624219 13414.0 15018.0 1054.0 \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Define a dictionary with the census variables to be added to the dataframe\n",
"dict_var = {}\n",
"acs_col_list = df_acs.columns[2:] # These are the census variables of interest\n",
"\n",
"# Loop on the rows of the school file.\n",
"for c in acs_col_list:\n",
" dict_var [c] = [] # Make an empty list for each column. \n",
" # One element will be added to each list in \n",
" # the dictionary for each school# For variables which are either FRACTIONS or MEDIAN VALUES, we take the \n",
"# MEAN across the tracts. For other values (corresponging to actual number of \n",
"# respondants) we take the SUM.\n",
"\n",
"for i in range (0, len (df_sch)):\n",
" geoid_list= df_sch.ix [i][9:9+n_tracts]\n",
" for i, c in enumerate (acs_col_list):\n",
" if i in [9, 10, 11, 18, 19, 20, 21, 22]: mode = 'avg'\n",
" else: mode = 'sum'\n",
" dict_var[c].append (calc_multitract_var (df_acs, var = c, geoid_list=geoid_list, mode = mode))\n",
"\n",
"df_tract_avg = pd.DataFrame(data = dict_var) \n",
"df_tract_avg.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Concatenate the tract-averaged data with the school outcome data"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.concat ([df_sch, df_tract_avg], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>NAME</th>\n",
" <th>DBN</th>\n",
" <th>STREET</th>\n",
" <th>ZIPCODE</th>\n",
" <th>LAT</th>\n",
" <th>LON</th>\n",
" <th>COUNTY</th>\n",
" <th>HOOD</th>\n",
" <th>DISPLAY_NAME</th>\n",
" <th>GEOCODE00</th>\n",
" <th>...</th>\n",
" <th>MEDIAN_AGE</th>\n",
" <th>MEDIAN_INCOME</th>\n",
" <th>MEDIAN_MONTHLY_HOUSING_COSTS</th>\n",
" <th>NATIVE_AMERICAN</th>\n",
" <th>NATIVE_CITIZEN</th>\n",
" <th>NON_CITIZEN</th>\n",
" <th>RENT_INCOME_RATIO</th>\n",
" <th>SAME_HOUSE</th>\n",
" <th>TOTAL_POP?</th>\n",
" <th>WHITE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Academy for Scholarship and Entrepreneurship: ...</td>\n",
" <td>11X270</td>\n",
" <td>921 East 228th Street</td>\n",
" <td>10466</td>\n",
" <td>40.888215</td>\n",
" <td>-73.852720</td>\n",
" <td>Bronx County</td>\n",
" <td>Wakefield</td>\n",
" <td>921, East 228th Street, Wakefield, Bronx, Bron...</td>\n",
" <td>36005040400</td>\n",
" <td>...</td>\n",
" <td>36.44</td>\n",
" <td>25765.4</td>\n",
" <td>1343.6</td>\n",
" <td>100.0</td>\n",
" <td>9036.0</td>\n",
" <td>2888.0</td>\n",
" <td>0.641336</td>\n",
" <td>14214.0</td>\n",
" <td>16193.0</td>\n",
" <td>1452.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Astor Collegiate Academy</td>\n",
" <td>11X299</td>\n",
" <td>925 Astor Avenue</td>\n",
" <td>10469</td>\n",
" <td>40.859900</td>\n",
" <td>-73.860322</td>\n",
" <td>Bronx County</td>\n",
" <td>Morris Park</td>\n",
" <td>Christopher Columbus High School, 925, Astor A...</td>\n",
" <td>36005032400</td>\n",
" <td>...</td>\n",
" <td>36.36</td>\n",
" <td>25270.2</td>\n",
" <td>1155.8</td>\n",
" <td>55.0</td>\n",
" <td>12243.0</td>\n",
" <td>4581.0</td>\n",
" <td>0.556566</td>\n",
" <td>17749.0</td>\n",
" <td>20013.0</td>\n",
" <td>8233.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Banana Kelly High School</td>\n",
" <td>08X530</td>\n",
" <td>965 Longwood Avenue</td>\n",
" <td>10459</td>\n",
" <td>40.817601</td>\n",
" <td>-73.897985</td>\n",
" <td>Bronx County</td>\n",
" <td>Melrose</td>\n",
" <td>965, Longwood Avenue, Melrose, Bronx, Bronx Co...</td>\n",
" <td>36005008500</td>\n",
" <td>...</td>\n",
" <td>29.28</td>\n",
" <td>14537.0</td>\n",
" <td>898.0</td>\n",
" <td>30.0</td>\n",
" <td>17741.0</td>\n",
" <td>5358.0</td>\n",
" <td>0.765690</td>\n",
" <td>22121.0</td>\n",
" <td>26072.0</td>\n",
" <td>3313.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Belmont Preparatory High School</td>\n",
" <td>10X434</td>\n",
" <td>500 East Fordham Road</td>\n",
" <td>10458</td>\n",
" <td>40.859840</td>\n",
" <td>-73.888295</td>\n",
" <td>Bronx County</td>\n",
" <td>Belmont</td>\n",
" <td>500, East Fordham Road, Belmont, Bronx, Bronx ...</td>\n",
" <td>36005038700</td>\n",
" <td>...</td>\n",
" <td>26.14</td>\n",
" <td>11822.4</td>\n",
" <td>1063.6</td>\n",
" <td>110.0</td>\n",
" <td>14542.0</td>\n",
" <td>5529.0</td>\n",
" <td>1.334316</td>\n",
" <td>17105.0</td>\n",
" <td>22229.0</td>\n",
" <td>6731.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Bronx Academy of Health Careers</td>\n",
" <td>11X290</td>\n",
" <td>800 East Gun Hill Road</td>\n",
" <td>10467</td>\n",
" <td>40.875549</td>\n",
" <td>-73.861423</td>\n",
" <td>Bronx County</td>\n",
" <td>Williams Bridge</td>\n",
" <td>800, East Gun Hill Road, Williams Bridge, Bron...</td>\n",
" <td>36005037200</td>\n",
" <td>...</td>\n",
" <td>34.52</td>\n",
" <td>24536.6</td>\n",
" <td>1281.8</td>\n",
" <td>154.0</td>\n",
" <td>10037.0</td>\n",
" <td>2031.0</td>\n",
" <td>0.624219</td>\n",
" <td>13414.0</td>\n",
" <td>15018.0</td>\n",
" <td>1054.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 115 columns</p>\n",
"</div>"
],
"text/plain": [
" NAME DBN \\\n",
"0 Academy for Scholarship and Entrepreneurship: ... 11X270 \n",
"1 Astor Collegiate Academy 11X299 \n",
"2 Banana Kelly High School 08X530 \n",
"3 Belmont Preparatory High School 10X434 \n",
"4 Bronx Academy of Health Careers 11X290 \n",
"\n",
" STREET ZIPCODE LAT LON COUNTY \\\n",
"0 921 East 228th Street 10466 40.888215 -73.852720 Bronx County \n",
"1 925 Astor Avenue 10469 40.859900 -73.860322 Bronx County \n",
"2 965 Longwood Avenue 10459 40.817601 -73.897985 Bronx County \n",
"3 500 East Fordham Road 10458 40.859840 -73.888295 Bronx County \n",
"4 800 East Gun Hill Road 10467 40.875549 -73.861423 Bronx County \n",
"\n",
" HOOD DISPLAY_NAME \\\n",
"0 Wakefield 921, East 228th Street, Wakefield, Bronx, Bron... \n",
"1 Morris Park Christopher Columbus High School, 925, Astor A... \n",
"2 Melrose 965, Longwood Avenue, Melrose, Bronx, Bronx Co... \n",
"3 Belmont 500, East Fordham Road, Belmont, Bronx, Bronx ... \n",
"4 Williams Bridge 800, East Gun Hill Road, Williams Bridge, Bron... \n",
"\n",
" GEOCODE00 ... MEDIAN_AGE MEDIAN_INCOME \\\n",
"0 36005040400 ... 36.44 25765.4 \n",
"1 36005032400 ... 36.36 25270.2 \n",
"2 36005008500 ... 29.28 14537.0 \n",
"3 36005038700 ... 26.14 11822.4 \n",
"4 36005037200 ... 34.52 24536.6 \n",
"\n",
" MEDIAN_MONTHLY_HOUSING_COSTS NATIVE_AMERICAN NATIVE_CITIZEN NON_CITIZEN \\\n",
"0 1343.6 100.0 9036.0 2888.0 \n",
"1 1155.8 55.0 12243.0 4581.0 \n",
"2 898.0 30.0 17741.0 5358.0 \n",
"3 1063.6 110.0 14542.0 5529.0 \n",
"4 1281.8 154.0 10037.0 2031.0 \n",
"\n",
" RENT_INCOME_RATIO SAME_HOUSE TOTAL_POP? WHITE \n",
"0 0.641336 14214.0 16193.0 1452.0 \n",
"1 0.556566 17749.0 20013.0 8233.0 \n",
"2 0.765690 22121.0 26072.0 3313.0 \n",
"3 1.334316 17105.0 22229.0 6731.0 \n",
"4 0.624219 13414.0 15018.0 1054.0 \n",
"\n",
"[5 rows x 115 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Finally clean up some of column names, and eliminate some that will not be used"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_c = pd.DataFrame() # c -> 'concise'\n",
"\n",
"# Build list of columns to copy\n",
"c_list = ['NAME','DBN','STREET','ZIPCODE','LAT','LON','COUNTY','HOOD','DISPLAY_NAME']\n",
"c_list = c_list + ['GEOCODE' + str (i).zfill(2) for i in range (0, n_tracts)]\n",
"c_list = c_list + ['2+_RACES','ASIAN','BLACK','DIFFERENT_HOUSE','DIFFERENT_HOUSE_ABROAD',\\\n",
" 'DIFFERENT_HOUSE_DIFFERENT_CITY_SAME_STATE','DIFFERENT_HOUSE_SAME_CITY',\\\n",
" 'DIFFERENT_HOUSE_US_DIFFERENT_STATE','FOREIGN_BORN_INCLUDING_NATURALIZED',\\\n",
" 'MEDIAN_AGE','MEDIAN_INCOME','MEDIAN_MONTHLY_HOUSING_COSTS','NATIVE_AMERICAN',\\\n",
" 'NATIVE_CITIZEN','NON_CITIZEN','SAME_HOUSE','TOTAL_POP?','WHITE','FRAC_MINORITY',\\\n",
" 'RENT_INCOME_RATIO','FRAC_MOVED','FRAC_NONCITIZEN','FRAC_FOREIN_BORN']\n",
"for c in c_list: df_c[c] = df[c]\n",
"\n",
"\n",
"# Copy and rename school outcome data\n",
"old_c_list = ['Total Cohort','Total Grads - % of cohort',\\\n",
" 'Total Regents - % of cohort','Total Regents - % of grads','Advanced Regents - % of cohort',\\\n",
" 'Advanced Regents - % of grads','Regents w/o Advanced - % of cohort',\\\n",
" 'Regents w/o Advanced - % of grads','Local - % of cohort','Local - % of grads',\\\n",
" 'Dropped Out - % of cohort','Q_Total Grads - % of cohort','Q_Total Regents - % of cohort',\\\n",
" 'Q_Total Regents - % of grads','Q_Advanced Regents - % of cohort',\\\n",
" 'Q_Advanced Regents - % of grads','Q_Regents w/o Advanced - % of cohort','Q_Local - % of cohort',\\\n",
" 'Q_Local - % of grads','Q_Still Enrolled - % of cohort','Q_Dropped Out - % of cohort']\n",
"\n",
"new_c_list = ['TOTAL_COHORT','GRADS_%','REGENTS_%_COHORT','REGENTS_%_GRADS'\\\n",
" ,'ADV_REGENTS_%_COHORT','ADV_REGENTS_%_GRADS','REG_REGENTS_%_COHORT','REG_REGENTS_%_GRADS'\\\n",
" ,'LOCAL_%_COHORT','LOCAL_%_GRADS','DROPPED_OUT_%','Q_GRADS_%',\\\n",
" 'Q_REGENTS_%_COHORT','Q_REGENTS_%_GRADS','Q_ADV_REGENTS_%_COHORT',\\\n",
" 'Q_ADV_REGENTS_%_GRADS','Q_REG_REGENTS_%_COHORT','Q_LOCAL_%_COHORT',\\\n",
" 'Q_LOCAL_%_GRADS','Q_STILL_ENROLLED_%','Q_DROPPED_OUT_%']\n",
"\n",
"for old_c, new_c in zip (old_c_list, new_c_list):\n",
" df_c[new_c] = df[old_c]\n",
" \n",
"\n",
"#There are some empties -- drop rows with NaN\n",
"df_c = df_c.dropna()\n",
"\n",
"# Save the 'concise' dataframe\n",
"fp_out = os.path.join (bp_data, 'df_C_sch_acs_NTract=' + str (n_tracts).zfill(2) + '.csv')\n",
"df_c.to_csv (fp_out)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"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
}
| gpl-3.0 |
joshnsolomon/phys202-2015-work | assignments/assignment06/InteractEx05.ipynb | 1 | 8032 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Interact Exercise 5"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from IPython.html.widgets import interact, interactive, fixed\n",
"from IPython.html import widgets\n",
"from IPython.display import display, SVG"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Interact with SVG display"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"[SVG](http://en.wikipedia.org/wiki/Scalable_Vector_Graphics) is a simple way of drawing vector graphics in the browser. Here is a simple example of how SVG can be used to draw a circle in the Notebook:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [],
"source": [
"s = ' <svg width=\"100\" height=\"100\"> <circle cx=\"50\" cy=\"50\" r=\"20\" fill=\"aquamarine\" /> </svg>'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg height=\"100\" width=\"100\"> <circle cx=\"50\" cy=\"50\" fill=\"aquamarine\" r=\"20\"/> </svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SVG(s)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Write a function named `draw_circle` that draws a circle using SVG. Your function should take the parameters of the circle as function arguments and have defaults as shown. You will have to write the raw SVG code as a Python string and then use the `IPython.display.SVG` object and `IPython.display.display` function."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"nbgrader": {
"checksum": "ff346dfaabec3ce8812bb0d03cf3951b",
"solution": true
}
},
"outputs": [],
"source": [
"def draw_circle(width=100, height=100, cx=25, cy=25, r=5, fill='red'):\n",
" \"\"\"Draw an SVG circle.\n",
" \n",
" Parameters\n",
" ----------\n",
" width : int\n",
" The width of the svg drawing area in px.\n",
" height : int\n",
" The height of the svg drawing area in px.\n",
" cx : int\n",
" The x position of the center of the circle in px.\n",
" cy : int\n",
" The y position of the center of the circle in px.\n",
" r : int\n",
" The radius of the circle in px.\n",
" fill : str\n",
" The fill color of the circle.\n",
" \"\"\"\n",
" \n",
" a = '<svg width=\"'+str(width)+'\" height=\"'+str(height)+'\"> <circle cx=\"'+str(cx)+'\" cy=\"'+str(cy)+'\" r=\"'+str(r)+'\" fill=\"'+fill+'\"/></svg>'\n",
" display(SVG(a))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg height=\"100\" width=\"100\"> <circle cx=\"10\" cy=\"10\" fill=\"blue\" r=\"10\"/></svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"draw_circle(cx=10, cy=10, r=10, fill='blue')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "6d760b87a2567cb9b9c7a9e2825cacfa",
"grade": true,
"grade_id": "interactex05a",
"points": 4
}
},
"outputs": [],
"source": [
"assert True # leave this to grade the draw_circle function"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Use `interactive` to build a user interface for exploing the `draw_circle` function:\n",
"\n",
"* `width`: a fixed value of 300px\n",
"* `height`: a fixed value of 300px\n",
"* `cx`/`cy`: a slider in the range [0,300]\n",
"* `r`: a slider in the range [0,50]\n",
"* `fill`: a text area in which you can type a color's name\n",
"\n",
"Save the return value of `interactive` to a variable named `w`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [],
"source": [
" w=interactive(draw_circle, width=fixed(300), height=fixed(300), cx=(0,300,1), cy=(0,300,1), r=(0,50,1), fill='red');"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "5993721946f31406b1b7aac42ddd5ce4",
"grade": true,
"grade_id": "interactex05b",
"points": 4
}
},
"outputs": [],
"source": [
"c = w.children\n",
"assert c[0].min==0 and c[0].max==300\n",
"assert c[1].min==0 and c[1].max==300\n",
"assert c[2].min==0 and c[2].max==50\n",
"assert c[3].value=='red'"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Use the `display` function to show the widgets created by `interactive`:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg height=\"300\" width=\"300\"> <circle cx=\"25\" cy=\"25\" fill=\"red\" r=\"5\"/></svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(w)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "eeb509517655f5e40f0bbf0ae8705e72",
"grade": true,
"grade_id": "interactex05c",
"points": 2
}
},
"outputs": [],
"source": [
"assert True # leave this to grade the display of the widget"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Play with the sliders to change the circles parameters interactively."
]
}
],
"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": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
pawarren/Fake-News-Echo-Chambers | demonstration.ipynb | 1 | 54927 | {
"cells": [
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /Users/chuyi/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# note that honeypot dataset is more than just tweets, hence the different name\n",
"from loader import load_michigan_tweets, load_political_tweets, load_honeypot_data, load_michigan_unretreived_tweet_ids\n",
"import datetime\n",
"from dateutil.parser import parse\n",
"from collections import Counter\n",
"import numpy as np\n",
"import pandas\n",
"import re\n",
"import nltk\n",
"from nltk.corpus import stopwords \n",
"from nltk.stem.wordnet import WordNetLemmatizer\n",
"import string\n",
"import gensim\n",
"from gensim import corpora\n",
"import snap\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.cluster import SpectralClustering\n",
"from sklearn import metrics\n",
"import seaborn\n",
"\n",
"nltk.download('stopwords')\n",
"# \"import snap\" compiles to another library, not SNAP, in Python 3."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# loading the honeypot data takes a minute or three. \n",
"\n",
"michigan_tweets = load_michigan_tweets() # a list of dictionaries\n",
"print(\"Loaded Michigan tweets.\")\n",
"poltical_general_tweets, political_keyword_tweets = load_political_tweets() # dataframes\n",
"print(\"Loaded Political tweets.\")\n",
"# dataframes and lists of \n",
"content_polluters, content_polluters_tweets, content_polluters_followings_every_hour_since_collected_at, legitimate_users, legitimate_users_tweets, legitimate_users_followings_every_hour_since_collected_at = load_honeypot_data()\n",
"print(\"Loaded Honeypot data.\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"79004\n",
"63277\n",
"142281\n"
]
}
],
"source": [
"unretrieved_michigan_tweet_ids = load_michigan_unretreived_tweet_ids()\n",
"print(len(unretrieved_michigan_tweet_ids))\n",
"print(len(michigan_tweets))\n",
"print(len(michigan_tweets) + len(unretrieved_michigan_tweet_ids)) # to-do: figure out why this is 142,281 tweets instead of the expected 142,249 tweets"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sample_tweet = michigan_tweets[0]\n",
"# print(sample_tweet)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Some helper functions.\n",
"# Setting: User A retweets a tweet by user B.\n",
"\n",
"# The timestamp of user A's retweet.\n",
"def get_timestamp(tweet):\n",
" return tweet['created_at']\n",
"\n",
"# The content of user A's tweet / retweet.\n",
"def get_content(tweet):\n",
" return tweet['text']\n",
"\n",
"# A list of URLs present in the content of user A's tweet / retweet.\n",
"def get_urls(tweet):\n",
" urls = set()\n",
" for url in tweet['entities']['urls']:\n",
" urls.add(url['expanded_url'])\n",
" if 'retweeted_status' in tweet and 'entities' in tweet['retweeted_status']:\n",
" for url in tweet['retweeted_status']['entities']['urls']:\n",
" urls.add(url['expanded_url'])\n",
" return urls if urls else None\n",
"\n",
"# The hashtag present in user A's retweet.\n",
"def get_hashtags(tweet):\n",
" hashtags = set()\n",
" for hashtag in tweet['entities']['hashtags']:\n",
" hashtags.add(hashtag['text'])\n",
" return hashtags\n",
"\n",
"# The tweet ID of user A's retweet.\n",
"def get_tweet_id(tweet):\n",
" return tweet['id']\n",
"\n",
"# The tweet ID of user B's tweet.\n",
"def get_derived_tweet_id(tweet):\n",
" if 'retweeted_status' not in tweet:\n",
" return None\n",
" return tweet['retweeted_status']['id']\n",
"\n",
"# User A's user ID.\n",
"def get_user_id(tweet):\n",
" return tweet['user']['id']\n",
"\n",
"# User B's user ID.\n",
"def get_derived_user_id(tweet):\n",
" if 'retweeted_status' not in tweet:\n",
" return None\n",
" return tweet['retweeted_status']['user']['id']\n",
"\n",
"# User A's display name.\n",
"def get_display_name(tweet):\n",
" return tweet['user']['name']\n",
"\n",
"# User A's Twitter handle.\n",
"def get_handle(tweet):\n",
" return tweet['user']['screen_name']\n",
"\n",
"# The number of followers of User A.\n",
"def get_num_followers(tweet):\n",
" return tweet['user']['followers_count']\n",
"\n",
"# The number of friends of User A.\n",
"def get_num_friends(tweet):\n",
" return tweet['user']['friends_count']\n",
"\n",
"# The number of posts of User A.\n",
"def get_num_posts(tweet):\n",
" return tweet['user']['statuses_count']\n",
"\n",
"# The number of retweets that User A has performed.\n",
"def get_num_retweets(tweet):\n",
" return tweet['retweet_count']\n",
"\n",
"# A tuple: (the tweet ID of User A's retweet, the tweet ID of User B's tweet).\n",
"# Returns None if the tweet passed in as an argument is not a retweet.\n",
"def get_tweet_to_derived_tweet_edge(tweet):\n",
" if not get_derived_tweet_id(tweet):\n",
" return None\n",
" return (get_tweet_id(tweet), get_derived_tweet_id(tweet))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(u'Tue Nov 01 01:43:18 +0000 2016',\n",
" u'RT @PAMsLOvE: #Huma Abedin Got Three Paychecks From State Dept., The Foundation, And #Clinton -Affiliated Consulting Firm https://t.co/edMA\\u2026',\n",
" {u'http://townhall.com/tipsheet/mattvespa/2016/10/31/ms-moneybags-huma-abedin-was-drawing-three-paychecks-from-state-the-foundation-and-clintonaffiliated-consulting-firm-n2238864'},\n",
" {u'Clinton', u'Huma'},\n",
" 793267136978358272L,\n",
" 793259419911933952L,\n",
" 28569011,\n",
" 20897273,\n",
" u'MAGA Save The USA',\n",
" u'KldudePap',\n",
" 2143,\n",
" 4919,\n",
" 71701,\n",
" 35,\n",
" (793267136978358272L, 793259419911933952L))"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Usage examples\n",
"\n",
"get_timestamp(sample_tweet), \\\n",
"get_content(sample_tweet), \\\n",
"get_urls(sample_tweet), \\\n",
"get_hashtags(sample_tweet), \\\n",
"get_tweet_id(sample_tweet), \\\n",
"get_derived_tweet_id(sample_tweet), \\\n",
"get_user_id(sample_tweet), \\\n",
"get_derived_user_id(sample_tweet), \\\n",
"get_display_name(sample_tweet), \\\n",
"get_handle(sample_tweet), \\\n",
"get_num_followers(sample_tweet), \\\n",
"get_num_friends(sample_tweet), \\\n",
"get_num_posts(sample_tweet), \\\n",
"get_num_retweets(sample_tweet), \\\n",
"get_tweet_to_derived_tweet_edge(sample_tweet)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Hashtags used by the Oxford paper.\n",
"\n",
"pro_trump_hashtags = set(['AmericaFirst','benghazi','CrookedHillary','DrainTheSwamp','lockherup','maga3x','MAGA','MakeAmericaGreatAgain','NeverHillary','PodestaEmails','projectveritas','riggedelection','tcot','Trump2016','Trump','TrumpPence16','TrumpTrain','VoterFraud','votetrump','wakeupamerica'])\n",
"pro_hillary_hashtags = set(['Clinton','ClintonKaine16','democrats','dems','dnc','dumptrump','factcheck','hillary2016','Hillary','HillaryClinton','hillarysupporter','hrc','ImWithHer','LastTimeTrumpPaidTaxes','NeverTrump','OHHillYes','p2','strongertogether','trumptape','uniteblue'])\n",
"neutral_hashtags = set(['Election2016','Elections2016','uselections','uselection','earlyvote','iVoted','Potus'])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Populating some data structures.\n",
"# For now, we're lumping everything together; later, we can distinguish between Trump, Hillary, and Neutral.\n",
"\n",
"tweet_id_to_user_id = {}\n",
"tweet_id_to_content_and_urls = {}\n",
"all_hashtags = set()\n",
"user_id_to_handle = {}\n",
"edges = [] # list, not set, because we want to allow multiple edges\n",
"\n",
"timestamps = []\n",
"dates = []\n",
"formatted_time = []\n",
"\n",
"for t in michigan_tweets:\n",
" user_id = get_user_id(t)\n",
" tweet_id = get_tweet_id(t)\n",
" derived_tweet_id = get_derived_tweet_id(t)\n",
" derived_user_id = get_derived_user_id(t)\n",
" \n",
" tweet_id_to_user_id[tweet_id] = user_id\n",
" if derived_tweet_id and derived_tweet_id not in tweet_id_to_user_id:\n",
" tweet_id_to_user_id[derived_tweet_id] = derived_user_id\n",
" \n",
" tweet_id_to_content_and_urls[tweet_id] = get_content(t), get_urls(t)\n",
" all_hashtags.update(get_hashtags(t))\n",
" user_id_to_handle[user_id] = get_handle(t)\n",
" \n",
" timestamp = get_timestamp(t)\n",
" timestamps.append(timestamp)\n",
"\n",
" dates.append(str(parse(timestamp).date()))\n",
" \n",
" formatted_time.append(parse(timestamp).strftime(\"%m-%d-%H\"))\n",
"\n",
"all_hashtags = list(all_hashtags)\n",
"\n",
"hashtag_occurrences = []\n",
"for t in michigan_tweets:\n",
" edge = get_tweet_to_derived_tweet_edge(t)\n",
" if edge:\n",
" edges.append((tweet_id_to_user_id[edge[0]], tweet_id_to_user_id[edge[1]]))\n",
" hashtags = get_hashtags(t)\n",
" hashtag_occurrences.append([1 if ht in hashtags else 0 for ht in all_hashtags])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are a total of 12364 hashtags in the dataset.\n"
]
}
],
"source": [
"print 'There are a total of %s hashtags in the dataset.' %(len(all_hashtags))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The average edge weight between connected users (i.e., one of whom has retweeted the other at least once) is 1.257103.\n"
]
}
],
"source": [
"print('The average edge weight between connected users (i.e., one of whom has retweeted the other at least once) is %f.' % (len(edges) / float(len(set(edges)))))\n",
"# The reason this is not equal to one is because user A may retweet user B multiple times."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cm = np.corrcoef(np.array(hashtag_occurrences), rowvar=False)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(12364, 12364)\n",
"0.813243391677\n"
]
},
{
"data": {
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yCFpdrwHuBfZGxP3p9V1kgfjTdGpL+ndfWt5pXM88433aGHMIWlX1HYAR8RTwuKQ3pqIL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\nDkAzaywHoJk1lgPQzBrLAWhmjfX/AW1UxUxDaA4CAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b365190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"print cm.shape\n",
"\n",
"plt.imshow(cm, interpolation='nearest')\n",
"plt.colorbar()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"seaborn.clustermap(cm, metric=\"correlation\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x10b2faf10>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10b2fa990>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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var6ZzQd6AJfFlP3AzPZ191cB3P3N0N77gf9VpFyWtvYCTiFK1NsUBZ6NKTvd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\nABvSbnAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1280d7ad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"date_counts = Counter(dates)\n",
"histogram = pandas.DataFrame.from_dict(date_counts, orient='index')\n",
"plt = histogram.plot(kind='bar')\n",
"plt.legend([\"Number of Tweets\"])\n",
"\n",
"formatted_time_counts = Counter(formatted_time)\n",
"histogram = pandas.DataFrame.from_dict(formatted_time_counts, orient='index')\n",
"every_n = 12 # two x-axis ticks per day\n",
"plt = histogram.plot(kind='bar')\n",
"tick_locations = plt.xaxis.get_ticklocs()\n",
"tick_labels = [label.get_text() for label in plt.xaxis.get_ticklabels()]\n",
"_ = plt.xaxis.set_ticks(tick_locations[::every_n])\n",
"_ = plt.xaxis.set_ticklabels(tick_labels[::every_n])\n",
"plt.legend([\"Number of Tweets\"])\n",
"\n",
"# The first graph shows the number of tweets per day.\n",
"# The second graph shows the number of tweets per hour. The axis ticks are 12 hours apart, but the data is hourly."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Counter({'fake': 235, 'bias': 133, 'conspiracy': 121, 'satire': 109, 'political': 65, 'unreliable': 56, 'clickbait': 35, 'junksci': 32, 'hate': 29, 'rumor': 10, 'reliable': 3, 'Conspiracy': 1, 'fake news': 1, 'fake ': 1, 'rumor ': 1, 'state': 1})\n"
]
}
],
"source": [
"# Setting up labeled_sites_to_type, which is a map from site to manual categorization by opensources.\n",
"\n",
"labeled_sites = set()\n",
"labeled_types = []\n",
"labeled_sites_to_type = {}\n",
"# Data from https://github.com/BigMcLargeHuge/opensources/blob/master/sources/sources.csv.\n",
"with open('data/opensources.csv', 'r') as f:\n",
" for line in f:\n",
" content = line.split(',')\n",
" labeled_sites.add(content[0])\n",
" labeled_types.append(content[1])\n",
" labeled_sites_to_type[content[0]] = content[1]\n",
"\n",
"labeled_types_frequencies = Counter(labeled_types)\n",
"labeled_types = set(labeled_types)\n",
"print(labeled_types_frequencies)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Creating helper function for topic modeling.\n",
"\n",
"def word_tokens_and_ids_from_tweets(tweet_ids):\n",
" tweets_contents = [tweet_id_to_content_and_urls[ti][0] for ti in tweet_ids]\n",
" \n",
" punctuation = set(string.punctuation)\n",
" stop_words = stopwords.words('english')\n",
" def clean(contents):\n",
" lower = [i for i in contents.lower().split()]\n",
" stop_free = ' '.join([i for i in lower if i not in stop_words])\n",
" punc_free = ''.join([ch for ch in stop_free if ch not in punctuation])\n",
" return punc_free.split()\n",
" tweets_contents_cleaned = [clean(tc) for tc in tweets_contents]\n",
" \n",
" dictionary = corpora.Dictionary(tweets_contents_cleaned)\n",
" return dictionary\n",
" \n",
"dictionary = word_tokens_and_ids_from_tweets(tweet_id_to_content_and_urls.keys()) # tweet_id_to_content_and_urls's keys exclude derived tweets"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(296, 1), (1776, 1), (4400, 1), (7149, 1), (8060, 1), (10154, 1), (14167, 1)]\n"
]
}
],
"source": [
"def vector_from_tweet(tweet_id):\n",
" content = tweet_id_to_content_and_urls[tweet_id][0]\n",
" return dictionary.doc2bow(content.lower().split())\n",
"\n",
"print (vector_from_tweet(793269953927536640L))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
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"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 |
KJE2001/seminars | 05_particle_in_a_box.ipynb | 1 | 16745 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"<figure>\n",
" <IMG SRC=\"gfx/Logo_norsk_pos.png\" WIDTH=100 ALIGN=\"right\">\n",
"</figure>\n",
"\n",
"# Particle in a box\n",
"*Roberto Di Remigio*, *Luca Frediani*\n",
"\n",
"The [particle in a box] model is among the simplest, exactly solvable models in quantum mechanics.\n",
"In the one-dimensional case, we assume a particle of mass $m$ to be confined into a box of length $L$.\n",
"The confinement is achieved by means of a potential energy operator that is zero inside the box and infinite outside, as in the Figure below.\n",
"\n",
"<figure>\n",
" <IMG SRC=\"gfx/Infinite_potential_well-en.svg\">\n",
"</figure>\n",
"\n",
"In practice, this means that the particle **cannot** escape the box: a result that we would have obtained also\n",
"from classical mechanics.\n",
"How does the quantum particle behave? We need to find the **eigenfunctions** and **eigenvalues** of the **Hamiltonian operator**, that is we have to solve the following ordinary differential equation:\n",
"\\begin{equation}\n",
"-\\frac{\\hbar^2}{2m}\\frac{\\mathrm{d}^2}{\\mathrm{d}x^2} \\psi_n(x) = E_n\\psi_n(x)\n",
"\\end{equation}\n",
"with **boundary conditions**:\n",
"\\begin{equation}\n",
"\\begin{aligned}\n",
" \\psi_n(0) &= 0 \\\\\n",
" \\psi_n(L) &= 0\n",
"\\end{aligned}\n",
"\\end{equation}\n",
"Thus acceptable solutions are of the form:\n",
"\\begin{equation}\n",
"\\psi_n(x) = \\sin(\\frac{n\\pi x}{L}) \\quad\\quad \\forall n \\neq 0\n",
"\\end{equation}\n",
"with energies:\n",
"\\begin{equation}\n",
"E_n = \\frac{h^2n^2}{8mL^2} \\quad\\quad \\forall n \\neq 0\n",
"\\end{equation}\n",
"\n",
"[particle in a box]: https://en.wikipedia.org/wiki/Particle_in_a_box"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Exercise 1: Normalization\n",
"\n",
"The wavefunction(s) given above as solution to the particle in a box problem are not normalized:\n",
"\\begin{equation}\n",
"\\left\\langle \\psi_n | \\psi_n \\right\\rangle = \\int \\mathrm{d}x \\psi_n^*(x)\\psi_n(x) = |A|^2 \\neq 1\n",
"\\end{equation}\n",
"Find the normalization constant."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Exercise 2: Ground-state and probabilities\n",
"\n",
"Given the **normalized** ground-state wavefunction for a particle confined to a one-dimensional box of length $L$, suppose the box is $10.0\\,\\,\\mathrm{nm}$ long. What is the probability that the particle is:\n",
"1. Between $a = 4.95\\,\\,\\mathrm{nm}$ and $b=5.05\\,\\,\\mathrm{nm}$\n",
"2. Between $a = 1.95\\,\\,\\mathrm{nm}$ and $b=2.05\\,\\,\\mathrm{nm}$\n",
"3. Between $a = 9.90\\,\\,\\mathrm{nm}$ and $b=10.0\\,\\,\\mathrm{nm}$\n",
"4. In the right half of the box\n",
"5. In the central third of the box\n",
"\n",
"How can we generalize to the excited states?"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Exercise 3: Eigenfunctions and probability distributions\n",
"\n",
"Write a Python function to plot the eigenfunctions of the particle in a box model. The function should accept the quantum number $n$, the length of the box $L$ and a NumPy array of $x$ values as arguments:\n",
"\n",
"```Python\n",
"def eigenfunction1D(n, L, x):\n",
" \"\"\" Normalized eigenfunction for the 1D particle in a box.\n",
" \n",
" n -- the quantum number\n",
" L -- the size of the box\n",
" x -- the NumPy array with the x values\n",
" \"\"\"\n",
"```\n",
"Once this function is defined, we can obtain the respective probability distribution by taking its square:\n",
"```Python\n",
"x = np.linspace(0, 10.0, 1000)\n",
"eig = eigenfunction1D(1, 10.0, x)\n",
"prob = eigenfunction1D(1, 10.0, x)**2\n",
"```\n",
"and plot both of them with:\n",
"```Python\n",
"plt.plot(x, eig)\n",
"plt.plot(x, prob)\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "NotImplementedError",
"evalue": "You need to write this function!",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNotImplementedError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-20-0680baa768c7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0meig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0meigenfunction1D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[0mprob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0meigenfunction1D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-20-0680baa768c7>\u001b[0m in \u001b[0;36meigenfunction1D\u001b[1;34m(n, L, x)\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;33m-\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mNumPy\u001b[0m \u001b[0marray\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mx\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \"\"\"\n\u001b[1;32m---> 13\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'You need to write this function!'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNotImplementedError\u001b[0m: You need to write this function!"
]
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"# make sure we see it on this notebook\n",
"%matplotlib inline\n",
"\n",
"def eigenfunction1D(n, L, x):\n",
" \"\"\" Normalized eigenfunction for the 1D particle in a box.\n",
" \n",
" n -- the quantum number\n",
" L -- the size of the box\n",
" x -- the NumPy array with the x values\n",
" \"\"\"\n",
" raise NotImplementedError('You need to write this function!')\n",
"\n",
"\n",
"x = np.linspace(0, 10.0, 1000)\n",
"eig = eigenfunction1D(1, 10.0, x)\n",
"prob = eigenfunction1D(1, 10.0, x)**2\n",
"\n",
"plt.plot(x, eig)\n",
"plt.plot(x, prob)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Exercise 4: Normalization of linear combinations of eigenfunctions\n",
"\n",
"A linear combination is defined as:\n",
"\\begin{equation}\n",
"\\Psi(x) = \\sum_{i=0}^{N} c_i \\psi_i(x)\n",
"\\end{equation}\n",
"where $c_i$ are the coefficients and $\\psi_i(x)$ are the eigenfunctions of the particle in a box.\n",
"Is $\\Psi(x)$ normalized? If not, how can we normalize it? Write a Python function that returns the normalization constant given a vector of coefficients $c_i$ in the linear combination.\n",
"\n",
"```Python\n",
"def normalize(coeffs):\n",
" \"\"\" Normalization constant for a linear combination of 1D particle in a box eigenfunctions\n",
" \n",
" coeffs -- a NumPy array with the coefficients\n",
" \"\"\"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "NotImplementedError",
"evalue": "You need to write this function!",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNotImplementedError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-19-b1e8fecf6838>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[0mcoeffs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mnormalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcoeffs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m<ipython-input-19-b1e8fecf6838>\u001b[0m in \u001b[0;36mnormalize\u001b[1;34m(coeffs)\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mcoeffs\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;33m-\u001b[0m \u001b[0ma\u001b[0m \u001b[0mNumPy\u001b[0m \u001b[0marray\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcoefficients\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \"\"\"\n\u001b[1;32m---> 11\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'You need to write this function!'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNotImplementedError\u001b[0m: You need to write this function!"
]
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"# make sure we see it on this notebook\n",
"%matplotlib inline\n",
"\n",
"def normalize(coeffs):\n",
" \"\"\" Normalization constant for a linear combination of 1D particle in a box eigenfunctions\n",
"\n",
" coeffs -- a NumPy array with the coefficients\n",
" \"\"\"\n",
" raise NotImplementedError('You need to write this function!')\n",
" \n",
" \n",
"coeffs = np.array([0.5, 0.5])\n",
"normalize(coeffs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Exercise 5: Energy of linear combinations\n",
"\n",
"The **expectation value** of an operator $O$ on the wavefunction $\\Psi$ is defined as:\n",
"\\begin{equation}\n",
"\\bar{O} = \\frac{\\langle\\Psi|O|\\Psi\\rangle}{\\langle\\Psi|\\Psi\\rangle} = \\langle\\Psi|O|\\Psi\\rangle\n",
"\\end{equation}\n",
"where the last equality holds only if $\\Psi$ is normalized.\n",
"The energy is the expectation value of the Hamiltonian operator:\n",
"\\begin{equation}\n",
"\\bar{H} = E = \\langle\\Psi|H|\\Psi\\rangle\n",
"\\end{equation}\n",
"\n",
"Given $\\Psi$ a linear combination of particle in a box eigenfunctions, calculate its respective energy.\n",
"**Hint**: remember that $H\\psi_i(x) = E_i\\psi_i(x)$ and that the $\\psi_i(x)$ are normalizd. You can also assume $\\Psi$ to be already normalized.\n",
"\n",
"Write a Python function to calculate the energy of such linear combinations. The function should accept the coefficients $c_i$ of the linear combination, the mass $M$ of the particle and the length $L$ of the box as arguments.\n",
"\n",
"```Python\n",
"def energy(coeffs, M, L):\n",
" \"\"\" Return energy of a linear combination of 1D particle in a box eigenfunctions.\n",
"\n",
" coeffs -- the coefficients of the linear combination\n",
" M -- the mass of the particle\n",
" L -- the size of the box\n",
" \"\"\"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "NotImplementedError",
"evalue": "You have to write this function",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNotImplementedError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-28-e1ce043dc3f2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[0mcoeffs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0menergy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcoeffs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m<ipython-input-28-e1ce043dc3f2>\u001b[0m in \u001b[0;36menergy\u001b[1;34m(coeffs, M, L)\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mL\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;33m-\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msize\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \"\"\"\n\u001b[1;32m----> 8\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'You have to write this function'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[0mcoeffs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNotImplementedError\u001b[0m: You have to write this function"
]
}
],
"source": [
"def energy(coeffs, M, L):\n",
" \"\"\" Return energy of a linear combination of 1D particle in a box eigenfunctions.\n",
"\n",
" coeffs -- the coefficients of the linear combination\n",
" M -- the mass of the particle\n",
" L -- the size of the box\n",
" \"\"\"\n",
" raise NotImplementedError('You have to write this function')\n",
"\n",
"coeffs = np.array([0.5, 0.5])\n",
"energy(coeffs, 1.0, 10.0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Exercise 6: Linear combinations of eigenfunctions\n",
"\n",
"We can now try to plot linear combinations of eigenfuntions and their corresponding probability densities.\n",
"Define the appropriate function to do so. Your function should use your previous implementation of `eigenfunction1D` to achieve its purpose."
]
}
],
"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 |
CognitiveBuilder/HelloCognitiveWorld | code/WatsonToneAnalyzerDSX.ipynb | 1 | 27191 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import json\n",
"from watson_developer_cloud import ToneAnalyzerV3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ask for credentials"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"conversation username:\n",
"········\n",
"conversation password:\n",
"········\n"
]
}
],
"source": [
"# not the best approach here but it will do\n",
"import getpass\n",
"print(\"conversation username:\")\n",
"username = getpass.getpass()\n",
"\n",
"print(\"conversation password:\")\n",
"password = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a ToneAnalyzer connector object"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tone_analyzer = ToneAnalyzerV3(\n",
" username=username,\n",
" password=password,\n",
" version='2016-05-19')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Function for parsing ToneAnalyzer JSON response"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def parse_toneanalyzer_response(json_data):\n",
" \"\"\"Parses the JSON response from ToneAnalyzer to return\n",
" a dictionary of emotions and their corresponding score.\n",
"\n",
" Parameters\n",
" ----------\n",
" json_data: {dict} a json response from ToneAnalyzer (see Notes)\n",
"\n",
" Returns\n",
" -------\n",
" dict : a {dict} whose keys are emotion ids and values are their corresponding score.\n",
"\n",
" Notes\n",
" -----\n",
" for an example of json see type pytest_data/tones_1.json\n",
" \"\"\"\n",
" emotions = {}\n",
" for entry in json_data['document_tone']['tone_categories']:\n",
" if entry['category_id'] == 'emotion_tone':\n",
" for emotion in entry['tones']:\n",
" emotion_key = emotion['tone_name']\n",
" emotion_value = emotion['score']\n",
" emotions[emotion_key] = emotion_value\n",
" return(emotions)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{u'Anger': 0.118448, u'Joy': 0.666785, u'Fear': 0.044258, u'Sadness': 0.099872, u'Disgust': 0.090883}\n"
]
}
],
"source": [
"json_response = tone_analyzer.tone(text=\"I'm so pumped up by rocking that whole demo!\")\n",
"print(parse_toneanalyzer_response(json_response))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load `textbooks.csv` into a pandas dataframe"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>asin</th>\n",
" <th>reviewText</th>\n",
" <th>title</th>\n",
" <th>price</th>\n",
" </tr>\n",
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" <tbody>\n",
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" <th>0</th>\n",
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"3 3 0070428077 This book serves as an excellent introduction ... \n",
"4 4 0070428077 Intelligent machines can be characterized, at ... \n",
"\n",
" title price \n",
"0 Machine Learning 50.27 \n",
"1 Machine Learning 50.27 \n",
"2 Machine Learning 50.27 \n",
"3 Machine Learning 50.27 \n",
"4 Machine Learning 50.27 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from io import StringIO\n",
"import requests\n",
"import json\n",
"import pandas as pd\n",
"\n",
"# @hidden_cell\n",
"# This function accesses a file in your Object Storage. The definition contains your credentials.\n",
"# You might want to remove those credentials before you share your notebook.\n",
"def get_object_storage_file_with_credentials_ed0baafd744e4452b0ef8e582e0f83a3(container, filename):\n",
" \"\"\"This functions returns a StringIO object containing\n",
" the file content from Bluemix Object Storage.\"\"\"\n",
"\n",
" url1 = ''.join(['https://identity.open.softlayer.com', '/v3/auth/tokens'])\n",
" data = {'auth': {'identity': {'methods': ['password'],\n",
" 'password': {'user': {'name': 'member_536659497418808f634cd60a91b6c12706d9d46a','domain': {'id': 'c00ac61e4791413396fb2d1701473203'},\n",
" 'password': 'u1a4)e~Do#-PEyj#'}}}}}\n",
" headers1 = {'Content-Type': 'application/json'}\n",
" resp1 = requests.post(url=url1, data=json.dumps(data), headers=headers1)\n",
" resp1_body = resp1.json()\n",
" for e1 in resp1_body['token']['catalog']:\n",
" if(e1['type']=='object-store'):\n",
" for e2 in e1['endpoints']:\n",
" if(e2['interface']=='public'and e2['region']=='dallas'):\n",
" url2 = ''.join([e2['url'],'/', container, '/', filename])\n",
" s_subject_token = resp1.headers['x-subject-token']\n",
" headers2 = {'X-Auth-Token': s_subject_token, 'accept': 'application/json'}\n",
" resp2 = requests.get(url=url2, headers=headers2)\n",
" return StringIO(resp2.text)\n",
"\n",
"df_data_1 = pd.read_csv(get_object_storage_file_with_credentials_ed0baafd744e4452b0ef8e582e0f83a3('WatsonTest', 'textbook_reviews_formatted.csv'))\n",
"df_data_1.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Process all reviews via ToneAnalyzer"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_map = {}\n",
"\n",
"for index,row in df_data_1.iterrows():\n",
" #print(row['reviewText'])\n",
" response = tone_analyzer.tone(text=row['reviewText'])\n",
" data_map[row[0]] = parse_toneanalyzer_response(response)\n",
" #print(data_map[row[0]])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_data_1['joy'] = df_data_1['Unnamed: 0'].map(lambda x : data_map[x].get('Joy',0.0))\n",
"df_data_1['anger'] = df_data_1['Unnamed: 0'].map(lambda x : data_map[x].get('Anger',0.0))\n",
"df_data_1['fear'] = df_data_1['Unnamed: 0'].map(lambda x : data_map[x].get('Fear',0.0))\n",
"df_data_1['sadness'] = df_data_1['Unnamed: 0'].map(lambda x : data_map[x].get('Sadness',0.0))\n",
"df_data_1['disgust'] = df_data_1['Unnamed: 0'].map(lambda x : data_map[x].get('Disgust',0.0))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td>0.096077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111844714X</th>\n",
" <td>181.5</td>\n",
" <td>113.76</td>\n",
" <td>0.411284</td>\n",
" <td>0.182884</td>\n",
" <td>0.108055</td>\n",
" <td>0.290764</td>\n",
" <td>0.048933</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0123747651</th>\n",
" <td>38.0</td>\n",
" <td>26.75</td>\n",
" <td>0.407968</td>\n",
" <td>0.092021</td>\n",
" <td>0.149078</td>\n",
" <td>0.312175</td>\n",
" <td>0.062388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0321246268</th>\n",
" <td>94.0</td>\n",
" <td>20.56</td>\n",
" <td>0.402369</td>\n",
" <td>0.246772</td>\n",
" <td>0.076671</td>\n",
" <td>0.528781</td>\n",
" <td>0.098681</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1441998896</th>\n",
" <td>211.5</td>\n",
" <td>30.40</td>\n",
" <td>0.396361</td>\n",
" <td>0.138575</td>\n",
" <td>0.090078</td>\n",
" <td>0.261942</td>\n",
" <td>0.101475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1449303714</th>\n",
" <td>219.0</td>\n",
" <td>40.27</td>\n",
" <td>0.391862</td>\n",
" <td>0.195448</td>\n",
" <td>0.081838</td>\n",
" <td>0.471926</td>\n",
" <td>0.053419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1449361323</th>\n",
" <td>242.5</td>\n",
" <td>31.89</td>\n",
" <td>0.381402</td>\n",
" <td>0.095837</td>\n",
" <td>0.081755</td>\n",
" <td>0.309667</td>\n",
" <td>0.073352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>026201243X</th>\n",
" <td>78.0</td>\n",
" <td>48.12</td>\n",
" <td>0.351389</td>\n",
" <td>0.222803</td>\n",
" <td>0.070772</td>\n",
" <td>0.324663</td>\n",
" <td>0.058890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0470526823</th>\n",
" <td>143.0</td>\n",
" <td>28.26</td>\n",
" <td>0.338254</td>\n",
" <td>0.176769</td>\n",
" <td>0.079936</td>\n",
" <td>0.349070</td>\n",
" <td>0.057814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0071344446</th>\n",
" <td>23.0</td>\n",
" <td>30.39</td>\n",
" <td>0.331174</td>\n",
" <td>0.102919</td>\n",
" <td>0.073207</td>\n",
" <td>0.378181</td>\n",
" <td>0.071350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0262013193</th>\n",
" <td>83.5</td>\n",
" <td>38.58</td>\n",
" <td>0.326654</td>\n",
" <td>0.137201</td>\n",
" <td>0.100862</td>\n",
" <td>0.251379</td>\n",
" <td>0.093282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0123814790</th>\n",
" <td>63.0</td>\n",
" <td>22.50</td>\n",
" <td>0.325618</td>\n",
" <td>0.085292</td>\n",
" <td>0.072206</td>\n",
" <td>0.267864</td>\n",
" <td>0.046464</td>\n",
" </tr>\n",
" <tr>\n",
" <th>061572499X</th>\n",
" <td>155.0</td>\n",
" <td>2.99</td>\n",
" <td>0.324619</td>\n",
" <td>0.210164</td>\n",
" <td>0.147587</td>\n",
" <td>0.366334</td>\n",
" <td>0.137660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111866146X</th>\n",
" <td>192.5</td>\n",
" <td>26.77</td>\n",
" <td>0.286817</td>\n",
" <td>0.154239</td>\n",
" <td>0.094443</td>\n",
" <td>0.278869</td>\n",
" <td>0.097225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0750676132</th>\n",
" <td>161.5</td>\n",
" <td>27.85</td>\n",
" <td>0.247697</td>\n",
" <td>0.125717</td>\n",
" <td>0.163543</td>\n",
" <td>0.262248</td>\n",
" <td>0.114711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0615684378</th>\n",
" <td>148.5</td>\n",
" <td>23.36</td>\n",
" <td>0.168138</td>\n",
" <td>0.108297</td>\n",
" <td>0.068295</td>\n",
" <td>0.401975</td>\n",
" <td>0.025670</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 price joy anger fear sadness \\\n",
"asin \n",
"0262193981 89.0 58.49 0.602187 0.103348 0.050392 0.339367 \n",
"1461471370 267.0 56.47 0.592355 0.075750 0.041199 0.184450 \n",
"1617290181 296.0 29.48 0.580712 0.118559 0.062267 0.266279 \n",
"1782161406 309.5 18.49 0.558162 0.108174 0.094054 0.437589 \n",
"0321321367 100.0 59.07 0.556551 0.133806 0.186068 0.294485 \n",
"0387952845 136.0 18.60 0.532165 0.109921 0.081381 0.234393 \n",
"1449367615 257.5 31.20 0.516575 0.138582 0.054572 0.255586 \n",
"1420067184 199.5 40.50 0.514658 0.123667 0.084937 0.456737 \n",
"0387310738 114.0 25.60 0.508823 0.143709 0.107897 0.336323 \n",
"0387848576 128.0 45.45 0.494179 0.092361 0.080211 0.206236 \n",
"0123748569 49.5 20.99 0.482101 0.085481 0.066819 0.260143 \n",
"1449358659 228.0 26.67 0.468830 0.095979 0.082347 0.458306 \n",
"0201157675 71.5 15.53 0.455495 0.074267 0.051183 0.181266 \n",
"0070428077 10.0 50.27 0.453624 0.119111 0.074131 0.263882 \n",
"1479324183 281.0 2.99 0.450030 0.082509 0.088774 0.276648 \n",
"0120884070 30.5 13.85 0.444960 0.084643 0.081928 0.291862 \n",
"1782162143 316.5 20.44 0.443479 0.131125 0.060903 0.276250 \n",
"1617291560 304.0 42.49 0.436357 0.093300 0.065419 0.274450 \n",
"1439810184 205.5 34.00 0.422965 0.166792 0.042912 0.156668 \n",
"1107422221 169.5 120.65 0.420399 0.108897 0.085122 0.252813 \n",
"111844714X 181.5 113.76 0.411284 0.182884 0.108055 0.290764 \n",
"0123747651 38.0 26.75 0.407968 0.092021 0.149078 0.312175 \n",
"0321246268 94.0 20.56 0.402369 0.246772 0.076671 0.528781 \n",
"1441998896 211.5 30.40 0.396361 0.138575 0.090078 0.261942 \n",
"1449303714 219.0 40.27 0.391862 0.195448 0.081838 0.471926 \n",
"1449361323 242.5 31.89 0.381402 0.095837 0.081755 0.309667 \n",
"026201243X 78.0 48.12 0.351389 0.222803 0.070772 0.324663 \n",
"0470526823 143.0 28.26 0.338254 0.176769 0.079936 0.349070 \n",
"0071344446 23.0 30.39 0.331174 0.102919 0.073207 0.378181 \n",
"0262013193 83.5 38.58 0.326654 0.137201 0.100862 0.251379 \n",
"0123814790 63.0 22.50 0.325618 0.085292 0.072206 0.267864 \n",
"061572499X 155.0 2.99 0.324619 0.210164 0.147587 0.366334 \n",
"111866146X 192.5 26.77 0.286817 0.154239 0.094443 0.278869 \n",
"0750676132 161.5 27.85 0.247697 0.125717 0.163543 0.262248 \n",
"0615684378 148.5 23.36 0.168138 0.108297 0.068295 0.401975 \n",
"\n",
" disgust \n",
"asin \n",
"0262193981 0.038366 \n",
"1461471370 0.040237 \n",
"1617290181 0.031640 \n",
"1782161406 0.061358 \n",
"0321321367 0.101630 \n",
"0387952845 0.053619 \n",
"1449367615 0.038545 \n",
"1420067184 0.066764 \n",
"0387310738 0.057385 \n",
"0387848576 0.055056 \n",
"0123748569 0.049576 \n",
"1449358659 0.049835 \n",
"0201157675 0.052204 \n",
"0070428077 0.047276 \n",
"1479324183 0.060684 \n",
"0120884070 0.069567 \n",
"1782162143 0.034436 \n",
"1617291560 0.040101 \n",
"1439810184 0.057470 \n",
"1107422221 0.096077 \n",
"111844714X 0.048933 \n",
"0123747651 0.062388 \n",
"0321246268 0.098681 \n",
"1441998896 0.101475 \n",
"1449303714 0.053419 \n",
"1449361323 0.073352 \n",
"026201243X 0.058890 \n",
"0470526823 0.057814 \n",
"0071344446 0.071350 \n",
"0262013193 0.093282 \n",
"0123814790 0.046464 \n",
"061572499X 0.137660 \n",
"111866146X 0.097225 \n",
"0750676132 0.114711 \n",
"0615684378 0.025670 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_data_1.groupby('asin').mean().sort_values(by='joy', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2 with Spark 2.1",
"language": "python",
"name": "python2-spark21"
},
"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 |
radovankavicky/PyDataBerlin2017 | present/PyData Berlin 2017 (presentation).ipynb | 1 | 20936 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "5405a770-262e-4bfd-ade8-061d9fb82c05"
}
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"nbpresent": {
"id": "e45f0568-3df8-43bc-8928-424404d2b30e"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sbn\n",
"import bokeh as bok\n",
"import plotly as plot"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"nbpresent": {
"id": "09dde386-d226-4de6-beb3-5ad2216d6a35"
}
},
"outputs": [],
"source": [
"# Customization\n",
"sbn.set() # matplotlib defaults\n",
"# Any Tweeks that normally go in .matplotlibrc, etc., should explicitly go here\n",
"plt.rcParams['figure.figsize'] = (12, 8)\n",
"%config InlineBackend.figure_format='retina'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "7afd62dd-0f35-4cbb-8e3f-7b8ab2c6f105"
}
},
"outputs": [],
"source": [
"from IPython.display import IFrame"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"nbpresent": {
"id": "d0da6701-79a5-4119-8349-0580487648d2"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"700\"\n",
" height=\"350\"\n",
" src=\"http://www.nbs.sk/en/home\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x13a066f128>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"IFrame('http://www.nbs.sk/en/home', width=700, height=350)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "bb7e0b03-a65c-4d72-9d96-a12c5345196e"
}
},
"outputs": [],
"source": [
"dframe= pd.read_csv('macrostat.csv')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"nbpresent": {
"id": "039533c2-63ba-4a92-bd24-eaec1d5a068e"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Q/Time;GDP Expenditure method</th>\n",
" <th>GDP - Expenditure method</th>\n",
" <th>Constant prices</th>\n",
" <th>SA</th>\n",
" <th>GDP - Gross domestic product</th>\n",
" <th>[SK</th>\n",
" <th>r</th>\n",
" <th>sa</th>\n",
" <th>mil. Eur]</th>\n",
" <th>SO SR</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1Q1993;7687.3711884008</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <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>1</th>\n",
" <td>2Q1993;7687.3711884008</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <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>2</th>\n",
" <td>3Q1993;7794.2256479195</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <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>3</th>\n",
" <td>4Q1993;7884.6386654354</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <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>4</th>\n",
" <td>1Q1994;8003.6967092835</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <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",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Q/Time;GDP Expenditure method GDP - Expenditure method Constant prices \\\n",
"0 1Q1993;7687.3711884008 NaN NaN \n",
"1 2Q1993;7687.3711884008 NaN NaN \n",
"2 3Q1993;7794.2256479195 NaN NaN \n",
"3 4Q1993;7884.6386654354 NaN NaN \n",
"4 1Q1994;8003.6967092835 NaN NaN \n",
"\n",
" SA GDP - Gross domestic product [SK r sa mil. Eur] SO SR \n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN NaN NaN "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Tip use .head()\n",
"dframe.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"nbpresent": {
"id": "795de9c4-f5c8-45b8-87af-6fb98ed259f9"
}
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'DataFrame' object has no attribute 'r'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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| mit |
ivanslapnicar/FESBMat | src/PageRank.ipynb | 1 | 44101 | {
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"# PageRank\n",
"\n",
"Prema članku [C, Moler, Google PageRank][Mol11].\n",
"\n",
"\n",
"[Mol11]: https://www.mathworks.com/moler/exm/chapters/pagerank.pdf \"C, Moler, 'Google PageRank', mathWorks, 2011.\"\n",
"\n",
"Algoritam koristi teoriju grafova i linearnu algebru."
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"Graf $\\mathcal{G}$ ima $n$ čvorova i $m$ grana. \n",
"\n",
"Matrica susjedstva $G$ grafa $\\mathcal{G}$ je definirana s\n",
"\n",
"$$\n",
"g_{ij}=\\begin{cases} 1, \\text{ako postoji grana iz čvora $j$ u čvor $i$},\\\\\n",
"0, \\text{inače} \\end{cases}\n",
"$$\n",
"\n",
"Na primjer, neka je zadan graf $\\mathcal{G}$"
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"using Graphs\n",
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"source": [
"G=simple_graph(3,is_directed=true)\n",
"add_edge!(G,1,2)\n",
"add_edge!(G,1,3)\n",
"add_edge!(G,2,3)\n",
"GraphViz(to_dot(G),\"neato\",\"svg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matrica susjedstva je \n",
"\n",
"$$\n",
"G=\\begin{bmatrix} 0 & 0 & 0 \\\\ 1 & 0 & 0\\\\ 1& 1& 0\\end{bmatrix}\n",
"$$\n",
"\n",
"Definirajmo vektor $e_i$ kao $i$-ti stupac jedinične matrice. Neka je $x=G \\cdot e_i$.\n",
"Tada je\n",
"\n",
"$$\n",
"x_{j}=\\begin{cases} 1, \\text{ako postoji grana iz čvora $i$ u čvor $j$},\\\\\n",
"0, \\text{inače} \\end{cases}\n",
"$$\n",
"\n",
"Na primjer (objasnite zašto):\n",
"\n",
"$$\n",
"G\\cdot e_1 = \\begin{bmatrix} 0 & 0 & 0 \\\\ 1 & 0 & 0\\\\ 1& 1& 0\\end{bmatrix} \\cdot\n",
"\\begin{bmatrix}1 \\\\ 0 \\\\ 0\\end{bmatrix} = \\begin{bmatrix} 0 \\\\ 1 \\\\ 1 \\end{bmatrix}, \\quad\n",
"G\\cdot e_2 = \\begin{bmatrix} 0 & 0 & 0 \\\\ 1 & 0 & 0\\\\ 1& 1& 0\\end{bmatrix} \\cdot\n",
"\\begin{bmatrix}0 \\\\ 1 \\\\ 0\\end{bmatrix} = \\begin{bmatrix} 0 \\\\ 0 \\\\ 1 \\end{bmatrix}, \\\\\n",
"G\\cdot e_3 = \\begin{bmatrix} 0 & 0 & 0 \\\\ 1 & 0 & 0\\\\ 1& 1& 0\\end{bmatrix} \\cdot\n",
"\\begin{bmatrix}0 \\\\ 0 \\\\ 1\\end{bmatrix} = \\begin{bmatrix} 0 \\\\ 0 \\\\ 0 \\end{bmatrix}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Graf web-a\n",
"\n",
"Zamislimo web sa šest stranica povezanih linkovima kao na slici:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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"text/plain": [
"IJuliaPortrayals.GraphViz(\"digraph graphname {\\n1\\n2\\n3\\n4\\n5\\n6\\n1 -> 2\\n1 -> 6\\n2 -> 3\\n2 -> 4\\n3 -> 4\\n3 -> 5\\n3 -> 6\\n4 -> 1\\n6 -> 1\\n}\\n\",\"fdp\",\"svg\")"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"G=simple_graph(6,is_directed=true)\n",
"add_edge!(G,1,2)\n",
"add_edge!(G,1,6)\n",
"add_edge!(G,2,3)\n",
"add_edge!(G,2,4)\n",
"add_edge!(G,3,4)\n",
"add_edge!(G,3,5)\n",
"add_edge!(G,3,6)\n",
"add_edge!(G,4,1)\n",
"add_edge!(G,6,1)\n",
"GraphViz(to_dot(G),\"fdp\",\"svg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Koristit ćemo `sparse` zapis matrice susjedstva u kojem se zapisuju samo indeksi retka i stupca i vrijednost elementa."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"nbpresent": {
"id": "339964f2-eba2-40c6-bc72-08687a1d486e"
}
},
"outputs": [
{
"data": {
"text/plain": [
"6×6 sparse matrix with 9 Int64 nonzero entries:\n",
"\t[2, 1] = 1\n",
"\t[6, 1] = 1\n",
"\t[3, 2] = 1\n",
"\t[4, 2] = 1\n",
"\t[4, 3] = 1\n",
"\t[5, 3] = 1\n",
"\t[6, 3] = 1\n",
"\t[1, 4] = 1\n",
"\t[1, 6] = 1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i = vec([ 2 6 3 4 4 5 6 1 1])\n",
"j = vec([ 1 1 2 2 3 3 3 4 6])\n",
"G=sparse(i,j,1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"nbpresent": {
"id": "a61a812c-8380-4789-9685-819fdb925442"
}
},
"outputs": [
{
"data": {
"text/plain": [
"6×6 Array{Int64,2}:\n",
" 0 0 0 1 0 1\n",
" 1 0 0 0 0 0\n",
" 0 1 0 0 0 0\n",
" 0 1 1 0 0 0\n",
" 0 0 1 0 0 0\n",
" 1 0 1 0 0 0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full(G)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6×6 Array{Float64,2}:\n",
" 0.0 0.0 0.0 1.0 0.0 1.0\n",
" 1.0 0.0 0.0 0.0 0.0 0.0\n",
" 0.0 1.0 0.0 0.0 0.0 0.0\n",
" 0.0 1.0 1.0 0.0 0.0 0.0\n",
" 0.0 0.0 1.0 0.0 0.0 0.0\n",
" 1.0 0.0 1.0 0.0 0.0 0.0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Prebacimo G u \"realne\" brojeve (Float64)\n",
"G=map(Float64,G)\n",
"full(G)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "303bcbc1-fb7d-41a5-9d65-e599645bcca6"
}
},
"source": [
"Od ove matrice se napravi matrica slučajne šetnje (_random walk_) na grafu sa sljedećim uvjetima:\n",
"\n",
"- vjerojatnost da pratimo neki od linkova je $p$,\n",
"- vjerojatnost da idemo na neku slučajno odabrano stranicu je $1-p$,\n",
"- ako pratimo neki od linkova, svaki izbor je jednako vjerojatan,\n",
"- ako idemo na neku slučajnu stranicu, svaki izbor je jednako vjerojatan.\n",
"\n",
"Google koristi $p=0.85$ ?"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6×6 Array{Float64,2}:\n",
" 0.0 0.0 0.0 1.0 0.0 1.0\n",
" 0.5 0.0 0.0 0.0 0.0 0.0\n",
" 0.0 0.5 0.0 0.0 0.0 0.0\n",
" 0.0 0.5 0.333333 0.0 0.0 0.0\n",
" 0.0 0.0 0.333333 0.0 0.0 0.0\n",
" 0.5 0.0 0.333333 0.0 0.0 0.0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Konačna sparse matrica G\n",
"p = 0.85\n",
"c=sum(G,1)\n",
"n=size(G,1)\n",
"for j=1:n\n",
" if c[j]>0\n",
" G[:,j]=G[:,j]/c[j]\n",
" end\n",
"end\n",
"full(G)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Neka je $e$ vektor sa svim elementima jednakim $1$, \n",
"\n",
"$$\n",
"e=\\begin{bmatrix} 1 \\\\ 1 \\\\ \\vdots \\\\ 1 \\end{bmatrix}.\n",
"$$\n",
"\n",
"Matrica $A$ je matrica slučajne šetnje po zadanom grafu, tzv. _Markovljeva matrica_:\n",
"\n",
"$$\n",
"A=pG+ e\\cdot z \\tag{1}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1×6 Array{Float64,2}:\n",
" 0.025 0.025 0.025 0.025 0.166667 0.025"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Definirajmo vektor z\n",
"ee=ones(n)\n",
"z = ((1-p)*(c.!=0) + (c.==0))/n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"nbpresent": {
"id": "799e14b7-f4a0-4f55-a8f8-fc821ce0fd93"
}
},
"outputs": [
{
"data": {
"text/plain": [
"6×6 Array{Float64,2}:\n",
" 0.025 0.025 0.025 0.875 0.166667 0.875\n",
" 0.45 0.025 0.025 0.025 0.166667 0.025\n",
" 0.025 0.45 0.025 0.025 0.166667 0.025\n",
" 0.025 0.45 0.308333 0.025 0.166667 0.025\n",
" 0.025 0.025 0.308333 0.025 0.166667 0.025\n",
" 0.45 0.025 0.308333 0.025 0.166667 0.025"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Definirajmo matricu A\n",
"A=p*G+ee*z"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matrica $A$ ima sljedeća svojstva:\n",
"\n",
"- $0<a_{ij}<1$, \n",
"- zbroj elemenata svakog stupca je $1$.\n",
"\n",
"Drugo svojstvo možemo zapisati kao \n",
"\n",
"$$\n",
"e^T A = e^T\n",
"$$\n",
"\n",
"odnosno\n",
"\n",
"$$\n",
"e^T (I-A)=0.\n",
"$$\n",
"\n",
"Transponiranje jednadžbe daje \n",
"\n",
"$$(I-A^T) e=0.$$\n",
"\n",
"Radi se o homogenom sustavu koji ima netrivijalno rješenje pa iz\n",
"Kronecker-Capelli-jevog teorema slijedi \n",
"\n",
"$$\\mathrm{\\mathop{rang}}\\, (I-A^T)<n.$$ \n",
"\n",
"No, tada je i \n",
"\n",
"$$\\mathrm{\\mathop{rang}}\\, (I-A)<n$$\n",
"\n",
"pa i homogeni sustav \n",
"\n",
"$$(I-A)x=0 \\tag{2}\n",
"$$\n",
"\n",
"ima netrivijalno rješenje za koje vrijedi \n",
"\n",
"$$\n",
"Ax=x. \\tag{3}\n",
"$$\n",
"\n",
"Vektor $x$ s ovim svojstvom zove se _vektor stanja_ Markovljeve matrice. \n",
"Vrijedi $x_i>0$, a $x$ možemo odabrati tako da je $\\sum x_i=1$.\n",
"Naime, ako je $Ax=x$, onda je i $A(\\alpha x)=\\alpha x$.\n",
"\n",
"> Za tako odabrani $x$, element $x_i$ je __težina__ ili __rang__ stranice $i$.\n",
"\n",
"> Težina stranice daje vjerojatnost da se tijekom šetnje po webu posjeti stranica $i$ pa su stranice s većom težinom važnije. \n",
"\n",
"Homogeni sustav (2), odnosno jednadžbu (3), možemo riješiti na nekoliko načina."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Uvrštavanjem oblika (1) u jednadžbu (3) dobivamo\n",
"\n",
"$$\n",
"(pG + e \\cdot z)x=I\\cdot x\n",
"$$\n",
"\n",
"odnosno\n",
"\n",
"$$\n",
"(I-pG)x=e(z\\cdot x)\\equiv \\gamma e. \\tag{4}\n",
"$$\n",
"\n",
"$\\gamma=z\\cdot x$ je nepoznati broj pa uzmimo $\\gamma=1$.\n",
"\n",
"Ako je $p<1$, onda je matrica $I-pG$ regularna (dokaz preskačemo) pa sustav (4) ima jedinstveno rješenje $x$. PageRank vektor je onda vektor\n",
"\n",
"$$\n",
"\\frac{x}{\\sum x_i}.\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6-element Array{Float64,1}:\n",
" 9.411 \n",
" 4.99967\n",
" 3.12486\n",
" 4.01024\n",
" 1.88538\n",
" 5.88505"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x=(I-p*G)\\ee"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6-element Array{Float64,1}:\n",
" 0.321017 \n",
" 0.170543 \n",
" 0.106592 \n",
" 0.136793 \n",
" 0.0643118\n",
" 0.200744 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x/=sum(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Možemo i pomnožiti $e$ s inverznom matricom (ne preporuča se):\n",
"\n",
"$$\n",
"y=(I-pG)^{-1} e \\\\\n",
"x=\\frac{y}{\\sum y_i}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6-element Array{Float64,1}:\n",
" 0.321017 \n",
" 0.170543 \n",
" 0.106592 \n",
" 0.136793 \n",
" 0.0643118\n",
" 0.200744 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"M=inv(full(I-p*G))\n",
"x=M*ee\n",
"x/sum(x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "96822e37-d2f5-4503-9655-e6040556107e"
}
},
"source": [
"Ni jedan od prethodnih načina nije moguć za jako velike matrice. Umjesto toga, rješenje jednadžbe $Ax=x$ se može dobiti iterativnom metodom na sljedeći način:\n",
"\n",
"- krenimo u slučajnu šetnju od početnog vektora $x_0=\\begin{bmatrix} 1/n \\\\ 1/n \\\\ \\vdots \\\\ 1/n \\end{bmatrix}$ (možemo uzeti i prethodni vektor težina).\n",
"\n",
"- formiramo vektore \n",
"\n",
"$$\n",
"x_1=A\\cdot x_0 \\\\\n",
"x_2=A\\cdot x_1 \\\\\n",
"x_3=A\\cdot x_2\\\\\n",
"\\vdots\n",
"$$\n",
"\n",
"- stanemo kada se vektor $x$ stabilizira, odnosno kada je \n",
"\n",
"$$\n",
"A\\cdot x\\approx x.\n",
"$$\n",
"\n",
"- izračunamo konačni PageRank vektor $\\displaystyle\\frac{x}{\\sum x_i}$.\n",
"\n",
"Matrica $A$ se nikad ne formira već se umnožak $A$ računa kao \n",
"\n",
"$$\n",
"Ax=(pG)\\cdot x + e \\cdot (z\\cdot x).\n",
"$$"
]
},
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"data": {
"text/plain": [
"myPageRank (generic function with 1 method)"
]
},
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"metadata": {},
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"source": [
"function myPageRank(G::SparseMatrixCSC{Float64,Int64},steps::Int)\n",
" p=0.85\n",
" c=sum(G,1)/p\n",
" n=size(G,1)\n",
" for i=1:n\n",
" G.nzval[G.colptr[i]:G.colptr[i+1]-1]./=c[i]\n",
" end\n",
" e=ones(n)\n",
" x=e/n\n",
" z = vec(((1-p)*(c.!=0) + (c.==0))/n)\n",
" for j=1:steps\n",
" x=G*x+(z⋅x)\n",
" end\n",
" x/sum(x)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
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"id": "f0505634-c456-47dd-a4fe-0a6a86388b81"
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"outputs": [
{
"data": {
"text/plain": [
"6-element Array{Float64,1}:\n",
" 0.321024 \n",
" 0.170538 \n",
" 0.106596 \n",
" 0.136795 \n",
" 0.0643103\n",
" 0.200737 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"myPageRank(G,15)"
]
},
{
"cell_type": "markdown",
"metadata": {
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"id": "c9bf7ecc-5c51-4cb3-9747-dd4bda11560f"
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},
"source": [
"### [Stanford web graph](http://snap.stanford.edu/data/web-Stanford.html)\n",
"\n",
"Riješimo malo veći testni problem."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
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"id": "637112da-d944-4bce-a751-e8a95f9014a2"
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"outputs": [
{
"data": {
"text/plain": [
"2312497×2 Array{Int64,2}:\n",
" 1 6548\n",
" 1 15409\n",
" 6548 57031\n",
" 15409 13102\n",
" 2 17794\n",
" 2 25202\n",
" 2 53625\n",
" 2 54582\n",
" 2 64930\n",
" 2 73764\n",
" 2 84477\n",
" 2 98628\n",
" 2 100193\n",
" ⋮ \n",
" 281849 165189\n",
" 281849 177014\n",
" 281849 226290\n",
" 281849 243180\n",
" 281849 244195\n",
" 281849 247252\n",
" 281849 281568\n",
" 281865 186750\n",
" 281865 225872\n",
" 281888 114388\n",
" 281888 192969\n",
" 281888 233184"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"W=readdlm(\"web-Stanford.txt\",Int)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"nbpresent": {
"id": "cd7c4a66-da5a-4f42-8cd0-f4cff46120df"
}
},
"outputs": [
{
"data": {
"text/plain": [
"281903×281903 sparse matrix with 2312497 Float64 nonzero entries:\n",
"\t[6548 , 1] = 1.0\n",
"\t[15409 , 1] = 1.0\n",
"\t[17794 , 2] = 1.0\n",
"\t[25202 , 2] = 1.0\n",
"\t[53625 , 2] = 1.0\n",
"\t[54582 , 2] = 1.0\n",
"\t[64930 , 2] = 1.0\n",
"\t[73764 , 2] = 1.0\n",
"\t[84477 , 2] = 1.0\n",
"\t[98628 , 2] = 1.0\n",
"\t⋮\n",
"\t[168703, 281902] = 1.0\n",
"\t[180771, 281902] = 1.0\n",
"\t[266504, 281902] = 1.0\n",
"\t[275189, 281902] = 1.0\n",
"\t[44103 , 281903] = 1.0\n",
"\t[56088 , 281903] = 1.0\n",
"\t[90591 , 281903] = 1.0\n",
"\t[94440 , 281903] = 1.0\n",
"\t[216688, 281903] = 1.0\n",
"\t[256539, 281903] = 1.0\n",
"\t[260899, 281903] = 1.0"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"S=sparse(W[:,2],W[:,1],1.0)"
]
},
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{
"name": "stdout",
"output_type": "stream",
"text": [
" 1.891961 seconds (846.68 k allocations: 542.596 MB, 14.03% gc time)\n"
]
}
],
"source": [
"@time x100=myPageRank(S,100);"
]
},
{
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"execution_count": 20,
"metadata": {
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"id": "cc0d1a66-a69a-478f-8db8-e6a850725631"
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{
"data": {
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"2.3491380170668298e-7"
]
},
"execution_count": 20,
"metadata": {},
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],
"source": [
"x101=myPageRank(S,101)\n",
"maxabs((x100-x101)./x100)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
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{
"data": {
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"([89073,226411,241454,262860,134832,234704,136821,68889,105607,69358 … 281647,281700,281715,281728,281778,281785,281813,281849,281865,281888],[0.0113029,0.00926783,0.00829727,0.00302312,0.00300128,0.00257173,0.00245371,0.00243079,0.00239105,0.00236401 … 5.33369e-7,5.33369e-7,5.33369e-7,5.33369e-7,5.33369e-7,5.33369e-7,5.33369e-7,5.33369e-7,5.33369e-7,5.33369e-7])"
]
},
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],
"source": [
"sortperm(x100,rev=true), sort(x101,rev=true)"
]
},
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"font-family": "Montserrat",
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"color": "headingColor",
"font-family": "Montserrat",
"font-size": 3
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"color": "headingColor",
"font-family": "Montserrat"
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"h6": {
"color": "headingColor",
"font-family": "Montserrat"
},
"h7": {
"color": "headingColor",
"font-family": "Montserrat"
},
"li": {
"color": "mainColor",
"font-family": "Open Sans",
"font-size": 4
},
"p": {
"color": "mainColor",
"font-family": "Open Sans",
"font-size": 4
}
},
"text-base": {
"color": "mainColor",
"font-family": "Open Sans",
"font-size": 4
}
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"82f1c284-7368-467d-81a6-51fa90d6c991": {
"backgrounds": {
"backgroundColor": {
"background-color": "backgroundColor",
"id": "backgroundColor"
}
},
"id": "82f1c284-7368-467d-81a6-51fa90d6c991",
"palette": {
"backgroundColor": {
"id": "backgroundColor",
"rgb": [
17,
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]
},
"headingColor": {
"id": "headingColor",
"rgb": [
238,
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]
},
"linkColor": {
"id": "linkColor",
"rgb": [
231,
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]
},
"mainColor": {
"id": "mainColor",
"rgb": [
238,
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]
}
},
"rules": {
"a": {
"color": "linkColor"
},
"h1": {
"color": "headingColor",
"font-family": "Montserrat",
"font-size": 7
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"color": "headingColor",
"font-family": "Montserrat",
"font-size": 5
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"color": "headingColor",
"font-family": "Montserrat",
"font-size": 3.75
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"color": "headingColor",
"font-family": "Montserrat",
"font-size": 3
},
"h5": {
"color": "headingColor",
"font-family": "Montserrat"
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"h6": {
"color": "headingColor",
"font-family": "Montserrat"
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"color": "headingColor",
"font-family": "Montserrat"
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"li": {
"color": "mainColor",
"font-family": "Open Sans",
"font-size": 4
},
"p": {
"color": "mainColor",
"font-family": "Open Sans",
"font-size": 4
}
},
"text-base": {
"color": "mainColor",
"font-family": "Open Sans",
"font-size": 4
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}
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}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
ruxi/ruxi.github.io | notebooks/TODO.ipynb | 1 | 3205 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!date #last update"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Required:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os.path, gitpath #pip install git+'https://github.com/ruxi/python-gitpath.git'\n",
"os.chdir(gitpath.root()) # changes path to .git root\n",
"#os.getcwd() #check current work directory"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TODO"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"by Ruxi\n",
"\n",
"Feb 8, 2016"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tasks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- install jekyll\n",
"- create js folder\n",
"- make webapps.html"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Version control"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Saving"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"py_commit_msg = \"\"\"\n",
"templating py_commit_msg\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"templating py_commit_msg\n",
"[master 1e560fa] templating py_commit_msg\n",
" 2 files changed, 14 insertions(+), 10 deletions(-)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"To [email protected]:ruxi/ruxi.github.io.git\n",
" b295fba..1e560fa master -> master\n"
]
}
],
"source": [
"%%bash -s \"$py_commit_msg\"\n",
"echo $1\n",
"git add --all :/\n",
"git commit -a -m \"$1\" #message from py_commit_msg\n",
"git push origin master"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"http://github.com/ruxi/ruxi.github.io"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pulling"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"git fetch --all\n",
"git pull origin master\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python3.4",
"language": "python",
"name": "python3.4"
},
"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 |
tensorflow/docs-l10n | site/ko/tutorials/structured_data/preprocessing_layers.ipynb | 1 | 30017 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "zg02FZzDyEqd"
},
"source": [
"##### Copyright 2019 The TensorFlow Authors.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "2mapZ9afGJ69"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sMYQvJuBi7MS"
},
"source": [
"# Keras 전처리 레이어를 사용한 구조적 데이터 분류"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8FaL4wnr22oy"
},
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td><a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/structured_data/preprocessing_layers\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">TensorFlow.org에서 보기</a></td>\n",
" <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ko/tutorials/structured_data/preprocessing_layers.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Google Colab에서 실행</a></td>\n",
" <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ko/tutorials/structured_data/preprocessing_layers.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">GitHub에서 소스 보기</a></td>\n",
" <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ko/tutorials/structured_data/preprocessing_layers.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">노트북 다운로드</a></td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nna1tOKxyEqe"
},
"source": [
"이 튜토리얼에서는 구조적 데이터(예: CSV의 표 형식 데이터)를 분류하는 방법을 보여줍니다. [Keras](https://www.tensorflow.org/guide/keras)를 사용하여 모델을 정의하고, [전처리 레이어](https://keras.io/guides/preprocessing_layers/)를 CSV의 열에서 모델 훈련에 사용되는 특성으로 매핑하는 브리지로 사용합니다. 이 튜토리얼에는 다음을 위한 전체 코드가 포함되어 있습니다.\n",
"\n",
"- [Pandas](https://pandas.pydata.org/)를 사용하여 CSV 파일을 로드합니다.\n",
"- [tf.data](https://www.tensorflow.org/guide/datasets)를 사용하여 행을 일괄 처리하고 셔플하는 입력 파이프라인을 빌드합니다.\n",
"- Keras 전처리 레이어를 사용하여 CSV의 열에서 모델을 훈련하는 데 사용되는 특성으로 매핑합니다.\n",
"- Keras를 사용하여 모델을 빌드, 훈련 및 평가합니다."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h5xkXCicjFQD"
},
"source": [
"참고: 이 튜토리얼은 [특성 열의 구조적 데이터 분류하기](https://www.tensorflow.org/tutorials/structured_data/feature_columns)와 유사합니다. 이 버전은 `tf.feature_column` 대신 새로운 실험용 Keras [전처리 레이어](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing)를 사용합니다. Keras Preprocessing Layer는 더 직관적이며 배포를 단순화하기 위해 모델 내에 쉽게 포함될 수 있습니다."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZHxU1FMNpomc"
},
"source": [
"## 데이터세트\n",
"\n",
"PetFinder [데이터세트](https://www.kaggle.com/c/petfinder-adoption-prediction)의 단순화된 버전을 사용합니다. CSV에는 수천 개의 행이 있습니다. 각 행은 애완 동물을 설명하고 각 열은 속성을 설명합니다. 이 정보를 사용하여 애완 동물의 입양 여부를 예측합니다.\n",
"\n",
"다음은 이 데이터세트에 대한 설명입니다. 숫자 열과 범주 열이 모두 있습니다. 이 튜토리얼에서 사용하지 않는 자유 텍스트 열이 있습니다.\n",
"\n",
"열 | 설명 | 특성 유형 | 데이터 형식\n",
"--- | --- | --- | ---\n",
"유형 | 동물의 종류(개, 고양이) | 범주형 | 문자열\n",
"나이 | 애완 동물의 나이 | 수치 | 정수\n",
"품종 1 | 애완 동물의 기본 품종 | 범주형 | 문자열\n",
"색상 1 | 애완 동물의 색상 1 | 범주형 | 문자열\n",
"색상 2 | 애완 동물의 색상 2 | 범주형 | 문자열\n",
"MaturitySize | 성장한 크기 | 범주형 | 문자열\n",
"FurLength | 모피 길이 | 범주형 | 문자열\n",
"예방 접종 | 애완 동물이 예방 접종을 받았습니다 | 범주형 | 문자열\n",
"불임 시술 | 애완 동물이 불임 시술을 받았습니다 | 범주형 | 문자열\n",
"건강 | 건강 상태 | 범주형 | 문자열\n",
"회비 | 입양비 | 수치 | 정수\n",
"설명 | 이 애완 동물에 대한 프로필 작성 | 텍스트 | 문자열\n",
"PhotoAmt | 이 애완 동물의 업로드된 총 사진 | 수치 | 정수\n",
"AdoptionSpeed | 입양 속도 | 분류 | 정수"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vjFbdBldyEqf"
},
"source": [
"## TensorFlow 및 기타 라이브러리 가져오기\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "S_BdyQlPjfDW"
},
"outputs": [],
"source": [
"!pip install -q sklearn"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LklnLlt6yEqf"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from tensorflow.keras import layers\n",
"from tensorflow.keras.layers.experimental import preprocessing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TKU7RyoQGVKB"
},
"outputs": [],
"source": [
"tf.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UXvBvobayEqi"
},
"source": [
"## Pandas를 사용하여 데이터 프레임 만들기\n",
"\n",
"[Pandas](https://pandas.pydata.org/)는 구조적 데이터를 로드하고 처리하는 데 유용한 여러 유틸리티가 포함된 Python 라이브러리입니다. Pandas를 사용하여 URL에서 데이터세트를 다운로드하고 데이터 프레임에 로드합니다."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qJ4Ajn-YyEqj"
},
"outputs": [],
"source": [
"import pathlib\n",
"\n",
"dataset_url = 'http://storage.googleapis.com/download.tensorflow.org/data/petfinder-mini.zip'\n",
"csv_file = 'datasets/petfinder-mini/petfinder-mini.csv'\n",
"\n",
"tf.keras.utils.get_file('petfinder_mini.zip', dataset_url,\n",
" extract=True, cache_dir='.')\n",
"dataframe = pd.read_csv(csv_file)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3uiq4hoIGyXI"
},
"outputs": [],
"source": [
"dataframe.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "C3zDbrozyEqq"
},
"source": [
"## 목표 변수 만들기\n",
"\n",
"Kaggle 대회에서의 작업은 애완 동물이 입양되는 속도를 예측하는 것입니다(예: 첫 주, 첫 달, 첫 3개월 등). 튜토리얼을 위해 단순화해 봅시다. 여기에서는 입양 속도를 이진 분류 문제로 변환하고 단순히 애완 동물이 입양되었는지 여부를 예측합니다.\n",
"\n",
"레이블 열을 수정한 후, 0은 애완 동물이 입양되지 않았음을 나타내고 1은 입양되었음을 나타냅니다."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wmMDc46-yEqq"
},
"outputs": [],
"source": [
"# In the original dataset \"4\" indicates the pet was not adopted.\n",
"dataframe['target'] = np.where(dataframe['AdoptionSpeed']==4, 0, 1)\n",
"\n",
"# Drop un-used columns.\n",
"dataframe = dataframe.drop(columns=['AdoptionSpeed', 'Description'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sp0NCbswyEqs"
},
"source": [
"## 데이터 프레임을 훈련, 검증 및 테스트로 분할하기\n",
"\n",
"다운로드한 데이터세트는 단일 CSV 파일입니다. 이를 훈련, 검증 및 테스트 세트로 분할합니다."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qT6HdyEwyEqt"
},
"outputs": [],
"source": [
"train, test = train_test_split(dataframe, test_size=0.2)\n",
"train, val = train_test_split(train, test_size=0.2)\n",
"print(len(train), 'train examples')\n",
"print(len(val), 'validation examples')\n",
"print(len(test), 'test examples')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "C_7uVu-xyEqv"
},
"source": [
"## tf.data를 사용하여 입력 파이프라인 만들기\n",
"\n",
"다음으로 데이터를 셔플하고 일괄 처리하기 위해 [tf.data](https://www.tensorflow.org/guide/datasets)로 데이터 프레임을 래핑합니다. 매우 큰 CSV 파일(메모리에 적합하지 않을 정도로 큰 파일)을 사용하는 경우, tf.data를 사용하여 디스크에서 직접 읽을 수 있습니다. 이 튜토리얼에서는 다루지 않습니다."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7r4j-1lRyEqw"
},
"outputs": [],
"source": [
"# A utility method to create a tf.data dataset from a Pandas Dataframe\n",
"def df_to_dataset(dataframe, shuffle=True, batch_size=32):\n",
" dataframe = dataframe.copy()\n",
" labels = dataframe.pop('target')\n",
" ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))\n",
" if shuffle:\n",
" ds = ds.shuffle(buffer_size=len(dataframe))\n",
" ds = ds.batch(batch_size)\n",
" ds = ds.prefetch(batch_size)\n",
" return ds"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PYxIXH579uS9"
},
"source": [
"이제 입력 파이프라인을 생성했으므로 반환되는 데이터의 형식을 확인하기 위해 호출해 보겠습니다. 출력을 읽기 쉽게 유지하기 위해 작은 배치 크기를 사용했습니다."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tYiNH-QI96Jo"
},
"outputs": [],
"source": [
"batch_size = 5\n",
"train_ds = df_to_dataset(train, batch_size=batch_size)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nFYir6S8HgIJ"
},
"outputs": [],
"source": [
"[(train_features, label_batch)] = train_ds.take(1)\n",
"print('Every feature:', list(train_features.keys()))\n",
"print('A batch of ages:', train_features['Age'])\n",
"print('A batch of targets:', label_batch )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "geqHWW54Hmte"
},
"source": [
"데이터세트가 데이터 프레임의 행에서 열 값에 매핑되는 열 이름의 사전(데이터 프레임에서)을 반환하는 것을 볼 수 있습니다."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-v50jBIuj4gb"
},
"source": [
"## 전처리 레이어의 사용을 시연합니다.\n",
"\n",
"Keras 전처리 레이어 API를 사용하면 Keras 네이티브 입력 처리 파이프라인을 빌드할 수 있습니다. 3개의 전처리 레이어를 사용하여 특성 전처리 코드를 보여줍니다.\n",
"\n",
"- [`Normalization`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing/Normalization) - 데이터의 특성별 정규화입니다.\n",
"- [`CategoryEncoding`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing/CategoryEncoding) 카테고리 인코딩 레이어입니다.\n",
"- [`StringLookup`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing/StringLookup) - 어휘의 문자열을 정수 인덱스로 매핑합니다.\n",
"- [`IntegerLookup`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing/IntegerLookup) - 어휘의 정수를 정수 인덱스로 매핑합니다.\n",
"\n",
"[여기](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing)에서 사용 가능한 전처리 레이어의 목록을 찾을 수 있습니다."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "twXBSxnT66o8"
},
"source": [
"### 숫자 열\n",
"\n",
"각 숫자 특성에 대해 Normalization() 레이어를 사용하여 각 특성의 평균이 0이고 표준 편차가 1인지 확인합니다."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OosUh4kTsK_q"
},
"source": [
"`get_normalization_layer` 함수는 특성별 정규화를 숫자 특성에 적용하는 레이어를 반환합니다."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "D6OuEKMMyEq1"
},
"outputs": [],
"source": [
"def get_normalization_layer(name, dataset):\n",
" # Create a Normalization layer for our feature.\n",
" normalizer = preprocessing.Normalization(axis=None)\n",
"\n",
" # Prepare a Dataset that only yields our feature.\n",
" feature_ds = dataset.map(lambda x, y: x[name])\n",
"\n",
" # Learn the statistics of the data.\n",
" normalizer.adapt(feature_ds)\n",
"\n",
" return normalizer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MpKgUDyk69bM"
},
"outputs": [],
"source": [
"photo_count_col = train_features['PhotoAmt']\n",
"layer = get_normalization_layer('PhotoAmt', train_ds)\n",
"layer(photo_count_col)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "foWY00YBUx9N"
},
"source": [
"참고: 숫자 특성(수백 개 이상)이 많은 경우, 먼저 숫자 특성을 연결하고 단일 [normalization](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing/Normalization) 레이어를 사용하는 것이 더 효율적입니다."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yVD--2WZ7vmh"
},
"source": [
"### 범주 열\n",
"\n",
"이 데이터세트에서 Type은 문자열(예: 'Dog'또는 'Cat')으로 표시됩니다. 모델에 직접 문자열을 공급할 수 없습니다. 전처리 레이어는 문자열을 원-핫 벡터로 처리합니다."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LWlkOPwMsxdv"
},
"source": [
"`get_category_encoding_layer` 함수는 어휘의 값을 정수 인덱스로 매핑하고 특성을 원-핫 인코딩하는 레이어를 반환합니다."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GmgaeRjlDoUO"
},
"outputs": [],
"source": [
"def get_category_encoding_layer(name, dataset, dtype, max_tokens=None):\n",
" # Create a StringLookup layer which will turn strings into integer indices\n",
" if dtype == 'string':\n",
" index = preprocessing.StringLookup(max_tokens=max_tokens)\n",
" else:\n",
" index = preprocessing.IntegerLookup(max_tokens=max_tokens)\n",
"\n",
" # Prepare a Dataset that only yields our feature\n",
" feature_ds = dataset.map(lambda x, y: x[name])\n",
"\n",
" # Learn the set of possible values and assign them a fixed integer index.\n",
" index.adapt(feature_ds)\n",
"\n",
" # Create a Discretization for our integer indices.\n",
" encoder = preprocessing.CategoryEncoding(num_tokens=index.vocabulary_size())\n",
"\n",
" # Apply one-hot encoding to our indices. The lambda function captures the\n",
" # layer so we can use them, or include them in the functional model later.\n",
" return lambda feature: encoder(index(feature))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "X2t2ff9K8PcT"
},
"outputs": [],
"source": [
"type_col = train_features['Type']\n",
"layer = get_category_encoding_layer('Type', train_ds, 'string')\n",
"layer(type_col)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j6eDongw8knz"
},
"source": [
"종종 모델에 숫자를 직접 입력하지 않고 대신 해당 입력의 원-핫 인코딩을 사용합니다. 애완 동물의 나이를 나타내는 원시 데이터를 고려합니다."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7FjBioQ38oNE"
},
"outputs": [],
"source": [
"type_col = train_features['Age']\n",
"category_encoding_layer = get_category_encoding_layer('Age', train_ds,\n",
" 'int64', 5)\n",
"category_encoding_layer(type_col)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SiE0glOPkMyh"
},
"source": [
"## 사용할 열 선택하기\n",
"\n",
"여러 유형의 전처리 레이어를 사용하는 방법을 살펴보았습니다. 이제 레이어를 모델을 훈련하는 데 사용합니다. [Keras 함수형 API](https://www.tensorflow.org/guide/keras/functional)를 사용하여 모델을 빌드합니다. Keras 함수형 API는 [tf.keras.Sequential](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) API보다 더 유연한 모델을 생성하는 방법입니다.\n",
"\n",
"이 튜토리얼의 목표는 전처리 레이어를 처리하는 데 필요한 전체 코드(예: 메커니즘)를 보여주는 것입니다. 모델을 훈련하기 위해 몇 개의 열이 임의로 선택되었습니다.\n",
"\n",
"요점: 목표가 정확한 모델을 빌드하는 것이라면 자신의 더 큰 데이터세트를 시도하고 포함할 가장 의미 있는 특성과 표현 방법에 대해 신중하게 고려하세요."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Uj1GoHSZ9R3H"
},
"source": [
"이전에는 입력 파이프라인을 보여주기 위해 작은 배치 크기를 사용했습니다. 이제 더 큰 배치 크기로 새 입력 파이프라인을 생성해 보겠습니다.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Rcv2kQTTo23h"
},
"outputs": [],
"source": [
"batch_size = 256\n",
"train_ds = df_to_dataset(train, batch_size=batch_size)\n",
"val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)\n",
"test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Q3RBa51VkaAn"
},
"outputs": [],
"source": [
"all_inputs = []\n",
"encoded_features = []\n",
"\n",
"# Numeric features.\n",
"for header in ['PhotoAmt', 'Fee']:\n",
" numeric_col = tf.keras.Input(shape=(1,), name=header)\n",
" normalization_layer = get_normalization_layer(header, train_ds)\n",
" encoded_numeric_col = normalization_layer(numeric_col)\n",
" all_inputs.append(numeric_col)\n",
" encoded_features.append(encoded_numeric_col)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "1FOMGfZflhoA"
},
"outputs": [],
"source": [
"# Categorical features encoded as integers.\n",
"age_col = tf.keras.Input(shape=(1,), name='Age', dtype='int64')\n",
"encoding_layer = get_category_encoding_layer('Age', train_ds, dtype='int64',\n",
" max_tokens=5)\n",
"encoded_age_col = encoding_layer(age_col)\n",
"all_inputs.append(age_col)\n",
"encoded_features.append(encoded_age_col)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "K8C8xyiXm-Ie"
},
"outputs": [],
"source": [
"# Categorical features encoded as string.\n",
"categorical_cols = ['Type', 'Color1', 'Color2', 'Gender', 'MaturitySize',\n",
" 'FurLength', 'Vaccinated', 'Sterilized', 'Health', 'Breed1']\n",
"for header in categorical_cols:\n",
" categorical_col = tf.keras.Input(shape=(1,), name=header, dtype='string')\n",
" encoding_layer = get_category_encoding_layer(header, train_ds, dtype='string',\n",
" max_tokens=5)\n",
" encoded_categorical_col = encoding_layer(categorical_col)\n",
" all_inputs.append(categorical_col)\n",
" encoded_features.append(encoded_categorical_col)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YHSnhz2fyEq3"
},
"source": [
"## 모델 생성, 컴파일 및 훈련하기\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IDGyN_wpo0XS"
},
"source": [
"이제 엔드 투 엔드 모델을 만들 수 있습니다."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6Yrj-_pr6jyL"
},
"outputs": [],
"source": [
"all_features = tf.keras.layers.concatenate(encoded_features)\n",
"x = tf.keras.layers.Dense(32, activation=\"relu\")(all_features)\n",
"x = tf.keras.layers.Dropout(0.5)(x)\n",
"output = tf.keras.layers.Dense(1)(x)\n",
"model = tf.keras.Model(all_inputs, output)\n",
"model.compile(optimizer='adam',\n",
" loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
" metrics=[\"accuracy\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f6mNMfG6yEq5"
},
"source": [
"연결 그래프를 시각화해 보겠습니다.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Y7Bkx4c7yEq5"
},
"outputs": [],
"source": [
"# rankdir='LR' is used to make the graph horizontal.\n",
"tf.keras.utils.plot_model(model, show_shapes=True, rankdir=\"LR\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CED6OStLyEq7"
},
"source": [
"### 모델 훈련하기\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OQfE3PC6yEq8"
},
"outputs": [],
"source": [
"model.fit(train_ds, epochs=10, validation_data=val_ds)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "T8N2uAdU2Cni"
},
"outputs": [],
"source": [
"loss, accuracy = model.evaluate(test_ds)\n",
"print(\"Accuracy\", accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LmZMnTKaCZda"
},
"source": [
"## 새로운 데이터로 추론하기\n",
"\n",
"요점: 전처리 코드가 모델 자체에 포함되어 있기 때문에 여러분이 개발한 모델은 이제 CSV 파일에서 행을 직접 분류할 수 있습니다.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4xkOlK8Zweeh"
},
"source": [
"이제 Keras 모델을 저장하고 다시 로드할 수 있습니다. TensorFlow 모델에 대한 자세한 내용은 [여기](https://www.tensorflow.org/tutorials/keras/save_and_load)에서 튜토리어를 따르세요."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QH9Zy1sBvwOH"
},
"outputs": [],
"source": [
"model.save('my_pet_classifier')\n",
"reloaded_model = tf.keras.models.load_model('my_pet_classifier')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "D973plJrdwQ9"
},
"source": [
"새 샘플에 대한 예측값을 얻으려면, `model.predict()`를 호출하면 됩니다. 다음 두 가지만 수행해야 합니다.\n",
"\n",
"1. 배치 차원을 갖도록 스칼라를 목록으로 래핑합니다(모델은 단일 샘플이 아닌 데이터 배치만 처리함).\n",
"2. 각 특성에 대해 `convert_to_tensor`를 호출합니다."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rKq4pxtdDa7i"
},
"outputs": [],
"source": [
"sample = {\n",
" 'Type': 'Cat',\n",
" 'Age': 3,\n",
" 'Breed1': 'Tabby',\n",
" 'Gender': 'Male',\n",
" 'Color1': 'Black',\n",
" 'Color2': 'White',\n",
" 'MaturitySize': 'Small',\n",
" 'FurLength': 'Short',\n",
" 'Vaccinated': 'No',\n",
" 'Sterilized': 'No',\n",
" 'Health': 'Healthy',\n",
" 'Fee': 100,\n",
" 'PhotoAmt': 2,\n",
"}\n",
"\n",
"input_dict = {name: tf.convert_to_tensor([value]) for name, value in sample.items()}\n",
"predictions = reloaded_model.predict(input_dict)\n",
"prob = tf.nn.sigmoid(predictions[0])\n",
"\n",
"print(\n",
" \"This particular pet had a %.1f percent probability \"\n",
" \"of getting adopted.\" % (100 * prob)\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XJQQZEiH2FaB"
},
"source": [
"요점: 일반적으로 더 크고 복잡한 데이터세트를 사용한 딥 러닝을 통해 최상의 결과를 얻을 수 있습니다. 작은 데이터세트로 작업할 때는 의사 결정 트리 또는 랜덤 포레스트를 강력한 기준으로 사용하는 것이 좋습니다. 이 튜토리얼의 목표는 구조적 데이터를 처리하는 메커니즘을 보여주기 위한 것이므로 향후 자체 데이터세트를 처리할 때 시작점으로 사용할 수 있는 코드를 살펴보았습니다."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "k0QAY2Tb2HYG"
},
"source": [
"## 다음 단계\n",
"\n",
"구조적 데이터의 분류에 대해 자세히 알아보는 가장 좋은 방법은 직접 시도해 보는 것입니다. 처리할 데이터세트를 찾고 위와 유사한 코드를 사용하여 분류하도록 모델을 훈련할 수 있습니다. 정확성을 높이려면 모델에 포함할 특성과 표현 방법을 신중하게 고려하세요."
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "preprocessing_layers.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
msmexplorer/msmexplorer | notebooks/Fs-Peptide-Example.ipynb | 1 | 9592 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modeling dynamics of FS Peptide\n",
"\n",
"This example shows a typical, basic usage of the MSMExplorer command line to plot the modeled dynamics of a protein system."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"from msmbuilder.example_datasets import FsPeptide\n",
"from msmbuilder.featurizer import DihedralFeaturizer\n",
"from msmbuilder.decomposition import tICA\n",
"from msmbuilder.preprocessing import RobustScaler\n",
"from msmbuilder.cluster import MiniBatchKMeans\n",
"from msmbuilder.msm import MarkovStateModel\n",
"\n",
"import numpy as np\n",
"\n",
"import msmexplorer as msme\n",
"\n",
"rs = np.random.RandomState(42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Fs Peptide Data\n",
"This dataset consists of 28 molecular dynamics trajectories of Fs peptide\n",
"(Ace-A_5(AAARA)_3A-NME), a widely studied model system for protein folding.\n",
"\n",
"Each trajectory is 500 ns in length, and saved at a 50 ps time interval (14\n",
"$\\mu$s aggegrate sampling)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"trajs = FsPeptide(verbose=False).get().trajectories"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extract Backbone Dihedrals\n",
"\n",
"The raw (x, y, z) coordinates from the simulation do not respect the translational and rotational symmetry of our problem. A Featurizer transforms cartesian coordinates into other representations. Here we use the DihedralFeaturizer to turn our data into phi and psi dihedral angles. Observe that the 264*3-dimensional space is reduced to 84 dimensions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"featurizer = DihedralFeaturizer(types=['phi', 'psi'])\n",
"diheds = featurizer.fit_transform(trajs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preprocess Data\n",
"\n",
"RobustScaler removes the median and scales the data according to the Interquartile Range (IQR). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scaler = RobustScaler()\n",
"scaled_data = scaler.fit_transform(diheds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Perform Dimensionality Reduction\n",
"\n",
"tICA is similar to principal component analysis (see \"tICA vs. PCA\" example). Note that the 84-dimensional space is reduced to 2 dimensions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tica_model = tICA(lag_time=10, n_components=2, kinetic_mapping=True)\n",
"tica_trajs = tica_model.fit_transform(scaled_data)\n",
"\n",
"ax, side_ax = msme.plot_trace(tica_trajs[0][:, 0], window=10,\n",
" label='tIC1', xlabel='Timestep')\n",
"_ = msme.plot_trace(tica_trajs[0][:, 1], window=10, label='tIC2',\n",
" xlabel='Timestep', color='rawdenim', ax=ax,\n",
" side_ax=side_ax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Perform Clustering\n",
"\n",
"Conformations need to be clustered into states (sometimes written as microstates). We cluster based on the tICA projections to group conformations that interconvert rapidly. Note that we transform our trajectories from the 2-dimensional tICA space into a 1-dimensional cluster index."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"clusterer = MiniBatchKMeans(n_clusters=12, random_state=rs)\n",
"clustered_trajs = clusterer.fit_transform(tica_trajs)\n",
"\n",
"_ = msme.plot_voronoi(clusterer, xlabel='tIC1', ylabel='tIC2')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Construct MSM\n",
"\n",
"We can construct an MSM from the labeled trajectories and see how much it is perturbing the raw MD populations of a microstate.\n",
"\n",
"In a large sampling regime, we should see a decorrelated cloud of points in the plot below. See [this thread](https://github.com/msmexplorer/msmexplorer/issues/94) for a discussion on how to interpret this plot."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"msm = MarkovStateModel(lag_time=1, n_timescales=5)\n",
"assigns = msm.fit_transform(clustered_trajs)\n",
"\n",
"_ = msme.plot_pop_resids(msm, color='tarragon')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also plot the implied timescales. Remember that the timescales in the `MarkovStateModel` object are expressed in units of time-step between indices in the source data supplied to the `fit()` or `fit_transform()` methods."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_ = msme.plot_timescales(msm, ylabel=r'Relaxation Time (frames)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Those are the timescales of an MSM built at a lag time of 1 frame (for this dataset, each frame represents 50 ps).\n",
"\n",
"Let's build several MSMs at lag times separated in log space to get a feel for when the MSM starts to have a Markovian behaviour."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"msm_list = [\n",
" MarkovStateModel(lag_time=x, n_timescales=5, verbose=False)\n",
" for x in [1, 10, 1e2, 1e3, 5e3, 9e3]\n",
"]\n",
"\n",
"for msm in msm_list:\n",
" msm.fit(clustered_trajs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_ = msme.plot_implied_timescales(msm_list,\n",
" xlabel=r'$\\tau$ (frames)',\n",
" ylabel='Relaxation Times (frames)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is a tradeoff between the MSM accuracy and the fact that we have a limited amount of data. From the plot above we can see that using a lag time of around 1000 frames (or 50 ns) to build an MSM is appropriate (timescales have leveled off and there is a separation between the first and the second longest timescales).\n",
"\n",
"We can inspect this timescales more closely now and express them in units of time:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"msm = msm_list[3] # Choose the appropriate MSM from the list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for i, (ts, ts_u) in enumerate(zip(msm.timescales_, msm.uncertainty_timescales())):\n",
" timescale_ns = ts * 50 / 1000\n",
" uncertainty_ns = ts_u * 50 / 1000\n",
" print('Timescale %d: %.2f ± %.2f ns' % ((i + 1), timescale_ns, uncertainty_ns))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Free Energy Landscape\n",
"\n",
"From our MSM and tICA data, we can construct a 2-D free energy landscape."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = np.concatenate(tica_trajs, axis=0)\n",
"pi_0 = msm.populations_[np.concatenate(assigns, axis=0)]\n",
"\n",
"\n",
"# Free Energy Surface\n",
"ax = msme.plot_free_energy(data, obs=(0, 1), n_samples=10000,\n",
" pi=pi_0, gridsize=100, vmax=5.,\n",
" n_levels=8, cut=5, xlabel='tIC1',\n",
" ylabel='tIC2', random_state=rs)\n",
"\n",
"# MSM Network\n",
"pos = dict(zip(range(clusterer.n_clusters), clusterer.cluster_centers_))\n",
"_ = msme.plot_msm_network(msm, pos=pos, node_color='carbon',\n",
" with_labels=False)\n",
"\n",
"\n",
"# Top Transition Pathway\n",
"w = (msm.left_eigenvectors_[:, 1] - msm.left_eigenvectors_[:, 1].min())\n",
"w /= w.max()\n",
"cmap = msme.utils.make_colormap(['rawdenim', 'lightgrey', 'pomegranate'])\n",
"_ = msme.plot_tpaths(msm, [4], [0], pos=pos, node_color=cmap(w),\n",
" alpha=.9, edge_color='black', ax=ax)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"hide_input": false,
"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 |
aaronta/illinois | unit_testing/unit_testing.ipynb | 2 | 26658 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Unit Testing"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Unit testing is the process of breaking a program into small pieces and testing thoroughly each piece.\n",
"\n",
"A unit is generally a function or class in a program."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Why should we do unit testing?\n",
"* verification and validation of code\n",
"* prevent bugs from being introduced by yourself or others\n",
"* personal sanity (prevents computational hubris)\n",
"* it's super duper easy to do but probably saves hours of headache\n",
"* lets you add cool things to your projects repo\n",
"<img src=\"https://raw.githubusercontent.com/dwyl/repo-badges/master/highresPNGs/build-passing.png\" width=\"150\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Two schools of thought. Create unit tests before writing code (test driven developement) or create unit tests after writing code to make sure it works correctly.\n",
"\n",
"Unit tests are run periodically, generally after changes to code base, to make sure changes haven't introduced new bugs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For python, like most languages, there are several frameworks for building unit tests. \n",
"\n",
"* [Unittest](https://docs.python.org/2/library/unittest.html)\n",
"* [Nosetests](http://nose.readthedocs.io/en/latest/)\n",
"* [pytest](https://docs.pytest.org/en/latest/)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Paul has briefly discussed nosetests before at hacker (https://github.com/walternathan6754/illinois/blob/master/sphinx/sphinx.pdf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unit tests are generally functions that test the output or performance of other functions or modules. This is often checked with assert statements that are evaluated."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pytest"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a simple example, we will start with a function to divide two numbers (don't ask why we need this function to be made, we just do!)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"def divide(numerator, denominator):\n",
" \"\"\" function to perform division of two numbers. This should not perform\n",
" integer division\n",
" \n",
" Raises:\n",
" ZeroDivisionError: raised if denominator is zero\n",
" \"\"\"\n",
" return numerator/denominator\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As the doc string explains, the function should not perform integer division, and should raise a ZeroDivisionError if the denominator is zero.\n",
"\n",
"Then, we could build a test function to test division of two ints"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"def test_divide_ints():\n",
" \"\"\"test division of two integers 4 and 2\"\"\"\n",
" assert divide(4,2) == 2\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These two functions are in the file simple_example.py. To perform execute the test function, in terminal perform"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"$ py.test -v simple_example.py\n",
"$ python -m pytest -v simple_example.py\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"====================================================== test session starts =======================================================\n",
"platform darwin -- Python 2.7.11, pytest-2.8.1, py-1.4.30, pluggy-0.3.1 -- /Users/Nathan/anaconda/bin/python\n",
"cachedir: .cache\n",
"rootdir: /Users/Nathan/Documents/illinois/unit_testing, inifile: \n",
"collected 1 items \n",
"\n",
"simple_example.py::test_divide_ints PASSED\n",
"\n",
"==================================================== 1 passed in 0.00 seconds ====================================================\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Either of those lines works for executing pytest on the included file. If no file is included, pytest will run on all files titled test_* in the current directory and all of the sub directories. It is worth noting that py.test could also be replaced with nosetests (a different unittesting module for python) and these codes will still work!\n",
"\n",
"Pytest will run on all functions named test_* in the files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Seperating Tests"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this simple case, it is okay to include both the function and the test in the same file. However, for a larger project this is less viable. Thus, the functions and the tests can be seperated "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"project\\\n",
" src\\\n",
" __init__.py\n",
" divide.py\n",
" test\\\n",
" test_divide.py\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The __init__.py file is important in the src folder because this allows python to import the files to the test files.\n",
"\n",
"Also, it is important to add the project directory to the PYTHONPATH."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"$ export PYTHONPATH=$PYTHONPATH;\\path_to_project\\\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can build many more tests into the test\\test_divide.py file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"from divide import divide\n",
"\n",
"import pytest\n",
"\n",
"def test_divide_ints():\n",
" assert divide(4,2) == 2\n",
"\n",
"def test_divide_floats():\n",
" assert divide(5.0, 2.0) == 2.5\n",
"\n",
"def test_zero_division():\n",
" with pytest.raises(ZeroDivisionError) as e_info:\n",
" divide(4.0,0.0)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This includes three tests. One to check division of intergers, one to test division of floats, and one to check that an exception is raised when zero is provided as a denominator."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"$ py.test -v test/test_divide.py\n",
"\n",
"Nathans-iMac:unit_testing Nathan$ py.test -v test/test_divide.py \n",
"====================================================== test session starts =======================================================\n",
"platform darwin -- Python 2.7.11, pytest-2.8.1, py-1.4.30, pluggy-0.3.1 -- /Users/Nathan/anaconda/bin/python\n",
"cachedir: test/.cache\n",
"rootdir: /Users/Nathan/Documents/illinois/unit_testing/test, inifile: \n",
"collected 3 items \n",
"\n",
"test/test_divide.py::test_divide_ints PASSED\n",
"test/test_divide.py::test_divide_floats PASSED\n",
"test/test_divide.py::test_zero_division PASSED\n",
"\n",
"==================================================== 3 passed in 0.01 seconds ====================================================\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, you may notice that this does not check that integer division is not performed. And in fact, our divide function actually does perform integer division (I am running python 2).\n",
"\n",
"Maybe, someone notices this and decides to fix this. And maybe, they really really really love numpy, so the fix they make is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"import numpy\n",
"\n",
"def divide(numerator, denomator):\n",
" \"\"\" function to perform division of two numbers. This should not perform\n",
" integer division\n",
" \n",
" Raises:\n",
" ZeroDivisionError: raised if denominator is zero\n",
" \"\"\"\n",
" return numpy.float64(numerator)/numpy.float64(denomator)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's not the worst fix ever. It corrects the integer division. However, it does break the test for zero division"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"$ py.test -v test/test_divide_numpy.py\n",
"\n",
"====================================================== test session starts =======================================================\n",
"platform darwin -- Python 2.7.11, pytest-2.8.1, py-1.4.30, pluggy-0.3.1 -- /Users/Nathan/anaconda/bin/python\n",
"cachedir: test/.cache\n",
"rootdir: /Users/Nathan/Documents/illinois/unit_testing/test, inifile: \n",
"collected 3 items \n",
"\n",
"test/test_divide_numpy.py::test_divide_numpy_ints PASSED\n",
"test/test_divide_numpy.py::test_divide_floats PASSED\n",
"test/test_divide_numpy.py::test_zero_division FAILED\n",
"\n",
"============================================================ FAILURES ============================================================\n",
"_______________________________________________________ test_zero_division _______________________________________________________\n",
"\n",
" def test_zero_division():\n",
" with pytest.raises(ZeroDivisionError) as e_info:\n",
"> divide(4.0,0.0)\n",
"E Failed: DID NOT RAISE\n",
"\n",
"test/test_divide_numpy.py:13: Failed\n",
"------------------------------------------------------ Captured stderr call ------------------------------------------------------\n",
"/Users/Nathan/Documents/illinois/unit_testing/src/divide_numpy.py:10: RuntimeWarning: divide by zero encountered in double_scalars\n",
" return numpy.float64(numerator)/numpy.float64(denomator)\n",
"=============================================== 1 failed, 2 passed in 0.15 seconds ===============================================\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So you can see the function did not raise the zero division error. Interesting thing about numpy.float64 variables, they can be divided by zero and produce NaN instead of an error.\n",
"\n",
"Our unit tests allowed us to know immediately that our new code broke other functioning protions of our code (which can be very costly to commit if it is a large project)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Verbosity in Pytest"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These examples are in test/test_verbose_fails.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are making tests, it might be very useful to include messages with your assertions when they fail."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"from divide import divide\n",
"\n",
"import numpy\n",
"\n",
"def test_integer_division():\n",
" assert divide(1,2) != 0, \"performed integer division\"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, when integer division is performed, the assertion will tell us why this is an issue"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"============================================================ FAILURES ============================================================\n",
"_____________________________________________________ test_integer_division ______________________________________________________\n",
"\n",
" def test_integer_division():\n",
"> assert divide(1,2) != 0, \"performed integer division\"\n",
"E AssertionError: performed integer division\n",
"E assert 0 != 0\n",
"E + where 0 = divide(1, 2)\n",
"\n",
"test/test_verbose_fails.py:6: AssertionError\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice there is two addition bits of verbose information. Our message of \"performed integer division\" and pytest telling us that divide(1,2) = 0, which I thought was cool"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When performing a test that tests dictionaries and lists, pytest will print the exact difference between the two objects"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"def test_lists():\n",
" left_list = [1,2,3,4]\n",
" right_list = [2,2,3,4]\n",
" assert left_list == right_list\n",
"\n",
"def test_dictionaries():\n",
" left_dic = {'item1': 1, 'item2':2, 'item3':3}\n",
" right_dic = {'item1': 2, 'item2':2, 'item4':3}\n",
" assert left_dic == right_dic\n",
"\n",
"def test_numpy_arrays():\n",
" left_array = numpy.array([1,2,3,4])\n",
" right_array = numpy.array([2,2,3,4])\n",
" assert numpy.array_equal(left_array, right_array) == True\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"$ py.test -v test\\test_verbose_fails.py\n",
"\n",
"___________________________________________________________ test_lists ___________________________________________________________\n",
"\n",
" def test_lists():\n",
" left_list = [1,2,3,4]\n",
" right_list = [2,2,3,4]\n",
"> assert left_list == right_list\n",
"E assert [1, 2, 3, 4] == [2, 2, 3, 4]\n",
"E At index 0 diff: 1 != 2\n",
"E Full diff:\n",
"E - [1, 2, 3, 4]\n",
"E ? ---\n",
"E + [2, 2, 3, 4]\n",
"E ? +++\n",
"\n",
"test/test_verbose_fails.py:11: AssertionError\n",
"_______________________________________________________ test_dictionaries ________________________________________________________\n",
"\n",
" def test_dictionaries():\n",
" left_dic = {'item1': 1, 'item2':2, 'item3':3}\n",
" right_dic = {'item1': 2, 'item2':2, 'item4':3}\n",
"> assert left_dic == right_dic\n",
"E assert {'item1': 1, ...2, 'item3': 3} == {'item1': 2, '...2, 'item4': 3}\n",
"E Common items:\n",
"E {'item2': 2}\n",
"E Differing items:\n",
"E {'item1': 1} != {'item1': 2}\n",
"E Left contains more items:\n",
"E {'item3': 3}\n",
"E Right contains more items:\n",
"E {'item4': 3}\n",
"E Full diff:\n",
"E - {'item1': 1, 'item2': 2, 'item3': 3}\n",
"E ? ^ ^\n",
"E + {'item1': 2, 'item2': 2, 'item4': 3}\n",
"E ? ^ ^\n",
"\n",
"test/test_verbose_fails.py:16: AssertionError\n",
"_______________________________________________________ test_numpy_arrays ________________________________________________________\n",
"\n",
" def test_numpy_arrays():\n",
" left_array = numpy.array([1,2,3,4])\n",
" right_array = numpy.array([2,2,3,4])\n",
"> assert numpy.array_equal(left_array, right_array) == True\n",
"E assert <function array_equal at 0x103c972a8>(array([1, 2, 3, 4]), array([2, 2, 3, 4])) == True\n",
"E + where <function array_equal at 0x103c972a8> = numpy.array_equal\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that pytest prints the common items, the differing items, and what items are only in the left container and the right container. \n",
"\n",
"Sadly, this doesn't work on numpy arrays though :("
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setup and Teardown"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The previous examples are all very simple. Most likely, in a real setting, the functions created will rely on a certain state of the program.\n",
"\n",
"To setup a certain state before a test function is performed, pytest can run functions with the name\n",
"```python\n",
"def setup_module(module):\n",
"```\n",
"or \n",
"```python\n",
"def setup_function(function):\n",
"```\n",
"\n",
"and the state can be destroyed with \n",
"```python\n",
"def teardown_module(module):\n",
"```\n",
"or \n",
"```python\n",
"def teardown_function(function):\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As an example, I will make a simplier version of how I used this in a project.\n",
"\n",
"Imagine a package that simulates power cycles. At some point in the code, the thermal efficiency needs to be calculated from various properties of the cycle components. In order to not have to run the code until the point where the efficiency needs to be computed (which could be hours), setup functions could set the state of the code to be able to test the function. \n",
"\n",
"This example is in src\\power_efficiency.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"\"\"\"module that computes the power efficiency of a cycle\"\"\"\n",
"\n",
"input_power = 0\n",
"output_power = 0\n",
"\n",
"def compute_efficiency():\n",
" \"\"\"Computes the power efficiency of a thermal cycle\n",
" Raises:\n",
" ValueError: if power is negative\n",
" \"\"\"\n",
" if output_power < 0:\n",
" raise ValueError\n",
" \n",
" return output_power/input_power\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code computes the efficiency of the cycle from the input and output power, which would be set elsewhere in the program.\n",
"\n",
"Obviously, without some setup, testing the compute_efficiency function would always result in a ValueError.\n",
"\n",
"So, using the setup_module we can set the state before testing the function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"import power_efficiency as pe\n",
"\n",
"def setup_module(module):\n",
" print \"\"\n",
" print \"module setup\"\n",
" pe.input_power = 100. # kJ\n",
" pe.output_power = 30. # kJ\n",
"\n",
"def teardown_module(module):\n",
" print \"\"\n",
" print \"module teardown\"\n",
" pe.input_power = 0. # kJ\n",
" pe.output_power = 0. # kJ\n",
"\n",
"def test_input_power():\n",
" print \"test input power\"\n",
" assert pe.input_power == 100.\n",
"\n",
"def test_compute_efficiency():\n",
" print \"\\ntest efficiency\"\n",
" assert pe.compute_efficiency() == 0.3 # it's not very efficient\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I added prints to the functions so that when the tests are run, we can see the order of the functions called."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"$ py.test -v -s test/test_compute_efficiency.py\n",
"\n",
"====================================================== test session starts =======================================================\n",
"platform darwin -- Python 2.7.11, pytest-2.8.1, py-1.4.30, pluggy-0.3.1 -- /Users/Nathan/anaconda/bin/python\n",
"cachedir: test/.cache\n",
"rootdir: /Users/Nathan/Documents/illinois/unit_testing/test, inifile: \n",
"collected 2 items \n",
"\n",
"test/test_compute_efficiency.py::test_input_power PASSED\n",
"test/test_compute_efficiency.py::test_compute_efficiency PASSED\n",
"\n",
"==================================================== 2 passed in 0.01 seconds ====================================================\n",
"Nathans-iMac:unit_testing Nathan$ py.test -v -s test/test_compute_efficiency.py \n",
"====================================================== test session starts =======================================================\n",
"platform darwin -- Python 2.7.11, pytest-2.8.1, py-1.4.30, pluggy-0.3.1 -- /Users/Nathan/anaconda/bin/python\n",
"cachedir: test/.cache\n",
"rootdir: /Users/Nathan/Documents/illinois/unit_testing/test, inifile: \n",
"collected 2 items \n",
"\n",
"test/test_compute_efficiency.py::test_input_power \n",
"module setup\n",
"test input power\n",
"PASSED\n",
"test/test_compute_efficiency.py::test_compute_efficiency \n",
"test efficiency\n",
"PASSED\n",
"module teardown\n",
"\n",
"\n",
"==================================================== 2 passed in 0.01 seconds ====================================================\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The -s collects the prints and orders them on screen. From the output, you can see that the module setup is called once, then the two tests functions are called and passed. Then the module teardown is called. \n",
"\n",
"These functions can also be very useful if data needs to be read into a program. The setup can read the data from file and then the test functions can be performed. \n",
"\n",
"The difference between module and function setups, is function setups are called before every test function and module setups are called once.\n",
"\n",
"Then I use the teardown function to undo the changes done by the setup function."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### pytest fixtures"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another method of setting up a state of a program before performing tests is pytest fixtures.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"import power_efficiency as pe\n",
"import pytest\n",
"\n",
"@pytest.fixture\n",
"def set_power():\n",
" print \"\\nset the power\"\n",
" pe.input_power = 100. # kJ\n",
" pe.output_power = 30. # kJ\n",
"\n",
"@pytest.fixture\n",
"def set_negative_power():\n",
" print \"\\nset negative output power\"\n",
" pe.input_power = 100. # kJ\n",
" pe.output_power = -30. # kJ\n",
"\n",
"def test_input_power(set_power):\n",
" print \"test input power\"\n",
" assert pe.input_power == 100.\n",
"\n",
"def test_compute_efficiency(set_power):\n",
" print \"test efficiency with setup\"\n",
" assert pe.compute_efficiency() == 0.3 # it's not very efficient\n",
"\n",
"def test_negative_power(set_negative_power):\n",
" print \"test efficiency with negative power\"\n",
" with pytest.raises(ValueError) as e_info:\n",
" pe.compute_efficiency()\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To use fixtures, pytest needs to be imported.\n",
"\n",
"Then before setup functions, a @pytest.fixture is placed.\n",
"\n",
"This allows for multiple setups to be created. Then the test function takes these fixtures as inputs."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```bash\n",
"$ py.test -v -s test/test_pytest_fixtures.py\n",
"\n",
"====================================================== test session starts =======================================================\n",
"platform darwin -- Python 2.7.11, pytest-2.8.1, py-1.4.30, pluggy-0.3.1 -- /Users/Nathan/anaconda/bin/python\n",
"cachedir: test/.cache\n",
"rootdir: /Users/Nathan/Documents/illinois/unit_testing/test, inifile: \n",
"collected 3 items \n",
"\n",
"test/test_pytest_fixtures.py::test_input_power \n",
"set the power\n",
"test input power\n",
"PASSED\n",
"test/test_pytest_fixtures.py::test_compute_efficiency \n",
"set the power\n",
"test efficiency with setup\n",
"PASSED\n",
"test/test_pytest_fixtures.py::test_negative_power \n",
"set negative output power\n",
"test efficiency with negative power\n",
"PASSED\n",
"\n",
"==================================================== 3 passed in 0.02 seconds ====================================================\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So depending on the if the test took set_power or set_negative_power as an input, the state of the module was different. This allows for testing with different states."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more on how to start unit testing I recommend downloading any open package and look through their testing codes. Online examples I have found have all been far too simple."
]
},
{
"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
}
| bsd-3-clause |
martin-hunt/hublib | examples/UI_Demo.ipynb | 1 | 104776 | {
"cells": [
{
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"jupyter_dashboards": {
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"outputs": [
{
"data": {
"application/javascript": [
"\n",
"requirejs.undef('filepicker');\n",
"\n",
"define('filepicker', [\"@jupyter-widgets/base\"], function(widgets) {\n",
"\n",
" var FilePickerView = widgets.DOMWidgetView.extend({\n",
" render: function(){\n",
" this.file = document.createElement('input');\n",
" this.file.setAttribute('class', 'fileinput');\n",
" this.file.setAttribute('id', this.cid);\n",
" this.file.multiple = this.model.get('multiple');\n",
" this.file.required = true;\n",
" this.file.setAttribute('type', 'file');\n",
" this.file.setAttribute('style', 'display:none');\n",
"\n",
" this.label = document.createElement('label');\n",
" this.label.setAttribute('for', this.cid);\n",
" this.label.setAttribute('style', 'border: 1px solid; border-radius: 5px; display: inline-block; padding: 6px 12px');\n",
"\n",
" this.icon = document.createElement('i');\n",
" this.icon.setAttribute(\"class\", \"fa fa-upload\");\n",
"\n",
" if (this.file.multiple) {\n",
" this.labelstr = \" Upload Files\";\n",
" } else {\n",
" this.labelstr = \" Upload File\";\n",
" }\n",
" this.label.innerHTML = this.labelstr;\n",
" this.label.prepend(this.icon);\n",
" this.el.appendChild(this.label);\n",
" this.el.appendChild(this.file);\n",
" this.listenTo(this.model, 'change:send', this._send_changed, this);\n",
" this.listenTo(this.model, 'change:reset', this._reset, this);\n",
" this.update();\n",
" },\n",
"\n",
" events: {\n",
" // List of events and their handlers.\n",
" 'change': 'handle_file_change'\n",
" },\n",
"\n",
" _reset: function() {\n",
" this.label.innerHTML = this.labelstr;\n",
" this.label.prepend(this.icon);\n",
" this.file.removeAttribute(\"disabled\");\n",
" },\n",
"\n",
" _send_changed: function() {\n",
" var that = this;\n",
" var send = this.model.get('send');\n",
" var fnum = send[0];\n",
" var offset = send[1];\n",
" var chunk_size=64*1024;\n",
" var reader;\n",
"\n",
" if (fnum == -1) {\n",
" // ignore\n",
" return\n",
" }\n",
"\n",
" if (offset == 0) {\n",
" this.model.set('sent', -1);\n",
" this.touch();\n",
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"\n",
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" var binary = '';\n",
" var bytes = new Uint8Array( buffer );\n",
" var len = bytes.byteLength;\n",
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" binary += String.fromCharCode( bytes[ i ] );\n",
" }\n",
" return window.btoa( binary );\n",
" }\n",
"\n",
" var reader_done = function (event) {\n",
" // chunk is finished. Send to python\n",
" if (event.target.error == null) {\n",
" var b64 = tob64(event.target.result);\n",
" that.model.set('data', b64);\n",
" that.model.set('sent', offset);\n",
" that.touch();\n",
" } else {\n",
" console.log(\"Read error: \" + event.target.error);\n",
" that.model.set('data', '');\n",
" that.model.set('sent', -2);\n",
" that.touch();\n",
" }\n",
" that.touch();\n",
" }\n",
" \n",
" var chunk_reader = function (_offset, _f) {\n",
" // console.log('CR' + ' ' + _f + ' ' + _offset);\n",
" reader = new FileReader();\n",
" var chunk = _f.slice(_offset, chunk_size + _offset); \n",
" reader.readAsArrayBuffer(chunk);\n",
" reader.onload = reader_done;\n",
" }\n",
" \n",
" // OK. request next chunk\n",
" chunk_reader(offset, this.files[fnum]);\n",
" },\n",
" \n",
" \n",
" handle_file_change: function(evt) {\n",
"\n",
" var _files = evt.target.files;\n",
" var filenames = [];\n",
" var file_readers = [];\n",
" this.files = [];\n",
"\n",
" for (var i = 0; i < _files.length; i++) {\n",
" var file = _files[i];\n",
" console.log(\"Filename: \" + file.name);\n",
" console.log(\"Type: \" + file.type);\n",
" console.log(\"Size: \" + file.size + \" bytes\");\n",
" this.files.push(file);\n",
" filenames.push([file.name, file.size]);\n",
" };\n",
" \n",
" // Set the filenames of the files.\n",
" this.model.set('filenames', filenames);\n",
" this.touch();\n",
"\n",
" // update the label\n",
" if (filenames.length == 0) {\n",
" this.label.innerHTML = this.labelstr;\n",
" this.file.removeAttribute(\"disabled\");\n",
" } else if (filenames.length == 1) {\n",
" this.label.innerHTML = \" \" + filenames[0][0];\n",
" this.file.setAttribute('disabled', 'true');\n",
" } else {\n",
" this.label.innerHTML = \" \" + filenames.length + \" files selected\";\n",
" this.file.setAttribute('disabled', 'true'); \n",
" };\n",
" this.label.prepend(this.icon);\n",
" },\n",
" });\n",
"\n",
" // Register the FilePickerView with the widget manager.\n",
" return {\n",
" FilePickerView: FilePickerView\n",
" };\n",
"});\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os,sys\n",
"sys.path.insert(0, os.path.abspath('..'))\n",
"import hublib.ui as ui"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 4,
"hidden": false,
"row": 0,
"width": 4
},
"report_default": {}
}
}
}
},
"source": [
"## Number (Float) Input with Units"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 4,
"height": 4,
"hidden": false,
"row": 0,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "52afe99d250a4f50b6a7330bfa95a01f",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Number</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Number(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Longitudinal Young\\'s Modulus\\nValues must be in units of gigapascal.\\nMin: 0.0\\tMax: 500.0\\n\" data-container=\"body\">E1</div>', layout=Layout(flex=u'2 1 auto')), BoundedFloatText(value=138.0, layout=Layout(width=u'auto'), max=500.0), HTMLMath(value=u'$\\\\mathrm{GPa}$', layout=Layout(min_width=u'6ch'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'5px', width=u'auto'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"e1 = ui.Number(\n",
" name='E1',\n",
" description=\"Longitudinal Young's Modulus\",\n",
" units='GPa',\n",
" min=0,\n",
" max=500,\n",
" value=138\n",
")\n",
"e1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"hidden": true
},
"report_default": {}
}
}
}
},
"outputs": [],
"source": [
"def my_cb(x, y):\n",
" print(\"CB:\", x == e1)\n",
" print(\"New:\", y)\n",
"def my_cb2(x, y):\n",
" print(\"CB2:\", x == e1)\n",
" print(\"New:\", y)"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 8,
"height": 4,
"hidden": false,
"row": 0,
"width": 4
},
"report_default": {}
}
}
}
},
"source": [
"You can control the width. But it is generally better to use Layout."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 4,
"hidden": false,
"row": 4,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
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"application/vnd.jupyter.widget-view+json": {
"model_id": "91fe526530eb4d929e957defd4409f5f",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Number</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Number(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Longitudinal Young\\'s Modulus\\nValues must be in units of gigapascal.\\nMin: 0.0\\tMax: 500.0\\n\" data-container=\"body\">E1</div>', layout=Layout(flex=u'2 1 auto')), BoundedFloatText(value=138.0, layout=Layout(width=u'auto'), max=500.0), HTMLMath(value=u'$\\\\mathrm{GPa}$', layout=Layout(min_width=u'6ch'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'5px', width=u'30%'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"e2 = ui.Number(\n",
" name='E1',\n",
" description=\"Longitudinal Young's Modulus\",\n",
" units='GPa',\n",
" min=0,\n",
" max=500,\n",
" value=138,\n",
" cb=my_cb,\n",
" width='30%'\n",
" )\n",
"e2"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 4,
"height": 4,
"hidden": false,
"row": 4,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('CB:', False)\n",
"('New:', 170.0)\n"
]
}
],
"source": [
"e2.value=170"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 8,
"height": 4,
"hidden": false,
"row": 4,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [],
"source": [
"e2.min = 150"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"hidden": true
},
"report_default": {}
}
}
}
},
"outputs": [],
"source": [
"e2.cb = my_cb2"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 4,
"hidden": false,
"row": 8,
"width": 4
},
"report_default": {}
}
}
}
},
"source": [
"## Integer Input\n",
"Integer inputs do not have units"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 4,
"height": 4,
"hidden": false,
"row": 8,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d6cf30c151a7423ab300a652c8fcbcd8",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Integer</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Integer(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Number of Loops to Run\\nMin: 0\\tMax: 500\\n\" data-container=\"body\">Loops</div>', layout=Layout(flex=u'2 1 auto')), BoundedIntText(value=1, layout=Layout(width=u'auto'), max=500), HTMLMath(value=u'', layout=Layout(min_width=u'6ch'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'5px', width=u'auto'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"e2 = ui.Integer(\n",
" name='Loops',\n",
" desc=\"Number of Loops to Run\",\n",
" min=0,\n",
" max=500,\n",
" value=1\n",
" )\n",
"e2"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 8,
"height": 4,
"hidden": false,
"row": 8,
"width": 4
},
"report_default": {}
}
}
}
},
"source": [
"## Checkbutton"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 4,
"hidden": false,
"row": 12,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b5ccf88b810143c6879ef764e9ea95be",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Checkbox</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Show the Advanced Options\" data-container=\"body\">Advanced Options</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"check = ui.Checkbox('Advanced Options', desc='Show the Advanced Options', value=False)\n",
"check"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 4,
"height": 4,
"hidden": false,
"row": 12,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"check.value"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 8,
"height": 4,
"hidden": false,
"row": 12,
"width": 4
},
"report_default": {}
}
}
}
},
"source": [
"## Radiobutton"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 6,
"hidden": false,
"row": 16,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "650f7495e29942b7b9370d5db4f8d9bb",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Radiobuttons</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Radiobuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), RadioButtons(index=2, layout=Layout(width=u'initial'), options=('peanut', 'walnut', 'almond', 'pecan'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"r = ui.Radiobuttons(\n",
" name='Nut',\n",
" description=\"Type of nut to eat.\",\n",
" value='almond',\n",
" options=['peanut', 'walnut', 'almond', 'pecan']\n",
")\n",
"r"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 4,
"height": 4,
"hidden": false,
"row": 16,
"width": 4
},
"report_default": {}
}
}
}
},
"source": [
"## Togglebutton"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 8,
"height": 5,
"hidden": false,
"row": 16,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7df41ea94bbc4c98930535619cb4edc5",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Togglebuttons</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(index=2, layout=Layout(width=u'initial'), options=('peanut', 'walnut', 'almond', 'pecan'), style=ToggleButtonsStyle(button_width=u'initial'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tb = ui.Togglebuttons(\n",
" name='Nut',\n",
" description=\"Type of nut to eat.\",\n",
" value='almond',\n",
" options=['peanut', 'walnut', 'almond', 'pecan'],\n",
" )\n",
"tb"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 4,
"height": 4,
"hidden": false,
"row": 20,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "44565c3029b647129bd156af0b25b8d9",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Dropdown</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(index=2, layout=Layout(min_width=u'10ch', width=u'auto'), options=('peanut', 'walnut', 'almond', 'pecan'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dd = ui.Dropdown(\n",
" name='Nut',\n",
" description=\"Type of nut to eat.\",\n",
" value='almond',\n",
" options=['peanut', 'walnut', 'almond', 'pecan']\n",
")\n",
"dd"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 8,
"height": 4,
"hidden": false,
"row": 21,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ea518756754c46db935ce18c6ecec099",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>String</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"String(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Name (First and Last)\" data-container=\"body\">Name</div>', layout=Layout(flex=u'2 1 auto')), Text(value=u'<name>', layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xstr = ui.String(name=\"Name\", description='Name (First and Last)', value='<name>')\n",
"xstr"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 4,
"hidden": false,
"row": 22,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "83021b677a934f91a1ce7231151aeaf7",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Text</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Text(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Experiment Description\" data-container=\"body\">Description</div>', layout=Layout(flex=u'2 1 auto')), Textarea(value=u'', layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# FIXME. Width\n",
"\n",
"xtxt = ui.Text(name=\"Description\", description='Experiment Description', value='')\n",
"xtxt"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 4,
"height": 4,
"hidden": false,
"row": 24,
"width": 4
},
"report_default": {}
}
}
}
},
"source": [
"# Grouping\n",
"Collections of UI elements can be collected in Tabs and Forms."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 8,
"height": 4,
"hidden": false,
"row": 25,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1641241f14194146a24d29da11a2f891",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Tab</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Tab(children=(Number(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Longitudinal Young\\'s Modulus\\nValues must be in units of gigapascal.\\nMin: 0.0\\tMax: 500.0\\n\" data-container=\"body\">E1</div>', layout=Layout(flex=u'2 1 auto')), BoundedFloatText(value=138.0, layout=Layout(width=u'auto'), max=500.0), HTMLMath(value=u'$\\\\mathrm{GPa}$', layout=Layout(min_width=u'6ch'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'5px', width=u'auto')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(index=2, layout=Layout(width=u'initial'), options=('peanut', 'walnut', 'almond', 'pecan'), style=ToggleButtonsStyle(button_width=u'initial'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), String(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Name (First and Last)\" data-container=\"body\">Name</div>', layout=Layout(flex=u'2 1 auto')), Text(value=u'<name>', layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), _titles={0: 'E1', 1: 'Nut', 2: 'Name'})"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ui.Tab([e1,tb,xstr])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 7,
"hidden": false,
"row": 26,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "69491973a66c4d5bb5e2365d4acc1f07",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Group</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Group(children=(Number(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Longitudinal Young\\'s Modulus\\nValues must be in units of gigapascal.\\nMin: 0.0\\tMax: 500.0\\n\" data-container=\"body\">E1</div>', layout=Layout(flex=u'2 1 auto')), BoundedFloatText(value=138.0, layout=Layout(width=u'auto'), max=500.0), HTMLMath(value=u'$\\\\mathrm{GPa}$', layout=Layout(min_width=u'6ch'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'5px', width=u'auto')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(index=2, layout=Layout(width=u'initial'), options=('peanut', 'walnut', 'almond', 'pecan'), style=ToggleButtonsStyle(button_width=u'initial'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), String(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Name (First and Last)\" data-container=\"body\">Name</div>', layout=Layout(flex=u'2 1 auto')), Text(value=u'<name>', layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'auto'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ui.Form([e1,tb,xstr])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 4,
"height": 9,
"hidden": false,
"row": 28,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bb79d501ed4144208dc67771d8526d64",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Group</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Group(children=(HTML(value=u\"<p style='background-color: #DCDCDC; font-size: 150%; padding: 5px'>My Parameters</p>\", layout=Layout(flex=u'2 1 auto')), Number(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Longitudinal Young\\'s Modulus\\nValues must be in units of gigapascal.\\nMin: 0.0\\tMax: 500.0\\n\" data-container=\"body\">E1</div>', layout=Layout(flex=u'2 1 auto')), BoundedFloatText(value=138.0, layout=Layout(width=u'auto'), max=500.0), HTMLMath(value=u'$\\\\mathrm{GPa}$', layout=Layout(min_width=u'6ch'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'5px', width=u'auto')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(index=2, layout=Layout(width=u'initial'), options=('peanut', 'walnut', 'almond', 'pecan'), style=ToggleButtonsStyle(button_width=u'initial'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), String(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Name (First and Last)\" data-container=\"body\">Name</div>', layout=Layout(flex=u'2 1 auto')), Text(value=u'<name>', layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'auto'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f1 = ui.Form([e1,tb,xstr], name='My Parameters')\n",
"f1"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 8,
"height": 6,
"hidden": false,
"row": 29,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a068972e7f804e688514a849eb81ce8a",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Group</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Group(children=(HTML(value=u\"<p style='background-color: #DCDCDC; font-size: 150%; padding: 5px'>More Parameters</p>\", layout=Layout(flex=u'2 1 auto')), Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Show the Advanced Options\" data-container=\"body\">Advanced Options</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(index=2, layout=Layout(min_width=u'10ch', width=u'auto'), options=('peanut', 'walnut', 'almond', 'pecan'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Text(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Experiment Description\" data-container=\"body\">Description</div>', layout=Layout(flex=u'2 1 auto')), Textarea(value=u'', layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'50%'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f2 = ui.Form([check, dd, xtxt], name='More Parameters', width='50%')\n",
"f2"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 11,
"hidden": false,
"row": 33,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cef82018cd3642f0ac26a8bd9e3d6f21",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Tab</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Tab(children=(Group(children=(HTML(value=u\"<p style='background-color: #DCDCDC; font-size: 150%; padding: 5px'>My Parameters</p>\", layout=Layout(flex=u'2 1 auto')), Number(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Longitudinal Young\\'s Modulus\\nValues must be in units of gigapascal.\\nMin: 0.0\\tMax: 500.0\\n\" data-container=\"body\">E1</div>', layout=Layout(flex=u'2 1 auto')), BoundedFloatText(value=138.0, layout=Layout(width=u'auto'), max=500.0), HTMLMath(value=u'$\\\\mathrm{GPa}$', layout=Layout(min_width=u'6ch'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'5px', width=u'auto')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(index=2, layout=Layout(width=u'initial'), options=('peanut', 'walnut', 'almond', 'pecan'), style=ToggleButtonsStyle(button_width=u'initial'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), String(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Name (First and Last)\" data-container=\"body\">Name</div>', layout=Layout(flex=u'2 1 auto')), Text(value=u'<name>', layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'auto')), Group(children=(HTML(value=u\"<p style='background-color: #DCDCDC; font-size: 150%; padding: 5px'>More Parameters</p>\", layout=Layout(flex=u'2 1 auto')), Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Show the Advanced Options\" data-container=\"body\">Advanced Options</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(index=2, layout=Layout(min_width=u'10ch', width=u'auto'), options=('peanut', 'walnut', 'almond', 'pecan'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Text(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Experiment Description\" data-container=\"body\">Description</div>', layout=Layout(flex=u'2 1 auto')), Textarea(value=u'', layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'50%'))), _titles={0: 'My Parameters', 1: 'More Parameters'})"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ui.Tab([f1,f2])\n",
"\n",
"# FIXME: What about a Form without a title? Box? Group? or Form(name='')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 8,
"height": 14,
"hidden": false,
"row": 35,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "59c2fd67b9f44b69963a221ccf940b73",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Group</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Group(children=(Group(children=(HTML(value=u\"<p style='background-color: #DCDCDC; font-size: 150%; padding: 5px'>My Parameters</p>\", layout=Layout(flex=u'2 1 auto')), Number(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Longitudinal Young\\'s Modulus\\nValues must be in units of gigapascal.\\nMin: 0.0\\tMax: 500.0\\n\" data-container=\"body\">E1</div>', layout=Layout(flex=u'2 1 auto')), BoundedFloatText(value=138.0, layout=Layout(width=u'auto'), max=500.0), HTMLMath(value=u'$\\\\mathrm{GPa}$', layout=Layout(min_width=u'6ch'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'5px', width=u'auto')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(index=2, layout=Layout(width=u'initial'), options=('peanut', 'walnut', 'almond', 'pecan'), style=ToggleButtonsStyle(button_width=u'initial'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), String(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Name (First and Last)\" data-container=\"body\">Name</div>', layout=Layout(flex=u'2 1 auto')), Text(value=u'<name>', layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'auto')), Group(children=(HTML(value=u\"<p style='background-color: #DCDCDC; font-size: 150%; padding: 5px'>More Parameters</p>\", layout=Layout(flex=u'2 1 auto')), Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Show the Advanced Options\" data-container=\"body\">Advanced Options</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(index=2, layout=Layout(min_width=u'10ch', width=u'auto'), options=('peanut', 'walnut', 'almond', 'pecan'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Text(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Experiment Description\" data-container=\"body\">Description</div>', layout=Layout(flex=u'2 1 auto')), Textarea(value=u'', layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'50%'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'auto'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# FIXME: What about colors for forms? everything?\n",
"ff = ui.Form([f1,f2])\n",
"ff"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 4,
"height": 9,
"hidden": false,
"row": 37,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e41ec397d1264a5fa89fd995917a6362",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Tab</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Tab(children=(Group(children=(Number(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Longitudinal Young\\'s Modulus\\nValues must be in units of gigapascal.\\nMin: 0.0\\tMax: 500.0\\n\" data-container=\"body\">E1</div>', layout=Layout(flex=u'2 1 auto')), BoundedFloatText(value=138.0, layout=Layout(width=u'auto'), max=500.0), HTMLMath(value=u'$\\\\mathrm{GPa}$', layout=Layout(min_width=u'6ch'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'5px', width=u'auto')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(index=2, layout=Layout(width=u'initial'), options=('peanut', 'walnut', 'almond', 'pecan'), style=ToggleButtonsStyle(button_width=u'initial'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), String(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Name (First and Last)\" data-container=\"body\">Name</div>', layout=Layout(flex=u'2 1 auto')), Text(value=u'<name>', layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'auto')), Group(children=(Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Show the Advanced Options\" data-container=\"body\">Advanced Options</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(index=2, layout=Layout(min_width=u'10ch', width=u'auto'), options=('peanut', 'walnut', 'almond', 'pecan'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Text(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Experiment Description\" data-container=\"body\">Description</div>', layout=Layout(flex=u'2 1 auto')), Textarea(value=u'', layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'auto'))), _titles={0: 'My Parameters', 1: 'More Parameters'})"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f1 = ui.Form([e1,tb,xstr])\n",
"f2 = ui.Form([check, dd, xtxt])\n",
"ui.Tab([f1,f2], titles=['My Parameters', 'More Parameters'])\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 4,
"hidden": false,
"row": 44,
"width": 4
},
"report_default": {}
}
}
}
},
"source": [
"## Disable UI Elements\n",
"You can disable individual elements or all the elements in a Form or Tab."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"hidden": true
},
"report_default": {}
}
}
}
},
"outputs": [],
"source": [
"ff.disabled = True"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"hidden": true
},
"report_default": {}
}
}
}
},
"outputs": [],
"source": [
"ff.disabled = False"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 4,
"height": 4,
"hidden": false,
"row": 46,
"width": 4
},
"report_default": {}
}
}
}
},
"source": [
"## Visibility\n",
"\n",
"You can make elements visible or invisible."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"hidden": true
},
"report_default": {}
}
}
}
},
"outputs": [],
"source": [
"#tb.visible = False"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"hidden": true
},
"report_default": {}
}
}
}
},
"outputs": [],
"source": [
"#tb.visible = True"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 4,
"hidden": false,
"row": 48,
"width": 4
},
"report_default": {}
}
}
}
},
"source": [
"## Callbacks\n",
"\n",
"Each non-grouping UI element can callback a function you provide when the value changes."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
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"col": 8,
"height": 5,
"hidden": false,
"row": 49,
"width": 4
},
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}
}
}
},
"outputs": [
{
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"application/vnd.jupyter.widget-view+json": {
"model_id": "0b80d113a9614a0bb029dfb8c9c2aad3",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Togglebuttons</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(index=2, layout=Layout(width=u'initial'), options=('peanut', 'walnut', 'almond', 'pecan'), style=ToggleButtonsStyle(button_width=u'initial'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'50%'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def something_changed(name, value):\n",
" print(\"%s changed to %s\" % (name, value))\n",
"\n",
"tbc = ui.Togglebuttons(\n",
" name='Nut',\n",
" description=\"Type of nut to eat.\",\n",
" value='almond',\n",
" options=['peanut', 'walnut', 'almond', 'pecan'],\n",
" cb=something_changed,\n",
" width='50%'\n",
" )\n",
"tbc"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 5,
"hidden": false,
"row": 54,
"width": 12
},
"report_default": {}
}
}
}
},
"source": [
"# Interactive UI\n",
"\n",
"We can use callbacks to modify the UI dynamically.\n",
"\n",
"### Disabling Elements"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 11,
"hidden": false,
"row": 59,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "356a7950fa78477f8f04acdd49e89612",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Group</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Group(children=(Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Show the Advanced Options\" data-container=\"body\">Advanced Options</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(index=2, layout=Layout(width=u'initial'), options=('peanut', 'walnut', 'almond', 'pecan'), style=ToggleButtonsStyle(button_width=u'initial'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Nut Options\" data-container=\"body\">Options</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(disabled=True, layout=Layout(width=u'initial'), options=('None', 'Toasted', 'Roasted', 'Glazed', 'Salted'), style=ToggleButtonsStyle(button_width=u'initial'), value='None')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'auto'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def set_advanced(name, val):\n",
" opt.disabled = not val\n",
"\n",
"adv_check = ui.Checkbox('Advanced Options', desc='Show the Advanced Options', value=False, cb=set_advanced)\n",
"\n",
"opt = ui.Togglebuttons(\n",
" name='Options',\n",
" description=\"Nut Options\",\n",
" value='None',\n",
" options=['None', 'Toasted', 'Roasted', 'Glazed', 'Salted'],\n",
" disabled=True, # initial state\n",
" )\n",
"ui.Form([adv_check, tb, opt])"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 4,
"height": 4,
"hidden": false,
