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coecms/ARCCSSive | examples/arccssive_example.ipynb | 1 | 8825 | {
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from ARCCSSive import CMIP5"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"db=CMIP5.connect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pass to the session \"outputs\" method some arguments to search for data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"results = db.outputs(experiment='historical',mip='day')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The \"outputs\" method returns the corresponding rows from the Instance table as result.\n",
"We use the count() method to check how many Instances we got back"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2734"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results.count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NB the search is very fast no matter how many rows we get back or how many fields we use as arguments. This is because we are not actually accessing the instance objects yet."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = db.outputs(experiment='rcp45',model='CNRM-CM5',mip='Amon',variable='tas', ensemble='r1i1p1')\n",
"results.count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We loop through the result object to find out the available versions and other Instance fields"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Available versions for ensemble r1i1p1\n",
"\n",
"v20111006 from /g/data/ua6/unofficial-ESG-replica/tmp/tree/pcmdi9.llnl.gov/thredds/fileServer/cmip5_data/cmip5/output1/CNRM-CERFACS/CNRM-CM5/rcp45/mon/atmos/Amon/r1i1p1/tas/1\n",
"\n",
"v20111006 from /g/data/ua6/unofficial-ESG-replica/tmp/tree/esg.cnrm-game-meteo.fr/thredds/fileServer/esg_dataroot1/CMIP5/output/CNRM-CERFACS/CNRM-CM5/rcp45/mon/atmos/tas/r1i1p1\n",
"\n",
"v20111006 from /g/data/ua6/unofficial-ESG-replica/tmp/tree/esg.cnrm-game-meteo.fr/thredds/fileServer/esg_dataroot1/CMIP5/output1/CNRM-CERFACS/CNRM-CM5/rcp45/mon/atmos/Amon/r1i1p1/v20111006/tas\n",
"\n",
"drstree path is /g/data1/r87/DRSv2/CMIP5/CNRM-CM5/rcp45/mon/atmos/r1i1p1/tas/latest\n"
]
}
],
"source": [
"for o in results:\n",
" print(\"Available versions for ensemble \" + str(o.ensemble))\n",
" print(\"\")\n",
" for v in o.versions: \n",
" print(str(v.version) + \" from \" + str(v.path))\n",
" print(\"\")\n",
" print(\"drstree path is \" + str(o.drstree_path()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another search this time without specifying the model"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"106"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = db.outputs(experiment='rcp45',mip='Amon',variable='clt')\n",
"results.count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Which models?"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"models=set()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for o in results:\n",
" models.add(str(o.model))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['ACCESS1-0', 'ACCESS1-3', 'BNU-ESM', 'CCSM4', 'CMCC-CM', 'CMCC-CMS', 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'CSIRO-Mk3L-1-2', 'CanESM2', 'EC-EARTH', 'FGOALS-g2', 'FIO-ESM', 'GFDL-CM3', 'GFDL-ESM2G', 'GISS-E2-H', 'GISS-E2-H-CC', 'GISS-E2-R', 'GISS-E2-R-CC', 'HadCM3', 'HadGEM2-AO', 'HadGEM2-CC', 'HadGEM2-ES', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR', 'IPSL-CM5B-LR', 'MIROC-ESM', 'MIROC-ESM-CHEM', 'MIROC4h', 'MIROC5', 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NorESM1-M', 'inmcm4']\n"
]
}
],
"source": [
"print(sorted(models))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from ARCCSSive.CMIP5.Model import Instance, Version"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"new_results=results.filter(Instance.model=='MIROC5').filter(Version.path.contains(\"/data1\"))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(u'v20111202', 'is latest? ', False, 'last checked on', u'15/04/2016')\n",
"\n",
"(u'v20120710', 'is latest? ', None, 'last checked on', None)\n",
"\n",
"Latest available version on raijin is\n",
"\n",
"/g/data/ua6/unofficial-ESG-replica/tmp/tree/aims3.llnl.gov/thredds/fileServer/cmip5_css02_data/cmip5/output1/MIROC/MIROC5/rcp45/mon/atmos/Amon/r3i1p1/clt/1\n",
"\n",
"[u'clt_Amon_MIROC5_rcp45_r3i1p1_200601-210012.nc']\n",
"\n",
"(u'clt_Amon_MIROC5_rcp45_r3i1p1_200601-210012.nc', u'5fb250da-24ac-4d8f-87f7-9d629a78a049')\n",
"\n",
"(u'v20110801', 'is latest? ', False, 'last checked on', u'15/04/2016')\n",
"\n",
"(u'v20120710', 'is latest? ', None, 'last checked on', None)\n",
"\n",
"Latest available version on raijin is\n",
"\n",
"/g/data/ua6/unofficial-ESG-replica/tmp/tree/aims3.llnl.gov/thredds/fileServer/cmip5_css02_data/cmip5/output1/MIROC/MIROC5/rcp45/mon/atmos/Amon/r1i1p1/clt/1\n",
"\n",
"[u'clt_Amon_MIROC5_rcp45_r1i1p1_200601-210012.nc']\n",
"\n",
"(u'clt_Amon_MIROC5_rcp45_r1i1p1_200601-210012.nc', u'52e0391e-d08f-4f05-9b05-aea803b277d7')\n",
"\n",
"(u'v20120710', 'is latest? ', None, 'last checked on', None)\n",
"\n",
"Latest available version on raijin is\n",
"\n",
"/g/data/ua6/unofficial-ESG-replica/tmp/tree/aims3.llnl.gov/thredds/fileServer/cmip5_css02_data/cmip5/output1/MIROC/MIROC5/rcp45/mon/atmos/Amon/r2i1p1/clt/1\n",
"\n",
"[u'clt_Amon_MIROC5_rcp45_r2i1p1_200601-210012.nc']\n",
"\n",
"(u'clt_Amon_MIROC5_rcp45_r2i1p1_200601-210012.nc', u'0495b62b-6394-45d6-a1d6-6fd44c529115')\n",
"\n"
]
}
],
"source": [
"for o in new_results:\n",
" for v in o.versions:\n",
" print(v.version,\"is latest? \",v.is_latest,\"last checked on\",v.checked_on)\n",
" print(\"\")\n",
" if v==o.latest()[0]:\n",
" print(\"Latest available version on raijin is\")\n",
" print(\"\")\n",
" print(v.path)\n",
" print(\"\")\n",
" print(v.filenames())\n",
" print(\"\")\n",
" f=v.files[0]\n",
" print(f.filename,f.tracking_id)\n",
" print(\"\")\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/automaton.infiltration.ipynb | 1 | 127363 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# _`automaton`_`.infiltration`\n",
"\n",
"Create the (accessible part of the) infiltration product of two automata. In a way the infiltration product combines the conjunction (synchronized) and the shuffle product.\n",
"\n",
"Preconditions:\n",
"- all the labelsets are letterized\n",
"\n",
"See also:\n",
"- [conjunction](automaton.conjunction.ipynb)\n",
"- [shuffle](automaton.shuffle.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Examples"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
]
},
{
"data": {
"application/javascript": [
"IPython.load_extensions(\"AutomatonD3Widget\")"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\{\\ldots\\}\\rightarrow\\mathsf{Series}[\\{\\ldots\\}\\rightarrow\\mathbb{Z}]$"
],
"text/plain": [
"letterset<char_letters()>, seriesset<letterset<char_letters()>, z>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import vcsn\n",
"c = vcsn.context('lal_char, seriesset<lal_char, z>')\n",
"std = lambda exp: c.expression(exp).standard()\n",
"c"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following simple example aims at emphasizing that the transitions of the infiltration combine those of the shuffle and the conjunction products."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"text/plain": [
"mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(x)>, z>>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = std(\"<x>a\"); x"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
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"mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xy)>, z>>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = std(\"<y>a\"); y"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
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"Don't be mistaken though: if in this example the sum of the shuffle and conjunction products indeed match the infiltration product, this no longer applies to larger automata. In the following example (which nicely highlights the features of these three types of product) the transition from $(1, 0)$ to $(2, 1)$ would be missing."
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"tuple_automaton<mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xy)>, z>>, mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(x)>, z>>, mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xy)>, z>>>"
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"### Associativity\n",
"This operator is associative."
]
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"x = std('<x>a')\n",
"y = std('<y>a')\n",
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"text/plain": [
"tuple_automaton<mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xyz)>, z>>, tuple_automaton<mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xy)>, z>>, mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xy)>, z>>, mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xy)>, z>>>, mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xyz)>, z>>>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = x.infiltration(y).infiltration(z); a"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
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"text/plain": [
"tuple_automaton<mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xyz)>, z>>, mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xy)>, z>>, tuple_automaton<mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xyz)>, z>>, mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xy)>, z>>, mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xyz)>, z>>>>"
]
},
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],
"source": [
"b = x.infiltration(y.infiltration(z)); b"
]
},
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{
"data": {
"text/plain": [
"True"
]
},
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"source": [
"a.strip().is_isomorphic(b.strip())"
]
},
{
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"metadata": {},
"source": [
"### Variadicity"
]
},
{
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"metadata": {},
"source": [
"As a convenience, `infiltration` is variadic: it may accept more than two arguments. However, it's (currently) only a wrapper around repeated calls to the binary operation (as can be seen by the parentheses in the state names below)."
]
},
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"text/plain": [
"tuple_automaton<mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xyz)>, z>>, tuple_automaton<mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xy)>, z>>, mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xy)>, z>>, mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xy)>, z>>>, mutable_automaton<letterset<char_letters(a)>, seriesset<letterset<char_letters(xyz)>, z>>>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c = x.infiltration(y, z); c"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c.strip().is_isomorphic(a.strip())"
]
}
],
"metadata": {
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"display_name": "Python 3",
"language": "python",
"name": "python3"
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| gpl-3.0 |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/ml_ops/stage2/mlops_experimentation.ipynb | 1 | 118788 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "copyright"
},
"outputs": [],
"source": [
"# Copyright 2021 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": {
"id": "title:generic,gcp"
},
"source": [
"# E2E ML on GCP: MLOps stage 2 : experimentation\n",
"\n",
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/mlops_experimentation.ipynb\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/mlops_experimentation.ipynb\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\\\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/stage2/mlops_experimentation.ipynb\">\n",
" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
" Open in Vertex AI Workbench\n",
" </a>\n",
" </td>\n",
"</table>\n",
"<br/><br/><br/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "overview:mlops"
},
"source": [
"## Overview\n",
"\n",
"\n",
"This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 2 : experimentation."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dataset:bq,chicago,lbn"
},
"source": [
"### Dataset\n",
"\n",
"The dataset used for this tutorial is the [Chicago Taxi](https://www.kaggle.com/chicago/chicago-taxi-trips-bq). The version of the dataset you will use in this tutorial is stored in a public BigQuery table. The trained model predicts whether someone would leave a tip for a taxi fare."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "objective:mlops,stage2,tabular"
},
"source": [
"### Objective\n",
"\n",
"In this tutorial, you create a MLOps stage 2: experimentation process.\n",
"\n",
"This tutorial uses the following Vertex AI:\n",
"\n",
"- `Vertex AI Datasets`\n",
"- `Vertex AI Models`\n",
"- `Vertex AI AutoML`\n",
"- `Vertex AI Training`\n",
"- `Vertex AI TensorBoard`\n",
"- `Vertex AI Vizier`\n",
"- `Vertex AI Batch Prediction`\n",
"\n",
"The steps performed include:\n",
"\n",
"- Review the `Dataset` resource created during stage 1.\n",
"- Train an AutoML tabular binary classifier model in the background.\n",
"- Build the experimental model architecture.\n",
"- Construct a custom training package for the `Dataset` resource.\n",
"- Test the custom training package locally.\n",
"- Test the custom training package in the cloud with Vertex AI Training.\n",
"- Hyperparameter tune the model training with Vertex AI Vizier.\n",
"- Train the custom model with Vertex AI Training.\n",
"- Add a serving function for online/batch prediction to the custom model.\n",
"- Test the custom model with the serving function.\n",
"- Evaluate the custom model using Vertex AI Batch Prediction\n",
"- Wait for the AutoML training job to complete.\n",
"- Evaluate the AutoML model using Vertex AI Batch Prediction with the same evaluation slices as the custom model.\n",
"- Set the evaluation results of the AutoML model as the baseline.\n",
"- If the evaluation of the custom model is below baseline, continue to experiment with the custom model.\n",
"- If the evaluation of the custom model is above baseline, save the model as the first best model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "recommendation:mlops,stage2,tabular"
},
"source": [
"### Recommendations\n",
"\n",
"When doing E2E MLOps on Google Cloud for experimentation, the following best practices with structured (tabular) data are recommended:\n",
"\n",
" - Determine a baseline evaluation using AutoML.\n",
" - Design and build a model architecture.\n",
" - Upload the untrained model architecture as a Vertex AI Model resource.\n",
"\n",
"\n",
" - Construct a training package that can be ran locally and as a Vertex AI Training job.\n",
" - Decompose the training package into: data, model, train and task Python modules.\n",
" - Obtain the location of the transformed training data from the user metadata of the Vertex AI Dataset resource.\n",
" - Obtain the location of the model artifacts from the Vertex AI Model resource.\n",
" - Include in the training package initializing a Vertex AI Experiment and corresponding run.\n",
" - Log hyperparameters and training parameters for the experiment.\n",
" - Add callbacks for early stop, TensorBoard, and hyperparameter tuning, where hyperparameter tuning is a command-line option.\n",
"\n",
"\n",
" - Test the training package locally with a small number of epochs.\n",
" - Test the training package with Vertex AI Training.\n",
" - Do hyperparameter tuning with Vertex AI Hyperparameter Tuning.\n",
" - Do full training of the custom model with Vertex AI Training.\n",
" - Log the hyperparameter values for the experiment/run.\n",
"\n",
"\n",
" - Evaluate the custom model.\n",
" - Single evaluation slice, same metrics as AutoML\n",
" - Add evaluation to the training package and return the results in a file in the Cloud Storage bucket used for training\n",
" - Custom evaluation slices, custom metrics\n",
" - Evaluate custom evaluation slices as a Vertex AI Batch Prediction for both AutoML and custom model\n",
" - Perform custom metrics on the results from the batch job\n",
"\n",
"\n",
" - Compare custom model metrics against the AutoML baseline\n",
" - If less than baseline, then continue to experiment\n",
" - If greater then baseline, then upload model as the new baseline and save evaluation results with the model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "install_mlops"
},
"source": [
"## Installations\n",
"\n",
"Install *one time* the packages for executing the MLOps notebooks."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "install_mlops"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"# The Vertex AI Workbench Notebook product has specific requirements\n",
"IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\")\n",
"IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
" \"/opt/deeplearning/metadata/env_version\"\n",
")\n",
"\n",
"# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
"USER_FLAG = \"\"\n",
"if IS_WORKBENCH_NOTEBOOK:\n",
" USER_FLAG = \"--user\"\n",
"\n",
"ONCE_ONLY = False\n",
"if ONCE_ONLY:\n",
" ! pip3 install -U tensorflow==2.5 $USER_FLAG\n",
" ! pip3 install -U tensorflow-data-validation==1.2 $USER_FLAG\n",
" ! pip3 install -U tensorflow-transform==1.2 $USER_FLAG\n",
" ! pip3 install -U tensorflow-io==0.18 $USER_FLAG\n",
" ! pip3 install --upgrade google-cloud-aiplatform[tensorboard] $USER_FLAG\n",
" ! pip3 install --upgrade google-cloud-pipeline-components $USER_FLAG\n",
" ! pip3 install --upgrade google-cloud-bigquery $USER_FLAG\n",
" ! pip3 install --upgrade google-cloud-logging $USER_FLAG\n",
" ! pip3 install --upgrade apache-beam[gcp] $USER_FLAG\n",
" ! pip3 install --upgrade pyarrow $USER_FLAG\n",
" ! pip3 install --upgrade cloudml-hypertune $USER_FLAG\n",
" ! pip3 install --upgrade kfp $USER_FLAG\n",
" ! pip3 install --upgrade torchvision $USER_FLAG\n",
" ! pip3 install --upgrade rpy2 $USER_FLAG"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "restart"
},
"source": [
"### Restart the kernel\n",
"\n",
"Once you've installed the additional packages, you need to restart the notebook kernel so it can find the packages."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "restart"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"if not os.getenv(\"IS_TESTING\"):\n",
" # Automatically restart kernel after installs\n",
" import IPython\n",
"\n",
" app = IPython.Application.instance()\n",
" app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2721ef0202d9"
},
"source": [
"## Before you begin\n",
"\n",
"### Set up your Google Cloud project\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
"\n",
"1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
"\n",
"1. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). \n",
"\n",
"1. If you are running this notebook locally, you will need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
"\n",
"1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
"Cloud SDK uses the right project for all the commands in this notebook.\n",
"\n",
"**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "project_id"
},
"source": [
"#### Set your project ID\n",
"\n",
"**If you don't know your project ID**, you may be able to get your project ID using `gcloud`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_project_id"
},
"outputs": [],
"source": [
"PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_project_id"
},
"outputs": [],
"source": [
"if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
" # Get your GCP project id from gcloud\n",
" shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n",
" PROJECT_ID = shell_output[0]\n",
" print(\"Project ID:\", PROJECT_ID)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_gcloud_project_id"
},
"outputs": [],
"source": [
"! gcloud config set project $PROJECT_ID"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "region"
},
"source": [
"#### Region\n",
"\n",
"You can also change the `REGION` variable, which is used for operations\n",
"throughout the rest of this notebook. Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.\n",
"\n",
"- Americas: `us-central1`\n",
"- Europe: `europe-west4`\n",
"- Asia Pacific: `asia-east1`\n",
"\n",
"You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n",
"\n",
"Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "region"
},
"outputs": [],
"source": [
"REGION = \"[your-region]\" # @param {type:\"string\"}\n",
"if REGION == \"[your-region]\":\n",
" REGION = \"us-central1\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "timestamp"
},
"source": [
"#### Timestamp\n",
"\n",
"If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "timestamp"
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "648aa9824ac6"
},
"source": [
"### Authenticate your Google Cloud account\n",
"\n",
"**If you are using Vertex AI Workbench Notebooks**, your environment is already\n",
"authenticated. Skip this step.\n",
"\n",
"**If you are using Colab**, run the cell below and follow the instructions\n",
"when prompted to authenticate your account via oAuth.\n",
"\n",
"**Otherwise**, follow these steps:\n",
"\n",
"1. In the Cloud Console, go to the [**Create service account key**\n",
" page](https://console.cloud.google.com/apis/credentials/serviceaccountkey).\n",
"\n",
"2. Click **Create service account**.\n",
"\n",
"3. In the **Service account name** field, enter a name, and\n",
" click **Create**.\n",
"\n",
"4. In the **Grant this service account access to project** section, click the **Role** drop-down list. Type \"Vertex AI\"\n",
"into the filter box, and select\n",
" **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
"\n",
"5. Click *Create*. A JSON file that contains your key downloads to your\n",
"local environment.\n",
"\n",
"6. Enter the path to your service account key as the\n",
"`GOOGLE_APPLICATION_CREDENTIALS` variable in the cell below and run the cell."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "535223fa4b84"
},
"outputs": [],
"source": [
"# If you are running this notebook in Colab, run this cell and follow the\n",
"# instructions to authenticate your GCP account. This provides access to your\n",
"# Cloud Storage bucket and lets you submit training jobs and prediction\n",
"# requests.\n",
"\n",
"import os\n",
"import sys\n",
"\n",
"# If on Vertex AI Workbench, then don't execute this code\n",
"IS_COLAB = False\n",
"if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
" \"DL_ANACONDA_HOME\"\n",
"):\n",
" if \"google.colab\" in sys.modules:\n",
" IS_COLAB = True\n",
" from google.colab import auth as google_auth\n",
"\n",
" google_auth.authenticate_user()\n",
"\n",
" # If you are running this notebook locally, replace the string below with the\n",
" # path to your service account key and run this cell to authenticate your GCP\n",
" # account.\n",
" elif not os.getenv(\"IS_TESTING\"):\n",
" %env GOOGLE_APPLICATION_CREDENTIALS ''"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bucket:mbsdk"
},
"source": [
"### Create a Cloud Storage bucket\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"When you initialize the Vertex SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n",
"\n",
"Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bucket"
},
"outputs": [],
"source": [
"BUCKET_NAME = \"gs://[your-bucket-name]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_bucket"
},
"outputs": [],
"source": [
"if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
" BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_bucket"
},
"source": [
"**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_bucket"
},
"outputs": [],
"source": [
"! gsutil mb -l $REGION $BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "validate_bucket"
},
"source": [
"Finally, validate access to your Cloud Storage bucket by examining its contents:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "validate_bucket"
},
"outputs": [],
"source": [
"! gsutil ls -al $BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "set_service_account"
},
"source": [
"#### Service Account\n",
"\n",
"**If you don't know your service account**, try to get your service account using `gcloud` command by executing the second cell below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_service_account"
},
"outputs": [],
"source": [
"SERVICE_ACCOUNT = \"[your-service-account]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_service_account"
},
"outputs": [],
"source": [
"if (\n",
" SERVICE_ACCOUNT == \"\"\n",
" or SERVICE_ACCOUNT is None\n",
" or SERVICE_ACCOUNT == \"[your-service-account]\"\n",
"):\n",
" # Get your service account from gcloud\n",
" if not IS_COLAB:\n",
" shell_output = !gcloud auth list 2>/dev/null\n",
" SERVICE_ACCOUNT = shell_output[2].replace(\"*\", \"\").strip()\n",
"\n",
" if IS_COLAB:\n",
" shell_output = ! gcloud projects describe $PROJECT_ID\n",
" project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
" SERVICE_ACCOUNT = f\"{project_number}[email protected]\"\n",
"\n",
" print(\"Service Account:\", SERVICE_ACCOUNT)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "setup_vars"
},
"source": [
"### Set up variables\n",
"\n",
"Next, set up some variables used throughout the tutorial.\n",
"### Import libraries and define constants"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_aip:mbsdk"
},
"outputs": [],
"source": [
"import google.cloud.aiplatform as aip"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "import_tf"
},
"source": [
"#### Import TensorFlow\n",
"\n",
"Import the TensorFlow package into your Python environment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_tf"
},
"outputs": [],
"source": [
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "import_tft"
},
"source": [
"#### Import TensorFlow Transform\n",
"\n",
"Import the TensorFlow Transform (TFT) package into your Python environment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_tft"
},
"outputs": [],
"source": [
"import tensorflow_transform as tft"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "import_tfdv"
},
"source": [
"#### Import TensorFlow Data Validation\n",
"\n",
"Import the TensorFlow Data Validation (TFDV) package into your Python environment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_tfdv"
},
"outputs": [],
"source": [
"import tensorflow_data_validation as tfdv"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "init_aip:mbsdk,all"
},
"source": [
"### Initialize Vertex AI SDK for Python\n",
"\n",
"Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "init_aip:mbsdk,all"
},
"outputs": [],
"source": [
"aip.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_NAME)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "accelerators:training,prediction,ngpu,mbsdk"
},
"source": [
"#### Set hardware accelerators\n",
"\n",
"You can set hardware accelerators for training and prediction.\n",
"\n",
"Set the variables `TRAIN_GPU/TRAIN_NGPU` and `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n",
"\n",
" (aip.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
"\n",
"\n",
"Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
"\n",
"Learn more about [hardware accelerator support for your region](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators).\n",
"\n",
"*Note*: TF releases before 2.3 for GPU support will fail to load the custom model in this tutorial. It is a known issue and fixed in TF 2.3. This is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "accelerators:training,prediction,ngpu,mbsdk"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"if os.getenv(\"IS_TESTING_TRAIN_GPU\"):\n",
" TRAIN_GPU, TRAIN_NGPU = (\n",
" aip.gapic.AcceleratorType.NVIDIA_TESLA_K80,\n",
" int(os.getenv(\"IS_TESTING_TRAIN_GPU\")),\n",
" )\n",
"else:\n",
" TRAIN_GPU, TRAIN_NGPU = (aip.gapic.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
"\n",
"if os.getenv(\"IS_TESTING_DEPLOY_GPU\"):\n",
" DEPLOY_GPU, DEPLOY_NGPU = (\n",
" aip.gapic.AcceleratorType.NVIDIA_TESLA_K80,\n",
" int(os.getenv(\"IS_TESTING_DEPLOY_GPU\")),\n",
" )\n",
"else:\n",
" DEPLOY_GPU, DEPLOY_NGPU = (None, None)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "container:training,prediction"
},
"source": [
"#### Set pre-built containers\n",
"\n",
"Set the pre-built Docker container image for training and prediction.\n",
"\n",
"\n",
"For the latest list, see [Pre-built containers for training](https://cloud.google.com/ai-platform-unified/docs/training/pre-built-containers).\n",
"\n",
"\n",
"For the latest list, see [Pre-built containers for prediction](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "container:training,prediction"
},
"outputs": [],
"source": [
"if os.getenv(\"IS_TESTING_TF\"):\n",
" TF = os.getenv(\"IS_TESTING_TF\")\n",
"else:\n",
" TF = \"2.5\".replace(\".\", \"-\")\n",
"\n",
"if TF[0] == \"2\":\n",
" if TRAIN_GPU:\n",
" TRAIN_VERSION = \"tf-gpu.{}\".format(TF)\n",
" else:\n",
" TRAIN_VERSION = \"tf-cpu.{}\".format(TF)\n",
" if DEPLOY_GPU:\n",
" DEPLOY_VERSION = \"tf2-gpu.{}\".format(TF)\n",
" else:\n",
" DEPLOY_VERSION = \"tf2-cpu.{}\".format(TF)\n",
"else:\n",
" if TRAIN_GPU:\n",
" TRAIN_VERSION = \"tf-gpu.{}\".format(TF)\n",
" else:\n",
" TRAIN_VERSION = \"tf-cpu.{}\".format(TF)\n",
" if DEPLOY_GPU:\n",
" DEPLOY_VERSION = \"tf-gpu.{}\".format(TF)\n",
" else:\n",
" DEPLOY_VERSION = \"tf-cpu.{}\".format(TF)\n",
"\n",
"TRAIN_IMAGE = \"{}-docker.pkg.dev/vertex-ai/training/{}:latest\".format(\n",
" REGION.split(\"-\")[0], TRAIN_VERSION\n",
")\n",
"DEPLOY_IMAGE = \"{}-docker.pkg.dev/vertex-ai/prediction/{}:latest\".format(\n",
" REGION.split(\"-\")[0], DEPLOY_VERSION\n",
")\n",
"\n",
"print(\"Training:\", TRAIN_IMAGE, TRAIN_GPU, TRAIN_NGPU)\n",
"print(\"Deployment:\", DEPLOY_IMAGE, DEPLOY_GPU, DEPLOY_NGPU)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "machine:training,prediction"
},
"source": [
"#### Set machine type\n",
"\n",
"Next, set the machine type to use for training and prediction.\n",
"\n",
"- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure the compute resources for the VMs you will use for for training and prediction.\n",
" - `machine type`\n",
" - `n1-standard`: 3.75GB of memory per vCPU.\n",
" - `n1-highmem`: 6.5GB of memory per vCPU\n",
" - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
" - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
"\n",
"*Note: The following is not supported for training:*\n",
"\n",
" - `standard`: 2 vCPUs\n",
" - `highcpu`: 2, 4 and 8 vCPUs\n",
"\n",
"*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "machine:training,prediction"
},
"outputs": [],
"source": [
"if os.getenv(\"IS_TESTING_TRAIN_MACHINE\"):\n",
" MACHINE_TYPE = os.getenv(\"IS_TESTING_TRAIN_MACHINE\")\n",
"else:\n",
" MACHINE_TYPE = \"n1-standard\"\n",
"\n",
"VCPU = \"4\"\n",
"TRAIN_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n",
"print(\"Train machine type\", TRAIN_COMPUTE)\n",
"\n",
"if os.getenv(\"IS_TESTING_DEPLOY_MACHINE\"):\n",
" MACHINE_TYPE = os.getenv(\"IS_TESTING_DEPLOY_MACHINE\")\n",
"else:\n",
" MACHINE_TYPE = \"n1-standard\"\n",
"\n",
"VCPU = \"4\"\n",
"DEPLOY_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n",
"print(\"Deploy machine type\", DEPLOY_COMPUTE)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "find_dataset:bq"
},
"source": [
"### Retrieve the dataset from stage 1\n",
"\n",
"Next, retrieve the dataset you created during stage 1 with the helper function `find_dataset()`. This helper function finds all the datasets whose display name matches the specified prefix and import format (e.g., bq). Finally it sorts the matches by create time and returns the latest version."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "find_dataset:bq"
},
"outputs": [],
"source": [
"def find_dataset(display_name_prefix, import_format):\n",
" matches = []\n",
" datasets = aip.TabularDataset.list()\n",
" for dataset in datasets:\n",
" if dataset.display_name.startswith(display_name_prefix):\n",
" try:\n",
" if (\n",
" \"bq\" == import_format\n",
" and dataset.to_dict()[\"metadata\"][\"inputConfig\"][\"bigquerySource\"]\n",
" ):\n",
" matches.append(dataset)\n",
" if (\n",
" \"csv\" == import_format\n",
" and dataset.to_dict()[\"metadata\"][\"inputConfig\"][\"gcsSource\"]\n",
" ):\n",
" matches.append(dataset)\n",
" except:\n",
" pass\n",
"\n",
" create_time = None\n",
" for match in matches:\n",
" if create_time is None or match.create_time > create_time:\n",
" create_time = match.create_time\n",
" dataset = match\n",
"\n",
" return dataset\n",
"\n",
"\n",
"dataset = find_dataset(\"Chicago Taxi\", \"bq\")\n",
"\n",
"print(dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "load_dataset_user_metadata"
},
"source": [
"### Load dataset's user metadata\n",
"\n",
"Load the user metadata for the dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "load_dataset_user_metadata"
},
"outputs": [],
"source": [
"import json\n",
"\n",
"try:\n",
" with tf.io.gfile.GFile(\n",
" \"gs://\" + dataset.labels[\"user_metadata\"] + \"/metadata.jsonl\", \"r\"\n",
" ) as f:\n",
" metadata = json.load(f)\n",
"\n",
" print(metadata)\n",
"except:\n",
" print(\"no metadata\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_automl_pipeline:tabular,lbn"
},
"source": [
"### Create and run training pipeline\n",
"\n",
"To train an AutoML model, you perform two steps: 1) create a training pipeline, and 2) run the pipeline.\n",
"\n",
"#### Create training pipeline\n",
"\n",
"An AutoML training pipeline is created with the `AutoMLTabularTrainingJob` class, with the following parameters:\n",
"\n",
"- `display_name`: The human readable name for the `TrainingJob` resource.\n",
"- `optimization_prediction_type`: The type task to train the model for.\n",
" - `classification`: A tabuar classification model.\n",
" - `regression`: A tabular regression model.\n",
"- `column_transformations`: (Optional): Transformations to apply to the input columns\n",
"- `optimization_objective`: The optimization objective to minimize or maximize.\n",
" - binary classification:\n",
" - `minimize-log-loss`\n",
" - `maximize-au-roc`\n",
" - `maximize-au-prc`\n",
" - `maximize-precision-at-recall`\n",
" - `maximize-recall-at-precision`\n",
" - multi-class classification:\n",
" - `minimize-log-loss`\n",
" - regression:\n",
" - `minimize-rmse`\n",
" - `minimize-mae`\n",
" - `minimize-rmsle`\n",
"\n",
"The instantiated object is the DAG (directed acyclic graph) for the training pipeline."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_automl_pipeline:tabular,lbn"
},
"outputs": [],
"source": [
"dag = aip.AutoMLTabularTrainingJob(\n",
" display_name=\"chicago_\" + TIMESTAMP,\n",
" optimization_prediction_type=\"classification\",\n",
" optimization_objective=\"minimize-log-loss\",\n",
")\n",
"\n",
"print(dag)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "run_automl_pipeline:async,tabular"
},
"source": [
"#### Run the training pipeline\n",
"\n",
"Next, you run the DAG to start the training job by invoking the method `run`, with the following parameters:\n",
"\n",
"- `dataset`: The `Dataset` resource to train the model.\n",
"- `model_display_name`: The human readable name for the trained model.\n",
"- `training_fraction_split`: The percentage of the dataset to use for training.\n",
"- `test_fraction_split`: The percentage of the dataset to use for test (holdout data).\n",
"- `validation_fraction_split`: The percentage of the dataset to use for validation.\n",
"- `target_column`: The name of the column to train as the label.\n",
"- `budget_milli_node_hours`: (optional) Maximum training time specified in unit of millihours (1000 = hour).\n",
"- `disable_early_stopping`: If `True`, training maybe completed before using the entire budget if the service believes it cannot further improve on the model objective measurements.\n",
"\n",
"The `run` method when completed returns the `Model` resource.\n",
"\n",
"The execution of the training pipeline will take upto 180 minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "run_automl_pipeline:async,tabular"
},
"outputs": [],
"source": [
"async_model = dag.run(\n",
" dataset=dataset,\n",
" model_display_name=\"chicago_\" + TIMESTAMP,\n",
" training_fraction_split=0.8,\n",
" validation_fraction_split=0.1,\n",
" test_fraction_split=0.1,\n",
" budget_milli_node_hours=8000,\n",
" disable_early_stopping=False,\n",
" target_column=\"tip_bin\",\n",
" sync=False,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "start_experiment"
},
"source": [
"### Create experiment for tracking training related metadata\n",
"\n",
"Setup tracking the parameters (configuration) and metrics (results) for each experiment:\n",
"\n",
"- `aip.init()` - Create an experiment instance\n",
"- `aip.start_run()` - Track a specific run within the experiment.\n",
"\n",
"Learn more about [Introduction to Vertex AI ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata/introduction)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "start_experiment"
},
"outputs": [],
"source": [
"EXPERIMENT_NAME = \"chicago-\" + TIMESTAMP\n",
"aip.init(experiment=EXPERIMENT_NAME)\n",
"aip.start_run(\"run-1\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_tensorboard_instance"
},
"source": [
"### Create a Vertex AI TensorBoard instance\n",
"\n",
"Create a Vertex AI TensorBoard instance to use TensorBoard in conjunction with Vertex AI Training for custom model training.\n",
"\n",
"Learn more about [Get started with Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_tensorboard_instance"
},
"outputs": [],
"source": [
"TENSORBOARD_DISPLAY_NAME = \"chicago_\" + TIMESTAMP\n",
"tensorboard = aip.Tensorboard.create(display_name=TENSORBOARD_DISPLAY_NAME)\n",
"tensorboard_resource_name = tensorboard.gca_resource.name\n",
"print(\"TensorBoard resource name:\", tensorboard_resource_name)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_input_layer:tabular"
},
"source": [
"### Create the input layer for your custom model\n",
"\n",
"Next, you create the input layer for your custom tabular model, based on the data types of each feature."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_input_layer:tabular"
},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Input\n",
"\n",
"\n",
"def create_model_inputs(\n",
" numeric_features=None, categorical_features=None, embedding_features=None\n",
"):\n",
" inputs = {}\n",
" for feature_name in numeric_features:\n",
" inputs[feature_name] = Input(name=feature_name, shape=[], dtype=tf.float32)\n",
" for feature_name in categorical_features:\n",
" inputs[feature_name] = Input(name=feature_name, shape=[], dtype=tf.int64)\n",
" for feature_name in embedding_features:\n",
" inputs[feature_name] = Input(name=feature_name, shape=[], dtype=tf.int64)\n",
"\n",
" return inputs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "make_input_layer:tabular"
},
"outputs": [],
"source": [
"input_layers = create_model_inputs(\n",
" numeric_features=metadata[\"numeric_features\"],\n",
" categorical_features=metadata[\"categorical_features\"],\n",
" embedding_features=metadata[\"embedding_features\"],\n",
")\n",
"\n",
"print(input_layers)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_binary_classifier:tabular"
},
"source": [
"### Create the binary classifier custom model\n",
"\n",
"Next, you create your binary classifier custom tabular model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_binary_classifier:tabular"
},
"outputs": [],
"source": [
"from math import sqrt\n",
"\n",
"from tensorflow.keras import Model, Sequential\n",
"from tensorflow.keras.layers import (Activation, Concatenate, Dense, Embedding,\n",
" experimental)\n",
"\n",
"\n",
"def create_binary_classifier(\n",
" input_layers,\n",
" tft_output,\n",
" metaparams,\n",
" numeric_features,\n",
" categorical_features,\n",
" embedding_features,\n",
"):\n",
" layers = []\n",
" for feature_name in input_layers:\n",
" if feature_name in embedding_features:\n",
" vocab_size = tft_output.vocabulary_size_by_name(feature_name)\n",
" embedding_size = int(sqrt(vocab_size))\n",
" embedding_output = Embedding(\n",
" input_dim=vocab_size + 1,\n",
" output_dim=embedding_size,\n",
" name=f\"{feature_name}_embedding\",\n",
" )(input_layers[feature_name])\n",
" layers.append(embedding_output)\n",
" elif feature_name in categorical_features:\n",
" vocab_size = tft_output.vocabulary_size_by_name(feature_name)\n",
" onehot_layer = experimental.preprocessing.CategoryEncoding(\n",
" num_tokens=vocab_size,\n",
" output_mode=\"binary\",\n",
" name=f\"{feature_name}_onehot\",\n",
" )(input_layers[feature_name])\n",
" layers.append(onehot_layer)\n",
" elif feature_name in numeric_features:\n",
" numeric_layer = tf.expand_dims(input_layers[feature_name], -1)\n",
" layers.append(numeric_layer)\n",
" else:\n",
" pass\n",
"\n",
" joined = Concatenate(name=\"combines_inputs\")(layers)\n",
" feedforward_output = Sequential(\n",
" [Dense(units, activation=\"relu\") for units in metaparams[\"hidden_units\"]],\n",
" name=\"feedforward_network\",\n",
" )(joined)\n",
" logits = Dense(units=1, name=\"logits\")(feedforward_output)\n",
" pred = Activation(\"sigmoid\")(logits)\n",
"\n",
" model = Model(inputs=input_layers, outputs=[pred])\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "make_binary_classifier:tabular"
},
"outputs": [],
"source": [
"TRANSFORM_ARTIFACTS_DIR = metadata[\"transform_artifacts_dir\"]\n",
"tft_output = tft.TFTransformOutput(TRANSFORM_ARTIFACTS_DIR)\n",
"\n",
"metaparams = {\"hidden_units\": [128, 64]}\n",
"aip.log_params(metaparams)\n",
"\n",
"model = create_binary_classifier(\n",
" input_layers,\n",
" tft_output,\n",
" metaparams,\n",
" numeric_features=metadata[\"numeric_features\"],\n",
" categorical_features=metadata[\"categorical_features\"],\n",
" embedding_features=metadata[\"embedding_features\"],\n",
")\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "visualize_model"
},
"source": [
"#### Visualize the model architecture\n",
"\n",
"Next, visualize the architecture of the custom model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "visualize_model"
},
"outputs": [],
"source": [
"tf.keras.utils.plot_model(model, show_shapes=True, show_dtype=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "save_model:gcs"
},
"source": [
"### Save model artifacts\n",
"\n",
"Next, save the model artifacts to your Cloud Storage bucket"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "save_model:gcs"
},
"outputs": [],
"source": [
"MODEL_DIR = f\"{BUCKET_NAME}/base_model\"\n",
"\n",
"model.save(MODEL_DIR)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "upload_model:vertex,base_model"
},
"source": [
"### Upload the local model to a Vertex AI Model resource\n",
"\n",
"Next, you upload your local custom model artifacts to Vertex AI to convert into a managed Vertex AI Model resource."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "upload_model:vertex,base_model"
},
"outputs": [],
"source": [
"vertex_custom_model = aip.Model.upload(\n",
" display_name=\"chicago_\" + TIMESTAMP,\n",
" artifact_uri=MODEL_DIR,\n",
" serving_container_image_uri=DEPLOY_IMAGE,\n",
" labels={\"base_model\": \"1\"},\n",
" sync=True,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "construct_training_package"
},
"source": [
"### Construct the training package\n",
"\n",
"#### Package layout\n",
"\n",
"Before you start training, you will look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
"\n",
"- PKG-INFO\n",
"- README.md\n",
"- setup.cfg\n",
"- setup.py\n",
"- trainer\n",
" - \\_\\_init\\_\\_.py\n",
" - task.py\n",
" - other Python scripts\n",
"\n",
"The files `setup.cfg` and `setup.py` are the instructions for installing the package into the operating environment of the Docker image.\n",
"\n",
"The file `trainer/task.py` is the Python script for executing the custom training job."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "construct_training_package"
},
"outputs": [],
"source": [
"# Make folder for Python training script\n",
"! rm -rf custom\n",
"! mkdir custom\n",
"\n",
"# Add package information\n",
"! touch custom/README.md\n",
"\n",
"setup_cfg = \"[egg_info]\\n\\ntag_build =\\n\\ntag_date = 0\"\n",
"! echo \"$setup_cfg\" > custom/setup.cfg\n",
"\n",
"setup_py = \"import setuptools\\n\\nsetuptools.setup(\\n\\n install_requires=[\\n\\n 'google-cloud-aiplatform',\\n\\n 'cloudml-hypertune',\\n\\n 'tensorflow_datasets==1.3.0',\\n\\n 'tensorflow==2.5',\\n\\n 'tensorflow_data_validation==1.2',\\n\\n ],\\n\\n packages=setuptools.find_packages())\"\n",
"! echo \"$setup_py\" > custom/setup.py\n",
"\n",
"pkg_info = \"Metadata-Version: 1.0\\n\\nName: Chicago Taxi tabular binary classifier\\n\\nVersion: 0.0.0\\n\\nSummary: Demostration training script\\n\\nHome-page: www.google.com\\n\\nAuthor: Google\\n\\nAuthor-email: [email protected]\\n\\nLicense: Public\\n\\nDescription: Demo\\n\\nPlatform: Vertex AI\"\n",
"! echo \"$pkg_info\" > custom/PKG-INFO\n",
"\n",
"# Make the training subfolder\n",
"! mkdir custom/trainer\n",
"! touch custom/trainer/__init__.py"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "transform_feature_spec"
},
"source": [
"#### Get feature specification for the preprocessed data\n",
"\n",
"Next, create the feature specification for the preprocessed data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "transform_feature_spec"
},
"outputs": [],
"source": [
"transform_feature_spec = tft_output.transformed_feature_spec()\n",
"print(transform_feature_spec)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "read_tfrecords_func"
},
"source": [
"### Load the transformed data into a tf.data.Dataset\n",
"\n",
"Next, you load the gzip TFRecords on Cloud Storage storage into a `tf.data.Dataset` generator. These functions are re-used when training the custom model using `Vertex Training`, so you save them to the python training package."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "read_tfrecords_func"
},
"outputs": [],
"source": [
"%%writefile custom/trainer/data.py\n",
"\n",
"import tensorflow as tf\n",
"\n",
"def _gzip_reader_fn(filenames):\n",
" \"\"\"Small utility returning a record reader that can read gzip'ed files.\"\"\"\n",
" return tf.data.TFRecordDataset(filenames, compression_type=\"GZIP\")\n",
"\n",
"\n",
"def get_dataset(file_pattern, feature_spec, label_column, batch_size=200):\n",
" \"\"\"Generates features and label for tuning/training.\n",
" Args:\n",
" file_pattern: input tfrecord file pattern.\n",
" feature_spec: a dictionary of feature specifications.\n",
" batch_size: representing the number of consecutive elements of returned\n",
" dataset to combine in a single batch\n",
" Returns:\n",
" A dataset that contains (features, indices) tuple where features is a\n",
" dictionary of Tensors, and indices is a single Tensor of label indices.\n",
" \"\"\"\n",
"\n",
" dataset = tf.data.experimental.make_batched_features_dataset(\n",
" file_pattern=file_pattern,\n",
" batch_size=batch_size,\n",
" features=feature_spec,\n",
" label_key=label_column,\n",
" reader=_gzip_reader_fn,\n",
" num_epochs=1,\n",
" drop_final_batch=True,\n",
" )\n",
"\n",
" return dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "read_tfrecords"
},
"outputs": [],
"source": [
"from custom.trainer import data\n",
"\n",
"TRANSFORMED_DATA_PREFIX = metadata[\"transformed_data_prefix\"]\n",
"LABEL_COLUMN = metadata[\"label_column\"]\n",
"\n",
"train_data_file_pattern = TRANSFORMED_DATA_PREFIX + \"/train/data-*.gz\"\n",
"val_data_file_pattern = TRANSFORMED_DATA_PREFIX + \"/val/data-*.gz\"\n",
"test_data_file_pattern = TRANSFORMED_DATA_PREFIX + \"/test/data-*.gz\"\n",
"\n",
"for input_features, target in data.get_dataset(\n",
" train_data_file_pattern, transform_feature_spec, LABEL_COLUMN, batch_size=3\n",
").take(1):\n",
" for key in input_features:\n",
" print(\n",
" f\"{key} {input_features[key].dtype}: {input_features[key].numpy().tolist()}\"\n",
" )\n",
" print(f\"target: {target.numpy().tolist()}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "test_model_input"
},
"source": [
"#### Test the model architecture with transformed input\n",
"\n",
"Next, test the model architecture with a sample of the transformed training input.\n",
"\n",
"*Note:* Since the model is untrained, the predictions should be random. Since this is a binary classifier, expect the predicted results ~0.5."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "test_model_input"
},
"outputs": [],
"source": [
"model(input_features)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "train_model_func"
},
"source": [
"## Develop and test the training scripts\n",
"\n",
"When experimenting, one typically develops and tests the training package locally, before moving to training in the cloud.\n",
"\n",
"### Create training script\n",
"\n",
"Next, you write the Python script for compiling and training the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "train_model_func"
},
"outputs": [],
"source": [
"%%writefile custom/trainer/train.py\n",
"\n",
"from trainer import data\n",
"import tensorflow as tf\n",
"import logging\n",
"from hypertune import HyperTune\n",
"\n",
"def compile(model, hyperparams):\n",
" ''' Compile the model '''\n",
" optimizer = tf.keras.optimizers.Adam(learning_rate=hyperparams[\"learning_rate\"])\n",
" loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)\n",
" metrics = [tf.keras.metrics.BinaryAccuracy(name=\"accuracy\")]\n",
"\n",
" model.compile(optimizer=optimizer,loss=loss, metrics=metrics)\n",
" return model\n",
"\n",
"def warmup(\n",
" model,\n",
" hyperparams,\n",
" train_data_dir,\n",
" label_column,\n",
" transformed_feature_spec\n",
"):\n",
" ''' Warmup the initialized model weights '''\n",
"\n",
" train_dataset = data.get_dataset(\n",
" train_data_dir,\n",
" transformed_feature_spec,\n",
" label_column,\n",
" batch_size=hyperparams[\"batch_size\"],\n",
" )\n",
"\n",
" lr_inc = (hyperparams['end_learning_rate'] - hyperparams['start_learning_rate']) / hyperparams['num_epochs']\n",
"\n",
" def scheduler(epoch, lr):\n",
" if epoch == 0:\n",
" return hyperparams['start_learning_rate']\n",
" return lr + lr_inc\n",
"\n",
"\n",
" callbacks = [tf.keras.callbacks.LearningRateScheduler(scheduler)]\n",
"\n",
" logging.info(\"Model warmup started...\")\n",
" history = model.fit(\n",
" train_dataset,\n",
" epochs=hyperparams[\"num_epochs\"],\n",
" steps_per_epoch=hyperparams[\"steps\"],\n",
" callbacks=callbacks\n",
" )\n",
"\n",
" logging.info(\"Model warmup completed.\")\n",
" return history\n",
"\n",
"\n",
"def train(\n",
" model,\n",
" hyperparams,\n",
" train_data_dir,\n",
" val_data_dir,\n",
" label_column,\n",
" transformed_feature_spec,\n",
" log_dir,\n",
" tuning=False\n",
"):\n",
" ''' Train the model '''\n",
"\n",
" train_dataset = data.get_dataset(\n",
" train_data_dir,\n",
" transformed_feature_spec,\n",
" label_column,\n",
" batch_size=hyperparams[\"batch_size\"],\n",
" )\n",
"\n",
" val_dataset = data.get_dataset(\n",
" val_data_dir,\n",
" transformed_feature_spec,\n",
" label_column,\n",
" batch_size=hyperparams[\"batch_size\"],\n",
" )\n",
"\n",
" early_stop = tf.keras.callbacks.EarlyStopping(\n",
" monitor=hyperparams[\"early_stop\"][\"monitor\"], patience=hyperparams[\"early_stop\"][\"patience\"], restore_best_weights=True\n",
" )\n",
"\n",
" callbacks = [early_stop]\n",
"\n",
" if log_dir:\n",
" tensorboard = tf.keras.callbacks.TensorBoard(log_dir=log_dir)\n",
"\n",
" callbacks = callbacks.append(tensorboard)\n",
"\n",
" if tuning:\n",
" # Instantiate the HyperTune reporting object\n",
" hpt = HyperTune()\n",
"\n",
" # Reporting callback\n",
" class HPTCallback(tf.keras.callbacks.Callback):\n",
"\n",
" def on_epoch_end(self, epoch, logs=None):\n",
" hpt.report_hyperparameter_tuning_metric(\n",
" hyperparameter_metric_tag='val_loss',\n",
" metric_value=logs['val_loss'],\n",
" global_step=epoch\n",
" )\n",
"\n",
" if not callbacks:\n",
" callbacks = []\n",
" callbacks.append(HPTCallback())\n",
"\n",
" logging.info(\"Model training started...\")\n",
" history = model.fit(\n",
" train_dataset,\n",
" epochs=hyperparams[\"num_epochs\"],\n",
" validation_data=val_dataset,\n",
" callbacks=callbacks\n",
" )\n",
"\n",
" logging.info(\"Model training completed.\")\n",
" return history\n",
"\n",
"def evaluate(\n",
" model,\n",
" hyperparams,\n",
" test_data_dir,\n",
" label_column,\n",
" transformed_feature_spec\n",
"):\n",
" logging.info(\"Model evaluation started...\")\n",
" test_dataset = data.get_dataset(\n",
" test_data_dir,\n",
" transformed_feature_spec,\n",
" label_column,\n",
" hyperparams[\"batch_size\"],\n",
" )\n",
"\n",
" evaluation_metrics = model.evaluate(test_dataset)\n",
" logging.info(\"Model evaluation completed.\")\n",
"\n",
" return evaluation_metrics"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "train_model_local"
},
"source": [
"### Train the model locally\n",
"\n",
"Next, test the training package locally, by training with just a few epochs:\n",
"\n",
"- `num_epochs`: The number of epochs to pass to the training package.\n",
"- `compile()`: Compile the model for training.\n",
"- `warmup()`: Warmup the initialized model weights.\n",
"- `train()`: Train the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "train_model_local"
},
"outputs": [],
"source": [
"os.chdir(\"custom\")\n",
"\n",
"import logging\n",
"\n",
"from trainer import train\n",
"\n",
"TENSORBOARD_LOG_DIR = \"./logs\"\n",
"\n",
"logging.getLogger().setLevel(logging.INFO)\n",
"\n",
"hyperparams = {}\n",
"hyperparams[\"learning_rate\"] = 0.01\n",
"aip.log_params(hyperparams)\n",
"\n",
"train.compile(model, hyperparams)\n",
"\n",
"warmupparams = {}\n",
"warmupparams[\"start_learning_rate\"] = 0.0001\n",
"warmupparams[\"end_learning_rate\"] = 0.01\n",
"warmupparams[\"num_epochs\"] = 4\n",
"warmupparams[\"batch_size\"] = 64\n",
"warmupparams[\"steps\"] = 50\n",
"aip.log_params(warmupparams)\n",
"\n",
"train.warmup(\n",
" model, warmupparams, train_data_file_pattern, LABEL_COLUMN, transform_feature_spec\n",
")\n",
"\n",
"trainparams = {}\n",
"trainparams[\"num_epochs\"] = 5\n",
"trainparams[\"batch_size\"] = 64\n",
"trainparams[\"early_stop\"] = {\"monitor\": \"val_loss\", \"patience\": 5}\n",
"aip.log_params(trainparams)\n",
"\n",
"train.train(\n",
" model,\n",
" trainparams,\n",
" train_data_file_pattern,\n",
" val_data_file_pattern,\n",
" LABEL_COLUMN,\n",
" transform_feature_spec,\n",
" TENSORBOARD_LOG_DIR,\n",
")\n",
"\n",
"os.chdir(\"..\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eval_model_local"
},
"source": [
"### Evaluate the model locally\n",
"\n",
"Next, test the evaluation portion of the training package:\n",
"\n",
"\n",
"- `evaluate()`: Evaluate the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "eval_model_local"
},
"outputs": [],
"source": [
"os.chdir(\"custom\")\n",
"\n",
"from trainer import train\n",
"\n",
"evalparams = {}\n",
"evalparams[\"batch_size\"] = 64\n",
"\n",
"metrics = {}\n",
"metrics[\"loss\"], metrics[\"acc\"] = train.evaluate(\n",
" model, evalparams, test_data_file_pattern, LABEL_COLUMN, transform_feature_spec\n",
")\n",
"print(\"ACC\", metrics[\"acc\"], \"LOSS\", metrics[\"loss\"])\n",
"aip.log_metrics(metrics)\n",
"\n",
"os.chdir(\"..\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_model_get"
},
"source": [
"### Retrieve model from Vertex AI\n",
"\n",
"Next, create the Python script to retrieve your experimental model from Vertex AI."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_model_get"
},
"outputs": [],
"source": [
"%%writefile custom/trainer/model.py\n",
"\n",
"import google.cloud.aiplatform as aip\n",
"\n",
"def get(model_id):\n",
" model = aip.Model(model_id)\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_task_py"
},
"source": [
"### Create the task script for the Python training package\n",
"\n",
"Next, you create the `task.py` script for driving the training package. Some noteable steps include:\n",
"\n",
"- Command-line arguments:\n",
" - `model-id`: The resource ID of the `Model` resource you built during experimenting. This is the untrained model architecture.\n",
" - `dataset-id`: The resource ID of the `Dataset` resource to use for training.\n",
" - `experiment`: The name of the experiment.\n",
" - `run`: The name of the run within this experiment.\n",
" - `tensorboard-logdir`: The logging directory for Vertex AI Tensorboard.\n",
"\n",
"\n",
"- `get_data()`:\n",
" - Loads the Dataset resource into memory.\n",
" - Obtains the user metadata from the Dataset resource.\n",
" - From the metadata, obtain location of transformed data, transformation function and name of label column\n",
"\n",
"\n",
"- `get_model()`:\n",
" - Loads the Model resource into memory.\n",
" - Obtains location of model artifacts of the model architecture.\n",
" - Loads the model architecture.\n",
" - Compiles the model.\n",
"\n",
"\n",
"- `warmup_model()`:\n",
" - Warms up the initialized model weights\n",
"\n",
"\n",
"- `train_model()`:\n",
" - Train the model.\n",
"\n",
"\n",
"- `evaluate_model()`:\n",
" - Evaluates the model.\n",
" - Saves evaluation metrics to Cloud Storage bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_task_py"
},
"outputs": [],
"source": [
"%%writefile custom/trainer/task.py\n",
"import os\n",
"import argparse\n",
"import logging\n",
"import json\n",
"\n",
"import tensorflow as tf\n",
"import tensorflow_transform as tft\n",
"from tensorflow.python.client import device_lib\n",
"\n",
"import google.cloud.aiplatform as aip\n",
"\n",
"from trainer import data\n",
"from trainer import model as model_\n",
"from trainer import train\n",
"try:\n",
" from trainer import serving\n",
"except:\n",
" pass\n",
"\n",
"parser = argparse.ArgumentParser()\n",
"parser.add_argument('--model-dir', dest='model_dir',\n",
" default=os.getenv('AIP_MODEL_DIR'), type=str, help='Model dir.')\n",
"parser.add_argument('--model-id', dest='model_id',\n",
" default=None, type=str, help='Vertex Model ID.')\n",
"parser.add_argument('--dataset-id', dest='dataset_id',\n",
" default=None, type=str, help='Vertex Dataset ID.')\n",
"parser.add_argument('--lr', dest='lr',\n",
" default=0.001, type=float,\n",
" help='Learning rate.')\n",
"parser.add_argument('--start_lr', dest='start_lr',\n",
" default=0.0001, type=float,\n",
" help='Starting learning rate.')\n",
"parser.add_argument('--epochs', dest='epochs',\n",
" default=20, type=int,\n",
" help='Number of epochs.')\n",
"parser.add_argument('--steps', dest='steps',\n",
" default=200, type=int,\n",
" help='Number of steps per epoch.')\n",
"parser.add_argument('--batch_size', dest='batch_size',\n",
" default=16, type=int,\n",
" help='Batch size.')\n",
"parser.add_argument('--distribute', dest='distribute', type=str, default='single',\n",
" help='distributed training strategy')\n",
"parser.add_argument('--tensorboard-log-dir', dest='tensorboard_log_dir',\n",
" default=os.getenv('AIP_TENSORBOARD_LOG_DIR'), type=str,\n",
" help='Output file for tensorboard logs')\n",
"parser.add_argument('--experiment', dest='experiment',\n",
" default=None, type=str,\n",
" help='Name of experiment')\n",
"parser.add_argument('--project', dest='project',\n",
" default=None, type=str,\n",
" help='Name of project')\n",
"parser.add_argument('--run', dest='run',\n",
" default=None, type=str,\n",
" help='Name of run in experiment')\n",
"parser.add_argument('--evaluate', dest='evaluate',\n",
" default=False, type=bool,\n",
" help='Whether to perform evaluation')\n",
"parser.add_argument('--serving', dest='serving',\n",
" default=False, type=bool,\n",
" help='Whether to attach the serving function')\n",
"parser.add_argument('--tuning', dest='tuning',\n",
" default=False, type=bool,\n",
" help='Whether to perform hyperparameter tuning')\n",
"parser.add_argument('--warmup', dest='warmup',\n",
" default=False, type=bool,\n",
" help='Whether to perform warmup weight initialization')\n",
"args = parser.parse_args()\n",
"\n",
"\n",
"logging.getLogger().setLevel(logging.INFO)\n",
"logging.info('DEVICES' + str(device_lib.list_local_devices()))\n",
"\n",
"# Single Machine, single compute device\n",
"if args.distribute == 'single':\n",
" if tf.test.is_gpu_available():\n",
" strategy = tf.distribute.OneDeviceStrategy(device=\"/gpu:0\")\n",
" else:\n",
" strategy = tf.distribute.OneDeviceStrategy(device=\"/cpu:0\")\n",
" logging.info(\"Single device training\")\n",
"# Single Machine, multiple compute device\n",
"elif args.distribute == 'mirrored':\n",
" strategy = tf.distribute.MirroredStrategy()\n",
" logging.info(\"Mirrored Strategy distributed training\")\n",
"# Multi Machine, multiple compute device\n",
"elif args.distribute == 'multiworker':\n",
" strategy = tf.distribute.MultiWorkerMirroredStrategy()\n",
" logging.info(\"Multi-worker Strategy distributed training\")\n",
" logging.info('TF_CONFIG = {}'.format(os.environ.get('TF_CONFIG', 'Not found')))\n",
"logging.info('num_replicas_in_sync = {}'.format(strategy.num_replicas_in_sync))\n",
"\n",
"# Initialize the run for this experiment\n",
"if args.experiment:\n",
" logging.info(\"Initialize experiment: {}\".format(args.experiment))\n",
" aip.init(experiment=args.experiment, project=args.project)\n",
" aip.start_run(args.run)\n",
"\n",
"metadata = {}\n",
"\n",
"def get_data():\n",
" ''' Get the preprocessed training data '''\n",
" global train_data_file_pattern, val_data_file_pattern, test_data_file_pattern\n",
" global label_column, transform_feature_spec, metadata\n",
"\n",
" dataset = aip.TabularDataset(args.dataset_id)\n",
" METADATA = 'gs://' + dataset.labels['user_metadata'] + \"/metadata.jsonl\"\n",
"\n",
" with tf.io.gfile.GFile(METADATA, \"r\") as f:\n",
" metadata = json.load(f)\n",
"\n",
" TRANSFORMED_DATA_PREFIX = metadata['transformed_data_prefix']\n",
" label_column = metadata['label_column']\n",
"\n",
" train_data_file_pattern = TRANSFORMED_DATA_PREFIX + '/train/data-*.gz'\n",
" val_data_file_pattern = TRANSFORMED_DATA_PREFIX + '/val/data-*.gz'\n",
" test_data_file_pattern = TRANSFORMED_DATA_PREFIX + '/test/data-*.gz'\n",
"\n",
" TRANSFORM_ARTIFACTS_DIR = metadata['transform_artifacts_dir']\n",
" tft_output = tft.TFTransformOutput(TRANSFORM_ARTIFACTS_DIR)\n",
" transform_feature_spec = tft_output.transformed_feature_spec()\n",
"\n",
"def get_model():\n",
" ''' Get the untrained model architecture '''\n",
" global model_artifacts\n",
"\n",
" vertex_model = model_.get(args.model_id)\n",
" model_artifacts = vertex_model.gca_resource.artifact_uri\n",
" model = tf.keras.models.load_model(model_artifacts)\n",
"\n",
" # Compile the model\n",
" hyperparams = {}\n",
" hyperparams[\"learning_rate\"] = args.lr\n",
" if args.experiment:\n",
" aip.log_params(hyperparams)\n",
"\n",
" metadata.update(hyperparams)\n",
" with tf.io.gfile.GFile(os.path.join(args.model_dir, \"metrics.txt\"), \"w\") as f:\n",
" f.write(json.dumps(metadata))\n",
"\n",
" train.compile(model, hyperparams)\n",
" return model\n",
"\n",
"def warmup_model(model):\n",
" ''' Warmup the initialized model weights '''\n",
" warmupparams = {}\n",
" warmupparams[\"num_epochs\"] = args.epochs\n",
" warmupparams[\"batch_size\"] = args.batch_size\n",
" warmupparams[\"steps\"] = args.steps\n",
" warmupparams[\"start_learning_rate\"] = args.start_lr\n",
" warmupparams[\"end_learning_rate\"] = args.lr\n",
"\n",
" train.warmup(model, warmupparams, train_data_file_pattern, label_column, transform_feature_spec)\n",
" return model\n",
"\n",
"def train_model(model):\n",
" ''' Train the model '''\n",
" trainparams = {}\n",
" trainparams[\"num_epochs\"] = args.epochs\n",
" trainparams[\"batch_size\"] = args.batch_size\n",
" trainparams[\"early_stop\"] = {\"monitor\": \"val_loss\", \"patience\": 5}\n",
" if args.experiment:\n",
" aip.log_params(trainparams)\n",
"\n",
" metadata.update(trainparams)\n",
" with tf.io.gfile.GFile(os.path.join(args.model_dir, \"metrics.txt\"), \"w\") as f:\n",
" f.write(json.dumps(metadata))\n",
"\n",
" train.train(model, trainparams, train_data_file_pattern, val_data_file_pattern, label_column, transform_feature_spec, args.tensorboard_log_dir, args.tuning)\n",
" return model\n",
"\n",
"def evaluate_model(model):\n",
" ''' Evaluate the model '''\n",
" evalparams = {}\n",
" evalparams[\"batch_size\"] = args.batch_size\n",
" metrics = train.evaluate(model, evalparams, test_data_file_pattern, label_column, transform_feature_spec)\n",
"\n",
" metadata.update({'metrics': metrics})\n",
" with tf.io.gfile.GFile(os.path.join(args.model_dir, \"metrics.txt\"), \"w\") as f:\n",
" f.write(json.dumps(metadata))\n",
"\n",
"get_data()\n",
"with strategy.scope():\n",
" model = get_model()\n",
"\n",
"if args.warmup:\n",
" model = warmup_model(model)\n",
"else:\n",
" model = train_model(model)\n",
"\n",
"if args.evaluate:\n",
" evaluate_model(model)\n",
"\n",
"if args.serving:\n",
" logging.info('Save serving model to: ' + args.model_dir)\n",
" serving.construct_serving_model(\n",
" model=model,\n",
" serving_model_dir=args.model_dir,\n",
" metadata=metadata\n",
" )\n",
"elif args.warmup:\n",
" logging.info('Save warmed up model to: ' + model_artifacts)\n",
" model.save(model_artifacts)\n",
"else:\n",
" logging.info('Save trained model to: ' + args.model_dir)\n",
" model.save(args.model_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "test_package_locally"
},
"source": [
"### Test training package locally\n",
"\n",
"Next, test your completed training package locally with just a few epochs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "test_package_locally"
},
"outputs": [],
"source": [
"DATASET_ID = dataset.resource_name\n",
"MODEL_ID = vertex_custom_model.resource_name\n",
"!cd custom; python3 -m trainer.task --model-id={MODEL_ID} --dataset-id={DATASET_ID} --experiment='chicago' --run='test' --project={PROJECT_ID} --epochs=5 --model-dir=/tmp --evaluate=True"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "warmup_base_model"
},
"source": [
"### Warmup training\n",
"\n",
"Now that you have tested the training scripts, you perform warmup training on the base model. Warmup training is used to stabilize the weight initialization. By doing so, each subsequent training and tuning of the model architecture will start with the same stabilized weight initialization."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "warmup_base_model"
},
"outputs": [],
"source": [
"MODEL_DIR = f\"{BUCKET_NAME}/base_model\"\n",
"\n",
"!cd custom; python3 -m trainer.task --model-id={MODEL_ID} --dataset-id={DATASET_ID} --project={PROJECT_ID} --epochs=5 --steps=300 --batch_size=16 --lr=0.01 --start_lr=0.0001 --model-dir={MODEL_DIR} --warmup=True"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mirrored_intro"
},
"source": [
"## Mirrored Strategy\n",
"\n",
"When training on a single VM, one can either train was a single compute device or with multiple compute devices on the same VM. With Vertex AI Distributed Training you can specify both the number of compute devices for the VM instance and type of compute devices: CPU, GPU.\n",
"\n",
"Vertex AI Distributed Training supports `tf.distribute.MirroredStrategy' for TensorFlow models. To enable training across multiple compute devices on the same VM, you do the following additional steps in your Python training script:\n",
"\n",
"1. Set the tf.distribute.MirrorStrategy\n",
"2. Compile the model within the scope of tf.distribute.MirrorStrategy. *Note:* Tells MirroredStrategy which variables to mirror across your compute devices.\n",
"3. Increase the batch size for each compute device to num_devices * batch size.\n",
"\n",
"During transitions, the distribution of batches will be synchronized as well as the updates to the model parameters."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_custom_pp_training_job:mbsdk"
},
"source": [
"### Create and run custom training job\n",
"\n",
"\n",
"To train a custom model, you perform two steps: 1) create a custom training job, and 2) run the job.\n",
"\n",
"#### Create custom training job\n",
"\n",
"A custom training job is created with the `CustomTrainingJob` class, with the following parameters:\n",
"\n",
"- `display_name`: The human readable name for the custom training job.\n",
"- `container_uri`: The training container image.\n",
"\n",
"- `python_package_gcs_uri`: The location of the Python training package as a tarball.\n",
"- `python_module_name`: The relative path to the training script in the Python package.\n",
"- `model_serving_container_uri`: The container image for deploying the model.\n",
"\n",
"*Note:* There is no requirements parameter. You specify any requirements in the `setup.py` script in your Python package."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_custom_pp_training_job:mbsdk"
},
"outputs": [],
"source": [
"DISPLAY_NAME = \"chicago_\" + TIMESTAMP\n",
"\n",
"job = aip.CustomPythonPackageTrainingJob(\n",
" display_name=DISPLAY_NAME,\n",
" python_package_gcs_uri=f\"{BUCKET_NAME}/trainer_chicago.tar.gz\",\n",
" python_module_name=\"trainer.task\",\n",
" container_uri=TRAIN_IMAGE,\n",
" model_serving_container_image_uri=DEPLOY_IMAGE,\n",
" project=PROJECT_ID,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cleanup:trainer"
},
"outputs": [],
"source": [
"! rm -rf custom/logs\n",
"! rm -rf custom/trainer/__pycache__"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tarball_training_script"
},
"source": [
"#### Store training script on your Cloud Storage bucket\n",
"\n",
"Next, you package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tarball_training_script"
},
"outputs": [],
"source": [
"! rm -f custom.tar custom.tar.gz\n",
"! tar cvf custom.tar custom\n",
"! gzip custom.tar\n",
"! gsutil cp custom.tar.gz $BUCKET_NAME/trainer_chicago.tar.gz"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "run_custom_pp_training_job:test"
},
"source": [
"#### Run the custom Python package training job\n",
"\n",
"Next, you run the custom job to start the training job by invoking the method `run()`. The parameters are the same as when running a CustomTrainingJob.\n",
"\n",
"*Note:* The parameter service_account is set so that the initializing experiment step `aip.init(experiment=\"...\")` has necessarily permission to access the Vertex AI Metadata Store."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "run_custom_pp_training_job:test"
},
"outputs": [],
"source": [
"MODEL_DIR = BUCKET_NAME + \"/testing\"\n",
"\n",
"CMDARGS = [\n",
" \"--epochs=5\",\n",
" \"--batch_size=16\",\n",
" \"--distribute=mirrored\",\n",
" \"--experiment=chicago\",\n",
" \"--run=test\",\n",
" \"--project=\" + PROJECT_ID,\n",
" \"--model-id=\" + MODEL_ID,\n",
" \"--dataset-id=\" + DATASET_ID,\n",
"]\n",
"\n",
"model = job.run(\n",
" model_display_name=\"chicago_\" + TIMESTAMP,\n",
" args=CMDARGS,\n",
" replica_count=1,\n",
" machine_type=TRAIN_COMPUTE,\n",
" accelerator_type=TRAIN_GPU.name,\n",
" accelerator_count=TRAIN_NGPU,\n",
" base_output_dir=MODEL_DIR,\n",
" service_account=SERVICE_ACCOUNT,\n",
" tensorboard=tensorboard_resource_name,\n",
" sync=True,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "delete_job"
},
"source": [
"### Delete a custom training job\n",
"\n",
"After a training job is completed, you can delete the training job with the method `delete()`. Prior to completion, a training job can be canceled with the method `cancel()`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "delete_job"
},
"outputs": [],
"source": [
"job.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "model_delete:mbsdk"
},
"source": [
"#### Delete the model\n",
"\n",
"The method 'delete()' will delete the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "model_delete:mbsdk"
},
"outputs": [],
"source": [
"model.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hp_tuning"
},
"source": [
"## Hyperparameter tuning\n",
"\n",
"Next, you perform hyperparameter tuning with the training package. The training package has some additions that make the same package usable for both hyperparameter tuning, as well as local testing and full cloud training:\n",
"\n",
"- Command-Line:\n",
" - `tuning`: indicates to use the HyperTune service as a callback during training.\n",
"\n",
"\n",
"- `train()`: If tuning is set, creates and adds a callback to HyperTune service."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "train_custom_job_machine_specification"
},
"source": [
"### Prepare your machine specification\n",
"\n",
"Now define the machine specification for your custom training job. This tells Vertex what type of machine instance to provision for the training.\n",
" - `machine_type`: The type of GCP instance to provision -- e.g., n1-standard-8.\n",
" - `accelerator_type`: The type, if any, of hardware accelerator. In this tutorial if you previously set the variable `TRAIN_GPU != None`, you are using a GPU; otherwise you will use a CPU.\n",
" - `accelerator_count`: The number of accelerators."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "train_custom_job_machine_specification"
},
"outputs": [],
"source": [
"if TRAIN_GPU:\n",
" machine_spec = {\n",
" \"machine_type\": TRAIN_COMPUTE,\n",
" \"accelerator_type\": TRAIN_GPU,\n",
" \"accelerator_count\": TRAIN_NGPU,\n",
" }\n",
"else:\n",
" machine_spec = {\"machine_type\": TRAIN_COMPUTE, \"accelerator_count\": 0}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "train_custom_job_disk_specification"
},
"source": [
"### Prepare your disk specification\n",
"\n",
"(optional) Now define the disk specification for your custom training job. This tells Vertex what type and size of disk to provision in each machine instance for the training.\n",
"\n",
" - `boot_disk_type`: Either SSD or Standard. SSD is faster, and Standard is less expensive. Defaults to SSD.\n",
" - `boot_disk_size_gb`: Size of disk in GB."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "train_custom_job_disk_specification"
},
"outputs": [],
"source": [
"DISK_TYPE = \"pd-ssd\" # [ pd-ssd, pd-standard]\n",
"DISK_SIZE = 200 # GB\n",
"\n",
"disk_spec = {\"boot_disk_type\": DISK_TYPE, \"boot_disk_size_gb\": DISK_SIZE}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "worker_pool_hpt"
},
"source": [
"### Define worker pool specification for hyperparameter tuning job\n",
"\n",
"Next, define the worker pool specification. Note that we plan to tune the learning rate and batch size, so you do not pass them as command-line arguments (omitted). The Vertex AI Hyperparameter Tuning service will pick values for both learning rate and batch size during trials, which it will pass along as command-line arguments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "worker_pool_hpt"
},
"outputs": [],
"source": [
"CMDARGS = [\n",
" \"--epochs=5\",\n",
" \"--distribute=mirrored\",\n",
" # \"--experiment=chicago\",\n",
" # \"--run=tune\",\n",
" # \"--project=\" + PROJECT_ID,\n",
" \"--model-id=\" + MODEL_ID,\n",
" \"--dataset-id=\" + DATASET_ID,\n",
" \"--tuning=True\",\n",
"]\n",
"\n",
"worker_pool_spec = [\n",
" {\n",
" \"replica_count\": 1,\n",
" \"machine_spec\": machine_spec,\n",
" \"disk_spec\": disk_spec,\n",
" \"python_package_spec\": {\n",
" \"executor_image_uri\": TRAIN_IMAGE,\n",
" \"package_uris\": [BUCKET_NAME + \"/trainer_chicago.tar.gz\"],\n",
" \"python_module\": \"trainer.task\",\n",
" \"args\": CMDARGS,\n",
" },\n",
" }\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_custom_job:mbsdk"
},
"source": [
"## Create a custom job\n",
"\n",
"Use the class `CustomJob` to create a custom job, such as for hyperparameter tuning, with the following parameters:\n",
"\n",
"- `display_name`: A human readable name for the custom job.\n",
"- `worker_pool_specs`: The specification for the corresponding VM instances."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_custom_job:mbsdk"
},
"outputs": [],
"source": [
"job = aip.CustomJob(\n",
" display_name=\"chicago_\" + TIMESTAMP, worker_pool_specs=worker_pool_spec\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_hpt_job:mbsdk"
},
"source": [
"## Create a hyperparameter tuning job\n",
"\n",
"Use the class `HyperparameterTuningJob` to create a hyperparameter tuning job, with the following parameters:\n",
"\n",
"- `display_name`: A human readable name for the custom job.\n",
"- `custom_job`: The worker pool spec from this custom job applies to the CustomJobs created in all the trials.\n",
"- `metrics_spec`: The metrics to optimize. The dictionary key is the metric_id, which is reported by your training job, and the dictionary value is the optimization goal of the metric('minimize' or 'maximize').\n",
"- `parameter_spec`: The parameters to optimize. The dictionary key is the metric_id, which is passed into your training job as a command line key word argument, and the dictionary value is the parameter specification of the metric.\n",
"- `search_algorithm`: The search algorithm to use: `grid`, `random` and `None`. If `None` is specified, the `Vizier` service (Bayesian) is used.\n",
"- `max_trial_count`: The maximum number of trials to perform."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_hpt_job:stage2"
},
"outputs": [],
"source": [
"from google.cloud.aiplatform import hyperparameter_tuning as hpt\n",
"\n",
"hpt_job = aip.HyperparameterTuningJob(\n",
" display_name=\"chicago_\" + TIMESTAMP,\n",
" custom_job=job,\n",
" metric_spec={\n",
" \"val_loss\": \"minimize\",\n",
" },\n",
" parameter_spec={\n",
" \"lr\": hpt.DoubleParameterSpec(min=0.001, max=0.1, scale=\"log\"),\n",
" \"batch_size\": hpt.DiscreteParameterSpec([16, 32, 64, 128, 256], scale=\"linear\"),\n",
" },\n",
" search_algorithm=None,\n",
" max_trial_count=8,\n",
" parallel_trial_count=1,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "run_hpt_job:mbsdk"
},
"source": [
"## Run the hyperparameter tuning job\n",
"\n",
"Use the `run()` method to execute the hyperparameter tuning job."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "run_hpt_job:mbsdk"
},
"outputs": [],
"source": [
"hpt_job.run()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "best_trial:mbsdk"
},
"source": [
"### Best trial\n",
"\n",
"Now look at which trial was the best:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "best_trial:mbsdk"
},
"outputs": [],
"source": [
"best = (None, None, None, 0.0)\n",
"for trial in hpt_job.trials:\n",
" # Keep track of the best outcome\n",
" if float(trial.final_measurement.metrics[0].value) > best[3]:\n",
" try:\n",
" best = (\n",
" trial.id,\n",
" float(trial.parameters[0].value),\n",
" float(trial.parameters[1].value),\n",
" float(trial.final_measurement.metrics[0].value),\n",
" )\n",
" except:\n",
" best = (\n",
" trial.id,\n",
" float(trial.parameters[0].value),\n",
" None,\n",
" float(trial.final_measurement.metrics[0].value),\n",
" )\n",
"\n",
"print(best)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "delete_hpt_job"
},
"source": [
"### Delete the hyperparameter tuning job\n",
"\n",
"The method 'delete()' will delete the hyperparameter tuning job."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "delete_hpt_job"
},
"outputs": [],
"source": [
"hpt_job.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "save_hpt"
},
"source": [
"### Save the best hyperparameter values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "save_hpt"
},
"outputs": [],
"source": [
"LR = best[2]\n",
"BATCH_SIZE = int(best[1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_custom_pp_training_job:mbsdk"
},
"source": [
"### Create and run custom training job\n",
"\n",
"\n",
"To train a custom model, you perform two steps: 1) create a custom training job, and 2) run the job.\n",
"\n",
"#### Create custom training job\n",
"\n",
"A custom training job is created with the `CustomTrainingJob` class, with the following parameters:\n",
"\n",
"- `display_name`: The human readable name for the custom training job.\n",
"- `container_uri`: The training container image.\n",
"\n",
"- `python_package_gcs_uri`: The location of the Python training package as a tarball.\n",
"- `python_module_name`: The relative path to the training script in the Python package.\n",
"- `model_serving_container_uri`: The container image for deploying the model.\n",
"\n",
"*Note:* There is no requirements parameter. You specify any requirements in the `setup.py` script in your Python package."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_custom_pp_training_job:mbsdk"
},
"outputs": [],
"source": [
"DISPLAY_NAME = \"chicago_\" + TIMESTAMP\n",
"\n",
"job = aip.CustomPythonPackageTrainingJob(\n",
" display_name=DISPLAY_NAME,\n",
" python_package_gcs_uri=f\"{BUCKET_NAME}/trainer_chicago.tar.gz\",\n",
" python_module_name=\"trainer.task\",\n",
" container_uri=TRAIN_IMAGE,\n",
" model_serving_container_image_uri=DEPLOY_IMAGE,\n",
" project=PROJECT_ID,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "run_custom_pp_training_job:full"
},
"source": [
"#### Run the custom Python package training job\n",
"\n",
"Next, you run the custom job to start the training job by invoking the method `run()`. The parameters are the same as when running a CustomTrainingJob.\n",
"\n",
"*Note:* The parameter service_account is set so that the initializing experiment step `aip.init(experiment=\"...\")` has necessarily permission to access the Vertex AI Metadata Store."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "run_custom_pp_training_job:full"
},
"outputs": [],
"source": [
"MODEL_DIR = BUCKET_NAME + \"/trained\"\n",
"FULL_EPOCHS = 100\n",
"\n",
"CMDARGS = [\n",
" f\"--epochs={FULL_EPOCHS}\",\n",
" f\"--lr={LR}\",\n",
" f\"--batch_size={BATCH_SIZE}\",\n",
" \"--distribute=mirrored\",\n",
" \"--experiment=chicago\",\n",
" \"--run=full\",\n",
" \"--project=\" + PROJECT_ID,\n",
" \"--model-id=\" + MODEL_ID,\n",
" \"--dataset-id=\" + DATASET_ID,\n",
" \"--evaluate=True\",\n",
"]\n",
"\n",
"model = job.run(\n",
" model_display_name=\"chicago_\" + TIMESTAMP,\n",
" args=CMDARGS,\n",
" replica_count=1,\n",
" machine_type=TRAIN_COMPUTE,\n",
" accelerator_type=TRAIN_GPU.name,\n",
" accelerator_count=TRAIN_NGPU,\n",
" base_output_dir=MODEL_DIR,\n",
" service_account=SERVICE_ACCOUNT,\n",
" tensorboard=tensorboard_resource_name,\n",
" sync=True,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "delete_job"
},
"source": [
"### Delete a custom training job\n",
"\n",
"After a training job is completed, you can delete the training job with the method `delete()`. Prior to completion, a training job can be canceled with the method `cancel()`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "delete_job"
},
"outputs": [],
"source": [
"job.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "get_experiment"
},
"source": [
"### Get the experiment results\n",
"\n",
"Next, you use the experiment name as a parameter to the method `get_experiment_df()` to get the results of the experiment as a pandas dataframe."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "get_experiment"
},
"outputs": [],
"source": [
"EXPERIMENT_NAME = \"chicago\"\n",
"\n",
"experiment_df = aip.get_experiment_df()\n",
"experiment_df = experiment_df[experiment_df.experiment_name == EXPERIMENT_NAME]\n",
"experiment_df.T"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "review_builtin_metrics"
},
"source": [
"## Review the custom model evaluation results\n",
"\n",
"Next, you review the evaluation metrics builtin into the training package."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "review_builtin_metrics"
},
"outputs": [],
"source": [
"METRICS = MODEL_DIR + \"/model/metrics.txt\"\n",
"! gsutil cat $METRICS"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "delete_tensorboard"
},
"source": [
"### Delete the TensorBoard instance\n",
"\n",
"Next, delete the TensorBoard instance."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "delete_tensorboard"
},
"outputs": [],
"source": [
"tensorboard.delete()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "reload_model"
},
"outputs": [],
"source": [
"vertex_custom_model = model\n",
"model = tf.keras.models.load_model(MODEL_DIR + \"/model\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "serving_function:chicago"
},
"source": [
"## Add a serving function\n",
"\n",
"Next, you add a serving function to your model for online and batch prediction. This allows prediction requests to be sent in raw format (unpreprocessed), either as a serialized TF.Example or JSONL object. The serving function will then preprocess the prediction request into the transformed format expected by the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "serving_function:chicago"
},
"outputs": [],
"source": [
"%%writefile custom/trainer/serving.py\n",
"\n",
"import tensorflow as tf\n",
"import tensorflow_data_validation as tfdv\n",
"import tensorflow_transform as tft\n",
"import logging\n",
"\n",
"def _get_serve_features_fn(model, tft_output):\n",
" \"\"\"Returns a function that accept a dictionary of features and applies TFT.\"\"\"\n",
"\n",
" model.tft_layer = tft_output.transform_features_layer()\n",
"\n",
" @tf.function\n",
" def serve_features_fn(raw_features):\n",
" \"\"\"Returns the output to be used in the serving signature.\"\"\"\n",
"\n",
" transformed_features = model.tft_layer(raw_features)\n",
" probabilities = model(transformed_features)\n",
" return {\"scores\": probabilities}\n",
"\n",
"\n",
" return serve_features_fn\n",
"\n",
"def _get_serve_tf_examples_fn(model, tft_output, feature_spec):\n",
" \"\"\"Returns a function that parses a serialized tf.Example and applies TFT.\"\"\"\n",
"\n",
" model.tft_layer = tft_output.transform_features_layer()\n",
"\n",
" @tf.function\n",
" def serve_tf_examples_fn(serialized_tf_examples):\n",
" \"\"\"Returns the output to be used in the serving signature.\"\"\"\n",
" for key in list(feature_spec.keys()):\n",
" if key not in features:\n",
" feature_spec.pop(key)\n",
"\n",
" parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)\n",
"\n",
" transformed_features = model.tft_layer(parsed_features)\n",
" probabilities = model(transformed_features)\n",
" return {\"scores\": probabilities}\n",
"\n",
" return serve_tf_examples_fn\n",
"\n",
"def construct_serving_model(\n",
" model, serving_model_dir, metadata\n",
"):\n",
" global features\n",
"\n",
" schema_location = metadata['schema']\n",
" features = metadata['numeric_features'] + metadata['categorical_features'] + metadata['embedding_features']\n",
" print(\"FEATURES\", features)\n",
" tft_output_dir = metadata[\"transform_artifacts_dir\"]\n",
"\n",
" schema = tfdv.load_schema_text(schema_location)\n",
" feature_spec = tft.tf_metadata.schema_utils.schema_as_feature_spec(schema).feature_spec\n",
"\n",
" tft_output = tft.TFTransformOutput(tft_output_dir)\n",
"\n",
" # Drop features that were not used in training\n",
" features_input_signature = {\n",
" feature_name: tf.TensorSpec(\n",
" shape=(None, 1), dtype=spec.dtype, name=feature_name\n",
" )\n",
" for feature_name, spec in feature_spec.items()\n",
" if feature_name in features\n",
" }\n",
"\n",
" signatures = {\n",
" \"serving_default\": _get_serve_features_fn(\n",
" model, tft_output\n",
" ).get_concrete_function(features_input_signature),\n",
" \"serving_tf_example\": _get_serve_tf_examples_fn(\n",
" model, tft_output, feature_spec\n",
" ).get_concrete_function(\n",
" tf.TensorSpec(shape=[None], dtype=tf.string, name=\"examples\")\n",
" ),\n",
" }\n",
"\n",
" logging.info(\"Model saving started...\")\n",
" model.save(serving_model_dir, signatures=signatures)\n",
" logging.info(\"Model saving completed.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "construct_serving_model"
},
"source": [
"### Construct the serving model\n",
"\n",
"Now construct the serving model and store the serving model to your Cloud Storage bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "construct_serving_model"
},
"outputs": [],
"source": [
"os.chdir(\"custom\")\n",
"\n",
"from trainer import serving\n",
"\n",
"SERVING_MODEL_DIR = BUCKET_NAME + \"/serving_model\"\n",
"\n",
"serving.construct_serving_model(\n",
" model=model, serving_model_dir=SERVING_MODEL_DIR, metadata=metadata\n",
")\n",
"\n",
"serving_model = tf.keras.models.load_model(SERVING_MODEL_DIR)\n",
"\n",
"os.chdir(\"..\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "test_serving_model:tfrec"
},
"source": [
"### Test the serving model locally with tf.Example data\n",
"\n",
"Next, test the layer interface in the serving model for tf.Example data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "test_serving_model:tfrec"
},
"outputs": [],
"source": [
"EXPORTED_TFREC_PREFIX = metadata[\"exported_tfrec_prefix\"]\n",
"file_names = tf.data.TFRecordDataset.list_files(\n",
" EXPORTED_TFREC_PREFIX + \"/data-*.tfrecord\"\n",
")\n",
"for batch in tf.data.TFRecordDataset(file_names).batch(3).take(1):\n",
" predictions = serving_model.signatures[\"serving_tf_example\"](batch)\n",
" for key in predictions:\n",
" print(f\"{key}: {predictions[key]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "test_serving_model:jsonl,chicago"
},
"source": [
"### Test the serving model locally with JSONL data\n",
"\n",
"Next, test the layer interface in the serving model for JSONL data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "test_serving_model:jsonl,chicago"
},
"outputs": [],
"source": [
"schema = tfdv.load_schema_text(metadata[\"schema\"])\n",
"feature_spec = tft.tf_metadata.schema_utils.schema_as_feature_spec(schema).feature_spec\n",
"\n",
"instance = {\n",
" \"dropoff_grid\": \"POINT(-87.6 41.9)\",\n",
" \"euclidean\": 2064.2696,\n",
" \"loc_cross\": \"\",\n",
" \"payment_type\": \"Credit Card\",\n",
" \"pickup_grid\": \"POINT(-87.6 41.9)\",\n",
" \"trip_miles\": 1.37,\n",
" \"trip_day\": 12,\n",
" \"trip_hour\": 6,\n",
" \"trip_month\": 2,\n",
" \"trip_day_of_week\": 4,\n",
" \"trip_seconds\": 555,\n",
"}\n",
"\n",
"for feature_name in instance:\n",
" dtype = feature_spec[feature_name].dtype\n",
" instance[feature_name] = tf.constant([[instance[feature_name]]], dtype)\n",
"\n",
"predictions = serving_model.signatures[\"serving_default\"](**instance)\n",
"for key in predictions:\n",
" print(f\"{key}: {predictions[key].numpy()}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "upload_serving_model:vertex,labels"
},
"source": [
"### Upload the serving model to a Vertex AI Model resource\n",
"\n",
"Next, you upload your serving custom model artifacts to Vertex AI to convert into a managed Vertex AI Model resource."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "upload_serving_model:vertex,labels"
},
"outputs": [],
"source": [
"vertex_serving_model = aip.Model.upload(\n",
" display_name=\"chicago_\" + TIMESTAMP,\n",
" artifact_uri=SERVING_MODEL_DIR,\n",
" serving_container_image_uri=DEPLOY_IMAGE,\n",
" labels={\"user_metadata\": BUCKET_NAME[5:]},\n",
" sync=True,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "evaluate_serving_model"
},
"source": [
"### Evaluate the serving model\n",
"\n",
"Next, evaluate the serving model with the evaluation (test) slices. For apples-to-apples comparison, you use the same evaluation slices for both the custom model and the AutoML model. Since your evaluation slices and metrics maybe custom, we recommend:\n",
"\n",
"- Send each evaluation slice as a Vertex AI Batch Prediction Job.\n",
"- Use a custom evaluation script to evaluate the results from the batch prediction job."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "evaluate_serving_model"
},
"outputs": [],
"source": [
"SERVING_OUTPUT_DATA_DIR = BUCKET_NAME + \"/batch_eval\"\n",
"EXPORTED_JSONL_PREFIX = metadata[\"exported_jsonl_prefix\"]\n",
"\n",
"MIN_NODES = 1\n",
"MAX_NODES = 1\n",
"\n",
"job = vertex_serving_model.batch_predict(\n",
" instances_format=\"jsonl\",\n",
" predictions_format=\"jsonl\",\n",
" job_display_name=\"chicago_\" + TIMESTAMP,\n",
" gcs_source=EXPORTED_JSONL_PREFIX + \"*.jsonl\",\n",
" gcs_destination_prefix=SERVING_OUTPUT_DATA_DIR,\n",
" model_parameters=None,\n",
" machine_type=DEPLOY_COMPUTE,\n",
" accelerator_type=DEPLOY_GPU,\n",
" accelerator_count=DEPLOY_NGPU,\n",
" starting_replica_count=MIN_NODES,\n",
" max_replica_count=MAX_NODES,\n",
" sync=True,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "custom_eval_script"
},
"source": [
"### Perform custom evaluation metrics\n",
"\n",
"After the batch job has completed, you input the results and target labels to your custom evaluation script. For demonstration purposes, we just display the results of the batch prediction."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "custom_eval_script"
},
"outputs": [],
"source": [
"batch_dir = ! gsutil ls $SERVING_OUTPUT_DATA_DIR\n",
"batch_dir = batch_dir[0]\n",
"outputs = ! gsutil ls $batch_dir\n",
"errors = outputs[0]\n",
"results = outputs[1]\n",
"print(\"errors\")\n",
"! gsutil cat $errors\n",
"print(\"results\")\n",
"! gsutil cat $results | head -n10"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_model:async"
},
"outputs": [],
"source": [
"model = async_model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "automl_job_wait:mbsdk"
},
"source": [
"### Wait for completion of AutoML training job\n",
"\n",
"Next, wait for the AutoML training job to complete. Alternatively, one can set the parameter `sync` to `True` in the `run()` method to block until the AutoML training job is completed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "automl_job_wait:mbsdk"
},
"outputs": [],
"source": [
"model.wait()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "evaluate_the_model:mbsdk"
},
"source": [
"## Review model evaluation scores\n",
"\n",
"After your model training has finished, you can review the evaluation scores for it using the `list_model_evaluations()` method. This method will return an iterator for each evaluation slice."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "evaluate_the_model:mbsdk"
},
"outputs": [],
"source": [
"model_evaluations = model.list_model_evaluations()\n",
"\n",
"for model_evaluation in model_evaluations:\n",
" print(model_evaluation.to_dict())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "custom_vs_automl_compare"
},
"source": [
"## Compare metric results with AutoML baseline\n",
"\n",
"Finally, you make a decision if the current experiment produces a custom model that is better than the AutoML baseline, as follows:\n",
" - Compare the evaluation results for each evaluation slice between the custom model and the AutoML model.\n",
" - Weight the results according to your business purposes.\n",
" - Add up the result and make a determination if the custom model is better."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "store_model_metadata"
},
"source": [
"### Store evaluation results for custom model\n",
"\n",
"Next, you use the labels field to store user metadata containing the custom metrics information."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "store_model_metadata"
},
"outputs": [],
"source": [
"import json\n",
"\n",
"metadata = {}\n",
"metadata[\"train_eval_metrics\"] = METRICS\n",
"metadata[\"custom_eval_metrics\"] = \"[you-fill-this-in]\"\n",
"\n",
"with tf.io.gfile.GFile(\"gs://\" + BUCKET_NAME[5:] + \"/metadata.jsonl\", \"w\") as f:\n",
" json.dump(metadata, f)\n",
"\n",
"!gsutil cat $BUCKET_NAME/metadata.jsonl"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cleanup:mbsdk"
},
"source": [
"# Cleaning up\n",
"\n",
"To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
"project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
"\n",
"Otherwise, you can delete the individual resources you created in this tutorial:\n",
"\n",
"- Dataset\n",
"- Pipeline\n",
"- Model\n",
"- Endpoint\n",
"- AutoML Training Job\n",
"- Batch Job\n",
"- Custom Job\n",
"- Hyperparameter Tuning Job\n",
"- Cloud Storage Bucket"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cleanup:stage2"
},
"outputs": [],
"source": [
"delete_all = False\n",
"\n",
"if delete_all:\n",
" # Delete the dataset using the Vertex dataset object\n",
" try:\n",
" if \"dataset\" in globals():\n",
" dataset.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the model using the Vertex model object\n",
" try:\n",
" if \"model\" in globals():\n",
" model.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" if \"BUCKET_NAME\" in globals():\n",
" ! gsutil rm -r $BUCKET_NAME"
]
}
],
"metadata": {
"colab": {
"name": "mlops_experimentation.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
bobobo80/python-crawler-test | test_juptyer/Untitled.ipynb | 1 | 4641 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import jieba\n",
"import jieba.analyse"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Building prefix dict from /usr/lib/python3/dist-packages/jieba/dict.txt ...\n",
"Dumping model to file cache /tmp/jieba.cache\n",
"Loading model cost 4.225765705108643 seconds.\n",
"Prefix dict has been built succesfully.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"欧亚 0.7300142700289363\n",
"吉林 0.659038184373617\n",
"置业 0.4887134522112766\n",
"万元 0.3392722481859574\n",
"增资 0.33582401985234045\n",
"7000 0.25435675538085106\n",
"139.13 0.25435675538085106\n",
"2013 0.25435675538085106\n",
"4.3 0.25435675538085106\n",
"实现 0.19900979900382978\n",
"综合体 0.19480309624702127\n",
"经营范围 0.19389757253595744\n",
"亿元 0.1914421623587234\n",
"在建 0.17541884768425534\n",
"全资 0.17180164988510638\n",
"注册资本 0.1712441526\n",
"百货 0.16734460041382979\n",
"零售 0.1475057117057447\n",
"子公司 0.14596045237787234\n",
"营业 0.13920178509021275\n"
]
}
],
"source": [
"s = \"此外，公司拟对全资子公司吉林欧亚置业有限公司增资4.3亿元，增资后，吉林欧亚置业注册资本由7000万元增加到5亿元。吉林欧亚置业主要经营范围为房地产开发及百货零售等业务。目前在建吉林欧亚城市商业综合体项目。2013年，实现营业收入0万元，实现净利润-139.13万元。\"\n",
"for x, w in jieba.analyse.extract_tags(s, withWeight=True):\n",
" print('%s %s' % (x, w))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pymongo\n",
"\n",
"db = pymongo.MongoClient().mfw_crawler\n",
"\n",
"logs_col = db['logs-10030']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"logs = logs_col.find(limit=100)\n",
"log_text = logs[100]['text_content']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"酒店 0.46736945056688317\n",
"瑞吉 0.45575359981730207\n",
"亚龙湾 0.4412697011788857\n",
"度假 0.43266867344250104\n",
"三亚 0.38344752602247173\n",
"童宝 0.18029812740025136\n",
"石梅湾 0.17528984608357773\n",
"威斯汀 0.16109965051344782\n",
"加井岛 0.13021531423351487\n",
"万宁 0.12376458398563049\n",
"机场 0.05020585518062841\n",
"先生 0.04661958579254294\n",
"我们 0.041201600771977376\n",
"海鲜 0.03655796618903225\n",
"房间 0.03408986349707582\n",
"超级 0.03180804026170926\n",
"大堂 0.025862971516099707\n",
"免税店 0.024761701110808543\n",
"不错 0.02333225989078341\n",
"凤凰 0.02296848112530792\n"
]
}
],
"source": [
"for x, w in jieba.analyse.extract_tags(log_text, withWeight=True):\n",
" print('%s %s' % (x, w))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"酒店 1.0\n",
"三亚 0.7191685618778029\n",
"度假 0.6677786528348395\n",
"瑞吉 0.6086505021735603\n",
"亚龙湾 0.601503165777763\n",
"是 0.44183442295425013\n",
"去 0.2597696020883801\n",
"加井岛 0.25331023474428765\n",
"吃 0.2404843438640427\n",
"有 0.22952313579170258\n"
]
}
],
"source": [
"for x, w in jieba.analyse.textrank(log_text, withWeight=True):\n",
" print('{} {}'.format(x, w))"
]
}
],
"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 |
TomTranter/OpenPNM | examples/simulations/Berea Sandstone - Effective Permeability.ipynb | 1 | 98306 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Berea Sandstone Simulation Using PoreSpy and OpenPNM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The example explains effective permeabilty calculations using PoreSpy and OpenPNM software. The simulation is performed on X-ray tomography image of [BereaSandstone](https://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/micro-ct-images-and-networks/berea-sandstone/). The calculated effective permeablity value can compared with value report in [Dong et al](https://www.semanticscholar.org/paper/Pore-network-extraction-from-images.-Dong-Blunt/31fbb0362bd02e483c8b1f19f944f9bf15095a80). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Start by importing the necessary packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import imageio\n",
"import scipy as sp\n",
"import numpy as np\n",
"import openpnm as op\n",
"import porespy as ps\n",
"import matplotlib.pyplot as plt\n",
"np.set_printoptions(precision=4)\n",
"np.random.seed(10)\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load BreaSandstone Image file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Give path to image file and load the image. Please note image should be binarized or in boolean format before performing next steps."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"path = '../fixtures/ICL-Sandstone(Berea)/'\n",
"file_format = '.tif'\n",
"file_name = 'Berea'\n",
"file = file_name + file_format\n",
"fetch_file = os.path.join(path, file)\n",
"im = imageio.mimread(fetch_file)\n",
"im = ~np.array(im, dtype=bool)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Confirm image and check image porosity\n",
"Be patient, this might take ~30 seconds (depending on your CPU)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#NBVAL_IGNORE_OUTPUT\n",
"fig, ax = plt.subplots(1, 3, figsize=(12,5))\n",
"ax[0].imshow(im[:, :, 100]);\n",
"ax[1].imshow(ps.visualization.show_3D(im));\n",
"ax[2].imshow(ps.visualization.sem(im));\n",
"ax[0].set_title(\"Slice No. 100 View\");\n",
"ax[1].set_title(\"3D Sketch\");\n",
"ax[2].set_title(\"SEM View\");"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.19645303125\n"
]
}
],
"source": [
"print(ps.metrics.porosity(im))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extract pore network using SNOW algorithm in PoreSpy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The SNOW algorithm (an accronym for Sub-Network from an Oversegmented Watershed) was presented by [Gostick](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.96.023307). The algorithm was used to extract pore network from BereaSandstone image."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"____________________________________________________________\n",
"Beginning SNOW Algorithm\n",
"Converting supplied image (im) to boolean\n",
"Peforming Distance Transform\n",
"Applying Gaussian blur with sigma = 0.4\n",
"Initial number of peaks: 10878\n",
"Peaks after trimming saddle points: 4315\n",
"Peaks after trimming nearby peaks: 4315\n",
"____________________________________________________________\n",
"Extracting pore and throat information from image\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 5478/5478 [00:50<00:00, 109.51it/s]\n"
]
}
],
"source": [
"#NBVAL_IGNORE_OUTPUT\n",
"resolution = 5.345e-6\n",
"net = ps.networks.snow(im=im, voxel_size=resolution)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import network in OpenPNM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The output from the SNOW algorithm above is a plain python dictionary containing all the extracted pore-scale data, but it is NOT yet an OpenPNM network. We need to create an empty network in OpenPNM, then populate it with the data from SNOW:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"ws = op.Workspace()\n",
"proj = op.Project()\n",
"pn = op.network.GenericNetwork(name=file_name, project=proj)\n",
"pn.update(net) # Fills 'pn' with data from 'net'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can print the network to see how the transferred worked:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"openpnm.network.GenericNetwork : Berea\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.area 5478 / 5478 \n",
"2 pore.centroid 5478 / 5478 \n",
"3 pore.coords 5478 / 5478 \n",
"4 pore.diameter 5478 / 5478 \n",
"5 pore.equivalent_diameter 5478 / 5478 \n",
"6 pore.extended_diameter 5478 / 5478 \n",
"7 pore.inscribed_diameter 5478 / 5478 \n",
"8 pore.label 5478 / 5478 \n",
"9 pore.surface_area 5478 / 5478 \n",
"10 pore.volume 5478 / 5478 \n",
"11 throat.area 9523 / 9523 \n",
"12 throat.centroid 9523 / 9523 \n",
"13 throat.conduit_lengths.pore1 9523 / 9523 \n",
"14 throat.conduit_lengths.pore2 9523 / 9523 \n",
"15 throat.conduit_lengths.throat 9523 / 9523 \n",
"16 throat.conns 9523 / 9523 \n",
"17 throat.diameter 9523 / 9523 \n",
"18 throat.direct_length 9523 / 9523 \n",
"19 throat.endpoints.head 9523 / 9523 \n",
"20 throat.endpoints.tail 9523 / 9523 \n",
"21 throat.equivalent_diameter 9523 / 9523 \n",
"22 throat.inscribed_diameter 9523 / 9523 \n",
"23 throat.length 9523 / 9523 \n",
"24 throat.perimeter 9523 / 9523 \n",
"25 throat.total_length 9523 / 9523 \n",
"26 throat.volume 9523 / 9523 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.all 5478 \n",
"2 pore.back 187 \n",
"3 pore.bottom 196 \n",
"4 pore.boundary 1163 \n",
"5 pore.front 189 \n",
"6 pore.internal 4315 \n",
"7 pore.left 179 \n",
"8 pore.right 206 \n",
"9 pore.top 206 \n",
"10 throat.all 9523 \n",
"11 throat.boundary 1160 \n",
"12 throat.internal 8363 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
],
"source": [
"print(pn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check network health"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remove isolated pores or cluster of pores from the network by checking it network health. Make sure ALL keys in network health functions have no value. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"key value\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"disconnected_clusters []\n",
"isolated_pores []\n",
"trim_pores []\n",
"duplicate_throats []\n",
"bidirectional_throats []\n",
"headless_throats []\n",
"looped_throats []\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
],
"source": [
"h = pn.check_network_health()\n",
"op.topotools.trim(network=pn, pores=h['trim_pores'])\n",
"h = pn.check_network_health()\n",
"print(h)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assign pore scale model "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assign conduit shape to calculate flow from pore i to pore j. In this simulation spherical pore and cylinderical throats geometry is considered. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"mod = op.models.geometry.throat_endpoints.spherical_pores\n",
"pn.add_model(propname='throat.endpoints', model=mod)\n",
"mod = op.models.geometry.throat_length.conduit_lengths\n",
"pn.add_model(propname='throat.conduit_lengths', model=mod)\n",
"mod = op.models.geometry.pore_area.sphere\n",
"pn.add_model(propname='pore.area', model=mod)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assign geometry"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"geo = op.geometry.GenericGeometry(network=pn, pores=pn.Ps, throats=pn.Ts)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assign phase"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example air is considered as fluid passing through porous channels. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"air = op.phases.Air(network=pn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assign physics"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"phys_air = op.physics.Standard(network=pn, phase=air, geometry=geo)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assign Algorithm and boundary conditions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select stokes flow algorithm for simulation and assign dirichlet boundary conditions in top and bottom faces of the network."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"perm = op.algorithms.StokesFlow(network=pn, project=proj)\n",
"perm.setup(phase=air)\n",
"perm.set_value_BC(pores=pn.pores('top'), values=0)\n",
"perm.set_value_BC(pores=pn.pores('bottom'), values=101325)\n",
"perm.run()\n",
"air.update(perm.results())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculate effective permeability"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Caclulate effective permeablity using hagen poiseuille equation. Use cross section area and flow length manually from image dimension. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The value of K is: [944.4486] mD\n"
]
}
],
"source": [
"resolution = 5.345e-6\n",
"Q = perm.rate(pores=pn.pores('bottom'), mode='group')\n",
"A = (im.shape[0] * im.shape[1]) * resolution**2\n",
"L = im.shape[2] * resolution\n",
"mu = air['pore.viscosity'].max()\n",
"delta_P = 101325 - 0\n",
"K = Q * L * mu / (A * delta_P)\n",
"print('The value of K is:', K/0.98e-12*1000, 'mD')"
]
}
],
"metadata": {
"@webio": {
"lastCommId": null,
"lastKernelId": null
},
"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": "3.8.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
mmathioudakis/pycon2016polarization | pycon_nx.ipynb | 1 | 202153 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img style=\"float: right;\", src='img/cover.jpg'/>\n",
"\n",
"# visualizing discussions on twitter with networkx\n",
"\n",
"kiran garimella, aalto university\n",
"\n",
"michael mathioudakis, aalto university\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## social media\n",
"<img style='float: right; height:300px;' src='img/social_media.jpg'/>\n",
"#### users generate digital content\n",
"status updates, blog posts, pictures, videos, reviews, ...\n",
"#### users interact\n",
"comments, likes, ratings, re-posts\n",
"#### digital traces\n",
"we can observe human interactions at global scale"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img style='float:right; width: 250px' src='img/twitter_logo.png'/>\n",
"## twitter\n",
"\n",
"#### microblogging platform\n",
"users post short messages, '__tweets__'\n",
"\n",
"#### since 2006, 300m + active users\n",
"\n",
"#### tweets, retweets, replies"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"show examples of real tweets, retweets, and replies"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img style='float:left; width:250px;' src='img/trump_tweet.png'>\n",
"<img style='float:left; width:250px;' src='img/greenwald_tweet.png'>\n",
"<img style='float:left; width:250px;' src='img/leme_tweet.png'>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"these are specific examples\n",
"\n",
"can we learn something from the stucture of people's interactions?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"we'll do that by visualizing __graphs__"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"graphs?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img style='float: right; width: 350px' src='img/generic_graph.png'/>\n",
"\n",
"## graphs!\n",
"\n",
"\n",
"\n",
"#### what is a graph?\n",
"\n",
"data structure\n",
"\n",
"two types of elements: nodes and edges"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"__todo__ include an example of a very simple social network"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### what are graphs used for?\n",
"represent social connections between people ...\n",
"\n",
"<img style='float: left; width: 350px' src='img/example_friends.png'/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"... or represent networks\n",
"e.g., road networks, computer networks\n",
"\n",
"<img style='float: left; width: 350px' src='img/road_network.png'/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### terminology\n",
"\n",
"_graph_ vs _network_"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## graphs with networkx\n",
"\n",
"python library\n",
"\n",
"create, process, visualize graphs\n",
"\n",
"development started in 2004\n",
"\n",
"mainly developed in 2014"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## building a graph"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img style='float: right; width: 400px' src='img/example_friends.png'/>\n",
"\n",
"let's build earlier example"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<img style='float: right; height: 200px;' src='img/example_friends.png'/>"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"import networkx as nx\n",
"\n",
"# initialize\n",
"graph = nx.Graph()\n",
"\n",
"people = ['jere', 'ella', 'miika', 'anniina', 'mikko', 'olli', 'laura', 'maria']\n",
"connections = [('jere', 'ella'), ('ella', 'anniina'), ('ella', 'miika'),\n",
" ('mikko', 'ella'), ('anniina', 'mikko'), ('laura', 'jere'),\n",
" ('olli', 'jere'), ('jere', 'maria'), ('miika', 'mikko'),\n",
" ('maria', 'laura'), ('olli', 'laura')]\n",
"\n",
"# add all nodes\n",
"for node in people:\n",
" graph.add_node(node)\n",
"\n",
"# add all edges\n",
"for node_a, node_b in connections:\n",
" graph.add_edge(node_a, node_b)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## visualizing a graph"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"def get_pyplot_ax(rows = 1, columns = 1, figsize = (7, 7)):\n",
" \"\"\" helper function \"\"\"\n",
" \n",
" import matplotlib.pyplot as plt\n",
" \n",
" fig, ax = plt.subplots(rows, columns, figsize = figsize)\n",
" \n",
" if rows == 1 and columns == 1:\n",
" ax.axis('off')\n",
" elif rows == 1 or columns == 1:\n",
" for subax in ax:\n",
" subax.axis('off')\n",
" elif rows > 1 or columns > 1:\n",
" for i in range(rows):\n",
" for j in range(columns):\n",
" ax[i][j].axis('off')\n",
" \n",
" return fig, ax"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
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eDYqiuMxms9doNAbMZrNXURRX7969G1RVrSSiu4loeGQSrRBxRUSpCD+He5yZ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\nR0REtvW/gv5B0Mi839EAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bb3e2e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = get_pyplot_ax()\n",
"\n",
"nx.draw_networkx(graph, ax = ax, node_size = 2300, node_color = 'white')\n",
"\n",
"fig.savefig('img/example_friends.png', dpi = 300)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"image/png": 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CpQZmNp0wG/NnTQZc9AjwjIY/syco5MbJzKaY2VWE6YKZtLNDCkLYzQT6zezKOGqszU8J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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a648c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = get_pyplot_ax()\n",
"\n",
"nx.draw_networkx(graph, ax = ax, node_size = 2300, node_color = 'black', node_label = None)\n",
"\n",
"fig.savefig('img/generic_graph.png', dpi = 300)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## add color to nodes"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"image/png": 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9A3dPAdTHefLOicU3eeOeSdTPreORsOsQkYDuybUgM0z52T2nsVrzRcKF5qzb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\nERFxLYWciIi4lkJORERc6/8BYSK9zhNaZKYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f8db208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"boy_color = 'green'\n",
"girl_color = 'orange'\n",
"\n",
"color_by_gender = {\n",
" 'jere': boy_color, 'miika': boy_color, 'mikko': boy_color, 'olli': boy_color,\n",
" 'ella': girl_color, 'anniina': girl_color, 'laura': girl_color, 'maria': girl_color\n",
"}\n",
"\n",
"# create a list of color per node\n",
"colors = [color_by_gender[node] for node in graph.nodes_iter()]\n",
"\n",
"fig, ax = get_pyplot_ax()\n",
"nx.draw_networkx(graph, ax = ax, node_size = 2300, node_color = colors);\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## custom labels"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"image/png": 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+/Sg8enZw35rrewu+gufezex4dfVEAa1Kn4koF7Ixg9kxB78UxoBDqeAubjWz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f8ee278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = get_pyplot_ax()\n",
"\n",
"labels = {}\n",
"for person in people:\n",
" labels[person] = person.upper()\n",
"nx.draw_networkx(graph, labels = labels, ax = ax, node_size = 2300, node_color = colors);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## fixed node positions"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"image/png": 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MsUUPY9JgCXQaVPX1BHycZktPzzwF1MJyVfViUrQe0MYYT62PMmPGU1QUQiHZ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111820710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pos = nx.layout.spring_layout(graph)\n",
"\n",
"fig, ax = get_pyplot_ax(rows = 1, columns = 2, figsize = (12, 5))\n",
"\n",
"left_plot = ax[0]\n",
"right_plot = ax[1]\n",
"\n",
"nx.draw_networkx(graph, pos = pos, ax = left_plot, node_size = 2300,)\n",
"nx.draw_networkx(graph, pos = pos, ax = right_plot, node_size = 2300, node_color = colors)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## analysis of a graph"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### degrees"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"anniina has 2 friends\n",
"ella has 4 friends\n",
"olli has 2 friends\n",
"mikko has 3 friends\n",
"maria has 2 friends\n",
"jere has 4 friends\n",
"laura has 3 friends\n",
"miika has 2 friends\n"
]
}
],
"source": [
"number_of_friends = graph.degree()\n",
"\n",
"for person in number_of_friends:\n",
" print(\"{} has {} friends\".format(person, number_of_friends[person]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img style='float:left; width: 400px;' src='img/example_friends.png'/>"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"from clustering import spectral_clusters\n",
"partition = spectral_clusters(graph, 2)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 1, 1, 1, 0, 0, 0], dtype=int32)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"partition"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"group_colors = []\n",
"for pos, name in enumerate(graph.nodes_iter()):\n",
" side = partition[pos]\n",
" color = 'blue' if side == 1 else 'grey'\n",
" group_colors.append(color)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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ZrX79+skHH3zQdfXq1b8D/sfteprLZld6gIicDPxeVU9wu5bmEJEpwCJV/YPb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\nTkREAkshJyIigaWQExGRwFLIiYhIYP0/1my2MEV1944AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11162ff60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = get_pyplot_ax()\n",
"nx.draw_networkx(graph, ax = ax, node_color = group_colors, node_size = 2300);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# back to twitter\n",
"\n",
"* the twitter data\n",
"* retweets and replies"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### dataset\n",
"\n",
"* tweets collected using the streaming api (1% sample)\n",
"* can also be collected from the REST api"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### a tweet"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"{u'contributors': None, u'coordinates': None, u'created_at': u'Fri Mar 25 11:52:14 +0000 2016', u'entities': {u'hashtags': [{u'indices': [0, 3], u'text': u'RT'}, {u'indices': [4, 11], u'text': u'Follow'}, {u'indices': [12, 23], u'text': u'TopStories'}], u'media': [{u'display_url': u'pic.twitter.com/IN2KVsc1fI', u'expanded_url': u'http://twitter.com/KTM_Riders/status/713332663755509761/photo/1', u'id': 713332663633883136, u'id_str': u'713332663633883136', u'indices': [114, 137], u'media_url': u'http://pbs.twimg.com/media/CeZERzaUsAAXPMP.jpg', u'media_url_https': u'https://pbs.twimg.com/media/CeZERzaUsAAXPMP.jpg', u'sizes': {u'large': {u'h': 683, u'resize': u'fit', u'w': 1024}, u'medium': {u'h': 400, u'resize': u'fit', u'w': 600}, u'small': {u'h': 227, u'resize': u'fit', u'w': 340}, u'thumb': {u'h': 150, u'resize': u'crop', u'w': 150}}, u'type': u'photo', u'url': u'https://t.co/IN2KVsc1fI'}], u'symbols': [], u'urls': [{u'display_url': u'bit.ly/1jyqSVe', u'expanded_url': u'http://bit.ly/1jyqSVe', u'indices': [90, 113], u'url': u'https://t.co/sAPChiEEcY'}], u'user_mentions': []}, u'extended_entities': {u'media': [{u'display_url': u'pic.twitter.com/IN2KVsc1fI', u'expanded_url': u'http://twitter.com/KTM_Riders/status/713332663755509761/photo/1', u'id': 713332663633883136, u'id_str': u'713332663633883136', u'indices': [114, 137], u'media_url': u'http://pbs.twimg.com/media/CeZERzaUsAAXPMP.jpg', u'media_url_https': u'https://pbs.twimg.com/media/CeZERzaUsAAXPMP.jpg', u'sizes': {u'large': {u'h': 683, u'resize': u'fit', u'w': 1024}, u'medium': {u'h': 400, u'resize': u'fit', u'w': 600}, u'small': {u'h': 227, u'resize': u'fit', u'w': 340}, u'thumb': {u'h': 150, u'resize': u'crop', u'w': 150}}, u'type': u'photo', u'url': u'https://t.co/IN2KVsc1fI'}]}, u'favorite_count': 0, u'favorited': False, u'filter_level': u'low', u'geo': None, u'id': 713332663755509761, u'id_str': u'713332663755509761', u'in_reply_to_screen_name': None, u'in_reply_to_status_id': None, u'in_reply_to_status_id_str': None, u'in_reply_to_user_id': None, u'in_reply_to_user_id_str': None, u'is_quote_status': False, u'lang': u'en', u'place': None, u'possibly_sensitive': False, u'retweet_count': 0, u'retweeted': False, u'source': u'<a href=\"http://dlvr.it\" rel=\"nofollow\">dlvr.it</a>', u'text': u\"#RT #Follow #TopStories Trump Aides Plot 'Two-Phase' Strategy to Win Potential Contested\\u2026 https://t.co/sAPChiEEcY https://t.co/IN2KVsc1fI\", u'timestamp_ms': u'1458906734776', u'truncated': False, u'user': {u'contributors_enabled': False, u'created_at': u'Wed Jun 06 08:28:15 +0000 2012', u'default_profile': False, u'default_profile_image': False, u'description': u'The Dirt Rider Club', u'favourites_count': 0, u'follow_request_sent': None, u'followers_count': 3218, u'following': None, u'friends_count': 3782, u'geo_enabled': False, u'id': 600799231, u'id_str': u'600799231', u'is_translator': False, u'lang': u'en', u'listed_count': 110, u'location': u'North America', u'name': u'KTM Rider Club', u'notifications': None, u'profile_background_color': u'131516', u'profile_background_image_url': u'http://abs.twimg.com/images/themes/theme14/bg.gif', u'profile_background_image_url_https': u'https://abs.twimg.com/images/themes/theme14/bg.gif', u'profile_background_tile': True, u'profile_image_url': u'http://pbs.twimg.com/profile_images/2375931627/795f1dvdi168knsqo2ch_normal.jpeg', u'profile_image_url_https': u'https://pbs.twimg.com/profile_images/2375931627/795f1dvdi168knsqo2ch_normal.jpeg', u'profile_link_color': u'009999', u'profile_sidebar_border_color': u'EEEEEE', u'profile_sidebar_fill_color': u'EFEFEF', u'profile_text_color': u'333333', u'profile_use_background_image': True, u'protected': False, u'screen_name': u'KTM_Riders', u'statuses_count': 162712, u'time_zone': u'Central Time (US & Canada)', u'url': u'https://twitter.com/KTM_Riders', u'utc_offset': -18000, u'verified': False}}"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### a retweet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img style='float:right; width:400px;' src='img/trump_tweet.png'>\n",
"__original tweet author__ realDonaldTrump\n",
"\n",
"__retweet author__ terry_golfing\n",
"\n",
"__text__ RT @realDonaldTrump: REPEAL AND REPLACE OBAMACARE!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### a reply"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img style='float:right; width:400px;' src='img/trump_tweet.png'>\n",
"__original tweet author__ realDonaldTrump\n",
"\n",
"__reply author__ tonyposnanski\n",
"\n",
"__text__ @realDonaldTrump have a better plan than the one you presented"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### building graph for retweets"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"dataset = []\n",
"tweet = None\n",
"def is_retweet(tweet):\n",
" pass\n",
"def get_retweet_author(tweet):\n",
" pass\n",
"def get_original_author(tweet):\n",
" pass\n",
"def contains_topic(tweet, topic):\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"topic = '#obamacare'\n",
"for tweet in dataset:\n",
" if contains_topic(tweet, topic) and is_retweet(tweet):\n",
" from_who = get_retweet_author(tweet)\n",
" to_who = get_original_author(tweet)\n",
" graph.add_edge(from_who, to_who)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### visualizations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img style='float:left; width: 400px;' src='img/germanwings_nocolor.png'/>\n",
"<img style='float:left; width: 400px;' src='img/nepal_nocolor.png'/>\n",
"<p style=\"clear: both;\">\n",
"<img style='float:left; width: 400px;' src='img/beefban_nocolor.png'/>\n",
"<img style='float:left; width: 400px;' src='img/russia_march_nocolor.png'/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# what happens when people disagree?\n",
"\n",
"we notice a difference...\n",
"\n",
"retweet graphs for controversial topics contain two well-separated clusters"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### clustering\n",
"\n",
"we cluster each graph like before"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"partition = spectral_clusters(graph, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"to find sides __if__ they exist"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### visualize with clusters"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"group_colors = []\n",
"for pos, name in enumerate(graph.nodes_iter()):\n",
" side = partition[pos]\n",
" color = 'blue' if side == 1 else 'red'\n",
" group_colors.append(color)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"nx.__draw_networkx__(graph, ax = ax, __node_color = group_colors__, node_size = 2300);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<img style='float:left; width: 400px;' src='img/germanwings.png'/>\n",
"<img style='float:left; width: 400px;' src='img/nepal.png'/>\n",
"<p style=\"clear: both;\">\n",
"<img style='float:left; width: 400px;' src='img/beefban.png'/>\n",
"<img style='float:left; width: 400px;' src='img/russia_march.png'/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# reply networks\n",
"\n",
"what about reply networks?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<img style='float:left; width: 400px;' src='img/men_germanwings.png'/>\n",
"<img style='float:left; width: 400px;' src='img/men_nepal.png'/>\n",
"<p style=\"clear: both;\">\n",
"<img style='float:left; width: 400px;' src='img/men_beefban.png'/>\n",
"<img style='float:left; width: 400px;' src='img/men_russia_march.png'/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# summary\n",
"\n",
"* __intro to networkx__ a python library to create, process, visualize graphs\n",
"* __application__ twitter discussions\n",
" * controversial and non-controversial topics\n",
" * retweet and reply graphs\n",
" * retweet graphs are clearly separated for controversial topics\n",
" * replies also from one side to the other\n",
" * consistent pattern for many instances we've seen\n",
"* graphs can offer __insights__ into human interactions\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# more about networkx\n",
"\n",
"* directed graphs\n",
"* more layout algorithms\n",
"* centrality and clustering measures\n",
"* more at http://networkx.github.io/\n",
"\n",
"slides at https://github.com/mmathioudakis/pycon2016polarization"
]
}
],
"metadata": {
"anaconda-cloud": {},
"celltoolbar": "Slideshow",
"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 |
RuiShu/cvae | notebooks/monte_carlo_kld.ipynb | 1 | 5502 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check Gaussian Criterion"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"require 'criteria/KLDCriterion'\n",
"require 'criteria/GaussianCriterion'\n",
"require 'utils/Sampler'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"kld = nn.KLDCriterion\n",
"gauss = nn.GaussianCriterion\n",
"sampler = nn.Sampler()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mu = torch.randn(3,4)\n",
"lv = torch.randn(3,4):pow(2):log()\n",
"pmu = torch.zeros(3,4)\n",
"plv = torch.zeros(3,4)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"10.784102064081\t\n"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kld:forward({pmu, plv}, {mu, lv})"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"10.790013963543\t\n"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"-- D(posterior || prior) = E[log posterior - log prior]\n",
"log_posterior = 0\n",
"log_prior = 0\n",
"N = 10\n",
"for i = 1,N do\n",
" code = sampler({mu, lv})\n",
" log_posterior = log_posterior + gauss:forward({mu, lv}, code)/N\n",
" log_prior = log_prior + gauss:forward({pmu, plv}, code)/N\n",
"end\n",
"print(log_posterior - log_prior)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-0.91893853320467\t\n"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zero_mu = torch.zeros(1)\n",
"zero_logv = torch.zeros(1)\n",
"code = torch.zeros(1)\n",
"print(gauss:forward({zero_mu, zero_logv}, code))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check Gradient"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"mu = torch.randn(3,4)\n",
"lv = torch.randn(3,4):pow(2):log()\n",
"pmu = torch.zeros(3,4)\n",
"plv = torch.zeros(3,4)\n",
"code = sampler({mu, lv})\n",
"mu = torch.randn(3,4)\n",
"lv = torch.randn(3,4):pow(2):log()\n",
"h = 1e-5\n",
"dmu, dlv = unpack(gauss:backward({mu, lv}, code))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-1.0892229340698e-09\t\n",
"1.0344436418563e-09\t\n",
"1.1293965762604e-11\t\n",
"-4.725304592057e-10\t\n",
"6.4559291246269e-10\t\n",
"-1.1422819357065e-09\t\n",
"1.1262029087078e-09\t\n",
"-1.5933434571735e-09\t\n",
"3.1108386977508e-09\t\n",
"-1.7533992036078e-09\t\n",
"5.0998139045078e-10\t\n",
"-7.0252781370073e-10\t\n"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i = 1,mu:size(1) do\n",
" for j = 1,mu:size(2) do\n",
" mu[{i, j}] = mu[{i, j}] + h\n",
" fph = gauss:forward({mu, lv}, code)\n",
" mu[{i, j}] = mu[{i, j}] - h - h\n",
" fmp = gauss:forward({mu, lv}, code)\n",
" mu[{i, j}] = mu[{i, j}] + h\n",
" print((fph - fmp)/2/h - dmu[{i, j}])\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-4.5784531721438e-10\t\n",
"2.8162148169031e-09\t\n",
"-7.4037234676361e-10\t\n",
"-8.7991369746021e-10\t\n",
"-6.3426597307625e-11\t\n",
"8.5557852469442e-10\t\n",
"-1.2577909269673e-09\t\n",
"8.821449126728e-11\t\n",
"-4.4890757777694e-11\t\n",
"-1.2077188138448e-09\t\n",
"9.8273034154772e-10\t\n",
"-6.3421907725569e-09\t\n"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i = 1,lv:size(1) do\n",
" for j = 1,lv:size(2) do\n",
" lv[{i, j}] = lv[{i, j}] + h\n",
" fph = gauss:forward({mu, lv}, code)\n",
" lv[{i, j}] = lv[{i, j}] - h - h\n",
" fmp = gauss:forward({mu, lv}, code)\n",
" lv[{i, j}] = lv[{i, j}] + h\n",
" print((fph - fmp)/2/h - dlv[{i, j}])\n",
" end\n",
"end"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "iTorch",
"language": "lua",
"name": "itorch"
},
"language_info": {
"name": "lua",
"version": "20100"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
tetsuyasu/jupyter_tfbook | Chapter03/MNIST single layer network.ipynb | 1 | 69192 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[MSL-01]** 必要なモジュールをインポートして、乱数のシードを設定します。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/tetsu/.pyenv/versions/miniconda2-latest/envs/py35/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"\n",
"np.random.seed(20160612)\n",
"tf.set_random_seed(20160612)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[MSL-02]** MNISTのデータセットを用意します。"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
"Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[MSL-03]** 単層ニューラルネットワークを用いた確率 p の計算式を用意します。"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"num_units = 1024\n",
"\n",
"x = tf.placeholder(tf.float32, [None, 784])\n",
"\n",
"w1 = tf.Variable(tf.truncated_normal([784, num_units]))\n",
"b1 = tf.Variable(tf.zeros([num_units]))\n",
"hidden1 = tf.nn.relu(tf.matmul(x, w1) + b1)\n",
"\n",
"w0 = tf.Variable(tf.zeros([num_units, 10]))\n",
"b0 = tf.Variable(tf.zeros([10]))\n",
"p = tf.nn.softmax(tf.matmul(hidden1, w0) + b0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[MSL-04]** 誤差関数 loss、トレーニングアルゴリズム train_step、正解率 accuracy を定義します。"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"t = tf.placeholder(tf.float32, [None, 10])\n",
"loss = -tf.reduce_sum(t * tf.log(p))\n",
"train_step = tf.train.AdamOptimizer().minimize(loss)\n",
"correct_prediction = tf.equal(tf.argmax(p, 1), tf.argmax(t, 1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[MSL-05]** セッションを用意して、Variableを初期化します。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"sess = tf.InteractiveSession()\n",
"sess.run(tf.global_variables_initializer())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[MSL-06]** パラメーターの最適化を2000回繰り返します。\n",
"\n",
"1回の処理において、トレーニングセットから取り出した100個のデータを用いて、勾配降下法を適用します。\n",
"\n",
"最終的に、テストセットに対して約97%の正解率が得られます。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step: 100, Loss: 2674.854492, Accuracy: 0.921100\n",
"Step: 200, Loss: 2180.231445, Accuracy: 0.933400\n",
"Step: 300, Loss: 1980.723145, Accuracy: 0.938600\n",
"Step: 400, Loss: 1829.058350, Accuracy: 0.943200\n",
"Step: 500, Loss: 1411.336182, Accuracy: 0.954900\n",
"Step: 600, Loss: 1390.591553, Accuracy: 0.958900\n",
"Step: 700, Loss: 1294.857422, Accuracy: 0.961000\n",
"Step: 800, Loss: 1259.285645, Accuracy: 0.962500\n",
"Step: 900, Loss: 1251.906860, Accuracy: 0.961600\n",
"Step: 1000, Loss: 1164.970215, Accuracy: 0.963500\n",
"Step: 1100, Loss: 1148.403320, Accuracy: 0.964600\n",
"Step: 1200, Loss: 1125.479248, Accuracy: 0.963500\n",
"Step: 1300, Loss: 1056.904053, Accuracy: 0.968300\n",
"Step: 1400, Loss: 1005.674744, Accuracy: 0.969500\n",
"Step: 1500, Loss: 1082.055664, Accuracy: 0.967400\n",
"Step: 1600, Loss: 1016.453430, Accuracy: 0.968100\n",
"Step: 1700, Loss: 931.991821, Accuracy: 0.971800\n",
"Step: 1800, Loss: 926.841797, Accuracy: 0.972200\n",
"Step: 1900, Loss: 1050.114502, Accuracy: 0.969500\n",
"Step: 2000, Loss: 1009.382874, Accuracy: 0.968800\n"
]
}
],
"source": [
"i = 0\n",
"for _ in range(2000):\n",
" i += 1\n",
" batch_xs, batch_ts = mnist.train.next_batch(100)\n",
" sess.run(train_step, feed_dict={x: batch_xs, t: batch_ts})\n",
" if i % 100 == 0:\n",
" loss_val, acc_val = sess.run([loss, accuracy],\n",
" feed_dict={x:mnist.test.images, t: mnist.test.labels})\n",
" print ('Step: %d, Loss: %f, Accuracy: %f'\n",
" % (i, loss_val, acc_val))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[MSL-07]** 最適化されたパラメーターを用いて、テストセットに対する予測を表示します。\n",
"\n",
"ここでは、「０」〜「９」の数字に対して、正解と不正解の例を３個ずつ表示します。"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x1080 with 60 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"images, labels = mnist.test.images, mnist.test.labels\n",
"p_val = sess.run(p, feed_dict={x:images, t: labels}) \n",
"\n",
"fig = plt.figure(figsize=(8,15))\n",
"for i in range(10):\n",
" c = 1\n",
" for (image, label, pred) in zip(images, labels, p_val):\n",
" prediction, actual = np.argmax(pred), np.argmax(label)\n",
" if prediction != i:\n",
" continue\n",
" if (c < 4 and i == actual) or (c >= 4 and i != actual):\n",
" subplot = fig.add_subplot(10,6,i*6+c)\n",
" subplot.set_xticks([])\n",
" subplot.set_yticks([])\n",
" subplot.set_title('%d / %d' % (prediction, actual))\n",
" subplot.imshow(image.reshape((28,28)), vmin=0, vmax=1,\n",
" cmap=plt.cm.gray_r, interpolation=\"nearest\")\n",
" c += 1\n",
" if c > 6:\n",
" break"
]
}
],
"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": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
speignier/suppositoire | .ipynb_checkpoints/test_neural_network-checkpoint.ipynb | 1 | 5822 | {
"metadata": {
"name": "",
"signature": "sha256:53a76d031d072cf6ce8f40edfc0ed0b1f9cb713d25eb7ad918245b0c5aaa8253"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"#!/usr/bin/env python\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import pdb\n",
"import csv\n",
"from dataset import *\n",
"from collections import Counter\n",
"import numpy as np\n",
"import operator\n",
"import matplotlib.pyplot as pl\n",
"import pandas as pd \n",
"from bs4 import BeautifulSoup \n",
"import re\n",
"from nltk.corpus import stopwords\n",
"from gensim.models import word2vec\n",
"from names_to_arrays import *\n",
"from sknn.mlp import Classifier, Layer"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#streets -> 10D vectors\n",
"d=data(\"../../data/List_of_Streets_and_Intersections.csv\",categorical=[\"streetname\",\"from_st\",\"to_st\"])\n",
"params_model = {\"window\":5, \"min_count\":1, \"workers\":5}\n",
"new_data = []\n",
"for i in d.df.index:\n",
" a = d.df.loc[i,]\n",
" new_data.append(review_to_words(\" \".join(map(str,list(a))),keepcaracters = \"[^0-9A-Z]\",stops = ABREVIATIONS))\n",
"model = word2vec.Word2Vec(new_data, size = 10, **params_model)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING:gensim.models.word2vec:consider setting layer size to a multiple of 4 for greater performance\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['CNN' 'streetname' 'from_st' 'to_st' 'cardinal' 'addrange' 'limits'\n",
" 'location' 'theOrder' 'LF_FADD' 'RT_FADD' 'LF_TADD' 'RT_TADD' 'FROM_CNN'\n",
" 'TO_CNN']\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\"\n",
"training_set=data(\"../../data/train.csv\",\n",
" categorical=[\"PdDistrict\",\"Resolution\",\"Address\",\"Dates\",\"DayOfWeek\"],\n",
" measure= [\"X\",\"Y\"],\n",
" hidden_cluster=[\"Category\"],\n",
" header = 0,\n",
" index=None)\n",
"\"\"\"\n",
"training_set=data(\"../../data/train.csv\",\n",
" categorical=[],\n",
" measure= [\"X\",\"Y\"],\n",
" hidden_cluster=[\"Category\"],\n",
" header = 0,\n",
" index=None)\n",
"training_set.standardize_table()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"training_set.convert_cluster_membership_to_numerical([\"Category\"])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X = training_set.df[training_set.features].values\n",
"Y = training_set.df[training_set.hidden_cluster].values\n",
"X = X[1:1000]\n",
"Y = Y[1:1000]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"output_dimensionality = len(np.unique(Y))\n",
"\"\"\"\n",
"nn = Classifier(\n",
" layers=[\n",
" Layer(\"Linear\", units=2),\n",
" Layer(\"Tanh\",units=output_dimensionality ),\n",
" Layer(\"Softmax\",units= output_dimensionality)],\n",
" learning_rate=0.02,\n",
" n_iter=10)\n",
"\"\"\"\n",
"from sknn.platform import gpu32\n",
"nn = Classifier(\n",
" layers=[\n",
" Layer(\"Maxout\", units=2,pieces = 2),\n",
" Layer(\"Softmax\",units= output_dimensionality)],\n",
" learning_rate=0.001,\n",
" n_iter=25)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#from sknn.platform import gpu32\n",
"z = nn.fit(X,Y)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"h = z.predict(training_set.df[training_set.features].values[1:10000])\n",
"y = training_set.df[training_set.hidden_cluster].values[1:10000]\n",
"y = np.transpose(y)[0]\n",
"s = 0\n",
"for i,hh in enumerate(h):\n",
" if hh != y[i]: s += 1\n",
"print s "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"7298\n"
]
}
],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 72
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
monaen/CellClassification | shape/analysis_shape_classification.ipynb | 1 | 59767 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## This ipynb shows the results of shapes classification\n",
"\n",
"* labels:\n",
" - circle --> 0\n",
" - rectangle --> 1\n",
" - triangle --> 2"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import os, sys\n",
"import matplotlib.pyplot as plt\n",
"from pylab import *\n",
"import glob\n",
"import collections\n",
"import random\n",
"import math\n",
"from PIL import Image, ImageDraw\n",
"%matplotlib inline\n",
"\n",
"\n",
"caffe_root = '../../../'\n",
"import caffe\n",
"from caffe import layers as L, params as P"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## define workspace paramsworkspace\n",
"workspace='examples/mywork/shape/'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## params setting\n",
"Numtrain = 6000\n",
"Numval = 1000\n",
"Numtest = 2000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### analysis function definition"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def calcu_loss_acc(net, batch_size = 1, Numval = 0):\n",
" ''' calculate the average loss and accuracy of dataset (default: validation dataset)\n",
" \n",
" input solver(Caffe solver)\n",
" batch_size\n",
" Numval(default = 0)\n",
" \n",
" return avg_loss, \n",
" avg_accuracy\n",
" '''\n",
" \n",
" # batch_size = net.blobs['data'].num\n",
" _loss = []\n",
" _accuracy = []\n",
" for i in range(Numval/batch_size):\n",
" rs = net.forward()\n",
" #print rs\n",
" _loss.append(rs['loss'].tolist())\n",
" _accuracy.append(rs['accuracy'].tolist())\n",
" \n",
" avg_loss = mean(_loss)\n",
" avg_accuracy = mean(_accuracy)\n",
"# print 'avg_loss: ', avg_loss, 'avg_accuracy ', avg_accuracy\n",
" \n",
" return avg_loss, avg_accuracy"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def calcu_ave_acc(model_def, model_weights, Numdata):\n",
" net = None\n",
" caffe.set_mode_gpu()\n",
" net = caffe.Net(model_def, # defines the structure of the model\n",
" model_weights, # contains the trained weights\n",
" caffe.TEST) # use test mode (e.g., don't perform dropout)\n",
" \n",
" \n",
" batch_size = net.blobs['data'].num\n",
" tlab_result = np.array([])\n",
" ground_truths = np.array([])\n",
" for i in range(Numdata/batch_size):\n",
" rs = net.forward()\n",
" tlab_result = np.append(tlab_result, rs['prob'].argmax(1))\n",
" ground_truths = np.append(ground_truths, net.blobs['label'].data)\n",
" \n",
" ave_acc = float(sum(tlab_result == ground_truths))/float(len(tlab_result))\n",
" print \"average accuracy: \", ave_acc\n",
" return ave_acc, tlab_result, ground_truths"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## calculate tp, tn, fp, fn\n",
"def acc_prec_recall(tlab_result, ground_truths):\n",
"\n",
" types = set(tlab_result)\n",
" if len(tlab_result) != len(ground_truths):\n",
" assert len(tlab_result) == len(ground_truths), 'The length of predicted results and ground truth labels are not match!'\n",
"\n",
" N = len(tlab_result) # N = tp + tn + fp + fn\n",
"\n",
" precision = []\n",
" recall = []\n",
"\n",
" for _type in types:\n",
"\n",
"\n",
" ind_tlab = np.where(tlab_result == _type)[0] # 37\n",
" ind_truth = np.where(ground_truths == _type)[0] # 39\n",
" ind_flab = np.where(tlab_result != _type)[0] # 163\n",
" ind_false = np.where(ground_truths != _type)[0] # 161\n",
"\n",
"\n",
" tp_list = [i for i in ind_tlab if i in ind_truth]\n",
" fp_list = [i for i in ind_tlab if i not in ind_truth]\n",
" tn_list = [i for i in ind_flab if i in ind_false]\n",
" fn_list = [i for i in ind_flab if i not in ind_false]\n",
" tp = float(len(tp_list))\n",
" fp = float(len(fp_list))\n",
" tn = float(len(tn_list))\n",
" fn = float(len(fn_list))\n",
"\n",
"\n",
" precision.append(tp/(tp+fp))\n",
" recall.append(tp/(tp+fn))\n",
"\n",
" return precision, recall"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the work root now is: /media/jupiter/mengnan/caffe-master\n"
]
}
],
"source": [
"## change the work root !!!\n",
"os.chdir(caffe_root)\n",
"print \"the work root now is: \", os.getcwd()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3 types of shapes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* average accuracy: **validation**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## params setting\n",
"val_net_path = \"train_val_5layers_bn_shape.prototxt\"\n",
"test_net_path = \"train_test_5layers_bn_shape.prototxt\"\n",
"deploy_net_path = \"deploy_5layers_bn_shape.prototxt\"\n",
"caffemodel_path = \"model/5layers_bn_iter_1200.caffemodel\"\n",
"model_val = os.path.join(workspace + val_net_path)\n",
"model_test = os.path.join(workspace + test_net_path)\n",
"model_deploy = os.path.join(workspace + deploy_net_path)\n",
"model_weights = os.path.join(workspace + caffemodel_path)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"average accuracy: 0.947\n",
"The val images accuracy is: 0.947\n"
]
}
],
"source": [
"## calculation val\n",
"ave_acc, tlab_result, ground_truths = calcu_ave_acc(model_val, model_weights, Numval)\n",
"print 'The val images accuracy is: ',ave_acc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* precision and recall of each cell type: **validation**"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"precision: [('circle', 0.9749216300940439), ('rectangle', 0.9721362229102167), ('triangle', 0.8994413407821229)]\n",
"recall: [('circle', 0.886039886039886), ('rectangle', 0.9843260188087775), ('triangle', 0.9757575757575757)]\n"
]
}
],
"source": [
"cell_types = ['circle', 'rectangle', 'triangle']\n",
"\n",
"precision, recall = acc_prec_recall(tlab_result, ground_truths)\n",
"print 'precision: ', zip(cell_types, precision)\n",
"print 'recall: ', zip(cell_types, recall)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* average accuracy: **test**"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"average accuracy: 0.935\n",
"The test images accuracy is: 0.935\n"
]
}
],
"source": [
"## calculation test\n",
"ave_acc, tlab_result, ground_truths = calcu_ave_acc(model_test, model_weights, Numtest)\n",
"print 'The test images accuracy is: ',ave_acc"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"precision: [('circle', 0.9668874172185431), ('rectangle', 0.966412213740458), ('triangle', 0.8812415654520918)]\n",
"recall: [('circle', 0.8755622188905547), ('rectangle', 0.9518796992481203), ('triangle', 0.9775449101796407)]\n"
]
}
],
"source": [
"cell_types = ['circle', 'rectangle', 'triangle']\n",
"\n",
"precision, recall = acc_prec_recall(tlab_result, ground_truths)\n",
"print 'precision: ', zip(cell_types, precision)\n",
"print 'recall: ', zip(cell_types, recall)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## now we test the network with a generated shape\n",
"\n",
"* The following part shows the network performance for classifying a randomly generated shape. We first generate three types of shapes (circle, rectangle, triangle) and then classify them individually and get the result. \n",
"\n",
"* remember our label assignments are:\n",
" 1. circle --> 0\n",
" 2. rectangle --> 1\n",
" 3. triangle --> 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### random shape generation functions definition"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def draw_triangle(img, cxy, r, fill, fuzzy=0):\n",
" x, y = cxy[0], cxy[1]\n",
" ax = x\n",
" ay = y-r\n",
" bx = x + math.floor(r*math.cos(math.pi/6))\n",
" by = y+math.floor(r*math.sin(math.pi/6))\n",
" cx = x - math.floor(r*math.cos(math.pi/6))\n",
" cy = y+math.floor(r*math.sin(math.pi/6))\n",
" if fuzzy > 0:\n",
" ax = math.floor(ax * random.uniform(fuzzy,1.0))\n",
" ay = math.floor(ay * random.uniform(fuzzy,1.0))\n",
" bx = math.floor(bx * random.uniform(fuzzy,1.0))\n",
" by = math.floor(by * random.uniform(fuzzy,1.0))\n",
" cx = math.floor(cx * random.uniform(fuzzy,1.0))\n",
" cy = math.floor(cy * random.uniform(fuzzy,1.0))\n",
"\n",
" pts = [(ax, ay), (bx, by), (cx, cy)]\n",
" draw = ImageDraw.Draw(img)\n",
" draw.polygon(pts, fill, outline=None)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def draw_circle(img, cxy, r, fill, bb=False, fuzzy=0):\n",
" draw = ImageDraw.Draw(img)\n",
" tlx=cxy[0]-r\n",
" tly=cxy[1]-r\n",
" brx=cxy[0]+r\n",
" bry=cxy[1]+r\n",
" if fuzzy > 0:\n",
" tlx=math.floor(tlx * random.uniform(fuzzy,1.0))\n",
" tly=math.floor(tly * random.uniform(fuzzy,1.0))\n",
" brx=math.floor(brx * random.uniform(fuzzy,1.0))\n",
" bry=math.floor(bry * random.uniform(fuzzy,1.0))\n",
" #print (\"Fuzzy is set. (%d,%d,%d,%d) => (%d,%d,%d,%d)\" %\n",
" # (cxy[0]-r,cxy[1]-r,cxy[0]+r,cxy[1]+r, tlx, tly, brx, bry))\n",
" if bb:\n",
" draw.rectangle([tlx, tly, brx, bry],fill,outline=None)\n",
" else:\n",
" draw.ellipse([tlx, tly, brx, bry],fill,outline=None)\n",
" del draw"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def BgColor():\n",
" # fixed color: white\n",
" bcA = 225\n",
" # fixed color: grey\n",
" bcB = 150\n",
" # fixed color: darkgrey\n",
" bcC = 80\n",
" # pick one, or use a fixed one:\n",
" #bgcolor = bcA\n",
" bgcolor = random.choice([bcA, bcB, bcC])\n",
" \n",
" return bgcolor\n",
"\n",
"def FgColor():\n",
" fgcolor = random.randint(0,250)\n",
" #fgcolor = 100\n",
" return fgcolor\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def GenShapeFA(shape, crmin, crmax, n, size, clipok, fuzzy=0):\n",
" for i in range(0,n):\n",
" bgcolor = BgColor()\n",
" img = Image.new('L', (size,size), bgcolor)\n",
" imgcx = size/2\n",
" imgcy = size/2\n",
" if clipok:\n",
" cdelta = size/2\n",
" else:\n",
" cdelta = size/2 - crmax\n",
" cx = imgcx + random.randint(-cdelta,cdelta)\n",
" cy = imgcy + random.randint(-cdelta,cdelta)\n",
" if clipok:\n",
" # this line may clip\n",
" r = random.randint(crmin,crmax)\n",
" else:\n",
" # I want r to always fall within image boundary\n",
" maxr_noclip = min([cx, size-cx, cy, size-cy])\n",
" rmax = min([crmax, maxr_noclip])\n",
" rmin = min([crmin, rmax])\n",
" r = random.randint(rmin,rmax)\n",
" \n",
" fgcolor = FgColor()\n",
" if shape == \"rectangle\":\n",
" prefix=\"s\"\n",
" draw_circle(img, (cx, cy), r, fgcolor, bb=True, fuzzy=fuzzy)\n",
" elif shape == \"circle\":\n",
" prefix=\"c\"\n",
" draw_circle(img, (cx, cy), r, fgcolor, bb=False, fuzzy=fuzzy)\n",
" elif shape == \"triangle\":\n",
" prefix=\"t\"\n",
" draw_triangle(img, (cx, cy), r, fgcolor, fuzzy=fuzzy)\n",
" elif shape == \"arc\":\n",
" prefix=\"a\"\n",
" draw_arc(img, (cx, cy), r, fgcolor, fuzzy=fuzzy)\n",
" \n",
" # img.save(outdir + \"/\" + prefix + \"_%04d_%03d_%03d\" % (i,fgcolor,bgcolor) + \".jpg\")\n",
" return img"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### parameters setting\n",
"\n",
"* #### parameters"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"size=305\n",
"clipok=False\n",
" \n",
"# shapes are defined relative to a bounding/inscribing circle\n",
"crmin=40\n",
"crmax=70\n",
"nrcircle=1\n",
"fuzziness=0.8\n",
"\n",
"classes = ['circle', 'rectangle', 'triangle']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* #### load model"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"net = None\n",
"caffe.set_mode_gpu()\n",
"\n",
"net = caffe.Net(model_deploy, # defines the structure of the model\n",
" model_weights, # contains the trained weights\n",
" caffe.TEST) # use test mode (e.g., don't perform dropout)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# set the input shape\n",
"net.blobs['data'].reshape(1, # batch size\n",
" 3, # 3-channel (BGR) images\n",
" 305, 305) # image size is 305x305"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* #### generate random shape\n",
"\n",
" 1. we first generate the shape and\n",
" 2. then change the format to fit the caffe requirement\n",
" - format: float32\n",
" - color channel: BGR"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"run_control": {
"marked": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(305, 305, 3)\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAEACAYAAABLUDivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJKEAklAASSiAJBRA+gF66xWoarbtbQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f976c0287d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = GenShapeFA(\"rectangle\", crmin, crmax, nrcircle, size, clipok, fuzzy=fuzziness)\n",
"img = np.asarray(img)\n",
"img = np.tile(img, (3,1,1))\n",
"img = img.astype('float32')/255\n",
"img = img.transpose(1,2,0)\n",
"plt.imshow(img)\n",
"plt.axis(\"off\")\n",
"print img.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* change the color channel to BGR"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean-subtracted values: [('B', 158.98750144903136), ('G', 158.98750144903136), ('R', 158.98750144903136)]\n"