"row": 50,
"width": 4
},
"report_default": {}
}
}
}
},
"source": [
"### Hiding Elements Dynamically"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 4,
"height": 11,
"hidden": false,
"row": 59,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4fde5ae82ef0401c8e65dd4fa82d8ebc",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Group</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Group(children=(Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Show the Advanced Options\" data-container=\"body\">Advanced Options</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'50%')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Type of nut to eat.\" data-container=\"body\">Nut</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(index=2, layout=Layout(width=u'initial'), options=('peanut', 'walnut', 'almond', 'pecan'), style=ToggleButtonsStyle(button_width=u'initial'), value='almond')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Nut Options\" data-container=\"body\">Options</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(disabled=True, layout=Layout(visibility=u'hidden', width=u'initial'), options=('None', 'Toasted', 'Roasted', 'Glazed', 'Salted'), style=ToggleButtonsStyle(button_width=u'initial'), value='None')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', visibility=u'hidden', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'auto'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def show_advanced(name, val):\n",
" opt.visible = val\n",
"\n",
"adv_check = ui.Checkbox('Advanced Options', desc='Show the Advanced Options', value=False, width='50%', cb=show_advanced)\n",
"opt.visible = False\n",
"ui.Form([adv_check, tb, opt])"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 8,
"height": 4,
"hidden": false,
"row": 59,
"width": 4
},
"report_default": {}
}
}
}
},
"source": [
"# A More Complicated Example"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 8,
"height": 12,
"hidden": false,
"row": 63,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7dba26fd625b412b93cd5d9474129695",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Group</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Group(children=(HTML(value=u'<p data-toggle=\"popover\" title=\"Not a day of the week!\" style=\\'background-color: #DCDCDC; font-size: 150%; padding: 5px\\'>Sundae</p>', layout=Layout(flex=u'2 1 auto')), Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Ice Cream Flavor.\" data-container=\"body\">Ice Cream</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(layout=Layout(min_width=u'14ch', width=u'auto'), options=('Vanilla', 'Strawberry', 'Chocolate', 'Pistachio'), value='Vanilla')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Radiobuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Ice Cream Topping\" data-container=\"body\">Topping</div>', layout=Layout(flex=u'2 1 auto')), RadioButtons(layout=Layout(width=u'initial'), options=('None', 'Fudge', 'Caramel', 'Marshmallow'), value='None')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Brownie on the bottom?\" data-container=\"body\">Brownie</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(layout=Layout(width=u'initial'), options=('No', 'Yes'), style=ToggleButtonsStyle(button_width=u'initial'), value='No')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Cherry on Top?\" data-container=\"body\">Cherry</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'50%'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ice_cream = ui.Dropdown(\n",
" name='Ice Cream',\n",
" description=\"Ice Cream Flavor.\",\n",
" value='Vanilla',\n",
" options=['Vanilla', 'Strawberry', 'Chocolate', 'Pistachio'],\n",
" )\n",
"topping = ui.Radiobuttons(\n",
" name='Topping',\n",
" description=\"Ice Cream Topping\",\n",
" value='None',\n",
" options=['None', 'Fudge', 'Caramel', 'Marshmallow'],\n",
" )\n",
"cherry = ui.Checkbox('Cherry', desc='Cherry on Top?', value=False)\n",
"brownie = ui.Togglebuttons('Brownie', desc=\"Brownie on the bottom?\", value='No', options=['No', 'Yes'])\n",
"sundae = ui.Group([ice_cream, topping, brownie, cherry], name='Sundae', desc='Not a day of the week!', width='50%')\n",
"sundae"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 8,
"height": 4,
"hidden": false,
"row": 75,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 6,
"hidden": false,
"row": 70,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f4f1faa8834b4b559b18cffffa3b9bfc",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Group</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Group(children=(Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Pizza Crust\" data-container=\"body\">Crust</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(layout=Layout(min_width=u'15ch', width=u'auto'), options=('Thin', 'Hand-Tossed', 'Pan', 'Gluten-Free'), value='Thin')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Sauce Type\" data-container=\"body\">Sauce</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(layout=Layout(min_width=u'11ch', width=u'auto'), options=('Regular', 'Robust', 'Pesto'), value='Regular')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"\" data-container=\"body\">Extra Cheese</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'50%'))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"crust = ui.Dropdown(\n",
" name='Crust',\n",
" description=\"Pizza Crust\",\n",
" value='Thin',\n",
" options=['Thin', 'Hand-Tossed', 'Pan', 'Gluten-Free'],\n",
" )\n",
"sauce = ui.Dropdown(\n",
" name='Sauce',\n",
" description=\"Sauce Type\",\n",
" value='Regular',\n",
" options=['Regular', 'Robust', 'Pesto'],\n",
" )\n",
"cheese = ui.Checkbox('Extra Cheese', value=False)\n",
"pizza = ui.Form([crust, sauce, cheese], name='', width='50%') # collapsible\n",
"pizza\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 4,
"height": 8,
"hidden": false,
"row": 70,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "46a2cd8a4d9f44eb8e8d7e5d1fda25d6",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Tab</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Tab(children=(Group(children=(Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Pizza Crust\" data-container=\"body\">Crust</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(layout=Layout(min_width=u'15ch', width=u'auto'), options=('Thin', 'Hand-Tossed', 'Pan', 'Gluten-Free'), value='Thin')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Sauce Type\" data-container=\"body\">Sauce</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(layout=Layout(min_width=u'11ch', width=u'auto'), options=('Regular', 'Robust', 'Pesto'), value='Regular')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"\" data-container=\"body\">Extra Cheese</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'50%')), Group(children=(HTML(value=u'<p data-toggle=\"popover\" title=\"Not a day of the week!\" style=\\'background-color: #DCDCDC; font-size: 150%; padding: 5px\\'>Sundae</p>', layout=Layout(flex=u'2 1 auto')), Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Ice Cream Flavor.\" data-container=\"body\">Ice Cream</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(layout=Layout(min_width=u'14ch', width=u'auto'), options=('Vanilla', 'Strawberry', 'Chocolate', 'Pistachio'), value='Vanilla')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Radiobuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Ice Cream Topping\" data-container=\"body\">Topping</div>', layout=Layout(flex=u'2 1 auto')), RadioButtons(layout=Layout(width=u'initial'), options=('None', 'Fudge', 'Caramel', 'Marshmallow'), value='None')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Brownie on the bottom?\" data-container=\"body\">Brownie</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(layout=Layout(width=u'initial'), options=('No', 'Yes'), style=ToggleButtonsStyle(button_width=u'initial'), value='No')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Cherry on Top?\" data-container=\"body\">Cherry</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'50%'))), _titles={0: '', 1: 'Sundae'})"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ui.Tab([pizza, sundae])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"col": 0,
"height": 8,
"hidden": false,
"row": 76,
"width": 4
},
"report_default": {}
}
}
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3680a34a70f6486eae9052d541cccf7f",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"HBox(children=(Group(children=(HTML(value=u'<p data-toggle=\"popover\" title=\"Not a day of the week!\" style=\\'background-color: #DCDCDC; font-size: 150%; padding: 5px\\'>Sundae</p>', layout=Layout(flex=u'2 1 auto')), Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Ice Cream Flavor.\" data-container=\"body\">Ice Cream</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(layout=Layout(min_width=u'14ch', width=u'auto'), options=('Vanilla', 'Strawberry', 'Chocolate', 'Pistachio'), value='Vanilla')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Radiobuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Ice Cream Topping\" data-container=\"body\">Topping</div>', layout=Layout(flex=u'2 1 auto')), RadioButtons(layout=Layout(width=u'initial'), options=('None', 'Fudge', 'Caramel', 'Marshmallow'), value='None')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Brownie on the bottom?\" data-container=\"body\">Brownie</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(layout=Layout(width=u'initial'), options=('No', 'Yes'), style=ToggleButtonsStyle(button_width=u'initial'), value='No')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Cherry on Top?\" data-container=\"body\">Cherry</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', padding=u'0 10px 0 0', width=u'100%')), Group(children=(Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Pizza Crust\" data-container=\"body\">Crust</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(layout=Layout(min_width=u'15ch', width=u'auto'), options=('Thin', 'Hand-Tossed', 'Pan', 'Gluten-Free'), value='Thin')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Sauce Type\" data-container=\"body\">Sauce</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(layout=Layout(min_width=u'11ch', width=u'auto'), options=('Regular', 'Robust', 'Pesto'), value='Regular')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"\" data-container=\"body\">Extra Cheese</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'50%'))))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# HBox to do multicolumns\n",
"# Accordion (collapsible form)\n",
"# Form -> Group??\n",
"import ipywidgets as widgets\n",
"layoutL=widgets.Layout(\n",
" display='flex',\n",
" flex_flow='column',\n",
" align_items='stretch',\n",
" width='100%',\n",
" padding='0 10px 0 0'\n",
" )\n",
"layoutR=widgets.Layout(\n",
" display='flex',\n",
" flex_flow='column',\n",
" align_items='stretch',\n",
" width='50%',\n",
"# padding='0 0 0 5px'\n",
" )\n",
"sundae.layout = layoutL\n",
"pizza.layout = layoutR\n",
"widgets.HBox([sundae, pizza])\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
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"height": 4,
"hidden": false,
"row": 84,
"width": 12
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"report_default": {}
}
}
}
},
"source": [
"# maybe smart layout? check widths and adjust size accordingly\n",
"Use width% or\n",
"widgets.HBox([sundae, pizza], size=[2,1])\n"
]
},
{
"cell_type": "code",
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{
"data": {
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"u'50%'"
]
},
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}
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"pizza.layout.width"
]
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}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f8fc7841f32b4494a7ff041135d3424f",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Accordion</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Accordion(children=(Group(children=(Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Pizza Crust\" data-container=\"body\">Crust</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(layout=Layout(min_width=u'15ch', width=u'auto'), options=('Thin', 'Hand-Tossed', 'Pan', 'Gluten-Free'), value='Thin')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Sauce Type\" data-container=\"body\">Sauce</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(layout=Layout(min_width=u'11ch', width=u'auto'), options=('Regular', 'Robust', 'Pesto'), value='Regular')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"\" data-container=\"body\">Extra Cheese</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', width=u'50%')), Group(children=(HTML(value=u'<p data-toggle=\"popover\" title=\"Not a day of the week!\" style=\\'background-color: #DCDCDC; font-size: 150%; padding: 5px\\'>Sundae</p>', layout=Layout(flex=u'2 1 auto')), Dropdown(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Ice Cream Flavor.\" data-container=\"body\">Ice Cream</div>', layout=Layout(flex=u'2 1 auto')), Dropdown(layout=Layout(min_width=u'14ch', width=u'auto'), options=('Vanilla', 'Strawberry', 'Chocolate', 'Pistachio'), value='Vanilla')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Radiobuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Ice Cream Topping\" data-container=\"body\">Topping</div>', layout=Layout(flex=u'2 1 auto')), RadioButtons(layout=Layout(width=u'initial'), options=('None', 'Fudge', 'Caramel', 'Marshmallow'), value='None')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Togglebuttons(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Brownie on the bottom?\" data-container=\"body\">Brownie</div>', layout=Layout(flex=u'2 1 auto')), ToggleButtons(layout=Layout(width=u'initial'), options=('No', 'Yes'), style=ToggleButtonsStyle(button_width=u'initial'), value='No')), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto')), Checkbox(children=(HTML(value=u'<div data-toggle=\"popover\" title=\"Cherry on Top?\" data-container=\"body\">Cherry</div>', layout=Layout(flex=u'2 1 auto')), Checkbox(value=False, layout=Layout(width=u'initial'))), layout=Layout(border=u'solid 1px lightgray', display=u'flex', flex_flow=u'row', justify_content=u'space-between', padding=u'3px', width=u'auto'))), layout=Layout(align_items=u'stretch', display=u'flex', flex_flow=u'column', padding=u'0 10px 0 0', width=u'100%'))), _titles={u'1': 'Sundae', u'0': 'Pizza'})"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"accordion = widgets.Accordion(children=[pizza, sundae])\n",
"accordion.set_title(0, 'Pizza')\n",
"accordion.set_title(1, 'Sundae')\n",
"accordion"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
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"version": 1,
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},
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"source": [
"accordion.selected_index=None\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
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},
"outputs": [
{
"data": {
"text/plain": [
"['world', 'martin', 'hunt']"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = ['hello', 'world', 'martin', 'hunt']\n",
"a[1:]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
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{
"ename": "AttributeError",
"evalue": "'list' object has no attribute 'appendleft'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-38-ff4166901048>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappendleft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'123'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'appendleft'"
]
}
],
"source": [
"a.appendleft('123')\n",
"a"
]
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| mit |
tboggs/fretboard | TUTORIAL.ipynb | 1 | 18972 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
" =============================================================================\n",
"E E||---|---|-G-|---|-A-|---|---|-C-|---|-D-|---|-E-|---|---|-G-|---|-A-|---|---|\n",
"B ||-C-|---|-D-|---|-E-|---|---|-G-|---|-A-|---|---|-C-|---|-D-|---|-E-|---|---|\n",
"G G||---|-A-|---|---|-C-|---|-D-|---|-E-|---|---|-G-|---|-A-|---|---|-C-|---|-D-|\n",
"D D||---|-E-|---|---|-G-|---|-A-|---|---|-C-|---|-D-|---|-E-|---|---|-G-|---|-A-|\n",
"A A||---|---|-C-|---|-D-|---|-E-|---|---|-G-|---|-A-|---|---|-C-|---|-D-|---|-E-|\n",
"E E||---|---|-G-|---|-A-|---|---|-C-|---|-D-|---|-E-|---|---|-G-|---|-A-|---|---|\n",
" =============================================================================\n",
" 3 5 7 9 12 15 17 19 \n",
"```\n",
"\n",
"# The fretboard python module\n",
"\n",
"The `fretboard` module is a relatively simple python module for displaying notes and scales relative to a guitar's\n",
"fretboard. It currently supports ASCII output.\n",
"\n",
"## Notes\n",
"\n",
"Before getting to scales and displays, it is useful to know how the module handles representations of musical notes.\n",
"The `fretboard` module doesn't deal with the acoustic pitch of notes; rather it represents notes in terms of their number of semitones relative to a C reference.\n",
"\n",
"\n",
"### Creating notes by name\n",
"Notes can be created either by naming the note and optionally appending \"#\" or \"b\" to indicate a sharp or flat note."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"C\n"
]
}
],
"source": [
"from fretboard import *\n",
"c = Note('C')\n",
"print (c)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"C#"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Note('C#')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Db"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Note('Db')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"C"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Note('B#')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you like, you can use multiple sharps or flats."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(C#, D, C)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Note('B##'), Note('B###'), Note('B##b')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating relative notes\n",
"\n",
"Notes can also be createde by specifying an interval (in number of semitones) relative to another note. For example, F is 5 semitones above C."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"F\n"
]
}
],
"source": [
"f = c + 5\n",
"print (f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding 12 semitones gets us back to the same note."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"F\n"
]
}
],
"source": [
"ff = f + 12\n",
"print (ff)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Intervals\n",
"\n",
"We can also take the difference of two notes, which yields the number of semitones between the notes."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"17\n"
]
}
],
"source": [
"print (ff - c)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the interval printed above is the total number of semitones (which can span more than one octave) and can be positive or negative. If we want the to know the simple (within-octave) interval from a note up to the next instance of another note, the `interval` method (or function) can be used)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
}
],
"source": [
"print (c.interval(f))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7\n"
]
}
],
"source": [
"print (f.interval(c))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scales\n",
"\n",
"The main purpose of this module is to display scales over a fretboard diagram. Lets take a look at the `Major` scale in the key of C."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[C, D, E, F, G, A, B]\n"
]
}
],
"source": [
"c_maj = Major('C')\n",
"print (c_maj)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also create different modes of the diatonic scale. For example, the minor scale is just the sixth mode of the diatonic scale."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[C, D, D#, F, G, G#, A#]\n"
]
}
],
"source": [
"c_min = Diatonic('C', mode=6)\n",
"print (c_min)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is also a `Minor` scale class so you can save some typing (and don't have to remember which mode of the major scale it is). In addition to the `Major` and `Minor`, there are also classes for `HarmonicMinor`, `Pentatonic`, and `Blues` scales."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[A, C, D, E, G]\n"
]
}
],
"source": [
"a_pmin = MinorPentatonic('A')\n",
"print (a_pmin)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `Blues` scale adds a flat fifth (the blue note) to the pentatonic minor."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[A, C, D, Eb, E, G]\n"
]
}
],
"source": [
"a_blues = Blues('A')\n",
"print (a_blues)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To make the scales useful, we have the ability to test for membership."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'C' in a_blues"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'B' in a_blues"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also get the interval of a given note with respect to the scale."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"m7\n"
]
}
],
"source": [
"print (a_blues.get_interval_name('G'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Displaying Notes and Scales\n",
"\n",
"The `fretboard` module supports printing fretboard diagrams to the terminal output (`stdout`) via the `console` submodule. The quickest way to display simple scale diagrams is visa the `show_scale` function."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" =============================================================================\n",
"E E||-F-|---|-G-|---|-A-|---|-B-|-C-|---|-D-|---|-E-|-F-|---|-G-|---|-A-|---|-B-|\n",
"B B||-C-|---|-D-|---|-E-|-F-|---|-G-|---|-A-|---|-B-|-C-|---|-D-|---|-E-|-F-|---|\n",
"G G||---|-A-|---|-B-|-C-|---|-D-|---|-E-|-F-|---|-G-|---|-A-|---|-B-|-C-|---|-D-|\n",
"D D||---|-E-|-F-|---|-G-|---|-A-|---|-B-|-C-|---|-D-|---|-E-|-F-|---|-G-|---|-A-|\n",
"A A||---|-B-|-C-|---|-D-|---|-E-|-F-|---|-G-|---|-A-|---|-B-|-C-|---|-D-|---|-E-|\n",
"E E||-F-|---|-G-|---|-A-|---|-B-|-C-|---|-D-|---|-E-|-F-|---|-G-|---|-A-|---|-B-|\n",
" =============================================================================\n",
" 3 5 7 9 12 15 17 19 \n"
]
}
],
"source": [
"console.show_scale(Minor('A'))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" =============================================================================\n",
"D M2||---|M3-|-4-|---|-5-|---|M6-|---|M7-|-R-|---|M2-|---|M3-|-4-|---|-5-|---|M6-|\n",
"A M6||---|M7-|-R-|---|M2-|---|M3-|-4-|---|-5-|---|M6-|---|M7-|-R-|---|M2-|---|M3-|\n",
"G 5||---|M6-|---|M7-|-R-|---|M2-|---|M3-|-4-|---|-5-|---|M6-|---|M7-|-R-|---|M2-|\n",
"D M2||---|M3-|-4-|---|-5-|---|M6-|---|M7-|-R-|---|M2-|---|M3-|-4-|---|-5-|---|M6-|\n",
"A M6||---|M7-|-R-|---|M2-|---|M3-|-4-|---|-5-|---|M6-|---|M7-|-R-|---|M2-|---|M3-|\n",
"D M2||---|M3-|-4-|---|-5-|---|M6-|---|M7-|-R-|---|M2-|---|M3-|-4-|---|-5-|---|M6-|\n",
" =============================================================================\n",
" 3 5 7 9 12 15 17 19 \n"
]
}
],
"source": [
"console.show_scale(Major('C'), tuning='D A D G A D', fmt='interval')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are various optional `show_scale` arguments that can be used to customize the display. The `fmt`\n",
"argument can be any of the following:\n",
"\n",
"- \"note\" (default) - display the name of each note\n",
"- \"interval\" - display name of the interval of each note with respect to the scale key\n",
"- `<text char>` - display the specified character (e.g., '*') for each note\n",
"- `<callable>` - display the return value of the callable oject applied to each `Fret` object of the display\n",
"\n",
"If a callable object is given for `fmt`, it is passed a `Fret` object that has the following attributes:\n",
"\n",
"- `note` - the `Note` object associated with the fret\n",
"- `string` - the number of the string associated with the fret\n",
"- `number` - the number of the fret (zero indicates open string)\n",
"\n",
"For example, let's display A Minor Pentatonic with \"R\" displayed for the root note and \"*\" for all others."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" =============================================================================\n",
"E *||---|---|-*-|---|-R-|---|---|-*-|---|-*-|---|-*-|---|---|-*-|---|-R-|---|---|\n",
"B ||-*-|---|-*-|---|-*-|---|---|-*-|---|-R-|---|---|-*-|---|-*-|---|-*-|---|---|\n",
"G *||---|-R-|---|---|-*-|---|-*-|---|-*-|---|---|-*-|---|-R-|---|---|-*-|---|-*-|\n",
"D *||---|-*-|---|---|-*-|---|-R-|---|---|-*-|---|-*-|---|-*-|---|---|-*-|---|-R-|\n",
"A R||---|---|-*-|---|-*-|---|-*-|---|---|-*-|---|-R-|---|---|-*-|---|-*-|---|-*-|\n",
"E *||---|---|-*-|---|-R-|---|---|-*-|---|-*-|---|-*-|---|---|-*-|---|-R-|---|---|\n",
" =============================================================================\n",
" 3 5 7 9 12 15 17 19 \n"
]
}
],
"source": [
"scale = MinorPentatonic('A')\n",
"console.show_scale(scale, fmt=lambda f: 'R' if f.note == scale.root else '*')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's display scale intervals for Box 2 of the A Minor Pentatonic scale."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" =============================================================================\n",
"E ||---|---|---|---|---|---|---|m3-|---|-4-|---|---|---|---|---|---|---|---|---|\n",
"B ||---|---|---|---|---|---|---|m7-|---|-R-|---|---|---|---|---|---|---|---|---|\n",
"G ||---|---|---|---|---|---|-4-|---|-5-|---|---|---|---|---|---|---|---|---|---|\n",
"D ||---|---|---|---|---|---|-R-|---|---|m3-|---|---|---|---|---|---|---|---|---|\n",
"A ||---|---|---|---|---|---|-5-|---|---|m7-|---|---|---|---|---|---|---|---|---|\n",
"E ||---|---|---|---|---|---|---|m3-|---|-4-|---|---|---|---|---|---|---|---|---|\n",
" =============================================================================\n",
" 3 5 7 9 12 15 17 19 \n"
]
}
],
"source": [
"console.show_scale(scale, fmt=lambda f: scale.get_interval_name(f.note) if f.number in range(7, 11) else None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `show_scale` function is a convenient wrapper around the `Console` class. To have greater control\n",
"over the display, we can work with a Console object directly. We'll create one and change the fill characters to be just empty space."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" =============================================================================\n",
"E E|| | | G | | A | | | C | | D | | E | | | G | | A | | |\n",
"B || C | | D | | E | | | G | | A | | | C | | D | | E | | |\n",
"G G|| | A | | | C | | D | | E | | | G | | A | | | C | | D |\n",
"D D|| | E | | | G | | A | | | C | | D | | E | | | G | | A |\n",
"A A|| | | C | | D | | E | | | G | | A | | | C | | D | | E |\n",
"E E|| | | G | | A | | | C | | D | | E | | | G | | A | | |\n",
" =============================================================================\n",
" 3 5 7 9 12 15 17 19 \n"
]
}
],
"source": [
"c = Console()\n",
"c.fret_fill_char = ' '\n",
"c.fret_empty_fill_char = ' '\n",
"c.display_scale(scale)\n",
"c.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `Console` class also has a `display_fret` method that allows us to control display of individual frets. Let's use that to display a C chord."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" =====================\n",
"E E||---|---|---|---|---|\n",
"B ||-C-|---|---|---|---|\n",
"G G||---|---|---|---|---|\n",
"D ||---|-E-|---|---|---|\n",
"A ||---|---|-C-|---|---|\n",
"E x||---|---|---|---|---|\n",
" =====================\n",
" 3 5 \n"
]
}
],
"source": [
"c = Console()\n",
"for (s, f) in [(1, 0), (2, 1), (3, 0), (4, 2), (5, 3)]:\n",
" c.display_fret(s, f)\n",
"c.display_fret(6, 0, 'x')\n",
"c.show(fmax=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, the `Console.show` method prints the display to `stdout`. To get the display string directly, use the `get_display` method instead."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" =====================\n",
"E E||---|---|---|---|---|\n",
"B ||-C-|---|---|---|---|\n",
"G G||---|---|---|---|---|\n",
"D ||---|-E-|---|---|---|\n",
"A ||---|---|-C-|---|---|\n",
"E x||---|---|---|---|---|\n",
" =====================\n",
" 3 5 \n"
]
}
],
"source": [
"text = c.get_display(fmax=5)\n",
"print (text)"
]
}
],
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
gher-ulg/divand.jl | examples/open boundary conditions.ipynb | 1 | 264418 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Open boundary conditions which mimic an infinite domain in $\\mathtt{DIVAnd}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## General idea"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Manipulate the metrics of the boxes surrounding an open sea such that the applied boundary condition is artifically pushed further away, similar to the idea of absorbing boundary conditions. Criteria for success: The analysis with data points in the center should not be different if the open boundary is 1 or 20 length scales away. \n",
"\n",
"Mathematical criteria (need to show it is close to the desired property from above): The background variance field should be as uniform as possible and not increase near the open boundary (as it currently does with the zero normal gradient condition)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1D testing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First tests reveal that a good way to adapt the metrics $p_m$ near the boundaries is to fix values such that $l p_m \\sim 2$ where $l$ is the local length scale. This amount to move the last grid point two length scales away in terms of metrics used in the numerical finite differences."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For example, testing for different values of $\\alpha$ to calculate $p_m$= $1 \\over \\alpha l $ we observe a clear minimum in the variance of $\\mathrm{diag} (\\mathbf{B})$. If we use this value and look at the analysis with the adapted boundary condition and the analysis in a much larger domain, the differences are indeed very small.\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"indmin(vj) = 96\n",
"indmin(rj) = 81\n",
"alen = 1.92\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x0000000003874F60>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000001BDDF048>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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NGwcA8Pb2RuPGjRXyt6IDU510lI/rhAkTUK9ePYSFheHdd9+V70+O4+TbLyoqwuDBg3Hy5Em0aNECY8aMgb6+Pk6fPo0ZM2bg6tWr2L59e6Xy6+LFi+jbty8KCwvh4+ODpk2bIjo6Gh4eHkqrmWzevBnTpk2DsbExRowYASsrK0RERGDFihU4cuQILly4AFNTU4U0vvfee7h69SoGDhwIXV1dHDhwAFOmTIGuri5u3ryJnTt3YvDgwejTpw8OHTqExYsXw8jICJ999plg+9OmTUPr1q3Rs2dP2NjY4OnTp/jjjz8wbtw4xMTEYNGiRaL7RYymjmt14gQAo0ePxvnz5zFgwACYmprijz/+wMqVK5GWloatW7cKllm7di0+/fRT1KtXD+PHj4eZmRlOnDgBNzc3mJmZVSqeZbdfWFiIvn374vnz53j33XdRUFCAkJAQ+Pr64sSJE9i4cSOuXbuGAQMGQCqVYv/+/Zg5cyasrKzkF36ZsWPHIiQkBPb29pg8eTI4jsPvv/+OadOm4cKFC9i5c6dG02Jubo7AwEBER0cjLCwMw4YNk58rZc8Zsf2+bds2TJkyBfr6+hg6dCgaNWqEe/fuYevWrTh8+DCuXLkiaJ6jzjkiJjAwEHFxcQgODoaHh4e8EC77VxPXLlV5FRQUhISEBEH15LLVKqOiotCvXz+8ePECXl5e8PHxQXp6Og4ePAh3d3ccPHgQ/fv3r1RaZ82ahQ0bNsDW1lZ+joeFheHKlSsoKChQeChQ9z4aGxuLzp074+2338aECRMQFxeH0NBQeHp64uLFi+jfvz/MzMzw3nvv4dmzZwgJCcHAgQMRExMj2Mdnz57FypUr4enpCV9fXxgbG+PevXv47bffcOjQIVy8eBHvvPNOhenW5L1MXX/++Sc4jkOnTp3kYePGjcMXX3yBHTt2YMaMGQrL7Ny5E4wxTJgwQR6m7n3m/v376Ny5M5o3b46xY8fi5cuXMDU1RUpKClxcXJCdnY2BAwfC19cXeXl5iI2Nxa5duzBjxgzUq1dPvh6xc+HIkSPw9fUFAPj6+sLBwQGRkZHYtGkTDh06hPPnzwsKQrW9P2Rp8PHxwfXr1zFgwAB4e3vLq/nK4hcWFoYjR45gyJAhmDp1Ki5evIjg4GDEx8fj22+/Re/evdGjRw9MmjQJf//9Nw4fPozY2FjcunVLvq2q5q+2nDp1ChzHwcvLS2Fa48aN4ezsjHv37iE2NlbQxE5T6zp9+jQAiC4zYMAA+XrLY4zJq4oXFxfD0dERvXv3hqWlpcK8spc8YvE3NzdHvXr18PDhQ8THxysUzMUcOnQIHMcpVBtWNy3lRUVF4caNG2jcuDH69OlT4fwysqri5QvlmtrHBQUF2LlzJ3iex6RJkxSmt2rVCnfv3sXRo0fx/vvvy8Nzc3MRHh4OQ0NDdO3aVR5uZWWFBg0aICYmBvfv3xcUvmNiYnDv3j15m+yy25ClKS8vT9DMS9anV9++fRXixRjD0aNHMWzYMMG0w4cPg+M40WWqkhZVDh06BAAKx4s6adGKqnwev3HjBtPT02M8z7Nx48ax0NBQFh8fr3KZb7/9lnEcxwYPHsyKi4vl4WlpaczR0ZHxPM8uXbokD1dW1VxVNcDg4GDR6pQVVdnjOI55enoKwj788EPGcRybOnWqIPz+/fvMzMyM6evrC9IcEREhr9a2Y8cOwTKyqivTp09XGoeylKV9/PjxjOM45uDgwB49eiQPLy4uZj169GA8z7Nr165VahvKqgzJ0vDhhx+ykpISefh///3HdHR0WKtWrRTWpSx/NZkOsW0o298yCxcuZBzHsVmzZgnSUlJSwiZOnMh4nmeHDh0SXba85s2bM57n2eHDhwXh69evl+dZ2byMj49nUqmUmZmZsZiYGMEy06ZNYxzHsSlTpgjCPTw8GMdxzNXVlWVmZsrDHz58yPT09Fi9evVYkyZNWHJysnzaixcvWP369ZmVlZXgvJItV15hYSHr3bs309PTY48fP1bYfvlzS5PHtbpx4jiOubi4sBcvXsjDc3NzmZOTE9PR0WGpqany8Li4OKanp8csLS0FVc4YY8zHx0dl1VMxsmvTsGHDBNWuzp07J69Cp2x/dejQQbCuPXv2yNOSm5srSIuLiwvjeZ6FhIRUKy2y/VW+iraqc0VWZars9SwmJobp6ekxZ2dnwfHGGGOnTp1iEomEDR8+XBBe1XNEFWXpYExz1y5l21B1jykqKmJNmzZlBgYGCtXdkpOTmZ2dHbO1tRUcK8pcvHiRcRzHnJ2dBcd2fn4+69q1K+M4TuGap+59lOd5tmzZMsG6lixZIj+Gp02bJpi2c+dOxnEcmz17tiA8LS2NZWdnK6Tl1q1bzNjYmA0cOFAQXhP3MlVkaQ8MDJT/Zs+ezdzd3eXndfn0eHl5MZ7n2b///quwvrfffpvp6+uzZ8+eycOqep8pu0++/PJLhW1s2LCB8TzPNmzYoDAtNzdXUDVS7LzOzs5mFhYWTEdHh124cEGwvKzqp5eXlyC8pvaHMrLrfNu2bQV5KyOr/qmrq6tw3vXt25dxHMcsLS0F10/GmGj+VyV/X7x4wRYuXCg4fir63bx5s1JpHjFiBON5noWGhopOHzx4MON5nh0/flwr62rQoAHjeV40vxljzNjYmPE8z16+fCkPk117y/44jmP6+vrsq6++UljH559/zjiOY/PmzVOY9uLFC/l5cOLECYXpRUVF8ryfOXMma9++PeN5nvXp00eharE6aRHz4YcfMp7n2fLly1XOV17//v0Zz/Nsy5YtgnBN7WPZs0P//v1Fp9+5c4fZ2dkxXV1dNnToUDZ//nw2bdo0Zm9vz+zs7ETzd//+/UxfX5+ZmpoyPz8/tmDBAjZu3DhmYmLCOnXqJPqs9umnnzKe55mDgwObPn06mz9/Phs8eDDT1dVlY8aMETzXMMZYXl6e/FrbuXNnNnv2bDZ79mzm4uLCjI2NRfNZnbTI/PLLLywwMJDNmTOH9evXj0kkEtakSRN2//79aqelPE1UNa9SwZux0p1ma2srP/FkFz5vb2+FBy/GGHNycmISiUShEMIYY1u3bmUcx7GJEyfKw2qz4F1QUMCMjIyYqampvD5/WV999ZVC2wTZQ1yPHj0U5i8sLGS6urqsU6dOSuNQlqqHFZ7n2bZt2xSWCQoKYhzHsY0bN1ZqG6oeXo2NjUXb7vfs2ZPxPM9ycnIE4eoUvKuajqoWvEtKSpilpSWztbVVKJAyVnrR53mejRo1SmFaeRcuXBB9OcNY6YOJrA1R2bz85ptvlD5YPX/+nJmamjJDQ0PBA7rs2D59+rTCMr169WI8z7Pg4GCFaRMmTGA8zysUzpQJDQ0VbUOjquCtieO6OnE6deqUwjILFy5kPM+zo0ePysNk+S5WYHv48CGTSCRqFbxjY2MVpjVp0oTxPC/an4WnpyfT09MTPIj36dOH8TzP/vrrL4X5w8PDFdoVqZMWTRW8P/nkE8bzPPvjjz8U5meMMW9vb6arqysvtKhzjqiibsG7KtcudQreYWFhjOM4NnfuXNHp69atYzzPs2PHjlWYxkmTJindJ7K4lb/mqXsfbdKkieBYZIyxhIQEeZ6VL3wWFxczXV1d1qtXrwrTITN06FBmYGAgaH9YE/cyVWQP9mK/1q1bKxTUGPvfQ275fXz9+nWFtp/q3GdkeWJjYyP6gmbDhg2M4ziFh3gxYuf17t27GcdxbOzYsQrzFxUVscaNGzOe51liYqI8vKb2hzKyc07s2ZGx/xW8/fz8FKbt2LGDcRzHPDw8FKbJ+opYvHixPKwq+Vv2JUllf5XtR6Nfv34qH9zHjBnDeJ5ne/fu1cq6ZB/PxI5bxhizs7NjPM+zlJQUedjBgwdZcHAwi4uLY/n5+SwxMZFt3bqV2djYMI7j2BdffCFYh+y+INbGW/YBQlka8/LyFPLez89P4flT3bSUl52dzUxNTZmenp7gZX5FZMdTx44dFdpea2ofy84PZQV4xhhLSkqSvyST/aRSKZs3b55oOYax0v1jb28vWMbGxob9+OOPCvcLmc2bNzNDQ0PBMp06dVJaIM7Pz2cfffSRwnkycuRIdvv2bY2lhTHGunTpIlimc+fO7MGDB0rnr2payqrxNt5AafWlhIQEnDhxAl9//TWGDBkCxhjCwsIwdOhQQVWs7OxsPHjwALa2tqJVUmXVEG/cuFGNb/aac/fuXeTm5qJt27ai1aZ79eoFxphofDt27KgQpqOjA2tra421HRLbhqxdiya20axZM9G2+5rcBqDddMTExODZs2cwMTHBkiVLsGjRIsFv7dq1MDAwEG2XWF5UVBQAiPbGzPO8vKfGsmTHhqenp8I0c3NztG/fHnl5eaLD2Yjli62tLQDIx14uy87ODkBpr+RlJSYmYvr06fIhGfj/b6/q4+MDAEqHWRCjqeNa3ThV9liR5bvYvmrcuLGg/VdlmZubi/bgWtE+KSoqErSnvHHjBnieF+3/omfPnpBIJIJrijbSUlmydtkREREK586iRYvw5MkTFBcXy5sbqXOOaIO2r12yPg3i4uJE8+Xq1atgjFXquqJq/7q7uyu046vOfbRdu3YKVZJlx6+zszOMjIwE03ieh7W1tcI1BQCOHj2KIUOGwNbWFnp6evJz+PDhw8jPz1c6RIwYbd/LZIqLi+W/7OxsXLlyBVZWVnj//ffx1VdfCeb19vaGmZkZdu/eLWifHRwcDI7jMH78eHlYde4zbdu2Fe2xeujQoTA2Nsa0adPg6+uLLVu2KB2qR0xUVBQ4jhO990gkEvnxVtnnF23sD2XKVvsXo4l7Y1Xy18HBASUlJYLjp6JfbY6wom3Dhg2Dn58fHBwcoKenh4YNG8Lf3x9Hjx6Frq4uvvvuOzx79kw+v5ubGyZNmoQXL16gTZs28Pf3x5w5c9C5c2cEBQWhZcuWACDab5NUKpXn/aNHjxAcHIw///wTHTt2FO2PpLr27NmDrKwsDBs2TN7EoSKhoaEICAiAra0tfvvtN62MCnL//n2cOXMG1tbW8vbH5d24cQNdu3ZFXl4ezp8/j6ysLCQmJmLx4sVYvXo1unTpgqysLMEyu3btQp8+fdCzZ0/cvn0bubm5uH37Nnr37o3p06dj9OjRCtuZOXMmpk+fjsDAQCQmJiIrKwvnzp0DAPTv3190BJJ+/fohLCwM+/btQ3p6OtLT07F3716cPXsWrq6uCv2iqJMWmUuXLqG4uBjp6ek4efIkGGPo0KED/vzzz2qnRRuq1MZbRiKRoE+fPvK2EIwx/Pbbb5gwYQJ27NgBb29vDB06VN7xhY2Njeh6ZOEvXrxQJxoaV534KmvfrKOjo7HhacS2IWtbooltqEqDprahbDua2sbTp08BlHaqoGzYCADIycmpcF0ZGRngOE7pcDhvvfWW6DKAeseQ2LAwsnwRa9crm1a2o7HY2Fh06tQJGRkZ6N69O7y8vGBmZgaJRIK4uDhs374d+fn5onETo4njujpxKtsWvuy2AeGxIst3VfuqqjdtZW2pZdtXtb/K7pOMjAxYWFgotAMDSq+l9evXF3TooY20VJbs/Pnuu++UzsNxnLzzFXXOEW3Q9rVLli8HDhxQOk/ZfFFF1f6VHQ9i86tzTRE7hmUPiaqO7/KdF65btw4BAQGwsLBA37595Z0NyvoquHXrVrWvK5q+z5RnYGAAFxcXhIaGomHDhli5ciU++ugjeSFNX18fI0eOxC+//IKTJ0/Cy8sLhYWF2Lt3Lxo0aCBov1+d+4yyc8Le3h7Xrl1DYGAgjh8/jt9//x2MMTRq1Ahz5swRbXtelqafX7S9P8pSNeQcx3Eq73+VvTdWN381RRZf2f4qTxZemc6c1FmXmZkZnj59ioyMDNE27bJlKtOXSPv27eHq6oqLFy/i0qVLgo7Efv75Z7i6umLLli3Yv38/OI5D165dERERgSVLluDOnTsVFnRtbGwwbtw4ODs7o2vXrvj444/l7Xc1lZaff/4ZHMfhww8/rDC9QGnHcaNHj8Zbb72F06dPi7bQKXkmAAAgAElEQVRR18Q+lnX2JtapGlB6Xo4cORJPnz4VDM1laGiIuXPnIiUlBevWrcPatWvx9ddfAyi9Xk2cOBHt2rUTdMzo7OyMHTt24M6dO9i/fz+mTZsmf1EXHByMH374AZ9++qmgPyE3NzccPnwYTZo0wfz58+Hn5wdDQ0MAwOzZs3Hu3DkcOnRIcEyMGDECUqkU7777LubOnStvf69OWsTUq1cPvXv3lve7MW7cOMTHx8v7TFEnLdqgVsG7PI7j4Ovri1u3buGbb77BqVOnMHToUPnBJ9abJgB5D7mVOcFlb8ZKSkoU3pJpquCuyfiS2iHbN97e3iofkiu7LsYYUlNTRaeLHSdljyHZW92ytH0MrV69Gs+fP0dwcLBCh1J79+4V7eVZ22oiTrL8TE1NFc13Zed0TTAzM8OzZ89QXFyscAOVvaUt+4KhNtMi23ZmZqbC11Bl81f1HHkVmZmZgeM4hQcJddcFlO7f8jUqZMdD+R56gdq7LxUXF2PRokWwsbHBjRs3FB6WL168qJXtaouZmRmaN2+OGzduICoqSl7wBgA/Pz9s2bIF27dvh5eXF44cOYKnT58iICBAcO5W5z6jrINAoLRn4ZCQEJSUlODmzZv466+/sGHDBnzyyScwNjYW1CgUSxdAzy+qVDZ/MzIy5J3HVpa3t3elxoVv3rw5ACjtpFjWE7Ozs7NW1tW8eXNcvHgRMTEx6Ny5s2D+lJQU5OTkoFGjRoKOp1SRFZLEXjJNmjRJtFOwv//+GzzPi9ZWENO5c2eYm5sr9B5e3bTcvHkTkZGRaNKkSaU6Vdu/fz/GjBkDW1tbnDp1SmHs8rLxAtTfx4WFhdixYwc4jhPNPwC4c+cOHjx4gI4dOwrGw5bx9PTE999/j8jISHnYyZMnUVhYKFrbiuM49OjRA1FRUYiMjJTPc/ToUXAcJxjbWsba2hotWrRAdHQ07t69i/bt28uXASC6jKxGTtl4qZMWVczMzNC1a1eEhYXh33//lR9n6qRFG6pc1VwV2RcgWTUtY2NjNG3aFElJSXjw4IHC/LK3HWLViMqTvc1KTExUmHbt2jXRZSQSSZXe1jZv3hyGhoa4efOm6DAasvhW9mJBtEMikYAxJrpvW7RoAXNzc1y+fLnab+pl+/nMmTMK00pKSnD+/HmF8Pbt24sOLwGU3syjo6Ohr68vWqDSBNl5Nnz4cIVpERERVXqQ0JSaiFOHDh3AGBPdV7GxsaLXjZrSvn17lJSUKAyDBZQeW8XFxYJrYG2mpUuXLgAgGlcx6pwjdZWsYFW2mrFMly5dwBirdL6ooirPzp07p3Dd0uR9VB3p6el48eIF3NzcFArdOTk58uYGrxJZ9emSkhJBuJubG5o1a4awsDBkZWVh+/bt4DhOoRqxJu8zYnieR/v27fHZZ59hz549YIzh4MGDKpdRde8pLi6WV6ek55eK8/fFixdYvHhxlX7R0dGV2rasyeLx48cVpsXGxiImJgYODg4V9miu7rpULfPHH38AgHz4pooUFRXJz39lhdDyIiIikJCQgMGDB4vWGhOTnZ2NzMxMhVpj1U3LTz/9JB/2rCK7d+/G+++/j4YNG+LMmTMq01vdfRwaGoq0tDT07dtXtLkbAHkNI2VNfGS16MoOuSVbpvyQWTW5jKr5q5KWisiaL5Y9ZtRJizZUqeC9d+9e/PXXX6IPJikpKfIqG2Xfpvj7+6OkpASfffaZ4CaXnp6OJUuWgOM4lW9xZVxdXcEYw5YtWwTh4eHh2Lt3r+gylpaWSEtLq3QVOF1dXYwZMwaZmZkK7b8ePHiA9evXQ09PT+XQNET7ZMNXiFW3lUgkmDFjBh4/fowZM2YgLy9PYZ6UlJRKtcV0c3ND8+bNcfbsWUH1JgDYsGGD6EPw2LFjoaurKzr9yy+/RGZmJsaNGyfaxk8TZBfp8g9fJ06cUBh+q6bURJzGjBkjz/ey47cyxjBnzhyFB+ya5O/vD8YYFixYgJcvX8rDX758ifnz54PjOEycOFEeXptp+fjjj6Gjo4OAgACFMTCB0jfxZQvT6pwjdZWq68qwYcPQtGlTbNy4EceOHRNd/vLly6LXm/JkY6svXbpU0H42Ly8PCxYsEF1GU/dRdVhZWcHQ0BCRkZGCr1pFRUWYOXNmldp2q0M2ZnX5McHVdfDgQcTGxkJXVxdubm4K0/38/JCXlyff123atFEYlkaT9xmZqKgo0Rf+si/YFdVAeffdd2FhYYGQkBBcuXJFMG3t2rWIjY1F3759BcPEaRrP81pp76oJVclfbbbx7tmzJ1q2bImzZ8/KhzACSq/vc+fOBcdxmDp1qmCZly9f4u7duwovXdVZ14QJEyCVSvHDDz8I7i/Pnz/Ht99+qzCWdXZ2tuiX28LCQsyaNQsJCQlo2bIlXFxcBNPF2uPGx8dj0qRJkEqlCsMf/vPPP6LP6oWFhZg+fTpKSkoUxoWualrKys3NRUhICHR0dCq8dm7fvh1+fn5wdHTEmTNnKhwCTZ39UpasLKUs7gDQunVrmJubIyEhQeE56sWLF1i1ahU4jhN8ye/evTuA0iZTf//9t2CZ6OhoHDhwABzHCYYB7d69OxhjWLNmjcL589NPP+HRo0ewsbHB22+/rbDMokWLBOXFkpISeVXxsvFSJy2JiYl48uSJaN5s3rwZ165dg729vWCYS3XSog1Vqmp+5coVrFu3Dm+99Rbc3d3lb2tiY2Nx9OhR5OXl4d1335V3mAQAc+bMwbFjxxAWFoa2bdti4MCByM3Nxf79+5GWloZ58+aJ3vzKmzBhAlatWoVly5YhOjoab7/9NmJiYnD8+HEMHz5ctLpX7969cf36dXh5eaFHjx6QSqVo27at6KDuMsuXL8e5c+fwww8/4OrVq/D09ERaWhr279+P7OxsbNy4sVLjDhLt6dq1KwwNDfH9998jPT1d3mZu5syZMDExwVdffYVbt25h8+bNOHz4MHr16gU7Ozs8efIE9+7dw4ULF/Dtt99W6qvz1q1b0a9fP/j4+GD48OFwcnJCdHQ0Tp06hQEDBii80XRwcMD333+Pjz/+GB06dMDIkSPRoEEDnDlzBpcuXcLbb7+N5cuXayVfgNLxsoOCguDr6wtfX1/Y2trin3/+wYkTJzBy5EilL6m0SRtxKv/yz8HBAcuXL8ecOXPQvn17jBo1Sj72dUZGBtq0aaNwo6kpo0ePRlhYGPbv349WrVrh3XffBcdxOHjwIOLi4vDee+/hvffe01paxF6UKtO8eXNs27YNEydORKtWrdC/f384OzujsLAQCQkJOHfuHKysrASdElX1HKmrevfujf3798Pb2xsDBw6EgYEBHBwcMHbsWOjo6CA0NBT9+/fHoEGD4Obmhnbt2sHQ0BCJiYm4du0aYmNjkZycXGEVTTc3N8yYMQM//PADWrduDV9fX/k43hYWFqJtdDV1H1UHx3GYOXMmVqxYgXfeeQfDhg1DQUEBTp8+jefPn8PT01P0K6umyF40iPWRUJFFixbJ/5+Tk4P//vsPx44dA8dxWLZsmWi1xnHjxuHrr7/GwoULUVRUJOhUrSxN3meA0rHCN2/eDHd3dzRt2hT16tXDgwcPcPjwYejr62PWrFmC+cuf10ZGRti2bRtGjhyJnj17YsSIEbC3t0dkZCROnjwJW1tb/PTTT5WKizpk8VFnP1VmvdVVUf5+8sknGtlORXieR1BQEHr37i2/J9rb2yM8PByRkZFwd3dXiIvsWdTDw0MwLrU663J0dMSqVaswa9YsuLi4YNSoUdDT08OBAweQlJQk7wRN5unTp/KCdcuWLWFjY4O0tDScPn0asbGxsLKyQkhIiEI6J06ciPj4eHTo0AEWFhaIjY3FoUOHUFRUhF27dsnHVJbZunUrgoKC0K1bNzg4OMDc3ByPHz/GyZMn5c2uVq1aVa20lBUSEoLMzEz4+vqqbGseERGBiRMngjEGDw8PbNu2TWEec3Nzwfmpzn6RuX//PiIiImBtbY0hQ4YojZeenh6+//57+Pv7Y/Lkydi7dy/at2+P58+f49ChQ0hPT0fXrl0FLyw7deoEf39/BAUFoVOnTvD29oaDgwNiY2MRFhaGwsJCBAQECK5Z06ZNw549e3Dr1i04Oztj6NChMDc3R1RUFE6dOgUdHR1s3LhRUHNxxYoVuHTpEnbs2IHr16/LC/Lh4eH477//YGVlhaVLl1YrLVFRURgxYgS6du0KJycnWFtb4+nTp7h8+TL+/vtvmJqaYufOnYJ4qZMWoPT+K+vX4/z582CMYeXKldi5cyeA0pee5ccrV6kqXaA/evSI/fjjj2z48OGsRYsWzMzMjEmlUmZra8sGDRrE9uzZI7pcfn4+W7ZsGXvnnXeYoaEhMzU1ZT169GD79u1TmDcuLo7xPM/8/f0Vpv33339s0KBBzNTUlJmYmDBPT0927tw5pUPm5OTksGnTprFGjRoxXV1dxvO8YHgTnudFh03JyMhg8+fPZ87OzkxfX5/Vq1ePeXl5iQ4HFBERwXieFwxbUZajoyNr0qSJ6LTKpn38+PFMIpGIjple0fbLUzYkj7K8ULV9ZWlTlo4PPviAcRzHrKysmIGBAevcuTP7888/Vaaj7Db+/PNP1qtXL2ZmZsYMDAyYkZERMzAwkA8HUD5+u3btYn369GGWlpZMKpWyhg0bsu7du7Ply5cLxiytSFRUFBswYAAzNTVlpqamrF+/fuzy5ctK81IWVy8vL2ZhYcH09fVZs2bN2Pz581lGRobCvB4eHkwikYhuW9W+V7b9S5cusd69ezMLCwtmamrKunfvzg4dOqQ0j8W2r8njWlNxklE1RNbevXtZx44dmYGBAbOysmIffPABS05OVrm+qqZP3f21adMm1qlTJ2ZkZMSMjIyYi4sL27Rpk9I4VCUtyvKxouHElMX1n3/+YRMmTGCOjo5MX1+fWVpasnfeeYd99NFHosPeqXOOiFF13Gnq2qVsG8XFxeyLL75gTZs2lQ9TU36YtLS0NLZgwQL2zjvvMCMjI2ZiYsKcnZ3ZiBEj2J49e5QOayNm48aN8vGh7ezs2IwZM1hmZqbSY09T91HGVOeZ2PaLi4vZ2rVrWatWrZihoSGzsbFhfn5+LCEhQTSPNXkvCwgIUDq0oDJiwz3p6uoyW1tb5u3tXeFQMH379pUPZfPkyROV81b2PlPRPrl69SqbNm0aa9euHbO0tGSGhoasWbNmbOLEiQpji6s6r69fv86GDx/OrKysmFQqlY9Xm5ycrDCvJp8tbt68yTiOYx988EGl5mdM9bWUMdXpVBU/sbyuSv7WhNu3b7ORI0eyBg0aMH19fda8eXO2aNEiwXjiMrK0Kjtnq7IumSNHjjAPDw9mamrKjI2Nmaurq8KwnowxlpmZyWbNmsW6du3KbGxsmFQqZSYmJqxdu3bs888/Z2lpaaLr37FjB3N3d2f169dnUqmU2dvbswkTJrA7d+6Izn/x4kU2efJk1rp1a2ZhYcF0dXWZpaUl6969O1uzZo3Ksbgrm5ayOnfuzCQSifwZVBnZMajqp2zYYnX2y7x58xjP8wpDtClz7tw55uPjw2xtbZmenh4zMTFhLi4ubOXKlQrjnsts376deXp6CvK5b9++7NdffxWdPycnhy1ZsoS1b9+eGRsbMz09PWZnZ8fee+89du3aNdFl4uLi2NSpU5mTkxPT19dnBgYGzNnZmc2aNYs9fvy42mlJSEhgn332GevSpQt76623mJ6eHjM1NWXt2rVjc+fOVfqMr05aZMPLKvuJDX+qCseYhl4nElKB0aNHy4dhcHJyQnBwMK5evYqIiIgKv9YEBQVh0qRJ6NevH4YOHQqJRIK7d+/Czs4Os2fPrqEUEEIIqUkdOnSAVCqVD+lG6qb169dj9uzZ+Oeff9CiRYvajg4hhNRJVPAmNeLq1avo0qULVq9ejYCAAAClHR20bt0a1tbWKjthio+Px9tvv40pU6ZgzZo1NRVlQgghtSgzMxOWlpYIDQ1VWe2S1D5fX1/50LKEEELEabYxDiFKHDhwADo6OoLeI6VSKSZOnIgvvvgCSUlJgmFdytq0aRNKSkrk7fVycnIqNdQRIYSQV5epqanCmOKkbqru8J2EEPIm0OhwYoQoEx0dDWdnZxgbGwvCXV1d5dOVCQ8PR4sWLXD06FE0atQIJiYmsLS0xNdff62xjlcIIYQQQgghRFvoizepEcnJyaK99drY2IAxhsePHytd9t69e5BIJPD398e8efPQpk0bhIaG4ptvvkFxcbGgd0RCCCGEEEIIqWuo4E1qxMuXLyGVShXCZUPvlB3fuLzs7GwwxrBixQrMmTMHAODt7Y2nT59i3bp1+Pzzz5VWPU9PT8eJEyfg6OgIAwMDDaSEEEIIIYRo0suXLxEXFwcvLy/Ur1+/tqNDiFZQwZvUCAMDA+Tn5yuE5+XlyaerWjY3N1cw1jFQ2kv6iRMncOPGDbi7u4sue+LECYwdO7YaMSeEEEIIITVh165dGDNmTG1HgxCtoII3qRE2Njai1cmTk5MBALa2tkqXtbW1xf3792FtbS0It7KyAmMMz58/V7qso6MjgNILecuWLdWI+ZsrICAAa9eure1ovFIoz9RD+VZ1lGfqoXyrOsoz9VC+Vc3t27cxduxY+XMbIa8jKniTGtGuXTtEREQgOztb0MHa5cuXwXEc2rVrp3TZjh074v79+0hKShJckJOSksBxHBo0aKB0WdmX9JYtW6JDhw7VT8gbxMzMjPKsiijP1EP5VnWUZ+qhfKs6yjP1UL6ph5oFktcZ9WpOaoSvry+Kiorw888/y8MKCgoQHByMLl26yIcSS0lJwd27d1FcXCyfb9SoUWCMYevWrfIwxhiCgoJgYWGBjh071lxCCCGEEEIIIaSK6Is3qRGurq4YMWIEFixYgNTUVDg5OSE4OBjx8fEICgqSzzd//nzs2LEDcXFxsLe3BwAMGzYMvXv3xrJly5CWloa2bdvi999/x8WLF/Hzzz9DV1e3tpJFCCGEEEIIIRWigjepMTt37sRXX32FXbt24fnz52jTpg2OHj2Kbt26yefhOA48r1gRIywsDF9++SX27duH7du3o3nz5ti9e7dCh2uEEEIIIYQQUtdQwZvUGD09PaxYsQIrVqxQOk9QUJDgC7iMoaEh1qxZgzVr1mgziqSM0aNH13YUXjmUZ+qhfKs6yjP1UL5VHeWZeijfCCHlcYwxVtuRIERboqKi0LFjR0RGRlInJ4QQQgghdRA9r5E3AXWuRgghhBBCCCGEaBEVvAkhhBBCCCGEEC2igjchhBBCCCGEEKJFVPAmhBBCCCGEEEK0iArehBBCCCGEEEKIFlHBmxBCCCGEEEII0SIqeBNCCCGEEEIIIVpEBW9CCCGEEEIIIUSLqOBNCCGEEEIIIYRoERW8CSGEEEIIIYQQLaKCNyGEEEIIIYQQokVU8CaEEEIIIYQQQrSICt6EEEIIIYQQQogWUcGbEEIIIYQQQgjRIip4E0IIIYQQQgghWkQFb0IIIYQQQgghRIuo4E0IIYQQQgghhGgRFbwJIYQQQgghhBAtooI3IYQQQgghhBCiRVTwJoQQQgghhBBCtIgK3oQQQgghhBBCiBZRwZvUmIKCAsybNw92dnYwNDREly5d8Ndff1W43Pbt28HzvMJPIpHgyZMnNRBzQgghhBBCCFGfTm1HgLw5/Pz8EBoaioCAADg5OSE4OBgDBw5EREQE3NzcVC7LcRyWLFkCR0dHQbi5ubkWY0wIIYQQQggh1UcFb1Ijrl69in379mH16tUICAgAAIwbNw6tW7fG3Llzcf78+QrX0b9/f3To0EHbUSWEEEIIIYQQjaKq5qRGHDhwADo6Opg8ebI8TCqVYuLEibh06RKSkpIqtZ7s7GyUlJRoK5qEEEIIIYQQonH0xZvUiOjoaDg7O8PY2FgQ7urqKp9uZ2endHnGGDw8PJCdnQ09PT14eXlh9erVcHJy0mq8CSGvl9TsVPj86oPEF4lIy3mKIlYEBgZ9HT1YGlqioWlDhI4KhZWRVW1HlbzhXr4Erl0DsrKAevWATp0AXd3ajhUhhBB1UcGb1Ijk5GTY2NgohNvY2IAxhsePHytd1tDQEBMmTICnpydMTU0RGRmJ1atXo1u3boiKilJZYCeEEJnU7FQ0XdcMOUVZCtOyCwuQnZGN+Ix4OK13wv2Z96nwTWrFv/8C33wD/P47kJ//v3BjY8DfH/jiC8CKDk1CCHnlUFVzUiNevnwJqVSqEK6vry+frsyIESOwdetWjB07FkOHDsWiRYtw4sQJpKenY+nSpVqLMyHk9ZCanQr3be5ouNpBtNBdXlZBFuzX2sN9mzue5NDICaRmFBQAn38OvPMOcPFiaeE7OhpISir98v3JJ0BwMNCsGRAaWtuxJYQQUlVU8CY1wsDAAPllX93/v7y8PPn0qujWrRs6d+5cqeHICCFvtuH7fHAh8QKKoHgNUia/OB8XEi/Aab0TFb6J1qWnA56ewKpVpQXue/eAOXOAtm0BW1vAxQVYsgR4+BDo2xfw8QEWLgQYq+2YE0IIqSyqak5qhI2NjWh18uTkZACAra1tldfZqFEjxMTEVGregIAAmJmZCcJGjx6N0aNHV3m7hJBXQ2p2Kobv88GlxCsApzidBw9diS4YYyhiRShhih03ZhVkYfi+4TjvX/HIC4SoIzER6NMHeP4cOH8e6NxZ+byWlsD+/cCKFcCCBUBOTmlhnRM5vgmpq0JCQhASEiIIy8jIqKXYEFJzqOBNakS7du0QERGB7OxsQQdrly9fBsdxaNeuXZXX+fDhQzRo0KBS865du5aGIiPkDZKanYpmG5ohqyBLodAtlUjhYusi6ETtSc4TDN83HNcfX0d+sfDL+JVHV+C+zZ06XSMal5IC9O4NFBYCly4BxtapcN0yBLdSb4ExBh2JDopLisEYg56OHiwN/r8DwBmhMDKywsyZpYXxBQtqOyWEVJ7Yh4+oqCh07NixlmJESM2gquakRvj6+qKoqAg///yzPKygoADBwcHo0qWLvIO0lJQU3L17F8XFxfL50tPTFdb3xx9/IDIyEgMGDNB+5AkhrxyfX31KC91l6PA66NaoGxICEnDe/7ygEG1lZIXz/ueREJAAEz0TwXJFrAgXEi9g+L7hNRJ38mbIygK8vEq/Wu87kgq/M+5w+N4B1x5fQ35xPgpKCpBbmCv/f3ZBaed/siYQo/yf4OuvS9uF791b26khhBBSEfriTWqEq6srRowYgQULFiA1NRVOTk4IDg5GfHw8goKC5PPNnz8fO3bsQFxcHOzt7QEAbm5uaN++PVxcXGBmZobIyEgEBQXBwcEBC+g1PyGkDNlwYZcTryhM62zXucIq41ZGVrg/8z6G7xuOK0lXUFRSJJ92JYm+fBPNKC4G3n8feJCaCucvfdDjd8WaFqrImkCcCzyPBw+AiRNLO2Vr1UqLkSaEEFIt9MWb1JidO3fik08+wa5duzBr1iwUFxfj6NGj6Natm3wejuPA88LD8r333sP9+/exbNkyzJw5EydPnsSUKVNw9erVSlc1J4S8GXx+Le1IrRhFgnATPROEjqpcV9Cyr9+d7YSNbYtK6Ms30YzAQOCPPwCHz3xw4+mFKhW6Za4kXUH3IHcsWfMEjRsDvr6lX88JIYTUTRxj1CcmeX3J2gxFRkZSG29C3gBN1zXFwxcP5X/r8DrobNdZra/Usnbf5b98SyVSJAQk0Fdvopbjx4EBA4AFS1OxpsRBtNDNgYMuryto462sA0ATPRMcG3Af/bpZYfx4YOPGGkgEIRpGz2vkTUBfvAkhhLwWUrNT8TgjTRAmq16uTiFZ2Zfv/OJ8+upN1JKaCowbV1rwPvOWj0KhWyqRolujbkiZk4L8r/KR83kO8r7MQ/5X+Uj+NBndGnWDDi9sJZhVkIV5UcOxciXw44/AyZM1mSJCCCGVRQVvQgghr4Uhu3yQx/7XoVpVqperEjoqFFKJVBAma+9NY3yTymIM+OgjgBmm4ukwd1xNEvZDIKtJoexFkbIXQUDp8bjHwB09BjzB5MlU5ZwQQuoiKngTQgh5paVmp8J9mzuupwgLMg2MGmikOriVkRVcbF0EYdTem1TV7t3AwYNAg499cDXlgqD5AgC42LpU6ngNHRWq2PN+SREuJl5A9uDhSE0Fli7VaNQJIYRoABW8CSGEvNJkHaoxTliQsTG20dg2QkeFilbzvf74On31JhV69Aj4+GPAxy8VsfnXBdNkw9xVpfO/+zPvix6PL4qSsWAB8N13wJ07Gos+IYQQDaCCNyGEkFfa46xkwd9VLchUBrX3JupiDJg8GTAyAh65KbbrVqcfAmXHY1pOGiZ8/AT29sD06aXbJoQQUjdQwZsQQsgrKzU7FY9faK5DtYqItfdOzk5WMjchwN69wPFzqTCf7Y7IVMV23dV5QVS+2nlWQRbeDxuODRuAU6eAffvUXjUhhBANo4I3IYSQV9aQXT7Ih+Y7VFNGrL13UmYSdbRGRGVkALNnA5bTfPBftvrtupWxMrJCA6MGgrArSVewNNkdA0c8waefArm5aq+eEEKIBlHBmxBCyCvrTpLwa7OmOlRTRdbeW48v/fKdX5xPHa0RUV9+CWSWpCLLpHrtulUp35eBrOO/VI/hSEsD1q2r9iYIIYRoABW8CSGEvHJSs1PR4Qd3ZLEkQbgmO1RTRta+tqGZnSCcOlojZUVGlo6rbT3DBwUl1W/XrYyyjv+eFyVj6lRg+XIgPb3amyGEEFJNVPAmhBDyyvH51Qc3nl4AdEsLNFKJVOMdqlWkfCGfOlojMiUlwKRZqdCf5o5Eptl23eUp62gtKTMJl992R7H+E3zzjcY2RwghRE1U8CaEEPLKiU0TVjG3M7XTWodqylBHa0SZnTuBaGcf5NbXfLtuZWRfvmXHZH5xPq6mXIDltOH48Ufg4UONb5IQQkgVUMGbEOujGTkAACAASURBVELIKyUlKxWpWcKezGuiinl51NEaEfPyZWnbbiMb7Q9zV5bsy7edqbAJBG+WjPr1S+NECCGk9lDBmxBCyCul188+KNatuZ7MVaGO1kh5338PpGSnAoY1N8xdWeVfQj19mYbZXz1BSAjw999a3TQhhBAVqOBNCCHklcEY8CC15nsyV0ZZR2tU5fzNlJYGLFsGNJjug5yi2nk5JDa29+96w9G4MbBoUY1EgRBCiAgqeBNCCHklpGanovUadxTo1XxP5hUpH4e0nDSqbv4Gmr8kFbnvuSNNT9ihWk2+HBIb2/vq4yuQTHbHb8ef4ObNGokGIYSQcqjgTQgh5JXg86sP/suu3Z7MlRH7ykjVzd8sMTHAthwfFNtdQBETdqhW0y+HxMb2vl9wAdIPhtNXb0IIqSVU8CaEEPJKePCk9nsyV0bsKyNVN3+zLFgASMxrtkM1ZZSN7W1ql4zffweio2s0OoQQQkAFb0IIIa+AlKxUpGXXfk/mqpSPT2IG9XD+pjgUnopQM3dwpsJmEDXVoVp5ysb2bvaWDZycqK03IYTUBip4E0IIqfN6b6k7PZkrU34c5cIS6uH8TcAYMPaQD+BwAUWoW80gQkeFopNtJ0glUkglUhSxQsz6/AkOHgS19SaEkBpGBW9CCCF1Wl3ryVwZZeMoX398nb56v8Z+2ZuKLJPrgrC60gzCysgKehI95BfnI784H1eTriKkZDgcHYHly2s1aoQQ8sahgjchhJA6KzU7Fe+sdUe+bt3ryVyZ8nHLL86nr96vqYICYOZ5H3mHfzJ16fgs39dASk4y5s4Ffv0VuHevliJFCCFvICp4E0IIqbN8fvXBv1l1sydzZUJHhcqrm8tQR2uvp02bgDwd4b6VSqR16vgs/xIgKTMJO3XdYWn/BCtX1lKkCCHkDUQFb0IIIXVWbHrd7clcGSsjK7jYugjCkjKpo7XXTUxSKub85w6JubA2houtS506Psv3PZBfnI9LSRdg5D8c27cDSUkVrIAQQohGUMGb1JiCggLMmzcPdnZ2MDQ0RJcuXfDXX39VeT2TJ08Gz/MYOnSoFmJJCKkLUrNT4b7NHSk5r04V87JkhR29MoUd6mjt9dJrsw+KbC+gmK/btTGU9T0Ak2QYGQGrV9dOvAgh5E1DBW9SY/z8/PD9999j3LhxWL9+PXR0dDBw4EBcvHix0uu4fv06tm/fDgMDAy3GlBBS23x+9cGFxAsoqeOFGmVkhZ2G5Qo7VOX89RAfDyRlvlq1Mcq/tHr6Mg3+M55g82YgPb2WIkUIIW8QKniTGnH16lXs27cPy5cvx/LlyzFp0iSEh4fDwcEBc+fOrfR6Zs2aBT8/P1hZ1c0HG0KIZpQvoNb1Qo0yYu1rqcr5q+/ThangDOv2uPLlhY4KhYmeifzvrIIsnLcrrYGxfn1txYoQQt4cVPAmNeLAgQPQ0dHB5MmT5WFSqRQTJ07EpUuXkFSJRmY7duzAv//+i6VLl2ozqoSQOsBST1iIqeuFGmVkVc51QFXOXxdRUcBvvA+YtG6PK1+elZEVGhg1EISl5ydjyhRgwwYgM7OWIkYIIW8IKniTGhEdHQ1nZ2cYGxsLwl1dXeXTVcnOzsb8+fPxxRdf0NduQl5zqdmpSHxcABRKIZVI4WrnWucLNcrIqpw3Mqcq568DxoA5cwBdy7o/rrwYsRoY553dkc2eYNOmWooUIYS8IajgTWpEcnIybGwUv1jZ2NiAMYbHjx+rXH7RokUwNDTEJ598oq0oEkLqiGEhPkiRXAN085FfnA9dXveVKNSoYmsivP6l5aRRdfNX0O6wVJx2dAdMXu1O/8r2cH4t9QIspw3H998DeXm1HEFCCHmNUcGb1IiXL19CKpUqhOvr68unKxMTE4P169fju+++g66urtbiSAipG+4lC78mvg5fh8Xa11J181dLURHw4V8+gMMFFLJXu9O/8j2c61kkIzUV2LGjliJGCCFvAJ3ajgB5MxgYGCA/P18hPO//X6+r6qV81qxZcHd3x7vvvqv29gMCAmBmZiYIGz16NEaPHq32OgkhmpWanQrvvT54VpgkuDu9Kl8TVZG1r80q+F+74NfhhcKbZN0vqXhpfl0QJuv071VjY2yDh88fyv9+UZiGgSOeYNUqK0ycCEgktRg58toLCQlBSEiIICwjI6OWYkNIzaGCN6kRNjY2otXJk///y5atra3ocqdOncKJEyfw+++/Iz4+HgDAGENRURFevnyJ+Ph4WFhYwMTERHR5mbVr16JDhw7VTAUhRJt8fvXBpaQL8juTVCKFi63LK/M1sSLlCzuyHs5DR4W+8lXpX3fZ2cCCaB/ARvgC+VV9KRQ6KhRO653kL4KyCrLwqNtw3P/1PH7/HfD1reUIktea2IePqKgodOzYsZZiREjNoKrmpEa0a9cOMTExyM7OFoRfvnwZHMehXbt2osslJiaC4zh4e3ujcePGaNy4MZo0aYLHjx8jPDwcTZo0QVBQUE0kgRCiZclZr8cQYsrI2tfq8dTD+atm9WqgUF94fEol0lf2pZBYD+dZSIanJ7B8eWkncoQQQjSLCt6kRvj6+qKoqAg///yzPKygoADBwcHo0qUL7OxK25ulpKTg7t27KC7+P/buPC6qcv8D+GcGcNhxQW0AcUNzJRcCFFRcciGXclCkcimveStLqeuWP7vmtVLT9LpVWopKl1QiW7BFc8cFwcwydwOVZdyREWQ9vz9OMzrMsApzZvm8X695wTxzZuY7h8PMfM/zPN+nBADQv39/fP3119i+fbvexdPTE08++SS2b9+OYcOGSfKaiKj2qDVqZN21rHWRq0s7v9bHgxXOLcnvl9SYfzUUdvX1C6oFeAVY9Emhsv9fSlclZs4EUlOB3bslCoqIyIpxqDmZRGBgIEaNGoXZs2dDrVbDz88PMTExSE9P1+uxnjVrFjZt2oS0tDT4+vrCx8cHPj4+Bo83depUNG3alEk3kZVQbVUhv9Sy1kWuqbJDzrUVzi05ibNmT32mQqlPku66tUyBSIhMwMgtI3Hl7hXczr+Nq3ev4j8IRcfABCxa1AT9+0sdIRGRdWGPN5nM5s2bMW3aNMTGxmLq1KkoKSlBYmIiQkJCdNvIZDLI5ZUfljKZDDKZrC7DJSIT+uu6Za6LXBOscG45/vwTUN+zzikQujXm3ZshtzAX6TnpSLqShCLVSOzcKfZ8ExFR7WHiTSZTr149LFq0CBkZGcjLy8ORI0cwYMAAvW02bNiA4uJi+Pr6VvhYly5dwjfffFOX4RKRCeVfNxz2aq2Mza89mnEUoetDuba3mZk2Rw2Zq3VPgSg71aFIkYWWLYHFiyUKiIjISjHxJiIiyag1anRbFYrbxVfhKHNDc4/mFrUuck2VTd6KS4tZaM3M7NkD7GygglDPuqdAlD0WM3MzIPtHKLbtuIaLFyUKiojICjHxJiIiyai2qvDrzSSgQTruC7nwcfeximG8ldFWOLeX65daYaE181BSArz1FqDwtP4pENpjUWH3oNr+paIk2D83EkuWSBwcEZEVYeJNRESSuXJHP7GxlcRTO782yDtIr11baI2ktSpGjV+7hEJw069kbm3DzIEHx6K3u361fVevLGzYAGRnSxQYEZGVYeJNRESSUGvUyM6x7vmzlWGhNfOj0QDTj6mA5kkoLC0AIFYyt/YpEGX/94ocrsPO4xpWrJAoICIiK8PEm4iIJDHifyoUyq17/mxljBVas5Vef3P1zmI1ijxT9NqspZJ5RcqeBNIU5aL+yyOxZg1w966EgRERWQkm3kREJInzWdY/f7YqyvY0ZtzNYIVziVy5Avw3UwU4FOi128JIDGMngewbZCEvD/j0U4mCIiKyIky8iYjI5O7fB3KzbGcJsYpoi1vVkz8obsUK59KYPRuAu/4JIYWdwmZGYpT9H1TnZaDBW6FY8vE1FBSUcyciIqoSJt5ERGRSao0anZaFosjxKlzsbWcJsfJoi1v5eOgXt+KQc9P64YAaX9QLhdxDv6BagFeAzYzEMFbh/JpjEq71HYnYWImDIyKycEy8iYjIpEZuUeFiobiE2L1i21lCrDJlextZ4dx0BAGI/EosqFYM2ymoVlZ5Fc6dm2bhww/FZdaIiKhmmHgTEZFJXVTb5hJilWGFc+l8+SWQK+gfh7ZQUK08ZU8C+TVV4uxZ4JtvJAqIiMgKMPEmIiKTyc5V43qebS8hVh5jxa2OZhxlobU6diFLjfH7QiGvb/1rdleVdsh5qwatENIsBDv/kYA+fYBFi8TRAUREVH32UgdARES246nPVCh1sO0lxCqidFXi0u1LuuvFpcW6QmsHXzooYWTWq88aFYqUSbrrCjsFArwCbPq41A45f9jMmUB4OLBvHxAWJk1cRESWjD3eRERkMhevcQmximh7Gu3l+ufFORy/buxLVSNTsL01u6tCrVEjdH0oWq9ojdD1oejW6xr8/YGFC6WOjIjIMjHxJiKiOqfWqNF1ZSjy7TictyLansYg7yC9dq7tXfsEARgea5trdleFaqsKSVeScOn2JSRdSYJq60jMnAn89BNw4oTU0RERWR4m3kREVOdUW1U4cStJl+TYYsXo6tD1fINre9eVL78E7pba7prdlSk7yiJLk4XRo4EWLYDFi6WJiYjIkjHxJiKiOnf5NitGV4e257tZff1lnVIyU9jrXQvKK6hmS2t2V6Zsz7/SVQl7e+Ctt4AtW4BLl8q5IxERGcXEm4iI6lzRLcMv8VQ5Lzf9/VRQUsBe71qgLahWKucIjPKUrWyu3TcvvQQ0bAgsXSpxgEREFoZVzYmIqM6oNWoM/0KF7LyrqFfPDUqPhvBx92GCU0UJkQnwXeaLgpIH85BZaO3R7DpcfkE1esBYZXMAcHYG3ngDeP994N//BppwgAARUZWwx5uIiOqMaqsKydlJQP10FCIXPu4+HGJeDU1cmiDAK0CvjYXWaq64GHgmjgXVHtVrrwF2dsDKlVJHQkRkOZh4ExFRncm4a1igiapHO+TXQcZCa49CrVGjzQehuFf/qF47C6pVX8OGwMsvA6tWAbm5UkdDRGQZmHgTEVGdUGvUyMq5rtfGnsXqY6G12vH0JhXSSpMAu2K9dhZUq5noaECjAdatkzoSIiLLwMSbiIjqxLNfqlCAB91hbvXc2LP4CMqetGChtaoTBODUZf3RFvZyexZUewTNmgHPPw989BFQWCh1NERE5o+JN5lMYWEhZs6cCW9vbzg7OyM4OBi7du2q9H4HDhzAiBEj4OvrCycnJyiVSgwZMgSHDh0yQdREVFPns/UTncYujdmz+AgSIhOgsFPotXHoftUsXqPGfZn+6Isg7yDWG3hEM2YAGRnA//4ndSREROaPiTeZzPjx47F8+XKMHTsWK1asgL29PcLDwytNoM+dOwc7Ozu88sorWLNmDaZPnw61Wo3evXvj559/NlH0RFRVao0aIZ+H4maB/hrJHGb+aFhorWYuXADePqECHDn6orZ16AAMHw4sWgSUlkodDRGReZMJgiBIHQRZv+TkZAQHB2Pp0qWIjo4GABQUFKBTp05o2rQpDh6s3jIu+fn5aNWqFbp27YodO3aUu93x48fRvXt3pKamolu3bo/0GoioakLXhyLpSpLuusJOgQCvACREJrB38RFdu3cNI7eMxLHMFBQ+tMRYSLMQLodlRGaOGu3+rUKux1FA/mBud6sGrXDxjYsSRmY9Dh0CQkKA7duBESOkjoYsFb+vkS1gjzeZRHx8POzt7TFp0iRdm0KhwMSJE3H48GFkZGRUcG9DTk5OaNy4Me7cuVPboRLRI8rK1R/+rF0jmUn3o9MWWvNxZ6G1qui5QoXcBkl6STfA0Re1qWdPIDQUWLhQnEtPRETGMfEmkzhx4gTatm0LV1dXvfbAwEDd7ZXJzc3FzZs3cfbsWbz99ts4deoUBgwYUCfxElHNKYr0kxomObWPhdYq98sRNdILUvTaWFCtbsyaBRw5Ahw4IHUkRETmi4k3mURWVhaUSsMv30qlEoIgIDMzs9LHGD16NBo3boz27dvjo48+wuTJk/F///d/dREuEdVQdq4a6VcKIStRQGGnQKB3IJOcOmCs0NrRjKOc7/03jQYYulkFOBTotbOgWt0IDwc6dRLnehMRkXFMvMkk8vPzoVAoDNodHR11t1dm0aJF2LlzJ9avX48ePXqgsLAQRUVFtR4rEdVc/3Uq5DU8BsGuAAUlBXCQOzDJqQPGCq0VlxYj6UqSzfd8qzVqtF4QivuNjuq1K+wUPAlUR2QyscL5jh3AyZNSR0NEZJ6YeJNJODk5oaCgwKD9/v37utsr4+/vj/79+2PChAn4+eefcfToUbz44ou1HisR1YwgABfV+vO7udxV3UmITEBIsxDYy+312m19vnevVSpcc0oC7PTndQd4BfAkUB0aMwbw9QUWL5Y6EiIi82Rf+SZEj06pVBodTp6VJX4p9/LyqtbjOTg4YPjw4Vi0aBEKCgqM9qY/LDo6Gh4eHnptUVFRiIqKqtbzEpFxao0aA9apUODAJcRMRVtorWwVee18b1urcq7WqBG+UYXzeUcBuwft9nJ7BHkHsbe7jjk4AG+9Bbz5JrBgAdCihdQRkbmKi4tDXFycXltOTo5E0RCZDhNvMokuXbpg79690Gg0egXWjhw5AplMhi5dulT7MfPy8iAIAnJzcytNvJctW8blKYjqkGqrCn/cTQIcxOsPLyFGdSshMgG+y3xR8NDyYtr53ra0hNuIOBWO30jSS7qBB/O6qe5NnCgm3R98AHz6qdTRkLky1vGhXU6MyJpxqDmZREREBIqLi7F27VpdW2FhIWJiYhAcHAxvb3FpnOzsbJw9exYlJSW67a5fv27weHfu3MFXX30FX19feHp61v0LIKIK/XWdS4hJhfO9gay7ahy7ygrmUnNxAaZPB9avB9LSpI6GiMi8sMebTCIwMBCjRo3C7NmzoVar4efnh5iYGKSnp2PDhg267WbNmoVNmzYhLS0Nvr6+AIAhQ4bAx8cHQUFBaNKkCdLT0xETE4OsrCxs3bpVqpdERH9Ta9RQa67rfaJwiLlpJUQmYOSWkTiacRTFpQ/mNmvne1v7CZCgj1QolRuvYE6m9eqrwJIlwHvvAevWSR0NEZH5YI83mczmzZsxbdo0xMbGYurUqSgpKUFiYiJCQkJ028hkMsjl+oflxIkTcfv2bSxfvhyvvvoq1q5di+7du2Pfvn149tlnTf0yiKiMgZ+rUGKfq7vuVs+NvYwmpp3vHeQdpNdu7et7qzVqtFsciiulrGBuLlxcxArnMTHAX39JHQ0RkfmQCYIgSB0EUV3RzhlKTU3lHG+iOuI0qzXuO13SXW/VoBUuvnFRwohs17V71wzme9vJ7BHsE2SV8739l4fi95wkg/aQZiHs7ZZQXh7QsiUwbBjw2WdSR0OWgN/XyBawx5uIiGpErVGj8/JQ3LdnJXNzYWy+d4lgnfO9D55Q4/ebnNdtjpydgZkzxV7vS5cq3ZyIyCYw8SYiohpRbVXhj5wkwEHsXVXYKZj0mIHy1vc+elWsdG7pa3yrNWoEfhKKXl81B+yNz+u2tp59S/TPfwKenmKVcyIiYuJNREQ1dImVzM1SefO9i//u+fZb4WfRyffwL1Q4pk4ySLo5r9u8ODsDs2YBmzYBFznzhIiIiTcREVVfdq4a13L1l/rjEHPzUl7Pd25hrsUOOz+VrsaxjBSjtwV4BfCkj5mZPBlo3Ji93kREABNvIiKqgf5rVShxYCVzc1ZezzdgmcPOb94EApeoINgZ9nRzioN5cnICZs8We73PnJE6GiIiaTHxJiKiahEE4IJaf5h5Y5fG7G00UwmRCXCr56bXZmnDzlPPqtHs36HIa2C4bNjl6Muc4mDGJk8GfHyAuXOljoSISFpMvImIqMrUGjU6LA1FYT1WMrcUTVya4MIbFyx22PmeZDWe3NgG+Y2TALtivds4vNz8KRTAu+8C8fHAsWNSR0NEJB0m3kREVGUjt6hw5h4rmVuaioadHzHTYedqjRqdPgpFv2+bQ1Dk6t3GZcMsy9ixQIcOwNtvSx0JEZF0mHgTEVGVnc9mJXNLZmzYeYkZDjvPuquG79I2OJX74CTPw7hsmGWxswPeew/YtQv45RepoyEikgYTbyIiqpK0G2rcyGMlc0tW2bBz32W+kvZ+qzVqBK8Nhc+S5ihErtFtWMjPMo0YAQQFicXWBEHqaIiITI+JNxERVUnYxyoI9VjJ3NJVNOy8oKRAkt5vtUaN0PWhaPZRcxzNSkKpnWEvt3Zaw4U3LrCn2wLJZMAHH4jzvL/+WupoiIhMj4k3ERFV6tYt4MotVjK3JsaGnWuZsvdbrVHDb0UbJF1JQpFgmHAD4kkeVi+3fH37AgMHAnPmAMXFlW9PRGRNmHgTEVGF1Bo1/JeFotSFlcytycPDzhV2CoPbtb3fdZWAa4eVey9pDk2R8WHl7OW2Pu+/L67pvXGj1JEQEZmWfeWbEBGRLRu6WYUM+yTddYWdAgFeARxmbgW0w86v3bsGvxV+yC00TIC1CbhyqReaufvAx90HCZEJj5QI/3ZBjcAv2ojzuGWGtz98jDHhti7duwORkeK63pGRgKur1BEREZkGE28iIqrQmYwswOHBdW0lc7Ie2t7vkVtGIiUzBQUlhkO+S4USpOekIz0nHV5LveBTxSRcrVFDtVWFy7ev4Pq9mygoKYaAQkBuvMKWWz039nBbuYULgXbtgMWLgfnzpY6GiMg0mHgTEZFRao0agzeooAGHmNuCh3u/K0rAAaDkoSS86RIlHGQOAATYy+1RIpRAEATYy+pBIdRHTmkWBFnJgzuXM8mNvdy2o0ULIDoaWLIEmDQJaNZM6oiIiOoe53gTEZFRqq0qnLj1YB1l7XxbDjG3btoE/HL05XKLr+krRZFQgCKhEPkleSgs/fv3Ug3uCFf1k+5ysHia7Zk9G3BzA95+W+pIiIhMg4k3EREZdSFbv4q5dog5EyPb8HDxteYezWEns6v152DxNNvl7g785z9AbKy4xBgRkbXjUHMiIjKQflONa/euA/UetHGIue3R9n4D0A1Bv3r3Kq7evYoSofKe7LLkkMPBTiwY8MRjT+C7qO+YcNuwiROBVavEYecHDohrfRMRWSsm3kREZKD3ahWEeg8qXLvVc+MQcxtXXhJ+I+8GikuLxXnddvYoKRXneNezr4f6ivrIKchBQ6eGtVINnayLnR3w0UfAU08B8fHAqFFSR0REVHeYeBMRkZ6MDODK7Syg/oO2xi6NmTCRzsNJONGjGDAAePppYMYMYNgwwNFR6oiIiOoG53gTEZGOWqNG15WhEFxZyZyITGPJEuDqVfEnEZG1YuJNJlNYWIiZM2fC29sbzs7OCA4Oxq5duyq93+7duzFx4kQ8/vjjcHFxQevWrTFp0iRkZ2ebIGoi2zLwcxWuOyUB9qxkTkSm0a4d8OabwHvvAX/9JXU0RER1g4k3mcz48eOxfPlyjB07FitWrIC9vT3Cw8Nx6NChCu83c+ZM7Nu3DyNHjsTKlSsRFRWFrVu3olu3brh27ZqJoieyfll31fj9ZopeGyuZE5EpzJ0LeHoCU6dKHQkRUd3gHG8yieTkZGzZsgVLly5FdHQ0AGDs2LHo1KkTZsyYgYMHy58ruGzZMoSGhuq1DRo0CH369MGqVaswf/78Oo2dyFaErlJBsCvQa+MQcyIyBVdXYPlyICIC+O47cb43EZE1YY83mUR8fDzs7e0xadIkXZtCocDEiRNx+PBhZGRklHvfskk3APTq1QsNGzbE6dOn6yReIluTnQ38dV1/3W6FnYJDzInIZEaOBAYNAt54A8jLkzoaIqLaxcSbTOLEiRNo27YtXF1d9doDAwN1t1fHvXv3oNFo4OnpWWsxEtkqtUaNzssMC6oFeAVwiDkRmYxMBqxcCWRmAu+/L3U0RES1i4k3mURWVhaUSsMhq0qlEoIgIDMzs1qPt2zZMhQVFWHMmDG1FSKRzeq/VoUbziyoRkTSa9MGmDUL+PBD4Nw5qaMhIqo9TLzJJPLz86FQKAzaHf9esDM/P7/Kj7V//37Mnz8fkZGR6NOnT63FSGSL0m6oceoOC6oRkfmYNQvw9gZeew0QBKmjISKqHUy8ySScnJxQUFBg0H7//n3d7VVx5swZjBw5Ev7+/li3bl2txkhki3qtVAEsqEZEZsTJCVizBti1C9i4UepoiIhqB6uak0kolUqjw8mzssRiTl5eXpU+xpUrVzBw4EA0aNAAiYmJcHFxqfLzR0dHw8PDQ68tKioKUVFRVX4MImtz+jRwNScLaPCgjQXViMgcDB4MjB0LREeLBdeMzFYjCxUXF4e4uDi9tpycHImiITIdJt5kEl26dMHevXuh0Wj0CqwdOXIEMpkMXbp0qfD+t27dwsCBA1FcXIy9e/eiadOm1Xr+ZcuWoVu3bjWKncgaZeaoEfSpCnBjQTUiMk/LlgE//QS8+iqQkCAWXyPLZ6zj4/jx4+jevbtEERGZBoeak0lERESguLgYa9eu1bUVFhYiJiYGwcHB8Pb2BgBkZ2fj7NmzKCkp0W2Xl5eHIUOGICsrCzt27ECrVq1MHj+RtQlZoUJuAxZUIyLz1aiROOR8+3Zg2zapoyEiejTs8SaTCAwMxKhRozB79myo1Wr4+fkhJiYG6enp2LBhg267WbNmYdOmTUhLS4Ovry8A4LnnnsOxY8cwceJEnDp1CqdOndJt7+rqihEjRpj89RBZsoMn1EgrSAEcHrRpC6oREZkTlUq8TJkC9OsHcBVRIrJUTLzJZDZv3oy5c+ciNjYWt2/fhr+/PxITExESEqLbRiaTQS7XH4jx22+/QSaTYf369Vi/fr3ebc2bN2fiTVQNxcXAkA0qoCELqhGRZVi1CujQAZg2DYiNlToaIqKakQkCF2og66WdM5Samso53kQAFi0CZmW2BhpeYVw9ZgAAIABJREFU0rUp7BS4HH2Zc7uJyGxt2gSMHw98+y0wbJjU0VBt4/c1sgWc401EZCP2paox+3wo7BqwoBoRWZaxY4Gnnwb+8Q/g2jWpoyEiqj4m3kRENqC4GBi6UQWhWRJKZCyoRkSWRSYDPv8cEATgpZfEn0REloSJNxGRlVNr1Gj5n1Bo6h/Va9cWVGNvNxFZgqZNgfXrgcRE4JNPpI6GiKh6mHgTEVm5AetUuCpPAuyK9dpZUI2ILM3QoeK63m++CZw+LXU0RERVx8SbiMiK/XlZjT9upei12cvtOcSciCzWhx8CLVoAzz0HFBRUujkRkVlg4k1EZKUEAQhdoQLs9b+ZBnkHcYg5EVksZ2fgf/8DTp0C5s6VOhoioqph4k1EZIXUGjVavx+K287687oVdgr2dBORxevaFXjvPWDJEuCnn6SOhoiocky8iYis0OANKvxVbDivm0uHEZG1eOstYPBgccj55ctSR0NEVDEm3kREVubuXeBUepZeG+d1E5G1kcuBzZsBNzdg1CjO9yYi88bEm4jIipSWAqNfUqOo3nW9ds7rJiJr1KgREB8PnDghVjonIjJXTLyJiKzIe+8BP7mrAEWurs2tnht7uonIagUEACtWAGvWAF98IXU0RETGMfEmIrISsdvVeCctFPLm+gXVGrs0Zk83EVm1l18Gxo4Vf546JXU0RESGmHgTEVmB8+eBCTtUgG8SSqFfUE3pqpQoKiIi05DJgE8+AVq3Bp59Frh1S+qIiIj0MfEmIrJwGg0wbIwaJY+l6LWzoBoR2RJnZ+Drr8WkOyICKCqSOiIiogeYeBMRWbCSEuCFF4Dz3VSAnX5JXxZUIyJb07o1kJAAHDwIvPoqIAhSR0REJGLiTURkwf75lhrfNAoFfPTndSvsFOzpJiKb1Ls3sHYt8NlnwEcfSR0NEZHIXuoAiIioZpYvBz67qwKaJ6G0zG0BXgHs6SYimzVhAnD2LDB9OtCmDTB8uNQREZGtY483EZEFSkgAoueqYefLed1ERMa89x7wzDPAc88Bv/4qdTREZOuYeBMRWZgd+9SI2BEK+ZvNUSLjvG4iImPkcmDzZqBdO2DIEODCBakjIiJbxsSbiMiCnDsHjPifCkKzJJTK9ZNuzusmItLn4gLs2AF4eABPPQVkZEgdERHZKibeREQWIi0NCBuqRknTFKO3c143EZGhJk2AnTuB4mJg0CCu8U1E0mDiTURkATIygP79gdtPqSDYGfZ0c143EVH5fH2Bn38GsrOB8HBAo5E6IiKyNUy8iYjM3PXrQNjTalwdGIqipobLhl2Ovsx53URElWjfHvjxR+DUKWDkSOD+fakjIiJbwsSbTKawsBAzZ86Et7c3nJ2dERwcjF27dlV6v+zsbMyaNQv9+vWDu7s75HI59u/fb4KIiaR3+7Y4LzE9WIXCx5JQIhTr3c7h5UREVRcQAHz7LXDggFjxPD9f6oiIyFYw8SaTGT9+PJYvX46xY8dixYoVsLe3R3h4OA4dOlTh/c6ePYsPP/wQmZmZ8Pf3h0wmM1HERNK6dQvoN0yNU0GhKPXS7+nmsmFERDXTty/w/ffA/v3i+t55eVJHRES2gIk3mURycjK2bNmChQsXYuHChfjHP/6BX375Bc2bN8eMGTMqvG9AQABu3ryJM2fOIDo62kQRE0lLrQZCB6txoncbFHsZ9nRz2TAioprr31+sdn74MPD008C9e1JHRETWjok3mUR8fDzs7e0xadIkXZtCocDEiRNx+PBhZFSwvoeLiwvq169vijCJzMKVK0Dv3sCFbipAkat3G3u6iYhqR1iYOOc7JUVc5zs3t9K7EBHVGBNvMokTJ06gbdu2cHV11WsPDAzU3U5EwMWLQM+n1Ejvbzi8HGBPNxFRbQoNFaud//abWE/jxg2pIyIia8XEm0wiKysLSqXSoF2pVEIQBGRmZkoQFZF5+eMPoFcv4EZ/FQqaGg4vd6vnxp5uIqJa1qMH8MsvwKVLQM+ewF9/SR0REVkjJt5kEvn5+VAoFAbtjo6OutuJbNnOnUBICNCgmRrCYyl6t2mHl1944wJ7uomI6kBAgDjfWxDERPz4cakjIiJrYy91AGQbnJycUFBQYNB+/+9FNJ2cnOr0+aOjo+Hh4aHXFhUVhaioqDp9XqKq+Owz4J//UsPtHypcbJCCghL9/xXt8HIiIqo7rVsDhw4BQ4cCffoA8fHAoEFSR2V94uLiEBcXp9eWk5MjUTREpsPEm0xCqVQaHU6elZUFAPDy8qrT51+2bBm6detWp89BVF2lpcCcOcDChcBjb6uQXS8JKNHfRmGn4PByIiITadwY2L0biIwUE/DVq4GXX5Y6KutirOPj+PHj6N69u0QREZkGh5qTSXTp0gXnzp2DRqPRaz9y5AhkMhm6dOkiUWRE0rh3DxgzBli0CHh3iRq3nVKMbhfgFcDh5UREJuTiAmzfDkyaBEyeDLzyClBYKHVURGTpmHiTSURERKC4uBhr167VtRUWFiImJgbBwcHw9vYGAGRnZ+Ps2bMoKSkp76GILN65c0BwMPD9HjUeXxSK9/ObGwwvV9gpuGwYEZFE7O2BNWuAtWuBzz8H+vUDsrOljoqILBmHmpNJBAYGYtSoUZg9ezbUajX8/PwQExOD9PR0bNiwQbfdrFmzsGnTJqSlpcHX11fXvmDBAshkMpw6dQqCIGDTpk04cOAAAGDOnDkmfz1ENfX118CECYBSCbT7twq/3kwy2EZhp8Dl6Mvs6SYiktikSUDHjoBKJRZgS0gA/l4JlYioWph4k8ls3rwZc+fORWxsLG7fvg1/f38kJiYiJCREt41MJoNcbjgQ45133oFMJtNto03WZTIZE2+yCMXF4nzuxYvFL3ALV6rR6XMOLyciMnc9ewKpqeJ7d69ewIcfAq+/Dvz9tYSIqEpkgiAIUgdBVFe0xTpSU1NZXI0kk54OjBsHJCUB//eBGrsaqZCSaVi9XGGnQIBXABIiE5h4ExGZmYICYOZM4L//BZ5+GtiwQSzGRo+O39fIFnCONxFRHREEYPNmwN8fuJClRvsloVhY0BxJV5KMJt2Xoy/j4EsHmXQTEZkhhQJYvhz4/nvg6FHgiSeAX36ROioishRMvImI6sCtW+JyNOPGAcOGAc3+pcIfOYYJtxaHlxMRWYannwZOngQ6dACeegqYPh3Iz5c6KiIyd0y8iYhq2Y4dQOfOwM6dwJdfAks/UePENePzuVm9nIjI8iiVwM8/i0tCrlwJdOkC/F3zlYjIKCbeRES1JDtb7OV++mmxCu7uZDVW3gtF8+XlLxfG4eVERJZJLhd7u0+cADw9gd69gSlTgNxcqSMjInPEquZERI+otBRYt04sulOvHhAbC/QfrkbbVW2QW2j4DYzLhRERWY927YD9+4HVq4HZs4HvvhN7wYcNY+VzInqAPd5ERI8gNVXs5fjnP8WlZvalqvFxQSha/Le50aQb4HxuIiJrY2cHvPEG8McfQPv2wIgRQHg4cPas1JERkblg4k1EVAMZGcCECcCTTwJ37gB79wLv/1eNoNg2RquWA5zPTURk7Vq2BH74Adi+XUy6O3USh6PfvSt1ZEQkNSbeRETVkJcHvPsu0LatWERtzRpxfl+fPoBqq6rcXm63em6cz01EZANkMrHH+88/gXnzxM+JNm3E4ecFxhe2ICIbwMSbiKgK7t8H/vtfoHVr4P33xQI658+LQ8zt/66WkaXJMriftpf7whsXmHATEdkQR0dgzhzgzBlx2Pm0acDjjwMxMUBJidTREZGpMfEmIqpAQYFYMKd1a+Ctt4AhQ8QvUYsWAR4e4jZqjRqh60ORcTdD777s5SYiombNgA0bgN9/BwICgBdfFJec3LKFCTiRLWHiTURkRG6u2MPt5ycWzBkwQEy4168X5/A9TLVVpTevm73cRERUVocOQHw8cOwY4OsLjBkjVkRft45D0IlsARNvIqKHZGYCs2aJPRT/+hfQt684T2/jRjEJN6bsEHNvd2/2chMRkVEBAcCPPwLJycATTwCTJ4sndJcsAXJypI6OiOoKE28isnmCABw9CowfD7RoAXz8MfDyy8BffwGbNolz8iqidFVWeJ2IiKisJ58Ue8BPnxanMb39NuDtDbzyirgsGRFZFybeRGSzcnOBTz4BunYFgoOBffuADz4ALl8GFi8GfHyq9jgJkQkIaRaCVg1acbkwIiKqlscfBz7/HEhPF5ce++YbcQ54nz7A1q0chk5kLeylDoCIyJRKS4H9+4HYWLGwTV4eMGyYmHAPHAjY2VX/MZu4NMHBlw7WfrBERGQzlErg3/8We76//hpYtQqIjAQaNACeew6YMAHo3l1croyILA97vInIJvz5JzB7tjiUvG9fYPducQ53ejqwfbs4zK8mSTcREVFtcnAARo8WTxL/+ac49SkhQRya3rmzuKrGpUtSR0lE1cXEm4iskiAAKSniGqodO4qXTz8Fnn4aSEoCLl4UexaqOpyciIjI1Nq3BxYuFKdA/fAD0KkT8O674hKXAQFMwoksCYeaE5HVKCoCDhwQh+ht3w5cvQo0bPhgKPngwUC9elJHSUREVD329uJn2ODBwL17wI4d4vzvd98VV+Lo1Ek8sRweDvTsKW5PROaF/5ZEZLEEATh3Dti5U7zs2SMWTPPxAZ55Bhg5EujVi19AiIjIeri4AKNGiReNRuwJT0wENmwQe8Dr1wcGDRKT8KeeEueOE5H0+HWUiCyGIIjD7ZKSxDnaO3eK1x0cxDP8M2eKXzZYfIaIiGyBq+uDJLy0VJxitWOHmIiPHy9u8/jjQFiYeOnTh4k4kVSYeBOR2SouBn77TUy0tZeMDPG2Dh2AZ58Vz+b36SN++SAiIrJVcjkQGChe5s0D1Gpg794Hl08/Fbdr2xYIDQWCgsRLx44cGUZkCvw3IyKzUFQkVm9NTQWOHxcvJ04A+fnivOyAAHE5lZAQsXe7cWOpIyYiIjJfTZuKy5FFRorXs7PFSul79gCHDwMxMWIvubOzOFIsKEj8rH3iCaBNG670QVTbmHiTyRQWFmLu3LmIjY3F7du34e/vjwULFmDAgAGV3jcnJwfTp0/H9u3bkZeXh8DAQCxduhRdu3Y1QeRUm0pLgbQ0McnWXv74Q7wUFIhDxNu1A7p1AyIixC8C3bsDjo5SR05ERGS5HntMXKZs9Gjx+r174knuo0eB5GSxWNuSJeJtjo5iwbYnnhAvnTuLn81Nm3IqF1FNMfEmkxk/fjwSEhIQHR0NPz8/xMTEIDw8HHv37kXPnj3LvZ8gCAgPD8fvv/+OGTNmoFGjRlizZg3CwsJw/PhxtG7d2oSvgqpCEIAbN8QlTi5dEpfuOnNGTLLPnBF7sQHAzU0cMt65MzBunJhgP/EEh40TERHVNRcXsQBpr14P2m7cAE6eFKd5/fabmJhv3gwUFoq3u7uLc8bbthV/ai9+fuLjEVH5ZIIgCFIHQdYvOTkZwcHBWLp0KaKjowEABQUF6NSpE5o2bYqDBw+We9+tW7dizJgx+Oqrr/Dss88CAG7cuIG2bdsiPDwcsbGx5d73+PHj6N69O1JTU9GtW7fafVFWLi4uDlFRUUZvKykRh6xlZIiXq1eB9PQHifalS2J1ca1GjcQP5g4d9C8+PtZ15ryifUbl436rPu6zmuF+qz7us5qxpv1WVAScPy+uInL27IPLuXNioq7l6Qm0aAE0by7+fPh3Hx+x2np5n/n8vka2gD3eZBLx8fGwt7fHpEmTdG0KhQITJ07EnDlzkJGRAW9vb6P3/eqrr/DYY4/pkm4A8PT0xOjRo/HFF1+gqKgIDg4Odf4arJ0gAHfvAtevi5dly+KQnx+F69eBzEwxudYm2dnZYvKtVa8e0KwZ0Lo10KMH8Pzz4u+tWgEtWwIeHtK9LlOypi9apsT9Vn3cZzXD/VZ93Gc1Y037zcHhwQnzsm7dEpPwCxfEE/Dp6eJ0sm++EVcd0faUA4BCIVZUVyoBL68HvyuV4rB3ImvHxJtM4sSJE2jbti1cy4whDgwM1N1eXuL966+/Gj37GRgYiHXr1uHcuXPo2LFj7QdtgQRBHMadkwPcuSP+LO/3mzfFBPvGjQc/H/6ABIBjx4AGDcQPRR8f8UP3qafE3729H/z09LSunmsiIiKqXMOG4gn3Hj0MbystFU/Up6WJJ+6zssQT+VlZ4uXsWfHnzZsmD5tIEky8ySSysrKgNLJwpFKphCAIyMzMrPC+ffr0MXpfAMjMzDT7xLukRExqCwr0L8ba8vLEM7/an1X9XZtYFxUZj0EmE+dmeXiIl0aNxMrgjz8uJs6NG+tf/vUv4PvvxTPdRERERNUhl4s9215eFW9XUADs2gUMHWqauIikwsSbTCI/Px8KhcKg3fHvUtX52mpb1byvIAgV3lfriy+A3bvFdaGLi8VEWPt7Ta6XbSsqqjipfnhYdlXJZGKhEmdn8WfZ3xs1Anx9H7R7eIjzp7SJddnfXV3FD8GqUiiYdBMREVHd0g5BJ7J2TLzJJJycnFBQUGDQfv/+fd3tNbmvTCar8L7apHz16tNwcBDXpKyti/bx5HLxdwcHca6z9me9eoC9/YPfq9ru5CQu46FQPPrw7aIicQj5w8VPqionJwfHjx9/tABsDPdZzXC/VR/3Wc1wv1Uf91nNcL9Vz+nTpwFU3BFDZOmYeJNJKJVKo8PJs7KyAABeFYxDUiqVuu2qe9+0tDQAQEHBCzCSu1MlunfvLnUIFof7rGa436qP+6xmuN+qj/usZrjfqi8tLQ0hISFSh0FUJ5h4k0l06dIFe/fuhUaj0SuwduTIEchkMnTp0qXC+xpbbuzIkSNwdnZG27Zty73voEGDEBsbixYtWlTYM05ERERE0sjPz0daWhoGDRokdShEdYbreJNJaNfxXrJkCd58800AQGFhITp16oTGjRsjKSkJAJCdnY2cnBz4+fnBzs4OgLiOd1RUFLZt24aRI0cCeLCO95AhQ/DFF19I86KIiIiIiIiqgIk3mUxkZCS2b9+OadOmwc/PDzExMUhJScHu3bt1w4omTJiATZs2IS0tDb6+vgCA0tJShIaG4tSpU/jXv/4FT09PrFmzBleuXMGxY8fQpk0bKV8WERERERFRhTjUnExm8+bNmDt3LmJjY3H79m34+/sjMTFRby6PTCaDvEzpbblcjh9++AHTp0/HypUrkZ+fj8DAQGzatIlJNxERERERmT32eBMRERERERHVoWqs6ktERERERERE1cXEm6zS7t27MXHiRDz++ONwcXFB69atMWnSJGRnZxvd/tChQwgNDYWLiwuUSiWmTp2Ke/fumThq6WVnZ2PWrFno168f3N3dIZfLsX//fqPbhoWFQS6XG1zCw8NNHLW0qrPPAB5rFdm4caPRY8rOzg7Xrl2TOjzJFRYWYubMmfD29oazszOCg4Oxa9cuqcMya/v27Sv3mEpOTpY6PMndu3cP//73vzFkyBA0atQIcrkcmzZtMrrtmTNnMHjwYLi5uaFRo0YYN24cbty4YeKIzUNV99uLL75o9Pjr0KGDBFFLKyUlBVOmTEGnTp3g6uqK5s2bIzIyEufPnzfYlscaWSvO8SarNHPmTNy+fRujRo1CmzZtcOnSJaxcuRKJiYk4ceIEmjRpotv2xIkTGDBgADp06IBly5bh6tWr+PDDD3HhwgUkJiZK+CpM7+zZs/jwww/Rpk0b+Pv74/Dhw+VuK5PJ0KxZMyxcuBAPz1ipaF11a1SdfcZjrXIymQz/+c9/0KJFC732+vXrSxOQGRk/fjwSEhIQHR2tK1AZHh6OvXv3omfPnlKHZ9amTZuGgIAAvTY/Pz+JojEfN27cwH/+8x80b95ct+ynMRkZGejVqxcaNGiAhQsXIjc3Fx9++CH++OMPJCcnw97etr5OVnW/AYCjoyM+//xzvc9JDw8PE0RpXhYtWoRDhw5h1KhR8Pf3R3Z2NlauXIlu3brh6NGjupMRPNbIqglEVujAgQMGbfv37xdkMpkwd+5cvfYhQ4YI3t7egkaj0bV99tlnglwuF3bu3FnnsZoTjUYj3L59WxAEQYiPjxfkcrmwb98+o9uGhYUJnTt3NmV4Zqk6+4zHWsViYmIEuVwupKamSh2K2Tl69Kggk8mEjz76SNd2//59wc/PTwgJCZEwMvO2d+9eQSaTCV999ZXUoZilwsJCQa1WC4IgCCkpKYJMJhM2btxosN0rr7wiuLi4CFevXtW17dq1S5DJZMK6detMFq+5qOp+mzBhguDm5mbq8MzS4cOHhaKiIr228+fPC46OjsLYsWN1bTzWyJpxqDlZpdDQUIO2Xr16oWHDhjh9+rSuLTc3F7t27cLYsWPh4uKiax83bhxcXFywdetWk8RrLlxcXKrds1hSUmLTQ6Wrus94rFWPRqNBaWmp1GGYjfj4eNjb22PSpEm6NoVCgYkTJ+Lw4cPIyMiQMDrLoNFoUFJSInUYZsXBwUFvBFh5EhISMHToUHh7e+va+vfvj7Zt29rke1dV95tWaWkpcnNz6zAi8xccHGzQW+3n54eOHTvqfS/jsUbWjIk32Yx79+5Bo9HA09NT1/b777+juLgY3bt319vWwcEBXbp0wa+//mrqMC3KuXPn4OLiAjc3NyiVSrzzzjsoLi6WOiyzxGOtagRBQFhYGNzd3eHs7IwRI0bgwoULUocluRMnTqBt27ZwdXXVaw8MDNTdTuV78cUX4e7uDkdHR/Tr1w+pqalSh2QxMjMzce3aNYOh+oB4/PG9q2J5eXlwd3eHh4cHGjVqhClTptj0yeqy1Gq17nsZjzWydpwoQTZj2bJlKCoqwpgxY3RtWVlZkMlkUCqVBtsrlUocPHjQlCFaFD8/P/Tr1w+dO3fGvXv3EB8fjwULFuD8+fOIi4uTOjyzw2Otcs7OznjxxRfRt29fuLu7IzU1FUuXLkVISAiOHz+u1wNia7Kysso9dgRBQGZmpgRRmb969eohIiIC4eHh8PT0xJ9//oklS5agd+/eOHToEJ544gmpQzR7WVlZAFDu8Xfr1i0UFRXBwcHB1KGZPS8vL8yYMQPdunVDaWkpfvzxR6xZswYnT57E3r17IZfbdv9XbGwsMjIysGDBAgA81sj6MfEmsycIAgoLC6u0rUKhMNq+f/9+zJ8/H5GRkejTp4+uPT8/v9z7OTo66m63RLWx3yqybt06vevPP/88Jk+ejM8++wzR0dG6njhLUpf7zJqPNWNqsi9HjRqFUaNG6dqHDx+OgQMHonfv3njvvfewZs2aOonVEuTn55d77GhvJ0M9evRAjx49dNeHDh0KlUoFf39/zJ49Gzt27JAwOstQ2XuXdhsmQ4bee+89veujR49GmzZt8H//93+Ij4/H6NGjJYpMemfOnMGUKVMQEhKCcePGAeCxRtbPtk+1kUXYv38/nJycKr04Ozvj3LlzBvc/c+YMRo4cCX9/f4Nk0cnJCQBQUFBgcL/79+/rbrdEj7rfauKtt96CIAgWu8RRXe4zaz7WjKmtfRkSEoKgoCCLPaZqi5OTU7nHjvZ2qprWrVtjxIgR2LNnj16laTKusveuh7ehykVHR0Mmk9n0e5parcbTTz+NBg0aYNu2bZDJZAB4rJH1Y483mb127dohJiamStuWHZ505coVDBw4EA0aNEBiYqJeUSvt9oIg6IY3PSwrK8uil8Z6lP1WU82aNQMA3Lp1q1Yez9Tqcp9Z87FmTG3uy2bNmtXaySFLpVQqjQ4n1x5P1nb81LVmzZqhsLAQ9+7dM5g3T/q0/5/lvXc1bNiQPZDV4OjoiEaNGlns5+Sjunv3LgYPHoy7d+/i4MGDeOyxx3S38Vgja8fEm8xe06ZNdcOQquPWrVsYOHAgiouLsXfvXjRt2tRgm06dOsHe3h4pKSmIiIjQtRcVFeHEiROIjIx8pNilVNP99iguXrwIAGjcuLFJn7e21OU+s+ZjzZja3JeXLl2y2GOqtmjXCtZoNHqJ4pEjRyCTydClSxcJo7M8Fy9ehKOjI5PuKvDy8kLjxo2RkpJicFtycjKPvWrSaDS4ceOGTb6nFRQUYOjQobhw4QJ++eUXPP7443q381gja8eh5mSV8vLyMGTIEGRlZWHHjh1o1aqV0e3c3d0xYMAAxMbG6lUZ3bRpE+7du2fT868qkpuba3T+7oIFCyCTyTBo0CAJojJvPNYqd+PGDYO2HTt2IDU1FUOGDJEgIvMRERGB4uJirF27VtdWWFiImJgYBAcH23ThuYoYO6Z+++03fPfdd3yfqgaVSoXvv/9eb9m6X375BefOneN7VzkKCgqg0WgM2ufPnw8ANveeVlpaitGjR+Po0aOIj48vtw4MjzWyZjKBE5zICj3zzDP49ttvMXHiRISFhend5urqihEjRuiu//rrrwgJCUH79u3x8ssv48qVK/joo48QFhZmk4V3tMnzqVOn8OWXX+Kll15Cy5YtAQBz5swBAOzbtw9RUVGIioqCn58f8vPzkZCQgMOHD2Py5Mk2VwSrKvsM4LFWmbZt26Jr164ICAiAh4cHUlNTsWHDBnh7eyM5Odkme4geFhkZie3bt2PatGnw8/NDTEwMUlJSsHv3boSEhEgdnlnq378/nJyc0LNnTzRp0gSnTp3CunXroFAocOjQIYMeN1u0evVq3LlzBxkZGfjkk08wcuRIdO3aFQDwxhtvwM3NDVevXkW3bt3g4eGBqVOnIjc3F0uWLIGvry+Sk5NtcvhvZfvt1q1b6Nq1K6KiotCuXTsAwI8//ogffvgB4eHh+P7776UM3+SmTZuGFStWYPjw4XpFNLWef/55AOCxRtZNILJCLVq0EORyudFLy5YtDbZPSkoSQkNDBWdnZ6Fp06bCG2+8IWg0Ggkil55MJjO63+zs7HTb/PXXX0JkZKTQqlUrwdnZWXB1dRWefPJJYd26dRJGLp2q7DMtHmvlmzt3rtCtWzehQYMGgkLy3LbgAAAgAElEQVShEFq0aCFMmTJFuHbtmtShmYWCggJhxowZgpeXl+Dk5CQEBQUJO3fulDoss7Zy5UohODhY8PT0FOrVqyd4e3sL48ePFy5evCh1aGajos/L9PR03XZ//vmnMHjwYMHV1VVo2LChMG7cOJv+36xsv925c0cYN26c0LZtW8HV1VVwcnISOnfuLCxatEgoLi6WOnyTCwsLK3d/yeVyvW15rJG1Yo83ERERERERUR3iHG8iIiIiIiKiOsTEmyQhl8vRr1+/Onv8mJgYyOVybNq0qVYeb8WKFejYsSOcnZ0hl8uxYsUKALX3OsLCwiCXm+e/48aNG2t1X0plwoQJkMvluHz5siTPf+HCBTz77LNQKpWQy+Vo2LAhAGDevHmQy+XYv3//Iz3+vn37IJfLdYV7zE2LFi3KLXJoKdLT0yGXy/HSSy9JHUqdsabXWN5rqei9oK7f6ytSk/+RuLg4dOvWDe7u7pDL5XjzzTdr/FjGSP2+WRFzf8+rqtr6DCDjzPn7FdkeHolUJXK53ODi6OiIli1bYsKECThz5ky1Hk8mk0Emk9VRtLX7+F9++SWmTZsGJycnREdHY968eQgODtZ7rkclk8kMPhjM6UtFXf6tTKWuj7mKlJaWYsSIEfjxxx8xbNgwzJs3D7Nnz671uIw9lrl86bCGYwiQ9jii2lHe39AU7/WVxVUdR44cwQsvvACNRoNXX30V8+bNw+DBg3WPVVufTWUfx5pOzpgDKd9TtCfWy17c3d3RvXt3fPDBB8jPz5ckttpi7PsVkVS4jjdVmUwmw7x586AtC5CTk4Pk5GRs2rQJCQkJOHjwIPz9/av0WKdPn4azs3OdxTpy5Ej06NEDSqXykR8rMTERMpkMiYmJBmuBnzlzplZex+bNm5GXl/fIj0PlW7hwIWbPni3Jskt//fUXTp8+jcmTJ+Pjjz/Wu+31119HVFQUfH19H+k5goKCcPr0aXh6euq1M1GsPd7e3jh9+jQ8PDykDoUeQXnvBaZ4r69NiYmJAMTPj6CgIL3bdu/eXSvPIeX7pq2orc+AR9GlSxc888wzAMQTxdnZ2fjuu+8wZ84c/PTTT9izZ4/Ffo7w+xWZEybeVC1z5841aHvjjTewevVqLF++HOvXr6/S47Rt27a2Q9Pj5uYGNze3WnmszMxMADD4IgbU3uvw8fExaGPdw9rVtGlTo39DU9CuR2rsRFDDhg11w84fhaOjY53/X9k6e3t77mMrUN57gSne62tTRe8r2uUMH5WxfcXPptpVW58Bj6JLly5455139NoWL16Mzp0748CBAzhw4AB69+4tUXSPxtj3KyKpcOwFPbKBAwdCEARcv369yvcxNl/u4XlO8fHxCAoKgouLCxo1aoSoqCjdl6KqKG9esnbeW15eHqZPn47mzZvD0dERbdq0weLFi/W2fffddyGXy7Fnzx4IgqAbgmVnZ1frr6PscOAXX3wR/fr1040yePi5y84Di4uLQ9++fdGgQQM4OTmhQ4cOeO+991BYWFjl/QUAFy9exKhRo9CwYUO4uroiJCREt7Z0eV+0jh8/DpVKhaZNm8LR0REtWrTAa6+9huzsbINttXMF09PTsWrVKnTs2BFOTk5o2bIlPvjgA91227ZtQ1BQEFxdXdG0aVO8/vrruH//vsHjbd++HWPHjsXjjz8OV1dXuLq6IiAgACtXrjQar7G5ig8PmUxPT8eYMWPQuHFjODk54cknn9T1KD0KuVyuW0v+4b+ldgpBefP7tMfWzZs38fLLL8PLywuOjo7o1KkTYmJiDJ6n7NQE7Wvbv3+/3vFr7JjNyMjAlClT0Lp1azg6OsLT0xMjRoxASkpKtV/vqlWr0KlTJzg5OcHHxwevv/467t69W+72hYWFWLhwIfz9/eHi4gIPDw/07t0b27ZtM9j24b/XpUuXEBERAU9PT7i7u2PQoEE4deoUAODGjRu6febk5ITAwEDs3bvX4PGysrIwf/58hIaGQqlUQqFQwNvbG88//zxOnz5d4fM/7OFj69NPP4W/vz+cnJzw2GOPYfLkyRW+/tqMqTrHsEajwZtvvolmzZrByckJ7du3x7Jly1BaWlrlWAH94y41NRWDBw9G/fr10bBhQ0RERODq1asAgEuXLmHMmDFo0qQJnJ2d0a9fP5w8edLoY2ZnZ+O1115Dy5YtoVAo0KRJE6hUKhw/frxWXkvZ94KavtcDQElJCdasWYMePXrAw8MDLi4u6NatG1avXl3u+2Z1/0fK0n6+xcTEQBAEtGjRQhev9jUZm+P98Ofinj170LdvX7i7u8PDwwNDhw41OmXM2L5q1aoVZDKZrpaK9lL28/ann35CeHg4GjduDEdHR/j5+WHGjBnIycmp8msFgGvXrmHixIl47LHH4OzsjK5du1Zac+TChQsYN24cfHx8dP9D48ePx4ULFwy2ffg9OC4uDgEBAXBxcYG3tzfeeust3Wfp7t270bdvX3h4eKBhw4YYN24cbt26ZfB4e/fuxcsvv4yOHTvCw8MDzs7O6Ny5M+bPn4+CgoIKn/9h1f0MqG3u7u548sknAUDv+127du2gUCiMvnYAWLRoEeRyOdasWaPXXp3PmYf3yf/+9z8EBwfDzc1N75j+9ttv0b9/f91+8fb2RlhYmMGosvKmWwmCgE8++QSBgYFwc3ODq6srAgMD8cknnxj935X670HWgT3e9Mh27twJmUyme4OuKe2Q2NWrV+O7777D8OHDERYWhqNHj2LLli04efIkTpw4AQcHhyo/nrG2oqIiDBo0CFlZWQgPD4e9vT22b9+OWbNmoaCgQNer37dvX8hkMmzYsAGXL1/WDbOvbLhVTV5H2eHAzz77rO6LTVhYmC5xA8QvVFovvfQSYmJi0KxZM0RERKB+/fo4cuQI5s6di927d2Pnzp1Vmtt04cIFBAcH4/bt2wgPD8cTTzyhKwY2ePBgo6/5+++/R0REBAAgIiICzZs3R2pqKj7++GN8++23OHjwIJo3b27wGt966y3s27cPw4YNw6BBg/Dtt99izpw5KCwsRIMGDTB79mw8++yz6N27N3bu3InVq1ejtLQUq1ev1nv+2bNnw87ODsHBwfD29kZOTg52796NqVOnIiUlBRs3bjT6dzEmLS0NgYGBaN26te7L1JYtW/DMM89g165d6NOnT6X7sDzz5s1DWlqawd9S+7OiuO7cuYOQkBAoFAqMGjUKBQUF2LZtG1566SXY2dlh7Nix5T5v/fr1MW/ePIPjF9A/ho4fP46BAwfizp07GDRoEFQqFW7cuIHt27cjNDQU27dv180brczUqVOxcuVKeHl5YfLkyXBwcMA333yDo0ePorCwEAqFQm/7oqIiDBw4EPv370f79u0xZcoU5OXlIT4+HpGRkfjtt9+wYMECg+f566+/EBQUhA4dOuDFF19EWloaEhIS0LdvXxw6dAiDBw+Gh4cHxowZg1u3biEuLg7h4eE4d+6cXu/H/v37sXjxYvTt2xcRERFwdXXF+fPn8dVXX+Hbb7/FoUOH0Llz50pft/ZvOH36dPz888+6Y3vPnj1Yt24dLl68iF27dlVpH9Y0puocw4WFhejXrx9SUlLQpUsXvPDCC7hz5w4WLFiAffv2VSnOspKTk7Fw4UKEhYXh5Zdfxu+//46EhAScOnVKdyy1b98e48ePR3p6Or766isMHDgQly5d0hvCnZaWhpCQEGRnZ6Nfv3547rnncOXKFWzbtg2JiYlISEhAeHj4I72Wsv9zNX2vLy4uxtChQ/Hzzz+jXbt2eP755+Ho6Ig9e/bg9ddfR3JyssH7UHX/R4zp2rUr5s2bh6+//honT57E1KlTUb9+fQDQ/SwvdplMhu+++w7ffPMNwsPD8corr+DPP/9EYmIiUlJS8Oeff+r1vhrbVzk5OVi+fLne8GRA7DXVevfdd/Huu++iUaNGGDp0KJo0aYKTJ09iyZIl+OGHH3D48GG4urpW+lpv3ryJHj16IC0tDb169UJISAiysrLwyiuv4KmnnjL6Oo8dO4YBAwbg3r17GD58ODp06IAzZ84gNjYW33zzDX755Rd0797d4DWuWLECP/74I5555hn07dsXP//8M5YtW4Zbt25hxIgRGDNmDIYOHYrJkyfj0KFDiI2Nxc2bNw1Obi1atAhnz55Fz549MXToUNy/fx9JSUmYN28e9u3bh127dunFXVefAY8qJycHx44dg1wuR9euXXXtEyZMwJw5cxAXF4fXXnvN4H4bN26EQqHAc889p2ur7ueMdp8sWbIEu3btwrBhw9CvXz/dCaq1a9fin//8J5RKJYYPHw5PT09cu3YNJ0+eRExMDF555RWDxyrrhRdeQFxcHHx9fTFp0iTIZDJ8/fXXePXVV5GUlITNmzcb3EfKvwdZCVMvHE6WSSaTCXK5XJg3b57u8uabbwqhoaGCXC4XRowYIWg0mmo9Xt++ffXa5s2bJ8hkMsHDw0M4deqU3m3PPfecIJfLhW3btlXp8WNiYgS5XC5s3LhRr71FixaCXC4Xhg4dKty/f1/Xfu3a/7N35/FRVff/x993kpCEEKIskRAJSFhcyiKbC7EgVhFULCBiqqiAUL+tBalVoj8rWkRRKKiI4gqkaAQhCLh8VVCRfY3SLxUEIQFiEogESAImJLm/P6YzZDKTlcya1/Px8CE5d+7MuXfuPTOfOed8zlHzggsuMC+88EKzpKTEYZ/+/fubFovFrcfh6jW++eYb0zAM85lnnnH52vPnzzcNwzDvuOMOs6ioyGHbM888Y1osFvOVV15xuW9FN954o2mxWMw5c+Y4lK9cudL+3pc/lwUFBWazZs3M4OBgc8OGDQ77vPjii6ZhGObAgQMdyu+//37TMAzzkksuMbOysuzlJ06cMFu0aGFGRESY0dHR5t69e+3biouLzcsvv9wMCwszjx075vB8Bw4ccHks9913n2mxWMytW7c6vb7FYjEzMjLsZenp6fbjmzp1qsPjP//8c9MwDPOWW25x+Tq1UdV7+fTTT5sWi8Vcu3atQ7mtXuPHjzfLysrs5f/5z3/M4OBg84orrqjRa1R1/ZaUlJjx8fFmeHi4uW7dOodtWVlZZmxsrNm6dWuzuLi42mPcuHGjaRiG2alTJ/PEiRP28qKiIvOaa66xv/flPffcc6ZhGOatt95qlpaW2suPHTtmv1c3bdpkLy//fj3//PMOzzV16lTTMAyzWbNm5p/+9CeHbf/6179MwzDMv/71rw7lx44dc9lu7dq1y2zSpIk5ePBgh3Lb648ePdqh3HZtt23b1jxy5Ii9vLS01Pztb39rWiwWc9u2bS7PW0V1rVNtruFp06aZhmGYI0aMcHquZs2amRaLxekYK2O77iwWi5mSkuKwbezYsfb3xNX75aqNuummm1y+v5s2bTKDg4PNFi1amIWFhed1LK7aAtOsfVs/ZcoU0zAMc+LEiQ73aFlZmTl27FjTYrGYK1eutJfX5R6pSmXHYZrWz7qKz7VgwQLTMAwzJCTE/Prrrx22Pf7446bFYjFnzJhR7WtUdh/YfPXVV6ZhGGZCQoJ56tQph20LFy50eS9WZty4cabFYjEfeeQRh/IdO3aYISEhpsVicWrzLr30UpfX45IlS0zDMMzLLrvModz2mX3BBRc4fP4UFRWZV1xxhRkUFGS2aNHCqY20fW5+//33DuUHDx50eSxPPfWUabFYzCVLlji9fn18BtSF7Zq48sor7d/tnnrqKfOPf/yjGRsba0ZGRpqvv/66wz5Hjhwxg4KCzN69ezs937Zt25zux7p8ztjekyZNmjidX9M0zZ49e5phYWFmbm6u07ZffvnF4W9X9/X7779vGoZh9urVyzx9+rS9/PTp02avXr1cXj+eeD8Q+Ai8USO2BsfVf7/5zW+cGqiaPF9lAetTTz3l9Pivv/7aNAzDfPTRR2v0/NUF3q6CNlvAVjFYrmvgXZvjqEvg3b17d7NRo0bmyZMnnbaVlpaaLVq0MK+66iqX+5Z35MgR0zAMMz4+3uHDpGLdyp/L9957zzQMw7znnnucHl9SUmJecsklpsViMQ8fPmwvt32Bmz9/vtM+Y8aMsf+wU5HtR4Rvv/222mMxTesXMsMwnIKQqr5AXnLJJS6PvW3btmbLli1r9LpVqWvg3aRJEzM/P99pn379+pkWi8UhAKlL4L1ixQrTMAzzsccec7n95ZdfNi0Wi/nZZ59Ve4wPPPCAy3uufN0qBgIdOnQwg4KCzB9//NFpn3feecc0DMMcO3asvcz2frVv397p/Tp06JD9nFUMXEtLS82QkBBzwIAB1R6HzZAhQ8zw8HCHH+KqCrwtFov57rvvOj2P7QeyuXPn1vi161Kn2lzDHTp0MIODg122g7b2q7aBd79+/Zy2ffvtt5W+XxkZGaZhGOaYMWPsZba2qF27dk4/gJqmaY4aNcq0WCzmv/71r/M6lvoIvMvKyszmzZubrVu3dvjRyObEiROmxWIxR44caS+ryz1SlboG3vfee6/T4w8ePOjyB4y6BN6///3vTYvFYv7nP/9xuf3KK680L7roomqP7+zZs2ZERIQZFRXlFMCXr1v5Nm/Dhg32oN+V6667zrRYLA4BoO06mTJlitPj//GPf5iGYZj333+/07aFCxeaFovFTE5OrvZYTNMaEFZs02yvXx+fAXVhuyYq+3539913mz/88IPTfrYfyCq+x3/+859Ni8Vifvzxx/ayunzO2N6Tij+42PTs2dNs0qSJmZeXV+0xurqvf/e735kWi8VcvXq10+PXrFljGoZh3nDDDQ7lnng/EPgYao5aKS0ttf/7zJkz2r17tyZPnqw//OEP2r17t6ZOnXpez28YhsMQMJs2bdpIkvLy8s7r+SUpKirKZeKZ+nwNdx/HmTNntGvXLrVs2VKzZ8922m6apkJDQ13OCa0oLS1NkpSQkOByOFb//v2d5p7t3LlThmHo+uuvd3p8UFCQfvvb3+pf//qX0tLSnBKbuDovrVu3liT16NHDaZstm65trqjN8ePH9eKLL+qzzz7TgQMHVFhYaN9mGIY98VBNdO/e3eWxt2nTRps3b67x89S3jh07uhyOWf46Op9My5s2bZJkHd77zDPPOG3ft2+fTNPUDz/8UO1wc9t15CoBT0JCgsN8Wck6L/enn37SxRdfrI4dOzrtY5tPa3ve8ly9X7ZrqFOnToqIiHDYZrFYdNFFFzldQ5I1M/S8efO0Y8cO5ebmqqSkxL7NMAzl5ubWOClffd3zdalTTa9h23mPi4tz2Q7279/f5bVQnarua1d1c3Vf297r6667zul6kazXxKJFi5SWlmZfRssdx1ITP/74o44fP65OnTq5/NwzTVPh4eEObXBt7xF3cfdn7ObNmxUSEqIlS5a43F5cXKxjx44pLy9PF154YaXPs2fPHp0+fVq//e1vXSZL7d+/v9NQflseAFefTZL1GtqwYYPS0tKUkJBgL6/sM7u6zybTNJ3aldOnT+ull17SRx99pB9//FH5+fn2aT61/Wxy92eAzX333eeQHPfYsWNavXq1JkyYoFWrVmnt2rUOUwnuv/9+ffnll1q4cKGmT58uyTp16IMPPlB0dLQGDRpkf2xdP2eMKqYw3n333frb3/6myy+/XHfddZf69eunvn37Oq3qUZm0tDRZLBaX08j69eunoKAgl589nno/ELgIvFFn4eHh6tWrl1JTU3XxxRfrxRdf1IMPPnjey47Y5qiVFxxsvVTLB/71+fz1/RqVvU59vUZeXp49oV1V63xXN0dRkj3RTWXBRatWrSrdp7Ll2mzlJ06ccNrmaikm23mpatvZs2cdXr9Xr17KyMhQnz59dN9996lZs2YKDg7WiRMn9NJLL7lMYlOZqq6J2iabqk/uvlZ/+eUXSdLSpUsrfYxhGCooKKj2uaq6joKCgpy+ENX3NWQLWipb6is4ONjhGpKkl19+WZMmTVKzZs104403Ki4uTo0bN7bP9du1a9d5X0e1fa/qWqeaXsN1ud9rorb3te39qnhfSzW/Jtx1LDVhu3f27dtXZRtc/gfB2t4j7mAYhstrxfZ+1Mfn3y+//KLS0tJqP5sKCgqqDLzr+tlkGEaV15BpmrX6bDIMo8afTSUlJbr++uu1bds2denSxZ7s0JbT5emnn663zyap/r6vVNSyZUslJibq9OnTGjdunB5//HF99tln9u1Dhw5V06ZNtWjRIj3//PP23AHHjx/XX//6V4fcMufzOVPZPTxp0iS1bNlSr732mubMmaOXX35ZkjVonjFjhssfUco7efKk/TtDRbZ70VXCYG+9HwgcBN44b1FRUercubPS0tK0c+dO1vv0ANuXgCuvvLJOmaddPVdOTo7L7a4ylNv2cbVNsmZmLv+4+vbWW2/Zfz2vuMTd5s2b9dJLL7nldQNNVFSUDMPQypUrdcstt5z3c0nW66h88jbJ+mUkNzfX3itQ/vHeuoZKS0v1zDPPKCYmRmlpaYqOjnbYvnHjRre8rrfrVJf73VNqe01481hsrz106NAqAwpX+9T0HvFXUVFRMk1Tubm55/08Uu0/m0zTrPIaqiyQrg8rVqzQtm3bNGbMGL399ttO9X366afd8rruYlsffuvWrQ7lYWFhuvPOO/XOO+/oyy+/1E033aSFCxfKMAzde++9Do89n8+ZqjoP7rnnHt1zzz06deqUNm7cqOXLl+udd97RzTffrD179qh58+aV7hsVFaXjx4+rtLTUaaSJ7V5s2rRpreoK1ATLiaFe2IanebN3MNBU1QMRERGhK664Qrt373b5y31t2LKVrl+/3uUSGl9//bXLfUzTdLlEU2lpqdatWyfJ9fC8+vDTTz/JMAwNGzbMaZurOjVktuvI1Xt79dVXyzRNp6kEdWF7r11lkl63bp3TddykSRPFx8crMzNTP/30k9M+X331lSTXw2LrQ25urk6cOKFrr73WKcAtLCysdOkqd/JEnZo0aaIOHTooMzNTBw8edNru6n73lPJtkavPkq+++kqGYdivNW8ey6WXXmpfRaKmvVy1vUd8VXW947YVMmoy1akql156qRo3bqzvvvtO+fn5Ttu//vprp8DMdg1V9jlga1fc9dm0f/9+GYahoUOHOm3zx8+mqr7b3X///TJNUwsXLlRubq7+93//V926dVPXrl0dHlefnzOuNG3aVDfffLPeeOMN3X///Tp+/Hi1r3XllVeqrKzM5ePWrl2r0tJSt332oGEj8MZ5++ijj3Tw4EGFhITo2muv9XZ1Aobt19ry606X99e//lVFRUUaPXq0y3VRT5w44XKOUkWxsbG68cYbdfDgQb366qsO21asWOHyg+n3v/+9mjVrppSUFG3ZssVh2+zZs3Xw4EHdeOONTvO760u7du1cBv5paWmaPn16jYbYn+/rl1/b1pdVdR3dfvvtio+P19y5cx2GEZa3efNml+uoV2T7EjZt2jSHeaK//vqrHn/8cZf7jBkzRmVlZXr00Ucdvtjl5uZq6tSpMgxDo0ePrva168K2nvSOHTschgOXlJRowoQJ591b58t1Gj16tEpLSzV58mSHH2QOHjyoOXPmuP3+qYytLUpPT3fKXbFlyxalpKSoWbNmDkGNt44lKChIf/nLX/Tzzz/rL3/5i8t7JDs72yH4rMs94osuvPBCGYZRafs3adIkmaapcePG2UcplHf69Gmnzw1XgoODdffdd+vUqVNOPcXbt2/X+++/77RP37591blzZ61fv17Lli1z2LZ06VKtX79enTt3dpjfXZ8q+2w6cOCAkpKS3H5v2dasro8gt6yszD56zNWc+WuvvVYdO3bUihUrNG/ePJ09e1b333+/0+Pq83PGprIfMWyjI6qbZz1mzBiZpqnHH39cZ86csZefOXPG/j6NHTu2xvUBaoqh5qiV8okxCgsL9Z///EefffaZDMPQ888/r5YtW3qxdo5c9fD5k86dOys2NlYffPCBgoOD1bZtW/swrjZt2mj06NHauXOnXnvtNcXHx2vgwIGKi4vT8ePHdfDgQX377bcaM2aMXnvttWpfa+7cubrmmmv08MMP6/PPP7ev4/3RRx9pyJAhWrlypcPjIyIi9O677+rOO+9Uv379NGLECMXFxWnHjh364osv1Lp1a82bN6/ezkXF9/Lee+/VjBkzNHHiRH311Vfq2LGj9u3bp48//ljDhw/XBx98UG+vXVl9DMNwOT/M19xwww368MMPNXToUA0ePFjh4eFq27at7rnnHgUHBys1NVU333yzbrnlFl177bXq3r27GjdurMOHD2vbtm06ePCgsrKyFBYWVuXrXHvttfrLX/6iV199Vb/5zW90xx132Ncobtasmcs5l3/729/02WefacWKFerWrZsGDx6s06dP68MPP9SxY8c0efJkt/2YZxiGJkyYoBdeeEFdunTR7bffruLiYn399dfKy8vT9ddf7/EeKk/V6ZFHHtFHH32kZcuWqUePHho4cKDy8vL04Ycfql+/flqxYsX5H0wdzZs3TwkJCXrsscf0xRdfqFevXjp06JCWLl2qoKAgzZ8/3yF5njeP5e9//7t27dqlN954Q6tWrdKAAQMUGxuro0ePat++fdqwYYOee+45XXbZZZLqdo/Ut/r4XIyIiNBVV12ldevW6Z577lGnTp0UFBSkIUOGqEuXLhowYIBeeOEFPf744+rYsaMGDx6sSy65RAUFBcrIyNDatWt13XXX6dNPP632tZ577jmtWbNGL730krZt26aEhAT9/PPPWrJkiW655RaX7+/ChQt10003aeTIkbr99tt16aWXas+ePVqxYoWioqKUnJxcq+OtzTm77bbb1KFDB82aNUu7du3SlVdeqYyMDH3yySe69dZbffazKS0tzeH73dGjR/XVV19p7969io6O1gsvvOByv3vvvVd///vfNXXqVIWEhDis3W1Tn58zNkOHDlWTJk109dVX23/sWI4SwcsAACAASURBVLdunbZt26bevXvrd7/7XZX7JyYmasWKFfrwww91xRVX6Pe//70Mw9BHH32k9PR03XXXXbrrrrtqVBegNnz/WyN8gu1X2vLJUoKCgtSyZUvdfvvteuihh+xZiF3Zvn27FixYoG+++Ubp6ekyTVO7d+/Wvn37XGY0rujUqVOSpJSUFC1ZskR9+vTRP//5T/uwsqrqXNPyqlT1XLV9vsr2qVhmsVj00UcfKSkpSUuXLrVnRr3uuuvs8wDnzJmjQYMGad68eVqzZo1OnDihZs2aKS4uTpMnT9bdd99dozp16NBBW7ZsUVJSklavXq21a9eqa9euWrFihXJycrRq1SqnfYYMGWL/YvnFF1/o5MmTatWqlf70pz/pySefdJkUpa6/9lfcLyYmRuvXr1dSUpI2bNigL774QpdeeqnmzZunAQMGaPHixTU6x7ayqupVcduJEyeUmZmphIQEe8bbmh5DbY6/tvWqbJ8HHnhAhw4d0gcffKAZM2aopKRE/fr10z333CNJ6tKli77//nvNmjVLH3/8sRYsWCCLxaKYmBj16NFDU6dOrXHSp5dfflmdO3fW3Llz9eabb6p58+YaNmyYpk2bpq5duzrVLSQkRKtXr9asWbP0/vvv69VXX1VwcLC6d++uV155RXfeeWetzkttz9mzzz6r6Ohovf3223rzzTcVFRWlm266SVOnTtVTTz1Vq+ulutet6Xtfn3WqrG6NGjXSmjVr9PTTT2vx4sV65ZVX1K5dOz311FO6/fbbtXLlynq7Vmu77ZJLLtH27dv17LPP6tNPP9XatWvVtGlTDR48WE888YTT8M+6HkttPx9c1TU4OFjLly/Xe++9pwULFuiTTz5RQUGBWrZsqUsuuUTTpk1zaoNre49Upy5tRFWPr+k+ixYt0qRJk/T555/rgw8+kGmaatOmjbp06SJJevTRR9W3b1+98sorWr9+vVauXKmoqCjFxsbqwQcfVGJiYk0OT82bN9fGjRv1xBNPaNWqVdqxY4c6d+6sN954Q3FxcS7f3z59+mjbtm169tlntXr1an388cdq0aKF7r77bj355JM1+s5R3fGX31Z+e+PGjfX1118rKSlJ33zzjdavX6/27dtrypQpevjhhyv9bKrJc9ekXrt371a7du109dVX1+g1bM+za9cu7dq1y14WFhamdu3aadKkSXr00UcrTXJ277336umnn1ZJSYluu+22Sj8r6vNzRpJeeOEFff7550pLS9Nnn32msLAwtW3bVjNmzNCDDz7oNG/b1bn64IMP1L9/f7377rt68803JUmXXXaZHn30UT344IMuz1Nt3w+gIsP0925B+IURI0Zo48aNGjFihLp27ars7GzNmTNHBQUF2rJliy6//PJK9zVNUwkJCfr3v/+txx57TM2bN9drr72mQ4cOaefOnYqPj/fgkaChW7lypYYOHapPP/1UAwcO9HZ1AADQrl271L17d73++uv64x//6O3qAHCBwBsesXnzZvXq1cth+NP+/fvVpUsXjRgxosqhX0uWLNFdd92lZcuW2ef25ebmqlOnTho8eLAWLVrk9voDNn/729/0zTffnHc2eQAA6surr76q5557Tunp6WrUqJG3qwPABQJveFWvXr1kGIa2bdtW6WNGjhypdevW6eeff3Yof/DBB/Xee+/p+PHj9jUyAQAAAMDXkNUcXpWTk1PtvJ60tDSXS3/06dNHp0+f1o8//uiu6gEAAADAeSPwhtcsWrRImZmZ1WaOzMrKcpnt1VZWsSccAAAAAHwJWc3hFXv27NFDDz2kvn376t57763ysWfOnFFoaKhTeVhYmEzTdFiDsaLc3Fx9/vnnateuncLDw8+73gAAAKhfZ86cUXp6ugYOHFirDOeAPyHwhsfl5OTolltu0YUXXqgPP/yw2iUYwsPDVVRU5FT+66+/yjCMKgPqzz//3L5sEgAAAHzXokWLarwUKuBvCLzhUadOndLNN9+sU6dOaf369ZWuDVleTEyMsrKynMptZVWtpdyuXTtJ1ob8sssuq1ulG6hJkyZp9uzZ3q6GfxgzRvr+e02S5HDGunWT3n3XS5XyH1xrtcc5qxvOWw39t02TdK5doz2rFa612vnhhx90zz332L+3AYGIwBseU1RUpFtvvVX79+/XmjVr1Llz5xrt1717d61fv96pfPPmzWrcuLE6depU6b623vDLLrvMZYI2VC4qKopzVlNffCENG6aojRvVo/xCEfn5EuewWlxrtcc5qxvOWw3l59v/GSWpR2iotZ2LjvZenfwM11rdMC0QgYzkavCIsrIy3XnnndqyZYuWLl2qPn36uHxcdna29u7dq9LSUnvZHXfcoZycHKWmptrLcnNztXTpUg0ZMoSlxOB90dHS+vXSBRc4lh87Jh096p06AUBt5eRICQlSZqZjea9eBN0AcJ7o8YZH/PWvf9WqVas0ZMgQ5ebm6r333nPYbpvPk5SUpOTkZKWnpysuLk6SNfB+6aWXNHr0aO3evVstWrTQa6+9prKyMj399NOePhSgcr17S5s2nestys+Xhg2zBuUA4OuGD5c2bDj3d2io1LixVO6HbwBA3RB4wyO+//57GYahVatWadWqVU7bbYG3YRiyWBwHYlgsFn322Wd69NFHNWfOHJ05c0Z9+vRRcnKyOnbs6JH6AzUSGiq1bOkwTFMu8hMAgE+q2F7FxkpXXEFvNwDUA4aawyO+/vprlZaWVvqfzfz581VSUmLv7baJiorSm2++qaNHjyo/P19r1qzRlVde6enDaFASExO9XQW/k5iYKFVcc97FGvRwxLVWe5yzuuG8VcNF+8U5qxvOG4CKDNMsnwkICCw7d+5Uz549tWPHDpKcwDOOHrUOLz98WMrLk5o1ky6+2DpUk14jAL4qJ0e67TZp1y7r3926SatW0W7BI/i+hoaAHm8AqE+2RGtt2liHnGdkWOdMDhvm7ZoBQOWGD5e2bZOKiqz/hYQQdANAPSLwBgB3qDhXkrneAHwZbRYAuBWBNwC4A3O9AfgT2iwAcCuymgOAO6SmWoeXZ2VZv8CyHA8AX0abBQBuRY83ALhDdLS0bJn1C2xWlvUL7dGj3q4VADjKyZESEqRrrrH+vWmTNU8F87sBoF4ReAOAuwwfbk2sduAACdYA+CbaKQDwCAJvAHAXkhUB8HW0UwDgEQTeAOAuJCsC4OtopwDAI0iuBgDuQrIiAL6OdgoAPIIebwBwl+hoa5KijRutf19zjTWJEUnWAHgbSdUAwKMIvAHA3UheBMDX0C4BgEcReAOAu5G8CICvoV0CAI8i8AYAdyN5EQBfQ7sEAB5FcjUAcDeSFwHwNbRLAOBRBN4A4G62JGsA4CtolwDAoxhqDgCeYMsgHB9PZnMA3kV7BAAeR+ANAJ5ABmEAvoL2CAA8jsAbADyBDMIAfAXtEQB4HIE3AHgCGYQB+AraIwDwOJKrAYAnkEEYgK+gPQIAjyPwBgBPIIMwAF9BewQAHsdQc3hMYWGhpkyZokGDBql58+ayWCxKTk6u8f47duzQrbfeqpiYGEVGRqpbt26aM2eOysrK3FhrAAAAADg/BN7wmNzcXE2dOlV79uxR9+7dZRhGjffduXOn+vbtq0OHDikpKUmzZs1SfHy8Jk6cqEceecSNtQbqGcv4APAW2h8A8BqGmsNjWrdurezsbEVHR2vHjh3q3bt3jfedN2+eDMPQunXrFBUVJUkaN26c+vfvrwULFmj27NnuqjZQv2zL+EjWpXyGDWPIJwDPoP0BAK+hxxseExISoujo6Drtm5+fr7CwMHvQbdOqVSuFh4fXR/UAz2AZHwDeQvsDAF5D4A2/0L9/f506dUrjx4/Xnj17dOjQIc2bN08fffSRnnjiCW9XD6g5lvEB4C20PwDgNQw1h18YN26cdu/erTfeeENvv/22JCk4OFivvvqqxo8f7+XaAbXAMj4AvIX2BwC8hsAbfsFisSg+Pl4333yz7rzzToWGhiolJUUPPfSQWrVqpSFDhni7ikDNsIwPAG+h/QEAryHwhl+YPn265syZo3379qlx48aSpDvuuEMDBgzQn//8Z916662yWJg5AT+Sk2NNdFS+56mOORAAoFq0OQDgVQTe8Auvv/66BgwYYA+6bYYMGaJHHnlE6enpat++faX7T5o0ySkxW2JiohITE91SX6BaZBcG4Em0OfARKSkpSklJcSg7efKkl2oDeA6BN/xCTk6OSktLncrPnj0rSSopKaly/9mzZ6tHjx5uqRtQJ2QXBuBJtDnwEa46Pnbu3KmePXt6qUaAZzA2Fz4nOztbe/fudQi0O3XqpC+//FJ5eXn2srKyMi1evFiRkZGKj4/3RlWBuiO7MABPos0BAK+ixxseNXfuXJ04cUKZmZmSpJUrV+rw4cOSpAkTJigyMlJJSUlKTk5Wenq64uLiJElJSUkaNWqU+vTpo/Hjxys8PFzvv/++0tLSNG3aNAUFBXntmIA6IbswAE+izQEAryLwhkfNnDlThw4dkiQZhqHly5dr+fLlkqRRo0YpMjJShmE4JUr7wx/+oJYtW+r555/XzJkzderUKXXu3FlvvPGGHnjgAY8fB3DeyC4MwJNocwDAqwi84VEHDx6s9jHz58/X/PnzncpvvPFG3Xjjje6oFgAAAAC4DXO8AcBbcnKkhAQpPt76/6NHvV0jAIGGdgYAfAKBNwB4i215nwMHrP8fNszbNQIQaGhnAMAnEHgDgLewvA8Ad6OdAQCfQOANAN7C8j4A3I12BgB8AsnVAMBbWN4HgLvRzgCATyDwBgBvYXkfAO5GOwMAPoGh5gAAAAAAuBGBNwB4G8v9AHAH2hYA8BkE3gDgbSz3A8AdaFsAwGcQeAOAt7HcDwB3oG0BAJ9B4A0A3sZyPwDcgbYFAHwGWc0BwNtY7geAO9C2AIDPIPAGAG9juR8A7kDbAgA+g6HmAOALyD4MoL7QngCAzyHwBgBfQPZhAPWF9gQAfA6BNwD4ArIPA6gvtCcA4HMIvAHAF5B9GEB9oT0BAJ9DcjUA8AVkHwZQX2hPAMDnEHgDgC8g+zCA+kJ7AgA+h6HmAAAAAAC4EYE3APgSlgECcD5oQwDAJxF4A4AvYRkgAOeDNgQAfBKBNzymsLBQU6ZM0aBBg9S8eXNZLBYlJyfX6jlWr16tG264QRdccIGaNm2qXr166cMPP3RTjQEvYBkgAOeDNgQAfBKBNzwmNzdXU6dO1Z49e9S9e3cZhlGr/efPn6+BAweqUaNGev755zVz5kz169dPhw8fdlONAS9gGSAA54M2BAB8ElnN4TGtW7dWdna2oqOjtWPHDvXu3bvG+2ZkZOihhx7SxIkTNWvWLDfWEvAylgECcD5oQwDAJxF4w2NCQkIUHR1dp31ff/11lZWV6ZlnnpFkHbYeERFRn9UDfAPLAAE4H7QhAOCTGGoOv7BmzRpdeuml+uSTT9SmTRtFRkaqefPmeuqpp2SaprerB9QvshIDqC3aDQDwafR4wy/s27dPQUFBGjNmjCZPnqyuXbsqNTVVzz77rEpLSzVt2jRvVxGoP7asxJI1M/GwYfRgAaga7QYA+DQCb/iFgoICmaapF154QX/7298kSUOHDtUvv/yil19+WU888QRDzxE4yEoMoLZoNwDApzHUHH4hPDxcknTXXXc5lCcmJurMmTNKS0vzRrUA9yArMYDaot0AAJ9Gjzf8QuvWrbV//35ddNFFDuXR0dEyTVN5eXlV7j9p0iRFRUU5lCUmJioxMbHe6wqcN7ISA6gt2g34iZSUFKWkpDiUnTx50ku1ATyHwBt+oWfPntq/f78yMzPVrl07e3lmZqYMw1DLli2r3H/27Nnq0aOHm2sJ1BOyEgOoLdoN+AlXHR87d+5Uz549vVQjwDMYag6fk52drb1796q0tNReNnLkSJmmqXfeecdeZpqm5s+fr2bNmtFYAwAAAPBZ9HjDo+bOnasTJ04oMzNTkrRy5UodPnxYkjRhwgRFRkYqKSlJycnJSk9PV1xcnCTp9ttv1w033KDnn39ex44dU7du3bR8+XJt3LhRb775pkJCQrx2TIDb5ORYMxWXHzoaHe3tWgHwJbQTAOAXCLzhUTNnztShQ4ckSYZhaPny5Vq+fLkkadSoUYqMjJRhGLJYnAdjrFixQk8++aQWL16shQsXqnPnznrvvfecEq4BAYPlgQBUh3YCAPwCgTc86uDBg9U+Zv78+Zo/f75TeePGjTVr1izNmjXLHVUDfA/LAwGoDu0EAPgF5ngDgK9ieSAA1aGdAAC/QI83APgqlgcCUB3aCQDwCwTeAOCrWB4IQHVoJwDALzDUHAB8WU6OlJAgxcdb/3/0qLdrBMBX0D4AgN8g8AYAX2bLWHzggPX/w4Z5u0YAfAXtAwD4DQJvAPBlZCwGUBnaBwDwGwTeAODLyFgMoDK0DwDgN0iuBgC+jIzFACpD+wAAfoPAGwB8GRmLAVSG9gEA/AZDzQHAH5C9GIAN7QEA+B0CbwDwB2QvBmBDewAAfofAGwD8AdmLAdjQHgCA3yHwBgB/QPZiADa0BwDgd0iuBgD+gOzFAGxoDwDA79DjDQD+wJa9eONG69/XXENSJaAhyslxDrqjo71dKwBANQi8AcCfkFQJaNhoAwDALxF4A4A/IakS0LDRBgCAXyLwBgB/QlIloGGjDQAAv0RyNQDwJyRVAho22gAA8EsE3gDgT2xJ1gA0TLQBAOCXGGoOAP4mJ8ea0Tw+nszmQEPBfQ8Afo3AGwD8DVmNgYaH+x4A/BqBNwD4G7IaAw0P9z0A+DUCb3hMYWGhpkyZokGDBql58+ayWCxKTk6u03ONGzdOFotFQ4YMqedaAn6ArMZAw8N9DwB+jcAbHpObm6upU6dqz5496t69uwzDqNPzbN++XQsXLlR4eHg91xDwE6mpUt++Uvv21v+T1RgIfNz3AODXCLzhMa1bt1Z2drYOHjyoF198UaZp1ul5Jk6cqPvuu0/R0dH1XEPAT9iyGm/caP37mmtItgQEspwc5yXE+AwEAL9C4A2PCQkJOe9gOTk5Wbt379a0adPqqVaAHyPZEtAwcK8DgN8j8IbfKCgoUFJSkv7f//t/9HYDEsmWgIaCex0A/B6BN/zGM888o8aNG+vhhx/2dlUA30CyJaBh4F4HAL8X7O0KADXx448/6pVXXtHixYsVEhLi7eoAviE11XneJ4DAw70OAH6PwBt+YeLEiUpISNDvf//7Ou0/adIkRUVFOZQlJiYqMTGxPqoHeEd0tLRsmXX+Z1aW9Ys5SZeAwJGTc+7+jomRNm3i/obfS0lJUUpKikPZyZMnvVQbwHMIvOHzvvrqK33++edavny5MjIyJEmmaaqkpERnzpxRRkaGmjVrpsjIyEqfY/bs2erRo4enqgx4ji3pkmRNvDRsmDXjOQD/x/2NAOSq42Pnzp3q2bOnl2oEeAaBN3ze4cOHZRiGhg4d6lBuGIYyMzPVvn17zZ49WxMmTPBSDQEvIukSELi4vwEgYBB4w+dkZ2fr5MmT6tChg4KCgnTDDTdo+fLlTo8bN26c2rVrpyeffFK/+c1vvFBTwAfExFh7wsr/DSAwcH8DQMAg8IZHzZ07VydOnFBmZqYkaeXKlTp8+LAkacKECYqMjFRSUpKSk5OVnp6uuLg4XXzxxbr44oudnmvixIm66KKLdNttt3n0GACfYku6dPiwlJcnHTkiJSQw1xvwdzk5UnGxFBpq/btbN5KqAYAfI/CGR82cOVOHDh2SZB0qvnz5cntv9qhRoxQZGSnDMGSxVL/SnWEYMgzDrfUFfF50tHXOZ0KCdOiQlJ8vZWQwFxTwd8OHS9u2nfs7JIQf0wDAjxF4w6MOHjxY7WPmz5+v+fPnV/u4A+WH3wENHXNBgcDCPQ0AAaX6bkUAgO+rOPeTuaCAf+OeBoCAQo83AAQC21xv23q/zAUF/Bv3NAAEFHq8ASAQREdLy5ZZv6BnZVm/sB896u1aAaitnBxrzoZrrrH+vWmTNV8D87sBwK8ReANAoBg+XNqwwbr80IYN1uAbgH/hPgaAgETgDQCBgmRMgP/jPgaAgETgDQCBgmRMgP/jPgaAgETgDQCBIjVV6ttXiouTIiOlI0esc0WZ6w34h5wcqbhYCg21/tenD0nVACBAEHgDQKCIjrYmYWrTRsrPlzIymCMK+JPhw6Vt26SiIut/ISEkVQOAAEHgDQCBhjmigH/i3gWAgEXgDQCBhjmigH/i3gWAgEXgDQCBJjVV6t373DzRs2eZ5w34Mtva3UeOWPMztG1rzdfA/G4ACBgE3gAQaKKjpUaNzs0T3bqVed6AL7Ot3Z2RYc3PcPHF1nwNzO8GgIBB4A0AgYi5ooD/4H4FgIBH4A0AgYi5ooD/4H4FgIBH4A0AgYg1vQHfx9xuAGgwCLwBIBCxpjfg+5jbDQANBoE3AAQy5o4Cvov7EwAaDAJvAAhkFeeKHjvGcHPAF+TkWO/H8pjbDQABi8AbAAJZaqp17qhNfj7DzQFfMHy49X60iYxkbjcABDACbwAIZNHRUsuWjmUMZwW8r+J92LIlc7sBIIAReANAoKs4fDUzkwzngLfYMplnZjqWM8wcAAIagTcABDrb0mKhoda/i4rIcA54iy2TeVGR9e/QUJYQA4AGgMAbHlNYWKgpU6Zo0KBBat68uSwWi5KTk2u071dffaWxY8eqc+fOioiIUHx8vMaNG6fs7Gw31xoIALalxWJjHcsZcg54XsX7LjaWJcQAoAEg8IbH5ObmaurUqdqzZ4+6d+8uwzBqvO/kyZO1du1aDRs2THPmzFFiYqKWLFmiHj166CjDZYGaqTiUlaGtgOdxHwJAgxTs7Qqg4WjdurWys7MVHR2tHTt2qHfv3jXed/bs2UpISHAoGzhwoPr166dXX31V//jHP+q7ukDgSU21Di8/fFjKy5OOHLHONU1NpbcN8IScHKm4+Ny0j27dGGIOAA0EPd7wmJCQEEXX8ct9xaBbkq677jo1a9ZMP/zww/lWDWgYbEPO27SxLmOUkcFcb8CThg+Xtm2zzu8uKpJCQvjRCwAaCAJv+K3CwkIVFBSoRYsW3q4K4F8qzjHdvp0M54A72TKZb9niWE6eBQBoMAi84bdmz56ts2fP6q677vJ2VQD/UnFOaVERvd6AO9kymZeUOJYzvxsAGgwCb/ilb7/9Vv/4xz80cuRI9evXz9vVAfxLauq5OaY29LwB7lPx/goOZgkxAGhgCLzhd/bs2aNhw4apa9eueuutt7xdHcD/REdLvXo5lmVmWofCMuQcqF85OdKxY45lV13FEmIA0MCQ1Rx+5fDhw7rpppt04YUX6pNPPlFERESN9ps0aZKioqIcyhITE5WYmOiOagK+z5bhfPv2c4mebInW1q/3du2AwDF8uDWZoU1kJD3daNBSUlKUkpLiUHby5Ekv1QbwHAJv+I3jx4/rpptuUklJib755htddNFFNd539uzZ6tGjhxtrB/gZW4bz+HjpwIFz5Qw5B+pXxXuqZUt6utGguer42Llzp3r27OmlGgGewVBz+Jzs7Gzt3btXpaWl9rLTp09r0KBBysrK0qeffqr27dt7sYZAAKmY3OnYMYabA/XBlsk8M9OxnIRqANAg0eMNj5o7d65OnDihzP9+EVm5cqUOHz4sSZowYYIiIyOVlJSk5ORkpaenKy4uTpL0hz/8Qdu2bdPYsWO1e/du7d692/6cTZo00e233+75gwECQWqq1KHDuaGw+fkMNwfqgy2TuU1oqDW3AsPMAaBBIvCGR82cOVOHDh2SJBmGoeXLl2v58uWSpFGjRikyMlKGYchicRyM8f3338swDL377rt69913Hba1bduWwBuoq+ho69DX8nNQt2yx9tSlpjIkFqiLnBxr/oTyYmP5QQsAGjCGmsOjDh48qNLSUpf/2Xq358+fr5KSEvvf1e13oPz8VMALcgpylPBuguJfiVfCuwk6WuhnQ7UrDn0tKTmXaA1A7Q0fbk1YWJ6fDTH3+3YNAHwMgTcAnKfhS4Zrw+ENOpB3QBsOb9CwxX4WsKamWtcUDq4wCGr7duZ7A7Vhm9e9ZYtjeWio3w0x9/t2DQB8DIE3AJynrIKsKv/2ebYM51dd5VheVESvN1AbtnndJSWO5b16+d20Db9v1wDAxxB4A8B5imkSU+XffiM11dozV55tvjc930DlKuvpDg62jibxs95uKYDaNQDwEQTeAHCeUkemqm+bvmp/YXv1bdNXqSP970u2JGuPXK9ejmXM9waqV1lP91VXWUeT+FlvtxRA7RoA+AiymgPAeYqOiNb6MQGSrTg11Rpkb9niGETY5nv7YQABuJWrDObBwdag2w97um0Cql0DAB9AjzcA4BzmewO14yqDuR/3dAMA3IPAGwDgjPneQNUCKIM5AMD9CLwBAM6Y7w1ULYAymAMA3I/AGwDgWmXre9PzjYausnndfprBHADgfgTeAADXKpvvbev57tCB4BsNi214edu2zOsGANQKgTcAoGqV9Xzn5zPsHA2LbXh5xaCbed0AgGoQeAMAqlZZz7fEsHM0HK6Gl9swrxsAUA0CbwBAzaSmSpGRjmUMO0egq2p4eWgo87oBADVC4A0A9SSnIEcJ7yYo/pV4JbyboKOFARaIRkdL+/dXPuw8Lo7ebwSWnBypY8fKh5cfOhSQ87oDvi0DAC8g8AaAejJ8yXBtOLxBB/IOaMPhDRq2OADnP1c17LyoiN5vBIbyvdz5+a4fE8DDyxtEWwYAHkbgDQD1JKsgq8q/A4qrYec29H7Dn1XVyy01iOHlDaotAwAPIfAGgHoS0ySmyr8DSvlh56Ghztvp/Ya/qUkvd2RkwA4vL69BtWUA4CEE3gBQT1JHpqpvm75qf2F79W3TV6kjA7dHTNK5YeeHDtH7Df9VPuCurpd7//6ADrhtGlxbBgAeEFz9QwAANREdEa31N/45LQAAHFxJREFUY9Z7uxqeZ+v9HjbMutxSxcClfO93Awlc4Cdsw8or6+GWrD8qNbDrtsG2ZQDgRvR4A0A9arDZgOn9hj+pybDyBtbLLTXg9gsAPIDAGwDqUYPPBlzTud+tWklhYdbs6ATh8ITywXZsbPXDyhvAXO6KGnz7BQBuxFBzAKhHZAPWud7vo0etw8td9SiapjXo2brV2gveq5c1S3QDCnLgQTUZUi41yGHl5dF+AYD70OMNAPWIbMDlVNf7bWPrBW/dWmrXjqHoqB85OVKfPtaRFTExVQfdDXBYuSu0XwDgPgTe8JjCwkJNmTJFgwYNUvPmzWWxWJScnFzj/U+ePKnx48crOjpaTZo00YABA5SWlubGGgO1Z8sGHBcVp8hGkTpy6kjDnitZfu53dQF4aamUkWENwmNirAETw9FRE+WD7NBQa89127bStm3WH3ZMs/J9G8gSYdXJKchRcWmxQoNCFRoUqj6xfchmDgD1iMAbHpObm6upU6dqz5496t69uwzDqPG+pmlq8ODB+uCDDzRhwgTNmDFDx44dU//+/fXTTz+5sdZA7diyAbdp2kb5xfnKOJnBXEnJMQDv3dsaHFXVBpSVWQMm23D0Nm2kJk0IxuE4V9t2TcTEnAuyi4ulggLX87dtgoKs+9PLbTd8yXBt+3mbikqLVFRapBBLiKIjOC8AUF+Y4w2Pad26tbKzsxUdHa0dO3aod+/eNd73ww8/1KZNm7Rs2TINHTpUkjRixAh16tRJU6ZM0aJFi9xVbaBOmCtZiehoayAtVT0HvKLiYut/Nlu3WhO0hYRIwcHW3nJJ6tZNWrWKQCoQ5ORIw4dLhw9Lv/wilZRYe65LSqw/zNRFaCj5BCpBmwUA7kWPNzwmJCRE0XX8orNs2TK1atXKHnRLUosWLXTnnXdqxYoVOnv2bH1VE6gXzJWsgfJzwNu2tfZC1oZpWoPx06cde8dbtbIGWBERjkOPmT/uW1z1XNveqzZtzmUeP3RIKiw815td26DbMKzP26cPQ8qrQJsFAO5Fjzf8Qlpamnr06OFU3qdPH7311lv68ccfdcUVV3ihZoBrqSNTdev7t2pXzi5J0tmyszpaeJShmxXZhqBL1oB42DDpyBEpN9fas1lcXPX8XFdsAXn5HnLb8OOMDOeectOs/N+NGkkXXCDl5Z3rcW3USGreXLr44obVc1pZD3Rtzl357ZX1XNveq7owDOt721DfozrIKcjR8CXDdeTUEUU2ilSz8Ga6uOnFzO8GgHpG4A2/kJWVpX79+jmVx8RYf5H/+eefCbzhU6IjotUoqJGKSq3zTLdmbtWwxcO0fsx6L9fMh5UPwm3qKxgvr7LAvLJ/VwwCywXxJRddpJIg61OWBknBZc7/LgmWToVb1PR0mUJcbK/v/dz12iGmFOzqtNf23NUni8UaaEtMM6gj29rdNl0v6ko7BQBuQOANv3DmzBmFusiGHBYWJtM0debMmSr3f/RR6cIL3VU7wLXvOmVJ5S7b7/Zn6Y47vFcfX/RrcI42xw3XmeDDKgr+RaZRIlOmDDNYMkplypRlQCOFlF6gs0GSaUgtCoO1MqVEXXNMyZRKgqSQMimkVKrlYPXzFiwp+L/Ty1W+87b8v4ulyOIKPbuVPbY+9nPXa3tRiWT/geNssHQ8XDrS1NAdI0J1rEnZf6+Z/5PmxlmvGdN2zeTJNEokSVFnuunajFUKKyUwL492Cr4gL8/bNQDcj8AbfiE8PFxFLjLU/vrrrzIMQ+Hh4VXun5Y2ScHBUQ5lMTGJiolJrNd6AuWFFMVIoQfsf58JztSatgnqvjdVoSWB++W/KCRH33Uarl8bHVbxf4Np/TcYCi65QGeD8+xlplEiWZwDPFPnekZLVazSoHM9p0cjpavHO79uywJp1ftS1xw5BOS2YK1RqRRW6rwfvK9iYH0iVIoqkvLCpSNNpWEjpWNNKu5lSjpT7q/KrxlJyovYqk8ubSXDDJFhBss0SqX//shj+7fFbKSQkuYKK764wdynZ4IzHcpDimLqPNIfqImsrBRlZaU4lJWUnPRSbQDPIfCGX4iJiVFWlnOGVVtZ69atq9x/9erZLueIA+50tDBVwxYP0/aft6uotEhlQUU60XSDzg4bpq/9fChnTkGObku5Tbtydsk0TQUHBau0rFSmaarELFGZ6RxMl6pYpcHu+0Z/rInrgNymqsC8sn/bgsALfpUaVTfkugEpHyjX5txV3H68ysDaDSymTBU7BOlOAXtwgX4Ny9Da3q0UYglxuLYbBTdS8/Dm9jnQ/pyzIeHd4TpRboh5aFCoerXu9d/j8mLF0AAk/ve/c3bu3KmePXt6pzqAhxB4wy90795d6yvO/ZS0efNmNW7cWJ06dfJCrYCq2db0jn8lXgfyzvV8b/95u18lWrMlXzp86rB+Of2LSspKVFxaLFPnos/isnqeu1tPDBkKsYSoUXAjhcZcoBv+nKeSshKnHwsq+3ej4Ea6IPQC5f16br+YM0F6771fFXvSVPPTqnbudKDM8S6xBcpRhu6+O0zZjctqfe6qOuehZaVO15U3mTJVXFbscG0XFxeroLhAGSczFDMzRiFB1vnl3Vp106rEVX51T2//ebtDWWzTWOZ2A4AbEXjD52RnZ+vkyZPq0KGDgv67vNAdd9yhZcuWKTU1VcOGDZMk5ebmaunSpRoyZIhCbMl1AB8U0yTGIfAuKi3y2URrNQmy3cUii0KCQs47gCsts44nd2sw9FztHh5Zx5ep637ueu1ISW0lpde5NlU7WnhUwxYP05FTR5R7OrdWP5JUdc244xouU5lD8sRWM1vZf+Tx9V7x4UuG2+tuw/JhAOBeBN7wqLlz5+rEiRPKzLTOKVu5cqUOHz4sSZowYYIiIyOVlJSk5ORkpaenKy4uTpI18H7ppZc0evRo7d69Wy1atNBrr72msrIyPf300946HKBGUkemKm52nMMX3S2ZW5TwboLXv5hXHDJe2TDxuiofTLsKoP0hSIHn2EaJ1LejhUfty/tVF7CfLT3rFJTWhL2HvFyvuK8F47Yf1rZkbnEoDw0KZfkwAHAzAm941MyZM3Xo0CFJkmEYWr58uZYvXy5JGjVqlCIjI2UYhiwWi8N+FotFn332mR599FHNmTNHZ86cUZ8+fZScnKyOHTt6/DiA2oiOiFav1r0cluwpKSvRhsMbPNrz7Y7ebNtQ7kCdB4vAEB0Rra3jttbosVUF6bX9YcpVMO7NIeoVlw6z6dW6F/cpALiZYZrnsxgq4NtsyTp27NhBcjV4lW0I7ZbMLSopK7GXB1uCdVXsVW4LUMv3aJ9vkN3I0kghQSH2+vvbvFbgfLkaCl8fP141Cmrk9vsppyBHbV9q69Cb7+72B6gpvq+hIaDHGwA8wDaENuHdBJc93x1e6aD9E/bXy5ff+gq2bcPEJYJsQHI9FL5iD3lth6ubMlVUWqStmVvVZlYbhQaHqll4s3obMWIb6WJbXaG8q2Kv8slcEwAQiAi8AcCDUkemuuz5zi/OV9zsuHLL+dTsy3Z9DR8vn/2bYeJAzbkaxl4xGK/pEHXbsPT84vx6GZaeU5CjjnM6Kr8432kb87oBwLMIvAHAgyrr+Zas2c43HN6g1v9srYubXuwy+K2PZGgMGQfcq2IwXtch6nXJnF7TES/M6wYAzyLwBgAvSB2Zqg6vdHDZE1VqlirjZIZDj9f5Zlz21DxSAM6qG6Je01EqVSVrq2kbERoUah9ZAwDwHAJvAPCC6Iho7Z+wX8MWD3M599KmfI9XcXFxrV6DYBvwXeV7xcv3iB85dUSlZmmNn6c2bURko8h6yyUBAKgdAm8A8BJbL9jRwqOV9n7XBsnQAP9UvkfcHZnTy/dy0yYAgHcQeAOAl5Xv/a5pjxfJ0IDAVJPM6TXJ7cCIFwDwLQTeAOADquvxss3fJMgGGp6aJGujjQAA30bgDQA+xlWPFwDY0EYAgP+xeLsCAAAAAAAEMgJvAAAAAADciMAbAAAAAAA3IvAGAAAAAMCNCLwBAAAAAHAjAm8AAAAAANyIwBsAAAAAADci8AYAAAAAwI0IvAEAAAAAcCMCbwAAAAAA3IjAGwAAAAAANyLwBgAAAADAjQi8AQAAAABwIwJveExxcbEmT56s2NhYNW7cWFdffbVWr15do3137NihW2+9VTExMYqMjFS3bt00Z84clZWVubnWAAAAAHB+CLzhMffdd59eeukljRo1Sq+88oqCg4M1ePBgbdy4scr9du7cqb59++rQoUNKSkrSrFmzFB8fr4kTJ+qRRx7xUO0bnpSUFG9Xwe9wzuqG81Z7nLO64bzVHuesbjhvACoi8IZHbN26VYsXL9b06dM1ffp0PfDAA1qzZo3atm2rxx57rMp9582bJ8MwtG7dOk2cOFHjxo1Tamqqfvvb32rBggWeOYAGiC8Ntcc5qxvOW+1xzuqG81Z7nLO64bwBqIjAGx6xdOlSBQcHa9y4cfay0NBQjR07Vps2bVJmZmal++bn5yssLExRUVEO5a1atVJ4eLjb6gwAAAAA9YHAGx7x3XffqVOnTmrSpIlDeZ8+fezbK9O/f3+dOnVK48eP1549e3To0CHNmzdPH330kZ544gm31hsAAAAAzlewtyuAhiErK0sxMTFO5TExMTJNUz///HOl+44bN067d+/WG2+8obfffluSFBwcrFdffVXjx493W50BAAAAoD4QeMMjzpw5o9DQUKfysLAw+/bKWCwWxcfH6+abb9add96p0NBQpaSk6KGHHlKrVq00ZMiQKl9Xkn744YfzPIKG5+TJk9q5c6e3q+FXOGd1w3mrPc5Z3XDeao9zVject9qxfU+r6vsg4O8M0zRNb1cCga9Lly5q1aqVvvzyS4fyH374QVdccYXeeOMNh/nf5U2fPl1z5szRvn371LhxY3v5gAEDtG/fPmVkZMhicT1r4r333tM999xTfwcCAAAAt1i0aJHuvvtub1cDcAt6vOERMTExLoeTZ2VlSZJat25d6b6vv/66BgwY4BB0S9KQIUP0yCOPKD09Xe3bt3e578CBA7Vo0SK1a9eORGwAAAA+6MyZM0pPT9fAgQO9XRXAbQi84RHdu3fXN998o4KCAocEa5s3b5ZhGOrevXul++bk5Ki0tNSp/OzZs5KkkpKSSvdt0aIFv5wCAAD4uL59+3q7CoBbkdUcHnHHHXeopKREb775pr2suLhYCxYs0NVXX63Y2FhJUnZ2tvbu3esQaHfq1Elffvml8vLy7GVlZWVavHixIiMjFR8f77kDAQAAAIBaoscbHtGnTx+NGDFCjz/+uHJyctShQwctWLBAGRkZmj9/vv1xSUlJSk5OVnp6uuLi4uxlo0aNUp8+fTR+/HiFh4fr/fffV1pamqZNm6agoCBvHRYAAAAAVIvAGx7zr3/9S3//+9+1aNEi5eXlqWvXrvrkk08chhYZhuGUKO0Pf/iDWrZsqeeff14zZ87UqVOn1LlzZ73xxht64IEHPH0YAAAAAFArZDUHAAAAAMCNmOMNAAAAAIAbEXgjIH311VcaO3asOnfurIiICMXHx2vcuHHKzs52+fiNGzcqISFBERERiomJ0cSJE1VYWOjhWntfdna2kpKSNGDAADVt2lQWi0Xffvuty8f2799fFovF6b/Bgwd7uNbeVZtzJnGtVWXhwoUur6mgoCAdPXrU29XzuuLiYk2ePFmxsbFq3Lixrr76aq1evdrb1fJpa9eurfSa2rp1q7er53WFhYWaMmWKBg0apObNm8tisSg5OdnlY/fs2aObb75ZkZGRat68ue69917l5uZ6uMa+oabnbfTo0S6vv8svv9wLtfau7du366GHHtJvfvMbNWnSRG3bttXIkSO1b98+p8dyrSFQMccbAWny5MnKy8vTiBEj1LFjRx04cEBz5szRJ598ou+++07R0dH2x3733Xf63e9+p8svv1yzZ8/WkSNHNGPGDO3fv1+ffPKJF4/C8/bu3asZM2aoY8eO6tq1qzZt2lTpYw3DUJs2bTR9+nSVn7FS1Zrsgag254xrrXqGYWjq1Klq166dQ/kFF1zgnQr5kPvuu0+pqamaNGmSPUHl4MGD9c033+jaa6/1dvV82sMPP6xevXo5lHXo0MFLtfEdubm5mjp1qtq2bWtf9tOVzMxMXXfddbrwwgs1ffp05efna8aMGfq///s/bd26VcHBDevrZE3PmySFhYXpnXfecficjIqK8kAtfcsLL7ygjRs3asSIEeratauys7M1Z84c9ejRQ1u2bLH/GMG1hoBmAgFo3bp1TmXffvutaRiG+fe//92hfNCgQWZsbKxZUFBgL3v77bdNi8Vifvnll26vqy8pKCgw8/LyTNM0zaVLl5oWi8Vcu3aty8f279/f7NKliyer55Nqc8641qq2YMEC02KxmDt27PB2VXzOli1bTMMwzFmzZtnLfv31V7NDhw5m3759vVgz3/bNN9+YhmGYy5Yt83ZVfFJxcbGZk5NjmqZpbt++3TQMw1y4cKHT4/7nf/7HjIiIMI8cOWIvW716tWkYhvnWW295rL6+oqbn7f777zcjIyM9XT2ftGnTJvPs2bMOZfv27TPDwsLMUaNG2cu41hDIGGqOgJSQkOBUdt1116lZs2b64Ycf7GX5+flavXq1Ro0apYiICHv5vffeq4iICC1ZssQj9fUVERERte5ZLC0tbdBDpWt6zrjWaqegoEBlZWXerobPWLp0qYKDgzVu3Dh7WWhoqMaOHatNmzYpMzPTi7XzDwUFBSotLfV2NXxKSEiIwwiwyqSmpurWW29VbGysveyGG25Qp06dGmTbVdPzZlNWVqb8/Hw31sj3XX311U691R06dNAVV1zh8L2Maw2BjMAbDUZhYaEKCgrUokULe9m///1vlZSUqGfPng6PDQkJUffu3ZWWlubpavqVH3/8UREREYqMjFRMTIyeeuoplZSUeLtaPolrrWZM01T//v3VtGlTNW7cWLfffrv279/v7Wp53XfffadOnTqpSZMmDuV9+vSxb0flRo8eraZNmyosLEwDBgzQjh07vF0lv/Hzzz/r6NGjTkP1Jev1R9tVtdOnT6tp06aKiopS8+bN9dBDDzXoH6srysnJsX8v41pDoGOiBBqM2bNn6+zZs7rrrrvsZVlZWTIMQzExMU6Pj4mJ0fr16z1ZRb/SoUMHDRgwQF26dFFhYaGWLl2qZ599Vvv27VNKSoq3q+dzuNaq17hxY40ePVrXX3+9mjZtqh07duif//yn+vbtq507dzr0gDQ0WVlZlV47pmnq5//f3v2FNPWGcQD/zkrdWImmWYqhZmZQ1qRQW5hQmJpo+G+IsKAgb8SUQIjqRvQikC6KJFrQCC+CJKJsCWGZlJUo/QFFpBVhJoUM0ZlNxfd3tdF+m2m549mf7wd2cc77Th8enp3x7Jzznm/fZIjK+wUHB6O0tBT5+fmIjIzE4OAgmpubkZWVhZ6eHuzZs0fuEL3e2NgYACxafxaLBXNzc1i3bt1qh+b1YmJiUF9fj7S0NCwsLKCjowMtLS348OEDurq6EBQU2Oe/WltbMTo6isbGRgCsNfJ/bLzJ6wkhMDs7u6y5ISEhbvd3d3ejoaEBOp0Ohw4dcuyfmZlZ9H2hoaGOcV/kibz9icFgcNqurKxEVVUVbt68ibq6OseZOF8iZc78udbc+ZdclpWVoayszLG/sLAQOTk5yMrKQlNTE1paWiSJ1RfMzMwsWjv2cXKVmZmJzMxMx3ZBQQFKSkqQmpqKc+fOwWQyyRidb1jq2GWfw2bIVVNTk9N2eXk5tm/fjgsXLqCtrQ3l5eUyRSa/oaEhVFdXQ6vVQq/XA2Ctkf8L7J/ayCd0d3dDqVQu+VKpVBgeHnZ5/9DQEIqLi5GamurSLCqVSgCAzWZzed+vX78c475opXn7F2fPnoUQwmcfcSRlzvy51tzxVC61Wi3S09N9tqY8RalULlo79nFanm3btqGoqAjPnj1zWmma3Fvq2PX7HFpaXV0dFApFQB/Tvn//jmPHjiE8PBx3796FQqEAwFoj/8cz3uT1UlJSYDQalzX3/5cnjYyMICcnB+Hh4Xj06JHTolb2+UIIx+VNvxsbG/PpR2OtJG//Ki4uDgBgsVg88vdWm5Q58+dac8eTuYyLi/PYj0O+asuWLW4vJ7fXk7/Vj9Ti4uIwOzuL6elpl/vmyZn987nYsSsiIoJnIP9CaGgoNm7c6LPfkys1OTmJ3NxcTE5O4sWLF9i8ebNjjLVG/o6NN3m96Ohox2VIf8NisSAnJwfz8/Po6upCdHS0y5xdu3Zh7dq16OvrQ2lpqWP/3Nwc3r17B51Ot6LY5fSveVsJs9kMAIiKilrV/+spUubMn2vNHU/m8tOnTz5bU55if1aw1Wp1ahRfv34NhUKBvXv3yhid7zGbzQgNDWXTvQwxMTGIiopCX1+fy1hvby9r7y9ZrVaMj48H5DHNZrOhoKAAHz9+RGdnJ3bs2OE0zlojf8dLzckv/fz5E3l5eRgbG4PJZEJiYqLbeRs2bMCRI0fQ2trqtMro7du3MT09HdD3X/3J1NSU2/t3GxsboVAocPToURmi8m6staWNj4+77DOZTOjv70deXp4MEXmP0tJSzM/P48aNG459s7OzMBqNyMjICOiF5/7EXU29f/8eDx8+5HHqL5SUlKC9vd3psXWdnZ0YHh7msWsRNpsNVqvVZX9DQwMABNwxbWFhAeXl5Xjz5g3a2toWXQeGtUb+TCF4gxP5oePHj+PBgwc4deoUsrOzncbUajWKiooc22/fvoVWq8XOnTtx+vRpjIyM4PLly8jOzg7IhXfszfPAwADu3LmDkydPIiEhAQBw/vx5AMDz589RUVGBiooKJCUlYWZmBvfu3cOrV69QVVUVcItgLSdnAGttKcnJydBoNNi3bx/CwsLQ39+PW7duITY2Fr29vQF5huh3Op0O9+/fR21tLZKSkmA0GtHX14enT59Cq9XKHZ5XOnz4MJRKJQ4cOIBNmzZhYGAABoMBISEh6OnpcTnjFoiuXbuGiYkJjI6O4vr16yguLoZGowEA1NTUYP369fj69SvS0tIQFhaGM2fOYGpqCs3Nzdi6dSt6e3sD8vLfpfJmsVig0WhQUVGBlJQUAEBHRwceP36M/Px8tLe3yxn+qqutrcWVK1dQWFjotIimXWVlJQCw1si/CSI/FB8fL4KCgty+EhISXOa/fPlSHDx4UKhUKhEdHS1qamqE1WqVIXL5KRQKt3lbs2aNY87nz5+FTqcTiYmJQqVSCbVaLfbv3y8MBoOMkctnOTmzY60t7uLFiyItLU2Eh4eLkJAQER8fL6qrq8WPHz/kDs0r2Gw2UV9fL2JiYoRSqRTp6eniyZMncofl1a5evSoyMjJEZGSkCA4OFrGxseLEiRPCbDbLHZrX+NP35ZcvXxzzBgcHRW5urlCr1SIiIkLo9fqA/mwulbeJiQmh1+tFcnKyUKvVQqlUit27d4tLly6J+fl5ucNfddnZ2YvmKygoyGkua438Fc94ExEREREREUmI93gTERERERERSYiNNxEREREREZGE2HgTERERERERSYiNNxEREREREZGE2HgTERERERERSYiNNxEREREREZGE2HgTERERERERSYiNNxEREREREZGE2HgTERERERERSYiNNxEREREREZGE2HgTERERERERSYiNNxEREREREZGE/gM632+QnzQ3igAAAABJRU5ErkJggg==",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000001B9B06D8>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000001BFE5240>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"alen = 1.62\n"
]
},
{
"data": {
"text/plain": [
"3-element Array{Any,1}:\n",
" PyObject <matplotlib.lines.Line2D object at 0x000000001C079828>\n",
" PyObject <matplotlib.lines.Line2D object at 0x000000001C079AC8>\n",
" PyObject <matplotlib.lines.Line2D object at 0x000000001C07E358>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"push!(LOAD_PATH,\"C:/JMB/DIVAnd.jl/src\"); using DIVAnd;cd(\"C:/JMB/DIVAnd.jl\");cd(\"examples\");include(\"DIVAnd_optimizepmn1Da.jl\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In practise the exact value for $\\alpha$ is depending on the domain size $L$ and grid resolution compared to the length scales $l$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Optimal $ \\alpha $ as a function of $L \\over l$ (domain size $L$ over length scale $l$) and resolution $ l p_m$ (length scale $l$ over grid spacing $1/p_m$). The optimal valueof $\\alpha$ is used to force the last and first metric to be $p_m$= $1 \\over \\alpha l $ "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2D verification"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check effect on $\\mathtt{DIVAndgo}$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Within $\\mathtt{DIVAndrun}$ so that windowing can be done efficiently? In order to avoid copying a tuple, work at the level where the pmn's are actually used ? Always allow to force use of unmodified metrics."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.5.0",
"language": "julia",
"name": "julia-0.5"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-2.0 |
CompPhysics/ComputationalPhysics2 | doc/Projects/2021/Project2/Project2VMC/ipynb/Project2VMC.ipynb | 2 | 19677 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- dom:TITLE: Project 2, Variational Monte Carlo studies of electronic systems. Deadline June 1, Spring 2021 -->\n",
"# Project 2, Variational Monte Carlo studies of electronic systems. Deadline June 1, Spring 2021\n",
"<!-- dom:AUTHOR: [Computational Physics II FYS4411/FYS9411](http://www.uio.no/studier/emner/matnat/fys/FYS4411/index-eng.html) at Department of Physics, University of Oslo, Norway -->\n",
"<!-- Author: --> \n",
"**[Computational Physics II FYS4411/FYS9411](http://www.uio.no/studier/emner/matnat/fys/FYS4411/index-eng.html)**, Department of Physics, University of Oslo, Norway\n",
"\n",
"Date: **Apr 7, 2021**\n",
"\n",
"Copyright 1999-2021, [Computational Physics II FYS4411/FYS9411](http://www.uio.no/studier/emner/matnat/fys/FYS4411/index-eng.html). Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
"\n",
"\n",
"## Introduction\n",
"\n",
"The aim of this project is to use the Variational Monte\n",
"Carlo (VMC) method to evaluate \n",
"the ground state energy, onebody densities, expectation values of the kinetic and potential energies \n",
" and single-particle denisties of \n",
"quantum dots with $N=2$, $N=6$, $N=12$ and $N=20$ electrons. These are so-called closed shell systems.\n",
"\n",
"\n",
"## Theoretical background and description of the physical system\n",
"\n",
"We consider a system of electrons confined in a pure two-dimensional \n",
"isotropic harmonic oscillator potential, with an idealized total Hamiltonian given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:finalH\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\label{eq:finalH} \\tag{1}\n",
"\\hat{H}=\\sum_{i=1}^{N} \\left( -\\frac{1}{2} \\nabla_i^2 + \\frac{1}{2} \\omega^2r_i^2 \\right)+\\sum_{i<j}\\frac{1}{r_{ij}},\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where natural units ($\\hbar=c=e=m_e=1$) are used and all energies are in so-called atomic units a.u. We will study systems of many electrons $N$ as functions of the oscillator frequency $\\omega$ using the above Hamiltonian. The Hamiltonian includes a standard harmonic oscillator part"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\hat{H}_0=\\sum_{i=1}^{N} \\left( -\\frac{1}{2} \\nabla_i^2 + \\frac{1}{2} \\omega^2r_i^2 \\right),\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and the repulsive interaction between two electrons given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\hat{H}_1=\\sum_{i<j}\\frac{1}{r_{ij}},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with the distance between electrons given by $r_{ij}=\\vert \\boldsymbol{r}_1-\\boldsymbol{r}_2\\vert$. We define the \n",
"modulus of the positions of the electrons (for a given electron $i$) as $r_i = \\sqrt{r_{i_x}^2+r_{i_y}^2}$.\n",
"\n",
"### Project 2 a):\n",
"\n",
"In exercises a-f we will deal only with a system of\n",
"two electrons in a quantum dot with a frequency of $\\hbar\\omega = 1$. \n",
"The reason for this is that we have exact closed form expressions \n",
"for the ground state energy from Taut's work for selected values of $\\omega$, \n",
"see M. Taut, Phys. Rev. A **48**, 3561 (1993).\n",
"The energy is given by $3$ a.u. (atomic units) when the interaction between the electrons is included.\n",
"If only the harmonic oscillator part of the Hamiltonian is included,\n",
"the so-called unperturbed part,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\hat{H}_0=\\sum_{i=1}^{N} \\left( -\\frac{1}{2} \\nabla_i^2 + \\frac{1}{2} \\omega^2r_i^2 \\right),\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"the energy is $2$ a.u.\n",
"The wave function for one electron in an oscillator potential in two dimensions is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\phi_{n_x,n_y}(x,y) = A H_{n_x}(\\sqrt{\\omega}x)H_{n_y}(\\sqrt{\\omega}y)\\exp{(-\\omega(x^2+y^2)/2}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The functions $H_{n_x}(\\sqrt{\\omega}x)$ are so-called Hermite polynomials, discussed in connection with project 1 while $A$ is a normalization constant. \n",
"For the lowest-lying state we have $n_x=n_y=0$ and an energy $\\epsilon_{n_x,n_y}=\\omega(n_x+n_y+1) = \\omega$.\n",
"Convince yourself that the lowest-lying energy for the two-electron system is simply $2\\omega$.\n",
"\n",
"The unperturbed wave function for the ground state of the two-electron system is given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\Phi(\\boldsymbol{r}_1,\\boldsymbol{r}_2) = C\\exp{\\left(-\\omega(r_1^2+r_2^2)/2\\right)},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with $C$ being a normalization constant and $r_i = \\sqrt{r_{i_x}^2+r_{i_y}^2}$. Note that the vector $\\boldsymbol{r}_i$ \n",
"refers to the $x$ and $y$ position for a given particle.\n",
"What is the total spin of this wave function? Find arguments for why the ground state should have\n",
"this specific total spin. \n",
"\n",
"### Project 2 b):\n",
"\n",
"We want to perform a Variational Monte Carlo calculation of the ground state of two electrons in a quantum dot well with different oscillator energies, assuming total spin $S=0$ using the Hamiltonian of\n",
"Eq. ([1](#eq:finalH)). \n",
"Our trial wave function which has the following form"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:trial\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\psi_{T}(\\boldsymbol{r}_1,\\boldsymbol{r}_2) = \n",
" C\\exp{\\left(-\\alpha\\omega(r_1^2+r_2^2)/2\\right)}\n",
" \\exp{\\left(\\frac{ar_{12}}{(1+\\beta r_{12})}\\right)}, \n",
"\\label{eq:trial} \\tag{2}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $a$ is equal to one when the two electrons have anti-parallel spins and $1/3$ when the spins are parallel. Finally, $\\alpha$ and $\\beta$ are our variational parameters. Note well the dependence on $\\alpha$ for the single-particle part of the trial function. It is important to remember this when you use higher-order Hermite polynomials.\n",
"Find the analytical expressions for the local energy.\n",
"\n",
"\n",
"### Project 2 c):\n",
"\n",
"Your task is to perform a Variational Monte Carlo calculation\n",
"using the Metropolis algorithm to compute the integral"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto1\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\langle E \\rangle =\n",
" \\frac{\\int d\\boldsymbol{r}_1d\\boldsymbol{r}_2\\psi^{\\ast}_T(\\boldsymbol{r}_1,\\boldsymbol{r}_2)\\hat{H}(\\boldsymbol{r}_1,\\boldsymbol{r}_2)\\psi_T(\\boldsymbol{r}_1,\\boldsymbol{r}_2)}\n",
" {\\int d\\boldsymbol{r}_1d\\boldsymbol{r}_2\\psi^{\\ast}_T(\\boldsymbol{r}_1,\\boldsymbol{r}_2)\\psi_T(\\boldsymbol{r}_1,\\boldsymbol{r}_2)}.\n",
"\\label{_auto1} \\tag{3}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the expectation value of the energy using both the analytical expression for the local energy and numerical derivation of the kinetic energy. Compare the time usage between the two approaches.\n",
"Perform these calculations without importance sampling and also without the Jastrow factor. For the calculations without the Jastrow factor and repulsive Coulomb potential, your energy should equal 2.0 a.u. and your variance should be exactly equal to zero.\n",
"\n",
"### Project 2 d):\n",
"\n",
"Add now importance sampling and repeat the calculations from the previous exercise but use only\n",
"the analytical expression for the local energy. Perform also a blocking analysis in order to obtain the optimal\n",
"standard deviation. Compare your results with the those without importance sampling and comment your results.\n",
"\n",
"### Project 2 e):\n",
"\n",
"Using either the steepest descent method or the conjugate gradient method, find the optimal variational \n",
"parameters and perform your Monte Carlo calculations using these. \n",
"In addition, you should parallelize your program using MPI and set it up to run on Smaug.\n",
"\n",
"### Project 2 f):\n",
"\n",
"Finally, we wil now analyze and interpret our results for the two-electron systems.\n",
"Find the energy minimum and discuss your results compared with the analytical solution from\n",
"Taut's work, see reference [1] below. Compute also the mean distance\n",
"$r_{12}=\\vert \\boldsymbol{r}_1-\\boldsymbol{r}_2\\vert$ (with $r_i = \\sqrt{r_{i_x}^2+r_{i_y}^2}$) between the two electrons for the optimal set of the variational parameters.\n",
"With the optimal parameters for the ground state wave function, compute the onebody density. Discuss your results and compare the results with those obtained with a pure harmonic oscillator wave functions. Run a Monte Carlo calculations without the Jastrow factor as well\n",
"and compute the same quantities. How important are the correlations induced by the Jastrow factor?\n",
"Compute also the expectation value of the kinetic energy and potential energy using $\\omega=0.01$, $\\omega=0.05$,\n",
"$\\omega=0.1$, $\\omega=0.5$ and $\\omega=1.0$. Comment your results. Hint, think of the virial theorem. \n",
"\n",
"\n",
"### Project 2 g):\n",
"\n",
"The previous exercises have prepared you for extending your calculational machinery to other systems.\n",
"Here we will focus on quantum dots with $N=6$ and $N=12$ electrons.\n",
"\n",
"The new item you need to pay attention to is the calculation of the Slater Determinant. This is an additional complication\n",
"to your VMC calculations. \n",
"If we stick to harmonic oscillator like wave functions,\n",
"the trial wave function for say an $N=6$ electron quantum dot can be written as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto2\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\psi_{T}(\\boldsymbol{r}_1,\\boldsymbol{r}_2,\\dots, \\boldsymbol{r}_6) = \n",
" Det\\left(\\phi_{1}(\\boldsymbol{r}_1),\\phi_{2}(\\boldsymbol{r}_2),\n",
" \\dots,\\phi_{6}(\\boldsymbol{r}_6)\\right)\n",
" \\prod_{i<j}^{6}\\exp{\\left(\\frac{a r_{ij}}{(1+\\beta r_{ij})}\\right)}, \n",
"\\label{_auto2} \\tag{4}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $Det$ is a Slater determinant and the single-particle wave functions\n",
"are the harmonic oscillator wave functions for the $n_x=0,1$ and $n_y=0,1$ orbitals. \n",
"Similarly, for the $N=12$ quantum dot, the trial wave function can take the form"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto3\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\psi_{T}(\\boldsymbol{r}_1,\\boldsymbol{r}_2, \\dots,\\boldsymbol{r}_{12}) = \n",
" Det\\left(\\phi_{1}(\\boldsymbol{r}_1),\\phi_{2}(\\boldsymbol{r}_2),\n",
" \\dots,\\phi_{12}(\\boldsymbol{r}_{12})\\right)\n",
" \\prod_{i<j}^{12}\\exp{\\left(\\frac{ar_{ij}}{(1+\\beta r_{ij})}\\right)}, \n",
"\\label{_auto3} \\tag{5}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case you need to include the $n_x=2$ and $n_y=2$ wave functions as well.\n",
"Observe that $r_i = \\sqrt{r_{i_x}^2+r_{i_y}^2}$. Use the Hermite polynomials defined in project 1. Reference [5] gives benchmark results for closed-shell systems up to $N=20$. \n",
"\n",
"\n",
"Write a function which sets up the Slater determinant. Find the Hermite polynomials which are needed for $n_x=0,1,2$ and obviously $n_y$ as well. Compare the results you obtain with those from project 1.\n",
"Compute the ground state energies of quantum dots for $N=6$ and $N=12$ electrons, following the same set up as in the previous exercises for $\\omega=0.01$, $\\omega=0.05$,\n",
"$\\omega=0.1$, $\\omega=0.5$, and $\\omega=1.0$.\n",
"The calculations should include parallelization, blocking, importance sampling and energy minimization using the conjugate gradient approach or similar approaches.\n",
"To test your Slater determinant code, you should reproduce the unperturbed single-particle energies\n",
"when the electron-electron repulsion is switched off. Convince yourself that the unperturbed ground state energies for $N=6$ is $10\\omega$ and for $N=12$ we obtain $28\\omega$. What is the expected total \n",
"spin of the ground states?\n",
"\n",
"### Project 2 h):\n",
"\n",
"With the optimal parameters for the ground state wave function, compute again the onebody density. Discuss your results and compare the results with those obtained with a pure harmonic oscillator\n",
"wave functions. Run a Monte Carlo calculations without the Jastrow factor as well\n",
"and compute the same quantities. How important are the correlations induced by the Jastrow factor?\n",
"Compute also the expectation value of the kinetic energy and potential energy using $\\omega=0.01$,\n",
"$\\omega=0.05$, $\\omega=0.1$, $\\omega=0.5$, and $\\omega=1.0$. Comment your results.\n",
"\n",
"### Project 2 i):\n",
"\n",
"The last exercise is a performance analysis of your code(s) for the case of $N=6$ electrons. Make a performance analysis by timing your serial code\n",
"with and without vectorization. Perform several runs with the same number of Monte carlo cycles and compute an average timing analysis\n",
"with and without vectorization. Comment your results. Use at least $10^6$ Monte Carlo samples. \n",
"\n",
"Compare thereafter your serial code(s) with the speedup you get by parallelizing your code, running either OpenMP or MPI or both.\n",
"Do you get a near $100\\%$ speedup with the parallel version? Comment again your results and perform timing benchmarks several times in order \n",
"to extract an average performance time. \n",
"\n",
"\n",
"\n",
"### Literature\n",
"\n",
"1. M. Taut, Phys. Rev. A **48**, 3561 - 3566 (1993).\n",
"\n",
"2. B. L. Hammond, W. A. Lester and P. J. Reynolds, *Monte Carlo methods in Ab Initio Quantum Chemistry*, World Scientific, Singapore, 1994, chapters 2-5 and appendix B.\n",
"\n",
"3. B. H. Bransden and C. J. Joachain, Physics of Atoms and molecules, Longman, 1986. Chapters 6, 7 and 9.\n",
"\n",
"4. A. K. Rajagopal and J. C. Kimball, see Phys. Rev. B **15**, 2819 (1977).\n",
"\n",
"5. M. L. Pedersen, G. Hagen, M. Hjorth-Jensen, S. Kvaal, and F. Pederiva, Phys. Rev. B **84**, 115302 (2011)\n",
"\n",
"## Introduction to numerical projects\n",
"\n",
"Here follows a brief recipe and recommendation on how to write a report for each\n",
"project.\n",
"\n",
" * Give a short description of the nature of the problem and the eventual numerical methods you have used.\n",
"\n",
" * Describe the algorithm you have used and/or developed. Here you may find it convenient to use pseudocoding. In many cases you can describe the algorithm in the program itself.\n",
"\n",
" * Include the source code of your program. Comment your program properly.\n",
"\n",
" * If possible, try to find analytic solutions, or known limits in order to test your program when developing the code.\n",
"\n",
" * Include your results either in figure form or in a table. Remember to label your results. All tables and figures should have relevant captions and labels on the axes.\n",
"\n",
" * Try to evaluate the reliabilty and numerical stability/precision of your results. If possible, include a qualitative and/or quantitative discussion of the numerical stability, eventual loss of precision etc.\n",
"\n",
" * Try to give an interpretation of you results in your answers to the problems.\n",
"\n",
" * Critique: if possible include your comments and reflections about the exercise, whether you felt you learnt something, ideas for improvements and other thoughts you've made when solving the exercise. We wish to keep this course at the interactive level and your comments can help us improve it.\n",
"\n",
" * Try to establish a practice where you log your work at the computerlab. You may find such a logbook very handy at later stages in your work, especially when you don't properly remember what a previous test version of your program did. Here you could also record the time spent on solving the exercise, various algorithms you may have tested or other topics which you feel worthy of mentioning.\n",
"\n",
"## Format for electronic delivery of report and programs\n",
"\n",
"The preferred format for the report is a PDF file. You can also use DOC or postscript formats or as an ipython notebook file. As programming language we prefer that you choose between C/C++, Fortran2008 or Python. The following prescription should be followed when preparing the report:\n",
"\n",
" * Use canvas to hand in your projects, log in at <http://canvas.uio.no> with your normal UiO username and password.\n",
"\n",
" * Upload **only** the report file! For the source code file(s) you have developed please provide us with your link to your github domain. The report file should include all of your discussions and a list of the codes you have developed. The full version of the codes should be in your github repository.\n",
"\n",
" * In your github repository, please include a folder which contains selected results. These can be in the form of output from your code for a selected set of runs and input parameters.\n",
"\n",
" * Still in your github make a folder where you place your codes. \n",
"\n",
" * In this and all later projects, you should include tests (for example unit tests) of your code(s).\n",
"\n",
" * Comments from us on your projects, approval or not, corrections to be made etc can be found under your Devilry domain and are only visible to you and the teachers of the course.\n",
"\n",
"Finally, \n",
"we encourage you to work two and two together. Optimal working groups consist of \n",
"2-3 students. You can then hand in a common report."
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
| cc0-1.0 |
Gjacquenot/AcousticBEM | Jupyter/Test InteriorHelmholtzSolver2D.ipynb | 1 | 21141 | {
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"text": [
"Test Problem 1\n",
"==============\n",
"\n",
"Density of medium: 1.205 kg/m^3\n",
"Speed of sound: 344.0 m/s\n",
"Wavenumber (Frequency): 7.30602942695 (400.0 Hz)\n",
"\n",
"index Potential Pressure Velocity Intensity\n",
" 1 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -1.6416e-01+ 6.9307e-03i 0.0000e+00\n",
" 2 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -5.0028e-01+ 8.1443e-03i 0.0000e+00\n",
" 3 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -8.3287e-01+ 9.1791e-03i 0.0000e+00\n",
" 4 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -1.1622e+00+ 1.0142e-02i 0.0000e+00\n",
" 5 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -1.4864e+00+ 1.1011e-02i 0.0000e+00\n",
" 6 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -1.8032e+00+ 1.1634e-02i 0.0000e+00\n",
" 7 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -2.1078e+00+ 1.1451e-02i 0.0000e+00\n",
" 8 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -2.4897e+00+ 2.4934e-03i 0.0000e+00\n",
" 9 1.5946e-02+ 0.0000e+00i 0.0000e+00+ 4.8292e+01i -4.0429e-02+ 2.0195e-02i 4.8762e-01\n",
" 10 4.7771e-02+ 0.0000e+00i 0.0000e+00+ 1.4467e+02i 4.2439e-01+ 9.6985e-03i 7.0156e-01\n",
" 11 7.9397e-02+ 0.0000e+00i 0.0000e+00+ 2.4045e+02i 7.2599e-01+ 2.3330e-03i 2.8048e-01\n",
" 12 1.1069e-01+ 0.0000e+00i 0.0000e+00+ 3.3523e+02i 1.0135e+00+ -3.4407e-03i -5.7671e-01\n",
" 13 1.4152e-01+ 0.0000e+00i 0.0000e+00+ 4.2861e+02i 1.2947e+00+ -8.7741e-03i -1.8803e+00\n",
" 14 1.7177e-01+ 0.0000e+00i 0.0000e+00+ 5.2020e+02i 1.5710e+00+ -1.4252e-02i -3.7069e+00\n",
" 15 2.0130e-01+ 0.0000e+00i 0.0000e+00+ 6.0962e+02i 1.8457e+00+ -2.0460e-02i -6.2366e+00\n",
" 16 2.2998e-01+ 0.0000e+00i 0.0000e+00+ 6.9651e+02i 2.3253e+00+ -2.2042e-02i -7.6760e+00\n",
" 17 2.2998e-01+ 0.0000e+00i 0.0000e+00+ 6.9651e+02i 2.3253e+00+ -2.2044e-02i -7.6770e+00\n",
" 18 2.0130e-01+ 0.0000e+00i 0.0000e+00+ 6.0962e+02i 1.8457e+00+ -2.0463e-02i -6.2374e+00\n",
" 19 1.7177e-01+ 0.0000e+00i 0.0000e+00+ 5.2020e+02i 1.5710e+00+ -1.4254e-02i -3.7076e+00\n",
" 20 1.4152e-01+ 0.0000e+00i 0.0000e+00+ 4.2861e+02i 1.2946e+00+ -8.7765e-03i -1.8808e+00\n",
" 21 1.1069e-01+ 0.0000e+00i 0.0000e+00+ 3.3523e+02i 1.0135e+00+ -3.4452e-03i -5.7746e-01\n",
" 22 7.9397e-02+ 0.0000e+00i 0.0000e+00+ 2.4045e+02i 7.2604e-01+ 2.3265e-03i 2.7970e-01\n",
" 23 4.7771e-02+ 0.0000e+00i 0.0000e+00+ 1.4467e+02i 4.2440e-01+ 9.6945e-03i 7.0127e-01\n",
" 24 1.5946e-02+ 0.0000e+00i 0.0000e+00+ 4.8292e+01i -4.0426e-02+ 2.0192e-02i 4.8755e-01\n",
" 25 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -2.4897e+00+ 2.4945e-03i 0.0000e+00\n",
" 26 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -2.1078e+00+ 1.1451e-02i 0.0000e+00\n",
" 27 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -1.8032e+00+ 1.1633e-02i 0.0000e+00\n",
" 28 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -1.4864e+00+ 1.1011e-02i 0.0000e+00\n",
" 29 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -1.1622e+00+ 1.0141e-02i 0.0000e+00\n",
" 30 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -8.3287e-01+ 9.1783e-03i 0.0000e+00\n",
" 31 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -5.0027e-01+ 8.1434e-03i 0.0000e+00\n",
" 32 0.0000e+00+ 0.0000e+00i 0.0000e+00+ 0.0000e+00i -1.6415e-01+ 6.9296e-03i 0.0000e+00\n",
"\n",
"\n",
"Sound pressure at the sample points\n",
"\n",
"index Potential Pressure Magnitude Phase\n",
"\n",
" 1 1.5896e-02+ 1.1894e-04i -3.6019e-01+ 4.8141e+01i 1.2763e+02 dB 1.5783\n",
" 2 4.8183e-02+ 3.9962e-05i -1.2102e-01+ 1.4592e+02i 1.3726e+02 dB 1.5716\n",
" 3 4.8183e-02+ 4.0005e-05i -1.2116e-01+ 1.4592e+02i 1.3726e+02 dB 1.5716\n",
" 4 1.4338e-01+ -2.5899e-04i 7.8433e-01+ 4.3423e+02i 1.4673e+02 dB 1.5690\n",
" 5 6.4991e-02+ -1.4397e-05i 4.3603e-02+ 1.9682e+02i 1.3986e+02 dB 1.5706\n",
"\n",
"\n",
"Test Problem 2\n",
"==============\n",
"\n",
"Density of medium: 1.205 kg/m^3\n",
"Speed of sound: 344.0 m/s\n",
"Wavenumber (Frequency): 7.30602942695 (400.0 Hz)\n",
"\n",
"index Potential Pressure Velocity Intensity\n",
" 1 -3.4277e-04+ 7.7101e-04i -2.3350e+00+ -1.0381e+00i -1.6678e-01+ 0.0000e+00i 1.9471e-01\n",
" 2 -2.9864e-04+ 8.3379e-04i -2.5251e+00+ -9.0444e-01i -4.9964e-01+ 0.0000e+00i 6.3083e-01\n",
" 3 -2.4640e-04+ 8.9745e-04i -2.7179e+00+ -7.4621e-01i -8.3041e-01+ 0.0000e+00i 1.1285e+00\n",
" 4 -1.4529e-04+ 9.5503e-04i -2.8923e+00+ -4.4002e-01i -1.1577e+00+ 0.0000e+00i 1.6743e+00\n",
" 5 5.3350e-05+ 1.0012e-03i -3.0321e+00+ 1.6157e-01i -1.4802e+00+ 0.0000e+00i 2.2441e+00\n",
" 6 4.2715e-04+ 1.0303e-03i -3.1203e+00+ 1.2936e+00i -1.7965e+00+ 0.0000e+00i 2.8029e+00\n",
" 7 1.1381e-03+ 1.0371e-03i -3.1408e+00+ 3.4468e+00i -2.1054e+00+ 0.0000e+00i 3.3063e+00\n",
" 8 2.8794e-03+ 1.0218e-03i -3.0944e+00+ 8.7203e+00i -2.4054e+00+ 0.0000e+00i 3.7216e+00\n",
" 9 1.9385e-02+ 9.0112e-04i -2.7290e+00+ 5.8707e+01i 1.4501e-01+ 0.0000e+00i -1.9787e-01\n",
" 10 4.8831e-02+ 1.1228e-03i -3.4003e+00+ 1.4789e+02i 4.3443e-01+ 0.0000e+00i -7.3859e-01\n",
" 11 7.9042e-02+ 1.3995e-03i -4.2383e+00+ 2.3938e+02i 7.2204e-01+ 0.0000e+00i -1.5301e+00\n",
" 12 1.0918e-01+ 1.6971e-03i -5.1398e+00+ 3.3064e+02i 1.0066e+00+ 0.0000e+00i -2.5870e+00\n",
" 13 1.3885e-01+ 2.0031e-03i -6.0662e+00+ 4.2052e+02i 1.2870e+00+ 0.0000e+00i -3.9038e+00\n",
" 14 1.6775e-01+ 2.3090e-03i -6.9928e+00+ 5.0803e+02i 1.5621e+00+ 0.0000e+00i -5.4617e+00\n",
" 15 1.9541e-01+ 2.6040e-03i -7.8861e+00+ 5.9181e+02i 1.8306e+00+ 0.0000e+00i -7.2182e+00\n",
" 16 2.2053e-01+ 2.8544e-03i -8.6446e+00+ 6.6789e+02i 2.0915e+00+ 0.0000e+00i -9.0401e+00\n",
" 17 2.2053e-01+ 2.8544e-03i -8.6446e+00+ 6.6789e+02i 2.0915e+00+ 0.0000e+00i -9.0402e+00\n",
" 18 1.9541e-01+ 2.6040e-03i -7.8863e+00+ 5.9181e+02i 1.8306e+00+ 0.0000e+00i -7.2183e+00\n",
" 19 1.6775e-01+ 2.3091e-03i -6.9931e+00+ 5.0803e+02i 1.5621e+00+ 0.0000e+00i -5.4619e+00\n",
" 20 1.3885e-01+ 2.0032e-03i -6.0666e+00+ 4.2052e+02i 1.2870e+00+ 0.0000e+00i -3.9040e+00\n",
" 21 1.0918e-01+ 1.6973e-03i -5.1403e+00+ 3.3064e+02i 1.0066e+00+ 0.0000e+00i -2.5872e+00\n",
" 22 7.9042e-02+ 1.3997e-03i -4.2390e+00+ 2.3938e+02i 7.2204e-01+ 0.0000e+00i -1.5304e+00\n",
" 23 4.8831e-02+ 1.1229e-03i -3.4008e+00+ 1.4788e+02i 4.3443e-01+ 0.0000e+00i -7.3872e-01\n",
" 24 1.9385e-02+ 9.0130e-04i -2.7296e+00+ 5.8706e+01i 1.4501e-01+ 0.0000e+00i -1.9791e-01\n",
" 25 2.8792e-03+ 1.0219e-03i -3.0948e+00+ 8.7196e+00i -2.4054e+00+ 0.0000e+00i 3.7222e+00\n",
" 26 1.1379e-03+ 1.0372e-03i -3.1412e+00+ 3.4462e+00i -2.1054e+00+ 0.0000e+00i 3.3067e+00\n",
" 27 4.2700e-04+ 1.0304e-03i -3.1206e+00+ 1.2932e+00i -1.7965e+00+ 0.0000e+00i 2.8032e+00\n",
" 28 5.3238e-05+ 1.0013e-03i -3.0323e+00+ 1.6123e-01i -1.4802e+00+ 0.0000e+00i 2.2442e+00\n",
" 29 -1.4537e-04+ 9.5508e-04i -2.8925e+00+ -4.4025e-01i -1.1577e+00+ 0.0000e+00i 1.6743e+00\n",
" 30 -2.4645e-04+ 8.9748e-04i -2.7180e+00+ -7.4636e-01i -8.3041e-01+ 0.0000e+00i 1.1285e+00\n",
" 31 -2.9867e-04+ 8.3381e-04i -2.5252e+00+ -9.0453e-01i -4.9964e-01+ 0.0000e+00i 6.3084e-01\n",
" 32 -3.4278e-04+ 7.7102e-04i -2.3350e+00+ -1.0381e+00i -1.6678e-01+ 0.0000e+00i 1.9471e-01\n",
"\n",
"\n",
"Sound pressure at the sample points\n",
"\n",
"index Potential Pressure Magnitude Phase\n",
"\n",
" 1 1.5612e-02+ 1.1364e-03i -3.4415e+00+ 4.7282e+01i 1.2750e+02 dB 1.6435\n",
" 2 4.8070e-02+ 1.3404e-03i -4.0592e+00+ 1.4558e+02i 1.3724e+02 dB 1.5987\n",
" 3 4.8070e-02+ 1.3403e-03i -4.0591e+00+ 1.4558e+02i 1.3724e+02 dB 1.5987\n",
" 4 1.3979e-01+ 1.8714e-03i -5.6677e+00+ 4.2335e+02i 1.4651e+02 dB 1.5842\n",
" 5 6.3939e-02+ 1.4465e-03i -4.3807e+00+ 1.9364e+02i 1.3972e+02 dB 1.5934\n",
"\n",
"\n",
"Test Problem 3\n",
"==============\n",
"\n",
"Density of medium: 1.205 kg/m^3\n",
"Speed of sound: 344.0 m/s\n",
"Wavenumber (Frequency): 7.30602942695 (400.0 Hz)\n",
"\n",
"index Potential Pressure Velocity Intensity\n",
" 1 -1.8138e+00+ 7.9567e-04i -2.4097e+00+ -5.4929e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 2 -1.8275e+00+ 7.4568e-04i -2.2583e+00+ -5.5346e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 3 -1.8556e+00+ 7.5399e-04i -2.2835e+00+ -5.6198e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 4 -1.8950e+00+ 8.0267e-04i -2.4309e+00+ -5.7389e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 5 -1.9379e+00+ 8.6302e-04i -2.6137e+00+ -5.8688e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 6 -1.9762e+00+ 9.1275e-04i -2.7643e+00+ -5.9849e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 7 -2.0044e+00+ 9.4877e-04i -2.8733e+00+ -6.0703e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 8 -2.0193e+00+ 9.6760e-04i -2.9304e+00+ -6.1153e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 9 -2.0204e+00+ 9.2985e-04i -2.8161e+00+ -6.1187e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 10 -2.0186e+00+ 8.8954e-04i -2.6940e+00+ -6.1133e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 11 -2.0161e+00+ 8.5965e-04i -2.6034e+00+ -6.1058e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 12 -2.0144e+00+ 8.3928e-04i -2.5418e+00+ -6.1005e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 13 -2.0143e+00+ 8.3059e-04i -2.5154e+00+ -6.1004e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 14 -2.0160e+00+ 8.3388e-04i -2.5254e+00+ -6.1055e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 15 -2.0185e+00+ 8.5333e-04i -2.5843e+00+ -6.1129e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 16 -2.0202e+00+ 8.8823e-04i -2.6900e+00+ -6.1182e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 17 -2.0192e+00+ 9.2868e-04i -2.8125e+00+ -6.1151e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 18 -2.0043e+00+ 9.1315e-04i -2.7655e+00+ -6.0701e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 19 -1.9761e+00+ 8.7927e-04i -2.6629e+00+ -5.9848e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 20 -1.9378e+00+ 8.3053e-04i -2.5153e+00+ -5.8686e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 21 -1.8949e+00+ 7.6983e-04i -2.3314e+00+ -5.7387e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 22 -1.8556e+00+ 7.1998e-04i -2.1805e+00+ -5.6196e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 23 -1.8274e+00+ 7.1074e-04i -2.1525e+00+ -5.5344e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 24 -1.8137e+00+ 7.5863e-04i -2.2975e+00+ -5.4927e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 25 -1.8102e+00+ 8.6940e-04i -2.6330e+00+ -5.4823e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 26 -1.7821e+00+ 8.3670e-04i -2.5339e+00+ -5.3969e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 27 -1.7313e+00+ 7.0239e-04i -2.1272e+00+ -5.2433e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 28 -1.6816e+00+ 4.9580e-04i -1.5015e+00+ -5.0927e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 29 -1.6816e+00+ 5.0315e-04i -1.5238e+00+ -5.0928e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 30 -1.7314e+00+ 7.2462e-04i -2.1945e+00+ -5.2436e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 31 -1.7822e+00+ 8.6929e-04i -2.6326e+00+ -5.3973e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
" 32 -1.8104e+00+ 9.0778e-04i -2.7492e+00+ -5.4827e+03i 0.0000e+00+ 0.0000e+00i -0.0000e+00\n",
"\n",
"\n",
"Sound pressure at the sample points\n",
"\n",
"index Potential Pressure Magnitude Phase\n",
"\n",
" 1 -1.7777e+00+ 8.2638e-04i -2.5027e+00+ -5.3838e+03i 1.6860e+02 dB -1.5713\n",
" 2 -1.7777e+00+ 8.1342e-04i -2.4634e+00+ -5.3837e+03i 1.6860e+02 dB -1.5713\n",
" 3 -1.9841e+00+ 8.7267e-04i -2.6429e+00+ -6.0089e+03i 1.6956e+02 dB -1.5712\n",
" 4 -1.9841e+00+ 8.5916e-04i -2.6020e+00+ -6.0088e+03i 1.6956e+02 dB -1.5712\n",
" 5 -1.8452e+00+ 8.5066e-04i -2.5762e+00+ -5.5882e+03i 1.6892e+02 dB -1.5713\n"
]
}
],
"source": [
"from HelmholtzSolver2D import *\n",
"from ExampleBoundaries import Square\n",
"\n",
"c = 344.0 # speed of sound [m/s]\n",
"rho = 1.205 # density of air [kg/m^3]\n",
"frequency = 400.0 # frequency [Hz]\n",
"\n",
"\n",
"# Test Problem 1\n",
"# Dirichlet boundary condition with phi = sin(k/sqrt(2)*x) * sin(k/sqrt(2)*y)\n",
"#\n",
"solver = HelmholtzSolver2D(*(Square()))\n",
"\n",
"boundaryCondition = BoundaryCondition(solver.aElement.shape[0])\n",
"boundaryCondition.alpha.fill(1.0)\n",
"boundaryCondition.beta.fill(0.0)\n",
"\n",
"k = frequencyToWavenumber(frequency)\n",
"\n",
"boundaryCondition.f[:] = np.sin(k/np.sqrt(2.0) * solver.aCenters[:,0]) \\\n",
" * np.sin(k/np.sqrt(2.0) * solver.aCenters[:,1])\n",
"\n",
"boundaryIncidence = BoundaryIncidence(solver.aElement.shape[0])\n",
"boundaryIncidence.phi.fill(0.0)\n",
"boundaryIncidence.v.fill(0.0)\n",
"\n",
"interiorPoints = np.array([[0.0250, 0.0250],\n",
" [0.0750, 0.0250],\n",
" [0.0250, 0.0750],\n",
" [0.0750, 0.0750],\n",
" [0.0500, 0.0500]], dtype=np.float32)\n",
"\n",
"interiorIncidentPhi = np.zeros(interiorPoints.shape[0], dtype=np.complex64)\n",
"\n",
"boundarySolution = solver.solveInteriorBoundary(k, boundaryCondition, boundaryIncidence)\n",
"interiorPhi = solver.solveInterior(boundarySolution, interiorIncidentPhi, interiorPoints)\n",
"print \"Test Problem 1\"\n",
"print \"==============\\n\"\n",
"print boundarySolution\n",
"printInteriorSolution(boundarySolution, interiorPhi)\n",
"\n",
"\n",
"# Test Problem 2\n",
"# von Neumann boundary condition such that phi = sin(k/sqrt(2) * x) * sin(k/sqrt(2) * y)\n",
"# Differentiate with respect to x and y to obtain outward normal:\n",
"# dPhi/dX = k/sqrt(2) * cos(k/sqrt(2) * x) * sin(k/sqrt(2) * y)\n",
"# dPhi/dY = k/sqrt(2) * sin(k/sqrt(2) * x) * cos(k/sqrt(2) * y)\n",
"boundaryCondition.alpha.fill(0.0)\n",
"boundaryCondition.beta.fill(1.0)\n",
"c = k / np.sqrt(2.0)\n",
"for i in range(solver.aCenters.shape[0]):\n",
" x = solver.aCenters[i, 0]\n",
" y = solver.aCenters[i, 1]\n",
" if (x < 1e-7):\n",
" boundaryCondition.f[i] = -c * np.cos(c * x) * np.sin(c * y)\n",
" elif (x > 0.1 - 1e-7):\n",
" boundaryCondition.f[i] = c * np.cos(c * x) * np.sin(c * y)\n",
" elif (y < 1e-7):\n",
" boundaryCondition.f[i] = -c * np.sin(c * x) * np.cos(c * y)\n",
" else:\n",
" boundaryCondition.f[i] = c * np.sin(c * x) * np.cos(c * y) \n",
"\n",
"boundarySolution = solver.solveInteriorBoundary(k, boundaryCondition, boundaryIncidence)\n",
"interiorPhi = solver.solveInterior(boundarySolution, interiorIncidentPhi, interiorPoints)\n",
"print \"\\n\\nTest Problem 2\"\n",
"print \"==============\\n\"\n",
"print boundarySolution\n",
"printInteriorSolution(boundarySolution, interiorPhi)\n",
"\n",
" \n",
"# Test Problem 3\n",
"# The test problem computes the field produced by a unit source at\n",
"# the point (0.5,0.25) within the square with a rigid boundary.\n",
"# The rigid boundary implies the bondary condition v=0.\n",
"# The test problem computes the field produced by a unit source at\n",
"# the point (0.5,0.25) within the square with a rigid boundary.\n",
"# The incident velocity potential is given by {\\phi}_inc=i*h0(kr)/4\n",
"# where r is the distance from the point (0.5,0.25)\n",
"boundaryCondition.alpha.fill(0.0)\n",
"boundaryCondition.beta.fill(1.0)\n",
"boundaryCondition.f.fill(0.0)\n",
"\n",
"p = np.array([0.05, 0.025], dtype=np.float32)\n",
"for i in range(solver.aCenters.shape[0]):\n",
" r = solver.aCenters[i] - p\n",
" R = norm(r)\n",
" boundaryIncidence.phi[i] = 0.25j * hankel1(0, k * R)\n",
" if solver.aCenters[i, 0] < 1e-7:\n",
" boundaryIncidence.v[i] = -0.25j * k * hankel1(1, k * R) * (-r[0] / R)\n",
" elif solver.aCenters[i, 0] > 0.1 - 1e-7:\n",
" boundaryIncidence.v[i] = -0.25j * k * hankel1(1, k * R) * ( r[0] / R)\n",
" elif solver.aCenters[i, 1] < 1e-7:\n",
" boundaryIncidence.v[i] = -0.25j * k * hankel1(1, k * R) * (-r[1] / R)\n",
" elif solver.aCenters[i, 1] > 0.1 - 1e-7:\n",
" boundaryIncidence.v[i] = -0.25j * k * hankel1(1, k * R) * ( r[1] / R)\n",
" else:\n",
" assert False, \"All cases must be handled above.\"\n",
" \n",
"for i in range(interiorIncidentPhi.size):\n",
" r = interiorPoints[i] - p\n",
" R = norm(r)\n",
" interiorIncidentPhi[i] = 0.25j * hankel1(0, k * R)\n",
" \n",
"boundarySolution = solver.solveInteriorBoundary(k, boundaryCondition, boundaryIncidence)\n",
"interiorPhi = solver.solveInterior(boundarySolution, interiorIncidentPhi, interiorPoints)\n",
"print \"\\n\\nTest Problem 3\"\n",
"print \"==============\\n\"\n",
"print boundarySolution\n",
"printInteriorSolution(boundarySolution, interiorPhi)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Copyright (C) 2017 Frank Jargstorff\n",
"\n",
"This file is part of the AcousticBEM library.\n",
"\n",
"AcousticBEM is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n",
"\n",
"AcousticBEM is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n",
"\n",
"You should have received a copy of the GNU General Public License along with AcousticBEM. If not, see http://www.gnu.org/licenses/."
]
}
],
"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
}
| gpl-3.0 |
mldbai/mldb | container_files/demos/Image Processing with Convolutions.ipynb | 1 | 52158 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Image Processing with Convolutions\n",
"\n",
"In image processing, most image filters and image transformation use convolutions. Convolutions modify the original matrix of pixels through a pointwise multiplication with a kernel or filter matrix. Wikipedia describes <a href=\"https://en.wikipedia.org/wiki/Kernel_(image_processing)\">convolutions on images</a> as:\n",
"\n",
"> Convolution is the process of multiplying each element of the image with its local neighbors, weighted by the kernel. For example, if we have two three-by-three matrices, one a kernel, and the other an image piece, convolution is the process of flipping both the rows and columns of the kernel and then multiplying locationally similar entries and summing. The [2,2] element of the resulting image would be a weighted combination of all the entries of the image matrix, with weights given by the kernel: \n",
"\n",
"> \n",
"\n",
"Amongst the suite of applications of convolutions, image blurring and sharpening as well as edge detection are the most common. In this demo, we will use MLDB query to efficiently transform images. To do so, we will use the [MNIST database of handwriten digits](http://yann.lecun.com/exdb/mnist/).\n",
"\n",
"In this demo, we will use the [jseval](../../../../doc/#builtin/sql/ValueExpression.md.html#jseval) function to execute JavaScript code inline with SQL, and the [SQL Expression Function](../../../../doc/#builtin/functions/SqlExpressionFunction.md.html) to persist and reuse the same JavaScript code."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"The notebook cells below use `pymldb`'s `Connection` class to make [REST API](../../../../doc/#builtin/WorkingWithRest.md.html) calls. You can check out the [Using `pymldb` Tutorial](../../../../doc/nblink.html#_tutorials/Using pymldb Tutorial) for more details."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pymldb import Connection\n",
"mldb = Connection()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"... And other Python librairies"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import random\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from IPython.display import display, Latex\n",
"from ipywidgets import widgets, interact"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading the data\n",
"\n",
"A pickled version of the dataset is available on the [deeplearning.net website](http://deeplearning.net/tutorial/gettingstarted.html).\n",
"\n",
"The dataset has been unpickled and saved in a public Amazon's S3 cloud storage. Check out MLDB's [Protocol Handlers](../../../../doc/#builtin/Url.md.html) for Files and URLS for more details on loading remote ressources."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<Response [201]>\n"
]
}
],
"source": [
"data_url_mnist = 'file://mldb/mldb_test_data/digits_data.csv.gz'\n",
"\n",
"print mldb.put('/v1/procedures/import_digits_mnist', {\n",
" \"type\":\"import.text\",\n",
" \"params\": {\n",
" \"dataFileUrl\": data_url_mnist,\n",
" \"outputDataset\": \"digits_mnist\",\n",
" \"select\": \"{* EXCLUDING(\\\"785\\\")} AS *, \\\"785\\\" AS label\",\n",
" \"runOnCreation\": True,\n",
" }\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Taking a random image and starting image manipulation\n",
"\n",
"Similarly to the first few steps in the [Real-Time Digits Recognizer](../../../../doc/nblink.html#_demos/Real-Time%20Digits%20Recognizer.ipynb) demo, we will display random MNIST digits from the test set. At each refresh, we get a randomly selected row using the [`sample` function in a SQL From Expression](../../../../doc/#builtin/sql/FromExpression.md.html)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc50371e9d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = mldb.query(\"\"\"\n",
" SELECT * EXCLUDING(label) \n",
" FROM sample(\n",
" (select * from digits_mnist where rowHash() % 5 = 0),\n",
" {rows: 1}\n",
" )\n",
"\"\"\")\n",
"\n",
"image = data.as_matrix().reshape(28, 28)\n",
"plt.imshow(image)\n",
"plt.gray()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defining the convolution function\n",
"\n",
"A discrete convolution can be defined mathematically as:\n",
"\n",
"$newPixel[i,j] = \\sum_{y=i}^{i+r}\\sum_{x=j}^{j+r}oldPixel[x,y] \\cdot weight[x,y]$\n",
"\n",
"where the $weight[]$ matrix (see 'kernelDict' dictionary in a couple of cells below) defines the type of image manipulation and $r$ is the area of effect. Imagine a \"square box\" centered at the pixel that you want to transform. The kernel weighted sum of \"old pixels\" in the \"square box\" gives you a \"new pixel\".\n",
"\n",
"As seen in the code below, each new pixel in the convolved picture is the weighted sum of the the pixel and its neighboring pixels where the weights are the values in the kernel matrix. \n",
"\n",
"Doing convolutions with custom function of type [SQL Expression Function](../../../../doc/#builtin/functions/SqlExpressionFunction.md.html) and [jseval](../../../../doc/#builtin/sql/ValueExpression.md.html) for inline definition of functions using Javascript allows us to process large amounts of data using the optimizations inherent to MLDB. Convolutions are typically very time consuming operations with \n",
"$O(n\\cdot r^2)$ complexity in this case where n is the number of features and r is the radius (i.e. neighboring pixels).\n",
"\n",
"There were two steps to creating the function below:\n",
"* JsConvolutionExpr used a jseval built-in function where all the logic resides\n",
"* A 'convolution' SQL Expression Function is created, allowing us to call 'convolution' with a simple [mldb.query](../../../../doc/#builtin/sql/Sql.md.html)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<Response [201]>\n"
]
}
],
"source": [
"# JavaScript code loosely based on Ivan Kuckir's blog post: http://blog.ivank.net/fastest-gaussian-blur.html\n",
"\n",
"def create_convolution():\n",
" \n",
" JsConvolutionExpr = \"\"\"\n",
" jseval('\n",
" var row_val = val;\n",
" var dim = Math.sqrt(row_val.length);\n",
" var radius = Math.sqrt(kernel.length);\n",
" \n",
" \n",
" /*************************************\n",
" ******** Function Definition *********\n",
" **************************************/\n",
"\n",
" // input 1D list, output 1D list, pixel matrix dimensions\n",
" function convolution(inList, outList, width, height, radius) {\n",
"\n",
" for (var i = 0; i < height; i++)\n",
" for (var j = 0; j < width; j++) {\n",
" var newPixel = 0;\n",
" var indexW = 0;\n",
" \n",
" for (var yr = i; yr < i + radius; yr++)\n",
" for (var xr = j; xr < j + radius; xr++) {\n",
" \n",
" var y = Math.min(height - 1, Math.max(0, yr));\n",
" var x = Math.min(width - 1, Math.max(0, xr));\n",
" \n",
" newPixel = newPixel + inList[y * width + x] * weights[indexW];\n",
" indexW ++;\n",
" }\n",
" \n",
" new_value = newPixel;\n",
" outList[i * width + j] = new_value;\n",
" }\n",
" return outList;\n",
" } // End of convolution\n",
" \n",
" //Assuring that the 1d row is in the right order\n",
" function arrangeMatrix(inList) {\n",
" \n",
" var length = inList.length;\n",
" var data = new Array(length);\n",
" for (var i = 0; i < length; i++) {\n",
" data[parseInt(inList[i][0][0])] = inList[i][1];\n",
" }\n",
" return data\n",
" }\n",
" \n",
" /*************************************\n",
" ********** Using Functions ***********\n",
" **************************************/\n",
" \n",
" var weights = arrangeMatrix(kernel); // filter matrix\n",
" var matrix = arrangeMatrix(row_val); // my picture\n",
" var convolvedMatrix = [];\n",
" \n",
" convolution(matrix, convolvedMatrix, dim, dim, radius);\n",
"\n",
" return convolvedMatrix;',\n",
" 'val, kernel', \n",
" valueExpr, kernel\n",
" ) AS *\n",
" \"\"\"\n",
"\n",
"\n",
" print mldb.put(\"/v1/functions/convolution\", {\n",
" \"type\": \"sql.expression\",\n",
" \"params\": {\n",
" \"expression\": JsConvolutionExpr,\n",
" \"prepared\": True\n",
" }\n",
" })\n",
" \n",
"create_convolution()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This function will used in the interactive menu in the next section. We will take the image of the digit that we have seen before and apply different filters. You will need to load the cells in this notebook to make it work."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using the convolution function\n",
"\n",
"We will first define the type filters or kernels that we want to try."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"kernelDict = {\n",
" 'Right Sobel': [-1, 0, 1, -2, 0, 2, -1, 0, 1], \n",
" 'Detect Edges': [1, 1, 1, 1, -8, 1, 1, 1, 1],\n",
" 'Sharpen': [0, -1, 0, -1, 5, -1, 0, -1, 0],\n",
" 'Box Blur': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],\n",
" 'Approximated Gaussian Blur': [0.0625, 0.125, 0.0625, 0.125, 0.25, 0.125, 0.0625, 0.125, 0.0625]\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def convolutionFunc(image_processing):\n",
" \n",
" SQL_Expr = \"\"\"\n",
" SELECT convolution({\n",
" valueExpr: %(data)s,\n",
" kernel: %(kernel)s\n",
" }) AS *\n",
" \"\"\" % {\n",
" \"data\": data.values[0].tolist(),\n",
" \"kernel\": kernelDict[image_processing]\n",
" }\n",
"\n",
" convolvedData = mldb.query(SQL_Expr)\n",
" image = convolvedData.as_matrix().reshape(28, 28)\n",
" plt.imshow(image)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Choose an image processing option from the drop-down menu\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc5011b5910>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"options=('Right Sobel', 'Detect Edges', 'Sharpen', 'Box Blur', 'Approximated Gaussian Blur')\n",
"interact(convolutionFunc, image_processing=kernelDict.keys(), );\n",
"print \"Choose an image processing option from the drop-down menu\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I found the 'Detect Edges' convolution particularly useful when training image recognition models. This can be useful in many Machine Vision applications."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convolutions with MLDB's TensorFlow plug-in\n",
"\n",
"Not everyone will want to code their own convolutions from scratch (such as with the `create_convolution()` function above). In fact, given the myriad of tools available, it may save you time and effort to use external librairies. MLDB has integrated the TensorFlow Open Source Library for Machine Intelligence allowing us to leverage some of the great Computer Vision APIs and GPU accelaration that it offers. Let's get started with the same images as before.\n",
"\n",
"First, I reshape my image and kernel lists into 4D tensors in the NHWC tensor format. Then, I use the [`tf_Conv2D`](../../../../doc/builtin/sql/ValueExpression.md.html#builtinfunctions), the TensorFlow operator that is exposed as an MLDB built-in function directly in SQL."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_ = data.values[0].reshape(1, 28, 28, 1).tolist() \n",
"# image input must be a [batch, in_height, in_width, in_channels] shaped tensor"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def TensorFlowConvolution(image_processing):\n",
" \n",
" kernel = np.asarray(kernelDict[image_processing]).reshape(3, 3, 1, 1).tolist() \n",
" # kernel must be a [filter_height, filter_width, in_channels, out_channels] shaped tensor\n",
" strides = [ 1, 1, 1, 1]\n",
" SQL_Expr = \"\"\"\n",
" SELECT tf_Conv2D(\n",
" {input: %(data)s, filter: %(kernel)s}, \n",
" {T: { type: 'DT_FLOAT'}, padding: 'SAME', strides: %(strides)s })\n",
" AS *\n",
" \"\"\" % {\n",
" \"data\": data_,\n",
" \"kernel\": kernel,\n",
" \"strides\": strides\n",
" }\n",
" \n",
" convolvedData = mldb.query(SQL_Expr)\n",
" image = convolvedData.as_matrix().reshape(28, 28)\n",
" plt.imshow(image)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Choose an image processing option from the drop-down menu\n"
]
},
{
"data": {
"image/png": 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r6HU5LOfc3KKXdodaDcT5otdIf7fbRaPRSPr3jUYD1Wp1oQ6WyWSSROztq56r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\nSgiFT0gJofAJKSH/HyDj6gG/VikbAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc50323a950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"options=('Right Sobel', 'Detect Edges', 'Sharpen', 'Box Blur', 'Approximated Gaussian Blur')\n",
"interact(TensorFlowConvolution, image_processing=kernelDict.keys(), );\n",
"print \"Choose an image processing option from the drop-down menu\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are a few definitions:\n",
"* Batch: The data is sometimes split into batches to parallelize Image Processing.\n",
"* Strides: Stride is a step that the \"square box\" (as described in 'Defining the Convolution Function' section above) will take. A stride has shape [stride_batch, stride_width, stride_height, stride_channel] so [1, 1, 1, 1] will shift the box one pixel at the time for each batch and each channel.\n",
"* Padding: As you may have noticed, sometimes the \"square box\" has elements outside the picture (i.e. for pixels at the boundries of the image). The 'SAME' padding allows the convolution algorithm to go beyond picture borders. Pixels in the padding area will typically be zero and the output image will have the same size as the input image. 'VALID' padding does not allow the \"square box\" to go beyond the picture boundries. In this case, the output picture size will be smaller. For moreinformation, see [this article](http://radio.feld.cvut.cz/matlab/toolbox/images/linfilt4.html).\n",
"* Channel: we have only one channel here because it is grayscale. For images with colors (i.e. RGB), we have 3 channels. See ImageMagick's [Color Basics and Channels](http://www.imagemagick.org/Usage/color_basics/) article for more information."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Where to next?\n",
"\n",
"Now you can move on to the [Real-Time Digits Recognizer](../../../../doc/nblink.html#_demos/Real-Time Digits Recognizer) demo where we'll show the machine learning steps to follow to build *MLPaint*, the real-time digits recognizer plugin.\n",
"\n",
"Otherwise, check out the other [Tutorials and Demos](../../../../doc/#builtin/Demos.md.html)."
]
}
],
"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
}
| apache-2.0 |
mykespb/jupyters | mp-nettemp3-fru-procwords.ipynb | 1 | 15846 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mikhail Kolodin. Project: Internet temperature. 2015-12-15 1.4.1\n",
"\n",
"IPython research for internet temperature. We use now only fontanka.ru website, later other sites and methods will be added.\n",
"\n",
"Version with database recording. Now full archive of headers since 2000.\n",
"\n",
"Here we count good and bad words in the database. No more downloading info from websites."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import datetime\n",
"now = datetime.datetime.now()\n",
"import time\n",
"\n",
"import sqlite3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Part I. Get database with data and correct it. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#db = \"mp-nettemp3-fru-2015.db\"\n",
"db = \"mp-nettemp3-fru-2000-2015.db\"\n",
"conn = sqlite3.connect(db)\n",
"cur = conn.cursor()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<sqlite3.Cursor at 0x7f8d804c2dc0>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conn.execute (\"alter table netdata add dtyear int\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total records: (512351,)\n"
]
}
],
"source": [
"cur.execute (\"select count(*) from netdata\")\n",
"print (\"total records: {}\" .format(cur.fetchone()))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"We have data for 5378 days\n"
]
}
],
"source": [
"rc = cur.execute (\"select distinct substr(ndate, 1, 10) from netdata\")\n",
"cnt = 0\n",
"for r in rc: cnt += 1\n",
"print (\"We have data for {} days\" .format(cnt))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cur.execute (\"update netdata set dtyear = substr(ndate, 1, 10)\")\n",
"conn.commit()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Part II. get good and bad words and strore them locally."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"goods, bads = \"words-good.txt\", \"words-bad.txt\""
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open(goods) as good:\n",
" goodw = good.read().split()\n",
"goodw.sort()\n",
"goodw = tuple(goodw)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Good words: ('абонент', 'автобус', 'автодорог', 'акварел', 'актер', 'актрис', 'артист', 'балет', 'безопасн', 'бесплатн', 'библиоте', 'блюз', 'богатство', 'богаты', 'вальс', 'велосипед', 'верност', 'вернул', 'верны', 'возлюбленн', 'волейбол', 'восстанов', 'вручен', 'встреч', 'выделени', 'выдели', 'выжил', 'выпущен', 'высажен', 'высокоскоростно', 'выставк', 'выставочн', 'выступи', 'выступлени', 'выяснил', 'галере', 'гаранти', 'гимнази', 'гирлянд', 'график', 'гуляни', 'гулять', 'детск', 'дзюдо', 'диплом', 'дирижер', 'добилась', 'добился', 'добро', 'добры', 'доволен', 'довольн', 'договор', 'доплачива', 'дорог', 'друг', 'дружествен', 'друзья', 'завершен', 'застрахова', 'защита', 'защитил', 'здоров', 'зелен', 'знаменательбн', 'игруше', 'изготов', 'изучени', 'изучит', 'имущество', 'институт', 'интернет', 'информатик', 'исследовали', 'исследовани', 'историческ', 'история', 'карнавал', 'картин', 'кинотеатр', 'кинофестивал', 'книг', 'книжн', 'коллекци', 'компьютер', 'концерт', 'корректиров', 'костюм', 'круглогодичн', 'круглосуточн', 'культур', 'курорт', 'лауреат', 'легенда', 'лучше', 'лучши', 'лыжни', 'лыжни', 'лыжны', 'любоваться', 'мастер', 'материн', 'матч', 'медал', 'международн', 'метро', 'мозаи', 'молочн', 'музее', 'музей', 'музею', 'музея', 'музыка', 'мясно', 'наград', 'надежн', 'наилучш', 'написал', 'наук', 'науч', 'нашел', 'нашли', 'новы', 'ноутбук', 'нравится', 'обмен', 'обнаружил', 'обыграл', 'одобр', 'оздоровлени', 'озеленени', 'опрашива', 'опрос', 'оптимальн', 'оптимиз', 'организован', 'орден', 'оркестр', 'освоен', 'открыл', 'открыти', 'отпустил', 'отремонтирова', 'охран', 'памят', 'певец', 'певиц', 'певчески', 'пешеход', 'писател', 'победит', 'подар', 'подключ', 'подновили', 'подписан', 'покажет', 'показыв', 'пользовател', 'полюбоваться', 'помирил', 'помог', 'помощ', 'популяр', 'посетит', 'посмотреть', 'поставк', 'поставл', 'почет', 'почтен', 'почтил', 'поэзи', 'поэт', 'праздник', 'праздничн', 'празднова', 'предприяти', 'преми', 'призер', 'призов', 'приятн', 'программи', 'продовольств', 'продукт', 'путешеств', 'радост', 'разработа', 'разреш', 'раритет', 'расширен', 'регат', 'реставра', 'решил', 'рождени', 'салат', 'салон', 'самбо', 'свадеб', 'свадьб', 'свобод', 'сервис', 'скоростно', 'скульпт', 'слетать', 'совершенств', 'создадут', 'создани', 'социальн', 'спасает', 'спасают', 'спасени', 'спектакл', 'спорт', 'стадион', 'стимулирова', 'стихи', 'стихов', 'стихотворени', 'строител', 'студен', 'счастлив', 'счасть', 'танец', 'танцев', 'театр', 'тенор', 'тимуров', 'товар', 'трамва', 'транспорт', 'тренер', 'троллейбус', 'туризм', 'турист', 'убедил', 'уверенн', 'удалось', 'удач', 'удовольстви', 'улучшен', 'умственны', 'университет', 'услуг', 'усовершенствован', 'успех', 'успеш', 'устрое', 'фабрик', 'фантаст', 'фестивал', 'фотограф', 'фотокорреспондент', 'футбол', 'хокке', 'хокку', 'хорош', 'художествен', 'художни', 'чемпион', 'шахмат', 'шашечны', 'шашки', 'школ', 'экскурси', 'юбиле', 'язык')\n"
]
}
],
"source": [
"print (\"Good words:\", goodw)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(bads) as bad:\n",
" badw = bad.read().split()\n",
"badw.sort()\n",
"badw = tuple(badw)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bad words: ('авария', 'агресси', 'арест', 'атака', 'атакова', 'банкрот', 'бастовать', 'бастующи', 'беда', 'бедственн', 'беженец', 'беженц', 'бездейств', 'безработ', 'беспоряд', 'беспризорн', 'бестви', 'болеет', 'болезн', 'болеют', 'бомб', 'бомж', 'взорва', 'взрыв', 'взятк', 'взяточни', 'вирус', 'военизированны', 'военны', 'возбужден', 'война', 'вооружени', 'воровств', 'вред', 'вынужден', 'генерал', 'героин', 'горел', 'гранат', 'грипп', 'давлени', 'депортаци', 'депортирован', 'дестабилиз', 'дискриминаци', 'домушник', 'жалоб', 'жалуются', 'забастовк', 'заболе', 'завал', 'задержан', 'зажор', 'заминирова', 'запрет', 'запрещен', 'заражен', 'застави', 'застрелил', 'затоплен', 'затор', 'землятрясени', 'избиени', 'избил', 'избит', 'казни', 'казнь', 'контрабанд', 'конфликт', 'коррумпирован', 'коррупци', 'криминал', 'кровав', 'кровь', 'ликвидир', 'лимит', 'лишили', 'лохотрон', 'мафии', 'мафиоз', 'мафия', 'мешает', 'мешать', 'миномет', 'наводнени', 'наган', 'наказани', 'напавш', 'нападени', 'наркодилер', 'наркоман', 'наркоти', 'нарушен', 'нацизм', 'национализм', 'националист', 'нацист', 'неблагополуч', 'недовольн', 'недополуч', 'незаконн', 'несанкционирован', 'нехватк', 'обанкротил', 'обвинил', 'обвинител', 'обвиня', 'обокрал', 'обстрел', 'оглуш', 'огнестрельно', 'ограбил', 'ограблен', 'ограничени', 'ограничить', 'опасност', 'опасны', 'опоздал', 'опоздани', 'оружейн', 'оружи', 'оскверн', 'отсуди', 'падени', 'парализован', 'перебо', 'перевернул', 'перелом', 'пистолет', 'плесен', 'побег', 'поврежден', 'погиб', 'погубил', 'подкуп', 'подозр', 'подорвал', 'подрал', 'подтоплены', 'пожар', 'покончил', 'покушени', 'помех', 'поножовщин', 'порнографи', 'пострада', 'потерпевши', 'потеря', 'потоп', 'похитил', 'похищен', 'похмель', 'правонарушен', 'пресечени', 'преступн', 'претензи', 'принудит', 'принужден', 'пробит', 'пробк', 'провал', 'простуд', 'протест', 'пулево', 'пуля', 'разбит', 'разворовывани', 'развратн', 'разруха', 'разруш', 'ранен', 'расстроенны', 'расстройств', 'растрат', 'расхищение', 'револьвер', 'рецесси', 'рухнул', 'сатанизм', 'сатанист', 'сбил', 'сбитого', 'сбитый', 'скандал', 'скончал', 'скорь', 'следстви', 'смертельн', 'смертн', 'смерть', 'снаряд', 'солдат', 'спад', 'спам', 'столкновени', 'столкнул', 'страшны', 'танк', 'террор', 'траур', 'тревог', 'тревож', 'тротил', 'троян', 'трудност', 'труп', 'туберкулез', 'тунеяд', 'тюремн', 'тюремны', 'тюрьм', 'убиенн', 'убийц', 'убил', 'убит', 'убытки', 'убыток', 'уволен', 'увольнени', 'угнал', 'угнан', 'угнать', 'уголовн', 'угонщи', 'угрожа', 'угроз', 'ужас', 'украден', 'украсть', 'умер', 'умира', 'уничтож', 'упадок', 'упадочны', 'уплотнени', 'уплотнительн', 'ухудшается', 'ухудшени', 'ущерб', 'форточни', 'хищени', 'цензур', 'шпион', 'штраф', 'эвакуир', 'экстремизм', 'экстремист', 'энцефалит', 'эпидеми')\n"
]
}
],
"source": [
"print (\"Bad words:\", badw)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Part III. Process add data in database, \n",
"set wpos, wneg, mark as counters for good and bad words in each record."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<sqlite3.Cursor at 0x7f8d804c2e30>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cur.execute (\"select *, rowid from netdata\")"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"toshow = 10\n",
"shown = 0\n",
"\n",
"for row in cur.fetchall():\n",
" header = row[3].lower()\n",
" dthere = row[1]\n",
" cpos = cneg = 0\n",
" for w in goodw:\n",
" if w in header:\n",
" cpos += 1\n",
" for w in badw:\n",
" if w in header:\n",
" cneg += 1\n",
" mark = cpos - cneg\n",
" rid = row[-1]\n",
" cur.execute (\"update netdata set wpos=?, wneg=?, mark=? where rowid=?\", (cpos, cneg, mark, rid))\n",
" if shown < toshow:\n",
"# print (\"update: rowid={5}, dt={4}, header={0}, wpos={1}, wneg={2}, mark={3}\" .format(header, cpos, cneg, mark, dthere, rid))\n",
" shown += 1"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"conn.commit()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"conn.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
dataDogma/Computer-Science | Courses/DAT-208x/DAT208x - Week 3 - Section 2 - Methods.ipynb | 2 | 10078 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Banner goes here]()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lecture: Methods\n",
"-- --\n",
"\n",
"Following are some of the functions used formarly in this course:\n",
"\n",
"\n",
"+ `Max()`\n",
"\n",
"+ `len()`\n",
"\n",
"+ `round()`\n",
"\n",
"+ `sorted()`\n",
"\n",
"Let's learn a few more:\n",
"\n",
"+ Get index in a list: ?\n",
"\n",
"+ Reversing a list: ?\n",
"\n",
"\n",
"**Note**: all the data structures in python are called `objects`.\n",
"-- --\n",
"Python has _built-in methods_ which informaly are:\n",
"\n",
"`Functions` that belong to `python objects`, e.g. A python object of type `string` has `methods`, such as:\n",
"\n",
"+ `capitalize` and\n",
"\n",
"+ `replace`\n",
"\n",
"Further, `objects` of type `float` have \"specific methods\" depending on the `type`.\n",
"-- --\n",
"Syntax: \n",
"\n",
"+ `object.method_name( <arguments> )`\n",
"\n",
"\n",
"+ The `.(dot)` is called `address off operator` which uses `reference` of specified method to be searched from the standard library of python, which contains list of functions and methods.\n",
"-- --\n",
"To sum things up: \n",
"\n",
"_In Python, everything is an `object`, and each object has a `specific method` associated with it, depending on the `type of object`._\n",
"\n",
"**Note:**\n",
"\n",
"+ Some methods can also, `change` the `objects` they are called on.\n",
" - e.g. The `.append()` method!\n",
"\n",
"\n",
"+ Consequently, some don't and thus a caution is needed while usig such methods!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lab: Methods\n",
"-- --\n",
"**Objective:**\n",
"\n",
"+ Get to know different kinds of methods.\n",
"\n",
"\n",
"+ Understand the nuances that come packaged with methods.\n",
"\n",
" - by practising them on data types such as `string` and `list`.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-- --\n",
"+ String Methods -- 100xp, status: Earned\n",
"\n",
"\n",
"+ List Methods -- 100xp, status: Earned\n",
"\n",
"\n",
"+ List Methods II -- 100xp, status: Earned\n",
"-- --"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1. String Methods -- 100xp, status: earned**"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"poolhouse\n",
"\n",
"POOLHOUSE\n",
"\n",
"3\n"
]
}
],
"source": [
"\"\"\"\n",
"Instructions: \n",
"\n",
" + Use the upper() method on room and store the result in room_up.\n",
" Use the dot notation.\n",
" \n",
" + Print out room and room_up. Did both change?\n",
" \n",
" + Print out the number of o's on the variable room by calling count()\n",
" on room and passing the letter \"o\" as an input to the method.\n",
"\n",
" - We're talking about the variable room, not the word \"room\"!\n",
"\"\"\"\n",
"# string to experiment with: room\n",
"room = \"poolhouse\"\n",
"\n",
"# Use upper() on room: room_up\n",
"room_up = room.upper()\n",
"\n",
"# Print out room and room_up\n",
"print(room)\n",
"print( \"\\n\" + room_up )\n",
"\n",
"# Print out the number of o's in room\n",
"print(\"\\n\" + str( room.count(\"o\") ) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**2. List Methods -- 100xp, status: earned**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Other Python data types alos have many common method's associated with them, some of these methods are exclusive to some data types.\n",
"\n",
"A few of them we will be experimenting on them:\n",
"\n",
"+ `index()`, to get the index of the first element of a slist that matches its input.\n",
"\n",
"\n",
"+ `count()`, to get the number of times an element appears in a list.\n",
"-- --"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method_descriptor:\n",
"\n",
"count(...)\n",
" S.count(sub[, start[, end]]) -> int\n",
" \n",
" Return the number of non-overlapping occurrences of substring sub in\n",
" string S[start:end]. Optional arguments start and end are\n",
" interpreted as in slice notation.\n",
"\n",
"\n",
"===================================================\n",
"===================================================\n",
"Help on method_descriptor:\n",
"\n",
"index(...)\n",
" S.index(sub[, start[, end]]) -> int\n",
" \n",
" Like S.find() but raise ValueError when the substring is not found.\n",
"\n",
"\n",
"The index of the element 20.0 is: 2\n",
"\n",
"The number of times 14.5 occurs is: 0\n"
]
}
],
"source": [
"\"\"\"\n",
"Instructions:\n",
"\n",
" + Use the index() method to get the index of the element\n",
" in areas that is equal to 20.0. Print out this index.\n",
" \n",
" + Call count() on areas to find out how many times 14.5\n",
" appears in the list. Again, simply print out this number.\n",
" \n",
"\"\"\"\n",
"# first let's look more about these methods\n",
"help(str.count)\n",
"print(2*\"\\n===================================================\")\n",
"help(str.index)\n",
"# Create list areas\n",
"areas = [11.25, 18.0, 20.0, 10.75, 9.50]\n",
"\n",
"# Print out the index of the element 20.0\n",
"print( \"\\nThe index of the element 20.0 is: \" + str( areas.index( 20 ) ) )\n",
"\n",
"# Print out how often 14.5 appears in areas\n",
"print(\"\\nThe number of times 14.5 occurs is: \" + str( areas.count( 14.5 ) ) )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3. List Methods II -- 100xp, status: earned**\n",
"-- --\n",
"Most list methods will change the list they're called on. E.g.\n",
"\n",
"+ `append()` : adds and element to the list it is called on.\n",
"\n",
"\n",
"+ `remove()`: removes the \"1st element\" of a list that matches the inuput.\n",
"\n",
"\n",
"+ `reverse()` : reverse the order of the elements in the list it is called on.\n",
"-- --"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method_descriptor:\n",
"\n",
"append(...)\n",
" L.append(object) -> None -- append object to end\n",
"\n",
"=====================================================\n",
"Help on method_descriptor:\n",
"\n",
"remove(...)\n",
" L.remove(value) -> None -- remove first occurrence of value.\n",
" Raises ValueError if the value is not present.\n",
"\n",
"=====================================================\n",
"Help on method_descriptor:\n",
"\n",
"reverse(...)\n",
" L.reverse() -- reverse *IN PLACE*\n",
"\n"
]
}
],
"source": [
"\"\"\"\n",
"Instructions: \n",
"\n",
" + Use the append method twice to add the size of the\n",
" poolhouse and the garage again:\n",
" \n",
" - 24.5 and 15.45, respectively.\n",
" \n",
" - Add them in order\n",
" \n",
" + Print out the areas.\n",
" \n",
" + Use the reverse() method to reverse the order of the\n",
" elements in areas.\n",
" \n",
" + Print out the area once more.\n",
"\"\"\"\n",
"# Let's look at the help on these methods\n",
"help( list.append )\n",
"print(\"=====================================================\")\n",
"help( list.remove )\n",
"print(\"=====================================================\")\n",
"help( list.reverse )\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"The new list contains two new items: [11.25, 18.0, 20.0, 10.75, 9.5, 24.5, 15.45]\n",
"\n",
"The new list has been reversed: [15.45, 24.5, 9.5, 10.75, 20.0, 18.0, 11.25]\n"
]
}
],
"source": [
"# Create list areas\n",
"areas = [11.25, 18.0, 20.0, 10.75, 9.50]\n",
"\n",
"# Use append twice to add poolhouse and garage size\n",
"areas.append( 24.5 )\n",
"areas.append( 15.45 )\n",
"\n",
"# Print out areas\n",
"print(\"\\nThe new list contains two new items: \" + str( areas ) )\n",
"\n",
"# Reverse the orders of the elements in areas\n",
"areas.reverse()\n",
"\n",
"# Print out areas\n",
"print(\"\\nThe new list has been reversed: \" + str( areas ) )"
]
}
],
"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",
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}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
festivalhopper/music-transcription | notebooks/onset_detection_convnet_1d.ipynb | 1 | 310534 | {
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from keras import backend as K\n",
"from keras.callbacks import EarlyStopping\n",
"from keras.models import Sequential\n",
"from keras.layers import Activation, Conv1D, Dense, Dropout, Flatten, MaxPooling1D\n",
"from keras.wrappers.scikit_learn import KerasClassifier\n",
"\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"import numpy as np\n",
"\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"from onset_detection.metrics import onset_metric\n",
"from onset_detection.read_data import read_data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:149: UserWarning: No truth found for AR_Lick11_FN.wav, skipping file.\n",
" warn('No truth found for ' + wav_file + ', skipping file.')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:149: UserWarning: No truth found for AR_Lick11_KN.wav, skipping file.\n",
" warn('No truth found for ' + wav_file + ', skipping file.')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:149: UserWarning: No truth found for AR_Lick11_MN.wav, skipping file.\n",
" warn('No truth found for ' + wav_file + ', skipping file.')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:151: UserWarning: Skipping non-wav file data\\IDMT-SMT-GUITAR_V2\\dataset2\\audio\\desktop.ini\n",
" warn('Skipping non-wav file ' + path_to_wav)\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:149: UserWarning: No truth found for FS_Lick11_FN.wav, skipping file.\n",
" warn('No truth found for ' + wav_file + ', skipping file.')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:149: UserWarning: No truth found for FS_Lick11_KN.wav, skipping file.\n",
" warn('No truth found for ' + wav_file + ', skipping file.')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:149: UserWarning: No truth found for FS_Lick11_MN.wav, skipping file.\n",
" warn('No truth found for ' + wav_file + ', skipping file.')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:149: UserWarning: No truth found for LP_Lick11_FN.wav, skipping file.\n",
" warn('No truth found for ' + wav_file + ', skipping file.')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:149: UserWarning: No truth found for LP_Lick11_KN.wav, skipping file.\n",
" warn('No truth found for ' + wav_file + ', skipping file.')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:149: UserWarning: No truth found for LP_Lick11_MN.wav, skipping file.\n",
" warn('No truth found for ' + wav_file + ', skipping file.')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Career SG\\fast\\country_folk\\audio\\country_1_150BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Career SG\\fast\\metal\\audio\\metal_3_135BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Career SG\\fast\\rock_blues\\audio\\rock_1_120BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Career SG\\fast\\rock_blues\\audio\\rock_2_115BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Ibanez 2820\\slow\\classical\\audio\\classical_8_100BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Ibanez 2820\\slow\\jazz\\audio\\jazz_1_160BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Ibanez 2820\\slow\\jazz\\audio\\jazz_2_170BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Ibanez 2820\\slow\\jazz\\audio\\jazz_3_120BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Ibanez 2820\\slow\\jazz\\audio\\jazz_4_70BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Ibanez 2820\\slow\\jazz\\audio\\jazz_5_80BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Ibanez 2820\\slow\\jazz\\audio\\jazz_6_150BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Ibanez 2820\\slow\\jazz\\audio\\jazz_7_140BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Ibanez 2820\\slow\\jazz\\audio\\jazz_8_110BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Ibanez 2820\\slow\\reggae_ska\\audio\\reggae_2_100BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:170: UserWarning: Skipping data\\IDMT-SMT-GUITAR_V2\\dataset4\\Ibanez 2820\\slow\\rock_blues\\audio\\rock_5_100BPM.wav: no truth csv\n",
" warn('Skipping ' + path_to_wav + ': no truth csv')\n",
"D:\\Users\\Michel\\Documents\\FH\\module\\8_IP6\\git\\onset_detection\\read_data.py:14: UserWarning: Cannot handle stereo signal (data\\IDMT-SMT-GUITAR_V2\\dataset3\\audio\\pathetique_poly.wav), skipping file.\n",
" warn('Cannot handle stereo signal (' + path_to_wav + '), skipping file.')\n"
]
}
],
"source": [
"active_datasets = {1, 2, 3, 4}\n",
"X_parts, y_parts, y_start_only_parts, ds_labels = read_data(active_datasets)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X_parts_train, X_parts_test, y_parts_train, y_parts_test, y_start_only_parts_train, y_start_only_parts_test, ds_labels_train, ds_labels_test = train_test_split(\n",
" X_parts, y_parts, y_start_only_parts, ds_labels, test_size=0.2, random_state=42\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(940179, 111)\n",
"(940179,)\n",
"(940179,)\n",
"(940179,)\n",
"(231747, 111)\n",
"(231747,)\n",
"(231747,)\n",
"(231747,)\n"
]
}
],
"source": [
"X_train = np.concatenate(X_parts_train)\n",
"X_test = np.concatenate(X_parts_test)\n",
"y_train = np.concatenate(y_parts_train).ravel()\n",
"y_test = np.concatenate(y_parts_test).ravel()\n",
"\n",
"y_start_only_train = np.concatenate(y_start_only_parts_train)\n",
"y_start_only_test = np.concatenate(y_start_only_parts_test)\n",
"\n",
"ds_labels_flat_train = []\n",
"for y_part, ds_label in zip(y_parts_train, ds_labels_train):\n",
" ds_labels_part = np.empty(len(y_part), dtype=np.int8)\n",
" ds_labels_part.fill(ds_label)\n",
" ds_labels_flat_train.append(ds_labels_part)\n",
"ds_labels_flat_train = np.concatenate(ds_labels_flat_train).ravel()\n",
"\n",
"ds_labels_flat_test = []\n",
"for y_part, ds_label in zip(y_parts_test, ds_labels_test):\n",
" ds_labels_part = np.empty(len(y_part), dtype=np.int8)\n",
" ds_labels_part.fill(ds_label)\n",
" ds_labels_flat_test.append(ds_labels_part)\n",
"ds_labels_flat_test = np.concatenate(ds_labels_flat_test).ravel()\n",
"\n",
"print(X_train.shape)\n",
"print(y_train.shape)\n",
"print(y_start_only_train.shape)\n",
"print(ds_labels_flat_train.shape)\n",
"print(X_test.shape)\n",
"print(y_test.shape)\n",
"print(y_start_only_test.shape)\n",
"print(ds_labels_flat_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5.48495118402e-19\n",
"1.0\n",
"2.3806596501e-05\n",
"1.03017544869\n"
]
}
],
"source": [
"ss = StandardScaler()\n",
"X_train = ss.fit_transform(X_train)\n",
"X_test = ss.transform(X_test)\n",
"print(X_train.mean())\n",
"print(X_train.std())\n",
"print(X_test.mean())\n",
"print(X_test.std())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(940179, 111, 1)\n",
"(231747, 111, 1)\n",
"(111, 1)\n"
]
}
],
"source": [
"input_dim = X_train.shape[1]\n",
"X_train = X_train.reshape(X_train.shape[0], input_dim, 1)\n",
"X_test = X_test.reshape(X_test.shape[0], input_dim, 1)\n",
"input_shape = (input_dim, 1)\n",
"print(X_train.shape)\n",
"print(X_test.shape)\n",
"print(input_shape)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def create_model(nb_filter=32, filter_length=8, padding='same', input_shape=(111, 1),\n",
" pool_size=2,\n",
" n_conv_layers=1, dropout=True):\n",
" model = Sequential()\n",
" model.add(Conv1D(nb_filter, filter_length, padding=padding, input_shape=input_shape))\n",
" model.add(Activation('relu'))\n",
" for i in range(0, n_conv_layers - 1):\n",
" model.add(Convolution1D(nb_filter, filter_length, padding=padding))\n",
" model.add(Activation('relu'))\n",
" model.add(MaxPooling1D(pool_size=pool_size))\n",
" if dropout:\n",
" model.add(Dropout(0.25))\n",
"\n",
" model.add(Flatten())\n",
" model.add(Dense(128))\n",
" model.add(Activation('relu'))\n",
" if dropout:\n",
" model.add(Dropout(0.5))\n",
" model.add(Dense(1))\n",
" model.add(Activation('sigmoid'))\n",
"\n",
" model.compile(loss='binary_crossentropy',\n",
" optimizer='adam',)\n",
" \n",
" return model\n",
"\n",
"\n",
"def fit_predict(clf):\n",
" clf.fit(X_train, y_train)\n",
" y_train_predicted = clf.predict(X_train).ravel()\n",
" y_test_predicted = clf.predict(X_test).ravel()\n",
" \n",
" model = clf.model\n",
" for layer in model.layers:\n",
" print(layer.get_config())\n",
" print('TRAIN')\n",
" print(classification_report(y_train, y_train_predicted))\n",
" print(onset_metric(y_train, y_start_only_train, y_train_predicted))\n",
" print('TEST')\n",
" print(classification_report(y_test, y_test_predicted))\n",
" print(onset_metric(y_test, y_start_only_test, y_test_predicted))\n",
" print('')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 940179 samples, validate on 231747 samples\n",
"Epoch 1/10\n",
" 94208/940179 [==>...........................] - ETA: 77s - loss: 0.2157"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-10-6cc24a7d0cc1>\u001b[0m in \u001b[0;36mfit_predict\u001b[0;34m(clf)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfit_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0my_train_predicted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0my_test_predicted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mD:\\ProgramFiles\\Anaconda3_64\\lib\\site-packages\\keras\\wrappers\\scikit_learn.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, **kwargs)\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msearchsorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_classes_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mKerasClassifier\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 202\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mD:\\ProgramFiles\\Anaconda3_64\\lib\\site-packages\\keras\\wrappers\\scikit_learn.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, **kwargs)\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0mfit_args\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mhistory\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mD:\\ProgramFiles\\Anaconda3_64\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m 843\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 844\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 845\u001b[0;31m initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m 846\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 847\u001b[0m def evaluate(self, x, y, batch_size=32, verbose=1,\n",
"\u001b[0;32mD:\\ProgramFiles\\Anaconda3_64\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m 1483\u001b[0m \u001b[0mval_f\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1484\u001b[0m \u001b[0mcallback_metrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1485\u001b[0;31m initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m 1486\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1487\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mD:\\ProgramFiles\\Anaconda3_64\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m 1138\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'size'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1139\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1140\u001b[0;31m \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1141\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1142\u001b[0m \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mD:\\ProgramFiles\\Anaconda3_64\\lib\\site-packages\\keras\\backend\\theano_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 1092\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1094\u001b[0;31m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1095\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1096\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mD:\\ProgramFiles\\Anaconda3_64\\lib\\site-packages\\theano\\compile\\function_module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 882\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 883\u001b[0m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 884\u001b[0;31m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0moutput_subset\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 885\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_subset\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moutput_subset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 886\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mD:\\ProgramFiles\\Anaconda3_64\\lib\\site-packages\\theano\\gof\\op.py\u001b[0m in \u001b[0;36mrval\u001b[0;34m(p, i, o, n)\u001b[0m\n\u001b[1;32m 870\u001b[0m \u001b[1;31m# default arguments are stored in the closure of `rval`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 871\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mrval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode_input_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode_output_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 872\u001b[0;31m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 873\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 874\u001b[0m \u001b[0mcompute_map\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"clfs = [\n",
" KerasClassifier(\n",
" build_fn=create_model,\n",
" batch_size=1024, nb_epoch=500,\n",
" validation_data=(X_test, y_test),\n",
" callbacks=[EarlyStopping(monitor='loss', patience=4)],\n",
" nb_filter=256, input_shape=input_shape,\n",
" ),\n",
" KerasClassifier(\n",
" build_fn=create_model,\n",
" batch_size=1024, nb_epoch=500,\n",
" validation_data=(X_test, y_test),\n",
" callbacks=[EarlyStopping(monitor='loss', patience=4)],\n",
" filter_length=16, input_shape=input_shape,\n",
" ),\n",
" KerasClassifier(\n",
" build_fn=create_model,\n",
" batch_size=1024, nb_epoch=500,\n",
" validation_data=(X_test, y_test),\n",
" callbacks=[EarlyStopping(monitor='loss', patience=4)],\n",
" input_shape=input_shape, n_conv_layers=2,\n",
" ),\n",
" KerasClassifier(\n",
" build_fn=create_model,\n",
" batch_size=1024, nb_epoch=500,\n",
" validation_data=(X_test, y_test),\n",
" callbacks=[EarlyStopping(monitor='loss', patience=4)],\n",
" input_shape=input_shape, dropout=False,\n",
" ),\n",
"]\n",
"\n",
"clfs2 = [\n",
" KerasClassifier(\n",
" build_fn=create_model,\n",
" batch_size=1024, nb_epoch=500,\n",
" validation_data=(X_test, y_test),\n",
" callbacks=[EarlyStopping(monitor='loss', patience=4)],\n",
" input_shape=input_shape, nb_filter=64, filter_length=16, n_conv_layers=2,\n",
" ),\n",
"]\n",
"\n",
"for clf in clfs2:\n",
" fit_predict(clf)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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yjBsHHDsGvPcecNddwMqVQAOv5SEDIxbGHaA5rzASC2MvlHE3Zw7XNfXrc1/Rvz/w5ZfA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Do/8pt9+SVDyAYP5or3CSfELoYBACNGcMVl8GD35zj0UK5qjx/vTyhpPAoKmDC3f//UPO2KU60a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\nCx3XKVaM2Y8PPcSyPgMHujuDNR7t3g289BJn6rRpwxIt8VRrOz+xyHBMB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hhzlTGmHoBX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\nuaOK9GIvEe3YEd3zcnJ40RPKli28MI5mOrX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shJTjVazITX3CXVZ9//3c7b1/\nfzdalbu77+bE6KhRzj2ntfyMZmRwQ9l4mzxNyIAjwIuNI0fyr3GTkcELgW3bsmrcxEq3biyU/803\nwe/34488uFWowJlqr9Svz8FLONkmAWlpzKJwe7mGhK5uXe7sHumy6m++4bIMNzLTwg04LlzIpdh+\nqTfile+/50Xp0KHO724ereuv54VG9v5mLf/ftat/Z1XFO6edxgvF4cNZs+aWW3iRuH496/EMHMhl\nX6VLM7vtlVd40fzNN8xyjEUm4UknMZs4WMBxyxYGfIYPz7/IvBfatOGkTX47l3pp/HgGlufPZ03D\nqlWPH3Abw/qMgWWvv/zCgEvXrlkbCcbrhJQxwIsvMqPhpZciu9A4/3xuVjJ7tv/ODX5nLbMbr7jC\nmUBDiRKcRHnrLfbp9HR3JuNPPpm76C5YEPx+1jLYFotJyuRkfi4XLQp+v9RUjk/Dzd7LqUwZBs5y\nLqv+5Rdm0734IjdXSknx92qEGjVOXFK9YQO/R5LhuHEjg4wAz5e1a3PyZuJE4L33oi9vc/nlLKOz\nfXto9//kE/49/BBECWwck9sE5+zZvL1Dh9i3Sxg3+fDDrL6bn2++4cTOvffGttxW8eLcpGbKlOhK\n0WU3bRqDrRMn+qM+f7gSNuBYrhwDia+/fvzs3tatPPH88588EZcsye3qb7019jtjNW7MCHj24tE5\nLV6cFaT78ENvl8EULsz6K+HsyBmQlsaTiR8vtgoqY4C+fTnojeRCc/JkZva4kTUSbsAxNZUXk35a\nqhhrgdmvc85hxrbflC7NQdqrr2YN5L74ghMqibicWpyRlAQ8+SSDia+9lrXs76abuIz5qquAN95g\nP9q2jYOyCy+MbRvbtGE2R167oY4dywHogAGxbVeo2rTh5Gt+gQkv1asX2qSEMQwyrl7N8V96Ondl\nrl6dNZziVaVK7OfR/A7VqjGII+H56COeq0aPdi4g0r07++bLL/N86Nbqn06dGMzp2jXv49PPP3P1\nUqwSApo0YcAxWHmmwJjOCV278vn27OH/589nzddt27iZ1ODB/gh0BVOjBif4Dx7Mui0wRg43w7FK\nFfa5detYnuGKK3htlp7OST0n3ovA7tahlDY7cIABb6/rNwbUqcOs+Jw7yQNcTl2vXnwGfBJB164M\nNg4axIz9/Nx3H895ffu637ac+vXjBkp33RX9pny//spEkh49Yr+5plMS+vK8f38urenfn530nHP4\n1bUrl4KWKsUL848+4gVNrJ18MgcDM2bk3hknTeIJt3p1LneI9UVUblq14kVe4MQdikOHeFLXcmr/\nGTCAmcCTJoX3uIwMztz06OHOju4VKzIIGkog9I8/uKNevC6Xc8qbb/JYNnGif7MFb7qJtewCSx0n\nT2bR42uu8bRZEgduuokTcP/6Fzcf2rWLu04//zwzvy64wLuLxhYtONmRW5bj7t3MohkyxL+7Ap93\nHgfGfq/jGI6kJF5kfP89swLHjPG6RRKvxo/n+KJxY+ees21boFgxBsMrV+Zn0A033six2rJlXE7/\n1FMnbnC1fDm/xyrg2LQpa5GtXp37zzdv5ufWqYBj587MjP/gAwYgWrdmkG3FCgaX4kH16hx3Z3/P\nNmzgdWxycnjPFdjksUsXZt0PHco+4OTmj5UqMZB7zz3ADz8Ev+/y5fz7eF2/MSCvjWP272ewWsup\nvVO5Mq913n03/00Fv/ySK2Tuu8+d69T8BCbLP/2UgepIWctr9SJFoqur6rWEDjgGlqGcfDLrLvTq\nxXTULVuyMh3vuYcXu15swgIwPXj9+uNPIpmZXL5x440cMM+bx5OKH7RsycFKfsshsvvsM85gKeDo\nP+XKMQA/YUJ4mwF9/DFnXHr1cqddgRnbQFH1YN5+O2tZY0G1cyeXGfbsyeCHX7VowQDja68x0D1t\nGtvs1fFX4ku9elwa06oVMx39olQpoEGD3AN2Eyeyr992W+zbFY62bRnIjWRTOD8rUgS4+WZmVYlE\nYuNG4H//c/Y5ixZlLcd9+9xd6m8Mz7Hr1nHS5u67WQdt2bKs+wQSGkqXdq8d2TVowM9lXnUcU1PZ\nbqc+s+eey8DigAFc5vjgg1waG6vf1wlVq3Kcm31Zdbg7VAeULMkA98GDvL586il3lpsG6hb36XNi\nkDu7xYvZJr+URCpfntdGOQOOCxbwWrZzZ2/aJdS7N4OJ1vJznVdZsP/+l5Ms118f2/Zl17Ilr3tG\njmRQPRKvvsqx5UsvxdcxK6eEDjgCjIb/+CMDJI89xhmds8/2ulVZmjbl7FRgt+r9+7nUYvRoRsYD\nAVO/qFSJy9nCWVadlsYPSbVq7rVLIjd0KGcgQyksHpCSwgHqFVe406bAICqUZdWTJzP7IJ6Xy0Vr\n5EgO6J56yuuWBJeUxA0Lpk4FZs1iloOWU0siaNuWx9BDh7JuO3iQNVVvvNH/x6e2bbkJQSg72ooU\nJM2aZWU9Oal7d36PxWR8cjI33vriCwaXGjZkIH7nztjVbww49VS+frCAY61awOmnO/eaN93EoNaC\nBVzZFm/ld4oWZfAk+8YxGzZEFnAEuBrmu+8YEHFLsWLMRktPDx6w/+QTlkbxy8SzMQxk5dw4ZuZM\nbmoT6/JrcqJLLuGxrGtXBrQHDmQwOGDpUgbTH3jA+3715JMsW/HCC+E/duNG4I47uDy7XTvHmxZT\nrh1yjTGljDGTjTF7jDG7jTEvG2OK5fOY14wxmTm+EmiRz4mKFGFds7ffZj2Ra65hJPu995ix5Me6\nIi1b8oMcak2CtDQuRYm3E3xB0bAhg8Hjx4d2/wMHmCncu7d7/bNMGdYrzS/guHEjl+u7lWkZDxYv\n5gzYY4/FR12Zfv24VH7oUG74oYkISQRt2jBb6dNPs26bNIlB9REjvGtXqOrX5wW533erFom1wYPd\ned7ARlft27vz/LmpVYurjiZM4HXHxRdzaXGsN3Rs2pSBpiNHjr89M5MBR6dL5AwaxFU58bpxFMA6\njtkDjhs3Rr6pzvnnOxvQzUvdutyp98EHc9/5+fBh9ke/1G8MyLlxzNGjXJKv5dT+UawY6zS/8goD\n2/XrZy3fv/deblzUrZunTQTAzN1+/Rj8DGe/hMxMTpSUKsU64PHOzRDQFABVADQF0BbA1QBCie/O\nBVAWQLljXz3daqBfdOvGmabq1XlCXLIE6NjR61blrVUrLnXNry4HAOzdy1kILaf2L2MY/Pngg9Ay\nCj/4APjrL3eDfMaEtnHMlCmc+S2og4BDhziQbtCA2Qrx4KKLGOTevp19yI+TKiLhqlqVqycCy6oz\nMjiz3bVrfGREFCrEyUQFHEWOF2kWWX4KFeIFZaw3U0xKYhD1++9ZisZaZ+tThqJJE+Dvv09ctrp6\nNTOtnQ4MGhP/Y43q1bk5WUYGgxGbNrnXN530n/8wWNqnz4m7C3/5JZMY/BZwrFOHdaIDO4EvWcL/\nF9RrDb8yhsfQ5cvZj2rVYqm8jz5ikNsviU4PPsj2hVOaY/x4/h6vvhp+nVY/cuVPYYz5B4CWAPpb\na7+y1i4DMAxAD2NMfjk4h6y1O6y1vx/7CmN7kvjUogVnms49l8G5GjW8blFwjRpxgDRvXv73XbyY\nJ0cFHP3t+uuB007jBgz5SUnhrKXbmxjlF3C0lm3p1Ik7wBZETzzBkhHPP++fE2soBg5kqQgva6uI\nOMkYZjkGAnbvvcfP5siR3rYrHG3aMACwfbvXLRERt5Uty8ygvXu52iCWatfmmDPnsuqFC7m6pUGD\n2LYnHtSowSz6H3/kirjDhyPPcIylk09mP9u0icGg7D75hJupVa/uTdvyUqsWvwcC4jNnckIxcLv4\ny+WX82/Vvj3w+OM8vnTo4HWrspQvz7HguHFZQexgPvqIn5WhQ+M7Kzs7ty5R6wPYba3NlvyNVAAW\nQH6J+9cYY7YbY9YZYyYYY2KQ9O2tU07hrN7SpeyUflesGHcTC6WOY1oaA6l+2GFb8lasGGeJXn6Z\ndcfy8scf3FggFnX3KlQIHnBcuZI7lBXUGoC//AI8/DBw113+KbYdqj59WNOkQgWvWyLinLZtmfn/\n448scdC4cfzsggpw9YIxPMaLSMFQtGjsX7NQISYv5Aw4pqby+uKUU2LfJr8LBOW+/joraBEPGY4A\n608+8QR32V2wIOv2xYu54qVQIe/alpsyZbixTmBZ9cyZXHkYTxP7BU2JEqzp/847TEbxW0bzXXdx\nP4t//zvv+6xcyYnfJk34eX/ssdi1z21ufXTKAfg9+w3W2gwAu479LC9zAdwAoAmAkQAaAfjQGL91\nG+eddZY7u4S5pWVLbsQTLDgFcDDRtKn/PvhyosGDWUD8rbfyvs+MGTz5Xned++2pWDH4LtUpKcCZ\nZzpf6ydePPooJyjuvdfrloTPGO5eKJJImjZlNsfIkbxQyZnN4XdlyjBAmttu2yIiTmralLtlBzZ7\nOHSIAahEyehxWunSTOD4+uusyfh4CTgCwJAhHK/368flyUePMtHGb8upAwIbx6xcyWsR7U7tf8Zw\nc+CLL/a6JScqVoxJItOmcQl4dj//zBJTNWpwwnr6dH42igXd+SS+hDWnYIx5FECwIbQF6zZGxFo7\nPdt/1xhjVgH4CcA1AD4K9tjhw4cjOcci9549e6Jnz4QvAemJVq2Au+9mgfy8Aj6//w6sWuXekrKp\nU6di6tSpx922Z0/sV+AnSt+rj2EXzAAAIABJREFUXJmB5PHjgb59c79PSgrvU6aM++2pWBHYvRvY\ns+fE+hUZGdzpuEcPb2ZG/dD3vvhiOOrWTUaPHlm3xWO/k9D5od8lyvHODcWL8+Lpvfe4GVKLFl63\nKHxt2wJjxnAzh5NP5m3qd+IFP/Q7QH3PLU2acFnw0qUMMn72GWv8+WES2Q99L7d+d+aZPbFyZU8U\nLw6ccUZ8lRM66STgtde4ImfIEODOO1nH068Bx9q1WXPv3Xd5DRKLdvq13+l454y+fbmsesQIxk+2\nb2cQ8oUXeF39wgsMyAfGXrESk35nrQ35C0BpABfl81UIQD8AO3M8NgnAEQAdw3zN3wEMCPLzmgBs\nenq6ldjJzLT27LOtHTEi7/tMnWotYO3WrbFrV3p6ugUD3zVtGP0skq9E7HsffMC/2fLlJ/7sp5/4\nsylTYtOWzz7j633zzYk/W7iQP/v889i0JRSx6nuBfte8eeL0O4lcrPtdIh3v3PDUUzw2TZ7sdUsi\n89VXbP/HHwe/n/qdeEFjvMSRmWltmTLW/vOf/P+oUdaecYa1GRnetisvfjjm3XeftWeeaW2/ftbW\nqePiL+uiadN4jmnY0NpTT7X20CGvW5S71FS2s3Rpa3v18q4dfuh34pwFC9ivrrvO2qJFrS1Z0trR\no63dt8/rlh3P6X4X1pJqa+1Oa+0P+XwdBfAZgJLGmOzbnzQFYAAsz/XJc2GMOQcMcm4Lp53iPmOY\n6RZs45i0NKBKlfioSynUujUzC8ePP/FnU6ZwNjVWO6gHlorkVsdx8mTWBb3iiti0xY9GjPC6BSKS\nU9++nLGORdkJN9Sowc0ktFu1iLjJGGY5Buo4pqZymbXq5OWtRg1gxw4uRY+n5dTZde8O9OzJzNYG\nDWK/S3uoatbk9507tTu1OKd5c9ZpnDULGDaMy6nvucebWrqx5Mph3Vq7DsB8AC8ZY+oYYxoCeBbA\nVGvtb4H7HdsYpuOxfxczxjxujKlrjKlgjGkKYCaAH449l/hMy5bAmjXAli25/zxQv1HiR1ISazm+\n9RYHNQH22I7QnTvH7qBYtiwLh+cMOB44wKLAvXoV7NqgZ57pdQtEJKfTTwdGjfJfEfxQnXQSB8Oq\n4ygibmvaFEhP5zjvyy/9sZzazwIbx3z/fXzsUJ2X8ePZ/vbtvW5J3kqVYmJDkSK83hVxyowZwNat\nwOjR7GcFgZvzSNcDWAfuTj0bwGIAg3LcpzKAQKGADACXA3gfwPcAXgLwJYCrrbVHXGynRKhZM16c\n5LZb9YYN/FLAMf70789A3iuvZN2Wns4BTix3hDYm952qP/gA+OsvBhxFRMRZbdpwMjHYpl0iItFq\n0gTIzATuv5/ftWFMcOedlxWgiNcMR4C/w/r1wO23e92S4Nq142qFEiW8bokkkqJFOTldkLgWcLTW\n/mmt7W2tTbbWlrLWDrDW7s9xnyRr7RvH/n3QWtvKWlvOWnuKtfZ8a+1ga+2O3F9BvHb66VzSmlvA\nMS2Nwchrrol5syRKpUtzM5bnn+fmLACXMJcrx8FhLFWseGLAMSWF/a5y5di2RUSkIGjenBmaWlYt\nIm46/3yO8958k2O6ChW8bpG/GcNl1UB8ZzgCXFHld2PHAm+84XUrROKfKmVIVFq2BBYuBI4ePf72\ntDSgVi2gZElv2iXRGTqU2S1z5vBv69WO0DkDjn/8AcydG9tMSxGRgiQ5mUvd/v7b65aISKJr2lTZ\njeEILKuO5wxHESlYFHCUqLRqBfz5J2uvBFgLLFqk5dTxrHZtoG5d4LnnGDzevt2bIF/OgOOMGexf\n3bvHvi0iIgXFu+8CI0d63QoRSXSBawUFHEPTqBEnhRRwFJF4EadlzcUv6tRhLY5584D69Xnb6tXA\n778r4Bjvhg4FbrgB2L8f+Mc/snZsi6WKFYHdu4E9ezjAmjwZaNECKFMm9m0REREREed06AD85z9A\n69ZetyQ+tG8P/PorN1UUEYkHynCUqCQlsd5T9jqOaWnc1athQ+/aJdG79lruhLx0KbMbvdgROjCD\nu2kTNyFaulSbxYiIiIgkgmLFgIceAk491euWxAdjuOmEiEi8UMBRotayJZdU79zJ/6elAQ0aaPAQ\n7045Bbj5Zv77+uu9aUMg4LhxIzBlCgemnTp50xYRERERERERCY0CjhK1li1Z8Dk1lRuMfPKJllMn\nilGjGED2aje8smUZ+NywgbtTd+rEoKOIiIiIiIiI+JdqOErUzj4buOwyLqs+7zzgr78UcEwUxYoB\nTZp49/rGABUqAO+9B6xbB4wd611bRERERERERCQ0CjiKI1q14pLXSpWA007jLsciTqhYkcHsMmW0\ni6GIiIiIiIhIPNCSanFEy5bcNe3FF4FGjYBCCmWLQwJ1HHv0UL8SERERERERiQcKOIojrrySu6Zt\n2aLl1OKsQMBRu1OLiIiIiIiIxAflC4kjTjkFuOYa4MMPFXAUZ3XuDOzZA9Sp43VLRERERERERCQU\nrmU4GmP+bYxZaozZZ4zZFcbjHjTG/GqM2W+MWWiMudCtNoqzevUCatYELr3U65ZIIrn4YuDRR7mB\njIiIiIiIiIj4n5tLqk8GMB3AxFAfYIy5B8CtAAYCuALAPgDzjTGFXWmhOOr664H0dAWGRERERERE\nREQKMteWVFtrHwAAY0zfMB52O4CHrLWzjz32BgDbAXQCg5ciIiIiIiIiIiLiY77ZNMYYUwlAOQBp\ngdustXsBLAdQ36t2iYiIiIiIiIiISOh8E3AEg40WzGjMbvuxn4mIiIiIiIiIiIjPhbWk2hjzKIB7\ngtzFAqhirf0hqlZFYPjw4UhOTj7utp49e6Jnz56xborEyNSpUzF16tTjbtuzZ0/M26G+V/D4oe+p\n3xU86nfiBfU78YIf+h2gvlcQ+aHvqd8VPOp34oVY9DtjrQ39zsaUBlA6n7v9bK09mu0xfQGMtdae\nns9zVwLwE4Dq1tpvs93+MYCvrbXD83hcTQDp6enpqFmzZmi/iCSsFStWoFatWgBQy1q7ws3XUt+T\n7GLV99TvJDv1O/GC+p14QWM88YqOeeIF9TvxgtP9LqwMR2vtTgA7o33RPJ57gzHmNwBNAXwLAMaY\n0wDUBTDejdcUERERERERERERZ7lWw9EYc64xphqACgCSjDHVjn0Vy3afdcaYjtke9jSA/xhj2htj\nqgJ4A8AWAO+71U4RERERERERERFxTlgZjmF6EMAN2f4fSMdsDGDxsX9XBvD/hQKstY8bY4oCeAFA\nSQCfAmhtrT3sYjtFRERERERERETEIa4FHK21/QD0y+c+Sbncdj+A+91plYiIiIiIiIiIiLjJtSXV\nIiIiIiIiIiIiUvAo4CgiIiIiIiIiIiKOUcBRREREREREREREHKOAo4iIiIiIiIiIiDhGAUcRERER\nERERERFxjAKOIiIiIiIiIiIi4hgFHEVERERERERERMQxCjiKiIiIiIiIiIiIYxRwFBERERERERER\nEcco4CgiIiIiIiIiIiKOUcDxmKlTp3r6eLXB2eeIF354v9QG59oQLxLl/VYb4o8f3i+1wbnniBeJ\n8H6rDfHHD++X2uBcG+KJH94vtcG554gXifB+qw3Ocy3gaIz5tzFmqTFmnzFmV4iPec0Yk5nj60O3\n2phdInQMtSH++OH9Uhuca0O8SJT3W22IP354v9QG554jXiTC+602xB8/vF9qg3NtiCd+eL/UBuee\nI14kwvutNjivkIvPfTKA6QA+A3BTGI+bC+BGAObY/w852ywRERERERERERFxi2sBR2vtAwBgjOkb\n5kMPWWt3uNAkERERERERERERcZkfazheY4zZboxZZ4yZYIw53esGiYiIiIiIiIiISGjcXFIdibkA\n3gGwAcAFAB4F8KExpr611ubxmFMAYO3atVG98J49e7BixQrPHq82OPMc2frBKVE1IjRR9z2v3y+1\nwbnHx7Dvqd+pDf8vnvod4P37pTY48xzqd2qDF8+hMZ7a4NXjdcxTG7x4DvU7tcGL53C831lrQ/4C\nA4CZQb4yAFyU4zF9AewK53WyPbbSsedtHOQ+1wOw+tJXjq/rI+lzYfZP9T195fblat+D+p2+cv9S\nv9OXF1/qd/ry4ktjPH159aVjnr68+FK/05cXX470O3Osk4XEGFMaQOl87vaztfZotsf0BTDWWhvR\n0mhjzO8ARllrXwrSppYANgI4GMlrSEI5BUBFAPOttTvdfCH1PckhJn1P/U5yUL8TL6jfiRc0xhOv\n6JgnXlC/Ey842u/CCjhG9AJRBByNMecA2ASgo7V2tuONExEREREREREREUe5tmmMMeZcY0w1ABUA\nJBljqh37KpbtPuuMMR2P/buYMeZxY0xdY0wFY0xTADMB/ABgvlvtFBEREREREREREee4uWnMgwBu\nyPb/QNXKxgAWH/t3ZQDJx/6dAeDyY48pCeBXMND4X2vtERfbKSIiIiIiIiIiIg5xfUm1iIiIiIiI\niIiIFByuLakWERERERERERGRgkcBRxEREREREREREXGMAo4iIiIiIiIiIiLiGAUcRURERERERERE\nxDEKOIqIiIiIiIiIiIhjFHAUERERERERERERxyjgKCIiIiIiIiIiIo5RwFFEREREREREREQco4Cj\niIiIiIiIiIiIOEYBRxEREREREREREXGMAo4iIiIiIiIiIiLiGAUcRURERERERERExDEKOIqIiIiI\niIiIiIhjFHAUERERERERERERxyjgKCIiIiIiIiIiIo5xNeBojLnKGDPLGLPVGJNpjOmQz/0bHbtf\n9q8MY0wZN9spIiIiIiIiIiIiznA7w7EYgJUAhgCwIT7GAqgMoNyxr7Ostb+70zwRERERERERERFx\nUiE3n9xaOw/APAAwxpgwHrrDWrvXnVaJiIiIiIiIiIiIW/xYw9EAWGmM+dUYs8AY08DrBomIiIiI\niIiIiEho/BZw3AZgEICuALoA2AzgY2NMdU9bJSIiIiIiIiIiIiEx1oZaWjHKFzImE0Ana+2sMB/3\nMYBN1tq+efy8NICWADYCOBhlMyX+nQKgIoD51tqdbr6Q+p7kEJO+p34nOajfiRfU78QLGuOJV3TM\nEy+o34kXHO13rtZwdMgXABoG+XlLAJNj1BaJH70ATHH5NdT3JDdu9z31O8mN+p14Qf1OvKAxnnhF\nxzzxgvqdeMGRfhcPAcfq4FLrvGwEgJSUFFSpUiXiFxk+fDjGjh3r2ePVBmeeY+3atejduzdwrF+4\nbCMQXd/z+v1SG5x7fAz73kZA/U5toHjqd4D375fa4MxzqN+pDV48h8Z4aoNXj9cxT23w4jnU79QG\nL57D6X7nasDRGFMMwIXgRjAAcL4xphqAXdbazcaYRwGUDyyXNsbcDmADgDVgKucAAI0BNA/yMgcB\noEqVKqhZs2bEbU1OTvb08WqDs8+B2KSDR933/PB+qQ3OteEYt/ue+p3akBvf9zvAH++X2uDcc0D9\nTm3w4DmgMZ7a4EEbjtExT22I+XNA/U5t8OA54FC/czvDsTaAjwDYY19jjt0+CcBNAMoBODfb/Qsf\nu095APsBfAugqbV2scvtFBEREREREREREQe4GnC01n6CIDthW2v75fj/EwCecLNNIiIiIiIiIiIi\n4p48g4EiIiIiIiIiIiIi4VLA8ZiePXt6+ni1wdnniBd+eL/UBufaEC8S5f1WG+KPH94vtcG554gX\nifB+qw3xxw/vl9rgXBviiR/eL7XBueeIF4nwfqsNzjPWWq/bEBVjTE0A6enp6U4VA5Y4tmLFCtSq\nVQsAallrV7j5Wup7kl2s+p76nWSnfideUL8TL2iMJ17RMU+8oH4nXnC63ynDUURERERERERERByj\ngKOIiIiIiIiIiIg4RgFHERERERERERERcYwCjiIiIiIiIiIiIuIYBRxFRERERERERETEMQo4ioiI\niIiIiIiIiGMUcBQRERERERERERHHKOAoIiIiIiIiIiIijnE14GiMucoYM8sYs9UYk2mM6RDCY64x\nxqQbYw4aY34wxvR1s40iIiIiIiIiIiLiHLczHIsBWAlgCACb352NMRUBzAaQBqAagHEAXjbGNHev\niSIiIiIiIiIiIuKUQm4+ubV2HoB5AGCMMSE8ZDCAn621I4/9/3tjzJUAhgNY6E4rRURERERERERE\nxCl+q+FYD0BqjtvmA6jvQVtERBLOkSPA4sVet0JEREREREQSmd8CjuUAbM9x23YApxljinjQHhGR\nhDJnDtCoEbBundctERERERERkUTlt4CjiIi4aONGfk/NmUsuIiIiIiIi4hBXazhG4DcAZXPcVhbA\nXmvtoWAPHD58OJKTk4+7rWfPnujZs6ezLRTfmDp1KqZOnXrcbXv27Il5O9T3Ch4/9L1I+93Wrfye\nmgrceqtbrRM3xHO/k/ilfide8EO/A9T3CiI/9D31u4JH/U68EIt+Z6zNd/NoZ17ImEwAnay1s4Lc\nZzSA1tbaatlumwKgpLW2TR6PqQkgPT09HTVr1nS62RJnVqxYgVq1agFALWvtCjdfS31PsotV34u2\n311/PTB1KnDaacDOnUAhv007SVjipd9JYlG/Ey9ojCde0TFPvKB+J15wut+5uqTaGFPMGFPNGFP9\n2E3nH/v/ucd+/qgxZlK2hzx/7D6PGWMuNsYMAdANwFNutlNEpKDYuhU4/3xg717gq6+8bo2IiIiI\niIgkIrdrONYG8DWAdAAWwBgAKwA8cOzn5QCcG7iztXYjgLYAmgFYCWA4gP7WWlUbExFxwNatQIcO\nzHBUHUcRERERERFxg6uL6ay1nyBIUNNa2y+X2xYDqOVmu0RECiJrGXCsUAFo3BhISwP+8x+vWyXi\nH998AyQnAxUret0SEREREZH4pl2qRUQKiN27gYMHgbPPBpo1A5YtA/bt87pVIv5gLdCxI9Cnj9ct\nERERERGJfwo4iojk47ffgNWrgcxMr1sSncAO1YGA4+HDwJIl3rZJxC/WrAE2beJnIj3d69aIiIiI\niMQ3BRxFRPLx1ltAnTrMgIpn2QOOF18MlC+vOo4iAXPmAEWLsuTAuHFet0ZEREREJL4p4Cgiko9V\nq4BLLgGSkrxuSXQCAcezzgKMYZajAo4iNGcOPxO33QZMmwZs2+Z1i0RERERE4pcCjiIi+Vi9Grjs\nMq9bEb2tW4EyZYDChfn/Zs2AlSuBHTu8bZeI13btYk3Ttm2B/v2BIkWAiRO9bpWIiIiISPxSwFFE\nJIjMTNZ2S5SA49lnZ/2/aVN+X7TIm/aI+MX8+UBGBgOOyclAv34MOB486HXLRERERETikwKOIiJB\n/PIL8PffiRlwLF+eS8W1rFoKujlzgOrVsz4fw4YBO3cCU6Z42y4RERERkXilgKOISBCrV/N71are\ntsMJOQOOAJdVL1wY/xviiEQqIwOYN4/ZjQGVKwPt2gFPP63PhoiIiIhIJBRwFBEJYtUqLrHMGaiL\nR3kFHDdtAn7+2Zs2iXht+XJmM2YPOALAHXfw8//RR960S0RECqbDh71ugYiIMxRwFBEJIrBhjDFe\ntyQ6hw5xc5icAcdGjbj7tpZVS0E1ezZwxhnAFVccf3vjxsxsfvppb9olIiIF0/ffe90CERFnKOAo\nIhJEouxQvW0bv+cMOJ52GgMtaWmxb5OIH8yZA7RuzcB7dsYwy3H2bGD9em/aJiIiBc+qVV63QETE\nGTEJOBpjhhpjNhhjDhhjPjfG1Aly30bGmMwcXxnGmDLBXmPHDufbLSIF25EjwLp1iRFw3LqV33Nb\nGt6sGQOOmZmxbZOfZWYCTz0F7N3rdUvETZs3A99+e+Jy6oDrrwdKlwaefTa27RIRkYIrUD9cEtt9\n9wFLl3rdChF3uR5wNMZ0BzAGwH0AagD4BsB8Y8wZQR5mAVQGUO7Y11nW2t+DvY4OzCLitB9/ZB2d\nghBw3LULWLkytm3ys/R0YMQIYMYMr1sibpozh5mNLVvm/vNTTgEGDwZefRX488/Ytk1ERAomXdcm\nvu+/Bx58kNcaAkybxuuR/fu9bok4LRYZjsMBvGCtfcNauw7ALQD2A7gpn8ftsNb+HvjK70XWrHGg\npQXcu+8Cv/7qdStE/COwpCVRAo6nngqULHniz+rVA4oWVR3H7AIzzsuXe9sOcdecOUDDhrl/LgIG\nD+bEw6uvxq5dIiJScG3d6t/Ve4sWAXXqaGObaD37LFCmDNCjh9ct8d7EiVxRkpYGfPyx160Rp7ka\ncDTGnAygFoD/rw5mrbUAUgHUD/ZQACuNMb8aYxYYYxrk91qaCYrOr78C3boB//uf1y0R8Y/Vq4Fy\n5bihRLwL7FCd2+Y3hQtz8xgFHLMEAo6ff+5tO8Q9Bw5wcNuuXfD7nXUW0LMn8MwzwNGjsWmbiIgU\nbH6d8Jw6FfjqK+DTT71uSfzaswd4/XVg0CCgSBGvW+Mdaxl7GDIEuO02oEIFYN48r1slTnM7w/EM\nAEkAtue4fTu4VDo32wAMAtAVQBcAmwF8bIypHuyF1qxR/bFozJjBD/177+l9FAlIlA1jgKyAY16a\nNePg8eDB2LXJr6xlwLF8eZ5b/v7b6xaJGz7+mEHHvOo3Znf77cCmTcCsWa43S0RECrhSpfwZcLQW\nWLiQ/54zx9u2xLNXXwUOHQJuucXrlnjHWmDkSGDUKOCBB4CxY7mBnwKOicd3u1Rba3+w1r5krf3a\nWvu5tbY/gGXg0uw87d/PzR0kMtOmAeeey0xHP57gRLxQ0AKOBw8Cy5bFrk1+tXEjd/UeNowTMF99\n5XWLxA2zZwMVKwJVquR/35o1gauvBp5+2vVmiYj4xnffaSLSC5dd5s8VFj//zMm3ChUUcIxURgbw\n3HPAdddxYrsgysgABgwAnnwSGDcO+O9/uQKrVStg/Xrgp5+8bqE4qZDLz/8HgAwAZXPcXhbAb2E8\nzxcAGga/y3D06pWMc8/NuqVnz57o2bNnGC9TMG3YwJPam29yk4R33wXqB1vw7hNTp07F1KlTj7tt\nz549MW/H8OHDkZycfNxt6nvx78ABFnLOLeDoh74Xbr/bupW1GvNy2WXAmWdyWXWTJk62NP4sWcLv\n/fsDjzzC4+M113jaJADx2e/8ylpeLLVrl3uZgdzccQfQpQs3FKpVy932+Yn6nXjBD/0OKNh9b9Ei\noEULYOBAYMIEr1sTO37oe5s3D8eWLclo3z7rHOWHfpeayo3WHnwQ6NuXwaHKlT1tUtz58EMGbqdM\nOf52P/S7WBzvDh0CevUCZs4E3ngD6NMn62dNmgCFCjHLcehQx15SgohJv7PWuvoF4HMA47L934DL\npO8O4zkWAHg7j5/VBGArVUq3t9xiJQKjR1t76qnW/vWXtQMHWnv++dZmZnrdqsikp6dbcJfzmtb9\nvl0TgE1PT4/dLygx89VX1gLWfv55aPePVd+LpN9lZlpbpIi1Tz8d/H49elhbp07IT5uwBg2ytkoV\n/rtxY2s7dfK2PcH4ud/52erV/HzPnRv6Y44etbZiRWt793avXfFC/U68oDFe7GzaZO0ZZ1hbqpS1\nhQtbu3mz1y3yVqyPeRMnplvA2u++i+EvGYJrr7W2QQNeMxYubO3Yse68ztGj1h454s5ze61pU2vr\n1g3tvol2rv37b2tbtOA1ycyZud/nmmusbdfO1WZIPpzud7FYUv0UgAHGmBuMMf8A8DyAogBeBwBj\nzKPGmEmBOxtjbjfGdDDGXGCMudQY8zSAxgCeC/Yil12mpcCRmjYNaN8eKF4c6NqVsy7ffON1q6Sg\n+Owz1kfr14/9r0ULZuJdeimX+ZcsySWPsa4tGtiI6tJLY/u6bti1izOKwZZUA1xW/dVXwO7dsWmX\nXy1dyp2LAfbFzz9nRpwkjjlzuDN7OJmrSUmciZ8/37VmiYh47sABZnMXKwasWMHrg8cfj+y51q0D\nnn/e2fYVBJdcwsxGPy2rzszkRmvNmrFPNGrk3rLqgQN5bZpo1qzhe3jbbV63JPZ27waaN2fpprl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83Bg9b+5z/WFi3KUcV777URZQaG6sABfl7eesvZ/T76KEsvw+X1KOTC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\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dnYGegqhbN9ap+/BDv1vijdq1ucDQc88x8BirbduAVauCd5ParRsD9+vX5//c//4D7riD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wC3AEgDkAygp9Y6Z+VqQRAEQRAEQRAEQRAEQRBciK0ajlrrpfBh1NRad8/lvWUAYu1slyAIgiAI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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1bb5cd30a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = clfs2[0].model\n",
"for layer in model.layers:\n",
" config = layer.get_config()\n",
" if config['name'].startswith('convolution1d'):\n",
" # squeeze = remove dimensions with shape 1\n",
" filters = np.squeeze(layer.get_weights()[0]).T\n",
" w = 8\n",
" h = config['nb_filter'] // w\n",
" fig, axarr = plt.subplots(h, w, figsize=(w*2, h*2), sharex=True, sharey=True)\n",
" for i in range(h):\n",
" for j in range(w):\n",
" _ = axarr[i, j].plot(filters[i*w + j])\n",
" break"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
nkmk/python-snippets | notebook/pillow_invert.ipynb | 1 | 1532 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from PIL import Image, ImageOps"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"im = Image.open('data/src/lena.jpg')\n",
"im_invert = ImageOps.invert(im)\n",
"im_invert.save('data/dst/lena_invert.jpg', quality=95)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"im = Image.open('data/src/horse.png').convert('RGB')\n",
"im_invert = ImageOps.invert(im)\n",
"im_invert.save('data/dst/horse_invert.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
kerimlcr/ab2017-dpyo | ornek/osmnx/osmnx-0.3/examples/08-example-line-graph.ipynb | 1 | 239440 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Street network analysis when a street is a node\n",
"\n",
"In some traditions of street network research, street becomes a node. The edges are connected when these streets touch each other. In OSMnx, basically, the intersections of streets become nodes and streets become edges. Therefore, in order to build a network according to the previous tradition, you have to modify the network that OSMnx generates properly. Fortunately, NetworkX has line_graph() function that does exactly that operation. This example demonstrates the process of transforming a city's street network into a line graph using OSMnx and calculating and coloring the closeness centrality for this line graph."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import osmnx as ox, networkx as nx, matplotlib.cm as cm, matplotlib.colors as colors\n",
"%matplotlib inline\n",
"ox.config(log_console=True, use_cache=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a street network using OSMnx"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# get the network and project it\n",
"G = ox.graph_from_place('Piedmont, California, USA', network_type='drive')\n",
"G_projected = ox.project_graph(G)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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YxsfHcfjwYdy+fRthYWHzsieBMFeQ8H8CwQimyrQxNb29vUhOTgYw4VaaKmYlKioKZWVl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\nTdedAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12c5ac50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot it: nodes in this network are street junctions\n",
"fig, ax = ox.plot_graph(G_projected, node_size=8, node_color='k', node_zorder=2, \n",
" edge_color='gray', edge_linewidth=0.5, edge_alpha=0.8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Let's convert the graph into a line graph so that the streets become nodes in the new graph."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# NetworkX's line_graph() creates a line graph\n",
"L=nx.line_graph(G_projected)\n",
"\n",
"# calculate closeness of each street from the line graph\n",
"closeness = nx.closeness_centrality(L)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### We are going to color streets in the original graph with their closeness centralities."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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7mm3cF2JXsDH7FM4qLcv7PUqwrvlPzkzY+wJlJgPbhryKponlGLbk7mRtzhe0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kAAAg\nAElEQVTt9yDmC1ntuFL8BFWWXNydOtPGbxvRAYdx1txa75hOmrmoBDM5pW83aW1/ZWDvJwlv74va\nvAOr9PdoOkmShaTCq6cb0d5fXi3q2ACiqMHH/Q1s2CjQP0dC4fOcz2qHwvQNFjQodEvoEhRHhNvc\nBp+TrVlXM6aG+zqeQZb6r1IGXfzXo5T/uUEyW8s5nb+A+IJHsQkCXX0/xUlu/waq+F9ZYV51uLx+\nRy4qiXQZic1mJFb/ncPrb4h7o0ahFGFXbuO0zVpowR5ajJ9mIj45B4DBXe2ru5Oar2f0K8v5bOcx\nZKLIS1OHseHpWXi56Jq0DpvNxsQ759Jn7GQqq0xM6d+R7x+fhUbZ8PFxn7AQhoaGk2es5KkfdzRq\n/mhfL8oNJgYOHES3hxZDYMPxTy8PvGp0vXmk/uqxrk4qenqGUWKpYldmkt1rKjNX82zsGhacXEPF\npTwKkzKJzb4xsy2u5Apzjy/h3cvL8B0Shk9Hb24Z0pd7Iu9yqDjiaL9pgMDPuY7/MHT3GIYVkaNF\n6xzuey1R7vdSY1NQVLUWq1ROrv5JErJbkVH2ATVSFT7q4bQPOE6474+olZ0bHhDwd30Eg02F1bCm\n+QUuNbPQimbyyz5ouG0zkK1/lAprBWHOE9Co7K/J5aydzPx7SunXey3l2V9hRY1M9wJdguKJcLc/\nA05vzkMndyZM18nhtctFLYIAhVW7AMip3MP+rCnsSe9DUfU2BNGZHn6r8NHUfUL5VyRJQm84Cahx\nc6pdc+9aBvrOB2Sc1a91eP0NIRfleKm0lJnrTrtvoYWm0mL8NBNpeXqUchmuuvoDVSVJ4s1Ne5nw\n5tdk68vpFh7IvpfvY0rf5lF9f37VVrZ+u4qi2P1M6xXNS5OHOdT/s8nj8VCo+CEpkc+PnHB4/hHt\nokCADcfj6RDqh9lkJSmvsN4+Ee6eRHq7k1pUQrK+/jL+L3e/BWzwRtzuBteyduv3dJkxkmE/vsTe\nggS0Gi1PbHqXwcunsbxo+x/t0irzePDUWyxKeJeSmgJC1ZH8/MJ6HlozCIIcV1yPcO6Aj1JGXFkK\nVqnh+ibX0s1jIkrBypnSow7Pey1yUc2lxBAenHSad7+IJLdyIwIigbpZdAi6QJD3ChRyf4fGFEU5\nknI8WqGCwsrVTVrfX/FxfZgqyQmhuv4YpeagynCQjMpfcZW74+9+Y7xMQ1w4byMz3UqpYR5dgs4R\n5X6jm7AhBEF0+Nn4nTZeb1NRpmDa+Ge5/UE/zhXOx2C+gFweQITbM4wIOYSXuptDY7760Sw2fpZB\ngLburMtrUYoagrX9sdoqiNPbV0TREcprTOgcdAe20IIjtBg/zURZpRFv9/pPbc6n5zFs0TLWHD6L\nSiHnnTljWfnwNHRqpybPb7ZYmPneOrbHpxE1fi4hY2azJTaFbH2ZQ+PIZSIb7pyBSpTx9uFDLDvq\nWHzN1N4dwQa7E5IZ27UNksXCmgMNF1l7rt8gQOCl/b/V2y7C1ZMonQ8Z1cUk6GuX0YjXZ3LHwc+5\n56n5nP1uF9UnU3my9a3sHbGIt0c/RLu23SiuyePH7MM8feYjHju7lBxjOt7KQN7s+CwfdHsCf7Un\nAequyIVyLpWfr3We+ujr2QuDJHKkyLG6PTJRQZTWl2xjNRU19RuNtSFJErkV64nNGsSpfWeIP21m\n589GwtwW0DbwEr4er9Wqt2UvgR4vYrbJqKxsWh2hvyITlRicxuAmllFUsaFZx74WSTKTVDgfAYjx\nXlGv0SJJEqWlN9bU2frLWLb+6sWwHo4bPb/jpwrFIBkoMzt+j53knqjLFnL6YBW7txSjUXajX9DP\nDA/ZRbT7XXa58H6n2pLLoaw7WfzEOla/k48tr5fdfQf5PQHIOFb4KSZLwzIh9lJoLKWypoYwbcOC\nrC200FhajJ9mICmjgBqTgejA2r+sFovEc6t/YeYH6yiuqmZQmwj2v/IAIzvH1NreUXJLyhm5+CvO\nZ+QR6etJ3IbPeX3hY1gsEre/vxaj2bEdZriHO9vmzMJJlPHGoYO8u8/+4F03jQp3jYrkgmIGRwWT\ntOo13r5vFgZD/YUMh4ZF4K5VcTwzmwpz/QG1L3QZgQ1YfOp619xPWXGM3fMO/zj+GUmVGbS6dwx+\nk3vSd3RPbg/7sz7NwrZzAYEVaWtJrk7EWe7BwtbzWdZzIa1c/gzMHu43FYB9BY5Xhx7pNwO5ILGn\noH5jrjZ6eY5GQuRIof0uBbNFT1LhM5zOaktuydNI1jRmzuvJgrdCePQlD3Ta2c2iNaeQuWOU98OF\nbEqr7Re5tAdf9+cx20SMFR8367jXkln0EJVSFeHOt6Ny6lBv24nTR+Pt7c2hQ9c//8FB7YlppcBg\nql/Etz66uY8GBLbnNM7NN2zgHaxc9wovLQ+j3HQe2w11kOrHbCnlRO6D7M4YRYk5jvmvRDL9MV/0\nbp/ZPYZW7kYXz7sAMz9lv+DYBdTDx0nbAYFbAro025gttPBXWoyfZuDRx57gzJoXqMy9cMNnxy5l\nMOilz/npTCIuKie+un8yH90zHpWyeUosHb6Yxq2vrkRfZWB0l1ZsWTAbjVLJzH5duK13W8orjMz6\n2PH4kUgvT36acydamYJPTp5gwXb7qyt3DgmgLCOFNzb+giRZqDHXYE9FhXu6dccmwdJD9Wc79fMP\nx0fpwtnSTHKryvg4cTeDdi5lyfn1FJj0RGmD+KLnfcQ+s4IBj91GgrEUvenPAGJ/tSfDfYagFT24\nP2IOq3u/TE+vG4s7hmmjsNrcyDeedTjGRS3X0VrnQ7qhiiJTjkN927uORi1KxJU2fGKmNxwiLmcc\n8dldqDR8B0ho1BPpEHiS3pE7uWveSwT42rhY3HwaWn7uS7DaBIrKXm24sQMoZZ6UyXpx4dhlTsdt\nbNaxASqr95BZvRc3uRe+7vVXla6oKaS46gSSzXrDvXdSXI1jM5rjGr2Wju7DcJG7klx5jjxD43Tn\n7pr+AhP6v4GElUPZd2CoQ5H9WixSNWcKFrAzYzD5hkM4ia509X6dDxYmc9+Tw6i0ZJBc+q3da+jp\nNRuNPIACwxlSKhovU/I7BwrOsSMnATelgvGBfZo8Xgst1EWL8dMMWGrMgI2955KpNl7dgRnNFh76\nciv3frGJCqOJ27q14cDS++kZU3vKd2NYtvM4D3y5BYtV4tkJg3lz9vX++ldvH0WrUB+SMotYsM5x\n0cxwLw923zMXd6WKjYkJ/OO7zXYZAV6lWaR9+yEfLX2RVnMX0vfhpWg0Dbta7u3cHaVCxg8XExuc\nZ25YF7JfXkGrYX35Jm03BquRXh5t2Tb4GdYNfJCunlez7u6OHI7VJvBR0vWnN/NjJvNtn1cY5V9/\nZl2YtjcK0cjZ0mMNrv+vjPQbjw2BX3Ici2MRRZEYbRB5ZjN6U+YNn0uSmbSSf3IqsytphTOx1JxF\nkAXh6/oK3YISae39AU7yq9XAo13nYrBpqTBsbLYgZa0yhkqxDTopgWrzlWYZ83fys6czanIeQ4fd\nwZnilZSa05tlXEkyklT0KDIEYny+rvcUzGApZ1fmPJ57348Dl75l4MDri4w6q6+ePFYadzZpTbcF\nzscGLE95hiJjRqPGCHGZTCv3B7DaTBzImoS5jixBSbKQUPQ6v6b1I6vyZ2SCE+09nmFk2EECnccC\n0NvvY0RknC+2X0IDYGzQWyAI7M59FYsD2Xrl5ip+zTnFR5d+YHH8t0w58AZPnV6LiMAbnWc1y0ll\nCy3UhWzx4sWL/92L+P9OhcyXLHkoisAQfjudhJuLmjkfrSelQI+XTsPyB6cyfUBnhGtruTcBSZJ4\ndPk2NhyOx0kh46sHpzCqS6ta207o3pYNx88Rn5KHVqOkc2iAQ3NpnZTM7NKJ7ecTiS/OZ29SClM7\ntkcU674WZ4WMFWvX4xHdjs49+xHi48ltPRrO+hIEgfSKEs7nFqBWKejuf2PNpCJDFY8d+pEVRw5Q\nvHY7lqIy7nvkMZb1nce44C44K66XN4hyDmJjxl4uVxQyK3wwMsGxF6qfKoRY/TbyTKX08hzuUF9f\nVQj7C34g21DAKN9xDt1/lUzJqdLTyCmilUt/AKrMySQVPUlGyTOYzEew2Uw4OfUn0vsLwjwW4KK6\n8RkTBIEiUwYK6ylMQgjuqsZXH78WURaMzbSZYlMqHrrJzTImgFbty5bflhHVTYdv7wwulm4itWIv\nRmspKpkbarl7o8ZNybsbvSWVSNc5uOnqlg85X7KdE3l3oxXzsClGMiz8RgkHUVRTUrUBizUNV+2d\nyMTGKZB7OgWiEp1IqozlpP4XymvyCNa0RyE6FgPoqe6B2VpIsfEs6eVrEQQBZ2UMMkFJtSWXRP17\nxBY8jd4UhygoiHb7Bz39vsBDfX2mmVxUIwoK8g1HKTbFEuZin76WWu5KpaWUIuM5ik2ZRLsMabDP\nC3HfsCh+M3vyE4kryeZyRSEVNUaiXDx5vfMddPW0L2u2hRYaS0uF5yZilSSmPL0Sb3cdVRj4/u0n\nEWQKYmY/xaxB3Xlq/MBm3cGUVxuZ/s+1ZOnL8HHR8t2TMxtMj88tLWfsayuxWCS+eGAyfaIdP32y\nWCUmrlrDhZJCQrQu/PSP2fWmzw9Y8jmVBhNySeD2fp14avwgu+YpMxrp+sWnuGtVnJr34B9/z6wo\n4dmjOziSlYnNBq4qJaMFHXe070nHjvVnyn1+eSsrrpzg3ui+zIusv5ZNbbx2YS5WSc/CtpuQORBM\nCrAu7T12FsbzSORsung0/KPwO5IksShhOlRZebDdNHLLPsVmTUUQwCq446m9nVC3x5DZEbxssBRx\nLrsHJiGcASF7HFp/fVzJ7oGKfHz84lHIml6J/HcSS7dxqOBtQrX9CdR0I7VyL/mGeMCGmzKMMN0Q\nwp2H4O4UYdd4v/z6PeNum8qsf/iy8rO8WtsYLOX8lvU4CukoJpuKELdn6OJZt7yCvnIthSVPoVaN\nIMT768Zc5h9cKDvAtuyPMf6rIKVSUKBTuHJrwHwidPbHvVwoeoOU8nVXdbOwcfVg/+rrXURGiMsk\n2nk8i9iAJMXO9NuotGTR0+dNgpxH1tv2dyRJYtWVSZitJYwL+YQATfs6254vTWXu0S/xcHJifFAP\n2rkFE6j2IkjthcpB3b4WWmgsLeeKTeRIXCo5hWX06hhKSnIuFkMVFkMlz08czDMTBzer4fPOJ1/Q\n6+6nydKX0SMyiB0vzrOrLpC/mwuf3Xt1F/fQsi3kljpeQE8uE/lh7h0MCAwlo6qcwZ8vp6iqbmXn\nSD9PamokTDVWYgK87Z7HVaWiZ0gg+koje9JSuFRSwMSfvqH/hmUczszES6PmjYEjiL/jMd6cOa9B\nwwdgbsQYnEQbmzKO272Oa2ntPBC5WMPhol0O9x0TMAsRiZ35jmV91UgVJK27xNI+m/nwwweQrGnI\nFO0I8VpJj+A4IjwW2mX4AKjlXlhkPVDbktEbExy+hrrQ6h5EKVjJKWne2J8Yl1spy5V4d8kaZEXt\nGBv8CdMjttDb+3FUMjfO6lexJX02m9NmEVu0nBJTSr0xZeczPsBSA7mFRixS1Q2fXyj5mR3pw1Ha\njmIUOjI05Nd6DR8AD91MBMGdauNuLNaSJl1vW9eBPNN6LaP87iRAFYqTTEWJuYjVaYvJqb5s/zhe\nCxgZso9o17vxUnXDWRGKj7o3HTwXMDrsJB28XmzQ8AHo6/8xIBBbuAjJTjeWKIqMDrr6HOzIfr5e\nF+uJ4suAwEMxt/BgzFgG+XQkyjmgxfBp4W+lxe3VRN5ZvZeySiNH49OwIWf8zOlUBrTnXHYJdw3r\n1myurk82/sTjc2dSnHiap555ljdnj63X9fRXgjxc0WqUHExI48fYRGb264xc5mhtEoEJHdqSVVRK\nbH4u62LjuKVVDO7qG2sb5ZVVcvDIcWQyGYtnjkGttL9mR0dfPz757GPWfbuK9bYK8owGglyceXPA\nKN7uP5YOno7Vp5GLMlKr0jlXWkyEzoUInWMSJEGaCI4Wb6bApKev12iH+qpkWs6V7iGlqpShPkNQ\nyupXHc+vPs3B3IUcLfyYSycKuXi0jAFDezJt9C6CXO9Do7DvtOOGdcgjKK9eT4Epk2CXxqvGX4tG\n2Ymiiq8QrBdw0T2I4KBLsTZKTWkczF/K6vcOs3NFFmnFZ5k5+W4UogZvdVuiXcfQynUcOoU/lTV5\nJFf8SmLZFlIr92C0lOAkc0Ulc//je6c3HEHpu5pRE/0YMEFHVc0ZApyvbgSM1gp+yZxPZfVyJET8\nXJ5haODLOMnsdWOpMJj2YLJk4qq5rUnXLQgiwZq2dPMYTV+vSQSro4kr209i+RH6eI63+38rE9V4\naXoT7DyBMNcZBDnfhpuqg0P3Rilzo0Yqo8h4lnLzZYKcb7Grn7PCh0JTOiWmRKqsZYTpag9YLjCV\nsifvIk4yG8P87CuwaQ/ZVXreSdjOV5cP8GNmLGmV+bRxDUTVog/WQi20GD9NIC1Hzwdr92OqsSJX\niLzz+HjunziYzNIqEtLzKayoYlD7yCbPs3j9Tr45fB5zVSnayFasWvxMo7LFOoUGkFFaxvmUXA4k\npTKtT+MKK45sFU2NycKR7Ey+O3uO/qGh+LlcrweUfvE8ny94CFNuCksWPGX32Efz0nj6+A8kvvMF\nxkupRPbuwlfT5rKk90ii3ew/Qfor7V3D+C7tMCmVmUwJGdhwh2twkqk4WnwQo5RBL8+JyEXHiq8p\nBBunSi8is5XQ1vVGlXVJsnBev4L9uc+TULaVSoseV4Un00c+zoL57zPx1keRNzKu5He0igCulG9G\nLl3A2/ku5GL9Rpg9CIJAeU0RGutxyiUVzir7a8T8lZTyXfyWs5Az+m8oq8nGN9CVqiozfWe5MTh6\n7nWbCIWowVv1uyE0HmdFAJU1+Vyp2HnVEKrYjcGqRy3zIKv0XUyWDPq1Xku5+Rhl5ku4KKPIqE7m\nWO7dqEjGIHRgaNBawp0dyy7SOHWluOJLzDUXcHOehyg0vV7X73g4BVBqzibTkEyRKZV2ro49s03F\nW9WHtIqNlJgT8VZ3R6OwL1YwXNeXuJLNFBrPE+E8BHUt8hpRzgFsyjzIxbJ8hvi2xcOpfi0xe9iR\nfZY7968mXl9MblUVmZVVnCzM49srR2nr5k2YrvHvjhb+O2kxfhqJwWCge//h6HNT8Y9qz9pX59Ah\n4uqJxOB2kXy7P5ZzaXnc0rUVbg1Ufa4Lo9nCzPfWcjAxDVetmhnTp5KmcSPK04PWAT6NGnN4+yh2\nX7xCUkYhmaVlDGvfuMDCvuGhuCqc2JeWyvfnEmjr7UOE55/CkKlFRazfsJGwDu2Zf1f96uaFhYWs\n2PkjzyXv5+uU45RZqolo05YZQ29h48KlhLrULThpL1q5mhP6eC6WlTLINwZPJ1eH+ldaDBQYYzFY\n1UQ71x3PUBuB6ih2528l35THSL/xf/y9wpzF0fwlHMx/nazqM1hsZoLUHRgS8CrdvB/BS90eFxcX\nh+aqjxqbAov5N4prDPhp7Y8/qg+dqhelFZ9hqrmAu4tjCt8WycSpwk/ZnfsiyRW7MUkVeChDGeCz\ngLHtlhA80Ibgcpkam4ogbe2G+lVDqA3RrmNo/S9DqMpSwJWKnVws24JCkFFWkIOrc2vkMg17dp3m\n5ee/xStyHzo3Ob7OTzMscKkDpz1/RcRg2o/Zko5LE09//kqMrjdnSn4hy5CCn1MoXqrgZh2/PgRB\nwFPVlbSKLeRVHSDKdbZdp9iiIMNdGc6V8t9IrTpGJ48ptbaLcfbjp+w4jhZdYEZY4w27s/pU5h1e\nxbfJ55CL8HqPW/io13T+0aoPLkobh/Mz2JV9kTsje6KUtVSMbuFPWoyfRvLxim1sWPU+hpIcTv22\nHj+PP3cvoigQ5uvOr7FJHLqYxsxBjhfrSs3XM/HNr8kuKadNoA9bnpmDKAr8fD4JP52Oga3DG732\ncd3asuF4PPEpeQR7u9HKgZica+kc6E+kmzs7Lifz46VE/DQ62vlfVeL+MTmNNP8YFj/yAO3961bn\n/j75HMNGjuCHT5ZhDnMnNCKCrwfcyfOjpjBywOBmcxsChGq82ZZ9hmxDNmMCHDulCNZEcbDwe4rN\nRfT3duxHThAEcg0JJFbpaecSQqU5nv25CzlVvIISczZOohNt3W5jRNCHRLtNRCO/ObtUN2UHUsuW\nY7YkEuRyf7P8b0VBQZHxAs62OAxCNBpl7VmH11JqTmdf7hIO5r9JvjEBm00iVNuXWwL/SSfPObg6\nXQ3ID9Z04Yz+OwqNl+jiPr3B9SpE9b8ModG0dp2AJFnYvH0HT0y4QmLqftoNzOarNwo4uqsCNz8X\nHpqwj3CXfk26fpWiO/rKZZhrEnDTzUUUG7fRqQ1BEIjUdeeU/hcuVRyno9sgVLKmaf85glruS4U5\nhRLzBUzWIvy19iUtuDsFk1kdT5n5MlabQJD2RtdWkMaLxPJUEkoLsNiM9PB0rOBrgaGUe46s4MOE\nY+iNZnr4eLOi/xx6e8cgCAJKUU43zwgE0cTBvGwE0UAfn+YpKtvCfwctAc+N4HxSDusPpdN68Fy2\nbvsJD5cbA0+HdYqmY6Q/WYVlfL2n4WJ117LzbBKT3vqGcqOJKb3as/7JO1Ap5X9kV1ms1iat/2hy\nOpUGMwJQaWqaeODYdq35dupk5ILIc7/t4uODVzWpYjNysAEjW994siRJEh/EHaTjt+/z5L4dCGFB\nKH3d8YpQY3HK4f7YD5l56CPWpx3DIjXtWq+lo3sUwRoNp4pzKTFXONTXSeaEVh6JRcqirMbxANcR\nPpM59OyvTBk3nF8zlqA3Z+OlDGaY/2JmRP1Gd+8nUTRBdsIeRFFEo5qARigntXx9s40b6L4Ei02k\ntPydetulVOzm+9TpfJ82i6zqUziJWrp43MnsqF0MD3wDnfL6WC4nmYZg3XC2vBvHsKldMNn5rFok\nE7HFqziu30mp4AwC6NS+dPB6jY/fXc/dz4UxcoYLktS0QGW4+j/1cnkaAYlc/ZNNHu+v+KhCGO57\nBzW2Gj69/BBZ1TcK8t5Muvu8hlxQk1qxmXKz/cUYxwa9ioCSs/pvqKopqrXNa53moJGLfJNymJzq\n+jX9rmVtyiGG/PIhpwqLaOXuwpbhc1kz8AFCa3FtzYseiijYOFaYZvf4Lfxv0JLq7iCl5dWMf3QZ\nJouVdx+fQN+udQegllYZGfb8FwjAntfuw0XTcJzF21v3s/pALKIgsGTaCMb3avfHZ7sTkpn/3Y/M\n7tGZ58Y1zm3x0c4jLNtxHEGAF6YOu6rF1QxcKihk8up1GCQLw0LD2fHdemTu3qQu/+iPNpU1Zpae\n/I3Nly9iqpGQiTAsLJxXeo3ET+tCvqGMlVf2s78ggTJLKYIA2ERCNQGMD+rB5JAeyEVZk9a5LfsQ\nS8/9zLig1rzQvn533F85WPgrhwo/IFo3iSkh8+zqU2rOYU/eRyTkn2ZJv9/ABhtO38eo1i+iVTpW\nc6k5MFlKOJvdDbMQzICQ+itpO8KVvHHorKfReP6Ms+rP+jEWyURs0TIulf+ISbqaHeihDKWb572E\nOjfs7rBYa1Bp1FjNVl7bOY8nhn6Ak6x2I7HCnMOJomXElZ3AIAloRBs93IfQRnUnHq4+f5wcFVQf\n4VDuQ6hlbowK2d0sGZlJWW2RbOWE+vyCugHZjMZwuHADv+VfLZbZx3Msw33n/W1FAHOr9nE073G0\ncn9Ghf5sd7+E0p84mPcWbk5RTA9fXmubXbmxLDy7kUhnd77rf2NNpb+yPvUIL8X+hlou8Fq3sYwJ\n6lpve0mSaL15KX39fFnV/z67197Cfz8tbi8HkCSJmc98TUm1gXtu68344Z3qba9SylHKZRy9mM7Z\ntFwm9G5XZ1uLReKuj9azM/4yWqWCtY/NoF+bsOvaXMjOZ9fFZHqFBdEnOtThtT+wfCtbjySgcpKz\n8uFpDG3XfIXEvLRaJrVvy+b4BM6cOUn2NyupTk7kxeeeI6eqjEcPbuPZA78SX1CIXCYwo0171o6e\nwZSoDuiUVwNFdQoV/X1aMSt8ALcG9KS6RiLPUE6+qYAT+kRWpexld+5FLJKN1i7+iHVksEiSREpF\nIWqZAsVfjKVoXRAbM/ZxuaKQO8MH1TlGbQSpw9hfsImSmgIGeE+ot21a5Ql+yFzMvsJvKTLn46LW\nMnB4D3pOsBHVahht3Oyrn9LcyEU1GVXHUUvxKJ2GoFH4Nc+48lZYDOsoMSfhoZtOuTnzX66tt8g3\nnsdmsxKi7c2owHfo7HkXbk72Pb+iKOOkVw5ePb2I6lXNkaJNpFTGk1+YzycffITgnk6yZSv78j9n\nb+Fmsow5aESRgV5jmRTyNqHO/dGodNe5zLSKYCrNlyg2JWKy5uCvHdr065eFUGn4kbKq7xAEN1SK\nTs3qsg3RtiNYHcPF8iOkVSdyovgHZIJAoKp1s85TG87KMIqMpyg1JwHgre5uVz8fVQxXKo5QarqM\nk8wTX/WNLtFIZ39O6xO5UFaIViHS0a1ud/6O7LMsOPkLapnA5mH30tM7usE1lJqr+SrpGG3dPRgT\n1HyZZS38/6fl5McBnnhjE4cvpNOndQjvL6w9kK82xixZTk5ROe/fO47BHW7M/sotKWfGu2vRVxkI\n93ZnzeMz0KluzBzZeDyel7bv5rHBfblvmP0xK+XVRqa9v4acwnJ8PHRsfPwO3HU3x8VisUos2bmD\n919cRIduHfC5bRBn8/Ox2QTc1Eru6dCdB9v3cWjXau+JUEJpFt+mHOJwcQJWzNhs0N29Le93u/O6\n+T6+tIlvUk/zUMwA5kQ4lrr+cdJCymvOcnfEMvzU16fMS5LEaf0GTug3U26pBGz4KH0Z6DOXaJdB\nSJLEV5dHASLzon/5t5XvLzLGk5Z/KzXyfvQJdFz3rS7iL/dh+ZeXCBnSHdeoq0K2KlFHG9fxdPK8\nG7kdNWZqY+rhRfTyaEsvT4FjxVswWSvY/fEl9n2RTOfbApnyemdUgkSIJojO7qpfpsoAACAASURB\nVBOJdB7V4P9Wkiz8kj4Yk1TF4IAVeKibLqKpr1xLQckzCEggaHDRzMTXbSFiM2TW/Y5FMvNj9nuc\nKzuKhA2V6ER/78n09Zx6U58ns1TJz2lDsdmsjA7dgcrOuLQqi55vr0wBZNwVuQUn+Y0xS+XmKsbs\new3JZuOnwc/hXkv219qUQ7x8Zg9yEdYOnkNHd/uM56SyHG7dtZypETG82vV2u/q08L9By8mPnazY\ndJQtB8/h7+bMilfvcGi31ad1KOsPxXHwQipzhnS/rj7PoQupzP5wPVWmGkZ3iWHZg1NwUtSelXA6\nNYtDV9IZHBNut0zFhax8Jr3zLSXlBnq2DmHDYzPRON28uheiKHDvgqcxqMA8tBd5VVUEuuh4ue8w\nPhw0jp6+IQ7vVO05Efo6ZT/bco6SbshDREYX1xiqLTWkGbJIryxjqN+fp24d3SJZk7aPK5U53BHm\nmPvQSdSRXLmfIpOJTu69ATBaKtiT9yHbst/kctVZaiQzkdq2TA5eQn+fuXg6hQFXA1jzjDkYLAkI\ngi/+mn9PAKZG7suV8h+QSwl46WYjb2KQrslSxYH8T3h3+VFWvpFJZmoxY6Z2p7/vUwz0f4EAbXdE\noXHuyhrJwlcpP9PXqx3jgsbT3nUgecYrWF3KsBTLeOiBWdzZ/TmG+T9MO7fb8FRF2/V8CYKIp6oz\n6RXbyK3a/a9spqYZD2plB1y1szFbczHXJGKuOUVxxcdUmU7gpGiPQubVpPHhajZVG9f+9PK4lYqa\nPHKN6VypOsfx4q14KQNvWkaYTFDiJHqQW72PQsNxwl2n2tVPKaqxIZJrOEm24SJt3G6sGeQkU+Km\nVLE//zJnSpKYENz7j89SKvK569BXfJ+aiFousHbwXXSw0/ABiC9NZ3tGIgP9Q+nj3RLw3MKftBg/\ndnD8bCqvrtyFSi5n3Vt3oVE7Vs/DXachR1/O+bQ8ckrLGdrxqrvpsx1HWbLxN2w2GwsmDuaJcQPr\nfXEfS87kWFomI1pH0S6o7gyq39l6KoH5y37AUmNl7ojuvD7jlpt2RC5JEqvizjB33Vouvv8Rpstp\ndJ00ms/HTGVRrxG08Wh4vfZQlyEE0NE1igejx7K4w1TGBnVhemhfvkk5QK6hhNkRf2aqyEU5lytS\nOF+qJ8bZgzCd/UUT/dRB7M3fQpkll3Yu3dietZQdeZ+RY0xDLoh0dhvC7SFv0MnjNjS16FAFqtsR\nX7KefFMqXepIA/47sNhU1Jh3UmiuwF/nmGbZ7xQak/k5eyk78j4l05iCm48GU7GZEdMDeXDINjxV\nDbslGpyjqoT33noDF1sxOS572ZO/kkqLnsntHmXRPR/RtdUw1PLGlULQKPwx1GRQbLpAtSWFAF3T\nXZEyUYOLZiwezg8jYcNck4jFconSyq8pqdqAIKhwkndo8vdQLipp49qfPp7jKLfkkVZ8meWfribK\ntz3BfmFNvo7acFe1JadqN2Xmy6jl3rg52acTF6jpxMWyXZSaknBTRuPhdKO8ThvXEA4WxnGhrIAA\njY5QrS/Pxa5nUewuCg0mevh4s2bgPwh3duw98nPWGY4XZDMtvCOtXR0rbtrCfzctbq8GyCsqZ8qT\nK7BIEl8+P42OrYMaNY4kSQxc8BmVBjPrn53Je9sPc+RSOiqFnGUPTKZTeMMnOb+7ve7p050nxgyo\nt+3SLXvYcCAOUSbw1uwxjOx4c3Y9ZouFt44d4ruz8RhMFhCgLO4kYa4unPrwi5sypyPMOPgh6YZs\ndg99BfU15fPzDEWM3/9PopxdWNPvOYfGvO+rSaxb+DMjHomm+6RgXOXO9PKcQhf3yXa5HtalPkS1\nOY5bgpcRqGnj8DU1B5IkcSSzEwJW+gSfd8hlklD6M0eL1lJovpqh4yZ3pofHBLp6TKfIdI5fs+6j\nm9ejtHWf0ej1VdYY2Ftwhs83ruTnx7/CPUjDP/ffRyvnPrRx7Y+70rEq33UhSRI7MoZitJbR3/8z\nfDS9G+7kIBWGvRSWvY65JgEBG6BCp7kVH9eXUMgbfxpUUH2WpLJfSKs+z8ZVcWxbmkDrfq24eCix\n+Rb/F4yWQn5JvwVRkDE2bB9yOzMU9aZMNqTdiUxQMzfqh1pdoIXGUsb8uIScTSfQ9eyALSQQX42c\n17pPYIBP474n43a/R1JpBUdvfRJ3p6YVCm3hvwvHywT/D2GusTD3hTXUSFaenDGk0YYPXE2JfX3O\nGGY/9zp9RozFd8BYQoKC+e6JmXbH34zu1IqXftzNkSsZPFFHG4tFYs5n6zn3LxX3NY9OJ8LHs9Hr\nrgu9oZpF+/bwa9JlrBYbMrnA2LYxlGJgv6uFD0f/Z/jXu3lEkpGTze7cBG4N/jOuw0/tRXs3T86X\nFpNSkU2Ec/27QqtkYXfBZk7rt5Fx8QIVhSayEyp49eGnaO3qWMBsf58H2Jl1PwfzP2N6+IeNuq6m\nIooiOvUkJONKrpStJtp9Tr3tTZYqDhd+SXzZPgxSDQI2QtRhDPK5hyDtnxk3PupO+Kq7cqFkDa1c\nJyFzUKH8SmUO69J3c6joHDWShcAuoYycPZ7bR4xmbkTzZ+uIokhfv0/Ykz2LE/lPMiZ0r136V47g\nrB6Cs3oINZYiCsqWUFn9E5XV31NRvQmFvA1erk/iqmk49qzQcI6ksp9JqzpHjqmUKumqK1EpWBkw\nPIzkYyU4DWpFtcWARt589YauRSX3prXbP7hYuoxjeY/RP+BLu/p5OAXTxnUiiWWb2ZmzlDFBL9/Q\nxlvlRlScgVPrj+CXksHCr5fxUKuRjY5lii1O5VJJBW09XFsMnxZuoOXkpx7uXbSOuNRcRnSNZulj\nzVO9Nah1R7IvnaPtrTM598Nqh7/YvV/+lGpLDbEvPYxcfn3f/NIKbn9/LfqyasL8PVj3yAy0quZ9\nkaeUFPP8nt84mZ6NzQZqJznTO3fkmd79UcrltPvmfWokiaS76jLP/l4SSrO49+RH9HLvwLvdZ133\nWaw+kftPfE1f70De7za/1v6VNeX8mLOSK5X7kAkmrDY53vIufL/rOAP7yujsO4TbAu2X7/idFZen\nYpUKuCNyOxp508v7NwaztYLYrM7UCP4MCDlUa5tCYzL78j8ltSoRK6AURNq49GKQ74No5bUb1bnV\np/gtez49vZ+ilZt9rr18YwlfXdnO3oKzaOROjPDtzij/HkTrgm56NhNAXOErXCnfTICmH739P76p\nc0mSREnVN+grPsdqybgawC/ocFZPwMftOeSyq+7SIsMFLpf/RGplPNmmkuuMHX+lhhBNDNEuwwnS\n9kcU5WzL/oV3E3czJbgN82P+cVOvYUf6LVRb8unj9wH+WvsqNF9Vfp+M2arntuCPCKylandOTg4T\nHhlKl7HOPDD+Izp7NO4kLrtKz4Q9n1Juktg6/G7auDV+49rCfyctxk8dvP/1XtbtPkOYtxvr3r6r\n2TIpjh4/yeSnFuHRaQCbX5hHjJ9j1Xxf2bKbtbHx3Nu3O4+P/tP1dTw5gwe/3Iq5xsrIbtG8PXNM\ns2Z/HMvKYPG+vVzOv+rq8HBW81CvXszu0PmPebYc3Mfcd99g+KzpfD/5rmabu6n027kQV7kzPw+9\n0b018cDL5BsM/Dr0BZwVf+4Os6tT+TFnGUWmeGSChGTT0tZ1FGP8Z6EUnZh3cjGRukKUQi4Px6yp\ns/ZMXZwq2ky8/l0CtBO4pRHGU3NxNHs2Sss+gr0346P5M4W5NtdWd48JdPOYhijWf2Bss9n4Nete\nqi2FjA/biEyoW1bAZrOxPecoX1z5EckmMSloILeHDMFZcXMLPv4VSZLYmTGSamsRvXzeJNB51N8y\nr6kmlfzSJVQb9yJQw5pvqvnyCzOTX+uEd+erEjYKwYqfUk2oOoZo12EEawfWeg8kSWLq4WcwWCW2\nDngD5U0U9Cw3J/Nb5lQUgoaxYfsbfCZ+J9eQwA8ZD6IU3bgrckut76gCYx7Lr/wDG2qebrMWhYMn\ncWkV+Uzeu4xKs8SjHXrzYKt/T1mJFv6zaQl4roXfDl/kgw0H0SoVfPf2XTg5oEjeEMFBgXTt3Ztf\n466w69xl5gzs6tDOtldkCMsPnOJ8Tj5z+3dDJoqs2n+Khd/uQJJsPD5+AAvGNZ8sRLK+mAnr1rDy\n1Bn0VdWEeLnx5qhRvDF8FJ39/K+b59apk8nbc4gCBZzWWtAqFES6eP4tO/f6+DEzluKaUuaGD71h\nLUrRxoGCFCqtevp7d+RsyWHWpL/Gaf06DNY85KIPA7zvZkbIAtq6dkMuyhEEgczqfM6XZeCuLMLD\nKQBflWMCtn6qVpwqXk+ZOYUuHo5lDzYnOmVr9FXfUmhKw0czlgP5n7At+w3Olx/DYK0mWB3KbQFP\nM9z/UQI19qmDC4KAWu5FUtkmtHJ/PFW1S14YrWYWnV/Fpqz9dHSL4M1O9zHQpxNO/wYNJkEQ8NMM\nJKV8A7nV+whznthkMVl7kMvccdVOwMP5UUTRhc8/3cOZExVEtlcxulckI/zmMTbwWbp5zSDCZRhu\nTuF13gNBELBRydHiLJwVJtq5tr5p63aSeWC05FNsOkeVJZ1A3Qi7+l2r/F5pLSW8FuV3rVxHsdlC\nWc1pkiqy6ephv/ZXUlkOU/Z+RVWNjWc7DeDemMYF89tLtdHMi1/9wuJVv7Jqx0kupRcwtGvUv/2d\n10LDtBg/fyEtq5j5b21CFGD5kpn4ezsmgGkP4d4eXMor5FxiCnv37WXK8IF2n9LIZSKFpVWczcnl\nQkYB+xNSWL33DAqFyBf3T+K2rs0XQLvl0gXmfL+JcoOZjoG+fDl+As/0G0ike+3ZNV9cjqe6xojf\n6EGkGY38lJLEp+eOsjcnGbko0trN+9/yUjhfmkNadTbtXcIJ0l7vqmnlHMKKI5vYvWwLue7byRb2\nUSOV46xoxfigpxkXeB8h2hvTp2tsFnbmn6G1s5wKSyEd3ex7+f+OIAgUmPKorjmHTfAiQHPzfqjq\nQy335oe9a1n4wBHihe0YfPMRgPauvZkW8hrdPafh2ohK1M6KILKqDpNrOEEr18k3/GAbrCZejF/B\n6ZIk5kdP5OHoSX/7ac9fUcrckAky8g3HSCvfiLe6O2p58xSBbAhBENA4dWPs6Pvp3seJW2+JxVVM\nJdjlLpwUN2ZH1YVa7sS27JMEa9X08ux2E1cMvuqBpJR/R4npAr6afqjl9mViRer6c7ZkE4XG84Tr\nBqOpRfk9RteRQ0X7qLYm4KnsgLeq4fsgSRJjf/uIyhqJl7oOYU6UfVpkjeVKdjFTF3/NxZxC5KKI\nZLNxKbeIiyn5jO7970lkaMF+WrS9rsFoNHPPknVYbTae/8dIokMbp5xuD6/dfgs5uzey+d2XWfD6\nmw71fXHCEFwMVax88h5WvfcOHi5qfll4N72i7H9JNsSxrAye+mkHgiDw0bixbJ0+iw6+9b+ATDHh\ndHjsAS498jI7J81lYkwrdE5KYnPzeHLfDqK+eYdbf1zJ6kunsUhSs621IUb6X5Ub2JEbf93fS0xl\nvHbha9LXHyFr40kOfXMeP1U/7o9awSMx7xHtXLf0RyfXGERE5GIEGdXnKDXnObyuQb73YrWJxOnX\nONy3Obl4LJpLZ6pJ2FPIcJ9ZPN5qK2MCX0SraHwmkiAIdPCYS2VNNmkVu677zGAx8Xz8V8SVJvNs\nmxlMDBrwH7NTjnGfR1v3e6ixmdiXPZfY/IVIkuVvm9/V1ZXJ4xYT47cRgNSieUgOfFcSyy4DEKJu\nnmy4+hBFkV5+/wTgWN5jdq9TFOUM838BbDZ+yl5Q59h3hL6EzSayJet1rHbcg88v/0aJSWJ6ZFvu\niKg/G7ap7Dh+kelLV1NpruH2fh05+OFDHHh/Pu4qFYcvpmG2/H3PTAuNo8X4uYZ5i9ZRbjIzaWAH\nxg5qf9PmydaXMWbpCnRBrXDy8mNLYSmVJrPd/UVR5OFebakpLaaqIJU27f3wdmm+I/oyo5G7t2wB\nYPWUyYyJblipO64wF6vNRifvqwZSK3cf3h8wnrMzHuXAtHuZ0aY9HioV5woKeOHQbqK/fodRP3zF\nlwnHMN7kF0V/7xhsNoH40jQALpSl8NCpdxl38FV+yU0kcnIP+k0ZwpcLtjA34nk8nRreweoUGmKc\nw0itkgEC58v2OLwujdwVnbITNimH7L9ZsPJaXnvxS6Yt7cbIR1vRxWOK3fEbDRGsHYCbMpJz+lVI\ntqsCtdUWIwviv+RcaQrPtb2DEX72SSX8nbT2eIiB/p+jFLWkVf7C9rS+pJdv/lvXoFX1wEU9Gquk\nJ6f0Jbv7fbnhGwwZRQzyaZpavb14q7vjrxmA0aonQf+u3f0inPvio+6CwZLLmeINtbYJ0oYRrhuH\nUixhTfoHDY659spplDIbz3UYb/c6GsM7a/eycOUOAF6bcwvPzhqGKIrI5SKy3HhSdq/m9Hn7RWBb\n+PfQEvD8L1759Be2H7tImyBvVr12502bZ/e5yzyz4mcsFolberXC01fLl7Gx9A4I4ts77aua+jtb\ndvzKq2fiKVHK8FPp2DhrOgGuLk1e48jVK7lSUMKDfXvyZJ/+dvVZfHQ3q87H8t7gMUyMrlvDLK+q\nnM8TjrEjPYnc8ipAQBAgwt2V8RFtubtND5yVzScH8DvDv3+exC176Xhbd8o8r44fotExJ3w4twT0\nbdSYa9N/5ruMHUwLUlNak8sD0cvrDe6tjezqi+zIvAeNsgszwj9quMNNYk/+Co4Xf08vz8kM9W2+\nTKG0it84mPcCA/1exUvdjwVxX5JYkcHzbWcx2Oc/W2tJkiTOF79OSvlmJCQ6ej5OlNvsPz4rMp4k\ns/JXioxnMFhykWxmwAaIqOV+hOjG0sb9/kYnHkiSmfPZ7bHZTLQLOIO8gWKO+/fvZ/DgwbgGe1Ca\nYb9KelOxSEZ+ThuM1WZmdOguVHVkAf4Vg6Wcb65MBETmRv+AspaaQZIk8ebFOYhCEbeHfEiYrvbC\nmZfLcxm78ysGBwTwZd+bk+lmsUjc+9Z6zmbmoVMoWPnM7UQG/ZmwIkkSWk9fjKVFfLdhM7dPnXhT\n1tFC89AS8wNs2RXHip+O46ZWseatu5DLbs6B2Nvb9vPOxv2AjeemD+WxsQPoFxnG92fOc7G4iKER\nEfg436h9UxdtoqKY168PF3MKiC/K59szZwl1caOVT+PdFc/v2cXBK+l0CvLlg1tutbvf6yf2U2yo\n5oMhtyKv52WvUzoxODCSee16MqdtVwSZRL6hgrSyMo7mZPHZueNsSomnwFhJW3df1PKmBb8mlGbx\nyrnNHFr+PdnfHaO8vIoRtw1kaYfZ3B89nijnxssBKEQ5O/OP0sezJ9mGo6hlrgQ6GLvjovDmbMkO\nTJYrtHadgFJ2c+qzNESwph1HizZQaEqjj9e0ZhvXRRlKesVuLmSdYmV2Lpersnix7Z0M+g83fOCq\n685XOxB/7WAyKraya/evWJVZXKx8hwT9+2RUbqfMnEiNVIpcdEYjD0At90cUZBgseRQbT3O57FtU\nMl/cnBwvMioIMkRBR4VxDwbzBTx0k+ttr9VqOX78OFPHTWbo0KaLtdqLKMhRiq7kVh+koiaZYOex\ndvVTiE5YbBL5htMUGlOJcb0xOFkQBFwVIaRW7aasRkYHt561jvXOnnXEnrvAEwNvo5Wr4zFqDZFX\nXM7kRatILS4lwtOdTa/cha/H9RvNf/5wgOQyGV26deWN55/4j3HltlA7//MnP7v2nWDh57+hdFKz\n/s27CPK7UZKgqZgtFu7+ZCPnk/NQqxWseGQqba6RpziZlsWM7zYS4+7Jz/fNbtQc62LjWbRnN1Zs\njI9uzTu33eLwjnNXSjL3b9mGTq3k+L33o5Lb7/5ovfI9nGQy4mY/4ujSAagwG/nq4km2XblAamkZ\nV59KGwEuOkaFRnN/u974ae071aowG/j88m525Z2l0lqBIEBNZhXJ3+wlaEIf4h/7qlFr/CtWm5XZ\nx16gvWskEdp0MqvPMzv8XXxUdStT18ZZ/U/EFr2Oj/oWbg1+oVnW1hi+z1jC5crj3BbwNO3dHNM8\nq4/VPy1lzviX8BkYw6b139PP++a5lG8WH3z1OI/d8z7dhriwcFkUGnkAHk4dCNQNx19zY+q5RTJy\ntvB1Miq3Y0Mixm0uHTwb991IyOqKRSqkld8eVMqmS4bcLLal9AVsjIs46lC/lZcnYpL0TAz58jrl\nd0mSSCiP5VDhVqosJ/FRjWR2+JO1juHi7UFFUQknYk/To0vXWts0luPn03n0062YJYlRnWN49d7R\nN7xbiyuqGfncl8jlIvvfehCVsqV+8H86/9N3KC4ujlFD+6B2D+CXHXtviuGTUVTKne+to6zcSGhA\n7YrtPcKCCHd1I6mkmHR9KaEeN2Y/NMSMrh3pHRLE7WvX80NyIuatVj6eZH9hxsKqSub/uB1BhLVT\npzlk+ORWlmO0WP6I92kMzkoVj3cawOOdBlBtMfNN4mk2X0ngsl7PynNnWXnuDN5aNcNDonigQx9C\nna+/V5IksS37DGtTD5JlzEMQbGCT0dY5knuihtFrRCR3t/6So0kJlJmrcVU2PbNIJsgY6tuDbdn7\nGR94DwWmNL5Lf4GpIYvxV9v/I9XRbTQnij4mp3ovVmkBsmaKuXGUEX4PcDn5OAcLVzeb8VNtKedc\neRyCAJ09Iv9fGj4AbdsNxCf4U9p2D2dC+NEGq0DLRRXdfZfQyn0ue7LuIKl0JTLBibYejlepDvJ4\ni7SiuaQXz6eV/6+NvYSbjqtTNMXGeCySwSGx3BEBi9h65WG2X3qVQRHPcVK/hyxDHBYpG4V4NR5Q\nsgVxa0DdVcinjptIQkICUWGObTwaYsX243yy/QgAT00ayMyRtWfQPbNyOzarjSemD2oxfP6f8D99\nl5ydnXF1daFdu5j/Y++8w6Ooujj87mzJpvdGegglhJLQO0gvCtJ7EwQVEUVBUMGC2EFEQASk994h\n9BJ6DySBFNJ778lmd/b7I6IiCdlNgqAf7/P4+JDM3HtmszNz7rnn/A7tm3lV+/jHA8P4cHVpfk+v\nlnX5ekT5EvbvtW/DOwcOMv9UAIsG6L7d9Fc8bKy4MHkSLZcs48iDMOKzsnGyqLhUXxRF+m3dTIla\nw8edOuBjq1+V266wIABecvGslN1/x0im4I36rXijfitUGjWbw2+xLTSQkLQ0NofcZXPIHSyNlLSy\ntMP4XiS5vpYEq+IQJWq0WrBV2NDfpSUjPFojE/7sJp516Bb35v1CvyupnFy6qVpsHejSjdMp11gU\ntpsP637MocSvWR/1Ph3sxtLMqq9O3cwFQcDL9BWicjdxIXUD7ezHVott+mKusMPWwJ3U4iiSix5g\nr6za3zO3JIMNUbOxaKjlyoOT+NZ4uhU4T5Na9Z355VQ9rIzG6NX+wlThTjfXPfjHvMK9zBXUthit\nl2MAYG7UBQOZF0UlweQWnsPU8Pn8HEVtMQAC+rU0cTb25ecJwTy4fYGp2yNwqGWCWpQjE5xwNmpE\nM6vO1DR98nbyb7/9Vmm7y0IURd5fvI8zwQ8wkMr4ZWp/fGuXrRKdV1jMzfvxWFoaMaRto2q14wVP\nj//rai9PT0/S09MJOHui2sf+ds9pPlhxAFHUMnt45yc6PgC9fGpjIJESEBVdpXllUoHPu3RCC7y7\n71CFx4uiyLj9u0nMzKVtTTfG++lffXM6LhKAfl66dXnWB4VUxpg6TTn4ymuEj/mAHzp0p6G9Hbnq\nAjYvWsz89z7mwJLVyAU5XeyasbvdR+ztOJ0xNds94vgAWBiVOoLBBSl8fafiz0YXzOUmzPQeT2px\nJl+FbKWT/SxqmjTjZPJKNkZ9SJYqWadx2tmPR62Vcy97Z7XYVVk6200A4FjisiqNk6VKYU3kTLJK\nUhnmNocmLh2RSit2BJ9XisRcAORS/asqDWW2eJoNATRE5eyp1PyuNqUtN44GjCYy+uk1Lq0sRep0\nsopDMRDMK5XgLZEqQJBgY9iG7o7z+NB7H9O9lzPUbXKFjk91I4oig+as5UxIJPamJuz/6rVyHR+A\nnw+eBxGGdnz+89he8Cf/184PUK0tIKA0v2fEws1sPnETQyM5m2cMZ2Cr8vVi/oqvgwM5JSruJadW\nyYbe9eriamLGjZREbsUnlnlMYl4uH57wx/eXJQSER2NnbsyqPv0rNd/9jDSUMhn2xk+vR5Uoimx6\ncJ0V4ee5VxiPoNRg3KQ2FrXdmDlkMie7fMbnjQZib1h+pKtpvx7U2zID54Ed2Bx5iR3R16vFNh/z\nmsxrMIUiTTEfBf6KRtuc3jWmkVoczYao6WSXpFQ4hlwwwM6wLVIyCco6VS12VQYP08YYSS2ILQyi\nSJ1XqTHSixNYEzmTAnUuI92/wMNEt+//84xKU/pZKCqp+mymKFUAL9ZkVup8I4UPean9GNIzisZN\n6nMmtif30uZSpE6o1HjVSWZREMdj+6NFS13LyjWffeu3Dnx2rhszu35HA4um1f5c1od5a48TmZ5F\nQxd79n89ARvzJxehHLseiiCVMK5Ts3/IwhdUB//3zk91EpeeTZc5KwiKSMLD2Yrjn0+krpPuW0jj\nmjcGCSw7f6XKtnzfqwcAHxw4/MfP8lQq5l8MoM1vy2n76wp23AqiSK3mZZ86nHvt9SdWaZVHgVpF\njqoYT/Pqz5cCuJYaw+gz6/HZ9Q2f3/QnMjcDF2MLZjbsTPTnv5F5P4opA5/ckfyvtkqNDHi3Tlek\nEoG5gfsIzirbOfwrJSUlFBYWPvGYumYeLG48i+ZW9VkffYB1Ubfp6fgRKrGQHTGfoxZLKpznJYd3\nELUSrqbpl5AdmnuXZeHf8PGdibx/axSzAsezJOxLMlVpeo3zkObW/QAtJ1L0TwxPKYphbeQs1KKK\n0R5f4vKMlKurm2Kx1PkxEHSvxvwrgqQ0w0BL5etL6nl+jltNA2r7GpGlSiQ4exeHontxLKodd1M+\nJE8VVumxK4MoqrmaPItT8SNRiTm4mvSipsWwyo0lqDAxebYK3wCxKZnsUyXDTwAAIABJREFUuRyE\nqULByhlDH2se/XdyCorIyirE09m6wmNf8Hzxf53zU50s37iVn/xvIDN34OVW3swb3kPvMTrV9sRA\nIuVExANEUazS6qepqzMNbey5cvMq45YsJsHchPCUDNCCRAK1HawZ4+vHYO/6VZrnwIP7ALSq4Vbp\nMf5OalEeP949jX9cCDklpXkEZnIDejh78279jtgqK/cCeoitkRnfNx3CtKubGXv+N453/QCzJ2gL\nNW7cmPj4eEJCQrC3L18A0Vxhyqx64zmXep3FYVv49cEx3qw5jV1xc7mSvovWtkOeaJe5whZDuQ9F\nJXc5l7ymwtyf48n7OJq0m2KxCACZRI6R1JhCTSGheUF8ETSVYa4TaW6tn8x/C6sBnEvdwI34Y/Sw\nn6LzdlViYQQbouYglcgZ4/EVtsrqUxz/JxFFkaySLFKKk0krTidDlcHe3Vu5uD6Cbxak0LAS+oES\nSj9DLZpK22VpacnBs4PJKArA13E3GUWXic3dRVpxOKG5RwnN9cdEaoKTcQdqWryJUv70Opkn5J3g\nesocSrQFKARTmtl/i73R4326dEElFlAiFmEm16/J89Ng/ubTaCXwyYguOjkzWwNuA9DZ9/mtwntB\n2bxwfqqBo6cDmDRyKFKlMYt2H+WtHpUTzRMEgR61vNgbep/1V28zpoVflez6rF1rfMePZo1Egvtn\nn+NUw44BPvWY1LgZRvLq6fh8NKp0tdm/VtXyfdSiyNqwy2yMuE5sfhYAMolAazt33vFpTxObqr9I\nVWLpi0cCdHH0ZmzNtqx5EMDQs79yoNOUcp3AiLQUigsL0Gh0e3G1s22CgMA391YRmS/iadyE65kH\naGkzsMIE6L4uX7Mpcjj3s37DUGpGU5uytyJ/vD+bqIJwBAR8zPzoYt8XT5M/y4QvpZ1ka+xvbIxZ\nxoP8+wx1naiT7QCpqjjCz6tZOekQN8f0ZteqIxWeE1sQwqaoL1BKjRjlPhcrg+rXWqlORFFkc+wm\n7uUGUagpQCUWo9GqELVqQOTvEi1n9t0i4kouZ47foU9VnB9t1dq6yISH27pFOJsNxdlsKKIoklrg\nT1TORlKKgrmfc4j7OQcxl1ngZNwFD4s3MdBRePBJaMQiUgr8Oei/g/1H/Bn8tgP1HAfSyOaTKi2g\nTiWtQIuWhhbdq2xjVVCrRS7ci8ZUoaBr84pV7QFO3g5HCwxt9yLf59/GC+enihSoVMzZfxljl1oo\nLexZc/wa7ep50sC1cmXfH3fryP7QUL49c45+Db0xM6y82rGPmwsOjRqRo1KxfMggutbSX2itIgJT\nk5AKAj7WujU1/DsBSREsDjnHrfR4NL9LTnmaWjPGqzlDPf2qde//TlYcWi00t3EHYJpPNwIz47iR\nGcW0a9tY2Hxomee99NNcAmMSSZODrq/01ja+eJm4cijxHK97duRBwnxSi6MrrKAykVsy0G0lO6LG\ncDt9Abcy1uJq0onWtqMxkZduLV5JP0NUQTi2Bg5Mqz0XI9njkbCWNp2oaeLNgtBPuJh+ipiCB7xb\n+zMUwqPfp3x1NoFZFwjNvU1cYRQZqmzUWkjKz0WrhZjs+4TnXsfLtPwmmTczjnIocRnmcltGuX+J\nueLZr+Arwj/Zn/PpR9BqQZAokEoUGEpNUQpGGEqNMJGZYCY3x1xugaXcEpuPPTjT1J8RE8dWar6H\njV2rEvkpHeehE/VnSxhBELA36Ym9SU9EUSQ5fw+R2ZtIK44gOHsnIdk7sJDb4mLSGzeL8cgF/XLz\n8lRh3E2dQ1JRCCISvvn8PhF3Cjm3LwvPeus4fHgEtsqalbqe5MJwArP8kUnktLAu+/77pzh0MRg1\nWro11v05GZWQgYmJAnPj6lelf8HT5YXzUwVEUWT4/M0UaAU++G4xNazNWbr/AqPnb+HL0d3p3UT/\nzr5Wxka827oVCy5cYOTG7eybUPlWGzKZjN179jBi+w7W3rxd7c5P6YozHxezisvpH5Kbm0tqSQFL\nwi5wIiGMfHVpTzNLA0NedvHhHZ/2WFSDBk9ZtobmJGIoGGBh8Of4K1uPoeuxBZxICmZN+HnGej2+\nrJ/eujNj03cxcM9mDg8eS03zilfREomE9raNWRW5BwOhVPFWF+cHwMrAif7uqzmZ+CNZxbeIy9vO\n1tztaFCAaMbP799EpYULu66W6fg8xFbpyOf1f+Gn0DnEFUYz/cJrXJ0findzD+r2cyK9OI1CUU1p\nLAxkErBSmONk6MbIkeNZ2N+VowWL2RIzj4Eu06lr9ui2hloswT9xBdczj+Bh3IgBLtMxklW9vco/\nQUReae+lT+p9h5NhxS5tRH44PgNrY25auesTfm97oqVqkZ8/IkeSshcFgiDgaNofR9P+iKKKuNzN\nROfsJF0VQ2bmWu5mrsFK4YSraT9czUY/sWxfIxZyJ+VDovLPIgIWMitqGHfi888L2LhlP/47gwi9\nE8O2qAkopOa4m7SlpfVoTBW65TkWqfPZHD0dLVpecZ75TJOcAXadCwQtjO1Ztor030nOyqWoSI1v\nXaenbNkLngYvnJ8qMH3tQR6kZtDQxYHPhnYDoKa9FdNXHeTjNUd4kJLBlJ76x8jfatcC//thBKWn\nMufQCb7o1bnSNrZ0dcXCQMmVuDhUajUKPcQLK+J8QjRaoIldxTd/kVrNdycPMLffMARrM1znT0Eh\nSOnoUJN363fEx/LpdqFeev8sJZTQw/HRCIZMkLK1/SR6nljIghB/6ls40fT3yNBDOjrXZE77Dnxx\n5gyv7FzH6WGvY2dYcd5RY0tvVkXu4UFeaZ+lArXulT42Bi4Mdl+AKIrczNxPRO5Z8tUJZGUnE3Q0\nFgBJoQwqkIxRCAqm1/2GbTEr2RiwgYt7rhN87T7jevTGRGaIq5EbXqY+NDBvhaPh3wTirGGs5is2\nRX3B9phv6eM0hUaWpd/FnJJ0tsd8Q3zhfVrbDKCT/UidNI2eH0qjjHKJbveD+vdIi1zP3m0Pkfxe\nW6LVVi3yo9H+XnIvVOxgCIICV/MxuJqPQSMWEp29mtjcfaSr4klLX8Lt9CVYG7jjbjaYGiYD/nCE\nRFEkJmcVQRnLKNZqMJWa4mf3JTZGpbljdftBq66DmDdsJr52tbA3lJFSFERYzkFCsw9iKLOltmkX\nmloPR1mOcx6ac54D8d+h1qpoavUqtUwrlypQnUQkZmBqoMDJTrfF3N7LwUiA9vWrV1jxBf8ML5yf\nSrLc/zLHAsOxMTVizZQ/E1k7N6zF5hkjGPPjFlYeukJEYjoLxr6s96pm8+jBtP15BZsCA6lrZ8Pw\nppUXz+rvU49VN27w65WrTGlduaTEstj3oFRvpHfN8it6DkaF8Oudy9xNSUWVkY1Gq0XQajGVGXC4\n2yTsdWxZURUK1CrWRAQgIPBxo8f1luwMzVjaYiSvX1rDG5fWcbjLtMeSql/zaU5mUSE/X75Czx2r\nOTd0UoV5U65GjpjLTbibE41cYqBTyfvfEQSBJtZ9aWJd2qk6rySPXT+NxMvEHiurJze6/Ct9nUZz\nqsV52ryfy+udJzOq0VidvpNKqQkj3T9nS8yX7I3/iUvp+zCWWRBXcA8tWga6zKCeuW7Nb58nHtZc\nCeVEUP6O5nfnR6GHwOGjPEx4rlrkRyPmA1pkgn45PFLBEE/Lt/C0fIsSdRaR2cuJyztCanEkKanf\nIU39FnO5HTLBkGxVPMVaDXKJhIaWY/C0eOex78q9nEtYe5jR2W0I9SzaIYoiITlHCczcRYYqgsCs\nzWz3/4XNn4QzYEp7eg9rj7HMikIxl4jcSxRqcgEJLawH0cH+tSp9JtVFsVqNfQVl7X8lIDgSLdCn\nefmNnF/w/PKiNq8SnL4TwWL/CxjIpWz7YMRjVQF1athy+NPx2FoZc/pmBAO/30CRSl3OaGVjpFCw\nc8ww5BKBT4+f5HRYZKXtndqmNRJgU2Bgpccoi6tJcUiATs6PrnxCMlN4/cQO6q6Zz+RjBwhMSsHW\n2IipnbsQGxPLyDULyC0pptORpZxPqvx16YIoiow5twaVVsUw91YYycp+ebWw9eSdul0pFtUMPbsM\nUXz8JfV+kw7086lLem4xL+9ZW+Yxf0UikdDIojaBWaHYK72IzLuJWMWV/7XMG9j4OtH9pcebQD6J\nhaFLUciLeGPyLMb0fE0vZ1whNWSE22f0dJyEUmpCkSYXH/O2TPCc/690fOCvJee6NZ/U/J4sX1nn\n56GTVfXITz4gqdIWkVxmQW3rGXRyO0lPtyPUM++PmdyWnJJU0oqjkUnkeJl2pofbKbys3i1zrtiC\nUCRoqWXWAih10n0sejDMYzmTah2mmfVEkkMkpMbmce3SbYJzTnM1Yxd3s45RrMnHybAu4z2XPTXH\nJy8vj7GvTWLw2NlM+2w7a7ZdJDEl+4nnaLRaTA11V6eOSsxAqZRhbfrsS/T/TlxcHImJFct4/D/z\nIvKjJ5HJGUxbewAJsPLNgdiYlb1SsDQx4sjsCYz+eQvBD5Lp/vlKtk0fgb2F7smGHjZWrBnSn1Fb\ndvDGrr3sGTuCuvb6J5OaKBQ0sLcnMCmZ4OQU6tnr176iPOJzc7A2NEIQBLKKC1l0O4C94fdIzy8E\nJBgZyHilthfTfNvhYf5nlOK3ziP5NeQC8++eZNy5jaxtP5JW9u7VYtNfuZEWy/Tr20lVZeFmaM+H\nDZ5cTTKhVjtuZ8RyJuUeY44uY03XSY+Vef/Y4RUScnO4HJPI8MNb2NJ7+BPHbGPjx9nUGxjK/Igr\n3MGV9F20tBlU6WtauW4VoRH3afH9BzqfE5IdTlzhFaTYM9JjYKXmlQpymln3ppm1bh27n3t+T64X\ndHR+1L87LfJKOz8Pt8uqFvkpVichUH3bi0qZA3VtZqOvGlNKcTLGUkWZn4dMUNDMZhgbvxyMsXs7\n3BvbMMrjB3JUySCRUMuk9VPP7zl48CBrVy/H2NSBJm3f4/qVSFatOYdCKcPaxgRnJ0sa1nOhT9eG\nWFoYkVdQjFYCFia6tx4pKlZjZvL8JTpnZmbi7e2NSqPl4o07NK77YluuLF5EfvSgoEjFyIWbUWtE\nPh3UhUYeT06UlMkENr03nF4tvcnKKeTluasJjNbPG2/h7sL3vXqg1moZtG4LKbmVU92d1qYNSODb\ns2crdf7fuZeeTFFOLpaGSrrsWoHf+sWsun2LrMIimtRwZHXPfgSPfo+fO/R9xPF5yCTv1qxqNxwt\n8HrAFtQVRFF0ISgoiKnffc4b5zbQ5tC3jLmwgpTiLJpb1mLnS7opz/7UbCjKiCw2vDwF+/Z+jA/Y\nwI9BJ7mUEvmHjZt7DcPVyoRL0fGEZDx5K6uFdUM8jZ3ZnxBOTZOWnEpZzenkNRRrCvS+viK1is0f\nbSB42SXSQjN0Pu+3yOVogclelesq/l/kYeRHoue2l4G0cs7Pnzk/lf+e5xcHohIzMDV4torZJaKK\nPLUKW4MnL8SkUikvdWuHYKRBLlFSx7wddcza/iOJzb1792bKlKks/GkBW9ZM4o1JL+Hb2BWloZzk\nxGyuXY5k1eqz9B2+mMGvL2PA6x9RUpCLl5ONznMoFTIKClVP8Soqh1KpxNLaDsHIjHELd/LhrwdQ\nqfXbefh/4EXkR0dEUWTI/I3kFqsY2qYR/Vs10Pncr0b0wMvRmkV7AhizYAufj+xGn2a67xP3behN\nTGYWP128xMsrN3B68msYKfR7CLfzcMfMwICLsbGoRbFSas4PuZEYT6/hQ0k+cx7V2+MwbFAHN0tz\nRnr7Ms67mc5jt3XwZLC7H9uibvLrvfNMrqdfw0a1KLIvOogjsfcIzEjg1vT5FIfH4jytH7YdfWlo\n7saM+t1pZK272JsgCAzzaMVVlqAS1VxIjeBCagTLQ88hQYKHiQ2vujZiXMPGfHbyNP7R9/G2Kj+S\nJpUITK09gvdvzScgzYQOth24mL6N65n7cTf2w07pga2BGzUM62ImL//BK4oiM+58Q4OpbUmKKiHI\nqARdxPSDskJRibFYyr2pZVY9jWefN4JTk7mUGEcjO0eaOOgmRvCH86PjHOLvTousko/MP0rUq1Dq\nHpv1HSDBxXxapceoDq6k70eLBFejirVwfC17EF0QxOX03fR2mvoPWFeKiYkJixYt/OPfw/o2Y1jf\nP++YxJRsjp8N4diZYC6cOUDEnd1Yezbknd8+0XkOe2tTImLSqixIW90YGhoSExXB5ZBoPlx5CP87\nYZx7P5Kf3uhLM+9/p/Do0+CF86Mj+6+GEJ2ehZnSgJn9O+p9/mudmuFuZ8kHKw8wZ91RHqRk8G5v\n3V/2Uzq0Ii47h50hwfT9bSP+k8bofcP19fZm/a1brLx6jTda6FbO+ZCswkJ+vHqBfREhZJYUkv97\nu4b2bq4sGfmWTtVPZTHLtys7om6xJvSqzs6PSq3mrQu7OJf4APXDl5JEwKFrK1KMlbRt2YXNr7xV\nKXsAImzkuCz9mOU9huJr48y55HCupcdwJTWKB3mpLAg+Tuy8jRQGxyLdv73C8TxNnJntM5FvQlax\nPVZCX6cRGMtSiC+4Q2juRR6m3zoqa+Nn2Yv6Fp2Q/q0K6fPgRcQWJNNjZC8i0xxYEXqFYbWaYiAt\n/xbOy8uja5uOyM21HPf/utKfx/OKKIoMP7CdS4mxf/xsWtM2vNNE96R+iY7Bbw3qUk2gSr7kJL8/\naisb+RFFNVlFl1EIZpgZVkJlsYrklqQTkLqdu9lXSFPloJBIaG1bcS/AemYd2B+/gIi8a/+Albrj\naGfOqIEtGdG/OS1HhZOQUIM3xw7Xq0VFTUdrIqLTCIxJwtf9+RP1bOHtxsnvJ/HdllOs2rmPdi2b\nMGLMeH5b9M2zNu254PlxV59zuvrVwtHClJyiYvp8tYaCIv3DnZ3qe7F1xgiUhnJWH7nGlJV7K0ya\n/Svf9ulOC0cnInOyGLVhh97zT/s98Xn9zVs6HS+KIrvuBdF9y2r81i9m7f0b5JQU08rOFbthA2iw\n6Fu2v/9ppR0fABO5gpa27mSVFHAiPrTC4zOKCmh/YAmnEsIxlMkZ7NmIvd3GcX/ITB4s3ozLrNcI\nEZ7ch+tJhGWncig6DAtLM7o518XByIxBHo35tumrnOr5Lhd7T2ewa1PUmfmIxcUcjr6q07iNLb35\nyW8GtUxd2RRziZUPIrma6UyhpjvWBv2pZdobtVbFocSF/BYxmaTC8D/O/TlsHYHZYTgqrfmqwQe8\nU78dSYW5bH/w5L9jdnY2aWFppAXl4mH49FodPCuG7t/GpcRYapiYMr1ZW5RSGQuuned0TMVJ9JLf\nYz7pOvY/K8grIDcpv9K2/tnbq3LOT1TGLEQ02Bj3rbQN+pKtSuFg/FK+CxnHl8GTOJV6gsySHNyM\navB2ra+xVFQs5CoIAjYGLuSqMyvdKPdpsnhnAGozW974dAlzZ3+o17kNPUrlOS6ERD0Fy6oHQRCY\nObwznT3NKM5JY/dBf7q8vZRb4fHP2rRnjkSr1Va+097/GaIo8vrSnVx9EIepgYJN04bjZqt/Q8+s\n/CIGfbee1Iw83J2s2DJtBEqFbkE4URTp/utaInOy6F/Xm+/66tdDrO/6DdxNSeHQ6FHUsS17zz48\nI53vLp3lTHwUxahBCy6G5oz28WOcbxOCU1Pos289XRy9WPlyP73mL4sHOel09/8FV2MrTvQqP2KT\nqyqi/f4l5JQU082pNr+0ezx5d8SJjVxKjWZ759E0ttX/hd9h32JicnNY2XEgnZ3KFoXsvH0FYbFJ\n1LAqpsBaYIBrMz5t9IpO42u1WsLzYrmWEURCYQqxhclE5yeg1mqwUZjT3cGTtOJT5Kuz6ewwgfBc\nCVvjDmMmM+bXJvNQyhRotVqGnlxPXH4WJ3u/9cToT1BQEEZGRnh4/LeSHif67+FoVDge5pacGDwO\nQRC4lhTHoL1bkAkCp4dOwOkJgoTXM66zMvJHJJKHuc8PN8D+uhEmKXWSJLBj5H7SQjO5ef0mjRrp\nLzuRo4rkeGx/ahh3o6XDt3qdm1d0k8DkAcglJjRxvoEgVE/AXqvV0r17d0KSghm3aixKQyXqIg1b\n3t+EmQO0n9UAkCCTgKuhE02tuuJn2R2pnvOfT93K6ZT1tLcdSju7kdVie3Xx0rtLKVCVcObHN1Ea\n6JdKkJqdR/dZKzAxNuD0t288V1tff0cURRoOeweJWQ0Mi5XY25mx/4cJz9qsZ8rz+9d6DhEEgd/e\nHsTQ1o3ILVLR79t1XLgXpfc4FsZKDs8eT/2aDkTFZ9DtsxUkZubobMPe8SOwUijZFRLCglPn9Zp7\n6u86P9+dPffIz4vUahZcPk+Ltb/QZccqjsaHI5VIeNWtHgFDJ3Ju1EReb1yaz7MluLRkvl+dqvXz\neoinmTX1LRyJyc/gm1vHyzxGFEX6Hl1NTkkxwzx9y3R8AKbULy29XnQ3QG87lgYHEJObS0sH53Id\nn7XB14hIzaaxlxsnR87BWGrIjuirbI3ULawvkUioZerKMLeevF93DAv9ZrCl1XfM9B6PvdKGjTE3\nyShpjqtxI958+z1Gevcn/14GP/l9gvL3Mn2JRMI7PqXRnx2Rt584n4+Pz3/O8Zlx+ghHo8KxNzLB\nf+Cf279NHZz5rE0nSkSRV3atf2KSZxOrJgxyHo+zYR1qGHrhoPTE3sAdWwMXrA2csFI4Yim3x0xu\ng6nMCgNLI2RGciI1cZWyubIih6Ioci+1tBy8jt2v1eb4AKhUKi5dukRCSCIp2YFklNwmJv4iIWdC\nubEvAk8jF4a6jGdu/c28UWshTa176+34ADSxfBmA4Gz978mnTV6xChtTI70dHwBbcxO6tahDXl4x\nX+849RSsqz4EQWDk0AFIDQyxdDQhOSWH+zH66479l3gR+akkW8/f5qudJwH4oE97RnUsv/fRk/hk\nsz/7LwSjkEtZPmUAfh66SaWn5ObRZdlqCjRqZnVox/hWTXWe0/fnxRSq1YRMfYczMVEsunaB2xlJ\niBItglZCfUt73m7Skm6eZXcqbr9hBbEF2YRNmFalxOm/kqMqou2BnyhQl9DGzoMV7Yag+EtE44NL\n+9kddYdmti5s6fzklh8Nt/+ASqsheOB0nVdjaUX5tNr9M4JEwvX+UzEpo8t7nqqIxmuXIGq1XBr1\nJjaGxkTlptPv9GI0iKxpNZ7GVWjAqtVqOZBwlpUPdpF6MIw7Wy6RF53Jqh1rGTdg9GPHDjm5jvj8\n7AqjP/8l5l08zYrAa1gYKDk3fAKmZfydpp44yN7wEBrZOrC3f/VEGt6+/jFJBWl4mrnxbcNZSP7e\n+bQC8lUJ+Mf2xsHoJVo7LtDpHK1WS8v2TmRlprN0Rx1MTd0pjUxJSp0pSWlkylThg7PZGIwV+ie0\nh4eHU1xcjI9PaQGGWlQzevlLONjasGDAbr3HK4+fQ8eQW5LBTO/d1erAVZXuH/xKWl4Bm2aNoI6b\n/hIgKrWajjOWUVxcwr65r+FkpXurn3+arPwiOs78BWsLI/Ki8/Gt78yvMwY/a7OeGdLPPvvss2dt\nxL+R+q4ONPV05tDNe5wLiSIxM5eXGujf3K9TAy+USjkXgqPYdzkIe2szvJ0qvgmNDRT0rFubrbfu\ncDoqCkdjU3wcdbt5Lwbe4eKiRSy+dp6D6iySCvOwMTBmnHdj1vQewOgGjalpWbaCrCiKfHn5NDWU\nprzuq0u9kW4YSGUM8vDlcGwIwdlJrLx/CblEoImtC3uj7vLT3XNYKAw52H080gocmvtZKYRkp2Bl\nYEQja90SEUec3EBCfh6zm3amuZ1bmceMPrKN6IxcprVsRUeX0r+1hYER3uY1OBwfyMH4QF518cNE\nrrtQ2l+RSCTUMXPHXmrFvAEzUWUXsWTHciYOeFwITiKR4Ghkxobw6zgYmtLA6vlLuKxuvr9yjmW3\nr2Isl3NiyGtYKcsWl+vpWZtDD+4TnJ5KakE+nd0q13Tzr+xPPI5KqyJDlU1NEzdqGOrXyFetLSA8\newMmcjdcTCveqj515ijf/NKXE/ujSEvR0GO4KYIyi2JNOsWadIo0aRRpUinSpJCtCiY2dyOxOavJ\nLLxInuoO2cU3SS88S3bRdRRSaxTSstXArayssLP787khSARuKg7j4GhFK5s+el3jk8gsjiehKBxL\nhT32hlX/e1QXTlZmHL0RxuHL9xjYoSEGOqYfPEQqCDhYm3LiRjhXImIZ3LbySvxPG6VCxsFb90lK\ny8XCwIDY+CyG92iCXPZvaktTfbzY9qoCzWq5sG/mWMyNlOy5GsSohZtRq/VPaBzXqSk/TeqDRCLh\n8/VHmb9PNy0ed2tLdoweikwi8JH/MfxDwso9VqVWsyLgKl1+/I3j+05SGHqf7ItX6OxYk8P9xnJ1\nzFtMb9W+wpYNZ2KiECVaWji66HWNumCjNOFUr8mM9WqBRivy/d2T1P50IuNmvo9EFNnaeaROvcnm\nNOmGBAnzA8/olFC+P/out1KT8LKwZEztsh26E7FhXIqOx8XKhLd9H6226eBQm3fqdkMlqhl8Zhkq\nsfKaGqIosjruAPXf78jIDyfw1oDXyz22jb0Hja2dWRpygWLNf1vHY8mNSyy5eRlDmZyjg8ZhZ/Tk\nJPs9/UZgqlCwMeQ2O+7frfL8alGNDAF7pS1Lw9cRla/f9tdDYUJdqr0K1MmMnTCI5d8F8+bHfly/\neYMhLcLp6hHy2H+d3e5S32Ye1sqmaLUa0oouE5WziYisX4nMXk141i9ciH+ZS3G9UKl114bSN7JV\nEc1tBgBabmUdrdZxq0qnprUZ26kJBeoSRn65Ua8ClIf0buqNl5sN4dFpHLgW8hSsrD6GtmsEWnBw\ntUAralm6W7+0if8SL5yfKuJkbY7/p+PxtLXidkwSPb5cSWae/gJ2HXxqsm3mKAwN5aw/dp3JK/bo\ndCN6O9ixfsgABImEKXsPcOFBzCO/Px0ayZAVm2n8+WJ+PBRAQloODTt3YuInn3Jj30F+e7k/3uUk\nPpfFnvvBAAysW1+/C9QRQRD42K8rV/pMo519TR4s3krm1qMYhCb0sabmAAAgAElEQVTgZa6bnVZK\nI7o51yFPXcyykItPPLZIrWbm5UMIEgmrOgwp8xi1KPLu8YNIJLCq+4Ayj5lQuy3dHBuQHB+H32uv\nVlpa/vOg5WSVZDFw7DDWf7PiicdKJBKm1m9HYkEOOyvI/fk3E5uTzQ9XAzCQSjk6aMwTE5kfYiRX\nsPvVEQhaLa/PnMGCVSurZINGq0EqyPjIezJyQc5Hd75jfdQuckt0q2ASfi91F3myk5paeJVTccPp\n94Y1rfvZ8/EbJ2jo41f+uIIMR9P+NHbcwEvut2jncgI/u8XUt5lHI7sFeFt/hLmiNrklD7iWWPb3\n++9o+bMarrqwUjiiFIxJ/EsV4/PCO4Pb09zTmdiMLAZN+ZLCQv2rRRdO7IMgwLxNx59rQcEhrRqB\nBHLVJQgygQNng561Sc+M52fz9V+MkULBrg9H8e6q/ZwOekCPub+xdsoQ6jrrt4fs5WDN4U8nMPj7\nDZwPjKTft+vY8v5wDCsQNGzm7syyfq8wcdc+xm3fxc99enEkKIzTIZEUFZagBSzNDHnZty5vd2iJ\nmWHlJdmvJccj1Upo6fR0S6fNFEp8TJ0wf7UbmphYCjzsyFUVlZnjURbfNu/F8fhQFt49xwCPhtgb\nld1WZOrFXRSUaBjv3QQXk7Ir92YEHCS3UM2QBj7UsizfAfu80Sus/uhbMo9fp584nkvrDulk60N2\nxp7kemYQ1gaWzK6nWyVGG3sP/KydWBpygYEeviik/70Q9pcXT6EFvmnfDRczC53PC8tMRx0dT9ae\nw0z3P0nHV3pUqgIQIO56NEojJUo/Oe/WHsvu+KPsTzjGkcRTOBs5IsSXELrvGhMm98HW0QyNtgQR\nNRpRhYiGjLR0fpgRhV+bIuSvzUTUqhG1KkRKAAmp8Sq+n3mUlr0Fuvf3ZsjI9/HovRupsX6SGkpZ\nDZSyR7dAnc1GcTVhAFnFQeSp7mGiqLiZRXU7PwBuxg24n3uZmPw7uBrrLhL7TzBrZBfa9R/LrUv7\neCn8Gpf89+l1vpOVOf06NGTXqUA+3XSUr0f3ekqWVo2HOkaGBnJa+bpz/toD/C/fo3sLfRuc/Pt5\n4fxUE4IgsGhCX34+eJ4VJ64w7MdNfDOyF939yq4aKg8LYyWHPnmN15ZsIzA8kW6f/caWD4ZXmEjX\nwt2Frh41ORoZwVu7DyLPB4VcSvv6HrzbqQ11HfTvCfZ3RFEksSgXJ0Ozp17WmZifw5Lbl7Hu0IJG\njq9wIz0O43KakpaFqULJJ35d+Oz6UUae2sSx3o+3t7iWEsPRmHBsDY34yLfsRqHB6UnsDrqHubGC\nr9uUn6uRpyqize4lGLRugWVxERnNXVh4bz/v1tWtBP6XsB0cTDyDTJCx0PcDnT/f0uhPe8ae2cyO\nyNsM92qs03n/Jm6mJCKTCPSrrZsq+rZ7d/juyjnSCguQuDhSo3dXNK5GjDy7mvlNB9LTzVuv+WNi\nYjg2eRdShYxJnkYIv+dISCSgpoSoghgu/3yS6CNhiJKrDP/g8aKFa+ezuHgoi+h7hXQccuyx3588\nnsblk7Hk5RnTvm8SKSmr+HVyNPscOrJv01mMZbq3XSgLV9PX2bphHFfNJjB8+Aw0YjEabdHv/6kQ\ntcVotMXk5eVycPpZrGvZUXPWX4Ux/+4MSf72r4p/ryrOY9cnt7npMoUdi05X6Xr+TkFBAV27diU5\nV823i5bTpWltzHXo0xWdmM7nq49yKyYJhbUjciMzElRGfL3jJLMGdtLLho8GvoT/1Xv4X7rP691a\n4OlQdt7ks+RsSCRowcnajPd7t+P8tQcs33PhhfPzgqozpXcbvBysmbXpCNPXHyQiKY23erbWawyZ\nTGDd1KHM2XKUveeD6PvlWpZN7k/Tmo+vWg8HhbLi3BXux6WiFUEmB7UhSMwEDk4cjbOl7ivliniY\n79Pc4ekL5o09vh2NKPJ1mx7MeGcKCTeCiGs9EldX3aupRtduyu6ouwRmJPBj4Bnea9jhj9+Josgb\nATsBWNZ+YLnOxoSju9Fq4ecur5R7jCiK9PNfQ3aRmomvvMy0j7+jz6n5rAq7gq2BGSM8OpR5HoBK\nVDPz9iLC8iIxlCr5wXcalgYVb+v8lbb2HtTWGPDB1KnU+XIRTfzK3yb5N1KsViOvIKIliiKr797g\n5xuXyCouQgK0dHTh2w7dcHvrQ47G3uPdK9uZdn0bCQXdGe/dUuf57ezscGrpgsJcSUu7BsgFOQpB\nhkxS+n+5REHDt904b3WcsRP64WbpjIAcqaBAKpEjkyjwGyzBNG8vzZs2oIVzC6QSQ2SCITJBSbFY\nQHGf3qhUEvp3n4KlcQ7p8ee5F3CDCINMNob1w8mkCZ0cP8NQpvv9nF0cQUjGIrKLrpAan8b8D5OQ\nSJJo0H46SsOyv8v3bxdy+2gKRlezuPVm9eaDpEbmcWNPHIGKRLQ/aas1rygjI4PLly+jFWR8uekY\n87acxFghx8fFnjnjulHD5tHFY1hsKl+sPUpQXAqgxdHMlO8/m0bDTT/Qf+5atp24jVojMntI2Yui\nshAEgbmje/De0n28u3wf++aMq7brqy6W+18q7afYpTlOtha4ulgRE5tBTFImrg76a9b9m3lR6v6U\nCI5LZuyibRSVqOncoCbzx75cqWjJutPXWbDrLBIJTB/YkeHt/AhPSWfBiQAuhEajKtagBewsjXm1\niQ+T2jZjzeWbLDh/AWOZnD1jh+NhU3alh75M9T/A3pgQNvQYTFuXsiuiqoNNoTeZFXCUeja2HO7z\nGgauNVDFJvLFtjXMHjRGr7FyVUU037OIElHNkZ4T8TIvXUHPveHPqpAb9HDz4pe2ZXdZX3jzHAsv\nXqathzMbeg4t85iQzCTGndxKUl4RzR0d2Nat1L74ggwGnPmJQrXIt0360aPG4xGZ5VtW8+nCudSZ\n3Jq6db1Z4Pc+RjL9tiQ1WpEzKbeYMv09gtefxbtrR4KPPt+aI/rSZ+d6AtOSOTpoDLWtHo1gqtRq\n5l87z4bgW+SXlCABOrp48E2H7tgbP5oUfSstnlFn11JCCYuaDaGbi+6r3devTkYuSFnaZFF1XNIj\nHIx5m+KS87iYvU0z2/F//Pz4iaNkSG+jcgmgSJOBBBmtbKdSz/LVcsdSi0WEZq4iPm8nWk0CEglo\nMEYmOLPm23jMTK2YPKMfUsEAqcQAQWKATFAikxgilRgiFZTs3HIUVw9PmrUs/c7+/QXxmEq1+OgR\n4hNUrKcuHoG1jR2/vHZYtw9HD27evElKdhHh6Rqu3Y8lLDGNgpISBImE4R38eHdwe0KiUvhy/THu\nJ6YBWpwtzZkxtCNtG/1ZgZaZV0DfL9aQm1vMK+3q8cXw7nrZMeKHTQRHJPPNxN56R/6fJt/sPsXm\nE7ewszHh2GelhRSnboQxc+F+Gvk4sfxD3XLC/iu8cH6eIhm5BQz6fgOpefl42Vuz6b3hOis5/5Wz\nwZG8t2IfCVeOk3rrNE79x2Ls4olSKaN9XQ/e7dwGd+tHvfYfTpxj2dVrKASBlQP70dqz6g3t2qz7\nlcSiXMInTHtq2155KhWNtyxCLWq5OPgNziVE8u7+bWjzM7GvW5MbA/Vv6ngoJoQp53djZ2jC+T5v\nE1+QTcd9y1DKZNwc8N4jekIPSSnMo9W6ZcikEq6PmYJJGVVw60OvMufSCbRa6OlRkyVtBzzyudzP\njmdEwK9otFp+bTWK5talD0KVqGbBvY0snPQFyQEPaD61F5cXHtTrmkpENceTr7El5gQJhWlY5Rhw\nbGEAxq1aEjTnS8wMKp/X9bxxPCqcCf57MJbLmde2K13cvYjISmf57av4R4WjFkWkEgk9PGoxr11X\nLJTlb3eEZ6fxyokluCvtONzrTZ1tmHh1MrKn4PycSfyG9IKtiBJP+rtvL/e+CszYytW0XxC1Gvys\nx9LUZvwjv0/MP09Y5i8UqG4hlWgQtVKUigbUsphEDZOO1WpzVZgX1BdTmT3v1Fn+j8x35GIIczce\no1Cj+d2LK03ndre2YOaITjSvV/YiLju/iL5frCYnp4hRPZrwXt/2Os8Zl55Nn9mrMDcz5NQ3b1TL\ndVSVxUcusOLAZYyNFRycPQ5Lkz9lInq++ysZmfns+XECjlb6RZ3/zbzQ+XmKGBrIGdHOjwv3ormX\nkMr2C4H08KuDqaFuOjCiKLLryl2WHb9Eel4BGYHnKUyKxtDVnbkTR7FkRF96+NTGwujxh31rTzes\nDQw5+SCS3UEhRKdm0rl2TQShcqFmURT56soZnAzNGO+ru6Civrx2YjsPsrKY6teKLi61eP3ELvIk\nEjrWb0BYThpmciV+NroJQT6klrktt9MTCM5K5nDMPXZG3CJTVcyC1q/gbVl2f6KhBzeRlFPAFx06\n0dT+8W2+uPwsxp3YgUwQWNdlEG/Wb/tYGN9GaUYjqxociAvkcPwdXnKozYW023wUuJjIgjgcfFxx\nq1kTeWdnrI0tqG1asXxAsUbFgYTzzAtex4nk6zgorXi71gDe8xuGdeMGnE/PIDo/g941/zt7+J4W\nVmQVF3E1KZ4jUWEsvXWZLffuEJaZjoFUyhgfPza/PJg+tbxRyuRPHMtKacSm0BukleTylrfuL7T9\nCYcQJAK9a/Ss6uX8wbXUtSTm/oZaYsur7tuQScvPabM3rE8t057cy9lHYuE15BITTOV2BKZ9x52U\nWaTmb0etSUQi2ONkNppmjsvwNB+KqcK92uytDs6mbsZQak5z65f/kfm8XGwZ0aUJCcnZyCUCbrYW\nzBvXg6mDO+BkW/4WolIho3+bBmw4fZOgqCTGd2um8zadmZGSO3HJhEenYmisoJHHs9XgWnP6Gkv2\nXECplLH7o9HYmj8aEbUyN+LM1XDCEtLo3bp6VPv/DbwodX/KyGQCG98bxitNvMnKL+KVr9ZwI+LJ\nGiE3IuOZ8OsOmnz4M59vPU5kYgaO1mZ88cN8Zi1eiZVPS5YdvVRhKfyIZr7sHDUMC4WSvaH38V2w\nmG+Pn0Wt0V/L4p/I98lVFREQH4OtkRHv+bYjMC2RuNwc6tvY8XObvghI+DHwbKW0OJa3H4SvlRMX\nfljO6UHv4Z5RxMtuZSfQ7gq/w52EVGrbWTKibtkJxBPPbEet0fJ1q260cSxfWbeFTR3m+fUl/UoY\njbzr89nqb1BrNfSp0ZGDA5fh/9VmWjk15OfQHVxIK1+PpkBdxNaYE4y8NJcl4buxV1ryVYOJLGky\njXa2DREkAuPrN8XMSI5/aARZRZVv7vo88lmbTpwbNoFBtX1oXcOVnh61+KVrH4LGvcPs1i/ppP/0\nEAdDc0pQo67E96i6CMk6TFT2T5RgQi/XjSikZQs2/hVThT393VYRdCaHNrVGMuPrRmTkb0NLESbK\nzjR32ks391P4WE9BJlSc7PtM0GoRJP9sRaJSIWPexF5snDOSFTOG4Ftbt2eYmZGSrk1rU1KsYduF\nQL3mnDeyBxKphGX7L1bqeVVdbL8YyMKd55ArpGz9cCSOlo9Hdnq1qoe5hRGXrgYTk5j6DKx8Nrxw\nfv4h5o3owbSX26HSaBi3ZDs7/nYzJWflMnvbUVp/spSxP2/jyv1YDA1k9GlRj2NzJuD/8XimvNyR\nryaPp7NPTTLzC/li78kK523o5MClqZMY2aAhGq2WFdev03D+z7y9Yz8J2br1E4M/9X361dWt4qYy\nLLgVgFYLY+uVOhyfXi6tipnTvBMWSiN6uNQlT13MryGX9B5bJgis7TgMMacArVrDlJrNyzzu0qVL\nDPZtSdYBf1Z1L7t/WEJ+NsGp6XhZWTDA07fCuSWSEjRhkRQmZMG9fNa3nMskr/4IgoBUkPJJvTF4\nmTrzVfA6grOjHjk3pySfdZFHGHlpLisfHKCmiRPzfd/mR793aGbt/chqVBAE3mvRhuLkNAZ9/ZnO\nn82/BRczC75/qSebXhnML9360tOzdqW2X12NLZFISvO1ngWxeVe5kzYbDQq6OK/BVK57Jaa5wgWj\nlLbkZpQQFCKhptWXdHO7QSvHxVgYPD/5JU/ioeDjv4Fpr7YHCaw7dl2v88yNlbzcuh5FhSX8uP/Z\n9DQ7dOMe8zadQCoTWP/+UNyf0IS7fysXbu2cS4sWuhcC/Nt5Ue31DzK2c1NqOlgzdfU+vth+gm17\nD9LA24uA+BwS00sdEalUoFltFyZ3b0ljj7JXKD8M60XbucvYfvkOQ1s2oq7jkx+eMqnAZ70681G3\nDnx97Cy7goI5EhHOkfBwLBVKmrs40ae+N51r10QmLftlci05HkErobVT9Ss7P2R3RDBSQcIb9VuS\nVVTIzZQk7I2NaeFQmq/0dYueHIm7x093zjGkpm+57Q3KY0rAbmzeHspI+zr071V22D0mJgZNfgG1\n1VqcTcuWF1gXehWQMLpOxWXlWao8fgzdQa2xLZjUYwjvDZyEkeJRuw1lBnzZ4HWm3viJ2XdWMtaj\nJzVNnDifdof9Cecp1BTT2ro+w926UsfsyblbY+s34a2efTgel4B/6050765fsub/A7XN7fBPgZtp\n8TTQsf1JdZFWFMGFpClIgFYOP2Oj1L/Vw9xZP6OsFYV13SzMDPye627if0UURa7tiMbH1wzKbhv4\n3GFtakRN11L15gdJ6XqVr388uBP7zwdxNvAB7+uRM1RV1GqRDzce4sTVMCRSCSvfGVhhy6Seresj\nSGXkq/5/UoBfOD//MO18PNg5fRQDP57P9t++YreJOd4TP8XD0YqR7fwY0Lx+hQ8zhUzG/OG9eWP1\nHt5YvZuTMyfo9ABUyGR82rMTn/bsxJ7AINZdu829tFT8H0Tg/yACiRYsDQypZ2dLx5ru9GngjZWx\n0T+i7xOdk0lmYSFNHGogEwTmXitNJH6zQYs/jjFVKHm3fjsWBJ6l75HVnOnzps72XE+J43TiA+zN\nLJjXq/yqhsGDB+Pl5UWdOnXKPeZEXDgSiZaBOkR9vgpei0ar5W3v/vTrVn7Ju6XClK8aTmJe8FoW\nhe0AQEBCBzs/hrl2xsNE95f0zIlvcNTfnwYNni8huecFX2snCIOQrH828lOozuZE3DikqPCxmYer\nSeV64wmCwLAe0zmdNJs7mdt5yfHDara06qjFEu7lBBGSe4fI/EiSi1IIOnMf/8/vcMkjgXnlF6w9\nd7zVuxXvL93P/D1nWfJGP53PU8hkKOQy8gqLn6J1jxIUm8ybv+wiJ6cICwtDfn1rAHVqVBxZdHNz\nY8ycFdwJSSIqKQN3h+qpEH6eeeH8PAM87K04/NV7eB3ZjNzKge3vj6BODf3UoNvV8aBdHXfO3Y/i\n24NnmfVKR73Of7WhD682LN3CuhoVx/6ge1yNiycmO5uAuBgC4mL48vRZjGRyZHExpAQco9PUd/Sa\nQx9+uXsJkDCiTqlGzaHIUAxkUsbUbfLIcW83aMuV1FgCkiKZeHYHKzvq1pX4rXOlHaqXtutf4bGN\nG5cf0YnJzSAiM4sapsYYVSC6mJ6ezu4lW/DoUp9+L5Xv+DzE2ciWpU3eJyIvnnRVNm5GjjgY6v8Q\nmjN7NnNmz9b7vP8XfG2d0GohMjf9H5tTFEUOxIxBRi6u5m/jbVG1xGlno9Kig3x1SnWYVyXSi1MJ\nzL5BWO594grjSFdlUqgpQfuH0KEWA0FKXT8vwl8KoVYLj2dqr7681MALI2MFV4NjEEVRrwWg0kBG\nQZF+Kt2VJSg2mZE/bEar0dKjZR2+Gt5DL1sj4rKQyqXYWz65b95/hRfOzzPCzs6GrQcO8eG6w8zd\neZINU8rWkXkSC0e8Qtsvf2HjhZsMbt6AmvaVUxRt5u5MM/c/t9iiM7LYeyeYgMgYwtLTuX/sMAXB\nd5EF3avU+LpwIjYCqSChn0c9AtMSKShR09HFvcybd03HIbTZu4STCeEsC7rIGz6tnjj2T7cDSCnK\no72DB03tKp+wLYoiY09tQdTC7KadKzz+rXkfEL4ygNyINC68FEpru4pzMiQSCV6mznjx9IUk/18x\nkimQaqUkFeqe81YVclRJ+Me+jpQ4jAw6PaLlU1k02iIABMk/9whXq9XM+upDBCctNm0sSSpOJqck\nH/VfdkoEwFSmpIaJA26GbtQ188HHrBGGstKtXpOfNJSI/75k/Jf8vDgYEMy+qyG82kL3vEcXewvu\nhiVxOTSGFrWrLjdSHslZuYxbuBWtqOWr13rSq7Hu1Z6iKDLuqy0U5qt4qXVtDA10V9L/N/PC+XmG\n9PSty48HAgiMTORefAp1K9iX/TtKhYxvBvdg6oYDTFq9m+MzdesHVRFuVha806E173QoVaZunJ5K\ntKMT86bPqJbx/05ifg4p+fn42NghCAK/BV8FYFTdspWKBUFgX49xtNu7hO9vn6KGsRl93Mt+IGUV\nFfBz0HnkEilLdIj6lEdCfjajTmziQWYuLWvUoIfLk0tCVaKa+EYybNp64dCrPp/cXYa70pNv/EZj\nZ/j/o6XxvGIsVZKt1r8BsT7kliRzPulbcorPIkWDibIHXWrMq5axU4tDATCW6ffM0IcCdT7XMy9x\nK+sm0QUx3LsWwcFPD6EwUzD8+DAMBCk2CgtqGNbAy8QLH3NfnJVuT4w2mMmsSC2O0DuC8qzp06Ie\nBwOCufUgXi/n55OhXRg6dwNfbTvJ3k/GPhXbClUqBn23geJiDZNfba2z46NWi2w5eZMNh6+RmZ6P\nu5s1X0wov4XPf40Xzs8zZu7Qbry+dCczNx5mzwz91Ivhf+ydd3gUVduH75kt2fTeCYHQQ+i99yYd\nqYKCvQF2VLC+6md/sSCCoBRFelWkCoReQwsJJIT03vvWme+PBQQhye4mCLzmvq5cC9k5Z2aSzZnf\nOed5fg/0D2tEpwZBHItLZu72g7w0uHuNXp8kSZS4uNJizDg8Pe9MrZrZR3YAAk+GmWMgDqQmohQF\n+gZWHAzqbe/ET70n8Mjelbx4eDNJJQVMD+t2y3FP79+ASZZ4vXUfnKooEHs7tEYDs49vZVPcJSRJ\npktgACv6Tqmy3ZfRa1HWceXtxZ8yNKAbs08vJ1F3hQmHP2Cwb3deC624XEYtdx5POycSyizbMpJl\nmZMLj6NUSRwN+h4ZGWTpqvOxjIyE2StWwiTrKTGkUGKIRinnIQiA4EdL71do5GJ5qYSqSCo5BkCg\nQ7sqjrSc1LJkjuUd4mJxNGnlGZSadFyr0aUSRJq3aUz8xETcQtz5rs13OCgdrT6HtyaAHH0cKeWJ\n1HW8f7a/6niakx/yiq1btWoS4E3DYHPAdEJWHvV8ajaWRpIkxn/xK0VFWoZ1C+Wp/p2qbgT8sOUI\nP208imySQYB2reoy76Ux/6oxqVb83GU6NaxLA39P4tJzORKTSJfG1peNmDd1BN0/WMBP4ScZ27EF\nQVUUQbWGA/GJyDK0D7LOWNBSivVa9iXH46axY3RIGFllJeSWlxHm5VvlH2JXv3psGjiNcbt/5r/n\nwjmYHs8PvcaSXFLAuivn2bNqPSf2hBM2YypPNrcuhfNQ+hXmXTjIyYx0DCZw1Sj5oNNARtSrOog4\nozyPnRmncFTaMb3RKERRZHXP19icfJJ5MRvZmR3O7yt2EnrZni9few8np3/HHvu9RKC9KwnaTNJK\nCwlwrPzvJT09ndM/ngQg9unvcXSuOlVblp1QqzrSyHUMTd1qPuPu5IU9OAbIBDtZ97k2SUaMsoG9\n+/aQqkuFZkbiSuLI0eVjuG72L+OosKORUzDNXELp6N6VQAdzlqfdnA9JKouzebutjn19oosOcLnk\n4n0lfnxdnZCBwjKt1W0Ht2/Cd4k5HIiKr3Hx8/TCDSSn5dO6SSAfTbLsczZ39T5Wbo1ArVEyYkAL\nnhzeCXdn6zJn/xeoFT/3AJ9MHsK4L37h3dW72Pm29VtXDmo1/xkzgNdXb+fJH9ez/bXHauzaNl+I\nBmBUmHWVsC3l3eO7kWR4tqV5xvJTtDmNfHQDy5xGwzz92Tf8WcbuXM6xzAQC+3ZBlmQCp08iYe0W\n9EnpBKYXW9RXUnEec8/vZ3dSHMU6IyDj6WDHpMateLlFb4tnRe+cX4aMzItNbi53MTKoPUMD2/B/\n5zfwzZw3OHjkCo1d/HnllVcs6reWWzl06BCDBw/m+enT+eTjj6tucJWGLt4cyo/hZHYyI6oQPwEB\nAfR/uweiSqZLyPsIiAiCaH5FwJyXZ/6eQlDiadcQjdK5mndWMZ9/8x9mvfA7fR4Lpuy1MXCDaPnr\ni+uvwrXXq/HHZYUG3hp0ABmYuGMi9i52eKidCXYIppVba9q7d7keo/N3QhxDSC6P40zBOTp7Wp+t\nFuLUGDIhufyK1W3vJqIoIohQUmZ95laIr1nwpOQU1ug1vbdmFycuJBPg68pPz9/ek+zvSJLE2p1n\nsNMo2fDZY3i7/XsnXrXi5x6gSYA3rRsEcCYujd9ORTO8nfVCY1ibZqw6eo7TiWks2HOMZ/patvxZ\nFaeS0xAFga7BNe/vU6AtZ0tcNPYqBU+Fmk0HtyXEIAAPNak6jfwavg7OHBj1PF8f282LR95FALat\n/Y28jQN47eeFHMpMIj43i/qet8ZHpJTks+jiUXYlXSatuBQQ0KgE+gcH80KLnlb7wBzJiSKmOJW6\nDt4M8Ls1a0wpKnin1TjqvmxgzZIVjBgxwqr+a7mZxKQkSkpK+GXvXj6xol2Yhz8kwtncNEbUC6vy\n+MYDGqA3GGjo0tf2i60hUsQjCCK4eLigUXghIFwVYiKCoLhJnInXXxUIKBAEEUkp03hAPApBzZy2\nb9DMtWp7jWu0cgsjPGcX5wsv2CR+gh0aIMuQrU2zuu3dRqEQKbUhcysj3zz58qlBobFk70k27o/E\n2dmOta9Nsfj3t37/OUwGiaG9mv+rhQ/Uip97hk8nD2HwBz/y6aZ9NokfgAWPjqL7Bwv4btcRRrUN\nxc+terNPSZJILy4mwOXO+PtM2PErBpPMi226IIoiZUY9iUWFBDo7V5lGfjtmduzHf99+gj7+DQgJ\nDCIkMAi3+Xqyf/yF9vGpNHl0NEFOLihFBeVGAwlFBeSVmUg3CE8AACAASURBVOMaFCK08PHiqdDO\nDA0Ktel+JUnik6hVALzfYlqlx04b+xDTxj5k9TlquZmHJk3iP2fOofCoOh5NkiQuFWZxJCORY8mx\nXJkxj481izi38B0EhXlZRJbNUTzX105kGcloYsdDa5C0Bj5O+AovL687eEeVU6TPon5/A/MvTOCZ\npqts6iMi/zBDP0mgs8cgmru3tKptmGuo2SagNMGmcytFJYJgR5Hhn7MZqClUSgVancHqdmlXxU8d\nz4priVnD7vOxfL3hAGq1grWzpuCosXys/P1gFDLwxLB/j5NzRdSKn3sEfzcXejYPITzyCkv2neTR\n3tYXD3XS2PH2qH68s34Xkz6ex44PXkFtQ5DvNe5kvM8Xp8O5mJtLC28fprc0Z5V9eiocWYYxDWwr\noSEIAq2bhrLhg29xyijihRdeJDPADZWvF57NG5BTVk5akXl1B2TslCLt/X0Z36A1Y+q3RClWz3b/\nt7SjFBrK6OYVSn2n2xdMraXmade2DcfjUkguLCTI1ZUyo54TWckcz0okuiCTpNJccvUlaGU9CGZZ\nYywqRZ+ZjyCAvTELe8cbt3kEBOTrpUNkkwnZJCFJssXFLe8U+7MWIgA9/WzP7DxXcByAdu63JghU\nhUpUohbtydXZXgPKXuFMuemfsRmoSezUSpvET3ZBCQB1vasfixmdksmsxVsRRIEfXxh321pdlXE5\nMRtnFzt8Pe7ctuz9Qq34uYf4aOIger6zgO93HGFqz7Y2rT482CGMT7/9jn0/LyDsyG5i9v9p8/Vs\nuerrU9PxPudy0pl35hj2KgUrB5n9jfRGI79ePItaITLjqhiyBfukXIqj4li0bBm/13NG3SiQ1Yf2\nMfqqoCoz6lEKImpFzX/0D2SfB2B6o5E13nctFRMkGVg792PaH/0Dn2l9MGDkRo0iSCJOCg2BGl9C\nnL1o6RFAJ99gunkvpjg9n5zo3dTrWYmvkkJGX6xDNslkZ2ffsaxHS0guO42AilA32zPHksouIyAS\n7GBbjQlPtTfp2iSMkhGlaP3fkavKizJjDnpJj1q8fzxl7O1UFJdaH/OTU1QKQF2vimtrWUJWYQlT\n565BkmT+79EhtAz2t6r9+SvpGHUmWrawPqnmf5Fa8XMP4eKgYUTHUDYdvcCXWw/w2vCqXYFvx+sP\nDmHcr4vIl5T0+3wxHz04kM4h1htsnUxJrfF4H73RyJSdq5FlWNB3FM5qDQCvHNqK3iTxeFhbqyp0\n30ixXsupIA1+Myby6rCJ1G/QgHbegQS7/DXo2LKdZinJZdkoBBF/h7v3cPy3MXXzOrbt34UhJ4eS\ny3E0Ug4j0N6Vxq6+tPEMpItfffwdbz87HvZ2P2JPRTP3kflo7DQ3reqYA5mFq/+Gsq8yKMzJo2lT\ny83jappSYx46UxHemopLr1hCiTEfR4WrzVvZwY7BZOiSiCqKoaWbZYkJN+KjCSRDe5GEkjgau9yZ\nRIo7gZO9mgyT9RXaC0rKQcCq7am/ozcar3r5GHlupOVePjey5s/TAIzpVVv2BmrFzz3HW2P6svXk\nRVbuP8OMQd3QqK3/FT04cgSPfbeKwwnJpOcW8/gP63F0VBMa6EvvpvUZ2aoZ7o6VpzZKkkRGUQn+\nLs41Gu/zxN71FGr1jG/SnN5XfXy2J15iS9wl3DV2vNXe9oDSZw+uxwS8/fR0nmtu/ZJ+dVGJyqt+\nL7X8E7yxewfhmfHU79iJN3oMYEr/Afj4WG76F9arOV6dlNR3a1jlCkSbAY0pNqRX95KrRXThXgQB\nGjpX/7OtqIYzdAvXMI7lHeBswTmbxE9dhwacK/iTK6UX7yvx4+ZkDzLkFpfhaUVqeF5RGUpV9bbU\nf9pzksLCcvp3bMTTA2xLZjl+IQlRKdCtxf1jMXAn+fc4Gt0nqJVKHundFqNJ4v31u23u56UHeqDU\nQfvgADo0DkJvMHE8JpnPt+yn2wcLaffePMZ9/ytzdx3kSk7eLe0PJSQhyTLt6tRc1esfo04QnpxA\noLMTn3Yx1zaKysvkub1bEAVYPnC8zULreFYShzMT8XdwvivCB8BRqUHCevFTpC8jtSwHSbJ+Vvm/\nxrx585g0fTqHkxLJK6/YgflMejpr4s7jLNhx4OEnefmhyVYJH4A6DsEIAlwsjK7yWLOvzd39/eTq\nEgAIsLctJu4vBORq3Etbt5bIMsSV2pau3sjJLHhSyhNsvoa7QWhdXwAORMVb3CY9v4iCwnLq+FQv\n3uf3E9EgwPvjB9jU/lJSFvl5ZTQO8flXGRlWRu3Kzz3IjMFdWXnwDH+cusis4T1xd7LegKp5kC+i\nKJCZV8KOOeZaQsl5hWw+G8Wh2EQuZ+RyITGTqMRMFv95AqVKpI6XK+3qBTK0ZVPWnIxA0utqLN7n\nZFYKHxzbi1qhYP0D5tTMnPJSxmxdgUmS+br3UFp6WbeHfQ1Jknj+4AYA5nd/sFrXOX78eJKTk9m5\ncyfOztYFBTor7QEo0ZfhpK76d7Yp5TBL43eRpzcHRApAM5cgvmjzFE5KjdXXfr9jMBiYMWMGAPt9\nHVD5eiPIoBYU2CtUuKo0uNnZo5dMXCwyuzN/0muQzdukTZyacSp/J+cKT9PSvVWlx4qCAmwQtjVJ\noT4NWQZfe9tida6hEtWUmUpsbm+v1KAU7cjSZdjU3tcuAFkWyNbdX+nuXZoGs3zbSY7HJFlc4mLO\n8u0gw+ODOlbr3Dq9EYVSxEljZ1P7r9fuRwCee7CHVe2MRon0tHyC6v7vbeXXip97EFEUmTGkG59u\n3MebK3ew4MnRNvWjUSsp1f4VoBfk4cr0Pl2Y3sdcCLSoXMvWyEvsiYojOjWLhIx8EtLzWbs/gthF\n/wfItH75+WrfT562jMnbV4MMP/Yfg7+jC0V6LYM2/0S5wcDMNl0YFWL7bPbL8/vJ1ZXRL6Ahraz0\n5bkRWZbZvO0P9KVlZGdnWy1+XFVmwZOhy6dhBeLHKBlZlvAn65IOUGrSIQChLkF4qJ2JKU4lqiiZ\nR458xppus20KJr2fUalULF68mK3nztK4Sz/SS4vJKislX1dGsUFPankRieUFAHirHfmk50D6NWho\n8/naebRnRZKCyMJTwLRKj1Wgum4WeLcoMeYgCCJqsXpuvH6aIBJKL1Koz8NVbZvjsJvKg1xdhk01\nukRRRCU6Uai/+xXpraFdSB0USpE9EZeRJld93wlZeZy5lIqXpyND21dvEunv4UxWbgnlej32Vmbw\nZuYVExGZjJOLHZ2aWR77WVBQxqNTf6CwuJyXXhjE8JG3+pbdz/y7Rtf7iMk92vDD7mMcjk4gJbfw\nem0Za1ApFegMpgrfd7HXMKlDKyZ1MM96jSaJvTFx/HbyHLECKEQFCkX19qpTSgoZvHkJWqORl9p1\no2dgCFF5mUzYtpIinZ6RDZvxSpueNvefoy3hh+gjqEUFX3cbVa1rFQSBfnPf4VzWFQLqWh8g7qY2\nm4ZlavNp6HyzPYDWqGde7Ba2pZ/EIJtQINDbuwUvNR2Nu/ovkTX77BIO5kQx/dR8FnSYWa37uR95\n/PHHqX7Nc8tQikq87OqSo4unQF+Am7piH5bqxMjUFFpTIUqh+iuCoS7tSCy7yL7srYwMfNimPuo6\nBJGrTyehLIkQp3pWt/dUB5ChvYTWqEVzn6xyKpUiw7uFsik8km9+P8SLIypfRZmzfDuyDLMn9Kv2\nuUP8PTgXm86pK2l0b1rPqrbPf7EOySQzfbzl42x2dhGPTVtEabkeEFi0aN//nPip3fy7h5kzpi8y\nMGvFHza1l2UQrbAlUSpEBjRrxMQeHQh6czZPLFqMg4Pts8zovCz6bVhMsU7Hw81a46NxZOiWpQzZ\ntIwinY5HQlvzTc/qORw/s389Jllmdut+NZLJFdq0GZqGdYjKy7S6rYfanFWUrfvLxj5fX8zb55Yx\nJPwttqQdQ0BgZEBntvX6kP+0fOQm4QPwYYup1Hf0JaoomY8u2GZiV4vldPXsgyDAtozfKz1OIaoQ\nBDBK1vu81BRGuRyNwjpfl9vRw3sgIkoO5WxHL1lfqwog1Nm8khGRf9am9vWdmiAIcLbghE3t7xZv\njO2DUiWy8s/TGI0Vx02tOniGqLhMAn1d6d2i4gLNltI8yOwbdibBuq3CeRsOkpyST2gTP0b3tCzL\nKzkpl6kPL6S0XM/wB8xO+54e1hexvdepFT/3MANbNSbQ04XIhAwuJFv/MDaaTChsCG7bFBWNqFLx\nYGvLS0z8nc+W/8TAH+eiNRpRKxT8HH2GNw/tIjInkzrOzqwcMpEPOg+0uX+AvWmXichNJdjJjUea\nWG8KeTtCXMx725G51sczeNmZV+dydYUklWYx89R8Rh34D+HZkWgUah6tP4CdvT/ilWYPoqlAqImi\nyA/tZ+KqcmBHxilWJu6z+V5qqZpePn2QZZGz+ccrPe7ayo/ORrFQE8jIKITqC3y1qKG390gkjKxM\nWmBTH+08WiPLcKkk1qb2LV3NMTBRxWdsan+3UCuVPNi7JQa9iS82h9/2mC3HL/DZr3tRqUS+eqZm\nPL96hYYgA+Hn4yxus2z7cZZvOo7aXsk3L42xqE3spQyeeOJHtHojU6d0pW5dDxCgR4/q2Svci9SK\nn3ucDyeaK/W+uXK71W0NRgmNyvrl+hOpKQhA7xDbUiJfnv8tr099nMzvfgZAIYq08vHjyRbtODju\nKQ6Ne5au/tUz2pIkiZePbAFgYc9x1errRkI9zBkdMYU2ONjma4n+5A/mr1rClKOfc6YgHne1E680\nGcO23h/yaMhAi+Ij7JRqfuz4EmpByZd/Lmfumh+sv5ZaLEIlKvG0C6bMlE2RoWLXYeVV8WOQrDe5\nq0nkGoo7Guw3Fo3oxPnCo2TaUGfLXe2GKCjJsLFGV4hjI2RZJLXM8of5vcIrI3qhslOwds8ZzlxJ\nvf797MISHpm7ineX7ERUCPz06gQa+tVMoLCPqxM+Xk7EJudQrq+6vtjK3RHM//UgajsFK/7zMC4O\nVW8t7v3zAs9NX4bBKPH8M32Z+lgvTJL58+bqal/te7jXqBU/9zjtG9ShUaAXCRl5HLhoeYolgEmS\n0KhVVp8ztbgIHwcnqwMZjZLEtK3rWF2ShrpeHVr07Mq+sU9w8eGX2TJsKm916EeQc/VcTq/xfsQu\nCvVahtdtRmNX7xrpE6ClpznjLLm4wKp2BzNjePb7/5K1O5rLq49Qx96Tj1tMY1OPdxlZp4tVfWVp\nC1iVtB93tRPn31jHyxOeZlO49eK3Fsvo5NEdQYDwrH0VHnNtxUVnunsrPyAgyzWTbi+KImPrPIks\ny8yNeZ1srfUeRi4qd0qMtpWpEEURjcKVIuP9FfQM5tifL54cjizD09+sJzW3gLdX7GDw7EVExqTj\n7+PCz69PIqxuzZa4Gdm5OUiw+M/KtwoPnI3jq5/3oVQrWPb+FOr6Vj7mSpLEe2+v54OPtiDLMrNe\nHcKD481eQvVDzGNr3JX77/dUFbXi5z7g08kPAPD+Gst9f/RGI5Ik4+xgXWrk6dQ0TLJMa3/r0s6T\niwrotOJ79mZeoW5gMEmR0Zxavob6LjWfIqk3GllxOQJ7hYrPOw2v0b497B0QJIH0cssG9UJ9GY/s\n/5Fp4asROzan2zPj+W3Ran7t+gbdfCzPYDNKRlYnhjPx0MeMPfQR65IPkKkrwL9XM1xa1uGbwp0U\n6Sv2vbmfkCSJ31K3MPvci7xxdiY/xM2n3Hj37i3A3lzaotBwq9/VNZSCeRKhl8r/kWu6HVcrjdVY\nf63dOzPYbyIGSc+XMbOsFkCBmgDAREa5bQ/GYMemgIHY4qp9lu41ejSvz/NjupGwfwv1g+uzbvNu\nNBoVbz8ygD/ef5xmdXxr/JyP9zVv7YdHVuyvVFSm5c15v4MAi96aSIOAysff5KRcxj34LfsPx+Lu\n6sCSn55k8AN/2T60aVsfUYB94ZcoKvzfGH+ucfdTGGqpkoZ+nrRtGEjE5VQ2nbjAqA5VP1RPXV2O\nbehrnfjYcCEKgAeaWO4lsv5iJLMObceIxNCgJszrP/yOGmllaUuQZJlufvVs9nipDDtBRb6+tNJj\nUkvzWJd4kh+ij6MzQQNXZ77tPIHGD1vvVbQ6MZzFV3aguxpMW8/RhzF1ujHErz12/dQsiN3Kr0n7\nmHbsS9Z0e5OzeSlsTD5BTFEq2fo8BCB75Qlck7X8tmYD7u41s7p2J9CZdHwU/RaFhhRkWQRELhQd\n4O3Is3zU4hvsFLb5mFSHzKsPfVdVxWnfyqsO0Ab5f2Pb6xoD/Mw2GtszVvFFzCzeaDoXd7VlVeub\nODchqvgsJ/MjGGY/2Opzd/PsT0zxEQ7m7KKR8/3j9HyNx/t35F1tNobSAvzdYffnz93Rcc9OqUSp\nEsnILa7wmKc+XYNBZ+LxsV0IrVe5APt9SwRffbMTSZLp2a0x77w/+pbrVypFxj/YgVVrjzNu3Dya\nNPJl4KAWPDCs9X1vllgrfu4TPn1oCAM/WMwXW8ItEj97Lpj30rs0ti5l+2hyMgIwsFHV/imSJPHC\nn1vZkhSNApHPug5mQrOWVp3PFlbs20HaewvZNrgbYZkJlBsNCIKAm1rD8827MbVRe5v+MCVJIq44\nl5Kj50mNiWdOnQBKBBNFei3FBh3FBh15Oi3FegNGyTwPt1fCu2378HBD612lrxSn89LpH8g3lCAi\nMMS/Pc82HHo9Zf4azzQaSlJZNhtWr8Xv9VBCpvdB4+uCLAnYCY6ATOzWw+iyioiMjKRHD+uMzP5J\nvor5jEJDCoH2ocxo+Br2Sg2L4xZwviicdSmrmBw89R+/poj848gydPSouGzAtZWfu73tdSeMFm8U\nQN9dfp+3Qr+1qF0Xzw5sSF3DmYJzDAuwXvw0cQ5DRsmVkkir294r7N2xlT6ffIYc2uSOi4Ff9p/G\naJDo0PL2tRbnbzxIfHwOjRr48NSIirfay8rKeOmFj4i6KGKvcWL27GH07R9W4fFPPdsPnd7Izp2R\nREanEXkxnW/n7ebVV4cwYOD9WyesVvzcJ/i6OdOnRQP2nItjwa6jPDOgc6XHH4tJAmBAS+vcYJML\nC/HUOFS5opJVWsKYzStI1hbiqXJg/YiHqO9mm2GaJUTlZzL3XDiHMhPI3LofXUwSea5OePZsRwMX\nD4yyRFJJIR9E7Obzs/t4oG5TZrfuj4fm9qn6kXnpbEu+yPHsJK4U5VJs0GO8Wl4i5ZffMOUUsqRV\nAxxam39+oiCjFAUclAoauLpS39mD5m7+PNKwO45K61cr9mWe5b3IFUjIdPJowtvNH8KlElfo95o/\nxPzf3qIwMpWQnu359OXX6OQVcn3APbltOJdjYu9p4bMrYyep5VG4qeswq+nb178/rf4TvHL2CCfy\nwpkU9PA/OqNMLE0gpTwae4UHvvYVz5SVV2N+/lcCnv/OAL/RXCo+R0JZFMdzw+noWXVRZW+NFypR\nQ3JZok3nFEURL3UQObp4ig2FOKuqVwLibtAoqA5tWrcnOj+H+Jw86nvdmTEwNa+QrzcfRFAIvD/h\n1hIXF+LTWbbpOGqNkgWvja2wH0mS6D94KkcOrCMwsAMHD2ylXv2qYyZnvDCIGS8MIiuzkOVLD7B9\nVyRfzd1RK35q+Wf4cOIgekYtYP62IzT296JvWMWrMyk5hXi4VC1ibuRiVjYGWaKlX+XLpbvjL/Ps\n3s3oZRM9fOqxdOhYlHfggZVaXMDAUSNIKc7DY+YEBIUCB6WKUQ8/hH+7Pjw3dhIBAX85Oudpy/hP\nxE52pFxiQ3wkG+IjcVap8bJ3xEGhwijLZJeXUKDXIt1QgFSjUOKjccTb3okABxfK3plFeVwiM6e/\nRJCrN772rqhr0G35xys7WBa/GxGBt0InMdC/avOwtyKX0eDFAfheNrLxvcXY2d0suNq3bUf7tu1q\n7BpvZHv6Xo7lnkIn6TFIBkyY8FF7E7cxiq51O/Low49W2Ue2Lovf0pYDKl5uPOem95SikuYunbhQ\ndIDw7H308bW9uK01xBbHMu/yRwjApLpPVHqsbBC4fDibsuHmshBGyYBe0mKQ9OilcgySDqOsRy/p\nMEg6DJKBnxesQuOoYvjkgYB89TMnI8nSVREjI8vS1X+Zvycjg2x+lWTJfAwyRpOJ9Z/G4ObjQJ0X\nfsIkm5AwIcvSDa/S9f+bz3H1/7KEjAlZlq8fYz6f9Fc7JATZwJaZ+/g5bTeXTsTh5lax6eM16tjX\nJb40hlxdHp521j/4u3kNYkvaAtYkL+HxkBetbn8vMLN7F579/Tc+/XM/CyZUz2j1duiNRh76ciVG\ng8TsSX1vydzSG41M/9xc3ufTF0bgVEFmV1FxOU+/+gslgi/OLnV4ePI4i4TPjfj4uvLq68M4dPgy\nOr3Rthu6R6gVP/cRTho7Fj/zII9+t5aXl/7Ozy9MpEXQrRkFF5IzMZokWliZbbAu0rz8PKhxxatF\nb+3fxc+xpxERmNOuD0+17mDdTVSBUZL4IfIYy6JPk5aVQ0r4YUAmxCTwVd8J9Aq4ahjW/9a2HhoH\nvuo6CkmS+OVyBL9ejiCtrIjE4nyuZmyiUSgJcHAmzMOPXv4NGBLUDBf13waL7jV6Szfxa8JelsXv\nRiOqmNfuORq71KmyzZbUwxzNvURI0xBWPzrnH1sZ2Z1xgJXJ69BL5vgns14UEIArVy6y+a21LBYW\nkNsqm+dCn8dBeXsjNEmS+O+ljwATE4Kevq2T8sS6U5hz/iDbMzbecfFTZChiReJSoouOAjDEfzJt\n3NtU2mbJZ6tYN/84l554ge7Tw7gWflwRhWml/DB7GwDFHRNQ21dvqM2NL2L/0ngEEbo8vAbRGvfS\n2yDftID0V+GO1POFlBfpycvLs0j8dPRoT0JZDHuywhkXZH0Znu7e/fg9/WcuFh9DL+lRi9X3Mfqn\nGdCsEc7b1IQnJdhU7qMqHv9uHYWF5Qzp3IQJ3W6tQffC3I2UlegY3i+Mrs3r3baPrJxiJj//I+UG\nI0FeDfBt9iRvvGl76SK93oidunru/3ebWvFzn9E2pA6fPjyUWcu3Mu3bNWx+feotpS+Whp8EYEhr\n64ypDicmgwzDmjW95b0inZYHN/9KTEkOzgo7Vg2dSJh3zWU0xBRk89HxfRxMTcRokhEECKsbxORl\nC1mXcIZCtcjWpOi/xE8liKLII43b80jjmjE+rCn+zDjNgrg/UItKlnV+FX/7qmfKqWXZzL20CaWg\n4Lt20/8x4bM9fR+/JP6CIIi0dm3PEyFTcL0hFim9NIuUaelIjjrOlsfwfMSL9PHpxkN1p9xSk2x5\n4hLKTFmEOLWlm/ftt+VcVC4EO4SRXH6e7y9/wxMhz6GqwdU2SZI4knuIXZlbydMnIQgyKtGZJ0Je\npJlLaJXtnZqocPK1p3nrxjR0bIJCUKAQlOYvUYlSUF3/UohKFN4qIp9JRe2oZEzI5KuSUUAURMwC\nUkAQzK83fY9r31Nc1VcCIiLUFfD9LBRPb0+GBPVHxHx+UVQiIl6/HkEQUaBEKSoRUSIK4k3XKaIw\nf1XwOUpb/SzZ+amEhIRY9HPt5d2N1cm/crrgrE3iB6CzxyAO527gt9TVPBhkW7mNu83wJk34Neo8\nS45F8HiXmht3Pt20j/Ox6QQHevDJlAdueX/FrlOcOp+Mn58Lsx++zYwQc1HUx19cRrnewJQRHQjf\nfJZ8pYirW/VqxFXmcH0/UCt+7kMGt25MZmExX27ez7j//sKOOY9fXwpNzM5n5+kYHDQqBrdubFW/\nCQX5uGs0aP62VXYsLZmp29dRjoE2bv6sGjHplmNsQZIklkSf4scLJ0ktKgYEXDRqxoSG8mqbHjhf\nXZGZpS1jwNaFrIs/h14y8lXXml9avtNEFibwnwsrERGY1/Y5i4SPJElMPzUfkywxJ3QCPpp/Jotr\n+Y5f2aj9DWc/Vz5r+R/87H1uOcbf0YfwH3by5MmXkSQBpcLErsyDHMg+Qn3Hutgp1NiJduxbsY/I\n4xH0m92b51u9VOl5n2kwk/ejZnGx+AgvnzmKUnBALdpjr3DESemCl50vwwJG4mVnWTYSQGpZChtT\n1xJbchbQIcvgrPKnv88wenn3tkhMmiQj7j3teaXvZN4LW2TxuQ8/F0WpKZ4OHsOwr4H6Va1eqzoO\np7p4+Hti8qg4m+jvOCgdcFC62GSUeI1hAeM4nPsbx/N2Mjpw8n2ZRfRy3x6svHCeZadOM7R5E346\ndoqTKamUGQy0DQjgpV5d8XZ2qrqjG9h5NoaVu09j76BixUsTb3k/JiWbb1fsR6lWsGTOxAp/bk+9\n/DP5ZVoGdw/lqak92bjoAD4BVa/qVUaDEG8io9OIOBlP2/a2meHebWrFz33K1F7tyCgo5pfw04z+\nfDnb5jyGWqlk+k+bkWX4YNIgqwaR+Lx8dJKJDj43b8N8cewA8yKPADAjrAuvdqp+QO3mzZt5dfE8\nDIN7ItuZHwpNvTx5sXV3Bgc3RhBuXtL30Diwd/iz9N+6gC2JUWhNRhb0qDio714jU5vPC6cWIiPz\nYYupNHW9fbbG33knchm5+hJ6+4QxyL9mtxcr4o11n/DpuDdxqevKgbNHbit8ruGosqeDezciCvfR\nxXME9soidmftJ7r4Cte2hXYs2kVJWgnDJqhQda98uHFSOfFpi3msS1lNZFEEZcYitFIxZaY88g0S\nyeXnicjfja+mEU/Uf77CAGWdScfW9N84lruPclMuggAiDjR37cXowHF42lln/7A3ayMmGdq4V55k\n8HdauLbnRH4c+7LCGRIwyKq2dwuloLS6en0Dx4ZEFkVwpSTBpiKnSlFJa7c+nCnYyZa01YyqM8nq\nPu42bvYamji5cmTLetrHRKEJCgLZ/FdwuTCPtVGRPNuhIy/3sWxPPSE7nzeW/IGgEPjphfE4aW6O\n8dMbjTzz8RpkSebD5x7A0/X2wmr+T/uIS8ulWbAPb70ylEMHLiFLMi3bVs9hf/qMATzz/HLenL2W\nZ57qw8gx7e470Vorfu5jXh/Zm4yCYv48e5nxc39lEQHoWgAAIABJREFUTKfmJGbmExrsy4AW1mV5\nrTtvjvcZ0NC8rZRTVsaj29ZxrjADe0HFssFj6RRg2UO7Kt5//30unz6Np78Xzj1aIihlLmszmX50\nPYpjAi4qDUFObtRz9qCek7v51dmDzYMeZczOZWyLOk3b5evY8u4X1KlTdczM3USSJJ4+8Q0G2ciz\nDYfSw6filNIb2Z52gv3ZF/BSO/Ne80fu8FWamXvpV04pLuFc34NWndvR0q1q75XnGo3n0eOH+TNr\nJ8s6fcWk4ClIkoRO0lJsLGbg4u78sv979GFFbE9fwWD/yZX2J4oi4+tOYjw3PwD1kp6TeSf4I30D\nmdpYPox+iabOHXms/lPYK83L96fzT7MtfRPp2suIgoQki/hrmjDUfxSt3G2rU1dqLGR7xkZUgsBA\n3wlWte3v24+juas4nnng/hE/ovWO8N28OhNZFMGerHCbxA/A2KCpnCnYy+HcrQwLGHfL1un9gFd6\nAnk7tqG5GM3CtRsY37oFaoXIqtPn+GTffuafOI4MvFKFANLqjTz831WYjDLvPjyAZoG3TkCe/2I9\npcU6hvUNo2/b24/18YnZrPz9JBqlgm8/Nq8c/bnTPM4PHmp73UaAxk0DeOLRHvy09ADffv8nCxft\nxV6jRpZlTCYJo0lCkmTzlywjXw0ys7dTMW5sB6Y9fudXMavi/vuE1XITc6cO56FvVhKZkMHnm/aj\nVIh8+6j1ldL/OHwEY3kZJ3OT+W7FUdK1RcgCNHbyYv3Ih3Cxq/6y/fVrnjuXZZvWM2n605jUCkqN\nekoNesqMekqMegp05SSV5HMqO4XfEi/cNA91Uqgp/P0ASb8foFtqEuc27sbVrnp713eSH+K2kacv\noatnMyYF97aoTUZ5Hp9dXItCEPm23Z01TrvG+uQ9/Jl5BEcvV3ovm8x/W79qUTs7hZoeXn04nLeD\nRXGbeLbRWERRxF50wF7pwMNDpjGi33A+ufgS2zN+I9ixCc1crI+JUItqunp1o6tXN07mHWdV0hJi\nSo4x69wJ7EQXDHI517a1HBRedPbszQP+w6plmihJEoviPsIgy4yr8xD2Suu2Ldzt3Fk9/SSZpzfT\n5cAQBraz3gvqn8YcNwQGyWhxzFV79zYslAWiiqJsPq9aVNPWvR8R+dvZl7Wd/n7DbO7rbnHF2xOn\ntu2Y8/RTPNLhrwD6h9q1pm+jBgxctJTvTxynX+MGtA6s2Ax12rw1FBdrGdm9OWM63TpZ+mnrcc5G\npVIn0I23p92+OLQkSbz87lpkWebdl4ei0ZgDyXOyipCB5i2qP2l8aEo3hgxtzffzdnPseBx6vREE\nUCpEHNRqVEoFKpUCtVqJWq1AEATi4rNZvuIwzs6a6yU07ha14ud/gF+mT+C1FX9wPiGD+U+OxtvF\nukE6Li6OfW+9icLJiY315iDKAiGOHjwW1o4pYZVnwdhCr1696NXLMuWvMxlJKsknoTifhJI8kksK\nKJziwpq8Auz6NmLAn58zt91kuvlWbcr4T1OgL2FVUjhqUcn7YVMsamOO8/kOoywxq+lYAh1qrm5Z\nRRzPvcDShI2IgoAJE0+FjKWhs+XmmE82GM3BnHAO5uzj2Ua3bke6qj2Z3vBdvrj0Fkvjv+Ld5t/j\noHS2+Xrbe3SkvUdH/kj/nUPZeyg1FaASNNR3bM2owLEEOlR/YM/VZTL/8rvk6AsJsvenu7flZVQk\nSWJ1/HEWRB8hJxeMeomn9/7Ck6p83gh74J7eHrhWwFVn0qISLRtHlKISd7U3efpsSo1lOCptm4wM\n9R/LqbztnMo/cN+JnxOpKaTpTHR7cTpvPDztlvf9XJxZ9OBIJq9ZxxNrN3B0xrMoFTd/Dkq1eqbN\nW0NsQjYNg734z8RbhU1UQiYL1x5EZafgx9m3xgFd4+uFf5JdVEaXlvXp0fmv2E8XVwezI3xWEb5+\n1Yv7AXB3d2T225ZXrs/JLmbipO/4ZcXhWvFTS/URRZEvH7Z9sHBzc6NhgwaUuzjxfa+RDK7f6J4Z\noO0UShq5etPoxuKl7eHrCU/x/cVwFsXtZvrJpYwN6sKclkPv3oXehjfOLkFC5pkGQ7FTWpbC+2HU\nr2Tpiujq1ZRhgdbFmNhCUmkGH0WZg3hlZIYF9GJ4oHVL0kpRSahzKy6WHuNkbjTtPW/dLgt0aMBQ\n/zH8lr6BLy69yutNvsJOWb1K0Q/4D+MB/5p9SKaXJbAnaz2nCk5ikmVaubZkar1ZFrWVJImvo3az\n9NIpinUyoiAz7KvXGObZmHnpJ1gac5qognR+6flkjV5zTXLdyVrS4YTlk6g+Pn3YkLqaNckbeLS+\nZUL/7zirXNEoPMjRJd6RlPE7xZX8XB7bsAEQ+HjA7VdiADrVq8vops3YeCmalzZt5dsH/xLUJy4n\nM33hJnTlRhrV82b5zPG3tC/X6Xnu07Ugw6czh+PufHuRGR2TxoadZ3FQK/lozs3JIfVCvDm+/xIn\nj11h6Miq/cVqGi9vZ5wdNfeER1Ct+KkFT09PYmNi7vZlWM2zTXvR3achTx9bwvqUIxzPucLP3Z/A\nRW37QzWhJIuD2Zc4m5/AlZJstCYDdgolnnZONHUJYKB/S1q4VR0s+HXMJqKKkgiw92RsXcuCHPdk\nnGZ35lnc1Q582GKazfdgKWnl2bx85ktMsglREGjnEcoTIWNs6mugfzcuXj7G3qzjtxU/AP39xpFW\nHs+pgtP838XpvNj4E9zVd35l60YkSSKx7CJZuhRydOkU6HPIN+RSoM+j2FiKVjIAAhpRweS6T9DO\no7dF/R7OiuXlI5vIKjVgr4JxDRszq9VgPO3MAuIhqQ+j93zH8ax0zuQm0tqzegGndwrV1ZifcivL\neAzx68/G1PUczTtis/gB8NPUI7EsgnKpDEcLV57uJuujI3lz2y4kk8xTXdvTxi+g0uM/HTaI/QkJ\nbIuLZV/sFXo3CuHLLfv5edcpAKYNbs+Lw26fVPLM5+soL9UzZlArure4vRWB0SjxynvrkYGPXh+J\nWn3zI37QAy1ZvfQg61cfuyvi5xqyfGecyq2hVvzUcl/TwiOQPwfM4vHDS7lYkkSfXZ/QxbMJs8IG\nU9fJMsfZYzkxfHVxG7HF2dfNEAFEAZSCiEmWSCjJ41RuMivij+GkUtHVqyGPN+hDI5ebB7syo5ZZ\nZ37iXGE8jgo7vm77tEXXkKMt5KOoVYgIfNPmuTsa8GmUjKxN2cPKxK1IsoRKVBDk4MdrTaahEGyb\nbSeUmFOdgx0qH/wfqT8Lu8RvOJx3mPcvzKC+YzBt3bujM2nRmkpxUDrRxXOw1fE1lZFSdpm9WRtJ\nKL1Mnr6IW91JZFSCiIPSnvqO9enpPYxQ144W9Z2rK2HG4VUcTc9EFGBkSEM+bj8GjfLmwGG1qGRg\nYDNiC46RoS2skfu6E1zb9tJbWcZDKSpp5hJGVNEZTuadpr2HbdvlBtkct2Uv3rtxfGA2Y33m983s\nvXQFhULks2GDGNOs6pqLoiiyZPyDjFz+C+/v2sPqPWc5cj4Bjb2K+c+Oom3I7bdsv990mIsxGQTX\n9eT1yf0q7H/O/22kSKtjSI9mdGh7awp6UF0vGjTz50p0Ol99/gcvvnard9Cdxt/PlYuxGZSV6XFw\nuHumlrXip5b7Hnulml97PsWiSwdYcmU/h/OiGBkehbPCETeVE94aZzztnHBQqHFU2eGgUOOjcaaB\nyYHZ4UvI8jf/AQY6uNLExZ+W7kF082xKA5e/HLJLjFqOZsewJfUkJ3MT2Zkezc70KFxUdjR09Cb8\ng6XoZD3Br/RDEAUC7T1Z2H5mpfW6riFJEs+fmodBNvFi45EEO1XPPPJkXhRrknaRqcvFKJm4sSKU\njEypsQz5qgQoTcin9GQmX73/Gg7V8KI5knsKWYbevlUHM08InkkTlzb8kb6SK6WJXClNuun9LWnr\n8FS7MdhvHB08Kx7oq0KSJFYnf8uxvCPICCgFAR87T+o5NsLHLhBvjT8+dnXwsguwKctpd9oFZhzc\njM4Ijdydmdd1Ao1ca874825wbeVHa7K+htlDdccx5/wZtqZvt1n86EzlgHBPb3ldzstl0po15BeX\n4+3qyOrxEwh2s9yDK9TPBztRQX5GKUfyivH1dmbD64/gqLm9EDhzOZWlG4+i1ihZPLvijMO9B6I5\ndPoKXi6OvPnikAqP++93U5k8+mu2rj+JvYOap5+/vTninaJFyyAuXs5k/75oBj9wq2P1P0Wt+Knl\nf4Ynm/TgySY92J4SyYLYfWSU55OizSJFl3nb46OfWYg2OYfO3z3L8ilv3bKKcyNOSg39/VvS399c\ntf5AZjQrEg9yNj+Z40mxXN55AoAWMwcxKXQgU+pZXqLh84trSNcW0MGjIWOCbPdRiitO4fOLy0gp\nzwBAuOoQfCNmn+G/nFwS5p0k4cQl1gSv5bVXbKutNPOzd1n837l0mT0cn86WPQRau/egtXsPksti\nuVJyAQeFM/YKRzK0yZwuOERaeSa/Ji2mrmMjfDWWB19fI60snu/jPqDIWI6L0p4pwTNo4lJzy/yf\nnPuDRRdOoxDgg459mdyw4ira/wS55SWsSzjP3rQYYouy0ErmmAovOyeGBjVnZvOeFhmTqq6Wl9BL\nequvIdA+AHuFM4llV2yO2dFLWuDeLZvw0+mTfLxnP7IEg5s34pvBQ62+z/LychLmz0eldKLdpKfZ\n/OY0NOrb/27KtHpmfmGu2/Xfl0bdUtfrGkXF5Xzw9TZEQeCbjyZUek0ODmp++PlpHps4n/U/H+b4\n4Vg+mTsZbx8Xq+7DVrp2bcTaDSc5eyapVvzUUktNMrhOGIPr/JUiWqQvJ7WskEJ9OcWGckqMOlJK\n8/m2ZThJUhTFTkrSyvMqFT9/p4dvM3r4mmNb9JKRZZo2zI/bSomdiYZOltdUO5F7ka3pp3BRafi4\nVeXFNStjd8Zxvo79GVkGlajAKJvw03gRaO+D8nqphGtFFKChU116+bTn3T7+rMpZhVtQ1Z4+f6dE\nr+WZ/evYvHUb5ZnFRJ3J5IEd3/Je6+F09K1nUR9BDo0IcvjLpySMzvT3G0dU4QkWXvmSRXEf81bz\n7626rmO5O1mdvARJluns0YUJQTNqdCVhxpGVbI2Px12jZEXfR2jqVnHa8o0U6ssBcLAw+L0qEovz\n+CpyP/syYikxahEEc80ujajCTWWPJMtklBeyOPYQyy4fY02faTT3qPxar9XW0lkZ83ONFq4tOZF/\niBP5EXTytN7SwCDrEYR7T/zojUamblzP8YQUVEoFnw8fxPDG1v/NSJLEtPnLKE2IRVSo2DDr4QqF\nD8CTn6xGV2Zg0rB2dGhW8STghTmr0UsST0/sTt06VZt4evu4sGrLi7z+4q9cikxhyuivGTyqHc/O\n6H89Lb4m0WsNZGYUkpGax+XYFGIubkKbdw5f56tiThAQRQFBwFz25eqXwaRn87blTJwygvHjbw0C\nrw6CfC9EHtVSy10ipiiVyYe+RyUK7Bvw7m2rt2uNenZnniVLW0hHz0aEVRDwHJEfy8sRPyAIAgva\nT6eJS9UrFg8d/j9SyvP5seMLNHK2LUX7z8wTfBWz/OqKjkyQvR+P1B9OJ48Wt7hl/52DZ68w8/vN\njOvagjcfsXz5+0B6HE/+uQGtQSbQTskgowMRvhJX9OkABKq9eaf1MNp7BKFSWb+lBLAo7gMii6Jo\n6tyQR+q9iqPStdLjIwuOsi1jFSnlGagEkWn1ZhLmVrMZc+EZl3h0zzqCXOz5Y9DzOKos9xEa8ee3\nxBTkc2707Nt+znLLSziYmUBSST72CiXd/EJo5n7zNlpCcR5LY06wMzWabF0xggBKFDRz9ae3f0PG\n1m+Fv+NfM3it0chn5/bwS9wxFILIit5TaetV8edsc8omdmetZVzQM/SsoA5bZaSUpvHWhTk0cGrK\n26GvW91+zrnHMWHik5ZLrW57pzifmcHD69ZRUqanjqcLaydMxNvR+pg0o1Fi7MIVXMrMQZcQj0a0\nJ2rppxUe/9WacFb+foqGId6seK/immc/rznKwlUHaRzkxU9fT7P6urb/cYZ5n29DX25AUAiEtqrL\nlEd70K5DCLIs33YMKSvTcfxADAmXM8nPKaEgv5TigjJKirWUlerQlenR6wwY9CYkk4kbzdpyS+I5\nmbASO6UTfRrNqPTackqucDJ5NUFBQSQlJVV6rLXUrvzU8q+msUsgE+q159f4k2xMPsqE4Jszs1Yn\nHGDuxZ3or0dC7yXY0ZVvOzxGHYeba0y1dW/EW80n8p8Lq5h+aj4/d56FXyU1vKIKE0kpz6Oxc4DN\nwmdf5im+ilkOV4VPb58OvNh4MgoLZ8+tGwWCLBOfnmfxOcuMeh7bvR6TJDOrQzeeC/vrZxaVn8Hb\npzYTW57C4NFD0Z5J4MypCJo1s36WPLX+a3wW/RIXiy/zduQzdPLoSnOXDvhr6uGm9iKt/AqxJedI\nKrvM5ZJLFBu1gEygvR+P138TT7uaj79ZG38KEFjYfZJVwueHzas4sHAFzac9gMEksSs5isOZ8Vwo\nSCe1rJBioxbp76HYF8zCJsDeDaUokl5eRJlJZ54dI9Dc1Z/HmnRmWFBohStbGqWSd9oOpKmrD29F\n/Ma0/Ss4OuKlClef1FdNIfUm67e9AOo4BqBROJFQGmdTexMmFPfQttfP507z/s69IMP4tmH8X7+K\nU9kro0SrY/i85WQUldDc3wetxpWUrIoD309EJ7Fy6ynsHFQseqPiOB+93siSNYdRCQLffFSx709l\nDH6gNf0HtuTX5QfZvPY4kRGJvBmRSEL6AeJS9jGw25P4ePsRfnwVDfw64ygEoy+//edDEAVEpYhK\nrUTjoMbNS42DowYnF3tc3R1w83DEzaMvew560rhRMzq264aMjCyZXaAlyZwJdu1Lrzew8Xc3HpxQ\n84HZteKnln89j9bvx6/xJ/gt5dRN4mdFQjhfRu1ELYo8VL89IU6+/JF6moi8VCYe/JYNPV/GR3Pz\nakR/v3Zkawv5Pu4PHjv+X9Z0mY1TBUHPcy+Z9/JfbGxbNezwzAi+jFmGeU9Lppd3e15sPMWqjC0n\nBzsUgkh6XpHFbT6O2I3BCC+163yT8AEIdfdjff+nuZifQc/SNZTqDZSWllrc942oRQ1vNf+ew9l/\nsCV9FYdzD3M49/DVd2VujGdSCQItXcIYEfgo3hrLty+tRcT8s7XkZ1yk1/J99EF+S7pAxJtzKY9O\nJDbIh9bCp1yfTMtgJ6rw1bgQ7OhOMzc/Qlw8KdZrOZqdyPn8NJLLzMLUTqGivWcwo4NbMqpeGGqF\n5cP3+AatuVyUw9K4Izy+fxUr+96+ZIpKuBbzY33A8zU81B6klSfb3P5eYUP0Bd7fuRelQmTB6JH0\nrmdbAc+MwmJGfvczRVodvRrXZ8GUUTz+/TqSMwopKNPi9rc4nqIyLS/P3QTAN6+OwaGSbagefUZw\nKfYys2d/iZOT7QkLSqXII4/15JHHenIxOpUVSw9ycfnvSJKRpJRU4q7EkpgajbZIpkerhtRv4kfT\nFkE0Cg3AJ8AVvwB3PLycUSotE65TnrY8kWHUpK623lal1IqfWv71eGqc8bV3JKYoG6NkRCkqSSrJ\n4qvoXWgUCtb1eIEAB/M++pigLvwUt5t5l/Yy6eDXbO49C6e/ZUlNqteXbF0h61IOM/X4F6zsevtt\njpjiNPw0boS5WT+o7s+K4IuYpVf/J9PTux0vNbFO+FzD2U5Nfmm5xccfzUhCFGRmhFW8LdLU3Y+E\nI6fJzc0lKKh6NeG6ej9AJ8+BROTvI0ObTL4+m2JjEe5qL+o5NKaBc0t8Nf9MjbfuHvVY9uF3zIxN\nZ9u7397y/qWCTJbEHOdgZhxZ2mK4ukrT6rGxmCIuEzLsAUz2asLc/enhF0JX3/oVBiI/0axmg6hn\nt+nP78mRnMpNpMyov+3qj1ph/p7BhoDnv6h8q7WqlvdCHMb+xHhe/2MHoiiw9qGJtPC1PI7vRi6m\nZzNx0Sp0RiPj27fg/RHmreWmgd6cjE5m74U4Rne4OUX+if9bjV5rZNqYzrRuGFhh30fPxXPqxF5M\nBi09u1R8nLU0bRbIS68P5fDeC7RqOYDfdnzEpIH/h0pWsXbHXELDmtTYue4mteKnllqA3j5NWZ0Y\nwfexO3iqwQCeObEYkyzzccsHrwufazzWoD9Z2kLWJJ5m0sGv2Njz1Vt8eWY2GU2WroD92VE8fWIu\nP3Z45aatiYzyPGSggZNlwbLXyCzP45vYlZwtuHh1L16mh1dbXm7ysMVbXX/Hy8WRuCzLt72cVGok\nmSqDiB0cHHBwqBm/FoWopIPnP5uSezuSImIo+uMguw6doVlzP+o4ulPPyQOTLHM2L4Uig/b6tlR9\nJy9GBbfk0cadLMq0qi6SJPF7/CXWX7qA1mjAx9GJTv51mNi0Fcqrv6ux9VqzIOYAiy8eZWZYz1v6\nsLu28iPbLn7Mq2O2ShjbhVNNcSE7iyfWm1defhw72mbhcyQuiSd/3oBJkpnZtyvP9v6rnMOI9qH8\nsjuCraeibxI/n674k8SkXJo29uPZURWveGj1Rl7/5jdC+z+HVFTCsQsFdK5mtYiiojI2bD3NqYgE\nVv/8Efk58Ywd/Q7bfj9Bdkoe3bs+8D8jfKBW/NRSCwBPNBjA5pQzLIk7wpK4w4BAb98G9Pe/fSrm\nG80fJEtbyL7MK0w7+h3LO9+aUfRhy0d59sTXXChK4bWzP/Blm2euvxdV+P/snXd8VFXXtq85U9J7\nDxACJPReQu+99yoIgiBSLCAWFH0EbKhYUBEE6dJ771VCJ4QkQBqk994nM2e+PwIqpk0mE8DvPddP\nijN777MnTLlnrbXvFQGAh0Xxjs0lcTv1ASvDdhKbl4BM9veJro6OLZhf/2WDhQ9ANUcbQpNSiUpM\no4Zz+UfVVbn5ZPv6kTgqA2ersouQ/38it1DN75o4bEf0oFmzpqhV5kRkpxCRkwKAQianjUNNJni1\nYkD1Bs/cq2bw3s0Exic9ddvB+8F8fP4M9RwdGFq3AS3tPdBk5LLu8J4SxU9ceDwH55+Dlx0ZPd2w\n0zWySguY5xf7icnKZPQfWxG1OpYN6ktnD0+D1jngd4/39xwD4LNhfRjR8unoTj13J8zMldwKiSFP\nrcZMpeLS3XD2nLiDmYWKXxcU75H3TxZ8u488jYbJYwZw8PAddh+5zYj+Lajhpr/fEEBCYiZ/7LrK\nn5dDSEnMAop++vl5mWi1asKDoxgxsjcabQFzPj1RobX15c7VMK6evcfD4HhMzVTU9HKmqU9tmrf3\nqtLXkCR+JCQoSn3t7fI2PwcfIyI3mfaOdZlRp3eZc75pMYUpV34iID2RebfW8X3racXG/NxqLhOv\nfMn11DC+CNzKB43GAxD62BG5jmXZ9SlqUcNH/j8TlBkK/C16zOSmvOw5mD6u7REMdGV+gld1R84F\nhXP7QYxe4sf/py2knLtM45w8fIYPp7VLdXzcqtOpRk2sTQyvO3jRefnEdvLlWha89yELfYp8nDSi\nSEp+NlqdDlczq+dmzuefFE9gfCLuNlas6T8cT2tbQtNT2fngLsfDQ7mXmMy9xEsAJKzYzcMHIfxR\nuz0TRj9dTHvm8AUiLsWyWzhIv4HDGOjevcJ70b0QiauKk1WQz8CNGylQa5jXraNejs0l0b5vf277\n3cHz5dn8NuMlOnt7ljjupW4tWHPkGrPX7ufbiQN578eDIINf3huNmUnpdT5nrwVz9X4krjaWzJ/S\nk0Y1XVm64ijT3l7N9pUzsbMr+QtJdm4+fnej8A+IJiAwmoiIFHKyCoq8vwQZNWo50rt7Qwb1a4pC\nPou0tDSSEjX06LELbZ6Gld+fokefVljbGCeae/tyCN8t2k3iY9H1BN8LD9j2+0UUgow69V2ZvWgo\ndRtXLnVeEpL4kZB4jIuZHYubjdd7vCAI/N5uFiMvfsuFxId8FrCTDxuPLj7G5x3GXv6Mo/E3cTK1\n4dU6A4jMTQSgYRnH4dPUmbx+43NytDkoBQUanYaaFu4Mce9GJ6cWqAxwJS4JMT2aO2vfY9mjPxly\nfHe549v26E14RCTmdb0IyEwgICuB9aE3QQdKBGyVZlSzsKaOrQNNHF1p616devaOL7Rrb3l87HuC\nWxlReJo6/CV8ABSCgIv5szGHK4vlNy4BMr7rMYCGDkXRxKZOrjR1cmVJp94k5maz48FdIjLS+KOB\nF4mpKRTYF9/30CnjOZt+FbeOnqx9uJMLydf5pOFczCvQhLbo1NrzT19VhKScbAZt3kx2rprxrZoy\nu41hFgmLDp/khq8vmqxMJjauU6rwAZjbryOHrt/H99pdGqz6CYcaPsyfOZqGnmWfUlz89a9kpGXT\noWEfxr67jpTMXArU6fgeWEa1Q9/QtfdCBEGGTgcajRZRKyJqdYiijn/U2WNqrqReAzcG9G3KwD5N\nUSiefn1aWVnh4QFJydGs/uE4+/fe4f05m/ll0wyDfjb/5PblED6YsQ6Ahk1rMHhCO1q09yIrI4/A\nWxHcvhzC7SthPAiM5Y1xK2nVrg5LVr9i1PcQSfxISFQChaBga8c3GXL+a3ZH+uFsYsN076ePw5oq\nVKxrO5/xvl+y8dFZnE1tickrSpW4m5ZsSCaKIm/d/oZsTQ5KQY5KUDDPeyKdHFuW691TURws5Oi0\nGqITY/Qa/8b06Zyrbs5wj0Ys6dyb85EPuR4Xzb3URCKzM0hV55CUnoNfRhy7IwLgJih1cibXa8n7\n7bv+VX/yX2FD4E02h9/ASjBl/8DJz3s7JRKeloYgQFv3kr8hO5tbMqdFUQG1u60VP/hfxqJm8SJZ\npYUpXmOa0dm+G4/y7hGa/Yip19/nLe9XaOfYXK+9VMY67olX1bPk3KOHvL5vP+pCLf0aerO0R9kR\n35IQRZFpf+zlSnAk9WfOJysllRMxGXxUzrw9CybRuM9wEu5cQJ2XTURyLx7GpVDL7en3haCIeLac\nuc2FWwFc2vU9yMDS2ROF0hQzlRIrRwvkChVaR9ijAAAgAElEQVQmKnMUCvljh20ZFhYmKJVyVCoF\ntrbm1K7lRKMG1WjfuhbW1vpFcFQqFXMWDObOrWhCQhPYu/UKw8dXzj/riwXbQAbfrJ9O41Z/H/iw\ndbCiRm1n+o1qA8Dd6+F88c42bl4N592XV/PN5pmlLVlhJPEjIVFJzBWmbOv4BsMvLOfXkPM4m9ow\ntMbT1Yf2Jtasbv0mU68t59v7ezERFFgpTEr9JrPw7k+kqtNQCQpsVFZ83mQubmZV0wF9wrjRLD37\nIU4u+hVfN3V2Ax2EpqdgqVIx0KseA72eLoRUazTcSYznelw0/snxnI0NY03wdbYE+zG7aXtmtfD5\nT0SCtt/3Y7HfCZTIOTRwKlYvaFovJTcXc5V+kcDo7CJbA0+r4inOQm1RWwwLhRk/tvyYrZEH2R19\njK+DV9M6qQkL6k0vt+muiPifift89ed5fvO9iUwG7/XowoxWbQxa5609h7kSHIm7ozUHZs7mzY0H\n+TMykm2+dxjXvvQWDhamKs5s+IlJr88j1d6bfTeD2HczCDkylIKAqNNRKIroHv9ABZ0cz5adqWZv\ny8pPJ9Oojttfr6PC395BLpdX2evq658mMW7oD6z66TSdezXC0cnKoHWunA8gOTWVtu0bPSV8SqJJ\nm9psPvs+U/t9Q4BfJL5ngmjfo6FB1/03L/67j4TEfwBHUxs2dHgdpSCwJOAAlxLvFRtT09KFdxuM\nKiooFAvxtCge3taIGj4NWE1gZggKmQJBJrCo4YwqEz5PcKtek/RCjV5jFYKAiUxBXG5WqWNUCgVt\n3Kszq1U7fu07jLuT3mRC7eYU6rR843+B5ptWsDXwjrG2b3RyC9XMOrOXD24eQUDGph4TqGFt+7y3\nVSrVbazJzldz4lFIuWPDMouijk0diotdjaghIzQZ4bHf4niPwSxv9hE2SmtupN3llevv8yArvMz1\ndejAyNFJYxMcHo6dpwdffPg/TFUKtk8YZ7DwCYxL4KR/KLZWppyc8woWKhXLxvZH0MFPZ3zLnV+r\nVi0uHdtLwOYv+Wpifzp61cTd1horUxPsLMyo6+JAz4Z1+H7qYG7++BYPb57n0sn9NPGu9pTQUSqV\nVfqFwtbBktlv9UErirw7a6PB6wwe1pfzD36iTQ/96ngEQWDJr1MA2LbqjMHX/TdS5EdCwkjUtnTl\nZ5/JzLy6nnk3t7Cxw2vUt3n6Bd7TqQXz//ycxAsPGLz4fQAC0sO4knKX8Oxo7mWFo9EVIpfJ0eq0\nLKg/mdqWVe9h4+loS3xENqlZudhblR8Ot1eakazO1Xt9lULB51378EG7Lrx//jhHo4P54Npxlt/6\nk6UdetO3jnf5i1QhoihyKiqUs1Fh+CXHEpyTiE6mw15hwd7+k19o4QPwS+8h9PpjPQvOHqPX5Dpl\nfgjevX4TpasDt5OjCUyPJzQjmYisdBJys3mwcx+xm49j/prA9F8HAeBh4caaVp/zc9hmziVdYeHd\nb+jj0pnptUpuoPmid0y6kRjFmA1fkh4RhVLUsmXsKJq5GG6M+dXJCwAsH/13k1N7K3OczC1Iz9e/\nR5ogCPTzqU8/n/oG76WqGTyqDScO3+H+g3g2/XaeSdO7Vmh+gN89crJzEBBY+/khqrlVp23P8gvL\na9R2RqmUkxhbuit2RZHEj4SEEWll78XS5iNYeHs3U6/8xnetXqJALOR43B3806KIz8smcucNsgKi\nWddsAxe1N9H9o62BgICADLlMYJbXWDroWWdRWRpVd+FKZDQX7z1kqE/5b0bVLK2JS80it1CNuVL/\nRohWJqb83GcoiTnZzD9zhItJj3jt/F48rtqyrGs/2lWreAf3yrLgwmEORAVSKHsc+dKBk9KK8V4t\nmNO8w3+iRqmOrQMD6npz+EEIn1w+zZJOJdetrNyyifD//YhpIy/Gm/2zPYcOuSBDtLYHuRxL56cF\ntyAIzPV+me7O7fji3q+cSLjIzbQAPm30VrGopCATijqsvoB8eesMa0MvI2/kQa+vPiIIkVknD/Ln\nxNcMXvN2RCxWlia093z6uasR/zvpv4rw5YqJjBmwnM3rL9Kzf2Pc9Wik+oRhA8eRp8mgU5NheFTz\nYOms9Sz+fTotOtYtd66JiYL8/MLKbP0pXvxXtYSEkZk1axaW1lZcvn69Stbv69aSeQ37kK/V8vq1\nTbx1YxtHY+6RkJdNdXNr3Kb2pc60Drj3aoaN0goL+d8naexNbBjk3pVfWn1IH1fjOvyWRXvvomat\n18Oi9Rpf19YJZHAjTr8i6X/jbGHJpsFjOD9iOi1s3YjMT2fc8W3037me1Dz9I0qVZc6ZfeyOvoNC\nJjDEvTFrOo3h/th3uTpmLm+17PSfED5P+LbbACxMFGzy9+dqbPH2EhpR5MeoIOT2NlRvWJfx9Rqz\nyKcb2/uOI2D8PMImvsf3b7+Lx6rP6TR6aInXaGxTl/U+y2hm04AUdRpzb/+PPdHHnxqjkCleuOPu\n6QW59Du8mrVhlzEVVGzsMpGT7y7Bx6sh0XmZnAgvP11YEndj49EUirT0KB45yiwowNbsxawRqwwW\nlqa8s3AwoqhjwezNes9bPGsjFoXVcbCoAWpnYpNycK5mz+IZvxMflVLu/Px8DeYWxus4/995ZUtI\nGImAB/fJycpm6s6N5Gv0q3OpKC95dmVzh5n0c6/P6JrN+c1nCh80GIqJzhqNey2cu9Un5c9QlAiI\n6Hin3mR2dviWdT6LmV5nJC6lnAKrKlp5VQMdBMcn6zW+qXNRvdKNeMPEzxNq2tqxd8QkDgyahLeF\nI/eyEumxYy2ZBfqnCwzlTnw0h2MCsJdbcHPMW3zfbQg9PLxQPQM35qrAVKFg3aARALxyZA+5hU+7\nNM84tZc8d1tm7txIyI7DfNF2INPqt6WtS00sKxC9UwoKPm40lze8piCXydkSuZ95fp+Rqc4GisTP\ni8Tp6FA6HviRsNxEGlq54zv0Ldq7egKwvEd/AD67cs6gtXf5BQAw4F/Ox6IoUqgTcbEyrCj4Rad7\nvya0aFmTxKQsVi4/Vu74r+ZvxfeYP03qtic2OZTtJz7HxEyJfQ178nPV+F8pvxGuRisazWMIJPEj\n8X+QI/sPMHr1DxTU8KT39rWoq0gA1bF0pZFVbW4kJDLx3DbeuXqC6wnJuFuqeLT8An6fHeTe0Vss\nbTKbrs6tMZUb71tNRVEpFJjJFcSk69fgtL17UYg/KCXRKNdv6uzGybFTGV+7GeliHqP3bzXKuqVx\nKzycNg0aEfP+j3zfYcgzaT/xLPBxrcG0Fi3JLdDQb+f6v57bCTnZnIkLxVFpyY/dBpW7jj51O12d\nfVjT+nM8zasTkRvDqzc/4HziNRRCkdu4KIrlrFC1iKLIAt+DzLy8jUKdlrn1urG/39SnhF4tW3vq\nWzvxKCedoKSECl/jSngUyKBf/adr1uLSi/q6OVoa78P6RWPpt+MxNVGwZs12dm07VOKYSyfuMqnL\nZ5zbexMbJytWH38HlakKdw8Hug1oxoOgWOQKgZiHZb+PZKYXNUe2c7Q02v7//3jFS0hUAEtLS3ZM\nf4PJR3ZwISyK/rvXc3z0VKOkOLIL89kcfplDkQGEpGeiEYucS6pZmdLL3Zsp3p2oYeHAr1FWrN+y\ngbUTv6aulWflH5QRcLa0ICpDv4LCmrZ2yHQyHmWmGXUPX3Try96HgaQWVE3qKyknmzePHeHPB/fR\n5KsRdFBQUDXi93mxqH137qUkcjkimu7b13J67DS+vH4eZDC/Racyi6ErWqNipbTk2+YL2Rt9gi2R\n+/kxdD0WMhPUWQWodYWYYlL+IlVATE4m405tJF6djpXcjE3dJ9LIvmTzwEUduvHSsZ0sunSa3cMn\nVOw6qRk42JgXixbaWZiBDtLzqj6CWRm2rj5HYlw67h4OdOjRgGo1HfWeqzJVMnBYXV5/ayFjJ2xi\n8Ofv4uhqBzLQFmpJik1Dq9aCDBq2rc2nq6ZgbvF3GtDdwx51vgalTIYoli22Hz6IB8DJzXgHDyTx\nI/F/lg0DxjDmwBauR8QzZM8GDo2YbNBR0bSCHNaHXuJoVBAPM3MQdTIEmY5a1hb0q9GAyV4dcTB5\nOvw9c+ZMZs40nmGXMajt7EBEZgYPE1MxN1Px/ek/uRMZh7pQg62FGc7WlrjbWuHpaIeXowPK1Gwe\nhkXD2PLXrgiFOm2F0jBQVM+y5e4dYjIzkMlkmCmUmCjkyJARlZFBdFYG95KTSM4tElXuLi7MP3yI\n5dd9+fD8aXp6Pd/TZv8mqyCf4OQU4rOzSczJITk3l9S8XNLy82jjXp1XWrQo87n6x6CxTD6yi/MP\nI+i6dQ0otMh1AmPrNqmS/Q6v3od2Di34JPB7ji/aReSJEHrs7smMYcVbvlQ1u8P8+fDmITSIdHCs\nw5quo1HJS/+o61TDExdTS24mx5Ccm4ujns14b0RGo9XoaF6juGWAuYkKC4WS2zGxnA4MpWcjr7/u\n02q1AMjlhvfjMwYXjgewYcXJv/7/hy82U83Dge2nPtdr/rXLIezZ7o+TXT2sTS0RBDmxD4t6y8lk\nYGVnQbP2Xrw0tzcedYr3MMzKyEMmA02hFgursh3EYyOK0vFOrsbrJSiJH4n/02wbNJ5hezdzNzaJ\nkfu3sHvoS3oJoAx1Dr/cP8vJmAdEZuWh08mQCzrq2lkz2KMxE2t3wFL53yp2bFzdmf2H9zHlx99J\nFuVFHvgCCIKMuOQs7vF0aPrR2pUUJiXhFS/HqY431uYmOFha4GpjSW0ne4Y2a0iNUvoMlYYoimjR\nYa3S/2d3KjyMuUcPUvD4Q6U0FIJAbVs7ZrRqw5hGRSLgSGwUwakpZBXkvxAGhpn5+cw7fozTUWF/\nGdv9m2ORoXx99RIj6jZkYZeuWKpKFoobBoxixom9HA96QMaZc7i0qF+lPjBuZk6sbv0Z3sJBZDLY\nErWfxslN6eBomH9ORdGIIjMv7OJcYjByBP7XrD8T67XSa+7c5u346OopPrl0mp/7DNZrzp47gQAM\nadqgxPu/HtWf2dsOMGf7QZQImMgVKHQifj8twdnSnHtBQZjrKbSqgmsX7gNga2+BczVLft62DELh\npYFWtPJpQLf+TWjeulaJz5nTR+/w1dKDIFOw9Y/t9OzXtMLXjwhNwNHRiuSsPKztLMoc6+ZRVAOZ\nEG28SLMkfiT+TyMIAvuGT6T/rvX4RScy8cgO/hg0rtTxUTkpfOp3kIux0WhEGUpBRxMHO4bXbMZY\nz7aYKIzTb+t5kBPmT/Te9aTeqE2r2e8yq2d7xrRsjCAIiKJIXGYWDxKSCUtKJTI1jV3XWxF3PwhH\nNzfUGg0xyQVEJWT8lTr55bgvTnaWfDWqH+1q63eEPTU/H2RgZ6JfL6nozAxeP7wfUadjXKMmdKxR\ndJ08jZY8jRpR1FHLzo4Gjk44WRSvF+hVuw7BqSlsDwzg1Zat9bpmVaDWaPjk7Bl2BAegRYeDyowe\nNWtjb26Go5kFzhbmuFha4mBmzuqbNzgQdp8/Hviz7f5dpjdrxQedS/ZbWd1nOL3Ozef0wePI7t6D\nT7/Xaz+VOas1/rO53J57BVNrE74NXkdkbgzjPIZVYsXyWb52Faui7yA2dMdZZc32ni9Tw0r/FMlL\njZrx+fXzHIsM1lsI/xkWiUyAXt51Sry/e6M6HJz9MssOnyc0KYWcwkIKCgrR5OWRJWooLDTesW1D\n6D20JRdPBJCemkNqShY25kUn1pIiMjkReZ0Tu64jE2TYOVtTt3F1OvRoQMfu9dm+6SxbN91CLshY\n+vVYWrf3KudKxVGrNdy5Fo6npyPJDxPxblK2l1nDlp6AjrPH7tK5fxN8upYsOCuCJH4k/s8jCAKH\nR02h9/a1+D6MYeqxXfzeb1SxcadiA5jz5z4KRRluFibMqN+eCbXb/1Xg+V9nSO8ebGnZkpY9erPm\nvad9TwRBoJqtDdVsbehRr+jNfsmQPsXWUGs0hCWlciMyhsP+97n7MJ5pv+3ms3F9GNasfP+giIyi\nb3aOZvp9I/7+yp9odTo+6dKdyc1b6jXnn0xq2oJfblzj9MOw5yJ+RFFkxbWr/HLrKgU6LWaCgnlt\nOjK9del7+bpvP74S+7Dqxg1W3LzCKv8bXI6KYvuoMZiXEAUa1mcwV85dYujA8iMaghGcaWSCgMra\nhPfrz+a7kNXsjD5ORG4MC+q+bvTIU6Y6j1l7fmbLqwuQKRXMOb2D7zsOrfB1BEFgVtO2fON3iQkH\nd3Jw1KQyx8dlZpGYmo2Xu0OZ16rj6sCqaSOeui1t/iuIooiNjfFSOIbQzKc2e69+THBANDcuhTA0\nvQPDJ3XA2c2Wa5dCuHgqgCC/SJJi07lyKpArpwKZHHuA+NRA2jR+hc3bl1C3oWHmkMd23yArIw8L\ncxUqEwW16pe9jkql4LV3+rPq66MMGTKCWnWduXD5FEql4V82JfEjIUFRSuT46Ffovm0NZ0MimKPY\nz0+9/vY62Rruy8c3TiMX4MeOAxlY/dmYDz5LvL29uXXzZqXWUCkUNHBzpoGbM5PatuBubDzjf9nG\nJ7tP0aNuHazL8T3RVPCE0I3YWGTApKaG/Xu4WFoiA9Ly8gyaXxl2BgSw9M9zZGgKUCAwtVFLFnbR\nr/GrIAi87uPDpObNGbNzG3fTEvD5fRVbho6imdvTNSjX01JwfGkMn0yYWlUPpUSqm7nzS4ulvOv/\nGddSA3n7zqd81mQBlorKn9hRixq+9D/KtrA7qEUNVl2aYe5oz4+dhxu85tzW7TkYdh//9HhW3rrK\n6y3bljp2xYWithUTfSr+vLOzK95T7XkhCAL1m3pQv+nTkdkO3erTodvfTtMh92JZs+oM/n8cRSaT\n8d7HQw0WPvf9o1j73TEaNK2B36VgOvRtgkJZ/hfI4ZM7I8q1jH55GUm3gklPT8fJyfC2P9JRdwmJ\nx6gUCk6OnYqTjRmH74Xy7vkjAKy4d4JF109jqpCxo8fL/18Kn6qiibsrs3q3Q1Mo8tXx8+WOb+ZS\n1DRV31NkCnnRW1hlIgqCTEau5tmlIC5FPKL92lUsOH+czMICBnrWxW/6bD7u1r3CJw4tVSqOvPQy\n0xq1JFujZuSebWT+q6VCUHIiKuTUeg4futYqK35quZRG1rWIzktixo0PuJxsuLmoRtSy8v5Z2hxY\nxqaQO5jIBT5q0wvPN8dTY9LASu9325CxqJCz7OZFwtJSSx13OigUuULG6OaNK33N/wLZeWpu3Yyg\ncf0R3Lr1gJGjhxi0TlR4Eh/P3oidgyW2Fio0hRomvd1Pr7kajZYt667SpuE0NqzdWSnhA5L4kZB4\nCnOlitPjpmJnacK2P6/R7K0JLL92ERsTOQd7z6Cp/bNvv/BfZ2ZnH2RyGVfDirsO/xtThQIFArE5\npTdN/SfultbogLsJ8QbtTSOKaHU67E31qzGqDKGpKfTdtJ6JB3cTl5dNW+fqXJn8Gj8PHFxq0bK+\nLOrWnfd8OqNB5M1jR/66XRRFkgtyqW5pXaH1RCO2p1AIChY3XsBLHgMpFLV8G7yOqdff4ffwP0gp\neFrk5qTkkZPydxSuUNRwOyaQ1gO74DV9CM32fcHyu3+iEUWm1mvB9SHvMcW7E+rIRDJDIiq9V3sz\nc77vNhARHQP2bMA/Ia7YmHvxiWRkF9DUw61KCsj3799P165dualHFParr76mc8dRDO//NcP7f8Ok\nsT/x8cKdXLkSajSfpYiIZD6YV+S7tfTrcTRvXvEaH4DkhAw+nLkeQZDRvLkHV04GMOntfrh76idi\nlry7nexcNWPG9GfilBHlTygHKe0lIfEvrFSmnBo3Fa/e3Yn68xrVx/fixLo9xY6rS+iHIAgoFQJ5\nenaNt1aYkKbWLw01vVVrLkVFsPTiObaPKr1QvTS2BhR1lq9pW3VREY0o8t7J4+wJCUIH1LNx4Pt+\nA2lQyW+u/+Z1Hx/W3rnJ2eiHhCQn4+3oyMnwolNjLVyLH8cuCZkRu7H/u8XFiOoD6eDYht/CtnA3\nI5TD8Zc4HH8JAVDIBDR5hewfugedDvy3voXOTIVGBxn+UQQeuYjc3ppafdpgrpRjo1SxLyKAbWF3\nKMgvIPi9Feg0WuImfoCbm36PtTQGetXjvYzOfHXrIsMObOHXnkPpU/tvK4QVF3yRATM6+VTqOqUx\n/6vlhPle4OChw7RqVfpptYKCAt5//10Aurb1xNrKmcSYDBIi07hy9j6CQqB6LUeGj/Gh/4BmBgm1\n9PRcZk//HW2hljffH4hPW8OET1ZGHh/N3EB2Zh59BzVj39rzDH65E2Ne76nX/Mvn7uF7ORR7Owve\n/siwqNO/kcSPhEQJ2JuZ88u89/hO/Jplcz6RhE8liExNQ12gpZaLfrUezmaWPMhKQhTFct+wO3t4\n4mphyfXYGC5HRdKhhv6RuTMPw1h8/ixymYx3O3TWe15FyFar6b9lA1G5mVgrTPi2V196V6Gn0De9\n+jHl8B5G79lO75p1OPowBHQwrUXFi8ENp3QB5WrqzKJGb6MRNZxJvMTVlNukqNPJ1xagRYupvTk6\nnQ4nS0uszC2wUZpj37c+0Rp3ZG5uRNma8Cgrk9SCAkzkciyVSpzNzIlu6oUur8Bo9TSzWrWjhrUt\nb5w7xIzT+1iU1Z1pzYqK0H1DIzExUdDNq5ZRrvVPEjOykbXrgZdrDRa8M7/MsSYmJqxbt47IiDg+\n/uSDv27394/i4L6b3LnxkMjQRH747BArvztB976NmfF6T6ys9LN0KCjQMH3yavJz1Iyd3JFBg1sY\n9JgK8gv53xubiIlIZvj4duz85RSdBzTjtY+H6SW2c3ML+OLjvchkMr76aZLRom2S+JGQKIVxI0Yw\nbkTlw6v/13lvd1Hjy5ld9fum7GFly/3sJB5mpFPHzr7c8Sv6D2LMrm1M2b+bH/oNpL9XyR2i8zUa\nojMyuBUfy+57gVyPjUEGfNWrH9WsK5YW0ofEnGz6bdlIamEeXd09WTd0eJX67AB0q1WLkd4N2R0S\nxM7QQNDBmLqNaehcsrvx80IhKOjj2o0+rt2eul2zU4NOJ7K+7dKnJ5RTWtMuT0ZmYT6mpsbzahrs\nXR83SyvGH97O4mtnORMRjmeujLycfLo3Lb8LuSH8b+8p5FbWfL7wAywsyva+AZgyZUqx25o2rUHT\npjUASEnOZOVPp7h87gHH997i5CE/Zs3rx5BhZfsfqdUaZkz9jfTkbLr2acT0Gd0NejxajZYvFmwj\n6HYkYyZ3ZPevp2nWwZt3lr+EXK7fa2HhnM0UFGoZP6kDNUswSzQUSfxISEhUGfN3HcE/PI7qzjb0\naajfB0Y9O0dOxIVwMy5aL/HTyr0aS7r3YtHZU8w+chALpYrq1tbkFRYSm51V6gkyezMz1g8dSeMq\nEAZhqakM3r6ZXG0ho70b8XVf/Yo6jcG3ffvzTodOPExLo6mrq0H1REYs+akghqXdiprIGH/Trd2q\ncWb0VEbu/4MTxw+T/MtGrJq0pNPwtUa/FsCFh4+wUZowonX5thD64OBozUf/G4Eoivyx+TKbfzvP\ngrcWMXVqICNHvoWraw2USgUqlQKViQITlYKsrHwunb1HXnYBjVvVZNEnhp2g0+l0/LhkP1fO3Wfk\nxPbsX3uemnXdWPTrK6hM9JMeh3dfJygohurV7Xhlln4pMn2RxI+EhITREUWRsb9tJehRIrY2Zuyc\nWXLPpMSsbDZdu01YaiqiqMPJ0oL0/BwyDp3jSK6KMQ31c46d0KQZHWt48N6pE/gnxhOckowMGY4W\n5jiYmaMUBMyUSuzMzKhmac34Jk2obedgzIf8F/vvBfHOmeMU6kRmN2/Lgk6dDF7rSmQkh+8HE56a\nRmZBPo1dXBhQry4da3qUGUVys7LCzYCO4oIRa34MxRAJU5XbrmFjy6UJM/AOCAK5HLm5BUsOnmX7\nrbtsfnk01kaMNgkyAWUVRAcFQWDI8Facux3G1cAHpKdF8uf5y7g5NSt5vEKg58CmvL/Q8PqaDStO\ncXzPTQaOas2p7Vexc7RkyfrpWOiZdktNyeKn5ceRCwJfr5xs8D5KQxI/EhISRuf1P/YT9CiROtUc\n2PHaBEyVxd9qNl27zeJzZ4u1ccj2v0P6nqPs8L3D+tlv6X3NmrZ2bBtl5EZjFeDA/ft8dfkCMblZ\nCMhY3KkHLzc3rE7it2vX+eGyL3nafxSJ6yAoOYkdgQHIZTJm+7TlzU4djLT7F4Oip0LF5Y+AUCWR\nnye8dfoI1K3J3MN7eK9tV2btOMCDmGQ6fbua9ZNH0rJ6NaNcx9XSkujMTKOs9U/2H/Pju3VnKRRF\nOg18jT7tHZkwdhT5BRry8wrJy1WTl1dIfkEhggzatvNGoTBchO3f4su2387RvX8TbpwMQCbI+Gzj\na9g76Z9eXjBzA1qtyJy3++JQgXn6IokfCQkJo/Ld6T+5GPgQFwcr9rw+8S8vnn+y5vJ1vrx0EZVM\nzrtdOtGzbh3kgsCjlHS2u1dn3f17mHnX5cNDJ/hsUHEn6RcJURSZsHsnVxKikQFN7F34acAgatpW\nvAO1RhR5ZeceLkdFohQEenjWYkzTJrStUR1zlYqzYeGcCAnh8INgfrx6hX1B91g/eiQ17YzX7dpY\nGHZc3rAQjiCTVZn0Sc7N5WhkMNYKE77vPQhBENg/YxI/nL/MyjNXmb/nKGffeNUo12pRw43IoAx2\nXb3LqLaVb0Kr0YjMXbQN/5BY5ILArHGdeGlk6eaNxuDiiQB+/eowPp29eeQfSVZ6Ll9tnaX3kXaA\njb+eISo6jfr13Rgytmr2K4kfCQkJoxGamMKa09cwM1Wyd1bJwuen8758f9UXE5mcfZMn4u38d/rJ\n3caaHX53cR4zDgtByfagAHrX86Kbd+1n+TAqxPDtW7mTGo+HhQ1/jBhN9Uq0LRix6Q8CkxOpbm3N\n7pcm4GjxdJuP3t5e9Pb24uMe3Zmxdz/XYmLo9fs6xjRuzKe9e1bYJLE0KhNFeR6ZM0EmQ1dFhUof\nXTiJKNPxXpsuT6Ua3+zagTMPwngQk/SXTUAAACAASURBVMz9hCTqu1TeumB+v87sunCBj778isHb\n1mJiYmLwWplZeUx+ewOJGTl4uNixYvEYHB2q9tRqSFAMXy/cSf2m1SlIyyU6PInFv0/Hu0kNvdeI\nfJjElg1/olIIfPFT2W1GKoNkcighIWE0Pth7DJ0OvntpELbmxY0D/3fkFN9f9cVMUHBgytPC5wmB\nCQkIOtg0dhQy4LWNmwmJiHwGu684x0NDuJMaT11rB85Nnlop4TP98y+4HeBPPXtHzr46tZjw+SdW\npqZsHT+WHwcNxFShYFvAXVqu+Jm9AYEGXx8MjbsYm4qLGJUgr7K01+W4SEyQM7FxcWf3d3oVWSR8\neeqCUa5la2FK4rE9PNi3hdc+WmzwOvn5aka9/huJ6dl0bl6LLSteqXLhA7B55RnMLEzoPbAZ/ldC\nmbNkJC066X8yThRF3pu9EZ1Ox7ufDMPC0nj1VP9GEj8SEhJG4X58EkGPEqnmZE2XEjxQVv95jc0B\n/tgoTTg54xXqOBUXPtHR0Vz4+AOSt+3ijY0HMUnMJ3TZ5zRq1IioxJRn8TAqxJpbN0EHP/cfZPAx\n9gfJSXT+6EPWfLiQhPXr+WPcGL3XGli/HrfnzmZso8bkaTTMXPY1Fu7uTF/xHUFJCQbt53kiM1B+\nyWVClUif5NxcMjT51LctOarT0dMDZJCYmV3pa+Wq1fT9eT1Wzdpg4VWfC/kydl8PMGitz348Sk5B\nIUO6N+bLD0dUucXCE0ICY2jTqS5xj5IR5AK9RlXMCPKHzw+RkpaLT7s6dOllnBNvpSGlvSQkJIzC\nirOXAfhoUI9i9/2yYSMfrd+AS58BnJz9CvalRDUWrdmJOjkRGTIy8vIxVZmiMLdEplQx8NuNLBnZ\nh6E+VfumWBHS8vMQkOHt6FjhuX9GRvK/82cIyUhBa67CrLYXDVu1xrac5q//RiEIfN6vD3M7tqfV\noCHkxsWx89gRTpgXItfJcDKxwNvWEQulCkFWJC9kMhmCTIaADJlMhkxWJDwCA++QuHYjoXNV0Lxr\nhR+TMTBExIjoqiRqtSngNshgQO16Jd4flpIGOqjlWDlzxcz8fPr/soGUjFz6jxrBOyuWM3r1Vj46\neJKmNVzwdtU/pZaZlcfZ66FYm5mw4PVnWy/n7GZLcGAMQ0e1IU+dxc2Lgfh00692KdAvgqOH/LAw\nV/Hxsqo/uCCJHwkJCaPwpMC1nktxIfDFx4tIi4xk/rjxpQqfQzfu4ZcALSYuYM9nc/D0eOzW/Pk8\ntl68zRdHzrNwzwnOBoXx/RTjWNxXFidzC0IzU3mYlqZ349B994JY5nuJ2Nyi/mVe1va0rdOUHdOt\nmdnVcKdpNysrXn//fX6u5cnEUSPIlGsJTE0gPj+L+MRHeq2Rfvwk+f73uXHgOLyq/0k74yEzSP1U\nVb3P6Ygw0MGEUiwXfB8VpWObuBvuFZWUnc3AXzaSmVNAv2Z1+X5EUYPWb0b2563dh1l59hrLx+vf\ntHXF72fRAVNGtntmEZ8njJnahaXzt7Lyxz2cj/6dngP28PbMH+jQtT5dejfC3LzkGiaNRstH87YB\nsPjrcahUVS9NJPEjISFhFMyUSgDS8/JxtXm6vmDFDz/g6+vLO1NKLmCMSEzjf+uPI8hl7P72fTyd\nnzY3HN+5BZ0b1Oaln7dy8kEYvg8iaF+vZtU8kAowsUkzfBOiePv4EfaNe6nUcdlqNauuX2NTwB3S\nNfmgg5ZObizt3pOGzi787+hpAGo5VC6CoDQxxaJJE3p516dfvb/baCTmZJNXWIiIDq2oe/yniE6n\nQwS0ooiogyOeTfjGwoyeL0+p1D4MRQYgq7iQMWYj1n8SmpmCtcKkVC+f8OSizu+1Hcs34yyJmPQM\nBv26idy8Qkb7NGbpwN5/3densRfKPQKXwyvWsPXs1RBUcjmjBz/LliZFdOjZkOUbZ/Dbd3s57W+C\nXG7GFd8wrviGsfyLQ1hamOBZy4nW7erQa1BznF2LauTen72RnDw1ffo3oUkrz2eyV0n8SEhIGIf8\nHNL9r5GQNpD6/wrTDxs2jGHDhpU47V50Aq98uwOtVscnU/oUEz5PqO5ow+pXRzJixWa+OXSB3fWq\n7iSIvgysV4/vrl7GLyWeAVs2Mr99R7p71kIQBCLS0zkWGszOe4GEZaSik4Ggk9GjRm0+69HrKRPC\npu6ubLnjz62oWHrWrWPwfgofu1mr/nXKztlCv75qMbkZWPftirVz5U8uVaXvTvFrGV4vVBqxWZnk\niRqaOZTeKLW9Zw22XfHnfOhD+tSrWM+28JRUhv+6mQK1lmldWvFuzy5P3S8IAq6WlsRmZem95sUr\nIeQVaujSsvYzj/o8oX7TGny77g2+XP06CoWCsOB4Th/xx+/GQ2KiUwkIiCYgMIb1a84j6nK4dX8r\ntla16dx6OPMWDX1m+5TEj4SEhFG4vm0TsUcOschcyYWdG/Wac/x2MAvXHkEn6pg+pC3D2pZdz1Ov\nmhMuFhaEJL84xc8Hxr3EiB1bCUpPYtrRfX+nbZ58FuughoU1I+s34tVWrUtsN9HNuxYcAb/YuErt\nRSNqAVDJ5QbNlz8+p66tzFH3SokQw9Jeoq7kFiaV4eTDUJBBh2qlN8vtWdcLZHD9UUyF1g5NTmHE\nqi2o1Vrm9mrP7M7tShxXqNUil+kvYjbsugI6HTNeqppGvRVB+TgS7FXPDa96fwvI1JQsTh/259rl\nEM5fOk9aZhTItPy2Y/YzFWyS+JGQkDAKb057hTu3A0kQHfhm33neGVZ2weyvR31ZffBKkfvrq/3p\n37K+XtdpXM2V0yFh3ItKoEGN59+w01yl4tjEydyKjWXjHT8iM9MBsDM1o5mLKxOaNCvz2DqAvbk5\npgoFAfGVO6FVqH0S+THsrV3++MOnqmpoysNQh+eqiDIFJicC0MateqljFHIBF1tLIpPSCU9JpbZD\n+emvkKQi4VNYqGV+305M79CmxHG5ajUpeXmY6vlvmZ2Tz/2IRJxsLanlUfEC/GeFvYMVo1/uSIFO\nh9+dKDr6TOO39QueSZ3PP5HEj4SEhFEYNWIEnXv0ZsSSDWw5fgutKPLeiOLdoBPSs3hv/RHuPIjF\nxFTB2rfH0MhDfxHTtUEtToeEcexO8Ashfp7Q0t2dlu7uBs/vWNOD0+HhnAt9SLcSrAL0QfNX2suw\nyI/wOMqgLaUZ7IuKTmd8j6KwjNSi2izXsv9NP+zfjbl/HGLmtv2cmP1KmWNFUWTShh0UFmpZ0L8z\n09q1LnXsezuOUagTmda2uL9QSazefAkdMLq/YS1VniWXLj5g46qzqMyUbN/1Pfb2+qVljYnk8yMh\nIWE0XGyt2PfxFCwsVGw76ccnf5xAfPxBmpady5xV++i/cA3+D2Jxc7bm4KevVEj4APRtXhd0cC0s\nqioewnPjjS7tQQcr/7xq8Bp/iR+FgWmvx58IVVVAXC5V2KaiosTlZKGQCZgqyo4R9KnnTZOaLkQm\nprPu6s0yx355+iLpWfn0alynTOETlpjCqeAw7FSmvNGrfbl7FUWRo+cDUchkjB1aciTpRSE8PJGl\nC3chE2R8+/Ok5yJ8QIr8SEhIGBknG0v2fjyZkUs2cuDCXTauXIG9sws6l0agAxsbM+aP6sqg1g0M\nWt/SzAQLhZKHyWlG3vnzpaGrM7amptxNMDz19aTg2cTQyM/j78OVqfnRFGrIicsEPQIWGlFDvlZD\ngaihQFtI5LUI8jLToeQSmGdKZmEB5nKlXmNXjR1Gl+W/sezoBeq7ONHes3idUGB8Apsu38bERME3\nwweUud4724+gA74Y0UevOphvfz1FbqGGvu3rVaohaVWTmZnLmzPWIWpEFnw6jPoNjNMQ1hAk8SMh\nIWF0nKwtOfHZDCYvXsGOS4eIEOT0WbiSKX3bMK6TfmH8sqjpYEtQQhK5ajXmJRQQ/1exNzMjs6DA\n4PnaSqa9/K9cJfrtxfw+pCe+iWGIOtDqRESdDp0OtDrd4+Pxuse36RB1RTU3T8ZE/byRLF9/un+i\nwb6tN7rHFTlPfn/8H/B0HzCdVuTWW7vQabSEjg7Fy8tL731bKFRoMW6qLl+rwcm07FqtJzhYmLPq\npWG8unEPr27ay1s9OzCtXSsEQUAURXb7B/HJgVPodDqWDOlVZjTJLyKWe0nJ1Lazo1uD8k/+xcSl\nsf/sXcyVCha+UbaoMhSNRiTgfgyJyVlotSJdO9TF3KxirzuNRuS1KWvIz1EzdkpHevepfOPWyiCJ\nHwkJiSrBVKVg6+I3qStPx97JhbfnTDfa2q08qxGUmMRp/zAGGxhBehHR/eN3Q/g77WXYW3t02EPE\nrByyHkYTkZH1lxt00Z///LsM4bErtExWdEpMIQjIkKGwNkemELCyssbBxBq5TEAukyGXyR//XUAh\nCMhlcpSCgOLx7QpBTuwQH9SpWXh4lH7CqiRczawJzkkgLicTNwtrgx77v9HotNio9Hfb7lS7Jp8M\n6cmSQ2f49vgllp+8hKmJkkKNFk2hiEyAL0f2Y0jjsp+vC/ecAODzEeW7M8fFJdCocWtUVi6sWbXG\nqFGfzKw8dh64yRnfYCIT0p6SlktXHsfF1oKpYzswqHfJBpD/5p03NpESl0HbLnV59bXiLvDPGkn8\nSEhIVBmCILDk00+Nvm6/FvXYdM2P80H/f4mf2MxMrFSGd/J+In5MDBQ/IydNZENGFOO69Ob7fsMN\nWuMND3dOT7/D7j7z8LSqmPnfvdfjSVNnoqpgNK+mlR0kw52UWKOIn2y1Gp0MHPSM/DxhfMumDGpY\nj8XHz+AfE09qdh5KlUDnxp680aU9Ne3LNrH0DYkkPC2NBk6ONK9ZdqF1ZHwa4+f8QEZqNCb5OXz1\n22k27rlGi4bV6dC6Dh3a1MFEjxNU2dn5RMenExuXTmxCOn6B0QQ/SiQlK6+oilwHLnaWtGpUAydH\nK9RqDVf9HvEwNpUvVp1ALgj079m4zGss//oIQbcjqVbLkcVfjC53T88CSfxISEj852jq4UrKlTNs\nO3uYqZ1aUd/T5bmZuhmLwPgECkQt7d1rGLyGViyKGv3b5FBf5IKAiZcnchPDBRiAoDS05siwgucO\nrp5seniNczFh9PPQzzKhLMLTinyknMwtKjzXytSEr4f2N+i6H+8/CcCXo/qVOe7g+bssW3kSneDA\nlPnf0r5xfS7eTOBhXCpHLt3jyKV7oNMhPI7IKRUCKoUCpVKOulBDvlpDoUZEW8oxOaUgUKeaPb07\nN2DkwJbFUlxzgNj4dMbMWcv6nb5lip+9u69zdM9NLG3NWPn7tBfmdSqJHwkJif8cOp2OxPNH0OlE\nRn34I2Y2zliqVLjYWlLLzZ6mtd2wk+fhZGNKmzYv9umXJ2y+cQeAkc0Mb9yqrWTaS/n4g+l5nfaS\nyR6HGipI92peoINbKdFG2Ud4elExvZuFVTkjjcfZoDCiMjNp6upCPbfSHbYXrzzKsTNByBUyPnqj\nHwM6Fz1fZkwpqqu5HRDBxSth3AuLIzungHy1hgK1hvxCDdn5apRyATMTJY42KiwsTLCxNMPOxgwH\nO0ucHa1o28ITj+oO5e7X1dkaHZRZ+3PzRjgrvz2OQiXnl7WvYmr64tTnSeJHQkLiP4dcLmfr1q0c\nvXgL5wbtCY9PJTEjm7DEVEKTUjl++z7+6z5E0IlERUXh5lZ6i4IXhcuPIhGQ0aee/oW+/0arE0FX\n1OndEBTPW/wYGPlRCAI2CnOic9ONso+orAwAaljbGGU9fVh86AwAX5UR9dl27CbHzgRhbWPKmqUT\nqO76dBpNoRBo07wWbZob5hNVEQLuxYIMatUoWSjFRKfy0fxtIIPPv5uAm7ttle+pIkjiR0JC4j/J\n2LFjGDt2zFO3iaLI/YhErgQ+4strrfBwtMBOz27rz5u47CxqWNtUKi2gqaQ54ROTw+clfgQMi/wA\neFk7cSM1gqS8bJzMKucdE5FRJKI8bZ7Nc+eYfzBxOdm0ruZOrVJ62wH8ceAGgiBjyzeTcbB9Pv44\nT/C9FQ5Ai8bF07S5uWpmv/o7WrWW2e8OoEVLz2e8u/J5MZJvEhISEkZAEAQa1nJl6qB2BPv5curU\nKUxL6cj9IhGTnoEOqKNHe4Sy0Fayx5XieYsfmeE+zZ1daiOTwe6wu5Xex/WEaGQ68HEvvbWFMfns\nyFlkOvhydNm1PukZedjZmz934QNwPzQegI5tih/HnzdnI7kZeQwc3Zqhw1s9663phSR+JCQkJJ4z\nt6OLGprq0xuqLDRi5USLXCgSH/+1mh+A0XWaotPBqZiQSu0hLC2ViJx03M2sDU4fVoR9NwNJysul\nXc0aVLcvO82mVAjk5Kj/ck1/nqRm5CLTgb3d00Ls/r0Ywu7FUa22E2/OM6zw+1kgiR8JCQmJ50xQ\nQlETzYaupRe66oMo6irV4+rJh732Oaa9DL2yi4UV5oIJwZmGO2SLosjLh3eiAz7u8Gy8aJYdv4hM\nB1+M6lvuWJ8WnhTkFfLGF7tRazTPYHelI4pFtpUazdNC7PBBP2TAtJnF+/q9SEjiR0JCQuI5E5qc\nCkCLaoY3RoXHaa9KqJ/nXfD8pObIUOpZO5MjFpCUl13huaIo8uqxfUTnZ9LRpSb9antXai/6sP2q\nP6kFeXSp7YmrTfknyz6dMwBHZytu+0XSe/JPLPn1GNm5+VW+z5JoXM8NZLDsl+NP3Z6dVbQfj1IK\noV8UJPEjISEh8Zy5sH0rKccOUU2PD8Cy0IoiskqoH7kRa35EA+qPKlPzA9CjmjcyGewIvVOhecGp\nybTb/CunY8NwUVmwfsCISu1DX7Zf8wcdfD6y/KgPFFkY7F3xKqOGtEQmg2OnA+n3yi+8+cVuLly+\nTl5eXhXv+G/mTu2BjZkJhy8EMufDrSQmZwHg6FT0HL5169Ez24shSOJHQkJC4jmiVqt5sG8naefP\n8PDhw0qtJeqMk/YyRLg8oTLiqzKnvQDGPK77ORMbqtf40w/D6L19Hb13ryOhIIfe1by4PHGmwT5J\nFSUtLw+lIOBgpb+TtCAIvD2pO2c2vsGMSZ2xtDLh+KHDdO3oQ92mnThz9UEV7vhvzM1UbP5hCi52\nVtwOjmXEzNVMemMdpnZmFKizWLNq2zPZh6FIR90lJCQkniMqlYrec+fh9zCCTBOzSq2lNZr4+W+m\nvRzNLDEXTAjJSix1zIXIR6y+c53ridHk6zSggzqW9izq0J3uNWtX6voVJVutLrPJaVkIgsDkIT5M\nHuLDkDeWEnxNhUZmzsfLDvKZ+TF6dKrH7PFdsLWuWIuOimBvZ8me317j2JkAft/hS3hsKuEHrnE3\nZCtZt6LZvbszI0cOrbLrVwZJ/EhISEg8Z955/TXm7DrExqu3aVHd8LofUad7fGLKMJ67z08l014A\nXlaO3PC7TlJ2Bk6WNkSkp7H1nj/nox8RlpFCAVoALAUV/avVZZ5PR+raO1b6uoZQqNViWYleblBU\nqxSvtKbl3GXseWciv2y9xJUb4Rw9EcDRkwHUqePMa+M60aGFfsJuy5Yt5OfnM23aNL330K9HY/r1\naExCUiYnzwexLPwUYUH57N91XxI/EhISEhIl06tuHcwUCo7dD+HT/HysDfQmKor8GC4gnnfkpzJ7\nf0LeiWtEf/crXqfu4jxuOGqd9q8GnbZKU7q61GJOy3Y0c3n+rt+i7m97AUPZcPom+aKWwc3r4+Hu\nwJfzhyKKIrtP+rH1wA1CQxN5d+kezK1MaNeqFlNHtMezWsnFyFt2XGLixEmAjr59+1K9esV8jlyc\nrJk4qh0TRx3l5XE/E/colfv3YqjfoFqlHmNVIIkfCQkJieeMIAi82bU9X56+yPSt+9g6eYxBTs+i\nTqQywRPVE/FTibqbyiCXySq1f4BCS2tkSiVWLi7YqUzxtnWkXy1vhtdrhGUFu8VXNeZKBdkFaoPn\n5xWo+fmYLwpkLBjV7a/bBUFgdN+WjO7bkrDIJFZsPs/tu5GcOXef0+fuY26por63K+2b16Jf50bI\n0fLpl4e5feMRtb1706F9LapVq5xgmffeIBbM2sjqn0+z/KeXK7VWVSCJHwkJCYkXgKntW/PHTX9u\nx8QxaNUmPhvcW+8UmEYUyVWrSb1/n5zcHIP3ILwgNT8aUYtCMKwzfExNN+quXMb9aW8Zc2tVghgf\nTVTQXQoKpmNiUvH01ztrD6MWtbzWwwdbi5Lrxep4OPH9wlGIosi56yHsPHqLByEJ3L4dye3bkXz5\n7WYCLvyMk1MjuvWZyY6tO3FytK7sQ6N5i5rYOFgQ6BdJfr76hWpqCpL4kZCQkHhhODZrCuPWbcM/\nLoGx67ejEARUcjmiTvfXL92TXzw+F6UDZCCq1YT99CPodAxu1JxPRw+jpSG+QTrQPXfxIxokfs5F\nPiRfo6G3Z/GWCy8CuflqDl8N4vTNUO4/SuDuto3kJ8cw86MvWff1JxVa6+StYC4GP8LZ0pzZgzuW\nO14QBHq0rUePtvUAiElM59ile6z7fRc6nQ5LKyVb179Wqd5y/6bPwGbs2niZwwf8GDnGx2jrGgNJ\n/EhISEi8ICgEgV3TJnA2OIxdfoHcjolDoxVRCAJyQYZCEFDIBRQyOUq5gEIuRyUXUMrlKOVyhK49\nSM3M4F5GLmPXbcfN1pJ3e3ZmUIP6FdrH80p7CY9rfgp1WkxRVnj+L35XAB1vtuxg5J0ZztV7kfxx\n+iYBoXFkZuYXiUvAxFRB825DCAm8wdVMOcv3nGfeiK56rZmelcsHW44hyOC3OaMM2lc1Z1smDmrD\n1rWX6NJrEcf2f2BU4QMwcnQbdm68zJlTAZL4kZCQkJAom+5169C9rgHRi5eKzPluxcTy+anz+EfH\nM2/3UZZYnmVsy6a0rlGNVtXcsSin9uV5pb3kwt9pr//X3p1HR1XffRx/35nJJCELSwhkgQAKaIAU\nZF+lIFRFpBYsslmUgtWqRXxaRa0rfaTL8xw3bC3KIorFulChFMqiEgIFJIGyQ0JMQiAkITsZksnM\nnf4RQC0BsjIh83mdw8lk5t7f7zvwB59zf1tNmaZJUnYWof4B9AhvW9+l1aqeyfPeJzXtNAA2u5Xr\nYlozKK4j44fGEdO28sT4U/nF/Gj+MpbGJ1HiKOf5aT+4bLsJ+7/m8SWrcZpufnHrYDpF1H4n5Vf+\nuB53hcmUySMIDKzbqrOqhLUOJTDYn8y0vHpvu64UfkREmpje0VF8PH0yJ4qKeHrtRrYfy+Ct+J1A\n5VMHq9UgyN9OWHAg0c2b0yU8jO4RbegbHUVFVg5l9trP+XCVl1OWful9di7FNE3OZOeTv/MweQPP\nYHgsuDwmbo+J26z86TTPv/Z8630PbtNk0erPKPg8gXGzZtS69vr01Dv/IDXtNB1jwnh2+mi+d13V\nQ5ARrUL57Jn7mDB/GZ8kHmBHSgbP3jOaQbEdvnPdqfxi/vDJl2w4cAwDeHDUAGbeNqDW9TkcTjas\nP4A90I9Z04fVup0raRvVgvTkbFwuE5ut8eyrrPAjItJERTdvzruTJpDvcLD2SDIHs3NIzSsgq6iY\nAkcZ6bmFpOUUsjU5HYCyzAyyFr5GXkwU13uKL2qvOguxst5cjmPnXoakFhE8IA44v2ez55vBtP96\nsOQBDANO/mYRZQdSGZjlT/CAPjX6rpnPvIw7r4A+93h/ZVFWfjGfbz9KaPNAPnz+3isOJ7VpEcyG\neQ/w0JufkJh+kp/9+RMCbX60Dm6Gn81KbnEpJU4nGNA8wJ+FP59AbEzdnm794Y11mC6TKT8ZUu/D\nXd/W+YYI0o9ms3dPOr37dmqwfmpK4UdEpIlr1awZU2/qWeVn2SVnSDxxkn1Zp0g6GEhuaCgh7aNo\nF9j8O9dVNRBW1ehYeWQk5QFHiGgbScugFlgMCwYGFsO4sJTdguXcawOL8c3rbX17klPipE//3kR2\n6oDl3H2WC9dWtmE1LBfeP/9nz/QpBGRmc+fIq3Ma++W88lE8eGDOPTdXO1gE2G0smXMPRzNz+b9P\nN3PwRDYni0owPR6a+fkRGxnO5GG9GDewW53DSnGxg81fHiYw2J97Jw6sU1uX4nS62Lf3OKfz89iX\n8jFvL3Txp74vNkhftWF4vDWtX0RE5Fse3bSKvx9M5p/TptO1pXd2Xa4r0zQZ8tAbWK0GCX/8hbfL\nqdIz81aybctRHvr5SCbe1e+iz8+UlLFx436OHMkiM7OA/ILK7ROsFgOrzYLVasEwKrekNCzGhdcY\nBoVFDgoKHZRXuMAwOHViF4cPfkxUxPWcyKremWtXg578iIhIo2A7t9Td6XZ5uZLaW7R2Jy6nm7G3\nxF2V/lJTc8jNLaFv305YrVd+IuRymWz/VwrNQvy/E3zOlJbx3tIENnx+kIJiBxd2m/R4LkxE95xb\nqubhv5/6ffOL1WIQEhRAp5gwOnUKJ6b9EJ5/uoh2kbH18G3rj8KPiIg0Cn7W8+Gn5qu9GosPN+7G\nsBjMubt6y9brIn7LEV54YSUAUyYPYubMK/e5et0eTJfJLSO7XXhvxQf/4u2l8ZiA1YDOHcMZNLAz\nffp2JPbGaOz+dYsKG1bdxfH0vEY16VnhR0REGoXzmxxWmKaXK6mdA2nZFBaepWdsNM2q2NF406ZN\nhIWF0atXrzr3VVpazhtvbKBddEsyTxSQmlq9FXZHUk4BMGJY5d5Pe3an8+clm7HbrEydPIhJkwZi\nt9dvNIjtEc3x9Dx2f3WMfoO61GvbtaXwIyIijYLfueEVp3ltDnu9tWorBvDAnYMu+uzo0aOMGjUK\nP3sAZ0pKahwwXC6Tpe8nsGfvcdzuMg7v30zZ2TYMG9aPzBMF3H33xXN3qtIuqhUAiXvSuel7Mcz7\n388AeP2VqdxwYy12BK+GIcNvZP2avWz54rDCj4iIyLfZrJVHWjhd1+aw18HUU9gDbAyIjbnos+jo\naLrc2JviMj+e++1n/Pa5CdVut6CwlJ/89B1KC8/iAY5nJHAsZQ0tQtvjqnAwfsLt9O7dsVpt3T2u\nN4sXx/P+8s3847O15BeHMqjPt1FHqAAACjhJREFUdQ0WfIALgefQvswG66OmGsfgm4iI+Dy/C8Ne\n12b4KSt3ERRY9e7ZQUFBHD2USFz/SSQdOF6jdh9/cgVnCs8yYnQ31q55nOXL5xHTrjuFxcdJTFzI\nvdOqv9lhQICdBx8cwcF/f8TKT+dTUrCXF18aX6N6aspmsxASGkBWVmGD9lMTCj8iItIoOK/R0HOe\nq8JNQMDlzyTrdn0EDmcF6RnVO/Lhyy1HSEvJpVOXNjw7dxwBAXYG9L+JlNTddIiIIyL4Blq1rNmO\n3BPv6sfQwQOx+4cydHC/ep/jU5UOncJxlrsoyD/T4H1Vh8KPiIg0ColZJwAPQ6Mbz07ANeHnZ6XU\nUX7Za8bc0gMMg49XJ16xvcIiB7/9/d8xLAbznvvRf/Xlx4yJT9Iz8k4K8xw1rnXFijcZcvPTnM6r\n/zO9qnJTv8p/0y/+uf+q9HclCj8iIuJ1TpeLwzl5NA/yJ/gKB682Vq1aNKO4pIyiM2WXvOaW4d2w\nANuTvr5sWydPFXLv/Qspd1QwZeogoqNaXnTN+X19XK6aTxC3221EhoeSlVuCw+Gs8f01NfIHPQD4\nasexBu+rOhR+RETE6+Z/tRmXy8Ok7j28XUqt3TPqJjxuD3fMmkt6enqV17hMk8yj61m5bDbDRsxm\nxoOLWfbBNtLSc0lJzeHjv+3igUeWMm3aW5wpKmPsD3sx876bq2zr/AEN1dncsCpjbovDA7z26rpa\n3V8dZWVOtsQf4cO/7mDHgYUsePsxSkpKGqy/6tJqLxER8aqN6cm8m7QHu93CL/s1/OaADWXqLX14\ndcEitv7tT/T6cjWL3/uU24fGEhBgJ/NUAe+v2cWqrQcoPJOD21VOcVE+aSk5LE3OYemi+AvteIBW\nbUJ4+KGRjLz5yjsju9212xdp8tTBLF+xg02bDzH7sdto1qx6T9yOZ+SxZEk8Z886sdtttGoVREhI\nAPl5peTln6Gw0EFxyVnyCx2UOSuPuTBNN2fP5uF2V+BwOAgJCalVzfVF4UdEROqN2+1m/fr1DBky\nhNDQS0/ENU2Tg/k5/G5HPAlfH8diwOKxP8LWgCeMN6Rtu1NZvGoHZUYLmoW1IygqlpeXb+Ll9zdh\ncO5oCMPA32Ll1y/8nuFxrenTpzclZ8pYuSqJlNQcLBaDjjGtuXV0dyLbtrhin3b/ysnVZ0trN2xl\nsViYNLE/S5Zv47VX1/HU0+Mue73T6eKpuR+StO/4N8dfVOXckRhBgXY6xoRxQ9dI+vXrxLLX/Tic\n9DVBgTWboN0QFH5ERKTeLFiwgMcee4yg/n0Iv3cS5468BINzh19W/uoxvzkfKjw0kFdGjWFIdEcv\nVFx7x47nsvDjbfzrYDpnXS7weIiKbMeyLduIaB3KinVJJB3OpMLtJiw0iBH9OjPu+3HY/b75rzck\nOICfTBlcq/73HtrKF6mL+PSTKJ6Kq90hqtPuHcKKj3ay8ctD3H13f7p0jaj6ux7LZs7jH1By1kmb\nVsE88vBo4nq2p6jQQdbJQoqKSomIbEFMTGtatgyqso3d8TeSsb+AvYlpDB7h3bO+dKq7iIjUm4SE\nBCZMmUzE2NG0HzkcEw+mx4PHU/nT9IDpMbFZrHRu1Yrbr+vKuOu7XbnhRiQ5PZdfvfo3ThQUg2EQ\nYLUxqHsHZo0fTJcO4VelBo/HQ4/owRzM2s6QHmNI2Lem1m1tTTjCr19cib+fjeeeGcfgIV0vfGaa\nJn9Zvo3F723F9HgYMzqOXz1xR6362bRmDy/NXsjEmbfy8Nwf1rre+qDwIyIiUg3OChcvvrWODYlH\n8QDd2rdh5l0DGdan81WvJfNYDjOGPU+pJY8Qwlm+62XCIprXur0lizfz3l+248FDUKCdyLbNcbtN\nTmQV4nSb+FkMnn1qHMOG31jrPtat/Zzbx4yiU0wsqekHat1Ofbg2B1dFRESuohfm/Z7eY37G+qSj\nhAb6s+Dx8bw7b5pXgg+An92K1bBx14Q7MTwW5j/ybp3au3/GcJa881Nu6tEel8skJS2XtMx8bDYL\nP/h+LH/98JE6BR+AmA4RhIQE039QXJ3aqQ968iMiInIZycnJdO3aFQyDF95czbM/ux2Llydmezwe\nZgx+EavVQrnLJCcjh6cXzWD47X29WteVmKbp9b87UPgRERG5LLfbzRNPPEFwaHNefP45b5dzwb7t\nKcz98evYA/z4PP09Sl2FJO5KpGdv7z9ZaewUfkRERK5RyXszWL0knvlvP0lBaQ6joqaw6utFV+W8\nrmuZwo+IiMg1zul08ruH3mbrR7u4Y9YtPPr/07xdUqOm8CMiItIEuFwuxkc/DIbBqlNvebucRs37\ns45ERESkzmw2G31v7YnTUc7ODXu9XU6jpvAjIiLSREz5n7EArFm82cuVNG4KPyIiIk1E554xYBhk\nZ5z2dimNmsKPiIhIE+IX4EfR6RJvl9GoKfyIiIg0IYHBAThKznq7jEZNGwGIiIg0IW1jWpOcmMqu\njfvpO6pHte5J+uIga5fFk5mSTUV5BdfHtWf01KH0Hdm9gav1Di11FxERaUJS/p3Bo8NfwOpn47EF\n9zPqnkGXvNY0TX419g8cSDhc+YZhYBjgMSujwfCJg3hy4cxGcSRFfVL4ERERaWJW/nkjC+f+BY/b\npH1sNHPfeYDr42K+c01psYNHhs8j69gporpGMuf1++g+sPKg1n3bknn5/j9RlF1E39t68Zu/zvbG\n12gwCj8iIiJNUE5mPi9Ne5OUpFQAuvS5jk5x7TEwyErLYX/CEUyXm14jevDyyjkXPd1xuVzc3+sp\ncjNO88hr9zH2/uHe+BoNQuFHRESkCdu9+RCv/mIp2V/nfOf9oJbBTPrlWH786K2XvDc/p4ipXefQ\npkM47/77dw1d6lWj8CMiIuIDck/mk52Rh9tlEnVdOOFRrap136wBz3L8cCYfpr5B87DgBq7y6mha\nM5hERESkSuFRregxsAs9h95Q7eADMGrKYPDAqrc/b8Dqri6FHxEREbmkO6ZXzvWJX/mVlyupPwo/\nIiIicknBLZoR3TWK44cy+ej1dd4up14o/IiIiMhlzftoNn4BdhY8/Q7d2/dm1crV3i6pTjThWURE\nRK4oK+00Y/qPZ0/uFtrYYpg8dAaz5k+iQ49ogoOvrYnQCj8iIiJSLQUFBTw8fTaFSW7cRX7sLYsn\n18wgYWsCAwcO9HZ51aazvURERKRaWrZsyQerlgFwcMdRfnjnTnLzwel0ermymtGTHxEREamViooK\nCgoKaNOmjbdLqRGFHxEREfEpWu0lIiIiPkXhR0RERHyKwo+IiIj4FIUfERER8SkKPyIiIuJTFH5E\nRETEpyj8iIiIiE9R+BERERGfovAjIiIiPkXhR0RERHyKwo+IiIj4FIUfERER8SkKPyIiIuJTFH5E\nRETEpyj8iIiIiE9R+BERERGfovAjIiIiPkXhR0RERHyKwo+IiIj4FIUfERER8SkKPyIiIuJTFH5E\nRETEpyj8iIiIiE9R+BERERGf8h/hhrFAE/JZtQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e3ecf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# list of edge values for the orginal graph\n",
"ev = [closeness[edge + (0,)] for edge in G_projected.edges()]\n",
"\n",
"# color scale converted to list of colors for graph edges\n",
"norm = colors.Normalize(vmin=min(ev), vmax=max(ev))\n",
"cmap = cm.ScalarMappable(norm=norm, cmap=cm.viridis)\n",
"ec= [cmap.to_rgba(cl) for cl in ev]\n",
"\n",
"# color the edges in the original graph with closeness centralities in the line graph\n",
"fig, ax = ox.plot_graph(G_projected, node_size=5, node_color='k', node_zorder=2,\n",
" edge_color=ec, edge_linewidth=1.5, edge_alpha=1)"
]
},
{
"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": 1
}
| gpl-3.0 |
probml/pyprobml | notebooks/book1/11/splines_cherry_blossoms.ipynb | 1 | 5323 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e4f7a48e",
"metadata": {},
"outputs": [],
"source": [
"# splines in 1d\n",
"# We use the cherry blossom daa from sec 4.5 of \"Statistical Rethinking\"\n",
"# We use temperature as the target variable, to match a draft version of the book,\n",
"# https://github.com/Booleans/statistical-rethinking/blob/master/Statistical%20Rethinking%202nd%20Edition.pdf\n",
"# The published version uses day of year as target, which is less visually interesting.\n",
"# This an MLE version of the Bayesian numpyro code from\n",
"# https://fehiepsi.github.io/rethinking-numpyro/04-geocentric-models.html\n",
"\n",
"\n",
"import numpy as np\n",
"\n",
"np.set_printoptions(precision=3)\n",
"import matplotlib.pyplot as plt\n",
"import math\n",
"import os\n",
"import warnings\n",
"\n",
"try:\n",
" import pandas as pd\n",
"except ModuleNotFoundError:\n",
" %pip install -qq pandas\n",
" import pandas as pd\n",
"\n",
"from scipy.interpolate import BSpline\n",
"\n",
"from scipy import stats\n",
"\n",
"try:\n",
" from patsy import bs, dmatrix\n",
"except ModuleNotFoundError:\n",
" %pip install -qq patsy\n",
" from patsy import bs, dmatrix\n",
"\n",
"try:\n",
" import sklearn\n",
"except ModuleNotFoundError:\n",
" %pip install -qq scikit-learn\n",
" import sklearn\n",
"from sklearn.linear_model import LinearRegression, Ridge\n",
"\n",
"\n",
"# https://stackoverflow.com/questions/61807542/generate-a-b-spline-basis-in-scipy-like-bs-in-r\n",
"\n",
"\n",
"def make_splines_scipy(x, num_knots, degree=3):\n",
" knot_list = np.quantile(x, q=np.linspace(0, 1, num=num_knots))\n",
" knots = np.pad(knot_list, (3, 3), mode=\"edge\")\n",
" B = BSpline(knots, np.identity(num_knots + 2), k=degree)(x)\n",
" # according to scipy documentation\n",
" # https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.BSpline.html\n",
" # if degree = k, ncoef = n, nknots = n + k + 1\n",
" # so if k=3, ncoef = nknots - 4\n",
" # where nknots = num_knot + 6 (because of 3 pad on left, 3 on right)\n",
" # so ncoef= num_knots + 6 - 4 = num_knots + 2\n",
" return B\n",
"\n",
"\n",
"def make_splines_patsy(x, num_knots, degree=3):\n",
" knot_list = np.quantile(x, q=np.linspace(0, 1, num=num_knots))\n",
" # B = bs(x, knots=knot_list, degree=degree) # ncoef = knots + degree + 1\n",
" B = bs(x, df=num_knots, degree=degree) # uses quantiles\n",
" return B\n",
"\n",
"\n",
"def plot_basis(x, B, w=None):\n",
" if w is None:\n",
" w = np.ones((B.shape[1]))\n",
" fig, ax = plt.subplots()\n",
" ax.set_xlim(np.min(x), np.max(x))\n",
" for i in range(B.shape[1]):\n",
" ax.plot(x, (w[i] * B[:, i]), \"k\", alpha=0.5)\n",
" return ax\n",
"\n",
"\n",
"def plot_basis_with_vertical_line(x, B, xstar):\n",
" ax = plot_basis(x, B)\n",
" num_knots = B.shape[1]\n",
" ndx = np.where(x == xstar)[0][0]\n",
" for i in range(num_knots):\n",
" yy = B[ndx, i]\n",
" if yy > 0:\n",
" ax.scatter(xstar, yy, s=40)\n",
" ax.axvline(x=xstar)\n",
" return ax\n",
"\n",
"\n",
"def plot_pred(mu, x, y):\n",
" plt.figure()\n",
" plt.scatter(x, y, alpha=0.5)\n",
" plt.plot(x, mu, \"k-\", linewidth=4)\n",
"\n",
"\n",
"def main():\n",
" url = \"https://raw.githubusercontent.com/fehiepsi/rethinking-numpyro/master/data/cherry_blossoms.csv\"\n",
" cherry_blossoms = pd.read_csv(url, sep=\";\")\n",
" df = cherry_blossoms\n",
"\n",
" display(df.sample(n=5, random_state=1))\n",
" display(df.describe())\n",
"\n",
" df2 = df[df.temp.notna()] # complete cases\n",
" x = df2.year.values.astype(float)\n",
" y = df2.temp.values.astype(float)\n",
" xlabel = \"year\"\n",
" ylabel = \"temp\"\n",
"\n",
" nknots = 15\n",
"\n",
" # B = make_splines_scipy(x, nknots)\n",
" B = make_splines_patsy(x, nknots)\n",
" print(B.shape)\n",
" plot_basis_with_vertical_line(x, B, 1200)\n",
" plt.tight_layout()\n",
" plt.savefig(f\"figures/splines_basis_vertical_MLE_{nknots}_{ylabel}.pdf\", dpi=300)\n",
"\n",
" # reg = LinearRegression().fit(B, y)\n",
" reg = Ridge().fit(B, y)\n",
" w = reg.coef_\n",
" a = reg.intercept_\n",
" print(w)\n",
" print(a)\n",
"\n",
" plot_basis(x, B, w)\n",
" plt.tight_layout()\n",
" plt.savefig(f\"figures/splines_basis_weighted_MLE_{nknots}_{ylabel}.pdf\", dpi=300)\n",
"\n",
" mu = a + B @ w\n",
" plot_pred(mu, x, y)\n",
" plt.xlabel(xlabel)\n",
" plt.ylabel(ylabel)\n",
" plt.tight_layout()\n",
" plt.savefig(f\"figures/splines_point_pred_MLE_{nknots}_{ylabel}.pdf\", dpi=300)\n",
"\n",
"\n",
"main()"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
| mit |
SatoshiNakamotoGeoscripting/SatoshiNakamotoGeoscripting | Lecture 11/.ipynb_checkpoints/Satoshi Nakamoto Lecture 10 Jupyter Notebook-checkpoint.ipynb | 1 | 8068 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Team: Satoshi Nakamoto <br>\n",
"Names: Alex Levering & Hèctor Muro <br>\n",
"Lesson 10 Exercise solution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import standard libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from numpy import mean\n",
"import os\n",
"from os import makedirs,chdir\n",
"from os.path import exists"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import non-standard libraries (install as needed)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from osgeo import ogr,osr\n",
"import folium\n",
"import simplekml"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Optional directory creation"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"if not exists('./data'):\n",
" makedirs('./data')\n",
"\n",
"#chdir(\"./data\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Is the ESRI Shapefile driver available?"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ESRI Shapefile driver IS available.\n",
"\n"
]
}
],
"source": [
"driverName = \"ESRI Shapefile\"\n",
"drv = ogr.GetDriverByName( driverName )\n",
"if drv is None:\n",
" print \"%s driver not available.\\n\" % driverName\n",
"else:\n",
" print \"%s driver IS available.\\n\" % driverName"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define a function which will create a shapefile from the points input and export it as kml if the option is set to True."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def shpFromPoints(filename, layername, points, save_kml = True):\n",
" spatialReference = osr.SpatialReference()\n",
" spatialReference.ImportFromProj4('+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs')\n",
" ds = drv.CreateDataSource(filename)\n",
" layer=ds.CreateLayer(layername, spatialReference, ogr.wkbPoint)\n",
" layerDefinition = layer.GetLayerDefn()\n",
" \n",
" point = ogr.Geometry(ogr.wkbPoint)\n",
" feature = ogr.Feature(layerDefinition)\n",
" \n",
" kml = simplekml.Kml()\n",
" for i, value in enumerate(points):\n",
" point.SetPoint(0,value[0], value[1])\n",
" feature.SetGeometry(point)\n",
" layer.CreateFeature(feature)\n",
" kml.newpoint(name=str(i), coords = [(value[0],value[1])])\n",
" ds.Destroy() \n",
" if save_kml == True:\n",
" kml.save(\"my_points.kml\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the file and layer name as well as the points to be mapped. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"filename = \"wageningenpoints.shp\"\n",
"layername = \"wagpoints\"\n",
"pts = [(51.987398, 5.665777),\n",
" (51.978434, 5.663133)]\n",
"shpFromPoints(filename, layername, pts)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define a function to create a nice map with the points using folium library."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def mapFromPoints(pts, outname, zoom_level, save = True):\n",
" mean_long = mean([pt[0] for pt in pts])\n",
" mean_lat = mean([pt[1] for pt in pts])\n",
" point_map = folium.Map(location=[mean_long, mean_lat], zoom_start = zoom_level)\n",
" for pt in pts:\n",
" folium.Marker([pt[0], pt[1]],\\\n",
" popup = folium.Popup(folium.element.IFrame(\n",
" html='''\n",
" <b>Latitude:</b> {lat}<br>\n",
" <b>Longitude:</b> {lon}<br>\n",
" '''.format(lat = pt[0], lon = pt[1]),\\\n",
" width=150, height=100),\\\n",
" max_width=150)).add_to(point_map)\n",
" if save == True:\n",
" point_map.save(\"{}.html\".format(outname))\n",
" return point_map"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Call the function specifying the list of points, the output map name and its zoom level. If not False, the map is saved as an html"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'module' object has no attribute 'Marker'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-10-f5c9b82dd1ed>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmapFromPoints\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"SatoshiNakamotoMap\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzoom_level\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m<ipython-input-9-72a9562c9893>\u001b[0m in \u001b[0;36mmapFromPoints\u001b[1;34m(pts, outname, zoom_level, save)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mpoint_map\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfolium\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmean_long\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmean_lat\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzoom_start\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzoom_level\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mpt\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpts\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m folium.Marker([pt[0], pt[1]], popup = folium.Popup(folium.element.IFrame(\n\u001b[0m\u001b[0;32m 7\u001b[0m html='''\n\u001b[0;32m 8\u001b[0m \u001b[1;33m<\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m>\u001b[0m\u001b[0mLatitude\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m<\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m>\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mlat\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m<\u001b[0m\u001b[0mbr\u001b[0m\u001b[1;33m>\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'module' object has no attribute 'Marker'"
]
}
],
"source": [
"mapFromPoints(pts, \"SatoshiNakamotoMap\", zoom_level = 6)"
]
}
],
"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"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
eggie5/ipython-notebooks | clearaccessip/Proposal - Alex Egg.ipynb | 1 | 69624 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ClearAccess IP Proposal\n",
"\n",
"IP Database Semantic Search. We will propose a method to search a database of patent filings and return a listed ranked by semantic relevance. \n",
"\n",
"## Latent Semantic Analysis\n",
"\n",
"* Semantic analysis adds structure to unstructured text. It helps uncover hidden relationships and automatically standardises/normalises metadata across large document collections.\n",
"* Latent Semantic Analysis (LSA) is a framework for analyzing text using matrices\n",
"* Find relationships between documents and terms within documents\n",
"* Used for document **classification**, **clustering**\n",
"\n",
"## Vectorizing text\n",
"* Most **machine-learning** and statistical algorithms only work with structured, tabular data\n",
"* A simple way to add structure to text is to use a document-term matrix\n",
"* Named Entity Recognition - Extraction of People, Places, Organisations, Times, Email addresses.. from text.\n",
"\n",
"## Document-term matrix\n",
"\n",