]
}
],
"source": [
"# load the mean for subtraction\n",
"mu = np.load(os.path.join(workspace, 'data/shape_mean.npy'))\n",
"mu = mu.mean(1).mean(1) # average over pixels to obtain the mean (BGR) pixel values\n",
"print 'mean-subtracted values:', zip('BGR', mu)\n",
"\n",
"# create transformer for the input called 'data'\n",
"transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
"\n",
"transformer.set_transpose('data', (2,0,1)) # move image channels to outermost dimension\n",
"transformer.set_mean('data', mu) # subtract the dataset-mean value in each channel\n",
"transformer.set_raw_scale('data', 255) # rescale from [0, 1] to [0, 255]\n",
"transformer.set_channel_swap('data', (2,1,0)) # swap channels from RGB to BGR"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"transformed_image = transformer.preprocess('data', img)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: rectangle\n"
]
}
],
"source": [
"# copy the image data into the memory allocated for the net\n",
"net.blobs['data'].data[...] = transformed_image\n",
"\n",
"### perform classification\n",
"output = net.forward()\n",
"\n",
"output_prob = output['prob'][0] # the output probability vector for the first image in the batch\n",
"\n",
"print 'predicted class is:', classes[output_prob.argmax()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Next, we integrate all the process into a function and make it clear"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def generate_shape(choice):\n",
" img = GenShapeFA(choice, crmin, crmax, nrcircle, size, clipok, fuzzy=fuzziness)\n",
" img = np.asarray(img)\n",
" img = np.tile(img, (3,1,1))\n",
" img = img.astype('float32')/255\n",
" img = img.transpose(1,2,0)\n",
" plt.imshow(img)\n",
" plt.axis(\"off\")\n",
" \n",
" return img"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def classify_shape(transformer, img): \n",
" transformed_image = transformer.preprocess('data', img)\n",
" # copy the image data into the memory allocated for the net\n",
" net.blobs['data'].data[...] = transformed_image\n",
"\n",
" ### perform classification\n",
" output = net.forward()\n",
"\n",
" output_prob = output['prob'][0] # the output probability vector for the first image in the batch\n",
"\n",
" print 'predicted class is:', classes[output_prob.argmax()]\n",
" \n",
" return"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### see the classification performance !!!\n",
"\n",
"* circle"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: circle\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAEACAYAAABLUDivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACtdJREFUeJzt3d1vkwUfh/Fv7WCsDGzZBlkHik4ZrQiVU6OoGTNblplo\ngiYajeGIGP+WJZ4YjzRGEyPhQEzMzAYSIzOoqGSgAxJwLo5tZYYAw07Z+hw8sXl42Ph1ffu1d6/P\nEVua9ruFXbt7ry+hbDYrALiX+7wHAKh+hAKAiVAAMBEKACZCAcBEKACYCAUAE6EAYCIUAEyEAoCJ\nUAAwNXjd8LvvvsuTTABHhw4dCuV7WY4oAJgIBQAToQBgIhQATIQCgIlQADARCgAmQgHARCgAmAgF\nABOhAGAiFABMhAKAiVAAMBEKACZCAcBEKACYCAUAE6EAYCIUAEyEAoCJUAAwEQoAJrf39QBWcuXK\nldy/Z2dndfz48aKur729Xfv27ct9HIvFtG7duqKus94QClSFTCaj0dFRSdKZM2dKet1XrlzRJ598\nkvv44Ycf1oYNG7R161bt2LGjpLcVVIQCrs6dO6cTJ04om83qn3/+qchtXrp0SZI0Njam4eFh3X//\n/Xrttdcqctu1ilCg4i5duqRTp05penradcfS0pL+/vtvpdNpDQ4OSpJeeOEFbdq0SdFo1HVbtSEU\nqJgzZ85ocnJSFy9e9J6yos8++0xtbW2KRqPq7+/3nlM1CAXKamlpSdlsVu+88473lLyl0+ncUUZ3\nd7eSyaTC4bD3LFeEAmVz9uxZnT9/Xr///rv3lIKNjIxoZGRE+/fv1wMPPKCNGzd6T3JBKFAWQ0ND\n+vXXX71nlMzw8LBaWlr0+uuve09xQShQUn/88Yc+/fRT7xllMTc3p8HBQe3cuVM9PT11dXeEUKAk\nZmZmNDExoZMnT3pPKbvx8XGtWbNGra2tSqVS3nMqgodwoySmpqbqIhL/Ghsb0/fff+89o2I4okBR\nrl27pvfff997houbN29qcHBQfX196urq8p5TVhxRoGAzMzM6cuSI9wx3X3zxhX744QfvGWXFEQUK\nks1mdfjw4Yo97LraffPNN2poaAjsOQtCgVVLp9P66KOPvGdUlWw2q6+++kqNjY1KJBLec0qOux5Y\nldnZWR09etR7RtUaGhrSqVOnvGeUHEcUyFs2m9XHH3/sPaPqjY6OKhKJaNeuXQqFQt5zSoJQIC/T\n09M6fPiw94yaMTIyolAopF27dnlPKQnueiAvv/zyi27fvu09o6YMDw97TygZQgHT1NRUyV91ql4E\n5XwOocA9jY2N6fz5894zata1a9d0+vRp7xlFIxS4p++++04///yz94yaNTc3p6+//tp7RtEIBVZ0\n8uRJXb9+3XtGINTSC/csh1BgWbdu3dLk5KT3jMBYWlrShQsXvGcUjFBgWX/99dcd76+B4mSzWf32\n22/eMwpGKLCsDz/80HtC4Jw7d65mXxaQUOAu3377rfeEwKrVZ5kSCtzl3zfIQelNTEx4TygIocAd\nTp8+rdnZWe8Zgfbee+95T1g1QoGcTCajs2fPes8IvIWFhZr7PhMK5CwuLurPP//0nhF4i4uLmpub\n856xKoQCObX2W66WpdNp3bp1y3tG3ggFckZHR70n1I3JyUnduHHDe0beCAUkqab+0wZFLX3PCQUk\nSV9++aX3hLrz+eefe0/IG6EAYCIU0Pz8vBYWFrxn1KWZmRnvCXkhFNDly5d5kJWToaEh7wl5IRQA\nTIQCgIlQADARCgAmQgHARCgAmAgF4GhxcVGZTMZ7holQAI4WFhaUTqe9Z5gIBeAoEolo27Zt3jNM\nhAKAiVAAMBEKACZCAcBEKACYCAUAE6GAYrGY1q9f7z2jLj3yyCPeE/JCKKCOjg5t2rTJe0ZdevLJ\nJ70n5IVQADARCkiSurq6vCfUnWQy6T0hb4QCkqTHH3/ce0LdSaVS3hPyRiiQ09LS4j2hbjQ1NWnN\nmjXeM/JGKJDz0ksveU+oG4lEoqZOIBMKACZCgZz169fr6aef9p4ReJFIRPv27fOesSqEAnfYvn27\nNm7c6D0j0J599lnvCatGKHCHlpYWrVu3zntGoO3YscN7wqoRCtzl1Vdf9Z4QWLV6wphQYFnd3d3e\nEwLnwQcfVFtbm/eMghAKLGvr1q1qaGjwnhEosVhMTU1N3jMKQiiwrFgspr1793rPCIxwOFyTJzH/\nRSiwoj179vD08xJ55ZVXvCcUhWNLrKi5uVmtra0Kh8O6fv2695yatHbtWjU3N2vz5s3eU4rCEQXu\n6cUXX1Rvb6/3jJq1bds2vfHGG94zikYoYNqyZUtNPSW6muzfv997QkkQCpjC4bDa2toUCoW8p9SU\njo6Omv0rx//jHAXysnfvXjU1NWloaMh7Sk1IJBLq6enxnlEyHFEgb4lEQgMDA94zql5HR4d6enp0\n333B+fEKzleCiujs7NQzzzzjPaNqPfTQQzpw4ECgIiERChTgiSee4MhiGclkUv39/d4zyoJQoCCd\nnZ3q6+sL3G/OQsXjcT3//POBfdh7ML8qVERXV5cymYyOHz/uPcVVV1eX+vr6vGeUFaFAUfbs2aPN\nmzfrwoUL+vHHH73nVFQkEtHAwIBaW1u9p5Qdx40oWnt7u9ra2hSNRr2nVExzc7NisZja29tr6tW0\nC8URBUoimUwqmUzqxIkT+umnn7znlNWWLVvU399fVy8ZSChQUk899ZRSqZSOHDkSyCeSvfnmm2ps\nbAzMIy7zxV0PlFQ4HFY0GtXBgwcVj8cD8zT1eDyuAwcOKBqN1l0kJI4oUEYvv/yypqamdPXqVR07\ndsx7TkESiYTi8bh2797tPcUVoUBZxeNxxeNx7dy5U8eOHdP4+Lj3pLy99dZbamho4LEiIhSokLVr\n16q3t1e9vb06evSopqenNT8/7z3rLp2dnXrsscfU2dnpPaWqEApU3MDAgKampjQ/P6+JiQmNjY25\n7olEInruueckSY8++qjrlmpFKOAiHo9L+u8PZnd3t27cuKEPPvhAknT79u2y3nY4HFYoFNLu3btr\n7q39vBAKVIUNGzbo7bffliSNjIzkPn/z5k1dvny56Ovevn177uNUKlUXj6YsJUKBqvO/bz60sLCg\nq1evrnjZTCaj8fFxpVKpFS/T2NhIGIpEKFDVGhsb1dHRcc/LcOKx/Pi7DwAToQBgIhQATIQCgIlQ\nADARCgAmQgHARCgAmAgFABOhAGAiFABMhAKAiVAAMBEKACZCAcBEKACYCAUAE6EAYCIUAEyEAoCJ\nUAAwEQoAJkIBwEQoAJgIBQAToQBgIhQATIQCgIlQADARCgAmQgHARCgAmAgFABOhAGAiFABMhAKA\niVAAMBEKACZCAcBEKACYCAUAE6EAYCIUAEyEAoCJUAAwEQoAJkIBwEQoAJgIBQAToQBgIhQATIQC\ngIlQADARCgAmQgHARCgAmAgFABOhAGAiFABMhAKAiVAAMBEKACZCAcBEKACYCAUAE6EAYCIUAEyE\nAoCJUAAwEQoAJkIBwEQoAJgIBQAToQBgIhQATIQCgIlQADARCgAmQgHARCgAmAgFABOhAGAiFABM\nhAKAiVAAMBEKACZCAcBEKACYCAUAE6EAYCIUAEyEAoCJUAAwEQoAJkIBwEQoAJgIBQAToQBgIhQA\nTIQCgIlQADARCgAmQgHARCgAmAgFABOhAGAiFABMoWw2670BQJXjiAKAiVAAMBEKACZCAcBEKACY\nCAUAE6EAYCIUAEyEAoCJUAAwEQoAJkIBwEQoAJgIBQAToQBgIhQATIQCgIlQADARCgAmQgHA9B+2\nDRWu4zvL8wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f97644f3a10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = generate_shape(\"circle\")\n",
"classify_shape(transformer, img)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: circle\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAEACAYAAABLUDivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9764490cd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = generate_shape(\"circle\")\n",
"classify_shape(transformer, img)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: circle\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAEACAYAAABLUDivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f97643b7110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = generate_shape(\"circle\")\n",
"classify_shape(transformer, img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* rectangle"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: rectangle\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAEACAYAAABLUDivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDfo9TyEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f97643539d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = generate_shape(\"rectangle\")\n",
"classify_shape(transformer, img)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: rectangle\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAEACAYAAABLUDivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noQCSUABJKIAkFEASCiD9Ap/3IE2KBghkAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f976427da50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = generate_shape(\"rectangle\")\n",
"classify_shape(transformer, img)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: rectangle\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAEACAYAAABLUDivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f97641ad6d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = generate_shape(\"rectangle\")\n",
"classify_shape(transformer, img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* triangle"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: triangle\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAEACAYAAABLUDivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUADRfwAA/mYhth0WKgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f976407fa50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = generate_shape(\"triangle\")\n",
"classify_shape(transformer, img)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: triangle\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAEACAYAAABLUDivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9764188f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = generate_shape(\"triangle\")\n",
"classify_shape(transformer, img)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: triangle\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAEACAYAAABLUDivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9764286450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = generate_shape(\"triangle\")\n",
"classify_shape(transformer, img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### wrong classification examples\n",
"\n",
"* sometimes the model output the wrong answer, since the whole accuracy cannot achieve 100 percent. like what I showed below. And actually each w"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: circle\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAEACAYAAABLUDivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmEYzFcYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f97642ac7d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = generate_shape(\"triangle\")\n",
"classify_shape(transformer, img)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: circle\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f9764690c90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = generate_shape(\"triangle\")\n",
"classify_shape(transformer, img)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: circle\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAEACAYAAABLUDivAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJKEAklAASSiAJBRAEgog/QLh5ha39dtE8AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f97644548d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = generate_shape(\"rectangle\")\n",
"classify_shape(transformer, img)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted class is: triangle\n"
]
},
{
"data": {
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},
"metadata": {},
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],
"source": [
"img = generate_shape(\"circle\")\n",
"classify_shape(transformer, img)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
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| mit |
idekerlab/sdcsb-advanced-tutorial | tutorials/Lesson_1_Introduction_to_cyREST.ipynb | 1 | 230986 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"# SDCSB Tutorial\n",
"### Advanced Cytoscape: Cytoscape, IPython, Docker, and reproducible network data visualization workflows\n",
"\n",
"Friday, 4/17/2015 at Sanford\n",
"\n",
"\n",
"### Lesson 1: Introduction to cyREST\n",
"\n",
"by [Keiichiro Ono](http://keiono.github.io/)\n",
"\n",
"----\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Welcome!\n",
"This is an introduction to cyREST and its basic API. In this section, you will learn how to access Cytoscape through RESTful API.\n",
"\n",
"### Prerequisites\n",
"* Basic knowledge of RESTful API\n",
" * [This is a good introduction to REST](http://www.restapitutorial.com/)\n",
"* Basic Python skill - only basics, such as conditional statements, loops, basic data types.\n",
"* Basic knowledge of Cytoscape\n",
" * Cytoscape data types - Networks, Tables, and Styles.\n",
"\n",
"### System Requirments\n",
"This tutorial is tested on the following platform:\n",
"\n",
"#### Client machine running Cytoscape\n",
"* [Java SE 8](http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html)\n",
"* [Cytoscape 3.2.1](http://cytoscape.org/download.php)\n",
"* Latest version of [cyREST app](http://apps.cytoscape.org/apps/cyrest)\n",
"\n",
"#### Server Running IPython Notebook\n",
"* Docker running [this image](https://registry.hub.docker.com/u/idekerlab/vizbi-2015/)\n",
"\n",
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Import Python Libraries and Basic Setup\n",
"\n",
"### Libraries\n",
"In this tutorial, we will use several popular Python libraries to make this workflow more realistic.\n",
"\n",
"* NumPy\n",
"* SciPy\n",
"* Pandas\n",
"* igraph\n",
"* NetworkX\n",
"* etc.\n",
"\n",
"#### Do I need to install all of them?\n",
"_NO_. Because we are running this notebook server in Docker container with all dependencies.\n",
"\n",
"#### HTTP Client\n",
"Since you need to access Cytoscape via RESTful API, HTTP client library is the most important tool you need to understand. In this example, we use [Requests](http://docs.python-requests.org/en/latest/) library to simplify API call code.\n",
"\n",
"#### JSON Encoding and Decoding\n",
"Data will be exchanged as JSON between Cytoscape and Python code. Python has built-in support for JSON and we will use [it](https://docs.python.org/2/library/json.html) in this workflow.\n",
"\n",
"### Basic Setup for the API\n",
"At this point, there is only one option for the cy-rest module: port number.\n",
"\n",
"#### Change Port Number\n",
"By default, port number used by cy-rest module is __1234__. To change this, you need set a global Cytoscape property from _**Edit → Preserences → Properties...**_ and add a new property __resr.port__.\n",
"\n",
"\n",
"## What is happing in your machine?\n",
"\n",
"#### Mac / Windows\n",
"\n",
"\n",
"\n",
"#### Linux\n",
"\n",
"\n",
"\n",
"__Actual Docker runtime is only available to Linux operating system and if you use Mac or Windows version of Docker, it is running on a Linux virtual machine (called boot2docker).__ \n",
"\n",
"#### URL to Access Cytoscape REST API\n",
"We assume you are running Cytoscape desktop application and IPython Notebook server in a Docker container we provide. To access Cytoscape REST API, use the following URL:\n",
"\n",
"\n",
"```url\n",
"http://IP_of_your_machine:PORT_NUMBER/v1/\n",
"```\n",
"\n",
"where __v1__ is the current version number of API. Once the final release is ready, we guarantee compatibility of your scripts as long as major version number is the same.\n",
"\n",
"\n",
"\n",
"### Check your machine's IP\n",
"\n",
"* For Linux and Mac:\n",
"```bash\n",
"ifconfig\n",
"```\n",
"* For Windows:\n",
"```\n",
"ipconfig\n",
"```\n",
"\n",
"## Viewing JSON\n",
"All data exchanged between Cytoscape and other applications is in JSON. You can make the JSON data more humanreadable by using browser extensions:\n",
"\n",
"* [JSONView for Firefox](http://jsonview.com/)\n",
"* [JSONView for Chrome](https://chrome.google.com/webstore/detail/jsonview/chklaanhfefbnpoihckbnefhakgolnmc)\n",
"\n",
"If you prefer command-line tools, [jq](http://stedolan.github.io/jq/) is the best choice.\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# HTTP Client for Python\n",
"import requests\n",
"\n",
"# Standard JSON library\n",
"import json\n",
"\n",
"# Basic Setup\n",
"PORT_NUMBER = 1234 # This is the default port number of CyREST"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"## Don't forget to update this line! This should be your host machine's IP address."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# IP address of your PHYSICAL MACHINE (NOT VM)\n",
"IP = '137.110.137.158'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"BASE = 'http://' + IP + ':' + str(PORT_NUMBER) + '/v1/'\n",
"\n",
"# Header for posting data to the server as JSON\n",
"HEADERS = {'Content-Type': 'application/json'}\n",
"\n",
"# Clean-up\n",
"requests.delete(BASE + 'session')\n",
"\n",
"# Utility function to display JSON (Pretty-printer)\n",
"def pp(json_data):\n",
" print(json.dumps(json_data, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Test Cytoscape REST API\n",
"\n",
"### Check the status of server\n",
"First, send a simple request and check the server status.\n",
"\n",
"#### Roundtrip between JSON and Python Object\n",
"\n",
"Object returned from the requests contains return value of API as JSON. Let's convert it into Python object. JSON library in Python converts JSON string into simple Python object."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"memoryStatus\": {\n",
" \"maxMemory\": 28217,\n",
" \"totalMemory\": 3089,\n",
" \"freeMemory\": 2393,\n",
" \"usedMemory\": 695\n",
" },\n",
" \"apiVersion\": \"v1\",\n",
" \"numberOfCores\": 8\n",
"}\n"
]
}
],
"source": [
"# Get server status\n",
"res = requests.get(BASE)\n",
"status_object = res.json()\n",
"print(json.dumps(status_object, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And of course, you can access this API from other tools, including web browsers.\n",
"\n",
"Click the following URL:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://137.110.137.158:1234/v1/\n"
]
}
],
"source": [
"print(BASE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How cyREST works?\n",
"\n",
"\n",
"\n",
"Basic mechanism of cyREST is very simple. It accesses __resources__ in Cytoscape with standard HTTP verbs: POST, GET, PUT, and DELETE. The URL above means _\"give me status of cyREST server.\"_\n",
"\n",
"And once you store the return values in Python object, you can access them through standard Python code:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"v1\n",
"695\n"
]
}
],
"source": [
"print(status_object['apiVersion'])\n",
"print(status_object['memoryStatus']['usedMemory'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are comfortable with these, you are ready to go!\n",
"\n",
"----\n",
"## 3. Import Networks from various data sources\n",
"There are many ways to load networks into Cytoscape from REST API:\n",
"\n",
"* Load from files\n",
"* Load from web services\n",
"* Send Cytoscape.js style JSON directly to Cytoscape\n",
"* Send edgelist\n",
"\n",
"### 3.1 Create networks from local files and URLs\n",
"\n",
"Let's start from a simple file loading examples. The __POST__ method is used to create new Cytoscape objects. For example,\n",
"\n",
"```bash\n",
"POST http://localhost:1234/v1/networks\n",
"```\n",
"\n",
"means create new network(s) by specified method. If you want to create networks from files on your machine or remote servers, all you need to do is create a list of file locations and post it to Cytoscape."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" {\n",
" \"networkSUID\": [\n",
" 23066\n",
" ],\n",
" \"source\": \"http://chianti.ucsd.edu/cytoscape-data/galFiltered.sif\"\n",
" },\n",
" {\n",
" \"networkSUID\": [\n",
" 21958\n",
" ],\n",
" \"source\": \"file:////Users/kono/prog/git/sdcsb-advanced-tutorial/tutorials/data/yeast.json\"\n",
" }\n",
"]\n"
]
}
],
"source": [
"# Small utility function to create networks from list of URLs\n",
"def create_from_list(network_list, collection_name='Yeast Collection'):\n",
" payload = {'source': 'url', 'collection': collection_name}\n",
" server_res = requests.post(BASE + 'networks', data=json.dumps(network_list), headers=HEADERS, params=payload)\n",
" return server_res.json()\n",
"\n",
"\n",
"# Array of data source. \n",
"network_files = [\n",
" #This should be path in the LOCAL file system! \n",
" 'file:////Users/kono/prog/git/sdcsb-advanced-tutorial/tutorials/data/yeast.json',\n",
" # SIF file on a web server\n",
" 'http://chianti.ucsd.edu/cytoscape-data/galFiltered.sif'\n",
" \n",
" # And of course, you can add as many files as you need...\n",
"]\n",
"\n",
"# Create!\n",
"res_json = create_from_list(network_files)\n",
"print(json.dumps(res_json, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What Happened?\n",
"\n",
"\n",
"\n",
"1. Send list of resource (file) locations as URL from this notebook\n",
"1. cyREST interpret the requrest for Cytoscape\n",
"1. Cytoscape uses its own data file loaders from the resource locations in the list\n",
"1. Cytoscape returns list of new networks created in the session.\n",
"\n",
"### What is _SUID_?\n",
"__SUID__ is the unique identifier for all graph objects in Cytoscape. You can access any objects in current session as long as you have its SUID. For the example above, you can access the new network SUIDs by:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[23066, 21958]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_network_suids = []\n",
"for new_network in res_json:\n",
" sample_network_suids.append(new_network['networkSUID'][0])\n",
"\n",
"sample_network_suids"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that __Cytoscape may creates multiple networks from a single network resource__. This is why you need index number after __networkSUID__. In this tutorial, all network resource (file) contains only one network, so you can just use __0__ to access the result."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Where is my local data file?\n",
"This is a bit trickey part. When you specify local file, you need to absolute path \n",
"\n",
"On Docker container, your data file is mounted on:\n",
"\n",
"```\n",
"/notebooks/data\n",
"```\n",
"\n",
"However, actual file is in:\n",
"\n",
"```\n",
"PATH_TO_YOUR_WORKSPACE/vizbi-2015-cytoscape-tutorial/notebooks/data\n",
"```\n",
"\n",
"Although you can see the data directory on _/notebooks/data_, __you need to use absolute path to access actual data from Cytoscape__. You may think this is a bit annoying, but actually, this is the power of container technology. You can use completely isolated environment to run your workflow. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 Create networks from public web services\n",
"\n",
"\n",
"\n",
"There are many public data sources and web services for biology. If the service supports Cytoscape-readable file formats, you can directly specify the query URL as the network resource location. For example, the following URL calls [KEGG REST API](http://www.kegg.jp/kegg/docs/keggapi.html) and load the [TCA Cycle pathway diagram for human](http://www.genome.jp/kegg-bin/show_pathway?org_name=hsa&mapno=00020&mapscale=&show_description=show).\n",
"\n",
"* [KEGG PATHWAY: hsa00020 Citrate cycle (TCA cycle) - Homo sapiens (human)](http://www.genome.jp/dbget-bin/www_bget?pathway+hsa00020)\n",
" * REST API to download the pathway in KGML format: http://rest.kegg.jp/get/hsa00020/kgml\n",
"\n",
"__Hand-drawn pathway diagram in KEGG:__\n",
"\n",
"\n",
"----\n",
"### Warning: You need to install [KEGGScape App](http://apps.cytoscape.org/apps/keggscape) to Cytoscape before running the following cells!\n",
"You can just click the link above and press __Install__ to directly install the app from the web.\n",
"\n",
"----\n",
"\n",
"#### Loading external data directly from API\n",
"If the data format is supported in Cytoscape, you can import it programmatically by passing the resource location (URL) to Cytoscape:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" {\n",
" \"networkSUID\": [\n",
" 24231\n",
" ],\n",
" \"source\": \"http://rest.kegg.jp/get/hsa00020/kgml\"\n",
" }\n",
"]\n"
]
}
],
"source": [
"# Resource location as URL\n",
"tca_cycle_human = 'http://rest.kegg.jp/get/hsa00020/kgml'\n",
"\n",
"# Pass it to Cytoscape\n",
"pp(create_from_list([tca_cycle_human], 'KEGG Metabolic Pathways'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and now your Cytoscape window should look like this:\n",
"\n",
"\n",
"\n",
"\n",
"### Connect multiple web services\n",
"OK, this is not so interesting because it can be done manually from GUI if we want. But what happens if you need to check hundreds of resources and filter the results? You can easily handle such problems if you know how to write your workflow as notebook (code).\n",
"\n",
"In this example, we will do the following:\n",
"\n",
"* Send simple query to fild list of pathways involving cancer (use [TogoWS](http://togows.dbcls.jp/))\n",
"* Convert the result tomake it readable for other web service (KEGG API)\n",
"* Import some of the result into Cytoscape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" \"path:map05200\\tPathways in cancer\",\n",
" \"path:map05202\\tTranscriptional misregulation in cancer\",\n",
" \"path:map05205\\tProteoglycans in cancer\",\n",
" \"path:map05206\\tMicroRNAs in cancer\",\n",
" \"path:map05210\\tColorectal cancer\",\n",
" \"path:map05212\\tPancreatic cancer\",\n",
" \"path:map05213\\tEndometrial cancer\",\n",
" \"path:map05215\\tProstate cancer\",\n",
" \"path:map05216\\tThyroid cancer\",\n",
" \"path:map05219\\tBladder cancer\",\n",
" \"path:map05222\\tSmall cell lung cancer\",\n",
" \"path:map05223\\tNon-small cell lung cancer\",\n",
" \"path:map05230\\tCentral carbon metabolism in cancer\",\n",
" \"path:map05231\\tCholine metabolism in cancer\"\n",
"]\n"
]
}
],
"source": [
"# Find pathways involving cancer\n",
"res = requests.get('http://togows.org/search/kegg-pathway/cancer/1,50.json')\n",
"pp(res.json())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This raw result needs some work to make it usable in other services. In the following cell, Python creates URLs from the list of pathway ID/pathway name pair:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" \"http://rest.kegg.jp/get/hsa05200/kgml\",\n",
" \"http://rest.kegg.jp/get/hsa05202/kgml\",\n",
" \"http://rest.kegg.jp/get/hsa05205/kgml\",\n",
" \"http://rest.kegg.jp/get/hsa05206/kgml\",\n",
" \"http://rest.kegg.jp/get/hsa05210/kgml\",\n",
" \"http://rest.kegg.jp/get/hsa05212/kgml\",\n",
" \"http://rest.kegg.jp/get/hsa05213/kgml\",\n",
" \"http://rest.kegg.jp/get/hsa05215/kgml\",\n",
" \"http://rest.kegg.jp/get/hsa05216/kgml\",\n",
" \"http://rest.kegg.jp/get/hsa05219/kgml\",\n",
" \"http://rest.kegg.jp/get/hsa05222/kgml\",\n",
" \"http://rest.kegg.jp/get/hsa05223/kgml\",\n",
" \"http://rest.kegg.jp/get/hsa05230/kgml\",\n",
" \"http://rest.kegg.jp/get/hsa05231/kgml\"\n",
"]\n"
]
}
],
"source": [
"# Convert to URLs. This can be done with for loop, but for simplicity, we use map function.\n",
"\n",
"# Extract ID portion of entries\n",
"path_ids = list(map(lambda x: x.split('\\t')[0], res.json()))\n",
"\n",
"# Make it consumable by KEGG API (Convert to list of URLs)\n",
"path_url_human = list(map(lambda x: 'http://rest.kegg.jp/get/' + x.replace('path:map', 'hsa') + '/kgml', path_ids))\n",
"\n",
"pp(path_url_human)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Let's pick first 3 result and import actual pathways."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" {\n",
" \"networkSUID\": [\n",
" 26142\n",
" ],\n",
" \"source\": \"http://rest.kegg.jp/get/hsa05205/kgml\"\n",
" },\n",
" {\n",
" \"networkSUID\": [\n",
" 25694\n",
" ],\n",
" \"source\": \"http://rest.kegg.jp/get/hsa05202/kgml\"\n",
" },\n",
" {\n",
" \"networkSUID\": [\n",
" 24511\n",
" ],\n",
" \"source\": \"http://rest.kegg.jp/get/hsa05200/kgml\"\n",
" }\n",
"]\n"
]
}
],
"source": [
"# This may take a while...\n",
"pp(create_from_list(path_url_human[0:3], 'KEGG Metabolic Pathways'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Discussion\n",
"The pipeline above is just a toy example, but you can automate your data preparation and import part if you use Python. You can try other web services to make it more realistic. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Understand REST Principles\n",
"We used modern best practices to design cyREST API. All __HTTP verbs__ are mapped to Cytoscape resources: \n",
"\n",
"| HTTP Verb | Description |\n",
"|:----------:|:------------|\n",
"| GET | Retrieving resources (in most cases, it is Cytoscape data objects, such as networks or tables) |\n",
"| POST | Creating resources | \n",
"| PUT | Changing/replacing resources or collections |\n",
"| DELETE | Deleting resources |\n",
"\n",
"This design style is called [Resource Oriented Architecture (ROA)](http://www.infoq.com/articles/roa-rest-of-rest).\n",
"\n",
"Actually, basic idea is very simple: mapping all operations to existing HTTP verbs. It is easy to understand once you try actual examples.\n",
"\n",
"#### GET (Get a resource)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://137.110.137.158:1234/v1/networks\n",
"[\n",
" 21958,\n",
" 24231,\n",
" 23066,\n",
" 25694,\n",
" 26142,\n",
" 24511\n",
"]\n"
]
}
],
"source": [
"# Get a list of network IDs\n",
"get_all_networks_url = BASE + 'networks'\n",
"print(get_all_networks_url)\n",
"res = requests.get(get_all_networks_url)\n",
"pp(res.json())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://137.110.137.158:1234/v1/networks/21958\n",
"http://137.110.137.158:1234/v1/networks/21958/nodes/count\n",
"http://137.110.137.158:1234/v1/networks/21958/nodes\n",
"http://137.110.137.158:1234/v1/networks/21958/tables/defaultnode.csv\n"
]
}
],
"source": [
"# Pick the first network from the list above:\n",
"network_suid = res.json()[0]\n",
"get_network_url = BASE + 'networks/' + str(network_suid)\n",
"print(get_network_url)\n",
"\n",
"# Get number of nodes in the network\n",
"get_nodes_count_url = BASE + 'networks/' + str(network_suid) + '/nodes/count'\n",
"print(get_nodes_count_url)\n",
"\n",
"# Get all nodes\n",
"get_nodes_url = BASE + 'networks/' + str(network_suid) + '/nodes'\n",
"print(get_nodes_url)\n",
"\n",
"# Get Node data table as CSV\n",
"get_node_table_url = BASE + 'networks/' + str(network_suid) + '/tables/defaultnode.csv'\n",
"print(get_node_table_url)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise 1: _Guess_ URLs\n",
"If a system's RESTful API is well-designed based on ROA best practices, it should be easy to guess similar functions as URLs.\n",
"\n",
"__Display a clickable URLs for the following functions:__\n",
"\n",
"1. Show number of networks in current session\n",
"1. Show all edges in a network\n",
"1. Show full information for a node (can be any node)\n",
"1. Show information for all columns in the default node table\n",
"1. Show all values in default node table under \"name\" column"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Write your answers here..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### POST (Create a new resource)\n",
"To create new resource (objects), you should use __POST__ methods. URLs follow ROA standards, but you need to read API documents to understand data format for each object."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" {\n",
" \"SUID\": 27043,\n",
" \"name\": \"Node created at 2015-04-15 20:40:33.608301\"\n",
" },\n",
" {\n",
" \"SUID\": 27044,\n",
" \"name\": \"Node created at 2015-04-15 20:40:33.608346\"\n",
" }\n",
"]\n"
]
}
],
"source": [
"# Add a new nodes to existing network (with time stamps)\n",
"import datetime\n",
"\n",
"new_nodes =[\n",
" 'Node created at ' + str(datetime.datetime.now()),\n",
" 'Node created at ' + str(datetime.datetime.now())\n",
"]\n",
"\n",
"res = requests.post(get_nodes_url, data=json.dumps(new_nodes), headers=HEADERS)\n",
"new_node_ids = res.json()\n",
"pp(new_node_ids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### DELETE (Delete a resource)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Response [200]>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Delete all nodes\n",
"requests.delete(BASE + 'networks/' + str(sample_network_suids[0]) + '/nodes')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Response [200]>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Delete a network\n",
"requests.delete(BASE + 'networks/' + str(sample_network_suids[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### PUT (Update a resource)\n",
"__PUT__ method is used to update information for existing resources. Just like POST methods, you need to know the data format to be posted."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Response [200]>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Update a node name\n",
"new_values = [\n",
" {\n",
" 'SUID': new_node_ids[0]['SUID'],\n",
" 'value' : 'updated 1'\n",
" },\n",
" {\n",
" 'SUID': new_node_ids[1]['SUID'],\n",
" 'value' : 'updated 2'\n",
" }\n",
"]\n",
"requests.put(BASE + 'networks/' + str(network_suid) + '/tables/defaultnode/columns/name', data=json.dumps(new_values), headers=HEADERS)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.3 Create networks from Python objects\n",
"And this is the most powerful feature in Cytoscape REST API. __You can easily convert Python objects into Cytoscape networks, tables, or Visual Styles__\n",
"\n",
"#### How does this work?\n",
"Cytoscape REST API sends and receives data as JSON. For networks, it uses [Cytoscape.js style JSON](http://cytoscape.github.io/cytoscape.js/) (support for more file formats are comming!). You can programmatically generates networks by converting Python dictionary into JSON.\n",
"\n",
"#### 3.3.1 Prepare Network as Cytoscape.js JSON\n",
"Let's start with the simplest network JSON:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Empty network has SUID 27063\n"
]
}
],
"source": [
"# Manually generates JSON as a Python dictionary\n",
"def create_network():\n",
" network = { \n",
" 'data': {\n",
" 'name': 'I\\'m empty!'\n",
" },\n",
" 'elements': {\n",
" 'nodes':[],\n",
" 'edges':[]\n",
" }\n",
" }\n",
" return network\n",
"\n",
"\n",
"# Difine a simple utility function\n",
"def postNetwork(data):\n",
" url_params = {\n",
" 'collection': 'My Network Colleciton'\n",
" }\n",
" res = requests.post(BASE + 'networks', params=url_params, data=json.dumps(data), headers=HEADERS)\n",
" return res.json()['networkSUID']\n",
"\n",
"\n",
"# POST data to Cytoscape\n",
"empty_net_1 = create_network()\n",
"empty_net_1_suid = postNetwork(empty_net_1)\n",
"print('Empty network has SUID ' + str(empty_net_1_suid))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Modify network dara programmatically\n",
"Since it's a simple Python dictionary, it is easy to add data to the network. Let's add some nodes and edges:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n"
]
}
],
"source": [
"# Create sequence of letters (A-Z)\n",
"seq_letters = list(map(chr, range(ord('A'), ord('Z')+1)))\n",
"print(seq_letters)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Option 1: Add nods and edges with for loops\n",
"def add_nodes_edges(network):\n",
" nodes = []\n",
" edges = []\n",
" \n",
" for lt in seq_letters:\n",
" node = {\n",
" 'data': {\n",
" 'id': lt\n",
" }\n",
" }\n",
" nodes.append(node)\n",
" for lt in seq_letters:\n",
" edge = {\n",
" 'data': { \n",
" 'source': lt, \n",
" 'target': 'A' \n",
" }\n",
" }\n",
" edges.append(edge)\n",
" network['elements']['nodes'] = nodes\n",
" network['elements']['edges'] = edges\n",
" network['data']['name'] = 'A is the hub.'\n",
"\n",
"# Option 2: Add nodes and edges in functional way\n",
"def add_nodes_edges_functional(network):\n",
" network['elements']['nodes'] = list(map(lambda x: {'data': { 'id': x }}, seq_letters))\n",
" network['elements']['edges'] = list(map(lambda x: {'data': { 'source': x, 'target': 'A' }}, seq_letters))\n",
" network['data']['name'] = 'A is the hub (Functional Way)'\n",
"\n",
"# Uncomment this if you want to see the actual JSON object\n",
"# print(json.dumps(empty_network, indent=4))\n",
"\n",
"net1 = create_network()\n",
"net2 = create_network()\n",
"\n",
"add_nodes_edges_functional(net1)\n",
"add_nodes_edges(net2)\n",
"\n",
"networks = [net1, net2]\n",
"\n",
"def visualize(net):\n",
" suid = postNetwork(net)\n",
" net['data']['SUID'] = suid\n",
" # Apply layout and Visual Style\n",
" requests.get(BASE + 'apply/layouts/force-directed/' + str(suid))\n",
" requests.get(BASE + 'apply/styles/Directed/' + str(suid))\n",
"\n",
"for net in networks:\n",
" visualize(net)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, your Cytoscpae window should look like this:\n",
"\n",
"\n",
"\n",
"### Embed images in IPython Notebook\n",
"cyRest has function to generate PNG image directly from current network view. Let's try to see the result in this notebook."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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NcNn9Mj097aQBWMGrcz0DURfXcyFZnIKxv8f1nyMLIdd7kIJnSq79XUnPXGAo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8wqcakJXpUZzciXSBrMCHB+5WDVckDk0oT4FgPnxXJ4ruaYts+F4NaQDZwuB/imBmIZoEclLFrN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\niq9y7zIvAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"\n",
"Image(url=BASE+'networks/' + str(net1['data']['SUID'])+ '/views/first.png', embed=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction to Cytoscape Data Model\n",
"Essentially, writing your workflow as a notebook is a programming. To control Cytoscape efficiently from Notebooks, you need to understand basic data model of Cytoscape. Let me explain it as a notebook...\n",
"\n",
"First, let's create a data file to visualize Cytoscape data model"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting data/model.sif\n"
]
}
],
"source": [
"%%writefile data/model.sif\n",
"Model parent_of ViewModel_1\n",
"Model parent_of ViewModel_2\n",
"Model parent_of ViewModel_3\n",
"ViewModel_1 parent_of Presentation_A\n",
"ViewModel_1 parent_of Presentation_B\n",
"ViewModel_2 parent_of Presentation_C\n",
"ViewModel_3 parent_of Presentation_D\n",
"ViewModel_3 parent_of Presentation_E\n",
"ViewModel_3 parent_of Presentation_F"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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pbm6OT9JlXg4wvN/3loVDvkpKSjI66Og9LDbs9lpBQUE+RxIAgK4/7I/7D87K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\nL099AAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = [\n",
" 'file:////Users/kono/prog/git/sdcsb-advanced-tutorial/tutorials/data/model.sif'\n",
"]\n",
"\n",
"# Create!\n",
"res = create_from_list(model)\n",
"model_suid = res[0]['networkSUID'][0]\n",
"\n",
"requests.get(BASE + 'apply/layouts/force-directed/' + str(model_suid))\n",
"Image(url=BASE+'networks/' + str(model_suid)+ '/views/first.png', embed=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mode, View Model, and Presentation\n",
"\n",
"#### Model\n",
"Essentially, __Model__ in Cytoscape means networks and tables. Internally, Model can have multiple View Models.\n",
"\n",
"#### View Model\n",
"State of the view.\n",
"\n",
"This is why you need to use __views__ instead of view in the API:\n",
"\n",
"```\n",
"/v1/networks/SUID/views\n",
"```\n",
"\n",
"However, Cytoscape 3.2.x has only one rendering engine for now, and end-users do not have access to this feature. Until Cytoscape Desktop supports multiple renderers, best practice is just use one view per model. To access the default view, there is a utility method _first_:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You can access (default) network view from this URL: http://137.110.137.158:1234/v1/networks/27307/views/first\n"
]
}
],
"source": [
"view_url = BASE + 'networks/' + str(model_suid) + '/views/first'\n",
"print('You can access (default) network view from this URL: ' + view_url)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Presentation\n",
"Presentation is a stateless, actual graphics you see in the window. A View Model can have multiple Presentations. For now, you can assume there is always one presentation per View Model.\n",
"\n",
"----\n",
"\n",
"### What do you need to know as a cyREST user?\n",
"CyREST API is fairly low level, and you can access all levels of Cytoscpae data structures. But if you want to use Cytoscape as a simple network visualization engine for IPython Notebook, here are some tips:\n",
"\n",
"#### Tip 1: Always keep SUID when you create any new object\n",
"__ALL Cytoscape objects, networks, nodes, egdes, and tables have a session unique ID, called SUID__. When you create any new data objects in Cytoscape, it returns SUIDs. You need to keep them as Python data objects (list, dict, amp, etc.) to access them later.\n",
"\n",
"#### Tip 2: Create one view per model\n",
"Until Cytoscape Desktop fully support multiple view/presentation feature, keep it simple: one view per model.\n",
"\n",
"#### Tip 3: Minimize number of API calls\n",
"Of course, there is a API to add / remove / update one data object per API call, but it is extremely inefficient!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3.3.2 Prepare Network as edgelist\n",
"Edgelist is a minimalistic data format for networks and it is widely used in popular libraries including NetworkX and igraph. Preparing edgelist in Python is straightforward. You just need to prepare a list of edges as string like:\n",
"\n",
"```\n",
"a b\n",
"b c\n",
"a c\n",
"c d\n",
"d f\n",
"b f\n",
"f g\n",
"f h\n",
"```\n",
"\n",
"In Python, there are many ways to generate string like this. Here is a naive approach:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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PYWZmpnd2M1H7bytVLjrWHkYVa+kZC0Y+OlH1SfYwAgAQJ6U/2GDj00QCnBkH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Qqt9euatq4Wt3z3MA/+/xzLdJiuGoDYOyndTk06pqajsSAnVbqo/u8qLVVfi7071FhSUjKk+Tb6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\nRqKZvo9ql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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_str = ''\n",
"n = 0\n",
"while n <100:\n",
" data_str = data_str + str(n) + '\\t' + str(n+1) + '\\n'\n",
" n = n + 1\n",
"\n",
"# Join the first and last nodes\n",
"data_str = data_str + '100\\t0\\n'\n",
"\n",
"# print(data_str)\n",
"\n",
"# You can create multiple networks by running simple for loop:\n",
"for i in range(3):\n",
" res = requests.post(BASE + 'networks?format=edgelist&collection=Rings!', data=data_str, headers=HEADERS)\n",
" circle_suid = res.json()['networkSUID']\n",
" requests.get(BASE + 'apply/layouts/circular/' + str(circle_suid))\n",
"\n",
"Image(url=BASE+'networks/' + str(circle_suid) + '/views/first.png', embed=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise 2: Create a network from a simple edge list file\n",
"Edge list is a human-editable text file to represent a graph structure. Using the sample data abobe (edge list example in 3.3.2), create a new network in Cytoscape from the edge list and visualize it just like the ring network above. \n",
"\n",
"__Hint__: Use Magic!"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Write your code here..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Discussion\n",
"In this section, we've learned how to generate networks programmatically in Python. But for real world problems, it is not a good idea to use low level Python code to generate networks because there are lots of cool graph libraries such as NetworkX or igraph which provide high level graph APIs. In the next session, let's use them to analyze real network data sets.\n",
"\n",
"## Continues to [Lesson 2: Visualization](Lesson_2_Visualization.ipynb)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
xmnlab/pywim | notebooks/AccuracyCalculationCOST323.ipynb | 2 | 138334 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true,
"toc": "true"
},
"source": [
"# Table of Contents\n",
" <p><div class=\"lev1 toc-item\"><a href=\"#Accuracy-Calculation-COST-323\" data-toc-modified-id=\"Accuracy-Calculation-COST-323-1\"><span class=\"toc-item-num\">1 </span>Accuracy Calculation COST 323</a></div><div class=\"lev2 toc-item\"><a href=\"#Description-of-the-accuracy-calculation-sheet\" data-toc-modified-id=\"Description-of-the-accuracy-calculation-sheet-11\"><span class=\"toc-item-num\">1.1 </span>Description of the accuracy calculation sheet</a></div><div class=\"lev2 toc-item\"><a href=\"#Source\" data-toc-modified-id=\"Source-12\"><span class=\"toc-item-num\">1.2 </span>Source</a></div><div class=\"lev3 toc-item\"><a href=\"#Tolarance-classes\" data-toc-modified-id=\"Tolarance-classes-121\"><span class=\"toc-item-num\">1.2.1 </span>Tolarance classes</a></div><div class=\"lev2 toc-item\"><a href=\"#Test\" data-toc-modified-id=\"Test-13\"><span class=\"toc-item-num\">1.3 </span>Test</a></div><div class=\"lev1 toc-item\"><a href=\"#References\" data-toc-modified-id=\"References-2\"><span class=\"toc-item-num\">2 </span>References</a></div>"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Accuracy Calculation COST 323"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"The accuracy calculation can be consulting in \\cite{jacob1998european}. In the \n",
"accuracy calculation sheet provided by COST 323, we can extract the follow\n",
"information:"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Description of the accuracy calculation sheet\n",
"\n",
"Initial verification: (Yes=1, No=0)\n",
"\n",
"Sheet Fields (the header columns stay in line 8 and 9 into the sheet): \n",
"\n",
"* SYSTEM/Entity (column A/Index), \n",
"* Number (column B) [Input], \n",
"* Identified (column C) [Input], \n",
"* Mean (column D) [Input], \n",
"* Std deviat (column E) [Input], \n",
"* p_o (column F), \n",
"* Class (column G), \n",
"* d (column H), \n",
"* d_min (column I) [Input/Minimization Solver Output],\n",
"* d_c (column J),\n",
"* class (column K), \n",
"* p (column L) - related to column I, \n",
"* p (column M) - related to column H, \n",
"* p' (column N), \n",
"* Accepted (column O)\n",
"\n",
"Test plan\n",
"\n",
"* \"r1\"=full repeatability\n",
"* \"r2\"=extended repeatability\n",
"* \"rr1\"=limited reproducibility\n",
"* \"rr2\"=full reproducibility\n",
"\n",
"Env\n",
"\n",
"* \"I\"=environmental repeatability\n",
"* \"II\"=environmental limited reproducibility\n",
"* \"III\"=environmental full reproducibility\n",
"\n",
"Description:\n",
"\n",
"* Number: number of measured vehicles/axles/etc. which are valid and kept for the\n",
"test (may be less than those of the test plan)\n",
"* Identified: percentage of the total number of measured vehicles/axles/etc. \n",
"which are valid and kept for the test (enter by hand)\n",
"* Mean, Std Deviation: mean and standard deviation of the individual relative \n",
"errors with respect to the static reference loads\n",
"* p_o: minimum required level of confidence within the class tolerance d, i.e. \n",
"of the confidence interval [-d;d]\n",
"* d_min: for each criterion, tolerance (half width of the confidence interval) \n",
"which exactly corresponds to the minimum required level of confidence p_o\n",
"* d_c: \"best acceptable interpolated class for the given criterion\", i.e. \n",
"tolerance on the GW which exactly corresponds to the d_min of the given \n",
"criterion"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Source"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"from scipy.stats import norm\n",
"from scipy.optimize import root, fsolve\n",
"from scipy import stats\n",
"\n",
"import numba as nb\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def calc_min_confidence(\n",
" data: pd.DataFrame, test_plan: str, env_condition: str\n",
"):\n",
" \"\"\"\n",
" =100*(2*NORMDIST(\\\\\n",
" IF(\\$B\\$5=\"r1\";2,675/IF(\\$D\\$5=\"III\";1,1;IF(\\$D\\$5=\"II\";1,05;1));\\\\\n",
" IF(\\$B\\$5=\"r2\";2,36/IF(\\$D\\$5=\"III\";1,1;IF(\\$D\\$5=\"II\";1,05;1));\\\\\n",
" IF(\\$B\\$5=\"RR1\";2,155/IF(\\$D\\$5=\"III\";1,1;IF(\\$D\\$5=\"II\";1,05;1));\\\\\n",
" IF(\\$B\\$5=\"RR2\";2/IF(\\$D\\$5=\"III\";1,1;IF(\\$D\\$5=\"II\";1,05;1));0))\\\\\n",
" ))-TINV(0,05;B9-1)/SQRT(B9);0;1;TRUE())-1)\n",
" \n",
" \"\"\"\n",
" # data = data.copy()\n",
" \n",
" def _calc(v: float):\n",
" \n",
" if env_condition=='I':\n",
" _v = 1 \n",
" elif env_condition=='II':\n",
" _v = 1.05\n",
" elif env_condition=='III':\n",
" _v = 1.1 \n",
" else:\n",
" raise Exception('INVALID_ENV_CONDITION')\n",
" \n",
" if test_plan=='R1':\n",
" _v = 2.675/_v\n",
" elif test_plan=='R2':\n",
" _v = 2.36/_v\n",
" elif test_plan=='RR1':\n",
" _v = 2.155/_v\n",
" elif test_plan=='RR2': \n",
" _v = 2/_v\n",
" else:\n",
" raise Exception('INVALID_TEST_PLAN')\n",
"\n",
" # in isf, the probabity is divided by 2 \n",
" # because in excel TINV is 2-side tail\n",
" return 100 * (\n",
" 2 * norm.cdf(_v-stats.t.isf(0.05/2, v-1)/np.sqrt(v), 0, 1)-1\n",
" )\n",
" \n",
" test_plan = test_plan.upper()\n",
" env_condition = env_condition.upper()\n",
" \n",
" data['min_confidence'] = data.number.apply(_calc)\n",
" \n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def calc_best_acceptable_class(\n",
" data: pd.DataFrame, initial_verification: bool\n",
") -> pd.DataFrame:\n",
" \"\"\"\n",
" \n",
" \"\"\"\n",
" # data = data.copy()\n",
" # IF(M$4=0;1;1,25)\n",
" factor = 1.25 if initial_verification else 1\n",
" best_acceptable_class = []\n",
" \n",
" # gwv \n",
" # =I9∗IF(M$4=0;1;1,25)\n",
" best_acceptable_class.append(data.loc['gwv', 'min_tolerance'] * factor)\n",
" \n",
" # group of axles \n",
" # =IF(I10<10; 0,7∗I10∗IF(M$4=0;1;1,25); I10∗IF(M$4=0;1;1,25)−3)\n",
" v = data.loc['group_axles', 'min_tolerance']\n",
" v = v*0.7*factor if v < 10 else v*factor-3\n",
" best_acceptable_class.append(v)\n",
" \n",
" # single axle\n",
" # =IF(I11<15;\n",
" # I11∗(I11∗IF(M$4=0;1;1,25)+97)∗IF(M$4=0;1;1,25)/168;\n",
" # I11∗IF(M$4=0;1;1,25)−5)\n",
" v = data.loc['single_axle', 'min_tolerance']\n",
" v = v*(v*factor+97)*factor/168 if v < 15 else v*factor-5\n",
" best_acceptable_class.append(v)\n",
" \n",
" # axle of a group\n",
" # =IF(I12<20;I12∗IF(M$4=0;1;1,25)/2;(I12∗IF(M$4=0;1;1,25)−10))\n",
" v = data.loc['axle_group', 'min_tolerance']\n",
" v = v*factor/2 if v < 20 else v*factor-10\n",
" best_acceptable_class.append(v)\n",
" \n",
" data['best_acceptable_class'] = best_acceptable_class\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def calc_classification(data: pd.DataFrame) -> pd.DataFrame:\n",
" \"\"\"\n",
" =IF(OR(J9<=5;J9>7);ROUNDUP((J9/5);0)*5;7)\n",
"\n",
" \"\"\"\n",
" def _calc(v: float):\n",
" # =IF(OR(J9<=5;J9>7);ROUNDUP((J9/5);0)*5;7)\n",
" return np.ceil(v/5)*5 if v<=5 or v>7 else 7\n",
" \n",
" data['class_value'] = data.best_acceptable_class.apply(_calc)\n",
" \n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def resolve_class_name(data: pd.DataFrame) -> pd.DataFrame:\n",
" \"\"\"\n",
" =IF(K9<=5;CONCATENATE(\"A(\";TEXT(K9;\"0\");\")\");\\\\\n",
" IF(K9<=7;CONCATENATE(\"B+(\";TEXT(K9;\"0\");\")\");\\\\\n",
" IF(K9<=10;CONCATENATE(\"B(\";TEXT(K9;\"0\");\")\");\\\\\n",
" IF(K9<=15;CONCATENATE(\"C(\";TEXT(K9;\"0\");\")\");\\\\\n",
" IF(K9<=20;CONCATENATE(\"D+(\";TEXT(K9;\"0\");\")\");\\\\\n",
" IF(K9<=25;CONCATENATE(\"D(\";TEXT(K9;\"0\");\")\");\\\\\n",
" CONCATENATE(\"E(\";TEXT(K9;\"0\");\")\")))))))\n",
" \"\"\"\n",
" def _resolve(v: int):\n",
" c = (\n",
" 'A' if v<=5 else\n",
" 'B+' if v<=7 else\n",
" 'B' if v<=10 else\n",
" 'C' if v<=15 else\n",
" 'D+' if v<=20 else\n",
" 'D' if v<=25 else\n",
" 'E'\n",
" )\n",
" return '%s(%s)' % (c, int(v))\n",
" \n",
" data['class_name'] = data.class_value.apply(_resolve)\n",
" return data\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def calc_delta(data: pd.DataFrame, initial_verification: bool):\n",
" \"\"\"\n",
" \n",
" \"\"\"\n",
" d = []\n",
" # factor\n",
" # IF(M$4=0;1;0,8)\n",
" factor = 0.8 if initial_verification else 1\n",
" \n",
" # gwv \n",
" # =K9*IF(M$4=0;1;0,8)\n",
" d.append(data.loc['gwv', 'class_value']*factor)\n",
" \n",
" # group of axles\n",
" # =IF(K10<7;K10/0,7;IF(K10<30;K10+3;K10*1,1))*IF(M$4=0;1;0,8)\n",
" v = data.loc['group_axles', 'class_value']\n",
" v = v/0.7 if v<7 else v+3 if v<30 else v*1.1 \n",
" d.append(v*factor)\n",
"\n",
" # single axle\n",
" # =IF(K11<10;K11*(85-K11)/50;IF(K11<25;K11+5;6*K11/5))*IF(M$4=0;1;0,8)\n",
" v = data.loc['single_axle', 'class_value']\n",
" v = v*(85-v)/50 if v<10 else v+5 if v<25 else 6*v/5\n",
" d.append(v*factor)\n",
"\n",
" # axle of group\n",
" # =IF(K12<10;2*K12;IF(K12<25;K12+10;6*K12/5+5))*IF(M$4=0;1;0,8)\n",
" v = data.loc['axle_group', 'class_value']\n",
" v = 2*v if v<10 else v+10 if v<25 else 6*v/5+5\n",
" d.append(v*factor)\n",
" \n",
" data['d'] = d\n",
" return d"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def calc_confidence_level(data: pd.DataFrame) -> pd.DataFrame:\n",
" \"\"\"\n",
" * Number (column B) [Input], \n",
" * Identified (column C) [Input], \n",
" * Mean (column D) [Input], \n",
" * Std deviat (column E) [Input], \n",
" * p_o (column F), \n",
" * Class (column G), \n",
" * d (column H), \n",
" * d_min (column I) [Input/Minimization Solver Output],\n",
" * d_c (column J),\n",
" * class (column K), \n",
" * p (column L) - related to column I, \n",
" * p (column M) - related to column H, \n",
" * Accepted (column O)\n",
" \n",
" =100*(\n",
" 1-TDIST((H9/E9-D9/E9)-TINV(0,05;B9-1)/SQRT(B9);B9-1;1)-\n",
" TDIST((H9/E9+D9/E9)-TINV(0,05;B9-1)/SQRT(B9);B9-1;1)\n",
" )\n",
" \"\"\"\n",
" def _calc(v: pd.Series) -> pd.Series:\n",
" return 100*(\n",
" 1-stats.t.sf(\n",
" (v.d/v['std']-v['mean']/v['std'])-stats.t.isf(0.05/2, v.number-1)/\n",
" np.sqrt(v.number),\n",
" v.number-1\n",
" )-stats.t.sf(\n",
" (v.d/v['std']+v['mean']/v['std'])-stats.t.isf(0.05/2, v.number-1)/\n",
" np.sqrt(v.number),v.number-1\n",
" )\n",
" )\n",
" data['confidence_level'] = data.T.apply(_calc)\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def resolve_accepted_class(data: pd.DataFrame) -> str:\n",
" \"\"\"\n",
" O12 = MAX(K11:K12)\n",
" \n",
" =IF(O12<=5;CONCATENATE(\"A(\";TEXT(O12;\"0\");\")\");\\\\\n",
" IF(O12<=7;CONCATENATE(\"B+(\";TEXT(O12;\"0\");\")\");\\\\\n",
" IF(O12<=10;CONCATENATE(\"B(\";TEXT(O12;\"0\");\")\");\\\\\n",
" IF(O12<=15;CONCATENATE(\"C(\";TEXT(O12;\"0\");\")\");\\\\\n",
" IF(O12<=20;CONCATENATE(\"D+(\";TEXT(O12;\"0\");\")\");\\\\\n",
" IF(O12<=25;CONCATENATE(\"D(\";TEXT(O12;\"0\");\")\");\\\\\n",
" CONCATENATE(\"E(\";TEXT(O12;\"0\");\")\")))))))\n",
" \"\"\"\n",
" v = data['class_value'].max()\n",
" c = (\n",
" 'A' if v<=5 else\n",
" 'B+' if v<=7 else\n",
" 'B' if v<=10 else\n",
" 'C' if v<=15 else\n",
" 'D+' if v<=20 else\n",
" 'D' if v<=25 else\n",
" 'E'\n",
" )\n",
" return '%s(%s)' % (c, int(v))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def solver_min_tolerance(data: pd.DataFrame) -> pd.DataFrame:\n",
" \"\"\"\n",
" * Number (column B) [Input], \n",
" * Identified (column C) [Input], \n",
" * Mean (column D) [Input], \n",
" * Std deviat (column E) [Input], \n",
" * p_o (column F), \n",
" * Class (column G), \n",
" * d (column H), \n",
" * d_min (column I) [Input/Minimization Solver Output],\n",
" * d_c (column J),\n",
" * class (column K), \n",
" * p (column L) - related to column I, \n",
" * p (column M) - related to column H, \n",
" * Accepted (column O)\n",
"\n",
" =100*(\n",
" 1-\n",
" TDIST((I9/E9-D9/E9)-TINV(0,05;B9-1)/SQRT(B9);B9-1;1)-\n",
" TDIST((I9/E9+D9/E9)-TINV(0,05;B9-1)/SQRT(B9);B9-1;1)\n",
" )\n",
" \"\"\"\n",
" \n",
" for i in data.index:\n",
" s = data.loc[i, :]\n",
" _number = s['number']\n",
" _mean = s['mean']\n",
" _std = s['std']\n",
" _min_confidence = s['min_confidence']\n",
" _factor = stats.t.isf(0.05/2, _number-1)/np.sqrt(_number)\n",
" _dof = _number-1\n",
" \n",
" func = lambda _min_tolerance: _min_confidence-100*(\n",
" 1-\n",
" stats.t.sf(\n",
" (_min_tolerance/_std-_mean/_std)-_factor, _dof)-\n",
" stats.t.sf(\n",
" (_min_tolerance/_std+_mean/_std)-_factor, _dof)\n",
" )\n",
" \n",
" try:\n",
" data.loc[i, 'min_tolerance'] = fsolve(func, [1])[0]\n",
" except:\n",
" data.loc[i, 'min_tolerance'] = np.nan\n",
" \n",
" return data"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Tolarance classes "
]
},
{
"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>B</th>\n",
" <th>C</th>\n",
" <th>D+</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>gwv</th>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>10</td>\n",
" <td>15</td>\n",
" <td>20</td>\n",
" <td>25</td>\n",
" <td>inf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>load_group_axles</th>\n",
" <td>7</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" <td>18</td>\n",
" <td>23</td>\n",
" <td>28</td>\n",
" <td>inf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>load_single_axle</th>\n",
" <td>8</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>20</td>\n",
" <td>25</td>\n",
" <td>30</td>\n",
" <td>inf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>load_axle_group</th>\n",
" <td>10</td>\n",
" <td>14</td>\n",
" <td>20</td>\n",
" <td>25</td>\n",
" <td>30</td>\n",
" <td>35</td>\n",
" <td>inf</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B+ B C D+ D E\n",
"gwv 5 7 10 15 20 25 inf\n",
"load_group_axles 7 10 13 18 23 28 inf\n",
"load_single_axle 8 11 15 20 25 30 inf\n",
"load_axle_group 10 14 20 25 30 35 inf"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 'A', 'B+', 'B', 'C', 'D+', 'D', 'E'\n",
"tolerance_table = pd.DataFrame(\n",
" [], columns=('A', 'B+', 'B', 'C', 'D+', 'D', 'E'),\n",
" index=(\n",
" 'gwv', 'load_group_axles', 'load_single_axle', 'load_axle_group'\n",
" )\n",
")\n",
"tolerance_table.loc['gwv',:] = [5, 7, 10, 15, 20, 25, np.inf]\n",
"tolerance_table.loc['load_group_axles',:] = [7, 10, 13, 18, 23, 28, np.inf]\n",
"tolerance_table.loc['load_single_axle',:] = [8, 11, 15, 20, 25, 30, np.inf]\n",
"tolerance_table.loc['load_axle_group',:] = [10, 14, 20, 25, 30, 35, np.inf]\n",
"tolerance_table"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"## Test"
]
},
{
"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>number</th>\n",
" <th>valid_measures</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>gwv</th>\n",
" <td>86</td>\n",
" <td>96.15</td>\n",
" <td>-2.27</td>\n",
" <td>6.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>group_axles</th>\n",
" <td>66</td>\n",
" <td>96.15</td>\n",
" <td>1.00</td>\n",
" <td>8.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>single_axle</th>\n",
" <td>197</td>\n",
" <td>95.50</td>\n",
" <td>-3.92</td>\n",
" <td>7.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>axle_group</th>\n",
" <td>169</td>\n",
" <td>96.00</td>\n",
" <td>-0.19</td>\n",
" <td>10.07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" number valid_measures mean std\n",
"gwv 86 96.15 -2.27 6.09\n",
"group_axles 66 96.15 1.00 8.44\n",
"single_axle 197 95.50 -3.92 7.66\n",
"axle_group 169 96.00 -0.19 10.07"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_plan = 'RR2'\n",
"env_condition = 'I'\n",
"initial_verification = False\n",
"\n",
"cols = ['number', 'valid_measures', 'mean', 'std']\n",
"indx = ['gwv', 'group_axles', 'single_axle', 'axle_group']\n",
"\n",
"data = pd.DataFrame(\n",
" [[86, 96.15, -2.27, 6.09],\n",
" [66, 96.15, 1.00, 8.44],\n",
" [197, 95.50, -3.92, 7.66],\n",
" [169, 96.00, -0.19, 10.07]],\n",
" columns=cols, index=indx\n",
")\n",
"\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minimum confidence calcuation Test ... SUCCESS\n"
]
}
],
"source": [
"print('Minimum confidence calcuation Test', end=' ... ')\n",
"\n",
"calc_min_confidence(\n",
" data=data, \n",
" test_plan=test_plan, \n",
" env_condition=env_condition\n",
")\n",
"\n",
"np.testing.assert_allclose(\n",
" data['min_confidence'].values,\n",
" np.array([92.584, 92.060, 93.704, 93.542]),\n",
" atol=0.001 # becouse round function\n",
")\n",
"print('SUCCESS')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best acceptable class Test ... SUCCESS\n"
]
}
],
"source": [
"print('Best acceptable class Test', end=' ... ')\n",
"data['min_tolerance'] = [\n",
" 13.0293783341772,\n",
" 17.2176428723888,\n",
" 17.0913086307026,\n",
" 20.2663837816549,\n",
"]\n",
"\n",
"calc_best_acceptable_class(\n",
" data=data, initial_verification=initial_verification\n",
")\n",
"\n",
"np.testing.assert_allclose(\n",
" data['best_acceptable_class'].values,\n",
" np.array([13.029, 14.218, 12.091, 10.266]),\n",
" atol=0.001 # because round function\n",
")\n",
"print('SUCCESS')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Class Value Test ... SUCCESS\n"
]
}
],
"source": [
"print('Class Value Test', end=' ... ')\n",
"\n",
"calc_classification(data=data)\n",
"\n",
"np.testing.assert_allclose(\n",
" data['class_value'].values,\n",
" np.array([15, 15, 15, 15])\n",
")\n",
"print('SUCCESS')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Class Name Test ... SUCCESS\n"
]
}
],
"source": [
"print('Class Name Test', end=' ... ')\n",
"\n",
"resolve_class_name(data=data)\n",
"\n",
"np.testing.assert_array_equal(\n",
" data['class_name'].values,\n",
" np.array(['C(15)', 'C(15)', 'C(15)', 'C(15)'])\n",
")\n",
"print('SUCCESS')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"delta Test ... SUCCESS\n"
]
}
],
"source": [
"print('delta Test', end=' ... ')\n",
"\n",
"calc_delta(data=data, initial_verification=initial_verification)\n",
"\n",
"np.testing.assert_allclose(\n",
" data['d'].values,\n",
" np.array([15, 18, 20, 25])\n",
")\n",
"print('SUCCESS')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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>number</th>\n",
" <th>valid_measures</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min_confidence</th>\n",
" <th>min_tolerance</th>\n",
" <th>best_acceptable_class</th>\n",
" <th>class_value</th>\n",
" <th>class_name</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>gwv</th>\n",
" <td>86</td>\n",
" <td>96.15</td>\n",
" <td>-2.27</td>\n",
" <td>6.09</td>\n",
" <td>92.583588</td>\n",
" <td>13.029378</td>\n",
" <td>13.029378</td>\n",
" <td>15.0</td>\n",
" <td>C(15)</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>group_axles</th>\n",
" <td>66</td>\n",
" <td>96.15</td>\n",
" <td>1.00</td>\n",
" <td>8.44</td>\n",
" <td>92.059850</td>\n",
" <td>17.217643</td>\n",
" <td>14.217643</td>\n",
" <td>15.0</td>\n",
" <td>C(15)</td>\n",
" <td>18.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>single_axle</th>\n",
" <td>197</td>\n",
" <td>95.50</td>\n",
" <td>-3.92</td>\n",
" <td>7.66</td>\n",
" <td>93.704239</td>\n",
" <td>17.091309</td>\n",
" <td>12.091309</td>\n",
" <td>15.0</td>\n",
" <td>C(15)</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>axle_group</th>\n",
" <td>169</td>\n",
" <td>96.00</td>\n",
" <td>-0.19</td>\n",
" <td>10.07</td>\n",
" <td>93.541785</td>\n",
" <td>20.266384</td>\n",
" <td>10.266384</td>\n",
" <td>15.0</td>\n",
" <td>C(15)</td>\n",
" <td>25.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" number valid_measures mean std min_confidence \\\n",
"gwv 86 96.15 -2.27 6.09 92.583588 \n",
"group_axles 66 96.15 1.00 8.44 92.059850 \n",
"single_axle 197 95.50 -3.92 7.66 93.704239 \n",
"axle_group 169 96.00 -0.19 10.07 93.541785 \n",
"\n",
" min_tolerance best_acceptable_class class_value class_name \\\n",
"gwv 13.029378 13.029378 15.0 C(15) \n",
"group_axles 17.217643 14.217643 15.0 C(15) \n",
"single_axle 17.091309 12.091309 15.0 C(15) \n",
"axle_group 20.266384 10.266384 15.0 C(15) \n",
"\n",
" d \n",
"gwv 15.0 \n",
"group_axles 18.0 \n",
"single_axle 20.0 \n",
"axle_group 25.0 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confidence Test ... SUCCESS\n"
]
}
],
"source": [
"print('Confidence Test', end=' ... ')\n",
"calc_confidence_level(data=data)\n",
"\n",
"np.testing.assert_allclose(\n",
" data['confidence_level'].values,\n",
" np.array([96.276, 93.461, 97.260, 97.902]),\n",
" atol=0.001\n",
")\n",
"print('SUCCESS')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accepted Class Test ... SUCCESS\n"
]
}
],
"source": [
"print('Accepted Class Test', end=' ... ')\n",
"\n",
"np.testing.assert_equal(\n",
" resolve_accepted_class(data=data), \n",
" 'C(15)'\n",
")\n",
"print('SUCCESS')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minimum Tolerance Solver Test ... CPU times: user 36.7 ms, sys: 3.33 ms, total: 40 ms\n",
"Wall time: 54.3 ms\n",
"SUCCESS\n"
]
}
],
"source": [
"print('Minimum Tolerance Solver Test', end=' ... ')\n",
"\n",
"data.min_tolerance = 0\n",
"\n",
"%time solver_min_tolerance(data=data)\n",
"\n",
"np.testing.assert_allclose(\n",
" data.min_tolerance.values, \n",
" np.array([\n",
" 13.0293783341772,\n",
" 17.2176428723888,\n",
" 17.0913086307026,\n",
" 20.2663837816549\n",
" ]), atol=1e-5\n",
")\n",
"print('SUCCESS')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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MMWOBsQAREREmOjo6PaNnSlxcHJ6cX0ZpTvfKSM572xlmrN3Dm79s5I2lydxZ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81ecfea7f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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7TsS5OzWllBO0YKlsq9G9RZj3UjPe6VSd7bEXaP/Jat6ft5OrCXq0pZQncv1U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\nJEkryICVJEmStIL/A/ksDVOP2qbNAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81ecf72908>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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lWkEBfjzXrCzLB4Rxf9XCjI3cS8vhK/hls05YqlRKeZp8OgFTredTgc4u6rQB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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81eae68d68>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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tLCzMeFJkZKRH20svGqdnJRXnsl3HTPMRkabU0J9NzwlrzN7j5zI2sJv4wvr0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\n8YmIiK5L8YmIiK77G3fs1O6h1cdWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81ead41358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# show graphical visualization about the solver function to achieve the \n",
"# mininum tolerance\n",
"\n",
"# data sheet value\n",
"data_y = [\n",
" 13.0293783341772,\n",
" 17.2176428723888,\n",
" 17.0913086307026,\n",
" 20.2663837816549\n",
"]\n",
"\n",
"_delta = 1e-5\n",
"\n",
"lims = [\n",
" (data_y[0]-_delta, data_y[0]+_delta),\n",
" (data_y[1]-_delta, data_y[1]+_delta),\n",
" (data_y[2]-_delta, data_y[2]+_delta),\n",
" (data_y[3]-_delta, data_y[3]+_delta),\n",
"]\n",
"\n",
"for k, s in data.T.items():\n",
" _number = s['number']\n",
" _mean = s['mean']\n",
" _std = s['std']\n",
" _min_confidence = s['min_confidence']\n",
" \n",
" _factor = stats.t.isf(0.05/2, _number-1)/np.sqrt(_number)\n",
" _dof = _number-1\n",
"\n",
" func = lambda _min_tolerance: _min_confidence-100*(\n",
" 1-\n",
" stats.t.sf(\n",
" (_min_tolerance/_std-_mean/_std)-_factor, _dof)-\n",
" stats.t.sf(\n",
" (_min_tolerance/_std+_mean/_std)-_factor, _dof)\n",
" )\n",
" \n",
" xlim = lims.pop(0)\n",
" x = np.linspace(xlim[0], xlim[1], 1000)\n",
" \n",
" ax = plt.figure().gca() \n",
" \n",
" data_func = pd.DataFrame({k: [func(xi) for xi in x]}, index=x)\n",
" data_func.plot(ax=ax)\n",
" \n",
" ax.plot(data_y.pop(0), 0, 'o', label='excel', color='black')\n",
" ax.plot(fsolve(func, [1]), 0, 'o', label='fsolve', color='green')\n",
" \n",
" ax.legend()\n",
" \n",
" locs, labels = plt.xticks()\n",
" plt.setp(labels, rotation=45)\n",
" \n",
" plt.grid(True)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# References\n",
"\n",
"(<a id=\"cit-jacob1998european\" href=\"#call-jacob1998european\">JACOB and O'Brien, 1998</a>) B. JACOB and E.J. O'Brien, ``_European Specification on Weigh-In-Motion of Road Vehicles (COST323)_'', SECOND EUROPEAN CONFERENCE ON WEIGH-IN-MOTION OF ROAD VEHICLES, HELD LISBON, PORTUGAL 14-16 SEPTEMBER 1998, 1998.\n",
"\n"
]
}
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| mit |
theideasmith/theideasmith.github.io | _notebooks/.ipynb_checkpoints/ODE N-Dimensional Test 1-checkpoint.ipynb | 1 | 36398 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Point Charge Dynamics\n",
"## Akiva Lipshitz, February 2, 2017\n",
"\n",
"Particles and their dynamics are incredibly fascinating, even wondrous. Give me some particles and some simple equations describing their interactions – some very interesting things can start happening. \n",
"\n",
"Currently studying electrostatics in my physics class, I am interested in not only the static force and field distributions but also in the dynamics of particles in such fields. To study the dynamics of electric particles is not an easy endeavor – in fact the differential equations governing their dynamics are quite complex and not easily solved manually, especially by someone who lacks a background in differential equations. \n",
"\n",
"Instead of relying on our analytical abilities, we may rely on our computational abilities and numerically solve the differential equations. Herein I will develop a scheme for computing the dynamics of $n$ electric particles en masse. It will not be computationally easy – the number of operations grows proportionally to $n^2$. For less than $10^4$ you should be able to simulate the particle dynamics for long enough time intervals to be useful. But for something like $10^6$ particles the problem is intractable. You'll need to do more than $10^12$ operations per iteration and a degree in numerical analysis. \n",
"\n",
"\n",
"\n",
"## Governing Equations \n",
"\n",
"Given $n$ charges $q_1, q_2, ..., q_n$, with masses $m_1, m_2, ..., m_n$ located at positions $\\vec{r}_1, \\vec{r_2}, ..., \\vec{r}_n$, the force induced on $q_i$ by $q_j$ is given by \n",
"\n",
"$$\\vec{F}_{j \\to i} = k\\frac{q_iq_j}{\\left|\\vec{r}_i-\\vec{r}_j\\right|^2}\\hat{r}_{ij}$$\n",
"\n",
"where \n",
"\n",
"$$\\hat{r}_{ij} = \\vec{r}_i-\\vec{r}_j$$\n",
"\n",
"Now, the net *marginal* force on particle $q_i$ is given as the sum of the pairwise forces\n",
"\n",
"$$\\vec{F}_{N, i} = \\sum_{j \\ne i}{\\vec{F}_{j \\to i}}$$\n",
"\n",
"And then the net acceleration of particle $q_i$ just normalizes the force by the mass of the particle:\n",
"\n",
"$$\\vec{a}_i = \\frac{\\vec{F}_{N, i}}{m_i}$$\n",
"\n",
"To implement this at scale, we're going to need to figure out a scheme for vectorizing all these operations, demonstrated below. \n",
"\n",
"We'll be using `scipy.integrate.odeint` for our numerical integration. Below, the function `g(y, t, q, m, n, d, k)` is a function that returns the derivatives for all our variables at each iteration. We pass it to `odeint` and then do the integration. "
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import numpy.ma as ma\n",
"from scipy.integrate import odeint\n",
"mag = lambda r: np.sqrt(np.sum(np.power(r, 2)))\n",
"\n",
"def g(y, t, q, m, n,d, k):\n",
" \"\"\"\n",
" n: the number of particles\n",
" d: the number of dimensions \n",
" (for fun's sake I want this \n",
" to work for k-dimensional systems)\n",
" y: an (n*2,d) dimensional matrix \n",
" where y[:n]_i is the position\n",
" of the ith particle and\n",
" y[n:]_i is the velocity of \n",
" the ith particle\n",
" qs: the particle charges\n",
" ms: the particle masses\n",
" k: the electric constant\n",
" t: the current timestamp\n",
" \"\"\"\n",
" y = y.reshape((n*2,d))\n",
" v = np.array(y[n:])\n",
"\n",
" # rj across, ri down\n",
" rs_from = np.tile(y[:n], (n,1,1))\n",
" \n",
" # ri across, rj down\n",
" rs_to = np.transpose(rs_from, axes=(1,0,2))\n",
"\n",
" # directional distance between each r_i and r_j\n",
" # dr_ij is the force from j onto i, i.e. r_i - r_j\n",
" dr = rs_to - rs_from\n",
" \n",
" # Used as a mask\n",
" nd_identity = np.eye(n).reshape((n,n,1))\n",
" \n",
" # Force magnitudes\n",
" drmag = ma.array(\n",
" np.sqrt(\n",
" np.sum(\n",
" np.power(dr, 2), 2)), \n",
" mask=nd_identity)\n",
"\n",
" # Pairwise q_i*q_j for force equation\n",
" qsa = np.tile(q, (n,1))\n",
" qsb = np.tile(q, (n,1)).T\n",
" qs = qsa*qsb\n",
" \n",
" # Directional forces\n",
" Fs = (1./np.power(drmag,2)).reshape((n,n,1))\n",
" \n",
" # Net Force\n",
" Fnet = np.sum(Fs, 1)\n",
" \n",
" # Dividing by m to obtain acceleration vectors\n",
" a = np.sum(Fnet*dr, 1)\n",
"\n",
" # Sliding integrated acceleration\n",
" # (i.e. velocity from previous iteration)\n",
" # to the position derivative slot\n",
" y[:n] = np.array(y[n:])\n",
" \n",
" # Entering the acceleration into the velocity slot\n",
" y[n:] = np.array(a)\n",
" # Flattening it out for scipy.odeint to work\n",
" return y.reshape(n*d*2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's define our time intervals, so that odeint knows which time stamps to iterate over. "
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"t_f = 10\n",
"t = np.linspace(0, 20, num=t_f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some other constants"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Number of dimensions\n",
"d = 2\n",
"# Number of point charges\n",
"n = 3\n",
"# charge magnitudes, currently all equal to 1\n",
"q = np.ones(n)\n",
"# masses\n",
"m = np.ones(n)\n",
"\n",
"# The electric constant \n",
"# k=1/(4*pi*epsilon_naught)\n",
"# Right now we will set it to 1\n",
"# because for our tests we are choosing all q_i =1. \n",
"# Therefore, k*q is too large a number and causes \n",
"# roundoff errors in the integrator. \n",
"# In truth:\n",
"# k = 8.99*10**9\n",
"# But for now:\n",
"k=1."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We get to choose the initial positions and velocities of our particles. For our initial tests, we'll set up 3 particles to collide with eachother. "
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"r1i = np.array([-2., 0.5])\n",
"dr1dti = np.array([2.,0.])\n",
"\n",
"r2i = np.array([30.,0.])\n",
"dr2dti = np.array([-2., 0.])\n",
"\n",
"r3i = np.array([16.,16.])\n",
"dr3dti = np.array([0, -2.])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And pack them into an initial state variable we can pass to odeint. "
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y0 = np.array([r1i, r2i, r3i, dr1dti, dr2dti, dr3dti]).flatten()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Fun Part – Doing the Integration\n",
"\n",
"Now, we'll actually do the integration"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Python/2.7/site-packages/ipykernel/__main__.py:51: RuntimeWarning: divide by zero encountered in divide\n"
]
}
],
"source": [
"# Doing the integration\n",
"yf = odeint(g, y0, t, args=(q,m,n,d,k)).reshape(t_f,n*2,d)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
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LA/mZV7kU1T7saMYMaN8eOnXy/RymT4dXX/UTQ0m4KYGQuDqo7kE8d95z/ND7\nB44/6HiumXANLYe3ZPy88eS63KDDEyExK4sb/vyZx6t2ZBHNVPsQsXQpdO0KxxwDq1f7GojPP/fz\nOoiAEggpI033asqrF73KrOtn0XjPxlz65qWkPJvCh4s/1PTYEhznoFcvEg45hJf2XwdsCn3tw6pV\n0KePvyrm1Km+z8OcOXD22bpmhexICYSUqdb1W/NBtw/4qudX1E6szTmvnEO7F9vx1Y9fBR2ahNHY\nsTB5MjZiBL1vuZzq1TuFtvZh40Y/JLNxY/+yPPAALF4MPXtClSpBRyflkRIICcSJB5/Il1d+yUeX\nfcTGrRtp92I7zhp3Fpm/ZgYdmoTF2rVwyy1w6aVwxhmkpfXg4osPC13tw/bt8Oyz0KSJTxquucY3\nXwwYAElJQUcn5ZkSCAmMmXFm4zP57rrveP3i11n2+zJSnk3h4tcvZsHqBUGHJ5XdgAGwdSs88QTg\nR2SMHTs8NLUPzvkZI1u1guuv9x0lFyzwL8demr5FikEJhAQuwRK4qMVFzLthHs+f9zzT/zedlsNb\nctW7V/HjHz8GHZ5URl9/7adNfOgh2G+/oKMpc998AyedBJ07wwEHwMyZMG4cNGgQdGRSkSiBkHKj\nakJVerbpyaIbF/Fkxyf5YPEHNH26Kf0+6sfKP1cGHZ5UFtu2QVqaH06QlhZ0NGVq4ULo0gWOPx7+\n/NNfNfPTT/2VM0VKSgmElDvVq1anT9s+LO27lHtOvoeX5rxEw6ENuXPSnfyx+Y+gw5OKbsgQf33p\nkSND0ztwxQro1QtatoTvvoMxY/yVMzt21MgKKT0lEFJu1UqsxR0n3cGyfsvo17YfT377JA2GNGDQ\nvwexcevGoMOTiuinn/wczH36hOK0e8MGuPdeP7Li1Vf9tSsWLoTLL4cEffvLLtJbSMq9PZL24KEO\nD7G071K6t+rOwC8G0mhoI56e/jRbc7YGHZ5UJH36wO67w/33Bx1JXG3bBsOH+8Rh0CBf+7B0KfTv\nDzVqBB2dVBZKIKTCqF+rPk+d/RSL+izizMZn0u/jfjR7uhkvzX6JnNycoMOT8u7dd+G993wTRp06\nQUcTF87Bm2/6poobb4Qzz4RFi+DRR2HPPYOOTiobJRBS4Ry6+6G82PlF5vaaS/J+yVz57pW0GtGK\nt+a/pVktpWB//ulrH846y/cirIS++sp3jrzoImjYEGbNgpdegoMPDjoyqayUQEiF1WKfFrx5yZtM\nv2Y6B9VbtJuiAAAgAElEQVQ9iC6vdeHo547mk6WfKJGQHd13H/z2Gzz9dKXrNTh/Ppx/PrRr56e1\n+OwzP7riyCODjkwqOyUQUuEdfcDRTOw+kS+u+ILEKol0fLkjp445la//+3XQoUl58P33MHgwDBzo\nT80riV9+gWuvhcMP94c4bpy/cmaHDkFHJmGhBEIqjVMOPYWpV01lQuoE1mav5YTnT+DcjHOZs2JO\n0KFJUHJz/VwPTZv6aasrgfXr4a67fAfJt96Cxx/3M0h266aRFVK29HaTSsXM6NS0E7Oun8UrF77C\ngtULaP1Ma7q92Y3FaxYHHZ6UtdGj/bSLI0dCBZ+ieutWGDoUGjXySUO/fn5kxU03QfXqQUcnYRTX\nBMLMTjKz98zsf2aWa2bnFVDmfjP7xcw2mdmnZtY4njFJOCRYAqmtUvnhhh94ttOzTPlxCs2HNef6\nCdfz8/qfgw5PysKqVXDbbXDllb6DQAXlHIwfD82bQ3o6nHeev0rmww/7EakiQYl3DcRuwGygN/C3\nXm1mdhtwI3A9cAywEZhoZhX7VEHKjWpVqnFtyrUs7rOYR05/hDfnv0njoY25ZeItrN60OujwJJ76\n9/cdJh95JOhISm3yZGjb1l8wtHlzmDPHV6oceGDQkYnEOYFwzn3snBvonHsHKKjrcz/gAefcBOfc\nPKAHsD/QOZ5xSfgkVUvi5uNuJqtfFrefeDvPZT5HgyENuHfyvazfsj7o8CTWvvgCxo71ycM++wQd\nTYnNnQvnnOOvkAk+kXj/fd9hUqS8CKwPhJk1AOoDk/KWOefWA98CxwUVl1RudarX4Z5T7iGrXxZp\nKWn8a+q/aDikIY99/RjZ27KDDk9iYcsWP/XiCSdAz55BR1MiP/8MV13lh2AuXOibLr79Fk4+OejI\nRP4uyE6U9fHNGvkvs7gysk4kbvauuTePnvEoS/os4aIWF3H7pNtp/FRjnvnuGbblbAs6PNkVjz7q\nexeOHFlhhiX88Qfcfjs0aQITJvjOkj/8AJdcUummrZBKpDx+uowC+kuIxMMBdQ5gZKeRzO89n/aH\ntqfXB71oPqw5r8x9hVyXG3R4UlJLlsCDD/ohmxWgvn/LFj9FRaNGPmno39/nPjfeWOEHjUgIWFnN\n2GdmuUBn59x7kccNgKVAa+fc91HlJgOznHPphewnGZjZrl076tatu8O61NRUUlNT43QEEgbfr/ye\nu7+4m/cWvkerfVvx4KkPcm7TczGdBpZ/zvmLPyxc6C/XvdtuQUdUqNxcf3XMO+/0Fwi95hp/1cz9\n9gs6MqlMMjIyyMjI2GHZunXrmDJlCkCKcy5zV/YfWAIRWfYL8KhzbnDkcR18E0YP59zrhewnGZg5\nc+ZMkkNwOV4JxrSfp3HHpDv4YvkXHHvgsTx06kO0b9A+6LBkZ8aP98MVJkyATp2CjqZQkybBP/4B\nmZl+CuqHH/YjLETKQmZmJikpKRCDBCLe80DsZmZHmlnryKKGkccHRR4/CdxlZueaWStgDPAz8G48\n4xIpyrEHHsukHpP49PJPycnN4dQxp3L62NOZ/r/pQYcmBVm3zs+odMEF5TZ5mDPHV5Ccdppvnvjq\nK3jnHSUPUnHFuw/EUcAsYCa+X8PjQCZwH4Bz7hHgKeAZ/OiLJOAs59zWOMclUiQz47SGp/HtNd/y\ndte3+WXDL7Qd1ZYLx1/If1b9J+jwJNpdd/krbg4ZEnQkf/PTT3DFFdCmDSxb5i+3/fXXcOKJQUcm\nsmviPQ/El865BOdclXy3q6LK3Ouc2985V9M519E5tySeMYmUlJnR+bDOfJ/2PWM6j2H2itm0GtGK\nHm/3YNnvy4IOT777DoYNg/vvh4MOKrp8Gfn9d7j1Vn8Zjo8/huHDYd48uPBCjayQyqE8jsIQKZeq\nJFTh8iMvZ8GNC3j67Kf5NOtTmj3djN4f9ObXDb8GHV445eTA9dfDEUdAnz5BRwPA5s1+JGnDhjBi\nhB+euXSpv6ZXtWpBRycSO0ogREoosUoiNxx9A0v7LuWB9g+QMS+DRkMbcdunt7E2e23Q4YXL8OEw\naxY88wxUrRpoKDk5MGYMNGvmk4bUVJ843HMP1KoVaGgicaEEQqSUalaryW0n3kZWvyxuOe4Whs0Y\nRoMhDXhwyoP8ufXPoMOr/H75xY+DvP56f8GIgDjnmyiSk31fh6OP9pNADR8O9eoFFpZI3CmBENlF\nu9fYnQdOfYClfZfSs3VPHpjyAA2HNGTItCFs3r456PAqr5tugqQkeOihwELIzITTT4ezzoI6dXzn\nyDfe8P0eRCo7JRAiMVKvVj2ePPNJFvdZzLlNz+XmT26m6VNNGZ05mu2524MOr3L56CN4/XV44gnY\nY48yf/ply6BbN0hJ8RUh774LU6bAcbqKj4SIEgiRGDu47sGMPn80P9zwA8cddBzXTLiGlsNb8tp/\nXtP02LGQnQ29e0OHDv5XvAytXg3p6b6fw+TJ8Oyz8P33cN55Glkh4aMEQiROmu3djPEXjSfzukwa\n7dGIrm905ahnj+KjxR9RVjPAVkr//Cf873++k0EZ/Wpv2uRnjGzUCEaP9h0jFy+Ga68NvO+mSGCU\nQIjEWZv92vDhZR8y5cop7Ja4G2e/cjbtXmzHVz9+FXRoFc/8+fDII36YQxl0NMjJgeef9081cKDv\nJLl0qe+7WY4vtSFSJpRAiJSRkw45iSlXTuHDbh+ycetG2r3YjrPHnc2sX2cFHVrF4Bz06gWHHAID\nBsT9qT74AI48Eq6+Gk44wecuQ4fCPvvE9alFKgwlECJlyMw4q8lZfHfdd7x20Wtk/Z5F8rPJXPL6\nJSxcvTDo8Mq3sWPhyy9900WNGnF7munToX17f0mNffbxj8ePh8aN4/aUIhWSEgiRACRYAhe3vJh5\nN8xj9HmjmfbzNFoMb8HV717NT+t+Cjq88mfNGrjlFj870+mnx+UpliyBSy7xU0qsXu1rID7/3M/r\nICJ/pwRCJEBVE6pyVZurWNxnMYM7DmbCogk0eaoJN318E6s2rgo6vPJjwADYts0P24yxVav8LNjN\nm/t5HEaP9lfOPPtsjawQ2RklECLlQPWq1enbti9Z/bIY2G4gL85+kYZDGnLX53fxx+Y/gg4vWFOn\nwqhRfsKo+vVjttuNG+HBB/3IijFj4IEH/MiKq66CKlVi9jQilZYSCJFypFZiLe5sdydZ/bK48Zgb\neeKbJ2g4pCH/+ve/2LRtU9Dhlb1t2/xVqI45xk9ZHQPbt/v5G5o08UnDtddCVpav5EhKislTiISC\nEgiRcmjPpD0ZdNoglvZdSrdW3bj7i7tpNLQRw6YPY2vO1qDDKztPPukvLDFy5C5XCzjnZ4xs1crn\nIu3bw4IFvlVkr71iFK9IiCiBECnH9qu9H0+f/TQLb1zIGY3OoM9HfWj2dDPGzBlDTm5O0OHF148/\nwr33Qt++0KbNLu3qm2+gXTvo3BkOOAC++w7GjYMGDWITqkgYKYEQqQAa7NGAlzq/xNxec2lTvw1X\nvHMFR4w8grfnv115Z7Xs29df5+L++0u9i0WLoEsXOP542LDBXzXz00/9NSxEZNcogRCpQFru25K3\nur7F9Gumc0DtA7jwtQs5ZtQxfLr008qVSLzzDrz3HgwZArVrl3jzlSvhhhugRQuYMcN3kszMhI4d\nNbJCJFaUQIhUQEcfcDSfXP4Jn/f4nKoJVTnj5TM4dcypfPPfb4IObdf9+acfV3n22XDhhSXe9L77\n/MiKjAwYNMjXQlx+OSTo204kpvSREqnA2jdoz9dXfc17l77Hmk1rOP754zkv4zy+X/l90KGV3r33\n+omjnn662NUF27bBiBF+tsiHH/YzXi9dCv37x3XSSpFQUwIhUsGZGec2O5fZabMZd+E4fvjtB1qP\nbM1lb13GkrVLgg6vZObM8SMvBg4sVg9H5+Ctt+Dww/0Vvs84AxYuhEcfhT33LIN4RUJMCYRIJZFg\nCXRr1Y35veczstNIJi+fzGFPH8b1E67n5/U/Bx1e0XJz/ZwPzZrBzTcXWXzqVH+Rqy5dfK4xa5bv\n63DIIWUQq4gogRCpbKpVqcZ1KdexpM8S/nXav3hz/ps0HtqY/p/0Z/Wm1UGHV7hRo2DaNN8WkZhY\naLEFC/xwzBNPhM2b/aiKjz/2V84UkbKjBEKkkkqqlsQtx99CVr8sbj/xdp6d+SwNhzTk3sn3sn7L\n+qDD29GqVXDbbdCzp5+woQC//uongDr8cN/SMW6cn8/htNPKOFYRAZRAiFR6darX4Z5T7iGrXxbX\npVzHoH8PouGQhjz29WNkb8sOOjzvllv8MIlHHvnbqg0bfJeIxo3hjTd8/4YFC6BbN42sEAmSPn4i\nIbF3zb157IzHWNJ3CRe1uIgBnw2g8VONeea7Z9iWsy24wD7/HF5+2WcGe+/91+KtW/1AjEaN/Ko+\nffzIivR0qF49uHBFxFMCIRIyB9Y5kJGdRrLgxgW0P7Q9vT7oRfNhzXll7ivkutyyDWbLFj/m8qST\n4MorAT+y4vXXoWVLPxllp05+LodBg2D33cs2PBEpnBIIkZBqvGdjXr7wZWanzablvi257K3LaD2y\nNe8tfK/sZrV85BF/KcwRIyAhgS+/hGOPhUsugaZNfV+H55+Hgw4qm3BEpPiqBh0AgJn1BvoD9YE5\nQB/n3IxgoxIJhyPqHcG7l77LtJ+nccekOzj/1fNpe0BbHurwEKc2OPWvcl279iYzcxkJCX//2sjN\n3U5ycgPGjx9W/CdesgT++U/o35//0JIB58L778NRR/lWjfbtY3F0IhIvgScQZtYVeBy4DpgOpAMT\nzaypc64cjzkTqVyOPfBYJvWYxKRlk7hj0h10GNOBUw49hcP3OZykakmsS/6J5atqsj27HWxLgu1J\nsK0mbEsiMeFzzm1bjbkr55JULYmkqkkkVUuiZrWaVK9SHTDWroUVK/zt118cJzzYm7rV96PXvLt5\n4wg/f8Orr8LFF6tzpEhFYEFfgMfMpgHfOuf6RR4b8F9gqHPub12yzSwZmDlz5kySk5PLNliRkHDO\n8e7Cdxn67VBWb1pN9vZssrdl8+tvK8mtUhWqbS7Bzgy21/BJx7aasD2JS+ZvYfxnP3HhWa2Y3Ogg\nDt4viZZNk9ituk868hKQpKqRx/mSkp2tr5ZQDaskV8yKea2PhF5mZiYp/nK0Kc65zF3ZV6A1EGZW\nDUgBHspb5pxzZvYZcFxggYmEnJnR+bDOdD6s8w7Lhw4dxYABRnb2VVB1M1TLhmqboGo2VMumSvVs\n9qi3id33yabuntnU3jOb3XbPJmn3TVSvlU3ibtnUzvmd+18ezcxjD6HG1Ydz8vZsNm3bwH83rCL7\nd5+oZG/PZtO2TX/9vTVna7FjT7CE4ichxUxKdraPKglVYv3y/+WEE9owYUIy2dlX/21dUtIo+vSJ\n21OLFCnoJoy9gSrAynzLVwLNyj4cEdmZtLQeDB58JsuX9/BNGNurUa/OP/n440EcdFA19tijGM0P\nN94I2xJIef3fvHLggcV63pzcnL9qQfLuN23btMOy6ISjwGVRj9dmry1wH3nLclxOsV+TagnVipWE\n1Kxa/KQkb1mHS45jr2eu5edl58D2Or4mxyUAW6lX7xXS0j4udpwisRZ0AlEYA4JtWxGRv0lMTCQ9\nvRsDBowhO/tqkpJe5I47mtO6dbXi7WDGDBg+HJ54AoqZPABUSahCrcRa1EqsVcrIS2ZbzrbSJyrR\n20Qer/xz5U7Lu5193V0CsN//Px45i6R135Ge3o3EnUz5LRJvgfaBiDRhbAK6OOfei1r+IlDXOXdB\nAdskAzPbtWtH3bp1d1iXmppKampqfIMWCbmtW7fSrNmZLF/+Poce2omFCz8u3g/Z9u1wzDF+oocZ\nM6BqeT1/KVvOObbmbC00UVmfvZ6r0+7gt9/7Q7VtML8Th9brVvzXXUIrIyODjIyMHZatW7eOKVOm\nQEXvA+Gc22ZmM4EOwHvwVyfKDsDQnW07ePBgdaIUCUBeLcQ//tGpZGfBw4fD7NnwzTdKHqKYGdWr\nVqd61erswR4Flrmry0oGDNhOdvY1JCWNUu2DFEtBJ9VRnSh3WXkYLPUEcJ2Z9TCzw4CRQE3gxUCj\nEpFCpaX14OKLDyMtrUfxNvjf/+Cuu/zlutu2jW9wlVBaWg/q1RsHbIr0fSjm6y4SR4EnEM6514Bb\ngPuBWcARQEfn3G+BBiYihUpMTGTs2OHFPwtOT4ekJHjooaLLyt/k1fpUr17CWh+ROCoX9YjOueHA\n8KDjEJE4+Ogjf3GLceN0MYtdkJbWgxkzMlX7IOVGuUggRKSS2rQJeveG004DdXDeJXm1PiLlhRII\nEYmff/4TfvkFJk6ESjI7pIh4gfeBEJFK6ocf4NFH4fbboUmToKMRkRhTAiEisecc9OoFhx4Kt90W\ndDQiEgdqwhCR2BszBqZMgU8/hRo1go5GROJANRAiEltr1kD//tCtm+88KSKVkhIIEYmtAQNg2zZ4\n/PGgIxGROFIThojEzqxZMGoUjBgB9esHHY2IxJFqIEQkdhYs8PeXXRZsHCISd0ogRCR2NNeDSGgo\ngRCR2EmIfKU4F2wcIhJ3SiBEJHbyaiByc4ONQ0TiTgmEiMSOaiBEQkMJhIjEjmogREJDCYSIxI5q\nIERCQwmEiMROXg2EEgiRSk8JhIjEjpowREJDCYSIxI6aMERCQwmEiMSOaiBEQkMJhIjEjmogREJD\nCYSIxI5qIERCQwmEiMSORmGIhIYSCBGJHTVhiISGEggRiR01YYiEhhIIEYkd1UCIhIYSCBGJHfWB\nEAkNJRAiEjtqwhAJDSUQIhI7asIQCY24JRBmdoeZTTWzjWa2tpAyB5nZB5EyK8zsETNTUiNSUakJ\nQyQ04vljXQ14DRhR0MpIovAhUBU4FrgCuBK4P44xiUg85dVAqAlDpNKLWwLhnLvPOTcEmFtIkY7A\nYcBlzrm5zrmJwN1AbzOrGq+4RCSOVAMhEhpBNhccC8x1zq2OWjYRqAu0DCYkEdklSiBEQiPIBKI+\nsDLfspVR60SkolEThkholKipwMweBm7bSREHNHfOLdqlqPx+dio9PZ26devusCw1NZXU1NRdfGoR\nKTXVQIiUGxkZGWRkZOywbN26dTHbf0n7GjwGvFBEmaxi7msFcHS+ZfUi9/lrJv5m8ODBJCcnF/Op\nRKRMKIEQKTcKOqnOzMwkJSUlJvsvUQLhnFsDrInJM8M3wB1mtndUP4gzgHXADzF6DhEpS2rCEAmN\nuI12MLODgD2BQ4AqZnZkZNUS59xG4BN8ojDWzG4D9gMeAJ52zm2LV1wiEkeqgRAJjXgOl7wf6BH1\nODNy3x6Y4pzLNbNO+HkivgY2Ai8C98QxJhGJJ81EKRIacUsgnHM9gZ5FlPkv0CleMYhIGdO1MERC\nQ9NGi0jsqAlDJDSUQIhI7KgJQyQ0lECISOyoCUMkNJRAiEjsqAlDJDSUQIhI7KgJQyQ0lECISOyo\nCUMkNJRAiEjsqAZCJDSUQIhI7KgGQiQ0lECISOyoE6VIaCiBEJHYUROGSGgogRCR2FEThkhoKIEQ\nkdhRE4ZIaCiBEJHYUROGSGgogRCR2FEThkhoKIEQkdhRE4ZIaCiBEJHYyWvCUA2ESKWnBEJEYkc1\nECKhoQRCRGJHCYRIaCiBEJHYUROGSGgogRCR2FENhEhoKIEQkdhRAiESGkogRCR21IQhEhpKIEQk\ndlQDIRIaSiBEJHaUQIiEhhIIEYkdNWGIhIYSCBGJHdVAiISGEggRiR1dTEskNJRAiEjs6HLeIqER\ntwTCzA4xs1FmlmVmm8xssZnda2bV8pU7wsymmFm2mf1oZrfGKyYRiTM1YYiERtU47vswwIBrgaXA\n4cAooCbwDwAzqw1MBD4BrgdaAS+Y2e/OuVFxjE1E4kFNGCKhEbcEwjk3EZ8c5FluZo8BaUQSCKA7\nUA242jm3HZhvZm2Am/HJhohUJGrCEAmNsu4DsTuwNurxscCUSPKQZyLQzMzqlmlkIrLrVAMhEhpl\nlkCYWWPgRmBk1OL6wMp8RVdGrRORikR9IERCo8RNGGb2MHDbToo4oLlzblHUNgcAHwHjnXPPF/UU\nUfspVHp6OnXr7lhJkZqaSmpqahG7F5G40URSIuVGRkYGGRkZOyxbt25dzPZvroRnCma2F7BXEcWy\n8polzGx/4Avga+dcz3z7egmo7Zy7MGrZKcAkYE/n3N+O1MySgZkzZ84kOTm5RLGLSBkwg2efhWuv\nDToSEcknMzOTlJQUgBTnXOau7KvENRDOuTXAmuKUjdQ8fA7MAK4qoMg3wINmVsU5lxNZdgawsKDk\nQUQqCDVhiFR68ZwHYj9gMvATftTFvmZWz8zqRRV7BdgKPG9mLcysK9AXeDxecYlInCUkKIEQCYF4\nzgNxBtAwcvtvZJnh+zZUAXDOrTezjsDTwHfAauBe59zoOMYlIvFkpj4QIiEQz3kgXgJeKka5ucDJ\n8YpDRMqYaiBEQkHXwhCR2FINhEgoKIEQkdgyUw2ESAgogRCR2FIThkgoKIEQkdhSE4ZIKCiBEJHY\nUhOGSCgogRCR2FIThkgoKIEQkdhSE4ZIKCiBEJHYUhOGSCgogRCR2FIThkgoKIEQkdhSE4ZIKCiB\nEJHYUg2ESCgogRCR2FINhEgoKIEQkdhSJ0qRUFACISKxpSYMkVBQAiEisaUmDJFQUAIhIrGlJgyR\nUFACISKxpSYMkVBQAiEisaUmDJFQUAIhIrGlGgiRUFACISKxpT4QIqGgBEJEYktNGCKhoARCRGJL\nTRgioaAEQkRiS00YIqGgBEJEYktNGCKhoARCRGJLTRgioaAEQkRiS00YIqGgBEJEYishQU0YIiEQ\n1wTCzN41sx/NLNvMfjGzMWa2X74yR5jZlEiZH83s1njGJCJxphoIkVCIdw3E58DFQFPgQqAR8Hre\nSjOrDUwElgHJwK3AvWZ2TZzjEpF4UQIhEgpV47lz59yQqIf/NbNBwNtmVsU5lwN0B6oBVzvntgPz\nzawNcDMwKp6xiUicqAlDJBTKrA+Eme0JXAZMjSQPAMcCUyLJQ56JQDMzq1tWsYlIDKkGQiQU4p5A\nmNkgM/sTWA0cBHSOWl0fWJlvk5VR60SkolECIRIKJU4gzOxhM8vdyS3HzJpGbfII0Bo4HcgBxhb1\nFJF7fQOJVERqwhAJhdL0gXgMeKGIMll5fzjn1gJrgSVmtgDfF6Ktc+5bYAVQL9+2+0bu89dM7CA9\nPZ26dXds5UhNTSU1NbXoIxCR+FENhEi5kJGRQUZGxg7L1q1bF7P9lziBcM6tAdaU8vmqRO6rR+6/\nAR6M6lQJcAaw0Dm306McPHgwycnJpQxDROJGCYRIuVDQSXVmZiYpKSkx2X/c+kCY2dFm1tvMjjSz\ng83sVOAVYDE+cSDyeCvwvJm1MLOuQF/g8XjFJSJxpiYMkVCIZyfKbPzcD58BC4DngNnAKc65bQDO\nufVAR+BQ4DvgUeBe59zoOMYlIvGkGgiRUIjbPBDOuXlAh2KUmwucHK84RKSMKYEQCQVdC0NEYktN\nGCKhoARCRGJLNRAioaAEQkRiSwmESCgogRCR2FIThkgoKIEQkdhSDYRIKCiBEJHYMlMNhEgIKIEQ\nkdhKSFANhEgIKIEQkdhSE4ZIKCiBEJHYUhOGSCgogRCR2FIThkgoKIEQkdhSE4ZIKCiBEJHYUhOG\nSCgogRCR2FIThkgoKIEQkdhSDYRIKCiBEJHYUh8IkVBQAiEisaUmDJFQUAIhIrGlJgyRUFACISKx\npSYMkVBQAiEisaXLeYuEghIIEYkt1UCIhIISCBGJLSUQIqGgBEJEYktNGCKhoARCRGJLNRAioaAE\nQkRiS/NAiISCEggRiS3NAyESCkogRCS21IQhEgpKIEQkttSEIRIKSiBEJLbUhCESCmWSQJhZopnN\nNrNcMzsi37ojzGyKmWWb2Y9mdmtZxCQicaImDJFQKKsaiEeAn4EdvlXMrDYwEVgGJAO3Avea2TVl\nFJeIxJqaMERCoWq8n8DMzgJOB7oAZ+db3R2oBlztnNsOzDezNsDNwKh4xyYicaAmDJFQiGsNhJnV\nA57FJwrZBRQ5FpgSSR7yTASamVndeMYmInGiJgyRUIh3E8YLwHDn3KxC1tcHVuZbtjJqnYhUNGrC\nEAmFEicQZvZwpDNkYbccM2tqZn2B2sC/8jYt7lNE7vUNJFIRqQlDJBRK0wfiMXzNws4sA9rjmyi2\nmO2QO3xnZuOccz2BFUC9fNvuG7nPXzOxg/T0dOrW3bGVIzU1ldTU1CJCE5G4UhOGSLmQkZFBRkbG\nDsvWrVsXs/2bi9MH3cwOBOpELdof37+hCzDdOfeLmaUBDwL1nHM5ke0eAjo751oUst9kYObMmTNJ\nTk6OS+wisgtuuAGmTYPMzKAjEZF8MjMzSUlJAUhxzu3ShzRuozCccz9HPzazjfjmiSzn3C+Rxa8A\nA4HnzexfQCugL9AvXnGJSOz17tqVZZmZVE1I4PqVK2m+eTM3NWsGwPbcXBokJzNs/PiAoxSRWIr7\nMM58dqjucM6tN7OOwNPAd8Bq4F7n3OgyjktEdkGbE04gecIErs7+/8FW7y1aBMCopCTo0yeo0EQk\nTspsKmvn3I/OuSrOue/zLZ/rnDvZOVfTOXewc+6xsopJRGKjR1oa4+rVY1u+5VuBV+rVo0daWhBh\niUgc6VoYIrLLEhMT6ZaezpikpB2Wj0lKolt6OomJiQFFJiLxogRCRGIify2Eah9EKjclECISE/lr\nIVT7IFK5KYEQkZjJq4XYhGofRCo7JRAiEjN5tRCdqldX7YNIJVfWwzhFpJLrkZZG5owZqn0QqeSU\nQIhITCUmJjJ87NigwxCROFMThoiIiJSYEggREREpMSUQIiIiUmJKIERERKTElECIiIhIiSmBEBER\nkRJTAiEiIiIlpgRCRERESkwJhIiIiJSYEggREREpMSUQIiIiUmJKIERERKTElECIiIhIiSmBEBER\nkTWliEAAAAoYSURBVBJTAiEiIiIlpgRCRERESkwJhIiIiJSYEggREREpMSUQIiIiUmJKIERERKTE\nlECUYxkZGUGHUCZ0nJWLjrPyCcuxhuU4YyWuCYSZLTez3Khbjpn9I1+ZI8xsipllm9mPZnZrPGOq\nSMLyZtZxVi46zsonLMcaluOMlapx3r8D7gKeAyyybEPeSjOrDUwEPgGuB1oBL5jZ7865UXGOTURE\nREop3gkEwJ/Oud8KWdcdqAZc7ZzbDsw3szbAzYASCBERkXKqLPpADDCz1WaWaWb9zaxK1LpjgSmR\n5CHPRKCZmdUtg9hERESkFOJdAzEEyATWAscDg4D6QP/I+vpAVr5tVkatW1fAPmsAzJ8/P9axljvr\n1q0jMzMz6DDiTsdZueg4K5+wHGsYjjPqt7PGru7LnHMl28DsYeC2nRRxQHPn3KICtu0JjARqOee2\nmdlEIMs51yuqTAtg7k720Q0YV6KgRUREJNplzrlXdmUHpamBeAx4oYgy+WsV8nwbec5DgcXACqBe\nvjL7Ru5XUrCJwGXAcmBzEXGIiIjI/6uB/w2euKs7KnEC4ZxbA6wp5fO1AXKBVZHH3wAPmlkV51xO\nZNkZwELnXEHNF3nPv0tZk4iISIh9HYudxK0TpZkda2b9IvM8NDCzy4AngLFRycErwFbgeTNrYWZd\ngb7A4/GKS0RERHZdiftA/F975x+kVVXG8c8XCMxsZcYVaMtQE0gbqAE1i9ASRZNxHHNEagzUciyj\nJiel7MdIWkpUaCQ75e8f00yGJqOigxoNYwqjboUNJKPjlhXCyGhK4ACxpz+e8y6n6y68d9+X93Lv\nPJ+Zd3bfc5677/Pde99znnvuOeep+w/bcsxOYBwwDOgG7gKuDyHsTOzGAzcCxwGbgUUhhJ/sE6cc\nx3Ecx2kK+yyAcBzHcRynunguDMdxHMdxcuMBhOM4juM4uSlVACFpjKSlkl6V9IakJySdlLE5TNIy\nSVslbZS0QFKpdAJImi5ptaRtkl6T9NtMfSV0AkgaKunPMeHahExdqZOtSRot6RZJL8Vz+YKkeZLe\nkbErtc4akr4iqTvqWC3puKJ9agRJV0p6WtKbkjZJul/S2IzNMEmL4467WyTdK2lEf39zfydq7pG0\nMCmrjEZJHZLujlq2SVojaWLG5mpJG2L9Y5KOKsrfgSBpkKRrknbnRUnf7cOuIZ1l63CWAYOBTwIT\ngTXAstqFHDvQh7HlqScAs4ELgKsL8HXASDoHm3B6K5Zg7OMkS1erojNhAfBPbBOyXpJka93Y+b4C\nmCfpiy33cOB8EEskdzFwDHAZ8CXghzWDiugkrqL6KXAVtmR7DbBcUnuhjjXGFODnwEeBU7DcPY9K\nemdicwMwHTgHOBHoAO5rsZ9NIQZ8F2PnLqUSGiUNB54EtgOnAUcD3wBeT2y+CczBEjweD2zFruOh\nLXd44HwL8/9SrA2aC8yVNKdm0BSdIYRSvIBDsD0kJidlB8Wyk+P7TwM7gfbE5hLs4hhStIY6dQ4G\n/gFcsAeb0uvMaFkbL/IeYEJS92VsZc6QpOw6YF3Rfjeo+XLgxarpBFYDP0veCwsM5xbtWxM1tsfr\n9BPxfRvWGZ2d2IyLNscX7W9ObQcB64GTgd8DCyuocT6wci82G4DLkvdtwFvAjKL9z6HzQeDmTNm9\nwF3N1FmaEYhgG0g9D8ySdKCkIdid3CagK5qdAPwlhLA5OXQ5cDDwoVb62wATsegeWQKyDZIelm3x\nXaMKOpE0ErgJy8r6Vh8mVU22NhzLD1Oj9DrjI5lJwO9qZcFapceBjxXl1z5gODZSVjt/k7CRwFT3\neuBlyqd7MfBgCGFFpvxYqqPxTOBZSb+Jj6T+mI70SToCy8OUan0T20W5TFqfAqZKGgMg6cPAZGzk\numk6SxNARE7FOtgtWIfzdeD0sHtjqlG8fQvsNDlXGTgSu3O7CnskMR0bWVgZh9+gGjrBtkTvDCH8\nqZ/6qujsJT5jnIPlhKlRBZ3t2OhZXzrKomGPSBI2lP+HEMK6WDwK2BEb35RS6ZY0E/gIcGUf1SOp\ngMbIkdiI33ps1+NfAIsknR/rR2EBYtmv4/nAPcDzknZgN9k3hBB+HeuborPwAELSdXHCTn+vXcmk\npU5M4GRs46mlwEPxTnZvFLrhRQ6dtXPygxDC0ti5Xoj5f24dH1UKnZK+Brwb+FHt0Ho/Iv4shc7M\nMe8FHgHuCSHctrePiD/LvlGLKL+GGp3YPJbP1mFbGt2S3ocFRueHZJO/eg6lJBoTBgFdIYTvhRDW\nhBBuAm7Ggoo9UTat5wGfA2Zi85FmA1dI+vxejsulc1+n866HupJzSZoKnAEMDyFsjeVzJE3D/jkL\nsORc2VnfteCiv+RcraLeJGQd8ffenKshhB2SXgLeH4vKrrMb+BQ2dL/dbux6eVbSr0IIFzKwZGut\nIldSOUkdwArs7vWSjN3+rLNeNgO76FtHWTT0i6QbsfZnSghhQ1K1ERgqqS1zh14m3ZOAQ4Eu7f4y\nDgZOjJPuTgeGlVxjjVdI2tbIX4HPxN83Yp3oSP5f2wigv5HS/ZEFwLUhhCXx/VpJh2MjTHfTJJ2F\nBxChzuRcyaznbHTUw+679lXAtyW1J/MDpgFvAOsokBw6u7AJS+OICU/i8+XDgb9Hsyro/CrwnaSo\nA3vuPwN4OpblTrbWKurVCb0jDyuAZ4CL+jDZb3XWSwhhZ7x2pwIPQO+Q/1RgUZG+NUoMHs4CTgoh\nvJyp7gL+i+m8P9qPxYL9Va30swEex1Z7pdyBdazzgX9hk7bLrLHGk1jbmjKO2LaGELolbcS0Pgcg\nqQ1bhbO4hX42yoHsoa9sms6iZ4vmmFV6CJbFcwkwARgD/BhL6T0+2gzClh89Em1Ow6Kra4r2P6fW\n67EJSqcCY4FbsMj54CrpzGgezdtXYbRhM4XvxIaOzwP+A3yhaH9z6HoPlrr+MSxIGll7VUln1DED\nm5s0C1tV80ssyDq0aN8a0NSJzUGakp474ICMTTe2vHwS1kk9UbTvDeruXYVRJY3YhNDt2J34B7Bh\n/i3AzMRmbrxuz8QCq6XxOzy0aP9z6Lw99iFnxLb17Nh/XttMnYULzflPmRg7zVeBf8eLeFrG5jDg\nodgAb8KesQ8q2vecOgdjQ1CvRJ3LgaOrpjOjZzQ2BD4hUz4eWAlsi1+Iy4v2Naeu2VFX+uoBdlVJ\nZ6LjUuBvMZBYBRxbtE8N6unp4/ztAmYlNsOwvSI2x85oCTCiaN8b1L0iE0BURmPsVJ+L37W1wEV9\n2MzDgvptsf09qmi/c2p8F5b9uhvb3+EF4Ptklvk3qtOTaTmO4ziOk5vCV2E4juM4jlM+PIBwHMdx\nHCc3HkA4juM4jpMbDyAcx3Ecx8mNBxCO4ziO4+TGAwjHcRzHcXLjAYTjOI7jOLnxAMJxHMdxnNx4\nAOE4juM4Tm48gHAcx3EcJzceQDiO4ziOk5v/AYv7CAgQVmdoAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x116d64950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"\n",
"fig = plt.figure()\n",
"#ax = fig.add_subplot(111, projection='3d')\n",
"ax = fig.add_subplot(111)\n",
"ys1 = yf[:,0,1]\n",
"xs1 = yf[:,0,0]\n",
"\n",
"xs2 = yf[:,1,0]\n",
"ys2 = yf[:,1,1]\n",
"\n",
"xs3 = yf[:,2,0]\n",
"ys3 = yf[:,2,1]\n",
"\n",
"ax.plot(xs1[:1], ys1[:1],'bv') \n",
"ax.plot(xs1[-1:], ys1[-1:], 'rv') \n",
"ax.plot(xs2[:1], ys2[:1], 'bv') \n",
"ax.plot(xs2[-1:], ys2[-1:], 'rv') \n",
"ax.plot(xs3[:1], ys3[:1], 'bv') \n",
"ax.plot(xs3[-1:], ys3[-1:], 'rv') \n",
"\n",
"# minx = np.min(y[:,[0,2],0]) \n",
"# maxx = np.max(y[:,[0,2],0]) \n",
"\n",
"# miny = np.min(y[:,[0,2],1]) \n",
"# maxy = np.max(y[:,[0,2],1])\n",
" \n",
"ax.plot(xs1, ys1) \n",
"ax.plot(xs2, ys2) \n",
"ax.plot(xs3, ys3) \n",
"\n",
"\n",
"# plt.xlim(xmin=minx, xmax=maxx)\n",
"# plt.ylim(ymin=miny, ymax=maxy)\n",
"\n",
"plt.title(\"Paths of 3 Colliding Electric Particles\")\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
diazmazzaro/UC2K17_DEV | demos/05_jupyter/InfoUser+Tags.ipynb | 1 | 11690 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from arcgis.gis import GIS\n",
"gis = GIS(\"https://ags-enterprise4.aeroterra.com/arcgis/\", \"PythonApi\", \"test123456\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"9item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
" <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
" <a href='https://ags-enterprise4.aeroterra.com/arcgis//home/user.html?user=PythonApi' target='_blank'>\n",
" <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n",
" </a>\n",
" </div>\n",
"\n",
" <div class=\"item_right\" style=\"float: none; width: auto; overflow: hidden;\">\n",
" <a href='https://ags-enterprise4.aeroterra.com/arcgis//home/user.html?user=PythonApi' target='_blank'><b>Python Api</b>\n",
" </a>\n",
" <br/><br/><b>Bio</b>: \n",
" <br/><b>First Name</b>: Python\n",
" <br/><b>Last Name</b>: Api\n",
" <br/><b>Username</b>: PythonApi\n",
" <br/><b>Joined</b>: May 08, 2017\n",
"\n",
" </div>\n",
" </div>\n",
" "
],
"text/plain": [
"<User username:PythonApi>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"me = gis.users.me\n",
"me"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'public'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"me.access"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Miembro de 4 grupo/s\n"
]
}
],
"source": [
"user_groups = me.groups\n",
"print(\"Miembro de \" + str(len(user_groups)) + \" grupo/s\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Posee 8 item/s\n"
]
}
],
"source": [
"user_items = me.items()\n",
"print(\"Posee \" + str(len(user_items)) + \" item/s\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Web AppBuilder']\n",
"['TRACK']\n",
"['TRACK']\n",
"['WebApp']\n",
"['Aggregate Points']\n",
"['Big Data Analytics', 'AggregatePoints']\n",
"['Aggregate Points']\n",
"['Big Data Analytics', 'AggregatePoints']\n"
]
}
],
"source": [
"for item in user_items:\n",
" try:\n",
" print(item.tags)\n",
" except:\n",
" print(item)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['TRACK', 'UC2017']"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"item = gis.content.get('b2c97de923214dfca44ee71420a56e54')\n",
"item.tags"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"item.update(item_properties={'tags':'TRACK,UC2017,DEVELOPERS'})"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['TRACK', 'UC2017', 'DEVELOPERS']"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"item.tags"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'TRACK_date'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"item.title"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'C:\\\\temp\\\\metadata.xml'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"item.download_metadata(save_folder=r'C:\\temp')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'C:\\\\temp\\\\thumbnail.JPEG'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"item.download_thumbnail(save_folder= r'C:\\temp')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
lpryszcz/jupyter | lpryszcz/hello_world.py.ipynb | 1 | 1220 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 Hello world!\n",
"1 Hello world!\n",
"2 Hello world!\n",
"3 Hello world!\n",
"4 Hello world!\n",
"5 Hello world!\n",
"6 Hello world!\n",
"7 Hello world!\n",
"8 Hello world!\n",
"9 Hello world!\n"
]
}
],
"source": [
"for i in range(10):\n",
" print \"%s Hello world!\" % i"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"For more, have a look at: https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks."
]
}
],
"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
}
| gpl-3.0 |
Neuroglycerin/neukrill-net-work | notebooks/superclass_hierarchy/Using superclass predictions.ipynb | 1 | 41600 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/afs/inf.ed.ac.uk/user/s13/s1320903/Neuroglycerin/neukrill-net-work\n"
]
}
],
"source": [
"cd .."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"calculate_normalisation_stats.py predictions.pkl\r\n",
"check_test_score.py pylearn2_cpu_model_function_builder.py\r\n",
"\u001b[0m\u001b[01;34mdata\u001b[0m/ README.md\r\n",
"generate_hlf_cache.py \u001b[01;34mrun_settings\u001b[0m/\r\n",
"generate_prior_weighted_csv.py settings.json\r\n",
"generate_prior_weighted_csv.py~ \u001b[01;32mstart_script.sh\u001b[0m*\r\n",
"generate_prior_weighted_csv.pyc test_attr.py\r\n",
"\u001b[01;32mimage_hack.sh\u001b[0m* test_priors.py\r\n",
"LICENSE \u001b[01;32mtest.py\u001b[0m*\r\n",
"logistic_sgd3.py test.py~\r\n",
"\u001b[01;34mmisc\u001b[0m/ train_attr.py\r\n",
"mlp3.py train_bow.py\r\n",
"mlp.py \u001b[01;32mtrain.py\u001b[0m*\r\n",
"mnist_train.py \u001b[01;34myaml_templates\u001b[0m/\r\n",
"model.pkl y_pickle\r\n",
"\u001b[01;34mnotebooks\u001b[0m/\r\n",
"\u001b[m"
]
}
],
"source": [
"ls"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Welcome to the HoloViews IPython extension! (http://ioam.github.io/holoviews/)\n",
"Available magics: %compositor, %opts, %params, %view, %%labels, %%opts, %%view\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f503ed38090>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f503ed389d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f503ed387d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pylearn2.utils\n",
"import pylearn2.config\n",
"import theano\n",
"import neukrill_net.utils\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import holoviews as hl\n",
"%load_ext holoviews.ipython\n",
"import sklearn.metrics\n",
"import pickle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Model with col_norms set to a higher value and augmentations turned on. We saved its pickle with predictions across all superclass vectors."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(\"predictions.pkl\", \"rb\") as f:\n",
" predictions = pickle.load(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get first parent predictions out."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"superclasses = predictions[:,121:(121+38)]\n",
"s = np.sum(superclasses, axis = 1)\n",
"any((s < 0.9999 )*(s>1.00001))"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"import neukrill_net.utils as utils\n",
"settings = neukrill_net.utils.Settings(\"settings.json\")\n",
"import neukrill_net.encoding as enc\n",
"hier = enc.get_hierarchy(settings)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get those superclasses that only have classes as children."
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"import neukrill_net.taxonomy as t\n",
"only_leaf_children = []\n",
"\n",
"layer = t.TaxonomyLayer(1)\n",
"\n",
"for s in hier[1]:\n",
" flag = True\n",
" for key, values in t.superclasses.items():\n",
" for i, v in enumerate(values):\n",
" if v == s and i != 1:\n",
" flag = False\n",
" if flag:\n",
" if s not in only_leaf_children:\n",
" only_leaf_children.append(s)"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['acantharia',\n",
" 'appendicularians',\n",
" 'calanoid',\n",
" 'chaetognaths',\n",
" 'cydippid',\n",
" 'decapods_all',\n",
" 'detritus',\n",
" 'diatoms',\n",
" 'euphausiids_all_ages',\n",
" 'fish',\n",
" 'no_class',\n",
" 'oithona',\n",
" 'other_hydromedusae',\n",
" 'physonect',\n",
" 'pluteus',\n",
" 'pteropods',\n",
" 'radiolarian',\n",
" 'rocketship',\n",
" 'seastar',\n",
" 'sphaeronectes',\n",
" 'sub_hydromedusae1',\n",
" 'sub_hydromedusae2',\n",
" 'sub_protists',\n",
" 'trichodesmium',\n",
" 'tunicate',\n",
" 'unknown']"
]
},
"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"only_leaf_children"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a list with keys being indices of a superclass (first parent) and values being all of the classes in it. "
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import neukrill_net.taxonomy as t\n",
"layer = t.TaxonomyLayer(1)\n",
"\n",
"backmap = {}\n",
"for i, c in enumerate(settings.classes):\n",
" j = int(np.where(np.array(hier[1]) == layer[c])[0])\n",
" if hier[1][j] in only_leaf_children:\n",
" try:\n",
" backmap[j] += [i]\n",
" except:\n",
" backmap[j] = [i]"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"{0: [0, 1, 2],\n",
" 1: [4, 5, 6, 7],\n",
" 2: [14, 15, 16, 17, 18, 19, 20, 21, 22, 26],\n",
" 4: [10, 11, 12],\n",
" 8: [29, 30],\n",
" 9: [32, 92, 94, 95],\n",
" 10: [33, 34, 35, 49],\n",
" 11: [36, 37],\n",
" 13: [47, 48],\n",
" 14: [50, 51, 52, 53, 54, 55],\n",
" 17: [8, 9],\n",
" 18: [24, 25],\n",
" 19: [58, 59, 66, 67, 68, 69, 70, 71, 74, 75, 76, 77],\n",
" 21: [104, 105],\n",
" 23: [38, 39, 40, 41, 45],\n",
" 25: [87, 88, 89],\n",
" 26: [90, 91],\n",
" 27: [97, 98],\n",
" 28: [42, 43],\n",
" 31: [99, 100, 101],\n",
" 32: [57, 60, 61, 62],\n",
" 33: [63, 64, 65, 72, 73],\n",
" 34: [82, 83, 85, 86],\n",
" 35: [108, 109, 110, 111],\n",
" 36: [113, 114, 115, 116, 117],\n",
" 37: [118, 119, 120]}"
]
},
"execution_count": 168,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"backmap"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class_predictions = predictions[:, 0:121]\n",
"nr = class_predictions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make a large array for class predictions for all images. Replace those predictions that correspond to only_leaf_children superclasses with its prediction weighted by the class prior. For other classes, use their own predictions."
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1000\n",
"2000\n",
"3000\n",
"4000\n",
"5000\n",
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"127000\n",
"128000\n",
"129000\n",
"130000\n"
]
}
],
"source": [
"for index,r in enumerate(superclasses):\n",
" if index%1000 == 0:\n",
" print index\n",
" for i,j in enumerate(r):\n",
" priors = []\n",
" if i in backmap:\n",
" d = sum([settings.class_priors[k] for k in backmap[i]])\n",
" for a in backmap[i]:\n",
" priors.append(settings.class_priors[a]/d)\n",
" nr[index,backmap[i]] = j*np.array(priors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Write a .csv file."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"u'1.jpg'"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"os.path.basename(settings.image_fnames['test'][0])"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
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" u'10099.jpg',\n",
" u'100990.jpg',\n",
" u'100991.jpg',\n",
" u'100992.jpg',\n",
" u'100993.jpg',\n",
" u'100994.jpg',\n",
" u'100996.jpg',\n",
" u'100998.jpg',\n",
" u'101.jpg',\n",
" u'1010.jpg',\n",
" u'10100.jpg',\n",
" u'101000.jpg',\n",
" u'101001.jpg',\n",
" u'101002.jpg',\n",
" u'101004.jpg',\n",
" u'101005.jpg',\n",
" u'101006.jpg',\n",
" u'101008.jpg',\n",
" u'101009.jpg',\n",
" u'10101.jpg',\n",
" u'101010.jpg',\n",
" u'101011.jpg',\n",
" u'101012.jpg',\n",
" u'101013.jpg',\n",
" u'101014.jpg',\n",
" u'101015.jpg',\n",
" u'101016.jpg',\n",
" u'101017.jpg',\n",
" u'101018.jpg',\n",
" u'10102.jpg',\n",
" u'101020.jpg',\n",
" u'101021.jpg',\n",
" u'101023.jpg',\n",
" u'101024.jpg',\n",
" u'101025.jpg',\n",
" u'101026.jpg',\n",
" u'101028.jpg',\n",
" u'10103.jpg',\n",
" u'101030.jpg',\n",
" u'101031.jpg',\n",
" u'101032.jpg',\n",
" u'101033.jpg',\n",
" u'101034.jpg',\n",
" u'101035.jpg',\n",
" u'101036.jpg',\n",
" u'101037.jpg',\n",
" u'101038.jpg',\n",
" u'101039.jpg',\n",
" u'10104.jpg',\n",
" u'101041.jpg',\n",
" u'101042.jpg',\n",
" u'101043.jpg',\n",
" u'101044.jpg',\n",
" u'101045.jpg',\n",
" u'101046.jpg',\n",
" u'101047.jpg',\n",
" u'101048.jpg',\n",
" u'101049.jpg',\n",
" u'10105.jpg',\n",
" u'101050.jpg',\n",
" u'101051.jpg',\n",
" u'101052.jpg',\n",
" u'101053.jpg',\n",
" u'101054.jpg',\n",
" u'101056.jpg',\n",
" u'101058.jpg',\n",
" u'10106.jpg',\n",
" u'101060.jpg',\n",
" u'101061.jpg',\n",
" u'101062.jpg',\n",
" u'101063.jpg',\n",
" u'101064.jpg',\n",
" u'101067.jpg',\n",
" u'101068.jpg',\n",
" u'101069.jpg',\n",
" u'10107.jpg',\n",
" u'101071.jpg',\n",
" u'101072.jpg',\n",
" u'101073.jpg',\n",
" u'101075.jpg',\n",
" u'101078.jpg',\n",
" u'101079.jpg',\n",
" u'101080.jpg',\n",
" u'101081.jpg',\n",
" u'101084.jpg',\n",
" ...]"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"names = [os.path.basename(n) for n in settings.image_fnames['test']]"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import neukrill_net.utils as utils\n",
"utils.write_predictions('superclass_predictions.csv', nr, names, settings.classes)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"utils.write_predictions('class_predictions.csv', class_predictions, names, settings.classes)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
wcmckee/artcontrol-api | scrapnot.ipynb | 1 | 25048 | {
"metadata": {
"name": "",
"signature": "sha256:ee127d30edf76db02b4dec4b4a76dd25eb4686f7e26ea9b14364f7352c57d897"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Scrapnot\n",
"========\n",
"<h2>testing</h2>\n",
"<h3>this is heading 3</h3>\n",
"blah blah blah\n",
"--------------\n",
"\n",
"\n",
"This is a scrapbook for exploring some python ideas and modules that i plan to take into other notebooks.\n",
"*Test!*\n",
"What I really need to start doing with these notebooks is a daily notebook. At the end of the week the blog .\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import markdown"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import requests\n",
"import json\n",
"import xmltodict"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hcpux = requests.get('http://feeds.feedburner.com/HamiltonComputerClub?format=xml')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 76
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cerz = hcpux.text"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 77
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 77
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hamx = xmltodict.parse(cerz)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 78
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for ha in hamx['rss']['channel']:\n",
" print ha"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"title\n",
"link\n",
"description\n",
"lastBuildDate\n",
"language\n",
"sy:updatePeriod\n",
"sy:updateFrequency\n",
"generator\n",
"atom10:link\n",
"feedburner:info\n",
"feedburner:browserFriendly\n",
"item\n"
]
}
],
"prompt_number": 79
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"staz = hamx['rss']['channel']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"alket = staz.keys()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(alket)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 94,
"text": [
"12"
]
}
],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"savlis = []"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 95
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print alket[3]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"lastBuildDate\n"
]
}
],
"prompt_number": 96
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"savcal = staz.values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 97
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"staz"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 103,
"text": [
"OrderedDict([(u'title', u'Hamilton Computer Club'), (u'link', u'http://hamiltoncomputerclub.org.nz'), (u'description', u'Exchange information and share skills'), (u'lastBuildDate', u'Thu, 01 May 2014 10:20:40 +0000'), (u'language', u'en-US'), (u'sy:updatePeriod', u'hourly'), (u'sy:updateFrequency', u'1'), (u'generator', u'http://wordpress.org/?v=3.9.1'), (u'atom10:link', [OrderedDict([(u'@xmlns:atom10', u'http://www.w3.org/2005/Atom'), (u'@rel', u'self'), (u'@type', u'application/rss+xml'), (u'@href', u'http://feeds.feedburner.com/HamiltonComputerClub')]), OrderedDict([(u'@xmlns:atom10', u'http://www.w3.org/2005/Atom'), (u'@rel', u'hub'), (u'@href', u'http://pubsubhubbub.appspot.com/')])]), (u'feedburner:info', OrderedDict([(u'@uri', u'hamiltoncomputerclub')])), (u'feedburner:browserFriendly', None), (u'item', [OrderedDict([(u'title', u'Windows RIP XP Long live 8.1'), (u'link', u'http://hamiltoncomputerclub.org.nz/windows-rip-xp-long-live-8-1/'), (u'comments', u'http://hamiltoncomputerclub.org.nz/windows-rip-xp-long-live-8-1/#comments'), (u'pubDate', u'Wed, 09 Apr 2014 21:38:34 +0000'), (u'dc:creator', u'Rod Aldridge'), (u'category', u'Link dump'), (u'guid', OrderedDict([(u'@isPermaLink', u'false'), ('#text', u'http://hamiltoncomputerclub.org.nz/?p=417')])), (u'description', u'Microsoft makes Windows free for select devices, announces universal Windows apps By Shawn Knight on April 2, 2014 http://www.techspot.com/news/56240-microsoft-makes-windows-free-for-select-devices-announces-universal-windows-apps.html Microsoft on Wednesday revealed plans to make Windows free for manufacturers to use on smartphones and tablets with screen sizes under nine inches. Furthermore, a future version of Windows for the \\u2026 <a href=\"http://hamiltoncomputerclub.org.nz/windows-rip-xp-long-live-8-1/\"> Continue reading <span class=\"meta-nav\">→ </span></a>'), (u'wfw:commentRss', u'http://hamiltoncomputerclub.org.nz/windows-rip-xp-long-live-8-1/feed/'), (u'slash:comments', u'0')]), OrderedDict([(u'title', u'Python'), (u'link', u'http://hamiltoncomputerclub.org.nz/python/'), (u'comments', u'http://hamiltoncomputerclub.org.nz/python/#comments'), (u'pubDate', u'Fri, 21 Mar 2014 12:49:39 +0000'), (u'dc:creator', u'wcmckee'), (u'category', [u'Software', u'code', u'python', u'teach']), (u'guid', OrderedDict([(u'@isPermaLink', u'false'), ('#text', u'http://hamiltoncomputerclub.org.nz/?p=391')])), (u'description', u'My name is William Mckee. I did a talk in February regarding the Python programming language. I found Bruce through the New Zealand Python users group mailing list. There had been much discussion in the post regarding getting some Python meetups/classes happen. Auckland, Wellington, and Christchurch have monthly Python meetups. \\u2026 <a href=\"http://hamiltoncomputerclub.org.nz/python/\"> Continue reading <span class=\"meta-nav\">→ </span></a>'), (u'wfw:commentRss', u'http://hamiltoncomputerclub.org.nz/python/feed/'), (u'slash:comments', u'0')]), OrderedDict([(u'title', u'Beware fake-capacity USB drives'), (u'link', u'http://hamiltoncomputerclub.org.nz/350/'), (u'comments', u'http://hamiltoncomputerclub.org.nz/350/#comments'), (u'pubDate', u'Mon, 21 Oct 2013 07:48:44 +0000'), (u'dc:creator', u'Bruce Kingsbury'), (u'category', u'Security'), (u'guid', OrderedDict([(u'@isPermaLink', u'false'), ('#text', u'http://hamiltoncomputerclub.org.nz/?p=350')])), (u'description', u'Various Trademe members have comment that there are listings for very cheap 128GB, 256GB and 512GB USB Memory Keys appearing on the site. Please be aware that there is a large number of fake USB Memory Keys being sold on eBay in the USA and from China which the firmware \\u2026 <a href=\"http://hamiltoncomputerclub.org.nz/350/\"> Continue reading <span class=\"meta-nav\">→ </span></a>'), (u'wfw:commentRss', u'http://hamiltoncomputerclub.org.nz/350/feed/'), (u'slash:comments', u'0')]), OrderedDict([(u'title', u'Found on the web 2013/10'), (u'link', u'http://hamiltoncomputerclub.org.nz/201310/'), (u'comments', u'http://hamiltoncomputerclub.org.nz/201310/#comments'), (u'pubDate', u'Mon, 30 Sep 2013 00:42:11 +0000'), (u'dc:creator', u'Rod Aldridge'), (u'category', u'Link dump'), (u'guid', OrderedDict([(u'@isPermaLink', u'false'), ('#text', u'http://hamiltoncomputerclub.org.nz/?p=307')])), (u'description', u'Some items which you may find interesting or useful Some HOWTO’s and other hints mostly from PC World, some security and privacy alerts, software, some general news, Microsoft news, insight into censorship in China, explanation of Application Programming Interfaces (APIs) and Encryption. Finally some light relief (this year’s IgNoble Prizes). \\u2026 <a href=\"http://hamiltoncomputerclub.org.nz/201310/\"> Continue reading <span class=\"meta-nav\">→ </span></a>'), (u'wfw:commentRss', u'http://hamiltoncomputerclub.org.nz/201310/feed/'), (u'slash:comments', u'0')]), OrderedDict([(u'title', u'Potential NSA Involvement in a NIST RNG Standard'), (u'link', u'http://hamiltoncomputerclub.org.nz/potential-nsa-involvement-in-a-nist-rng-standard/'), (u'comments', u'http://hamiltoncomputerclub.org.nz/potential-nsa-involvement-in-a-nist-rng-standard/#comments'), (u'pubDate', u'Mon, 30 Sep 2013 00:22:02 +0000'), (u'dc:creator', u'Bruce Kingsbury'), (u'category', u'Tidbits'), (u'guid', OrderedDict([(u'@isPermaLink', u'false'), ('#text', u'http://hamiltoncomputerclub.org.nz/?p=296')])), (u'description', u'In August 2007, a young programmer in Microsoft\\u2019s Windows security group stood up to give a five-minute turbo talk at the annual Crypto conference in Santa Barbara. It was a Tuesday evening, part of the conference\\u2019s traditional rump session, when a hodge-podge of short talks are presented outside of the \\u2026 <a href=\"http://hamiltoncomputerclub.org.nz/potential-nsa-involvement-in-a-nist-rng-standard/\"> Continue reading <span class=\"meta-nav\">→ </span></a>'), (u'wfw:commentRss', u'http://hamiltoncomputerclub.org.nz/potential-nsa-involvement-in-a-nist-rng-standard/feed/'), (u'slash:comments', u'0')]), OrderedDict([(u'title', u'New website and blogs'), (u'link', u'http://hamiltoncomputerclub.org.nz/new-website-blogs/'), (u'comments', u'http://hamiltoncomputerclub.org.nz/new-website-blogs/#comments'), (u'pubDate', u'Tue, 17 Sep 2013 12:26:55 +0000'), (u'dc:creator', u'Virginia Schnauer'), (u'category', [u'Notices', u'website']), (u'guid', OrderedDict([(u'@isPermaLink', u'false'), ('#text', u'http://hamiltoncomputerclub.org.nz/?p=231')])), (u'description', u'I met with Bruce today and have cleaned up some loose end on your new look website. Members can now become contributors or post comments and have conversations. I suggest you all start here by commenting your likes and dislikes regarding look, format and content. It would be great to \\u2026 <a href=\"http://hamiltoncomputerclub.org.nz/new-website-blogs/\"> Continue reading <span class=\"meta-nav\">→ </span></a>'), (u'wfw:commentRss', u'http://hamiltoncomputerclub.org.nz/new-website-blogs/feed/'), (u'slash:comments', u'1')]), OrderedDict([(u'title', u'PC World Magazine'), (u'link', u'http://hamiltoncomputerclub.org.nz/pc-world-magazine/'), (u'comments', u'http://hamiltoncomputerclub.org.nz/pc-world-magazine/#comments'), (u'pubDate', u'Sat, 14 Sep 2013 09:24:32 +0000'), (u'dc:creator', u'Virginia Schnauer'), (u'category', [u'Tidbits', u'IT', u'magazines']), (u'guid', OrderedDict([(u'@isPermaLink', u'false'), ('#text', u'http://hamiltoncomputerclub.org.nz/?p=190')])), (u'description', u'End of a era PC World magazine has closed down. It is available online http://www.pcworld.co.nz/. The site now offers sections on smartphones and tablets, computers, gadgets, a business centre, home entertainment, cameras, and a forum It is intended for the IT trade publications to continue.'), (u'wfw:commentRss', u'http://hamiltoncomputerclub.org.nz/pc-world-magazine/feed/'), (u'slash:comments', u'0')]), OrderedDict([(u'title', u'First article'), (u'link', u'http://hamiltoncomputerclub.org.nz/first-article/'), (u'comments', u'http://hamiltoncomputerclub.org.nz/first-article/#comments'), (u'pubDate', u'Sun, 01 Sep 2013 11:06:21 +0000'), (u'dc:creator', u'Bruce Kingsbury'), (u'category', [u'Notices', u'website']), (u'guid', OrderedDict([(u'@isPermaLink', u'false'), ('#text', u'http://hamiltoncomputerclub.org.nz/?p=49')])), (u'description', u'This is the new website of the Hamilton PC computer club. It’s registered and hosted on wordpress by onlydomains.com. The theme at time or writing is wp386, a throwback to the old BBS days. I’m sure everybody on the committee is going to absolutely loath it which should encourage them \\u2026 <a href=\"http://hamiltoncomputerclub.org.nz/first-article/\"> Continue reading <span class=\"meta-nav\">→ </span></a>'), (u'wfw:commentRss', u'http://hamiltoncomputerclub.org.nz/first-article/feed/'), (u'slash:comments', u'3')])])])"
]
}
],
"prompt_number": 103
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"savcal()[0:7]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 98,
"text": [
"[u'Hamilton Computer Club',\n",
" u'http://hamiltoncomputerclub.org.nz',\n",
" u'Exchange information and share skills',\n",
" u'Thu, 01 May 2014 10:20:40 +0000',\n",
" u'en-US',\n",
" u'hourly',\n",
" u'1']"
]
}
],
"prompt_number": 98
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for ket in alket:\n",
" #print staz[ket]\n",
" print staz['title']\n",
" savlis.append(staz[ket])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hamilton Computer Club\n",
"Hamilton Computer Club\n",
"Hamilton Computer Club\n",
"Hamilton Computer Club\n",
"Hamilton Computer Club\n",
"Hamilton Computer Club\n",
"Hamilton Computer Club\n",
"Hamilton Computer Club\n",
"Hamilton Computer Club\n",
"Hamilton Computer Club\n",
"Hamilton Computer Club\n",
"Hamilton Computer Club\n"
]
}
],
"prompt_number": 87
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"opd = json.dumps(hamxy)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'hamxy' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-88-470a3306a967>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mopd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhamxy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'hamxy' is not defined"
]
}
],
"prompt_number": 88
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"savopd = open('cpuclu.json', 'w')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 89
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"savopd.write(opd)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'opd' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-90-b9cbea379c27>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msavopd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'opd' is not defined"
]
}
],
"prompt_number": 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"savopd.close()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"zopa = open('cpuclu.json', 'r')"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print zopa.read()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"myjson = zopa.read()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sjson = json.dumps(hamx)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print sjson"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cerz"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"markdown.to_html_string('*testing one two three*')"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"html = markdown.markdown('testing123!')"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print html"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import os"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"posfol = (\"/home/will/hamiiltoncomputerclub.org.nz/static/posts\")\n",
"blotil = (\"wcmckee\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"os.makedirs(posfol)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 72
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 72
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ls"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\u001b[0m\u001b[38;5;27m~\u001b[0m/ dh.py oceanconnor.ipynb README.md testing123\r\n",
"aklmcam.ipynb example.xls prnsav redTube.py tlchome.html\r\n",
"aklmcam.py hcpux pyblen.py redTube.pyc tlc.ipynb\r\n",
"artcontrol.ipynb home.html pynztacam.ipynb result tlc.py\r\n",
"artcontrolme.wordpress.2014-02-10.xml ipython_log.py pynztacam.py salecus.ipynb tpb.ipynb\r\n",
"artcontrol.py ipython_log.py.001~ pyssh.ipynb salecus.py Untitled0.ipynb\r\n",
"config ipython_log.py.002~ pyssh.py salecus.pyc uploads.html\r\n",
"cpuclu.json LICENSE pywgit.ipynb savPrn wcmckee.rst\r\n",
"dhcpd.leases linkz pywgit.py scrapnot.ipynb\r\n",
"dh.ipynb myfile.txt pywgitz.py scrapnot.py\r\n"
]
}
],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rstz = open((blotil + '.rst'), 'w')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rstz.write('"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "EOL while scanning string literal (<ipython-input-75-0019d967869f>, line 1)",
"output_type": "pyerr",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-75-0019d967869f>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m rstz.write('\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m EOL while scanning string literal\n"
]
}
],
"prompt_number": 75
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"conv = markdown.markdownFromFile"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandoc"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pandoc.PANDOC_PATH = ('/usr/bin/pandoc')"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"panout = pandoc.Document.OUTPUT_FORMATS"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for pa in panout:\n",
" print pa"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "raw",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"panin = pandoc.Document.INPUT_FORMATS"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"panin"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for nai in panin:\n",
" print nai"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nai"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"doc = pandoc.Document()\n",
"doc.markdown = '''\n",
"# I am a tag\n",
"\n",
"*omg this is a test!\n",
"\n",
"[artcontrol](https://artcontrol.me)\n",
"'''"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print doc.html"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
anandha2017/udacity | nd101 Deep Learning Nanodegree Foundation/DockerImages/14_intro_to_deep_neural_networks/notebooks/02_save_and_restore_tensorflow_models/04 Finetuning (Naming Error).ipynb | 1 | 22785 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Remove the previous weights and bias\n",
"tf.reset_default_graph()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"save_file = '04model.ckpt'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Two Tensor Variables: weights and bias\n",
"weights = tf.Variable(tf.truncated_normal([2, 3]))\n",
"bias = tf.Variable(tf.truncated_normal([3]))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"saver = tf.train.Saver()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Save Weights: Variable:0\n",
"Save Bias: Variable_1:0\n"
]
}
],
"source": [
"# Print the name of Weights and Bias\n",
"print('Save Weights: {}'.format(weights.name))\n",
"print('Save Bias: {}'.format(bias.name))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" saver.save(sess, save_file)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Remove the previous weights and bias\n",
"tf.reset_default_graph()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Two Variables: weights and bias\n",
"bias = tf.Variable(tf.truncated_normal([3]))\n",
"weights = tf.Variable(tf.truncated_normal([2, 3]))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"saver = tf.train.Saver()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Load Weights: Variable_1:0\n",
"Load Bias: Variable:0\n"
]
}
],
"source": [
"# Print the name of Weights and Bias\n",
"print('Load Weights: {}'.format(weights.name))\n",
"print('Load Bias: {}'.format(bias.name))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Restoring parameters from 04model.ckpt\n"
]
},
{
"ename": "InvalidArgumentError",
"evalue": "Assign requires shapes of both tensors to match. lhs shape= [3] rhs shape= [2,3]\n\t [[Node: save/Assign = Assign[T=DT_FLOAT, _class=[\"loc:@Variable\"], use_locking=true, validate_shape=true, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](Variable, save/RestoreV2)]]\n\nCaused by op 'save/Assign', defined at:\n File \"/opt/conda/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"/opt/conda/lib/python3.6/runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in <module>\n app.launch_new_instance()\n File \"/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n app.start()\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 477, in start\n ioloop.IOLoop.instance().start()\n File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\n super(ZMQIOLoop, self).start()\n File \"/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py\", line 888, in start\n handler_func(fd_obj, events)\n File \"/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n return fn(*args, **kwargs)\n File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n self._handle_recv()\n File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n self._run_callback(callback, msg)\n File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n callback(*args, **kwargs)\n File \"/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n return fn(*args, **kwargs)\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n return self.dispatch_shell(stream, msg)\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\n handler(stream, idents, msg)\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n user_expressions, allow_stdin)\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2717, in run_cell\n interactivity=interactivity, compiler=compiler, result=result)\n File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2821, in run_ast_nodes\n if self.run_code(code, result):\n File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2881, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"<ipython-input-10-18da33d742f9>\", line 1, in <module>\n saver = tf.train.Saver()\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\", line 1139, in __init__\n self.build()\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\", line 1170, in build\n restore_sequentially=self._restore_sequentially)\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\", line 691, in build\n restore_sequentially, reshape)\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\", line 419, in _AddRestoreOps\n assign_ops.append(saveable.restore(tensors, shapes))\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\", line 155, in restore\n self.op.get_shape().is_fully_defined())\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/state_ops.py\", line 271, in assign\n validate_shape=validate_shape)\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/gen_state_ops.py\", line 45, in assign\n use_locking=use_locking, name=name)\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py\", line 767, in apply_op\n op_def=op_def)\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\", line 2506, in create_op\n original_op=self._default_original_op, op_def=op_def)\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\", line 1269, in __init__\n self._traceback = _extract_stack()\n\nInvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [3] rhs shape= [2,3]\n\t [[Node: save/Assign = Assign[T=DT_FLOAT, _class=[\"loc:@Variable\"], use_locking=true, validate_shape=true, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](Variable, save/RestoreV2)]]\n",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1138\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1139\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1140\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1120\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1121\u001b[0;31m status, run_metadata)\n\u001b[0m\u001b[1;32m 1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 90\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[0;34m()\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[1;32m 467\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mInvalidArgumentError\u001b[0m: Assign requires shapes of both tensors to match. lhs shape= [3] rhs shape= [2,3]\n\t [[Node: save/Assign = Assign[T=DT_FLOAT, _class=[\"loc:@Variable\"], use_locking=true, validate_shape=true, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](Variable, save/RestoreV2)]]",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-175f9a1061ed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# Load the weights and bias - ERROR\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36mrestore\u001b[0;34m(self, sess, save_path)\u001b[0m\n\u001b[1;32m 1546\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Restoring parameters from %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1547\u001b[0m sess.run(self.saver_def.restore_op_name,\n\u001b[0;32m-> 1548\u001b[0;31m {self.saver_def.filename_tensor_name: save_path})\n\u001b[0m\u001b[1;32m 1549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1550\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 787\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 788\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 789\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 790\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 791\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 995\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 996\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 997\u001b[0;31m feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m 998\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 999\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1130\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1131\u001b[0m return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1132\u001b[0;31m target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m 1133\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1134\u001b[0m return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1150\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1151\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1152\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1154\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\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[0;31mInvalidArgumentError\u001b[0m: Assign requires shapes of both tensors to match. lhs shape= [3] rhs shape= [2,3]\n\t [[Node: save/Assign = Assign[T=DT_FLOAT, _class=[\"loc:@Variable\"], use_locking=true, validate_shape=true, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](Variable, save/RestoreV2)]]\n\nCaused by op 'save/Assign', defined at:\n File \"/opt/conda/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"/opt/conda/lib/python3.6/runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in <module>\n app.launch_new_instance()\n File \"/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n app.start()\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 477, in start\n ioloop.IOLoop.instance().start()\n File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\n super(ZMQIOLoop, self).start()\n File \"/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py\", line 888, in start\n handler_func(fd_obj, events)\n File \"/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n return fn(*args, **kwargs)\n File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n self._handle_recv()\n File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n self._run_callback(callback, msg)\n File \"/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n callback(*args, **kwargs)\n File \"/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n return fn(*args, **kwargs)\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n return self.dispatch_shell(stream, msg)\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\n handler(stream, idents, msg)\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n user_expressions, allow_stdin)\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2717, in run_cell\n interactivity=interactivity, compiler=compiler, result=result)\n File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2821, in run_ast_nodes\n if self.run_code(code, result):\n File \"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2881, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"<ipython-input-10-18da33d742f9>\", line 1, in <module>\n saver = tf.train.Saver()\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\", line 1139, in __init__\n self.build()\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\", line 1170, in build\n restore_sequentially=self._restore_sequentially)\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\", line 691, in build\n restore_sequentially, reshape)\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\", line 419, in _AddRestoreOps\n assign_ops.append(saveable.restore(tensors, shapes))\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/saver.py\", line 155, in restore\n self.op.get_shape().is_fully_defined())\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/state_ops.py\", line 271, in assign\n validate_shape=validate_shape)\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/gen_state_ops.py\", line 45, in assign\n use_locking=use_locking, name=name)\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py\", line 767, in apply_op\n op_def=op_def)\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\", line 2506, in create_op\n original_op=self._default_original_op, op_def=op_def)\n File \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\", line 1269, in __init__\n self._traceback = _extract_stack()\n\nInvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [3] rhs shape= [2,3]\n\t [[Node: save/Assign = Assign[T=DT_FLOAT, _class=[\"loc:@Variable\"], use_locking=true, validate_shape=true, _device=\"/job:localhost/replica:0/task:0/cpu:0\"](Variable, save/RestoreV2)]]\n"
]
}
],
"source": [
"with tf.Session() as sess:\n",
" # Load the weights and bias - ERROR\n",
" saver.restore(sess, save_file)"
]
},
{
"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 |
dominicmeroux/Reading-In-and-Analyzing-Calendar-Data-by-Interfacing-Between-MySQL-and-Python | .ipynb_checkpoints/Reading in and Analyzing Calendar Data by Interfacing Between MySQL and Python-checkpoint.ipynb | 1 | 13886 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import Packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from icalendar import *\n",
"from datetime import date, datetime, timedelta\n",
"from __future__ import print_function\n",
"import mysql.connector\n",
"from mysql.connector import errorcode\n",
"import pickle\n",
"import csv\n",
"import pandas\n",
"from pandas.io import sql\n",
"import numpy as np\n",
"import os\n",
"import re\n",
"import glob\n",
"import pytz\n",
"import calendar_parser as cp \n",
"# for calendar_parser, I downloaded the Python file created for this package\n",
"# https://github.com/oblique63/Python-GoogleCalendarParser/blob/master/calendar_parser.py\n",
"# and saved it in the working directory with my Python file (Jupyter Notebook file). \n",
"# In calendar_parser.py, their function _fix_timezone is very crucial for my code to \n",
"# display the correct local time. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Establish Connection with MySQL (optional approach)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"User = # MySQL Username\n",
"Password = # MySQL password\n",
"Host = # MySQL host\n",
"cnx = mysql.connector.connect(user=User, password=Password, host=Host)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cursor = cnx.cursor()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Approach / Code modified from MySQL Connector web page\n",
"DB_NAME = \"CalDb\"\n",
"\n",
"# 1) Creates database if it doesn't already exist\n",
"# 2) Then connects to the database\n",
"def create_database(cursor):\n",
" try:\n",
" cursor.execute(\n",
" \"CREATE DATABASE {} DEFAULT CHARACTER SET 'utf8'\".format(DB_NAME))\n",
" except mysql.connector.Error as err:\n",
" print(\"Failed creating database: {}\".format(err))\n",
" exit(1)\n",
"\n",
"try:\n",
" cnx.database = DB_NAME \n",
"except mysql.connector.Error as err:\n",
" if err.errno == errorcode.ER_BAD_DB_ERROR:\n",
" create_database(cursor)\n",
" cnx.database = DB_NAME\n",
" else:\n",
" print(err)\n",
" exit(1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create table specifications\n",
"TABLES = {}\n",
"TABLES['eBike'] = (\n",
" \"CREATE TABLE IF NOT EXISTS `eBike` (\"\n",
" \" `eBikeName` varchar(10),\"\n",
" \" `Organizer` varchar(100),\"\n",
" \" `Created` datetime NOT NULL,\"\n",
" \" `Start` datetime NOT NULL,\"\n",
" \" `End` datetime NOT NULL\"\n",
" \") ENGINE=InnoDB\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating table eBike: OK\n"
]
}
],
"source": [
"# If table does not already exist, this code will create it based on specifications\n",
"for name, ddl in TABLES.iteritems():\n",
" try:\n",
" print(\"Creating table {}: \".format(name), end='')\n",
" cursor.execute(ddl)\n",
" except mysql.connector.Error as err:\n",
" if err.errno == errorcode.ER_TABLE_EXISTS_ERROR:\n",
" print(\"already exists.\")\n",
" else:\n",
" print(err.msg)\n",
" else:\n",
" print(\"OK\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Obtain current count from each calendar to read in and add additional entries only\n",
"cursor.execute(\"SELECT COUNT(*) FROM eBike WHERE eBikeName = 'Gold'\")\n",
"GoldExistingCount = cursor.fetchall()\n",
"\n",
"cursor.execute(\"SELECT COUNT(*) FROM eBike WHERE eBikeName = 'Blue'\")\n",
"BlueExistingCount = cursor.fetchall()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
}
],
"source": [
"#print(GoldExistingCount[0][0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Read in Calendar Data"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Declare lists\n",
"eBikeName = []\n",
"Organizer = []\n",
"DTcreated = []\n",
"DTstart = []\n",
"DTend = []\n",
"Counter = 0"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Open first e-bike calendar, appends data, then repeats for second calendar. \n",
"# A future modification I am working on is to bring this into one loop such that \n",
"# as many calendars as desired for a specific table can be read in from one folder. \n",
"#\n",
"# Additionally, I plan to look into potential for using the .ics url to read in\n",
"# calendar data so that the file does not need to be updated each time this code\n",
"# is run for analysis of calendar data. \n",
"b = open('Gold.ics','rb')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cal = Calendar.from_ical(b.read())\n",
"\n",
"timezones = cal.walk('VTIMEZONE')\n",
"\n",
"for k in cal.walk():\n",
" if k.name == \"VEVENT\":\n",
" Counter = Counter + 1\n",
" if Counter > GoldExistingCount[0][0]:\n",
" eBikeName.append('Gold')\n",
" Organizer.append( re.sub(r'mailto:', \"\", str(k.get('ORGANIZER') ) ) )\n",
" DTcreated.append( cp._fix_timezone( k.decoded('CREATED'), pytz.timezone(timezones[0]['TZID']) ) )\n",
" DTstart.append( cp._fix_timezone( k.decoded('DTSTART'), pytz.timezone(timezones[0]['TZID']) ) )\n",
" DTend.append( cp._fix_timezone( k.decoded('DTEND'), pytz.timezone(timezones[0]['TZID']) ) )\n",
"\n",
"b.close()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Resetting 'Counter' to 0 and opening next calendar...\n",
"Counter = 0\n",
"b = open('Blue.ics','rb')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cal = Calendar.from_ical(b.read())\n",
"\n",
"timezones = cal.walk('VTIMEZONE')\n",
"\n",
"for k in cal.walk():\n",
" if k.name == \"VEVENT\":\n",
" Counter = Counter + 1\n",
" if Counter > BlueExistingCount[0][0]:\n",
" eBikeName.append('Blue')\n",
" Organizer.append( re.sub(r'mailto:', \"\", str(k.get('ORGANIZER') ) ) )\n",
" DTcreated.append( cp._fix_timezone( k.decoded('CREATED'), pytz.timezone(timezones[0]['TZID']) ) )\n",
" DTstart.append( cp._fix_timezone( k.decoded('DTSTART'), pytz.timezone(timezones[0]['TZID']) ) )\n",
" DTend.append( cp._fix_timezone( k.decoded('DTEND'), pytz.timezone(timezones[0]['TZID']) ) )\n",
"\n",
"b.close()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Now that calendar data is fully read in, create a list with data in a format for \n",
"# entering into the MySQL database. \n",
"# \n",
"# At this point, if the MySQL Connector component is not desired, other approaches \n",
"# include creating a Pandas dataframe or something else.\n",
"# For reference, a Pandas dataframe could be created with the following command: \n",
"# df = pandas.DataFrame({'ORGANIZER' : Organizer,'CREATED' : DTcreated, 'DTSTART' : DTstart,'DTEND': DTend})\n",
"eBikeData = []\n",
"for i in range(len(DTcreated)):\n",
" eBikeData.append((eBikeName[i], Organizer[i], DTcreated[i], DTstart[i], DTend[i]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MySQL Connection to Push Out and Read In Data"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Insert calendar data into MySQL table eBike\n",
"cursor.executemany(\"INSERT INTO eBike (eBikeName, Organizer, Created, Start, End) VALUES (%s, %s, %s, %s, %s)\", \n",
" eBikeData)\n",
"cnx.commit()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Find emails associated with reservations created at latest 7 days ago\n",
"cursor.execute(\"SELECT Organizer, Start FROM eBike WHERE DATEDIFF(Start, CURDATE()) >= 7\")\n",
"WeeklyEmail = cursor.fetchall()\n",
"print(WeeklyEmail)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Find total e-bike rides by user\n",
"cursor.execute(\"SELECT Organizer, COUNT(*) AS Total_Rides FROM eBike GROUP BY Organizer ORDER BY Total_Rides DESC;\")\n",
"TotalRides_by_User = cursor.fetchall()\n",
"print(TotalRides_by_User)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cursor.close()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cnx.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Desired Features (planned future improvements)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Features to add within the next week: Total Trips by Reservation Time, Total Trips by Weekday, \n",
"# Average and Maximum Hours by Weekday, Average and Maximum Utilization by Weekday, \n",
"# Find how far in advance reservations are created"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Generate reports from SQL query results"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# SINGLE LOOP READ-IN FEATURE\n",
"\n",
"# Enter desired directory where .ics files are contained\n",
"#path = '/Users/dmeroux/Documents/Calendar_Data_Extraction_V1.0'\n",
"#for infile in glob.glob( os.path.join(path, '*.ics') ):\n",
"# b = open(infile,'rb')\n",
"# cal = Calendar.from_ical(b.read())\n",
"# for k in cal.walk():\n",
"# if k.name == \"VEVENT\": \n",
"# if str(type(k.decoded('DTSTART'))) == \"<class 'datetime.datetime'>\":\n",
"# Organizer.append( re.sub(r'mailto:', \"\", str(k.get('ORGANIZER') ) ) ) # email address of organizer\n",
"# DTcreated.append( datetime.timestamp(k.decoded('CREATED') ) ) # reservation created date\n",
"# DTstart.append( datetime.timestamp(k.decoded('DTSTART') ) ) # reservation start date\n",
"# DTend.append( datetime.timestamp(k.decoded('DTEND') ) ) # reservation end date\n",
"# b.close()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# URL FEATURE"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Option to work with Pandas Dataframe (this code runs properly)\n",
"################################### ISSUE TRANSFERRING 'SUMMARY' INTO MYSQL \"TOO LONG\"\n",
"#df = pandas.DataFrame({'ORGANIZER' : Organizer,'CREATED' : DTcreated, 'DTSTART' : DTstart,'DTEND': DTend})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Add use case with PySpark\n",
"# FOR PYSPARK APPROACH, TRY CONNECTING TO MYSQL https://www.supergloo.com/fieldnotes/spark-sql-mysql-python-example-jdbc/"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
Cyb3rWard0g/HELK | docker/helk-jupyter/notebooks/sigma/proxy_ua_frameworks.ipynb | 1 | 5711 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploit Framework User Agent\n",
"Detects suspicious user agent strings used by exploit / pentest framworks like Metasploit in proxy logs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Content\n",
"```\n",
"- title: Exploit Framework User Agent\n",
" id: fdd1bfb5-f60b-4a35-910e-f36ed3d0b32f\n",
" status: experimental\n",
" description: Detects suspicious user agent strings used by exploit / pentest framworks\n",
" like Metasploit in proxy logs\n",
" references:\n",
" - https://blog.didierstevens.com/2015/03/16/quickpost-metasploit-user-agent-strings/\n",
" author: Florian Roth\n",
" logsource:\n",
" category: proxy\n",
" product: null\n",
" service: null\n",
" detection:\n",
" selection:\n",
" c-useragent:\n",
" - Internet Explorer *\n",
" - Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; InfoPath.2)\n",
" - Mozilla/4.0 (compatible; Metasploit RSPEC)\n",
" - Mozilla/4.0 (compatible; MSIE 6.1; Windows NT)\n",
" - Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1)\n",
" - Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0)\n",
" - Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0)\n",
" - Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0; Trident/4.0; SIMBAR={7DB0F6DE-8DE7-4841-9084-28FA914B0F2E};\n",
" SLCC1; .N\n",
" - Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)\n",
" - Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US) AppleWebKit/525.13 (KHTML,\n",
" like Gecko) Chrome/4.0.221.6 Safari/525.13\n",
" - Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0; MAAU)\n",
" - Mozilla/5.0\n",
" - Mozilla/4.0 (compatible; SPIPE/1.0\n",
" - Mozilla/5.0 (Windows NT 6.3; rv:39.0) Gecko/20100101 Firefox/35.0\n",
" - Sametime Community Agent\n",
" - X-FORWARDED-FOR\n",
" - DotDotPwn v2.1\n",
" - SIPDROID\n",
" - Mozilla/5.0 (Windows NT 10.0; Win32; x32; rv:60.0)\n",
" - Mozilla/6.0 (X11; Linux x86_64; rv:24.0) Gecko/20140205 Firefox/27.0 Iceweasel/25.3.0\n",
" - '*wordpress hash grabber*'\n",
" - '*exploit*'\n",
" condition: selection\n",
" fields:\n",
" - ClientIP\n",
" - c-uri\n",
" - c-useragent\n",
" falsepositives:\n",
" - Unknown\n",
" level: high\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Querying Elasticsearch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from elasticsearch import Elasticsearch\n",
"from elasticsearch_dsl import Search\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialize Elasticsearch client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"es = Elasticsearch(['http://helk-elasticsearch:9200'])\n",
"searchContext = Search(using=es, index='logs-*', doc_type='doc')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run Elasticsearch Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s = searchContext.query('query_string', query='c-useragent.keyword:(Internet\\ Explorer\\ * OR Mozilla\\/4.0\\ \\(compatible;\\ MSIE\\ 6.0;\\ Windows\\ NT\\ 5.1;\\ SV1;\\ InfoPath.2\\) OR Mozilla\\/4.0\\ \\(compatible;\\ Metasploit\\ RSPEC\\) OR Mozilla\\/4.0\\ \\(compatible;\\ MSIE\\ 6.1;\\ Windows\\ NT\\) OR Mozilla\\/4.0\\ \\(compatible;\\ MSIE\\ 6.0;\\ Windows\\ NT\\ 5.1\\) OR Mozilla\\/4.0\\ \\(compatible;\\ MSIE\\ 7.0;\\ Windows\\ NT\\ 6.0\\) OR Mozilla\\/4.0\\ \\(compatible;\\ MSIE\\ 8.0;\\ Windows\\ NT\\ 6.0;\\ Trident\\/4.0\\) OR Mozilla\\/4.0\\ \\(compatible;\\ MSIE\\ 7.0;\\ Windows\\ NT\\ 6.0;\\ Trident\\/4.0;\\ SIMBAR\\=\\{7DB0F6DE\\-8DE7\\-4841\\-9084\\-28FA914B0F2E\\};\\ SLCC1;\\ .N OR Mozilla\\/5.0\\ \\(compatible;\\ Googlebot\\/2.1;\\ \\+http\\:\\/\\/www.google.com\\/bot.html\\) OR Mozilla\\/5.0\\ \\(Windows;\\ U;\\ Windows\\ NT\\ 5.1;\\ en\\-US\\)\\ AppleWebKit\\/525.13\\ \\(KHTML,\\ like\\ Gecko\\)\\ Chrome\\/4.0.221.6\\ Safari\\/525.13 OR Mozilla\\/5.0\\ \\(compatible;\\ MSIE\\ 9.0;\\ Windows\\ NT\\ 6.1;\\ WOW64;\\ Trident\\/5.0;\\ MAAU\\) OR Mozilla\\/5.0 OR Mozilla\\/4.0\\ \\(compatible;\\ SPIPE\\/1.0 OR Mozilla\\/5.0\\ \\(Windows\\ NT\\ 6.3;\\ rv\\:39.0\\)\\ Gecko\\/20100101\\ Firefox\\/35.0 OR Sametime\\ Community\\ Agent OR X\\-FORWARDED\\-FOR OR DotDotPwn\\ v2.1 OR SIPDROID OR Mozilla\\/5.0\\ \\(Windows\\ NT\\ 10.0;\\ Win32;\\ x32;\\ rv\\:60.0\\) OR Mozilla\\/6.0\\ \\(X11;\\ Linux\\ x86_64;\\ rv\\:24.0\\)\\ Gecko\\/20140205\\ \\ \\ \\ \\ Firefox\\/27.0\\ Iceweasel\\/25.3.0 OR *wordpress\\ hash\\ grabber* OR *exploit*)')\n",
"response = s.execute()\n",
"if response.success():\n",
" df = pd.DataFrame((d.to_dict() for d in s.scan()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show Results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
seg/2016-ml-contest | DiscerningHaggis/Discerning_Haggis_Facies_Classification_sub2.ipynb | 1 | 41345 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# *Discerning Haggis* 2016-ml-contest submission\n",
"\n",
"**Author:** [Carlos Alberto da Costa Filho](http://www.researchgate.net/profile/Carlos_Da_Costa_Filho), University of Edinburgh\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.colors as colors\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"\n",
"import seaborn as sns\n",
"sns.set(style='whitegrid',\n",
" rc={'lines.linewidth': 2.5,\n",
" 'figure.figsize': (10, 8),\n",
" 'text.usetex': False,\n",
" # 'font.family': 'sans-serif',\n",
" # 'font.sans-serif': 'Optima LT Std',\n",
" })\n",
"\n",
"from pandas import set_option\n",
"set_option(\"display.max_rows\", 10)\n",
"pd.options.mode.chained_assignment = None\n",
"\n",
"from sklearn import preprocessing\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn.metrics import confusion_matrix\n",
"from scipy.stats import truncnorm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convenience functions"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def make_facies_log_plot(logs, facies_colors):\n",
" #make sure logs are sorted by depth\n",
" logs = logs.sort_values(by='Depth')\n",
" cmap_facies = colors.ListedColormap(\n",
" facies_colors[0:len(facies_colors)], 'indexed')\n",
" \n",
" ztop=logs.Depth.min(); zbot=logs.Depth.max()\n",
" \n",
" cluster=np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)\n",
" \n",
" f, ax = plt.subplots(nrows=1, ncols=6, figsize=(8, 12))\n",
" ax[0].plot(logs.GR, logs.Depth, '-g')\n",
" ax[1].plot(logs.ILD_log10, logs.Depth, '-')\n",
" ax[2].plot(logs.DeltaPHI, logs.Depth, '-', color='0.5')\n",
" ax[3].plot(logs.PHIND, logs.Depth, '-', color='r')\n",
" ax[4].plot(logs.PE, logs.Depth, '-', color='black')\n",
" im=ax[5].imshow(cluster, interpolation='none', aspect='auto',\n",
" cmap=cmap_facies,vmin=1,vmax=9)\n",
" \n",
" divider = make_axes_locatable(ax[5])\n",
" cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
" cbar=plt.colorbar(im, cax=cax)\n",
" cbar.set_label((17*' ').join([' SS ', 'CSiS', 'FSiS', \n",
" 'SiSh', ' MS ', ' WS ', ' D ', \n",
" ' PS ', ' BS ']))\n",
" cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
" \n",
" for i in range(len(ax)-1):\n",
" ax[i].set_ylim(ztop,zbot)\n",
" ax[i].invert_yaxis()\n",
" ax[i].grid()\n",
" ax[i].locator_params(axis='x', nbins=3)\n",
" \n",
" ax[0].set_xlabel(\"GR\")\n",
" ax[0].set_xlim(logs.GR.min(),logs.GR.max())\n",
" ax[1].set_xlabel(\"ILD_log10\")\n",
" ax[1].set_xlim(logs.ILD_log10.min(),logs.ILD_log10.max())\n",
" ax[2].set_xlabel(\"DeltaPHI\")\n",
" ax[2].set_xlim(logs.DeltaPHI.min(),logs.DeltaPHI.max())\n",
" ax[3].set_xlabel(\"PHIND\")\n",
" ax[3].set_xlim(logs.PHIND.min(),logs.PHIND.max())\n",
" ax[4].set_xlabel(\"PE\")\n",
" ax[4].set_xlim(logs.PE.min(),logs.PE.max())\n",
" ax[5].set_xlabel('Facies')\n",
" \n",
" ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([])\n",
" ax[4].set_yticklabels([]); ax[5].set_yticklabels([])\n",
" ax[5].set_xticklabels([])\n",
" f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)\n",
"\n",
"def accuracy(conf):\n",
" total_correct = 0.\n",
" nb_classes = conf.shape[0]\n",
" for i in np.arange(0,nb_classes):\n",
" total_correct += conf[i][i]\n",
" acc = total_correct/sum(sum(conf))\n",
" return acc\n",
"\n",
"\n",
"adjacent_facies = np.array([[1], [0,2], [1], [4], [3,5], [4,6,7], [5,7], [5,6,8], [6,7]])\n",
"\n",
"def accuracy_adjacent(conf, adjacent_facies):\n",
" nb_classes = conf.shape[0]\n",
" total_correct = 0.\n",
" for i in np.arange(0,nb_classes):\n",
" total_correct += conf[i][i]\n",
" for j in adjacent_facies[i]:\n",
" total_correct += conf[i][j]\n",
" return total_correct / sum(sum(conf))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load, treat and color data\n",
"We try smoothing the data using several windows."