"We will model each patent document as a row in a matrix:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Initial import statements\n",
"import sklearn\n",
"import numpy\n",
"from __future__ import print_function\n",
"from sklearn.decomposition import TruncatedSVD\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn.preprocessing import Normalizer\n",
"from sklearn import metrics\n",
"from sklearn.cluster import KMeans, MiniBatchKMeans\n",
"import pandas as pd\n",
"import warnings\n",
"# Suppress warnings from pandas library\n",
"warnings.filterwarnings(\"ignore\", category=DeprecationWarning,\n",
"module=\"pandas\", lineno=570)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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>cool</th>\n",
" <th>data</th>\n",
" <th>football</th>\n",
" <th>fun</th>\n",
" <th>great</th>\n",
" <th>learning</th>\n",
" <th>like</th>\n",
" <th>machine</th>\n",
" <th>python</th>\n",
" <th>science</th>\n",
" <th>statistics</th>\n",
" <th>super</th>\n",
" <th>watch</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Machine learning is super fun</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Python is super, super cool</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Statistics is cool, too</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Data science is fun</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Python is great for machine learning</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>I like football</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Football is great to watch</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cool data football fun great \\\n",
"Machine learning is super fun 0 0 0 1 0 \n",
"Python is super, super cool 1 0 0 0 0 \n",
"Statistics is cool, too 1 0 0 0 0 \n",
"Data science is fun 0 1 0 1 0 \n",
"Python is great for machine learning 0 0 0 0 1 \n",
"I like football 0 0 1 0 0 \n",
"Football is great to watch 0 0 1 0 1 \n",
"\n",
" learning like machine python \\\n",
"Machine learning is super fun 1 0 1 0 \n",
"Python is super, super cool 0 0 0 1 \n",
"Statistics is cool, too 0 0 0 0 \n",
"Data science is fun 0 0 0 0 \n",
"Python is great for machine learning 1 0 1 1 \n",
"I like football 0 1 0 0 \n",
"Football is great to watch 0 0 0 0 \n",
"\n",
" science statistics super watch \n",
"Machine learning is super fun 0 0 1 0 \n",
"Python is super, super cool 0 0 2 0 \n",
"Statistics is cool, too 0 1 0 0 \n",
"Data science is fun 1 0 0 0 \n",
"Python is great for machine learning 0 0 0 0 \n",
"I like football 0 0 0 0 \n",
"Football is great to watch 0 0 0 1 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example = [\"Machine learning is super fun\",\n",
"\"Python is super, super cool\",\n",
"\"Statistics is cool, too\",\n",
"\"Data science is fun\",\n",
"\"Python is great for machine learning\",\n",
"\"I like football\",\n",
"\"Football is great to watch\"]\n",
"vectorizer = CountVectorizer(min_df = 1, stop_words = 'english')\n",
"dtm = vectorizer.fit_transform(example)\n",
"pd.DataFrame(dtm.toarray(),index=example,columns=vectorizer.get_feature_names\n",
"()).head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Each row represents a document -- **this would be patent document**. Each column represents a word. So each document is a N-dim (13 in this case) vector.\n",
"* Each entry equals the number of times the word appears in the document\n",
"* Note: order and proximity of words in documents is NOT accounted for. Called a \"bag of words\"\n",
"representation. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'cool',\n",
" u'data',\n",
" u'football',\n",
" u'fun',\n",
" u'great',\n",
" u'learning',\n",
" u'like',\n",
" u'machine',\n",
" u'python',\n",
" u'science',\n",
" u'statistics',\n",
" u'super',\n",
" u'watch']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get words that correspond to each column\n",
"vectorizer.get_feature_names()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Example: \"machine\" appears once in the first document, \"super\" appears twice in the second document, and\n",
"\"statistics\" appears zero times in the third document."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Singular value decomposition and LSA"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Fit LSA. Use algorithm = “randomized” for large datasets\n",
"lsa = TruncatedSVD(2, algorithm = 'arpack')\n",
"dtm_lsa = lsa.fit_transform(dtm)\n",
"dtm_lsa = Normalizer(copy=False).fit_transform(dtm_lsa)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Each LSA component is a linear combination of words"
]
},
{
"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>cool</th>\n",
" <th>data</th>\n",
" <th>football</th>\n",
" <th>fun</th>\n",
" <th>great</th>\n",
" <th>learning</th>\n",
" <th>like</th>\n",
" <th>machine</th>\n",
" <th>python</th>\n",
" <th>science</th>\n",
" <th>statistics</th>\n",
" <th>super</th>\n",
" <th>watch</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>component_1</th>\n",
" <td>0.280004</td>\n",
" <td>0.035353</td>\n",
" <td>0.033417</td>\n",
" <td>0.223993</td>\n",
" <td>0.178307</td>\n",
" <td>0.338085</td>\n",
" <td>0.004555</td>\n",
" <td>0.338085</td>\n",
" <td>0.391281</td>\n",
" <td>0.035353</td>\n",
" <td>0.038169</td>\n",
" <td>0.672310</td>\n",
" <td>0.028861</td>\n",
" </tr>\n",
" <tr>\n",
" <th>component_2</th>\n",
" <td>0.365270</td>\n",
" <td>-0.064548</td>\n",
" <td>-0.298349</td>\n",
" <td>-0.168056</td>\n",
" <td>-0.478428</td>\n",
" <td>-0.366379</td>\n",
" <td>-0.082792</td>\n",
" <td>-0.366379</td>\n",
" <td>0.001036</td>\n",
" <td>-0.064548</td>\n",
" <td>0.101363</td>\n",
" <td>0.424306</td>\n",
" <td>-0.215557</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cool data football fun great learning \\\n",
"component_1 0.280004 0.035353 0.033417 0.223993 0.178307 0.338085 \n",
"component_2 0.365270 -0.064548 -0.298349 -0.168056 -0.478428 -0.366379 \n",
"\n",
" like machine python science statistics super \\\n",
"component_1 0.004555 0.338085 0.391281 0.035353 0.038169 0.672310 \n",
"component_2 -0.082792 -0.366379 0.001036 -0.064548 0.101363 0.424306 \n",
"\n",
" watch \n",
"component_1 0.028861 \n",
"component_2 -0.215557 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(lsa.components_,index = [\"component_1\",\"component_2\"],columns =\n",
"vectorizer.get_feature_names())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Each document is a linear combination of the LSA components"
]
},
{
"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>component_1</th>\n",
" <th>component_2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Machine learning is super fun</th>\n",
" <td>0.957024</td>\n",
" <td>-0.290007</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Python is super, super cool</th>\n",
" <td>0.856484</td>\n",
" <td>0.516174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Statistics is cool, too</th>\n",
" <td>0.563355</td>\n",
" <td>0.826215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Data science is fun</th>\n",
" <td>0.704171</td>\n",
" <td>-0.710030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Python is great for machine learning</th>\n",
" <td>0.717284</td>\n",
" <td>-0.696781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>I like football</th>\n",
" <td>0.099136</td>\n",
" <td>-0.995074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Football is great to watch</th>\n",
" <td>0.235618</td>\n",
" <td>-0.971846</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" component_1 component_2\n",
"Machine learning is super fun 0.957024 -0.290007\n",
"Python is super, super cool 0.856484 0.516174\n",
"Statistics is cool, too 0.563355 0.826215\n",
"Data science is fun 0.704171 -0.710030\n",
"Python is great for machine learning 0.717284 -0.696781\n",
"I like football 0.099136 -0.995074\n",
"Football is great to watch 0.235618 -0.971846"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(dtm_lsa, index = example, columns = [\"component_1\",\"component_2\"])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"([0.95702439393037975,\n",
" 0.85648370973755417,\n",
" 0.56335489155638685,\n",
" 0.7041710879980152,\n",
" 0.71728390345517168,\n",
" 0.099136388426341912,\n",
" 0.23561831047045345],\n",
" [-0.29000742994307804,\n",
" 0.51617405490221724,\n",
" 0.82621502416713011,\n",
" -0.71003033655449643,\n",
" -0.69678102861954549,\n",
" -0.9950738547915835,\n",
" -0.97184567281593071])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xs = [w[0] for w in dtm_lsa]\n",
"ys = [w[1] for w in dtm_lsa]\n",
"xs, ys"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10a477450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot scatter plot of points\n",
"%pylab inline\n",
"import matplotlib.pyplot as plt\n",
"figure()\n",
"plt.scatter(xs,ys)\n",
"xlabel('First principal component')\n",
"ylabel('Second principal component')\n",
"title('Plot of points against LSA principal components')\n",
"show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Geometric picture"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"data": {
"image/png": 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4OG6//XYqVqyYazGZ3BPIKax4YKyqLgMQkfZAAtAuhHEZYzKQnJzMHXfcwaRJ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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a477e50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot scatter plot of points with vectors\n",
"%pylab inline\n",
"import matplotlib.pyplot as plt\n",
"plt.figure()\n",
"ax = plt.gca()\n",
"ax.quiver(0,0,xs,ys,angles='xy',scale_units='xy',scale=1, linewidth = .01)\n",
"ax.set_xlim([-1,1])\n",
"ax.set_ylim([-1,1])\n",
"xlabel('First principal component')\n",
"ylabel('Second principal component')\n",
"title('Plot of points against LSA principal components')\n",
"plt.draw()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* We have reduced dimension from 13-dim to 2-dim (and have lost some info)\n",
"* Similar docs point in similar directions. Dissimilar docs have perpendicular (orthogonal) vectors. \"Cosine\n",
"similarity\"\n",
"* Can use cosine similarity for search: which doc has the smallest angle with search term?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Document similarity using LSA"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Compute document similarity using LSA components\n",
"similarity = np.asarray(numpy.asmatrix(dtm_lsa) * numpy.asmatrix(dtm_lsa).T)\n",
"pd.DataFrame(similarity,index=example, columns=example).head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Summary\n",
"\n",
"With aforementioned methods in place we now have a framework to load documents into our database and then query them using semantic search. This general method is what I would apply to the Patent database search problem definition.\n",
"\n",
"## Improvements and next steps:\n",
"\n",
"* Vectorize with TFIDF (term-frequency inverse document-frequency: uses overall frequency of words to weight\n",
"document-term matrix)\n",
"* Use LSA components as features in machine learning algorithm: clustering, classification, regression\n",
"* Alternative dimensionality reduction: Isomap, Random Matrix Methods, Laplacian Eigenmaps, Kernel PCA (cool\n",
"names!)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
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"name": "ipython",
"version": 2
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"version": "2.7.11"
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},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
bsmithyman/scientific-python-examples | Analytical Helmholtz.ipynb | 1 | 197021 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analytical Helmholtz\n",
"\n",
"Brendan Smithyman | October, 2015\n",
"\n",
"This notebook implements a very simple class to model the whole-space point-source response of the 2D or 3D Helmholtz equation. We compare the different responses produced when using either $H_0^{(1)}$ (i.e., the Hankel function of the zeroth order of the first kind) or $H_0^{(2)}$ (the Hankel function of the zeroth order of the second kind). These are complex conjugates of each other, and so one of the resulting wavefields is the time-reversed response (after a sign flip)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting Setup\n",
"\n",
"The following block contains setup for plotting defaults. Note that we explicitly set the font to 'Bitstream Vera Sans', which is the default in most cases. If you don't have this font on your system, you could comment out the last few lines, or replace it with a font that you prefer to use."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Plotting imports and defaults\n",
"\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
"%matplotlib inline\n",
"\n",
"from IPython.display import set_matplotlib_formats\n",
"set_matplotlib_formats('png')\n",
"matplotlib.rcParams['savefig.dpi'] = 300\n",
"\n",
"# Font options\n",
"font = {\n",
" 'family': 'Bitstream Vera Sans',\n",
" 'weight': 'normal',\n",
" 'size': 8,\n",
"}\n",
"\n",
"matplotlib.rc('font', **font)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports\n",
"\n",
"Here, we import the necessary libraries to do the computations: `numpy`, which is the main scientific & numerical computing library in Python, and the `hankel1` and `hankel2` functions from `scipy.special`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.special import hankel1, hankel2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Class Definition\n",
"\n",
"Here, we build a class called `AnalyticalHelmholtz`, which inherits from `object`.\n",
"\n",
"The `__init__` method is called automatically when we *instantiate* the class (i.e., make a new object based on `AnalyticalHelmholtz`). The `__init__` method expects one or two inputs: 1) a required input (dictionary) called `sc`, which contains the configuration for the system, and 2) an optional input called `hf`, which defaults to the `hankel` function from `scipy.special`.\n",
"\n",
"When running `__init__`, Python first stores some properties from the `sc` parameter, and determines whether it is going to run in 2D mode or 3D mode. At the end of this method, a member object `_hf` is created, which stores a reference to the chosen Hankel function (from `hf`).\n",
"\n",
"When an object of class `AnalyticalHelmholtz` is called like a function, Python executes the `__call__` method. This uses whichever Green's function was set up during `__init__`, and returns a wavefield. The distance from the source is calculated for each point in the grid (from the $x$ and $z$ locations passed into the function call), and the response is returned as a `numpy` array (based on the configuration in the `sc` object from when the `AnalyticalHelmholtz` class was instantiated).\n",
"\n",
"NB: `k` is a property, which behaves like a variable bound to the instance, but actually runs some code inside the `k` method when its value is asked for. This way, it can be calculated and stored based on the `omega` and `c` properties."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class AnalyticalHelmholtz(object):\n",
" '''\n",
" Implements the analytical Green's functions for the 2D and 3D\n",
" Helmholtz equation(s).\n",
" ''' \n",
"\n",
" def __init__(self, sc, hf = hankel1):\n",
"\n",
" self.dimension = sc.get('dimension', 2)\n",
" self.omega = sc['omega']\n",
" self.c = sc['c']\n",
"\n",
" if self.dimension == 3:\n",
" self.Green = self.Green3D\n",
" else:\n",
" self.Green = self.Green2D\n",
"\n",
" self._x, self._z = np.mgrid[\n",
" sc['xorig']:sc['xorig']+sc['dx']*sc['nx']:sc['dx'],\n",
" sc['zorig']:sc['zorig']+sc['dz']*sc['nz']:sc['dz']\n",
" ]\n",
" \n",
" self._hf = hf\n",
"\n",
" def Green2D(self, x):\n",
"\n",
" return -0.5j * self._hf(0, self.k*x)\n",
"\n",
" def Green3D(self, x):\n",
"\n",
" return (1./(4*np.pi*x)) * np.exp(1j*self.k*x)\n",
"\n",
" def __call__(self, x, z):\n",
"\n",
" return np.nan_to_num(self.Green(np.sqrt((x - self._x)**2 + (z - self._z)**2)))\n",
" \n",
" @property\n",
" def k(self):\n",
" if getattr(self, '_k', None) is None:\n",
" self._k = self.omega / self.c\n",
" return self._k"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Object Instantiation\n",
"\n",
"The following cell contains the `systemConfig` dictionary, which we build up from a series of keys and values. I make fairly liberal use of *whitespace* here to keep the values nicely-organized. By changing these values, you can change the behaviour of the `AnalyticalHelmholtz` objects that use this configuration.\n",
"\n",
"There are two `AnalyticalHelmholtz` objects created here. Note that they are roughly the same (they use the same `systemConfig` object), except that one of them uses the `hankel1` function $H_0^{(1)}$ and the other uses the `hankel2` function $H_0^{(2)}$. At this point, the `__init__` methods have been called for both objects, but no wavefields have been constructed. However, the `AnalyticalHelmholtz` objects know *how* to generate wavefields when asked."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"systemConfig = {\n",
" 'c': 2000.,\n",
" 'omega': 200.,\n",
" 'dimension': 2,\n",
" 'nx': 100,\n",
" 'nz': 200,\n",
" 'dx': 1.,\n",
" 'dz': 1.,\n",
" 'xorig': 0.,\n",
" 'zorig': 0.,\n",
"}\n",
"\n",
"ah1 = AnalyticalHelmholtz(systemConfig, hankel1)\n",
"ah2 = AnalyticalHelmholtz(systemConfig, hankel2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wavefield Modelling\n",
"\n",
"Here, we set two variables (`xs` and `zs`), which contain the coordinates for the source. Then, we produce two wavefields: `u1`, which uses our `ah1` object and `u2`, which uses our `ah2` object. These wavefields are `numpy` arrays, with dimensions and shapes dependent on the `systemConfig` we used."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xs = 15.\n",
"zs = 15.\n",
"\n",
"u1 = ah1(xs, zs)\n",
"u2 = ah2(xs, zs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting\n",
"\n",
"We create a `dict` called `plotopts`, which contains a number of default settings for the `imshow` function. We then use the `matplotlib` object-oriented interface to build up a figure. We have 4 subplots, each of which includes a call to `plt.imshow` to plot the appropriate wavefield, and a call to `plt.title` to label it.\n",
"\n",
"NB: The call to `fig.tight_layout` rearranges the plots so that their axis labels are not overlapping."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
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afgdJH5J0U2o5AGwWOA5gCo4D2C5wHEABfgPYPnAcQAGOA5jCLT1bYGZfralA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\nNqnyAHqwLMVV8w4cKOeZZJP9G1MX/Ty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G4qqdq9v5OF18bJDauVWTCuC2bffj8rYNjvYM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bLWJG7a08GfHL5FutvyVXS+cQ9ZWPuz7m7Z7Cp5d/Z/hKaPv2uiij4t9aLQegA2hcHGhcLD40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\n9VO7n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4vH3GHzydbPj702nD1XJ8omPz+TTWqbvtYS5acHEYsVABqAxo1tCxonvI+0yc/P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\n137JlXT99DUm+2IXP8SuDATwApErAyKXB0TO3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"text/plain": [
"<matplotlib.figure.Figure at 0x10e9a7a50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plotopts = {\n",
" 'cmap': cm.bwr,\n",
" 'vmin': -1.,\n",
" 'vmax': 1.,\n",
" 'extent': [\n",
" systemConfig['xorig'],\n",
" systemConfig['xorig'] + systemConfig['dx'] * systemConfig['nx'],\n",
" systemConfig['zorig'] + systemConfig['dz'] * systemConfig['nz'],\n",
" systemConfig['zorig'],\n",
" ],\n",
"}\n",
"\n",
"fig = plt.figure()\n",
"\n",
"ax = fig.add_subplot(1,4,1, aspect=1)\n",
"plt.imshow(u1.real.T, **plotopts)\n",
"plt.title('$\\mathscr{R}\\{ H_0^{(1)} \\}$')\n",
"\n",
"ax = fig.add_subplot(1,4,2, aspect=1)\n",
"plt.imshow(u2.real.T, **plotopts)\n",
"plt.title('$\\mathscr{R}\\{ H_0^{(2)} \\}$')\n",
"\n",
"ax = fig.add_subplot(1,4,3, aspect=1)\n",
"plt.imshow(u1.imag.T, **plotopts)\n",
"plt.title('$\\mathscr{I}\\{ H_0^{(1)} \\}$')\n",
"\n",
"ax = fig.add_subplot(1,4,4, aspect=1)\n",
"plt.imshow(u2.imag.T, **plotopts)\n",
"plt.title('$\\mathscr{I}\\{ H_0^{(2)} \\}$')\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"1. Modify the `AnalyticalHelmholtz` class to take the source location as a `tuple`, rather than as two separate numbers; i.e., $(xs, zs)$.\n",
"2. Modify the `AnalyticalHelmholtz` class to simulate a free-surface response at the top of the model. To do this, you will need to add a second *ghost source*, with the same $x$-position as the main source, but with a $z$-position reflected around the top of the model (and the opposite sign).\n",
"3. Add a *property* to the `AnalyticalHelmholtz` class that returns the `extent` to use for plotting purposes (i.e., that tells the plotting code what the boundaries of the model are).\n",
"4. Add appropriate labels to the $x$- and $z$-axes of the plots."
]
},
{
"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 |
webdog/watson-2016 | reports/data-vis-sample.ipynb | 1 | 78140 | {
"cells": [
{
"cell_type": "code",
"execution_count": 264,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"#For the sake of cleanliness, ignore warnings in the notebook\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 265,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['.ipynb_checkpoints',\n",
" 'closed_issue_comments.csv',\n",
" 'closed_merged_prs.csv',\n",
" 'data-vis-sample.ipynb',\n",
" 'issue_assignees.csv',\n",
" 'issue_contrib.csv',\n",
" 'open_closed_merged_prs.csv',\n",
" 'positive_negative_pr_contributions.csv',\n",
" 'repo_maintenance.csv',\n",
" 'sentiment_analysis.csv']"
]
},
"execution_count": 265,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.listdir()"
]
},
{
"cell_type": "code",
"execution_count": 267,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib as plt\n",
"import seaborn as sns\n",
"\n",
"pd.set_option('max_columns', 50)\n",
"#Inline imaging\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 268,
"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>net_negative</th>\n",
" <th>net_equal</th>\n",
" <th>deletions</th>\n",
" <th>additions</th>\n",
" <th>number</th>\n",
" <th>net_positive</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3391</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3390</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3388</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>36</td>\n",
" <td>3387</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3386</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" net_negative net_equal deletions additions number net_positive\n",
"0 0 0 1 1 3391 0\n",
"1 0 0 2 3 3390 1\n",
"2 0 0 1 3 3388 2\n",
"3 0 0 0 36 3387 36\n",
"4 0 0 1 1 3386 0"
]
},
"execution_count": 268,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('positive_negative_pr_contributions.csv', delimiter='|')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 269,
"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>net_negative</th>\n",
" <th>net_equal</th>\n",
" <th>deletions</th>\n",
" <th>additions</th>\n",
" <th>net_positive</th>\n",
" </tr>\n",
" <tr>\n",
" <th>number</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>3391</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3390</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3388</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3387</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3386</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" net_negative net_equal deletions additions net_positive\n",
"number \n",
"3391 0 0 1 1 0\n",
"3390 0 0 2 3 1\n",
"3388 0 0 1 3 2\n",
"3387 0 0 0 36 36\n",
"3386 0 0 1 1 0"
]
},
"execution_count": 269,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Define the index. inplace=True maintains the same DataFrame object rather than creating a new one\n",
"df.set_index('number', inplace=True)\n",
"df.head()\n",
"#print(users[(users.age == 40) & (users.sex == 'M')].head(3))\\\n",
"#print(num_pos)"
]
},
{
"cell_type": "code",
"execution_count": 270,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" net_positive\n",
"number \n",
"3268 172\n",
"3244 72\n",
"3227 62\n",
"3210 109\n",
"3135 60\n",
"3112 128\n",
"3109 113\n",
"3035 406\n",
"3017 75\n",
"2924 129\n",
"2907 80\n",
"2839 66\n",
"2783 238\n",
"2774 535\n"
]
}
],
"source": [
"#Take a single column assign to new dataframe object.\n",
"new_df = df[['net_positive']]\n",
"#Observe that the DF's value is greater than 50\n",
"print(new_df[(new_df.net_positive > 50)])\n",
"#Create a list comprehension\n",
"nums = [i for i in new_df['net_positive'] if i > 50]"
]
},
{
"cell_type": "code",
"execution_count": 341,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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tNr0hM38UEbdRf45RDfirzNwSEb8BvA84AvwwM38zIj4A/A/wU8fPBeyujvdDYCAz/zAi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\noiTrUrYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1240cd080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_1 = new_df[(new_df.net_positive > 50)]\n",
"ax1= sns.boxplot(data=x_1, orient=\"v\", notch=False)\n",
"#Set the xlim to whatever the maximum plus 10 ticks to show any outliers in the set\n",
"#This data represents when PRs have net positive to the codebase greater than 100 lines of code\n",
"#ax.set(xlim=(0, max(nums) + 10))\n"
]
},
{
"cell_type": "code",
"execution_count": 289,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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WLAGYMEYmMtfqrUVWjjGyWwDHsqzrgHOAbwGt4x5vBAaAIXKBNvn+abW2Ns5O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\nMw6SvN0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x125664fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Jointplot with a linear regression (Defined )\n",
"g = sns.jointplot(x=df.index, y=(new_df['net_positive']), kind=\"reg\")"
]
},
{
"cell_type": "code",
"execution_count": 293,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#hexplot\n",
"\n",
"df1 = pd.read_csv('issue_assignees.csv', delimiter=\"|\")\n",
"df1.set_index('assigned_name', inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 363,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<bound method JointGrid.plot of <seaborn.axisgrid.JointGrid object at 0x1230650b8>>"
]
},
"execution_count": 363,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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0Fgl9pHjMsEwNOo2t1wAAANC1hI3iof37agRbrwEAACAMCRnFmekpGjsiS0kEMQAAAMKQ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\nmksGZ0AAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x124b7b9b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax2 = sns.jointplot(x=new_df.index[:25], y=new_df.net_positive[:25], kind=\"hex\", ylim=(0,100), size=10)\n",
"ax2.plot"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
toros-astro/ProperImage | docs/source/tutorial/Tutorial04.ipynb | 1 | 11944749 | null | bsd-3-clause |
tensorflow/docs-l10n | site/en-snapshot/tutorials/distribute/multi_worker_with_estimator.ipynb | 1 | 16582 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Tce3stUlHN0L"
},
"source": [
"##### Copyright 2019 The TensorFlow Authors.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "tuOe1ymfHZPu"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MfBg1C5NB3X0"
},
"source": [
"# Multi-worker training with Estimator\n",
"\n",
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/distribute/multi_worker_with_estimator\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/distribute/multi_worker_with_estimator.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/distribute/multi_worker_with_estimator.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/distribute/multi_worker_with_estimator.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
" </td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-_ZO8y69hs-N"
},
"source": [
"> Warning: Estimators are not recommended for new code. Estimators run `v1.Session`-style code which is more difficult to write correctly, and can behave unexpectedly, especially when combined with TF 2 code. Estimators do fall under [compatibility guarantees](https://tensorflow.org/guide/versions), but will receive no fixes other than security vulnerabilities. See the [migration guide](https://tensorflow.org/guide/migrate) for details."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xHxb-dlhMIzW"
},
"source": [
"## Overview\n",
"\n",
"Note: While you can use Estimators with `tf.distribute` API, it's recommended to use Keras with `tf.distribute`, see [multi-worker training with Keras](multi_worker_with_keras.ipynb). Estimator training with `tf.distribute.Strategy` has limited support.\n",
"\n",
"\n",
"This tutorial demonstrates how `tf.distribute.Strategy` can be used for distributed multi-worker training with `tf.estimator`. If you write your code using `tf.estimator`, and you're interested in scaling beyond a single machine with high performance, this tutorial is for you.\n",
"\n",
"Before getting started, please read the [distribution strategy](../../guide/distributed_training.ipynb) guide. The [multi-GPU training tutorial](./keras.ipynb) is also relevant, because this tutorial uses the same model.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MUXex9ctTuDB"
},
"source": [
"## Setup\n",
"\n",
"First, setup TensorFlow and the necessary imports."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bnYxvfLD-LW-"
},
"outputs": [],
"source": [
"import tensorflow_datasets as tfds\n",
"import tensorflow as tf\n",
"\n",
"import os, json"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-xicK9byC7hi"
},
"source": [
"Note: Starting from TF2.4 multi worker mirrored strategy fails with estimators if run with eager enabled (the default). The error in TF2.4 is `TypeError: cannot pickle '_thread.lock' object`, See [issue #46556](https://github.com/tensorflow/tensorflow/issues/46556) for details. The workaround is to disable eager execution."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5dJ6UYrGDsVs"
},
"outputs": [],
"source": [
"tf.compat.v1.disable_eager_execution()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hPBuZUNSZmrQ"
},
"source": [
"## Input function\n",
"\n",
"This tutorial uses the MNIST dataset from [TensorFlow Datasets](https://www.tensorflow.org/datasets). The code here is similar to the [multi-GPU training tutorial](./keras.ipynb) with one key difference: when using Estimator for multi-worker training, it is necessary to shard the dataset by the number of workers to ensure model convergence. The input data is sharded by worker index, so that each worker processes `1/num_workers` distinct portions of the dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dma_wUAxZqo2"
},
"outputs": [],
"source": [
"BUFFER_SIZE = 10000\n",
"BATCH_SIZE = 64\n",
"\n",
"def input_fn(mode, input_context=None):\n",
" datasets, info = tfds.load(name='mnist',\n",
" with_info=True,\n",
" as_supervised=True)\n",
" mnist_dataset = (datasets['train'] if mode == tf.estimator.ModeKeys.TRAIN else\n",
" datasets['test'])\n",
"\n",
" def scale(image, label):\n",
" image = tf.cast(image, tf.float32)\n",
" image /= 255\n",
" return image, label\n",
"\n",
" if input_context:\n",
" mnist_dataset = mnist_dataset.shard(input_context.num_input_pipelines,\n",
" input_context.input_pipeline_id)\n",
" return mnist_dataset.map(scale).cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4BlcVXMhB59T"
},
"source": [
"Another reasonable approach to achieve convergence would be to shuffle the dataset with distinct seeds at each worker."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8YFpxrcsZ2xG"
},
"source": [
"## Multi-worker configuration\n",
"\n",
"One of the key differences in this tutorial (compared to the [multi-GPU training tutorial](./keras.ipynb)) is the multi-worker setup. The `TF_CONFIG` environment variable is the standard way to specify the cluster configuration to each worker that is part of the cluster.\n",
"\n",
"There are two components of `TF_CONFIG`: `cluster` and `task`. `cluster` provides information about the entire cluster, namely the workers and parameter servers in the cluster. `task` provides information about the current task. The first component `cluster` is the same for all workers and parameter servers in the cluster, and the second component `task` is different on each worker and parameter server and specifies its own `type` and `index`. In this example, the task `type` is `worker` and the task `index` is `0`.\n",
"\n",
"For illustration purposes, this tutorial shows how to set a `TF_CONFIG` with 2 workers on `localhost`. In practice, you would create multiple workers on an external IP address and port, and set `TF_CONFIG` on each worker appropriately, i.e. modify the task `index`.\n",
"\n",
"Warning: *Do not execute the following code in Colab.* TensorFlow's runtime will attempt to create a gRPC server at the specified IP address and port, which will likely fail. See the [keras version](multi_worker_with_keras.ipynb) of this tutorial for an example of how you can test run multiple workers on a single machine.\n",
"\n",
"```\n",
"os.environ['TF_CONFIG'] = json.dumps({\n",
" 'cluster': {\n",
" 'worker': [\"localhost:12345\", \"localhost:23456\"]\n",
" },\n",
" 'task': {'type': 'worker', 'index': 0}\n",
"})\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qDreJzTffAP5"
},
"source": [
"## Define the model\n",
"\n",
"Write the layers, the optimizer, and the loss function for training. This tutorial defines the model with Keras layers, similar to the [multi-GPU training tutorial](./keras.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WNvOn_OeiUYC"
},
"outputs": [],
"source": [
"LEARNING_RATE = 1e-4\n",
"def model_fn(features, labels, mode):\n",
" model = tf.keras.Sequential([\n",
" tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28, 28, 1)),\n",
" tf.keras.layers.MaxPooling2D(),\n",
" tf.keras.layers.Flatten(),\n",
" tf.keras.layers.Dense(64, activation='relu'),\n",
" tf.keras.layers.Dense(10)\n",
" ])\n",
" logits = model(features, training=False)\n",
"\n",
" if mode == tf.estimator.ModeKeys.PREDICT:\n",
" predictions = {'logits': logits}\n",
" return tf.estimator.EstimatorSpec(labels=labels, predictions=predictions)\n",
"\n",
" optimizer = tf.compat.v1.train.GradientDescentOptimizer(\n",
" learning_rate=LEARNING_RATE)\n",
" loss = tf.keras.losses.SparseCategoricalCrossentropy(\n",
" from_logits=True, reduction=tf.keras.losses.Reduction.NONE)(labels, logits)\n",
" loss = tf.reduce_sum(loss) * (1. / BATCH_SIZE)\n",
" if mode == tf.estimator.ModeKeys.EVAL:\n",
" return tf.estimator.EstimatorSpec(mode, loss=loss)\n",
"\n",
" return tf.estimator.EstimatorSpec(\n",
" mode=mode,\n",
" loss=loss,\n",
" train_op=optimizer.minimize(\n",
" loss, tf.compat.v1.train.get_or_create_global_step()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "P94PrIW_kSCE"
},
"source": [
"Note: Although the learning rate is fixed in this example, in general it may be necessary to adjust the learning rate based on the global batch size."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UhNtHfuxCGVy"
},
"source": [
"## MultiWorkerMirroredStrategy\n",
"\n",
"To train the model, use an instance of `tf.distribute.experimental.MultiWorkerMirroredStrategy`. `MultiWorkerMirroredStrategy` creates copies of all variables in the model's layers on each device across all workers. It uses `CollectiveOps`, a TensorFlow op for collective communication, to aggregate gradients and keep the variables in sync. The [`tf.distribute.Strategy` guide](../../guide/distributed_training.ipynb) has more details about this strategy."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "1uFSHCJXMrQ-"
},
"outputs": [],
"source": [
"strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "H47DDcOgfzm7"
},
"source": [
"## Train and evaluate the model\n",
"\n",
"Next, specify the distribution strategy in the `RunConfig` for the estimator, and train and evaluate by invoking `tf.estimator.train_and_evaluate`. This tutorial distributes only the training by specifying the strategy via `train_distribute`. It is also possible to distribute the evaluation via `eval_distribute`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BcsuBYrpgnlS"
},
"outputs": [],
"source": [
"config = tf.estimator.RunConfig(train_distribute=strategy)\n",
"\n",
"classifier = tf.estimator.Estimator(\n",
" model_fn=model_fn, model_dir='/tmp/multiworker', config=config)\n",
"tf.estimator.train_and_evaluate(\n",
" classifier,\n",
" train_spec=tf.estimator.TrainSpec(input_fn=input_fn),\n",
" eval_spec=tf.estimator.EvalSpec(input_fn=input_fn)\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XVk4ftYx6JAO"
},
"source": [
"## Optimize training performance\n",
"\n",
"You now have a model and a multi-worker capable Estimator powered by `tf.distribute.Strategy`. You can try the following techniques to optimize performance of multi-worker training:\n",
"\n",
"* *Increase the batch size:* The batch size specified here is per-GPU. In general, the largest batch size that fits the GPU memory is advisable.\n",
"* *Cast variables:* Cast the variables to `tf.float` if possible. The official ResNet model includes [an example](https://github.com/tensorflow/models/blob/8367cf6dabe11adf7628541706b660821f397dce/official/resnet/resnet_model.py#L466) of how this can be done.\n",
"* *Use collective communication:* `MultiWorkerMirroredStrategy` provides multiple [collective communication implementations](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/distribute/cross_device_ops.py). \n",
" * `RING` implements ring-based collectives using gRPC as the cross-host communication layer. \n",
" * `NCCL` uses [Nvidia's NCCL](https://developer.nvidia.com/nccl) to implement collectives. \n",
" * `AUTO` defers the choice to the runtime.\n",
" \n",
" The best choice of collective implementation depends upon the number and kind of GPUs, and the network interconnect in the cluster. To override the automatic choice, specify a valid value to the `communication` parameter of `MultiWorkerMirroredStrategy`'s constructor, e.g. `communication=tf.distribute.experimental.CollectiveCommunication.NCCL`.\n",
"\n",
"Visit the [Performance section](../../guide/function.ipynb) in the guide to learn more about other strategies and [tools](../../guide/profiler.md) you can use to optimize the performance of your TensorFlow models.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AW0Hb2xM6EGX"
},
"source": [
"## Other code examples\n",
"\n",
"1. [End to end example](https://github.com/tensorflow/ecosystem/tree/master/distribution_strategy) for multi worker training in tensorflow/ecosystem using Kubernetes templates. This example starts with a Keras model and converts it to an Estimator using the `tf.keras.estimator.model_to_estimator` API.\n",
"2. [Official models](https://github.com/tensorflow/models/tree/master/official), many of which can be configured to run multiple distribution strategies.\n"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"Tce3stUlHN0L"
],
"name": "multi_worker_with_estimator.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
phoebe-project/phoebe2-docs | development/tutorials/23_24_gaussian_processes.ipynb | 2 | 1160 | {
"cells": [
{
"cell_type": "markdown",
"id": "65334961-1d16-4b9c-99fb-abb9c7ca507f",
"metadata": {},
"source": [
"# 2.3 - 2.4 Migration: new backends for Gaussian Processes\n",
"\n",
"As of 2.4, the gaussian_process feature, which wrapped around `celerite`, has been replaced with the GP implementation in `scikit-learn` and `celerite2`. Adding a gaussian process feature now requires a `kind='sklearn'` or `kind='celerite2'` to be provided.\n",
"\n",
"For more see the [GPs tutorial](gaussian_processes.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ecbe6e0f-696b-4fed-ace9-cb80c05d484a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| gpl-3.0 |
statsmaths/stat665 | lectures/lec22/.ipynb_checkpoints/notebook20-checkpoint.ipynb | 3 | 5254 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Word embeddings\n",
"Import various modules that we need for this notebook (now using Keras 1.0.0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: pylab import has clobbered these variables: ['copy']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n"
]
}
],
"source": [
"%pylab inline\n",
"\n",
"import copy\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from keras.datasets import imdb\n",
"from keras.models import Sequential\n",
"from keras.layers.core import Dense, Dropout, Activation\n",
"from keras.optimizers import SGD, RMSprop\n",
"from keras.utils import np_utils\n",
"from keras.layers.convolutional import Convolution1D, MaxPooling1D, ZeroPadding1D, AveragePooling1D\n",
"from keras.callbacks import EarlyStopping\n",
"from keras.layers.normalization import BatchNormalization\n",
"from keras.preprocessing import sequence\n",
"from keras.layers.embeddings import Embedding"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the MNIST dataset, flatten the images, convert the class labels, and scale the data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# I. Example using word embedding\n",
"We read in the IMDB dataset, removing the 25 most common words, and using the next 500 most commonly used terms."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"(X_train, y_train), (X_test, y_test) = imdb.load_data(path=\"imdb.pkl\", nb_words=500,\n",
" skip_top=25, maxlen=100, test_split=0.2)\n",
"X_train = sequence.pad_sequences(X_train, maxlen=100)\n",
"X_test = sequence.pad_sequences(X_test, maxlen=100)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'Embedding' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-ed320527dfcd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEmbedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\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 4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'Embedding' is not defined"
]
}
],
"source": [
"model = Sequential()\n",
"\n",
"model.add(Embedding(500, 25, input_length=100))\n",
"model.add(Dropout(0.25))\n",
"\n",
"model.add(Flatten())\n",
"\n",
"model.add(Dense(256))\n",
"model.add(Dropout(0.25))\n",
"model.add(Activation('relu'))\n",
"\n",
"model.add(Dense(1))\n",
"model.add(Activation('sigmoid'))\n",
"\n",
"model.compile(loss='binary_crossentropy', optimizer='rmsprop', class_mode='binary')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.fit(X_train, y_train, batch_size=batch_size,\n",
" nb_epoch=nb_epoch, show_accuracy=True,\n",
" validation_data=(X_test, y_test))"
]
}
],
"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-2.0 |
rsignell-usgs/ipython-notebooks | files/ROMS_subset_velocity.ipynb | 1 | 392832 | {
"metadata": {
"name": "COAWST_subset_velocity"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": "Subset ROMS data using a lon/lat bounding box"
},
{
"cell_type": "code",
"collapsed": false,
"input": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import path \n\nimport netCDF4",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": "def bbox2ij(lon,lat,bbox=[-160., -155., 18., 23.]):\n \"\"\"Return indices for i,j that will completely cover the specified bounding box.\n \n i0,i1,j0,j1 = bbox2ij(lon,lat,bbox)\n \n lon,lat = 2D arrays that are the target of the subset\n bbox = list containing the bounding box: [lon_min, lon_max, lat_min, lat_max]\n\n Example\n ------- \n >>> i0,i1,j0,j1 = bbox2ij(lon_rho,[-71, -63., 39., 46])\n >>> h_subset = nc.variables['h'][j0:j1,i0:i1] \n \"\"\"\n bbox=np.array(bbox)\n mypath=np.array([bbox[[0,1,1,0]],bbox[[2,2,3,3]]]).T\n p = path.Path(mypath)\n points = np.vstack((lon.flatten(),lat.flatten())).T \n n,m = np.shape(lon)\n inside = p.contains_points(points).reshape((n,m))\n ii,jj = np.meshgrid(xrange(m),xrange(n))\n return min(ii[inside]),max(ii[inside]),min(jj[inside]),max(jj[inside]) \n ",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": "#url = 'http://geoport.whoi.edu/thredds/dodsC/coawst_2_2/fmrc/coawst_2_2_best.ncd'\nurl = 'http://geoport.whoi.edu/thredds/dodsC/coawst_4/use/fmrc/coawst_4_use_best.ncd'\n#url = 'http://testbedapps-dev.sura.org/thredds/dodsC/alldata/Shelf_Hypoxia/tamu/roms/tamu_roms.nc'\n#url = 'http://tds.ve.ismar.cnr.it:8080/thredds/dodsC/ismar/model/field2/run1/Field_2km_1108_out30min_his_0724.nc'\n#url='http://tds.ve.ismar.cnr.it:8080/thredds/dodsC/field2_test/run1/his'\n\nnc = netCDF4.Dataset(url)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": "#nc.variables.keys()",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": "lon_rho = nc.variables['lon_rho'][:]\nlat_rho = nc.variables['lat_rho'][:]",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": "bbox = [-71., -63.0, 41., 44.]\ni0,i1,j0,j1 = bbox2ij(lon_rho,lat_rho,bbox)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": "#tvar = nc.variables['ocean_time'] # usual ROMS\ntvar = nc.variables['time'] # USGS COAWST FMRC Aggregation",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": "h = nc.variables['h'][j0:j1, i0:i1]\nlon = lon_rho[j0:j1, i0:i1]\nlat = lat_rho[j0:j1, i0:i1]\nland_mask = 1 - nc.variables['mask_rho'][j0:j1, i0:i1]\n",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": "plt.pcolormesh(lon,lat,ma.masked_where(land_mask,-h),vmin=-350,vmax=0)\nplt.colorbar()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": "<matplotlib.colorbar.Colorbar instance at 0x49be368>"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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QQF1dHZ/5zMlVPyft0j333MNDDz2kK6MCPPvss1RWVjJ37tyTHuD+++/Xv5ub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"text": "<matplotlib.figure.Figure at 0x41c55d0>"
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Now subset the velocity. This is a bit trickier with ROMS because\n# the U and V velocities are staggered",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": "def rot2d(x, y, ang):\n '''rotate vectors by geometric angle\n \n This routine is part of Rob Hetland's OCTANT package:\n https://github.com/hetland/octant\n '''\n xr = x*np.cos(ang) - y*np.sin(ang)\n yr = x*np.sin(ang) + y*np.cos(ang)\n return xr, yr",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": "def shrink(a,b):\n \"\"\"Return array shrunk to fit a specified shape by triming or averaging.\n \n a = shrink(array, shape)\n \n array is an numpy ndarray, and shape is a tuple (e.g., from\n array.shape). a is the input array shrunk such that its maximum\n dimensions are given by shape. If shape has more dimensions than\n array, the last dimensions of shape are fit.\n \n as, bs = shrink(a, b)\n \n If the second argument is also an array, both a and b are shrunk to\n the dimensions of each other. The input arrays must have the same\n number of dimensions, and the resulting arrays will have the same\n shape.\n \n This routine is part of Rob Hetland's OCTANT package:\n https://github.com/hetland/octant\n \n Example\n -------\n \n >>> shrink(rand(10, 10), (5, 9, 18)).shape\n (9, 10)\n >>> map(shape, shrink(rand(10, 10, 10), rand(5, 9, 18))) \n [(5, 9, 10), (5, 9, 10)] \n \n \"\"\"\n\n if isinstance(b, np.ndarray):\n if not len(a.shape) == len(b.shape):\n raise Exception, \\\n 'input arrays must have the same number of dimensions'\n a = shrink(a,b.shape)\n b = shrink(b,a.shape)\n return (a, b)\n\n if isinstance(b, int):\n b = (b,)\n\n if len(a.shape) == 1: # 1D array is a special case\n dim = b[-1]\n while a.shape[0] > dim: # only shrink a\n if (dim - a.shape[0]) >= 2: # trim off edges evenly\n a = a[1:-1]\n else: # or average adjacent cells\n a = 0.5*(a[1:] + a[:-1])\n else:\n for dim_idx in range(-(len(a.shape)),0):\n dim = b[dim_idx]\n a = a.swapaxes(0,dim_idx) # put working dim first\n while a.shape[0] > dim: # only shrink a\n if (a.shape[0] - dim) >= 2: # trim off edges evenly\n a = a[1:-1,:]\n if (a.shape[0] - dim) == 1: # or average adjacent cells\n a = 0.5*(a[1:,:] + a[:-1,:])\n a = a.swapaxes(0,dim_idx) # swap working dim back\n\n return a",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": "#start=datetime.datetime(2012,1,1,0,0)\n#start = datetime.datetime.utcnow()\n#tidx = netCDF4.date2index(start,tvar,select='nearest') # get nearest index to now\ntidx = -1\ntimestr = netCDF4.num2date(tvar[tidx], tvar.units).strftime('%b %d, %Y %H:%M')",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": "zlev = -1 # last layer is surface layer in ROMS\nu = nc.variables['u'][tidx, zlev, j0:j1, i0:(i1-1)]\nv = nc.variables['v'][tidx, zlev, j0:(j1-1), i0:i1]",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": "lon_u = nc.variables['lon_u'][ j0:j1, i0:(i1-1)]\nlon_v = nc.variables['lon_v'][ j0:(j1-1), i0:i1]\nlat_u = nc.variables['lat_u'][ j0:j1, i0:(i1-1)]\nlat_v = nc.variables['lat_v'][ j0:(j1-1), i0:i1]",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": "lon=lon_rho[(j0+1):(j1-1), (i0+1):(i1-1)]\nlat=lat_rho[(j0+1):(j1-1), (i0+1):(i1-1)]\nmask = 1 - nc.variables['mask_rho'][(j0+1):(j1-1), (i0+1):(i1-1)]\nang = nc.variables['angle'][(j0+1):(j1-1), (i0+1):(i1-1)]",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": "# average u,v to central rho points\nu = shrink(u, mask.shape)\nv = shrink(v, mask.shape)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": "# rotate grid_oriented u,v to east/west u,v\nu, v = rot2d(u, v, ang)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": "from mpl_toolkits.basemap import Basemap\nbasemap = Basemap(projection='merc',llcrnrlat=39.5,urcrnrlat=46,llcrnrlon=-71.5,urcrnrlon=-62.5, lat_ts=30,resolution='i')\nfig1 = plt.figure(figsize=(10,8))\nax = fig1.add_subplot(111)\n\nbasemap.drawcoastlines()\nbasemap.fillcontinents()\nbasemap.drawcountries()\nbasemap.drawstates()\nx_rho, y_rho = basemap(lon,lat)\n\nspd = np.sqrt(u*u + v*v)\nh1 = basemap.pcolormesh(x_rho, y_rho, spd, vmin=0, vmax=1.0)\nnsub=2\nscale=0.03\nbasemap.quiver(x_rho[::nsub,::nsub],y_rho[::nsub,::nsub],u[::nsub,::nsub],v[::nsub,::nsub],scale=1.0/scale, zorder=1e35, width=0.002)\nbasemap.colorbar(h1,location='right',pad='5%')\ntitle('COAWST Surface Current: ' + timestr);",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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KpW6qaPfu3Xrz+kOGDGH58uUcOHAAjUZDdHQ0oaGhODo60rZtWz777DMePXqERqMhPDxc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\nGc6p6q+oSMEUvCusZs8cNxN5u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DffoS/BXizI/GuTYOF8GDACSbFUlp3WdlBJBIDvTAXV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\nrsEvxIYSE0Xr/Qv448og2CR5BDzpFbmYu7w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apHJ5keS5mQDY7ZjLquvj2k/Aeej0vPi3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5r8PlnZzsaYkuTsGu/ht6puW/X7VHkeqoUlHmFR6Ixo0+8gNB+iPgKT1Z2IKfGEuppTc465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\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R5alTpw5GYwHF3a6metl660/+m4rn9eVfHmq+DiZVhOSrHwXANogcb8MQGkJRjgN7Sgbx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\nKBQKhULxh1BiQqFQKBQKxR/ifwEb3xwvRX0qIAAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x526f850>"
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
}
],
"metadata": {}
}
]
} | unlicense |
nkmk/python-snippets | notebook/digit_int.ipynb | 1 | 6510 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"i = 9876"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9876\n"
]
}
],
"source": [
"print(i)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'int'>\n"
]
}
],
"source": [
"print(type(i))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"s = str(i)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9876\n"
]
}
],
"source": [
"print(s)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n"
]
}
],
"source": [
"print(type(s))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n"
]
}
],
"source": [
"print(len(s))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'int'>\n"
]
}
],
"source": [
"print(type(len(s)))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n"
]
}
],
"source": [
"print(len(str(i)))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"i_minus = -9876"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"s_minus = str(i_minus)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-9876\n"
]
}
],
"source": [
"print(s_minus)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
}
],
"source": [
"print(len(s_minus))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9876\n"
]
}
],
"source": [
"print(abs(i_minus))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n"
]
}
],
"source": [
"print(len(str(abs(i_minus))))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6\n"
]
}
],
"source": [
"print(s[-1])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"8\n"
]
}
],
"source": [
"print(s[-3])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# print(s[-10])\n",
"# IndexError: string index out of range"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n"
]
}
],
"source": [
"print(type(s[-1]))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6\n"
]
}
],
"source": [
"print(int(s[-1]))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6\n"
]
}
],
"source": [
"print(str(i)[-1])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6\n"
]
}
],
"source": [
"print(int(str(i)[-1]))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"s = '9,675'"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9675\n"
]
}
],
"source": [
"print(s.replace(',', ''))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n"
]
}
],
"source": [
"print(type(s.replace(',', '')))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n"
]
}
],
"source": [
"print(len(s.replace(',', '')))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6\n"
]
}
],
"source": [
"print(s.replace(',', '')[-3])"
]
}
],
"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": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
bbglab/adventofcode | 2018/ferran/day04/repose_record.ipynb | 1 | 3837 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 1"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## parse table"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# parse table\n",
"\n",
"l = ! cat input.txt | tr '\\n' ';'\n",
"l = l[0].rstrip(';').split(';')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def parselem(entry):\n",
" s = entry.split('] ')\n",
" day, time = s[0][1:].split(' ')\n",
" action = ' '.join(s[1].split(' ')[:2])\n",
" day = int(''.join(day.split('-')))\n",
" time = int(''.join(time.split(':')))\n",
" return day, time, action"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"events = sorted(list(map(parselem, l)), key=lambda x: (x[0], x[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## fill out table"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"def sleepdict(events):\n",
" start, end = 0, 0\n",
" acc = {}\n",
" for day, time, action in events:\n",
" if action not in ['falls asleep', 'wakes up']:\n",
" guard = action.split(' ')[1]\n",
" if guard not in acc:\n",
" acc.update({guard: np.zeros(60)})\n",
" if action == 'falls asleep':\n",
" start = time\n",
" if action == 'wakes up':\n",
" end = time\n",
" vector = np.zeros(60)\n",
" vector[start: end] = 1\n",
" acc[guard] += vector\n",
" return acc"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"98680"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sleeps = sleepdict(events)\n",
"sortlist = sorted([(k, v) for k, v in sleeps.items()], key=lambda x: sum(x[1]), reverse=True)\n",
"int(sortlist[0][0][1:]) * np.argmax(sortlist[0][1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 2"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mostfreq = []\n",
"for guard, arr in sleeps.items():\n",
" mostfreq.append((guard, np.argmax(arr), max(arr)))"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9763"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mostfreq = sorted(mostfreq, key=lambda x: x[2], reverse=True)\n",
"int(mostfreq[0][0][1:]) * mostfreq[0][1]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:adventofcode]",
"language": "python",
"name": "conda-env-adventofcode-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": 2
}
| mit |
fastai/course-v3 | nbs/dl2/02a_why_sqrt5.ipynb | 1 | 13744 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Does nn.Conv2d init work well?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Jump_to lesson 9 video](https://course19.fast.ai/videos/?lesson=9&t=21)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"from exp.nb_02 import *\n",
"\n",
"def get_data():\n",
" path = datasets.download_data(MNIST_URL, ext='.gz')\n",
" with gzip.open(path, 'rb') as f:\n",
" ((x_train, y_train), (x_valid, y_valid), _) = pickle.load(f, encoding='latin-1')\n",
" return map(tensor, (x_train,y_train,x_valid,y_valid))\n",
"\n",
"def normalize(x, m, s): return (x-m)/s"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"torch.nn.modules.conv._ConvNd.reset_parameters??"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_train,y_train,x_valid,y_valid = get_data()\n",
"train_mean,train_std = x_train.mean(),x_train.std()\n",
"x_train = normalize(x_train, train_mean, train_std)\n",
"x_valid = normalize(x_valid, train_mean, train_std)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(torch.Size([50000, 1, 28, 28]), torch.Size([10000, 1, 28, 28]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train = x_train.view(-1,1,28,28)\n",
"x_valid = x_valid.view(-1,1,28,28)\n",
"x_train.shape,x_valid.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(50000, tensor(10))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n,*_ = x_train.shape\n",
"c = y_train.max()+1\n",
"nh = 32\n",
"n,c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"l1 = nn.Conv2d(1, nh, 5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x = x_valid[:100]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([100, 1, 28, 28])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def stats(x): return x.mean(),x.std()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([32, 1, 5, 5])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l1.weight.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((tensor(-0.0043, grad_fn=<MeanBackward1>),\n",
" tensor(0.1156, grad_fn=<StdBackward0>)),\n",
" (tensor(0.0212, grad_fn=<MeanBackward1>),\n",
" tensor(0.1176, grad_fn=<StdBackward0>)))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats(l1.weight),stats(l1.bias)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t = l1(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(0.0107, grad_fn=<MeanBackward1>),\n",
" tensor(0.5978, grad_fn=<StdBackward0>))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats(t)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(0.0267, grad_fn=<MeanBackward1>),\n",
" tensor(1.1067, grad_fn=<StdBackward0>))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"init.kaiming_normal_(l1.weight, a=1.)\n",
"stats(l1(x))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch.nn.functional as F"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def f1(x,a=0): return F.leaky_relu(l1(x),a)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(0.5547, grad_fn=<MeanBackward1>),\n",
" tensor(1.0199, grad_fn=<StdBackward0>))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"init.kaiming_normal_(l1.weight, a=0)\n",
"stats(f1(x))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(0.2219, grad_fn=<MeanBackward1>),\n",
" tensor(0.3653, grad_fn=<StdBackward0>))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l1 = nn.Conv2d(1, nh, 5)\n",
"stats(f1(x))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([32, 1, 5, 5])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l1.weight.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"25"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# receptive field size\n",
"rec_fs = l1.weight[0,0].numel()\n",
"rec_fs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(32, 1)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nf,ni,*_ = l1.weight.shape\n",
"nf,ni"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(25, 800)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fan_in = ni*rec_fs\n",
"fan_out = nf*rec_fs\n",
"fan_in,fan_out"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def gain(a): return math.sqrt(2.0 / (1 + a**2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1.0,\n",
" 1.4142135623730951,\n",
" 1.4141428569978354,\n",
" 1.4071950894605838,\n",
" 0.5773502691896257)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gain(1),gain(0),gain(0.01),gain(0.1),gain(math.sqrt(5.))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.5788)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.zeros(10000).uniform_(-1,1).std()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5773502691896258"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1/math.sqrt(3.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def kaiming2(x,a, use_fan_out=False):\n",
" nf,ni,*_ = x.shape\n",
" rec_fs = x[0,0].shape.numel()\n",
" fan = nf*rec_fs if use_fan_out else ni*rec_fs\n",
" std = gain(a) / math.sqrt(fan)\n",
" bound = math.sqrt(3.) * std\n",
" x.data.uniform_(-bound,bound)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(0.5603, grad_fn=<MeanBackward1>),\n",
" tensor(1.0921, grad_fn=<StdBackward0>))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kaiming2(l1.weight, a=0);\n",
"stats(f1(x))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(0.2186, grad_fn=<MeanBackward1>),\n",
" tensor(0.3437, grad_fn=<StdBackward0>))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kaiming2(l1.weight, a=math.sqrt(5.))\n",
"stats(f1(x))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class Flatten(nn.Module):\n",
" def forward(self,x): return x.view(-1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m = nn.Sequential(\n",
" nn.Conv2d(1,8, 5,stride=2,padding=2), nn.ReLU(),\n",
" nn.Conv2d(8,16,3,stride=2,padding=1), nn.ReLU(),\n",
" nn.Conv2d(16,32,3,stride=2,padding=1), nn.ReLU(),\n",
" nn.Conv2d(32,1,3,stride=2,padding=1),\n",
" nn.AdaptiveAvgPool2d(1),\n",
" Flatten(),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y = y_valid[:100].float()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(0.0875, grad_fn=<MeanBackward1>),\n",
" tensor(0.0065, grad_fn=<StdBackward0>))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t = m(x)\n",
"stats(t)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"l = mse(t,y)\n",
"l.backward()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(0.0054), tensor(0.0333))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats(m[0].weight.grad)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"init.kaiming_uniform_??"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for l in m:\n",
" if isinstance(l,nn.Conv2d):\n",
" init.kaiming_uniform_(l.weight)\n",
" l.bias.data.zero_()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(-0.0352, grad_fn=<MeanBackward1>),\n",
" tensor(0.4043, grad_fn=<StdBackward0>))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t = m(x)\n",
"stats(t)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(0.0093), tensor(0.4231))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = mse(t,y)\n",
"l.backward()\n",
"stats(m[0].weight.grad)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!./notebook2script.py 02a_why_sqrt5.ipynb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
JohnCrickett/PythonExamples | Identifiers.ipynb | 1 | 2998 | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python Identifiers\n",
"Python’s identifiers are case-sensitive names for entities in the source code, i.e. for functions, classes and variables.\n",
"\n",
"Some identifiers have a reserved meaning:\n",
"\n",
"| Reserved Pattern | Example | Meaning |\n",
"|:--|:--|:--|\n",
"|_* (leading underscore) | _do_something | Weak \"internal use\" indicator, e.g. from M import * does not import objects whose name starts with an underscore. |\n",
"| \\__ * (double leading underscore) | \\__common_name | Any identifier of the form \\__spam (at least two leading underscores, at most one trailing underscore) is textually replaced with _classname\\__spam, where classname is the current class name with leading underscore(s) stripped. Used to avoid name conflicts. |\n",
"| __*__ (double leading and trailing underscore) | \\__init\\__ | \"magic\" objects or attributes that live in user-controlled namespaces. E.g. \\__init\\__ , \\__import\\__ or \\__file\\__ . Never invent such names; only use them as documented. |"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# TODO:\n",
"\n",
"Add some examples of identifiers and the above rules\n",
"\n",
"Add some examples of errors when using keywords.\n",
"\n",
"Show nicely formatted keyword list:\n",
"\n",
"```python\n",
"False\n",
"None\n",
"True\n",
"and\n",
"as\n",
"assert\n",
"break\n",
"class\n",
"continue\n",
"def\n",
"del\n",
"elif\n",
"else\n",
"except\n",
"finally\n",
"for\n",
"from\n",
"global\n",
"if\n",
"import\n",
"in\n",
"is\n",
"lambda\n",
"nonlocal\n",
"not\n",
"or\n",
"pass\n",
"raise\n",
"return\n",
"try\n",
"while\n",
"with\n",
"yield\n",
"```"
]
},
{
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"execution_count": null,
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"source": []
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| mit |
AllenDowney/HeriReligion | archive/heri17.ipynb | 1 | 328965 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import print_function, division\n",
"\n",
"%matplotlib inline\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import thinkbayes2\n",
"import thinkplot\n",
"\n",
"import statsmodels.formula.api as smf\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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>noneall</th>\n",
" <th>fatherall</th>\n",
" <th>motherall</th>\n",
" <th>attendedall</th>\n",
" <th>nonemen</th>\n",
" <th>fathermen</th>\n",
" <th>mothermen</th>\n",
" <th>attendedmen</th>\n",
" <th>nonewomen</th>\n",
" <th>fatherwomen</th>\n",
" <th>motherwomen</th>\n",
" <th>attendedwomen</th>\n",
" <th>bornagain</th>\n",
" <th>evangelical</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</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>1966</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>66.1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>59.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>74.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1967</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1968</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>91.7</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>90.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>93.7</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1969</th>\n",
" <td>13.6</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>91.0</td>\n",
" <td>15.7</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>89.2</td>\n",
" <td>2.3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>93.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1970</th>\n",
" <td>10.7</td>\n",
" <td>NaN</td>\n",
" <td>3.1</td>\n",
" <td>89.0</td>\n",
" <td>11.9</td>\n",
" <td>NaN</td>\n",
" <td>2.8</td>\n",
" <td>87.4</td>\n",
" <td>9.1</td>\n",
" <td>NaN</td>\n",
" <td>3.3</td>\n",
" <td>90.9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" noneall fatherall motherall attendedall nonemen fathermen \\\n",
"year \n",
"1966 NaN NaN NaN 66.1 NaN NaN \n",
"1967 NaN NaN NaN NaN NaN NaN \n",
"1968 NaN NaN NaN 91.7 NaN NaN \n",
"1969 13.6 NaN NaN 91.0 15.7 NaN \n",
"1970 10.7 NaN 3.1 89.0 11.9 NaN \n",
"\n",
" mothermen attendedmen nonewomen fatherwomen motherwomen \\\n",
"year \n",
"1966 NaN 59.0 NaN NaN NaN \n",
"1967 NaN NaN NaN NaN NaN \n",
"1968 NaN 90.0 NaN NaN NaN \n",
"1969 NaN 89.2 2.3 NaN NaN \n",
"1970 2.8 87.4 9.1 NaN 3.3 \n",
"\n",
" attendedwomen bornagain evangelical \n",
"year \n",
"1966 74.0 NaN NaN \n",
"1967 NaN NaN NaN \n",
"1968 93.7 NaN NaN \n",
"1969 93.0 NaN NaN \n",
"1970 90.9 NaN NaN "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('heri17.csv', skiprows=2, index_col='year')\n",
"df[df.columns] /= 10\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['time'] = df.index - 1966\n",
"df['time2'] = df.time**2"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def MakeErrorModel(df, y, formula, n=100):\n",
" \"\"\"Makes a model that captures sample error and residual error.\n",
"\n",
" df: DataFrame\n",
" y: Series\n",
" formula: string representation of the regression model\n",
" n: number of simulations to run\n",
"\n",
" returns: (fittedvalues, sample_error, total_error)\n",
" \"\"\"\n",
" # make the best fit\n",
" df['y'] = y\n",
" results = smf.ols(formula, data=df).fit()\n",
" fittedvalues = results.fittedvalues\n",
" resid = results.resid \n",
"\n",
" # permute residuals and generate hypothetical fits\n",
" fits = []\n",
" for i in range(n):\n",
" df['y'] = fittedvalues + np.random.permutation(results.resid)\n",
" fake_results = smf.ols(formula, data=df).fit()\n",
" fits.append(fake_results.fittedvalues)\n",
"\n",
" # compute the variance of the fits\n",
" fits = np.array(fits)\n",
" sample_var = fits.var(axis=0)\n",
" \n",
" # add sample_var and the variance of the residuals\n",
" total_var = sample_var + resid.var()\n",
"\n",
" # standard errors are square roots of the variances\n",
" return fittedvalues, np.sqrt(sample_var), np.sqrt(total_var)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def FillBetween(fittedvalues, stderr, **options):\n",
" \"\"\"Fills in the 95% confidence interval.\n",
" \n",
" fittedvalues: series\n",
" stderr: standard error\n",
" \"\"\"\n",
" low = fittedvalues - 2 * stderr\n",
" high = fittedvalues + 2 * stderr\n",
" thinkplot.FillBetween(fittedvalues.index, low, high, **options)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def PlotModel(y, fittedvalues, sample_error, total_error, **options):\n",
" \"\"\"Plots confidence intervals and the actual data\n",
" \"\"\"\n",
" FillBetween(fittedvalues, total_error, color='0.9')\n",
" FillBetween(fittedvalues, sample_error, color='0.7')\n",
" thinkplot.Plot(fittedvalues, color='0.5')\n",
" thinkplot.Plot(y, **options)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def Plot(df, y, formula, **options):\n",
"\n",
" fittedvalues, sample_error, total_error = MakeErrorModel(df, y, formula)\n",
" PlotModel(y, fittedvalues, sample_error, total_error, **options)\n",
"\n",
" thinkplot.Config(xlim=[1965, 2017])"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD+xyVF0yzufmAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9805b29b90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"import seaborn as sns\n",
"sns.set_style('whitegrid')\n",
"sns.set_context('talk', font_scale=1.3)\n",
"\n",
"current_palette = sns.color_palette()\n",
"sns.palplot(current_palette)\n",
"BLUE, GREEN, RED, PURPLE, YELLOW, SKY = current_palette"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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mpnc0sUhRDAwMaJ9rampgs9nm/L4kSXC5XHlZZMJASERERCtePB6ftc9wpsbGxnB8LIxU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\n6tB69wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9805d20e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"formula = 'y ~ time + time2'\n",
"y = df.noneall\n",
"Plot(df, y, formula, color=BLUE, label='None')\n",
"thinkplot.Config(ylabel='Percent', loc='upper left')"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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XX3q93nlnO98ZCUFWFEQiEdj0IirN6Ufra9euhd1uz/tYlophkoiIiCgHhSpKDgDBYDCr\nMHmGJyBh3BOHKIqQJAmrK2IQBKCtrQ01NTUFGctSMUwSERERnUKhipID6ULjgUBgznFFUfDWcAhx\nKY5YLIYaawIOUwqNjY1oamrK+ziWi2GSiIiIaBGFDJLzFSbPOOqNY8IbRTQShQBgdUUMVVVVaG9v\nL9nO7fkwTBIREREtoFBFyTPX9ng8815bURTsPhhAJByBAgX19jiqnWasW7dOU0ESKJMwqSgKfvSj\nH6Gvrw933XVXzp977LHHcNlll2HDhg3Ytm0bfvCDHxSkHhQRERGtPIUMkgDg8/kgSdK85455Yzjm\nCUJWZOgFBWtqUujp6YHBYCjIWE6H9kZ0ErfbjTvuuANut3tJv4CPPPIIHnzwQXzta1/DBRdcgP37\n9+Puu+/G7OwsvvrVrxZwxERERFTuCtndBgBCoRDC4fC852RZxmt7jqjf7XIksKF3HSwWS0HGcro0\nPzP5+OOPo7q6Gr/85S9zrqMkSRIeeeQR7NixA1dddRWam5tx6aWX4pZbbsHPf/5zeDyeAo+aiIiI\nylmhuttkru33++c9pygKDg6OYGLmeIg9v2cVKioqCjKWfNB8mLz66qvxT//0T3A4HDl/5u2330Yk\nEsHWrVuzjm/duhWpVAqvvPJKvodJREREK0Q8Hi9IdxsgXV5ovsLkGUeOHMHQ0SDk907XV9vQ1erK\n6dqCIJRkPaXmw2RLS8uSP3Po0CEAmNPwvLm5GTqdTj1PREREdCJJkhZcx3i6MhtuFprxdLvdOHr0\nKLzR9JNYs9mMs7vqcgqIgiCgvr4eOl3xo13J1kxKkoSjR48u+p6Ojo5lXTvTHP3kqvA6nQ5ms3lO\n8/RTKVSB0lLKtIEqRDsoKjzev/LG+1e+eO/K26nuXzKZLNiMJAD4/X7EYrF5zwUCAYyMjEBMCJiN\nG2A0GGGz2dDWYD7lBiBBEFBbWwuj0QhRFIs+O1myMDk4OIjt27fP+wMrigJBEDAwMFCCkc21f//+\nUg+hYIaHh0s9BDoNvH/ljfevfPHelbf57p/BYIDFYilYEBNFccENN7FYDENDQ+lH4BErdIIOFosF\nLqcAJSkissjSTUEQ4HA4EA6HEQ6Hlzxhlg8lC5MbNmzAgQMHCnLtzCLVcDiMyspK9Ximn2ZVVdWS\nrtfX15fX8WlBPB7H8PAwurq6YDabSz0cWiLev/LG+1e+eO/K20L3L5VKFezRduZ7RVGct492PB7H\n2NgYAECn0yOYsMNZ4YROp8P61grY7Yvv4K6trc26bktLS0EC8WITa5ovDbQcXV1dUBQF4+PjaG5u\nVo+PjY1BURSsXbt2SdfT6lb8fDCbzSv651vpeP/KG+9f+eK9K28n3r9UKoVkMlmw+o3JZBJ+v3/e\nzTHJZBIHDx5EIpGAIAiIpkwwWCqg1+lhNAhorbdCp1s4GNbW1sLpdGYdK+Ts6kI0vwFnOTZu3IjK\nykq89NJLWcdfeOEFGI1GXHzxxSUaGREREWlFKpUqWJtEIL1sb3p6et5albIsY3BwENFoFED6cbXO\nvhoGfTrUtjVYYNAvHApramrmBMlS0XyY9Pv98Hq96jb6eDwOr9cLr9erLqDds2cPLr/8crz22msA\nAKPRiJtvvhlPPfUUnn76aUxMTOC5557Dww8/jOuuuy7r0TcRERGdeQrZbzvD5/PNu9lHURQcOnQI\ngUBAPdbW3gFP6Hh47GyyLnjd6upqTdWd1Pxj7u3bt2NyclJ9/dxzz+G5554DANx777248sorIYoi\nRkdHEYlE1Pdde+210Ov1ePTRR/GNb3wD9fX1+NznPocbb7yx6D8DERERaUemTWIhg+Ts7OyCG26O\nHTuW1UBl9erVEFGBRDIIAHDa9HBVGuf9bGVlpeYmxTQfJk9+VD2fTZs2zbvze8eOHdixY0chhkVE\nRERlSpKkgtZjjMViC3a4mZ6exvj4uPra5XKhpaUFL/5pRj3W2WSdd91jRUUFqqur8z/g06T5x9xE\nRERE+aAoCmw22ynrNp6OZDKJ6enpec8Fg8GsskSVlZXo7OxETJIx4T++m7yjce4jbqfTiZqamvwP\nOA8YJomIiGjFUxQFkiRBr9cX7DtkWYbH45k3rEYiERw4cEB9tG6z2dDd3Z3uzDcZA9574t5YY4LD\nmj1Gh8Oh2SAJMEwSERHRCqcoCkRRLOiMJJDecDNfvUpRFDEwMKDu6jaZTOjp6YHBYICiKBiZPN5p\n7+RZSbvdjtra2pL03M4VwyQRERGtWJkguVA/7HwJBAJZG4EzEokEBgYG1JBpMBjQ09OjFk33hZII\nvtfixqAXsKb+eDF1u92OurrcenOXEsMkERERrUjFCpKRSCSrzE9GKpXCgQMH1H7cgiCgu7s7q2PN\nyOTxXt2t9WYYDeloZrPZyiJIAgyTREREtAJlalMXOkhKkgSv1zvv9w8ODmb1yl63bl1WWZ+UrODw\n1PEwmaktabPZ4HK5yiJIAgyTREREtALF43EkEomCfkcqlYLH45lTr1JRFIyMjGBm5ni5n/b2dtTW\n1ma975g3DimR/qzdokNjtansgiTAMElEREQrTDGCpKIo8Hg88858HjlyJKsoeXNzM5qamua8b/iE\nR9wdTdayDJIAwyQRERGtIPF4fN4d1fl2YlvnE01NTeHo0aPq6/r6erS2ts55nyjJOOY9/vnetmrU\n19eXXZAEGCaJiIhohShWkFxo57bf78ehQ4fU11VVVejo6Jg3IB52x5B5Ot5Ya8W6juayDJIAwyQR\nERGtAJIkFSVILrRze3Z2FoODg+prh8OhFiWfz/BE+hG3Tq/D+/tWlW2QBBgmiYiIqMxJkjTvI+d8\ni8fj8+7cjkajOHDggFoU3WKxoKenZ8FuOzOhBGZCSej0OtisFvS0abe7TS4YJomIiKhsJRKJogTJ\nZDI5787teDyO/v5+dSOO0WhEb28vjEbjgtcamYpBp9PBbDKja3UlLKbCtXgsBoZJIiIiKkuJRAKi\nKJ76jadJlmVMT0+r7RBP/P7+/n718bper0dPTw8sFssi11Iw5pHSHXAEYENHdUHHXgwMk0RERFR2\nihUkFUWBz+ebM/uZSqUwMDAwp7uNw+FY9HresAIZRkAA7BYj2hqdBRt7sTBMEhERUVkpVpAEgGAw\nOGfntizLOHDgAMLhsHps7dq1qKqqWvRaZrMZk0Ed8N5em962Kuh0+dt4s9AazUJjmCQiIqKyUcwg\nOd/ObUVRMDQ0hGAwqB7r6OhAXV3dotcym83QmStw6Njx9op9eXzErdPpYLVaS7Ir3FD0byQiIiJa\nhmIFSbc/hl/9/hCSkohzu5xorDYBSAfJQ4cOwefzqe9tbW1FY2PjotezWCzwRoz4ze8OIfneju+G\naitcVda8jLeUQRJgmCQiIqIyUKwgKSVSePZ3h+H2hQAF+J+3/ehtsWFjpxPHjo7D7Xar721qakJz\nc/Oi1zOazNh7JIk/DR3/nNGgwyXnr8rLeAVBgNVqXbCeZTEwTBIREZGmFfPR9m/fmYDHnw6SAAAF\n6B+PYnDcjzqDG470JCVcLhfa2toWnQ1MKga8sjeMSV9UPVbjNOPKD7ahtnLhHd9LUeogCTBMEhER\nkYYVM0iOTobwxv5JKHI6SVY5DAiEk4hLcfgjUUzDiSaniL5WKzo7OxcNkv4I8McDAcTix8sJrWup\nxEcuaIE5T3UlLRZLyTbdnIhhkoiIiDSpmEFSlJL4z5eHIafSaxpXu8zYdnYV3jrgwav707u5FQCe\nmBPWUBWaozKqHHNnBBVFweBkAntGY+oxnSDggxub8L71dXlb12g2mxctjF5M3M1NREREmlPMIAkA\nz/3hEILhdC1Jk1HA5vUVmJ2dRTJ4GH31s3CakjDoDXA47JgJpfD/vuHF/rEI5BM64khJGX8YiGDP\n4eNB0mY24OpLOvD+HlfegqTJZILJZMrLtfKBM5NERESkKcUOkvuGp7B3xK++vqC7AqlETO23bTEA\nG1cnYahqwN6xGGQZkGXgraEQjkyL2NJbiZQM/L4/BDFxvI5kc50dV3xgDZy2/M0gGgwGTQVJgGGS\niIiINESSpKL02s6YCYbx3Kvj6us19RbUO2X09w+o7RNNJhP6+nphsViwpsGOV/YH4A+le3F7Agn8\n+nUf9Ho9dAajGiTPW1eHPzu3CXp9/h4C6/V6WCyWkpUAWggfcxMREZEmFDtISpKE/+8PhxCLp9dJ\nWkw6nNNmwsDAABKJBID0TGBvb6/ab7vKYcDl76/F2e12ZDKdDCEdJAEY9Tp87KJW/Pn7mvMaJEtd\nS3IxnJkkIiKikovH45AkqWjfl0wm8ea+MYxMHC/bc16nBYeGD6jj0Ov16Onpgc1my/qsXidgY6cT\nq10WvHYwglgiHfCqnWZ84uI1eStGnpGpJanFIAkwTBIREVGJFTtIyrKMoxNuvLJvRj3WWm9EyDOi\nrtXU6XRYv349nE7ngtdZ01SFc3o7sGfYj0RSxsZ1dbDkqexPhhaKkp8KwyQRERGVjCiK6iPlYlAU\nBT6fD6/s9UGUMo+3BTiSRxET07uwBUHAunXrUFlZueB1HA4HamtrIQgCzl/vKth4tVJLcjEMk0RE\nRFR0iqIgHo8XNUgCQCQSwRFvAqNuUR1HvckHSYyo71m7di1qamoWvIbT6URNTU3BHztbLBYYDNqP\natofIREREa0opQqSwWAQgdko3jj4XpCEAocuCJMcVN/T1dWFurq6Ba9RUVGxaNDMF4vFopmi5Kei\n3QfwREREtOIoilL0R9sAEA6HEQgE8NahGOJJGQoUJMUwXObj6ybb29tRX1+/4DWKFSRNJlPZBEmA\nYZKIiIiKJBMkk8lkUb83Go3C6/XisFvEhC8JKOnH3atsMzDo0h1s1qxZg6ampgWvUVlZWZQgaTQa\nYTabC/49+cTH3ERERFRwiqIgFouphcCLJR6PY3p6GtF4CrsHQ1CgIBKNoMoYQqUlHWpXr16N5ubm\nBa9RVVWFqqqqgo/VYDCUXZAEODNJREREBVaqIJlIJOB2u5FIyvjd3gCkRHpmVEiJaKlI79xuampC\nS0vLgteorq4uSpDUanebXDBMEhERUcHIsoxoNFr0IJlMJuF2u5FKpfCH/UG4AxJiYgySJKG9KgK9\nDqivr0dbW9uCAa6mpmbR8kD5ouXuNrngY24iIiIqCFmWEYvFIMty0b/X4/EgkUjgzaEQxjwiRDH9\n32pnBE5zEnV1LnR2di4Y4Orq6uBwOAo+1nIPkgDDJBERERVAZkZSUZSifq+iKPB4PJAkCQPjUQyM\nRyGKImKxGBrsIuptIqqr69DV1TVvgBMEAXV1dbDb7QUfazl0t8kFwyQRERHlVSqVQiwWK0mQ9Hq9\nEEURh90xvDkUgiiKiMaiqLEm0FIRg93uxLp16+YNcIIgoL6+HlZrfntrz2elBEmAYZKIiIjyKJlM\nQhTFogdJAPD7/YhEIpiakfCH/UE1SDpMSXRUR1BVWYmWlpZ5A5xOp0N9fT0sFktRxloObRJzVf5x\nmIiIiDQhmUyWZEYSAAKBAEKhEALhJP733RlEY+kgaTXIWFcbQVVlBbq7uxcMkg0NDUUNkuXQJjFX\nK+cnISIiopJJJBIQRbEk353pbhMVU3jhT36EIjFEo1EYdQrW1YZRXelAT0/PvGsk9Xo9GhoaYDKZ\nijJWs9lcVt1tcsEwSURERKdFkiTE4/GSfHemu42UkPHCn2YwE0wHSb0ArKsNo67ajp6eHuj1+jm7\nyg0GAxoaGooW7kwmU9FCazExTBIREdGyxeNxSJJUku+OxWKYnp5GSlbw270BuP0RRKIRCAC6asJo\nqLGip6dn3kfKRqMRDQ0NRXvcbDKZyrK7TS4YJomIiGjJFEVBPB5HIpEoyffH43F4PB7Isow/9gcx\nNhVCJBIBALRXR7Gq1oze3t4Fg2RjY2PRNsAYjcYVOSOZwTBJRERES6Io6baEyWSyJN8vSRLcbjcU\nRcE7I2EcHJ9Vg+TqChGtdYYFg6TBYEB9fX3RgmSm33Y5FyU/FYZJIiIiylmp+mxnZPptp1Ip7BuL\n4O2hGTVI1tsldNbr0NfXN+86SIvFArPZXLQgWc79tpeCYZKIiIhyUqr2iBmZfttRMYE/9AdxaCKE\nSDgCBQqTjXw6AAAgAElEQVSqLAmsb1QWDJI2mw3V1dXwer1FGatery/7Nom5YpgkIiKiUypVV5sT\nv9/tdmPCG8Xv9gUQCIlqkHSYktiwSsaGDX3zrk10OByora0t2mzqSui3vRQMk0RERLSoUgdJWZbh\ndrvx9tAM/jQSRlyS1CDZYI9jbYOCsxYIkhUVFaiuri5asMu0STxTgiTAMElERESLKGUxciC9RnPs\nyCReeNuNSZ8ESZIQiUSg18lor4qiqVqPvr6+ecvuVFVVoaqqqmhjFQQBNpttRfTbXgqGSSIiIppX\nKYuRA+kg+U7/OF58241YXIYkSQhHwnCYkuisjqDKaUFf3/wzkrW1tXA6nUUba2ZG8kwLkgDDJBER\nEc2jlMXIASCVkvE/rw5j90E/oABxKY5IJIJVThGrnCLsNuu8QVIQBNTV1cFutxd1vBaLpWi7xLWG\nYZKIiIhUpS5GDgCzEQm/ePEgjnrSJX/iUhxSLIzu2ggqLUlYrQsHyfr6elit1qKO12q1Fq2Tjhad\nuT85ERERZSl1DUkAGJ0M4f/+7xAi0fSsaDwehz4VRF99BCa9ApvNNm/5H51Oh4aGhqK3LLRYLGd0\nkAQYJomIiAilryEJAOFYAk+/OISY+F6QlOKoNvjQVCVCEAC73Y7e3t45QVKv16OhoaHoLQstFsu8\nNS3PNAyTREREZ7hSl/7J+O2bo2qQhBxHi9WNCnO6ZeNCQdJgMKChoaHooc5sNjNIvifnMOl2uyGK\nItasWQMA8Pv9ePLJJxEIBPCRj3wE73vf+wo2SCIiIiqMZDIJURRLHiSPTXnx7rAPACDGRTRbptUg\n6XA40NPTMye8mUwmNDQ0FH3ji9lsLvosqJbltH993759uPzyy/H8888DSK9f+NSnPoUHH3wQjz/+\nOD7zmc9g9+7dBR0oERER5ZckSZqYkQwGg/jjngmkUukgKSRCqLKkNwA5HI55ZyQtFgsaGxuLHiRN\nJhOD5ElyCpMPPvgguru78clPfhIA8Oyzz2JsbAzf+MY38Oqrr+Liiy/Go48+WtCBEhERUX4oigJR\nFEtaQzIjGAzi2JQXB49GIYoiotEomitiEATA6XSit7d3zgYXu92OhoaGotd0NBqNRd/gUw5yugt7\n9+7F9ddfr1aRf+mll9DW1oarr74a1dXV2LFjB/r7+ws6UCIiIjp9mSBZytI/GbOzs5iZmcHe0TAi\nkRiisSjsxhQqzUk4nU709PTMCZJOpxN1dXVFb1doNBphsViK+p3lIqcwGQqFUFdXByC9SPfNN9/E\n1q1b1fNOpxOBQKAwIyQiIqK8kGUZ0WgUyWSy1EPB7Ows/H4/IrEk3h3yISbGAADNFTFUVVXOOyNZ\nVVWF2traogdJg8HAGclF5BQm6+rqMD4+DgB45ZVXEI1GsWXLFvX8xMQEKisrCzNCIiIiOm2pVArR\naLSkpX8yQqEQ/H4/FEXBb98aQzSW7v3tMKXQ1piekTx5LWRtbW1R+2xnGAwGWCyWogfYcpLTbu4P\nfOAD+N73vof+/n48//zzaGhowEUXXQQgvav7sccew3nnnVfQgRIREdHyaGXHNpAOkj6fD4qiYG//\nEIYnj8+S9q42Yf36tVlrIQVBgMvlgs1mK/pY9Xo9g2QOcpqZvPXWW9Hc3Iyf/OQniEQiuO+++9Sp\n5/vuuw8jIyO46aabCjpQIiIiWjqt7NgGgHA4DJ/PB1mWcfDgQewfiyAzKlelCReemx0kM11tShUk\nrVYrg2QOcpqZrKurw89+9jPMzs7CYrFkbYn/9Kc/jVtuuQWrVq0q2CCJiIhoabTQY/tE4XAYXq8X\nqVQKBw8ehMcXhCeSXiJnNpux9bzmrCBZqq42me9mkMzdkjrgVFRUzDl21lln5W0wREREdPpkWYYo\niiXtsX2iE4PkwMAAZmdnMRGyQkG6XmRrQwWaa49vcDEajWhoaChJz2sGyaVb8C5de+21S7qQIAj4\n6U9/etoDIiIiouVLpVIQRVETG22A42skk8kk+vv7EQ6HIaUETEfMsFqtsFgs2NjpVMObxWKBy+Uq\nejFyIP1YnUFy6RYMk++8807WL6Ysy2opAb1eD1mW1fUXVquVu7mJiIhKLJFIIB6Pa2J9JHC8/E8i\nkUB/fz8ikQgAYCJkgdVqSwfHSiOaatKPsm02G1wuV0nCnE6ng81mY5BchgXD5N69e9X/P3LkCG67\n7TZ88pOfxIc//GHU1NQAAKampvA///M/ePrpp/HQQw8VfrREREQ0r3g8DkmSSj0MVTAYxMzMDOLx\nOPr7+xGLpetIxpMCoqiBxZJ+rH1OhwOCIMDpdKKmpqZkQZIzksuX02KEb33rW/jEJz6htlPMaGxs\nxLXXXgtRFHHPPffgkUceKcggiYiIaH6ZjjZaKESeEQgEEAgEEI1G0d/fnxVyk5YWmKR0/HBVpWcl\nq6urS/aEMxMki92acSXJ6Vdu9+7d6O3tXfD8xo0b8dZbb+VtUERERHRqsiwjFotpKkjOzMwgEAgg\nHA5j3759apAUBAGr13TBHT4+j7WxwwmXy8UgWeZy+tVTFAUHDx5c8PzBgwc1s9CXiIjoTJDpaKOV\nHdtAupFJMBhEMBjE/v37s/Za9PT04NisGZm40FBtwjnrV8PhcJRkrIIgMEjmSU6PuTdv3oydO3ci\nFoth8+bNcLlcANK/aV5++WU8/PDD2LRpU0EHSkRERGmJRAKiKJZ6GFn8fj9mZ2fh8/kwODiobgIy\nGAzo6emBYLBheGIaQDrIfeiC9pIUI898v81mY5DMk5zC5N13340bbrgB999//5zFqYqioLW1Ff/4\nj/9YkAESERFRmtYKkQPpMfn9foRCIbjdboyMjKjnTCYTent7YbPZ8MeBIBQFEHQCOlbXoKO5+H22\ngfTSAJPJxCCZRzmFycbGRjz77LN45ZVX8O6778Lr9QIAqqurcdZZZ2Hr1q0wGo0FHSgREdGZTGuF\nyIF0kPT5fAiHwzh27BjGxsbUcxaLBb29vbBYLAjFkhiZiEGn18FkMuHis5tKsnNaEATEYjEGyTzL\nubS8Xq/H1q1bsXXr1kKOh4iIiE6SSqU00187Q1EUeL1ehMNhjI2NYWJiQj1nt9vR29sLo9EIRVGw\n51AEOp0eJrMJLfUOtDYWf52kIAgwmUzc41EAC4bJZ599dskXu/LKK09rMERERJRNkiTE4/FSDyOL\nLMuYnp5GNBrFyMgIPB6Peq6iogLd3d0IicDh0RBG3THEkzqYzOnC5Bed1VD08WY222hpecBKsmCY\n/PKXvwxBENR/BZ04HT3fMYBhkoiIKF+0uD4SSM+SejwexGIxDA4Owu/3q+fM9hokLKvwX7sDmI2m\nH8cbTUYYTem4sabRidaG4s5KZoKkXq/X3K/lSrFgmNy5c6f6/8lkEo888ggcDgc+9KEPweVyQVEU\nTExM4IUXXoCiKPjiF79YlAETERGtdJn6kVp7JJtMJuHxeBCNRnHw4EEEg0HEkzr4YkaIqEIqaoMw\nHU2/WUhvwNHr9bCY9FjfWoUPbmwq6nhPDJJUOAuGyY9+9KPq/z/wwAPo6+vDd7/73Tnvu+mmm3Dz\nzTfjzTffxMUXX1yYURIREZ0hkskkRFHU1PpIIF2OyO12IxqNYmBgAEe8EqbCToQlPSwWS7odIdJP\nLAVBgN1uQXdrNdavqUJbowN6fXE3vTBIFk9Od/aZZ57Bxz72sQXPX3311ctaY0lERETHSZKkuY02\nQHpcU1NTmJ2dxd69e3HEK2HYb0dY0sNmtalBUqcD2pts+D+XdOGW7WfhLy5qRWdzBYPkCpfTbm6f\nz7do8/hkMpm1ZoKIiIhyp8X+2hmiKMLj8SAQCODAgQNIJJI4NuuEAAE2uw0Wsxmras1oa7Bg7epK\nNK9qKGmIY5AsvpzCZFtbG3bt2oWzzjoL9fX1Weempqbwox/9CC0tLQUZIBER0UqWSqUgiqLm1kcC\nQDQaxfT0NLxeL4aGhiDLMmZEI8SUAQ6HA1aLCVduroPNoofdbkddXV1J6kdmMEiWRk5h8tZbb8Vt\nt92Gbdu2oaOjQ22n6PP5MDQ0BEVRcN999xV0oERERCuNFsv+ZITDYXi9XkxOTuLw4cMAAEUBpiI2\nOJ1OGPQGrG+xwWbRo7KyElVVVQySZ6icwuSHP/xhPP744/jpT3+KPXv2qL+pampqsG3bNvzN3/wN\nNm/eXNCBEhERrRRafqwNQO2xPT4+jmPHjqnHo4odeksV9Do99HoBva121NbWwul0lnC0DJKllnMH\nnPPOOw/nnXdeIcdCRES04mn5sTYAzMzMYGZmBiMjI5ienlaP2+0OeGON0OvS417fYsealiZYrdZS\nDRUAg6QW5BwmAeDo0aN488034fF4oNPpsGrVKmzatAl1dXWFGh+A9L/gHnroITz00EP4+Mc/jnvv\nvfeUn7nkkkuyWjtlVFRU4I033ijEMImIiBaVSCQgimKphzGvTJ/tYDCIAwcOIBgMqueqq6thr12D\nd/fMAgAMBh0ueX+HJoKkzWZjr+0SyylMplIpfOUrX8Ezzzwz519SBoMB119/Pb7whS8UZIButxt3\n3HEH3G43DIYlZV9cf/31uO6667KOlXI9BxERnZm02s0mQ5ZleDwezM7OYmBgAJFIRD3X0NCA9vZ2\nPP/WDABA0Al4f28TqittpRpuehwMkpqRUzrbtWsXfvnLX+KKK67Atm3b1A04brcbv/nNb/DII4+g\nqakJf/3Xf533AT7++OOorq7Gv/zLv2Dbtm1L+qzVakVtbW3ex0RERJQrrXazyUgmk3C73QgGgxgY\nGMjaENTS0oLVq1dj0i/BG0yku9lYzLhwQ2MJRwzodDpYrVYGSY3IKUz+6le/wg033DBvy8S/+Iu/\nwLe//W089dRTBQmTV199NcsOERFRWUokEojH45orQp4Rj8fh8Xjg9/tx8OBBdUOQIAjo6OhAQ0MD\nFEXBnsNhGAwGGE1GnN1VA6fNWLIxM0hqT0534ujRo9iyZcuC57dt26bu8M43BkkiIio3md3aWmyL\nmBGNRjE1NYXJyUn09/erQVKn06G7uxsNDQ0AAHcggZkoYDQZodcJuKC3frHLFhSDpDblNDNpMBgQ\nCoUWPC9J0pJ3UUmShKNHjy76no6OjiVd82T79u3DjTfeiAMHDgAANm/ejFtvvRXNzc1Luo5WF0uf\njsxjDK3WN6PF8f6VN96/8pXLvZNlGZIkaTZEAkAoFILf78eRI0eySv+YTCZ0d3fD4XBAlmXodDoM\nTaWg1+uhKAp61lTBZtaVpKSRTqeDwWBYtCPfqfDPXmHkFCb7+vrw1FNPYevWrTAas6e2E4kEHn/8\ncfT19S3piwcHB7F9+/Z5N8QoigJBEDAwMLCka56otrYWkUgE1113HVpaWjAyMoKdO3fiU5/6FH71\nq1+hqqoq52vt379/2ePQuuHh4VIPgU4D71954/0rXwvdO6PRCLPZrNnNnoqiIBqNIhKJYHx8HDMz\nM+o5q9WKjo4OCIKASCQCvV6PaNKEscn0ZJIgAB31uqxyQcWSSqXy2rOcf/byK6cwecMNN+Czn/0s\nLr30Ulx88cVoamoCkG6l+PLLL2N6ehqPPvrokr54w4YN6oxhITz99NNZr7u6utDR0YErrrgCTz75\nJG666aacr7XUoFwO4vE4hoeH0dXVBbPZXOrh0BLx/pU33r/ytdC9UxQFkiRpdpMNcLz0TyqVwtjY\nGEKhkPpUsaqqCuvWrVNfm0wm1NXV4T9fOQqT2QQA6G2rQueapqKPW6fTwWQy5SWg88/e8i02sZZT\nmPzgBz+IBx54APfffz9+/vOfZ51bu3Ytvv71r5dFB5y1a9fCYDDA7XYv6XMWi6VAIyo9s9m8on++\nlY73r7zx/pWvE+9dMpmEKIrQ6XSaXcuXSqXg9XoxMzODgYEBiKKohrPGxka0t7err202G+rq6jDl\nj2HcE4EgCBAgYMtZTUsu0Xe69Ho9rFZr3md6+Wcvv3L+XXHppZfi0ksvxeTkJNxuNwRBQFNTE+rr\nS7cQdyHDw8PYtWsXbrjhBnR1danH9+/fj2Qyifb29hKOjoiIVoLMbOTprOErhkQiAY/HA6/Xm7Vj\nGwDa2trQ1NSkhrWKigpUV1dDEAS8ts+jvq+nrQrVFcWdyStUkKT8W/I/MZqamtTH3MXg9/shyzIU\nRVGLvnq9XgCA0+mE2WzGnj178KUvfQlf/epXceGFF2LVqlXYvXs3+vv78aUvfQltbW04dOgQvvOd\n76CxsRFXXXVV0cZPREQrT6Z2ZCqVKvVQFhWLxTA9PY2pqSmMjIyoaw51Oh3WrVuHmpoaAOlSQDU1\nNWqP7SlfFCMT6W43AgRc2FfciSMGyfJS3PnqZdi+fTsmJyfV18899xyee+45AMC9996LK6+8EqIo\nYnR0VK3Yb7PZ8OSTT+KBBx7A3XffDa/Xi8rKSmzZsgW33347KioqSvKzEBFR+TMYDIjH40V/5LtU\ns7Oz8Pl8OHLkSFb1FJPJhPXr18PhcABIB0uXy5XVGvG1/cdnJde1VqK2sniPhA0GAywWC4NkGdH2\nnwQAL7300infs2nTpjk7vxsbG3Pq4U1ERJSLzGPtUvejPpUTe2wPDw/D5/Op52w2G3p6etTNJ0aj\nEfX19VmVWqZnYhg6erwv9+YizkpqfTc8zU/zYZKIiKjUMptstPBY2+1Phz2DTkClw4QKe/o/h9UA\nWZYxPT2NYDCIAwcOZPXYzuzYzsyoWiwWuFyuOXWiXz1xVrKlEq7q4oRnBsnyxTBJRES0gMxa/UQi\nUeqhwO2P4o973Rg+NjvveQEKDEIKBiGBcHAaeiUFs8EEk15GS1Mdute1Qf/ebnOHw4Ha2to5wc0b\nEDE4fnxW8sK+hsL9QCcwmUws1VPGGCaJiIjmkUqlIIpiyWtHTvmi+OM+N0YWCJFAeqySJCEejyMa\niUKBHoAeAgRYbVZ4jlrw9jEP7BY96qrtcFXrUTnpRoXdhMr3ZjadNiNe3e+GgvQmnc7mCjTUFH5W\n0mw2w2QyFfx7qHByCpN33XXXKd8jCAIcDgd6e3vxkY98hPWbiIioLGml5M+kL4pX97rVXdUnWtdS\niUq7CcGIhGl/GL6AiGg0mtX+VxB0cDjsMBrS6yEVAJKshzuQgDvgn3NNnSBAPqHDzOYNhZ+VZJBc\nGXIKk88884w6FX5yKyNBELKOCYKAhx56CP/+7/+uNoknIiIqB1qYjVwoRAoQsK61Epv76uGqtkKW\n5fc22iQxNHQEXv0MpJQO8ZQOgsGG6rpGxFM6RMQUIqIMWTAsWlT9xCDZ3uREU62tYD8jkF6zeXKL\nZipPOYXJ119/HTfffDPq6uqwfft2tVL+6Ogonn76aczOzuKb3/wm4vE4Xn75ZfzzP/8zfvCDH+A7\n3/lOocdPRER02hRFQSKRQDweL9kYJrwR/HGvG4ff64WdoYbIDfVwVaUfOyeTSXg8HszOzmJgYACx\nWAx6HWDVyVhVX6l2fAPSs38ulwsKdJiNSAhGJIQiCQTf+//ZsIRgJIGImF4XajLo8cGNha0nbbVa\nNV9aiXKX0528//770dXVha997WtZx1etWoWLLroIX//61/HEE0/gjjvuQFtbG3Q6HR5++OFCjJeI\niCivZFku+U7t/31rAm8enM46JkBA95pKbO5rQF3V8aVjmULkMzMzczraNDc3o7W1VX2aaLfbUVdX\np76urbQsWDMymZQRiiZgtxpgMurnfc/pEgQBFouFQXKFyamJ6H//93/jsssuW/D8pZdeil//+tfq\n697eXgSDwQXfT0REVGqZtZHRaLSkQXJ8KpwVJAUIWL+mCv/PX6zDFVvWqEFSURQEAgG43W5MTEyg\nv79fDZI6nQ5r167FmjVr1OBYXV0Nl8uVc6kdg0GH6gpzQYMkZyRXppzuqCRJmJiYWPC8x+NBIBBQ\nXw8NDaGuru70R0dERFQAWpiNBNIB8bfvHP/7tbXBgQ+9r3nO7KEsy/B6vQiHwxgdHcXU1JR6zmQy\nobu7W22FKAgCXC4XbLbCrnlcikyQPLmmJa0MOYXJc889Fzt37oTJZMKWLVvUXp6hUAivv/46fvjD\nH2LdunUAgF//+tfYuXMnLr300sKNmoiIaBm0sDbyRAOjAbhnYgAAg16Hyy9sQYU9e3ezJEnweDyI\nRqM4ePAgQqHjayrtdjvWr1+v1mg0GAyor6/X1A5pnU4Hq9W66OYfKm85hcl/+Id/wGc+8xn8/d//\nPQCo/7LI/IvOYrHgvvvuAwD84Q9/QE1NDW677bZCjJeIiGhZtDIbmZFMyvj9u8dnGM/vrpsTJMPh\nsNoacXBwMKtcUV1dHTo7O9W/kxfqaFNKDJJnhpzCZFdXF55//nk8++yz2LNnD/x+PxRFQWVlJXp6\nenDllVeivj7du/PWW29FfX0910QQEZEmaG02MuPtQS9mo+lwaDUbsKn3eA9sRVHg9/sxOzsLt9uN\nw4cPZ5XhW7NmDVatWqWuh1yoo00p6fV6WK1WTY2JCiPnxOd0OnHNNdec8n2rVq06rQERERHlSyqV\nQjwe18xsZEYsnsRrJ/TAvmhDAyym9IxiMpnE9PQ0YrEYDh06BI/n+PsMBgPWrVuHqqoq9VhNTQ0q\nKiqKN/gcGAwGWCwWBskzRM5hUpIk/Nd//Rd2794Nj8cDnU6HpqYmbNmyBR/+8If5G4aIiDRDq7OR\nGa/t8yCeSAfcaqcZ53Sl9yKIoojp6Wl1fWQ4HFY/Y7fb0d3drXaY0+l0cLlcsFoL3/JwKYxGI8xm\nM3PBGSSnMOn3+/GZz3wGQ0NDAKCufZBlGT//+c9x/vnnY9euXZr7DU1ERGceLXSxWcxMKI53hrzq\n6w+e0wSdTkAgEEAgEFDXRyYSCfU9LpcLnZ2d6t+/RqMR9fX1musgYzKZ1M1AdObIKUz+8Ic/hNvt\nxje/+U1ccskl6m7u6elp/OY3v8H3v/99PPjgg7jzzjsLOlgiIqKFKIqCeDyeFcK06PfvTiElp9c/\nNtfZ0bHKru7WnpycxNjYmLo+UhAEtLW1obGxUZ3ps1qtcLlcmtvUwj7bZ66cwuTvfvc7fOELX8D2\n7duzjrtcLnz605+GJEl4/PHHGSaJiKgkMo+0T9ykokUT3ggOjh+vy3xhbw0mJychSRJGRkbg9R6f\nsTQajeju7s5aD1lZWYmqqirNPUJmn+0zW05h0uv1Yu3atQue37BhQ1YBVSIiomKQZRnxeDyrpaBW\nKYqCl9+ZVF+31pmgS84iHIvh4MGDiEaj6jmn04l169apj4wFQUBdXR3sdnvRx30q7GpDOc2RO51O\nHDt2bMHzU1NTauV9IiKiQjuxFWI5BEkAGDk2i6PTkfTmIElCz2ojvF4v9uzZkxUkGxoa0NfXl1WI\nvKmpSXNBUhAE2Gw2BknKbWZy06ZNeOihh7BhwwZ0dnZmnRseHsaDDz6ICy64oCADJCIiOpHWN9jM\nJ5WS8fI7k0ilUpAkCd3NVninxrOe6ul0OrS3t6OhoUE9psVC5ACLkVO2nMLkbbfdhk9+8pP42Mc+\nhjVr1qCpqQlAekZydHQU1dXV+OIXv1jQgRIR0ZktMxt5YheYcrFnxA+3L4xkMgmdIMMQG8OU/3jZ\nH4vFgu7u7qzZx4qKClRXV2tufaRer4fFYmGQJFVOvxM6Ojrw7LPPYvv27Ugmk3jzzTfx1ltvQZZl\n7NixA8888wxaW1sLPVYiIjpDJRIJRCKRsgySkaiIF98YRTKZhJSQYFc8iMeOB8na2lqcffbZapDM\nrI+sqanRXJA0GAyckaQ5cl7o0NzcjHvuuaeQYyEiIsqi1Q42uYpEInjh9VFExARisRjkRBSuivT6\nSEEQ1Kd9mdBoMBjgcrk0WauRxchpIXn5p8WRI0fw+c9/Ph+XIiIigqIoEEUR0Wi0LIOkLMuYnp7G\n2JEp7Dk8i1AoDFEUsbpChE5IF/fesGFDVn9ti8WCpqYmTQZJk8nE9oi0oLxswQoGg3jxxRfzcSki\nIjqDKYqCZDJZFjUjFyKKIrxeL5LJJF7tn8bMTBCyIsNmTKHWKqGqqgpr167Nqsuo1fWRAGtI0qlx\nPz8REWlCuT/SVhRFbYeoKAoGho9i30gImUjcUhHDmjWtaG5uVkOjlutHCoIAi8XC0j90SvwdQkRE\nJVUubRAXI0kSvF4vJElCPB7H0NAwdh9KQXnvr9kam4yLzu9GZWWl+hmj0QiXy6XJFoSCIMBqtWqu\nJBFpE8MkERGVxEp4pA0As7OzmJmZgaIo8Pv9GB4exrGADsG4DQBgNBhw2ebVqKy0qZ+x2Wyoq6vT\n5K5o1pCkpWKYJCKiosuEyHIqPH6yVCoFr9eb3qUtyxgbG8Pk5CRm4waMB9PB0Wqx4uyuGjTUHA+S\n1dXVWTOUWqLX62G1WjW5dpO0a8EwuWfPnpwvMjIykpfBEBHRylZOvbQXE41GEQwGkUqlEIvFMDg4\niEgkgnhSwIjfDkGng8NuR0O1Fe9fVwEgHdTq6upgtVpLPPr5GQwG7timZVkwTF599dU5/4ZSFIW/\n+YiIaEErYV0kkA7D4XAYsVgMgiBgenoahw4dgizLkBVg2O+AoDejwm6DzWzAn51TDYNegNlshsvl\n0uxmFpPJpMmSRFQeFvxdffPNNzMgEhHRaVEUBYlEApIklfW6SACIxWLweDwQRRFmsxmjo6Pwer0A\nAEUBRgN2KEYn7GYzdIKAD55VBYdFD6fTqcluNhks/UOna8EwecsttxRzHEREtMKshHWRQHo20u/3\nIxwOQ5ZlRCIRDA4OIh6Pq++ZSTghGaph0af/Wt3UXYGmGjNqa2vhcDhKNfRFccc25Ys259uJiKhs\nlXu9yBPFYjH4fD4kk0koioJjx45hdHQUOp1OnWnU2+rhi9ph0KdfdzVb0ddWgfr6ek2W/QG4Y5vy\ni2GSiIjyYqVsrgHSP8vMzAxCoRCAdFeb4eFhtSA5kN5Q09DcjtdGFAjvlSZ3VRrx5+etgstVq9mg\nxhI3RFoAACAASURBVB3blG8Mk0REdFpkWYYkSWW/uSbj5NnI6elpHD58OGum1el0oqNzLV7aG4GU\nSIdnq1mHv9zagYb66lIN/ZSMRiPMZjODJOUVwyQRES2LoiiQJAmSJJV6KHlx8mxkIpHAoUOH4PP5\n1PcIgoDGxka0t7fjDwNhBMLpIKnX63D1h9ahsb6qJGPPBXdsU6EwTBIR0ZKspB3aGaIowuv1qo/o\nZ2ZmMDIykhWULRYLOjs7odPp0D8uYtyT3oCjN+hxxQc6sKZJu0GSO7apkBgmiYgoJyul/eGJTtyp\nDaQ3D42NjWFqairrfQ0NDWhra4MgCBg6EsC7h+OAIMBoNGJTbyPO7qotxfBPiTu2qRgYJomIaFGZ\nEClJUtmX+TlRJBKB3+9X10KGw2EMDQ0hFoup7zEajejs7ERNTQ0AYCYk4fXBGKDTw2w2o62pAn92\nblNJxn8qer0eFotFsxuBaOVgmCQionmt1BCZTCbh9/sRjUYBQC35c+TIkawZ15qaGnR2dqqPh6Px\nFH67NwBZEWA1m1HttOCKLWug12svrLE1IhUTwyQREWVZqSFSURSEQiEEAgH154rFYhgeHlY33QDp\nGb22tjbU19erYWwmlMBL7wYQT+lgMBhgNOjxiYvbYLNo769RbrShYtPenwIiIiqJlRoiAUCSJPh8\nPrVrjaIomJycxPj4eNbP6nQ60dXVBavVqh475ovj9/tnodMZYTAACVnCZZua0VBjnfM9pSQIAiwW\ni2b7f9PKxd9xRES0YlofnkxRFASDwaxi4/PNRgqCgJaWFjQ3N2c9Gh48GsVbI1EYjOlONka9gG1n\n1aG7tbK4P8gp6HQ6WCwWbrShkmCYJCI6g63UECklUkgkJARm/GoxdUVRMDExgSNHjmT9vHa7HV1d\nXbDb7eoxRVHwzqEIBickGN5bM+m0GfGJD7QCiRC0hBttqNQYJomIzjAr+XE2ABwcn8Ezvx2BnEqh\na5UV61tsMEDCyP/f3p1HyVXWeQP/3q32vffudGffQyA7EEjCqsgiQXT0BZmROToqmHFQzPwTZRg1\nOjg6oscjKsNRjo5gMIMovm/CZggBQiAQliQk6X3vqu7a97rP+0fl3q7qququ7lR3bb/POXW6+9bS\nt/qp7vr2s/yec+cyeiPnzZuHlpaWtCAWTzC8/mEAfaNxtaev0WHAzm0LoNNwGBkpnTBJO9qQUkBh\nkhBCqkSlh0jGGDxeH545dBbRaLLcz8meAN4+MwotvKg3hGHRAhyXvTcSAMJRGUdOh+DyJdSAtqTF\nghsvb4NGEkpq33GtVguNRlPs0yCEwiQhhFS6StyxZiJlgc17HWMIhpNBMpFIIBAMIB6PIwgRYyET\n9JKMNQvMWLlqHjRS+vzCQAQ49H4Q/vD4Htwbl9dh+7om8Hzp9PzRQhtSauiVSAghFUqWZcRiMcRi\nsYoNkbIsw+12w+v1QpYZ3usMgIEhEo7AxHvAiYA7npzzKAoiJL0B55wieo44sbTZgOWtBph0Ajxh\nAX97141ILNljy4HD1RuasX55bTGfXgae56HX62l+JCkpFCYJIaTCyLKMaDSqLjypVBN3sOkaDmPM\nF0EwEARYDK0NAQg8EEkIiEqNcIa0OH9TRGMM73cF8EFPEEvm2dDvikA+H7glkcfNW+djcYulWE8t\nK5ofSUoVhUlCCKkQiURC7YmsZLFYDC6XC+FwWD0Wj8dx5N0BeL3JY83mCAQ+OTfy4vNzI6MxGWcH\nQjjVE4Q/lIAgCJA0Enqd449j0ku4bfvCkqshSfMjSSmjMEkIIWWMMYZEIoFoNKr20FUqWZbh8Xjg\n9XrThu3HxsZw7L0ujLiTYUvggGZzDG1tbWhublaHhDUSj1VtRqxsM8IX1eJkTxDdQ371cepteuzc\nvgAWY+mENpofScoBvToJIaQMKYtqYrFYRa7Mnsjv92NsbCwtMEejUXR2dmJkxImuUbN6fH4tjw3r\n16btYqPQarWora2FJElYuwwYGQvhvY4xCDyHS1fXZyzKKSaqH0nKBYVJQggpI9WwqCZVJBLB6Oio\nug0ikAzSw8PD6OrqQjwehy8qwh8VwHE8TEY9rrm0BfoJe2ZzHAebzQaLxZI257DOrsdV9tIa0gZo\nfiQpLxQmCSGkDChD2aVU5zCbYDiO9v7kyup8CDyHhc0WGCaEv0QigbGxMfj9/rTjoVAI586dg9fr\nVY/1+3TQarTQG/RY0WqEccJjaTQa1NbWls2cQ51OB+n8rjuElAMKk4QQUqKUIuOxWKws5kN2Dvjw\nvy93Ihaf3rC71ajBnR9ZCoNOBGMMXq8XHo8nbfhelmX09fWht7c3rUc2BgOYZIdRksBxwOq29CLk\nFosFdru9LHr4OI6DXq+n/bVJ2aEwSQghJUZZlR2Px8tmKPtUlxvPvtqNRJ49kqk8gSj+dLgLN17a\nCK/HnbEa3e12o6OjA6FQSD3GcRyam5txdtQEKZi8/cJGHcyG5NuaKIqora2FTqe7gGc1d2h+JCln\nFCYJIaQElFsvZKo3Tzvx4pv9YEgGSbNewoIm8xT3AhIyw8lON2RZxtkeF/4S9+PSFVb1+kgkgs7O\nTrhcrrT7mUwmLF68GFFZg74z49etmW8CkOyNtNlsZRPMNBoNNBpNWfSeEpINhUlCCCmicl5QwxjD\n4RODeO39YfVYjUWH269amFd5nUQiAa0Qw5F3RwAAH/aGYDdJWNqsw8DAAHp6etKGugVBQFtbGxob\nG8FxHN56z61e11avRZ1dj9raWmi12gI+y9lDZX9IpaBXMCGEzDHGGARBQCQSKdsC47LMcOBoL95t\nH1WPNdcacdv2BdBrJ39rkWUZXq8XXq8Xi+t59Dfq0DmYLBz+ynsu9HePQotA2n3q6uowf/58dRGN\nNxhHx9B4sfHL1jSiubmxbHr3aFibVBIKk4QQMkcSiQTi8TgikQgMBgNkWS7LMBGPy3jmlS6c7Rtf\nUb242YKbr5gPScz9fBhjCAQCafUiOY7D5SutcPsi6B32IRqN4v2AiFV1PHSiDIPBgIULF8JqtaY9\n1vtdAYABHM9hyTw7li9qmp0nOwuo7A+pNBQmKwRjDG9/OAKXJ4zL1zbBoKOyEoSUAqW4eDweVwNU\nuQ1npwpH4th/qBO9I+M9h2sWOnD95hYIQu4gGQqFMDY2hmg0mnZclmUMDQ7ALvehJ24EwCEuczg7\nZsZH19vQOq85I3QFwwmcGwhDlERIkoQrLm4u6HOcLRzHQavVUtkfUnEoTFYIpzuMZw63AwAi0Thu\nvGJRkc+IkOqlbHGohMhK4QvGsO/Fdjg948PLm1fWY9sluYeXo9EoxsbG0lZiK1JXaQscsLTGj1NO\nMyRJC41ej7OjerTOy3zM0/0RSBoNeJ5HS60R8+qNmTcqMTzPQ6/Xl2VPNCFToTBZIWLx8dWfHQPe\nSW5JCJktpbiYJpGQ4QnEMOoNY9QbUS9jvggYA+wWLRxmLRyW8YvNpMnoZXR5wtj3Yge8wfGexR3r\nmrFpZV3W7xuPx+F2uzOKjgNAMBhEV1cXxsbG0o7X27RonFePtzuTAbx3JIK32/1Ytzi5MpzjOGj1\nZnSO+NRQtmV1fckPF9OwNql0FCYrRL3DAIHnkJAZRr1hBMMxGuomZA7Isox4PJ42jF0sLk8Y/c7g\n+cCYDI9ufxTyJME25Iyj35m+2IXnOFhNGjVkmg0SXn1vGKFoXL3+o5e2YvVCe8bjJRIJdXHNxEAd\ni8XQ29uLwcHBtOsEQUBraysaGxvB8zyizIsPuoIAgHc7ArAZRaxa6IDD4cDrH7gQTyRXeNfb9FjU\nPHUJomKhYW1SLShMVghR4FHvMGDg/JvCoCuIRS3WKe5FCJmJUgqQQHJY/eV3BvH6B8NT3zgPMmMY\n8yV7L8/1p18nCTxuuXI+FjVb0u+TskI7tZyPct3Q0BB6enoyhv3r6+vR1taWttXh+iVmuP1x9Lui\n4DgOb54LY8VSMxKMx1sfOtXblXKvJA1rk2pCYbKCNNea1DDZN+KnMElIAZVqUXFZZnjuWB/eOevK\neRuzQYLDrE0OaVt0qLFoYTdrwXFQh7xHvRG4vBGMeSNpQ9mp9BoRt+1YgOba8TmKyvaHXq834+fC\nGMPY2Bi6uroy5kxaLBYsXLgQRmPmfEee47BtjQ0H3/EhGAEYBzx9qBPL2qyIxJLfw2HWYllraf6N\no2FtUm0oTFaQ5loj3jz/+cCEYStCyPQpAVK5lJpEQsazr/XgVNd48e6WWiPaGk2oOT//0W7WQiPl\n3uvZYtRk7FYTjSUw5oumhMwwOHC4/KIGOCzJguCMMfj9frjd7qzhOhgMoqOjAx6PJ+24TqfD/Pnz\n4XA4coYtjUaDpqYa/J8G4PH/dwaRWAK+UAxvnh7vldy8qh48X1phjYqQk2pFr/gK0lxnUj/vd2ZO\neieETK3UA6QiHpfxp1e6cC6l1uOqBXZ8dMu8SUv05EMjCWhw6NHg0Gdcp9SKdLvdWX8+sVgM3d3d\nGBoaSjsuCALmzZuHpqamnEO/HMfBZrPBYrGcn28I3HLFfOx7sUPdqhFI9rSuWmC7oOdYaFSEnFQz\nCpMVpNamh8BziMbicPvC8PrDsJh0xT4tQkpeqc2BnEo0lsD+Q53oHhr/p/GSpTW4dmPLrA6tBoNB\njI2NZd21J5FIoL+/H/39/Rk/w4aGBrS1tU26EMVgMMDhcGT06i1oMuOq9U144a3xyZubVtZfcGAu\nJNpbm1Q7CpMVROA5NNYY0DXgAWMM7b0uLGuzQxRFiKJI/zETkkLZjSYej2csGClloUgcf3ypA/2u\noHpsy6p6XHnx7G0lGAwG4fF4EIlEMq5TFtf09vZmhEyr1YoFCxZknRepEEURDocDBoMh523WL6+F\nJxDFm6edaLDrsXaxY+ZPpoBoWJuQJPoNqDBNNUZ0DSTnKA26gljcYkEikUAkEoEgCGqwJKTaKIXE\nlQA5VR3IUCQOfzCGOnvmUG+x+EMx7HuxAyPu8cUs2y5uwpbV9QX/XowxhEIhuN3ujF1rlOtdLhe6\nu7sRDofTrtPr9Zg/fz7sdnvOgMtxHCwWC6xW65T/6HIch6s3tGDzynrotUJJ9EoKggCj0Ui9kYSA\nwmTFaaod/+9+cDR99WQikVCDpSzL0Gg0kGUZjDH6g0gqjhIeUy/5CobjeOwvpxGMxHHxkhpct2l2\nh4/z4Q1E8eQL7RjzjfcOXruxBeuW1Rb0+zDG1J7IXCHS4/Ggq6sLgUD6Qj+NRoO2tjbU1dVN+vPK\nNaQ9FZOhNOo1hsNhGtYmJAWFyQrTVDM+nDToCuYMirIsQ6vVqsFSEAT1wvM8/ZEkZYcxBlmW1d7H\nC5n7eKrLjWAkubjknbMumPQiLr+osVCnOm2j3giefOEcfMHkMPJkRcNnSgmRbrc765xIAPD7/ejq\n6spYoS2KIlpaWiZdXAMkS+Y4HA7o9aXT2zsdPM9Dq9Xm/PkQUq0oTFaYGqsOGlFANJ5AMBKHLxiD\nxaiZ9D6pq1eB5JASz/MQRZHCJSlpysIZpeexUFsYtvenb0n6yrtDMBs0uKgIc/WGx0L4wwvtargV\neA43bZ1fsBqLyupsj8eTMySFQiF0d3fD5UqvZcnzPJqamtDS0jJpLyPP87DZbDCbzWX7t0RZZJNt\n3igh1Y7CZIXhOA4NDj16hpOrPAddoSnD5ESpw4PKY1K4JKVA6XlULrOxcCYaS6StklYcONoLo17M\n2PllNvUOB/DHv3Wohbolgcet2xZk1IWciXxDZF9fH0ZGRtKCOsdxqK+vR2tra9rONdmYTCbY7XYI\nQu5al6VM6Y2kueaE5Ea/HRWosSYlTI4GsaztwnowJoZLAOqQuLJKnMIlmQ2pr725WnXdPeRHQk4G\nJ4dZC1HkMTwWgswY/nS4C5+5djEaHLlXHhfKibMuPHesTz0XrSTgEzsWoqUu98rofMiyDL/fD6/X\nm7OOZjgcRm9vb0aIBICamhq0tbVNOVSt1WrhcDig1Wov6HyLiXayISQ/FCYrUKMj9yKcQlHe4JUJ\n+qlzLgVBoD++ZEaUnsfUHsi5dq7Pp36+tNWK9ctq8dsDZ+ENRhGLy3jqpQ7ccf1SWE3T6/HPVyIh\n46XjA2l7UBu0Im6/alHWIuL5P24CPp8v697ZislCpNVqRVtbG8zmyXtFRVGEzWYr65XOyYLp2knr\nYhJCxlGYrEBNNSlhcpJFOIWUq+eSwiXJRel1TA2OhZrzeCHnlDpfcnGLBSaDhNuvWojfHjiLSCyB\nQDiOfS+24/9cvwR6bWH/hAbDcTzzSlfaMHu9TY9bty2YcXiNx+PweDzw+/05f77hcBh9fX0YHh7O\nGiJbW1thsUw+vM/zPKxWq7p7TbminWwImT4KkxXIYpSg14gIReOIxBJw+6Owm+d2qInCJVEwxtRL\nanAsxULhw2Nh+EPJ+YN6jaj+Y1Zj1WHntgX4w4vtSMgMo74I9v+tE5+6ehFEsTChY2QshP2HOuEJ\njJfjWd5mw0e3zJt0b+1cotEoPB5PRvmeVJFIBL29vVlDpMViQWtrK6zWyafJcBwHs9kMq9VatvMi\nFUpvJP19ImR6KExWII7j0FijR8dAcrhu0BWc8zA5EYXLypMaECcGxtSvi93bOB2p+1wvbDaD58df\nl60NJnzssjY880oXAKDPGcBfXu3GLVfMv+DX74fdHjz7Wjdi8fGAfcXaRly6un7ajx0Oh+HxeBAK\n5Z7iMllPZL4hEgCMRiNsNlvZDwcLggCtVlv2YZiQYqEwWaEaHClhcjSElQsKV4+uECaGS57ns14o\nZM69XIGwnENivjpShrgXNWfODVwx3wZfMIaXjif3if6wx4OX3hrAVRuaZ/T9GGM4fGIQr743pB6T\nRB43Xd6GJfPyXzinrMz2er1ZC40rgsEg+vr64HQ6c4bIfIapdTod7HZ7WS+uUVBvJCEXjsJkhZo4\nb7LUybKcddiTQmZhKMFhql7EUhx6nivBcBwDrmRvHs9xOcvvbFxRC18wuU80ABw7PQKzUcLGFXXT\n+n6xuIxnXunBuf7xBT82kxY7ty1ArU2X12Moi2p8Pt+ki5W8Xi/6+vowNjaWcZ3ZbFZ7Iqf6vZIk\nCXa7fdJ9tMsF9UYSUjgUJitUaumSobEQZJmlDdmVi1whU6l9qQTLiZ9Xi3yGmZWvyeTa+71gSP6c\nmmsNORfXcByHHeua4QvG8GFPcieYF9/qh0kvYcV8W17fy+2P4pnXhhGIcmqAm99oxs1b2/Ja1BON\nRuH1ehEIBHK2LWMMbrcbfX198Hq9GddbrVa0tLTkFSJFUYTVaoXJZKqIf+SoN5KQwqIwWaHMBgkm\nvQR/KIZYXMaoN5J3b0c5yFb7MlVquFQuE78u5TeSqYaYo9EojEYjwuFwzlqBZHraU3oIF7dMtXKZ\nw42XtSEQakefM7nA5dlXu9VwOZXOAS+8vhg02uQK7Y3L67B9XdOk//AxxhAKheD1ehEOhye9ncvl\nQl9fX9bFNw6HAy0tLVOW+AGSvXdWq7Wsd65JRb2RhMwOCpMVrMGhh78vuTJ1cDRYUWFyKrl6NFNN\nDJbZgmaur2cqnx7EfOYjyrJcVT2wsy2RkNE5MB4m89nlRhR57Ny+AL87cBajvggSMsPpbnde309p\nX4Hn8JEtrVizKPc2jUqRcZ/PN+me0LIsY2RkBH19fRlhk+M41NXVobm5Oa8hakEQYLFYYDabK+Z1\npmyHWAmhmJBSU/JhMpFI4PHHH8dTTz2F3t5eOBwO7NixA7t27ZpyteF///d/44knnkBfXx/q6upw\nyy234Ctf+UrVbIvV6DCoq1MHXSGsWVTkEyoxF7KIJPUNaeKb08TrKn3RSiXoGwmqWxZajRrUWPNb\nWKI/X0z8dwfPqiWF8mXQCfjk1QvR2pA9uEajUfh8vknrQyq3GxwcxNDQUEbY5HkeDQ0NaG5uzmux\nDM/zsFgssFgsFRMieZ6HTqej3khCZlHJp6q9e/di37592LNnDzZv3owzZ85gz5496OzsxKOPPprz\nfr/4xS/w05/+FA888AC2bNmC999/H3v27IHX68W3vvWtOXwGxdNYM75bxtBo6S/CKSepb+4UEMvf\nubRV3NMrum01afD3NyxD73AAcp6vBQ4y9HwobaEcML4q2+fzIRKJTPoYgUAAAwMDWXerEUURjY2N\naGpqyqtsD8/zaq3ISgmRAPVGEjJXSjpM+v1+PPHEE/j85z+PT3ziEwCA1tZWdHd34/vf/z46Ozux\nYMGCjPtFo1H84he/wB133IHbbrsNANDS0oLh4WHs3bsXX/rSl1BfXz+XT6UoUrdVHB4LI5GQIQiV\n80ZBSKG0p9SXXNQy9VzCiQw6Ecva8i/lE4/HMTIyHhZjsZjaCznZ9AzGGMbGxtDf3591UY1Wq0Vj\nYyMaGxvz6omrpILjqWgXG0LmVkmHSZPJhEOHDkGnS5/rV1tbCwA5i/K+9dZbCAQC2L59e9rx7du3\n49vf/jYOHz6shsxKZtCJsBo18ASiiMsynJ7IBe3tS0glGvNFMOpLBjtJ4NFWb5qT78sYQzAYRDAY\nnHRBDZCc7jM8PIyBgYGstzWbzWhqakJNTU1evXBKT6TFYqmoEMlxHDQaDa3UJmSOlXSYBAC7PbPY\n9vPPPw+LxYLFixdnvU97ezsAoK2tLe14S0sLeJ5Xr68GDQ69uj3boCtIYZKQCVJ7JdsaTQXbHjGX\nWCwGj8cDt9uNcDg8ae9ZKBTC0NAQhoaGMioXcByHmpoaNDU15bUyG6jMOZEKURSh1Wor7nkRUg6K\nFiaj0Sh6e3snvc2iRZkrRvbv34+//vWv2LNnDzQaTdb7+XzJVZlGozHtOM/z0Gq16vXVoNFhUMuV\nDI6GcHGRz4eQUpNaNHxxHqu4Z0KWZQQCAQQCAYTDYXWP8mwYYxgdHcXQ0BDc7szV4aIooqGhAY2N\njXnvQKOU+DGZTBUXtjiOU+tGEkKKo2hh8sMPP8Ttt9+edSiCMQaO43Dy5Mm0408++SQeeOAB3Hnn\nnbjjjjvm6lSnHIIqJT6fD2NjYzCZTNDpdKizadTJ+QNOv1qTcOJHUl6o/QojGkugZ8in/o60NRgK\n+jMNh8MIBAIIBoNpi2SUeZGp8yMjkQiGh4cxPDycdUtEvV6PpqYm1NbWqkPTU5W/EkURFosFRqMR\nHMflVTKrnAiCAFEUJ605W2jKwqipFkiR0kTtNzuKFibXrFmDU6dO5X37hx9+GD/72c/w5S9/Gbt2\n7Zr0thZLsnfB7/enlQ+Kx+MIh8Ow2fLbpULx/vvvT+v2xaQUtHY6nRAEAbygQSQSAQcOfSNRDAwO\nQxTGA3y27dVI+aD2uzAdg0GEQsk3lRqLhHDAjXBmne9pSSQSiEQiiEQiUwacYDAIn88Hp9MJjyd7\nwXOLxYLa2lp1z+x8/rkVRRF6vR6iKCIUCuWcX16ulJ/xXAXIbM6ePVu0700uHLVfYZX8nEkA+PGP\nf4xf/vKX+M53vqOu6p7MkiVLwBhDd3c3Wlpa1ONdXV1gjGHp0qXT+v6rV6+e9jkXi9frxcDAQNox\nk45HIMIgCCIgmVBXk+x9GRsbg91ur5q6m5WE2q8wjnf0qbvQrFpUh7q66e2vrZBlGaFQCIFAANFo\nFBzHZSwcTBWJRNDX1we32632kKQuhJEkCfX19WhoaMh7KBtIruY2m83Q6/UVuwBFFEWIoli05xeJ\nRHD27FksWbJkWm1DSgO138xN1rFW8u9CTz/9NB555BE89NBDuPHGG/O6zyWXXAKr1YoXXngBl112\nmXr8ueeegyRJuPLKK6d1DpO9KZSabBP6ay0SfINhRBNRnG4fhFXfqD4n5Q8zKU/UfjPHGEPnYEAN\nJUvbbNP6WSrbG04cxs41J1HZK3t4eBijo6OIxWIQBCEtFFmtVjQ2NsJut+c9t5HjOBiNRpjN5op+\ncyy1cj9arbas3htIOmq/wirpd6FQKITvfe97uO6667BlyxY4nc606w0GAwwGA06cOIHdu3fjW9/6\nFi699FJIkoR77rkHDz30EJYtW4atW7finXfewSOPPIK77757yp1zKk2NRULHYHJobHA0iNHRUTDG\nEI/HYTab1flUhFSTwdEQAuHkjjF6rZhWlzUXxhgikYi6mCaf+YfBYBDDw8NwOp3qXMjU+ZOiKKKu\nrg6NjY3Q6/OvtqCU9zGbzRX9DwUtsCGk9JX0X6D33nsPbrcbBw4cwIEDBzKuv+eee3DvvfciHA6j\ns7MTgcD4ZKe77roLgiDgV7/6FR588EHU19fji1/8Ir7whS/M5VMoCTWW8T/CLm9ycQFjDOFwGMPD\nw5AkSQ3mOp2OgiWpCh1pu96YwfO5X/dKgAwGg3kt0InFYnA6nRgZGYHf7896G6PRiHnz5qGurm5a\nvW2SJKmLakqll2620A42hJSHkg6TmzZtyljRnc3mzZuz3u6OO+6Y01XfpcphEgEOAAM8wThicRmp\nG+EkEgn4fD74fD7wPA+DwQCj0UjBklS0c33jJYEWZSkJFI1GEQwGEQgEMva8zkbZnWZkZETt/Z9I\no9GgtrYWtbW1YIxNKxDq9XpYLJaq+L0stSFtQsjkSjpMksKQRB42owi3Pw4wwOWLo96avellWYbf\n74ff71eDpcFgqOgJ/aT6+EMxDJ7fr57nOCxoMquVEJRdafINkH6/H06nEy6XK2tJH47j4HA4UF9f\nD5vNppboSR1JyYXneXU+ZK66upWEhrQJKU8UJqtEjVlKhkkAo95YzjCZamKw1Ov1arCkHgNSzjpS\nCpU3OrQI+NxwhkJ5DWEr2yA6nU44nc6c9epMJhPq6+tRU1Mz7XCk0WjU+czV8rtGQ9qElC8Kk1Wi\n1irh3ECy1pzTFwMwvW0VU3fw4DgurceyWt7sSGVgjOF0V7IXUU7IsOnEvHbFCgaDcLlccDqdOes2\nKsPY9fX1MBimXtCTqlpWZU8kCAK0Wm1F7RFOSLWhMFklaszjTe3yTj18NxnGWFqwVHosDQYDwnlU\nTQAAIABJREFUBUtSkmKxmFq8OxAM4cOuUSQSyTmN82pzB7dwOKwGyFzD0qIowuFwoKamRh3Gng6N\nRgOTyVSRWx1ORtnetpJXohNSLei3uErYzRJ4HpBlwBdMIBIrzJZqypBfMBhUCzUbjUbo9XrqaSBF\nk0gkEA6HEQqFEA6H04avB0cjiJ8PkmaDAIth/HWq1I4cHR3F6OhozpXYgiDAbrejtrYWNptt2iFQ\nmRvY0NAAo9E4g2dYvjiOg0ajgSRJNKRNSIWgMFklBJ6DzShi1Hd+3qQvBkuBR9KUN+JQKKQGS6XH\nkoIlmU1K/UclPE62726fa/y6lprkL4HX61UDZK7tCnmeh91uR01NDex2+4xe08o/W1qtFi6Xq6qG\ns4FkWSONRlNVPbCEVAMKk1WkxiKpYdLljcMys53j8pIaLF0ulxosjUYjBUtSEMrQdTgcRjgczquA\nOGMMvc4IGBhisRhYyI9jx07nXLnNcRxsNhtqa2tnvHWlKIowmUwwGo3qQpx8FvpUEpoXSUhlozBZ\nRWotEs70JRcOuHwxLKybu/Ibyhv+6OgotFotjEYjDAYDzZcieVP2v1aGr6cbyCKRCHoHRzEwnKwb\nyXMMsYAbE2uVC4IAm80Gh8Mx4wCplNUymUxVvWUbzYskpDrQb3gFcTqd6OvrQygUgsFgyJiPlLoT\njtMbB1CcWm6RSASRSEQNlgaDAVqtFlqtluZQEZUydK2Ex8mGrrOJxWLweDzqJRwOY9CvRTSWrGRg\n0cbUIKnRaGC32+FwOGC1Wmc8DKvX62EymbL+/lUTmhdJSHWhMFkh/H4/nn/+eXXemCRJsFqtsFqt\nsNls0Gq1sBpFCAKQSADBSALhqIxiz/1XgiUwvihBp9NBp9NRuKwysiyrrwflks/QtSKRSMDr9arh\nMdvqa3d4/B+oBiuPlpYWOBwOmEymGb/WlHmQNDc4iepFElJ9KExWkNQ3XmVvYKfTCSDZY2K1WqEX\ntfDGOXAch1F/AjX2Yp1tJmW/cGUBhBIulYCp1Wpp4n6FYIwhHo+ri2UikUheO86kUorqK+HR5/Nl\n3cJQvT14hGU9DHoJkkbCti2NMGhnFv5oDnAmWlxDSPWiMFkhTCYTrrnmGrz77rvo6urKeGNWFsNE\nfHp4AjoIgoCOXj/qzIDZbC7J7ctSw6XH4wEAaLVatedD2XaNekBKE2MMsiwjkUiol3g8PqNeR+Xx\ngsGgGh69Xi8SiUTO23McB5PJpPbQu8MatAeTr6MaizjtIEkBMjtRFKHRaOhnQkgVozBZQerr67Fh\nwwbU1dUhGAzC7XbD7XbD5/Opb9xGTQIswBBPxNHvjODkyWG1B1ApnGw0GmEymUpm0rzMGHzBBMb8\ncUTjwbTrOI6DKIqQRBGSJEGUJIiCAHAcjDoJi1vMFDYLJDUcTgyJ2Y5PNyxmo/wjoVym6r00GAxq\neLRYLGmv4ZA8vm/2wsb8doCiAJmbIAjQaDQl83eCEFI89FegAinbshmNRrS0tECWZXUuGTfsQftY\n8nbBmAjGAI4bn7vocrnUx9HpdBkBc7bfUGNxGWP+OMb8cYz6Yurnym4leeEAnuPB8zwuXmLHNRtb\nIEkShYEssgXAyT6fTUrPo9/vh8/ng8fjmXLRjVarVecFWywWaDSanLdttGuwfa0N0biMJU3Zw6Sy\nB72yqxMN2WbieV5dXEMIIQCFyarA8zxsNhtsNhva2hjO+QYRCkcRjUahNTAkosGsvUjKELMy7xIY\nX62aGjIv5A132B3F4FhUDY6+UAKYRm7MigEykyHLMt48NQKTFMP8Bh14nockSRkXURQrpvdyOsFQ\nluVJ5xjOtmg0Cp/Pp4bHQCAwZWCVJAkWi0UNkNMtuzO/PvP2kiSp4ZEWfeWmjGBU0u8LIaQwKExW\nGY7jUGfVYlAGBF5AXUsL2up0CIVC8Pv96iUYDGYNGsrcy5GREfXxlHp6Su9lvj06Z/qDePUDb17n\nrdPwsJtFGLUCkMf7mNsfh9OTHBI9ctIDu1mExSCmrR5XKEPlykUQhIzPi/XmKcty1iFlJYgpbVQK\n4TAX5XwjkYj6+vL5fIhGo1PeVxAENTxardaCldzR6XRqgKQetslRmR9CyFQoTFahGouIwbFkoHJ5\n41jQwKvD4g0NDQCSASAQCCAQCKQFzIkYY+rtFMowe2rvZbYQ4Atm9kJxHGAxinCYRNjNUvKjSYR+\nmoslojEZfz7qgj+UQCzO8Ld33bhhYw1EIfPNkLHkbiiTzccTBEENlsrngiCA5/m0iyAI4Dgu55su\nYyxrb2GueYe5wqFSRkcUxaINxcqyjFgshmg0ql4mfh2NRqdVXFyj0cBkMsFsNsNsNsNkMhXk+Wk0\nGrXklE6no+HrPFCIJITki8JkFUotXu7yZg9QPM+rb+iKRCKhhkvlYygUyrgvY0wNoKmPNzFgrm4z\ngOOAWJzBbhbhMEmwGsWsgW+6NBKP7RfZ8NdjLsgyMOaL49gZHy5dYZnR4ynhLp/eNABp4VK5v9LL\nWOqUcD0xFGYLjheC53n19aAEx0LtVZ0aHmkbv+mhEEkImS4Kk1Wo3qoBz3FIINkTmC9lyNFiGQ9k\n8Xg8rffS7/dnXTQhyzJ8Ph98Pl/a4xmNRtiMRug4HfiEDrGoDnyB6knWWCRsXGrB0dPJofQPe4No\nsEtY2JDfSt4LoQTHUtqDWantODEkZguKhcRxnFqD0GAwqMGxkLvEUHi8cBQiCSEzRWGyChl1Ai5f\nZUFnvw/rlpinvsMkRFFU57MpYrFYRsDMFlCUHUu83sx5k6k74Sjz22YSFJbP02PIHUXXULIQ+qsf\neNUe0EqhDJ3nExILPadSCYmpl4nHZiOcKHVGldcHhceZSw37FCIJITNROe+oZFoWNuhQb0rAOAuh\nSpIkdfW4IhqNpg2P+/3+SYdJlYUySrHyVEqR5NRLajFzpfadMnfxspUWjPpi8AUTiCeS8yc/tin7\n/MlSkhoSJ4bCSCSiLpKKxWIFHz5XVrpP/DmnhkVJkuZs7qEkSWn/XFB4vHAUIgkhhUJhkswJjUYD\nh8MBh8MBIBmUotEoAoEAQqGQWoZI2V5vMvF4HPF4POuCIIVSC09ZlT3fLOHYqAAGDuEIhxffimLT\nUoNaGkhZyDJXb6qpPYnZ5icqx3OVylGCprLgJ1/i+eLu2UJialAs5gIVJeRQz+PsoOFsQkihUZgk\nRZG67/ZEsiynhcuJQTOfoVrlMVLVazXodBsAAO93BhHy9KPWkD78rqzQnupj6ort1I/ZVnJPnKcY\ni8UKPpdS2Y1kqt7EUgxlSnBM7WGmkFN4qf9g0c+XEFJIFCZJyeF5HgaDAQaDIeO61EUkkUgkY+hX\n+Txbj16dIQpfRIQrlNwlpdNtgFGKQy+NDxErq7YvdKVyoSgBYGJQFEURiUQCVqu1rHruUp+DEhyp\nTM/sohBJCJltFCZJWVGGQCVJgtFozHobZQhYqR2pDIvHYjE0NsXwyocJ+CMMsszQ6bNhTb0fTI7P\nadkeZagx16IV5ZJrGFupA6rX60smjKUWf08t/J56jMLM3KG9swkhc4X+ypCKkxpq9PrMMkCOuhie\nPeaC0nkZNzZh62qrGkInFg/P9lEZameMTfl5tgVD5dhLlG1noNTPy6V3tNIprzdqD0LIXKEwSaqO\n3Sxh83KLupXjuYEQGuwaLGnWV20vjhLAJ/Yopn5dbuG3mijtR9MGCCHFUJ3vnKTqLWnSY2gsivaB\n5CKd1097UGMRYTdV5j7NPM/nDIlzvZKdFA6tzCaElAIKkxXGaDSipaVFnSeYepmNeoTliuM4bFlu\ngcsbhycQRyIBvH7Ki49urCn2qU0bx3E5h6CVr6m3qrIIgqCWtaIQSQgpNgqTFSZ1gUo2yhZ/Stkc\nZWtEZT5gOe0hfaEkMbl/95+POiHLwLA7Bqcnilqrptinlia1V1EJikByblx9fT20Wi0FiipB8yEJ\nIaWIwmSVUcqE8DwPvV4Pm82WdZ7gxMUoky1IUT4vRzaTiAUNOnW4+4OeILbNcZjMtaAldQh6IqVo\nO/VMVb7UfxCph5kQUoooTJKslAn9+WKM5QyZU62ILrZVbUY1THYNheFfkoBJd+E9PxzHgef5rD2L\nVC6HTIWGsgkh5YLCJCkIZd7edIbfUoOlEi6zXSZeB2DKjxM/z/ZmrBxr0esxrz6EAVcE4IDO4QQu\nXWVN29Em10VZuDLxY7adcAiZivJPnCRJNJRNCCkbFCZJ0Si9dqVg23o99h/qAAC0D0Vx/WV2aCR6\nMydzg3apIYSUs9J4JyekyBa3mGE3J/cJj8QSeLd9rMhnRCodYwyCIMBgMMBoNFJ5H0JI2aIwSQiS\nw4sblteqX7912glZLo05naSyKHMh/X4/rcwmhFQECpOEnLd6oR06TfKN3e2P4Fyft8hnRCqFUlzc\nYDDAYDBU7U5LhJDKRGGSkPM0koCLl4wXLX/ztLOIZ0MqgbI/vNFohFarpV5IQkhFojBJSIp1y2rB\nn5+31jPsx6ArWOQzIuVGEARotVqYTCbo9XpaVEMIqXgUJglJYTZIWDHfpn5NvZMkH8pqbKPRCIPB\nAI1GQwGSEFI1KEwSMkHqQpxTXW74grEing0pVUqAVFZja7Xakil1RQghc4n+8hEyQWONAa31JgCA\nzBiOf0i9kyRJ2dowNUDSPEhCSLWjMElIFqm9k++cdSEaSxTxbEgxKQFSWUij0+koQBJCSAoKk4Rk\nsbjFAptJAwAIRxN4v4OKmFcTnuczAiQtpCGEkOwoTBKSBc9zWL+8Tv36zdPOtL2+SeWZOAeSAiQh\nhOSHwiQhOVy0yA7t+f25x3wRtPf7inxG1SEel/H2GRf6RgKz/r2UMj5Go5HmQBJCyAxRmCQkB40k\nYO0Sh/r1sVMjRTyb6pBIyHjyhXYcfKMXT77QDn+BV9JzHAdRFKHT6WAymdQyPrQKmxBCZo7+ghIy\nifUpRcy7h/wYGg0V+Ywq26G3B9HnTPZIyjKDKF7YnyglPGq1WnX4Wq/XQ5IkGr4mhJACoTBJyCQs\nRg2WtVrVr988Tb2Ts+VUlxvHUn6+V6xtVPdKnw5BENLmPur1emg0GgiCQAGSEEJmAYVJQqawccX4\nQpxTXe6CD70SYNQbwf99vUf9ekmLBZtX1U1yj3FKeNTr9erQtTL3kcIjIYTMPgqThEyhqdaAlloj\nACAhMxw/Q0XMCykaS+DplzsRi8sAAJtJixsua8sZBFPL9qSGR1p5TQghxUFhssIIgqC+uUqSRAsL\nCiS1d/KdM6OInw8+5MIwxnDwjT44PWEAgCjwuOWK+WnD20p41Ol06qprKttDCCGlQyz2CZDC4jgO\ngiCklTdhjCGRSECWZSQSCSQStJvLdC2ZZ4HVqIEnEEUoGsf7HWO4eGlNsU+r7L19xoUPOscLwl+7\nsQVNtUb1NSwIAv1DRAghJY7+SlcBZUVr6rwynU6HUCgEURQhiiK9YU8hWcR8fIvFY6dGIMtUxPxC\nDDiDePGtAXAcB57nsX5FAy67uE3teaSedUIIKQ/0l7pKcRyHeDyetmWcyWRSV78qw+S0iGHcRYsc\nahHzUV8EJ86NFvmMyovSa67RaABewrOv94HjeQiCgOY6M27cuphea4QQUoZomJuoOI5Te4kmkmU5\n66WathjUagRsWVWPQ+8MAABeOTGIlfNt0M6gfE0lUV43ymsn2+fKBUjWj9z34ml4A1EAgE4j4pNX\nL4V0gTUlCSGEFAeFSZIXnuezhkzGWM6gWYk2LK/F8TNO+IIxBCNxHD05jCsvbir2ac2aiYEwV1ic\njpff7sO5Prf69a3bF8Nu0RX61AkhhMwRCpPkgmRb8ANUbsgURR5XXtyEZ1/tBgAcO+XExUtqYDFq\ninxm05NvT2Khh53P9rpx6Hif+vUVFzdjWZu9oN+DEELI3KIwSWbFZCEzV9AslyHzVQtsePPUCIbG\nQognZLxyYgg3XNZa7NNS5QqIE8PiXPP4I9j/0lkwJNt5QZMF29eXzs+NEELIzFCYJHNqsnmZStBU\nwubEz0slcHIchx3rmvHEC+cAAO93jGH98lo0OPSz/n2Vnx1jDJFIRK2/mBoWS1E8IWPfC2cQisQB\nAGaDBrddtRQCX5rnSwghJH8UJknJSO0xm9ijqVDC5MTAOdllNrQ1mrC4xYJzfV4wMLx0vB+funrR\njMLcxGHlfHoTOY5DNBpVSzuVukPHe9E34gcA8ByH269eCpNeKvJZEUIIKYTSfxciJIUSqHL1bmaT\nGkAnfp76derHfD6/an0LOgZ8YDLQMxRA52AAi1ssWc85Vzgs5d7EQjrb61E/v3ZzG1obzEU8G0II\nIYVEYZJUvNQAWkhGoxGbVzfj2MkhAMDhd4exekkjDd1msXVtM/52vBerFtZgy+rGYp8OIYSQAqIw\nScgF2L5uHk6cdSIaS8DpDuHtD4exYUVDsU+r5KxeVIPVi2j7SUIIqURUJZiQC2DUS7ji4mb165fe\n7EUkRnufE0IIqR4UJgm5QFtWN6l1JgPhGI6c6C/yGRFCCCFzh8IkIRdIEnlcvXG8XuKr7w7A448U\n8YwIIYSQuUNhkpACuGhxLRprjACSNRVfequ3yGdECCGEzA0Kk4QUAMdxuG5zm/r1iTNODLoCRTwj\nQgghZG5QmCSkQBY2W7G0NbnPNAPDwaPdJbFjDyGEEDKbKEwSUkDXbmoDf76eZUe/B2d73UU+I0II\nIWR2UZgkpIDq7HqsX1Gvfn3waDcSMvVOEkIIqVwUJgkpsO3r5kEjJfcWVwqZE0IIIZWKwiQhBWbU\nS9i6dryQ+aHjfUU8G0IIIWR2UZgkZBZcuqZRLWQeisQRT8hFPiNCCCFkdtDe3ITMAkkUcNfHVuHV\nE/1Y2maHKND/bYQQQioThUlCZonDosONVywq9mkQQgghs4q6SwghhBBCyIxRmCSEEEIIITNGYZIQ\nQgghhMwYhUlCCCGEEDJjFCYJIYQQQsiMUZgkhBBCCCEzRmGSEEIIIYTMGIVJQgghhBAyYxQmCSGE\nEELIjFGYJIQQQgghM1by2ykmEgk8/vjjeOqpp9Db2wuHw4EdO3Zg165dsFqtOe939dVXo7+/P+O4\nxWLB0aNHZ/OUCSGEEEKqRsmHyb1792Lfvn3Ys2cPNm/ejDNnzmDPnj3o7OzEo48+Oul9//Ef/xF3\n33132jGO42bzdAkhhBBCqkpJh0m/348nnngCn//85/GJT3wCANDa2oru7m58//vfR2dnJxYsWJDz\n/nq9HjU1NXN0toQQQggh1aekw6TJZMKhQ4eg0+nSjtfW1gIAQqFQMU6LEEIIIYScV9JhEgDsdnvG\nseeffx4WiwWLFy8uwhkRQgghhBBF0cJkNBpFb2/vpLdZtGhRxrH9+/fjr3/9K/bs2QONRjPp/d97\n7z184QtfwKlTpwAAl112GXbt2oWWlpaZnzghhBBCCFEVLUx++OGHuP3227MuiGGMgeM4nDx5Mu34\nk08+iQceeAB33nkn7rjjjkkfv6amBoFAAHfffTdaW1tx7tw5/PCHP8RnPvMZ/OlPf4LNZsv7XMPh\ncN63LReRSCTtIykv1H7ljdqvfFHblTdqv9nBMcZYsU8iHw8//DB+9rOf4ctf/jJ27do1o8c4c+YM\nbr75ZuzatQtf/vKX87rPm2++OaPvRQghhBBSSTZs2JD1eMnPmQSAH//4x/jlL3+J73znO+qq7plY\nunQpRFHE0NBQ3vfJ9YMjhBBCCCFlsAPO008/jUceeQTf//738w6SZ8+exe7du3H27Nm04++//z7i\n8TgWLlw4G6dKCCGEEFJ1SjpMhkIhfO9738N1112HLVu2wOl0pl2CwSAA4MSJE7jhhhvw2muvAQCa\nm5vxxhtv4F/+5V9w+PBh9Pb24tChQ7j//vvR2NiI2267rZhPixBCCCGkYpT0MPd7770Ht9uNAwcO\n4MCBAxnX33PPPbj33nsRDofR2dmJQCAAADAYDPjd736HH//4x9izZw+cTiesViu2bt2Kr371q7BY\nLHP9VAghhBBCKlLZLMAhhBBCCCGlp6SHuQkhhBBCSGmjMEkIIYQQQmaMwiQhhBBCCJkxCpOEEEII\nIWTGKEyWuSeeeALr1q3DXXfdlXHdO++8g7vvvhvr1q3Dxo0bsWvXrrSC7fv378eKFSuwcuVKrFix\nIuPyxhtvqLf929/+hk996lNYu3YttmzZgq9//esYGRmZk+dYqS6k7QDA7Xbj3//933Hdddfhoosu\nwo4dO/Dd735XLZmloLabHRfafn6/Hw8++CCuvPJKXHTRRbj11lvx3HPPZTwWtV/h7d+/H7fddhvW\nrVuHHTt24Bvf+AYGBwfV6zs6OvDFL34RGzduxCWXXIK///u/x4kTJ9IeIx6P44c//CGuvvpqrFmz\nBh/5yEfw2GOPZXwvar/CK0T7AUA0GsWDDz6IFStW4Kc//WnW70XtlydGypLX62Vf+cpX2OWXX84u\nu+wy9tnPfjbt+pMnT7K1a9eyL3zhC+zkyZPs5MmTbOfOneyGG25g0WiUMcZYJBJhTqcz4/KTn/yE\nXX755czn8zHGGDt27BhbtWoV27t3L+vu7mZvvPEG27lzJ7vllltYPB6f8+de7grRdowx9nd/93fs\n2muvZUeOHGG9vb3s4MGDbPPmzey+++5Tb0NtV3iFar9Pf/rT7PLLL2cHDx5kPT097KGHHmIrV65k\nr732mnobar/Ce+yxx9iKFSvYr371K9bV1cVee+01dv3117ObbrqJRaNR5vV62datW9nnPvc5durU\nKXbq1Cl2//33s0suuYT19PSoj/PNb36Tbd68mR08eJB1d3ezffv2sTVr1rBf/vKX6m2o/QqvUO13\n5swZdvPNN7ObbrqJrVixgv3kJz/J+F7UfvmjMFmm/ud//ofdcccdbGhoiH384x/PeEO7//772YYN\nG5jf71eP9fT0sBUrVrCnnnoq5+O6XC62adMmtm/fPvXY3XffzXbu3Jl2u9OnT7Ply5ezv/zlLwV6\nRtWjEG137tw5tnz5cvbkk0+m3fc///M/2erVq1koFGKMUdvNhkK035EjR9jy5cvZ/v370+772c9+\nlt15553q19R+hbd161b2jW98I+3YgQMH2IoVK9jhw4fZz3/+c3bRRRexsbEx9fpoNMq2bt3KvvnN\nbzLGGBsYGGArV65kjz/+eNrjfO9732ObNm1ikUiEMUbtNxsK0X6MMXbfffexf/u3f2Mul4stX748\na5ik9ssfDXOXqW3btuE3v/kN6uvrs17/wQcfYOXKlTAajeqxefPmYfny5XjllVdyPu4PfvADtLW1\nqVtXRqNRvP7669ixY0fa7ZYtW4ampiYcOnTowp9MlSlk20mSlPa1RqMBO186ltpudhSi/T744ANw\nHIdNmzal3feaa67BW2+9hXA4TO03S5599lns2bMn7VhtbS2A5K5rL7/8MtauXQubzaZeL0kStm7d\nipdffhkAcPjwYTDGsH379rTH2b59O3w+H44fP07tN0sK0X4A8M///M/45je/mfE3VEHtNz0UJstU\nc3MzeD5384miCFHM3OCopqYGnZ2dWe/T1dWFp59+Grt27VKP9fT0IB6Po62tLeP28+bNQ3t7+/RP\nvsoVou0WLVqErVu34tFHH1Xb4OzZs/jjH/+IW2+9FTqdjtpulhSi/ZTrJ96upqYGsiyjp6eH2m+W\nWCwWmEymtGPPP/88BEHARRddhPb2drS2tmbcr7W1FQMDA4hEIujo6ADP85g3b17GbRhjaG9vp/ab\nJYVoPwBZ2yUVtd/0UJisUAsXLsTp06cRi8XUY4lEAmfOnIHf7896n5///OdYtmwZtm3bph7zer0A\nkNbLojAajfD5fAU+c5Jv2/3kJz9Bc3MzPvaxj2HNmjW46aabsH79ejz44IMAqO2KJZ/2W7hwIRhj\nGYsCTp48CSC5OIfab268+uqreOyxx3DXXXehoaEBXq8368/cYDAAAHw+H3w+H3Q6HTiOy3kbar+5\nMZP2ywe13/RQmKxQd955J8bGxvDggw/C6/XC4/HggQceQCQSydqtPzo6imeeeQb/8A//MPcnS9Lk\n23b/+q//ijNnzuBHP/oRnnrqKTz00EM4evQodu/eXcSzJ/m03xVXXIFFixbhBz/4AU6dOoVEIoGD\nBw/imWeeAZDZY0lmx4svvogvfelLuOqqq/D1r3+92KdDponar3RQmKxQGzZswH/8x3/gwIED2LJl\nC3bs2AGTyYRrrrkGNTU1Gbc/ePAgAOCqq65KO26xWAAga2+mz+eD1WqdhbOvbvm03YsvvogDBw7g\nu9/9Lm644QYsX74cN998M/bs2YM///nPeOutt6jtiiSf9uN5Ho888gjsdjt27tyJtWvX4te//jXu\nu+8+AMk5YNR+s+vJJ5/EvffeixtvvBEPP/ywOnXBarUiEAhk3N7n84HjOFgsFlgsFoTDYXV+skJp\nK5vNRu03yy6k/fJB7Tc99O9vBbv55pvxsY99DE6nEw6HA5Ik4dOf/jQuueSSjNsePHgQ69aty/hF\na2trgyRJ6OrqyrhPZ2cnrrnmmlk7/2o2Vdu1t7eD4zgsXbo07X6LFy9Wr//4xz9ObVck+fzutba2\n4ve//z1GR0chiiIsFgt+//vfw2q1oqmpCbFYjNpvlvzhD3/At771Ldx7772455570q5bvHhxzp95\na2srNBoNlixZos5tTZ1T19HRAY7jsGzZMvrbOYsutP3yQe03PdQzWaHOnDmDp556CoIgoKGhAZIk\nobOzEydOnMBHP/rRtNvKsoyjR49i/fr1GY+jrIJ76aWX0o4fP34cLpeLfqFmQT5t19zcDAA4d+5c\n2n2Vr1taWqjtiiSf9gsEAvjTn/6Erq4uOBwO9Z+4P//5z7j++usB0O/ebDl69CgeeOAB3HfffRlB\nBEiOzrz77rtwuVzqsWAwiCNHjqg/8yuvvBI8z+OFF15Iu+9zzz2HmpoarF27ltpvlhTi4IivAAAC\nqElEQVSi/fJB7Tc9wgMPPPBAsU+CTJ/H44Hf70cgEMD//u//QhRF7NixQ9395OzZs/jqV7+KSCSC\n1tZWnDlzBrt378amTZsy5kV2d3fj17/+NXbu3IlVq1ZlfK/58+fjV7/6FcbGxrBw4UKcO3cOe/bs\nwaJFi9RhOZK/QrRdc3Mznn76aRw5cgQLFiwAz/M4fvw49u7di7q6Onzta18Dz/PUdrOgEO3H8zzu\nueceHD58GCtXrkQoFMLDDz+M119/HT/4wQ9gNpsB0O/ebPinf/onzJs3D1/72tcQDAbTLrIsY82a\nNXj66afx2muvYeXKlfB4PPj2t7+N/v5+PPTQQzAYDDAYDPB6vXj88ccxf/586PV6/OUvf8EjjzyC\n3bt3Y82aNQCo/WZDIdoPAJxOJ4LBINxuN377299izZo1WL58OYLBICRJgiAI1H7TwLGJkz5IWfjs\nZz+LY8eOZb3unnvuwb333ov9+/fj0UcfRU9PD2w2Gz7+8Y/j3nvvzejmf/vtt/GZz3wG//Vf/4WP\nfOQjWR/ztddew49+9COcOnUKBoMB1157Le6///6855+QcYVqu/7+fvzoRz/CkSNH4PF4YLPZsG3b\nNnzta19LmxdLbVdYhWq/jo4OfPe738Xbb7+NRCKBjRs3Yvfu3epUBQW1X+H09/dP2qN06623Yu/e\nvejt7cXevXvx+uuvI5FIYMOGDdi9e3fatBLGGH76059i//79GBkZQWtrKz73uc/hk5/8ZNpjUvsV\nTiHbb8WKFRmr8Rlj4DgOv/nNb9QasNR++aEwSQghhBBCZozmTBJCCCGEkBmjMEkIIYQQQmaMwiQh\nhBBCCJkxCpOEEEIIIWTGKEwSQgghhJAZozBJCCGEEEJmjMIkIYQQQgiZMQqThBBCCCFkxv4/BdQ+\n26fwHOQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9805cf3d10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ps = df.noneall / 100\n",
"odds = ps / (1-ps)\n",
"log_odds = np.log(odds)\n",
"log_odds\n",
"Plot(df, log_odds, formula, color=BLUE, label='None')\n",
"thinkplot.Config(ylabel='Log odds')"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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iKLTWYDJhICQiIiKKkURdePpE4rqXcTAYxObNm/HXv/4Vhw8fRkZGBk4//XSsWbMGkydP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\nACA5QQgAkJwgBABIThACACT3BcjaxtiGXqEPAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9805c7c790>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"attend = 100-df.attendedall\n",
"Plot(df, attend, formula, color=GREEN, label='No attendance')\n",
"thinkplot.Config(ylabel='Percent')"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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RIIhpQ3Y3g67O2KgdE4xM5WQyCUEQoGmV1TaLqBwyMpsn0JJYVesxXduxqDju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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9805d206d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"diff = df.nonemen - df.nonewomen\n",
"diff = diff.loc[1973:]\n",
"Plot(df, diff, formula, color=PURPLE, label='Gender gap')\n",
"thinkplot.Config(ylabel='Difference (percentage points)')"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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0LbizSeD/tlYn1MNVIWuGKWz4eOzEfHy93QFF0eDslNDscKN4VPL3c6ZwodXg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\nwEUiY4wxxhgT4CKRMcYYY4wJ/As5O3DZ7NTYLQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9805d45510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"diff = df.nonemen - df.nonewomen\n",
"diff = diff.loc[1986:]\n",
"Plot(df, diff, formula, color=PURPLE, label='Gender gap')\n",
"thinkplot.Config(ylabel='Difference (percentage points)')"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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VK7Fy5cohvx4hhBCSS9k20h4Kb5lDabwd7Ilh9FhfXl9vpFDv25x6DtVBnp5M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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9805c01a10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"diff = df.nonemen - df.nonewomen\n",
"diff = diff.loc[1986:]\n",
"Plot(df, diff, 'y ~ time', color=PURPLE, label='Gender gap')\n",
"thinkplot.Config(ylabel='Difference (percentage points)')"
]
},
{
"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": []
}
],
"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 |
KeatingLab/PixelDB | notebook/.ipynb_checkpoints/Database_stat-checkpoint.ipynb | 1 | 257824 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import re\n",
"from scipy import stats\n",
"import seaborn as sns\n",
"\n",
"\n",
"\n",
"%pylab inline\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"myAmino = [\"R\",\"H\",\"K\",\"D\",\"E\",\"S\",\"T\",\"N\",\"Q\",\"C\",\"G\",\"P\",\"A\",\"V\",\"I\",\"L\",\"M\",\"F\",\"Y\",\"W\"]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1976"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Load PixelDB\n",
"path = \"/media/vince/Postdoc/PixelDB/\"\n",
"PixelDB = pd.read_csv(path+\"PixelDB.csv\")\n",
"len(PixelDB[\"name\"])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Entries in DB:', 1976)\n",
"('Receptor Cluster', 479)\n",
"('Unique Binding Mode', 702)\n",
"('Binding mode with 2 plus peptide', 271)\n"
]
}
],
"source": [
"print(\"Entries in DB:\",len(PixelDB[\"cluster_number\"]))\n",
"print(\"Receptor Cluster\",len(PixelDB[\"cluster_number\"].value_counts()))\n",
"print(\"Unique Binding Mode\",len(PixelDB[\"unique_id\"].value_counts()))\n",
"print(\"Binding mode with 2 plus peptide\",np.sum((PixelDB[\"unique_id\"].value_counts()) >= 2))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Some STAT on PixelDB"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"PixelDBecr = PixelDB.copy()\n",
"for uniid in list(np.unique(PixelDB[\"unique_id\"])):\n",
" sdf = PixelDB[PixelDB[\"unique_id\"] == uniid]\n",
" \n",
" \n",
" \n",
" if not np.sum((np.array(sdf[\"longest_continuous_core\"]) > 3) & (np.array(sdf[\"longest_continuous_ecr\"]) > 3)) > 0:\n",
" PixelDBecr = PixelDBecr[PixelDBecr[\"unique_id\"] != uniid]\n",
" continue\n",
" #break"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"PixelDBoecr = PixelDB[PixelDB[\"longest_continuous_ecr\"] > 3]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Unique Binding Mode', 124)\n",
"('Exosite and ECR complexes', 325)\n"
]
}
],
"source": [
"print(\"Unique Binding Mode\",len(PixelDBecr[\"unique_id\"].value_counts()))\n",
"print(\"Exosite and ECR complexes\",np.sum(PixelDBecr[\"longest_continuous_ecr\"] > 3))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Full\n",
"103\n",
"('Average Overlap of:', 'PFAM', 0.69063912129211225)\n",
"('Median Overlap of:', 'PFAM', 1.0)\n",
"('Average Nb of Binding mode:', 1.465553235908142)\n",
"('Median Nb of Binding mode:', 1.0)\n",
"(1, 32)\n",
"('More than one binding mode', 103)\n",
"('Total cluster', 479)\n",
"()\n",
"103\n",
"('Average Overlap of:', 'uniprot', 0.53466530207447838)\n",
"('Median Overlap of:', 'uniprot', 0.5)\n",
"('Average Nb of Binding mode:', 1.465553235908142)\n",
"('Median Nb of Binding mode:', 1.0)\n",
"(1, 32)\n",
"('More than one binding mode', 103)\n",
"('Total cluster', 479)\n",
"()\n",
"103\n",
"('Average Overlap of:', 'CATH', 0.9560795191863154)\n",
"('Median Overlap of:', 'CATH', 1.0)\n",
"('Average Nb of Binding mode:', 1.465553235908142)\n",
"('Median Nb of Binding mode:', 1.0)\n",
"(1, 32)\n",
"('More than one binding mode', 103)\n",
"('Total cluster', 479)\n",
"()\n",
"ECR\n",
"14\n",
"('Average Overlap of:', 'PFAM', 0.70000000000000007)\n",
"('Median Overlap of:', 'PFAM', 1.0)\n",
"('Average Nb of Binding mode:', 1.4090909090909092)\n",
"('Median Nb of Binding mode:', 1.0)\n",
"(1, 16)\n",
"('More than one binding mode', 14)\n",
"('Total cluster', 88)\n",
"()\n",
"14\n",
"('Average Overlap of:', 'uniprot', 0.46309523809523812)\n",
"('Median Overlap of:', 'uniprot', 0.34166666666666667)\n",
"('Average Nb of Binding mode:', 1.4090909090909092)\n",
"('Median Nb of Binding mode:', 1.0)\n",
"(1, 16)\n",
"('More than one binding mode', 14)\n",
"('Total cluster', 88)\n",
"()\n",
"14\n",
"('Average Overlap of:', 'CATH', 0.9642857142857143)\n",
"('Median Overlap of:', 'CATH', 1.0)\n",
"('Average Nb of Binding mode:', 1.4090909090909092)\n",
"('Median Nb of Binding mode:', 1.0)\n",
"(1, 16)\n",
"('More than one binding mode', 14)\n",
"('Total cluster', 88)\n",
"()\n"
]
}
],
"source": [
"#Is there a link between CATHdb and Binding mode\n",
"\n",
"for ikl in range(0,2):\n",
" torun = PixelDB\n",
" if ikl == 1:\n",
" print(\"ECR\")\n",
" torun = PixelDBecr\n",
" else:\n",
" print(\"Full\")\n",
"\n",
" \n",
" \n",
" \n",
"\n",
" for test in [\"PFAM\",\"uniprot\",\"CATH\"]:\n",
" CATHdbOver = []\n",
" BindingMode = []\n",
" for uniid in list(np.unique(torun[\"cluster_number\"])):\n",
" sdf = torun[torun[\"cluster_number\"] == uniid]\n",
"\n",
"\n",
"\n",
" BindingMode.append(len(sdf[\"unique_id\"].value_counts()))\n",
" if (len(sdf[\"unique_id\"].value_counts()) == 1):\n",
" continue\n",
" AllCATH = []\n",
"\n",
"\n",
"\n",
" AllCATHUni = []\n",
" for unid in np.unique(sdf[\"unique_id\"]):\n",
"\n",
" CATHuni = []\n",
" for cid in sdf[sdf[\"unique_id\"] == unid][test]:\n",
" #print(cid.split(\"_\"))\n",
" if str(cid) != \"nan\":\n",
" CATHuni += cid.split(\"_\")\n",
" AllCATHUni += cid.split(\"_\")\n",
"\n",
" AllCATH.append(list(set(CATHuni)))\n",
"\n",
" Tot = 0\n",
" Over = 0\n",
"\n",
" for i in range(0,len(AllCATH)):\n",
" for j in range(i+1,len(AllCATH)):\n",
" OverT = 0\n",
" for v in AllCATH[i]:\n",
" if v in AllCATH[j]:\n",
" OverT += 1\n",
" if (OverT != 0):\n",
" Over += 1\n",
" #else:\n",
" # print(i,j,AllCATH[i],AllCATH[j])\n",
" Tot += 1\n",
" CATHdbOver.append(float(Over)/float(Tot))\n",
" #if (CATHdbOver[-1] < 0.5):\n",
" # print(uniid,Over,Tot)\n",
" # print(AllCATH)\n",
"\n",
"\n",
"\n",
" #if len(sdf[\"unique_id\"].value_counts()) > 6:\n",
" # print(uniid,len(sdf[\"unique_id\"].value_counts()))\n",
" # print(pd.Series(AllCATHUni).value_counts())\n",
" #else:\n",
" # break\n",
"\n",
"\n",
" #break\n",
" # \n",
" #break\n",
" print(len(CATHdbOver))\n",
" print(\"Average Overlap of:\",test,np.mean(CATHdbOver))\n",
" print(\"Median Overlap of:\",test,np.median(CATHdbOver))\n",
" print(\"Average Nb of Binding mode:\",np.mean(BindingMode))\n",
" print(\"Median Nb of Binding mode:\",np.median(BindingMode))\n",
" print(np.min(BindingMode),np.max(BindingMode))\n",
" \n",
" print(\"More than one binding mode\",np.sum(np.array(BindingMode) > 1))\n",
" print(\"Total cluster\",len(BindingMode))\n",
" \n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Full\n",
"('1A7C', 'A', 2, array([19]))\n",
"('1EE4', 'A', 2, array([8]))\n",
"('1KL3', 'AD', 2, array([47]))\n",
"('1LVM', 'A', 2, array([76]))\n",
"('1NX0', 'A', 2, array([108]))\n",
"('1OV3', 'AB', 2, array([272]))\n",
"('1Q1S', 'C', 2, array([8]))\n",
"('1Q1T', 'C', 2, array([8]))\n",
"('1TOQ', 'CG', 2, array([229]))\n",
"('2R28', 'BA', 2, array([189]))\n",
"('2W73', 'AB', 2, array([189]))\n",
"('2YNR', 'A', 2, array([8]))\n",
"('3AGY', 'B', 2, array([155]))\n",
"('3AP1', 'AB', 2, array([208]))\n",
"('3AV9', 'AB', 2, array([24]))\n",
"('3AVA', 'AB', 2, array([24]))\n",
"('3AVB', 'AB', 2, array([24]))\n",
"('3AVC', 'AB', 2, array([24]))\n",
"('3AVF', 'BA', 2, array([24]))\n",
"('3AVG', 'AB', 2, array([24]))\n",
"('3AVH', 'AB', 2, array([24]))\n",
"('3AVI', 'AB', 2, array([24]))\n",
"('3AVJ', 'AB', 2, array([24]))\n",
"('3AVK', 'AB', 2, array([24]))\n",
"('3AVL', 'AB', 2, array([24]))\n",
"('3AVM', 'AB', 2, array([24]))\n",
"('3AVN', 'AB', 2, array([24]))\n",
"('3C27', 'B', 2, array([3]))\n",
"('3CYY', 'BA', 2, array([255]))\n",
"('3H5R', 'AB', 2, array([125]))\n",
"('3H8D', 'CD', 2, array([354]))\n",
"('3L3Q', 'A', 2, array([8]))\n",
"('3O6Q', 'AC', 2, array([346]))\n",
"('3RF3', 'BA', 2, array([291]))\n",
"('3TWW', 'AB', 2, array([461]))\n",
"('3WNE', 'AB', 2, array([24]))\n",
"('3WNF', 'AB', 2, array([24]))\n",
"('3WNG', 'AB', 2, array([24]))\n",
"('3ZIN', 'A', 2, array([8]))\n",
"('3ZIO', 'A', 2, array([8]))\n",
"('3ZIP', 'A', 2, array([8]))\n",
"('3ZIQ', 'A', 2, array([8]))\n",
"('3ZIR', 'A', 2, array([8]))\n",
"('3ZKE', 'IK', 2, array([122]))\n",
"('3ZQI', 'AB', 2, array([262]))\n",
"('4B8O', 'A', 2, array([8]))\n",
"('4CY2', 'A', 2, array([11]))\n",
"('4DS1', 'AC', 2, array([122]))\n",
"('4GUS', 'A', 2, array([103]))\n",
"('4MZ5', 'E', 2, array([8]))\n",
"('4MZ6', 'E', 2, array([8]))\n",
"('4R6O', 'GE', 2, array([63]))\n",
"('4RXH', 'B', 2, array([8]))\n",
"('4YNL', 'AB', 2, array([347]))\n",
"('4Z0Y', 'DB', 2, array([130]))\n",
"('4Z0Z', 'CB', 2, array([130]))\n",
"25\n",
"('Same receptor engage in multiple peptide binding:', 56)\n",
"ECR\n",
"('1KL3', 'AD', 2, array([47]))\n",
"('1Q1S', 'C', 2, array([8]))\n",
"('1Q1T', 'C', 2, array([8]))\n",
"('2YNR', 'A', 2, array([8]))\n",
"('3C27', 'B', 2, array([3]))\n",
"('3L3Q', 'A', 2, array([8]))\n",
"('3ZIN', 'A', 2, array([8]))\n",
"('3ZIO', 'A', 2, array([8]))\n",
"('3ZIP', 'A', 2, array([8]))\n",
"('3ZIQ', 'A', 2, array([8]))\n",
"('3ZIR', 'A', 2, array([8]))\n",
"('4B8O', 'A', 2, array([8]))\n",
"('4MZ5', 'E', 2, array([8]))\n",
"('4MZ6', 'E', 2, array([8]))\n",
"('4RXH', 'B', 2, array([8]))\n",
"3\n",
"('Same receptor engage in multiple peptide binding:', 15)\n"
]
}
],
"source": [
"#How often same PDB different binding mode\n",
"\n",
"for ikl in range(0,2):\n",
" torun = PixelDB\n",
" if ikl == 1:\n",
" print(\"ECR\")\n",
" torun = PixelDBecr\n",
" else:\n",
" print(\"Full\")\n",
" tot = 0\n",
" AllCluster = []\n",
" for uniid in list(np.unique(torun[\"pdb_id\"])):\n",
" sdf = torun[torun[\"pdb_id\"] == uniid]\n",
" if (len(sdf) == 1):\n",
" continue\n",
" for ch in list(np.unique(sdf[\"receptor_chain\"])):\n",
" ssdf = sdf[sdf[\"receptor_chain\"] == ch]\n",
" if (len(ssdf) == 1):\n",
" continue\n",
" print(uniid,ch,len(ssdf),np.unique(sdf[\"cluster_number\"]))\n",
" AllCluster += list(np.unique(sdf[\"cluster_number\"]))\n",
" tot += 1\n",
" #break\n",
" print(len(list(set(AllCluster))))\n",
" print(\"Same receptor engage in multiple peptide binding:\",tot)\n",
" #break"
]
},
{
"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>name</th>\n",
" <th>pdb_id</th>\n",
" <th>pubmed_id</th>\n",
" <th>resolution</th>\n",
" <th>uniprot</th>\n",
" <th>PFAM</th>\n",
" <th>CATH</th>\n",
" <th>receptor_chain</th>\n",
" <th>receptor_length</th>\n",
" <th>peptide_chain</th>\n",
" <th>...</th>\n",
" <th>surface_ss</th>\n",
" <th>interior_ss</th>\n",
" <th>unique_id</th>\n",
" <th>COREBINDING_aa</th>\n",
" <th>COREBINDING_ss</th>\n",
" <th>EXOSITE_aa</th>\n",
" <th>EXOSITE_ss</th>\n",
" <th>bs_loc_type</th>\n",
" <th>mean_seq_iden_in_bm</th>\n",
" <th>mean_seq_iden_not_bm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1467</th>\n",
" <td>5AJP_A_B_114_1.pdb</td>\n",
" <td>5AJP</td>\n",
" <td>25939779.0</td>\n",
" <td>1.65</td>\n",
" <td>Q10471</td>\n",
" <td>PF00535_PF00652</td>\n",
" <td>3.90.550.10</td>\n",
" <td>A</td>\n",
" <td>495</td>\n",
" <td>B</td>\n",
" <td>...</td>\n",
" <td>C:15;E:1;G:11;H:14;T:28</td>\n",
" <td>B:7;C:69;E:115;G:20;H:108;T:87</td>\n",
" <td>114_1</td>\n",
" <td>A:1;F:2;G:0;H:1;K:1;L:1;N:0;Q:1;R:1;S:0;V:1;W:...</td>\n",
" <td>C:6;E:4;G:0;H:0;T:1</td>\n",
" <td>A:1;F:0;G:1;H:1;K:0;L:0;N:1;Q:0;R:0;S:0;V:0;W:...</td>\n",
" <td>C:2;E:2;G:2;H:0;T:0</td>\n",
" <td>ALA_192_A_COREBINDING;ALA_402_A_EXOSITE;ARG_28...</td>\n",
" <td>0.991919</td>\n",
" <td>-1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 36 columns</p>\n",
"</div>"
],
"text/plain": [
" name pdb_id pubmed_id resolution uniprot \\\n",
"1467 5AJP_A_B_114_1.pdb 5AJP 25939779.0 1.65 Q10471 \n",
"\n",
" PFAM CATH receptor_chain receptor_length \\\n",
"1467 PF00535_PF00652 3.90.550.10 A 495 \n",
"\n",
" peptide_chain ... surface_ss \\\n",
"1467 B ... C:15;E:1;G:11;H:14;T:28 \n",
"\n",
" interior_ss unique_id \\\n",
"1467 B:7;C:69;E:115;G:20;H:108;T:87 114_1 \n",
"\n",
" COREBINDING_aa COREBINDING_ss \\\n",
"1467 A:1;F:2;G:0;H:1;K:1;L:1;N:0;Q:1;R:1;S:0;V:1;W:... C:6;E:4;G:0;H:0;T:1 \n",
"\n",
" EXOSITE_aa EXOSITE_ss \\\n",
"1467 A:1;F:0;G:1;H:1;K:0;L:0;N:1;Q:0;R:0;S:0;V:0;W:... C:2;E:2;G:2;H:0;T:0 \n",
"\n",
" bs_loc_type mean_seq_iden_in_bm \\\n",
"1467 ALA_192_A_COREBINDING;ALA_402_A_EXOSITE;ARG_28... 0.991919 \n",
"\n",
" mean_seq_iden_not_bm \n",
"1467 -1.0 \n",
"\n",
"[1 rows x 36 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sdf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
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"source": []
},
{
"cell_type": "code",
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"metadata": {
"collapsed": false
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"source": []
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{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
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"source": [
"\n"
]
},
{
"cell_type": "code",
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"metadata": {
"collapsed": true
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{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Full\n"
]
},
{
"data": {
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0jfcIwuHkeLuUpaXFTyhU+2bSpzP2PRdjmiKSo77km63kaCASFxEHz6zPrJbf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\ncaJgFNxOiSsvbOOeGxYjSXu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m9PoRn6EAer2Lvktba5ZVNK4xUBTlW6qqfromV7M546mU\ncXNh87Iz2kVkmjon1O+ip3vJldIIyO5W2pSPI4ql/1RGPqyBUQ/u8RrDHOsb5AdP72Hv0Zyw3P03\nL+WKZTlhuXy3uV3hBFFNp8EhsyxY7PLJC+lFTTcDKa3kdRIeH8dffx2HZY5KER2rTCq6XBPOChs1\nqxgYQHA5sbIamAZ46wisWXPG1p2cShwOCeXCNnZuOTbkGir9/yjjdZDQdHxTqF/JU80ZsoqirAU2\nAgVdAlVVT35jXJvTgrEZN43uABc2LyusP1PJGYKeEWss9HQPJ9TvMmvpQ6P2Hdsa1CnmHtAZ0yI4\njq9eN/LCcgfQDYtLlrRy740dNNQN/8E/eTjEa73RwnJUy7l8TCzunNeaU0ntfoZkZA+mnsCUA9TL\n64jqxTUQvsEY3mSiICAoyHLNpMdLzSqAs6Lu5FRz5Q2LEASBnccjZYV6LKAnmcHXcHKMwe8Bn2H0\ncCxy/QdszkGmmnFzOqLryaEZQYlt6V50PTnKZTS2NWhmRP/aSm1CD52I872ndnOkN0FDnZP7b1ZY\n1TF6n6xhsq0vVnIs2/pi3NzeSH/XaMMl6hHmGvvYwZKiY+Yc7ETWdfTwAHo0SmTD8zWXHh87qzgb\n6k5ONaIoctW6xVyQyvB/dx4uma4sAjO8tTG41WQT2YIkNiWZbMbN6YiW6oGSf24AFlqqB9m/AKjc\nGnQkI9uEZjWDX756gGdfP4JpWVy9fCYfWLuIOnfxm/xARhtlXEaSMS16Dj0Fo2YwOS4T3uaYNYMB\nox5LFBFMk8BAL6tfz1UXy8FGRI/nrJYePxsJeFy0eZwcTxULRs7wOGviIoLqYgY+4E+x217anMU4\nPDMopGkUIQxtz1GpNehI8sJ0/ceSfP+p3fSEUzQ3uPnorUtYNr9SQ/NyRglkdIjtLLntdWsF/QQL\nc3ZLkgi3zOS1K27lio3PEFi5EjOVqig9rkej5+xbvWlq6JkwYCG7Gk9Jp7pyPHT+HB5+5wg9qSwm\nQzMCj5OHaiiJXo1JeYQatr20sTkdkWUvsrt1TMxgaJu7dZSLqFJh2Uj8osivX9rPb9/KVZHWzfHR\ntKSJA4LBEssqm/tf6U3PT4IRQgAFNEvigDW75DEHzltGNtTEsmA7V/jrK0qPn4vKpMnBNH0Hn4Hs\nbmAoCC86qWtcQePsm0s2MzrZOEWRT18wj4Smk3bKuLO1CRqP5KS2vbSxOd0YKQLWpny8bDbRSJyS\niBKoGxXgHUumL8WRriidSQ25TqZ+aSPOBhdR0ywbTygca0w8NyOJhwSl02ANt4zmcrBz23GymsnS\nFatJbHiuaL9aSI+P/DwdFdo6nqrzjUTXDZ54dCszGt9iwbzjozeaWQb73kQQBBpn31LT61ZL+vhx\nor97GTOr4Zq/gPoVK/D5/Sxonp4eDie77aXNGcYoMbozPEg8krwI2IHOPhKxDL56Fws6mlmz9vcx\nzfS4dQZCGU+OqRlk9saIHksgigLNCwNIc30I4uhZwMh4wlj8DpmAQyKiGUXbRLkBRCeYo/3HXlL4\nSJLAV3SMlDYQMzkD07mzh2P1C2i96H3MOroZeeAYrkDDlKXHy3+eCxGrlNGYzvOV4olHtxLui7H6\nguLZYJ7EwG4Cs244aS4jM5Mhc/wYR/7hS2AMf/9xoA+QZ82i6RtfL3v8VJho20vINbOfbNtLm9OY\nkW9hokRJMbo7zruRRDZ5xhuHjRv2sWPzcMO+RCxTWL5q3eJCsLgUWcNkT3Rw1DrLssj0poh3hjGy\nJnNb63jfjR08dqKvZAQgH08oVYfglETOD/pLSloojUHqxOUM9m0etd4hGMzK9tDpLDYGnr4U4oiA\ndCKWIYGf/Q3X42uXmb+4mStv7ECYwkN2vM+zFGPf+lPJLP29gzS11rFl46EJn28ipJJZBkKD1HlT\nyHL5mZilxzG0OKKrUoxn6liGwYlHf0Di7W1YifLJCbG+Y/zy7z7HRZ/8c3wlvuupUG3by/XASnJz\n508D1QmS2Jy2jPxDlCSh6C1Mb4mxufHVwitwXoxu07E3yZrZM1qpVNMMDnT2ldx2sLOPy649r6JL\nYmwA2cgYxNQBMqE0kmiybvEhVs7sRzt8gEDdJYS14ofNeKJ1lSQtXn3+PMzBY7S19uN2Z8hknZzo\naSLV6cK3ME6q2YPhlpDSBp6+FIG9pdNUARIJnZ3bTiBI0qQfspU+z327e1l8/gwaW+oKn2mpt35d\nM0inhj/TciocB9TQuN9PNfT3Dpbr41OEoYNjEt4zTTOIRVJgQX3QU3bMRirFvj/9NOjlY1C6CD+7\nMUB/wIElZvjZK3/PTN9MPrvqD3HKJ68C+RlVVW8BDo9Y9ya57CKbM4xSf4gut0x/7/CbbiKWgZiL\ntsQSTszbPer4jJkBzmyl0mQim7vHEiTiGZKJLA1BT+nt2QQnEsfwyToxTSZ1fJB4VwRLt5gXjHLn\nsk5IuJkAACAASURBVC6a6tJDe3eyIm7wkvvSovMsaairKC9RTtJC0wwOdg4wmFiE2rUAtytLOuPE\nNHMPmmBXjIZ9cUyXiJgxR80IKjHWCE7EVx+JJejPDOAQ3Yjm6H2TgxpPPLoVh1NEubCNK29YVHIW\nMZZyD+pEPFvx+6mWptY6BAGSKQ+GIVacHSSivbjrqp8ZmKbJqy/sZc/24+ha7kZkp8iSofsf6+Y6\n8Oefq2gIIGcI+pqGH/omFkcTx/ja1m/zl5d+puqxVaKsMVAU5T7gC8A8RVEOj9jkBMbXVrU5LSn1\nh1juwegPz6Bntoollf9DOROVSr0+J756V8n79vldeEuU/mf1LF/b+m2OJ05gYmKmvWgHV2DGGhAk\ngXVLD7NmzmHGhAY44QiWHINVpf7cWEmLZCLLYCIXLzBNiWSq+KEomhZiqjjeUIm8EfQ3uKr21ef7\nWrwd2kV4eRg546E+PIOZR5YgjGmvrmVNdm45hmVaHNo3eceCIIDTNfWZqMfrpLGljv7eQY4ea2Xe\n3NKPNMsCf3C4S1s1RvJ3z3fxzrbRAWl96P4FQRg1A8v292MOVq5ZSTqhP1A6ZnE8cYJENlETl1HZ\nVxNVVX8CnA88Blw94ucScnEDmzOMStP5Ujizbhxa5VyB/nSYgXT5rJrTEYdDYkFHaQXR+R3NOBwS\nmmYQDafQhoK4X9v6bY4mjmFYJvqJeWR2rsGMNSAHIiy43F/SEGiWRLdUWi1UjQySnUTWUN6QjUeh\nE9ZHV7H4/PHrBvJGMP+ykDeUeV/9xg37io7J97UIZ8I54TR3ioGZBzk+Z0/Z6xzo6i/78lENlgXZ\nzMQMXTnu+sgqmlrr2LVnMelM6fdizQjg8vgxTZNX1nfx2CNv8NOHX+exR97glfVdmObwd2iaJr99\nVi0yBP+fvTePj6O+7/+fM3tfklbSytZh+fbHAtvY5jA2BhswNwklhHAEyNU2+aXtt82vSVrStE4o\nKd+StGnSJG36S5uGBBICuUg4zWmDMYdtjI3tkW9bsqxrpdXuau+Z3x+rlVbaQyNpJfmY5+PBA+/O\nauYzs7Pz/nzex+udzRGlc/CeAog0K6OOs6vCglbgSa2i0hoqzdy8qJtIUZSUEOKPgetJGwYNeJ90\n3YHBGUYx90g+4tYoCUt01M+91vI6d4hbJzK0KWfNVfOBtHskFIzh9tiYs6iaS9fP4/UXDwybGdcu\nctMmn0KNuIgfXooWrgBzHMucXViq2rn7vMuINdtx2Idfq34chDRnXl2ZYgHkYmQMWfbqLsOMujKu\n+8h59HRFqKpxDapZ+m5ejN1p4WhzF+FQP05Hul60P+IYdC/NGTCOhSYLR0a4kYr1tQh621FbRY7L\nCNL3YMHaPh24y/Kv3MaD2WziY5++mHAoxubnK2iofgmPux9JShudRKqCuSs/B+gLkG99+RAfFDEE\nkOvmciwSOZ9JmCDsMOGKpLCkoLo3gaSClmchIiNT7545rvMfiZ5sov8G5pAWqpOArwK3k9YsMjiD\nKOYeyUf5LBOVrnL80V6ssoWYmlsOD7Cnaz+3LoifUa6ijO7LqnXzhi37X3/xQM6Pfvv+VmLlc0me\nnA+ajKmyDcvsvUiWBBqQkILE1UYcNA87hpMI9mSMqCXXlaO361k+sg1ZMBjD5bIxZ1EVt965gm5/\nGJd7aDWXqaq96FI7C+eeJNbXjKalC6uSSZn2zjpU22WsuWo+wUBhl2GoL8bm55u58kaBLMtF+1ok\nbBESlii2WK7OvtNtTRuEcTJ3YOVWSlxuGzfctoJEYhm9Xd2oyS4qqmdhc6Tl2PUkHEBhQ5qN02UZ\nZsysVVXIbjdqKIQqwZYVLg7V2wm6ZDxhldltURaciOPtS+D35v6+at0zS5ZVpOdubFIUZTACJoSQ\ngG0lObrBlFJsVllV4yIeTQ6bJa+5aj6x1BU8ceAp9nU3FzQG/ljPGdvUxmIxDc7S8v3oQ2gcC3lJ\nhirBEsU65wNM3s7B7ZmZmePSj/Lupkcp95zCbo8Rjdo41VGFWU1CY+5xR+t6VoxChkwe2J+qJkjG\nAwQ73yLs351Tk5DJ1LFYVBrqWnD7jiDLAqfbistjJRzM/z0372nHZjezdsPCon0tXLgLuhdnz69k\n3y59bo1F59fQdiKQc09OFhaLCV9tDTDcraYn4QDyB8JHks+YzX3omxy5/0u8tkjjvcVDBjToMbHH\n42LPQme6mY0KqiyhoiEjU+ueyRdXfn6MZ1kYPcagVQhhVxQlswa2AQdLNgKDKaWQe2TNVfNJpbSc\n4NgfDm7irVPbi+5TRsZhPvPFzbJ/9Ck0TqINZEpIWL2nkOfuQTIPz/rInpldfO29vLFpH2372ujx\nS7jcTtYu8hCoKWN/b7/urmd6yTZkAJqawt/yHJFehVRCfxwn0qug1l2NxWLB7rAUNAYwNBO2Wgr3\ntbi44QJmqrNQdp8abM6SySZasrJelzHwlNm44vq0C2WyKpD1ojfhYLRVd2WNi7XX5KbvmhwOGv/l\nXzm29WFI5EkDlqR0TAaoMlfx+bX34E6WTX2dAWnX0CEhxBukA86rgN1CiEcAFEW5r6QjMphUCs0q\n09sY9nAp5hfORkUlkoyV/OacajI/+pN9UY6iESM985mDRG1qFifK2zkVbkdFzTszk2WZy687n0Ri\ncc61va5Bf5P68dLS/AdCnW+P+e+S8QDJWADJ7CUWLZ7imJ16W6yvhUmYuHT9fPp6IiANdO4aCMwX\nW31kmJM1g55oGul4yC6AczitBVfU2eMs9BmzWUYsm8naDblppRkCsSC9idElJrriXXzlX9/nW59b\nn87rLCF6jMFvBv7L8PvSDsFgOhg5q8xHMb9wNpW207vdZSyRIhCKUe62YSsyu0yqGm02GWUgujkD\nqEfChMS8RTO5b9UXCMVDtIZOUV/EV6vn2pYaVU3Q2zG64c5HJGLjnTc6WXKha1RXR/ZMeLS+FhaL\niaqa4dfIYjExT/jyPjQhvSKYbHdQMTJ6Rf7OdFFaJivrj+5ZAeRfUWcYuep2ua3UzfZyyfq5RBIq\niZSGrcA8oJjbbRgSyI4gX/zeVv7nb68qyTln0FOB/BMhxBJggaIovxVCVCiKMvoTwuC0JjtfGvIv\nxfXeoMt8S07L4HFKVXn85YPsbO7E3xejsszGikU+7rhqAaaBGVqmbeXR4wEe29RMTzCG12Fhviwj\n9ydyfvRuqxtRuUD/GEZ0RKsYpQvauM81ESQeHd/P8lRHFS1tvaxcYxrV1TEnj897rH0t8rkqGxdU\nsfTCBtxltmlzB0Faryi7AFPT0tXKv/3ZTj726YvzrqgzjFx125wWfr3lMA88sr3g/ZehWDvZYWig\nRtITr4MtPSxoyF/HMh70VCB/AbiL9Ir5t8DfCyF6FEV5sGSjMJgyRlYgW6wyGumimJEFRpkb9LVj\nW7Ek7CQs0WEFaFV275S3u4x3dxNpVnAsElirij+AHn/5IC++O5QF3d0XG3x9x9ULefZ4F7vbA5z4\noItoez+SLPGhy+bwoTVzSMSSw9wE42VkR7RiXdAmgsniwWqvIB4dZWY5gKals4laTs5kf/N8IEY8\nliro6rBYZRYvqy3JjL2Yq3I6yegV5cPfGSbSH8fhtI666susDB97sbng/Xf3hkU5f3fTnOt5fddJ\n4q42JFskrySH2u+BZPp+fHF7y9QaA9KG4FLgpYHXXyKdZmoYgzOQkfnSmQAf5OZOq6rKzGOLWbrX\nRSoik7BGiFf30nCRgytnX0alvWJKVgSxRAp/Zy/hhzeiZol4yW43cx/6JiZH7o8zGk+ys7kz532A\nnc1dOOeV8doH7fQ196AlVCxlVsqavJgbXWx7pTRqmcU6ohVTLR0PsmyhouZ8Oo7nziyzpR2SSWjv\n9HHoyCz6+4fqDDwD+fs5rg6PjbpGL2uvWYDNVlr9/OlwpxWjmF5RZoXQMEff/R5LpIref7etm5/j\nsgyFk4QOLUKT5yNZ+rHM34XsDA3WZaj9HuJ7Vw1+fkZFfkXd8aLn2w0qiqIKkY7sD/x77KWTBtOO\n3grkTMbIW68dZs/2k4AJCbDGnVhPOpl1sp6ZOqpaJ0q2m+fu9x7BpQ53X6ihEEfu/xIL/u17OX/b\n0xfDX8Dd0R2M8sJLh4l2R0GW8CyswDnLjSRJvNfWR+WOk4OaPhNRyyzWEW28RWfFaFh0M6FQjEhA\nQUsFSaSctLR4ON5ShyypqKo8rNAsm2z3z+k4a58KMnpF+QyCJKW36yUQKnz/9QSjBEIxarzDH+bl\nbhuVZTa6+2JoMQ/xvWvBHEd2BNOuoeTwe+WK5XW6x6MHPcbgkBBiI+AVQnwEuAPYW9JRGEwJeiuQ\nQ8EY/q4Qh/fnn9noUfYsBRk3jzsewqnmH7caChHv7s5xGXnLhn5YOWgQ7Y5i9dooW1yJ2Tn0M4hI\nWlrkbYS2z3jOuVhHtIkUneVDVVWef2ofe3dV0B9aSmUV1DbWIrvMSHJ3ujjNbWX+eRVYLDInDvUU\nzd+fjFn7ZDaq0YOqJkglgpgsnrz9CbL1ikZS6RubuzD7wT4Sr8dOuTs3FdtmMbFikY/XdhzDbYsT\nillJJK2owVx3qNNuoqq8tN+Pnrvxz4C/BFpJt7rcAvygpKMwmBL0ViCbLTLPPrmHSDiRd/toyp6l\nIHuZ3ZinFWU2kWYF6+o1w96zW81cXGfleMtejttnEBqR+eOeV4ZrThnSCMdsdiOYbMZzzlaTTFOF\nO29fgokUneXjjZcODqziAEx0dUJXZwdLLqzjjj+5JOchPJUP5qloVFMMTVPpaX1hsP7CZCnHUSHw\n1l+b09LyI/etzJtN9JH7Vo7pmJkHe3bMIMOKRdV5s9o0TeU6cZiLKvZglfsJRGwc7Kpg76lqTgVd\nRAZWBhazzP/93OoxjUcPeoxBCnhLUZRvAQghPsRgo9DxIYRwAHuAfyQdi/gp6TbebcC9iqKMX8nK\noCDFKpCzScTVYbGEkRRS9iwl2cvs4/YZRT87Ut8lFYnw1j1/wfJgkKVIvFvRxJbK5SRl86AbQDJJ\nOYYAwBOII6saqpwiYYliSaRlmcd7zsX6EpSKRCKFsjt/IZey+xSXrp+fY8SkaD/mUy1IDQ1gmdy0\n4OGGasj1pmkal1+TG0gtNT2tLwyrv0glAoOvR7a0zOgVjawzGA93XJXOOtvZ3EVPMIrXY2fFourB\n9/ONM9z1NvYBO+F1xri4sZ2LZrWjatAedPHfb19AIglPbTnC3dfk6hpNBL2dzrpIrwgArgI+Anxq\nAsf9KkMNch4Avq8oyhNCiH8CPg38xwT2bVCEkQFCsyU9M0oMZBNFowmSRQwB5E8vLDXZy+yQ1U2/\nbMuJGUA6iDzSRXTk/i+hhkJ0Wit4pmYNbfZqnMkIN/Vu53eV6QBc8GC6QtdW7cBkN5OKJol1RbjM\nYmPbrL30edtJ2iKDsszXzLha1zmPnHEX6ktQSvp6IwWNdyKu0tcbocqXXhmp8TjHH3qQeGsLqCrI\nMtb6Bhrv/yqytfQGXo+hmsx7SVUTRHrzK4NmKq8LuYz0BosLYZJl7t6wiNvWzR+1ziWVihHufi/v\nNkkCkwR15WE+c8ku/mvbCrbtPsVt6xcUrZsZK3qMwSJFUf4k80JRlC8IIV4b7wGFEIuBJuDpgbfW\nA58b+PfvgS9iGINJI19aH6TjCclEil/+z7sF/9bltjFv8dQUBI1cZv9g9q18/thvBmMHEkPZRNnE\nu7tJhPrZWnkBb3qXoEomzg8e5urOd3CqMbbXraQlagENggcCBA/1YbLJpGIqNpNE340n8LcdHdxf\nRpa5vX4/UHgmNporZGRfgpIymgJo1vbjDz1I/ERWexJVJX7iOMcfepA5Gx8o+dDGYqgmg1QiWFCa\nI5UITElLS5vFlBMsHklPy7NoBbS/spnhCeMwxwnFrXT3Rqgr4bXTYwwcQohKRVH8AEKIOtI1B+Pl\nW8CfA58ceO3Kcgt1APkF4A1KysgAYbnXQSKRKhhTcLmt3P7pCyeUcz9Whi+z4bHln+DiOgvX+GK4\nFjflrTPYt203j866iS6bF08izHWd21jQP+QWu8wR4PFolotG1UgNBIsvXVHDXv+beceyu3svt6Ru\nKJhKO54ewKWizOvAbJXzrugsVpmyge85GQymVwR5iLe2kAwGMXtK7DIag6EaD4l4kkBPpGDsQzLZ\nKayZLQ1sn15UNUG076iuz8oSzPD0c7LHipQsTV+HDHqMwQPABwPdzkxAHfCZ8RxMCHEfsFlRlKOZ\nVNUR6CrJ9HqdmM3jXx75fKevdMJkofecz7ugjre3HMl5//wV9TTOnnpV0r+860Ki8SQ9fTG8ZTbs\n1vy3bDSe5NHn9vO7PSqazcuKgML6rh3YtOHhrRtvWUX4oMpL75wgEktn+ditMhsumc2HrprBF57L\nX8XbE+3F5FbxuXOvYyKe5PhA9y5ZTWJL9RMzOVFlM8rudm78o6XYHPk7VZWKFZc08s7rR3PeX35J\nI3V1FQD0th1Nu4byoao4Qt1UzCttumJFuQOrzVSwIc3BfR0sWjxjUHFVL7FIgmd/u4ejB7voC0Rx\ne2zMXVDFjR9Zis0xZLBj/TFOFrQ4GpXlJmzO6X0eRIMdtCT6dD39VA26AlYqgBm+spI+y/TIUfxB\nCDGPoeY2+xVF6R/n8W4C5g2kqDYAMSAkhHAoihIB6oGTxXYA0NMz3sOnH4qdnaMLQp1NjOWcV6ye\nRSQSz9FgWbF61rReNzMQDETIN4J9x3r432f30dkbpabCzoY9vyuYgZTwzuQja23ctKqRzt4IaBo+\nrxObxUQ8Ei8ov+G1V5AKyXRGckcQ6InQ5w+zsOsdqsMnsCdDRM1uulyzOFh9Mb/5xU6uvrlpgleg\nOCvXNCJJEnvfayUUjOP2WJkrfKxc0zj4vSXdVWk1wnwGQZaJuKtITMJ3vGjJjGEB5Gx2vHmcRCKl\ne/WUccftf78tt2Byx0k+eO8k562oG+w1rKoysrkMNZmrBiqby+kNysjh6X0enPzFk/TXyzjdOsq3\nuqLce+gZ+srmkEyuHfNvspjx0CNH4QW+AtQqinKPEOJDQohtiqLkT0IvgqIod2Tt92vAUWANcBvw\ns4H/PzfW/RqUjtNVKiAf/dEEv3zlEJt3nUSS4IZVjdyydi7+nzfTtznXGJRdsR7ZlvZw2iwmGkb4\nW4vpwyytPr+gi8jpttLUt4PawL6h95IhGgdenzx2OYlEalKvoyzLXP9HS1h2SUPB783s8WCtbxge\nMxjAWt9QehfRAJdcMY/9u08VTEw4onTqruEY6Y4biaoyrNewLFtwehfnVXO1ly3KGzyeStRYjKf2\nxfDaZrLanWswVW1gwaBqqN0xEr86SbkK5T176fn148y4+96SjUWPm+hHwGukH9qQjhf8BLixRGPY\nCDwihPgscGxg3wbTzOkmFTCSnQc6+enzCr2hOA0+F5+6sYm5tWWosRjea67D6bbT8eZbqD09yF4v\nZRdehO/2O0fdbzFZ5kKY1CTVodwHLEB1+ASH+/onvS4jw2jfW+P9Xy2YTTRZRPvTGWojXWgZRraC\nLMRYenhnt+n01l8LGvSc2oNJ7h9sPHTwVQdiSfPgKmI66O/uYb/ZR6g5PTERNd2U22MEojaUjiq2\nHpzBn4aew9IdgehwY9q39Q18t31scIIzUfQYA5+iKN8VIt3kVlGUJ4UQfz7RAyuK8rWsl9dMdH8G\n5wZ94TiPvdjM2/s6MJskbr18LjdcOhsTGh2/eJTg9u2kevxYq6vwLF9BxdXXYPFW6v7BjCbLnI9k\nIIAlmn+5bk+G8NqTk16XoRfZamXOxgdIBoPEWlqwNUzeiiCDw2HivMC7lPcczXGhaZKM22PVdX3G\n0sM7u0hQ0+DFTVX0dK3AbosTjVkHJTmyVxHTQchkp8/sAk3ieWUeLx+YPVR9rJqojvVgbs0vnqdF\no8Q7O7E3NJRkLLrq4YUQFgbC8UKIGYB+kQ4DgwmQkZh2m03sUDr5+YsHCEUSzK8r45M3NlFfnb4V\n23/2CIFXXx76u65u4q+8DBrMuGfs/ZfGIstsLi/HXFlJsrs7Z1vU7Ka2qfG0c7WZPR7MTZMbx1Bj\nMZKBAD2bnqO2c8/g+9kutAO+VcwVPl3XZyw9vLOLBF/fdGBAYsJEfyR39TEWN1Wp8VZ68JpVelID\n9T6qiZ6sMY6WbFU6EXR9xuB7wDtArRDiKeAS0vIUBmco060Ro4fsPgDdfVH6m3sJdUawWmTuunoh\nV1/YgCynfwpqLEZgS/7Sl8CW1/DdfkfJltL5kG023CtW0vvippxt2rwm1ly7eNKOfTqipVJ0PvEL\nQjt3kPT7yavFDPjCJ7BtuFl33YreCnoY6jWcSKQ4ciDXSGej1001GdgsJi68YDYv7sh/TgGLh5hk\nxq7l6ltJdjsWX+lk0PVkE/1SCLEVWE06++eziqK0lWwEBlPGdGvEFB3bwCzSXF6ObLPx7PEu3mjv\nIdIaJniwFy2lYa20ceVlc7jm/PphfxtrbYVUgZzrVIpYayuOefMmdfyZeERo506Sfj9yhRf38hUs\nvPMupNPg2sY7O9EScSSLFavPN6nGsfOJXww3jAV0oR2pfpou9I3p3htZQW+xph/42oA7PdNrOfO5\n/lB8sGF9IfS6qSaLOzYsBFkabMKkoQISaJCUTOz2zOfivtwq6vI1a0v6PerJJnpSUZSPAk+U7KgG\n08J0FkYVYuQs0lxZieOCFexqvAj/nm4SvTEks0RZkxdHrYvjyTjxlDpM0iEZKp5eN9r2UiCZTNTc\n+XGqb/3oMKM2nWipFB2PP0bf1jfQotGhDTYb5ZetpeaOu5FM418Zdj73DIEXX6R8wwZ816fzSdRY\njNDOHbr+3lxZibm8fEzHHJntNntOJZ1doZxeyxn0uJYaF1YRTPZxoOsICyvmUukoXcMYPYyUrXjx\n5CY2H38HqbeSMnMH286Xkd+r5nx/GHs4is1XjWPZcl0JEWNBj5vooBDi06Qb2gyaWEVRDpd0JAaT\nSrFMjMmSpI6n4jlB2JErgJGzyHi3n9d3tHHkeAsqMjafgzLhxWRLjy1fHwDH3OKz/tG252O8rjTZ\nZsNaM/m9HvTQ+cQvCLz8Uu6GWIzAyy8hyTI1d358zPvta1Y49fBDg697nvwlPU/+kplfvh97hTft\nGtKBe8WKcRvMTNaUxWrO22s5+3OFXEsqKfw1x/ml6Vl++ebQ6sVldvH11V/GYZlat1FGtuKO8pux\nWCTe79yDP6ZSaSvDfOcSmmZtQAuGmLmgAX/f6NIVY0WPMbgjz3saMLnrboOSUiwTo9SS1Ck1xa8P\nPs37nR/QE+vFa6tgWXUTl+8M0T/gRjFXVuJcspTgtm2Df9du9fJszWpO2atxpGI4lszAUusepi6a\nrw+A2ePB2tBAvCVXasE6IltmtCyaqXKlTXY2jxqLEdyxvehngjt2UH3rR8f8QM42BCPfX/D9HxYM\npiPLoGmYK6twr1hR8pltITIuow/2HifVL5GwxAiXdXNy9gdo5lz3YjgZZuOb/8zDV3ytZGNIJFKc\nbOmlq62PeYt8uMsd9Ifig9XZ2ZOOohltDicmm42seXnJ0BMzmFvyoxpMOcWWy6WWpP71waeHFW75\nYz282rqVXn+Ydd3pNLlkdzd9r72a/jcyWyuXsc27BFWSWdJ3iKu63+XZVZ8hOCL4WKgPQONX/qFo\n/rxetc7JdqVNlWpoMhAgNcoMPdXjJxkIjGkl0/ncM0W3d7/yUsFgevkV6/Fee/2Uu9BkWeaSK2fz\ngvXXBEORnF7e+Qgn+/FHeibsMlJVlc0vKOx7b6gI8u3Nx3I+Z7LAeRfUD5t0jCWjrRRMb2TLYMrI\nLJfzUUpJ6ngqzvudH+TddrjeRmLEYVrt1fy48Wa2Vi7DneznYydf5OaONyircLNsTj1eqxkJ8FrN\nrKmpyOkDoMZixDs6QNOYs/EB5v3Ld6j/67/h4p/8N3M2PjD4gB1U68xIMWSpdWYYzZWWSExcGEzP\nOEqBubwcU2VxNU6Td+w++8CLL4663Xf7nVRsuAZzVTXIMuaqaio2XEPNXR/HWlMzLbGUQCyIP+kn\nbu8f1RBk2Oc/MOHjbn350DBDUIhUAna/28rLz+T/7QCEEkkOBcIEC7RSnSil7XBtcFozMhOjUMvD\niRCIBemJ5Rd7C7pMhB0mKkIp4pKZzVXLebe8CSSJlb37Wde9A9tACp1DNHHTgnquGagzGNkHYGTg\nWfZ4cK1YTvVtH8YhFmAtK4OMJo9Otc7JdqXljMMsITlNaP2pkquGyjYbnpUX5p2hZ/CsXDnmB3P5\nhg30PPnLottPx2B6uc1TUHeqEDMmOCtPJFIc3De6IcjmwJ5u1l03XLokrqr8cO8J2iNxVEBuPskM\nh5XPnjcLawldl4YxOIcopjtUqtqDYj86TziFK5LiiKOW52ouJWDxUBkPcEPHm8yKdgx+TrLbqbnz\nboCCfQCGBZ4lkJaaic06wsl938dsLSfeswxb5XokSSbW0lJUrTPW0oK5qWnSXWmD45DAtKYS01wX\nkseMFkySOhImeuI47vPOn9AxsvHdfieaqubJJrJTftll4/LZ+66/Mb8xGDBsVdcOiQmcTsH0YrpT\nhWgsmzWhY/aH4kTCY5/FN+85xfkrhtKnf7j3BG2RoRiBCrRF4vxw7wn+YsnsCY0xGz2ppbOBfwGq\nFEW5UgjxJ8CriqJMfA1lMC1k69eUOmBa7EfX2KKyqWo175ctRNJULu3ZzVr/Lsza8Ad1+drLMTkL\nNwMZmb5oWlOJZXnF4OtUso+O46/jjsSpbLgeW0NDUbVO20A5f7HMk1K40jLjMK2uGDZeqdyCvLyC\nmP0wbkpnDCSTiRl334vvto+VtM5g5pfvTweRzRJSmRnTinLkOgdymYW2vT8o2F94usnoS6WzdPKv\nXjNcXnvpqDIko+F0W3G4zGM2CIeVjkFjEEokaY/kDxa3R+KEEkncltLM6fXs5f8jXYX81wOvXH8r\nugAAIABJREFUFeC/gCtLMgKDaWUyAqaZH917R98iIMfxhFOUH61ib/ACwmVOamJ+buzYysyYH8+l\na4gcaCbZ48fsrdSVZZIMBIbSF80Sprn51VEivc2odVePSa1zMl1pZo8Ha2MDzM1fhNUfOoRXTZRc\nSVO22UqmXwPgWbiQxMY7CLe/iyarw7K9ivUX1kMskRq1ReR4GZmlYzWZ+e3BZ3mvcw/xgS5jVsnC\npXWX8NGFN0/4eBaLiQVNM3RVTGez9KKh76q9P0ahCIc6sN1dPnXGwKIoylNCiC8AKIqyuUBjGoMz\njMmqPTDJJm6bfR3LfvwabbEo25wX0uyeg0lOsa57B5f0fIAJDXNVNTPu/QRqPD6mNEtzeTmyx4Pa\n14fkNCF58t/GqUSA6Kmj2Ksadat1TraEd91f/Rltyn/m3aal+qakDeNE8bc8T7jrHTCBVEAdJ2OI\n9Rq2lKry+MsHB6twK8tsrFjk446rFmAqcQV3dpbOJ86/k7tScboi3WiahM9ZOeEVQTZrrppPMpnU\nFUTOMGfBkMTEDKcNGfIaBHlge6nQK1RXwZBQ3fnA6attbKCb8QZMVTVBKhHEZPEU/LEnenvZE63g\npeqLiJps1Ec6uLFjK1WJoSYj7guW0/WbJ4dVH7tXrMR3+51FK2Nlmw338pX0bX4VrT+FFkwileeO\nQwsmafnhP2EuT+939lc3kurv12V4Jk3C22pHkgvLi2nS6akVlUFVE4S7d436ubH2F3785YOD/a4B\nuvtivPhuC/3RJPdeJ0q+SsjGarJS556cbruyLLP++iYuu3oRJ1t6aW/p4+C+dgL+aN7P3/W5VcNe\nuy1mZjisw2IGGWY4rCVzEYH+tpfbSAvVvQ9UA/eUbAQG08ZYA6aaptLT+gKRXoVUIoDJUp7XP9wV\niPDIK63smXEZFjXBNZ1vsTKgDM0hZZnydevR0IalKia7uweDwqNVxs74+L2EdryLGgqROhJGzvLB\nD+7vUBCSWs5+J1utsxip2Ci5/zE/WMeW7jmVJGN+0EYveDJZyjFZ9GVGxRIpdjbn75W1dc8plOM9\n41olZDSZUv1h1GgMx9y5ky7XXQiLxcTsuVXMnlvFJZfPJdIfp6OtD39XPyeO+Fl2Uf2wFUE2nz1v\n1vBsIhjMJioleorOXhFCrACWkBaqa1YUJb9ZMzijsFhMzJnvZc/OUznb8gVMe1pfGNYxaqR/WNU0\nXtnRypOvHSIWT7HQHuPq/X+gIjlcj738ivX4PnoHR//hK3nHFdq5c9TKWMlkYt7D/8qxbzxA4s20\nTzaTnZMKJ0kdDqNtHf7g1bPfwc/GQ7SGTlHvnonbml/qYDxYHDMo1qA9vf10Rp9osqNCfxexkz09\ndBfRDsqsEgDu3rBo1P1pqRTtj/6Uvm1bIT7ccFkaZjH7K39f0gK/8eBwWpk9v5rZ82HFqsZh24L9\ncVo6QlS4renmTTVu/mLJ7HQwuT/G+Y3VRAORko9JTzbRFcBnFEX5xMDrTUKIf1QUZXPJR2MwZWTy\n9Gt37qRXmkuXZw5RkxNPmT1vwFRVE0R6c5UTIe0fPmlbzU+eP8iBlgAuu5l7bmpidZOPrif9aSXP\nEQHiRHd3QQ2bpM7KWNlqZe7XHyQZDNJ/4jjPJ97n4MmdnLJoOLwS81e4uHxnGHnguZv0d4+633gy\nzrd2/IC20ClUVGRkat0z+eLKz2M1T/wBYjY7MdtrSObp0Wy212A2F86iOh0w27xIshVNzb86kGQr\nrqrl6e5iAxQKCseTcb65/fu09rWDdS3Ei5/7zuYubls3v6jLSEulOPbg1/MmCwAkWk5w/KEHmbPx\ngaLHmg7iySQP/mQ7rZ3hYVMFCWiocfN3961kfrkLj9XMZMzG9biJ/gn4ZNbrzwI/BS6bhPEYkF/g\nrdRk5+kLuljQvYOYyUnNFaup27A65/OpRJBUIpD7viqxRXHz2rM7SKY0LlxUxT3XLqbcnZ5919z5\ncSpvuYV4TxtWby1mR3qWXawhjFlnZezgdXJ6eN58iFdP7Uw3ZUUi6DHx3uJ0ltG6HemViam8YtT9\nfmvHD2gNDfWiVVFpDZ3kWzt+wFcu+atRx6SHmeIznFL+m2S0g/QKQcJsr2Gm+ExJ9j+ZyLIFV+Vy\nQl25PYVN1ipmLv5jTKb0dz9aUPhbO37AyXAbkglM3nZS7cWVb3qCUQKhGDXewkaj4xePFjQEGeIt\nJ0pa4FcKUqrKF7+/lVAkNw1VA050hHjwke088OlVuX9cIvQYA0lRlIOZF4qiHBZCTLwu3yCHvAJv\nvnT/XZNcugBaPplhk5bCmQwS3bUD9bbbkG22YQqjJosHk6V8mEFo63Pxuz0LORV047bFuXHJQZbO\nSpLqbUNzpWeGw2IM/qEYQ7GGMKOpWY68ThW2cvoT+ZfNh+ttrNkVxpIafb+heIi2UK7LDKAtdIpQ\nPFQSl5Esm6lr+izJZD+JSDsWx4zTfkWQjbfhWpAY+F77kM1unBUCb8P1w2JHhYLCAB++oo620FBb\nFEtjMwAp/wxIOMjnjrKYZdzOwpMj3fLZmjZYaHi68NMXlLyGIJuWjjDB/jila2czHD3G4LgQ4p+B\nV0nHLq4HTkzSeM5p8gq8Dby+fdGHS3acYXn6I7f1+En4/QReezkny8e+ZiHh7ndJpCQ2H27k9SMN\naJrE8vp2rhOHcVhSpBIMiysUizEMawgzhjqDkdepkPwFDElg1Hjr8wals2MDrQOuoXykVwinEJUL\nio5tLJjNTsyeM08HUpLkdIyo7uqCWWXFgsI7m7tYtgzULGeIJGlYZytoDQeIfnApRMty/i6WUPn1\n5kPcc03+1PZkIECqt3gx2cDBBgsNTwdiiRTvNRfvxpbhhbdPcFdV6WJY2egxBp8Cvgh8nvSKZSvw\nN5MymnOYYgJvu7s+4Jb515fMZTSai6b3pU3D+glnsnHecjURNTvYf6CJnn4nHnuUG887QJMv133U\n36MgSfmLqwZz0E2WMWvYFLtO+fBEoe7iy6m/895h6ar5YgM1Th8y0rCHVAYZmXr3TN3HPReQZUvB\n1NFAKIa/QFC4JxjFRWXBa02q8GPpjffbuH39grxxA3N5Oeaqqvzy2VlYG2adVi6iQChGX78+Seqn\ntx3j6W3HqK928vefvAiruXSppaPmaSmKElUU5UHgw8AfAQ/DpMQvzmmKCbz5o70EYqXr1pWSzbDk\nYlJ5ctpdy5YR3p2bRx42m9nW5uPNXSvp6XfgnHmMsqWvI6rzj1lNBkhl1RQMO368l1R0yIBkNGz0\nZPkUu075WDl/NQ0f/2RO3UImNpBZCaionOpvRy6Q5z/DVUMkGcMf6UHxHyQUD+kew3QTT6l0R9Md\n4qaKcreNyrL836fXY6fO66U2T26/lrANuInyE0uodPb0592WcT0Ww9IwK6fQcLopd9uoKnCtCtHa\n1c83HtHXUU4verKJvgT8HZAxpZm8uNO7OuYMo5jAW6W9gnLbxGcyw3WIZuJYdBe+0HHmt72B1evF\nvWIF5euvIvDqK8P+7rCzjmdmrCbidyHZQ1jm7kHz9NIH9KkWKvIUiElmDxoaJHMfmmpfAv/vn2XG\nHfeO+RyKXSebyYbL7KAnFsDnrOS8yqZBaYxsisUGklqSWtcM2sOdqKhISDjNTqLJCF/b9s9D54dE\nnbu2ZFlGk0FK03j2eBd7e4L0JlJUWEyc5/VwQ2M1pgJN6kuFzWJixSLfsJhBhhWLqrFZTHxx5ef5\n5vbvczI8FDswWRLYbCqxWJHHS5GxD+9F3Y1cVo5j/nw8a9binL/gtFoRZCh2rYpxoiNEsD+Op0gc\nZSzoWWN8GlimKErxEL3BhCgm8La0+vySuIhG6hBFUmaOO+bhvvkiLr9ODAaNMy6kiGzlpeqL2FO2\nAElTcfkOkpp9GElOzzCTwIF4iosduT/c7eFeUmqKix25404dCRPevwv1jz42ZsG0Ytdpde3Fg7oz\n8+vr6OvJ76YoFhsAuH3hLYMxhO0d7/HGybcZqTWmoZU8y6jUPHOskzc7h1ZgvYkUWzt60TSND82Z\nfDXRO65Kx1d2NnfRE4zi9dhZsah68H2r2crfrfoCoXiIo4EWPFYnte6ZPK4d4ZUd+fV87FYTvorC\nK4fTUT5bD9nXqrsvisUkkUjld7Nm09IRomlOaeRL9BiDA4YhmBoys9jdXR/gj/ZSaa9gafX5eWe3\nY6WYDtGJljAp2YxMeqntumA5b711gE2+VYTNDmZEu7mxYyuHl8zkbXn4Q/SVaBy31U0tETyyTFBV\nORBP8Up0yAe60GqiTJJhQKo5tdUPkjzmLlsZil0nk2zC56zCZraSrpHMpd49Exk5r0HIxAbcVjdz\nyxv52b4nio6llFlGpSSeUtnRnd9Nt6O7j+tmVeftGFdKRjZ6LyQ+57a6WeJbPPj67g0LOdDSS0tH\nOOezly2dqUua4nSSzy5GdsZe9rU61d3Pvz35/qh/31Cg9/N40GMMdgshHiOdTTQ4P1IU5X9KNgoD\nYJTepxNErw5RbyjGzzor+aB2PSY1xfqu7VzSuxcZjYXBlThF07CH8PlVi9nSuY9gPIJblgipGtmT\n6JejcTZHYWZE5cPPdGOJpWc75kr9XbZG1l1M9Dq5rW5q3TOH1RNkqM2qONYTn5iMLKNS4I/Fian5\nZ5YxVcMfizPTaZ+SsWQavevFJMts/OTFPLapmZ0HuugNxanKqlE4GxjZnMlcWYlr2QWUr11HmcmE\nvbq8YJ16hvpqZ8lcRKDPGNSRnmJlVyJpgGEMJonJ6H06mg6Rw2Vhy/snefylA/THHDRE2rmxYyuV\niaHAdf+uXdx22zeGPYQDsSBbWrehAb0FHj5JoNUmEbbIVMTSJSqj5fzDUD3Brs49g3UXF/iWDK4A\nJnKdvrjy8wUrjTPo6Y51+mYZjRYTmNyYwUQxyTL3XreYj101eZLW08mw5kykM/YCr7xM4JV0Fp9k\ntzO3Zh2HrVMnT6JHm+hTQggZqFEUJX/UzeC0p1jjlqrZFfz7r3fzwdEerGaJq7vf4qIeJedxkS0T\nkXkI620n6ImCK5aWrdZTSwDwqwO/57XWrYOve2K9vNryOpqm8jHxR6OfdBGsZitfueSvimoQ6emO\nVVti7aJSUWmzYJOlvKsDmyxRaSttz4TJYqyrijMBPcVx8ViCLqn4ebd29Zc0gDyq01AIcRVwiLSb\nCCHEt4UQE3diG0w5q6+cx6LFCRyOGKBit8dQa5I8tb+dD472YPP2Ip//KnvW+dm80oU6whrIFeWo\nHgenQh28eOw1ToU6Bh+Yo7Fy/moWfv3/MueBb1Bz58eLSlRD2jW0tfWdvNu2ndpOPKUvL3s03FY3\nonJBwQf6RxbcxPqGtVTavMPel5Cod9cNW0mcTlhNMiuqcwu3AFZUl016vMCgMMWKPjOETA76zPmb\nNmVz5GT+uNB40KtNdCnwi4HX3wD+ADxdslEYTAmBtk0snP028xpkWnvKeOFgIy0dZdgsCSzzPkCq\nOokkQR9Sjq4PwPbqKN9+8x8HX//m0NOYJTOfX/Zp9voVuvq7B4OymQBtld07LLirh0giwt9vfYgE\nibzbY6kYXZHukmrQF9KDGhmfMEkynZHukquZTgY3NaaL6Pb2hgjEk5RbzZxX4eaGxurpHto5TbGi\nzwzuVISyZJi+UWTAPc7SrfD0GIOQoijtme5miqJ0CSFKMy0zmDJUNUF/r0JKlXjjaB2vHWokpcmc\nP7OTtYsO81i0h5HKKIcbnax+P0zEJnO43saWlbkPv6SW5Lu7/iv3eKhcWLOMS2ZexJyyhjFpK218\n82EiqeJ1jWosQby/Y8Kpgyk1xWP7f8Xurr2Ek/2UWctY7jufjy788LAxZ8cnKh3eQrs7rTBJEjfP\n9nFtQxXBRBKPxWysCE4DiulyZbBoKRaFTvCu97yCnzHJEnW+qc0miggh1gGSEMIL3MkEKpCFEA8D\nlw8c+yHgHdIqqCagDbhXUZTC4uYG4yKVCHKiK8lTH1xAe9CN2xbjpqZDNM3wk9I03HEpJwAcdMr8\n9pbZdJr6SZrHHnDc3vE+2zveR0aiVmeBlj/SQziZm1KYQVI11r8XIfb8dzg6hu5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SQiDED/TOt+2wtS52mZRpUdgUcqsa/agJ3zuAcRd76eu4mR33ZEY/xSGrC9iThEHTE5ZZ8LrJPl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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdbd79993d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Average seq iden in and out', 0.79647664200254031, 0.60648915632317968, 40)\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fdbd77a9b90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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WSdIkTndIUsEMaUkqmCEtSQUzpCWpYIa0JBXMkJakghnSklQwQ1qSCmZIS1LBDGlJKpgh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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdbd77edb50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Median seq iden in and out', 0.83052071224679525, 0.60353523261498387, 40)\n",
"('Max and min seq iden ', 0.37046378885187348, 0.99875930521091805, 40)\n"
]
},
{
"data": {
"image/png": 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x/drrY8ARzeftZMrwiV2A9YA9sO1aIzNzgkUm21a/m+RyY7ZpbWHvic3tLX3m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\nEWTY0xKLiI2A5zV/9h20GxG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"text/plain": [
"<matplotlib.figure.Figure at 0x7fdbd750fed0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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AzLwHOA3YIiJWbat8Wqipqw8Bb+sEOIDMvIvypXyC9Tn6IuIZlLPF3wvc2TXc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\nyBAnSZJUof8PLgtWIGtHlpAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdbd777f490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Is there a link between CATHdb and Binding mode\n",
"\n",
"for ikl in range(0,2):\n",
" torun = PixelDB\n",
" if ikl == 1:\n",
" print(\"ECR\")\n",
" torun = PixelDBecr\n",
" else:\n",
" print(\"Full\")\n",
" inbm = []\n",
" outbm = []\n",
" for uniid in list(np.unique(torun[\"cluster_number\"])):\n",
" sdf = torun[torun[\"cluster_number\"] == uniid]\n",
" sdf = sdf[sdf[\"mean_seq_iden_in_bm\"] > 0]\n",
" sdf = sdf[sdf[\"mean_seq_iden_not_bm\"] > 0]\n",
" \n",
" \n",
" \n",
" if (len(sdf) == 0):\n",
" continue\n",
" \n",
" #die\n",
" if (len(sdf[\"unique_id\"].value_counts()) == 1):\n",
" continue\n",
" plt.scatter(sdf[\"mean_seq_iden_in_bm\"]*100,sdf[\"mean_seq_iden_not_bm\"]*100)\n",
" inbm.append(np.mean(sdf[\"mean_seq_iden_in_bm\"]))\n",
" outbm.append(np.mean(sdf[\"mean_seq_iden_not_bm\"]))\n",
" #print(inbm[-1],uniid)\n",
" #if uniid == 7:\n",
" # die\n",
" \n",
" plt.xlabel(\"Mean sequence receptor within Binding mode\")\n",
" plt.ylabel(\"Mean sequence receptor not in Binding mode\")\n",
" plt.plot([-5,105],[-5,105])\n",
" plt.xlim((-5,105))\n",
" plt.ylim((-5,105))\n",
" plt.show()\n",
" #break\n",
" print(\"Average seq iden in and out\",np.mean(inbm),np.mean(outbm),len(inbm))\n",
" plt.hist(inbm,20)\n",
" plt.title(\"In binding mode\")\n",
" plt.show()\n",
" plt.hist(outbm,20)\n",
" plt.title(\"Between binding mode\")\n",
" plt.show()\n",
" print(\"Median seq iden in and out\",np.median(inbm),np.median(outbm),len(inbm))\n",
" print(\"Max and min seq iden \",np.min(inbm),np.max(inbm),len(inbm))\n",
" \n",
" figsize(10,10)\n",
" plt.hist(np.array(inbm)*100)\n",
" plt.xlim([0,100])\n",
" plt.xlabel(\"Average sequence idendity in binding mode (%)\", fontsize=18 )\n",
" plt.ylabel(\"Count\", fontsize=18)\n",
" plt.tick_params(axis='both', which='major', labelsize=18)\n",
" plt.show()\n",
" \n",
" plt.hist(np.array(outbm)*100)\n",
" plt.xlim([0,100])\n",
" plt.xlabel(\"Average sequence idendity between binding mode (%)\", fontsize=18 )\n",
" plt.ylabel(\"Count\", fontsize=18)\n",
" plt.tick_params(axis='both', which='major', labelsize=18)\n",
" plt.show()\n",
" \n",
" break\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#sdf[\"unique_id\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Full\n",
"('Unique Binding Mode', 702)\n",
"('Strc Per Binding Mode', 2.8148148148148149, 1.0, 1, 174)\n",
"('Uniprot Per Binding Mode', 1.4800569800569801, 1.0, 0, 31)\n",
"('CATH Per Binding Mode', 1.3190883190883191, 1.0, 0, 7)\n",
"('PFAM Per Binding Mode', 1.1182336182336183, 1.0, 0, 10)\n",
"ECR\n",
"('Unique Binding Mode', 124)\n",
"('Strc Per Binding Mode', 8.4112903225806459, 4.0, 2, 174)\n",
"('Uniprot Per Binding Mode', 2.774193548387097, 2.0, 0, 31)\n",
"('CATH Per Binding Mode', 1.3548387096774193, 1.0, 0, 4)\n",
"('PFAM Per Binding Mode', 1.2096774193548387, 1.0, 0, 10)\n",
"ECRdist\n",
"('Unique Binding Mode', 124)\n",
"('Strc Per Binding Mode', 2.620967741935484, 1.0, 1, 29)\n",
"('Uniprot Per Binding Mode', 1.5241935483870968, 1.0, 0, 14)\n",
"('CATH Per Binding Mode', 1.3306451612903225, 1.0, 0, 4)\n",
"('PFAM Per Binding Mode', 1.0887096774193548, 1.0, 0, 10)\n"
]
}
],
"source": [
"for ikl in range(0,3):\n",
" torun = PixelDB\n",
" if ikl == 0:\n",
" print(\"Full\")\n",
" if ikl == 1:\n",
" torun = PixelDBecr\n",
" print(\"ECR\")\n",
" if ikl == 2:\n",
" torun = PixelDBoecr\n",
" print(\"ECRdist\")\n",
"\n",
" UniquePFAM = []\n",
" UniqueUnip = []\n",
" UniqueCATH = []\n",
" \n",
" UniprotPerBindingMode = []\n",
" CATHPerBindingMode = []\n",
" PFAMPerBindingMode = []\n",
"\n",
" StrPerBindingMode = []\n",
"\n",
" for uniid in list(np.unique(torun[\"unique_id\"])):\n",
" sdf = torun[torun[\"unique_id\"] == uniid]\n",
" #print(sdf[\"sequence_alignment\"])\n",
" \n",
" StrPerBindingMode.append(len(sdf))\n",
" \n",
" Uniuni = []\n",
" for cid in sdf[\"uniprot\"]:\n",
" #print(cid.split(\"_\"))\n",
" if str(cid) != \"nan\":\n",
" Uniuni += cid.split(\"_\")\n",
" UniprotPerBindingMode.append(len(list(set(Uniuni))))\n",
" \n",
" \n",
" CATHuni = []\n",
" for cid in sdf[\"CATH\"]:\n",
" #print(cid.split(\"_\"))\n",
" if str(cid) != \"nan\":\n",
" CATHuni += cid.split(\"_\")\n",
" CATHPerBindingMode.append(len(list(set(CATHuni))))\n",
"\n",
" PFAMuni = []\n",
" for cid in sdf[\"PFAM\"]:\n",
" #print(cid.split(\"_\"))\n",
" if str(cid) != \"nan\":\n",
" PFAMuni += cid.split(\"_\")\n",
" PFAMPerBindingMode.append(len(list(set(PFAMuni))))\n",
"\n",
" #print(\"Binding mode with ECR and Core >= 4\",len(PixelDBecr[\"unique_id\"].value_counts()))\n",
" print(\"Unique Binding Mode\",len(np.unique(torun[\"unique_id\"])))\n",
" print(\"Strc Per Binding Mode\",np.mean(StrPerBindingMode),np.median(StrPerBindingMode),np.min(StrPerBindingMode),np.max(StrPerBindingMode))\n",
" print(\"Uniprot Per Binding Mode\",np.mean(UniprotPerBindingMode),np.median(UniprotPerBindingMode),np.min(UniprotPerBindingMode),np.max(UniprotPerBindingMode))\n",
" print(\"CATH Per Binding Mode\",np.mean(CATHPerBindingMode),np.median(CATHPerBindingMode),np.min(CATHPerBindingMode),np.max(CATHPerBindingMode))\n",
" print(\"PFAM Per Binding Mode\",np.mean(PFAMPerBindingMode),np.median(PFAMPerBindingMode),np.min(PFAMPerBindingMode),np.max(PFAMPerBindingMode))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Full\n",
"('Unique Cluster', 479)\n",
"('Strc Per Cluster', 4.1252609603340291, 1.0, 1, 288)\n",
"('Uniprot Per Cluster', 1.8455114822546972, 1.0, 0, 46)\n",
"('CATH Per Cluster', 1.3444676409185803, 1.0, 0, 7)\n",
"('PFAM Per Cluster', 1.2025052192066805, 1.0, 0, 10)\n",
"ECR\n",
"('Unique Cluster', 88)\n",
"('Strc Per Cluster', 11.852272727272727, 4.0, 2, 272)\n",
"('Uniprot Per Cluster', 3.5568181818181817, 2.0, 1, 42)\n",
"('CATH Per Cluster', 1.4090909090909092, 1.0, 0, 4)\n",
"('PFAM Per Cluster', 1.3181818181818181, 1.0, 0, 10)\n",
"ECRdist\n",
"('Unique Cluster', 88)\n",
"('Strc Per Cluster', 3.6931818181818183, 1.0, 1, 51)\n",
"('Uniprot Per Cluster', 1.9545454545454546, 1.0, 1, 16)\n",
"('CATH Per Cluster', 1.375, 1.0, 0, 4)\n",
"('PFAM Per Cluster', 1.2386363636363635, 1.0, 0, 10)\n"
]
}
],
"source": [
"for ikl in range(0,3):\n",
" torun = PixelDB\n",
" if ikl == 0:\n",
" print(\"Full\")\n",
" if ikl == 1:\n",
" torun = PixelDBecr\n",
" print(\"ECR\")\n",
" if ikl == 2:\n",
" torun = PixelDBoecr\n",
" print(\"ECRdist\")\n",
"\n",
" UniquePFAM = []\n",
" UniqueUnip = []\n",
" UniqueCATH = []\n",
" \n",
" UniprotPerBindingMode = []\n",
" CATHPerBindingMode = []\n",
" PFAMPerBindingMode = []\n",
"\n",
" StrPerBindingMode = []\n",
"\n",
" for uniid in list(np.unique(torun[\"cluster_number\"])):\n",
" sdf = torun[torun[\"cluster_number\"] == uniid]\n",
" #print(sdf[\"sequence_alignment\"])\n",
" \n",
" StrPerBindingMode.append(len(sdf))\n",
" \n",
" Uniuni = []\n",
" for cid in sdf[\"uniprot\"]:\n",
" #print(cid.split(\"_\"))\n",
" if str(cid) != \"nan\":\n",
" Uniuni += cid.split(\"_\")\n",
" UniprotPerBindingMode.append(len(list(set(Uniuni))))\n",
" \n",
" \n",
" CATHuni = []\n",
" for cid in sdf[\"CATH\"]:\n",
" #print(cid.split(\"_\"))\n",
" if str(cid) != \"nan\":\n",
" CATHuni += cid.split(\"_\")\n",
" CATHPerBindingMode.append(len(list(set(CATHuni))))\n",
"\n",
" PFAMuni = []\n",
" for cid in sdf[\"PFAM\"]:\n",
" #print(cid.split(\"_\"))\n",
" if str(cid) != \"nan\":\n",
" PFAMuni += cid.split(\"_\")\n",
" PFAMPerBindingMode.append(len(list(set(PFAMuni))))\n",
"\n",
" #print(\"Binding mode with ECR and Core >= 4\",len(PixelDBecr[\"unique_id\"].value_counts()))\n",
" print(\"Unique Cluster\",len(np.unique(torun[\"cluster_number\"])))\n",
" print(\"Strc Per Cluster\",np.mean(StrPerBindingMode),np.median(StrPerBindingMode),np.min(StrPerBindingMode),np.max(StrPerBindingMode))\n",
" print(\"Uniprot Per Cluster\",np.mean(UniprotPerBindingMode),np.median(UniprotPerBindingMode),np.min(UniprotPerBindingMode),np.max(UniprotPerBindingMode))\n",
" print(\"CATH Per Cluster\",np.mean(CATHPerBindingMode),np.median(CATHPerBindingMode),np.min(CATHPerBindingMode),np.max(CATHPerBindingMode))\n",
" print(\"PFAM Per Cluster\",np.mean(PFAMPerBindingMode),np.median(PFAMPerBindingMode),np.min(PFAMPerBindingMode),np.max(PFAMPerBindingMode))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1043"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(PixelDBecr)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"for cid in PixelDBecr[\"CATH\"]:\n",
" if str(cid) != \"nan\":\n",
" CATHuni += cid.split(\"_\")\n",
" \n",
"CATHPerBindingMode.append(len(list(set(CATHuni))))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1422 ----ACNDENYA\n",
"1423 GRHGAANDENY-\n",
"Name: sequence_alignment, dtype: object\n",
"1432 ARTMQTARKSTGGKAPRKQL------\n",
"1433 ARTMQTARKSTGGKAPRKQLATKAAR\n",
"Name: sequence_alignment, dtype: object\n",
"1435 ----TME-NLSRRLKVTGDLFD--IM------\n",
"1436 DGG-TME-NLSRRLKVTGDLFD--IMSG----\n",
"1437 --SST-MGQVGRQLAIIGD-DINRR---YDSE\n",
"Name: sequence_alignment, dtype: object\n",
"1438 -----LMRVQAHIRKRMVA\n",
"1439 ----SLMRVQAHIRKRMVA\n",
"1440 ---GSLLRVQAHIRKKMV-\n",
"1441 KNIPSLLRVQAHIRKKMV-\n",
"Name: sequence_alignment, dtype: object\n",
"690 --CGVPAIQ------P------------\n",
"691 --CGVPAIQ------PVL----------\n",
"692 --CGVPAIQ------PVLS---------\n",
"693 --CGVPAIQ------PVLS--G------\n",
"694 ADCGLRPLFEKKSLEDKT-ERELLESYI\n",
"Name: sequence_alignment, dtype: object\n",
"1453 ------P---PTLHELYDL\n",
"1454 DDYLWGLEAGEGISDLF-D\n",
"Name: sequence_alignment, dtype: object\n",
"1456 --------------CWTTRMSPPQQIC---------\n",
"1457 --------------CFTARMSPPQQIC---------\n",
"1458 ----KDIGAGPVASCFTTRMSPPQQICLN-------\n",
"1459 TQQAKDIGAGPVASCFTTRMSPPQQICLNSVVNTAL\n",
"Name: sequence_alignment, dtype: object\n",
"1464 -WDEDWDG-----------\n",
"1465 DWDEDWDGPKSSSYFKDSE\n",
"Name: sequence_alignment, dtype: object\n",
"1466 -----STCPA-------\n",
"1467 TT---PSPVPTTSTTSA\n",
"1468 --GTTPSPVPTTSTCSA\n",
"1469 --GTTPSPVPTTSTTSA\n",
"Name: sequence_alignment, dtype: object\n",
"1483 ---QEDIIRNIARHLAQVGDSMD--\n",
"1484 ----A-STKKLSECLKRIGDELDSN\n",
"1485 SESQEDIIRNIARHLAQVGDSMD-R\n",
"Name: sequence_alignment, dtype: object\n",
"695 ----------ARAE-VH---\n",
"696 ----------ARTAR-----\n",
"697 --------GCARSE-G----\n",
"698 --------GSARAE-PKM--\n",
"699 --------GAARAE-VHL--\n",
"700 --------GSARSE-GY---\n",
"701 --------GSARAE-VHL--\n",
"702 ---L-----CSRAR-PLV--\n",
"703 --------GAARAE-VYL-R\n",
"704 --QT----GSARSE-GY---\n",
"705 ----DGTCVAARTR-PV---\n",
"706 VNPT----GCARSE-PKMS-\n",
"707 LNPH----GAARAE-VY---\n",
"708 INPT----GCARSE-PKI--\n",
"Name: sequence_alignment, dtype: object\n",
"1488 RLEHFTKLRPKR-N-KKQQPT----\n",
"1489 YLAHPTRDRAKIQHSR-RPPTR---\n",
"1490 NLLHLTANRPKM-PGR-RLPGRFNG\n",
"Name: sequence_alignment, dtype: object\n",
"1504 -------------RR-RRIE-VNVELRKAKKDDQMLKRR-\n",
"1505 PRLSQYKSKYSSLEQSER-RRRLLELQKSKRLDYVNHARR\n",
"Name: sequence_alignment, dtype: object\n",
"726 --------S----KYI-TTIAGVMTLS--\n",
"727 ----------KYKQ-SVRLISLCQRLS--\n",
"728 ---R----R-RWKL-SFSIVSLCNHLTR-\n",
"729 -R-K------KWKQ-SVRLISLCQRLSR-\n",
"730 --------R-KW-QKTGHAVRAIGRLSS-\n",
"731 -R-R------RWKL-DFSIVSLCNHLTR-\n",
"732 -------A-IGFKK-LAEAVKFSAKLMGQ\n",
"733 ---R----R-KW-QKTGHAVRAIGRLSSS\n",
"734 ----RRREI-RFRV-LVKVVF-F------\n",
"735 FNAR----RK-L-KGAILTTMLAT-----\n",
"736 FNAR----RK-L-KGAILTTMLAT-----\n",
"Name: sequence_alignment, dtype: object\n",
"737 -----------KK-KATFRAITS-TLA-SSFK---R-R------\n",
"738 ----------ASPW-KSARLMVHTV-ATF--NSIK-----ER--\n",
"739 AR---------RKW-QKTGHAVRAI-GRL--S------------\n",
"740 --------RKKT-FKEVANAVKISA-S-L--M------------\n",
"741 -----------K-KKATFRAITS-TLA-SSFK---RRRS-----\n",
"742 --KKRFSF--K-K--S-FKLSGF-S-----------------FK\n",
"743 -----------H-M-GKVYAALMIF-D-FYKQ--NKTSRD----\n",
"Name: sequence_alignment, dtype: object\n",
"744 -------KFYATFLAAEYF-R-KF------KKR---\n",
"745 -------KFYATFLIQEYF-R-KF------KKR---\n",
"746 EVTVG--KFYATFLIQ--------------------\n",
"747 -------KFYATFLIQEHF-R-KF------MKRQEE\n",
"748 -----IPRLDTLILVKAMGHRK-RFGNPFR------\n",
"Name: sequence_alignment, dtype: object\n",
"749 --NHWQ-KIRTMVNL--PVISPFK------\n",
"750 ARK--EVI-RNKIRAIGK--M-ARVFSVLR\n",
"Name: sequence_alignment, dtype: object\n",
"1520 -LTLASKLK-----\n",
"1521 MLKLRQLQKKK-Q-\n",
"1522 MLKLRQLQK-KKQK\n",
"Name: sequence_alignment, dtype: object\n",
"1523 -S-MDDLLIRRLTDRNDKEA-HLNELF----------------\n",
"1524 MPRWKRHISEQLRRRDRLQRQAFEEIILQYN---------KLL\n",
"1525 DS-MDDLLIRRLTDRNDKEA-HLNELFQ--DNSGAIGGNI---\n",
"Name: sequence_alignment, dtype: object\n",
"1530 --DADTLLHFA--TES-TPD---------------------\n",
"1531 LGANDELISFKDEGE-QE--EKSSENSSAERDLADVKSSLV\n",
"Name: sequence_alignment, dtype: object\n",
"1541 A----RYGVSNTSINRK-\n",
"1542 A----RYGVSNTSINRKK\n",
"1543 -ARDSPYGLS-QGITK--\n",
"Name: sequence_alignment, dtype: object\n",
"755 --KR-RRHPS-------\n",
"756 -RKR-RRHPS-------\n",
"757 -RKR-RRHPSG------\n",
"758 --KR-RRHPSG------\n",
"759 ARKR-RRHPSG------\n",
"760 ----GRSRQPLVLGDNC\n",
"Name: sequence_alignment, dtype: object\n",
"761 AW---ASGN-LLTQAIRQQYYKPIDVDRMYGTIDSPKLEELF---\n",
"762 -IPAWA-SGNLLTQAIRQQYYKPIDVDRMYGTIDSPKLEELFNKS\n",
"763 -IPAWASGN-LLTQAIRQQYYKPIDVDRMYGTIDSPKLEELF---\n",
"764 PIPAWASGN-LLTQAIRQQYYKPIDVDRMYGTIDSPKLEELF---\n",
"Name: sequence_alignment, dtype: object\n",
"765 -----EEIFGEFE--\n",
"766 ------GEYVNIE-F\n",
"767 --PYP-EDYGDIEIG\n",
"768 YNPYP-EDYGDIEIG\n",
"Name: sequence_alignment, dtype: object\n",
"1544 ---SWYSY-PPPQR--A--------\n",
"1545 -TANILKPLMSPPSREEIMAT--LL\n",
"1546 RTANILKPLMSPPSREEIMATLL--\n",
"Name: sequence_alignment, dtype: object\n",
"1550 -PDWDFN----\n",
"1551 -EWGPWV----\n",
"1552 DDFGGFEAAET\n",
"Name: sequence_alignment, dtype: object\n",
"1556 -----KKPLDG--\n",
"1557 -----KGLGKGGA\n",
"1558 KSTGGKAPRKQ--\n",
"Name: sequence_alignment, dtype: object\n",
"1559 ----DHDAHA---\n",
"1560 GGGAVPTAKA---\n",
"1561 GGGGAPTAKAPSK\n",
"Name: sequence_alignment, dtype: object\n",
"774 -GSVVIVGRIILS---------\n",
"775 -GSVVIVGRIILS---------\n",
"776 KGSVVIVGRIVL----------\n",
"777 KGSVVIVGRIILS---------\n",
"778 KGSVVIVGRIVLSGKPAIIPA-\n",
"779 KGCVVIVGRIVLSGKPAIIPKK\n",
"780 KGSVVIVGRIVLSGKPAIIPKK\n",
"Name: sequence_alignment, dtype: object\n",
"781 -PSSTPC------------\n",
"782 -DDIVPC------------\n",
"783 TEDVVCC------------\n",
"784 QEREVPC------------\n",
"785 SECTTPC------------\n",
"786 -DELVYLLDGPGYDPIH--\n",
"787 -GRLVYLLDGPGYDPIHCD\n",
"Name: sequence_alignment, dtype: object\n",
"1575 ----DWDFLPP-\n",
"1576 SDDDFWVRVA-P\n",
"Name: sequence_alignment, dtype: object\n",
"1588 --AYDPARKLL----\n",
"1589 EKPYKEVTEDLLHLN\n",
"Name: sequence_alignment, dtype: object\n",
"789 --P----------TLQLPLA----\n",
"790 -------------KKDLRISC---\n",
"791 -------------NPGLKIPK---\n",
"792 -------K--P--DLRVVIPP---\n",
"793 -PK-------R--PTTLNLF----\n",
"794 -VV-------R--PGSLDLP----\n",
"795 ---P---R--P--TLQLPLA----\n",
"796 -PK-------R--PTTLNLF----\n",
"797 -------------AADLRISCNSK\n",
"798 -------R-RNLKGLNLNL-H---\n",
"799 ---R---RLQK---GNLPVR----\n",
"800 QRP--------RPTLQLPLA----\n",
"801 ----SLQN-RNTKNLSLDI-----\n",
"Name: sequence_alignment, dtype: object\n",
"802 --LSSL--AASSLAKRRQ-Q-------------\n",
"803 PQLKPI--ESSILAQRRV-R-------------\n",
"804 MKLSPP--SKSRLARRRA-L-------------\n",
"805 PQLKPI--EASILAARRV-R-------------\n",
"806 IKIKKIEDASNPLLLKRRKKARAL---------\n",
"807 -----------RLQERRG------SNVALMLDC\n",
"Name: sequence_alignment, dtype: object\n",
"808 ----RPPDLWIH\n",
"809 KRGNIPKPLNL-\n",
"Name: sequence_alignment, dtype: object\n",
"811 -RPYLPRP------\n",
"812 RPILLP-W-R----\n",
"813 -PRPLP-FP-----\n",
"814 NRLLLT-G------\n",
"815 NRLMLT-G------\n",
"816 KPPYLPRP------\n",
"817 RPPYLPRP------\n",
"818 -PPYLP-RPR----\n",
"819 RRPRLP-RPR----\n",
"820 RPPRLP-RPR----\n",
"821 -RPRLPRP-RP---\n",
"822 RPPRLPRP-RP---\n",
"823 -PPYLPRP-RPP--\n",
"824 --SYLP-RPTPP--\n",
"825 PRPYLP-RPRPPRP\n",
"Name: sequence_alignment, dtype: object\n",
"1607 GLRQAVTQ----\n",
"1608 ---APAKQLLNF\n",
"Name: sequence_alignment, dtype: object\n",
"1617 -GSIKK------\n",
"1618 SAKISKPLHIKT\n",
"Name: sequence_alignment, dtype: object\n",
"1625 VNFDD-IASSE----NLLHLTANRPKMPGRRLPG---\n",
"1626 -FIS-ELPS--EEGKKLEHFTKLRPKRNKKQQPTQAA\n",
"Name: sequence_alignment, dtype: object\n",
"836 ---CHPQNT------\n",
"837 ---SHPQNT------\n",
"838 ---CHPQF-C-----\n",
"839 --FSHPQNT------\n",
"840 -F-CHPQNT------\n",
"841 ---CHPQGPP----C\n",
"842 ---CHPQGPPC----\n",
"843 -C-CHPQCGAAYSC-\n",
"844 RC-CHPQCGAVEEC-\n",
"Name: sequence_alignment, dtype: object\n",
"1627 ----------MDDDFQL\n",
"1628 DLEMLAPYIPMDDDFQL\n",
"Name: sequence_alignment, dtype: object\n",
"1631 EISLPSDFEHTIHVGFDAVT-GEFT-----------\n",
"1632 EISAPSNFEHRVHTGFDQ-HEQKFTGLPRQWQSLIE\n",
"Name: sequence_alignment, dtype: object\n",
"850 ----------KQLSELL--------\n",
"851 ----------KILHRLLQ-------\n",
"852 -------H--QLLRYLL--------\n",
"853 -------S---LLKKLLD-------\n",
"854 ----------TLLQLLLG-----H-\n",
"855 -------H--KILHRLLQ-------\n",
"856 -------H--KILHRLLQ-E-----\n",
"857 -------K--I-LHRLLQDS-----\n",
"858 -------H--KILHRLL-QE-----\n",
"859 ----------HKLVQLLTT-----T\n",
"860 -----L----SLLQKLLL-A----T\n",
"861 --------AHKILHRLLQE------\n",
"862 ------RH--KILHRLLQE------\n",
"863 ----E-RH--KILHRLLQE------\n",
"864 -----ARH--KILHRLLQE------\n",
"865 ---E--RH--KILHRLLQEG-----\n",
"866 -------H--KILHRLLQEGSP---\n",
"867 ---E--RH--KILHRLLQEGSPS--\n",
"868 --LTE-RH--KILHRLLQEG-----\n",
"869 HSSLTERH--KILHRLLQ-------\n",
"Name: sequence_alignment, dtype: object\n",
"1650 WFEG-----YDNTFP-\n",
"1651 ----KSLTIYAQVQ-K\n",
"Name: sequence_alignment, dtype: object\n",
"870 ---SQRLVFNRPFLMFIVD---N-NILFLGKVNRP--------\n",
"871 --NSQRLVFNRPFLMFIVD---N-NILFLGKVNRP--------\n",
"872 --R-TIVRFNRPFLMIIVPT-DTQNIFFMSKVTNP-K------\n",
"873 --G-TIVRFNRPFLMIIVPT-DTQNIFFMSKVTNPKQ------\n",
"874 ---HPIIQIDRSFMLLILERS-TRSILFLGKVVNPTE------\n",
"875 -H-VLKFKVDHPFHFFIRHNK-SKTILFFGRFCCP-V------\n",
"876 -SIPPEVKFNKPFVFLMIEQN-TKSPLFMGKVVNP-T-Q----\n",
"877 --KPIILRFNQPFIIMIFDHF-TWSSLFLARVMNPV-------\n",
"878 --LHPIIQIDRSFMLLILERS-TRSILFLGKVVNPTEA-----\n",
"879 ---HPIIQIDRSFMLLILERS-TRSILFLGKVVNPTEA-----\n",
"880 ---PPVIKIDRPFHFMIYEET-SGMLLFLGRVVNPTLL-----\n",
"881 AVLYPQVIVDHPFFFLIRNRR-TGTILFMGRVMHPETM-----\n",
"882 ---TIRFSVDRPFHIVVRR---RGAILFLGSIADPH--DPGPA\n",
"Name: sequence_alignment, dtype: object\n",
"883 TVASS---------\n",
"884 TEAAAGDGGVMTGR\n",
"885 TEAAAGMGGVMTGR\n",
"886 TEAAAGTGGVMTGR\n",
"Name: sequence_alignment, dtype: object\n",
"0 -NLVPQ-----------V--A-T--V-\n",
"1 -VQQES---S----------F-V--M-\n",
"2 -IRYPK-----------T--F-G--W-\n",
"3 -GILGF-----------V--F-T--L-\n",
"4 -LPFEK-----------S--T-V--M-\n",
"5 -LPFDR-----------T-T--I--M-\n",
"6 -CINGV-----------V-W--T--V-\n",
"7 -EEFGR-----------A--A-S--F-\n",
"8 -LSSPV-----------T--K-S--F-\n",
"9 -RPQVP---L---------R--P--M-\n",
"10 -FQWMG-----------Y--E-L--W-\n",
"11 -FAPGN---Y----------P-A--W-\n",
"12 -KVAEL-----------V-W--F--L-\n",
"13 -SRYWA-----------I--R-T--R-\n",
"14 -GLCTL-----------V--A-M--L-\n",
"15 -CINGM-----------C--W-T--V-\n",
"16 -IMDQV---P----------F-S--V-\n",
"17 -FAPGF-----------F--P-Y--L-\n",
"18 -QFKDN-----------V--I-L--L-\n",
"19 -ALYNT---A----------A-A--L-\n",
"20 -SLFNT-----------V--A-T--L-\n",
"21 -KPIVV---L---------H--G--Y-\n",
"22 -VMAPR---T----------L-F--L-\n",
"23 -MHPAQ---T----------S-Q--W-\n",
"24 -SLFNT-----------V--A-T--LY\n",
"25 -RRIYD-----------L--I-E--L-\n",
"26 -NLVPT-----------V--A-T--V-\n",
"27 -ILMEH---I----------H-K--L-\n",
"28 -KTFPP---T----------E-P--K-\n",
"29 -SRRWR-----------R--W-N--R-\n",
" ... \n",
"144 -ELAAIG--I----------L-T--V-\n",
"145 -EPLPQ-G--------Q-LT--A--Y-\n",
"146 -RPQVP--L-------RP-M--T--Y-\n",
"147 -RPHER-NGF----------T-V--L-\n",
"148 -GHAEE---Y--------G-AET--L-\n",
"149 -HPVGD--A-------D-YF--E--Y-\n",
"150 -HPVGQA--D-------Y--F-E--Y-\n",
"151 -KAFSP-EV--------I--P-M--F-\n",
"152 -HPVAE-----------ADYF-E--Y-\n",
"153 -HEEAV--S---------VDR-V--L-\n",
"154 -HPVGE--A-------D-YF--E--Y-\n",
"155 -KAFNP-EI--------I--P-M--F-\n",
"156 -ALPHA-----------I--L-R-L--\n",
"157 -KGFNP-EVI----------P-M--F-\n",
"158 -HPVGD-----------ADYF-E--Y-\n",
"159 -TIAMEL--I-------R--M-I--K-\n",
"160 -HPVAE--A-------D-YF--E--Y-\n",
"161 -EECDS-E--------LE-IK-R--Y-\n",
"162 -AIMPA-R----------FY----PK-\n",
"163 -CPSQE--P--------MSIY-V--Y-\n",
"164 -RRLLR--G--------H-N----QY-\n",
"165 -LPEPL-P----QG--Q-AT--A--Y-\n",
"166 -LPEPA-P------QGQ-LT--A--Y-\n",
"167 -LPEPL-A----QG--Q-LT--A--Y-\n",
"168 -LPEPL-P----QG--Q-LT--A--Y-\n",
"169 -LPEPL-P----QG--A-LT--A--Y-\n",
"170 -LPEAL-P----QG--Q-LT--A--Y-\n",
"171 -RVEDV-----------TNT--AEYW-\n",
"172 -LPAV-V-G-L-----SPGEQ-E--Y-\n",
"173 -FLNKDL-E-VD-G--H-FV--T--M-\n",
"Name: sequence_alignment, dtype: object\n",
"174 FPTK-D-V----AL\n",
"175 VPLR-P-----MTY\n",
"176 YTVK-Y-----PNL\n",
"177 IDWF-D-----GKD\n",
"178 KVIT-F-I----DL\n",
"179 SAPD-T-----RPA\n",
"180 IDWF-E-----GKE\n",
"181 GGKK-K-----YRL\n",
"182 ALYN-F-----ATM\n",
"183 GGRK-K-Y----KL\n",
"184 IDWF-D-----GKE\n",
"185 GGKK-K-Y----QL\n",
"186 FEAN-G-----NLI\n",
"187 EIIN-F-E----KL\n",
"188 EQYK-F-Y----SV\n",
"189 KAFS-P-----EVI\n",
"190 YTVK-F-----PNM\n",
"191 VNDI-F-----EAI\n",
"192 RYGF-V-----ANF\n",
"193 SQYY-Y-N----SL\n",
"194 IQQS-I-E----RI\n",
"195 SSIE-F-----ARL\n",
"196 GGKK-K-Y----KL\n",
"197 RGYV-Y-----QGL\n",
"198 SEIE-F-----ARL\n",
"199 INFD-F-N----TI\n",
"200 RAKF-K-----QLL\n",
"201 SIIG-F-E----KL\n",
"202 AVFN-F-----ATM\n",
"203 RYPL-T-----FGW\n",
" ... \n",
"240 KAPF-N-F---ATM\n",
"241 EGPR-N-Q---DWL\n",
"242 ASNE-N-A--E-TM\n",
"243 YQLE-N-Y---CGL\n",
"244 KGPA-N-F---ATM\n",
"245 RQAS-L-S--I-SV\n",
"246 EEYL-Q-A---FTY\n",
"247 IGPG-R-A---FYA\n",
"248 SYVN-T-N--M-GL\n",
"249 LYLV-C-G---ERV\n",
"250 ASNE-H---M-ETM\n",
"251 ASNE-N-M---ETM\n",
"252 ELKR-K-M---IYM\n",
"253 KAVA-N-F--A-TM\n",
"254 KVPR-N-Q---DWL\n",
"255 ASNE-N-W---ETM\n",
"256 KAVY-N-L---ATM\n",
"257 KALY-N-F---ATM\n",
"258 ASNED--M--E-TM\n",
"259 SSLE-N-F-R-AYV\n",
"260 SQLK-N-N-AK-EI\n",
"261 RYPL-T-F--GWCF\n",
"262 RYPL-TLG---WCF\n",
"263 SSLE-N-F-A-AYV\n",
"264 IGPG-R-A-F-YTI\n",
"265 RGPG-R-A-F-VTI\n",
"266 KRWII--L-G-LNK\n",
"267 EENLL--D-F-VRF\n",
"268 VGYP-K-VKEE-ML\n",
"269 SGVE-N-P-GGYCL\n",
"Name: sequence_alignment, dtype: object\n",
"284 --Y---FINILTL\n",
"285 YELDEKFDRL---\n",
"Name: sequence_alignment, dtype: object\n",
"1670 FAAAVSAFAANMLSSVLKSEATSS---------\n",
"1671 FAAAVSAFAANMLSSVLKSEATSSIIKSVGETA\n",
"Name: sequence_alignment, dtype: object\n",
"1678 NEKNGPIIQNN-----KFEYKEDTIK\n",
"1679 ---E-TLTGQYDKNLV-TTVEEE-Y-\n",
"Name: sequence_alignment, dtype: object\n",
"1680 ----PQPVDSWV\n",
"1681 PQPV---DSWV-\n",
"Name: sequence_alignment, dtype: object\n",
"1684 QKFIARNRAPRVQ-----------\n",
"1685 QKFIARNRAPRVQIEYDVELYGAE\n",
"Name: sequence_alignment, dtype: object\n",
"888 -------RTTPV---\n",
"889 -------DETNL---\n",
"890 -------GETRL---\n",
"891 -------LDVPV---\n",
"892 -------RETQV---\n",
"893 -------IESDV---\n",
"894 -----E-AQTRL---\n",
"895 ------EQVSAV---\n",
"896 -----V-QDTRL---\n",
"897 -V-----KESLV---\n",
"898 ----RW-QDTRL---\n",
"899 ----RH-PTSII---\n",
"900 -----SYLVTSV---\n",
"901 --T---RRETQL---\n",
"902 -----H-REMAVDCP\n",
"903 ---ATV-RTYSC---\n",
"904 -NS-RV-QDSII---\n",
"905 ANS-RF-PTSII---\n",
"Name: sequence_alignment, dtype: object\n",
"1688 -----GDYM-N-M-\n",
"1689 FPLKRHDKVDDLSK\n",
"Name: sequence_alignment, dtype: object\n",
"1693 DFSIVGSLP--R--\n",
"1694 -FSIVGSLPRDFEL\n",
"Name: sequence_alignment, dtype: object\n",
"1697 ARTKQTA------\n",
"1698 ARTKQTARKSTGG\n",
"Name: sequence_alignment, dtype: object\n",
"966 ------KRYDREFLLGFQ--------------------------\n",
"967 -----KKRYSREFLLGF---------------------------\n",
"968 -----RIIYDRKFLMECR--------------------------\n",
"969 -----KKRYDREFLLGFQ--------------------------\n",
"970 --------YDREFLLDFQF-------------------------\n",
"971 ----GRIIYDRKFLMECR--------------------------\n",
"972 -----RIIYDRKFLMECRN-------------------------\n",
"973 ----GRIIYDRKFLMECRN-------------------------\n",
"974 ----TRIIYDRKFLMECR--------------------------\n",
"975 PGG-TRIIYDRKFLLDRRNS------------------------\n",
"976 PGG-TRIIYDRKFLMECRN-SP----------------------\n",
"977 ---PHMIRYNRDTLMTARDT--KRAPIPDEMLQEINRVAPDILI\n",
"Name: sequence_alignment, dtype: object\n",
"978 ---R--RKLPEI-\n",
"979 -APT--YSPPLPP\n",
"980 -APT--YSPPLPP\n",
"981 -APT--YPPPLPP\n",
"982 -APT--YPPPPPP\n",
"983 SL--ARRPLPPLP\n",
"Name: sequence_alignment, dtype: object\n",
"984 -PPPVP-P-------\n",
"985 -PPPVP-PR------\n",
"986 PPPALPSSAP---S-\n",
"987 --PALPSSAPSG---\n",
"988 PPPPLPSGPA---YA\n",
"989 -PPVIA-PRP-EHTK\n",
"Name: sequence_alignment, dtype: object\n",
"993 MSL----P-GRWKPK-\n",
"994 ---QLINTNGSWHI-N\n",
"Name: sequence_alignment, dtype: object\n",
"995 VEQH---HRRTDND------\n",
"996 ----RKRIHIGP--GRAFYT\n",
"Name: sequence_alignment, dtype: object\n",
"1003 ----RTQPDGQSFR\n",
"1004 ----RGCADGQSFR\n",
"1005 ---QRESPDGQSFR\n",
"1006 ---QRSPPDGQSFR\n",
"1007 --VARPPPIGAEVP\n",
"1008 PHLQRPPPIGQSFR\n",
"Name: sequence_alignment, dtype: object\n",
"1009 ----LPTLPKLP------SLS-----\n",
"1010 ---HTPRLPTLP------KR-V----\n",
"1011 AFVHMPTLPNLDF----------HKT\n",
"1012 AFVHMPTLPNLD-FHKT---------\n",
"1013 PLYTSPSLPNITLGL--P--------\n",
"1014 TLVSMPPLPGLDLK--------GS--\n",
"Name: sequence_alignment, dtype: object\n",
"288 ----------------ILHRLL-------\n",
"289 ----------------ILHRLLQ------\n",
"290 -------------K--ILHRLLQ------\n",
"291 -------------G--LLWDLLT------\n",
"292 -------------K--ILHRLLQ------\n",
"293 -------------S--AFSRLYT------\n",
"294 ------------P---MLMNLL-------\n",
"295 -------------K--ILHRLL-Q-----\n",
"296 ------------GA---FQNLFQ------\n",
"297 ----------------SLIDLLAD-----\n",
"298 ---------------QSLINLLAD-----\n",
"299 --------S----A---FSRLYTR-----\n",
"300 -------------K--ILHRLLQD-----\n",
"301 -------------K--ILHRLLQD-----\n",
"302 -------------K--ILHRLLQD-----\n",
"303 ------------HK--ILHRLLQ------\n",
"304 -------------S--ELLKYLTT-----\n",
"305 -------------A--ALAALLAA-----\n",
"306 --------P----A--ILYALLSS-----\n",
"307 -------------K--ILHRLLQE-----\n",
"308 --------H----K--ILHRLLQD-----\n",
"309 --------P----A--ILYALLSS-----\n",
"310 --------H----K--ILHRLLQ------\n",
"311 ------H------K--ILHRLLQE-----\n",
"312 --------H----K--ILHRLLQD-----\n",
"313 --------H----K--ILHRLLQE-----\n",
"314 -------------K--ILHRLLQE-----\n",
"315 ------------HK--ILHRLLQE-----\n",
"316 -------------K--ILHRLLQD-----\n",
"317 ------------PS--LLKKLLLA-----\n",
" ... \n",
"338 -----R--P----A--ILYALLSS-----\n",
"339 -----A--N----A--LLRYLLDKD----\n",
"340 -----N--H----P--MLMNLLK------\n",
"341 -------------S--LLLHLLKSQ----\n",
"342 --------H----K--ILHRLLQDS----\n",
"343 ------------HK--ILHRLLQD-S---\n",
"344 -----E--N----A--LLRYLLDK-----\n",
"345 ---G----N----A--ALRYLLGA-----\n",
"346 -----D--H----Q--LLRYLLDKD----\n",
"347 -----------KHK--ILHRLLQD-----\n",
"348 -----R--H---K---ILHRLLQ------\n",
"349 -----R--H---P---LLLRHLL------\n",
"350 ---E-R--H----K--ILHRLL-Q-----\n",
"351 ---K-N--H----P--MLMNLLK--D---\n",
"352 ----RP--C----S--ELLKYLTTN-D--\n",
"353 -K---N--H----P--MLMNLLK--D---\n",
"354 ----KN--H----P--MLMNLLK------\n",
"355 ------GLE----A--IIRKALM------\n",
"356 ------------HK--ILHRLLQD-SS--\n",
"357 --------H----K-K-LLQLLTCSS---\n",
"358 ----KN--H----P--MLMNLLK------\n",
"359 ---------K--HK--ILHRLLQD-SS--\n",
"360 ------------PS--LLKKLLLAP-A--\n",
"361 -K---N--H----P--MLMNLLK------\n",
"362 --------N----A--LLRYLLDRD--D-\n",
"363 ----KE--N---A---LLRYLLDK-----\n",
"364 T--E-R--H----K--ILHRLLQ------\n",
"365 --------H----K--ILHRLLQEGS-PS\n",
"366 SLTE-R--H----K--ILHRLLQE-----\n",
"367 SLTE-R--H----K--ILHRLLQE-----\n",
"Name: sequence_alignment, dtype: object\n",
"1033 ----DDHLL--\n",
"1034 ----DEDLLE-\n",
"1035 ---SDEDLLHI\n",
"1036 ---DDVPMVIA\n",
"1037 ---SDEDLLE-\n",
"1038 ----DEDLLHI\n",
"1039 --LLDDELMS-\n",
"1040 GFSDDVPMVIA\n",
"Name: sequence_alignment, dtype: object\n",
"1042 -K----G--LIDYYLM-------------\n",
"1043 -K----H--TLDIFFKPL-----------\n",
"1044 -----P-KHTLDIFFKPL-------T---\n",
"1045 ----RQT--SMTDFYHS------------\n",
"1046 KK----G--LIDYYLM-------------\n",
"1047 -----P-KHTLDIFFKPL-----------\n",
"1048 ---AFQA--KLDTFLWS------------\n",
"1049 ---RRQT--SMTDFYHSK-------RRLI\n",
"1050 --KRRQT--SMTDFFHS-KRRLIFS----\n",
"Name: sequence_alignment, dtype: object\n",
"1053 ---------KCVVM\n",
"1054 ---------GCVLS\n",
"1055 ---------KCVIM\n",
"1056 --------TKCVVM\n",
"1057 --------TKCVIF\n",
"1058 --------TKCVFM\n",
"1059 -----PTASACNIQ\n",
"1060 KKKSK---TKCVIM\n",
"Name: sequence_alignment, dtype: object\n",
"1061 -PTA---SACVLS\n",
"1062 D--DPTASACNIQ\n",
"Name: sequence_alignment, dtype: object\n",
"1074 ----E--MVRQARILAQATSDLVNAIKA--------\n",
"1075 -------ILEAAKSIAAATSALVKAASA--------\n",
"1076 ----V-VLINAVKDVAKALGDLISATK---------\n",
"1077 ---GR-PLLQAAKGLAGAVSELLRSAQP--------\n",
"1078 ----SRKLLSAAKILADATAKMVEAAK----G----\n",
"1079 ------PLLQAAKGLAGAVSELLRSAQPASA-----\n",
"1080 --TAKRQFVQSAKEVANSTANLVKTIKA--------\n",
"1081 ------DIDQMFSTLLGEMDLLTQSL-G---VDTLY\n",
"1082 RDDRRERIVAECNAVRQALQDLLSEYMG--------\n",
"Name: sequence_alignment, dtype: object\n",
"1084 ------ETFSDLWKLLP----\n",
"1085 ------ETFSDLWKLLP----\n",
"1086 ------LTFEHYWAQL----T\n",
"1087 -------TFSDLWKLLP----\n",
"1088 -------TFSDLWKLLPE---\n",
"1089 -------TFAEYWAQL---AS\n",
"1090 ------TSFAEYWNLLS-P--\n",
"1091 ------LTFEHWWAQL---TS\n",
"1092 ------ETFSDLWKLLPEN--\n",
"1093 CNCKAPETFLCYWRCLQ----\n",
"Name: sequence_alignment, dtype: object\n",
"1103 ------KILHRLLQD-\n",
"1104 -----HKILHRLLQ--\n",
"1105 G--LE-AIIRKALMGK\n",
"1106 SPGSR-EWFKDMLS--\n",
"Name: sequence_alignment, dtype: object\n",
"380 ----------------------------------------------...\n",
"381 ----------------------------------------------...\n",
"382 ----------------------------------------------...\n",
"383 ----------------------------------------------...\n",
"384 ----------------------------------------------...\n",
"385 ----------------------------------------------...\n",
"386 ----------------------------------------------...\n",
"387 ----------------------------------------------...\n",
"388 ----------------------------------------------...\n",
"389 --------------------------CY------------------...\n",
"390 ----------------------------------------------...\n",
"391 ----------------------------------------------...\n",
"392 ----------------------------------------------...\n",
"393 --------------------------I-------------------...\n",
"394 ----------------------------------------------...\n",
"395 ----------------------------------------------...\n",
"396 ----------------------------------------------...\n",
"397 ----------------------------------------------...\n",
"398 ----------------------------EPC---------------...\n",
"399 ----------------------------------------------...\n",
"400 ----------------------------------------------...\n",
"401 ----------------------------------------------...\n",
"402 ----------------------------------------------...\n",
"403 ----------------------------------------------...\n",
"404 ----------------------------------------------...\n",
"405 --------------------------SG------------------...\n",
"406 ----------------------------------------------...\n",
"407 ---------------------------------QECTPGQTKKQDC...\n",
"408 -CSPSGAICSGFGPPEQCCSGACVPHP-------------------...\n",
"409 ---------------------------E-VTCE---P-GTTFKDKC...\n",
"410 KCSPSGAICSGAGPPEQCCSGACVPHP-------------------...\n",
"Name: sequence_alignment, dtype: object\n",
"411 ------------ACGRR-----------------------------\n",
"412 -------------CGKK-LVT-------------------------\n",
"413 --------K--FQCGQK--T----------------------L---\n",
"414 -------------CGQK-T-L---------------------RP--\n",
"415 ------L-K--FQCGQK-T---------------------------\n",
"416 ------L-K--FQCGQK-TL--------------------------\n",
"417 -------TTCDGPCGVRFRQ----------------------N---\n",
"418 -----A------DCGLR-PLFEKKSLEDKTERELLESY--------\n",
"419 ---GEA------DCGLR-PLFEKKSLEDKTERELLESYI-------\n",
"420 ----EA------DCGLR-PLFEKKSLEDKTERELLESYI-------\n",
"421 -------------CGLR-PLFEKKQVQDQTEKELFESY-I------\n",
"422 ------------DCGLR-PLFEKKSLEDKTERELLESYI-------\n",
"423 -----A------DCGLR-PLFEKKSLEDKTERELLESYI-------\n",
"424 -------------CGLR-PLFEKKSLEDKTERELLESYI-------\n",
"425 ----EA------DCGLR-PLFEKKSLEDKTERELLESYI-D-----\n",
"426 ----EA------DCGLR-PLFEKKSLEDKTERELLESYID------\n",
"427 ----EA------DCGLR-PLFEKKSLEDKTERELLESYID-G----\n",
"428 -----A------DCGLR-PLFEKKSLEDKTERELLESY-I-D--GR\n",
"429 GSG-EA------DCGLR-PLFEKKSLEDKTERELLESYID-G--R-\n",
"Name: sequence_alignment, dtype: object\n",
"430 -------FEEIP--------\n",
"431 ------KYEPF---------\n",
"432 ------DFEEIP--------\n",
"433 ------DFEEIPEE------\n",
"434 -------FEGIPGE------\n",
"435 ------DFEEIP-E-Y----\n",
"436 ------DFEEIPGE------\n",
"437 -----GDFEEIPEE------\n",
"438 -------FEEIPEE------\n",
"439 -----GDFEEIP--------\n",
"440 -------YEPIPEEA-----\n",
"441 ------DFEEIPEE-Y----\n",
"442 ------DYEPIPEEAF----\n",
"443 ------DFEEIPGE-YL---\n",
"444 ------DFEEIPEE-YL---\n",
"445 -----GDFEEIPEE-YL---\n",
"446 ------DFEEIPEE-YLQ--\n",
"447 -----SDFEEFSLDDI--EQ\n",
"448 GGGGNGDYEPIPEEA-----\n",
"Name: sequence_alignment, dtype: object\n",
"449 ----SGKVPL-\n",
"450 ----SGKVPLS\n",
"451 DFLAEGGGV--\n",
"452 TVELQGVVP--\n",
"453 DFLAEGGGVR-\n",
"454 DFLAEGGGVR-\n",
"Name: sequence_alignment, dtype: object\n",
"459 GCQV-------------------------NYCP-----PVPCL---\n",
"460 --------------------ACSRYEVDCRGRG-----S-----AC\n",
"461 ----EDYAAIEASLSETFNT------AADPGRRLGEGSK----P--\n",
"Name: sequence_alignment, dtype: object\n",
"1124 KRWIIM-GLN-K--\n",
"1125 KRWIIL-GLN-K--\n",
"1126 -RYPL---TLGWCF\n",
"1127 -KLVALV-I-N-AV\n",
"Name: sequence_alignment, dtype: object\n",
"1140 PA-PFAAA----\n",
"1141 -KGEADALSLD-\n",
"1142 -KLKLLVVIRLK\n",
"Name: sequence_alignment, dtype: object\n",
"1149 -----GQVGRQLAIIGDDINR--------\n",
"1150 -A--ADPLGQALRAIGDEFETRFR-----\n",
"1151 -RPE-IWIAQEARRIGDEANAYYAR----\n",
"1152 -RPE-IWIAQEYRRIGDEFNAYYAR----\n",
"1153 -RPE-IWAAQELRRIGDEFNAYYR-----\n",
"1154 -RPE-IWIAQELRRIGDEFNAYYAR----\n",
"1155 -RPE-IWIAQELRRIGDEENAYYR-----\n",
"1156 GRPE-IWIAQELRRIGDEFNAYYA-----\n",
"1157 AS-T-KKLSECLKRIGDELDSNMELQRMI\n",
"Name: sequence_alignment, dtype: object\n",
"1158 ---------------------SHPQF-------\n",
"1159 ---------------------SHPQF-E-----\n",
"1160 ----------------------HPQF-E----K\n",
"1161 -------------------RCCHPQCGAVEEC-\n",
"1162 GHVVEGLAGELEQLRARLE--HHPQG-Q-----\n",
"Name: sequence_alignment, dtype: object\n",
"1163 ---HPQF-E-----\n",
"1164 ---HPQF-E----K\n",
"1165 -CCHPQCGAAYSC-\n",
"1166 RCCHPQCGAVEEC-\n",
"Name: sequence_alignment, dtype: object\n",
"1167 --------ETGTTNTATT--\n",
"1168 --------ETGTTNTATTAT\n",
"1169 SNPPCQTHETGTTNTATTAT\n",
"Name: sequence_alignment, dtype: object\n",
"466 -----A-------LDKWD-------\n",
"467 -----E-------LEKWAS------\n",
"468 -----E-------QDKWAS------\n",
"469 -----E-------ADKWQS------\n",
"470 -----E-------LDKWAG------\n",
"471 -----E-------LDHWAS------\n",
"472 -----E-------LDKWAN------\n",
"473 -----E-------LDKYAS------\n",
"474 -----E-------LDKWAS------\n",
"475 -----A-------LDKWAS------\n",
"476 -----E-------NDKWAS------\n",
"477 ----LE-------LDKWASL-----\n",
"478 ---LLE-------LDKWAS------\n",
"479 ----LE-------LDKWASLW----\n",
"480 ---LLE-------LDKWASLW----\n",
"481 --ELLE-------LDKWASL-----\n",
"482 --ELLE-------LDKWASLW----\n",
"483 EQELLE-------LDKWASLW----\n",
"484 ------DKKQKVHALFY----KLDI\n",
"Name: sequence_alignment, dtype: object\n",
"517 -LIN--TNGS-WHVN--------\n",
"518 QLIN--TNGS-WHIN--------\n",
"519 ----LPTPPTRE---PKKVAVVR\n",
"Name: sequence_alignment, dtype: object\n",
"523 VKAETRLNP--D--------LQPTE\n",
"524 ----NWFDITNWLWYIKKK------\n",
"525 ----NWFDITNWLWYIKKKK-----\n",
"Name: sequence_alignment, dtype: object\n",
"529 -AQ---SQRAP-DR-----\n",
"530 ETI---YNTT----LKY--\n",
"531 ---CKEWLST-AP----CG\n",
"Name: sequence_alignment, dtype: object\n",
"534 VQ-----------GSGAFGR-\n",
"535 --CGADSYEMEEDGVRK---C\n",
"Name: sequence_alignment, dtype: object\n",
"536 TDHG--A-E----\n",
"537 YTTSTRGDLAHVT\n",
"Name: sequence_alignment, dtype: object\n",
"540 --------------KKQKVHALFYK-\n",
"541 HFPICIFCCGCCHRSKCG--M--CCK\n",
"Name: sequence_alignment, dtype: object\n",
"544 --------------PGGGQIVGGVYLLPRR\n",
"545 SIQDLRRRFFLHHLIAEI------------\n",
"Name: sequence_alignment, dtype: object\n",
"546 PTSSE--QI----\n",
"547 -YLEDWIKYNNQK\n",
"Name: sequence_alignment, dtype: object\n",
"548 --LLTEVETPIR----------NEWG\n",
"549 GS------ATRELDELMASLSD----\n",
"Name: sequence_alignment, dtype: object\n",
"497 VCN---------PLTG--AL--LC------\n",
"498 ----AI-IGL-MVGGV---V----------\n",
"499 ---EEED--DD-MGFG---L----------\n",
"500 VCN---------PLTG--AL--LCSAAE--\n",
"501 ------------QLINTNG-SWH-----VN\n",
"Name: sequence_alignment, dtype: object\n",
"502 ----HQLDPAFG----\n",
"503 ----PKLEPW-KHP--\n",
"504 EPVDPKLEPW-KHPGS\n",
"Name: sequence_alignment, dtype: object\n",
"505 ----AEP--WTVRNEDL\n",
"506 KG-----VRI-GPGQ--\n",
"507 -FDSAEP--WTVRNED-\n",
"Name: sequence_alignment, dtype: object\n",
"508 ------LELDKWA-\n",
"509 -----LLELDKWA-\n",
"510 QLINTNGSWHI--N\n",
"Name: sequence_alignment, dtype: object\n",
"511 KLVF--FAEDV--------\n",
"512 ----NWWDITNWLWYIKKK\n",
"513 ----NWFDITNWLWYIKKK\n",
"Name: sequence_alignment, dtype: object\n",
"514 --MDWNM----HAA\n",
"515 TR-KSIHIGPG---\n",
"516 TR-KSIRIGPG---\n",
"Name: sequence_alignment, dtype: object\n",
"1192 GCCS-DPRCAW-R-------\n",
"1193 GC-CSTPPCAVLY---C---\n",
"1194 GCCS-LPPCALNNPKYC---\n",
"1195 GC-CSRPPCILNN---PDLC\n",
"Name: sequence_alignment, dtype: object\n",
"1212 ------SS-ETKRAARRPYK-----\n",
"1213 -----TK-PAIRRLARRGGV-----\n",
"1214 --QGITK-PAIRRLARRG-------\n",
"1215 ----LLSSSETKRAARRPYKPIAL-\n",
"1216 KL--LSSS-ETKRAARRPYKPIALR\n",
"Name: sequence_alignment, dtype: object\n",
"1224 -------------NGYENPTY--K-------\n",
"1225 -------------NGYENPTY--K-------\n",
"1226 --------------NFDNPVY--RK--T---\n",
"1227 --------S-I---NFDNPVY--Q----KTT\n",
"1228 VAPEERHLSKMQQNGYENPTYKFFEQM----\n",
"Name: sequence_alignment, dtype: object\n",
"1229 ----RLLEASADAN----\n",
"1230 ---Q-LTSYD--------\n",
"1231 ----QLTSYDCEVNAP-I\n",
"1232 -VVKLLLEHGADVSAQ--\n",
"1233 SVVEYLLQHGADVH----\n",
"1234 EVVKLLLEHGADVLAQD-\n",
"1235 EVVKLLLEHGADVDAQDK\n",
"Name: sequence_alignment, dtype: object\n",
"559 --------------GRPR----TTSFAE---\n",
"560 ---------G-----RPR----TTSFAE---\n",
"561 ----P---VL----AFQREGFGRQSMS----\n",
"562 -----IAA---G-R-TGR----RQAIHDI--\n",
"563 T-TYA-DF-IASGR-TGR----RNAI-----\n",
"564 -TTYA-DF-IASGR-TGR----RNAI---HD\n",
"565 T-TYA-DF-IASGR-TGR----RNAIHD---\n",
"566 T-TYA-DF-IASGR-TGR----RASIHD---\n",
"567 T-TYA-DF-IASGR-TGR----RNAIHD---\n",
"568 T-TYA-DF-IASGR-TGR----RNAIH----\n",
"569 T-TYA-DF-IASGR-TGR----RACIHD---\n",
"570 T-TYA-DF-IASGR-TGR----RNAIHD---\n",
"571 T-TYA-DF-IASGR-TGR----RNAIH----\n",
"Name: sequence_alignment, dtype: object\n",
"1236 ----F-SDIYKIREIADGLC--L-\n",
"1237 ------SDIYKIREIADGLC--L-\n",
"1238 ------WIAQELREIGDKFNAYYA\n",
"1239 -RP-EIWIAQEFRRIGDEFN-A--\n",
"1240 -QEQLLTLASILREDGKVFD----\n",
"1241 GSGTMENLSRRLKVTGDLFDIMSG\n",
"Name: sequence_alignment, dtype: object\n",
"1279 GSLHRVPLR-----------------------------------\n",
"1280 -AVVKVPLKKFKSIRETMKEKGL---------------------\n",
"1281 -AVVKVPLKKFKSIRETMKEKGLLGEFLRTHKYDPAWKYRFGDL\n",
"Name: sequence_alignment, dtype: object\n",
"1287 -----RGALLDQIRQGIQLNKT---------\n",
"1288 PS--PREQLMESIRKGKELKQI---------\n",
"1289 PS--PREQLMESIRKGKELKQI---------\n",
"1290 --TPQGEDMLNAIRRGVKLKKTTTNDRSAPR\n",
"Name: sequence_alignment, dtype: object\n",
"1293 ----------------VMEMEPE-T-MET--KSVID---S--\n",
"1294 -----------D----HMEMEPE-T-MET--KSVT-DYF---\n",
"1295 ----------------HMEMEPE-T-MET--KSVT-DYF---\n",
"1296 -------F--YMGT---CQDEPEQLD--DWNRI-AEL-----\n",
"1297 --Q-L-LH--SD----HMEMEPE-T-MET--KSVT-DYF-SK\n",
"1298 HPEPVASWMSEQ--RWAGEPEVMCT-LQH--KSIA-------\n",
"Name: sequence_alignment, dtype: object\n",
"1309 -----------WR-QDID-------\n",
"1310 L-----------DEETGEFL-----\n",
"1311 Q-----------NEENGEQE-----\n",
"1312 -MDLIDILWRQDI-DLGVSREVFDS\n",
"Name: sequence_alignment, dtype: object\n",
"1325 -NSTL-Q----\n",
"1326 VNSTLQ-----\n",
"1327 TSAVLQSG---\n",
"1328 -SAVLQSGF--\n",
"1329 TSAVLQSGFRK\n",
"Name: sequence_alignment, dtype: object\n",
"1335 S-----G---------SLANNIKKSTVIVKN\n",
"1336 --QSG-S----------LANNIKKSTVIVK-\n",
"1337 -IQ--SG---------SLANNIKKSTVIVKN\n",
"1338 -------RLHSEIQSGSLANNIKKSTVIVKN\n",
"Name: sequence_alignment, dtype: object\n",
"1339 -----EAQTRL\n",
"1340 -----YPTSII\n",
"1341 --QLAWFDTDL\n",
"1342 -N-SRWPTSIL\n",
"1343 AN-SRWPTSII\n",
"Name: sequence_alignment, dtype: object\n",
"1344 ----RNLF--GP\n",
"1345 -PVKRRLDLE--\n",
"1346 -SRHKKLMFK--\n",
"1347 PKPLKKLRFD--\n",
"Name: sequence_alignment, dtype: object\n",
"1349 -------KKVTFL-E-E-VTEYYIS-----------\n",
"1350 -------KKVTFL-E-E-VTEYYISGDE-DRK-G--\n",
"1351 -------KTVTWPEEGKLREYFYFELDETERVNV-N\n",
"1352 -------KKVTFL-E-E-VTEYYISGDE-DRK-GPW\n",
"1353 GAMGRKRKTVTWPEEGKLREYFYFELDETERVNV-N\n",
"Name: sequence_alignment, dtype: object\n",
"1368 --VFFAED-----\n",
"1369 --VFFAEDVGS--\n",
"1370 KLVFFAEDVGSNK\n",
"Name: sequence_alignment, dtype: object\n",
"1373 --LTGCGDIIA-E-----\n",
"1374 --YQGGGEEMA-L-P---\n",
"1375 --SYEGYEGYY-S--Q--\n",
"1376 PRDSYSGDALY-E--F--\n",
"1377 ----GGGEQLAINEL-IS\n",
"Name: sequence_alignment, dtype: object\n",
"1378 --------SFNLAPLGRR---\n",
"1379 ---E-L-EAYRLGPASA----\n",
"1380 ---GLA-LKYLLTPVN-----\n",
"1381 ------SH-FNLAPLGRRRV-\n",
"1382 ERLE-L-EAYRLGPAS----A\n",
"Name: sequence_alignment, dtype: object\n",
"614 -------------------------------RKRKFS---------...\n",
"615 -------------------------------RKRTWR---------...\n",
"616 -------------------------------RKRGYS---------...\n",
"617 -------------------------------RKRKWS---------...\n",
"618 -------------------------------QKRSFS---------...\n",
"619 ------------------------------LGKRKY----------...\n",
"620 ----------------------------L--GKRKRH---------...\n",
"621 ------------------------------VAKKYRN---------...\n",
"622 ----------------------------P-PKKKRK-V--------...\n",
"623 ----------------------------E-PSKRARPA----E---...\n",
"624 ------------------------------PVKKPKI----R----...\n",
"625 ----------------------------P-AAKRVKLD--------...\n",
"626 ----------------------------P-FKKKRRE----A----...\n",
"627 -------------------------AA-P-PKKKRKV----E----...\n",
"628 -----------------D--------G-P-TAKKLKTE----Q---...\n",
"629 ------------------------A-I-S-PSKRARP----AE---...\n",
"630 -------------------------GS-P-PKKKRKVG--------...\n",
"631 -------------------------------GKISKHWTGI-----...\n",
"632 ----------------DE---------EGGG-EEDQ-D-----FDL...\n",
"633 ---------------------DAQHAA-P-PKKKRKVE--------...\n",
"634 --SAKRKEP--E----PK--------G-S-TKKKAKT---------...\n",
"635 A--VKRPAA--T---KKA----------G-QAKKKKL---------...\n",
"636 SA-VKRPAA--T---KK---------A-G-QAKKKK-L----D---...\n",
"637 SA-VKRPAA--T----KK--------A-G-QAKKKKLD--------...\n",
"638 C--GKRSAE--G---SN--P------P-K-PLKKLRG---------...\n",
"639 P--RKRPL---E---WDE--D----EE-P-PRKRKRLW--------...\n",
"640 ---MSRRRHSDE---NDG--------G-Q-PHKRRKTS----D---...\n",
"641 R--KKRKTE--E---ES-PL------K-D-KAKKSKG---------...\n",
"642 ---MSRRRHSYE---NDG--------G-Q-PHKRRKTS----D---...\n",
"643 RR-RKRKREW-D---D---D-----DD-P-PKKRRRLD--------...\n",
"644 ---EKKKRT--VAEEDQL-HL----DG-Q-ENKRRRHD---S----...\n",
"Name: sequence_alignment, dtype: object\n",
"645 -------KKRKV-----\n",
"646 -------KKRKV-----\n",
"647 -------GKRKR-----\n",
"648 -------KKRKV-----\n",
"649 -------KKRREA----\n",
"650 -------SKRAR--PA-\n",
"651 ------KKKRKV-EY--\n",
"652 -----SPSKRAR--P--\n",
"653 -G--S--IIRKWN----\n",
"654 TV--L--GKRK------\n",
"655 SV--L--GKRKRH----\n",
"656 -------RKRTWRDAF-\n",
"657 ----H--RKRKFSDAF-\n",
"658 -R--Q--RKRKWSEAF-\n",
"659 --SQG--TKRSYEQME-\n",
"660 -------RKRGYSVAF-\n",
"661 --SRG--QKRSFSKAFG\n",
"Name: sequence_alignment, dtype: object\n",
"1387 -------LQQTQAQVDEVVDIMRVNVDKVLERD\n",
"1388 NLTSNRRLQQTQAQVDEVVDIMRVNVDKVLERD\n",
"Name: sequence_alignment, dtype: object\n",
"1392 --DLTVEKAADVTWEEEAEQTGVSHNLMITVDDDGTMRIKD-----...\n",
"1393 STDMWIERTADISWESDAEITGSSERVDVRLDDDGNFQLM------...\n",
"1394 GTDMWIERTADISWESDAEITGSSERVDVRLDDDGNFQLMN-----...\n",
"1395 ETDMWIERTADITWESDAEITGSSERVDVRLDDDGNFQLM------...\n",
"1396 --DMWIERTADISWESDAEITGSSERVDVRLDDDGNFQL-MNDPGA...\n",
"Name: sequence_alignment, dtype: object\n",
"1404 AAAAAAA----\n",
"1405 RPKPQQFFGLM\n",
"Name: sequence_alignment, dtype: object\n",
"1411 ----GLLDALDLAS\n",
"1412 -Q--GLLDALDLAS\n",
"1413 GHGQGLLDALDLAS\n",
"Name: sequence_alignment, dtype: object\n",
"663 ------VVKQNCLKLAT------------------\n",
"664 ----LQPFPQPELPY--------------------\n",
"665 ----QWIRVNIPKRI--------------------\n",
"666 -----AWRSDEALPLGS------------------\n",
"667 ----QVIILNHPGQISA------------------\n",
"668 ----AAYSDQATPLLLS------------------\n",
"669 ----STDYGILQINSRW------------------\n",
"670 ----SAVRLRSSVPGVR------------------\n",
"671 ----PKYVKQNTLKLAT------------------\n",
"672 ----PEVIPMFSALSEG------------------\n",
"673 ----RQLYPEWTEAQRL------------------\n",
"674 ----SGEGSFQPSQENP------------------\n",
"675 --A-DLIAYPKAATKF-------------------\n",
"676 ---GSDWRFLRGYHQY-------------------\n",
"677 --G-ELIGTLNAAKVPAD-----------------\n",
"678 ----LVEALYLVCGERGG-----------------\n",
"679 ---GSDARFLRGYHLYA------------------\n",
"680 --G-ELIGILNAAKVPAD-----------------\n",
"681 --V-SKWRMATPLLMQAL-----------------\n",
"682 -PV-SKMRMATPLLM--------------------\n",
"683 -PV-SKMRMATPLLMQA------------------\n",
"684 ----PEVIPMFSALSEG-A---------T------\n",
"685 -PV-SKMRMATPLLMQAL---------P-------\n",
"686 PVV-HFFKNIVTPRTPPP---------S-------\n",
"687 ----MNLPSTKVSWAAVG-----------GGGSLV\n",
"688 ----QHIRCNIPKRIGP-SKVATLVPR--------\n",
"Name: sequence_alignment, dtype: object\n"
]
}
],
"source": [
"UniprotPerBindingModeECR = []\n",
"CATHPerBindingModeECR = []\n",
"PFAMPerBindingModeECR = []\n",
"\n",
"\n",
"\n",
"for uniid in list(np.unique(PixelDBecr[\"unique_id\"])):\n",
" sdf = PixelDBecr[PixelDBecr[\"unique_id\"] == uniid]\n",
" print(sdf[\"sequence_alignment\"])\n",
" UniprotPerBindingMode.append(np.sum(np.unique(sdf[\"uniprot\"]) != \"nan\"))\n",
" CATHuni = []\n",
" for cid in sdf[\"CATH\"]:\n",
" #print(cid.split(\"_\"))\n",
" if str(cid) != \"nan\":\n",
" CATHuni += cid.split(\"_\")\n",
" CATHPerBindingMode.append(len(list(set(CATHuni))))\n",
" \n",
" PFAMuni = []\n",
" for cid in sdf[\"PFAM\"]:\n",
" #print(cid.split(\"_\"))\n",
" if str(cid) != \"nan\":\n",
" PFAMuni += cid.split(\"_\")\n",
" PFAMPerBindingMode.append(len(list(set(PFAMuni))))\n",
" #print(list(set(CATHuni)))\n",
" #die"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fdbd7408350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(CATHPerBindingMode,100)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IOError",
"evalue": "File /media/vince/Postdoc/PixelDB/PixelDB/other_files/pdbmap does not exist",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIOError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-20-b69a39a8ddc2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#Load PFAM\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mpdbmap_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"PDB\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Chain\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"unk\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"name\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"PFAM\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"uniprot\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"range\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mpdbmap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"/media/vince/Postdoc/PixelDB/PixelDB/other_files/pdbmap\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpdbmap_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdelimiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"\\t\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mpdbmap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpdbmap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"unk\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mpdbmap_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpdbmap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[0;32m 527\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[0;32m 528\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 529\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 530\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 531\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 293\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 294\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 295\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 296\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 297\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 610\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 611\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 612\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 613\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 614\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_options_with_defaults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 745\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 746\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 747\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 748\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 749\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 1117\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'allow_leading_cols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1118\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1119\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1121\u001b[0m \u001b[1;31m# XXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas/parser.c:3246)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader._setup_parser_source (pandas/parser.c:6111)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mIOError\u001b[0m: File /media/vince/Postdoc/PixelDB/PixelDB/other_files/pdbmap does not exist"
]
}
],
"source": [
"#Load PFAM\n",
"pdbmap_name = [\"PDB\",\"Chain\",\"unk\",\"name\",\"PFAM\",\"uniprot\",\"range\"]\n",
"pdbmap = pd.read_table(\"/media/vince/Postdoc/PixelDB/PixelDB/other_files/pdbmap\",names=pdbmap_name,delimiter=\"\\t\")\n",
"pdbmap = pdbmap.drop(\"unk\",axis=1)\n",
"pdbmap_name = list(pdbmap.columns.values)\n",
"for c in pdbmap_name:\n",
" pdbmap[c] = pdbmap[c].str.replace(';', '')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#cath-b-newest-all\n",
"cath_name = [\"PDB\",\"v\",\"CATH\",\"range\"]\n",
"cathb = pd.read_table(\"/media/vince/Postdoc/PixelDB/PixelDB/other_files/cath-b-newest-all\",delimiter=\" \",names=cath_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for ikl in range(0,3):\n",
" torun = PixelDB\n",
" if ikl == 0:\n",
" print(\"Full\")\n",
" if ikl == 1:\n",
" torun = PixelDBecr\n",
" print(\"ECR\")\n",
" if ikl == 2:\n",
" torun = PixelDBoecr\n",
" print(\"ECRdist\")\n",
" \n",
" UniquePFAM = dict()\n",
" for uniid in list(np.unique(torun[\"PFAM\"])):\n",
" if str(uniid) == \"nan\":\n",
" continue\n",
" for v in uniid.split(\"_\"):\n",
" #print(v)\n",
" if v not in UniquePFAM:\n",
" UniquePFAM[v] = 0\n",
" UniquePFAM[v] += 1\n",
" #break\n",
"\n",
" UniqueCATH = dict()\n",
" for uniid in list(np.unique(torun[\"CATH\"])):\n",
" if str(uniid) == \"nan\":\n",
" continue\n",
" for v in uniid.split(\"_\"):\n",
" #print(v)\n",
" if v not in UniqueCATH:\n",
" UniqueCATH[v] = 0\n",
" UniqueCATH[v] += 1\n",
"\n",
" UniqueUniprot = dict()\n",
" for uniid in list(np.unique(torun[\"uniprot\"])):\n",
" if str(uniid) == \"nan\":\n",
" continue\n",
" for v in uniid.split(\"_\"):\n",
" #print(v)\n",
" if v not in UniqueUniprot:\n",
" UniqueUniprot[v] = 0\n",
" UniqueUniprot[v] += 1\n",
"\n",
"\n",
" print(\"PDB has this many unique PFAM\",len(pdbmap[\"PFAM\"].value_counts()))\n",
" print(\"PixelDB has this many unique PFAM\",len(UniquePFAM))\n",
" print(\"Percentage = \",100.0*len(UniquePFAM) / float(len(pdbmap[\"PFAM\"].value_counts())))\n",
" #print(\"PixelDBecr has this many unique PFAM\",len(PixelDBecr[\"PFAM\"].value_counts()))\n",
"\n",
" print(\"PDB has this many unique uniprot\",len(pdbmap[\"uniprot\"].value_counts()))\n",
" print(\"PixelDB has this many unique uniprot\",len(UniqueUniprot))\n",
"\n",
" print(\"Percentage = \",100.0*len(UniqueUniprot) / float(len(pdbmap[\"uniprot\"].value_counts())))\n",
"\n",
" #print(\"PixelDBecr has this many unique uniprot\",len(PixelDBecr[\"uniprot\"].value_counts()))\n",
" print(\"PDB has this many unique CATH\",len(cathb[\"CATH\"].value_counts()))\n",
" print(\"PixelDB has this many unique CATH\",len(UniqueCATH))\n",
"\n",
" print(\"Percentage = \",100.0*len(UniqueCATH) / float(len(cathb[\"CATH\"].value_counts())))\n",
" \n",
" print(\"Most frequence Cath\")\n",
" for w in sorted(UniqueCATH, key=UniqueCATH.get, reverse=True)[0:3]:\n",
" print w, UniqueCATH[w]\n",
" print(\"Most frequence uniprot\")\n",
" for w in sorted(UniqueUniprot, key=UniqueUniprot.get, reverse=True)[0:3]:\n",
" print w, UniqueUniprot[w]\n",
" print(\"Most frequence PFAM\")\n",
" for w in sorted(UniquePFAM, key=UniquePFAM.get, reverse=True)[0:3]:\n",
" print w, UniquePFAM[w]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"allCarac = [\"resolution\",\"receptor_length\",\"peptide_length\",\"size_of_binding_mode\",\"longest_continuous_core\",\"longest_continuous_ecr\"]\n",
"\n",
"for v in allCarac:\n",
" AllMean = []\n",
" for uniid in list(np.unique(PixelDB[\"unique_id\"])):\n",
" sdf = PixelDB[PixelDB[\"unique_id\"] == uniid]\n",
" AllMean.append(np.mean(sdf[v]))\n",
" print(\"%30s Avg:%6.2f Std:%6.2f Median:%6.2f Min:%6.2f Max:%7.2f\" % (v,np.mean(AllMean),np.std(AllMean),np.median(AllMean),np.min(PixelDB[v]),np.max(PixelDB[v])))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"allCarac = [\"resolution\",\"receptor_length\",\"peptide_length\",\"size_of_binding_mode\",\"longest_continuous_core\",\"longest_continuous_ecr\"]\n",
"\n",
"for v in allCarac:\n",
" AllMean = []\n",
" for uniid in list(np.unique(PixelDBecr[\"unique_id\"])):\n",
" sdf = PixelDBecr[PixelDBecr[\"unique_id\"] == uniid]\n",
" AllMean.append(np.mean(sdf[v]))\n",
" \n",
" print(\"%30s Avg:%6.2f Std:%6.2f Median:%6.2f Min:%6.2f Max:%7.2f\" % (v,np.mean(AllMean),np.std(AllMean),np.median(AllMean),np.min(PixelDBecr[v]),np.max(PixelDBecr[v])))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"allCarac = [\"resolution\",\"receptor_length\",\"peptide_length\",\"size_of_binding_mode\",\"longest_continuous_core\",\"longest_continuous_ecr\"]\n",
"PixelDBoecr = PixelDB[PixelDB[\"longest_continuous_ecr\"] > 3]\n",
"for v in allCarac:\n",
" AllMean = []\n",
" for uniid in list(np.unique(PixelDBoecr[\"unique_id\"])):\n",
" sdf = PixelDBoecr[PixelDBoecr[\"unique_id\"] == uniid]\n",
" AllMean.append(np.mean(sdf[v]))\n",
" \n",
" print(\"%30s Avg:%6.2f Std:%6.2f Median:%6.2f Min:%6.2f Max:%7.2f\" % (v,np.mean(AllMean),np.std(AllMean),np.median(AllMean),np.min(PixelDBoecr[v]),np.max(PixelDBoecr[v])))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"PixelDB.sort([\"peptide_length\"],ascending=False).head(5)[[\"name\",\"size_of_binding_mode\",\"cluster_number\",\"receptor_length\",\"peptide_length\"]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"PixelDB.sort([\"receptor_length\"]).head(5)[[\"name\",\"size_of_binding_mode\",\"cluster_number\",\"receptor_length\",\"peptide_length\"]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def bootstrap(AllCore,AllECR,it=10000):\n",
" BtECR = dict()\n",
" BtCore = dict()\n",
" for lab in Label:\n",
" BtECR[lab] = []\n",
" BtCore[lab] = []\n",
" for it in range(0,it):\n",
" ECRMean = []\n",
" CoreMean = []\n",
" for aa in Label:\n",
"\n",
" index = np.random.randint(len(AllECR), size=len(AllECR[aa]))\n",
"\n",
" ECRMean.append(np.sum(np.array(AllECR[aa])[index]))\n",
" CoreMean.append(np.sum(np.array(AllCore[aa])[index]))\n",
" ECRMean = np.array(ECRMean)/np.sum(ECRMean)*100.0\n",
" CoreMean = np.array(CoreMean)/np.sum(CoreMean)*100.0\n",
" for i in range(0,len(ECRMean)):\n",
" BtECR[Label[i]].append(ECRMean[i])\n",
" BtCore[Label[i]].append(CoreMean[i])\n",
" return(BtCore,BtECR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"AllMeanData = dict()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"PixelDBoecr = PixelDB[PixelDB[\"longest_continuous_ecr\"] > 3]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"LegendLabel = [\"Solvent Exposure [0-9]\",\"Levy Classification\",\"Stride Classifcation\", \"Amino Acid compostion\"]\n",
"ToTest = [\"percent_exposed_alignment\",\"levy_alignment\",\"stride\",\"sequence_alignment\"]\n",
"\n",
"#LegendLabel = [\"Solvent Exposure [0-9]\",\"Stride Classifcation\"]\n",
"#ToTest = [\"percent_exposed_alignment\",\"stride\"]\n",
"\n",
"\n",
"for (alin,LL) in zip(ToTest,LegendLabel):\n",
" if alin not in list(PixelDBoecr.columns.values):\n",
" continue\n",
" AllMeanData[LL] = dict()\n",
" print(alin)\n",
" \n",
" AllECR = dict()\n",
" AllCore = dict()\n",
" \n",
" \n",
" SumCore = 0.0\n",
" SumECR = 0.0\n",
" \n",
" #Find Label\n",
" Label = []\n",
" for v in PixelDBoecr[alin]:\n",
" for i in range(0,len(v)):\n",
" if v[i] == \"-\":\n",
" continue\n",
" Label.append(v[i])\n",
" Label = sorted(list(set(Label)))\n",
" if alin == \"sequence_alignment\":\n",
" Label = myAmino\n",
" \n",
" df = pd.DataFrame()\n",
" \n",
" print(alin,Label)\n",
" count = 0\n",
" for uniid in list(np.unique(PixelDBoecr[\"unique_id\"])):\n",
" \n",
" Tecr = dict()\n",
" Tcore = dict()\n",
" Totecr = 0.0\n",
" Totcore = 0.0\n",
" \n",
" sdf = PixelDBoecr[PixelDBoecr[\"unique_id\"] == uniid]\n",
" \n",
" for (ecr,ali,lecr) in zip(np.array(sdf[\"core_ecr_alignment\"]),np.array(sdf[alin]),np.array(sdf[\"longest_continuous_ecr\"])):\n",
" \n",
" if lecr < 4:\n",
" continue\n",
" #print(ecr,ali[0])\n",
" #ali = ali[0]\n",
" #print(ali,lecr)\n",
" for i in range(0,len(ecr)):\n",
" #if alin == \"sequence_alignment\":\n",
" # if levy[i] != \"C\":\n",
" # continue\n",
" if ecr[i] == \"E\":\n",
" if ali[i] not in Tecr:\n",
" Tecr[ali[i]] = 0.0\n",
" Tecr[ali[i]] += 1.0\n",
" Totecr += 1.0\n",
" SumECR += 1.0\n",
" \n",
" if ecr[i] == \"C\":\n",
" if ali[i] not in Tcore:\n",
" Tcore[ali[i]] = 0.0\n",
" Tcore[ali[i]] += 1.0\n",
" Totcore += 1.0\n",
" SumCore += 1.0\n",
" #print(ali[i],ecr[i])\n",
" #break\n",
" \n",
" for aa in Label:\n",
" if aa not in Tecr:\n",
" Tecr[aa] = 0.0\n",
" #print(aa,Tecr[aa],Totecr)\n",
" if aa not in AllECR:\n",
" AllECR[aa] = []\n",
" \n",
" AllECR[aa].append(Tecr[aa] / float(Totecr))\n",
"\n",
" if aa not in Tcore:\n",
" Tcore[aa] = 0.0\n",
" if aa not in AllCore:\n",
" AllCore[aa] = []\n",
" AllCore[aa].append(Tcore[aa] / float(Totcore))\n",
" count += 1\n",
" df = df.append({'class': 'Core', 'AA': aa, 'percentage': Tcore[aa] / float(Totcore)}, ignore_index=True)\n",
" df = df.append({'class': 'ECR', 'AA': aa, 'percentage': Tecr[aa] / float(Totecr)}, ignore_index=True) \n",
" \n",
" #break\n",
" print(SumCore,SumECR)\n",
" ECRMean = []\n",
" CoreMean = []\n",
" \n",
" (BtCore,BtECR) = bootstrap(AllCore,AllECR,it=10000)\n",
" \n",
" \n",
" for aa in Label:\n",
" ECRMean.append(np.mean(BtECR[aa]))\n",
" CoreMean.append(np.mean(BtCore[aa]))\n",
" \n",
" \n",
" plt.scatter(CoreMean,ECRMean)\n",
" for i in range(0,len(Label)):\n",
" ttest = stats.ttest_rel(BtECR[Label[i]], BtCore[Label[i]])[1]\n",
" print(\"%2d %2s P.val=%10.8f Core:%6.2f ECR:%6.2f\" % (i,Label[i],ttest,CoreMean[i],ECRMean[i]))\n",
" #print(i,Label[i],ttest,\"CORE:%4.2f \" % (CoreMean[i]),\"ECR\",ECRMean[i])\n",
" if Label[i] not in AllMeanData[LL]:\n",
" AllMeanData[LL][Label[i]] = dict()\n",
" AllMeanData[LL][Label[i]][\"ECR\"] = [ECRMean[i],np.std(BtECR[Label[i]]) ]\n",
" AllMeanData[LL][Label[i]][\"Core\"] = [CoreMean[i],np.std(BtCore[Label[i]])]\n",
" if ttest < 0.05:\n",
" plt.text(CoreMean[i]+0.15,ECRMean[i]+0.15,Label[i])\n",
" \n",
" Lim = [-3,int(np.max(ECRMean+CoreMean)*1.1)]\n",
" #print(Lim)\n",
" #Lim = [-1,10]\n",
" plt.plot(Lim,Lim,c=\"black\")\n",
" plt.xlim(Lim)\n",
" plt.ylim(Lim)\n",
" plt.xlabel(\"Core \"+LL+\" %\")\n",
" plt.ylabel(\"ECR \"+LL+\" %\")\n",
" plt.show() \n",
" \n",
" sns.boxplot(x=\"AA\",hue=\"class\",y=\"percentage\",data=df)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Label = myAmino\n",
"Color = [\"red\"]*3+[\"blue\"]*2+[\"purple\"]*4+[\"black\"]*3+[\"green\"]*5+[\"yellow\"]*3\n",
"LL= \"Amino Acid buried\"\n",
"AllMeanData[LL] = dict()\n",
"for i in range(0,len(Label)):\n",
" plt.scatter(CoreMean[i],ECRMean[i],c=Color[i])\n",
" ttest = stats.ttest_rel(BtECR[Label[i]], BtCore[Label[i]])[1]\n",
" print(\"%2d %2s P.val=%10.8f Core:%6.2f ECR:%6.2f\" % (i,Label[i],ttest,CoreMean[i],ECRMean[i]))\n",
" \n",
" if Label[i] not in AllMeanData[LL]:\n",
" AllMeanData[LL][Label[i]] = dict()\n",
" AllMeanData[LL][Label[i]][\"ECR\"] = [ECRMean[i],np.std(BtECR[Label[i]]) ]\n",
" AllMeanData[LL][Label[i]][\"Core\"] = [CoreMean[i],np.std(BtCore[Label[i]])]\n",
" \n",
" \n",
" #print(i,Label[i],ttest,\"CORE:%4.2f \" % (CoreMean[i]),\"ECR\",ECRMean[i])\n",
" #if ttest < 0.05:\n",
" plt.text(CoreMean[i]+0.15,ECRMean[i]+0.15,Label[i],color=Color[i])\n",
"Lim = [-3,int(np.max(ECRMean+CoreMean)*1.1)]\n",
"#print(Lim)\n",
"Lim = [-1,15]\n",
"plt.plot(Lim,Lim,c=\"black\")\n",
"plt.xlim(Lim)\n",
"plt.ylim(Lim)\n",
"plt.xlabel(\"Core composition (%)\")\n",
"plt.ylabel(\"ECR composition (%)\")\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"AllECR = dict()\n",
"AllCore = dict()\n",
"AllSurf = dict()\n",
"AllInte = dict()\n",
"\n",
"\n",
"SumCore = 0.0\n",
"SumECR = 0.0\n",
"SumSurf = 0.0\n",
"SumInte = 0.0\n",
"\n",
"for aa in myAmino:\n",
" AllECR[aa] = []\n",
" AllCore[aa] = []\n",
" AllSurf[aa] = []\n",
" AllInte[aa] = []\n",
"Label = myAmino\n",
"for uniid in list(np.unique(PixelDBoecr[\"unique_id\"])):\n",
" sdf = PixelDBoecr[PixelDBoecr[\"unique_id\"] == uniid]\n",
" Tecr = dict()\n",
" Tcore = dict()\n",
" Tsurf = dict()\n",
" Tinte = dict()\n",
" Totecr = 0.0\n",
" Totcore = 0.0\n",
" Totsurf = 0.0\n",
" Totinte = 0.0\n",
" for v in sdf[\"EXOSITE_aa\"]:\n",
" for aa in v.split(\";\"):\n",
" sp = aa.split(\":\")\n",
" if sp[0] not in Tecr:\n",
" Tecr[sp[0]] = 0\n",
" Tecr[sp[0]] += float(sp[1])\n",
" Totecr += float(sp[1])\n",
" SumECR += float(sp[1])\n",
" for v in sdf[\"surface_aa\"]:\n",
" for aa in v.split(\";\"):\n",
" sp = aa.split(\":\")\n",
" if sp[0] not in Tsurf:\n",
" Tsurf[sp[0]] = 0\n",
" Tsurf[sp[0]] += float(sp[1])\n",
" Totsurf += float(sp[1])\n",
" SumSurf += float(sp[1])\n",
" for v in sdf[\"interior_aa\"]:\n",
" \n",
" for aa in v.split(\";\"):\n",
" sp = aa.split(\":\")\n",
" if sp[0] not in Tinte:\n",
" Tinte[sp[0]] = 0\n",
" Tinte[sp[0]] += float(sp[1])\n",
" Totinte += float(sp[1])\n",
" SumInte += float(sp[1])\n",
" \n",
" for v in sdf[\"COREBINDING_aa\"]:\n",
" for aa in v.split(\";\"):\n",
" sp = aa.split(\":\")\n",
" if sp[0] not in Tcore:\n",
" Tcore[sp[0]] = 0\n",
" Tcore[sp[0]] += float(sp[1])\n",
" Totcore += float(sp[1])\n",
" SumCore += float(sp[1]) \n",
" \n",
" Label = myAmino\n",
" for aa in Label:\n",
" if aa not in Tecr:\n",
" Tecr[aa] = 0.0\n",
" #print(aa,Tecr[aa],Totecr)\n",
" if aa not in AllECR:\n",
" AllECR[aa] = []\n",
" if (Totecr != 0):\n",
" AllECR[aa].append(Tecr[aa] / float(Totecr))\n",
" else:\n",
" AllECR[aa].append(0)\n",
"\n",
" if aa not in Tcore:\n",
" Tcore[aa] = 0.0\n",
" if aa not in AllCore:\n",
" AllCore[aa] = []\n",
" if Totcore != 0:\n",
" AllCore[aa].append(Tcore[aa] / float(Totcore))\n",
" else:\n",
" AllCore[aa].append(0)\n",
" \n",
" \n",
" if aa not in Tsurf:\n",
" Tsurf[aa] = 0.0\n",
" if aa not in AllSurf:\n",
" AllSurf[aa] = []\n",
" if Totsurf != 0:\n",
" AllSurf[aa].append(Tsurf[aa] / float(Totsurf))\n",
" else:\n",
" AllSurf[aa].append(0)\n",
" \n",
" if aa not in Tinte:\n",
" Tinte[aa] = 0.0\n",
" if aa not in AllInte:\n",
" AllInte[aa] = []\n",
" if Totinte != 0:\n",
" AllInte[aa].append(Tinte[aa] / float(Totinte))\n",
" else:\n",
" AllInte[aa].append(0)\n",
"ECRMean = []\n",
"CoreMean = []\n",
"SurfMean = []\n",
"InteMean = []\n",
"\n",
"(BtCore,BtECR) = bootstrap(AllCore,AllECR,it=10000)\n",
"\n",
"(BtInte,BtSurf) = bootstrap(AllInte,AllSurf,it=10000)\n",
"\n",
"Label = myAmino\n",
"for aa in Label:\n",
" ECRMean.append(np.mean(BtECR[aa]))\n",
" CoreMean.append(np.mean(BtCore[aa]))\n",
" SurfMean.append(np.mean(BtSurf[aa]))\n",
" InteMean.append(np.mean(BtInte[aa]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(SumCore,SumECR,SumSurf,SumInte)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"Color = [\"red\"]*3+[\"blue\"]*2+[\"purple\"]*4+[\"black\"]*3+[\"green\"]*5+[\"yellow\"]*3\n",
"\n",
"LL= \"Binding Site\"\n",
"AllMeanData[LL] = dict()\n",
"\n",
"\n",
"for i in range(0,len(Label)):\n",
" ttest = stats.ttest_rel(BtSurf[Label[i]], BtCore[Label[i]])[1]\n",
" print(\"%2d %2s P.val=%10.8f Core:%4.2f ECR:%4.2f Surf:%4.2f Inte:%.2f\" % (i,Label[i],ttest,CoreMean[i],ECRMean[i],SurfMean[i],InteMean[i]))\n",
" if Label[i] not in AllMeanData[LL]:\n",
" AllMeanData[LL][Label[i]] = dict()\n",
" AllMeanData[LL][Label[i]][\"ECR\"] = [ECRMean[i],np.std(BtECR[Label[i]]) ]\n",
" AllMeanData[LL][Label[i]][\"Core\"] = [CoreMean[i],np.std(BtCore[Label[i]])] \n",
" AllMeanData[LL][Label[i]][\"Surf\"] = [SurfMean[i],np.std(BtSurf[Label[i]])]\n",
" AllMeanData[LL][Label[i]][\"Inte\"] = [InteMean[i],np.std(BtInte[Label[i]])]\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"AllMean = [CoreMean,ECRMean,SurfMean,InteMean]\n",
"MeanLab = [\"CoreBinding\",\"Exosite\",\"Surface\",\"Interior\"]\n",
"AllMSE = dict()\n",
"AllComp = dict()\n",
"for (arr1,lab1) in zip(AllMean,MeanLab):\n",
" AllMSE[lab1] = dict()\n",
" AllComp[lab1] = dict()\n",
" for i in range(0,20):\n",
" AllComp[lab1][myAmino[i]] = arr1[i]\n",
" for (arr2,lab2) in zip(AllMean,MeanLab):\n",
" plt.scatter(arr1,arr2)\n",
" plt.xlabel(lab1)\n",
" plt.ylabel(lab2)\n",
" for i in range(0,20):\n",
" plt.text(arr1[i]+0.15,arr2[i]+0.15,Label[i],color=Color[i])\n",
" plt.xlim(0,20)\n",
" plt.ylim(0,20)\n",
" plt.plot([0,100],[0,100])\n",
" plt.show()\n",
" #AllMSE[lab1][lab2] = np.sqrt(np.mean(np.power(np.array(arr1)-np.array(arr2),2)))\n",
" AllMSE[lab1][lab2] = np.corrcoef(arr1,arr2)[0][1]\n",
" print(lab1,lab2,np.sqrt(np.mean(np.power(np.array(arr1)-np.array(arr2),2))),np.corrcoef(arr1,arr2)[0][1])\n",
"sns.clustermap(pd.DataFrame(AllMSE))\n",
"plt.show()\n",
"sns.clustermap(pd.DataFrame(AllComp))\n",
"plt.show()\n",
"\n",
"sns.heatmap(pd.DataFrame(AllComp).transpose()[myAmino].transpose(),annot=True)\n",
"plt.show()\n",
"#print(\"Core vs Exosite %.2f\" % (np.sqrt(np.mean(np.power(np.array(CoreMean)-np.array(ECRMean),2)))))\n",
"#print(\"Core vs Surface %.2f\" % (np.sqrt(np.mean(np.power(np.array(CoreMean)-np.array(SurfMean),2)))))\n",
"#print(\"Exosite vs Surface %.2f\" % (np.sqrt(np.mean(np.power(np.array(ECRMean)-np.array(SurfMean),2)))))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"AllECR = dict()\n",
"AllCore = dict()\n",
"AllSurf = dict()\n",
"AllInte = dict()\n",
"\n",
"\n",
"SumCore = 0.0\n",
"SumECR = 0.0\n",
"SumSurf = 0.0\n",
"SumInte = 0.0\n",
"\n",
"for aa in myAmino:\n",
" AllECR[aa] = []\n",
" AllCore[aa] = []\n",
" AllSurf[aa] = []\n",
" AllInte[aa] = []\n",
"Label = ['B', 'C', 'E', 'G', 'H', 'T', 'b']\n",
"for uniid in list(np.unique(PixelDBoecr[\"unique_id\"])):\n",
" sdf = PixelDBoecr[PixelDBoecr[\"unique_id\"] == uniid]\n",
" Tecr = dict()\n",
" Tcore = dict()\n",
" Tsurf = dict()\n",
" Tinte = dict()\n",
" Totecr = 0.0\n",
" Totcore = 0.0\n",
" Totsurf = 0.0\n",
" Totinte = 0.0\n",
" for v in sdf[\"EXOSITE_ss\"]:\n",
" for aa in v.split(\";\"):\n",
" sp = aa.split(\":\")\n",
" if sp[0] not in Tecr:\n",
" Tecr[sp[0]] = 0\n",
" Tecr[sp[0]] += float(sp[1])\n",
" Totecr += float(sp[1])\n",
" SumECR += float(sp[1])\n",
" for v in sdf[\"surface_ss\"]:\n",
" for aa in v.split(\";\"):\n",
" sp = aa.split(\":\")\n",
" if sp[0] not in Tsurf:\n",
" Tsurf[sp[0]] = 0\n",
" Tsurf[sp[0]] += float(sp[1])\n",
" Totsurf += float(sp[1])\n",
" SumSurf += float(sp[1])\n",
" for v in sdf[\"interior_ss\"]:\n",
" \n",
" for aa in v.split(\";\"):\n",
" sp = aa.split(\":\")\n",
" if sp[0] not in Tinte:\n",
" Tinte[sp[0]] = 0\n",
" Tinte[sp[0]] += float(sp[1])\n",
" Totinte += float(sp[1])\n",
" SumInte += float(sp[1])\n",
" \n",
" for v in sdf[\"COREBINDING_ss\"]:\n",
" for aa in v.split(\";\"):\n",
" sp = aa.split(\":\")\n",
" if sp[0] not in Tcore:\n",
" Tcore[sp[0]] = 0\n",
" Tcore[sp[0]] += float(sp[1])\n",
" Totcore += float(sp[1])\n",
" SumCore += float(sp[1]) \n",
" \n",
" for aa in Label:\n",
" if aa not in Tecr:\n",
" Tecr[aa] = 0.0\n",
" #print(aa,Tecr[aa],Totecr)\n",
" if aa not in AllECR:\n",
" AllECR[aa] = []\n",
" if (Totecr != 0):\n",
" AllECR[aa].append(Tecr[aa] / float(Totecr))\n",
" else:\n",
" AllECR[aa].append(0)\n",
"\n",
" if aa not in Tcore:\n",
" Tcore[aa] = 0.0\n",
" if aa not in AllCore:\n",
" AllCore[aa] = []\n",
" if Totcore != 0:\n",
" AllCore[aa].append(Tcore[aa] / float(Totcore))\n",
" else:\n",
" AllCore[aa].append(0)\n",
" \n",
" \n",
" if aa not in Tsurf:\n",
" Tsurf[aa] = 0.0\n",
" if aa not in AllSurf:\n",
" AllSurf[aa] = []\n",
" if Totsurf != 0:\n",
" AllSurf[aa].append(Tsurf[aa] / float(Totsurf))\n",
" else:\n",
" AllSurf[aa].append(0)\n",
" \n",
" if aa not in Tinte:\n",
" Tinte[aa] = 0.0\n",
" if aa not in AllInte:\n",
" AllInte[aa] = []\n",
" if Totinte != 0:\n",
" AllInte[aa].append(Tinte[aa] / float(Totinte))\n",
" else:\n",
" AllInte[aa].append(0)\n",
"ECRMean = []\n",
"CoreMean = []\n",
"SurfMean = []\n",
"InteMean = []\n",
"\n",
"(BtCore,BtECR) = bootstrap(AllCore,AllECR,it=10000)\n",
"\n",
"(BtInte,BtSurf) = bootstrap(AllInte,AllSurf,it=10000)\n",
"\n",
"for aa in Label:\n",
" ECRMean.append(np.mean(BtECR[aa]))\n",
" CoreMean.append(np.mean(BtCore[aa]))\n",
" SurfMean.append(np.mean(BtSurf[aa]))\n",
" InteMean.append(np.mean(BtInte[aa]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"AllMean = [CoreMean,ECRMean,SurfMean,InteMean]\n",
"MeanLab = [\"CoreBinding\",\"Exosite\",\"Surface\",\"Interior\"]\n",
"AllMSE = dict()\n",
"AllComp = dict()\n",
"for (arr1,lab1) in zip(AllMean,MeanLab):\n",
" AllMSE[lab1] = dict()\n",
" AllComp[lab1] = dict()\n",
" for i in range(0,len(Label)):\n",
" AllComp[lab1][Label[i]] = arr1[i]\n",
" for (arr2,lab2) in zip(AllMean,MeanLab):\n",
" #plt.scatter(arr1,arr2)\n",
" #plt.xlabel(lab1)\n",
" #plt.ylabel(lab2)\n",
" #for i in range(0,20):\n",
" # plt.text(arr1[i]+0.15,arr2[i]+0.15,Label[i],color=Color[i])\n",
" #plt.show()\n",
" AllMSE[lab1][lab2] = np.sqrt(np.mean(np.power(np.array(arr1)-np.array(arr2),2)))\n",
" print(lab1,lab2,np.sqrt(np.mean(np.power(np.array(arr1)-np.array(arr2),2))))\n",
"sns.clustermap(pd.DataFrame(AllMSE))\n",
"plt.show()\n",
"sns.heatmap(pd.DataFrame(AllMSE),annot=True)\n",
"plt.show()\n",
"sns.clustermap(pd.DataFrame(AllComp))\n",
"plt.show()\n",
"\n",
"sns.heatmap(pd.DataFrame(AllComp).transpose()[Label].transpose(),annot=True)\n",
"plt.show()\n",
"#print(\"Core vs Exosite %.2f\" % (np.sqrt(np.mean(np.power(np.array(CoreMean)-np.array(ECRMean),2)))))\n",
"#print(\"Core vs Surface %.2f\" % (np.sqrt(np.mean(np.power(np.array(CoreMean)-np.array(SurfMean),2)))))\n",
"#print(\"Exosite vs Surface %.2f\" % (np.sqrt(np.mean(np.power(np.array(ECRMean)-np.array(SurfMean),2)))))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"Color = [\"red\"]*3+[\"blue\"]*2+[\"purple\"]*4+[\"black\"]*3+[\"green\"]*5+[\"yellow\"]*3\n",
"\n",
"LL= \"Binding Site SS\"\n",
"AllMeanData[LL] = dict()\n",
"\n",
"\n",
"for i in range(0,len(Label)):\n",
" ttest = stats.ttest_rel(BtSurf[Label[i]], BtCore[Label[i]])[1]\n",
" print(\"%2d %2s P.val=%10.8f Core:%4.2f ECR:%4.2f Surf:%4.2f Inte:%.2f\" % (i,Label[i],ttest,CoreMean[i],ECRMean[i],SurfMean[i],InteMean[i]))\n",
" if Label[i] not in AllMeanData[LL]:\n",
" AllMeanData[LL][Label[i]] = dict()\n",
" AllMeanData[LL][Label[i]][\"ECR\"] = [ECRMean[i],np.std(BtECR[Label[i]]) ]\n",
" AllMeanData[LL][Label[i]][\"Core\"] = [CoreMean[i],np.std(BtCore[Label[i]])] \n",
" AllMeanData[LL][Label[i]][\"Surf\"] = [SurfMean[i],np.std(BtSurf[Label[i]])]\n",
" AllMeanData[LL][Label[i]][\"Inte\"] = [InteMean[i],np.std(BtInte[Label[i]])]\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"figsize(20,8)\n",
"Test = [\"Amino Acid\",\"Stride Classifcation\",\"Solvent Exposure [0-9]\",\"Binding Site\",\"Binding Site SS\"]\n",
"Test = AllMeanData.keys()\n",
"Label = Test\n",
"\n",
"Order = [\"Core\",\"ECR\",\"Surf\",\"Inte\"]\n",
"Col = [\"darkred\",\"darkblue\",\"gray\",\"black\"]\n",
"\n",
"pos = 0.0\n",
"for test in Test:\n",
" pos = 0.0\n",
" pos += 1.0\n",
" \n",
" sub = AllMeanData[test]\n",
" #print(len(sub))\n",
" Tlab = sorted(sub)\n",
" if len(Tlab) == 20:\n",
" Tlab = myAmino\n",
" Xlabpos = []\n",
" \n",
" for t in Tlab:\n",
" Tpos = []\n",
" \n",
" for o in Order:\n",
" if o not in sub[t]:\n",
" continue\n",
" plt.bar([pos],sub[t][o][0],color=Col[Order.index(o)])\n",
" plt.plot([pos+0.4,pos+0.4],[sub[t][o][0],sub[t][o][0]+sub[t][o][1]],color=\"black\")\n",
" \n",
" plt.plot([pos+0.1,pos+0.7],[sub[t][o][0]+sub[t][o][1]]*2,color=\"black\")\n",
" \n",
" Tpos.append(pos)\n",
" pos += 1.0\n",
" Xlabpos.append(np.mean(Tpos)+0.5)\n",
" pos += 1\n",
" #print(t)\n",
" #break\n",
" plt.xticks(Xlabpos, Tlab)\n",
" plt.title(test)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"AllMeanData.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mpl.style.use('seaborn-whitegrid')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"figsize(20,8)\n",
"Test = [\"Binding Site\",\"Amino Acid compostion\",\"Solvent Exposure [0-9]\"]\n",
"Label = [\"Receptor Amino Acid Composition (%)\",\"Peptide Amino Acid Composition (%)\",\"Solvent Exposure (%)\"]\n",
"Order = [\"Core\",\"ECR\",\"Surf\",\"Inte\"]\n",
"Col = [\"darkred\",\"darkblue\",\"gray\",\"black\"]\n",
"Color = [\"red\"]*3+[\"blue\"]*2+[\"purple\"]*4+[\"black\"]*3+[\"green\"]*5+[\"yellow\"]*3\n",
"pos = 0.0\n",
"for (test,lab) in zip(Test,Label):\n",
" pos = 0.0\n",
" pos += 1.0\n",
" \n",
" sub = AllMeanData[test]\n",
" #print(len(sub))\n",
" Tlab = sorted(sub)\n",
" if len(Tlab) == 20:\n",
" Tlab = myAmino\n",
" Xlabpos = []\n",
" \n",
" for t in Tlab:\n",
" Tpos = []\n",
" \n",
" for o in Order:\n",
" if o not in sub[t]:\n",
" continue\n",
" plt.bar([pos],sub[t][o][0],color=Col[Order.index(o)])\n",
" plt.plot([pos,pos],[sub[t][o][0],sub[t][o][0]+sub[t][o][1]],color=\"black\")\n",
" \n",
" plt.plot([pos-0.3,pos+0.3],[sub[t][o][0]+sub[t][o][1]]*2,color=\"black\")\n",
" \n",
" Tpos.append(pos)\n",
" pos += 1.0\n",
" Xlabpos.append(np.mean(Tpos))\n",
" pos += 1\n",
" #print(t)\n",
" #break\n",
" if test == \"Solvent Exposure [0-9]\":\n",
" Tlab = [\"[0-10[\",\"[10-20[\",\"[20-30[\",\"[30-40[\",\"[40-50[\",\"[50-60[\",\"[60-70[\"]\n",
" \n",
" plt.xticks(Xlabpos, Tlab)\n",
" plt.tick_params(axis='both', which='major', labelsize=18)\n",
" plt.xlabel(lab,size=18)\n",
" plt.ylabel(\"Residue distribution %\",rotation=90)\n",
" plt.show()\n",
" #break"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"#Relationship between buried and aa type\n",
"AllDF = []\n",
"for (t,name) in zip(range(0,3),[\"All\",\"Core\",\"ECR\"]):\n",
" Count = dict()\n",
" Tot = dict()\n",
" for aa in myAmino:\n",
" Count[aa] = dict()\n",
" Tot[aa] = 0\n",
" for i in range(0,9):\n",
" Count[aa][str(i)] = 0\n",
" torun = PixelDBecr\n",
" if t == 0:\n",
" torun = PixelDB\n",
" for (seq,bury,ecr,size) in zip(torun[\"sequence_alignment\"],torun[\"percent_exposed_alignment\"],torun[\"core_ecr_alignment\"],torun[\"size_of_binding_mode\"]):\n",
" #print(seq,bury)\n",
" for i in range(0,len(seq)):\n",
" if seq[i] == \"-\":\n",
" continue\n",
" if t == 1:\n",
" if ecr[i] == \"E\":\n",
" continue\n",
" if t == 2:\n",
" if ecr[i] == \"C\":\n",
" continue\n",
" if seq[i] not in Count:\n",
" Count[seq[i]] = dict()\n",
" if bury[i] not in Count[seq[i]]:\n",
" Count[seq[i]][bury[i]] = 0\n",
" #print(i,seq[i],bury[i])\n",
" Count[seq[i]][bury[i]] += 1.0/float(size)\n",
" Tot[seq[i]] += 1/float(size)\n",
" #break\n",
" NormC = dict()\n",
" for aa in Tot:\n",
" NormC[aa] = dict()\n",
" print(aa,Tot[aa])\n",
" for b in Count[aa]:\n",
" bt = b\n",
" if int(bt) > 4:\n",
" bt = \"5+\"\n",
" if bt not in NormC[aa]:\n",
" NormC[aa][bt] = 0\n",
" \n",
" NormC[aa][bt] += int(float(Count[aa][b]) / float(Tot[aa])*100.0+0.5)\n",
" print(name)\n",
" figsize(8,8)\n",
" df = pd.DataFrame(NormC)\n",
" df = df[myAmino]\n",
" AllDF.append(df)\n",
" sns.heatmap(df[myAmino],vmax=70,annot=True)\n",
" plt.title(name )\n",
" plt.ylabel(\"Amino acid binned exposure distribution\")\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.heatmap(AllDF[1]-AllDF[2],annot=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.scatter(PixelDBoecr[\"peptide_length\"],PixelDBoecr[\"longest_continuous_ecr\"])\n",
"plt.xlabel(\"Peptide Length\")\n",
"plt.ylabel(\"ECR Length\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.scatter(PixelDB[\"peptide_length\"],PixelDB[\"longest_continuous_ecr\"])\n",
"plt.xlabel(\"Peptide Length\")\n",
"plt.ylabel(\"ECR Length\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(PixelDBecr[PixelDBecr[\"longest_continuous_ecr\"]>=4][\"peptide_length\"].mean())\n",
"print(PixelDBecr[PixelDBecr[\"longest_continuous_ecr\"]>=4][\"peptide_length\"].min())\n",
"print(PixelDBecr[PixelDBecr[\"longest_continuous_ecr\"]>=4][\"peptide_length\"].max())\n",
"plt.hist(PixelDBecr[PixelDBecr[\"longest_continuous_ecr\"]>=4][\"peptide_length\"].values,25)\n",
"plt.tick_params(axis='both', which='major', labelsize=18)\n",
"plt.xlabel(\"Peptide Length\")\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Count = dict()\n",
"for (ecr,pl) in zip(PixelDBecr[\"longest_continuous_ecr\"],PixelDBecr[\"peptide_length\"]):\n",
" if ecr not in Count:\n",
" Count[ecr] = dict()\n",
" if pl not in Count[ecr]:\n",
" Count[ecr][pl]=0\n",
" Count[ecr][pl] += 1\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.heatmap(pd.DataFrame(Count).fillna(value=0).transpose(),annot=True)\n",
"plt.xlabel(\"Peptide Length\")\n",
"plt.ylabel(\"ECR Length\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Find longest ecr\n",
"PixelDB.sort([\"longest_continuous_ecr\"],ascending=False).head(5)[[\"name\",\"size_of_binding_mode\",\"unique_id\",\"receptor_length\",\"peptide_length\"]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Search for traf\n",
"tolook = \"2.60.210.10\"\n",
"for (v,uniid,seq) in zip(np.array(PixelDB[\"CATH\"]),np.array(PixelDB[\"unique_id\"]),np.array(PixelDB[\"sequence_alignment\"])):\n",
" if v == nan:\n",
" continue\n",
" if re.search(tolook,str(v)):\n",
" print(v,uniid,seq)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#ECR contact to receptor\n",
"myCut = 3\n",
"tot = 0\n",
"low = 0\n",
"for (exp,seq) in zip(np.array(PixelDB[\"percent_exposed_alignment\"]),np.array(PixelDB[\"core_ecr_alignment\"])):\n",
" #rint(exp,seq)\n",
" for i in range(0,len(exp)):\n",
" if seq[i] == \"-\":\n",
" continue\n",
" if (seq[i] == \"e\") or (seq[i] == \"E\"):\n",
" tot += 1\n",
" if int(exp[i]) <= myCut:\n",
" low += 1\n",
" #rint(i,exp[i],seq[i])\n",
"print(tot,low)\n",
"print(float(low)/float(tot))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"PixelDB.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"carac = pd.read_table(\"/media/vince/Postdoc/PixelDB/PixelDB/other_files/AllCp.dat\",names=[\"PDB\",\"Chain\",\"CP\",\"Tot_cont\"],delimiter=\"\\s\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"figsize(10,10)\n",
"plt.hist(carac[\"CP\"]*100,20)\n",
"plt.xlabel(\"Peptide surface contacting a symmetry-related complex (%)\", fontsize=18 )\n",
"plt.ylabel(\"Count\", fontsize=18)\n",
"plt.tick_params(axis='both', which='major', labelsize=18)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in range(1,100):\n",
" plt.scatter(i,np.sum(carac[\"CP\"]*100 > i)/float(len(carac[\"CP\"])))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in [5,10,15,20,50]:\n",
" print(i,np.sum(carac[\"CP\"]*100 > i)/float(len(carac[\"CP\"])),np.sum(carac[\"CP\"]*100 > i))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Most frequent PFAM in multiple binding mod\n",
"\n",
"\n",
"for bla in list(np.unique(PixelDB[\"cluster_number\"])):\n",
" sdf = PixelDB[PixelDB[\"cluster_number\"] == bla]\n",
" if (len(np.unique(sdf[\"unique_id\"])) < 10):\n",
" continue\n",
" UniqueCATH = dict()\n",
" for uniid in list(np.unique(sdf[\"CATH\"])):\n",
" if str(uniid) == \"nan\":\n",
" continue\n",
" for v in uniid.split(\"_\"):\n",
" #print(v)\n",
" if v not in UniquePFAM:\n",
" UniqueCATH[v] = 0\n",
" UniqueCATH[v] += 1\n",
" print(bla,len(np.unique(sdf[\"unique_id\"])),list(set(UniqueCATH)))\n",
" #break"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Does receptor cluster with multiple binding mode have more CATH\n",
"\n",
"for bla in list(np.unique(PixelDB[\"cluster_number\"])):\n",
" sdf = PixelDB[PixelDB[\"cluster_number\"] == bla]\n",
" UniqueCATH = dict()\n",
" for uniid in list(np.unique(sdf[\"CATH\"])):\n",
" if str(uniid) == \"nan\":\n",
" continue\n",
" for v in uniid.split(\"_\"):\n",
" #print(v)\n",
" if v not in UniquePFAM:\n",
" UniqueCATH[v] = 0\n",
" UniqueCATH[v] += 1\n",
" plt.scatter(len(np.unique(sdf[\"unique_id\"])),len(list(set(UniqueCATH))))\n",
" plt.xlabel(\"Number of binding mode\")\n",
" plt.ylabel(\"Number of CATH\")\n",
" #print(bla,len(np.unique(sdf[\"unique_id\"])),len(list(set(UniqueCATH))))\n",
" #break"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sorted(list(np.unique(PixelDBoecr[\"unique_id\"])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for clu in list(np.unique(PixelDB[\"cluster_number\"])):\n",
" #print(clu)\n",
" sdf = PixelDB[PixelDB[\"cluster_number\"] == clu]\n",
" if len(np.unique(sdf[\"unique_id\"])) < 5:\n",
" continue\n",
" print(list(np.unique(sdf[\"unique_id\"])))\n",
" #break"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.unique(sdf[\"unique_id\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import glob"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"#Get all simil Matrix\n",
"AllPair = []\n",
"NormCount = dict()\n",
"AllK = []\n",
"Fract = []\n",
"AverageSeqIden = []\n",
"for i in range(0,21):\n",
" myK = float(i)/float(20)\n",
" AllK.append(myK)\n",
" NormCount[myK] = 0\n",
"for f in glob.glob('/media/vince/Postdoc/PixelDB/PixelDB/clusters/*/*_simil.CSV'):\n",
" sdf = pd.read_table(f,sep=\"\\s\")\n",
" if len(sdf) == 1:\n",
" continue\n",
" np.fill_diagonal(sdf.values, -1)\n",
" keep = np.triu(np.ones(sdf.shape)).astype('bool').reshape(sdf.size)\n",
" allval = np.array(sdf.stack()[keep])\n",
" allval = list(allval[allval > 0])\n",
" AllPair += allval\n",
" AverageSeqIden.append(np.mean(allval))\n",
" toadd = 1.0 / float(len(allval))\n",
" Fract.append(np.sum(np.array(allval) > 0.7) / float(len(allval)))\n",
" #die\n",
" for v in allval:\n",
" myK = int(v*20.0)/20.0\n",
" #print(int(v*20.0)/20.0,len(allval))\n",
" NormCount[myK] += toadd\n",
" #if len(sdf) == 5:\n",
" # break"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(np.mean(AverageSeqIden))\n",
"plt.hist(np.array(AverageSeqIden)*100)\n",
"plt.xlabel(\"Average sequence idendity in receptor cluster (%)\", fontsize=18 )\n",
"plt.ylabel(\"Count\", fontsize=18)\n",
"plt.tick_params(axis='both', which='major', labelsize=18)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(np.mean(AverageSeqIden),np.median(AverageSeqIden),np.min(AverageSeqIden),np.max(AverageSeqIden))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.hist(Fract,20)\n",
"plt.title(\"Distribution of receptor within cluster >70% seq idendity\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"AllV = []\n",
"for myK in AllK:\n",
" AllV.append(NormCount[myK])\n",
"plt.bar(np.array(AllK)*100,AllV)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.hist(np.array(AllPair)*100,30)\n",
"plt.xlabel(\"Pairwise sequence identidy in cluster receptor of +2 complexes\")\n",
"plt.ylabel(\"Count\")\n",
"plt.tick_params(axis='both', which='major', labelsize=18)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"AllIn = []\n",
"AllBe = []\n",
"\n",
"\n",
"AllBetweenBindingMode =[]\n",
"\n",
"for clu in list(np.unique(torun[\"cluster_number\"])):\n",
" sdf = torun[torun[\"cluster_number\"] == clu]\n",
" if (len(sdf[\"unique_id\"].value_counts()) == 1):\n",
" continue\n",
" #if (len(sdf[\"unique_id\"].value_counts()) != 2):\n",
" # continue\n",
" for f in glob.glob(\"/media/vince/Postdoc/PixelDB/PixelDB/clusters/\"+str(clu)+\"/*_simil.CSV\"):\n",
" simildf = pd.read_table(f,sep=\"\\s\")\n",
" #print(f)\n",
" \n",
" allunid = list(np.unique(sdf[\"unique_id\"]))\n",
" InBindingMode = []\n",
" BetweenMode = []\n",
" for i in range(0,len(allunid)):\n",
" unidd1 = allunid[i]\n",
" for j in range(i,len(allunid)):\n",
" unidd2 = allunid[j]\n",
" df1 = sdf[sdf[\"unique_id\"] == unidd1]\n",
" df2 = sdf[sdf[\"unique_id\"] == unidd2]\n",
"\n",
" id1 = df1[\"pdb_id\"]+\"_\"+df1[\"receptor_chain\"]\n",
" id2 = df2[\"pdb_id\"]+\"_\"+df2[\"receptor_chain\"]\n",
" allval = []\n",
" for i1 in id1:\n",
" for i2 in id2:\n",
" allval.append(simildf[i1].transpose()[i2])\n",
" fract = np.sum(np.array(allval) > 0.7) / float(len(allval))\n",
" #print(i,j,unidd1,unidd2,fract)\n",
" if i == j:\n",
" InBindingMode += allval\n",
" else:\n",
" BetweenMode += allval\n",
" AllBetweenBindingMode += allval\n",
" fract = np.sum(np.array(InBindingMode) > 0.7) / float(len(InBindingMode))\n",
" #print(\"In binding mode\",fract)\n",
" AllIn.append(fract)\n",
" fract = np.sum(np.array(BetweenMode) > 0.7) / float(len(BetweenMode))\n",
" #print(\"Between binding mode\",fract)\n",
" AllBe.append(fract)\n",
" #break"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"simildf[id1].transpose()[id2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.hist(AllBetweenBindingMode)\n",
"plt.tick_params(axis='both', which='major', labelsize=18)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in range(80,101):\n",
" print(i,np.sum(np.array(AllBetweenBindingMode) > (float(i)/100)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.hist(AllIn,20)\n",
"plt.xlim((0,1))\n",
"plt.title(\"In binding mode\")\n",
"plt.show()\n",
"plt.hist(AllBe,20)\n",
"plt.title(\"Between binding mode\")\n",
"plt.xlim((0,1))\n",
"plt.show()\n",
"print(\"Average fraction >70% in Binding Mode\",np.mean(AllIn))\n",
"print(\"Average fraction >70% Between Binding Mode\",np.mean(AllBe))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"PixelDB.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"AllArrang = dict()\n",
"for v in np.array(PixelDB[\"core_ecr_alignment\"]):\n",
" vinit = v\n",
" v = v.replace(\"-\",\"\")\n",
" for i in range(0,len(v)):\n",
" vi = v\n",
" v = re.sub('C+', \"C\", v)\n",
" v = re.sub('E+', \"E\", v)\n",
" v = re.sub('e+', \"e\", v)\n",
" v = re.sub('c+', \"c\", v)\n",
" if v == vi:\n",
" break\n",
" if v not in AllArrang:\n",
" AllArrang[v] = 0\n",
" AllArrang[v] += 1\n",
" #print(vinit,v)\n",
"for w in sorted(AllArrang, key=AllArrang.get, reverse=True):\n",
" print w, AllArrang[w]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"AllArrang = dict()\n",
"for v in np.array(PixelDBoecr[\"core_ecr_alignment\"]):\n",
" vinit = v\n",
" v = v.replace(\"-\",\"\")\n",
" for i in range(0,len(v)):\n",
" vi = v\n",
" v = re.sub('C+', \"C\", v)\n",
" v = re.sub('E+', \"E\", v)\n",
" v = re.sub('e+', \"e\", v)\n",
" v = re.sub('c+', \"c\", v)\n",
" if v == vi:\n",
" break\n",
" vinit = vinit.replace(\"-\",\"\")\n",
" print(vinit,v)\n",
" if v not in AllArrang:\n",
" AllArrang[v] = 0\n",
" AllArrang[v] += 1\n",
" #print(vinit,v)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tot = 0\n",
"for w in sorted(AllArrang, key=AllArrang.get, reverse=True):\n",
" if (w[0] == \"E\") or (w[-1] == \"E\"):\n",
" tot += AllArrang[w]\n",
" #else:\n",
" print w, AllArrang[w]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(tot,len(PixelDBoecr))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"AllArrang = dict()\n",
"sdf = PixelDB[PixelDB[\"cluster_number\"] == 1]\n",
"for v in np.array(sdf[\"core_ecr_alignment\"]):\n",
" vinit = v\n",
" \n",
" v = v.replace(\"-\",\"\")\n",
" for i in range(0,len(v)):\n",
" vi = v\n",
" v = re.sub('C+', \"C\", v)\n",
" v = re.sub('E+', \"E\", v)\n",
" v = re.sub('e+', \"e\", v)\n",
" v = re.sub('c+', \"c\", v)\n",
" if v == vi:\n",
" break\n",
" if v not in AllArrang:\n",
" AllArrang[v] = 0\n",
" AllArrang[v] += 1\n",
" #print(vinit,v)\n",
"for w in sorted(AllArrang, key=AllArrang.get, reverse=True):\n",
" print w, AllArrang[w]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Amino acid composition\n",
"AllAAComb = dict()\n",
"sub = AllMeanData[\"Binding Site\"]\n",
"\n",
"for aa in sub:\n",
" if aa not in AllAAComb:\n",
" AllAAComb[aa] = dict()\n",
" for t in sub[aa]:\n",
" ti = t\n",
" if t == \"Core\":\n",
" ti = \"Core-binding\"\n",
" if t == \"ECR\":\n",
" ti = \"Exosite\"\n",
" if t == \"Inte\":\n",
" ti = \"Interior\"\n",
" if t == \"Surf\":\n",
" ti = \"NISR\"\n",
" AllAAComb[aa][ti] = sub[aa][t][0]\n",
"sub = AllMeanData[\"Amino Acid compostion\"]\n",
"\n",
"for aa in sub:\n",
" if aa not in AllAAComb:\n",
" AllAAComb[aa] = dict()\n",
" for t in sub[aa]:\n",
" AllAAComb[aa][t] = sub[aa][t][0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.DataFrame(AllAAComb)[myAmino].transpose()[[\"Core\",\"ECR\",\"Core-binding\",\"Exosite\",\"NISR\",\"Interior\"]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"LabelOr = [\"Core\",\"ECR\",\"Core-binding\",\"Exosite\",\"NISR\",\"Interior\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"colcol = [\"red\",\"blue\"]+[\"gray\"]*4\n",
"LabelOr = [\"Core\",\"ECR\",\"Core-binding\",\"Exosite\",\"NISR\",\"Interior\"]\n",
"sns.set(font_scale=1.4)\n",
"#sns.clustermap(df,row_cluster=False,row_colors = Color,col_colors=colcol)\n",
"pal = sns.light_palette(\"navy\", as_cmap=True)\n",
"sns.clustermap(df[LabelOr],row_cluster=False,col_cluster=False,cmap=pal,figsize=(12,12),linewidths=.5,annot=True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"colcol = [\"red\",\"blue\"]+[\"gray\"]*4\n",
"sns.set(font_scale=1.9)\n",
"#sns.clustermap(df,row_cluster=False,row_colors = Color,col_colors=colcol)\n",
"pal = sns.light_palette(\"navy\", as_cmap=True)\n",
"sns.clustermap(df,row_cluster=False,cmap=pal,figsize=(12,12),linewidths=.5,annot=True,metric=\"Correlation\", fmt=\".1f\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"figsize(20,8)\n",
"Test = [\"Binding Site\",\"Amino Acid compostion\",\"Solvent Exposure [0-9]\"]\n",
"Label = [\"Receptor Amino Acid Composition (%)\",\"Peptide Amino Acid Composition (%)\",\"Solvent Exposure (%)\"]\n",
"Order = [\"Core\",\"ECR\",\"Surf\",\"Inte\"]\n",
"Col = [\"darkred\",\"darkblue\",\"gray\",\"black\"]\n",
"Color = [\"red\"]*3+[\"blue\"]*2+[\"purple\"]*4+[\"black\"]*3+[\"green\"]*5+[\"yellow\"]*3\n",
"pos = 0.0\n",
"for (test,lab) in zip(Test,Label):\n",
" pos = 0.0\n",
" pos += 1.0\n",
" \n",
" sub = AllMeanData[test]\n",
" #print(len(sub))\n",
" Tlab = sorted(sub)\n",
" if len(Tlab) == 20:\n",
" Tlab = myAmino\n",
" Xlabpos = []\n",
" \n",
" for t in Tlab:\n",
" Tpos = []\n",
" \n",
" for o in Order:\n",
" if o not in sub[t]:\n",
" continue\n",
" plt.bar([pos],sub[t][o][0],color=Col[Order.index(o)])\n",
" #plt.plot([pos+0.4,pos+0.4],[sub[t][o][0],sub[t][o][0]+sub[t][o][1]],color=\"black\")\n",
" \n",
" plt.plot([pos+0.1,pos+0.7],[sub[t][o][0]+sub[t][o][1]]*2,color=\"black\")\n",
" \n",
" Tpos.append(pos)\n",
" pos += 1.0\n",
" Xlabpos.append(np.mean(Tpos)+0.5)\n",
" pos += 1\n",
" #print(t)\n",
" #break\n",
" if test == \"Solvent Exposure [0-9]\":\n",
" Tlab = [\"[0-10[\",\"[10-20[\",\"[20-30[\",\"[30-40[\",\"[40-50[\",\"[50-60[\",\"[60-70[\"]\n",
" \n",
" plt.xticks(Xlabpos, Tlab)\n",
" plt.tick_params(axis='both', which='major', labelsize=18)\n",
" plt.xlabel(lab,size=18)\n",
" \n",
" plt.show()\n",
" #break"
]
}
],
"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 |
mirjalil/DataScience | .ipynb_checkpoints/predictive-modeling-checkpoint.ipynb | 1 | 9130 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluation\n",
"\n",
" * Holdout method (2/3 for training, 1/3 for testing) \n",
" * Repeated holdout \n",
" * Cross-validation \n",
" * k-fold cross-validation \n",
" * startified k-fold cross-validation (equal representation of classes) \n",
" * leave-one-out \n",
" * Bootstrap (sample with replacement)\n",
" \n",
"Most commonly: 10 fold cross-validation\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preprocessing Steps\n",
"\n",
"\n",
"**Data matrix:** rows are instances/items/patterns and columns are features/attributes.\n",
"\n",
"### Statistical Noise \n",
" \n",
"\n",
" Common rule for removing outliers: $ \\text{mean} + 3\\times \\text{std} $ \n",
" \n",
" \n",
"### Dealing with missing values\n",
" \n",
" * Get rid of them\n",
" * Deleting the instances (records) \n",
" * Deleting features with high missing cases \n",
" \n",
" * Replacement with mean values \n",
" * Inference / imputations\n",
" * \n",
" \n",
"### Feature Transformation\n",
" \n",
" * Combining variables \n",
" * Scaling data \n",
" * Mean centering $x_{new} = x - \\text{mean}(x) $\n",
" * Z-score: mean centering and standardization $x_{new} = \\frac{x - \\text{mean}(x)}{\\text{std}(x)}$\n",
" * scale to range $[0 .. 1]$ by $x_{new} = \\frac{x - \\min(x)}{\\max(x) - \\min(x)}$\n",
" * Log-scaling $x_{new} = \\log(x)$\n",
" * Discretization\n",
" \n",
"### Feature selection\n",
" \n",
" * Reducing the features with low correletaions ot outcome \n",
" * Start from empty set of features and add 1 feature at a time while testing the performance on a sample set"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Statistical Modeling\n",
"\n",
"Basic assumptions: \n",
" * Variables are equally important \n",
" * Variables are statistically independent \n",
" \n",
"**Statistical Indepence:** knowledge about the value of one attribute, desn't tell us anything about the value of other attributes.\n",
"\n",
"### Naive Bayes Rule\n",
"\n",
" * $\\text{Pr}(\\text{Hypothesis}\\ |\\ \\text{Evidence})=\\displaystyle \\frac{\\text{Pr}(\\text{Evidence} \\ | \\ \\text{Hypothesis})\\times \\text{Pr}(\\text{Hypothesis})}{\\text{Pr}(\\text{Evidence})}$\n",
" \n",
"Naive Bayes assumption: evidence terms are conditionnaly independet from each other. As a result, evidence can be split into independent parts\n",
"\n",
"$$\\text{Pr}(\\text{Evidence} \\ | \\ \\text{Hypothesis}) = \\text{Pr}(\\text{E}_1 \\ | \\ \\text{Hypothesis}) \\times \\text{Pr}(\\text{E}_2 \\ | \\ \\text{Hypothesis}) \\times .. \\times \\text{Pr}(\\text{E}_d \\ | \\ \\text{Hypothesis})$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Decision Tree Induction\n",
"\n",
" * A method for estimating descrete valued functions \n",
" * Robust to noise and missing data \n",
" * Applicable to non-linear relationships \n",
" \n",
" #### Basic Frameork\n",
" \n",
" * Each node tests an attribute\n",
" * Each branch correpond to some possible attribute values \n",
" * Each leaf node assigns a class \n",
" \n",
" * Training a decision tree will include the minimum set of attributes in order to fit the data\n",
" \n",
" #### Construction of a decision tree\n",
" * First attribute is selected as a root node based on given criterion and branches are created\n",
" * The instances are split into subsets according to the condition on the node (Divide & Conqure)\n",
" * The procedure is repeated recursivly for each branch, by creating new nodes and dividing the data into subsets\n",
" * Stop when all the instances at the node have the same class (pure node = leaf node), or we run out of attribues on the path from to the node.\n",
" \n",
" #### Algorithmic Procedure\n",
" \n",
" * Loop while not done\n",
" * At each step, pick the best attribute to split the data\n",
" * For each value of the selected attribute, create branches (child nodes)\n",
" \n",
" ### Criterion for selecting the *best* attribute\n",
" \n",
" \n",
" #### Information gain \n",
" \n",
" Define entropy: $$\\text{entropy}(p_1, p_2, ..p_n) = -p_1\\log p_1 -p_2 \\log p_2 ... -p_n log p_n$$\n",
" \n",
" * Total entropy: the weigthed average entropy at each node \n",
" * Information Gain: the entropy before spliting at a node minus total entropy after splitting $$\\text{Gain} = \\text{entropy}_\\text{before splitting} - \\text{entropy}_\\text{after splitting}$$\n",
" \n",
" #### Gini Index\n",
" \n",
" ### Overfitting in Decision Trees\n",
" \n",
" #### Gain Ratio\n",
" \n",
" Information Gain divided by interinsic information\n",
" \n",
" * Inrinsic information: entropy of an attribute (not the class)\n",
" * The intrinsic informaiton of attribute $v_a$ is higher than $v_b$, if $v_a$ has higher number of possible values \n",
" \n",
" ### Pruning Decision Trees\n",
" \n",
" #### Pre-pruning\n",
" \n",
" #### Post-pruning\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"--------\n",
"\n",
" * **Example**\n",
" \n",
"| Index | Age | Marital Status | Highest Degree | Risk Factor |\n",
"|-------|-----|----------------|----------------|--------------|\n",
"| 1 | 22 | Single | BS | High |\n",
"| 2 | 27 | Married | MS | Low |\n",
"| 3 | 34 | Single | MS | High |\n",
"| 4 | 45 | Married | PhD | Low |\n",
"| 5 | 28 | Married | PhD | Low |\n",
"| 6 | 32 | Single | BS | High |\n",
"| 7 | 31 | Married | PhD | Low |\n",
"| 8 | 38 | Married | BS | High |\n",
"| 9 | 36 | Single | MS | Low |\n",
"| 10 | 41 | Married | MS | Low |\n",
"\n",
"\n",
"\n",
" * Initial Entropy:\n",
" \n",
" $\\text{entropy} = -\\frac{4}{10}\\log \\frac{4}{10} - \\frac{6}{10} \\log \\frac{6}{10} = 0.971$\n",
"\n",
" * Split on Marital Status: \n",
" \n",
" <img hight=100 width=250 src='figs/decision-tree/decision-tree-2.png'>\n",
" \n",
" Child 1: Single (3H, 1L) \n",
" $\\text{entropy}=-\\frac{3}{4}\\log \\frac{3}{4} - \\frac{1}{4}\\log \\frac{1}{4} = 0.658$ \n",
" Child 2: Married (1H, 5L) $\\text{entropy}=-\\frac{1}{6}\\log \\frac{1}{6} - \\frac{5}{6}\\log \\frac{5}{6} = 0.651$\n",
" \n",
" * Total entropy: $\\text{entropy}_{tot}=\\frac{4\\times 0.658 + 6\\times 0.651}{10} = 0.654$ \n",
" \n",
" * Information Gain for this node: $\\text{Gain} = 0.971 - 0.654 = 0.317$\n",
" \n",
" \n",
" \n",
" * Split on Degree: \n",
" \n",
" <img hight=100 width=300 src='figs/decision-tree/decision-tree-1.png'>\n",
" \n",
" Child 1: BS (3H, 0L) $\\text{entropy}=-\\frac{3}{3}\\log \\frac{3}{3} = 0$ \n",
" Child 2: MS (1H, 3L) $\\text{entropy}=-\\frac{1}{4}\\log \\frac{1}{4} - \\frac{3}{4}\\log \\frac{3}{4} = 0.811$ \n",
" Child 3: PhD (0H, 3L) $\\text{entropy}=-\\frac{3}{3}\\log \\frac{3}{3} = 0$ \n",
" \n",
" * Total entropy: $\\text{entropy}_{tot} = \\frac{0+4*0.811+0}{10} = 0.324$\n",
" * Information Gain for this node: $\\text{Gain} = 0.971 - 0.324 = 0.647$\n",
" \n",
" * Therefore, splitting on Highest Degree resules in greater information gain.\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
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| gpl-2.0 |
nerdcommander/scientific_computing_2017 | lesson17/Lesson17_team_improved.ipynb | 1 | 18608 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Unit 3: Simulation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lesson 17: Random processes, modeling, and plotting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Notebook Authors "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_(fill in your two names here)_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Facilitator: _(fill in name)_ \n",
"Spokesperson: _(fill in name)_ \n",
"Process Analyst: _(fill in name)_ \n",
"Quality Control: _(fill in name)_ \n",
"\n",
"If there are only three people in your group, have one person serve as both spokesperson and process analyst for the rest of this activity. \n",
"\n",
"At the end of this Lesson, you will be asked to record how long each Model required for your team. The Facilitator should keep track of time for your team."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computational Focus: Random Numbers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model 0: Context"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"0\\. ***Explain*** one application of computer generated random numbers to your field of science."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model 1: Python Random Number Generator\n",
"\n",
"***Type the following*** into a Jupyter code cell:\n",
"```python \n",
"import random\n",
"for i in range(10):\n",
" print(random.random())\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Critical Thinking Questions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1\\. What is the smallest value printed? The largest value printed?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2\\. If you run the for-loop a second time, is the same series of numbers generated? If not, what is the new range (min and max) of values?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3\\. After repeating the for-loop a number of times, describe (to the best of your ability) the range of numbers returned by the random function."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4\\. Based on the output returned by the random function, describe (to the best of your ability) the nature of the distribution of numbers generated. (Do they appear clustered around a particular value, or are they spread out uniformly over the range?) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model 2: Plotting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the previous model, we evaluated the output of our function by simply looking at list of numbers. In order to visually assess important numerical relationships, it is often helpful to create graphical displays of our data. `matplotlib` is a library for making 2D plots in python. `matplotlib` is a powerful and flexible object-oriented library, which makes it both useful and complex. To simplify some basic plots, we will make use of the `pyplot` interface that works on top of `matplotlib` and makes it easier to make plots more quickly. \n",
"\n",
"Here are some links to the documentation:\n",
"+ [matplotlib docs](http://matplotlib.org/)\n",
"+ [pyplot docs](http://matplotlib.org/api/pyplot_api.html)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def rand_mod2():\n",
" \"\"\"\n",
" produces a histogram of 10 random numbers\n",
" \"\"\"\n",
" import random\n",
" import matplotlib.pyplot as plt \n",
" numbers=[]\n",
" for i in range(10):\n",
" numbers.append(random.random())\n",
" plt.hist(numbers,10)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rand_mod2()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Critical Thinking Questions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"5\\. What would be appropriate labels of the x and y axis for the plot that displays?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"6a. In the cell below, modify the code in `rand_mod2()`, so that it increases the number of random numbers generated and plotted. Comment all changes and run an example."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## new code with comments\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## example\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"6b. Describe how the output plot generated changes when you increase the number of random numbers plotted."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"7\\. Describe how the output plot generated changes when you increase the value of the second parameter of the `pyplot` `hist()` method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## code, change second parameter plt.hist()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"8a. Consider again your answer to question 4 (the last question in the previous model). Based on the plot of the output returned by the random function, how does visualization of this data impact your original assessment of the nature of the distribution?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"8b. Use the `rand_mod2()` code to demonstrate."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"9\\. In general, describe what the `pyplot.hist()` method does with the series of random numbers to create this type of display."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model 3: Data and graph types"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before we get too far in making different types of graphs, we should consider what the correct type of graph might be for the data that we have. Table 1 below is a handy reference for appropriate graph types based on data type. \n",
"\n",
"\n",
"### Table 1\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Critical Thinking Questions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"10\\. Explain why it is appropriate that we used histograms to plot the results of our random number simulations in Model2."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"11\\. Run the code below to bring in the `genotype_height_weight.csv` data set (make sure the data is in the same directory as this notebook, or change the path below): "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"data = pd.read_csv('genotype_height_weight.csv')\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"12a. What are the variable types in the data set? Justify your answers."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"12b. What are the python data types of the variables in the data set? Use code to justify your answer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"13\\. If you wanted to see how many of each genotype there are, what type of graph would you make? Justify your answer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"14\\. If you wanted to see what the distribution of both height and weight, what type of graph would you make? Justify your answer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model 4: More plotting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will use various methods of the matplotlib library to visualize data in a variety of different ways. First we will consider the correct chart/graph types for different kinds of data. Then we will work through a few more examples of useful plots that can be created using `matplotlib`, `pyplot`, and if you have a DataFrame, `pandas`. \n",
"\n",
"Plotting in Python can get pretty complicated since the object oriented `matplotlib` is the basis for most plots, and most of the tools that we use (e.g. `pyplot` and `pandas`) actually sit on top of `matplotlib` and make your life easier (mostly). The advantages of this, are that there are lots of ways to customize plots and simple ones are pretty easy to make, but it can get complicated fast.\n",
"\n",
"For example, ***run the code in the cells below***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## displays a simple bar graph\n",
"import matplotlib.pyplot as plt\n",
"mylista = [1,2,3,4,5]\n",
"mylistb = [6,2,3,4,5]\n",
"plt.bar(mylista, mylistb)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Critical Thinking Questions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"15\\. What does each list in the code above do? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"16\\. What happens when you switch the position of the lists in the `plt.bar` call? "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"17\\. Explain what each argument inside plt.bar() does. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"18a. Experiment making bar charts with the lists below. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## put lists in memory\n",
"mylistc = [1,2,3,4,5]\n",
"mylistd = [6,2,3,4,5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## first, just make a simple bar graph\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## expt 1\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## expt 2\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"18b. what happens when the lists are different lengths? (run code, leave results, write interpretation) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"18c. what happens when items in a list repeats? (you need to consider/test/interpret 2 different scenarios - 1. when the list that is the first parameter repeats, 2. when the list that is the second parameter repeats - but in both cases the lists need to be the same length)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### shaping up our plots\n",
"below is a more realistic and sensibly labled bar chart:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## displays a slightly more complex\n",
"## and better labeled bar graph\n",
"cat = ['A', 'B', 'C', 'D', 'E'] # data categories\n",
"xcat = range(5) # x-axis \"markers\" - category order\n",
"count = [6,2,3,4,5] # heights of bars in order\n",
"plt.bar(xcat,count, align='center') # make plot with x-axis, markers centered\n",
"plt.xticks(xcat, cat) # relabel with real categories\n",
"plt.title('title')\n",
"plt.xlabel('x axis')\n",
"plt.ylabel('y axis')\n",
"plt.show() # draw the plot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"19\\. What is the difference between `cat` and `xcat`? Why do we need both?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"20\\. Explain what happens if you change the order of the values in `cat`? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"21\\. Copy, paste, and modify the code above to plot a barchart of the count of the three different genotypes in the `genotype_height_weight.csv` dataset (remember we brought it into memory above as the `data` DataFrame. (hint: if you remember back to the `pandas` lesson, `df.col_name.value_counts()` will count the values in a column (it returns a Series))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"22\\. Explain why a bar chart is an acceptable type of graph for this data visualization."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### plotting with `pandas` \n",
"The `pandas` library has some great, quick plotting tools for `Series` and `DataFrames`. These functions sit on top of `matplotlib` and just ease the use of DataFrames and columns from DataFrames as input for some useful chart types. The nice thing about this is that `panda'` feeds the data to `matplotlib` in a way that it can use it, and we can still use the formatting syntax that we've learned for `matplotlib` to make labels and titles (rather than having to learn a whole new, `pandas` specific one. \n",
"\n",
"This part of the `pandas` docs has a lot of good info on making graphs: \n",
"http://pandas.pydata.org/pandas-docs/stable/visualization.html \n",
"\n",
"***run*** the code below:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## makes the genotype bar chart\n",
"%matplotlib inline\n",
"data.genotype.value_counts().plot.bar()\n",
"plt.title('genotype count')\n",
"plt.xlabel('genotype')\n",
"plt.ylabel('count')\n",
"plt.xticks(rotation='horizontal')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"22\\. Does this graph match the one you made above? Look carefully... Explain differences and/or why we would ask this question (what is the advantage of the `pandas` version of this plot)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"23a. `pandas` tries to only use the appropriate data types for the plot type that you pick. In the example below, we pass the entire DataFrame to the `plot.box()`, which variable does it leave out and why?."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## run this code\n",
"data.plot.box()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"23b. This plot still looks like crap. Explain why this is **not** an appropriate graph?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"23c. Make and explain a much more appropriate graph to visualize the distribution of these 2 columns."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"24a. What type of graph would show the possible relationship of weight and height?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"24b. Make a graph that uses the `data` DataFrame to visualize the potential relationship of weight to height (height is the explanatory variable and weight is the response variable in this case and the response variable is on the y-axis by convention). Hint:\n",
"```python\n",
"df.plot.scatter(x='x_col', y='y_col')\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"24c. Label the scatter plot with better labels on both axes and a title."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Temporal Analysis Report"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How much time did it require for your team to complete each Model?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Model 1: "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Model 2:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Model 3:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Model 4:"
]
}
],
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
liulixiang1988/data-science-learning | ml/01-协同过滤.ipynb | 1 | 1889 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 协同过滤"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"协同过滤(collaborative filtering)是根据其他用户进行推荐。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. 如何寻找相似用户\n",
"\n",
"推荐的第一步就是找相似用户。加入用户对一些内容进行评分，那么可以根据他们对同样的数据进行打分来判断他们的相似。也就是计算距离。\n",
"\n",
"加入有数据：\n",
"\n",
"|千与千寻|大鱼海棠\n",
"--|--|--\n",
"小明|5|5\n",
"小强|2|5\n",
"小亮|1|4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1 曼哈顿距离\n",
"\n",
"曼哈顿距离(Manhattan Distance)是最简单的计算方法。在二维情况下，每个用户用$(x, y)$表示，因此$(x_1, y_1)$可能是小明，而$(x_2, y_2)$可能是神秘的`X`女士。于是他们的距离可以采用以下公式来计算：$$|x_1-x_2|+|x_2-y_2|$$\n",
"即分别计算$x$坐标和$y$坐标的差值的绝对值。\n",
"\n",
"### 1.2 欧氏距离\n",
"\n",
"勾股定理（毕达哥拉斯定理）：$$\\sqrt{(x_1-x_2)^2+(y_1-y_2)^2}$$\n",
"这里$x_1$、$x_2$分别表示用户1和用户2喜欢《千与千寻》的程度，而$y"
]
}
],
"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 |
CompPhysics/MachineLearning | doc/src/LectureNotes/_build/jupyter_execute/chapter1.ipynb | 1 | 26530 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to Applied Data Analysis and Machine Learning\n",
"\n",
"During the last two decades there has been a swift and amazing\n",
"development of Machine Learning techniques and algorithms that impact\n",
"many areas in not only Science and Technology but also the Humanities,\n",
"Social Sciences, Medicine, Law, indeed, almost all possible\n",
"disciplines. The applications are incredibly many, from self-driving\n",
"cars to solving high-dimensional differential equations or complicated\n",
"quantum mechanical many-body problems. Machine Learning is perceived\n",
"by many as one of the main disruptive techniques nowadays. \n",
"\n",
"Statistics, Data science and Machine Learning form important\n",
"fields of research in modern science. They describe how to learn and\n",
"make predictions from data, as well as allowing us to extract\n",
"important correlations about physical process and the underlying laws\n",
"of motion in large data sets. The latter, big data sets, appear\n",
"frequently in essentially all disciplines, from the traditional\n",
"Science, Technology, Mathematics and Engineering fields to Life\n",
"Science, Law, education research, the Humanities and the Social\n",
"Sciences.\n",
"\n",
"It has become more\n",
"and more common to see research projects on big data in for example\n",
"the Social Sciences where extracting patterns from complicated survey\n",
"data is one of many research directions. Having a solid grasp of data\n",
"analysis and machine learning is thus becoming central to scientific\n",
"computing in many fields, and competences and skills within the fields\n",
"of machine learning and scientific computing are nowadays strongly\n",
"requested by many potential employers. The latter cannot be\n",
"overstated, familiarity with machine learning has almost become a\n",
"prerequisite for many of the most exciting employment opportunities,\n",
"whether they are in bioinformatics, life science, physics or finance,\n",
"in the private or the public sector. This author has had several\n",
"students or met students who have been hired recently based on their\n",
"skills and competences in scientific computing and data science, often\n",
"with marginal knowledge of machine learning.\n",
"\n",
"Machine learning is a subfield of computer science, and is closely\n",
"related to computational statistics. It evolved from the study of\n",
"pattern recognition in artificial intelligence (AI) research, and has\n",
"made contributions to AI tasks like computer vision, natural language\n",
"processing and speech recognition. Many of the methods we will study are also \n",
"strongly rooted in basic mathematics and physics research. \n",
"\n",
"Ideally, machine learning represents the science of giving computers\n",
"the ability to learn without being explicitly programmed. The idea is\n",
"that there exist generic algorithms which can be used to find patterns\n",
"in a broad class of data sets without having to write code\n",
"specifically for each problem. The algorithm will build its own logic\n",
"based on the data. You should however always keep in mind that\n",
"machines and algorithms are to a large extent developed by humans. The\n",
"insights and knowledge we have about a specific system, play a central\n",
"role when we develop a specific machine learning algorithm. \n",
"\n",
"Machine learning is an extremely rich field, in spite of its young\n",
"age. The increases we have seen during the last three decades in\n",
"computational capabilities have been followed by developments of\n",
"methods and techniques for analyzing and handling large date sets,\n",
"relying heavily on statistics, computer science and mathematics. The\n",
"field is rather new and developing rapidly. Popular software packages\n",
"written in Python for machine learning like\n",
"[Scikit-learn](http://scikit-learn.org/stable/),\n",
"[Tensorflow](https://www.tensorflow.org/),\n",
"[PyTorch](http://pytorch.org/) and [Keras](https://keras.io/), all\n",
"freely available at their respective GitHub sites, encompass\n",
"communities of developers in the thousands or more. And the number of\n",
"code developers and contributors keeps increasing. Not all the\n",
"algorithms and methods can be given a rigorous mathematical\n",
"justification, opening up thereby large rooms for experimenting and\n",
"trial and error and thereby exciting new developments. However, a\n",
"solid command of linear algebra, multivariate theory, probability\n",
"theory, statistical data analysis, understanding errors and Monte\n",
"Carlo methods are central elements in a proper understanding of many\n",
"of algorithms and methods we will discuss.\n",
"\n",
"\n",
"\n",
"## Learning outcomes\n",
"\n",
"These sets of lectures aim at giving you an overview of central aspects of\n",
"statistical data analysis as well as some of the central algorithms\n",
"used in machine learning. We will introduce a variety of central\n",
"algorithms and methods essential for studies of data analysis and\n",
"machine learning. \n",
"\n",
"Hands-on projects and experimenting with data and algorithms plays a central role in\n",
"these lectures, and our hope is, through the various\n",
"projects and exercises, to expose you to fundamental\n",
"research problems in these fields, with the aim to reproduce state of\n",
"the art scientific results. You will learn to develop and\n",
"structure codes for studying these systems, get acquainted with\n",
"computing facilities and learn to handle large scientific projects. A\n",
"good scientific and ethical conduct is emphasized throughout the\n",
"course. More specifically, you will\n",
"\n",
"1. Learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;\n",
"\n",
"2. Be capable of extending the acquired knowledge to other systems and cases;\n",
"\n",
"3. Have an understanding of central algorithms used in data analysis and machine learning;\n",
"\n",
"4. Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications, from numerical integration to simulation of stock markets;\n",
"\n",
"5. Understand methods for regression and classification;\n",
"\n",
"6. Learn about neural network, genetic algorithms and Boltzmann machines;\n",
"\n",
"7. Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++, in addition to a basic knowledge of linear algebra (typically taught during the first one or two years of undergraduate studies).\n",
"\n",
"There are several topics we will cover here, spanning from \n",
"statistical data analysis and its basic concepts such as expectation\n",
"values, variance, covariance, correlation functions and errors, via\n",
"well-known probability distribution functions like the uniform\n",
"distribution, the binomial distribution, the Poisson distribution and\n",
"simple and multivariate normal distributions to central elements of\n",
"Bayesian statistics and modeling. We will also remind the reader about\n",
"central elements from linear algebra and standard methods based on\n",
"linear algebra used to optimize (minimize) functions (the family of gradient descent methods)\n",
"and the Singular-value decomposition and\n",
"least square methods for parameterizing data.\n",
"\n",
"We will also cover Monte Carlo methods, Markov chains, well-known\n",
"algorithms for sampling stochastic events like the Metropolis-Hastings\n",
"and Gibbs sampling methods. An important aspect of all our\n",
"calculations is a proper estimation of errors. Here we will also\n",
"discuss famous resampling techniques like the blocking, the bootstrapping\n",
"and the jackknife methods and the infamous bias-variance tradeoff. \n",
"\n",
"The second part of the material covers several algorithms used in\n",
"machine learning.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Machine Learning, a small (and probably biased) introduction\n",
"\n",
"\n",
"Ideally, machine learning represents the science of giving computers\n",
"the ability to learn without being explicitly programmed. The idea is\n",
"that there exist generic algorithms which can be used to find patterns\n",
"in a broad class of data sets without having to write code\n",
"specifically for each problem. The algorithm will build its own logic\n",
"based on the data. You should however always keep in mind that\n",
"machines and algorithms are to a large extent developed by humans. The\n",
"insights and knowledge we have about a specific system, play a central\n",
"role when we develop a specific machine learning algorithm. \n",
"\n",
"\n",
"Machine learning is an extremely rich field, in spite of its young\n",
"age. The increases we have seen during the last decades in\n",
"computational capabilities have been followed by developments of\n",
"methods and techniques for analyzing and handling large date sets,\n",
"relying heavily on statistics, computer science and mathematics. The\n",
"field is rather new and developing rapidly. Popular software libraries\n",
"written in Python for machine learning like\n",
"[Scikit-learn](http://scikit-learn.org/stable/),\n",
"[Tensorflow](https://www.tensorflow.org/),\n",
"[PyTorch](http://pytorch.org/) and [Keras](https://keras.io/), all\n",
"freely available at their respective GitHub sites, encompass\n",
"communities of developers in the thousands or more. And the number of\n",
"code developers and contributors keeps increasing.\n",
"\n",
"\n",
"Not all the\n",
"algorithms and methods can be given a rigorous mathematical\n",
"justification (for example decision trees and random forests), opening up thereby large rooms for experimenting and\n",
"trial and error and thereby exciting new developments. However, a\n",
"solid command of linear algebra, multivariate theory, probability\n",
"theory, statistical data analysis, understanding errors and Monte\n",
"Carlo methods are central elements in a proper understanding of many\n",
"of the algorithms and methods we will discuss.\n",
"\n",
"\n",
"The approaches to machine learning are many, but are often split into\n",
"two main categories. In *supervised learning* we know the answer to a\n",
"problem, and let the computer deduce the logic behind it. On the other\n",
"hand, *unsupervised learning* is a method for finding patterns and\n",
"relationship in data sets without any prior knowledge of the system.\n",
"Some authours also operate with a third category, namely\n",
"*reinforcement learning*. This is a paradigm of learning inspired by\n",
"behavioral psychology, where learning is achieved by trial-and-error,\n",
"solely from rewards and punishment.\n",
"\n",
"Another way to categorize machine learning tasks is to consider the\n",
"desired output of a system. Some of the most common tasks are:\n",
"\n",
"* Classification: Outputs are divided into two or more classes. The goal is to produce a model that assigns inputs into one of these classes. An example is to identify digits based on pictures of hand-written ones. Classification is often supervised learning.\n",
"\n",
"* Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.\n",
"\n",
"* Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.\n",
"\n",
"The methods we cover have three main topics in common, irrespective of\n",
"whether we deal with supervised or unsupervised learning.\n",
"\n",
"* The first ingredient is normally our data set (which can be subdivided into training, validation and test data). Many find the most difficult part of using Machine Learning to be the set up of your data in a meaningful way. \n",
"\n",
"* The second item is a model which is normally a function of some parameters. The model reflects our knowledge of the system (or lack thereof). As an example, if we know that our data show a behavior similar to what would be predicted by a polynomial, fitting our data to a polynomial of some degree would then determin our model. \n",
"\n",
"* The last ingredient is a so-called **cost/loss** function (or error function) which allows us to present an estimate on how good our model is in reproducing the data it is supposed to train. \n",
"\n",
"At the heart of basically all Machine Learning algorithms we will encounter so-called minimization or optimization algorithms. A large family of such methods are so-called **gradient methods**.\n",
"\n",
"\n",
"## A Frequentist approach to data analysis\n",
"\n",
"When you hear phrases like **predictions and estimations** and\n",
"**correlations and causations**, what do you think of? May be you think\n",
"of the difference between classifying new data points and generating\n",
"new data points.\n",
"Or perhaps you consider that correlations represent some kind of symmetric statements like\n",
"if $A$ is correlated with $B$, then $B$ is correlated with\n",
"$A$. Causation on the other hand is directional, that is if $A$ causes $B$, $B$ does not\n",
"necessarily cause $A$.\n",
"\n",
"These concepts are in some sense the difference between machine\n",
"learning and statistics. In machine learning and prediction based\n",
"tasks, we are often interested in developing algorithms that are\n",
"capable of learning patterns from given data in an automated fashion,\n",
"and then using these learned patterns to make predictions or\n",
"assessments of newly given data. In many cases, our primary concern\n",
"is the quality of the predictions or assessments, and we are less\n",
"concerned about the underlying patterns that were learned in order\n",
"to make these predictions.\n",
"\n",
"In machine learning we normally use [a so-called frequentist approach](https://en.wikipedia.org/wiki/Frequentist_inference),\n",
"where the aim is to make predictions and find correlations. We focus\n",
"less on for example extracting a probability distribution function (PDF). The PDF can be\n",
"used in turn to make estimations and find causations such as given $A$\n",
"what is the likelihood of finding $B$.\n",
"\n",
"\n",
"\n",
"## What is a good model?\n",
"\n",
"In science and engineering we often end up in situations where we want to infer (or learn) a\n",
"quantitative model $M$ for a given set of sample points $\\boldsymbol{X} \\in [x_1, x_2,\\dots x_N]$.\n",
"\n",
"As we will see repeatedely in these lectures, we could try to fit these data points to a model given by a\n",
"straight line, or if we wish to be more sophisticated to a more complex\n",
"function.\n",
"\n",
"The reason for inferring such a model is that it\n",
"serves many useful purposes. On the one hand, the model can reveal information\n",
"encoded in the data or underlying mechanisms from which the data were generated. For instance, we could discover important\n",
"corelations that relate interesting physics interpretations.\n",
"\n",
"In addition, it can simplify the representation of the given data set and help\n",
"us in making predictions about future data samples.\n",
"\n",
"A first important consideration to keep in mind is that inferring the *correct* model\n",
"for a given data set is an elusive, if not impossible, task. The fundamental difficulty\n",
"is that if we are not specific about what we mean by a *correct* model, there\n",
"could easily be many different models that fit the given data set *equally well*.\n",
"\n",
"\n",
"The central question is this: what leads us to say that a model is correct or\n",
"optimal for a given data set? To make the model inference problem well posed, i.e.,\n",
"to guarantee that there is a unique optimal model for the given data, we need to\n",
"impose additional assumptions or restrictions on the class of models considered. To\n",
"this end, we should not be looking for just any model that can describe the data.\n",
"Instead, we should look for a **model** $M$ that is the best among a restricted class\n",
"of models. In addition, to make the model inference problem computationally\n",
"tractable, we need to specify how restricted the class of models needs to be. A\n",
"common strategy is to start \n",
"with the simplest possible class of models that is just necessary to describe the data\n",
"or solve the problem at hand. More precisely, the model class should be rich enough\n",
"to contain at least one model that can fit the data to a desired accuracy and yet be\n",
"restricted enough that it is relatively simple to find the best model for the given data.\n",
"\n",
"Thus, the most popular strategy is to start from the\n",
"simplest class of models and increase the complexity of the models only when the\n",
"simpler models become inadequate. For instance, if we work with a regression problem to fit a set of sample points, one\n",
"may first try the simplest class of models, namely linear models, followed obviously by more complex models.\n",
"\n",
"How to evaluate which model fits best the data is something we will come back to over and over again in these set of lectures.\n",
"\n",
"\n",
"\n",
"\n",
"## Choice of Programming Language\n",
"\n",
"Python plays nowadays a central role in the development of machine\n",
"learning techniques and tools for data analysis. In particular, seen\n",
"the wealth of machine learning and data analysis libraries written in\n",
"Python, easy to use libraries with immediate visualization(and not the\n",
"least impressive galleries of existing examples), the popularity of the\n",
"Jupyter notebook framework with the possibility to run **R** codes or\n",
"compiled programs written in C++, and much more made our choice of\n",
"programming language for this series of lectures easy. However,\n",
"since the focus here is not only on using existing Python libraries such\n",
"as **Scikit-Learn**, **Tensorflow** and **Pytorch**, but also on developing your own\n",
"algorithms and codes, we will as far as possible present many of these\n",
"algorithms either as a Python codes or C++ or Fortran (or other languages) codes. \n",
"\n",
"\n",
"\n",
"\n",
"## Data handling, machine learning and ethical aspects\n",
"\n",
"In most of the cases we will study, we will either generate the data\n",
"to analyze ourselves (both for supervised learning and unsupervised\n",
"learning) or we will recur again and again to data present in say\n",
"**Scikit-Learn** or **Tensorflow**. Many of the examples we end up\n",
"dealing with are from a privacy and data protection point of view,\n",
"rather inoccuous and boring results of numerical\n",
"calculations. However, this does not hinder us from developing a sound\n",
"ethical attitude to the data we use, how we analyze the data and how\n",
"we handle the data.\n",
"\n",
"The most immediate and simplest possible ethical aspects deal with our\n",
"approach to the scientific process. Nowadays, with version control\n",
"software like [Git](https://git-scm.com/) and various online\n",
"repositories like [Github](https://github.com/),\n",
"[Gitlab](https://about.gitlab.com/) etc, we can easily make our codes\n",
"and data sets we have used, freely and easily accessible to a wider\n",
"community. This helps us almost automagically in making our science\n",
"reproducible. The large open-source development communities involved\n",
"in say [Scikit-Learn](http://scikit-learn.org/stable/),\n",
"[Tensorflow](https://www.tensorflow.org/),\n",
"[PyTorch](http://pytorch.org/) and [Keras](https://keras.io/), are\n",
"all excellent examples of this. The codes can be tested and improved\n",
"upon continuosly, helping thereby our scientific community at large in\n",
"developing data analysis and machine learning tools. It is much\n",
"easier today to gain traction and acceptance for making your science\n",
"reproducible. From a societal stand, this is an important element\n",
"since many of the developers are employees of large public institutions like\n",
"universities and research labs. Our fellow taxpayers do deserve to get\n",
"something back for their bucks.\n",
"\n",
"However, this more mechanical aspect of the ethics of science (in\n",
"particular the reproducibility of scientific results) is something\n",
"which is obvious and everybody should do so as part of the dialectics of\n",
"science. The fact that many scientists are not willing to share their codes or \n",
"data is detrimental to the scientific discourse.\n",
"\n",
"Before we proceed, we should add a disclaimer. Even though\n",
"we may dream of computers developing some kind of higher learning\n",
"capabilities, at the end (even if the artificial intelligence\n",
"community keeps touting our ears full of fancy futuristic avenues), it is we, yes you reading these lines,\n",
"who end up constructing and instructing, via various algorithms, the\n",
"machine learning approaches. Self-driving cars for example, rely on sofisticated\n",
"programs which take into account all possible situations a car can\n",
"encounter. In addition, extensive usage of training data from GPS\n",
"information, maps etc, are typically fed into the software for\n",
"self-driving cars. Adding to this various sensors and cameras that\n",
"feed information to the programs, there are zillions of ethical issues\n",
"which arise from this.\n",
"\n",
"For self-driving cars, where basically many of the standard machine\n",
"learning algorithms discussed here enter into the codes, at a certain\n",
"stage we have to make choices. Yes, we , the lads and lasses who wrote\n",
"a program for a specific brand of a self-driving car. As an example,\n",
"all carmakers have as their utmost priority the security of the\n",
"driver and the accompanying passengers. A famous European carmaker, which is\n",
"one of the leaders in the market of self-driving cars, had **if**\n",
"statements of the following type: suppose there are two obstacles in\n",
"front of you and you cannot avoid to collide with one of them. One of\n",
"the obstacles is a monstertruck while the other one is a kindergarten\n",
"class trying to cross the road. The self-driving car algo would then\n",
"opt for the hitting the small folks instead of the monstertruck, since\n",
"the likelihood of surving a collision with our future citizens, is\n",
"much higher.\n",
"\n",
"This leads to serious ethical aspects. Why should we opt for such an\n",
"option? Who decides and who is entitled to make such choices? Keep in\n",
"mind that many of the algorithms you will encounter in this series of\n",
"lectures or hear about later, are indeed based on simple programming\n",
"instructions. And you are very likely to be one of the people who may\n",
"end up writing such a code. Thus, developing a sound ethical attitude\n",
"to what we do, an approach well beyond the simple mechanistic one of\n",
"making our science available and reproducible, is much needed. The\n",
"example of the self-driving cars is just one of infinitely many cases\n",
"where we have to make choices. When you analyze data on economic\n",
"inequalities, who guarantees that you are not weighting some data in a\n",
"particular way, perhaps because you dearly want a specific conclusion\n",
"which may support your political views? Or what about the recent\n",
"claims that a famous IT company like Apple has a sexist bias on the\n",
"their recently [launched credit card](https://qz.com/1748321/the-role-of-goldman-sachs-algorithms-in-the-apple-credit-card-scandal/)?\n",
"\n",
"We do not have the answers here, nor will we venture into a deeper\n",
"discussions of these aspects, but we want you think over these topics\n",
"in a more overarching way. A statistical data analysis with its dry\n",
"numbers and graphs meant to guide the eye, does not necessarily\n",
"reflect the truth, whatever that is. As a scientist, and after a\n",
"university education, you are supposedly a better citizen, with an\n",
"improved critical view and understanding of the scientific method, and\n",
"perhaps some deeper understanding of the ethics of science at\n",
"large. Use these insights. Be a critical citizen. You owe it to our\n",
"society."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"```{toctree}\n",
":hidden:\n",
":titlesonly:\n",
":numbered: \n",
":caption: Supervised Learning\n",
"\n",
"chapter2.ipynb\n",
"chapter3.ipynb\n",
"chapter4.ipynb\n",
"chapter5.ipynb\n",
"chapter6.ipynb\n",
"chapter7.ipynb\n",
"chapter8.ipynb\n",
"chapter9.ipynb\n",
"chapter10.ipynb\n",
"chapter11.ipynb\n",
"```\n"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
} | cc0-1.0 |
zenoss/pywbem | docs/notebooks/pywbemmock.ipynb | 1 | 29669 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pywbem_mock Demonstration Notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table of contents for this notebook\n",
"\n",
"- [Table of Contents for this notebook](#Table-of-Contents-for-this-notebook)\n",
"- [Using pywbem_mock to create a simple repository](#Using-pywbem_mock-to-create-a-simple-repository)\n",
" - [Create a FakeWBEMConnection and repository](#Create-a-FakeWBEMConnection-and-repository)\n",
" - [Display the created repository](#Display-the-created-repository)\n",
" - [Execute WBEM operations on the mock repository](#Execute-WBEM-operations-on-the-mock-repository)\n",
" - [Create a new instance of the defined class](#Create-a-new-instance-of-the-defined-class)\n",
" - [Retrieve all instances of the class](#Retrieve-all-instances-of-the-class)\n",
" - [Retrieve the new instance from the mock server](#Retrieve-the-new-instance-from-the-mock-server)\n",
" - [Get the new instance with server defined path](#Get-the-new-instance-with-server-defined-path)\n",
" - [Retrieve all instances of the class](#Retrieve-all-instances-of-the-class)\n",
" - [Delete the new instance](#Delete-the-new-instance)\n",
" - [Executing a CIM method](#Executing-a-CIM-method)\n",
"- [Creating a repository from DMTF Schema](#Creating-a-repository-from-DMTF-Schema)\n",
" - [Add more classes to this repository](#Add-more-classes-to-this-repository)\n",
" - [Add more CIM objects](#Add-more-CIM-objects)\n",
" - [The association operations](#The-association-operations)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The pywbem client includes a package (`pywbem_mock`) to mock a WBEM server and allow the pywbem client to \n",
"execute WBEM operations against this fake WBEM server.\n",
"\n",
"This notebook contains examples of creating and using a simple repository and creating and using more complex repositories usingthe DMTF released Schema MOF files.\n",
"\n",
"For more detailed information on the package and its APIs see the pywbem client documentation on \n",
"[read-the-docs](https://pywbem.readthedocs.io/en/stable/mocksupport.html) which includes both an overview and API descriptions for the `pywbem_mock` package of pywbem."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using pywbem_mock to create a simple repository\n",
"\n",
"The pywbem mock package (`pywbem_mock`) allows a user of the pywbem client to mock a WBEM server so that pywbem WBEM request methods can be executed without having a WBEM server available;\n",
"\n",
"The pywbem mock support consists of the `pywbem_mock.FakedWBEMConnection` class that establishes a faked connection. That class is a subclass of `pywbem.WBEMConnection` and replaces its internal methods that use HTTP/HTTPS to communicate with a WBEM server with methods that operate on a local in-memory repository of CIM objects (the mock repository).\n",
"\n",
"As a result, the operation methods of `FakedWBEMConnection` are those inherited from WBEMConnection, so they have the exact same input parameters, output parameters, return values, and most of the server raised exceptions, as when being invoked on a WBEMConnection object against a WBEM server.\n",
"\n",
"Each `FakedWBEMConnection` object has its own mock repository. The mock repository contains the same kinds of CIM objects a WBEM server repository contains: CIM classes, CIM instances, and CIM qualifier types (declarations), all contained in CIM namespaces.\n",
"\n",
"Because `FakedWBEMConnection` operates only on the mock repository, the class does not have any connection-related or security-related constructor parameters.\n",
"\n",
"`FakedWBEMConnection` has methods that allow the user to add CIM classes, instances and qualifier types to its mock repository and view what is in the mock repository either by defined pywbem CIM objects (CIMClass, CIMInstance, etc. or by compiling MOF files containing the definition of CIM objects. The methods include:\n",
"\n",
"- `compile_mof_file()` - Compiles a MOF file into the fake repository\n",
"- `compile_mof_string()` - Compiles a string containing MOF into the fake repository \n",
"- `add_cimobjects()` - Inserts CIM bbjects defined with pywbem classes (CIMClass, CIMInstance, etc.) into the defined fake repository.\n",
"- `add_namespace()` - Defines a CIM namespace in the fake repository\n",
"- `display_repository()` - Display the namespaces, CIM classes, CIM instances, etc. that are in the Fake repository\n",
"\n",
"CIM instances in the repository can be modified or deleted by using the pywbem `WBEMConnection` operation methods such as `DeleteClass()` or `ModifyInstance()`. Classes and qualifier declarations can be deleted using the corresponding delete methods.\n",
"\n",
"The following cells demonstrate creating a FakeWBEMConnection, populating it repository, accessing the objects created in the repository and deleting the CIMInstances created.\n",
"\n",
"It also demonstrates defining and access a CIM method in the fake repository."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a FakeWBEMConnection and repository\n",
"\n",
"The following code demonstrates adding a simple set of qualifier declarations and a CIM class to the Fake repository by compiling their MOF definitions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pywbem\n",
"import pywbem_mock\n",
"\n",
"# MOF string defining qualifiers declarations, class, and instance\n",
"# This mof will be used throughout this notebook\n",
"mof = '''\n",
" Qualifier Key : boolean = false,\n",
" Scope(property, reference),\n",
" Flavor(DisableOverride, ToSubclass);\n",
" Qualifier Description : string = null,\n",
" Scope(any),\n",
" Flavor(EnableOverride, ToSubclass, Translatable); \n",
" Qualifier In : boolean = true, \n",
" Scope(parameter), \n",
" Flavor(DisableOverride, ToSubclass);\n",
"\n",
" [Description (\"This is a dumb test class\")]\n",
" class CIM_Foo {\n",
" [Key, Description (\"This is key prop\")]\n",
" string InstanceID;\n",
" [Description (\"This is some simplistic data\")]\n",
" Uint32 SomeData;\n",
" [Description (\"This is a method without parameters\")]\n",
" string Method1();\n",
" [Description (\"This is a second method with parameter\")]\n",
" uint32 Delete([IN, Description(\"blahblah\")]\n",
" boolean Immediate);\n",
" };\n",
"\n",
" instance of CIM_Foo as $I1 { InstanceID = \"I1\"; SomeData=3; };\n",
" '''\n",
"\n",
"# Create a faked connection (with a mock repository in full mode)\n",
"conn = pywbem_mock.FakedWBEMConnection(default_namespace='root/cimv2')\n",
"\n",
"# Compile the MOF string and add its CIM objects to the default namespace\n",
"# of the mock repository\n",
"conn.compile_mof_string(mof)\n",
"\n",
"print(\"Qualifier declarations and classes installed in Fake repository\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Display the created repository\n",
"\n",
"At any time, the data in the repository can be displayed using the method `FakeWBEMConnection.display_repository()`.\n",
"\n",
"This method includes a number of options to display selected namespaces, define the destination for the output, display only a summary of data in the repository, and define the output format for the displayed objects (mof, xml, repr)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"conn.display_repository()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Execute WBEM operations on the mock repository\n",
"\n",
"Once qualifier declarations, classes, and instances have been inserted into the mock repository they\n",
"can be retrieved using the `WBEMConnection` methods provided by `pywbem`.\n",
"\n",
"Thus, `WBEMConnection.getQualifier()` retrieves a single qualifier declaration. The method `tomof()` is\n",
"a method in pywbem cimobject classes CIMQualifierDeclaration, CIMClass, and CIMInstance and is an easy\n",
"way to display the objects returned from the repository in the standard DMTF language for CIM objects."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pywbem import CIMInstanceName, Error\n",
"## Perform operations on the faked connection:\n",
"\n",
"# Enumerate top-level classes in the default namespace (without subclasses)\n",
"classes = conn.EnumerateClasses();\n",
"\n",
"### Get the 'Description' qualifier type in the default namespace\n",
"qd = conn.GetQualifier('Description')\n",
"print(qd.tomof())\n",
"\n",
"### Enumerate subclasses of 'CIM_Foo' in the default namespace (without subclasses)\n",
"classes = conn.EnumerateClasses(classname='CIM_Foo')\n",
"for cls in classes:\n",
" print(cls.tomof())\n",
"\n",
"### Get 'CIM_Foo' class in the default namespace\n",
"my_class = conn.GetClass('CIM_Foo')\n",
"\n",
"### Get a specific instance of 'CIM_Foo' in the default namespace\n",
"keybindings = {'InstanceID': \"I1\"}\n",
"inst = conn.GetInstance(CIMInstanceName('CIM_Foo', keybindings))\n",
"print(inst.tomof())\n",
"### print the path of the returned instance\n",
"print(\"path:%s\" % inst.path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a new instance of the defined class\n",
"\n",
"Creating an instance primarily involves attaching properties with their name, value (and often type)\n",
"as a dictionary to a CIMInstance with the name of the class for the new instance. The method\n",
"`WBEMConnection.CreateInstance()` only requires the new instance object (and the namespace if the default\n",
"namespace is not being used) to manage the instance that is being created.\n",
"\n",
"The mocker creates the path for this instance and inserts the new instance into the mock\n",
"repository. Note that a successful `CreateInstance()` returns a CIMInstanceName for the new instance which can be used to access the new instance on the WBEM server."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pywbem import CIMInstance, Uint32\n",
"\n",
"p = {\"InstanceID\": \"I2\", \"SomeData\": Uint32(999)}\n",
"\n",
"newinst = CIMInstance(\"CIM_Foo\", properties=p)\n",
"new_path = None\n",
"try:\n",
" new_path = conn.CreateInstance(newinst)\n",
" print(\"Return path: %s\" % new_path)\n",
"except Error as er:\n",
" print(\"Exception on CreateInstance. exception=%s\" % er)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Retrieve the new instance from the mock server\n",
"\n",
"Here we retrieve the instance we just created from the existing connection and display the instance using the tomof() method.\n",
"\n",
"Note that we are retrieving the instance using the path we previously defined. Since the server returns the path of an instance it creates, that is the path object your should be using to retrieve an instance. Under some circumstances the server could change the path and return a path different that what eyou defined. The mocker does not change the path provided."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"myinst = conn.GetInstance(CIMInstanceName('CIM_Foo', keybindings={'InstanceID': \"I2\"}))\n",
"\n",
"print(\"path:%s\\n%s\" % (myinst.path, myinst.tomof()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get the new instance with server defined path\n",
"\n",
"Since `WBEMConnection.CreateInstance(` returned the path the server created for the instance we inserted into the repository, that path can also be used to retrieve the instance from the repository"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('Path to new instance %s' % new_path)\n",
"myinst2 = conn.GetInstance(new_path)\n",
"print(myinst2.tomof())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Retrieve all instances of the class\n",
"\n",
"In this case we display the returned instances using the string representation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"insts = conn.EnumerateInstances(\"CIM_Foo\")\n",
"for inst in insts:\n",
" print(\"path=%s\" % inst.path)\n",
" print(\"%s\" % inst)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Delete the new instance\n",
"\n",
"The WBEMConnection::DeleteInstance method deletes a single instance of an instanced given the instance path.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" conn.DeleteInstance(new_path)\n",
" for inst in insts:\n",
" print(\"path=%s\" % inst.path)\n",
"except Error as er:\n",
" print(\"Error with delete\")\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Executing a CIM method\n",
"\n",
"The mocker can execute CIM methods as if they were on a server. However, since there are no real providers\n",
"in the mocker, the user must define what a method would do in the server. This definition is provided to the mocker by defining a callback and installing it with the add_method_callback method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pywbem import CIMParameter\n",
"\n",
"# Definition of callback method. This must be registered to\n",
"# the mock environment with add_method_callback()\n",
"def method1_callback(conn, methodname, objectname, **params):\n",
" \"\"\"\n",
" Callback function that demonstrates what can be done, without being\n",
" really useful.\n",
" \"\"\"\n",
"\n",
" # Access input parameters\n",
" print('Callback received params: %r' % params )\n",
" print('Callback recieved object_name %s' % objectname)\n",
" ip1 = params['InputParam1']\n",
"\n",
" # Access the mock repository through the faked connection object.\n",
" cl = conn.GetClass(objectname)\n",
"\n",
" # Set return value == 0 and output parameters\n",
" rtn_val = 0\n",
" # setup output parameter that will be returned to caller\n",
" op1 = CIMParameter('OutputParam11', 'string', value='Some output data')\n",
" return rtn_val, [op1]\n",
"\n",
"more_mof = '''\n",
" Qualifier Out : boolean = false,\n",
" Scope(parameter),\n",
" Flavor(DisableOverride, ToSubclass);\n",
"\n",
" Qualifier Static : boolean = false, \n",
" Scope(property, method), \n",
" Flavor(DisableOverride, ToSubclass);\n",
"\n",
" class TST_Class {\n",
"\n",
" string InstanceID;\n",
"\n",
" [Static,\n",
" Description(\"Static method with input and output parameters\")]\n",
" uint32 Method1(\n",
" [IN, Description(\"Input param 1\")]\n",
" string InputParam1,\n",
" [IN (False), OUT, Description(\"Output param 1\")]\n",
" string OutputParam1);\n",
" };\n",
"'''\n",
"\n",
"# Compile the MOF string and add its CIM objects to the default namespace\n",
"# of the mock repository\n",
"conn.compile_mof_string(more_mof)\n",
"\n",
"# Register the method callback function to the mock repository, for the\n",
"# default namespace of the connection\n",
"\n",
"# try block allows excuting cell multiple times without exception\n",
"# because the method is already registered.\n",
"try:\n",
" conn.add_method_callback('TST_Class', 'Method1', method1_callback)\n",
"except ValueError as ve:\n",
" print(ve)\n",
"\n",
"\n",
"# Define a value for the Method Parameter IP1\n",
"params = [('InputParam1', 'someData')]\n",
" # Invoke static method Method1\n",
"result = conn.InvokeMethod('Method1', 'TST_Class', Params=params)\n",
"\n",
"print('Return value: %r' % result[0])\n",
"print('Output parameters: %r' % (result[1],))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating and accessing Associations\n",
"\n",
"TODO: Create the section. For now, see section [The association operations](#The#association#operations)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating a repository from DMTF Schema\n",
"\n",
"In addition to creating a repository with specifically defined classes, qualifier declarations and instances the repository can be created using the published DMTF schema as the source for qualifier declarations and classes. The DMTF schema is released on a regular basis with new functionality, fixes, etc. And is published on the DMTF web site. It includes the set of CIM qualifier declarations defined by the DMTF and the CIM classes that make up the CIM model. It is available in both MOF and XML formats. The method documented here uses the MOF files as the basis for creating CIM qualifier declarations and CIM classes in the mock repository.\n",
"\n",
"Using this method requires that you make the following decisions:\n",
"\n",
"- Which version of the published DMTF released schema you intend to use? The available Schema versions are listed on the [DMTF web site](https://www.dmtf.org/standards/cim)\n",
"\n",
"- Where in your local environment do you want to save and expand the schema. This should be a directory and serves both as the storage space for download and expansion and the location checked before download so that the schema is not downloaded each time it is used?\n",
"\n",
"- Use of the experimental schema for the defined DMTF schema release as well as the released schema.\n",
"\n",
"- The leaf classes in the class hiearchy you want to mock. The method takes responsibility for determining superclasses and other required classes ( ex. associations, embedded instances) defined in the MOF for any of the leaf classes you specify.\n",
"\n",
"The method `FakeWBEMConnection.compile_dmtf_schema()` performs a number of functions including:\n",
"\n",
"- Downloading the DMTF Schema defined by the version if it is not already downloaded.\n",
"- Creating a list of all classes that must be compiled based on the list of classes provided (the leaf classes required for the mock)\n",
"- Compiling all of the qualifier declarations defined in the CIM Schema.\n",
"- Compiling the defined classes and classes on which they depend into the repository.\n",
"\n",
"The following example defines a DMTF schema version, the destination directory, and the leaf classes of interest and then calls the compile_dmtf_schema method to create a mock repository. At the end of the operation, the mock repository is includes the complete DMTF set of qualifier declarations (there are only about 100 of them) and the classes you specified and their dependencies. The destination directory should also include the complete download from the DMTF and the expanded mof files for the qualifier declarations and classes.\n",
"\n",
"Note: that each time you call compile_dmtf_schema() it installs the complete set of qualifier declarations in the mock repository so that it is best to create a complete list of leaf classes required an call this method only once.\n",
"\n",
"*Warning*: Some of these cells create new objects on the server. Once those objects are created, they cannot simply be recreated on the server so the repeated execution of some of the cells without restarting (see Kernel in the menu) will cause exception returns from the server.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pywbem\n",
"import pywbem_mock\n",
"\n",
"import os\n",
"print(os.getcwd())\n",
"\n",
"# Defines the version of the DMTF schema to be installed.\n",
"# This demo is done with the schema already loaded into the pywbem development environment assuming that\n",
"# the user is using the docs/notebooks directory. This may change as pywbem updates the schema use\n",
"# in out tests\n",
"DMTF_TEST_SCHEME_VER = (2, 49, 0)\n",
"# location of schema 2.49.0 in pywbem 0.13.0 development\n",
"# code cloed from github\n",
"SCHEMA_DIR = os.path.join('..', '..', 'tests', 'schema')\n",
"print(SCHEMA_DIR)\n",
"\n",
"# An alternative would be to define your own schema and schema directory\n",
"#DMTF_TEST_SCHEME_VER = \"2.51.0\"\n",
"#SCHEMA_DIR = \".\"\n",
"\n",
"classnames = ['CIM_RegisteredProfile', 'CIM_Namespace', 'CIM_ObjectManager',\n",
" 'CIM_ElementConformsToProfile', 'CIM_ReferencedProfile']\n",
"\n",
"conn = pywbem_mock.FakedWBEMConnection(default_namespace='root/cimv2')\n",
"print(conn)\n",
"\n",
"conn.compile_dmtf_schema(DMTF_TEST_SCHEME_VER, SCHEMA_DIR,\n",
" class_names=classnames, verbose=False)\n",
"conn.display_repository()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Add more classes to this repository\n",
"\n",
"Subsequent to the installation of the DMTF schema we can add classes of our own to the repository.\n",
"This adds two dummy classes that really do not depend on the schema and simply demonstrates installing classes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mof= '''\n",
" class TST_Class1 {\n",
" [Key]\n",
" string InstanceID;\n",
" string Prop1;\n",
" };\n",
"\n",
" class TST_Class2 {\n",
" [Key]\n",
" string InstanceID;\n",
" string Prop2;\n",
" };\n",
"\n",
" [Association]\n",
" class TST_Association12 {\n",
" [Key]\n",
" TST_Class1 REF Ref1;\n",
" [Key]\n",
" TST_Class2 REF Ref2;\n",
" };\n",
"'''\n",
"\n",
"conn.compile_mof_string(mof)\n",
"# print the class returned from the server using the __str__ magic method and __repr__ magic method\n",
"print(conn.GetClass(\"TST_Class1\"))\n",
"print(repr(conn.GetClass(\"TST_Class2\")))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Add more CIM objects\n",
"\n",
"Now that we have classes in the repository we can add some CIM Instances. The following cell defines an instance of each of the new classes and also an assoiation instance that relates the instances of the classes.\n",
"\n",
"In this case, they are defined as instances of pywbem objects to demonstrate adding pywbem CIM objects to the repository.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ns = conn.default_namespace\n",
"\n",
"# Define a key for this instance\n",
"c1_key = pywbem.CIMProperty('InstanceID', type='string', value='111')\n",
"\n",
"# Create the path for the instance (its CIMInstanceName)\n",
"c1_path = pywbem.CIMInstanceName(\n",
" 'TST_Class1',\n",
" keybindings={c1_key.name: c1_key.value},\n",
" namespace=ns\n",
")\n",
"\n",
"# Create a CIMInstance of class TST_Class1\n",
"c1 = pywbem.CIMInstance(\n",
" 'TST_Class1',\n",
" properties=[\n",
" c1_key,\n",
" pywbem.CIMProperty('Prop1', type='string', value='1'),\n",
" ],\n",
" path=c1_path,\n",
")\n",
"\n",
"# Create a second instance\n",
"c2_key = pywbem.CIMProperty('InstanceID', type='string', value='222')\n",
"c2_path = pywbem.CIMInstanceName(\n",
" 'TST_Class2',\n",
" keybindings={c2_key.name: c2_key.value},\n",
" namespace=ns\n",
")\n",
"c2 = pywbem.CIMInstance(\n",
" 'TST_Class2',\n",
" properties=[\n",
" c2_key,\n",
" pywbem.CIMProperty('Prop2', type='string', value='2'),\n",
" ],\n",
" path=c2_path,\n",
")\n",
"\n",
"# Create keys and paths for CIMInstance 1 and 2\n",
"a12_key1 = pywbem.CIMProperty('Ref1', type='reference', value=c1_path)\n",
"a12_key2 = pywbem.CIMProperty('Ref2', type='reference', value=c2_path)\n",
"a12_path = pywbem.CIMInstanceName(\n",
" 'TST_Association12',\n",
" keybindings={\n",
" a12_key1.name: a12_key1.value,\n",
" a12_key2.name: a12_key2.value,\n",
" },\n",
")\n",
"\n",
"# Define the association instance\n",
"a12 = pywbem.CIMInstance(\n",
" 'TST_Association12',\n",
" properties=[\n",
" a12_key1,\n",
" a12_key2,\n",
" ],\n",
" path=a12_path,\n",
")\n",
"\n",
"# add all of the created CIM instances to the repository\n",
"conn.add_cimobjects([c1, c2, a12])\n",
"\n",
"# Get the instances from the repository and display them\n",
"returned_instances = conn.EnumerateInstances('TST_Class2')\n",
"for inst in returned_instances:\n",
" print(inst.tomof())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The association operations\n",
"\n",
"The relation between a source instance and it associated instances is defined with instances of an association class (CIMClass with Association qualifier) and can be accessed through the `References()` and `Associators()` WBEMConnection methods. These are powerful methods that provide the basis for navigating through the CIM model on a server.\n",
"\n",
"Note that in most cases an association can also be accessed directly by executing GetInstance or EnumerateInstances on the Association class. However, the power of the `References()` and `Associators()` methods is that the return instances within the class hiearchy defined by association instances including optionally accounting for subclasses, filtering of the roles (Reference property names) and the Association class name.\n",
"\n",
"Pywbem_mock accesses associations defined in the mock repository with the `WBEMConnection.Associators()` and `WBEMConnection.References()` methods and implements the same retrieval algorithms as a WBEM server to get\n",
"the correct instances based on the request input parameters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Display the instances of the test association.\n",
"returned_assoc_insts = conn.EnumerateInstances(\"TST_Association12\")\n",
"for inst in returned_assoc_insts:\n",
" print(inst.tomof())\n",
"\n",
"# Get the References for each of the target instances defined for\n",
"# the association.\n",
"cls_111_paths = conn.EnumerateInstanceNames('TST_Class1')\n",
"for path in cls_111_paths:\n",
" returned_refs = conn.References(path)\n",
" print('Refs for %s' % path)\n",
" for inst in returned_refs:\n",
" print(inst.tomof())\n",
" \n",
"# Get the associated instances for the target instances using the\n",
"# Associators operation\n",
"for path in cls_111_paths:\n",
" returned_assocs = conn.Associators(path)\n",
" print('Associations for %s' % path)\n",
" for inst in returned_assocs:\n",
" print(inst.tomof())\n",
" for inst in returned_assocs:\n",
" print('Returned association path: %s:' % inst.path)"
]
},
{
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| lgpl-2.1 |
CalebBell/fluids | docs/Examples/Crane TP 410 Solved Problems/7.33 Pump Affinity Rules.ipynb | 1 | 1827 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 7.33 Pump Affinity Rules"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given: For the 6\" impeller trim on the curve below, the pump produces 126' of head and 400 gpm while running at 3500 rpm. The brake horsepower is 17.5 hp in that case.\n",
"\n",
"Find the flow rate, head, and power of this operating point if the speed is changed to 1700 rpm."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(194.28571428571428 <Unit('gallon / minute')>,\n",
" 29.725714285714286 <Unit('foot')>,\n",
" 2.0053061224489794 <Unit('horsepower')>)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from fluids.units import *\n",
"head1 = 126*u.foot\n",
"Q1 = 400*u.gal/u.min\n",
"rpm1 = 3500*u.rpm\n",
"power1 = 17.5*u.hp\n",
"\n",
"rpm2 = 1700*u.rpm\n",
"\n",
"Q2 = rpm2/rpm1*Q1\n",
"head2 = head1*(rpm2/rpm1)**2\n",
"power2 = power1*(rpm2/rpm1)**3\n",
"\n",
"Q2, head2, power2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The values match those given in Crane."