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"perf = []\n",
"windows = []"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted accuracy: 79.157%\n",
"Predicted accuracy: 80.000%\n",
"Predicted accuracy: 80.723%\n",
"Predicted accuracy: 81.446%\n",
"Predicted accuracy: 79.157%\n",
"Predicted accuracy: 76.747%\n",
"Predicted accuracy: 79.880%\n",
"Predicted accuracy: 80.000%\n",
"Predicted accuracy: 82.771%\n",
"Predicted accuracy: 81.446%\n",
"Predicted accuracy: 78.916%\n",
"Predicted accuracy: 80.964%\n",
"Predicted accuracy: 79.759%\n",
"Predicted accuracy: 78.193%\n",
"Predicted accuracy: 80.000%\n",
"Done\n"
]
}
],
"source": [
"windows_new = list(range(15, 30, 1))\n",
"for window in windows_new:\n",
" validationFull = pd.read_csv('../validation_data_nofacies.csv')\n",
" training_data = pd.read_csv('../facies_vectors.csv')\n",
"\n",
" # Treat Data\n",
" training_data.fillna(training_data.mean(),inplace=True)\n",
" for col in ['GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE']:\n",
" training_data[col] = training_data[col].rolling(window=window).sum()\n",
" training_data.fillna(method='backfill',inplace=True)\n",
" training_data['Well Name'] = training_data['Well Name'].astype('category')\n",
" training_data['Formation'] = training_data['Formation'].astype('category')\n",
" training_data['Well Name'].unique()\n",
" training_data.describe()\n",
"\n",
" # Color Data\n",
" # 1=sandstone 2=c_siltstone 3=f_siltstone \n",
" # 4=marine_silt_shale 5=mudstone 6=wackestone 7=dolomite\n",
" # 8=packstone 9=bafflestone\n",
" facies_colors = ['#F4D03F', '#F5B041','#DC7633','#6E2C00',\n",
" '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']\n",
"\n",
" facies_labels = ['SS', 'CSiS', 'FSiS', 'SiSh', 'MS',\n",
" 'WS', 'D','PS', 'BS']\n",
" #facies_color_map is a dictionary that maps facies labels\n",
" #to their respective colors\n",
" facies_color_map = {}\n",
" for ind, label in enumerate(facies_labels):\n",
" facies_color_map[label] = facies_colors[ind]\n",
"\n",
" def label_facies(row, labels):\n",
" return labels[ row['Facies'] -1]\n",
"\n",
" training_data.loc[:,'FaciesLabels'] = training_data.apply(lambda row: label_facies(row, facies_labels), axis=1)\n",
" #make_facies_log_plot(\n",
" # training_data[training_data['Well Name'] == 'SHRIMPLIN'],\n",
" # facies_colors)\n",
"\n",
" correct_facies_labels = training_data['Facies'].values\n",
"\n",
" feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)\n",
" feature_vectors.describe()\n",
"\n",
" scaler = preprocessing.StandardScaler().fit(feature_vectors)\n",
" scaled_features = scaler.transform(feature_vectors)\n",
"\n",
" X_train, X_test, y_train, y_test = train_test_split(scaled_features,\n",
" correct_facies_labels, test_size=0.2)\n",
"\n",
" clf = MLPClassifier(solver='lbfgs', alpha=.1,\n",
" hidden_layer_sizes=(300,300,300))\n",
" clf.fit(X_train,y_train)\n",
" conf_te = confusion_matrix(y_test, clf.predict(X_test))\n",
" perf.append(100*accuracy(conf_te))\n",
" print('Predicted accuracy: %.3f%%' % (100*accuracy(conf_te),))\n",
"print('Done')\n",
"windows += windows_new"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f21821c5b00>]"
]
},
"execution_count": 181,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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2NN5dD5e68pCrLbW58vaQ52HAkVty5ozoXV0if6mxEm/9uAH55act1g/yCcAj\nox+EpqUvMo9XOPSyekfO5mJLba48hOwOBEFAXlG1S94q4o4YcOS2nDEjemeXyPcJ88PY8Sb8cOlr\nGE2WQ22jo0cgJeB2rN14DobKIrH9RpfV2zLk6ujZXGypzVWHkN2BK98q4q5UK1asWCF1EQDQ1taG\niooKhISEwM/PT+pyJHGlDyIiIuDt7fqPRLE3V9v+K5fI1ze1XW30akWr7jDOmo5YDEn6evngoZQH\nEKcci9c/yLJ8D4AGYxv2ZZUgVheE/lGBFq/1jwpErC4IZ4pr0XDN+3Th/lhy78hOf7l1WtsNvo8t\nbKnNlvdc4Wr7gLM469/TXRiNRtTU1PR6P+ARHFEnOrtEXhl8CeqBx6FQW85GEh8+CI+NnY9I/3A8\n8sq3Nl3yf2XI9XB+CXJPFCIxPg6jEqI7PdJx9q0FtgwHe9JDNXvLlW8VcXcMOKJOWFzurjTBO+Yk\nvCItH2kjmBW4PWYSHk6bDqVSieMFFb26RF6hUEAfGwqF0Q8JsaFd/jKT4lJ8W4aDPeWhmr3FWysc\nhwFH1Ikrl7srfBugvukYlH4NFq+bmwLQWjgC+vgxUCqVFu+54Wf38hJ5XoovL/z3dBwGHFEntEG+\nUGkN8B54HApVu9guCICpNBam4sGAoJLkEnleii8v/Pd0HKXUBRC5GlO7CQdrvoX6pizLcGv1QeuJ\nVJguxAOCqstL5Ltjj0vknfV9yDn47+k4DDiia1Q0VuG5XX/BV2e+s2hvrw1D8/FxMNdfPgfS3SXy\nyi6uA7DXJfLO+j7kHPz3dBwGHNFPjhqO47+/fhmnq4os2n2qE9B6cjRg8gFw+a/prmbRvzLLii7c\n8i/y7t5jC2d9H3IO/ns6Bs/Bkcczm834JHcHPsv70qI9UO2PJTcvQFIfvUteIs9L8eXFmltFqGcY\ncOTRapvr8NcD/0BO2UmL9sHaWDw17mGE+18+7+Gql8jzUnx56emtItQzDDjyWCfKz+CNjHWoNtZa\ntN81+OeYm3QPvFT88SByZ/wJJo8jCAJ2nPwWH2ZvhVkwi+2+Xj5YnDoX42JSJKyOiOyFAUcepanN\niLd+3IDMi1kW7f2DdHj6lkWIDuojUWVEZG8MOPIYZQ3lePWHVSiuM1i0TxgwFg+Nvh++Xj4SVUZE\njsCAI4+QX34af973Dupbrk655a30woJRv8LEuFt4Mp9IhhhwJHvfnc3AmkMfot18dVaSME0onrn1\nEcRpB0jVsVK7AAAgAElEQVRYGRE5EgOOZMtsNmNTzuf44sQ3Fu2DtbF45tbFCNEES1QZETkDA45k\nydjWjL8dWI9DJdkW7bfEjMajqXOh9lJLVBkROQsDjmSnvLES//vDKpyrvWjR/qvEqbhHfxfPtxHZ\nQBAE5BZWoqquGdogXwyLC3P5nyUGHMnKqYpCvLZ3NWpb6sU2tcobj4/9NW7uP0rCyojcV0ZOCdZv\nz7N4MKsuzB8Lpupdep5MTrZMsrGn6Ees2P2GRbiFaoLxfPrTDDciG2XklGDlhswOTx03VDZi5YZM\nZOSUSFTZjVkVcFu3bkV8fDwSEhIsvur1egDAN998gylTpiA5ORlz5sxBXl6eQ4omupZZMOOf2dvw\n9x/fg8lsEtsHhQ7AK5OWu82VkoIgIK+oGjlFTcgrqoYgCFKXRB5OEASs354Hcxe7olkA1u/Ic9l9\n1aohyilTpmDChAnicltbG+bPn4/09HScOXMGy5YtwwsvvIDk5GS89957WLRoEb799lv4+PAGWnKM\nZlML/v7jezhYfMyi/eb+o/DYmPnwcZOLSa4fAtqyv8othoBI3nILKzscuV3PUNGIvLNVGBYX5qSq\nes6qIzi1Wo2wsDDxv23btgEAli5dir1792Lw4MGYNm0a+vfvj6VLl6KiogJnzpxxSOFEVcYaPPft\nXzqE2y+HTcF/pS10q3Bz1yEgkrequuaerVfbs/WczeZzcLW1tVi3bh2WLVsGb29vhISE4MyZMzhy\n5AgEQcCWLVsQGBiImJgYe9ZLBAAoqS/D//v2zzhbc0Fs81Z64cm03+DexLuhVLjH6WV3HwIiedMG\n+fZsveCeredsNl9FuWnTJkRFRWHSpEkAgMmTJ2PXrl144IEHoFKpoFQq8c477yAwMNBuxRIBwJnK\nIrzyw1sW026F+AbhmVsXY3DYQAkrs567DwGRvA2LC4MuzL/bfVQX7g/9QK0Tq+o5m//M3bx5M+bO\nnSsu19TUoKKiAs899xw+/fRTzJgxA8uXL0dVVZVdCiUCgOzSfPzpu/+zCLe+QX3w8u2/c7twA9x/\nCIjkTaFQYMFUPZRd3O6mVAAL7ta77P1wNh3BZWdno6ysDJMnTxbb/vznP2Po0KG4//77AQDPP/88\n7rrrLnz22Wd46KGHevzZLS0taGpqsqUst2c0Gi2+epobbf+Bi0ew9uhHaBeuzik5KHQAnhqzEH4K\nX7fcb/x8evaLwc8Hbrl91vL0nwHA9fogaVAInrovCR/+5xRKq67W1EerwZw7hyBpUIjd982Wlha7\nfI5NAbd3716kpqZaDD/m5uZi3rx54rJCoUB8fDxKSqw7QW4wGGAwGG68oowVFRU57LMFQcC58lbU\nN7Uj0E+FARFql/vrq7PtP1RzHN9WHLBoG+TXH9NCf4YLBeedVJkDCAJCA1SobmjvchVtgBeEJgPy\n80udWJi0HPkz4C5cqQ8CFcAjd2pxrrwVDcZ2BGpUiIlQQ6GoRH5+pdTldcnmI7iUFMunHkdGRna4\nYvLs2bMYMWKEVZ+t0+kQEhJiS1luz2g0oqioCLGxsdBoNHb//IN5l/DBf06h7Jq/wqK0Gjx45xCM\n0Ufa/ftZq7PtFwQBW0582SHcbuk3GguS7oWXUiVFqXb1G4Tj9Y+y0Nl1JAoFsGDqMOhd4N/HGRz9\nM+AOXLkP9E76PjU1NXY50LEp4E6dOoVp06ZZtM2ePRu///3vkZiYiOTkZHzyyScwGAyYMWOGVZ/t\n4+MDPz8/W8qSDY1GY/c+yMgpwRsfZXW4Wq+syog3PsrC8vmpLnO/1ZXtbze3451Dm7D77H6L16fF\n34E5I2a43JGnrX42OhY+Pmqs35EHQ8U1UyGF+2PB3Z55H5wjfgbcjSf3gb2GZ20KuKqqKgQHWz5q\nZPLkyTAajVizZg3KysqQkJCAjRs3Qqt1zatrPElPL0W/OVHnMqHRYmrF/2Wsw+GSHIv2eSNn4e6h\nt0tUleOkDY/GzYk6HM4vQe6JQiTGx2FUQrTL/HsQuSObAu7YsWOdts+aNQuzZs3qVUFkf+52KXpj\naxP+mvEeTlYUiG0qhRKPjpmHCbFjJazMsRQKBfSxoVAY/ZAQG8pwI+olPk3AA7jTpej1pka8vP8t\nXKy/ekGFj0qNp29ZhJG6YRJWRkTuhgHnAdxlNoKS+jJ8ULwddaar97gFqv2xfMJjbnmPGxFJyz3m\nM6JeuTIbQXekno2gsOo8Xt73d4twC/fT4vmJyxhuRGQTBpwHcPXZCM5UFuGF7/4PDW1XbxbtHxyN\nFyc+g75BfSSpiYjcHwPOQ6QNj8by+anQhVseyenC/SW9ReBkRQFe+O5NNLZdvSx4cGgs/pS+FFo/\nz7wfkojsg+fgPMiVS9FzCytRXdcCbbAv9AO1kh255Zefxit73kKz6eq0PAM00Vh28yIEqLsfUiUi\nuhEGnIdRKBRIHBQudRk4XnYSr/7wNlraW8W2xIghuCNwHHy8+IBcIuo9DlGS02WV5uGVH96yCLdR\nukQ8kfobeCv5NxcR2Qd/m5BTHSk5jr/sW4M2s0lsG903CU+lLURbS5uElRGR3DDgyGkOXczC6/vX\nwXRNuI3tl4wn0xbCS6lCGxhwRGQ/DDhyigMXjuDNjHfRLpjFtnExo7Fk7K+hksETAYjI9TDgyOH2\nnz+Evx5YD/M14TZ+wBj8dsw8hhsROQwDjhxqT9GPeOvgBgjXPOzsZwPTsHj0g1AqeY0TETkOA44c\n5ruzGVh18H0IuBput8fdiodG3w+lguFGRI7FgCOH2FmwF+8c+tCi7c6bbsOCUfcy3IjIKRhwZHe7\nC/d3CLfJQ9Ixf+Qv+YwzInIaBhzZ1f7zh7D60AcWbdPi78CcETMYbkTkVAw4sptDF7PxtwPrLS4o\nmZFwJ+4fPp3hRkROx5MhZBfZpfl4Y/9ai/vc7hr8c4YbEUmGAUe9dqK8AK/tXW0x/dbPB47D/GSe\ncyMi6TDgqFcKq87hlR/+bjFx8rj+KXhk9BxeLUlEkuJvILLZ+ZqLePH7v8HY1iy2pUQPx+M3L+BN\n3EQkOf4WIpsY6i/hxe//iobWRrFteFQ8nhr3MLw4/RYRuQAGHFmtorEKL3z3Jmqa68S2oeGD8Myt\ni6FWeUtYGRHRVQw4skq1sRbPf/d/qGiqEtviQmPwP+Mfgy+fxE1ELoQBRz1W39KAF797E6UN5WJb\n/+Bo/OG2JfBTaySsjORAEATkFVUjp6gJeUXVFvdTEtmCN3pTjzS1GvHS93/DhTqD2NYnIAL/77Yn\nEOgTIGFlJAcZOSVYvz0PhsrL53S37K+CLswfC6bqkTY8WuLqyF3xCI5uqNnUgld+eAuF1efFtnA/\nLZ792X8hRBMsYWUkBxk5JVi5IVMMtysMlY1YuSETGTklElVG7o4BR91qa2/Da3tX42RFgdgW4huE\n//ezJxHur5WwMpIDQRCwfnsezF2MRpoFYP2OPA5Xkk0YcNQls2DGWwc3IqfshNgWqPbHH297ArrA\nSAkrI7nILazscOR2PUNFI/LOVnW7DlFnGHDUpQ+ytmL/+UPissbbF3+4bQliQvpKWBXJSVVd841X\nAlBV27P1iK7FgKNO7Tj5LXac3Ckueym98N+3Poo47QAJqyK50Qb59my94J6tR3QtBhx1sO98JjYe\n2ywuK6DA42N/jWGRQySsiuRoWFwYdGH+3a6jC/eHfiDP95L1GHBk4XjZSbz140aLtvnJv8S4mBSJ\nKiI5UygUWDBVD2UXD51QKoAFd+v5VAqyCQOOROdqivHavtUwXfPYm6lDb8fkIekSVkVylzY8Gsvn\np0IXbnkkpwv3x/L5qbwPjmzGG70JwOX5JV/e83eLJwPcGpOKOUkzJayKPEXa8GjcnKjD4fwS5J4o\nRGJ8HEYlRPPIjXqFAUdoaGnES3v+hmpjrdg2PGoofjtmHp/pRk6jUCigjw2FwuiHhNhQhhv1Gn97\nebhWUyv+d+8qXKwrFdsGhPTD07c8Ai8V//4hIvfFgPNgZrMZf/1xPU5cM0tJhJ8W/zPhMfh5c/Jk\nInJvDDgPJQgC1h/9BAeLj4ltAWp//P62JdBqQiSsjIjIPhhwHmrbia/xnzPfi8veKm/8bvyj6BvU\nR8KqiIjshwHngb4/ewCbsj8XlxUKBZ68+TcYGj5IwqqIiOyLAedhjhnysDrzfYu2haPuw5h+IyWq\niIjIMawKuK1btyI+Ph4JCQkWX/V6PQDg5MmTeOCBB5CUlIRp06bhxx9/dEjRZJtzNcV4ff87aBfM\nYts9+rtwx00TJKyKiMgxrAq4KVOmYN++fdi7dy/27duH3bt3Y8CAAZg/fz4aGhqwcOFCDB48GDt2\n7MCkSZPw+OOPo6qKj7lwBTXNdXj1h1VoNrWIbT+LTcOvEqdKWBURkeNYFXBqtRphYWHif9u2bQMA\nLF26FJ999hn8/f3xpz/9Cf3798eSJUsQGxuL48ePO6Rw6rnW9jb8ee8aVDRd/WMjqU8CFqXO4c20\nRCRbNt/JW1tbi3Xr1uHll1+Gt7c3MjMzkZ5uOWfhp59+2usCqXcEQcDqzA9wqrJQbOsb2Af/lfYQ\nvJQqCSsjInIsmy8y2bRpE6KiojBp0iQAwIULFxAaGopnn30Wt956K+677z4cOXLEboWSbbbmf4W9\n5w6Ky4Fqf/xuwm/hr/aTsCoiIsezOeA2b96MuXPnistNTU1Yt24dIiMjsW7dOowePRoLFy5EWVmZ\nXQol6x24cAQf5XwhLquUKjx9yyPoExAhYVVERM5h0xBldnY2ysrKMHnyZLFNpVIhISEBjz/+OAAg\nPj4e+/btw7Zt27Bo0aIef3ZLSwuamppsKcvtGY1Gi6+9UVRzAX8/8J5F26+H/xKxAX1dtn/tuf3u\nytP7wNO3H2AfAJdzwB5sCri9e/ciNTUVgYGBYltERATi4uIs1ouNjYXBYLDqsw0Gg9XvkZuioqJe\nvb/e1IiNF7ah1dwmto0JGY7wxkDk5+f3sjrH6+32y4Gn94Gnbz/APrAHm4/gUlIsn/A8cuRIZGZm\nWrQVFhZi6lTrLkPX6XQICfHMuRCNRiOKiooQGxsLjca2yY5bTK14Zf9baGi/epSWHDUMi1Nd/9E3\n9th+d+fpfeDp2w+wDwCgpqbGLgc6NgXcqVOnMG3aNIu2++67Dx988AH+/ve/Y9q0adi6dSuKi4s7\nrHcjPj4+8PPz7AsgNBqNTX1gFsxYtf8DFNUWi20DgvviqVsegq+3rz1LdChbt19OPL0PPH37Ac/u\nA3sNz9r0J31VVRWCg4Mt2qKjo/Huu+9i165dmDp1Kr7//nusXbsWkZGRdimUbuyT4zvwY/FRcTnY\nNwi/G/9btwo3IiJ7sekI7tixY522Jycn47PPPutVQWSbH4oO4rO8L8Vlb6UXnrnlEYT7ayWsiohI\nOq59UoZ65FRFYYcJlB8dMxdDwuO6eAcRkfwx4NxceWMlXtu7Gm1mk9g2Sz8Ztw4YI2FVRETSY8C5\nMWNbM179YRVqW+rFtpv7j8LsxCkSVkVE5BoYcG7KLJjx1wP/wPnai2LboNABeGzMfJe/HYCIyBn4\nm9BNbc79Nw6X5IjLWk0Inhm/GD5eagmrIiJyHQw4N3ToYjY25/5LXFarvPHftz4KrcYzb5AnIuoM\nA87NlNSX4W8/rrdoW5w6F3HaGIkqIiJyTQw4N9Lc1ow/710DY1uz2DZlyETcOiBVwqqIiFwTA85N\nCIKAtw++j+K6q/OzDYscggeTZkpYFRGR62LAuYkvTnyDA8VXHyAbpgnFf6UthIpP5SYi6hQDzg1k\nl+ZjU87n4rKX0gtP37IIwb5BElZFROTaGHAu7lJjJd7MeBeCIIhtD6Xch5vCYqUriojIDTDgXFir\nqRV/2bsG9a2NYtvtcbciPe4WCasiInIPDDgXJQgC3jm8CWdrLohtg8MGYsGoeyWsiojIfTDgXNR/\nznyPPUU/isvBPoF4etwieKu8JayKiMh9MOBc0KnKQmw4+qm4rFIo8dS4h6H140wlREQ9ZdMDT8lx\n6k2N+PDwx2gXzGLb3JGzoI8cLGFVRETuh0dwLsRkNmFb6bcWj78ZP2AM7hr8cwmrIiJyTww4F7Lp\n+DZcbL4kLseG9MOi0XOgUCgkrIqIyD0x4FzE7sL92HVuv7gcoPbHslse4eNviIhsxIBzAedqirHu\nyEcWbU/cvACRAeESVURE5P4YcBIztjXj5d2r0NbeJra1XRiMt9cbkJFTImFlRETujQEnIUEQ8MrO\nd1HdWiW2tVdHwGSIg6GyESs3ZDLkiIhsxICT0LeF+3Ci7ri4bG7xRWvhcACXLyoxC8D6HXkW81AS\nEVHPMOAkcq6mGP84/LG4LJgVaCtIAtotLyoxVDQi72zV9W8nIqIbYMBJwNjWjNf3r4VJMIltpuIh\nMDeEdrp+VW1zp+1ERNQ1BpyTCYKAtYf/CUP91fvd2qsjYCqN7fI92mBfJ1RGRCQvDDgn2312P/ae\nOyguK9o0FufdrqcL94d+oNZJ1RERyQcDzonO11zEu0eunndTKZS4d/B9UJo7v5lbqQAW3K3nTCZE\nRDbgZMtO0vzTebdr73e7f8QMTIsfg2i/EqzfkQdDxdUHm+rC/bHgbj3ShkdLUS4RkdtjwDnBlfNu\nJfVlYtuo6OG4e+hEAEDa8GjcnKjD4fwS5J4oRGJ8HEYlRPPIjYioFxhwTrD77H78cM15tzC/UDw2\nZh6UiqsjxAqFAvrYUCiMfkiIDWW4ERH1Es/BOVhn592eSnsIgT4BElZFRCR/DDgHam5rxhv713U4\n7zYkPE7CqoiIPAMDzkGunHe7WF8qtl173o2IiByLAecgu89m3PC8GxEROQ5/2zrA5fNuV5/vplQo\n8V9pC3nejYjIiRhwdtZiasUbGZbn3R4YMR1DwwdJWBURkedhwNnZ+1lbcLHumvNuukTcPfR2CSsi\nIvJMDDg7OlySg6/P7BGXQzXB+O3Y+TzvRkQkAf7mtZMaYy1WHdxo0fbYmPkI4nk3IiJJMODsQBAE\nvH1wI+paGsS2qUNvx4g+CRJWRUTk2RhwdvDV6e9wrDRPXI4N6Yf7hk+TsCIiImLA9dL5mov4IOsz\ncdlb5Y0n0n4Db5W3hFUREZFVAbd161bEx8cjISHB4qter7dYr7i4GMnJycjMzLRrsa6mtb0Nbx74\nB9rMJrFt/shZ6Bekk7AqIiICrHyawJQpUzBhwgRxua2tDfPnz0d6errFeitWrEBzc7N9KnRhm7K2\n4kJtibg8Kno4Jg2a0M07iIjIWawKOLVajbCwMHF5zZo1AIClS5eKbV988QWamprsVJ7rOmbIxb9P\n7xaXg32D8Gjqg3zMDRGRi7D5HFxtbS3WrVuHZcuWwdv78vmm6upq/OUvf8Hzzz8PQRDsVqSrqWuu\nx1sdbgmYh2DfIIkqIiKi69kccJs2bUJUVBQmTZoktq1cuRIzZ87ETTfdZJfiXJEgCFiV+T5qm+vE\ntsmDf46RumESVkVERNezOeA2b96MuXPnisv79+/H0aNH8dvf/tYuhbmqbwr24HBJjrjcPzgaDyTN\nlLAiIiLqjFXn4K7Izs5GWVkZJk+eDABoaWnBihUr8Nxzz0GtVveqoJaWFpc9h1dSX4YNRzeLy15K\nLywa+QBMLW0woa2bd/aM0Wi0+OppPH37AfaBp28/wD4ALueAPSgEG06Wvf3228jMzMT69esBAJmZ\nmZg3bx40Go147s1oNMLX1xczZszAihUrbviZTU1NyM/Pt7YUpzEJ7Xj/whe41Foptk0MvxmjQxIl\nrIqISL4SEhLg5+dn8/ttPoJLSUkRl5OSkvD1119brDNp0iS89NJLSEtLs+qzdTodQkJCbCnLoT7K\n224RbsMj4vHg2F/a9apJo9GIoqIixMbGQqPR2O1z3YWnbz/APvD07QfYBwBQU1MDg8HQ68+xKeBO\nnTqFadOuTkWlVqvRv3//DutFRkZCq9Va9dk+Pj69Smx7EwQB248dxFcF34ltgT4BWJL2a/hr/B3y\nPTUajUv1gbN5+vYD7ANP337As/vAXsOzNgVcVVUVgoODu11HDveDZeSU4N1/HUVt32+guObUYnrk\nFIRout9+IiKSlk0Bd+zYsRuu48rn03oiI6cEKzcchNegY1Cpr57wNJXF4JPMWsQFliBteLSEFRIR\nUXc42XInBEHA+u15UGgNUGnLxHaz0R9tF4bCLADrd+TJ+mZ2IiJ3x4DrRG5hJQx1lfAecPUoVDAr\n0FqQBJhVAABDRSPyzlZJVSIREd0AA64TlbVGqAfmQuF19d42U8kgCE2WU3FV1cp/QmkiInfFgOvE\n+bY8qELKxWVzYxBMhrgO62mDfZ1ZFhERWYEBd53yxkrsLP5KXBbMCrQWDgcEy67ShftDP9C6WyCI\niMh5GHDXMAtmrM58H0bT1aFHU/EQCMZAi/WUCmDB3XpZ3ApBRCRXNt0mIFffnPkBOWUnxWWdph+a\nTMNQiqtzY+rC/bHgbj1vESAicnEMuJ+U1l/CB1mfictqlTeW//xh9JkagdzCSlTXtUAb7Av9QC2P\n3IiI3AADDoDZbMbbBzeipb1VbJszYiZ0gZEAgMRB4VKVRkRENuI5OAD/Pr0LJyoKxOVhkUNw5+Db\nJKyIiIh6y+MDrrjOgH9mbxOXfb188OiYeVAqPL5riIjcmkf/Fm83t+OtHzegzWwS2+aN/CUi/cMk\nrIqIiOzBowNu24mvUVB1Tlwe2UePiXG3SFgRERHZi8cGXFF1MT7N/Ze47O+tweLUubxCkohIJjwy\n4EztJrx1cAPaze1i24JRv4LWz/WeJE5ERLbxyIDbnPdvnKspFpdT+yZh/IAxElZERET25nEBd6ay\nCJ/n/0dcDlT74+HRD3BokohIZjwq4Frb2/DWwQ0wC2ax7aHR9yPEN6ibdxERkTvyqID75Ph2XKwr\nFZfHxYxGWv8UCSsiIiJH8ZiAK6g6h+0nd4rLIb5BWDjqVxJWREREjuQRAWcyt2P1wfchCILYtmj0\nAwj0CZCwKiIiciSPCLgvTnyNc7UXxeVx/VMwum+ShBUREZGjyT7gLtaVYnPuv8XlALU/Foy6V8KK\niIjIGWQdcJef0P0BTNfMNfnr5NkI5lWTRESyJ+uA+/rMHpy85jE4SX30vKGbiMhDyDbgKhqrsCn7\nc3HZx8sHi3hDNxGRx5BlwAmCgLWHN6HZ1CK2PTB8OiL4GBwiIo8hy4Dbey4TRw254vLQsDjceROf\n0E1E5ElkF3B1zfV47+gn4rKX0guPjHkQSqXsNpWIiLohu9/6649+gvrWRnH5Hv1d6Bekk7AiIiKS\ngqwC7nBJDvadPyQuxwT3xYz4OySsiIiIpCKbgGtqM2LdoX+KywqFAotTH4SXykvCqoiISCqyCbhN\nWZ+j0lgtLk8ZMhE3hcVKVxAREUlKFgGXd+k0vi7YIy5H+YfjV4lTJayIiIik5vYB12pqxZrMDyza\nHkmdAx8vtUQVERGRK3D7gNuc928YGi6Jy+kDxyExKl7CioiIyBW4dcCdrb6AL058Iy6H+AbhwZH3\nSFgRERG5CrcNuPafHmJqFsxi20Mp9yNA7S9hVURE5CrcNuB2nPwWZ2suiMtj+yVjTL+RElZERESu\nxC0D7lJjJT7N3SEu+3trsHDUrySsiIiIXI3bBZwgCPjH4Y/Q2t4mts0dOQshmmAJqyIiIlfjdgGX\neTELRwzHxeX48EH42cA0CSsiIiJX5FYBZ2xrxvojV58UoFIo8fDoB6BUuNVmEBGRE1iVDFu3bkV8\nfDwSEhIsvur1egDAd999hxkzZiA5ORnTp0/Hrl277Frsp8d3WEzHNTV+EvoHR9v1exARkTxYNRPx\nlClTMGHCBHG5ra0N8+fPR3p6Ok6dOoUlS5Zg+fLlmDBhAvbs2YMnnngCW7ZswdChQ3tdaFH1Bfz7\n9G5xOcI/DLP0k3v9uUREJE9WHcGp1WqEhYWJ/23btg0AsHTpUmzfvh1paWmYM2cO+vfvjzlz5mDs\n2LH48ssve12k2WzGO4c2WdzztnDUrzgdFxERdcnmZ8nU1tZi3bp1ePnll+Ht7Y2ZM2eira2tw3oN\nDQ29KhAAdhbuxZmqInF5TL+RGBU9vNefS0RE8mVzwG3atAlRUVGYNGkSACAuLs7i9dOnT+PAgQN4\n4IEHelVgTXMdNmV/Li77evlgQfK9vfpMIiKSP5svP9y8eTPmzp3b6WtVVVVYsmQJUlJSMHHiRJuL\nA4CNx7agqc0oLt+bOBVhfqG9+kwiIpI/m47gsrOzUVZWhsmTO17kUVFRgQULFkChUODNN9+0+rNb\nWlrQ1NQEAMgrP4W95w6Kr8UEReO2vmPE1+XGaDRafPU0nr79APvA07cfYB8Al3PAHmwKuL179yI1\nNRWBgYEW7WVlZZg3bx5UKhXef/99hIZaf6RlMBhgMBhgMpvwjwufWbx2W9BonDp5ypaS3UpRUZHU\nJUjK07cfYB94+vYD7AN7sPkILiUlxaLNaDTioYcegre3NzZu3AitVmtTQTqdDiEhIfj85H9Q3VYn\ntqcPGIeJI26z6TPdhdFoRFFREWJjY6HRaKQux+k8ffsB9oGnbz/APgCAmpoaGAyGXn+OTQF36tQp\nTJs2zaJt9erVKC4uxsaNG2E2m1FRUQEA8PX1RUBAQI8/28fHB7XtDdhx5upN4sE+gZg7ahb81H62\nlOt2NBoN/Pw8Y1s74+nbD7APPH37Ac/uA3sNz9oUcFVVVQgOtpzc+Ouvv0ZzczPuvdfyCscZM2bg\nlVdeserz1x3+J0xmk7g8P/mX8PeQcCMiIvuwKeCOHTvWoc0eN3QDQE75CeSUnRCXh0cNxS0xqXb5\nbCIi8hwuN0vxVwV7xP/3UnphYcr9UCgUElZERETuyOUCrrGtUfz/mQl3IjowSsJqiIjIXblcwF2h\nC4jE9IQ7pS6DiIjclMsG3MKU+6BWeUtdBhERuSmXDLhbY1Ixok+C1GUQEZEbc7mA81H5YN7IWVKX\nQYJgpYAAAAslSURBVEREbs7lAm5W/F0I0QTfeEUiIqJuuFzADdEOlLoEIiKSAZcLOCIiIntgwBER\nkSwx4IiISJYYcEREJEsMOCIikiUGHBERyRIDjoiIZIkBR0REssSAIyIiWWLAERGRLDHgiIhIlhhw\nREQkSww4IiKSJQYcERHJEgOOiIhkiQFHRESyxIAjIiJZYsAREZEsMeCIiEiWGHBERCRLDDgiIpIl\nL6kLsJUgCMgtrERVXTO0Qb4YFhcGhUIhdVlEROQi3DLgMnJKsH57HgyVjWKbLswfC6bqkTY8WsLK\niIjIVbjdEGVGTglWbsi0CDcAMFQ2YuWGTGTklEhUGRERuRK3CjhBELB+ex7MQuevmwVg/Y48CEIX\nKxARkcdwq4DLLazscOR2PUNFI/LOVjmpIiIiclVuFXBVdc09W6+2Z+sREZF8uVXAaYN8e7ZecM/W\nIyIi+XKrgBsWFwZdmH+36+jC/aEfqHVSRURE5KrcKuAUCgUWTNVD2cXtbkoFsOBuPe+HIyIi9wo4\nAEgbHo3l81OhC7c8ktOF+2P5/FTeB0dERADc9EbvtOHRuDlRh9zCSlTXtUAb7Av9QC2P3IiISOSW\nAQdcHq5MHBQudRlEROSi3G6IkoiIqCcYcEREJEsMOCIikiWrAm7r1q2Ij49HQkKCxVe9Xg8AyMvL\nw7333ouRI0di9uzZyM3NdUjRREREN2JVwE2ZMgX79u3D3r17sW/fPuzevRsDBgzA/PnzYTQasWjR\nIqSmpuKzzz7DyJEj8cgjj6C5mdNmERGR81kVcGq1GmFhYeJ/27ZtAwAsXboU//rXv6DRaPDMM88g\nLi4Of/jDH+Dv74+vvvrKIYUTERF1x+ZzcLW1tVi3bh2WLVsGb29vZGdnIyUlxWKdUaNG4ejRo70u\nkoiIyFo2B9ymTZsQFRWFSZMmAQAuXbqEyMhIi3XCwsJQVlbWuwqJiIhsYPON3ps3b8aiRYvE5ebm\nZqjVaot11Go1Wltbe/R5ZrMZANDQ0GBrSW6vpaUFAFBTUwOj0ShxNc7n6dsPsA88ffsB9gFwNQeu\n5IKtbAq47OxslJWVYfLkyWKbj49PhzBrbW2Fr2/PHl1z5R+1oqICFRUVtpQlGwaDQeoSJOXp2w+w\nDzx9+wH2AXA5FwICAmx+v00Bt3fvXqSmpiIwMFBsi4qKQnl5ucV6FRUViIiI6NFnBgcHIzY2Fj4+\nPlAqeXseEZGnMpvNaGlpQXBwcK8+x+YjuOsvKElKSsLatWst2o4ePYrFixf3rBAvL4SFhdlSDhER\nyUxvjtyusOlQ6dSpU4iLi7Nou/POO1FfX4+XX34ZBQUFePHFF9HU1IS77rqr10USERFZy6aAq6qq\n6nDoGBAQgNWrV+PQoUOYNWsWcnJysHbt2h6fgyMiIrInhSAIgtRFEBER2Ruv5iAiIlliwBERkSwx\n4IiISJYYcEREJEsuEXCtra34/e9/j9TUVIwfPx7r16+XuiSn2rlzZ4fn7D355JNSl+UUra2tmDp1\nKjIzM8W24uJiLFiwAMnJybj77ruxb98+CSt0vM764MUXX+ywT3z44YcSVml/ZWVleOKJJzB27Fjc\ndtttWLlypTgbkqfsA931gSfsA+fPn8fChQuRnJyM9PR0vPvuu+Jr9tgHbJ6L0p5effVV5OXl4f33\n30dxcTF+97vfoW/fvrjjjjukLs0pzpw5g/T0dLz44ou4clGrj4+PxFU5XmtrK5YuXYozZ85YtD/2\n2GOIj4/Hli1bsHPnTjz++OP48ssv0adPH4kqdZyu+qCwsBDLli3DzJkzxTZ73PjqSp544gmEhIRg\n06ZNqKmpwe9//3uoVCo888wz+O1vf4uEhATZ7wPd9YHc9wFBELBo0SIkJSVh27ZtKCoqwtKlS9Gn\nTx9MmTLFPvuAILGmpiZhxIgRQmZmptj29ttvC3PnzpWwKudatmyZ8Prrr0tdhlOdOXNGmD59ujB9\n+nQhPj5eOHjwoCAIgrB//34hOTlZaG5uFtf99a9/Lfztb3+TqlSH6aoPBEEQJkyYIOzbt0/C6hyr\noKBAiI+PFyorK8W2HTt2CBMmTBAyMjI8Yh/org8EQf77wKVLl4SnnnpKaGxsFNsef/xx4U9/+pPd\n9gHJhyhPnDiB9vZ2jBw5UmxLSUlBdna2hFU5V0FBAQYOHCh1GU518OBBpKWl4eOPPxaPWoHL08AN\nGzbM4gg2JSUFx44dk6JMh+qqDxoaGlBWVobY2FjpinOwiIgIrF27Flqt1qK9vr4eWVlZHrEPdNYH\ngiCgvr7eY/aB119/HX5+fgCAw4cP49ChQxgzZozd9gHJhyjLy8sREhICL6+rpYSFhaGlpQXV1dUI\nDQ2VsDrnOHv2LH744QesWrUKZrMZv/jFL/DEE0/A29tb6tIc5v777++0vby83GOeK9hVHxQWFkKh\nUGDVqlXYs2cPQv5/e3fzktoahQH86RBYUBFEBA1T0jKUNIg+rJBoUBIOHNbAGjuIIiwogsKBDvoe\nOKgoiwj6DypoEERRg6w0SKREEsrAIrAIW3dwuXKU0/3K3PfuvX7gwK2D5evDXsq7N6u4GFarFWaz\nOcsVfp/CwkI0NzcnnxMR1tfX0dDQIJkMfLYGjY2NksjAz4xGIyKRCNra2tDR0QGHw5GRDAje4OLx\n+C/nyAH427Pk/s/u7u7w+voKmUyG2dlZhMNhTE1N4e3tDaOjo0KXl3Wf5UEKWfhDMBjEjx8/IJfL\n0dvbi+PjY4yNjaGgoADt7e1Cl/ctnE4n/H4/tre3sbKyIskMOJ1OXF1dYXt7GxcXF5LKwPz8PKLR\nKCYmJuBwODJ2HhC8wX02Rw4A8vPzhSgpq8rLy3F0dISioiIAgEqlwsfHB4aHhzEyMoKcnByBK8wu\nmUyGp6enlGP/ZK6gGJjNZhiNxmQmKisrcXNzg83NTVGe3FwuFzweD2ZmZqBQKCSZgfQ1UCgUksqA\nWq0GANjtdgwNDcFiseD5+TnlPf8mA4LvwZWVlSEWi6VMbo1Go8jLy0t+uWKX/jnlcjne3t4Qi8UE\nqkg4X50rKBbpmaioqMD9/b1A1XyfyclJrK6uwuVyJU/cUsvAr9YAEH8GHh8fsbu7m3JMoVDg/f0d\npaWlGcmA4A2uqqoKubm5KZuHJycnqKmpEbCq7Dk4OEB9fX1yojkA+Hw+FBcXS2L/MZ1Wq4XP50v5\nV396eppyEZLYzc3NwWq1phzz+/2iuxBpYWEBW1tbmJ6eThmrJaUMfLYGUshAOByGzWZLaWTn5+co\nKSmBXq/H5eXl1zOQics9v2p8fJxMJhN5vV7a2dkhvV5POzs7QpeVFS8vL9Ta2kqDg4MUDAZpf3+f\nDAYDLS0tCV1a1iiVyuQl8olEgkwmEw0MDND19TW53W7S6XQUiUQErvJ7/bwGXq+X1Go1LS8vUygU\noo2NDdJoNHR2diZwlZkTCASourqaZmdn6eHhIeUhlQz82RpIIQOJRIIsFgv19/dTIBCg/f19ampq\nIo/HQ4lEgrq6ur6cgf9Eg4vH42S326m2tpZaWlpobW1N6JKyKhAIUF9fH+l0OjIYDLS4uCh0SVmV\nfg9YKBSinp4e0mg0ZDKZ6PDwUMDqsiN9Dfb29qi7u5u0Wi11dnaK7gef2+0mlUqV8lAqlaRSqYiI\n6Pb2VvQZ+Ks1EHsGiH6/F85ms1FdXR0ZDAZyu93J1zJxHuB5cIwxxkRJ8D04xhhj7Dtwg2OMMSZK\n3OAYY4yJEjc4xhhjosQNjjHGmChxg2OMMSZK3OAYY4yJEjc4xhhjosQNjjHGmChxg2OMMSZK3OAY\nY4yJEjc4xhhjovQbyf+FhddagzUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2182971198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"p = np.poly1d(np.polyfit(windows, perf, 2))\n",
"f, ax = plt.subplots(figsize=(5,5))\n",
"ax.plot(windows, perf, 'o')\n",
"ax.plot(range(max(windows)+1), p(range(max(windows)+1)))"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted accuracy: 79.880%\n"
]
}
],
"source": [
"validationFull = pd.read_csv('../validation_data_nofacies.csv')\n",
"training_data = pd.read_csv('../facies_vectors.csv')\n",
"\n",
"# Treat Data\n",
"training_data.fillna(training_data.mean(),inplace=True)\n",
"for col in ['GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE']:\n",
" training_data[col] = training_data[col].rolling(window=25).sum()\n",
"training_data.fillna(method='backfill',inplace=True)\n",
"training_data['Well Name'] = training_data['Well Name'].astype('category')\n",
"training_data['Formation'] = training_data['Formation'].astype('category')\n",
"training_data['Well Name'].unique()\n",
"training_data.describe()\n",
"\n",
"# Color Data\n",
"# 1=sandstone 2=c_siltstone 3=f_siltstone \n",
"# 4=marine_silt_shale 5=mudstone 6=wackestone 7=dolomite\n",
"# 8=packstone 9=bafflestone\n",
"facies_colors = ['#F4D03F', '#F5B041','#DC7633','#6E2C00',\n",
" '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']\n",
"\n",
"facies_labels = ['SS', 'CSiS', 'FSiS', 'SiSh', 'MS',\n",
" 'WS', 'D','PS', 'BS']\n",
"#facies_color_map is a dictionary that maps facies labels\n",
"#to their respective colors\n",
"facies_color_map = {}\n",
"for ind, label in enumerate(facies_labels):\n",
" facies_color_map[label] = facies_colors[ind]\n",
"\n",
"def label_facies(row, labels):\n",
" return labels[ row['Facies'] -1]\n",
"\n",
"training_data.loc[:,'FaciesLabels'] = training_data.apply(lambda row: label_facies(row, facies_labels), axis=1)\n",
"#make_facies_log_plot(\n",
"# training_data[training_data['Well Name'] == 'SHRIMPLIN'],\n",
"# facies_colors)\n",
"\n",
"correct_facies_labels = training_data['Facies'].values\n",
"\n",
"feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)\n",
"feature_vectors.describe()\n",
"\n",
"scaler = preprocessing.StandardScaler().fit(feature_vectors)\n",
"scaled_features = scaler.transform(feature_vectors)\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(scaled_features,\n",
" correct_facies_labels, test_size=0.2)\n",
"\n",
"clf = MLPClassifier(solver='lbfgs', alpha=.1,\n",
" hidden_layer_sizes=(300,300,300))\n",
"clf.fit(X_train,y_train)\n",
"conf_te = confusion_matrix(y_test, clf.predict(X_test))\n",
"\n",
"print('Predicted accuracy: %.3f%%' % (100*accuracy(conf_te),))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Retrain and predict\n",
"Finally we train a neural network using *all* data available, and apply it to our blind test."
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"clf_final = MLPClassifier(solver='lbfgs', alpha=0.1,\n",
" hidden_layer_sizes=(300,300,300))\n",
"clf_final.fit(scaled_features,correct_facies_labels)\n",
"\n",
"validationFullsm = validationFull.copy()\n",
"for col in ['GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE']:\n",
" validationFullsm[col] = validationFullsm[col].rolling(window=25).sum()\n",
"validationFullsm.fillna(method='backfill',inplace=True)\n",
"\n",
"validation_features = validationFullsm.drop(['Facies', 'Formation', 'Well Name', 'Depth'], axis=1)\n",
"scaled_validation = scaler.transform(validation_features)\n",
"validation_output = clf_final.predict(scaled_validation)\n",
"validationFull['Facies']=validation_output\n",
"validationFull.to_csv('well_data_with_facies_DH_sub2.csv')"
]
},
{
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
keirl/bigdata | code/.ipynb_checkpoints/generate_driving_distances-checkpoint.ipynb | 1 | 5909 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from pyspark.sql import SparkSession\n",
"\n",
"spark = SparkSession \\\n",
" .builder \\\n",
" .appName(\"Calculate Distances\") \\\n",
" .getOrCreate()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import string\n",
"PATH_RAWDATA = '../rawdata/'\n",
"PATH_PROCESSEDDATA = '../processeddata/'\n",
"DIST_FN='distances.parquet'\n",
"DRIV_DIST_FN='driv_dist.parquet'\n",
"PATH_BING = '../rawdata/bing_results/'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"root\n",
" |-- z_id: integer (nullable = true)\n",
" |-- z_pop: integer (nullable = true)\n",
" |-- z_house_unit: integer (nullable = true)\n",
" |-- z_land: double (nullable = true)\n",
" |-- z_water: double (nullable = true)\n",
" |-- z_lat_d: double (nullable = true)\n",
" |-- z_long_d: double (nullable = true)\n",
" |-- z_lat_r: double (nullable = true)\n",
" |-- z_long_r: double (nullable = true)\n",
" |-- m_id: integer (nullable = true)\n",
" |-- name: string (nullable = true)\n",
" |-- UATYPE: string (nullable = true)\n",
" |-- m_pop: integer (nullable = true)\n",
" |-- m_house_unit: integer (nullable = true)\n",
" |-- m_land: double (nullable = true)\n",
" |-- m_water: double (nullable = true)\n",
" |-- m_lat_d: double (nullable = true)\n",
" |-- m_long_d: double (nullable = true)\n",
" |-- m_lat_r: double (nullable = true)\n",
" |-- m_long_r: double (nullable = true)\n",
" |-- dist: double (nullable = true)\n",
"\n",
"111727\n"
]
}
],
"source": [
"dfDist = spark.read.parquet(PATH_PROCESSEDDATA+DIST_FN)\n",
"dfDist.printSchema()\n",
"dfNearby = dfDist.where(dfDist.dist<60)\n",
"print(dfNearby.count())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/spark-2.0.2-bin-hadoop2.7/python/pyspark/sql/session.py:336: UserWarning: Using RDD of dict to inferSchema is deprecated. Use pyspark.sql.Row instead\n",
" warnings.warn(\"Using RDD of dict to inferSchema is deprecated. \"\n"
]
}
],
"source": [
"# https://realpython.com/blog/python/api-integration-in-python/\n",
"# http://docs.python-requests.org/en/master/user/quickstart/\n",
"import requests\n",
"import json\n",
"import time\n",
"from pyspark.sql import Row\n",
"import os\n",
"\n",
"\n",
"def request_distance(wp0,wp1):\n",
" values = dict()\n",
" values['wp.0']=wp0\n",
" values['wp.1']=wp1\n",
" values['distanceUnit']='mi'\n",
" f = open('../credentials/bing_map_key', 'r')\n",
" values['key']= f.readline()\n",
" f.close()\n",
" \n",
" r = requests.get('https://dev.virtualearth.net/REST/V1/Routes/Driving', params=values)\n",
" r.raise_for_status()\n",
" \n",
" return r\n",
"\n",
"\n",
"\n",
"def get_distance(row):\n",
" path = PATH_BING\n",
" fn_prefix = path+str(row['z_id'])+'_'+str(row['m_id'])\n",
" fn = fn_prefix+'.json'\n",
" \n",
" with open(fn_prefix+'_meta.json','w') as f:\n",
" json.dump(row,f)\n",
" \n",
" if os.path.isfile(fn):\n",
" with open(fn,'r') as f:\n",
" resp_json = json.load(f)\n",
" ddist = float(json.dumps(resp_json[\"resourceSets\"][0][\"resources\"][0][\"travelDistance\"], sort_keys=True, indent=4))\n",
"\n",
" else:\n",
" z_coords = str(row['z_lat_d'])+','+str(row['z_long_d'])\n",
" m_coords = str(row['m_lat_d'])+','+str(row['m_long_d'])\n",
" \n",
" try:\n",
" resp = request_distance(z_coords,m_coords)\n",
" resp_json = resp.json()\n",
" ddist = float(json.dumps(resp_json[\"resourceSets\"][0][\"resources\"][0][\"travelDistance\"], sort_keys=True, indent=4))\n",
" time.sleep(.400)\n",
" with open(fn_prefix+'.json','w') as f:\n",
" json.dump(resp_json,f)\n",
" except:\n",
" print('Error occured:', str(row['z_id'])+'_'+str(row['m_id']))\n",
" ddist = -1\n",
" \n",
" \n",
" record=row.asDict()\n",
" record['id'] = str(row['z_id'])+'_'+str(row['m_id'])\n",
" record['ddist']= ddist\n",
" print(record['id'],record['dist'],record['ddist'])\n",
" \n",
" return record\n",
"\n",
"\n",
"r = dfNearby.rdd.map(get_distance)\n",
"dfR=spark.createDataFrame(r)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dfR.coalesce(1).write.save(PATH_PROCESSEDDATA+DRIV_DIST_FN,mode='overwrite')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"nbformat": 4,
"nbformat_minor": 1
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| mit |
jsharpna/DavisSML | lectures/lecture3/lecture3.ipynb | 1 | 67934 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Linear Regression and Subset Selection\n",
"\n",
"## StatML: Lecture 3\n",
"\n",
"### Prof. James Sharpnack\n",
"\n",
"### Reading: \"The Elements of Statistical Learning,\" Hastie, Tibshirani, Friedman, Ch. 3 (ESL)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import linear_model\n",
"import scipy as sc"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Recall LinearRegression.fit\n",
"\n",
"*Throughout let $p < n$*\n",
"\n",
"Fit in OLS solves the following, on training set\n",
"$$\n",
"\\hat \\beta = (X^\\top X)^{-1} X^\\top y\n",
"$$\n",
"where $X,y$ are $n \\times p$ and $n$ arrays.\n",
"\n",
"#### Linear solve:\n",
"$$\n",
"(X^\\top X) \\hat \\beta = X^\\top y\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Recall LinearRegression.predict\n",
"\n",
"Apply predict to training set then\n",
"$$\n",
"\\hat y = X \\hat \\beta = X (X^\\top X)^{-1} X^\\top y\n",
"$$\n",
"is a projection of $y$ onto the column space of $X$. Projection in $n$-D space.\n",
"\n",
"Projections are idempotent,\n",
"$$\n",
"P := X (X^\\top X)^{-1} X^\\top\n",
"$$\n",
"has\n",
"$$\n",
"P P = X (X^\\top X)^{-1} X^\\top X (X^\\top X)^{-1} X^\\top = X (X^\\top X)^{-1} X^\\top.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img src=\"projection.png\" width=70%>\n",
"Image from wikipedia."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Exercise 3.1\n",
"\n",
"Suppose that we have a perfectly reasonable $n \\times p$ design matrix $X$, and $n$ response vector $y$ such that $X^\\top X$ is invertible. Suppose that we duplicate the columns of $X$ to make an $n \\times (2p)$ matrix. Suppose that we want to run OLS with the new data by finding solutions to the normal equation.\n",
"1. From the projection intuition above, what is the impact on $\\hat y$?\n",
"2. Show that a valid solution to the new normal equations is $\\tilde \\beta = \\frac 12 [\\hat \\beta; \\hat \\beta]$? \n",
"3. What form do other valid solutions take?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# STOP"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Answer to 3.1\n",
"\n",
"1. No impact on $\\hat y$, because there is no change in the column space of $X$.\n",
"2. Let $\\tilde X = (X,X)$ then \n",
"$$\\tilde X^\\top \\tilde X = [X^\\top X, X^\\top X; X^\\top X, X^\\top X]$$\n",
"This is not invertible! But normal equations are...\n",
"$$ \\tilde X^\\top \\tilde X \\tilde \\beta = \\tilde X^\\top y = [X^\\top y; X^\\top y] $$\n",
"Which gives,\n",
"$$\n",
"[X^\\top X, X^\\top X; X^\\top X, X^\\top X] \\frac{[\\hat \\beta ; \\hat \\beta]}{2} = \\frac 12 [X^\\top X \\hat \\beta + X^\\top X \\hat \\beta; X^\\top X \\hat \\beta + X^\\top X \\hat \\beta] = [X^\\top X \\hat \\beta;X^\\top X \\hat \\beta]\n",
"$$\n",
"but because $\\hat \\beta$ solves the original normal equations this is equal to \n",
"$$ = [X^\\top y; X^\\top y] = \\tilde X^\\top y.$$\n",
"3. There was nothing special about $1/2$ and we could repeat the arguments above with $[\\theta \\hat \\beta; (1-\\theta) \\hat \\beta]$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Regression by Successive Orthogonalization\n",
"#### ESL pg. 54\n",
"\n",
"0. Input $x_0=1, x_1, \\ldots, x_p$ columns of design matrix.\n",
"1. Init $z_0 = x_0 = 1$\n",
"2. For $j = 1,\\ldots,p$\n",
" - Regress $x_j$ on $z_0,\\ldots,z_{j-1}$ giving $$\\hat \\gamma_{j,l} = \\frac{z_l^\\top x_j}{z_l^\\top z_l},$$ $l = 0, \\ldots, j-1$ and $z_j = x_j - \\sum_{k=0}^{j-1} \\hat \\gamma_{j,k} z_k$\n",
"3. Regress $y$ on the residual $z_p$ to give $\\hat \\beta_p$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Regression by Successive Orthogonalization\n",
"\n",
"What does \"regress onto\" mean?\n",
"\n",
"Solving the normal equation \n",
"$$\n",
"Z^\\top Z \\hat \\gamma_j = Z^\\top x_j\n",
"$$\n",
"\n",
"where $Z$ has columns $z_0, \\ldots z_{j-1}$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Why is this any easier? \n",
"\n",
"\n",
"$Z$ is **orthogonal**, i.e. the columns are orthogonal, \n",
"$$\n",
"z_j^\\top z_k = 0, j\\ne k\n",
"$$\n",
"which means $Z^\\top Z$ is diagonal (easy to invert).\n",
"\n",
"\n",
"**Exercise. 3.2.1** Show that Successive Orthogonalization is equivalent to the Gram-Schmidt procedure for finding an orthonormal basis of column space of $X$. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Regression by Successive Orthogonalization\n",
"\n",
"Regress $y$ on the residual $z_p$ to give $\\hat \\beta_p$?\n",
"\n",
"We know that $z_p$ is the only basis element that contains $x_p$ and that regressing $y$ onto $Z$ is equivalent to regressing $y$ onto $X$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Why?\n",
"\n",
"We can write these matrices as \n",
"$$\n",
"X = Z \\Gamma\n",
"$$\n",
"where $Z$ is orthogonal and $\\Gamma$ is upper triangular.\n",
"Let $D$ be the diagonal matrix with $\\| z_j\\|$ on diagonal.\n",
"Then \n",
"$$\n",
"X = Z D^{-1} D \\Gamma = Q R\n",
"$$\n",
"for $Q = Z D^{-1}$ is $n \\times p$, $R = D \\Gamma$ is $p \\times p$.\n",
"\n",
"$Q$ is orthonormal ($Q^\\top Q = I$) and $R$ is upper triangular."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Regression by Successive Orthogonalization\n",
"\n",
"\n",
"- Normal eqn is\n",
"$$\n",
"X^\\top X \\hat \\beta = X^\\top y \\equiv R^\\top R \\hat \\beta = R^\\top (Q^\\top y)\n",
"$$\n",
"- Upper triangular matrices are easy to invert!\n",
"```\n",
"[1, 2] [a] = [4]\n",
"[0, 3] [b] [5]\n",
"```\n",
"- One of many decompositions that can make linear regression easy (after the decomposition is made)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Regression by Successive Orthogonalization\n",
"\n",
"0. Input $x_0=1, x_1, \\ldots, x_p$ columns of design matrix.\n",
"1. Init $z_0 = x_0 = 1$\n",
"2. For $j = 1,\\ldots,p$\n",
" - Regress $x_j$ on $z_0,\\ldots,z_{j-1}$ giving $$\\hat \\gamma_{j,l} = \\frac{z_l^\\top x_j}{z_l^\\top z_l},$$ $l = 0, \\ldots, j-1$ and $z_j = x_j - \\sum_{k=0}^{j-1} \\hat \\gamma_{j,k} z_k$\n",
"3. Regress $y$ on the residual $z_p$ to give $\\hat \\beta_p$\n",
"\n",
"- Only gives us $\\hat \\beta_p$! Not a great algorithm in its current form.\n",
"- Algorithm exposes the effect of correlated input $x_j$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Suppose that $y$ follows the linear model \n",
"$$\n",
"y = X \\beta + \\epsilon\n",
"$$\n",
"where $\\epsilon_i$ is iid normal$(0,\\sigma^2)$.\n",
"Then \n",
"\n",
"3. Regress $y$ on the residual $z_p$ to give $\\hat \\beta_p$\n",
"\n",
"Means $$\n",
"\\hat \\beta_p = \\frac{y^\\top z_p}{z_p^\\top z_p}\n",
"$$\n",
"If $\\|z_p\\|$ is small then this is instable (high variance), when does this happen?\n",
"$$\n",
"z_j = x_j - \\sum_{k=0}^{j-1} \\hat \\gamma_{j,k} z_k\n",
"$$\n",
"> If $x_p$ is correlated with $x_0,\\ldots,x_{p-1}$ (small residual when regressed onto) then $\\hat \\beta_p$ is instable. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Exercise 3.2.2\n",
"\n",
"Below is some code for generating a design matrix with correlated X variables, and the response vector. The rho parameter is the correlation of the X variables, so that $rho = 1$ means that they are perfectly correlated. Use the code below to generate the true beta once (also a parameter of `sim_corr_lm`) and set `sigma=1`. Choose a sequence of rho's: `Rhos = [0,.2,.4,.6,.8,.9,.95,.99]` and for each one run 100 trials of the following: simulate from the linear model, fit OLS, and save the first coefficient beta_1. For each rho in the list, calculate the variance of beta_1 and plot the variance as a function of rho."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"def sim_corr_lm(n,p,rho,beta,sigma):\n",
" \"\"\"\n",
" Simulate a design matrix with all columns having marginal correlation rho\n",
" \"\"\"\n",
" assert p < n and rho < 1 and rho >= 0, \"p must be less than n and rho in [0,1)\"\n",
" Sigma = (1 - rho)*np.eye(p) + rho*np.ones((p,p))\n",
" X = np.random.multivariate_normal(np.zeros(p),Sigma,n)\n",
" y = X @ beta + np.random.normal(0,sigma,n)\n",
" return X,y"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f8b01ffd4a8>]"
]
},
"execution_count": 51,
"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": [
"n, p, rho = 100, 2, .8\n",
"beta = np.random.normal(0,1,p)\n",
"sigma = 1.\n",
"X, y = sim_corr_lm(n,p,rho,beta,sigma)\n",
"plt.plot(X[:,0],X[:,1],'.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# STOP"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"## Solution to 3.2.2\n",
"def sample_coef_corr_lm(trials,**kwargs):\n",
" \"\"\"\n",
" Sample the OLS coefficients for rho correlated input\n",
" \"\"\"\n",
" beta_sim = []\n",
" for t in range(trials):\n",
" X,y = sim_corr_lm(**kwargs)\n",
" beta_sim += [linear_model.LinearRegression(fit_intercept=False).fit(X,y).coef_]\n",
" return np.array(beta_sim)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'beta variance')"
]
},
"execution_count": 62,
"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": [
"## Sample coefficients with different rho and plot variance of beta_1 as rho increases\n",
"Rhos = [0,.2,.4,.6,.8,.9,.95,.99]\n",
"coef_vars = [sample_coef_corr_lm(100,n=n,p=p,rho=rho,beta=beta,sigma=sigma)[:,0].var(axis=0) for rho in Rhos]\n",
"plt.plot(Rhos,coef_vars)\n",
"plt.xlabel('X correlation')\n",
"plt.ylabel('beta variance')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Solution to Exercise 3.2.1\n",
"\n",
"In Gram-Schmidt the projection operator is $$\\frac{\\langle x, z\\rangle}{\\langle z, z \\rangle} z,$$ and so the projection of $x_j$ onto the space spanned by $z_k$ is \n",
"$$ \\frac{x_j^\\top z_k}{z_k^\\top z_k} z_k = \\hat \\gamma_{j,k} z_k.$$\n",
"\n",
"The result is immediate from algorithm:\n",
"$$z_j = x_j - \\sum_{k=0}^{j-1} \\hat \\gamma_{j,k} z_k$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Singular value decomposition\n",
"\n",
"- Recall that QR decomposition was computed to make LinearRegression.fit easier.\n",
"- There are other decompositions that can also be used: Cholesky and Singular Value.\n",
"\n",
"**Singular Value Decomposition** (for $n > p$ and $X^\\top X$ invertible)\n",
"\n",
"$$\n",
"X = U D V^\\top,\n",
"$$\n",
"- U is orthonormal ($U^\\top U = I$) $n \\times p$\n",
"- V is orthonormal $p \\times p$\n",
"- D is diagonal\n",
"\n",
"\n",
"1. If X is singular, there is a non-zero vector $z$ such that $Xz = 0$, then an eigenvalue is $0$. This is equivalent to a residual in Succ. Ortho. being zero.\n",
"2. Computing SVD is more expensive then QR in general."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Singular Value Decomposition\n",
"\n",
"Suppose that we precomputed the SVD,\n",
"$$\n",
"X = UDV^\\top.\n",
"$$\n",
"Then the Gram matrix is \n",
"$$\n",
"X^\\top X = V D U^\\top U D V^\\top = V D^2 V^\\top,\n",
"$$\n",
"the Spectral decomposition.\n",
"\n",
"You should derive that \n",
"$$\n",
"\\hat \\beta = V D^{-1} U^\\top y.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Singular Value Decomposition\n",
"\n",
"The coefficient formula\n",
"$$\n",
"\\hat \\beta = V D^{-1} U^\\top y\n",
"$$\n",
"means that \n",
"$$\n",
"\\hat \\beta = \\sum_{j=0}^p d_j^{-1} (u_j^\\top y) v_j \n",
"$$\n",
"which means that \n",
"\n",
"> $\\hat \\beta$ is instable (high variance in some direction) if the eigenvalues are very small."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Exercise 3.3\n",
"\n",
"Below you can see some code for computing the SVD of $X^\\top X$. Using the same selection of `Rhos` for each `rho` compute the singular values and plot them in order from largest to smallest. You should get one line for each rho. What does this say about the stability of $\\hat \\beta$ as rho approaches 1."
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(100, 100) (8, 8) (8,)\n"
]
},
{
"data": {
"text/plain": [
"1.558768684874895e-12"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Simulate again\n",
"n, p, rho = 100, 8, .8\n",
"beta = np.random.normal(0,1,p)\n",
"sigma = 1.\n",
"X, y = sim_corr_lm(n,p,rho,beta,sigma)\n",
"\n",
"## SVD\n",
"U,d,Vt = sc.linalg.svd(X)\n",
"\n",
"## Check to make sure that we understand\n",
"print(U.shape, Vt.shape, d.shape)\n",
"np.abs(X - U[:,:p] @ np.diag(d) @ Vt).sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# STOP"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"## Sample coefficients with different rho and store eigenvalues and beta variances\n",
"Rhos = [0,.2,.4,.6,.8,.9,.95,.99]\n",
"res_mat = []\n",
"for rho in Rhos:\n",
" X, y = sim_corr_lm(n,p,rho,beta,sigma)\n",
" U,d,Vt = sc.linalg.svd(X)\n",
" res_mat += [d]"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## plot the ordered eigenvalues for each rho\n",
"colors = plt.cm.jet(np.linspace(0,1,len(Rhos)))\n",
"for col, rho, res_vec in zip(colors,Rhos,res_mat):\n",
" plt.plot(res_vec,label=str(rho),c=col)\n",
"plt.legend()\n",
"_ = plt.xlabel('eigenvalue index')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Ridge regression\n",
"\n",
"We can summarize the above statements as\n",
"> If X is nearly singular then $\\hat \\beta$ is instable"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"One solution is *Ridge regression*, $\\hat \\beta$ that solves\n",
"$$\n",
"\\min. \\sum_{i=1}^n (y_i - x_i^\\top \\beta)^2 + \\lambda \\sum_{j=1}^p \\beta_j^2.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Ridge regularization will \"pull\" $\\beta$ towards 0.\n",
"- $\\lambda$ is a tuning parameter and the second term is ridge penalty\n",
"- centered $X$ ($x_{i,j} \\gets x_{i,j} - \\bar x_j$ for all but intercept) then $\\hat \\beta = \\bar y$, can then remove the intercept from $y$ and perform ridge with $\\beta$ without intercept\n",
"- normalize $X$ ($x_{i,j} \\gets x_{i,j} / \\| x_j \\|$) makes all penalty terms on same scale"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Ridge regression\n",
"\n",
"Ridge objective (with no intercept)\n",
"$$\n",
"(y - X \\beta)^\\top (y - X \\beta) + \\lambda \\beta^\\top \\beta\n",
"$$\n",
"is proportional to \n",
"$$- 2 y^\\top X \\beta + \\beta^\\top (X^\\top X) \\beta + \\lambda \\beta^\\top \\beta = -2 (X^\\top y)^\\top \\beta + \\beta^\\top (X^\\top X + \\lambda I) \\beta.$$\n",
"\n",
"The ridge solution satisfies some new normal equations,\n",
"$$\n",
"(X^\\top X + \\lambda I) \\hat \\beta = X^\\top y.\n",
"$$\n",
"\n",
"> Ridge regularization always has a solution because $(X^\\top X + \\lambda I)^{-1}$ exists! But it is biased!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img src=\"ridge_bias.PNG\" width=70%>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Exercise 3.4\n",
"\n",
"Recall that we can write the OLS solution as \n",
"$$\n",
"\\hat \\beta = V D^{-1} U^\\top y .\n",
"$$\n",
"\n",
"Consider the solution to the ridge normal equation, \n",
"$$\n",
"(X^\\top X + \\lambda I) \\hat \\beta = X^\\top y.\n",
"$$\n",
"Show that it can also be solved with a similar form but where the singular values $d_j$ are augmented."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# STOP"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Solution to exercise 3.4\n",
"\n",
"If $X = U D V^\\top$ then \n",
"$$\n",
"X^\\top X + \\lambda I = V D^2 V^\\top + \\lambda I = V (D^2 + \\lambda I) V^\\top \n",
"$$\n",
"because when $p < n$ then $V V^\\top = I$.\n",
"So Ridge solution can be shown to be (exercise)\n",
"$$\n",
"\\hat \\beta = V (D^2 + \\lambda I)^{-1} D U^\\top y \n",
"$$\n",
"compare to OLS\n",
"$$\n",
"\\hat \\beta = V D^{-1} U^\\top y \n",
"$$\n",
"modifies the eigenvalues to be \n",
"$$\n",
"d_j \\gets \\frac{d_j^2 + \\lambda}{d_j}.\n",
"$$\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Subset selection\n",
"\n",
"Two motivations for selecting variables\n",
"1. Fewer variables can lead to lower risk\n",
"2. We want to discover a subset of variables with large effects"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"A model for *sparsity*\n",
"$$\n",
"y_i = x_i^\\top \\beta + \\epsilon_i\n",
"$$\n",
"where for some of the $j = 1,\\ldots,p$, $\\beta_j = 0$.\n",
"\n",
"**Def** Support of $\\beta$ is \n",
"$$\n",
"\\textrm{supp}(\\beta) = \\{j = 1,\\ldots,p : \\beta_j \\ne 0 \\}.\n",
"$$\n",
"then goal is to find supp$(\\beta)$ or $\\beta$ such that $|$supp$(\\beta)| \\le s$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Computational challenges of subset selection\n",
"\n",
"*Combinatorial ($L_0$) subset selection:* Select $S \\subseteq \\{1,\\ldots, p\\}$ s.t. $|S| \\le s$ and minimizes\n",
"$$\n",
"\\sum_{i=1}^n \\left(y_i - \\sum_{j \\in S} x_{i,j} \\beta_j \\right)^2\n",
"$$\n",
"\n",
"This optimization is NP hard in general!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"**Def** $L_0$ norm (not a norm), is $\\|\\beta\\|_0 = |{\\rm supp}(\\beta)|$.\n",
"\n",
"Then subset selection is\n",
"$$\n",
"\\min \\| y - X \\beta \\|^2 \\textrm{ s.t. } \\beta \\in \\mathbb R^p, \\| \\beta \\|_0 \\le s\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Greedy methods\n",
"\n",
"Basic idea: at each step, choose an action that improves empirical risk\n",
"\n",
"#### Forward Stepwise\n",
"- Input X standardized, $x_0 = 1$, y\n",
"- Let $S_0 = \\{0\\}$\n",
"- For $s = 1,\\ldots, p$:\n",
" - For $j \\notin S$:\n",
" $$ R_j = \\min \\| y - X \\beta \\|^2 \\textrm{ s.t. supp}(\\beta) = S_{s-1} \\cup \\{ j \\} $$\n",
" - Add minimizer of $R_j$ to $S_{s-1}$ update $S_s$ \n",
"\n",
"- Intermediate OLS steps can be slow\n",
"- Correlations can cause issues: add a variable early on only because it is correlated with significant vars, never lose it"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
blegat/Polyhedra.jl | examples/Double Description.ipynb | 2 | 28747 | {
"cells": [
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"struct CutoffPointIndex\n",
" cutoff::Int\n",
" index::Int\n",
"end\n",
"Base.show(io::IO, p::CutoffPointIndex) = print(io, \"p[$(p.cutoff), $(p.index)]\")\n",
"struct CutoffRayIndex\n",
" cutoff::Int\n",
" index::Int\n",
"end\n",
"Base.show(io::IO, r::CutoffRayIndex) = print(io, \"r[$(r.cutoff), $(r.index)]\")\n",
"struct DoubleDescriptionData{PointT, RayT, LineT, HST}\n",
" fulldim::Int\n",
" halfspaces::Vector{HST}\n",
" # Elements ordered by first halfspace cutting it off\n",
" points::Vector{PointT}\n",
" pz::Vector{BitSet}\n",
" cutpoints::Vector{Vector{PointT}}\n",
" cutpz::Vector{Vector{BitSet}}\n",
" pin::Vector{Vector{CutoffPointIndex}}\n",
" rays::Vector{RayT}\n",
" rz::Vector{BitSet}\n",
" cutrays::Vector{Vector{RayT}}\n",
" cutrz::Vector{Vector{BitSet}}\n",
" rin::Vector{Vector{CutoffRayIndex}}\n",
" lines::Vector{LineT}\n",
" cutline::Vector{Union{Nothing, LineT}}\n",
" lineray::Vector{Union{Nothing, CutoffRayIndex}}\n",
" nlines::Vector{Int}\n",
"end\n",
"function Base.show(io::IO, data::DoubleDescriptionData)\n",
" println(io, \"DoubleDescriptionData in $(data.fulldim) dimension:\")\n",
" println(io, data.points)\n",
" println(io, data.rays)\n",
" println(io, data.lines)\n",
" for i in reverse(eachindex(data.cutpoints))\n",
" println(io, \" Halfspace $i: $(data.halfspaces[i]):\")\n",
" if !isempty(data.cutpoints[i])\n",
" println(io, \" Cut points:\")\n",
" for j in eachindex(data.cutpoints[i])\n",
" println(io, \" $j: \", data.cutpoints[i][j], \" zero at: \", data.cutpz[i][j])\n",
" end\n",
" end\n",
" if !isempty(data.pin[i])\n",
" println(io, \" In: \", data.pin[i])\n",
" end\n",
" if !isempty(data.cutrays[i])\n",
" println(io, \" Cut rays:\")\n",
" for j in eachindex(data.cutrays[i])\n",
" println(io, \" $j: \", data.cutrays[i][j], \" zero at: \", data.cutrz[i][j])\n",
" end\n",
" end\n",
" if !isempty(data.rin[i])\n",
" println(io, \" In: \", data.rin[i])\n",
" end\n",
" if data.cutline[i] !== nothing\n",
" println(io, \" Cut line: \", data.cutline[i])\n",
" if data.lineray[i] !== nothing\n",
" println(io, \" Line ray: \", data.lineray[i])\n",
" end\n",
" end\n",
" if !iszero(data.nlines[i])\n",
" println(io, \" $(data.nlines[i]) uncut lines left\")\n",
" end\n",
" end\n",
"end\n",
"\n",
"function DoubleDescriptionData{PointT, RayT, LineT}(fulldim::Integer, halfspaces) where {PointT, RayT, LineT}\n",
" n = length(halfspaces)\n",
" return DoubleDescriptionData{PointT, RayT, LineT, eltype(halfspaces)}(\n",
" fulldim,\n",
" halfspaces,\n",
" PointT[],\n",
" BitSet[],\n",
" [PointT[] for i in 1:n],\n",
" [BitSet[] for i in 1:n],\n",
" [CutoffPointIndex[] for i in 1:n],\n",
" RayT[],\n",
" BitSet[],\n",
" [RayT[] for i in 1:n],\n",
" [BitSet[] for i in 1:n],\n",
" [CutoffRayIndex[] for i in 1:n],\n",
" LineT[],\n",
" Union{Nothing, LineT}[nothing for i in 1:n],\n",
" Union{Nothing, CutoffRayIndex}[nothing for i in 1:n],\n",
" zeros(Int, n)\n",
" )\n",
"end\n",
"function Base.getindex(data::DoubleDescriptionData, p::CutoffPointIndex)\n",
" if p.cutoff == 0\n",
" return data.points[p.index]\n",
" else\n",
" return data.cutpoints[p.cutoff][p.index]\n",
" end\n",
"end\n",
"function Base.getindex(data::DoubleDescriptionData, r::CutoffRayIndex)\n",
" if r.cutoff == 0\n",
" return data.rays[r.index]\n",
" else\n",
" return data.cutrays[r.cutoff][r.index]\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"isin (generic function with 2 methods)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function _bitdot_range(b1::BitSet, b2::BitSet, i, n)\n",
" count = 1 # They share the hyperplance `i`\n",
" for j in (i+1):n\n",
" if j in b1 && j in b2\n",
" count += 1\n",
" end\n",
" end\n",
" return count\n",
"end\n",
"function isadjacent(data, i::Integer, p1::CutoffPointIndex, p2::CutoffPointIndex)\n",
" pz1 = data.cutpz[p1.cutoff][p1.index]\n",
" pz2 = data.cutpz[p2.cutoff][p2.index]\n",
" n = length(data.halfspaces)\n",
" return _bitdot_range(pz1, pz2, i, n) + data.lines[i] + 1 == data.fulldim\n",
"end\n",
"function isadjacent(data, i::Integer, p::CutoffPointIndex, r::CutoffRayIndex)\n",
" pz = data.cutpz[p.cutoff][p.index]\n",
" rz = data.cutrz[r.cutoff][r.index]\n",
" n = length(data.halfspaces)\n",
" return _bitdot_range(pz, rz, i, n) + data.lines[i] + 1 == data.fulldim\n",
"end\n",
"function isadjacent(data, i::Integer, r::CutoffRayIndex, p::CutoffPointIndex)\n",
" return isadjacent(data, i, p, r)\n",
"end\n",
"function isadjacent(data, i::Integer, r1::CutoffPointIndex, r2::CutoffPointIndex)\n",
" rz1 = data.cutrz[r1.cutoff][r1.index]\n",
" rz2 = data.cutrz[r2.cutoff][r2.index]\n",
" n = length(data.halfspaces)\n",
" return _bitdot_range(rz1, rz2, i, n) + data.lines[i] + 2 == data.fulldim\n",
"end\n",
"function isin(data, i, p::CutoffPointIndex)\n",
" if p.cutoff == 0\n",
" return i in data.pz[p.index]\n",
" else\n",
" return i in data.cutpz[p.cutoff][p.index]\n",
" end\n",
"end\n",
"function isin(data, i, r::CutoffRayIndex)\n",
" if r.cutoff == 0\n",
" return i in data.rz[r.index]\n",
" else\n",
" return i in data.cutrz[r.cutoff][r.index]\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"add_index! (generic function with 4 methods)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"resized_bitset(data) = sizehint!(BitSet(), length(data.halfspaces))\n",
"function add_index!(data, cutoff::Nothing, p::AbstractVector)\n",
" push!(data.points, p)\n",
" push!(data.pz, resized_bitset(data))\n",
" return CutoffPointIndex(0, length(data.points))\n",
"end\n",
"function add_index!(data, cutoff::Integer, p::AbstractVector)\n",
" push!(data.cutpoints[cutoff], p)\n",
" push!(data.cutpz[cutoff], resized_bitset(data))\n",
" return CutoffPointIndex(cutoff, length(data.cutpoints[cutoff]))\n",
"end\n",
"function add_index!(data, cutoff::Nothing, r::Polyhedra.Ray)\n",
" push!(data.rays, r)\n",
" push!(data.rz, resized_bitset(data))\n",
" return CutoffRayIndex(0, length(data.rays))\n",
"end\n",
"function add_index!(data, cutoff::Integer, r::Polyhedra.Ray)\n",
" push!(data.cutrays[cutoff], r)\n",
" push!(data.cutrz[cutoff], resized_bitset(data))\n",
" return CutoffRayIndex(cutoff, length(data.cutrays[cutoff]))\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"add_adjacent_element! (generic function with 1 method)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function add_in!(data, i, index::CutoffPointIndex)\n",
" push!(data.pin[i], index)\n",
" if index.cutoff == 0\n",
" push!(data.pz[index.index], i)\n",
" else\n",
" push!(data.cutpz[index.cutoff][index.index], i)\n",
" end\n",
"end\n",
"function add_in!(data, i, index::CutoffRayIndex)\n",
" push!(data.rin[i], index)\n",
" if index.cutoff == 0\n",
" push!(data.rz[index.index], i)\n",
" else\n",
" push!(data.cutrz[index.cutoff][index.index], i)\n",
" end\n",
"end\n",
"function set_in!(data, I, el, index)\n",
" for i in I\n",
" if el in Polyhedra.hyperplane(data.halfspaces[i])\n",
" add_in!(data, i, index)\n",
" end\n",
" end\n",
"end\n",
"function add_element!(data, k, el)\n",
" cutoff = nothing\n",
" for i in reverse(1:k)\n",
" if data.cutline[i] !== nothing\n",
" el = line_project(el, data.cutline[i], data.halfspaces[i])\n",
" index = add_adjacent_element!(data, i - 1, el, data.lineray[i])\n",
" set_in!(data, i:k, el, index)\n",
" return index\n",
" end\n",
" if !(el in data.halfspaces[i])\n",
" cutoff = i\n",
" break\n",
" end\n",
" end\n",
" index = add_index!(data, cutoff, el)\n",
" set_in!(data, (index.cutoff+1):k, el, index)\n",
" return index\n",
"end\n",
"function add_adjacent_element!(data, k, el, parent)\n",
" index = add_element!(data, k, el)\n",
" # Condition (c_k) in [FP96]\n",
" if index.cutoff != parent.cutoff\n",
" addintersection!(data, index, parent)\n",
" end\n",
" return index\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"combine (generic function with 4 methods)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using LinearAlgebra\n",
"function combine(β, p1::AbstractVector, value1, p2::AbstractVector, value2)\n",
" λ = (value2 - β) / (value2 - value1)\n",
" return λ * p1 + (1 - λ) * p2\n",
"end\n",
"function combine(β, p::AbstractVector, pvalue, r::Polyhedra.Ray, rvalue)\n",
" λ = (β - pvalue) / rvalue\n",
" return p + λ * r\n",
"end\n",
"combine(β, r::Polyhedra.Ray, rvalue, p::AbstractVector, pvalue) = combine(β, p, pvalue, r, rvalue)\n",
"function combine(r1::Polyhedra.Ray, value1, r2::Polyhedra.Ray, value2)\n",
" # should take\n",
" # λ = value2 / (value2 - value1)\n",
" @assert 0 <= value2 / (value2 - value1) <= 1\n",
" # By homogeneity we can avoid the division and do\n",
" #newr = value2 * r1 - value1 * r2\n",
" # but this can generate very large numbers (see JuliaPolyhedra/Polyhedra.jl#48)\n",
" # so we still divide\n",
" newr = (value2 * r1 - value1 * r2) / (r2 - r1)\n",
" # In CDD, it does value2 * r1 - value1 * r2 but then it normalize the ray\n",
" # by dividing it by its smallest nonzero entry (see dd_CreateNewRay)\n",
" return Polyhedra.simplify(newr)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"add_if_adjacent! (generic function with 1 method)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function addintersection!(data, idx1, idx2)\n",
" if idx1.cutoff > idx2.cutoff\n",
" return addintersection!(data, idx2, idx1)\n",
" end\n",
" i = idx2.cutoff\n",
" if isin(data, i, idx1)\n",
" return\n",
" end\n",
" el1 = data[idx1]\n",
" el2 = data[idx2]\n",
" h = data.halfspaces[i]\n",
" newel = combine(h.β, el1, h.a ⋅ el1, el2, h.a ⋅ el2)\n",
" add_adjacent_element!(data, i - 1, newel, idx1)\n",
"end\n",
"function add_if_adjacent!(data, i::Integer, el1, el2)\n",
" # Condition (c_k) in [FP96]\n",
" if el1.cutoff != el2.cutoff\n",
" if isadjacent(data, i, el1, el2)\n",
" addintersection!(data, el1, el2)\n",
" end\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"double_description (generic function with 1 method)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_shift(el::AbstractVector, line::Line) = el + Polyhedra.coord(line)\n",
"_shift(el::Line, line::Line) = el + line\n",
"_shift(el::Ray, line::Line) = el + Polyhedra.Ray(Polyhedra.coord(line))\n",
"function line_project(el, line, h)\n",
" # (line + λ * cutline) ⋅ h.a == h.β\n",
" # λ = (h.β - line ⋅ h.a) / (cutline ⋅ h.a)\n",
" λ = (h.β - el ⋅ h.a) / (line ⋅ h.a)\n",
" return Polyhedra.simplify(_shift(el, λ * line))\n",
"end\n",
"function hline(data, line::Line, i, h)\n",
" value = h.a ⋅ line\n",
" if !Polyhedra.isapproxzero(value)\n",
" if data.cutline[i] === nothing\n",
" if value > 0\n",
" line = -line # Make `lineray` point inward\n",
" end\n",
" data.cutline[i] = line\n",
" cut = true\n",
" return true\n",
" else\n",
" line = line_project(line, data.cutline[i], hs)\n",
" end\n",
" end\n",
" data.nlines[i] += 1\n",
" return false\n",
"end\n",
"function double_description(hr::HRepresentation)\n",
" v = Polyhedra.dualfullspace(hr)\n",
" hps = Polyhedra.lazy_collect(hyperplanes(hr))\n",
" hss = Polyhedra.lazy_collect(halfspaces(hr))\n",
" data = DoubleDescriptionData{pointtype(v), raytype(v), linetype(v)}(fulldim(hr), hps, hss)\n",
" for line in lines(v)\n",
" cut = false\n",
" for i in reverse(eachindex(hps))\n",
" cut = hline(data, line, nhalfspaces(hr) + i, hss[i])\n",
" if cut\n",
" break\n",
" end\n",
" end\n",
" if !cut\n",
" for i in reverse(eachindex(hss))\n",
" cut = hline(data, line, i, hss[i])\n",
" end\n",
" if cut\n",
" break\n",
" end\n",
" end\n",
" if !cut\n",
" push!(data.lines, line)\n",
" end\n",
" end\n",
" # Add line rays after all lines are added so that the rays can be `line_project`ed.\n",
" # We only do that for halfspaces, hyperplanes do not create rays from cutoff lines.\n",
" # We use increasing index order since higher index may need the `lineray` of lower index.\n",
" for i in eachindex(hss)\n",
" line = data.cutline[i]\n",
" if line !== nothing\n",
" ray = Polyhedra.Ray(Polyhedra.coord(line))\n",
" data.lineray[i] = add_element!(data, i - 1, ray)\n",
" end\n",
" end\n",
" @assert isone(npoints(v))\n",
" add_element!(data, nhalfspaces(hr), first(points(v))) # Add the origin\n",
" for i in reverse(eachindex(hss))\n",
" if isempty(data.cutpoints[i]) && isempty(data.cutrays[i])\n",
" # Redundant, remove its contribution to avoid incorrect `isadjacent`\n",
" for p in data.pin\n",
" if p.cutoff == 0\n",
" delete!(data.pz, i)\n",
" else\n",
" delete!(data.cutpz[p.cutoff], i)\n",
" end\n",
" end\n",
" for r in data.rin\n",
" if r.cutoff == 0\n",
" delete!(data.rz, i)\n",
" else\n",
" delete!(data.cutrz[pr.cutoff], i)\n",
" end\n",
" end\n",
" continue\n",
" end\n",
" if i > 1\n",
" for p1 in data.pin[i], p2 in data.pin[i]\n",
" add_if_adjacent!(data, i, p1, p2)\n",
" end\n",
" for p in data.pin[i], r in data.rin[i]\n",
" add_if_adjacent!(data, i, p, r)\n",
" end\n",
" end\n",
" deleteat!(data.cutpoints, i)\n",
" deleteat!(data.cutpz, i)\n",
" if i > 1\n",
" for r1 in data.rin[i], r2 in data.rin[i]\n",
" add_if_adjacent!(data, i, r1, r2)\n",
" end\n",
" end\n",
" deleteat!(data.cutrays, i)\n",
" deleteat!(data.cutrz, i)\n",
" deleteat!(data.pin, i)\n",
" deleteat!(data.rin, i)\n",
" end\n",
" if isempty(data.points)\n",
" # Empty polyhedron, there may be rays left,\n",
" # Example 1: for 0x_1 + x_2 = -1 ∩ 0x_1 + x_2 = 1, the line (0, 1) is detected as correct\n",
" # Example 2: for 0x_1 + 0x_2 = 1, the lines (1, 0) and (0, 1) are detected as correct\n",
" # but since there is no point, the polyhedron is empty and we should drop all rays/lines\n",
" empty!(data.lines)\n",
" empty!(data.rays)\n",
" end\n",
" similar(v, data.points, data.lines, data.rays)\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Examples\n",
"\n",
"## Square"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"H-representation Polyhedra.Intersection{Float64,Array{Float64,1},Int64}:\n",
"4-element iterator of HalfSpace{Float64,Array{Float64,1}}:\n",
" HalfSpace([1.0, 0.0], 1.0)\n",
" HalfSpace([-1.0, 0.0], 1.0)\n",
" HalfSpace([0.0, 1.0], 1.0)\n",
" HalfSpace([0.0, -1.0], 1.0)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Polyhedra\n",
"h = HalfSpace([1.0, 0.0], 1.0) ∩ HalfSpace([-1.0, 0.0], 1.0) ∩\n",
" HalfSpace([0.0, 1.0], 1.0) ∩ HalfSpace([0.0, -1.0], 1.0)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.000197 seconds (155 allocations: 9.531 KiB)\n"
]
},
{
"data": {
"text/plain": [
"V-representation Polyhedra.Hull{Float64,Array{Float64,1},Int64}:\n",
"4-element iterator of Array{Float64,1}:\n",
" [-1.0, -1.0]\n",
" [1.0, -1.0]\n",
" [-1.0, 1.0]\n",
" [1.0, 1.0]"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time double_description(h)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.000984 seconds (1.51 k allocations: 70.188 KiB)\n"
]
},
{
"data": {
"text/plain": [
"V-representation Polyhedra.Hull{Float64,Array{Float64,1},Int64}:\n",
"4-element iterator of Array{Float64,1}:\n",
" [1.0, 1.0]\n",
" [-1.0, 1.0]\n",
" [1.0, -1.0]\n",
" [-1.0, -1.0]"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time doubledescription(h)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Triangle"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"H-representation Polyhedra.Intersection{Float64,Array{Float64,1},Int64}:\n",
"3-element iterator of HalfSpace{Float64,Array{Float64,1}}:\n",
" HalfSpace([1.0, 1.0], 1.0)\n",
" HalfSpace([1.0, -1.0], 0.0)\n",
" HalfSpace([-1.0, 0.0], 0.0)"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h = hrep([HalfSpace([ 1., 1], 1),\n",
" HalfSpace([ 1., -1], 0),\n",
" HalfSpace([-1., 0], 0)])"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.000179 seconds (141 allocations: 8.641 KiB)\n"
]
},
{
"data": {
"text/plain": [
"V-representation Polyhedra.Hull{Float64,Array{Float64,1},Int64}:\n",
"3-element iterator of Array{Float64,1}:\n",
" [0.0, 0.0]\n",
" [0.0, 1.0]\n",
" [0.5, 0.5]"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time double_description(h)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.000830 seconds (1.26 k allocations: 61.578 KiB)\n"
]
},
{
"data": {
"text/plain": [
"V-representation Polyhedra.Hull{Float64,Array{Float64,1},Int64}:\n",
"3-element iterator of Array{Float64,1}:\n",
" [0.5, 0.5]\n",
" [0.0, 0.0]\n",
" [0.0, 1.0]"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time doubledescription(h)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"H-representation Polyhedra.Intersection{Float64,SArray{Tuple{2},Float64,1,2},Size{(2,)}}:\n",
"3-element iterator of HalfSpace{Float64,SArray{Tuple{2},Float64,1,2}}:\n",
" HalfSpace([1.0, 1.0], 1.0)\n",
" HalfSpace([1.0, -1.0], 0.0)\n",
" HalfSpace([-1.0, 0.0], 0.0)"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using StaticArrays\n",
"sh = HalfSpace((@SVector [ 1, 1]), 1) ∩\n",
" HalfSpace((@SVector [ 1, -1]), 0) ∩\n",
" HalfSpace((@SVector [-1, 0]), 0)\n",
"shf = HalfSpace((@SVector [ 1, 1]), 1.0) ∩\n",
" HalfSpace((@SVector [ 1, -1]), 0.0) ∩\n",
" HalfSpace((@SVector [-1, 0]), 0.0)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.000688 seconds (8.06 k allocations: 165.719 KiB)\n"
]
},
{
"data": {
"text/plain": [
"V-representation Polyhedra.Hull{Rational{BigInt},SArray{Tuple{2},Rational{BigInt},1,2},Size{(2,)}}:\n",
"3-element iterator of SArray{Tuple{2},Rational{BigInt},1,2}:\n",
" Rational{BigInt}[0//1, 0//1]\n",
" Rational{BigInt}[0//1, 1//1]\n",
" Rational{BigInt}[1//2, 1//2]"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time double_description(sh)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.009481 seconds (43.65 k allocations: 923.055 KiB)\n"
]
},
{
"data": {
"text/plain": [
"V-representation Polyhedra.Hull{Rational{BigInt},SArray{Tuple{2},Rational{BigInt},1,2},Size{(2,)}}:\n",
"3-element iterator of SArray{Tuple{2},Rational{BigInt},1,2}:\n",
" Rational{BigInt}[1//2, 1//2]\n",
" Rational{BigInt}[0//1, 0//1]\n",
" Rational{BigInt}[0//1, 1//1]"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time doubledescription(sh)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.000447 seconds (250 allocations: 12.750 KiB)\n"
]
},
{
"data": {
"text/plain": [
"V-representation Polyhedra.Hull{Float64,SArray{Tuple{2},Float64,1,2},Size{(2,)}}:\n",
"3-element iterator of SArray{Tuple{2},Float64,1,2}:\n",
" [0.0, 0.0]\n",
" [0.0, 1.0]\n",
" [0.5, 0.5]"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time double_description(shf)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.002401 seconds (3.48 k allocations: 142.328 KiB)\n"
]
},
{
"data": {
"text/plain": [
"V-representation Polyhedra.Hull{Float64,SArray{Tuple{2},Float64,1,2},Size{(2,)}}:\n",
"3-element iterator of SArray{Tuple{2},Float64,1,2}:\n",
" [0.5, 0.5]\n",
" [0.0, 0.0]\n",
" [0.0, 1.0]"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time doubledescription(shf)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"H-representation MixedMatHRep{Float64,Array{Float64,2}}:\n",
"3-element iterator of HalfSpace{Float64,Array{Float64,1}}:\n",
" HalfSpace([1.0, 1.0], 1.0)\n",
" HalfSpace([1.0, -1.0], 0.0)\n",
" HalfSpace([-1.0, 0.0], 0.0)"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hmat = hrep([ 1 1\n",
" 1 -1\n",
" -1 0],\n",
" [1., 0, 0])"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.000121 seconds (130 allocations: 7.969 KiB)\n"
]
},
{
"data": {
"text/plain": [
"V-representation MixedMatVRep{Float64,Array{Float64,2}}:\n",
"3-element iterator of Array{Float64,1}:\n",
" [0.0, 0.0]\n",
" [0.0, 1.0]\n",
" [0.5, 0.5]"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time double_description(hmat)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.000910 seconds (1.67 k allocations: 84.406 KiB)\n"
]
},
{
"data": {
"text/plain": [
"V-representation MixedMatVRep{Float64,Array{Float64,2}}:\n",
"3-element iterator of Array{Float64,1}:\n",
" [0.5, 0.5]\n",
" [0.0, 0.0]\n",
" [0.0, 1.0]"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@time doubledescription(hmat)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.1.0",
"language": "julia",
"name": "julia-1.1"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.1.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
jeanpat/DeepFISH | notebooks/GenerateOverlappingChromosomes.ipynb | 1 | 1745223 | null | gpl-3.0 |
sserkez/ocelot | test/workshop/5_Genesis_preprocessor.ipynb | 2 | 486559 | {
"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"
},
"name": "",
"signature": "sha256:25cd0e9ddaa9beb942f9f865e15118811243cd58ea80fe8ebcc0adcde63d0c3b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*This notebook was created by [Svitozar Serkez](http://www.xfel.eu/organization/staff/serkez_svitozar/). Source and license info is on [GitHub](https://github.com/sserkez/ocelot/tree/dev/docs). August 2016. *"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Tutorial N5: Genesis preprocessor. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ocelot is not only a particle tracking code, but a simulation toolkit.\n",
"\n",
"Python is known to be a very good \"glue\" between different software\n",
"\n",
"Interface to other codes may be (and was) developed.\n",
"GENESIS interface is under active development and is currently used for studies\n",
"\n",
"-list of papers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## This example will cover the following topics:\n",
"* Initialization of the library\n",
"* Preparing the GENESIS simulation\n",
"* running many-stage statistical simulation\n",
"\n",
"### Requirements\n",
"* OCELOT - library\n",
"* numpy, scipy, matplotlib\n",
"* beam.txt - input beam file"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Import of modules"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# the output of plotting commands is displayed inline within frontends, \n",
"# directly below the code cell that produced it\n",
"%matplotlib inline\n",
"from __future__ import print_function\n",
"# this python library provides generic shallow (copy) and deep copy (deepcopy) operations \n",
"from copy import deepcopy\n",
"\n",
"# import from Ocelot graphical modules\n",
"import sys, os\n",
"from ocelot import *\n",
"from ocelot.utils.xfel_utils import *\n",
"from ocelot.gui.accelerator import *\n",
"from ocelot.gui.genesis_plot import *\n",
"#from ocelot.optics.elements import Filter_freq\n",
"\n",
"import numpy as np\n",
"from copy import copy\n",
"#import matplotlib.pyplot as plt\n",
"# load beam distribution"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"initializing ocelot...\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setting input parameters\n",
"electron beam energy and expected radiation photon energy"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"E_beam=8.5 #[GeV]\n",
"E_photon=250 #[eV]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating SASE3 lattice\n",
"with native ocelot objects"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# defining the undulator\n",
"lperiod=0.068\n",
"nperiods=73\n",
"und = Undulator(lperiod=lperiod, nperiods=nperiods, Kx=1.0);\n",
"und.Kx = Ephoton2K(E_photon, und.lperiod, E_beam)\n",
"\n",
"# defining of the drifts\n",
"d2 = Drift (l=4*und.lperiod)\n",
"d3 = Drift (l=7*und.lperiod)\n",
"\n",
"# defining of the quads\n",
"qf = Quadrupole (l=6*und.lperiod, k1=-7.3)\n",
"qd = Quadrupole (l=6*und.lperiod, k1=7.3)\n",
"qdh=deepcopy(qd)\n",
"qdh.l/=2\n",
"\n",
"# creating of the cell\n",
"extra_fodo = (und, d2, qdh)\n",
"cell_ps = (und, d2, qf, d3, und, d2, qd, d3)\n",
"l_fodo= MagneticLattice(cell_ps).totalLen/2\n",
"sase3 = MagneticLattice((und, d2, qd, d3) + 11*cell_ps)\n",
"\n",
"up = UndulatorParameters(und,E_beam)\n",
"up.printParameters()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Undulator parameters:\n",
"('L=', 4.964)\n",
"('gamma(electron)=', 16634.085776399123)\n",
"('K=', 8.8723675084034621)\n",
"('B[T]=', 1.397364688791348)\n",
"('test', 250.00000000000003)\n",
"Radiation parameters:\n",
"('w1(first harmonic, zero angle)=', 3.7981687858557933e+17, 'Hz/2pi', 250.00000000000003, '[eV]', 4.959367720369577e-09, 'm')\n",
"('Total energy loss [Gev]', 0.00044305322825621829)\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Load beam file"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"beamf = read_beam_file('beam.dat')"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['ZPOS', 'GAMMA0', 'DELGAM', 'EMITX', 'EMITY', 'BETAX', 'BETAY', 'XBEAM', 'YBEAM', 'PXBEAM', 'PYBEAM', 'ALPHAX', 'ALPHAY', 'CURPEAK', 'ELOSS']\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Plot beamfile"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig=plt.figure()\n",
"fig.set_size_inches((20,15))\n",
"plot_beam(fig, beamf)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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u4/YYFZ+ISNbYBdwYmqjyKwPaM9AfyWJu/34bcnxTpkBVlVOQ/dAhWL4cxo0L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nvr6e2bNnp7zSzKZNm1i7di0zZ85M7IsnSgAzZsxg586dLFu2jDlz5jBlyhRGjRrVo+eNYr8E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\nIiIiIiIi4g0Vq0RERERERERExBv/D/krLsjrq/cCAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xa712208>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Match beam file"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"beta_av = 20.0\n",
"beam=get_beam_peak(beamf)\n",
"beam.E=E_beam\n",
"rematch(beta_av, l_fodo, qdh, sase3, extra_fodo, beam, qf, qd)\n",
"beamf = transform_beam_file(beamf ,transform = [ [beam.beta_x,beam.alpha_x], [beam.beta_y,beam.alpha_y] ], energy_new = beam.E, emit_scale = 1.0)\n",
"beamf = cut_beam(beamf,[-2e-6, 2e-6])\n",
"\n",
"fig=plt.figure()\n",
"fig.set_size_inches((20,15))\n",
"plot_beam(fig, beamf)"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"********* calculating fodo parameters *********\n",
"fodo parameters:\n",
"('k*l=', array([ 0.05283898, 0.15461945]))\n",
"('f=', 18.925420799442076, 6.4674915975241269)\n",
"('kap1=', 3.0923890195166788)\n",
"('kap2=', 1.0567796727980598)\n",
"('betaMax=', array([ 26.4674916, 38.9254208]))\n",
"('betaMin=', array([ 13.5325084, 1.0745792]))\n",
"('betaMean=', array([ 20., 20.]))\n",
"********* *********\n",
"before rematching k=-7.300000 7.300000 beta=29.813443 19.628544 alpha=1.244151 -0.831963\n",
"after rematching k=0.259015 -0.259015 beta=24.470237 14.252958 alpha=1.325022 -0.880831\n",
"matching to slice 999\n",
"transforming\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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+MVNU5G9/BrGSkhKAcJIkUiAQoK2tjby8PObNm4e1Nu5Stsjnjhs3Ln2dzUDz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\n9Qww3jbnFmBS2HnzlVLDkiCfp/Dz85hU3S+4AC67zOpfcw2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vH3OLPKJ/8vUXBLfg5++l\np3QvD6Hq1QsKCmD1arj2WnjppRpf0lP6xxF7zqpUCAOMaKzqfxyFtvFdhaGGTcfX/oEH4KOPTATD\n3r1wxRXw2WeQlpbwWzuuu8P4XX+nUTrKuNdA1T6ttZ4eNp6LMUyFVA1USo0BxmqtBwT6pUDTsDnj\nMEnVh0Q7xzauo5VdEIQk8L//wXHHmXZ2NmzcaEIBhcrR2nif7Q6UoNm0iVYv9mTzXpNA8+frfqZL\nTpcqLiAIqY1SCq21qn6mkExkDyZ4hqlTQ6sSv/KKScIuRM0Li15g1LujALik1yW89LuaG/zcwNqC\ntXR4vEPI2CtH/53Cls244r0rALj0iEt54dwXIp3uHHPnwjHHQGmp6T/+OFx/vbMyCSmNG/ZgUZlj\nlVIDgR12Q1VgDIzX0812A1OAfsCntv4CTBJ1O82AN2Kc43vCvSz8hp/1d7Xu9hDA885LiKHK1frX\nBKWgW7dgd9OSb4OGqoYZDcltmhs8lnK6x4CfdQfRXxDchJ+fR0/qPnIkXHaZ1f/DH4yXVQ3wpP5x\nICTBeormrMrKPTRkfHfh7pDjrlj7AQPg1lut/vjxsHx5wm/rCt0dxO/6O021xiqlVF+wEq0rpbKU\nUp0JVOjTWhcAO5VSWbZzOmOSsD9gu9StwPiwyw/UWk+JcY4gCG6juNi8vSxHqgBGj81Y9f1PXwbb\nh7c8nDSVePduQRAEQUhp/vlPq/rurl1w6aVSUS0GQsIAU8BY1ahuowpjTTKbhBirwsMAXcNdd5nQ\nVoD9+2HUKMvTShBSkCpzVgUMUPMArZRSgP1/9ofKG1rrKUqpMUopjanqlwP0s3tbaa1nBgxdwwAF\n5ALn2+8XzRxBYmf9rL9rdf/oI9i2zbTbtYMTT0zIbVyrf22wGasWr18Y/F+5PF9VOSmpe5T4WXcQ\n/QXBTfj5efSs7o0bw6uvmlQFpaUwaxa8+SacH9ufGJ7Vv5akWoL19MIiGhXBnrrWWFa9LOoWWwOu\ny1lVTt26pjrggAFQUmKqXD7yCNx8c8Ju6RrdHcLv+jtNlcaqgNdUVK/2tdaTo5gzPR5zBEFwGfYQ\nwAsvTErCx5TBbqza/bMx91PRWCUIgiAIQg056ij485/h4YdNf/x4OOMMaNjQWbk8wP7i1Eqwzkcf\n0bgwzFiVmUVGWkaw71rPKoDevY2H1V13mf6dd8KZZ8JhhzkrlyAkAPmL0oP4PXbWz/q7Uvfdu+Hd\nd63+JZck7Fau1L+2dO8ebC5Wm4PtcGNVSuoeJX7WHUR/QXATfn4ePa/7+PGmAAzAypXGeBUDnte/\nhtjDAFPBs4rp02lSGDpUXRig69b+1luhXz/TLioy4YAlJQm5let0TzJ+199pxFglCELteOcdEzcP\ncPjhViy9EB0Bz6qSNPihkbUh7Nmyp1MSCYIgCELqkZMDjz1m9SdPNpWMhSpJqQTrRUXw3ns0Kgod\n9kzOqnIyMkw4YN2Ae9i8efDgg87KJAgJQHm19LCUTRYEl3DaaSZnFcADD4RWKhGiIyeHH+vs4LBr\nTbddk3as/fNaZ2USBBfghrLJQkVkDyZ4Fq1NxeLpgawj/frBt99CerqzcrmYMe+NYcpCU+tq0tBJ\njO031mGJasFHH8Fpp3HkGJh7kDWs/6op02Wk32N9D0ruLCE9zeXfiwcftPbdGRnwxRdwzDHOyiSk\nDG7Yg4lnlSAINWfTJvjkE6t/4YXOyeJlunXj+5ZWV/JVCYIgCEICUAoefRTqBcLZ5s+H555zViaX\nk1JhgG+9BYRWDCsnTaXRuG7jYH9P0Z4kCVULbroJjj7atIuLYdgwWLHCWZkEIY6IscqD+D121s/6\nu073qVOhrMy0TzgBOnZM6O1cp3+86NaNxa2sbq+WFY1VKat7FPhZdxD9BcFN+Pl5TBndO3YM9QIf\nPx527qz2tJTRP0ZSJgywtNSkrgAq8xWpLBTQtWufnm4qXebkmP7GjZCXB1u2xO0WrtU9Sfhdf6cR\nY5UgCDXHXgXw4oudk8PrdO8eaqwSzypBEARBSBzjxkGHDqa9dStMmOCoOG4mZTyrvv46aMTRGXUi\nTvFU3qpycnPh7behQQPTX78e/vhHE/IqCB5HjFUeJC8vz2kRHMXP+rtK959/hjlzTDsjA84/P+G3\ndJX+8STMs6pnq4rJ1VNW9yjws+4g+guCm/Dz85hSujdoAI88YvWffBJ++KHKU1JK/xgI8azK8LBn\n1X//G2zq8qqQYVRmrHL92p94ool2KGfaNLjnnrhc2vW6Jxi/6+80YqwSBKFm2H8pnnaa5YIsxMzO\nTq35NbBvyiiFg5sd7KxAgiAIgpDqDB8OJ59s2qWlcPXVJu+PEML+YsuzytNhgB98YLWbNIk4xZOe\nVeUMHQqjR1v9CRPgn/90TBxBiAdirPIgfo+d9bP+rtL9tdes9gUXJOWWrtI/jizJKgy2D92qyFAV\nq8+kqu7R4GfdQfQXBDfh5+cx5XRXCp54wqoEOGsW3HZbpdNTTv8oSYkwwPXrYfFi087IoG+X44OH\ncupbL1vtxqrdRbuDbc+s/VNPweDBVv/664N5umqKZ3RPEH7X32nEWCUIQuwsWWK5y9evD2ef7aw8\nHufH/b8G24dv0rB2rYPSCELqopQao5QarZSaqJSaqpTqE2HO8MCc4Uqpm5RSuU7IKghCEujZE+6+\n2+o/9li14YB+4pu137Bo46Jg37NhgB9+aLVPOIEHTnuYgxofROO6jXn/wveDhzztWQVQty5Mn25V\nCNQaRo6E1193Vi5BqCGRs8sJrsbvsbN+1t81utu9qs46Cxo1SsptXaN/nPlxy4/Bdo8twPLlFSor\npqru0eBn3UH0jxdKqTHAVK31rkA/F1iplOqrtV4UGBsI9Ndaj7ed9zEwONI1Bf/h5+cxZXUfPx4+\n+8x8SkvhL38JNW4ESFn9K0Frzcg3R4aMeTYM0L6ep51Gi4Yt+OWGXygqLaJBRoPgIbuxquBAQbDt\nqbVv2BDee88YrFatgqIiUwSpcWM488yYL+cp3ROA3/V3GvGsEgQhNrQOfUNz4YXOyZIi2I1Vh20B\nVqxwThhBSF2yyw1VAFrr1cAzwHjbnFuASWHnzVdKDUuCfIIgOEFamvGoSgv8WfTRR6H5jXzKqh2r\nWLsr1NPbk2GAJSXwySdW//TTAaiTVifEUAWQlZkVbO88sDMp4iWEFi2M8bVHD9MvK4MRI2DePGfl\nEoQYEWOVB/F77Kyf9XeF7nPnmjc1AFlZwV/6ycAV+ieAH7ZYIQc9KjFWparu0eBn3UH0jwcBL6qJ\nSqlOYYdWAn0Dc7KBQVrrX8LmrAJGIgj4+3lMad179QpNTn3jjRWSrae0/mHsKtzFvV/eW2Hck2GA\ns2fDzoDh6aCD4LDDKp3atH7TYHvHgR3BtifXvmNHmDkTOnUy/X37YMgQ+OabmC7jSd3jiN/1dxox\nVgmCEBt2r6rf/Q4yM52TJQXYeWAn63evB6BuCXTegQkDFAQhbgS8qE6NYIgaACwItDsDOsLp2wkY\ntARBSGHuvdeESgEsWwZPP+2sPA4y6p1RvPDdCxXGPelZZQ8BPP10k1i/ErLrZQfbnvasKqd1a+Ml\n2DRghNu+HU46yRQW0JF+3QmCu1Dao19UpZT2quyC4FlKS6FDB1NVBYyr/GBJ5VIbvln7Dcf+51gA\nem6Cxf8GunaVUEBBAJRSaK0r/8uidtfOJuBZpbVeE8hXNU1r3SxsXsRxPyN7MCFleeghuOUW087K\ngqVLoU0bZ2VKMjsP7KTpg00rjPdo0YMlf1iCqsLY40r69YMFgXcSb74Jw4dXOvXdZe9y7tRzARja\nfWhI8nVPM2+eMdRt3WqNnX02PPcc5ORUfp7gaxK5B4sWSbAuCEL0zJplGapatIBTTnFWnhQgJF/V\n5kBj1SooLBSvNcF35OfnJ9PlfhowWmu9xjaWHWFepDFBEFKR66+HKVPMC6OCAhg3Dl5+2Wmpksr/\n1v6vwtiNR9/I9Udf7z1D1aZNlqEqPR0GDapyesp5VpXTv78Jhxw5EubPN2PvvQcnnwxff520QkmC\nECsSBuhB/B4762f9HdfdXgXw/POhTnLt3Y7rnwBC8lWVBDZJZWUVPKtSUfdo8bPu4C/98/LymDBh\nQvATjlKqj1LqY6XU3Go+85RSUyu7j1JqHPC01vpt2/D2SqbnVHFM8Bl+eh7D8YXumZmh4X+vvgo/\nmpdKvtAf+GrNVyH907ueztC6Q+mQ1cEhiWrBp59a7WOOMd5yVRCSs2q/x3NWhdOlizFMXX+9NbZ4\nsXnx/P33lZ6WErrXAr/r7zRirBIEITqKi437dDkXXOCcLCmE3bOqR8Nc68CyZQ5IIwjuRmu9UGs9\nWGs9oJpPf611xKToSqnhwEqt9fTwaweONwk7JRuTZF0QBD9wyilw5pmmrTXcfbez8iSZWWtnBduP\nD3mcGRfN8J5HVTkzZ1rtU0+tdnrTepETrKcMmZnw+OMwebI1Nncu9O0Ld9wBBw44J5sgREByVgmC\nEB0ffABnnGHa7drBmjVWmWehxnR4rEOwNPSPuy/l0EdeNAfuvddsHATBx8Q7X0Ig/5TWWn9mH9Na\nzwy05wJjtNaLbMcnAj9rrafESw6vI3swIeWZNw8GDDBtpeC776BnT2dlShLNHmrG9v3GmfS3P//G\nQU0OcliiGqK1qYT366+mP2sWHHdclafsLtxNk4nmfUWDjAbsvW0ve4v28uJ3L9KtWTcGda46jNBT\nPPoo3HpraNXLbt1g0iQTHij4HjfkrJK/NAVBiA57COAFF4ihKg7sKtwVNFRlpGXQtdvR1kHxrBKE\nuKKU6gtQbqhSSmUppToTWunvVmB82KkDxVAlCD6jf3+TgBqM0eNPfzJFZlKcwpLCoKEqTaXRulFr\nhyWqBatWWYaqRo3gyCOrPaVR3Uakq3QA9hXvo6i0iAe/fpBr/nsNg18azLKtKbQ3u/FGY4Q9/nhr\nbMUK41l4xRWwbZtzsglCAPlr04P4PXbWz/o7pvv+/fDOO1bfoRDAVFv7pVuWBtvdm3Uno8fh1sEw\nY1Wq6R4LftYdRP94oJTKAuYBHyulypRSpZg8VCswOakACHhYTVVKDVNKDVdK3QSc74jQgivx8/Po\nO93vvtt4VQHk55M/dqyz8iSBTXs3BdutGrYiPc0Ybjy59vYQwBNPhIyMak9RSlVIsn7vl/cCoNHc\n88U9cRfTUQ49FL74wnhT2fN5PfecOfbqq+R//rlz8rkAT373UwgxVgmCUD3//S/s3m3a3bqZ2Hah\n1oTkq2rRAw45xDq4bJlJtC4IQq3RWhdordO01um2f8s/48PmTg983tJaP6y1/sUhsQVBcJLeveGu\nu6z+Sy/B6tXOyZMENu7ZGGx72qsKQo1VMVSvtidZ7/qPriHHdhftrrVYriMtDcaOhaVL4bzzrPEt\nW+Dii+HOO2Hz5srPF4QEIsYqD5KXl+e0CI7iZ/0d0/311632BRdYbxqTTKqtfQVjVfPmkBNw8ti7\nF9atCx5PNd1jwc+6g+gvCG7Cz8+jL3W/887gC7q84mIYHx4lnFps2L0h2G7TuE2w7bm1LysDu0fQ\nwIFRn2r3rAo3Tu0r3ldr0VxLmzbwxhvw7rsmN22AvK+/NvnaZsxwUDjn8Nx3P8UQY5UgCFWza1fo\nLyipAhg3ftwaZqxSqqJ3lSAIgiAIzpCeDk89ZfWnToXvv3dOngQT4lnV0MOeVUuWGM8gMC8Ce/WK\n+tQGGQ0qPba/eH9tJXM/Z58NP/wAf/iDNbZ5M5x1Flx1FezZ45xsgu8QY5UH8XvsrJ/1d0T3996z\nStn26gU9eiRfhgCptvY/bP4h2D6sxWGmUYmxKtV0jwU/6w6ivyC4CT8/j77V/eij4eyzyS/vT5jg\nnCwJ4tNVn9Lr3724+v+uDo7ZPas8t/b2EMCTT46pKNDCDQsrPZbSnlV2mjSBf/0LPviA/Jwca/yZ\nZ0x47DffOCdbkvHcdz/FEGOVIAhVY68CeOGFzsmRYuwp2sOagjUApKt0ujXrZg4ceqg1STyrBEEQ\nBMF57r7bak+fDgsrN2h4kVNfOpXvN4d6jLVp1KaS2R7AbqyKIQQQ4MSOJ1Z6bFfhrppK5E1OOw3+\n85/QXFYrV5oKgnfdBcXFzskm+AKltXZahhqhlNJelV0QPMO2bdC6NZSUmP7q1dCpk6MipQoLNiyg\n3zP9ADi42cEs+2PAMDVjhnG1BsjLC825IAg+QymF1tqZJHlCpcgeTPAl550Hb71l2mefbXL7pADr\ndq2j3WPtKoy/ef6bDO8x3AGJaklxscn/WR6utmIFdO1a9Tk2Zv06iyEvD4noRVWvTj323bYP5VDu\nVsfQGl55Ba691qQHKad/f/jTn0y1xY4dnZNPSAhu2IOJZ5UgCJXz1luWoeroo8VQFUd+2vpTsH1w\n84OtA/Ywyx9+QBAEQRAEFzBhglVg5r33YO5cR8WJFzNXz4w4bg8D9BTz5lmGqvbtoUuXmE4/vsPx\nrLtxHW+NeKvCsQMlBygoLIiHlN5CKbjkEpOv7aSTrPF58+DSS6FzZ+N9WFrqnIxCSiLGKg/i99hZ\nP+ufdN1dFgKYSmv/0zabsaqZzVjVqRM0CCT33LIlmCA0lXSPFT/rDqK/ILgJPz+PftYdIH/rVhgx\nwhr461+dEyaOVGqsauTRnFXhIYA18ILKrpdN60aRE8zbKyb6gZC179DB/HwfeggyMqzxsjJjzD31\nVFi/PtkiJhRPffdTkKiMVUqpMUqp0UqpiUqpqUqpPhHmDA/MGa6UukkplZuoOYIgJIHffoMvvjDt\ntDQ4/3xn5Ukxlm9bHmyHGKvS0kLzVol3lSAIgiC4g7/+1UrW/cEH8PXXzsoTB+zFXspJV+m0a1Ix\nNNAT1CJflZ2c+jkRx9fvTi1jTMykp8O4cbB8Odx8M3TrZh37/HNTKGjECHjpJRM+KAi1oNqcVUqp\nMcBUrfWuQD8XWAn01VovCowNBAZprcfbzvtYaz3Y1o/LHNu45EsQhETy97+bX0Jg3pR8/LGz8qQY\n/Z7px4INCwD4ctSXnNDxBOvgZZfBiy+a9pNPmhwBguBD3JAvQaiI7MEEX/P738PLL5t2584wfz5k\nZzsrUy3o+o+urNyxMmQsNzuXVdevckiiWrB/v1mLoiLTX7cO2rat0aW27N1Cy4dbVhh/+synuar/\nVbWRMrUoKYF77zWf8N8LZ50Fzz9vcogJnsMNe7BoPKuyyw1VAFrr1cAzwHjbnFuASWHnzVdKDY/T\nnGFRyCkIQjwp34gBXHyxc3KkIFrrUM8qe84qgMMOs9riWSUIgiAI7uGee6BxY9NetQpuucVZeWpJ\npAp3XXJiy/PkGmbPtgxVhxxSY0MVQNP6TSOOL926tMbXTEnq1DH5qj79FA4O28++/z706WOOST4r\noQZUaawKeFFNVEp1Cju0EugbmJON8Yb6JWzOKmBEnOaMrFYTH+H32Fk/65803b//HhYvNu169eB3\nv0vOfashVdZ+w54N7CkyyT+z62XTokGL0AkRjFWpontN8LPuIPoLgpvw8/PoZ93Bpn9uLkyebB2Y\nPNl4V3mUSAnDO2d3Dul7Zu2/+spqn3hirS5VJ61OxPEnvn2CK9+9kvd/er9W1/cKUa/9KafA0qWm\n8MDYsdb4r7+aCI22beGOO6CwMCFyJgrPfPdTlCqNVQEvqlMjGJAGAAsC7c5AJF/w7QQMWnGcIwhC\nMnjlFat9zjnQpIlzsqQgIZUAmx1csQRyuLFKwm0EQRAEwT2MGAFnnGHaWsPttzsrTw0pLCmkqLSo\nwnhuU4+mDLYbq044ofJ5teQ/i/7D8GnD+W3Xbwm7hydRCvr3h0mT4J13QsNjN2+Gv/3NVBdfsKDy\nawiCjWrDALXWn9n7AQ+ogUAgmQ1NgZ0RTt0J5MR5jgDk5eU5LYKj+Fn/pOheVgavvmr1XRQCmCpr\nb68E2L1Z94oTOnSAhg1Ne9s22Lw5ZXSvCX7WHUR/QXATfn4e/aw7hOmvFDz6qJVs/aOP4JtvHJGr\nNkTyqgJoVr9ZSN8Ta19SEroGCTRWARSXFfPdxu8Seg83UOO1P+ccWLQIxoyBlrbcX4sWQb9+0Lev\nSTni8heynvjupzBRVQMMYxowWmu9xjYWKatg+Fi85giCkEhmzYK1a007JweGDHFWnhQk3LOqAmlp\n0KOH1Ze8VYIgCILgLg4+GC66yOrffbdzstSQggORjVWe9KxauBD27jXtDh2gY8eE31I8q6qhY0d4\n5hlYvx7+8Q/IzLSOLVxoihX07Ws8E1eurPw6gm+JyVillBoHPK21fts2vL2S6Tm2Y/GaE8KoUaOY\nMGECEyZM4PHHHw+JKc3Pz0/ZfnnbLfKI/snrh/8MEnK/l18mH8gHGDkS6tb1l/5J6H+zwnrzd3Dz\ngyPPt1VOyX/nHR5//HHXyJ/svp/+fxf9K/YFwU34+bvpZ92hEv3vvNPT3lXhydXTVBondTyJU3JP\nCRn3xNonIATw9K6nB9ttG1dM1r5u97q43MfNxGXt09Phuutg3jzzt4XdaLVoEdx/P/TqFZqGxCV4\n4rufymito/oAw4FhlRwrBZqEjY0DPor3HNu49iuff/650yI4ip/1T7juBw5onZ2ttXHK1XrWrMTe\nL0ZSZe27PNFFMwHNBPR3G7+LPOnvf7fW4aqrUkb3muBn3bX2t/6B3/VR71Xkk5yP7MH8iZ9117oK\n/X//e+v39eDBSZWptsxcNTO4HznpuZP09n3bdVlZWYV5nlj7c8+11uHpp+NyyTU71+iRb4zUFz17\nkd5duFtPnj9ZX/b2ZcGf2eXvXB6X+7iZhKz9jh1a/+UvWmdmWmtmf4ZefdX8TeICPPHdTxBu2IMp\nI0fVKKUGBoT9zD6mtZ4ZaM8FxmitF9mOTwR+1lpPiecc27iORnZBEGLg7bdh2DDT7tTJlGQOT/4t\n1IrCkkIa3N+AMl2GQrH3tr3Uz6hfceKHH8LpgTd6xx1nwjMFwWcopdBay39CLkP2YIJgY/lyOPRQ\nk/MT4Ouv4dhjnZUpSt5Z9g6/m2oqPp/V/Szeu/A9hyWqIVqbvEhbt5r+Dz+EplOIIzOWz+Cs184C\nYHCXwXx0yUcJuY8v2LED3n8f7rsPVqwIPda5M9x7rylkkC1ZgZzADXuwasMAlVJ9wUq0rpTKUkp1\nJrRC363A+LBTB4YZmOI1RxCERPHyy1b74ovFUJUAVu1YRZk2G9oOWR0iG6oAjjjCai9ebG2CBUEQ\nBEFwD927wyWXWP0JExwTJVbsOauy6mU5KEktWbrUMlQ1a2aMhwnioMYHBduSs6qWNG0Kl14K8+eH\nPkNgXphffDG0aAF//KMxbAnJQWvX/LyrNFYppbKAecDHSqkypVQpJn/UCmwV+gIeVlOVUsOUUsOV\nUjcB59uvFa85gsTO+ln/hOq+eTO8Z3ujFv5LwwWkwtpXWwmwnNatoXlz0969m/ypUxMsmXtJhXWv\nDX7XXxDchJ+fRz/rDtXof8cdJi8PwCefGO8qD2CvBtikbpNK57l+7T//3GqfdFLcX7ba9W/XpF2w\nvW6X5KyKC40bw0svwerVcM89xohVTkkJPPUUtGoFgwfDk09ahaCSgOu/+zVh1y5jIHzrLXj4Ybj2\nWjjzTOjTBw46COrWNUW2XECdqg5qrQuIMgm71np6suYIgpAAXnzR/EIA475+yCHOypOiVFsJsByl\nTLLJzwLR11IlRRAEQRDcSbdu5iXfCy+Y/oQJxmjlcuwJ1j3tWfXZZ1b75JMTeqvmDZoSvdeoAAAg\nAElEQVRTN70uRaVFFBQWsLtwN40zGyf0nr6hUydTtODaa+GBB0zEx8aN5lhxsXmmPvkErr8exo0z\nFTjtydqFiqxfD198Ad9/b33WrHFaqqiJqRqg4A7y8vKcFsFR/Kx/wnTXGp591upfeWVi7lNLUmHt\nl29bHmwf3LwKYxUYY1WAPB+HAabCutcGv+svCG7Cz8+jn3WHKPS3e1d9+qknck2GhAFmVm6scvXa\nl5WB3fslAcYqu/5KqZBQwFSvCOjI2ufkwN//bgwtkydDv36hx8vK4MEHTV6rW26BBQvM3zIJwNXf\n/arYuNFUYOzUCS66yBj/ZsyI3lDV2B0GWDFWCYJgSi0vW2bajRrBiBHOypPC2MMAq/SsghBjFYsX\nJ0giQRAEQRBqTdeu8PvfW30P5K6ye1Y1yaw8DNDVfP89bN9u2i1bJiyxup22jdsG2xv3bEz4/XyL\nUjB6NMybZ0L/nnoKjjzSOr5+PTz0kDFm9egBH/k42X1pKSxZAlOmGKeDLl1MyGRxccW5GRnm5zV0\nqDFoPfIITJ8Oc+YYY9a+fSZU0AWIscqDpGTsbAz4Wf+E6T55stW+4AJjsHIhqbD2UeesgpAk6/mz\nZydKJNeTCuteG/yuvyC4CT8/j37WHaLU3+5dNXMmfPVVQmWqisKSQgpLCqucY89ZVVUYoKvX3p6v\nKi8vIcWBwvVv3ah1sJ3qxirXrH27dnDNNeYF+z/+YQyTdpYtg9NOM1XNX34Z9uyJy21do384W7YY\nT6k77oBBg0yer549YcwY+M9/jMGpnCOPhFtvhVdeMcbdPXtMxcz33zc/yxtvhN/9DgYMgA4doH4l\nxZ8coMqcVYIg+IAtW+C116z+6NHOyZLibN+/na37TLWa+nXq0z6rfdUn9OgBaWnG3XndOti7Fxo2\nTIKkgiD4hUAxnUFa67eclkUQPE+XLqa62XPPmf6ECcZolWSWb1vOcf85DoVi1hWzKn05FpJg3aue\nVeHGqiTgJ2OV60hLM95AV11l8ldNnQrvvAO7d5vjb79tPo0awciRcMUVcMwx3q5wrrWJsJg1C2bP\nNga7aHLZ9u4Nf/sbnH66Z/VXOkHxnYlGKaW9KrsguIr774fbbzftAQOMC6iQEGb/Nptjnj0GgF6t\nevHd1d9Vf1KPHqYkM5hfUEcdlUAJBcFdKKXQWntzh+UilFK5wBta6/4Rjg0E3gDK3SoWAJO01lOq\nuJ7swQShMlauhIMPNmE5YJIbn3hiUkU4cvKRzF0/F4Bj2x/L11dErk7Yd1JfFm5cCMDXV3zNse2P\nTZqMcaG01FRO3rnT9JcuTUqBoPu+vI87P78TgFuOu4WJgyYm/J5CFWzebAxY06ZFPt6rl6kyOGiQ\n9176bt9uDG7vvlv93NatjWHuqKNMsazjjjPGvRpS2R5MKTUG0EBXIBeYqLVeGMX1Yj5PPKsEwc8U\nF8O//231//Qn52TxAfZKgNWGAJbTq5dlrFq8WIxVgiBETcBINRZYBfSpYmpfYLvW2h1JKgTBy3Tp\nApddZkJxwHhX2avVJYFyQxXAt799G3FOaVkpS7cuDfarzaPpRn780TJUtWpljIRJQDyrXEbLlsbD\n6vrrTXGD116zcvGC2T+fe65JGn7llebvndxc5+StjLIyY9yeM8fIvGSJ+Y6XV2u3U7cu9O1rjFNH\nH20+7dsn3IMqYHCaWr5fCOwzViql+mqtF8X7PMlZ5UFcGzubJPysf9x1f+cd+O03027ZEs4/P77X\njzNeX/uYkquXE0iyng+wqNL/y1Mar697bfG7/kLN0Vqv1lqP11pPjmKuGKqiwM/Po591hxj1v/12\nqBPwCfj8c/MHqEOkp6VHHP9l5y8cKDkAQKuGrWjWoFml13Dt2n/zjdU+9tiE/aEuOas8wrHHwl13\nGQPPN9+Y/E12T6rdu+Hxx00xhGOPtTyWqvAUTrj+RUWwerUxsg0aBKecYvJLvfqqMVjZDVWDBsFj\njxnddu0y/z76qCmM1aFDskL9su37Ba31auAZYHwizhNjlSD4mX/8w2pffTVkZjoniw9Yvm15sB21\nsap3b6u9YEGcJRIEQRAEIe507my8q8pxsDJguopsrPpxy4/Bdo8Wia+glxDCjVVJolXDVsH2pr2b\nknZfIUqUMp5GzzxjDFfXXGMMVOWUlZnvznPPGY+revVgyBDjkbVtW+Ll0xo+/hgGDjTeXp07w6mn\nhuZfs3P44fDCCyZH1w03GN0c+Jst4A01USnVKezQSoyHdlzPA8lZJQj+ZeFC4z4K5u3fmjXQtm3V\n5wi1oue/e7Jk8xIAZl85m6PaRRHSt2mTiUEH88t0927rba0gpDiSsyp+KKVKtdYV/moN5KzKxuSR\nKMCEC86sKo+E7MEEIQpWr4bu3S3PiCTmrlJ3W/9tNq7bmF3jKzpOjn5vNM8ufBaAPw74I/88459J\nkS2uHHII/BTwWp81y+ToSQJrC9bS4fEOgPGy2vCXDUm5r1ALysqMgejRR43RpypatTLV0c87z1TG\n69nThN3VhD17TEhwfr6JZtm5E7ZuhR07Kj/n/PONh1XPniZ3bdOmNbt3LYm0B1NKnaK1/ixsbBqg\ntdYjq7hWjc6Tv3gEwa888ojVPv98MVQlmNKyUlZsWxHsH9w8Ss+qVq1MDPratXDggHlDFAgNFARB\niAPzgFxbzoiZSqmflVKDtNa/OCiXIHib3FzjXfWsMQjxt78lPdE6RA4DnDRvUtBQBR71rNq2zTJU\nZWRAv35Ju3XLhi2D7c17N1NaVlppuKXgEtLS4LTTzGf9evjhB3j9dXj5ZROKZ2fTJnjiCfMBaNHC\nPMtdupjvWlERFBZW/tm3D/bvNy+Y58yBgoKK8thp29bs9Xv1gksuceT/iWiJYHDKBgZSjYdUTc+T\nMEAP4qnY4QTgZ/3jpvuKFcbVtZwbbojPdROMl9d+7a61FJYWAmaTk10vO/qT+/UzOasA5s2Lt2iu\nx8vrHg/8rr+QWLTWBRGSm74J3OKEPG7Hz8+jn3WHGup/661WNa6PP3bkd3ikMMBJ8yeF9I9ud3SV\n13Dl2s+ebbX79DHe5wkiXP/MOpnk1M8BoEyXsXXf1oTd22lcufa1pW1bE3b37LPGkPTTT3Dvvcbg\nGeZBlQ+wZQs8/DD84Q8werQJK/zzn83z/de/msrqjzwCTz4JkyfDK6/A9OnGg6syQ1V2trnemjWw\nbp35Pj/zjKsNVZUwDRittV6TiPPEs0oQ/Mj99xt3WIDBg+HII52VxwfYKwHGXHGnf3+TDB9g/nyT\nEFIQBCFxrMRUEayUUaNG0alTJwCys7Pp3bs3eXl5gPXHjfRTq1+OW+TxhP5du5KflweffUYewP33\nkx+ovJwsectKysjPzw8e/++n/+W7jd8Fj4/JHUPBTwXQpnL9Fi1a5PjPv0I/kK8qH6B9e/PzTdD9\nIunfulFrtu/fDsD7+e8zeuhoZ38eCeovChT3cYs8ce8HjJ55d9wBd9xB/syZMGMGefPmwYEDLFq+\nHHbtsr5fgX9j6rdtS9748ebl84oV0LAheeeeC+npRp5Vq1zx88jPz+f5558HCP5+rwql1Djgaa31\n29VOruF5krNKEPzG6tXQrRuUlpr+V1/B8cc7K5MP+Me3/+D6D68H4Mo+VzLl7CnRn/zhh3D66aZ9\n5JHwbeQy1IKQakjOqvgRKWdVeelowqr0BEpMj9VaD6jkWrIHE4Ro+f770PD9JUvgsMMSekt7zqq2\njduy4roVZKZnkp6WzicrP2Hwy4MBOKLVESy62qOVhgcOhM8CkUVTp5qKaMm8/YsD+Wy1uf+HF3/I\nkK5Dknp/IUmUlMAHH5icU3v3mhDAunVNgvNIn3r1TJ6rBg3MvwcdZJ7/5FTqiytV7cGUUsMx+aam\nx3jNmM4TzypB8Bt//atlqDr5ZDFUJYkaVQIsx56H4bvvoLjYxMwLgiDUju3AzXZDVYB+wKcOyCMI\nqUfPnnD22fDee6Y/cSK89FLSbr9+93raPtKWBhkNmDNmDrN+nRU8dlz75CQkjzslJaEv7o45Juki\ntG7UOtiWioApTJ06cNZZ5iMAwcIsO+x5qJRSA7XWM+N9nuSs8iDh7r1+w8/611r3BQtCN0gOllKu\nCV5e+5+22cIAo02uXk6LFuS3CpRJLiw0SSF9hJfXPR74Xf9kopTKCrz1S0UqvB3VWhcAO5VSWcFJ\nSnXGJD19IImyeQY/P49+1h1qqf9tt1nt116DVatqLU8sFBQWsGHPBsa+P5YZK2YEx4/vEN0LS9et\n/ZIlxssFjOdK+/YJvV0k/Vs3tIxVG/dsTOj9ncR1a59k/K5/OEqpvmAlTA/smzpjS5QeGJumlOoU\ny3mREGOVIPgFreEvf7H6Z5/txSR+nsWes6p7s+6xX6C77RwfJlkXhHiglBqjlBqtlJqolJqqlOoT\nNqU/MFkpVRr4zFVKjXZC1ngQ2AyOKy8PHdD5JqVUk/I5WuspwIjAz2UcMAboF8HbShCEmnLUUSZs\nDYx3+0MPOSLGBz9/wIINCwDITM/k9G6nOyJHrQnkqwLg2GMdEaFVo1bBdiobqwShnMCLrXnAx0qp\nMqVUKcZDewWQY5uag3np1TnG8yre06s5ByRfgiDEyIwZlgtrerp5K3XIIc7K5BP2Fu2l0QONAFOV\nZ9/t+6ibXje2izzwgPVm9uqr4d//jrOUguA+4pmzKpCHaWq5EcaWr6lveTW8gIv6SmC7GGsqR/Zg\nglADPv8cTjnFtOvWNd5VBx2UkFvZc1ZVxojDRjD1vKkJuX/C+f3v4eWXTfvRR01ltiTz4ncvctk7\nlwFw4eEX8urwV5MugyAkEjfkDRXPKkHwAyUlMG6c1b/6ajFUJZGft/8cbHdu2jl2QxWE5q0SzypB\nqAkhScS11quBZ4Dx4RPFUCUIQtzJy4OjjzbtoiJT6j4BRGtIvujwixJy/6Rg96xyIF8VhOasEs8q\nQUgMYqzyIH6PnfWz/jXW/amnYNky027c2CRZ9yBeXXt7vqoahQAC+fv3W53Fi81G1yd4dd3jhd/1\njwcBL6qJ9vwJAVZSTb4EQbDj5+fRz7pDHPRXCm6/3epPmgTbttXumhE4UHKg2jnpKp1Tck+J+pqu\nWvuNG2HlStPOzIQ+4dHc8SdiziqfGKtctfYO4Hf9nUaMVYKQ6vzyS2hiz9tugxYtHBPHj9jzVcVc\nCbCcrCzo1Mm0i4pMGKcgCFER8KI6VWv9S9ihAcCCsLF+SqlhSqmBgfxOif9LSBAEf3DmmaaMPcC+\nffCvf8XlssWlxewtMgnH9xXvq3b+4S0Pp3Fm47jcO+l89ZXVPuooY7ByAL8YqwTBSSRnlSCkMlrD\nkCHwySemf/jhMH++yZUgJI3fv/17Xl5scitMGjqJsf3G1uxC558Pb75p2s88A2PGxElCQXAH+fn5\nIW8x77777oTlS1BKZWPlrFoTGMsCcstzWAXGfgYGRTB0+RbZgwlCLXjlFbjkEtNu0QLWrIH69Wt8\nua37ttL76d5s27+N/7vo/+jerDvtH6u6Ot6o3qN47pznanxPR/nTn+Cf/zTt22+H++5zRIzSslIy\n78ukVJcCcOD2A2TWccZwJgiJQHJWCYKQWJ591jJUpaWZvhiqkk5cPKsA+ve32t9+WwuJBMGd5OXl\nMWHChOAnwUwDRpcbqgC01gV2Q1WAN4FbEi2MIAg+YcQIaB8wJm3ZAi+8UKvLPTnnSdbtXseBkgMM\neXlItZ5V6SqdPx+d/ITkcePLL632CSc4JkZ6WjotG7YM9ldsX+GYLIKQqoixyoP4PXbWz/rHpPuK\nFXD99Vb/+uvhyCPjLlMy8eLaa63jk7MqP9+4u5cze3YtJfMOXlz3eOJ3/e0opfoopT5WSs2t5jNP\nKVVpmSul1Djgaa3121HcdiXQv9pZgi/w8/PoZ90hjvpnZIRWr3vkESgtrfHlftjyQ7BdUlZSqbHq\nsBaH8eHFHzJ/7Hx6teoV0z1cs/Y7d5q8nWBewh57bFJuW5n+R7Wz9mWT5k1KiizJxjVr7xB+199p\nxFglCKlIcbFxMd8X2LAceij87W/OyuRTNu/dzK5CU1iscd3GITkOYmbAAEhPN+0ff4SCgjhIKAje\nQWu9UGs9WGs9oJpPf631yEjXUEoNB1ZqraeHjecqpcqUUk2SoowgCP5l9GjIzjbtn3+Gd9+N+RJF\npUUMfmkwb/74Zsj4qh2rIs5v3qA5Q7oO4YjWR8R8L9fwv/+ZFBdgEqs3djbv1rUDrg22n1v0XFTJ\n7QVBiB4xVnmQvLw8p0VwFD/rH7Xut90Gc+aYdkaGyY9Qi3wIbsGLa2/3qjq4+cEoVbPQ77y8PGjY\n0ErMqrVvQgG9uO7xxO/6xxOl1EBgh91QFRgD2A7crLXeFXZaP+DTJIkouBw/P49+1h3irH/jxvCH\nP1j9v/895ku8svgVPln1SYXxr9Z8FWE2NGvQLOZ7lOOatbcnV09iCGBl+g/MHUibRm0A2Fu8l3W7\n1iVNpmThmrV3CL/r7zRirBKEVOPtt+Hhh63+ffclpayvEBl7vqqahgCGcMwxVttHoYCCUFuUUn0B\ntNafBfpZSqnOQPl4AbAzkGS9/JzOwEDggeRLLAhCSnPddVYe0dmzY34BNXf93Ijjn//yecTxnHo5\nMV3flbgkX1U5SimaN2ge7O8u2u2gNIKQeoixyoP4PXbWz/pXq/vPP8OoUVZ/6FC46aZEipRUvLj2\ny7ctD7Zrk1w9qLvdWPXNNzW+npfw4rrHE7/rHw8CBqh5wMeBUL9SjCfVCiD4F5zWegowQik1OpDX\nagzQL4K3leBT/Pw8+ll3SID+bdrARRdZ/fIKd1HSrH5kT6nvNn0XeX4tPKtcsfb798Ncm4Euicaq\nqvRvnGmFIu4uDDVWfbXmK2748Aa+3/R9okRLOK5Yewfxu/5OU8dpAQRBiBN798J558GuwN9UnTrB\niy+aBJSCY4SEAdamEmA5Rx9ttWfPhrIyWWNBqIaA11RUD4rWenKCxREEQTBcdx08/7xpT5sGjz0G\nLVpEdWpxWXFMt6rMuOUZ5swxOVkBDjkk6p9Tomlc12assnlW7Svex7lTz2X7/u3MXD2T7//gXYOV\nIDhFrf/CCbjRD4+HMEJ0+D121s/6V6p7WRlceil8F3ibVrcuvPkmNG2aNNmSgRfXPjxnVU0J6t6l\nCzQPuJzv3AnLl1d6TqrgxXWPJ37XXxDchJ+fRz/rDgnSv29f6yVUcTG89FLUp27fvz2mW2XXy45p\nvh1XrL2DIYBV6V+ZZ9WSzUuCa7Rk8xJ27N+RMPkSiSvW3kH8rr/TRGWsClTImVfJ4f7AZKVUaeAz\nVyk1OsI1hgfc6ocrpW5SSuXWZI4gCBG4/XaYbits9eST0K+fc/IIABSXFodU5emW0632F1XKl6GA\ngiAIgpCSjBljtadMsardVUMkY1W/NpXv/WpjrHIFDiVXr47KPKt+3PJjyLwftvyQNJkEIVWo0lgV\nMFI9AAwCqsrQ3BdoqrVOD5SMnhJ2nYFAf631FK31W1rrh4FJsc4RDH6PnfWz/hF1f+45mDjR6t9w\nQ+jGJ4Xw2tqv3rmakrISANo1aUfDug1rfK0Q3Y891mrPmlXja3oFr617vPG7/oLgJvz8PPpZd0ig\n/iNGQKNGpr10adQvoXYcqOipM2/sPC7vfXnE+Vn1siKOR4Pja19SEvpzOfHEpN6+ypxVdSv3rLIT\n3vcKjq+9w/hdf6ep0liltV6ttR4fTf6GapKP3kJFw9P8sPDByuYMq+7eguBbvvgCrrrK6p95Zmgl\nQMFR7JUA45Kvqpzjj7faX0UuUS0IgiAIggdo1AguuMDqT5lS+Vwb4Z5Vd5xwBwBPnfEU1/S/psL8\nrMyaG6scZ9Ei2LPHtNu3h44dnZXHRkgYYFHlxqofNotnlSDESsKz8gYq8AzSWv8SdmgVMCIwJ7uK\nOSMTLaPX8HvsrJ/1D9F96VIYNsxKNtmrF7z2GqSnOyJbMvDa2tvzVXVv1r1W1wrRfcAAyMw07RUr\nYOPGWl3b7Xht3eON3/UXBDfh5+fRz7pDgvUfbcugMnWqVSynCuw5kMb0HcO448YBUD+jPk+d+RS9\nW/cOme/pnFUO5quCanJWRetZtcWbnlWOr73D+F1/p4mXsaqfUmqYUmpgINeUPWSwMxAp+Ho7Jnww\n2jmCIJTz228wZAhsD7xVa9UK3n8fGjeu+jwhqSzfZiU/j6tnVWYmHHWU1RfvKkEQBEHwLkceCYcf\nbtr79hmDVTXYPaseHPQgTTKbhByvm143pF+bMEDHcWm+KojsWbW3aC/rdq8Lmbd0y9KkyiUIqUA8\njFXzgJVa6+la65mBXFNvKKU6BY7nADsjnLczcAygaRRzhAB+j531s/75+fmwYwecdhqsXWsGGzY0\nhqoOHRyVLRl4be3jVQkQIuhu36yluLHKa+seb/yuvyC4CT8/j37WHRKsv1Jw5ZVWf3LVGViKS4uD\nhhGFimiIqmCsqkUYoKNrr3Vofs4k56uCGHJWBdYkUvL7TXs3UVRaFHfZEo089/lOi+Bram2s0loX\naK0XhQ2/iclBVU4kv9PwsWjmCIK/KSyEs86CHwJx73XqmCqAAwY4K5cQEXvOqtqGAVbAbqyyu8cL\ngiAIguA9LrkE6gYMTHPnmk8l7DxgveNvWr8paarin3RluiykX69OvfjImWyWLYOtW027WTM49FBn\n5QkjxLMqEAYYKfk9wIbdG5IikyCkCnUSdN2VwNhAu6Jp2ZBjOxbNnAqMGjWKTp06AZCdnU3v3r2D\ncaXlVtBU7Ofl5blKHtE/Sf3SUvL+9S/4+mvMUch74QUYPNgd8kk/pL+nZA+b9m4CIENlsHrRajqf\n3LlW1y8nPz8fSkrIS0uDsjLyv/sOZswgb+hQ1+gfz375mFvkEf2Tr78guAU/fy/9rDskQf/mzWHk\nSHjpJdP/5z/hxRcjTrV77jSt1zTinP3F+0P6Sqkai+bo2ttfyB1/vPFCSzJV6R/uWXXfl/dx5+d3\nRpy7bvc6Oma7Jzl8NMhzn+e0CL5GaR0pVVSEiUqVaq3Tw8ZyMYapbHs1QKXUGGCs1npA+blA07A5\n4zBJ1YdEOyfs3jpa2QXB82gNY8bAs89aY489Bjfc4JxMQpXMXTeXI6ccCcBhLQ5jyTUJSKzZvz/M\nn2/aM2aYapCCkEIopdBaJ/8vE6FKZA8mCAli7lyTvwqMl9Wvv0KrVuwt2kvDug2D02b/Nptjnj0G\ngAFtBzBnzJwKlzr0qUNZtnVZsK//6tFn9pJL4JVXTPvhh/l/9s47PIqq++OfSe+d3juCdBBR1EgR\nGyDVRpGi/uwFUNHXV6yIBX1RbCCgoFIUrHQwgiAdpNeE0AnpCemb+f0x2TLb0rbMZu/neXi4c+fu\nzD2Znd27Z875HiZNcu98zNhxfgc95yo6ou1qtePwlcM2xy4dvpQR7Ue4amoCQbXQwhrMp5qvTwde\nMHUwldENWG+yvQdFRN2UWGBZJccIELmzXmn/yy/D118bIqp44QWvdFR50rV3pF4V2LDdNBXQg/42\nlcWTrrsz8Hb7BQIt4c33ozfbDi6yv0cPuP56pV1UhPzllwxePJioGVFMXjsZvZM4NS/V8JLo4IpF\nVlUHt157U11ON+hVgX37TSOr7DmqAObuncuUtVNIykhy1NScjrjvE9w9Ba+mMs4qC6+aLMtZQKYk\nSQbFPkmSmgN9gekmQ18Cppq9vK8sy3MrOUYg8D5mzoR33zVujx2r3hZoEtNKgK1jWjvnJH37Gtvr\n1jnnHAKBQCAQCFzHU08Zmkk/fMavx36lpLSED//5kJn/zFT6TZwdjSOsF9jJL3Gcs8ptJCcr0WWg\nFBTq0sX+eDdgqllVHmtPreWDfz7g6dVPO3FGAkHNwW4aYJkT6hGgBzAMRTh9J/CVlbQ/GaWqXwww\n3TzaSpKkofom0Az4UZbl05UdYzJWhKALaj7ffqs4p/TcfbciqO7v7745CSrEvT/ey9JDSwGYP3g+\nD3V+yPEnycmBmBgoKVG2L1+G2rUdfx6BwE1oIQRdYIlYgwkETqSoCBo2hCtX2FUfejxi3NUmtg1H\nnzzKM6ueYdaOWQBM7zudl3q/ZHGYiOkRhup04KFpgIsWwejRSrtfP00+mMsqyCJqhu2aYI0iGnE2\n+6xFv0deD4FXoYU1mF2B9bLIqffLO4gsy/brqypjljtijEDgNfz+O4wfb9zu3RuWLhWOKg/BtBJg\nm9jqpwFaJTxcSRfQl3TeuBHuu8855xIIBAKBQOB8AgJg5EiYPZucAPWu5KxkZFnmVMYpQ1/LmJZW\nD1MjIqtMUwBNpQ80RFhAmN3919a+1rqzSparJXovEHgD1dWsErgBb8+d9Qr7//4bRowAnU7Z7tgR\nfvuNhO3b3TsvN+Mp175ULlWnAcZWPw3Qpu39+hnbGnzi6Ag85bo7C2+3XyDQEt58P3qz7eBi+x98\nEIBcM2dVQUkBF3IucDL9pKHPlrOqpLTE0JYs1VwqhduuvQb0qsC+/b4+voT4h9jc375We6v9ecV5\n1Z2WSxD3fYK7p+DVCGeVQKA19u9X0v0KCpTtZs1g9WqIsh1iLNAW57LPGZ5oxoXEERsS67yT9e9v\nbK9bp1SOFAgEAoFA4Llcfz00bUpOoOWuhh81VBVxaRHdwuohGkU0MrTbxrV1+BSdzpUrcOSI0vb3\nh5493TsfO0QF2V6jd63XFT8fy2SmK3lXnDklgaBGIJxVHkh8fLy7p+BWarT9iYkwYABkZSnbderA\n2rVQrx5Qw22vAJ5iv2mpaEctEG3a3qOHkg4IcPYsnDxpfZwH4ynX3Vl4u/0CgZbw5vvRm20HF9sv\nSfDAAxaRVebUCa1jU+D7h2E/4Cv54iv5snj44mpNxy3XXi9xANC9OwQHu34OZZRnf+1Q23qhdcPq\nWo2uMq3oqGXEfR/v7il4NcJZJRBohbQ0uOMOuHRJ2Y6IUCKqWloP7xZoF1Nnlck2xysAACAASURB\nVNP0qvT4+4PpF2kNTQUUCAQCgcCrePBBC80qc1rEWI+qArix8Y0kPZPEuefP0bFORwdPzgV4gF6V\nnjqhdWzuiwqKomu9rhb9V66KyCqBoDyEs8oD8fbc2Rppf2EhDBkCx8t0jgID4ddfoXNn1bAaaXsl\n8BT7TcXVHRVZZdd201TA9esdcj4t4SnX3Vl4u/0CgZbw5vvRm20HN9jfrh05LRra3C0hMbHLRLuH\naBTZiLphdas9Fbdc+7/+MrbdqFcF5dtfJ8y+s6pD7Q4W/Z4SWSXu+wR3T8GrsVsNUCAQuABZVqr+\nmT5BWrQIbrnFfXMSVIujaY5PA7SLqcj6xo2KML+vr/PPKxAIBAKBwGnk9ugEGecs+vc+upd6YfXs\nOkk8mtRU2LtXafv6KhWxNYy9yKro4Gja1Wpn0S80qwSC8pFkDxXjlSRJ9tS5CwQqXn0V3nrLuP3e\nezBlivvmI6g2DWY24ELOBQCOP3mcVrGtnHtCWYaGDeGCck62bdO0EKlAUFEkSUKWZVHbW2OINZhA\n4Boe/WUiX+372qK/5NUSfH1q8EOpJUvgvvuUdq9esHWre+dTDh9u/ZDJ6yZb3af7r45SuZTr5lzH\n3kt7Df1Te0/lnb7vuGqKAkGl0cIaTKQBCgTuZN48taPq0UdhsvUvO4FnkFOYY3BU+fv40yy6mfNP\nKknqVMBVq5x/ToFAIBAIBE4lpyTPom9EuxE121EFav1N0/WNRrGVajmm0xh8JB/8fPzY8fAOJvcy\nrvGFZpVAUD7CWeWBeHvubI2xf/16xTml5/bb4dNPFceDDWqM7VXEE+w3LSfdMqal1XLFVaFc2++4\nw9j+4w+HnFMreMJ1dybebr9AoCW8+X70ZtvBPfbnFuUa2i/+De+vhdlNHnP5PFxquyxrzllVWc2q\nBYMXkPRMEgsGLzD0+fn4cX3D6w3bqflCs8oT8Hb73Y1wVgkE7uDQIRg2DEpKlO1OnWDpUvATMnKe\njjPE1SvEgAFGnapdu4xVJQUCgUAgEHgkOUU5hvZtp2DyVqg19wc3zsgFnDwJZ84o7fBwj5A1MNes\nqhNWh6ZRTZHMHkDHhcQZ2iKySiAoH+Gs8kDiTcvUeyEeb/+lS3DnnZCdrWzXrw+//658IZeDx9te\nTTzB/qOpzhFXL9f2qCi48Ubj9urVDju3u/GE6+5MvN1+gUBLePP96M22g3vsN42sCisqayxaBOnp\nLp2HS203jaqKjwd/f9ed2wbl2W8eWVU7tLbVcTHBMYZ2RkFGteflCsR9H+/uKXg1Hh3GUVJaQnJm\nMifTT3I2+yzp+elk5GeQV5yHv68/Ab4BhPiHUC+sHvXC69EgvAHNopsRERjh7qkLvJXcXLjrLuMT\no7AwJWWroe3SxALPwrQSYJvYNq49+V13waZNSvuPP+Chh1x7foFAIBAIBA4jp9AYWRXerC2cPwr5\n+TB/Pkya5MaZORGNpQBWhNjgWNV2dFC01XGRQZGGdlZBllPnJBDUBDzaWRX8djAlpSWVfl10UDTN\nopvRNKopTSObGtrNopT/QwNCnTBbx5GQkKBJL6+uVMf5nPMkZyZzOvM057LPIUkSQX5BBPsF0yCi\nAc2jm9MqphX+vlV/SqJV+8ulpESpbLJnj7Lt46NUO+ncucKH8FjbHYQn2O+sNMAK2X7XXfDii0p7\n7VooLtbEE8nq4gnX3Zl4u/0CgZbw5vvRm20H99hvmgYYPmo8/P2CsjFnDjz/vF2dU0fiMttLSmDj\nRuO2RpxV5dnv6+PLTY1vYvOZzTSLakajyEZWx0UGGp1V2YXZjp6mUxD3vXfb72482llVFUcVKGGX\nGRcz2HNxj9X9tUJqUTu0NuGB4YQHhBv+D/EPIdA3kEC/QIL8gircDvILItAv0GrbXjWPUrmUgpIC\nCkoKyC/OJ78kn/zifI5kH0E6LVFQUkCgXyBhAWGqeYYGhOIjVT3Ds1QupUhXRLGuWPm/tNiwnVGQ\nweXcy1y+epmzWWc5nXWa05mnSc5M5mz22QpdkyC/ILrU7cItTW6hf4v+3NjoRgL9Aqs8X49AluGp\np9TC159/rqQDCmoMulIdx9OOG7bbxLk4sqpdO2jSBJKTlTTTTZugb1/XzkEgEAgEAoFDUKUBjngQ\nJr+hROkfO6Y8/OzWzY2zcwI7dxplMho2hDYuXkdVg8XDF7P8yHJub3m7zeI6YQFhhnZOUQ66Ul3N\nr+woEFQDSZZld8+hSkiSJDMN6ofXp0V0C5pFNyMuOI7o4GhC/UMpKS2hSFdEdmE2F3MvcjH3Iuey\nz3E68zQFJQXunr4BX8lX5dgCDE6pQl1hlY8b4h9CWEAYYQFhhPiHICFRKpciI6Mr1VFcWqxyRpm2\nS+VSR5lXIYL9grm5yc30a96P+KbxdK7b2WEV1DTDjBnw0kvG7alT4Z133DcfgVNIykii+azmgCK2\neWmyG0TOn3pKqSoJ8PjjMHu26+cgEDgISZKQZdk1oQOCCiNJkuyp60eBwFOQZRm/N/0M6/LiV4vx\ne2g8LFyoDHjuOZg5040zdAJvvAGvvaa0x42DefPcOx8nEDE9whAxl/lipio1UCDQElpYg3m0RyB3\nam6lU/ZK5VJSrqaQlJHE6czTJGWq/0/OTKa4tNhJM7ZEJ+vIK84jrzjPocfVHzPlaopDj1setUNr\nK+mVUU1pHNEYXx9fCkoKyCnM4Uz2GY6mHuVc9jnVa/JL8llzag1rTq0BIDwgnBsa3UDnup3pULsD\n7Wu3p3FkY6KDoi2qalSWwpJC0vLTSM1LJS0vzdDOLMjE38dfSVn0D6ZBeAMaRzamcWTj6qeFLlyo\ndlQ98AC89Vb1jinQJM4SV68Uw4YZnVUrVsAnnygppwKBQCAQCFzOsdRjvLzxZbrV68bLN71c4dfl\nl+QbHFXBfsHKg9wHHzQ6qxYvhvffN1YCrgmsWWNsayQF0NFEBBqdVVmFWcJZ5WZOpZ9i9s7Z9Gve\njztbiYwXreHRzqqqOBF8JB/qhtWlblhdejXqZbFfV6rjYu5FMvIzyC7MJqcoh5zCHLILsykoKaBQ\nV6j8X1KoahfoLPtsji07TmFJITL2n0wG+gYS7B9MsF+w4f+SghJqR9cmyC+IQl0hOYU5hnnmFOVU\n2/ElIRHgG2AQqff38TdshweEUyesDnVC61A/vL7BMdU0qimNIxsT4h9S7vEv515my9ktrE9cz/rE\n9ZxIP6Han1OUo3Je6dGL5fsU+1Avth7hAeFEBEYQ6BeIhGSYOyhf8DlFynXLKcwhPT+d1LxUrhZf\nrfTfo3l0czrX7UyXul24sdGN9GzYs0J2AoqzYNw443Z8vPKUqIrOA2/Pm9a6/abOKkeLq1fY9ptu\nglq14MoVuHgR/vlHXSXQA9H6dXc23m6/QKAlvPl+9Gbboer2T/xtIn+f+ZvlR5ZzXYPr6Ne8HwCJ\nGYl8vedr7mh1B70b97Z4nam4uiF9rG9fqF0bUlKU7/g//4R+/apkT2VwybW/ckVZs4CyTr7tNuee\nrxI40v7IoEjO55wHykTWNe6r8vT7fvu57UQFRdmU5nhg+QPsOL+Dj7Z9xKVJlywqO3q6/Z6ORzur\nnIGvjy8NIxrSMML51dlkWaa4tFjlzAIMjqkgvyCr2lPl3TSlcil5xXnkFuWSW5TL1aKrSJKEhISP\n5IOP5KNyRJk7pZydO10nrA5DrxnK0GuGApCcmcz6xPVsPL2Rv07/ZfgANyevOI9TGacAOJF7wuoY\nZ5CYkUhiRiLLjywHwN/Hnx4NenBz45u5qclN3NjoRutPRdasgXvvBZ1O2e7QAZYvh8Aars/lxRxL\nc464eqXw9YUhQ+Crr5TtH3/0eGeVQCAQCASeyt9n/ja0lx5aanBWPfzbw2xM2sh7W98jdUqqai15\n+MphPtj6gWE7JjhGafj5KcV6Zs1Str/91iXOKpewapWi8Qpwww0QG2t/vIfiiSLrnsq3/37L2J/H\nEuAbwJbxW+hev7vFmB3ndxjaCacTuPfae105RUE5eLRmlafOXWAbWZZJykxix/kdHEw5yIGUA5xI\nO8GZrDNViooyx1fyJTYklriQOGKDjf9HB0ejK9UpKYtFOZzLPseZrDMkZyWXKxrvI/nQo34P+jXv\nR7/m/ejVsBeB23YqT4Ty85VBrVrB5s1Qp47dYwk8m/gF8fyV/BcAKx9YyR2t7nDPRNauhQEDlHaj\nRorguosqBgkEjkQLegkCS8QaTCCoONLrxo+wodcM5aeRP1n0r7h3Bfe0vQeAgpICWsxqwYWcC4b9\nfZr1YcOYDcrGrl3Qo4fSDgiAs2eVaCtPZ8QI5QEbwLvvGqsb1zBuX3S7IXvkjwf+sJl6VlhSSKlc\nSrB/sCunV6O4cd6NbD27FVDSL7NeyrIYY3offnPPN4zpNMZl89M6WliDicgqgaaQJInm0c1pHt1c\n1S/LMpkFmaRcTVGlPGYXZlOkKzKMAZCRCfILMqQJhgeGEx0UTVxIHBGBEZXSvSosKeTwlcPsu7SP\n7ee3s/nMZg5fOawaUyqXsv38draf387bm98m2CeQmxNLuK2zjnuOQvPwxrB+vXBUeQGmkVUurwRo\nyq23QnQ0ZGQoi9idO+G669w3H4FAIBAIBKTlpVntN9Vz3XJmi8pRBagzPrp3V77Td+yAoiL48kt4\n9VWnzNdlFBWp9aoGDnTfXJyMaQSdrciq5MxkeszpQUFJAZvGbaJz3c6uml6N4nLuZUM7uzCbs1ln\naRTZyOZ4LRVhEygI1V0PJCEhwd1TcDmSJBEdHE2buDbkHs/l1ma3MqjNIEZ1HMX4LuMZ32U8E7pO\nYELXCUzsOpFRHUcxuO1gbm12K93rd6dFTAsigyIrLdAe6BdIl3pdGNdlHF/c/QWHHj9EyuQUlo9c\nznPXP0fXel0NOll68ksLWdNUx6QB0OIZ6Px8CK8nzufA5QNU90m0N157U7Rsf2ZBJpdylep/gb6B\nNIls4tDjV8p2f38YPNi4/dNPDp2Lq9HydXcF3m6/I5Ek6WFJkiZLkjRFkqQlkiT1tTJmmCRJE8v+\nnyxJUjN3zFWgTbz5fvRm28Ex9qfl23dWZRdm8+J6y4iihuFm8iTPPGNsf/aZ4uxxIk6/9ps2QU6Z\nRlezZnDNNc49XyVxpP0RARGGdlaBZaQPwOR1k7mSd4WcohxGLhvpsHNXBS3e97Is89Xur/jPxv+Q\nkZ9hc1yDiAaq7YTTCXaPW1hSaNGnRfu9CRFZJRBUklqhtRhyzRCGXDMEgPT8dBJOJ7B+41zWHV/N\nyRi1Q+rfzKP8+9c0pv01jZYxLRnZbiQj2o+gU51O1a5uKNAOx1KNUVWtYls5XfutXIYNgwULlPZP\nPykh9eL9JvBiJEmaAnwpy3J22XYkkCFJUldZlveV9fUFusuyPNXkdWsB7Sj9CgQCjyU9P91q/7ns\nc8iyTL9v+7H74m6L/eY/uhk+HCZPVkTWL12CZcuUSoGeym+/GdsDB9bo9YppZFVWoXVnlamOknkh\nKgFsPrOZR39/FIDUvFS+uPsLq+PMnYG2/t56CnWWziqBexGRVR6It1ck0Jr9McExDN1bwGfPrOXE\nLJmkj+GrzVHcXT+eAN8A1diT6Sd55+936PJlF9p82ob/bPwP+y/vr3DEldZsdzVatt/Z4uqVtr1/\nfwgPV9qnTsHevQ6fk6vQ8nV3Bd5uvwO5F3hEvyHLchaQCJiqE78IfGn2ut2SJA11/vQEnoA334/e\nbDs4xn59GmCpXKrqP5N1hsSMRHZe2Gn1dRaFnwIC4PHHjdv/+59RnNwJOPXay7LaWXX33c47VxVx\npP0RgcbIKltpgBWuOu4CtHjff7TtI0P7y93mX9lGzJ1TphU2rWEtDVCL9nsTwlklEFSXr76CUaMM\nVf+axrbg4bl7+e3hP0mdksqS4Uu4t/29xrLDZZxIP8Hbm9+m0xedaDu7La9ufJWd53eiK9W5wwpB\nNTmaetTQbhvrpkqApgQGqlMBv/vOfXMRCLTBcOArs77mwG4wRFr1k2X5tNmYRBRHl0AgEFQafx9/\nQ1sfuaHXW9WTmJFoEIK2RoPwBpadjz5qrDC9cyds21b9ybqDI0cgKUlph4XBLbe4dz5OxrQaoK00\nwGA/Iapuj4o688z/vubOQfNgAaFZpT2Es8oD8fbcWc3YL8swbZqyWNB/2LVvr1T9a9oUgPDAcEa2\nH8ni4Yu5MuUKP9/7Mw90eMDCcXU87ThvbX6L6+ZeR+x7sdyz+B4+2f4Juy7sUuVPa8Z2N6Fl+50t\nrl4l20eNMra//x5K7Fe21Cpavu6uwNvtdxSyLJ/WpwACSJL0LvCuLMt/lnU1B6yFJqQDXV0wRYEH\n4M33ozfbDlW337yaW05hjoWz6nzOeTYkbTBs+0jqn2gWkVUAtWrBAw8Yt1991WnRVU699qZRVQMG\nKFFjGsOR9pumAS4/upyZ/8y0cKKYv2fMI/FciRbv+1D/0HLHyLJs8XfNKVJHVplXfM8tyrU4jhbt\n9yaEZpVAUBWKiuCRR+Cbb4x93bvD6tUQG2v1JUF+QQxuO5jBbQeTX5zPmlNrWHpoKb8e+5WrxVcN\n47IKs/jl2C/8cuwXQHkid23ta+lWrxvhueEEnwumQ50OmgoRFsCRK0cM7TaxbqwEaErfvlC3rqJn\ncekSbNigLAQFAi9GkqRhQH/gpCzLH5jsigEyrbwks2yfQCAQVBpzx9SFnAvEBFt+pCw7vMzQfvPW\nN3ll4yuG7Vqhtawf/PnnlbVoaanyHf/9956nXbV0qbFdg6sA6jFNA7yQc4FJaydx5MoRPr3zUx5Y\n/gAXci6o1pSg6DLVDq3t6qlqFnvOqmJdMV/s+oLi0mJ0sjpbxdxZZX5vmqYJrjqxii93f0lP/57E\nE1/9SQuqhFTd6mTuQpIk2VPnLvBwsrIU8eoNxidg9O8PP/4IERG2X2eD/OJ8Vp1cxYqjK9iQuIGL\nuRfLfY2P5EPbuLZ0qdtF+VevC53rdra6+BE4nyJdEaHvhBqe0ORMzbGInnMbzz8PH5Xl9o8aBQsX\nunc+AoEdEhISVE8xX3/9dWRZdorSriRJXYA5QB9ZlrPLxNXXyrLsazZuGPCVLMvWn0R4IWINJhBU\nDFmW8X3DF9kkaPOvh/6iRXQLGn5kJVoKkJC4PPky7T9rz5W8K/Ru3JvN4zbbPsmkSTBzptJu3hyO\nHlWqAnsChw8rWQmgpDReugRRUe6dk5PZkLiBfgv7WfTPHzyfcb+Ms/qaPY/soUu9LoCSjREVFOXV\nzqtXNrzCO3+/Y9iWXzPeX3N2z+GR3x+x9jI61O7AXa3uolVsK8Z2Gkt2YTYx7xl/O41oN4KlI5ZS\nKpfi+4ZxKaD7r84i2tEbkCTJaWuwiiIiqwSCynD2LNx5Jxw8aOwbPx6++KLKC4Ng/2CGXjOUodcM\nRZZljqUdY2PSRv5K/ovdF3ZzKuOUxWtK5VIOXznM4SuH+e6AUYuoSWQTutXvxi1NbqFPsz60r9Ve\nVBx0ASfSThgcVU0im2jHUQUwerTRWbV8OXz+uaIJIRBokPj4eJWY6euvv67aX+ZgmgFEl3MoCTgl\ny7JNrSlZlvdKkrQL+BGl2p/1Ml1KVJWtfQKBQGATnaxTOapAid6wV3WsXng9aoXWYuWDK1l5YiWj\nO462f5Jp05Tqv+npkJioRFeNHVv9ybuCRYuM7YEDa7yjCqBOWB2r/c+tec7may7kXKBLvS6sOLKC\noUuHEuAbwLEnj9E0qqmTZqltzAtYFemKDH22HFUAB1IOcCDlAADfHfiO+YPnq/br0wZT81JV/blF\nuaqIOIHr8D4XYQ3A23Nn3Wb/tm3Qs6faUfXmmzB3rsOeYEmSRNu4tjze43GWDF/CyadPkvFiBhvH\nbOT9/u/Tp3Yf2sa1RcK6Ayo5K5nlR5bzzOpn6PB5B+rPrM9TK59i69mtFa44qGW0+t4/dOWQod2+\ndnunnKPKtnfubHxqmZcHS5Y4bE6uQqvX3VV4u/2myLK8V5bl22RZ7lHOv+6mjipJkrpIkpQuSZL5\navMU0Fd/7LKx5mOiUETWBQKvvh+92Xaomv2muqN6cotyLdKPTGkS2QSA7vW7899b/kuz6Gb2TxIe\nDs+ZODreesvhGpVOufalperiL6Y6mxrDkfa3jWtrVUA9s8BaFrrC+ZzzAAxdqhSmLdIV2XVuORIt\n3vfFpcWqbXt/O1tsTNrI5zs/V/XlFOWQX5zPgn0Lqn18gWOokLNKkqRmZU8fbe0fJknSxLL/J0uS\nZPGp6qgxAoFb+PprpTrJxbIUPX9/+PZb+M9/wMmRS1FBUdza7FYm3zCZV695lSNPHCFnag5bx2/l\n0zs+ZUKXCXSt19XiKQPApdxLfLrzU26cdyMtP2nJJ9s/4WrRVStnEVSHw1cOG9rt4tq5cSZWkCQl\n+k/PnDnum4tA4F6WmAqslxGH2hG1B0Vo3ZRYYBkCgUBQSaxFUOUU5Vh1YulpHNm48id66iljVNLJ\nk/DDD5U/hqv5+284c0Zpx8TAHXe4dz4uws/Hz5DSV1HOZZ+z6DudedpBM/I8zO+fjPyMKh3n3S3v\nqrazC7MZ+eNIXlz/oqpfOKvch11nVZmTajrQD7B6V5VpPHSXZXmuLMs/lYmVfumMMQIF0xQJb8Sl\n9ufnw2OPwcSJiqg6KF+oa9Yo6VUuRm97aEAovRr14onrnmDuoLnsfmQ3uVNz2ffoPmbfOZth1wyz\n0K9KzEjk6dVP0/R/Tfli1xfoSnVWzqBttPred0VkVbVsHzPGWF1n+3bYv98hc3IVWr3ursLb7XcE\nZVFT1labDwMvmGy/BEw1G9NXluW5zpqbwLPw5vvRm22HqtlvLYIqtyjXbhqgPrKqUkRGWkZX6Ry3\nznPKtTdNAbz3Xk1WAdTjaPvbxrat1PgtZ7eQcjVF1eeqdbwW73vz+yqjQHFWFZQUVOu4qXmp/H78\nd4t+4axyH3adVbIsJ8myPFWWZXuP4l/E0qm0u0yQ1BFjhtqbo0DgNPbvVyr8ffGFsa9jR9i1C269\n1X3zsoG/rz+d6nbi8R6P8+PIH0mZnMKGMRuY0GUCUUFGDYDUvFQe++Mxus/pzv7LnuW00CqqyKpa\nGousAoiLgyFDjNsiukrghciyPFWSpCmSJE0v+/9zYJgsyytMxmwAlkiSNFQf5Q2McNukBQKBR1OV\nNMAqRVYBPP204rQCOH5cnWKnNQoK1FUANZwC6AyaR5sH8NpnY9JGGn3USNVnXunOmzC/fzYlbwIg\nOTO5Qq+/veXtVvsv5V6y2p9VkFWJ2QkcSbU0qyRJigT6ybJ82mxXIjCybExUNcfYFEf1VrSYO+xK\nnG5/cTG8/z706KFUKdEzciRs3QrN3JedWhnbfX186dOsD3MHzeX88+f59I5PVQugfZf20WNODz7c\n+qHH6Flp8b1fpCvieNpxw/Y1cdc45TzVtv3hh43tRYsU/SoPQYvX3ZV4u/2ORJbl98sewr0vy/Jj\nsiz/aWXM8rJ/P8my/IGVtYnAi/Hm+9GbbYeK259ZkMnLG17m0x2fWo2gyi3KtZsG2CSqCpFVoKQB\nPvuscXvqVMjNrdqxzHD4tf/jD6W6Nijr6l69HHt8B+No+yd2nWgQ7H7hhhe4tva15b7G3EGjL+zj\nbLR435v/LV5c/yJLDy0lMaN8eckQ/xBaxbSq1PlEZJX7qK7AenPA2q/cdKCrg8cIBM5FlpX0vs6d\n4YUXjGl/ISHw1VeweDGEhrp3jlUkxD+EJ657gqNPHOXNW980CDsW6YqYvG4y9/90P3nFnuO80BIn\n008aFgyNIxsTHhju5hnZ4NZboUULpZ2Zqe0nrgKBQCAQeCgfbv2Q6X9P56lVT7HqxCqL/TmFOc6J\nrAJ4/nmoW1dpX7gA06dX/VjO5KuvjO0HH3S6/qvWqBNWh72P7uX3+3/nzT5vsmzEMiIDIyt1DE+U\n83AURaWW98+UdVMq5KyKCIygQXiDSp1POKvcR3WdVTFY14DILNsHSnlpR4wRlKHF3GFX4nD7dTr4\n/Xe4+Wa4/XZ1NFXXrrBnjxKVooEv0uraHuwfzH9u/g97H91Lt3rdDP1LDi3h5vk3c+XqlWrO0Llo\n8b1/KMVEr6qWc/SqwAG2+/jA448bt//3P8VB6wFo8bq7Em+3XyDQEt58P3qz7WDb/rNZZ7l+7vWG\nddRbm98y7JuyborF+NxiJ2hW6YmIgHdNRKM/+ABOnar68cpw6LU/dgzWrlXaPj4wYYLjju0knPHe\nbx7dnLta30WAbwBt49py/KnjHHvyWIWdVq5KA9TifW8tMvFM1hmWHS6/FkqIfwgNIoSzylOorrMK\nlJLO5fU5aoxA4DhOnoQ331TCjwcOVKqS6AkNVVIB//kH2rRx3xydRJu4NmydsJXHuxudF7sv7qbv\nt31JzUt148w8D83rVZkyfrwxOvDQIdi40b3zEQgEAoHAw5m/bz7bz29n85nNjP15rGpfcWmxxXh7\nmlWjO44mMqhyETaWBxkNPXsq7aIieP316h3P0Xz4obE9cCA0beq2qWiJ2qG1aR3bmu+GVizyvbpi\n4p6Mrfvnr+S/yn1tsF8w9cPrV+p8C/5dwL0/3suWM1sq9TpB9fGr5uvTbfTHmOxz1BgLHnroIZqW\nfcBFRUXRuXNng/dXn19bE7dNc4e1MB+Psb+0lPjQUPjlFxK+/x6Sk4kvO47+iPH+/jBxIgm33gq1\nahFfVplEK/ab/w2qe7zZd83m2trX8sTKJ5CROZBygD7f9OHNlm8S6R/pdnudbb8jtlWVAMsiq5xx\nvn379vFsmRZFtY730EMkzJ6tbP/vf9C3r6b+nta2P/74Y6/5fBf2W24LBFoiISHBa9+b3mC7LMuU\nyqX4+vha7LNl/9JDRqHwVSct0/7MMdes6tOsD4NaD6JLvS7c1Pimqk3cFB8fmDkTbrxR2f7uO3jl\nlWo9fHXYtU9OhvnzjdumGlsaxpXv/Ttb3cm393xLZkEmpXIpz66x/jfKcmw/kQAAIABJREFUyM9A\nlmUkJ2d+aPG+t5dGWx4h/iGVTgNMzEgkMSORHed3kPRMUpXPLag8UkWFlSVJ0smybPHJLUmSDoiW\nZTnbpG8KimD6AEeOMTuv7Cmi0I5Gix8arqRS9peUwKZN8NNPsGIFXLxofVxcnBJ18vjj0KQa4ddO\nxlnXfuG/Cxn781jkMum4jnU6kjA2gejgaIefqzpo8b1/7WfXGhxW2yZso2fDnk45j8NsP3YM2paV\nTJYkOHgQ2mk7IkyL192VeLP9kiQhy7L7c7AFKsQaLN7d03ALNd12WZa5Z8k9rD65mo8HfMxjPR5T\n7bdl/7Clw1h+ZHmFz9OrYS8mdJnAxN8mAjCu8zjmDZ5Xrblb5bbbYN06pX3zzfDnn4ojqwo47No/\n9pixyvZNN8Fff2lCZqM83PXeLywp5N2/32XaX9Os7r/68lVC/EOcOget3PevJ7zO9vPbmdFvBs+t\neY4NSRuqdJxbmtzC7w/8Tvh0+xqzfj5+VkXsL026RJ2wOlU6t6ehhTVY1T6x1OxBEUg3JRZY5oQx\nAsTT5nLt1+mUXPiHH4Z69aBvX/jsM0tHVVAQDBoE338P587BjBmadlSB86796E6j+eaeb5BQPo/2\nX97PPUvu0VyIsdbe+8W6YlUlQGemATrM9jZt4O67lbYsK6mwGkdr193VeLv9guojSVIzSZJ22dk/\nTJKkiWX/T5YkyX1lbzWON9+PNcX2DYkbuP+n+1l7aq2q/0DKAX499itFuiIeX/k4F3PU60Zb9ldW\nzya3SK1ZFegbWKnXV5jp08G3LM5g0yZFq7KKOOTanz0LX39t3H7tNY9wVIH73vuBfoG8Fv8aYzqN\nsbo/Pd9mApLD0MJ9n3A6gWl/TWPVyVXc9f1dKsmSAN+ASh0rNCCUsIAwQzVGa8SFxPHHA39Y3ffv\n5X8rdb6aiCRJD5etGd6VJGmJJEldKvi6Sq81KuOssvVp8hIw1ayvryzLc50wRiCwzeXLyhdzixYw\nYADMnQupZvpLcXEwbhz8/DOkpcEvv8D990OgkxYKHsToTqOZP9gYmr0peROjV4ymVC5146y0zcn0\nkwY9ikYRjbRbCdCc//7X2F6yBI4ccd9cBAKB0yhzUk0H+gFWF5OSJPUFusuyPFeW5Z9kWf4A+NKV\n8xQIyuNM1pkKVfoy53jacaZvns72c9sByCvOY/iy4Sw+uJj7f7qfYp1RU+pE2gnVa9/b8p6hfTT1\nKBsSN1itwFZZh4G5ZlVlf2xXmG7dYKrJT6upUxW9Snfx2mtQXPb3vvFG6NPHfXPxMFrFtLLaP2+v\nEyLyNMgvR38xtM9mn1U5jG5ucrPd114Tdw2jOo4ybD/WXYmYtKdb1SiiEVFB1qWz91/er9oulUv5\nv9//j+vnXs/uC7vtzqUmIEnSw8CSsjXDSyg+nN2SJHUu53VVWmvYdVZJkhQpSdIUSZKWAnKZ52yy\nJEkGV6QsyxuAJZIkDdV7yYARpsdx1BiBgl7Pw1tR2S/LytOi++6DRo3g5ZeVfHhT6teHJ5+EhAS4\ndAnmzYPBgyHEuWGzzsDZ135s57G818+4OPvx8I88t/o5tJLuobX3vkqvqrbzKgGCg23v0QPuvFNp\ne0B0ldauu6vxdvsFVUeW5SRZlqfKsjzHzrAXsVww7pYkaagTp+axePP96C7b91zcQ9OPm9JiVgv+\nOl2+gHLK1RTe3vQ2438Zz7WfXcvLG1/mtkW3kV2Yzc9HfzZEQqXnp3Mi3eigMneG/Xn6T3SlOp74\n4wmumX0N/Rb244OtH1icryrOKlPNqkA/Jz4wffVV6Fz2G7KwEIYPh9zcSh+m2td+925YsMC4/cYb\nHhNVBe6/71vHtrba/1rCa+y6YDNo1iG423aAlSdX2tzXJtZSi+2FG14AQEJi4ZCFzOg3g4e7Psz0\nvtO5q9VdgBI9ZYtGkbadVeaRVXP3zOXL3V+y/fx23t78drm21ACiTGWbZFlOAr7CMujInCqtNewK\nrMuynAW8X86JkWW53ERtR40RCAAoLYVff1UiqXbssNwfG6tUQxk5UqmIUsUcfW9k8g2TOZd9jlk7\nZgEwa8csmkY15blez7l5ZtpDVQkwTtu6Txa89hqsLPvyX7xYibbSa1kJBAKvQJKkSBRt0NNmuxKB\newGxLhO4BFmWKS4tthplNP6X8QZNzdErRnPmuTM2j1NSWsKUdVP49t9vVf3ZhdkcTDnIwv0LVf0H\nLh8wpPCbO6tyi3LZfGYzn+36zND3zb/f8GLvF1Xj0vLSKmChkZyiHNdEVgEEBMCiRcpDqvx8OHpU\nkcn4/nvXOYtkGZ57TvkfFAkOEVVVKWw5qwC2n9tO9/rdXTgb15KUkaSS3DCnSaSlhMu4LuO4v8P9\nBPsF0yZOcWZ9NfAr1ZjwANvZEPYiq/69pHZWTUuYZmivOLrC5jFrAmVpe+9KkrTMbN1wCnjEzuuq\nvNYQv+A9EC3kDruN4mLik5Ph2mthyBBLR9UNN8DChYoG1UcfQa9eNcpR5YprL0kSMwfMZHi74Ya+\nSWsnVUo81Flo7b3vysgqh9t+3XVw++1KW+PRVVq77q7G2+0XOJXmgLXQ2XSgq4vn4hF48/1YFdtX\nHFnB7Ytu59djv9ocU6wr5oZ5NxD7Xqwq3UfPwZSDhvbZ7LM2j/P5zs+p9X4tC0eVnsSMRDYnb7Z5\n7MRMtbMqrziPQynqtLkjqUc4l33OsF1YUsjV4qs252SNgpIC8orzDNtO06zS0769UdQclAdUY8YY\nU/IqQLXe9z/8AJvL/u5+fvB+uXEQmsPd933LmJY295m+H52Bu22356gCaBJl6ayKCY6hc93OBkeV\nNexJd+idVb6SZVXQ42nHDRIp6fnpXMy1UbyrBlIWRdXfitOpB4r+uC2qvNaoOb/iBTWb0lLlyVDr\n1vDQQ2qNncBAeOQR+Pdf2LIFRo1SxNMFVcbXx5eFQxZyQ6MbAJCReXD5g2w7t83NM9MWBy4fMLTb\n13Kus8opvPaasf3DD7Bvn/vmIhAI3EEMYE0dOrNsn0BQZUrlUoYuHcqaU2sYvHiwTUmBn4/+zLZz\n28gtyuWeJfdY7NfJljpR5iw/spzHVz5uV+z8RNoJC8fSgZQDhrnuvbhXte9q8VVOpp+0OM66U+sM\n7YyCjHLnZo20fGM0llPTAPWMGaNEVOlZtAgefVQpSuRMUlLg6aeN2089pazlBZUiLCDM5j57Dtya\nwKXcS3b3W4usigku/+vLbmRVZCMCfAOYfMNki32FukJD8QXzFEx/H3/NSKc4C1mWN5puS5IUBfQF\nXrDzsiqvNYSzygPRQu6wS1m7VhGJHD0aTp8mQd8fHg4vvghJSfDll9Cxoxsn6Rpcee2D/IL45b5f\nDKKOBSUFDPphEKfST7lsDuZo6b1fWFKoetrjUZpVeq6/Hu5ScveRZZg0yRimryG0dN3dgbfbL3A6\n1nIdrOc/CLz6ftTbfjDlIBn55Ttp8ovzVdu2KgxfyLmg2q5KYZev935d7hi9Y8q8T5Zlbl90O1fy\nrqj25RXncTLD0lk1ae0kFuxbQJGuqMrV2Exf59Q0QFNmz1Y7rObPh/79IbP8aoZVft8/+aRS0Aig\ncWN4/fWqHcfNaPm+d7azyt22X7562e7+BhENVNtBfkH4+dhVOgLsO6vqhtUF4N1+7/Lddd/x870/\nqyp+61OGzT+7ikuLmb9vPosPLqaktKTcOdQQlgITZVlOLmdcldYa5V9JgcBdJCbC44/DmjXq/ogI\neOkleOwxiBLraWcSFxLHygdXcv3c60nLT+NK3hXu/P5Oto7fSmxIrLun51aOpB4xPO1tHt3c7lMv\nTfP++7B6tfJ0deNGRcdK78ASCAQ1HVu/tGPs7APgoYceomnTpgBERUXRuXNnQ7qI/seN2K5Z2wCf\n7fyMJ1Y+QZhfGEnPJREXEmdzfPse6oc42YXZBPsHW4xPTlT/xvlh9Q80CG5gM/3I2vlOXDxhdawp\n1pxViRmJLFq1iHWJ6yz2lZSWcDT1qEV/RkEG434ZR2ZBZpW1gn468pOhnZyYTEJBgmuu55dfknDm\nDKxZQzzAn3+S0KMHfPAB8YMH23z9vn37Knc+WSY+IQGWLTM8ZI6fMwfCwzXzfq7MdqXtd8J213pd\n2XNRybTqW7svG1I2AHD88nESEpz3/tlXFnXvLvt3HtmJPfZsV2efFZUUVejvYS8N8MKRC9BMaacc\nS6FzcGc61O5g0Kr9458/uKnJTRbOKoAJv04A4FT6KV65+RVNvH8rup2QkMCCskII+u93e0iSNAX4\nQpbl8sS6qrzWkDw1VE2SJNlT5y4oh5IS+N//lAom+SZP5UJC4PnnYcoUxWElcBlbz26lzzd9KNQp\n1Wt6N+7NutHrCPLz3nTLhf8uZMzPYwC4p+09rLjXg0UVH38cPv9cabduraTUilRagQaQJAlZlj2n\nZJSGkSRJJ8uyhQCHJEk6INq0uk/ZArSfLMsDbBxLrMG8FOl14+340o0vMb3fdJtjkzOTafq/pobt\nRUMWcTT1KGM7j1Vp8Ly35T1eXG8ULf9p5E8MajOIF9a9wMXciyw+uNiwz1fypeS/lhELDWc25HzO\n+SrZtGjIIkatGFX+QDOC/IJYMnwJgxcPrtJ59cwdOJcJXSdU6xiVQqdTdCpNo5yaN4cZM2DYsOoL\nr+fkKFW6V5pUcBs/Hr4uP/pNYJsTaScYtnQYkUGRLBuxjHof1gOU1LOC/xTgI9XMhKkHlz/I9we+\nByDUP9QilTdnag7h09WOJ/m18r+f3t/yPi+sN2auNYxoSEZ+Bg90eMBCjB3glQ2v8M7f7wDw6s2v\n8satb/DkyieZvXO21ePf0uQWEh5KID0/nZLSEmqH1i53TlrD3hpMkqRhgFzRAnlVWWuASAMUaI1T\np+DGG2HyZKOjysdH0aQ6eVL5chWOKpdzQ6MbWDR0ERLK59XfZ/5m7M9j0ZU6WetAw+y/vN/Q7lC7\ngxtn4gCmTTPeV8ePw1tvuXU6AoHApexBET81JRZY5oa5CDyIw6mH7e43FREHGLViFG9tfosxK8ao\n+rMKslTb+y/v55t93/DRto9UjipQ9KvMjyvLskUKX2XYfXG3oR0ZGEmd0DoWY2qF1KJrPbUOcEFJ\nQZXTAE1xiWaVKb6+yvf+118bHVOJiTBihFI1cM2aqksCHDwIffuqHVUDBsAnn1R72t5Oq9hW7H9s\nP5vHbaZuWF2DLlNxaTGXc+2nynkypppV1iIZA3wDDL9PKoN5ZNWzPZ8l66Usq44qULIo9JzKUCRR\nrEVW6TmQcoCjqUdpMLMBDWY2YOvZrZWeo1aRJKkvkGHqqCrrs0eV1hrCWeWB6MP1ahxLl0LXruoK\nfx07wvbtiiZVPeUJQo21vwK40/bh7Ybzfn9jBZelh5by8G8PV0lboqpo6dqbphO4wlnlVNtr14Z3\n3zVuz5ihRFdpBC1dd3fg7fYLHIat1fxLwFSzvr6yLM918nw8Em++H//880/VdnZhto2RCvkl+Vb7\n/zn3j0qE2FwU/WDKQVWanDnJmclMXT+V3vN6M+6XcVzKvUSRrqi86dvE1FnVo0EPQgNCLca0iGlB\nSZ46oisuJI60vDSLsbawpZHjMs0qc8aPVyoDmj4E3r1bqRR8yy2wZImyBk9Ntf++l2VYsUJxdnXq\nBDtN0rYmTYJff1WyIzwYLd73jSIaGdrO1K1yt+3lOav8ffyrdA+Z348BvgH4+lhW/9Pbb+qsSsxI\nRJZlu86q9Px0RiwbQUFJASWlJQxfOtzmWE9CkqSuYBRalyQpUpKk5phU9SvrWypJUlOTl1ZprSE0\nqwTuJz8fnn0WvjLxZPv7K099pkxR2gJN8Hyv50nOSuaTHcoTsvn75hPkF8TsO2cjVTds3MMwdVZ1\nrFMDxP0ffdRYXrqkBCZMgG3blDLTAoHAI5EkKRJ4BKWstCxJ0hJgJ/CVPhRfluUNZQvLoSgOrWbA\nCHfNWaBd8nTqiCZ7P9TAMrLKlKvFVw1aj1mF6siqpMwkDqYctPnaSWsnserkKgC2nN1Cy+iWNsdW\nBNOKXuEB4YT6Wzqr4kLi6BjWkf1Zxqjq1LzUSpWtv67BdWxI2mDRH+jr4sgqU0aOVCKhZsxQop8K\nykTwN29W/ulp2VJ5gOznB+3aQe/eSv/588r6fcsW9XF9fBRJjyefdI0dXkjjyMb8e1l5sHg26yzX\nNbjOzTNyDqZRY53rdlbt8/fxR5IkYkNiy/08Msc8sqo8h5eps2rbuW2ETQ+z+xkHqD7HKvNZoVXK\n1hS7UNYTEmAagvmeSTsGpUJgc+A0VH2tIX6FeCC2BCc9kiNHlC/KgyaLkubNlac53a2LVtYo+yuJ\nu22XJImPb/+YvOI8Q+Wdz3d9jr+PPx/f/rHTHVbutl9PWl6a4UsxyC9Ipb3hLJxuu48PzJmjPBUt\nLFSers6cCS/Yq0TrGrRy3d2Ft9svqDqyLGcB71dgXIU0JwTecz/mFObw5qY3ySzIZFCbQdzV6i6a\nd2oOJj6JU+mnKNIVqX7kybJsWAvY+yF35eoVg7PKPLJKLyJtC72jSs/+lP02Riol7P+v2/8ZtGb0\nRAVFGc5rOs+wgDBC/C2jgEL8Q5g2ZBrnfjvHt/9+azz3ZdvnNqdH/R5WnVVui6zSExsL770HzzwD\nb7+trAVK1FFk8SdPKnIcFaFvX/j4Y7j2WidM1j1o8b53VWSVO20v1hWTlq9EL/pIPrSNa6var0+h\nnT94PgMWKbJHi4YsqtCxrUVWWUNvf/3w+khIyGX+mfIcVTWRsjVFuZl5siwnoaT4mfdXeq0h0gAF\n7kGWlScx3bqpHVUjR8KePTYdVQL34yP58OXdX/JghwcNfbN2zGLirxO9RsPKNKqqXa12VsOGPZI2\nbeC114zb//kP7Nple7xAIBAIaiRf7PqC97e+z5w9cxj4w0A2Jm20iFzQyTpOpBmr8M3dM5foGdGM\nWTGGYl2x3R9zqXmphrZ5ZFVlScpIstrfILwBOx/eSZ9mfSz2metP6QkPCLeaBhjqH4q/rz/f3PMN\nNzS6wdC/79I+q8eJDIy06DP/oa3H5ZpVtmjQAD77TNGufPVVRWuqc+eKZTj4+yvOrj17YN26GuWo\n0iqNIo3OqjNZZ9w4E+eRcjXF0I4LibMQKdc7mPo3789v9//G0uFLue/a+yp07MpGVvn7+lMnzFLP\nTuBchLPKA3F37nC1SU+H4cOVtCO9iHpQkKJLtXgxRFp+wZvi8fZXA63Y7uvjy4J7FjCy/UhD37x9\n87jvp/soLCl02nm1Yv+By65PAXSZ7ZMnG53FxcVKRZ9s+7okzkYr191deLv9AoGW8Jb7MTEjUbW9\n+cxm1u9YbzHuWNoxAErlUl5c/yJZhVks3L+QqRum2o+sMhFENxdYN2VGvxmM7TTW7lxPZ5622v9w\n14dpHt3cqmOqVUwrQ2SXKeGB4TYjq/TXvnFkY0O/LWH38MBwi4rJwf7BDGhhWfTK7ZFV5jRrBm+8\nAatXw969cP48CW++CcuWwYIFMHo0tG8PN90E994LU6fCoUNKNFWXLtWvJqhBtHjfe4NmlaleVd2w\nusQGq4N1/H0UR6okSdzd+m5GtB9R4QfIFY2sMrW/QXgDq2NC/UPx81ES1m5qfJPNc4oqupVHOKsE\nrmX9eiXNaLlJFGD79oqo+iOP1MgvuJqKn48f3w39jnGdxxn6fjz8I4MXD+Zq0VU7r/R8XC2u7lL8\n/RWncXjZl/ipU4oIq/iCFQgEAq8hpyhHtZ2UmURakaWY+Ml0JTXs8JXDqsp4H2/7mPPZ520e3zSy\nyjwN0JT2tdqz4J4FzOg3w+YYWw4jf1/lh2x0cDQNIxqq9oX6h9I6trXFa2ylAZrqWDWOaGyx35xO\ndToRFRSl6gv0DeSTOz6hb7O+Fv2aplYtRaNq+HAYOxa+/VbJiti0SVkvvPMOtGrl7ll6HaaRVWez\n7Durfj/+O3d8dwfLDnlGkVdZlknPT+fyVaNeVZ3QOhb3pk6uekZHZSOrAIvPET0PdHiAnQ/vZNmI\nZcwbPM/m6+191gmsI5xVHogW86bL5dgxGDYM+veHc+eM/Y8/rlQN6VDxH/weab+D0Jrtfj5+zB00\nl2d7PmvoW3NqDQMWDXDKB7JW7DcN+3dVZJVLbW/RQl3w4Kef4M03XXd+M7Ry3d2Ft9svEGgJb7kf\nzSv9JWYkEhhn6VTRpwH+feZvVb9O1rH9/Habx79y1SSyyk4aoF7U+N7295Y/aTNMqxXXDaur2hcW\nEEab2DYWr7ElsB7iH2K49qZOAlPmDJyDn48fIf4hfHrnpxbHaRjRkFaxrfj1/l9V/Xqnmpbxlve9\nLbRof0Ujq5IykhixbASrT67moV8eqvQDZVfbLssyty26jdj3Yrnr+7sM/XXD6lpo4+YU5pi/vMJE\nBEaotsvTrALLyKqnr3uaL+76gg9u+4DOdTszvN1wmkc3t4iq1FMTRNZdjXBWCZxHaSn8+ScMHAht\n26qjqWJi4OefYfZsCA523xwF1cZH8mHmgJlMu2WaoW/L2S3EL4hXhe/WFEpKS1SRVV3qdnHjbJzI\nffepq/i89hosXeq++QgEAoHAZVhEVmUkcSHXstrWyQwlsmrzmc0W+3ac32Hz+PrIKl2pzsIxZkrT\nqKYANIlqwiNdHzH0W4uKMkfC+MPWXOsmNCDUurPKThqgnjqh1nVrhl0zjLPPneXipIs0jWrKqYxT\nqv1d6nWxOBZAblFuOZYIBJY0iGhgeI9fzLlIsa7Y6rgp66ZQUKJUecwrzlNVv9QiR1KPsD7RMuVY\nf9+Z3teFuqpLj5g7k32k8t0i5pFV93e4n0e7P6pyfPlIPrSKsR5peDrzNLO2z2Le3nkiJbCCCGeV\nB6K5vGlZhrQ02L4dvv9eyXO/7z6oWxf69IHff1ePHz1aCR8ePLhKp9Oc/S5Eq7ZLksRr8a/x0YCP\nDH3/Xv6X3vN62xQ+rQpasP9o6lHDl36jiEbEhlgUu3AKbrH9o4+Uqj56Ro2ClStdPg0tXHd34u32\nCwRawlvuR/OIhQs5F9h/1rLynT6yaveF3Rb77EV76FP3zJ1iptQPr0+wv/GB5ocDPuSRro/wZI8n\nefNW69G+TSKbAIqWzbguRpkCc2dVWECYzTRAa5FVoQGhhmtfK7SWxX4JicigSOqG1bWI2ADF6Wb6\nY1ivaxMRGEHnup2t2qIlvOV9bwst2h/gG2CIGJSRLQoggBKltPKEet229ezWSp3H1bYfSz1mtV9v\nqzWtuapgHqVVpCuyOk6lWRWhjqzSO9PNseVMf3vz2zyz+hkm/DqB347/VvHJejF+7p6AwIOQZSWF\nb/t2OHxYSe07flwpZZtZgZSvgQMVEcZevZw/V4FbePb6Z4kJjmH8L+PRyTpOZZyi9/zerB21lva1\n27t7eg7BNAXQExaY1cLPT4mmuuEG5X4vLoahQxWHVR/L6koCgUAgqBmYO5FkZE7mnrQYdz7nPHnF\neSq9qoqgd1bZkwzQpwDqCQsI48uBXwKw7tQ6q6+ZO2guB1MO0rVeV+qH1zf01w4xi6zyD6VNnPU0\nwPIiq8wdX6DoYplHZkzuNZkP/vkAgIVDFqr2LRq6iHl759G/eX+H/fgWeB+NIhsZUsvOZJ2hSVQT\n1f684jzyS/JVfVvPVc5Z5Wr0RRvM0TurIgIj7Dq5q0pForTMHdHWPgsAm5FVpo7C+3+6n6sv12yN\nX0cgnFUeiEtzhwsKlFS+336DP/6AM5UsjRoXByNGKOVs21guCqqCFvPGXYUn2D6m0xiigqIYuWwk\nhbpCLuRc4OYFN7PygZX0bNizWsfWgv17L+41tF2ZAug222NilMIIN98MSUlQWAiDBsGqVUolIBeg\nhevuTrzdfoFAS7jrfiyVS9l9YTdt4tpYjdxxNOVpwTQIb8D5HEVAvd+3/WyKnNtCnwZorxKgragF\nUCKdrNE6tjX9mvez6LeWBmgt+sFeGqD+2lv7gWpepQzglZtfISooitaxrenduLdqX+PIxkyLn2bV\nBi3i7d9DWrW/UUQjQ7qttUhG00IGerae3YosyxaRRbYoz/bDVw4T7BdMs+hmFTpeedhyVtUJU9IA\nwwPDwfG+KkpKS6z2m9rfrlY71T5bqYPX1Lqm3PPZq5YqMCLSAAWWXLoEX38N99wDsbFw553w+ef2\nHVUhIYpI+pAhMGUKfPEF7NoFly/DZ585zFEl8AwGtRnE6lGrDWVh0/PT6fttX/5M+tPNM6s+ey+Z\nOKvq1VC9KnMaNoQNG6BBWfjz1atw222WKb4CgUAgcAovb3iZ6+ZeR/vP2htS0Z2JPR2pmOAYbmtx\nm2H7n3P/GNq+ki+RgZHlHv989nlDxS9b2CoTD5Z6M3pqhVim6IH1NMCwgDCrwuvWHGGm54sJjrH4\nkRoTHGPxmqigKF65+RVGtB9h3QiBoJqoRNatVAS05kROz08n5WqKQ86/6sQq2n/WnhazWnDg8oHy\nX1AByksD1P+2cAQTukwAlHt1YOuB5Y5vHdua1+Nfp2u9rqx+cLXNcT3q93DYHL0d4azyQByeO1xc\nDFu2wOuvQ8+eUK8eTJwIv/wCeWZe37AwuPVWmDQJ5syBv/6CCxcgNxf271dE1N97Dx59FLp1Ax/H\nv8W0mDfuKjzJ9vim8fw59k/iQuIAuFp8lYE/DOSfs/+U80rbuNt+WZZVzipXpgG623aaNVMirOqU\nCcsWFCgO7W+/dfqp3W67m/F2+wUCLeGu+3HGlhkAnMs+x7pT69h1YZfTnFalcilXi22np9QPr887\nfd+hU51OFvsigyIt0veskZyVzN9n/rara2WaxmeONYdSWECYSuPKFIvIqjLnk3m6jr00QP2195F8\nDGsbPdacVTUJb/8e0qr9ppUpKxpZBXAi/USFz2HP9sGLFf1hGZnxv463OS63KFdVndMeNiOrQk0i\nqxzERwM+4pt7vmHbhG02ozXN7f/vLf9l9yO7GdBygM3jWksxtkYVjhcvAAAgAElEQVRhSdUF4r0F\n4azyRmRZ0ZyaNUtJ54mNhd69Ydo02GGlckvr1opz6s8/IT0dNm6EDz5QHFo336w4tyoYSirwLrrV\n78bmcZsNT0evFl/lju/uUKXSeRJnss4Y9DWig6INQq5eQ9u2imO7WVmot04HY8fCu+8qnysCgUCg\nMdLy0rhytXIpalrDXDh57M9j6TGnBz3n9nRKRanyqtM1CG9A3bC6/DPB8uFTRGCE3XSg0R1HG9qz\ndsziTJbtqP3KRlbZiqoC62mAYMVZFRhu9djmDizz49V0Z5VAmzSObGxoV8pZlVZxZ5U9ikuNFQit\nRXYBfLrjU8Knh3P93OtVqXaZBZksO7SMy7mXDX1peWk2oy31BY0cea+FB4YzptOYCjuXKkpFKgsC\nnEy31AEUqBHOKg+kSnnTWVmwbBmMG6ek8rRvr+hI/fYb5Jgl/vr6wi23KA6pY8eUfx98APHx4O/v\nCBOqhVbzxl2BJ9reNq4t68esNywiswqzuG3RbRy5cqTSx3K3/eZRVRXN93cE7rbdQIsWisOqQwdj\n39SpymdLoXOeEGnGdjfh7fYLBFXlUMohGn7UkPoz67P93HaHHNMd96N5pb2MggwA9l/ez+6LllX4\nqoOuVMdbm96yO0bvRAr2D2ZUx1GqfRGBETSNbGr1dfFN45nUa5Jhe1PyJrvOqspGVtkSO7a2Ty9q\n3iq2lUW/tciq0IBQ1bU3d4zVdGeVt38PadV+8zTArIIsvt7zNQdTDgLYdNRXxklSUdt1ss6i73z2\neZ5a9RQAOy/s5O8zfxv2DV86nJE/juTWb25FV6q8NjEj0ebx9Q6gd/u+i6/kC8CMfjMqNLfqUNVr\nH9+0/NcdTT1apWN7E8JZVZO5fBk++URxMsXGwsiRsGABXLxoObZxY5gwAX74Aa5cgYQEJZqqtfXS\nmwJBZWgb15Z1o9cRFRQFKE96+i3sR1JGkptnVjncJa6uOerVg02bFKe2nm++gf79IdX6UzyBQCBw\nNU+teoqCkgJKSksY+EP5eiRaxZ5DqlhXbHNfVViwbwHvb33fsN08ujl+Pup6TKZOpJggtZPGVmTV\nEz2e4McRP3Jt7Wvx91EefKZcTWHOnjmGMW3j2to8jzlWI6tCbUdWme/TO6TMRdyD/YLLrQYIls6v\nZlGOEZcWCCqDeRrgU6ueYuJvE+k9rzcZ+RmqyCrT9Nx3/n6HHnN68NPhnxw2F2sC5e9teU+1fSjl\nEKAUVtiQtAGAI6lH+HTHp4z7ZRyL9i8yjO1ar6uhbaqR1yKmBdsnbmfZiGU8d/1zDpu/o5k3aF65\n+lrnss+5aDaei3BWeSB286ZzcmDhQrj9diWC6umnFV0pnZm3Ozoahg1ThNNPnIDTp2HuXLjvPmWf\nhtFq3rgr8GTbO9XtxKoHVxkWmBdyLnDX93fZrQRkjrvtN/3B4Eq9KnC/7RZERcHatTDeRKNg82ZF\n9+5I5aPm7KE5212Mt9svEFSVUxmnDO3KVquzhTvuR3vOKmvRDBUhPT+d3vN602BmA5WW5MTfJqrG\nRQdF0zKmpaqvQYQxPS86WL1mjAiMsFrFb3yX8cSGxOLr42uzyp+5s6peeD2b8/f18SXQN1DVVzvE\ndmRVgG+AalsvAm/uEJMkicggS4H4UP9Q1bU3j6wees1Qm+euCXj795BW7a8TWsfgTE7NS2Xh/oWA\nksWw+uRqlbOqV8NeqtfuurDLEPVkD1PbS+VSm6nH1pxV285vU20fSVXWh/su7VP1P7vmWRbsW8Cs\nHbMMfb0b9WbZiGU82u1RZt0+SzW+W/1uDG83HH9f52f8VPXaN4tuxuXJl9k+0XZUb06RE8oa1jCE\ns6omUFSkpPPdd58ifjxmDKxZY+mg6t4d/vtf2LZNiZ768Uf4v/+Dli2F5pTAJVzf8Hp+vf9Xw6Lx\nSOoRRv440ma5WC0hyzI7L+w0bPdoICp9EBCgOLnfe8/4GZKYCL16KWLsAoFA4EYiAiPcPQWHYKs6\nFpSvL2WLUctHseXsFi7kXGD2ztk2x4UHhnNNnLoMu2lksXn6W0RghNUoI9PIpBYxLayeyzy1ztzB\nZI55KqC9yCqApcOX0qVuFz654xODSHPPBj1pGNEQgP7N+wMYtm3NHyDMP0y13STKyzQsBZrA18fX\nprbbpdxLKie9ubMK4GLuxQqvwXdd2EWLWS2o9X4t/jr9l8V+fSqfKeYPpA9dUSKrTGU1bNEsuhnD\n2w3ni7u/cLimlKsI9g+26ZwH+HL3l2xK3uS6CXkgwlnlgcTHxyul4//4Q3E21aunCKUvWQL5+erB\nN90EX3wBly7Bzp3Gin++vm6ZuyPQat64K6gJtvdp1od5g+YZtteeWsuzq5+t0Gvdaf/Z7LOGUr8R\ngRG0jnVtiqxmr70kwZQpsGIFhJQt5rOy4I474OuvHXIKzdruIrzdfoGgqjjDWeXq+1FXquN05mmb\n+3MKc1TtjUkby60wdSr9FKtOrjJs/3b8N0D5cWtOeEA44zqPw1fyJdQ/lHmD5tGzYU/D/uggdWRV\nZGCk1R9nps6e5lHWqwXqdWgqinkqoD3NKoAR7Uew59E9PHndk4a+QL9A1o9ez+w7Z7NwiBKVUiuk\nloWjLMQ/RHXtJ3SdYGgvHb60UvP2RLz9e0jL9pumAppyLO2YKrLqmlrXWBQUAGwKmuuJj4/nUu4l\n+n7bl9OZp0nLT2PIkiEWenOmUZ7ZhdnM2j7LorLf4SuHAdhzcY99o7BM0XUX1b329r6HzmWf45YF\nt7D/8v5qnaMm41f+EIEmSEuDfftg925Yt07Riykqsj722mvhwQfh/vuhiXjSI9AeD3Z8kGNpx3hz\n05sAzN45m451OvJIt0fcPDPb7DxvjKrqVq9bhSt9eA2DByvC63ffDefPQ0mJUjH01Cl46y3wEX8v\ngfaRJCkS6CfLsuOEPARuQ5/q5UqWHFzC1rNbmXTDJFWlrqpyIeeCquKWOfrIKlmW6b+wP9vPb+eO\nlnew8sGVNl+z7Zw6Nedq0VX+TPqT7MJsi7G+Pr4MbDOQc8+fIzwg3CKayVpkVWhAKC1jWqpEnIP9\ngg1tU+0cU2z96LaFRWSVnWqA9mgT10YVuSFJErVCanE+57yhz9dH7Ui7vuH1/Pt//1JQUsB1Da6r\n0nkFAkcQGxxrtf9o6lGVs6pWSC2e7vm0Repfal5quY7eZYeWqT4fMgoy+HqP+oGkaYTWm3+9yQf/\nfGBxnJSrKVy5eqVikVU1RAcu0DcQfx9/u5/jL6x7gdWjVrtwVp6D+PWgNWQZkpJg+XIlZW/QIEX8\nPC4O+vWDF18kYf36/2fvvuOjqtL/gX9OCp0kJEDoJBQRkRYiUkTpiG2VKhYsS1O+FlQExP0piwqi\nX13ddUVAV11R6a5fRYWAKAJKKCq6ghCSUKWlkFBCyvn9cWdu7szcKUlmMnfmfN6vV17MbTP3yWUm\nZ557znNcE1UtWwIzZgA//wzs2QPMnBm2iSqrjhuvDuEU+7P9n8WYTmP05Ye+eMjrnZZgxm8cAhiM\nhmlIXPtu3YAfftD+tZs3T0ucO/f6rICQiD2AVI/fn4QQE4UQTwghpgshlgkhBjntkgpgsRCi1PaT\nLoSYYPZcZH3OQ7f8UYzc0/vxk72f4PZVt+P17a9j2lf+Kfybmed5IhJ7zZN9Z/bhh6NabZQvDnzh\ncWiPvZewXaksxcD3B2LyZ5Pd7tukXhOkb0132W5WswoAHkx90GG98VqYDbOb3W82Hur5EFrEtEBU\nRBRWjfGeL3buWdWpcSevx/gqoY5rAsD52ndJ7KJMokr1v0NWjt8+eZGzvaf3OrzXG9ZpiHu73Ysm\n9Zo47GecMXD70e14M/1NnDl/Rl+3adMm096dzp9NZbJMf2yWqLJ7/6f38fuZ391ut7NKz6qqXnsh\nhD7s2B1/1VQMR+xZFWwXL2q9Edav177k/fgjkJfn27FXXgkMG6YltK65hj0XKKREiAi8+6d3sff0\nXvx84mdcKr2E0StGY+eknW7/8AbT9qPb9cdXNWO9KreaN9cKrd9+uzZUGQCWLwcOHwb+8x+gUeXu\nfBNVlRBiOoC3pJRnbcuxAHKFEClSSmO11xQAOfb9KHRdKnW8sXfy3EmH4uD+fq3blt2mL6/+bbVf\nntfTVO5Aec+qH444FvE9de6UXqC8pKwEEz6dgM2HNuONG95wSVbZnTh3wmWd2dBAI+dhgPZk1YSU\nCZiRNgPFZcVoUq8JakeX96xyLqT+/Z+/R8/mPSGEQMbDGSgoKjBNFjm7WHLRYfnKxld6PcZX7nqr\nEFmNux6kxvdzpIhE47qNERkRiU33bMLlb5S/B+29r44VHMN1716HiyUX8fzm55E2Pk1/r2bnZ7s8\n//FCk9nlffDE+ie87tOwTkPTiQ5CVUzNGI/DLc9dOleNZxNamN2oblICv/4KvPqqVtMlPl7rMfXi\ni8CmTe4TVTVragXSJ0xA/yVLtC9+e/YAL78MXHutUokqK48bD7Rwi712dG2sHL1Sn9r1YO5B3Pef\n+9zONBKs+MtkmcNsTMEorh5S175ePeCTT4CpU8vXbdsG9OoF7HNfKNidkIo9AFSP34/GAtDHGksp\n8wEcBDDYeUcmqsLD+eLzDstmyZiKcvd+3HNij8NyvRqOBbhzLuQgIycDFZWZ66Vnla1m1bYj2xzW\nG5NMC3csxHs/vYeDuQcx55s5Ffo9GG/OmMVuNgwQ0Aqzf3PvN5iYMhHLRy13GDrftUlXPJj6IFrF\ntsKqMatwdYur9dn1akTW8ClRBZTPLGbnrSB7RZidg8qfxSrHDlg7fl9u8Dat31QfytqhYQdM6F7e\nYdjeq2dj5kY9AXy04CgeX/c4AC12s2TVsYJjLuvc1ctLrJuIpvXcz+7pbGTHkT7vG2j+uPbe6ifa\nbzocOXvEJQmvOr/0rGKNBy9OntRmxlq/Xpvq/Zjrm9tBfDzQvbv2062b9tOhAxDFjnAUftontMc7\nf3oHo1eMBqANo/hn+j8xtedUL0dWn9/P/K6P1W9ctzFaxlSsroaSoqKAv/8daN8emDZNS9QfPAj0\n6QN8+inQt2+wz5DUMwqA863NNgB2muxLYeBCiePwY2+9hKoi92Kuw7J9OnkAOJR/CFe8cQXOFZ/D\n8lHLMbrTaJ+f92Cebz2rnJNVxwuPozu6o6SsBE+sK+/J8P2R710STGYa1GqAGpE18MKgFzzv5zQM\n0Jik692yN3q3dJ2BDADeuPENvAH3sxD6wjjsyN89odizikKFL8kq56G3Des01B/be1Y5l+L4Jusb\nlJSVICoiCtl5rskqs0R6waUC06Rx47qN8bfr/4ZB7zuPvDf3QOoDPu0XKrwlq84Xn8eb6W/iwbVa\nEv+3qb+5DGNXlb+64/hU40EIMVIIMcH27xNCCJfKab7sY3kXLwIbN2p1o1JSgMREreD5u++aJ6ra\nt9d6IKxcCWRlAadPa8mtl17SjuvUySFRZeVx09VB5fjDNfZRV4zCwz0f1penr59uOp49WPEbi6tf\n1ewq/Q5wdQrJay8E8Mgj2kyBtW1DQHJygEGDtM87H4Vk7H6kevz+IqXMMvaYEkLMBzBfSvm10649\nhBAjhBCDbO2Q7tV7puQvF4odk1UnCqves8rd+9F5ivaCogK9l/CsDbNwrlgb5nHPJ/e4HJuRk+Ey\ns5ad8Qthn5Z9XLYXXCrAxZKL+OXkLw7r7Ym5PSf2oKjUsbfD3tN7TV/L7ubLbsYfT/yB7EezHYqh\nm8Xu/MVUoPr+Po67cpz++NFevs0q7KsrGl3hsk7lz2KVYwesHb8vw+Wa13cc/tyobnlJBnuyyjiC\nAADOFZ/DLyd/wZcbvtR7Xxnf3843AwDtczC/KN9lfWytWAxMHojJPRzr4vVP6u+y76grRqFrk65e\nIqo+/rj2vvSsenCtVufvUP4hfLrv0yq/ZrjwZ1cdjzUebEVMU6WUswzr1gEYWpF9LKmkRJulb+NG\nYMMGrQbVRQ9d+OLitC9rQ4cCQ4YAyaGXjyPytwVDFmBj1kb8cvIXXCi5gPFrxuO7+79zuDsdLKxX\nVUV/+pM2zPnmm7WepkVFwJgx2jDmadO0pBYRtEZhoL8UCCFGAhgC4ICU0rkK7A4AyYYaVhuEEAeE\nEIOllFkBPTHyO197VmXnZaNB7QZev1B4knfRsYxDqSxF4aVClMpSbDtc3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p/f8TkiRITp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PiHSOJjQRAsxt8X/qbtrLYkpKolsf5F/Jn35DwerfKoTexJ19I5f+s8l+9cJjoumqtxV7kW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\nYII=\n",
"text": [
"<matplotlib.figure.Figure at 0xc62b898>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tapering the undulator\n",
"linear, quadratic,power-law..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def f1(n, n0, a0, a1, a2):\n",
" '''\n",
" piecewise-quadratic tapering function\n",
" '''\n",
" for i in xrange(1,len(n0)):\n",
" if n < n0[i]:\n",
" return a0 + (n-n0[i-1])*a1[i-1] + (n-n0[i-1])**2 * a2[i-1]\n",
" a0 += (n0[i]-n0[i-1])*a1[i-1] + (n0[i]-n0[i-1])**2 * a2[i-1]\n",
" \n",
" return 1.0\n",
"\n",
"tap_start=3 #number of undulators\n",
"lin_tap=0.01 #taper step\n",
"quad_tap=0.0\n",
"\n",
"n = 60\n",
"n0 = [0,tap_start,60]\n",
"a0 = und.Kx\n",
"a1 = [0,lin_tap*a0]\n",
"a2 = [0,quad_tap]\n",
"\n",
"taper_func1 = lambda n : f1(n, n0, a0, a1, a2)\n",
"\n",
"sase3= taper(sase3, taper_func1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"specify the run_dir - directory into which the experimental results will be saved"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"run_dir = 'gen_stst' #directory to dump data\n",
"run_id=0 # run number (subdirectory 'run_#') for statistical studies\n",
"try:\n",
" os.makedirs(run_dir)\n",
"except:\n",
" pass\n",
"launcher = get_genesis_launcher('genesis') # launcher object to start genesis"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"generate Genesis input object"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"inp = generate_input(up, beam, itdp=False)\n",
"inp.lattice_str = generate_lattice(sase3, unit = up.lw, energy = beam.E) #generate Genesis lattice based on Ocelot lattice object\n",
"inp.beam_file_str = beam_file_str(beamf)\n",
"#inp.beamfile = 'tmp.beam'\n",
"\n",
"inp.runid = run_id\n",
"inp.run_dir = run_dir\n",
"inp.ipseed = 17111+7*run_id # defines shot-noise, changes automatically \n",
"# below other Genesis parameters may be specified, like prad0, dgrid, etc....."