]
}
],
"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": "3.8.7"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
iamshang1/Projects | Misc/Python4ML/numpy_tutorial.ipynb | 1 | 4701 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#import numpy\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#create numpy array\n",
"x = [[1,2,3],[4,5,6]]\n",
"x = np.array(x)\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#array info\n",
"print(x.shape)\n",
"print(x.dtype)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#indexing\n",
"print(x[0,0])\n",
"print(x[:,0])\n",
"print(x[0,:])\n",
"print(x[0,1:3])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#setting values\n",
"print(x)\n",
"x[1,2] = 100\n",
"print(x)\n",
"x[1,:] = [101,102,103]\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#summary statistics\n",
"x = [[1,2,3],[4,5,6]]\n",
"x = np.array(x)\n",
"print(x.mean())\n",
"print(x.std())\n",
"print(x.min())\n",
"print(x.max())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#statistics for columns and rows\n",
"print(np.mean(x,0))\n",
"print(np.mean(x,1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#transformations\n",
"x_transpose = x.T\n",
"print(x_transpose)\n",
"\n",
"x_reshape = x.reshape(3,2)\n",
"print(x_reshape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#other ways to intialize arrays\n",
"ones = np.ones(shape=(3,5))\n",
"print(ones)\n",
"\n",
"zeros = np.zeros(shape=(3,5))\n",
"print(zeros)\n",
"\n",
"random = np.random.rand(3,5)\n",
"print(random)\n",
"\n",
"rand_ints = np.random.randint(low=0,high=10,size=(3,5))\n",
"print(rand_ints)\n",
"\n",
"seq = np.arange(start=0,stop=3,step=0.5)\n",
"print(seq)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#operations\n",
"a = np.arange(0,10).reshape(2,5)\n",
"b = np.arange(5,15).reshape(2,5) \n",
"elem_sum = a + b\n",
"print(elem_sum)\n",
"\n",
"print(a + 1)\n",
"print(a + np.random.rand(2,1))\n",
"print(a + np.random.rand(1,5))\n",
"\n",
"elem_product = a * b\n",
"print(elem_product)\n",
"\n",
"print(a * 2)\n",
"\n",
"dot = np.dot(a, b.T)\n",
"print(dot)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#normalize\n",
"x = np.random.rand(3,5)\n",
"x -= np.mean(x,0)\n",
"x /= np.std(x,0)\n",
"print(x)\n",
"print(np.mean(x,0),np.std(x,0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#concatenating arrays\n",
"a = np.zeros((2,5))\n",
"b = np.ones((2,5))\n",
"c = np.ones((2,5)) * 2\n",
"print(np.vstack((a,b,c)))\n",
"print(np.vstack((c,b,a)))\n",
"print(np.hstack((a,b,c)))\n",
"print(np.hstack((c,b,a)))\n",
"print(np.concatenate((a,b,c),axis=0))\n",
"print(np.concatenate((a,b,c),axis=1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#filtering and masking\n",
"a = np.random.rand(20)\n",
"print(a)\n",
"filter = a > 0.5\n",
"print(filter)\n",
"print(a[filter])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
hootnot/oanda-api-v20 | jupyter/streams.ipynb | 1 | 6147 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"[index](./index.ipynb) | [accounts](./accounts.ipynb) | [orders](./orders.ipynb) | [trades](./trades.ipynb) | [positions](./positions.ipynb) | [historical](./historical.ipynb) | [streams](./streams.ipynb) | [errors](./exceptions.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Streaming data (prices & events)\n",
"\n",
"The REST-V20 API offers *streaming prices* and *streaming events*. Both can be simply accessed as the next example will show."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'asks': [{'liquidity': 10000000.0, 'price': '1.05534'}, {'liquidity': 10000000.0, 'price': '1.05536'}], 'tradeable': True, 'instrument': 'EUR_USD', 'type': 'PRICE', 'closeoutAsk': '1.05538', 'closeoutBid': '1.05518', 'time': '2017-03-09T13:37:46.048197280Z', 'status': 'tradeable', 'bids': [{'liquidity': 10000000.0, 'price': '1.05522'}, {'liquidity': 10000000.0, 'price': '1.05520'}]}\n",
"{'asks': [{'liquidity': 1000000.0, 'price': '120.991'}, {'liquidity': 2000000.0, 'price': '120.992'}, {'liquidity': 5000000.0, 'price': '120.993'}, {'liquidity': 10000000.0, 'price': '120.995'}], 'tradeable': True, 'instrument': 'EUR_JPY', 'type': 'PRICE', 'closeoutAsk': '120.995', 'closeoutBid': '120.965', 'time': '2017-03-09T13:37:44.958026355Z', 'status': 'tradeable', 'bids': [{'liquidity': 1000000.0, 'price': '120.969'}, {'liquidity': 2000000.0, 'price': '120.968'}, {'liquidity': 5000000.0, 'price': '120.967'}, {'liquidity': 10000000.0, 'price': '120.965'}]}\n",
"{'asks': [{'liquidity': 1000000.0, 'price': '120.995'}, {'liquidity': 2000000.0, 'price': '120.996'}, {'liquidity': 5000000.0, 'price': '120.997'}, {'liquidity': 10000000.0, 'price': '120.999'}], 'tradeable': True, 'instrument': 'EUR_JPY', 'type': 'PRICE', 'closeoutAsk': '120.999', 'closeoutBid': '120.971', 'time': '2017-03-09T13:37:47.550550833Z', 'status': 'tradeable', 'bids': [{'liquidity': 1000000.0, 'price': '120.975'}, {'liquidity': 2000000.0, 'price': '120.974'}, {'liquidity': 5000000.0, 'price': '120.973'}, {'liquidity': 10000000.0, 'price': '120.971'}]}\n",
"{'asks': [{'liquidity': 10000000.0, 'price': '1.05527'}, {'liquidity': 10000000.0, 'price': '1.05529'}], 'tradeable': True, 'instrument': 'EUR_USD', 'type': 'PRICE', 'closeoutAsk': '1.05531', 'closeoutBid': '1.05510', 'time': '2017-03-09T13:37:49.349544372Z', 'status': 'tradeable', 'bids': [{'liquidity': 10000000.0, 'price': '1.05514'}, {'liquidity': 10000000.0, 'price': '1.05512'}]}\n",
"Stream processing ended because we made it stop after 3 ticks\n"
]
}
],
"source": [
"import json\n",
"import oandapyV20\n",
"import oandapyV20.endpoints.pricing as pricing\n",
"from oandapyV20.exceptions import StreamTerminated\n",
"from exampleauth import exampleauth\n",
"\n",
"accountID, access_token = exampleauth.exampleAuth()\n",
"client = oandapyV20.API(access_token=access_token)\n",
"\n",
"instruments = [\"EUR_USD\", \"EUR_JPY\"]\n",
"r = pricing.PricingStream(accountID=accountID, params={\"instruments\": \",\".join(instruments)})\n",
"\n",
"n = 0\n",
"stopAfter = 3 # let's terminate after receiving 3 ticks\n",
"try:\n",
" # the stream requests returns a generator so we can do ...\n",
" for tick in client.request(r):\n",
" print(tick)\n",
" if n >= stopAfter:\n",
" r.terminate()\n",
" n += 1\n",
" \n",
"except StreamTerminated as err:\n",
" print(\"Stream processing ended because we made it stop after {} ticks\".format(n))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'lastTransactionID': '7582', 'time': '2017-03-09T15:08:57.512620407Z', 'type': 'HEARTBEAT'}\n",
"{'lastTransactionID': '7582', 'time': '2017-03-09T15:09:02.594588344Z', 'type': 'HEARTBEAT'}\n",
"{'lastTransactionID': '7582', 'time': '2017-03-09T15:09:07.657436396Z', 'type': 'HEARTBEAT'}\n",
"{'lastTransactionID': '7582', 'time': '2017-03-09T15:09:12.721645564Z', 'type': 'HEARTBEAT'}\n",
"Stream processing ended because we made it stop after 3 ticks\n"
]
}
],
"source": [
"import json\n",
"import oandapyV20\n",
"import oandapyV20.endpoints.transactions as trans\n",
"from oandapyV20.exceptions import StreamTerminated\n",
"from exampleauth import exampleauth\n",
"\n",
"accountID, access_token = exampleauth.exampleAuth()\n",
"client = oandapyV20.API(access_token=access_token)\n",
"\n",
"instruments = [\"EUR_USD\", \"EUR_JPY\"]\n",
"r = trans.TransactionsStream(accountID=accountID)\n",
"\n",
"n = 0\n",
"stopAfter = 3 # let's terminate after receiving 3 ticks\n",
"try:\n",
" # the stream requests returns a generator so we can do ...\n",
" for T in client.request(r):\n",
" print(T)\n",
" if n >= stopAfter:\n",
" r.terminate()\n",
" n += 1\n",
" \n",
"except StreamTerminated as err:\n",
" print(\"Stream processing ended because we made it stop after {} ticks\".format(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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ikeyasu/til | cnn_dogs_cats/ConvNet-D-vgg16-large-dataset.ipynb | 1 | 7693575 | null | mit |
jdossgollin/CWC_ANN | Week05/notebooks/week02_mnist_mlp.ipynb | 1 | 9059 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MNIST MLP - copied from week 2\n",
"\n",
"You should already have gone through the `GettingStartedSequentialModels` notebook -- if not you'll be lost here!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from IPython.display import SVG\n",
"from keras.utils.vis_utils import model_to_dot\n",
"import time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're going to use some examples from [https://github.com/fchollet/keras/tree/master/examples](https://github.com/fchollet/keras/tree/master/examples).\n",
"There are tons more and you should check them out!\n",
"We'll use these examples to learn about some different sorts of layers, and strategies for our activation functions, loss functions, optimizers, etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simple Deep NN on the MNIST Dataset\n",
"\n",
"This examples is from [https://github.com/fchollet/keras/blob/master/examples/mnist_mlp.py](https://github.com/fchollet/keras/blob/master/examples/mnist_mlp.py).\n",
"It's a good one to start with because it's not much more complex than what we have seen, but uses real data!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import keras\n",
"from keras.datasets import mnist # load up the training data!\n",
"from keras.models import Sequential # our model\n",
"from keras.layers import Dense, Dropout # Dropout laters?!\n",
"from keras.optimizers import RMSprop # our optimizer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Typically it's good practice to specify your parameters together"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"batch_size = 128\n",
"num_classes = 10\n",
"epochs = 10 # this is too low "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now get the data.\n",
"It's nicely split up between training and testing data which we'll see can be useful.\n",
"We'll also see that this data treats the images as matrices (row is an observation, column is a pixel).\n",
"However, the input data _doesn't need to be a matrix_."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"(x_train, y_train), (x_test, y_test) = mnist.load_data()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(x_train.shape)\n",
"print(y_train.shape)\n",
"print(x_test.shape)\n",
"print(y_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The tutorial then makes a few changes to the data.\n",
"First, reshape it -- to make sure that the rows and columns are what we expect them to be.\n",
"Then, divide by 255 so that the values go from 0 to 1.\n",
"Such scaling is typically a good idea.\n",
"It also treats the $X$ values as `float32` which you don't have to worry about too much but makes computation a bit faster (at the expense of non-critical numerical detail)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_train = x_train.reshape(60000, 784)\n",
"x_test = x_test.reshape(10000, 784)\n",
"x_train = x_train.astype('float32')\n",
"x_test = x_test.astype('float32')\n",
"x_train /= 255\n",
"x_test /= 255\n",
"print(x_train.shape)\n",
"print(y_train.shape)\n",
"print(x_test.shape)\n",
"print(y_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As before we use the `to_categorical()` function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_train = keras.utils.to_categorical(y_train, num_classes)\n",
"y_test = keras.utils.to_categorical(y_test, num_classes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now define our model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Dense(512, activation='relu', input_shape=(784,)))\n",
"model.add(Dropout(0.2))\n",
"model.add(Dense(512, activation='relu'))\n",
"model.add(Dropout(0.2))\n",
"model.add(Dense(10, activation='softmax')) # remember y has 10 categories!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# comment this line if you don't have graphviz installed\n",
"SVG(model_to_dot(model).create(prog='dot', format='svg'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is a \"dropout layer\"?\n",
"See [Quora](https://www.quora.com/In-Keras-what-is-a-dense-and-a-dropout-layer):\n",
"\n",
"> Using “dropout\", you randomly deactivate certain units (neurons) in a layer with a certain probability $p$. So, if you set half of the activations of a layer to zero, the neural network won’t be able to rely on particular activations in a given feed-forward pass during training. As a consequence, the neural network will learn different, redundant representations; the network can’t rely on the particular neurons and the combination (or interaction) of these to be present. Another nice side effect is that training will be faster.\n",
"\n",
"We can use the `summary()` method to look at our model instead of the plot -- this _will_ work on your computer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.compile(loss='categorical_crossentropy',\n",
" optimizer=RMSprop(),\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's run our model.\n",
"Note that by giving it a name (`history = model.fit(...`) we'll be able to access some of its outputs.\n",
"We also use the `validation_data` argument to make it print out the model performance on validation data (which is __not__ used for fitting the model/calculating the back-propagation).\n",
"The `verbose=1` makes the model talk to us as it fits -- put 0 to make it run silently"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"start = time.time()\n",
"history = model.fit(x_train, y_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" validation_data=(x_test, y_test))\n",
"finish = time.time()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can score our model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print('Test loss:', score[0])\n",
"print('Test accuracy:', score[1])\n",
"print('training wall clock time: %5.2f min' % ((finish-start)/60.))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interpreting Validation Performance\n",
"\n",
"It's nice to see how our model performs on validation data.\n",
"This gives us a nice benchmark on how well the model generalizes to data that it hasn't used in training before.\n",
"However, there are some limitations.\n",
"\n",
"- In this case the validation score tells us how well our model does on new MNIST data in the same format as the original data. It doesn't tell us how good it is at image classification on other types of data.\n",
"- When comparing many models, if we select the model with the best validation score we should be aware that this is a form of overfitting!"
]
}
],
"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": 2
}
| mit |
iris-edu/ispaq | EXAMPLES/Example1_plotMetricsOverTime.ipynb | 1 | 6243 | {
"cells": [
{
"cell_type": "markdown",
"id": "unable-findings",
"metadata": {},
"source": [
"# Example 1 - Plot sample_rms for a target over time\n",
"The intent of this series of Jupyter Notebooks is to demonstrate how metrics can be retrieved from an ISPAQ sqlite database and provide some ideas on how to use or plot those metrics. \n",
"\n",
"This example plots a timeseries of a single metric over time.\n",
"\n",
"To run the example, it requires that there are sample_rms values\n",
"for 2020-10-01 through 2020-10-15 for IU.ANMO.00.BH1.M in a\n",
"database located at ../ispaq_example.db. To generate these values, you can run:\n",
"\n",
" python3 run_ispaq.py -M sample_rms -S ANMO --starttime 2020-10-01 --endtime 2020-10-16 --output db --db_name ispaq_example.db\n",
"This example will assume that the above command has already been run and the metrics already exist (it will take several minutes to run).\n",
"\n",
"\n",
"To begin, we need to import the necessary modules:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "alive-fault",
"metadata": {},
"outputs": [],
"source": [
"import sqlite3\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.dates import DateFormatter\n",
"import matplotlib.dates as mdates\n",
"import datetime"
]
},
{
"cell_type": "markdown",
"id": "built-shape",
"metadata": {},
"source": [
"Now we need to set some variables:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fuzzy-underwear",
"metadata": {},
"outputs": [],
"source": [
"db_name = '../ispaq_example.db'\n",
"metric = 'sample_rms'\n",
"startDate = '2020-10-01'\n",
"endDate = '2020-10-15'\n",
"target = 'IU.ANMO.00.BH1.M'\n",
"filename = f'example1_{target}_{startDate}_{endDate}.png'"
]
},
{
"cell_type": "markdown",
"id": "surgical-variable",
"metadata": {},
"source": [
"The first step is to create a query that will be used to retrieve the sample_rms (or whatever metric you are using in the code block above)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "regulated-convenience",
"metadata": {},
"outputs": [],
"source": [
"SQLcommand = f\"SELECT * FROM {metric} WHERE start >= '{startDate}' \" \\\n",
" f\"and start < '{endDate }' and (target like '{target}');\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "particular-scratch",
"metadata": {},
"outputs": [],
"source": [
"print(\"\\nCommand used to retrieve metrics from the sqlite database:\")\n",
"print(SQLcommand)"
]
},
{
"cell_type": "markdown",
"id": "documentary-terminal",
"metadata": {},
"source": [
"\n",
"Create a connection to the database and run the query, loading it into a pandas dataframe"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "controlling-glasgow",
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" conn = sqlite3.connect(db_name)\n",
" DF = pd.read_sql_query(SQLcommand, conn, parse_dates=['start','end'])\n",
" conn.close\n",
"except Exception as e:\n",
" print(f\"Unable to connect to or find the {metric} table in the database {db_name}:\\n{e}\")"
]
},
{
"cell_type": "markdown",
"id": "sixth-stack",
"metadata": {},
"source": [
"At this point, we have created a query to retrieve the metrics from the SQLite database, used sqlite3 to connect to the database, retreieved the metrics, closed the connection, and then ensured that the start times are in a datetime format for plotting purposes. \n",
"\n",
"This is what the dataframe looks like:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "brilliant-literacy",
"metadata": {},
"outputs": [],
"source": [
"print(DF)"
]
},
{
"cell_type": "markdown",
"id": "motivated-joseph",
"metadata": {},
"source": [
"For plotting purposes, we will create a new dataframe where each column (only one column in this case) is the metric and the associated values, and the index is the date of that value. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "caroline-contemporary",
"metadata": {},
"outputs": [],
"source": [
"plotDF = pd.DataFrame()\n",
"plotDF[metric] = DF['value']\n",
"plotDF.index=DF['start']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "returning-infrastructure",
"metadata": {},
"outputs": [],
"source": [
"print(plotDF)"
]
},
{
"cell_type": "markdown",
"id": "hybrid-richards",
"metadata": {},
"source": [
"Now we use that dataframe to produce a plot."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "hourly-basics",
"metadata": {},
"outputs": [],
"source": [
"ax = plotDF.plot(style='.', color='k', title=metric)\n",
"ax.xaxis.set_major_locator(mdates.DayLocator(interval=2))\n",
"plt.minorticks_off()\n",
"date_form = DateFormatter(\"%m-%d\")\n",
"ax.xaxis.set_major_formatter(date_form)\n",
"plt.gcf().autofmt_xdate()"
]
},
{
"cell_type": "markdown",
"id": "adjustable-spoke",
"metadata": {},
"source": [
"And save the plot for later viewing."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "western-collector",
"metadata": {},
"outputs": [],
"source": [
"plt.savefig(filename)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "chief-listing",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| lgpl-3.0 |
atcemgil/notes | fe588/FE588 Take Home Exam2 2017.ipynb | 1 | 27479 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the class we have implemented a polynomial object, that is extended as below.\n",
"\n",
"\n",
"\n",
"1. We show how to define the $+$ operator to implement the addition operation of two polynomials. The current implementation is incomplete as it can not add $p(x) = x^2 + 2x + 3$ and $q(x) = x+4$ if these are defined as arrays of different sizes. You should fix this\n",
"\n",
"2. Implement the substraction $-$ (method sub)\n",
"\n",
"3. Implement multiplication $*$ (method mul). Note that this is equivalent to the convolution of the coefficients\n",
"\n",
"4. Implement ploting the graph of the polynomial (method plot). In this function, you should use matplotlib"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2z^2 3z^1 1\n",
"[2, 3, 1]\n",
"0z^2 4z^1 4\n",
"[0, 4, 4]\n",
"--------------------\n",
"Result of p + q:\n",
"2z^2 7z^1 5\n",
"[2, 7, 5]\n",
"--------------------\n",
"Result of p - q:\n",
"Implement me\n",
"[]\n",
"--------------------\n",
"Result of p * q:\n",
"Implement me\n",
"[]\n",
"--------------------\n",
"implement me\n",
"We should see a plot here\n"
]
}
],
"source": [
"class polynomial(object):\n",
" def __init__(self, c, v):\n",
" self.coeff = c\n",
" self.v = v\n",
" def __repr__(self):\n",
" D = len(self.coeff)\n",
" for i in range(D):\n",
" if i<D-1:\n",
" print(self.coeff[i], end='')\n",
" print('{}^{} '.format(self.v, D-i-1), end='')\n",
" else:\n",
" print(self.coeff[i])\n",
" \n",
" return str(self.coeff)\n",
" def __add__(self,b):\n",
" \"\"\"Computes a+b and returns the result\"\"\"\n",
" ### Note: This code does not add polynomials \n",
" ### of different orders so you must fix this\n",
" D = len(self.coeff)\n",
" \n",
" coeff = []\n",
" for i in range(D):\n",
" coeff.append(self.coeff[i] + b.coeff[i])\n",
" \n",
" return polynomial(coeff, self.v)\n",
" def __sub__(self,b):\n",
" \"\"\"Computes a-b and returns the result\"\"\"\n",
" print('Implement me')\n",
" return polynomial([],self.v)\n",
" def __mul__(self,b):\n",
" \"\"\"Computes a*b and returns the result\"\"\"\n",
" print('Implement me')\n",
" return polynomial([],self.v)\n",
" def plot(self, number_of_points=100, left=-1, right=1):\n",
" \"\"\"\n",
" Evaluates the polynomial at number_of_points equally spaced \n",
" points between left and right and plots the result using matplotlib\n",
" \"\"\"\n",
" print('implement me')\n",
" return\n",
" \n",
" \n",
"p = polynomial([2,3,1], 'z')\n",
"print(p)\n",
"\n",
"q = polynomial([0,4,4], 'z')\n",
"# Your program must also work when we define more naturally\n",
"# q = polynomial([4,4], 'z')\n",
"print(q)\n",
"\n",
"print('--------------------')\n",
"print('Result of p + q:')\n",
"r1 = p + q\n",
"print(r1)\n",
"\n",
"print('--------------------')\n",
"print('Result of p - q:')\n",
"r2 = p - q\n",
"print(r2)\n",
"\n",
"print('--------------------')\n",
"print('Result of p * q:')\n",
"r3 = p * q\n",
"print(r3)\n",
"\n",
"print('--------------------')\n",
"# Generates a plot\n",
"p.plot()\n",
"print('We should see a plot here')\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy as sc\n",
"import pandas as pd\n",
"# \n",
"# import seaborn as sns\n",
"# sns.set(color_codes=True)\n",
"\n",
"# plt.figure(figsize=(5,5))\n",
"# df = pd.read_csv(u'data/wind_tribune.csv')\n",
"# sns.jointplot(x='wind_speed', y='production', data=df);\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([11, 9])"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"a = np.array([1,2,3,4,5,9,11])\n",
"\n",
"a[:-3:-1]"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.2z^5 -2z^4 -2z^3 -3z +1 \n",
"-1z^3 +4z^2 +4z -4 \n"
]
},
{
"data": {
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M5Ny5c6xfv952FOVDLl68SGxsLIcOHWLRokWUKVPGdiSv4+fnx4svvsh3333HjBkzbMex\nRgv/Mk2bNiUwMFCndZRLPfXUU3z22WdMmDCBkJAQ23G8VlRUFPXr12f48OFkZmbajmOFFv5lChYs\nSNOmTfnoo49sR1E+YtGiRbz66qv8+9//pkePHrbjeDURYfjw4Rw8eJCpU6fajmOFFv4VoqKi2L9/\nPykpKbajKC+XnJzMI488QoMGDXjjjTdsx/EJLVu2JCwsjJdffpnz58/bjuNyDil8EZkmIukisvey\n524WkZUisj/nfXFHjOVsUVFRACxfvtxyEuXNzpw5Q0xMDIGBgSxcuJACBQrYjuQTRISnnnqKtLQ0\nZs+ebTuOyznqCH86EHnFc0OB1caYasDqnMdur1KlStSsWVMLXzmNMYYePXqQkpJCQkKCbpvgYq1a\ntaJWrVq8+uqrPnfjI4cUvjFmPXDyiqfbApdOh88A2jliLFeIjo5m48aNnDx55V9Jqbx76623mDdv\nHi+//DLNmze3HcfniAhDhw4lJSWFpUuX2o7jUs6cwy9ljDmc8/ERoJQTx3Ko6OhoLl68yCeffGI7\nivIyW7Zs4YknniA6OppBgwbZjuOz2rdvzz/+8Q9GjhzpU3vsuOSkrcn+F73qv6qI9BSRJBFJOnbs\nmCviXFe9evUoVaqUTusohzpx4gQPPvgg5cqVY8aMGfj56ZoJWwICAhg0aBBbt25l3bp1tuO4jDO/\n446KSBmAnPfpV3uRMWayMSbYGBNcsmRJJ8bJPT8/P9q0acOKFSt8+qo85ThZWVnExcVx5MgR5s+f\nT/HiHrGGwavFx8dTunRpRo4caTuKyziz8JcB8TkfxwMeNVkWHR3N6dOn+eKLL2xHUV5g9OjRfPzx\nx4wZM4Z69erZjqOAwMBA+vbty2effcbXX39tO45LOGpZ5lwgEfiniBwSke7ASKCliOwHInIee4yW\nLVtSoEABndZRebZx40aeeuopOnTowH/+8x/bcdRlevbsSYECBRg/frztKC4h7nTCIjg42CQlJdmO\n8Yc2bdqwb98+vvvuO925UN2QEydOUKtWLfLnz8+OHTsoWrSo7UjqCt27dychIYFDhw557FSbiGw3\nxgRf73V61ugvPPDAAxw4cIBdu3bZjqI8kDGG+Ph40tPTmT9/vpa9m+rbty+//fYb06ZNsx3F6bTw\n/0Lbtm3x8/Nj8eLFtqMoDzR27Fg++ugjXnvtNerWrWs7jrqGWrVq0bhxY9566y2vv0GKFv5fKFmy\nJI0bN2bRokW2oygPs23bNoYOHUq7du3o06eP7TjqOvr3788PP/zg9efstPCvIyYmhq+//prk5GTb\nUZSHOH36NJ06daJMmTJMnTpVz/94gPvvv5+KFSsybtw421GcSgv/Oh544AEAndZRuWKM4bHHHiM1\nNZU5c+Zw8803246kciEgIIBevXqxbt06r94pVwv/OsqXL0+DBg208FWuTJs2jXnz5vHiiy/SqFEj\n23HU3/Dwww8TEBDAu+++azuK02jh50JMTAzbt28nNTXVdhTlxvbt20e/fv1o0aIFQ4d6xOaw6jKl\nS5embdu2TJ8+3WvviKWFnwsxMTGATuuoazt37hydOnWiUKFCzJw5U/fJ8VA9e/bkxIkTfPDBB7aj\nOIV+V+bCbbfdRs2aNXW1jrqmwYMHs3v3bqZPn07ZsmVtx1E3KCIigsqVKzN58mTbUZxCCz+XOnTo\nwKZNmzh06JDtKMrNLF++nPHjxzNgwADatGljO47KAz8/P3r06MGaNWvYv3+/7TgOp4WfSx07dsQY\nw/z5821HUW7kp59+4pFHHqFWrVo+teuiN3vkkUfw9/dnypQptqM4nBZ+LlWvXp06deqQkJBgO4py\nE5e2PD579ixz587V+9J6ibJlyxIVFcX06dO9bnt0Lfy/ITY2lm3btvHdd9/ZjqLcwOjRo1m9ejXj\nxo3j9ttvtx1HOVD37t1JT09nxYoVtqM4lBb+3/Dggw8C6FG+YuvWrTz99NP861//onv37rbjKAeL\njIykRIkSzJw503YUh9LC/xsqVqxIo0aNtPB93C+//ELnzp0pW7YskyZN0q0TvFC+fPno3Lkzy5Yt\n4+eff7Ydx2G08P+m2NhY9u7dy969e21HUZb06dOHAwcO8P7773vs/unq+uLj4zl//jzz5s2zHcVh\ntPD/pg4dOuDn5+dV3wQq9+bMmcPMmTN5+umnCQ8Ptx1HOVHt2rW58847vWpaRwv/bypVqhQtWrRg\n7ty5uNPdwpTzHThwgF69etGwYUOeeeYZ23GUk4kIcXFxJCYmes2afC38G9C5c2e+++47EhMTbUdR\nLnLhwgU6d+4MwOzZswkICLCcSLlCly5d8PPz85qjfC38G9C+fXsKFSrEjBkzbEdRLvLiiy+yefNm\nJk2aROXKlW3HUS5StmxZIiIimDVrFllZWbbj5JkW/g246aabaN++PfPmzePs2bO24ygnW79+PSNG\njCA+Pp5OnTrZjqNcrGvXrqSmpnrFb/ROL3wR+UFE9ojIThFJcvZ4rhIfH8/p06dZtmyZ7SjKiX7+\n+We6dOlClSpVGD9+vO04yoK2bdsSGBjoFcuxXXWE38wYU8sYE+yi8ZyuWbNmVKhQQad1vJgxhp49\ne3L48GHmzp3LTTfdZDuSsuCmm26iTZs2LFiwwONvcq5TOjfIz8+PLl268Omnn3L48GHbcZQTTJs2\njYULFzJ8+HDq1atnO46yqFOnThw9epTPP//cdpQ8cUXhG2CViGwXkZ4uGM9l4uPjycrKYvbs2baj\nKAdLTk6mX79+NG/enMGDB9uOoyxr3bo1QUFBHn/9jTh7LbmIlDPGpInIrcBKoK8xZv1ln+8J9ASo\nWLFiXU+7jWBISAi//voru3fv1kvsvURmZiYhISH8+OOP7N69W29oogB46KGHWLFiBUeOHCFfvny2\n4/yJiGzPzZS504/wjTFpOe/TgSVA/Ss+P9kYE2yMCS5ZsqSz4zjcww8/zN69e9m2bZvtKMpBnnzy\nSXbu3Ml7772nZa/+0KlTJ06ePMnq1attR7lhTi18EQkSkZsufQzcC3jVJjSdO3cmKCiISZMm2Y6i\nHOCTTz5hzJgx9O7dm+joaNtxlBu59957KVq0qEev1nH2EX4pYIOI7AK2Ah8ZY7xqg+kiRYoQGxtL\nQkICp0+fth1H5cHhw4eJj4/n7rvv5rXXXrMdR7mZAgUKEBMTw5IlSzh37pztODfEqYVvjPneGHNP\nztudxpgRzhzPlp49e/Lbb7/pyVsPdunuVWfOnCEhIYHAwEDbkZQbevDBB8nIyPDYaR1dlukAwcHB\n1K5dm0mTJumGah5q9OjRrFq1inHjxlGjRg3bcZSbat68OUWLFmXx4sW2o9wQLXwHEBEee+wxdu/e\nzdatW23HUX/T5s2b/7h7VY8ePWzHUW4sf/78REVFsXTpUi5cuGA7zt+mhe8gsbGxevLWA506dYrY\n2FjKly/P5MmTdWmtuq6YmBhOnDjBF198YTvK36aF7yBFihShc+fOJCQkcOrUKdtxVC4YY3j00Uc5\ndOgQCQkJFCtWzHYk5QFatWpFYGAgS5YssR3lb9PCd6BevXpx9uxZpk6dajuKyoXJkyezcOFCXn75\nZRo0aGA7jvIQQUFBREZGsnjxYo/bMlkL34Fq165N48aNeeuttzx+kyVvt3v3bgYMGECrVq144okn\nbMdRHiYmJoa0tDSSkjxrA2AtfAfr378/P/zwg26b7MbOnDnDgw8+SPHixZk5cyZ+fvpjoP6eqKgo\nAgICPG61jn6nO1jbtm2pVKkSb7zxhu0o6iqMMfTq1Yv9+/czd+5cbr31VtuRlAcqXrw4zZo1Y/Hi\nxR61FFsL38H8/f3p27cv69evZ+fOnbbjqCu89957vP/++zz//PM0adLEdhzlwWJiYti/fz9ff/21\n7Si5poXvBN27dycoKIhx48bZjqIus2fPHvr06UPz5s158sknbcdRHu7SXkvLly+3nCT3tPCdoFix\nYsTHxzNnzhyOHj1qO44CfvnlFzp06EDRokWZM2cO/v7+tiMpD1euXDnq1KnDhx9+aDtKrmnhO8mA\nAQP4/fff9SjfDVxab//tt9+SkJBAqVKlbEdSXiI6OprExESOHz9uO0quaOE7SbVq1ejQoQMTJkzQ\nXTQte+edd5g3bx4jRozQeXvlUFFRUWRlZfHJJ5/YjpIrWvhONGzYMDIyMnj77bdtR/FZW7du5fHH\nH6d169Z6q0LlcHXq1KFMmTIeM4+vhe9EtWvXJjIykrFjx/Lbb7/ZjuNzjh8/TocOHShbtiyzZs3S\n9fbK4fz8/IiKiuLTTz/l/PnztuNcl/4EONmwYcM4duwY06ZNsx3Fp1y8eJHY2FjS09NZtGgRN998\ns+1IyktFRUWRkZHhEZupaeE7WXh4OI0aNWL06NH8/vvvtuP4jOeee45Vq1YxYcIE6tSpYzuO8mIR\nEREEBgZ6xGodLXwnExGefPJJDh48yIwZM2zH8QkffPABI0aMoHv37nTv3t12HOXlChUqRPPmzVm+\nfLnbX3Wrhe8C9913H/Xr12f48OFkZmbajuPV9u3bR9euXalXrx5vvfWW7TjKR0RHR/Pdd9+RnJxs\nO8pf0sJ3ARHhpZde4uDBg0yePNl2HK91+vRp2rVrR6FChVi8eLHel1a5TJs2bQDcfnmmFr6LRERE\n0KRJE0aMGKErdpwgKyuLrl278v3337NgwQLKly9vO5LyIRUqVKBGjRp8+umntqP8JS18FxERRowY\nwdGjR3WqwQmeeeYZli9fztixY2ncuLHtOMoHRUZG8vnnn7v1AZ3TC19EIkUkRUS+FZGhzh7PnTVq\n1Ij77ruPV199Va++daA5c+bw8ssv8+ijj9K7d2/bcZSPioyMJDMzk88//9x2lGtyauGLiD8wAbgP\nqAHEikgNZ47p7l566SVOnjzJyJEjbUfxCtu2baN79+5/3GlMb0KubAkPD6dgwYKsWLHCdpRrcvYR\nfn3gW2PM98aY80AC0NbJY7q1OnXq0LVrV8aOHcuBAwdsx/Fohw4dol27dpQuXZqFCxeSP39+25GU\nDwsMDKRp06Y+XfjlgB8ve3wo5zmf9sorr+Dv78+gQYNsR/FYZ86cITo6moyMDJYtW0bJkiVtR1KK\nyMhIvvnmG7c9mLN+0lZEeopIkogkHTt2zHYclyhXrhxDhw5l0aJFbj3f564ubZuwZ88eFixYwN13\n3207klJAduEDbrtax9mFnwZUuOxx+Zzn/mCMmWyMCTbGBPvSUdp///tfKlSowIABA7h48aLtOB5l\n4MCBfPjhh4wfP/6PHzCl3EG1atWoUqWK207rOLvwtwHVRKSKiOQHOgHLnDymRyhYsCCjRo1i586d\nTJkyxXYcjzF27FjefPNNHn/8cXr16mU7jlJ/IiK0atWK1atXu+XumU4tfGPMBaAP8CmwD5hvjPnK\nmWN6ko4dO9KsWTOGDBnC4cOHbcdxewkJCQwcOJD27dszevRo23GUuqrIyEjOnDnDpk2bbEf5H06f\nwzfGfGyMqW6M+YcxZoSzx/MkIsKkSZM4d+4cffv2tR3Hra1Zs4a4uDgaN27M+++/r/ekVW6rWbNm\n+Pv7s2rVKttR/of1k7a+rlq1ajz77LMsWrSIpUuX2o7jlr788kvatWtH9erV+eCDD3SPHOXWihQp\nQoMGDbTw1dUNGjSIu+++m969e5ORkWE7jltJTk7m3nvvpVixYqxYsYLixYvbjqTUdbVo0YJt27Zx\n6tQp21H+RAvfDeTLl493332Xn376iYEDB9qO4zZSU1Np2bIlfn5+rFq1SjdEUx4jIiKCrKwst1t2\nrYXvJho0aMCQIUOYOnUqixYtsh3HuiNHjhAREcGZM2dYuXIl1atXtx1JqVwLCQmhUKFCbjeto4Xv\nRl544QXq1q3Lo48+yqFDh2zHsebIkSM0a9aMw4cP8/HHH1OzZk3bkZT6W/Lnz0/jxo1ZvXq17Sh/\nooXvRvLnz8+cOXPIzMwkPj6erKws25Fc7lLZ//jjj3z88ceEhobajqTUDWnRogX79u0jLS3t+i92\nES18N1O9enXefPNN1qxZ43M7ah45coTmzZv/Ufa6r73yZBEREUD2kmJ3oYXvhrp160anTp14+umn\n3f6WaY77KiV5AAALfUlEQVSSmppKeHg4qampWvbKK9SsWZMSJUq41Ty+Fr4bEhGmTJlCzZo1iY2N\nZf/+/bYjOVVKSgphYWEcP36clStXatkrr+Dn50fz5s1ZvXo1xhjbcQAtfLcVFBTEBx98QL58+Wjb\ntq3Xrs/fsWMH4eHhnD9/nnXr1tGwYUPbkZRymBYtWpCWlkZKSortKIAWvlurXLky8+fP55tvvqFj\nx45uuRlTXlyauilYsCBffPEF99xzj+1ISjnUpXl8d1mto4Xv5po1a8bEiRNZsWIF8fHxXrOV8sSJ\nE4mOjuaf//wnmzdv1nX2yitVqVKFihUrsm7dOttRAAiwHUBdX48ePThx4gRDhw6lePHiTJgwwWPv\n3XrhwgWGDBnCmDFjaN26NfPmzaNw4cK2YynlFCJCkyZNWLFiBcYY6z+3eoTvIYYMGcLgwYN55513\nGDp0qNucBPo7jh8/TqtWrRgzZgx9+vRh6dKlWvbK6zVt2pRjx46xb98+21H0CN+TjBw5koyMDEaN\nGkVGRgZvvfWWx2wTnJSURPv27Tl69CjvvfceDz/8sO1ISrlE06ZNAVi3bh01atSwmkWP8D2IiPD2\n228zZMgQJk6cyEMPPeT2J3KzsrIYNWoUDRs2xBjDhg0btOyVT6lSpQrly5d3i3l8PcL3MCLCyJEj\nKVGiBIMGDeLYsWMkJCTgjvcDTktLIy4ujjVr1hATE8PkyZO55ZZbbMdSyqVEhKZNm/LZZ59Zn8fX\nI3wP9d///pcZM2awceNG6tSpw5YtW2xH+kNWVhYTJ06kRo0abN68mXfffZeFCxdq2Suf1bRpU9LT\n00lOTraaQwvfg8XFxZGYmEi+fPkIDw/nzTfftL7h2t69ewkPD6dXr17UrVuXnTt30qNHD+urE5Sy\nqUmTJgDWp3W08D1c7dq12b59O61ataJ///6EhYWxd+9el+dIS0ujR48e3HPPPaSkpDB9+nRWr15N\ntWrVXJ5FKXfzj3/8g3Llylm/IYoWvhcoXrw4y5YtY+bMmXzzzTfUrl2bIUOGcOLECaePnZaWxuDB\ng7ntttuYOXMm/fr1Izk5mfj4eD2qVyrHpXn8devWWV1SrYXvJUSErl27kpyczEMPPcSoUaOoXLky\nQ4cOJT093aFjGWNISkqia9euVK5cmddff5327duTkpLC2LFjKVGihEPHU8obNGnShKNHj1rdV8dp\nhS8iz4tImojszHlr7ayx1P8rUaIE06dPZ+/evURHRzNq1CgqVKhATEwMixYt4ty5czf0dY0xfP31\n1zz//PPccccd1KtXjw8++IDevXvz7bff8v7771OlShUH/22U8h6Xr8e3RZz164WIPA+cMca8lts/\nExwcbJKSkpySx1elpKQwceJEEhISOHLkCEFBQTRo0IBGjRpRv359KlWqRNmyZbn55psREbKysjh7\n9iwHDx7kwIEDfPPNN2zatIn169dz9OjRPy4V79SpEx07dqRYsWK2/4pKeQRjDOXKlaNZs2bMnj3b\noV9bRLYbY4Kv+zotfN9w4cIF1q5dy9KlS9m0aRO7du3604oef39/jDFXXeVToUIFmjRpQnh4OG3a\ntKFcuXKujK6U13jwwQfZsmULqampDv26uS18Z1941VdE4oAk4AljzM9XvkBEegI9ASpWrOjkOL4r\nICCAli1b0rJlSwDOnDnD7t27SUtL46effiI9PR0/Pz8CAgIoUKAAFSpUoGrVqlStWpVSpUpZTq+U\ndwgLC2PBggUcPHjQSt/lqfBFZBVQ+iqfegp4BxgOmJz3rwPdrnyhMWYyMBmyj/DzkkflXuHChfVm\nI0q5WFhYGAAbN270vMI3xkTk5nUi8i7wYV7GUkopT1ezZk0KFy7Mhg0biI2Ndfn4zlylU+ayhw8A\nrr8aSCml3EhAQAChoaFs2LDByvjOXIc/SkT2iMhuoBnwuBPHUkopjxAeHs6ePXs4deqUy8d2WuEb\nY7oaY+42xtQ0xtxvjDnsrLGUUspThIWFYYwhMTHR5WPrlbZKKeVC9evXJyAggC+++MLlY2vhK6WU\nCwUFBVGnTh0r8/ha+Eop5WJhYWFs3bqVzMxMl46rha+UUi4WFhZGZmYm27dvd+m4WvhKKeVily7A\ncvU8vha+Ukq5WMmSJalevTqbNm1y6bha+EopZUFoaCiJiYkuvSGKFr5SSlkQEhLCsWPH+P777102\npha+UkpZEBoaCsDmzZtdNqYWvlJKWXDXXXdRuHBhl15xq4WvlFIW+Pv7U79+fS18pZTyBaGhoeza\ntYtff/3VJeNp4SullCWhoaFcvHgRV93aVQtfKaUsCQkJAXDZtI4WvlJKWXLLLbdQvXp1LXyllPIF\nrrwASwtfKaUsCg0NddkFWFr4SillkSvn8bXwlVLKIldegBXg9BGUUkpdk7+/P926daNq1apOH0sL\nXymlLBs3bpxLxsnTlI6I/EtEvhKRLBEJvuJzw0TkWxFJEZFWeYuplFIqr/J6hL8XiAEmXf6kiNQA\nOgF3AmWBVSJS3RhzMY/jKaWUukF5OsI3xuwzxqRc5VNtgQRjTKYx5gDwLVA/L2MppZTKG2et0ikH\n/HjZ40M5zymllLLkulM6IrIKKH2VTz1ljFma1wAi0hPoCVCxYsW8fjmllFLXcN3CN8ZE3MDXTQMq\nXPa4fM5zV/v6k4HJAMHBwa67uaNSSvkYZ03pLAM6iUgBEakCVAO2OmkspZRSuZDXZZkPiMghIBT4\nSEQ+BTDGfAXMB74GVgC9dYWOUkrZJa7YoS23ROQYkGoxQgnguMXxc0MzOoYnZATPyKkZHSMvGSsZ\nY0pe70VuVfi2iUiSMSb4+q+0RzM6hidkBM/IqRkdwxUZdfM0pZTyEVr4SinlI7Tw/2yy7QC5oBkd\nwxMygmfk1IyO4fSMOoevlFI+Qo/wlVLKR2jhX0FERotIsojsFpElIlLMdqYr/dW21LaJSGTOltjf\nishQ23muJCLTRCRdRPbaznItIlJBRNaKyNc5/5372850JREJFJGtIrIrJ+MLtjNdi4j4i8iXIvKh\n7SzXIiI/iMgeEdkpIknOGkcL/3+tBO4yxtQEvgGGWc5zNZe2pV5vO8jlRMQfmADcB9QAYnO2ynYn\n04FI2yGu4wLwhDGmBhAC9HbDf8dMoLkx5h6gFhApIiGWM11Lf2Cf7RC50MwYU8uZSzO18K9gjPnM\nGHMh5+FmsvcBcit/sS21bfWBb40x3xtjzgMJZG+V7TaMMeuBk7Zz/BVjzGFjzI6cj38hu6zcardZ\nk+1MzsN8OW9ud0JQRMoDbYAptrO4Ay38v9YN+MR2CA+i22I7mIhUBmoDW+wm+V85UyU7gXRgpTHG\n7TICbwCDgSzbQa7DkH2jqO05Owg7hU/e0zY3Wz6LyFNk/2o925XZLnH2ttTK/YlIYWARMMAYk2E7\nz5Vy9seqlXOea4mI3GWMcZtzIyISBaQbY7aLSFPbea4jzBiTJiK3AitFJDnnt1GH8snCv96WzyLy\nMBAFtDCW1q3e4LbUtuV6W2z110QkH9llP9sYs9h2nr9ijDklImvJPjfiNoUPNALuF5HWQCBQRETe\nN8Z0sZzrfxhj0nLep4vIErKnRx1e+DqlcwURiST7V8D7jTG/2c7jYbYB1USkiojkJ/u+xsssZ/I4\nIiLAVGCfMWaM7TxXIyIlL61gE5GCQEsg2W6qPzPGDDPGlDfGVCb7e3GNO5a9iASJyE2XPgbuxUn/\n49TC/19vATeR/WvVThGZaDvQla61LbVtOSe7+wCfkn2icX7OVtluQ0TmAonAP0XkkIh0t53pKhoB\nXYHmOd+DO3OOUt1JGWCtiOwm+3/0K40xbrvs0c2VAjaIyC6y7xvykTFmhTMG0ittlVLKR+gRvlJK\n+QgtfKWU8hFa+Eop5SO08JVSykdo4SullI/QwldKKR+hha+UUj5CC18ppXzE/wE4spyBsp+TpgAA\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f75b630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"\n",
"\n",
"class polynomial(object):\n",
" def __init__(self, c, v='x'):\n",
" self.coeff = c\n",
" self.v = v\n",
" def __repr__(self):\n",
" coeff = self.coeff\n",
" v = self.v\n",
" s = ''\n",
" D = len(coeff)\n",
" \n",
" first = True\n",
" \n",
" for i in range(D):\n",
" pw = D-i-1\n",
" pre = '+' if coeff[i]>0 else ''\n",
"\n",
" if first:\n",
" if pre=='+':\n",
" pre = ''\n",
" first = False\n",
" \n",
"\n",
" if pw == 0:\n",
" vname = ''\n",
" elif pw == 1:\n",
" vname = v\n",
" else:\n",
" vname = v + '^'+ str(pw)\n",
"\n",
" if coeff[i] != 0:\n",
" s += pre+str(coeff[i])+ vname + ' '\n",
" \n",
" return s\n",
" def __add__(self,b):\n",
" \"\"\"Computes a+b and returns the result\"\"\"\n",
" ### Note: This code does not add polynomials \n",
" ### of different orders so you must fix this\n",
" L_a = len(self.coeff)\n",
" L_b = len(b.coeff)\n",
" \n",
" coeff = self.coeff if L_a > L_b else b.coeff\n",
" short = self.coeff if L_a <= L_b else b.coeff\n",
" \n",
" for i in range(len(short)):\n",
" coeff[-1-i] += short[-1-i]\n",
" \n",
" return polynomial(coeff, self.v)\n",
" def __sub__(self,b):\n",
" \"\"\"Computes a-b and returns the result\"\"\"\n",
" print('Implement me')\n",
" return polynomial([],self.v)\n",
" def __mul__(self,b):\n",
" \"\"\"Computes a*b and returns the result\"\"\"\n",
" coeff = np.polymul(self.coeff, b.coeff)\n",
" return polynomial(coeff,self.v)\n",
" def deriv(self):\n",
" coeff = []\n",
" D = len(self.coeff)\n",
" for i in range(D-1):\n",
" pw = D-i-1\n",
" coeff.append(pw*self.coeff[i])\n",
" \n",
" if coeff == []:\n",
" coeff = [0]\n",
" \n",
" return polynomial(coeff, self.v)\n",
" \n",
" def plot(self, number_of_points=100, left=-1, right=1):\n",
" \"\"\"\n",
" Evaluates the polynomial at number_of_points equally spaced \n",
" points between left and right and plots the result using matplotlib\n",
" \"\"\"\n",
" x = np.linspace(left,right,number_of_points)\n",
" y = np.polyval(self.coeff, x)\n",
" plt.plot(x, y, 'k')\n",
" plt.show()\n",
" \n",
" return\n",
" \n",
" \n",
"p = polynomial([0.2, -2, -2, 0,-3,1], 'z')\n",
"print(p)\n",
"\n",
"q = polynomial([-1, 4,4,-4], 'z')\n",
"print(q)\n",
"\n",
"q.plot(left=-2.4, right=5)\n",
"\n",
"r = p+q"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.2z^5 -2z^4 -2z^3 -3z +1 \n"
]
},
{
"data": {
"text/plain": [
"4.0z^3 -24z^2 -12z "
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p = polynomial([0.2, -2, -2, 0,-3,1], 'z')\n",
"print(p)\n",
"\n",
"q = p.deriv().deriv()\n",
"q"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1x^4 +3x^3 -3x -1 "
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = polynomial([1,-1])*polynomial([1,1])*polynomial([1,3,1])\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ -0.2, 2.8, -4.2, -24.8, 11. , 35. , -24. , -16. , 12. ])"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.polymul(q.coeff, p.coeff)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2z^2 3z^1 1\n",
"[2, 3, 1]\n",
"0z^2 4z^1 4\n",
"[0, 4, 4]\n",
"--------------------\n",
"Result of p + q:\n",
"2z^2 7z^1 5\n",
"[2, 7, 5]\n",
"--------------------\n",
"Result of p - q:\n",
"Implement me\n",
"[]\n",
"--------------------\n",
"Result of p * q:\n",
"Implement me\n",
"[]\n",
"--------------------\n",
"implement me\n",
"We should see a plot here\n"
]
}
],
"source": [
"q = polynomial([0,4,4], 'z')\n",
"# Your program must also work when we define more naturally\n",
"# q = polynomial([4,4], 'z')\n",
"print(q)\n",
"\n",
"print('--------------------')\n",
"print('Result of p + q:')\n",
"r1 = p + q\n",
"print(r1)\n",
"\n",
"print('--------------------')\n",
"print('Result of p - q:')\n",
"r2 = p - q\n",
"print(r2)\n",
"\n",
"print('--------------------')\n",
"print('Result of p * q:')\n",
"r3 = p * q\n",
"print(r3)\n",
"\n",
"print('--------------------')\n",
"# Generates a plot\n",
"p.plot()\n",
"print('We should see a plot here')\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": "3.5.3"
},
"toc": {
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"toc_cell": false,
"toc_position": {},
"toc_section_display": "block",
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}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
powermundsen/ml2048 | src/2048-emulator/.ipynb_checkpoints/game_workbook-checkpoint.ipynb | 1 | 815787 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below are the *puzzle.py* and *logic.py* scripts.\n",
"\n",
"- *puzzle.py* handles the graphical part of the game and is also host to the game object, which conatins the **game matrix** and the **score** which we'll need in order to interface with it. It runs under *tkinter*.\n",
"\n",
"- *logic.py* handles the logic of the game as the name suggests. It includes what actions to take based on what keys are pressed and how to tranform the game matrix."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
"up\n",
"+ 0 = 0\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ 0 = 64\n",
"up\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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},
{
"name": "stdout",
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"text": [
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ 0 = 64\n",
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"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
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"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"up\n",
"+ 0 = 64\n",
"'d'\n",
"right\n",
"+ 16 = 80\n",
"up\n",
"+ 0 = 80\n",
"up\n",
"+ 0 = 80\n",
"up\n",
"+ 4 = 84\n",
"up\n",
"+ 4 = 88\n",
"up\n",
"+ 8 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
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"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
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"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
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"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
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"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
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"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
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"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
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"+ 0 = 96\n",
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"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"'s'\n",
"down\n",
"+ 0 = 96\n",
"up\n",
"+ 0 = 96\n",
"up\n",
"+ 4 = 100\n",
"up\n",
"+ 4 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n"
]
},
{
"name": "stdout",
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"text": [
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"up\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ 0 = 104\n",
"up\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
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"+ 0 = 104\n",
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"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n",
"up\n",
"+ 0 = 104\n"
]
},
{
"ename": "TclError",
"evalue": "can't invoke \"update\" command: application has been destroyed",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTclError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-17512d63ef3c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0mgamegrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey_down\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"'w'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0mgamegrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_idletasks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m \u001b[0mgamegrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\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/usr/lib/python3.5/tkinter/__init__.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1031\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1032\u001b[0m \u001b[0;34m\"\"\"Enter event loop until all pending events have been processed by Tcl.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1033\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'update'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1034\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mupdate_idletasks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1035\u001b[0m \"\"\"Enter event loop until all idle callbacks have been called. This\n",
"\u001b[0;31mTclError\u001b[0m: can't invoke \"update\" command: application has been destroyed"
]
}
],
"source": [
"# %load puzzle.py\n",
"from tkinter import *\n",
"from logic import *\n",
"from random import *\n",
"\n",
"SIZE = 500\n",
"GRID_LEN = 4\n",
"GRID_PADDING = 10\n",
"\n",
"BACKGROUND_COLOR_GAME = \"#92877d\"\n",
"BACKGROUND_COLOR_CELL_EMPTY = \"#9e948a\"\n",
"BACKGROUND_COLOR_DICT = { 2:\"#eee4da\", 4:\"#ede0c8\", 8:\"#f2b179\", 16:\"#f59563\", \\\n",
" 32:\"#f67c5f\", 64:\"#f65e3b\", 128:\"#edcf72\", 256:\"#edcc61\", \\\n",
" 512:\"#edc850\", 1024:\"#edc53f\", 2048:\"#edc22e\" }\n",
"CELL_COLOR_DICT = { 2:\"#776e65\", 4:\"#776e65\", 8:\"#f9f6f2\", 16:\"#f9f6f2\", \\\n",
" 32:\"#f9f6f2\", 64:\"#f9f6f2\", 128:\"#f9f6f2\", 256:\"#f9f6f2\", \\\n",
" 512:\"#f9f6f2\", 1024:\"#f9f6f2\", 2048:\"#f9f6f2\" }\n",
"FONT = (\"Verdana\", 40, \"bold\")\n",
"\n",
"KEY_UP_ALT = \"\\'\\\\uf700\\'\"\n",
"KEY_DOWN_ALT = \"\\'\\\\uf701\\'\"\n",
"KEY_LEFT_ALT = \"\\'\\\\uf702\\'\"\n",
"KEY_RIGHT_ALT = \"\\'\\\\uf703\\'\"\n",
"\n",
"KEY_UP = \"'w'\"\n",
"KEY_DOWN = \"'s'\"\n",
"KEY_LEFT = \"'a'\"\n",
"KEY_RIGHT = \"'d'\"\n",
"\n",
"class GameGrid(Frame):\n",
" score=0\n",
" def __init__(self):\n",
" self.score=0\n",
" Frame.__init__(self)\n",
"\n",
" self.grid()\n",
" self.master.title('2048')\n",
" self.master.bind(\"<Key>\", self.key_down)\n",
"\n",
" #self.gamelogic = gamelogic\n",
" self.commands = { KEY_UP: up, KEY_DOWN: down, KEY_LEFT: left, KEY_RIGHT: right,\n",
" KEY_UP_ALT: up, KEY_DOWN_ALT: down, KEY_LEFT_ALT: left, KEY_RIGHT_ALT: right }\n",
"\n",
" self.grid_cells = []\n",
" self.init_grid()\n",
" self.init_matrix()\n",
" self.update_grid_cells()\n",
" \n",
" #self.mainloop()\n",
" # while True:\n",
" # self.key_down(\"'w'\")\n",
" # self.update_idletasks\n",
" # self.update()\n",
"\n",
" def init_grid(self):\n",
" background = Frame(self, bg=BACKGROUND_COLOR_GAME, width=SIZE, height=SIZE)\n",
" background.grid()\n",
" for i in range(GRID_LEN):\n",
" grid_row = []\n",
" for j in range(GRID_LEN):\n",
" cell = Frame(background, bg=BACKGROUND_COLOR_CELL_EMPTY, width=SIZE/GRID_LEN, height=SIZE/GRID_LEN)\n",
" cell.grid(row=i, column=j, padx=GRID_PADDING, pady=GRID_PADDING)\n",
" # font = Font(size=FONT_SIZE, family=FONT_FAMILY, weight=FONT_WEIGHT)\n",
" t = Label(master=cell, text=\"\", bg=BACKGROUND_COLOR_CELL_EMPTY, justify=CENTER, font=FONT, width=4, height=2)\n",
" t.grid()\n",
" grid_row.append(t)\n",
"\n",
" self.grid_cells.append(grid_row)\n",
"\n",
" def gen(self):\n",
" return randint(0, GRID_LEN - 1)\n",
"\n",
" def init_matrix(self):\n",
" self.matrix = new_game(4)\n",
"\n",
" self.matrix=add_two(self.matrix)\n",
" self.matrix=add_two(self.matrix)\n",
"\n",
" def update_grid_cells(self):\n",
" for i in range(GRID_LEN):\n",
" for j in range(GRID_LEN):\n",
" new_number = self.matrix[i][j]\n",
" if new_number == 0:\n",
" self.grid_cells[i][j].configure(text=\"\", bg=BACKGROUND_COLOR_CELL_EMPTY)\n",
" else:\n",
" self.grid_cells[i][j].configure(text=str(new_number), bg=BACKGROUND_COLOR_DICT[new_number], fg=CELL_COLOR_DICT[new_number])\n",
" self.update_idletasks()\n",
" \n",
" def key_down(self, event):\n",
" if type(event) is str:\n",
" key = event\n",
" else:\n",
" key = repr(event.char)\n",
" print(key)\n",
" if key in self.commands:\n",
" self.matrix,done,newscore = self.commands[key](self.matrix)\n",
" self.score+=newscore\n",
" print(\"+\",newscore,\"=\",self.score)\n",
" if done:\n",
" self.matrix = add_two(self.matrix)\n",
" self.update_grid_cells()\n",
" done=False\n",
" if game_state(self.matrix)=='win':\n",
" self.grid_cells[1][1].configure(text=\"You\",bg=BACKGROUND_COLOR_CELL_EMPTY)\n",
" self.grid_cells[1][2].configure(text=\"Win!\",bg=BACKGROUND_COLOR_CELL_EMPTY)\n",
" if game_state(self.matrix)=='lose':\n",
" self.grid_cells[1][1].configure(text=\"You\",bg=BACKGROUND_COLOR_CELL_EMPTY)\n",
" self.grid_cells[1][2].configure(text=\"Lose!\",bg=BACKGROUND_COLOR_CELL_EMPTY)\n",
"\n",
"\n",
" def generate_next(self):\n",
" index = (self.gen(), self.gen())\n",
" while self.matrix[index[0]][index[1]] != 0:\n",
" index = (self.gen(), self.gen())\n",
" self.matrix[index[0]][index[1]] = 2\n",
"\n",
"#MOVE THIS TO A NEW FILE\n",
"gamegrid = GameGrid()\n",
"while True:\n",
" gamegrid.key_down(\"'w'\") #TEST MACHINE DECISION GOES HERE\n",
" gamegrid.update_idletasks\n",
" gamegrid.update()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# %load logic.py\n",
"#\n",
"# CS1010FC --- Programming Methodology\n",
"#\n",
"# Mission N Solutions\n",
"#\n",
"# Note that written answers are commented out to allow us to run your\n",
"# code easily while grading your problem set.\n",
"from random import *\n",
"\n",
"#######\n",
"#Task 1a#\n",
"#######\n",
"\n",
"# [Marking Scheme]\n",
"# Points to note:\n",
"# Matrix elements must be equal but not identical\n",
"# 1 mark for creating the correct matrix\n",
"\n",
"def new_game(n):\n",
" matrix = []\n",
"\n",
" for i in range(n):\n",
" matrix.append([0] * n)\n",
" return matrix\n",
"\n",
"###########\n",
"# Task 1b #\n",
"###########\n",
"\n",
"# [Marking Scheme]\n",
"# Points to note:\n",
"# Must ensure that it is created on a zero entry\n",
"# 1 mark for creating the correct loop\n",
"\n",
"def add_two(mat):\n",
" a=randint(0,len(mat)-1)\n",
" b=randint(0,len(mat)-1)\n",
" while(mat[a][b]!=0):\n",
" a=randint(0,len(mat)-1)\n",
" b=randint(0,len(mat)-1)\n",
" mat[a][b]=2\n",
" return mat\n",
"\n",
"###########\n",
"# Task 1c #\n",
"###########\n",
"\n",
"# [Marking Scheme]\n",
"# Points to note:\n",
"# Matrix elements must be equal but not identical\n",
"# 0 marks for completely wrong solutions\n",
"# 1 mark for getting only one condition correct\n",
"# 2 marks for getting two of the three conditions\n",
"# 3 marks for correct checking\n",
"\n",
"def game_state(mat):\n",
" for i in range(len(mat)):\n",
" for j in range(len(mat[0])):\n",
" if mat[i][j]==2048:\n",
" return 'win'\n",
" for i in range(len(mat)-1): #intentionally reduced to check the row on the right and below\n",
" for j in range(len(mat[0])-1): #more elegant to use exceptions but most likely this will be their solution\n",
" if mat[i][j]==mat[i+1][j] or mat[i][j+1]==mat[i][j]:\n",
" return 'not over'\n",
" for i in range(len(mat)): #check for any zero entries\n",
" for j in range(len(mat[0])):\n",
" if mat[i][j]==0:\n",
" return 'not over'\n",
" for k in range(len(mat)-1): #to check the left/right entries on the last row\n",
" if mat[len(mat)-1][k]==mat[len(mat)-1][k+1]:\n",
" return 'not over'\n",
" for j in range(len(mat)-1): #check up/down entries on last column\n",
" if mat[j][len(mat)-1]==mat[j+1][len(mat)-1]:\n",
" return 'not over'\n",
" return 'lose'\n",
"\n",
"###########\n",
"# Task 2a #\n",
"###########\n",
"\n",
"# [Marking Scheme]\n",
"# Points to note:\n",
"# 0 marks for completely incorrect solutions\n",
"# 1 mark for solutions that show general understanding\n",
"# 2 marks for correct solutions that work for all sizes of matrices\n",
"\n",
"def reverse(mat):\n",
" new=[]\n",
" for i in range(len(mat)):\n",
" new.append([])\n",
" for j in range(len(mat[0])):\n",
" new[i].append(mat[i][len(mat[0])-j-1])\n",
" return new\n",
"\n",
"###########\n",
"# Task 2b #\n",
"###########\n",
"\n",
"# [Marking Scheme]\n",
"# Points to note:\n",
"# 0 marks for completely incorrect solutions\n",
"# 1 mark for solutions that show general understanding\n",
"# 2 marks for correct solutions that work for all sizes of matrices\n",
"\n",
"def transpose(mat):\n",
" new=[]\n",
" for i in range(len(mat[0])):\n",
" new.append([])\n",
" for j in range(len(mat)):\n",
" new[i].append(mat[j][i])\n",
" return new\n",
"\n",
"##########\n",
"# Task 3 #\n",
"##########\n",
"\n",
"# [Marking Scheme]\n",
"# Points to note:\n",
"# The way to do movement is compress -> merge -> compress again\n",
"# Basically if they can solve one side, and use transpose and reverse correctly they should\n",
"# be able to solve the entire thing just by flipping the matrix around\n",
"# No idea how to grade this one at the moment. I have it pegged to 8 (which gives you like,\n",
"# 2 per up/down/left/right?) But if you get one correct likely to get all correct so...\n",
"# Check the down one. Reverse/transpose if ordered wrongly will give you wrong result.\n",
"\n",
"def cover_up(mat):\n",
" new=[[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]]\n",
" done=False\n",
" for i in range(4):\n",
" count=0\n",
" for j in range(4):\n",
" if mat[i][j]!=0:\n",
" new[i][count]=mat[i][j]\n",
" if j!=count:\n",
" done=True\n",
" count+=1\n",
" return (new,done)\n",
"\n",
"def merge(mat):\n",
" done=False\n",
" newscore=0\n",
" for i in range(4):\n",
" for j in range(3):\n",
" if mat[i][j]==mat[i][j+1] and mat[i][j]!=0:\n",
" mat[i][j]*=2\n",
" mat[i][j+1]=0\n",
" newscore=mat[i][j]\n",
" done=True\n",
" return (mat,done,newscore)\n",
"\n",
"\n",
"def up(game):\n",
" print(\"up\")\n",
" # return matrix after shifting up\n",
" game=transpose(game)\n",
" game,done=cover_up(game)\n",
" temp=merge(game)\n",
" game=temp[0]\n",
" done=done or temp[1]\n",
" newscore=temp[2]\n",
" game=cover_up(game)[0]\n",
" game=transpose(game)\n",
" return (game,done,newscore)\n",
"\n",
"def down(game):\n",
" print(\"down\")\n",
" game=reverse(transpose(game))\n",
" game,done=cover_up(game)\n",
" temp=merge(game)\n",
" game=temp[0]\n",
" done=done or temp[1]\n",
" newscore=temp[2]\n",
" game=cover_up(game)[0]\n",
" game=transpose(reverse(game))\n",
" return (game,done,newscore)\n",
"\n",
"def left(game):\n",
" print(\"left\")\n",
" # return matrix after shifting left\n",
" game,done=cover_up(game)\n",
" temp=merge(game)\n",
" game=temp[0]\n",
" done=done or temp[1]\n",
" newscore=temp[2]\n",
" game=cover_up(game)[0]\n",
" return (game,done,newscore)\n",
"\n",
"def right(game):\n",
" print(\"right\")\n",
" # return matrix after shifting right\n",
" game=reverse(game)\n",
" game,done=cover_up(game)\n",
" temp=merge(game)\n",
" game=temp[0]\n",
" done=done or temp[1]\n",
" newscore=temp[2]\n",
" game=cover_up(game)[0]\n",
" game=reverse(game)\n",
" return (game,done,newscore)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2+"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
JDTimlin/QSO_Clustering | data/photoz/SpIESHighzQuasarPhotoz2.ipynb | 1 | 156076 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Photo-z Determination for SpIES High-z Candidates\n",
"\n",
"Notebook that actually applies the algorithms from `SpIESHighzQuasarPhotoz.ipynb` to the quasar candidates."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"234416\n"
]
}
],
"source": [
"## Read in the Training Data and Instantiating the Photo-z Algorithm\n",
"\n",
"%matplotlib inline\n",
"from astropy.table import Table\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"#data = Table.read('GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_starclean.fits')\n",
"#JT PATH ON TRITON to training set after classification\n",
"data = Table.read('/Users/johntimlin/Catalogs/QSO_candidates/Training_set/GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_starclean_with_shenlabel.fits')\n",
"\n",
"#JT PATH HOME USE SHEN ZCUT\n",
"#data = Table.read('/home/john/Catalogs/QSO_Candidates/Training_set/GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_starclean_with_shenlabel.fits')\n",
"\n",
"\n",
"data = data.filled()\n",
"# Remove stars\n",
"qmask = (data['zspec']>0)\n",
"qdata = data[qmask]\n",
"print len(qdata)\n",
"\n",
"# X is in the format need for all of the sklearn tools, it just has the colors\n",
"Xtrain = np.vstack([ qdata['ug'], qdata['gr'], qdata['ri'], qdata['iz'], qdata['zs1'], qdata['s1s2']]).T\n",
"#y = np.array(data['labels'])\n",
"ytrain = np.array(qdata['zspec'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we are running on separate test data, we don't need to do a `train_test_split` here. But we will scale the data. Need to remember to scale the test data later!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"StandardScaler(copy=True, with_mean=True, with_std=True)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# For algorithms that need scaled data:\n",
"from sklearn.preprocessing import StandardScaler\n",
"scaler = StandardScaler()\n",
"scaler.fit(Xtrain) # Don't cheat - fit only on training data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Applying to Quasars Candidates\n",
"\n",
"Quasars candidates from the legacy KDE algorithm are in<br>\n",
"`GTR-ADM-QSO-ir-testhighz_kdephotoz_lup_2016_quasar_candidates.dat`\n",
"\n",
"Quasars candidates from the Random Forest Algorithm are in<br>\n",
"`GTR-ADM-QSO-ir_good_test_2016_out.fits`\n",
"\n",
"Quasar candidates from the RF, SVM, and/or bagging algorithms are in<br>\n",
"`GTR-ADM-QSO-ir_good_test_2016_out_Stripe82all.fits`<br>\n",
"\n",
"In the case of the latter file, this includes Stripe82 only. If we run on the other files, we might want to limit to Stripe 82 to keep the computing time reasonable."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#testdata = Table.read('GTR-ADM-QSO-ir_good_test_2016_out_Stripe82all.fits')\n",
"# TEST DATA USING 3.5<z<5 zrange ON TRITON\n",
"#testdata = Table.read('/Users/johntimlin/Catalogs/QSO_candidates/Final_S82_candidates_full/GTR-ADM-QSO-ir_good_test_2016_out_Stripe82all.fits')\n",
"\n",
"# TEST DATA USING 2.9<z<5.4 zrange ON HOME\n",
"testdata = Table.read('/Users/johntimlin/Catalogs/QSO_Candidates/photoz/SpIES_SHELA_Quasar_Canidates_Shen_zrange_JTmultiproc.fits')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2029149 12499\n"
]
}
],
"source": [
"#Limit to objects that have been classified as quasars\n",
"qsocandmask = ((testdata['ypredRFC']==0) | (testdata['ypredSVM']==0) | (testdata['ypredBAG']==0))\n",
"testdatacand = testdata[qsocandmask]\n",
"print len(testdata),len(testdatacand)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## If you want to compare ZSPEC to ZPHOT, use the cells below for test set"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"15891 15891\n"
]
}
],
"source": [
"## Test zspec objects with zspec >=2.9 and see how well the zphot matches with zspec\n",
"testdata = Table.read('/Users/johntimlin/Catalogs/QSO_candidates/Final_S82_candidates_full/QSOs_S82_wzspec_wcolors.fits')\n",
"\n",
"#Limit to objects that have been classified as quasars\n",
"#qsocandmask = ((testdata['ypredRFC']==0) | (testdata['ypredSVM']==0) | (testdata['ypredBAG']==0))\n",
"#qsocandmask = (testdata['ZSPEC'] >= 2.9)\n",
"\n",
"testdatacand = testdata#[qsocandmask]\n",
"print len(testdata),len(testdatacand)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scale the test data "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Xtest = np.vstack([ testdatacand['ug'], testdatacand['gr'], testdatacand['ri'], testdatacand['iz'], testdatacand['zs1'], testdatacand['s1s2']]).T\n",
"XStest = scaler.transform(Xtest) # apply same transformation to test data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Not currently executing the next 2 cells, but putting the code here in case we want to do it later."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Read in KDE candidates\n",
"dataKDE = Table.read('GTR-ADM-QSO-ir-testhighz_kdephotoz_lup_2016_quasar_candidates.dat', format='ascii')\n",
"print dataKDE.keys()\n",
"print len(XKDE)\n",
"XKDE = np.vstack([ dataKDE['ug'], dataKDE['gr'], dataKDE['ri'], dataKDE['iz'], dataKDE['zch1'], dataKDE['ch1ch2'] ]).T"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Read in RF candidates\n",
"dataRF = Table.read('GTR-ADM-QSO-ir_good_test_2016_out.fits')\n",
"print dataRF.keys()\n",
"print len(dataRF)\n",
"# Canidates only\n",
"maskRF = (dataRF['ypred']==0)\n",
"dataRF = dataRF[maskRF]\n",
"print len(dataRF)\n",
"\n",
"# X is in the format need for all of the sklearn tools, it just has the colors\n",
"XRF = np.vstack([ dataRF['ug'], dataRF['gr'], dataRF['ri'], dataRF['iz'], dataRF['zs1'], dataRF['s1s2']]).T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Instantiate Photo-z Algorithm of Choice\n",
"\n",
"Here using Nadaraya-Watson and Random Forests"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from astroML.linear_model import NadarayaWatson\n",
"model = NadarayaWatson('gaussian', 0.05)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<astroML.linear_model.kernel_regression.NadarayaWatson at 0x118a40310>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(Xtrain,ytrain)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
" max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
"modelRF = RandomForestRegressor()\n",
"modelRF.fit(Xtrain,ytrain)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Apply Photo-z Algorithm(s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Random Forest"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"zphotRF = modelRF.predict(Xtest)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Nadaraya-Watson"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"zphotNW = model.predict(Xtest)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Only need this if Xtest is too big"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from dask import compute, delayed\n",
"def process(Xin):\n",
" return model.predict(Xin)\n",
"\n",
"# Create dask objects\n",
"dobjs = [delayed(process)(x.reshape(1,-1)) for x in Xtest]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/johntimlin/anaconda/lib/python2.7/site-packages/astroML/linear_model/kernel_regression.py:52: RuntimeWarning: invalid value encountered in divide\n",
" return (K * self.y).sum(1) / K.sum(1)\n"
]
}
],
"source": [
"import dask.threaded\n",
"ypred = compute(*dobjs, get=dask.threaded.get)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The dask output needs to be reformatted.\n",
"zphotNW = np.array(ypred).reshape(1,-1)[0]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"testdatacand['zphotNW'] = zphotNW\n",
"testdatacand['zphotRF'] = zphotRF"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#TRITON PATH\n",
"#testdatacand.write('/Users/johntimlin/Catalogs/QSO_candidates/photoz/Candidates_photoz_S82_shenzrange.fits', format='fits')\n",
"#HOME PATH\n",
"#testdatacand.write('/home/john/Catalogs/QSO_Candidates/photoz/Candidates_photoz_S82_shenzrange.fits', format='fits')\n",
"\n",
"testdatacand.write('/Users/johntimlin/Catalogs/QSO_candidates/Final_S82_candidates_full/QSOs_S82_wzspec_wcolors_wphotoz.fits')\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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sbXFq2jR0W7ECAKCqro4eq1djY7t2WFq9Omr06oWlwcF4mpSEJ9bWn2Uev3ZExSgiIlJq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\nkSD/D2iI2JZ/EGViAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11782c410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from densityplot import *\n",
"from pylab import *\n",
"fig = plt.figure(figsize=(5,5))\n",
"hex_scatter(testdatacand['zphotNW'],testdatacand['ug'], min_cnt=10, levels=2, std=True, smoothing=1,\n",
" hkwargs={'gridsize': 100, 'cmap': plt.cm.Blues}, \n",
" skwargs={'color': 'k'})\n",
"plt.xlabel('zphot')\n",
"plt.ylabel('u-g')\n",
"#plt.xlim([-0.1,5.5])\n",
"#plt.ylim([-0.1,5.5])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 2., 1., 0., 3., 1., 1., 1., 5., 8.,\n",
" 4., 6., 6., 7., 2., 8., 12., 14., 13.,\n",
" 29., 29., 19., 30., 43., 40., 37., 28., 40.,\n",
" 46., 46., 53., 49., 60., 80., 71., 81., 98.,\n",
" 97., 97., 130., 165., 166., 181., 228., 316., 390.,\n",
" 435., 471., 392., 308., 200., 145., 121., 69., 119.,\n",
" 147., 123., 104., 58., 14., 13., 12., 7., 2.,\n",
" 2., 1.]),\n",
" array([ 0.24562 , 0.319459 , 0.393298 , 0.46713699, 0.54097599,\n",
" 0.61481499, 0.68865398, 0.76249298, 0.83633198, 0.91017097,\n",
" 0.98400997, 1.05784897, 1.13168796, 1.20552696, 1.27936596,\n",
" 1.35320495, 1.42704395, 1.50088295, 1.57472194, 1.64856094,\n",
" 1.72239994, 1.79623893, 1.87007793, 1.94391693, 2.01775592,\n",
" 2.09159492, 2.16543392, 2.23927291, 2.31311191, 2.38695091,\n",
" 2.4607899 , 2.5346289 , 2.6084679 , 2.68230689, 2.75614589,\n",
" 2.82998489, 2.90382388, 2.97766288, 3.05150188, 3.12534087,\n",
" 3.19917987, 3.27301887, 3.34685786, 3.42069686, 3.49453586,\n",
" 3.56837485, 3.64221385, 3.71605285, 3.78989184, 3.86373084,\n",
" 3.93756983, 4.01140883, 4.08524783, 4.15908682, 4.23292582,\n",
" 4.30676482, 4.38060381, 4.45444281, 4.52828181, 4.6021208 ,\n",
" 4.6759598 , 4.7497988 , 4.82363779, 4.89747679, 4.97131579,\n",
" 5.04515478]),\n",
" <a list of 1 Patch objects>)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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XjRkbERN7zCZMqezboHnz5tHS0tJIy5I04MaPh0cfhVdfra3fcAN88YuwZk05fWnkyH1w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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b0216d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from astroML.plotting import hist as fancyhist\n",
"fancyhist(testdatacand['zphotRF'], bins=\"freedman\", histtype=\"step\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
nick-youngblut/SIPSim | ipynb/bac_genome/fullCyc/.ipynb_checkpoints/Day1_addRich_rep10-checkpoint.ipynb | 1 | 1283676 | null | mit |
wtsi-medical-genomics/team-code | python-club/notebooks/python-club-5.ipynb | 1 | 13267 | {
"metadata": {
"name": "",
"signature": "sha256:712872538764a1e112f71ef9f7d9b03bfabcc3d9f5389b2279f12125fcf12ac6"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Solution to this week's problem"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def sum_numbers_i(n):\n",
" if n == 0:\n",
" return 0\n",
" else:\n",
" return n + sum_numbers_i(n - 1)\n",
"\n",
"def sum_numbers_ii(n):\n",
" return sum(range(n + 1))\n",
"\n",
"def sum_numbers_iii(n):\n",
" return n * (n + 1) / 2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit sum_numbers_i(500)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10000 loops, best of 3: 107 \u00b5s per loop\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit sum_numbers_ii(500)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"100000 loops, best of 3: 7.63 \u00b5s per loop\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit sum_numbers_iii(500)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1000000 loops, best of 3: 265 ns per loop\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Efficient file handling\n",
"\n",
"Lustre is designed for **big read/writes**. If you can read the complete file at once, then do so. Repeatedly reading a single file one line at a time can be slow.\n",
"\n",
"###Example\n",
"I have a file of 1's located in my scratch space. Reading it one line at a time:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def read_ones_ver1():\n",
" \"\"\"\n",
" Read the file one line at a time . Your allocated RAM better be big enough.\n",
" \"\"\"\n",
" with open('/lustre/scratch113/teams/barrett/users/dr9/ones.txt') as f:\n",
" for line in f:\n",
" times2 = int(line) * 2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Farm Results\n",
"\n",
"```\n",
"Fri Feb 13 12:17:18: Started on <bc-30-3-08>, Execution Home </nfs/users/nfs_d/dr9>, Execution CWD </nfs/users/nfs_d/dr9>;\n",
"Fri Feb 13 12:17:42: Done successfully. The CPU time used is 23.5 seconds.\n",
"Max Memory: 11 MB\n",
"Average Memory: 11.00 MB\n",
"```\n",
"But if I use the `read()` function, the whole file is read:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def read_ones_ver2():\n",
" \"\"\"\n",
" Read the whole file at once. Your allocated RAM better be big enough.\n",
" \"\"\"\n",
" with open('/lustre/scratch113/teams/barrett/users/dr9/ones.txt') as f:\n",
" lines = f.read()\n",
"\n",
" for line in lines:\n",
" times2 = int(line) * 2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Results\n",
"```\n",
"Fri Feb 13 12:16:04: Started on <bc-29-2-15>, Execution Home </nfs/users/nfs_d/dr9>, Execution CWD </nfs/users/nfs_d/dr9>;\n",
"Fri Feb 13 12:16:08: Exited with exit code 1. The CPU time used is 2.5 seconds.\n",
"Max Memory: 165 MB\n",
"Average Memory: 165.00 MB\n",
"```\n",
"\n",
"#VCF\n",
"Variant Call Format (VCF) files are text based files (though often compressed) that contain variant calls and associated information.\n",
"\n",
"The general structure is:\n",
"\n",
"```\n",
"##Meta-information lines\n",
".\n",
".\n",
".\n",
"#A header line\n",
"variant call 1\n",
"variant call 2\n",
".\n",
".\n",
".\n",
"variant call n\n",
"```\n",
"\n",
"###Meta-information\n",
"These lines contain information that includes:\n",
"\n",
"* the version of the VCF file\n",
"* what program created the file\n",
"* the format of some of the attributes contained within each variant call\n",
"\n",
"The following is an example of meta-information lines:\n",
"\n",
"```\n",
"##fileformat=VCFv4.0\n",
"##fileDate=20090805\n",
"##source=myImputationProgramV3.1\n",
"##reference=1000GenomesPilot-NCBI36\n",
"##phasing=partial\n",
"##INFO=<ID=NS,Number=1,Type=Integer,Description=\"Number of Samples With Data\">\n",
"##INFO=<ID=DP,Number=1,Type=Integer,Description=\"Total Depth\">\n",
"##INFO=<ID=AF,Number=.,Type=Float,Description=\"Allele Frequency\">\n",
"##INFO=<ID=AA,Number=1,Type=String,Description=\"Ancestral Allele\">\n",
"##INFO=<ID=DB,Number=0,Type=Flag,Description=\"dbSNP membership, build 129\">\n",
"##INFO=<ID=H2,Number=0,Type=Flag,Description=\"HapMap2 membership\">\n",
"##FILTER=<ID=q10,Description=\"Quality below 10\">\n",
"##FILTER=<ID=s50,Description=\"Less than 50% of samples have data\">\n",
"##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">\n",
"##FORMAT=<ID=GQ,Number=1,Type=Integer,Description=\"Genotype Quality\">\n",
"##FORMAT=<ID=DP,Number=1,Type=Integer,Description=\"Read Depth\">\n",
"##FORMAT=<ID=HQ,Number=2,Type=Integer,Description=\"Haplotype Quality\">\n",
"```\n",
"\n",
"###Header line\n",
"The header line has the following tab-delimited fields :\n",
"\n",
"```\n",
"CHROM\n",
"POS\n",
"ID\n",
"REF\n",
"ALT\n",
"QUAL\n",
"FILTER\n",
"INFO\n",
"```\n",
"As well as the standard fields, if genotype data is present in the file, these are followed by a FORMAT column header, then an arbitrary number of sample IDs. For example:\n",
"\n",
"```\n",
"#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA00001 NA00002 NA00003\n",
"```\n",
"\n",
"###Variant calls\n",
"The rest of the the file consists of variant calls with data filling in each of the header line fields. As with the header line, each entry is seperated by tabs. Furthermore, some of these entries are contain lists of information seperated by various characters (, and ;). An example of some variants calls:\n",
"\n",
"```\n",
"20 14370 rs6054257 G A 29 PASS NS=3;DP=14;AF=0.5;DB;H2 GT:GQ:DP:HQ 0|0:48:1:51,51 1|0:48:8:51,51 1/1:43:5:.,.\n",
"20 17330 . T A 3 q10 NS=3;DP=11;AF=0.017 GT:GQ:DP:HQ 0|0:49:3:58,50 0|1:3:5:65,3 0/0:41:3\n",
"20 1110696 rs6040355 A G,T 67 PASS NS=2;DP=10;AF=0.333,0.667;AA=T;DB GT:GQ:DP:HQ 1|2:21:6:23,27 2|1:2:0:18,2 2/2:35:4\n",
"20 1230237 . T . 47 PASS NS=3;DP=13;AA=T GT:GQ:DP:HQ 0|0:54:7:56,60 0|0:48:4:51,51 0/0:61:2\n",
"20 1234567 microsat1 GTCT G,GTACT 50 PASS NS=3;DP=9;AA=G GT:GQ:DP 0/1:35:4 0/2:17:2 1/1:40:3\n",
"```\n",
"\n",
"The examples cited are from the following pages which also contains more in depth information\n",
"* http://samtools.github.io/hts-specs/\n",
"* http://www.1000genomes.org/wiki/Analysis/Variant%20Call%20Format/vcf-variant-call-format-version-40\n",
"\n",
"\n",
"---\n",
"\n",
"#The problem\n",
"You are trying to find the variant call at position 1110696 along with it's ancestral allele in a VCF file.\n",
"\n",
"#Solutions\n",
"**Solution 1:** Write a parser to split each line by '\\t', count the number of columns until you reach your desired field of interest. If this column is semicolon delimited, split on this character. Search the results until you find datapoint.\n",
"\n",
"**Solution 2:** Use PyVCF\n",
"\n",
"---\n",
"\n",
"##PyVCF\n",
"\n",
"PyVCF is a convenient package that parses Variant Call Format files into objects for easy data access. For example, we can iterate over all of the lines in this file with the following code:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import vcf\n",
"\n",
"vcf_reader = vcf.Reader(open('/Users/dr9/Developer/PyVCF/vcf/test/example-4.0.vcf', 'r'))\n",
"\n",
"for record in vcf_reader:\n",
"\tprint record"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Record(CHROM=20, POS=14370, REF=G, ALT=[A])\n",
"Record(CHROM=20, POS=17330, REF=T, ALT=[A])\n",
"Record(CHROM=20, POS=1110696, REF=A, ALT=[G, T])\n",
"Record(CHROM=20, POS=1230237, REF=T, ALT=[None])\n",
"Record(CHROM=20, POS=1234567, REF=GTCT, ALT=[G, GTACT])\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The solution to our problem is therefore:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import vcf\n",
"\n",
"vcf_reader = vcf.Reader(open('/Users/dr9/Developer/PyVCF/vcf/test/example-4.0.vcf', 'r'))\n",
"\n",
"for record in vcf_reader:\n",
" if record.POS == 1110696:\n",
" print record.INFO['AA']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"T\n"
]
}
],
"prompt_number": 27
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each `Record` gives us all of the information with that variant call. This object has all sorts of methods and attributes to make our lives easier.\n",
"\n",
"###Exercise 1.\n",
"Use the `dir` function lets checkout all of the available attributes for a record.\n",
"\n",
"Hint:\n",
"The `for` loop above is not neccessary. We also just use: \n",
"\n",
"```\n",
"rec = vcf_reader.next()\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Exercise 2\n",
"Calcuate the proprtion of variant calls in `/lustre/scratch113/teams/barrett/users/dr9/vcfs/example-4.0.vcf` that pass the filters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can get a list of dictionaries containing the parsed sample column and record using the `sample`attribute:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"vcf_reader = vcf.Reader(open('/Users/dr9/Developer/PyVCF/vcf/test/example-4.0.vcf', 'r'))\n",
"record = vcf_reader.next()\n",
"for sample in record.samples:\n",
" print sample['GT']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0|0\n",
"1|0\n",
"1/1\n"
]
}
],
"prompt_number": 36
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The genotypes are represented by `Call` objects, which have three attributes\n",
"\n",
"* the corresponding Record site\n",
"* the sample name in sample\n",
"* a dictionary of call data in data\n",
"\n",
"As seen below:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"vcf_reader = vcf.Reader(open('/Users/dr9/Developer/PyVCF/vcf/test/example-4.0.vcf', 'r'))\n",
"record = vcf_reader.next()\n",
"call = record.genotype('NA00001')\n",
"print call.site "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Record(CHROM=20, POS=14370, REF=G, ALT=[A])\n"
]
}
],
"prompt_number": 37
}
],
"metadata": {}
}
]
} | gpl-2.0 |
gfeiden/Notebook | Daily/20150812_rgb_morphology_bcs.ipynb | 1 | 85574 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Morphology of the (s)RGB owing to Boundary Conditions\n",
"\n",
"A quick exploration of how different surface boundary conditions affect the morphology of the sub-giant-branch and red-giant-branch. \n",
"\n",
"## GS98 solar abundance distribution\n",
"\n",
"All the models adopt the Grevesse & Sauval (1998) solar abundance distribution. Boundary conditions considered include: \n",
" 1. Eddington grey $T(\\tau)$ approximation (Eddington 1926).\n",
" 2. Krishna-Swamy (1966) solar-calibrated grey $T(\\tau)$ approximation.\n",
" 3. Phoenix NextGen non-grey prescribed where $T(\\tau) = T_{\\rm eff}$ (Hauschildt 1999a,b; Dotter et al. 2007, 2008).\n",
" 4. Phoenix NextGen non-grey prescribed where $\\tau_{\\rm ross} = 1$ (Feiden, _this note_)\n",
" 5. Phoenix NextGen non-grey prescribed where $\\tau_{\\rm ross} = 10$ (Feiden, _this note_)\n",
" 6. Phoenix NextGen non-grey prescribed where $\\tau_{\\rm ross} = 100$ (Feiden, _this note_)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def loadTrack(filename):\n",
" trk = np.genfromtxt(filename, usecols=(0,1,2,3,4))\n",
" bools = [(point[0] > 3.0e7) for point in trk]\n",
" return np.compress(bools, trk, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#m1500_edd = loadTrack('files/trk/edd/m1550_GAS07_p000_p0_y26_mlt2.202.trk')\n",
"#m1500_ks66 = loadTrack('files/trk/ks/m1550_GAS07_p000_p0_y26_mlt2.202.trk')\n",
"m1500_dsep = loadTrack('files/trk/m150fehp00afep0.jc2mass')\n",
"m1500_teff = loadTrack('files/trk/m1500_GS98_p0_p0_T60.iso')\n",
"m1500_t010 = loadTrack('files/trk/m1500_GS98_p000_p0_y28_mlt1.884.trk')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x107e7fed0>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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F+kZWlPRYOoejE53DISK9RSqd+SQ7pi1/ATioZfqU5ghLkgjoHA4REamYVDpTC6SLHvqh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\nrOAwxhhjTNpZwWGMMcaYtPt/obxsbbw53K4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x105f78b90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1, figsize=(8., 8.))\n",
"\n",
"ax.set_xlabel('${\\\\rm Effective\\\\ Temperature\\\\ (K)}$', fontsize=22.)\n",
"ax.set_ylabel('$\\\\log(L / L_{\\\\odot})$', fontsize=22.)\n",
"ax.tick_params(which='major', axis='both', length=10., labelsize=16.)\n",
"\n",
"ax.grid(True)\n",
"ax.set_xlim(8000., 2000.)\n",
"\n",
"ax.plot(10**m1500_dsep[:,1], m1500_dsep[:,3], '-', dashes=(5.0, 5.0), lw=3, c='#069F74')\n",
"ax.plot(10**m1500_teff[:,1], m1500_teff[:,3], '-', dashes=(15.0, 10.0), lw=3, c='#800000')\n",
"ax.plot(10**m1500_t010[:,1], m1500_t010[:,3], '-', lw=3, c='#0473B3')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## GAS07 solar abundance distribution\n",
"\n",
"All models below adopt the Grevesse et al. (2007) solar abundance distribution. \n",
"\n",
"We first begin with models that are identical with the exception of the surface boundary conditions. This means that tracks computed with the grey approximation _do not_ adopt the solar-calibrated mixing length parameter."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"m1550_edd = loadTrack('files/trk/edd/m1550_GAS07_p000_p0_y26_mlt2.202.trk')\n",
"m1550_ks66 = loadTrack('files/trk/ks/m1550_GAS07_p000_p0_y26_mlt2.202.trk')\n",
"m1550_t050 = loadTrack('files/trk/m1550_GAS07_p000_p0_y26_mlt2.202.trk')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x107e24290>]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Dn0m/sv6FZMaKkfzHyTYLXQ3sT1KsbEqygt4vgeXufgkiIoN1EDDecvDLTqMU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\nI4RIJ1xlWNsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x105f78e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1, figsize=(8., 8.))\n",
"\n",
"ax.set_xlabel('${\\\\rm Effective\\\\ Temperature\\\\ (K)}$', fontsize=22.)\n",
"ax.set_ylabel('$\\\\log(L / L_{\\\\odot})$', fontsize=22.)\n",
"ax.tick_params(which='major', axis='both', length=10., labelsize=16.)\n",
"\n",
"ax.grid(True)\n",
"ax.set_xlim(8000., 2000.)\n",
"\n",
"ax.plot(10**m1550_edd[:,1], m1550_edd[:,3], '-', dashes=(5.0, 5.0), lw=3, c='#069F74')\n",
"ax.plot(10**m1550_ks66[:,1], m1550_ks66[:,3], '-', dashes=(15.0, 10.0), lw=3, c='#800000')\n",
"ax.plot(10**m1550_t050[:,1], m1550_t050[:,3], '-', lw=3, c='#0473B3')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
jgarciab/wwd2017 | class5/class5_stats.ipynb | 1 | 612298 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Working with data 2017. Class 5\n",
"## Contact\n",
"Javier Garcia-Bernardo\n",
"[email protected]\n",
"\n",
"## 0. Structure\n",
"## 1 Assumptions of t-test and regression\n",
"- Normality \n",
"- Independent and identically distributed (i.i.d.)\n",
"- Equal variance\n",
"- (for linear regression) Uncorrelated residuals\n",
"\n",
"\n",
"## 2. groups\n",
"- Compare one group vs one value\n",
"- Compare two groups \n",
"- Compare two paired groups\n",
" \n",
"## 3. Multiple groups\n",
"- ANOVA\n",
"- Multiple comparison (Tukey correction)\n",
" \n",
"## 4. Regressions\n",
"- Linear regression\n",
"- Logistic regression\n",
"- Machine learning"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: scikits.bootstrap in /root/.local/lib/python3.5/site-packages\r\n",
"Requirement already satisfied: scipy in /opt/anaconda/anaconda3/lib/python3.5/site-packages (from scikits.bootstrap)\r\n",
"Requirement already satisfied: numpy in /opt/anaconda/anaconda3/lib/python3.5/site-packages (from scikits.bootstrap)\r\n"
]
}
],
"source": [
"#Install something we'll need\n",
"!pip install --user scikits.bootstrap\n",
"\n",
"#Requires restarting the kernel"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<style>.container { width:90% !important; }</style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"##Some code to run at the beginning of the file, to be able to show images in the notebook\n",
"##Don't worry about this cell\n",
"\n",
"#Print the plots in this screen\n",
"%matplotlib inline \n",
"\n",
"#Be able to plot images saved in the hard drive\n",
"from IPython.display import Image \n",
"\n",
"#Make the notebook wider\n",
"from IPython.core.display import display, HTML \n",
"display(HTML(\"<style>.container { width:90% !important; }</style>\"))\n",
"\n",
"import seaborn as sns\n",
"import pylab as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import scipy\n",
"import scipy.stats\n",
"from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
"import scikits.bootstrap as bootstrap"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Assumptions of most tests\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.1 Normality\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://kanbanize.com/blog/wp-content/uploads/2014/07/Standard_deviation_diagram.png\" width=\"500\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(url=\"https://kanbanize.com/blog/wp-content/uploads/2014/07/Standard_deviation_diagram.png\", width=500)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**How to test it**\n",
"- QQ plot. Measure our quantiles vs hypothetical quantiles.\n",
"- Most tests are resistant to small deviations."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def qq_plot(x):\n",
" import scipy.stats\n",
" (osm, osr),(slope, intercept, r) = scipy.stats.probplot(x, dist='norm', plot=None)\n",
" plt.plot(osm, osr, '.', osm, slope*osm + intercept)\n",
" plt.xlabel('Quantiles',fontsize=14)\n",
" plt.ylabel('Quantiles Obs',fontsize=14) "
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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HvI2pvrmNXz/4Nhtb3iI06V0COe0QACcacrtv75oGTrDH7tsDHbN97Ya1jwDj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\nkiRJ6uBgSZIkqcN/5Byz/FrE7h8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0f8d9668d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.random.randn(100)\n",
"plt.subplot(2,2,1)\n",
"qq_plot(x)\n",
"\n",
"x = np.random.exponential(2,100)\n",
"plt.subplot(2,2,2)\n",
"qq_plot(x)\n",
"\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.2 Equal variance (if two or more groups)\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"http://goodsciencebadscience.nl/wp-content/uploads/2012/09/variances1.gif\" width=\"500\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(url=\"http://goodsciencebadscience.nl/wp-content/uploads/2012/09/variances1.gif\",width=500)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**How to test it**\n",
"- Levene test (it doesn't assume normality)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LeveneResult(statistic=40.098137331112042, pvalue=1.5876312204292832e-09)\n"
]
}
],
"source": [
"#random normal data (mean 0 std 1)\n",
"x = np.random.randn(100)\n",
"#random normal data (mean 0 std 2)\n",
"y = np.random.randn(100)*2\n",
"\n",
"#Test\n",
"print(scipy.stats.levene(x,y))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.3 i.i.d. (independent and identically distributed)\n",
"This comes from the data collection.\n",
"- Each observation is independent\n",
"- There are no subpopulations in the population"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.4 Uncorrelated residuals (for linear regression)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"These are the residuals\n"
]
},
{
"data": {
"text/html": [
"<img src=\"https://upload.wikimedia.org/wikipedia/commons/e/ed/Residuals_for_Linear_Regression_Fit.png\" width=\"500\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"These are the residuals\")\n",
"Image(url=\"https://upload.wikimedia.org/wikipedia/commons/e/ed/Residuals_for_Linear_Regression_Fit.png\",width=500)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://i.stack.imgur.com/RU17l.png\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(url=\"https://i.stack.imgur.com/RU17l.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.4 No or little multicolinearity (for linear regression)\n",
"- Your dependent variables are not measuring the same thing\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. t-test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.1 Sample against value\n",
"- Imagine you measure increase in productivity, and you want to see if the increase is significant"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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vfLpgL3sy8zGFFxHcOx1LVEHNOfuZRGxHk8EWqhsLfZSx0BZoP/l5\nHYzionyOZ+q59slDR/tZG2OJjomne9J5nDx6gIxdm5TR4APffju/0XNWq5nY2IhWayXqKkX/FnAJ\nYAN+AB6tU/9F0Q7IzMkjqHc6lq5HqbZZneURelaEckW0SYzJD1MYjty9Gc31FbE9VYFsjOQhuvvl\n6KEMyko9boioCEzmo9d36YUeDD0YeN2vGilaFKfmZMXhjVSdtxxrgm4waA4LtqOCyt0X1xgMkWHK\nYGhrqJ2GNkq1a6JzQg86J/TwszbGkzJ8DCsWfoWmOTmwdxsTLxzob5XaFIsWfc/FF19CdHQ0ixbV\nrttksZh56aXnf1V3jZTyPy2thxCiE7AZvcR8OVAuhPgc5aZsN2ScPsL7C7+m3JpT8wnjyEug6sgg\n3RWBqrnQllFGQxuksqKcQ1IvqzxoaPvfZQDoktiLzgk9yM0+wb70bcA0f6vUppg+/U98/PEXREdH\nM336nxqKb3BvFlcdtNziRoOUshC4t85wEnCigemKNkS5vZxv0heyKecnTK5PFmd5hJ4VUdSZmMhg\n3nhinH+VVJwzymhogxzYux2HQ+/2ltwBXBOgZ4ikDL+I1Uu+5eihvZSUFNO5s6o47inPPPMC3bp1\nB+Dpp5+vZTSYzSZefvmFu/2hl6sJ3kPANZ6uMarqprcYXQXUG/ylS1FpFe99t4t9xXuw9MzAFFxV\n44qwn+yPPasPaLpOfbpFe13F9VxRj1Hj+KqHMhraIOk79cahUdGx9Eg6z8/atB6DXUaD0+lg8+ZN\n9OnTarF7bZ6rrz77mTx58rW1zlmtZu64Y9rn1ceu5nKGp+MIIS5Grxj7f1LKlZ6uM7rqprcEkj6t\nqUthSSVP/+d7KrvuwNrlbBshR14CtqPJaFVndekUGcz/uyOVTpHeVYxtKTrqY2QEymhoY9hsVTXN\nm5KHXVjTr6EjkNizL7HxCeSfyWbjxnVMnXqrv1Vqk1xyyWg++ug/CJHc2JQJwLtAk/2zzwUhxLXA\nF8DvpJRferPWqKqb3mJ0FdBA1uV0YREvLPwCR79MLCY9INtZEa67Igq71Jo7pG8cD9wwBKfNTn6+\n3XDd3OnIj5Gn+niL10aDK13qXWAMUAx8I6V8qpG5EcAHwO1AspRyn9u5VegteO2czQnPkFKO8Fan\njsSBvdupqqwAYPD5Y/2sTeuiuyjGsG7FXNLStlJWVkp4uG/9JzoiWVlZgF6FMy8vt+bYajUxZsyU\nJNc0K7rREGOUHkKIscBnwM1SyuXerg+0qpiBpI/RumiaxpbsNL7YPQdnfIUe/OIwYz/VH/upvjWu\nCACrxcSLd42me2f9NerPe9SRHiOj8WWnYTZ69PM0IAFYKITIklK+5T5JCNENWAlsQA+qqosG3COl\n/MIHHTose9LWAxAR1Yk+56X4WZvWJ2X4RaxbMRebzcaGDeu54oor/a1Sm2Hq1GsxmUyYTCaefPL3\ndU8frnO8yQgdhBAW9IqyT/piMCj8x8mSLL7KmMOhosM1yfqOvK4uV4TeuqQ6M2Jo/87cM2UQ4SFq\nM7u94dUj6gpaGgZMkFKWACVCiDeAR9GLtbjTBfgDsBP4dSMiVdU5L7DZqpC7dNdEyrAxmM2WZla0\nP3r0HkBkdCwlRfmsXr1CGQ1e8PnnX7Fly2beeecNxo4dR6dO+maCyQQLFsyvzpTQ0DMZ3jNIjYuA\nZOAfQoh3XNerztYQUspjBl1X4SMV9gq+27+ENSfXQSOuCPcCTa5iYeTnl7bpb9SKhvHWDBwJZNap\n3LYNEEKICCllafWglHInsFMI0bsJedOEEE+iF3nZCDwgpaxbA1/h4mBGGpWV5QAMHtGxXBPVmM1m\nBgwayfaflrNx43oqKysICVG9KDyhX7/z6NfvPNasWcVDDz1Or166R8JqNfPGG6/f1Ro6SCnXovea\nUQQ4mqax7tg2Zsh5OCzlYALNacZ+sp/LFXH2YVQFmjoO3hoN8UB+nbHqFtmdgVI8Z49r/u3om13/\nBH4QQqRIKT2OljEifcXo1Bhv5VutJsxmE+nVronITvQbMASLuf5GTXVgpNlsxmQyYTGbGpzXGM2t\ncZdf3ZXSk3WeXK8x2XURQ1LZ/tNyysvL2bJlE5deeplH1zLycQ2050xTvPfeRw3KViiqySrN5n97\n53Cw6FCNiefI74LtyKAaV0Q10eFBymDoQPjicGoRl4KU8iH3YyHE/egGyHj0WAiPMDJ9xejUGE/l\n2+1lBAWZyHBlTQxLvZioqPAm14SGBhEWFozFGkR4uOdpTp6uCQ0N8mmdJ9erK7suA5IHExsbS35+\nPmvXruSGGxrsn9YoHeE50xwbNmxgyZIlFBQU4HQ6+eGHH2bUmaJJKVV6Sgejwl7JD5nLWX5sNU7N\n1aq+Igzb0UE4C7rWmx8ZFsT/3T6ytdVU+BFvjYYc9N0Gd+LR/ZE556KIlLJECJEHdPdmnRHpK0an\nxngrv6CglL27tlNRXgZA8pALKSurbHCu2WwmNDSIigob5eVVWKw0OrchmlvjLt/pdHq8zpPrNSa7\nLpWVdsaOHceCBfNZvnw5J07kEB7etBEFxj6ugfacaYrZs7/l9ddfqeld4uKWOtMaCl5WtFM0TWN7\nzi5m7Z9PQWWhPuY0Yz/VF/vJfrVcEap9dcfGW6NhC5AkhIiTUla7JUYD6VLKsibW1XoDEkJEAa8A\nL0sps1xjndGDJ72KaTAyfcXo1BhP5dvtGnvSNgAQERlNUv/BOJyNvae7vh04nWiahsOpNTG3Ps2v\nOSvffY4v16q/rmHZ9TRwaowbdxkLFsynoqKCVatWcdVVP/P4mh3hOdMUM2Z8RZ8+ffntbx+mZ88k\nQkODufHGa1SlrA5KdulpZuybS0b+/poxR4HLFVF51hhXxoICvDQapJRpQojNwCtCiCeAHsDjwGsA\nQoi96GmU692Wmajj0pBSFgshxgDvuNwSoNd+SJNSbvDtX2m/lJWVcTAjDYAhI8djsag4soEDk0lM\n7EZW1imWLVvsldHQ0cnKOsWLL07noov0PgBWqxkp5RE/q6VoZSodVbor4uhqHJoDAGdlmJ4VUccV\nodpXK6rxJQLqFnRjIQtYAXwmpXzfdW4gEAkghHhWCFEO7EXfadghhCgTQjzjmns9ujGxDziCHm7j\ncf35jsRPP63HbrcBMPyCS/ysTWBgMpm48spJAGzevJGCgrrxuYrG6NQphtBQlXHSUdE0jbTTu/jT\nhtdYcmQlDs2B5jRhO9GPyl3jlMGgaBKvAyGllCeBBiPPpJQWt7//AvylCTnHqe9HVTTA6tV6XGhc\nl270SBrgZ20Ch4kTJ/HFF5/hcDhYtWo5N9ygnk6ecNllV7Bq1QpSU0f7WxVFK3O6LIf/7f2O/YXu\nrojOLldE/eqqymBQ1EWV6wpwcnNz2bVLb4M9PPXShload1j69u1P//7ncfDgAZYtW6yMBg+5/fZf\n8vLLz/Pqq3/m8ssnkpiYwO23T61XXlRKme4P/RQtT5WjinkHlrHy+GowuWKHKkP1rIj8rjSUFKcM\nBkVDKKMhwFmxYklNJsGwVOWaqMvEiZM4ePAAO3fuICvrFImJ3fytUsBzww1XYzKZ0DSNBQvmVQ/v\namCqCp5p4xSWVvLPpUs5GboZgqsLNJmwZ/XFfrI/OOs/xFaLCdErRgU8KhpEGQ0BzpIlPwDQrWc/\n4ruoD8S6TJw4iQ8/fBdN01i8eCG//vU9/lYp4PnZz6bU2rGqU0bacIQQk4DPgRVSyttb67odjcN5\np/j7mi/Rok/XjDkK47EdSUGrqO2KUJkRCk9RRkMAc+jQQfbtywBg0LAxftYmMElISGTUqAvYsmUT\nCxbM45e/vKtDtQv3hWeffbHWcWuWkRZC/AG4Gz0AWmEAVQ4b8w8sY8WxHyHK3RWRjDM/AXdXhDIW\nFN6ijIYA5vvv5wJgtVpJHqqC1hpj8uRr2bJlE1lZp9i+fSujRl3gb5XaNK506H9LKQcbIL4cvbbL\nPwDvyocqmmVnTjr/S59DfmU+mKtdEX1croizb/fKWFD4ijIaApTKykqWLFkIwOjRFxEeEeVnjQKX\n8eMvIzIyipKSYhYunK+MBg8oLy8nLW0b2dmnAHj99VcedJ2yAtcCfYy4rpTynwBCCCPEd1gOn8ni\n9wu+pjToeM2Y7ooYhFYRCYDVbCIiLIg+iVHcNWWQMhYUPqGMhgBl7dofKSrSm4leeeXPvOoE1tEI\nCQnhyit/xpw5M/nxx5U89lgxUVHKyGqMkydP8Pjjv+PUqZNomlYd3/CO2xQTMMs/2jVPoDTYMrpJ\nmSecKS7l7ytmkRuyG1OQ7orQqkKwHU3GkZdItSsiOiKI6fdfRHSE8YZCINyXapQujeOrHl4bDUKI\nJPTqjWOAYuAbKeVTjcyNAD5A72SZLKXc53YuBHgbveZDCLAKvTV2XgOiOhzz538HQGJiN4YOHc7G\n9Gw/axTYTJlyHXPmzKSqqpLly1X6ZVN88smH5OXlMW3aL0hK6s2rr/4F4EX0T5h7gX9JKV/1p45N\nYXRTMG/xlz7bTu7i7+v+gy2sBBMuV0R2H+wnarsiOkUG868/TKBTZOt6gwLpcVK6tBy+7DTMBjYD\n04AEYKEQIktK+Zb7JCFEN/RulRtouPnNdGAEcCFQBnwMfIpeKbJDc+LEcbZt2wLoH4YqsK95Bg4U\nDBgwkP3797FgwTxlNDTBjh3b+e1vH+amm6YCVBsNc6WUO4UQ7wCbhBDrpJRr/alnYxjVFMxbjG5S\n1hi55XnMyJjLjpw9NTV9HUVxuiui/OwOW5DFzOB+cdx7TQpOm538fHur6Oev+6J08U0fb/HKaBBC\npALDgAlSyhKgRAjxBvAo8Fad6V2APwA7gV/XkWNBj6C+w1VhEiHEs0C6ECKxuolVR2XhQj133mw2\nc/XVqrK2p0yefB1vv/06UmaQnr6HlBQj4vjaPrm5OQwceDamwFWzwQogpcwXQkwH/gRc4ScVm8To\npmDe0lr62Jx2Fh5cwdKjK9FMeq8IrSoE2zGB40w3wIQJsDRQZ8Ef9yuQHielS8vh7U7DSCBTSlnk\nNrYNEEKICClljetdSrkT2CmE6N2AnP5ANLDdbb509aoYBSzwUq82Q+6ZM5SVlRKdF05RYVk9i9Nm\nszFv3hwAhg07n9KycvLz86isUp2Km2PSpMl8+OG7lJeXMXv2DFJS/uRvlQKS8PBw8vPPegGjoqIp\nLCzogf5aBshAfx0q/ERRWRWfLtjL4awi0MAReRpnt90QUqoXaNJM2LN6Yz9xXo0rIshq5s2HxxEe\nokLVFMbh7bMrHqjbGaj63aczeByvF+/6XVdWvkuOxxgRVGJkwMqefYexWWMJzS+jotJWU+2xmp1b\n1lBYqPezTx55BfuzbRTm28grKPKo2mG1K8NsNmMymbCY9R9PaW6Nu/zqVtaerPPkeo3Jrq+DCavV\nhNVa+/GJiYlm8uQpzJo1kxUrlvLww48SH3/26WTk42p0kFNLyh827Hw+/fRjkpKS6N//PJKSkti1\nq+AuYL5rymjAkL1s1xcDDQhyHd8IaFLK8CYXdjA+XbCXHQfPYAouJygpA0vc2ZgmR1GsXqCpvHaw\n7/kDuxAdEdymv8UqAh9fTNKWbH5wzrKMDCoxQnZ0dDjOkBgAwuq8TWqaxtYNSwHomtiTC8Zehtls\nxoyD0opywsM9D2QKDQ0iLCwYizXIq3WergkNDfJpnSfXqyu7LlWVwcTERBAbW7/Bzt1338msWTOx\n2+0sXvw9v/vd7+rNaWvPmZaW/9vf/oY777yTjz56lw8++IBrrpnCrl07bxBCbAHOAJcDi875Qg0g\npWzbUWAGUXdnobiiEmu3w1i7H8Rkqc6KCMZ2LLnGFVGN1WJiSL94Hr11BE5b68QtKDou3hoNOZzd\nJagmHv2bQ46XcqrXlrmNxwGn609vHCOCSowMWCkpqcCs2QgNDaKiovZOw+EDezhx9CAAYy6dQkWF\n3g67rLyKygobZWWVzco3m801ssvLq7BY8WhdNc2tcZfvrrsv16q7rjHZDa0pKCjFaq3/5TQ2NoHR\no8ewadNGvvrqK6ZO/QVBQboRYuTjanSQU0vK79cvmQ8++ISjR4+Qn1/KddfdzF//+tevgVvRP422\noMcpKVqJ6p0FAHN0LsGD92IO0zduNc2EIzsJ24nzwKE/l+vGLsRFh9IpMqTVgh0VHRdvjYYtQJIQ\nIs4tNXI0kC6lLGtiXV2H/CGgAN1vegxACDEECHZdw2OMDCoxQrbToYHrA9HpdOJwnr0161boAZCh\nYREMS72s5pzTqaE5qTW3iSvUyNY0DYdT83CdTvNrGtbdl2vVX9ew7HoaODXsdq3Rx+amm6ayadNG\nzpw5w7JlS7nyyp/VOt/WnjNGyB8wIJkBA5Kx251YrWaklLcLIe4HLFLKwnPXVNEc1bsLR7KLKSm3\nNeyKKI4lJGsYQZXRhIXo7jxVnEnhT7wyGqSUaUKIzcArQogngB7A48BrAEKIvcA9Usr1bstM1HFD\nSCmdQogPgWddW6Ll6CmYs6SU3uxYtBvycrPI2LUJgNSxVxEcEupnjdouY8ZcTI8ePTlx4jjffPM/\nJk6cpFqKe4ArI0pxjrgbA70Toph6+XnMXHmAI9nF9Oisu9RO5JbidGoUldnA5MSakElIj4OYLK6s\nCFswtqOCwTFDeew35/vz31EoauFLTMMtwEdAFlAIvCelfN91biAQCTUplH90jWvADiGEBvxZSjkd\neN41dwd6C975QHUp2w7HhlXfo2kaZrOZ0eOv9rc6AY3T6SQv70yTcyZPvpaPPnqPffsyWLp0MSNH\npmK1mujUSbnUn3uudi02s9nE8uVLZ9SZpkkpb209rdoP7q6GgpIzHD5VpBsHQEFJVa255qgzBPVJ\nr3FFoIE5ry+W08mIrnHcNWVQq+quUDSH10aDq67ClEbOWdz+/gvwlybk2ICHXT8dmuKi/JoAyMHn\njyUmroufNQpsSksKWZ2WTdeuVY3OCU8cRkRkJ0pLCvnk8/9QHtSdivJibo2JaDAWoiOxatXyhobr\nVsNSOb4+ciS7uNZxcbmt/qSgCoKSMrDGny1JE2rrzGNj76BXVHejVVQofEYl9AYA61fOw27TPwAv\nuUpVMvSE8IhoomPimpwzbuKNLP7uM04eO0Be7im6JPRoJe0Cm5kz59U6tlhM3HjjNf3Q3Y1TgUGu\n3wof6J0QRUHJ2Z2wqLCgmp0G3RVxBGuPAzWuCOzBJFSM5JErJhMTodySisBGGQ1+prSkiM1rfgBg\n0LALSejeUC0shS+kXnwVa5bOoqy0mB8Xz+SWXz3mb5UCgrr1PlyBkJlAJrBOCPF34M/AI62uXDvg\nrlzbyFwAAB+ISURBVCmDGoxpOFx8GHruxmZ11cbTTIxJHM3NA68mPKhj734p2g7KaPAz65Z/R1VV\nBQCXXqW+3LUkISFhXHTZtSxf8D8O7dvJiaMHgPP8rVZbYD7wJcpo8Ino8GAenTq85riwsojolD3Y\nstNqxvpEJ3GruIGkqJ7+UFGh8BllNPiRwvxcNq7WK2YnD7mA7kn9/axR++PCSyazbsV3VJSXsXrJ\nLO7/xSR/q9QWiHH9KJqhsKSSN75JI/NUEb0TaqdCOpwOfjy+jgWHl1Lh0OuXRASFc0P/yYzplorZ\npBrRKdoeymjwIysWfYPdVoXJZGbitXf4W512SWhYBBdfcSPLv/+SY5mSDRs2cMklE/2tll85fPhQ\nrWOr1cyYMbekoNdJ6Q+8DBw24tpCiCTgXWAMUAx8I6V8qulVgcvb32wnbX8uoGdKfLpgL49OHc6B\ngsN8I+dwslQPdDRh4uLuo7mu/9VEKFeEog2jjAY/kX3yKNs26lHs54++jK7dkvysUfvlosuuZdOa\nRRQX5vH+++8zduxl1PQU7oD86le3NlS3Ypfb3ybgHoMuPxvYDEwDEoCFQogsKWXdLrltgoPHa9fB\nyszN4bM9X7M5e1vNWO+oXtwqbqB3dK/WVk+haHG8Nhq8+aYghHgEvfZCInqL7MeklNtc51YBY9Eb\n41S/g2VIKUd4q1NbQ9M0Zv/vfZxOJ1ZrEJdfrdLhjSQ4OIQJk29j7lf/IjMzk7lz53D99Tf7Wy2/\n8bOfTallNJhMsGDB/P+gl+Q8A8yRUm5o6esKIVKBYcAEVyGpEiHEG+glq9uk0dC/Zyfy0isAJ5aE\no9h7HWRztp4pEWEN57r+P2Ns99HKFaFoN/iy0+DRNwUhxLXAC8Ak9G8xjwLfCyH6SymrO93dI6X8\n4lz+gbZI+o6N7EvXu4KPm3gjMXFd/axR++f80ZezfuVccrKO8+GH73LppROIiYn1t1p+4dlnX6x1\nbLWaeeON/9/encdHVZ0NHP9NErKQQEBARBYRhEcRFVDRIrjVDRRFXIvLqy0qryjuSIXWpVYR1Pq6\nVCtVUF+xvlXrgixaxKqgIKB1oTwssskiQkLIRiTMvH+cOzgMCcyayYTn+/n4iXPn3nOfhJlzzz33\nnPM8fHUdnLoXsFJVt4ZsWwiIiOSraqRZcutU+AqPoeMWbrqkJ/e9MpXvsz8jkLsVP+5RRJ8Dj+Xc\nTv0pyN49qZox6SyqRkOUdwrXAhNVdb537Hhvv4FAcPW5fW5t36qqbUx943kAmjVvRd/TBqc4on1D\nZmYmpw+8gskTHqS0tJRnnnmSUaN+l+qwUqq4uJiyslIKC5vWmDE0CVoAxWHbgjlsWgIRNRriTQ++\ntfwn/jplEas2lHLQAU0Yek43mubXnsdh0rTFu6zwOGnaYm69pAfl1eW8tWQaa5p+tnPfDk3bMeTQ\n8zm4Wd1OnU52avZoWCw1q0+xQOxxRNvTEM2dwtHAK8EXqhoQkS+BY/m50XCpiNwJtAc+A4ap6q6j\ntBqYf854iy1FLr1G/8FXk50dXSppE7t2B3Whf//+TJs2jalT3+H008/i6KOPTXVYdaqoaDMvvTSJ\nWbP+uctS3H6/fz3ue/mgqm6otYD4xX2jEG968Cfe+Hrn4MXi0ipemKH8/jfH17r/6h92Tcmx6oet\nzN30OX/7+m0qtlcCkN8oj18dOYjTOvUlIyN1F4Vkp2aPhsVSs/oUSyyibTREc6dQ274tvf9fBJQB\nQ3Cj0p4EpotIN1VtkPldv/rqSz6b8yEAR/Tqw+E9fkGUSSFNnIYNG8acOXMoKSnhoYfuZ9KkyTRu\nvOe7bJfromiP+2Rl+aiurmDLlnKqq90/6n777ZfSC0g41cWMHHkTRUVFtGq1P3369CU/P5+ysjJm\nz/54B25J9yEiMkBVPw8eJyKHAQNVdVycIfyIqxdCtcA9qow4UV286cGXrdmy2+vi4to7OTq0LqBo\nq1tLJaOgmMAhS3h+4c9VW992xzHokP40yS6gpKQy5rjikezU7BZLw4olNJ5oxTKmIZo7hVr3VdXh\noa+9tLxFQD9gVqQnSEZXTzK6kcrLyxk79g+AmwZ4weXDyczMxOff+4cnI8OHLwMyM/b+pw9epDIy\nMvD5fGRm+CI6Lmhvx4SWH0xlHclxkZyvtrKjjbE2GRkZNG/enJEjf8vo0aPYsGE9zzzzBCNH3rXH\n4zZt2szMeUp+QeEey87JyaKqqhq/3095WQln9jmUli0Tk0ck3s9kZWUld911O1lZjXjkkf+hT5++\nu5TdtGleOxE5G3gaeFNEDlfV4NW1OS7BXLyNhvlABxHZT1WDrbDewCJVrYi0kHjTg3fYv4Di0qpd\nXu+pvKv6H8qEwBes9H2Ov/lqgke2b9KW63oPoVVma6qrk5sSPVLJTs0eDYulZvUpllhE22iI5k6h\ntn2/pgaqWiYiRUBU2VqS2dWTqLIDgQAPPHAP33+/BoDBQ4ZRuJe8CaGqKrPJyW1E48aRP8rIzW1E\nXl42mVnRHRfpMbm5jWI6LpLzhZcda4zhfqpyH/fBg8/j448/ZPr06bz55hucdFI/zjrrrFqPq66u\noGWrVjRrHv5xrt2W4myaNctP+FiBWD+Tb731d0pLt/L222/Tvn3NU/9U9V0RORn4ErgBt5Q0uAt7\n3I8VVPVLEfkcGCsit+FyXdwCjI+37GiEL/O8p0yS/oCfL4sWsH7/6firXS9CXlYuAzudxSkH9aHF\nfk322EthTEMTbaMhmjuF+bhxDS8BiEgGbkzEBBFpAowF/hB8fioiLYFWQFRjGpLR1ZPobqQpU95i\nypQpABzVszfde50IwLZt2/FH0NNQUfkTVdu2U1FRtdd9MzIyyM1txLZt26ms/InMLCI6Lmhvx4SW\nHxp7LOcKP662sqONsTZVVe6p19atlYwYcTsLF37Bxo0/MHr0GNq27Ui7djVfTLdsKaey8ieyc2o/\nX3jslZU/sWVLecIyasb7mZw+fQbnnTeYgoL9drvIhXZTqup3IvIXYJCIjAWG4hZ7+lfcv4RzITAB\n2ACUAE+r6jMJKjsi4cs812ZFyWpeXfIP1pSu3bnt+AOOYdAhA2iSXWDTKM0+KapGw97uFERkMfBr\nVZ2D6+Z8RURewa3RcAewDZiqqlUicjzwhPdYAtzaD19GOz88mV09iSj766+/Yvz4sQC0bduegYN+\nRSDgnnn7/X52RDCowe8PEPAT0b7Bbn2/308gEGCHPxDhcc7ej/m5/NB9YjnX7sfVXHb0MdYSudcQ\n2bHDT0FBU+6++35GjBhGRUU5d955O3/+8wTy8wt2O666OoB/r+fbNXa/P0B1dSDhn81YP5MrVnzH\nkCFXRnrsLOBmYCNuOen1wH9HfdIaqOo64OxElJUsZT+V89byacxZP2/ntrYFbbi46yAOaXZwCiMz\nJvViaSpfiGssbAA+ACaF3Cl0AQoAVHUG8FvciOzNwC+BAaoavF07D9fluQRYBWQC58T2a9RPGzas\nZ8yYkWzfvp28vDzuv38sOTmW+ra+OOKIo7j2Wje0ZsWK5dxzzxiqqxvkGFzKy8tp2jTidBJFuO9j\nOW4cQzdVXZWs2OoLf8DPJ2s/477Pxu9sMORm5nJhl3O585gR1mAwhhgGQu7pTkFVM8Ne/wX4Sy37\nfo9rgDRIRUWbufXWGyguLsLn8zFmzH107tyF739YkOrQTIhLL72MVatWMHXqO8ydO4eHH36QkSNH\n16tZD4mQn1/Ali3hk5lq1QqoUNV9Zt3jVVvX8Kq+yarSNTu39T6gF4M6n01hTpMURmZM/WK5J5Kg\npGQLt98+YufAx2HDbqRfv5NSHJWpic/n47bbRrF+/Tq++GIBU6e+Q3Z2DrfcckdN+RnSVqdOnZk/\nfx4nnNAvkt0H4Hr/Gryy7eW8s3w6s9fNI4B7/HRg/gFc3HUQXZp3SnF0xtQ/Det2qh7YuPEHbrjh\nOpYtWwrAFVdcxa9+ZRks67NGjRrxwAPj6datOwBvvvka48b9sUE9qujb9ySmTHmLNWtW73E/b9XX\nXwPv1ElgKeIP+Jm9bi73fTaeT9bNJUCA3MwcLugykFHH3mQNBmNqYY2GBFq6dAnDh1/DqlUuq/DF\nFw9h6NCEjB8zSZafX8D48Y8hcigA7777NmPGjKSiomFMpzv33PMpLCzkxhuv4+OPP9ztfRHJEpFf\nAzOArcDDdRxinfqb/oPJi1+nfLub9HVs6578/vg7OLV9PzIzMvdytDH7Lns8kSAzZ77HQw/dz7Zt\nbuW4oUOHccUVVzeoLu6GrkmTpjz22J8ZPXokCxfOZ86cT7j22qu5/fZRwJ7XjqjvcnNzGTv2UW67\n7UZGjx5JYWEzOnc+hMaN8ykrKwXYBDTBDYI8V1U3pTTgJNtY4ZaVaZPfmku6DqJL884pjsiY9GCN\nhjiVlpby+OMPM2PGNACys7O57bZR9O/foCaC7DPy8wsYN+4xxo//IzNmTGP16pXcccdNnHDaBZx4\n+gVpPUDykEO68MILf2Py5BeZNWsmCxZ8Hvp2MfBXYLyq/pCaCOvONUdcydqydXQuPNh6FoyJgjUa\nYrRjxw6mTXuHCROeobjYrXPVuvUB3Hvvg3TrdniKozPxyM7O5q677uGww7rz5JN/oqqqig/enczS\nRQs56/yrad+xa6pDjFmzZs24/voRXH/9CCorKykvL6Np0ya0bdtqn5pPmN+oMV2bH5LqMIxJO9Zo\niNKOHTuYPfsjXnjhOZYuXbJz+xln9Ofmm++goGD3xYFM+vH5fAwefBFHHHEU9933O1atWsGaFYuZ\n8OiddO/Vl5PPvIj923RIdZhxycvLIy8vj6ys9O09McbUragbDSLSAbd64/FAKfCqqo6qZd8RwPXA\nAbhVIW9W1YXeeznA/+DWfMgBPsSlxt5zOsEU2bKlmJkz3+e1115l7dqf53IffHAnbrzxVo45pncK\nozPJ0qVLV8aNe4yn/voScz+eStW2Cr5Z+AnfLPyETnIUx584gEMO60lmdnaqQ00L3uyMV4AfVbVP\nquMxxkQnlp6GN4DPgUuB1sBUEdmgqo+F7iQiA4G7gTNxSapuAqaISGdVrQQeAHoCxwEVuOepE3Er\nRdYL69atZe7cuXz00Szmz5/Hjh07dr7XqtX+XH75VQwcOIisLOuwacgaNWpE7379+cUpA/lw+qvM\nn/0eO3ZU853+m+/03+Q1LqDbUcfR45i+HNihK41yEpNvoqERkSHAg8A3uMyZxpg0E9XVzrtLOBI4\nVVXLgDIReRTXIHgsbPdrgYmqOt87dry330AReR03F/xyb4VJRGQ0sEhEDggmsaorgUCAoqLNLF++\njGXLlrB8+VIWL17EmjVrdtu3a9dDueSSIZxyymnWWNjH5Bc05ewLr+GkMy9i/uz3mPfJdMq2FlNZ\nUcaCT2ey4NOZ+HwZtGnfiVat21G6tjtHHdWDTp0607hxYrNdpqkc3E3CdbibCWNMmon2qtcLWKmq\nW0O2LQRERPJVNXRS+9G4bkgAVDUgIl8Cx+JS7xYCX4S8ryJS6R33bpRxRe2DD95n2rR32bBhPRs2\nrKOqqvYMhu3bd+CUU07jtNPOoGNHW/RlX1fQpBknn3Ux/U4fzHdLvubbL2bzn6/mUllRRiDgZ93q\nZaxbvYx/f/7hzmMKCwtp3boNrVsfQIsWLSksLKRp08KdP3Nzc8nJySU3N4ecnFxycnLIzc0lKyuL\njIxMfL6snYnO0pWqTgQQkVSHYoyJUbSNhha4qVmhgmMQWuIS3Oxt35bee4Ea3i/23o9YZmb0g7j8\nfj/jxv2RiorwbN6QmZlJx44H063bYRx5ZE969TqGNm0OjPoctcnI9O2ctud+7j3rYEaGj8ptpZRt\n3XvugIyMDH6qyqKqqprKilIyM7MjOi5ob8eElr9LauwYzhV+XG1lRxtjbSrKS9m0aRM5OflRpZfe\nurWIyoqtZGTsvuZGm7YdadO2I6edPYSiTd+zTL9l9YrFbFy3mrLSLTv3KykpoaSkhCVLFkcVc7jM\nzEwyMjLIyMggMzNz52ufLwOfz0dOTg5Dh17HgAGRT/mN5TuUSvUl3mAc9SEei6VmFkvtYo0jlv71\naFYr2tu+8a585GvaNC+mAxcsSE3iqMHnxJKDQoATYjjuuBiO+UUMx9T1cbGeK1YdOS6WP+U+RkQu\nA14CQrtEfN7rq1X1xQScJubvfLLUp3gslppZLIkTbaPhR1wvQahgr8GPEe77tfeez3sderu/H7Ax\nypiMMfWAqr4MvJzqOIwxyRNt/8R8oIOI7BeyrTewSFXD+/rn48YnACAiGbgxEZ8B3+EeRYS+3x3I\n9o4zxhhjTD0TVaNBVb/ETbccKyJNxGX3uQW3bgMislhEgnOvnwauFJHjRCQPGANsA6aqqh94Fhgt\nIu1EpAVuCubrqhreY2GMaVgsIYsxaSqWMQ0XAhOADUAJ8LSqPuO91wUoAFDVGSLyW+D/gFa4xsYA\nVQ1OU/i9t++/gUxcKt7rY/w9jDH1nIgsBjrg6p0Mb7ZUABBV3X1+szGm3vGl+zQuY4wxxtSN+jH3\nwxhjjDH1njUajDHGGBMRazQYY4wxJiLWaDDGGGNMRKzRYIwxxpiIWKPBGGOMMRFJ69zO3sqUfwLO\nwP0uHwE3qer3CTzHMbhsnT+qap+97b+XsjrgFsI6HigFXlXVUfFHubP8M4EXgA9UdUiiyg0pvwMu\nBfqJwHZgOu7vvXWPB0ZW9lHAI8AxQCXwL6/sH+ItO+w8f/LKTViDWUT8QBVuzYFgroUJqnpTgsof\nDQwHmgCfAteo6qoElNsPeI9dc0VkAI1UNTPe8pNFRA7i58+hH5gH3KyqS1MQS9LroBhiSlidFcO5\nk1rHRRlLUuvDKGNJWt0ZQyxx1bXp3tMwCbdwVDfcwlLZwPOJKlxEhgCvA0sSVOQbwBqgI3AacL6I\n3JyIgkXkDtyHMlGx1uQdXKbS9rglwA8HHo63UBHJBmYAH+D+PbsDrfFWGk0UEekBXMGuF8lECABd\nVbWxquZ5PxPVYBgODMFVNm2ARbhVWOOmqh+HxNtYVRsD9wKvJqL8JHoTWAe0w32XtpK6mCeRxDoo\nWkmos6KVtDouGnVUH0YjKXVntBJR16Z7o2ENcLuqFqvqFuAZoG8Cy8/BpYqcF29BXuv/SOBOVS1T\n1eXAo8C18ZbtqcTlAVmeoPJ2ISKFuFU9f6uqlaq6DteKPzEBxTcG7gLGqup2Vd2Mq3y6J6BsAETE\nh1va/JFElRnCR/KWRr4VuEtVl3mfm5tVNSmVsHc3dCtwRzLKTwQRaQQ8jvubVKpqOTAZd9FOhWTX\nQdFKWJ0VrTqo46KR1PowGkmuO6MVd12b1o8nVHV42KYOwPoElj8RQEQSUVwvYGVYd9RCV7zke5Vf\nzFT1SUhYrDWVXwIMDdvcAVibgLK3EHJ3Ju6XuAr4W7xlhxiGq0gmA/cnsNygh7y8K02AvwO3xvtv\nKiIHAgcDLUTkW9wdwSzgv1V1U7wB1+A+4K+qGve/abKo6nZgYvC1iLTHLT+fkp6GZNdB0UpwnRWt\npNZx0Uh2fRiNZNadMcQSd12b1o2GUCLSEVfp1de7pBa4zJ6hiryfLYE6+0IlgndXcQNwTgLL7AAs\nxeUieRa4J0HltvbKSlbL/lPc2IArgU64fCtP4b6M8Wjn/bwQOBX3d3kd97cZHGfZu/C+P+fjutjT\ngohsAxoB/8A1ClMqDeqgZGtQdVyyJKPujCGGmOvaet1oEJHLgJfY9Rl0cKDZ1ar6orffobjnNBNV\ndVKiy0+gBpHdT0ROAN4GRqrqrESVq6qrgRwR6Yz7IP8vcFkCin4EeE5V1RtEl1CqekLoSxG5E3hb\nRK7x7oxjFfy8PBQcpCQidwNTRSRbVX+Ko+xww4E3VHVjAsuMSaTfS1XN9XpjHsE12vqlKpZY66Bk\nxZNCDaKOS5Zk1Z3RiqeurdeNBlV9GXh5T/uISG/gXWC8qo5LdPkJ9COuJR6qBe7LnjbpwEVkIK7S\nGu79/RJOVZd7MwbmiMgI77lbTETkl0Af4BpvU11UaitxLfj9ia8LcoP3sySsbJ9XdiJH6F+IG8+Q\nctF8L1V1nYjcAqwTkV6qurCuY4mnDkpGPCnUIOq4ZKmLujNasdS1aT0QUkS6AFNwz4+T+mVNgPlA\nB2+KVlBvYJGqVqQopqh4z+wnARck8kMvIqd4aZNDBbz/4r2bvgx3gV0tIj8CCwCfiGwUkYvjLBsR\n6SEi4aOgu+GmYK6Ls/jvcTMDeoRsOxg3ZSvesnfypmB1AN5PVJnJIiJdRWS1iDQP2Ry8646nVyfW\neNKpDkq2tK/jkiVZdWcMccRd19brnoYIPAU8q6ovJfk8cd+dquqXIvI5MFZEbgPa4qbOjY+37Log\nIpnABNzI6JkJLn4B0FREHsI9WysA7gY+UtXSOMu+BRgT8ro9bgzCUez+/DUWG4FrRWQjbopXR9xz\n7b+oalxTO1V1h4g8B4wWkY9x895/B7ykqv74wt5FT2CzqpYlsMxkWQZsAR4XkRtw6zQ86G3/Twri\nqas6KFp1/pgg3eu4ZEly3RmtuOtaXyCQ6CnrdUNE2gGr+Ll1FLqwzhmq+kkCzrEYdweWheuV2e6V\nL6q6JobyDsR9eE7GdTk/rap/iDdOr+xKL7ZG3qZqIODNvU9E+X1xi4BU8fPfOfgzpr9HWPmHA08C\nxwJlwEzcVLaEjkT3xjR8l8jFi7y/zUPAEcA23B3FmESMOfDmVT+CW6shC3gNuDGRd24iMgoYoqpH\nJqrMZPJmTDwB/BL3956Lu9Ov0zn5dVEHxRBTQuusGM6ftDouyjiSWh9GGUtS684Y4omrrk3bRoMx\nxhhj6lZaj2kwxhhjTN2xRoMxxhhjImKNBmOMMcZExBoNxhhjjImINRqMMcYYExFrNBhjjDEmItZo\nMMYYY0xErNFgjDHGmIhYo8EYY4wxEUn33BPGGLPPEpHGuAyu5wHdgUJckrNFwJu4vBjlqYswMiLy\nX8BE4KqQdOMrAb+qdkphaCaM9TQYY0waEpEjcY2D+4GvgF8DJwL/hcs4eR/wHxHplbIgayAi3UXE\nLyIdQja/DRwDvBOyzXIc1EPW02CMMWlGRFoC03EX1l6qujRsl6ki8hTwIfCuiPRQ1R/qOMza/JKw\nBoGqFpOYrLMmyazRYIwx6ecOoDUwuIYGAwCq+p2IDAOmAL8FbhaRScCVQHdVXRTc18v+ugJ4TVUv\nDtl+AjASOAFoAqwDPgLuUdUVIfsFyz3Qi+1CoCUuC+jjqvqMt98s4CRco2GliARUNVNErgKeJ+Tx\nRE1E5ADgXuAs4ABcJs2PgHtV9euQ/fK83/kCoL13vhXA/wKPxJu2fl9mjyeMMSb9XARsUtW39rST\nqk4F1nr7g7t4RnTBFJFjcGmT2wO/waW7Hg8MBt4XkfyQ3YPlvgTk4R6VXArsAJ4SkVO9/a7FNWIA\nzsGlZ44oLq93ZS4wyIvjVOBmoBvwqYj0CNn9WeAW4GlcA2OQ97uMxaWxNzGyngZjjEkj3sW6I+4i\nGImFwDki0irKU3UF/gmMUtVvvG2fikhH4Dbc+IlpIfv7gJWqen1IrDtwjYQLgA9UdamIbPbe/kZV\nV0cRz11AO+BkVf3Y2zZbRD4ClgAPAAO87ecA76vqkyHHzxKRb4GfojinCWONBmOMSS+F3s/SCPff\n6v1sFs1JVHUyMLmmt3ANhIPCtgeAv4dtCz46aRHNuWtxNrAmpMEQjPN7EZkL9AvZvAY41ZuV8Vpw\nBomqPp+AOPZp1mgwxpj0EmwENI9w/2BjoSSak4hIFnADcAnQxTufz3s7QM2Pt9eGvQ7e1SfiUXgH\nIFtE/DW8FwACItJMVbfgHqG8ihsnMUFEFuJ6Zl5UVU1ALPssG9NgjDFpRFXLcAMMe3oX9r3pCWxU\n1Y172MdXw7YXgUeB9cDVuPEHPYC791BOMgcYBgczHuXFEfpfT6AXUAagqstU9WigN/B7XEPrduBb\nb3CoiZH1NBhjTPp5HTfQ70rc3XSNRORs3IyGJ7xNwbv07LBd24YdV4DrYVioqoPD3hsUe9hxWQm0\nwY2FiKhxoqoLgAXAWBFpDbwPPCoiz6nq9qRF2oBZT4MxxqSf8UARMN6b5bAbEemEmz2wGXjQ2xwc\nhNg5bPeLwl5n4nofNoduFJHmwFDvZaw3ncELfrTHTwGa4hozoTH5ROQpEbnYe91FRJ71Fr/ayVun\n4l9ADlAQU+TGehqMMSbdqOoPInIubiXF2SLyPDAV+BHYDzgFuA43WHKAqm7wDv0Hrpv+D97Mhi3A\nQOCwsPJLRGQ+cIqI3ATMAw7FzWB4CtcIGSgi81T1syjDD457uFVEPgA+8F7X9Igk1Fhc4+Y5EWkL\nzAH2B4Z7v++skPLPBs4QkQeAb73tvXGrZU7zFpMyMbCeBmOMSUOq+inuYv8n3PTHV3AX0rdwF9Fx\nwOGq+nnYMb/BPaZ42fuvEe5iHL5WwsW4KZW/835eDlytqg/hHo/0AZ6LINTwcp/GNUKGAn/m5wGd\nNT1y2LlNVYtwF/6JwI241S4nem8PUNXXvP0qcOMvpgOjgPdwvRRX4f5W4b0qJgq+QMAWxjLGmIZA\nRNYA+cD+qlqd6nhMw2M9DcYY03C8jJtiOXRvOxoTC+tpMMaYBkJECnGZL5viHivMA4pV9T8pDcw0\nGNbTYIwxDYSqluDGGnwM/BH3PH9gSoMyDYr1NBhjjDEmItbTYIwxxpiIWKPBGGOMMRGxRoMxxhhj\nImKNBmOMMcZExBoNxhhjjImINRqMMcYYExFrNBhjjDEmItZoMMYYY0xE/h/9H2sHGCtsbwAAAABJ\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f60570e09e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"--------------------\n",
"Test for mean of group different than zero\n"
]
},
{
"data": {
"text/plain": [
"Ttest_1sampResult(statistic=19.561758393832346, pvalue=8.7108452542495534e-36)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from scipy.stats import norm\n",
"#Our sample, normally distributed with mean 0.1 and std 1\n",
"x = np.random.randn(100)+2\n",
"\n",
"#histogram\n",
"plt.subplot(2,2,1)\n",
"sns.distplot(x,kde=False,fit=norm)\n",
"\n",
"\n",
"plt.subplot(2,2,2)\n",
"qq_plot(x)\n",
"plt.show()\n",
"\n",
"\n",
"print(\"-\"*20)\n",
"print(\"Test for mean of group different than zero\")\n",
"scipy.stats.ttest_1samp(x,popmean=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.2 Two independent samples\n",
"- Imagine you measure productivity for a control group and a experimental group\n",
"- Imagine it's normally distributed \n",
"- You want to see if the productivity of the groups is different"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test for equal variance\n",
"LeveneResult(statistic=0.18372231239245079, pvalue=0.66865960819279313)\n"
]
}
],
"source": [
"from scipy.stats import norm\n",
"#Our sample, normally distributed with mean 0.1 and std 1\n",
"x = np.random.randn(100)+0.1\n",
"#Our other sample, normally distributed with mean 0.5 and std 1\n",
"y = np.random.randn(100)+0.5\n",
"\n",
"\n",
"#Equal variance?\n",
"#We are testing if we can do the test\n",
"#If significant it means that the variances are different\n",
"print(\"Test for equal variance\")\n",
"print(scipy.stats.levene(x,y))\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
]
},
{
"data": {
"image/png": 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J3sf6UdZoymdpU7CfLYFenGdVGZT4YC89d95Bu3UZ9toT2GtPYNgOmTXUz2u8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\naDIYDAaDwWBIAGM0GQwGg8FgMCTA/wcbfUEqRK9IhgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f60570335f8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"--------------------\n",
"Test for different means\n"
]
},
{
"data": {
"text/plain": [
"Ttest_indResult(statistic=-2.9602185045213134, pvalue=0.0034495814845479505)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#histogram\n",
"plt.subplot(2,2,1)\n",
"sns.distplot(x,kde=True)\n",
"sns.distplot(y,kde=True)\n",
"\n",
"\n",
"plt.subplot(2,2,2)\n",
"qq_plot(x)\n",
"qq_plot(y)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"print(\"-\"*20)\n",
"print(\"Test for different means\")\n",
"scipy.stats.ttest_ind(x,y)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.3 Two paired samples\n",
"- Imagine you measure productivity for a group before starting a new program and after the program (each individual has two observations)\n",
"- Imagine it's normally distributed\n",
"- You can test if the productivity has increased"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test for equal variance\n",
"LeveneResult(statistic=1.2464290960049443e-07, pvalue=0.9997186638758232)\n"
]
}
],
"source": [
"from scipy.stats import norm\n",
"#Our sample, normally distributed with mean 0.1 and std 1\n",
"x = np.random.randn(100)+0.1\n",
"#Our other sample, similar to x but adding 0.05 and some random noise\n",
"y = x+np.random.randn(100)/10 + 0.05\n",
"\n",
"\n",
"#Equal variance?\n",
"print(\"Test for equal variance\")\n",
"print(scipy.stats.levene(x,y))\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
]
},
{
"data": {
"image/png": 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E+mEVtm1gGDYH+49I0rTKKaWeAfwT8DygCGdg+CGl1B8Cltb6O7mMT6xdlxqr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\nxhhjTBqsaDLGGGOMScP/B81CUjFixl5NAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f60542de048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#histogram\n",
"plt.subplot(2,2,1)\n",
"sns.distplot(x,kde=True)\n",
"sns.distplot(y,kde=True)\n",
"\n",
"\n",
"plt.subplot(2,2,2)\n",
"qq_plot(x)\n",
"qq_plot(y)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WRONG: What would happen if we don't use paired t-test\n",
"Ttest_indResult(statistic=-0.36128847200154984, pvalue=0.71826902336070653)\n",
"--------------------\n",
"Test for different means\n"
]
},
{
"data": {
"text/plain": [
"Ttest_relResult(statistic=-5.1739506148333767, pvalue=1.2001340429502704e-06)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"WRONG: What would happen if we don't use paired t-test\")\n",
"print(scipy.stats.ttest_ind(x,y))\n",
"\n",
"print(\"-\"*20)\n",
"print(\"Test for different means\")\n",
"scipy.stats.ttest_rel(x,y)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.4 What happens if they don't have equal variance? (But normality)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"scipy.stats.ttest_rel?"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test for equal variance\n",
"LeveneResult(statistic=33.947129051276335, pvalue=3.405985034562977e-08)\n"
]
}
],
"source": [
"#Our sample, normally distributed with mean 0.1 and std 1\n",
"x = np.random.randn(100)+0.1\n",
"#Our other sample, normally distributed with mean 0.2 and std 2\n",
"y = np.random.randn(50)*2+0.2\n",
"\n",
"\n",
"#Equal variance?\n",
"print(\"Test for equal variance\")\n",
"print(scipy.stats.levene(x,y))\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
]
},
{
"data": {
"image/png": 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Uz08xykA4zrbHj3K6K4DtCZEqbcUuawdPdFKFs1SwlFRvA6mBGrDOnRao5aW4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\n0KSUUkoplQINmpRSSimlUvD/AXmMXoXsr0qzAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6057044828>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"WRONG: What happens if we try the test assumming equal variance\n",
"Ttest_indResult(statistic=-1.6473476178397546, pvalue=0.10160817641110562)\n",
"--------------------\n",
"Test for different means with different variance\n"
]
},
{
"data": {
"text/plain": [
"Ttest_indResult(statistic=-1.3503948008453872, pvalue=0.18178335336590215)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#histogram\n",
"plt.subplot(2,2,1)\n",
"sns.distplot(x,kde=True)\n",
"sns.distplot(y,kde=True)\n",
"\n",
"\n",
"plt.subplot(2,2,2)\n",
"qq_plot(x)\n",
"qq_plot(y)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"print(\"WRONG: What happens if we try the test assumming equal variance\")\n",
"print(scipy.stats.ttest_ind(x,y))\n",
"\n",
"print(\"-\"*20)\n",
"print(\"Test for different means with different variance\")\n",
"scipy.stats.ttest_ind(x,y,equal_var=False)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.5 What happens if they are not normally distributed (but equal variance)\n",
"- Use only when the number of observation in each sample is > 20 and you have 2 independent samples of ranks\n",
"- Mann-Whitney-U "
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test for equal variance\n",
"LeveneResult(statistic=0.31029262175336686, pvalue=0.5781297914261414)\n"
]
}
],
"source": [
"#Our sample, uniformly distributed \n",
"x = np.random.exponential(2,100)\n",
"#Our other sample, niformly distributed + 0.1\n",
"y = np.random.exponential(2,100)+0.1\n",
"\n",
"\n",
"#Equal variance?\n",
"print(\"Test for equal variance\")\n",
"print(scipy.stats.levene(x,y))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
]
},
{
"data": {
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cCTyWpi7b0urrQdOtP3KOY/qFi1bVONn8ViFm1IXmjNAd7p0riYqDgAceeCRl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\n+By1584AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f60541f5a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"#histogram\n",
"plt.subplot(2,2,1)\n",
"sns.distplot(x,kde=True)\n",
"sns.distplot(y,kde=True)\n",
"\n",
"\n",
"plt.subplot(2,2,2)\n",
"qq_plot(x)\n",
"qq_plot(y)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WRONG: What happens if we try t-test\n",
"Ttest_indResult(statistic=-0.47338561343706537, pvalue=0.63645958719886819)\n",
"--------------------\n",
"Test for different means with no normal distributions, equal variance and paired samples\n"
]
},
{
"data": {
"text/plain": [
"MannwhitneyuResult(statistic=4916.0, pvalue=0.41916777366941338)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"print(\"WRONG: What happens if we try t-test\")\n",
"print(scipy.stats.ttest_ind(x,y))\n",
"\n",
"print(\"-\"*20)\n",
"print(\"Test for different means with no normal distributions, equal variance\")\n",
"scipy.stats.mannwhitneyu(x,y)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.6 What happens if they are not normally distributed (but equal variance) and paired samples?\n",
"- Use only when the number of observation in each sample is > 20 and you have 2 independent samples of ranks\n",
"- Wilcoxon signed ranksum "
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test for equal variance\n",
"LeveneResult(statistic=0.23002853591535549, pvalue=0.63203201208956483)\n"
]
}
],
"source": [
"#Our sample, normally distributed with mean 0.1 and std 1\n",
"x = np.random.random(100)\n",
"#Our other sample, normally distributed with mean 0.05 and std 1 and some random noise\n",
"y = x+np.random.randn(100)/10 + 0.05\n",
"\n",
"#Equal variance?\n",
"print(\"Test for equal variance\")\n",
"print(scipy.stats.levene(x,y))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
]
},
{
"data": {
"image/png": 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DdHT4SU5O7nebY9UnpawELulnXc9+J2HsKcOfHdOAiglPX6mGe9PnUZ52AkSe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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f60540a4630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"#histogram\n",
"plt.subplot(2,2,1)\n",
"sns.distplot(x,kde=True)\n",
"sns.distplot(y,kde=True)\n",
"\n",
"\n",
"plt.subplot(2,2,2)\n",
"qq_plot(x)\n",
"qq_plot(y)\n",
"\n",
"plt.show()\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WRONG: What happens if we try t-test\n",
"Ttest_relResult(statistic=-7.0915815375897777, pvalue=1.9977883955834856e-10)\n",
"--------------------\n",
"Test for different means with no normal distributions and equal variance\n",
"WilcoxonResult(statistic=824.0, pvalue=4.9575571539880461e-09)\n"
]
}
],
"source": [
"\n",
"print(\"WRONG: What happens if we try t-test\")\n",
"print(scipy.stats.ttest_rel(x,y))\n",
"\n",
"print(\"-\"*20)\n",
"print(\"Test for different means with no normal distributions and equal variance\")\n",
"print(scipy.stats.wilcoxon(x,y))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.6 What happens if they are not normally distributed and not equal variance?\n",
"- You can only test if the distributions are different (booo)\n",
"- scipy.stats.mannwhitneyu(x,y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. More than two samples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ANOVA (Parametric) or Kruskal-Wallis (Non parametric)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Analysis of variance (ANOVA) is a collection of statistical models used to analyze the differences between group means and their associated procedures (such as \"variation\" among and between groups). \n",
"- ~ t-test\n",
"- If normality\n",
"\n",
"\n",
"The Kruskal–Wallis one-way analysis of variance by ranks (named after William Kruskal and W. Allen Wallis) is a non-parametric method for testing whether samples originate from the same distribution. \n",
"- ~ Mann-Whitney-U \n",
"- If no-normality but equal variance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.1 Compare if the groups are different"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test for equal variance\n",
"LeveneResult(statistic=2.0480092714597573, pvalue=0.13080921080653679)\n"
]
}
],
"source": [
"#Our sample, normally distributed with mean 0.1 and std 1\n",
"x = np.random.randn(100)+0.1\n",
"#Our other sample, normally distributed with mean 0.2 and std 1\n",
"y = np.random.randn(100)+0.2\n",
"#Our other sample, normally distributed with mean 0.3 and std 1\n",
"z = np.random.randn(100)+0.3\n",
"\n",
"\n",
"#Equal variance?\n",
"print(\"Test for equal variance\")\n",
"print(scipy.stats.levene(x,y,z))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
]
},
{
"data": {
"image/png": 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ohmkSTtlYXH/qyAXJxGltbaGp6SCJRJyioiI+97k7FwshDs+2XRLJXOH4kYFMonO4LWG6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\ni4Crj1jldu7elctnynPxpX05DMZieK76EP4N64m+/CJGJEL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"text/plain": [
"<matplotlib.figure.Figure at 0x7f6053f22470>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"--------------------\n",
"ONE-WAY ANOVA\n",
"F_onewayResult(statistic=1.9446821552695626, pvalue=0.1448496537094488)\n",
"--------------------\n",
"ONE-WAY KRUSKAL WALLIS\n",
"KruskalResult(statistic=3.3152000000000044, pvalue=0.19059586169236964)\n"
]
}
],
"source": [
"\n",
"#histogram\n",
"plt.subplot(2,2,1)\n",
"sns.distplot(x,kde=True)\n",
"sns.distplot(y,kde=True)\n",
"sns.distplot(z,kde=True)\n",
"\n",
"\n",
"plt.subplot(2,2,2)\n",
"qq_plot(x)\n",
"qq_plot(y)\n",
"qq_plot(z)\n",
"\n",
"plt.show()\n",
"\n",
"print(\"-\"*20)\n",
"print(\"ONE-WAY ANOVA\")\n",
"print(scipy.stats.f_oneway(x, y, z) )\n",
"print(\"-\"*20)\n",
"print(\"ONE-WAY KRUSKAL WALLIS\") #use if not-normal and equal variance\n",
"print(scipy.stats.mstats.kruskalwallis(x,y,z))\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.2 Problem with many groups (look-elsewhere bias)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Let's compare two identical samples, see how often we get false results\n",
"for i in range(100):\n",
" x = np.random.randn(100)\n",
" y = np.random.randn(100)\n",
" stat, pvalue = scipy.stats.ttest_ind(x,y)\n",
" if pvalue < 0.05: \n",
" print(i,pvalue)\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.3 Compare which groups are different"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.3.1 Bonferroni correction\n",
" - Corrects for the number of comparisons that you WILL make (Set p-value before the experiment. Don't cheat please)\n",
" - New threhold to consider something significant is $\\frac{0.05}{\\#\\ of\\ comparisons}$"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"New threshold (instead of 0.05) = 0.008333333333333333\n",
"x and y: Ttest_indResult(statistic=-1.5176204931996204, pvalue=0.13070511158476028)\n",
"x and z: Ttest_indResult(statistic=-1.2996294462939917, pvalue=0.19523907106939523)\n",
"x and w: Ttest_indResult(statistic=-1.8995627011104539, pvalue=0.058944076616144633)\n",
"y and z: Ttest_indResult(statistic=0.1720575490687388, pvalue=0.86356801701764385)\n",
"y and w: Ttest_indResult(statistic=-0.43104499058947698, pvalue=0.66690455874746379)\n",
"z and w: Ttest_indResult(statistic=-0.58344029803134156, pvalue=0.56026092381780823)\n"
]
}
],
"source": [
"#Our sample, normally distributed with mean 0.1 and std 1\n",
"x = np.random.randn(100)+0.1\n",
"#Our other sample, normally distributed with mean 0.2 and std 1\n",
"y = np.random.randn(100)+0.2\n",
"#Our other sample, normally distributed with mean 0.3 and std 1\n",
"z = np.random.randn(100)+0.3\n",
"#Our other sample, normally distributed with mean 0.3 and std 1\n",
"w = np.random.randn(100)+0.5\n",
"\n",
"print(\"New threshold (instead of 0.05) = \",0.05/6)\n",
"\n",
"#Compare x and y\n",
"print(\"x and y: \", scipy.stats.ttest_ind(x,y))\n",
"#Compare x and z\n",
"print(\"x and z: \", scipy.stats.ttest_ind(x,z))\n",
"#Compare x and w\n",
"print(\"x and w: \", scipy.stats.ttest_ind(x,w))\n",
"#Compare y and z\n",
"print(\"y and z: \", scipy.stats.ttest_ind(y,z))\n",
"#Compare y and w\n",
"print(\"y and w: \", scipy.stats.ttest_ind(y,w))\n",
"#Compare z and w\n",
"print(\"z and w: \", scipy.stats.ttest_ind(z,w))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.3.2 Tukey's HSD (honest significant difference) test. Best\n",
"- Assumes normality\n",
"- Much more power (less false negatives) than Bonferroni (bonferroni with many samples is hard)"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.stats.multicomp import pairwise_tukeyhsd"
]
},
{
"cell_type": "code",
"execution_count": 68,
"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>productivity</th>\n",
" <th>group</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>178</th>\n",
" <td>-0.913089</td>\n",
" <td>b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>-0.014540</td>\n",
" <td>a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td>2.065359</td>\n",
" <td>d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>1.224755</td>\n",
" <td>a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>0.239282</td>\n",
" <td>a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>0.982057</td>\n",
" <td>b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>0.081560</td>\n",
" <td>a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>0.972888</td>\n",
" <td>b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308</th>\n",
" <td>1.297314</td>\n",
" <td>d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>0.643592</td>\n",
" <td>b</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" productivity group\n",
"178 -0.913089 b\n",
"64 -0.014540 a\n",
"395 2.065359 d\n",
"53 1.224755 a\n",
"31 0.239282 a\n",
"122 0.982057 b\n",
"60 0.081560 a\n",
"174 0.972888 b\n",
"308 1.297314 d\n",
"132 0.643592 b"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"data/tukey_example.csv\")\n",
"df.sample(10)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Multiple Comparison of Means - Tukey HSD,FWER=0.05\n",
"============================================\n",
"group1 group2 meandiff lower upper reject\n",
"--------------------------------------------\n",
" a b 0.0542 -0.2883 0.3967 False \n",
" a c 0.1966 -0.1459 0.5392 False \n",
" a d 0.5083 0.1658 0.8509 True \n",
" b c 0.1424 -0.2001 0.485 False \n",
" b d 0.4541 0.1116 0.7967 True \n",
" c d 0.3117 -0.0308 0.6542 False \n",
"--------------------------------------------\n"
]
}
],
"source": [
"res2 = pairwise_tukeyhsd(df[\"productivity\"],df[\"group\"])\n",
"print(res2)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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7cDuP87p0B+J/t437X9rf91v98KZv5brhU5O17ZPuwvDprlXakekOeO4z5fYhue553yjd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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f604d65ef98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"res2.plot_simultaneous(comparison_name=None,xlabel='diffs',ylabel='Group')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4. Fitting distributions\n",
"## Why do we care about distributions?\n",
"- They tell us a lot about the mechanism generating our values\n",
"\n",
"- Normal distribution: \n",
" - Nothing weird going on (just normal)\n",
"- Lognormal distribution: \n",
" - Multiplicative process: \n",
" - The value depends on the multiplication of many normal variables.\n",
" - For example, each unit of time you can grow depending on some random factors.\n",
" - Many financial indicators are lognormally distributed\n",
" - Looks like a normal distribution if you take the logarithm of your values (np.log(x))\n",
"- Exponential distribution: \n",
" - Time between events (generated by poisson events)\n",
" - If the time between events follows an exponential distribution the events are independent\n",
" - It's a straight line if the y axis is in log scale\n",
"- Power-law distribution: \n",
" - Rich-get-richer mechanism: Your growth depends on your value -> Inequality is unavoidable\n",
" - The **tails** of some human and financial phenomena follow power-laws.\n",
" - The tail is a straight line in log-log scale (both y and x axis)\n",
" \n",
"- Binomial distribution: Number of successes out of a number of trials. Each event independent\n",
"- Poisson distribution: Number of successes in some time. Each event independent\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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xdt9rdBPDpYumvr4+3UUnInkZyZQDabNmzwdg/Ro9g05kKCqaCuC5paFo2mnO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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd36c12b198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import lognorm,norm,expon,powerlaw\n",
"x = np.random.lognormal(1,0.8,3000) \n",
"plt.subplot(2,2,1)\n",
"sns.distplot(x,fit=norm,kde=False)\n",
"plt.title(\"norm\")\n",
"\n",
"plt.subplot(2,2,2)\n",
"sns.distplot(x,fit=lognorm,kde=False)\n",
"plt.title(\"lognorm\")\n",
"\n",
"plt.subplot(2,2,3)\n",
"sns.distplot(x,fit=expon,kde=False)\n",
"plt.title(\"expon\")\n",
"\n",
"plt.subplot(2,2,4)\n",
"sns.distplot(x,fit=powerlaw,kde=False)\n",
"plt.title(\"powerlaw\")\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 5. Calculate Confidence Intervals with bootstrap methods\n",
"- How does it work?\n",
"- You have a sample (imagine 100 values)\n",
"- Do 10000 times: \n",
" - Take 100 values from the sample (with replacement)\n",
" - Calculate the mean (or std)\n",
"- Take the percentiles 2.5 and 97.5 of those 10000 values -> Those are your confidence intervals (kind-of)\n"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import scikits.bootstrap as bootstrap"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CI for mean with bootstrapping = [-1.13814925 0.09261081]\n",
"CI for std with bootstrapping = [ 2.77119667 3.62362368]\n"
]
}
],
"source": [
"#Bootstrap of mean and std\n",
"x = np.random.randn(100)*3 #this could be one of our columns\n",
"\n",
"CIs = bootstrap.ci(x, statfunction=np.mean,n_samples=100000) \n",
"print('CI for mean with bootstrapping = ', CIs)\n",
"\n",
"CIs = bootstrap.ci(x, statfunction=np.std,n_samples=100000) \n",
"print('CI for std with bootstrapping = ', CIs)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(0.33822769269829528, 3.1644168130907633)"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = np.random.randn(100)*3\n",
"np.mean(x),np.std(x)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CI for mean with bootstrapping = [-0.26813271 0.9691153 ]\n"
]
}
],
"source": [
"CIs = bootstrap.ci(x, statfunction=np.mean,n_samples=100000) \n",
"print('CI for mean with bootstrapping = ', CIs)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Ttest_1sampResult(statistic=1.0634891829804902, pvalue=0.29014734833454026)"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.ttest_1samp(x,popmean=0)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CI for p-value with bootstrapping = [ 0.00407042 0.96082875]\n"
]
}
],
"source": [
"#Bootsrap of p-value\n",
"def return_pvalue(x):\n",
" stat,pvalue = scipy.stats.ttest_1samp(x,0)\n",
" return pvalue\n",
"\n",
"CIs = bootstrap.ci(x, statfunction=return_pvalue, n_samples=10000) \n",
"print('CI for p-value with bootstrapping = ', CIs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# In class exercise \n"
]
},
{
"cell_type": "code",
"execution_count": 109,
"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>Company_name</th>\n",
" <th>Company_ID</th>\n",
" <th>Big3Share</th>\n",
" <th>Position</th>\n",
" <th>Revenue</th>\n",
" <th>Assets</th>\n",
" <th>Employees</th>\n",
" <th>MarketCap</th>\n",
" <th>Exchange</th>\n",
" <th>TypeEnt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>INVESCO LTD</td>\n",
" <td>BM40671R</td>\n",
" <td>17.85</td>\n",
" <td>1</td>\n",
" <td>7500000.0</td>\n",
" <td>NaN</td>\n",
" <td>7500.0</td>\n",
" <td>13123024.0</td>\n",
" <td>New York Stock Exchange (NYSE)</td>\n",
" <td>Bank</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ROYAL CARIBBEAN CRUISES LTD</td>\n",
" <td>LR30002MX</td>\n",
" <td>14.32</td>\n",
" <td>3</td>\n",
" <td>7500000.0</td>\n",
" <td>NaN</td>\n",
" <td>7500.0</td>\n",
" <td>16739323.0</td>\n",
" <td>New York Stock Exchange (NYSE)</td>\n",
" <td>Industrial company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>GENCO SHIPPING & TRADING LTD</td>\n",
" <td>MH30004AQ</td>\n",
" <td>0.14</td>\n",
" <td>31</td>\n",
" <td>350000.0</td>\n",
" <td>NaN</td>\n",
" <td>1500.0</td>\n",
" <td>43392.0</td>\n",
" <td>New York Stock Exchange (NYSE)</td>\n",
" <td>Industrial company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>EAGLE BULK SHIPPING INC</td>\n",
" <td>MH40003AQ</td>\n",
" <td>2.85</td>\n",
" <td>9</td>\n",
" <td>350000.0</td>\n",
" <td>NaN</td>\n",
" <td>750.0</td>\n",
" <td>26674.0</td>\n",
" <td>NASDAQ National Market</td>\n",
" <td>Industrial company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>POWERSHARES DB US DOLLAR INDEX BEARISH</td>\n",
" <td>US*S00100679</td>\n",
" <td>0.00</td>\n",
" <td>101</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>44120.0</td>\n",
" <td>NYSE ARCA</td>\n",
" <td>Industrial company</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Company_name Company_ID Big3Share Position \\\n",
"0 INVESCO LTD BM40671R 17.85 1 \n",
"1 ROYAL CARIBBEAN CRUISES LTD LR30002MX 14.32 3 \n",
"2 GENCO SHIPPING & TRADING LTD MH30004AQ 0.14 31 \n",
"3 EAGLE BULK SHIPPING INC MH40003AQ 2.85 9 \n",
"4 POWERSHARES DB US DOLLAR INDEX BEARISH US*S00100679 0.00 101 \n",
"\n",
" Revenue Assets Employees MarketCap Exchange \\\n",
"0 7500000.0 NaN 7500.0 13123024.0 New York Stock Exchange (NYSE) \n",
"1 7500000.0 NaN 7500.0 16739323.0 New York Stock Exchange (NYSE) \n",
"2 350000.0 NaN 1500.0 43392.0 New York Stock Exchange (NYSE) \n",
"3 350000.0 NaN 750.0 26674.0 NASDAQ National Market \n",
"4 NaN NaN NaN 44120.0 NYSE ARCA \n",
"\n",
" TypeEnt \n",
"0 Bank \n",
"1 Industrial company \n",
"2 Industrial company \n",
"3 Industrial company \n",
"4 Industrial company "
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"data/big3_position.csv\",sep=\"\\t\")\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PartA. Compare the number of employees of industrial vs financial companies"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"industrial = df.loc[df[\"TypeEnt\"]==\"Industrial company\"]\n",
"financial = df.loc[df[\"TypeEnt\"]==\"Financial company\"]"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f60542e7400>"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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BWa0/mFk5wXDIrbkEtH9/I7FY4V1Ii0TCVFSUFWR8ii13iUSCX/3q1wCcfPIpzJo1j717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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f60570f5128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Employees (like most is lognormally distributed -> we can convert it to a normal distribution and run our tests happily)\n",
"i = np.log(industrial[\"Employees\"].dropna())\n",
"f = np.log(financial[\"Employees\"].dropna())\n",
"\n",
"from scipy.stats import lognorm,norm\n",
"plt.figure(figsize=(10,3))\n",
"plt.subplot(1,2,1)\n",
"sns.distplot(df[\"Employees\"].dropna(),fit=lognorm,kde=False)\n",
"\n",
"plt.subplot(1,2,2)\n",
"sns.distplot(np.log(df[\"Employees\"].dropna()),fit=norm,kde=False)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 8.922658\n",
"2 7.313220\n",
"3 6.620073\n",
"23 3.761200\n",
"24 4.672829\n",
"Name: Employees, dtype: float64"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i.head()"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"31 7.725330\n",
"54 4.189655\n",
"56 3.218876\n",
"87 6.236370\n",
"91 3.891820\n",
"Name: Employees, dtype: float64"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.head()"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"LeveneResult(statistic=3.3037088094994504, pvalue=0.069226865802670651)"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Check if our distributions have the same variance\n",
"scipy.stats.levene(i,f)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f6053f657b8>"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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TQ27N/jpDGT6fSzBYpx8jmqFYhNhYgqZdR9N9xMYS+x7XtOsUnC0aeo2av0Qs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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6054071780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(i)\n",
"sns.distplot(f)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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P//DRiJOM1Utm8cOrjiI1aeCT9fE3Fb6eoZp0iYZS6m7gWuBUrXVIj008ni4ChzMJe4Jz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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6054331d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot histograms and qq_plots for i and f\n",
"qq_plot(i)\n",
"qq_plot(f)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"MannwhitneyuResult(statistic=233817.5, pvalue=3.205266258082931e-22)"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Run the correct statistical test to know if the \n",
"#number of employees of financial and industrial companies is different\n",
"scipy.stats.man(i,f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PartB. Compare the market capitalization for different types of companies"
]
},
{
"cell_type": "code",
"execution_count": 123,
"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>Company_name</th>\n",
" <th>Company_ID</th>\n",
" <th>Big3Share</th>\n",
" <th>Position</th>\n",
" <th>Revenue</th>\n",
" <th>Assets</th>\n",
" <th>Employees</th>\n",
" <th>MarketCap</th>\n",
" <th>Exchange</th>\n",
" <th>TypeEnt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>INVESCO LTD</td>\n",
" <td>BM40671R</td>\n",
" <td>17.85</td>\n",
" <td>1</td>\n",
" <td>7500000.0</td>\n",
" <td>NaN</td>\n",
" <td>7500.0</td>\n",
" <td>13123024.0</td>\n",
" <td>New York Stock Exchange (NYSE)</td>\n",
" <td>Bank</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ROYAL CARIBBEAN CRUISES LTD</td>\n",
" <td>LR30002MX</td>\n",
" <td>14.32</td>\n",
" <td>3</td>\n",
" <td>7500000.0</td>\n",
" <td>NaN</td>\n",
" <td>7500.0</td>\n",
" <td>16739323.0</td>\n",
" <td>New York Stock Exchange (NYSE)</td>\n",
" <td>Industrial company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>GENCO SHIPPING & TRADING LTD</td>\n",
" <td>MH30004AQ</td>\n",
" <td>0.14</td>\n",
" <td>31</td>\n",
" <td>350000.0</td>\n",
" <td>NaN</td>\n",
" <td>1500.0</td>\n",
" <td>43392.0</td>\n",
" <td>New York Stock Exchange (NYSE)</td>\n",
" <td>Industrial company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>EAGLE BULK SHIPPING INC</td>\n",
" <td>MH40003AQ</td>\n",
" <td>2.85</td>\n",
" <td>9</td>\n",
" <td>350000.0</td>\n",
" <td>NaN</td>\n",
" <td>750.0</td>\n",
" <td>26674.0</td>\n",
" <td>NASDAQ National Market</td>\n",
" <td>Industrial company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>POWERSHARES DB US DOLLAR INDEX BEARISH</td>\n",
" <td>US*S00100679</td>\n",
" <td>0.00</td>\n",
" <td>101</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>44120.0</td>\n",
" <td>NYSE ARCA</td>\n",
" <td>Industrial company</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Company_name Company_ID Big3Share Position \\\n",
"0 INVESCO LTD BM40671R 17.85 1 \n",
"1 ROYAL CARIBBEAN CRUISES LTD LR30002MX 14.32 3 \n",
"2 GENCO SHIPPING & TRADING LTD MH30004AQ 0.14 31 \n",
"3 EAGLE BULK SHIPPING INC MH40003AQ 2.85 9 \n",
"4 POWERSHARES DB US DOLLAR INDEX BEARISH US*S00100679 0.00 101 \n",
"\n",
" Revenue Assets Employees MarketCap Exchange \\\n",
"0 7500000.0 NaN 7500.0 13123024.0 New York Stock Exchange (NYSE) \n",
"1 7500000.0 NaN 7500.0 16739323.0 New York Stock Exchange (NYSE) \n",
"2 350000.0 NaN 1500.0 43392.0 New York Stock Exchange (NYSE) \n",
"3 350000.0 NaN 750.0 26674.0 NASDAQ National Market \n",
"4 NaN NaN NaN 44120.0 NYSE ARCA \n",
"\n",
" TypeEnt \n",
"0 Bank \n",
"1 Industrial company \n",
"2 Industrial company \n",
"3 Industrial company \n",
"4 Industrial company "
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"data/big3_position.csv\",sep=\"\\t\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:2: RuntimeWarning: divide by zero encountered in log\n",
" from ipykernel import kernelapp as app\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Company_name</th>\n",
" <th>Company_ID</th>\n",
" <th>Big3Share</th>\n",
" <th>Position</th>\n",
" <th>Revenue</th>\n",
" <th>Assets</th>\n",
" <th>Employees</th>\n",
" <th>MarketCap</th>\n",
" <th>Exchange</th>\n",
" <th>TypeEnt</th>\n",
" <th>logMarketCap</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>INVESCO LTD</td>\n",
" <td>BM40671R</td>\n",
" <td>17.85</td>\n",
" <td>1</td>\n",
" <td>7500000.0</td>\n",
" <td>NaN</td>\n",
" <td>7500.0</td>\n",
" <td>13123024.0</td>\n",
" <td>New York Stock Exchange (NYSE)</td>\n",
" <td>Bank</td>\n",
" <td>16.389879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ROYAL CARIBBEAN CRUISES LTD</td>\n",
" <td>LR30002MX</td>\n",
" <td>14.32</td>\n",
" <td>3</td>\n",
" <td>7500000.0</td>\n",
" <td>NaN</td>\n",
" <td>7500.0</td>\n",
" <td>16739323.0</td>\n",
" <td>New York Stock Exchange (NYSE)</td>\n",
" <td>Industrial company</td>\n",
" <td>16.633271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>GENCO SHIPPING & TRADING LTD</td>\n",
" <td>MH30004AQ</td>\n",
" <td>0.14</td>\n",
" <td>31</td>\n",
" <td>350000.0</td>\n",
" <td>NaN</td>\n",
" <td>1500.0</td>\n",
" <td>43392.0</td>\n",
" <td>New York Stock Exchange (NYSE)</td>\n",
" <td>Industrial company</td>\n",
" <td>10.678030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>EAGLE BULK SHIPPING INC</td>\n",
" <td>MH40003AQ</td>\n",
" <td>2.85</td>\n",
" <td>9</td>\n",
" <td>350000.0</td>\n",
" <td>NaN</td>\n",
" <td>750.0</td>\n",
" <td>26674.0</td>\n",
" <td>NASDAQ National Market</td>\n",
" <td>Industrial company</td>\n",
" <td>10.191445</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>POWERSHARES DB US DOLLAR INDEX BEARISH</td>\n",
" <td>US*S00100679</td>\n",
" <td>0.00</td>\n",
" <td>101</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>44120.0</td>\n",
" <td>NYSE ARCA</td>\n",
" <td>Industrial company</td>\n",
" <td>10.694668</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Company_name Company_ID Big3Share Position \\\n",
"0 INVESCO LTD BM40671R 17.85 1 \n",
"1 ROYAL CARIBBEAN CRUISES LTD LR30002MX 14.32 3 \n",
"2 GENCO SHIPPING & TRADING LTD MH30004AQ 0.14 31 \n",
"3 EAGLE BULK SHIPPING INC MH40003AQ 2.85 9 \n",
"4 POWERSHARES DB US DOLLAR INDEX BEARISH US*S00100679 0.00 101 \n",
"\n",
" Revenue Assets Employees MarketCap Exchange \\\n",
"0 7500000.0 NaN 7500.0 13123024.0 New York Stock Exchange (NYSE) \n",
"1 7500000.0 NaN 7500.0 16739323.0 New York Stock Exchange (NYSE) \n",
"2 350000.0 NaN 1500.0 43392.0 New York Stock Exchange (NYSE) \n",
"3 350000.0 NaN 750.0 26674.0 NASDAQ National Market \n",
"4 NaN NaN NaN 44120.0 NYSE ARCA \n",
"\n",
" TypeEnt logMarketCap \n",
"0 Bank 16.389879 \n",
"1 Industrial company 16.633271 \n",
"2 Industrial company 10.678030 \n",
"3 Industrial company 10.191445 \n",
"4 Industrial company 10.694668 "
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Create a new column that is the logarithm of \"MarketCap\", call it \"logMarketCap\"\n",
"df[\"logMarketCap\"] = np.log(df[\"MarketCap\"])\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 112,
"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>Big3Share</th>\n",
" <th>Position</th>\n",
" <th>Revenue</th>\n",
" <th>Assets</th>\n",
" <th>Employees</th>\n",
" <th>MarketCap</th>\n",
" <th>logMarketCap</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3882.000000</td>\n",
" <td>3882.000000</td>\n",
" <td>3.837000e+03</td>\n",
" <td>3.695000e+03</td>\n",
" <td>3.300000e+03</td>\n",
" <td>3.882000e+03</td>\n",
" <td>3882.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>10.323029</td>\n",
" <td>16.122875</td>\n",
" <td>3.467816e+06</td>\n",
" <td>9.155279e+06</td>\n",
" <td>1.066365e+04</td>\n",
" <td>5.706551e+06</td>\n",
" <td>-inf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.951140</td>\n",
" <td>34.876491</td>\n",
" <td>1.490323e+07</td>\n",
" <td>6.451795e+07</td>\n",
" <td>5.129945e+04</td>\n",
" <td>2.346929e+07</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-3.757200e+04</td>\n",
" <td>2.190000e+02</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-inf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.420000</td>\n",
" <td>1.000000</td>\n",
" <td>6.544700e+04</td>\n",
" <td>1.588285e+05</td>\n",
" <td>2.640000e+02</td>\n",
" <td>1.372448e+05</td>\n",
" <td>11.829521</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>10.365000</td>\n",
" <td>2.000000</td>\n",
" <td>3.867370e+05</td>\n",
" <td>8.707830e+05</td>\n",
" <td>1.327500e+03</td>\n",
" <td>6.494275e+05</td>\n",
" <td>13.383845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>17.197500</td>\n",
" <td>8.000000</td>\n",
" <td>1.830800e+06</td>\n",
" <td>3.674560e+06</td>\n",
" <td>6.243750e+03</td>\n",
" <td>2.733080e+06</td>\n",
" <td>14.820940</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>48.190000</td>\n",
" <td>165.000000</td>\n",
" <td>4.821300e+08</td>\n",
" <td>2.144316e+09</td>\n",
" <td>2.300000e+06</td>\n",
" <td>5.496596e+08</td>\n",
" <td>20.124810</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Big3Share Position Revenue Assets Employees \\\n",
"count 3882.000000 3882.000000 3.837000e+03 3.695000e+03 3.300000e+03 \n",
"mean 10.323029 16.122875 3.467816e+06 9.155279e+06 1.066365e+04 \n",
"std 7.951140 34.876491 1.490323e+07 6.451795e+07 5.129945e+04 \n",
"min 0.000000 1.000000 -3.757200e+04 2.190000e+02 1.000000e+00 \n",
"25% 2.420000 1.000000 6.544700e+04 1.588285e+05 2.640000e+02 \n",
"50% 10.365000 2.000000 3.867370e+05 8.707830e+05 1.327500e+03 \n",
"75% 17.197500 8.000000 1.830800e+06 3.674560e+06 6.243750e+03 \n",
"max 48.190000 165.000000 4.821300e+08 2.144316e+09 2.300000e+06 \n",
"\n",
" MarketCap logMarketCap \n",
"count 3.882000e+03 3882.000000 \n",
"mean 5.706551e+06 -inf \n",
"std 2.346929e+07 NaN \n",
"min 0.000000e+00 -inf \n",
"25% 1.372448e+05 11.829521 \n",
"50% 6.494275e+05 13.383845 \n",
"75% 2.733080e+06 14.820940 \n",
"max 5.496596e+08 20.124810 "
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Replace infinite values (log(0) = infinite) with nan\n",
"df = df.replace([np.inf, -np.inf], np.nan)\n",
"\n",
"#Drop na values if \"logMarketCap\" is na \n",
"df = df.dropna(subset=[\"logMarketCap\"])\n"
]
},
{
"cell_type": "code",
"execution_count": 114,
"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>Big3Share</th>\n",
" <th>Position</th>\n",
" <th>Revenue</th>\n",
" <th>Assets</th>\n",
" <th>Employees</th>\n",
" <th>MarketCap</th>\n",
" <th>logMarketCap</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3878.000000</td>\n",
" <td>3878.000000</td>\n",
" <td>3.833000e+03</td>\n",
" <td>3.692000e+03</td>\n",
" <td>3.296000e+03</td>\n",
" <td>3.878000e+03</td>\n",
" <td>3878.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>10.333677</td>\n",
" <td>16.036101</td>\n",
" <td>3.471048e+06</td>\n",
" <td>9.162528e+06</td>\n",
" <td>1.067561e+04</td>\n",
" <td>5.712437e+06</td>\n",
" <td>13.347133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.948320</td>\n",
" <td>34.789581</td>\n",
" <td>1.491066e+07</td>\n",
" <td>6.454366e+07</td>\n",
" <td>5.132941e+04</td>\n",
" <td>2.348068e+07</td>\n",
" <td>2.181133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-3.757200e+04</td>\n",
" <td>2.190000e+02</td>\n",
" <td>1.000000e+00</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.420000</td>\n",
" <td>1.000000</td>\n",
" <td>6.612000e+04</td>\n",
" <td>1.590505e+05</td>\n",
" <td>2.647500e+02</td>\n",
" <td>1.376925e+05</td>\n",
" <td>11.832778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>10.375000</td>\n",
" <td>2.000000</td>\n",
" <td>3.867670e+05</td>\n",
" <td>8.743075e+05</td>\n",
" <td>1.329500e+03</td>\n",
" <td>6.529460e+05</td>\n",
" <td>13.389249</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>17.200000</td>\n",
" <td>8.000000</td>\n",
" <td>1.837287e+06</td>\n",
" <td>3.679478e+06</td>\n",
" <td>6.279750e+03</td>\n",
" <td>2.737552e+06</td>\n",
" <td>14.822574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>48.190000</td>\n",
" <td>165.000000</td>\n",
" <td>4.821300e+08</td>\n",
" <td>2.144316e+09</td>\n",
" <td>2.300000e+06</td>\n",
" <td>5.496596e+08</td>\n",
" <td>20.124810</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Big3Share Position Revenue Assets Employees \\\n",
"count 3878.000000 3878.000000 3.833000e+03 3.692000e+03 3.296000e+03 \n",
"mean 10.333677 16.036101 3.471048e+06 9.162528e+06 1.067561e+04 \n",
"std 7.948320 34.789581 1.491066e+07 6.454366e+07 5.132941e+04 \n",
"min 0.000000 1.000000 -3.757200e+04 2.190000e+02 1.000000e+00 \n",
"25% 2.420000 1.000000 6.612000e+04 1.590505e+05 2.647500e+02 \n",
"50% 10.375000 2.000000 3.867670e+05 8.743075e+05 1.329500e+03 \n",
"75% 17.200000 8.000000 1.837287e+06 3.679478e+06 6.279750e+03 \n",
"max 48.190000 165.000000 4.821300e+08 2.144316e+09 2.300000e+06 \n",
"\n",
" MarketCap logMarketCap \n",
"count 3.878000e+03 3878.000000 \n",
"mean 5.712437e+06 13.347133 \n",
"std 2.348068e+07 2.181133 \n",
"min 1.000000e+00 0.000000 \n",
"25% 1.376925e+05 11.832778 \n",
"50% 6.529460e+05 13.389249 \n",
"75% 2.737552e+06 14.822574 \n",
"max 5.496596e+08 20.124810 "
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f6053f80860>"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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XFXBMgSSpKYaCgrlOgSRpmFaMugHatX7dB9ffcBP/8u1LZ26vmIAnPeGIYTZLkrRMGQoK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\nF+QAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6053fb6518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Find the distribution of \"MarketCap\" and \"logMarketCap\", fitting the rigth distribution\n",
"from scipy.stats import norm,lognorm,expon\n",
"sns.distplot(df[\"MarketCap\"],fit=lognorm,kde=False)"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f6053de8e10>"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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KC3Oj7aX3WuzUZu6INURsA8qNMUXW2shljPXAXmtt7wWed05foDEmH/gm8BVrbWt4WwnO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\nCw4PjCczaTFnAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6053e327b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Find the distribution of \"MarketCap\" and \"logMarketCap\", fitting the rigth distribution\n",
"from scipy.stats import norm,lognorm,expon\n",
"sns.distplot(df[\"logMarketCap\"],fit=norm,kde=False)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Kepe only three types of entities\n",
"df = df.loc[df[\"TypeEnt\"].isin([\"Financial company\",\"Industrial company\",\"Bank\"])]"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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hh8JMmjRlRLGNLRNpYe0LAYnI2KQkQkQqWsuhp7sDy4mMKL6nu2NduyYiGzHN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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6053ceecc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot distributions for each type\n",
"plt.figure(figsize=(6,4))\n",
"for typeent,group in df.groupby(\"TypeEnt\"):\n",
" sns.distplot(group[\"logMarketCap\"],kde=False,norm_hist=True,label=typeent)\n",
"\n",
" plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"F_onewayResult(statistic=9.6548583490128426, pvalue=6.5712383714430791e-05)"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Run ANOVA\n",
"bank = df.loc[df[\"TypeEnt\"] == \"Bank\"]\n",
"bank_values = bank[\"logMarketCap\"]\n",
"\n",
"ind = df.loc[df[\"TypeEnt\"] == \"Industrial company\"]\n",
"ind_values = ind[\"logMarketCap\"]\n",
"\n",
"fin = df.loc[df[\"TypeEnt\"] == \"Financial company\"]\n",
"fin_values = fin[\"logMarketCap\"]\n",
"\n",
"scipy.stats.f_oneway(bank_values,ind_values,fin_values)"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Multiple Comparison of Means - Tukey HSD,FWER=0.05 \n",
"====================================================================\n",
" group1 group2 meandiff lower upper reject\n",
"--------------------------------------------------------------------\n",
" Bank Financial company 0.6829 0.3149 1.051 True \n",
" Bank Industrial company 0.238 -0.0201 0.4962 False \n",
"Financial company Industrial company -0.4449 -0.7385 -0.1513 True \n",
"--------------------------------------------------------------------\n"
]
}
],
"source": [
"#Run Tukey test\n",
"res2 = pairwise_tukeyhsd(df[\"logMarketCap\"],df[\"TypeEnt\"])\n",
"print(res2)"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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79tiX5KCDrnEhlGj727DhLJ/rVcJ3+OqhrleHUet5n3322uoyIwbXLd0A9C9J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\nZ90zzjhzzlW91cEV3NVDXa8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LKCMxz2TmcsoH7ExzXY02cz3gHMp9/oX2wlQL2sz1N4Fr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\nSZIkqWoWrpIkSZKkqlm4SpIkSZKq9l9U1naEtuV1/AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f604d65e908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot tukey test\n",
"res2.plot_simultaneous(comparison_name=None,xlabel='diffs',ylabel='Group')\n",
"plt.show()\n"
]
}
],
"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
}
| gpl-3.0 |
google/skywater-pdk-libs-sky130_bag3_pr | workspace_setup/tutorial_files/1_flow_demo.ipynb | 1 | 66891 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Module 1: BAG Workflow Demo\n",
"Welcome to the BAG tutorial! In this module, you will test run a simple demo of a common-source amplifier design to get an idea of generator-based design methodology. This also serves to make sure you setup your workspace properly."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## BAG Workflow\n",
"\n",
"<img src=\"bootcamp_pics/1_flow_demo/flow_demo_1.PNG\" alt=\"Drawing\" style=\"width: 600px\"/>\n",
"\n",
"The above flow diagram outlines how circuit design is typically done with BAG. You will notice that it is largely similar to traditional manual design flow, with two major differences:\n",
"\n",
"* Designer focus on designing schematic/layout/testbench generators, instead of specific circuit instances.\n",
"* Layout is usually done before schematic.\n",
"\n",
"Discussions about the benefits of designing circuit generators instead of instances are outside of the scope of this tutorial, so I will assume you are already convinced. So, why do we design layout generators before schematic generators? There are several reasons:\n",
"\n",
"* Since BAG can easily automates layout and post-extraction simulations, there is almost no need for schematic only simulations.\n",
"* One schematic could correspond to many different layouts (each with a different floorplan strategy), whereas one layout corresponds to exactly one schematic.\n",
"* It is impossible to determine schematic details such as dummy transistors before layout is done.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## BAG Schematic Example\n",
"<img src=\"bootcamp_pics/1_flow_demo/flow_demo_2.PNG\" alt=\"Drawing\" style=\"width: 500px\"/>\n",
"\n",
"The above figure shows the schematic template used for a common-source amplifier schematic generator, you can find this schematic in Virtuoso in library `demo_templates` and cell `amp_cs`. Note that this is just like any other normal schematics, with the following differences:\n",
"\n",
"* Transistors are from the `BAG_prim` library. In this way this schematic can be ported across process by simply changing the `BAG_prim` library.\n",
"* Dummy transistors' ports are connected using wire stubs and net labels. This allows BAG to easy reconnect those ports if necessary.\n",
"\n",
"When BAG generates a new schematic, it will simply copy this schematic to a new library, then perform a set of modifications described by the schematic generator. The modifications could include:\n",
"\n",
"* Delete instances.\n",
"* Create new instances.\n",
"* Change the master of an instance.\n",
"* Reconnect instance terminals.\n",
"* Modify instance parameters.\n",
"* Add/Remove/Rename pins.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testbench Schematic Example\n",
"<img src=\"bootcamp_pics/1_flow_demo/flow_demo_3.PNG\" alt=\"Drawing\" style=\"width: 400px\"/>\n",
"The above figure shows a schematic template for a DC operating point testbench generator, which can be found in library `bag_testbenches_ec` and cell `amp_tb_dc`. It is just like the schematic template we seen before, but instead of a symbol view it has an ADEXL view. To generate a new testbench, BAG will copy and modify both the schematic and the ADEXL view and returns a `Testbench` object that can be used to control simulations from Python."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testbench ADEXL Setup\n",
"<img src=\"bootcamp_pics/1_flow_demo/flow_demo_4.PNG\" alt=\"Drawing\" style=\"width: 500px\"/>\n",
"The figure above shows the ADEXL view associated with a testbench template. ADEXL is used to enable parametric/process corner sweeps."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running Demo Work Flow\n",
"```python\n",
"def run_flow(prj, specs, dsn_name, lay_cls, sch_cls=None, run_lvs=True, lvs_only=False):\n",
" # generate layout, get schematic parameters from layout\n",
" dsn_sch_params = gen_layout(prj, specs, dsn_name, lay_cls)\n",
" # generate design/testbench schematics\n",
" gen_schematics(prj, specs, dsn_name, dsn_sch_params, sch_cls=sch_cls, check_lvs=run_lvs, lvs_only=lvs_only)\n",
"\n",
" if lvs_only:\n",
" # return if we're only running LVS\n",
" print('LVS flow done')\n",
" return\n",
"\n",
" # run simulation and import results\n",
" simulate(prj, specs, dsn_name)\n",
"\n",
" # load simulation results from save file\n",
" res_dict = load_sim_data(specs, dsn_name)\n",
" # post-process simulation results\n",
" plot_data(res_dict)\n",
"\n",
"```\n",
"Now that you have an rough idea of how BAG generates new schematics and testbenches, let's try to run the common-source amplifier design flow. To do so, simple select the code box below and press Ctrl+Enter to evaluate the Python code. If everything works fine, you should see output messages in the dialog box below the code box, and it should end with DC/AC/Transient simulation plots. Schematics, layouts, and testbenches should also be generated in the `DEMO_AMP_CS` library in Virtuoso, so you can take a look over there.\n",
"\n",
"The Python script simply performs the following:\n",
"\n",
"* Read a specification file to get schematic/layout/testbench/simulation parameters.\n",
"* Create a `BagProject` instance to perform various functions.\n",
"* Call the `run_flow()` method defined in Python module `xbase_demo.core` to execute the common source amplifier design flow.\n",
"\n",
"The `xbase_demo.core` module is defined in the file `$BAG_WORK_DIR/BAG_XBase_demo/xbase_demo/core.py`. You can take a look if you're interested, but the `run_flow()` method definition is reproduced above for your convenience. You can see it simply calls other methods to generate layout/schematics, run simulations, and post-process simulation results."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"creating BagProject\n",
"computing layout\n",
"ext_w0 = 1, ext_wend=1, ytop=2592\n",
"final: ext_w0 = 1, ext_wend=1, ytop=2592\n",
"{'s': WireArray(TrackID(layer=3, track=7, num=9, pitch=2), 1109, 1265, 0.001), 'd': WireArray(TrackID(layer=3, track=8, num=8, pitch=2), 1231, 1387, 0.001), 'g': WireArray(TrackID(layer=3, track=8, num=8, pitch=2), 915, 1071, 0.001)}\n",
"WireArray(TrackID(layer=3, track=8, num=8, pitch=2), 915, 1071, 0.001)\n",
"6.5\n",
"creating layout\n",
"layout done\n",
"computing AMP_CS schematics\n",
"creating AMP_CS schematics\n",
"running lvs\n",
"Running tasks, Press Ctrl-C to cancel.\n",
"lvs passed\n",
"lvs log is /users/erichang/projects/bag_gen/BAG2_cds_ff_mpt/pvs_run/lvs_run_dir/DEMO_AMP_CS/AMP_CS/lvsLog_20180906_102350my93d2vr\n",
"computing AMP_CS_tb_dc schematics\n",
"creating AMP_CS_tb_dc schematics\n",
"computing AMP_CS_tb_ac_tran schematics\n",
"creating AMP_CS_tb_ac_tran schematics\n",
"schematic done\n",
"setting up AMP_CS_tb_dc\n",
"running simulation\n",
"Running tasks, Press Ctrl-C to cancel.\n",
"simulation done, load results\n",
"setting up AMP_CS_tb_ac_tran\n",
"running simulation\n",
"Running tasks, Press Ctrl-C to cancel.\n",
"simulation done, load results\n",
"all simulation done\n",
"loading simulation data for AMP_CS_tb_dc\n",
"loading simulation data for AMP_CS_tb_ac_tran\n",
"finish loading data\n",
", gain=-3.822\n",
", f_3db=3.601e+09, f_unity=9.122e+09, phase_margin=107.7\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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F8G4oFwulUb2wpARwPfAmoBVYJ+kuM9ucqmNmn0mrfyXwivD5NODvgRWAAY+Fx7ZHFW+2+JiFS2lt7wPwbigXC1G2LM4DtprZNjMbBG4FLjlG/dXALeHzNwO/NLODYYL4JbAywlizJrUAko9ZuFSymOMtCxcDUSaLOcCOtO3WsOwlJC0AFgH3TeZYSZdLWi9pfVtbW1aCPlH1qaVVvRuq6O1s76O6PEFTdVm+Q3HuhEWZLMa6sHy8O9VWAT8ys+HJHGtmN5jZCjNb0dLScpxhZldNRQLwMQsXXDY7p9HvsXDxEGWyaAXmpW3PBXaNU3cVR7qgJnvslFKaKKG6POFjFu7wDXnOxUGUyWIdsETSIknlBAnhrtGVJJ0KNAGPpBWvAS6W1CSpCbg4LCsItRWlvrSqY/ehPl8dz8VGZFdDmVlS0hUEf+QTwI1mtknStcB6M0sljtXArZY2mZKZHZT0DwQJB+BaMzsYVazZ5pMJuoHkMO29Q8yqr8x3KM5lRWTJAsDM7gbuHlV2zajtL4xz7I3AjZEFF6HayjK/GqrI7escAGCmJwsXE34HdwTqK0t9zKLI7e3sB2BGfUWeI3EuOzxZRCA1TbkrXnvDlsWsBm9ZuHjwZBGB2gofsyh2e8KWxcw6TxYuHjxZRCBYh9u7oYrZvs5+yhMlNPoNeS4mPFlEoLailJ7BYYZHfLW8YrW3s58Z9RV+Q56LDU8WEUhNJuj3WhSvPZ39ftmsixVPFhHwZOH2dQ74ZbMuVjxZRKDu8GSCPm5RrFLdUM7FhSeLCKSmKffLZ4tT90CSnsFh74ZyseLJIgK+tGpx23MovGzWk4WLEU8WETicLHzMoijtS929XefdUC4+PFlEwMcsitv+nkEAmj1ZuBjxZBGBhqogWXT0erIoRge6g6k+mms9Wbj48GQRgcqyBFVlCTp6B/MdisuDA92DlAgaq/zubRcfk0oWkmokJaIKJk6aqsto95ZFUTrQM8C0mgpKSvzubRcfx0wWkkokfUDSzyXtA54BdkvaJOnLkpbkJszC01hdTnuPtyyK0f7uQZpry/MdhnNZlallcT9wMnA1MMvM5pnZDOACYC3wRUkfijjGgjStppx274YqSge6B5juycLFTKaV8t5oZi/pSwmXOP0x8GNJ3jE7hsbqMnZ19OU7DJcHB3oGeXlTY77DcC6rMrUsfhJ2Q9WMV2GsZOKgqbqcg96yKEoHuwe9ZeFiJ1Oy+DbwDuAFSbdJepck/y2YgKaacg71Dfk05UWmf2iYroGkXzbrYueYycLMfmpmq4EFwB3AR4Htkm6U9KZcBFiomqrLMIPOPm94FZOD4UUN02v8O5WLlwldOmtmfWZ2m5m9G7gYeAXwi0gjK3BN1cEfCx/kLi4HusNk4S0LFzMTShaSZkq6UtJDwJ3AvcA5EzhupaQtkrZKumqcOpdK2hxejntzWvmXJD0VPt4/wZ9nykgtp+nJorjs7wnu3vYxCxc3x7waStIngNXAqQTdUH9jZg9N5IXDm/euB94EtALrJN1lZpvT6iwhuCz31WbWLmlGWP424GzgLKACeFDSPWbWOdkfMF+mhd0Q7T3eDVVMUi2L5hpvWbh4yXTp7PnAF4FfmdnIJF/7PGCrmW0DkHQrcAmwOa3OJ4DrzawdwMz2heXLgAfNLAkkJW0EVgK3TzKGvPFuqOKUmhfKWxYubjJ1Q/0vM7t3vEShwNxxjp0D7Ejbbg3L0i0Flkp6SNJaSSvD8o3AWyRVS2oGLgLmjfH+l0taL2l9W1tbhh8lt7wbqjgd6BmksqyE6nKfFcfFS6aWxZcllQA/BR4D2oBK4BSCP+BvAP6eIBGMNtbEOKOvIy0FlgAXAnOB30o608zulXQu8HD4no8AL1kcwsxuAG4AWLFixZS6RrW2opSyhHx+qCKzv3uA6TUVSD4vlIuXYyYLM3ufpGXAB4E/B2YDvcDTwN3AdWbWP87hrRzdGpgL7Bqjztrwxr4XJG0hSB7rzOw64DqAcOD7ucn8YPkmicbqcp95tsh09A4dblU6FyeZWhaEA9KfP47XXgcskbQI2AmsAj4wqs6dBAPo3w27m5YC28LB8UYzOyBpObCc4AqsgtJUXXb4untXHDp6Bz1ZuFjKmCyOl5klJV0BrAESwI1mtknStcB6M7sr3HexpM3AMPC5MEFUEnRJAXQCHwoHuwtKU3W5d0MVmY6+IWY3VuU7DOeyLrJkAWBmdxN0V6WXXZP23IDPho/0Ov0EV0QVtOm15Ty7tzvfYbgc6ugd8kWPXCz5SnkRaq6tYH94KaWLv5ERo6N38PBl087FyUTv4P71RMrc0ZprK+joHWIwOdlbVFwh6hpIMmL4mIWLpUx3cFcC1UCzpCaOXA5bD5wUcWwFLzXz6IGeAWY3eD923B0Kx6cavBvKxVCmMYtPAp8mSAx/SCvvJJjKwx1DamnN/V2DniyKQEdfcOWbd0O5OMp0n8XXgK9JutLMvpGjmGKjuS5oWfi4RXFIXfnm3VAujiZ6NdQhSR8ZXWhmN2U5nlhpCbuh2jxZFIXUDZiN3rJwMTTRZHFu2vNKgmk+/gB4sjiG1JiFtyyKw6E+b1m4+JpQsjCzK9O3JTUA348kohipKk9QU55gf5ffxV0MUtPR+30WLo6O9z6LXoI5nFwGzXV+r0Wx6OgbpK6ilNKE377k4mdCLQtJ/8WRGWMTwOkU0NoS+eQ35hWPjt4hGrwLysXURMcsvpL2PAm8aGZjTUvuRmmuLeeF/T35DsPlgN+97eJsQu1lM3sQeAaoA5oA74SfoKBl4aerGHT0+fTkLr4mOt3HpcCjwPuAS4HfS3pvlIHFRXNtBe29gySHfcqPuAvWsvCWhYuniXZDfR44N7VGtqQW4FfAj6IKLC6a6yowg4M9g8yor8x3OC5CHb2DfiWUi62JXrZRkkoUoQOTOLaopW7M29flg9xxZmYc8m4oF2MTbVn8QtIa4JZw+/2MWqfCjW1WQ9Ca2HOonzPnNOQ5GheV3sFhRgzqKiNdIsa5vJnoTXmfk/Qe4DUEM8/eYGY/iTSymJgVdj3t6RxvqXIXB90DwUKONRWeLFw8ZZqi/JvAzWb2sJndAdyRm7Dio6WugkSJ2OvJIta6+oNkUevJwsVUpnGH54B/lvRHSV+SdFYugoqTRIloqa1g9yFPFnGWall4N5SLq2MmCzP7mpm9CngdcBD4f5KelnSNpKU5iTAGZjZUessi5roPtyx8gNvF00RvynvRzL5kZq8APgC8G3g60shiZFZ9BXu8ZRFr3QPBJILeDeXiaqI35ZVJeoekHwD3AM8CfxppZDEyu6HKk0XMpcYsvBvKxdUxk4WkN0m6EWgFLie4XPZkM3u/md2Z6cUlrZS0RdJWSVeNU+dSSZslbZJ0c1r5P4VlT0v6uiSNdXwhmFlfSddAkp6wX9vFT2rMwlsWLq4yfbL/FrgZ+GszOziZF5aUIFin+00EyWadpLvMbHNanSXA1cCrzaxd0oyw/Hzg1cDysOrvCMZNHphMDFPFrIbgxrw9nf2c3FKb52hcFHr80lkXc5nW4L7oBF77PGCrmW0DkHQrcAmwOa3OJ4Drzaw9fL/UXeJGsCJfOcF9HWXA3hOIJa9mhvda7D3kySKuugaSVJSWUF7qExu4eIrykz0H2JG23RqWpVsKLJX0kKS1klYCmNkjwP3A7vCxxsxeMqAu6XJJ6yWtb2tri+SHyIbUjXl++Wx8dfcnfbzCxVqUyWKsMQYbtV1KsOLehcBq4DuSGiWdQrDA0lyCBPN6Sa99yYuZ3WBmK8xsRUtLS1aDz6bDU3745bOx1T2Q9PEKF2tRJotWYF7a9lxg1xh1fmpmQ2b2ArCFIHm8G1hrZt1m1k1wBdYrI4w1UtXlpTRWl7Groy/fobiIdPcnqfWWhYuxKJPFOmCJpEWSyoFVwF2j6twJXAQgqZmgW2obsB14naRSSWUEg9sFfV/H3KYqWts9WcRVl7csXMxFlizMLAlcAawh+EN/u5ltknStpHeG1dYAByRtJhij+JyZHSBYJ+N54ElgI7DRzP4rqlhzYV5TNa3tvfkOw0Wkuz/pd2+7WIv0q5CZ3c2oqczN7Jq05wZ8Nnyk1xkGPhllbLk2t6mK+57Zh5lRwLeMuHEEYxaJfIfhXGT8Or8cmTetmoHkCG3dvghSHHUP+JiFizdPFjkyt6kKwMctYsq7oVzcebLIkblN1QDsOOjjFnEzkBxmcHjE77NwsebJIke8ZRFf3b7wkSsCnixypLq8lOk15Z4sYsgnEXTFwJNFDgX3Wng3VNwcXlLVu6FcjHmyyKG506p9zCKGDi+p6i0LF2OeLHJowbRqWtv7SA6P5DsUl0Xd3rJwRcCTRQ4taq4hOWI+bhEzPYM+ZuHiz5NFDi1uqQHghf09eY7EZZOPWbhi4MkihxY3BwsfPd/WnedIXDYdGbPwm/JcfHmyyKGmmnIaq8u8ZREz3f1JEiWissx/nVx8+ac7xxY113iyiJnugSQ15QmfINLFmieLHPNkET9d/UnqKr0LysWbJ4scW9xcw+5D/fSGV9C4wtc9MORXQrnY82SRY4vCQW5vXcSHT0/uioEnixw7eUZw+ezWfX5FVFwE05N7snDx5skixxY311JaIrbs6cp3KC5Lurxl4YqAJ4scKy8tYXFLjSeLGOnuT/q8UC72PFnkwamz6nnGk0VsBOtve7Jw8ebJIg9Om1XHzo4+uvqH8h2KO0HDI0bv4LB3Q7nYizRZSFopaYukrZKuGqfOpZI2S9ok6eaw7CJJG9Ie/ZLeFWWsuXTqzDoAnt3rrYtC5wsfuWIR2SdcUgK4HngT0Aqsk3SXmW1Oq7MEuBp4tZm1S5oBYGb3A2eFdaYBW4F7o4o1106dFSSLZ/Z0cc6CaXmOxp2Iw/NCecvCxVyULYvzgK1mts3MBoFbgUtG1fkEcL2ZtQOY2b4xXue9wD1mFptVg+Y0VlFTnvBB7hg4sv6238Ht4i3KZDEH2JG23RqWpVsKLJX0kKS1klaO8TqrgFsiijEvSkrEspPqeWrnoXyH4k5Q90Aw7uRjFi7uokwWY82qZqO2S4ElwIXAauA7khoPv4A0G3gZsGbMN5Aul7Re0vq2trasBJ0ry+c2smlXJ0O+al5B6x4YBqC2IpHnSJyLVpTJohWYl7Y9F9g1Rp2fmtmQmb0AbCFIHimXAj8xszEvGzKzG8xshZmtaGlpyWLo0Vs+t4GB5IgPchc474ZyxSLKZLEOWCJpkaRygu6ku0bVuRO4CEBSM0G31La0/auJWRdUyvK5QQPqiVbviipk3g3likVkycLMksAVBF1ITwO3m9kmSddKemdYbQ1wQNJm4H7gc2Z2AEDSQoKWyYNRxZhPC6dXU19Z6smiwB1eUtUvnXUxF+kn3MzuBu4eVXZN2nMDPhs+Rh/7R146IB4bklg+t5EnWjvyHYo7AX6fhSsWfgd3Hi2f28CWPV30DQ7nOxR3nLr7k1SXJ0iU+Cp5Lt48WeTRuQunkRwxHt/Rnu9Q3HHyeaFcsfBkkUfnLGxCgkdfOJjvUNxx8unJXbHwZJFH9ZVlLJtd78migPn05K5YeLLIs/MWTeMP29sZTPrNeYXIl1R1xcKTRZ79yaJp9A+N8KRP/VGQfElVVyw8WeTZeYumI8FDW/fnOxR3HIIBbr9728WfJ4s8m1ZTzvK5jTywZawJd91U19U/5NOTu6LgyWIKuHBpC4/v6KC9ZzDfobhJMDO/dNYVDU8WU8CFp7ZgBr95rrBmzi12fUPDjBjUeLJwRcCTxRSwfG4jTdVlPLjFk0UhOTzjrHdDuSLgyWIKSJSI1y1t4YFn20j6+hYFoyu1pKq3LFwR8GQxRaw8czYHewZ5ZNuBfIfiJqjHJxF0RcSTxRRx4akt1FaU8rONu/Mdipsg74ZyxcSTxRRRWZbg4mUzueep3X43d4Ho8paFKyKeLKaQt798Np39SX7rV0UVhNTCR36fhSsGniymkNec0kJzbTm3rduR71DcBHT2BUuqNlT5Hdwu/jxZTCHlpSW8b8U8fv3MPnYf6st3OC6Dzv5w/W3vhnJFwJPFFLP63PmMmHnrogB09gV3b5cm/NfIxZ9/yqeY+dOree2SFm55dLsPdE8BGp5EAAAN/klEQVRxnf1D1Pt4hSsSniymoD9/zSL2dg5wxx9a8x2KO4bOviHqfbzCFQlPFlPQa5c0s3xuA//2wPN+R/cU1ukzzroiEmmykLRS0hZJWyVdNU6dSyVtlrRJ0s1p5fMl3Svp6XD/wihjnUokccVFp7D9YC8/3bAr3+G4cXT2Jamv9JaFKw6RJQtJCeB64C3AMmC1pGWj6iwBrgZebWZnAJ9O230T8GUzOx04DyiqBR/eePpMXjangS+v2ULvYDLf4bgxdA14N5QrHlG2LM4DtprZNjMbBG4FLhlV5xPA9WbWDmBm+wDCpFJqZr8My7vNrDfCWKeckhJxzTuWsaezn289uC3f4bgxBC0L74ZyxSHKZDEHSL/+szUsS7cUWCrpIUlrJa1MK++QdIekxyV9OWypHEXS5ZLWS1rf1ha/u57PXTiNty2fzb8/+DzPt3XnOxyXZmTE6Or3loUrHlEmC41RZqO2S4ElwIXAauA7khrD8guAvwbOBRYDl73kxcxuMLMVZraipaUle5FPIde8fRmVZQn+6vaNPtg9hfQMJhkxfMzCFY0ok0UrMC9tey4werS2FfipmQ2Z2QvAFoLk0Qo8HnZhJYE7gbMjjHXKmllfyT+860w27Ojg679+Lt/huFBHr0/14YpLlMliHbBE0iJJ5cAq4K5Rde4ELgKQ1EzQ/bQtPLZJUqq58Hpgc4SxTmnvfPlJvPecuXz9vq3c86RPYT4VHAjXS2+uK89zJM7lRmTJImwRXAGsAZ4GbjezTZKulfTOsNoa4ICkzcD9wOfM7ICZDRN0Qf1a0pMEXVrfjirWQvCP7zqTs+Y18tnbN/KH7e35Dqfo7e8aAGB6TUWeI3EuNyK9z8LM7jazpWZ2spldF5ZdY2Z3hc/NzD5rZsvM7GVmdmvasb80s+Vh+WXhFVVFq7IswQ0fPocZ9RV89D8eZeOOjnyHVNQO9ITJotZbFq44+B3cBWRGfSW3fOKVNNaU8YFvr+XXT+/Nd0hFa3932A1V6y0LVxw8WRSYkxqr+OEnz2dRSw0fv2k937zvOb9KKg8OdA9SW1FKZdlLruh2LpY8WRSgWQ2V/PCT5/P25SfxlXuf5X3feoRn93blO6yicqBnwLugXFHxZFGgqsoTfH3VWXxt1Vlsa+th5b/+hqt+/AQ7O3zRpFzYc6ifGXXeBeWKh89VUMAkcclZc7hgSQvfuO85/nPti9y+fgdvPmMWH3rlAl65eDqJkrHujXQnasfBXl65eHq+w3AuZzxZxMC0mnL+/h1n8PELFnPTI3/k1kd3cM9Te2iuLefiM2ZxwSnNnLdoGtN9MDYrBpMj7O7sZ9606nyH4lzOeLKIkTmNVVz9ltP59BuWcv+Wffz8yd3c+fhObv79dgBObqnh9Nn1nD67nqUz65g/rZo5TVW+hvQk7WjvxQxPFq6o+F+JGKoqT/DWl83mrS+bzWByhCd3HmLttgM8vr2dDTs6+NkTR98FXl9ZykmNVTTXVtBQXUZjVRmN1WU0VpVTXZGgqix4VJYnqCxNUFWeoKK0hNISkSgRpSUlJBJK2z5SXlICQkhHJguThCAoU+F1kz218xAAp8+uy3MkzuWOJ4uYKy8t4ZwFTZyzoOlwWVf/EM/t62Znex87O/rYFT4O9gyy61AfHb1DdPQOMjJ62seIpRJKKpkcKQt2jC470TxzvIf3DA5TV1nK0pmeLFzx8GRRhOoqyzh7fhNnz28at87IiNE9mKRvcDh4DA3TP3Tk3/6hEYZH7KhHcsQYHhkJ/7XD/5oZFiYeA8zAOLoMs6P2cfj50WUcLjuxTHYihw8Nj/CaJS2UJfxiQlc8PFm4MZWUiPrKMp+C2zkH+H0WzjnnJsCThXPOuYw8WTjnnMvIk4VzzrmMPFk455zLyJOFc865jDxZOOecy8iThXPOuYx0onfCThWS2oAXT+AlmoH9WQonmzyuyfG4Jsfjmpw4xrXAzFoyVYpNsjhRktab2Yp8xzGaxzU5HtfkeFyTU8xxeTeUc865jDxZOOecy8iTxRE35DuAcXhck+NxTY7HNTlFG5ePWTjnnMvIWxbOOecy8mThnHMuo9gnC0krJW2RtFXSVWPsr5B0W7j/95IWpu27OizfIunNOY7rs5I2S3pC0q8lLUjbNyxpQ/i4K8dxXSapLe39P56276OSngsfH81xXF9Ni+lZSR1p+6I8XzdK2ifpqXH2S9LXw7ifkHR22r4oz1emuD4YxvOEpIclvTxt3x8lPRmer/U5jutCSYfS/r+uSdt3zM9AxHF9Li2mp8LP1LRwX5Tna56k+yU9LWmTpP8xRp3cfMaCJS/j+QASwPPAYqAc2AgsG1XnL4B/D5+vAm4Lny8L61cAi8LXSeQwrouA6vD5f0vFFW535/F8XQZ8c4xjpwHbwn+bwudNuYprVP0rgRujPl/ha78WOBt4apz9bwXuIVjy+5XA76M+XxOM6/zU+wFvScUVbv8RaM7T+boQ+NmJfgayHdeouu8A7svR+ZoNnB0+rwOeHeN3Miefsbi3LM4DtprZNjMbBG4FLhlV5xLge+HzHwFvkKSw/FYzGzCzF4Ct4evlJC4zu9/MesPNtcDcLL33CcV1DG8GfmlmB82sHfglsDJPca0GbsnSex+Tmf0GOHiMKpcAN1lgLdAoaTbRnq+McZnZw+H7Qu4+XxM5X+M5kc9mtuPK5edrt5n9IXzeBTwNzBlVLSefsbgniznAjrTtVl56og/XMbMkcAiYPsFjo4wr3ccIvjmkVEpaL2mtpHdlKabJxPWnYXP3R5LmTfLYKOMi7K5bBNyXVhzV+ZqI8WKP8nxN1ujPlwH3SnpM0uV5iOdVkjZKukfSGWHZlDhfkqoJ/uD+OK04J+dLQRf5K4Dfj9qVk89Y6fEeWCA0Rtnoa4XHqzORY4/XhF9b0oeAFcDr0ornm9kuSYuB+yQ9aWbP5yiu/wJuMbMBSZ8iaJW9foLHRhlXyirgR2Y2nFYW1fmaiHx8viZM0kUEyeI1acWvDs/XDOCXkp4Jv3nnwh8I5irqlvRW4E5gCVPkfBF0QT1kZumtkMjPl6RaggT1aTPrHL17jEOy/hmLe8uiFZiXtj0X2DVeHUmlQANBc3Qix0YZF5LeCHweeKeZDaTKzWxX+O824AGCbxs5icvMDqTF8m3gnIkeG2VcaVYxqosgwvM1EePFHuX5mhBJy4HvAJeY2YFUedr52gf8hOx1v2ZkZp1m1h0+vxsok9TMFDhfoWN9viI5X5LKCBLFD8zsjjGq5OYzFsWgzFR5ELScthF0S6QGxc4YVecvOXqA+/bw+RkcPcC9jewNcE8krlcQDOgtGVXeBFSEz5uB58jSQN8E45qd9vzdwFo7Mpj2QhhfU/h8Wq7iCuudSjDYqFycr7T3WMj4A7Zv4+jBx0ejPl8TjGs+wTjc+aPKa4C6tOcPAytzGNes1P8fwR/d7eG5m9BnIKq4wv2pL5I1uTpf4c9+E/Cvx6iTk89Y1k70VH0QXCnwLMEf3s+HZdcSfFsHqAR+GP7iPAosTjv28+FxW4C35DiuXwF7gQ3h466w/HzgyfCX5UngYzmO6/8Am8L3vx84Le3YPw/P41bgz3IZV7j9BeCLo46L+nzdAuwGhgi+yX0M+BTwqXC/gOvDuJ8EVuTofGWK6ztAe9rna31Yvjg8VxvD/+fP5ziuK9I+X2tJS2ZjfQZyFVdY5zKCi17Sj4v6fL2GoOvoibT/q7fm4zPm030455zLKO5jFs4557LAk4VzzrmMPFk455zLyJOFc865jDxZOOfcFJRpcsPjeL0vhZMgPiXp/ZM93pOFc6NImp42w+geSTvTth+O6D1fIek7x9jfIukXUby3m7K+S5bmC5P0NoKJEs8C/gT4nKT6ybyGJwvnRrHgLvWzzOws4N+Br6a2zez8iN72b4FvHCOmNmC3pFdH9P5uirExJjeUdLKkX4TzUP1W0mkTfLllwINmljSzHoL7QiaViDxZODcJkrrDfy+U9KCk2xWsn/HFcI2IR8O1DU4O67VI+rGkdeHjJX/sJdUBy81sY7j9urSWzOPhfgjmSfpgjn5UNzXdAFxpZucAfw382wSP2wi8RVJ1OH3KRRw9FUhGcZ9I0LkovRw4neDb3zbgO2Z2XrhAzZXAp4GvEbRMfidpPrAmPCbdCiC9X/qvgb80s4fCCeT6w/L1wD9G9tO4KS38LJwP/DBYRQEIpiNC0nsIZjQYbaeZvdnM7pV0LsF0JG3AI0ByMu/vycK547fOzHYDSHoeuDcsf5LgmxvAG4Flab/c9ZLqLFibIGU2wS9wykPAv0j6AXCHmbWG5fuAk7L/Y7gCUQJ0hN2jR7FggsGxJhlMr3MdcB2ApJsJ5kmb1Js7547PQNrzkbTtEY58ESsBXpU25jFnVKIA6COYowwAM/si8HGgClib1i9dGdZ1RciCqclfkPQ+OLyc6sszHEZYNyFpevh8ObCcI19uJsSThXPRupdgcjwAJL3kWyHB6menpNU52cyeNLMvEXQ9pZLFUo7urnIxJukWgu6iUyW1SvoYwZjVxySlJi6c6GqBZcBvJW0mGPf4kAWLvU2Yd0M5F63/Dlwv6QmC37ffEMwYepiZPSOpIa176tPhokTDwGaOrGJ3EfDz3IXu8snMVo+za9KX05pZP8EVUcfNZ511bgqQ9Bmgy8yOda/FbwgWKmofr45zUfFuKOemhv/L0WMgR5HUAvyLJwqXL96ycM45l5G3LJxzzmXkycI551xGniycc85l5MnCOedcRp4snHPOZfT/AWm1HuHK8LRdAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import os\n",
"\n",
"# import bag package\n",
"import bag\n",
"from bag.io import read_yaml\n",
"\n",
"# import BAG demo Python modules\n",
"import xbase_demo.core as demo_core\n",
"import xbase_demo.demo_layout.core as layout_core\n",
"\n",
"# load circuit specifications from file\n",
"spec_fname = os.path.join(os.environ['BAG_WORK_DIR'], 'specs_demo/demo.yaml')\n",
"top_specs = read_yaml(spec_fname)\n",
"\n",
"# obtain BagProject instance\n",
"local_dict = locals()\n",
"if 'bprj' in local_dict:\n",
" print('using existing BagProject')\n",
" bprj = local_dict['bprj']\n",
"else:\n",
" print('creating BagProject')\n",
" bprj = bag.BagProject()\n",
"\n",
"demo_core.run_flow(bprj, top_specs, 'amp_cs', layout_core.AmpCS, run_lvs=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" ## Conclusion\n",
" Congratulations! You successfully walk through a BAG design flow. In the following modules we will learn how to write simple layout and schematic generators in BAG."