],
"language": "python",
"metadata": {
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"******** FEL Parameters ********\n",
"ex= 1.38895798733e-07\n",
"ey= 2.13452762304e-07\n",
"rxbeam= 1.11421096629e-05 [m]\n",
"rybeam= 1.05416158084e-05 [m]\n",
"rel energy spread deta= 0.000162368283071 [m]\n",
"xlamd= 0.068\n",
"aw0= 6.27371123037\n",
"gamma0= 16634.1159848\n",
"Ip= 4445.33915304 beam peak current [A]\n",
"lambda0= 4.95934970746e-09\n",
"Pb= 3.77854513472e+13 beam power [W]\n",
"N= 153100.938649\n",
"rho= 0.00413668812369\n",
"power= 2577.28852613 equivalent shot noise power [W]\n",
"coupling parameter fc= 0.712953317588\n",
"gain length estimate lg= 0.755241144761\n",
"Rayleigh length estimate zr= 0.297846800745\n",
"\n",
"Ming Xie gain reduction estimates:\n",
"diffraction parameter etad= 2.53566982379\n",
"energy spread parameter etad= 0.0226614550422\n",
"gain length degradation lscale= 0.766336177728\n",
"**************************************\n",
"generating lattice file...\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"now all the genesis input files are created, such as lattice file, beam file, input file."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(inp.input())"
],
"language": "python",
"metadata": {
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" $newrun \n",
" aw0 = 6.27371123037 \n",
" xkx = 0\n",
" xky = 1\n",
" wcoefz = 0 0 0\n",
" xlamd = 0.068\n",
" fbess0 = 0.712953317588\n",
" delaw = 0\n",
" iertyp = 0\n",
" iwityp = 0\n",
" awd = 6.27371123037 \n",
" awx = 0\n",
" awy = 0\n",
" iseed = -1\n",
" npart = 2048\n",
" gamma0 = 16634.1159848\n",
" delgam = 2.70085285285\n",
" rxbeam = 1.11421096629e-05\n",
" rybeam = 1.05416158084e-05\n",
" alphax = 1.32502206016\n",
" alphay = -0.880831166291\n",
" emitx = 1.38895798733e-07\n",
" emity = 2.13452762304e-07\n",
" xbeam = 0.0\n",
" ybeam = 0.0\n",
" pxbeam = 0.0\n",
" pybeam = 0.0\n",
" conditx = 0.0\n",
" condity = 0.0\n",
" bunch = 0.0\n",
" bunchphase = 0.0\n",
" emod = 0.0\n",
" emodphase = 0.0\n",
" xlamds = 4.95934970746e-09\n",
" prad0 = 0\n",
" zrayl = 0.297846800745\n",
" zwaist = 2\n",
" ncar = 151\n",
" lbc = 0\n",
" rmax0 = 9.0\n",
" dgrid = 0.0\n",
" nscr = 0\n",
" nscz = 0\n",
" nptr = 40\n",
" nwig = 98\n",
" zsep = 20\n",
" delz = 4.0\n",
" nsec = 1\n",
" iorb = 0\n",
" zstop = 256.0\n",
" magin = 1\n",
" magout = 0\n",
" quadf = 0\n",
" quadd = 0\n",
" fl = 0\n",
" dl = 0\n",
" drl = 0\n",
" f1st = 0\n",
" qfdx = 0\n",
" qfdy = 0\n",
" solen = 0\n",
" sl = 0\n",
" ildgam = 5\n",
" ildpsi = 7\n",
" ildx = 1\n",
" ildy = 2\n",
" ildpx = 3\n",
" ildpy = 4\n",
" itgaus = 1\n",
" nbins = 4\n",
" igamgaus = 1\n",
" lout = 1 1 1 1 1 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0\n",
" iphsty = 1\n",
" ishsty = 1\n",
" ippart = 0\n",
" ispart = 0\n",
" ipradi = 0\n",
" isradi = 0\n",
" idump = 0\n",
" iotail = 1\n",
" nharm = 1\n",
" curpeak = 4445.33915304\n",
" curlen = 7e-06\n",
" ntail = -500\n",
" nslice = 1000\n",
" itdp = 0\n",
" isntyp = 0\n",
" iall = 0\n",
" itdp = 0\n",
" ipseed = 17111\n",
" iscan = 0\n",
" nscan = 3\n",
" svar = 0.01\n",
" isravg = 1\n",
" isrsig = 1\n",
" cuttail = -1.0\n",
" eloss = 0\n",
" version = 0.1\n",
" ndcut = -1\n",
" idmpfld = 0\n",
" idmppar = 0\n",
" ilog = 0\n",
" ffspec = 0\n",
" convharm = 1\n",
" ibfield = 0.0\n",
" imagl = 0.0\n",
" idril = 0.0\n",
" alignradf = 1\n",
" offsetradf = 0\n",
" multconv = 0\n",
" outputfile ='run.0.gout'\n",
" maginfile ='lattice.inp'\n",
" filetype ='ORIGINAL'\n",
" $end\n",
"\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(inp.lattice_str)"
],
"language": "python",
"metadata": {
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"# header is included\n",
"? VERSION= 1.00 including new format\n",
"? UNITLENGTH= 0.068 :unit length in header\n",
"AW 6.27371123037 73.0 0.0\n",
"AW 6.27371123037 73.0 17.0\n",
"AW 6.27371123037 73.0 17.0\n",
"AW 6.33644834267 73.0 17.0\n",
"AW 6.39918545498 73.0 17.0\n",
"AW 6.46192256728 73.0 17.0\n",
"AW 6.52465967959 73.0 17.0\n",
"AW 6.58739679189 73.0 17.0\n",
"AW 6.65013390419 73.0 17.0\n",
"AW 6.7128710165 73.0 17.0\n",
"AW 6.7756081288 73.0 17.0\n",
"AW 6.8383452411 73.0 17.0\n",
"AW 6.90108235341 73.0 17.0\n",
"AW 6.96381946571 73.0 17.0\n",
"AW 7.02655657802 73.0 17.0\n",
"AW 7.08929369032 73.0 17.0\n",
"AW 7.15203080262 73.0 17.0\n",
"AW 7.21476791493 73.0 17.0\n",
"AW 7.27750502723 73.0 17.0\n",
"AW 7.34024213953 73.0 17.0\n",
"AW 7.40297925184 73.0 17.0\n",
"AW 7.46571636414 73.0 17.0\n",
"AW 7.52845347645 73.0 17.0\n",
"AD 6.27371123037 17.0 73.0\n",
"AD 6.27371123037 17.0 73.0\n",
"AD 6.33644834267 17.0 73.0\n",
"AD 6.39918545498 17.0 73.0\n",
"AD 6.46192256728 17.0 73.0\n",
"AD 6.52465967959 17.0 73.0\n",
"AD 6.58739679189 17.0 73.0\n",
"AD 6.65013390419 17.0 73.0\n",
"AD 6.7128710165 17.0 73.0\n",
"AD 6.7756081288 17.0 73.0\n",
"AD 6.8383452411 17.0 73.0\n",
"AD 6.90108235341 17.0 73.0\n",
"AD 6.96381946571 17.0 73.0\n",
"AD 7.02655657802 17.0 73.0\n",
"AD 7.08929369032 17.0 73.0\n",
"AD 7.15203080262 17.0 73.0\n",
"AD 7.21476791493 17.0 73.0\n",
"AD 7.27750502723 17.0 73.0\n",
"AD 7.34024213953 17.0 73.0\n",
"AD 7.40297925184 17.0 73.0\n",
"AD 7.46571636414 17.0 73.0\n",
"AD 7.52845347645 17.0 73.0\n",
"QF -7.3438282371 6.0 77.0\n",
"QF 7.3438282371 6.0 84.0\n",
"QF -7.3438282371 6.0 84.0\n",
"QF 7.3438282371 6.0 84.0\n",
"QF -7.3438282371 6.0 84.0\n",
"QF 7.3438282371 6.0 84.0\n",
"QF -7.3438282371 6.0 84.0\n",
"QF 7.3438282371 6.0 84.0\n",
"QF -7.3438282371 6.0 84.0\n",
"QF 7.3438282371 6.0 84.0\n",
"QF -7.3438282371 6.0 84.0\n",
"QF 7.3438282371 6.0 84.0\n",
"QF -7.3438282371 6.0 84.0\n",
"QF 7.3438282371 6.0 84.0\n",
"QF -7.3438282371 6.0 84.0\n",
"QF 7.3438282371 6.0 84.0\n",
"QF -7.3438282371 6.0 84.0\n",
"QF 7.3438282371 6.0 84.0\n",
"QF -7.3438282371 6.0 84.0\n",
"QF 7.3438282371 6.0 84.0\n",
"QF -7.3438282371 6.0 84.0\n",
"QF 7.3438282371 6.0 84.0\n",
"QF -7.3438282371 6.0 84.0\n",
"\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(inp.beam_file_str)"
],
"language": "python",
"metadata": {
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"# \n",
"? VERSION = 1.0\n",
"? SIZE =521\n",
"? COLUMNS ZPOS GAMMA0 DELGAM EMITX EMITY BETAX BETAY XBEAM YBEAM PXBEAM PYBEAM ALPHAX ALPHAY CURPEAK ELOSS\n",
"-1.99578155155e-06 16636.8491332 1.86806646647 8.28193026263e-08 7.92424918988e-08 4.80748057486 3.80534285333 0.0 0.0 0.0 0.0 0.599355677268 0.712462798687 442.445184853 -28725.103293\n",
"-1.98809868869e-06 16636.8268689 1.86816506507 8.28118000853e-08 7.9281606531e-08 4.80025726799 3.80890738489 0.0 0.0 0.0 0.0 0.599792994783 0.71370350518 441.588856421 -28709.097391\n",
"-1.98041582583e-06 16636.8061321 1.83201981982 8.28122996212e-08 7.93198709845e-08 4.79181451684 3.81410553011 0.0 0.0 0.0 0.0 0.600111350976 0.715128676868 440.747637886 -28693.811591\n",
"-1.97273296296e-06 16636.7612831 1.89664444444 8.28251950231e-08 7.93520875772e-08 4.78298211531 3.82083584465 0.0 0.0 0.0 0.0 0.600517671324 0.71686418496 440.181218364 -28680.2052469\n",
"-1.9650501001e-06 16636.7647844 1.85702582583 8.28515996559e-08 7.93756996924e-08 4.77444896891 3.82896850927 0.0 0.0 0.0 0.0 0.601054693132 0.718934874804 439.967002789 -28668.1001577\n",
"-1.95736723724e-06 16636.7483645 1.86155975976 8.28872664019e-08 7.93918847538e-08 4.76651939666 3.83811519015 0.0 0.0 0.0 0.0 0.601660559938 0.721276033425 439.829129875 -28658.4091289\n",
"-1.94968437437e-06 16636.7246685 1.87150880881 8.29220801786e-08 7.94047926524e-08 4.75916026699 3.8481263806 0.0 0.0 0.0 0.0 0.602403175093 0.723879798273 440.671519844 -28651.4039871\n",
"-1.94200151151e-06 16636.7112544 1.87304284284 8.2956893151e-08 7.94216966739e-08 4.7524359789 3.85822191896 0.0 0.0 0.0 0.0 0.603209011644 0.726594142735 441.513834285 -28647.5007676\n",
"-1.93431864865e-06 16636.6990682 1.86482972973 8.29976057639e-08 7.94418049028e-08 4.7459407646 3.86836184788 0.0 0.0 0.0 0.0 0.604090142678 0.729324200863 443.207359892 -28646.9003866\n",
"-1.92663578579e-06 16636.6507064 1.85527197197 8.3030375322e-08 7.94696790087e-08 4.73919987419 3.8783353931 0.0 0.0 0.0 0.0 0.605039099788 0.732075426155 445.25345913 -28649.0983448\n",
"-1.91895292292e-06 16636.6382233 1.88051421421 8.30525915509e-08 7.95004882779e-08 4.73216672431 3.88835039864 0.0 0.0 0.0 0.0 0.605995408563 0.734831198835 447.674078975 -28654.6978687\n",
"-1.91127006006e-06 16636.60175 1.82969309309 8.30620999257e-08 7.95326997482e-08 4.72516269911 3.89833643115 0.0 0.0 0.0 0.0 0.607013420705 0.737457087752 450.822975382 -28662.2999406\n",
"-1.9035871972e-06 16636.5867332 1.86025565566 8.30667956012e-08 7.95578764149e-08 4.71783690682 3.90801544271 0.0 0.0 0.0 0.0 0.608076847688 0.739864446218 453.968053735 -28672.9899857\n",
"-1.89590433433e-06 16636.5645093 1.89921611612 8.30697983069e-08 7.95797876404e-08 4.71073034828 3.91697945652 0.0 0.0 0.0 0.0 0.609215806605 0.741867215671 457.888787684 -28684.7933405\n",
"-1.88822147147e-06 16636.5289336 1.88808198198 8.30757988504e-08 7.95967967428e-08 4.70375383424 3.92485526292 0.0 0.0 0.0 0.0 0.610531807194 0.74341967968 461.834244338 -28698.7973176\n",
"-1.88053860861e-06 16636.5123486 1.8577981982 8.30728969241e-08 7.96133032206e-08 4.69734310817 3.93168678173 0.0 0.0 0.0 0.0 0.612070903413 0.744662429195 466.005781459 -28713.2028045\n",
"-1.87285574575e-06 16636.458052 1.85403693694 8.30559127019e-08 7.96310867004e-08 4.69101657126 3.93764425684 0.0 0.0 0.0 0.0 0.613861489074 0.745656067203 470.320772968 -28728.6884188\n",
"-1.86517288288e-06 16636.4482911 1.8767963964 8.30322088964e-08 7.96456945195e-08 4.68382314624 3.94241223332 0.0 0.0 0.0 0.0 0.61571874785 0.746482214087 474.632382132 -28744.2941441\n",
"-1.85749002002e-06 16636.4238726 1.88838998999 8.30054000699e-08 7.96544999771e-08 4.67542226737 3.9458170136 0.0 0.0 0.0 0.0 0.61755409479 0.747190038265 478.913988843 -28759.1999612\n",
"-1.84980715716e-06 16636.3945626 1.89048958959 8.29845194518e-08 7.96576970217e-08 4.6664805074 3.94809554463 0.0 0.0 0.0 0.0 0.619313264698 0.747890904543 483.191015632 -28773.8863186\n",
"-1.84212429429e-06 16636.361523 1.88522132132 8.29619126369e-08 7.96641963655e-08 4.65771719742 3.94982097592 0.0 0.0 0.0 0.0 0.621139496728 0.748694156326 487.189765066 -28786.7927869\n",
"-1.83444143143e-06 16636.3124926 1.87035825826 8.29369046596e-08 7.96800970365e-08 4.64984323107 3.95124761158 0.0 0.0 0.0 0.0 0.623183522781 0.749745307861 491.144263223 -28798.9977261\n",
"-1.82675856857e-06 16636.2997049 1.86484174174 8.29058927405e-08 7.97083072781e-08 4.64304983697 3.95207954618 0.0 0.0 0.0 0.0 0.625509809786 0.750938966326 494.889657639 -28809.7018056\n",
"-1.81907570571e-06 16636.2583624 1.86957627628 8.28669289366e-08 7.97473709892e-08 4.63736583438 3.95237259909 0.0 0.0 0.0 0.0 0.627992048532 0.752266606067 498.41937864 -28819.3928029\n",
"-1.81139284284e-06 16636.2437271 1.86286886887 8.28233161391e-08 7.97929831206e-08 4.63259492019 3.95215657738 0.0 0.0 0.0 0.0 0.630518690365 0.753609139983 501.900711837 -28827.7968906\n",
"-1.80370997998e-06 16636.2219417 1.86295595596 8.27740999026e-08 7.9844600132e-08 4.6283599886 3.95119674125 0.0 0.0 0.0 0.0 0.632985365284 0.754906786955 505.035008154 -28835.1000156\n",
"-1.79602711712e-06 16636.1896353 1.8792036036 8.27367346138e-08 7.9895253077e-08 4.62479842616 3.94969044487 0.0 0.0 0.0 0.0 0.635548266808 0.756253596003 508.164101325 -28841.094447\n",
"-1.78834425425e-06 16636.1713786 1.89573913914 8.27180103587e-08 7.99428736325e-08 4.62153755346 3.94755989646 0.0 0.0 0.0 0.0 0.638211646169 0.757620566487 511.075388036 -28846.1971749\n",
"-1.78066139139e-06 16636.1196193 1.8384008008 8.27121010689e-08 7.99807931336e-08 4.61826882136 3.94485338254 0.0 0.0 0.0 0.0 0.640920564296 0.759045946755 513.916485475 -28850.5992028\n",
"-1.77297852853e-06 16636.114346 1.84494714715 8.27067006123e-08 8.00169058361e-08 4.61543970627 3.94169773188 0.0 0.0 0.0 0.0 0.643654686598 0.760520906376 516.701516067 -28854.2006315\n",
"-1.76529566567e-06 16636.0938574 1.83850540541 8.27098976424e-08 8.00473775289e-08 4.61328153917 3.93846669068 0.0 0.0 0.0 0.0 0.646486651233 0.762043942615 519.396012965 -28857.4975687\n",
"-1.7576128028e-06 16636.06771 1.85152342342 8.27269937594e-08 8.00787885406e-08 4.61252656409 3.93553877944 0.0 0.0 0.0 0.0 0.649581911244 0.763650495503 522.094015734 -28860.3989416\n",
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"1.92247850851e-06 16625.6700219 1.82130680681 5.71832944182e-08 3.29406557561e-08 23.5160387874 13.7357367026 0.0 0.0 0.0 0.0 1.21057978462 -0.883512510369 48.3871380431 -319.104955439\n",
"1.93016137137e-06 16625.6801708 1.84723943944 5.62968954902e-08 3.26832890422e-08 23.7000247684 13.8309920945 0.0 0.0 0.0 0.0 1.23193973998 -0.882893625147 43.2213017148 -284.400975818\n",
"1.93784423423e-06 16625.68974 1.86562432432 5.57241295796e-08 3.25109292793e-08 23.8225086206 13.8916534839 0.0 0.0 0.0 0.0 1.24595452529 -0.882356109563 38.4025160661 -252.998563814\n",
"1.9455270971e-06 16625.6984935 1.8696962963 5.52055959081e-08 3.23543591542e-08 23.9341378276 13.9485221285 0.0 0.0 0.0 0.0 1.25893040665 -0.881887995414 34.0780338278 -223.099296752\n",
"1.95320995996e-06 16625.7065221 1.88058038038 5.47333024613e-08 3.2213700733e-08 24.0348242887 14.0015907696 0.0 0.0 0.0 0.0 1.27078282356 -0.881483691647 29.7946223234 -196.056140933\n",
"1.96089282282e-06 16625.7141337 1.87038318318 5.43058992713e-08 3.20890164775e-08 24.1244476809 14.0508050669 0.0 0.0 0.0 0.0 1.28151767565 -0.881181938333 26.2947694889 -171.103310426\n",
"1.96857568569e-06 16625.7212471 1.84376956957 5.39224153225e-08 3.19799612318e-08 24.2038934541 14.0955058501 0.0 0.0 0.0 0.0 1.29120464656 -0.880962684279 22.7903679003 -148.681589596\n",
"1.97625854855e-06 16625.727554 1.84555155155 5.35839639357e-08 3.18868175951e-08 24.2731536704 14.1357116202 0.0 0.0 0.0 0.0 1.29982303503 -0.880798053268 19.8053638738 -128.495813273\n",
"1.98394141141e-06 16625.7333443 1.84240810811 5.32921262323e-08 3.18079881196e-08 24.3335061636 14.1710317316 0.0 0.0 0.0 0.0 1.30744968008 -0.880722487053 17.066162072 -110.197455137\n",
"1.99162427427e-06 16625.7387269 1.82275485485 5.30411869807e-08 3.17412497273e-08 24.3860778235 14.2021358444 0.0 0.0 0.0 0.0 1.31416114349 -0.880698420828 14.5334873274 -94.2283991465\n",
"1.99930713714e-06 16625.7435023 1.80836766767 5.28278794743e-08 3.16856207259e-08 24.4312998203 14.229112735 0.0 0.0 0.0 0.0 1.31997223317 -0.880741524214 12.4583731221 -79.5507689627\n",
"\n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "heading",
"level": 3,
"metadata": {
"collapsed": true
},
"source": [
"Genesis may be executed with the following command:"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"#g = run(inp,launcher)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"if \"run\" function is placed in a sctipt, the following post-processing code will be executed after the GENESIS simulation is finished\n",
"\n",
"the following two python scripts would start ocelot/genesis several-stage simulation for many independent runs\n",
"\n",
"Possible post-processing between stages:\n",
"* electron beam: propagation through chicane via second-order tracking +CSR (in development)\n",
"* radiation: hard X-ray Self-seeding\n",
"* radiation: soft X-ray Self-seeding (in development)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"exp_dir='/some_directory'\n",
"\n",
"run_number=10\n",
"run_ids = xrange(0,run_number)\n",
"\n",
"start_stage = 1\n",
"stop_stage = 4\n",
"\n",
"# set simulation parameters\n",
"# prepare electron beam file\n",
"\n",
"stage=1\n",
"if start_stage <= stage and stop_stage >= stage:\n",
" for run_id in run_ids: \n",
" run_dir = exp_dir + 'run_' + str(run_id) \n",
" #prepare input, specify parameters\n",
" #inp.ipseed = 17111*(run_id + 1)\n",
" #\n",
" #\n",
" #\n",
" #g = run(inp,launcher)\n",
" print('run #',run_id, ' of stage ',stage)\n",
" \n",
"stage=2\n",
"if start_stage <= stage and stop_stage >= stage:\n",
" for run_id in run_ids: \n",
" run_dir = exp_dir + 'run_' + str(run_id) \n",
" #prepare input based on stage 1 output\n",
" #inp.ipseed = 27222*(run_id + 1)\n",
" #\n",
" #inp.distfile = 'run.'+ str(inp.runid)+'.s1.gout.dist'\n",
" #\n",
" #g = run(inp,launcher)\n",
" print('run #',run_id, ' of stage ',stage)\n",
" \n",
"stage=3\n",
"if start_stage <= stage and stop_stage >= stage:\n",
" for run_id in run_ids: \n",
" run_dir = exp_dir + 'run_' + str(run_id) \n",
" #prepare input based on stage 1 output\n",
" #inp.ipseed = 37333*(run_id + 1)\n",
" #\n",
" #inp.distfile = 'run.'+ str(inp.runid)+'.s1.gout.dist'\n",
" #\n",
" #g = run(inp,launcher)\n",
" print('run #',run_id, ' of stage ',stage)\n",
" \n",
" #stage=n ........."
],
"language": "python",
"metadata": {
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"run # 0 of stage 1\n",
"run # 1 of stage 1\n",
"run # 2 of stage 1\n",
"run # 3 of stage 1\n",
"run # 4 of stage 1\n",
"run # 5 of stage 1\n",
"run # 6 of stage 1\n",
"run # 7 of stage 1\n",
"run # 8 of stage 1\n",
"run # 9 of stage 1\n",
"run # 0 of stage 2\n",
"run # 1 of stage 2\n",
"run # 2 of stage 2\n",
"run # 3 of stage 2\n",
"run # 4 of stage 2\n",
"run # 5 of stage 2\n",
"run # 6 of stage 2\n",
"run # 7 of stage 2\n",
"run # 8 of stage 2\n",
"run # 9 of stage 2\n",
"run # 0 of stage 3\n",
"run # 1 of stage 3\n",
"run # 2 of stage 3\n",
"run # 3 of stage 3\n",
"run # 4 of stage 3\n",
"run # 5 of stage 3\n",
"run # 6 of stage 3\n",
"run # 7 of stage 3\n",
"run # 8 of stage 3\n",
"run # 9 of stage 3\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"next: Tutorial N6: Genesis_postprocessor"
]
}
],
"metadata": {}
}
]
} | gpl-3.0 |
kylemede/DS-ML-sandbox | notebooks/DistributionMetrics.ipynb | 2 | 10489 | {
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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JGlMGvSS1nEEvSS1n0EtSyxn0ktRyBr0ktZxBL0ktZ9BLUssZ9JLUcnMGfZLVSQ4meT3J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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0a56f02f10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"values = np.random.normal(0, 1.0, 100000)\n",
"\n",
"plt.hist(values, 50)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculate the mean, variance, skew and kurtosis of a distribution."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0015159083534063444"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(values)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.9925178213867264"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.var(values)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-0.0027389027509418414"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import scipy.stats as sp\n",
"sp.skew(values)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-0.025480576885773765"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp.kurtosis(values)"
]
},
{
"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": 1
}
| gpl-3.0 |
menpo/menpo-admin | old_migrations/bump_anaconda_key.ipynb | 1 | 2175 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this to work you'll need to have the travis CLI installed and have already run login."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"import subprocess\n",
"from functools import partial\n",
"from utils import appveyor_op, apply_to_all_projects, replace_str\n",
"\n",
"working_dir = '/Users/jab08/_bump_condaci'\n",
"\n",
"new_av_enc_key = 'tsajM8eklrwmWI6OdAEKi3FMqIUXUCHS45rPJZ3bwO25PRWi5onvLdsNMk+8vYRS'\n",
"old_av_env_key = 'mw1Fz5a5C0lT4CXzsOCADoo/Xa9YymZI3yjVZNR8f5GwYrVAOC2YXxyEG6NaSWZY'\n",
"\n",
"new_key_secure = 'NOT_A_CHANCE'\n",
"\n",
"replace_key = partial(replace_str, old_av_env_key, new_av_enc_key)\n",
"appveyor_key_replace = partial(appveyor_op, replace_key)\n",
"\n",
"\n",
"def bump_repo(repo_dir, restart_travis=True):\n",
" os.chdir(repo_dir)\n",
" if appveyor_key_replace(repo_dir):\n",
" print('bumped {}'.format(repo_dir))\n",
" print(subprocess.check_output(['git', 'commit', '-am', 'bump appveyor anaconda key']))\n",
" print(subprocess.check_output(['git', 'push', 'origin', 'master']))\n",
" print(subprocess.check_output(['travis', 'env', 'set', 'BINSTAR_KEY', new_key_secure]))\n",
" if restart_travis:\n",
" print(subprocess.check_output(['travis', 'restart']))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"apply_to_all_projects(working_dir, bump_repo)"
]
}
],
"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 |
IST256/learn-python | content/lessons/10-HTTP/WMC2-Sending-Data.ipynb | 1 | 8529 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Watch Me Code 2: Ways to send data over HTTP \n",
"\n",
"Examples using the httpbin.org service\n",
"\n",
"https://httpbin.org/\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import requests"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'args': {'age': '45', 'name': 'mike'},\n",
" 'headers': {'Accept': '*/*',\n",
" 'Accept-Encoding': 'gzip, deflate',\n",
" 'Host': 'httpbin.org',\n",
" 'User-Agent': 'python-requests/2.22.0'},\n",
" 'origin': '71.176.79.102, 71.176.79.102',\n",
" 'url': 'https://httpbin.org/get?name=mike&age=45'}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# In the URL query string, HTTP GET\n",
"url = \"https://httpbin.org/get?name=mike&age=45\"\n",
"response = requests.get(url)\n",
"response.json()\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'args': {'age': '45', 'name': 'mike'},\n",
" 'headers': {'Accept': '*/*',\n",
" 'Accept-Encoding': 'gzip, deflate',\n",
" 'Host': 'httpbin.org',\n",
" 'User-Agent': 'python-requests/2.22.0'},\n",
" 'origin': '71.176.79.102, 71.176.79.102',\n",
" 'url': 'https://httpbin.org/get?name=mike&age=45'}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# same example but don't create the querystring by hand. use a dict\n",
"querystring = { 'name' : 'mike', 'age' : 45 }\n",
"url = \"http://httpbin.org/get\"\n",
"response = requests.get(url, params = querystring)\n",
"response.json()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'args': {},\n",
" 'headers': {'Accept': '*/*',\n",
" 'Accept-Encoding': 'gzip, deflate',\n",
" 'Api-Key': 'testing',\n",
" 'Host': 'httpbin.org',\n",
" 'Id': '345876',\n",
" 'User-Agent': 'python-requests/2.22.0'},\n",
" 'origin': '71.176.79.102, 71.176.79.102',\n",
" 'url': 'https://httpbin.org/get'}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# make a request, adding data to the header\n",
"\n",
"# NOTE: all header values MUST be strings!!! AND they are case-insensitive as per the HTTP protocol spec.\n",
"header = { 'api-key' : 'testing', 'id' : '345876' }\n",
"url = \"http://httpbin.org/get\"\n",
"response = requests.get(url, headers = header)\n",
"response.json()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'args': {'age': '45', 'name': 'mike'},\n",
" 'headers': {'Accept': '*/*',\n",
" 'Accept-Encoding': 'gzip, deflate',\n",
" 'Api-Key': 'demo',\n",
" 'Host': 'httpbin.org',\n",
" 'User-Agent': 'python-requests/2.22.0'},\n",
" 'origin': '71.176.79.102, 71.176.79.102',\n",
" 'url': 'https://httpbin.org/get?name=mike&age=45'}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# here's a combination of querystring plus headers:\n",
"querystring = { 'name' : 'mike', 'age' : 45 }\n",
"header = { 'api-key' : 'demo'}\n",
"url = \"http://httpbin.org/get\"\n",
"response = requests.get(url, params = querystring, headers = header)\n",
"response.json()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'args': {},\n",
" 'data': 'this is a lot of data.this is a lot of data.this is a lot of data.this is a lot of data.this is a lot of data.this is a lot of data.this is a lot of data.',\n",
" 'files': {},\n",
" 'form': {},\n",
" 'headers': {'Accept': '*/*',\n",
" 'Accept-Encoding': 'gzip, deflate',\n",
" 'Content-Length': '154',\n",
" 'Host': 'httpbin.org',\n",
" 'User-Agent': 'python-requests/2.22.0'},\n",
" 'json': None,\n",
" 'origin': '71.176.79.102, 71.176.79.102',\n",
" 'url': 'https://httpbin.org/post'}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# here's an example of a post\n",
"payload = \"this is a lot of data.this is a lot of data.this is a lot of data.this is a lot of data.this is a lot of data.this is a lot of data.this is a lot of data.\"\n",
"url = \"http://httpbin.org/post\"\n",
"response = requests.post(url, data = payload)\n",
"response.json()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'args': {},\n",
" 'data': '',\n",
" 'files': {},\n",
" 'form': {'age': '45', 'name': 'Mike', 'status': 'married'},\n",
" 'headers': {'Accept': '*/*',\n",
" 'Accept-Encoding': 'gzip, deflate',\n",
" 'Content-Length': '31',\n",
" 'Content-Type': 'application/x-www-form-urlencoded',\n",
" 'Host': 'httpbin.org',\n",
" 'User-Agent': 'python-requests/2.22.0'},\n",
" 'json': None,\n",
" 'origin': '71.176.79.102, 71.176.79.102',\n",
" 'url': 'https://httpbin.org/post'}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# here's another post, with a python dict, because there are key-values the post uses form.\n",
"person = { 'name' : 'Mike', 'age' : 45, 'status' : 'married' }\n",
"url = \"http://httpbin.org/post\"\n",
"response = requests.post(url, data = person)\n",
"response.json()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'args': {'id': '1'},\n",
" 'data': '',\n",
" 'files': {},\n",
" 'form': {'age': '45', 'name': 'Mike', 'status': 'married'},\n",
" 'headers': {'Accept': '*/*',\n",
" 'Accept-Encoding': 'gzip, deflate',\n",
" 'Api-Key': '32871549',\n",
" 'Content-Length': '31',\n",
" 'Content-Type': 'application/x-www-form-urlencoded',\n",
" 'Host': 'httpbin.org',\n",
" 'User-Agent': 'demo'},\n",
" 'json': None,\n",
" 'origin': '71.176.79.102, 71.176.79.102',\n",
" 'url': 'https://httpbin.org/post?id=1'}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# this one uses a POST, payload, querystring and headers to show these can all be combined!\n",
"person = { 'name' : 'Mike', 'age' : 45, 'status' : 'married' }\n",
"header = { 'api-key' : '32871549', 'user-agent' : 'demo' }\n",
"querystring = { 'id' : 1 }\n",
"url = \"http://httpbin.org/post\"\n",
"response = requests.post(url, data = person, params = querystring, headers=header )\n",
"response.json()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
},
"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,
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"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
RyanAlberts/Springbaord-Capstone-Project | Instacart_EDA_Categories__General.ipynb | 1 | 631141 | {
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import re\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns\n",
"sns.set_style(\"whitegrid\")\n",
"import calendar"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>product_id</th>\n",
" <th>product_name</th>\n",
" <th>aisle_id</th>\n",
" <th>department_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Chocolate Sandwich Cookies</td>\n",
" <td>61</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>All-Seasons Salt</td>\n",
" <td>104</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Robust Golden Unsweetened Oolong Tea</td>\n",
" <td>94</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Smart Ones Classic Favorites Mini Rigatoni Wit...</td>\n",
" <td>38</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Green Chile Anytime Sauce</td>\n",
" <td>5</td>\n",
" <td>13</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" product_id product_name aisle_id \\\n",
"0 1 Chocolate Sandwich Cookies 61 \n",
"1 2 All-Seasons Salt 104 \n",
"2 3 Robust Golden Unsweetened Oolong Tea 94 \n",
"3 4 Smart Ones Classic Favorites Mini Rigatoni Wit... 38 \n",
"4 5 Green Chile Anytime Sauce 5 \n",
"\n",
" department_id \n",
"0 19 \n",
"1 13 \n",
"2 7 \n",
"3 1 \n",
"4 13 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First, let's import requisite files\n",
"orders = pd.read_csv('../Instacart_Input/orders.csv')\n",
"prior_set = pd.read_csv('../Instacart_Input/order_products__prior.csv')\n",
"train_set = pd.read_csv('../Instacart_Input/order_products__train.csv')\n",
"aisles = pd.read_csv('../Instacart_Input/aisles.csv')\n",
"departments = pd.read_csv('../Instacart_Input/departments.csv')\n",
"products = pd.read_csv('../Instacart_Input/products.csv')\n",
"\n",
"products.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>aisle_id</th>\n",
" <th>aisle</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>prepared soups salads</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>specialty cheeses</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>energy granola bars</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>instant foods</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>marinades meat preparation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>other aisle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>packaged meat</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>bakery desserts</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>pasta sauce</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>kitchen supplies</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" aisle_id aisle\n",
"0 1 prepared soups salads\n",
"1 2 specialty cheeses\n",
"2 3 energy granola bars\n",
"3 4 instant foods\n",
"4 5 marinades meat preparation\n",
"5 6 other aisle\n",
"6 7 packaged meat\n",
"7 8 bakery desserts\n",
"8 9 pasta sauce\n",
"9 10 kitchen supplies"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# To avoid confusion down the road, I am changing the names of two aisles\n",
"# 'missing' and 'other', which share the same string values as their parent department \n",
"\n",
"aisles.loc[5,'aisle'] = 'other aisle'\n",
"aisles.loc[99,'aisle'] = 'missing aisle'\n",
"aisles.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>product_id</th>\n",
" <th>product_name</th>\n",
" <th>Category</th>\n",
" <th>Sub_Category</th>\n",
" <th>aisle_id</th>\n",
" <th>department_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>349</th>\n",
" <td>350</td>\n",
" <td>Mixed 12 Pack Lion's Share Ale</td>\n",
" <td>alcohol</td>\n",
" <td>alcohol >> beers coolers</td>\n",
" <td>27</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>378</th>\n",
" <td>379</td>\n",
" <td>Super Dry Beer</td>\n",
" <td>alcohol</td>\n",
" <td>alcohol >> beers coolers</td>\n",
" <td>27</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>429</th>\n",
" <td>430</td>\n",
" <td>Born Yesterday Pale Ale</td>\n",
" <td>alcohol</td>\n",
" <td>alcohol >> beers coolers</td>\n",
" <td>27</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>469</th>\n",
" <td>470</td>\n",
" <td>Lite Beer</td>\n",
" <td>alcohol</td>\n",
" <td>alcohol >> beers coolers</td>\n",
" <td>27</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>735</th>\n",
" <td>736</td>\n",
" <td>Beer, IPA</td>\n",
" <td>alcohol</td>\n",
" <td>alcohol >> beers coolers</td>\n",
" <td>27</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" product_id product_name Category \\\n",
"349 350 Mixed 12 Pack Lion's Share Ale alcohol \n",
"378 379 Super Dry Beer alcohol \n",
"429 430 Born Yesterday Pale Ale alcohol \n",
"469 470 Lite Beer alcohol \n",
"735 736 Beer, IPA alcohol \n",
"\n",
" Sub_Category aisle_id department_id \n",
"349 alcohol >> beers coolers 27 5 \n",
"378 alcohol >> beers coolers 27 5 \n",
"429 alcohol >> beers coolers 27 5 \n",
"469 alcohol >> beers coolers 27 5 \n",
"735 alcohol >> beers coolers 27 5 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The aisles.csv and departments.csv files more useful when merged into the products.csv\n",
"\n",
"# Departments appear to represent larger groups, with sub-groups represented by aisle, \n",
"# not unlike a physical store. \n",
"\n",
"# Personally, I can more easily conceptualize this thusly:\n",
"# 'departments' == Categories, and 'aisles' == Sub-Categories.\n",
"\n",
"# Merge Aisle and Department information into Product df\n",
"\n",
"aisle_names = aisles['aisle']\n",
"dept_names = departments['department']\n",
"products['Category'] = dept_names[products\n",
" ['department_id'].values-1].values\n",
"products['Sub_Category'] = dept_names[products\n",
" ['department_id'\n",
" ].values-1\n",
" ].values + \" >> \" + aisle_names[products['aisle_id'\n",
" ].values-1].values\n",
"\n",
"cols = ['product_id',\n",
" 'product_name',\n",
" 'Category',\n",
" 'Sub_Category',\n",
" 'aisle_id',\n",
" 'department_id',\n",
" ]\n",
"products = products[cols]\n",
"\n",
"\n",
"products.sort_values(by=['Category', 'Sub_Category']).head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>product_id</th>\n",
" <th>product_name</th>\n",
" <th>Category</th>\n",
" <th>Sub_Category</th>\n",
" <th>aisle_id</th>\n",
" <th>department_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6727</th>\n",
" <td>6728</td>\n",
" <td>Fun Size Chocolate Candy</td>\n",
" <td>missing</td>\n",
" <td>missing >> missing aisle</td>\n",
" <td>100</td>\n",
" <td>21</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" product_id product_name Category Sub_Category \\\n",
"6727 6728 Fun Size Chocolate Candy missing missing >> missing aisle \n",
"\n",
" aisle_id department_id \n",
"6727 100 21 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Example of missing category / aisle \n",
"products[products['product_id'] == 6728]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Things to note:\n",
"\n",
" * product_id seems to increase proportionally to with department_id | aisle_id (when sorted, low product_ids first, high product_ids last), which might be useful later\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Product_Count</th>\n",
" <th>_Product_Count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Category</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>alcohol</th>\n",
" <td>1054</td>\n",
" <td>--</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alcohol >> beers coolers</th>\n",
" <td>--</td>\n",
" <td>385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alcohol >> red wines</th>\n",
" <td>--</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alcohol >> specialty wines champagnes</th>\n",
" <td>--</td>\n",
" <td>95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alcohol >> spirits</th>\n",
" <td>--</td>\n",
" <td>195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alcohol >> white wines</th>\n",
" <td>--</td>\n",
" <td>147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>babies</th>\n",
" <td>1081</td>\n",
" <td>--</td>\n",
" </tr>\n",
" <tr>\n",
" <th>babies >> baby accessories</th>\n",
" <td>--</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>babies >> baby bath body care</th>\n",
" <td>--</td>\n",
" <td>132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>babies >> baby food formula</th>\n",
" <td>--</td>\n",
" <td>718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>babies >> diapers wipes</th>\n",
" <td>--</td>\n",
" <td>187</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bakery</th>\n",
" <td>1516</td>\n",
" <td>--</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bakery >> bakery desserts</th>\n",
" <td>--</td>\n",
" <td>297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bakery >> bread</th>\n",
" <td>--</td>\n",
" <td>557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bakery >> breakfast bakery</th>\n",
" <td>--</td>\n",
" <td>226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bakery >> buns rolls</th>\n",
" <td>--</td>\n",
" <td>195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bakery >> tortillas flat bread</th>\n",
" <td>--</td>\n",
" <td>241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>beverages</th>\n",
" <td>4365</td>\n",
" <td>--</td>\n",
" </tr>\n",
" <tr>\n",
" <th>beverages >> cocoa drink mixes</th>\n",
" <td>--</td>\n",
" <td>223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>beverages >> coffee</th>\n",
" <td>--</td>\n",
" <td>680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>beverages >> energy sports drinks</th>\n",
" <td>--</td>\n",
" <td>294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>beverages >> juice nectars</th>\n",
" <td>--</td>\n",
" <td>792</td>\n",
" </tr>\n",
" <tr>\n",
" <th>beverages >> refrigerated</th>\n",
" <td>--</td>\n",
" <td>675</td>\n",
" </tr>\n",
" <tr>\n",
" <th>beverages >> soft drinks</th>\n",
" <td>--</td>\n",
" <td>463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>beverages >> tea</th>\n",
" <td>--</td>\n",
" <td>894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>beverages >> water seltzer sparkling water</th>\n",
" <td>--</td>\n",
" <td>344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>breakfast</th>\n",
" <td>1115</td>\n",
" <td>--</td>\n",
" </tr>\n",
" <tr>\n",
" <th>breakfast >> breakfast bars pastries</th>\n",
" <td>--</td>\n",
" <td>173</td>\n",
" </tr>\n",
" <tr>\n",
" <th>breakfast >> cereal</th>\n",
" <td>--</td>\n",
" <td>454</td>\n",
" </tr>\n",
" <tr>\n",
" <th>breakfast >> granola</th>\n",
" <td>--</td>\n",
" <td>185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>personal care >> first aid</th>\n",
" <td>--</td>\n",
" <td>240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>personal care >> hair care</th>\n",
" <td>--</td>\n",
" <td>816</td>\n",
" </tr>\n",
" <tr>\n",
" <th>personal care >> muscles joints pain relief</th>\n",
" <td>--</td>\n",
" <td>172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>personal care >> oral hygiene</th>\n",
" <td>--</td>\n",
" <td>565</td>\n",
" </tr>\n",
" <tr>\n",
" <th>personal care >> protein meal replacements</th>\n",
" <td>--</td>\n",
" <td>325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>personal care >> shave needs</th>\n",
" <td>--</td>\n",
" <td>198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>personal care >> skin care</th>\n",
" <td>--</td>\n",
" <td>245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>personal care >> soap</th>\n",
" <td>--</td>\n",
" <td>525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>personal care >> vitamins supplements</th>\n",
" <td>--</td>\n",
" <td>1038</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pets</th>\n",
" <td>972</td>\n",
" <td>--</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pets >> cat food care</th>\n",
" <td>--</td>\n",
" <td>499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pets >> dog food care</th>\n",
" <td>--</td>\n",
" <td>473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>produce</th>\n",
" <td>1684</td>\n",
" <td>--</td>\n",
" </tr>\n",
" <tr>\n",
" <th>produce >> fresh fruits</th>\n",
" <td>--</td>\n",
" <td>382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>produce >> fresh herbs</th>\n",
" <td>--</td>\n",
" <td>86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>produce >> fresh vegetables</th>\n",
" <td>--</td>\n",
" <td>569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>produce >> packaged produce</th>\n",
" <td>--</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>produce >> packaged vegetables fruits</th>\n",
" <td>--</td>\n",
" <td>615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snacks</th>\n",
" <td>6264</td>\n",
" <td>--</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snacks >> candy chocolate</th>\n",
" <td>--</td>\n",
" <td>1246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snacks >> chips pretzels</th>\n",
" <td>--</td>\n",
" <td>989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snacks >> cookies cakes</th>\n",
" <td>--</td>\n",
" <td>874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snacks >> crackers</th>\n",
" <td>--</td>\n",
" <td>747</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snacks >> energy granola bars</th>\n",
" <td>--</td>\n",
" <td>832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snacks >> fruit vegetable snacks</th>\n",
" <td>--</td>\n",
" <td>356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snacks >> ice cream toppings</th>\n",
" <td>--</td>\n",
" <td>85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snacks >> mint gum</th>\n",
" <td>--</td>\n",
" <td>168</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snacks >> nuts seeds dried fruit</th>\n",
" <td>--</td>\n",
" <td>582</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snacks >> popcorn jerky</th>\n",
" <td>--</td>\n",
" <td>316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snacks >> trail mix snack mix</th>\n",
" <td>--</td>\n",
" <td>69</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>155 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Product_Count _Product_Count\n",
"Category \n",
"alcohol 1054 --\n",
"alcohol >> beers coolers -- 385\n",
"alcohol >> red wines -- 232\n",
"alcohol >> specialty wines champagnes -- 95\n",
"alcohol >> spirits -- 195\n",
"alcohol >> white wines -- 147\n",
"babies 1081 --\n",
"babies >> baby accessories -- 44\n",
"babies >> baby bath body care -- 132\n",
"babies >> baby food formula -- 718\n",
"babies >> diapers wipes -- 187\n",
"bakery 1516 --\n",
"bakery >> bakery desserts -- 297\n",
"bakery >> bread -- 557\n",
"bakery >> breakfast bakery -- 226\n",
"bakery >> buns rolls -- 195\n",
"bakery >> tortillas flat bread -- 241\n",
"beverages 4365 --\n",
"beverages >> cocoa drink mixes -- 223\n",
"beverages >> coffee -- 680\n",
"beverages >> energy sports drinks -- 294\n",
"beverages >> juice nectars -- 792\n",
"beverages >> refrigerated -- 675\n",
"beverages >> soft drinks -- 463\n",
"beverages >> tea -- 894\n",
"beverages >> water seltzer sparkling water -- 344\n",
"breakfast 1115 --\n",
"breakfast >> breakfast bars pastries -- 173\n",
"breakfast >> cereal -- 454\n",
"breakfast >> granola -- 185\n",
"... ... ...\n",
"personal care >> first aid -- 240\n",
"personal care >> hair care -- 816\n",
"personal care >> muscles joints pain relief -- 172\n",
"personal care >> oral hygiene -- 565\n",
"personal care >> protein meal replacements -- 325\n",
"personal care >> shave needs -- 198\n",
"personal care >> skin care -- 245\n",
"personal care >> soap -- 525\n",
"personal care >> vitamins supplements -- 1038\n",
"pets 972 --\n",
"pets >> cat food care -- 499\n",
"pets >> dog food care -- 473\n",
"produce 1684 --\n",
"produce >> fresh fruits -- 382\n",
"produce >> fresh herbs -- 86\n",
"produce >> fresh vegetables -- 569\n",
"produce >> packaged produce -- 32\n",
"produce >> packaged vegetables fruits -- 615\n",
"snacks 6264 --\n",
"snacks >> candy chocolate -- 1246\n",
"snacks >> chips pretzels -- 989\n",
"snacks >> cookies cakes -- 874\n",
"snacks >> crackers -- 747\n",
"snacks >> energy granola bars -- 832\n",
"snacks >> fruit vegetable snacks -- 356\n",
"snacks >> ice cream toppings -- 85\n",
"snacks >> mint gum -- 168\n",
"snacks >> nuts seeds dried fruit -- 582\n",
"snacks >> popcorn jerky -- 316\n",
"snacks >> trail mix snack mix -- 69\n",
"\n",
"[155 rows x 2 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's list the Category and Aisle counts, grouped alphabetically, arranged hierarchically.\n",
"\n",
"data = {'Product_Count' : products['Category'].value_counts(),\n",
" '_Product_Count' : products['Sub_Category'].value_counts()\n",
" }\n",
"\n",
"counts_df = pd.DataFrame(data=data).fillna(value='--')\n",
"counts_df.index.names = ['Category']\n",
"counts_df.sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Product_Count</th>\n",
" <th>_Product_Count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Category</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>missing</th>\n",
" <td>1258</td>\n",
" <td>--</td>\n",
" </tr>\n",
" <tr>\n",
" <th>missing >> missing aisle</th>\n",
" <td>--</td>\n",
" <td>1258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>other</th>\n",
" <td>548</td>\n",
" <td>--</td>\n",
" </tr>\n",
" <tr>\n",
" <th>other >> other aisle</th>\n",
" <td>--</td>\n",
" <td>548</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Product_Count _Product_Count\n",
"Category \n",
"missing 1258 --\n",
"missing >> missing aisle -- 1258\n",
"other 548 --\n",
"other >> other aisle -- 548"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# products categorized as'missing' or 'other' account for 1,806 products,\n",
"# roughly 3.634% of the 49,688 total products. Not exactly negligible.\n",
"\n",
"counts_df.sort_index()[99:103]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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zc4FnfOUr6jMobHsbNmxQTEyMDh06pBMnTlj/cGDv9+5mevToccMl44tS2Pcl//2/0eck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\n9fXVsGHDdPr0aZu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gQQ0YMEC5ubnWmqFDhyo+Pl7vvPOOxo4dq9jYWI0cOfKOHRsAAACA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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119e13fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(14,10), ncols=1)\n",
"\n",
"products['Category'].value_counts().plot(kind='bar')\n",
"_= ax.set_title('Categories by Total Products', size=22)\n",
"_= ax.set_ylabel('Count', size=20)\n",
"_= ax.tick_params(labelsize=16)\n",
"plt.xticks(ha='right', rotation=55);\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Let's plot sub-category (aisle) as a proportion of category (department)\n",
"\n",
"#sns.barplot(x = stacked_bar_data.Group\n",
"#products.pivot('','')[].plot(kind='bar', stacked=True)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"548"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1 = {'_Product_Count' : products['Sub_Category'].value_counts()\n",
" }\n",
"data2 = {'Product_Count' : products['Category'].value_counts()\n",
" }\n",
"\n",
"\n",
"d1_counts_df = pd.DataFrame(data=data1)\n",
"d2_counts_df = pd.DataFrame(data=data2)\n",
"data1['_Product_Count'].value_counts().sum()\n",
"d2_counts_df['Product_Count']['other']"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>No. of Sub-Categories</th>\n",
" <th>Parent</th>\n",
" <th>Products in Parent Category</th>\n",
" <th>Products in Sub-Category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>beers coolers</th>\n",
" <td>None</td>\n",
" <td>alcohol</td>\n",
" <td>1054</td>\n",
" <td>385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red wines</th>\n",
" <td>None</td>\n",
" <td>alcohol</td>\n",
" <td>1054</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>specialty wines champagnes</th>\n",
" <td>None</td>\n",
" <td>alcohol</td>\n",
" <td>1054</td>\n",
" <td>95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>spirits</th>\n",
" <td>None</td>\n",
" <td>alcohol</td>\n",
" <td>1054</td>\n",
" <td>195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>white wines</th>\n",
" <td>None</td>\n",
" <td>alcohol</td>\n",
" <td>1054</td>\n",
" <td>147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>baby accessories</th>\n",
" <td>None</td>\n",
" <td>babies</td>\n",
" <td>1081</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>baby bath body care</th>\n",
" <td>None</td>\n",
" <td>babies</td>\n",
" <td>1081</td>\n",
" <td>132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>baby food formula</th>\n",
" <td>None</td>\n",
" <td>babies</td>\n",
" <td>1081</td>\n",
" <td>718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>diapers wipes</th>\n",
" <td>None</td>\n",
" <td>babies</td>\n",
" <td>1081</td>\n",
" <td>187</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bakery desserts</th>\n",
" <td>None</td>\n",
" <td>bakery</td>\n",
" <td>1516</td>\n",
" <td>297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bread</th>\n",
" <td>None</td>\n",
" <td>bakery</td>\n",
" <td>1516</td>\n",
" <td>557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>breakfast bakery</th>\n",
" <td>None</td>\n",
" <td>bakery</td>\n",
" <td>1516</td>\n",
" <td>226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>buns rolls</th>\n",
" <td>None</td>\n",
" <td>bakery</td>\n",
" <td>1516</td>\n",
" <td>195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tortillas flat bread</th>\n",
" <td>None</td>\n",
" <td>bakery</td>\n",
" <td>1516</td>\n",
" <td>241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cocoa drink mixes</th>\n",
" <td>None</td>\n",
" <td>beverages</td>\n",
" <td>4365</td>\n",
" <td>223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coffee</th>\n",
" <td>None</td>\n",
" <td>beverages</td>\n",
" <td>4365</td>\n",
" <td>680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>energy sports drinks</th>\n",
" <td>None</td>\n",
" <td>beverages</td>\n",
" <td>4365</td>\n",
" <td>294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>juice nectars</th>\n",
" <td>None</td>\n",
" <td>beverages</td>\n",
" <td>4365</td>\n",
" <td>792</td>\n",
" </tr>\n",
" <tr>\n",
" <th>refrigerated</th>\n",
" <td>None</td>\n",
" <td>beverages</td>\n",
" <td>4365</td>\n",
" <td>675</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" No. of Sub-Categories Parent \\\n",
"beers coolers None alcohol \n",
"red wines None alcohol \n",
"specialty wines champagnes None alcohol \n",
"spirits None alcohol \n",
"white wines None alcohol \n",
"baby accessories None babies \n",
"baby bath body care None babies \n",
"baby food formula None babies \n",
"diapers wipes None babies \n",
"bakery desserts None bakery \n",
"bread None bakery \n",
"breakfast bakery None bakery \n",
"buns rolls None bakery \n",
"tortillas flat bread None bakery \n",
"cocoa drink mixes None beverages \n",
"coffee None beverages \n",
"energy sports drinks None beverages \n",
"juice nectars None beverages \n",
"refrigerated None beverages \n",
"\n",
" Products in Parent Category \\\n",
"beers coolers 1054 \n",
"red wines 1054 \n",
"specialty wines champagnes 1054 \n",
"spirits 1054 \n",
"white wines 1054 \n",
"baby accessories 1081 \n",
"baby bath body care 1081 \n",
"baby food formula 1081 \n",
"diapers wipes 1081 \n",
"bakery desserts 1516 \n",
"bread 1516 \n",
"breakfast bakery 1516 \n",
"buns rolls 1516 \n",
"tortillas flat bread 1516 \n",
"cocoa drink mixes 4365 \n",
"coffee 4365 \n",
"energy sports drinks 4365 \n",
"juice nectars 4365 \n",
"refrigerated 4365 \n",
"\n",
" Products in Sub-Category \n",
"beers coolers 385 \n",
"red wines 232 \n",
"specialty wines champagnes 95 \n",
"spirits 195 \n",
"white wines 147 \n",
"baby accessories 44 \n",
"baby bath body care 132 \n",
"baby food formula 718 \n",
"diapers wipes 187 \n",
"bakery desserts 297 \n",
"bread 557 \n",
"breakfast bakery 226 \n",
"buns rolls 195 \n",
"tortillas flat bread 241 \n",
"cocoa drink mixes 223 \n",
"coffee 680 \n",
"energy sports drinks 294 \n",
"juice nectars 792 \n",
"refrigerated 675 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data3 = { 'No. of Sub-Categories' : {},\n",
" 'Products in Sub-Category' : {},\n",
" 'Parent' : {},\n",
" 'Products in Parent Category' : {}\n",
" }\n",
"\n",
"for i in range(len(d1_counts_df['_Product_Count'])):\n",
" #print counts_df.index[i], counts_df['_Product_Count'][i]\n",
" match = re.findall(r'([\\w ]+)( >> )([\\w ]+)', d1_counts_df.index[i])\n",
" parent, child = match[0][0], match[0][2]\n",
" if parent in data3['No. of Sub-Categories']:\n",
" data3['No. of Sub-Categories'][parent] += 1\n",
" data3['Products in Sub-Category'][child] = d1_counts_df['_Product_Count'][i]\n",
" else:\n",
" data3['No. of Sub-Categories'][parent] = 1\n",
" data3['Products in Parent Category'][parent] = d2_counts_df['Product_Count'][parent]\n",
" data3['Products in Sub-Category'][child] = d1_counts_df['_Product_Count'][i]\n",
" \n",
" \n",
" if child not in data3['Parent']:\n",
" data3['Parent'][child] = parent\n",
" data3['Products in Parent Category'][child] = d2_counts_df['Product_Count'][parent]\n",
"\n",
"\n",
"stacked_category_data = pd.DataFrame(data=data3)\n",
"stacked_category_data['Parent'\n",
" ] = stacked_category_data['Parent'\n",
" ].fillna('None')\n",
"stacked_category_data['Products in Sub-Category'\n",
" ] = stacked_category_data['Products in Sub-Category'\n",
" ].fillna('N/A')\n",
"\n",
"stacked_category_data['No. of Sub-Categories'\n",
" ] = stacked_category_data['No. of Sub-Categories'\n",
" ].fillna('None')\n",
"\n",
"stacked_category_data.sort_values(by=['Parent', 'Products in Parent Category'])[21:40]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Parent Products in Sub-Category\n",
"alcohol beers coolers 385.0 1\n",
" red wines 232.0 1\n",
" specialty wines champagnes 95.0 1\n",
" spirits 195.0 1\n",
" white wines 147.0 1\n",
"babies baby accessories 44.0 1\n",
" baby bath body care 132.0 1\n",
" baby food formula 718.0 1\n",
" diapers wipes 187.0 1\n",
"bakery bakery desserts 297.0 1\n",
" bread 557.0 1\n",
" breakfast bakery 226.0 1\n",
" buns rolls 195.0 1\n",
" tortillas flat bread 241.0 1\n",
"beverages cocoa drink mixes 223.0 1\n",
" coffee 680.0 1\n",
" energy sports drinks 294.0 1\n",
" juice nectars 792.0 1\n",
" refrigerated 675.0 1\n",
"dtype: int64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's plot Sub_Category product groups (aisle) \n",
"# as a proportion of total products in Parent Category (department)\n",
"\n",
"\n",
"newstacked = stacked_category_data.groupby(['Parent', \n",
" stacked_category_data.index,\n",
" 'Products in Sub-Category'\n",
" ]).size()[21:40]\n",
"newstacked"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"aisles_only = stacked_category_data.sort_values(by=\n",
" ['Parent',\n",
" 'Products in Parent Category'])[21:]\n",
"Top15 = aisles_only.sort_values(by='Products in Sub-Category', \n",
" ascending=False)[:15]\n",
"Bottom15 = aisles_only.sort_values(by='Products in Sub-Category', \n",
" ascending=False)[119:]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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lACgQrHQBAFDA5s2bp3fffVfVqlXTnj17tGnTJiUlJemFF17Q2rVrVadOncJO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88/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\niIiIPEL/A16UXsQ6Ig0lAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a11a990>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plotting top and Bottom 15 Sub-Categories by Product Count\n",
"\n",
"f, ax = plt.subplots(figsize=(14,5))\n",
"\n",
"Top15['Products in Sub-Category'].plot(kind='bar')\n",
"_= ax.set_title('Top 15 Sub-Categories by Count', size=22)\n",
"_= ax.set_ylabel('Count', size=20)\n",
"_= ax.tick_params(labelsize=16)\n",
"plt.xticks(ha='right', rotation=55);"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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avHix2rRpo3r16umpp57Szp07nV0SAAAAgGKs2ASqZcuW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\n7Jii6NjvAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a1547d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Notice that the larger Sub-Categories reflect more vague grouping criteria, \n",
"# e.g. \"missing\" isn't exactly specific\n",
"# which suggest less helpful groupings for making predictions\n",
"# as opposed to the smaller sub-categories below\n",
"\n",
"f, ax = plt.subplots(figsize=(14,5))\n",
"Bottom15['Products in Sub-Category'].plot(kind='bar')\n",
"_= ax.set_title('Bottom 15 Sub-Categories by Count', size=22)\n",
"_= ax.set_ylabel('Count', size=20)\n",
"_= ax.tick_params(labelsize=16)\n",
"plt.xticks(ha='right', rotation=55);"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>order_id</th>\n",
" <th>product_id</th>\n",
" <th>add_to_cart_order</th>\n",
" <th>reordered</th>\n",
" <th>user_id</th>\n",
" <th>eval_set</th>\n",
" <th>order_number</th>\n",
" <th>order_dow</th>\n",
" <th>order_hour_of_day</th>\n",
" <th>days_since_prior_order</th>\n",
" <th>product_name</th>\n",
" <th>Category</th>\n",
" <th>Sub_Category</th>\n",
" <th>aisle_id</th>\n",
" <th>department_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>33120</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>202279</td>\n",
" <td>prior</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>8.0</td>\n",
" <td>Organic Egg Whites</td>\n",
" <td>dairy eggs</td>\n",
" <td>dairy eggs >> eggs</td>\n",
" <td>86.0</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>28985</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>202279</td>\n",
" <td>prior</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>8.0</td>\n",
" <td>Michigan Organic Kale</td>\n",
" <td>produce</td>\n",
" <td>produce >> fresh vegetables</td>\n",
" <td>83.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>9327</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>202279</td>\n",
" <td>prior</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>8.0</td>\n",
" <td>Garlic Powder</td>\n",
" <td>pantry</td>\n",
" <td>pantry >> spices seasonings</td>\n",
" <td>104.0</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>45918</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>202279</td>\n",
" <td>prior</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>8.0</td>\n",
" <td>Coconut Butter</td>\n",
" <td>pantry</td>\n",
" <td>pantry >> oils vinegars</td>\n",
" <td>19.0</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>30035</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>202279</td>\n",
" <td>prior</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>8.0</td>\n",
" <td>Natural Sweetener</td>\n",
" <td>pantry</td>\n",
" <td>pantry >> baking ingredients</td>\n",
" <td>17.0</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" order_id product_id add_to_cart_order reordered user_id eval_set \\\n",
"0 2 33120 1.0 1.0 202279 prior \n",
"1 2 28985 2.0 1.0 202279 prior \n",
"2 2 9327 3.0 0.0 202279 prior \n",
"3 2 45918 4.0 1.0 202279 prior \n",
"4 2 30035 5.0 0.0 202279 prior \n",
"\n",
" order_number order_dow order_hour_of_day days_since_prior_order \\\n",
"0 3 5 9 8.0 \n",
"1 3 5 9 8.0 \n",
"2 3 5 9 8.0 \n",
"3 3 5 9 8.0 \n",
"4 3 5 9 8.0 \n",
"\n",
" product_name Category Sub_Category aisle_id \\\n",
"0 Organic Egg Whites dairy eggs dairy eggs >> eggs 86.0 \n",
"1 Michigan Organic Kale produce produce >> fresh vegetables 83.0 \n",
"2 Garlic Powder pantry pantry >> spices seasonings 104.0 \n",
"3 Coconut Butter pantry pantry >> oils vinegars 19.0 \n",
"4 Natural Sweetener pantry pantry >> baking ingredients 17.0 \n",
"\n",
" department_id \n",
"0 16.0 \n",
"1 4.0 \n",
"2 13.0 \n",
"3 13.0 \n",
"4 13.0 "
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now let's try to find some category trends. First, let's check out the alcohol category. \n",
"\n",
"master_set = pd.merge(pd.merge(prior_set, orders, on='order_id', how='right'), products,\n",
" on='product_id',\n",
" how='left')\n",
"master_set.head()"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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06dJkz56dX3/9lcjISHx9fZk8efITb9/JyYk5c+bQpUsXdu7cyWuvvUZAQAB+fn5ER0dz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12d114350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Let's plot a heatmap of the count of products ordered in the alcohol category by hour and day\n",
"\n",
"category_five = master_set[master_set['department_id'] == 5.0]\n",
"cat_heatmap_data = pd.DataFrame(category_five[['order_dow', 'order_hour_of_day']].groupby(['order_dow', \n",
" 'order_hour_of_day']\n",
" ).size()\n",
" ).reset_index()\n",
"cat_heatmap_data = cat_heatmap_data.pivot(index='order_hour_of_day', \n",
" columns='order_dow', \n",
" values=0)\n",
"\n",
"f, ax = plt.subplots(figsize=(16,12))\n",
"_= ax.set_title('Alcohol Category:\\n Time-Series Trend', size=22)\n",
"_= ax.set_ylabel('Hour of Day', size=20, labelpad=15)\n",
"_= ax.set_xlabel('Day of Week', size=20, labelpad=15)\n",
"_= ax.tick_params(labelsize=16)\n",
"sns.heatmap(cat_heatmap_data, ax=ax, annot=True, fmt=\"d\");"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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m6NChrFy5EhsbG86dO6cdMnnnzh3tKp/169enePHihIeH06hRIypWrKjt0YuNjaVMmTJc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\n4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lIZkcMSUGrUOPt6EDG4C2aDEXtXJ9o9MgQHDxdMRhPVl8otReLhL9fg6ONOzKQB7P6/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\nRERERPyQkj0RERERERE/9D+eiAgaWTrAsQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x122b9fe50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bananas = master_set[master_set['product_id'] == 24852]\n",
"cat_heatmap_data = pd.DataFrame(bananas[['order_dow', 'order_hour_of_day']].groupby(['order_dow', \n",
" 'order_hour_of_day']\n",
" ).size()\n",
" ).reset_index()\n",
"cat_heatmap_data = cat_heatmap_data.pivot(index='order_hour_of_day', \n",
" columns='order_dow', \n",
" values=0)\n",
"\n",
"f, ax = plt.subplots(figsize=(16,12))\n",
"_= ax.set_title('Organic Bananas:\\n Time-Series Trend', size=22)\n",
"_= ax.set_ylabel('Hour of Day', size=20, labelpad=15)\n",
"_= ax.set_xlabel('Day of Week', size=20, labelpad=15)\n",
"_= ax.tick_params(labelsize=16)\n",
"sns.heatmap(cat_heatmap_data, ax=ax, annot=True, fmt=\"d\");"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
sumitsahrawat/IHaskell | ihaskell-display/ihaskell-widgets/Examples/Bool Widgets.ipynb | 5 | 6837 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The `Bool` Widgets\n",
"\n",
"+ CheckBox\n",
"+ ToggleButton"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These widgets can be used to represent a Boolean value. The idea is pretty simple, the widget can be in one of two states which represent the two boolean values.\n",
"\n",
" Checked / On : True\n",
" Unchecked / Off : False"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"{-# LANGUAGE OverloadedStrings #-}\n",
"{-# LANGUAGE FlexibleContexts #-}\n",
"import IHaskell.Display.Widgets\n",
"import Data.Text (pack, unpack)\n",
"import Text.Printf (printf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simple demonstration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"-- Check box\n",
"chk <- mkCheckBox\n",
"\n",
"-- Toggle button\n",
"tgb <- mkToggleButton\n",
"\n",
"-- Valid widget: Displaying booleans conveniently\n",
"vld <- mkValidWidget"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below, we represent one boolean using a checkbox, and the other using a toggle button. The logical and (`&&`) of the two is displayed below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"-- Display the widgets\n",
"chk\n",
"tgb\n",
"vld"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `BoolValue` field represents the underlying boolean value."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"setField chk Description \"Bool 1: \"\n",
"setField tgb Description \"Bool 2\"\n",
"\n",
" -- Cosmetic changes\n",
"setField vld Description \"Bool 1 && Bool 2\"\n",
"\n",
" -- And (&&) the two values, and send output to html widget\n",
"setHandler w = setField w ChangeHandler $ do\n",
" b1 <- getField chk BoolValue\n",
" b2 <- getField tgb BoolValue\n",
" setField vld BoolValue (b1 && b2)\n",
"\n",
"setHandler chk\n",
"setHandler tgb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extended example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try to create a graphical 8-bit-binary to decimal converter. We'll represent seven bits using `ToggleButton` widgets, and the negative bit using a `CheckBox`. The binary number is represented using 1+7-bit sign-and-magnitude representation for simplicity.\n",
"\n",
"Boxes are used to layout the widgets in an appealing manner, and the output widget is used to display the result."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"-- First, some library functions\n",
"import Control.Monad (replicateM, forM_)\n",
"import Data.IORef\n",
"import IHaskell.Display (plain)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we create a `CheckBox` and seven `ToggleButton`s."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sign <- mkCheckBox\n",
"bits <- replicateM 7 mkToggleButton\n",
"\n",
"setField sign Description \"Negative\"\n",
"forM_ bits $ \\t -> do\n",
" setField t ButtonStyle PrimaryButton\n",
" setField t BorderRadius 20"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we create a `FlexBox` to hold the widgets, and an `HTMLWidget` to display the output."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"box <- mkFlexBox\n",
"out <- mkHTMLWidget\n",
"\n",
"-- Sub-containers\n",
"box1 <- mkFlexBox\n",
"setField box1 Children [ChildWidget sign, ChildWidget out]\n",
"box2 <- mkFlexBox\n",
"setField box2 Children (map ChildWidget $ reverse bits)\n",
"\n",
"-- Add widgets to the container\n",
"setField box Children (map ChildWidget [box1, box2])\n",
"setField box Orientation VerticalOrientation\n",
"\n",
"-- Add some UI chrome\n",
"setField box BoxStyle InfoBox\n",
"setField box BorderRadius 20\n",
"setField out BorderStyle GrooveBorder\n",
"setField out BorderRadius 20\n",
"setField out BorderWidth 4\n",
"setField out Width 100\n",
"setField out Height 30\n",
"setField out Margin 10\n",
"setField sign Padding 10\n",
"setField box2 Padding 10\n",
"setField box2 Pack BaselineLocation\n",
"\n",
"-- Display the container\n",
"box"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we implement the logic of our converter, and make it send the output to the `HTMLWidget` we created above."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import Control.Arrow (first, second)\n",
"\n",
"-- Mutable value, with a sign bit\n",
"val <- newIORef (0 :: Int, False)\n",
"\n",
"-- Helper function to redraw output\n",
"refresh :: (Int, Bool) -> IO ()\n",
"refresh (x, b) = \n",
" let val = x * if b then (-1) else 1\n",
" fmt = \"<div align=\\\"center\\\"><b>%d</b></div>\"\n",
" in setField out StringValue (pack $ printf fmt val)\n",
"\n",
"setField sign ChangeHandler $ do\n",
" -- Change sign for value\n",
" modifyIORef val (second not)\n",
" -- Redraw output\n",
" readIORef val >>= refresh\n",
"\n",
"forM_ (zip bits (iterate (*2) 1)) $ \\(t, n) -> do\n",
" setField t Description \"0\"\n",
" setField t ChangeHandler $ do\n",
" f <- getField t BoolValue\n",
" setField t Description (if f then \"1\" else \"0\")\n",
" modifyIORef val (first $ if f then (+n) else (\\x->x-n))\n",
" readIORef val >>= refresh"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Haskell",
"language": "haskell",
"name": "haskell"
},
"language_info": {
"codemirror_mode": "ihaskell",
"file_extension": ".hs",
"name": "haskell",
"version": "7.10.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
rockingdingo/reinforcement-learning | DP/Policy Iteration.ipynb | 3 | 11202 | {
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pprint\n",
"import sys\n",
"if \"../\" not in sys.path:\n",
" sys.path.append(\"../\") \n",
"from lib.envs.gridworld import GridworldEnv"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pp = pprint.PrettyPrinter(indent=2)\n",
"env = GridworldEnv()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Taken from Policy Evaluation Exercise!\n",
"\n",
"def policy_eval(policy, env, discount_factor=1.0, theta=0.00001):\n",
" \"\"\"\n",
" Evaluate a policy given an environment and a full description of the environment's dynamics.\n",
" \n",
" Args:\n",
" policy: [S, A] shaped matrix representing the policy.\n",
" env: OpenAI env. env.P represents the transition probabilities of the environment.\n",
" env.P[s][a] is a (prob, next_state, reward, done) tuple.\n",
" theta: We stop evaluation one our value function change is less than theta for all states.\n",
" discount_factor: lambda discount factor.\n",
" \n",
" Returns:\n",
" Vector of length env.nS representing the value function.\n",
" \"\"\"\n",
" # Start with a random (all 0) value function\n",
" V = np.zeros(env.nS)\n",
" while True:\n",
" delta = 0\n",
" # For each state, perform a \"full backup\"\n",
" for s in range(env.nS):\n",
" v = 0\n",
" # Look at the possible next actions\n",
" for a, action_prob in enumerate(policy[s]):\n",
" # For each action, look at the possible next states...\n",
" for prob, next_state, reward, done in env.P[s][a]:\n",
" # Calculate the expected value\n",
" v += action_prob * prob * (reward + discount_factor * V[next_state])\n",
" # How much our value function changed (across any states)\n",
" delta = max(delta, np.abs(v - V[s]))\n",
" V[s] = v\n",
" # Stop evaluating once our value function change is below a threshold\n",
" if delta < theta:\n",
" break\n",
" return np.array(V)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def policy_improvement(env, policy_eval_fn=policy_eval, discount_factor=1.0):\n",
" \"\"\"\n",
" Policy Improvement Algorithm. Iteratively evaluates and improves a policy\n",
" until an optimal policy is found.\n",
" \n",
" Args:\n",
" env: The OpenAI envrionment.\n",
" policy_eval_fn: Policy Evaluation function that takes 3 arguments:\n",
" policy, env, discount_factor.\n",
" discount_factor: Lambda discount factor.\n",
" \n",
" Returns:\n",
" A tuple (policy, V). \n",
" policy is the optimal policy, a matrix of shape [S, A] where each state s\n",
" contains a valid probability distribution over actions.\n",
" V is the value function for the optimal policy.\n",
" \n",
" \"\"\"\n",
" # Start with a random policy\n",
" policy = np.ones([env.nS, env.nA]) / env.nA\n",
" \n",
" while True:\n",
" # Implement this!\n",
" break\n",
" \n",
" return policy, np.zeros(env.nS)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Policy Probability Distribution:\n",
"[[ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]]\n",
"\n",
"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\n",
"[[0 0 0 0]\n",
" [0 0 0 0]\n",
" [0 0 0 0]\n",
" [0 0 0 0]]\n",
"\n",
"Value Function:\n",
"[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
"\n",
"Reshaped Grid Value Function:\n",
"[[ 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0.]]\n",
"\n"
]
}
],
"source": [
"policy, v = policy_improvement(env)\n",
"print(\"Policy Probability Distribution:\")\n",
"print(policy)\n",
"print(\"\")\n",
"\n",
"print(\"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\")\n",
"print(np.reshape(np.argmax(policy, axis=1), env.shape))\n",
"print(\"\")\n",
"\n",
"print(\"Value Function:\")\n",
"print(v)\n",
"print(\"\")\n",
"\n",
"print(\"Reshaped Grid Value Function:\")\n",
"print(v.reshape(env.shape))\n",
"print(\"\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AssertionError",
"evalue": "\nArrays are not almost equal to 2 decimals\n\n(mismatch 87.5%)\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0.])\n y: array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-55581f8eb5c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Test the value function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mexpected_v\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_array_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected_v\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_array_almost_equal\u001b[0;34m(x, y, decimal, err_msg, verbose)\u001b[0m\n\u001b[1;32m 914\u001b[0m assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,\n\u001b[1;32m 915\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Arrays are not almost equal to %d decimals'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m precision=decimal)\n\u001b[0m\u001b[1;32m 917\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 918\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision)\u001b[0m\n\u001b[1;32m 735\u001b[0m names=('x', 'y'), precision=precision)\n\u001b[1;32m 736\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcond\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 737\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 738\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 739\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 2 decimals\n\n(mismatch 87.5%)\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0.])\n y: array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])"
]
}
],
"source": [
"# Test the value function\n",
"expected_v = np.array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])\n",
"np.testing.assert_array_almost_equal(v, expected_v, decimal=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
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],
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| mit |
ProfessorKazarinoff/staticsite | content/code/altair/altair_test.ipynb | 1 | 122690 | {
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{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
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"
},
"metadata": {
"jupyter-vega": "#eb3c64b2-8f92-4556-a63f-585bf7179c9a"
},
"output_type": "display_data"
}
],
"source": [
"import altair as alt\n",
"\n",
"# Uncomment/run this line to enable Altair in JupyterLab/nteract:\n",
"# alt.enable_mime_rendering()\n",
"\n",
"# load data as a pandas DataFrame\n",
"cars = alt.load_dataset('cars')\n",
"\n",
"alt.Chart(cars).mark_point().encode(\n",
" x='Horsepower',\n",
" y='Miles_per_Gallon',\n",
" color='Origin',\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import altair as alt"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Acceleration</th>\n",
" <th>Cylinders</th>\n",
" <th>Displacement</th>\n",
" <th>Horsepower</th>\n",
" <th>Miles_per_Gallon</th>\n",
" <th>Name</th>\n",
" <th>Origin</th>\n",
" <th>Weight_in_lbs</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>12.0</td>\n",
" <td>8</td>\n",
" <td>307.0</td>\n",
" <td>130.0</td>\n",
" <td>18.0</td>\n",
" <td>chevrolet chevelle malibu</td>\n",
" <td>USA</td>\n",
" <td>3504</td>\n",
" <td>1970-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11.5</td>\n",
" <td>8</td>\n",
" <td>350.0</td>\n",
" <td>165.0</td>\n",
" <td>15.0</td>\n",
" <td>buick skylark 320</td>\n",
" <td>USA</td>\n",
" <td>3693</td>\n",
" <td>1970-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>11.0</td>\n",
" <td>8</td>\n",
" <td>318.0</td>\n",
" <td>150.0</td>\n",
" <td>18.0</td>\n",
" <td>plymouth satellite</td>\n",
" <td>USA</td>\n",
" <td>3436</td>\n",
" <td>1970-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>12.0</td>\n",
" <td>8</td>\n",
" <td>304.0</td>\n",
" <td>150.0</td>\n",
" <td>16.0</td>\n",
" <td>amc rebel sst</td>\n",
" <td>USA</td>\n",
" <td>3433</td>\n",
" <td>1970-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10.5</td>\n",
" <td>8</td>\n",
" <td>302.0</td>\n",
" <td>140.0</td>\n",
" <td>17.0</td>\n",
" <td>ford torino</td>\n",
" <td>USA</td>\n",
" <td>3449</td>\n",
" <td>1970-01-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Acceleration Cylinders Displacement Horsepower Miles_per_Gallon \\\n",
"0 12.0 8 307.0 130.0 18.0 \n",
"1 11.5 8 350.0 165.0 15.0 \n",
"2 11.0 8 318.0 150.0 18.0 \n",
"3 12.0 8 304.0 150.0 16.0 \n",
"4 10.5 8 302.0 140.0 17.0 \n",
"\n",
" Name Origin Weight_in_lbs Year \n",
"0 chevrolet chevelle malibu USA 3504 1970-01-01 \n",
"1 buick skylark 320 USA 3693 1970-01-01 \n",
"2 plymouth satellite USA 3436 1970-01-01 \n",
"3 amc rebel sst USA 3433 1970-01-01 \n",
"4 ford torino USA 3449 1970-01-01 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cars = alt.load_dataset('cars')\n",
"cars.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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" border: 1px dotted gray;\n",
"}\n",
"\n",
".vega-embed .vega-actions a {\n",
" margin-right: 6px;\n",
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"</style>\n"
]
},
"metadata": {
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{
"data": {
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"
},
"metadata": {
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},
"output_type": "display_data"
}
],
"source": [
"alt.Chart(cars).mark_point().encode(\n",
" x='Weight_in_lbs',\n",
" y='Acceleration',\n",
" color='Cylinders:N'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv('data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Time (s)</th>\n",
" <th>Position (m)</th>\n",
" <th>Force (N)</th>\n",
" <th>Speed (mm/min)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.2</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>1.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.4</td>\n",
" <td>0.000011</td>\n",
" <td>0</td>\n",
" <td>8.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.6</td>\n",
" <td>0.000057</td>\n",
" <td>0</td>\n",
" <td>16.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.8</td>\n",
" <td>0.000124</td>\n",
" <td>6</td>\n",
" <td>24.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.0</td>\n",
" <td>0.000218</td>\n",
" <td>19</td>\n",
" <td>31.62</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time (s) Position (m) Force (N) Speed (mm/min)\n",
"0 2.2 0.000000 0 1.71\n",
"1 2.4 0.000011 0 8.57\n",
"2 2.6 0.000057 0 16.95\n",
"3 2.8 0.000124 6 24.10\n",
"4 3.0 0.000218 19 31.62"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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"
},
"metadata": {
"jupyter-vega": "#ff3596ea-a24f-4ed6-867b-698a56fbef70"
},
"output_type": "display_data"
}
],
"source": [
"alt.Chart(df).mark_line().encode(\n",
" x='Position (m)',\n",
" y='Force (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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
britram/qof | pytools/iat-analysis.ipynb | 1 | 1978 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"ipfix_file = \"../test/tsdag-nonat-191800.ipfix\""
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import ipfix\n",
"import qof\n",
"import pandas as pd\n",
"\n",
"ipfix.ie.use_iana_default() # loads IANA default IEs from module core definitions\n",
"ipfix.ie.use_5103_default() # loads reverse IEs for RFC5103 biflows\n",
"ipfix.ie.use_specfile(\"qof.iespec\") # loads enterprise-specific IEs for QoF\n",
"\n",
"ipfix.types.use_integer_ipv4() # accelerate dataframe processing of per-IP stuff"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = qof.dataframe_from_ipfix(ipfix_file, (\"flowStartMilliseconds\", \"flowEndMilliseconds\",\n",
" \"minTcpIOTMilliseconds\", \"reverseMinTcpIOTMilliseconds\",\n",
" \"maxTcpIOTMilliseconds\", \"reverseMaxTcpIOTMilliseconds\",\n",
" \"minTcpChirpMilliseconds\", \"reverseMinTcpChirpMilliseconds\",\n",
" \"maxTcpChirpMilliseconds\", \"reverseMaxTcpChirpMilliseconds\",\n",
" \"packetDeltaCount\", \"reversePacketDeltaCount\", \n",
" \"tcpSequenceLossCount\", \"reverseTcpSequenceLossCount\"))\n",
"df = qof.derive_duration(df)\n",
"df = qof.drop_lossy(df)"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
quantopian/alphalens | alphalens/examples/intraday_factor_synthetic_data.ipynb | 2 | 5419914 | null | apache-2.0 |
ambimanus/kaggle | 02-digits/knn.ipynb | 1 | 91518 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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dkpOT1TPPPHPf7aOiovTv0bx5c1PontXjUbduXbVkyRK1ZMkSdeLEiTQ5qPfK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\nIAiCj/J/kbbbeFyU8loAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f908f19f450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"np.random.seed(0)\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"plt.rcParams['image.cmap'] = 'gray'\n",
"\n",
"size = 10\n",
"\n",
"# load data\n",
"train = np.loadtxt('train.csv', dtype=int, delimiter=',', skiprows=1)\n",
"labels, data = train[:,0], train[:,1:]\n",
"\n",
"# plot some random images\n",
"fig, axarr = plt.subplots(size, 10)\n",
"for num in range(10):\n",
" idx_num = np.argwhere(labels==num).flatten()\n",
" idx = np.random.choice(idx_num, size=size, replace=False)\n",
" sample = data[idx]\n",
" for i in range(size):\n",
" axarr[i, num].imshow(sample[i].reshape((28, 28)))\n",
" axarr[i, num].axis('off')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=-1, n_neighbors=3, p=2,\n",
" weights='uniform')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.neighbors import KNeighborsClassifier as KNC\n",
"\n",
"knc = KNC(n_neighbors=3, n_jobs=-1)\n",
"knc.fit(data, labels)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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RhpsBHcv5XMwp6KFFskqLUCvfm2++GQiLEvfff/9kN7aBp5LNzMzMDEhRxHCZZZaJpkrW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\naGZmZmYNfGFoZmZmZoAvDM3MzMyswf8BhXF9IAFp2M0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f908c64f550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# load test data\n",
"test = np.loadtxt('test.csv', dtype=int, delimiter=',', skiprows=1)\n",
"# visualize some images\n",
"fig, axarr = plt.subplots(1, size)\n",
"for i in range(size):\n",
" axarr[i].imshow(test[i].reshape((28, 28)))\n",
" axarr[i].axis('off')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted labels: [2 0 9 9 3 7 0 3 0 3]\n"
]
}
],
"source": [
"# predict labels\n",
"prediction = knc.predict(test)\n",
"print 'Predicted labels:', prediction[:size]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"out = np.stack((np.arange(1, len(prediction) + 1), prediction), axis=-1)\n",
"np.savetxt('prediction.csv', out, fmt='%d', delimiter=',',\n",
" header='ImageId,Label', comments='')"
]
}
],
"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 |
byque/programacion_en_python | a-primeros_pasos/primeros_pasos.ipynb | 1 | 856 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"¡Hola mundo Python!\n"
]
}
],
"source": [
"print(\"¡Hola mundo Python!\")"
]
},
{
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
philippbayer/cats_dogs_redux | Statefarm.ipynb | 1 | 845628 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/ubuntu/statefarm\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using gpu device 0: Tesla K80 (CNMeM is disabled, cuDNN 5103)\n",
"/home/ubuntu/anaconda2/lib/python2.7/site-packages/theano/sandbox/cuda/__init__.py:600: UserWarning: Your cuDNN version is more recent than the one Theano officially supports. If you see any problems, try updating Theano or downgrading cuDNN to version 5.\n",
" warnings.warn(warn)\n",
"Using Theano backend.\n"
]
}
],
"source": [
"%cd /home/ubuntu/statefarm\n",
"from theano.sandbox import cuda\n",
"cuda.use('gpu0')\n",
"%matplotlib inline\n",
"from __future__ import print_function, division\n",
"path = \"/home/ubuntu/statefarm/\"\n",
"#path = '/home/ubuntu/statefarm/sample/'\n",
"import utils; reload(utils)\n",
"from utils import *\n",
"from IPython.display import FileLink\n",
"\n",
"import skimage\n",
"from skimage import exposure, io\n",
"#import cv2\n",
"import glob\n",
"import matplotlib.image as mpimg\n",
"#from imutils.object_detection import non_max_suppression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# First, make the validation set with *different* drivers"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 725 p002\n",
" 823 p012\n",
" 876 p014\n",
" 875 p015\n",
" 1078 p016\n",
" 1237 p021\n",
" 1233 p022\n",
" 1226 p024\n",
" 1196 p026\n",
" 848 p035\n",
" 651 p039\n",
" 605 p041\n",
" 591 p042\n",
" 724 p045\n",
" 835 p047\n",
" 1011 p049\n",
" 790 p050\n",
" 920 p051\n",
" 740 p052\n",
" 794 p056\n",
" 809 p061\n",
" 820 p064\n",
" 1034 p066\n",
" 346 p072\n",
" 814 p075\n",
" 823 p081\n",
"Got 22424 pics\n"
]
}
],
"source": [
"%%bash\n",
"cut -f 1 -d ',' driver_imgs_list.csv | grep -v subject | uniq -c\n",
"lines=$(expr `wc -l driver_imgs_list.csv | cut -f 1 -d ' '` - 1)\n",
"echo \"Got ${lines} pics\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"fastai's statefarm has 3478 pics in validation set and 18946 in training, so let's get something close to that"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training has 18587\n",
"Validation has 3837\n"
]
}
],
"source": [
"import csv\n",
"import os\n",
"to_get = set(['p081','p075', 'p072', 'p066', 'p064'])\n",
"with open('driver_imgs_list.csv') as f:\n",
" next(f)\n",
" for line in csv.reader(f):\n",
" if line[0] in to_get:\n",
" if os.path.exists('train/%s/%s' %(line[1], line[2])):\n",
" os.popen('mv train/%s/%s valid/%s/%s'%(line[1], line[2], line[1], line[2]))\n",
"\n",
"import glob\n",
"print('Training has', len(glob.glob('train/*/*jpg')))\n",
"print('Validation has', len(glob.glob('valid/*/*jpg')))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# now starts the actual work"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 18587 images belonging to 10 classes.\n",
"Found 3837 images belonging to 10 classes.\n"
]
}
],
"source": [
"batch_size = 64\n",
"\n",
"gen_t = image.ImageDataGenerator(rotation_range=15, height_shift_range=0.05, \n",
" shear_range=0.1, channel_shift_range=20, width_shift_range=0.1)\n",
"\n",
"trn_batches = get_batches(path+'train', gen_t, batch_size=batch_size)\n",
"val_batches = get_batches(path+'valid', batch_size=batch_size*2, shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from vgg16bn import Vgg16BN\n",
"model = vgg_ft_bn(10)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model.compile(optimizer=Adam(1e-3),\n",
" loss='categorical_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/3\n",
"18587/18587 [==============================] - 556s - loss: 2.9649 - acc: 0.4286 - val_loss: 3.0638 - val_acc: 0.3797\n",
"Epoch 2/3\n",
"18587/18587 [==============================] - 558s - loss: 1.9663 - acc: 0.5787 - val_loss: 2.9769 - val_acc: 0.4193\n",
"Epoch 3/3\n",
"18587/18587 [==============================] - 557s - loss: 1.8001 - acc: 0.6075 - val_loss: 3.0384 - val_acc: 0.4277\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f6cc79c8d90>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit_generator(trn_batches, trn_batches.N, nb_epoch=3, validation_data=val_batches, \n",
" nb_val_samples=val_batches.N)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/3\n",
"18587/18587 [==============================] - 548s - loss: 1.7652 - acc: 0.6239 - val_loss: 3.4739 - val_acc: 0.4446\n",
"Epoch 2/3\n",
"18587/18587 [==============================] - 544s - loss: 1.7735 - acc: 0.6368 - val_loss: 3.7832 - val_acc: 0.4279\n",
"Epoch 3/3\n",
"18587/18587 [==============================] - 543s - loss: 1.7729 - acc: 0.6399 - val_loss: 3.9534 - val_acc: 0.4154\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f6cc670d390>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.optimizer.lr = 1e-5\n",
"model.fit_generator(trn_batches, trn_batches.N, nb_epoch=3, validation_data=val_batches, \n",
" nb_val_samples=val_batches.N)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"last_conv_idx = [i for i,l in enumerate(model.layers) if type(l) is Convolution2D][-1]\n",
"conv_layers = model.layers[:last_conv_idx+1]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"conv_model = Sequential(conv_layers)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 18587 images belonging to 10 classes.\n"
]
}
],
"source": [
"trn_batches = get_batches(path+'train', gen_t, batch_size=batch_size, shuffle=False)\n",
"\n",
"conv_feat = conv_model.predict_generator(trn_batches, trn_batches.nb_sample)\n",
"conv_val_feat = conv_model.predict_generator(val_batches, val_batches.nb_sample)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<type 'numpy.ndarray'>\n"
]
}
],
"source": [
"save_array(path+'results/conv_val_feat.dat', conv_val_feat)\n",
"save_array(path+'results/conv_feat.dat', conv_feat)\n",
"#print(type(conv_feat))\n",
"\n",
"conv_feat = load_array(path+'results/conv_feat.dat')\n",
"conv_val_feat = load_array(path+'results/conv_val_feat.dat')\n",
"print(type(conv_feat))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"#print(conv_layers[-1].output_shape)\n",
"def get_bn_layers(p):\n",
" return [\n",
" MaxPooling2D(input_shape=conv_layers[-1].output_shape[1:]),\n",
" Flatten(),\n",
" Dropout(p),\n",
" Dense(512, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(p),\n",
" Dense(512, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(p),\n",
" Dense(10, activation='softmax')\n",
" ]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p = 0.8"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 18587 images belonging to 10 classes.\n",
"Found 3837 images belonging to 10 classes.\n",
"Found 79726 images belonging to 1 classes.\n"
]
}
],
"source": [
"(val_classes, trn_classes, val_labels, trn_labels, \n",
" val_filenames, filenames, test_filenames) = get_classes(path)\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bn_model = Sequential(get_bn_layers(p))\n",
"bn_model.compile(Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 18587 samples, validate on 3837 samples\n",
"Epoch 1/1\n",
"18587/18587 [==============================] - 11s - loss: 3.1639 - acc: 0.2381 - val_loss: 1.4330 - val_acc: 0.5176\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f6cb53c44d0>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bn_model.fit(conv_feat, trn_labels, batch_size=batch_size, nb_epoch=1, \n",
" validation_data=(conv_val_feat, val_labels))\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 18587 samples, validate on 3837 samples\n",
"Epoch 1/7\n",
"18587/18587 [==============================] - 11s - loss: 1.4675 - acc: 0.5147 - val_loss: 1.2066 - val_acc: 0.6161\n",
"Epoch 2/7\n",
"18587/18587 [==============================] - 11s - loss: 0.9823 - acc: 0.6595 - val_loss: 1.2035 - val_acc: 0.6224\n",
"Epoch 3/7\n",
"18587/18587 [==============================] - 11s - loss: 0.7936 - acc: 0.7255 - val_loss: 1.2357 - val_acc: 0.6046\n",
"Epoch 4/7\n",
"18587/18587 [==============================] - 10s - loss: 0.6630 - acc: 0.7732 - val_loss: 1.0442 - val_acc: 0.6729\n",
"Epoch 5/7\n",
"18587/18587 [==============================] - 11s - loss: 0.5988 - acc: 0.7995 - val_loss: 1.0698 - val_acc: 0.6643\n",
"Epoch 6/7\n",
"18587/18587 [==============================] - 10s - loss: 0.5507 - acc: 0.8176 - val_loss: 1.0956 - val_acc: 0.6586\n",
"Epoch 7/7\n",
"18587/18587 [==============================] - 10s - loss: 0.4997 - acc: 0.8299 - val_loss: 1.1512 - val_acc: 0.6529\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f6cbf72d2d0>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bn_model.optimizer.lr = 1e-7\n",
"bn_model.fit(conv_feat, trn_labels, batch_size=batch_size, nb_epoch=7, \n",
" validation_data=(conv_val_feat, val_labels))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Let's predict on the test set\n",
"\n",
"The following is mashed together from fast.ai"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 79726 images belonging to 1 classes.\n"
]
}
],
"source": [
"test_batches = get_batches(path+'test', batch_size=batch_size, shuffle=False, class_mode=None)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"conv_test_feat = conv_model.predict_generator(test_batches, test_batches.nb_sample)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That took *forever* (one hour? didn't time it perfectly)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"preds = bn_model.predict(conv_test_feat, batch_size=batch_size*2)\n",
"subm = do_clip(preds,0.93)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"subm_name = path+'results/subm.gz'"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"classes = sorted(trn_batches.class_indices, key=trn_batches.class_indices.get)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"submission = pd.DataFrame(subm, columns=classes)\n",
"submission.insert(0, 'img', [a[4:] for a in test_filenames])\n",
"submission.head()\n",
"submission.to_csv(subm_name, index=False, compression='gzip')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<a href='/home/ubuntu/statefarm/results/subm.gz' target='_blank'>/home/ubuntu/statefarm/results/subm.gz</a><br>"
],
"text/plain": [
"/home/ubuntu/statefarm/results/subm.gz"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import FileLink\n",
"FileLink(subm_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Private score: 0.94359\n",
"# Public score: 1.18213\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bn_model.save_weights(path+'models/bn_model.h5')\n",
"bn_model.load_weights(path+'models/bn_model.h5')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bn_feat = bn_model.predict(conv_feat, batch_size=batch_size)\n",
"bn_val_feat = bn_model.predict(conv_val_feat, batch_size=batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try something else - can I look at what the model predicts for the training set?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's have a look at the images with a 'bad' maximum probability, around 50% - \n",
"how many training pictures do we have with bad probabilities?"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.99996793"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.max(bn_feat[:,1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Give me all training pictures that don't have a class 'probability' above 90%"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 1, 11, 16, ..., 18577, 18582, 18583]),)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.where(np.amax(bn_feat, axis=1) < 0.9)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def check_training_picture(bn_feat, filenames, number):\n",
" print(bn_feat[number,:])\n",
" print(filenames[number])\n",
" plt.imshow(mpimg.imread('train/' + filenames[number]))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.5061 0.0131 0.0147 0.0492 0.0204 0.0084 0.3025 0.0023 0.0652 0.0181]\n",
"c0/img_101091.jpg\n"
]
},
{
"data": {
"image/png": 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KoY+oz+UwInjfLOc3OZ35cL4CEUKMxLwNY10az0qiYcXksjABm0twTI6UY6Qn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\nDYjakaSKVCZI1QRGPSlIiOAFG8/bEVChMbpCJorZtKCfLtKxGV//0tf58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HJjsM0nn7/A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\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lQAhFtjrN8Z62MRzdvcv1dkN/fSnXroQoR96vteaNN96A2vH7hOOPnRHVKQJVqCLvFaJs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\nhuPdAUSEK83ozpzRtI18Vr0k81o1RRqAcETnylCp901t8FjCjSV2rQ2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WWI2jOe5sAsuQ\nAfs9pYRMYyUkGcxz/enMAuVBzkP40bl4ZClGEDmE4MGc0LYtdrsGwwjEcQLHhLZpEUEYhkGgOpqZ\nbaTMaPb8M82lMj4lEXbMJd65XsW2B2rYzNiNyhdI2Ykwx6n6vsc4uKrcQzIlpTYyzQ2X9YHnnNG1\nDTabTrKMnQjN0/GIL55/juuXryTb1guRQtL9iBiFTMGUhUJ6gaSG1day3P9snE3ThMGN0iaNugV8\naT/NwyzzhftC05EDO4HK531Vzd8Z77FGMCzObeewsMK5uH3t+dYoQ026QDSHfMo1VgmHNfxcI2q1\np1o3CSjPHTO64oIwSeWU0IZQ+IjBQIqitFx2YE7ahk5qdVNOWi41e8Prezg31l78uyiuN3mKWeeJ\nnFPHAiDvEHMCSBMwaeb0NddoygnJGreXE731UgC8vxL9HwH8I6vX/gsAvwPgP2bm3yeiTwH8GQB/\nDwBIEon+SUgcFQD+d0i78D8D4L/Rz/xpAL8I4G+96eTe48Gi/vceChWu4bqaAcheF47FM4dYPchz\nC+CnhX3X0J8RBVinktvbWzBz4catN4/1D1xfF7MQjZsXKopUY266uYTYQBiBfGjACsG1TYuXL14g\np4QPnj7D8XSCI8Jmty1MR+tEGruHnKUGU+jbqEAyOlFzcguK/L4HawEoXrbNyc3NDaacsL+4kH6j\n204SSZx5AcK5KqUYVtKSNQdVYjxMEuMxpVJ70Db/RjNnc8vMYJeWz5gJQesUTYECpDEqg7lnKj9T\nhs4FmAByTvug+rmsqBbEdj3ee2SXEE0hgEDJldpdXz4rqfoF9KiRPfhNEgAAIABJREFUFYV/C4EA\nqPYRSm0tSBijGucFmiuCUvuYqjCW1mRWI2rJI4TNZoeuk16ObdjCw2EcB7x68RI3NzcI3iP1Axzr\nfCuUbYtBCCbM+GNsug4+EG4Pd6Kku7bUEAJKqTlOyGHJBgRg0TWpQP5Dv1ivAm2XiViEGoAlQ5UZ\nWXWcs8DLesyaD7lWlufg2PozdQxdEnmWcdpaNtjftm7rPQgsk96Wx11CuD54qfFkRhwjpmHEFCd4\ni9cjC8qB2QFBlubrRChMSbZnCrGD3UVlfNTnr+d+/dn1eEjZ5sqDzzECJP1fh3EEzHj2EiwQuScr\ntPEOzUqh55wxjF8xYxEzHwD8dv0aER0AvGDm39GXfg3Av0dE34eUuPxlAB8D+G/1GDdE9J8D+CtE\n9ArALYD/FMBv8B9CZu6bRq1w1rGDelPgzAN7V+X4vvj6ufOsv2/Zul3X4fLyUjyDcQTTTDj/pnOW\nDQeIgHKabGHKQhVEYAaxh/OW3erBjvHBBx/g5vVrvHp9jcv9BTKzwH0JCG2DdtOBTtq42VfzmEmY\nj7A08pxZf27Wo0SEDF4YAetNV0gSPDD2PYZhwHa3w9Xjx2g3AkmR5dFH5fyUL0NPh1lxaa/J+e2F\nArV18dCQ1mJL9iMiD9aYYFTFlrNmrVYC3Tkhv8g54+5wQM5JYsLOo+syDkpVFqcJU5zQ5BbEWmif\nElIIpc0YokMwb1AmDbD55Xm9C5wpEK95juKZ1jFSUWYJc39TANJhA1o/yGLRJ57ZazhZyZWSbFCD\ntttUiE/GMA64vX6FVy9e4Hh3WyBpe97GymPGl8XQvJfkq6ZpENpZidW11Jm5lFf5Tasef1U7WCue\n8sxnpWYxvWQGRsXqY7yxayVqiqnOCwh+vR6ozEk5VxWzq5UrsFQo60QzSyAC5hpSO0aZw5VXa7+b\nbANQ9if0fas9HacJY5wwTlNZS2uPsT7OwmDWz6yTqOz66p/1qI/1TvLrLQrWZEQIAYfDocyDc8Jm\nJcMYp2hpXL7H+CoYixYahpn/EyLaAfjPIGQL/wuAP8tzjSgA/FuQ9m//FYRs4a8D+Ne/gmt577FW\nnKWpc2VFMR6GIxZDLeb62D/tqBd/bT2bJ7TdbrHb7eDHEWOck3vqmtK11VZDdqSYXtaOChQMOhMP\nx5I1LMHFe49xirh6/BgxRtze3mK/32NQGHicJoEzg/QHBM0lO9IyythyUITlHP1AIRk3C7Gex3pz\nmeCx5JumaTCmiP50wjAMuHh0ic1uh+1uK9eilnWZC3OA1YOqDaXZe5ufwdo7mD8MSGsoI27w5Wcp\n8cnaEDvP3mQIEne2TS0KImDTbTGMylxEDm0nxBt932OKEeMwottshA+VLHNVnlWeItI4wnkH3zZA\n8AjZa5kF1ZcsyUiljEXvWZ87Lz6XQeSEWjBnhKZBygljPyE0XmKMGUIujowcNTMzy8PMidD4FsEL\nBO+pRU4TUhxxc3ON29vXyDGi9QFCCoH5eWSGb+U+U86QshChKowpAg7C8KUkJajuQ24xIw0DwMvk\nQfMIc6X0aLVPTMFypWAAi2EuFVPtQa4h1dpTrJGhWrkYiXr9nkz9UnEyz+Uta090ncy3/md7Zd3U\nIuaZxU2ywzOGIWIapZ8nkpTkMAmcayiBrQ/mquWd5jk0evyaaOTceMgTtZ9rB+Kh4yyOaRCzGlPk\nXPFEbZ6sYYELzSxTSRCTOgT2DqcD8BUoUWb+Z8+89pcA/KU3fGcA8G/ovz/0IQvJjPL5oZgVaDEs\nW2S11cgs3KTr453/m2B2mG2K+v1zXuW7jPp7tRDPOZfM1M2mQ0cb3NzcLCxAsxZNoMcY4ajqRpKF\nsMCZ0s155uhQ6I8zi3DzHpkSvCbrhCbg8uoRTseTlhQ4jaEZuw6h226QU8Y0CRl3cM0MWWVJ6nFE\nyMoz6oi05ECB1pUVbfOxrgF1zmHrPaLCXTfXr/H6+jUeP77CB88+ABkyyICrYj2s6TJF+NrzWW3e\n2vJeWMIa5DXFK2tIeWczlXMyJzASyLFmNs9xsXEYkTNwsd+jaVr0wxHDMMC1HTjl0rbtNPQYp3GR\neWmN1zkLAw0DAl31HqETblF2KFD1bG2rocBC8ybeyLzmxKCRTeOJJBHDyRwF53E83OHq8RWGcULo\nJIHN4Nc4aRcVrTts2i0YhHGcsNs2CN5hShHXL17gxedfwEOSeOr78s5rwb9DzqZZTVkIuTq81P+R\nc0BOsyLQeLDF9lKMYCdJUc4rHK3euKy3WeADQNRnTZbsRrOXaArdSpfq9bDuALOGJWt5UMO29d6u\njbV67Qn06ACeS3keqiqoz1sjOcbtXJ+38XPuA4ASey/HyWpIw6gTgeBcMUodpIYYOZcON/LWmevH\neRn40Hys53A91h5rbSzYeZumKfI/54TNZiP30LbYPXqE/W4vxtY4Ybff4+bVK9ze3mLohSkO75A+\n+zPBnSsP5v4DrZM/ai7V5feWxNFvfKjrvx94yHbsd7/288ew+0kpSdp21+HRo0c4HA6LpCNLtijH\n0ytlg3ANyjCPVE+RkJGJ4JwKxSQF1z6Q1H4RwQPY7XeIMeLu7k5ajnVdgZs9JI4dmgahaTBOQ2ni\nnJJwd4KoZOcyZoF1bo7OvVbDrIE0I7UjDP2A1y9fYRpGXF5e4uLqEbz3SDFKVmfbwAehUCPtSKNn\nORt7Px+f9bDelKTW7PyvEhBlgy+ZlQgeOY0AAykxHGdpon7qMYwjHObG2ACUci1iU9awtJnK3iNC\nSyDAhQLRtY3A5fXtmFfDJMkViYGkrcLUQJphbRGiUnZCxdvojwMeXUJ5jT36OMJRhnehNFYWr7FB\nEzoQZuINzhOOhxvc3rzGcDpIM/hhRGvKw54xG1OSGnY69RnSjBsshpzBkaUWuva+1FAQVic1GnVP\nyPzPEOs8PVVCXPl/DZfOcuEcoUj93NchgNpTrZXmGuqtj1tfSw2h1sdcl5atlbb9rMumUkqImsl3\nD3plgNTQdWziQRENNUA41+UnM3nCOU/a5q4e6zl7E9R77jj198qx7G/LN/BekjHbAIIkyTkiPLq6\nQrvbIXuCdy12uz2mYcSjx08QmgYvX7xEf+rfeB02foaUKC2IByQJoVnENepRQzJ19ts5eLQexSel\nZZD6q4B262OtLb2UMlLfI4SA/X6PpmlwOByKh2AWWdJMy1KgbNy3zCDJ20BSaxdZOErhHWJmiZ+o\nkGOdR3IOXuf32bNnOB6PuLu7WyT/1NfddkI64FKCm6QFE4GEWYZz2YjvjKWcmx8ADXlsLy8xDiP6\nuyNiP+DU97h89Aj7i700th6lW4n3rsRjCdAKkOXGvq88Zb34qquMFuGAM7R8BsgZiDGLx+1pIYRz\nFtYYWYONwKZR4sghBPTHk8Kc8zqsDaKsSi9otvbkHNI4gL2HixH9MMB3LQILUcE8tYosZBYFyjyr\nfbXauX4MDuZOl+czTROGfsD2Uix5ByAreXfjW8Q8qLfWogkdHHmdm4yYpfb47va1Eo579bK4QMhM\nklmcUoLUrqhmB2kzds3UVSM4TmPZyxylfMiude3pnUu6qf+e46WGNiw/Z8/CaBwNql0fxxSkna8m\n669h2HM5DOv9LWuOCnFBrYDrLGO7dkMF1vdax2dz1o4+NkcWh7Z9YPfNDLBS7rE8D7am6AqzOwCa\n7rpwJs4pSlvPCwPhS6J054btX/LSN9YFL518knDoxhjx4tVL/PK3voUPv/UtfPLxjwHv0W23cCA0\nTYucgJ8cP3mn8/1MKNH1AipdPLD0PuuHurAUH8gnuadMH1Cc55TuVxUvnX8ycpqVf9M0pWfoMAwF\nfrq3eFVgCew3Q5kZwlgjdZSEjCTevAoobxB4kLKE4IURZLffo2lbXF9fgw+HAkUKRDcXezsn3SCc\nmwmqo5yuCGy7t7UiPjOZdWKvvQRikixO5zFNIw53dxiGAf3phMtHl0WhEwFI1TrAXL9XW8drr8Fp\nqYZBuXP8yzyOCv1wkrlYhwrMsIdmiZfGxsEL16dmRTJLL8RJqf/MywzeFLkI9Ng0cNoDcooRYZqk\nXq8h7UNZx3mz9PMsOoIA8ohFcFaKNGfBg3WtAAA5sfC3F3vEmEoT9JSkzCVOEtdtNgHwDTIInoVU\nfEwJtzfXePnFFxj7AZ16s8Rx9thtfTIqz9QME2kzZvSGNdzuQ0CyRuukhhEvUaUaWTKlsdj7uL93\ny/tnYMkajlx4oryEMc/Bmmt4dX3s9bpfeIvlWd7PaC3zUXm03vvCplWIQaymTOe6GFh2TO2YA62l\nt31gnuw6/rv26NdjrUDXc/LQ9951MLg0GjeE8XA4SKJXCGKAMkCeMKaIi0ePcPXkgNPhWAw87xyu\nrq5wd3uD16+v33rOnwklWkMvBuFKzEreXy/MewuVlx7ogwqwNvfx0y2Gdx12rSCA9T5PJ4lR7vd7\nXFxcFK90GAYpQ5ksxqLfj1EhVbl881IlX0RqEYkFFiMn8ZGUMyh4BFYmHh9K55mcM775zW/i+vq6\nsJ1Ywtain2brSpC/hoJoARPdt2bvD8L6kXj1elLO8ETotjsc8oQpRlxfX6Mfejy6eoTtboOonl/S\nInYmKj1Z639rq1mERoKVeXjlUAWML7biuiUH51CgX30D3nnEmNWTcRjHiNbJufYXFzgdDoAXYXB3\nOEg9HksyimuaQsQQQhAYPU7SlzHGwisbU0QbmnJOMBcv2IwWE5xFoAJFkUryl8bVVZEQSJRnCBjj\nhDZ0iG7C2A8YhwE5CbTbNhswUym/YhbigtPhgFN/RGgcutCgH6M87szFkzCVPz9/JbOIAs1SoGJ0\nnFMatn5cZbwVkgNNhDEo9Ny+OueJrmWEJSat2bpsmBxZe4S18jGDbWFgYVZkxUggFI+qrCss4601\nYrGGdA3BWJS4lGYQahjKh/Xi57iofEjLpCClJELRILWh0DUu9d7n5WOtQM9Bt1+FvLTSzwQWI9M7\ndNsNhjhhs93io48+QtM0+PFPfoIf/O738fLVDb770UfYbHeYxglxkNCK9w32+8ufVSVaCTsIdOZ0\n8xTYtlha8o21VVT/be8DClPYeqo+R86VGFJpr0PCM/mQR/Umj/UctLOGhx+671HbEOWc8erVK+x2\nO+x2OzjnCperJw/vpYRENqS2lUJVTC4yROIgWe+LGSTM7zKPWYWx8xh9xqQE+ZuuwzhN2O12GKNY\nvMf+hMgZTfAivJymnzdN8YQs7sUpKZuSPiNrkA2Uer3VBFneSXnuGZJE4liTpZyDR4DvAmJMGPsR\nXwwvcHG5x26/E2L/5AU59AQ0ui5UKElmjpQ42MwQATkrgws7QP9lgpaziHBxRBpPJMAJAsIsgGNK\ntVCUPpAxRnBm7Ldb7C8vpYXUMMI10p8SzJI8w62sRScGFDUeoQ3ohwFIDJoYNLIkVQUGYF6XCGNH\nnV4LgdlUVip4HJXZJFDWmlrnhPjdBcRRWli1vkGaJvggRtEUI2JK2Gy2IB+UEUxKjQjANJzw+voa\n/ekEABinCcM0ovEODIlpigfJKOlfWepPZ+Xj4MhrUpQkEjlN2CLvwFn73HISKNIL2w6cL3s0MyOn\niBRHMcLMI9R7L4YFTIFn3fvKAKb9OjlnzVDVrjiaeCPkEGpY6wK1LOBSNkISEjC2NNv7i84xPGdw\n1+Umpii7rpsVrXrZ5J1mTM+I2qTZtQD0PbvPyutn3fXMsP+APMc5SkjUknVm5MJeKwtoKZ5sB8/7\nlOx/s8Hz4HjT21zJBzK0hEvjgdubWyADDh4vv3iFZ0+f4aNf+mWcJsbx9TWOt7fYbjbIMcIrN/CU\nRvjm3dTj11qJnnP/hUBBf9dMyRo+fCj4/RDkasLOjm7Lymm2X7E8dfNxKdKXjbNOLlgrxLVldu78\n567vbAyFlokHXlP/j8cjvPeF6ed0OhUI0FmdmPaZnCdPdIYVoBAg8TNSgeAAxwTKwpSDxpUgfmnq\n3Qi9IDmH6eZG6OAU+uuatsSaAUbTdmjaTujIUhLY2BRGfe9zUu5873llfAjGKsw3zCBVgkEFjG8D\n2qbFOI54/fIG1y9e4/Hjx/jGN74BOMJwGhB2bmZuyln7WpplzypQCY5aFT5O4FuwUCoSWT26FHbD\nQygmhFSd1OqqPSHnZHVlXT9DikLCTh6xz2i6DvHugOHUo2tabHYbMEizaIHsGRQ0iS4z0mkCIYEC\nAy2DnK31iBQntK32pswOTHZ9dU8eKDWeGAflP+eQSMoBTocRl1d73N5do9u1AIQQPgHiKZPGiCFE\nAY6B4XhAfzxgGgY4JimlCg6ZSfp+qvNDCsGBWfld5R+JNlEhPiGnAO8bNI3HZrvB6XjEFJMYidMI\nDo2qAjUCoD1AWaglHLh4XQStW64yhSFPvFKiBNLWbwSGUTaadCBNaiHOKnNMUVWMOIZQs/l28m+G\nSeeG2rArOCOjbF+vYWcmwpQTRuUbtrI300a2r13Oxftcw8Ks/MVws2fuFMHLBcYzeWrHpDeQ09R/\nGQRcf/cNg+/LR1cdkPQEmRjMUv7kPCn5hijG490J1y9e4+Xza/zSdz/Cn/rud/Hbv/3bmI5HtGrs\nFsPBKCTfYXytlShgFtn8ABzN+HxdumKffdvPWjmVjLUqM8/+1UTuxSMF3VOYdbxj4elmvvfaH8bc\nGNRkStNilNYRxuKVNcxq3+OMEsc7F4Mxy1cEgwMmaQzdaCZuKRnyvljLUz8UxVHT11n6vV1PcH5R\nx/em+VlDQ/UzXD8Pe81gfTMsbm5ucHd3hw8++ACXVxdISYv1NatT/nDKbepEwDhXWntmzlAWeIA0\nG5aqxt9VnOuewFutAft7moQr12Bwu+YZRmSJUzoh+TbP3jcBeewxjSN6P8DHAJfnDHRAIT01norq\nZpIuMMQAG+xcQROY1zS8CP1xHAHeSclQzuhPPaYxomk7SZayDiX6b4pixB0PR/TDgK1vKiRnaVjW\nhm4h37e9BGgN83Jd2p4f1Hi1uysGkLnu1XCOkNP59WXXsfCA3cxbu45f2t7JOc882GeOV/9OWK6F\n9bqwOSAIl/ZcaxzKvtlsNnDOYdR6yIiZMYnZDIOZUAOquDhz8VbrtbmWSQbFr+Hsc8b/Qw7Jm+bi\ny4xz3zWFlln5yTmBc0RKsq5DE3AajvjBD3+AX/mFX8Wzb34Dr29vAe/w6NEjjNpXNkO9+XcYX3sl\nCiwtHBHq80IvQv6M4LK/65/rzxissR6WJemcw+XlJXLOGPoeWeG5uozh3MNmLL3ScwkID8G7X2bU\nHs92KxR9vbL82DnOKXPbLOtkjFq4kHMgp153rmpsoQTaCjkREbrQFN5bIqlz3Gw2peC8ZBLnDFZo\njlSR1tdUP0OrJ63naiFwzwiG+qcRVtzc3ODTTz/F65sNLp/shad3s1EKRCdKMmmsGCQMTDnVYXBB\nvIikptKuCeLE57zkPj0nqAjAME0lvpyIsNvthNBiGIqhAQBpikhR4FRPDghSF9p1HeI4IQ4Rw9jD\nDw1oknn1SgJBRNo+LAn8nQnaabZaj1r+VBRogkEBgQR6PRwOeHS1R3AeKUVM4wRAEs2IBdJ0QKGE\ni6OUQt3d3SHHBNauIMQzqlMrjvVeBNQwonn+UkpA0kxx79G1HY64Q86MoMiJeS1S4pCRY5QG0Wfi\nmOuxWGtE957fes8YDIszQvicHOJqf9Xwbb1niQjBe2lDWM2DGcDrDF+qFqUAMzMJQX0dOKNA1/N9\n7h7eNGfvo0Tf9P7bxrnzSDKkPO9AkkWcYjJwCpETKBD68YQf/OAH+PZ3voOPf/M3sdlu8fzzz3F1\ndVX6sr6rev/aK9F1cN25MGd96nhIQdbvvUmJ1jWD9jlrq3NxcYH9fg9mxqlp0Z9O6PuZh/MhL3Ot\nQB/6+VXMz5wEI5t7HEdst9ui2EyB1ZvDEo8AevA6ytzlDEwKYzJrbd9SiflGSjGGKZbOJsMw4KRx\nMfPuTYC4EARac3ObsFrILDbyqntGfW31M1gXxwMoHe/HccSjR4/kOfZHXL96hf1uj13O2oVF4Ooc\ngsZanMZtVXiRxkZVgSIDWedDkpTm+aj5VgEsDS5gTsVXT7RpGlBohJWJ+qI40pRnkgon7Y9d1yFu\nE/KU0McTYpowTgPcMJdkNE0j2bRjggsGmQuMyzAlTzBzARbXA1DiiUmUa5osoSaiH44Y+xGNb9A0\nLTgLcxNli20LdWF/PKE/HUsrvXV7sPo51mujfnaSzCbZxdM0AV4bQHuP/cUeh9tbMSRiQmiyMtcI\nuXqKSWKhhWnn4XDKOuvW1lbtidV7Zu2ZvmnfVC/ck0kLgwEovMDEM1uXrQPbv3adKSVEXiJg6+sv\nP+m8jLpvOGgsWMe5etUvM35aRboeAk0DBKfzlUEQWs+YIghA226Qwfjxjz/GL3z0S+i2G/TDAOeE\nW1eUaMY4vVujsq+9EgWw8DqFw9TdE6bnvFH7/SGYzca5hei9x36/x36/L9lubdsAPDdlfptHuX6/\nHg9Bvef+fmisYSbbVJbBaxmdptCsjq0oLhbYpxYMZ61TVZzSL1REr9W/RUxgAE3OQGZsNpvFvRFJ\nhxKzpCf1DtqmkcSgCuJlnonfi3cMAHwfVjJlXHs1a6+DiND3fYHDTGB2XYecSWsZ77C/uMDF5UWp\nxxRKPwf2DHiuFCIBzkmcGCyemM4hSDyxddeOei5KUoh66JYN2zQNcky4vLzEeOoLgbpQ7VjccjYi\n27bB1DWCjExaEjNlyRjOknnYtBGxkK4LnMuZMdcKqQIlpQakDGZrWUWSjKPdkxwDU0o43J0Qx4jN\ntkOgRjrXuCBhAZbPjUko5aZJugEREZAk4Snl+zWcpqhqGktg3vNmdFCMIIUr7b0Yo7S9aqMeI4Ad\nz/zNdrx3EN5vMrZrJVvvsbcdZ74fy9aZXy8t0lYkMGuj2MqdavRLynaktaOG3hchL4OPqX4BDxv8\n5WNn5MlDsu1N9/6287zt++trqP/2sBp2gssQAylD2hxywqbrsNt1GGJC//oOd7e3ePbsGW5ubvDh\nh9+WkjJILoU1oXjb+FopURGqy5hlPWpB/ybL6k0K9Nwx6wVvQv3y8rIQGJTFm4RWqmka3N3dLTzS\n+joeslbr66lhnbWXdW4oavXGsfbCTdk3TYPtdlu8VIsLeN9I7ISXsd31wrVry5akAICcK7WnRCRe\nVRULtbikecPGB9z3vWTKei+1jiFU9G8Sb0zG78qsCSL53rM9V2Re/6y9QgClzZklXzSuQ+NbDNOI\n4TRgHCdcXFzg4uICriFw0mN4oUEjU+h6DgcAbvm8TLjWpRZEVAr2AaDXVnfM0rHH4NkJIy4uLvDF\nZ89L3A+ZEMeI2Ab4KcK3Xup3g0fXtRi7FqfpiClGuDEiTgkxZTiSutMTDUhpkszssv6TeI+k0TOW\npB6Ch3Nc3jNPJKcJh7sDfJBM4xA6BN8iRSn5Ec/VuEo9+uMJp+MRYz+AU8aUE5rQYBpmVqZ6r9l8\n1slyRYkwYxrFSMsEhM221H1ut1ucDkcQ6WcKciFZsDnWZSZYxFZrD/Oct2X3b1DqtPJY1op+PdYw\nbQ3H1vKm7CkLSwEFeVgfpz5PvfbrY9XXVq5FsoTuvXdfRi2P89DxZhTj/j2fk3sPGRtvGut7qpVy\nrmRmSgkhJnhH2gQe4BSRhgH7/R43hx53t3f4xrMP8Plnz7HdbDC+HpCjNnM4VwlwZnytlOh6LBaY\nwSpm2T0w3scDLd/BcrHknEvGq2WiAoLDO/WCjaPRPKv18d8laP2QMWBj6a2+8bYXw75nnpmxrVxd\nXaHrOuGOHAYhDFdC9WWq/X34y4RQYi69Gcu80rzpBrWYjWnGFILBtVa0fzqd0HadeF2OsNlskadJ\nEmdCQM4JMcr1u5WXMgvM8/d+7uf6NfHuGJe7C4zTiNu7W7zoX2DoB+x2OzRdi+ADqJsTdhhi6HWb\njaKhVOAyOM1srAwSW7f2e9/3uLu9RbfflZ6xk0LvDiIULi8v8eLFC7RtizgJ8QU84BoHCgIjh6YB\n7QlxEqNkjBMwDBjHDXYpl13vvZeEGmZVdqywnXqnbPPi9DUThozMCc5LLu/xcIAL4oG7oLFUJoCl\nMThBYqJJCfT7U49RDQQPKoQI61F7ZOW5VIomRUmOIieZwtM4Imy2CBWbUMziWcRpgg+N7ru1sc2L\nxDq7z4fGm4z0Gto9p8DOfXfdUs32g6E5gBp5QCm7WceL1+tXyDVmj7N85sxrwH25eN5TvO+Fn5ub\nc3Kofna1J/2uHmlOSyP+ITiZvJQC2fFTjsgxoWmktI5yRjqdgLYDGOhPJ/z8t7+DOI6YBjHs4jTJ\n2nqgH/N6fK2VaD3KIsgPa5OHvJX6tTd9D5it17pZtn3GO1fWWdd1ZWMsGuBWi/mhc62h5/Woz1k+\n+w5wyvp+asvQe4/r62t0XTfTBt4dS+cJiyfWHWLOzlGSnjeOK75hKGCoCs+OY6wiljTjvS8dS4Zp\nhFcyeQdpqpshnic5gRUdIO3XlF2+Fiz1dT30bB+E0iGJGcEFOBDa0ODRxSPEnHB3d0CKCZePHiG5\nBMoRvpkNKR+CrA2tW/RBGoHDA0gCV1sooPa0pmkqRBV12YLBzZYIttvt8Pz5c5xOJ+yCsEQNwwmR\nJzxuHqMLEvsjAM2mxXa/Q7rrMY4RwzBimhKaRsgtgg8Y4gTrUmRemfxuAkpqGGWtzHuLAHjnkVX5\nOy81eTJnqqiylXOwGqI08yZPU2EjWscU62dm629tcORSaibniDFiIoe2adGoUmqaBmkQ+H8YR4RW\n2KuIzhvODyFE63HOY6sV25s+f2+tkczb2lCoY6F2jKRJWrUsqY2yWh64ao8+pCz1lXuvnTOS1wq0\nvv4vM95VeZ4bbzpngmSYMzLSOIIc0ASPQITTkPHhB4/w6tUd7l69QvAb8BQRyGHXbvDZJz/BdrMF\nxQyXWdonvsP4WivRGvqc/373oH5dw7m2GhffqUpc6s/VlieB+QARAAAgAElEQVQzm8taYKB1HKe+\n5rcJ9rVntd4k9eb9MorUzmXXY3E2i43udjs8ffYM4zAWBqTaAqzPXV83MFvLpYZU70HYRCroRb1g\nO67FPruuA4gKcYRdV12uZL83TQNo/Ln2mOs5rp/Ru3iijgidlwzQUWOwbdMgsMSQb25v8erlSzx9\n+hRdKwlaPkg8mEjqU8X7nOc3pqSkFfNaqrOk+75HzrnEQq2kwQwO0vmwZLbD4SBzS8DxeEIfezgP\nPHr2CN2mAYjRtg32+y2myDgNCeM4YZxGdKlF8B4heAz9KHWmMBAnIWW+V089Z7zLM8ycCyVhShGZ\nCZ12r/HkAUgvVWYlMNdawxilybPF2oUNieeesqthZVi1EmXWNm4kxk5MEVNOCOQwTSMa58sa8d4j\nQ0j8NymC/Czyyh6r1ss59qJ61Htzvfbr19/HE63Xnn3+HCQrHvOyrv0hOXJOMZ5/7+HPrY5YPltf\nV521P6+V+99eh4PeV4nWsupN381O+sA6lu5bfiIgZ0xjxp/+7i/gn/rVX8Gv/w+/jk9fXgPI4Cnh\nsx9/gjxOQGgQaZyTGKefAU+0Tgt/VytyPR5SnG/7zloxeu9LXdY6Pb0WAPb5d11Ca8X50177etTJ\nNDVB9vF4BNGAy4tLtG2Lly9fFiF+TqE7APnMtUhkzS4Yi1iQKbzD4VBiycySSNO0LW7ubguseX19\njd1uNx+XqhrMKnPTnkttwBSof31tD2xmE/bee7Q+INMscNrQ4PHVFYZRjItmL82mhR5xLvmRDOOZ\nKStsGrRdW5QhAPGUUsLd3V25bxOeMcZSspE1KWscRzgiPHnyROOlQLNp0McTbm5u4FrAbzxCcyme\nWNsolDVhnCRmOU3yjBGkftfu13QJK1wLY1XiOmxSNcEOXojnnUNoGgCs0L9frG9i0qztjBilmXzf\n96KUARj3JtGcXLNen+c8RUcyN0xA4gxkC00kxBCBShkxC9VgShnuTLKIKdIaXbI9/T5jrUTP3c85\nL7b+rv1e4Mhaqa/kTj0v536eO+f7vvYu412Uorz98LW/bayV9UOyj5w2K/AOPCV9rhlPL3f40Y8+\nwfWfeolvf+tDPH/5GjklfPjNb5a49pMnT/D69WspLUsJMf4Jzc49Z8mtPdI3jtW8q/NY3ju3FKj6\nMOGcpcVFgZZjUPU545ycEZRS67c4CrNW5gldoXx+ZlkqF4zqYCSespDEv9vCDIUuRQ7gnUeeIhy0\ndVRmoU2jhLvDDbquw8XlDilJjNISKawBtXiZlYfPDI6ahEWhsMMgZTCrYDJBS6Sk4RPyJF4ptwmu\nCSWuzMy4uLjANE24vb3Fdrst8DIRIevvOQsBAoCq0Hz2NkKOAAw9EOiyzv0hMgOHMMEvWwkSAPLI\nmUDUILSSDPT5x8/x+OkTPLq6QrdpEJoO8A7b/RbtppO6NAI2rUfjNeu5IYzTiHE4CBXjKI22Ox/Q\n+AZ+c4EEB247bJ48xl2eQDkhXDQYj0dM6YTNFoh3L+Fii/3GI0eP4/UNjtsdtmGL0Epj7nYX0I4R\nYxoxDEfcHRLajkB+j/3lJW6Ot0ic0Gw6HA8j3KBsW1qSA5YsT1kTsUxFHh2a0GlyzwgfAggbgBvE\nTGBKIMqy9nNGYKB1hCZn+GlCyEJU70lWYxKKIgBSTuGcJDm1rTTrTglFIRJ5qdNN4q03CaBE4Dgh\nJYBCK+s8BEyTR4oTHDHi4Rb7IFm7Pk7wOSFnBmuY2LL6mVnCQpzmtZvNOJO8h0BSUgOI5yNZoUpZ\nmIW1KLNkRTeNlCcBBOeh2bRWdkPomqDOQELUfrEm49aZE0y1/FtmK9v6BXhBL3jOyywyE7GUNdVJ\nm/cVMWAN3AEoExBK20QTdgQgsyj+pNfHEGpDxloBypzJ8RXGLoxIrIaQNGVnNxREkIsgVtlX9idA\n04QWAKaEJnhEBrjx+Px4QmgC/vrf/jtgZgybBhwjfvjJP8AHH3yAZhewu9rix88/VkOYkOhPYHbu\nV+F5/f9l1F1C6nv6Ku7xq5ony9aNUWo7rb5wGAbc3t4iWw3lCtounvnKuj53jeb1rdEEDwb8XLrU\ntq2QxKvVaJ6ilafYOe0Y5lXU9aWBtLB/bjuu6ex1C7aZEeddxuXlJb74/Auc+h4fffcXsb+4QLfb\nIiaBOhvtzxnjpApY4qJ9L4xCOTNcCJJ5HgK6toNT+sOu67DzGzgixJzAWWpdN7stvANuh9fImeGV\nhzjGhNevb7Dd7bAPHp5lPlp9bkapKHMCQMti+tMI71s0bYM0DGdhSHuuNpeJNYvXCbWavc7MSssn\nEG5mo8MTPWTGjsHV8/GXcKaNdTcdubYlNGiJOPbMrdHCmoCg5qNdr0XQ+++bh9Z3uS437wFDqAAs\nPNycM/phUON8hoDflhj3vtdXX+P/196bxlyWpPldvycizl3eJdfKWnupdnW7Zzzd9HhmbAR4bGNb\nYBmNDQINIyMsQMgyBskgJJsRQkZ8ACSExeZBSIgPtrElg2WWT2OPBwGe8dCe6Vncs/X0Nl3VVZXV\nub3bXc6JhQ9PxLlxz3vvm29mVVdmZd5HdSvfe+5ZIuJExLP/n/oe1uYydJxfo5ehkkmsN96ofAMr\na1xM60FRxU0gUlwXmaFL0UJSX2rvcamOfSnpQCEETILZySlfv/+AT3ziE7z0wi2+8uu/oRrouvX6\nQvpIMdFCl7GNP+1UR2jCagHVppHaZ3EpKspp1iDeLyMteXgppZ6ZGmOYTqe89NJLvelDpezV5lEH\nIMGKkRljtPj1sI1qRyyqIEkCXiDFFTzfsO5rGae2bXt/am3eL2kjpT0iimUq1YZZxsyk82a7bWN3\nzm2Q4PqN64QYeO+997hB4jBGxBmtRtIlxJZISc3FjClCslg3xiG9GbdpRkzGU6J1xOSxOfgHwFlH\niIFYzIyjhr2DfWazGV3QGqQ2WU7PTnnw4AEuw8GZDMM4GU80IjZp9KGmHAVGzYiTozPGo4Azo75g\n9ZCh1NaelJLi3BrVJI0x+JiBH6IWvc5vOZvz1VKSkvpPS8S6qza3TS4LEVljfCuT3moOFddJmRdF\nyCran8uBXmXjrNNRhsx5mzl0MAH6cRhG4tZtMkZ96mUe1hH6hfH3sRNicrDcesTqZdfvJutcXzC7\noo1uJVmhVNX3OffsBGsWsX48WCuYvo2Rdp3P1jmjoCRGNXndK9QCJbmob0qai23yOBMjyawLKY9C\n/ZzN+1Lvgsr3CSFy48YNjo6O+oA+RXd6BgOLaon1cZzTTyPVGthQ4n8YEx0yXjV1fXCoRyEEkBUI\nQF3yaX9/n5s3bzKfzzk6OuoDqfocxky1ZL3NTzTsc4wRPApL55KCkRfgBTE040mv0XVRfao1LnDN\naGs0IIkhY9+yBjAuDAMrtvvXh5t8KcPVNA3ee9781re4cfMmh1cPGU+nxORxjSOKmv1SytVrRg6X\n33vR5p1ziBths6lLEgqN5wMpaUpPyChGKSXG4zGt95gYIeQUmxhY5Jqp48lY/ZdiGDUN08mU5XKh\nwWJ2zmQ8ZTwaEYLHdx1SRRnXeMU14EHPOLCkEIkZ1F6KhpHNsuoCyybOXI0nCnifLQnB09hxfu+r\nIKP+nVTjXPsGdW7J2jvq/aRVLEIdpFWAF7xXBl7wZmsUpJT9X2s+zS1UmyVrc2oxXdbruFhPyryu\nNcxeQ7Xr6FUXBzdVPqG1oKCyNxb/dlRrQD7HSB2stHp+iKE3vz6MNo1I2vBj7XFK2b1V1mESNdUG\nn/EzkwLFN06xsjsfaZxAbpMhZb93fow8+v5fB2kVsJYEGNcQQ2Q8HvHe7dvcuXu3P6fk5V6GPlJM\n9Fmi4SYBj554fE5yz857qWTGjRPhkrOjRyAaRLeGEJjNZr059+bNm3290hrftZh018w31bzP1hpI\nJU8xm1dFpXjddNcRisp9C9NumgZpl321mgIcsXrGejBKMiX3cFhSqvTv4bp/rb2sIoLJptMRt999\nlwdH9zk4OGDvYJ/RWGH7bIbc0yC0HJXqNI2mcao1GVRKj17BxjGa5tI4LeHlnMO6Bmum3L99n8l0\ninVWNdKzUxpjWeZ808lkomOL5PShcc6v9SwWcy2iPr1CSsrcoKMZ5N9ts5C4jJVMCL0WVRipNdkU\nVwmCpS5pzxAH5sptPKs2+a9M9ishqbSnnmdF4xyCHZTfhmZgTZm5HOXeEMjVX/I8xZqVpUH0TMfK\ndFu076HWWFwPxdJSt2uTSdcUXLuqNfQlDFdKBn0SUHlQ0fJKRZz8NyU+4NEDM9O2v4u2TmakaPxE\nMxox2Z8ynkxocn3bk5MTju4/YNl1WGtomlVMQ0gas2CsENb2jUfXRM+ZsBGcaxTcZTrl/r37EJMK\n7om+ZOBl6KPFRDf4Ch5X09omxbwfze1R/RXbjq1Jt1V3hxrr8Lo1Jjy416VMVefaA8h55l6k7pKa\nYYzpMYQLUEPt66wBFXy9ufVcdL2PIReoxhoN1IA+BSJ4r0n61vYJ5UUDLRi4y+WSg4ODfgMzxmgV\nGR/6qNe+GkbOXa3Hpt6Q6zEsfw+/G2NyEr9ec3BwwGx2xrfu/jYITKcTpodXmGaYyNFoRJP9oCxb\n5mczRqMRB/v7jCcTTAw4I3SAtY7gPYuuxTXKrNq2o13O8bk8m0TBjRrsIveJxHw+5/TkJPuys2nb\nNVCA7b0ntJ5gW67sH/DgwQmvvnKdbtautKoNZsIyRiEjwBB1cy6mSGJSEAgpgT0dgchoMqVdznt/\nthQBK0VKzbjaHF+nPHUZ7apEb4uskIuGDAnWhdKCilWYUjHrlsjo2s87XCNDYUlEBZyUBUmA0XjE\nuJkwn89XWropKGNpVRUk/7aJMTqzEjZrk2PdL22nmj6HtAoqyi6DlNaqBuWZunatnl9MqOv327g3\nJvV9appazcY1J9UHT4iRUTOiW3ps03Bw9QovvfwSL7/yCteuXcNlzGyTMY+dVeHv3bff5s533uP2\nO+9ydPcewspqJVbwXVorS7Zt/ytWpSGtW/bKO9AoeWttv1/VSokKXM+gOfd5oXXJX879tv3CC+9K\nMfdsFfsfk2KMnJ6eMp1OuXHjBovFggcPHvRm1LJxqR9C8WBThu4b+oVJZUNOJFmXIMsmW6T2nkmO\nmjW/qfee+/fvM5lM2Nvbw1pL27aMbINzWvNSNUefF0rqJXn9N64tpvrvc6OqlsW1t2QRDvb22Z9O\nNQq3XTI/OcV3Hd1igbUOZy0mo+do4FTD4vSM8XjC4ZVrTPfVX9q1HT55mmaFXLNs1VxrMPgQ8DH0\ngkKMkdh1hK5TjNq2VSBus6pyUtKUionTOYegOMIS14W1dXdBtbGXnTSlXHdKaJdLYkqMxiP1bScF\nICmMoUbjuQwNTavbBM+aaZY+LRaL/lg5r7gjSs5z7dPcZAUaClEimh8bUTPtOMNTxhhpRqvavDFG\nrRZjPrh11o//JminvKZTiVXd+tj37/4air0R1CQck86vxvH57/8Brl+7zvUb1zXn2+Sxtgac6yPo\n25iw0z1e/8xn+cTveIO733mPd956k6/8+m+wPDtjPG5olx3jSZMtJY9Hw3m08vn3J/QfHeNHUzh2\nTPQpo3rh6ktct81f+GIvtD+U6f8IvmTp/7exjTUVDSPGyGQy4ebNmyyXS46OjrDWMp1OFTyhgvgr\n/UlpBUPWb9glhrYIEylp0Esq6QdqHhQjhI7eF1oY67Vr1/qcxJQ0qnWZtEyX7RnYeq3ZGDNsZNTQ\n/eEmuqnfpTpL+b1oAWpoM+xNphwe7KtGXd5dynB3UbV1HxPtWeT03j3EGG7ceoXrL1zHjUcEAmJz\nvVep8IZJLGftKuUq+9+Kb9Z7T7tYspzNsaI5qyK6B1vRItyh6+hsR2M1zWQ5XzCyo56pDN/RmsTf\ndyXpGKScTlG0O9SnXQpce+9psjl663QbjGM5Vvsu1Yy/PhdrS0xhjoVZjsfjNcSj2mdarrHW9prx\nJkvD8FkmC2tFGCgac+32iFGZbU2bNPvL0Oqa7darc+P3yE9Z0dY9JvPnItuXZ8SouMi3Xn6JT3/m\n01x56ZO9UOtFNOhtZHWiZtN5JKmZtvMslksssH/1Bm/sH7B3eIVf+cUvMT85JhktWnB5D+WGZqd0\nbk6ra6l3tPbHgD5S+rIy0I6JPoVUv+hVqPf6b5sm+kUm6jXt9rIrbMtiGvpiYSXxz2azPj1jMpkw\nHo85O9N8yBL5VpvLtmoZCVXzkvpLY6o2CYkaXFNMcLK6V61RlOellDLcXrPW3gLkUG+oKWmObIm1\nqN/FRk2IgYScd5gCRxeDp2sDk+mIUQVUYUyOsm1W4ArLtsV3Ld+5/R5tt+TqjWu46YiRGxODJxq9\nb+mncRbJ8Iol2KjgERftv10sddNvSiWciokkYblc4Kzm/46aUMqFbpxnxcy6EgaKLzJhjEYQa58j\nYg2+89gEiYjHk0LAORUCLkNDDaJos/V7KAxyxWBX5fTqe9R5xbWG3ftZB37ITaZiyUzAOAtGWPoO\nR8I0jpAire/6KFREzo3X4zDQ9esu0EQpc1S/b36WbFnSD+cWIqjaOTg1xMjVa1d57fVP8uprr6qV\no5mAsXij97bWIaUGbq/9Za+tExo3QmLALxd473n145+gcQ2/+PNf5OTeAzzC6H0o9cO9qhYC4YK1\n/Uz6RJ9x2rxJr/4Pj+ez3cT03i8NtRRYMaGSDjOZTBiNRhweHjKZTDT4pe3WmF29aa3dn3XBoR6b\nc34lWfmT6mjcyUTRhEpEsWAIoTBg6TUIIyUdBpWwxZwrXrxtocVhGbak5i0rRhF8tHnMTk9J0wnX\nrl2jaVyPHFSiR52xWhc1wjImTo+OseOGqUSwgnVqSSjtKOH3xUSZUlIoP3G9cFBA633XZQjClVlT\nNTJQmF/PfD5nOjnoNarS12Edz3OaWokuz+9/2bUgCt2YUtIajqIR1rFtETFryFNrr1Hk3HPq7+tp\nLivGVt5jsYSUcSnzYDi3aq22fK8j0Ov2lH97Zm0NIWo6VglgK/M9JrWMlPczZNb1PS9L6+OdNjDB\nYVpKfubWWNrLPf9cO4sWOjjv1ou3eP3Tb7C3v6/QintT5t4zGluca7DWIMb2BcpTDnxMJoc/eq91\nO0NAxCBOo8VvvfwK3/ePfYEv/6N/xOnRg0u1eRsNIRR7LTOWggvSW3R7XPC0eT/eRM89E71Is3sa\nKCYPqaCJ6EfKbn+Otkf3rpu9NrrfN1zEuVWjFt6VJtKfYGRNowN6HF7nHKPRiOvXr3N8fKxVPNpW\nARQapxUTUuiLqaekfjZTFQEm6MaVYiTm4A5BtGC6ES0E7kP2NWpJMDERayzWOq0ZnqMjgg/4NpAo\nlTss1mnEXhKw0dKEJufaxb7nMQcOaSpGHs9xkxdgIiUhmpghACGEREqazN4Yw/LMcxwXXL91nYOD\nq7ShIxk0KCkF7HgEsznNPCge7NmCsXHEZGn2xjhniClo4IVJSGyJXUe3XKrW7hqt3xkgRUMURwyW\n2GpwpnFCSNrvZewwWCR5umVgsTiFdIWU9vv5Mtz86zWSxJNEc+2iKOyeAMkvSARsIxA0rzU5Q7Ka\nwuB9wjVTEAdiFCoyJYyJWFF0ohhDZvgrF4S1DcbkMQ+ZOVpbbNkgiRAhpAAodmrsWroUsc5iBZxT\n/7yCcgWMs0RRH5gJI0JswXhSDBoshSBi1OVrDMaOiMDIWZzVYuNt12V4ODXPm7wZ55Ad1dajHiwm\nydSvnbKBrxh3CJ54UR3LtGmNl02/X6FrK7a8tzpgTIqrZoNwKBtSvAQQo5HYnffEaLj12mu8/LGP\nE92YeXKMJ3u0bp9mOlLtU0THDgOpQRCN3MZA0mhmYxusmRCTJ4YWQgd+SWyXvPTSq/j5gi998R8S\nnSiEpXUIZQ6oHzikbK0TshNovT9rbqLehMvK6lL5kQNhZfa/ZMz2R4uJyvmX+/i3erJ5ppftR0pl\nwx7e4Nwd34fXAODy2ur5ZcpG02+dclAk/Bs3btC2Laenp8znc0LUSElLjt5tfQYfkD6Xs88xK4NQ\nNgJjlNFV5bIC9ObNEoVpcjRuyhtbyqWzQqsaiHVOJWQRsJpX2TjXp6EUM5mJklNwVkzUVxoHoltn\nYl2LSSlh3QhJwunpGUvfcuulF7l6/RrNdEwXPfPlnCYlwjjh/ZKIlkFbzBe4XB4uhqBJ51ajlRvn\niEFh99quo122TKcHmqgugeViie88YTqhaVS4qGdJiEH9ssEzOz2ja5ekyflgorr2af2vGmpzNZ2S\nohQjvmtpl3pNFMFH1e5Du6TNJfaEShCUvJFxHsGLwYauMH3KNApecUpqVixwjsbkdJsQlSEH1iwU\n6h7Q6kAigrEGiYYe9q/IiGR/LwU3WJk8aRW9HUOgxOrJMD2I8z5L1RLPa3SrDX6T372+47kLz638\ntdEbvLN6LFftOX/f88dS319jDNeu3eCFW7eADN0nVsH9JfuWM2MSkR5coQisiM5DBeHI7ywIwSQt\nVBADSTqsc9x68SU+873fwze+/lsEn8vvtSu4yEFv83+Pr23nnq6nBz2EPlpM9Dmmi/yd+Yz3da/N\nQsU2H8rgfnrTc8frtIGySbS5PuaVK1dwznF2dsZisegja0ejUX9tbWbbFjFazq2fUQeR1Ck2zmrB\n6uBiHxgSyAw3b7BWJAvKmlPaS+opQdGSzepY0SKGTHP4d0paKxVrmM1nvPnWWxyfnvDqJz7G4ZUr\nK0zUBF1UeXrRtngSo/0pxhtcsLlmLX2x86Zx2ceoPsgYA86NmEwm+G6mgBTOat5kI6QcCGURQlKE\noeQjs7Mz5rM5Vw4rU21ue100oPzbf/RAf40xq7Ju1lqiwCIGorGE2ULr1C7bSiDSuVMgAofP2URD\nVJ+US+b188SI4jRzvipLHVxkjJoaV0AHhcmuzKKwKqRujMHESIwJnyvYgAZqkU3lVoyacmXzmriI\ntrkMHpcuY0LetO63nW+cg6ja49Vr1xARRRuKWoBALQBaUzhG1VpN9g+LRcfaOpwb5bqfKuSkFPEm\nIaJWltQ4iCqwHFy7xme+53t4cHSX22/dzlHAto/61gYX4ePJWBN3TPQjRPWGDIPN7BE09Msu1Lp+\n5OPcs25XYWgFwL4OPCpYsnU9zdontslvuva8WMZFNzgylmyMgdYHgtVk9s6GFYqNCOIcKfiMoOLx\nZJOXaxDr1J8jBpsUZSWWIr1Cn7+7ZnIuzRmAU5RrAhrJOd3b42x+xttvv82sXfL6p15nNB7jjCM5\nMBNDSIk2BrroWSwX2LGlSQ0mgUuqgRWQ8T5QQsjaNzmC12aQgpQjVtWUmYyOpbWWzgdS1khnx6f4\nm34NHm6YqL7pvdfzsQ5q6qHtQsAHHeOQ/dMagZR65iMVCGsdHDYUwmDlB5aYtevM5HoNKJtPY1hh\ntaqGWfpU+Vuz0KUFH4o7wVRzbn0eq3Uinp+TeR9fEw4egS6a6xfRtvNk8PvDGOlD20cO7HIN0709\njLEsFgvG0z21GOVKOiF0+Bw9VHJqDQrIT47IVQ9HLtZOJCbRSVki5qVBUsDHhO+WiBvziU9+int3\nH9C1HZNmtDa+UjfyCfDRHRP9iFGticHAJPM+JtB3I/iopt6smfM4C+LReDxmOtWanLPZjJOTk94M\nW0vJveawjZFWfSgab2EUxaTsmgrgvoIo9Dli1XuvzCupibNgr4pkCPVGc1xJKwbWsF5Lsm5D/a+Y\nErADxloODg4Razl+cMTXvvo1Xv3Ya1y7dg2SEDCMUmQSI4t2yWK5ZMq+lvxKoqZLEo0xeL9K0A8h\nZiQY6fNGNU+3U+i04HNKZ47wRWi9mnxHruH05ISu6/oI35X58zyu7FDbHjLc3ozfB/H4bJJegdQX\n0+bwfQ5NjsN33edheoWltMYiJue+xpB9mgVXN/TXFCZq6numlcZZwNBDqFuVA7JQ7ar4VE3WPntN\nmtXaTDE+Ig9dCZqPsw43nb+NiV5W69z4DHG4ZsTe/gGRtAo6y5/gO4K3eKvxCsYY9ZmnANESJSAx\nQDTKWEXACpJy/rhxJKODGq3DNIHWdyRrePGVV3nlY3d485vf0Lzo4nNWaYwSEf0kuOhzwUQv0rye\nVEDRNs3tMrTuI8rlwKrKFo9yr4vbCEOR+lzAQVmcl9g2Yow9LKAxRk17OfF9b2+Pw8NDmqbh9PR0\nhV8p0v9dpy+sCRMx/ytoya4cZSG5hJfkhRm6Thd70AAkFyPGGkbOgXMsKxORzwzAZY2q+AVLqoTC\n2xmCXyH81GbG0l8dxwSiYPDFxwqB6XQPYw0P7t1HgMloTNM4sJbxaKz1P4PvzeUKs1ck/MJwVlpl\nSkVQ0bHyUavwxBQYJUdKokzF6zxR/NJE8oGxazg9Oe0LoReLgJrsVubcTZ+hNlq/s1hMxjl2Q4B2\nuQQynqtUtV5zX+r3W8+zeq7pOwhYnPo0c/3TIGoKL5kg5b2llPpcXde41fsJ+psTFaoSnq5bPVvN\njQboMHmKGdbbkhJZG8vzjhInsHkdnO9bufS8pelRqV//W567rT3D62sXSmmktY7RZIJtGiSXaOxj\nBkIgBq+l3IwjiidmVDLvPY0xpGjUD5+0YLu+tewzFRWEIhC8IYlB3BjbJIJPuNGY1z72cd5+69s5\nBsIQxSOpRNXX6/88ytpQy3/Y2BpjSOF8kNImei6Y6DZ6Ugz0UehSppYsCZsLXvgH1deN2sIj3L9O\nbSnaS9M0fSHcpmm4efMmi8WiL7c2mUx6BlwqxpR26Ead21SFbIiepFpCeZZJGcx9FdxhQkZBci4v\naIUkK5pFAZEwxrC/r1VTiim6wIYVRlJwdMu/tV+2ZyEpkQhEDDF6nG24euUaD+4/4Ku/9VU++clP\nYp0WunbGMm5G+Bjo2pbRKJetinqPlIZjXkyXCplH0rFtu7hKu7E2R4BGiBqpHDsPMeKXbY85e5F/\nbujrHVpH6uOkpED11btYLBZQMdV8wdrYDZ9X31fIfnL9uOwAAB6ISURBVNDedJoq4SQHEeXzS6H0\n+vpSWKEIHEAWlISYN/hCMaqvjiQkY7Bme9jKGjPacs6ma4oPthZKttFW8+1AeL7o+UNNv37mJiHZ\nOaf+TdswGU8xxiloQgxIhBQ6vF+VxQuyyCZcsPk9J+sQCSQs+vJTb/5OKddbLmNgLMY4hICxjiSW\nKMKNF17g8OpVzh4c9f6UJFWb87odWuku62se9l33qocjJT3XTPRZo4dteg9jdOdNPRuigjfcFx7Z\nBbTWpoK/27YtAJPJpMfiPTo64vT0tF/MhdY2+sh6de3SJiGnS0iWVLOJNeq1ZXPwIeCaoGbMrD2V\nosMFzFz9eIn9/X1SUtzgUS60XMZuaNZcN3lqs3IsIqSAMY4YPaPRmP29Ax7cv0+7XPLx115j72Cf\n5WyOMYITSzdfEkYWEQfGUfzVtS9N/Z4xlwLziNhc2FyZqPcea1TL7NoWi8W3LdGrmbVrW4X+22Iy\nHzLOWlAo/azL1XVdByKrCNYQe6Ztyo7HasMvpt9yj23ztX9+DycJiTqwSLUkQdTfO2BMIYPm9zm6\nziFpdY4y1jK/8jOrKO1eqx4Kkhtb+2hUM4APQ8jfZlkqFoEa57oLkS5ETIw4VsJjzKZcY8BbwYeE\niUljCSIEhM4scUmB6AMdFoVJdGLQqKNSqUUwLkdht54oAWsbum7G3v4+N2/d4sHdu/gQaKyhr/zb\nx2+s92eTlv1B046JPitUSeNDprnJp3X+8k1scLPJeaNf9hGauslnWGuoJb90b2+PF154gcPDQx48\neMBsNiOl1NeKhOwrFdczzvNjUpmmbMlBjcQAXhujp+YNvDBqW0ykxuJE8wuTDzy4e4+9vT329vbw\nbYcdrQqB1yawWvrVjUZLfRkpKTGimqgbkaLnYG8fZyy3b9/GxsRLr7ysWpC1jMYjQoj4ZUeKnsZO\nsI2pNnVFDSpaaHl+MevGFFh2Gfw9aVk1UgZCyAwhek/sPMfHx7zyyivnTIv1PFkzU1fHC0OtgQvM\nIHm9mNOLC7/8VpuHyxypS6+tabxJg4OKH9sIiKk1KdVr1tJNpNIgB88TEZaLJSpsdKx8o6UGqF6U\nUNAIYgVI/xDN8bI0XLOPykQfRROtaZOPtDDOuh6w7zw0U60jm32gihKd53oMhGA0DQvAWJL1YCyK\n6qFlDSXquUTN97Q2YZsRGKHrloQUsRiMcXiTrSq2wRtL13muXrsO1iEban0KEcRuXIPfTYFkx0Sf\nESp+syGDgsefQCn1PKY6dj6gJG068cL7rrezr/AyKAl1cnLC2dkZh4eH3Lhxo9dMS1WPPuhngylx\nY58rpmoAX2kuBdXINy5rpDmv1FpGTUNyDb7raFzD/GzGcrHg6tWrfYmuelOtS8D1YyaQTIFdDNl/\najRAwjb40DGZTHjxxRe5953btMsl167foMkpP25skaAg3ilExEJmQ5VmmIjBs1wuCCFiZJnL2RXM\nWkMgkBJYYxXkwgeOHxxx/OCYxWzO8dFRj/ZTaLghlWND4aE2p4Jqoo1IRosSNRkXTFvjzt2vRDpv\nY+DlWPF5hxgxIWh9sJzmQloPVtrE6CRbLQrGbkoJCWSzYilIUD879c9Nai9cEyQ/qA16KIi9n/te\n9tr6ndWVc2qhJaWMF5xzrsEQEkiMio0cFJhAJOGtYM0EiRF8JIlX14VYouuIxhCDzcXphUXUmAOM\nBoWR8ZUlREIXiD5BFIxzHB8dc+XqNfb392lPI8QSLa9C08P6XSsVHyR9tJhoZV65iD5oqeNRBn34\n7Ee59iJz7GXv2TO0/pwSXbjuf8o37hnLxuc+pK1F5hfWn3dhoHCq/1j5MI0Io6ZRc08IBO9pcrHq\nk1zSa29vj1u3bnF6eqoQgtl/N2qKHzPb2qr81jJ2MYMyaISmogmRTX7ZS6N5kynDhudcUJvoU2JG\noxFd59nb2yfGyL1795kcTLNWLFqyUTKcmQhi80aYcoh/6XvUsAxrR3SLBSEGxuMxvus4mO5jrt/k\n3Xdus5gvuXL9GocpMpUpTMdYsUiyGmxhqv0jqW8J1EfsQ0BIhKQA9iPTaJkq3+FjqwAJMbKcLbh/\n5x6zkzmxi/izOfjMmGwaTJxqbgw0pnrjXQd798TgCQlsSgpBGANiKt9jsWQUBolGEpPUz1b6p37D\n0tccFBUVwpFUakHG7G/NFYRS1L/zIqifE7N/PJnA2IyIuc6X3i9/1uaslm1LSdaWWOr38OqgrJCI\niia7YREM1sLDzbnrS/+8Fnn+ZRWBZHh+WSMqRJZI9fo9lgpHavbO5c6CxQaDcYIko1YddB5GEZIN\n4GKOZPa5ypGwTAkfPU3wJCy+cUyaMSYJy07zhiVDU2o1oUjoPCkLeot2xmx+xpXplOlkwuzoPo1U\nfcndW7lONvvqV+O4SfDvd7M1H/vD6KPFRB+TPgzfwlNBPTj1ijGqqbVs7Kuk8GLeKptKOXfNLJQ2\nLcr8hHJNeW5cmdKMXOzPyk2k7GoprTSfFIJu8KJRfaUtBZ+0aZo1f2nXdfjQaU1Np5Ga6vNyvQao\nfVVNrfhNREqUJUiKpKBStYRAaFu8bbAx4RI0Wbu01tEYNTGGrmM0ngDC2dmM6XTK3v5+H/zUx/wY\no6YvE7RqRIIUVn1tnEOSgy7ikoEEh1ev4yPcfu9dTo6P8aHj9h3PrVu3uPHCdUbjPY2OTB0mCBJQ\nhhASxkLKsIBJIsmCsQ1YS4iCX0ZssDRJOLl7xHvffJfTuyc0tsGII5wtYdHhJmPoQq6KvO6/NGXT\nyu9zCApfa6aajN+RYmLkRoR2jjNG51zxZQt9ket+1sSQhaLCyPR3KxAlZPkraqEAyZjJKSFisM7m\nnOSI9FY/6QPvVkwvIUk1qNT4MjNADEkcSKxMwgkN5dV1kaJknNyyfFQb7tdLImu151aAzrktW9K5\nNTggk2uPlnHeRiJx8B1CWEXwF1O/2Eb7mwUv77uVRpqjkF3OrbYxYiQiyUMQIjmugITBZOznRJCE\npEDyLUSPTZ7ReEq36LLJVWjnkTCaQjJE3/a1fXuhOq0i3ZeLGT6ekVLHYhE5ONjj3m19pysLxipV\nqfw9DFBbGy85L+n3fFVE0a42mIw30XPBRHek9LDE+SHVDLbQRdfXm+dWk+qW685tCAN833LOYrGg\n6zoODw85PDwkxsjx8THz+RzvfV8hpCyiEqiijV9vZxmP4abVB0sEQ4wa/FPXMNWanDa7ejQCtus6\njh48oBmNVDPNGr4IPeRg36YSDpFEN/AMFlFAAgzClcND2nbJydkJ3bKjbZe89eab3L9/j1fnc164\n+QJ2v+l9oSlErZ4ipoezUxOq0f09REJMhM5z/OCEs+Nj3nv7NmdHp4hRUPXGjfC+Yz6fM9nfW6u4\nkcr4VNrjpnlR+ihSKqd4Ukw94yzRzq7pgUt7v2yIq1SaOrCo/ndYMHxYjmw0UrSm5XJJ27bnzJLD\nOVVo21w4JyAQSWiKRYkm1U07gqyeISKr97phnLYdu9iMu2pbvdaGAOspqal0SK4Zl45nxlmCwlYm\nXQ1EUyqQmcNiEevPiZhUBRiFgISAB0QCEcF4T0wak2CcpWm0KL1iEDd5jcXempK8z4hEga5rWaKV\ngXzX4ayuK2tND7JS02XedX9s2yhLyTFOF5xVjetDz9jRR55qpjZcfBcyul6yW7/Xw5jjB+3MH/oX\nCyNtmob9/X2uXLnCeDzuj9dpEiVQSINZbL/3DzWn0t4YY29qjDHhExAT0QSitYQspTvnGLmGaeM4\nOTkhGd3AF4tFn6rjxqPVBiealqF4ryGHvogGWkgO8ZeUsWc7nLW8cP0mMQROzk5oRpoG9OBsDily\n48pVYqyS3TMj0pDkRIrk4zmII0QO9q/w4M4DvvXmW1zZO8BH1c6dcwrwb4TOa3DRjRdvqSYliiXb\nI8zk8SjjtcnnWAQXay3tcomJWlwgpki7bNeqefX3HLzv8s6HTGxIdST0eDzWurE5/7hxjja0D51b\n9X1q3/aw/uTwuqGveNOcGtKm4t81XaSFluPDtbwpLqDe/NeibKmq2EAu0GD7a0ut3xK8JyK94FP3\nt4/INiu1WqJajxJtbx7GGE2HCR1GGrrlUvF3raNddtmiUqxFMTc7Er2n7TTAcD6bY0zXm2wJ1Xwf\n0DYmeu68LdcW4U8FRgUieRjtmOgzTtuYxcM2Jz0fNvtdHu15j3L96uJ1v0RhcAXlKCVFJSqmp+l0\nynQ6ZTabMZ/P19IvVqg7m9tZf+/bJxoFmlBIMzFCiDaDAyjeZwmIODw81PZ0HaOmwWftKKaEaxzG\n2gxpV7QbqyZBsvYiUSuhhAAIzml7R41w4+p1rXozX+KcJUji7P4J773zHV4dv5jTC3K0ZARQrTSh\ngVpd68HA9Ws3mJ+e8fWvfJVRM+EL3/+DvPv2O3z5l36FxdmMUTNmsVzAaMrJ6akWPyepadSscHLV\nirp5TtQbedmIuralkUgyTvNdfaf1OMu1rDbD4SY9tGhs2hyHzKHNaToppb5Q+1AbHW605d+agdYa\n4fozVVsSMfnfShMdzNfN5tjzTHQ49y5cI9mPubI9ZudNrznnEc3Ocl0fTRYuFoAG6azSiEwPjWit\nZW9/vxdCSwCfMYbReEy3QbMPISr4R8o+aRFSWqqZnAZBzb3BB4yxxG5lNhdB3RnFtBogEYhBfeeK\nsRyRpEyVEJnPZ5yenPRlzIZjk+L6u922z21jopv+fhh9pJhojH1ZuvdN2wbp/WpPjzL4j0ohJKz9\n4LS7bQFL65rFJZ53XmFdu1+55/uhojlHH+lMB6yiK0MILBYL9vf3OTg4YDQacXx83G8EvWS5pW31\nd/03kZ28SBSSjUgUoNNC3xg639J2iSnCMi2ZTqfs7+1xfHLCqGnovKebz2l8ozi2zlZtyKAOebNL\nkrVHMWDU96dpBZErV64QQuCtt96km7cQtSxXbDsWiyWpmEi7ohlA8imDJ3i6tlXL+NTz5ttvc3p0\nyhu/8+N0Hl548TVe//Scr/zqr9F2LbZxhJRYLBfEFDO0m0K3YbLvKSWkzskcjGsNE5iSArU7q37h\nGKNG1DpHSit3Qc+gBybhTXMnpaTFvmtGnH8vzLOc13WdgsOzEsIe9v7rv4daX/lN86dXABplzqS0\nfn+7YbO6aB3UTHTrPiJC1y41arto0VlALAKFiPRFxjvv6UKL6bKrQwoDk77qjHWu1zq993QFIcw5\nnFRuiGosagvA6pNNvklIElT4AmIn2EYU6hGFAJT8LqLkeIiowWIr+EAP0SvuctdiJRLajrOjE06P\nT4ghYp3piz+WVXsZLXQT1fthyQuPG8zFm+gjxUS/i/zpQnoU39538zkxPjoTvSjAZwh2fV6jWPfV\nrNqxft22Xpfzysa6zZR10YYx3EDbxRI3UqZYUmNKW2azWY8idOPGDVJKzGazVX7paNJvMkONYe05\n0JuLROiB0q21RB/oYuo3rNlshnOOeZrTNR3TyUS1uLQ6x3vNxzTW9pVCIGGt+oIkQbIZjSiX6UoS\nsY2j7QKT6ZSXX36F27dv4xddDv1XAQEE33p826mGKyjyThfo5otcuspy9/Z3ePfb72Cl4fr1F+gC\nEAMf//jrnJ2c8dY3v65pLwk67zk5PeXK9WsquAiMcjk2ybUbiwluExVN5uDggPt37ygalfecHJ8w\naxc0exP8stVNKqoJ24hgGtvPvWJ1qItrG2MUgWnZ9hjM5f1vytus/X1DgW6bhjqcuw+br+vMdehT\njRvn15CGPtHhbz2zQo0NbdvhRqO+vFhZCwUUgZQIqEnfjZq+6EIZy2Jq78es6nPsn6MVaTQ3Nvb+\n6qGZPWXc6RgjkoOVrNEc6NQmrEsENFVsOZ+DMYwnQhQQcUQfcqGHbOGInug9oV3iMzMzSSE7g/fc\nu3OHdrHIwtwqODKxeT8bWhT6dY+6KOr3Xb/LskZ3iEU7upCGG0ChNeaY1v0vH8TzLnVun48wPL66\nVy0VA/2mWzbx6XTa55fevXt3Ldq3NgOea5faLFmhoGpATcgamBHFETXWAK5vQ8wRhmIMe5Mpre84\nOzvDNY1G/+ZSb9ZqlHRMCXEOI1rDNIQAUbQEW64YnKKHaLly9SrLtuXOu+8B0LYt7dJjRTFwY0gQ\nA0u/YG+yz7fe+RZ379xhMpnSzmaELtB1geuvfIy96QFtF3M6Ucf3ff7znDy4x+zsBBHH8ckJZ7MZ\nV1+4QTMZq1l6NMIVxhKTmuUGm1Uxp6qZL/TH7x8f9RtTcobZvSMQy95kj26xZOyc+p7yfrWN8QwF\nv6FWNJxnKWkuaX3+NhqabretCZHCp9QvV86v/Yqr68zg2ssL45ue389TQWEqMzMcj8e90NGbw1PC\njSdrDHQ0Gp8zV2tKUGYsxmBs9exsNU6542YD03G4td9SygXRAZGI5AjobrlUIdI5urZFgKYxfWR8\nSImUNdDlckGKimzVdVrZKbRLZiennBwdszedMp/Nch3bKu1RCks9/z43jeOFY7zh2m20Y6I72jhZ\nJEu0xVx1oYlpC71fpruN6vYMNeSyoXZd14Opj0YjXnzxRWbzBfP5quTamvmr3mSTbpIi0m8giCaV\np7xBJJulcbOKEi0gC814pAE1jePw8DAjBemtvfe9/2llelNJ3AikqAkkMSq4NgWNSYRrN25w7727\nxKAMUZKmJbRtS/AR14zZm+5z97073H77XWIXCEtPWGhQhjjLtSvXickQkgYRmRQhLfnUG5/ml37h\nHzK9csgn33iDvb097ty9w3W5ySRDHWoxbc3pLf6x8j5AtdUyjl2nUb4nZ2e889abvPL66/zIP/8n\nWMwXfONrX+Pn/v7PcPqdu4xHE7qo2kiIK9D78i42oSOVuVWbE8v73DZXHkbD55VrhxpqzeDqT3n2\nwxjmw9py0b2tCMlYlkbhHEv0bA8YUWuZWaN01vYMF6nS2lAhzqgksNF8vtb/AWNfFQ0QmoIEVu5j\ncsHzhAqjIRAlYIymmqXgCd5gjUNisTpombwYfNY6O5bLRR/Y0y1b7t65q66MUKUSlbb08m6FTLWF\n+nVdfb9MjMg22jHR55i2+Q9Wiwjg8TXRTZrE5c/VZ29odX+vTT6rQmVzKfVL27bl6rUbNI1G0Hrv\n+0jaWosoZpxYpVvUbTQ5GtEHr7iBln4Dj1XZNds4xkxoxiNGoxENRquyeK8arRZrIblS9NkhkqOK\nRXM/iTkfMhp88NjRiGbU4I1jmfNmU1JfeQyR5DRy+Z23vo2fdzQjRwoRZ4XGGKKbcvPmC4hYjDje\n+vZv8+KtG8TgaX3H937ud/HGZz/Hpz79Bl2KfPnXfpVvvfkm+4eHTKdTHYOU9LmtPzf2Xdcxm80Y\njUZ473PJuzNuvPYqv/v3/CB7hwfM2yWf/fz3cTaf8w9+6qfBWbrFEnJx7PLZNDf77wNGVn4r76Fn\nZBlI4DK0TXPZdA6AZB9xCeRJVY6r5A3dyPno3odpw/Xvw6hasRac4+zkBONsBtcPjCbjtfuU+bRi\nrppaEkKgR13KQVAh+3L7zPIiR1bCiWq2ipnba5ylT8FjgumF7uIfFomIaOnAmAKCVb94ULNv8B3z\nTivkxND1NWglYzyHXPRchbeWu7dvc3Z0xP7+PqH1vdKZSLmgPT1YA2nVh23vUIRzjBTOC1GXoY8K\nE51ANi1cwtm7IUVqw0mb73Opax/xnpe+/GHPTuoXfZTnXHjPtAJlSEmzF8WUxOVIFSfB0EdajvWT\nccNzHoXpDuXHuOWeKYLPpsSNvo583CefNzhYdJ7FfMFy0WKzZjoejfBtx2K5JPiQlU3dGEsu4GqY\nssmuSgdQU2Eg4TFGfZ0F+cU5h1jD7OQU22gRbhDN08xtbbN2ad3KP6UbU6nNGCCG3kxsSPi2xRrL\neDri9OyMB3ePsM6RYi4qvvDc/c5dumWLaxzJh1wRw2JzIJMT4fjeXZYhMTs+4ptH9/jYq7e4+507\nHO5P8G3L13/rqxxcPeTW9etcu3qFlBlT8J5Jrv/qyEzEmr7/Rgxt12qQCtKD35tG0W5mxyeMrOP0\nwTGTpsE2I5azGVYsPgRGzq027+xn6823VJtfSr2GkqqtsGhD1IyI85CF9flD0+mQ2Z33pWaUQTQQ\nptQXX81j+ufbCtowXbCx1wLBerSxBjHFXOov5vSTlP3IxSfaLpfKKHMUte86sKlPVwmSryuVT6om\nCKFnlqWP5XuIoY92JSXwoW9fLDfy2s7gFBnLW0tyrhcyEEMygjGOSALjNMo8RorLOgZFOCLnZaYU\nCRkCdDGf8eDBfcJyqdWcQhZYu0SURCwJYwIiBok6V7YrCKs+JjSaWfc+fY91MFF1/eTcjep7Pq4K\n+2GSiPxJ4H9+0u3Y0Y52tKMdPXf0r6SU/vq2Hz8qTPQm8M8C3wQWT7Y1O9rRjna0o+eAJsDrwE+m\nlO5uO+kjwUR3tKMd7WhHO3oa6QOCLtjRjna0ox3t6PmjHRPd0Y52tKMd7egxacdEd7SjHe1oRzt6\nTNox0R3taEc72tGOHpM+EkxURP5tEfmGiMxF5OdE5Pc86TZ9ECQiPywi/4eIfFtEooj88Q3n/Cci\n8raIzETk74rIpwe/j0XkL4vIHRE5EZH/VURe/PB68fgkIj8uIl8UkWMRuS0if1tEfueG857lMfgz\nIvLLInKUPz8rIn90cM4z2/8hich/kNfCXxocf2bHQET+Yu5z/fm1wTnPbP8LicirIvJXcx9meV38\nwOCcp24cnnomKiL/MvBfAn8R+N3ALwM/KSIvPNGGfTC0D/wS8GfZAM8jIn8B+HeAPw38XuAM7fuo\nOu2/Av454F8Efj/wKvC3vrvN/sDoh4H/FvjHgT8CNMDfEZFpOeE5GIM3gb8A/ADwg8BPA/+7iHwv\nPBf97ykLx38aXeP18edhDL4MvAS8nD+/r/zwPPRfRK4BPwMs0XTG7wX+feB+dc7TOQ41asfT+AF+\nDvivq+8CvAX8+Sfdtg+4nxH444NjbwP/XvX9CjAHfrT6vgT+heqcz+Z7/d4n3afHGIMXctt/3/M6\nBrn9d4F//XnqP3AA/Cbwh4D/C/hLz8scQBWEL13w+zPd/9ze/xz4vx9yzlM5Dk+1JioiDSqd/71y\nLOnI/BTwTzypdn0YJCKfQiXSuu/HwP/Hqu8/hEI31uf8JvAtPprjcw3VyO/B8zcGImJE5MeAPeBn\nn7P+/2Xg/0wp/XR98Dkag89kt87XROSvicjH4bnq/48APy8ifzO7dr4kIv9m+fFpHoenmomimokF\nbg+O30YH9Fmml1GGclHfXwLaPJm2nfORIBER1BTz91NKxR/0XIyBiHxORE5QKfonUEn6N3l++v9j\nwPcDP77h5+dhDH4O+NdQM+afAT4F/D8iss/z0X+A3wH8W6g14p8B/nvgvxGRfzX//tSOw0cFgH5H\nzz79BPC7gH/qSTfkCdBvAF8ArgL/EvBXROT3P9kmfTgkIh9Dhac/klLqnnR7ngSllH6y+vplEfki\n8NvAj6Jz43kgA3wxpfQf5e+/LCKfQ4WKv/rkmvVweto10Ttoqd6XBsdfAt798JvzodK7qP/3or6/\nC4xE5MoF5zz1JCL/HfDHgD+YUnqn+um5GIOUkk8pfT2l9Isppf8QDaz5czwf/f9B4BbwJRHpRKQD\n/gDw50SkRbWIZ30M1iildAR8Bfg0z8ccAHgH+PXBsV8HPpH/fmrH4almolky/QXgD5dj2ez3h4Gf\nfVLt+jAopfQN9MXXfb+CRrKWvv8CWtWyPuez6MT7Bx9aY98HZQb6J4B/OqX0rfq352UMNpABxs9J\n/38K+Dxqzv1C/vw88NeAL6SUvs6zPwZrJCIHKAN9+zmZA6CRuZ8dHPssqpE/3XvBk47KukTU1o8C\nM+BPAd8D/A9o9OKtJ922D6Bv++im8f1oBNm/m79/PP/+53NffwTdaP434LeAUXWPnwC+AfxBVKr/\nGeD/fdJ9u2T/fwINYf9hVFosn0l1zrM+Bv9p7v8ngc8B/xm6Efyh56H/W8ZkGJ37TI8B8F+g6Rif\nBP5J4O+iGvjN56H/uf0/hMYE/DjwBvAngRPgx572efDEB++SA/xn0TJoc1Si+KEn3aYPqF9/AGWe\nYfD5n6pz/mM0tHsG/CTw6cE9xmiu5Z086f4X4MUn3bdL9n9T3wPwpwbnPctj8D8CX89z+13g75AZ\n6PPQ/y1j8tNUTPRZHwPgb6Bpe3M0kvSvA596Xvpf9eGPAb+S+/irwL+x4Zynbhx2pdB2tKMd7WhH\nO3pMeqp9ojva0Y52tKMdPc20Y6I72tGOdrSjHT0m7Zjojna0ox3taEePSTsmuqMd7WhHO9rRY9KO\nie5oRzva0Y529Ji0Y6I72tGOdrSjHT0m7Zjojna0ox3taEePSTsmuqMd7WhHO9rRY9KOie5oRzva\n0Y529Ji0Y6I72tGOdrSjHT0m7Zjojna0ox3taEePSTsmuqMd7WhHO9rRY9L/D6uiI6DP2d5kAAAA\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff5d3acd250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"check_training_picture(bn_feat, filenames, 22)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is marked as class0 - \n",
"\n",
"```\n",
"c0: normal driving\n",
"c1: texting - right\n",
"c2: talking on the phone - right\n",
"c3: texting - left\n",
"c4: talking on the phone - left\n",
"c5: operating the radio\n",
"c6: drinking\n",
"c7: reaching behind\n",
"c8: hair and makeup\n",
"c9: talking to passenger\n",
"```\n",
"\n",
"That hand is probably confusing, but it's mostly the correct class."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 5.4387e-01 9.9393e-04 6.4095e-06 3.1461e-05 3.1368e-04 1.3539e-03 3.0034e-05\n",
" 6.8933e-04 7.3784e-05 4.5264e-01]\n",
"c0/img_102087.jpg\n"
]
},
{
"data": {
"image/png": 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Mh0M+80efZfvqJS5dPE8IyZ82HA6p65qqrBkMEv3rxEBYEELAFSWGRDOXZU1RliCGoAHv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\nEoFxpJs9jNWkaR+rU6wxhFEECErlEeLE8Oef+ys+9NBDJIAjA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mT8UTyaUUhFo14n7fdZ\na3Z47tnn6LY6VAoliocPM12dwGhNmG2sWxfwFXcuu3D509+KMPbq5qXjW/HZVz6PoWlzpadfdbgr\nnGV2OQGPoPQl/yy1ln9MPmez5+1gDhlqnS4XFxdZWFri5eNHWVpe5Mtf/gL333OY82fPEuAwSjI2\nUsbEEqMk/bSPMdoDjtIEkXXOtcb5HpdZ7d8vqcypNA4ZhGhtwDmUklidYoyh1mjS7fVJjUEKKJXK\nlEfKXhUIiQoihAoQMuBTf/hHpNL30u11uySpFx3obHbodtpcqG1w9vx5jh99mXptDaUEYSAJlEel\n7t67Fy0FqlBAKukRvDqmn/SJ0z6FsJD9fIJCGFEZqTJ7+90cvu8eZFFilGBiaoaXj53jK3/9ZbRO\nmd0xwfrKAmePvMjSpQuYtI+SFoxm3549hFKSJin1ep3jp45zyy23Mb1zhumdMzRankebJhqFY2R0\nhE6zSW+zjXQGIwJQImOgOJxwmKwvq8lkRVXg+bKByrNuPkMGMDZWYXV9HatTlPIzwjjN2vmzdFsN\nWs06195wIyOTk4hAbTETLDjpsdl544yc/yKyCDSQW7bADjWlGMgDZsYWIZB8B6Zzg1IBWSz69IAz\nyEKAkx4VN75jym+qMutAMoDBCxj8GH45frP5u2/W1F6ulnE5sOZyLuiVBAvyx53LwDYmcxCcRIrQ\n48asz2tZDdKAkl5P0rdE83uBlALrfJozj1QDij4VpMLMsHm5QIFH+CoVUYgsqdbeUOdF+Uw2TYqt\n1K0xHv2mQo2xBhFswcgRoK3GGZfxUiUqKHj+aeYh2tQMJAQREKjQF/QFCCTSigG/FJMplAhw1mAz\nJ0Ck2vcojCKsykEeKVJY4m4H3elQDSOaJJw+doZiWGbXrp08+dILHL7tLsrFiIpQhHmhyw2j8sSr\n3M/Ht4KXfJVGVHwzn321s3jLuRyg3N03uwKGj+YwYovfl6sLSeGNgRfQEFlJS2YRmkEWJFYI+mlC\nGBVInGFlY5WllWUWFy/SabbodHrUlhY4f+w4O4qjjAgJSYIxfYgEzbiZCSUYEmNJRdYUOgh9swZr\nPCDJeedTBQrT62cNHSRKRTQ7PUYrYxgnGRkdpdts8dxTz3Dw5pv4F//6v+XA3lleeuEJ/vD3fpeF\nSxdptHpoNQJBGUXEgf3XcezURX7qF36ZiR07sM4Sx33GKiUqhYhSGIAUyCDgDXe/gbHxCcbGJqiO\njTE2PkYSd/nc5/6cbi+mqAJSIzF43dfEpKQ2oagcI6MlklSTdPvofoKerrDzlht9m8DUcub4Gf72\ni19AJD2u3zPDpXOnOXvqOGsLF6iMlohjQ6e1ycX5BcandjA3N0fsJMmZ09RbDZ4/cYQ777yXQlTk\nwpkjLC8tUS4WmKiOEThDvVbHJV7qU2aIdyFDj93AoggBgxIeSStsipUOnCDVFhkU2Ox0MMDY+BjF\nkTLtdmcwa2XSBKC3tsmp+iJJa42DN97ErrnrEGqMxFhkIDF4UYyCLHotXhUShP56CxzSaS/ZIBQJ\n0da8zPZclc1R78h/JxpRFWSea5azxm+0hVKBA3NzZIIWr8lxudG8asSuGH799sh2YKDZMtY++mNQ\nXxRC4rDbxOnz4w0Dj5Ta4pIOIlQhfF9PfJrXWl/THO7jN0hda+t1bS1ZjSEr5mfeaN69T6MHn5n3\nN3Vuq1l3jh5mKJWcf6/8tcP3Pc/V/z9NU6SQjI1N0Gy2iJubBEFEsVDE2YSZnVMUQknca9Npl+i2\n24xGRQ7f9QYfCQvf3HxrXF7LHs5mfPtTwOKbMG5XGzC7gWcuh75ujkMdet3Vfi4QuO2/o3CexhEI\nlWUv/JFNVrJQkQThPODHJizNz7O0vEyrs0m9VmN9bZlabZ2kn9JvtwmEpd9r02u3GR0porXzEaH1\n1Iw8Y6GkyjbTnHPuQUI4hxGSTq9LeWycWr2FCgPCQonRoMjIaBWpIrS1nDl/gbe/5738T//m3zAz\nM0NiNHMH99NqbfLZP/0TxhKLUWVqmzG1XpfRIGRkcpLJkQpr9QYf+NAHufbaOQIlmJ4cI1CCiclx\nokKBKCoRRkVUEKGkwjmNTnocef555ucvUCyWBms0TRKMNqhs7ecYg3ztri6tsrq4QigC1lfW+NLf\nfIHAaqYnJ1m4cJGTx4+yfOk8QSDYMbubifEJjh87RrPZ5Pix44xWq+w/MIe2lvNnz9Frd9Fac/Hi\nRVaWl6lWqxSjkGKxSL22nimLbVHcBvNFiCG/TILzVUyZpxuEjyS1ySl6ksmJHQQqYnPzHIUg8LVk\nmfE6pSDu9zhz/Bira+scaraZu+EWRid2gEmJCl4VSkmFypTVjLHeSZMiZ0Z5X9nlFBiZ0WJEtk/m\n6d/vwJqo82xqT3sQHpmldUq302dsfILKVIV4bfPbfZr/4PiHVH+u+HqGDeaWscwf8yAdL77ggTzb\nCcawxSPNJ4d12/uLeiPmP2uYIyqkxG3T3s2kAwdcOYbOzW2j1Ayf43BPU5F5d9u/gx2IQmyBmbZH\n8sOGc9gJ8IbYZxestQQqoFwq0W53qG3UqIxOZH1MI0ZkwMyOcUqhoNuqY4zj1MkTHJidZc/MHqz0\nUoRBIK8ibsvNyLdP3ENc7Wc7ruL7bL126/iXj8sj9G98VOEuM/Z5qcLhhUKG5gehwArf8m6jtsHq\n6goLiwvML84TpwnlcpFarcZmY41Wvemvf+og6bLZqLGxvkZldB9SKLQxPqthtJ+3IgMGYXzqD4Hn\nfEZYYenEGlEoc+TYSYQMecvb3k6SWhrNNrG2yDDixSMv8Pb3PMSv/Mq/JCgWqCXQ7vaw/ZgH3/Yu\nzs8v8+jXnuaa62/j7TffjpGWz/7Z5zh5/hLjYxNobfnwD3+YytgoFkMQKeIkJopUhkwHYyWpcwgZ\nkCYpldEqUzumOXfuDKKI77oEpHHib2lKkKaoSCHxTrI3bJqNpTWiIOThL30JG8fs3TfL0sWzfP3p\nJ2ivLXi6jFEkBOy79nrq7R6qsEYSx7zw0gluuP56Dl5zA61mj6SSUK1WeeaZpyiVSuzevRuJo7PZ\nptvtAp5nq3XixRGG50q+FznfhALnQUXG+BSuBw9aduzcxa233MFNN9/ApUsXWV5eJtUxEgnC15+l\nUJ4ChKG1ssDzT3Rp1OrcdMcb2L33AEnXIFTg36MirLM4pwmkwrgsyhSZS++ygCM38IAQOR3QcbWu\n4uvKiBqtsXmlXwLGIi10dI+dExPcdfcbeOKvH/l2n+arjmHDoa4An341MNKVqS8M6o3OkXWNz/tz\nqkEdVGvtU9zCTxpjDJbtIgg5XyqPMPOOB0JuafZ61O/we3xk7Fx+39+8J+fT5j7d64E+1rqM65kO\nJABz4FO+8Lci0fxYlsuRzVvt1tSAC2utHnxfYcTAmNfrdaKozGi1TBQWGRktUy6G2DSmUCwBhnar\nwdGjxxirjBOMVnHKd5G5wtW57G8+/r82olefShHuao3oN3HMYcM4CF+zEsiQ1p7L8kDf+Hgw/Bv5\nAMQjIbVOfLQgBWmiadTXWV1bYWlpiYVLl6jVNthstUjTBGcNF7sder0+gUtI+z2kChFIquUCu6bH\nESYh6feIigW63R5KKozxou84iXAGcu1aobAI4mwDb6Qpq6urfM/b38UNN9xEo9Xm+MkzJE4wNjnN\n1597ntvvupuf/6VfIhaCR594lv/9Dz7DRn2ND//A+/nYD7yfB7//vTxz8iKt1FIcn+ItD72LD33k\no/z+p/+Qxx9+hHMvHeV/++3f4ad+6uME1VFiwBSK9J3xDrIQoHwaMTYaoRSxs+zaPYtJDcZ4QQEl\nvQyA1r5Eg3MoIbHCIZ0gkIrRqMyls+epra7RqTfYO7ODtYVLvPTs07Rr6xy87lpwmovLq9z/5jdz\nx+13YBCcOXUa4RxGG7SFONbsn7uGyfFxVlaW2Gy1mJqeRghBuVRi8dI8WmuibO2CL7UMolC29gc3\nsKUOi8AJSaIBUWDu4By33Hwbd955J/v37SMMCszMvMD582cYGSnSNw7hLDZr4SiEd7hMu8mZI89S\nX1nh7vveyOyNN1MYHQVr0HEXFZURCDyTVGKVT4cP5FKHwGXDpbZhUOY3Gq8rjTxrNM5YjLbE/dT3\nmVQKhKRQLnHbnbe9zr7RNx7/kGHNRQqGwTu5OEE+CQZefjYup8DkqDVgYDSHj51PWK31IP06DEwa\noIOzG/gOMsa4LP1r0dqitcnu66ylm912Dvm5xXFMkiSD8768Xpy/P1eLSVMP9Bh+fRzH7Jieplgq\nUiyWKESR/23QlCIFNqHTamC1phCEnD1zkvXaOhqHFo70lb/20M1edvtWDHeVt2/l2DKgTriBqHsu\nqWexWPGNb0Y4rPS0FC09ylVnj5tA0HMxZxcv8PATj/D4E4/w3PNPc/78KVZWF2k11mm3atTXl2jV\n17H9DqRdSPoEzhBgUCahUg6Z2TFBp7PJ0uICWhuSVNOPU3qx9sR/ywCIhxBYIUhRdFJDvZdw5MQp\nPv7TP8f//Ku/xX/2E5/AhUXWWy3CkQovnjhJdWqa3/rt36IwPskjTz7Lx/7zj9PW8IMf/XHe/6EP\nYkTIvkM3csNtd9FDcnF1nUuNJsH0BJ/8uZ/k3/7O7/KOD36QP/zUp/nXv/HbpKnGCEHfWFKhSIVC\nE5Ci6AOxlBTDIgZJdWyCJEnRSTpoSKHw8nRKSEIVDCLUPJMUSsVzTz1Da2OdfTM76NRrvPDMM9TX\nVnH9HuNjYxw+fJjy+BSz++b4/ne+m/sf/B66icYSsH/uWrMYVSgAACAASURBVFShxEvHTrB/7hru\ne+BBarUaJo4ZGxtjamqKNE3pdrsDRzrPKL3StcqMKBKL8jVdQDtBP7Hs3LWX73/nezh8z30oFfH8\nkRd47LGvUigUqVTG6XYTrM06TSmJFOB0n9BppNNEwlJfusSjf/N5nn/qcWpL84ROU5QSZX1LRb9H\nZqtWCMxgjue8e4MxOisNOaw1V21EX2eRaOrTBdJPmlK5jI77RIWITneTO+++nSAKcTZFBQGdjmZ0\npEi3279i5Pf/x3i11O2wEcnHKzq1COFVeLL7xgx3ZPHek7GWIHssVwAyxiDBo1OFR97mkWH+2ZcL\nIQxHx7khlUIhhRvwQJ3bEm4Wg3Sen4hRFG2jw2ylWvMWbWrwfN4FJk3TwXnEcYxzbkCJsdYShP51\nOVUmn9RJkhDH8eA9hUJIGGZqJlbS7nSYnJrmnsOHuXhhaXD+SZLS6bQx2iCEolAeRWFwQvD8s19n\n98xuZBgSXEGpxH+23RaNAYirRPB9c+OVi/dKNKPUpAM1lvxaeI/a18HzWqC/VM4jm2Fo/mR1ooF0\nnb9W1lmMswRKkRrtCejW42O9OIUdzD+BB5b5rID1Le2sZXFpkVOnTrHZ7lIaqbBv/z4mp6bQaUKv\n36XX79FqNVivbdDpdPwG1mvSaDRo1Rsk/Z7nASZ9Qgku66Ur0oQ0iQkDhcqkIZUUTE2Msbi0zKX5\nebrdmKkdM4iiIkCSWk99EoBxDicDEm3paEPPQB/Fr/7ab/L+9z7Epk44fvIkf/vYY5SKZTaaLVaW\nlvmdT38awgKf/tTv86/+l1/nEz/zc9z7PW/h05/6FOfPneZnf/LHmdgxw/5rD7HSjmn3Y+qdmE7i\nf9PRyTF+4Zd/gV17d/OZz/yfLNZX+fhP/ST7Dh4g0SCVIHUmq8sD1rEpHKFzjFarhIUCQqqsf2pe\nPtFemUtrAmuJgtCnya0jFo5iFDBeGaHTrPPSc8+wsbpAqCRWwplTp5menuTaa6/jb7/yMA8+8Gaa\njU2McTT6bTZeeJEojBipjnH7G+5meWmJpaUlopERioUC1loajcZg3xiUW7Kmrdv3vWyOCUkc94ii\ngl+7QrB7zwEe+sCHeNc7383CpQUefeQRnnzicRbmL7Br106kEISBwpB1bcHzEfLuVQqwJkY5ie62\nOPLVR1lfXuTe+x9k94GDqOKoX7NGIpTEGUuSMRZUJomar6vhrJffq65utb6ujKhO+ug0oVgOhups\nhm7SQRIyUorYsXOS9fkVn2KIJHHc/3af9lWPV/d8xCteN4jSrI/4RJZK9d6UL9w7lwOEtsv+DWvf\nAln9cftjA5CRFEPau5maEFvdWrZSub6V2TA9Z7g+u/X67BsNPb5N1o+t53NjnxvdOI7pdDpsbGwQ\nx/Ggjjo+XsXa0BtgLTFa02w22bd/P1pLNjs9SsUyKvBOh3MwWi5TLkQUghDT75MkXU6fOc5NN9+K\nweCXxpbBFHL42titx9FcPuxVerCvdq2vKMSRgacG6VYBKsgFtP1zOQhMDkH3HQ6dGVtU9posJQjQ\nTWKMMYRhSKhCNBYjDakxJC5AoxHWYCxoZ7Cp9alc00MKWFtb44knn+Txxx9neWmJJElYXFzEWstb\n3/pWHnjwzRTHy3RNn97agu+KEvdZuHQRrWPiJKZRq7OxscFUISLp99Cp71DiMmfQg380IlMK8hSr\n0CfdhY8wCoWQ/fv3ooKIdruNCorsni0TGwfGoKTfjONeHxeADYsUiwVeOvcyv/6b/5YHHniABE0/\nTfjTz32OhaVlbrzxZp598kn++3/2z7n3DXfxf332c/yvv/WbfOCHfpA33H8vqdaoMOKJp5/Fpj+K\nDAvcfOutvHj6DC6ApaU1rjt4LTIQlAIoT1T4iZ/+OIk0/P6nP8WpM6f4Z7/yK9xy260kVoBSWAdx\nknr0sNSUwUst5eWNPA1pLFYbrM6Aeo5tafsoEFQrZeJum2ef/hor58+xf24vU+PjXJi/RKuxweNf\ne4r3/dBHeOrrz/HffPKTnD56FPp9oulplFLcevMtfPhHfoRut8tjX/sqcRwjJVmppMbFixczh/My\nINEANZ43uBYZwl/hpCIxDisUUzt38873vJf3PvR+ClGRL33ly6yvrrGxUfNcXZNSKkZ0OgYhAwR2\nAAzzn+XRtk4olPA8VJN0WHjxeb68scYdh9/IDXe8gZGxKawDE1tSZwkCD2J0mGw/yIITKXCZQIR1\nFuu2Z+1ebbyujOjISJFCJEniGKkUm5ubYBOcjumKmFK5wr79e1g6tzIA1Kep8TzG76DhDZW/77Ou\nLgMLgTHemOY9JHxt020zWl53Vg4ZtleKxeeebiC2ape+vpl7gdub3ebvGX6/HETIw5/HNt3d4c/N\n6xPDqFzwad5Op0Or1aLVagFQqVQIgoB+v591oMlqt72cTiEwFubmDlBrtGg22hTLEVIIAqGIVJh1\ng/Dyb3Gvw4kTL3Po+msoBkVeuTQuqw+S6/xeaaG9Msvg97lvbFw98OGV83Xw227hxQZI51yIQyqP\nqDUYXBatIHxnE4fxlUzpOb8pPi2+0dhgfW2dRrNBt9sldQk4QRxrWu0O7W6XF44epbXZyfRJFfVG\nE9dvIAX0+33K5TL79u7lzvvu5qabbmJ2dpZCoUgURWht6HS6rK0tI7C0W01Wl5fodjbpbrawRiNw\nFFxKvx2TxDE61f4LWuel1wbNE7IUuopwMsi0arKo2DrGx8cxRnBxfolWs0mpVCYaKVIqleh3NtFO\nEJQKEEa0Ys3Syip3velN6GKZxKZoa7lw8RJPPPUUhVKRE6dO8uAb7+eh972Xp557jn/+i7/AnQ+8\nESEsf/PXf8WP/tjH2b9/P/PnTnF+fpHrrj3I7OwMO3dOsVJr0qw1McZiI0Vba6zyNKof/4kfJywG\nfPp3fpf/4Rd/kf/653+ZG2+9leJomU6aem6lSTDOIkOFDEJUpkiU04Hy9ehrokPTM8dLSE2Sdjh9\n+hTr68uoUKDjmLm5OcanJnn5xAlq6+t87eG/54E3v5nHn3iCKAxJOr5jzQ9++MPccecdrDc2eOyr\nX+X5l18cRH+VapUkc2KFHQJKZkhca7Ksl7SQCfJrbUAKVBDinGCsMsab3/w9/MiHf4Q4Tvl3v/Fr\nfP3JpxgZGSHVMaVSAWcNhYKnxghXIHch82Ts1nLKQDJSUMBgFGwuXuKphzvU6w1uuftexqdnCItl\nhHM+k6IExmxFoUGm2LYVAFx9TfR1ZUR3TE+ya9cONjsxQgYYkwIhUVBEiZSpqTHe8tbv47nHn8MY\ngdFb7buulD59rY0rX7RX9qS5EvqWAc0lmwg278hiMmGOLSNKJpYwbMCUUllqbggR67Y6v+SRKBmq\nzdrckNosJZhHtZkwg3UIsdX/M6+VDqdPcjpNPnGH65+B9KCkNE2J45hms0maplSrVcbHx6lUKjSb\nzezxGK3LRFFE4AKkUCTxBqm2JLFjds8+xsemqLUbXhRbhQz61ltDKEHHPWprXer1Nfbs2Dv47YdT\nq9aaLJ1rBo+7V/FWLw8mrzY2dZd9Zj5UrvWZGWPnHFoaJFsp/FjHgwYBoQzRqWazt8nF5XnOLZxn\nZWWFNPGt71qtFs8feZ7jx4+zsrJCFEaUy2WKoxFRFBEVyhgnCcICM7N7uf+BN/Pgg99Lr5/y8KOP\nsHr+ZSYnxpmcmODmW27h9ttvp1go0G63WV9fZ62+TqfTIenHhEi67U021tZoNxs4nYJOSLpdnEl9\nXd+kpBTQOh2kCKWUftMbAn94UG2IJfB1q/waWEsYFhkZGWGsWqVe22R+fp69hw4yPTZNt9fBmJTy\nyAg9bTh59iwf+8f/FR/98Y+T4BteO+v43Oc/z5mzZxgpj9Bpt/knv/B/oI3h3/32b1HduYNqZZQw\nFGy0mrTbm4RKMlIusrKyzq2HDlKtjLBndhfrjQb9VptmvUG1Mk3iBKl0xDZlpFzgv/jYx2iurvHw\nl/6W/+7nf4kf+/gneNcHHsJK6CZ9cCm9NPZGXwhUGPq+ujbHQeSZIZt70kOMJIeQhtZmjflLZ7E6\nxumYxYWLXLi0i/seeBPd1NJL4dKFizxbeJpr9u6jubqKLha49rpDvOlND1BvNvjiV75Mt9clLEb0\nVrqMVka4+aabOBMGHD/6MoFUHtE6yKBsOdQic+4QkiAqUCyVKJdHKZRKHLr+Bn7gh3+Y6R07+IM/\n+AP+6rOfZWLHTpbPnEY6TakYkSQ9osj3e9VJrqSdeQwiE2gR4IRH+yIdMo1BCArlUVKT8vLzz7C8\ntsad9z7ATbfeDs531wnCwPPV5bAozdaizdfU1YzXlRHt9jrYzDvRxhEEBXSqKRQi77WUIt75jnfw\ne7/1aTbWGwghCENe8QO93kduALd6fGYemsgN8Rb9xFo3aLE68LAGEekweu7V67fD/FMpty8U8CAi\n8OlCKf2attliF3helkdlDqkYDUWZue5ujgpWSpGmmjiJBwjckZERqtUq1WqVQqEwMK7tdpso8oAD\nay3CeKRnoVhCtttASD9Jmd29j92ze9lYX8cmKWEU+RZLzlIsRsRJTKIT1leX2T29K/vBhn+D/Pvm\nBtRu+w0uvz6Xp+B9afJq5uCVX2PsFk0oL2XMbyxx4dJFXjhyhKPHjrG0uAT4lLVOU/r9PhsbGzT7\nbURRURkdpTo2RhgEgw13z/69HLrxel//TFOCokAbS6U6wfTMbg5ecz3X3XATO3ftI44Nm81Nvvft\n38908S1UR8pY59BGs7y2yvraOv24jzWWWr1GvV4nRCB7Mf1ej7TXx8Q90Ck2jRFG43SKcg4lBR2T\nIoSPPqWQWc/XwDsOzmW0FE/dt3bL1TDW18usjQmCAnv27CEKG6ysrtLtx34jLhbo9TS9JGZxdZ3p\nmV3cfOvtJM7RSTQTKuDF4y/y6KOPIJWi1+kwNzfH3IED/PEf/zHPfv0Zbr/zTnrdTWaCPUjh6LRb\nbLaanD11inazjsFRLhaYnp5kYrxKp9djeWGRmT1TGAwaQSQCtNVUKiN88qd/hoqK+MJjz/EfPvPv\nqW1u8pZ3vp2wXMShSbtdCs6QDDbzLdBfjnTNS/TewdhygI1J0SZhx8w0SSmiXasRd/scP36cPfvm\nuPOuu1mpNbFxwsUzZ5gaH+PwPYepNRu8+6H30et2+Puv/j3rG2uMjY+T6ASkZN/cHJVqlfX1jYEm\n7eVzNneUPU0uAKnYObPbC0mMTzBSrXL48GFuufVmVlY3ePrpp5nevQtrLVOTk3TaDV8jTmN6PYsS\noL2LT0bLYIABGFpzFgikxTlJKEEGEusEtYV5Hv/Kl1nfqHHH3XdTqY7R6bQJiqVtmBBjzBbyN3dO\nrmK8roxos9Gjtr5JuTRCv98h1W2QHcAwNV2EqM/OyQPccNs4jz+6gXJQCop0OxohAxwWo7RHGwLq\n/4USy7diaG3ZKiHkrXzkVTkAQogsdZIZvBRwjjCS/nGnsEbipJcUw3kx7lxI279XIaVvE4U1KGm2\noXVzQzIgxxvfgi1QARJFFhAgpSCQQRY5OY+qtlvGOK9/OgdGC0zq65nCSRKbYrUjFBJpnSefFwsU\ni2XGp6aYnJyg2Wxy7OhzbLaalItlZCro1Xu4UBOFATYydOgTTAj6OsAUDCtukbmRIrunp1haWKTd\nbDJSLOGsI+73qVTHSHt9jr14gpuvvxkjPWgiSRIKhYg4TShE0TZj6HAIWcw2Mh8pGpM3+ZaD+rGQ\nkpQUK4YEI7Be4UkKnHZ0uh0arQabnU1i20WbFGEFrW6Lc+fO8dhjj/Hss8+ysrLm6zk5UCKTQRwZ\nrzA5Ocn01A5GR0cpheNUcOxSc6ggJMk6kYCiWCxSLJaZnJxkx45d7N69m717D1AdqxBUZiiVRhBK\nYrSh0485f+4STzx9hqTbobGxztve9n0UtWPxxDlq6xv0222Sfh+TpMRJj063Qy/ukxpN7Cwy7XtE\ntdb0ez2EwytQOYdJPVhEKYWIMjS5dSjhuZOJTjMutC9QKKFQNsVJh8lSmsJ4EJzVDgKNkiHlSDI+\nUqK/sMIXXzxGbBx7r7ueVEKtXWKyMsO//9Mv8mOTBxmbKKNtzJe++GUuvvwSqtej140p7zlIZ6PD\n0ZdOsnvXHqRJUDIErYlcSrexwvR0hak9s/z9C8f43ve9C+lg/+69nH75KJ3U0Om0iHsx4UgR5yCR\njlAGdC0UJ8b58Cd+gsX6r/Ly0aM8/Jd/QryxyEPvfz9RGBDohKTXQeqUbtylWPKau0HWQ1elGqEN\nrtdHRVHWicav6X49YrIyy/X33sTK/CU2VpdJeh3q9Q2+8KW/4cM/+lEeeNNdPPV4j7OnT/P8iRe5\n64E38X0f+iCjMzN84Qt/w/zCAsVAUrAxQdxlbP8BDt1xF+vNNqfOnMMZQRBJr0AkGUTHQT9ABRIj\nLKnpUhqvUKiGuKIiVgG7d+5j5/5bOHpqgSeffBwnK+zafwuXLp5lZAI6uk6SdCgHBUYLVYgDrF33\nJlT5WDTN9i4lRBacOySS1OwkCAySFBlvUCZktLyXkpri3KMv41ba3HXPLeyeO0Bfe2Pf131UQZJi\nCYKIQCoCFyJfCdO/4nhdGdEUQaPdot1uImXCxFjEaDlitGIphxrZb+FKKffeew/PPXOGXkvQi2Ok\nDLfVo66cMPv2j7wkua3zidiKdi5PN2zjizqRvXb7Ma3x+f+81ugGWsL5sTLOVG4Yh1SMZOaR5XSS\ngdgDbts5WGcHqeDh88vPfficBhxUkXFKEeg8LYUcGNh+3MMZx/hkiUJUZKRSpTI2Rmos586f5+Kl\necbHKuzevYeNxVV6/U4m2WUxJsUCKoogCCnLEGcdZ06f5vbbbmPf7B5OnTjB2mabQhigVMBmu4HF\nsLGxSrNZJ6iOIwSUyyMkJqEQFTFO+3Z85PNH4tAYfFpVokD5mmnf9DHaEBWKpGnKZtqm3mxw+sxp\njh8/zsL8Av1+H+ss9XqdxcVFFhYW6G52KU9XKBaLA8CV0RqpFJPTu9k9e2CQ6iyMVigUSlRGK4yN\njVEujzIyMkqlMkqlMsbo6ChjY+OMVEeJCgVvPAslCmEJqQKsyjMQAUoExDoGWaavDWtrG1yaX+T0\n6TMsLSyAMUyOV6itrfL1p55kUrTptFroOMbEMXG3h7Xa00uSmMQkWPBNAZJelt4DnaZIREZ8zxyx\nPNLcNl+GpTG3S2W6PLPhHOT8SucwDvrdHkmySZyk3mEsFCgUSyxcXGBx/UmC0Sq7566lOlrBGcNf\nfvbP2Wg0KIcpR597hqTXR6UpRmvOnj3L2kaNiclpZmZ3EyqdCQRIlldW2Gy1mNk1w8GDczz99NMe\ndCUc01OTzM7O0lvqYrtdlNZUFXSNpag8sj01hkgpZqen+eTP/Sy/9uu/wYsvvczTTz1NvVbnHe98\nB1OTY7TjLvV6nUJUIEmSAerdZGvJGjNI61rnMFi00RgcQTHi0M03Uq2MMD5eod1sUp0c48zZ0zz6\n6KO8+6H3cvMtt7K6toFViv0H5ti/fz/PHTnCmdNnKEgf/cXdmEIQERUirrv2WhbOn6fT3kTl5QXB\ndlBTsUCSxr4XsYE00fT6fawIiAojXHtwP/v3zPD8s8+wOn+JSjGiFEbU1iKwParVKkk3L6n6lmcq\nKGJsis6TXqHMOO+XpYtIEQLS1OJcSKDGuP++d3D9Dffx7JGjPPHsV9lYX+cNb7qX2UM3oUKFjATC\neqtgjUEbnyI2V1kCfF0ZUQtEJUW1LBktBExWJROjgmKQIJ3GpH3qGzWuv+kGZBjhVIpNHaESOLOF\nIHPutUknvRLFZZiWMHhsm6Xcqofm0n4ZdI3sDXjk7HYgkX8uq4nm/FB8fcm5TPJKiIw/ZQZGztNq\nzDYwUP53S+lDDFLIW8Z+uGYiBlGtdV4E3KODE6SUpKnnA0fFEjKMQAUIFWKcoFarcXFhEScVhdII\nQgVExQjR8d9bBj7CCZTyUWYcI2QXGbYYHR/l/Nlz3HDoesaqVc6snKRYLFIulxBGUCgXaLbWqdXX\nmJneicWRYjHZd8nTiHEa0+q0aG+2cdKChTRNaLabXJpf4KWXXmZ9fYNGo0mapHR7XTbaTZbXVujV\ne6gwoDRWYmRkhDAKMVoTRRHX3XIDlWqVXuI3ahUE7JieZnx8nNFKhWq1SqlUolQsUq2OURmboVwa\noVKpUB4pE4YFLwWnPNLWOQdSYD3mFhBYITHWeiMmlCfoE6C1pdVPWa8tcerUaS6cv0i316PT7tDd\nbBEIS1lZNhvrXDjVpyHapHHfp8RTjU4SX7e1htRqjNP5zMRpndXxHDbVAxrHII3mHMIJ35ggc9zy\n7WsbcG1ovvm5JbDZgvYNsw3aWBLtPzuKCujAMTY1yT179tJLDZu9hJl9eykXC3SbDZ6/dIliaYTJ\n8ZBOowmpxsYpSir6/T7GGu6+5x7KY0XOnDpK0o9ZXV1ldXWVr3zlK/zQRz7qATfOYVJLaPssX5rn\nmce/xupGn+NS0q6vcejWm6lUypSrVSrlEcbLJaSDnk7Zt2snP/Sh/4RmvcVmp83pU6fp97rcf+9h\n5vbPstloYpKUUhiSugxhLrbWXV4ptJlDa60lFY6eSTFSsGv/Xt9zV0lUIaAbxywszfPII3/PW9/2\ndg6t1zl47SEO33MfCwvLvPTCy9hEMzJWJe22SfoxlVKFXhxTKhaobazj6WgBXjpv2KmGWKcY5wik\n1/Uulkrsmplhds8+5uau446bDtGuL3PsyDOkm3WKAlIpqRRDOv0OUjqvd2sFqBCnLLEtIEQB51Kc\n1UjnBt1rtnY7B6KPCgqkCWhTZHbfTRzcfx+lcB/f96bruenGwzz2xBd5+tHnuG6zz8233YKwCmsE\nqXNExaLXUxbeub+a8foyoraLkm12TE8wu2MUqZsUZIruttBJQtztsNZyiGiUykSVzeY6IsgpB2IA\nzLiq0tS3cbwCaMOWAYUcKLVd1J4sPSoEvpuNcuAp2UM8TR/Z5EeVQvgG3DIYHEM6MfD0xWXnA1uR\n5OVDCDdA3gohBlxCDzLK+ua+Qg83d2NzZaOEPDIuFIoUCiWQCuMEvTSlu7rOyVMn6HT77J87yFi1\nSprEqED5GmewZai9kffIW6NT4l4XFUhcalhamGfP7t2kcY8LF86hdY+oFCEjR6x71Jt12iZmY2Od\n5dVVrLV0+j021tf56tce54knnmB9fZ2wUMDIfsahhW63Q7vdZe/efdn5FwnDiGKxyFh5hpmD1xAV\nIow2WSp1B9VqlampKaanp7N6b5GgOEGxUKQYFQiCkF7c82hkqQhESI6uxZaG5oC/Zkr569/PQJvS\nI3HQwqOtjfUUFSd8yt1Y6PR6LCwvcuHCBebPLFCr1eh0Oggh0XFMp9WgHClMUSHSmM1ajyCMsUmC\ncA6rNcIYosBr4TpjsU4PnCSTppnSjsoMu5/TeX0e5xDq/6buzYMsu+77vs855y5v7X2brWcwGAwG\nCwHuFClKIkVrIWVLEeWSqKpEZauksh3bSaVStitVcVmJYquSqtiMZKmiOJETybEVhVosqiQRpCRu\noAiKBEAABDDADGfr6em9+613OVv+OPe+1zNcRIq2Qp6prul+7771nnt+5/f9fX/f75Tsdu+cu7du\nNVFirqTZwr/wuaIkohUlQeDDWrSxNNopabtNN0pZixr0s5yNL34R7UP9d269w+jgiKPtXXxZorxD\niAijNRu3N3joda9FNAQ3Nq+Ra83B7i7r6+tsb++wMD9PrCIef/QRfv/3fo9Pf/JjvPDs59j84qu0\nW8uMioxP/cmHUWmCajZpxzFJt8tDFx/ikYcf4fSJk9x39hSnT5xi/dRpXrl6hfvWz7K3v8OffOSP\necsbX0szUTTiBKv1pFYnVcV6rcVUqot/cs3Gin4+5sbWJhfvO8/yqVOMi5xcl5y7/wLjImdzc4dn\nXniJR177Bi488ABCxrzyyqvs3NmmmTQRxkPpQHu6zTa7O7e5dvUK+3u7RCpYwnlbIwdVICUoEcVp\njAfitMncwhLWWsajAafXVlhd7PKHv/8HPP3kH3P29GmipEukGsy2G+R58FeVUYTTUHpJ6SW5iYmT\nGClKrM8r8wVDTcWra8MqKvFeIWSTVK1w8YHvYqbzIGWe4m3EhbOv4/WPv47nLj/F73z41znY2eId\n3/PdCKGI0wStNQ6LkwRG8dcwvqWC6GJLs74kWeyUtBJP0pTYAoalReee/f2S7V6fublZHrh0nhtf\n3COoOLq76JLimzSQ3tsiEnB+ddd7Pd6qUo+qa6GqSdxNaql3hzUyEa612mVFcryXS0oBUoUmd+cm\nnRpTBpuv2L7TxW2S3SKm/Z6Sqme1zkIruJlKZMKHjY01bvJ+vLdVTVgE4QYVoeK4qmULer0BO3t7\nXLl6jU67RbPdRSqFjGPSVoOObYOzYO30ipqQqyxlnqONRs3NcfP6dTCGMydPUeYjbm3eREQwzoY0\nmwnXNq7xc//LL/Dcs5+nNxrQbLYnWR1SkTQbdBaWaLZaJN2IbrdLp92l251hfn4BKQSNZivAqTNz\nzM3O0Z1bojM3S6vVJlExzVYzmAt7N2lREQiEV+AbWBkCRqxiknQGKwRjpxH4iqmbTCC0CbnD16xI\nMSkNlC7QMFASJ5OJSYP3nqNxwc1bt3nl1VfZ2tmm1+sRl4ay1OhSg3OYogCr8drgywJ0TpGP0U2P\ncBYlREUQsoH8VLEnJS4Q2CqSm6gm3wSZOM4wFyGTUlFCpBTW2aq2XM3RyVwNYhc1yShUlsM0FVEU\n2hOdJI4VHk2hTcjcSs3hYAdkhPUe4wRSBQcPU5R0I8nNjW2KwYBUyNC2JDxZlvPCF17k8be9je78\nPKU1jLIMJWNWllcojWA8GnHf2XX2trZ46skneeGZp7H5mLPnzmIPcubSGVSa4uMY1WhQOih6Q57+\n9FP82ceeRMoYfE53ZobllVXiRkoEdNIm2WjIC59/NBGZkQAAIABJREFUjtlui0RFlHk+UftS1bVU\nFgXG6ADxVkIYALnR7B0dsrG9RZTEnFpeZmHtBIXW5OMxi8traAvDccnKidMkjTbXbtzkxee/QCNJ\niL3DaUOqInJb4J2nGcdsbtxkNBxgywKbJEhfs2XDNe0JClXeOQqjWTq5xuLKGqsry7SaKXOzHbLh\nEU6PObkyy1wrDl6uGJpJhPC2IrkFMlEzadCZbRJHq4BhPD6kzAzI4Hc8NYyonFcSSZZp2q1TzM0+\nxPkL3wHiBGnSYG6uQUJGQwgurl/kjY89ygc/+Lv8aSJ49HWPkXRaqLSBFRKv3MQv+c8b31JB9OyS\n5L5liYwyTD5CA8YojgqFcy1Ms01xNCButnn7O9/FR574DMZBXD1+moke8y38C46v1EP0H5IFPFlY\nJzXEkNmFwCWm7SJV35SYCMO74HhAqDvq0lRtKFUmWQUmdQzOBRAqiCEY7qbQT2uyU+bd8c9qrUVG\ngbjlPHjj8d5WWekU6lGRQFALTpuQiRI8QOvAHqmYOEoQQmKtx1hLXgw4OOpx1BsyNz+PJGQ4SdoA\nBDJNiNQcVudk4/Gkxoa3mLLAOo+KbFC2GgxI05iNmzeROF7zmkcZjPsc9o4QscJkGTKO2R+OaS4s\ncOahS8zPL7K0tkqaNDh1bp3FxWVmZmeYm51jZnExQKxpk1jGYRMi/OTziMpxyLroWP0moCKFY9Ku\nUp8/RKjHBG/cGOshlYrce6RKQUDpK1az8hRFTjNtYky44K1zpFGCl4JxoemkMSB49eYOH/7wR3j8\n8cd4zWsusbVzwJUrV9m4vcHWzhZFEZjQaVFSliU6D8ImWEMxHGJswVIrZabVZLu3T+4tkQzhEmtD\nEPUu7DMihZdh8+pdRY4Lq2xFKAIZK4QIHo8TuLZyDpJCTZimoiorCBnE7qRSlDrISCIkKg7VaGs8\nMlIYU0G61tEfjcm0m4i7YwNJaZwXWOsYDsY8cPESNhuzt3WHYjQiMhbpPYXRCCF55cWXUFFEd26O\nnf1DyiwjMhHWGBYXF/n808+SjTNMWeLKkpPLSwwOIB8PaQo3eV0ZKYS1AV1otkFGCFFvOgPBLBIS\nYRzlKCNGIpKUcpRxMB4ivEFUJSnnPHHFjK3Vqny9XohQDhmNxsSNlJ/+W3+Lz/zpn3J7Z5fTJ9bI\ni4KdO3dodLqcXk95y3e8gyRt4RA8/9wL5NmYdpoGZWRrcdoSqcDmXlyYZdA7YjToIwj+vaEMZCfs\neykEVptACJSC9XP30+7OIOMWS8vLrKyssr+/Qzbs0UwjGqlitjXL9sGI2Zku89k8h8N9BoMhkLI0\n32TtzBkOdgVJIrjyynPEcRNnRxX8Um8hw1pWFDlSzWBsm8de+900O+uMyoSF2TmS1DPbNfzL9//P\nDMsNzr2mi/Qlzzz1JAf7m7z9ne+gu7CE8SAScSxAf/XxLRVEl9qWTlRAHGNEwrAUbOwM2Nge47yg\n1Z5HtiK2DnqcPncfJ0+dYP/OAcWwJBJqQkQIYNBUEPmbZXy1AHwc4q2zy7r+WVuh1T2a4QH14wLM\n62zt0KKCITa1liVI7ytYV1RQW4ABv1Kb1PHAHv4+9oJ3tX/YENgJi0T9GUMGW4vkM6nR1uQkURNF\nTMl46MhKjS5LamGEmbk5Oq0WcaTAK7RRaG9IZGjQtmWJEArvg+Yv1mO0xVmNtwZXxpTjMWkS0Wyk\nfNub3srzL7/IlevXmF9aQEVN/ot/8A+ZmZml1e7Q7LQRVpHOdRAqmqivSBSaeiGTlIR2i0hGd7PX\nPFX95p7zWzGxazTu+HB4VIg7ZNqRxorC1U44EqUiSpsTN1JGJiNWMUooYmJGtkBYQRRH5AI++Psf\n5ld/7dfpttq88S3fxo1b29y6eZPDw0PycYbUHqldcPXIhjitcYXGliXeaoRzmFKTDQekMhQJqiIc\nxpng82oDNdsJgqqMrOBWIUJtrJINPC68Uf9eo0Re1iIc4cvxlRxhbasX5hQIKYlieUybuaoTOiiN\nIRvnDEdjBoMRuXFkWc5oPA7EHOuQShHHQaaymQhGgwPGwwE4ixAebywCBSo4qnSaMTtHLsjVuRHG\nO5I4YW35BEoIdFGA0aA1QhuUNUTOIpxFSo9wCuktyju8CU4kAQ3zgcnuw2ZLeiabDSEEsZBYIRHO\n4kPRN8yNmognpj3w3jlcVUax1kKpefiBB3nDY49w8YGLfOJjH2PnzhbtmTk645yiNJw+cZKZuUXS\nRoNXL7/K9uYms90OrsyrAGIJfdCVbKjTjEd9bJmjpEBFAm9dtZb4qnQgAjPXWZJmh9LB9n4fIyLe\n8MY3kmvDrdsboKA/HmKcZy2dodXpoJxH7kU4C0Y7rC5wxtNK29BtEsUQiZjS1R0Fx5YcBHgJqoVU\nc6zf9zhr64/i01ki2aIUGUnL84v/6y/wwotPUYhtdkaQZz1azZSNV17iQ4ND3vDWt3Hfgw+RF2Py\ncf/LL4D3jG+pIKoiR5woDCmDkeXqzV12DjWFa1JYydhaUpmQjXqsraRcevxR/mjjI6EWZqueRaY1\nxm9k/GX1nR5n5t5dK50GsHoXygQaDTvgoLXr8V5O1E2CkkgS6gtKhgDrK9tyGRYoWdWtvAkX5r2k\npuMfvYaCa+GFGrYVYmrTJsRxAlJNdKqfp+rLsvX94X0LYSiyMYU2jEsNKiaJFWkSsby0QCuN8dYQ\nC4GPI3COJFIo4Rlbi9FB/lAEDiHWaCbScc6SqIjB0YDbG1s0210efugxssJx9cZ1Gs0ZLr7pLYAi\niVNGRYGPZFC8iVIUwRTdEL47d/x8SEFZIVx3QR1fJoZ6X9WvxfRvACkDKKdtIMWpWJK7Simqkvqz\nghDQvQIVkzuLLguSOCFSMSmSjPAeLj7yMD/7T/8pJ06ssru9x+/+zm+xMDdPGilclmGzEWacBfeb\nfIAxDluWWG1w1hBV50+Xhk63RRQlCBFadLAejEU4H6BdUbO9w1yaNuGHD1j3AU/q+PLY/FLRZM4L\nISem4y58sdTMcKkiAhSryQtNaYIqmVKKclywt3/Izu4evcEAXTriOPhezi7OkKQJcRyT5zntdpvl\npQUuX77MoN8HQruNdS4EdO/p7R8E0pRQPP7463jhs0/TiJusrq1hvSQbj5DCU2ZZMIDPx7iyhCLH\nVeSCCIfyHukcCIOwilq0Q7hgci9dAIREJWMcrtOISEUYq6tNYCWb7mo2+1RZx3kfbifc1222+ZPf\n+SA/f+IU73jnO1Eq4qjfp5U2WF47gRSKtVNncNahS82tW7coijFznRajUU6kJM4ZvKjKIxHEUuGd\nBe9QUkw0fIFpQiIEUnoKU7IwN0fcaHF0NKDrIroLKwwKw+5RH5UkWKXolyXRYICMOiAluTY4DwKF\nKw1eOxoqZWZhBRU5GnFMNg4oWXBVDITHyjEUJ7p0ZtZ59HXfydr6JXp9SbMJ3QXFv/53v8RnnnmC\nxA2ZmZEcHm2ihKHbbuOsx5uM5z73aYbjIecfeoToy6qRfen4lgqimVAc5oI7O/tsbA4YFQrjGrS7\n80TOo2RCFAtWl5eJ0xbv/oEf4FMfejL0AX2j+O3/X6Nan48H0OP/wzSgS2qd3FBjtMYiYjmpLxkd\nvPVE5EiEQIqIWg7OehdUjqhguHtSo7qv83jQPE5qqSdzOGZqIl6vn1JWfqSOY++5hjwFyBB0Qy3V\nIoVHRJJIChpRRNpukTSbtFot5udmaKQJpsyRQmJ9RKQkSRT0NY0OTFGtA7RnraYsDCpyGJ0jbBvZ\naKK1Jhtn3Lp5h5Pr66yunuHq9duouE1vVBDFCVHU4srNDbb3D1hcWWVhZZVWqx1gayFpSBnqczU7\nmWPT7K7NxpeffL6CwCYZaShYkgpBUbOCrSPCVwv7dGKMjKM/OCJSccjafND41XrEzMwsKhJgPc1W\nB+sMV65eZX93lzu3b5MPepxcWkYPh7hxhigKpLVQ5ijnUc5gnANj0FWWmWc50fxs2JRNYFoPsmIe\nVMX3WsFGiqBHKlxF/pD36DMzJcQAk1r73XPubv/dEEhDT7V1gZAUi6A5u7ezx7UbN9nbPwAEjUaD\npdk5okgho1AMNtYyyPr0+j0WFuYwpmBvf5uizIkJNf8A8ITX3blzh8EoJ41T1hZX2Jxf4OTaKZI4\noT/KA2FmOAoG4nmONxblgoC/PiYPEJKluhWsjpQghUH4aLLHqiWahQu9tF6F0ouxDuGmyk01mej4\n91OXBpxzKOM5d+Eiv/3//Ab/9ld/jXd87/dy8tQpCm3wztOamUM7SJVkf2eXw71dEinQxZg0lnir\n8d6EcxrqIzhtApmvOaZXfVfHJ/mU4Ggx1tDqdFhcXkWrJiJpsX3QZzCAQa5JVYJPEqwX+GYLJxLG\n45zDXh+8JBIRKEkqYxoyJnIC4RxoTQxYYSvyYv3qIT2Sco5Tpx7m9NlHaM/NYRuCTgc+9uQH+bPn\nPkTaGSGyo4CEeQ1SEynL0kKX8xfOQ9Lgs09+nCiOaKSNL3vN3ju+pYLokfZc2+mzcatHkafMzayB\nTJGRIvWe7uwMTim63Vl87rh48UHWHzjLK8+9XAO4hBSrbsX4xqLqvdno16q1+PWNKXR7byY6bSuZ\naumKioARFuQgzyclVatK9ZjI0Gj4Sm3GBnF1LwK8FFXwkA8XyN3Bsnr8l3wHd+vnTvgi8jh8W5Gh\nxJSFWWv4eldbOIXHSSmCY4O3NJsNGkLS6szSaDWD9V0FlQk83lmSJCGNIpJIgTX4ZgMbOQ4Pe2Tj\nrGpNEThbUnoHNjRmF1FBFpe0u46rV25AFNNpLwAxn/jUp7lxc4NHHn0N129t8PTzz3PhwUs89Mhj\nPPjwI3TbTQrjKtJa6HGcNH1PXCyOf1O1AdTxM1v/L8O5cMHiTcmUologkyjiaNjHGMN4PGZ/f5+9\nvT36/T5RY5ZIKdbWVllZWUFrx/bObSSKs2dTjLXs7OwyGByRZUM2btygEcVsb2+iR11aSGyeIa1B\nGY0vSrwrQ3bsHMKaQNaqHFpGWY5HoSrmpJQSFQVpPmF9gMt9xYIXooK9JVIG8QSUDEhHXYevSWkT\nRu6xmnGFakxIcMe+Med8aD+QkjiO6A0G7O/tc/PmBtt7B6Rpg053hnarg9DgTCDgaGuqn6C1PDs3\nw87eDodHhyBDO5lzVZYFeGvZunGLrTv7nDq3ytzsHEvLy8RxEvxKhWKcjynzAlOU6KLA6hJfEa1M\n1VNc+d6gCCiDCFYr1WbUh68igDbTGeMcIgo1YyPC2lXbnCHuZjGH2y3OS6wP7WjlcMTizCxve9tb\nuXz1Gu98xzu5fOVVRlmGLjUz7Q55UZJGCb3DQ3qHhzQThTcZkQrrQmhTmrbNtdsN1s+cQnnYvr15\njKl/t2mCqoEHAe3uLDMuotmdpdmd42Bng63dA1YXOoyNwwlFicBqy+FgyGFvQLsRU+QFykW0kgbd\ndpuWmKU32A2bYGdQstJNxk+4Bl5IGs0lzqxfonQxB72MzmKXnB5/9LHfwYtDSr/PA/ctMRjsY4Y5\nCwvzzM91GI8zYuU5dfoEn/jox3j2M59m9eTJr7IuT8e3VBDd2pPYpEHUbNGc7TI3t8R4HJwglJJo\nb7FasbW5QyIhk/A973k7r37hZax1xHHIxlQExn7jsO5/6KD55UQVgrMAQNWMjjh2W1i4w/UoEJWo\ngpOOWhLB4gPkaD1eepJIIb0PrEtnJ9NQJQonxYSQFOqoYdfrqANnFbDhLjcFKUSlAhUgR0/IrEIU\nnbIrnQ8yjNLV/bChvQaCznFocwGEwDhDbjwzrQaNZkqjIYiUQylBGkdI4YiUQIgIpdLAKJRgWgZr\nLEXRZzgcUZaGRtpCCYmVLmQY0pCVIyIdk/ome71dCgvdhQWasy2cdDz6yMPcuLnBr/3f/46//Xf/\nPt/1PT8QCDdW0hAKU0AiJbkNfbWJgkgIcu+IhcDVgu9A3X4RMrgANRsbWkCGwyF5npNlOZubtxmP\nMk6unuXBixdZmWsxLDQ3bu3y0ksvBh/HpQUuPPggczOzxI0ZBIKyKBkOhrz84stEUpEkCddeuUqe\n5+RZ0GEd9g/ZvbXB2vIyQhtsUZCPhyFI4ihMQV5mxCIENedtQC0q7oAgtFNoD7LRRpghSkkiSRBU\nEDrUrAnZoVIRUimQob4ranKQrOaEDHPC1/VVBMeJ6LLy/RQi6D8HEqgITh1WoEhAQr834Na1m9y6\ndYvBaEwjTek0UoTVZL1DYhdjcZRWY5wFJegPe9x/4X7SJGJ78za2zGk6UeHGAcTzXqLiCG0KRr09\nXDHPjdu3AxcjD5tOU+QU4xHSleisjynHWGconaH0oc0HookQQg1TqooVHzb0QdnHEdx3PDWPIGT0\nUgq0N/io0k42Ve0RsDi0Mzhpg7iFCOfKOYNWET5KIEp46JFHeOXVqxwd9cA6GkmKcOCFQ5dj9vfu\nkCpBLBSeCGEsysY47yhMGTaGkWJm9RydpVOs+YSXXrqMzjOiSOGsRaoYFScY70gjT1QK8tEYhaDT\n7LC6doJz951nZ+smSbtBe75L2m5jnMQLj3Y5mR7R6jbDJjcKWs4idqyfX2Nl/hTPv3CAthmI2mXF\n4WXoD3bERDJh9czrWT35OL5oYI/GtDsJH/itX2F8+yXi5Ij1s2s0Om1uH2zRilsszC0RRQ1ULLl+\na4eHHnszadqkt7lB3jv8mtbtryuICiH+G+CHgUtABnwK+Efe+1fuOe6/B34KmAOeBP6O9/7KsftT\n4J8DPwakwIeA/9x7v/PV38As7e4pRmONR3J76wCpJEkaWiKGWUmRabrtLoPRLkZZvv073sKv/qtf\nw5WOwX5JoyGrmgeIbzZm0T3jbuJOfePkzskO3vlA+MfbqYVPRc5wvmopEYC1SGvwLsZoTaBJCrwI\nmrNWhMcF+ywzMSyZ9qvWb0EcK+RRwUpuEmCllGEB5XgmqibvX0USKVVFLgo9r1IqogkZKvBU4iT0\nmzWaKXGsiCJBHCviSGKtoW6HIUqpOhSJ4pS00WJne5e8LGjEzWDX5CWNTjMIOQiJdprD3iGZsTQ6\nGQ88/AjdhSXkwQEnTp0gWTnB+37sx3nPDxZ0Z5d48fIVnnv+JbQ2vOf73kkUt7mzeYfMGvb393ns\n0Ut052ZoNCO0DV6cxmusCyozWWZwvrIgExXUKSTN9gxx2qI945mZW6AoSqSOeemlK+yvrNDpdLh0\n6QEuXXoACKzULHOMxgVHm7eQQjLo9dnc3ORgb5+Zbjecp1CspSxDdmnyEf29feabLRpRjC5KdFmi\nJFhnJlmaUIoINSH3BIhMgArbAeMcUZIi4hilIFIgnMeLqv7sQxCVdTAWHitEHTPDBvCYhZ6v51D9\n1wTeF8iKQKdUjPdU7R0eZ8MmbX9/n5dffpmdqpc3UYpYSqwuKzUfj3ExFktpDdrbIL0oYGlpgSLP\nGQ0CoUjZ8F6sD5tARwjeLh9z7eorqFbK5uYdvI9pxg2cscFBqMjR4yFFPqQsMqw2aGOCnm9l3RUw\nr4D8iCrjrJuxpZdhiyWqzaokIEJKhRKHCNCww6NNMOam2sCqSCHjwAZ33lKb5eEt47IkjmMWV1YZ\nZ2N29vaCqEcUB7atc6gkQZcZg/5ROL3Ohc2L8+Bk+PGCoizQmWVZJviowdkLF1l87lm2bt6otLSn\n/d/SC4zzNBoNekdHALQ6wYM0iSMG/UPSNKbTadBpt1GqRbfbJUtKbt/OmJ3tYgpNIleZaXXx0nM0\n2Gf97BksY7wIGbJQx75TGeN8yszcKc6ffZxOY5HIRyw0Uq48/We89NRHSf2Y1aUukbTcuHWdoixZ\nnu2iRPBJjqIGt7e26XRmaDYbFOMhejz8mtbprzcT/Q7gF4DPVo/9OeAJIcRD3vusWjT/EfD3gJ8A\nrgP/A/Ch6piyep73A+8GfgToA78I/Gb1/F9xjEeO/sDiScBJFhYWaDSapGlSrfCeMpeMBn2SpInV\nfdJmwne96+380R9+olJvaWNNWSnbFF/nx//mGBNZviqIWhv66qZOFzXMy+S4WnmoLCFVMVQ5qIwU\nQvrK/DvUORyV7ZQTYaNRLQS1+HwN00I1kR1Vv9i0p/Q4BDzRAZYSZx0qVsRxhFIxEOS7BJUQhKcS\nWFckaciq6v7TKIqI4/guhmccx6g4raBEQ+0/qrVmNBrRWmhXC7oiShKyYoAzmiRNyUvD6OCQx87d\nz9lz5+kNR0gVsbm5TX/7iLUTp1mYX6E/zJjpzNJKGnz0Ux/l4x/5Y37sR/86S4tLPPPcZc6sr/NH\nT3ySd//Au9m6vcUoG3Lf+bN0ZlIcMUkESTdsDKw11bmyeAeRSkEqSq3pNGeY7Si2N/a4/sXrFFlB\nmqYM+8sgBL2jHnkR6nB5ljMa9EnjhEGvz8bGLebn5sn6/WpxLyeyjcIZnMnJh0PGgyHKw+HBAatz\ns4g4CoxVBM5YjPd3MWeNNghR6yY7yrKc3B+pIO4QOG2uyrBCQK0FHapJUP1e3Va1reBFBf9Wh1Xk\nmilzLVSY87ygLIMQhxRBSejOnW2uXr3K4eEhzrmJt2y4FgKkKRB4C8YbRKSIo5jSadrNBjPdGXa3\ndzg6OgqbTGoxywBHewFxmuARfObPPsvmwUEIQFETW2rKIqfIM/JsTDkeV3KDJVaXWFMJIEzKGmJS\nDz5ezxTVBlXAXddMfd34CsJFBDJRWZaUWoc6ZQ2jEuBtGS5GvA8EwkgKtC6JpCBJIlrNlOGgJIpC\nHdtVtnKDwaDyXK4Z+1PiUnBokpRlyWGvx+poxP7BAW9+43fz3X/le/g/f/mXkVIQR3HlAgVRFCPi\nmLxwjPsDjPd0mk3m52ZppQmHuzvEfkw2m1IMhrTbKa0kZa47yzP9z2KNqxyFmhirufzKdW7euMb9\n5z7N/t4eRTFGCI2SDpxHyhjjmjjd5uyZx3nkvtcgbUwaSdpK89Qnfp9scIe5RRBmwK2rd7BesDC/\nRBRHWGdRPkIqQZFnSOlpNxsMlEBMW+W/6vi6gqj3/j3H/xZC/A1gB3gD8Mnq5v8S+Fnv/e9Vx/wE\nsA38J8BvCCFmgJ8E3ue9/1h1zN8EXhJCvNl7/5mv9Prt7iIrq+sYA1Io2q0uUgqMCcLXQgQVlt7R\nIY04x5YDjnqOv/aD7+IPf+dPiJsp5VAjVYvg2bj79Xz8b8pRX3zHGbA1GQmYLniRmvwd9DdDO0Ik\nqlqMDEQNRF3PAipv0vp1AhGIIEWomEK/TH1Aj8sA1qMWna/rN1LWKkahfhpcXEL2Et6br0pq001C\nHTT9PYu890F8IJIxzglMWaC1Jo7j4PRSlqSdBngYjDIK4+m2u5w/f5H5hUWUilk7s45H0h9lgGJj\n4w4HLuKpp55FO7jwwMOcXj/LpQcu4krNH3/kw/zi+9/PP/wH/zUnF1Z5+dkXmJ2f4zOffIq1Eyf4\n3OefYdQveeDSgxz2KsusyGNMiTVm8v045yjKgkhFIARlWTI3M8vooM/27U0GB4esrKzgS40xll6v\nVzGsYZxlZEdHOGcZDAbkozFlFKG9YDQakmc5SRSHWrS3WFMgjMUVBcJZ8uEQXZTEUiK9IJEKhZzU\n3VSlblWniYEUJtBaB5itVnPwMPVzrMT3XSg71Hq4Qkx7iyfylF9SSglF5eCoF+rx4VgoS42vrPeO\nej1u3Nzg1q3bQVM2TUnTdPK+JyIl1dw0zmGrftXgblOwvr6OkpJe74gsyyoRc0fNknAiVK/NcMjD\nb30bFy7cj6mQFWcspigxRRZagXRBWeSYssRbi3UGX10LxoaSgxRy4sE7CZbHfr9341l/T8GMvN7A\nVLJ+zuKFxItwXRgdRDFwAY3yPrTo6Dyn1WkHgl1eko3Hk+c11kzajkajUSCuRSrwh5yo4OwpF8NW\nz9c7OuTlYZ+f/umf5PWPP8aHn/gDNq7foG65UlKipOKgyHEiZuW+c3Tm5kB65udn6DQT9GiIJGO4\nu8t8s0Wz0Sb1itl2h1bSYH94gLCOVncWbUOSc3h0wNPPfpZICFTsK43mEhkJhGygdZNmcoL1k6+n\nKeYRVrC2uMDG9Rd49ZXPIP0R7TRiND7Cu5LlpZUgCOOrLN8ZEBJjSryzYWNoDfwlmXLPVfPuAEAI\ncR+wBvzRscnQF0I8BbwV+A3gjdXrHj/mshDiZnXMVwyiCMXu/hFlYVhcXML2+5RlzmDQ4/DogPF4\nhNSaVmzJVY/leUm3KThx+gzveNdDfPyJF7EiQgmBrxxTvhWGv+f3iV1PTfKpod2KmTMhrPgQwJIk\nIYqmhB5fa5NW4vLSg57ocQYvx9AWxIRNiwiF/LAAhF47Jhe+AGpiUKjETsX0Q12rhnsjFXrLjLYI\nL4kiWfWmVoxF72vtB7x16KLEuwoGQ1D4HGctjbSBiELwsdogVfWBq584jpmdmwuSfIRFodmcoTMT\n02q1aLW7dDtzoCIOD/ukbcfJE6e4vrHBE3/4YU4+9Dpe/4Y3sn/U5+a1G3zhhZd47eOvpdNIuXj+\nPgZ72/zar/wKf+Mn/g5l/4jDYsz27ZuoN78ZMy558bkv0Gp2sN5xe3OTZkNhdEGWBTF2Y0xwcUGQ\nNlKUVIxGI7bVNn5cMDg4QjdyEqFCA35ZMhqNQg+gCIFFZCPG4zEHBwfMzsxgxhkSgRmOMUVO1Ggg\nlcKYgmzUIxYgjEFZj/KOMhvTqKDXSEQBiiOwo6NaLEJIvDfEKrAl6wDlq41W0N6tgsLEn6uCZCc1\n9AC74X0l6u0nUH4Na4c6YDiHgeAzJZtFUTD33t7e5tr162ze3sJaR5qmk7at46bKkxEK9rQaLVQS\nIyIFCpaXlxkMBgz7A4zWodWnQltcNROdhFPnL/Dmt7wZpRTDLMNJiTfgihJbFjhdYIoitLaUBVZr\ndFFiyqKC7aPq65gGyFq7WlTkquOB9fhy5L1HKoH0EmumNfT6OVT1I6rgOoGNnccZG+aBNrz68ssM\nxmMarSayql9qb4kaDay1ZHmYj5K6L5cpmYhjeWd/AAAgAElEQVRanEWgkpjBcIDWBaXRrJ06w30P\nPsT1GzcpjCVWDinBFgUmjrjwwCXuv/Qo7W6XwXBEt9ui2UiYaTeIjUUUBaeX1yjKmGI45tBYWknC\nVlaAttDuMugdoXWO90HMxXmLxCKj8P4sAmsEQnQ4d+5xVpYeRGcxM902nWabpz73cQZml07Tcjjq\ncdQ/ACkorKXMNZFSCK1pd2bodGcqUXsd4gM2+N5+DeMvHERF+JbfD3zSe/9idfMaYRXdvufw7eo+\ngFWg9N7f28l6/JgvO1559TLraFrNNv3ePnhPno9Cq4MKASDxQ5Y6MfPzEadOtug0PUuzhr/30+/l\nmSdfZGwd3meU2n3T0qruYuEeg0XrRaJmNJalJkniSSBTURR27xWsUz9XYNFFk+zN+qkTS8gQbQWh\nBqZpsKoC3FQAHymnF2pNIxRUMJ2vcI8KfiLEMill1ataP4VEiqCgUzMhnXNh8ZW1vi4oVcF8NfOw\ndpGpIC1rLc46Go1GyDidRmhXwY5hEzA7N0ccJ6EY7EMWlUtB2mzSiFOsA20dwlu09dzZvs6FSykn\nVta4fetD7GeCrdt3uP+BBzl35ixxkuKtZabdZq7T5uTaKs8+8zTPP/0pHrpwis987nNEScrWzSvM\ntprc2tzgxpVZGu02Gzev007jIM6uQ00rQOslQkoaaYpUKmSmeU6sLYO9PXy3y0AIlLUURcF4PA7a\nnt5jtKbpDcNBn/7uHnPNBmYUzlUxHoTnCdsepLDYPEN5hyJ8ZqcNttQ4bZBRTN2ScnzOEE5xEKwX\nwdw9wKohsEmCOIFwLmRHVSoX5kjdLxpIZyFLomKRB/UsAXedd2enpYfQzRA2uqUxbG7e4ZXLr7C/\nv1+R0cJIkgSYegYfZ4mHHaBCW8t4WGC9pTPbpdlssr+3E2zZKt9bqnnsfciG4zjizW/9Nk6fOcNQ\nl2BDhueMwemCoHBeYouMMhtT5hmj4QCTZzgbtIKjZie8F/xdwb02d+BYqePeEcoSMihBVXNlgvJA\nEIyIovD46toQCKTzCOfRZc7i4hxJHJGmEXFcowFuAuUG6U0zXV+qzLem+tdrkJSSJI4Z7OwQddps\n7e6i0pT3/NAP8/RnP8fw8BBDKFUYrZlbOc2lhx8ibgTLSu8dsRIkkUABrSTBFIOAbJQBwx8Ph8RS\nUuZjmnOzKAneW5IkDqTRskRKgfUa6S3GBgU0az1x0mL99P00GrNoF6OaTa5v3eSVjcvML85x6uwa\nrRlB1BAkrQZJ2sZ7x+bNa7z00mWsilg+eZL27Ax5UaCiqFobBdZ82dNz1/hGwsgvAQ8D3/4NPMfX\nNYSwNBuSOHJI4ek0UnTLY7RCSEMcCVYSyeqCYn5OIEUf6SzlSPCGx8/zbW98gGc+d5lezxBHYL8p\nvVzCON7E7Lyf9NwlzQbdbpc0Tdnd3aW0BqUUsVLBrcb7AGtaU8GgYlInqiX74jjG2gDLQWDuKhEW\nNu8d3loKoyth+pAhSKjEGKqsQwpstVDVCNykTwyCM0fF0q24TQHiMyEAO+cC9GUqU26lEFHN4ITg\nJCNDA7/3eGvxtcuH9xighLCACIfwASb21UZDKUW73QYfFnddOnylLKSNDaST0uC8pj8Y0hsMONw/\nYO3ECQ73thFjy8HuLq++fJn5pRXWTpzi4Ucf5sTqCufOrXPj6qu0GikvPv8ZFua+k2K4h45jhodd\nGu02Nu+xvXGF9swsvZ1bZDIKbR7h5E42REIInFKTc+O9J8syXFlQDME2G4x6R2hdUuQ5Wmu895Sl\nJhKO4cEBlAWNOEgsah0WGWxgyxprSZTAGo23QdIuURJdFIF97YLjTW2Obv3UQ7Y2/wawJjBbJzXR\nOArKV86GTZtzNU0o/F3tsrw4Rk6iAkv8FOINfcEmrNsqRqrpMYPRGOcc16/f4MaNW+G14+SuVpA6\n2H+54ar5WRQlSMGZc+s89NAliqIgG41DvRdR1TBDELFC4qXi7IX7OXvffQyyMboSNnDW4rXGmwJX\n5pg8Ix+NGPaO2Ny4xWjQJ8KTxBHeGhpRipIx1liMCp7GwrmKSHfsOnee40vRFN4FXYmg15/TVpDu\nxFIwXGw4Uwmp+HACbF4iA+YbmOnGTNqLwubWVhudcM3VRgX+2EakRuqUVERxTDEqsOOMp59+lve+\n96/zI+97Hy+//DL/+ud/niwvmF2cJx9n6F4/QMjFISpOwgYcz97uDqPhgFY3xWmHNgXOSwbDESJS\nGB0i1vLSIroscM7QbqUMBwOQpp5dYT2UIGXQWZ5pJswudFCxwzqNaxie/cJnWTq7wMn1E8zMKZwo\nsRJErLh46UHe/tY3c2pxlg984AP8s3/yM5gLiuW1E5w8c4allWWuvPwF5LQK8VXHXyiICiH+JfAe\n4Du893eO3bVFmN+r3J2NrgLPHDsmEULM3JONrlb3fcXx1G9/lM8/8WmooAfnLG/4ztfx2m9/GGty\nWq0WC/MNum2LFAVGF0gBQ1OgRMab3/Z6PvLRyxgLaQPsNzmvKLStKHylVJI2GiwuLjI7O8vy8jJC\nCG7evImUkjRNkZVaiRDTTLNu8tdahx1lkhAnCa4IF2Icx0Qy3B8phZIxgmDtVWpDIBJJRHw39h3g\nr6lqimQa+GtY1040fYOKiZShn7DuGw2ZMRUxROGp9XarxxyDuupF/XimUcOicdQMi1IUjqsJJoaw\nOOICIzgSwU81kpIiy9kabjIYDvFA3Giydesmlx68wGsuXeSFK7dYXFnACYXJhmzeuk6WDbjv3DnO\nnz/HW77tLVz94qvcvHaF3Yv3Mz/bYWd/n6O9LVaiNVw+JOtFSKcx417Iso6xGKsPgfceLaq6mQxK\nP2acYbMBpc4omzFCpyFDy8d4E1jJsXeYoqQYDULQKwuQAl3kOFOgdY6zach6jcfqokIHQFUSj8F3\nNTC0lYxQcYQv8lC3nigETetjilATtdYScay+JxVCRaH53lf2dt4HxSEvqn5RmGgDU2eOdTtVtfkz\nHpOH18/zgu3tHTY2Njg4OEQXmrTRCJs6Zybz+/j40pYzQbvTJm0mCKU4ceIESimyLGMwGGCtCZCe\ntxgfQAuhJCqJecOb34SMI8ZlifUeqeIgyF/mSGMQznC0v8u1q6+yf+cOupLCi6PQwiLimLIsiVRC\nXAnqo3XQzLW2soOTyOP96sdQp0nppfYKtYENbKwhihqTzWIcx2EeVYIqzjlcqQOBzWhGwxEiDpwI\n4yzWBdhY26BAFAhBU3JXaFebQvk1qS+OYtqtiEG/x+effhbtHHHc4H3/2U/wySef5IvPPoPFk7Qa\nGG0YHuzTnpvnaH+X+aUlsnzEc8/fIm406I9GZL0+UjTR2jLMhiytLNEfDWl3gvH49s4W/YMDOq0W\n3pYgbJhLk75hD4SyU3+4w8c+/u9501tKHn78+7m1d5OR22T1bIekbRmUGaV1NGdm0c7xyWeeQ6QJ\nP/W+9/ITP/lTDLTlY3/wB+zduM5v/pt/w53N23jnKc1/JO3cKoD+EPBd3vub90zia0KILeBdwHPV\n8TPAWwgMXIDPERTT3gX8dnXMg8A68Kdf7bW//z/9q6ytnw5VC2fptBvYMscUI2a7LdZPnmSxa5AM\niNIW7XgenVt0kfDi1T3S7jLd5ZTDfskw9yRf74f/SxpfIm5Q1XxUBfnJYzvKUO+MJnWoemepqDMb\nWwWg4EoQVZCvdcEMen19HSU8w/4A7x1JFJEkCcbEDPojjHXIKqAdf2+uZhlW9c7jOf00mB7Ltibv\nu/5cdX2tzp4ttnLgCMShCCmnJKXaHLzO2Gy1sbDWglVEcSWioaZkqpq1XLFbwDnSJEFJSf/wkNFg\niBCCmUoCr390wNLsLD/47u9j+//6t7SURyQR2ks0ltGgx/b2HbQteeD+83zP930fv/6//29cu3qd\n06dPY4otRr0+dm4RX5YUgz7KOvRgSFpnm3Xg8VM4GwL8WUssOm0oswElYGZaNGJBWWiK8WjyHTjn\ncIXBFAUzc7NYXYKSmLLEWoPWJcaEjZPWxeR350zIPCKJdhbtLMa7sHlSMUVRBBedOML7wLat6+h1\nhlq3sYjKCF5U8CDOVk369SYriGuoeDo76gwrTZuh71bbybksdIFSgX17/fp1rl69Rp5lNBpt2p0u\nxhiMcUjulp6s58iXqmwFEYtxlpGXBV54oiRi0O8zGAygMtgWPniaSiFxUjAzO8vy6iqFLqnbN6w2\nOGNJVYQl487GTb7wwgsc7OwghWd2ZgbhXSW2YBHqeA10ypSfljD8ZA7UfIWa31A/Lpzn4N9rjCEv\ngnB+UuHfgrAu4D3YwHMwJkCqwjpsqZFVvdPqYFIeuAmyEvUI7YFRHBrnhZKTz1uxa7BeEsURiYtD\nWSFp8Pxzz+OtJ8Ny4YGLfPf3fS+XP/80Bh82YoMxg91dkiRht7dJHCtu3LhGMe5jhA8ZZxRz2B9z\neDRmfmEBpyS98ZDltVX6gx7DYZ8kjsjHIxppSmlLgkRK1WMrQglBRA5Pxvb2izzzvCJqRxRmiEx6\nCDXgcDjG+QhUm6w3RqaS3Gqu3b6FUIH9/94f/XG680tc/+JVfuj738NTn/o4/+N/9zM0lCQfDP7c\n9frr7RP9JeDHgR8ERkKI1equnvc+r35/P/DfCiGuEFpcfhbYAP59Ndn7Qoj/A/jnQohDYAD8PPDk\nV2PmAkgf04haREoivMPlQY7rxOIic3MthLPcuL3P1s4N7mxeZ29nj62tguGhJo47XHroLXzn9/8o\nH/zdD4IbQPalu9lvllHvRktXZXPeMc4zjLMYZ+kPBxz1jojTJLiiGF3VOqC2AgvBKlyQtV2SqWog\nUaQwUtLr9Zjttmk2m5RV+4SvWh7SNCV2QZxeSIk7Vr8JvpQBhlJeTftAj40pz6de9HwlQQgoP1Gt\nqXtc8eG8KqWC4LYDEVMt3gEitG5aE3YVe85iJ9CL9KDiaaAtCh1gaqHwXjIsSlypGY9GCC9YWlpE\neEuZj1hbWiIb9Dl/5hSPPXSBnf1D2rNtxtqTO8iM5c6dTfIyx5iSpeUl/tpffS8vvvQS3sd4Ldnb\nOWJ1ucSVHicsPrIo7fE6x0o/OSdUdWkh6p7RmoBDZUOnK9KXJRIO7TSqEgLHBch9NB5hnabbaeG9\nRREk10Jt2AZPTydwTqOUQClJqTV5WVDLcWijKY0mjgLRpWZRB9jYTs5fzdQ0piK41OxvIYISEQov\nFVK6qt4tKmlEF/xGhSQImlPB62Oo+piDJZ6ntI4bN25wZ/MOw8EIFcV0ZuYC/FoURFGMI7Bfa1Wh\n6Vz70mtZKsnq2hppM2aUZaSNBlJKRpVMn6hKHdhgmYYKjNX7HriAF1DoEqES8GLiPFRmI25deZVn\nn3mafDxibW2FmW4HV2rybEgxFlhbIoRAJcldyMmE51DVSOvWlmMX/eS6DZtGgTVBpq92aAq2g4HL\noKSsLrDqHDmHNQZdBiee/f19OrNdBBFaG0Qc4Y0Jgdd5rFKkjQZpkmJcjhehQD2BeSt0JIoiYhsH\nE4TYMhgO+I3f/C1+5Ed+GKckP/je93Ll5Rf58Af+X5YW5+kmMeOjQ1qdDvlwyMHeDvv7u0jh2Nrb\nZ77dQCZNjoYH3Nnb5+TZMxwNB7S7bTyOra07WKORMmwWdVHg46D8hJeIirBVFhkikmjbY/nceR55\n7Qm0u07a8IyyO3iTk8QxRdlEyjbaOKy0lOWI4WhAXrVAJZ0u1ivWz55ndmGRtNFGRTHGlHwt4+vN\nRP82IY/+6D23/03gV6vJ8j8JIVrALxPYu58A3n2sRxTgvyLw2D9AEFv4Q+Dv/nkvnmeGrTu7DPt9\nRoMeB3vbHB1sc9Tbo8iHjAZDSimJm4qFecnFC/fzyOvexNL8fZw7+xjeN1k/dz/Xdg743BNP4DFT\nJKV+kbquUA0PX7Lr/Y89jjP2lAgyAhMyCnB0eMjhwQFChEBXaB0cP6psQUjPhBnpa4asmGRzQZtW\n0mq1uH17B7c6x+ryCs4YrNVIpYhUgq+a5x0hmcOLSr2oXhgCG/B43+jxce/C5pzDVixbj0N5ifMO\nIRUVhWXyvEqBwE6Cs0DgxLTeee/n8lXt0zkfGtidx5iw8NmKaKULQ/+ohy41rUaDxYUFpIfSFLQb\nXZaXFtjb3+Hs+fOcPX2Cg8MD5udniXKDG+eMyzFJ3GA0GtIfpAxHQ9pRi3NnLzAcjIijlPEow2mH\ndAJfOKzUFMOcSJVEavqeqb5FLwQiCpCasxbrHNqE8119y2EBL8sgNmADMURrjbNB8rDZaobgRt3e\nMW07ElJOA58UGBc0R5MkoRbVMEYHQQMpSNMGZVlQ6pKanh1IXVNdY2NtgM5FIG3VBKJg+l65fHpX\ntbqA1hZRnTvvQmtBkPQT5HnJ0eERg8GAWxu3GQwHpHGDNE3RxpJlGXEcrPGM1lMyzr1Z55e7jqpr\nIMsy+r0+63Nz6FIzHA4xxpDIYE6glAq+wyLMzNOnTgUCVRQgWSEqu7Wy5Orll3nh6T/DGcOlBy8x\n0+0wODqk0AZZZYayktmkIvK5Y0LtHLu27215mfxU7ymOosl5tMZgjQkWaGLq3eurAvKUhOdwJpy3\nwdFRKNkIEcQ/KmTIiWCmbZWlGcWhvKNLnJUVps0kM5YiWCZGMkgsJpFBpQ3+xT/7OR59/et48OL9\nPPTQw/zjf/xP+OLLX+Dq5cvc11mkf3RA0mpihWR/d5tB7wilJIPhgHaacOfWTW7c2CBKmiyvrbK3\nv8vK6ipXXrlMkeWkaYTJNd1mM6hucZw1LCesNOsNS6vLfPvb30ScJOTjHocHuzRaHm1HZGNIG2sM\n+0cQt5BeMB4PmOk0IVJIB3e2thmXBXPdGb5w+TKXX30VL1XgAXwN4+vtE/2amDje+58Bfuar3F8A\nf7/6+ZrHE098hMZMB6kUjWaDk6dPcWr1DK9dW+XkqVP8f+S9ebBt2X3X91lr7eHM545v7tfdrwdZ\nrcFSSzK2sY0dHEiApEiMnRCMoUiRosIfwcVgyB8poCp/UAyZKpBUuUg5ldEhjnC5HMC2LIFsGckt\nyZK71epBb373vTvfM+1pDfnjt/Y5571+LT0BodJkd726792+95y991l7/abvMBwN8TpnY+s8vd4I\nk3TIu30SY0iM5+xkn3nu+KN/6sf56mufo7l1jA7ga8i1ipqqKoouC8BAG40PT5aR/Is4HnqwALN8\nAMOSy2fCSgEo1JaOSQk2xGARCDaIJFtiYrGnBGnmHE3jKaZzNjY2UEqxORpyejLHNgeMRiOUTqic\nERWgLBd0bgy8yosZsGTRgUSLticuYBO7cueAZdu4fbiXABXVVtbC4dNovJP2s1EGH+SP8iIqXtka\nYwxZJtcgiYESxanEkKbpMsMPUf3FVZbaKmZzS9OA85q6tjSHR+gA48hzXBQFzig6gy4bl8/hewkT\nV3BcTPnI930vv/CZz1AkGbvnn2L33JisM+f4ZMLs9IyNfMD2uXPcPr5HcA35wJDPAvbojGK6T6KE\n8pHqhLo8E9m2xKwJRahlwmabFZIaQNsa4zzz2ZymdtSZBxIS47G2IjQNxjbcPztma2sLpwK1b0gD\nNFbQy6lSlLMZeZ5D0Oi0EyNaTT2bMEhBVXNUosiGPQg1qVEUIYiMG4LGdcGig0cZg0ejsh5NEJ9T\nV1ckxghy1SkBtFkl99tB4xwqGHx0EAKWSVxVVcxmM05OTjg+Phb/SwwdlYlvpnNkSkm3IkIk0xb6\n+5jjMYUorrG89dZboDwqwLNPPcX08Bhf1WRKNkmtRS5U+wxPoD8eM+hu4BoRMMh0oJMqTo8O+cZr\nr/K13/oy4+GAK1eu0M07VFWFSROoFF4ZHJagEtk7YsBP4udujPihilrXihvqlAGdxPmkliDnQXto\n5gWmaTDOkuHRIZBquReJVlGaU4BdjbVUTc2irkiVIHpnBw+gGpPlXdJUfGa9dcsOkMWRJRnTxsko\noG5N2GuUF4BaKAtCVbIICcN+jyzvsH/nAX/6j/6H/Jd/57/hO7/zg5x//gX+/H/1N/lL/+lfZHBW\n0DQNJ8cPSPOcUCbsf/1Vnn3uBS4Mx9x44xvsHxyQj8Z87Lt/B5efewaH48uvfJGTB4diqNCIW8+8\nsYQkwUROp0od3UGKdVAtHBubF/jgRz+G0jmn0ymhqdHaUMwdzuc4YFHOKBpLno8I9Gis4sMvfYxx\nCFQo7t24BUHRHYyZFwve94EP86GXP8pvfe6z71xUjzn+P0ryePzxo//BH+fl3/k9DMcjyZ4IAqhJ\nE6wVuS2d5Ji0g7XQOJEYawBlUnYvX2U+O+Rf+6E/wH/2ny/4L37yL3Cyf8Jgs8P8pKCXZyggVYY6\nVjEq6CdGaf3zHm3LZ/1Yh8Cvg2zeHZW4El4IQZC4AEGvZnCNg3lRkGUZnV6PNM85OTmhcY7xeCzB\nLUno5R1pJ4VWEUmqHdsI8rfT7Qg3zInR9zogor2eR2dVj6tY22tp553ee6wPgvBUSlrHsQLVxmCi\neENblRitsY1HGS+flVIsFnOKcsFsXlEWIow+TCO/Lk8wWULSyRkOh/RGfbqdjlSlZcXJ8TEbOxd5\n//tf4u0bdwhqn51zF7l8+TKd7oBvLBbcv3+f0WjE5sYGr7/2KhfP7dDpdAhe9HDzPCfLRARAWqMK\npbLlPbIRIPTQnHENTCUVqMU5K5ZTXiyrWneRqixo6prBYLBUmGmi76qKpHdAqlvn0UaqRedkvuas\nRZtW4Se2W8OK+L9sw69/Rt7hCDJvVV4q0hDQwS+BLtINEB1k8YSs8U07l5fgWRQFd+/eFZGIssYY\nRSfPcc0jAv1rz8OjIh6PW0ePHitXE8fWxibGJDR1TVVVy9aozHtF+tI5z+WnnoriHyqO0T1lVfH6\n61/jN7/wBZ579mkunb9AlmVURRmfqbB8v/WjDZyt2tY7nuX2PKNEY4hcbeUNmIBtKmxTY63cN+/c\nEjGvIghNtVVvnHO2f4IX790He3sUxYI077C5tYNKEkySgjHUrmE6n3J2fEQ5n4naUlOLeXkU0/eu\nwdaVyA0mGVVVgUkZjIbcevVV/syf/I/4Wz/9d7j2vud46X0vcWF7l0zN0M5STWbc3dsjKMXRoqRO\nMjY2NqmCZ3zuHJeuXOITn3iZ6fEpv/JLv8T923cE5a1b4JDc1SWMKBqZd7tdhuNN5mXNCy+8j42t\nTWbzOWVRSOfEOaytZbylRJfb+xDvpZg6fOzll6l84Pj4lH/ya5/j3MXLHB2dkBnNeGuXH/jB382N\nN17n7M6db7nO3lNB9NpLH2Hn6ouEEJazwXnZYGclJjGkaZcsyXEYSDQ6NdRNoKpKVJpyenRMv5Px\nletv8oM//PvI/uqUv/vf/TRv/vbr6Ewzr2tSo/G2Fh5WJrJQ/7KOx20Mj1amq6rr4Qd2uQlHpRb5\nngQ4HdGXKlWRtqAoihLngoBItGF7e1faeHUjRH5l6OS5dE6MRgdpaXkv4AztpRVrvaVVQVo/z4cs\nm97lWK9UYQUW0lqLs6OS+U/wYmZsQsC0iFEQyL4xuLomSRNpfwUv7bvJWQRiNCgFnV6XLIpFpN2U\nJMvIux26vY4EUBRYh/UVp0cnkHS49swzDIZb5IMN0Cm9wZjReIssTbn+9je4desWl5++Srfb5fDw\nkJ3N8ZLPKZumWvYZ1xV1Hr3+9e/5EGeesY3nrVu14a3FKAkKxWKBt04SncaKTVawkfIgc0gAo9Il\nCliqlQgyCVJptS1Ao0NMSoQ2ISbaIv0mLd0A0YqtsQ0uUdRNg1OeVAPOxYBtKcqKxgd8ENF6DUwm\nE46Pjzk6OmI+K/BBOhedjsD7bDSrftwaedzfn+gIq3u8vb0trd2I/l1fp+04AAUXLpwnKEnYrBOR\ng1/95V/i4M4dnrp6heeuPYeta6EhRXk/66ys4W+x1oGHnhHWEiaR2dSRbhJAeapK1LectZRlIdKc\nRi2D/3qyJUHUYptaZE29xTcNo34XnGXvzm0e3N+jPxgw3twk73SoGsv+3h1ODg9oajGkkErUSgC1\njXighhATaE3e64tCk9Kk/R53bt/mP/4Tf5If/xN/jGeee4bvuPYSt+zbbAwHXO73GFy/zt179zja\n2+ML//TzvPTxj/OJ7/9+nnnmaXTwPLhzh1/8+V/gzttv0ck7NN5FT2P5AFv9joCmahq6aUaWdTBJ\nwlNXzjEej5jNpsznhWAmbIO3TpDnMYhaGtK8i7MNJtXs3b/PW2++yXMXt/nlf/wbNA4Oj8/Y3DTM\nnOX0+Jjx1nlefP+H+MK/akF00VhK61FG44LCBw1JRppkgsKrLWezCbPZgrKqqGqHCzKnODs9Ik8U\ndTlBB8fr5rd56tw5fvIv/RS//A//Eb/+mX/C/u09bFmxEqJ0j521/L91LEWc12kQ8I7g9PgsXNFy\nIqUSXW0QxkR0r07lsmKlUFWVzEeNJk1S0Z32Aip5sL+PsxW9bh6RwTKLaIUb2llke35tDbEeKB4F\nVayjFB+tLETub8VsFnSuXr7OOsWl5S+2r5l18kidCFR1zXQ2xXlHt5czHI2pa0F1iusL6CxFZwlJ\nlsZKREyVXZPI7KjxnCUnvP3GW9zeO2DnwhV2L10BnTAYbXL16afpdXvcvnWL09NTxuMxN76xz9bG\nCG308r7KBbL82gbD9c+zlTRs74H3nmBrqTiRWW4ITjax2BFo6oq6FlNpYwxVVS1BY23QaN9HkpSA\nwi3t5ZYSjZGk751HJ1ExCkF0e2sliPoVrcgohW/5nkoCpEPI8sE6bF0uZ1ZpIp6j08mUk4Mj9vf3\nmUwmS5RvlmXvSKKUVu9YF+uf//paeqJDxfuroNfrURQFx0dHUfDCL59tYwx14xlvbLC5tSEzZ2tJ\nspRf+dQvc/nyJX7v7/4hrr/15rLqt1Y6BG1yE0JYIdZ5ONld/9O+50OJMfLMrubloIOiqARV3X4e\ny2fK6GgrFznZsZPgbIN3Dc5KK1YrTwHBnW4AACAASURBVGYU/VEf11ScnJ1xtD/l6HCfblcCUbWY\nUS3OmJycUhcLsE6E7ZWYiBst7jIqiAjFfNKQ5F28BxtR7yf39/nbf+1v8od+/I/wI7//D5L++2OC\n87zw/POMx0O++Mor/Nqv/RPu3rnDaNjnheeeYzGf85uf+3U+9+lf5fTwgDRJqIuCfjfH1nVEKsuD\nEwg4NFneYWNzUxKiRJ7dyXRCXTXiMNRYSSC9wzvpmnkFaENTFSRGUy4WbI+HPHXlMp/69Vf4P37u\n/2Jj6xwbWY+9vQd0O7nwquua3YtPPdEye08F0a+//hp7p8fLDWM+n9PpdABFWZagwCiLdxaTpjTW\nU1khVOdpQlXMUL4hSxTlYs5dRB7r4tWr/NiP/xFuvPk2t9++ToLiwd099u7eBZS0S/4lHG0AXQ+i\n623b9Y3kUaWTZUWzlF4Ly8RYXqLdtAOoEMUSpN2WhCTKsUFtG5EHsw1n0zMa12U4GJBmGSq6e/gg\naNu828UHT10J+OKh82A1F22//26bX7vhtEF0KTywOu0luArnovWpaJ16hG7hnMMrmY+a1DDMBlS1\nVKF5J6OzRId6dGowiREUb/QuLQsJZmmaoTPw0xl3bt+i8YbTkxNOZwvy7oC002M8HnPhwgWeSZ7l\n9p07pJ2cQb9PWZaSTdvmISH09vrbwNpyWdt71c51pdXagLWoIChmJdkQeJnrOW+FiO48W7ubQsuw\nDls3VHW1nLclbTvXSvA0RgBGOhI1Tdv+i8+SNkk00BYQkfMWQ8A7KxVmRNgpnQmNo9sVGJhzIqQe\nq7FWfm8+nXN//4BbN29RLQqUWo0WWt7jMuDwTlrX445vtxJtgUV5N2d7e5uqquh2xYw9uLD8fLQB\njGbn3C7D8QinFUU557Uvv06WJvzA930/X/3yK9y5dZtLFy/QNI0kL7Wg2U2S0FT1O8FOj1Scj3aV\n2p/RWj4PDaBCbOkHrBXwmFy7dJS8kg7NaqYuT4EPQfizvsG7mo5R+ERz8+Z1XnzxRc7vbmObktnC\nSbVaOZoKMqPZGo0Y5Bmz6Rmz04mAC31AzMM1IVKXlBLOeFVMSbIOobIknS6uKLBo/sf//qf5B3/v\nk1z7xHfze3/PD3N+5wKD7oAPvO8lnr70FEeH+3z6V36ZX/7Ff8QXfuPXmZ8e46oFBFCJIk/NKoCu\nPnW5T9qwtb3N9vYunV4PpQ1pljKfzUWU38VulpUg6pwFxLu1co7hxibzs1MKG/iu7/leZmcT/uJf\n+ItcvfYcznlm0wnGpDRVRTfLaDRknd4TrbP3VBB95dc/Q2dzA2tlMxQsnWzOeZ7T7XbJE0+aiE+l\nDZ6gDFqLz2NCwCiwCjIUPhEDw3lV0E1zvuODH+A7v/M76aUZb772NX7mp38Ggo1OMR1ms9mSJO69\n411iwhMd65VcGzTXgTmP8iLfbRa0nENGtFr8rtybsArATWNjtmsEmOMFwRtCkHmiipu5QIdIk5y6\nLlgsjpjPF1y8eJFuV1CgTWujpoSe0el18X4161vOXiMvtT1/aX25h2af7df1Clyyeh+tw8zD2X1L\nD4h/B6ito9vN0dqQ5RmzxZSAJ80Exetp5cuEO4iGJDXoRDiuta1RKNIkxzYWowxHe3vcvnGLj3z8\nuzk4m2N0gm0aPCV3plOOj4+5cOECFy9dYjGd8NRTVykXM/r9PocP9tna3KIsS5IkjyjKVTWyLtTf\nUkpWsyzRQW3J8j54vLPLasXVNScnxyRaMxwMqMpyadidJAkKljKJK1cfaSunJiFYMaRuqoos74iW\nalWRpJmIDlhxw8F70WuN7XrBE4N1NcF50rxDMzkhTzOCF2tCZRJmswX37t3jzp17nE1nqKDI0tUa\nWF/Dj67lx83O1+fF65iAx/3O+vMQ/4F2iuFgSFVVHBzsy0xv7fdkzi7KQMPxkKquyIcZd67f5Ojo\nkN/xXd/Fr3zql7CLAoKjKsuI/F5V/Ta2x3mkO7M8n7VzWw0jVohio0Wf2HtIothJcNKyx3uqYoFt\nGkwiOADRtk7RRqOU8Eh9cNimiklPQwiQJinz+YwvfemLfOADH2BnawvvHdOmwtuVobZRntGgz+7m\nmNnWlJPDI6aTM5q6xRhowGOMPJ/OCuAo0RAa4W8G5Nz29/bZ/8V/wG/8w1/ib43HbG6O0YgGtrMV\ns8kZ88kEgidJFJ3BiDxNqRYFzcIKTYsQldFA6ZQ00fQ2dxmOxugkISDYgdlsRl03srdFGl9wcq7B\ntzKmnsRokWasKra2d1lMTvibf+Ovce7cOTSBxfSMpirp5F200thiTidLmS8KnuR4TwXRbhrYGXdI\nk4TEJNSNPNBtVhtcgWoalNEkeU4nT0lSs3QX6GYdMmPAWSn/o2EuyLKeFXMOZwt6WUba7XD12ae4\n/vY36HQ6XLt2jS9/+bcIQRxQkuSfr9G7Xpk9GkCWKM130dVsj4c3oQj9Xnv9dvGv3i/qmyoVh/eI\nGLxvJd88KoqiBwUhGmmfnM6ZL26wtbXJaDgU8nWWy3YQPJ28uwz6WSa81bIUWPqyrcnjbdIerUDa\nzV8MhvU7NtYW3br+O1LVQZbnjMZ9qqZkMhUxrJU9uQbVopaVOEAoaFyDrS3driil1E2DQfG5X/t1\n9u7eJe++yoc/9t3cfrBPkvXoZKJzO5/Peeutt9jd3WXY77GxscFMBfr9Pgexqp5Op/QHIxEwSM1D\nQeDRFmX7NYQg4gWipYd3NvJIoalrQd82lo3NDYIPsjk1VipPHzfosPKtDG373nup4L3FN1KdBufw\niJRfWltMKj8rLyGzUBVYOYTE/2ebmqqpSbMOdbWgnE0Z97u8+fW32Lt7j7PTGc57klR0T7/ZrPBJ\nj/V7tr6O1tfQo/PlAKQmY3tnG2stZ2dnyyCq1lS2nHPoRLOzu40LjuPjE9544w2+7we+n9e++tvc\nun6d7/2uj/H28eFyPvyQa8y7nPPy/B7qLrV/iM+PoKHTNAUtnQajE6xrcE6SVYHYBXSANMuWYwjb\nNBRFsRLf91ZauQSCVjgn3q9Ht25xo9/nAx/4AJsbI8pSNJili2FJjMbZmkUjlfr42WeZTibc39tj\nejaJlBdNK9FIRJcrL9WeQQT8dWgtD6VTVJ2dcO9oH1vXYNvEGzqdDptbu4y3RmxsbzLo9Tg9OOL2\nzeucnZxijPgEu2BJ0pTBYEB3Y4ter08WubfOCf2pVdBq14etAiaJAhquFv93DNVixnAwopMa7t68\nTlE5ku4IHcRQ3CYJLstFtc2kuCqhLv8VDKKXLuxy4coFknZ2GLl2Cmlr+OAx7YwNsfZq2gzfelxZ\nUziHrRpBD2pN3dQixOwsVSn2RsYHXFly6eolbt+5yXQ65eTkhB/6oR/iV3/102SZiuCbf/ZrWQcC\nrVegjwbRbwY2av+++rr+sy0dYFWttxWfIN1aqoXMPiSzFuUZCVKaxGQknYQ0FXTggweHHB2dirzi\n1lYMqB2MTjFGxOEFlZrhvX9INHv9utcri/bc259bn+UFolVb+7NtBytuW95ZEgVpEEEI6+xS1F0p\nJZS3EIXPxZtjuYWF4LCuRpGgE8OiWNDrGYw2XL9+g+tvvc3meIP7e/cZ37jBix/8ME1QzIqKJEkY\njUaUZcnR8TFNVdLNROmpPxgsr7+qKrwPUpGa7oqWtDYHXG/3th6OLfcvjRKIIXiausZ7R11WOGfZ\n3NzAJMnyHrcbS3ssN3jvV5t8DKhrH4as/SZyRZME72UcEuK5EGeiQSmcs3g0VV2KxVjwPDg4JNew\nPR6xd3+fyWRKkhhSlS6DzZMqVH8zKb/15GO9c7F+rM/j21mwU8KRFOMC4c3SBmStly3dwfYGWTfH\nJIZXPv85PvihD7K1tcX169/gfc9do6kqhoM+8/kcbdKlKEnLoX3csQyirLd2H3Pd3oKQvOR1tcbW\nDa5plu10hbQphecrQhK6NZRwYsFmbU3TVKKdHKQDBwq05u7du2xvb3Pu/C6j4ZCDgwOcrQle/DvT\nRECUi0VFpQWI8/Szz7P/4AGHR0cCXiOQ5ALes058ZhWOIHXo8lN05ZRO3iFNUlSSkA67aKMxSUqe\nZXQ6Hfq9Hmm3gw2BpvFcuHiFRCfc0jcFFNg4BsMBvUFfJlTKEFCYJMUHeaacbe8NS03p1HRpqgpn\nGxIDKE9TOS5eucJ4a5v7R8cURYkyKc3slFCmaCNYiCbJxL9Yp5RaU8+/tVoRvMeCqAtC3K5juyk1\nSZvOLUESRJks5z1NVPKRgb6iKRtsWeFqqV6n0zMaZ2lcTWVrEfleLOhmOaGu2B5tkGQZuoE7d+7w\n8ssv8/zz1/ja194gSd59dvMkx+NauI9WXd8KQPGOABpl2gTtp6C1Llu2QFkCINYDeAvBb7PydpNK\ntMF5ER83OsPZmqq0LOZnnJ7M2N7eZHf3HJ2dPr1+QlWJiTSI24eN6ijttTonJPDHJQHt9ftlQF/d\ni/U5cXvebUXivV+2uJqmYV74SHSP6UMrqo+4cDjvRATAOYzJsL4iz2MV6ixH+wd89dVXyZKUbqeL\ncvDqb7/KcGuHp59/kXMXLnE2m3J8ckK318PNZkwmE+rMMO73GI2GDIfD5fU7a6Owwepa14PAejBd\nVjdOEIZZlCi0TU1VldR1SVmUbIxGDIcDZtXqd9p2cHtPV0FZ2oyr2oclf9F7L7xE76SNnUQxB+/i\ns7Qi8oMWoXblhfyuxIDg4OiIp86fI6g2sRTxBWstWhvh/D2h8su7BdFHq/d1nMB6IH0H8hnI+8Ol\nXm7TNNIWNSYiTlfjlIsXL9Lr9rh77y5VU/PxT3ycL3zhN0nThOPjI7aHAw4O9rl44RJNHFmwbNE/\nnm72uLZzq04Vv7m6Lu8JugVx+SiuEelIjYwblNFok4rjDysTh/aZsU2NrSrBQxiNUgZMQne0wezk\nhG/cuMFgNGRza4uiKJjNZiL/6GxUrUpJTSpzz0ba+OcvPUW3P+L+3h6L6US08k2IXadEQE2AKLlC\nQJHoBq1kppokCXmekWTyJ887GJNQuIZi7kjzDkYFpqFkuLHJ+/tD3nrrTW7duU3QJf3NLfr9PtZ6\nmkaMCpQSFaWmEeEMmfdLReqbOXVVkxjNcNDF6MCg38Moxa2bNziZTMi6A6BCqwSvlNxPY3DG4NMc\nbVICiip6sH6r4z0VRD3QeLHvsY0lOIGZ26bBNpKV2NpR1Q2NbWi8o6pLmroh+ICtLL52hEYCbkqN\n10paZHhUIgLSZVmQKrh3/y7D8YDju8eEAK+99ho/+qM/ys/8zM9w5wmgz9/seFwLd10Xd706e7dq\n9KGv8o/YOXtkMxIkzrKrFrQIHXhrHwrmPghIpw2sKqrUhBCNkYPMVFvLsYODExYL4f3t7I7igpas\n/+7de2xsjIGHhReMfjgQKqWWc9NHK4n1irxNNtp56joIq23taKMwaS5o1UjQd95HXE6AYHFW0dBg\nkowkcaRZj8VigTYJDw4OefXVrzGbzOjnXbxzUo0n8NnPfpaDswnXnn+R8xcvci5NBW0aAuU8UBRz\ntHdkieHpp59mb29PBAXmszVlmdUs/FF+6AoYFqgrCZpJp4NSCJ3CeyaTCdZWPPfsCxitmc/ntICs\nR8X5H72HhAjCajd8H2hcI5m9k01Y180SfRraVm57jloTcHgMoWnYPzjgyuVLvPTSS1zY2mD/3l2q\n6E1pnV3yGb21mCfMNd275IyPBtJHE69Hj+X6QtHr9ej2esyms4e0o9fXmjGGXr/HZDrh+vVvMBwM\n6Ha73Lp1Cwg888wz9HtdDCr6wMb3jZX2u13eajzRPkft8/zwM22i3CDOkSQCvrLOEUIMjBEcKbPQ\nJHZSpPOm2/eIa8C6mkSbZbXc7Q2w1qG04vjggLt373Lt2jX6gwFFWQq1hdYRJaC0J1VriW8IjMZj\nev0+927d4fDoAWkcizhvY1sn+toFi8Lgg6WsLNSFCCbUC9IsJ0lzOr0e3e5AEMA6R3uooi6xyjPy\nLOfa8y9g8pwbt27y4P4+F65cptPpsCgWaK3pdDp0Ohnj8ZjT01Pm80KqUuvo9zbY3t6mk2c0VUFd\nL5hPpxwcHkhXIu9SzKeYJI2uSlKMqSQVlaKqRJucLO9g6ydzKHlPBdHy/hFnZbOE9M9ms2XbsP1e\nKCuwdlmdtBl3CGL31SruEAKZE36aimAZay3zpqEsS+q6Xm52ioQkTfjt17/O989n/Kk/92f4K3/l\nLwOeXt5hdiK0GVuLwbX3cRypidqlAVRrii30EJM8DH2HVVb96GwH1gBEa/PA9pCs26OVXbaYpepc\nQhpYonYB5eNGE7RYmEVuqQoSlPJEaC1lUVCVDXUtm2IgRJEAvQS6FIuSG9dv8+B+n7quGY2mlNWC\nprEMBxCyaHUVHVqcF49EY1pQhzy8QJT/i+jfIOAoSyBNBbSiVYrR2SpIeE1QmtJWJCSYYChOpiKJ\n51sfVbBOKqSaGoWm1xswndX0ez1CMJSl4+69G9zb28PahsFgTNPIXDEJjp6qcGXB5NZb3KoW1Gdn\nfPCjH2OjN2L/8AGn1lHXjrPS0e0kmN4m6WBOM5nSVDM2BwkHp8cMN8R5p1zUEDTOBoyS9amVEjQs\nHucSnDOM8iHGa0JwFNMZZ2dTzp8/T2e0xdl8Lq29EKJRShSJj+tBxe6LjDQdjlWQdRqsVuADRkOi\nG5Sd4vwcnWSkSlEFhfUQ4ppoI4VBkhE7C8yPaz744sexTcUr//Q1vBoQtBWBeHxsmz609IiQ6rje\n4tpskeN+Rf9Zb4XK89peo4jFm8Q89HPrz0ye51KZeU9ve4OQahZNgdYiQlBHYfmA+N0O+mOGeY/Z\nyRlnxyf0Rpb9vbuMN/p87GO/n363z9HBIf2dS5R1TaZYIp1NQBSWlMaiOJvOCCEwGo0eCvohxHFH\nDG7yoSkSDE2VYlKDDY6kowjKUdRnOFeLsEkNueqR6A4mFV639g5tRe7TVRbtAzSgfEaadVChQYcG\nX9WMEsWxrdCJ4cGdW5w7d47+5jbHVUM43ieJEo1yJFHfWqF0VKnShrST8fRT59BUHB4e4ZViPN6I\nz5pcmzGtGYWAF7Ux4KCe1TTaEsKcM3XKYDhke3uXtKtELSrP8UnKwhUsXEGv1+PKc0/jU8W9e/e4\nffs2l86fE2BjU7FwDYQeIQQuXLiA9547d+6Q9lI+fO19HDzY5/TwhLPZFOc9nW6XgJHiwdq47zSk\nxsv3qhpnLDpNSfMcG0rq0NDYkic53lNB9K033yTrdWJ1EVuV7WA/PkQdrVEttSIIcq5FlLaIUWmz\nOUzjloNpaZ2I/2a7+S75ew5QgqL7+U/+ff7sT/1Z/uhP/ASf/Lmf44//8Z/gf/6Z/4kHe/eIuAui\n+MtSO1SZaHukdazkFCGIa/q32xJ+HILxn/V4RxXEqlJqmiYi35olpH+9clo/lBKkXJIYTk5O0EZE\nHBZFQZoZOp0WhBQeQhu3yU1biT58fQ+37dqff3SOHEIQTqP32LhZtX/qqsS6ZllZ1bZkMBiyWJQ4\nB6enE87O7jKZzFkUCwgKYxKsF6eLVfVsyNKUw8NDVNohyQe8/upr7F68xM72jqhmacPx6SlVWZGn\nKd1ulzRNqeoKFVrbLx5qubajh9ijim05acsmSUKv1yNJEqanMxaLBUop+v0+zjkWi8WKMtPO5UKI\n+q8qgoPi7Nh7PKLylBgjxughUEcyv2ZFmarKIqKErSAcfUBpqY6UfBCgFLPJGalWvHJ6wp2b17nx\njbfxtsFoaaPzLi1OEPqGDLhBtRPTsDLYXl8bj4KHWo/Z1jh5vS0+Go2kYlksmC8WkiinKVVZURaF\nbKDtzzvhZda1pdfvC13iTKzu3rx5g6tPPcX7P/ASn/7VT/Olr32Rnc3tWJmJ/rM2Gq3ELLqK87hu\nt8v7X3o/WgtXeB47GS2wyERuo9Z66bMK8lqmNdVwHmfL5b4lACOPTE1XY5i2k9Gi4ds9rh1zBEcE\nEOqHWt5FUXLj5k2ef+n9jEcj7OQEZW308Y1yhGuJTft5hBDo9/pcffoZ8k6X6VSqufF4E200Z6en\nLArpRAQvXSuFAKG6JsVF4YOApq5rbt68ic4SxttbbG5uxmdIL1vvvV6Pq1evkiQJd+7c4eDggM3N\nTTY2NoQfOpmIs01ZcuHCheXzNp9NuXfvDrPZnP5wiFKBui6XPshojfcGbxXBSGANaJTXaIQ9kHWl\n5ezfZdb96PGeCqK2rOMmoORB0kbmOWtyV4W1uLjx20Ysoeq6kfZeEIksH6NdgrQ/W+h2kui4qa82\neufEJqpuGnqjPneu3+Tn/s+f48/91J/nta+/zitf/jL/9o/8Qf72f/nfokStizRLIx1ANgvJqNdG\n76FFjD75sd6iexxK8XHV67fzerAKoOtztsdVv48G0RBYbmD7+w/o9XucO7fL/sF9qipD65V/qFq2\nnNvZoF5lvmvvFSI6tQWIWCuyg+Aj3zAF5PU8REh7WHItj4+P6HTzZTLU6XRIE8P+3j6XLj7F9bdv\nEUKK91CUJcakpGmrUKXwWir2EALayO8XkzmnJ6cMN3a4d+8OQWsC23R7A3Z3dzDGcHp6GgFWHbr9\nHk0xF73TNBWUbGOXHY72UKwoTbZpqOua4XCIMQnz+ZzJdEJZlozHY4bDIfP5XKTrvFomj+3rrLeF\npSKVGahCS/sRuxQzF1CG2L0FH5a6u0opDCpagwWaqPLT8kqd9zhnuP2Nt5hMJwRn0QoSKYMRvSkp\nOZeVZDxDpQAtSa7GLM9bKUUT1hSddFudxsQprg+TJKIJHe3ggNg9Sbh46RJZlrG/v08AyqoSEn9V\n46LyU2v+sj4P7vf7TCYTzl+8QGUtByfHfP1rr3N0dsLevT3u3b3HlfOXuHr1Kq999SsirO4cRV2S\naM3G1iZbW1ukJuHk5IT7e3vCYR+N6OSKLM8wRoBnaZ6jk1QcU5QSn1JEC1srSLWito0YeTc1wbqo\nYLQaazwkj2kt3hGT3po0lUDtMaJ9a1YJZ5t03bt9m93Llzh38RLl2TGLySSeSyK4itg9WF9P3ntq\nH8g6fbZ2zqOTPLrrNPR6PXqDMZhMkulUMBlJkgqa1iR0k4QkzcjyjnzW1jMvxdd1MV+wubUp1btf\n4SJ6vR7nz0kF+vabbwiIr2nY2toiSVOKomB/b4/T01PKoiAAJ/4uwTk63RTbFCKakSSEoCI6XROC\ndA28NTKmUKCTFAMkmUb5QPBOqvsnON5TQTQ0lnpexLmEixuJXw3gnUM5KxJkXnikPkjAFMRmK0b0\ncH+prXB8pAg8jJQFrR1GKcr5Aozmi6+8wuc//3n+3R/5Ef763/jrvPAd38ELH/kA3/jKa4J0S1MJ\nvmqFvFwCXLzYQem1QdGTVpTrAXT93/EyHhuYv9lrr79O+/UhXupaJbDOf3s0WIcQuHTpEr1el6KY\n41zD/fv3KcuS4D1lmdHrdRkOh2idLLPoFYhotUFATAy0LORHz3X976tzs8vzEsu3ht3dXZ566jJK\nK9I04dKli2yMN/nLf/mvsphbjo9mnLtwgbr2GCMtMuvBeUgiRD5EjohCCPFZmjCfTZhNzxgow73b\nt1gUUy5dvkKv12dzc4O6rll4j8YxHI04Lhf4EMjTXDbGVt3GtkL8AvTCR5Rs3ZBlGaPxGOfsMuNO\n05Rz584BMJ/P451wbUG3bK+HWNkqhOISoUyIRZpCx2arUdIqVwQxpsazWMwJzlNVNfP5nMV8TlNX\nNK6VcFxJOfpG3HdaE/XgBL2pFCjE83P5eUFMnAJKycYl59wasMsmn+LE0NuvUK8mSWRTj2sxyzOS\nPENpQxOR9UqJetL+oZgo5J0OSiu6ri+8v2hiIIHELp936x06FaDOrCz47uef5+d/4RfY2d7h1s2b\nNNby9htvcnp4zOV/6xIqGH7ri69Q1rJWu/0eG+Mx/V6P48Nj7t/fYzaZCggotg5VR7obKwGR1R6j\ntBKOKFKJmkSRpsLhDo2N3rDSHTNakaTJUtdXEkZZS86ueKsrjV5Bpxut0akIaQTryPKcsrIc3H/A\nhUuX2dne5X4lhUZA47w4J0li55Z7ZQDK4FG2Jsk7bO2eoyhKrG2oygrrIe8NcGiK6b6YvBtDmmck\nUVHOOosrC9Iso9PpMRgN2VUiCXl6fEJdVjLP7HTAB4q5cJ83xxu88MKL3L59h+OTM6wLnD9/nq3t\nHerGcvBgP+7dshdoBd5KNWx0HGrEvRwVIlJf45RHBUNQK1NymTAoEu9IzJPhyt9TQfT08EjsnNoW\nRiMtUdYChSGg2hmTMRBh48v2r1o2kKJxs1/+eluFPjqjdE1AG0WaZ5R1zXwy43/46b/Ln/7J/4Tv\n+p7v5Suvvca/82M/xn99/W/ga4sNisZ5nLMYtKh9sFbFqYcDwbdzrP/eQ+1Vpd6BJ/pWr7F+tO2z\n9ezepIko+hDw8V7LjO2drbr79/fo93t4L+dTlhXOBcqyiptySVHUjEZDOp3OslXZto7ba2mF2VW7\n2cRESLMWeJ1FNXI2JklEySdSdBKj6eR9rly5xHg85ujogNpZ6rLi2kee49ozL/D1r78B5GRpVwS2\ntaZqakwkclsnQUgrcbpwzuEJdPMMk8LZ6Qm9wRCjOhSLOUcH+5TDEeONTYbDARqYzycMBkNODg7F\nCcWY1RhBi87pqiSXaxeT6obN8QitFIuyXKJKN7c26cZWZVsVLsFKgdgqI3JFV824EIhgIbtca41t\nKMuC06MjrG2Y9XtopZhMJixOJji/4t1pLRWsUqzM2bWGTguAseAsaWJkHutdDJBheX1Cxhd92rai\nTNM8OonoZYLbRIk+pVZG1lqbqB0bNW+TDJ11lgEoic+5956002X3wkWSJOHevXsYQQBRl5UAV8Lq\nEUmyFFs1SwrFZDZltLlBWZakRcFoOASgWhQCSmws5WJO1dRknZTBYMTu9g6z2Yw3336b6WSCrRvS\nTo5SiqqqVuYP0U5Oh7AU3VBIZjdAnAAAIABJREFUYm+MwSlxi9JaEpCmKmmqkqoosHVFEoUMTLIK\nxq0Gdgjt31cgPalEo3H3Gq86TVIJLHnO6ekps+mMc1tbnJ6dYf2Uh+bTSzlKFz9/UUsKIYikoNbk\n3T4XNjeZz+ecnZ3JaCwobClFRG0bMp+TpKmgub2nscIhr6oGnST0hwN2d3cZDAacnp6yv7/PxsYG\nw+Ew7iMlTdMwGo155hlp7U4mEw4ODuj1emht+Pf+8B/m0qVL/PwnP8neW68T4nhu6YrTSlfGUYRU\npZqgjLTQUSjnhCLpo9ymc6j0ycLjeyqIlvPFss0Da9IC8cmQ7DssU18fVtJirdyZLJPVQoHVXqYf\n0e5cR+9Z56BuJGtPDHdu3OIXf+H/5t/4A7+Pn/3Zn0WZhE989/fw2V/6FFrL6w76Q/r9PqPhkMVs\nzr27d5dUjMS8E2H4bgH1cf//0SpyfYaxfrTJw0MtX/3ugbsNVOso4daYt46Z8XrF2iYok8kZi8U8\nCt7L99M0ie1z8N5yUp8xmczYGI/Y2dWMRyMSk5BlYiklfL6GqipJU73c+A1GXDa86Lkab2hpKymB\noPwSjVs3jp2dLQaDPnUtD+B4Y0SSGh7sPeBjL3+EL37xKxAsb7z+Ohs757hw/jKHJ8cy73JBhDQC\nkUAuAcQgbZ9UG45PThiNNhgPhxgN5WIexwWK0cY4CmDXlPOa0cYGdbmIDiwseW0aaTcqFHmWURUF\nxaJgd2eHLMsoq4rpVEBSWZbR6/WYLxZLXmgLHlpWNe1n3a5/LQHLe4cKAaM1VVkynUw4PT1hMRe0\nqvKO0+NW4cqRq3QJ8Fqut0d14QOEIIlPgsz88VJFigFH2xMREXpB2SkiOog075BkMkfWLRUDUF5e\ns9fvk+d5NBKYyDwr0VTTOd3hgDwRAZU06u+uU57KshT+pDHLpKKp66V8XhtwZcQgiWJQsLG9xeHR\nEdP5nCtXr+Jr6WyZoFBVRWoMve0tsjRbqne9/fbbnJ4Kb1roSEZUrdpK11qssSQuIUlXc+f1GW8I\nId4eBVh0gPnZBGLQlQ65Q+mULEsfEhuR58/S+rxWVcXOjowVRGw9wdcN3W5PniNtqK1FpYbFbM5i\nOqN/9Wm6/RHzeSkAQ1djXWs6IB2V9jA6XxYcKlJYzs6mbG5u0O8POT4+wrmA7/UoihJQzBYLsk6P\nvNuRZaQsLvrJWhf9fStxI9rd2aUsCmbzGd5KJyd4j1aasq7oDfpcffoq9+7d4+DBPrPZjF6vxx/6\nsR/l5Y9+lPv7D/jkW18HpZZro2ka0jRdFglaa5QH5xuCCUvutjLCzSVt4whyH57geE8FUQJR3b89\nHh8M1oPNuwamb/L/Hv09CRpa5NScRYcEheZLn3+F5557ng998CP87//Lz/Lyhz/CuYvia/r8c8+R\npdJnn5yecuvWLS5dvszR4QFV5VARyv7PezxUVT5hJfqtrnu5SRsjbSZApwkmS5ezl2VANqJekuiW\npuIAHaXM2pnw6vWddRwcHDOZzNjd3WF7e5uNjQ3yYU5d12RZzmAwoKoWgk5co2+sZ/jriYVOFGnk\njY1HQzp5TreTU9eKjY0x/X4PrTSL2ZSL58/LBqWlfZnOZhQbBSY1FPOSrJNJ8LGu3falyI/zxeAd\neao52r/P7u42Wb+L9xZvExYLEb7v9fv0wwDlHU1dYZuK2WQavV21zOy9UEiSxFAuxDR6sCbUUEQ5\nv263S384iJXqw+osrR5ui+4lBHQEMKk4H66rivlixmw+ZTadUZaLKCMoaGBBZkd1oriIVp/Xuy8o\nad2uVb1tUGjHIUEqUNaChQuBVBuSLCfvdAVwF9HqAEblUlkFz+lkyny+4MUXX+TixQtcv36dmzdu\notOULO9ipGxbzXC1wQfP7bt72Kah0+0w6A9kvuX8st0dFw3OC6rZJImAfLRmOptRlSWuaZicnWGU\nxtY1m+cu8Obrb9Dp9rj27LNUdcHd23eo41q0dSMJeAuEC6tb9yja+NFjhW2QqrksK+qFVKDBOWSu\nHN111rqLEhRUTGLl33mei5l50+BjwgIsFY6Wn50PoIQqGFD0+kOSfCKUrjyTYOI1PjqhtFiDNFmB\nu9rrmc1qjo8P2d3d5dy5XZLEcBomlFVNVdW44JnOZmSdnDTLZB5OwHvQTUNTN5ydnTGfz0WZqNtl\nM91ksVgwm04ZjUYiq6gUwVd0Oj0uX36KNM3Z29ujPxjy9dff4O9/8uf5zS+8Qt7rUhYFtbPL6tkr\nkSrMMqnE67ohyzN85Je2vFMVwDcW145B/lVs5z7p8e1Bdp7g9dRqw060QUzBoDyb8el/9Cle/q5P\nsDnY4tbNu3z047+DLE9IlCZNDbdv3uQrX/oSCnjxhRcoFrOHW3nr7/OY77VHG9weV42uvvH43/t2\n3mf9Z8SnUB6aNE1J05ROp4OYezdLq6ZWdrFF2Am4x0v1r1qd3oDShiyXTNpay717D7h374Dd3S0u\nXLjA5uYm3W4X5yxaB4zpCHBsTXxBFrxZVsfteY3HwyWqleUs2pMmCVUpCiahtgwGY1SQ2ViSpmR5\nSrefM+6MOPn6Edor8JAoedjX9l2U9xAU3TznZDLl4P4evdFIfioVvqAo2hi00nT7fQgRkFOXzKeT\nVQVvHZ08x9YNxWIhm8fGRnRlqakrUUYaDAZ0e71lO/Ohdv5D88+Ad57aW1wjyk3z2ZzJ2RmLxQyP\ndAgMUT/Zu0i9ks9JRapN4Mmyb/DLHLalUnnfzi6jLFykQkllGgOlSUjznKybg06klasFyeKDi6/j\nl8jSj778UT70oQ/x+c9/nsl0ynw+J+vkMkcOMs/VUSwkzzJ2zknrsGkaXGNxC1G2aWfbIYp9OO9A\nS8XivWO0tcuXfuvLJGnKjevXufbs8xiluXL5Cv3+gAf379PtDygWM87OTigXC5n5tZJ71qO8SPM9\n+hyuB1D5uqLvAFhvIWhSEyjnM0LwuKrGNzU4jzIJaZqslLiWXF4VW/syCtjY2FrytFtgkCeQpokA\nsawYbkhqC8EKp747GNLtDSmLOUbnaEq8szhCdIZx+MZSuYevR8VW+mKx4MaNG+zs7HD+/HlSfQ6H\n4vj4WKzn4jOQKEjSJN5/wAd0KlSnqiyZNKdUi4LNzU2GPQF7FbM5vX4fS6CuK4KWufjO+XMUVcm5\nixc4m035e//b/4rudrl6+Slu3rzBzu55gvecnZ5QlRUhKOqiJjUJSZJRW4eOWASTyn5eNTXGJLja\niyuR+v9pEJXB8JP97JNOI0Mcyhsl8HpbW4KHvNfj1pvXSXTGs89d40tf+Qo3UoHH51nCyeERb3/9\ndZqmZmtjg7Ozk2Xb8bHv8y7BbT2APtraXf8d9YRX9M3ep31dyfBX+4HWMrsyce6S5TmdToeqrrFN\nHX0HJcuDsGw7KaVxtK4KisWipNvN4/tJJTaZzDg8/G2GwxHj8YjxeMzGRp8kUQwGg4eEGdbh/S0Y\nKc8lkOZ5TlWVbG0JZD5J5AFfLGZARtPUKDyXLm1y/dYRqfE0TUUIjjRLSTIDOJQxBB+pIu0diJu2\n0eAV9Htd7u/dY/fyZYbjMVopqXjiHCdNDKlJyLsd+tUA6oLp2alk3P0+aSJG8idHR/S6XS5dvBjR\nuZIwZFHhpbU6exTY5ZwjMSvruqaqKQshnS8WC2azGcEJrSXLEwFGJdEtppHZa+1FoShoQeJq9W2g\nxsMjM/gIlFFofCByTGMLDdk80yxjMBoxHI1QJpE2sNLiTEIgVRlVLV6+3TwntZbPfPaz/ONf+zWS\nJOHcxYtMp1PSLHvINg+I4MLFWhCXjdFHJHLwUo36uPad9+hU5nVFVYmM43ROXRSM+wN0gCsXL2LL\nmtmiwGQpZ6ennJ6doLyjk+Z47wiN7AvWB1T8E1nXKxCWD8v3bdd9i2No17RSAa0Vs9kU11iaqlqu\nJ2N0DKLy+8uZeGgDaqAoSs6dy1bc9qBpaUY6Gti3aPh2HVlrKYoSj0InCTrJhKKU5XinscjkRwfA\ni45zu394IoZBafJUM58v2Lt7C1sVXLy0wcWLFzDGcP/BAwDKpkY3KVkmghGJMhgU+CCJSBRMqaNX\na7/fZzwey78XC1SvE9v1FU1j6XQ67Oye4/nnX2B7ewdMwosvvo9+bri1d48Pf/QjlGXFvTt3GA2G\n/K7f9QPcv7vHpz71qSjOMkdpz2AwopN3ROt6IoL23ouk45OWY++5IPovusp8ksMFTxJVZ2xd08v7\nbG5uMysKyqLm7vWbnB2fYhPFbD5jMBpx/GCfs/v3ohoznJ6ecnJ8GOdhouf5pMd6AP1mleW3iVH6\n1u8Ze5k+BNl0gLCWgSadDp1uV9qIES1dlCW2cbFSTKXqCaJbCoo8T5dzvsFggHOOZ555hnv37nF4\neIj3Ajw4OHCMRl12dnbo9/tCUYlAjZaz2soVptGVJE0TxsMBg26PYC2j4ZByMaOpKoxSkDqapuJ3\nft/3sv/zv0hRWg4P9wkGTJpi8SQqJUsen4GqNqZGAFNVWW7dvMEHP/SdaGKrOcvjxqYJJmBMSt7t\noN2Izc1NEfKoakIaKGZztNYMI4ilFYkALVV9kkhL1gqoSxnBvDrnBIQULEVRUBWlyMQ1DfP5fIlo\nTJJEjACCIA2Xn2li6HY6lIlmNptBEMS4954nTL6RMNGiGqW68t7jQkx0jCYxhkRrBr0uvX6PLOuS\n97vk/Z7Qv3QL8FjN4tUjn20bLHVstwYEgKSUptvr0u/3Vz8XwrJaWywWLOaCNvZ2pW/bKgx570kS\nOc+z6Zyf+GN/jIs7F/k9P/zDdLtd6qKk1xXAVfAiw7d3f5/hoIdSwk1WSqoo5yw6tqvVWiW61C+O\nesjee8x6Vcp64irPx2w2pakKbF1Liz4mpCZNlklym0S17VxrJVj2er3lvdNeE6UxBGgUROEo0QYH\ny5FFUVSUjQjLiLKWw2Qp2iLXpEVjOEkE4NVSCVs8iW1ktNPJheO7v/8A66bs7Oyytb1NYy1nk2kU\nxgl0up6eUqSpIUlTabeHQK8ntmOtqX1RFIKA7nZFD7tJMSYKbgQicChhe3uH+XwhqkxKM5kX6DQH\nk3E6PcarhP54kx/8oX8day37B8fcuH4dpTVJ4hkM+kIVAwaDHmenU7x7lJr1zY/3VhBdYfbf9fh2\nguwTV6JNQPdSbFXT7fd4+tlrPP30M9y6dYvyesWiLBiqAeVkRqgrbn31qxA8aZ4vZ03BOXScG65h\n/p/g5FVE87K2acWfVY9cgw4PVaMh/keIvxeIlUasaNvgG0EocqKeEFhmxz5qD3ttSLKUxCRLhaPY\n68VoQ2o0ncGQrDdgNpkyOZtgvSc3CRsbGyzmC6qqhMpjtCI1GVubOyzKBSdnEzZ3d7lw5QreeY6P\nj1kUC+aHp9w7PCbPMjbGY7Y3t9jY2KCbd2JbVNCijQp08i6dXofuoMusLBgYxXhjxMnJAcFZcA3z\n6QyjE973/DVeet9z3Lp7n739Cd41zIqC0cY249EOi0Uh5PAQ0aKBOHNbc1lxln6ecny4z907N3n2\nuRcwSovRgZivxepCYzo9Uq3Y8YG6cRwfPGByeoLRivPnL9Dt95gWBSgd4fYqtrwiXzfIA11Xwntr\nuaSUZ7JRR2H1LFH0dzZl4/YegqXXTQGhiZiWuhAdWpI8QSWGoljQRIH2JJh3gNGg3ezX1mSeyrav\nzbKlakyy7AgkWUqW5sIVTGUe1gJ+dCJViNKrmamPIKUQAkVRCCrT2bjWAK8YDzfiXC3qPWuZvbvG\norzCW0ttS6q6FpWmusESsEq6B6odL4Ro1pxkVAFU3uEzv/lb/NRP/jA/+Hv+Tb78G79Bamo+85lP\nS5UE2ODp5AkEB96RaC1VfBAlNB/b6tIOD8vn1SPB1cX/E6cFERQpv+frmk5qKGczXCUc0aW+tdKY\nNENp0XNVPqJy4yw0eE9ZleSdnCTLhMNrHQkJ3ousffBOBNZ9kH6+t6RJjgmOsphQxVl7K5KhkcRf\nRGLMEj9iUHilcUpjlcZFH1rr3BJD8P+w9+ZRsiV3fecnIu6WW2Xt9bZ+3a+73+tVqIVEaweMwRiY\nA8cweGTwOfhgYMy+WIdl5sCMGbBnzswfMwePfGZ8wDM2MNhIgAEJbBZLLAItjdRaut/Sr/vttVdl\n5XbXiJg/Im5mVnV160nqwaMluutVVVbmzbz3RsRv+/6+3ygI2dzepagsa2snWF5dwwrFfq/HeDym\n0g6Nn8QJjTghEJI4jpwYdhTSP+ijtdO4XV+/w8LCAt1u12vAaoIoxEqoiorhcERapDx39TJVNubq\nlUsU4zGBVNy+vU6a5iStOayM2R8VrC4vsXrmXj70kY+RZxmLS21GaUGVly7y9KC8MAwoKweAupvx\nuWVEZ2pULzVewWBsMoI4Ih9lJO0mFx59hLW1NYIkYn51kcbOBsN0wO7eFgpBUDfoCgG+LaSugxwy\nkscY0ZeASR3+i99QmdkU3MPeBZ7UqezMS+yL3/sQIMlg7DRFLL3nKaTrv6vKCi1K16YQeESmUHh8\nAvh6XCgV3aUl17+V5azfuUOgFKvLK2itGfYH2DQlLwoGoxG3bt1m9eQJhumYcVGAdXRqa6ur3HPf\nWe5s3ubOnTv0Dkbs7vfZ3Nqj02zRTBosLS1xYm2NpNui0YqxWFTUQKNIq5JmIGl3O6gwYGdr06Fe\nZRPTrOjMd3ni1Y9z8sxJ/vLDHyErSuIwohE1aTXnqUoYZLuEYYgKQ0xZYfSkkoTAEvqQQyrDjWvP\ns7S0xNLSKUrt+v6wksq6SMWKABUmtDoLrKxpdrY3KYqM1eUVojh0xhIBMsBgCYSTaCt9bVNXFbmn\ntqyRpcZYEl2ShCFBEk1u5VG5Of8gdgYZWudAAmuJWi06nu1Ga03RGzuCAz/1rHG9h7NCBUopaMbO\nMCtFHMeT9H39nAm6O4iwMoFJ2tJ9tpql6hBqvHBE9dFcQnfOzeEsyyYpT+NbF3Lte1fLkiovqPIM\ntKbIM6zVFLpkeXXFMUYJMFJgpDtvIYSrhYYRKopIi4q1M2e4vnFAf1TwEz/zs/z9b/lmxllGHIcI\nbaiqnLJyjpGpDMqvwcnK8whufDp82gLm74HwDVvCSwxS6+I6gFZoLQGK3sEBZZ5TFtW0Z125NCsy\ndChmXbnIyRisdutaa8384pyL4rGOUck6BLuUAbYyhGFEymhS20+ikEgKynQ4iSyFEC76RPhuhlqE\nAMf0JCRaOKkxiaCyUNkKIacpZmkhiBoMRilmc4sTJ07QnV8gy4uJAECWpgyVotvukESuLGSlJIkj\nwqUFAp8hSdOU/kHPBSRJExnWzqDBSklpSoSC/qAHcYCtCoR20mbZaIQMGhQVWBlRGIGK2xwMM3qD\nlGYzxiCR1tX0q0J7HV7LYDwiaTXJPh+5c/9zDV1WtObaPPLII6ysrDAcDNjZ3p4wZQQ+7YZ1CvV3\nN44xop9lPvZTAo+OPK9+bg1UOAqfL6tygv50zEIaIbSnEjOTFgtgAhYaDofEQUgUhpy7/34yT0+n\ntWY4HhFZzSAbETVixnnGrds3mV+cJxunpKMxZ8+eJVaSy888S384oNIlkQqIQ0dWvbO9g9GGzc0t\nNje36M51OXv+Xk6fPsOoKKHMOXHyDJ1Wg7Q0nDp7H+sbW+TjlE6nCUqRFTmduTnmFhdQUcSlS89z\n4/Ymuzu3MVXO3PwiRRy79JVHIsuZ8qi7e05SqxGEpFnOC1eu0mrNE8ZtH7F6Ug3cxqmFRIYR8wvL\nLK2cQCGYX1xCKQeCMdZidOUMZ5U6AQXPtFWnIkMsQRg4vUQkgjmXMveRELjm+sm9FQ6IZRFQ9176\n+ufsnJvlbC5Xi0OCAUEQTCMRKV1frhDEYeRZqBzopZ5H9XwBn3auNEZnPvMx7TGun2ONKxVYa5Ba\nk+dO9NoYTZZmh+ZlDWbLKu/0GYOtSiekYIwTpq4qrNUsLMzRajVgoF3fON7hA5fitRW2KBiNU+ba\nbe7cusG73/N7dKKQPE2JjUE1Y7LUtRWpKHYC6R6cc3SN1enoY9fcjMFFW6wyuJDQ3zOlKPKc8WhE\nUeQYU3nH104ifKUUCJ8iNpOjOadKKRbmF5yyiUelIqVTfQlcr7wMAh8Fuyg8jCMIXbtd4cGBdXpa\n+AxP7SCDdwcC6dL91lWp6i+MI8AHh3gOcCn5g/0eSRzTnesyP9dlOBySZRlKOCM8GAxIpQPVBYEj\nkoiiiPn5+QmL1HA49A5e6pxFIbDSEWRIBMJAHMYuyyIdJ0AsBXGSYHx9Hiy9/T06jQYf++hHCCQ0\noghhnCJROhphtUUKRaFLoiQhjGPOPvgAlz760WP30NnxRSN6N8NY2m3XZnDlyhX29/dJfV+fNYYg\nCqmbeY8uouMYftx4hY3oBP8yXdQvZ0SP/j6rXVoTIDhJOT1JI9ebpANE1MAhd4w8z4miyBkNz8YT\nBiFxo0HkF4gKA7Zu36CyliIbEcUJWZFjjeWhCxfo7e6xt73D9s3bjNKUIAoIRUhVavIypQwUS0tL\nHkCU0+8PGI9TtoYHPPvc89xz9h4ee9VjrKydIstSNvb6nFhZ5v7zj/DclefISo0oSowURElCXuSc\nPnWSJGmQ5zlXLt1Cp2OUMIRhQlmVSFUTnTujeejKCUGApBFG9HZ2Wb91i9P33AdBhNUGKwzGSlAC\nZSW5hkajyX33n+cFaxmmQ4R1fYzUqOayRFbp5B5Fvpcy9ALXYsYAFoFDQyOEQ3EKAWpGsHqSpldY\nXP1PySl/s/L9lkoFyFqkXVmXMnWnRxhGBEodooA0xhAWjuwkz3NGgzGj0Wgq/+afW/O5TlKyM3NT\nTriYzaTX2Hi+5rqHM89zl7b2ZYja4DqmHpdKVgik52UV1qAwvrYNVCW2LN21mXAqO0SvqTRJ5GLy\nvTu3WbtwgXf9+q9z7dKzYDRSwt7urkOU+t5SO1P7PeqsHv1yf9BTUgxPJefqo+68a1MohSXLMwdy\n8xEd3tFVQeDI9uV0DdZEQlobssyRhLTarck+4GqgjtwCn5KVocJgqYzrrw7iyBnV0snuVbo65BzU\nblpdt62HS+G79S88aAzvpBhAaFDWOTxxFLG3s+vcvbk5h8Ct9wkhKMuKyjhh8TAMJ3tlo9Gg2+2y\nuLjoxLm1JktL0BpdlggJIgA82HNpcckBPeOEKhy75aArt1alJZSC3a1NRgc9bt24xuJ8l0BZxoM+\nZVE4FHkgqSoNQtKeX+DJN7+JNE2/aERfqRHEIfv7++zv7U2AHbWHHMQRRZ47RGUQelTXdLwUGOiV\nH1Ops0/1frPG8rj+tRq4IJQgVKGPIBw5szG+huoJJYT3iutNU5cODQnQabcRQjDXmSOKIjrdLovL\nj5OmKbfu3KbXO0BIwcFBj9s3bxAgOXf2LKFQXH3+KoPhAGsF3W6bMIwYZ6lb3H4jT/MMUQjiKIZx\nwV89/QmeufwcD154gCeeeDWNOCYIEzqLJzlzTrC/vuE4cYOYxW6XXm8PO045sbLMl33pqwiVZWtz\ni3S0i1EO9BQ1p+ASeHHKXVSaUCg0Fes3rtNut1lYXkO7/J3b7K3ESoWKEipjaC0ssbJ2iqtXnmU8\n7BMIQez5ayMlCcLmIaILcBGck8Fyn0AFCqsah6LIGtAjmKKYhXD8rEIEKDmVuZOT1Kz0vK0uctUy\ndyn8qqKsKtJ8QJqmjNN0YtTS8Zgo01jPR1t/PmvsFD09uVZmQkhee3o1YnX6szuO9cxVdYrUEXZE\naONqbrUwhFIhNYGh9CnH2jCBpZlEJHGIrkqHSRCWqVKRUxwJowiT50htKLKc557+GF/39V/HWqvJ\nJz/yFP29HZTWEDhFkrKqnJYm9hCCb4rANS+KRI0n8tfafSnt6BfxCjeuJGNRwjmVWTqiLHNPMjJV\nOXL6oS4yrFtarICycMxTKydOOqBYUbp2Ie3QwAbrgIDS15+VxHqllSBy4tal53I2VeVJ8d1tspN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6cpJGma0my2UV66a1Isq3dznDyZEI7VqTnXobe7i64EN99/i1P3n+XCI4/R6iwA0kVqMsBq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pB5zvLx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\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pHaFtEcjLEmviXFjnUJnCeXcVUtA6DVfXIyZRDQ/CK7w1eBWxUZfIXSCnRktqhXcNEsvN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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff5d392d950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"check_training_picture(bn_feat, filenames, 45)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This doesn't have any 'good' class, everything is low, which is weird - could be the not-straight head angle, but who knows. I just realised that some pictures have a blue tape-like thing on the driver window (see above and below), some pictures don't have that sheet, which is probably confusing.\n",
"\n",
"# TODO: find a way to mask that window"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1.4622e-01 1.4535e-03 1.7558e-05 1.5187e-04 7.7130e-05 3.3243e-03 9.7658e-06\n",
" 2.4722e-04 4.6495e-04 8.4804e-01]\n",
"c9/img_39396.jpg\n"
]
},
{
"data": {