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
rsignell-usgs/notebook | UGRID/.ipynb_checkpoints/plot_mesh-Copy1-checkpoint.ipynb | 1 | 175693 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# plot a ugrid mesh"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.tri as tri\n",
"import pyugrid"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#url = 'http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/mwra/fvcom'\n",
"url = 'http://geoport.whoi.edu/thredds/dodsC/usgs/vault0/models/tides/vdatum_fl_sab/adcirc54.ncml'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ug = pyugrid.UGrid.from_ncfile(url)\n",
"lon = ug.nodes[:,0]\n",
"lat = ug.nodes[:,1]\n",
"nv = ug.faces\n",
"triang = tri.Triangulation(lon,lat,triangles=nv)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import cartopy.crs as ccrs\n",
"import matplotlib.pyplot as plt\n",
"from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
"\n",
"\n",
"def make_map(projection=ccrs.PlateCarree()):\n",
" fig, ax = plt.subplots(figsize=(8, 6),\n",
" subplot_kw=dict(projection=projection))\n",
" ax.coastlines(resolution='10m')\n",
" gl = ax.gridlines(draw_labels=True)\n",
" gl.xlabels_top = gl.ylabels_right = False\n",
" gl.xformatter = LONGITUDE_FORMATTER\n",
" gl.yformatter = LATITUDE_FORMATTER\n",
" return fig, ax"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fe7d82ae890>,\n",
" <matplotlib.lines.Line2D at 0x7fe7d81a1810>]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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wyLhxxnq3bpSaBgxgcPSlS7SNNWxIO1bx4lQn2u08j2tsXfLkIlmyiOTPL1Ku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9YHDxJUT5wgEHTtqqpTv/ceGX337py4x4/z2K+/5j1fucLtx46xbxYL+dy+fWqyN2xIwL17V4hK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\nB1W4qVChQoWKYof/D5Z/hrOh9WvLAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe7d82ae850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))\n",
"#fig, ax = make_map(projection=geodetic)\n",
"fig, ax = make_map()\n",
"\n",
"kw = dict(marker='.', linestyle='-', alpha=0.85, color='darkgray')\n",
"kw = dict(linestyle='-',color='red')\n",
"ax.triplot(triang, **kw) # or lon, lat, triangules;\n",
"#ax.set_extent([-84, -78, 25, 32])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Yr1TiUXIyGnXsiKbNm6Np06bw9PTM08/h8uXLWLp0KaZPn/5Oz42JiUH9+vWR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\nWkFtMlj282HQRj4FkHs0ghiRN6/UCgqSnu3by2OPPCLTAXkIkFKA+IE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IAAAU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\ngQ+yOfiIuQ984AMf+MAH2Rz+D64QwOvkOgjmAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe7d8117ad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import mplleaflet\n",
"#geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))\n",
"#fig, ax = make_map(projection=geodetic)\n",
"fig, ax = make_map()\n",
"\n",
"kw = dict(marker='.', linestyle='-', alpha=0.85, color='darkgray')\n",
"kw = dict(linestyle='-',color='red')\n",
"ax.triplot(triang, **kw) # or lon, lat, triangules;\n",
"ax.set_extent([-87.5, -82.5, 29.4, 31])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from cartopy.io.img_tiles import MapQuestOpenAerial, MapQuestOSM, OSM\n",
"geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))\n",
"\n",
"fig = plt.figure(figsize=(12,8))\n",
"tiler = MapQuestOpenAerial()\n",
"ax = plt.axes(projection=tiler.crs)\n",
"\n",
"bbox=[-71, -69.3, 42, 42.8]\n",
"#ax = plt.axes(projection=ccrs.PlateCarree())\n",
"ax.set_extent(bbox)\n",
"ax.add_image(tiler, 8)\n",
"\n",
"#ax.coastlines()\n",
"kw = dict(marker='.', linestyle='-', alpha=0.85, color='darkgray', transform=geodetic)\n",
"ax.triplot(triang, **kw) # or lon, lat, triangules\n",
"#ax.set_extent()\n",
"gl = ax.gridlines(draw_labels=True)\n",
"gl.xlabels_top = False\n",
"gl.ylabels_right = False\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"gist_id": "",
"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 |
ES-DOC/esdoc-jupyterhub | notebooks/ncar/cmip6/models/sandbox-1/land.ipynb | 1 | 173498 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Land \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: NCAR \n",
"**Source ID**: SANDBOX-1 \n",
"**Topic**: Land \n",
"**Sub-Topics**: Soil, Snow, Vegetation, Energy Balance, Carbon Cycle, Nitrogen Cycle, River Routing, Lakes. \n",
"**Properties**: 154 (96 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/land?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**: 2018-02-15 16:54:22"
],
"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', 'ncar', 'sandbox-1', 'land')"
],
"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 --> Conservation Properties](#2.-Key-Properties--->-Conservation-Properties) \n",
"[3. Key Properties --> Timestepping Framework](#3.-Key-Properties--->-Timestepping-Framework) \n",
"[4. Key Properties --> Software Properties](#4.-Key-Properties--->-Software-Properties) \n",
"[5. Grid](#5.-Grid) \n",
"[6. Grid --> Horizontal](#6.-Grid--->-Horizontal) \n",
"[7. Grid --> Vertical](#7.-Grid--->-Vertical) \n",
"[8. Soil](#8.-Soil) \n",
"[9. Soil --> Soil Map](#9.-Soil--->-Soil-Map) \n",
"[10. Soil --> Snow Free Albedo](#10.-Soil--->-Snow-Free-Albedo) \n",
"[11. Soil --> Hydrology](#11.-Soil--->-Hydrology) \n",
"[12. Soil --> Hydrology --> Freezing](#12.-Soil--->-Hydrology--->-Freezing) \n",
"[13. Soil --> Hydrology --> Drainage](#13.-Soil--->-Hydrology--->-Drainage) \n",
"[14. Soil --> Heat Treatment](#14.-Soil--->-Heat-Treatment) \n",
"[15. Snow](#15.-Snow) \n",
"[16. Snow --> Snow Albedo](#16.-Snow--->-Snow-Albedo) \n",
"[17. Vegetation](#17.-Vegetation) \n",
"[18. Energy Balance](#18.-Energy-Balance) \n",
"[19. Carbon Cycle](#19.-Carbon-Cycle) \n",
"[20. Carbon Cycle --> Vegetation](#20.-Carbon-Cycle--->-Vegetation) \n",
"[21. Carbon Cycle --> Vegetation --> Photosynthesis](#21.-Carbon-Cycle--->-Vegetation--->-Photosynthesis) \n",
"[22. Carbon Cycle --> Vegetation --> Autotrophic Respiration](#22.-Carbon-Cycle--->-Vegetation--->-Autotrophic-Respiration) \n",
"[23. Carbon Cycle --> Vegetation --> Allocation](#23.-Carbon-Cycle--->-Vegetation--->-Allocation) \n",
"[24. Carbon Cycle --> Vegetation --> Phenology](#24.-Carbon-Cycle--->-Vegetation--->-Phenology) \n",
"[25. Carbon Cycle --> Vegetation --> Mortality](#25.-Carbon-Cycle--->-Vegetation--->-Mortality) \n",
"[26. Carbon Cycle --> Litter](#26.-Carbon-Cycle--->-Litter) \n",
"[27. Carbon Cycle --> Soil](#27.-Carbon-Cycle--->-Soil) \n",
"[28. Carbon Cycle --> Permafrost Carbon](#28.-Carbon-Cycle--->-Permafrost-Carbon) \n",
"[29. Nitrogen Cycle](#29.-Nitrogen-Cycle) \n",
"[30. River Routing](#30.-River-Routing) \n",
"[31. River Routing --> Oceanic Discharge](#31.-River-Routing--->-Oceanic-Discharge) \n",
"[32. Lakes](#32.-Lakes) \n",
"[33. Lakes --> Method](#33.-Lakes--->-Method) \n",
"[34. Lakes --> Wetlands](#34.-Lakes--->-Wetlands) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties \n",
"*Land surface key properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of land surface model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.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 **Type:** STRING **Cardinality:** 1.1\n",
"### *Name of land surface model code (e.g. MOSES2.2)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.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. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General description of the processes modelled (e.g. dymanic vegation, prognostic albedo, etc.)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.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": [
"### 1.4. Land Atmosphere Flux Exchanges\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Fluxes exchanged with the atmopshere.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.land_atmosphere_flux_exchanges') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"water\" \n",
"# \"energy\" \n",
"# \"carbon\" \n",
"# \"nitrogen\" \n",
"# \"phospherous\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.5. Atmospheric Coupling Treatment\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the treatment of land surface coupling with the Atmosphere model component, which may be different for different quantities (e.g. dust: semi-implicit, water vapour: explicit)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.atmospheric_coupling_treatment') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.6. Land Cover\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Types of land cover defined in the land surface model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.land_cover') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"bare soil\" \n",
"# \"urban\" \n",
"# \"lake\" \n",
"# \"land ice\" \n",
"# \"lake ice\" \n",
"# \"vegetated\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.7. Land Cover Change\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe how land cover change is managed (e.g. the use of net or gross transitions)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.land_cover_change') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.8. Tiling\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general tiling procedure used in the land surface (if any). Include treatment of physiography, land/sea, (dynamic) vegetation coverage and orography/roughness*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.tiling') \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. Key Properties --> Conservation Properties \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Energy\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe if/how energy is conserved globally and to what level (e.g. within X [units]/year)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.conservation_properties.energy') \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. Water\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe if/how water is conserved globally and to what level (e.g. within X [units]/year)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.conservation_properties.water') \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. Carbon\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe if/how carbon is conserved globally and to what level (e.g. within X [units]/year)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.conservation_properties.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": [
"# 3. Key Properties --> Timestepping Framework \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Timestep Dependent On Atmosphere\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is a time step dependent on the frequency of atmosphere coupling?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestep_dependent_on_atmosphere') \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": [
"### 3.2. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Overall timestep of land surface model (i.e. time between calls)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.timestepping_framework.time_step') \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. Timestepping Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General description of time stepping method and associated time step(s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestepping_method') \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. Key Properties --> Software Properties \n",
"*Software properties of land surface code*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Repository\n",
"**Is Required:** FALSE **Type:** STRING **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.land.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": [
"### 4.2. Code Version\n",
"**Is Required:** FALSE **Type:** STRING **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.land.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": [
"### 4.3. Code Languages\n",
"**Is Required:** FALSE **Type:** STRING **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.land.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": [
"# 5. Grid \n",
"*Land surface grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of the grid in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.grid.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": [
"# 6. Grid --> Horizontal \n",
"*The horizontal grid in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general structure of the horizontal grid (not including any tiling)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.grid.horizontal.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. Matches Atmosphere Grid\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does the horizontal grid match the atmosphere?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.grid.horizontal.matches_atmosphere_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": [
"# 7. Grid --> Vertical \n",
"*The vertical grid in the soil*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general structure of the vertical grid in the soil (not including any tiling)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.grid.vertical.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": [
"### 7.2. Total Depth\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The total depth of the soil (in metres)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.grid.vertical.total_depth') \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. Soil \n",
"*Land surface soil*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of soil in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.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. Heat Water Coupling\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the coupling between heat and water in the soil*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_water_coupling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.3. Number Of Soil layers\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of soil layers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.number_of_soil layers') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.4. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the soil scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.prognostic_variables') \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. Soil --> Soil Map \n",
"*Key properties of the land surface soil map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General description of soil map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.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": [
"### 9.2. Structure\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil structure map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.structure') \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. Texture\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil texture map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.texture') \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. Organic Matter\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil organic matter map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.organic_matter') \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. Albedo\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil albedo map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.albedo') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.6. Water Table\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil water table map, if any*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.water_table') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.7. Continuously Varying Soil Depth\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does the soil properties vary continuously with depth?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.continuously_varying_soil_depth') \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": [
"### 9.8. Soil Depth\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil depth map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.soil_depth') \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. Soil --> Snow Free Albedo \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Prognostic\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is snow free albedo prognostic?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.snow_free_albedo.prognostic') \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": [
"### 10.2. Functions\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If prognostic, describe the dependancies on snow free albedo calculations*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.snow_free_albedo.functions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"vegetation type\" \n",
"# \"soil humidity\" \n",
"# \"vegetation state\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.3. Direct Diffuse\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If prognostic, describe the distinction between direct and diffuse albedo*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.snow_free_albedo.direct_diffuse') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"distinction between direct and diffuse albedo\" \n",
"# \"no distinction between direct and diffuse albedo\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.4. Number Of Wavelength Bands\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If prognostic, enter the number of wavelength bands used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.snow_free_albedo.number_of_wavelength_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": [
"# 11. Soil --> Hydrology \n",
"*Key properties of the land surface soil hydrology*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General description of the soil hydrological model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.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": [
"### 11.2. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of river soil hydrology in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.time_step') \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. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil hydrology tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.tiling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.4. Vertical Discretisation\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the typical vertical discretisation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.vertical_discretisation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.5. Number Of Ground Water Layers\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of soil layers that may contain water*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.number_of_ground_water_layers') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.6. Lateral Connectivity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Describe the lateral connectivity between tiles*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.lateral_connectivity') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"perfect connectivity\" \n",
"# \"Darcian flow\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.7. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *The hydrological dynamics scheme in the land surface model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Bucket\" \n",
"# \"Force-restore\" \n",
"# \"Choisnel\" \n",
"# \"Explicit diffusion\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Soil --> Hydrology --> Freezing \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. Number Of Ground Ice Layers\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *How many soil layers may contain ground ice*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.freezing.number_of_ground_ice_layers') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.2. Ice Storage Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the method of ice storage*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.freezing.ice_storage_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.3. Permafrost\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the treatment of permafrost, if any, within the land surface scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.freezing.permafrost') \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. Soil --> Hydrology --> Drainage \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General describe how drainage is included in the land surface scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.drainage.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": [
"### 13.2. Types\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Different types of runoff represented by the land surface model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.drainage.types') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Gravity drainage\" \n",
"# \"Horton mechanism\" \n",
"# \"topmodel-based\" \n",
"# \"Dunne mechanism\" \n",
"# \"Lateral subsurface flow\" \n",
"# \"Baseflow from groundwater\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 14. Soil --> Heat Treatment \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General description of how heat treatment properties are defined*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_treatment.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": [
"### 14.2. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of soil heat scheme in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_treatment.time_step') \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. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil heat treatment tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_treatment.tiling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.4. Vertical Discretisation\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the typical vertical discretisation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_treatment.vertical_discretisation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.5. Heat Storage\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Specify the method of heat storage*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_treatment.heat_storage') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Force-restore\" \n",
"# \"Explicit diffusion\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.6. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Describe processes included in the treatment of soil heat*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_treatment.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"soil moisture freeze-thaw\" \n",
"# \"coupling with snow temperature\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 15. Snow \n",
"*Land surface snow*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 15.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of snow in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.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. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the snow tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.tiling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.3. Number Of Snow Layers\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of snow levels used in the land surface scheme/model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.number_of_snow_layers') \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. Density\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Description of the treatment of snow density*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.density') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"constant\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.5. Water Equivalent\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Description of the treatment of the snow water equivalent*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.water_equivalent') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.6. Heat Content\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Description of the treatment of the heat content of snow*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.heat_content') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.7. Temperature\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Description of the treatment of snow temperature*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.temperature') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.8. Liquid Water Content\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Description of the treatment of snow liquid water*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.liquid_water_content') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.9. Snow Cover Fractions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Specify cover fractions used in the surface snow scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.snow_cover_fractions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"ground snow fraction\" \n",
"# \"vegetation snow fraction\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.10. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Snow related processes in the land surface scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"snow interception\" \n",
"# \"snow melting\" \n",
"# \"snow freezing\" \n",
"# \"blowing snow\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.11. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the snow scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.prognostic_variables') \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. Snow --> Snow Albedo \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 16.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe the treatment of snow-covered land albedo*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.snow_albedo.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"prescribed\" \n",
"# \"constant\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.2. Functions\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If prognostic, *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.snow_albedo.functions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"vegetation type\" \n",
"# \"snow age\" \n",
"# \"snow density\" \n",
"# \"snow grain type\" \n",
"# \"aerosol deposition\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 17. Vegetation \n",
"*Land surface vegetation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 17.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of vegetation in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.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": [
"### 17.2. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of vegetation scheme in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.time_step') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.3. Dynamic Vegetation\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there dynamic evolution of vegetation?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.dynamic_vegetation') \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": [
"### 17.4. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the vegetation tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.tiling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.5. Vegetation Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Vegetation classification used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.vegetation_representation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"vegetation types\" \n",
"# \"biome types\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.6. Vegetation Types\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *List of vegetation types in the classification, if any*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.vegetation_types') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"broadleaf tree\" \n",
"# \"needleleaf tree\" \n",
"# \"C3 grass\" \n",
"# \"C4 grass\" \n",
"# \"vegetated\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.7. Biome Types\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *List of biome types in the classification, if any*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.biome_types') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"evergreen needleleaf forest\" \n",
"# \"evergreen broadleaf forest\" \n",
"# \"deciduous needleleaf forest\" \n",
"# \"deciduous broadleaf forest\" \n",
"# \"mixed forest\" \n",
"# \"woodland\" \n",
"# \"wooded grassland\" \n",
"# \"closed shrubland\" \n",
"# \"opne shrubland\" \n",
"# \"grassland\" \n",
"# \"cropland\" \n",
"# \"wetlands\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.8. Vegetation Time Variation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *How the vegetation fractions in each tile are varying with time*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.vegetation_time_variation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"fixed (not varying)\" \n",
"# \"prescribed (varying from files)\" \n",
"# \"dynamical (varying from simulation)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.9. Vegetation Map\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If vegetation fractions are not dynamically updated , describe the vegetation map used (common name and reference, if possible)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.vegetation_map') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.10. Interception\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is vegetation interception of rainwater represented?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.interception') \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": [
"### 17.11. Phenology\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Treatment of vegetation phenology*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.phenology') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic (vegetation map)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.12. Phenology Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *General description of the treatment of vegetation phenology*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.phenology_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": [
"### 17.13. Leaf Area Index\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Treatment of vegetation leaf area index*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.leaf_area_index') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prescribed\" \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.14. Leaf Area Index Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *General description of the treatment of leaf area index*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.leaf_area_index_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": [
"### 17.15. Biomass\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Treatment of vegetation biomass *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.biomass') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.16. Biomass Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *General description of the treatment of vegetation biomass*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.biomass_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": [
"### 17.17. Biogeography\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Treatment of vegetation biogeography*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.biogeography') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.18. Biogeography Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *General description of the treatment of vegetation biogeography*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.biogeography_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": [
"### 17.19. Stomatal Resistance\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Specify what the vegetation stomatal resistance depends on*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.stomatal_resistance') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"light\" \n",
"# \"temperature\" \n",
"# \"water availability\" \n",
"# \"CO2\" \n",
"# \"O3\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.20. Stomatal Resistance Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *General description of the treatment of vegetation stomatal resistance*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.stomatal_resistance_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": [
"### 17.21. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the vegetation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 18. Energy Balance \n",
"*Land surface energy balance*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 18.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of energy balance in land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.energy_balance.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": [
"### 18.2. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the energy balance tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.energy_balance.tiling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.3. Number Of Surface Temperatures\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The maximum number of distinct surface temperatures in a grid cell (for example, each subgrid tile may have its own temperature)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.energy_balance.number_of_surface_temperatures') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.4. Evaporation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Specify the formulation method for land surface evaporation, from soil and vegetation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.energy_balance.evaporation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"alpha\" \n",
"# \"beta\" \n",
"# \"combined\" \n",
"# \"Monteith potential evaporation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.5. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Describe which processes are included in the energy balance scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.energy_balance.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"transpiration\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 19. Carbon Cycle \n",
"*Land surface carbon cycle*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 19.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of carbon cycle in land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.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": [
"### 19.2. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the carbon cycle tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.tiling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 19.3. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of carbon cycle in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.time_step') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 19.4. Anthropogenic Carbon\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Describe the treament of the anthropogenic carbon pool*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.anthropogenic_carbon') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"grand slam protocol\" \n",
"# \"residence time\" \n",
"# \"decay time\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 19.5. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the carbon scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 20. Carbon Cycle --> Vegetation \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 20.1. Number Of Carbon Pools\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Enter the number of carbon pools used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.number_of_carbon_pools') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 20.2. Carbon Pools\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the carbon pools used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.carbon_pools') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 20.3. Forest Stand Dynamics\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the treatment of forest stand dyanmics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.forest_stand_dynamics') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 21. Carbon Cycle --> Vegetation --> Photosynthesis \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 21.1. Method\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the general method used for photosynthesis (e.g. type of photosynthesis, distinction between C3 and C4 grasses, Nitrogen depencence, etc.)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.photosynthesis.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 22. Carbon Cycle --> Vegetation --> Autotrophic Respiration \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 22.1. Maintainance Respiration\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the general method used for maintainence respiration*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.maintainance_respiration') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.2. Growth Respiration\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the general method used for growth respiration*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.growth_respiration') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 23. Carbon Cycle --> Vegetation --> Allocation \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 23.1. Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general principle behind the allocation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.2. Allocation Bins\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Specify distinct carbon bins used in allocation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_bins') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"leaves + stems + roots\" \n",
"# \"leaves + stems + roots (leafy + woody)\" \n",
"# \"leaves + fine roots + coarse roots + stems\" \n",
"# \"whole plant (no distinction)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.3. Allocation Fractions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how the fractions of allocation are calculated*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_fractions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"fixed\" \n",
"# \"function of vegetation type\" \n",
"# \"function of plant allometry\" \n",
"# \"explicitly calculated\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 24. Carbon Cycle --> Vegetation --> Phenology \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 24.1. Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general principle behind the phenology scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.phenology.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 25. Carbon Cycle --> Vegetation --> Mortality \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 25.1. Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general principle behind the mortality scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.mortality.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 26. Carbon Cycle --> Litter \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 26.1. Number Of Carbon Pools\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Enter the number of carbon pools used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.litter.number_of_carbon_pools') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.2. Carbon Pools\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the carbon pools used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.litter.carbon_pools') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.3. Decomposition\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the decomposition methods used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.litter.decomposition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.4. Method\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the general method used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.litter.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 27. Carbon Cycle --> Soil \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 27.1. Number Of Carbon Pools\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Enter the number of carbon pools used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.soil.number_of_carbon_pools') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.2. Carbon Pools\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the carbon pools used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.soil.carbon_pools') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.3. Decomposition\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the decomposition methods used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.soil.decomposition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.4. Method\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the general method used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.soil.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 28. Carbon Cycle --> Permafrost Carbon \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 28.1. Is Permafrost Included\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is permafrost included?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.is_permafrost_included') \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": [
"### 28.2. Emitted Greenhouse Gases\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the GHGs emitted*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.emitted_greenhouse_gases') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 28.3. Decomposition\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the decomposition methods used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.decomposition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 28.4. Impact On Soil Properties\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the impact of permafrost on soil properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.impact_on_soil_properties') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 29. Nitrogen Cycle \n",
"*Land surface nitrogen cycle*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 29.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of the nitrogen cycle in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.nitrogen_cycle.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": [
"### 29.2. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the notrogen cycle tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.nitrogen_cycle.tiling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 29.3. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of nitrogen cycle in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.nitrogen_cycle.time_step') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 29.4. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the nitrogen scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.nitrogen_cycle.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 30. River Routing \n",
"*Land surface river routing*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 30.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of river routing in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.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": [
"### 30.2. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the river routing, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.tiling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.3. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of river routing scheme in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.time_step') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.4. Grid Inherited From Land Surface\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the grid inherited from land surface?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.grid_inherited_from_land_surface') \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": [
"### 30.5. Grid Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *General description of grid, if not inherited from land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.grid_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": [
"### 30.6. Number Of Reservoirs\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Enter the number of reservoirs*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.number_of_reservoirs') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.7. Water Re Evaporation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.water_re_evaporation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"flood plains\" \n",
"# \"irrigation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.8. Coupled To Atmosphere\n",
"**Is Required:** FALSE **Type:** BOOLEAN **Cardinality:** 0.1\n",
"### *Is river routing coupled to the atmosphere model component?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.coupled_to_atmosphere') \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": [
"### 30.9. Coupled To Land\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the coupling between land and rivers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.coupled_to_land') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.10. Quantities Exchanged With Atmosphere\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If couple to atmosphere, which quantities are exchanged between river routing and the atmosphere model components?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.quantities_exchanged_with_atmosphere') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"heat\" \n",
"# \"water\" \n",
"# \"tracers\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.11. Basin Flow Direction Map\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *What type of basin flow direction map is being used?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.basin_flow_direction_map') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"present day\" \n",
"# \"adapted for other periods\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.12. Flooding\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the representation of flooding, if any*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.flooding') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.13. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the river routing*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 31. River Routing --> Oceanic Discharge \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 31.1. Discharge Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Specify how rivers are discharged to the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.oceanic_discharge.discharge_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"direct (large rivers)\" \n",
"# \"diffuse\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.2. Quantities Transported\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Quantities that are exchanged from river-routing to the ocean model component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.oceanic_discharge.quantities_transported') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"heat\" \n",
"# \"water\" \n",
"# \"tracers\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 32. Lakes \n",
"*Land surface lakes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 32.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of lakes in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.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": [
"### 32.2. Coupling With Rivers\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Are lakes coupled to the river routing model component?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.coupling_with_rivers') \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": [
"### 32.3. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of lake scheme in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.time_step') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.4. Quantities Exchanged With Rivers\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If coupling with rivers, which quantities are exchanged between the lakes and rivers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.quantities_exchanged_with_rivers') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"heat\" \n",
"# \"water\" \n",
"# \"tracers\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.5. Vertical Grid\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the vertical grid of lakes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.vertical_grid') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.6. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the lake scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 33. Lakes --> Method \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 33.1. Ice Treatment\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is lake ice included?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.method.ice_treatment') \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": [
"### 33.2. Albedo\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe the treatment of lake albedo*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.method.albedo') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 33.3. Dynamics\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Which dynamics of lakes are treated? horizontal, vertical, etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.method.dynamics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"No lake dynamics\" \n",
"# \"vertical\" \n",
"# \"horizontal\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 33.4. Dynamic Lake Extent\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is a dynamic lake extent scheme included?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.method.dynamic_lake_extent') \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": [
"### 33.5. Endorheic Basins\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Basins not flowing to ocean included?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.method.endorheic_basins') \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": [
"# 34. Lakes --> Wetlands \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 34.1. Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the treatment of wetlands, if any*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.wetlands.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": [
"### \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 |
SystemsBiologyUniandes/Cytometry | Visualizing the data/Visualization Examples.ipynb | 1 | 2386684 | null | mit |
google/intelligent_annotation_dialogs | exp2_IAD_prob.ipynb | 1 | 38409 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright 2018 Google LLC\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",
" https://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "iTueGwFWcDK2"
},
"source": [
"# Experiment 1: fixed detector in many scenarios\n",
"\n",
"This notebook contains the code for computing the performance of the fixed strategies in various scenarios. The full experiment is described in Sec. 5.2 of CVPR submission \"Learning Intelligent Dialogs for Bounding Box Annotation\". Please note that this notebook does not reproduce the experiment since the starting detector is too strong, there is no re-training, and there are only two iterations being done."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "1VacJd0VNcb3"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"import math\n",
"import gym\n",
"\n",
"import pandas as pd\n",
"from gym import spaces\n",
"\n",
"from sklearn import neural_network, model_selection\n",
"from sklearn.neural_network import MLPClassifier\n",
"\n",
"from third_party import np_box_ops\n",
"import annotator, detector, dialog, environment"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "OwMFtKv4MmMB"
},
"source": [
"To specify the experiments, define: \n",
"\n",
"* type of drawing\n",
"* desired quality of bounding boxes"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "VxqklQG_PQml"
},
"outputs": [],
"source": [
"# desired quality: high (min_iou=0.7) and low (min_iou=0.5)\n",
"min_iou = 0.7 # @param [\"0.5\", \"0.7\"]\n",
"# drawing speed: high (time_draw=7) and low (time_draw=25)\n",
"time_draw = 7 # @param [\"7\", \"25\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "edB2VJm7nsI_"
},
"source": [
"Other parameters of the experiment"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "iuyNuLctrDi7"
},
"outputs": [],
"source": [
"random_seed = 80590 # global variable that fixes the random seed everywhere for replroducibility of results\n",
"\n",
"# what kind of features will be used to represent the state\n",
"# numerical values 1-20 correspond to one hot encoding of class\n",
"predictive_fields = ['prediction_score', 'relative_size', 'avg_score', 'dif_avg_score', 'dif_max_score', 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
"\n",
"time_verify = 1.8 # @param"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Oiv682TsBLDf"
},
"source": [
"# Load all data"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "WXBNH4EY3zYF"
},
"outputs": [],
"source": [
"# Download GT:\n",
"# wget wget https://storage.googleapis.com/iad_pascal_annotations_and_detections/pascal_gt_for_iad.h5\n",
"# Download detections with features\n",
"# wget https://storage.googleapis.com/iad_pascal_annotations_and_detections/pascal_proposals_plus_features_for_iad.h5\n",
"\n",
"download_dir = ''\n",
"ground_truth = pd.read_hdf(download_dir + 'pascal_gt_for_iad.h5', 'ground_truth')\n",
"box_proposal_features = pd.read_hdf(download_dir + 'pascal_proposals_plus_features_for_iad.h5', 'box_proposal_features')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "fwlNbTtUBqht"
},
"source": [
"# Initialise the experiment"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "28W__WtSBvMt"
},
"outputs": [],
"source": [
"annotator_real = annotator.AnnotatorSimple(ground_truth, random_seed, time_verify, time_draw, min_iou)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "ptK6iHEGc8UJ"
},
"outputs": [],
"source": [
"# better call it image_class_pairs later\n",
"image_class = ground_truth[['image_id', 'class_id']]\n",
"image_class = image_class.drop_duplicates()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "cylItnHrT-4n"
},
"source": [
"Select the trainig and testing data according to the selected fold. We split all images in 10 approximately equal parts and each fold includes these images together with all classes present in them."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "7jN8cxbd_o_k"
},
"outputs": [],
"source": [
"unique_image = image_class['image_id'].drop_duplicates()\n",
"\n",
"# divide the images into exponentially growing groups\n",
"im1 = unique_image.iloc[157]\n",
"im2 = unique_image.iloc[157+157]\n",
"im3 = unique_image.iloc[157+157+314]\n",
"im4 = unique_image.iloc[157+157+314+625]\n",
"im5 = unique_image.iloc[157+157+314+625+1253]\n",
"\n",
"# image_class pairs groups are determined by the images in them\n",
"image_class_array = image_class.values[:,0]\n",
"in1 = np.searchsorted(image_class_array, im1, side='right')\n",
"in2 = np.searchsorted(image_class_array, im2, side='right')\n",
"in3 = np.searchsorted(image_class_array, im3, side='right')\n",
"in4 = np.searchsorted(image_class_array, im4, side='right')\n",
"in5 = np.searchsorted(image_class_array, im5, side='right')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "v1PJckyYGfP3"
},
"source": [
"# Batch 1: Annotate 3.125% of data with strategy X"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "IABYNoFcEfRw"
},
"outputs": [],
"source": [
"the_detector = detector.Detector(box_proposal_features, predictive_fields)\n",
"image_class_current = image_class.iloc[0:in1]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"height": 300,
"output_extras": [
{
"item_id": 1
},
{
"item_id": 7
}
]
},
"colab_type": "code",
"executionInfo": {
"elapsed": 1349,
"status": "ok",
"timestamp": 1516022147699,
"user": {},
"user_tz": -60
},
"id": "LmwTDdPtGfP6",
"outputId": "e16dac03-0cdb-4c9e-b0fe-6065db7bb690"
},
"outputs": [
{
"data": {
"application/javascript": [
"window[\"32907156-f9f6-11e7-8447-5065f3390f23\"] = colab.output.setOutputHeight(300, false, {\"interactive\": true});\n",
"//# sourceURL=js_cdfe1c1d05"
],
"text/plain": [
"<IPython.core.display.Javascript at 0xf4e4810>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running 241 episodes with strategy X\n",
"Episode 0: D\n",
"Episode 1: D\n",
"Episode 2: D\n",
"Episode 3: D\n",
"Episode 4: D\n",
"Episode 5: D\n",
"Episode 6: D\n",
"Episode 7: D\n",
"Episode 8: D\n",
"Episode 9: D\n",
"Episode 10: D\n",
"Episode 11: D\n",
"Episode 12: D\n",
"Episode 13: D\n",
"Episode 14: D\n",
"Episode 15: D\n",
"Episode 16: D\n",
"Episode 17: D\n",
"Episode 18: D\n",
"Episode 19: D\n",
"Episode 20: D\n",
"Episode 21: D\n",
"Episode 22: D\n",
"Episode 23: D\n",
"Episode 24: D\n",
"Episode 25: D\n",
"Episode 26: D\n",
"Episode 27: D\n",
"Episode 28: D\n",
"Episode 29: D\n",
"Episode 30: D\n",
"Episode 31: D\n",
"Episode 32: D\n",
"Episode 33: D\n",
"Episode 34: D\n",
"Episode 35: D\n",
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"Episode 228: D\n",
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"Episode 230: D\n",
"Episode 231: D\n",
"Episode 232: D\n",
"Episode 233: D\n",
"Episode 234: D\n",
"Episode 235: D\n",
"Episode 236: D\n",
"Episode 237: D\n",
"Episode 238: D\n",
"Episode 239: D\n",
"Episode 240: D\n",
"total_reward = -1687\n",
"average episode reward = -7.0\n"
]
}
],
"source": [
"%output_height 300\n",
"\n",
"env = environment.AnnotatingDataset(annotator_real, the_detector, image_class_current)\n",
"print('Running ', len(env.image_class), 'episodes with strategy X')\n",
"\n",
"total_reward = 0\n",
"new_ground_truth_all = []\n",
"all_annotations = dict()\n",
"\n",
"for i in range(len(env.image_class)):\n",
" print('Episode ', i, end = ': ')\n",
" state = env.reset(current_index=i)\n",
" agent = dialog.FixedDialog(0)\n",
" done = False\n",
" while not(done):\n",
" action = agent.get_next_action(state) \n",
" if action==0:\n",
" print('V', end='')\n",
" elif action==1:\n",
" print('D', end='')\n",
" next_state, reward, done, coordinates = env.step(action)\n",
" state = next_state\n",
" total_reward += reward\n",
"\n",
" dataset_id = env.current_image\n",
"\n",
" # ground truth with which we will initialise the new user\n",
" new_ground_truth = {}\n",
" new_ground_truth['image_id'] = dataset_id\n",
" new_ground_truth['class_id'] = env.current_class\n",
" new_ground_truth['xmax'] = coordinates['xmax']\n",
" new_ground_truth['xmin'] = coordinates['xmin']\n",
" new_ground_truth['ymax'] = coordinates['ymax']\n",
" new_ground_truth['ymin'] = coordinates['ymin']\n",
" new_ground_truth_all.append(new_ground_truth)\n",
"\n",
"\n",
" if dataset_id not in all_annotations:\n",
" current_annotation = dict()\n",
" current_annotation['boxes'] = np.array([[coordinates['ymin'], coordinates['xmin'], coordinates['ymax'], coordinates['xmax']]], dtype=np.int32)\n",
" current_annotation['box_labels'] = np.array([env.current_class])\n",
" all_annotations[dataset_id] = current_annotation\n",
"\n",
" else:\n",
" all_annotations[dataset_id]['boxes'] = np.append(all_annotations[dataset_id]['boxes'], np.array([[coordinates['ymin'], coordinates['xmin'], coordinates['ymax'], coordinates['xmax']]], dtype=np.int32), axis=0)\n",
" all_annotations[dataset_id]['box_labels'] = np.append(all_annotations[dataset_id]['box_labels'], np.array([env.current_class])) \n",
"\n",
" print()\n",
"\n",
"print('total_reward = ', total_reward) \n",
"print('average episode reward = ', total_reward/len(env.image_class))\n",
"\n",
"new_ground_truth_all = pd.DataFrame(new_ground_truth_all)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "6LxSGM47Gs0g"
},
"source": [
"# Batch 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Yk_PHZ6Q8pYh"
},
"source": [
"Starting from Batch 3 the code will be just repeated."
]
},
{
"cell_type": "code",
"execution_count": 0,
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"outputs": [],
"source": [
"ground_truth_new = pd.DataFrame(new_ground_truth_all)\n",
"annotator_new = annotator.AnnotatorSimple(ground_truth_new, random_seed, time_verify, time_draw, min_iou)"
]
},
{
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"execution_count": 11,
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Running 241 episodes with strategy V3X\n"
]
},
{
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"0: VVVD\n",
"1: V\n",
"2: V\n",
"3: V\n",
"4: V\n",
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"10: VVV\n",
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"19: VV\n",
"20: V\n",
"21: VVVD\n",
"22: VVVD\n",
"23: V\n",
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"28: VV\n",
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"32: V\n",
"33: VVVD\n",
"34: V\n",
"35: VV\n",
"36: VVVD\n",
"37: VVVD\n",
"38: V\n",
"39: VV\n",
"40: VVVD\n",
"41: VVVD\n",
"42: VVVD\n",
"43: VV\n",
"44: V\n",
"45: V\n",
"46: V\n",
"47: V\n",
"48: VVV\n",
"49: V\n",
"50: VVVD\n",
"51: V\n",
"52: V\n",
"53: VVVD\n",
"54: VVV\n",
"55: VVVD\n",
"56: VVV\n",
"57: V\n",
"58: V\n",
"59: VVVD\n",
"60: V\n",
"61: VVVD\n",
"62: V\n",
"63: V\n",
"64: V\n",
"65: VVVD\n",
"66: VVVD\n",
"67: V\n",
"68: VV\n",
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"74: VVVD\n",
"75: VVVD\n",
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"81: VV\n",
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"85: VV\n",
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"90: VVVD\n",
"91: V\n",
"92: VVVD\n",
"93: VVVD\n",
"94: V\n",
"95: VVVD\n",
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"98: V\n",
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"100: V\n",
"101: V\n",
"102: VVVD\n",
"103: VVVD\n",
"104: VVVD\n",
"105: V\n",
"106: VVVD\n",
"107: V\n",
"108: V\n",
"109: V\n",
"110: V\n",
"111: VVVD\n",
"112: V\n",
"113: V\n",
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"118: V\n",
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"121: V\n",
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"126: V\n",
"127: VVVD\n",
"128: V\n",
"129: V\n",
"130: V\n",
"131: V\n",
"132: VVVD\n",
"133: V\n",
"134: V\n",
"135: VVV\n",
"136: VVVD\n",
"137: VVVD\n",
"138: V\n",
"139: VVVD\n",
"140: V\n",
"141: V\n",
"142: VVV\n",
"143: V\n",
"144: VVVD\n",
"145: VVV\n",
"146: V\n",
"147: V\n",
"148: V\n",
"149: V\n",
"150: V\n",
"151: V\n",
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"153: VVVD\n",
"154: V\n",
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"160: V\n",
"161: VV\n",
"162: VV\n",
"163: V\n",
"164: VVVD\n",
"165: VVVD\n",
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"167: VVVD\n",
"168: V\n",
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"179: VVVD\n",
"180: V\n",
"181: VVVD\n",
"182: VVVD\n",
"183: VVVD\n",
"184: VVVD\n",
"185: V\n",
"186: VV\n",
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"190: V\n",
"191: V\n",
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"193: V\n",
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"195: V\n",
"196: VV\n",
"197: VVVD\n",
"198: V\n",
"199: VV\n",
"200: VVVD\n",
"201: V\n",
"202: V\n",
"203: V\n",
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"205: V\n",
"206: VVVD\n",
"207: VVVD\n",
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"209: VVVD\n",
"210: V\n",
"211: V\n",
"212: VVV\n",
"213: V\n",
"214: V\n",
"215: VV\n",
"216: VVVD\n",
"217: VVVD\n",
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"223: VVVD\n",
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"228: V\n",
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"231: V\n",
"232: V\n",
"233: V\n",
"234: VVVD\n",
"235: VVVD\n",
"236: VVVD\n",
"237: V\n",
"238: V\n",
"239: V\n",
"240: VV\n",
"Average episode reward = -5.18423236515\n"
]
}
],
"source": [
"# @title Collect data for classifier\n",
"env = environment.AnnotatingDataset(annotator_new, the_detector, image_class_current)\n",
"\n",
"print('Running ', len(env.image_class), 'episodes with strategy V3X')\n",
"\n",
"%output_height 300\n",
"total_reward = 0\n",
"\n",
"data_for_classifier = []\n",
"\n",
"for i in range(len(env.image_class)):\n",
" print(i, end = ': ')\n",
" agent = dialog.FixedDialog(3)\n",
" state = env.reset(current_index=i)\n",
"\n",
" done = False\n",
" while not(done):\n",
" action = agent.get_next_action(state)\n",
" next_state, reward, done, _ = env.step(action)\n",
" if action==0:\n",
" state_dict = dict(state)\n",
" state_dict['is_accepted'] = done\n",
" data_for_classifier.append(state_dict)\n",
" print('V', end='')\n",
" elif action==1:\n",
" print('D', end='')\n",
" state = next_state\n",
" total_reward += reward\n",
"\n",
" print()\n",
"\n",
"print('Average episode reward = ', total_reward/len(env.image_class))\n",
"\n",
"data_for_classifier = pd.DataFrame(data_for_classifier)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"cellView": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"height": 105,
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"colab_type": "code",
"executionInfo": {
"elapsed": 22900,
"status": "ok",
"timestamp": 1516022174199,
"user": {},
"user_tz": -60
},
"id": "64kJJVp5AtHp",
"outputId": "906636c8-f041-4406-c50d-973d5163df1a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross-validating parameters' values... This might take some time.\n",
"best score = -0.613398861011\n",
"best parameters = {'hidden_layer_sizes': (20, 20, 20, 20, 20), 'activation': 'relu', 'alpha': 0.01}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n"
]
}
],
"source": [
"# @title Train classification model (might take some time)\n",
"\n",
"#model_mlp = neural_network.MLPClassifier(alpha = 0.0001, activation = 'relu', hidden_layer_sizes = (50, 50, 50, 50, 50), random_state=602)\n",
"#model_for_agent = model_mlp.fit(data_from_Vx3X[predictive_fields], data_from_Vx3X['is_accepted'])\n",
"np.random.seed(random_seed) # for reproducibility of fitting the classifier and cross-validation\n",
"\n",
"print('Cross-validating parameters\\' values... This might take some time.')\n",
"\n",
"# possible parameter values\n",
"parameters = {'hidden_layer_sizes': ((20, 20, 20), (50, 50, 50), (80, 80, 80), (20, 20, 20, 20), (50, 50, 50, 50), (80, 80, 80, 80), (20, 20, 20, 20, 20), (50, 50, 50, 50, 50), (80, 80, 80, 80, 80)), 'activation': ('logistic', 'relu'), 'alpha': [0.0001, 0.001, 0.01]}\n",
"model_mlp = neural_network.MLPClassifier()\n",
"# cross-validate parameters\n",
"grid_search = model_selection.GridSearchCV(model_mlp, parameters, scoring='neg_log_loss', refit=True)\n",
"grid_search.fit(data_for_classifier[predictive_fields], data_for_classifier['is_accepted'])\n",
"print('best score = ', grid_search.best_score_)\n",
"print('best parameters = ', grid_search.best_params_)\n",
"# use the model with the best parameters\n",
"model_for_agent = grid_search.best_estimator_"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "q00bs0Ns5J_o"
},
"source": [
"**Now is the time to retrain the detector and obtain new box_proposal_features. This is not done in this notebook.**"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "Nx2VHs2iG_Eg"
},
"outputs": [],
"source": [
"image_class_current = image_class.iloc[in1:in2]\n",
"the_detector = detector.Detector(box_proposal_features, predictive_fields)\n",
"agent = dialog.DialogProb(model_for_agent, annotator_real)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"cellView": "code",
"colab": {
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"elapsed": 5293,
"status": "ok",
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"outputId": "c3a50b6f-7656-404b-a924-59888aec03a8"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running 250 episodes with strategy IAD-Prob\n"
]
},
{
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"text": [
"intelligent dialog strategy\n",
"0: V\n",
"1: V\n",
"2: V\n",
"3: V\n",
"4: V\n",
"5: V\n",
"6: V\n",
"7: VV\n",
"8: V\n",
"9: V\n",
"10: D\n",
"11: V\n",
"12: V\n",
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"33: VVD\n",
"34: VVVVD\n",
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"70: VVVVD\n",
"71: V\n",
"72: D\n",
"73: D\n",
"74: V\n",
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"76: V\n",
"77: D\n",
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"87: D\n",
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"102: V\n",
"103: D\n",
"104: D\n",
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"109: VD\n",
"110: V\n",
"111: D\n",
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"115: VD\n",
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"122: VV\n",
"123: D\n",
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"126: D\n",
"127: D\n",
"128: V\n",
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"148: D\n",
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"154: VD\n",
"155: VVD\n",
"156: D\n",
"157: D\n",
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"169: VD\n",
"170: V\n",
"171: VD\n",
"172: VD\n",
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"174: V\n",
"175: D\n",
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"186: D\n",
"187: V\n",
"188: D\n",
"189: VD\n",
"190: V\n",
"191: V\n",
"192: V\n",
"193: V\n",
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"195: V\n",
"196: V\n",
"197: V\n",
"198: VD\n",
"199: V\n",
"200: V\n",
"201: V\n",
"202: D\n",
"203: D\n",
"204: VV\n",
"205: D\n",
"206: D\n",
"207: VV\n",
"208: VVVVVVVVVVVVVVVVVVVVVVD\n",
"209: D\n",
"210: V\n",
"211: VD\n",
"212: D\n",
"213: V\n",
"214: D\n",
"215: V\n",
"216: V\n",
"217: VVD\n",
"218: V\n",
"219: VV\n",
"220: V\n",
"221: V\n",
"222: V\n",
"223: D\n",
"224: VD\n",
"225: V\n",
"226: V\n",
"227: V\n",
"228: V\n",
"229: D\n",
"230: VVVVVVD\n",
"231: V\n",
"232: VVVVVVVVVVVVVVVV\n",
"233: V\n",
"234: VD\n",
"235: D\n",
"236: D\n",
"237: VVVD\n",
"238: V\n",
"239: V\n",
"240: V\n",
"241: V\n",
"242: V\n",
"243: VV\n",
"244: V\n",
"245: VD\n",
"246: V\n",
"247: VD\n",
"248: VVD\n",
"249: V\n",
"total_reward = -1050.0\n",
"average episode reward = -4.2\n"
]
}
],
"source": [
"# @title Annotating data with intelligent dialog\n",
"env = environment.AnnotatingDataset(annotator_real, the_detector, image_class_current)\n",
"\n",
"print('Running ', len(env.image_class), 'episodes with strategy IAD-Prob')\n",
"\n",
"%output_height 300\n",
"print('intelligent dialog strategy')\n",
"\n",
"total_reward = 0\n",
"# reset the gound truth because the user only needs to annotate the last 10% of data using the detector from the rest of the data\n",
"new_ground_truth_all = []\n",
"\n",
"for i in range(len(env.image_class)):\n",
" print(i, end = ': ')\n",
" state = env.reset(current_index=i)\n",
"\n",
" done = False\n",
" while not(done):\n",
" action = agent.get_next_action(state)\n",
" if action==0:\n",
" print('V', end='')\n",
" elif action==1:\n",
" print('D', end='')\n",
" next_state, reward, done, coordinates = env.step(action)\n",
" state = next_state\n",
" total_reward += reward\n",
"\n",
" dataset_id = env.current_image\n",
"\n",
" # ground truth with which we will initialise the new user\n",
" new_ground_truth = {}\n",
" new_ground_truth['image_id'] = dataset_id\n",
" new_ground_truth['class_id'] = env.current_class\n",
" new_ground_truth['xmax'] = coordinates['xmax']\n",
" new_ground_truth['xmin'] = coordinates['xmin']\n",
" new_ground_truth['ymax'] = coordinates['ymax']\n",
" new_ground_truth['ymin'] = coordinates['ymin']\n",
" new_ground_truth_all.append(new_ground_truth)\n",
"\n",
"\n",
" if dataset_id not in all_annotations:\n",
" current_annotation = dict()\n",
" current_annotation['boxes'] = np.array([[coordinates['ymin'], coordinates['xmin'], coordinates['ymax'], coordinates['xmax']]], dtype=np.int32)\n",
" current_annotation['box_labels'] = np.array([env.current_class])\n",
" all_annotations[dataset_id] = current_annotation\n",
"\n",
" else:\n",
" all_annotations[dataset_id]['boxes'] = np.append(all_annotations[dataset_id]['boxes'], np.array([[coordinates['ymin'], coordinates['xmin'], coordinates['ymax'], coordinates['xmax']]], dtype=np.int32), axis=0)\n",
" all_annotations[dataset_id]['box_labels'] = np.append(all_annotations[dataset_id]['box_labels'], np.array([env.current_class])) \n",
"\n",
" print()\n",
"\n",
"print('total_reward = ', total_reward) \n",
"print('average episode reward = ', total_reward/len(env.image_class))\n",
"\n",
"new_ground_truth_all = pd.DataFrame(new_ground_truth_all)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"default_view": {},
"last_runtime": {
"build_target": "",
"kind": "local"
},
"name": "exp2_IAD_Prob_open_sourcing",
"provenance": [
{
"file_id": "0Bz6XXZ1741KYUXRsNUI0c18wQVU",
"timestamp": 1505896405546
}
],
"version": "0.3.2",
"views": {}
},
"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": 1
}
| apache-2.0 |
buntyke/TRo2017 | Experiments/Exp7/experiment1.ipynb | 1 | 7413 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Experiment 7: TRo Journal\n",
"---\n",
"\n",
"In this experiment, the generalization of cloth models to unseen T-shirts of the mannequin is verified. The evaluation is performed using RMSE, NRMSE, Pearson correlation as the parameters. In this notebook, the MRD cloth models are trained excluding one T-shirt for test inference."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# import the modules\n",
"import sys\n",
"import GPy\n",
"import csv\n",
"import numpy as np\n",
"import cPickle as pickle\n",
"import scipy.stats as stats\n",
"import sklearn.metrics as metrics\n",
"from matplotlib import pyplot as plt\n",
"\n",
"%matplotlib notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Loading\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# load all the files and create Data\n",
"trackPath = '../Data/Tracks/'\n",
"mocapPath = '../Data/MocapData/'\n",
"kinectPath = '../Data/KinectData/'\n",
"\n",
"nShr = 4\n",
"nPos = 6\n",
"names = []\n",
"for sInd in range(nShr):\n",
" for pInd in range(nPos):\n",
" names.append('K1S%dP%dT1' % (sInd+1,pInd+1))\n",
"\n",
"# create directory for results\n",
"dName = '../Models/Exp7'\n",
"if not os.path.exists(dName):\n",
" os.makedirs(dName)\n",
" \n",
"Data = pickle.load(open('../Data/Data.p','rb'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# set the overall parameters for bgplvm\n",
"qDim = 15\n",
"\n",
"# dimensions for kinect and mocap\n",
"qDims = [10,5]\n",
"qDVals = [np.arange(0,qDims[0]), np.arange(qDims[0],qDims[0]+qDims[1])]\n",
"\n",
"# set the number of inducing inputs\n",
"nInducing = 100"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# main loop\n",
"samplingFreq = 2\n",
"\n",
"# optimization variables\n",
"SNR0 = 1000\n",
"SNR1 = 100\n",
"trainIters = 1500\n",
"initMod0Iters = 500\n",
"initMod1Iters = 500\n",
"initVardistIters = 2000"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"# loop over the kinect keys\n",
"kinectExt = 'C'\n",
"kinectDim = 7500\n",
"kinectKey = 'Cloud'\n",
"\n",
"mocapDim = 8\n",
"mocapExt = 'T'\n",
"mocapKey = 'TopCoord'\n",
" \n",
"keys = [kinectKey,mocapKey]\n",
"dims = [kinectDim, mocapDim]\n",
"YNames = [kinectKey, mocapKey]\n",
"expName = '%s%s' % (kinectExt,mocapExt)\n",
"\n",
"ValInd = [[6,7,14,15,22,23],[4,5,12,13,20,21],[2,3,10,11,18,19],[0,1,8,9,16,17]]\n",
"TestInd = [[0,1,2,3,4,5],[6,7,8,9,10,11],[12,13,14,15,16,17],[18,19,20,21,22,23]]\n",
"TrainInd = [[8,9,16,17,18,19],[0,1,14,15,22,23],[4,5,6,7,20,21],[2,3,10,11,12,13]]\n",
"\n",
"for sInd in range(nShirts):\n",
" valData = {}\n",
" testData = {}\n",
" trainData = {}\n",
"\n",
" valInd = ValInd[sInd]\n",
" testInd = TestInd[sInd]\n",
" trainInd = TrainInd[sInd]\n",
" \n",
" print 'Cycle:%d' % (sInd+1)\n",
" print valInd, testInd, trainInd\n",
" \n",
" for key,dim in zip(keys,dims):\n",
" trD = np.empty((0,dim))\n",
" for ind in trainInd:\n",
" trD = np.concatenate((trD,Data[names[ind]][key][::samplingFreq,:]),axis=0)\n",
" trainData[key] = trD\n",
" \n",
" # choosing the training dataset\n",
" nSamples = trainData[kinectKey].shape[0]\n",
" trainList = [trainData[kinectKey], trainData[mocapKey]]\n",
" \n",
" # initializing the latent space \n",
" scales = []\n",
" inputX = np.zeros((nSamples,qDim))\n",
"\n",
" for qD,qDV,Y in zip(qDims, qDVals, trainList):\n",
" x,frcs = GPy.util.initialization.initialize_latent('PCA',qD, Y)\n",
" scales.extend(frcs)\n",
" inputX[:,qDV] = x\n",
" \n",
" scales = np.asarray(scales)\n",
" print scales\n",
" \n",
" # setting up the kernel\n",
" mrdKernels = []\n",
"\n",
" for Y in trainList:\n",
" mrdKernels.append(GPy.kern.RBF(qDim, variance=1., lengthscale=1./scales, ARD = True))\n",
" \n",
" # initializing MRD model\n",
" mrdModel = GPy.models.MRD(trainList, input_dim=qDim, num_inducing=nInducing, kernel=mrdKernels, \n",
" X=inputX, name='%s%d%d' % (expName,sInd,pInd+1))\n",
" print 'Setup Model!'\n",
" \n",
" # Phase 1: Optimizaition by fixing variance parameters\n",
" var0 = mrdModel.Y0.Y.var()\n",
" var1 = mrdModel.Y1.Y.var()\n",
"\n",
" mrdModel.Y0.rbf.variance.fix(var0)\n",
" mrdModel.Y1.rbf.variance.fix(var1)\n",
"\n",
" mrdModel.Y0.Gaussian_noise.variance.fix(var0/SNR0)\n",
" mrdModel.Y1.Gaussian_noise.variance.fix(var1/SNR1)\n",
"\n",
" mrdModel.optimize(messages=True, max_iters=initVardistIters)\n",
" \n",
" # Phase 2: Optimize each model individually\n",
"\n",
" # constrain space 0\n",
" mrdModel.Y1.constrain_fixed()\n",
" mrdModel.optimize(messages=True, max_iters=initMod0Iters)\n",
"\n",
" # constrain space 1\n",
" mrdModel.Y0.constrain_fixed()\n",
" mrdModel.Y1.unconstrain_fixed()\n",
" mrdModel.Y1.rbf.variance.fix(var1)\n",
" mrdModel.Y1.Gaussian_noise.variance.fix(var1/SNR1)\n",
" mrdModel.optimize(messages=True, max_iters=initMod1Iters)\n",
" \n",
" # Phase 3: Optimize the model without any constraints\n",
"\n",
" # training without constraints\n",
" mrdModel.Y0.unconstrain_fixed()\n",
" mrdModel.Y1.unconstrain_fixed()\n",
" mrdModel.optimize(messages=True, max_iters=trainIters)\n",
" \n",
" print 'Training Done!'\n",
" \n",
" # plot the learned model\n",
" mrdModel.plot_scales(sharex=True,sharey=False,titles=YNames)\n",
" mrdModel.plot_latent(which_indices=[0,1])\n",
" \n",
" # save the model\n",
" mrdModel = pickle.dump(mrdModel, open('../Models/Exp7/%s%d.p' % (expName,sInd+1),'wb'))\n",
" \n",
" print 'Saving Done!'"
]
}
],
"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"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
tensorflow/docs-l10n | site/ja/io/tutorials/colorspace.ipynb | 1 | 11216 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Tce3stUlHN0L"
},
"source": [
"##### Copyright 2020 The TensorFlow IO Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "tuOe1ymfHZPu"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qFdPvlXBOdUN"
},
"source": [
"# 色空間変換"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MfBg1C5NB3X0"
},
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td><a target=\"_blank\" href=\"https://www.tensorflow.org/io/tutorials/colorspace\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\"> TensorFlow.orgで表示</a></td>\n",
" <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ja/io/tutorials/colorspace.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\"> Google Colab で実行</a></td>\n",
" <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ja/io/tutorials/colorspace.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">GitHub でソースを表示{</a></td>\n",
" <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ja/io/tutorials/colorspace.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">ノートブックをダウンロード/a0}</a></td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xHxb-dlhMIzW"
},
"source": [
"## 概要\n",
"\n",
"コンピュータビジョンでは、選択した色空間がモデルの性能を大きく左右することがあります。最も一般的な色空間は`RGB`ですが、多くの場合は`YUV`、`YCbCr`、`XYZ (CIE)`などの他の色空間に切り替えると、モデルの性能が向上します。\n",
"\n",
"`tensorflow-io`パッケージは、画像データの準備や拡張に使用できる色空間変換 API のリストを提供しています。"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MUXex9ctTuDB"
},
"source": [
"## セットアップ"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "upgCc3gXybsA"
},
"source": [
"### 必要なパッケージをインストールし、ランタイムを再起動する"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uUDYyMZRfkX4"
},
"outputs": [],
"source": [
"!pip install tensorflow-io"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VSkY6UAxODOq"
},
"source": [
"### サンプル画像をダウンロードする\n",
"\n",
"このチュートリアルで使用する画像例は[雪の中の猫](https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg)ですが、任意の JPEG 画像で置き換えても構いません。\n",
"\n",
"以下のように画像をダウンロードし、`sample.jpg`としてローカルディスクに保存します。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "baeqVsglPQUI"
},
"outputs": [],
"source": [
"!curl -o sample.jpg -L https://storage.googleapis.com/download.tensorflow.org/example_images/320px-Felis_catus-cat_on_snow.jpg\n",
"\n",
"!ls -ls sample.jpg"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "J0ZKhA6s0Pjp"
},
"source": [
"## 使い方"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yZmI7l_GykcW"
},
"source": [
"### 画像ファイルを読み込む\n",
"\n",
"画像を読み取り、形状が`(213, 320, 3)`の`uint8`テンソルにデコードします。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nS3eTBvjt-O5"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import tensorflow_io as tfio\n",
"\n",
"image = tf.image.decode_jpeg(tf.io.read_file('sample.jpg'))\n",
"\n",
"print(image.shape, image.dtype)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IGnbXuVnSo8T"
},
"source": [
"画像は以下の方法で表示できます。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0rLbVxuFSvVO"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure()\n",
"plt.imshow(image)\n",
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "86qE8BPl5rcA"
},
"source": [
"### RGB からグレースケールに変換する\n",
"\n",
"`tfio.experimental.color.rgb_to_grayscale`を使用して`RGB`画像を`Grayscale`に変換し、チャンネル数を 3 から 1 に減らすことができます。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "eEa0Z5U26Ep3"
},
"outputs": [],
"source": [
"grayscale = tfio.experimental.color.rgb_to_grayscale(image)\n",
"\n",
"print(grayscale.shape, grayscale.dtype)\n",
"\n",
"# use tf.squeeze to remove last channel for plt.imshow to display:\n",
"plt.figure()\n",
"plt.imshow(tf.squeeze(grayscale, axis=-1), cmap='gray')\n",
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ineBzDeu-lTh"
},
"source": [
"### RGB から BGR に変換する\n",
"\n",
"画像ソフトやカメラのメーカーによっては`BGR`を好む場合がありますが、`tfio.experimental.color.rgb_to_bgr`を使用して BGR に変換することができます。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LfZo0XaaAaeM"
},
"outputs": [],
"source": [
"bgr = tfio.experimental.color.rgb_to_bgr(image)\n",
"\n",
"print(bgr.shape, bgr.dtype)\n",
"\n",
"plt.figure()\n",
"plt.imshow(bgr)\n",
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nXd776xNIr_I"
},
"source": [
"### RGB から CIE XYZ に変換する\n",
"\n",
"`CIE XYZ`（または `CIE 1931 XYZ`）は、多くの画像処理プログラムで使用されている一般的な色空間です。以下では`tfio.experimental.color.rgb_to__xyz`を使用して、RGB から`CIE XYZ`に変換しています。`tfio.experimental.color.rgb_to_xyz`は`[0, 1]`の範囲の浮動小数点入力を想定しているため、追加の前処理が必要なので注意してください。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kLEdfkkoK27A"
},
"outputs": [],
"source": [
"# convert to float32\n",
"image_float32 = tf.cast(image, tf.float32) / 255.0\n",
"\n",
"xyz_float32 = tfio.experimental.color.rgb_to_xyz(image_float32)\n",
"\n",
"# convert back uint8\n",
"xyz = tf.cast(xyz_float32 * 255.0, tf.uint8)\n",
"\n",
"print(xyz.shape, xyz.dtype)\n",
"\n",
"plt.figure()\n",
"plt.imshow(xyz)\n",
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7rhLvOSZB0k0"
},
"source": [
"### RGB から YCbCr に変換する\n",
"\n",
"最後に、多くのビデオシステムでは`YCbCr`がデフォルトの色空間です。`YCbCr`への変換は、`tfio.experimental.color.rgb_to_ycbcr`を使用して行います。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UyFMBK-LDDnN"
},
"outputs": [],
"source": [
"ycbcr = tfio.experimental.color.rgb_to_ycbcr(image)\n",
"\n",
"print(ycbcr.shape, ycbcr.dtype)\n",
"\n",
"plt.figure()\n",
"plt.imshow(ycbcr, cmap='gray')\n",
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VuL8rrnhwab4"
},
"source": [
"さらに面白いことに、`YCbCr`は各成分が知覚的に意味のある情報を持つ`Y'`（ルマ）、`Cb`（青色差クロマ）、`Cr`（赤色差クロマ）という成分に分解することができます。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wvur-gtyxjwG"
},
"outputs": [],
"source": [
"y, cb, cr = ycbcr[:,:,0], ycbcr[:,:,1], ycbcr[:,:,2]\n",
"\n",
"# Y' component\n",
"plt.figure()\n",
"plt.imshow(y, cmap='gray')\n",
"plt.axis('off')\n",
"plt.show()\n",
"\n",
"# Cb component\n",
"plt.figure()\n",
"plt.imshow(cb, cmap='gray')\n",
"plt.axis('off')\n",
"plt.show()\n",
"\n",
"# Cr component\n",
"plt.figure()\n",
"plt.imshow(cr, cmap='gray')\n",
"plt.axis('off')\n",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"Tce3stUlHN0L"
],
"name": "colorspace.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
xlbaojun/Note-jupyter | 05其他/pandas文档-zh-master/检索 ,查询数据.ipynb | 1 | 345362 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# 检索，查询数据\n",
"\n",
"这一节学习如何检索pandas数据。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Python和Numpy的索引操作符[]和属性操作符‘.’能够快速检索pandas数据。\n",
"\n",
"然而，这两种方式的效率在pandas中可能不是最优的，我们推荐使用专门优化过的pandas数据检索方法。而这些方法则是本节要介绍的。"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 多种索引方式"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"pandas支持三种不同的索引方式：\n",
"* **.loc** 基于label进行索引，当然也可以和boolean数组一起使用。‘.loc’接受的输入：\n",
"* * 一个单独的label，比如5、'a'，注意，这里的5是index值，而不是整形下标\n",
"* * label列表或label数组，比如['a', 'b', 'c'] \n",
"* **.iloc** 是基本的基于整数位置(从0到axis的length-1)的，当然也可以和一个boolean数组一起使用。当提供检索的index越界时会有IndexError错误，注意切片索引(slice index)允许越界。\n",
"* **.ix** 支持基于label和整数位置混合的数据获取方式。默认是基本label的. .ix是最常用的方式，它支持所有.loc和.iloc的输入。如果提供的是纯label或纯整数索引，我们建议使用.loc或 .iloc。\n",
"\n",
"\n",
"以 .loc为例看一下使用方式：\n",
"\n",
"**对象类型** | **Indexers**\n",
"\n",
"Series | s.loc[indexer]\n",
"\n",
"DataFrame | df.loc[row_indexer, column_indexer]\n",
"\n",
"Panel | p.loc[item_indexer, major_indexer, minor_indexer]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 最基本的索引和选择"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"最基本的选择数据方式就是使用[]操作符进行索引，\n",
"\n",
"**对象类型** | ** Selection** | **返回值类型**\n",
"\n",
"Series | series[label],**这里的label是index名** | 常数\n",
"\n",
"DataFrame| frame[colname],**使用列名** | Series对象，相应的colname那一列\n",
"\n",
"Panel | panel[itemname] | DataFrame对象,相应的itemname那一个"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"下面用示例展示一下"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04',\n",
" '2000-01-05', '2000-01-06', '2000-01-07', '2000-01-08'],\n",
" dtype='datetime64[ns]', freq='D')"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates = pd.date_range('1/1/2000', periods=8)\n",
"dates"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>0.177302</td>\n",
" <td>-1.546113</td>\n",
" <td>-0.952839</td>\n",
" <td>0.008122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>-1.049451</td>\n",
" <td>0.137660</td>\n",
" <td>1.987125</td>\n",
" <td>1.246595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>-0.978006</td>\n",
" <td>0.532418</td>\n",
" <td>-0.847118</td>\n",
" <td>2.461356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-04</th>\n",
" <td>-0.798626</td>\n",
" <td>-1.377614</td>\n",
" <td>0.776687</td>\n",
" <td>0.342846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-05</th>\n",
" <td>-0.095023</td>\n",
" <td>-1.861150</td>\n",
" <td>0.134254</td>\n",
" <td>0.661625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-06</th>\n",
" <td>0.249859</td>\n",
" <td>-0.664076</td>\n",
" <td>0.881349</td>\n",
" <td>1.945613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-07</th>\n",
" <td>0.995785</td>\n",
" <td>0.674858</td>\n",
" <td>-0.809902</td>\n",
" <td>-0.543680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-08</th>\n",
" <td>0.295963</td>\n",
" <td>-0.698917</td>\n",
" <td>0.932431</td>\n",
" <td>1.440893</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2000-01-01 0.177302 -1.546113 -0.952839 0.008122\n",
"2000-01-02 -1.049451 0.137660 1.987125 1.246595\n",
"2000-01-03 -0.978006 0.532418 -0.847118 2.461356\n",
"2000-01-04 -0.798626 -1.377614 0.776687 0.342846\n",
"2000-01-05 -0.095023 -1.861150 0.134254 0.661625\n",
"2000-01-06 0.249859 -0.664076 0.881349 1.945613\n",
"2000-01-07 0.995785 0.674858 -0.809902 -0.543680\n",
"2000-01-08 0.295963 -0.698917 0.932431 1.440893"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(np.random.randn(8,4), index=dates, columns=list('ABCD'))\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"<class 'pandas.core.panel.Panel'>\n",
"Dimensions: 2 (items) x 8 (major_axis) x 4 (minor_axis)\n",
"Items axis: one to two\n",
"Major_axis axis: 2000-01-01 00:00:00 to 2000-01-08 00:00:00\n",
"Minor_axis axis: A to D"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"panel = pd.Panel({'one':df, 'two':df-df.mean()})\n",
"panel"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"我们使用最基本的[]操作符"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"2000-01-01 -0.562650\n",
"2000-01-02 -1.062558\n",
"2000-01-03 -1.194126\n",
"2000-01-04 0.936506\n",
"2000-01-05 -1.196422\n",
"2000-01-06 1.436726\n",
"2000-01-07 0.329280\n",
"2000-01-08 0.857815\n",
"Freq: D, Name: A, dtype: float64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = df['A'] #使用列名\n",
"s#返回的是 Series"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"Series使用index索引"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"1.436726247472784"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[dates[5]] #使用index名"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>-0.505721</td>\n",
" <td>-0.769480</td>\n",
" <td>0.857238</td>\n",
" <td>2.124492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>-1.005630</td>\n",
" <td>1.230158</td>\n",
" <td>-0.849252</td>\n",
" <td>-0.512871</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>-1.137198</td>\n",
" <td>0.960215</td>\n",
" <td>1.531157</td>\n",
" <td>0.940112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-04</th>\n",
" <td>0.993435</td>\n",
" <td>-0.300830</td>\n",
" <td>1.046579</td>\n",
" <td>1.142567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-05</th>\n",
" <td>-1.139493</td>\n",
" <td>-0.819572</td>\n",
" <td>0.168454</td>\n",
" <td>-0.310352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-06</th>\n",
" <td>1.493655</td>\n",
" <td>0.409088</td>\n",
" <td>-1.424400</td>\n",
" <td>-1.870575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-07</th>\n",
" <td>0.386208</td>\n",
" <td>0.825722</td>\n",
" <td>-0.845220</td>\n",
" <td>-0.275965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-08</th>\n",
" <td>0.914743</td>\n",
" <td>-1.535301</td>\n",
" <td>-0.484555</td>\n",
" <td>-1.237407</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2000-01-01 -0.505721 -0.769480 0.857238 2.124492\n",
"2000-01-02 -1.005630 1.230158 -0.849252 -0.512871\n",
"2000-01-03 -1.137198 0.960215 1.531157 0.940112\n",
"2000-01-04 0.993435 -0.300830 1.046579 1.142567\n",
"2000-01-05 -1.139493 -0.819572 0.168454 -0.310352\n",
"2000-01-06 1.493655 0.409088 -1.424400 -1.870575\n",
"2000-01-07 0.386208 0.825722 -0.845220 -0.275965\n",
"2000-01-08 0.914743 -1.535301 -0.484555 -1.237407"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"panel['two']"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
" **也可以给[]传递一个column name组成的的list，形如df[[col1,col2]], 如果给出的某个列名不存在，会报错**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>-0.562650</td>\n",
" <td>-1.226827</td>\n",
" <td>0.149550</td>\n",
" <td>2.333782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>-1.062558</td>\n",
" <td>0.772811</td>\n",
" <td>-1.556939</td>\n",
" <td>-0.303581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>-1.194126</td>\n",
" <td>0.502868</td>\n",
" <td>0.823470</td>\n",
" <td>1.149401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-04</th>\n",
" <td>0.936506</td>\n",
" <td>-0.758176</td>\n",
" <td>0.338892</td>\n",
" <td>1.351857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-05</th>\n",
" <td>-1.196422</td>\n",
" <td>-1.276918</td>\n",
" <td>-0.539233</td>\n",
" <td>-0.101062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-06</th>\n",
" <td>1.436726</td>\n",
" <td>-0.048258</td>\n",
" <td>-2.132088</td>\n",
" <td>-1.661286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-07</th>\n",
" <td>0.329280</td>\n",
" <td>0.368375</td>\n",
" <td>-1.552907</td>\n",
" <td>-0.066676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-08</th>\n",
" <td>0.857815</td>\n",
" <td>-1.992648</td>\n",
" <td>-1.192243</td>\n",
" <td>-1.028118</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2000-01-01 -0.562650 -1.226827 0.149550 2.333782\n",
"2000-01-02 -1.062558 0.772811 -1.556939 -0.303581\n",
"2000-01-03 -1.194126 0.502868 0.823470 1.149401\n",
"2000-01-04 0.936506 -0.758176 0.338892 1.351857\n",
"2000-01-05 -1.196422 -1.276918 -0.539233 -0.101062\n",
"2000-01-06 1.436726 -0.048258 -2.132088 -1.661286\n",
"2000-01-07 0.329280 0.368375 -1.552907 -0.066676\n",
"2000-01-08 0.857815 -1.992648 -1.192243 -1.028118"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>-1.226827</td>\n",
" <td>-0.562650</td>\n",
" <td>0.149550</td>\n",
" <td>2.333782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>0.772811</td>\n",
" <td>-1.062558</td>\n",
" <td>-1.556939</td>\n",
" <td>-0.303581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>0.502868</td>\n",
" <td>-1.194126</td>\n",
" <td>0.823470</td>\n",
" <td>1.149401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-04</th>\n",
" <td>-0.758176</td>\n",
" <td>0.936506</td>\n",
" <td>0.338892</td>\n",
" <td>1.351857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-05</th>\n",
" <td>-1.276918</td>\n",
" <td>-1.196422</td>\n",
" <td>-0.539233</td>\n",
" <td>-0.101062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-06</th>\n",
" <td>-0.048258</td>\n",
" <td>1.436726</td>\n",
" <td>-2.132088</td>\n",
" <td>-1.661286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-07</th>\n",
" <td>0.368375</td>\n",
" <td>0.329280</td>\n",
" <td>-1.552907</td>\n",
" <td>-0.066676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-08</th>\n",
" <td>-1.992648</td>\n",
" <td>0.857815</td>\n",
" <td>-1.192243</td>\n",
" <td>-1.028118</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2000-01-01 -1.226827 -0.562650 0.149550 2.333782\n",
"2000-01-02 0.772811 -1.062558 -1.556939 -0.303581\n",
"2000-01-03 0.502868 -1.194126 0.823470 1.149401\n",
"2000-01-04 -0.758176 0.936506 0.338892 1.351857\n",
"2000-01-05 -1.276918 -1.196422 -0.539233 -0.101062\n",
"2000-01-06 -0.048258 1.436726 -2.132088 -1.661286\n",
"2000-01-07 0.368375 0.329280 -1.552907 -0.066676\n",
"2000-01-08 -1.992648 0.857815 -1.192243 -1.028118"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['B', 'A']] = df[['A', 'B']]\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"## 通过属性访问 把column作为DataFrame对象的属性"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"可以直接把Series的index、DataFrame中的column、Panel中的item作为这些对象的属性使用，然后直接访问相应的index、column、item"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"sa = pd.Series([1,2,3],index=list('abc'))\n",
"dfa = df.copy()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 2\n",
"c 3\n",
"dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sa"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sa.b #直接把index作为属性"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>-1.226827</td>\n",
" <td>-0.562650</td>\n",
" <td>0.149550</td>\n",
" <td>2.333782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>0.772811</td>\n",
" <td>-1.062558</td>\n",
" <td>-1.556939</td>\n",
" <td>-0.303581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>0.502868</td>\n",
" <td>-1.194126</td>\n",
" <td>0.823470</td>\n",
" <td>1.149401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-04</th>\n",
" <td>-0.758176</td>\n",
" <td>0.936506</td>\n",
" <td>0.338892</td>\n",
" <td>1.351857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-05</th>\n",
" <td>-1.276918</td>\n",
" <td>-1.196422</td>\n",
" <td>-0.539233</td>\n",
" <td>-0.101062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-06</th>\n",
" <td>-0.048258</td>\n",
" <td>1.436726</td>\n",
" <td>-2.132088</td>\n",
" <td>-1.661286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-07</th>\n",
" <td>0.368375</td>\n",
" <td>0.329280</td>\n",
" <td>-1.552907</td>\n",
" <td>-0.066676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-08</th>\n",
" <td>-1.992648</td>\n",
" <td>0.857815</td>\n",
" <td>-1.192243</td>\n",
" <td>-1.028118</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2000-01-01 -1.226827 -0.562650 0.149550 2.333782\n",
"2000-01-02 0.772811 -1.062558 -1.556939 -0.303581\n",
"2000-01-03 0.502868 -1.194126 0.823470 1.149401\n",
"2000-01-04 -0.758176 0.936506 0.338892 1.351857\n",
"2000-01-05 -1.276918 -1.196422 -0.539233 -0.101062\n",
"2000-01-06 -0.048258 1.436726 -2.132088 -1.661286\n",
"2000-01-07 0.368375 0.329280 -1.552907 -0.066676\n",
"2000-01-08 -1.992648 0.857815 -1.192243 -1.028118"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfa"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"2000-01-01 -1.226827\n",
"2000-01-02 0.772811\n",
"2000-01-03 0.502868\n",
"2000-01-04 -0.758176\n",
"2000-01-05 -1.276918\n",
"2000-01-06 -0.048258\n",
"2000-01-07 0.368375\n",
"2000-01-08 -1.992648\n",
"Freq: D, Name: A, dtype: float64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfa.A"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
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" <th>2000-01-02</th>\n",
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" <th>2000-01-04</th>\n",
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" <td>0.338892</td>\n",
" <td>1.351857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-05</th>\n",
" <td>-1.196422</td>\n",
" <td>-1.276918</td>\n",
" <td>-0.539233</td>\n",
" <td>-0.101062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-06</th>\n",
" <td>1.436726</td>\n",
" <td>-0.048258</td>\n",
" <td>-2.132088</td>\n",
" <td>-1.661286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-07</th>\n",
" <td>0.329280</td>\n",
" <td>0.368375</td>\n",
" <td>-1.552907</td>\n",
" <td>-0.066676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-08</th>\n",
" <td>0.857815</td>\n",
" <td>-1.992648</td>\n",
" <td>-1.192243</td>\n",
" <td>-1.028118</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2000-01-01 -0.562650 -1.226827 0.149550 2.333782\n",
"2000-01-02 -1.062558 0.772811 -1.556939 -0.303581\n",
"2000-01-03 -1.194126 0.502868 0.823470 1.149401\n",
"2000-01-04 0.936506 -0.758176 0.338892 1.351857\n",
"2000-01-05 -1.196422 -1.276918 -0.539233 -0.101062\n",
"2000-01-06 1.436726 -0.048258 -2.132088 -1.661286\n",
"2000-01-07 0.329280 0.368375 -1.552907 -0.066676\n",
"2000-01-08 0.857815 -1.992648 -1.192243 -1.028118"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"panel.one"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 2\n",
"c 3\n",
"dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sa"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"a 5\n",
"b 2\n",
"c 3\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sa.a = 5\n",
"sa"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"a 5\n",
"b 2\n",
"c 3\n",
"dtype: int64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sa"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"dfa.A=list(range(len(dfa.index))) # ok if A already exists"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>0</td>\n",
" <td>-0.562650</td>\n",
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" <td>2.333782</td>\n",
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" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>1</td>\n",
" <td>-1.062558</td>\n",
" <td>-1.556939</td>\n",
" <td>-0.303581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>2</td>\n",
" <td>-1.194126</td>\n",
" <td>0.823470</td>\n",
" <td>1.149401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-04</th>\n",
" <td>3</td>\n",
" <td>0.936506</td>\n",
" <td>0.338892</td>\n",
" <td>1.351857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-05</th>\n",
" <td>4</td>\n",
" <td>-1.196422</td>\n",
" <td>-0.539233</td>\n",
" <td>-0.101062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-06</th>\n",
" <td>5</td>\n",
" <td>1.436726</td>\n",
" <td>-2.132088</td>\n",
" <td>-1.661286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-07</th>\n",
" <td>6</td>\n",
" <td>0.329280</td>\n",
" <td>-1.552907</td>\n",
" <td>-0.066676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-08</th>\n",
" <td>7</td>\n",
" <td>0.857815</td>\n",
" <td>-1.192243</td>\n",
" <td>-1.028118</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2000-01-01 0 -0.562650 0.149550 2.333782\n",
"2000-01-02 1 -1.062558 -1.556939 -0.303581\n",
"2000-01-03 2 -1.194126 0.823470 1.149401\n",
"2000-01-04 3 0.936506 0.338892 1.351857\n",
"2000-01-05 4 -1.196422 -0.539233 -0.101062\n",
"2000-01-06 5 1.436726 -2.132088 -1.661286\n",
"2000-01-07 6 0.329280 -1.552907 -0.066676\n",
"2000-01-08 7 0.857815 -1.192243 -1.028118"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfa"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>0</td>\n",
" <td>-0.562650</td>\n",
" <td>0.149550</td>\n",
" <td>2.333782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>1</td>\n",
" <td>-1.062558</td>\n",
" <td>-1.556939</td>\n",
" <td>-0.303581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>2</td>\n",
" <td>-1.194126</td>\n",
" <td>0.823470</td>\n",
" <td>1.149401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-04</th>\n",
" <td>3</td>\n",
" <td>0.936506</td>\n",
" <td>0.338892</td>\n",
" <td>1.351857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-05</th>\n",
" <td>4</td>\n",
" <td>-1.196422</td>\n",
" <td>-0.539233</td>\n",
" <td>-0.101062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-06</th>\n",
" <td>5</td>\n",
" <td>1.436726</td>\n",
" <td>-2.132088</td>\n",
" <td>-1.661286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-07</th>\n",
" <td>6</td>\n",
" <td>0.329280</td>\n",
" <td>-1.552907</td>\n",
" <td>-0.066676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-08</th>\n",
" <td>7</td>\n",
" <td>0.857815</td>\n",
" <td>-1.192243</td>\n",
" <td>-1.028118</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2000-01-01 0 -0.562650 0.149550 2.333782\n",
"2000-01-02 1 -1.062558 -1.556939 -0.303581\n",
"2000-01-03 2 -1.194126 0.823470 1.149401\n",
"2000-01-04 3 0.936506 0.338892 1.351857\n",
"2000-01-05 4 -1.196422 -0.539233 -0.101062\n",
"2000-01-06 5 1.436726 -2.132088 -1.661286\n",
"2000-01-07 6 0.329280 -1.552907 -0.066676\n",
"2000-01-08 7 0.857815 -1.192243 -1.028118"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfa['A'] = list(range(len(dfa.index))) # use this form to create a new column\n",
"dfa"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**注意：**使用属性和[] 有一点区别：\n",
"\n",
"如果要新建一个column，只能使用[]\n",
"\n",
"毕竟属性的含义就是现在存在的！不存在的列名当然不是属性了\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"You can use attribute access to modify an existing element of a Series or column of a DataFrame, but be careful; if you try to use attribute access to create a new column, it fails silently, creating a new attribute rather than a new column."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"使用属性要注意的：\n",
"* 如果一个已经存在的函数和列名相同，则不存在相应的属性哦\n",
"* 总而言之，属性的适用范围要比[]小"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 切片范围 Slicing ranges"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"可以使用 [] 还有.iloc切片，这里先介绍使用[]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**对于Series来说，使用[]进行切片就像ndarray一样，**"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"2000-01-01 -1.226827\n",
"2000-01-02 0.772811\n",
"2000-01-03 0.502868\n",
"2000-01-04 -0.758176\n",
"2000-01-05 -1.276918\n",
"2000-01-06 -0.048258\n",
"2000-01-07 0.368375\n",
"2000-01-08 -1.992648\n",
"Freq: D, Name: A, dtype: float64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"2000-01-01 -1.226827\n",
"2000-01-02 0.772811\n",
"2000-01-03 0.502868\n",
"2000-01-04 -0.758176\n",
"2000-01-05 -1.276918\n",
"Freq: D, Name: A, dtype: float64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[:5]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"2000-01-01 -1.226827\n",
"2000-01-03 0.502868\n",
"2000-01-05 -1.276918\n",
"2000-01-07 0.368375\n",
"Freq: 2D, Name: A, dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[::2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"2000-01-08 -1.992648\n",
"2000-01-07 0.368375\n",
"2000-01-06 -0.048258\n",
"2000-01-05 -1.276918\n",
"2000-01-04 -0.758176\n",
"2000-01-03 0.502868\n",
"2000-01-02 0.772811\n",
"2000-01-01 -1.226827\n",
"Freq: -1D, Name: A, dtype: float64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[::-1]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"[]不但可以检索，也可以赋值"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"s2 = s.copy()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"s2[:5]=0 #赋值"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 0\n",
"5 5\n",
"dtype: int64"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"对于DataFrame对象来说，[]操作符按照行进行切片，非常有用。"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>0.177302</td>\n",
" <td>-1.546113</td>\n",
" <td>-0.952839</td>\n",
" <td>0.008122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>-1.049451</td>\n",
" <td>0.137660</td>\n",
" <td>1.987125</td>\n",
" <td>1.246595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>-0.978006</td>\n",
" <td>0.532418</td>\n",
" <td>-0.847118</td>\n",
" <td>2.461356</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2000-01-01 0.177302 -1.546113 -0.952839 0.008122\n",
"2000-01-02 -1.049451 0.137660 1.987125 1.246595\n",
"2000-01-03 -0.978006 0.532418 -0.847118 2.461356"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[:3]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-08</th>\n",
" <td>0.295963</td>\n",
" <td>-0.698917</td>\n",
" <td>0.932431</td>\n",
" <td>1.440893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-07</th>\n",
" <td>0.995785</td>\n",
" <td>0.674858</td>\n",
" <td>-0.809902</td>\n",
" <td>-0.543680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-06</th>\n",
" <td>0.249859</td>\n",
" <td>-0.664076</td>\n",
" <td>0.881349</td>\n",
" <td>1.945613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-05</th>\n",
" <td>-0.095023</td>\n",
" <td>-1.861150</td>\n",
" <td>0.134254</td>\n",
" <td>0.661625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-04</th>\n",
" <td>-0.798626</td>\n",
" <td>-1.377614</td>\n",
" <td>0.776687</td>\n",
" <td>0.342846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>-0.978006</td>\n",
" <td>0.532418</td>\n",
" <td>-0.847118</td>\n",
" <td>2.461356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>-1.049451</td>\n",
" <td>0.137660</td>\n",
" <td>1.987125</td>\n",
" <td>1.246595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>0.177302</td>\n",
" <td>-1.546113</td>\n",
" <td>-0.952839</td>\n",
" <td>0.008122</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2000-01-08 0.295963 -0.698917 0.932431 1.440893\n",
"2000-01-07 0.995785 0.674858 -0.809902 -0.543680\n",
"2000-01-06 0.249859 -0.664076 0.881349 1.945613\n",
"2000-01-05 -0.095023 -1.861150 0.134254 0.661625\n",
"2000-01-04 -0.798626 -1.377614 0.776687 0.342846\n",
"2000-01-03 -0.978006 0.532418 -0.847118 2.461356\n",
"2000-01-02 -1.049451 0.137660 1.987125 1.246595\n",
"2000-01-01 0.177302 -1.546113 -0.952839 0.008122"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[::-1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 使用Label进行检索"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**警告**：\n",
"\n",
"\n",
".loc要求检索时输入必须严格遵守index的类型，一旦输入类型不对，将会引起TypeError。"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-01</th>\n",
" <td>0.478902</td>\n",
" <td>0.234646</td>\n",
" <td>0.628231</td>\n",
" <td>0.480590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-02</th>\n",
" <td>0.744357</td>\n",
" <td>0.234170</td>\n",
" <td>0.555582</td>\n",
" <td>0.117715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-03</th>\n",
" <td>0.612064</td>\n",
" <td>0.104215</td>\n",
" <td>0.674296</td>\n",
" <td>0.842351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>0.823353</td>\n",
" <td>0.829003</td>\n",
" <td>0.501923</td>\n",
" <td>0.388439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-05</th>\n",
" <td>0.810892</td>\n",
" <td>0.192622</td>\n",
" <td>0.606018</td>\n",
" <td>0.581612</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2016-01-01 0.478902 0.234646 0.628231 0.480590\n",
"2016-01-02 0.744357 0.234170 0.555582 0.117715\n",
"2016-01-03 0.612064 0.104215 0.674296 0.842351\n",
"2016-01-04 0.823353 0.829003 0.501923 0.388439\n",
"2016-01-05 0.810892 0.192622 0.606018 0.581612"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 = pd.DataFrame(np.random.rand(5,4), columns=list('ABCD'), index=pd.date_range('20160101',periods=5))\n",
"df1"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "TypeError",
"evalue": "cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with these indexers [2] of <type 'int'>",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-71-f18421f8c4a7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1284\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\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[0;32m 1285\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1286\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1287\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1288\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1398\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1399\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1400\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_slice_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1401\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mis_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1402\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getbool_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_get_slice_axis\u001b[1;34m(self, slice_obj, axis)\u001b[0m\n\u001b[0;32m 1306\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1307\u001b[0m indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop,\n\u001b[1;32m-> 1308\u001b[1;33m slice_obj.step, kind=self.name)\n\u001b[0m\u001b[0;32m 1309\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1310\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\tseries\\index.pyc\u001b[0m in \u001b[0;36mslice_indexer\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m 1503\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1504\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1505\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mslice_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1506\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1507\u001b[0m \u001b[1;31m# For historical reasons DatetimeIndex by default supports\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\indexes\\base.pyc\u001b[0m in \u001b[0;36mslice_indexer\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m 2698\u001b[0m \"\"\"\n\u001b[0;32m 2699\u001b[0m start_slice, end_slice = self.slice_locs(start, end, step=step,\n\u001b[1;32m-> 2700\u001b[1;33m kind=kind)\n\u001b[0m\u001b[0;32m 2701\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2702\u001b[0m \u001b[1;31m# return a slice\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\indexes\\base.pyc\u001b[0m in \u001b[0;36mslice_locs\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m 2877\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2878\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2879\u001b[1;33m \u001b[0mstart_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_slice_bound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'left'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2880\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2881\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\indexes\\base.pyc\u001b[0m in \u001b[0;36mget_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m 2816\u001b[0m \u001b[1;31m# For datetime indices label may be a string that has to be converted\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2817\u001b[0m \u001b[1;31m# to datetime boundary according to its resolution.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2818\u001b[1;33m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_slice_bound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mside\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2819\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2820\u001b[0m \u001b[1;31m# we need to look up the label\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\tseries\\index.pyc\u001b[0m in \u001b[0;36m_maybe_cast_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m 1458\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1459\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_float\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1460\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalid_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'slice'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1461\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1462\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\indexes\\base.pyc\u001b[0m in \u001b[0;36m_invalid_indexer\u001b[1;34m(self, form, key)\u001b[0m\n\u001b[0;32m 1115\u001b[0m \"indexers [{key}] of {kind}\".format(\n\u001b[0;32m 1116\u001b[0m \u001b[0mform\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\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-> 1117\u001b[1;33m kind=type(key)))\n\u001b[0m\u001b[0;32m 1118\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1119\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with these indexers [2] of <type 'int'>"
]
}
],
"source": [
"df1.loc[2:3]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"输入string进行检索没问题"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-02</th>\n",
" <td>0.744357</td>\n",
" <td>0.234170</td>\n",
" <td>0.555582</td>\n",
" <td>0.117715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-03</th>\n",
" <td>0.612064</td>\n",
" <td>0.104215</td>\n",
" <td>0.674296</td>\n",
" <td>0.842351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>0.823353</td>\n",
" <td>0.829003</td>\n",
" <td>0.501923</td>\n",
" <td>0.388439</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2016-01-02 0.744357 0.234170 0.555582 0.117715\n",
"2016-01-03 0.612064 0.104215 0.674296 0.842351\n",
"2016-01-04 0.823353 0.829003 0.501923 0.388439"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.loc['20160102':'20160104']"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"细心地你一定发现了，index='20160104'那一行也被检索出来了，没错，**loc检索时范围是闭集合[start,end]**.\n",
"\n",
"整型可以作为label检索，这是没问题的，不过要记住此时整型表示的是label而不是index中的下标！"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
".loc操作是检索时的基本操作，以下输入格式都是合法的：\n",
"* 一个label，比如：5、'a'. 记住这里的5表示的是index中的一个label而不是index中的一个下标。\n",
"* label组成的列表或者数组比如['a','b','c']\n",
"* 切片，比如'a':'f'.注意loc中切片范围是闭集合！\n",
"* 布尔数组"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"a 1.270268\n",
"b 1.015481\n",
"c 0.380879\n",
"d 0.965170\n",
"e -0.218055\n",
"f 0.224802\n",
"dtype: float64"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1 = pd.Series(np.random.randn(6), index=list('abcdef'))\n",
"s1"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"c 0.380879\n",
"d 0.965170\n",
"e -0.218055\n",
"f 0.224802\n",
"dtype: float64"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1.loc['c':]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"1.0154808822674235"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1.loc['b']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"loc同样支持赋值操作"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"a 1.270268\n",
"b 1.015481\n",
"c 0.000000\n",
"d 0.000000\n",
"e 0.000000\n",
"f 0.000000\n",
"dtype: float64"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1.loc['c':]=0\n",
"s1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"再来看看DataFramed的例子"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>0.500635</td>\n",
" <td>2.515980</td>\n",
" <td>0.968653</td>\n",
" <td>-0.764951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>0.911650</td>\n",
" <td>-2.208888</td>\n",
" <td>0.389002</td>\n",
" <td>0.296063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>-0.326533</td>\n",
" <td>-0.548483</td>\n",
" <td>-0.225515</td>\n",
" <td>0.561847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>0.061768</td>\n",
" <td>-0.299833</td>\n",
" <td>-1.081881</td>\n",
" <td>-1.389517</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e</th>\n",
" <td>0.440465</td>\n",
" <td>-0.332527</td>\n",
" <td>1.633278</td>\n",
" <td>-0.096852</td>\n",
" </tr>\n",
" <tr>\n",
" <th>f</th>\n",
" <td>1.023751</td>\n",
" <td>-0.562649</td>\n",
" <td>-0.284983</td>\n",
" <td>1.629945</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"a 0.500635 2.515980 0.968653 -0.764951\n",
"b 0.911650 -2.208888 0.389002 0.296063\n",
"c -0.326533 -0.548483 -0.225515 0.561847\n",
"d 0.061768 -0.299833 -1.081881 -1.389517\n",
"e 0.440465 -0.332527 1.633278 -0.096852\n",
"f 1.023751 -0.562649 -0.284983 1.629945"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 = pd.DataFrame(np.random.randn(6,4), index=list('abcdef'),columns=list('ABCD'))\n",
"df1"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>0.500635</td>\n",
" <td>2.515980</td>\n",
" <td>0.968653</td>\n",
" <td>-0.764951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>0.911650</td>\n",
" <td>-2.208888</td>\n",
" <td>0.389002</td>\n",
" <td>0.296063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>-0.326533</td>\n",
" <td>-0.548483</td>\n",
" <td>-0.225515</td>\n",
" <td>0.561847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>0.061768</td>\n",
" <td>-0.299833</td>\n",
" <td>-1.081881</td>\n",
" <td>-1.389517</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" A B C D\n",
"a 0.500635 2.515980 0.968653 -0.764951\n",
"b 0.911650 -2.208888 0.389002 0.296063\n",
"c -0.326533 -0.548483 -0.225515 0.561847\n",
"d 0.061768 -0.299833 -1.081881 -1.389517"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.loc[['a','b','c','d'],:]"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>0.500635</td>\n",
" <td>2.515980</td>\n",
" <td>0.968653</td>\n",
" <td>-0.764951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>0.911650</td>\n",
" <td>-2.208888</td>\n",
" <td>0.389002</td>\n",
" <td>0.296063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>-0.326533</td>\n",
" <td>-0.548483</td>\n",
" <td>-0.225515</td>\n",
" <td>0.561847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>0.061768</td>\n",
" <td>-0.299833</td>\n",
" <td>-1.081881</td>\n",
" <td>-1.389517</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"a 0.500635 2.515980 0.968653 -0.764951\n",
"b 0.911650 -2.208888 0.389002 0.296063\n",
"c -0.326533 -0.548483 -0.225515 0.561847\n",
"d 0.061768 -0.299833 -1.081881 -1.389517"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.loc[['a','b','c','d']] #可以省略 ':'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"使用切片检索"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>0.061768</td>\n",
" <td>-0.299833</td>\n",
" <td>-1.081881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e</th>\n",
" <td>0.440465</td>\n",
" <td>-0.332527</td>\n",
" <td>1.633278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>f</th>\n",
" <td>1.023751</td>\n",
" <td>-0.562649</td>\n",
" <td>-0.284983</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"d 0.061768 -0.299833 -1.081881\n",
"e 0.440465 -0.332527 1.633278\n",
"f 1.023751 -0.562649 -0.284983"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.loc['d':,'A':'C'] #注意是闭集合"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"A 0.500635\n",
"B 2.515980\n",
"C 0.968653\n",
"D -0.764951\n",
"Name: a, dtype: float64"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.loc['a']"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"使用布尔数组检索"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"A True\n",
"B True\n",
"C True\n",
"D False\n",
"Name: a, dtype: bool"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.loc['a']>0"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>0.500635</td>\n",
" <td>2.515980</td>\n",
" <td>0.968653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>0.911650</td>\n",
" <td>-2.208888</td>\n",
" <td>0.389002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>-0.326533</td>\n",
" <td>-0.548483</td>\n",
" <td>-0.225515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>0.061768</td>\n",
" <td>-0.299833</td>\n",
" <td>-1.081881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e</th>\n",
" <td>0.440465</td>\n",
" <td>-0.332527</td>\n",
" <td>1.633278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>f</th>\n",
" <td>1.023751</td>\n",
" <td>-0.562649</td>\n",
" <td>-0.284983</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"a 0.500635 2.515980 0.968653\n",
"b 0.911650 -2.208888 0.389002\n",
"c -0.326533 -0.548483 -0.225515\n",
"d 0.061768 -0.299833 -1.081881\n",
"e 0.440465 -0.332527 1.633278\n",
"f 1.023751 -0.562649 -0.284983"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.loc[:,df1.loc['a']>0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"得到DataFrame中的某一个值, 等同于df1.get_value('a','A')"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0.50063542438780895"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.loc['a','A']"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0.50063542438780895"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.get_value('a','A')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 根据下标进行检索 Selection By Position"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"pandas提供了一系列的方法实现基于整型的检索。语义和python、numpy切片几乎一样。下标同样都是从0开始，并且进行的是半闭半开的区间检索[start,end)。如果输入 非整型label当做下标进行检索会引起IndexError。\n",
"\n",
"\n",
".iloc的合法输入包括：\n",
"* 一个整数，比如5\n",
"* 整数组成的列表或者数组，比如[4,3,0]\n",
"* 整型表示的切片，比如1:7\n",
"* 布尔数组\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"看一下Series使用iloc检索的示例："
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 -0.280654\n",
"2 -0.687606\n",
"4 -1.195345\n",
"6 -0.384770\n",
"8 -0.590466\n",
"dtype: float64"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1 = pd.Series(np.random.randn(5),index=list(range(0,10,2)))\n",
"s1"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 -0.280654\n",
"2 -0.687606\n",
"4 -1.195345\n",
"dtype: float64"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1.iloc[:3] #注意检索是半闭半开区间"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"-0.38477022333948063"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1.iloc[3]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"iloc同样也可以进行赋值"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 0.000000\n",
"2 0.000000\n",
"4 0.000000\n",
"6 -0.384770\n",
"8 -0.590466\n",
"dtype: float64"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1.iloc[:3]=0\n",
"s1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"DataFrame的示例:"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>2</th>\n",
" <th>4</th>\n",
" <th>6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.357496</td>\n",
" <td>0.007987</td>\n",
" <td>-0.373388</td>\n",
" <td>0.713999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1.256285</td>\n",
" <td>-0.366258</td>\n",
" <td>-0.980229</td>\n",
" <td>-1.377265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.256183</td>\n",
" <td>-0.405019</td>\n",
" <td>-0.740001</td>\n",
" <td>0.854734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-1.122789</td>\n",
" <td>-1.652925</td>\n",
" <td>-2.109178</td>\n",
" <td>0.714779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-1.105426</td>\n",
" <td>0.183194</td>\n",
" <td>-0.418197</td>\n",
" <td>1.454595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1.287264</td>\n",
" <td>0.318804</td>\n",
" <td>0.532221</td>\n",
" <td>1.124164</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 2 4 6\n",
"0 0.357496 0.007987 -0.373388 0.713999\n",
"2 -1.256285 -0.366258 -0.980229 -1.377265\n",
"4 -0.256183 -0.405019 -0.740001 0.854734\n",
"6 -1.122789 -1.652925 -2.109178 0.714779\n",
"8 -1.105426 0.183194 -0.418197 1.454595\n",
"10 1.287264 0.318804 0.532221 1.124164"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 = pd.DataFrame(np.random.randn(6,4),index=list(range(0,12,2)), columns=list(range(0,8,2)))\n",
"df1"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>0</th>\n",
" <th>2</th>\n",
" <th>4</th>\n",
" <th>6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.357496</td>\n",
" <td>0.007987</td>\n",
" <td>-0.373388</td>\n",
" <td>0.713999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1.256285</td>\n",
" <td>-0.366258</td>\n",
" <td>-0.980229</td>\n",
" <td>-1.377265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.256183</td>\n",
" <td>-0.405019</td>\n",
" <td>-0.740001</td>\n",
" <td>0.854734</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" 0 2 4 6\n",
"0 0.357496 0.007987 -0.373388 0.713999\n",
"2 -1.256285 -0.366258 -0.980229 -1.377265\n",
"4 -0.256183 -0.405019 -0.740001 0.854734"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.iloc[:3]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"进行行和列的检索"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>4</th>\n",
" <th>6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.980229</td>\n",
" <td>-1.377265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.740001</td>\n",
" <td>0.854734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-2.109178</td>\n",
" <td>0.714779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-0.418197</td>\n",
" <td>1.454595</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 4 6\n",
"2 -0.980229 -1.377265\n",
"4 -0.740001 0.854734\n",
"6 -2.109178 0.714779\n",
"8 -0.418197 1.454595"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.iloc[1:5,2:4]"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>2</th>\n",
" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.366258</td>\n",
" <td>-0.980229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-1.652925</td>\n",
" <td>-2.109178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.318804</td>\n",
" <td>0.532221</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2 4\n",
"2 -0.366258 -0.980229\n",
"6 -1.652925 -2.109178\n",
"10 0.318804 0.532221"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.iloc[[1,3,5],[1,2]]"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>2</th>\n",
" <th>4</th>\n",
" <th>6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1.256285</td>\n",
" <td>-0.366258</td>\n",
" <td>-0.980229</td>\n",
" <td>-1.377265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.256183</td>\n",
" <td>-0.405019</td>\n",
" <td>-0.740001</td>\n",
" <td>0.854734</td>\n",
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"</table>\n",
"</div>"
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"text/plain": [
" 0 2 4 6\n",
"2 -1.256285 -0.366258 -0.980229 -1.377265\n",
"4 -0.256183 -0.405019 -0.740001 0.854734"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.iloc[1:3,:]"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <thead>\n",
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" <th>2</th>\n",
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" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>0.007987</td>\n",
" <td>-0.373388</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>-0.366258</td>\n",
" <td>-0.980229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.405019</td>\n",
" <td>-0.740001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-1.652925</td>\n",
" <td>-2.109178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.183194</td>\n",
" <td>-0.418197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.318804</td>\n",
" <td>0.532221</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
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"text/plain": [
" 2 4\n",
"0 0.007987 -0.373388\n",
"2 -0.366258 -0.980229\n",
"4 -0.405019 -0.740001\n",
"6 -1.652925 -2.109178\n",
"8 0.183194 -0.418197\n",
"10 0.318804 0.532221"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.iloc[:,1:3]"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"-0.36625813479137037"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.iloc[1,1]#只检索一个元素"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"注意下面两个例子的区别："
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 -1.256285\n",
"2 -0.366258\n",
"4 -0.980229\n",
"6 -1.377265\n",
"Name: 2, dtype: float64"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.iloc[1]"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>0</th>\n",
" <th>2</th>\n",
" <th>4</th>\n",
" <th>6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1.256285</td>\n",
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"</table>\n",
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"text/plain": [
" 0 2 4 6\n",
"2 -1.256285 -0.366258 -0.980229 -1.377265"
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"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"df1.iloc[1:2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**如果切片检索时输入的范围越界，没关系，只要pandas版本>=v0.14.0, 就能如同Python/Numpy那样正确处理。**\n",
"\n",
"注意：仅限于 切片检索"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'b', 'c', 'd', 'e', 'f']"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = list('abcdef')\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"['e', 'f']"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[4:10] #这里x的长度是6"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[8:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"s = pd.Series(x)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 a\n",
"1 b\n",
"2 c\n",
"3 d\n",
"4 e\n",
"5 f\n",
"dtype: object"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"4 e\n",
"5 f\n",
"dtype: object"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.iloc[4:10]"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"Series([], dtype: object)"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.iloc[8:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
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" <td>-0.372023</td>\n",
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" <td>1.030279</td>\n",
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" A B\n",
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"4 1.305473 -0.973037"
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"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
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"source": [
"df1 = pd.DataFrame(np.random.randn(5,2), columns=list('AB'))\n",
"df1"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
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"outputs": [
{
"data": {
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"text/plain": [
"Empty DataFrame\n",
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"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
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"source": [
"df1.iloc[:,2:3]"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
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"source": [
"df1.iloc[:,1:3]"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" A B\n",
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"execution_count": 109,
"metadata": {},
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"source": [
"df1.iloc[4:6]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"上面说到，这种优雅处理越界的能力仅限于**输入全是切片**，如果输入是越界的 列表或者整数，则会引起IndexError"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "IndexError",
"evalue": "positional indexers are out-of-bounds",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-110-496782e5248f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1284\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\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[0;32m 1285\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1286\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1287\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1288\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1558\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1559\u001b[0m \u001b[1;31m# validate list bounds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1560\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_valid_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1561\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1562\u001b[0m \u001b[1;31m# force an actual list\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_is_valid_list_like\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1497\u001b[0m \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1498\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1499\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"positional indexers are out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1501\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mIndexError\u001b[0m: positional indexers are out-of-bounds"
]
}
],
"source": [
"df1.iloc[[4,5,6]]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
" 输入有切片，有整数，如果越界同样不能处理"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "IndexError",
"evalue": "single positional indexer is out-of-bounds",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-111-a87dc3221dcf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1282\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1283\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1284\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\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 1285\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1286\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m 1503\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1504\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1505\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1506\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1507\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_has_valid_tuple\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 137\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Too many indexers'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 139\u001b[0m raise ValueError(\"Location based indexing can only have [%s] \"\n\u001b[0;32m 140\u001b[0m \"types\" % self._valid_types)\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_has_valid_type\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1471\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1472\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1473\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_valid_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1474\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mis_list_like_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1475\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_valid_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_is_valid_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1485\u001b[0m \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1486\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1487\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"single positional indexer is out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1488\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
]
}
],
"source": [
"df1.iloc[:,4]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 选择随机样本 Selecting Random Samples"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"使用sample()方法能够从行或者列中进行随机选择，适用对象包括Series、DataFrame和Panel。sample()方法默认对行进行随机选择，输入可以是整数或者小数。"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 1\n",
"2 2\n",
"3 3\n",
"4 4\n",
"5 5\n",
"dtype: int64"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = pd.Series([0,1,2,3,4,5])\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"dtype: int64"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.sample()"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"5 5\n",
"1 1\n",
"3 3\n",
"0 0\n",
"2 2\n",
"4 4\n",
"dtype: int64"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.sample(n=6)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"4 4\n",
"1 1\n",
"0 0\n",
"dtype: int64"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.sample(3) #直接输入整数即可"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"也可以输入小数，则会随机选择N*frac个样本, 结果进行四舍五入"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"5 5\n",
"2 2\n",
"4 4\n",
"dtype: int64"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.sample(frac=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "ValueError",
"evalue": "Only integers accepted as `n` values",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-117-24c34e460c2e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#必须输入frac=0.5\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36msample\u001b[1;34m(self, n, frac, replace, weights, random_state, axis)\u001b[0m\n\u001b[0;32m 2555\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2556\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfrac\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2557\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Only integers accepted as `n` values\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2558\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfrac\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2559\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfrac\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0maxis_length\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: Only integers accepted as `n` values"
]
}
],
"source": [
"s.sample(0.5) #必须输入frac=0.5"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"5 5\n",
"4 4\n",
"0 0\n",
"2 2\n",
"1 1\n",
"dtype: int64"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.sample(frac=0.8) #6*0.8=4.8"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"5 5\n",
"4 4\n",
"2 2\n",
"dtype: int64"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.sample(frac=0.7)# 6*0.7=4.2"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"sample()默认进行的无放回抽样，可以利用replace=True参数进行可放回抽样。"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 1\n",
"2 2\n",
"3 3\n",
"4 4\n",
"5 5\n",
"dtype: int64"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"1 1\n",
"3 3\n",
"5 5\n",
"4 4\n",
"2 2\n",
"0 0\n",
"dtype: int64"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.sample(n=6,replace=False)"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"5 5\n",
"5 5\n",
"5 5\n",
"5 5\n",
"3 3\n",
"1 1\n",
"dtype: int64"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.sample(6,replace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"默认情况下，每一行/列都被等可能的采样，如果你想为每一行赋予一个被抽样选择的权重，可以利用weights参数实现。\n",
"\n",
"注意：如果weights中各概率相加和不等于1，pandas会先对weights进行归一化，强制转为概率和为1！"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 1\n",
"2 2\n",
"3 3\n",
"4 4\n",
"5 5\n",
"dtype: int64"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = pd.Series([0,1,2,3,4,5])\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"example_weights=[0,0,0.2,0.2,0.2,0.4]"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"2 2\n",
"5 5\n",
"3 3\n",
"dtype: int64"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.sample(n=3,weights=example_weights)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"example_weights2 = [0.5, 0, 0, 0, 0, 0]"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"dtype: int64"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.sample(n=1, weights=example_weights2)"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "ValueError",
"evalue": "Fewer non-zero entries in p than size",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-128-933f0356437e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexample_weights2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#n>1 会报错，\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36msample\u001b[1;34m(self, n, frac, replace, weights, random_state, axis)\u001b[0m\n\u001b[0;32m 2567\u001b[0m \"provide positive value.\")\n\u001b[0;32m 2568\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2569\u001b[1;33m \u001b[0mlocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis_length\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2570\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlocs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mis_copy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2571\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.RandomState.choice (numpy\\random\\mtrand\\mtrand.c:16370)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: Fewer non-zero entries in p than size"
]
}
],
"source": [
"s.sample(n=2, weights=example_weights2) #n>1 会报错，"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**注意**：由于sample默认进行的是无放回抽样，所以输入必须n<=行数，除非进行可放回抽样。"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 1\n",
"2 2\n",
"3 3\n",
"4 4\n",
"5 5\n",
"dtype: int64"
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "ValueError",
"evalue": "Cannot take a larger sample than population when 'replace=False'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-130-d71dc28cbaa3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#7不行\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36msample\u001b[1;34m(self, n, frac, replace, weights, random_state, axis)\u001b[0m\n\u001b[0;32m 2567\u001b[0m \"provide positive value.\")\n\u001b[0;32m 2568\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2569\u001b[1;33m \u001b[0mlocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis_length\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2570\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlocs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mis_copy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2571\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.RandomState.choice (numpy\\random\\mtrand\\mtrand.c:16292)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: Cannot take a larger sample than population when 'replace=False'"
]
}
],
"source": [
"s.sample(7) #7不行"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"3 3\n",
"1 1\n",
"4 4\n",
"2 2\n",
"2 2\n",
"1 1\n",
"1 1\n",
"dtype: int64"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.sample(7,replace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"如果是对DataFrame对象进行有权重采样，一个简单 的方法是新增一列用于表示每一行的权重"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
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"source": [
"df2 = pd.DataFrame({'col1':[9,8,7,6], 'weight_column':[0.5, 0.4, 0.1, 0]})\n",
"df2"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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"execution_count": 135,
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"source": [
"df2.sample(n=3,weights='weight_column')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"对列进行采样, axis=1"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
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"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3 = pd.DataFrame({'col1':[1,2,3], 'clo2':[2,3,4]})\n",
"df3"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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"source": [
"df3.sample(1,axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"我们也可以使用random_state参数 为sample内部的随机数生成器提供种子数。"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
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"metadata": {},
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"source": [
"df4 = pd.DataFrame({'col1':[1,2,3], 'clo2':[2,3,4]})\n",
"df4"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"注意下面两个示例，输出是相同的，因为使用了相同的种子数"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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"source": [
"df4.sample(n=2, random_state=2)"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
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"source": [
"df4.sample(n=2,random_state=2)"
]
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{
"cell_type": "code",
"execution_count": 141,
"metadata": {
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"editable": true
},
"outputs": [
{
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"source": [
"df4.sample(n=2,random_state=3)"
]
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{
"cell_type": "code",
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"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 使用赋值的方式扩充对象 Setting With Enlargement"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"用.loc/.ix/[]对不存在的键值进行赋值时，将会导致在对象中添加新的元素，它的键即为赋值时不存在的键。\n",
"\n",
"对于Series来说，这是一种有效的添加操作。"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 2\n",
"2 3\n",
"dtype: int64"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"se = pd.Series([1,2,3])\n",
"se"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 2\n",
"2 3\n",
"5 5\n",
"dtype: int64"
]
},
"execution_count": 144,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"se[5]=5\n",
"se"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"DataFrame可以在行或者列上扩充数据"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B\n",
"0 0 1\n",
"1 2 3\n",
"2 4 5"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfi = pd.DataFrame(np.arange(6).reshape(3,2),columns=['A','B'])\n",
"dfi"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"dfi.loc[:,'C']=dfi.loc[:,'A'] #对列进行扩充"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"0 0 1 0\n",
"1 2 3 2\n",
"2 4 5 4"
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfi"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"dfi.loc[3]=5 #对行进行扩充"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"0 0 1 0\n",
"1 2 3 2\n",
"2 4 5 4\n",
"3 5 5 5"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfi"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 标量值的快速获取和赋值"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"如果仅仅想获取一个元素，使用[]未免太繁重了。pandas提供了快速获取一个元素的方法：at和iat. 适用于Series、DataFrame和Panel。\n",
"\n",
"如果loc方法，at方法的合法输入是label，iat的合法输入是整型。"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.iat[5]"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0.24985887518963862"
]
},
"execution_count": 155,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.at[dates[5],'A']"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"-0.79862582029106743"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iat[3,0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"也可以进行赋值操作"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>0.177302</td>\n",
" <td>-1.546113</td>\n",
" <td>-0.952839</td>\n",
" <td>0.008122</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>-1.049451</td>\n",
" <td>0.137660</td>\n",
" <td>1.987125</td>\n",
" <td>1.246595</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>-0.978006</td>\n",
" <td>0.532418</td>\n",
" <td>-0.847118</td>\n",
" <td>2.461356</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-04</th>\n",
" <td>-0.798626</td>\n",
" <td>-1.377614</td>\n",
" <td>0.776687</td>\n",
" <td>0.342846</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-05</th>\n",
" <td>-0.095023</td>\n",
" <td>-1.861150</td>\n",
" <td>0.134254</td>\n",
" <td>0.661625</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-06</th>\n",
" <td>0.249859</td>\n",
" <td>-0.664076</td>\n",
" <td>0.881349</td>\n",
" <td>1.945613</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-07</th>\n",
" <td>0.995785</td>\n",
" <td>0.674858</td>\n",
" <td>-0.809902</td>\n",
" <td>-0.543680</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-08</th>\n",
" <td>0.295963</td>\n",
" <td>-0.698917</td>\n",
" <td>0.932431</td>\n",
" <td>1.440893</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-09</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D 0\n",
"2000-01-01 0.177302 -1.546113 -0.952839 0.008122 NaN\n",
"2000-01-02 -1.049451 0.137660 1.987125 1.246595 NaN\n",
"2000-01-03 -0.978006 0.532418 -0.847118 2.461356 NaN\n",
"2000-01-04 -0.798626 -1.377614 0.776687 0.342846 NaN\n",
"2000-01-05 -0.095023 -1.861150 0.134254 0.661625 NaN\n",
"2000-01-06 0.249859 -0.664076 0.881349 1.945613 NaN\n",
"2000-01-07 0.995785 0.674858 -0.809902 -0.543680 NaN\n",
"2000-01-08 0.295963 -0.698917 0.932431 1.440893 NaN\n",
"2000-01-09 NaN NaN NaN NaN 7.0"
]
},
"execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.at[dates[-1]+1,0]=7\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 布尔检索 Boolean indexing"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"另一种常用的操作是使用布尔向量过滤数据。运算符有三个:|(or), &(and), ~(not)。\n",
"\n",
"**注意**：运算符的操作数要在圆括号内。"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"使用布尔向量检索Series的操作方式和numpy ndarray一样。"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 -3\n",
"1 -2\n",
"2 -1\n",
"3 0\n",
"4 1\n",
"5 2\n",
"6 3\n",
"dtype: int64"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = pd.Series(range(-3, 4))\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"4 1\n",
"5 2\n",
"6 3\n",
"dtype: int64"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[s>0]"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 -3\n",
"1 -2\n",
"4 1\n",
"5 2\n",
"6 3\n",
"dtype: int64"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[(s<-1) | (s>0.5)]"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"3 0\n",
"4 1\n",
"5 2\n",
"6 3\n",
"dtype: int64"
]
},
"execution_count": 162,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[~(s<0)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"DataFrame示例："
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>0.177302</td>\n",
" <td>-1.546113</td>\n",
" <td>-0.952839</td>\n",
" <td>0.008122</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-06</th>\n",
" <td>0.249859</td>\n",
" <td>-0.664076</td>\n",
" <td>0.881349</td>\n",
" <td>1.945613</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-07</th>\n",
" <td>0.995785</td>\n",
" <td>0.674858</td>\n",
" <td>-0.809902</td>\n",
" <td>-0.543680</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-08</th>\n",
" <td>0.295963</td>\n",
" <td>-0.698917</td>\n",
" <td>0.932431</td>\n",
" <td>1.440893</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D 0\n",
"2000-01-01 0.177302 -1.546113 -0.952839 0.008122 NaN\n",
"2000-01-06 0.249859 -0.664076 0.881349 1.945613 NaN\n",
"2000-01-07 0.995785 0.674858 -0.809902 -0.543680 NaN\n",
"2000-01-08 0.295963 -0.698917 0.932431 1.440893 NaN"
]
},
"execution_count": 165,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['A'] > 0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"利用列表解析和map方法能够产生更加复杂的选择标准。"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>one</td>\n",
" <td>x</td>\n",
" <td>1.952594</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>one</td>\n",
" <td>y</td>\n",
" <td>0.964196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>two</td>\n",
" <td>y</td>\n",
" <td>0.752335</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>three</td>\n",
" <td>x</td>\n",
" <td>0.897060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>two</td>\n",
" <td>y</td>\n",
" <td>-0.120268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>one</td>\n",
" <td>x</td>\n",
" <td>-0.114347</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>six</td>\n",
" <td>x</td>\n",
" <td>-0.690658</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 one x 1.952594\n",
"1 one y 0.964196\n",
"2 two y 0.752335\n",
"3 three x 0.897060\n",
"4 two y -0.120268\n",
"5 one x -0.114347\n",
"6 six x -0.690658"
]
},
"execution_count": 169,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = pd.DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],\n",
" 'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],\n",
" 'c' : np.random.randn(7)})\n",
"df2"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"criterion = df2['a'].map(lambda x:x.startswith('t'))"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>two</td>\n",
" <td>y</td>\n",
" <td>0.752335</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>three</td>\n",
" <td>x</td>\n",
" <td>0.897060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>two</td>\n",
" <td>y</td>\n",
" <td>-0.120268</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"2 two y 0.752335\n",
"3 three x 0.897060\n",
"4 two y -0.120268"
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2[criterion]"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>two</td>\n",
" <td>y</td>\n",
" <td>0.752335</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>three</td>\n",
" <td>x</td>\n",
" <td>0.897060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>two</td>\n",
" <td>y</td>\n",
" <td>-0.120268</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"2 two y 0.752335\n",
"3 three x 0.897060\n",
"4 two y -0.120268"
]
},
"execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2[[x.startswith('t') for x in df2['a']]]"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>three</td>\n",
" <td>x</td>\n",
" <td>0.89706</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"3 three x 0.89706"
]
},
"execution_count": 173,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2[criterion & (df2['b'] == 'x')]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"结合loc、iloc等方法可以检索多个坐标下的数据."
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>x</td>\n",
" <td>0.89706</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" b c\n",
"3 x 0.89706"
]
},
"execution_count": 174,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.loc[criterion & (df2['b'] == 'x'), 'b':'c']"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 使用isin方法检索 Indexing with isin"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"isin(is in)\n",
"\n",
"对于Series对象来说，使用isin方法时传入一个列表，isin方法会返回一个布尔向量。布尔向量元素为1的前提是列表元素在Series对象中存在。看起来比较拗口，还是看例子吧："
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"s = pd.Series(np.arange(5), index=np.arange(5)[::-1],dtype='int64')"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"4 0\n",
"3 1\n",
"2 2\n",
"1 3\n",
"0 4\n",
"dtype: int64"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"4 False\n",
"3 False\n",
"2 True\n",
"1 False\n",
"0 True\n",
"dtype: bool"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.isin([2,4,6])"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"2 2\n",
"0 4\n",
"dtype: int64"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[s.isin([2,4,6])]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Index对象中也有isin方法."
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"4 0\n",
"2 2\n",
"dtype: int64"
]
},
"execution_count": 179,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[s.index.isin([2,4,6])]"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"2 2.0\n",
"4 0.0\n",
"6 NaN\n",
"dtype: float64"
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[[2,4,6]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"DataFrame同样有isin方法，参数是数组或字典。二者的区别看例子吧："
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ids</th>\n",
" <th>ids2</th>\n",
" <th>vals</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>b</td>\n",
" <td>n</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>n</td>\n",
" <td>n</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ids ids2 vals\n",
"0 a a 1\n",
"1 b n 2\n",
"2 f c 3\n",
"3 n n 4"
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'n'],\n",
" 'ids2':['a', 'n', 'c', 'n']})\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"values=['a', 'b', 1, 3]"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ids</th>\n",
" <th>ids2</th>\n",
" <th>vals</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ids ids2 vals\n",
"0 True True True\n",
"1 True False False\n",
"2 False False True\n",
"3 False False False"
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isin(values)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"输入一个字典的情形："
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"values = {'ids': ['a', 'b'], 'vals': [1, 3]}"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ids</th>\n",
" <th>ids2</th>\n",
" <th>vals</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ids ids2 vals\n",
"0 True False True\n",
"1 True False False\n",
"2 False False True\n",
"3 False False False"
]
},
"execution_count": 186,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isin(values)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"结合isin方法和any() all()可以对DataFrame进行快速查询。比如选择每一列都符合标准的行:"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"values = {'ids': ['a', 'b'], 'ids2': ['a', 'c'], 'vals': [1, 3]}"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"row_mark = df.isin(values).all(1)"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ids</th>\n",
" <th>ids2</th>\n",
" <th>vals</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ids ids2 vals\n",
"0 a a 1"
]
},
"execution_count": 189,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[row_mark]"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"row_mark = df.isin(values).any(1)"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ids</th>\n",
" <th>ids2</th>\n",
" <th>vals</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>b</td>\n",
" <td>n</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ids ids2 vals\n",
"0 a a 1\n",
"1 b n 2\n",
"2 f c 3"
]
},
"execution_count": 199,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[row_mark]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## where()方法 The where() Method and Masking"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"使用布尔向量对Series对象查询时通常返回的是对象的子集。如果想要返回的shape和原对象相同，可以使用where方法。\n",
"\n",
"使用布尔向量对DataFrame对象查询返回的shape和原对象相同，这是因为底层用的where方法实现。"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"3 1\n",
"2 2\n",
"1 3\n",
"0 4\n",
"dtype: int64"
]
},
"execution_count": 205,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[s>0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"使用where方法"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"4 NaN\n",
"3 1.0\n",
"2 2.0\n",
"1 3.0\n",
"0 4.0\n",
"dtype: float64"
]
},
"execution_count": 206,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.where(s>0)"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ids</th>\n",
" <th>ids2</th>\n",
" <th>vals</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ids ids2 vals\n",
"0 NaN NaN NaN\n",
"1 NaN NaN NaN\n",
"2 NaN NaN NaN\n",
"3 NaN NaN NaN"
]
},
"execution_count": 207,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df<0]"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ids</th>\n",
" <th>ids2</th>\n",
" <th>vals</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ids ids2 vals\n",
"0 NaN NaN NaN\n",
"1 NaN NaN NaN\n",
"2 NaN NaN NaN\n",
"3 NaN NaN NaN"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.where(df<0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"where方法还有一个可选的other参数，作用是替换返回结果中是False的值，并不会改变原对象。"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ids</th>\n",
" <th>ids2</th>\n",
" <th>vals</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ids ids2 vals\n",
"0 2 2 2\n",
"1 2 2 2\n",
"2 2 2 2\n",
"3 2 2 2"
]
},
"execution_count": 214,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.where(df<0, 2)"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ids</th>\n",
" <th>ids2</th>\n",
" <th>vals</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>b</td>\n",
" <td>n</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>n</td>\n",
" <td>n</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ids ids2 vals\n",
"0 a a 1\n",
"1 b n 2\n",
"2 f c 3\n",
"3 n n 4"
]
},
"execution_count": 211,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ids</th>\n",
" <th>ids2</th>\n",
" <th>vals</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>b</td>\n",
" <td>n</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>n</td>\n",
" <td>n</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ids ids2 vals\n",
"0 a a 1\n",
"1 b n 2\n",
"2 f c 3\n",
"3 n n 4"
]
},
"execution_count": 215,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.where(df<0, df) #将df作为other的参数值"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"你可能想基于某种判断条件来赋值。一种直观的方法是："
]
},
{
"cell_type": "code",
"execution_count": 216,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"4 0\n",
"3 1\n",
"2 2\n",
"1 3\n",
"0 4\n",
"dtype: int64"
]
},
"execution_count": 216,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2 = s.copy()\n",
"s2"
]
},
{
"cell_type": "code",
"execution_count": 217,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"s2[s2<0]=0"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"4 0\n",
"3 1\n",
"2 2\n",
"1 3\n",
"0 4\n",
"dtype: int64"
]
},
"execution_count": 218,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"默认情况下，where方法并不会修改原始对象，它返回的是一个修改过的原始对象副本，如果你想直接修改原始对象，方法是将inplace参数设置为True"
]
},
{
"cell_type": "code",
"execution_count": 223,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.randn(6,5), index=list('abcdef'), columns=list('ABCDE'))\n",
"df_orig = df.copy()"
]
},
{
"cell_type": "code",
"execution_count": 226,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df_orig.where(df < 0, -df, inplace=True);"
]
},
{
"cell_type": "code",
"execution_count": 227,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>-1.196045</td>\n",
" <td>1.488123</td>\n",
" <td>-1.258859</td>\n",
" <td>-1.072124</td>\n",
" <td>1.186134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>-0.981695</td>\n",
" <td>-0.895986</td>\n",
" <td>-0.342973</td>\n",
" <td>1.387443</td>\n",
" <td>-1.254906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>-1.051246</td>\n",
" <td>1.700564</td>\n",
" <td>-1.647643</td>\n",
" <td>0.569339</td>\n",
" <td>-0.292161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>-0.668880</td>\n",
" <td>-1.762922</td>\n",
" <td>0.886166</td>\n",
" <td>-0.057681</td>\n",
" <td>-0.106489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e</th>\n",
" <td>-1.049452</td>\n",
" <td>-0.427708</td>\n",
" <td>0.111594</td>\n",
" <td>0.524640</td>\n",
" <td>0.372802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>f</th>\n",
" <td>-0.320219</td>\n",
" <td>0.179891</td>\n",
" <td>0.638859</td>\n",
" <td>-1.704587</td>\n",
" <td>0.199829</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E\n",
"a -1.196045 1.488123 -1.258859 -1.072124 1.186134\n",
"b -0.981695 -0.895986 -0.342973 1.387443 -1.254906\n",
"c -1.051246 1.700564 -1.647643 0.569339 -0.292161\n",
"d -0.668880 -1.762922 0.886166 -0.057681 -0.106489\n",
"e -1.049452 -0.427708 0.111594 0.524640 0.372802\n",
"f -0.320219 0.179891 0.638859 -1.704587 0.199829"
]
},
"execution_count": 227,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_orig"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**对齐**"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"where方法会将输入的布尔条件对齐，因此允许部分检索时的赋值。"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df2 = df.copy()"
]
},
{
"cell_type": "code",
"execution_count": 232,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df2[df2[1:4] >0]=3"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.196045</td>\n",
" <td>-1.488123</td>\n",
" <td>1.258859</td>\n",
" <td>1.072124</td>\n",
" <td>-1.186134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>-1.387443</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3.000000</td>\n",
" <td>-1.700564</td>\n",
" <td>3.000000</td>\n",
" <td>-0.569339</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>-0.886166</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e</th>\n",
" <td>1.049452</td>\n",
" <td>0.427708</td>\n",
" <td>-0.111594</td>\n",
" <td>-0.524640</td>\n",
" <td>-0.372802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>f</th>\n",
" <td>0.320219</td>\n",
" <td>-0.179891</td>\n",
" <td>-0.638859</td>\n",
" <td>1.704587</td>\n",
" <td>-0.199829</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E\n",
"a 1.196045 -1.488123 1.258859 1.072124 -1.186134\n",
"b 3.000000 3.000000 3.000000 -1.387443 3.000000\n",
"c 3.000000 -1.700564 3.000000 -0.569339 3.000000\n",
"d 3.000000 3.000000 -0.886166 3.000000 3.000000\n",
"e 1.049452 0.427708 -0.111594 -0.524640 -0.372802\n",
"f 0.320219 -0.179891 -0.638859 1.704587 -0.199829"
]
},
"execution_count": 233,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2"
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df2 = df.copy()"
]
},
{
"cell_type": "code",
"execution_count": 235,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.196045</td>\n",
" <td>1.196045</td>\n",
" <td>1.258859</td>\n",
" <td>1.072124</td>\n",
" <td>1.196045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>0.981695</td>\n",
" <td>0.895986</td>\n",
" <td>0.342973</td>\n",
" <td>0.981695</td>\n",
" <td>1.254906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>1.051246</td>\n",
" <td>1.051246</td>\n",
" <td>1.647643</td>\n",
" <td>1.051246</td>\n",
" <td>0.292161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>0.668880</td>\n",
" <td>1.762922</td>\n",
" <td>0.668880</td>\n",
" <td>0.057681</td>\n",
" <td>0.106489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e</th>\n",
" <td>1.049452</td>\n",
" <td>0.427708</td>\n",
" <td>1.049452</td>\n",
" <td>1.049452</td>\n",
" <td>1.049452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>f</th>\n",
" <td>0.320219</td>\n",
" <td>0.320219</td>\n",
" <td>0.320219</td>\n",
" <td>1.704587</td>\n",
" <td>0.320219</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E\n",
"a 1.196045 1.196045 1.258859 1.072124 1.196045\n",
"b 0.981695 0.895986 0.342973 0.981695 1.254906\n",
"c 1.051246 1.051246 1.647643 1.051246 0.292161\n",
"d 0.668880 1.762922 0.668880 0.057681 0.106489\n",
"e 1.049452 0.427708 1.049452 1.049452 1.049452\n",
"f 0.320219 0.320219 0.320219 1.704587 0.320219"
]
},
"execution_count": 235,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.where(df2>0, df2['A'], axis='index')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**mask**"
]
},
{
"cell_type": "code",
"execution_count": 236,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"4 NaN\n",
"3 NaN\n",
"2 NaN\n",
"1 NaN\n",
"0 NaN\n",
"dtype: float64"
]
},
"execution_count": 236,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.mask(s>=0)"
]
},
{
"cell_type": "code",
"execution_count": 237,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>NaN</td>\n",
" <td>-1.488123</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1.186134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1.387443</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>NaN</td>\n",
" <td>-1.700564</td>\n",
" <td>NaN</td>\n",
" <td>-0.569339</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.886166</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.111594</td>\n",
" <td>-0.524640</td>\n",
" <td>-0.372802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>f</th>\n",
" <td>NaN</td>\n",
" <td>-0.179891</td>\n",
" <td>-0.638859</td>\n",
" <td>NaN</td>\n",
" <td>-0.199829</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E\n",
"a NaN -1.488123 NaN NaN -1.186134\n",
"b NaN NaN NaN -1.387443 NaN\n",
"c NaN -1.700564 NaN -0.569339 NaN\n",
"d NaN NaN -0.886166 NaN NaN\n",
"e NaN NaN -0.111594 -0.524640 -0.372802\n",
"f NaN -0.179891 -0.638859 NaN -0.199829"
]
},
"execution_count": 237,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.mask(df >= 0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## query()方法 The query() Method (Experimental)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"DataFrame对象拥有query方法，允许使用表达式检索。\n",
"\n",
"比如，检索列'b'的值介于列‘a’和‘c’之间的行。\n",
"\n",
"**注意：** 需要安装numexptr。"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"n = 10"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.randn(n, 3), columns=list('abc'))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.122199</td>\n",
" <td>0.930233</td>\n",
" <td>0.032165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.701562</td>\n",
" <td>-0.264389</td>\n",
" <td>-0.218722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.249641</td>\n",
" <td>-0.491504</td>\n",
" <td>-0.505626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.737489</td>\n",
" <td>-1.021851</td>\n",
" <td>-0.133149</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.824726</td>\n",
" <td>0.610772</td>\n",
" <td>-0.618181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1.776072</td>\n",
" <td>0.179174</td>\n",
" <td>-0.221257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.203382</td>\n",
" <td>0.170864</td>\n",
" <td>0.311583</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.184993</td>\n",
" <td>2.168807</td>\n",
" <td>-0.525213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-1.569373</td>\n",
" <td>-0.422874</td>\n",
" <td>0.025034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-0.250066</td>\n",
" <td>-0.523624</td>\n",
" <td>-0.068660</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 0.122199 0.930233 0.032165\n",
"1 0.701562 -0.264389 -0.218722\n",
"2 1.249641 -0.491504 -0.505626\n",
"3 0.737489 -1.021851 -0.133149\n",
"4 0.824726 0.610772 -0.618181\n",
"5 1.776072 0.179174 -0.221257\n",
"6 0.203382 0.170864 0.311583\n",
"7 -0.184993 2.168807 -0.525213\n",
"8 -1.569373 -0.422874 0.025034\n",
"9 -0.250066 -0.523624 -0.068660"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-1.569373</td>\n",
" <td>-0.422874</td>\n",
" <td>0.025034</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"8 -1.569373 -0.422874 0.025034"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df.a<df.b) & (df.b<df.c)]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-1.569373</td>\n",
" <td>-0.422874</td>\n",
" <td>0.025034</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"8 -1.569373 -0.422874 0.025034"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('(a < b) & (b < c)') #"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"### MultiIndex query() 语法"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"对于DataFrame对象，可以使用MultiIndex，如同操作列名一样。"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"n = 10"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"colors = np.random.choice(['red', 'green'], size=n)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"foods = np.random.choice(['eggs', 'ham'], size=n)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"array(['green', 'red', 'red', 'red', 'green', 'red', 'green', 'red',\n",
" 'green', 'red'], \n",
" dtype='|S5')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"colors"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"array(['ham', 'eggs', 'ham', 'ham', 'ham', 'ham', 'ham', 'eggs', 'ham',\n",
" 'eggs'], \n",
" dtype='|S4')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"foods"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"index = pd.MultiIndex.from_arrays([colors, foods], names=['color', 'food'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.randn(n,2), index=index)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>color</th>\n",
" <th>food</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>green</th>\n",
" <th>ham</th>\n",
" <td>-0.302117</td>\n",
" <td>-0.329496</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">red</th>\n",
" <th>eggs</th>\n",
" <td>1.511928</td>\n",
" <td>0.516101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ham</th>\n",
" <td>-0.496161</td>\n",
" <td>-2.188031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ham</th>\n",
" <td>-0.675945</td>\n",
" <td>-1.174039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>green</th>\n",
" <th>ham</th>\n",
" <td>-0.045403</td>\n",
" <td>1.232689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red</th>\n",
" <th>ham</th>\n",
" <td>-0.629616</td>\n",
" <td>0.898270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>green</th>\n",
" <th>ham</th>\n",
" <td>-1.107152</td>\n",
" <td>-0.608575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red</th>\n",
" <th>eggs</th>\n",
" <td>-2.190136</td>\n",
" <td>0.267058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>green</th>\n",
" <th>ham</th>\n",
" <td>0.463844</td>\n",
" <td>0.753210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red</th>\n",
" <th>eggs</th>\n",
" <td>-2.202780</td>\n",
" <td>-0.489497</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1\n",
"color food \n",
"green ham -0.302117 -0.329496\n",
"red eggs 1.511928 0.516101\n",
" ham -0.496161 -2.188031\n",
" ham -0.675945 -1.174039\n",
"green ham -0.045403 1.232689\n",
"red ham -0.629616 0.898270\n",
"green ham -1.107152 -0.608575\n",
"red eggs -2.190136 0.267058\n",
"green ham 0.463844 0.753210\n",
"red eggs -2.202780 -0.489497"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>color</th>\n",
" <th>food</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">red</th>\n",
" <th>eggs</th>\n",
" <td>1.511928</td>\n",
" <td>0.516101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ham</th>\n",
" <td>-0.496161</td>\n",
" <td>-2.188031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ham</th>\n",
" <td>-0.675945</td>\n",
" <td>-1.174039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ham</th>\n",
" <td>-0.629616</td>\n",
" <td>0.898270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>eggs</th>\n",
" <td>-2.190136</td>\n",
" <td>0.267058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>eggs</th>\n",
" <td>-2.202780</td>\n",
" <td>-0.489497</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1\n",
"color food \n",
"red eggs 1.511928 0.516101\n",
" ham -0.496161 -2.188031\n",
" ham -0.675945 -1.174039\n",
" ham -0.629616 0.898270\n",
" eggs -2.190136 0.267058\n",
" eggs -2.202780 -0.489497"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('color == \"red\"')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"如果index没有名字，可以给他们命名"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df.index.names = [None, None]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>green</th>\n",
" <th>ham</th>\n",
" <td>-0.302117</td>\n",
" <td>-0.329496</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">red</th>\n",
" <th>eggs</th>\n",
" <td>1.511928</td>\n",
" <td>0.516101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ham</th>\n",
" <td>-0.496161</td>\n",
" <td>-2.188031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ham</th>\n",
" <td>-0.675945</td>\n",
" <td>-1.174039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>green</th>\n",
" <th>ham</th>\n",
" <td>-0.045403</td>\n",
" <td>1.232689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red</th>\n",
" <th>ham</th>\n",
" <td>-0.629616</td>\n",
" <td>0.898270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>green</th>\n",
" <th>ham</th>\n",
" <td>-1.107152</td>\n",
" <td>-0.608575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red</th>\n",
" <th>eggs</th>\n",
" <td>-2.190136</td>\n",
" <td>0.267058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>green</th>\n",
" <th>ham</th>\n",
" <td>0.463844</td>\n",
" <td>0.753210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red</th>\n",
" <th>eggs</th>\n",
" <td>-2.202780</td>\n",
" <td>-0.489497</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1\n",
"green ham -0.302117 -0.329496\n",
"red eggs 1.511928 0.516101\n",
" ham -0.496161 -2.188031\n",
" ham -0.675945 -1.174039\n",
"green ham -0.045403 1.232689\n",
"red ham -0.629616 0.898270\n",
"green ham -1.107152 -0.608575\n",
"red eggs -2.190136 0.267058\n",
"green ham 0.463844 0.753210\n",
"red eggs -2.202780 -0.489497"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">red</th>\n",
" <th>eggs</th>\n",
" <td>1.511928</td>\n",
" <td>0.516101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ham</th>\n",
" <td>-0.496161</td>\n",
" <td>-2.188031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ham</th>\n",
" <td>-0.675945</td>\n",
" <td>-1.174039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ham</th>\n",
" <td>-0.629616</td>\n",
" <td>0.898270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>eggs</th>\n",
" <td>-2.190136</td>\n",
" <td>0.267058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>eggs</th>\n",
" <td>-2.202780</td>\n",
" <td>-0.489497</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1\n",
"red eggs 1.511928 0.516101\n",
" ham -0.496161 -2.188031\n",
" ham -0.675945 -1.174039\n",
" ham -0.629616 0.898270\n",
" eggs -2.190136 0.267058\n",
" eggs -2.202780 -0.489497"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('ilevel_0 == \"red\"')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"ilevl_0意思是 0级index。"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### query() 用例 query() Use Cases"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"一个使用query()的情景是面对DataFrame对象组成的集合，并且这些对象有共同的的列名，则可以利用query方法对这个集合进行统一检索。"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.165333</td>\n",
" <td>-1.033593</td>\n",
" <td>-0.350963</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.194190</td>\n",
" <td>-1.150226</td>\n",
" <td>0.394567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.596311</td>\n",
" <td>0.731163</td>\n",
" <td>-0.076441</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.581372</td>\n",
" <td>-0.181338</td>\n",
" <td>-0.537066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.400650</td>\n",
" <td>-0.557857</td>\n",
" <td>0.899903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.953411</td>\n",
" <td>-0.339584</td>\n",
" <td>-0.326526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.624137</td>\n",
" <td>2.367149</td>\n",
" <td>1.713632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-1.082154</td>\n",
" <td>0.656102</td>\n",
" <td>-0.085876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.403841</td>\n",
" <td>0.049296</td>\n",
" <td>-1.450854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-1.545236</td>\n",
" <td>-0.277590</td>\n",
" <td>0.670381</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 0.165333 -1.033593 -0.350963\n",
"1 1.194190 -1.150226 0.394567\n",
"2 2.596311 0.731163 -0.076441\n",
"3 -0.581372 -0.181338 -0.537066\n",
"4 -0.400650 -0.557857 0.899903\n",
"5 -0.953411 -0.339584 -0.326526\n",
"6 -0.624137 2.367149 1.713632\n",
"7 -1.082154 0.656102 -0.085876\n",
"8 0.403841 0.049296 -1.450854\n",
"9 -1.545236 -0.277590 0.670381"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(np.random.randn(n, 3), columns=list('abc'))\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1.094352</td>\n",
" <td>-0.298453</td>\n",
" <td>-1.033678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.433829</td>\n",
" <td>0.453419</td>\n",
" <td>0.025845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.073075</td>\n",
" <td>-0.807078</td>\n",
" <td>-0.301511</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.349793</td>\n",
" <td>1.206156</td>\n",
" <td>-0.409708</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.182058</td>\n",
" <td>-0.751007</td>\n",
" <td>-1.877473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.073020</td>\n",
" <td>0.615864</td>\n",
" <td>1.196358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.359860</td>\n",
" <td>1.054296</td>\n",
" <td>0.174546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-1.139869</td>\n",
" <td>-0.005851</td>\n",
" <td>0.100370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-0.250069</td>\n",
" <td>-0.380308</td>\n",
" <td>-0.632730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-1.041732</td>\n",
" <td>0.077047</td>\n",
" <td>0.368696</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.769995</td>\n",
" <td>0.593472</td>\n",
" <td>1.192260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>-0.863597</td>\n",
" <td>-0.064093</td>\n",
" <td>1.033223</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 -1.094352 -0.298453 -1.033678\n",
"1 0.433829 0.453419 0.025845\n",
"2 -0.073075 -0.807078 -0.301511\n",
"3 -0.349793 1.206156 -0.409708\n",
"4 -1.182058 -0.751007 -1.877473\n",
"5 0.073020 0.615864 1.196358\n",
"6 -0.359860 1.054296 0.174546\n",
"7 -1.139869 -0.005851 0.100370\n",
"8 -0.250069 -0.380308 -0.632730\n",
"9 -1.041732 0.077047 0.368696\n",
"10 0.769995 0.593472 1.192260\n",
"11 -0.863597 -0.064093 1.033223"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = pd.DataFrame(np.random.randn(n+2, 3), columns=df.columns)\n",
"df2"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"expr = '0.0 <= a <= c <= 0.5'"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"[Empty DataFrame\n",
" Columns: [a, b, c]\n",
" Index: [], Empty DataFrame\n",
" Columns: [a, b, c]\n",
" Index: []]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"map(lambda frame: frame.query(expr), [df, df2])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Python中query和pandas中query语法比较 query() Python versus pandas Syntax Comparison"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 2 1 2\n",
"1 2 6 1\n",
"2 0 0 3\n",
"3 1 7 3\n",
"4 7 7 6\n",
"5 8 7 6\n",
"6 1 8 9\n",
"7 4 7 1\n",
"8 2 8 3\n",
"9 4 9 7"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(np.random.randint(n, size=(n, 3)), columns=list('abc'))\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"6 1 8 9"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('(a<b) &(b<c)')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"6 1 8 9"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df.a < df.b) & (df.b < df.c)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"query()可以去掉圆括号, 也可以用and 代替&运算符"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"6 1 8 9"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('a < b & b < c')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"6 1 8 9"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('a<b and b<c')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### in 和not in 运算符 The in and not in operators"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"query()也支持Python中的in和not in运算符，实际上是底层调用isin"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df = pd.DataFrame({'a': list('aabbccddeeff'), 'b': list('aaaabbbbcccc'),\n",
" 'c': np.random.randint(5, size=12),\n",
" 'd': np.random.randint(9, size=12)})\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a</td>\n",
" <td>a</td>\n",
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" <tr>\n",
" <th>1</th>\n",
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" <td>a</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>b</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>b</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>d</td>\n",
" <td>b</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>d</td>\n",
" <td>b</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>e</td>\n",
" <td>c</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>e</td>\n",
" <td>c</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 a a 2 8\n",
"1 a a 2 2\n",
"2 b a 1 1\n",
"3 b a 1 3\n",
"4 c b 0 8\n",
"5 c b 1 2\n",
"6 d b 0 1\n",
"7 d b 1 7\n",
"8 e c 4 2\n",
"9 e c 1 7\n",
"10 f c 4 1\n",
"11 f c 3 0"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a</td>\n",
" <td>a</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>a</td>\n",
" <td>a</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>b</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>b</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 a a 2 8\n",
"1 a a 2 2\n",
"2 b a 1 1\n",
"3 b a 1 3\n",
"4 c b 0 8\n",
"5 c b 1 2"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('a in b')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a</td>\n",
" <td>a</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>a</td>\n",
" <td>a</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>b</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>b</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 a a 2 8\n",
"1 a a 2 2\n",
"2 b a 1 1\n",
"3 b a 1 3\n",
"4 c b 0 8\n",
"5 c b 1 2"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.a.isin(df.b)]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" a b c d\n",
"6 d b 0 1\n",
"7 d b 1 7\n",
"8 e c 4 2\n",
"9 e c 1 7\n",
"10 f c 4 1\n",
"11 f c 3 0"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[~df.a.isin(df.b)]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
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" <th>c</th>\n",
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"text/plain": [
" a b c d\n",
"0 a a 2 8\n",
"3 b a 1 3\n",
"4 c b 0 8\n",
"5 c b 1 2"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('a in b and c < d') #更复杂的例子"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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" <thead>\n",
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],
"text/plain": [
" a b c d\n",
"0 a a 2 8\n",
"3 b a 1 3\n",
"4 c b 0 8\n",
"5 c b 1 2\n",
"6 d b 0 1\n",
"7 d b 1 7\n",
"9 e c 1 7"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.b.isin(df.a) & (df.c < df.d)] #Python语法"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### ==和列表对象一起使用 Special use of the == operator with list objects"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"可以使用==/!=将列表和列名直接进行比较，等价于使用in/not in.\n",
"\n",
"三种方法功能等价： ==/!= VS in/not in VS isin()/~isin()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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" <th>10</th>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 a a 2 8\n",
"1 a a 2 2\n",
"2 b a 1 1\n",
"3 b a 1 3\n",
"4 c b 0 8\n",
"5 c b 1 2\n",
"6 d b 0 1\n",
"7 d b 1 7\n",
"8 e c 4 2\n",
"9 e c 1 7\n",
"10 f c 4 1\n",
"11 f c 3 0"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('b==[\"a\", \"b\", \"c\"]')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
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" <tr>\n",
" <th>11</th>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 a a 2 8\n",
"1 a a 2 2\n",
"2 b a 1 1\n",
"3 b a 1 3\n",
"4 c b 0 8\n",
"5 c b 1 2\n",
"6 d b 0 1\n",
"7 d b 1 7\n",
"8 e c 4 2\n",
"9 e c 1 7\n",
"10 f c 4 1\n",
"11 f c 3 0"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.b.isin([\"a\", \"b\", \"c\"])] #Python语法"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
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"text/plain": [
" a b c d\n",
"0 a a 2 8\n",
"1 a a 2 2\n",
"2 b a 1 1\n",
"3 b a 1 3\n",
"5 c b 1 2\n",
"7 d b 1 7\n",
"9 e c 1 7"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('c == [1, 2]')"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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"text/plain": [
" a b c d\n",
"4 c b 0 8\n",
"6 d b 0 1\n",
"8 e c 4 2\n",
"10 f c 4 1\n",
"11 f c 3 0"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('c != [1, 2]')"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
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" <th>3</th>\n",
" <td>b</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>d</td>\n",
" <td>b</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>e</td>\n",
" <td>c</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 a a 2 8\n",
"1 a a 2 2\n",
"2 b a 1 1\n",
"3 b a 1 3\n",
"5 c b 1 2\n",
"7 d b 1 7\n",
"9 e c 1 7"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('[1, 2] in c') #使用in "
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>d</td>\n",
" <td>b</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>e</td>\n",
" <td>c</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"4 c b 0 8\n",
"6 d b 0 1\n",
"8 e c 4 2\n",
"10 f c 4 1\n",
"11 f c 3 0"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('[1, 2] not in c')"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a</td>\n",
" <td>a</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>a</td>\n",
" <td>a</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>b</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>b</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>d</td>\n",
" <td>b</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>e</td>\n",
" <td>c</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 a a 2 8\n",
"1 a a 2 2\n",
"2 b a 1 1\n",
"3 b a 1 3\n",
"5 c b 1 2\n",
"7 d b 1 7\n",
"9 e c 1 7"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.c.isin([1, 2])] #Python语法"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 布尔运算符 Boolean Operators"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"可以使用not或者~对布尔表达式进行取非。"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.809874</td>\n",
" <td>0.825521</td>\n",
" <td>1.029453</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.051787</td>\n",
" <td>0.918937</td>\n",
" <td>1.154500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.335353</td>\n",
" <td>0.231090</td>\n",
" <td>-1.512497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.176560</td>\n",
" <td>-0.966830</td>\n",
" <td>2.052055</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.074463</td>\n",
" <td>0.166296</td>\n",
" <td>0.576796</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.082201</td>\n",
" <td>-0.900843</td>\n",
" <td>-0.374039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.519903</td>\n",
" <td>0.041034</td>\n",
" <td>-0.642189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.483423</td>\n",
" <td>-0.845009</td>\n",
" <td>0.190998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.822515</td>\n",
" <td>-0.926675</td>\n",
" <td>-0.165761</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-0.884488</td>\n",
" <td>1.118452</td>\n",
" <td>-1.248411</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 0.809874 0.825521 1.029453\n",
"1 -0.051787 0.918937 1.154500\n",
"2 0.335353 0.231090 -1.512497\n",
"3 1.176560 -0.966830 2.052055\n",
"4 -0.074463 0.166296 0.576796\n",
"5 -0.082201 -0.900843 -0.374039\n",
"6 1.519903 0.041034 -0.642189\n",
"7 -0.483423 -0.845009 0.190998\n",
"8 0.822515 -0.926675 -0.165761\n",
"9 -0.884488 1.118452 -1.248411"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(np.random.randn(n, 3), columns=list('abc'))\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df['bools']=np.random.randn(len(df))>0.5"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>bools</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.809874</td>\n",
" <td>0.825521</td>\n",
" <td>1.029453</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.051787</td>\n",
" <td>0.918937</td>\n",
" <td>1.154500</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.335353</td>\n",
" <td>0.231090</td>\n",
" <td>-1.512497</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.176560</td>\n",
" <td>-0.966830</td>\n",
" <td>2.052055</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.074463</td>\n",
" <td>0.166296</td>\n",
" <td>0.576796</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.082201</td>\n",
" <td>-0.900843</td>\n",
" <td>-0.374039</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.519903</td>\n",
" <td>0.041034</td>\n",
" <td>-0.642189</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.483423</td>\n",
" <td>-0.845009</td>\n",
" <td>0.190998</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.822515</td>\n",
" <td>-0.926675</td>\n",
" <td>-0.165761</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-0.884488</td>\n",
" <td>1.118452</td>\n",
" <td>-1.248411</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c bools\n",
"0 0.809874 0.825521 1.029453 True\n",
"1 -0.051787 0.918937 1.154500 True\n",
"2 0.335353 0.231090 -1.512497 True\n",
"3 1.176560 -0.966830 2.052055 False\n",
"4 -0.074463 0.166296 0.576796 False\n",
"5 -0.082201 -0.900843 -0.374039 False\n",
"6 1.519903 0.041034 -0.642189 False\n",
"7 -0.483423 -0.845009 0.190998 False\n",
"8 0.822515 -0.926675 -0.165761 False\n",
"9 -0.884488 1.118452 -1.248411 False"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>bools</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.809874</td>\n",
" <td>0.825521</td>\n",
" <td>1.029453</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.051787</td>\n",
" <td>0.918937</td>\n",
" <td>1.154500</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.335353</td>\n",
" <td>0.231090</td>\n",
" <td>-1.512497</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c bools\n",
"0 0.809874 0.825521 1.029453 True\n",
"1 -0.051787 0.918937 1.154500 True\n",
"2 0.335353 0.231090 -1.512497 True"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('bools')"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>bools</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.176560</td>\n",
" <td>-0.966830</td>\n",
" <td>2.052055</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.074463</td>\n",
" <td>0.166296</td>\n",
" <td>0.576796</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.082201</td>\n",
" <td>-0.900843</td>\n",
" <td>-0.374039</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.519903</td>\n",
" <td>0.041034</td>\n",
" <td>-0.642189</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.483423</td>\n",
" <td>-0.845009</td>\n",
" <td>0.190998</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.822515</td>\n",
" <td>-0.926675</td>\n",
" <td>-0.165761</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-0.884488</td>\n",
" <td>1.118452</td>\n",
" <td>-1.248411</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c bools\n",
"3 1.176560 -0.966830 2.052055 False\n",
"4 -0.074463 0.166296 0.576796 False\n",
"5 -0.082201 -0.900843 -0.374039 False\n",
"6 1.519903 0.041034 -0.642189 False\n",
"7 -0.483423 -0.845009 0.190998 False\n",
"8 0.822515 -0.926675 -0.165761 False\n",
"9 -0.884488 1.118452 -1.248411 False"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('not bools')"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>bools</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c bools\n",
"3 True True True True\n",
"4 True True True True\n",
"5 True True True True\n",
"6 True True True True\n",
"7 True True True True\n",
"8 True True True True\n",
"9 True True True True"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('not bools') == df[~df.bools]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"表达式任意复杂都没关系。"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>bools</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.074463</td>\n",
" <td>0.166296</td>\n",
" <td>0.576796</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c bools\n",
"4 -0.074463 0.166296 0.576796 False"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shorter = df.query('a<b<c and (not bools) or bools>2')\n",
"shorter"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>bools</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.074463</td>\n",
" <td>0.166296</td>\n",
" <td>0.576796</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c bools\n",
"4 -0.074463 0.166296 0.576796 False"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"longer = df[(df.a < df.b) & (df.b < df.c) & (~df.bools) | (df.bools > 2)]\n",
"longer"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>bools</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c bools\n",
"4 True True True True"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shorter == longer"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### query()的性能"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"DataFrame.query()底层使用numexptr，所以速度要比Python快,特别时当DataFrame对象非常大时。\n",
"\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 重复数据的确定和删除 Duplicate Data"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"如果你想确定和去掉DataFrame对象中重复的行，pandas提供了两个方法：duplicated和drop_duplicates. 两个方法的参数都是列名。\n",
"* duplicated 返回一个布尔向量，长度等于行数，表示每一行是否重复\n",
"* drop_duplicates 则删除重复的行"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"默认情况下，首次遇到的行被认为是唯一的，以后遇到内容相同的行都被认为是重复的。不过两个方法都有一个keep参数来确定目标行是否被保留。\n",
"* keep='first'(默认)：标记/去掉重复行除了第一次出现的那一行\n",
"* keep='last': 标记/去掉重复行除了最后一次出现的那一行\n",
"* keep=False: 标记/去掉所有重复的行"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>one</td>\n",
" <td>x</td>\n",
" <td>2.126953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>one</td>\n",
" <td>y</td>\n",
" <td>0.570685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>two</td>\n",
" <td>x</td>\n",
" <td>-0.718881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>two</td>\n",
" <td>y</td>\n",
" <td>0.044910</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>two</td>\n",
" <td>x</td>\n",
" <td>0.376090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>three</td>\n",
" <td>x</td>\n",
" <td>-0.205828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>four</td>\n",
" <td>x</td>\n",
" <td>0.336854</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 one x 2.126953\n",
"1 one y 0.570685\n",
"2 two x -0.718881\n",
"3 two y 0.044910\n",
"4 two x 0.376090\n",
"5 three x -0.205828\n",
"6 four x 0.336854"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = pd.DataFrame({'a': ['one', 'one', 'two', 'two', 'two', 'three', 'four'],\n",
" 'b': ['x', 'y', 'x', 'y', 'x', 'x', 'x'],\n",
" 'c': np.random.randn(7)})\n",
" \n",
"df2"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 True\n",
"2 False\n",
"3 True\n",
"4 True\n",
"5 False\n",
"6 False\n",
"dtype: bool"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.duplicated('a') #只观察列a的值是否重复"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 True\n",
"1 False\n",
"2 True\n",
"3 True\n",
"4 False\n",
"5 False\n",
"6 False\n",
"dtype: bool"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.duplicated('a', keep='last')"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>one</td>\n",
" <td>x</td>\n",
" <td>2.126953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>two</td>\n",
" <td>x</td>\n",
" <td>-0.718881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>three</td>\n",
" <td>x</td>\n",
" <td>-0.205828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>four</td>\n",
" <td>x</td>\n",
" <td>0.336854</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 one x 2.126953\n",
"2 two x -0.718881\n",
"5 three x -0.205828\n",
"6 four x 0.336854"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.drop_duplicates('a')\n"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>one</td>\n",
" <td>y</td>\n",
" <td>0.570685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>two</td>\n",
" <td>x</td>\n",
" <td>0.376090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>three</td>\n",
" <td>x</td>\n",
" <td>-0.205828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>four</td>\n",
" <td>x</td>\n",
" <td>0.336854</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"1 one y 0.570685\n",
"4 two x 0.376090\n",
"5 three x -0.205828\n",
"6 four x 0.336854"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.drop_duplicates('a', keep='last')"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>three</td>\n",
" <td>x</td>\n",
" <td>-0.205828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>four</td>\n",
" <td>x</td>\n",
" <td>0.336854</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"5 three x -0.205828\n",
"6 four x 0.336854"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.drop_duplicates('a', keep=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"可以传递列名组成的列表"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 False\n",
"2 False\n",
"3 False\n",
"4 True\n",
"5 False\n",
"6 False\n",
"dtype: bool"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.duplicated(['a', 'b']) #此时列a和b两个元素构成每一个检索的基本单位，"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>one</td>\n",
" <td>x</td>\n",
" <td>2.126953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>one</td>\n",
" <td>y</td>\n",
" <td>0.570685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>two</td>\n",
" <td>x</td>\n",
" <td>-0.718881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>two</td>\n",
" <td>y</td>\n",
" <td>0.044910</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>two</td>\n",
" <td>x</td>\n",
" <td>0.376090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>three</td>\n",
" <td>x</td>\n",
" <td>-0.205828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>four</td>\n",
" <td>x</td>\n",
" <td>0.336854</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 one x 2.126953\n",
"1 one y 0.570685\n",
"2 two x -0.718881\n",
"3 two y 0.044910\n",
"4 two x 0.376090\n",
"5 three x -0.205828\n",
"6 four x 0.336854"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"也可以检查index值是否重复来去掉重复行，方法是Index.duplicated然后使用切片操作(因为调用Index.duplicated会返回布尔向量)。keep参数同上。"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df3 = pd.DataFrame({'a': np.arange(6),\n",
" 'b': np.random.randn(6)},\n",
" index=['a', 'a', 'b', 'c', 'b', 'a'])\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>0</td>\n",
" <td>0.370944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1</td>\n",
" <td>0.392321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2</td>\n",
" <td>-0.999154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3</td>\n",
" <td>-0.236476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>4</td>\n",
" <td>-0.318244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>5</td>\n",
" <td>1.164510</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b\n",
"a 0 0.370944\n",
"a 1 0.392321\n",
"b 2 -0.999154\n",
"c 3 -0.236476\n",
"b 4 -0.318244\n",
"a 5 1.164510"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([False, True, False, False, True, True], dtype=bool)"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3.index.duplicated() #布尔表达式"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>0</td>\n",
" <td>0.370944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2</td>\n",
" <td>-0.999154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3</td>\n",
" <td>-0.236476</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b\n",
"a 0 0.370944\n",
"b 2 -0.999154\n",
"c 3 -0.236476"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3[~df3.index.duplicated()]"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3</td>\n",
" <td>-0.236476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>4</td>\n",
" <td>-0.318244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>5</td>\n",
" <td>1.164510</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b\n",
"c 3 -0.236476\n",
"b 4 -0.318244\n",
"a 5 1.164510"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3[~df3.index.duplicated(keep='last')]"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3</td>\n",
" <td>-0.236476</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b\n",
"c 3 -0.236476"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3[~df3.index.duplicated(keep=False)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 形似字典的get()方法"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Serires, DataFrame和Panel都有一个get方法来得到一个默认值。"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 2\n",
"c 3\n",
"dtype: int64"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = pd.Series([1,2,3], index=['a', 'b', 'c'])\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.get('a')"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"-1"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.get('x', default=-1)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.get('b')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## select()方法 The select() Method"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Series, DataFrame和Panel都有select()方法来检索数据，这个方法作为保留手段通常其他方法都不管用的时候才使用。select接受一个函数(在label上进行操作)作为输入返回一个布尔值。"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.randn(10, 3), columns=list('ABC'))"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.768992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.126865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.508768</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.847265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.537149</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.610245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.626082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.813772</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.300097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1.368777</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A\n",
"0 0.768992\n",
"1 -0.126865\n",
"2 -0.508768\n",
"3 -0.847265\n",
"4 -1.537149\n",
"5 0.610245\n",
"6 -0.626082\n",
"7 0.813772\n",
"8 0.300097\n",
"9 1.368777"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.select(lambda x: x=='A', axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## lookup()方法 The lookup()方法"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"输入行label和列label，得到一个numpy数组，这就是lookup方法的功能。"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.456863</td>\n",
" <td>0.261728</td>\n",
" <td>1.313254</td>\n",
" <td>0.105114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1.041107</td>\n",
" <td>1.067823</td>\n",
" <td>1.156849</td>\n",
" <td>-1.306408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1.186713</td>\n",
" <td>-1.078201</td>\n",
" <td>0.561977</td>\n",
" <td>-0.107848</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.496749</td>\n",
" <td>0.246163</td>\n",
" <td>0.496875</td>\n",
" <td>0.334775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.076146</td>\n",
" <td>-0.459081</td>\n",
" <td>-0.646699</td>\n",
" <td>-0.143237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.337840</td>\n",
" <td>1.284264</td>\n",
" <td>-1.327627</td>\n",
" <td>0.139834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.161458</td>\n",
" <td>0.284919</td>\n",
" <td>-1.969045</td>\n",
" <td>-0.129893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.361869</td>\n",
" <td>-1.292803</td>\n",
" <td>0.204441</td>\n",
" <td>0.066561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-1.518187</td>\n",
" <td>-1.247654</td>\n",
" <td>-0.988123</td>\n",
" <td>-0.353545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.391405</td>\n",
" <td>0.097482</td>\n",
" <td>-0.190093</td>\n",
" <td>-0.074410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1.023540</td>\n",
" <td>-1.345222</td>\n",
" <td>0.537438</td>\n",
" <td>-1.357927</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>-0.108617</td>\n",
" <td>-0.888801</td>\n",
" <td>-0.142176</td>\n",
" <td>-0.143029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.996520</td>\n",
" <td>0.569428</td>\n",
" <td>0.078876</td>\n",
" <td>-0.645631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>-0.845302</td>\n",
" <td>-0.642925</td>\n",
" <td>1.089828</td>\n",
" <td>0.645551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2.205784</td>\n",
" <td>-0.763532</td>\n",
" <td>1.763455</td>\n",
" <td>-0.873189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.645096</td>\n",
" <td>0.828053</td>\n",
" <td>-1.405876</td>\n",
" <td>0.974612</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.444146</td>\n",
" <td>1.831544</td>\n",
" <td>0.439983</td>\n",
" <td>-0.108334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>-0.126371</td>\n",
" <td>-0.196340</td>\n",
" <td>-0.411644</td>\n",
" <td>0.414911</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>-0.721539</td>\n",
" <td>-1.932596</td>\n",
" <td>-1.595068</td>\n",
" <td>-1.966388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.060839</td>\n",
" <td>0.731949</td>\n",
" <td>-0.082693</td>\n",
" <td>1.665486</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"0 1.456863 0.261728 1.313254 0.105114\n",
"1 -1.041107 1.067823 1.156849 -1.306408\n",
"2 -1.186713 -1.078201 0.561977 -0.107848\n",
"3 0.496749 0.246163 0.496875 0.334775\n",
"4 -1.076146 -0.459081 -0.646699 -0.143237\n",
"5 -0.337840 1.284264 -1.327627 0.139834\n",
"6 0.161458 0.284919 -1.969045 -0.129893\n",
"7 -0.361869 -1.292803 0.204441 0.066561\n",
"8 -1.518187 -1.247654 -0.988123 -0.353545\n",
"9 0.391405 0.097482 -0.190093 -0.074410\n",
"10 1.023540 -1.345222 0.537438 -1.357927\n",
"11 -0.108617 -0.888801 -0.142176 -0.143029\n",
"12 0.996520 0.569428 0.078876 -0.645631\n",
"13 -0.845302 -0.642925 1.089828 0.645551\n",
"14 2.205784 -0.763532 1.763455 -0.873189\n",
"15 0.645096 0.828053 -1.405876 0.974612\n",
"16 0.444146 1.831544 0.439983 -0.108334\n",
"17 -0.126371 -0.196340 -0.411644 0.414911\n",
"18 -0.721539 -1.932596 -1.595068 -1.966388\n",
"19 0.060839 0.731949 -0.082693 1.665486"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dflookup = pd.DataFrame(np.random.randn(20, 4), columns=list('ABCD'))\n",
"dflookup"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.26172828, 0.56197743, -1.07614596, 0.28491916, -0.35354466])"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dflookup.lookup(list(range(0,10,2)), ['B','C','A','B','D'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Index对象 Index objects"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"pandas中的Index类和它的子类可以被当做一个序列可重复集合(ordered multiset)，允许数据重复。然而，如果你想把一个有重复值Index对象转型为一个集合这是不可以的。创建Index最简单的方法就是通过传递一个列表或者其他序列创建。"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'e', u'd', u'a', u'b'], dtype='object')"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"index = pd.Index(['e', 'd', 'a', 'b'])\n",
"index"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'d' in index"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"还可以个Index命名"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'something'"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"index = pd.Index(['e', 'd', 'a', 'b'], name='something')\n",
"index.name"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"index = pd.Index(list(range(5)), name='rows')"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"columns = pd.Index(['A', 'B', 'C'], name='cols')"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.randn(5, 3), index=index, columns=columns)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>cols</th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" <tr>\n",
" <th>rows</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.344614</td>\n",
" <td>1.756430</td>\n",
" <td>-0.648766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.085694</td>\n",
" <td>0.181015</td>\n",
" <td>-0.679165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.487108</td>\n",
" <td>-0.546367</td>\n",
" <td>-0.159887</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.124834</td>\n",
" <td>0.034413</td>\n",
" <td>0.863767</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.531960</td>\n",
" <td>1.210601</td>\n",
" <td>0.703460</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"cols A B C\n",
"rows \n",
"0 2.344614 1.756430 -0.648766\n",
"1 0.085694 0.181015 -0.679165\n",
"2 -0.487108 -0.546367 -0.159887\n",
"3 0.124834 0.034413 0.863767\n",
"4 -0.531960 1.210601 0.703460"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"rows\n",
"0 2.344614\n",
"1 0.085694\n",
"2 -0.487108\n",
"3 0.124834\n",
"4 -0.531960\n",
"Name: A, dtype: float64"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['A']"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 返回视图VS返回副本 Returning a view versus a copy"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"当对pandas对象赋值时，一定要注意避免链式索引(chained indexing)。看下面的例子："
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">one</th>\n",
" <th colspan=\"2\" halign=\"left\">two</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>first</th>\n",
" <th>second</th>\n",
" <th>first</th>\n",
" <th>second</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a</td>\n",
" <td>b</td>\n",
" <td>c</td>\n",
" <td>d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>e</td>\n",
" <td>f</td>\n",
" <td>g</td>\n",
" <td>h</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>i</td>\n",
" <td>j</td>\n",
" <td>k</td>\n",
" <td>l</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>m</td>\n",
" <td>n</td>\n",
" <td>o</td>\n",
" <td>p</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" one two \n",
" first second first second\n",
"0 a b c d\n",
"1 e f g h\n",
"2 i j k l\n",
"3 m n o p"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfmi = pd.DataFrame([list('abcd'),\n",
" list('efgh'),\n",
" list('ijkl'),\n",
" list('mnop')],\n",
" columns=pd.MultiIndex.from_product([['one','two'],\n",
" ['first','second']]))\n",
"dfmi"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"比较下面两种访问方式："
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 b\n",
"1 f\n",
"2 j\n",
"3 n\n",
"Name: second, dtype: object"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfmi['one']['second']"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 b\n",
"1 f\n",
"2 j\n",
"3 n\n",
"Name: (one, second), dtype: object"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfmi.loc[:,('one','second')]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"上面两种方法返回的结果抖一下，那么应该使用哪种方法呢？答案是我们更推荐大家使用方法二。\n",
"\n",
"dfmi['one']选择了第一级列然后返回一个DataFrame对象，然后另一个Python操作dfmi_with_one['second']根据'second'检索出了一个Series。对pandas来说，这两个操作是独立、有序执行的。而.loc方法传入一个元组(slice(None),('one','second')),pandas把这当作一个事件执行，所以执行速度更快。"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 为什么使用链式索引赋值为报错？"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"刚才谈到不推荐使用链式索引是出于性能的考虑。接下来从赋值角度谈一下不推荐使用链式索引。首先，思考Python怎么解释执行下面的代码？"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"dfmi.loc[:,('one','second')]=value\n",
"#实际是\n",
"dfmi.loc.__setitem__((slice(None), ('one', 'second')), value)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"但下面的代码解释后结果却不一样："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"dfmi['one']['second'] = value\n",
"# becomes\n",
"dfmi.__getitem__('one').__setitem__('second', value)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"看到__getitem__了吗？除了最简单的情况，我们很难预测他到底返回的是视图还是副本(哲依赖于数组的内存布局，这是pandas没有硬性要求的)，因此不推荐使用链式索引赋值！\n",
"\n",
"而dfmi.loc.__setitem__直接对dfmi进行操作。"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"有时候明明没有使用链式索引，也会引起SettingWithCopy警告，这是Pandas设计的bug~"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def do_something(df):\n",
" foo = df[['bar', 'baz']] # Is foo a view? A copy? Nobody knows!\n",
" # ... many lines here ...\n",
" foo['quux'] = value # We don't know whether this will modify df or not!\n",
" return foo"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 链式索引中顺序也很重要"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"此外，在链式表达式中，不同的顺序也可能导致不同的结果。这里的顺序指的是检索时行和列的顺序。"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>one</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>one</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>two</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>three</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>two</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>one</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>six</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a c\n",
"0 one 0\n",
"1 one 1\n",
"2 two 2\n",
"3 three 3\n",
"4 two 4\n",
"5 one 5\n",
"6 six 6"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfb = pd.DataFrame({'a' : ['one', 'one', 'two',\n",
" 'three', 'two', 'one', 'six'],\n",
" 'c' : np.arange(7)})\n",
"dfb"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\python27\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"dfb['c'][dfb.a.str.startswith('o')] = 42 #虽然会引起SettingWithCopyWarning 但也能得到正确结果"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\python27\\lib\\site-packages\\ipykernel\\__main__.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" from ipykernel import kernelapp as app\n"
]
}
],
"source": [
"pd.set_option('mode.chained_assignment','warn')\n",
"dfb[dfb.a.str.startswith('o')]['c'] = 42 #这实际上是对副本赋值！"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"正确的方式是：老老实实使用.loc"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>aaa</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>bbb</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ccc</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B\n",
"0 aaa 1\n",
"1 bbb 2\n",
"2 ccc 3"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfc = pd.DataFrame({'A':['aaa','bbb','ccc'],'B':[1,2,3]})\n",
"dfc"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>bbb</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ccc</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B\n",
"0 11 1\n",
"1 bbb 2\n",
"2 ccc 3"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfc.loc[0,'A'] = 11\n",
"dfc"
]
}
],
"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
}
| gpl-2.0 |
agarbuno/metodos-analiticos | Lecture_5_Stream_Algorithms.ipynb | 2 | 21394 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Lecci\u00f3n 6 - Mining Streams"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Objetivo\n",
"El objetivo de esta lecci\u00f3n es entender como funcionan distintos algoritmos para miner\u00eda de flujos de datos, es decir informaci\u00f3n que no est\u00e1 almacenada sino se va adquiriendo de forma incremental.\n",
"\n",
"Nota: Esta lecci\u00f3n est\u00e1 basada en el Cap\u00edtulo 4 del libro Mining of Massive Datasets. Rajaraman, Leskovec and Ullman 2012 y en las notas del curso de M\u00e9todos Anal\u00edticos del 2013 por Felipe Gonzalez."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Miner\u00eda de flujos\n",
"\n",
"Existen diversas razones por las que es necesario minar flujos de datos en tiempo real en memoria, en lugar de almacenarla para realizar consultas en otro momento, por ejemplo, \n",
"\n",
"* el flujo de informaci\u00f3n es tan grande que no es pr\u00e1ctico almacenarla\n",
"* el flujo de informaci\u00f3n es tan r\u00e1pido que si movieramos la informaci\u00f3n a disco perder\u00edamos capacidad de procesarla\n",
"\n",
"No obstante queremos tener la capacidad de analizar esta informaci\u00f3n conforme se genera.\n",
"\n",
"Algunas procesos interesantes que podemos aplicar sobre estos flujos son por ejemplo:\n",
"\n",
"* muestrear aleatoriamente un flujo\n",
"* filtrar objetos no deseados\n",
"* estimar el n\u00famero de elementos distintos utilizando estructuras de datos probabil\u00edsticas\n",
"\n",
"Y algunos otros m\u00e1s simples:\n",
"\n",
"* ventanas de tiempo\n",
"* res\u00famenes acumulados (m\u00e1ximo, m\u00ednimo, promedio, \u00bfmediana?)\n",
"\n",
"A todo esto, \u00bfcu\u00e1les son algunos ejemplos de flujos?\n",
"\n",
"* datos de sensores\n",
"* flujos de im\u00e1genes\n",
"* logs de internet\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Res\u00famenes acumulados\n",
"\n",
"Comencemos por explorar la forma m\u00e1s simple de an\u00e1lisis de flujos.\n",
"\n",
"\u00bfCu\u00e1les son los algoritmos para encontrar estadisticas descriptivas sobre un flujo?\n",
"\n",
"* m\u00e1ximo\n",
"* m\u00ednimo\n",
"* promedio\n",
"\n",
"##### Estad\u00edsticas de orden\n",
"\n",
"Las estad\u00edsticas de orden tienen una caracter\u00edstica especial, esta es, que tienen que conocer el orden de los elementos para poder contestar a la pregunta, \u00bfCu\u00e1l es el valor del elemento que est\u00e1 a la mitad?\n",
"\n",
"##### Ventanas de tiempo\n",
"\n",
"Otra posibilidad para consultar informaci\u00f3n sobre un flujo, es hacer consultas sobre ventanas de tiempo de tama\u00f1o $n$ que en general representan los $n$ elementos m\u00e1s recientes del flujo. O guardar un numero de elementos arbitrarios que permitan consultar los datos para un periodo de tiempo $t$.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Muestreo de flujos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Supongamos que queremos tomar una musestra de un flujo para posteriormente realizar operaciones sobre de ella, \u00bfC\u00f3mo hacemos para que la muestra sea representativa del flujo total? La estrategia de muestreo depende de que es lo que se quiere muestrear. Por ejemplo, dado un sistema que recibe informaci\u00f3n de m\u00faltiples sensores.\n",
"\n",
"* Muestrar eventos que tienen una duraci\u00f3n mayor a D, se pueden muestrear los eventos directamente\n",
"* Muestrar la proporci\u00f3n de sensores que tienen eventos de duraci\u00f3n mayor a D, muestrear los eventos\n",
"\n",
"En el primer caso es sencillo realizar el muestreo, generar un n\u00famero aleatorio entre 0 y $n$ donde $n$ es el inverso de la proporci\u00f3n $1/n$ que se quiere muestrear.\n",
"\n",
"El segundo caso sin embargo, requiere que podamos identificar cuales son los usuarios que estamos muestreando, para que tomemos en cuenta todas las observaciones que les corresponden.\n",
"\n",
"\u00bfC\u00f3mo podemos utilizar hashing para no tener que almacenar una lista con todos los usuarios que s\u00ed debemos muestrear?\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import random\n",
"\n",
"NUM_USERS = 10000\n",
"\n",
"rnd = random.Random(11)\n",
"events = list()\n",
"for i in range(1000000):\n",
" duration = 0\n",
" if rnd.random() > 0.2:\n",
" duration = rnd.randint(0, 200 - 1)\n",
" else: \n",
" duration = rnd.randint(200, 1000 - 1)\n",
" events.append((rnd.randint(0, NUM_USERS - 1), duration))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def sample(elements, pct):\n",
" sampled = list()\n",
" for elem in elements: \n",
" r = rnd.randint(0,int(1/pct))\n",
" if r == 0:\n",
" sampled.append(elem)\n",
" return sampled\n",
"\n",
"def proportion(sampled, threshold):\n",
" count = 0\n",
" for event in sampled:\n",
" if event[1] > threshold:\n",
" count += 1\n",
" print \"Proporci\u00f3n de eventos > %s: %.4f\" % (threshold, count / float(len(sampled)))\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Contar eventos con duraci\u00f3n mayor a 200\n",
"proportion(events, 200)\n",
"sampled = sample(events, .01)\n",
"proportion(sampled, 200)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Proporci\u00f3n de eventos > 200: 0.1992\n",
"Proporci\u00f3n de eventos > 200: 0.2004"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def sampled_per_user(data):\n",
" r = rnd.randint(0, len(data))\n",
" user = data[r][0]\n",
" filtered = [_ for _ in data if _[0] == user]\n",
" proportion(filtered, 200)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Que proporci\u00f3n de los eventos de un usuario aleatorio fueron mayores a 200\n",
"for i in range(5):\n",
" sampled_per_user(sampled)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Proporci\u00f3n de eventos > 200: 0.0000\n",
"Proporci\u00f3n de eventos > 200: 0.5000\n",
"Proporci\u00f3n de eventos > 200: 0.0000\n",
"Proporci\u00f3n de eventos > 200: 0.0000\n",
"Proporci\u00f3n de eventos > 200: 0.0000\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podemos observar que el n\u00famero de eventos mayores a 200 para los usuarios muestreados no corresponde a los de su distribuci\u00f3n. Ya que los eventos que la muestra se hace sobre todos elementos sin tomar en cuenta que algunos eventos para un usuario suceden con menor probabilidad\n",
"\n",
"#### \u00bfC\u00f3mo podemos obtener una muestra representativa por usuario?\n",
"\n",
"Nuestro objetivo es muestrear todos los eventos de la proporci\u00f3n correcta de los usuarios."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def sample_users(elements, pct):\n",
" seen = dict()\n",
" sampled = list()\n",
" for elem in elements: \n",
" user = elem[0]\n",
" if not user in seen:\n",
" r = rnd.randint(0,int(1/pct))\n",
" seen[user] = (r == 0)\n",
" if seen[user]:\n",
" sampled.append(elem)\n",
" return sampled"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"user_sampled = sample_users(events, 0.01)\n",
"proportion(user_sampled, 200)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Proporci\u00f3n de eventos > 200: 0.1956\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Que proporci\u00f3n de los eventos de un usuario aleatorio fueron mayores a 200\n",
"for i in range(5):\n",
" sampled_per_user(user_sampled)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Proporci\u00f3n de eventos > 200: 0.1684\n",
"Proporci\u00f3n de eventos > 200: 0.1759\n",
"Proporci\u00f3n de eventos > 200: 0.1909\n",
"Proporci\u00f3n de eventos > 200: 0.1731\n",
"Proporci\u00f3n de eventos > 200: 0.2809\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"El problema con este enfoque es que tenemos que mantener la informaci\u00f3n sobre todos los usuarios que hemos observado hasta el momento, para poder determinar si los tenemos que muestrear o no.\n",
"\n",
"Una alternativa eficiente en memoria es... utilizar hashes. Si el hash del usuario corresponde a la cubeta 0 lo muestreamos de otra forma no."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def sample_users_hash(elements, pct):\n",
" sampled = list()\n",
" for elem in elements: \n",
" if hash(elem[0]) % int(1/pct) == 0:\n",
" sampled.append(elem)\n",
" return sampled"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"user_sampled_hash = sample_users_hash(events, 0.01)\n",
"proportion(user_sampled_hash, 200)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Proporci\u00f3n de eventos > 200: 0.1990\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in range(5):\n",
" sampled_per_user(user_sampled_hash)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Proporci\u00f3n de eventos > 200: 0.2222\n",
"Proporci\u00f3n de eventos > 200: 0.2300\n",
"Proporci\u00f3n de eventos > 200: 0.2182\n",
"Proporci\u00f3n de eventos > 200: 0.2589\n",
"Proporci\u00f3n de eventos > 200: 0.3196\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En este caso la funci\u00f3n hash funciona como un generador de n\u00fameros aleatorios que siempre mapea al mismo usuario al mismo n\u00famero aleatorio.\n",
"\n",
"En general se puede obtener una muestra de una fracci\u00f3n $a/b$ aplicando hashes a $b$ cubetas y muestreando aquellos cuyo hash sea menor a $a$."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def sample_hash(elements, a, b):\n",
" sampled = list()\n",
" for elem in elements: \n",
" if hash(elem[0]) % b < a:\n",
" sampled.append(elem)\n",
" return sampled"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sampled_hash = sample_hash(events, 1, 100)\n",
"proportion(sampled_hash, 200)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Proporci\u00f3n de eventos > 200: 0.1990\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in range(5):\n",
" sampled_per_user(sampled_hash)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Proporci\u00f3n de eventos > 200: 0.1731\n",
"Proporci\u00f3n de eventos > 200: 0.1630\n",
"Proporci\u00f3n de eventos > 200: 0.1532\n",
"Proporci\u00f3n de eventos > 200: 0.1727\n",
"Proporci\u00f3n de eventos > 200: 0.2190\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from collections import defaultdict\n",
"\n",
"def get_proportion(sampled, threshold):\n",
" count = 0\n",
" for event in sampled:\n",
" if event[1] > threshold:\n",
" count += 1\n",
" return count / float(len(sampled)) \n",
"\n",
"def proportion_per_user(sdata):\n",
" per_user = defaultdict(int)\n",
" per_user_total = defaultdict(int)\n",
" for event in sdata:\n",
" per_user[event[0]] += (1 if event[1] > 200 else 0)\n",
" per_user_total[event[0]] += 1\n",
"\n",
" sm = 0\n",
" for user, user_events in per_user.items():\n",
" sm += user_events / float(per_user_total[user])\n",
" \n",
" print \"Promedio de proporci\u00f3n de eventos por usuario > %s: %.2f @%s\" % (200, sm / len(per_user), len(per_user))\n",
" \n",
"proportion_per_user(sampled)\n",
"proportion_per_user(user_sampled)\n",
"proportion_per_user(user_sampled_hash)\n",
"proportion_per_user(events)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Promedio de proporci\u00f3n de eventos por usuario > 200: 0.20 @6327\n",
"Promedio de proporci\u00f3n de eventos por usuario > 200: 0.20 @98\n",
"Promedio de proporci\u00f3n de eventos por usuario > 200: 0.20 @100\n",
"Promedio de proporci\u00f3n de eventos por usuario > 200: 0.20 @10000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Filtrado"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Otro proceso com\u00fan a aplicar sobre flujos es el filtrado, nos gustar\u00eda tener un proceso para dejar pasar ciertos registros y no otros, por ejemplo filtrar las direcciones peligrosas en un navegador. Este proceso se complica cuando \n",
"\n",
" * El flujo es muy grande y no podemos mantener en memoria la membres\u00eda de todos los datos del conjunto\n",
" * No queremos acceder al disco cada vez que queremos hacer una consulta.\n",
"\n",
"Suponiendo que tenemos 1 megabyte de memoria disponible para almacenar esta informaci\u00f3n podr\u00edamos almacenar hasta 1 millon de registros de direcciones peligrosas utilizando un bit por direcci\u00f3n, utilizando... una funci\u00f3n hash para mapear cada direcci\u00f3n a uno de los bits.\n",
"\n",
"Cuando queremos validar una direcci\u00f3n, \n",
"\n",
"* Aplicamos una funci\u00f3n hash a la direcci\u00f3n\n",
"* Checamos si dicha direcci\u00f3n est\u00e1 en nuestro arreglo de memoria\n",
"* Si encontramos la direcci\u00f3n en el arreglo mandamos una p\u00e1gina alertando al usuario.\n",
"\n",
"Es posible que al utilizar este m\u00e9todo algunas p\u00e1ginas que no son peligrosas mapen a un bit encendido. En cuyo caso tendremos un falso positivo.\n",
"\n",
"#### El filtro de Bloom\n",
"\n",
"Un filtro de bloom consiste en:\n",
"\n",
"1. Un arreglo de $n$ bits\n",
"1. Una colecci\u00f3n de $k$ funciones hash que mapean de manera \u00fanica el valor a uno de los $n$ los buckets\n",
"1. Un conjunto $S$ de $m$ llaves\n",
"\n",
"El objeto de esta funci\u00f3n es dejar pasar aquellos elementos cuyo valor est\u00e9 en el conjunto y no rechazar la mayor\u00eda de los que no est\u00e1n en el conjunto.\n",
"\n",
"En el caso de las p\u00e1ginas maliciosas lo utilizamos de forma inversa pero el principio es el mismo.\n",
"\n",
"* Para construir el filtro, inicializamos el arreglo con ceros\n",
"* Para cada valor $s$ en $S$\n",
" * Aplicamos cada una de las $h_k$ funciones a $s$\n",
" * Cambiamos a 1 cada uno de los bits designados por las funciones $h_k$\n",
"* Para probar si un nuevo elemento est\u00e1 en el filtro, aplicamos el mismo procedimiento y verificamos que todos los bits esten encendidos.\n",
"* Si alguno de los bits no est\u00e1 encendido entonces es seguro que $s$ no estaba en el conjunto.\n",
"* En cambio, si todos los bits est\u00e1n prendidos es probable sea un falso positivo debido a una colisi\u00f3n\n",
"\n",
"##### An\u00e1lsis\n",
"\n",
"A continuaci\u00f3n analizamos las propiedades estad\u00edsticas del filtro. La cantidad que nos interesa conocer es cual es la probabilidad de un falso positivo, como funci\u00f3n de $n$ la longitud del filtro, $m$ el n\u00famero de elementos en el conjunto y $k$ el n\u00famero de funciones.\n",
"\n",
"La probabilidad de que un bit no sea 1 para un elemento y una funci\u00f3n $h_k$ es:\n",
"\n",
"$$1 - \\frac{1}{n}$$\n",
"\n",
"De que no sea 1 para ninguna de las funciones $h_k$ es:\n",
"\n",
"$$\\left(1 - \\frac{1}{n}\\right)^k$$\n",
"\n",
"La probabilidad de que despu\u00e9s de insertar $m$ elementos un bit a\u00fan sea 0 es:\n",
"\n",
"\n",
"$$\\left(1 - \\frac{1}{n}\\right)^{km}$$\n",
"\n",
"\n",
"Por lo tanto la probabilidad de que un elemento sea 1 despu\u00e9s de insertar $m$ elementos es:\n",
"\n",
"\n",
"$$1 - \\left(1 - \\frac{1}{n}\\right)^{km}$$\n",
"\n",
"\n",
"Cuando insertamos un nuevo elemento cada uno de los las $k$ posiciones tiene una probabilidad de ser uno igual a $1 - \\left(1 - \\frac{1}{n}\\right)^{km}$ por lo tanto la probabilidad de que las $k$ posiciones sean 1 es:\n",
"\n",
"$$\\left[1 - \\left(1 - \\frac{1}{n}\\right)^{km}\\right]^k \\approx \\left(1 - e^{-km/n}\\right)^k$$\n",
"\n",
"Esta aproximaci\u00f3n asume independencia entre cada una de las probabilidades, si bien esto no es estrictamente correcto para valores grandes de $n$ y valores chicos de $k$ la aproximaci\u00f3n es buena. Ver http://people.scs.carleton.ca/~kranakis/Papers/TR-07-07.pdf\n",
"\n",
"Por lo tanto podemos concluir que la probabilidad de falsos positivos disminuye conforme utilizamos arreglos de bits m\u00e1s grandes (valores m\u00e1s grandes de $n$) e incrementa conforme agregamos m\u00e1s elementos al conjunto (valores m\u00e1s grandes de $m$).\n",
"\n",
"##### N\u00famero \u00f3ptimo de funciones\n",
"\n",
"Dado $n$ y $m$ el valor de $k$ que minimiza la probabilidad de falsos positivos es:\n",
"\n",
"$$k = \\frac{n}{m}\\text{ln }2$$\n",
"\n",
"El n\u00famero de bits $n$, requerido para mantener esta probabilidad de falsos positivos es:\n",
"\n",
"$$n = -\\frac{m\\text{ ln }p}{(\\text{ln }2)^2}$$\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
blanton144/exex | docs/notebooks/light-2.ipynb | 1 | 495466 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Light 2 Numerical and Data Analysis Answers"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy.interpolate as interpolate\n",
"import astropy.io.fits as fits\n",
"import matplotlib.pyplot as plt\n",
"import requests"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Identify Balmer absorption lines in a star\n",
"\n",
"*Author: Nicholas Faucher*\n",
"\n",
"Download an optical spectrum of an A star. Identify \n",
" all Balmer absorption lines that are apparent in that spectrum."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Data downloaded from https://doi.org/10.5281/zenodo.321394\n",
"\n",
"Referenced in https://iopscience.iop.org/article/10.3847/1538-4365/aa656d/pdf\n",
"\n",
"Fluxes are normalized to the flux at 8000 Å"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"def find_nearest(array, value):\n",
" index = (np.abs(array - value)).argmin()\n",
" return index\n",
"\n",
"def find_local_min(array, index):\n",
" min_index = np.argmin(array[index-25:index+26])\n",
" return min_index + index - 25\n",
"\n",
"balmer_series = np.array((6562.79, 4861.35, 4340.472, 4101.734, 3970.075, 3889.064, 3835.397))\n",
"balmer_labels = [r'H$\\alpha$', r'H$\\beta$', r'H$\\gamma$', r'H$\\delta$', r'H$\\epsilon$', r'H$\\zeta$', r'H$\\eta$']\n",
"\n",
"hdul = fits.open('A0.fits')\n",
"data = hdul[1].data\n",
"loglam = data['Loglam']\n",
"lam = 10**loglam\n",
"flux = data['Flux']\n",
"\n",
"mask = lam < 8000\n",
"\n",
"plt.figure(figsize=(15,8))\n",
"\n",
"plt.plot(lam[mask],flux[mask])\n",
"\n",
"for i in range(len(balmer_series)):\n",
" index = find_nearest(lam, balmer_series[i]) # finds the closest wavelength index to current balmer series\n",
" min_index = find_local_min(flux, index) # finds the local minimum near current index\n",
" plt.text(lam[min_index]-30,flux[min_index]-0.3, balmer_labels[i], fontsize=10) # puts the appropriate label near each local minimum\n",
"\n",
"plt.xlabel('Wavelength (Angstroms)', fontsize=14)\n",
"plt.ylabel('Normalized Flux', fontsize=14)\n",
"plt.title('Balmer Absorption Lines for an A star', fontsize=14)\n",
"\n",
"plt.savefig('balmer.png', dpi=300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Identify Balmer emission lines in a galaxy\n",
"\n",
"*Author: Kate Storey-Fisher*\n",
"\n",
"Download an optical spectrum of a star forming galaxy. Identify all Balmer emission lines that are apparent in the spectrum. Zooming in on Hα or Hβ, visually compare the Balmer absorption (in the stellar continuum) to the emission."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data ###\n",
"\n",
"This is an optical spectrum of a galaxy in SDSS. The data and more info can be found here: https://dr12.sdss.org/spectrumDetail?mjd=53794&fiber=6&plateid=2214"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"request_template = 'https://dr13.sdss.org/optical/spectrum/view/data/format=fits/spec=lite?plateid={plate}&mjd={mjd}&fiberid={fiberid}'\n",
"request = request_template.format(plate=2214, fiberid=6, mjd=53794)\n",
"r = requests.get(request)\n",
"fp = open('spec-2214-53794-0006.fits', 'wb')\n",
"fp.write(r.content)\n",
"fp.close()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"hdu = fits.open('spec-2214-53794-0006.fits')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"header = hdu[0].header\n",
"data = hdu[1].data\n",
"z = 0.0657799 #Redshift at link above"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"wl = 10**data['loglam']\n",
"flux = data['flux']\n",
"model = data['model']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Balmer Series\n",
"\n",
"The Balmer series are lines due to transitions to the n=2 level of hydrogen. The wavelengths of the first few lines are given below.\n",
"\n",
"The next line, H_epsilon, is outside of the region of our spectrum."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#Balmer series\n",
"halpha = 6564.5377\n",
"hbeta = 4861.3615\n",
"hgamma = 4340.462\n",
"hdelta = 4101.74\n",
"lines = [halpha, hbeta, hgamma, hdelta]\n",
"labels = [r'H$_{\\alpha}$', r'H$_{\\beta}$', r'H$_{\\gamma}$', r'H$_{\\delta}$']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Find the wavelength at which the lines are observed, due to redshifting."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#Shifted\n",
"lines_shifted = np.empty(len(lines))\n",
"for i in range(len(lines)):\n",
" lines_shifted[i] = lines[i]*(1+z)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The H$\\alpha$ line is clear, but the others are hard to see when looking at the full spectrum."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 936x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(13, 7))\n",
"plt.plot(wl, flux)\n",
"plt.plot(wl, model, color='black') \n",
"plt.xlabel('Wavelength $\\lambda$ ($\\AA$)')\n",
"plt.ylabel('Flux $f_\\lambda$ ($10^{-17}$ erg cm$^{-2}$ s$^{-1}$ $\\AA$)')\n",
"\n",
"for line, label in zip(lines_shifted, labels):\n",
" plt.axvline(line, color='red', alpha=0.7)\n",
" plt.annotate(label, xy=(line, 25), xytext=(line, 25), size=16)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Zooming in\n",
"\n",
"We see that the H$\\alpha$ line is very strong, and the H$\\beta$ line also has a clear emission peak.\n",
"H$\\gamma$ and H$\\delta$ do not appear to have emission that is significant relative to the noise. The black \n",
"lines in these plots are the model fit by the spectroscopic pipeline in SDSS, so it does not necessarily\n",
"faithfully represent the true galaxy spectrum."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Zooms\n",
"width = 100\n",
"fig, axarr = plt.subplots(2,2, figsize=(15, 10))\n",
"plt.subplots_adjust(hspace=0.3)\n",
"count = 0\n",
"for i in range(2):\n",
" for j in range(2):\n",
" line = lines_shifted[count]\n",
" wf = [(w, f, m) for w, f, m in zip(wl, flux, model) if (w<line+width) and (w>line-width)]\n",
" wlcut = [tup[0] for tup in wf]\n",
" fluxcut = [tup[1] for tup in wf]\n",
" modelcut = [tup[2] for tup in wf]\n",
"\n",
" axarr[i,j].set_title(labels[count], size=20)\n",
" axarr[i,j].plot(wlcut, fluxcut)\n",
" axarr[i,j].plot(wlcut, modelcut, color='black')\n",
" axarr[i,j].axvline(line, color='red', alpha=0.7)\n",
" axarr[i,j].set_xlabel('Wavelength $\\lambda$ ($\\AA$)')\n",
" axarr[i,j].set_ylabel('Flux $f_\\lambda$ ($10^{-17}$ erg cm$^{-2}$ s$^{-1}$ $\\AA$)')\n",
"\n",
" count += 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Absorption to Emission\n",
"\n",
"Zooming in further on the H$\\beta$ line to visually inspect it, the model (black) has clear emission and clear absorption. The absorption is in the underlying stellar continuum spectrum and reflects the presence of neutral, but excited, hydrogen gas in the stellar atmospheres. The absorption feature is believable in the data itself (blue), but it is less obviously real, because of the noise. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Flux $f_\\\\lambda$ ($10^{-17}$ erg cm$^{-2}$ s$^{-1}$ $\\\\AA$)')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"width = 30\n",
"fig = plt.figure(figsize=(10, 7))\n",
"count = 1\n",
"line = lines_shifted[count] #H_beta\n",
"wf = [(w, f, m) for w, f, m in zip(wl, flux, model) if (w<line+width) and (w>line-width)]\n",
"wlcut = [tup[0] for tup in wf]\n",
"fluxcut = [tup[1] for tup in wf]\n",
"modelcut = [tup[2] for tup in wf]\n",
"\n",
"plt.title(labels[count], size=20)\n",
"plt.plot(wlcut, fluxcut)\n",
"plt.plot(wlcut, modelcut, color='black')\n",
"plt.axvline(line, color='red', alpha=0.7)\n",
"plt.xlabel('Wavelength $\\lambda$ ($\\AA$)')\n",
"plt.ylabel('Flux $f_\\lambda$ ($10^{-17}$ erg cm$^{-2}$ s$^{-1}$ $\\AA$)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Estimate dust extinction\n",
"\n",
"*Author: Jiarong Zhu*\n",
"\n",
"Find the SDSS optical spectra and images for the two galaxies\n",
"UGC 10227 (a typical-looking disk galaxy observed at high inclination)\n",
"and MCG -01-53-020 (a typical-looking disk galaxy observed at low\n",
"inclination). A major difference in observing galaxies at these\n",
"inclinations is the resulting amount of dust extinction. For a\n",
"standard reddening law, how much extinction do you need to explain\n",
"the first galaxy spectrum as a reddened version of the second?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we open the appropriate spectra, which can be found using the search facilities on SkyServer. Using the plate, MJD, and fiber numbers there, we construct the URL to download the data:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"UG = fits.open('https://dr16.sdss.org/sas/dr16/sdss/spectro/redux/26/spectra/lite/1056/spec-1056-52764-0308.fits')\n",
"MCG = fits.open('https://dr16.sdss.org/sas/dr16/sdss/spectro/redux/26/spectra/lite/0637/spec-0637-52174-0403.fits')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's construct arrays with the rest-frame wavelength and the flux. We will not concern ourselves with the overall normalization of the flux in this step."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"z_UG = UG[2].data['Z'][0]\n",
"z_MCG = MCG[2].data['Z'][0]\n",
"lam_UG = UG[1].data['loglam'] - np.log10(1. + z_UG)\n",
"lam_MCG = MCG[1].data['loglam'] - np.log10(1. + z_MCG)\n",
"f_UG = UG[1].data['flux']\n",
"f_MCG = MCG[1].data['flux']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot both, and we see that for UGC 10227, which is seen edge-on, it is a much redder spectrum than MCG -01-53-020, which is seen face-on. But many of the small scale features of the spectra are similar: the 4000 Angstrom break, with its Calcium H and K lines, the G band features redward of 4000 Angstromes, Na D line, and the TiO bands. Not all the features are quite the same. MCG -01-53-020 has a weaker Mg b line and does not have evident H$\\alpha$ emission. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.plot(10.**lam_UG, f_UG, label='UGC 10227 (edge-on)')\n",
"plt.plot(10.**lam_MCG, f_MCG, label='MCG -01-53-020 (face-on)')\n",
"plt.xlabel('wavelength')\n",
"plt.ylabel('flux')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We want to put these functions on the same wavelength grid. For our purposes, a simple 3rd-order spline interpolation scheme will be sufficient. Note that for more demanding purposes, a more accurate interpolation, or avoiding interpolation altogether, could be necessary. Whenever you interpolate, you usually cause error covariances between the output pixels and a loss of information."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"f_MCG_interp_func = interpolate.interp1d(lam_MCG, f_MCG, kind='cubic',\n",
" fill_value='extrapolate')\n",
"f_MCG_interp = f_MCG_interp_func(lam_UG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's just check that the interpolation didn't do anything silly."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.plot(10.**lam_UG, f_UG, label='UGC 10227 (edge-on)')\n",
"plt.plot(10.**lam_UG, f_MCG_interp, label='MCG -01-53-020 (face-on)')\n",
"plt.xlabel('wavelength')\n",
"plt.ylabel('flux')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can just divide the two arrays on the same wavelength grid to get some estimate of the extinction (here quantified in magnitude units). "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"A = - 2.5 * np.log10(np.abs(f_UG / f_MCG_interp)) # abs() is used here to avoid invalid(negative) points"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.plot(10.**lam_UG, A)\n",
"plt.xlabel('$\\lambda$ in Anstroms')\n",
"plt.ylabel('extinction $A_{\\lambda}$ in mag')\n",
"#plt.plot(lam,lam*A)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will estimate the total dust extinction under the assumption that the extinction follows the law:\n",
"\n",
"$$\\frac{A(\\lambda)}{A_V} = \\left(\\frac {\\lambda} {5500 \\mathrm{~Angstrom}} \\right)^{-1}$$\n",
"\n",
"This is an approximation of more detailed extinction laws estimated from stellar absorption studies; e.g. Cardelli, Clayton, and Mathis (1989).\n",
"\n",
"It is important to realize that $A(\\lambda)$, despite being a logarithmic measure of extinction, is multiplicatively related to $A_V$, due to the fact that the extinction is exponentially related to optical depth. It is this property that allows us to use the shape of the spectrum to determine the absolute level of extinction."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will take a crude approach and just bracket $A_V$ with three values (0.5, 1, and 2), showing that $A_V \\sim 1$ reproduces the shape of the ratio between the spectra, and that therefore $A_V \\sim 1$ is about the actual level of extinction in UGC 10227."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"AV = 1.\n",
"Amodel_10 = AV * (5500. / 10.**lam_UG)\n",
"AV = 0.5 \n",
"Amodel_05 = AV * (5500. / 10.**lam_UG)\n",
"AV = 2.0 \n",
"Amodel_20 = AV * (5500. / 10.**lam_UG)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.plot(10.**lam_UG, A - Amodel_05, label='Residuals from A_V = 0.5')\n",
"plt.plot(10.**lam_UG, A - Amodel_10, label='Residuals from A_V = 1.0')\n",
"plt.plot(10.**lam_UG, A - Amodel_20, label='Residuals from A_V = 2.0')\n",
"plt.xlabel('$\\lambda$ in Anstroms')\n",
"plt.ylabel('extinction $A_{\\lambda}$ in mag')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Indeed, $A_V=1$ works reasonably well. The fact that the residuals are actually near zero is a coincidence related to the fact that the galaxies are approximately the same stellar mass and redshift."