"image/png": 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h4y67+3vs7EFZlJRVHZSAxtAYSV16cCpIUqJBSmhMQzFdspxdUtWB2FQU0ev1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k0eaUF5UURzaClZ76JEpQhRp9b5gMmiwL7SGq2imIQyI0w1idWzj96mfQDbd3gk2hRRPSnxTpUI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WTMYbPHblKqU17N27T/ItsRX+o3Oe5WzO/p6Im3dtK9lfQlCnSvqCQYk5+7AsCV7E3/d2HrAx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\nhZg5rJ4UpNWAMRQ2my3ElDNCQ3C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L+GAe0/anjVnlaUqWSK6m0ppskDMYDqVsFhGdGIE0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QhBVK9GI4wtsiKPxSrpqzebBmsKjCk4ODjGWk3TNqyXC1LwiPeJZ1QaQhAPycmkZlzXbKoR\nfbuRRTw4zs7PhAWsC7q+x9qCqqrQCtr1gq5taFcbSqspSkO3WvLo7a9juo6ikOrTaLDayGhFMZLE\nMCTG4wmbGAhJoZIEAGUiye8EPPZHgnZQrvjVorOGL8jPaFQEZSOuk+ChrOXazZsYa4lKenVFWeK8\noyrtY+TFJ/E15JeIJhCC49b1a7zy8vNcOzpivZxTlgadIk4pUgpMjo545c4dPvbRj7FcLfCbjbCd\n244+OMaViFS4NBgWiDvTAHMOJgcxilB823a8c37BYr7IYhKJ+XzJerVhvdpgbcFkPBFpQzPGGElS\njTUYbbaVYN93xBgoC5Ot+PJ71nvM8Setc3skoH1WMuwIQtsKc9DpTWzh2iGAqiHpiSHPjA8zppEY\n86hNSiS/V6V+wPZNG0Q/TID5vdw+aGHfX/w/rInvk97PLuPdv8+uUttCGlmBQysl1Q8Dk1UCqTEG\nWxRU4xrXNkL8GcTkE/gw7K+4KRRWgZc+gajwmG0FClnwO0WBdo1UgjoLNgzEzphnzJSIoEp/Kwft\nfTbp9n0O0C4ik6aukLSujmMolQ2qk0ETd30rnVWPipKujzjg9p2nuXHnLk2IdD4SjUVbS2kLdOdA\nidLRAGHFFDk4mPHw0SOMUty8dsLF6Slf+q3f5tPf/d2sm4a6qlj1HTEICcp7z3KxZLlacXR0xMHx\nEWUZWK/XOO/FV9M5MUHOzMzlaiVIAFK51XXN4WzK+eUFoe9RpUWjqIrEwwcXrNdrzGyGUZrQCyuV\n6Og2K3QIcnvX07YC7StbUCjoVnLefNd3fJLVfMX5ozm3b9+i7Rq6LnHnznOs145ln2g6+fxaJ5Z7\nVhfopMAFDqqa7mJO4Xt0gvXikmVdU9Y1KkYh6dgS1zUURYlKWsYjshZt33UE70QYIXk0kZQcRI93\nDtc76rqmtDYHBDnH275nuVpRVTUoRVWPCN7he0eZJQCbILaBFsvi/Jyw3lAZg8rMS6NFYGC4PrQy\nBBWzVV94TFmrKIptBSOs9cdVuZRSj8GtKSUJ1jnRjDESlYHC0PeecjTmxs1bkoAlmZm0hUVbgTi1\nNY+tI9IX9o+pdZESm/Wc5597no+88tLQeaWqSiaTEcn3TCZjjFZUByccHMw4uzgXK7usxDSuKxQV\nvndbdauh+h6g0MHEfXh95xxv37vH+UbaDt57lssVXdPhg4wTaQXtZkVKMJ5MCM5jSiEQBufQRuZw\nne/RRmHLYrv+KJXZ7lqLvJ9WO9m/91k/r1ahw6gKOYAO0O02gLIb/Rrm5AcxloFT4p1Hty0pq1t9\nmO2bKoj+7hBQfm+23+2g/l6B+WpylPZOFGIgGblgYtrBGC43yAdywyBL9ljGGxMxKUIWG1AqZFuh\nSEqDsfV+UB9mP8U5Q6nHh87fV8c47WbytpXj3vtOe79/0JZSotBZkF4LGzkZRVT5fZQFd+88y/G1\nG2ycw3Q9pipFPEGBNZpxXdNuNqKQUxR418NIhuK1AkJEoZmNal797S/x8osv8fyLL9H7HtOPZWwk\nOEKzAa3o+57lcoFSIoV2dHhAyHOtvfeS0JQFIUjQbduGWS1iFyF46lGJDw7ve5RKzC97mq5js1ph\ntabQBhUAWozWVCqAj/ggFmq2KEXfFSUzwxh0rIkxMhpZjuopRdSE1hM6kTkb2Yprh9fQ6w48aAOl\nMRRaEyOMtMG6wKgomRqLKiyb9QbXNCwvL1CrVSbLaFksXbZzi0EgTQXeO/pO5jjLvPDqFEneo0gQ\nIqHvMUpTVRXe9/Rdt+1Xta4nJEUAbt64SbfZsF7MiSpxGQLJe6rRmH694p2vv5klSTLAr7IZQj7/\ntDHYssKFSOM8gSSwrtYS0JKh0rsRrf2RLXmKLNc3JGxKEVLEaIstCxH7QLG4mKMiPPPcC1y7eZM+\neCIKCxTZqQbfvgu+HWaxh2s4xoh3HXfvPsXzLzybOQ+R4D22LEUmsRChFKMNISWWyxXERNRhS2Ky\n1lBYQ7CGrm0BaLuetmsxRghaKQUSgUlVc35xzuuvv8Z8uSSWNWVZMJtMuXH9OkeHR5wcHTGpay7O\n57z+6qu8/vobdJsVk9mMzrekaDBlCYgpgPcuo1Bpi3pt2c/b2ebHZ8y3AvE8Xpxc/Q4IArCLi1sF\nJA1byFbWSjJ0HrdrodHSB41eUIk/8JXoN8v2exH4rxJvhgvNB49JBo0ipojPVanSGpVnGuVCF5GG\nkPKqomSRSillSTyZhTMmEWV0CmtT7hUNPc2s9JEfp6xAW0pWrOx0kfczV5hJyUl9ld27z9jNN2RY\neHfTfp/oKqHAaBHzViaJf7jVqKLAucTx9Vs8/dwLJFWy2KxZbjZMrUFZIzOH1jKuSzZlQd+smY2O\nabsObQ93iUOMKK0oywrXO37+H/8TTm5c5+jomFhqur6l76X3GYLP2r2ey0vp3U0mYybTA+p6xMRa\nkgLnHKNRJfJ2TUvTKkprMSjKusDHDuc7FJGL81OWqw3eOSbjCSZJ/zHRo5NBRUf0ET9k3HEEqZB+\nl1IoU+D7DSkqoh5xNJ0xqWuSD5iUMDHh2p5CW1TcoLw4BNUjy/HBlAebFYVRUunUM64fzOg3kfVi\nTmE0RkMInqIsRCEpCgISfdZb1gbve9arJX3foWKUBEZr2e/eURqbK9Ge0ahiNpkSfJ8VqYQ1G1PC\nx8DsYJbVfRST6YT7X3vEO+/cZ1JYTIq8/fWvc/7wgYyAyCxWTgAFGfExUGDRhcWmWqpQpTGmyPq4\nUrEaY7fn2FVIdzdvq/NjdEZqBqu0xGK5RpcVz77wHB/79m9jNJ7Q+oA2FoymLEvKopA52b3zfNiG\nQDr4bp4cH/PKKy9xcHBA27YcHhyIBnVuqWyh196xXJ2jlWY2neKVJAiqHKGNoaoqUmlJKdD0HSgZ\nEyKPhiidqKqC9XrFG2+8yoOH9zk+OeHpF1/kxo3r3Lx+g7qumdZjTo6OqaqK5+4+Qwiev/3jP84/\n+Ps/iy2gns5wiDJYitD3rSSee8n4AI0P71cSr/TYNb510XpC0Ly6Lg7w7BA8B93cOFSkA3y77f/G\nd32+KSZ8dN8Kov8itn8RlfKQRaUUiT6IeHMUDz+GnoExFGWBLQq0c8Ley0y/YRRK6Z3Asw8CcZWZ\n6aqUSPLtLyiSledqdEj9cgCF3UUy/Dz0TFWOju8KpPLLY8dwv+odft//efsaKfdlE2T/LBIKU1hm\nR8ckXaBsydG1gvlyQde1jOyYEDyh77DViLoqaRcLjDqSIWsF2ojmrHceg4UQmIwnnM0v+an/7Sf5\ncz/2earDayRkwa3rmvV6lRWVZNQjes9yvmC12mCLkunBjHo8pqwqRqOKyXjM6nLBar5AA2U1QhcJ\nWxrQ4jHp+wDJUxhDoRDYFoXT4LyjaVqIkdLsSGAimh+xVYXSsN6sAQjKU09GGKto27WQooxGpchy\nccl6dUlRAVoMlQ+vzbhYnIIRf0p0EiPy+Sndek1fj0nTGSSxJdPZxopcUSiVQHk2bcdqtUAFj1V6\nO0yfQgJPdhXxdF3PdDphOp0yX1zkfjNbmBTk3Lucz0nOcXH6kK++/joqJCazEWePHvLV11+F4Elo\nmRUkO4BkqB80LolxQVFVVFo8UneiIPIWbDZl3vq1Kr0dyRhgR4bkUGtsJpF573HeU89m3L5+i2/7\nxLczHk8IKWW9a8lMjbWUZUG3fjILfYD3Y4yMx2NeeeUVytGIiGY6OwBtxLEpBDabNX3f0zYt6/Wa\ndr4SeDklSmvRJEajikQS8wIF1bhidjwjpcTl5SUPHz4UYYwYqUYVSkdefOk5Pvu9n+HatesEbbG2\nkM+nHnMwnTIZjYghsrh8yGw64S/92J9mebbgV37ti8RgscWI1jmSFmWqRMIYC1vNbbWznVNCCY57\nx+D9tmE92D92Mfc6t6TbgcOx1x+VnmheI688n1JqF2y/NSf6L2b7/Qukj0Meg7lsJqkKjKPiFja1\n1mIKiwue0PidgXMOnGpbOe7mxfK8Mj6FvLBolBKjbhFsUNvHxSuLwH6/9r2OyftVok96yH7G+Nhj\n0/Zhu+BqNNPJjPligZ02TA5GFFoxHo9puxbnegpdEUJP8IZJPeI0eKLrCd4RUpQgWhRE57ORN0TX\nceP6Nd546y1+4ed/ns/+iR9hVNe5D+ZJKdI0G1Qe/5FjEkFbQgwsl3OWqwXlqOJgdkA9GjEej3Bd\ng82LuSkM08MJtjB0XlwsrFYU1mC1FnhZaVmcQqRxPQWWojD4PuAJFKYQiD1Cm3pWzYLRaISOBlUo\nWt/SB5nVCylQ2ILzi3OC8SgT6WPA0zM+qDk4OQSjSBr64LGVxbmWFAPB98KGVQPDMW0Vk2KUYxiC\no3fdNlCF4LBK43zMyJ7Cu4RVgWazwR/OGI0qRqMRznt8rmhjDOiiZLla4V3PejHnK1/6Mv1yzu2T\nI5bzS1579VUWlxeUSokHpZb2QFKJmE9xbW2ukO121jNlRZz95G9Y5A2DZnTM+5xwLhAjmQdgt+el\nc46YFNVozPU7d7l947bAxj5CEUUmMicZ1lpG9YjlxePn+LAVRUHXdVhrefrppzk6PgY62TVtaNpO\noNi2ZT6f47xA5c1qjWk9zlpc33J0dIgtlPTTzQSbZMTGWst6s2Q2m/Kxj7/CRz76EouFjA7NZrP8\nPoWhrLSmHM+oayF+uWaD73vAo5KTWVvXcHI45S//5b/A1++9xTsPT5keiTlA8J30RrWmKOx23Rhc\nl4wVmpnB4DMrO+5d70P/9mqysX+bVKwyLhP3zAP2iUUpxJ1x93bZ2SF7BgV66Fv/AaxEn1SRvPtO\n38ATfsh4p2K6ii1e3TF2DNUn7MATI4J63z/v7pYXge1XEh+84bfty6UMy+4NLA8nnFYyWG0Uo6qi\n6Vtiym4eJmf4aXsqSYJNJKJwMaFUInnp45D7jlqrnTwgCaMSKommrdpWqmwrUPLJKQPoORHVUgHH\nXNEqrfNhSSL3xd5jAXXlX5IShV5FCgsY0Eq8Rg8OJpjxmNfeeodbTz9L365ZOYU2mjjAdH2P1oq6\nylkDkVXTYIuCZrGhMBarDb3S2YopUWUf17vXb/Jbv/4bHN96jk/94U/lj11R1WNMUeBci+86Ugxo\nZYhKgzFCsCAR+sBqviL2kSwCRwzgUiQpzdHxCSEmFIIAKJ0THJXwOHwAaxSd8wLv6kyawKMjaB2p\nrMJHxyZXxyJbF9AG2s0GWxTE5HIvPLHeLJgcHuA6EUYsTIm1JdduXAOtcCniVaSY1vSpxaWWtluh\nlM+qVOLt6b2cgz5FdEhilO2jnIPeCymNiEqRGHqI0idLMRFdYrNeUY+vU40mLFYbMS1ICWUUpUm4\nzQa3XvP2a6/SLy44nIzpN2u+9vprPLp/X+BBJYmjVwatLcpYjC6256ZUlRaJteldUpW7NkncKhmJ\nZ6kjBVEostZiVYlWco1E8liYtdTjmkkxojQFbe+piooQElgDXmfnEsv08ISHb70lfq9aEaOQ1EIQ\nQuCiWXN4eIPbt67TdxsKE2m7lma93FZXy+WS+eUlg5m1azt0FOQg9Im+LEmTEb7rcV0p7jEKlFEc\nHt9AAct1Rz2qObl2G2M0fe/QRlNWIsRirEEbjXc9TYatbTUS3WojzjKXTUsIgfrwkKPrJ3z94akw\nkYG2bXOLqAAsKBE/kVlumedWmWw3cPWHxDyltEXVhh6pOD5l3kWQOXWtFFZXW9Lj1t4sCFEwbcd6\nhjlRpF8PDKYY0iqVc43dVN/7bt9UQXTYrjbhr24fJjZ+QxSghLirq+H1pUJ7V/R7QmB/r31JcQ/2\nfJ893n7Gim21mfb+3+1Tyj1zgcjy02e9Srmb1oayKGE8ZrVaCRMNuRC1khERtuSl3DPNL5FiJAWF\nVqI9K414UWORgNpLgNxS5mVfSGpru5RQIkQs8ZSoBRHLUg3Zx3Jo6OfgmXbHSKqEvQANQhKoFBSK\nEGTxGNdjxpXFxUBpIjevHbDqAxcbh4mGybgWiDpEuq5jVMjQd1GVdK7lcDwV9qIS+LbZNLjgMRiS\nF0JMaRSHk5pf/oVf5LlnnuHo2gkohbZGtEd1HiD3Xj4iU4iB9MCAdI7e9aReoDowNE3DdDZl3fTU\n9Sx7haectEg/u08BkyASGSfFpm3FtkrLZ2ONoqosVaElcVitcD5Qj8eSfMVIcD1tu2GsxwQl+X8I\nEedaDsfPErtE0lIJRQXXr90QstRizqrbMJqNsZUiaUfvN4TYkzBoCoIXKDwpBUb6S9EnVBC2dd/2\npCjJiyIRfY93jZBhQsSWlk3TcOhlZKjr5b0ZlbBG4bs1cb3i3quvcXH/Hgfjmm45597Xv8bDe29h\ntGJcjwlZEaewk8fcVoYEfH+mEzXYYQEM4gJZgCCrIAUftuo7Ol9QhdEYlSCJmElEFK+sNZjKUiZD\n3zooxAxCKxHYN0g1HDEcXr8pC3kCa0we9pdrPQSHtZqD2QRrFV2zJmmBIlMSM2nXOyHFbTZbwiDe\nQ+4zGq1xbUO3rjBK0bWdKAVVJVFpOifVmy0LIhYXFEkZTGFz8qaprIzDGJu2ULbRVoKj67crXdNH\nzs7OKVTFou0IyIyvUgXeexlFi4oULVCgTIEupD8sJC2NLqx4jNqdp3CIEe13BKshiA6Jzv6XSdl9\nJw5jOgHlfTZFz4FYSRJHUjmQZi4Fco1pJQF9a1L7Ads3ZRD9/d6SIgcI+X031/i4a4PE0CtQw7tv\nyk+y9/3DVsTvlzhcgaKGSt3vLQgMnqFKUxWVzMyFSJ+6bfDcn9McZk5RsmhrFD6/mUG9ViOBI2kR\ntNfG7iUXA2tpCIshK8QodMqWU0qyQa3yjF6GV1TWy01JSAImQ2/DYii2VQIpR6NyZq8wpWU8m5GU\nZr3eUFY1t596hkXT8ejNt1BaYa3o+4Yonobz1ZLD6YzJZELTNLTdBucUx8cnTA4mPDp7hC0NYvQt\nbFOjDaOyYNN5fvInfoLP/6XPMzk6wqe49YbU2opwdszzhnlUSBZAsaFbL1cCZ2UCyemjUy4uLnjp\npZcAYVbbTLcX0krUfUUAACAASURBVEXMpC3E6DmrsAwQfmHF1Hi9WrOYz+ldz3R2sCVvbDYbLi4u\n3kVgAVitVvn7EmUz8aaylJWlKjXR98QQqasK7yLOBSGfBami9EBkyj0nFROu7wltSwyeFCQwJO8I\neYxhEPEQgocmRDEY8ClRFBUoQ1laMdJuGpbzc9YPT7l/722MEsGKt9/6Gt1qzXg8YTyqKLQcm6qS\n/qEQk3YXWkpINRL9bkEOO8jYe79jF+feRsxok8pjXmn7RJIExpiEuBd2vdWQAs6LBZvWoImUGpJR\nhJAIKjKdThmNRrTNekvaG9oggwDHZDLJIv6ehCdlqLzvelzfbyHXwcmlyApag3ynd2KTZgpLURU4\nV4qmtBwI6rrm6OiI0Wi0PReG59s3MojR412G10cmjwTJKE7fOzbrhuATDx68xfn5BWVV5iDYy/Oq\nXc9ZYFyDzT3m/RnOYcbTWJPFXmSt2A+Ww/jeUJlug2iQzz54ty22hs9w1/7KON5jt+3G5obHfdjO\n3LeC6IfYrgamJ8HJ+yff/pYGqPddz/mNvf6Tqu+rgfOxn7dsW2Hs9X1PlbO7oijxXkSZg5IKNYZA\nJA80a43VMnu5Q4slGMYI3gn8Nsw3kqLAqNpKBjcMisIWJtlW6Smfcklvv3J3JMNTwyyegj0BMKl2\nhxN7J4k4CFaHpJiOx0wnY7S2JGW5WF4wOrrB9Vs3SZcrZmfnLFcrgveMp1O81vQ9eNfT9T1KJ5RO\nJAIXiwW3nrpJHwxH14+4d+8dxvVYFt9coSiVmI3HvPnWV/mZf/gP+Tf+wp8j+gjWkDBEbYFI1IlC\ny/iHEHmEQR1C4HB2IC46SqGOjnjt9ddwITCZzZjP55RlSd/3uZe400BWCKmIJLBikaUYtVLMLy+Z\nz+cYYzk4ONhKQPq+5/L8nM1qxcHBARphdlqtWK83Qk4qZf9671guF4xSzdSOKAvFzWsHdE0QYYNN\nRwxJGK3KEMUjDGVEzMCgsdriYwcxEFxP9A7XdyTvhTgVdoYNpERICh+S6PJqgy3kuUpbEX3HerHk\n8vyCs7ffwpJwXc+jizNC75iMa3SqULlNoQs5P6zZLXHDQrkvJLBNMH33WG9tWFCHK25nn52vdb2X\nPO9dk2nvsYGIij3eKbSWvrYtLNHLGJZXmrquePruU7zx+mt0bSsCEihCkOp1Uo+pipL1ao3RCZ9c\nlrHbwZo73V62RJ2YmcNKqz00SxFDwPeO0bhmPB5zODtgMpFAPhhTKDJRKs81g1S0vUvCJncepXqC\nD3gfaduOrutom5626fjiF/85m82G0agmhMR6vcnJ+Y6Rq82e61CeR90nCdncsywKsWW0hWhWkxMe\nUkIrQTqSCkJkixHD0Jd3j4tGPBYwM7lor6V21RHmG+G2fCuIfohNGyPyXLk3qnR2OkkpZ68xY55P\nIEWrJ6K83/D2eIYkH/BVb8jhNrm//G7zY4YFuCxLtNH0XUefM1fvxbsx5RMuxDyor4adTwJ95P5c\nVIqUnFwUOtemKhDRWJPt07TOzgtyEPYPgdr72r4PtnXrQG8ClXJg0BlqjtuLMQFGG4xRVNZQlYbp\ndCLzhUnhXOD0YsEPf/aHQJfYsub68QnBeZIXacSQEEWhkNi0DQoxCbZWc3A0Y9NuCCrwh77zO3jn\n0QP62FPaUmD1hFTOKfBtH/sIr375t/nNX3mOb/vUp0ApQsxQkRJoypqIUZLQdF0HUT6XqhBZwpRk\npvTmzZsoq2mahq7rGCQBi0IUgYjZ41FrgmtBJQprkHaj43IuggwpJWYHU6pRkas/xXq9YjmfyyiK\nUnjnKKyBBKvFgqoUFaC2bblYzmm7BlNZTq6dcDCbcTAZ0TU9l+cXMoOLQWMkeCLzdVoKDorCyBhN\n3+KDp91s6LuG4DIRyaVtRUWSXmBMMrOoiwqFIbiOwpRE5+k2G5kLvVzgu47QtSwXc5LvGY8qUUBS\nwpAttJGqqXf0od1eo0PF4rLrTIxD3zNhgnusWtFcuW63C7ACZbaojZzPgsIkxXbRJoGPjuAlGbRI\nD8hbReNaOp2wSmEVHB4cMhrVtO1ma+Mm4g8FdV0D4FwPVqpBlV93X5ovZGLUsCYUZYGxGoPKxB2b\n9z/l9onwJEpbUGiD76RarKoKs03KNH2uNIVgpcSXNib6pmXjB3GGiHeeRw8e8OUvf5nf+I3fRCst\n1olK430QRm7u3Q+VqFKQQsQjY2GKXavO5+OqUkIZAxSZMGgxeZwoJECnLR9BIclKUIKiDZX5sPY9\nNiKTHpeQfZIj2Iddt78pg+j7Zwm7g/NBfdP4Ab3V/Tsr+eQzU01tPRhjjOLnGTJbT++Ufa4ODr+v\nAMF7vrba+/HxinMf+oHHK+XhYUVRSFDM77UoCqb1IScn1wG56JerFZvNhmazYbVayUB8FI1dpdie\n3EPv1Gpy1eDRQfoZkSC9BqWxWotOqRpCY8pwK1KxoKQKRaOSQKvSck6yAictFko6ZMJHAoTYodFE\ntZsvK6ymqMtsdmylsNGWzabHo/nEH/pOGhdI2nJyckLXdZxfXLJYbVDGEJLGR49re4xOjCpLco6D\no2OSgtZ13Lp7i0/+4e/k137119DJbGfqjLYEPBdnD7l+dMhP/eTf5/D4kOde/rgECCTxKEoDSP+1\nbdsMH8bt96oSIXKd4OTkBKxiuVyijN5WSoUVaDgNn33KEnY5I++ahuVyxeXlJXU94uT4mLKsaDeN\nVE8hcXlxSYyBaTmVbN05QmFJMbBcLqlKEZjYbFZ0zYbOdcQuMj8/5/jokJOTE1SMtJsGawsGZnSM\nEWssxRaaC+iowDtc07BeXtKs16QQsFoxsqUwoL0neJ9l2kQcY4BTU5Kq5+TwgPX8gna5pFstWZyd\n4jcrCJ4iJ7QaKMoSkliENe06Q4KR3u8CzRDgnoQsCd0r/xuu171rb0iP05DpDWf20F81RiwC820h\nBgg9pADBghYij9OJxotYvnc9BEfRiZhAYUUNSGXIv65H1KORwPVqSNrTVgJvSAr2k2prxXS+KDKL\nPmYlMb17z8kHmvWaCyOuT2VRbt+ntZayLLHWbiu5pmlyIBJ3m6Hy7HuPc47FYsXlxSVf/epX+eIX\nf4vlsuHw+IDNpiElRYy5RasygVAJYWtImAfJw2Gd8c6h4877U2tNH/S27QHSc1dGELQ0zJQizz0c\nj30x+g+aMR22LQLxBLTxvbZvqiAqQeP9g962j7f3mPcq09XebVfnE7fPt4V3ElVVUtc1Xddt1USe\n/DjFyckJp6dn2G2DPGXWW789QZ8UFPerTemrxMdOgqv7un//dwk4576Bzv22pmkoy5LO9YzHY4qy\n5Gg85u5zz+K8JwS5YC4vLzk9PWV5cUnXtvjeZZsrUbJJCSziA0iIRB1zfykRQqSqdvsKUGiDKYTd\nFzMwtj+Tp3SuPreEoZi/BlWk4RiQ7aoUVVVQ12NGowplFKOioHMeZUo8igfnc773B36Y+uCYZevp\nvMc5yZqVKXj6uRd44aWXwSi++trrvP76l+mbFdonptMKUIwmY7xO3Hv0iM/+4A/wtbfu8eCte5wc\nHOGaDqMTMbRU1qKN4mgy4ef+0f/Fn752E4eljRFTjfHRUdVG5vjadrsYpLw4brPgJK47RyfHzA4O\ncM5hjcH3jrZpWPd9FsnwVKNiS57o+56zszO6ruPg4ICjoyOKomCzycEEzfxyTvKeSV2jY8CoApKQ\nUHzXsby84HA6JiZZ0KuyRGUyk9f/D3lv+mNZkp73/SLibHfNpfat9x4OR7P1zFBDkaJESbDkT6Is\nCpJsQJZkAwYE/wmGYX3yv2HDXwzTgGSTGnq4mLRFkDPycIbTe3U3e6vq2nO/21li8Yc3zrknb2X1\nVHMswU0GUJWZN0/ee5aIeLfnfR4lsm6LBZPBEFcKCYS1DSbREBxGQ5ZqmrqmLFc0dcnR4T5Hhwf4\npsZ4J+hLJH1uopPZr3H5SKxgUISmITOGerFgfrDPo7t3uH/7Q1bHh6hGUsLeOXCexjsa7wlOCB6s\ntdIH6D3Wn+ZV7ZdF+uuonxJtx+MqKrJRtypEHW1eD9TQrsH5fM5opCgGI1xwuNWKerni2FrqpmK1\nXFCVS5qmIq08WZrSNNIX6uqG4BzbkymDvKCpG2l/cg0xBO7mTOust/2rSZKQpylBg1KBNDUkUYrN\nNZYKMfAn8znL1RJbNQTruXTpEkVRYLTBW8d8VTI7OYktOy2Yp5G2miimUJYVd+/c5e7de9y+fZtP\n7hyQGJiMxzjrUcrEvbIl5Ze1GxA0syS6Ygq9sXgtPbQmSWRe1A3eOSGM8UaoBKOQvKR5xZlsTbJr\nST68j/3TK9n/oMt0dXu11v1q0WP75lmR6ZPG586IbhqQxw9aT+qzjE1/bPYx9n/fJzX2IeBswCSO\nvCiYLxbYuOhaT6cFGziPIPSynOn2NqvVivPnz2OM4fDwEI80Yyu91tJURneRbf9aAVzzdA+zv0H0\njap1Ui9QSj63LEtJicQoKHFCCJCmKRhDkWZcmk45d+0a1WzGcr7g5OiIo6MDZrMZtrE03qGcI01F\njlh7y6BI4vUHrPVAI5PdGJSWVKx3Hh0JAXRspUFJrajlze3qUAqU9tEJiZJrKNJUGr6zLI2pyECq\nwCiRsQrasFjVJMWQV/7yX6FqPGVjqa0nzweMpztMdi7xzPMvM9o+D1rxl145x/Mvv8RqfsTR/gOO\njo5JkhSShGlxjr3DQ8ra8so3v8m/uX0X7wJZlkMjYtxGA95xYWebR/v7/P5vfZe/8bf/Y5ZlBXhW\ny8A03Za+wKI4NWdaBqc2XYaKrFJpwmw+YzGbMxmPGY9GLOcLvHOkkUQd51jMZpycnGCtZXd7m93d\nXdlEqkpSts5xfHxMWVaMRyNJ52vdITwJgaosqVYlV69c6XJYWksPpIsIbm8js5LzzI6POTo6RKnA\nIMtwtmJZW5pqRVM3rFYLFosTFvMTmqoi6YHLvNIxCySgotCLOFIjlIm2XPLg7m3wnvu3P2J+dMAn\n77/L0aMHmODwTR2b6oUD1Tkbqdx6DF4xTevcWhOynVvqjH3jrJijfa2/AbdbRIuG77+otUa3tcq6\noWRGkWfQwGK1YrmsWC6XWFdH+sMSvCXxkjrNsoRBUVDVFdtb24xH4y6CFkfTE4Jbk6j3Mlz06o1K\nCdivL8jgnGM+n6ONGLFVXTEcD3GNIzEyL5Mkoa5r4Xpumq4eGoIQx88Xx4QQKMuKhw8fcvfuPax1\nbG9PuHzlm2y/+z7vvfchbbnRNhbbOEwqAKQ2EjVmneFSQWqitLVcJY6kjiBCSelCCBbR6jZoFaic\n9JwqrUgT4dx1TggSvPMRACX/klguOvXc+imF3u+grxhzxqQ4Y/y5M6JntYucFZqrmAo6a2x6roII\nDZ23Wtc1WZZJATxJeioppaRPnePg4IDJZCKRRYwMylL4Kauqommq9eYZJ0zf890sdD/NOMtRMLE3\nM4QgMkXOEbIMY3NCYjB4fHBYr2jaek7UE8zGI8bTCZevXRFU58E++3t77O09ol6V1N7jVISul1LX\nKJQSweAgzDkqOAgGH9NkOoupWIw0u2uD0r5b/LLAVEw/uVOtCa2nLZG9bJI6LqKmrtDpgJW1LBYl\nv/w3/w6jyTYPDk+oPCyqhodH+5g0Z7IzZFE7TO1ivGtROmO0vYt1lqPZgqBS6sayNd6itDNef+tt\nvv7lr/LlL3/En77xDjujCaBITDQEeEIdOL815earf8L161f58je+xfFqiUk1VbmgyHO2t7ex1lKt\nSkFVhvVz08Jigc6kFpYkCYf7Bzy4/4Cfeflltre2eHDvPtPRWBrsDx6wXC7RWnH+/C7j8Zi6riRC\njJHu8fEh89mC4WAYnQ4Xe15b5QorbTBNxdZ0HNmWYg1MQeWF1D64Bt802OBZzWZSLzVQDBJQnv39\nPY6OjnCN8P1636CVNK9LG6i0uPj4HH0PFCM1xQSCI1FCZH4vUgrWixnvvvEq+3dvY7xFxWisrd17\nF07r48KpDfOp186Zr6lTX2mriWpdV4NoQ+OcVXEDdt6xmJWs5ssIKzCsFiuctdRNjbMV4CRl39Ro\nYwQUF9mctqZTBsVASPabBq0lVSlMYy0YS760vdp9OTGP9JO71rnwNs41T+MaFqulEI4kIk5R1zUP\nHz6kKAr5e+/JsozpdMpsNuPR3h6BkqOjYx49EvH36XTEtWvXmU63mEwmfOUrX+LXfu1fcfPtO0Li\n4dZuSH99JzFz47xFK4kqkyQR5aXoAKRZJinyto5dVYKATgw6b3t01+xY2nu0jxiVsG6DOfU8u/nw\nkwn9Psv++7kyom1K71OHWpvRvkHpFNR7r3e3dCNfvpn+bSJ6DEShQJrUQ6fPmWQp48mY7Z1zfHL7\njqDarGV/f5/JZMJisaCqa+YLoVkrikImUExfCMNGiDVaOTEfvCBoP8P9Oas+2h/ee+q6Zl6uMHkG\nqSH1DoUhaLBKuHKN0dIO4wElvLvTYoetczs8++LzVKuSk+Mjjg4Pmc/nVNUKO19Q1w1VOaPMEoo8\no8hTfGIg0ptlSYKPm4EiFv+DAwwhaOFdVUb6Sgmd992yI2mtSFODQqL9NJWaqA7S86nSgtVqxdVn\nnuH5F1/i0f4Bs9JyuChZljXjxDDZ2qF2inff/5hrznDl+jUIDhVqVKgwecF4awdXOVxTcXR8QjIo\nKOuGxarkm9/6Nvc+vEtdW/LIQpSYBI2mXJUkOTx/4xo//N4fce78LucuX2bZVFQxTZqmaeRMzaQ9\nIQI6QhCDo4zGAScnJ5zf3WE4HvHo3gOGwyFZkjI/OmaxWHB8fMxqPiPLMkajEYPBQKK4SHBf1zUn\nJyfMZjMyk4r0lff4RurOrXPoG8vs6JhEKYo046SRGmybYg/W4Wjh/5I7a6oKrQMmSVEqUFcrTmZH\nlKs5uk3FBytRQVAop7uIU1an/CzsNVlUV1E0oSbJEiE3SQzz40PeeeN17r73Lt5VTIqUUFfYEPA9\nrEHbQNXFGz2gidLrTM6n18XMY6+s+5XX4ywnfhOh275mNCznc+rKkuiEclWjgMxozHBIlhlMohkM\nttFak+cZi8WCLMuYTCZ476jrOpZlHOAFJ9Cu915Q0dY827qpc64F1EdUq8WghATeS+ReVxUnR0cM\n8lwUiYIIyHfYDe+p0pS6LKlWS+7efZ8kTbl25RIXL1xkPJ4yHA7xXqLT527c4K/94rd55+a/xjbS\nm9nWMNt70hpSHfm7lRJ6w9lsxqoSx9J6J/2pWcZ4IjSZk60p3jlWZUm5WpLnGUUxEIc8ZqSCb3Bd\ni1Jzyoj2n/2TItHHUvd/HiPRpzKiZ4xN47k2oqcLzk+sPUZWnSRJmG5vwe11StY6Ibh+7vnnGRQj\nDo9OqKoKG2ufh4eHnMT+uzRNsU4WRpaLenpfgqj7PCXpehe8eFtPeY2bk6BLSSNOhI4ReVmVFHVB\nWmTCKuNFPcMhvW5RiE/QuHFjN2lKZqRlYzKd8uzzz8rm4QS17JdzVsslj/Yecv/uXQ729zg8PEIr\naUof5BlpmlCMxx0Aom338NbH/KGgcFuAkVp7OV2EEZwjjcTdOtalfHBdbbGuar761a9TVhUHRwuO\nVw237z1ia3eXrYtXGI3GPLz3gA9v32cwPcfFa9dRaBKTcnJ0yOHxCVlWkOQJ54YF737wAeViyWAw\n5M7d+3z55S9y9fp1Pr75PnmWkuYpOE9ZrhiPxljrWa6WDPKE7/zG/8av/uP/lNH2FBuE8H65XAp5\n93AkHnmed+ChJEIXdSrR98nJjKOjI65cvUKWptLyMB5z8+23WcznbE2Srr+vfd4g5Pb7+/s8fPiQ\nra0tRuMxLUK7jeKdc3EDFJ3ZdYQf6fq6NKCNqWMfU89t/dCTZ7nU0asl3sf6qBMHqaVqc40Fq09H\ni0rm/Hg8ZlAMUEro8gjrmqYKgbfffIMP33gVlWhSFKGuCbahcULnJzy6YjwVpzNO8iVE4oJ1mjds\nbOy9FfRU66wd/b2i+9mva2kmphCHgwEqlLjak2pDU9cMRyN2z2+R5xk+WLwqTuE9Lly4wNbWFicn\nJz3NTGm90vSBL3KNcvaqM+bex/sTPXKlVOek13Uda5IyH6qqYj6fd6WG1UrI8NM0ZbVacXh4iNaa\n0XDEz/+Vn+fq1auxNOQYj8Y0jeBoHzx4wHxxzAsvPs94PO5aydI0k8Bmo8ykkLRtUy2po/pQu095\n77HOMV8sWCyXcj9V4NqzzzAYDChtzXxecnJyxGAwYDQaAZEVqaFLRct8P/2M4ndPfK6n99A/h5Ho\n5ugbvPZrX5Ny87i1oxpTMbanDqDWXop1QfqYIgdjkihGozHnds/x8ksv8f6fvs/h0TFaa8aTHb72\nyjd45rlnqVYr/vov/xIff/wx7733HsvFEpMaiIgx56UpOs2MGKQgnr4CktiI3O/90hD7EU8DkPrX\n3F6foAJ7x7V1qC69JJ5/YhLsqkGlFVkxIpwsUWMhazeJjt6rkGOHzGCTBJVkeG3waUqSZHitqX1c\npCpBp5rkwhRF4OIzz3PpG0AQ6aamWrGYL1gsZtRlhT88lrpLucKWosWZGEWeJhitGBS5cHM2jYgo\nJwlOCbggMQaVKbz2NKHpAFplMUFlGSfWcuNrX6cZZTw6fMDh0ZzZ0YKdtGDkaoZDgw8r5of3KA/v\ncv/9hEuThOn2NsvQcDI7pnSBpa3RfsnEBH7mCy/x4P59Dg8OWTjHo/17fOXbX+Wtj96mNo5JM0Kp\nQJIYqrJBG8UgS2mUJnWKV7//Q375b/0tEq3RzqGSjKa0HFQnDMYjAUYVCVlEJAcCtjbUtePeg3ss\nyoa/dP0GK9dglWO6PSFLNT5LuHHxWSFYry02NHgcdV2yv7fPYrbg/HTCdDJBKbChljRg0LgajBGj\nXDvPw4NDnnnheZrEYJsSW63Is5zQVOiyjq6UR9ua2q2Yz/bRLmGQjtBBkwRNrgyEBoUBLz2tvgko\nG8g8ODSVs6gsIRsNGY0neO8prcU3Fuc82gnQaRAcjz7+iL2bb5LaEmUDXhtWCnyakyjpJY2LuRsq\n/iwRbwAkhRy8rHOpOfci1t5o49VN47g5tPVkiaZyHmccAWgIse4XWyxqafRPfBLLpYrK19hgGZ+f\nMt7dxiWGmbcxQxbBVU2gmA659tx16maF0pZECyBIBwsh4giCeDMScEcS9rYcEJAWNOvAGEGIOycl\nHevAJATnIWhwsFpVHBwdCVWll5LMaDRgNBgymYxRSnh0z53bZVSIuajrhkSl2Frq0nVdE7zj5ORI\nUOXRUTOmbasRHm6jE7TOsBZJMVuPIZArxaqusN4RtCIbFFy+cB6UYm9/j/lsRkLCnY9usbW1xbVr\n16QE4T31YoWyUTbOObS1uOUcmpJMtW1NQoBBTMG3VJoge2/rqIhztS6J+SchjzbG59qIfhowqD/6\nyNc1ElIYMlwHXW/z5Ibd3THHxzMa65hsjdjd2WYwGGAbx/e//31m8zlZlmFry4svvsjzzz/PYrXk\n5PiYh/fvcevWLUGlbQjt9gv/Z/clrRu928Xcplb6XnYfxdvVQWN2oouye6CD/qbgnMMFARkUA5FG\nqqqKIjHCyBeL9es6ZMszKhykApfXEPrOiqaqpQ6XRLk0jaS+tYIrOqZvlGZ3NKGtaVWrJXW54tYn\nt1ieHHN4cMByuWC2WJFmhmKQC02d95gYRbnFQhrStXxO0zRMdgxuVTHc2eXZF1/i9t37OJNyeDwj\nLwoW85LJZIs8y7n38CFaa5597lkuXr4mpANGo2zMFPiMOj6bo8NjjE64cuUaWhnu3btHYhK+9a1v\n8cor3+B3f/d32b26zXK5INHSSuC8Jc1ymrJiZ2ebD95/j+n2Ft/+q7+ED4HBaMTxYsVkMuZ4uZDX\nBjlploIXcXOPZrFY8PFHH3Ph4gXGk5x6WRO8sMt88We/xO1bH1M3QgvXOmnHR0fcufMJidJMJlKn\nMsZgnQBSQozoHZF1KUmYz2c0dSPApeWSqlqhgvQllosFVVWSJSkEoSoEZO7bpltXpzMpgbqJqEoQ\nYW7ncU1DMRoy3pqSFjnWr0lAXCM1WN80aN8wXy556603mZ2csO4gjv8HeFKA8KT1v3nMWUay/9KT\njom/bL8BAi540lNvtD4PrXXEP4hRHQ6H7O7uCqevtZ34NHFfKMs5Fy9eJ1GKJpIGdP5/6zP0Mmft\neW7uBUopUGH9TKRQiNJaWkJCbOXxUjJp21jyPGM8HnPu3C47O9tcuHCe0WjYRY5NNcfGlqHgFdZK\n29ZyuWR//4iyLLl16x6LxaK7j977Tm+1/y/eoDjXZB4UwwE7589x7tJFrl67ype/+lWMMbz6xuv8\n2+/+TpeZWS6XXLt2rXv/sizXpazI4NSC987K0LUP6iwMydPMoc3xuTKiZ92Qzejs07zIzdRtDNZO\nrUtt4ODwmKs3rnL5yhUWizl3bn0CHDAeTdg/OJSJ6AMmSzk+PuaHP/ohe/v7zI6PKJdLqfe0kknO\nramregAZ7x43opsGtD3XU8byjNc+bbL0/2aNOhNE5mpVkg8GqKYRhhMFLQm3Uqrr4WwBC9KakQCi\n6CHNbxHwYzKUCrgQCLH5PlhJCzYaTBRh/vD4hDQx5GlGOhyhR2O+cOVK7IOUOnFqDHVTi+JM5C2V\nmlHO1tZWhzBVWlFXNYnKOV7MmZ6/wMp70vGUzGSkywbnFMFo8tGAV998HZTGJBnPPPcCFy9fZTge\nSYrdNaRJQmMNpQ9or8kHY8rKoVTNc8+9yHAw5YMP3ue1197ir/3S3+D2rbvcevcDrly6KEbGNigC\nZbkiNQYXapIU3nrzNV764he5euMGx7OS6dYWlfOMJ1s0tqZxAdVIC4BRCt8IyMN5x3PPPYOzENAk\naYJznslkymQyZXl8gk4Lyqri8HCP2ckh48GQ8WjEoBgCOm7gRDkBqTF7rwBpEdnfe4TW0pZga+nd\nNEph64bZ3hEhQgAAIABJREFUbEa1WqFHhRhSJQQNznnSyDe8yUnbRn0CHpNPVMYwHQ0hTaidZTWv\nqRsBzHgnxPSJSQTIVNW8+9ZbfPLxx+LoKklV6jiXPRptAuaMZf7Yphg+G7jorHVz1nC9zJV1llQV\np/5OMkHx2B7P63A47PYCpVQHnHG26YzA1nQqbEXW9UobkY5uIzLqO+Ktw91+D767d0CX7tZa47QH\nF6+B0BGuZFnOhQsXuHLlCnmeEoJEncZEdHxoieSJdVCh+qtrS13B7Lji1R+/Sxn5ebvzUhGT0kPg\nByWAysViyXK1FNUYBZ/c/oQ7d+7ywx/+kFu3bvH3f/VXef6ZZzn52tf5wz/4A4zSrOYLDvcPGI/H\nGCVtgz4+L1uWHTbgacYm/qX9epaBfdL4XBvRswzNWWMTbRvfTNK60VML8e+HgyGv/NIr5EXBe+//\nKXdv3+HKhfNcunSJWx/dlkgjTbHOMxgU3L17l6quBVUZvNRCTCvMG5FoG4CfdrPZHJsG9CyjuXk/\nut8Ruppn/97071v7/i2FXFML96ZJBIiSKCUtKTFiOX0uRtBzWq9TR0p3zoeP/KQqkjdrAhYBP5hY\n41Uo6iyhRlF6SJzIe81qTwakSU6WpDTGoIoJeZ5112KMEX3HJMUlBocwFoWBRYWUZ559AZXmDIPn\n+mRCmmWUZU21LHn/nfcwOiG1OVeuXSVJC65ff4YsH1JWNQ6i6HYmhkwbFidLnEP0HktLVTluPPMc\naZpz8+2bEBT/9J/+M/7tb/82f/SHf8h4NGBrPGQ+PyGEQJEPqeoVu9tTHu4f8n/85m/yz/7L/4rh\ncIANoHVClqckaY4PVijigmw24/FQ9CNffImLF88zmy2FecakeFdjsozzFy/z4dERt+9+QrmaY+sV\nW9Mxo+FAnKQIWAJBoQc8Rkm6CmS+H5cl+3t7nL90kSQxLE4W2GpF7T22aSiXC6q6pMgSVGzIl/qt\nlwiY02C2NEnwcdv2zqODvDYcDHHBs6orykZq7/P5nEQLw5DSChUcwdbcu32b9999B1uWArxRQlSh\nohKO0grjw2MwoP767gw6nOob76+Dx/eI9TH9tfXYcXL3ovoQ4uApacNAS5tSoK1HulPv2VI4Jsma\nX1qiukDVVFw8f4Eiz6irFa0SCURGHhWj0g0HO37AYxu/AsF3EToHRCLUdj7IMa0ARAgtc5iOTGAp\n1sq5DgYFaZowO3pEVVYopXEOZrN5jEgDs5MVN29+yMcfPcR7MSxtCUlHp7wFkLXnUUYSh6IQUFzV\niAh7sJbhZMyv/N1f4cb1G/z3//Jf8syN57h24wZ37txhMBxydHTUdUb0a96tQ/A0YzP4+EkZzSeN\nz5URPWucFamdNfpMFCATyK0BbWI0FHzxy19ksVry6huv89LLL/PCiy9yeO8+H334cffgUEhefrFk\nPl8CEXUY379FYMI6394HNvykcdZCPuv6ftI1t5PL9Ix6CAGNkuZka6mqijTLpO5iDCFNRF7Ftykf\n6CRhAvEahWg8KEXb7BmUIagoOCy5WwH9xEWrjRhdk6Zd2jkojUU8cqsM3imcSshMRlNLI72AjxIS\nDNYbGmfAyfNME40PCS7NqOY1TteUTUM6WzDZ2iZPMibbuxzN52xNpmzt7hCUohgUmNgTJzJsumsF\nUiphkA/IdofUtfTzCTE3lGXNc8+9QGIy3n77bYriA/7m3/6PuHL9Kt/9ze8wWy4pBjkaKOuSLB+w\nXMzYmY65v7/Hb/z6r/MP/uE/4HBeYYohzobITixGrmocdd2wOym4fPECw/Ggo2IjgKtqQlBoNIPh\nhGw04M7Ne0yGBVuTCcF7VotlTOMJaO1Ue4Z2mMj4FAIcHh2zXMy5cPHL2Kbm+PgIv5J0WCCIIosP\neC+N9k2TYCOwy7t12qxjzmHNEmO0Ed7XLKNxlrKspfXAOxaLeQScJaRGobyjaWr2PvqI13/4x1SL\nOcVoSKJENUUpQyvGbZRCyDceN4SbTufTAkM+04iVE+Ja6gs36yBmU3e/iwUiY2hiv2q7Jp21RO59\njFFUpeXixQtCaNLSXMI6GpU70Gez7t7PaH1qX4N1+reNQLt9Iq5Ho9qUc/vMPPP5ggcPHmCtZWtb\nSDvyPO962G3jqKqGLMtQSkQgZrOS5bLhnZt/yve+9yNsbUlTyVB576IsXWs4Txso771cj3cCaNKK\nIsuomgbfWP6n/+F/ZDAoMGnOJ7dvs7O9TZamBOfI05TlfE5qTAc8dJHpanOcvXc+xaP+8xiJPmmc\nFX091e+7iRS5WIG333qb+cmcF7/4Mru7u7z15pvc//g2AHmWM9maMhyNcC6QZSmj0YiqrkmylGFR\niAxWHH2Y9WMgKL1mqumnWU8d8ymp6/61SWpa4YN9LFJvU8RdE7aOwsHek2cZOEntmiwlaeuoSnhv\ntUnWqV0lDd0+yP0isu5IFIyQz6vTDd9JEg1o2y8BKL9W1VDKxEjVoEMaKcsM3mmMzhCRTA3K4K18\nH1z7GQneaRSGhQ3oIAjfxivKRUnjjiiKgpPZjOnOLtPplKxImU63GI2nAmIQWhdaLtz2noneomGY\npAyHIzFgAVZlRdPsMxqPuXb9Bj9+9XWcb/j611/BGM3/+mv/M3k+pbYNaZrgmposTanqFee2prz7\n9pv8+Icv8qWvfZ15uSSYlBDZXKyrsbamSFI+eP8OyigmW1vUtY9zaE3OYQMYk3DhyhUGoyFVXbFY\nePJIHJ8Yg0kU1kt6XZ6FpqqaqF0qhPPHJyfkec5kOKCpKmbHx6hGeqAJUJVLScc3NcPhoHu+eZ4z\nO1kyjGLkIQgn89HhIQZFkRckMVV5MpsRlMJ5x3wxx7oGBRR5RrAWrQyp1uwf7PP2a6+yOD7EGEWW\nKFwjmZ04cwQ040PXJ7mZtenX/0NoGa7M42WcM8aT1lXXd9kxBBlcJHUwaYbWksUZDIeRHABBD7ct\nPWpNUdm+X7seXexnrBtLlqWMBwOcqyF4KYeE1gjSCZ4rtb627rxj1K17+4by4czysYpp1DUpu2RF\nrLWcnMwIQbIE4/0xV65ejuA9aWNazFeirAORAa1kf/8R7717ix/84FWWi5I0yeRRRRal/v022sS2\nplREr4Mc03Lzai00l6kRFPP+o70uWtVKqDBNzKL1n0sWe/C9c9JaEwOmvijI49nI03iT9pg1k5ha\neyQ/Yfy5MKJ/1tHy4LS5jQAsF0sGowG3Pr7FB3/6fmxDkXrghfMXOHfhPD4g9cRiwM7OOZTW0uNU\nlVFS6TR70JNARP2vn3U8tug33uusQn57XokxqEQEp9s2H9c02CYhadKu3ijaxqpbsCG0UafuUjIh\npmhMmypqP08rMYLKiOBuPN2MRGpcyohxlQQSsfqKit9DTDeF9l9U91BRezT+LBFxlKpzQiSASSjL\nitWqZGe6xfmLFxkOBuSjjDTNcUFkvEIQzl7vpYYkMLMQff/1ObT3t7YNdQODPGd7Z4sv/uwX+ZPX\n3mAwHPHciy/zK3/vP+E3f/1/ZzwcEHygqRtybSiSjNpadqdjfvs3v8PV69eoHEx3z1E3NWVd4b2l\nKHLqumJ2csLVG9eiyIFkDVSIgideIgAboBgNeOb553nzxz8i1wNcWVKkCY33whqldawVBUIQ4vCy\nbnBReWPv0UOuPfssw+GQm++9K+0XqcFoqKuG4K38fRA6taqq4hxYK24URUG9KgkhMB6PyZOU1Mim\nvJjNKcuSxrnIlCXAKIIj1Qgi3AcOHz7ivTff5PDhQxKlyIzGOUuSaIKz3ecREPHvjXWwuZbW6+Cn\ni0T766n93sWNvxgNyYYDdJaeXl+uFQlYR+UtB21VVZ0coVIKG9uJ6rrk8sUbZHnKfLaKepgeQY+2\nkeg6bSZxahupxtJK/F7FlK8KdADBNrW9BvTEsktoJxU0jWM+X0RCGM1gUHS1xdFoJO1KlbTmFUXO\no0cPefjwEWVZM54UXL92mQ/ev/2p+1l3znFOwRoHIqclc1uhePGFF1gul7imQSuFt64719h7EFvc\nItlGlARsP+MnBVRnHfNnARXBXyAjetZNbfvLelMUpRTVokQliiLLaQ/w3nN0dIT1Qv3XemR1XeO8\njwtCHmSb3mr7/zY9oXhCP5UBPSs9sRnJwhqd208lp8aQ5zkt8EmIwOVrq2KPT+Km1Xv/eJ88QYym\nSTrWJ6MBYsQaU70tkECbNt2kKVSE/SsFysjfxxSTADJi2gfREGwNpvIqnowgCsWF9rTE3D6yo6i2\nzhZhGFonZFmOSVPhIbYNIDUdMAQfvXyEz1MkSYSirPXk21jIJEbulXdkWcr5C+d5/qUv8Nobb1EU\nBV/+6te598kd3nrtx+R5LowzzmMSIe03wZGbwO9997v8vX/0j5itFqg0xdUV+aAgz1IO9h6RFynb\n29NTzhhK0rjSx6nAe8qm5uKVy3zy0TbzowOmRQHtHAQwAWJ0VFeVMAY5T7ksmc3npEnKC88+y6MH\n9znY22MwGpHlKSoEmkZQsyiFc7ZLUaapaFEas848iOJIQpok6CAaqOVqRVWWNHWN1obRaESWimB1\nXS4pTIL2juODPT54+ya3336bNLYK4R3OWayPBiM4VBAWrDY829wsz8Q9/JRjc+2kqfA158WArMjB\naGpvqeqaxlrSJBHj5UUUYFNlqWkaUi/rTkWtz5YicGdnixC88AGLGntnaLrVrDYiUNYReDvaaxeZ\nO43WwhbWRm+hXXsIOEvuLZGsQUUu5jXiVWvNcDgQrlqvmU5HVFXJgwf38d5z/cZlfu7nfo6vfuWQ\nX/tf/jW3b93FdjXhjXMMPBYdtvtjmqZdSSBNEiajMSeHR/hGUv1J5NPVWkeFKKndBu+FfMNJu02b\nAdicA/3X2kiUnlE/a0992vEXxohujjbK6STlopHIkgSVSsrAVjXaaIIXhG1d1zx48ACTJEwmW8Je\nlGRiNKOOoeulUOFsgIJ41X/2xf7klNQandctpjOoDVtVEIIYEOdaSSNHGh2A+EZd6iiEWI8JreMR\nN4hoIFMVFWto76mKHq/qFmNQCtOqSURt0ECIUasiGEH9BgVaBVAuRlP9erZEiRKPiBukrOw5chMM\nuEBjG6bTbYpiiAsBk2R4XYPWaJ1ACDRNJPlUGqXipqV659XebyX3Ah+FjlXA40lTw9Ubz3F0dMzb\n77zH7vQbfOUrX+fdt29Sriq2t7ZY1CshC1DSynBua8Intz7g1T/+AT/3C7/AwWxOZhSTYSG6pssF\nzz/3PHmmWaycpGON1ASDDmhjxIhK/o7heMTLX3iZP/neH+Gto0EkwZA2QElRaYW1FcFJ32RVrvDW\n8uKLLzAaj/mT114lywVU5WPtri6XBCeKIk0TpdjQJJGScDWX1NxqtSKEQJ7nrBYLFssV1aqMxN+K\nLM1Jk1RIyBH5L5Nn+LJk/9Eeb/74R9x65z1MkpAbjXUW7xuy1KzbMcL6/oP0TotB+emyOT9p9Df4\nLJP2jyzLIc5ljzyPNKYo0zSVc7QxkkzTjn+2Bc1AG4nROdqT6ZiiyFnOF9EJt3GP9zG1q0D7VpOh\nM6ptmaTjE+jVHE004m2KN/TaTOhpjGplUEFKGs4LaKwVfFguRYyiLEvG4wnTwZTRaEJjl+ye2+Xc\nuW22traZz0puPHOFv/ztb3Dr9p0OHyLLKaL3k4TAmiBexT3BeR/b4qTVznuPs5Z7d+5yfHSMQtSK\n+riOFqzZAiThtNTdmfvtxvchtOIWP/34C2NEz/IyVC/S8ojz5JyjKWu0ViSJEdUPHRcUOtYTAkdH\nRyyWK4bDEUkmNIB4Dz0pp8e9n58+jduOx+u7uqu3PMkj7+5BBOy08k1aRwamRsAjpknxuV9rKrYR\nQD+RFj1aWQwiJQWy9uNaFwOnW8q+CDaKNj1o1uhopaLrLGAvKUd4hHcwRNhWm9hSEDxB6y71ihOB\ncB89a6s0rnFkaSr0et5LfTfp1Z6NwXgFQXUp/dAq5hAIRp2KwhWBoNaRkQ/E1o1tvvilv8Q7b7zO\n/fuPuLi7xbd+7tv84PvfgyBpbo2iqWuKQcGyWrE1GfG97/0hF69c4cLlqwyHQ4o8Z1ZX7G5vMxoP\nKctWH1Lujfg1cnOMUdgQGAyH1MslFy5e5NLlyxzcucvWcBijf9k0bXQI8jRFpYpFBMJduHCBq1eu\n8Mknt9FKMxqKs9E0FaAi3ZxHq6g4pDVVWZPmOcPhkD0XJarqWpDT2lBWVdcXqZSgnVOTQNAS8ShF\ntVhycLDHh+/c5Na779C0qjYKnBV1F+HEJSJU43W3c9nLc2ifVX8tPL7Gf7qNMstEtaltTRGRiZbG\nYb0clJKe2zzLMEHRKGkVAh6rhXb4B0KnLfuFF54lSzPmsxOKPKdqhHe4W3E9Q6l6/8t3ao15YB1R\nmbhqQozcTcz4eMV6LYaAQUCE1vaNjxjVpmmEMN9a0iQlmZxjOp2i9JDJtIgIbQ/KUVYLfGhIEmjs\n+r60z6SlQm2NaJIkZFG71jqL9rFWHAkjjo6OCCF00m5tGrzlGW+N6qYSUD8jB+tswpP24n5E/xci\nEnU2gGkL9Tp6TXEzbnfCNhfYG4qzoj7F4+JH4vwlRkyCcxLzoBQt9kSkmuRjQtOwnJ2QJonoGUIH\nAAhhnacnGthT5+A3z4f1RtHllnnsWp64YQSpw+j+39LWw2TZi+cmtbIsz7HRc9OR1QUnHLShZZHJ\nLCpNQAe8EhLAQAAPPsqeaaPQJsWS9Qr0crNM1JwUoFJkZFKiytIaVhV5XFEqtta4iE7UEFN4SiFI\nYKTnU6HAm662o5X06zkCIUitaWUrdJKgDGRpitGKoEdyBS6glUcngeCtXDcuGm0khdktuuhRx1S1\nsJ0onIPlqsEv7nD58mWG33yFg/09DlY1L33tFV67+RZKKerFIQNj8Apc1ZBphTGeqlrw/T/8Hf7F\nf/0vODxpMFqzWjbs7FxiXi8QLmFJdyuvY70Iac3VCo0nhBE6MSSF4trzLwkBfJbgvGdQJGiVMCoK\nqrrGOyutFH5JNply+bln2Z/PeHRwwHA4FOSnczSNpWmE6k8FAahoF6Dx+KpmuJWJUR5PsNrQWEdA\nUTWWZdVItJoPIgcq6ODImhUER1NXfHDzbd5841WqxRycI9EBo2SNeh9R9GhcVOxY91M7upaRdorH\n9Kbq0pyba0rQz977U5mFxJg496OqiNZ4xHgroBgMGMR/nfEJAtJqMyxeteQt8jnOepokQCII90YF\nvFHgc/wqkKc5TVmTOFlrHlHD2drdZffcNk2zQhtP0yzQice7BqVsdAJEiQkNqU5j2rKtryhhw2qd\nz7jeBI8nkaiPHN9dVKcjuj54XCThQIe45iLYyKQ0NjBflIxImC9rjuYVhycrdnZGFMWE/f37ki1w\nmnKxJFUGXzaoyC0eaHvj1+17TdNwfHzMdHubUVFQnpxQWivHek+WGWzsm82ztHMSiJmrLBNAY9M0\nEdgYEBFzEU7YRG0/WdJM9pN19k50T6VcG2t4TxnnfK6MaLvx9qV/gHi960hJbVieJ96LMz2PNobq\nHXaGYe7qVSHQnJU6bT2iMzygzzLUE8/z046X/0I05u1okZTj8aSrR7S9VTjb1Um98x25dQgBHxza\nmw7wEPCoxOOtIyRBmE9iD6lSikStEW59D0+hou8QugnbPZ1+ysVLrTP0KtZd+iXObbk2JS0YRA+U\nNgsmtbvJZIJJEpQ2vbdX3fsoBSoCowitPqSgFiXQ7vOTtnOiD5cPDIcDjo+OSLOcazduYOsaZxu+\n9OWv8Ef/9++zPR5QW9EBlXunMEFT5Bn7jx7xG7/xXb79C7/ErU/uMxpvUdcVQTlJG7epvL6hkFWO\nItBYoUNcNQ275y5w9dp1DvceUgxyYU2y4vGHEGiAw5NDRqMRly9fxnvPo0ePhDkqRpAANFVM77ec\nq1DWwkU6HI5IckGktny9B/sHjAdDsjTDaI2NfXpFmpEmBl9VNOWCWx9/zBuvvcrRwwcixxWEB7Z9\nfhoiCcTTjCdldR7/+w543YaOCqraYrSW+ivS06oSzSDPyfOcoihiG8f6/doZIHNMxfUtn+i70HQ9\nBzUq9lML0YjTGqUC5WrFYDTCRnGHK1eukBqDtXXMwMS+Tq1RyqC7D4n8uW3vaMwEtfvgGhkfPe9e\nurvdP/oIfaATt0cjmAPWrYDtMdYK1/dqteL45Ji7dw2Bc+xsj9A6YX9vn0E+ZrmseO3V1whnPYNA\n10qXZRnL5ZKjoyO2xmMGgwGNbSCI9F5jLSYR8o0OPas1WZ51YuGbYtttkPC0++tZXQ/9c11/fbr5\n+DkzoqfD+P8/jBZ40DZRd8rrGwv8z9IjGr/5TOey+XPfmLXggp2dnU6suEUPKyUTWDdNl6bLsoyg\nDcZ5nBYNUbQgbrXzYOgYavo12M22gCdd5yaa+NR5P+HS+/ezHS7ElI7YVAH+DAqGoyGNbdBJgrMu\n9u11phCFRuu45QVPUEJmj/coLUa6b+dVW0hqa6QoEdnO0i6FX+Q5JIZr168zm8/YmgxjtCxRnQ+S\nmitMhrUVr/3JjyiGEzwJL39hC6U1LvKkxhPtjL4YT3XqPjhr8QGKPOfZ519gdnRAmuZkeUGgBuho\n53Z3RS5tsVhw+7YgKUejUcc6lCQJmIRlucK6JioVibZp7RwXrl4QPd35nEExZLFYEGyQWmxdYctV\nFGBPcFVJtbIsjo+4+aMf8dG776ETzXBUsJzNpP0ptBIHPqYezxZbeBxE97TKLPFhhXVKR6HIUkEP\n20awDqPRkMFkijGGNE27NdF+9qmaWvtO/c+N68w7TzDrthbtpeyA1gSj8SY6cyEwPznh3O4uVy5d\nRdN09wJAGRXZwtZzIERpQOfW87HN5MSQ4sx05GYas92v+mnWflqz/X177evXJcPWNLUQZRjPYrli\nsVgRXMLxbM6rr39AnvRSzWrtyDvnSPx6L6qripMQyBKDC0KML/3YcT/RMfUcqUSHw2GkmxQR+v65\n9o3q/xfjswY9nysjCo9f4NPmsf99HLd5bJ/QoT95z6pTmk9RZzllSH8KI7r5egih87DbTd97jw2e\n1GQiuh28pPe0Fpkv7/Ct+oeuYyrWQUgkxRQ83p+NQO5ff1sTUonpNqEQvyqluugSoo1SENTpmsXm\nPW5fc219zGgxos4xGAwEbVw3ZDptb8Spe9xGxiIIEoEXTqGMRyvf80pjHNLLfLSbsg+qJ+kGVV2z\nNR5z+co1vvy1r/Pe22+yPRmT64xYQYqGNJAnKboY8O5bb/H3/+E/JslHzBfLU6jO7iPPWNhpolmW\nFpMkLOYnDIYjdi9cYnZ0SAiQJGbdUxeEdq6pa2YnMy6cv8B0MunEj9M0RqyrGg4PcH6OdT62DsH9\nRw9JigGXLl9CZynVw5L9B49IlaZZragWcwaJAdsIQXyoee/dm7zz+ussD4/QyuPritKW5KlB49dk\n6rHm6c+Y62fV9sVune6tfprRdyYFaTvoos6kGDw2fzc/v0vvt8auLdcgc07oQCVVqrVGOx/rpYZg\nDNZonLUs5nO00vzsz3yJ6XSKnT9ABY8KIUr5tkY/tobFdaq16dZGvCBaJRvWJph+Gad/Le0+1K7H\n/j1po75+kNL2txpjSJKUra0pV65cZjBMMcqjdYoxkvG4c/ee6KKrx5+JlJA8VV2RZjk7OzsoYxgO\nCranE/b29vjww49orGVra0tYvaxDKaLWaBpLDY20TEVHv3V2ngQo+qzjLADS04zPnRHtexxnIU/P\nGk/y0J762DOO24yizvLy+sduLtBO/+9TzuFJ0ehn3Tj6hqfVnWyL9s4LSMZ7T1PXhKVitVqRZClJ\nZAfBSb+gdw5lLSiNVWvaN9EBXT+LPnlEPzWklAJrH7++M4aOtdPNwv+mPiEQWYckVSdG1LOzs0Md\nWw+0Ff5YTNLL0AjLUiT6jekzDdqhvMPRdBFGiwo+db7xEaRZgnVeAvREGsePTk7IjOKf/NN/zu//\nzm/xe7/722SDbQptYj1TPjZVCtc0zFeHHOzt8dyLW1ib4ozQ3AERUNJmDNaOowhcQ17kVK4hKE1t\nA8+98BKv//hHrOqKVBtCcGSJcO4eHx1TliVFUXTk9N57lotF51gZDLZxaJOQ6EDTCPL8ZDbjzbdf\n54OPPhS0qvO4qiIET7mYQVMzGeQMipz7n3zMD77/Pe59/DFEkBE+UGSJ9IB6B14QqKozhu4JMIGf\n7Bhuzof+I1pT8NEWDRmMhFZxOBx2kZg/43POWrdtHa37KcT5155LiKQHSkBlyihwguHQWlFVJYaM\nn/3Sl7h29TJNbJHT3ktErto2L+mlFv9CobWPhPEqpnXlszw67iX6dNq6d/5n3Z8QNrl2ObW++sa0\nrsV5rmshm0dlkWWpwbpAlibcfOcDkpQznf62dNTnE3fWcnh4wEe3RGNVJZorN67xhS98gaOjI/b2\n9qRPlUBVrXBl3aGl+9m+PrCou87HzuCzjfXcerrjP2dG9HRkcpaX+h96bBqEzQj5LMPaT5n0j90c\nIYSuBrb5WU+bctjcBNqaQpt6NmkCRpMVuWwkRnc9W7auMEbhjUEjdR1UK5cWuk09eAEkZbHm0SLw\n2k26f52+l9Lpe8X9+yUbSfS8e1Hnk2oZQUVlkeBpGksxKGKtxa5rvii8gzSS13f3JXr8OoqBJyYT\nkJFTnRAytCg/6OV2u80hSVICKqJSNbPZjMsXznP/4SN+/hf/KltbW/zg//oDDvYO2NnZprENSSKg\nIeccwXl+41//K/7JP/8vGEym1NpTVY2It8dIcpNsPEkSGi/tLAEwaY6rSybjLUbTbY7391BpIDWG\nxXxOXTedx75aLJjPZhRF0bUJuKhz21I8tsbeaNm0iizt7rWtV5igsaWIszflEu0t1YFl7/4d/vSt\nNznafyTalxpMVAvBNjRW8tkq9GK6z5g+66cb+4Zgcy4pkF7T2ILSRp1FUayzGHFjVhtsR6f0fTfW\nUlc6wgUzAAAgAElEQVR/i3l2067HDaOrlcakBuuC1JJtQ6hKrl67zBd/5kUIjuAkckeFyMbUMmmq\nyOolpPs+yuRpr1A6Gg+ZhutzWp/pqfPvX0d7z9r11Hew23XR3tN2TrTtb3t7DwnBYhLpHS3LFbbx\nnJwccf/hAbbrwvEdFsF3OAoxzGVZopRivlyS5TKnXIyyDw4OeO+997hy5QpFUfDw4cMO3Zwp00XG\n7XnWdf2YAT1r9NPV/Z/P2lP6xzzt+JwZ0c08/Z/dgD5t2vYn/f1Zhu0nHQuPP9gnnlO3WB9/z6e5\n/s1jvPek6bp/zBiDydIuomyh5E0jdZpAEEWJNCPz4g1rH/CqwThR/NCRpms+nzMajTh37hwhBB48\nePCYccyyQloYeukib91j90Mpjde9+lJvc9y8p54gDe/OUlY1Fy5dhJjW9d5TB+nVc95K7Sg6CsJv\nHOfTKWOdxMjBxrS3jxGGbBDr6wHhenCgU7QWD3k8nVLWDcoFSuv4wpe+xNVLl/mtf/Ob3P3kFtPx\nGOsatEpQ3jMeDjieL3nj1Vf58te+xkJ5tqY767keDYRRkdwiCEOTQdF4L+AobVAmoWwaLly6zN3b\nt1CDnP3jY7I0FTLyIMjQVZSLmp2coJT03bX8qHiJSF2kLmwFjxUSBYXYwnXr3ffZe/iA/b2HrOZz\n6uUJtlzRlEt8tSIxiiS2UWjVFnXbSbgZLcQUpHo6nIP0bq8d0T5gpk3vtc80zQshvhgMyLKcJIkb\newsWVJEPqFd62HRQ+1kmgpy/j8Vq3bZaNVbAQ3lO2tYZg2GxmIM2zKoTrHdcun6Zb/7cN0gSRV1W\n6FCjTUAHFYUJ4v3QvesKAYeJc69t+GqdazG+oV0LSp3aLvrrrzWQ/fJK3wn5NIPivWMxm5EaQ5qn\nnJzMUAiO4gf/z484OlmQ5xlNYztykvYzlRb8gXPCkbsmVhBSekELy73f29vDWcvW9jaTyYRyVWJS\ng/HrzGM/hftZsDGt09B+v5nl6p/zZxmfKyP6NBf40xrHzzLOMqRnHdN6+6ei1D+DA7AZ5X6W82z/\nVgr4w84wd5tDZMERKjyxGEYHqtLjnScNgvAMSnrQdJqTKkNqDInWWCWcqoeHh5RlyeXLl3nxxRdZ\nrVY8evSIk5OTeB/WqXgX6xztOZ76J6RBp1L2T3JctBFUaFCQ5zlXr14VsJGLTFKxB87FXrRAIM0L\nYdHJC7RJhGGph8JOkhRtPImxNLaRnkUCayYZeR/pZ1WdHdBxU7BNzbAYsCxLUmMYXzjPr/7n/xm/\n9Z3vcPON18mVZppN5J5by6go+OTDD/jmN7/B4WLFvfndqO24Q9OIXJi0IIiSirUOrxyu7aVUmqAS\nVlXN7rmLbO+eo17MAKm12djXWRSFUPVFSrcWpNGmyerlkjzLCEZh64rl4SGT8RC8aILeu3OHN19/\ng4cffYQtS3BO0J3Bo/GkKqCyFNc0wo0aWWV6sxHYbFbpZvjTzWc54ccYgdqoPcsyES5IEorRtEN1\ndmlFbTbfjbP6TjexDCGE6ADI+g1R+k9rLej0cJrGrnQKNRgxn8+w2jA+N+RrX/8q+SClWS0QyWwR\nwrYhKtPoNXm9CnGuq9hvCrRwY9/Ofx+kfSU6k9GrO7Vu+rXQ1lhullw2Dcpj0atSVNWK/f2GJM2p\n6gbnArduf8KHH36CMpq6bVVBossQItLXGLQyrFar6MSnooDTWIwWh1nHNU+Ah3fvMx6M2BqOcWWN\nCi3qeJ0l6Asf9B2fDXftP8j4c2dE4aczpGe9/6e93+aGvjkJN4vV3ca/MXGf9Dn9s/m0+s+TrqWf\nOgUxonVdk8SNpZ8Wb99TFpiCJrYfaINqGpwP6CTFpCnDImcwGoo8GZImrqqK4XBIWZbcvHmTa9eu\ncfnyZba2ttjf32d/f5+6svggUlst8s7H+o7ptcls3pd+uums34EY3PFkiveBqqmx1gnJt491mPar\n86TRqKAMaR7Txr1UkQ8BrRJ0lqGNRKPWNoSoIOLbTYJKDIIPglaGyJCUUjlLohOsd+wvZ+zubvN3\n/t7f5dyFc/zB7/4ueV2zvb3NYlVim4aDvUe8+sM/5sVvfYuyrDk+Pubw8Ijr12+I/F4j9SkREAgo\n7SPBRIifK/WzunG8+OJL/MH/+TvkBk4WJYRI8J013TUmxqCzvJsv3jqGw0IcEu8oEk0KzPcesX//\nAe+9dZM7tz4iOIdxliQaHmFjdLETI4B3aKOFnSoI+Or0UKe+79oi1Fmd24+PFk2+iQJPEomcB4NB\nN7etSrooBxWj2HYdhf6esjYqn2ZEVe9aRKIs/o1zOK0J1uGNkFPo4ZiPPvyA8xd2Ob875cK5LbLR\ngKA9eaoB0VRN2kDcyHvbsL5DElBHUJ4WdqH2dzbIOlUICC709pzNaLCPYD2l//opRrRvaOUfUbj9\nGGVSHj064MOP7pDmCdZ62R+0wOeCl7mplCCNkzTBWsfO7jnOnTvH8WzGg3v3CHEd+naNx889Pjqi\nqWvp543KQe259DVa+0b0afbpfx/jc2VEn2Z8Wp77acZnNaL93296b+2DbfP4/SL4k6LWx86n1xP3\nWY3oWaOuaxaLBcOYxlNKdYY6wKkNxllp+ajVitVqRTEcMd3eZZAXDIqCQZGjdIILUqPb3t4myzIO\nDw9J05S7d+8ym83Y2tpiMpnw3HPPcfDogLIsWa1Wkjb064Xc+pKS/QunFnz/HvfTd0opbE8p5/Ll\nyxFQVOM6cRb5e6mJ5YQAOk0wSYbzARqLNokgclGReEBBZFsyUW7JmIQQHMJfKxGgMiGqpUi9uNVb\nVSiauiHNUlKTYpXicLUgD/ALf/2XuLS7y3d//d9weHRAlg0YDYfMViveeP11nn/lFbYmU4lkVysO\nDw4YjydSkFRKOFrR2FBLn6UxNM5JdGYS5ss5O5MtnPfcuvMJg2JAmuRdb+dgMCDPc7z3ndRVmxUw\nNELuoGFxeMTHtz/h9vvvc/+DD2iWK0ajEWVZkkUaQx883gVCFIEGocJDKZqoqJG0YLCwfsbSatmZ\nit7/P3m09ek28mxT85KyzbrUdAgRQC4fHVP0a6FyqW3Gc1aPp3DPGkqxZrKilScTXdEAUbrPYAzc\nuv+AL73yDf7b/+6/4dzulH/3h7/HH/z+71DPFwx1YJymjAYJobboRONCq3piRTNbG7mXOho0H9Ba\niB2CguBlrSQq4BApv7aN60nnv7mP9PeizXJTS5wPQkihvI8EN6ICdO/eA5LU0DQe6xx5nsf17E5l\nulxEKQ+GAy5evLgGLdmGRGkIa4E3B+A8y9mcPM8IcQ6VvnzM+J8FKkJtsgT8+x+fQyP6KZGoaqEK\n0UXkJ4X3T3e7ffRSNykY2tdCu/HTeo5xszjl3SEyYN6jvO+YWNp0DTyuFQjRiCrWoLsgG4Jvj25Z\nW1jXdc4abeN+8J66LDGJYVAUUudSbRO3koUJkkJRkmJydU1lA4PxlGIwZDieko/GJHlBkuWYJMFF\ncdzBcIAxmo9vfcwgH0it9OSE4BxFUbB78TwKhXWWxXzBbDbrhHSdc7LRerlupVQ0aQLaUITI3CPG\nNRKd4RXYEJiMx1y7cYOyriKHcfTOlY5pL7oNVqD4iuAtrgl4b9GulWkzJFHD0ocQ+5Ol+T2gUSGR\nerEP0gzvPcHFDdlo4SMGskFB44RAm0zI6xOTsKwdX/jq13AOvvud7+Dqmp3hiEwpVvMT3vzxj/j5\nn/9F5qul0OalCfP5gsQkko7Nc4zWLCtpGnRJQDlN0GCdBQJaw3PXr/Heqz/i4ksvMxwO+X/Je+9g\n27K7vvOzwg4n3fBy6PBarVYHBEKpJSEJSUgIZAEmGowpF8ZTMwgYMCYzGGwwVJmZorAxhqHM1MAU\nUx4MDNhkZIRaiCQUUOzceh1efjedtMMK88dae599zzv3vdfqZkBmdZ1+9567zz5r773WL35/31+v\n1wv1oEqS6NA9p64ryukes+mU+XzOZOcyF86dY7o7Zm97m91z5xHGoJIELT2z3W20VDgRShCa/dYA\nnBwh3Ow6BmRYtPtXNb67M8VirV+zbhd7qhlBWYR2cHmeMxj0SZLQgkuI8MyaELvWqt2bQSnUcTpL\npSx0jLnrRLwaelBBw1QZmzF44rr2uNJQVTPuftlr+M7v/15qBTul53Wf/0UcOnqc3/rlX2J35zL9\nDYXUAuU1NoZja+ep47oOqYFgMIV9anHChJSKC51NQvmZbBtvBzYxu08ONvdvf4Ssc92uC6JqykZC\n6ZqJTQiklDhjSXRGr9fn3KWzzCsTo0eCVKfUNkQjlOiEm70I+y7NOXHiJMdPnGBne4vZdIZSoUxO\niNAqr5GvMho76xsbXL50GVvXQSbIcE+aZ9U0LhfxOsR1pH0j34RYrLfmWO8X+mLx2D0HnWt5fHop\nUdGJc3TfbpVVXBQALLpfHJx/XHGyFaUnviEIbFoOichk4xdp/vYByaVb3/H0iItAotoyk1ZBckA4\nQnSn2Q23dt6PISXlr/1oOLWPlxX7EdY1pizx3uLRoR4zEuVKF0JB2ku8rUB4Kgc67TEcbJAO1hhs\nHqG/sQ4ytExKspRpVaJ1AiL0wczzHtZEonvrqGdzfGXYMzWJTsjSlHTQ48hoQJakIXQTuTWL+Zyi\nrrFAaQ3SBR5cjaI2NYnWoUOL8OgkobAW4wx3nDiBIYQ1jQvCwLjYizOugdpUnUezuN8NeElKGQjm\nVADrgEDpBJ1msQWcRGoNIiALNQopQDZGgACPwjpHaS1CCWSakThJInJwjrkHjOQFL3kZX3n4KP/3\nL/yfDI2hn2qkNZz9+Ed40W23ovMhRVlReQBJLQzGGryp6fcyUhk841CVLjDSYnyJsCWu9BxbW+OJ\nj36Mpz/6MbSWsWA9dBqxtqKczrHWYMsqCCrr0NRMxmMAUqXRxgYvxJZID0kSSBLqdt2F9dttTk0M\ny4WlFz3UZv3Rye0vLfVVu3R5S4R8vSRNgmedZVkg5e9YsftSKb5u32v4hnxrlC3WwbLSPEiJWjxO\neFKC0aqcxxtPrlOmpYVUcXV3xl333Ms3/ot/iVhbY3tvTOIU1Uxx76vfzMlbz/Cffv7fc/7Jhzhz\n8gi5dzhjQAnSnsJWjtp5QltAReIShJdY4SjSOXgZS2I81oQSEeljPl54DKEus0nLSKkQvttbtYNE\nFoH6z9mmB2cjLgVlFZWXdzhnQGTINGN7WrC1O0GnEUAFgCMSQKF8oEudVxakJsl7DNY2OXnL7Sgl\nyXoDpuMp3kNtLdIHhqw8DzXs1lpKU7M3meClwHjX9mn2vsNU5Gz0PxqGset5311D4trVtj/Kt3rt\nHTQ+vZTo3+A4KGTbhAqf7UiSpI3tL593/1i24om/+xXv3dzwUgTwiAjWuVK6RR4GLxRMVLi1dTiV\nsL5xiI2jRxisbdBfX6c/WqNyFpdorFLoJKASIWzmzc3D7Gxvh1yGh8o6nLAUkylzQoPePMvo5Tlz\nEcjK8yyj3+sxGg5BKYzzzGczZtNpaKtlSrQMedRAExdaNlWu4vDhw2xuboa8a1UFuL1zkc2oufIF\nu0nD0NPc9wahGvg4Y1NyqfBCobRClQVSBnSs0MkC0aoDzaFKFFbZNhpinUNHaL+UEurICRw96dpZ\ntNLceuZW3vTmN/N7v/lbHB6Fhu/b8zkf/9jHefXrP5f57gQpVOgSpDSmrtgrC4q5ZrS2gSCUsdQ6\nzFn5UJoznk45efo0r/mcz+E9v/5rgGfcGJVNiYkzgdA9du3Ag4oEF6qhXOuG+IjBkM5SXZX7v2a9\n3QSO4aChYnNva0MHHa01eX9EmuVtmmTVdy2nPvaBg64zr+5x1+YEg/JVHds8hPJhMp6iR+tsXdni\n5Z/zOr7zB3+IYtBna2ebLMsRSjItSs5dKbn15C2849u+i1/6jz/F2Uc/wZm1IbKfgQ1eX56mJCY0\nvADRtrBDhCblTWlZ0+cYaAF7IWKiMQ2ugW74swn1NtdF2+cvXlr0QJtrFaG/7XjMdDpHyECLOJlM\nmc2moWvNilHUkKSirWMerq8hlaKsSzKRUVQFWT9nuleSxtKpJFnkYJv0wu7ubsjda4135sDn+Dc9\n/k4o0YMV1M0pv9UAILFPmrQh25vUp6u6C6wcq/606vhnsaaU1qEfIrQbqukejw8dQoTzVM4gdcpw\n/TCHTp5idOgI+fo6/Y1NsuEQYQ0Wi4l1gILQmd5Ix6HDhzHWokR8r6ww1gUi+Ag+qVyBrUIxfqk1\nk3jzsixDpxk6SVgbDNlcW8dUga3kyuXLjPf26GV5m+MTQnDmzJm2JjQ8n2stzaoq4kYNwiTwlQYj\nqK6bGrQQulVSBos/MrYIqZA6iZ6oItEpLkkovSDJ0kV7JhWQmkougpNCCJxq+IiDNS28p6hLhEh4\n7RvfwHg85q/e/wGEqegPBzz40IPcec+95IMR87ImzXr42BBaKEFlauazCTrrkeYJlpS5kNTekeU5\n9XgPm0ve8gVfyHt+93dgOo6L07c5KC+i5KR1P1AqlDthXdveb3mJRdNxX/j1wD22yuA76PgV79W1\njfMKyOJer4fUWejKI/aDzbr/dgFo3TDtyil2jIXlPbmsUIVzKEFcEyLQ0zlBtjHk6oXLfO5XfBXf\n+j3fh0gzJtMJ/eGA3b0xG5uHyAd99navcHE85+hok2/+3h/iF/7jz/Lhd/8Oxw5tcHRzHVsWYEwI\nn9oA7LPC4VRYs4kMxBneNwjjptbTRC8yll6JRRgb360r7xoPYpE3bm9N7O4iNc7W7OzssbW1S117\nlA5lbDbmP4HWCdg3FJTGkOYZa4c28FKh85Ss3yNRmkuXL4EMDSqcrTHGMhj0qeu6NU6dc20+dvk5\nPVsFurzWbgRCerbjWSlRIcQ3Au8AzsS3Pgb8sPf+dzvH/DDwPwAbwHuBd3jvH+38PQN+AvhqIAN+\nD/gm7/2lT/0ybmruq9591p+91vtcRrbd3Hy8960l3UC1DzruZt4Tnf/faKS9HJUmQZUIEUARLvJ8\niqBYrXOUDjZG6xw7fQubR0+QjzbIhmvINMcpRZImeFMxnUxJjSfrZ0gJprYMB0N6+YCyLBFCkfb6\nYWMURQy9EAvFHcY5nHHIWMNXlhWICUIE0EhT59fLc06dOsXVK1cYj8eMJxOcc9xy34s4cuQI0+mU\ntbXQzLq2foUn2gvvuViXGp+dJ8L/nQ9hSe/Aerw3EMPdztTgTAjx2pBLtKXAiSwgLGNJRZomoVdq\nU9sqm6blAayDAOdD0/As7WNMzaSqeeNb38rVnR2eeeopBgK0lvzVBz/Aaz/388jToMy9hyTRJGkI\n45rK4r3B1QJvLdILitkc5z29NGVWVtxyxx3cedcLOftXH2jve6PYos3Eor2XD8rTdphzmjVHky5Z\neKQ3u95WHbcSRLfidEpJdCRKyLNgWFm3EKRNreOyh9m8Vnmqq7znZVDNQZ6oxpMQjAiLoHIwd57Z\neMqbvuYf8o+/6VsweZ/xbI6TnvFkQm84oHYWDGwcOclkss3k0g4vPH2Kr/gn30wxu8qjn/godm+P\nw3mOsJ5UKbwMkfpahBbzziuElyi1qLPWWmONiwQnnrpelC11b2qjLGnBVDFMi4vlKJFpyYc+pWVZ\ncfnyVba2dnAOej3d3vfGU+zWmXaHTBK8cwzW13FCMhiOuP/Vr2I2m/Hxj36Eq9tXGQ16rA36pEpz\n7tx5kiShLKu2drx5JkKEumW5FKr926RIb443bzGeAr4HeBnwcuAPgd8QQtwLIIT4HuBbgP8RuB+Y\nAr8nhEg75/hJ4O3AVwCfC5wCfvU5XMP/b+OaB3HN3/bD4Vd5md0N2kXurhreN2E3v+8lPde8ltMB\nq+bRzCXJ0qaqO3hJSx0RLB6LY7C+wclbz3D81tvJ19aQWUaSZcTirqA4pMLUNbPJFG9DO7ayKCmj\nsqyqCudD7lInCf2sR6ZTBLJt55SlGVJI6qKirkNjYGMsRVGwt7fHxYsXOXv2LI89/jiz+ZzjJ05w\n8tQp8n4PoRVnzpyJAJPBopBbLFqtLfKdoRZNSkGeZ6xvrHHk6GGOHDnMoUObrK2PGAz6ZFmKkhEy\nJnzgeY3es3QGbIWrC+piRlVWzGdzpuMJs/GE6WRKNSuoixITc43WhDZN1rlINBHI8MvaonSKkRKr\nNG9529sZFxXbOztsHtrk0UcfpSxm9POcXpoyGvZbBaqThH6eIglNtqv5DJxlbTikrEqq2jArS6xU\n3PWiF4Xr8QZMHcAw3oExYE0Au8VrVUKihIwts+Q+b8AvvW5u0+zPN3cRlteu9wgqiO3mQJL3BgwG\nIwaDEUmah/eXvMZVBuiNhGxXCS+zQa3yRNvLiXvWiaBAx8Yyt/DlX/9P+fYf+lcwWOPqfM5uVSET\nTa/fx1iLkIokH1AC2cZhbLbGJ7en7Pqcb//eH+He+1/D1VlNQSg7StIsGkyhBCu8JFqH7jNaa5Ik\n/JvlKVmWkaYJWgdjTaAQqKg8mzCubz3RkFMMLF7OQkPSL4RkNit46qlzXL68jXNBqRZzsw/JW9cm\nNKhYESKvnSPrD+iPRhjnuP3MGU6ePMmFC+d5+KMfRQnBcNRnMh1TVRVJohmNRqEEK3J6N2BD7327\nBp9LGPfZhIGfbcj4WXmi3vvfWnrrB4QQ7wBeDXwC+DbgR7z3vwkghPjHwEXgS4FfFkKsAd8AfI33\n/t3xmH8CfEIIcb/3/i+ezXxWjZBo7nqGy15kVxleq7yWN9D+49ujQp3edUjkr5nX0sZvkvyNRZQk\ni04gy/O5aXt/STg1515QxwUrMskzbETrOrPINSDBOBvaESnFqdtu58TttzFY38DrlLQ/CEoJiasM\nO5Mxs+mEvd1d+mh2rl5FSklZldTFHCEluzvbbf4oyzOODjfp5T3KsqKoSuraYKwLIdA0JUuavKRY\nhMx8k+sJnrIVMFhfY7ixzqFDh8jynK2trdAQ2oSQJzLmOzu1hMG7CmQIAoWpHd7VUbhrslSTZ/1g\nkVtHXVdUVR2MDGnjqWLuWIjO7w58MApM5XBWIerAjNQYSUonKK2QDd1fTEBX1lKXNaQJ6aDPW//e\n2/izB97JbD4lSxM+8qG/4gvf9nZq64NX7UM4fjqfoerQVcdbi6lqnLHMJlOElxjnSLRmUszZOHqY\nbJAz35uE5y+C8AyNn0PuymH3hfqaMF3LZMS1irMLzFle551FSFODedCabdYqQrSUklJJsixjOBqF\ndUpMPYiA3jZxjXa5lLvrvlGOTWeWZnQjP132mu58ruehOA8y7VGUFU6nCCX4xn/2HXzu2/4ee9ax\nNZmSDQdtc2mHp9fro5OYPhGK2kI+2gRnubg3J1UlX/P17+B9L7iHP/iV/4we5vTShER48DVSGHpp\nhnMSjI50fDVCJEhZxzymwzkRyeI1VSQxCW3t3JJcCO0OnbMIFcIj1gYavStXttje3sFZF/PiTdRs\nf715EjvhrPLoRmvr9AdDlNL0B0P6/T7PPPMM5545h7E1Wgn2treZ7k2Y7U1J05Tjx4+zs7OLMXUL\numzKAsPzW83HDfvJJFY9v4MiDjcafy1KtDtE0CD/AOgDfyKEuAM4Afy3ziT2hBB/DrwG+GXgFfE7\nu8c8JIR4Mh7znJXoQWMfCXr4iVX3aGWY6aDz+Rtvumc7lhVhzL7e1FhlADRCA4JFqpRE6IbAmbaV\nGSL2EdUSh+fIkcPc/sI7WTt8NPCyylArWZcVzu0xm4WSCO9qRF1TWYeZTQIFoDPMCN6eJHDDFkVB\nOR9TbE/p9XoA9NeG3Hr6NvrD4T4F00DYA4tJtJCVRGmN0orhcEiSJtS1QUjJZDZrhUdj19jOQ2sU\nchAm4a4a42NuJwhZKdWCVUoqkiwjyXrk3mBrgzUGY6oQ9o55J0mcnw89V8NmNiHsJkBahYtw/dDs\nXIdrTMI8PUSUq2Be1mRJwj0v/iwe+shfsrW1Sz/PePyRR/nY6Y9w9Nhx9mYzvPSgJJWtSawlTXPS\nvEeqFLauCe3dQhjZOU9hDclowNQYnPBIJShKQz9NqOtF/1QnBDaGKvetqe6/opMXPUC+XCt4mrzr\n/rEKJCKStG22nOf9mHdTnVxpYwSvMDSvswevl/Ns5rLqb8vlLkFpa3bmFbO6pqdyvutf/xgve93r\n2ZoV7E13UXlGbQ29fh9hDVqlaKVIpIo5cxXDrYo07aFQXJ3Ocb2MN73tKxFO8Qe//svoPCcXnixV\nDDMNwuMQOCMxdRPCpg13NsNa0za3aKI6wbMLeVbvAvBPSoUxjqKYUZQl83nBbDZjPiuwNsiJztPi\nelJIax0JQBxra2sMhmvkec6srBmN1jn3zDN8/KMf48KFc8hY1mJqQ9w6Ifplmx66B3uAK9NYYn86\n7aBxM8d8quNZK1EhxIuBPwVyYAx8WVSEryHssYtLH7lIUK4Ax4HKe793nWP+Wsa1BAeeplNGd6za\nkGJpE4fjaHlMF8d8aqGGZqwCSTzXEaxuUCp4Tr1eL+TsPAvh5jxKK4xzSB/yeUeOH+PoiWN4neGE\nAuuY7u1RGoMAyvkcW5eBAlBKKhc9A62DcjA1sz0HUpKkCZmSmAiln8ymHD5yhBfedRdrGxugJNY5\nKhvrRX1AjEoP2GCDKqFJhSJNUyrvqOqKugpWa6L0oj2SDJ6sVknriTZE9lolLfq2NjVlWeKspSws\nSSLp9wcIIaiqisIEUI2K6FytE1KfYU0dXg2q2tdttMCHuhu8DbV8zgW3WQiBVw6nNMIolE1AqwBC\nEiJ6xp6iqMizhFe+8pX8/u+9kzzvgSv543e/m5e87OVY4bECVKpRmWZNpW0ZQ9JwtlqHsYZEaaTW\nOO/4zFe8nDtefB9PfPwTlNM5gzxjOpsFTl0f+n+EnKdHebEI18bl3s2H0rxvb3J9XuewZUXmYr6t\n1+vR7/evG0XqCs/r7ZXlsKwQYh8T0fKxNwrjlQ7mZcWZ+17M//Rd38Nt997HU5evUksNMdQqBRc3\n8sQAACAASURBVAhTI+MalIiQFvCgRWDnKYsaZzz9fo5yh9iZzki05dVveTu9YZ/f+bX/h81UcaiX\nA4JBngECleYURcl8VrR9YmHR3crF1oRKJVhbxGbrYU1LqZlNZ5FU3lMUBbOiaLFfznmk1EjZYS1r\nHqBfLd8a8gqA0WjExsYGMsnY3dmjNxiS5zlbl69y7pmncc6QJxpbVbi6QklJHSk5i6JoG1bcSNEd\nFCE86HOr3v8bAxbF8SDwEmAd+ErgF4UQn/u8zeivcSxv2uvd3GbD7Veq3U0tW+nyfD2QmxEK1xur\nPNEmD9Jcb57nge+T/eG4uqpAS5wLaFOlNaWpKcqK2jjm84qdnTHzeYEpSqR3KFzLOmKkCyHLRDMY\nDkjSNFAGigCG0FpgCkNRG4bDIc4Ldvem9NfW6fdyrPBgEnxd4azB20APGJSMJMlTkjxH91KEVFhn\nUXmKVgJfGsqyjCHBoDCNix5ookmSLISEfN128cizHr18gHOO+XxOWZbM5yVpmiF1ig5wo8Bo5C3C\nC3pZD5FmWBsUadMmyvlQJylomqD4BSjVNz1EVQArSRNyZFohopeaJhl5kiFi/fDtZ27n9jO38fhD\nT9Dvjai9RQJ33PlCdmdjirrCYPHGYQ2URQFAkgQmKUGgnXPeUNcVo9GIf/4D38+//Gf/nEtnn6a0\nFqQKrDiEEDkylm6YBTjOdZd1BCEtROmqnOaKdetvnEFt86Vp0nZbkVq1IV6EaEFwn8rOWE5xdF/L\ne3cVAX0TVjTGMK9Kzrz0lXz3v/hBRsdP8szWDjZJUWmGkAJTVawN+pHOUqNEIO5QHjQeTM0g65Fq\ny3xWUMwKNtYGpEnGpZ0d/EbOS17/eVSm4o9++zfQ85K1Q5v4ynL8+DHm3sYUjUGIhfJsjPiGZajB\nFjnnmU4njPdCJ5+iKKlrG5+xwO/DDPhFeUtrQbUEggfeW+c8g0GfjY0NkiRhXpRolbC5vsFsb8KF\nc+fxxpFqhQJcVeNNjZC6DalPJpOVz+ugZ9lNiTWy7aD8OPwt80R9gCw+Hn/9oBDifkIu9McJd/44\n+73R48AH488XgFQIsbbkjR6Pf7vuqOsFq0UzlBLo5FqPcnlc6/YfbLWsVqCrj735YOuNR3d+z5cn\nGhZa8JqaWrJGrInmO2P+Ic17GG+xwPkLF7Cf+AQzYymrmqq01LUlTzO0F2QRqENlEEpRU1HM51Sm\nwtsjbB45RJ6mocuId+gkoYxk7WVVUW1vU3vL3mzC+uYG2aBP3u+hEo1ERXBEYOCpncWWZSi90Yre\nIEURSklkqlHKLqjrTAgdEzdobS1lZQI6VqR4P0XKQDzQ8Kxubh7COcd4HIwEpTUqT9AqReAxdY0z\nFWVVkyiJiuUVzprQKi7mka0L+dFA+xc5YzwBtONcUMrW451DOo2XAqU0pnbUsqLf6yNEyAXee+99\nXDp3CU2KTwVnP/lJXvGqVzHaXKf2ltJV1Nu7eC+ojaOqa7yXDAdDEh2YvJ2rEcJhvOFFd7+IL/m6\nr+P/+Hc/jdneJVEaE8uBQhONWJPIc42nPLvR5LeSJEHGVmVNiLJbx7vv345ReJAybMYqjMBB3uby\n+12vqAG7fNYrX813/vCPka1tcHE6R6UZRsjAuywEveEgQKO8Q8ayJ92WTQUDz5kK4QT9Xg5CUhbQ\nyzXrm+tMyilaK17z+jez1kt456/9Cle3drnr9Gkmu2PEMJR1NeVUWgdO2hBhWdwna20E5V1iPC5i\nmCQY/0pFLl4Ro1/x+kLExnfSHjf3/NbX19jc3CTP8xDWtZ7jx45S15anPvlJTKS/xAXDOBDvA51c\nbVEUByrAVc9yeQ7PVV4a61r52JqJN3nK56NOVAKZ9/4JIcQF4M3AhwEikOhVwE/HY98PmHjM/xuP\nuRu4jRAivu5I4qLsjmZTQYyvRynQKDjvu8pOLH4WROqy/Z6kdTaCWQLwwzmHQu97SN0az7YfRXPe\nVTj9zsOQDZip6QaxNKyzqAh+sZHSq9thAhZqe7UwEPt+NN7jlaI0FplovE4YWLVAWfrAt5kkGuEF\nmcpCOHRu2Xr8SUQSiAXymI/EleHULlB6CQVeWLSZIxyU5YzzT0/QCg4dOYaWitmsJM0EaTqgsnOs\nreilPepyj8keOFegdjU6y8nyjCTJ0GmCioXYUkqkSMBKqrnDmyIQ4SuBUgkileR5oB20xpCbHkX0\nTBtyBWsqnKvb9+azbfZ2g9czHA7p9XqsjQZImTOeFtS1oDaeNAslJUYqyqrAKdBSIRMNZPhYHiSl\niWUmkX4sJHvaZ2QRNMxXwrnAlyolQoZQmFOKytckScLE9Lj1jnvYOPxRzp99nNEgZ3frKn/2nt/j\ns+9/DVk2QJKhN6PANCEcVlU1DoPKUvI80PxNJhOm4x2enqW86au/gc943efzyQc/zjOPPcxDH/pL\nJpfPY6cl21tXqeZjvAYhoTQK4zVKDzA1CByKGkmN9hZLQGgvRz9WgTqkVO0xLu6rAL4NylNnGWmW\nIVGxn2YwLhorRMpA8Oi97+SkYyOHWObRncMyMX2zx7ulZMvK2bqaREq096FxPGClpEAwR2J1wkvf\n+na+8bv/FyZ5n3N7e8gkeM59wv0JsLXIaCUlXjmEljjlkVpghW89wFYI4KhdTVV7UtkjHWxypaqY\nornvlV/Iztzz5//tt9HzCcdGQ9JZRSJTZF2hrER5SaJTpkBNzdQVXJ7OuPzkhMuXL1OWIboCxFKR\n2PoskmrQvd/QckJfw4rmAyNabR0qTaiMxSlFfzhi88Qp8rTHZHcP7xOOHB5QlzOefPJJnCnJE4U1\n8xgdAesFHg2EJgjeOeoIJlIdDMd+sXat3PdR1oapBmSx7AA2u2Aj2X5erFS6gVd5P44kAKeuncry\neLZ1oj8G/A7wJDAC/hHwBuCt8ZCfJCB2HwU+CfwI8DTwG9ACjX4e+AkhxDYhp/rvgPf65wGZG79j\n38/dzRavIeawRKssm78LIfZtyAWic9HVYHnzPR8e4/UsrADM+NTPLYREiLBddRIK6U0nZCVl6LDQ\ngFHaMgQlW3tgeUEuL0LvPa5hlRESKTzT6ZTDh8EaQ5amTGczhsMhw+EQ72lLXhZUZGEYY6nrGarW\nJDaEYZuyFe/BxybBXlTt/MG0DcG11qRpCOE2c254ecsyKNGmBdi8LCiqkt3xHv1+n9FoxGg0oj8Y\ngcwoyzKCQALQIknS4HXWllTo8Dt1S1QhjYxArbjGumFDArJzeTQCo2mALKTk8KFj1MZy73338fQn\nH0UrRZpmPP7445w6cyf90SGS3oCslwVyCS0YDAZIOcd7mM/nzGYzer0eJ06coNjcYDabMSkrjp6+\ngzvvvAtZVbhizHxnm/l4hw+//3383M/+HLOL50jS0CFECIG3NYH80CP99T2Fg9fgYr0IEQxYKSVJ\nlra0fd77Nkd8UMnXcxnLYdx2LnFkWUY5mZGlGus8VV1jpcRIxXxnzOd9zdfybd/7few5z940IEob\nFHlzrmburWF+kyFEqQXFPDRMSJMhWmumkylTnfH6N7yFcvcqH/jTB8hVwsg7TF0wLwqUSimKAovE\n1o6qNFy5dIXHnniC6VXTopOb628IQZaRycuy7ECZ5hdAOutC+ubEiRMcOnSIna1dnHNtI/nLly9T\nFEX7fTcaN3PMQR5nV85373k31HszozmuIX2QUuHcjTsLPVtP9BjwC8BJYJfgcb7Ve/+H8WJ+XAjR\nB/53AtnCe4C3ee871b98O8F9+xUC2cLvAt/8LOfRjmvAASwWwnJoFpYARr6poQr3vluz2STWhZAo\nFrH3g777oGT3zcy9q8i7yu25jjZHEI2FRmF1eU5FQx0mYt8/KUGKhYfvYllBtOJXL0qPry3WuEg+\nLZiNJ0z29hiM1rC1RRjLbDxl88QxIFi+Ol0o0oYOL8xTX7Nhwt9Cb0wR6d+kVJHdxMRQlCL0QyX+\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pTQURPJP3eiidYp1jOpsym81RJBRVQZJodDbkslGcuuvF/OhP/Sz/6lu/mUfe/+ckqcYZ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3upREY8XB+j1HjlZ62g1Zrap6WT+4pmgRd8D7bKHp9LiGKtFdpmQJAoH1AoH7FK4WI4p+iCAltX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\nB8T/A3yBu0rUeJYfAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff5d3a81310>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"check_training_picture(bn_feat, filenames, 17421)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is marked as 'talking to passenger', but it may as well be c0, driving normally."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kick out the 'bad' pictures\n",
"I believe that these low quality marks 'confuse' the network, so a network trained without those pictures should work slightly better."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1580\n",
"8.50056491096\n"
]
}
],
"source": [
"to_remove = np.where(np.amax(bn_feat, axis=1) < 0.9)[0]\n",
"print(len(to_remove))\n",
"print(1580./18587*100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1580 pictures are 'weird', which is not that much compared to our 18587 pictures (roughly 8.5%)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"to_remove_files = set([filenames[index] for index in to_remove])\n",
"list(to_remove_files)[:5]\n",
"\n",
"out = open('weird_files.txt', 'w')\n",
"for f in to_remove_files:\n",
" out.write('%s\\n'%f)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/ubuntu/statefarm/\n"
]
},
{
"data": {
"text/plain": [
"['c4/img_41361.jpg',\n",
" 'c8/img_51539.jpg',\n",
" 'c0/img_87801.jpg',\n",
" 'c4/img_10249.jpg',\n",
" 'c4/img_87180.jpg']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(path)\n",
"%pwd\n",
"\n",
"to_remove_files = [x.rstrip() for x in open('/home/ubuntu/statefarm/train/weird_files.txt')]\n",
"len(to_remove_files)\n",
"to_remove_files[:5]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"mkdir: cannot create directory ‘weird_ones’: File exists\n",
"mkdir: cannot create directory ‘weird_ones/train’: File exists\n",
"mkdir: cannot create directory ‘/home/ubuntu/statefarm/weird_ones/train/c0’: File exists\n",
"mkdir: cannot create directory ‘/home/ubuntu/statefarm/weird_ones/train/c1’: File exists\n",
"mkdir: cannot create directory ‘/home/ubuntu/statefarm/weird_ones/train/c2’: File exists\n",
"mkdir: cannot create directory ‘/home/ubuntu/statefarm/weird_ones/train/c3’: File exists\n",
"mkdir: cannot create directory ‘/home/ubuntu/statefarm/weird_ones/train/c4’: File exists\n",
"mkdir: cannot create directory ‘/home/ubuntu/statefarm/weird_ones/train/c5’: File exists\n",
"mkdir: cannot create directory ‘/home/ubuntu/statefarm/weird_ones/train/c6’: File exists\n",
"mkdir: cannot create directory ‘/home/ubuntu/statefarm/weird_ones/train/c7’: File exists\n",
"mkdir: cannot create directory ‘/home/ubuntu/statefarm/weird_ones/train/c8’: File exists\n",
"mkdir: cannot create directory ‘/home/ubuntu/statefarm/weird_ones/train/c9’: File exists\n"
]
}
],
"source": [
"%%bash\n",
"mkdir weird_ones\n",
"mkdir weird_ones/train\n",
"for i in {0..9}; do mkdir /home/ubuntu/statefarm/weird_ones/train/c${i}; done\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/ubuntu/statefarm/train\n"
]
}
],
"source": [
"%cd /home/ubuntu/statefarm/train\n",
"for l in glob.glob('*/*jpg'):\n",
" if l in to_remove_files:\n",
" os.popen('mv %s ../weird_ones/train/%s'%(l, l))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"17008\n"
]
}
],
"source": [
"%%bash\n",
"find . -type f | wc -l"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK we removed the weird ones."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"path = \"/home/ubuntu/statefarm/\"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 17007 images belonging to 10 classes.\n",
"Found 3837 images belonging to 10 classes.\n"
]
}
],
"source": [
"batch_size = 64\n",
"\n",
"gen_t = image.ImageDataGenerator(rotation_range=15, height_shift_range=0.05, \n",
" shear_range=0.1, channel_shift_range=20, width_shift_range=0.1)\n",
"\n",
"trn_batches = get_batches(path+'train', gen_t, batch_size=batch_size)\n",
"val_batches = get_batches(path+'valid', batch_size=batch_size*2, shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from vgg16bn import Vgg16BN\n",
"model = vgg_ft_bn(10)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model.compile(optimizer=Adam(1e-3),\n",
" loss='categorical_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/3\n",
"17007/17007 [==============================] - 504s - loss: 2.8008 - acc: 0.4544 - val_loss: 3.0788 - val_acc: 0.4042\n",
"Epoch 2/3\n",
"17007/17007 [==============================] - 514s - loss: 1.8587 - acc: 0.5992 - val_loss: 3.4269 - val_acc: 0.3774\n",
"Epoch 3/3\n",
"17007/17007 [==============================] - 518s - loss: 1.6381 - acc: 0.6416 - val_loss: 3.3901 - val_acc: 0.3977\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7fbf9f73a850>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit_generator(trn_batches, trn_batches.N, nb_epoch=3, validation_data=val_batches, \n",
" nb_val_samples=val_batches.N)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/3\n",
"17007/17007 [==============================] - 519s - loss: 1.5947 - acc: 0.6538 - val_loss: 3.3720 - val_acc: 0.4522\n",
"Epoch 2/3\n",
"17007/17007 [==============================] - 518s - loss: 1.5708 - acc: 0.6681 - val_loss: 3.4160 - val_acc: 0.4339\n",
"Epoch 3/3\n",
"17007/17007 [==============================] - 515s - loss: 1.6429 - acc: 0.6689 - val_loss: 4.0929 - val_acc: 0.3912\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7fbf9e3ebfd0>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.optimizer.lr = 1e-5\n",
"model.fit_generator(trn_batches, trn_batches.N, nb_epoch=3, validation_data=val_batches, \n",
" nb_val_samples=val_batches.N)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"last_conv_idx = [i for i,l in enumerate(model.layers) if type(l) is Convolution2D][-1]\n",
"conv_layers = model.layers[:last_conv_idx+1]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"conv_model = Sequential(conv_layers)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 17007 images belonging to 10 classes.\n"
]
}
],
"source": [
"trn_batches = get_batches(path+'train', gen_t, batch_size=batch_size, shuffle=False)\n",
"\n",
"conv_feat = conv_model.predict_generator(trn_batches, trn_batches.nb_sample)\n",
"conv_val_feat = conv_model.predict_generator(val_batches, val_batches.nb_sample)\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"#print(conv_layers[-1].output_shape)\n",
"def get_bn_layers(p):\n",
" return [\n",
" MaxPooling2D(input_shape=conv_layers[-1].output_shape[1:]),\n",
" Flatten(),\n",
" Dropout(p),\n",
" Dense(512, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(p),\n",
" Dense(512, activation='relu'),\n",
" BatchNormalization(),\n",
" Dropout(p),\n",
" Dense(10, activation='softmax')\n",
" ]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p = 0.8"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 17007 images belonging to 10 classes.\n",
"Found 3837 images belonging to 10 classes.\n",
"Found 79726 images belonging to 1 classes.\n"
]
}
],
"source": [
"(val_classes, trn_classes, val_labels, trn_labels, \n",
" val_filenames, filenames, test_filenames) = get_classes(path)\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bn_model = Sequential(get_bn_layers(p))\n",
"bn_model.compile(Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 17007 samples, validate on 3837 samples\n",
"Epoch 1/1\n",
"17007/17007 [==============================] - 10s - loss: 3.1612 - acc: 0.2461 - val_loss: 1.4841 - val_acc: 0.4636\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7fbf9193c910>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bn_model.fit(conv_feat, trn_labels, batch_size=batch_size, nb_epoch=1, \n",
" validation_data=(conv_val_feat, val_labels))\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 17007 samples, validate on 3837 samples\n",
"Epoch 1/7\n",
"17007/17007 [==============================] - 10s - loss: 1.3785 - acc: 0.5421 - val_loss: 1.3588 - val_acc: 0.5351\n",
"Epoch 2/7\n",
"17007/17007 [==============================] - 10s - loss: 0.9063 - acc: 0.6846 - val_loss: 1.2480 - val_acc: 0.5994\n",
"Epoch 3/7\n",
"17007/17007 [==============================] - 10s - loss: 0.6948 - acc: 0.7599 - val_loss: 1.2294 - val_acc: 0.6237\n",
"Epoch 4/7\n",
"17007/17007 [==============================] - 10s - loss: 0.5743 - acc: 0.8016 - val_loss: 1.2193 - val_acc: 0.6286\n",
"Epoch 5/7\n",
"17007/17007 [==============================] - 10s - loss: 0.5032 - acc: 0.8301 - val_loss: 1.2701 - val_acc: 0.6268\n",
"Epoch 6/7\n",
"17007/17007 [==============================] - 10s - loss: 0.4660 - acc: 0.8380 - val_loss: 1.2784 - val_acc: 0.6213\n",
"Epoch 7/7\n",
"17007/17007 [==============================] - 10s - loss: 0.4307 - acc: 0.8546 - val_loss: 1.3651 - val_acc: 0.6028\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7fbf9193d410>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bn_model.optimizer.lr = 1e-5\n",
"bn_model.fit(conv_feat, trn_labels, batch_size=batch_size, nb_epoch=7, \n",
" validation_data=(conv_val_feat, val_labels))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 17007 samples, validate on 3837 samples\n",
"Epoch 1/7\n",
"17007/17007 [==============================] - 10s - loss: 0.4004 - acc: 0.8684 - val_loss: 1.5176 - val_acc: 0.5968\n",
"Epoch 2/7\n",
"17007/17007 [==============================] - 10s - loss: 0.3883 - acc: 0.8726 - val_loss: 1.4288 - val_acc: 0.6216\n",
"Epoch 3/7\n",
"17007/17007 [==============================] - 10s - loss: 0.3537 - acc: 0.8803 - val_loss: 1.3639 - val_acc: 0.6213\n",
"Epoch 4/7\n",
"17007/17007 [==============================] - 10s - loss: 0.3494 - acc: 0.8837 - val_loss: 1.3312 - val_acc: 0.6375\n",
"Epoch 5/7\n",
"17007/17007 [==============================] - 10s - loss: 0.3299 - acc: 0.8908 - val_loss: 1.3274 - val_acc: 0.6343\n",
"Epoch 6/7\n",
"17007/17007 [==============================] - 10s - loss: 0.3047 - acc: 0.9002 - val_loss: 1.2876 - val_acc: 0.6534\n",
"Epoch 7/7\n",
"17007/17007 [==============================] - 10s - loss: 0.3059 - acc: 0.8999 - val_loss: 1.2306 - val_acc: 0.6664\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7fbf9193cf10>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bn_model.optimizer.lr = 1e-7\n",
"bn_model.fit(conv_feat, trn_labels, batch_size=batch_size, nb_epoch=7, \n",
" validation_data=(conv_val_feat, val_labels))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 79726 images belonging to 1 classes.\n"
]
}
],
"source": [
"test_batches = get_batches(path+'test', batch_size=batch_size, shuffle=False, class_mode=None)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"conv_test_feat = conv_model.predict_generator(test_batches, test_batches.nb_sample)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That took *forever* (one hour? didn't time it perfectly)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"preds = bn_model.predict(conv_test_feat, batch_size=batch_size*2)\n",
"subm = do_clip(preds,0.93)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"subm_name = path+'results/subm_woweird.gz'"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"classes = sorted(trn_batches.class_indices, key=trn_batches.class_indices.get)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"submission = pd.DataFrame(subm, columns=classes)\n",
"submission.insert(0, 'img', [a[4:] for a in test_filenames])\n",
"submission.head()\n",
"submission.to_csv(subm_name, index=False, compression='gzip')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<a href='/home/ubuntu/statefarm/results/subm_woweird.gz' target='_blank'>/home/ubuntu/statefarm/results/subm_woweird.gz</a><br>"
],
"text/plain": [
"/home/ubuntu/statefarm/results/subm_woweird.gz"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import FileLink\n",
"FileLink(subm_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Private score: 0.92506\n",
"# Public score:\t1.11814"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# RESULTS\n",
"Interestingly, the validation accuracy and validation loss is VERY similar, almost identical to the above. The training accuracy is slightly better.\n",
"\n",
"# TODO: Fix the validation problems too"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TRYING OUT CUTTING FROM PICTURES"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'numpy.ndarray' object has no attribute 'save'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-45-e509eadee8d4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m#plt.imshow(img)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;31m#cv2.imshow('hi', img)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'save'"
]
}
],
"source": [
"hog = cv2.HOGDescriptor()\n",
"hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())\n",
"img = glob.glob('train/*/*jpg')[100]\n",
"\n",
"img = cv2.imread(img)\n",
"\n",
"(rects, weights) = hog.detectMultiScale(img, winStride=(4, 4), padding=(8, 8), scale=1.05)\n",
"for (x, y, w, h) in rects:\n",
" cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)\n",
"rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])\n",
"pick = non_max_suppression(rects, probs=None, overlapThresh=0.5)\n",
"for (xA, yA, xB, yB) in pick:\n",
" cv2.rectangle(img, (xA, yA), (xB, yB), (0, 255, 0), 2)\n",
"#plt.imshow(img)\n",
"#cv2.imshow('hi', img)\n",
"img.save('test.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 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 |
ViBOT-Erasmus/B31XI-SI-Ensemble-Classifiers | 06-ensemble-classifiers.ipynb | 2 | 2325537 | null | gpl-2.0 |
midnighteuler/projecteuler | src/Prob9.ipynb | 1 | 1937 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<pre>\n",
"A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,\n",
"\n",
"a^2 + b^2 = c^2\n",
"For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.\n",
"\n",
"There exists exactly one Pythagorean triplet for which a + b + c = 1000.\n",
"Find the product abc.\n",
"</pre>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Have $a + b + c = 1000$ and $a^2 + b^2 = c^2$\n",
"\n",
"We can reduce this with a little algebra:\n",
"\n",
"$$a^2 = c^2 - b^2 = (c + b)(c - b)$$\n",
"\n",
"\n",
"So\n",
"$$a^2 = (1000 - a)(1000 - a - 2b)$$\n",
"\n",
"\n",
"Simplifying:\n",
"$$\n",
"a + b - \\frac{ab}{1000} = 500\n",
"\\\\ b = \\frac{1000 (500 - a)}{1000 - a}\n",
"$$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in range(2, 500):\n",
" if 1000*(500 - i) % (1000 - i) == 0:\n",
" a = i\n",
" b = (1000*(500 - i))/(1000 - i)\n",
" c = 1000 - a - b\n",
" break\n",
"print \"a:\",a, \"b:\",b, \"c:\",c\n",
"print a**2 + b**2, c**2\n",
"print a*b*c"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"a: 200 b: 375 c: 425\n",
"180625 180625\n",
"31875000\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
QuantifyingUncertainty/GMHPhotoReceptor.jl | notebooks/PhotoReceptor1_4params.ipynb | 1 | 17415 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is an example Notebook for the PhotoReceptor model. Please run each cell in sequence. \n",
"\n",
"OPERATION:\n",
"- Run a cell by pressing the **Play** button (the black triangle) in the toolbar above.\n",
"- Note that the execution of a cell may take a while, and will be confirmed by a printout.\n",
"- To remove all printed output and figures, select **Cell/All Output/Clear** at the top.\n",
"\n",
"TROUBLESHOOTING:\n",
"- If the output of a cell contains a warning (the box turns pink), re-run to see if it disappears. \n",
"- If the warning does not go away when re-running, try to proceed with the next cells. \n",
"- If further commands go wrong, select **Kernel/Restart** at the top.\n",
"- You can also re-start by selecting **File/Close and Halt**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"###Load the PyPlot package\n",
"import PyPlot\n",
"println(\"PyPlot package loaded successfully\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following cell, you can specify the number of parallel processes to run the MCMC with. The way to do this differs when running the notebook on a single computer vs. when running this notebook on a cluster of different computers (for more information on clusters see [Preparing an AWS Cluster](http://quantifyinguncertainty.github.io/#6-preparing-an-aws-cluster)).\n",
"\n",
"1. To run the MCMC not in parallel (in a single Julia process), set RUNPARALLEL=false.\n",
"\n",
"2. To run the MCMC in parallel on a single machine, set RUNPARALLEL=true and RUNONCLUSTER=false. You can set how many additional processes to run with by setting the NPROCS variable. It is recommended not to make NPROCS larger than the total number of CPU cores on your machine (defined by Julia global variable Sys.CPU_CORES).\n",
"\n",
"3. When running this notebook on a cluster, set RUNPARALLEL=true and RUNONCLUSTER=true. Set the xxx.xxx.xxx.xxx values to the private IP addresses of the slave machines you have started (add as many **slaveip** entries to **machvec** as required)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"RUNPARALLEL = true\n",
"RUNONCLUSTER = false\n",
"\n",
"if RUNPARALLEL\n",
" println(\"Starting additional Julia processes\")\n",
" NPROCS = min(16,Sys.CPU_CORES) #do not make larger than CPU_CORES\n",
" if nprocs() < NPROCS\n",
" addprocs(NPROCS-nprocs(),topology=:master_slave)\n",
" end\n",
" println(\"Number of Julia processes: \",nprocs())\n",
"\n",
" if RUNONCLUSTER \n",
" println(\"Starting additional Julia processes on the cluster\")\n",
" slaveip1 = \"[email protected]\"\n",
" slaveip2 = \"[email protected]\"\n",
" machvec = [(slaveip1,:auto),(slaveip2,:auto)]\n",
" addprocs(machvec,topology=:master_slave)\n",
" println(\"Total number of Julia processes in cluster: \",nprocs())\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"###Import first on the master process before importing on all slaves to avoid parallel race conditions\n",
"import GeneralizedMetropolisHastings\n",
"import GMHPhotoReceptor\n",
"\n",
"###The following statement makes the GeneralizedMetropolisHastings core code available on all processes\n",
"@everywhere using GeneralizedMetropolisHastings\n",
"@everywhere using GMHPhotoReceptor\n",
"\n",
"println(\"GMH modules loaded successfully\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to change how many iterations are run, you can do so in the following box by changing\n",
"- nproposals: number of proposals evaluated in parallel in the Generalized Metropolis Hastings algorithm\n",
"- niterations: number of total iterations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#a single core performs between 50-60 model evaluations per second\n",
"#this can help you to estimate the overall time per iteration\n",
"#for instance, 100 proposals per process would make each iteration take about 2 seconds\n",
"nproposalsperprocess = 200 \n",
"nproposals = nproposalsperprocess*nworkers() #the more cores available, the more proposals we can execute in parallel\n",
"\n",
"#MCMC iteration specifications\n",
"nburnin = 200\n",
"niterations = 10\n",
"ntunerperiod = 10\n",
"\n",
"###Settings of the model\n",
"numvilli1 = 30000\n",
"\n",
"#specify the values that determine the priors on the parameters\n",
"latencylocation = (2.0,3.5) #uniform distribution with (low,high) values\n",
"latencyscale = (0.2,0.7) #uniform distribution with (low,high) values\n",
"refractorylocation = (4.0,6.0) #uniform distribution with (low,high) values\n",
"refractoryscale = (1.5,2.5) #uniform distribution with (low,high) values\n",
"bumpamplitude = (3.0,5.0) #uniform distribution with (low,high) values\n",
"bumpshape = (log(3.0),0.3) #lognormal distribution with (location,scale) values; variable can vary roughly between 2.0 and 4.0, but becomes increasingly penalized outside\n",
"bumpscale = (log(2.5),0.3) #lognormal distribution with (location,scale) values; variable can vary roughly between 1.5 and 3.5, but becomes increasingly penalized outside\n",
"\n",
"photonfilename = \"../data/naturallight.jld\"\n",
"photons1 = photonsequence(photonfilename)\n",
"current1 = lightinducedcurrent(photonfilename)\n",
"\n",
"modelpolicy1 = policy(:photoreceptor) #4-parameter model with stochastic lognormal latency and refractory parameters and fixed bump parameters\n",
"params1 = parameters(:photoreceptor,modelpolicy1,latencylocation,latencyscale,refractorylocation,refractoryscale)\n",
"\n",
"####Variance for a normal, additive noise model, estimated from previous runs\n",
"variance1 = [3600.0]\n",
"\n",
"println(\"==========================================\")\n",
"println(\"Simulation parameters defined successfully\")\n",
"println(\"==========================================\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"###Create a PhotoReceptor model\n",
"model1 = model(:photoreceptor,params1,photons1,current1,variance1,numvilli1,modelpolicy1)\n",
"\n",
"###Show the model\n",
"println(\"==========================\")\n",
"println(\"Model defined successfully\")\n",
"println(\"==========================\")\n",
"show(model1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"###Plot the measurement data\n",
"PyPlot.figure(\"PhotoReceptor1\") ; PyPlot.clf()\n",
"PyPlot.subplot(211)\n",
"PyPlot.plot(dataindex(model1),measurements(model1);label=\"measured\",linewidth=2,color=\"yellow\")\n",
"PyPlot.xlabel(\"Time (s)\")\n",
"PyPlot.ylabel(\"Current (nA)\")\n",
"PyPlot.xlim(dataindex(model1)[1],dataindex(model1)[end])\n",
"PyPlot.title(\"Light-Induced Current\")\n",
"PyPlot.grid(\"on\")\n",
"PyPlot.legend(loc=\"upper right\",fancybox=\"true\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"###Evaluate the model for a set of random parameter values and plot the fit\n",
"###You can execute this cell as many times as you like\n",
"###Parameter values are drawn from the prior distributions on the parameters\n",
"numevaluations = 100\n",
"paramvals = GeneralizedMetropolisHastings.initvalues!(trait(:initialize,:prior),params1,zeros(Float64,length(params1)))\n",
"println(\"Evaluating the model $numevaluations times\")\n",
"evaldata = evaluate!(model1,paramvals,numevaluations)\n",
"logposteriorvals = zeros(numevaluations)\n",
"for i=1:numevaluations\n",
" logposteriorvals[i] = loglikelihood(model1,evaldata[:,i])+logprior(params1,paramvals,Float64)\n",
"end\n",
"meanevaldata = mean(evaldata,2)\n",
"stdevaldata = std(evaldata,2)\n",
"\n",
"PyPlot.figure(\"PhotoReceptor Model Evaluations\")\n",
"PyPlot.subplot(211)\n",
"PyPlot.plot(dataindex(model1),evaldata)\n",
"PyPlot.plot(dataindex(model1),meanevaldata;label=\"mean\",linewidth=1,color=\"black\")\n",
"PyPlot.xlim(dataindex(model1)[1],dataindex(model1)[end])\n",
"PyPlot.legend(loc=\"upper right\",fancybox=\"true\")\n",
"PyPlot.ylabel(\"Current (nA)\")\n",
"PyPlot.title(\"Variability in Light-Induced Current for 100 Model Evaluations\")\n",
"PyPlot.subplot(212)\n",
"PyPlot.plot(dataindex(model1),stdevaldata)\n",
"PyPlot.plot(dataindex(model1),mean(stdevaldata,2),linewidth=1)\n",
"PyPlot.xlim(dataindex(model1)[1],dataindex(model1)[end])\n",
"PyPlot.xlabel(\"Time (s)\")\n",
"PyPlot.ylabel(\"STD of Current (nA)\")\n",
"PyPlot.grid(\"on\")\n",
"\n",
"PyPlot.figure(\"PhotoReceptor Measured vs Model Fit\")\n",
"PyPlot.subplot(211)\n",
"PyPlot.plot(dataindex(model1),measurements(model1);label=\"measured\",linewidth=2,color=\"yellow\")\n",
"PyPlot.plot(dataindex(model1),meanevaldata;label=\"mean\",linewidth=1,color=\"black\")\n",
"PyPlot.xlim(dataindex(model1)[1],dataindex(model1)[end])\n",
"PyPlot.xlabel(\"Time (s)\")\n",
"PyPlot.ylabel(\"Current (nA)\")\n",
"PyPlot.legend(loc=\"upper right\",fancybox=\"true\")\n",
"PyPlot.title(\"Comparison of Mean vs Measured Light-Induced Current\")\n",
"\n",
"println(\"Evaluation paramater values: \")\n",
"display(paramvals)\n",
"println(\"Mean Log-Posterior for these parameters: \",mean(logposteriorvals))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"###Create a sampler, either a Metropolis sampler with normal proposal density\n",
"###or an Adaptive Metropolis sampler with normal proposal density\n",
"#sampler1 = sampler(:mh,:normal,0.01,4)\n",
"sampler1 = sampler(:adaptive,0.01,4)\n",
"println(\"============================\")\n",
"println(\"Sampler defined successfully\")\n",
"println(\"============================\")\n",
"show(sampler1)\n",
"\n",
"###Create a tuner that scales the proposal density\n",
"#tuner1 = tuner(:scale,ntunerperiod,0.1,:erf) #use for Metropolis sampler\n",
"tuner1 = tuner(:monitor,ntunerperiod) #use for Adaptive Metropolis sampler\n",
"println(\"==========================\")\n",
"println(\"Tuner defined successfully\")\n",
"println(\"==========================\")\n",
"show(tuner1)\n",
"\n",
"###Create a Generalized Metropolis-Hastings runner (which will default to Standard MH when nproposals=1)\n",
"runnerpolicy1 = policy(:mh,nproposals;model=:stochastic,initialize=:prior,store=:all)\n",
"runner1 = runner(runnerpolicy1,niterations,nproposals;numburnin = nburnin)\n",
"println(\"===========================\")\n",
"println(\"Runner defined successfully\")\n",
"println(\"===========================\")\n",
"show(runner1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"###Run the MCMC (can take quite a bit of time)\n",
"println(\"=======================\")\n",
"println(\"Run the MCMC simulation\")\n",
"println(\"=======================\")\n",
"@time chain1 = run!(runner1,model1,sampler1,tuner1)\n",
"println(\"=========================\")\n",
"println(\"Completed MCMC simulation\")\n",
"println(\"=========================\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"###Show the result of the simulations\n",
"show(chain1)\n",
"\n",
"nparas = numparas(model1)\n",
"meanparamvals = mean(samples(chain1),2)\n",
"stdparamvals = std(samples(chain1),2)\n",
"\n",
"println(\"Results of the MCMC simulation:\")\n",
"for i=1:nparas\n",
" println(\"mean $(parameters(model1)[i].key): $(meanparamvals[i])\")\n",
" println(\"std $(parameters(model1)[i].key): $(stdparamvals[i])\")\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"println(\"================\")\n",
"println(\"Plotting results\")\n",
"println(\"================\")\n",
"\n",
"###Plot the average model results in the data window\n",
"PyPlot.figure()\n",
"modeldata = evaluate!(model1,vec(meanparamvals))\n",
"PyPlot.subplot(211)\n",
"PyPlot.plot(dataindex(model1),measurements(model1);label=\"measured\",linewidth=2,color=\"yellow\")\n",
"PyPlot.plot(dataindex(model1),modeldata;label=\"model\",linewidth=1,color=\"black\")\n",
"PyPlot.xlim(dataindex(model1)[1],dataindex(model1)[end])\n",
"PyPlot.xlabel(\"Time (s)\"); PyPlot.ylabel(\"Current (nA)\")\n",
"PyPlot.legend(loc=\"upper right\",fancybox=\"true\")\n",
"PyPlot.title(\"Model vs Measured Data + Log-Posterior over Samples\")\n",
"\n",
"###Plot the logposterior values across samples\n",
"PyPlot.subplot(212)\n",
"PyPlot.plot(1:numsamples(chain1),logposterior(chain1,model1))\n",
"PyPlot.xlabel(\"Samples\")\n",
"PyPlot.ylabel(\"Log-Posterior\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"PyPlot.figure()\n",
"PyPlot.subplot(211)\n",
"PyPlot.plot(dataindex(model1),measurements(model1);label=\"measured\",linewidth=2,color=\"yellow\")\n",
"PyPlot.plot(dataindex(model1),meanevaldata;label=\"model\",linewidth=1,color=\"black\")\n",
"PyPlot.xlim(dataindex(model1)[1],dataindex(model1)[end])\n",
"PyPlot.ylabel(\"Current (nA)\")\n",
"PyPlot.legend(loc=\"upper right\",fancybox=\"true\")\n",
"PyPlot.title(\"Comparison of model and measured data - random from prior\")\n",
"PyPlot.subplot(212)\n",
"PyPlot.plot(dataindex(model1),measurements(model1);label=\"measured\",linewidth=2,color=\"yellow\")\n",
"PyPlot.plot(dataindex(model1),modeldata;label=\"model\",linewidth=1,color=\"black\")\n",
"PyPlot.xlim(dataindex(model1)[1],dataindex(model1)[end])\n",
"PyPlot.xlabel(\"Time (s)\")\n",
"PyPlot.ylabel(\"Current (nA)\")\n",
"PyPlot.legend(loc=\"upper right\",fancybox=\"true\")\n",
"PyPlot.title(\"Comparison of model and measured data - mean of posterior\")\n",
"PyPlot.figure()\n",
"PyPlot.subplot(211)\n",
"PyPlot.plot(dataindex(model1),measurements(model1)-modeldata;label=\"measured-model\",linewidth=1,color=\"black\")\n",
"PyPlot.xlim(dataindex(model1)[1],dataindex(model1)[end])\n",
"PyPlot.xlabel(\"Time (s)\")\n",
"PyPlot.ylabel(\"Current (nA)\")\n",
"PyPlot.legend(loc=\"upper right\",fancybox=\"true\")\n",
"PyPlot.title(\"Difference of measured and model data\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i=1:nparas\n",
" PyPlot.subplot(220 + i)\n",
" leftlim = meanparamvals[i]-3*stdparamvals[i]\n",
" rightlim = meanparamvals[i]+3*stdparamvals[i]\n",
" binsize = stdparamvals[i]/4\n",
" bins = leftlim:binsize:rightlim\n",
" h = PyPlot.plt[:hist](vec(getindex(samples(chain1),i,:)),bins)\n",
" PyPlot.grid(\"on\")\n",
" PyPlot.title(\"$(parameters(model1)[i].key)\")\n",
" PyPlot.ylabel(\"Samples\")\n",
" PyPlot.xlim([leftlim,rightlim])\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"###Only run this box if you want to shut down all worker processes\n",
"println(\"Pre processes running: \",procs())\n",
"if nprocs() > 1\n",
" rmprocs(workers())\n",
" sleep(1.0)\n",
"end\n",
"println(\"Post processes running: \",procs())"
]
},
{
"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": 0
}
| mit |
mitliagkas/graphs | wdc/Scratch.ipynb | 1 | 28372 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Eigendecomposition for subset of PLD"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Get the subset of the graph"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this segment we load the dataset and keep only a tiny subset"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Load first partition of PLD"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#data = sc.textFile('/var/datasets/wdc/pld-arc')\n",
"data = sc.textFile('/var/datasets/wdc/x0')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Keep only nodes with ID less than 1e4"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"small = data.map(\n",
" lambda line: [int(x) for x in line.split('\\t')]\n",
" ).filter(\n",
" lambda (source, destination): source<1e4 and destination<1e4\n",
" )"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Persist result"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"small.cache()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"PythonRDD[2] at RDD at PythonRDD.scala:37"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"small.count()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"2419"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"small.take(10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"[[70, 105],\n",
" [86, 2106],\n",
" [107, 103],\n",
" [115, 903],\n",
" [115, 6975],\n",
" [201, 2211],\n",
" [201, 4185],\n",
" [201, 4271],\n",
" [201, 4490],\n",
" [201, 4491]]"
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Do the eigenvector calculation"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Initialize First Iteration"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = 1000"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pyspark.accumulators import AccumulatorParam\n",
"class VectorAccumulatorParam(AccumulatorParam):\n",
" def zero(self, value):\n",
" return [0.0] * len(value)\n",
" def addInPlace(self, val1, val2):\n",
" for i in xrange(len(val1)):\n",
" val1[i] += val2[i]\n",
" return val1\n",
"xnew = sc.accumulator([0.0]*p, VectorAccumulatorParam())"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = sc.broadcast([1.0]*p)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x1 = small.map(lambda (source, dest): (dest,1)).reduceByKey(add)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x1.cache()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"PythonRDD[9] at RDD at PythonRDD.scala:37"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x1.count()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"746"
]
}
],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Approximate iteration"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"approxjoin = x1.filter(lambda (k,v): v>2).join(small)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x2 = approxjoin.map(lambda (k,v): (v[1], v[0])).reduceByKey(add)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x2.take(5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 50,
"text": [
"[(7424, 43), (8640, 8), (6368, 870), (7448, 37), (1304, 25)]"
]
}
],
"prompt_number": 50
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Exact iteration"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x2 = x1.join(small).map(lambda (k,v): (v[1], v[0])).reduceByKey(add)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x2.cache()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 52,
"text": [
"PythonRDD[124] at RDD at PythonRDD.scala:37"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x2.count()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"498"
]
}
],
"prompt_number": 56
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Third iteration"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x3 = x2.join(small).map(lambda (k,v): (v[1], v[0])).reduceByKey(add)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x3.cache()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 58,
"text": [
"PythonRDD[138] at RDD at PythonRDD.scala:37"
]
}
],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x3.count()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 59,
"text": [
"447"
]
}
],
"prompt_number": 59
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x3.filter(lambda (k,v): v>0).take(4)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 61,
"text": [
"[(6696, 2117), (6352, 25289), (6368, 25289), (6880, 2064)]"
]
}
],
"prompt_number": 61
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Fourth Iteration"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x4 = x3.join(small).map(lambda (k,v): (v[1], v[0])).reduceByKey(add)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x4.cache()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 63,
"text": [
"PythonRDD[153] at RDD at PythonRDD.scala:37"
]
}
],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x4.count()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 64,
"text": [
"445"
]
}
],
"prompt_number": 64
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Show top domains"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x4.filter(lambda (k,v): v>10000).take(4)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 67,
"text": [
"[(6696, 25411), (6352, 734252), (6368, 734252), (6880, 24852)]"
]
}
],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"names = sc.textFile('/var/datasets/wdc/pld-index')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def indexline(line):\n",
" parts = line.split('\\t')\n",
" return (int(parts[1]), parts[0])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"index = names.map(indexline)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"index.cache()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 71,
"text": [
"PythonRDD[160] at RDD at PythonRDD.scala:37"
]
}
],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"index.take(3)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 73,
"text": [
"[(0, u'0-------------------------------------------------------------0.com'),\n",
" (1, u'0-------------------------------------------------------------0.dk'),\n",
" (2, u'0-----0.org')]"
]
}
],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"topnodes = index.join(x4.filter(lambda (k,v): v>7e5))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"ERROR:py4j.java_gateway:An error occurred while trying to connect to the Java server\n",
"Traceback (most recent call last):\n",
" File \"/home/migish/workspace/spark/python/lib/py4j-0.8.1-src.zip/py4j/java_gateway.py\", line 424, in start\n",
" self.socket.connect((self.address, self.port))\n",
" File \"/usr/lib/python2.7/socket.py\", line 224, in meth\n",
" return getattr(self._sock,name)(*args)\n",
"error: [Errno 111] Connection refused\n"
]
},
{
"ename": "Py4JNetworkError",
"evalue": "An error occurred while trying to connect to the Java server",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mPy4JNetworkError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-87-a8a9e4be9c66>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtopnodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m7e5\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/home/migish/workspace/spark/python/pyspark/rdd.pyc\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, other, numPartitions)\u001b[0m\n\u001b[1;32m 1038\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1039\u001b[0m \"\"\"\n\u001b[0;32m-> 1040\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mpython_join\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumPartitions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1042\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mleftOuterJoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumPartitions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/migish/workspace/spark/python/pyspark/join.pyc\u001b[0m in \u001b[0;36mpython_join\u001b[0;34m(rdd, other, numPartitions)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0mwbuf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvbuf\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwbuf\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_do_python_join\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrdd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumPartitions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdispatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/migish/workspace/spark/python/pyspark/join.pyc\u001b[0m in \u001b[0;36m_do_python_join\u001b[0;34m(rdd, other, numPartitions, dispatch)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mvs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0mws\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mvs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupByKey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumPartitions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatMapValues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mdispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/migish/workspace/spark/python/pyspark/rdd.pyc\u001b[0m in \u001b[0;36munion\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 429\u001b[0m \"\"\"\n\u001b[1;32m 430\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jrdd_deserializer\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jrdd_deserializer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m rdd = RDD(self._jrdd.union(other._jrdd), self.ctx,\n\u001b[0m\u001b[1;32m 432\u001b[0m self._jrdd_deserializer)\n\u001b[1;32m 433\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mrdd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/migish/workspace/spark/python/pyspark/rdd.pyc\u001b[0m in \u001b[0;36m_jrdd\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1441\u001b[0m broadcast_vars = ListConverter().convert(\n\u001b[1;32m 1442\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jbroadcast\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pickled_broadcast_vars\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1443\u001b[0;31m self.ctx._gateway._gateway_client)\n\u001b[0m\u001b[1;32m 1444\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pickled_broadcast_vars\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1445\u001b[0m \u001b[0mclass_tag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prev_jrdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassTag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/migish/workspace/spark/python/lib/py4j-0.8.1-src.zip/py4j/java_collections.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(self, object, gateway_client)\u001b[0m\n\u001b[1;32m 475\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgateway_client\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 476\u001b[0m \u001b[0mArrayList\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJavaClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'java.util.ArrayList'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgateway_client\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m \u001b[0mjava_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mArrayList\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 478\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0melement\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[0mjava_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melement\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/migish/workspace/spark/python/lib/py4j-0.8.1-src.zip/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 665\u001b[0m \u001b[0margs_command\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 666\u001b[0m \u001b[0mEND_COMMAND_PART\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 667\u001b[0;31m \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gateway_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 668\u001b[0m return_value = get_return_value(answer, self._gateway_client, None,\n\u001b[1;32m 669\u001b[0m self._fqn)\n",
"\u001b[0;32m/home/migish/workspace/spark/python/lib/py4j-0.8.1-src.zip/py4j/java_gateway.py\u001b[0m in \u001b[0;36msend_command\u001b[0;34m(self, command, retry)\u001b[0m\n\u001b[1;32m 359\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mPy4J\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 360\u001b[0m \"\"\"\n\u001b[0;32m--> 361\u001b[0;31m \u001b[0mconnection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_connection\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 362\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/migish/workspace/spark/python/lib/py4j-0.8.1-src.zip/py4j/java_gateway.py\u001b[0m in \u001b[0;36m_get_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[0mconnection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeque\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 317\u001b[0;31m \u001b[0mconnection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_connection\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 318\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/migish/workspace/spark/python/lib/py4j-0.8.1-src.zip/py4j/java_gateway.py\u001b[0m in \u001b[0;36m_create_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 322\u001b[0m connection = GatewayConnection(self.address, self.port,\n\u001b[1;32m 323\u001b[0m self.auto_close, self.gateway_property)\n\u001b[0;32m--> 324\u001b[0;31m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\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 325\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/migish/workspace/spark/python/lib/py4j-0.8.1-src.zip/py4j/java_gateway.py\u001b[0m in \u001b[0;36mstart\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0;34m'server'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 430\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mPy4JNetworkError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 433\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\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[0;31mPy4JNetworkError\u001b[0m: An error occurred while trying to connect to the Java server"
]
}
],
"prompt_number": 87
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"TODO: Plot the masses on first 3 steps for top nodes"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
tensorflow/tpu | tools/colab/mnist_tpuestimator.ipynb | 1 | 336782 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"\u003ca href=\"https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab/mnist_tpuestimator.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "N6ZDpd9XzFeN"
},
"source": [
"##### Copyright 2018 The TensorFlow Hub Authors.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "KUu4vOt5zI9d"
},
"outputs": [],
"source": [
"# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License.\n",
"# =============================================================================="
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "xqLjB2cy5S7m"
},
"source": [
"## MNIST Estimator to TPUEstimator\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "39I6y3QJsb6t"
},
"source": [
"## Overview\n",
"\n",
"This notebook will show you how to port an Estimator model to TPUEstimator.\n",
"\n",
"All the lines that had to be changed in the porting are marked with a \"TPU REFACTORING\" comment.\n",
"\n",
"You do the porting only once. TPUEstimator then works on both TPU and GPU with the `use_tpu=False` flag.\n",
"\n",
"This notebook is hosted on GitHub. To view it in its original repository, after opening the notebook, select **File \u003e View on GitHub**."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "j_k5L8WB9KWa"
},
"source": [
"Note: This notebook starts with the code included in the [MNIST Estimator](https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab/mnist_estimator.ipynb) notebook. This conversion enables your model to take advantage of Cloud TPU to speed up training computations."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "avJXF3ofBAmg"
},
"source": [
"## Learning objectives\n",
"\n",
"In this notebook, you will learn how to port an Estimator model to TPUEstimator."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "SOMDaCK97FIq"
},
"source": [
"## Instructions"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "_I0RdnOSkNmi"
},
"source": [
"\u003ch3\u003e\u003ca href=\"https://cloud.google.com/tpu/\"\u003e\u003cimg valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/tpu-hexagon.png\" width=\"50\"\u003e\u003c/a\u003e \u0026nbsp;\u0026nbsp;Train on TPU\u003c/h3\u003e\n",
"\n",
" 1. Create a Cloud Storage bucket for your TensorBoard logs at http://console.cloud.google.com/storage and fill in the BUCKET parameter in the \"Parameters\" section below.\n",
" \n",
" 1. On the main menu, click Runtime and select **Change runtime type**. Set \"TPU\" as the hardware accelerator.\n",
" 1. Click Runtime again and select **Runtime \u003e Run All** (Watch out: the \"Colab-only auth for this notebook and the TPU\" cell requires user input). You can also run the cells manually with Shift-ENTER.\n",
"\n",
"\u003ch3\u003e\u003ca href=\"https://cloud.google.com/ml-engine/\"\u003e\u003cimg valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/mlengine-hexagon.png\" width=\"50\"\u003e\u003c/a\u003e \u0026nbsp;\u0026nbsp;Deploy to Cloud Machine Learning (ML) Engine\u003c/h3\u003e\n",
"1. At the bottom of this notebook you can deploy your trained model to ML Engine for a serverless, autoscaled, REST API experience. You will need a GCP project name for this last part.\n",
"\n",
"TPUs are located in Google Cloud, for optimal performance, they read data directly from Google Cloud Storage (GCS)."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Lvo0t7XVIkWZ"
},
"source": [
"## Data, model, and training"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "qpiJj8ym0v0-"
},
"source": [
"### Imports"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"colab_type": "code",
"id": "4Ru_7bw-xyoP",
"outputId": "03b2474c-c1c6-4431-a264-1c01440d5f1d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensorflow version 1.13.1\n"
]
}
],
"source": [
"import os, re, math, json, shutil, pprint, datetime\n",
"import PIL.Image, PIL.ImageFont, PIL.ImageDraw # \"pip3 install Pillow\" or \"pip install Pillow\" if needed\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from matplotlib import pyplot as plt\n",
"from tensorflow.python.platform import tf_logging\n",
"print(\"Tensorflow version \" + tf.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "aBDGQWkbLGvh"
},
"source": [
"### Parameters"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "_V_VbLELLJCS"
},
"outputs": [],
"source": [
"BATCH_SIZE = 32 #@param {type:\"integer\"}\n",
"BUCKET = 'gs://' #@param {type:\"string\"}\n",
"\n",
"assert re.search(r'gs://.+', BUCKET), 'You need a GCS bucket for your Tensorboard logs. Head to http://console.cloud.google.com/storage and create one.'\n",
"\n",
"training_images_file = 'gs://mnist-public/train-images-idx3-ubyte'\n",
"training_labels_file = 'gs://mnist-public/train-labels-idx1-ubyte'\n",
"validation_images_file = 'gs://mnist-public/t10k-images-idx3-ubyte'\n",
"validation_labels_file = 'gs://mnist-public/t10k-labels-idx1-ubyte'"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "qhdz68Xm3Z4Z"
},
"outputs": [],
"source": [
"#@title visualization utilities [RUN ME]\n",
"\"\"\"\n",
"This cell contains helper functions used for visualization\n",
"and downloads only. You can skip reading it. There is very\n",
"little useful Keras/Tensorflow code here.\n",
"\"\"\"\n",
"\n",
"# Matplotlib config\n",
"plt.rc('image', cmap='gray_r')\n",
"plt.rc('grid', linewidth=0)\n",
"plt.rc('xtick', top=False, bottom=False, labelsize='large')\n",
"plt.rc('ytick', left=False, right=False, labelsize='large')\n",
"plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n",
"plt.rc('text', color='a8151a')\n",
"plt.rc('figure', facecolor='F0F0F0')# Matplotlib fonts\n",
"MATPLOTLIB_FONT_DIR = os.path.join(os.path.dirname(plt.__file__), \"mpl-data/fonts/ttf\")\n",
"\n",
"# pull a batch from the datasets. This code is not very nice, it gets much better in eager mode (TODO)\n",
"def dataset_to_numpy_util(training_dataset, validation_dataset, N):\n",
" \n",
" # get one batch from each: 10000 validation digits, N training digits\n",
" unbatched_train_ds = training_dataset.unbatch()\n",
" v_images, v_labels = validation_dataset.make_one_shot_iterator().get_next()\n",
" t_images, t_labels = unbatched_train_ds.batch(N).make_one_shot_iterator().get_next()\n",
" \n",
" # Run once, get one batch. Session.run returns numpy results\n",
" with tf.Session() as ses:\n",
" (validation_digits, validation_labels,\n",
" training_digits, training_labels) = ses.run([v_images, v_labels, t_images, t_labels])\n",
" \n",
" # these were one-hot encoded in the dataset\n",
" validation_labels = np.argmax(validation_labels, axis=1)\n",
" training_labels = np.argmax(training_labels, axis=1)\n",
" \n",
" return (training_digits, training_labels,\n",
" validation_digits, validation_labels)\n",
"\n",
"# create digits from local fonts for testing\n",
"def create_digits_from_local_fonts(n):\n",
" font_labels = []\n",
" img = PIL.Image.new('LA', (28*n, 28), color = (0,255)) # format 'LA': black in channel 0, alpha in channel 1\n",
" font1 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'DejaVuSansMono-Oblique.ttf'), 25)\n",
" font2 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'STIXGeneral.ttf'), 25)\n",
" d = PIL.ImageDraw.Draw(img)\n",
" for i in range(n):\n",
" font_labels.append(i%10)\n",
" d.text((7+i*28,0 if i\u003c10 else -4), str(i%10), fill=(255,255), font=font1 if i\u003c10 else font2)\n",
" font_digits = np.array(img.getdata(), np.float32)[:,0] / 255.0 # black in channel 0, alpha in channel 1 (discarded)\n",
" font_digits = np.reshape(np.stack(np.split(np.reshape(font_digits, [28, 28*n]), n, axis=1), axis=0), [n, 28*28])\n",
" return font_digits, font_labels\n",
"\n",
"# utility to display a row of digits with their predictions\n",
"def display_digits(digits, predictions, labels, title, n):\n",
" plt.figure(figsize=(13,3))\n",
" digits = np.reshape(digits, [n, 28, 28])\n",
" digits = np.swapaxes(digits, 0, 1)\n",
" digits = np.reshape(digits, [28, 28*n])\n",
" plt.yticks([])\n",
" plt.xticks([28*x+14 for x in range(n)], predictions)\n",
" for i,t in enumerate(plt.gca().xaxis.get_ticklabels()):\n",
" if predictions[i] != labels[i]: t.set_color('red') # bad predictions in red\n",
" plt.imshow(digits)\n",
" plt.grid(None)\n",
" plt.title(title)\n",
" \n",
"# utility to display multiple rows of digits, sorted by unrecognized/recognized status\n",
"def display_top_unrecognized(digits, predictions, labels, n, lines):\n",
" idx = np.argsort(predictions==labels) # sort order: unrecognized first\n",
" for i in range(lines):\n",
" display_digits(digits[idx][i*n:(i+1)*n], predictions[idx][i*n:(i+1)*n], labels[idx][i*n:(i+1)*n],\n",
" \"{} sample validation digits out of {} with bad predictions in red and sorted first\".format(n*lines, len(digits)) if i==0 else \"\", n)\n",
" \n",
"# utility to display training and validation curves\n",
"def display_training_curves(training, validation, title, subplot):\n",
" if subplot%10==1: # set up the subplots on the first call\n",
" plt.subplots(figsize=(10,10), facecolor='#F0F0F0')\n",
" plt.tight_layout()\n",
" ax = plt.subplot(subplot)\n",
" ax.grid(linewidth=1, color='white')\n",
" ax.plot(training)\n",
" ax.plot(validation)\n",
" ax.set_title('model '+ title)\n",
" ax.set_ylabel(title)\n",
" ax.set_xlabel('epoch')\n",
" ax.legend(['train', 'valid.'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Lzd6Qi464PsA"
},
"source": [
"### Colab-only auth for this notebook and the TPU"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "both",
"colab": {},
"colab_type": "code",
"id": "MPx0nvyUnvgT"
},
"outputs": [],
"source": [
"IS_COLAB_BACKEND = 'COLAB_GPU' in os.environ # this is always set on Colab, the value is 0 or 1 depending on GPU presence\n",
"if IS_COLAB_BACKEND:\n",
" from google.colab import auth\n",
" auth.authenticate_user() # Authenticates the backend and also the TPU using your credentials so that they can access your private GCS buckets"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "bLZKp8bWZdmh"
},
"source": [
"### TPU detection"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"colab_type": "code",
"id": "qQpdD_DSZjNY",
"outputId": "5821e7d9-c7bc-4eb0-94ed-fe43c6d38e02"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on TPU ['10.65.19.2:8470']\n"
]
}
],
"source": [
"#TPU REFACTORING: detect the TPU\n",
"try: # TPU detection\n",
" tpu = tf.contrib.cluster_resolver.TPUClusterResolver() # Picks up a connected TPU on Google's Colab, ML Engine, Kubernetes and Deep Learning VMs accessed through the 'ctpu up' utility\n",
" #tpu = tf.contrib.cluster_resolver.TPUClusterResolver('MY_TPU_NAME') # If auto-detection does not work, you can pass the name of the TPU explicitly (tip: on a VM created with \"ctpu up\" the TPU has the same name as the VM)\n",
" print('Running on TPU ', tpu.cluster_spec().as_dict()['worker'])\n",
" USE_TPU = True\n",
"except ValueError:\n",
" tpu = None\n",
" print(\"Running on GPU or CPU\")\n",
" USE_TPU = False"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Lz1Zknfk4qCx"
},
"source": [
"### tf.data.Dataset: parse files and prepare training and validation datasets\n",
"Please read the [best practices for building](https://www.tensorflow.org/guide/performance/datasets) input pipelines with tf.data.Dataset."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "F7yKy0bRh6R0"
},
"outputs": [],
"source": [
"def read_label(tf_bytestring):\n",
" label = tf.decode_raw(tf_bytestring, tf.uint8)\n",
" label = tf.reshape(label, [])\n",
" label = tf.one_hot(label, 10)\n",
" return label\n",
" \n",
"def read_image(tf_bytestring):\n",
" image = tf.decode_raw(tf_bytestring, tf.uint8)\n",
" image = tf.cast(image, tf.float32)/255.0\n",
" image = tf.reshape(image, [28*28])\n",
" return image\n",
" \n",
"def load_dataset(image_file, label_file):\n",
" imagedataset = tf.data.FixedLengthRecordDataset(image_file, 28*28, header_bytes=16)\n",
" imagedataset = imagedataset.map(read_image, num_parallel_calls=16)\n",
" labelsdataset = tf.data.FixedLengthRecordDataset(label_file, 1, header_bytes=8)\n",
" labelsdataset = labelsdataset.map(read_label, num_parallel_calls=16)\n",
" dataset = tf.data.Dataset.zip((imagedataset, labelsdataset))\n",
" return dataset \n",
" \n",
"def get_training_dataset(image_file, label_file, batch_size):\n",
" dataset = load_dataset(image_file, label_file)\n",
" dataset = dataset.cache() # this small dataset can be entirely cached in RAM, for TPU this is important to get good performance from such a small dataset\n",
" dataset = dataset.shuffle(5000, reshuffle_each_iteration=True)\n",
" dataset = dataset.repeat() # Mandatory for TPU for now\n",
" dataset = dataset.batch(batch_size, drop_remainder=True) # drop_remainder is important on TPU, batch size must be fixed\n",
" dataset = dataset.prefetch(-1) # prefetch next batch while training (-1: autotune prefetch buffer size)\n",
" return dataset\n",
"\n",
"#TPU REFACTORING: training and eval batch sizes must be the same: passing batch_size parameter here too\n",
"# def get_validation_dataset(image_file, label_file):\n",
"def get_validation_dataset(image_file, label_file, batch_size):\n",
" dataset = load_dataset(image_file, label_file)\n",
" dataset = dataset.cache() # this small dataset can be entirely cached in RAM, for TPU this is important to get good performance from such a small dataset\n",
" #TPU REFACTORING: training and eval batch sizes must be the same: passing batch_size parameter here too\n",
" # dataset = dataset.batch(10000, drop_remainder=True) # 10000 items in eval dataset, all in one batch\n",
" dataset = dataset.batch(batch_size, drop_remainder=True)\n",
" dataset = dataset.repeat() # Mandatory for TPU for now\n",
" return dataset\n",
"\n",
"# instantiate the datasets\n",
"training_dataset = get_training_dataset(training_images_file, training_labels_file, BATCH_SIZE)\n",
"validation_dataset = get_validation_dataset(validation_images_file, validation_labels_file, 10000)\n",
"\n",
"# For TPU, we will need a function that returns the dataset\n",
"\n",
"# TPU REFACTORING: input_fn's must have a params argument though which TPUEstimator passes params['batch_size']\n",
"# training_input_fn = lambda: get_training_dataset(training_images_file, training_labels_file, BATCH_SIZE)\n",
"# validation_input_fn = lambda: get_validation_dataset(validation_images_file, validation_labels_file)\n",
"training_input_fn = lambda params: get_training_dataset(training_images_file, training_labels_file, params['batch_size'])\n",
"validation_input_fn = lambda params: get_validation_dataset(validation_images_file, validation_labels_file, params['batch_size'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "_fXo6GuvL3EB"
},
"source": [
"### Let's have a look at the data"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 181
},
"colab_type": "code",
"id": "9aYl2IYXiycl",
"outputId": "5c2d818b-4ebd-49b3-9f08-24f2938892e8"
},
"outputs": [
{
"data": {
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799P+/fuJ4yojfeTl5UlW95EjR8SHZk3PgdLSUlq8eLF4jX/99deSya6JsrIy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\nYII=\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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1En3Iffny5ULD472LmmbXrl0EgHr37i1quYmJiWRlZVUtJKV169YUGBio1Lkn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\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"N = 24\n",
"(training_digits, training_labels,\n",
" validation_digits, validation_labels) = dataset_to_numpy_util(training_dataset, validation_dataset, N)\n",