]
},
{
"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
}
| bsd-3-clause |
DhashS/Olin-Complexity-Final-Project | reports/03_approximation_algorithms.ipynb | 2 | 40546 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from graphgen import EUC_2D\n",
"from algs import minimum_perfect_matching #suboptimal, use Blossom V if you need to actually use it\n",
"from algs import brute_force\n",
"import networkx as nx\n",
"import itertools as it\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Christofides algorithm\n",
"\n",
"1. Create a minimum spanning tree T of G.\n",
"1. Let O be the set of vertices with odd degree in T. By the handshaking lemma, O has an even number of vertices.\n",
"1. Find a minimum-weight perfect matching M in the induced subgraph given by the vertices from O.\n",
"1. Combine the edges of M and T to form a connected multigraph H in which each vertex has even degree.\n",
"1. Form an Eulerian circuit in H.\n",
"1. Make the circuit found in previous step into a Hamiltonian circuit by skipping repeated vertices (shortcutting)\n",
"\n",
"\n",
"The Christofides algorithim is one of the key algorithms regarding the travelling salesman problem. It has a $\\frac{3}{2}$ bound on it's apprioximation, meaning the tour it produces, at most, is $\\frac{3}{2}$ worse than the optimal tour. Here's a demonstration on a random Euclidean graph. The Christofides algorithm is one of the first approximation algorithms, formtive in the building of the field."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def Christofides(G):\n",
" T = nx.algorithms.minimum_spanning_tree(G)\n",
" O = {n for n, d in T.degree(T.nodes_iter()).items() if d%2 == 1}\n",
" induced_subgraph = nx.Graph(G.subgraph(O)) \n",
" M = minimum_perfect_matching(induced_subgraph)\n",
"\n",
" T.add_weighted_edges_from([(u,v,M[u][v]['weight']) for u, v in M.edges()])\n",
" H = T\n",
" \n",
" seen = set()\n",
" tour = []\n",
" for u, _ in nx.eulerian_circuit(H):\n",
" if not u in seen:\n",
" tour.append(u)\n",
" seen.add(u)\n",
" return tour"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"G = EUC_2D(10)\n",
"opt_christofides = Christofides(G)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"([0, 7, 8, 5, 9, 3, 6, 4, 2, 1], 11.674609901636719)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cost = 0\n",
"for u, v in zip(opt_christofides, opt_christofides[1:]):\n",
" cost += G[u][v]['weight']\n",
"opt_christofides, cost"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 (8, 7, 6, 0, 4, 2, 1, 3, 9, 5)\n",
"Name: opt_tour, dtype: object"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"opt, _ = brute_force(G)\n",
"opt.opt_tour"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 8.792924\n",
"Name: cost, dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"opt.cost"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 13.189386\n",
"Name: cost, dtype: float64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1.5*opt.cost #upper bound on cost"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Lin-kernighan ($K$-opt heuristic)\n",
"\n",
"The Lin-kernighan heuristic is a way to \"settle\" a tour into a local optimum by investigating small, local changes in the tour. The locality of the change is the parameter $k$, which specifies how many 2-node pairs to swap and check if the cost is lower.\n",
"\n",
"\n",
"This is what a 2-opt switch looks like. If the new tour is lower cost than the unperturbed tour, we store it again and run the whole process over. Speeding this up and \"boosting\" out of local minima to better solutions can be achieved with higher $k$.\n",
"\n",
"\n",
"\n",
"Let's run the two-opt until there's no improvement."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def two_opt(G, tour):\n",
" from functools import reduce\n",
" #Consider all 2-node groups in the tour (Exhaustive enumeration)\n",
" cost = lambda t: sum([G[u][v]['weight'] for u, v in zip(t, t[1:])])\n",
"\n",
" def inner(i_tour):\n",
" for i in range(len(i_tour)):\n",
" for i2 in range(len(i_tour)):\n",
" if i<i2:\n",
" if i2-i < 2:\n",
" continue\n",
" tour_c = i_tour[:]\n",
" tour_c[i:i2] = list(reversed(i_tour[i:i2]))\n",
" if cost(tour_c) < opt:\n",
" return tour_c #not done yet, have to re-run since \"bubble\" has to be re-evaluated\n",
" return True #No advantageous swap\n",
" old_res = tour\n",
" opt = cost(tour)\n",
" it_opts = []\n",
" while True:\n",
" res = inner(old_res)\n",
" if res == True:\n",
" return (old_res, it_opts)\n",
" if type(res) == list:\n",
" it_opts.append(res)\n",
" old_res = res\n",
" opt = cost(res)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[43, 18, 10, 16, 15, 21, 39, 3, 13, 33, 47, 29, 25, 22, 46, 48, 20, 34, 17, 5, 32, 4, 9, 27, 8, 35, 45, 41, 38, 37, 40, 11, 0, 2, 23, 28, 30, 14, 26, 19, 42, 24, 31, 36, 1, 12, 7, 44, 6, 49]\n",
"Pre $K$-opt: 80.0384254487\n",
"Post $K$-opt cost: 23.4920708742\n"
]
}
],
"source": [
"G = EUC_2D(50)\n",
"res, ts = two_opt(G, list(range(len(G)))) #naive seed tour\n",
"print(res) #That looks different! We've settled into a local minima\n",
"cost = lambda t: sum([G[u][v]['weight'] for u, v in zip(t, t[1:])])\n",
"print(\"Pre $K$-opt: \", cost(range(len(G))))\n",
"print(\"Post $K$-opt cost: \", cost(res))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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FO+ST9McXStXW2W92LAAARgyFFLCgxLgIfWNxviSps2dQv39+l9weHp8HAAhO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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc7a0253dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.plot([cost(t) for t in ts]) #Let's see how cost drops per iteration of 2-opt\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For Euclidean case, $\\frac{\\texttt{Tour}_{k-opt}}{\\texttt{Tour}_{opt}} \\leq O(\\log |V|)$."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Arora and JSB Mitchell PTAS"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Sanjeev Arora and JSB Mitchell proposed a PTAS for the Travelling salesman problem. After looking at the problem for a while and understanding the algorithm intuitivley, the best I can provide is a rationale for a lack of implementation. \n",
"\n",
"https://pdfs.semanticscholar.org/7056/caeba3fa0d4064d03ef53fc2090974b70c71.pdf\n",
"\n",
" The Traveling Salesman Problem (TSP) is perhaps\n",
" the most famous optimization problem in the set NP-hard.\n",
" Many problems that are natural applications in computer\n",
" science and engineering can be modeled using TSP and\n",
" therefore, researchers are searching implementations focusing\n",
" on the quality of the produced solution and their\n",
" computational time. An innovative Polynomial-Time\n",
" Approximation Scheme (PTAS) for the Euclidean TSP was\n",
" given by Sanjeev Arora. To date, there is no evidence that it\n",
" can be implemented to be practically useful\n",
" \n",
" \n",
" Experimental results show that Arora´s PTAS is practically feasible. Table I and Table II show that in spite of its good performance, it seems that our approach must be improved to generate more approximate solutions. In most cases the significant theoretical results are lost due to implementation decisions. We think the quality of the solutions had to do with implementation aspects linked to data structures and the need to give more hints about which portals must be used by the tour.\n",
" \n",
" \n",
"This is a team with a substantial amount of knowlege behind them. Hopefully this provides a sense of the scale of the task"
]
},
{
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
twosigma/beaker-notebook | test/ipynb/scala/Spark_no_UI_option.ipynb | 2 | 1726 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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 --noUI\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",
"\"Pi is roughly \" + 4.0 * count2 / NUM_SAMPLES"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"spark.sparkContext.stop()"
]
}
],
"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": "2.12.8"
},
"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 |
blegat/Polyhedra.jl | examples/Computing controlled invariant sets.ipynb | 2 | 5628 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook shows how to use [CDD](https://www.inf.ethz.ch/personal/fukudak/cdd_home/) to compute controlled invariant sets for an hybrid system.\n",
"We consider the `cruise_control.jl` example of HybridSystems.jl which comes from [this paper](https://dl.acm.org/citation.cfm?id=2461378)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"include(Pkg.dir(\"HybridSystems\", \"examples\", \"cruise_control.jl\"));"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"const va = 15.6\n",
"const vb = 24.5\n",
"const vc = 29.5\n",
"const v = (va, vb, vc)\n",
"const U = 4.\n",
"const m0 = 500\n",
"const T = 2\n",
"const N = 10\n",
"const M = 1\n",
"const H = 0.8;"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"macro _time(x)\n",
" quote\n",
" y = @timed $(esc(x))\n",
" # y[1] is returned value\n",
" # y[2] is time in seconds\n",
" y[2]\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"using Gurobi\n",
"lpsolver = optimizer_with_attributes(Gurobi.Optimizer, \"OutputFlag\" => 0);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"function liftu(S, sys::HybridSystems.DiscreteLinearControlSystem)\n",
" [sys.A sys.B] \\ S\n",
"end\n",
"function new_constraint(hs, S, q, t)\n",
" @assert source(hs, t) == q\n",
" σ = symbol(hs, t)\n",
" r = target(hs, t)\n",
" ABset = liftu(S[1], hs.resetmaps[σ])\n",
" project(ABset, 1:statedim(hs, q))\n",
"end\n",
"function new_constraints(hs, S, q)\n",
" map(t -> new_constraint(hs, S, q, t), out_transitions(hs, q))\n",
"end\n",
"function add_hrep!(S, h::HalfSpace)\n",
" # I was using LP cycling errors when using CDD's LP solver\n",
" if issubset(S, h) # If S ⊆ h, then adding h will not change affect S\n",
" false\n",
" else\n",
" push!(S, SimpleHRepresentation(reshape(h.a, 1, length(h.a)), [h.β]))\n",
" true\n",
" end\n",
"end\n",
"function add_constraint!(S, P)\n",
" added = count(map(h -> add_hrep!(S, h), ineqs(P))) + count(map(h -> add_hrep!(S, h), eqs(P)))\n",
" removehredundancy!(S) # CDD throws LP cycling error\n",
" added\n",
"end\n",
"function add_constraints!(S::Polyhedron, Ps::Vector{<:Polyhedron})\n",
" sum(P -> add_constraint!(S, P), Ps)\n",
"end\n",
"function set_iteration!(hs, S)\n",
" Ps = map(q -> new_constraints(hs, S, q), states(hs))\n",
" added = add_constraints!.(S, Ps)\n",
" @show added\n",
"end\n",
"function iterate!(hs, S, nit)\n",
" map(i -> (gc(); @_time set_iteration!(hs, S)), 1:nit)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Mmax = 1\n",
"nit = 2\n",
"t = zeros(Mmax, nit)\n",
"Hs = Vector{HybridSystem}(Mmax)\n",
"CIS = Vector{Vector{Polyhedron}}(Mmax)\n",
"for m in 1:Mmax\n",
" Hs[m] = cruise_control_example(N, m, vmin = 5., v=(va, vb, vc), U=U, H=H, sym=false, m0=500);\n",
" I0 = Hs[m].invariants;\n",
" @show nineqs(I0[1])\n",
" CIS[m] = deepcopy(I0);\n",
" @show m\n",
" t[m, :] = iterate!(Hs[m], CIS[m], nit)\n",
" @show t[m, :]\n",
"end\n",
"t"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Mmax = size(t, 1)\n",
"nit = 1\n",
"previt = size(t, 2)\n",
"t = [t zeros(Mmax, nit)]\n",
"totit = size(t, 2)\n",
"for m in 1:Mmax\n",
" t[m, previt+(1:nit)] = iterate!(Hs[m], CIS[m], nit)\n",
" @show t[m, :]\n",
"end\n",
"t"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import Plots\n",
"Plots.pyplot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"D = [1, 2]\n",
"Plots.plot(project(Hs[1].invariants[1], D))\n",
"Plots.plot!(project(CIS[1][1], D))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Plots.savefig(\"dist_trailerspeed.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"t"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.6.4",
"language": "julia",
"name": "julia-0.6"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
jplourenco/bokeh | examples/plotting/notebook/line_server.ipynb | 46 | 1962 | {
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
"This IPython Notebook contains simple examples of the line function. \n",
"\n",
"To clear all previously rendered cell outputs, select from the menu:\n",
"\n",
" Cell -> All Output -> Clear"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"from bokeh.plotting import figure, show, output_notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*To run these examples you must execute the command `python bokeh-server` in the top-level Bokeh source directory first.*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"output_notebook(url=\"http://localhost:5006/\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"N = 80\n",
"x = np.linspace(0, 4*np.pi, N)\n",
"y = np.sin(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"p = figure()\n",
"p.line(x,y, color=\"tomato\", line_width=2)\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
mne-tools/mne-tools.github.io | dev/_downloads/8ea2bfc401dbdff70c284d271d62fa8c/label_from_stc.ipynb | 1 | 5690 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Generate a functional label from source estimates\n\nThreshold source estimates and produce a functional label. The label\nis typically the region of interest that contains high values.\nHere we compare the average time course in the anatomical label obtained\nby FreeSurfer segmentation and the average time course from the\nfunctional label. As expected the time course in the functional\nlabel yields higher values.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Author: Luke Bloy <[email protected]>\n# Alex Gramfort <[email protected]>\n# License: BSD-3-Clause"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.minimum_norm import read_inverse_operator, apply_inverse\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nfname_inv = (\n data_path / 'MEG' / 'sample' / 'sample_audvis-meg-oct-6-meg-inv.fif')\nfname_evoked = data_path / 'MEG' / 'sample' / 'sample_audvis-ave.fif'\nsubjects_dir = data_path / 'subjects'\nsubject = 'sample'\n\nsnr = 3.0\nlambda2 = 1.0 / snr ** 2\nmethod = \"dSPM\" # use dSPM method (could also be MNE or sLORETA)\n\n# Compute a label/ROI based on the peak power between 80 and 120 ms.\n# The label bankssts-lh is used for the comparison.\naparc_label_name = 'bankssts-lh'\ntmin, tmax = 0.080, 0.120\n\n# Load data\nevoked = mne.read_evokeds(fname_evoked, condition=0, baseline=(None, 0))\ninverse_operator = read_inverse_operator(fname_inv)\nsrc = inverse_operator['src'] # get the source space\n\n# Compute inverse solution\nstc = apply_inverse(evoked, inverse_operator, lambda2, method,\n pick_ori='normal')\n\n# Make an STC in the time interval of interest and take the mean\nstc_mean = stc.copy().crop(tmin, tmax).mean()\n\n# use the stc_mean to generate a functional label\n# region growing is halted at 60% of the peak value within the\n# anatomical label / ROI specified by aparc_label_name\nlabel = mne.read_labels_from_annot(subject, parc='aparc',\n subjects_dir=subjects_dir,\n regexp=aparc_label_name)[0]\nstc_mean_label = stc_mean.in_label(label)\ndata = np.abs(stc_mean_label.data)\nstc_mean_label.data[data < 0.6 * np.max(data)] = 0.\n\n# 8.5% of original source space vertices were omitted during forward\n# calculation, suppress the warning here with verbose='error'\nfunc_labels, _ = mne.stc_to_label(stc_mean_label, src=src, smooth=True,\n subjects_dir=subjects_dir, connected=True,\n verbose='error')\n\n# take first as func_labels are ordered based on maximum values in stc\nfunc_label = func_labels[0]\n\n# load the anatomical ROI for comparison\nanat_label = mne.read_labels_from_annot(subject, parc='aparc',\n subjects_dir=subjects_dir,\n regexp=aparc_label_name)[0]\n\n# extract the anatomical time course for each label\nstc_anat_label = stc.in_label(anat_label)\npca_anat = stc.extract_label_time_course(anat_label, src, mode='pca_flip')[0]\n\nstc_func_label = stc.in_label(func_label)\npca_func = stc.extract_label_time_course(func_label, src, mode='pca_flip')[0]\n\n# flip the pca so that the max power between tmin and tmax is positive\npca_anat *= np.sign(pca_anat[np.argmax(np.abs(pca_anat))])\npca_func *= np.sign(pca_func[np.argmax(np.abs(pca_anat))])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"plot the time courses....\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.figure()\nplt.plot(1e3 * stc_anat_label.times, pca_anat, 'k',\n label='Anatomical %s' % aparc_label_name)\nplt.plot(1e3 * stc_func_label.times, pca_func, 'b',\n label='Functional %s' % aparc_label_name)\nplt.legend()\nplt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"plot brain in 3D with mne.viz.Brain if available\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"brain = stc_mean.plot(hemi='lh', subjects_dir=subjects_dir)\nbrain.show_view('lateral')\n\n# show both labels\nbrain.add_label(anat_label, borders=True, color='k')\nbrain.add_label(func_label, borders=True, color='b')"
]
}
],
"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": 0
} | bsd-3-clause |
HaFl/ufldl-tutorial-python | Softmax_Regression.ipynb | 1 | 26090 | {
"cells": [
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import scipy.optimize\n",
"import time\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn.datasets import fetch_mldata"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data\n",
"First get and preprocess the data. This time, we will use the complete data set and not just the samples for the numbers **`0`** and **`1`**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get data: contains 70k samples of which the last 10k are meant for testing"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mnist = fetch_mldata('MNIST original', data_home='./data')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prepare for concat"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_all = mnist.target[:, np.newaxis]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Intercept term to be added"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"intercept = np.ones_like(y_all)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before the next step, we need to define this util function which normalizes the data."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def normalize_features(train, test):\n",
" \"\"\"Normalizes train set features to a standard normal distribution\n",
" (zero mean and unit variance). The same procedure is then applied\n",
" to the test set features.\n",
" \"\"\"\n",
" train_mean = train.mean(axis=0)\n",
" # +0.1 to avoid division by zero in this specific case\n",
" train_std = train.std(axis=0) + 0.1\n",
" \n",
" train = (train - train_mean) / train_std\n",
" test = (test - train_mean) / train_std\n",
" return train, test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, normalize the data (zero mean and unit variance)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_normalized, test_normalized = normalize_features(\n",
" mnist.data[:60000, :],\n",
" mnist.data[60000:, :],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Concat **`intercept`**, **`X`**, and **`y`** so that shuffling is easier in a next step"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_all = np.hstack((\n",
" intercept[:60000],\n",
" train_normalized,\n",
" y_all[:60000],\n",
"))\n",
"test_all = np.hstack((\n",
" intercept[60000:],\n",
" test_normalized,\n",
" y_all[60000:],\n",
"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Shuffle the data. As mentioned in the [Logistic_Regression](http://nbviewer.ipython.org/github/HaFl/ufldl-tutorial-python/blob/master/Logistic_Regression.ipynb) notebook already, I don't think it's needed, but let's stick with the ufldl tutorial here."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.random.shuffle(train_all)\n",
"np.random.shuffle(test_all)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, get train and test data sets"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_X = train_all[:, :-1]\n",
"train_y = train_all[:, -1]\n",
"\n",
"test_X = test_all[:, :-1] \n",
"test_y = test_all[:, -1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Softmax Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define some helpful variables and initial random theta values for all classes."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"m, n = train_X.shape\n",
"k = np.unique(train_y).size\n",
"theta = np.random.rand(n, k) * 0.001"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This **`indicator_mask`** will come in handy when computing the gradient later on."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"indicator_mask = np.zeros((train_X.shape[0], theta.shape[1]), dtype=np.bool)\n",
"for i, idx in enumerate(train_y):\n",
" indicator_mask[i][idx] = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a helper function to keep the code DRY. It computes the probabilities of all classes for all samples."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def probs(theta, X, y):\n",
" if theta.ndim == 1:\n",
" theta = theta.reshape((theta.size / k, k))\n",
" values = np.exp(X.dot(theta)) \n",
" sums = np.sum(values, axis=1)\n",
" return (values.T / sums).T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The cost function of Softmax Regression. We could actually use the **`indicator_mask`** here instead of the loop at the end, but that would be computational overkill."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def cost_function(theta, X, y):\n",
" log_probs = np.log(probs(theta, X, y))\n",
" cost = 0\n",
" for i in range(m):\n",
" cost -= log_probs[i][y[i]]\n",
" return cost"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The gradient function of Softmax Regression."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def gradient(theta, X, y):\n",
" gradient_matrix = -X.T.dot(indicator_mask - probs(theta, X, y))\n",
" return gradient_matrix.flatten()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alright, let's run the optimization. 100 iterations are enough here."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization took 195.31115102767944 seconds\n"
]
}
],
"source": [
"J_history = []\n",
"\n",
"t0 = time.time()\n",
"res = scipy.optimize.minimize(\n",
" fun=cost_function,\n",
" x0=theta,\n",
" args=(train_X, train_y),\n",
" method='L-BFGS-B',\n",
" jac=gradient,\n",
" options={'maxiter': 100, 'disp': True},\n",
" callback=lambda x: J_history.append(cost_function(x, train_X, train_y)),\n",
")\n",
"t1 = time.time()\n",
"\n",
"print('Optimization took {s} seconds'.format(s=t1 - t0))\n",
"optimal_theta = res.x.reshape((theta.size / k, k))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the evolution of **`J`** (to make sure we did the right thing)."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10d5d00f0>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10d5b9f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(J_history, marker='o')\n",
"plt.xlabel('Iterations')\n",
"plt.ylabel('J(theta)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Even the **`optimal_theta`** still results in a relatively high cost value **`J`**. That's not necessarily a bad sign, but rather the consequence of the model being more complex than e.g., Logistic Regression."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def accuracy(theta, X, y):\n",
" correct = np.sum(np.argmax(probs(theta, X, y), axis=1) == y)\n",
" return correct / y.size"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training accuracy: 0.9435833333333333\n",
"Test accuracy: 0.9235\n"
]
}
],
"source": [
"print('Training accuracy: {acc}'.format(acc=accuracy(res.x, train_X, train_y)))\n",
"print('Test accuracy: {acc}'.format(acc=accuracy(res.x, test_X, test_y)))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
pgr-me/metis_projects | 05-lights/03_analyze_countries.ipynb | 1 | 8968 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import geopandas as gpd\n",
"import pandas as pd\n",
"import pickle\n",
"\n",
"from sklearn.linear_model import LinearRegression\n",
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf\n",
"\n",
"from library.analyze import countries_regressions"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AFG\n",
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}
],
"source": [
"# import cleaned dataframe\n",
"df = pd.read_pickle('data/geo/pickles/zonal_stats_c_norm.pickle')\n",
"\n",
"# calculate regression stats for each country\n",
"df_regression = countries_regressions(df).sort_values(by='r', ascending=False)\n",
"\n",
"# save outputs to csv\n",
"df_regression.to_csv('data/country_stats.csv')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYR\n"
]
}
],
"source": [
"# syria case\n",
"\n",
"# import cleaned dataframe\n",
"df_syria = pd.read_pickle('data/geo/pickles/zonal_stats_c_norm_syr.pickle')\n",
"\n",
"# calculate regression stats for each country\n",
"df_syria_regression = countries_regressions(df_syria).sort_values(by='r', ascending=False)\n",
"\n",
"# save outputs to csv\n",
"df_syria_regression.to_csv('data/country_stats_syria.csv')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SSD\n",
"AGO\n"
]
}
],
"source": [
"# angola and south sudan cases\n",
"\n",
"# import cleaned dataframe\n",
"df_agossd = pd.read_pickle('data/geo/pickles/zonal_stats_c_norm_agossd.pickle')\n",
"\n",
"# calculate regression stats for each country\n",
"df_agossd_regression = countries_regressions(df_agossd).sort_values(by='r', ascending=False)\n",
"\n",
"# save outputs to csv\n",
"df_agossd_regression.to_csv('data/country_stats_agossd.csv')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>beta</th>\n",
" <th>intercept</th>\n",
" <th>r</th>\n",
" <th>r_adj</th>\n",
" <th>p_beta</th>\n",
" <th>p_int</th>\n",
" <th>c_beta_low</th>\n",
" <th>c_beta_high</th>\n",
" <th>c_int_low</th>\n",
" <th>c_int_high</th>\n",
" </tr>\n",
" <tr>\n",
" <th>country</th>\n",
" <th></th>\n",
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" <td>-0.733977</td>\n",
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" <td>-0.050667</td>\n",
" <td>1.756995</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" beta intercept r r_adj p_beta p_int \\\n",
"country \n",
"AGO 1.017198 -0.066976 0.862810 0.858523 2.361394e-15 0.024607 \n",
"SSD 0.240826 0.853164 0.105244 -0.118445 5.304519e-01 0.058750 \n",
"\n",
" c_beta_low c_beta_high c_int_low c_int_high \n",
"country \n",
"AGO 0.871145 1.163251 -0.124813 -0.009138 \n",
"SSD -0.733977 1.215629 -0.050667 1.756995 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_agossd_regression"
]
}
],
"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 |
farr/PleiadesStars | QPGP_notes.ipynb | 1 | 855224 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pylab as pl\n",
"from george import GP, kernels\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# What this notebook is about\n",
"\n",
"In my research recently I have made extensive use of quasi-periodic Gaussian Process (QPGP for short) models, where the covariance function, or kernel, is the product of two terms: one strictly periodic and one monotonically decreasing function of Euclidean distance in the input space (which is typically 1-D for the applications I'm looking at). Specifically, I usually combine the exponential (sine squared) periodic kernel introduced by e.g. Mackay (1998) with the familiar squared exponential kernel, because random draws from the resulting composite kernel look remarkably similar to the stellar light curves I am working with. That being said, many other combinations are possible.\n",
"\n",
"Despite having used this type of GP quite a bit, I still have a fairly hazy understanding of how the parameters of the kernel (i.e. the hyper-parameters of the GP) relate to properties of the resulting time-series, or vectors, that we might be able to relate to: for something like the period it is fairly obvious, but for things like number of wiggles per period (for want of a better expression), evolutionary time-scale, or even amplitude, things are more murky, and sometimes surprising (to me at least). What I really would like to understand better, ultimately, is the relationship between the hyper-parameters and the physical parameters of stellar surface structures, such as magnetically active regions and star spots, that give rise to the observed variability in the light curves I am studying. Physical properties of interest include active region or spot lifetimes, differential rotation, distribution of active regions over the stellar surface... But as a first step, in this notebook I am going to try to explore the relationship between the GP hyper-parameters and the basic properties of the resulting time-series."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The building blocks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The squared exponential kernel\n",
"\n",
"This is defined as:\n",
"\n",
"$$k_{\\mathrm{SE}} (r) = V \\exp{\\left( -\\frac{r^2}{2l^2} \\right)},$$\n",
" \n",
"where, if our input variable is $t$, $r \\equiv |t-t'|$, $V$ controls the variance of the resulting time-series, and $l$ is their length scale (also known as input scale). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Alternative parametrisations\n",
"\n",
"Many alternative parametrisation are sometimes used, for example one can use an amplitude (or output scale) $a = \\sqrt{V}$ instead of the variance hyper-parameter. Similaryl, one can use an inverse length scale $\\eta \\equiv l^{-1}$, or a \"metric\" $m \\equiv 0.5 l^{-2}$ (the latter is used in *george*, for example), instead of the length scale $l$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### What does it actually look like?\n",
"\n",
"Let's plot the covariance function for a few different values of $V$ and $l$, and then random draws from the corresponding GPs."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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Nsh2GiHiZCiURERERF9SpU4eOHTvaDkNEvMzfWyXU0iAiYpla75xSnhIRsUyt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\nIiIiIiIiIiIiIiIiIiIiIjI0/D8axgxIiFArfAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fecfa90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"r = np.r_[0:5:251j]\n",
"def expsquared(l, r):\n",
" return np.exp(-(r/l)**2/2)\n",
"pl.figure(figsize=(14,5))\n",
"cols = ['b','g','r']\n",
"lss = ['-', '--', ':']\n",
"l = [0.1,0.5,2]\n",
"V = [0.1,0.5,2]\n",
"ax1 = pl.subplot(121)\n",
"for i in range(len(l)):\n",
" pl.plot(r,1*expsquared(l[i],r), label = r\"$l=$%s\" % repr(l[i]), c = cols[i])\n",
"pl.legend(loc=0)\n",
"pl.title(r\"Squared exponential kernel ($V=1$)\")\n",
"pl.xlabel(r\"$r$\")\n",
"pl.ylabel(r\"$k_{\\mathrm{SE}}(r)$\")\n",
"ax2 = pl.subplot(122, sharex = ax1, sharey = ax1)\n",
"for i in range(len(V)):\n",
" pl.plot(r,V[i]*expsquared(1,r), label = r\"$V=$%s\" % repr(V[i]), c = 'k', ls = lss[i])\n",
"pl.legend(loc=0)\n",
"pl.title(r\"Squared exponential kernel ($l=1$)\")\n",
"pl.xlabel(r\"$r$\")\n",
"pl.xlim(0,3)\n",
"pl.ylim(0,2.1);"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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JsdRUw9vcuMFYWJjZh5YsAHK9i4h96QghZcDByhJ9y7VI4IbsqPdj4txgoDxxmC5hWVnA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\nJEmSJEmSJEmSJEmSJEmSJEmSJEm1+B9rR/SJj5QTGQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ffc40d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"t = r - 2.5\n",
"pl.figure(figsize=(14,5))\n",
"for i in range(len(l)):\n",
" if i == 0:\n",
" ax1 = pl.subplot(131)\n",
" else: \n",
" axc = pl.subplot(1,3,i+1, sharex = ax1, sharey = ax1)\n",
" for j in range(len(V)):\n",
" gp = GP(V[j] * kernels.ExpSquaredKernel(l[i]**2))\n",
" gp.compute(t,yerr=1e-3)\n",
" sam = gp.sample(t).flatten()\n",
" pl.plot(t,sam,c=cols[i],ls=lss[j], label = r\"$V=$%s\" % repr(V[j]))\n",
" pl.title(r\"$l=$%s\" % repr(l[i]))\n",
" pl.legend(loc=0)\n",
" pl.xlabel(r\"$t$\")\n",
"pl.xlim(-2.5,2.5)\n",
"pl.ylim(-3,3);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So as we'd expect, $V$ controls the amplitude and $l$ the \"wigglyness\" of the time-series. But can we be a bit more specific?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### What does $l$ mean exactly?\n",
"\n",
"It would be useful to understand the relationship between $l$ and the typical number of turning points, or zero crossings, per unit time. I don't have a good mathematical insight into this, but I can try to get at it empirically... Warning: the next cell takes a while to run."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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c5/Z+AE4DOAdgWtltfgDKV9/wrgqWdP++8O+hQ0KrYREM9Y3PWrlS2MvVyQno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\nMcYYY4wxxhhjjDHGGGOMMWv2/zvxMadUDydVAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110c07b50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dt = np.r_[0:20:501j]\n",
"T = dt[-1]-dt[0]\n",
"logl = np.r_[-1:0.5:15j]\n",
"l = 10.0**logl\n",
"n = len(l)\n",
"nsim = 100\n",
"ntp = np.zeros((n,nsim))\n",
"def turningPoints(lst):\n",
" dx = np.diff(lst)\n",
" return (dx[1:] * dx[:-1] < 0)\n",
"nzc = np.zeros((n,nsim))\n",
"def zeroCrossings(lst):\n",
" z = 0\n",
" si = np.sign(lst)\n",
" return (si[1:] * si[:-1] < 0)\n",
"for i in range(n):\n",
" gp = GP(kernels.ExpSquaredKernel(l[i]**2))\n",
" gp.compute(dt,yerr=1e-3)\n",
" sam = gp.sample(dt, size = nsim)\n",
" for j in range(nsim):\n",
" tmp = sam[j,:].flatten()\n",
" tp = turningPoints(tmp)\n",
" ntp[i,j] = tp.sum()/float(T)\n",
" zc = zeroCrossings(tmp)\n",
" nzc[i,j] = zc.sum()/float(T)\n",
"ntp_mean = np.mean(ntp, axis=1)\n",
"nzc_mean = np.mean(nzc, axis=1)\n",
"for i in range(n):\n",
" pl.plot(np.ones(nsim)*l[i], ntp[i,:].flatten(), 'k.')\n",
" pl.plot(np.ones(nsim)*l[i], nzc[i,:].flatten(), 'r.')\n",
"pl.plot(l, ntp_mean, 'ko')\n",
"pl.plot(l, 10.0**(-0.25-np.log10(l)), 'k-', label = 'turning points')\n",
"pl.plot(l, nzc_mean, 'ro')\n",
"pl.plot(l, 10.0**(-0.5-np.log10(l)), 'r-', label = 'zero crossings')\n",
"pl.xlabel(r\"$l$\")\n",
"pl.legend(loc=0)\n",
"pl.loglog()\n",
"pl.xlim(0.08,4);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, it looks like, for this kernel, the number of turning points per unit time is $n_{\\mathrm{TP}} \\approx 0.56/l$ and the number of zero crossings per unit time is $n_{\\mathrm{ZC}} \\approx 0.31/l$. Where these constants come from, I'm not sure, but the $1/l$ dependency makes sense, and also the fact that there are fewer zero crossing than turning points."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### What does $V$ mean exactly?\n",
"\n",
"$V$ should be equal the variance of the time series, this is kind of obvious. But I'm less clear about the relationship between the amplitude, or output scale ($\\sqrt{V}$) and the actual peak-to-peak amplitude of the time-series."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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stlFm1DHG8Pvf/569e/dy6NAhHnroISZMmNDgnGHDhrFs2TKuvPJKXn31VQD27t3LxRdf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\niIhIAvl/PZUVjaY+pDgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11019cf90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dt = np.r_[0:20:501j]\n",
"T = dt[-1]-dt[0]\n",
"l = 1.0\n",
"logV = np.arange(10) \n",
"V = 10.0**logV\n",
"n = len(V)\n",
"nsim = 100\n",
"amp = np.zeros((n,nsim))\n",
"var = np.zeros((n,nsim))\n",
"for i in range(n):\n",
" gp = GP(V[i] * kernels.ExpSquaredKernel(l**2))\n",
" gp.compute(dt,yerr=V[i]*1e-3)\n",
" sam = gp.sample(dt, size = nsim)\n",
" for j in range(nsim):\n",
" tmp = sam[j,:].flatten()\n",
" var[i,j] = tmp.var()\n",
" amp[i,j] = tmp.max() - tmp.min()\n",
"var_mean = np.mean(var, axis=1)\n",
"amp_mean = np.mean(amp, axis=1)\n",
"for i in range(n):\n",
" pl.plot(np.ones(nsim)*V[i], var[i,:].flatten(), 'k.')\n",
" pl.plot(np.ones(nsim)*V[i], amp[i,:].flatten(), 'r.')\n",
"pl.plot(V, var_mean, 'ko')\n",
"pl.plot(V, V, 'k-', label = 'variance')\n",
"pl.plot(V, amp_mean, 'ro')\n",
"pl.plot(V, 10.0**(0.56+0.5*np.log10(V)), 'r-', label = 'pk2pk amplitude')\n",
"pl.legend(loc=0)\n",
"pl.xlabel(r\"$V$\")\n",
"pl.loglog()\n",
"pl.xlim(0.1,1e10);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So the variance is equal to $V$, as expected. The amplitude is given by $A \\approx 3.6 V^{0.5}$; once again the index is as expected but I'm not sure where the constant comes from."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The exponential(sine squared) kernel\n",
"\n",
"We now move to the strictly periodic kernel, which forms the second building block of our QPGP kernel. I use the exponential(sine squared) kernel, which is defined (once again following RW) as:\n",
"\n",
"$$k_{\\mathrm{P}} (r) = V \\exp{\\left( -\\frac{2 \\sin^2 (\\pi r/P)}{l^2} \\right)},$$\n",
"\n",
"where $P$ is the period, $V$ controls the variance, and $l$ is a length or input scale. However, $V$ and $l$ don't have exactly the same interpretation as in the squared exponential kernel."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Alternative parametrisations\n",
"\n",
"Once again, alternative parametrisations are sometimes used: in the past I have sometimes used $l' \\equiv l/2$, and *george* uses $\\Gamma \\equiv 2 / l^2$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### What does it actually look like?\n",
"\n",
"Let's plot the covariance function for a few different values of $P$, $V$ and $l$, and then random draws from the corresponding GPs."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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sIDExEW3atNE6DKuaM2cOTp8+bfL+9913H+rVq6diRLYtISEBy5YtM3l/V1dX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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11032cf90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def expsine2(P, l, r):\n",
" return np.exp(-2*(np.sin(np.pi*r/P))**2/l**2)\n",
"lws = [1,2,3]\n",
"cols = ['b','g','r']\n",
"lss = ['-', '--', ':']\n",
"P = [0.3,1,3]\n",
"l = [0.5,2,5]\n",
"V = [0.1,0.5,2]\n",
"pl.figure(figsize=(14,5))\n",
"ax1 = pl.subplot(131)\n",
"for i in range(len(P)):\n",
" pl.plot(r,1*expsine2(P[i],1,r), label = r\"$P=$%s\" % repr(P[i]), lw = lws[i], c = 'k', ls = '-')\n",
"pl.legend(loc=0)\n",
"pl.title(r\"ES2 kernel ($l=1,V=1$)\")\n",
"pl.xlabel(r\"$r$\")\n",
"pl.ylabel(r\"$k_{\\mathrm{SE}}(r)$\")\n",
"ax2 = pl.subplot(132, sharex = ax1, sharey = ax1)\n",
"for i in range(len(l)):\n",
" pl.plot(r,1*expsine2(1,l[i],r), label = r\"$l=$%s\" % repr(l[i]), lw = 1, c = cols[i], ls = '-')\n",
"pl.legend(loc=0)\n",
"pl.title(r\"ES2 kernel ($P=1,V=1$)\")\n",
"pl.xlabel(r\"$r$\")\n",
"ax3 = pl.subplot(133, sharex = ax1, sharey = ax1)\n",
"for i in range(len(V)):\n",
" pl.plot(r,V[i]*expsine2(1,1,r), label = r\"$V=$%s\" % repr(V[i]), lw = 1, c = 'k', ls = lss[i])\n",
"pl.legend(loc=0)\n",
"pl.title(r\"ES2 kernel ($P=1,l=1$)\")\n",
"pl.xlabel(r\"$r$\")\n",
"pl.xlim(0,3)\n",
"pl.ylim(0,2.1);"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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YUWEYpjDguYS5miju4W0AcNNNN+HTTz/F9ddfj7Vr1+Kpp57y+7OcTieefvppZGRkYNGi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9+rFxAUZH85BedAV8Xcpa8v8Omn9pVFEARBEAShruIq7mSXciFPmACcOMFZ/FmzmOMj1B2K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nhbu7zHSYMWMG3n77bQDAuXPnMHfu3EJf365dO/Tr1w9RUVHo2LEj0tLS9M999dVXiIyMxPr16wu8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\niIiIiIiIiIiIiIjInv0/xYEjhFk0FdcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110a944d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pl.figure(figsize=(14,15))\n",
"for i in range(len(P)):\n",
" for j in range(len(l)):\n",
" if (i == 0) & (j == 0):\n",
" ax1 = pl.subplot(331)\n",
" else: \n",
" axc = pl.subplot(3,3,i*3+j+1, sharex = ax1, sharey = ax1)\n",
" Gamma = 2.0/l[j]**2\n",
" for k in range(len(V)):\n",
" gp = GP(V[k] * kernels.ExpSine2Kernel(Gamma,P[i]))\n",
" gp.compute(t,yerr=1e-3)\n",
" sam = gp.sample(t).flatten()\n",
" pl.plot(t,sam,lw = lws[i], c=cols[j],ls=lss[k], label = r\"$V=$%s\" % repr(V[k]))\n",
" if i == 0: \n",
" pl.title(r\"$l=$%s\" % repr(l[j]))\n",
" if j == 0: \n",
" pl.ylabel(r\"$P=$%s\" % repr(P[i]))\n",
" if i == len(P)-1:\n",
" pl.xlabel(r\"$t$\")\n",
" if j == len(l)-1:\n",
" pl.legend(loc=0)\n",
"pl.xlim(-2.5,2.5)\n",
"pl.ylim(-3,3);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can immediately see things are going to be a little more complicated than before. Obviously, $P$ controls the period, but the shape and range of the covariance function, and hence the \"wigglyness\", variance and even mean of the resulting time-series, depend on both $V$ and $l$ in an interconnected way.\n",
"\n",
"Before we delve into that any deeper, though, a short aside..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2-D interpretation\n",
"\n",
"According to RW, this type of kernel was introduced by Mackay (1998), by mapping the one-dimensional variable $t$ onto a 2-D space $\\mathbf{u}(t)=(\\cos(t),\\sin(t))$, and using the squared exponential kernel in $\\mathbf{u}$-space. Noting that $(\\cos(t)-\\cos(t'))^2+(\\sin(t)-\\sin(t'))^2=4 \\sin^2 (r/2)$, one obtains:\n",
"\n",
"$$k(r) = \\exp{\\left( -\\frac{2 \\sin^2 (r/2)}{l^2} \\right)}.$$\n",
"\n",
"(As $\\sin^2(x)$ has period $\\pi$, the above expression produces random periodic functions with period $2\\pi$, the modified expression I used enables me to generate random periodic functions with arbitrary period $P$.)\n",
"\n",
"This provides some insight into the \"meaning\" of $l$: it is still a length scale, but in $\\mathbf{u}$ space, not in the time domain itself. In other words, this type of GP sets up a probability distribution over smooth, 2-D random functions in $\\mathbf{u}$-space with characteristic length scale $l$. Note however that those functions are defined only over a unit circle in $\\mathbf{u}$-space, and must symmetric under 180 degree rotation. The same functions are also smooth, 1-D random periodic functions in $t$-space. Let's see if we can illustrate that. "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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WUxMZYPJkJzfc0D0m87bb2lm0SMavpZyHToZw2F5GL3cgnHaP6kRCJFwWOn+k\nlAE04ybCBpp5iBWqY21HirU4MbD4M5sIx6rxcWTxE7n8KTLI//t/WUydqgNRolGLn/60nXA4ud6d\nit1YMhtWftg9+/OHb4FLJkWJzDCSLH7GYHJoI4d23mUjm2hWHatLshZrpwArgdXATTt5fjrQAiyJ\nffyy35LtwhvU8SFNgF2l/4zhOGWZDpFBdF3jvvuyuy6H/ve/ER580K82lDqp1YYZUXjkPHueSBSY\nfiOUT1QaSYj+djpDOBwfDgxMWnmQhaojdUnGYs0B3I/d2I0HzgXG7eR184FJsY9f9Vu6naghxD2s\nwxG7/PltSpgklz9FBpo0ycXVV2d13b/55hZWrowoTKREyrVhPHweBAJ2sZZTAqepjSOECi50rmc8\nBbTjIsomqnmbT1XHApKzWDsCWANswJ44/ixwxk5elzTdVk+ymXYigMVQPNzEcNWRhFDmzjtzmDhR\nA6L4/SZ33NGiOlJ/S602bPOXsPgV6Nwf8fJnweNTGkkIVYZRyHmMITt2OXQ+i+lIgtmhyVisDQE2\n97i/JfZYTxZwFLAUeAP73asSK2njJarRsXBjcQPDcSXlj1WI/uFwaNx9dx4ejwVY/Otf7cydm1GX\nQ1OqDeP+syEStRMd8l0Yd7yyKEIkg5OZwAi86JhEqOUN3lIdKSmrit6MSP4MqAAmAn8BZic00S5E\nMfkdq7vuTyKf6RSriCJEUjnxRC+nnebGvq4G11zTSEeHufsvSh8p04bxzgOweYX9z6Q74Zw/gZYc\nHX5CqJKFh1lMppgm3ET4msVs6vF/vQpOpWffuSrsRqxTBfY7057aetx+E3gQKAIadzzYbbfd1nV7\n+vTpTJ8+PU4xYRGNrKQV0HCjcydj43ZsIVLdo48WM29ekKYmk6+/jvLCC+1cfHF8x3JWVlZSWVkZ\n12PGQdzasES2X4SD8Nrvu+/PvANKh8fv+EKksPGMYyL78TVf4ibEx7zBUK6P+3l624Yl41soJ/A1\ncAJQDXyMPUC356InZUAt9jvYI4DnYacDxSzLSszSAVFMTmcBzUSwgFkM5UZGJeRcQqSqX/+6mV/+\nshkwyc21qKoaQW5u4jr0NbtXSHW7Fq82LGHtFwBPXA9v32ffLhoCv/sCcooSdz4hUsw2tvAM9xAl\nQh6tnMBl7McxCT3nrtqwZLwMGgWuBd4GlgPPYTdyP4x9AJwFfAl8DvwZOKe/Q77FVtoIoGHgRuNq\nmVQgxDceWrq2AAAgAElEQVTcfHMBBQUGYNDWZvLAA9/o/E5Hyd+G1a6H95/u3lJq1m+kUBNiB2WU\nM4WJFNGIiyireBGDkJIsqt+BJlrC3pl+h/doja0eeQNj+d52Vz2EEJ1eeKGNWbOqAQu3GwKBMeh6\nYpqeJOlZi5fE9az99QqY+4j9kxpzFNz+PuiyLZ4QOwrRwRxuIEQ9PvxM4gaGcmrCzpdKPWtJ72FW\ndxVqRbg5mYGKEwmRvGbMyGbkSAdgEQ5bXHNNjepImW395zDvcXupDhOY8RMp1ITYBQ/ZjGcq2fjR\nsNjE/URp7/ccUqztpQBR5lKNjglYXM4ocnCpjiVE0vJ6df7whwFd9196qZXGxuhuvkIk1BsPQDhi\nX/4cNw2OmKk6kRBJbSwXUQDk0YpJPS3M7fcMUqztpedYTx1+dCxG4uMkBqmOJETSO+OMXI44wgNY\n1NWFuf32baojZaata+CdR+1CTXfDRXepTiREShjNFbiI4iVALXf0+/mlWNsLLYSZw0b02LZSZ1CB\nB7l8IMSeOJ0aP/pRIZ0j2p98spEtW8KqY2Wel3sUZ8MOgv0OV5dFiBQygG9RRIRs/EAjAZ7t1/NL\nsbYX5lNNEyE0YCg+ZjJUdSQhUsYFFxRy6KEewKC5OcLjjzeojpRZjCj85x/dS/b+8EGlcYRIJQ5y\nGcyV+Ggnjxb81p/79fxSrPVSGIMne6xgfCYj0NJm0pkQ/ePHPy6ls1q4665tNDXJ2LV+88iP7bFq\nBjDiYBg1SXUiIVJKDmeSbXlxWAauyBKM6D/77dxSrPXScppoJYCGiRud06RXTYi9dt55RZSU2EMH\nWltN3nkn4zZ5VyPohwWv2oWaBcz8KTiScQMbIZKXgzIKI2eSH2jFEw1j+f/Rb+eWYq2XHuUrNCx0\nLM5jFLr0qgmxT+68cxD2mhEGv/jF5j29XMTDW4/Ctk327YoDYOr31eYRIkU5XT/HGS5AD4Cj8T0I\nLuyX80qx1guraGIjjegYOIHjGKw6khAp64wzCsjKsgCTDRtCvPee9K4lVDQCr//N7lUzgGnnSK+a\nEPtI00pxBY/B1QqaCWz7a7+cV4q1XniTdRhYaMCxDGQIOaojCZGyBg1yc8klJQCYJjz4oCzjkVDr\nlsK6r+zLn55sOPdm1YmESG35v4Z2oBVoeA4SuYdvjBRre9BIgEq2AKCjcZps1i5En11xxQDcbg2w\nmD27ntWrA6ojpa/H7+i86gzHnQuaDOEQok+840CbaP9NdYRg7W8Sfkop1vbgS+qIEEbDZAA+DqBY\ndSQhUt7EidkceKAbMIhGLd58MyM2eO9/QT98saB7YsEp/6M6kRCpT3NCzv9ALXYPW9UrCT+lFGt7\n8CIr0AEHFrMYrTqOEGnjl78sp7PL5667NqmOk55efgCaG2JbS02BA45UnUiI9FBxGVAGIaDqU6ip\nTOjppFjbjSraqI5t2OpAYxJlihMJkT6OPjoPn8+eaFBTE+bzz9tUR0ovpgnzXrJ71UzgGNkDVIi4\ncfjAN9XuWTOANbMTejop1nZjHuuJxK4fHMYgyshWHUmItFFa6mbWrFIAolGLhx+uVpwozWzbDF9+\nZBdqDhec+xPViYRIL2P+DzqANuCrJxN6KinWdiGKyftswIGJA5OTGKE6khBp5+KLB6HrABYvvriN\ntjbZ0SBuXnwYwtgfh51E7ActhIiXkoPAOdTe8rilCVa+mrBTyV/vLjTgp4F2NEx04FAGqo4kRNqZ\nNq2AvDwAg/r6MCtXdqiOlD7en2P3qlnACeeoTiNE+nFmwaDToQm7h21Z4iYaSLG2C/9mOXpsx4Lj\nGIZDflRCJMRllw3CrigM/vSn9arjpIeli+DrL+wfa1EZnHiW6kRCpKcpP4aoEyLAwucgmJixt1KB\n7ISFxQrquu5PlF41IRLm+OOLsK8jmHz0UTOGkfgFJtPekoX2LLUIsP/B4M1SnUiI9FQ4HCIF9ri1\ncBiqVyXkNFKs7cR6mthEE2CRg5upDFcdSYi0deqppVRUeAFYty5AZWWD4kRp4Nm/dS+E+70fqk4j\nRPrSNJj4AwgALSa8dW9CTiPF2k6spAYHBjoGoylCk03bhUioadOKYrcsKivrlWZJedWbYPNGu1jz\n+OCgyaoTCZHeDjoN/Jp9geDz9yESjvsppFjbiUVsQMNEw+IohqqOI0Ta+973BtC50/js2TWq46S2\nD96FjoBdrI0/FMoGq04kRHo74DgIZ0MLsGkj1KyL+ymkWNtBE342UYcDCx86kyhXHUmItHfccSUU\nFjoAi5UrW1i9ul11pNT17xftsWphYMa5qtMIkRkmzbB71sLA2y/E/fBSrO2gmubYQrhQSBYFyMBc\nIRItP9/F6NFeIEo0arBkSbPqSKnri6V2sWYB4yapTiNEZhh9FDRiTzRYPD/uh5dibQcfsLbr9hRG\nKkwiRGY599zuXuynn5a9QvfJ+/Ogeqt9CXTIKDh0iupEQmSGGZcAHvuN0gfzobkxroeXYm0HW6hH\nx0DDZCQlquMIkTEmTSrour1ypewTui+M1V8TDEEoDNbYCarjCJE5srLBUwx+IGpBTXy3z5NirYdG\n2tlIHRoWLjQOY5jqSEJkjGnTSiko0AGDVaua+fJLuRS6tyIvPIdp2nu4myefoTqOEJllyqkQBFoN\neD6+e4VKsdZDLa04MNExGUK+6jhCZJyDD86jc4+kNWukd21vWKEQwZWrCYYhjBPt8CNVRxIio1hT\nT8IKgRWF0GdL4npsKdZ6WMpGADRgDGVqwwiRgaZOLcaeUmXw+utVquOkFHPdOsJbqolaYHqz0ceM\nVR1JiIxiHXQYHe3Q0QGh9z+M67GlWOthDd3rOx0sl0CF6Hennz4EPdYqLVxYt/sXi+34P/kEvwF+\nA6yp01THESLjaMOGEy0fjmlCwB8ivGxZ3I4txVoPNTShxS7BFJKjOo4QGWfIEB+mad9et07WWtsb\nwYULiWAvLYz0qgnR7zSHg2D5SBqDEAiZhD7/PG7HlmItZjP1BAigY1FKNuUU7fmLhBBxNXiwj/Hj\ncwGTUCjM++/Lbga91frf/9prcmoaWWedpTqOEBnJecopRLDnGTRUVsbtuFKsxbTi77pdgE9hEiEy\n24gRPuz+IYuqKv+eXi5ignV1hLB/co7iYtVxhMhIjhEjaMNewSO4enXcjivFWsyXsckFAKMYqDCJ\nEJnt6KNLu26/+aZMMugN/6efEqqttQdxDBqEe6Qs6C2ECjknnkgACAHbFizAjEbjclwp1mJaaEMn\nio7BMEr3/AVCiISYNKm7V6iqSsat9UakvZ2gphEAnKNGqY4jRMZy+HxYxcWEAMvpxPDH5+qAFGsx\n66hGw0LDZKCssSaEMvvtl0vn8h0ffbRNdZyU0PzOOxiWhQW4DzhAdRwhMpbudmMNG0YAaA8GqZ83\nLz7HjctRUlyYCCZhnJj4cDJQJhcIoUxFRQ4lJR7snchNmptDqiMlvY6qKkLYg5pzjjpKdRwhMlrO\noYcSxV7eO9jQEJdjSrEGNNGOnyAAGhZe3IoTCZG5PB4H2dkAETo6Qnz9tWw7tSctS5d2/eegFxTs\n6eVCiARyDhxIAAgA9Z98EpdjSrEGtNA9Lmao7FwghHKTJnWPG5Vxa7tnWRZt1dVEgIjHQ8Gxx6qO\nJERGK5k2DUOzV21tWLs2Lsd0xuUoKa62azFc8OFVnEYIUVLS/Xe4alWLwiTJz4pE8Dc0YACuSATN\n4VAdSYiMprnddFgWAI7Nm+NyTOlZA+poQMdCx6IcWZ9ICNUOPrgI+6Keyddfx2fMR7pqXbmSkGEQ\nxV7jyZkju68IoVLJ4YdjOJ0YQOOmTRiRSJ+PKcUa4MePjoGGST7ZquMIkfHKynx0FmuNjUHVcZKa\nGYkQxl7XySnj1YRQTtM0TI+HMGBaFpre91JLijXsnjUN0LEokD1BhVCuvNxH5/IdK1fWq46T1Go/\n/hgDu7R1lZerjiNExnN4PDgLCzGAYDBI7ZIlfT6mFGuAYW99jIZGCYWK0wghKipycbk0wMQ0LdVx\nkpoZWww3CGQNG6Y6jhACyB4yxB4Jr+s4vH0fC783EwyygHOBA2Nf13mdog34CHghdj/l1NIIWFhY\n6Giq4wiR8TweJ5GI3ZysWdNMNGridMp7y52p//JLLOxV6Rz5sqC3EMnAmZdn73BsmtQsXkzphAl9\nO14vX3ciMB6YA/x9h+c04CDgBmAusLRPifqZiYUHDQMDNx4KZfcCIZQrKvJSWuqlri5Ibq5TirXd\nMIzOawNQMG6c0ixCCFveqFGY774LgO7x9Pl4vWn9vMAG4D5gLWy3y7kP+w3dUuAe7EEmKSVAAD8d\naJiECUq/mhBJQNc1/P4gEKWtLciWLW2qIyWtlvXruxbEjQZlMoYQycDSNKLYRVH9ypV9Pl5virUg\nsAb4BXAqMKPHcwcAx/e4/1WfEyngQcOBRTE5OJA1ioRIBsOG5QIWDgex8WtiZ0KRCBEgpGkUyr6g\nQiSFglGjuoYnGIaxp5fv0d5cV3gFGAFcCbwGPAIcDKT0ctl+Al0TDLovJgghVLMbuCiGEaGmpkN1\nnKTVEVsQ17Qswu2y24MQycCIRjEAA+iore3z8famWFsBPAj8Ert37RagBrtwi7dTgJXAauCmXbzm\nvtjzS4FJ+3qingWaD9++HkYIEWdFRd0zqDonG6SIfmu/AMKhECZgaBo5snSHEEkhe8CArtuRQKDP\nx+tNseYBSnrcfzP2ubNQ+7THc0P7nAgcwP3YDd547BmoO46a/TawHzAauAJ4aF9PZs8ANdExcMiI\nNSGShtvdPSTBMFJm+Y5+bb8AorF38CbEZfFNIUTfOXtMKjD7aQeDEDAFOA97+Y6dKcRudOKxyM8R\n2GPkNgAR4FngjB1eczrwROz2R0AB7NsO7B20o2GhgYxWEyKJ6LqFXYIY1NamzASDfm2/AIJtbVjY\nl0EtK2WKWiEyRrCl7/sb93bpjteBQcCNwADsGaIu7MuxfmAL9hi2eOy4PAToufPpFmByL15TDmzb\n25Nl48OBiYWFB9fefrkQIkEGB5bzvzxAMwXkuWfs+QuSQ7+2XwCubHuLPN3tJqtQFvUWIhk4s7r7\ntkKtrX0/3l68divwmz6fcc96+9Zwx2uW+/SWsoVmiPWsGYT35RBCiAQoWTWHXNrJpZ01c+Zw8hl9\nGtrVX/q1/QIINNgb3ZvhMP6GBnwlJXv4CiFEoq19552u2/FYumNvirVdGYs9mDZeqoCKHvcrsN95\n7u415bHHvuG2227ruj19+nSmT5++3fMFFKChxXrW3PscWggRX6XlJUQb7Spm4rEH7vQ1lZWVVFZW\n9muuPejX9gvAV1JCqLUVp9e73aBmIYQ6FUcdxad//SsA+buZ+NPbNmxfirXTsMdcdF4zPASYuA/H\n2ZXF2ANvhwPVwNnYg3R7+jdwLfZ4kClAM7u4hNCzsduZVlqxYrtkBej7jA0hRHyUF2tswO6CytN2\nviTFjgXM7bff3h/Rdqdf2y+AQHMzYC+I21ZdLZdChUgCVo+11XIGDdrl63rbhu1LsTYBuAt78CzA\n+ftwjN2JYjdkb2OP+X8Me9mQH8aefxh4A3tG1RqgA7hkX09WSCHO2L6gLtnXXoik4XTbPd2aplE0\ncqTiNL3Wr+0XQM6AAQQbG9FdLinUhEgSvuLirtvZcRiasC/F2hfAOuhaoOyTPqf4pjfpXiKk08M7\n3L82HicK4MeKDRcJ4o/HIYUQcRBqbLQbKMvqGpeVIvqt/QII++12y4xEaK+pIXfw4HgdWgixj9qq\nqroKLDMU6vPx9qUr6WbgM+C92MeTfU6hUAGFaLGxviYptfCmEGmta20iTaNgxAi1YZJYz3Fqss6a\nEEnCMNCwh3HklJb2+XD70rP2W+ylPDqd2ucUCtkFmomOKbNBhUgi0c5Vvy0LMw5766Urh8PR9a47\n3CHbcgmRDCJtbV0FltPV92XB9qVYe32H+ym9GV0WWV3rrIVox8RAl+VxhVAuVFuLjv3O1FdUpDpO\n0vIVFHQVa22bN+/2tUKI/hGqqekqsLLj0H71tlibjb1DwUTgD9izlzqNpw+rb6vmwIkHFxECuHBJ\noSZEknC4XDgAh9eLKydHdZyk5SspwYm9xInukPZLiGRgRSJdS2bkDBzY5+P1doDDz4BaYCPwEnBD\n7LEPgHv7nEIhJ040TDQgSpAm6lRHEiLjBRoaunrWCAZxS7G2S1m5uWjYjXnDsmWq4wghgPZ169Cx\n/y577hO6r3rbs9a56O0q7Gnoy7EnGRyJve5aSsunkHpq0NGlZ02IJKA5HDi9XoxgkNyhQ9Gd8Vi/\nOz0VjRnT3WqZMklKiKQQDtvDOHSdogMO6PPh9mXqUBCYjr0BcTt92CYlWThxARYmBrVUq44jRMZr\nWLYMIxgEkEJtDxwOR1fPWtOKFarjCCGAwJo1eACPaUIcJkjtS7HWiF2s/RK4HNi/zykUK2UADkwc\nmBhda/0KIVQxw+GuzTOLx45VmiXZDTziCFy6jhMIp9Z6dEKkJcs00YJBdMDt81E0se+bPO3LW9aF\nsQ+AR/qcIAm4Yj1rGhYNbAVSYsNoIdJW4/LlXYPmPTJebbesaBTNNHEAHRs2qI4jRMaL+v0YtbV4\nAS0QQNO0PX7NnsgKisAgKnBgomPRRr3qOEJkvPb167sWlBxwyCGq4yS1gZMnk52VhQsw6+sx4rBa\nuhBi3wU2bMClaehA4dixOLzePh9TBoMAbrpnarTRpDCJEAKgffPmrkHz3oICpVlSgQ729YFgcLsN\npIUQ/S9cX4/LsnBhz9aOB+lZAwYyvOu2fRlUCKFSw9KlXbdLDztMYZLkpzudlO2/Py7AHY1S/+GH\nqiMJkdHaFi7EBTiAwgkT4nJMKdYAN16cONCJYhHGkj1ChVAqGts2SXe5yIrDvnrpLqeiAi/gBczY\nxu5CCDWitbU4ATeQvX985mBKsQZkkU0WXjQswrSzjU2qIwmRsVrXriVUVWXPpHK5yB06VHWkpFc0\nfjw69jv5xrlzVccRIqMFFi3CjT00IXfMmLgcU4q1mBIG4cDEiUkE2QxZCFXCTU3our08deH48arj\npITCI48kG8gBjE3yZlMIVSzTxNi6FQfgcrnImRSf1SWkWIsppKRrXactXRs2CCH6W01lJXpsKYqC\nkSNVx0kJvrIye1sbILB4seo4QmQso6kJa+NGe0HcSATPkCFxOa4UazFD6R4EWM8WhUmEyGyNS5d2\nLdsx+FvfUh0nJeQecgh5RUX4AE9jI5E62eNYCBXCK1fiwC6ucg8/HC1OO7BIsRbjJRt7YVyTZtly\nSghlmr/8suu2t6REYZLUoXs8uHNy8AKeQIDI5s2qIwmRkYKffIIbyAJ8cRxvK8VazCBG48OHjoWf\nOlkcVwgFQo2NtC1fjga4fD7KTztNdaSUUXrCCfgAHxB++23VcYTISJHKyu6etTi2X1KsxWhoeLG3\ntbGw8NOiOJEQmSfc1ASGgRPw5uWhOxx7/Bph844Zg9cBeW5g5XLVcYTISOaaNbgBD+AeNixux5Vi\nrYdyOmeeWazlE6VZhMhEG/71LzDtdQ7Lpk9XGybF5Jw+A58LnDo45knPmhD9zdi4EdfKr3BrkJWd\nRdZxx8Xt2FKs9TCQ0egYODBpYqPqOEJknNZly7pulx59tMIkqUcfNBhXUS65uZCtdUC9TDIQol9t\n2YTu0PA6wDtmdFwPLcVaD4PYL7Z8h0UNKxSnESLz1P7nP13jPQYcc4zqOClFKyjAe9B4NB30qB/+\nK4vjCtGfzFdfwuu0cDsh67hpcT22FGs9+CgijxIcmGhEqWOt6khCZIzW5csxWlrQAN/AgeTFaeXv\nTKKd/G3IxV4d99N5quMIkVH0BfPQNHA4NRwzvhvfY8f1aClOR6eEzqm2FtUs2+3rhRDx0/Dhh+jR\nKE6gcNw4HF6v6kipZ8JEu1X3AEtlQ3ch+pNjzQq8HvC6LRz7j43rsaVY28Foui+9bJRJBkL0m6oX\nXgDsxXCHXnih2jCp6thTobzU3tF920rYILuxCNEvXnkGiKLpoB0xGQYNjuvhpVjbQR6DANAxaKcG\ng6jiREKkP8s0aVvevdxEnuwJum/cbigfBtlArgkb5OqAEP1iySJwAG5g0qFxP7wUazsoYDClVKBh\nEaGVjXykOpIQaa/unXeIVFXZC0mOGkXR5MmqI6WuMy6we9ZcwNy/q04jRGaY84y9Oa8DOP/yuB9e\nirWdGMBYHBg4MamTcWtCJFzTBx+gYbdzxUceqTpOajtwKmRpUAhs+xSC7aoTCZHe1q+Cjla7WCvK\nh6Ej4n4KKdZ2YhRHx5bwgC18rDSLEJmg5oUXupbsKD/vPNVxUtvoQ2HocLvyDdXCGrk6IERCzXkW\njIjdgB11POTmx/0UUqztRAFDyaIIgCAtbJPeNSESpmXJEvxr1qABnqIiCqVnre+mnAkFQB6w5J+q\n0wiR3t75lz0zSgdmJmZylBRrO+HEwwDGAxYaJlUsVh1JiLRV/9ZbXVtM5R9xBK6CAsWJ0sDEE+3W\n3QdsrlQcRog01tEOm1bFNgMFDkvMYt5SrO3COE7DgYWOxXrexcJUHUmItFT98MNo2G9MR9x4o+o4\n6WHsSTBslD3RILQRVv5bdSIh0tMTfwDLtBuw42ZAYUlCTiPF2i7kU44DNzoGJn5a2Kw6khBpJ7B+\nPZFt23ACLreb3IkTVUdKD7oDRhxt96wVAFveUp1IiPRjWbDoTbuScgBHfSthp5JibReceBnJsbGJ\nBgareEVxIiHST9Vf/oIWDKIBZTNn4ikrUx0pfRz7f5CFPUNt1WOq0wiRftZ9BasW239jeTnw/asS\ndiop1najgqMhVq5t5RPCtKkNJEQaMfx+ap9/3l6yQ9MoO+cc1ZHSS8FoKKyITTQIw8YXVScSIr28\n+Q+7RHACh08DhyNhp5JibTcGMBEfJYBJhBZZc02IOGr/7DPCVVUAOPLzKf1ufDc+znhOD0y40G7l\ns4DN/1CdSIj0UvmS/felA9+9MqGnkmJtD/bjOzgw0TFZxXOq4wiRNjbfeWfXeoZDfvhDpVnS1ujL\noTDbHrvW9ja0r1CdSIj08O7TULvR7lkbMgIOPyGhp5NibQ8GcwRg7xXawXoC1CpOJETqi9TV0fHJ\nJ/bEAqB05kzVkdJTznAomGDvFVpoQPOrqhMJkR7ee9auoJzAUd8GT1ZCTyfF2h7kUs4QJqMDJkFW\n84LqSEKkvK1/+QvRpiYA8qZPJ1f2Ak2ccbfbxZobaPyD6jRCpL6aDfDR63YF5XbBpb9K+CmlWOuF\nCo7vul3Fe4RoVphGiNRmtLVR91j37MTS889XmCYD5B0JnhJ7okFOE7Q/qzqREKlt9p+7dywYezjk\nJn4hbynWemEwx5DNEMAiSgs1LFQdSYiU1TpvHtHqahyAZ8AABl52mepI6c2RB4OvxtIhmg1G+CHV\niYRIXUYU5j9L12bGF97SL6eVYq2X9uOs2EQDi9XIrCoh9lX1z37WtWPBoBtuUB0nM+RdSjQ/HyMX\not73MY3/qE4kRGp66W5o3mY3YMPGwsTp/XJaKdZ6aTDT0HACEKKJBpYqTiRE6vF/+inhtWvtiQUO\nB6UXJmbTY7ED51DwnUTI4aLFm0Ob9rjqREKknnAQ3nuye8eCky8Bt7dfTi3FWi+5yGYks2L3TFbw\nN6V5hEhFVT//OVYkAkDJFVfgLi9XnChzON334XfnEtWdtOlvEWWD6khCpJYFL0HVCrtXrWwonH5d\nv51airW9UM63cOBEx6SdFdTyoepIQqSM9vnz6Zg7177jdFJy6aVqA2UYTRuEU5tMCwW0UMBW7lYd\nSYjUEQnDM//PLtQcwLRz+q1XDaRY2ys5lFPOSWhYaETZiGzfIkRvNdx7L7pp4gCKZs7Ed+ihqiNl\nnGx+i4mDAF628QlhGlVHEiI1LKuEbevs2w4nXPT7fj29FGt7aSTn4MaHjkUzi6ljgepIQiQ9/4IF\ndLzyiv2mNDubslv6ZwaV2J6Xg3AxHT/Z+IG1/FF1JCFSw6NX07Xlylm/6PfTS7G2l3wMoZQpXffX\n8Q9MogoTCZHcLNOk7tZbu/Y7zps2jawDD1QdKyNpaJRwGQY6LeSxjqW0sFZ1LCGS24Ln7F41B5Dl\ngVMTuw/oziRbsVYEvAusAt7BXsZxZzYAXwBLgI/7JVkPY/kxDrIAizaW08gn/R1BiJQR/PRT/P+J\nLRXhcDD48ceV5kmwpG/DiphCDsfz/9m77/CoyvSN49+ZTCqEEHpHLIAFEZViw9gVO1iwd3d17WVd\nXVdRd9fV1Z9rW3UtyNorCGJDJCirYKFYaCIgvUNISJ85vz/eCYkhgZDMzHvOmftzXbkyLckzmXDz\nzHnLiRBkC0F+0nQOkfo5Drw1AgKOObI29HbI7ZjwMtzWrP0JE3Q9gYnR63VxgDygH0RP3plAqTSn\nI8eTQoQUHOZxPw7hRJch4gmrL7xw6+XsM84g1LatxWrizhMZ1oOzKSGbIrKZy/cs54dElyDiDWP/\nASvnmkatRSs42s4m3m5r1k4BRkUvjwJO285jA9u5L+56cQ1BQgSIUMkaVvORzXJEXKn4/fepmD+f\nFCAlEKD9o4/aLinePJFhbdiXdhxGBSE2kcMUPiGiN5wiv1WwBj59qnpftdP+CK06WynFbc1ae2B1\n9PLq6PW6OMCnwLfAFQmoaxtB0tidPxDEIYDDLzyMQ8RGKSKu5DgO66+6ipRIhCDQ+o47SG1f3z9p\n3/BMhg3gXMK0o5IQC1nGHG30LfJb790P65eay133huOutVZKyMLPnAB0qOP2P9e67kQ/6nIIsBJo\nG/1+c4Ev6nrgiBEjtl7Oy8sjLy9vp4rdng6cyFJepIINhNnCfP5OL+6M2fcX8bKNt95KeNky86a0\nZUtaXhv7oMvPzyc/Pz/m33cHEpZh8cyv5rRldw7kS2ZQQRrv8Qk96E0WWTH7GSKetfAbmPhE9fUh\nN6xQnDQAACAASURBVEF67P9tNDTDrA4l1mEuZh7HKqAjMAnovYOvuRsogjrXoDuOU19WxsY6JjOb\nWwngECKLvowki13j+jNF3K5y0SJW9u9PeP16AFo/9xzZCdgENxAIgN1ci2WGxT2/IkR4gH+ygTJK\nyeRYDuJkDo3rzxRxvUgE/m8IfP+xebu15xFwx2cJ+dH1ZZjbhkHHAhdFL18EjKnjMVlAdvRyM+BY\nsDc7tjWHkssAgjhEKOJn7rVViohrbLr6aog2aukDB9L8/PMtV5QwnsqwIEFO4kS2kEMZabzHTFaz\nyUYpIu4x7XX48WNzOSsbzrV/tg+3NWv/AI7BLHs/MnodoBMwPnq5A2a4YCYwDXgfs0TeigAp9OBa\nTOxFKGEWK3nJVjki1pW88ALlH31EEEgFWj34IIH0dNtlJYrnMqwP+9CRNhSSzSZyeZgvCWv+rSSr\njcvh9WurTys16GzYZX/bVbluGDTW4j6MUGUpT7E8enL3VNrSh5dJo11CfraIW0TWrWP9wIGEFy7E\nAbKuu46cBK4AdcEwaCwlLL8KKOJy3qOcEOWkcSMHcCzdEvKzRVzl5Stg8nNm+LP1rvDX2ZCauDeb\nXhkG9ayOnE8G3QGoYA0Luc9yRSKJV3jDDUQWLjRnK+jalea33267JGmAHJozjH0ophnlpPFP5lBA\nue2yRBJr7gSY+pxplUIhOOuBhDZq26NmLUZCZNOD2wGHIGEKyWcNr9suSyRhyl57lfLXXtl6PfuR\nR0jpUNeiSXGjs9mXVmRQSDbryOF65hOudzGriM8UroaXzzeNWjqw/4lwwBm2q9pKzVoM5TCQjpyz\n9fjlCv5DGcut1iSSCJFVqyi5+y8EAxAMQuZFF5IxbJjtsmQnBAjwTwZRQRoVhJgCvEWB7bJEEmP8\nn6BojemKcrvA+S/arug31KzFWGd+RzpdADMcumibrZdE/GfL1b8jsmghgQCEOnei+V//ZrskaYRu\nNOdCOlJIC0qdDG6JbGCpU2G7LJH4+v4tmPFidFFBCpxyP2TVd1pfO9SsxViIluzK/YBDChFK+JoV\nPGK7LJG4KXvkISrfH7v1erP/PEewSxeLFUlT3Eh3upJOcXkWBaUtOKW0kC0JWuggknDr5sF7l1QP\nf/Y/Cw5031ZDatbioDn70Zk/EAACRFjPy2zhW9tlicRcZOZ0yh/+B8GgGf7MuP5G0o4/wXZZ0kSv\n0Ym0cBrhihR+2NCMOzZqKw/xoYoS+OD3ULnF7DPUoTcMfcZ2VXVSsxYnHbiEZvQliINDEb9yExGK\nbZclEjNOWRnll55HcON6M/y5bx8y/3K37bIkBrqSyl2pzagsaE64NI3H1gR5p0BH18Rn/jcCluVH\nj6plwsmPQXr2Dr7IDjVrcRIkgx78C8gAoJLVLCT+p9sRSZTy84bhzJ9LMACpGalkvfo2gZwc22VJ\njFydmsnxaSHYAhQEOG8e/KT3m+IX896Abx6sHv7Mux12O8Z2VfVSsxZHqbRnV8wh1QARSvmW1Zq/\nJj4QfuyfRCZ8tPV66vOvENyjp8WKJB5ebQd7BoFKKNsYYPh0WFtmuyqRJlo3A764zpyhIBXodSoc\n4u7FgGrW4qw5g2jHNQRxovPXHmczE2yXJdJozrT/EbnrjwSdMAApv7+W0NAzLVcl8dA8GOD57gFa\nVwJh+HE1XP0NhDWFTbyqfBN8filURLfpaNMTjnkCAu5uh/xyWpb6JOx0LTuyiOGUMo0ADpBCDz4m\nnd1tlyWyc1YsgwE9qSgqMdf36kNoykwCQXcEnU43FR//WQK/+wYoABy4sy/cd6DtqkQa4bMh8OuH\nUA4EQ3DKFGg/0HZVW+l0U5Z150XS2CV6LcwSzqKcJTZLEtk5q1fBcYOgtIRQCqR06kDo4/+5plGT\n+LmyG9zcHYgAG+FvE+HpGbarEtlJ06+AVR+aoc8M4KhXXNWobY9SNkGCZNCZpwjREXBwWMdqriXM\nBtuliexY4Wa46jxYac7IEWjbjuBLYwhku3PllMTeX/eDUzoCYXBK4Y73YcJ821WJNNDyp+HX581i\nghSg312w61m2q2owNWsJlEFv2jOCIEGCOJTxHWu4AUcnTBY3C4fhlt/BlM/M9UAARjwIB3jjHanE\nRkYIXjgSdm0BFMHGAjj9WVi01nZlIjuw/j2YdxWkOabr6T4E+nhrmyE1awmWzXG05wHMCd8jlPIp\na7nZdlki9bv7Rhj7uplFEQTuexiGX2S7KrGgdSZ8dxG0zAA2w5b1cMA9sKHIdmUi9Sj8Cn4+3VzO\nBDofAAePd/2Cgtq8Va1P5HAWrbll6wzCLbzDWm6yWpNIne69BUY+bpIiCFx1I/z+RttViUUts+Cr\nayEnDaiAjStgv5tg2TrblYnUUvIFLD0KAo5p1Fr2gf0+tF1Vo6hZs6Ql15PNJdFrDsW8wibut1qT\nyG/852F4tsa+gGecB3950F494hq928ErV0DrVMCBpYth6J2wXEOi4hYVX8P68yG1xMxTa74b9H4d\n0trarqxR/LLEvT6uWfpen1VcSBkfR18IhxxuJofbLFclSe+5R+C+myAMOMCgwfDGRAiFbFe2Xdq6\nI7FemQwXPABOERCGg3rDe/+Etq1sVybJLOzMJbDxKIKlK6AYCDSHzpMhY3/bpe1QfRnml1Crj+vD\nzqwLvYhSPiSIqTWbP9KCP1quTJLWyMdgxPXV1wcMhjcmmTO1u5yatcR7bSKcezew2Vzfd3eY+ipk\nZlgtS5JU2FnGlkg/sgrXE6pwwEmD3FmQ2tt2aQ2ifdZcKkCA1jxNJsdtva2QBylghL2iJHk9dT/c\nW6NRO+RIeH6sJxo1seOco2Dkn6JXiuH7b2G/42FzodWyJAmVM5eVDMZhM8XNQ5Q0awG5Uz3TqG2P\nEtgFgmTRiqdI51jAnEe0hCco4EYc3P/OWnzigVvh0RHmPV0AOPgIeOINaKGTs8v2XXwSPHUnZKYA\nYZg/EwYeCYt/tV2ZJIsiprGEc4gEtlASyCQc6EZq5tuQ2s92aTGhZs0lgmTTmpGkkxc9JVUlJYyk\ngKtxKLFdnvhZZSXcdx08/xBUlptGrf/B8Ox70KqN7erEI35/Fjx4O4QiQATm/gAnnQyzZtmuTPxu\nHZNZxK2UUESYIBXB1mSmjCQUOMZ2aTHjl7kd9fHEnI/aNnI+ZbwfveaQyQlk82+CaNauxNiWQrjr\ndzDuterbBubBS5OsldQUmrNm37tjzcJhZwvgQKfO8M7bMGiQ7crEj5YxmYXcTjpbyKSUIFnszttk\nsJvt0hpFc9Y8pCUvksllgEMKESp4n82cRoRVtksTP9m4Dv5wKoyv0aidfB48/5G9msTzhp4CE96D\n1FRzfcVyGDw4wgcfROwWJr7i4DCbd/mafxAhQhkZlNCVnnzo2UZte9SsuVCAEC14kGb8MbppfASH\nbylgAGEW2i5P/GDtSjh7f/hmUvWGtxdfD/e/AGnptqsTjzvqSJicD+3aAYSpqCjjxBNLefJJnVpP\nmi5CmC94kW94HQiwiRxS6cu+jCSNDrbLiwu/DBfUx5PDCDWV8Ayl/BmzWQxAGs15i1T8MxYvCTbz\nS7jiaCgrMXuoBUNw3d/gMu9vF6NhUHeZPh0uvLCCn36qwGRYmEsvzeD557VoRRqnkgpe41GK+IkQ\nlWRSTFf2YgA3kumDqULaZ83DyhnDFq4H1hKIRAg6KaQFbiIjeJ/t0sRrXnwQHr8TKirM9ZxWcMvD\ncOrFVsuKFTVr7rNihcOwYSVMnVoIVAClHHhgGh9/3JlWrVJslycesoLVjOQl0lhCCpU0o5Q96M/h\nXEEK7t6wu6HUrHlcmDkUOscTjKwCxyEYjpASGEJG6ssEAi1slyduV1YKd18An46GyjBEgOyW8Mwn\nsE9/29XFjJo1dwqHHYYNK+C99zYAlUAF3boFGTWqK3l5zW2XJx4widl8xGeE2UhLCkinnIM4nkMZ\nYru0mFKz5gOOE2ZL5Eic8P8IRp9XINCb9NAzpAQPtVyduNbc6XDPJbDg++rTR+3RB0ZNhcws29XF\nlJo1d3v00Y3ccMNqoBwIk5PjcPPN7fnLXzrbLk1cqoww/2E605lOKmVks5kQKVzOcPagh+3yYk7N\nmk84TgXl4bupDD9QdQNBpwWpodsIpd5utzhxnzcfg+fuMys/wTRqZ1wFNz3iy4UEatbc74svtjBk\nyCKKiooxh3gjnHxyLs88sxsdO6bZLk9cZA6beZzvWcZKWlBAKpW0JYvbOZ1csm2XFxdq1nymMjyG\nssrzCYa3EAQCEQgGTyIl4wkCwe62yxPb1q+Cf10Hn75VfTQtJQR/eQFOvMB2dXGjZs0bliwp56yz\nfmbatM2Yhs1h993TeOCBHgwd2tZ2eeICL7KWV5hPOUXksBlwOJbOXMKBNMN/bzSrqFnzoUhkNpXl\nl+OEvyIlHL0x0I1g+n0E0y60WptYNOlteOZ2WLqg6v9B2G1fuGsU9NrPdnVxpWbNO4qKwtxzz1Ie\nemg5ZuGBQyBQydVXd+bvf9+DFi1SbZcoFix2Krk7so6pKWu2Hk3LpJJb2J3j2IWgb/55103Nmk85\nThHh8nuh7CGInkc0EIFg6FzIepBAUHNBksamdfCfP8HYF6j6W8ABTr8arvwrtMi1WV1CqFnznnHj\nNnDllXNZtaoY07RB377Z/P3vPRkypJ3d4iShnimM8HBoAxtDxbRK3USACAcS5A52YS+SYyGdmjWf\ni1SMI1J6E0QWVB9lC3aHjD8TyLjCam2SABNegVH3wNKfzbAnQLsucM3/wZFnWi0tkdSsedOiRSXc\neut83nlnRfQWh2DQ4YorunL//b3JzdVcNj+bVwK3rIrwfkqEZq02EUqrJCNQyrWhDK4PtKU5ybPF\ni5q1JOBEVhEpuYNg2cjqGyNA6gkEmj0MoT2t1SZxsnIRPH0zTB5dfVsEOOJsuOoB6JBc8xfVrHmX\n4zg8++xSbr99Phs2lGH+kGGPPZpxxx17cPHFXe0WKDEXduBv8+GxAlifGoHWEQLBMP1yyvhbszSO\nD2bYLjHh1KwlEafsTSi5E8I/E6ias+TkQuYlkP0ABPyxeWBScxwY9RcY/yxsWFPjaFo3uPReOP4i\nq+XZombN++bNK+L22+cxevSK39x+zDFtePzxPvTqpX3Z/OCTZfCnWTAjDOQCaRDIjHBNJ4cRuUFa\npfjln/HOUbOWZJzIOii5B4r/TSAc2TqFiZRdIfvvkHm21fqkCaaNh2dvgmXzq1d6OsDxl8Ml90Cb\nTpYLtEfNmn+8+OJS7r57HkuWlGy9rWXLEGee2Yl//7sPoZBObe1Fa7bAZePh4wKoSMM0akEY0BHu\n2w2ObWm7QrvUrCWr8klQeBeUTzHXHSAchNR9IPdlSO9jtTzZCSsWwIPnwM8zIVxpbnOA3Q+Ei+6F\nASdYLc8N1Kz5y8qVpdx//888+eRiIpHq21u1SuVvf+vF5Zd3V9PmEWUVcMsYeHE2FKUCLYEQNGsO\ndx0I1+0GGckzNa1eataSmVMBxSOh8A6oXB9t2DCf0wdD639Bej/LRUq9lsyGF2+Fbz4w03iqhjwz\ns+Giv8KQKyEt+eZ21EXNmj9NmbKeu++ex2efrd96WyBgjrQ9+eS+nHJKe5o10/QON1q7CV7Mhzve\nhcqWQAamUQvARQfC7f2hV5IfTatJzZpAeAUUPQmbHzDnh6ziAFlDIed6yBxsrTypZf40eP9xmPyK\nuV7VZAOcciOcfoOZoyZbqVnzr3DYYfToldx4408sW1YavTUIBOnRI5M779yN88/vTFqajrS5wbqN\n8NxYuP9d2BwC0tl6NG3g7vDP0+Awxdc21KxJtcpFsPEuKHrZXK9ahBABsk6GVn+ELJ1r1JqF0+Hd\nv8G096AiXD3fEGDAqXDxP6HzHtbKczM1a/63eXMFr7++nBtumE1JyW9f6r33zuGSSzpz883JtQra\nTbYUw72PwUufwspCoAWQDoEW0KEVPHslHLs3pGrIs05q1mRblSth3cVQPAXCxdVHbQiZuWxtH4Ss\ngyHor5N9u1J5KSz8Fl66BRbPhPKy6iY6JQN27w83/Bfa72K5UHdTs5Y8SkvDPPLIYh56aBEbNlQA\nKZgjbdCpUxrXXtuNSy7pRPv22qMtEX79Fe57EEZ/BhvKME1aEMiGLp3g0T/AKQdBSE3adqlZk/qV\n/wzrboHCj8ApN7dFoh8pHaD1DZB7IaR2tFmlPxWuh89HwcePwepff3sULRCCPkfDBQ9Cdy0EaQg1\na8knHHa4/fZ5vPHGGpYsKa9xTwqBQIBLL+3EhRe2Z/DgHGs1+tlbb8FLL8G48UAzIDP6OQT79IGr\nh8NVybMvd5OpWZMdK/0ONj4FBaOgsrJ6aBQgpSU0Oxw63AfpvSCod6uNFq6ENQvgnbvgx3zYvNbc\nXtUgE4DDzoNjfg+9D7FXpwepWUtehYWVPPnkUp58chnLlpVjjrSZo22BAPTv35zrruvISSe1IidH\nixGaYt26ME89VcZrrwWYMyez+o5mQBAOPAiuvAQuOwuCmkK4U9SsScOVL4BNr8Gav0O4tPr2qmYi\ntSO0uQJanQcZPW1V6T3rfoX//Re+eAHWLjbNcGWN+0PpcMJNcNgF0EVnm2gMNWuyeXMln3yynjvu\nWMyiRRVUVtb8c0ghGAxwwQVtOP30XE49tZW1Or2mstJh5MgNjBlTxgcfVALNMUs7zTSZUAgOPgTu\nvgcGH2Kuy85TsyaNs2EUrH8Gir/6bWPhAE46pO8O7a6G7MOgmYbqtrFqLvz8BXz2JKz8GcqKze1b\nzywB7NIfDrsIjv2DxUL9Qc2a1DRvXjF33rmEjz/eRGFhBH5zjskgnTqlc/rpLTn11JYceWQ2KUm6\na359CgoqmTx5AyNHrmfKlFLWrUvBNGlVnwN06dKcE05I48EHoaW24GgyNWvSNEWfw8Z3Yc2TQBgi\nTvWRtqqhu2b9IPcUyD0esg9IztNaRSKw9DuYNwmmvw1LZ0BF5bZH0YIpMPgKOGAY7HO0rWp9R82a\n1GXp0jJGj17Pww+vZvnycsJhgBA1/1R69Eijf/8srriiDX37ZtK2baqtcq1auLCQH3/czFNPLebb\nb8tZt87B7LuRDqQBaQQCGfTvn8Ef/pDL0KFpNG/ul39y9qlZk9hwwlDwIax9BjZOgMqy6knxVXPc\nHCCtBzTrCx0ugOZ9IMvHW02sXwQrvofvXjXN2eqfa6yspbqhDYag19FwyMXQ/wzTsElMqVmTHVm6\ntIx7713OhAlb+PXXijof06xZgAMOyOLMM3PZd99MDjusedXflu8UF1fw1VermThxNRMmrGTGjM2E\nw6mYo2fZmCWd6UCA/fZrxTHHtOSuu9rRvLnyKx680qydCYwAegP9gen1PO544F+Yv6bngAfqeZzC\nLp7KlkDhVFj5OBTPgYr1tY62RTkpkLYLtDwMWvSH9qdDWmtvLlKoLIeyzfDDW7B8FsyfaOaihaOh\nX3VmiK1nGciB9r0h7zrYdRC03dVS4cnBBc1aLDNM+RVHJSURpk3bwosvrmfSpEKWLKm7cQPo0iWV\nHj3SOPHEbA49NJv9929GZqb3Zs47jkNZWZgPPljEjz+u5733FrNkSTHr1pVj5p8FMX+SqUCIQCCN\nnj1zOeusjpxwQhsGDcr2bdPqFl5p1npj/pt/BriZuoMuBZgHHA0sB74BzgHm1PFYhV2iVG6GDeNg\n/VjYkA9la2rcx29XlgbSwHEg52Botie0GgQ5B0BmV0h10fL60gIoXAXLpsGyb2D1T7D4K3AipmkL\nU0djCmS1gx4Hwb5DYZ8h0LyNnfqTkAuatVhmmPIrgT7+eDNTp25h1Kh1rFpVSUlJ7d+9CbJAwMRX\nnz7p9OyZwXHHtaB9+zSOOiqXtLQAqanuaOJKSiooLw/zwQc/s3p1MZ9+upgff9zIr78WUnOlbPVn\nc/QsNzeLLl2yuOiiPcjLa8cBB+RafBbJxyvNWpVJ1B90BwF3Y96ZAvwp+vkfdTxWYWfLxsmw+RtY\n8zpsWWqat5pNTdURqK3Xg2a+V9bukNoKcg+E9LbQog9kdYXUXMjqBClZ5qSATeU4UFkKhUuhfAts\n/AXWzYPCFbBqOmzZAGvngxOCSHSyWdUQL/z2/KoZrSCnK+x/DnTdH3od0/T6pFFc0KxViUWGKb8s\nWbq0nAkTNjNuXAHffVfM0qUVVL/rhOoAiGz9HAxGaNEiRPfuGey9dxYdO6YxaFALgsEAAwe2JBCA\njh0zYnJkynEcAoEAv/yygZSUIF9/vZzi4gpmzlzJggUb+fXXAn7+eT2OA+XlYarn51V9rm7UQqEU\nunfP4ZhjunLwwZ05/fRuNG+enPP13KC+DPPiDPDOwNIa15cBAy3VIvXJPdx8dL/FbP+xaQps/h7W\nTYCCbyGywRyhguiqyOjlogXm88avq49aVTVFoeZQsQWa7QaRMGR0gUAQ0nJMExdxTFNXXgSp2VC2\nAUgxw5aVxWZ+XdEqiARhwwJIaw5lhRAJVC+YqFK1GCBSY1VAAPP90ptDlwGw6+HQZSB0PwjSm8Xr\nNyn+owxzua5d07j00jZceqk5Kj59ejGLFpXy5pvrmTOnhB9+KCYYhEikunmLRGDTpko2bSpk1qyN\n1GzoUlIiOE6EtLQIOTlBMjIidOqUQTAI7dunUVERJjc3FcdxCAYjpKYGKSqqICsrhbVrt5CS4rBq\nVRFlZZVs2lTK+vVFOI5DYWE5oVCQyspIPc+ErfUBhEIBKisdDj64I717t+K00/Zgr71as9tuLhrR\nkDrZaNYmAB3quP0OYFwDvl5vNb0mJQNaH20+etxkGqBwCaweC6UrYO0EKF4OBbPNClIn2iDVfBML\nUFlkPm9eYBqrgkXVP6PqTW/tif01/1oqa30uL4zeX8efVDB6pK9tL8juArsfBdkdYc/TITUTQh6c\nbyexogxLMvvvn8X++2cxbJjZl62goJJ580r46qtCvv56EwsWFDNzZgHBYIDS0qoQqg6wcNiEU2mp\nQ2lpMRDh118LqHlkznzUdfSu9lLy3w5L1NeopaYGadYslb32ast++3Vgn306cOyxu9KuXRbZ2elN\n/p1IYtlo1po6RrQc6FrjelfMO9M6jRgxYuvlvLw88vLymvjjpcmCIQhmQ5fzzPXdbzWfwyVQshI2\n/2SOsG35FTbNNk3VpjlmQULpWvPZKa91aia2/S+w9m1V11NSzYKAzNZm7lmrvSC1GbTuA807Qtu9\noFVPyOkOqRlx+iVILOTn55Ofn5/oH5uwDFN+uVNOTogBA7IZMCAb6ARAeXmEysoIEydupKwszNSp\nG1mxooRFi4pYubKYwsIyNm0qJTU1RFlZOSkpAcLh2qNddQUZW+fJBYMBIpEAKSmmGUtLC9GlSzYt\nW2bSp087WrRIZ/Dg7mRkhDj88O4EgwFSUtwxh07q1tAMc8PcjrpMAm4BvqvjvhBmcu5RwArga7TA\nIDk4EShZDQSgaLG5rWwjlG+CYCpsWQnpbaBoOTTrDCVroFlXM3Sa1d40ZIFUaN7ZDJ9mtdP2GT7j\nsjlrTc0w5ZfPrF9fRlpagB9/3EROTipz5myiRYsQS5YUkZERpKionMrKMFlZIQoLy+ncuTkbNpTQ\ns2cu69cXs/febSgurqRHj5akpAR0hMyHvLLA4HTgMaANUADMAE7AvHV5Fjgx+rgTqF72/jxwfz3f\nT2EnkkRc0KzFMsOUXyJJxivNWqwp7ESSiAuatVhSfokkmfoyTIPZIiIiIi6mZk1ERETExdSsiYiI\niLiYmrUoC8v/rdDz9I9keI6QPM+zqZLh95QMzxH0PP0kVs9RzVpUMvzRgJ6nnyTDc4TkeZ5NlQy/\np2R4jqDn6Sdq1kRERESSgJo1ERERERfzy35E9ckHDrddhIgkzGQgz3YRMZKP8ksk2fgpw0RERERE\nREREREREREREREREREQ85UzgJyAM7L+dxx0PzAV+Bm5LQF2x1gqYAMwHPgFa1vO4xcD3wAzg64RU\n1nQNeW0ei94/C+iXoLpibUfPMw8owLx2M4A7E1ZZ7LwArAZ+2M5j/PBaxory67cW4738guTIMOWX\n4fXX0ZreQE9gEvWHXQqwANgFSAVmAnsmorgYehD4Y/TybcA/6nncIkwwekVDXpshwAfRywOBqYkq\nLoYa8jzzgLEJrSr2DsMEWH1h54fXMpaUX7/ltfyC5Mgw5ZfR5NcxmfdZm4t5t7Y9AzB/aIuBCuB1\n4NT4lhVzpwCjopdHAadt57Fe2sqlIa9Nzec+DfOuvH2C6ouVhv4Neum1q8sXwMbt3O+H1zKWlF/b\n8tq/gWTIMOWX0eTXMZmbtYboDCytcX1Z9DYvaY85PEv0c31/IA7wKfAtcEUC6mqqhrw2dT2mS5zr\nirWGPE8HOBhzeP0DYK/ElJZQfngtE0355W7JkGHKL6PJr2MopuW4zwSgQx233wGMa8DXO7EtJ27q\ne55/rnXdof7ndAiwEmgb/X5zMe8W3Kqhr03td2xeeU2rNKTe6UBXoBg4ARiDGSLzG6+/ljtL+fVb\nfsovSI4MU35Va9Lr6Pdm7Zgmfv1yzB9Rla6Yjthttvc8V2OCcBXQEVhTz+NWRj+vBUZjDl+7Oewa\n8trUfkyX6G1e0pDnWVjj8ofAvzHzdzbEt7SE8sNrubOUX/7NL0iODFN+GV5/HV1hEnBAPfeFgF8w\nkyPT8O4E3aoVOH+i7gm6WUB29HIz4H/AsfEvrUka8trUnNQ5CO9NzoWGPc/2VL9rG4CZH+JFu9Cw\nCbpefS3jQfnlzfyC5Mgw5Zfh9dfRqtMxY8glmHdtH0Zv7wSMr/G4E4B5mEmStyeywBhphZnLUXvp\ne83nuSvmH9FM4Ee88zzrem1+F/2o8kT0/llsf4sDN9vR8/wD5nWbCXyJCQOveQ1YAZRj/l1eij9f\ny1hRfnk/vyA5Mkz5ZXj9dRQRERERERERERERERERERERERERERERERERERERERERERERERERKHk/\nDQAAIABJREFUERERERER8Zf0WtczrFQhIiKyk4K2CxBJgJOoPndglS7A0RZqERER2Slq1sTvOgIt\ngHXR63sCd2DO0bYXkGmpLhERERHBNGY1G7KrMScWBtgNc8JdERER19KRNfGKVOA+4Ezg/4BmwM3A\nqcAtmL/lg4ATgWHAedGvaweURC+fAFyGGQLtAPwC9ElM+SIiIo2jZk284hJgGfAWZgjzj8AXwHvA\ncuBizNy0cmAs8GP062ouJPgQWAE8C6yK3haKc90iIiJNomZNvKIfsCh6+d/AgVTPQ1sD9AceB84G\nplPdpKXW+B4dqG7SqmTFo1gREZFYUbMmXvE90DN6uTMwF+gWvd4lev/JwOVUD4cChGt8j/7A19HP\nVU1aJH4li4iINJ2aNfGKpzFN2nDgMODPwMGYI2mto/d3iF4/Cfgg+nXFNb7Hiuj3yI7eHgAKE1C7\niIiIiNTjFiC3nvv6Ypo7EREREbEkB7iynvtuQkeXRUTE5VJsFyASZ2VUz08rqHH73sCvbLvgQERE\nRERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERE\nREREREREkk1XYBLwE/AjcF09j3sM+BmYBfRLTGkiIiIi0gHYL3q5OTAP2LPWY4YAH0QvDwSmJqY0\nEREREaltDHBUrdueBs6ucX0u0D5hFYmIiIgkSNB2ATuwC2aIc1qt2zsDS2tcXwZ0SVBNIiIiIgkT\nsl3AdjQH3gauB4rquD9Q67pT+wG77bab88svv8ShNBEREZGYm0X1VLCt3HpkLRV4B3gZMwxa23LM\nQoQqXaK3/cYvv/yC4zj6iOPH3Xffbb0GP3/o96vfsR8+9DvW79gPH4n4HQN962qK3NisBYDngdnA\nv+p5zFjgwujlQcAmYHX8SxMRERFJLDcOgx4CnA98D8yI3nYH0C16+RnMStAhwAJgC3BJgmsUERER\nSQg3NmtTaNgRv2viXYjsWF5enu0SfE2/3/jT7zj+9DuOP/2O48/m77j2JH2/caJjwCIiIiKuFggE\noI7ezI1z1kREREQkSs2aiIiIiIupWRMRERFxMTVrIiIiIi6mZk1ERETExdSsiYiIiLiYmjURERER\nF1OzJiIiIuJiatZEREREXEzNmoiIiIiLqVkTERERcTE1ayIiIiIupmZNRERExMXUrImIiIi4mJo1\nERERERdTsyYiIiLiYmrWRERERFxMzZqIiIiIi6lZExEREXExNWsiIiIiLqZmTURERMTF1KyJiIiI\nuJiaNREREREXU7MmIiIi4mJq1kRERERcTM2aiIiIiIupWRMRERFxMTVrIiIiIi6mZk1ERETExdSs\niYiIiLiYmjURERERF1OzJiIiIuJibm3WXgBWAz/Uc38eUADMiH7cmZiyRERERBIrZLuAeowEHgf+\nu53HTAZO2eF3OuUUyMiA3r3hmGPgkEMg6NYeVURcbdEiGDcOZs2CtWuVLSISG4sWwZgx9d7t1mT5\nAti4g8cEGvSdLr0UTj8dwmG46ioTrG++CY7T5CJFJElMmQLHHQcDB8L338OAAcoWEWm6mtny00/1\nPqxhDY8duwDjgD513Hc48C6wDFgO3ALMruNxjlMzOB0HJk6Em2+GXXaBF16A1q1jXLaI+MbGjXDd\ndZCfD/fcA+edB+np2z5O2SIiO6OebAkEAlBHb+bWYdAdmQ50BYqBE4AxQM+6HjhixIitl/Py8sg7\n+mj4+mu4/XbTyX7yCey6awJKFhFP+fprOPNMOPVUmDMHmjev/7GBAChbRKQhamRL/n/+Q/60aXD/\n/dv9Eq8eWattEXAAsKHW7b89slbbU0/BX/9qOts99mhclSLiP+PGmWHOZ5+F007b+a9XtohIXXaQ\nLX47stYeWAM4wADME6vdqO3YVVdBKGTGi6dMgU6dYluliHjPM8/AiBEwfryZm9YYyhYRqa0J2eLW\nZu01zLy0NsBS4G4gNXrfM8AZwFVAJWYodHijf9IVV8DKlTBsmHkXXNd8FBFJDo8+Co89Bl98Abvv\n3rTvpWwRkSpNzBY3D4PGwvaHQatEIjB0KHTuDE8+Gf+qRMR9nn8e7r0XPv8cunePzfdUtojITmRL\nfcOgataqFBTAfvvB44/DSSfFtyoRcZc33oCbboJJk6BnnWuVGk/ZIpK8djJb1Kw1xOefw/DhZh+l\nNm3iV5WIuMfkyXDWWTBhAuy7b3x+hrJFJPk0IlvUrDXUjTfCpk0wcmR8KhIR91iwAA49FF5+2Wy9\nEU/KFpHk0chsUbPWUIWFsPfe8N//Ql5eXIoSERfYtAkOOgiuvx5+//v4/zxli0hyaEK2qFnbGe+8\nA/fdB9On61x/In5UWQlDhsCee5pVWomibBHxtyZmS33NmtKiLkOHQlYWvPKK7UpEJB6uvx5SUuDh\nhxP7c5UtIv4Wp2zRkbX6/O9/cO65MG8eZGTEtioRseeJJ8wZBr78EnJyEv/zlS0i/hSDbNGRtZ11\nyCHQr5/55YuIP3z8sTkN1Lhxdho1ULaI+FGcs0VH1rZnzhwYPBjmz4fc3NhVJSKJN2cOHH64mTd2\n2GH2a1G2iPhDDLNFR9YaY8894dRT4ZFHbFciIk2xdq3ZkPbBB+03aqBsEfGLBGWLjqztyIIFZgnu\nwoWQnR2bqkQkcUpL4cgj4Ygj4G9/s11NNWWLiLfFIVu0dUdTDB8O/fvDzTc3/XuJSOJEInDOORAI\nwKuvum+7DGWLiDfFKVvUrDXFjBnmMOfChZCe3vTvJyKJcccd5pQvEye6c+WlskXEm+KULZqz1hT9\n+pnzev33v7YrEZGGev55ePNNGDPGnY0aKFtEvMhCtujIWkNNngyXXw5z55oN70TEvcaPh0svNSdQ\n79XLdjXbp2wR8Y44Z4uOrDXV4MHQujW8/77tSkRkez77DC65xOx35PZGDZQtIl5hMVvUrDVUIADX\nXguPP267EhGpz5dfwtlnw1tvwYABtqtpGGWLiPtZzhYNg+6MsjLo3h0mTTL7JImIe3z+OZxxhpn/\ndfzxtqvZOcoWEfdKYLZoGDQW0tPhyivhySdtVyIiNY0bZ8L01Ve916iBskXErVySLTqytrOWL4c+\nfWDxYmjRIrbfW0R23qhRcNttMHasd4Y+66JsEXEXC9miI2ux0rkzHH20ltqL2FZRAddfD/fdZyb+\nerlRA2WLiFu4MFvUrDXGNdfAE09ArI/aiUjDrFhhTvPyyy/w7bew1162K4oNZYuIXS7NFjVrjXHY\nYebUElOm2K5EJLk4DowcCfvtB8cea4YnWra0XVXsKFtE7HB5toRsF+BJgQBcdpnZxfiww2xXI5Ic\n5s83W1ysXQuffGJC1W+ULSKJ54Fs0ZG1xrrgAnOqiYIC25WI+NuyZWal5CGHmDld06a5MkxjRtki\nkhgeyhY1a43Vrh0ccwy8/rrtSkT8x3HMJpQXXmjOndm6tXn3e+utkJpqu7r4UraIxI9Hs0XNWlNc\ndhk895ztKkT8Y/Nm+Pe/oW9fuPhi8y7355/h/vshN9d2dYmjbBGJLY9ni/ZZa4pwGHr0MJvm9e0b\nv58j4nczZsDTT8Obb5rhiN//Ho44wky2T0bKFpHY8Fi2aJ+1eEhJMSd1ff5525WIeE9FBbz8Mgwa\nBKedBt26wezZ5tx7Rx3l2jBNCGWLSOP5MFt0ZK2pFi2CgQPN7uMuHu8WcY2iIvNO99FHoWdPuOEG\nGDLENChSTdkisnN8kC06shYvPXpAr17w0Ue2KxFxt0jE7M7fuzd8843Zx2jiRDj5ZE+FacIoW0Qa\nJgmyRfusxcL555tDriefbLsSEXeaP99sSQHw9ttmeEJ2TNkisn1Jki06shYLZ50FH3+sfZFE6vLq\nq2Yfo4sugq++8m2YxoWyRaR+SZQtbm3WXgBWAz9s5zGPAT8Ds4B+iSiqXrm55lxi77xjtQwRV6ms\nhKuughEjYMIEuPpqT07stUrZIrKtJMwWtz67kcDx27l/CLA7sAdwJfBUIorarvPPh5desl2FiDuU\nlsKwYWaS/HffuXZXcE9QtohUS9JscWuz9gWwcTv3nwKMil6eBrQE2se7qO068UT4/ntYssRqGSLW\nlZebMM3IMBN9s7NtV+RtyhYRI4mzxa3N2o50BpbWuL4M6GKpFiM9Hc44w4yhiySrSMScxiUtzUyM\nT0uzXZH3KVtEkj5bvNqswbb7kMR5Q7UGuOACM1wR773dRNzqrrtg6VJ47TXtDRZLyhZJdkmeLV7d\numM50LXG9S7R27YxYsSIrZfz8vLIy8uLX1UHHwzFxTBzJvSzu+ZBJOFGjzbveL/5xgxTSOwoWySZ\n+Thb8vPzyc/P3+Hj3HwGg12AcUCfOu4bAlwT/TwI+Ff0c23xP4NBbX/5iwnVhx9O7M8VsWnhQrNs\n/v33YcAA29X4k7JFklGSZUt9ZzBwa7P2GnA40AazhcfdQNVxz2ein5/ArBjdAlwCTK/j+yS+WZs3\nD/LyYNky3+ycLLJd4TAcfjgMHQo33WS7Gv9StkiyScJsqa9Zc+sw6DkNeMw1ca+iMXr1gg4d4PPP\n4YgjbFcjEn+PPAKhkDkPn8SPskWSjbJlKy8vMHCvs8+GN96wXYVI/C1dCv/4B7zwgu83pXQFZYsk\nC2XLb7h1GDRWEj8MCmazvoEDYcUK865AxK+GDzcnT66xkEfiSNkiySJJs6W+YVC1q/HQo4f5+Owz\n25WIxM/kyTB1Kvzxj7YrSR7KFkkGypZtqFmLFw1XiJ9VVsJ118E//wlZWbarSS7KFvEzZUudNAwa\nL0uXQt++sGpV0u20LEngqafgzTfNEZ6A32PEZZQt4mdJni0aBk20rl1hr73gk09sVyISW6Wl8Ne/\nwkMPJWWYWqdsEb9SttRLzVo8DR+u4QrxnxdeMLvoH3CA7UqSl7JF/EjZUi+/t672hkHBDFPsuSes\nXOm7U2RIkiorgz32gLffTordxF1L2SJ+o2wBNAxqR4cO5l3Chx/arkQkNl58EfbeO6nD1BWULeI3\nypbtUrMWb2efDa+/brsKkaYrL4f774e77rJdiYCyRfxD2bJDatbibdgw+Ogj2LLFdiUiTfPqq7D7\n7nDQQbYrEVC2iH8oW3ZIzVq8tWljdhzXcIV4mePAY4/BzTfbrkSqKFvED5QtDaJmLRHOOMNMmhTx\nqi+/hKIiOO4425VITcoW8TplS4NoNWgirFkDPXualVuZmbarEdl5w4ebIYrrr7ddidSkbBGvU7b8\nhlaD2tSunVm5pU0sxYtWrDB/uxdfbLsSqU3ZIl6mbGkwNWuJouEK8aqnn4ZzzoGcHNuVSF2ULeJV\nypYG0zBooqxYAfvsY4Yr0tNtVyPSMGVl0L07TJpkNmEV91G2iBcpW+qkYVDbOnUy5/ObONF2JSIN\nN26c+btVmLqXskW8SNmyU9SsJdKwYfDOO7arEGm4kSPhkktsVyE7omwRr1G27BQNgybSkiWw//5m\nuCI11XY1IttXNby2bBlkZdmuRrZH2SJeomypl4ZB3aBbN9htN8jPt12JyI699JI5YqMwdT9li3iJ\nsmWnqVlLNA1XiBc4jhmm0JJ671C2iBcoWxpFzVqiDRsGo0dDOGy7EpH6TZ1qQvXgg21XIg2lbBEv\nULY0ipq1RNttN+jcGb74wnYlIvV78UXzzjfg92mtPqJsES9QtjSKmjUbNFwhblZaCm+9BRdcYLsS\n2VnKFnEzZUujqVmz4Ywz4N13IRKxXYnItj76CPr2hS5dbFciO0vZIm6mbGk0NWs29OoFublm7F7E\nbV5/3ZxcWbxH2SJupmxpNDVrtuh8fuJGW7bAhx+a4TTxJmWLuJGypUnUrNlyxhlmbombNu0VGTfO\nrNJq08Z2JdJYyhZxI2VLk6hZs2XvvSEjA7791nYlItU0TOF9yhZxI2VLk6hZsyUQ0HCFuMumTTBp\nEpx2mu1KpCmULeI2ypYmU7Nm07BhJlA1XCFuMGYMHHkk5OTYrkSaStkibqJsabLQTjw2EzgH6BP9\nuiwgAhQC04C3otelofr1M2E6axbst5/taiTZvfEGXHSR7SokFpQt4ibKliZr6BbCRwN7AeOBX+r4\nHvsCRwETgVkxqOt44F9ACvAc8ECt+/OA94CF0evvAH+t4/s4jtvfWd5yC2Rmwn332a5EktmmTeZk\n4CtWQPPmtquRWFC2iBsoW3ZKwJzZYZverCHDoBnAYuAxtm3UABxMg/Z/QGWjK6yWAjyBadj2whzN\n27OOx00G+kU/6mrUvGHYMLOJpYhN48dDXp7C1E+ULeIGypaYaEizVgosqHG9Q43LWbUe+1OTK4IB\n0Z+3GKgAXgdOreNx/jix2MCB5p3H3Lm2K5Fk9u67MHSo7SoklpQt4gbKlpjYmQUGdwAnACfXuG1v\n4IiYVgSdgaU1ri+L3laTAxyMOaL3AeYInDcFg3D66XoHLPYUF8Onn8LJJ+/4seIdyhaxTdkSMzvT\nrI0GegC/B8YBzwL7AYfHuKaGTDKbDnQF+gKPA2NiXENi6eTLYtMnn8CBB0Lr1rYrkVhTtohNypaY\n2ZnVoHOiH4uADzHDof0xjVMsLcc0YlW6Yo6u1VRY4/KHwL+BVsCG2t9sxIgRWy/n5eWRl5cXozJj\n6LDDYMkSWLQIevSwXY0kGw1T+JeyRWxStuxQfn4++fn5O3xcQ+Z9pQPZwLoGPLYbsKQBj9ueEDAP\ns7p0BfA1ZpHBnBqPaQ+swRyFGwC8CexSx/dy/2rQKpdfDnvtBTfdZLsSSSYVFdC+PfzwA3SuPdtA\nfEHZIjYoWxqlKatBy4BBwLmYvdbqkgtcCXRvZH01VQLXAB8Ds4E3MI3a76IfAGcAPwAzMVt8eP8c\nFhquEBvy86FnT4WpnylbxAZlS0w1dEXlacAq4EigHWY7j1QgDBRjhimfBQriUGNTeOfIWlkZdOgA\ns2dDx462q5Fk8Yc/mD2QbrvNdiUSL8oWsUHZ0ij1HVlraLP2CPAK8C1mG433YlZZfHmnWQM4/3w4\n+GC4+mrblUgycBzYZRf44ANz8m/xL2WLJJKypdGaMgwKZvXnnzGT+W8EbgGOY9stNaQphg7VcIUk\nzo8/mu0d9vLuzjfSQMoWSSRlS8w1ZmPZmzFH2PYG9gE6YYZBH8csDHATbx1ZKy42wxS//AJt2tiu\nRvzuH/+A5cvh8cdtVyLxpmyRRFK2NFpTj6zV9DDmVE//Bq7GzGebApzShPoEICsLjjkGxo61XYkk\ng/ffhxNPtF2FJIKyRRJJ2RJzjWnW6lIO6JwmsTB0qHYcl/hbv94sqXfjvoMSH8oWSQRlS1zEqll7\nFzOvTZrqpJPg889h82bblYifffQRHHEEZGTYrkQSRdkiiaBsiYtYNWsSKy1awODBMH687UrEzzRM\nkXyULZIIypa4ULPmRlq5JfFUWQkffwxDhtiuRBJN2SLxpGyJGzVrbnTKKTBhglnBJRJrX35p9kDS\nzuLJR9ki8aRsiRs1a27Upg0ceKB5hyISa+PHm/lLknyULRJPypa4UbPmVjqfn8TL++8rUJOZskXi\nRdkSN43ZFNdLvLUpbk0rV5rdn1evhrQ029WIXyxaBIMGmb+voN6rJSVli8SDsiUmYrkpriRCx44m\nUCdOtF2J+Mn48Wbyr8I0eSlbJB6ULXGl36qbabhCYk3DFALKFok9ZUtcaRjUzRYtggEDzGHlUMh2\nNeJ1RUXQqRMsW2b23JLkpWyRWFK2xIyGQb2oRw/o1g2++MJ2JeIHEyea/6AVpqJskVhStsSdmjW3\nGzZM5/OT2NDO4lKTskViRdkSdxoGdbu5c+Goo2DpUk3clMZzHLNR5eTJsMcetqsRN1C2SCwoW2JK\nw6Be1bs3tGwJ06bZrkS8bMYMyM5WmEo1ZYvEgrIlIdSsecHQoRqukKYZP17DFLItZYs0lbIlIdSs\neUHVMnuvD+mKPVpWL3VRtkhTKVsSQs2aF/TtC4EAzJpluxLxotWrYf58OPRQ25WI2yhbpCmULQmj\nZs0LAgEzXKFNLKUxPvwQjj5apxaSbSlbpCmULQmjZs0rtOO4NJaGKWR7lC3SWMqWhNHWHV4RiUDX\nrvDpp7DnnrarEa8oL4d27cxQRbt2tqsRN1K2SGMoW+JCW3d4XTColVuy8774wmzRoDCV+ihbpDGU\nLQmlZs1LtOO47CwNU0hDKFtkZylbEkrNmpcceqjZbXzRItuViFdoDyRpCGWL7CxlS0KpWfOSUAhO\nPVXvgKVh5s+HLVtgv/1sVyJup2yRnaFsSTg1a16juSXSUFXvfAN+X0ckMaFskYZStiScmjWvOeoo\nmDMHVqywXYm4neaUyM5QtkhDKVsSTs2a16SlmXc0Y8bYrkTcrKAAvvnG/Acs0hDKFmkIZYsVata8\nSDuOy4588gkccgg0a2a7EvESZYvsiLLFCjVrXnTccfDtt7Bune1KxK3efx9OPtl2FeI1yhbZEWWL\nFW5t1o4H5gI/A7fV85jHovfPAvolqC53yMqCY4+FsWNtVyJuFA7DBx9oTonsPGWLbI+yxRo3Nmsp\nwBOYhm0v4Byg9jlQhgC7A3sAVwJPJbJAV9BwhdRn6lTo1Am6dbNdiXiRskXqo2yxxo3N2gBgAbAY\nqABeB06t9ZhTgFHRy9OAlkD7BNXnDieeaE73UVBguxJxm3HjNEwhjadskfooW6xxY7PWGVha4/qy\n6G07ekyXONflLi1awODBZv6ASE2aUyJNoWyR+ihbrAnZLqAOTgMfV3s3vjq/bsSIEVsv5+XlkZeX\n16iiXOmss+DNN+G882xXIm6xaBGsXQv9+9uuRLxM2SK1KVviIj8/n/z8/B0+zo3bDw8CRmDmrAHc\nDkSAB2o85mkgHzNECmYxwuHA6lrfy3GchvZ+HlRQYOYO/PortGxpuxpxg8ceg5kz4YUXbFciXqZs\nkdqULQkRMGeF2KY3c+Mw6LeYhQO7AGnA2UDtpUljgQujlwcBm9i2UfO/nBw44ghtYinVNKdEYkHZ\nIrUpW6xyY7NWCVwDfAzMBt4A5gC/i34AfAAsxCxEeAa4OvFlusTw4fDGG7arEDfYvNms1jrmGNuV\niB8oW6SKssU6Nw6DxpK/h0EBioqgc2dYuBBat7Zdjdj09tvw3HPw0Ue2KxE/ULZIFWVLwnhpGFR2\nRvPmZtfxd9+1XYnYpmEKiSVli1RRtlinZs0Phg+H11/f8ePEv7SzuMSDskWULa6gZs0PTjgBpk+H\n1cm3xkKipk2Djh2he3fblYifKFtE2eIKatb8IDPT7Dr+9tu2KxFbxo7VMIXEnrJFlC2uoGbNLzRc\nkbwcx8wrOv1025WIHylbkpeyxTXUrPnFscfC7NmwbJntSiTRZs+G0lI44ADblYgfKVuSl7LFNdSs\n+UVaGpx6qjlFjCSX0aPhtNMg4PedeMQKZUvyUra4hpo1Pzn3XHj1VdtVSKKNHg1Dh9quQvxM2ZKc\nlC2uoWbNT444Alatgp9+sl2JJMqvv8KSJXDoobYrET9TtiQfZYurqFnzk5QUOO88eOkl25VIoowZ\nY1ZqhUK2KxE/U7YkH2WLq6hZ85sLLoBXXjEbGYr/jR6tlVqSGMqW5KJscRU1a36zzz7Qti3k59uu\nROJt7VqYMUMnV5bEULYkD2WL66hZ86MLL9RwRTIYPRqOPx4yMmxXIslC2ZIclC2uo2bNj845x8w3\n2LLFdiUST2+8AWefbbsKSSbKluSgbHEdNWt+1L49HHKIeXck/rR6NXz3nTl3o0iiKFv8T9niSmrW\n/OqCCzRc4Wdvv23O2ZiZabsSSTbKFn9TtriSmjW/OvVU+OYbWLHCdiUSDxqmEFuULf6mbHElNWt+\nlZlpll1r13H/Wb4cfvwRjjvOdiWSjJQt/qVscS01a3524YUwciQ4ju1KJJbeegtOOQXS021XIslK\n2eJPyhbXUrPmZ4MHQ0UFfPWV7UokljRMIbYpW/xJ2eJaatb8LBCAK6+E//zHdiUSKwsXwoIFcPTR\ntiuRZKZs8R9li6vppF9+d9FFsMcesHEj5Obarkaa6qWXYPhwSE21XYkkO2WLvyhbXE1H1vyubVuz\nX87LL9uuRJrKceC//zX/SYrYpmzxD2WL66lZSwZVwxWaDOxtU6aY078ccIDtSkQMZYs/KFtcT81a\nMsjLg9JSmDrVdiXSFKNGmXe+gYDtSkQMZYs/KFtcz++vjOPoHZ/x0EMwa5Z2Hveq4mLo0sXsgdSp\nk+1qRKopW7xN2eIqAdMwb9ObaYFBsrjsMth1V1i5Ejp2tF2N7KwxY2DAAIWpuI+yxduULZ6gYdBk\nkZsL554LTz1luxJpjFGjzEakIm6jbPE2ZYsnaBg0mcybZzazXLxYJ+n1kl9+gYEDYelSvW7iTsoW\nb1K2uE59w6A6spZMevWCAw/UOf285pln4OKLFabiXsoWb1K2eIaOrCWbTz+FG2+E77/Xyh8vKC2F\nbt3gf/8zG5CKuJWyxVuULa6kI2tiHHWU2RPp009tVyIN8dZb0K+fwlTcT9niLcoWT1GzlmwCAbjt\nNrjvPm1k6QVPPQVXXWW7CpEdU7Z4i7LFU9zWrLUCJgDzgU+AlvU8bjHwPTAD+DohlfnJOeeYZfb5\n+bYrke2ZOdNM/D3pJNuViDSMssUblC2e47Zm7U+YZq0nMDF6vS4OkAf0AwYkpDI/CYXgL3+Be+6x\nXYlsz5NPwhVXmNdLxAuULd6gbPEct80CnQscDqwGOgD5QO86HrcIOBBYv4PvpwUG9amshD33hOee\ng8MPt12N1LZyJey9t9kSoW1b29XI/7d3pzFSVVkAx/9ItwuiqKgwCgqYiGCIghNFxWFxG3smIEZN\nhPjB7YMLalwyKtGgRlFJxEQlBidM1CFOVAQGDaigHVwiirIJAmLAjCOLBkEYt26658MpBsM0UDT1\n6r169f8lnepqyqqTl/b0efeee6+KZ27JNnNLplXKAoNORKFG4bHTLl7XDMwG5gPXlSE6FQgxAAAK\nDklEQVSu/KmpgdGjvQPOqieegJEjTaaqPOaWbDO3VKQ0RtbeIkbNdjYaeA44/Dc/20j0se3sd8Ba\n4KjC+40C3m3hdY6s7c72O+AJE+D889OORttt2gQnnACffALduqUdjbT3zC3ZZG7JvCydDbq7/3O3\nT3+uIwqyDbt43drC47fAVKJvraVijTFjxvzv+0GDBjFo0KC9CjbXampg3Di49dY4iNn+hWx45hmo\nqzOZqnKZW7LJ3JI59fX11BexICdrPWuPEX1ojxKLCw7j/xcZtAPaAluAg4lVo/cXHnfmyNqeNDfD\nBRfA0KEwalTa0ejnn6F7d3jzTejTJ+1opNYzt2SLuaUi7GpkLWvF2hHAS8BxxPYclwObgGOAZ4E/\nAT2AVwuvrwEmA2N38X4Wa8VYuhQGD4Zly+DII9OOpro9/TTMnAmvvZZ2JNK+M7dkh7mlIlRKsVZq\nFmvFuvnm6DOZMCHtSKrX1q2xm/jrr0O/fmlHI5WGuSV95paKYbGm3du4MZZzT5kCZ52VdjTV6YEH\nYjn95MlpRyKVjrklfeaWimGxpj2bMgXuuSd2tz7ooLSjqS4bNkDv3vDxx9FXIuWJuSU95paKYrGm\n4owYAYceGquGVD6jRkHbtrEHkpRH5pZ0mFsqisWaivPDD3DaaTFsfsUVaUdTHVaujOmhzz93o0rl\nl7ml/MwtFcdiTcVbuDA2spw1K5KrktPUBEOGwMUXx55UUp6ZW8rH3FKRKuW4KWXBqafCxIkwbBis\nXp12NPk2aRL89JP7UKk6mFvKx9ySK24rrZYNHx4H/g4ZAvX1cPzxyX/md9/FbueLF8fKpe+/j6/G\nRujQAQ47LBpk+/aNry5dko8pSWvXRtP17NnRUyJVA3NL8swtueM0qHbvySfhscdgxoy4Ky6l5mb4\n7DOYPj2+vvgiPqNPHzjpJOjYEQ4/PI6q2bw5kuuqVbBgQZxtd9RRMcR/ySUxpdKmwn6dL7sMTjwR\nHnoo7Uik8jO3JMfcUrHsWVPrvfIKXH89jB0L11yzb4mrsRHef39HEt22LaZEhg2Dc86B2tri3qep\nCebPh2nT4KWXYpXZjTdG43K7dq2Pr1yefx4eeQQ+/RQOPDDtaKR0mFtKz9xS0SzWtG+WLYORI+Nu\ndNy4vWsO3ro1zqObPj120D7uuB1J9JRT9v2utakp3v/pp+Gjj+D22yO5Hnzwvr1vUlasgAED4O23\nPaNPMreUjrml4lmsad81NkZz8MMPQ48ecPnlce5fz54xnQBxN/vVVzHt8OGHMGdO3OGdeWYk0KFD\nI6EmZelSuP9+mDsX7roLbrgB9t8/uc/bW5s3wxlnwB13wLXXph2NlA3mln1nbskFizWVTkND3MVO\nmwYffABr1sRUQZs2sGULdOoU59D17Qvnnht3eu3blzfGJUvgzjtjxdnjj0NdXfp9Jw0N8UelRw94\n6ql0Y5GyyNzSOuaW3LBYU3IaGmDTpmjqPeSQ7Bwn09wMM2fCbbfFirPx4+PYlbRiufpqWL8+pmyK\n7Z+Rqpm5pbhYzC254T5rSk5tbayeOvro7CRTiLvdurq4E66rg4ED4aabYhl/OTU1xZTJihXw8ssm\nU6lY5pbdM7dUDYs15V9tLdxyCyxfHs979Yrpi19/Tf6zf/017nqXLIld27PamCxp75lbVCYWa6oe\nHTtGP8fcudGc3Ls3TJ0a0whJWL8eLrwwpnHeeCN6byTlj7lFCbNYU/Xp1SuamCdMgPvug/7943kp\nE+usWdCvH5x9NkyZ4l2vVA3MLUqICwxU3ZqaYmPOBx+EAw6IKY1LL219f8yXX8Lo0bEn08SJcN55\npY1XUmUwt6gVXGAgtWS//WJPp0WL4N57YfJk6No1EuucOfDLL3t+j23b4ozDESPg9NPj7nrJEpOp\nVM3MLSohR9akna1eDS+8EEvzly2LjSZ79oyz9jp1ioORt2yJPaAWL4b33ouDn6+8Eq66Kg6FlqSd\nmVu0B+6zJrXGd9/BvHmxa/rKlfG8sTH2fOraFU4+OTbm7No17UglVRJzi1pgsSZJkpRh9qxJkiRV\nIIs1SZKkDLNYkyRJyjCLNUmSpAyzWJMkScowizVJkqQMs1iTJEnKMIs1SZKkDLNYkyRJyjCLNUmS\npAyzWJMkScqwrBVrlwFLgW1Av9287o/AcuAL4C9liEuSJCkVWSvWlgDDgbm7eU1b4CmiYOsNXAH0\nSj40taS+vj7tEHLN65s8r3HyvMbJ8xonL81rnLVibTmwcg+vOR1YBawBGoB/AMOSDUu7YoJIltc3\neV7j5HmNk+c1Tp7F2t45FvjXb55/XfiZJElS7tSk8JlvAZ1b+Pk9wIwi/vvm0oYjSZKUXW3SDmAX\n3gFuBz5t4d/6A2OInjWAu4Em4NEWXrsKOCGB+CRJkkptEXBq2kEU6x3gtF38Ww3wJdAN2B9YiAsM\nJEmSymI40Y/2E7AOmFn4+THA67953UXACmLk7O5yBihJkiRJkiTlmpvmJmsSsJ7YF0/J6Eq0AywF\nPgNuTjecXDoQmEe0UiwDxqYbTm61BRZQ3AIy7b01wGLiGn+Ubii5dRjwCvA5kSv6pxtOPrQlpke7\nAbXY05aEc4C+WKwlqTM7mkzbE9P+/h6XXrvCYw3wITAgxVjy6jZgMvDPtAPJqdXAEWkHkXPPAVcX\nvq8BOpQ7gErcZ21P3DQ3ee8C36cdRM6tI240ALYSd3THpBdObv1YeNyfuNHbmGIsedQFqAP+SnZ3\nH8gDr21yOhADFJMKzxuBzeUOIo/FmpvmKm+6ESOZ81KOI4/2I4ri9cS087J0w8md8cCdxPZKSkYz\nMBuYD1yXcix51B34FvgbsZ3Ys+wYkS+bPBZrbpqrPGlP9ErcQoywqbSaiOnmLsAfgEGpRpMvfwY2\nEL1Ujvwk52ziZu4i4EZiFEilUwP0AyYUHv8D3FXuIPJYrP2baM7erisxuiZVmlpgCvB3YFrKseTd\nZmJ7oN+nHUiOnAUMJXqqXgSGAM+nGlE+rS08fgtMJVqBVDpfF74+Ljx/hSjatI/cNLc8uuECgyS1\nIf6wjU87kBw7kljlBXAQMBc4N71wcm0grgZNQjvgkML3BwPvAxekF05uzQVOLHw/hpZPTFIruGlu\nsl4EvgF+IfoDr0o3nFwaQEzRLSSmkRaw44g1lUYfogdlIbH1wZ3phpNrA3E1aBK6E7+/C4ktfvx7\nl4xTiJG1RcCrpLAaVJIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSVJG9QLuSTsISdUj\njwe5S1KSBhPHf0lSWVisSVLxLgKuAboAnVOORZIkSS2YkXYAkqqLI2uSVLzOwLq0g5BUXdqmHYAk\nVZDBwGYid24CGtINR1I1cGRNkor3DXAs0B74MeVYJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSpNL6Lz8L2gZNKKvvAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113317090>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"t = np.r_[0:2*np.pi:401j]\n",
"x = np.cos(t)\n",
"y = np.sin(t)\n",
"l = 1.0\n",
"Gamma = 2/l**2\n",
"gp = GP(kernels.ExpSine2Kernel(Gamma, np.pi))\n",
"gp.compute(t,yerr=1e-3)\n",
"f = gp.sample(t, size = 1)\n",
"pl.figure(figsize=(10,10))\n",
"ax1 = pl.subplot(221)\n",
"pl.scatter(x,y,s=10,c=t,edgecolors='none')\n",
"pl.xlabel(r\"$\\cos(t)$\")\n",
"pl.ylabel(r\"$\\sin(t)$\")\n",
"pl.title(r\"$t$\")\n",
"ax2 = pl.subplot(222, sharex = ax1, sharey = ax1)\n",
"pl.scatter(x,y,s=10,c=f,edgecolors='none')\n",
"pl.xlim(-1.1,1.1)\n",
"pl.ylim(-1.1,1.1)\n",
"pl.title(r\"$f(t)$\")\n",
"ax3 = pl.subplot(212)\n",
"pl.plot(t,f,'r-')\n",
"pl.ylabel(r\"$f(t)$\")\n",
"pl.xlabel(r\"$t$\")\n",
"pl.xlim(0,2*np.pi);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we move through the time domain, we rotate around the circle in $\\mathbf{u}$-space, and observe the value of the function at the corresponding location. This offers (for me) fresh insight as to how modelling the light curve of a rotating star with this particular kind of GP relates to the actual surface features on the star. Note that one full rotation of the star corresponds to half a rotation in $\\mathbf{u}$-space. The observed function value at any given time is the stellar flux integrated over the stellar disk, i.e. the \"intrisic\" surface contrast multiplied with a visibility function and integrated over the visible hemisphere.\n",
"\n",
"Consider a point on the stellar surface with latitude $\\chi$ and longitude $\\phi_0$, where the meridian ($\\phi_0=0$) is taken to face the observer at $t=0$. If we ignore limb-darkening, the visibility function is simply the cosine of the angle $\\beta$ between the line joining the centre of the star to the observer, and that joining the centre of the star to the point under consideration:\n",
"\n",
"$$\\cos \\beta = \\cos i \\sin \\chi + \\sin i \\cos \\chi \\cos \\phi,$$\n",
"\n",
"where $\\phi = \\phi_0 + 2 \\pi t / P$ (here $P$ is the stellar rotation period). So, if the \"intrinsic\" stellar surface contrast is $C(\\chi,\\phi_0)$, the observed flux at time $t$ is\n",
"\n",
"$$f(t) = \\int_{-\\pi/2}^{\\pi/2} \\mathrm{d}\\chi \\int_{0}^{2 \\pi} \\mathrm{d}\\phi_0 V(\\chi,\\phi_0) C(\\chi,\\phi_0),$$\n",
"\n",
"where $V(\\chi,\\phi_0) = \\max(\\cos \\beta,0)$, ensuring the integral covers the visible hemisphere only.\n",
"\n",
"<font color=red> Now relate this to $x$ and $y$ in $\\mathbf{u}$-space</font>.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Other periodic kernels\n",
"\n",
"This isn't by any means the only way to create a periodic kernel. To start with, one could use, presumably, any other valid kernel in $\\mathbf{u}$-space, or construct a different periodic warping of $t$. <font color=red>This may be worth investigating a little more...</font> \n",
"\n",
"Furthermore, another commonly used periodic kernel is the cosine kernel:\n",
"\n",
"$$ k(r) = \\cos \\left(\\frac{2 \\pi r}{P} \\right).$$\n",
"\n",
"This is simpler, in that it doesn't have a scale hyper-parameter, but the big difference is that the covariance is forced to go to $-1$ for $r = nP + 0.5$, i.e. observations in antiphase have to be anticorrelated. In other words, this kernel is appropriate only for modelling harmonic signals that can be described just by sines and cosines with period $P$. This is very much appropriate for (e.g.) acoustic stellar oscillations, which is why Brewer & Stello (2009) recommend using it in that context, but it doesn't fit the bill for rotational modulation of evolving active regions.\n",
"\n",
"By contrast, with the exponential(sine squared) kernel I am using, the covariance only drops to a value somewhere between 0 and 1 for $r = nP + 0.5$, and this is what enables it to model more complex periodic signals, including for example active star light curves displaying a characteristic \"double dip\" feature, which is commonly observed.\n",
"\n",
"In principle one could construct periodic kernels even more specifically tailored to any particular task, and if we had more insights into the dynamics of the problem (i.e. the equations of motion governing the system we're trying to model) this would definitely be the way to go. However, I personally have no such insight in the context of active star light curves. For my purposes, the exponential(sine squared) kernel does a fine job in all the applications I've used it for so far, with a remarkably small number of hyper-parameters, so I'm sticking with it..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Variance and mean\n",
"\n",
"One interesting and somewhat surprising property of this kernel is that, even if $V=1$, the variance of the resulting time-series isn't necessarily unity, and their mean isn't zero.\n",
"\n",
"This is because, depending on the value of $l$ (or, equivalently, $\\Gamma$), the covariance function doesn't necessarily go to zero -- ever. Specifically, the minimum value of the covarinace function, which occurs when $r = nP + 0.5$, such that $\\sin^2(2 \\pi r / P)$ = 1, is \n",
"\n",
"$$\\min(k_{\\mathrm{P}}) = V \\exp \\left( \\frac{-2}{l^2} \\right) = V \\exp(-\\Gamma),$$\n",
"\n",
"while of course $\\max(k_{\\mathrm{P}}) = V$, so that\n",
"\n",
"$$\\mathrm{range}(k_{\\mathrm{P}}) = V \\left[ 1 - \\exp \\left( \\frac{-2}{l^2} \\right) \\right] = V \\left[ 1 - \\exp(-\\Gamma) \\right].$$\n",
"\n",
"So is it this range that controls the actual variance and mean of the time-series? Let's test that."
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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33eTJ/KfQVHy8GLcnRBioLUkPPfSQ1r7qTf2GG27Q2jft8LW4uTo1f/xjbJvP\nAmufeMKs6Bw5AtZaPClUVVVxFHgCmAw0Rf9/ArNiFRYynyC9pqYmqfjYILEqMavVPy8vjyeffDLt\n9joT23XoVsDCqrOzk+7ubg4ePMiMGTN8OUd5eXnsdW2yRrAkVq5cSXNzM08//XRG1kaLKBlBX3jh\nBU+O6VXvgR/CvN5QkG699Vb27dvn+jihqPgIESZux4mrqSivvDK+PyQ1NXBfeumlWvsmGr72Isrw\nNXWuTrRXJw/IAx5zcD5rCMFRoANoj/5fEE0fHRaZSA0rxEBmpYw3DINFixal3d6aB3n48GHteZCp\nqA+zhTYab8Lqw2gvfX5+PmtSLDHgJlYdO3Ys9lpnoWvIfDrykmjjXcTBkgjJkhiEuUfwnHPOCboI\noWQ1CITdZcBi4GFgdpJtglwPSYjTNDY2xhYSa25u1t4fZSGy6dOnO963qalJ+9x/+td/TbyAKBjG\nI4+kPN+kSZO0zlVWVmYA/RbCc1puEQ4MzAVM7cQpkFiVVH5+vtbCkNa9w8mClKnMmjXLAIwJEyZk\n9eKotbW1thYIdROrwN5C14l4sYCpurDovHnzbJe1pKRE6zzl5eUJFzA966yzHJXbL2rZ8vLygi5O\nKNXU1KjXyTG/e3weB1qAL3P6VAAhQsnLoVGbN2/W2t4acjBo0CCWLVtmb6dBg2LD1ybcfnvs4yPA\nWUOG9FV90mTu2bZtm1ZZrRbD+Ow427dv1zqOEAGTOOWS2qr+8ssvp93eygTW09PD3LlzPSuHNQRr\nw4YNWd27q07IT5V22atYpQ57s8OLuVTq96hzjGnTpmmd54MkSzqMGzdO6ziZVFNTE3QRQqmjo4P6\n+nrXx3FS8VkCvAvEPyVdAryKuZzHN+O+difwEwfnEiLjvBwa9bOf/Uxr+3PPPRcwb9ZJh4A8+2y/\nuTooQxb40Y9iw9cqgC6NzDWDBg3SKmsi+fn5rF271vVxhHBJ4lQGWffJ0tJSnnvuubTbq40l559/\nvmfl0B2CFdb015MmTQLSJw9wE6vUIdlTpkzR2teLIYXq+dNVltU5L7pzc9TzqMI2FNIaLlpcXBxL\nDy/6u+eeezxJ9e2k4rMUM3ioCjADxiXAWOBq4OOYz18/ANYCW5wXU4jM8XL88j/8wz9obV9RUQEk\naMFTenWIn+x6/Hhfr87Xv97vS1b2HTuStYwlk2i8dW9vLzfddJPWcYTwgcSpDJo9ezZFRUVMmTIl\n9gCXitrriq6fAAAgAElEQVTD4OUkc91scX5ll3PLbvKAqqoqVqxY4aiBrjiaDCc/P5/vf//7Wvta\nP7Py8nLuv/9+7XNDX2UmLy8vbWNZssqLHcnSWYdtMWtruYUwJ10IWpDr+GwCuuI+mwLsAHYDJzHH\nSl8GfBX4DPAF4B8dl1IIj9hp4fvNb34TC4bXXnutq/PNnBm/wk1qtbW11NTUMB2oOuOMpL06/Wbv\npFgzZ2fcuj6p6AbPzZs3U1JSwtatW/t9/uyzz2odRwgfSJzKoMcff5wTJ07Q3t5u656pNrLceuut\nnpVDd+hXWDM+ZiJ5gDW8rLe3V3u4oVWZOHLkCLfccouj80+cOBEwE2Lcfffdts4H+kPrklWadBM6\n+O29994DzIVd/crkl+10nmdS0U+RkdgwYK/y/i1gKnAzcPrqiXHUNKpNTU2SLlT4ZtWqVXR2dgKw\nYMECHnvssdO2sRbjA2cLieXn58dWi/6Xf/kX2/udKinhl9ZaPAcPxhcqZQUnmXTdwhUVFRw+fJji\n4mLtNKHqatqqFStWaJdTBKOtrW0grUvjKk6BxKpkrDk7AFu2pO80U4e6feYzn+GjJGuQ6dqxYweR\nSIQ33niD7u7utI05y5cvp6WlhcWLF4dqTpDast3S0uLLPdXNcEO1Mqb+7HVYaYkrKyvTpjQ3lGyn\nunNRCwsL+eijj/rFZTB7KdVYHzS7mfwGIitOpZrvlgkj6T92+vPAz5X3X8RmIEEy5YgMKioqSptJ\nBiW7Sk1NjfY51P2LioqSb/jMM8kzsIFxfWmp0b56tavzp8vWc9VVVxlFRUVGY2Ojdhak0tLS2HnU\nzG5Os/yI4JFbWd1G4l2cAolVSRUXFxuAkZ+fbytLG3EZtryiZgqrq6vz7LiZNnfuXAMwGhoaUt6X\nb7zxRqOxsdGYO3eu9v07Ly9PKxOfqrKy0vX9/swzz7R9DDdlnTp1ar9sadb/XmYT9IJaTifZZAcC\n6++CkGR12wcMV94Px2xNs0VWwxaZkul1Hk5rDUs1VyfOsuPHeerHOs9lp0uXraetrS02RGXBggVa\nx1a/N7X1MOhWGaGvra1tICxg6ipOgcSqZCZMmACYw6bSDVuK52VPi6H0DFhlyka1tbXU1tamvTZu\n5igZLnpRrLmjEyZMsJ99NE5XV99I1HTD19Q4ppuER+1ZWrNmDSUlJWzZsoXx48drHcdvVjm9XtQ3\nl9x0002MHTs2sPOPpH9LWgTYGf28CHOC6MdtHivoSqQYQOys84DSEllZWal1/P+zaJHxt2BcBsbf\ngrGgpiZ5r86PfhTbb1FjY8JtFjU2an+PavnTtY7Z6QFLRm3ltfLrl5eXG7t379YuswgHcrvHx02c\nAolVSdntobCg3KOGDh3qWTms9WUGDx6c1fchu+vz1NfXx+KU7ver/gx013Dr6uoympubXa2VpLOO\njzqiYNOmTVrnmT59elb0pOj0gA10BNDj8xDwHHAu5njpG4AezAmiTwKvAL8F/uqmYEL4oa6uLpZA\nIJGNa9bQADQCDcApjVWCf9rayp/vuYffA48BvweWJpqrkyADW080w068Uw7m9ahZldK1jrnpAbNa\n4Xp7ezl8+DBgTnZduHCh1nGE8IHEqQyy20NhUTNCNjQ0eFYOa07j0aNHky8HkAWsuaUFBQXceeed\nSbc7GI0v3d3drrJp6q43l4nkCypD6Z2aPTvVGsOnS5opNWTUERQyaiL3BV15FD5wM/bYT+qK05df\nfnm/r7WvXm3886hR/XpcroD082x+/euUc3WurK5OWy7H507AWhW9oKAg7ThmNyudq71F6j/dniMR\nHuRWj4/Xgv7xhJY638/O37+1vddzLXR7nsIqEonYmquEct8966yztM6h7nvvvfdq7Wu3R8ru+Zua\nmlJuq87x0e3xUXunwvpcYhiGUVdXJ6MmbCIH4pSxaNEiY8OGDUFfS+EhL26MfsjPz086DOzbF1+c\nsOJy55w5px8oRUUn/t/1NofLta9ebTSA0QhGAxhnDR7s6Hu0GzQNwzBGjx5tRCIRo7q6Wvtmqw5V\nUP9JxSf7bNiwwVi0aFFOBBQfSaxKAs0H8IqKitj29fX12udL9gD7sY99zIhEIsaQIUOy+uFRvYen\nup/a3S4R9WeWn5+vta8XFUz1/CUlJSm3tYZVO/k+VWF9LjGM7BmSF6RcilNBX0vhAzdjj/1EirHl\nKefZpOrV+bu/MwzDMK6ornbc45OofCkzwqWgEwzdZEGqqqqK7aueU8YnZy9yIKD4KOgfT2jpPoDr\nZoGLl+wBVu0Z0O0BCRO7c5WsnjM7vfvxvOpFcUrn/KBXsU4mzD2CYS5bWJx33nlqRkHHvMrqJkQ/\nI0aMAMyxx2Edax2/sFmyeTat7e3wpS/1//DIkb6qzeOPA9D41a/y5Uj/pbH+Hpj51a86Kp/TbEfT\np08HYNy4cTz44IMpt1Wz6Zx77rla57HGJBcUFMRW4c7Pz+d73/ue1nGEENnNuudccMEFae854C4L\nHCRfeNQw9J6H7CxoHQRrvZmjR4+mXCDUWvvl1KlTzJkzR+sc9fX1gDkHZvjw4Wm27q+qqooVK1a4\nysh34YUXxl4/8MADKbdV54Sp++nSnYuWSWEuW1h0dnbSrTHvOsyCrkQKhVdjYMPaeoHScvTEE0/0\n+9rL3/pW8l4dm70Y8Vndhmr+fls9MG7Gvuu0xqlD/3R7fJINdcvm9TMGOqTHJ5WgfzyhpdsD4CZL\nV6rzWfMO7fYkhXXoE8r9NNX8F3W72tparXME3Tum84xQUlLiOC6qzzRDhw4N7ciEVPOPhcnKHksO\nxCkZNx0iXgUCawx3YWFhaBYKi59DU1lQkHJuzgwwziwr0z4PSjAqLy/X2terscx2VVdXG4BRVlam\nPSRRHd6i/pM5Ptknl8ZO+0hiVRJqg42dioza4JJufocO3UUgs2FIdqrro243ZMgQx+cIouJz3XXX\nGbW1tcasWbPSVnzczAlTn2nUxrqwVS7cLC0xUDz00ENGeXl5TsSpoK+lUCRqhXHSC4RyUy0uLvaz\nyLYkypp22r/rrzcMw10wid9f9/ebDAej3bt3G/X19Y6CvhVQxo8fH5uYOX78+FD18Ak95EBA8VHQ\nP57QQrlv2Zko72Z+SSqDBg0ywP6cl7BOKLd7fdTrrjsnVK186v4MvBgZovZwpOt9sVr6nTTQWZXb\nioqKWI9PGDOnKXNXQtcbFTbkQJwK+hoKRaIhBE56gVBuyFVVVX4V154UlZ27Zs1KsLnziovb/dX9\n0qX4DJr6u+LFZFcRPHIgoPjI9fX1I51uGFL0oty34ocQJzJp0iRfKhy6PQPqQ3GYHoTtXh/1uuvG\nWTdD3bwYGaLTw3HVVVcZRUVFRmNjo/bvuFq5tUY4eP175wU3S0sMNORAnAr6Goo0nMzXQbkhRyIR\nn0sYZ+VK26mmFzU2piy77rjp+P11f7/V/bI5K5HITuRAQPGR6+vrx5ySMMxT+cQnPqFVBrdzQJNV\n9nSzxalzPsI09MluhQwlXuiOrFD3jc9wmo4Xc3jVYWfpKj5ufsfVslqVi7DNPTYMbzLlDRS4jFOS\n1U2ktWPHDiKRCG+88YajjBq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YJxXAqVOnJrxPqhUqOz1qKHGqqKhIuxx+sdsb46bHp6am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\nGyMJb4VxOUmgKIzdwN2UOFACMacGx5wKz5wKz6wKJ1VO9bOqWxp7gbN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"text/plain": [
"<matplotlib.figure.Figure at 0x112b74e90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dt = np.r_[0:10:501j]\n",
"T = dt[-1]-dt[0]\n",
"P = 1.0\n",
"n = 20\n",
"nsim = 50\n",
"logV = np.random.uniform(0,5,size=n) \n",
"logGamma = np.random.uniform(-1,1,size=n) \n",
"V = 10.0**logV\n",
"Gamma = 10.0**logGamma\n",
"ran = V * (1-np.exp(-Gamma))\n",
"l = np.sqrt(2/Gamma)\n",
"var = np.zeros((n,nsim))\n",
"msq = np.zeros((n,nsim))\n",
"for i in range(n):\n",
" gp = GP(V[i] * kernels.ExpSine2Kernel(Gamma[i],P))\n",
" gp.compute(dt,yerr=V[i]*1e-3)\n",
" sam = gp.sample(dt, size = nsim)\n",
" for j in range(nsim):\n",
" tmp = sam[j,:].flatten()\n",
" var[i,j] = tmp.var()\n",
" msq[i,j] = (tmp.mean()**2)\n",
"var_mean = np.mean(var, axis=1)\n",
"pl.figure(figsize=(14,5))\n",
"ax1 = pl.subplot(121)\n",
"for i in range(n):\n",
" pl.plot(np.ones(nsim)*ran[i], var[i,:].flatten(), 'k.')\n",
"pl.plot(ran, var_mean, 'ro')\n",
"pl.plot(ran, 10.0**(-0.25+np.log10(ran)), 'r-')\n",
"pl.title('variance')\n",
"pl.xlabel(r\"$V$ $\\exp(-\\Gamma)$\")\n",
"pl.loglog()\n",
"ax2 = pl.subplot(122, sharex = ax1, sharey = ax1)\n",
"for i in range(n):\n",
" pl.plot(np.ones(nsim)*ran[i], msq[i,:].flatten(), 'k.')\n",
"pl.title('mean squared')\n",
"pl.xlabel(r\"$V$ $\\exp(-\\Gamma)$\")\n",
"pl.loglog()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see the variance does scale like $V \\exp(-\\Gamma)$, as expected, though there is a proportionality factor of 0.56, the origin of which I don't understand. The mean (or rather, the range of the squared means) doesn't quite scale with $V \\exp(-\\Gamma)$; the dependence on $V$ and $\\Gamma$ must be a bit different. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <font color=red> DONE TILL HERE</font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### What does $l$ mean now?\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the squared exponential kernel, the covariance ranges from 1 to 0 and falls to $e^{-0.5}$ when $\\Delta t=l$. The range sets the variance of the variations and $l$ their length scale. How the lenght scale relates to the number of turning points per unit time is something I'll want to investigate later.\n",
"\n",
"In the periodic kernel we're considering, the covariance ranges from 1 to $e^{-\\Gamma}$. The variance of the variations therefore scales as $1-e^{-\\Gamma}$. \n",
"\n",
"The covariance falls to $e^{-0.5\\Gamma}$ times its full range when $\\Gamma \\sin^2 \\left(\\frac{\\pi \\Delta t}{P} \\right) = 0.5 \\left( 1-e^{-\\Gamma} \\right)$, i.e. $\\frac{\\Delta t}{P} = \\frac{1}{\\pi} \\arcsin\\left( \\sqrt{\\frac{1-e^{-\\Gamma}}{2\\Gamma}}\\right)$. This gives an estimate of the length scale of the variations within a period. </s></font>\n",
"\n",
"NB1: For small $\\Gamma$, the covariance never approaches zero, and this leads to functions which have non-zero mean, even over an infinite interval. Something similar happens with the squared exponential kernel for large $l$, but over a finite time interval $\\Delta t \\sim < l$.\n",
"\n",
"NB2: for very small $\\Gamma$, the variations look close to sinusoidal with period $P$.\n",
"\n",
"Let's see if all this holds out in practice, by drawing samples from periodic and squared exponential GP priors with the appropriate parameters."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from george import GP, kernels\n",
"import time\n",
"def compare_per_se(P, Gamma, dt):\n",
" gp_per = GP(kernels.ExpSine2Kernel(Gamma,P))\n",
" gp_per.compute(dt,yerr=1e-3)\n",
" seed = int(time.time())\n",
" np.random.seed(seed)\n",
" sam_per = gp_per.sample(dt,size=1)\n",
" pl.plot(dt,sam_per.T,'k-', label = 'Per')\n",
" pl.title(r\"$P=$%s, $\\Gamma=%s$\" % (repr(P), repr(Gamma)))\n",
" pl.xlabel(r\"$t$\")\n",
" pl.ylabel(r\"$f$\")\n",
" a_effective = np.sqrt(1 - np.exp(-Gamma))\n",
" l_effective = (P/np.pi) * np.arcsin(np.sqrt((a_effective**2/2/Gamma)))\n",
" print \"amplitude=%.3f, length scale=%.3f\" % (a_effective, l_effective)\n",
" gp_se = GP(a_effective**2 * kernels.ExpSquaredKernel(l_effective**2))\n",
" gp_se.compute(dt,yerr=1e-3)\n",
" np.random.seed(seed)\n",
" sam_se = gp_se.sample(dt,size=1)\n",
" pl.plot(dt,sam_se.T,'k--', label = 'SE')\n",
" pl.legend(loc=0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"amplitude=1.000, length scale=0.072\n"
]
},
{
"data": {
"image/png": 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ffvtN5/qRI0cwefJkJCQkCB4YopTyjoYPHz4s6G7GubdyIxwHBwcMHToUWVlZ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\nUHYN9MQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x105b5fa90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"compare_per_se(1.0,10.0,dt)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"amplitude=0.308, length scale=0.242\n"
]
},
{
"data": {
"image/png": 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yXCozMxORkZGIjo7GtWvXUK5cOfj5+aFFixaoWrWq2eW5lNVqvbkukpKSUK5c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\nEREZ9v/g5M6dvwIhWwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1066a1210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"compare_per_se(1.0,0.1,dt)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"amplitude=0.795, length scale=0.190\n"
]
},
{
"data": {
"image/png": 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7Wn44zJgxA9HR0QadSyw+Pj4IDAxEUlKSZOds2RZ79+5FXFycbN+0Y2JikJqa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\nwwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1069f9fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"compare_per_se(1.0,1.0,dt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This seems to give reasonable results for a wide range of $\\Gamma$: the solid and dashed lines have similar amplitudes and length scales. Good!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quasi-periodic kernel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can construct a quasi-periodic kernel by multiplying the periodic (ExpSine2) kernel by a squared exponential (or any other kernel, but let's stick to squared exponential for now). This is defined as:\n",
"$k_{\\mathrm{QP}}(t,t') = \\exp \\left[ - \\Gamma \\sin^2 \\left(\\frac{\\pi \\Delta t}{P} \\right) - \\frac{(\\Delta t)^2}{2L^2}\\right]$. Let us take a look at the covariance function."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"solid: Gamma=0.5, dashed: Gamma=5\n",
"blue: L=0.3, green: L=3, red: L=30\n"
]
},
{
"data": {
"image/png": 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MG92bIG5pBUmwl5LCl4X9+wMA7mfex/CNwxHh2QytW/RHbFYsto/Zjtk9PsbZmo7ITFNL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\nMTbJUHtxCa4Ytf1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zzWBLsGcPf56T7s30vXf3Itw/HHUr63BXVJqSriRf0V5NgEeAP8xR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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x106e7d910>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def expsine2_SE(Gamma, P, L, dt):\n",
" return np.exp(-Gamma*(np.sin(np.pi*dt/P))**2 - dt**2/(2*L**2))\n",
"dt = np.r_[0:5:301j]\n",
"Gammas = [0.5,5]\n",
"Ls = [0.3,3,30]\n",
"lss = ['-', '--']\n",
"cols = ['b','g','r']\n",
"for i in range(len(Gammas)):\n",
" Gamma = Gammas[i]\n",
" ls = lss[i]\n",
" for j in range(len(Ls)):\n",
" L = Ls[j]\n",
" col = cols[j]\n",
" pl.plot(dt,expsine2_SE(Gamma,1,L,dt), \\\n",
" label = r\"$\\Gamma=$%s, $L=$%s\" % (repr(Gamma), repr(L)), \\\n",
" ls = ls, c = col)\n",
"#pl.legend(loc='lower right')\n",
"pl.title(r\"ExpSine2_SE with $P=1$\")\n",
"pl.xlabel(r\"$\\Delta t/P$\")\n",
"pl.ylabel(r\"$k_{\\mathrm{Per}}(t,t')$\")\n",
"pl.xlim(0,5)\n",
"pl.ylim(0,1.1)\n",
"print \"solid: Gamma=%s, dashed: Gamma=%s\" % (Gammas[0], Gammas[1])\n",
"print \"blue: L=%s, green: L=%s, red: L=%s\" % (Ls[0], Ls[1], Ls[2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For large $L$ (if $L \\gg T$, where $T$ is the time span of the data), the behaviour is essentially the same as for the strictly periodic case. \n",
"\n",
"For small $L$ (if $L \\ll P$), there are no subsidiary maxima, and the behaviour will look much like a squared exponential. However, if $\\Gamma$ is small, it can still influence the length scale. <font color=\"red\">_Probably need to double check this also_ <s> The covariance ranges for 0 to 1 but falls to $e^{-0.5}$ when $\\Gamma \\sin^2 \\left(\\frac{\\pi \\Delta t}{P} \\right) + \\frac{(\\Delta t)^2}{2L^2}= 0.5$, i.e. when $(\\Delta t)^2 = \\frac{L^2 P^2}{2 \\pi^2 L^2 \\Gamma + P^2}$. In the extremes, the length scale can thus be approximated as $\\Delta t \\approx \\frac{P}{\\pi} \\frac{1}{\\sqrt{2\\Gamma}}$ or $\\Delta t \\approx L$, whichever is the smaller.</s></font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<font color=red>_This has an error in it too, started to correct it but need to follow through._\n",
"Now, what is the condition for there to be secondary maxima? Set $\\frac{\\partial}{\\partial \\Delta t} \\left[ k_{\\rm QP}(t,t') \\right]=0$, this implies $\\sin(2 \\pi \\Delta t / P) = - \\frac{P^2 \\Delta t}{\\pi \\Gamma L^2}$. <s>This always has a solution at $\\Delta t = 0$, but at least two solutions for non-zero $\\Delta t$ exist if $L^2 / \\Gamma > 3 P / 4$. (I get this by requiring that $- \\Gamma \\Delta t / L^2$ be $>-1$ at the first minimum of the sinusoid, which occurs at $\\Delta t = 3 P / 4$.) The first is a minimum and the second a maximum. </s></font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<font color=\"red\">_And probably will need to edit this too..._<s>If that condition is satisfied, then we genuinely have what we might call a quasi-periodic process. The number of subsidiary maxima is $n = \\mathrm{floor} \\left(\\frac{1}{4} + \\frac{L^2}{P \\Gamma} \\right)$. This is the maximum number of cycles for which the process \"remembers\" anything of what it was doing $n$ cycles ago (i.e. for which the covariance for $\\Delta t = nP$ is larger than the covariance for $\\Delta t = nP \\pm \\epsilon$).</s></font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is not, however, what we would normally think of as the overall evolutionary timescale of the process: that is given by the time interval for which the covariance has decayed by $e^{-0.5}$, which we can approximate as $L$. This approximation ignores the oscillatory component of the covarinace function and focuses on the exponential envelope only, but that's ok, because the evolutionary timescale is only constrained if the time span of the data is significantly longer than $L$, which is equivalent to saying that the periodic wobbles are small compared to the overall decay."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The approximation derived in the periodic case for the length scale of the variations within a cycle can still be used, though we should note that the smaller $L$ is, the worse an approximation it will be. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, provided $L^2 / \\Gamma > 3 P / 4$ and the time span $T$ of the observations exceeds $P$ (i.e. there are subsidiary minima and maxima in the covariance function as evaluated over $\\Delta t \\le T$), the minimum of the covariance occurs at $\\Delta t = (n-1/2)P$ where $n=\\mathrm{floor}(T/P)$, at which point $k_{\\rm QP} \\approx \\exp \\left( - \\Gamma - \\frac{T^2}{2L^2} \\right)$. So the variance of the functions this process generates, when evaluated over time span $T$, is approximately given by $1-\\exp \\left( - \\Gamma - \\frac{T^2}{2L^2} \\right)$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next I should look at how well this bears out in practice. But I've run out fo time for today."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def compare_QP_se(P, Gamma, L, dt):\n",
" gp_QP = GP(kernels.ExpSine2Kernel(Gamma,P) * kernels.ExpSquaredKernel(L**2))\n",
" gp_QP.compute(dt,yerr=1e-3)\n",
" seed = int(time.time())\n",
" np.random.seed(seed)\n",
" sam_QP = gp_QP.sample(dt,size=1)\n",
" pl.plot(dt,sam_QP.T,'k-', label = 'Per')\n",
" pl.title(r\"$P=$%s, $\\Gamma=%s$,$L=$%s\" % (repr(P), repr(Gamma), repr(L)))\n",
" pl.xlabel(r\"$t$\")\n",
" pl.ylabel(r\"$f$\")\n",
" if (L**2/Gamma) < (3*P/4):\n",
" print 'No secondary covariance maxima: process is not periodic'\n",
" l_effective = min(P/np.pi/np.sqrt(2*Gamma), L)\n",
" a_effective = 1.0\n",
" l_evol = np.nan\n",
" else:\n",
" l_evol = L\n",
" a_effective = np.sqrt(1 - np.exp(-Gamma))\n",
" l_effective = (P/np.pi) * np.arcsin(np.sqrt((a_effective**2/2/Gamma)))\n",
" print \"amplitude=%.3f, length scale=%.3f, evolutionary length scale %.1f\" % (a_effective, l_effective, l_evol)\n",
" gp_se = GP(a_effective**2 * kernels.ExpSquaredKernel(l_effective**2))\n",
" gp_se.compute(dt,yerr=1e-3)\n",
" np.random.seed(seed)\n",
" sam_se = gp_se.sample(dt,size=1)\n",
" pl.plot(dt,sam_se.T,'k--', label = 'SE')\n",
" if np.isfinite(l_evol):\n",
" gp_se2 = GP(a_effective**2 * kernels.ExpSquaredKernel(l_evol**2))\n",
" gp_se2.compute(dt,yerr=1e-3)\n",
" np.random.seed(seed)\n",
" sam_se2 = gp_se2.sample(dt,size=1)\n",
" pl.plot(dt,sam_se2.T,'k:', label = 'Evol')\n",
" pl.legend(loc=0)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No secondary covariance maxima: process is not periodic\n",
"amplitude=1.000, length scale=0.300, evolutionary length scale nan\n"
]
},
{
"data": {
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j4/OsQyLcv38fjRs31kUvRwOICAsWLMD//vc/xeS7K4n3338fvXr1wgsvvCCJ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\nN5eGmZnl9l8MFY6aTyz1qAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x106fe1fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"compare_QP_se(1.0, .5, .3, dt) # should be aperiodic with length scale ~ L"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No secondary covariance maxima: process is not periodic\n",
"amplitude=1.000, length scale=0.101, evolutionary length scale nan\n"
]
},
{
"data": {
"image/png": 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A5OWgH7YvoTAx0APLly/HL7/8AplMpm9TDJrbt2+jrq6uVdinZ8+eKmsNFRUVoVu3bmrn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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x106f90c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"compare_QP_se(1.0, 5, .3, dt) # should be aperiodic with shorter length scale ~ 1/pi/sqrt(2Gamma)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"amplitude=0.627, length scale=0.216, evolutionary length scale 3.0\n"
]
},
{
"data": {
"image/png": 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jIyP0798fDg4O2LBhA4CSEMbNmjWT+URGRurYag6n5pOcnAwbGxsAJVvf33//fRw7dgx/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\nLVuwceNGJCUlCWxd9XDt2jV069YNhoaGgujr2bM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"text/plain": [
"<matplotlib.figure.Figure at 0x106e16690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"compare_QP_se(1.0, .5, 3, dt) \n",
"# Should be periodic with evol timescale ~ L, total variance slightly < 1, \n",
"# a few wiggles per period with smaller than overall variance"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"amplitude=0.997, length scale=0.102, evolutionary length scale 3.0\n"
]
},
{
"data": {
"image/png": 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IeHg4nnnmGSQnJ5vk/nfu3JFFg2c9g8HSIqi0iYYlrmu0trYiMTERQ4YMkUVDpuNJS0vD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\n0BBCCKHY/wP1JGCpllRFhgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x104f583d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"compare_QP_se(1.0, 5, 3, dt) \n",
"# Should be periodic with evol timescale ~ L, total variance slightly ~ 1, \n",
"# many wiggles per period with bigger variance"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"amplitude=0.627, length scale=0.216, evolutionary length scale 10.0\n"
]
},
{
"data": {
"image/png": 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4d++efLDIjBkz0LJlS5Un1tMVPz8/nD9/Htu3b5c0jtmzZ8PS0rLKB4Pv3bsH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\nM7PcXBpmZpabS8PMzHJzaZiZWW7/AHEQQ8Qw+V3eAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10530a2d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"compare_QP_se(1.0, .5, 10, dt) "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"amplitude=0.997, length scale=0.102, evolutionary length scale 10.0\n"
]
},
{
"data": {
"image/png": 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Gf3bq1ClkZmbCz88PMTExqk04ckpSVFVVSZYksGeugeHqy96mInM+gx49eqCw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\nQggBSQZCCCEgyUAIIQQkGQghhADw/3griWh/o4UCAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10523edd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"compare_QP_se(1.0, 5, 10, dt) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All seems to behave as expected... Pfew!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is encouraging because it means we have a way of relating the parameters of the QP GP kernel as defined above to properties of the time series that are themselves closely related to physical things we might be tryin to model like the lifetime and longitudinal distribution of active regions on a star."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"source": []
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],
"metadata": {
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"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
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"name": "ipython",
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"file_extension": ".py",
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"name": "python",
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"pygments_lexer": "ipython2",
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| mit |
blab/antibody-response-pulse | bcell-array/.ipynb_checkpoints/alva_machinery_event_OAS-checkpoint.ipynb | 1 | 7979 | {
"metadata": {
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"signature": "sha256:8f0ee114d2286a136b65af1655ce2bdf813b6ba61448e4ee876a29a30fa1ecc1"
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Infectious Pulse\n",
"https://github.com/blab/antibody-response-pulse/\n",
"\n",
"## Homemade machinery for solving partial differential equations\n",
"### Runge-kutta algorithm for a array of coupled partial differential equation "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"'''\n",
"author: Alvason Zhenhua Li\n",
"date: 03/23/2015\n",
"\n",
"Home-made machinery for solving partial differential equations\n",
"'''\n",
"import numpy as np\n",
"\n",
"# define RK4 for an array (3, n) of coupled differential equations\n",
"def AlvaRungeKutta4XT(pde_array, initial_Out, minX_In, maxX_In, totalPoint_X, minT_In, maxT_In, totalPoint_T, event_tn_In):\n",
" global eventName\n",
" # primary size of pde equations\n",
" outWay = pde_array.shape[0]\n",
" # initialize the whole memory-space for output and input\n",
" inWay = 1; # one layer is enough for storing \"x\" and \"t\" (only two list of variable)\n",
" # define the first part of array as output memory-space\n",
" gOutIn_array = np.zeros([outWay + inWay, totalPoint_X, totalPoint_T])\n",
" # loading starting output values\n",
" for i in range(outWay):\n",
" gOutIn_array[i, :, :] = initial_Out[i, :, :]\n",
" # griding input X value \n",
" gridingInput_X = np.linspace(minX_In, maxX_In, num = totalPoint_X, retstep = True)\n",
" # loading input values to (define the final array as input memory-space)\n",
" gOutIn_array[-inWay, :, 0] = gridingInput_X[0]\n",
" # step-size (increment of input X)\n",
" dx = gridingInput_X[1]\n",
" # griding input T value \n",
" gridingInput_T = np.linspace(minT_In, maxT_In, num = totalPoint_T, retstep = True)\n",
" # loading input values to (define the final array as input memory-space)\n",
" gOutIn_array[-inWay, 0, :] = gridingInput_T[0]\n",
" # step-size (increment of input T)\n",
" dt = gridingInput_T[1]\n",
" # starting\n",
" # initialize the memory-space for local try-step \n",
" dydt1_array = np.zeros([outWay, totalPoint_X])\n",
" dydt2_array = np.zeros([outWay, totalPoint_X])\n",
" dydt3_array = np.zeros([outWay, totalPoint_X])\n",
" dydt4_array = np.zeros([outWay, totalPoint_X])\n",
" # initialize the memory-space for keeping current value\n",
" currentOut_Value = np.zeros([outWay, totalPoint_X])\n",
" for tn in range(totalPoint_T - 1):\n",
" # secondary period of virus-1\n",
" eventName = event_tn_In[0, 1] \n",
" if tn > int(totalPoint_T*(event_tn_In[1, 0]/(maxT_In - minT_In))):\n",
" eventName = event_tn_In[1, 1]\n",
" # setting virus1 = 0 if virus1 < 1\n",
" if gOutIn_array[0, 1, tn] < 1.0:\n",
" gOutIn_array[0, 1, tn] = 0.0\n",
"# eventName = event_tn_In[1, 1]\n",
" ## timepoint of 1st-infection of virus-2\n",
" if tn == int(totalPoint_T*(1.0/2)):\n",
" gOutIn_array[0, 2, tn] = 16.0 # set which virus infection \n",
" gOutIn_array[0, 1, tn] = 16.0 # set which virus infection \n",
" eventName = event_tn_In[0, 1] \n",
" ## setting virus2 = 0 if virus2 < 1\n",
" if gOutIn_array[0, 2, tn] < 1.0:\n",
" gOutIn_array[0, 2, tn] = 0.0 \n",
" # keep initial value at the moment of tn\n",
" currentOut_Value[:, :] = np.copy(gOutIn_array[:-inWay, :, tn])\n",
" currentIn_T_Value = np.copy(gOutIn_array[-inWay, 0, tn])\n",
" # first try-step\n",
" for i in range(outWay):\n",
" for xn in range(totalPoint_X):\n",
" dydt1_array[i, xn] = pde_array[i](gOutIn_array[:, :, tn])[xn] # computing ratio \n",
" gOutIn_array[:-inWay, :, tn] = currentOut_Value[:, :] + dydt1_array[:, :]*dt/2 # update output\n",
" gOutIn_array[-inWay, 0, tn] = currentIn_T_Value + dt/2 # update input\n",
" # second half try-step\n",
" for i in range(outWay):\n",
" for xn in range(totalPoint_X):\n",
" dydt2_array[i, xn] = pde_array[i](gOutIn_array[:, :, tn])[xn] # computing ratio \n",
" gOutIn_array[:-inWay, :, tn] = currentOut_Value[:, :] + dydt2_array[:, :]*dt/2 # update output\n",
" gOutIn_array[-inWay, 0, tn] = currentIn_T_Value + dt/2 # update input\n",
" # third half try-step\n",
" for i in range(outWay):\n",
" for xn in range(totalPoint_X):\n",
" dydt3_array[i, xn] = pde_array[i](gOutIn_array[:, :, tn])[xn] # computing ratio \n",
" gOutIn_array[:-inWay, :, tn] = currentOut_Value[:, :] + dydt3_array[:, :]*dt # update output\n",
" gOutIn_array[-inWay, 0, tn] = currentIn_T_Value + dt # update input\n",
" # fourth try-step\n",
" for i in range(outWay):\n",
" for xn in range(totalPoint_X):\n",
" dydt4_array[i, xn] = pde_array[i](gOutIn_array[:, :, tn])[xn] # computing ratio \n",
" # solid step (update the next output) by accumulate all the try-steps with proper adjustment\n",
" gOutIn_array[:-inWay, :, tn + 1] = currentOut_Value[:, :] + dt*(dydt1_array[:, :]/6 \n",
" + dydt2_array[:, :]/3 \n",
" + dydt3_array[:, :]/3 \n",
" + dydt4_array[:, :]/6)\n",
" # restore to initial value\n",
" gOutIn_array[:-inWay, :, tn] = np.copy(currentOut_Value[:, :])\n",
" gOutIn_array[-inWay, 0, tn] = np.copy(currentIn_T_Value)\n",
" # end of loop\n",
" return (gOutIn_array[:-inWay, :])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# eventName = (1.0/10)*(101 + np.tanh(-maxT_In/4 + gridingInput_T[0])*101)[tn]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# min-max sorting\n",
"def AlvaMinMax(data):\n",
" totalDataPoint = np.size(data)\n",
" minMaxListing = np.zeros(totalDataPoint) \n",
" for i in range(totalDataPoint):\n",
" # searching the minimum in current array\n",
" jj = 0 \n",
" minMaxListing[i] = data[jj] # suppose the 1st element [0] of current data-list is the minimum\n",
" for j in range(totalDataPoint - i):\n",
" if data[j] < minMaxListing[i]: \n",
" minMaxListing[i] = data[j]\n",
" jj = j # recording the position of selected element\n",
" # reducing the size of searching zone (removing the minmum from current array)\n",
" data = np.delete(data, jj)\n",
" return (minMaxListing)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
}
],
"metadata": {}
}
]
} | gpl-2.0 |
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