"display_digits(training_digits, training_labels, training_labels, \"training digits and their labels\", N)\n",
"display_digits(validation_digits[:N], validation_labels[:N], validation_labels[:N], \"validation digits and their labels\", N)\n",
"font_digits, font_labels = create_digits_from_local_fonts(N)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KIc0oqiD40HC"
},
"source": [
"### Estimator model\n",
"If you are not sure what cross-entropy, dropout, softmax or batch-normalization mean, head here for a crash-course: [Tensorflow and deep learning without a PhD](https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd/#featured-code-sample)."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "M9s9yHq7jMCV"
},
"outputs": [],
"source": [
"# This model trains to 99.4% sometimes 99.5% accuracy in 10 epochs\n",
"\n",
"# TPU REFACTORING: model_fn must have a params argument. TPUEstimator passes batch_size and use_tpu into it\n",
"#def model_fn(features, labels, mode):\n",
"def model_fn(features, labels, mode, params):\n",
"\n",
" is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n",
"\n",
" x = features\n",
" y = tf.reshape(x, [-1, 28, 28, 1])\n",
"\n",
" y = tf.layers.Conv2D(filters=6, kernel_size=3, padding='same', use_bias=False)(y) # no bias necessary before batch norm\n",
" y = tf.layers.BatchNormalization(scale=False, center=True)(y, training=is_training) # no batch norm scaling necessary before \"relu\"\n",
" y = tf.nn.relu(y) # activation after batch norm\n",
"\n",
" y = tf.layers.Conv2D(filters=12, kernel_size=6, padding='same', use_bias=False, strides=2)(y)\n",
" y = tf.layers.BatchNormalization(scale=False, center=True)(y, training=is_training)\n",
" y = tf.nn.relu(y)\n",
"\n",
" y = tf.layers.Conv2D(filters=24, kernel_size=6, padding='same', use_bias=False, strides=2)(y)\n",
" y = tf.layers.BatchNormalization(scale=False, center=True)(y, training=is_training)\n",
" y = tf.nn.relu(y)\n",
"\n",
" y = tf.layers.Flatten()(y)\n",
" y = tf.layers.Dense(200, use_bias=False)(y)\n",
" y = tf.layers.BatchNormalization(scale=False, center=True)(y, training=is_training)\n",
" y = tf.nn.relu(y)\n",
" y = tf.layers.Dropout(0.5)(y, training=is_training)\n",
" \n",
" logits = tf.layers.Dense(10)(y)\n",
" predictions = tf.nn.softmax(logits)\n",
" classes = tf.math.argmax(predictions, axis=-1)\n",
" \n",
" if (mode != tf.estimator.ModeKeys.PREDICT):\n",
" loss = tf.losses.softmax_cross_entropy(labels, logits)\n",
"\n",
" step = tf.train.get_or_create_global_step()\n",
" # TPU REFACTORING: step is now increased once per GLOBAL_BATCH_SIZE = 8*BATCH_SIZE. Must adjust learning rate schedule accordingly\n",
" # lr = 0.0001 + tf.train.exponential_decay(0.01, step, 2000, 1/math.e)\n",
" lr = 0.0001 + tf.train.exponential_decay(0.01, step, 2000//8, 1/math.e)\n",
" \n",
" # TPU REFACTORING: custom Tensorboard summaries do not work. Only default Estimator summaries will appear in Tensorboard.\n",
" # tf.summary.scalar(\"learn_rate\", lr)\n",
" \n",
" optimizer = tf.train.AdamOptimizer(lr)\n",
" # TPU REFACTORING: wrap the optimizer in a CrossShardOptimizer: this implements the multi-core training logic\n",
" if params['use_tpu']:\n",
" optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)\n",
" \n",
" # little wrinkle: batch norm uses running averages which need updating after each batch. create_train_op does it, optimizer.minimize does not.\n",
" train_op = tf.contrib.training.create_train_op(loss, optimizer)\n",
" #train_op = optimizer.minimize(loss, tf.train.get_or_create_global_step())\n",
" \n",
" # TPU REFACTORING: a metrics_fn is needed for TPU\n",
" # metrics = {'accuracy': tf.metrics.accuracy(classes, tf.math.argmax(labels, axis=-1))}\n",
" metric_fn = lambda classes, labels: {'accuracy': tf.metrics.accuracy(classes, tf.math.argmax(labels, axis=-1))}\n",
" tpu_metrics = (metric_fn, [classes, labels]) # pair of metric_fn and its list of arguments, there can be multiple pairs in a list\n",
" # metric_fn will run on CPU, not TPU: more operations are allowed\n",
" else:\n",
" loss = train_op = metrics = tpu_metrics = None # None of these can be computed in prediction mode because labels are not available\n",
" \n",
" # TPU REFACTORING: EstimatorSpec =\u003e TPUEstimatorSpec\n",
" ## return tf.estimator.EstimatorSpec(\n",
" return tf.contrib.tpu.TPUEstimatorSpec(\n",
" mode=mode,\n",
" predictions={\"predictions\": predictions, \"classes\": classes}, # name these fields as you like\n",
" loss=loss,\n",
" train_op=train_op,\n",
" # TPU REFACTORING: a metrics_fn is needed for TPU, passed into the eval_metrics field instead of eval_metrics_ops\n",
" # eval_metric_ops=metrics\n",
" eval_metrics = tpu_metrics\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "DeIxkrv9Wihg"
},
"outputs": [],
"source": [
"# Called once when the model is saved. This function produces a Tensorflow\n",
"# graph of operations that will be prepended to your model graph. When\n",
"# your model is deployed as a REST API, the API receives data in JSON format,\n",
"# parses it into Tensors, then sends the tensors to the input graph generated by\n",
"# this function. The graph can transform the data so it can be sent into your\n",
"# model input_fn. You can do anything you want here as long as you do it with\n",
"# tf.* functions that produce a graph of operations.\n",
"def serving_input_fn():\n",
" # placeholder for the data received by the API (already parsed, no JSON decoding necessary,\n",
" # but the JSON must contain one or multiple 'image' key(s) with 28x28 greyscale images as content.)\n",
" inputs = {\"serving_input\": tf.placeholder(tf.float32, [None, 28, 28])} # the shape of this dict should match the shape of your JSON\n",
" features = inputs['serving_input'] # no transformation needed\n",
" return tf.estimator.export.TensorServingInputReceiver(features, inputs) # features are the features needed by your model_fn\n",
" # Return a ServingInputReceiver if your features are a dictionary of Tensors, TensorServingInputReceiver if they are a straight Tensor"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "RxpRgF874-ix"
},
"source": [
"### Train and validate the model, this time on TPU"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 3094
},
"colab_type": "code",
"executionInfo": {
"elapsed": 46943,
"status": "ok",
"timestamp": 1552677762774,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 420
},
"id": "TTwH_P-ZJ_xx",
"outputId": "4328d8c2-29a5-4a63-b8f4-365f8a97018d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:Estimator's model_fn (\u003cfunction model_fn at 0x7f35edda0f28\u003e) includes params argument, but params are not passed to Estimator.\n",
"INFO:tensorflow:Using config: {'_model_dir': 'gs://gm-bucket/mnistjobs/job-2019-03-15-19:21:55', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
"cluster_def {\n",
" job {\n",
" name: \"worker\"\n",
" tasks {\n",
" key: 0\n",
" value: \"10.18.219.10:8470\"\n",
" }\n",
" }\n",
"}\n",
", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_service': None, '_cluster_spec': \u003ctensorflow.python.training.server_lib.ClusterSpec object at 0x7f35edd3fe48\u003e, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': 'grpc://10.18.219.10:8470', '_evaluation_master': 'grpc://10.18.219.10:8470', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_tpu_config': TPUConfig(iterations_per_loop=234, num_shards=None, num_cores_per_replica=None, per_host_input_for_training=2, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None), '_cluster': \u003ctensorflow.python.distribute.cluster_resolver.tpu_cluster_resolver.TPUClusterResolver object at 0x7f35fe32c8d0\u003e}\n",
"INFO:tensorflow:_TPUContext: eval_on_tpu True\n",
"INFO:tensorflow:Querying Tensorflow master (grpc://10.18.219.10:8470) for TPU system metadata.\n",
"INFO:tensorflow:Found TPU system:\n",
"INFO:tensorflow:*** Num TPU Cores: 8\n",
"INFO:tensorflow:*** Num TPU Workers: 1\n",
"INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, -1, 5500052682987470911)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 6028402093884317175)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, 1015491410753059348)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 9892516086734793051)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 17179869184, 6204465791995677744)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 17179869184, 3155068735017297531)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 17179869184, 14411619360662689937)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 17179869184, 11467280455001446031)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 17179869184, 5770875602062311761)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 17179869184, 1289596734364493101)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 17179869184, 4967910847423677762)\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Colocations handled automatically by placer.\n",
"INFO:tensorflow:Calling model_fn.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/losses/losses_impl.py:209: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n",
"INFO:tensorflow:Create CheckpointSaverHook.\n",
"INFO:tensorflow:Done calling model_fn.\n",
"INFO:tensorflow:TPU job name worker\n",
"INFO:tensorflow:Graph was finalized.\n",
"INFO:tensorflow:Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"INFO:tensorflow:Saving checkpoints for 0 into gs://gm-bucket/mnistjobs/job-2019-03-15-19:21:55/model.ckpt.\n",
"INFO:tensorflow:Initialized dataset iterators in 0 seconds\n",
"INFO:tensorflow:Installing graceful shutdown hook.\n",
"INFO:tensorflow:Creating heartbeat manager for ['/job:worker/replica:0/task:0/device:CPU:0']\n",
"INFO:tensorflow:Configuring worker heartbeat: shutdown_mode: WAIT_FOR_COORDINATOR\n",
"\n",
"INFO:tensorflow:Init TPU system\n",
"INFO:tensorflow:Initialized TPU in 7 seconds\n",
"INFO:tensorflow:Starting infeed thread controller.\n",
"INFO:tensorflow:Starting outfeed thread controller.\n",
"INFO:tensorflow:Enqueue next (234) batch(es) of data to infeed.\n",
"INFO:tensorflow:Dequeue next (234) batch(es) of data from outfeed.\n",
"INFO:tensorflow:loss = 0.13176687, step = 234\n",
"INFO:tensorflow:Enqueue next (234) batch(es) of data to infeed.\n",
"INFO:tensorflow:Dequeue next (234) batch(es) of data from outfeed.\n",
"INFO:tensorflow:loss = 0.12924866, step = 468 (0.539 sec)\n",
"INFO:tensorflow:global_step/sec: 434.272\n",
"INFO:tensorflow:examples/sec: 111174\n",
"INFO:tensorflow:Enqueue next (234) batch(es) of data to infeed.\n",
"INFO:tensorflow:Dequeue next (234) batch(es) of data from outfeed.\n",
"INFO:tensorflow:loss = 0.026477287, step = 702 (0.238 sec)\n",
"INFO:tensorflow:global_step/sec: 981.384\n",
"INFO:tensorflow:examples/sec: 251234\n",
"INFO:tensorflow:Enqueue next (234) batch(es) of data to infeed.\n",
"INFO:tensorflow:Dequeue next (234) batch(es) of data from outfeed.\n",
"INFO:tensorflow:loss = 0.0033653756, step = 936 (0.239 sec)\n",
"INFO:tensorflow:global_step/sec: 981.105\n",
"INFO:tensorflow:examples/sec: 251163\n",
"INFO:tensorflow:Enqueue next (234) batch(es) of data to infeed.\n",
"INFO:tensorflow:Dequeue next (234) batch(es) of data from outfeed.\n",
"INFO:tensorflow:loss = 0.022911828, step = 1170 (0.238 sec)\n",
"INFO:tensorflow:global_step/sec: 982.213\n",
"INFO:tensorflow:examples/sec: 251446\n",
"INFO:tensorflow:Enqueue next (234) batch(es) of data to infeed.\n",
"INFO:tensorflow:Dequeue next (234) batch(es) of data from outfeed.\n",
"INFO:tensorflow:loss = 0.030684669, step = 1404 (0.237 sec)\n",
"INFO:tensorflow:global_step/sec: 987.057\n",
"INFO:tensorflow:examples/sec: 252687\n",
"INFO:tensorflow:Enqueue next (234) batch(es) of data to infeed.\n",
"INFO:tensorflow:Dequeue next (234) batch(es) of data from outfeed.\n",
"INFO:tensorflow:loss = 0.07946512, step = 1638 (0.238 sec)\n",
"INFO:tensorflow:global_step/sec: 983.899\n",
"INFO:tensorflow:examples/sec: 251878\n",
"INFO:tensorflow:Enqueue next (234) batch(es) of data to infeed.\n",
"INFO:tensorflow:Dequeue next (234) batch(es) of data from outfeed.\n",
"INFO:tensorflow:loss = 0.0021402398, step = 1872 (0.243 sec)\n",
"INFO:tensorflow:global_step/sec: 963.441\n",
"INFO:tensorflow:examples/sec: 246641\n",
"INFO:tensorflow:Enqueue next (234) batch(es) of data to infeed.\n",
"INFO:tensorflow:Dequeue next (234) batch(es) of data from outfeed.\n",
"INFO:tensorflow:loss = 0.017131934, step = 2106 (0.240 sec)\n",
"INFO:tensorflow:global_step/sec: 976.482\n",
"INFO:tensorflow:examples/sec: 249979\n",
"INFO:tensorflow:Enqueue next (234) batch(es) of data to infeed.\n",
"INFO:tensorflow:Dequeue next (234) batch(es) of data from outfeed.\n",
"INFO:tensorflow:loss = 0.0011579404, step = 2340 (0.230 sec)\n",
"INFO:tensorflow:global_step/sec: 1015.67\n",
"INFO:tensorflow:examples/sec: 260011\n",
"INFO:tensorflow:Saving checkpoints for 2340 into gs://gm-bucket/mnistjobs/job-2019-03-15-19:21:55/model.ckpt.\n",
"INFO:tensorflow:Stop infeed thread controller\n",
"INFO:tensorflow:Shutting down InfeedController thread.\n",
"INFO:tensorflow:InfeedController received shutdown signal, stopping.\n",
"INFO:tensorflow:Infeed thread finished, shutting down.\n",
"INFO:tensorflow:infeed marked as finished\n",
"INFO:tensorflow:Stop output thread controller\n",
"INFO:tensorflow:Shutting down OutfeedController thread.\n",
"INFO:tensorflow:OutfeedController received shutdown signal, stopping.\n",
"INFO:tensorflow:Outfeed thread finished, shutting down.\n",
"INFO:tensorflow:outfeed marked as finished\n",
"INFO:tensorflow:Shutdown TPU system.\n",
"INFO:tensorflow:Loss for final step: 0.0011579404.\n",
"INFO:tensorflow:training_loop marked as finished\n",
"INFO:tensorflow:Calling model_fn.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py:2655: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Deprecated in favor of operator or tf.math.divide.\n",
"INFO:tensorflow:Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2019-03-15T19:22:23Z\n",
"INFO:tensorflow:TPU job name worker\n",
"INFO:tensorflow:Graph was finalized.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use standard file APIs to check for files with this prefix.\n",
"INFO:tensorflow:Restoring parameters from gs://gm-bucket/mnistjobs/job-2019-03-15-19:21:55/model.ckpt-2340\n",
"INFO:tensorflow:Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"INFO:tensorflow:Init TPU system\n",
"INFO:tensorflow:Initialized TPU in 8 seconds\n",
"INFO:tensorflow:Starting infeed thread controller.\n",
"INFO:tensorflow:Starting outfeed thread controller.\n",
"INFO:tensorflow:Initialized dataset iterators in 0 seconds\n",
"INFO:tensorflow:Enqueue next (1) batch(es) of data to infeed.\n",
"INFO:tensorflow:Dequeue next (1) batch(es) of data from outfeed.\n",
"INFO:tensorflow:Evaluation [1/1]\n",
"INFO:tensorflow:Stop infeed thread controller\n",
"INFO:tensorflow:Shutting down InfeedController thread.\n",
"INFO:tensorflow:InfeedController received shutdown signal, stopping.\n",
"INFO:tensorflow:Infeed thread finished, shutting down.\n",
"INFO:tensorflow:infeed marked as finished\n",
"INFO:tensorflow:Stop output thread controller\n",
"INFO:tensorflow:Shutting down OutfeedController thread.\n",
"INFO:tensorflow:OutfeedController received shutdown signal, stopping.\n",
"INFO:tensorflow:Outfeed thread finished, shutting down.\n",
"INFO:tensorflow:outfeed marked as finished\n",
"INFO:tensorflow:Shutdown TPU system.\n",
"INFO:tensorflow:Finished evaluation at 2019-03-15-19:22:34\n",
"INFO:tensorflow:Saving dict for global step 2340: accuracy = 0.9931, global_step = 2340, loss = 0.027747009\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2340: gs://gm-bucket/mnistjobs/job-2019-03-15-19:21:55/model.ckpt-2340\n",
"INFO:tensorflow:evaluation_loop marked as finished\n",
"INFO:tensorflow:Calling model_fn.\n",
"INFO:tensorflow:Running infer on CPU\n",
"INFO:tensorflow:Done calling model_fn.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:205: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
"INFO:tensorflow:Signatures INCLUDED in export for Classify: None\n",
"INFO:tensorflow:Signatures INCLUDED in export for Regress: None\n",
"INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default']\n",
"INFO:tensorflow:Signatures INCLUDED in export for Train: None\n",
"INFO:tensorflow:Signatures INCLUDED in export for Eval: None\n",
"INFO:tensorflow:Restoring parameters from gs://gm-bucket/mnistjobs/job-2019-03-15-19:21:55/model.ckpt-2340\n",
"INFO:tensorflow:Assets added to graph.\n",
"INFO:tensorflow:No assets to write.\n",
"INFO:tensorflow:SavedModel written to: gs://gm-bucket/mnistjobs/job-2019-03-15-19:21:55/mnist/temp-b'1552677756'/saved_model.pb\n"
]
}
],
"source": [
"EPOCHS = 10\n",
"\n",
"# TPU_REFACTORING: to use all 8 cores, increase the batch size by 8\n",
"GLOBAL_BATCH_SIZE = BATCH_SIZE * 8\n",
"\n",
"# TPU_REFACTORING: TPUEstimator increments the step once per GLOBAL_BATCH_SIZE: must adjust epoch length accordingly\n",
"# steps_per_epoch = 60000 // BATCH_SIZE # 60,000 images in training dataset\n",
"steps_per_epoch = 60000 // GLOBAL_BATCH_SIZE # 60,000 images in training dataset\n",
"\n",
"MODEL_EXPORT_NAME = \"mnist\" # name for exporting saved model\n",
"\n",
"# TPU_REFACTORING: the TPU will run multiple steps of training before reporting back\n",
"TPU_ITERATIONS_PER_LOOP = steps_per_epoch # report back after each epoch\n",
"\n",
"tf_logging.set_verbosity(tf_logging.INFO)\n",
"now = datetime.datetime.now()\n",
"MODEL_DIR = BUCKET+\"/mnistjobs/job\" + \"-{}-{:02d}-{:02d}-{:02d}:{:02d}:{:02d}\".format(now.year, now.month, now.day, now.hour, now.minute, now.second)\n",
"\n",
"# TPU REFACTORING: the RunConfig has changed\n",
"#training_config = tf.estimator.RunConfig(model_dir=MODEL_DIR, save_summary_steps=10, save_checkpoints_steps=steps_per_epoch, log_step_count_steps=steps_per_epoch/4)\n",
"training_config = tf.contrib.tpu.RunConfig(\n",
" cluster=tpu,\n",
" model_dir=MODEL_DIR,\n",
" tpu_config=tf.contrib.tpu.TPUConfig(TPU_ITERATIONS_PER_LOOP))\n",
" \n",
"# TPU_REFACTORING: exporters do not work yet. Must call export_savedmodel manually after training\n",
"#export_latest = tf.estimator.LatestExporter(MODEL_EXPORT_NAME, serving_input_receiver_fn=serving_input_fn)\n",
" \n",
"# TPU_REFACTORING: Estimator =\u003e TPUEstimator\n",
"#estimator = tf.estimator.Estimator(model_fn=model_fn, config=training_config)\n",
"estimator = tf.contrib.tpu.TPUEstimator(\n",
" model_fn=model_fn,\n",
" model_dir=MODEL_DIR,\n",
" # TPU_REFACTORING: training and eval batch size must be the same for now\n",
" train_batch_size=GLOBAL_BATCH_SIZE,\n",
" eval_batch_size=10000, # 10000 digits in eval dataset\n",
" predict_batch_size=10000, # prediction on the entire eval dataset in the demo below\n",
" config=training_config,\n",
" use_tpu=USE_TPU,\n",
" # TPU REFACTORING: setting the kind of model export we want\n",
" export_to_tpu=False) # we want an exported model for CPU/GPU inference because that is what is supported on ML Engine\n",
"\n",
"# TPU REFACTORING: train_and_evaluate does not work on TPU yet, TrainSpec not needed\n",
"# train_spec = tf.estimator.TrainSpec(training_input_fn, max_steps=EPOCHS*steps_per_epoch)\n",
"# TPU REFACTORING: train_and_evaluate does not work on TPU yet, EvalSpec not needed\n",
"# eval_spec = tf.estimator.EvalSpec(validation_input_fn, steps=1, exporters=export_latest, throttle_secs=0) # no eval throttling: evaluates after each checkpoint\n",
"\n",
"# TPU REFACTORING: train_and_evaluate does not work on TPU yet, must train then eval manually\n",
"# tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)\n",
"estimator.train(training_input_fn, steps=steps_per_epoch*EPOCHS)\n",
"estimator.evaluate(input_fn=validation_input_fn, steps=1)\n",
" \n",
"# TPU REFACTORING: exporters do not work yet. Must call export_savedmodel manually after training\n",
"estimator.export_savedmodel(os.path.join(MODEL_DIR, MODEL_EXPORT_NAME), serving_input_fn)\n",
"tf_logging.set_verbosity(tf_logging.WARN)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9jFVovcUUVs1"
},
"source": [
"### Visualize predictions"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 574
},
"colab_type": "code",
"id": "yJNQlhN2jjYm",
"outputId": "6c3c6eed-4030-44a8-acd3-73d95954953d"
},
"outputs": [
{
"data": {
"image/png": 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W7OxsxMXFce1L27dvL5hWZejr68PR0RFAWTv6usLS0pL73KBBA8F0du/ejZ9/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\nTkSuQmCC\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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AzaguK0NZRiae/rgDJoMHgt/GqMFnfrF3HyKmzWQ7IqWpaSjPzGxxqENjsZ4+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\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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t2hCARq1MzKfc1McYJ6peiftFI//am6urK8XHx0s6s/f9998LmXKA6pR7Yg8a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\nDAaDwWAYCWaMMxgMBoPBYDAYRuL/A17rmpZn0tO9AAAAAElFTkSuQmCC\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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q7ePjA7VaLcr5qqqqWpw7vLKyEn5+fuA4DmvXrm1xGXJzc5GQkIDY2Fi9oSlS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\nrxgO4xXTAAAAAElFTkSuQmCC\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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NpTnG+M6dO/XCwMSaneBDC9avXy/K/p5HXV0dKisr8Y9//ENI/CIi7Nq1S/Tp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\nnMFgMBgMBoPBMBH/Dy8WZa9yvv4IAAAAAElFTkSuQmCC\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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FClRVVaGqqopX/dLSUnz99dfc92thYYH09HReNe7du4devXqBiGBnZ4eSkhIAwKRJk3id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\nQmCC\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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pqaktzkt68OCB4FHlV6KaWuKwqKgI/v7+8Pf3FxwYLi4u2LdvHx4/fozly5cL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\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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hFxcX/u/x48fz5z9//rxauk4PD4+30m0sz549ox07dlB8fDwZGRkREdHs2bNp+PDhouip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\nBoPBYDAkghnjDAaDwWAwGAyGRPw/wSVnWejeDd4AAAAASUVORK5CYII=\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"# recognize digits from local fonts\n",
"# TPU REFACTORING: TPUEstimator.predict requires a 'params' in ints input_fn so that it can pass params['batch_size']\n",
"#predictions = estimator.predict(lambda: tf.data.Dataset.from_tensor_slices(font_digits).batch(N),\n",
"predictions = estimator.predict(lambda params: tf.data.Dataset.from_tensor_slices(font_digits).batch(N),\n",
" yield_single_examples=False) # the returned value is a generator that will yield one batch of predictions per next() call\n",
"predicted_font_classes = next(predictions)['classes']\n",
"display_digits(font_digits, predicted_font_classes, font_labels, \"predictions from local fonts (bad predictions in red)\", N)\n",
"\n",
"# recognize validation digits\n",
"predictions = estimator.predict(validation_input_fn,\n",
" yield_single_examples=False) # the returned value is a generator that will yield one batch of predictions per next() call\n",
"predicted_labels = next(predictions)['classes']\n",
"display_top_unrecognized(validation_digits, predicted_labels, validation_labels, N, 7)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5tzVi39ShrEL"
},
"source": [
"## Deploy the trained model to ML Engine\n",
"\n",
"Push your trained model to production on ML Engine for a serverless, autoscaled, REST API experience.\n",
"\n",
"You will need a GCS bucket and a GCP project for this.\n",
"Models deployed on ML Engine autoscale to zero if not used. There will be no ML Engine charges after you are done testing.\n",
"Google Cloud Storage incurs charges. Empty the bucket after deployment if you want to avoid these. Once the model is deployed, the bucket is not useful anymore."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "3Y3ztMY_toCP"
},
"source": [
"### Configuration"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "both",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"colab_type": "code",
"id": "iAZAn7yIhqAS",
"outputId": "86bd5098-1022-4ff6-cc04-9f0a4832f1e3"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved model directory found: gs://stagingtemp/mnistjobs/job-2018-11-30-03:15:01/mnist/1543547747/\n"
]
}
],
"source": [
"PROJECT = \"\" #@param {type:\"string\"}\n",
"NEW_MODEL = True #@param {type:\"boolean\"}\n",
"MODEL_NAME = \"estimator_mnist_tpu\" #@param {type:\"string\"}\n",
"MODEL_VERSION = \"v0\" #@param {type:\"string\"}\n",
"\n",
"assert PROJECT, 'For this part, you need a GCP project. Head to http://console.cloud.google.com/ and create one.'\n",
"\n",
"#TPU REFACTORING: TPUEstimator does not create the 'export' subfolder\n",
"#export_path = os.path.join(MODEL_DIR, 'export', MODEL_EXPORT_NAME)\n",
"export_path = os.path.join(MODEL_DIR, MODEL_EXPORT_NAME)\n",
"last_export = sorted(tf.gfile.ListDirectory(export_path))[-1]\n",
"export_path = os.path.join(export_path, last_export)\n",
"print('Saved model directory found: ', export_path)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "zy3T3zk0u2J0"
},
"source": [
"### Deploy the model\n",
"This uses the command-line interface. You can do the same thing through the ML Engine UI at https://console.cloud.google.com/mlengine/models.\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "nGv3ITiGLPL3"
},
"outputs": [],
"source": [
"# Create the model\n",
"if NEW_MODEL:\n",
" !gcloud ml-engine models create {MODEL_NAME} --project={PROJECT} --regions=us-central1"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "o3QtUowtOAL-"
},
"outputs": [],
"source": [
"# Create a version of this model (you can add --async at the end of the line to make this call non blocking)\n",
"# Additional config flags are available: https://cloud.google.com/ml-engine/reference/rest/v1/projects.models.versions\n",
"# You can also deploy a model that is stored locally by providing a --staging-bucket=... parameter\n",
"!echo \"Deployment takes a couple of minutes. You can watch your deployment here: https://console.cloud.google.com/mlengine/models/{MODEL_NAME}\"\n",
"!gcloud ml-engine versions create {MODEL_VERSION} --model={MODEL_NAME} --origin={export_path} --project={PROJECT} --runtime-version=1.10"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "jE-k1Zn6kU2Z"
},
"source": [
"### Test the deployed model\n",
"Your model is now available as a REST API. Let us try to call it. The cells below use the \"gcloud ml-engine\"\n",
"command line tool but any tool that can send a JSON payload to a REST endpoint will work."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "zZCt0Ke2QDer"
},
"outputs": [],
"source": [
"# prepare digits to send to online prediction endpoint\n",
"digits = np.concatenate((font_digits, validation_digits[:100-N]))\n",
"labels = np.concatenate((font_labels, validation_labels[:100-N]))\n",
"with open(\"digits.json\", \"w\") as f:\n",
" for digit in digits:\n",
" # the format for ML Engine online predictions is: one JSON object per line\n",
" data = json.dumps({\"serving_input\": digit.tolist()}) # \"serving_input\" because that is what you defined in your serving_input_fn: {\"serving_input\": tf.placeholder(tf.float32, [None, 28, 28])}\n",
" f.write(data+'\\n')"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 301
},
"colab_type": "code",
"id": "n6PqhQ8RQ8bp",
"outputId": "cce754dd-c46a-42d1-8baa-4eb2ec30a846"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"\u003cFigure size 936x216 with 1 Axes\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"# Request online predictions from deployed model (REST API) using the \"gcloud ml-engine\" command line.\n",
"predictions = !gcloud ml-engine predict --model={MODEL_NAME} --json-instances digits.json --project={PROJECT} --version {MODEL_VERSION}\n",
"\n",
"predictions = np.array([int(p.split('[')[0]) for p in predictions[1:]]) # first line is the name of the input layer: drop it, parse the rest\n",
"display_top_unrecognized(digits, predictions, labels, N, 100//N)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "2a5cGsSTEBQD"
},
"source": [
"## What's next\n",
"\n",
"* Learn about [Cloud TPUs](https://cloud.google.com/tpu/docs) that Google designed and optimized specifically to speed up and scale up ML workloads for training and inference and to enable ML engineers and researchers to iterate more quickly.\n",
"* Explore the range of [Cloud TPU tutorials and Colabs](https://cloud.google.com/tpu/docs/tutorials) to find other examples that can be used when implementing your ML project.\n",
"\n",
"On Google Cloud Platform, in addition to GPUs and TPUs available on pre-configured [deep learning VMs](https://cloud.google.com/deep-learning-vm/), you will find [AutoML](https://cloud.google.com/automl/)*(beta)* for training custom models without writing code and [Cloud ML Engine](https://cloud.google.com/ml-engine/docs/) which will allows you to run parallel trainings and hyperparameter tuning of your custom models on powerful distributed hardware.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "XXSk0bENYB7-"
},
"source": [
"## License"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "hleIN5-pcr0N"
},
"source": [
"\n",
"\n",
"---\n",
"\n",
"\n",
"author: Martin Gorner\u003cbr\u003e\n",
"twitter: @martin_gorner\n",
"\n",
"\n",
"---\n",
"\n",
"\n",
"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",
" http://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License.\n",
"\n",
"\n",
"---\n",
"\n",
"\n",
"This is not an official Google product but sample code provided for an educational purpose\n"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [
"N6ZDpd9XzFeN"
],
"name": "MNIST Estimator to tpuestimator.ipynb",
"provenance": [],
"toc_visible": true,
"version": "0.3.2"
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
esa-as/2016-ml-contest | Bird_Team/Facies_classification_3.ipynb | 2 | 106201 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Facies classification using Machine Learning\n",
"\n",
"#### Bird Team: PG+AC"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"from pandasql import sqldf\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import KFold, cross_val_score,LeavePGroupsOut, LeaveOneGroupOut, cross_val_predict\n",
"from sklearn.metrics import confusion_matrix, make_scorer, f1_score, accuracy_score, recall_score, precision_score\n",
"import numpy as np\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.colors as colors\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"pd.options.mode.chained_assignment = None"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"set(['SHRIMPLIN', 'Recruit F9', 'SHANKLE', 'CHURCHMAN BIBLE', 'NOLAN', 'NEWBY', 'LUKE G U', 'CROSS H CATTLE'])\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Facies</th>\n",
" <th>Formation</th>\n",
" <th>Well Name</th>\n",
" <th>Depth</th>\n",
" <th>GR</th>\n",
" <th>ILD_log10</th>\n",
" <th>DeltaPHI</th>\n",
" <th>PHIND</th>\n",
" <th>PE</th>\n",
" <th>NM_M</th>\n",
" <th>RELPOS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2793.0</td>\n",
" <td>77.45</td>\n",
" <td>0.664</td>\n",
" <td>9.9</td>\n",
" <td>11.915</td>\n",
" <td>4.6</td>\n",
" <td>1</td>\n",
" <td>1.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2793.5</td>\n",
" <td>78.26</td>\n",
" <td>0.661</td>\n",
" <td>14.2</td>\n",
" <td>12.565</td>\n",
" <td>4.1</td>\n",
" <td>1</td>\n",
" <td>0.979</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2794.0</td>\n",
" <td>79.05</td>\n",
" <td>0.658</td>\n",
" <td>14.8</td>\n",
" <td>13.050</td>\n",
" <td>3.6</td>\n",
" <td>1</td>\n",
" <td>0.957</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2794.5</td>\n",
" <td>86.10</td>\n",
" <td>0.655</td>\n",
" <td>13.9</td>\n",
" <td>13.115</td>\n",
" <td>3.5</td>\n",
" <td>1</td>\n",
" <td>0.936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>A1 SH</td>\n",
" <td>SHRIMPLIN</td>\n",
" <td>2795.0</td>\n",
" <td>74.58</td>\n",
" <td>0.647</td>\n",
" <td>13.5</td>\n",
" <td>13.300</td>\n",
" <td>3.4</td>\n",
" <td>1</td>\n",
" <td>0.915</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Facies Formation Well Name Depth GR ILD_log10 DeltaPHI PHIND \\\n",
"0 3 A1 SH SHRIMPLIN 2793.0 77.45 0.664 9.9 11.915 \n",
"1 3 A1 SH SHRIMPLIN 2793.5 78.26 0.661 14.2 12.565 \n",
"2 3 A1 SH SHRIMPLIN 2794.0 79.05 0.658 14.8 13.050 \n",
"3 3 A1 SH SHRIMPLIN 2794.5 86.10 0.655 13.9 13.115 \n",
"4 3 A1 SH SHRIMPLIN 2795.0 74.58 0.647 13.5 13.300 \n",
"\n",
" PE NM_M RELPOS \n",
"0 4.6 1 1.000 \n",
"1 4.1 1 0.979 \n",
"2 3.6 1 0.957 \n",
"3 3.5 1 0.936 \n",
"4 3.4 1 0.915 "
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filename = './../training_data.csv'\n",
"training_data = pd.read_csv(filename)\n",
"print(set(training_data[\"Well Name\"]))\n",
"training_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"set(['CRAWFORD', 'STUART'])\n",
"(830, 10)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Formation</th>\n",
" <th>Well Name</th>\n",
" <th>Depth</th>\n",
" <th>GR</th>\n",
" <th>ILD_log10</th>\n",
" <th>DeltaPHI</th>\n",
" <th>PHIND</th>\n",
" <th>PE</th>\n",
" <th>NM_M</th>\n",
" <th>RELPOS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2808.0</td>\n",
" <td>66.276</td>\n",
" <td>0.630</td>\n",
" <td>3.3</td>\n",
" <td>10.65</td>\n",
" <td>3.591</td>\n",
" <td>1</td>\n",
" <td>1.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2808.5</td>\n",
" <td>77.252</td>\n",
" <td>0.585</td>\n",
" <td>6.5</td>\n",
" <td>11.95</td>\n",
" <td>3.341</td>\n",
" <td>1</td>\n",
" <td>0.978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2809.0</td>\n",
" <td>82.899</td>\n",
" <td>0.566</td>\n",
" <td>9.4</td>\n",
" <td>13.60</td>\n",
" <td>3.064</td>\n",
" <td>1</td>\n",
" <td>0.956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2809.5</td>\n",
" <td>80.671</td>\n",
" <td>0.593</td>\n",
" <td>9.5</td>\n",
" <td>13.25</td>\n",
" <td>2.977</td>\n",
" <td>1</td>\n",
" <td>0.933</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2810.0</td>\n",
" <td>75.971</td>\n",
" <td>0.638</td>\n",
" <td>8.7</td>\n",
" <td>12.35</td>\n",
" <td>3.020</td>\n",
" <td>1</td>\n",
" <td>0.911</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Formation Well Name Depth GR ILD_log10 DeltaPHI PHIND PE \\\n",
"0 A1 SH STUART 2808.0 66.276 0.630 3.3 10.65 3.591 \n",
"1 A1 SH STUART 2808.5 77.252 0.585 6.5 11.95 3.341 \n",
"2 A1 SH STUART 2809.0 82.899 0.566 9.4 13.60 3.064 \n",
"3 A1 SH STUART 2809.5 80.671 0.593 9.5 13.25 2.977 \n",
"4 A1 SH STUART 2810.0 75.971 0.638 8.7 12.35 3.020 \n",
"\n",
" NM_M RELPOS \n",
"0 1 1.000 \n",
"1 1 0.978 \n",
"2 1 0.956 \n",
"3 1 0.933 \n",
"4 1 0.911 "
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"well_data = pd.read_csv('./../validation_data_nofacies.csv')\n",
"print(set(well_data[\"Well Name\"]))\n",
"print(well_data.shape)\n",
"well_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"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>Facies</th>\n",
" <th>Formation</th>\n",
" <th>Well Name</th>\n",
" <th>Depth</th>\n",
" <th>GR</th>\n",
" <th>ILD_log10</th>\n",
" <th>DeltaPHI</th>\n",
" <th>PHIND</th>\n",
" <th>PE</th>\n",
" <th>NM_M</th>\n",
" <th>RELPOS</th>\n",
" <th>origin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2808.0</td>\n",
" <td>66.276</td>\n",
" <td>0.630</td>\n",
" <td>3.3</td>\n",
" <td>10.65</td>\n",
" <td>3.591</td>\n",
" <td>1</td>\n",
" <td>1.000</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2808.5</td>\n",
" <td>77.252</td>\n",
" <td>0.585</td>\n",
" <td>6.5</td>\n",
" <td>11.95</td>\n",
" <td>3.341</td>\n",
" <td>1</td>\n",
" <td>0.978</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2809.0</td>\n",
" <td>82.899</td>\n",
" <td>0.566</td>\n",
" <td>9.4</td>\n",
" <td>13.60</td>\n",
" <td>3.064</td>\n",
" <td>1</td>\n",
" <td>0.956</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2809.5</td>\n",
" <td>80.671</td>\n",
" <td>0.593</td>\n",
" <td>9.5</td>\n",
" <td>13.25</td>\n",
" <td>2.977</td>\n",
" <td>1</td>\n",
" <td>0.933</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2810.0</td>\n",
" <td>75.971</td>\n",
" <td>0.638</td>\n",
" <td>8.7</td>\n",
" <td>12.35</td>\n",
" <td>3.020</td>\n",
" <td>1</td>\n",
" <td>0.911</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2810.5</td>\n",
" <td>73.955</td>\n",
" <td>0.667</td>\n",
" <td>6.9</td>\n",
" <td>12.25</td>\n",
" <td>3.086</td>\n",
" <td>1</td>\n",
" <td>0.889</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>NaN</td>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2811.0</td>\n",
" <td>77.962</td>\n",
" <td>0.674</td>\n",
" <td>6.5</td>\n",
" <td>12.45</td>\n",
" <td>3.092</td>\n",
" <td>1</td>\n",
" <td>0.867</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>NaN</td>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2811.5</td>\n",
" <td>83.894</td>\n",
" <td>0.667</td>\n",
" <td>6.3</td>\n",
" <td>12.65</td>\n",
" <td>3.123</td>\n",
" <td>1</td>\n",
" <td>0.844</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>NaN</td>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2812.0</td>\n",
" <td>84.424</td>\n",
" <td>0.653</td>\n",
" <td>6.7</td>\n",
" <td>13.05</td>\n",
" <td>3.121</td>\n",
" <td>1</td>\n",
" <td>0.822</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>NaN</td>\n",
" <td>A1 SH</td>\n",
" <td>STUART</td>\n",
" <td>2812.5</td>\n",
" <td>83.160</td>\n",
" <td>0.642</td>\n",
" <td>7.3</td>\n",
" <td>12.95</td>\n",
" <td>3.127</td>\n",
" <td>1</td>\n",
" <td>0.800</td>\n",
" <td>test</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Facies Formation Well Name Depth GR ILD_log10 DeltaPHI PHIND \\\n",
"0 NaN A1 SH STUART 2808.0 66.276 0.630 3.3 10.65 \n",
"1 NaN A1 SH STUART 2808.5 77.252 0.585 6.5 11.95 \n",
"2 NaN A1 SH STUART 2809.0 82.899 0.566 9.4 13.60 \n",
"3 NaN A1 SH STUART 2809.5 80.671 0.593 9.5 13.25 \n",
"4 NaN A1 SH STUART 2810.0 75.971 0.638 8.7 12.35 \n",
"5 NaN A1 SH STUART 2810.5 73.955 0.667 6.9 12.25 \n",
"6 NaN A1 SH STUART 2811.0 77.962 0.674 6.5 12.45 \n",
"7 NaN A1 SH STUART 2811.5 83.894 0.667 6.3 12.65 \n",
"8 NaN A1 SH STUART 2812.0 84.424 0.653 6.7 13.05 \n",
"9 NaN A1 SH STUART 2812.5 83.160 0.642 7.3 12.95 \n",
"\n",
" PE NM_M RELPOS origin \n",
"0 3.591 1 1.000 test \n",
"1 3.341 1 0.978 test \n",
"2 3.064 1 0.956 test \n",
"3 2.977 1 0.933 test \n",
"4 3.020 1 0.911 test \n",
"5 3.086 1 0.889 test \n",
"6 3.092 1 0.867 test \n",
"7 3.123 1 0.844 test \n",
"8 3.121 1 0.822 test \n",
"9 3.127 1 0.800 test "
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# concat train and test for processing \n",
"well_data[\"origin\"] = 'test'\n",
"training_data[\"origin\"] = 'train'\n",
"df = pd.concat([well_data,training_data],axis=0,ignore_index=True)[list(training_data.columns)]\n",
"df['Well Name'] = df['Well Name'].astype('category')\n",
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"session\n",
"depth\n",
"add avgs of feat\n",
"add distances feat.\n",
"lag lead\n",
"rolling\n",
"special window features for NM_M\n",
"filling na\n",
"Vectorizing Formation text data\n",
"Finished preparing data. Now ready for ML ignition!\n"
]
}
],
"source": [
"# add some features based on the well data. \n",
"\n",
"# nb points : can be correlated with how soft soil is ? \n",
"print(\"session\")\n",
"sessionsize = df.groupby([\"Well Name\",'Formation']).size().reset_index()\n",
"sessionsize.columns = [\"Well Name\",'Formation','formation_size']\n",
"df = pd.merge(df,sessionsize,how='left',on = [\"Well Name\",'Formation'])\n",
"\n",
"# depth : \n",
"print(\"depth\")\n",
"sessionsize = df.groupby([\"Well Name\",'Formation'])[\"Depth\"].min().reset_index()\n",
"sessionsize.columns = [\"Well Name\",'Formation','minimum_depth']\n",
"df = pd.merge(df,sessionsize,how='left',on = [\"Well Name\",'Formation'])\n",
"\n",
"sessionsize = df.groupby([\"Well Name\",'Formation'])[\"Depth\"].max().reset_index()\n",
"sessionsize.columns = [\"Well Name\",'Formation','maximum_depth']\n",
"df = pd.merge(df,sessionsize,how='left',on = [\"Well Name\",'Formation'])\n",
"\n",
"df['formation_depth'] = df[\"maximum_depth\"] - df[\"minimum_depth\"]\n",
"\n",
"df[\"soft_indic\"] = df['formation_depth'] / df[\"formation_size\"]\n",
"\n",
"# add avgs of feat\n",
"print(\"add avgs of feat\")\n",
"list_to_avg = ['Depth', 'GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE', 'NM_M', 'RELPOS']\n",
"for val in list_to_avg : \n",
" df[val + \"_min\"] = df.groupby([\"Well Name\",'Formation'])[val].transform(np.min)\n",
" df[val + \"_max\"] = df.groupby([\"Well Name\",'Formation'])[val].transform(np.max)\n",
" df[val + \"_mean\"] = df.groupby([\"Well Name\",'Formation'])[val].transform(np.mean)\n",
" df[val + \"_var\"] = df.groupby([\"Well Name\",'Formation'])[val].transform(np.var) \n",
"\n",
"# add distances feat. = an attempt at regulariation.\n",
"print(\"add distances feat.\")\n",
"for val in list_to_avg : \n",
" df[val + \"_min_dist\"] = df[val] -df[val + \"_min\"]\n",
" df[val + \"_max_dist\"] = df[val] -df[val + \"_max\"]\n",
" df[val + \"_mean_dist\"] = df[val] -df[val + \"_mean\"]\n",
" \n",
"# add lag and lead !\n",
"print(\"lag lead\")\n",
"list_to_lag = ['Depth', 'GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE', 'NM_M', 'RELPOS']\n",
"for val in list_to_lag:\n",
" for lag in range(1,11):\n",
" df[val+'_lag_'+str(lag)]=df[val]-df.groupby(\"Well Name\")[val].shift(periods=lag)\n",
" df[val+'_lead_'+str(lag)]=df[val]-df.groupby(\"Well Name\")[val].shift(periods=-lag)\n",
"\n",
"# adding some Formation lag and lead. \n",
"for lag in range(1,3):\n",
" df['Formation'+'_lag_'+str(lag)]=df.groupby(\"Well Name\")['Formation'].shift(periods=lag)\n",
" df['Formation'+'_lead_'+str(lag)]=df.groupby(\"Well Name\")['Formation'].shift(periods=-lag)\n",
" df['Formation'+'_lag_'+str(lag) + 'equal'] = (df['Formation'+'_lag_'+str(lag)] == df[\"Formation\"]).astype(int)\n",
" df['Formation'+'_lead_'+str(lag) + 'equal'] = (df['Formation'+'_lead_'+str(lag)] == df[\"Formation\"]).astype(int) \n",
"\n",
"print(\"rolling\")\n",
"#Add rolling features\n",
"list_to_roll = ['Depth', 'GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE', 'NM_M','RELPOS']\n",
"window_size = [5,10,15,20,50]\n",
"for w in window_size:\n",
" for val in list_to_roll:\n",
" df[val+'_rollingmean_'+str(w)]=df.groupby(\"Well Name\")[val].apply(\n",
" lambda x:x.rolling(window=w,center=True).mean())\n",
" df[val+'_rollingmax_'+str(w)]=df.groupby(\"Well Name\")[val].apply(\n",
" lambda x:x.rolling(window=w,center=True).max())\n",
" df[val+'_rollingmin_'+str(w)]=df.groupby(\"Well Name\")[val].apply(\n",
" lambda x:x.rolling(window=w,center=True).min())\n",
" df[val+'_rollingstd_'+str(w)]=df.groupby(\"Well Name\")[val].apply(\n",
" lambda x:x.rolling(window=w,center=True).std())\n",
" \n",
"print(\"special window features for NM_M\")\n",
"def NM_M_distance(x,how,target):\n",
" length = len(x)\n",
" rank = np.empty(length)\n",
" count = -1\n",
" NM_M = x[\"NM_M\"].values\n",
" if how==\"up\":\n",
" order = range(length)\n",
" elif how==\"down\":\n",
" order = range(length-1,-1,-1)\n",
" for i in order:\n",
" if ((NM_M[i] != target) & (count>-1)):\n",
" count+=1\n",
" rank[i] += count\n",
" elif NM_M[i] == target:\n",
" count=0\n",
" else:\n",
" rank[i] = count\n",
" rank = pd.DataFrame(rank.astype(int), columns=[\"NM_M_Rank_Target_+\"+str(target)+\"_\"+how], index = x.index)\n",
" return(rank)\n",
"df[\"NM_M_Rank_Target_1_up\"]=df.groupby([\"Well Name\"]).apply(NM_M_distance,how=\"up\",target=1)\n",
"df[\"NM_M_Rank_Target_2_up\"]=df.groupby([\"Well Name\"]).apply(NM_M_distance,how=\"up\",target=2)\n",
"df[\"NM_M_Rank_Target_1_down\"]=df.groupby([\"Well Name\"]).apply(NM_M_distance,how=\"down\",target=1)\n",
"df[\"NM_M_Rank_Target_2_down\"]=df.groupby([\"Well Name\"]).apply(NM_M_distance,how=\"down\",target=2)\n",
"\n",
"print(\"filling na\")\n",
"df = df.groupby([\"Well Name\"], as_index=False).apply(lambda group: group.bfill())\n",
"df = df.groupby([\"Well Name\"], as_index=False).apply(lambda group: group.ffill())\n",
"\n",
"print(\"Vectorizing Formation text data\")\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"list_formation = ['Formation',\n",
" 'Formation_lag_1',\n",
" 'Formation_lead_1',\n",
" 'Formation_lag_2',\n",
" 'Formation_lead_2']\n",
"for l in list_formation:\n",
" cv = CountVectorizer()\n",
" counts = cv.fit_transform(df[l].values)\n",
" cols = [c+\"_\"+l for c in cv.get_feature_names()]\n",
" counts = pd.DataFrame(counts.toarray(),columns = cols)\n",
" df = df.drop(l,axis = 1)\n",
" df = pd.concat([df,counts],axis=1)\n",
"\n",
"print(\"Finished preparing data. Now ready for ML ignition!\")"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#tokeep =['Facies','origin','Formation','Formation_lag_1','Formation_lead_1','Formation_lag_2','Formation_lead_2',\n",
"# 'Well Name','Depth','GR','ILD_log10','DeltaPHI','PHIND','PE','NM_M','RELPOS']\n",
"#nums = ['Depth','GR','ILD_log10','DeltaPHI','PHIND','PE','NM_M','RELPOS']\n",
"#tokeep = tokeep + [x+'_lag_1' for x in nums] +[x+'_lead_1' for x in nums]\n",
"#df = df[tokeep]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CV performance"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"clf = RandomForestClassifier(\n",
" max_depth = 10,\n",
" n_estimators = 100,\n",
" max_features=0.1,\n",
" min_samples_leaf=25,\n",
" min_samples_split=50,\n",
" class_weight='balanced',\n",
" oob_score=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ytrain = df[(df['origin']=='train')&(df['Well Name']<>'NOLAN')]['Facies']\n",
"yvalid = df[(df['origin']=='train')&(df['Well Name']=='NOLAN')]['Facies']\n",
"xtrain = df[(df['origin']=='train')&(df['Well Name']<>'NOLAN')].drop(['Well Name','origin','Facies'],axis=1)\n",
"xvalid = df[(df['origin']=='train')&(df['Well Name']=='NOLAN')].drop(['Well Name','origin','Facies'],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
" criterion='gini', max_depth=10, max_features=0.1,\n",
" max_leaf_nodes=None, min_impurity_split=1e-07,\n",
" min_samples_leaf=25, min_samples_split=50,\n",
" min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
" oob_score=True, random_state=None, verbose=0, warm_start=False)"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf.fit(xtrain,ytrain)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.773162939297\n",
" precision recall f1-score support\n",
"\n",
" 1.0 0.00 0.00 0.00 4\n",
" 2.0 0.82 0.66 0.73 118\n",
" 3.0 0.62 0.82 0.70 68\n",
" 4.0 0.27 0.21 0.24 28\n",
" 5.0 0.47 0.49 0.48 47\n",
" 6.0 0.25 0.13 0.17 30\n",
" 7.0 0.00 0.00 0.00 4\n",
" 8.0 0.72 0.49 0.58 116\n",
" 9.0 0.00 0.00 0.00 0\n",
"\n",
"avg / total 0.63 0.54 0.57 415\n",
"\n",
"0.539759036145\n"
]
}
],
"source": [
"preds = clf.predict(xvalid)\n",
"from sklearn.metrics import classification_report\n",
"print(clf.oob_score_)\n",
"print classification_report(yvalid, preds)\n",
"print(f1_score(yvalid, preds,average=\"micro\"))"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# this time let's use all the training set \n",
"groups = df[(df['origin']=='train')][\"Well Name\"]\n",
"ytrain = df[(df['origin']=='train')]['Facies']\n",
"yvalid = df[(df['origin']=='test')]['Facies']\n",
"xtrain = df[(df['origin']=='train')].drop(['Well Name','origin','Facies'],axis=1)\n",
"xvalid = df[(df['origin']=='test')].drop(['Well Name','origin','Facies'],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cv=LeaveOneGroupOut().split(xtrain, ytrain, groups)\n",
"y_pred = cross_val_predict(clf, xtrain, ytrain, cv=cv, n_jobs=-1)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 1.0 0.64 0.36 0.46 259\n",
" 2.0 0.58 0.58 0.58 738\n",
" 3.0 0.56 0.67 0.61 615\n",
" 4.0 0.51 0.69 0.59 184\n",
" 5.0 0.37 0.22 0.28 217\n",
" 6.0 0.50 0.42 0.46 462\n",
" 7.0 0.31 0.45 0.37 98\n",
" 8.0 0.51 0.53 0.52 498\n",
" 9.0 0.60 0.66 0.63 161\n",
"\n",
"avg / total 0.53 0.53 0.53 3232\n",
"\n",
"0.532487623762\n"
]
}
],
"source": [
"print(classification_report(ytrain, y_pred))\n",
"print(f1_score(ytrain, y_pred,average=\"micro\"))"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature ranking:\n",
"1. feature 403 lm_Formation (0.025752)\n",
"2. feature 381 PE_rollingmean_50 (0.024209)\n",
"3. feature 289 NM_M_rollingmean_10 (0.021777)\n",
"4. feature 257 NM_M_rollingmean_5 (0.018710)\n",
"5. feature 404 sh_Formation (0.018616)\n",
"6. feature 318 PE_rollingmax_15 (0.018099)\n",
"7. feature 350 PE_rollingmax_20 (0.017107)\n",
"8. feature 2 ILD_log10 (0.015412)\n",
"9. feature 360 RELPOS_rollingstd_20 (0.014475)\n",
"10. feature 239 GR_rollingmin_5 (0.013867)\n",
"11. feature 17 GR_min (0.012606)\n",
"12. feature 382 PE_rollingmax_50 (0.012416)\n",
"13. feature 241 ILD_log10_rollingmean_5 (0.011988)\n",
"14. feature 1 GR (0.011731)\n",
"15. feature 419 lm_Formation_lead_1 (0.011285)\n",
"16. feature 21 ILD_log10_min (0.011224)\n",
"17. feature 296 RELPOS_rollingstd_10 (0.011138)\n",
"18. feature 237 GR_rollingmean_5 (0.011138)\n",
"19. feature 273 ILD_log10_rollingmean_10 (0.011086)\n",
"20. feature 317 PE_rollingmean_15 (0.011065)\n",
"21. feature 35 PE_mean (0.011052)\n",
"22. feature 6 NM_M (0.010820)\n",
"23. feature 369 ILD_log10_rollingmean_50 (0.010532)\n",
"24. feature 305 ILD_log10_rollingmean_15 (0.010221)\n",
"25. feature 39 NM_M_mean (0.010044)\n",
"26. feature 243 ILD_log10_rollingmin_5 (0.009469)\n",
"27. feature 385 NM_M_rollingmean_50 (0.009135)\n",
"28. feature 328 RELPOS_rollingstd_15 (0.009055)\n",
"29. feature 349 PE_rollingmean_20 (0.008771)\n",
"30. feature 242 ILD_log10_rollingmax_5 (0.008643)\n",
"31. feature 48 GR_min_dist (0.008514)\n",
"32. feature 31 PHIND_mean (0.008388)\n",
"33. feature 286 PE_rollingmax_10 (0.008388)\n",
"34. feature 321 NM_M_rollingmean_15 (0.008347)\n",
"35. feature 412 sh_Formation_lag_1 (0.007454)\n",
"36. feature 285 PE_rollingmean_10 (0.007174)\n",
"37. feature 337 ILD_log10_rollingmean_20 (0.007114)\n",
"38. feature 278 DeltaPHI_rollingmax_10 (0.006858)\n",
"39. feature 376 DeltaPHI_rollingstd_50 (0.006857)\n",
"40. feature 49 GR_max_dist (0.006849)\n",
"41. feature 353 NM_M_rollingmean_20 (0.006425)\n",
"42. feature 277 DeltaPHI_rollingmean_10 (0.006424)\n",
"43. feature 4 PHIND (0.006408)\n",
"44. feature 393 NM_M_Rank_Target_1_up (0.006360)\n",
"45. feature 271 GR_rollingmin_10 (0.005924)\n",
"46. feature 254 PE_rollingmax_5 (0.005879)\n",
"47. feature 253 PE_rollingmean_5 (0.005586)\n",
"48. feature 22 ILD_log10_max (0.005536)\n",
"49. feature 420 sh_Formation_lead_1 (0.005492)\n",
"50. feature 258 NM_M_rollingmax_5 (0.005457)\n",
"51. feature 411 lm_Formation_lag_1 (0.005441)\n",
"52. feature 57 PHIND_min_dist (0.005375)\n",
"53. feature 50 GR_mean_dist (0.005299)\n",
"54. feature 435 lm_Formation_lead_2 (0.005228)\n",
"55. feature 269 GR_rollingmean_10 (0.005179)\n",
"56. feature 287 PE_rollingmin_10 (0.005146)\n",
"57. feature 371 ILD_log10_rollingmin_50 (0.005132)\n",
"58. feature 310 DeltaPHI_rollingmax_15 (0.005041)\n",
"59. feature 250 PHIND_rollingmax_5 (0.005020)\n",
"60. feature 362 Depth_rollingmax_50 (0.004984)\n",
"61. feature 358 RELPOS_rollingmax_20 (0.004901)\n",
"62. feature 264 RELPOS_rollingstd_5 (0.004878)\n",
"63. feature 307 ILD_log10_rollingmin_15 (0.004688)\n",
"64. feature 339 ILD_log10_rollingmin_20 (0.004617)\n",
"65. feature 5 PE (0.004368)\n",
"66. feature 342 DeltaPHI_rollingmax_20 (0.004280)\n",
"67. feature 251 PHIND_rollingmin_5 (0.004199)\n",
"68. feature 8 formation_size (0.004191)\n",
"69. feature 361 Depth_rollingmean_50 (0.004150)\n",
"70. feature 24 ILD_log10_var (0.004093)\n",
"71. feature 53 ILD_log10_mean_dist (0.004055)\n",
"72. feature 275 ILD_log10_rollingmin_10 (0.004005)\n",
"73. feature 247 DeltaPHI_rollingmin_5 (0.004002)\n",
"74. feature 249 PHIND_rollingmean_5 (0.003885)\n",
"75. feature 23 ILD_log10_mean (0.003850)\n",
"76. feature 245 DeltaPHI_rollingmean_5 (0.003834)\n",
"77. feature 309 DeltaPHI_rollingmean_15 (0.003788)\n",
"78. feature 11 formation_depth (0.003778)\n",
"79. feature 16 Depth_var (0.003691)\n",
"80. feature 274 ILD_log10_rollingmax_10 (0.003402)\n",
"81. feature 18 GR_max (0.003364)\n",
"82. feature 238 GR_rollingmax_5 (0.003362)\n",
"83. feature 341 DeltaPHI_rollingmean_20 (0.003343)\n",
"84. feature 210 RELPOS_lead_1 (0.003259)\n",
"85. feature 28 DeltaPHI_var (0.003191)\n",
"86. feature 329 Depth_rollingmean_20 (0.003186)\n",
"87. feature 281 PHIND_rollingmean_10 (0.003101)\n",
"88. feature 301 GR_rollingmean_15 (0.003024)\n",
"89. feature 246 DeltaPHI_rollingmax_5 (0.003002)\n",
"90. feature 396 NM_M_Rank_Target_2_down (0.002976)\n",
"91. feature 211 RELPOS_lag_2 (0.002969)\n",
"92. feature 294 RELPOS_rollingmax_10 (0.002961)\n",
"93. feature 3 DeltaPHI (0.002889)\n",
"94. feature 330 Depth_rollingmax_20 (0.002885)\n",
"95. feature 43 RELPOS_mean (0.002884)\n",
"96. feature 10 maximum_depth (0.002867)\n",
"97. feature 383 PE_rollingmin_50 (0.002838)\n",
"98. feature 213 RELPOS_lag_3 (0.002801)\n",
"99. feature 52 ILD_log10_max_dist (0.002753)\n",
"100. feature 322 NM_M_rollingmax_15 (0.002738)\n",
"101. feature 380 PHIND_rollingstd_50 (0.002732)\n",
"102. feature 212 RELPOS_lead_2 (0.002719)\n",
"103. feature 209 RELPOS_lag_1 (0.002681)\n",
"104. feature 367 GR_rollingmin_50 (0.002661)\n",
"105. feature 279 DeltaPHI_rollingmin_10 (0.002652)\n",
"106. feature 348 PHIND_rollingstd_20 (0.002646)\n",
"107. feature 394 NM_M_Rank_Target_2_up (0.002599)\n",
"108. feature 104 GR_lead_8 (0.002599)\n",
"109. feature 303 GR_rollingmin_15 (0.002580)\n",
"110. feature 392 RELPOS_rollingstd_50 (0.002548)\n",
"111. feature 388 NM_M_rollingstd_50 (0.002485)\n",
"112. feature 51 ILD_log10_min_dist (0.002430)\n",
"113. feature 108 GR_lead_10 (0.002423)\n",
"114. feature 384 PE_rollingstd_50 (0.002417)\n",
"115. feature 298 Depth_rollingmax_15 (0.002328)\n",
"116. feature 215 RELPOS_lag_4 (0.002321)\n",
"117. feature 359 RELPOS_rollingmin_20 (0.002320)\n",
"118. feature 9 minimum_depth (0.002317)\n",
"119. feature 255 PE_rollingmin_5 (0.002267)\n",
"120. feature 124 ILD_log10_lead_8 (0.002243)\n",
"121. feature 235 Depth_rollingmin_5 (0.002236)\n",
"122. feature 27 DeltaPHI_mean (0.002197)\n",
"123. feature 344 DeltaPHI_rollingstd_20 (0.002181)\n",
"124. feature 378 PHIND_rollingmax_50 (0.002133)\n",
"125. feature 262 RELPOS_rollingmax_5 (0.002110)\n",
"126. feature 351 PE_rollingmin_20 (0.002093)\n",
"127. feature 326 RELPOS_rollingmax_15 (0.002089)\n",
"128. feature 335 GR_rollingmin_20 (0.002061)\n",
"129. feature 355 NM_M_rollingmin_20 (0.002056)\n",
"130. feature 218 RELPOS_lead_5 (0.002055)\n",
"131. feature 366 GR_rollingmax_50 (0.001998)\n",
"132. feature 331 Depth_rollingmin_20 (0.001980)\n",
"133. feature 389 RELPOS_rollingmean_50 (0.001967)\n",
"134. feature 368 GR_rollingstd_50 (0.001943)\n",
"135. feature 228 RELPOS_lead_10 (0.001942)\n",
"136. feature 333 GR_rollingmean_20 (0.001940)\n",
"137. feature 283 PHIND_rollingmin_10 (0.001934)\n",
"138. feature 282 PHIND_rollingmax_10 (0.001932)\n",
"139. feature 372 ILD_log10_rollingstd_50 (0.001930)\n",
"140. feature 20 GR_var (0.001928)\n",
"141. feature 68 RELPOS_mean_dist (0.001906)\n",
"142. feature 370 ILD_log10_rollingmax_50 (0.001892)\n",
"143. feature 62 PE_mean_dist (0.001890)\n",
"144. feature 220 RELPOS_lead_6 (0.001882)\n",
"145. feature 263 RELPOS_rollingmin_5 (0.001842)\n",
"146. feature 167 PHIND_lag_10 (0.001829)\n",
"147. feature 291 NM_M_rollingmin_10 (0.001815)\n",
"148. feature 352 PE_rollingstd_20 (0.001812)\n",
"149. feature 336 GR_rollingstd_20 (0.001762)\n",
"150. feature 347 PHIND_rollingmin_20 (0.001707)\n",
"151. feature 316 PHIND_rollingstd_15 (0.001703)\n",
"152. feature 225 RELPOS_lag_9 (0.001684)\n",
"153. feature 217 RELPOS_lag_5 (0.001683)\n",
"154. feature 61 PE_max_dist (0.001682)\n",
"155. feature 374 DeltaPHI_rollingmax_50 (0.001676)\n",
"156. feature 44 RELPOS_var (0.001670)\n",
"157. feature 266 Depth_rollingmax_10 (0.001668)\n",
"158. feature 436 sh_Formation_lead_2 (0.001665)\n",
"159. feature 379 PHIND_rollingmin_50 (0.001664)\n",
"160. feature 315 PHIND_rollingmin_15 (0.001627)\n",
"161. feature 128 ILD_log10_lead_10 (0.001622)\n",
"162. feature 216 RELPOS_lead_4 (0.001600)\n",
"163. feature 100 GR_lead_6 (0.001593)\n",
"164. feature 338 ILD_log10_rollingmax_20 (0.001565)\n",
"165. feature 306 ILD_log10_rollingmax_15 (0.001559)\n",
"166. feature 106 GR_lead_9 (0.001548)\n",
"167. feature 45 Depth_min_dist (0.001543)\n",
"168. feature 223 RELPOS_lag_8 (0.001543)\n",
"169. feature 46 Depth_max_dist (0.001536)\n",
"170. feature 297 Depth_rollingmean_15 (0.001527)\n",
"171. feature 373 DeltaPHI_rollingmean_50 (0.001523)\n",
"172. feature 226 RELPOS_lead_9 (0.001517)\n",
"173. feature 233 Depth_rollingmean_5 (0.001503)\n",
"174. feature 219 RELPOS_lag_6 (0.001502)\n",
"175. feature 363 Depth_rollingmin_50 (0.001502)\n",
"176. feature 33 PE_min (0.001496)\n",
"177. feature 56 DeltaPHI_mean_dist (0.001494)\n",
"178. feature 187 PE_lag_10 (0.001480)\n",
"179. feature 272 GR_rollingstd_10 (0.001475)\n",
"180. feature 25 DeltaPHI_min (0.001448)\n",
"181. feature 162 PHIND_lead_7 (0.001426)\n",
"182. feature 345 PHIND_rollingmean_20 (0.001419)\n",
"183. feature 37 NM_M_min (0.001408)\n",
"184. feature 127 ILD_log10_lag_10 (0.001406)\n",
"185. feature 346 PHIND_rollingmax_20 (0.001406)\n",
"186. feature 429 a1_Formation_lead_2 (0.001376)\n",
"187. feature 29 PHIND_min (0.001362)\n",
"188. feature 98 GR_lead_5 (0.001354)\n",
"189. feature 377 PHIND_rollingmean_50 (0.001350)\n",
"190. feature 311 DeltaPHI_rollingmin_15 (0.001341)\n",
"191. feature 107 GR_lag_10 (0.001329)\n",
"192. feature 325 RELPOS_rollingmean_15 (0.001308)\n",
"193. feature 224 RELPOS_lead_8 (0.001290)\n",
"194. feature 299 Depth_rollingmin_15 (0.001283)\n",
"195. feature 234 Depth_rollingmax_5 (0.001279)\n",
"196. feature 265 Depth_rollingmean_10 (0.001274)\n",
"197. feature 99 GR_lag_6 (0.001273)\n",
"198. feature 0 Depth (0.001271)\n",
"199. feature 47 Depth_mean_dist (0.001263)\n",
"200. feature 66 RELPOS_min_dist (0.001254)\n",
"201. feature 334 GR_rollingmax_20 (0.001241)\n",
"202. feature 30 PHIND_max (0.001220)\n",
"203. feature 125 ILD_log10_lag_9 (0.001216)\n",
"204. feature 166 PHIND_lead_9 (0.001209)\n",
"205. feature 312 DeltaPHI_rollingstd_15 (0.001179)\n",
"206. feature 54 DeltaPHI_min_dist (0.001164)\n",
"207. feature 97 GR_lag_5 (0.001158)\n",
"208. feature 105 GR_lag_9 (0.001148)\n",
"209. feature 164 PHIND_lead_8 (0.001139)\n",
"210. feature 304 GR_rollingstd_15 (0.001138)\n",
"211. feature 102 GR_lead_7 (0.001111)\n",
"212. feature 26 DeltaPHI_max (0.001107)\n",
"213. feature 410 b5_Formation_lag_1 (0.001104)\n",
"214. feature 67 RELPOS_max_dist (0.001091)\n",
"215. feature 288 PE_rollingstd_10 (0.001088)\n",
"216. feature 59 PHIND_mean_dist (0.001088)\n",
"217. feature 320 PE_rollingstd_15 (0.001081)\n",
"218. feature 14 Depth_max (0.001073)\n",
"219. feature 60 PE_min_dist (0.001053)\n",
"220. feature 101 GR_lag_7 (0.001042)\n",
"221. feature 32 PHIND_var (0.001038)\n",
"222. feature 365 GR_rollingmean_50 (0.001010)\n",
"223. feature 221 RELPOS_lag_7 (0.000995)\n",
"224. feature 144 DeltaPHI_lead_8 (0.000994)\n",
"225. feature 19 GR_mean (0.000990)\n",
"226. feature 41 RELPOS_min (0.000986)\n",
"227. feature 227 RELPOS_lag_10 (0.000976)\n",
"228. feature 36 PE_var (0.000970)\n",
"229. feature 313 PHIND_rollingmean_15 (0.000964)\n",
"230. feature 165 PHIND_lag_9 (0.000961)\n",
"231. feature 214 RELPOS_lead_3 (0.000956)\n",
"232. feature 163 PHIND_lag_8 (0.000944)\n",
"233. feature 343 DeltaPHI_rollingmin_20 (0.000937)\n",
"234. feature 364 Depth_rollingstd_50 (0.000937)\n",
"235. feature 13 Depth_min (0.000920)\n",
"236. feature 146 DeltaPHI_lead_9 (0.000908)\n",
"237. feature 308 ILD_log10_rollingstd_15 (0.000875)\n",
"238. feature 123 ILD_log10_lag_8 (0.000855)\n",
"239. feature 248 DeltaPHI_rollingstd_5 (0.000843)\n",
"240. feature 117 ILD_log10_lag_5 (0.000842)\n",
"241. feature 158 PHIND_lead_5 (0.000840)\n",
"242. feature 319 PE_rollingmin_15 (0.000840)\n",
"243. feature 295 RELPOS_rollingmin_10 (0.000839)\n",
"244. feature 160 PHIND_lead_6 (0.000832)\n",
"245. feature 119 ILD_log10_lag_6 (0.000815)\n",
"246. feature 168 PHIND_lead_10 (0.000805)\n",
"247. feature 267 Depth_rollingmin_10 (0.000803)\n",
"248. feature 120 ILD_log10_lead_6 (0.000795)\n",
"249. feature 356 NM_M_rollingstd_20 (0.000793)\n",
"250. feature 270 GR_rollingmax_10 (0.000771)\n",
"251. feature 7 RELPOS (0.000766)\n",
"252. feature 391 RELPOS_rollingmin_50 (0.000752)\n",
"253. feature 183 PE_lag_8 (0.000732)\n",
"254. feature 327 RELPOS_rollingmin_15 (0.000721)\n",
"255. feature 222 RELPOS_lead_7 (0.000704)\n",
"256. feature 15 Depth_mean (0.000700)\n",
"257. feature 113 ILD_log10_lag_3 (0.000691)\n",
"258. feature 413 a1_Formation_lead_1 (0.000675)\n",
"259. feature 397 a1_Formation (0.000669)\n",
"260. feature 185 PE_lag_9 (0.000663)\n",
"261. feature 126 ILD_log10_lead_9 (0.000657)\n",
"262. feature 357 RELPOS_rollingmean_20 (0.000645)\n",
"263. feature 34 PE_max (0.000644)\n",
"264. feature 55 DeltaPHI_max_dist (0.000626)\n",
"265. feature 340 ILD_log10_rollingstd_20 (0.000625)\n",
"266. feature 252 PHIND_rollingstd_5 (0.000590)\n",
"267. feature 122 ILD_log10_lead_7 (0.000587)\n",
"268. feature 181 PE_lag_7 (0.000576)\n",
"269. feature 421 a1_Formation_lag_2 (0.000572)\n",
"270. feature 179 PE_lag_6 (0.000570)\n",
"271. feature 395 NM_M_Rank_Target_1_down (0.000539)\n",
"272. feature 324 NM_M_rollingstd_15 (0.000527)\n",
"273. feature 58 PHIND_max_dist (0.000518)\n",
"274. feature 375 DeltaPHI_rollingmin_50 (0.000517)\n",
"275. feature 121 ILD_log10_lag_7 (0.000513)\n",
"276. feature 142 DeltaPHI_lead_7 (0.000505)\n",
"277. feature 156 PHIND_lead_4 (0.000497)\n",
"278. feature 188 PE_lead_10 (0.000488)\n",
"279. feature 111 ILD_log10_lag_2 (0.000477)\n",
"280. feature 157 PHIND_lag_5 (0.000475)\n",
"281. feature 256 PE_rollingstd_5 (0.000472)\n",
"282. feature 261 RELPOS_rollingmean_5 (0.000461)\n",
"283. feature 314 PHIND_rollingmax_15 (0.000459)\n",
"284. feature 115 ILD_log10_lag_4 (0.000432)\n",
"285. feature 112 ILD_log10_lead_2 (0.000414)\n",
"286. feature 390 RELPOS_rollingmax_50 (0.000413)\n",
"287. feature 145 DeltaPHI_lag_9 (0.000411)\n",
"288. feature 182 PE_lead_7 (0.000402)\n",
"289. feature 96 GR_lead_4 (0.000397)\n",
"290. feature 177 PE_lag_5 (0.000386)\n",
"291. feature 147 DeltaPHI_lag_10 (0.000382)\n",
"292. feature 293 RELPOS_rollingmean_10 (0.000379)\n",
"293. feature 110 ILD_log10_lead_1 (0.000372)\n",
"294. feature 186 PE_lead_9 (0.000354)\n",
"295. feature 206 NM_M_lead_9 (0.000351)\n",
"296. feature 151 PHIND_lag_2 (0.000347)\n",
"297. feature 276 ILD_log10_rollingstd_10 (0.000346)\n",
"298. feature 280 DeltaPHI_rollingstd_10 (0.000343)\n",
"299. feature 118 ILD_log10_lead_5 (0.000336)\n",
"300. feature 103 GR_lag_8 (0.000329)\n",
"301. feature 402 b5_Formation (0.000320)\n",
"302. feature 155 PHIND_lag_4 (0.000318)\n",
"303. feature 12 soft_indic (0.000318)\n",
"304. feature 92 GR_lead_2 (0.000310)\n",
"305. feature 323 NM_M_rollingmin_15 (0.000309)\n",
"306. feature 94 GR_lead_3 (0.000295)\n",
"307. feature 148 DeltaPHI_lead_10 (0.000295)\n",
"308. feature 284 PHIND_rollingstd_10 (0.000285)\n",
"309. feature 114 ILD_log10_lead_3 (0.000278)\n",
"310. feature 201 NM_M_lag_7 (0.000268)\n",
"311. feature 197 NM_M_lag_5 (0.000244)\n",
"312. feature 154 PHIND_lead_3 (0.000240)\n",
"313. feature 90 GR_lead_1 (0.000233)\n",
"314. feature 387 NM_M_rollingmin_50 (0.000229)\n",
"315. feature 244 ILD_log10_rollingstd_5 (0.000226)\n",
"316. feature 171 PE_lag_2 (0.000209)\n",
"317. feature 93 GR_lag_3 (0.000209)\n",
"318. feature 173 PE_lag_3 (0.000191)\n",
"319. feature 152 PHIND_lead_2 (0.000190)\n",
"320. feature 184 PE_lead_8 (0.000189)\n",
"321. feature 143 DeltaPHI_lag_8 (0.000185)\n",
"322. feature 302 GR_rollingmax_15 (0.000183)\n",
"323. feature 95 GR_lag_4 (0.000182)\n",
"324. feature 178 PE_lead_5 (0.000181)\n",
"325. feature 91 GR_lag_2 (0.000176)\n",
"326. feature 180 PE_lead_6 (0.000165)\n",
"327. feature 292 NM_M_rollingstd_10 (0.000149)\n",
"328. feature 161 PHIND_lag_7 (0.000148)\n",
"329. feature 139 DeltaPHI_lag_6 (0.000143)\n",
"330. feature 159 PHIND_lag_6 (0.000135)\n",
"331. feature 136 DeltaPHI_lead_4 (0.000133)\n",
"332. feature 38 NM_M_max (0.000131)\n",
"333. feature 140 DeltaPHI_lead_6 (0.000128)\n",
"334. feature 109 ILD_log10_lag_1 (0.000122)\n",
"335. feature 176 PE_lead_4 (0.000118)\n",
"336. feature 240 GR_rollingstd_5 (0.000115)\n",
"337. feature 207 NM_M_lag_10 (0.000109)\n",
"338. feature 150 PHIND_lead_1 (0.000109)\n",
"339. feature 132 DeltaPHI_lead_2 (0.000104)\n",
"340. feature 405 a1_Formation_lag_1 (0.000103)\n",
"341. feature 138 DeltaPHI_lead_5 (0.000102)\n",
"342. feature 172 PE_lead_2 (0.000098)\n",
"343. feature 137 DeltaPHI_lag_5 (0.000098)\n",
"344. feature 430 b1_Formation_lead_2 (0.000093)\n",
"345. feature 170 PE_lead_1 (0.000089)\n",
"346. feature 409 b4_Formation_lag_1 (0.000086)\n",
"347. feature 386 NM_M_rollingmax_50 (0.000083)\n",
"348. feature 141 DeltaPHI_lag_7 (0.000082)\n",
"349. feature 196 NM_M_lead_4 (0.000081)\n",
"350. feature 133 DeltaPHI_lag_3 (0.000076)\n",
"351. feature 116 ILD_log10_lead_4 (0.000072)\n",
"352. feature 65 NM_M_mean_dist (0.000069)\n",
"353. feature 169 PE_lag_1 (0.000066)\n",
"354. feature 135 DeltaPHI_lag_4 (0.000061)\n",
"355. feature 131 DeltaPHI_lag_2 (0.000059)\n",
"356. feature 134 DeltaPHI_lead_3 (0.000054)\n",
"357. feature 354 NM_M_rollingmax_20 (0.000051)\n",
"358. feature 153 PHIND_lag_3 (0.000049)\n",
"359. feature 417 b4_Formation_lead_1 (0.000039)\n",
"360. feature 174 PE_lead_3 (0.000038)\n",
"361. feature 202 NM_M_lead_7 (0.000031)\n",
"362. feature 129 DeltaPHI_lag_1 (0.000029)\n",
"363. feature 175 PE_lag_4 (0.000026)\n",
"364. feature 89 GR_lag_1 (0.000026)\n",
"365. feature 149 PHIND_lag_1 (0.000024)\n",
"366. feature 130 DeltaPHI_lead_1 (0.000015)\n",
"367. feature 236 Depth_rollingstd_5 (0.000000)\n",
"368. feature 332 Depth_rollingstd_20 (0.000000)\n",
"369. feature 232 Formation_lead_2equal (0.000000)\n",
"370. feature 231 Formation_lag_2equal (0.000000)\n",
"371. feature 414 b1_Formation_lead_1 (0.000000)\n",
"372. feature 229 Formation_lag_1equal (0.000000)\n",
"373. feature 208 NM_M_lead_10 (0.000000)\n",
"374. feature 408 b3_Formation_lag_1 (0.000000)\n",
"375. feature 230 Formation_lead_1equal (0.000000)\n",
"376. feature 190 NM_M_lead_1 (0.000000)\n",
"377. feature 415 b2_Formation_lead_1 (0.000000)\n",
"378. feature 416 b3_Formation_lead_1 (0.000000)\n",
"379. feature 434 b5_Formation_lead_2 (0.000000)\n",
"380. feature 433 b4_Formation_lead_2 (0.000000)\n",
"381. feature 432 b3_Formation_lead_2 (0.000000)\n",
"382. feature 431 b2_Formation_lead_2 (0.000000)\n",
"383. feature 290 NM_M_rollingmax_10 (0.000000)\n",
"384. feature 428 sh_Formation_lag_2 (0.000000)\n",
"385. feature 427 lm_Formation_lag_2 (0.000000)\n",
"386. feature 426 b5_Formation_lag_2 (0.000000)\n",
"387. feature 425 b4_Formation_lag_2 (0.000000)\n",
"388. feature 268 Depth_rollingstd_10 (0.000000)\n",
"389. feature 300 Depth_rollingstd_15 (0.000000)\n",
"390. feature 424 b3_Formation_lag_2 (0.000000)\n",
"391. feature 423 b2_Formation_lag_2 (0.000000)\n",
"392. feature 422 b1_Formation_lag_2 (0.000000)\n",
"393. feature 260 NM_M_rollingstd_5 (0.000000)\n",
"394. feature 40 NM_M_var (0.000000)\n",
"395. feature 418 b5_Formation_lead_1 (0.000000)\n",
"396. feature 259 NM_M_rollingmin_5 (0.000000)\n",
"397. feature 203 NM_M_lag_8 (0.000000)\n",
"398. feature 42 RELPOS_max (0.000000)\n",
"399. feature 407 b2_Formation_lag_1 (0.000000)\n",
"400. feature 72 Depth_lead_2 (0.000000)\n",
"401. feature 73 Depth_lag_3 (0.000000)\n",
"402. feature 74 Depth_lead_3 (0.000000)\n",
"403. feature 75 Depth_lag_4 (0.000000)\n",
"404. feature 76 Depth_lead_4 (0.000000)\n",
"405. feature 77 Depth_lag_5 (0.000000)\n",
"406. feature 78 Depth_lead_5 (0.000000)\n",
"407. feature 79 Depth_lag_6 (0.000000)\n",
"408. feature 80 Depth_lead_6 (0.000000)\n",
"409. feature 81 Depth_lag_7 (0.000000)\n",
"410. feature 82 Depth_lead_7 (0.000000)\n",
"411. feature 83 Depth_lag_8 (0.000000)\n",
"412. feature 84 Depth_lead_8 (0.000000)\n",
"413. feature 85 Depth_lag_9 (0.000000)\n",
"414. feature 86 Depth_lead_9 (0.000000)\n",
"415. feature 87 Depth_lag_10 (0.000000)\n",
"416. feature 88 Depth_lead_10 (0.000000)\n",
"417. feature 71 Depth_lag_2 (0.000000)\n",
"418. feature 70 Depth_lead_1 (0.000000)\n",
"419. feature 191 NM_M_lag_2 (0.000000)\n",
"420. feature 398 b1_Formation (0.000000)\n",
"421. feature 406 b1_Formation_lag_1 (0.000000)\n",
"422. feature 205 NM_M_lag_9 (0.000000)\n",
"423. feature 204 NM_M_lead_8 (0.000000)\n",
"424. feature 189 NM_M_lag_1 (0.000000)\n",
"425. feature 401 b4_Formation (0.000000)\n",
"426. feature 400 b3_Formation (0.000000)\n",
"427. feature 399 b2_Formation (0.000000)\n",
"428. feature 200 NM_M_lead_6 (0.000000)\n",
"429. feature 192 NM_M_lead_2 (0.000000)\n",
"430. feature 199 NM_M_lag_6 (0.000000)\n",
"431. feature 198 NM_M_lead_5 (0.000000)\n",
"432. feature 195 NM_M_lag_4 (0.000000)\n",
"433. feature 194 NM_M_lead_3 (0.000000)\n",
"434. feature 63 NM_M_min_dist (0.000000)\n",
"435. feature 64 NM_M_max_dist (0.000000)\n",
"436. feature 193 NM_M_lag_3 (0.000000)\n",
"437. feature 69 Depth_lag_1 (0.000000)\n"
]
}
],
"source": [
"importances = clf.feature_importances_ \n",
"indices = np.argsort(importances)[::-1]\n",
"print(\"Feature ranking:\")\n",
"feature = list(xtrain.columns.values)\n",
"for f in range(xtrain.shape[1]):\n",
" print(\"%d. feature %d %s (%f)\" % (f + 1, indices[f], feature[indices[f]], importances[indices[f]]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Apply to test"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# this time let's use all the training set \n",
"ytrain = df[(df['origin']=='train')]['Facies']\n",
"yvalid = df[(df['origin']=='test')]['Facies']\n",
"xtrain = df[(df['origin']=='train')].drop(['Well Name','origin','Facies'],axis=1)\n",
"xvalid = df[(df['origin']=='test')].drop(['Well Name','origin','Facies'],axis=1)\n",
"well_name_valid = df.loc[(df['origin']=='test'),\"Well Name\"]"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
" criterion='gini', max_depth=10, max_features=0.1,\n",
" max_leaf_nodes=None, min_impurity_split=1e-07,\n",
" min_samples_leaf=25, min_samples_split=50,\n",
" min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
" oob_score=True, random_state=None, verbose=0, warm_start=False)"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf.fit(xtrain,ytrain)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"preds = clf.predict(xvalid.values)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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dxdxhnXXsrkuRfFxS1pc69ib+eHWByt1K4qyqG/SMIvN2aSP2ffty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\nAaL88M/ArJzMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11f972750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"well = \"CRAWFORD\"\n",
"depth = xvalid.loc[well_name_valid== well ,\"Depth\"]\n",
"predictions = pd.Series(preds).loc[well_name_valid==well]\n",
"plt.plot(depth,predictions)\n",
"plt.axis([2950,3175, 1, 9])\n",
"plt.grid(b=True, which='major', color='r', linestyle='--')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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EwwhCekyEvykQGEFIgl0MJIjxptKNo2atFwij2WafcF0gVBVBmHONGX0OmAl/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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11de1a150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"well = \"STUART\"\n",
"depth = xvalid.loc[well_name_valid== well ,\"Depth\"]\n",
"predictions = pd.Series(preds).loc[well_name_valid==well]\n",
"plt.plot(depth,predictions)\n",
"plt.axis([2800,3050, 1, 9])\n",
"plt.grid(b=True, which='major', color='r', linestyle='--')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xvalid['Facies']=preds\n",
"xvalid.to_csv('XmasPreds_3.csv')"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"test_1 = pd.read_csv('XmasPreds_1.csv')[\"Facies\"]\n",
"test_3 = pd.read_csv('XmasPreds_3.csv')[\"Facies\"]"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 830\n",
"unique 2\n",
"top True\n",
"freq 645\n",
"Name: Facies, dtype: object"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(test_1==test_3).describe()"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
nkmk/python-snippets | notebook/numpy_array_equal_array_equiv.ipynb | 1 | 6732 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2]\n"
]
}
],
"source": [
"a = np.arange(3)\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2]\n"
]
}
],
"source": [
"b = np.arange(3)\n",
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 2 3]\n"
]
}
],
"source": [
"c = np.arange(1, 4)\n",
"print(c)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(np.all(a == b))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.all(a == c))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(np.array_equal(a, b))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.array_equal(a, c))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(np.array_equiv(a, b))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.array_equiv(a, c))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0. 1. 2.]\n"
]
}
],
"source": [
"b_f = np.arange(3, dtype=float)\n",
"print(b_f)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(np.array_equal(a, b_f))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(np.array_equiv(a, b_f))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1. 1. 1.]\n"
]
}
],
"source": [
"ones = np.ones(3)\n",
"print(ones)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.array_equal(ones, 1))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(np.array_equiv(ones, 1))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 1 2]\n",
" [0 1 2]\n",
" [0 1 2]]\n"
]
}
],
"source": [
"a_2d = np.array([[0, 1, 2], [0, 1, 2], [0, 1, 2]])\n",
"print(a_2d)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.array_equal(a_2d, b))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(np.array_equiv(a_2d, b))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[nan 1. 2.]\n"
]
}
],
"source": [
"a_nan = np.array([np.nan, 1, 2])\n",
"print(a_nan)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[nan 1. 2.]\n"
]
}
],
"source": [
"b_nan = np.array([np.nan, 1, 2])\n",
"print(b_nan)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.array_equal(a_nan, b_nan))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.array_equiv(a_nan, b_nan))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.all(a_nan == b_nan))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
jbwhit/jupyter-best-practices | notebooks/Advanced-Notebook-Tricks.ipynb | 2 | 12531 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Advanced Notebook"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:23.187051Z",
"start_time": "2018-09-19T03:46:21.591470Z"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"from pandas.tools.plotting import scatter_matrix\n",
"from sklearn.datasets import load_boston\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"sns.set_context('poster')\n",
"sns.set_style('whitegrid')\n",
"plt.rcParams['figure.figsize'] = 12, 8 # plotsize\n",
"\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# BQPlot\n",
"\n",
"Examples here are shamelessly stolen from the amazing: https://github.com/maartenbreddels/jupytercon-2017/blob/master/jupytercon2017-widgets.ipynb"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:25.068331Z",
"start_time": "2018-09-19T03:46:24.977343Z"
}
},
"outputs": [],
"source": [
"# mixed feelings about this import\n",
"import bqplot.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:25.342029Z",
"start_time": "2018-09-19T03:46:25.339250Z"
}
},
"outputs": [],
"source": [
"x = np.linspace(0, 2, 50)\n",
"y = x**2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:25.690593Z",
"start_time": "2018-09-19T03:46:25.611706Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "da366ea5c4854a878e81f7866db0b757",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Figure(axes=[Axis(scale=LinearScale()), Axis(orientation='vertical', scale=LinearScale())], fig…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"scatter = plt.scatter(x, y)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:26.036888Z",
"start_time": "2018-09-19T03:46:26.026009Z"
}
},
"outputs": [],
"source": [
"fig.animation_duration = 500\n",
"scatter.y = 2 * x**.5"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:26.170123Z",
"start_time": "2018-09-19T03:46:26.157341Z"
}
},
"outputs": [],
"source": [
"scatter.selected_style = {'stroke':'red', 'fill': 'orange'}\n",
"plt.brush_selector();"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:26.372518Z",
"start_time": "2018-09-19T03:46:26.365512Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scatter.selected"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:26.670651Z",
"start_time": "2018-09-19T03:46:26.664068Z"
}
},
"outputs": [],
"source": [
"scatter.selected = [1,2,10,40]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ipyvolume"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:27.785075Z",
"start_time": "2018-09-19T03:46:27.159720Z"
}
},
"outputs": [],
"source": [
"import ipyvolume as ipv"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:27.833311Z",
"start_time": "2018-09-19T03:46:27.830148Z"
}
},
"outputs": [],
"source": [
"N = 1000\n",
"x, y, z = np.random.random((3, N))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:28.192266Z",
"start_time": "2018-09-19T03:46:28.019468Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bc19bce70fe5456dba5e546911f6362f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Figure(camera=PerspectiveCamera(fov=46.0, position=(0.0, 0.0, 2.0), quaternion=(0.0, 0.0, 0.0, …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = ipv.figure()\n",
"scatter = ipv.scatter(x, y, z, marker='box')\n",
"ipv.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:28.745291Z",
"start_time": "2018-09-19T03:46:28.730850Z"
}
},
"outputs": [],
"source": [
"scatter.x = scatter.x - 0.5"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:29.284267Z",
"start_time": "2018-09-19T03:46:29.276971Z"
}
},
"outputs": [],
"source": [
"scatter.x = x"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:30.041956Z",
"start_time": "2018-09-19T03:46:30.029671Z"
}
},
"outputs": [],
"source": [
"scatter.color = \"green\"\n",
"scatter.size = 5"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:30.135152Z",
"start_time": "2018-09-19T03:46:30.127245Z"
}
},
"outputs": [],
"source": [
"scatter.color = np.random.random((N,3))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:30.638520Z",
"start_time": "2018-09-19T03:46:30.631700Z"
}
},
"outputs": [],
"source": [
"scatter.size = 2"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:32.443482Z",
"start_time": "2018-09-19T03:46:30.696718Z"
}
},
"outputs": [],
"source": [
"ex = ipv.datasets.animated_stream.fetch().data"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:32.453403Z",
"start_time": "2018-09-19T03:46:32.446965Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(6, 200, 1250)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex.shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:33.289185Z",
"start_time": "2018-09-19T03:46:33.278110Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(6, 200, 313)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex[:, ::, ::4].shape"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:36.369201Z",
"start_time": "2018-09-19T03:46:35.089776Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "279aa56f861746c2bb20f3b52aa9a399",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Figure(animation=200.0, camera=PerspectiveCamera(fov=46.0, position=(0.0, 0.0, 2.0), quaternion…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ipv.figure()\n",
"ipv.style.use('dark')\n",
"quiver = ipv.quiver(*ipv.datasets.animated_stream.fetch().data[:,::,::4], size=5)\n",
"ipv.animation_control(quiver, interval=200)\n",
"ipv.show()\n",
"ipv.style.use('light')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2018-06-06T23:37:45.115960Z",
"start_time": "2018-06-06T23:37:45.113181Z"
}
},
"outputs": [],
"source": [
"ipv.style.use('light')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2018-06-07T02:27:04.492537Z",
"start_time": "2018-06-07T02:27:04.489578Z"
}
},
"outputs": [],
"source": [
"quiver.geo = \"cat\""
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:40.265215Z",
"start_time": "2018-09-19T03:46:40.200758Z"
}
},
"outputs": [],
"source": [
"N = 1000*1000\n",
"x, y, z = np.random.random((3, N)).astype('f4')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:40.686040Z",
"start_time": "2018-09-19T03:46:40.606619Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b75b9c50d7064d14ba016f24bb44c3eb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Figure(camera=PerspectiveCamera(fov=46.0, position=(0.0, 0.0, 2.0), quaternion=(0.0, 0.0, 0.0, …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ipv.figure()\n",
"s = ipv.scatter(x, y, z, size=0.2)\n",
"ipv.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:44.157821Z",
"start_time": "2018-09-19T03:46:43.704660Z"
}
},
"outputs": [],
"source": [
"ipv.save(\"3d-example-plot.html\")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-19T03:46:48.551626Z",
"start_time": "2018-09-19T03:46:48.325843Z"
}
},
"outputs": [],
"source": [
"!open 3d-example-plot.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"hide_input": false,
"kernelspec": {
"display_name": "dspy3",
"language": "python",
"name": "dspy3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
},
"toc": {
"base_numbering": 1,
"nav_menu": {
"height": "48px",
"width": "252px"
},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"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 |
Housebeer/Natural-Gas-Model | Backup/.ipynb_checkpoints/Matching Market v7-checkpoint.ipynb | 1 | 69161 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Matching Market"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"This simple model consists of a buyer, a supplier, and a market. \n",
"\n",
"The buyer represents a group of customers whose willingness to pay for a single unit of the good is captured by a vector of prices _wta_. You can initiate the buyer with a set_quantity function which randomly assigns the willingness to pay according to your specifications. You may ask for these willingness to pay quantities with a _getbid_ function. \n",
"\n",
"The supplier is similar, but instead the supplier is willing to be paid to sell a unit of technology. The supplier for instance may have non-zero variable costs that make them unwilling to produce the good unless they receive a specified price. Similarly the supplier has a get_ask function which returns a list of desired prices. \n",
"\n",
"The willingness to pay or sell are set randomly using uniform random distributions. The resultant lists of bids are effectively a demand curve. Likewise the list of asks is effectively a supply curve. A more complex determination of bids and asks is possible, for instance using time of year to vary the quantities being demanded. \n",
"\n",
"## New in version 7\n",
"- preserved market throughout the whole model run (new one was created every time step)\n",
"- previously two books were created, now we keep only one (the book of the market)\n",
"- these improvements cut the run time in half (from 7 minutes to 3 minutes)\n",
"- The observer now only tells the market to start, market instruct its subscribers\n",
"\n",
"## Microeconomic Foundations\n",
"\n",
"The market assumes the presence of an auctioneer which will create a _book_, which seeks to match the bids and the asks as much as possible. If the auctioneer is neutral, then it is incentive compatible for the buyer and the supplier to truthfully announce their bids and asks. The auctioneer will find a single price which clears as much of the market as possible. Clearing the market means that as many willing swaps happens as possible. You may ask the market object at what price the market clears with the get_clearing_price function. You may also ask the market how many units were exchanged with the get_units_cleared function."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Agent-Based Objects\n",
"\n",
"The following section presents three objects which can be used to make an agent-based model of an efficient, two-sided market. "
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import random as rnd\n",
"import pandas as pd\n",
"import numpy as np\n",
"import time\n",
"import datetime"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# measure how long it takes to run the script\n",
"startit = time.time()\n",
"dtstartit = datetime.datetime.now()\n",
"\n",
"class Seller():\n",
" def __init__(self, name):\n",
" self.name = name\n",
" self.wta = []\n",
" self.prod = 2000\n",
" self.lb_price = 0\n",
" self.ub_price = 10\n",
"\n",
" # the supplier has n quantities that they can sell\n",
" # they may be willing to sell this quantity anywhere from a lower price of l\n",
" # to a higher price of u\n",
" def set_quantity(self):\n",
" n = self.prod\n",
" l = self.lb_price\n",
" u = self.ub_price\n",
" for i in range(n):\n",
" p = rnd.uniform(l, u)\n",
" self.wta.append(p)\n",
"\n",
" def get_name(self):\n",
" return self.name\n",
"\n",
" def get_asks(self):\n",
" return self.wta\n",
"\n",
" def clear_wta(self):\n",
" self.wta = []\n",
"\n",
"\n",
"class Buyer():\n",
" def __init__(self, name):\n",
" self.name = name\n",
" self.wtp = []\n",
" self.step = 0\n",
" self.base_demand = 0\n",
" self.max_demand = 0\n",
" self.lb_price = 0\n",
" self.ub_price = 10\n",
"\n",
" # the supplier has n quantities that they can buy\n",
" # they may be willing to sell this quantity anywhere from a lower price of l\n",
" # to a higher price of u\n",
" def set_quantity(self):\n",
" n = int(self.consumption(self.step))\n",
" l = self.lb_price\n",
" u = self.ub_price\n",
" for i in range(n):\n",
" p = rnd.uniform(l, u)\n",
" self.wtp.append(p)\n",
" \n",
" def get_name(self):\n",
" return self.name\n",
" \n",
" # return list of willingness to pay\n",
" def get_bids(self):\n",
" return self.wtp\n",
" \n",
" def clear_wtp(self):\n",
" self.wtp = []\n",
" \n",
" def consumption(self, x):\n",
" # make it initialise to seller\n",
" b = self.base_demand\n",
" m = self.max_demand\n",
" y = b + m * (.5 * (1 + np.cos((x/6)*np.pi)))\n",
" return(y)\n",
"\n",
"\n",
"class Book():\n",
" def __init__(self):\n",
" self.ledger = pd.DataFrame(columns = (\"role\",\"name\",\"price\",\"cleared\"))\n",
"\n",
" def set_asks(self,seller_list):\n",
" # ask each seller their name\n",
" # ask each seller their willingness\n",
" # for each willingness append the data frame\n",
" for seller in seller_list:\n",
" seller_name = seller.get_name()\n",
" seller_price = seller.get_asks()\n",
" for price in seller_price:\n",
" self.ledger=self.ledger.append({\"role\":\"seller\",\"name\":seller_name,\"price\":price,\"cleared\":\"in process\"},\n",
" ignore_index=True)\n",
"\n",
" def set_bids(self,buyer_list):\n",
" # ask each seller their name\n",
" # ask each seller their willingness\n",
" # for each willingness append the data frame\n",
" for buyer in buyer_list:\n",
" buyer_name = buyer.get_name()\n",
" buyer_price = buyer.get_bids()\n",
" for price in buyer_price:\n",
" self.ledger=self.ledger.append({\"role\":\"buyer\",\"name\":buyer_name,\"price\":price,\"cleared\":\"in process\"},\n",
" ignore_index=True)\n",
"\n",
" def update_ledger(self,ledger):\n",
" self.ledger = ledger\n",
" \n",
" def get_ledger(self):\n",
" return self.ledger\n",
" \n",
" def clean_ledger(self):\n",
" self.ledger = pd.DataFrame(columns = (\"role\",\"name\",\"price\",\"cleared\"))\n",
"\n",
"class Market():\n",
" def __init__(self):\n",
" self.count = 0\n",
" self.last_price = ''\n",
" self.book = Book()\n",
" self.b = []\n",
" self.s = []\n",
" self.buyer_list = []\n",
" self.seller_list = []\n",
" self.buyer_dict = {}\n",
" self.seller_dict = {}\n",
" self.ledger = ''\n",
" \n",
" def update_seller(self):\n",
" for i in self.seller_dict:\n",
" self.seller_dict[i].clear_wta()\n",
" self.seller_dict[i].set_quantity()\n",
"\n",
" def update_buyer(self):\n",
" for i in self.buyer_dict:\n",
" self.buyer_dict[i].step += 1\n",
" self.buyer_dict[i].clear_wtp()\n",
" self.buyer_dict[i].set_quantity()\n",
" \n",
" def add_buyer(self,buyer):\n",
" self.b.append(buyer)\n",
" self.buyer_list.append(buyer)\n",
" \n",
" def add_seller(self,seller):\n",
" self.s.append(seller) \n",
" self.seller_list.append(seller)\n",
" \n",
" def set_book(self):\n",
" self.book.set_bids(self.buyer_list)\n",
" self.book.set_asks(self.seller_list)\n",
" \n",
" def get_ledger(self):\n",
" self.ledger = self.book.get_ledger()\n",
" return self.ledger\n",
" \n",
" def get_bids(self):\n",
" # this is a data frame\n",
" ledger = self.book.get_ledger()\n",
" rows= ledger.loc[ledger['role'] == 'buyer']\n",
" # this is a series\n",
" prices=rows['price']\n",
" # this is a list\n",
" bids = prices.tolist()\n",
" return bids\n",
" \n",
" def get_asks(self):\n",
" # this is a data frame\n",
" ledger = self.book.get_ledger()\n",
" rows = ledger.loc[ledger['role'] == 'seller']\n",
" # this is a series\n",
" prices=rows['price']\n",
" # this is a list\n",
" asks = prices.tolist()\n",
" return asks\n",
" \n",
" # return the price at which the market clears\n",
" # this fails because there are more buyers then sellers\n",
" \n",
" def get_clearing_price(self):\n",
" # buyer makes a bid starting with the buyer which wants it most\n",
" b = self.get_bids()\n",
" s = self.get_asks()\n",
" # highest to lowest\n",
" self.b=sorted(b, reverse=True)\n",
" # lowest to highest\n",
" self.s=sorted(s, reverse=False)\n",
" \n",
" # find out whether there are more buyers or sellers\n",
" # then drop the excess buyers or sellers; they won't compete\n",
" n = len(b)\n",
" m = len(s)\n",
" \n",
" # there are more sellers than buyers\n",
" # drop off the highest priced sellers \n",
" if (m > n):\n",
" s = s[0:n]\n",
" matcher = n\n",
" # There are more buyers than sellers\n",
" # drop off the lowest bidding buyers \n",
" else:\n",
" b = b[0:m]\n",
" matcher = m\n",
" \n",
" # It's possible that not all items sold actually clear the market here\n",
" for i in range(matcher):\n",
" if (self.b[i] > self.s[i]):\n",
" self.count +=1\n",
" self.last_price = self.b[i]\n",
" \n",
" return self.last_price\n",
" \n",
" # TODO: Annotate the ledger\n",
" def annotate_ledger(self,clearing_price):\n",
" ledger = self.book.get_ledger()\n",
" for index, row in ledger.iterrows():\n",
" if (row['role'] == 'seller'):\n",
" if (row['price'] < clearing_price):\n",
" ledger.loc[index,'cleared'] = 'True'\n",
" else:\n",
" ledger.loc[index,'cleared'] = 'False'\n",
" else:\n",
" if (row['price'] > clearing_price):\n",
" ledger.loc[index,'cleared'] = 'True'\n",
" else:\n",
" ledger.loc[index,'cleared'] = 'False' \n",
" \n",
" self.book.update_ledger(ledger)\n",
" \n",
" def get_units_cleared(self):\n",
" return self.count\n",
" \n",
" def clean_ledger(self):\n",
" self.ledger = ''\n",
" self.book.clean_ledger()\n",
"\n",
" # not called yet\n",
" def action1(self):\n",
" #self.book.set_asks([sell for sell in obser1.seller_dict.values()])\n",
" #self.book.set_bids([buy for buy in obser1.buyer_dict.values()])\n",
" self.update_buyer()\n",
" self.update_seller()\n",
" self.set_book()\n",
" clearing_price = self.get_clearing_price()\n",
" self.annotate_ledger(clearing_price)\n",
" self.clean_ledger()\n",
" return clearing_price\n",
"\n",
"\n",
"class Observer():\n",
" def __init__(self, x, y, z):\n",
" self.wta = []\n",
" #self.god_info = period\n",
" self.init_buyer = x\n",
" self.init_seller = y\n",
" self.maxrun = z\n",
" self.hist_book = []\n",
" self.buyer_dict = {}\n",
" self.seller_dict = {}\n",
" self.timetick = 0\n",
" self.gas_market = ''\n",
" \n",
" def set_buyer(self, buyerinfo):\n",
" for name in buyerinfo:\n",
" self.buyer_dict[name] = Buyer('%s' % name)\n",
" self.buyer_dict[name].base_demand = buyerinfo[name]['b']\n",
" self.buyer_dict[name].max_demand = buyerinfo[name]['m']\n",
"\n",
" \n",
" def set_seller(self, seller_info):\n",
" for name in seller_info:\n",
" self.seller_dict[name] = Seller('%s' % name)\n",
" self.seller_dict[name].prod = seller_info[name][0]\n",
" \n",
" def set_market(self):\n",
" self.gas_market = Market()\n",
" #add suplliers and buyers to this market\n",
" for supplier in self.seller_dict.values():\n",
" self.gas_market.add_seller(supplier)\n",
" for buyer in self.buyer_dict.values():\n",
" self.gas_market.add_buyer(buyer)\n",
" self.gas_market.seller_dict = self.seller_dict\n",
" self.gas_market.buyer_dict = self.buyer_dict\n",
"\n",
" def run_it(self):\n",
" # Timing\n",
" # time initialising\n",
" startit_init = time.time()\n",
" \n",
" #initialise, setting up all the agents\n",
" first_run = True\n",
" if first_run:\n",
" #self.set_buyer(buyerinfo)\n",
" self.set_buyer(self.init_buyer)\n",
" self.set_seller(self.init_seller)\n",
" self.set_market()\n",
" first_run=False\n",
" \n",
" # time init stop\n",
" stopit_init = time.time() - startit_init\n",
" print('%s : init' % stopit_init)\n",
" \n",
" for period in range(self.maxrun):\n",
" # time the period\n",
" startit_period = time.time()\n",
" \n",
" self.timetick += 1\n",
" print('#######################################')\n",
" print(god_info[period][0])\n",
"\n",
" # real action on the market\n",
" clearing_price = self.gas_market.action1()\n",
" \n",
" # recording the step_info\n",
"\n",
" # since this operation can take quite a while, print after every operation\n",
" period_time = time.time() - startit_period\n",
" print('%s : period time' % period_time)\n",
" self.hist_book.append([god_info[period][0], clearing_price])\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example Market\n",
"\n",
"In the following code example we use the buyer and supplier objects to create a market. At the market a single price is announced which causes as many units of goods to be swapped as possible. The buyers and sellers stop trading when it is no longer in their own interest to continue. "
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>elec</th>\n",
" <th>indu</th>\n",
" <th>home</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>jan</th>\n",
" <td>1066</td>\n",
" <td>1572</td>\n",
" <td>4218</td>\n",
" </tr>\n",
" <tr>\n",
" <th>feb</th>\n",
" <td>941</td>\n",
" <td>1349</td>\n",
" <td>3490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mar</th>\n",
" <td>965</td>\n",
" <td>1416</td>\n",
" <td>2636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>apr</th>\n",
" <td>841</td>\n",
" <td>1215</td>\n",
" <td>1614</td>\n",
" </tr>\n",
" <tr>\n",
" <th>may</th>\n",
" <td>742</td>\n",
" <td>1285</td>\n",
" <td>1458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jun</th>\n",
" <td>673</td>\n",
" <td>1171</td>\n",
" <td>763</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jul</th>\n",
" <td>698</td>\n",
" <td>1229</td>\n",
" <td>603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aug</th>\n",
" <td>668</td>\n",
" <td>1169</td>\n",
" <td>709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sep</th>\n",
" <td>729</td>\n",
" <td>1207</td>\n",
" <td>1042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>okt</th>\n",
" <td>983</td>\n",
" <td>1362</td>\n",
" <td>1742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>nov</th>\n",
" <td>944</td>\n",
" <td>1371</td>\n",
" <td>2632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dec</th>\n",
" <td>994</td>\n",
" <td>1505</td>\n",
" <td>4301</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" elec indu home\n",
"jan 1066 1572 4218\n",
"feb 941 1349 3490\n",
"mar 965 1416 2636\n",
"apr 841 1215 1614\n",
"may 742 1285 1458\n",
"jun 673 1171 763\n",
"jul 698 1229 603\n",
"aug 668 1169 709\n",
"sep 729 1207 1042\n",
"okt 983 1362 1742\n",
"nov 944 1371 2632\n",
"dec 994 1505 4301"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
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{
"data": {
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mG2MKjDGFwHxgSosejQLg1uG30imoE0+tesrpUJTyeWvz1/LWlreYOmgqw+OG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\nzcjbSRZnAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1eee6d8b908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#not used anymore, construct time in with datetime and delete\n",
"\n",
"# is dictionary really suited? Sorted vs unsorted\n",
"# god_info is the the input for the model\n",
"# This is a example\n",
"'''\n",
"god_info = {\n",
" 'jan': {'home': (100, 0, 10), 'industry': (50, 0, 10), 'cat': (75, 0, 10)},\n",
" 'feb': {'home': (90, 0, 10), 'industry': (40, 0, 10), 'cat': (60, 0, 10)},\n",
" 'march': {'home': (100, 0, 10), 'industry': (50, 0, 10), 'cat': (75, 0, 10)}\n",
" }\n",
"#'''\n",
"\n",
"#read montly consumption data of 2010 into a dataframe\n",
"df = pd.read_csv('2010cbstestrun.csv', header=0, index_col=0)\n",
"df = df.transpose()\n",
"\n",
"#make an array of for the buyers from the csv in the following format\n",
"#buyerinfo = [time ['home', (100, 0, 10)], ['industry', (50, 0, 10)], ['cat', (75, 0, 10)]]\n",
"god_info = [[x, [['elec',(y,0,10)],['indu',(z,0,10)],['home',(u,0,10)]]] \n",
" for x,y,z,u in zip(df.index,df['elec'],df['indu'],df['home'])]\n",
"\n",
"#plot the 2010 monthly consumption data\n",
"df.plot();\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# make initialization dictionary\n",
"init_buyer = {'elec':{'b':400, 'm' : 673}, 'indu':{'b':400, 'm':1171}, 'home':{'b': 603, 'm': 3615}}\n",
"init_seller = {'natural gas' : (2000, 0, 10)} \n",
"\n",
"# make a history book to record every timestep\n",
"hist_book = []"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## run the model\n",
"To run the model we create the observer. The observer creates all the other objects and runs the model."
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0010006427764892578 : init\n",
"#######################################\n",
"jan\n",
"25.845173120498657 : period time\n",
"#######################################\n",
"feb\n",
"22.100587844848633 : period time\n",
"#######################################\n",
"mar\n",
"17.581023454666138 : period time\n",
"#######################################\n",
"apr\n",
"13.295471429824829 : period time\n",
"#######################################\n",
"may\n",
"10.505373001098633 : period time\n",
"#######################################\n",
"jun\n",
"9.160466194152832 : period time\n",
"#######################################\n",
"jul\n",
"10.20919418334961 : period time\n",
"#######################################\n",
"aug\n",
"13.159301280975342 : period time\n",
"#######################################\n",
"sep\n",
"17.351248025894165 : period time\n",
"#######################################\n",
"okt\n",
"21.565234184265137 : period time\n",
"#######################################\n",
"nov\n",
"24.900577783584595 : period time\n",
"#######################################\n",
"dec\n",
"26.21299433708191 : period time\n"
]
}
],
"source": [
"# create observer and run the model\n",
"# first data about buyers then sellers and then model ticks\n",
"obser1 = Observer(init_buyer, init_seller, 12)\n",
"obser1.run_it()\n",
"#get the info from the observer\n",
"hist_book = obser1.hist_book"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"# recording the total run\n",
"f = open('hist_book.csv', 'a')\n",
"for item in hist_book:\n",
" f.write('%s,%s\\n' % (item[0], item[1]))\n",
"f.close()\n",
"\n",
"# make a dataframe of clearing prices\n",
"df_hb = pd.DataFrame(hist_book)\n",
"df_hb = df_hb.set_index(0)\n",
"df_hb.index.name = 'month'\n",
"df_hb.rename(columns={1: 'price'}, inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Operations Research Formulation\n",
"\n",
"The market can also be formulated as a very simple linear program or linear complementarity problem. It is clearer and easier to implement this market clearing mechanism with agents. One merit of the agent-based approach is that we don't need linear or linearizable supply and demand function. \n",
"\n",
"The auctioneer is effectively following a very simple linear program subject to constraints on units sold. The auctioneer is, in the primal model, maximizing the consumer utility received by customers, with respect to the price being paid, subject to a fixed supply curve. On the dual side the auctioneer is minimizing the cost of production for the supplier, with respect to quantity sold, subject to a fixed demand curve. It is the presumed neutrality of the auctioneer which justifies the honest statement of supply and demand. \n",
"\n",
"An alternative formulation is a linear complementarity problem. Here the presence of an optimal space of trades ensures that there is a Pareto optimal front of possible trades. The perfect opposition of interests in dividing the consumer and producer surplus means that this is a zero sum game. Furthermore the solution to this zero-sum game maximizes societal welfare and is therefore the Hicks optimal solution.\n",
"\n",
"## Next Steps\n",
"\n",
"A possible addition of this model would be to have a weekly varying demand of customers, for instance caused by the use of natural gas as a heating agent. This would require the bids and asks to be time varying, and for the market to be run over successive time periods. A second addition would be to create transport costs, or enable intermediate goods to be produced. This would need a more elaborate market operator. Another possible addition would be to add a profit maximizing broker. This may require adding belief, fictitious play, or message passing. \n",
"\n",
"The object-orientation of the models will probably need to be further rationalized. Right now the market requires very particular ordering of calls to function correctly. "
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"it took us 212.11668634414673 seconds to get to this conclusion\n",
"in another notation (h:m:s) 0:03:32.116686\n"
]
}
],
"source": [
"# timeit\n",
"\n",
"stopit = time.time()\n",
"dtstopit = datetime.datetime.now()\n",
"\n",
"print('it took us %s seconds to get to this conclusion' % (stopit-startit))\n",
"print('in another notation (h:m:s) %s'% (dtstopit - dtstartit))"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
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zmdNxTBCyomC8xhrOnrciLZt1Gce48/K2RITZ/77G8+y3yniVNZw9a9qSVBpF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\njDGmlBUFY4wxpf4fYp9KhJNm9rAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1eee6ebd128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# print the run results\n",
"df_hb\n",
"df_hb.plot()\n",
"plt.ylabel('€ / unit');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time of last run\n",
"Time and date of the last run of this notebook file "
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"last run of this notebook:\n"
]
},
{
"data": {
"text/plain": [
"'Fri, 30 Jun 2017 13:31:20'"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# print the time of last run\n",
"print('last run of this notebook:')\n",
"time.strftime(\"%a, %d %b %Y %H:%M:%S\", time.localtime())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
deepmind/open_spiel | open_spiel/colabs/rcfr_pytorch.ipynb | 1 | 8450 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pyspiel \n",
"import tensorflow.compat.v1 as tf\n",
"import torch \n",
"import torch.nn as nn\n",
"\n",
"import algorithms.rcfr as rcfr_tf\n",
"import pytorch.rcfr as rcfr_pt\n",
"tf.disable_v2_behavior()\n",
"\n",
"tf.enable_eager_execution()\n",
"\n",
"_GAME = pyspiel.load_game('kuhn_poker')\n",
"_BATCH_SIZE = 12"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def tnsorflow_example(game_name, num_epochs, iterations):\n",
" game = pyspiel.load_game(game_name)\n",
"\n",
" models = []\n",
" for _ in range(game.num_players()):\n",
" models.append(\n",
" rcfr_tf.DeepRcfrModel(\n",
" game,\n",
" num_hidden_layers=1,\n",
" num_hidden_units=13,\n",
" num_hidden_factors=8,\n",
" use_skip_connections=True))\n",
"\n",
" buffer_size = -1\n",
" truncate_negative = False\n",
" bootstrap = False\n",
" if buffer_size > 0:\n",
" solver = rcfr_tf.ReservoirRcfrSolver(\n",
" game,\n",
" models,\n",
" buffer_size,\n",
" truncate_negative=truncate_negative)\n",
" else:\n",
" solver = rcfr_tf.RcfrSolver(\n",
" game,\n",
" models,\n",
" truncate_negative=truncate_negative,\n",
" bootstrap=bootstrap)\n",
"\n",
" def _train_fn(model, data):\n",
" \"\"\"Train `model` on `data`.\"\"\"\n",
" batch_size = 100\n",
" step_size = 0.01\n",
" data = data.shuffle(batch_size * 10)\n",
" data = data.batch(batch_size)\n",
" data = data.repeat(num_epochs)\n",
"\n",
" optimizer = tf.keras.optimizers.Adam(lr=step_size, amsgrad=True)\n",
"\n",
" @tf.function\n",
" def _train():\n",
" for x, y in data:\n",
" optimizer.minimize(\n",
" lambda: tf.losses.huber_loss(y, model(x), delta=0.01), # pylint: disable=cell-var-from-loop\n",
" model.trainable_variables)\n",
"\n",
" _train()\n",
"\n",
" # End of _train_fn\n",
" result = []\n",
" for i in range(iterations):\n",
" solver.evaluate_and_update_policy(_train_fn)\n",
" if i % 10 == 0:\n",
" conv = pyspiel.exploitability(game, solver.average_policy())\n",
" result.append(conv)\n",
" # print(\"Iteration {} exploitability {}\".format(i, conv))\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def pytorch_example(game_name, num_epochs, iterations):\n",
" game = pyspiel.load_game(game_name)\n",
"\n",
" models = []\n",
" for _ in range(game.num_players()):\n",
" models.append(\n",
" rcfr_pt.DeepRcfrModel(\n",
" game,\n",
" num_hidden_layers=1,\n",
" num_hidden_units=13,\n",
" num_hidden_factors=8,\n",
" use_skip_connections=True))\n",
"\n",
" buffer_size = -1\n",
" truncate_negative = False\n",
" bootstrap = False\n",
" if buffer_size > 0:\n",
" solver = rcfr_pt.ReservoirRcfrSolver(\n",
" game,\n",
" models,\n",
" buffer_size,\n",
" truncate_negative=truncate_negative)\n",
" else:\n",
" solver = rcfr_pt.RcfrSolver(\n",
" game,\n",
" models,\n",
" truncate_negative=truncate_negative,\n",
" bootstrap=bootstrap)\n",
"\n",
" def _train_fn(model, data):\n",
" \"\"\"Train `model` on `data`.\"\"\"\n",
" batch_size = 100\n",
" num_epochs = 20\n",
" step_size = 0.01\n",
" \n",
" data = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True)\n",
" loss_fn = nn.SmoothL1Loss()\n",
" optimizer = torch.optim.Adam(model.parameters(), lr=step_size, amsgrad=True)\n",
"\n",
" def _train(model, data):\n",
" for epoch in range(num_epochs):\n",
" for x, y in data:\n",
" optimizer.zero_grad()\n",
" output = model(x)\n",
" loss = loss_fn(output, y)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" _train(model, data)\n",
"\n",
" # End of _train_fn\n",
" result = []\n",
" for i in range(iterations):\n",
" solver.evaluate_and_update_policy(_train_fn)\n",
" if i % 10 == 0:\n",
" conv = pyspiel.exploitability(game, solver.average_policy())\n",
" result.append(conv)\n",
" # print(\"Iteration {} exploitability {}\".format(i, conv))\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tensorflow_rcfr = []\n",
"pytorch_rcfr = []\n",
"num_epochs, iterations = 20, 100\n",
"for _ in range(10):\n",
" tensorflow_rcfr.append(tnsorflow_example('kuhn_poker', num_epochs, iterations))\n",
" pytorch_rcfr.append(pytorch_example('kuhn_poker', num_epochs, iterations))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [i for i in range(10)]\n",
"tf_exploitability = [sum(tfe) for tfe in zip(*tensorflow_rcfr)]\n",
"pt_exploitability = [sum(pte) for pte in zip(*pytorch_rcfr)]\n",
"\n",
"plt.plot(x, tf_exploitability, label=\"tensorflow\")\n",
"plt.plot(x, pt_exploitability, label=\"pytorch\")\n",
"\n",
"plt.legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tensorflow_rcfr = []\n",
"pytorch_rcfr = []\n",
"num_epochs, iterations = 200, 100\n",
"for _ in range(10):\n",
" tensorflow_rcfr.append(tnsorflow_example('kuhn_poker', num_epochs, iterations))\n",
" pytorch_rcfr.append(pytorch_example('kuhn_poker', num_epochs, iterations))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [i for i in range(10)]\n",
"tf_exploitability = [sum(tfe) for tfe in zip(*tensorflow_rcfr)]\n",
"pt_exploitability = [sum(pte) for pte in zip(*pytorch_rcfr)]\n",
"\n",
"plt.plot(x, tf_exploitability, label=\"tensorflow\")\n",
"plt.plot(x, pt_exploitability, label=\"pytorch\")\n",
"\n",
"plt.legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tensorflow_rcfr = []\n",
"pytorch_rcfr = []\n",
"num_epochs, iterations = 20, 100\n",
"for _ in range(10):\n",
" tensorflow_rcfr.append(tnsorflow_example('leduc_poker', num_epochs, iterations))\n",
" pytorch_rcfr.append(pytorch_example('leduc_poker', num_epochs, iterations))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [i for i in range(10)]\n",
"tf_exploitability = [sum(tfe) for tfe in zip(*tensorflow_rcfr)]\n",
"pt_exploitability = [sum(pte) for pte in zip(*pytorch_rcfr)]\n",
"\n",
"plt.plot(x, tf_exploitability, label=\"tensorflow\")\n",
"plt.plot(x, pt_exploitability, label=\"pytorch\")\n",
"\n",
"plt.legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
philayres/babble-rnn | notebooks/network_highlights.ipynb | 1 | 795104 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Network Notes"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"generating encoded output \n",
"Training Iteration 27837 \n",
"using full set of frames \n",
"generating encoded output \n",
"Training Iteration 27838 \n",
"using full set of frames \n",
"generating encoded output \n",
"Training Iteration 27839 \n",
"using full set of frames \n",
"generating encoded output \n",
"Interrupt signal caught. Closing gracefully. Iteration: 27839 \n",
"saving config \n",
"\n",
"'\n"
]
}
],
"source": [
"# good 160 unit *3 network\n",
"network_model = \"func-28-1-3\"\n",
"iter1 = 27830\n",
"iter2 = 23550\n",
"codec = 3200\n",
"model_type = 'functional'\n",
"\n",
"output_fn_postfix1 = \"output_\" + str(0) + \"_\" + str(iter1)\n",
"output_fn_postfix1_mid = \"output_\" + str(1) + \"_\" + str(iter1)\n",
"\n",
"output_fn_postfix2 = \"output_\" + str(0) + \"_\" + str(iter2)\n",
"output_fn_postfix2_mid = \"output_\" + str(1) + \"_\" + str(iter2)\n",
"\n",
"\n",
"\n",
"from subprocess import check_output, call\n",
"\n",
"import os\n",
"import json\n",
"\n",
"home = os.environ.get('HOME')\n",
"if codec==3200:\n",
" codec_sub = '-3200'\n",
"else:\n",
" codec_sub = ''\n",
"\n",
"os.chdir(home + \"/store/c2gen/notebooks\")\n",
"\n",
"\n",
"cb = home + \"/store/c2gen/out/\"+network_model+\"/out-c2cb-\"+str(output_fn_postfix1)\n",
"call([\"bash\", home + \"/store/c2gen/c2towav.sh\", cb ])\n",
"cb = home + \"/store/c2gen/out/\"+network_model+\"/out-c2cb-\"+str(output_fn_postfix1_mid)\n",
"call([\"bash\", home + \"/store/c2gen/c2towav.sh\", cb ])\n",
"cb = home + \"/store/c2gen/out/\"+network_model+\"/out-c2cb-\"+str(iter2)\n",
"call([\"bash\", home + \"/store/c2gen/c2towav.sh\", cb ])\n",
"\n",
"import network_data as nd\n",
"from IPython.display import display, Markdown, Audio, Image\n",
"nh = home + \"/store/c2gen/out/\"+network_model+\"/log\"\n",
" \n",
"if os.path.isfile(nh):\n",
" with open(nh) as f:\n",
" res = f.readlines()\n",
" print(str.join(\"\",res[-12:]))\n",
"\n",
" try:\n",
" res = check_output([\"tail\", '-n 1', home + \"/store/c2gen/nohup/\"+network_model+\".out\"])\n",
" except:\n",
" try:\n",
" res = check_output([\"tail\", '-n 1', home + \"/store/c2gen/nohup-\"+network_model+\".out\"])\n",
" except:\n",
" res = ''\n",
" if res != '': \n",
" res = str(res).replace('\\\\n', \"\\n\")\n",
" res = str(res).replace('\\\\r', \"\\n\")\n",
" res = str(res).replace('\\\\x08', \"\")\n",
" res = res.split(\"\\n\")[-1]\n",
" print(res)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"start_iteration : 27839\n",
"num_iterations : 30000\n",
"fit_batch_size : 5\n",
"learn_next_step : False\n",
"gen_every_nth : 10\n",
"generate_num_outputs : 2\n",
"save_model_every_nth : 10\n",
"framelen : 13\n",
"frame_seq_len : 200\n",
"overlap_sequence : 1\n",
"seed_seq_len : 200\n",
"seed_start_index : 60\n",
"seq_step : 198\n",
"test_data_fn : test/critiquepracticalreason_00_kant_64kb.c2cb-3200\n",
"frame_prop_orig_scale : [1, 127, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31]\n",
"frame_prop_loss_scale : [1, 127, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31]\n",
"stateful : False\n",
"shuffle : False\n",
"limit_frames : 0\n",
"optimizer : {'name': 'RMSprop', 'params': {}}\n",
"generate_len : 200\n",
"model_filename : out/func-28-1-3/model-27839.h5\n",
"frame_len_ms : 20\n"
]
}
],
"source": [
"with open(home + \"/store/c2gen/out/\"+network_model+\"/config.json\") as f:\n",
" config = j = json.load(f)\n",
" for k,v in j.items():\n",
" print(k,\": \",v) \n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/markdown": [
"# func-28-1-3 Notes\n",
"\n",
"2018-01-29 15:11:06\n",
"\n",
"Run with arguments test/critiquepracticalreason_00_kant_64kb.c2cb-3200 --load-weights=out/func-28-1-2/weights-15000.h5\n",
"\n",
"## Description\n",
"\n",
"Added in two new LSTMs and reloaded weights from last run.\n",
"\n",
"Disabled training on the 3 initial LSTMs and on encoder and decoder\n",
"\n",
"Going well!\n",
"\n",
"Restarted at 3000, having reenabled trainable flag for \n",
"the initial LSTMs, allowing all LSTMs to refine themselves.\n",
"\n",
"Restarted at 5450. Added 2 more LSTMs on the end, disabling the training \n",
"of existing early and mid LSTMs.\n",
"\n",
"\n",
"Restarted at 9290 with a final wide LSTM on the end after disabling the training of all others\n",
"\n",
"Didn't work due to a shape mismatch. Trying two new lstms instead of one large\n",
"\n",
"Restarted at 12940 setting all LSTMs to trainable\n",
"\n",
"At 18130 added two more LSTMs, disabling all others from training.\n",
"\n",
"\n",
"At 20290 enabled training on all the LSTMs. \n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"try:\n",
" with open(home + \"/store/c2gen/out/\" + network_model + '/notes.md', 'r') as fh:\n",
" display(Markdown(fh.read()))\n",
" \n",
"except:\n",
" print(\"no markdown file found\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"if model_type != 'functional':\n",
" nd.model_config(network_model)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Columns: ['generator_TD_Dense_0_loss', 'model_2_loss_2', 'model_2_loss_1', 'loss']\n"
]
},
{
"data": {
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ALywVgxBCCCHqHptcoVj+oBRCCCFEeWwyuRFCCCGEKI8kN0IIIYSwK5LcCCGEEMKuSHIj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\nouBGREREUsr/AxnKJQghUAugAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd4e02c4b00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nd.plot_training_loss(network_model, 'mean abs', columns=['generator_TD_Dense_0_loss', 'model_2_loss_2', 'model_2_loss_1', 'loss'], weights=[20,1,1,10], start_index=0, y_max=4) "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch,generator_TD_Dense_0_loss,loss,model_1_loss,model_2_loss_1,model_2_loss_2\n",
"\n",
"0,0.0228834829662,0.0228834829662,0.403349318315,1.48986675234,1.28469138259\n",
"0,0.0228853921744,0.0228853921744,0.403349318315,1.4896422835,1.28469138189\n",
"0,0.023087434378,0.023087434378,0.403349318315,1.49151063209,1.28469138304\n",
"0,0.0229652069392,0.0229652069392,0.403349318315,1.49016173996,1.28469138127\n",
"0,0.023130009408,0.023130009408,0.403349318315,1.49165277819,1.28469138262\n",
"0,0.0230927647217,0.0230927647217,0.403349318315,1.49181504394,1.28469138241\n",
"0,0.0229178732956,0.0229178732956,0.403349318315,1.48996953423,1.28469138271\n",
"0,0.0230939418238,0.0230939418238,0.403349318315,1.4909953994,1.28469138184\n",
"0,0.0229480034374,0.0229480034374,0.403349318315,1.48991555011,1.28469138211\n",
"0,0.0230319671685,0.0230319671685,0.403349318315,1.49201913814,1.28469138284\n",
"\n",
"27830\n"
]
}
],
"source": [
"with open(home + \"/store/c2gen/out/\"+network_model+\"/training.log\") as f:\n",
" rl = f.readlines()\n",
" print(str.join(\"\",rl[0:1]))\n",
" print(str.join(\"\",rl[-10:]))\n",
" \n",
"\n",
"with open(home + \"/store/c2gen/out/\"+network_model+\"/gen_counter\") as f:\n",
" latest_iter = (int(f.readlines()[0]))\n",
" print(latest_iter)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Latest Iteration: 27830\n",
"3200 rate codec\n",
"\n"
]
},
{
"data": {
"image/png": 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7+mnCuPFMmB3+OB31O+uocnUnfK84IvJF4DpgjIg4begKomohJT4BfKSU2m4d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\n62hkU7bJW6upaG6lINLM7q1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7LuyoY7SMHz6dvix+36EXtgJ1xx9zXSIQfAoyJEjiC+pD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\n0Gt331oYb5+6LUs2TBzlezu+R09oCo9klHwITO6jNxIyZOV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PnNzR7g15TeTvE0iLnI1K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\n0eq6vgntbpIc2sjz0pJ/AJL0Cfmc2KP1yYeIU9Kfg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85pjnTnzp0xJpPpKWAsl2iwngEI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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd4a4d58630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"3200 rate codec\n",
"\n",
"Calculated diff for each parameter\n"
]
},
{
"data": {
"image/png": 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sqjXu+yHg9xh3tcUybum5Trcf93KsEx77HHS19DqmTY4xcHLP6ebOvRwkwS2B\nB5hIc/e+XOt0f7zuxzy67VG+u+q7JJ1BV4HzyNYzkCr1eUzM6Y4SS5cu7WppaQksWbKkO0p5/vz5\nXcXFxckpU6YkBlLq70gzmpbuamCOiByPUbaXAx/JPkhEyoDzgSs9+4oAn6q2u5/fBXzziEhtsYww\n2UuFelWYK38Ea34KpVPgvC/2OdaxuqxkMPRWZShX7uUpiRo+FniagzoTuCyjT+qaN3Q18KP1P+ru\nc9m8yzi+7PhhkC1z6dKx/EAVCATo6Oh4zbvv0Ucf3Z363Fupv9Fk1JSuqiZE5CbgKcAP/ExVN4nI\nDW77T9xDPwA8raqdnu7VwO/dJ8sA8CtVffLISW+xDD/dS4acAQZSNe8x74VVvY5plwwNHO8l8q7D\ndZyeS4aCmODXqdR378sOpFpTl7mS8UDngcNQul7Z0p9TyTss44dRndNV1ceBx7P2/SRr+wHggax9\nO4FFIyyexXJEyS5i32/u5TY3eKZoYq9jZifasPROb8tycuVeDqWUrnqVrnkPx8I81vIYIX8oY/za\njoEHO2WTK/eyVz7L+GG8BFJZLMcMA8693NZbCIR3rH7GGAxrfg7718D77z78scYgg8m9nFK6kz1K\nN9W+4uBynmp9ClrhlAmnIAivN7x+WEo313xz6rN9oBpf2DSQFssYoTdLt1eF2epOU/URoDMclYq6\neexmWPfgMAw0NsldVCD9EOR1L6eV7qEefTY3p6cY/2HmP/DQux9iatFUajsPR+nmDqSSYzyQajxi\nla7FMlbokQbS3e6tAH3MDdjUvpRu5piW3umRHGP3S5yQ3EnCSSndtLZLzelW0QJxs4a2PrKX0MQn\nWde4svu4JdVmmdbU4qnDZulmZ8ayP+34wipdi2WM0FtGqpyBMl5N3KtWtoFUg6GHpfvAu7mz9dMk\nnZ6WblA9q05cj8PTtQ+RN2E5XYkwpxeezsKqhZxUZdb1VhdVcyictooHS67cy6nP9rcdX9g5XYtl\njJBep5t67yMIKu4J5u/D0rXrdAdOb8tyEu5DjeSwdAFo2YNWnciOtnXEWxfxwPvuon3X+owEFKWh\nUtpj7cMu27EeSOX3+xfPmTOnO13XpZde2vTtb3+7bjRl6g+rdC2WsUJv7uVcN9Wop2KZ9mHpuk12\nWUn/OE5uazKZy72sHqW76Q8cCBXQGm8kGT4v57UuCZXQEe/AUQefDN7B2Fsg1bG+TjcvL8/ZsmXL\nG/0fOXbHvI7/AAAgAElEQVSw7mWLZYzQeyBVjoNjHkt3AIFUx/KNeaDkWqcLeOZ00+0Zlu5rv2Ti\nLz4AQDJ8fM5rXRIswVGHcDzcs3EAONnzzS52ne74w1q6FssYocecbl9WqtdVeaQDqVQz7/xHCb1F\nCPdr6QLBRJTSYAXtsYk5r3VJqASA9lg7xaHiQcumvVi6PpG+pvSPGF99+avTdzTvGNbqPSdWnBj+\nr7f9V595kqPRqG/+/PkLUtuf//znD1x77bXNffUZbazStVjGGNk5l/t1L/dh6Y7InK6TBP/Rd+vo\nzYWbSJoG6c3SBZr9AeaWnUYNuedYu5VuvJ0pTBmCbJ4HAo9/8lgPpBqP7uWj7z/HYhmnGJemevIl\nm/d+3ct9zemORO5lJ36UKt3cwUp9WbqvhN7K2bEVtPjgxNKTeY7ca6K9lu5wyjZW5nT7s0gtaeyc\nrsUyxhhQEfvYAAOpRmLJkDOqRVpGjIw5Xc+dMRW97HU5BzROTP3cXvJlWudfTGnSoTLfpOPMda1L\nQ6as65CVrreIvXdO12fndMcbR9/jqsUyThlU7uWo5+Y9kECq4Zz3S8b7P2Yc0q+l69F2AY0TI4j6\nAnTkFTPRcSgLlQPh3IFUh2npZibusEuGUmTP6b7jHe9o/dGPftR/ftRRxCpdi2WMkLqXZs/D5rZ0\nB7pOt48xhsph1IUdy/QWSJWKXs5epxsjgE+g3edjGlDuL8AoXSU7zCyldNtibUOULf3Z5l5Ok0wm\n1462DIPFupctljFG+t7fh6UbG2AgVV9jDBXnaLV0059zz+mm2wMaI0YQnwht7l20MmXD5LjWqYjl\nIVu65H4gsLmXxx9W6VosY4SeaSAztzOItoP4zecBJMewc7p9kz0vKqSvaa7cy2ZO11i6TZiHnrI+\n5s+DviAFgQI6vA9LvRHt6OHC78vStTp3fGGVrsUyRugxp+v0Y+nml5nPfa7THYFAqqNwTjfbRSvJ\n9INFbkvXzOmKCE3u9Q/FO3KOlaIkVEJrrLV/YW6bBg99KGNXj2IMLsf6kqHxyKgqXRG5UES2isgO\nEfn3HO3LRKRVRNa5r68NtK/FMu4YTBrIWCfkl7oH9G7ppvM4D5eQHJWWbvY19nkKGiSSPXMvp5Su\nT+CQEzX74m05x0pRkVdBS6RlYALtXJ4ln0c2G0g1rhm1QCoR8QN3A+8E9gOrReRPqpq90PlFVb1k\niH0tlnFDuuCBZrz3mhwjr8TtcIQt3WNC6aavaa51ugEnRhcBfCLUORGzL9YGVPa6hKciv4KmaNOQ\nMnqN9XW6loEzIEtXRCpEZKGIzBYZQrbu3CwFdqjqTlWNAb8B3ncE+losY5q0hZv5nkG0HUIlZkHp\nAJJjDOuN+Sh0L2frSfEo3Vy5lwMaJ+oGUtUmjFvZ7wZJ9XatK/IraI40w6OfgFvKch+UzP1A01sx\nBpt7efzRq6UrImXAjcAVQAioB/KBahFZCfxIVZ87jHNPA7xZTPYDZ+Y47mwReR2oAb6gqpsG0RcR\nuQ64DqC6uprly5cPSdiOjo4h9x1pxqpsVq7B4ThJQNiyZSvLwzvZ2JC+AT/33HMZ7s0zD+6grXQu\nE/Gxb/cudvXyfbbsNQoy3NV1WN+5oyMdALR2zSraSwfoJh1hhuu3jCY8c6bAipde4Gx3O+Ve3rJl\nC8vbdgAwI9JBTEO0NDfTGWwCoGbXVmAmGzduYkFJpIdc4aYw9R31sPNlgJxy+5IRznM/e9vjifTf\nwooVKyjLM38LnR1d1Mc7B3UNxurf/1AoLCw8PRwOv+bdt379+rxrr712Vltbmz8Wi8mZZ57Z8etf\n/3rPY489VnLFFVecMG3atFgsFpMPfOADTd///vcPHGmZ+3IvPwL8AjhXVTP+w0RkMXCViMxW1Z+O\noHyvAjNUtUNELgb+AMwZzACqei9wL8CSJUvUW+NyMCxfvpyh9h1pxqpsVq7BEVj5BOBw4ty5LDtr\nJmw9BGtWA3De+cvwp0wcJwkvNFIwdwk0rWLm9GnM7OX77FuxG97YRF5e/mF9Z+9NevFpp8KMs4Y8\n1nAyXL9lRzQBzzwFmCQYZ5+1FFaYNjf1MgsXnMSy06YBcHCVEIsEqawqY58a9/KsKRNgB8xfsIDi\n5m095Nq6fivPr3u+ezun3OEmeLFnu+/ZJ8GNkj7nbWdTVZwHQNnGl6goCrFs2dIBf9ex+vc/XNx4\n440zPv3pTx+88sorWwBWrVpVkGpbsmRJx3PPPbejra3Nd8oppyz4wAc+0HrOOecMrfTTEOnVVayq\n71TVX2YrXLdtrarefJgKtwaY7tk+zt3nPU+bqna4nx8HgiIyYSB9LZbxiqqyY8cO/v7Yr/C7N9qM\nOcf2OrNWtny6WTaUtU43HA+z9KGlPLHriWGpMtTYEeUTT3XyfPJUd7Chz+mqKmd9+2/c98LOIY8x\nEvRIjJHjO3rnUv0aI0YARzpwREj6AviTppZ6b+7eyoLKzB25jktE0p9f/D78+gpXvkw5VJX77ruP\nCZH9dk43i0OHDgVnzpwZS20vXbq0K/uY0tJS55RTTglv2bIl78hKN4BAKhF5G7BOVTtF5ErgLcAP\nVHXPYZ57NTBHRI7HKMzLgY9knXsycFBVVUSWYh4SGoGW/vpaLOON7iVDjrJnzx662lsolSjNWpip\nNFv2mvfyGTnndPe07aEr0cXXX/k6n5z1sBnzMJTuxto2kgq3JT7C+f7XD2tOt6kzRl1bhFsf38y1\n580e8jjDjfcSiki/SjcVvZwUd5lQIA+fWyu3t2tdmZeldJ0E+IOZ+7xKd92voKulx5g+Eerr66mp\nqaEiWM5BPb7f7zfS1H7lP6ZHt28f1tJ+eXPmhKd++9ZBF1K48cYbD1588cVzTz/99M4LLrig9cYb\nb2ycMGFCxpNpXV2d/7XXXiu65ZZbaodP4oExkKCoHwNhEVkEfB54E+N2PixUNQHcBDwFbAZ+q6qb\nROQGEbnBPexDwEYRWQ/cCVyuhpx9D1cmi2U0ke4lQ9DSYm62xWKWo2Tcx1vd+1D5TDfjfabSPdBp\npqm6El3DEkh1qM0oghqd4A6WhP1r4JUfDnqs/c1poyOeHAOFYF0GZummP/udGFENkhATPKXBQnxJ\nc526V3Al4/Dct2G/yVRYkV+BeH9IbyrPFIlo+nPjDnAVeXYxhj17jM1TGG9Fj8Jo8sPhM5/5TOOG\nDRs2XXrppU0vvPBCyRlnnDG/q6tLANasWVN80kknLbjgggvmfuYzn6lbsmRJpL/xhpuBLBlKuJbm\n+4AfqupPReQTw3Fy12X8eNa+n3g+/xDI+Z+dq6/FcjTgqHqUbqx7XzfNu8172XE53cu1HemH93t2\nXUVowhI08u4hy5NSlO0UctX8W/mlE4f7LzCNZ980pLEANta0cvqMiiHLNZz0WJKTQ5FJhnvZ5F5O\n4qZ1DBXyTLSG/GkPIYk55kHo6a/C338Mz38XjltKxaV3U+pdU/387XDht9ObTe38aO0mHvaeNB4G\n1R7ypZSuDyUUGUDCjRFmKBbpSDJr1qz4zTff3HjzzTc3zpkzZ+GaNWsKID2nO5qyDcTSbReRLwNX\nAn9xlwwF++ljsVgGSXqdbk9LN8NS3b8GquZAsAB8/h7rdGs6THjDlSddSdCXT6Bk82FZul5F+UzJ\nWZkKaZBu6/3N6ZiVutYjbmT0So/kEzlc6NmWrte97AsV85LTSLB0Axc9uZTZO38Bb/4t3WH/KqpV\nqPRa9zVrMsZ/vT1MONpj+hHiXT0s8YaGBiorjbs6NZdsMTzyyCOl0WhUAPbu3RtoaWnxe+d4R5uB\nWLofxsyXfkJV60RkBvC9kRXLYjl2SSYTtLcbCyqtdN2bbjIBe1fAyR802+LLaemeUHYCX1r6Jdbt\nb6A18sJhzenWtHiCOx3NVEi55iX7wKvA2yJjZ71vjzSLOYpIZARSOWZON0E7guAPlVCXPAR+OBDw\nM3vf740XYt7FsNU45AodZYbfM+0ZbsoY31EI5SomEe/q8VDQ0tLC7NmzaWpqwtfL2t5jgUgk4quu\nrj41tf3JT37y4P79+4Nf+MIXZuTl5TkA3/jGN/bPmDEj8frrr4+eoB4GonQ/q6pfSm2o6l4RWTiC\nMlksxxzNkWYaK+5H2j9EvCs915dyL3dP2x7cANE2mHWO2RZ/jznd2s5aphZPNf39k5BAmKQO3Rra\n39xFyAcxB3AAbyrDRGTASvf5bfX8cuUeZlYVsqcxTFvX2FEWPSzdXHO6Kb+gKgGNESVAXNopC5Uh\nvkIOxKKAn9pAgNnxhPFALLzUvH53DcQ6OD5Ymh6wK1PpJlTJdzwGWcXx0LwLzSqSEI1GiEQiTJ48\nmTfeeAPfUVr1aSA4jtNbab/92TsuueSS9ksuuWRoZZ6GkYG4l9+ZY99Fwy2IxXIs86c3/0Q0bxuh\nqueJtTUAUDJxGuXSRYAENG43B9ZvNe+T3Yd7X6bSDcfD7GjewZwKs5y92D8JgEcDn4A9K4YkW2Nz\nK5P87ryhKtqwPd2YGLjX7oVt9QDc/A9zTB3aMWTp9gyk6ilb95yua+nHNEhc26nMryQWyKfeb26n\ntQGPLTNpPhSb34B4mNniWaESbs7Im51EyXOVbmTW+bD0WiNbLHMZaXubyfFcVVWFIz58Onauo6V/\nelW6IvJJEdkAzBOR1z2vXcCGIyeixXL0M7FgIgASaCPWcpC8vDwmnrAQvyjv9K+n9Gdvg+Y90JKK\nXHaXqWe5l9fXryehCc6YfAYAxT4TcVwbCMDLdwxarlgkQhchSlPL9R0ltuvl9AHJaO6OOWjrijO1\nLJ8PnH4cJflB2iJjydLtP5Cq273sfucYARK0UZFfQV0ggLrtNQE/4QLjaaDqRHBr6RLrZHrS6+Z3\nMrwGjkKeWzxh19tvhUqzpEqzlG5bq3kAKi8vx/EFERu9PK7oy738K+AJ4DbAW8WnXVWbcnexWCxD\noSBgkub4Aq3EWw9y4syZhCon4yhU+CKIOiZquWUPFFebICpw1+mmle7qutX4xc/pk04HoNC1dGuC\nfti3atBytTeaoKxQMAEJEAeiDTvottcSAw+GaovEKck3ruiS/ABtXf1YaJFWeOor8I+3pSsqjRAZ\nS3J6UbpONMwf//tWCDfyPiBGkDjtVObPoDZc333c1pLjWX3KVzl/8XyefXEFx1f4OR4g1kl1LOsh\nJdwIhSYgyutebtRA92/sZC0tams1ijqldP3HsHt5PNKXe1lVdTcm/3K754WIVPbRz2KxDJKEW0rO\nH2jH6WpnypQpiD9EoxYRSNUYaT9gEmOUz0h3zHIvrzm4hgVVCygKFgGQJ6UEEiHeCIXMHGLjm4OS\nq63BrPmN5btq1lGivhAc72YIHoR7ua0rQWmBec4vzQ/2H0j18p3w2oOw+r5ByTwUerqXewZSddXu\nYcfqFRx6cwsAU2kgkKihwheiVkz/qpifnYUTUV+ARNFkXnjhBf73j26K+keuprJ5L+3eCkOdDd0f\nk5p2Lzc4fnB/Q7Ldy+3t+Hw+CgsLUV8Av1pLdzzRl9L9lfu+Fljjvq/1bFsslmEi6d7kizA30IqK\nChxV6pwSRPzE8UNbbU+l61mn25XoYkPDBpZMXpIxdkVXJWvy883G418YlFztzQcBqC+qMjtUifhC\nUDLFbA/S0i11Ld3SgkD/gVRxN/jLN/IrFAeyZCjRaeZSJ003+ZcX+bbT5oOKus3UaAyfKlMihbQm\nDgHQ2ppeP5t6LCoMN7En6Pk+4bTSNe5lo3TrHX+3pet1L/sEwuEwRUVFpqyfL4jPupfHFX3lXr7E\nfT9eVWe776nX2MnfZrEcBURjURY2LaQsbmrklpeXo4qrdIX9TDFKt3U/lHnSjnvW6a6vX0/CSbCk\nOq10HYUJXRXUBgPUVs837sxB0NZijq8rcYOBHIylW2TmoNc8+zwtb7wIK+7u7rP/jY1sfvn5nmNF\n4pQWuEp3IJZuSqEHC/o+zmXnunq2rznI8l9tZcUf3kRVOdQW4Y5ntmWUxsvFQDJSJTpN4Gt5levo\n88VRESoadlDrRKlOJimKFRJONhNzYt1rrQEO4s7ZA23eBb9t6UQmCVXKxJz3kOOHkFlepPFMS7ez\ns5PCQrfNF8RvA6nGFQOtpztNRM4WkfNSr5EWzGI5lmjY3cD81vksrTfVYsrLy3FUaXLMzbWBSmPl\nOvF0NCy4gVTGjnqzxbiOF1Qt6G52VKmMGDflrlBo0HmT29qMtZbMC6YGJOYLQtEEYo6P5//8V/J+\nc6mZe40bJfnwN/6dx+/suZS/rStBab7rXi4I9j+nm0qJGBhYTvqXfrudp3+6iU0v1PDqk3vobInx\nb4++zh3PbGfd/r5LEWau0809p5sItyPio6TczC8X+syYldEItckwU+MJ8uLm92pONudUugBB91SR\n4mp49lumgAWmhlC+E8NBaHF83e5l9czpOpq2dAHUHyRwDLuXCwsLT8/et379+rylS5fOmz9//oLZ\ns2cvvOKKK2YCPPbYYyUlJSWnpfZ//vOfn3LkJR5YwYPvYhJkvEGqthQo8MIIymWxHFNIyFg/fvWj\nQElJCY62EXP/RSPkdd+c8Yc8HdOBVM2RZgShIi+dWtFRKHTM2O2BEIQHsV430krbgTeBBWjIPJ9L\nyr1cNIlo0shW4HPndZMxCObnHEpVae9h6fajLAbjum7oor0p8/j2pgjhqLk2sUTfeZ4z3Ms+erV0\n80tKyHMfQAK+MFBCZayT2kQ7SxMJwnETqdyUaMpQui2UdH+e5Caz2Dvn7cx97TewdyUsfD+OG0gV\n84XocjRt4cd6Wrrl5eWusEH8mkBVM9JUHsuM29J+Ht4PzFPVi1X1Pe7rvSMtmMVyLJHwZBWKBZP4\n/X4cVZKYggYR8qAjh9L1BFI1R5opyyvD7/N3N6sqha5GaQ8ETRakv33TRAb3KVAUfnkpbR2ulRVy\nx/S4l6NO1jN7svegqs5YEkfJmNPtiCZI9uX2TS1HSvS/LKl2e1rBJX1RugoO0N7Y1Z3QIpEcjHtZ\n2Bb380TVOd37/AU7aW7ew5KqWvL85rdqc2+fxYkEhxJhpiSSBF1L91cHf8XGrRspKyujpKSEFtLR\n11MS5kFgZ7SBX5SWkDi02ciYUrr+PCKOA0E3e1U880GprSNMXaKQl7Y3oP4gPpR4vHevwe8ONrOn\na+BLu8Y74760H7ATk2v52PnVLJYjTDKZjpY9WHAQVe12efpIKV13WYrX3eoJpGqONlORn1lAwFGl\nyB26zeeHrkZTp3XKIljwvt4F2r8aatbQduL9sAXwd6UGNEq3eGK3pdtNH8ox5Uoucd3LZa7F29oV\np7IolLtTarwBWLw125q7P7dUrscJRGg4uJiAq3X7S8ThrUPgE+H+SAV/mvclLnrlJQDyqv9CxdY2\nlpZtIrrHBJc1i/kuKuCgTEskqEsWUEQFLdLMpsgm3jv3vdTV1dHSnla6qW+7rm4ND1VVUF23kn8E\nkmrcy3FfiIij4A+YB6ysJUPNUfj7fh+/2rWeCwtbTZKwrjChUM/r6KjyqTf2UBbws/XcU/q9jkPl\nb7/YPL2ppmNYS/tVTisOX/DPJx2Tpf3CwDoRuUdE7ky9Rlowi+VYIqV0/1wQZnXVCmo6arpdnj6S\nRummYmC9aRc97uWmSBOV+Zmr+RyFAhwCqnR4vY/xftzMnUbBt4Wq8QXA7zR2Dxj1hSC/jGjSn9kn\nK1GGejRZKmgq5V5OKdqmzj6e5VPKdgBK12vpOgFzfFNDKz43aKm/oC0lM/dymyO0Bopx3DIUEmxG\nYuY654XN2uV6QuSp0Okq9qmJBAGU93RdQ9AJMu2saVxyySWUl5fTSs91xiH3lJ3tZrykKvkaI+7P\nI5IqjBAs7PFbdWgeMcdHbWuEU1qeASBSt51cdLrjtCZ6LoE6WjkaSvv9yX1ZLJYRIuVejnfNBMx6\n20ORCkJVy4l0JelwPNZttnvZtXTbOtqY2zWXHTt20NHRQTweJ+kUkE+CtxbECYc9SjLexe7duyks\nLOTQoUMsXLgwc07QXT+6vytK0q/4nAZgikmi5MuDrhb8kjVPmrVmNxGPEcwzc7yp5UEp93JVkfk+\njR0xTpxETjTagQAajyBAR6yDVXWreMeMd2Qc99qaDbQ0tlMxqZTm+nRq3e2v1qAnT804f7dsSYe/\nbDjAJadOxe+TjOQYPhHyuho4K7yPDn8hRckmTt0ToDjLkn+97HQqtKY77ePURIJKCdNUt5Mpx01h\nzUGzsrK8vJw3KMHBtXKWfRmW30bIPWltVwMkE93rdBP+kHEvA4SKIJ5p6XZoiLia32p7shp8EI1l\nPVQk4/DGH+mY857cF3eYGYpFOpKM5dJ+/SpdVf3fkTq5iFwI/ADwA/er6ney2j8KfAkTad8OfFJV\n17ttu919SUzN38zFiRbLOCJl6SYi1RRICavrVrOuYS95k9bRWHcOpdGC9GJPf5Z72b1559XnkX8w\nnwfffDDdPud8KibX8cGJCQ5s8yjVRIQHHnige7OqqoopUzzBnO7Soo2N+9HgZHxOCzAFUdfSffij\nzCxuBY+FmG3pJmJppdsSNgq5rIel2/s8cLS5jnzg0I7NVF8AX3j+C7xc+zJ/+6e/ManQaOp4PM4f\nH3uUgqLpLFpwLvUv13X3V1+c1gZjJWZbunc/9yb/88w2gn4fF58ypceSoe/8/dMUOBH25VVzSud6\nluytoKiiJmOMFbP/hbz2Z6kN1OIDJieSnOBroIkZzMyfyYttLxJPxk2OZPx0UEQpnXDyB9Hnb6cy\naIKuDvgF2vaTBAqTXcT9+ca9DK6lmxnnEyaEa3Sz1ZnKdF+USCwr8Gv1T+HJL+G76A6gR4DvUc0j\njzxS+p73vKc9Ly9PvaX9Xn/99YGtPRthBhK9vIuM/yzD4a7VFRE/cDemoMJ+YLWI/ElV3/Actgs4\nX1WbReQi4F7gTE/721W1AYtlnOO4bsAkASYETmJl7UqaIq3EWxcR88WIe6JfM9zLPh8k4ySdJBLp\nGb06b98jnF25kW0UU16UTlWY7bKMRLK8bJ0NkF9OZ9QPeQHmxNrZLom0e9ldX1oc8CjNbEs3lt5O\nKdfKYqNsq9z3xj6Uri9uquvEO43r+NVDrwLpRCIAMfcc8VArFZMLUV96vGCx0uW6VbOXJ/11s1HO\nIbdIQXZyjALHXI92fxFB6STud9g8r5F3eOLPYr4gxf5KagN+JhIgAJT7OsibuYiq0lehGWJOjBkz\nTDKTRiqM0g3kI4E8PjLnUp4Kb6KmayM07SSpx1MRa6GzoDLD0pWsKkNx9RF1A8M2JaczPbCDSCzr\nOrrWsRzaCsVG6bYnkpQEsqYExjlHa2k/rwWZD/wTMBxpIJcCO1R1J4CI/AZ4H2ZpEgCq+orn+JXA\nccNwXotlzJGydB2EKv9JbOgyeZITHfOI520nST7GOen0XDLkJGmJtlAYL8QX9OHEHUpLSykuKuLN\nAwlOzjMP+EV5ngxJOZahZO6oh6IJROuDaKmfimQUJGmUbiB9/tJgWllv2PEKDWvTlYwSnjzDKeVa\n5Vq4FYWu0u3oXekG3OLs4lrQXQmzHfVY1Kmo3USwnaLKII4vrVwlL0kkbpRXfVPmQ8bGGpNdKula\nuOFkEqc8hK8lluFm7/Tn45cuHJ9SlFVCMSohfP5yagMBpjpCmHwK6cJXNsVVwRBLxqisrKSkMJ89\n/sUc374fggUkfSG6OjuZ2T6JlYEANL5JsmAW5bEW9pafQFdqTjevBGKdBEiSwCjMpASIJJT8ogPU\nRUrp0FCPh6b1xJgjAp2HwK23sD8S46TiMWHsDRvjsbTfQNzL2Sls7hCRtcDXDvPc0wDvPMB+Mq3Y\nbD6BKcDQLRrwjIgkgXtU9d5cnUTkOuA6gOrqapYvXz4kYTs6Oobcd6QZq7JZuQZOU1MThRTiIGjj\nZCgCUT/JzrnEC97AcYLEA0UEE+2sfX0j7buNcjm1uZVAooO/vvRXChOFSIFQkl9CcXExxU4rtQTY\nk1dNAQ0Uh9L3mz1vbgVO6N5ev349DQ1pp9Gi2h34nCCJeD4a9FORiLhKFyK+EI4E8GmC0mBaAW79\n5W9Z+EoTLDLjrnj5ZQonGDfw61ui5Plh5csvdh9fGIDXt+1keSDTbQvgT3Rxrptpqau9NeP3evnv\nL7M7tBsweYgBEGXV+pe6la7P5yMea6IzYdazbt3Y0D1GwmPWrnt9I3n1W7g9kk/szInkPV9HuDNt\nWcZ8IXwSJSlQSjFu+nm3LUg8WURdwM/0iJ9WSinQCPvqW6mqMkpz+UvLqQhUkF9Uwiu1hSwLwosr\nVjMvAs+s3U3Z9jjFp5Sxjxc4dMJplEWbaUjm0RaNsnz5ck5ujxBure9WuABJX5BwPEzR7B8gncfT\neOAd7Nixg04x36892c5X9v+Kd02o5KaDu6Da9Ht89VoOSmJM/v0fSwzEvfwWz6YPY/kOxEIeNkTk\n7Rile45n9zmqWiMik4C/isgWVe2RsMNVxvcCLFmyRJctWzYkGZYvX85Q+440Y1U2K9fAebHmRZJ1\nSRx8zJq8iB9f/CI/f2UPP9iyn6TEAT9SPBFa2ll8xllmyQ9AzUToUOYvms/6FeuZPHUyV3/oanw+\nH/LS/3Bv3UHIN4rI71mrOrmqDGrhkksu4bHHHmPq1KmZ12RTkqbyE+FQCA35KE/GEEmAKtET3onv\n0F8g2kZ5KG1h+cUh6nFfnnbqqUydOx+APx5cx6T2poxzVK9ZTkF5KcuWeW8xLoc2g1mtQ2HIx/nn\nnw+/MNunnn4qp0w0y18eeuih7i7llSXs8TXi9wUpKy9BD7QSd5Lg8xMROOdt5xII+s3yoaefBuDE\nufNZtvg4vvryGxCLoXk+SosLwTU14hLArw7qg9NLT4K2A93ni/pCFBRM5lCHn7ldQcIUEAeOmzET\nX3I9dMKSpUuYXjqdrq4uNtca9/i573gX+18MkCfmd5l2qIApx0eZWFpAgRMhOGEGSX+AZectg4Zf\nUsYIchQAACAASURBVNOUGaPkBPKRwF4AfMEWEipMmDCh+9ruaN4B+2FbKESFmt/nV6fO5tyKEoI+\nGZN//8cSA1Ge3/d8TmDmWS8bhnPXAJ4kshzn7stARE4F7gcu8lrdqlrjvh8Skd9j3NU2S5ZlXOIk\nHRSlNNpBfmsT+XvzCKlb2s29OSf8ReYfNsO9bJJjtHS2kOfkUVZeRiBVRL2thsnUE8iPEEsIPo93\nNBntACZQWlpKQUEBHS1tJDvj+Ivc+eJwA7smuI6nkI/yeIwS6aAgXk/0uLO7146eWp5WQgFxeP7k\nBaiGETLndBs6olQWZeYhqCwK5Q6kirbD7pc8snby+tr0v3Z75ABOciG1b+xD6s2a2XwpZf+BvTi+\nOCF/PkVFRbT6mog6Zs42KsrO1+opqSogWJ3OmhVz3bj5rktZ/T68QdyO+PCrQ0EgRll7PXWhKibH\nzG0o6gsS9YcI+3wEEkEi5HGKbOO5aAOJoPkNUq7wvIJC8iVGTP001zcQ1SAh4lTlxehoyyMWbaE4\natYaxwomEEkk2X6wnTl5xeQ7add4PnHaYuAv32WuTeQ4s043mvY4tMWM67xQHcob3mBi135aWxto\nL3pLjyVlliNPv+t0VfXtntc7VfU6Vd06DOdeDcwRkeNFJARcTtbSJBGZAfwOuEpVt3n2F4lISeoz\n8C5g4zDIZLGMCo7j4ODws79+h8u+ewO73vs+qlc+C4C6Sjfmd+fjcmSkamw2iqCqoirdFm2nUDoJ\nhqJ0dOZlWLqxTlOeLj8/n8LCQppereHQna+mGiHcyF4xiTY05KPSifNn3238vfVafJHW7rXBJcG0\n0uwoKeO267/C3083qdkT8bQiaOqMdc/npuhV6T7yie5qSK2xPJyuDn77k1u7m1/bcCM71zUQ+s0H\n+EjLNxDHT3l+NbW1tWgoSkF+AUVFRcTFIeZq0C5R/vqzN/jd99ZyqNbjPnbTQ+anihCEfGjGXVHx\nq8NlE7dS2bqd/XnV3S2O+Olwcwb5ksVEMb/PRbu+TVDMw0vMrRqkwQLyiREhxI9//GMihHiPfyUf\nm/0qJ0kHkXiU4ohRuonCSnzb23jn/7xAXSRIAWlvQoHEifny8Re4U5bqI6lCJJq+js3uOKlMZDfv\n/B5fe/GzbG7c3PNaW444vSpdEblSRPpqP0FEzumtvT9UNQHcBDwFbAZ+q6qbROQGEbnBPexrQBXw\nIxFZJyKpkoLVwEsish5YBfxFVZ8cqiwWy2jjOA6OKIWetaDBVnPzTEXkxsW1FDMsXTGBVG6e30lV\nnkWv0TYKg0bBRMJF+N2bcMIvJKPGqZSfn09RfiFdEiPZ6t649/0d1GF/volb1JCfCifBTDGK3d9l\n3lc2zsz4Dm6CJjbMX2zOkxW9nJ15akJxiIZcgVR7V3Z/bIvn4/c5xANpMz2hQkdzhElBU+Ah33Go\nLKnGcRxi/jYmTKmgrKyMDjcdZkihUyDhLsJobU1f426lm7J0Qz6C/nQwll8dColxXJ6Zy20JZia5\n6HJMQJo6pewMzOFNnUZlZC8BSQdSAWggn3xiRN18VAc0bXEWB2KEw60U128AIFlQha/RyFjb5adQ\novjc9WIL5U3a4yB+c96gREmQqXSbok0AFCq0FE/DnzSWb3a2Msvo0Jd7uQp4zQ2aWgvUY6KXTwTO\nBxqAfz+ck6vq48DjWft+4vl8DXBNjn47gUWHc26LZSyRWjKUsc8fAAWRCH4SJFJLZdwlQ8nWKBID\n0STtbUYpTJngWWsbbScvGCYCRDvLu62jWFBwknECgQh+Xwdl8S724VlSs/tlED91PvcmHfJR6sku\nVdpu5hibNZ994XKmFxqFX+DKFykuJlkQ7Fa6qkpjZ6x7mVCKyqIQzeEYjqPdmaMAdNJJyL6V7jWA\nfIWEP22lxxXCbelo65CvBeky9WVVlaKiwv/P3nlHSXbVd/5z732pYucw0xM0QdIoIaEIBoyEyNGW\nAEfANjbGxgZjMAe8a6+962PWYZ0wNsEGEby2F7BBGAwmSSKOhHKapInd07mrK7947/5xX3d1SxgP\nYQQa9fecOjNV9cJ971Xf7/2l74/+vj6inPiGM8lJR9OUhgEtaLV719qNUuJuikuPdIs5SQG4JmWX\ns4zKh7ersz6+GtNGAmnWT9XtcnN6Ga+MP4OTJz6tkG4qXXwRExoPA0yaXtehxEi69WnKfbaMyZSG\nQdukttnQHqdESJMi42IJrUFI63J2ZIQwGbNzC8RxTBwfYb5tF1QFoTgy/ETS+h2Agyf/E7nNDTyq\n+Hb9dP8SuBT4R2AEuDZ/P4V1915vjPnW2mMb2MAGviMEtZBKZ73AgdOxVqoUEU9nL2PzeQWd1phE\nM/32WwkP1DFxSqfZISNjsLomZhc18PJEp7AzhMg5e84d5rA+h7Ov/BLzn3kq18/9N0piGrHS1ODE\nXth0MfMdu4PxJMGa9nGDNSvoM/jsWY60h1c/XyHO/7jjl3jLWV9cLRmqdRLiVDNSfnhM1yfT5hHC\nFUszNk6cKkFhokt/ocTo2O7V7xMD/QePYIygKwTv2/V5vjT2MTZvtupT7Zkp+qslEmOvZziz46rL\nvDyo0zvfnV86wXvfeAvxijiGpyiKXjmVpxN2ususeObvqpy7bqyJts9IZUX6vZRpRnFMTOFh5U2p\n8Fbdy8oP1pGuJzOS9FIKqW3XR2FwVRnhaNNO0ePCLmwq5GNT9rkqEfMX7jt4c/oOPve5/8PeW5/P\nvqM35PcpZTaepYsl/uON45xpeCy29vu2MV1jTGaM+Zwx5veMMb9sjPkNY8y7jTFn3tPbwAZ+gFBR\nisytya8871UgJaprJ1glIoboCfqTdohPWGvMICFNidsxkRch5Zo/6aiJm5f0tKNxSOx3M84IgY5Y\ndIYYqlkC6mMOHaeYJIPZ+2H8IhtvFRkosY50+5ftWjtxBVPxEP9wJHc6BZbkzukcA3rCFbcdte7O\ni7f2r7vmlRjvwwUysuYi81mBr10xgJYCIWKG3Z4rOzXgpUvEokwzv94vZ5/lBc9+FoXjB6lEbfrL\nJZJ8ehvJ7L/nX2Kt1PYa0m3nC50o9zQYT1KkZ0UroxlWXeqZz5uu/ThvOvct68aaZtbD4Guffk8z\nIyyZlvJkppWYbiocAhJCXH78pS9/BOmq7Cn4ecKU8iurah0P1e2C4fnefQxt/yscYZ+nkJZ0pYyZ\nENYqXliw7vZ6HqKIJNSdMh2RgTEU3DOrRvc/w0prv3379j1w+PDh+9/4xjfOrXx3+eWXt/bt2/fA\nXXfd9eBHP/rRoa985Svf1yYNp4JTamK/gQ1s4PTCIJBaowUc33Ee7vg4TtdaUR7pOiIg6hAdXhG6\nsDHdrJ2hnYRkeppketo2G4iapHkf3CQJMClkElKpCHTEMgPkrXaRIiNDE81NkXVqZCPn0egahJuA\nEMxsu2719GMNO7nHrqRUCZkJrVqWGCjDGnJeSKyl/I3DiwSu5Alb+la/y9J0nRRkutKaLkspeIaW\n75J4Ept8nKDXpBQnRpC5TTIU7TVu6ebyHE67Tntxnv5KYdXSHdQCJWDxiBXuWKtOlbHG+jUppuxS\nyK3JrvQwJmFARdQyn0NyhI5aT1zCNFFG4GqXvkAwI2xMvRTb5xNnMcYYGpFZtXSHRseZMj0PgSc1\nnjkPX4fEwqXguojc0l1K7T0qiQjjz5OqCESCkGn+3HrXEsVNhDa0css+lIJlp0JNSQTgyEe10vMH\nhjOhtd8GNrCB043MILXmja9RLFT/iJ+vjuF0LdEGxlBkjYJU3CQ6DO6mEizahgeiCxcdWOTQNbYZ\nwMArX8F41iT2RtFak6YeWmsyJUilJMhiZvVmTE5mPhEPBke47gtv5BJ+jtJ9O2iF8yg314QubVs9\n/c7WERIlMFIQFEOgSmYEy2WHkRM/zxeKBa7tdJnLLd29h5e4dNsAfl7DW5ue4oY3vY7Lf+MPALj3\nznv4+u/+Na/+4z+l9K6LqUhYGiwQ1spoKZAkRKJnDacGEq+J22nTWZPr+Yrbf5WfYBP1+TkCV642\nMRDiOKOlrZzsDLHHyXi721vA5PxEs/F5RlrvZ2HiHbRLdo4+4fXzq8MJb9CCK44rmvMR9PXqkFWW\nIrIGRXwEgv6CWrV0y7F1B8dZzD/eeoLfvfF+PubFdIxPlBqOm1EyI1DCsKO8CxjFN10iGRBIuWrp\ndrDlTS4xiUyIVbJq5dqL6y1yzg4nueYri/zJ7gkAIiGpOxVqUuKY00u6n/3bv9i6cOLY99VqHN66\nvfOcX/mNx2Vrvw1sYAOnGxqk1kwPChLRRFUquJ0V0tWU6C3WTadBdLyJv7PP9lzVGSKVFLpdBl75\nCpzNm0hOnIAsJvJd3MTwZPMVkiwjk4JYegQ64lj7yQhlJ/UiXfYHx0hMyv1+gXuWFJ1QovL+c2tT\ncMpZl9C1yVxu0boyMyOYyXvufqpsY5O1uEO9k/DgTIMn7eyVMs0dPYLOUtJ5O58eOnCYJAqZvqcn\nIakDqB+roCUIUpYGlla/Swzg1nBJ6KyxdLsqolwcobW0iI5DtucZ2sJbphpI2viIICVa26Mh/zeK\nbLmUFz5IxVgr9bZCmbaEb1Rc2lFAy7HnuvSqj/CUKz7ElumjSN3E1QEBIUEQEIqADEWQx3KjLOLf\n77Mxamvp+tS7CXXKvDz+XbQRFB1JrEN8HRFJH18AuXxly9jnowhBQEcliPw+K2PWke5Z3bzPb76S\niJDUnCpLSuEZs5pRfabjMd/aTwjxBuD9WP2zv8O2rHirMeY/TvPYNrCBxw9y0l2BrFZxJm0oqqgz\nivTKXKITXUxaRu7oQ9zrYNIMgcAPI6rPeQ7h/Q+gmw0Yg9BVlJKUczjMfAaZErRlgUBH1JwhPEpA\nnRJdZl1bClR3UpbqCdJxcQYsQwXZ+rmp6VkyEMUEgSDTuS400Mmt50Ya8vXDCxgDV+3oJXg15qYR\nGKjPA0NMTs8zBMwfvJ+VdKnUEbSmiug9DoKYbl+M6koQGakRGNc2b2jnlu6Ll67mxsGbKFRG6HQW\naBzdj6MjUCD8iMARhHhMDbOuu0Gac7ZRY5CAW9/HUKzJkNzju0DKNwOfn0gKNPPZ8mRgXcjPuPNT\n3LGriWOK9FMHIZFSkEifQCegINEJWwasAVimS4hLLY9h327OpUEZT2oONe7Az7rEIkCnZtW93M5r\nfx1h73/HSSCx/x/MNG3RM+C0FDSFyPOpITIONdXPkpIERqPk6Wt28N1YpKcTP8yt/U7F0v0FY0wD\nK0AxALwC+N/ffpcNbGAD3wmEWU+6qlLBzd3LQ1m27g/1LG8nr3hSkXPmJ3mopDAmo0KLl1x2M07j\nbmSpiG7ZBJ/UhUozYZwa490ULQVtZUm37vo4sZ3di3SZ8yzpGrdOkhmiqITj2zNvfctvrhtv3S0T\ntxxExeFlZ/0WrizTNpZMurn1efeBkNd++A4cMi7e2s/kh99M7Xe2cdE9b+L1534VNXMvvlrgzid+\nmKnRkMXDvQZjiZS0Z4oYPKTRtB68F88oHAGjJ0O2tP8VYDWmO55YS3rT0Pm8bMdv0f5oCa2eBIB0\nUxYWF5k04xwbchB5KnJBSpYm/oUv7voHtLGLmvPCL/Gbxz/Islthn5shjKGhFEf9Ai1X4OSEXe60\nGF2YRuoGwhTppwlJBykEsQwIMhtrjbKIRpjwZuef2S7naJtgnSBIiI8UIe20gWdC6onLK/+8p8a1\nYumW0y5/8IGUhhutWrp1KWmvKaUy0qz29vWNoSsli6pKTSlK2jxuYrof/ehHq1EUCYC1rf1+0ONa\nwak8hRX/zfOBD+UCFo/sIbaBDWzgu4eGZI2rUFYruKGN4w7q9BGb78tjiyc8xXkmYxxrFTuHP4Ys\nnUOSu6bxYoZqCVIYxuKU+YpHU5Yo6JhISFRkLaUSHRZ8G4eUbi9T2vEVgdE4rJ+zlt0yYS2iUCXv\nyuPTzrdZsT5Lustl3hKbTnwDh+fhHvp3BlTd+nQlhMfuwBuypHLs3ALRHTOsdDDsLFyCTgWxtJZe\nmjTxjEcmIDMwULf3ZMW9PB7bxKRDzr0kSyc4d+QCImMTt1w0ZNaluxxIVup/nDSj238X87qCZjsK\n6Ob1ystumSWZMZ5lTDsOy8onkYKXHY859+rtzP/Fn9JNIkTWQOtxNjMDyYAlXREQ5PW5cRaz1Iq5\nVB0G4D3ZC/npTu9edvHpp0s7rTOhQ5qZt64hQ5PcSk67bDoJZXWUl13ez6fmIJYCYXqWbqBTDuby\nk1sTmFWw4PSRCEHR6DPSvXymtva7XQjxH8AO4G25/OIjK/k3sIENfA8QhE7PhawqVdyoizAZg1n2\nLfcY8xxmlcCQ2dIhrECVLBbR7Q5agHQ6VFo90g59SUvZXm/CpKg82alEh0XHxk3FOtJ18ZME6Vgi\nuEPv5lJ5CKEl0bKHHmtiMBhcmnnJy0yeMFWlzfbwIfo6x6ifPM6gPrnetxbWcQhJgVJhCFeuVnZQ\n7r8EuJtYWRbOfINvJLEwq9KOAA3Xnms8saRbH5xm8cBtLIRlQmcPjg4RAhSGFMmS4+dBYXD9GRKn\nS2gExtixryRmNdwCLRGzM02Zdhy6jiX/0dDwa9vH+PD0Ce7rc5EmRIoSZzEFsY8QEMsAV0cooYiz\nmMV2xLhscG97gsNyM4utnqu+i48QXdpJHV9HLJv1ybQhHhmKct7msNDWXLrN4VOrt6pH0AUdczK/\n99vTjBOOoplnW5e1PiMt3cdia79TcS+/Gqs8dYUxpoPNqfj50zqqDWzg8QYjeDDoldSovirCGH7/\nG++nar71GveNZ41zQDa4bssA5PWbSdhGlkrobkjoSySGNd336AaKhrSkq3SETK1l54g2ddXGGLHO\n0hWNjPfc9zaGL7FE8XVtuwZpo+i2HP5oyeEVu3+bScehjSX3ulJ0hKAiumRLs0g0lQ9djZt3XOgq\nSwy+THHyrGSlS1RKPVIYGLdCF4m09yQdS3AKLRwgWku6Ti4eEVv38kzQ5ENPWmJfNklHSLy8PWAg\n2lR3/08G0k9S5J/wx25EFm3TgK7oICKbPNWWAgMsuj6pgPHULni6JTtmVxuMNighMLmIhhDl3NLt\nrFq6KovwlGct3XZMn2nQyWw62sJyL3u6bXwEXQ4VIn59sM0fbm2xkt5VLDgMFD0i4TK+/QS6ZNja\nzGh84fd6D1TAypIqSBOmHIdAa3bEbWKhWc5J96kHDebWu9jADx6n0vBAY5/rjwohrsNKQO7+9ntt\nYAMb+M4guLW0efVd+dnPplMZ4LLZ/VTjR5Lu/9o9wU+OD/JV5xhHXYfpgq3pbS/N5aQb0fUVlWaK\nu4a072iOUXMtkZV0G2UitIAj1hOHDicQKuapnW/yBHUSrwpPb95O32ZrIDzIJv67+Hneu/vHmUok\nJxPJolvnoKdorWlj1JKSKm1Et0HVjfDSBotRgYPnvZoHzy9hhKBaGUflGcEmVIyPW/GMk+6VDPzo\nq3jur7yRDJuAlfgaTxiCtLjO0q1Jj4LW7F/4Gk4q+LI8xnQR7uw/SgeDrzOMTqi4M9QcOJl9kUDd\ngjf4NVS5F0OW2pqOWggiIQjzxcCmNBfPGLLk7RrQnQQlBDrPKFeqjIOGzU9ECohkj3SjLKbWiama\nJp3MZnwvNnuZ6MumRGqWuXMioOYITvrpqq7y5U8c549fejHay/BIibcZrkgiZoWmZHr3YOV++GlK\nTSoGM01BG4wAk7f2KwuDCh4f4hg/7PgvSVcI8T7gfcD1wIvy1wtP87g2sIHHGSSu7Gn+ipEhDr74\nBUgM/c1HplD83LBPoCQTwk6kk77dV+XuZYwh9BT9dUsWe3ML9Z5klEXXkttEOIckZX7I45uBDwYq\n7bMB2O3Ncak7xTDL6847LLp8uPssTujNHFM9kq0pSUv03kcCqqJDGai6duL//MzZeE+6kNqAR6oE\ngTdAoWrdo3GUUq7a6WiO5+EVy5z/lGtICy0iT5Jo8IyiGA6uWroJihaKojHsb9yGH0viPPJ1uG+O\nLuCbDNIIP7dKozVZ4J3qIUxm759Qvc/bQpBq+35zbunGeYKSq0G3E6SEVFliFrIKv/xleO7bVy1d\nqUN86dOKuwS6iytSsrxZwmLLHntnVmfRVIA2zb5eSdSKxOPAcJFr94yQuQYn1SBARYJpx2HIGJw8\n8W7lfjhas+S6pHIQPy9SFrliVklpnOr6Zg0b+MHgVJz8TzLGnH/aR7KBDTyuIXBUc9VVGGURL4/+\nhP3+GIVxQ9ySeLlUoWNS3PoxKFxEN3dFHg9y8Yw0ZO9DD3D/j+zhxtrz+Kva33BQR9ypd3OV3Mdx\nv7pKuru6tspjuc/ltnbAeORRFVUOAOc88Q6eP/kQlYPrE6h2pdY1urhwmNltbQaVZimT/OVwSFMK\nyAqgujSl5JNb9qFHffbsD3n96DATYR/nNWyjsMSBUvVehG8XA3L0TrxOES3AV1sxxjD//vu4fdN2\n+v3jxCamkAQsE9KUkl8dG+FlNZe2ERS1pn9ng4FySjuX2Jrsq7NtNiXAUMkCPGkXD1qYddeTNC/A\n67djGklT5h2HewKf38n5acXSjYO8XlkbslbCv50/xVyeRWxUBTbZXJ4LljQuksyJCGsd6tOf4Fb/\n3QC4/ZugabWoAfZkLeoUuWGwSGfwGNVM01ASIbsYoHn/LHv3/iznOAInM+iiQacFphxBkKb0S1iQ\ncp27vSklkeojyElX5s0b/KBMbbnA+H/1M9zAacepxHS/LoTYIN0NbOA0wgiJlD23Y5RFSMegd2e4\nniF0eutjLwvhoS8BMJ+7OJt5Ozo3a7HvyEGOb55gsjVBNY44YLbwkezpfGLbFcy7xVXS/dGaJZso\n8DjkeVwQdxnMXdFbzST93QiVJ+osDl7G0qEKO+63Qg9xeicAL+lP8IWxhAu4NavDfNx1mSyEnOyr\nc+hsny+VinxhR43l5VupVC4kdQTFYIZMWbdu5tfReopMCfr6dmCijPhwnT8o/xJHvK1EKNrdKpBx\n1HX4crHAu8u76CAoacP4ZXM8s19wWSHjmYQkStNymhSM4Wp5JSNifY1qGm6jXN9DsnzF6mcr8du/\n7t+1+tmm/LPEzRc8GmaOHef2iSVOjNt7r0V5dfutTUhNgCtDZJpQcFqU8nh7tWKTvWY79lhPSbvU\nTZlbSjZ56ldqNq4scks3xtC5aJHUEbipYX6zoV7dzEnHod9UuKZtz7/W3d6Sgro/zOeHngaA1PaY\n/bKLW+6NcwM/OJwK6X4QS7z7hRD3CCHuFUL8cOReb2ADZwqEwMhelvJqH9YBgxcbOnk7PwA/i+CB\nj9NJOjTyEpckd+36IgIMTddOsIOiwZKp8pCZ4JYtVxI6zirpXrv0DbRwaMuMmpLsyZqUU2sRBs2U\ntTbhfRe+jYWjOxg8cBxIEeIoz114Epe4inLevefFjYhCx9pSy2saL9xZsaQziEeS1BgdfT6pI3FS\nQ5oTUldLYIlMOqhEoru9jOuGUyYUAhOX8Aws54lYU0GdCEFJawr9Mc/f/UJeMyC50LH3pKPaFIFx\nM0BZrs8A7y7+FOX552PinlJWMe9XfMDvufnHVyxdx+4flD1uPb539XuDJJa2rEdrQyGBVlDAVRFK\n63WE2FcYwM9CIi1wJWwhoE8mNKWk2NzFk0NLoitt+6KgDdIw35zASQ1xv6EQt+lIybDxeFJoyXmd\npSskoVPiP4avBkDm7uUB0aU0VGEDP3icCun+PVYQ47n04rkvOp2D2sAGHm8wQpKuSURaaQmXDWnc\nRNNe07jeyxKYup3phz4PQF+WEeduUyng6aNHuNhMItH002YR6ytV0idULqHy6Qqfgo5pBwO0jCXo\nzWnGgLYu52otYW6kd04jXVSlQv2ZGaUd70KIhIvauxGpg5vP+TuSLkFuKddUz7K8x7ELiEDZ6aav\n/AxSR+CkhiQX7G9qD5UZtPDQ7WSVdJ3oIDcEJ6hJcIzCW7MSWPaWqTmGojFIbegfuApXK0r5+iRU\nTQpCsrd8LzcP37Hufms9gPBSTNaTC/Z4ZJP3sjE4xpA49rqKY0XubNy9+r2QJRKpiCcnOfiLr0cC\noQpQWYij11uhbuLQb2wYwNMx/7rta0x6GU0pKaSSSl6f649/ElXaT+Q1MVrSiPptTLdsaOYJb5sz\n8HIX8tpztIVAyxKDvo1Vr1i6AyYkPtIT3ThTcMa19ssxb4y50RhzxBhzbOX1/Ti5EOK5uQV9SAjx\n1m/xvRBC/FX+/T1CiEtPdd8NbOCxBC0l2cNItytLpEPgJZrlaIz/nvw8vxz/Bk6WQmGAqdvfC8Cu\nJCFek2t1+dAUL+B2BmgihWHRWNJ1VEDLtRN1Q+b6yFrRLO4AYCzJGBZ2kg6FYP9gmTed+5u8f9PP\nAVaw474XGGQwSZYN8YTOeZA65KJWbMlShvJuSDPykdr3bbeLI6ssT/UTC5eWFqtiFDVdtqRrArJW\nggkt6Xrde5iSTS4LIyba4wRrE7kFzHiSiTSluuQwPHQNXioJ8lVAR7UYFUW+0LeXed9afFtDh3jx\nqSBd+vrnAMmWpd08rdNlvLtn9dDXl3bwuiHrevaNIc2Vn0rjJe4uPLi6nVIBqWeof+rTLN2flyAp\nD0WGJxSxEPxt+iIenBnFbTkMSLsA6Qsn+cLIHXx64l6aUtKvUyp5YpR0WhS3vZ/YidCdASJTRGnw\nCxmLBbvNjqiDEXZR1MpFLzKgK8GIIjvL1pUtUyuXWTEZTtirgz6TcSa09rtTCPF/hRA/JYS4buX1\nvZ5YCKGAdwLPA84HfupbxI6fB5ydv14D/O13sO8GNvCYgRES/TD3ckv1k/YZnMQwW9vJh7Nn8Vl9\nJU4WwSU/w8lZa3HtjBNiCVPpBWhj/6R3m2mu8u4FYGmFdGVAnIsntIQl3bm+nbRGrFyil1QYzLsZ\nHR8usF8U+MTYM/js4LMAK9hxKJRgoLX8mwinD50p4rx8ZVOaMmYsuc3KR3ZMq4sIX2yitRQRSHZK\ngQAAIABJREFUE3CPY0lDR8NoE+NkhiSrQGZI63lmsIlRKP5uZo4tcR+lb1GyfHk35NKjMa7bh5eC\nn5vDmdNmzK1QVz09hDdPBURzLwQpaMTW3frTi5I/nmkysdwj3ZfJIV47buOivoE4b6WXDSWc8OZw\nc8+CI31ST7J86166gXVVZ7niVdkPiIXgLr0Lc2gn2cwsA3ls+Kx23nNYpaRCMGyi1eSnFYROShwF\nRPm9LLiaxXzVcU7UIMnVutq5V6GVx9WNLHLRgO0K5SSTSMAFJjb18XjAmdDarwBEWO3lFRjgX77H\nc18JHDLGHAYQQvwT8BLggTXbvAT4oDHGAN8QQvQLITYBZ53Cvt83vO4dL2LZmea9D/7X2/6g8MM6\nto1xnRrEOPiuv9r15jMfvp3nVALCs7rIaVvPuQKZxPzxiT04SuBo2JLHHeuVDoQ7mDAPoYThedjY\nY40Ko6kg+cw4I+ca2n3zfKDq8ts1h38vXMPXH1wEIwjNIP2qBShqKqAVGmI86s0Ofz3p8IqllEOR\nQqYjEBd457kFXpSO0DEzQMrmNGUCGw9dVC6sKc9RWtFUCXNLVbI7jnD+eMAdnsbgknZ24Xp30tdM\nOUEJASz+4wNIqRAmRmBJJdUanfZc3iUzQFvUuDyMSHTC6z98O68xEe64RBqDcNr0I6ir1uo+1Vyq\n0ijBfNuS5JXePr6Znksl6cU8J855ISR2rvaMIcolOg80bgHgfN9wdyhQokBUUNxXTmjufgEA4w/e\nBmdDoiVGCULjc8Kt4u2LcM4fBAd29k1w75rnP6Y7PLwwLBTzNNolPGEXOp6rWPI0Za05K1rmkLsZ\nMIQ52TZX4uiiwDfvizAopG4TYLV8p+YWmPjPfoDfI5Y+emBrMtP+vlqN7nipM/jSc8641n7/Jeka\nY06X+tQEsPaGTgJXncI2E6e4LwBCiNdgrWTGxsa46aabvuOBpqJLzXnUO0Bt4HGEE55gLUlNtEKa\nF2r8XBhjSfcyT1WW8InpIXYNjdKXJZTyWGAyOMP96R6Wk1F2Ly7xFL0PFJTK/Tx90WVLbYTxVj+L\ni+/lX/tSROn57IsHmMkeAL/MCWeMi+MvA5tYkgH1ZkosfBzRZKpZ5ObhHZxMbqOQDVBLNf824VJq\nXMNSZRvjxz7KUHacCWHrTdcmUgEMRUPMF2bJ6n1ckUBNuBxxHVCbMFkRQ4wBHkjP4QJASkUoARMj\nhA3S3p+OUtG9hLKhpYt5oftJhrQGNA8emUSMxQglKGlBqNqUmjPUVH11nxIxQ6LLzEgfzyjcyo0n\nn8mu+kk+ry9lRPfi0HfODzK8sJdW5QoaboSXL4due+hGfGG4tHMud8uDyKxMSAFx+TTR1yuMn9xL\nkTwLOdVEjsDRAV8aPY/zBq/iF1FEWcxljuQTa+7PVv1IdcLENGlHJdpKggYfmPMNm5OUwCQkqgB0\nCJXghvTZ7CrcZs+rC0xP1shGhnHSWXwBHafKoWOTHLzpJlqt1nc1Dz5W8IY3vGHxJS95SePjH/94\n9ZOf/GT/DTfcMPLAAw88AL3WflJK88Pc2i/ASkFeAHlHZcAY8wuncVzfNxhj3gO8B+Dyyy83V199\n9Xd8jKuvvpqbbrqJ72bfRwM/rGPbGNepQWvDkz9wIR0pkVmAViFLssNYtcXu+0vAMlOmJ2wgs4yb\n/tuz+dl//wcqJw9SyN2SbUfR94QK77vvGbx84S6uUvsA2H3OHjbf5dBohwhKrEinv2/Ts/m9bRcw\nNHMnXzlcZlKNcFVqjxX6FeIpu91opcGbfnQXT7zyF3nqRz7COIpC3KEuBJn2SII96KWXIfkcW7ES\nkq2HBa4G0iJzCEZjqzAV4lJTinImWdIeWsBXqxfyjfql7Lz/Xyicex3LnkCYBHLS9YmoZD0X7I0/\n8XOo935w9f1Nv/kUDtyQsIiD0g5CtRH3fpjmFZ3VbYpEvK0wx69t341rYtRAFedrmgXTR3lNQtI1\n11wDB2Jenl1KmP4lqbHXNZsusNPN2HT4CthxEJI+OqaAW6rxnFedR/tVb6J7vh3v7qLgHiMoqjJv\nv+GNTL3tFkpTN3NVfCuH1CKc17s/u83iI34XKRlJWmFJJZBAkGbMe4adce7qlpZ0I6H4vfTnuKQx\nDuM30e+U2fv6Z3LB//t7SGdxpeTu7U/n6c9+BYxdcFp+/9+NRXo68Vhv7fchYBx4DnAzsAXbW/d7\nxRSwdc37Lflnp7LNqey7gQ08JiAEqwlCjrYhpo8XJ7lv8VxmG7YE5z/8XrxRphl33f3nHK8fo4jV\n2gXoKMk9J1t87OCLuSfrzej77zhGYyFkdvMBmu4+RK5ipbJFHjrpMbs8j05LnHDHVpWMGskgpaXr\nAQi8Ggsnm9y7/8sAlIVDmlmrsKMLqCNNaqm1xLcaa+mGcn3w9ZwBSyqHu126xDS0z5KSlLXC5Nc8\nbUocdoscrFzMCbnA/qCJMDE6Txgq06VvTcclpR8W4A3rlJLcfWw83Or9LA7XWKOYSEFEDDOKp2PO\nPrHMpsgmGi2aKg3dEwI5/qFvsO/eQ9TmNBqfVAtE1mA2XGaPk1FOLLG6OiCWAarUom/XmL0n+SIh\nXa4TC0hMAZNoQEJsFwDtcs9NDjBqHmlwZSal0y1zMo/ZugksKLGqkrU8Z2O0ER7KaBpF+wzSGXjg\n5ikyxyZT+UawHMwwV2s/4hxnIs6E1n67jTEvE0K8xBjzASHE/wW+/H04923A2UKIHVjC/Engpx+2\nzY3Ar+Ux26uAujFmWggxfwr7bmADjwkIIQjyiF7Z67Jk4GjYz5/f8av0OR/gEuWxSJXNosOUKWJi\nmF18N/WkyC49smrptqTks0evBeBo98k0xee4X29mf8vhMuC27Z9m0UQobJKNTBc4eLTJtDdHlm7j\ndn8PjciWzXSjKmVtcxNLbp0jh45yrHk7BNAnJVmmQBvunx/CPdZAA82gwIRZBiqkKkIaw1sXa/xz\n3w52jc7CXMDXsiXG1Bxz8WaWynXOjQNOZHYamm053NzdxNPLAfe5t3LSH8tJ15JyRXQYMGvaHOpk\n/Y389JupJhFQRGbDwHG+ePl6oikQUdLjvOLkjfzCkU+ztW4t2CWqdHTCxcvbeUJ0IXIp4YtzD3Jw\n9CyKZUXmeDjJcQDOJmFzWGFTdxTHsXrZqavwyx0GX/Uqpg5/EThKomz2cpL46FyFyisH0IJ6cf3U\nW9EabQTx/DUIbwmneg+ZTul2K7QqNRJZIE1iOq5gMHeDd/NuUREBVR1xQhYIgOaxhC9/8wAXnXc2\nh/pvZ5tMibw6jWbM6Cn+Jh8rOFNb+638speFEBcCM/C9PztjTCqE+DXgs4AC3pf36n1t/v27gE9j\n+/geAjrk3Y3+s32/1zFtYAM/KPi5OVZ1EpYSEHnizqioMWuGUUbwLLOXG4JrSUJFLcsVoHRplXSX\nS1eweHyQi0eW2HzwbG6ov52vqkNMDVaJoy5Np0aSdJHa/vm66TyJA123hW6XOVzZyu9e80E48Fo6\nCirx3wO/gy+7dLM6zWgOAogWdtpBp5pm2hPRj4VDGYMwBiMEBW34qWaLpcIVbPFuxEMx7TS40z1C\nlk2QiH2cHVV4MHLIgBllo1cdp0tHRCw7MZiYTOaN3AkZyta0THp4n+GHvsjd0k5RbngeWsRMD03D\nmhSlAhFBZghyq/aClu1zu2QqdEj5rc8+wJ2jm2AcEmkXINq4hJFBpQsAbM4yClry05PX89kJa22G\nFOi2jzP2trey9612KiqJhFgIlgjoLFhLdvtrX434P3cQPkyso5JpapSJFp5jr7W8H20yssxDFRRJ\nq5/lXLikmEf5gnO2Q3oPXRyCLKK10o/ZeBgMz3zwIq7btQM18Q76nBpnjWz51j++xzAei639ToV0\n3yOEGAD+O9byLAO/8/04uTHm01hiXfvZu9b83wCvO9V9N7CBxyo8JKCpqBQSBfkEukXM85AZYQRD\nJjJMwSHqeiyllkiyeIhCnkj1EKMsdAc5f8wmkUpvExNtK4jwUDZNPa2DMIh0DgOoZIHIiUlVjMlK\nGCVw04cACJEUOjb6FIguiUiZlR0UhqqwxxepoYvLyro8FRJhbF1rKARBPq5D/cP4YgfnO0WOBgt0\niYkCG6PeFEnGE4cpYF56IBIO7NhLFjWY8qdQ6Rypa8miJLrr3bDZwyxd4EQ2waBehKyETAt0yutz\ngh2hGS7+CRe37Oeb41nAupdbYpGkrfjm0FlcTkK1bF36RjskSiPTeSQwlmZkxCQiw81j4F0KTB+9\njYGhS1mqLcMmGDRdYhGwoBT3feE42wC/r8zojp08mMtorqBiNPOsqV4xHtrYhcG2md0k8ghLqU3Q\nCvLWjLoo8eqGCBdXR5ALjRjtEAOODDkR3sfWyMd4LdKuOKUJfwOnF982piuEkEDDGFMzxtxijNlp\njBk1xrz7URrfBjbwuIBnrMuwlIswjJW6bEawTcwzaYYZ1imhSjAFRbfrsZTZP90w3EQhV4GaMoM0\nkwpnT2xHuZJn/cJF7CwWcHTKp8cdVhqeG22rJGS2jBzMJ+q0DErgJUcwwiU2AjevbizKDmBoB1BV\nhiGVV4Ykmtj0pnGdN4BfUY3y8/M1nDIf5NVcGmxl2asTy5iOa48xmqZM5PWmi0riDX6VLw3+G7ds\nuoUZ/xuorIYRPjVTZpAGY7pXcqm7vQSpFcyKIRpZQCccIVb9q58XNVzftKVDm6KbefH8Tev2W6JK\nR8CDfefzb+4l/J2YJs7rbk3mkqkMlS5SVYZCqkmJiUVKkNhrjAiYP3kPzaUFdMVapFUiMiF4WiiY\nutfGtEWgOOdJT6Xr5zHx6gvxMg/fWCu8d3EeQodoBOXJJmm2mYX8N6Ide12ahILWdIVCiAwh80WI\ncRk+r590YIY4iykuZRiVkMYbFRg/DPi2pJv30n3LozSWDWzgcQsnn1ALuY7xz1y1lc8576JftJg0\nIwzqiFAaS7phgcVYojCEcwVyvQWWEmsp7Rgf5rXvuJpzrhjnkmdcyaZoBuHVH3FOJWMKE9bNqbMS\nKEGJFkZ4RMIgbC4KnowoTh0gEQmBhE2prWcVqV5JhLZIrDs2F70iyF3g56m7eYjdXHbi30HAM175\nDJyLrAJfUTeY0HYMDWWt/YfDCJcpM8yEWGDM9GK0ycx8byPfWs5zYoipyGG2tQ2d9w12DLx/CX5v\nYYlvhdSUSHCIBByr2nj3EorjIj9X5qBJUdk8g45BaEMmUmJSgtzj0E7KNOuHmHzgPmQ5JUOsJqX9\n0VuvWpXKlIHDlS95KRe+xOoLtfteylWDPw5AgV6uj8l8hAlJlUKkIbCVaUdR1JqFkr13Ju4QGOhK\nQSELQSYYI8A4XP6ys9l8UZHBIGD7vdaFrnZ8y8vfwKOMU8le/rwQ4s1CiK1CiMGV12kf2QY28DiC\nzB1/iSihDMRJG1/ZxI8pM0w1bZFIgQkcDILprk+/MsRhgM6zf5c6uUt6oBdnTXdUKfR9BbfvTh4B\nHbMcWiKy7mVJiTZGePhpgaRgicwnwhEhqcwIhGEgzN2gqYE1JTxJfg0rpOvlIcZt4iiZcIn7n0qg\nDbcfupGFzFqsQTjPZjoUtaapNMiHxWkBgyXdrWKeAXrWbTK7hnRzd/WMrHJ/bRPaKHRgLcKKMHh6\nfQx1LdK8HCsVisn+vGEDDtN5MwZShSFFpgsMKY1WgpSESCQUc+MyYpgkneXL+/Zz1+6n0BH+qjZy\nMKAo54LQsmDvUSPtYnAIhOYrwZMBKIqepSuMhzARmZSQheCOM+U4bE5TTpTyBn1JRNFoQgFBFiFE\nAsYBBFPzSywvLlJqNlnRBomzH3g4cwOcGun+BDauegtwe/765ukc1AY28HiDyS3dz8gfJ1EVFjpz\nOMJm1s6bEYa7J0mkxORZr3OxRzVRaBSJtq7aeq5XvKmvR7p/euLdTO+6C7f/dkRSwGSrpfZoHbLc\ntMRlsuKqpYtwiURGuvUyADwilExIRIYnoT+3qEWiEXGPzOq5gISzwsMdO9aB4CTCZNz+5P/BxVrx\nzWNfZDmyLthCc4oRlhjINF2VIZS1Lk0yiC9sCY7QDpNmhLPlFE7eTemK/Zpkdk1tq2+t70k/ZbJp\ndZeykiVdTxgGowazvn0/P9gT2ACIja0+TATMlqw9MWc083ms2iQFIEZmNUYczdBSQmxiYlJK+eVr\nfwK3FPM31a3808TPUhflXkMCHdM/YO+ZDOw9aSYdjPQ5lxnmPesheGfl+tUxCe0hdJe2FyCyELwh\nTjoOE2nGVMmWAjlpTKANHWHwdQoiwRh7bf/8LzdSm5/HP3oUmXNtkj7S27GBRx//JekaY3Z8i9fO\nR2NwG9jA4wWZWSECh8Q/m9uXeoqmz10qUqnvRQuJ7nMRGJaNQ2FxE1oIQmNJNs1szK6/aI8135nn\naPMYV05u5tf/bYwf+cI16Ni6JisdA2i6Sd7GLgtACS7d/WcY4RHKiKRsY5oeMXuuuZ6EDB/Y9TN5\nY5dUQ9xzBye58L6zUhjbcvmnp/02MkjYwWG+1sq4fOdz2a9gefF2BjJNNWoz1Lmb/kwTqRgxFuOa\nEVqH3oTI9aGllkya4dXz/MKhK/mtT0Ayt8ZdnMeIa05GK8s1nR1rwRYwDKZNPld8En/ztL28eeY3\nADjpjfDBsRdw9As2xp0ah+U8Xl03Ls18etQd63IWGK7fX2OgnhCaFC0MQ3lsXZa34VUSalVLoHXK\nq7HtKI3wHUlmQLh2+1bSxQiPF3MLO7JjbHnK5/lfl76eK8bsQktpB6FDpvuGMVmXjFFOOg6jqaHh\nBSRC4acxBaMJhSEwiY3p5r+jUAsioNju4E4J/vmwYnDo6Y/43W3g0cepWLoIIS4UQrxcCPHKldfp\nHtgGNvB4Qpa7Zj0dk/h7ONGdY1YptHBppNYKjlWCEC36nSaZ22Tb4A40krYp4mtDloU4UlBwFalO\needd7wTgAs7CMwGFTJGFeTezPOkpyeUHjfYxUjDeV8hjuiHRgLUYfSKGuxmJ0HhCMDLWh8AgEoNY\nQ7qpWU+6Hi6DOy8B4Dwe4I7lFhedcx1GCGrRQUYzQyXTjBXaFDOFcNo4Zgkpy4AiWbHKjbV0VxBr\nhVuISeYsQeEWYWg3PPePyNwG7dRjVCwxlFoXdjHPom45VdwoYmCmmX/epe5Vcbr2GjIhaRl7zq7w\naBlL3ll3GwYHg8PFUcSiqTCbC4yM5TWzmTdK6AVEfgHPRDQprsZ0O/ESFI6TGINZUQ9LOxjhUzGT\nDMVNUid3P0vrYhaZh9Qh0/1DCBNTawiaSlKiiEdKqDyqSYeCNnSlxjcZiHS189BKglup3UakgkRL\npDzzcpfPyNZ+Qoj/Abwjf10D/DHw4tM8rg1s4HEFjZ10PR2R+OcCcJ/vkbhDkGfs3rvlFoanXseI\ntG7Ci869FC0kJ9MhPC0xJqQSOAgh+OrUV/nYwY+hhOKcvt10HYkHJI2LARjLtek1LUBYC8kR9LkK\nI3xiunRcO0n7hBQXI2I0rpEEfmArRaMMsSamm+YLB5l3OlKFISYqttznvNo+EilJj2b4yseQMZJA\nJTWMODWcLEA6LUTWWLVQw6Y9jtAOx8zY6nnmTD9uKSOZz0nXK4Hjw5Nei3GWiLMyf+S+lz888iEA\nfjTKOOmNkBwV7LjxQ7z2G1b1+CNjz+Hi+j6IDAJDZgRt4+HkbvKaKeJnMRiPTJ5D5u3BN3BZ9G5+\nx7eLicHc0k3UCAvYhcFTuIWCiXBzgt1/6E+Y2/Nmum6dNF+kdJMQI3yq5hhe0itrknkimY3pdpmr\nDAAZtQlbdpo44wSZIFIufUmbgjF0hca4mU2kyn9Hcd4kotjpsNwnUY9op3Dm4kxo7fdS4FpgJm9+\ncDHw+OgRtYENPEpYsRI9HaKVdVEuKkWkRjFmfWeyQi7scPZZT2R4927+pv5iGtkwQrWpFuykuxja\neOc/PP8fGBgZp2s0rgHd2c4Th3+frVN5LW9WxxU+IEAJAinRaoCuWGYmEPSlGT4xRobEGFyjkMbB\n1SCb6+tk09y9PJcHdYMdz2VzeTNvmSzwyXmbALW0/zBPGLECQsOZxFGGQbnIjoFRpNMC06bhWrc2\n+T2R2uGQ6fXHmTLDuKWUeD6PUXoliGy2kFE1lC5ylXiQyzoLLGx+J69cWOZr/ZdQnGoz8OBdGC24\n58tP5A+PX8/lh+5HaIErIEUS4jCW2eNmSM5uWLJriF8m6XsN+mHkVcSjoDWZtwXnxNsA+JGDF1A1\n7VVLd76Wd3tSDaI82S3MuiB9SmION+o1WlA56UrtASlhbpwubj0AwAe2vJogVcSOYiiuU9CGUGoi\nXyNEwlDawRcZcZ4jcP5H/h9/89o+nMcR6Z4Jrf26xhgthEiFEFVgjvW6xxvYwAa+RyRrLF2tbFLQ\nkpJ0sjGE6K6bMrVnLbyqO0qWzSIy0FkZ4bSpBvY4zdi6ULdXt6NHpjCAdu13TqQgTxKWuoGbN5w3\nUiDRaNVPJJqcDDQTwk7ekeqgAUe7dJsJvhGIh5Nubl0t5+nLm0uX0O/3ExvBXMXGm+cOHOCSTRPc\nxm0MJgoVaKRI2VQewtSnwMSrli45cYjMQa+xD2YZhJIgO9xBZyBkAFGTyeVFUuc4l2TnUhQxXlLn\n7GiJShLxtf5L+JHarRRnp9AI1OwiTpiwdKAEGHxPESYuGsm27jxTZZtQdd7SUe4b2IloKWRFrl7j\nCgLjUkTQChNuzuuCL57ro4828znpJgZmUx/pd7igm1Ae8InSECk8JAYn6iV2rZAuK25radBC8lDd\ning0/U0E3TqJUgxFNQJfE0pNWBQgE4rGUDTRqqVbHhoicgx96SlFEr9rfPzjH986Nzf3fbUaR0dH\nOz/2Yz92xrX2O5Un8U0hRD/wXmzm8h3A10/rqDawgccZ/MyWgVSpgnAIhE9NKlpmFKUifKc3MWfu\nMsZIimKAJE5QJkNk5dzSzUtS4gZSSIpukb5Re2zt5QlGjQZOXkMrs2XkSvMwJch0iJdWQRim/Bpb\ni9YQ6DiWxJV2acx3Lemu77m+Gpcey0lkwB9H5J17hLZZycuLNbb/6UcBGEpc/H7L/gP16dXjaGVJ\n16xYupndZm9yDWAlG1sVG1ONll26+4+SnTzG0/73N2g/9Bau0NY6dch4zqJV5Pp63yX0t2zS2Ge3\nX4lMU37pvk/SnCwQDCQEnkMbe3921k+uunm3N2YwnsQ52iK+rblaFrWCQDtUjeCTi3U+NeFSyAzb\nM8nNXLyavfwvyx5vn1a88ao9xLmlG2cdZH5vnHZhzREtN+SPB6G7RJ7P1/YfwteG1KlSDAukUjIS\n1ygYYzPN+xRCpJTcgJLuEK14TjyPhOxxZem+4Q1vWLz33nvvv+6665ZuueWWyhVXXLGn2+0K6LX2\nu/baa8/5oW3tZ4z51fy/7xJCfAaoGmN+OJSjN7CBMwRxdgU3Tv4Tf7Gzwt1ASXjUlKSRjSFEAz/o\nlfoMnqM5tlykm0CaZjhao0yZTLWp+D1Lt+yWkUIyvms3QkrkeXugBrrWoOrmmcFZHbGi3KQESdrF\nyypoYNGvsX34fJiEme1tWICgO0JjISQwAjAYwSr5rhDSz05O8Pv6p5G51O+XXv4lDiwf47p9MP1j\n1/C6vp/k11uLzId/j+wetNc0cz+MW03oX27dzn3X/xi3fTWPKScdmsD/3P5sfmTuAuqU6QwG+EQ0\njhcojkakXesSNlmJK/Wx1Xv1ovmbmPaGOVKY4AnPaHPz7j/j61+4n+cd28tzj+5FOpotT1vCdxT1\nPHHqaa94Gbfc0eTEcsREcx7jSpswlhpSZ40ClxGUSkUuShQ3KkuWn75wF5vOFbz1Pa/h3Oq1wPuZ\nTvIYdzpHN298ECV1HH+E+vwLSVs95ayfSv+Vr/B6OjLFA6RuEnkBXV9TyiQIwdnzx5Cdc/DNVwi0\nIZaatN/DBIaJTBPpNgvGWupCCDJjUOL0WrrfjUV6OvFYb+2HEOI6IcSfAb8O7Dq9Q9rABv5/e28e\nJ0d5HWo/p6r3nn0kjVaEhDYEYhESmzAIMDbm2gZ5wcbGS7yQxXGwr0lix4mDP+cmzoKdex3H38VO\nbG64seN4xcSxAxiBARswIIEQEqskJLSMRiPN2ktVnftHVXVXz/SMZqTpmR7pfX6/VlfXevpVT506\ny3vOycfuxHwWFl1OP/QKAFliHLJtunJ+TDfR2Fja9zB7UCdLf8HBKbrY6pKUBsQeJJ3wLZreQi+N\nCf+YRDpDx6Il5HL+Q73d1c+MVr87juCBpojFBEQougNkCr5CdpyDnJLJ4BCn3/bn1cYLTfQcHCw1\naJCkhRUYUcUgcxYvhTpNWMGGGekZnNW+HLTIq21Z7IsuopA8hWfblvr7Z2fR5pazoBfn93DBrCaw\ngz66RT8k15WJ+30QgZ6GNmJpl55XU3iORT7IVI7hcLa3jZ1BU4ez+l7gVy1nY+Exs91hcMnp7M+E\nlbNc0uvPJf7mz5KMWfQHSnfeiiUsnOGP3bzeTiRf9kzmfm9TadnzLBIzG1l9yN++bkA4fXYT8TlZ\nCm6cfdbsiv9jyz3IvoECfYUeHKeXhJWg7+BFiFN2GZxReJU4Dur5yl3cXvLJFLmESzwogrKysBEc\nfwpVWv0Hn1w2jVAg5UCz00+OGKIe/PObcNQjVmOlW0/Ue2u/sWQv/yPwO8AzwBbgt0Xkq7UWzGA4\nmXAlyd7kDM7oegnbKRLPKYdsi90HZ6JuP7H2stLdN7gLdRvoyzu4rofteWTtZkQUK+EnLEWVLsDK\nOetox1e0LzkzuM29sLRNNUks5ue35nK9tPTbKILtHGRROsktr8b5aucWAOxCI937B8h4vvKzE8rS\nWX4B/lDp5gI3baQnPA2JBmxvkIdzHZz1sH+u/a2L/fKNS15P6wXlvib9bp6mmI3r+EorPei7X/vS\nGTqCZu/dS9/pW7gDMTxH0KCQxQrZRUby/MIrzyR5pPkcWou9iOVndodKF0Cu+TBcegu5/A5rAAAg\nAElEQVTJmF2KG7c1ZVg0I0t72qKxmIPmsmv/yEC5apRHnFh7inNe9JO41mUDN7EtqKfssytzdGzn\nIHd13s1F314HeCTtGMW+tooM8PbiYZqkv+Q+sLwecok0uYQH+A9Dp+R34RDMQQ7qbhezCUQLxPPQ\nUuwjp3FscWHXI7jqnrBKN2ztF75uvfXWjp/97GdNy5cvP2P58uUrr7rqqmVha7+pljVkLIlUVwCn\nBx1/EJE7ANNGz2CYQBp6ihQHhIX5naTygzQ6LrsTNhddeyr3/OsgsRllRZF3B31LN+/gFl1EXJoS\nzXQBvQXfIh2qdNuSc1DLf9jfRQfilasTuW4COyYkLSHXfYBPfftb/PFHElzcavG6Fv9GPxA0jI/l\nm3h1xyHOj8XpSe2lyc1xw2VvZuuv7qPYlQT1m6oDpZhlSEKKDAK9gVXbmm6GD/0MGjpo3fLvpf22\nUmBdzKaYX0vfwRZw/KpZdqzI1c69/AVv5mDrKpJN/n3UKwqZoMPOQvFnhzyly/gtfg7Asr7nWNO1\nE2JxLIF8LMmjN/8l//Wr7Xx53ToAkvGyUmrPJvjkVcu4cblwysFOkme3MPhMD3Znjq6ecghQY800\nX72IVUta+HevyHln+tOa8o7//Ty7slG97XTyy8EHS5+bLAd3IFvqDgSQ8fLcmbyNp6/5Kz7/2ANY\nbi9r5+/l8aSDSDMxgdnv/iav/OAX0AvNgcIuJgTRAoligpZ8L4XGGKiCgOM5xKRSlhOF6djabyyP\nPy9C0PXaZ0GwzmAwTBC2COSUlngPbQPdzPAcum2L085upJAbINbeUrG/ulkG8i5e0cWxPZoT/vbu\nQb9KU2+xl8Z4WemmyODZoRL0UK9svTlugljMr/6c27uHeZ37aZYkM+wC/WHFqoC44yv/pplp5rvd\ndBR7yCRs2q0BCkGz+ZyGSrfyO6aG3G060o3QcQZkZ5DOlItfPGK7ZC1FLQs3v4hBy0ItpRDP0q5H\nEJRD0kY8qMHoOULaKtKRgnniK+it1qkA5DXGmj3bWTawGyy79CBwcPFKHpm7Ctv2s3yTMV+4LIOk\n4jZt2QTLZ6VItxfJJMBr87/T8y93l/8PrBR2U4Ls6g5et2Y+mSBzfCAsjWmXx9izW7CdA/S55XrR\nM+wcWlDUrcxIW5Y6zNtXvImYxLC8HqysTS7hkbdbmZtMEFt0Cdn5fsA8E1QjU3ERLfqW7qCvV8IM\n5qKC56Vx3OHNJAyTz1iUbiPwnIhsFJH7ga1Ak4jcJSJ31VY8g+HkwBLB8WI0xfM8vvX9rM6/hivC\n/iNBS7iGVMX+6mYD9zI4MZf5Db7SeoGvcPX3rx5m6WqPU1a6ouCVLZ/+HsHpP4J95AgzP/bXADTE\nsvQWeunOlZUMQIPtu3wTc9K4WMTwEBEKhQL5UOkG05+GWrpiVX6Huemy9U66/FDRa8GfP3QL2AKe\ngJtELI+++Ew+3vT3xGM227r2cvPioMl8kKh0u/13/En82xzWLIftJrrtRrq1kRSOX/DCipUeBJyg\nSlUoYzLmK6g2iRhCgUs2LR4ESvmeJ35dHtMh3ydksOgG2yMZ54nFJHLP4FG2amdafWjBwx2qDBMN\niAgtqVbE7aFr8etxLBhMzGBByv9/a1+1xN81cDmr5gGHeM6jtd9/UGrQXlRhz44/4l9e+CMeevFg\nVXkNk8tY3Mufm+iLBl2K/g04FdgBXK+q3UP2WQD8H6ADvxHo7ar6P4NttwIfBcLHxj8JmtobDNMS\nEThcaOD+fYtZvKCfDse/cW7ZEURyZrWXf+34/W/78g7qKcWYx2ULT+cnz8RxybGnbw/pWLqkdNVT\nnO4cXmuoBBWIoSqIKOolsT0PV8sKoSnZRE+hh0NBF6LzMqdw5WlrufLit9K5q4/nki7OqxYx8bBE\nyOfz2JavdDecv4Rv/orSdKHSd7QbwSsrmCvmnl3emGrlX/fsI6UeX5w5k0cG9vimsgdKAktccrEW\n9lgF4rbHK937cFttwKXo+grzbPdJAPZpG27c5rdO/Twf2/4vLJZ9aKB0Q5ncQI5QCYeWbhsRyz7s\nD4yHhjWTtRynbcpUL1cwWPDd3h+NN/P9IH2nt+XdJJLLQWwauu/0v//pf8iOu3N4sSFKN+nHyNtT\nbezJ9XJg3rmwFwZTM5mXDDK6Z80Ndk4DA7iO/7AQG3RpO+x/hzX6FH2Sxi220xg/zGkzG6rKa5hc\nxtLw4IHRXsd43U8D96nqUuC+4PNQHOBTqroSuBD4mIisjGz/sqqeE7yMwjVMaywRGuIDPNk9j8cT\npzPP8a2l53ZtJZFO47VWFoGztYG+3n5cLIoJpS3bQDG1rLR90BmkKeEn23h9RXAV144qXRAJm9on\nsCzw7PLNvznTRl+xr6R0r2p+D+9bfStzl7Zy9pULiKVjOOpbupZAoVCgIL7lt2TuzOA7VX7HviFu\n1EWZSC2FdAurCgWWFh0+39mF6ADYIB6oJoiJr716E2nilkVnXz/dgQ45EjRGCGmRPtyYzaOZMzlC\nI0kpYpcsXV+o0NINlXAy7ivu9mREAQZK11VKlm5YjxnAi1WPk4bu5XVnzS+tc+NziDWuY7DxjaV1\nZ7WvQh3FHTIuJPzv05pqJe710hX8HwymZzE7LJ4RdBoqBN/dDapoWT05Gvr9TO4+0hxS/8Hrwlm/\nYkHbpFc8NFRhqlLargXuCJbvAK4buoOq7lXVJ4PlXuA5YN7Q/QyGEwFLoC1oNP+KzGKu41tLL+57\nnnnLV3I4mDYz/6CvaeLFIr2HenDExklANhWjmDy94pyNiUY/i/ZLfq6JEwu0YNCJJpyWgpeimEyQ\niSfJBR7RloYZFe7lBqvSSrLEr0Bl4yGqOI5DLqhs5cXSwT6VWnd2orKlXnMsUt0pVXYvzysWmJNs\nRC0B1wMvQQo/gSmfcMlZO+nPe2D5t6+DduUDSbc24sTjflKXxklSxFIX7LJ72S25l/3PoaXbfsYV\n5RMFSjevggZjF7V0i0N0ZchgoHQzgVWqQWGK1VLZ0/jFu4J6yoF7ud3xC4iQ8Me6NdWK7fXSnff/\nDzy7meyh/fzDh97F//nzP6UgcXrtZlIaxy36+8QP9tOS963ersv/hoNzLgUgGy9nXRumlqlqO9Gh\nqmEJmn34LuQREZFTgXOBRyOrPx50O/oNvkXcXeVQROQm4CaAjo4ONm7ceEwC9/X1HfOxtaZeZTNy\njZ2B/gEa475ieYSVHFr4MWLOXRx0uyk0ZNn84jYAMvkZ3Ph0iu8VF7Cn7wVcK8NAGna8+AyDDVdy\ndl83L7r3AdC/s5+Hdz7AqTmbQlY5PE9hT/maudfehZV6Dad3JW5bmqza3PXJK1j0mkNPZy/dA908\nuc132TJIxZhte83BwSIuHk9v9pXJC8WZ/K/4h1m8LwO4vPjiC2ws7Cgd8ym1+AUJfhRUwHrggbKj\nTDyXsPGcALPcDHsLzzHgzkGJkxX/oaNfe3GsTijO46qmq/j/bvgZR9zTeLK4givzB/lObCkPypn0\nJ9OIp+Txla46eQ4d7mHbNn8cd+32B+Lhhx4iFRO6DvhKqf/Q/tL3TOS7uBgYdFwImj/0Ryzdrtzh\nqr+jTQf8B6bnn93MBTN+h5/G/fjrGdrKYm6nYfZsXtixlmd/5VcgLAjEN3XxlbhvhxzsybFl40YG\nDg2gTg+vdfn7eVaW/Q/eR3t/P539/fzJ2z/La4djpJw7OZzfB0A2B02FAZJa4PFtu0gP+pZwvHik\nJGs9/v5PJsakdEUkAYS+q+2qWhxt/+CYe4HZVTZ9NvpBVVVkaEG5ivM0AN8HPqGqYcDla8AX8P1k\nXwBuAz5U7XhVvR24HWDNmjW6fv36o4lelY0bN3Ksx9aaepXNyDV2Gjf/kq/n3oxl9ZN0XL55yjtY\nsvU/6Us7XPbma3mu59fQBc8uLPLVcz7Bf/6kEzffj9PQSLwhzjVXXErmvs2ckriBFwd9pfv+q96P\n9WqBzgeeZu67VtGe74HHylOFnN5VtLirOewWKaYStLa08OdX+1Pw+zd9jY2bN9I4u5HGwUaaG5sr\nxuzIpj088uwOAJYvPY1Htz9KItvMQ5l3csMlq+GBe1m+bBnrL1xY8T0vzRf50SN+nHrY/0F5Ng1n\n2HmecV4Cbz5YMZrFnwsrmZxfBMRq56OXfZR39vwXqSOn8ZW9a/nYF9/ET//HvRweLKIxO5i+FCdJ\ngWTMom3GLM5YeTo8vYmOjjnw6qtcdumlpBM2D/Zu5f5XX+Gc009j/aVB/Z/e/fAr8GJxNJiX3K8J\nLDxUhY7Fc1i/ftWw/8vup3bDk5u5fN2FzGUNdz/jFzy54KzrSGz5G7x4jrl7rmUg2D8vir0/x/IV\nNhyEGfNOZf369Tz/9PM88NQDuHHfta5WhsSO50vXefKUN1DMP0eL28hhx7dhmgZ8u3qudCGNZ1HI\nLoVOmNsSK413Pf7+j5VMJnPuwMBAhQth8+bNyY9+9KOn9vT02IVCQS644IK+b3/72zvvvvvuxhtu\nuOG0efPmFQqFgmzYsOHQbbfdtnekc9eKsRTHWA+8AHwV+EfgeRG59GjHqerrVfXMKq8fA/tFZE5w\n/jn4TRSqXTuOr3D/r6r+IHLu/arqqqqHXxP6/KN+U4OhjrEEvuRcjybaSBT8G3xjIc2emTk+uPkP\n+MGWv/P383IUOlpIO3n6ix5F2yZmW4gI8yyLvVr+k/67nd38/h7/nvJw3OGf93YNu+6MhsBdaguJ\neNn9GyZh/dv2f6Mt1VZFXsEJbh9OLmjiYsXQSM/Yoe5lgLYgdpq2Rr/1zNv9FAXLAWxE47Sq73od\nzG5G7EHyBWFpy1IyVoaY18HsphSJmIUd+ottAU/JaYKkOMS1OEJM1989nKfblo0UtCi5l61STNfD\nIoFLAmHTgR7e8/Vfl9zJIT2DvqXblI7THi/bNVnboqVlrX8ep5z5nA+9/mG/20hMF6AvF9bkT5Lt\nOUw85bvvE6oURWiMxLSbAk0+XzrZfaTAAfHP0Zaom9oQNedEaO13G/AGVb1MVS8F3gh8+Tivexfw\ngWD5A8CPh+4gfobDPwHPqeqXhmyLNh/egF8py2CYtoTKQFZeRkZPBeDiweWsa17LirYVpf3EG2Bw\nRiMNxUF6ExkcyyZu+3/GC9JJ9qYtfu/Uz3B41p/w1V0H+Ekxx2sp4ZN79vuKKGBGQ4L/dcO5ZBN2\nKACJiCKMTjf67bN+u6q8odIt5v07vVoxPIVAnw1LpAJIWBZ/uXQeP1uzbPjGd/wzXPxxAOYWHcQK\nHGpenA53APHyqJUBK4fjQdGF69uup8VezPxW/94ZKl31FNR3LwMkNFcxT7ecvRxOGQpiutlIclSg\ndAtYYAlJyz8mbcdY2JrhyV2HeeSlLrbtq5zL3DPoy92YirEkU1biWdvmjJW3MbPlJi64+g2sueZU\nWs5uY1+YwGYF/xeB0m1L+g87hfxebCtFqlDEUo/Fq33FHVcPx7Jpi8S03dXL6Fh9mPlWJ7u7Bzno\nNYD4Fb5OFk6E1n5xVd0eflDV5wML9Hj4IvBdEfkwsBO4HkBE5gLfUNVrgHXA+4BnRCQseBpODfob\nETkH3728Axh+VzAYphElo3DVGSx+6lHgNE5LLuTP3v45il6R1f+y2t+PHIMpocXL80piDp5YJIIC\nDwtbMzzaO8CcwTUUUwOlcz81N8HsZJwDhXJU6NKlM3nr2XP51sO+61NtIRHRkg1BMs+CxgW85bS3\nsPHVjRXyWgJO0HqvkIsqXcXTSityKB+aP7P6hjPfDkteD498xc/ejvvyepqkwcqTcn0Xq9h+7Ltn\nsMh52fP4Qb/HikW+9RcLv4Pnv0Klm/QGgylD/uZhlm6glNobokrX31gMlG9LEvYPQsfMJi5bNpPn\nH3wZgP09lY1qenJFEjbEbYsWu/wg02BbJBLtnLX6j0vr+rftx/lWEGiXUOn6DzyhpSvFfdh2loTn\nW9SLV69l+yMPEvNcHDvGTKstbE7Ewfetp+2nG5nvdNLVX2BPLonEDpK0q2daTxRbn/vjBf19z0+o\n1ZhtWDaw8vS/Pmlb+31DRNYHr6/jJy8dM6rapapXqurSwA19KFj/WqBwUdWHVFVU9ayhU4NU9X2q\nuirY9tZIUpbBMC0Jp66oxJmnOwD4whlX0eO4fGhL+b4jKAPOIG1J4UiqAU8t4oHSXZBJ0hcXNu2q\nzCl8cmYCRxVECL3P6cDCjbpj4xEt6Xi+O/LMGWeOKG9o6RYC97LaoaVbOR1nXKSa4Zq/Y+688xGr\n7BJNkyfmgmdlEMu/Xk+uiOsp+3pyzG8NlG5ozasGiVS+smlyuyHRUCV7udLSbati6VpBfePmhH9M\nQzLGBYvLLvfd3ZWGVM+gQyZW/u6hSz1jD7/dVozRCO5l2z2EZ2VIeC6tc+bSNsefxGE7RRw7xqxE\ne+kUxeB084PKXNu6QOz+mivdemLat/YDfhf4GPAHwedf4sd2DQbDBGGV4no2b3Hu5Eu9V7OnsZmv\n7TrAPV09nHnqrbQ629m2+9v0O/3MPfdMirv8m3lo6a7I+nHCX3RU/lk/0WZzJFegLW4TS8fp6S+S\nDhRBeNNf0ZTmnbPLiuR1817H25e+nT9Y/QdUw58yFCrdwKq2YqijaMm9fIw9XM//KK1rPwJfeV9p\nVZo8uBbiLmFh0mE7cGTQoaeguJ7S0eR/99lNaQYLHntOySKHCnRqZDpR2+LS93WGTBm6fMUsXjs8\nyNzmSG/bQOn+dOAn3NfZxa8Trwcgm4xx8WkzeM8Fp/Cvj+4arnRzRTIRX+Bdq5fyrT0HmZMc7iCs\nGKOwKUFQHCMaSy9KhrbGBi5+53uJp30Z7WIB144xIzELgkJahWAO9nLxpyN19nrEmg6SWnjJ8HGe\nQI7FIq0l07q1n6rmVfVLqvq24PVl9WuOGQyGCaIU0xWhyRrg3qffi6jHl3fuJybw40uu40vn+0qo\nv9jP3PPLXXQSQY/X85uz2ALPN5XjdytysNtz6XU9/uCUDpa0+1ZUJrB0Q6XzidPm8JZZ5bmymXiG\nWy++tWoSVSivE5jNhcGy0o26l6vFdMeKiDArW5bHQrE8wZNGTkn7eZM9uSK9Bf9aYSw2HhMWtmfQ\nhA2e8oJGpva3LY7EdCut8UUzsvzpm1eW2hECEPMV+Tn53Xxq5x00BcZiQzJGKm7zlxtWsWJ2Y3Wl\nG7F0l2RS/MXS+VUfQirGqBTT9ZVuU6IJO3A5F0jT1tzCinWXkQiUrlXI49gx2lLlGZeFoHjGMtlN\nS6D57czLJGedMezaJyr13tpvREtXRL6rqteLyDOEJWwiqOpZNZXMYDiJKN18RcCK0er0srK4n2cT\nczinMUPWtskHjecHigPMjsQeE7b/Z5yN2ZzTmOGJnnI8d0Mhxl+lfDftgnSilK2cKild/8Kh5Ttm\nea2ypXtg9w5fdDuG5zmRRKrj0LrAopb57AqWW6UPy1G8lJRcwT2DRXqDW2noFrZE8FR9N7oquzRS\nAqBtMZbfhAnH06M/FNhx3/os+NOVmpL+AeEDC8D81jS7u/3xfmJnN79+uYvenEMmPrbvbo3iXhYR\nmpItdOe6UCtDOnBPJ4LsZcnncFPNZFIzSqcoapggplywqI2fP7ufWPZlUrHqdaKnO2Frv/Dz7/7u\n7+7fvXt3/JZbbjklmfTLi4Wt/Z5++umpEzTCaO7lm4P3N0+GIAbDyYwEVYsEfAvLyfHh/if4Vts7\n+a15/k01GypdZ4C2lnLSZSpW/jP+0LwZ5Hpf43XP97O9yeLtrc08PRO6Cg5rm7I8FCjdRHADD2/6\n8dj4itP5Md2y8lm0aBF7iOFp8aiJVGPlg2e9iwce3wrARvdsbFfRmE0qSHo6PBCxdBvCHr7iK33x\n6zZ7UWde22KsI76CdD3v6A8FIhDPQFDhqSnh759Nlsd7fmuGR18+hKrywW8+Rm/OoTEZ44zqDoKq\nlyh/qLR0ARY0nlJSumFMOJ7yFagODuBk2yEVp7/prWSKOyloOV/ovRcspM89wGa6SNqTnqQ7KUzH\n1n4jKt0wOUlVd06eOAbDSUrYAEiAVBPkDvOe4jbes3Z5aZeElSAmMfqL/RVTWxIRpfv22W1c0610\n/cxXVq1vS/NPZ5Zr1ITH9eSc8vUoJz+Nlah7GeD9738/j9z5BKqMOk93PCxum4Xf1Azu13NpchTP\nFlIJQQS6+gv0lNzLyeCawXfxFwDo1GZmyhFINiDiK13H1bHJF0+XlG5jwgKUhgqlm6Y379Az6JBN\nxOjNOfTmHTLxsRX7i8ogQ6YMAZw/+zye7nwKFbs0t9mybGLJJF5vD05HDGloZKDlnTQfuYOCF8lQ\nXzaTw7bD0w9Byj4xLd3pyGju5V6quJVDVLWpJhIZDCchJe8yAskg+WeIS1BESMfTDBQHKqa2pGKV\nCTqx1vJxdmulhRO6YQ/1+2kZpZv++HRuRSJVKFvo2p0o93JFJjFguR5YwvYkNKfjPLDvMAsKim0J\nzelyO8Gwf4AEyv+N+b/mvWc18amITK6nY7PE4+myezllAS6ZZKV7GWD34QHmtqTYF0wfisZ0R6PS\nvRycN1meI31mu589Hivuqch+TqTSOD1H8OwYbjoLDJAQpeBWhi5zji9PMnZiWrrTkdEs3UYAEfkC\nsBf4F/x7w3uBOSMdZzAYxo8MtXQBqtwos/Es/cV+Monyn+4wpdueJj6/AS14xOdWNip4y9lzufPX\nO/ngxacCcMsblvPKwX5WL2xlPITFMfa4Tbz9Da8rrZuoRCqgwqIESPX51vlvEh6ZmPDkwV40LrRm\n4qUEKEvADZ8ggpoTh2jiUPqUkowQxnTHYulmYNAPBDclfaXbMMS9DP60oSNBUYw5zSmWto7tKaZ6\nIlXZ0r1o7kVIagn9zW8rxXTBV7p2oGDzYYUqUQpepdLNu/7D1Yka052OjMUH8lZVjTS+5Gsispka\n9Nk1GE5WKmK6yUDpVonDZWNZBhzfRZqKC7mikhyidCVu0fH75w47FmBmY5Jf3LK+9HnV/GYe/KPL\nxy+v+NLeU1zOJ09bXlqnWm6Ze0zzdCuuUXl8ps8l/XIvg4sbSSUspOAxqB5t2VTFMW54WMRlHirY\n6DzdMT0UxFLlmG7SBopkIw8881oCS7d7kK7+Au+7cCFfuO7MMTcUiH5HjaX9xK1k2YmYiWeYseiv\nONCfIxOpGBZPp4m5fvy2N/jtJCwoupVl8UNL17iX64exZE/0i8h7RcQWEUtE3gv011owg+Fkorql\nO/xGmYlnGCj6Sre9wb/5p+OTX/ggaiWGusASQWHCLN2heKpIzlc0+ZhAwWOgoBVu6KDvfXAAFeuh\nrOTGZekW/dtdU8q3RLMR93JLJk42YbOzq5/DA8VhLvGjER2j7qXvgPf9sDRPNyQZDHClezmFHbR/\nPFL0x6SapZtzcwhC3DreIoKGiWIsSvc9+GUa9wevdwbrDAbDBCFRpZAc2b2ciWfoD5RAS9ZXbtl4\neth+taZC6UasSE8noDjGCHgKDPoKphD3Ld3+4lClK7ihAF7Z0pVhlq439phuwNL2BAvbMyyfXbZE\nRYRFM7P8+mW/mURFGckxUDFGqRZYvH64CIHQUfdytqWNmBsoXccfk6Qlfkx3/lo4279F5508qVjq\nuL0OholjLMUxdqjqtao6Q1Vnqup1qkGdOoPBMCEIkZtiGNOrFtONZekPmp2nkn68Lm01D9uv1kQt\ntHB5WEx3fLOQjoqnigwEpSHjFhQ9ckUtJVGFMoTFI6MNQ0sPBlbE0h2LKR5Ruh2NCR74w8tZNCNb\nscvaU9t4fr+fbDV+SzeSvTyCOGF5zqilO2/FSmKBK/lwoHRTodL9yL2w4WuAb+meqNOFwG/tN3Td\n5s2bk+eff/7yFStWrFy8ePEZN9xww0KAu+++u7GxsfGccP2nPvWpKclNOmpMV0S+SfXiGFX71xoM\nhvFT4V4Ola3nDtsvG8+W3Mt2vBdoYTA/+TdVqVAW5WpannectZdHQRU07yIKmrAQIFdQmlJlpSsC\nDsMt3fKDgf/ujse9XDp59aeICxe3882HdwDjV7oVycsjyFNSupGnmAUrV2Fv9BsQh+7llGVRcCpj\nunk3f0Ir3WqErf1uvPHGwwCPPfZY6ckpLAPZ09NjrVq1auWGDRuOXHLJJQMjn23iGcuz6N3AfwSv\n+4AmoK+WQhkMJyuCQFic3h1ebbUipjvTn0J/yZIRuvbUEKuKsrDEn6M7UVOGoLJSlqeKekoLgiYi\nbQhTZdvBEsEJY7nRRCqr/GAA4TzdMQgQj8TVreo2yoWL2knYFjFLhlnBR6NabHwoYeekVGSHGQsW\nEgtiuoeD96Qtw6YMhe7lk4lp39pPVb8f/Swi3wYeqplEBsNJSNlapGzpOsPLxUZjuoP281x22XbO\nW/jfJkvMEpUx3fI6r6I4xvFfJ5u0GQwsOdfz48Xz1OJIROk2VbiXh08ZgrJFWTlPd5yWrlW9VGZz\nJs7Tt74B1XL3prESVbRHs3TdiMNRLIsb/vQLfG/TS6WYblqs0hShkMlyL3/iuV0LtvXnJrS134ps\nauDvTz/lpGztN5SlwKyJFsRgOJmR6Ht4k6xi6WZjWQpegVeOvMIT+59gXsO8YftMBlUTqSwmtDgG\nVLqoQ7f1XCy/oUFA1L0cjelWupcrE6nGVHsZKmK6I1m6AKm4PW6FG5UrKtswEYINRa8yyhdWqDpS\ndBEgZccoesPdyyfbdKFp39ovUplKgvd9wB+PetDRz9kG/BtwKn4T+utVtbvKfjvwm1a5gKOqa8Zz\nvMEwXaiM6Ybu5eGWblh/+eO/+DgAS1qXTIZ4w6joSBfJvK7sp3v814kqorAd32JiFe7lpnT5NiYi\nZYtQq8V0Q0vXK/UhHpWKmO74lerRsCrGsfqAvWlGM3cdOMzybKXyTAWJVd2OS8qySMYSw9zLg84g\n6Vjts9uPxSKtJdO9tV+jqjZF3pcNdTkfA58G7lPVpfhx4k+Psu/lQQP7Ncd4vA0gS20AABrZSURB\nVMFQ91SUgVz6Rr/o/fk3DdsvEyiBnT07ecvit/DBMz44eUJGGGnKkFZUpDp+rWtF3O5uUN9xhlg8\ne3m5yVmlpes/oQNIxTzdiPue8czTjVq6E690pco4DmVDRysvvG4VpzdUKs/GoPFDZ6FI2vbn4g6d\np9ud66Yl1cLJxLRt7RdFRFrx3cqlRy1VffA4rnstsD5YvgPYyPis5+M93mCoKypiuk1z4E/2VN0v\nE7G8rltyHdYIGbW1plosstRWbwLdy+E5YpaULF0RoT0Zx4pbeEVvSExXSko3aulKREYYR+3laBJS\nDZTuWNzLUFawUZqDdTlPaY9bJOzhlu6h3CFak+Mr8TmdONFa+wEgIh/Bb/M3H9gEXAj8CrjiOK7b\nEXYxwndXd4ywnwL3iogL/G9VvX2cxyMiNwE3AXR0dIy5PNtQ+vr6jvnYWlOvshm5xk5Xlx9aeuml\nl9jo7hpxv925cseyw88dZuP2jbUWDRg+Znt6y2bkr3/1CC0pi9f25CkUHZ7atBmAzZueYmDn8Smq\nfD4IualSDBKGdrzyMhtlN3Zc8Irw7FOP81rKfwrYty+P4wW3tYilu2vnDjZufI3dgdwFxyWfyx31\ndzB77y5WBMuPPPo4heTLY5J7rL+xAwNlIR979FFeyYz9IcpTEJpRBM3n2Ld/H3knX7quqy49hR6O\n7DtSIUs9/v6PlROqtV+Em4G1wK9V9XIRWQH85dEOEpF7gdlVNn02+kFVVUS0yn4Al6jqHhGZBdwj\nItuGWthHOZ5AUd8OsGbNGl2/fv3RRK/Kxo0bOdZja029ymbkGjt37nwcOg+wZMkS1l+yaMT9LtPL\nuLDrQpoSTZzSdMqkyTd0zF480AsP+3+K69atY2Zjkgd6n+XRA7tZddZZ8JvHWL16NeeNs5HCUDKP\n3Q+DA8RjNnnHA1WWnHYa6y87jfQTv6A4MMgbr7i01ADiv7qfQXd1+gdHEo8WL1rE+vVLeWF/KLeQ\nzaSP/jvYcgi2+4sXX3wJNI74fF/BWH9jrx4agAfvB+Ciiy4sNVAYK02/fIYjjsu8piaWtC7hns33\ncOlll2KJRedAJ+yC1StWs35FWZZ6/P2fTIxF6eZUNSciiEhSVbeJyPKjHaSqrx9pm4jsF5E5qrpX\nROYAB0Y4x57g/YCI/BA4H3gQGNPxBsP0QSL/jrKXCGfOOLP24oxBjpBokpLqxNZeDs9hW4LrVcaK\n0+k4PTJYMZfXEvDC61ZJpArldvUYimOMkr18rIylOMZoNMVsjjguMxIxEvgJeAW3QCqW4lDuEABt\nqbYJkdUwMYzFl7FbRFqAH+Fbmz8Gdh7nde8CPhAsfwD48dAdRCQrImF7wSzwBmDLWI83GKYTFdnL\n04DRay9PfCJVLKLBw9M2NSQgaQ1LRtIwPl4xT7dyypDqGMc6OyMizMTHz6uN43hoivkytcdjpaYG\nYTJVd96f0NGaOnFjutORsRTH2BAs3ioi9wPNwM+O87pfBL4rIh/GV+DXA4jIXOAbqnoNfpz2h8Ef\nSwz4V1X92WjHGwzTFRnyXu9Ur0gV1F72KtcfD+Ep7IjCC897wXlzeK7NQlUrE6XCy1adpztOJdc0\nt7xcA0t3rIlUI9EYTHtqT8RIFvz53WEyVXfOV7rG0q0vxvUrUtUHJuKiqtoFXFll/WvANcHyy8DZ\nQ/cZ7XiDYbpS0WVoGlBRqD/Qh7WZpzvc0g0XOxqSONk4OU9J25Hs75I5y7Bjxq10GyIx3CmapzsW\n2uM2Cdd3LxfdIqpaci8bS7e+mPhHN4PBMG5KTeynh86tGousRe3l8Bx2VOkGy03BlJn/PHiE39u6\nk43nL/f3DxRwNUu3WlGP0QWIKNqaxHSPz9ItBg84MxJxEkVf6R7KHeJdd7+LpmQTllg0Jya/C5Vh\nZKZmkp/BYKigZOlOrRhjZky1lyfg7hJeJmZLZJ2/HM5T/fqrfrbykz0Dfly5OYEMOIirw85jWeO0\ndKPUZJ5udHn8//uh0m2Px5iZ9htf/HzHz+nOd7OzZyfLW5dj10DueuGEbO1nMBhqT+l+O01M3dES\nqWpu6YaJVIHSfTXnxzAzloUIeK1J7AODVc9ToeTG+1BQg0Ikx5tIFRYMaYnbtMX9+PPdL99d2r52\n9trjlHD6cSK09jMYDDVGxjhlqF6ojEWG7/6UIXcipwwFd6hYFQs1tHQPFv0WB/2uR7cFJCys7srK\nTMcc0wVIBzHRGjwQVYuNj4e5Kd+l3GTbzM7M9ufnDnaWKpWt6Vgz2uEnJNO+tZ/BYJgEptmUoWo1\ng8N3L1Ku8XgpW7rR7GX/fWhpxCOOy2EruHZvZbedcj/d8roxy/c7D8HezeMRe8xEFe2xWLpfOf0U\nNh7qZVHG1x0z0zPZP7CfG0+/kYVNC3nd/NdNlKij8off27zg+X29E9rab9nsxoG/fcfZprWfwWCY\neMpThqaH1h2piT2UXZ4T2dqvcp5upaUb0uO49IYPLzmnYtvQ2stReY9K83xYUZuexcc7Zag1HmND\nRzk7OWz1uG7uOq5ffj2xGiR/1TvTvrWfwWCoPRUND6YBVStSBQtuMFF3oitSlddVZi+HHHFcesQD\nx4NiZWVYGXLs0OWposJNPwEPXHMb5rK5czPnzDrnuM81Ho7FIq0l07q1n8FgqD3TuTjG0AeGibR0\nR5unmxqi1Xsclx4BGXSHjWPVRKo6GOyKmO4EyPP+le/n8xd/vqIb1cnGCdHaz2Aw1JbpVgZyaFlF\nfzm0dCe+iX01SzdqbS9IJUqWrgxWupaj55EKJTf1g328tZeHcnr76Zzefvpxn2e6cEK29jMYDLVn\nusZ0q8UkHbcGMV17dItwYSpBr+NyBEUG3WHbp4OlWw/yTDemY2s/4142GOqAktU1TW68o9UyHtoN\n6Piu479Xq70M8DsMcMeqRTTHbV7NFcgH7uWhSNWHhKkf7HqTx1B7jNI1GOqA6RfTHXkKTjmmO3HX\nqTZPF2C9FHjjjGYabZvX8v40oaGZy9Fj6k3JVZvvbDixMUrXYKgHptkNt7rl6L+H2csTO093dDds\ndPpQVfdyqSlDeV09KLloElo9xJgNtcfEdA2GOiCM5epR9qsXqsdIJ97SLdVerjJPN8qCdKK8fdSY\nbn1ZuuCPk1G4Jw9G6RoMdUDpnjtNtO5oiVQTG9Mdm6V7cUtD+UPRG7a9erb1cYs3IVgidWF1GyYH\no3QNhjqgrHOnh9YdW0x34hKpRorphqzIpspyjHKe+rR0ZdqFFwzHzpTEdEWkTUTuEZEXgvdhXZZF\nZLmIbIq8ekTkE8G2W0VkT2TbNZP/LQyGiSO8/+v00Lmjtsor1V6egLtL1drLVc5riXDLqbN5g1W9\nfv1oDwlTjUj9WN3TjenY2m+qEqk+DdynqkuB+4LPFajqdlU9R1XPAc4DBoAfRnb5crhdVX86KVIb\nDDViusV0JXCJVp2nO0m1l4dyy6LZXBFLVd0WLY5RTgI7bvEmBEukbqzuE4Gwtd+2bdu2vvzyy89+\n8pOfPBBuW7NmTd+2bdu2btq06bnvfe977Q899NCkl+6aKqV7LXBHsHwHcN1R9r8SeElVd9ZUKoNh\niphuli6EyqLyM0RjuhNxDf/dtodb1FX3H+Gio3VFmmosqR9ZTgRMa7/qdKjq3mB5H9BxlP3fDXx7\nyLqPi8j7gd8An1LV7moHishNwE0AHR0dbNy48ZgE7uvrO+Zja029ymbkGjt79+YB2L59OxsHX55i\naYZTdcxUKRaLpfXbd/vzZHe96hcDeuiXvyRhH58y6erym8Ds31vuwPbM00+jr9lV5XpxV2VLv5Ct\nz24h2bmtJDfAwYOdNfsdjOc35nl+tvVk/SZr8vv/0ccWcGDrxFqNs1YOcN1XT7jWfjVTuiJyLzC7\nyqbPRj+oqorIiM/3IpIA3gp8JrL6a8AX8L1xXwBuAz5U7XhVvR24HWDNmjW6fv36sX+JCBs3buRY\nj6019SqbkWvs/PzQM7B7F0uXLWP9hQunWpxhVBsz+97/JJWMl9Z3PbEbtmymY84cePVVLrvsUpJD\nOgGNl+/ueQL27+OUBfNh1w4Azj3nbNYtmVFVrj2P7oStW4ad56xVq1h/ekdJbtfx6OiYxfr1q49L\nvpEYz28svvHniMik/Sbr8fc/kdx8881d1157bc+PfvSjpp/85Cct3/rWt2Zu3bp1K5Rb+1mWpSdc\naz9Vff1I20Rkv4jMUdW9IjIHODDSvsCbgCdVdX/k3KVlEfk6cPdEyGwwTBUl9/LUijEuhrpFwwSn\nmtRerojpjiZT9Y3VYs/14tK1rOlScXsUjsEirSWmtd9w7gI+ECx/APjxKPvewBDXcqCoQzYAwx9t\nDYZpROmmO42CukMTgGpTe7lK9vJoMd0RNlXr5mMSqU5MTGu/6nwR+K6IfBjYCVwPICJzgW+o6jXB\n5yxwFfDbQ47/GxE5B98w2FFlu8EwrZielq5UnYIzsbWX/fejzdMdKsPw89R3IpWZqHtsmNZ+Y0RV\nu/Azkoeufw24JvK5H2ivst/7aiqgwTDJlKYMTSOtO9KUoXI/3cmrSDV0/9HWlxo61YmeEzkB3MtT\nxHRs7WcqUhkMdUB5ytD00bojTRlyPG/CXLdjrb1cluHo6+vR0jVq9+TBKF2DoQ4ol4GcPgxLpIpY\nuhOl0EqWrn18lq5UkbOeYrp1IophEjBK12CoA0KlMI0M3VFjuhOndP33scd0Rz9P9Pj6sXTrQw7D\n5GCUrsFQR0wjnYuMkr08UXpk/NnLI8R0q7in66n2cp2IYpgEjNI1GOqA6RnTrb17eeLm6Q5friv3\ncp3IYqg9RukaDHXAdIzqDVUWVoV7eaKu4b/bY3QvjzxPt76nDNWL1W2oPVNVHMNgMESYng0Pqk/F\nqUUiVUVMd5S71tjm6Va+TzXG0j12pmNrP2PpGgx1QDl7efpoXRGpUIBRS3fiYrr++/FautH19RnT\nrQ9ZTgTC1n433njjYYDHHnssHW4Ly0D29PRYq1atWrlhw4Yjl1xyycBkymcsXYOhDpiWlq5VvdKT\n63kjttgbL6WY7nFOGapWI7p+3MtSN1b3iYBp7WcwGI5KacrQFMsxHny36HBl6Lg1mKcbMalHLY4x\nghlR77WX60T/HzN/9vCfLXix+8UJbe23pHXJwBfWfeGEa+1nLF2DoQ4ouZenkdYdaqFJydKd+ESq\nmtRerhOta9zLE8vNN9/c9cwzzzz7tre97dCDDz7YuHbt2hWDg4MC5dZ+V1555bITrrWfwWAYB6F7\neRrZuqPVXp4oJRIqxhO59vKJYOkei0VaS041rf0MBsNoTMeGB8NqL1sTP2WoWu3l402kqrspQ1b9\nyHIiYFr7GQyGozId77lD55fWtPbycRbHMLWXT0xMaz+DwXBMlGO608fUHSmm63gesdEm047rGv57\nbIxlIKdj7WXT2u/YmY6t/Yx72WCoA6bjlKHJrb08VvfyWGK69TVP1y8yMtVSGCaLKVG6IvJOEXlW\nRDwRWTPKfleLyHYReVFEPh1Z3yYi94jIC8F76+RIbjDUhlJMd4rlGA8j1V6eyC5DtZinK6V1xy3e\nhGANeXgxnNhMlaW7BXgb8OBIO4iIDXwVeBOwErhBRFYGmz8N3KeqS4H7gs8Gw7RlOlq6I9Vedt3a\n1l4eaxP7qAUZPSRcrhdFN/ThZRrheZ43LQWvNcG4eNW2TYnSVdXnVHX7UXY7H3hRVV9W1QLwHeDa\nYNu1wB3B8h3AdbWR1GCYHJIx/08xatHVOyPVXu4rOBM3ZWicXYai141ZFjHbH1eryvH1MtIyfacM\nbens7Gw2ircSz/Oks7OzGd+4HIZMZeKGiGwEblHV31TZ9g7galX9SPD5fcAFqvr7InJYVVuC9QJ0\nh5+rnOcm4CaAjo6O877zne8ck6x9fX00NDQc07G1pl5lM3KNnbyr/PvWfq5fmSVRh4q32pg9vs8h\nYcPZM/18zKKn3Lm1QH9RWdluc8Up8eO+7mt9Hps6Ha5aGOfOrQWSNtywIlFSrkPlyjvKnc8VaEgI\ns9JCU1LY2eNx3ZJ4SYE/vKfI5k6Xt5yWYEFjbeyO8fzGntzvIALnzpqcvNZj+f1ffvnlT6hqRSjw\niSeemBWLxb4BnInJD4riAVscx/nIeeedd2DYVlWtyQu4F1/TD31dG9lnI7BmhOPfAXwj8vl9wD8E\ny4eH7Ns9FpnOO+88PVbuv//+Yz621tSrbEau8VGvcqnWr2xGrvFzLLIBv9Ea6YqT7VWzRytVff1x\nnmIPsCDyeX6wDmC/iMxR1b0iMgcY/jRhMBgMBkOdUc8ugceBpSKySEQSwLuBu4JtdwEfCJY/APx4\nCuQzGAwGg2FcTNWUoQ0ishu4CPgPEfl5sH6uiPwUQFUd4PeBnwPPAd9V1WeDU3wRuEpEXgBeH3w2\nGAwGg6GumZKKVKr6Q+CHVda/BlwT+fxT4KdV9usCrqyljAaDwWAwTDT17F42GAwGg+GEwihdg8Fg\nMBgmCaN0DQaDwWCYJIzSNRgMBoNhkpjSilSTjYh0AjuP8fAZwMEJFGciqVfZjFzjo17lgvqVzcg1\nfo5FtoWqOrMWwpxsnFRK93gQkd/okDJo9UK9ymbkGh/1KhfUr2xGrvFTz7KdDBj3ssFgMBgMk4RR\nugaDwWAwTBJG6Y6d26dagFGoV9mMXOOjXuWC+pXNyDV+6lm2Ex4T0zUYDAaDYZIwlq7BYDAYDJOE\nUboGg8FgMEwSRumOARG5WkS2i8iLIvLpKZZlh4g8IyKbROQ3wbo2EblHRF4I3lsnQY5/FpEDIrIl\nsm5EOUTkM8H4bReRN06BbLeKyJ5g3DaJyDWRbZMim4gsEJH7RWSriDwrIjcH66d03EaRa0rHTERS\nIvKYiGwO5Pp8sH7Kf2ejyDblv7PgWraIPCUidwefp3zMDAHj7Xp/sr0AG3gJWAwkgM3AyimUZwcw\nY8i6vwE+HSx/GvjrSZDjUmA1sOVocgArg3FLAouC8bQnWbZbgVuq7DtpsgFzgNXBciPwfHD9KR23\nUeSa0jEDBGgIluPAo8CFUz1eR5Ftyn9nwfX+O/CvwN3B5ykfM/PyX8bSPTrnAy+q6suqWgC+A1w7\nxTIN5VrgjmD5DuC6Wl9QVR8EDo1RjmuB76hqXlVfAV7EH9fJlG0kJk02Vd2rqk8Gy734faLnMcXj\nNopcIzFZcqmq9gUf48FLqYPf2SiyjcSkySYi84H/BnxjyPWn/G/TYNzLY2Ee8Grk825GvyHVGgXu\nFZEnROSmYF2Hqu4NlvcBHVMj2ohy1MsYflxEng7cz6F7bUpkE5FTgXPxLaS6GbchcsEUj1ngJt0E\nHADuUdW6Ga8RZIOp/539PfBHgBdZVxdjZjBKdzpyiaqeA7wJ+JiIXBrdqKrK6E/ck0K9yBHha/gh\ngnOAvcBtUyWIiDQA3wc+oao90W1TOW5V5JryMVNVN/i9zwfOF5Ezh2yfsvEaQbYpHTMReTNwQFWf\nGGmfOvzbPKkwSvfo7AEWRD7PD9ZNCaq6J3g/APwQ3xW0X0TmAATvB6ZIvJHkmPIxVNX9wU3SA75O\n2YU2qbKJSBxfsf1fVf1BsHrKx62aXPUyZoEsh4H7gaupg/EaSbY6GLN1wFtFZAd+KOwKEbmTOhuz\nkxmjdI/O48BSEVkkIgng3cBdUyGIiGRFpDFcBt4AbAnk+UCw2weAH0+FfKPIcRfwbhFJisgiYCnw\n2GQKFt5wAjbgj9ukyiYiAvwT8JyqfimyaUrHbSS5pnrMRGSmiLQEy2ngKmAbdfA7G0m2qR4zVf2M\nqs5X1VPx71W/UNUbqYMxMwRMdSbXdHgB1+BndL4EfHYK5ViMn2m4GXg2lAVoB+4DXgDuBdomQZZv\n47vPivhxoA+PJgfw2WD8tgNvmgLZ/gV4Bnga/0YzZ7JlAy7Bd+s9DWwKXtdM9biNIteUjhlwFvBU\ncP0twOeO9nufxP/LkWSb8t9Z5HrrKWcvT/mYmZf/MmUgDQaDwWCYJIx72WAwGAyGScIoXYPBYDAY\nJgmjdA0Gg8FgmCSM0jUYDAaDYZIwStdgMBgMhknCKF3DSY2ItIjI70U+zxWR79XoWteJyOeC5Zki\n8mjQCeZ1tbjeOOT6OxG5YiplMBhOFsyUIcNJTVBr+G5VPfMou07EtR4B3qqqB0Xk3cDrVfUjVfaz\nVdWttTyR6y0Evq6qb5isaxoMJyvG0jWc7HwROC3offq3InKqBH14ReSDIvKjoP/oDhH5fRH574F1\n+msRaQv2O01EfhY0ofiliKwYehERWQbkA4V7Dn6rtWuD66ZFpE9EbhORzcBFIvI5EXlcRLaIyO1B\n1ShEZKOIfFlEfiMiz4nIWhH5gfh9Uv8icr0bxe/3uklE/ndQnN8WkW8F53xGRD4JoKo7gXYRmV3r\nwTYYTnaM0jWc7HwaeElVz1HVP6yy/UzgbcBa4H8AA6p6LvAr4P3BPrcDH1fV84BbgH+scp51QNg+\nbxPwOeDfgusOAlngUVU9W1UfAv5BVdcGFngaeHPkXAVVXQP8//jl/D4WyPlBEWkXkdOBdwHr1C/I\n7wLvxS/CP09Vz1TVVcA3I+d8MpDRYDDUkNhUC2Aw1Dn3q99jtldEjgA/CdY/A5wVdOa5GPj3wBgF\nvyH4UOYAnaNcx8VvOBByuYj8EZAB2vDLfobXDmt/PwM8q0HLNhF5Gb94/SXAecDjgUxp/AL3PwEW\ni8hXgP8A/ityvQPA3FHkMxgME4BRugbD6OQjy17ks4f/92MBhwOLcjQGgeZRtufCOK6IpPCt5TWq\n+qqI3AqkqsgUlScqkwB3qOpnhl5ERM4G3gj8DnA98KFgUyqQ0WAw1BDjXjac7PQCjcd6sPp9Z18R\nkXeC37EnUGxDeQ5YMsbThgr2YGBJv2OcYt0HvENEZgUytYnIQhGZAViq+n3gT4HVkWOWUe6IYzAY\naoRRuoaTGlXtAh4Okov+9hhP817gw0ES1LPAtVX2eRA4VyI+6FFkOozfi3UL8HP89pJjRlW34ivV\n/xKRp4F78N3b84CNIrIJuBP4DJR66S4BfjOe6xgMhvFjpgwZDJOEiPxP4Ceqeu9UyxJFRDYAq1X1\nz6ZaFoPhRMdYugbD5PGX+IlR9UYMuG2qhTAYTgaMpWswGAwGwyRhLF2DwWAwGCYJo3QNBoPBYJgk\njNI1GAwGg2GSMErXYDAYDIZJwihdg8FgMBgmif8H/Vn+YfQARaAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd4a561cb00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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l3L5pIWNWPMng4CBfmD8X1q9rbzvqqKMYPGa/2DGT7vst+06bzODgSYkyn9+4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\nUXA4HA5HG2cUHA6Hw9HGGQWHw+FwtPn/xrOZuLyXTwkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd4a4b8c6d8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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6hzlqZr0/BqGKq8UkkS4m5zXoYnJKcxQ+r45BaAVRigWhV8PqG0nlfKZ/u/98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\nYakWRJwYxPBYmk17+/mnF53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merrdmdylMlmVeY+f38zHX3EKy4+eSTqjuGF1J71Dxclm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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd49dedcb00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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cj4ExtAkCoRF02zTEzZtmm3msZBmZ78e1SD1krhMJqrVG9GJQICLK5Ou5HOG5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\n5jn/D3yazeJVtdH0AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd49dff0f98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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7qxx7OP06MJBmckjXCV73+OOEdj1Vd5/tSS38L27exGOpzczqEnYMKH70u1Us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q1wzN7B9ALcl51w2ckznOdNwN572Bds5tD+OmPHnoB/fifjZzZx6YG9nK+3Pb7/aZ6qrOGy5z7D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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd49de1ba20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"try:\n",
" print(\"Latest Iteration:\", latest_iter)\n",
" \n",
" output_fn_postfix = \"output_\" + str(0) + \"_\" + str(latest_iter)\n",
" output_fn_postfix_mid = \"output_\" + str(1) + \"_\" + str(latest_iter)\n",
" \n",
" chart_postfix = output_fn_postfix\n",
"\n",
" nd.plot_codec_params(network_model,chart_postfix, scale_up='orig')\n",
" nd.plot_codec_params(network_model,chart_postfix, scale_up='full', test_data_fn=config['test_data_fn'], test_seed_start=config['seed_start_index'])\n",
" nd.plot_spec_params(network_model,chart_postfix, params='Voicing')\n",
" nd.plot_spec_params(network_model,chart_postfix, params='Wo')\n",
" nd.plot_spec_params(network_model,chart_postfix, params='E')\n",
" nd.plot_spec_params(network_model,chart_postfix, params='LSPs', lsp_param=[0], test_data_fn=config['test_data_fn'], test_seed_start=config['seed_start_index'])\n",
"except FileNotFoundError:\n",
" print(\"File not found\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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DlI5hGIYRGWZeMwzDMCLDgg0m0bZtW+3UqVPe9VetWkXTpk3DEyhCyll2MPnjxuSPj1KQ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\nhDt2kmCfjp4pIgpTw5G7bk3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zXy8Pi6UxMIzS4YQ9N02tOGLX6MNoFs28lglVfRl4Oc2+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\nKR3DMAwjMv4f+ga/kfGoyMcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd49f92c358>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
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\" type=\"audio/x-wav\" />\n",
" Your browser does not support the audio element.\n",
" </audio>\n",
" "
],
"text/plain": [
"<IPython.lib.display.Audio object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"cb = home + \"/store/c2gen/out/\"+network_model+\"/out-c2cb-\"+str(output_fn_postfix)\n",
"call([\"bash\", home + \"/store/c2gen/c2towav.sh\", cb ])\n",
"\n",
"try:\n",
" nd.plot_audio_waveform(network_model, output_fn_postfix)\n",
" display(Audio(filename=home + \"/store/c2gen/out/\"+network_model+\"/out-c2cb-\"+str(output_fn_postfix)+codec_sub+\".wav\"))\n",
"except FileNotFoundError:\n",
" print(\"file not found\")\n",
"print()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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cutf1bzN/RbSrzyXkMDOd112s1eETFrMsxZqmukBliN9NonTXowOE1Ck2p5wP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\nHzY9tsnW5F46AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd4a55d0588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
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\" type=\"audio/x-wav\" />\n",
" Your browser does not support the audio element.\n",
" </audio>\n",
" "
],
"text/plain": [
"<IPython.lib.display.Audio object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"cb = home + \"/store/c2gen/out/\"+network_model+\"/out-c2cb-\"+str(output_fn_postfix_mid)\n",
"call([\"bash\", home + \"/store/c2gen/c2towav.sh\", cb ])\n",
"\n",
"try:\n",
" nd.plot_audio_waveform(network_model, output_fn_postfix_mid)\n",
" display(Audio(filename=home + \"/store/c2gen/out/\"+network_model+\"/out-c2cb-\"+str(output_fn_postfix_mid)+codec_sub+\".wav\"))\n",
"except FileNotFoundError:\n",
" print(\"file not found\")\n",
"print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Early Iterations\n",
"==="
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"output_0_27830\n",
"3200 rate codec\n",
"\n"
]
},
{
"data": {
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7+mnCuPFMmB3+OB31O+uocnUnfK84IvJF4DpgjIg4begKomohJT4BfKSU2m4d\n5+/AMcA2ERmqlNoqIkOB2jSOkZhcJhv5xWKUgpXP6zT80+7Uy0rKKGzbD40NMHiyNc4kRYTHuOsM\nCYjGSM3/ypAZgm7d/ww8D9xBfOLPPqXUrjSOuRE4SkR6oV27JwILgQbgMuAn1vM/0zhGYqLzSHMR\nI/W5uO+thvptcOrPof8YvaykgsK2Jj3XrdfA4O0N7TEJJIZENNvz9c25YsgMQYpUKaXWi8j17hUi\n0j9VZaqUeltEnkQ7WFqBxWhXbTnwhDW1ZgNwXir7D0002ShPFmlLEzz1Bf16xMzY8pIyilvqINIM\nvQZY2wfsu3aFTkiacHLGxO3cmIujIQGRFv1sLFJDhkhkkZ6OLgWoiFZ5B+v9mFQPqpT6HvA91+ID\naOs0N+RSkXpd3Bc9DJvegr6joPKQ2PKSMgojVmZ3r/7W5gF/+Htm6+fv7THZqmAsUkN4zLliyBBB\ntXZPt54Pzp04OSSfMdJX/w9e/iGMOAqu/E/8Omf9T9siDWNlNWyH8sFpidk1MBdHQ0iMRWrIEKHm\nCYjIMBE5RkSOtx/ZFizr5CtGWrcVqu6AHn3gpO+3H1rioUjD/OF3rk1PxK6CsTIMQcTV1jbnSkej\nK7ZRA0BEfoouyrAMsM03BbyaRbmyT75ipO89rpX31f+D/h7Gfpwi7Q9IsHIoLoOWBti1FkYdnTGR\nOy/m4mgIwI6PQn4t0l3r4NdHwKVPw9hO3to5y3SGNmphLNKzgIlKqU8ppT5tPc7ItmBZJ6eVjRwX\n98adWvl5KVGI75HYZ4S2nIP+8D166+eda6B2efqidnaMRWoIos1RWS6f58q6/+nnD57KnwydhGTb\nqOVWOk2Ymevr0LV1D2RZltySy8be7j9sUOUdZ4y0R29rbMAf3i539vov9eML82D4jJRF7fwYRWoI\noK2DWKT2/7a0In8yBDDvj8tH7Npcn9E2av2HlTee+LnJ3baNWiOwRETuFZFf249sC5Z18mWRqkhw\ndm27HokBFmmkDQ7UxS/b26GaNuQG542KsUgNQTgt0lzfdLU0wfx79DzWaE/U/Jf+7Oh0lTZqz1iP\nrkU02SjHMdJERdVtRdqjj36WgBiprUT7j9UxUogVu+9OKJNAYghJnGs3x8de+BD851Y9U6CDNxdP\nxXLMJh29jVpCRaqUeiQXguScfFqkBCjSgkL93HekfpYCf4t0/179PO4TsMBSpC9+G4pK4cjPpSVx\np8JYpIasdLtPAAAgAElEQVSw5Mu1qxQsuFe/fvHbseXGIk3Ik08+2fvTn/70vtLSUuVso/b+++/3\nTLx1bgiTtfsRHvduSqmUCzJ0CPI2j1QFx0gHTqB62GkMP/d2a4Hge+tsK9LRx2rL9e3fQ+t+eOaG\nbqZIjUVqCEm+XLv122D3ejj0s7D0b7HlHdQizRdduY2aM3OlB/BZoH92xMkh+apslChGWlDImvFX\nM9zO6pUCfyvLVqQ9+8OpP4Vlz8C+vMTa84yxSA0hiXPt5tAi3Wl5H6ddqMM279yv35tqZHF01jZq\nCZONlFI7HY/NSqm7gC7QZTqfMdIk+mUGxUhtRWrHU20l2qNv4v3WbdUNxbsCzguiUaSGIOJcuzk8\nV+yCKQPGQpmjApk5X7sEYVy7RzreFqAt1I7T8DFlrBM4JzFS52ETxEjdBMVIm/boZ1uR2m4ju/1a\nEHcdoj/7bXvDy9JRMa5dQ1jyFSPduQYKS/Tc8LKBseWmTGGXIIxCvNPxuhX4iGx3ZskF9gnc0WKk\n7QiIkTZs189lg/TzmfdAzQfh9m/fQFQv7PzzTk2ykSEs+YqR7loH/Q7WyYT2/zXXMhiyRpis3a5Z\nv0rl0iJNIkbqJqiyUcN2naxQYs2bLirRd7th3NV9Rug5p8v+2QUUqbFIDSHJl0W6ZwP0G6VfO5tL\nGIu0S+BruojIJSL+po2IjBWROdkRKwfYJ3AkAydy/XZ44nPQsNPnWI6LeySSuRhpw/Z4N1F0fIjP\nVGwp3+qF4WXpsBiL1BCSfJUIbNgRU6BOi9Scr12CIIt0ALBYRBahe5JuR2ftjgM+BuwAvpF1CbNG\nBi3SD57Sll3ZYDjt//yPBZmNkdbXxicu2OPDWGX2Prcs1nfphcXhZepoGIvUEJZ8ZO1GItZNr5ci\nNRZpV8DXNFJK/Qo4EngcGIRuun0ksBm4VCl1rlJqdU6kzAb2nWAmYqR28YTNPpnbcTG8tgzGSHe4\n4i0EK944mSzLuLUJtn2YhDwdEBMjNYTF6drN1U3X/j36ht3+r8bV1zXnq5Mu2UZNKdUG/Nd6dC2i\nrt0Mxki3vKufW5t1/HHA2Phj2a+TmToWNI+0oRZGzHRvEF6RjpgNG+dD9Ttw0OFJCBUCpWD7inAZ\nxJk4VuxN9o9n6Lzkw7XrTgoUgc88CE9eof+HezfrZhV29r0hjq7SRq1rkslkI6dV29oMz90Cdx8J\nTbvtAbH1kSQtUr+YZ6RNt2TztEhDunb7joTyyuzESVf+G+45KkdtooxFaghJPhVpueO/Ovr4mAy/\nnAL3dc2czkzQVdqodU0ymWzkVHR7NsBHVq/BA/ugZz+X6zGFGKmXldW4S++rXYw0rEWqQAph+Eyo\nXhBenrAcsIpyf/B3OOTczO/fiYmRGsKSD9eu2yIFR9MM60bebjrRQfjP7+4asWPThoy2URs4YlTj\nJ794c7dto9ZFyaBF6pxusnMtUUUZvcCnGSP1Uox25xe3OyiZZCMpgCGH6TluLe1u8tKjp1VdKRdZ\nwSZGaghLJA/TX+ptReq46bWvAftqciNDJ6ZLtFETkZuAh4B9wP3AEcA3lFIvZlm27BKmIEPjLnj2\nRjjlJ9BneOJ9gb6ztO82owlN7ukvyWbtBhzT7hYT2yCJZCOJfa59W6F/kn0ImhvhH9fAJ2+PJVy5\n5auv0Z+5IIv3bMYihT0b4YVb4czfaC+IwRvbtVvUI8euXYFeHiXK91olZIszavylTSqWYzbp6G3U\nwlzdrlBK1QEnA/2AS4GfZFWqXBAmRrp5ESx/Fv54VvCfznkh37nG0VkmAxapn6vWT1n6KV6v7aUA\neh+k39el4BFZ8xIsf0ZfwIPkW/GcVrpt2Sp+YSxS/nyB/p43ZcFN35WwXbuFpbmzSJsbdLs0502v\nfQ2o26yfywe3384A6DZqBw4cEABnG7V8y+UkTIzUNp8+BfxJKfWhSBdoWRCNkQZYpEU99PPO1fCX\ni+HCP3uPs/fRa4B27boVaVoxUp/pL/Y+3Uo5dIzUVqTD9PtUFKntVrZr/rr3b/PEpfq5Z3+4YZH3\nnXk6uLOiuxstTVBrTWFqPZBfWTo6bc36vC8sImfeC9Wm8xGc2JdQ2yItH5IbWTo4XbmN2iIReRE4\nGLhVRCqAzn+1CqNInRflNS/pjNyiEo9x1j76DNd9B6N/kk06KzadrF0/V629zEuRJhMj7W1NvbLv\njJOh1OqluN+j8L0t3xl3axd5SxP87yfw8o9g0mlw8Mesi1kGqF3uPHBm9plp9tXoouWZvokA2LIk\n9trrtzDEaGvWv0PYEEgmiLS1D23Y/1tbkfbonRtZOjhdto0acCW6gtFMpVQjUAJcnlWpckKIggz2\nH232tdB2ALZ94D3OVsYVQ3U8xP6T/OlsuG9ue4s06Ripl0VqK1LxGJ+ERVpaAaV9UrNIbbGCFOnw\nmTDnZvj4rTD2RFj4ADx6Diy4N/njebH2FXjsM47jdlBFeudE+PnY7Oy7+p3Ya6NIg2lr0Yo07DSx\nTOBlkUa9UnaIKRd9kQ3ZIkw/0gi668vxInIOujzguHQOKiJ9ReRJEVkhIstF5GgR6S8i/xWR1dZz\ndjMmwsRIncoA/DNQ7XHllda0FMcfdNdashojbbevJF27oOOkKSlS6zj7A1y7TvnOewSuelkXgnjr\n95m5eOzMe55BeLJlAVW/YyV7iVGkiWhr1uUww4ZAMkGkrX1SoPt/67wOtTbDgj/4/z9ql8Oq/2RW\nRkNaJLyii8iDwIPAucCnrcfpaR73V8ALSqlJwDRgOdrqnaeUGg/MI9t1fFWYO0FrTN+RUFwGuz/y\nGWZbpEP0Nk2uhtlxhXcUSZY2cu3AtdN2rt2wBRkc7dz6HwzbVyYhk70P60JkT8Vx798tX2kFDJsO\n0y+HvRt15aN0cV+gOqpFmk1q3oeDjtTuQaNIg2lrhoLi8NPEMkFQjDQ6xqHU598N//4qvPtH7/3d\ncxT8ufN3suxKhDGNjlJKzVBKXaaUutx6XJHqAUWkD3A88ACAUqpZKbUHOBN4xBr2CHBWqscIhX3i\nfvQ/WPmCzxhbGRTqeJ7fHWzEYZGCrjgUvyPH2LYUXLtJxkiTtUiHTdcJVdFKTA7aWuDv18BWj8B+\n0HF8LWZiZQN3ZmISuvu77GaKtLVZT30ZOF4nfxlFGozt2s1pjDSSnEXa3KCfG3ZkVy5DxgiT7TFf\nRKYopZZl6JgHozvJPCQi09CdZW4CKpVSW60xNUCl18YicjVwNUBlZSVVVVUpCXHEnt1ESxk8fj5V\nc//Zbkz/ne9xGLDo3Xc5rDXCtupNrPE43rDqlYwHlm7YwaEex3p30SKOtF7v3bOLgkgLiwLkrq+v\nj36umU1NNNTWssw1vqJuNdOBpUs/YOfWaOlJJtdup6KpkQUJvpfjWlvYUr2ZtVVV9N1dwuHAe88/\nxO7+R8aN67PnA454/y/s2fA+9eO+yavz/kOksDS2zhrn/h0qaz5kMvDWggXs7xk/Ja2wtZHjgLUL\n/8um2vSSLIZuWc1Ex/sFb79NY1kKiVNZwv4t51rvUz1f/ejZWM1sFWF5bQvDWws5sHktH4Q4hvMc\n62hkU7bJW6upaG6lINLM7q1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7LuyoY7SMHz6dvix+36EXtgJ1xx9zXSIQfAoyJEjiC+pD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\n0Gt331oYb5+6LUs2TBzlezu+R09oCo9klHwITO6jNxIyZOV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PnNzR7g15TeTvE0iLnI1K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\n0eq6vgntbpIc2sjz0pJ/AJL0Cfmc2KP1yYeIU9Kfg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85pjnTnzp0xJpPpKWAsl2iwngEI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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd49de23e80>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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l3L5pIWNWPMng4CBfmD8X1q9rbzvqqKMYPGa/2DGT7vst+06bzODgSYkyn9+4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\nUXA4HA5HG2cUHA6Hw9HGGQWHw+FwtPn/xrOZuLyXTwkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd4a5466908>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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6hzlqZr0/BqGKq8UkkS4m5zXoYnJKcxQ+r45BaAVRigWhV8PqG0nlfKZ/u/98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\nYakWRJwYxPBYmk17+/mnF53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merrdmdylMlmVeY+f38zHX3EKy4+eSTqjuGF1J71Dxclm\nVhCG6MmHkG2PwUqpesAXdaRpQUTVz4qDeWy+Nalrve8Svdb6VFEoi2kxUA/sA3YDu3DWhDjiUcr5\n8ok8pTaytZii14aGYLnvkGtNMAaRb8nRKBdTvkF0NuhYoospZB6EKZNz/alzMU12fnsx9bNExOmU\nB0Z9rg3vXMEYhBvDaCroYooIUmfyy9baXE9X/0QtiMlREAD/+KIT+dGV5/HZv3G8049HVOWNQo+y\ndfmTfNZisJ3q/7NJJ+HHmRbERDLiUpl4FoS2hsyBTTXEH6CAglBKvRI4j+ya1B8FHhWRO0TkC+UW\nrpx4QWopvB6EDlJHTpRT8WdSO+cr0oIIlU2fK3Buct09UfJMtNy3F8sIC1Jnyt/ICy/HOjkUq+xa\nZ9bRNTAW2q6ispiaCriYdP2d8CB1Hlma6+gaHJuQ203LPNEYhMmZi1pICKzrLFJBZHQMQnzvw8jW\nNjIHLq7LWL8JwcxiGp/AwtXmACG03Lf7qmfqp3wWRHUoiIIxCHe96fUi0gscdv9ejVNu43PlFa98\nOPpBz2+I2IfsXIl8xfqUUl6qXaH1IKDIdMGoGEnEqC4bg4jjYirNgjBdTMmEZK+Zky0S6/QlEyX/\nZE108s5XZDxFWxDjMeZBDMV0MYFjReSU2lDRM6nBsSDGUhkGRlPMbMhdfCYOUQkRE6G5voaTF8xk\nXZEWhBf3ihGD0O0glc5tl/nK4psT0IKB42KI7WLS3yWT+9lUUygG8SER+ZWIdAL34iiGzcDrgLkT\nubCIJEVknYj8yX0/V0TuFJFn3Nc5hc4xEXQpiETIcpka5bMMokem5uzoQutB6HPFJRHhLDXdXzn7\nU+DB0RZEzGIpwa+kj88+bCEWRBFumVKJ+h0n8EyHUqyym9fsWBBjEUuEmgzGdDGBk/s/ns7NYsob\ng3ALvnUNlB6HmKwspiArFs/h8c6eohS652JKFs5iCmbb6W2JRP55TaYSLm8MwnUxJarXgijURSwF\nfgO8QCl1glLqHUqp/1FKPaGikojj82Fgk/H+k8BdSqmTgLvc92VDQXZN6sgYhLm4T55y3yrrZgne\n1rD7HFoSPAIhfwwibEU58/MwJlqLyatb7yqBsGsqpWIroFKJyi6Z7CymdKa4gPv85nq6BkZDFUQw\nLjE4mqImIV7F2HzUJRM55yxU80qvcT2RTKbJjEGYrFjcQt9Iim1FzI3wXEwxFIRnQfiC1NnKCFGH\n+iyIiSiIdP4OXzffsDTxIyJIrZT6iFLqJqXUxOv0GojIIuBy4EfG5tcA17v/Xw+8djKvGcQr950v\ni8kYneWLQZiLkseJQRTTcUaZwtk4QngWU/4YxMRcTObILCHhmVNTOVFuskttKFVc5zivuY7RVCY0\nQyfHghhN0VRfE0sB1dZI7pKjmUIuJm1B5CqIroFR3vezNRwqkOVUrvXFz3HLgq9zV6GLQyYwOTNO\nDCJYAdY5NF+Q2rQgJhCD8FkQuZ/rgaIeHBSKWUwFcedBTDbfAj4BmGssLjAU0T4gtFaCiFwNXA2w\nYMEC2tvbSxIgnVGMjY2xc+dO0ulM6Hl27BhDKeez7u4RBgbD9+vrHyYxJrS3t3P48LDvM6Vg46ZN\nvm179+ylvT16IvrAwIB3nf0HRhgayr3uMx3ObOFVqx6gsTbbmLa62++//wGa68Ib2ZYe5wFY/+ST\nqD2FXRu9I/6HZHBomPb2dnZ0jkImw7NbtgDw4KoHaWlwGvu+/SOMDIf/XpPF7l3ho+Jnt26lXe0M\n/awUxlMp9uzeSXt7vGUz9+9y7sH6Z3NrBa1es4auZ7K/+dbOUZIqHet3So2OsGvvPu99e3s7g0PD\nHDgwGnl8j3vvHly7noaup73tAwMDfPrn7dy5I8Xc9L1ctiw6PvH0Duf7PPTQg8yKaFOlkFGKGTVw\n6yMbmT+QXbPabP9BtvU6bbf3UJcr08PMbwwfcXXscBTf1m3baJddgNMJ7+zs5NBAhoFhFXqdDR3Z\nmfirHnyYBU2JgnKFsXFn9jzbt22jPeNvk0NDTl8xOjIEwNp1j3ufpdOp2NcqVq5iqLiCEJFXAweU\nUo+JSFvYPkopJSKh+l0pdS1O4UBWrlyp2tpCT1GYv9xKfX0dixcvRu3YRth5HhraRM2uHbS1tXHj\n7sfo2z9AW9ulOfvNePw+jprbSFvbSn689RHo9i/ScvIpy+HJJ7z3xx57DG1t0ROE2tvbPXn+eOBx\ndg4fypHvmfu2weZNXHzxC33Bx45V22HzRi666KLQ1agAZmzrhkceZsXZZ3Hhia2RcmgO9o9C+1+9\n93V19bS1tXFf/0Zq9+5k+fJTYONTnH/Bhd6qY7/ds5aDqb7Q33WyeGhoE3Rsy9m+dNnxtLWdOGnX\nkbtuY8nixbS1nRpr/+Gn9vLj9WupmzkHOOj77Kyzz+HcJdnw2o27H2PueHi7CjL78ftomdsI+/YD\n0Onzf0YAACAASURBVNbWRv0jd7Pw6Lm0tZ0desx4OsO/tN/G3GOW0NZ2sre9vb2dmlkzgX2cf/ap\ntK1YFHnd7au2w6aNXPLCi2hpLL0qbBgrtz3C/oEx2tou9skWbDftTx/gquse5ZtvPgsefoJjjl4A\n+/Zw3vNf4BUCDLJqcCNs385xi5d6313dfivLli1lbF8fg11DtLVdknPchnuehc2OMj33vOdz4lHN\nkXLlY+dDHbBhAwCnnHQibRct833e/MT90N9Hy6yZ7B7o4/QzzoRHVwNQV1cb+1rFylUMU2FBXAT8\nrYi8CmgAZonIL4D9IrJQKbVXRBYCk7fKeQgZZcQg8qS5aksvX1DLmc0aHaQOTpgqbiZ11ES5qBhE\nrrsn91j/voXISXM1XEw6BTh4zWLdMqUQOQ+iCBfTswcG2Gas25xMCBee0MqMuuwov9iAe7O7boLp\nutHZcsH7MjKe8cpoFKK+JiQGkVGhZRw0tckELY21dIcEqXURv0L3qdh5M8WwYvEcvnv3M56rLYob\nVjvW2PrdfQBe1mC+dh5Mc80p3x+Rx2QmAkzEXWm6A8PnQTiv+ruY93baupiUUp8CPgXgWhAfU0q9\nXUS+ClwJXOO+3lxGGYDsBDgIn1IfLKER1RZ1uqezX+7nwUyISVkPIuJccUptFL0mdc5EOedVr4Mx\ndTGI8O1xazENjaV4/f88yOFAcb+Pv+IU/vFFWQuk2HkQujLrISMGUZtMMJrK5ASpx1IZb0GiQtSF\nKYgYirjVDZoH0UX8ClUv1T/nZGcxAZx93GwyCjbu7eO8pdGJkT1Dzj2a6Srf2pDAbpBgkDptxFJE\nojP9TAUxkRhE3PUg9JyOUZ+CKPmyk8pUxSDCuAa4UUTeA+zAWfu6LJgTxXSjDytuZ1oQ+deDMPfL\nvbPBEW0x91435Ec7DqGUs76vlk1/nnMA8SyIuDVm8pX7TrpzRCB3adVyD4KiOqy4g76b1u7m8PA4\n377ibE6Y77gRPvSrdazp8MeHip0HoTuxnsGs4tEKInhfxlKZWBlM+hxhWUyFkh7mNdXltSCGx/Ir\niHSRA4piOHG+E4bcfnDQpyB+s2Ynzx4Y4L0XH8/8mfUcdhWEDuxms5gKy21m3YHTbpzKCFEWhJEW\nOxELwpxJHZrF5Ka5uspuLJ29D9VQhwmmWEEopdqBdvf/buAllbiu+ZDqTiatVM6PYbpJnHTT8PP5\nqrnGsCCKufmCYwq/8QcPAdBxzeWebM65/Pt718/TrotekzpY7tuYKCeSnSjntyCq28WUySiuW7Wd\nMxfN5m/POsY713lL5vKXjft862kUOw+iud6JCZlrUtd65RT8so2mMzlLeUZRV5PwnVMpRb4V5TSt\nzfVs2tvn22bKMVJgZbJypbkCHNPSQG1SfKmuB4YyfOL2JwGns/7s35xG77Cj4HQnGqcWkzYEgmW/\nRaIX4gK/q2ci8yDShcp9u6+1oS6mki87qRzR60qXir5tIsYkr5BnRCm/ZZBvolz+GERQQcSXNZEI\nV0xRqarxJsoV51OOqsWkXS+hE+UyFZgHESF+oVIbn7zpSV7xrfvYdnCQ97xwme93WLG4hd6hcTq6\nncwSpZS7tkbxMQgT3QkE28JYKn4MImhBpDPKcW8WVBB1OS4m09NVyMVUrjRXcPzvi+c2sr3LiQM9\nvK2bT90/TDIhvGDZXG5cs5P+kXF6XQtCf//amsKWso79BWMR3pKjEcf5XEwTmHVpzqQPL/ftt4aq\nMQYxLRWE10ESHmDN7meW5I2OQSiFsSZ17ufFrBSVS5HVXL3Poxt21ByKKMz9apPZmefZwme515zS\neRBKuR177m+QzihuXLOTtFK85fnHcdnzFvo+P3aOs0yn7lCL/a0AGmuTOYOAaAWRLi4GYQZQXQui\n0GhzXnM9fSMpX5VSs98bLqQgyhiDAFjW2kxHl6OQv3HHFtIKPv2qU/nM5acyMJrip6s6PP+8pyB0\nBeE8xo8ZK3NenfdJLwZROEg9sRhE/mJ9XgwiGRaDqA4FUU0xiIphumdMF1MQ8+HLW6wvY+6Xe2Nz\nXExFyBp1XVPJ+fd34wF5zjmRNalrEgkji0nXYtIxCL+LaapKbSgFr/ufB2moSXLD1ef7PusdGiOj\n4J3nL+GqQNohZEd6QbdEUeVREkJzXQ39hjtIK4FgWxhLx49B1AcsiExGx78Ku5jAyapa6K5TbYoR\nrBAbJBvcjSVm0Rw/v4n7njnIus4eVncc4i3L63jPC517s3LJHL5+5xZvX60gw2YfB/EWDAoEqQu5\nmCYrBpGK6WLyspgmu079JDAtLQhvVIjpYgrPFNIPX75iff4YRGEXUzELkIiEd/bZVNXgAe7neRp2\nlPURhblfTTK7Al82zTXExVSk374UIleUyyjWdfby0LZubn58t+8zXZOoNWJFM68ybSCwWeyiMUE3\nU1hJdCgui6k26a/F5FgQhUf2uh6TGaguxsWkXa3lUvinHzOLsVSGf/3Deprqklx8bPa3+9obz2L5\n0dn5tHqU7VlkSjGWyrBlf3/OeT3XkuESBdPFFP6MjKULxyCUUmzYczjv9/LNpM4TpPaymAxFXW73\nbFyqRIzK4j2kpgURpiBUthaTEwsIbywZX7G+3M9zYhBFyJqImqcR8dDG6fSLXpPa2K82mbUgdBB/\nquZBRGcxZeX48K8e9605oGsSzWsKVxBBi7LY30rTHMjpzxeDKDXN1YlBFFbE2oI4aMQhTDkKuZjM\ncvblYMVxzsTBDXv6eOPK43yVAZa2NvG9t53jvfdcTMms1XrNbZt5+TfvY1fPkO+8wTRXn6tMot1T\n4zGquf72sV1c/p0HuGvT/sjvVWhFOf2T6rU+TEVdLS6maakgvCA1ZueWu59/TYNoCyITYkG0nTKf\nz7mLouQGqYuwIHDq9eReM7wRxYtBFBd09FkQRu0q7VrLziUx5atEue/w7ZmM4tiWGd4M2P+8bTNf\nuGUDX7hlA1+9w5khO39m+Ixg/V1TmawSNLfHJWhB6AVuQhVEsnC5E3CUjNmZZzwFUThIDX4LwhSj\nYJA6RiB8Ihw3dwbzmuoQgXddtDTn8xPmN3PT318IZC2IbBYTrN/tjOR3HvKXuUkHLAjTVZbvNxtP\nZzyLL8qCeObAgO81DPPYsLaqU3bnuvfnoLGwU7UoiGkZg/AFqSNMf70tfgzCH6SeUZv0ZobmprnG\nl1VEQhupv9JsFm2a5kvkKdbF5I9BZF0weqJceJrr1C05mlHOPTlncQvdA6M8tK2bxzp7SEh2tbAo\nC6Im4GIqdQ5AjgURYamOpYuzIHyrnSnlDGIKupic72pmMvldTAXSXDPlndMiIrz+3EUMj6VZMq+J\n7SH7HOW6BIMWREYpZs1w0oqDEx71vctavNoalMgqyeA8rzNqk/SPpiKD1Lrt5U+zNSzqyL2goSbJ\nrIYa9vePeNuqRD9MTwXhC1LnudHBGETUTQ5mO+n99bmDDbGYziaqoURbEPGDd/GD1Nkdk8aDqd1I\nYRVkyz3qDMplklbKjQslmFGbpIdxfn31+SREeM33VgEwe0Z4cbqgy7HUOQAzgxZEiIspk1GMp1V8\nBZH0y5C1IPIf11SXpKE24Sv5bcZDC2cx5a8YOxl8+lX561zp62sF4ZXayCjvXvYFFUQeF1OwMsLd\nm/dzx4b9XPP6MxlLZWiocxRElALwLIw8abBmsDtfCXoRxw24v6/6LIjp6WLSDz3Z0VdYQ4g7k9pZ\nk9r53/BIeY062IiKWQ8iqqEowkd1elv+LCa9b/GN0Ftg3UixzJb38D8QUzUPQinl3ZP/fusK/uWl\nJ7Ni8RxOXTgre2zEwYmAUternhT7wJ65qMX33nMxGW1IB0PjzoMIKpLsPcgvm14nuyvCxTRaKAZR\ngeVjC6E7ZP2bmTOpZ81wlLGeTKfJcTEZA6Ngmuu7f7qGXz3qVFsdT2dodGtxRSkAbx3pGPMwtJxR\nJMRJJNh/eMS3rRqYlgrCu1mGLzLsPpuBVpHolefMh9R81c9UsFhfURZExPaoIHBYymnusaW5TcBI\nL8xkXWtRK8pN5XoQevLiuUvm8uGXngTkdrBhZC0I530paa4AV5x3nO99XchMat3ZxU1zzVEQ7kS5\nOIq+dWZ9wMWUlaOgi6kC8aRCBAva1RoddEOt05l3B9a1CKa56udXXKs36gkZTzsuJoh2IWnrOJUn\nNdWcKJfPohcRWpvrfWnRU62QNdNSQZjF+vSzqR+YdZ09PLS12ytjYM5viLrH2hfv7If3GjnHooib\nHzXSjfIL621xZlKX0ghrfBaEXxFWPAZh/DZXnZ4NOmeUnsmde/27Pnopf/7QxTnbNbo9aN+z91sV\n2UO2NNZx099fSLPryQpzMenOrpg0VxN9rjiuvNamulALYmZ9TSwXU7Hff7LJcTHpiXJKeZ10V79f\nQejRfzbN1T2X22ZDi2AqxXg64ymdqCB1MqJ0iknatxZ27ufa4k6IeKnIGluLaQox5xCYwabDQ+P8\n3fcfBODOf7nEN0ov6GIKWBBCdHxjUiwIClkQ0efMlOg2AX9NIe1GipooV6ksppqEcPo8ozx3xo1B\nhHw/XZQviqTR8TivzvZSHthzl8zhqMYEA4cz3gh4IgoiuJ/2ccf5nec21bFhT7YeU8oVo7mhJkYW\nU/6S4pVAu5j0bHAvSO3GcQC6B4PlRFToq9Nmw5+RVEYxls7Q0uho9qD1r6mNUQvKDHDnm5eUkGwq\nsrmtGpieFgRGFpPhczbzxHuHx51Owr1Rjs8y/HwZlR1hmkFq/f9EYhCRBelUhAVhfP7wtm7u3XIw\nZ59Sc/vBHxzUrrUwF5Mqc+48RM8az+gYRLL462eVuvN+Iu44MAuyhbiYUsW5mIIWhO6A4ozuG+uS\n/hRZV4ym+sIKIp2Z+hGtdm1qZWDGIPTkwWC9qWB6q2k5C+G11dIZx4LQLqbgEq8a/ZunMoo/rNtN\nR8i62v61sPMHqecFFESVGBDTVEG49ypY7vuwEeQaHksHLIjwBpUtZIbvVUQiXUylZjGZmSSG7grs\nnzXFr7j2Ya78yepcmUt0m4CxFrBSpFWgmqvxQJjlR8qFeX7zd0orv1VX1DndJyJnJnWJT6yWsT6k\n1IYXgyhiwSCTVDrb4RWiodavILQCbK6vKRiDUCqbhDFVaJeSNw/CULhaQZjl1cGwHEJnUocncoyn\nM4ynlLdgVJSFoM/VPTjGP//6cT7wi8dy9kkVcDFpEiK0BLLq8nkAKsm0VBBmR2+6mMwGNjye9sUg\nooJauhHmuJjEiG9MYB6E2QmaHUSUX1if+/YN+3I+yx6be+641BoWhFKKpJir2PmvUe5RZza91j8n\nJOOluZZgQUTMpC7ZgvAUhM6KyXbGRbuYAr20VjBxZGuoTTKWyhgZPc72mQ01jKUzBfP5pzpoqr+j\ndjFphaGU8jrigcCE0mCaa9pwrUYlnWQtiPA5TBptWTzmrh0S1tZMF1PYPcqm24sX86g2pmUMwjcP\nwpsYBT1D+SyI8KCWNzszJEjtuZgmNJNajP/93yFfDMJcRrN/ZNy3bvVEgtRZiwvDxaRlyn7PYldh\nK4XILKa0ivx9CpEM3LNiS6Pnyui81noukmynoUfDxZT7NinWgtDXnFGX9L6XTuccc7eHUYmMtEKI\nCLVJMZRqVpFrRTkwmvKt4xFMcw0q+3ALwjnfjDodYwi3rrSi3+Omppop1Nl9FKcfM4vzj5/Hq888\nJvK7JQQaav331loQU0hYue+0Ul7NechaEJqoYn16H91xhk6UmyQLwnRVRaUe6k2m73R/34hvHzNI\nXyymi0mXIkkYSsMvX3k7FbO/NC+lR241E7AggpkvpX4XfVQiIdQlE77Ux4kGqXUnFUc03QHpeINu\nHtrXnq+SaJxV6ypBMiHZaq6JbAxCK8p0RvlnmgfmP5iu1YSE+5jSGcciaajJn8UU3B52D1MZxbzm\nev7t1aeFfq7PkAixIKpEP0xPBeFZEOArtWFOtOnoHmR//2jBLCZdgbHBbQDmvAlvolxOFlNpQQjf\nLFylv4Ef/SCbI9W9h/0KothaTCY1RvZIWulqrlqmYBZTZYLU4P8lxtLZjqBYomdSlyqjfnVGwGYh\nuGInygUtCK1s4rjStCLQcQjdPBrdkiDBpUxNqiGLCRyloJtYjS+LKSt7/2h2kGfWDHP2dbbr1Oyw\n53k8nWE8naG+NkFComMQwRIc4yG/XyqTiTVIEYnfBipNdUpVZnxBamOU3zM0zkz3gfnhvdt4Ymev\n1+HqBYOCbib9wGnzPBuzyPbtE6vmmv3fV9slyoKQXFdGUEFMZIUwM11TZyqZS3N614jIsppMvBhE\nYLt+WEuJQQRrc000SK1lTIpQEyjXnc1iiud/zklzTekYRHwXk7Yg9Pdq9LJ1ohVENcQgIKsUwMxi\nUj7rZ2AkG4cIBqlNZZ8vSJ3KKGqTCWoSicgspmBmYtjvl0qrmAoixIKoEh/TtFQQoWtSZxS9Q2Mc\nNave1/EGy3gH79uQu+C7vsFmLCLozw6eMw5mDCJjKKgoH7vpYtKN80t/2ugt+q7Po2UsFq/onMqu\nRRA290Kp8q1ApvFlMRnb9eiulFFvcO6KucjMRGRMiLuewwRcTDlZTDrNtSgXk3OM52IyYhBRKFWa\nNTbZmJ2tOfHQ7KzNQHVkmmvCDVKHdMJ6wFebTJBMSHQMIvBMj4dYGqmM8im1KBKC59KqNqalgtC3\nUsQoh6GcLKY5jXWeOQ6GDzlitKpHZPoYkez+WeXjb2QlepiAbOceNUJPGBZEY12SE+Y30TeSYtXW\nLuMcpQdeg/Mgol1MFZwHofBpiMlwMWXLfcd344Shj9JBVr+LyWk7cRVEsADgeBHfsyHoYvKC1K6L\nqaAFEUvEsmLeA60slHLauv4sngWhZ1LnXkM/z3XJBDVJyWNBFHYxOWXDo++t6eqtr63Orrg6pSoz\nZpA6aXQyPUNjtDTWMqMu+yBmg87+YzVBF5Me8YtxbM5EuSI6zuCDmW3sERaEu2ks5czeve6q5wP+\nmv9eyYESnnqzBo6WIWpFuXJ7JXyWnqEhxr1yDBMPUk/WPIhkQnJWhCvWgji2ZYbv/Xi6eBfTaCBI\n3RjDgqiEso+D2dmaxfLGM4o5jU6pivV7DnPlT1YzNJbySl2kc+6l85wqBd/66xZudIv0QdYjUJMU\n6muSvnWiTYIWQ1gwe3Q8k5OdFEaYBVElHiab5mq6mPb0DrNy6RwSu7JLCZrzICBEQbgNqjEnBiG+\nSXj/v73zjpLrqvP851ehs1IrR0uyZMvCsiVLtuXcjhjDYPAYBnbIwcsSzjBmYM3CMMwchmGGHYYw\nCyxpxjsEc1gzi4nGNmqcc1K2oiXbsrLU6ljp7h/v3fduvXoVXlV3VUl9v+f0qaoX7v31fffd3/1l\nE1FetSAz8bKMFsnmaha8icf8nYk50WsxvCaMYvG5wM4yT4KoQwbQvPaNr97CWQ2DEH/hgdE0UjvM\nNVTFVGEUWqKom2v5e7WEO+zGEWihtmIvpmZgEC5TiMfyU+mnMzmmdCQ51D/CF3+zBYAHtx0qSLGh\nmX7cC5RTfPXebXl96Pc5GY/RGqjgZyJYaS7MBjGcyZaMbzA1GVaCaCKY6b71Anyof4S+4QyLpnXl\nPexgltYgZ9cShGeDCHFzLbRBVE5r8L30o0LL2SAcDwqttx4JSbNQSxzEDV9/wBPtdTu/Wb+Phbf9\nmn3Hh+oeB5Fvg/AXgshtFkgQ1avjTLpiWoIwFpyRiBJEEOlcdAliKBXdBqFtTY2GpiGe5xjhzPUp\nnYXJ7vRrHHR31arlMAelIUPF1JqIeYF5QaQDauOw8RtOl2YQHq0U2peK1cuuN8Ylg/AmhiFBbHdL\nBy6a1pGnd/RtCvpe59zmfX2ks7kCG0RFXkxVGqkhf2cb1oyp1tJiMsCrfSNezd5acjElDaNb31Da\nSbXhzqJfPPsKAC8dHaqPm6sZB2EcT3leTNW1G49JXt1tqF3F5Li5xvI2C3q32h4hirbTCGaLFigX\nHgehJd/yXkwVkzhm0CpDU/LP5RSZnKK7I8Ag8G1/+44Ps79vOM970cnOHGKk1hJEQgpqgJsw1cbd\nnS0Fm0ClFCOZXEXuq6YnYLNhXDIIM1mf3mXucCOPF07tzBO39YMTj0HAkYEUb/jGg/z6+X2eztKz\nQejrKfSp14jysgWvNXXjpWwQIxnHQKZ3p9/+4w4u/cd1zv9fUxyEP2X6hjPey5Z3TUzqku67WPtR\ndPNhiMekIJJ61FRMxtwaTGdpicciSRDvu3SR991XpZW/r0DFFEmCaHy6b/DVm47U6hzLKUUqk6Oj\nNZ6nqhvJ5PI80S784n15Qa1CaQki6UkQRVRMxs3Tu1oLGGwqm0MpKpIgwp5fs9gg6s4gRGS+iKwT\nkU0islFE/sI93i0i94jINvdzyljRoKVDwX84Ow4OEI8J87s78jwUzB0gOBOyf9gpRbi/b9i3Qbi5\nW/JzMYUziChb9+IqpvJeTPGYuMbRoB0j/9ooMA2//SOZvFQbGqlMrk7pvg0VU4gNohIXwzDERUbN\nSO2pmNw4CHMhHhzJFE1vUQy3XnsGP/7ghYCZ7rs8ba2eikkHyuV7MZWSIJrOBiF+9H42p8jkcrTE\nY3QZXl4DqUwBA/Bdlovv2E0GUUqCMMdr+oTWgusqSaNSSjptEv7QEAkiA3xCKbUcWAt8RESWA7cB\n9ymllgL3ub/HBJ4EYex+tx/oZ/nsiSTjsbyJZUZGg/NQtXti33Dat0G06Ehq817nsxYbRHDymKqP\n8FxMbp9ZP4qzNeAhUcuuOPgiaJ/yvGuyuTq5uYYfj6J6CYPj/+58r0UdZ9IQEyfVhjkXBlLZPJVR\nJRARul19eyaCpNRu5GKCQhVTsZ0yOBuqZoik1hsuL1UG2s3VUaeadcYHQupJmyqmYv/OcMq0QcSL\n2iD0c1w4tYOJ7YmCd1yr8lorsUE0wdgWQ90ZhFJqn1Lqaff7CWAzMBe4Ebjdvex24E1jR4PzGZN8\n49s7Lzqt4NqgDUIpRcqtttI3lGE4nSUmvieK3tmYC3hwUY1SDyKInLFwhbWi6U1n/WymwV2Mn4sp\nOh1HAmUdTTdXjZG0ZhCRm4+E4qk2qo+kdto1vMVqUMeB/zziMXH96g0JIpXxUl1EgV6so6jSknFH\n0gtGUnteTCUYRLaItFpvJA0Vk+ltls7kSMZjTDUM1YOpbAGDyFcxhf9DpptrSyJW1Lsrk1OcO38y\nvZ+8ssB9GQpT8JRCGCXNEkndUDdXEVkIrAIeA2Yqpfa5p14FZha55xbgFoCZM2fS29sbud9dx51J\nMDw0zJNPPAHAhBaYfHw7vb078q49dvQovb297HjRiUR+4MGHODjkPPxtL77EpFahJQZ//OMfnbZ3\nOQvo3pf2sr19P51J2HNkMK/NbS9spXdoZ1H6+vv7vf9r5+78HPcPPfwwU9tjHDg4zNBgruD/337M\n+d/S2RyDA047Kuu38Yd169i1O41AVWO3OHaIs6fF2XDI6efggf088fjRvGueeX49qVSaV155md7e\nQ2HNjAo2H/Z3dwMDA+hXTb+smzdtouvIC5HbzWUz7HnpJXp7D/LCUaeP9c8/T+6V6NGu2UwaELZv\n38bxo1mODilv3PfuGyabVpGfwyv9zv+3c/eLAGzatJHOI1vL3peMwQs7d9Pbu4+hkRRxEZ58/FEA\nNm7ewvT+HaH3HTs2RFyqmy/VwJz/Jk70DQGQSad44IH7Adi+Ywcj6QyvvvIyathfpDe9sLMgX9LT\nz613Pp96ihcPhUsGm3c6MRHbNj5P39E0Rwf8d8yk6+ChITI5Z0wOHxyhfyCbR7N+Rju2baW3yLj2\nuf/P5s2bmHA0f54ODg5WPN7Fxms00DAGISJdwJ3Ax5VSfeZuVimlRCSUhSqlvgN8B2DNmjWqp6cn\nct9T9h6DRx6ivb2Nyy9ZCw+u472XLuG6q88EYOET69h92FnUp06dSk/PBex9ZDds3shFF1/sVI96\n5BHaJ3YzdXI7XYf2o+nYIjvghS3Mnzef665ezjtTW/j2H/MnyLJlZ9Jz/oKi9PX29nrt7XpoF2zZ\n5J274MK1zO/u4Md7nmRABunpuTzv3kl7jsKjD6OA7smT6Om5mElP9nJk2Kl4tfaSy3gqtZ34rp1E\nGrvf/RqAiy9YzZuvaWPtP9wHwOxZs7ho7Rlw/zrv0iVnLiO+eSML5s+np2d55X1ERPvOw/CEs8B1\ndXUC2kvLOX/uirPpec2syO22PXgvM2fNpKdnhdPHY4+yauW5XLxkWuS2/m3D3UCGM884gyPxw5zY\nf4KenisA+OaWR+iKCT09ayO1uevQADzYy5x582HnTs6p8P/seuAeps+aRU/PCn669fckE1muuOxS\n6L2HhYuX0HPJotD7vr7pITpaEvT0XBiJzmphzn8T39/xGBw5RFtrK1f2XAH3/JaFCxeR3b6NxQsX\n0D2Y5qn9ewCYOmsOuV0v5t0/f/EZ8Ox6zj9/Df1bDsC2kM1DxyTgMNddcRHPDW/l4N5jHi0mXd/c\n8gixGPT0XMTdR55na9+BPJo3vHwcHnyQ884p/mz+ZeNDcPwYK17zGnpWzPbeMYD2jo6K389i4zUa\naIgXk4gkcZjDj5RSP3cP7xeR2e752cCBsep/zuR2/uZPljO3K8a8KR18911r+PCVS7zzP/vQxZw7\nfzIQHiinxfG+4QzDqayXO968XnO3ZbMmFPQfLZI6YIMwjKdh7ZjXh6mYhlLZmlxQYyJ5xkCdtsCE\nY6Sug4rJ6CCsq2pVTPFYYSR1rfUgwlRMA6mMZwOIRF9AdVnps2xNxLh7436G01lyOZXn5VYspQS4\nHnNN4cXkGqkNG0Qm55SXTcZjeWU7zZQbGseG0t79xZ7nq25iy2ldrSUD5ZxMrc7YhamYhgPxUaUQ\nSktzaJga4sUkwPeBzUqprxin7gLe7X5/N/CLsaJh+oRW3nvJIqZ3OP/+tctn5j3I6RNaWbuoXYzp\nWgAAHNhJREFU26HXo9v5dIzULoMYcozU+bmb8h92mBdDlFetwIvJ0I2HvbPm9YkQBjHsLt7V6pRF\nnAygpm2mwAbheTHVz0gd1lO1i1pcZNQrynnJ+kwvplS2KhuE9ryLkqwPYMbENg6eGOHr920jq8jz\ncCsfSR2ZzFFHPOan1Nf0aON6Mh5jepdvg+gbThfcrwuChW1qNPb3jdDVmqAtGaelhJurmYgvEYsV\npNOpqBhUibnVJPyhIRLEJcA7gatE5Fn37wbgS8C1IrINuMb93TB4HhOS/6m9JgCOhzGIwMMO83GP\nlM01cK1XyKZIO+EShE9fsFJeVOgCQV0tvltvsK2Ux4TGdlXJaz+kq2o9b2IxKaxCVq3B2/0UN1DO\nTLUxMJKJ7MUERkLBiEkJ/8/7L+C8BZP58eN7GMo4hmvtXFHSi6lp6kH4EoSOhtbG4GRc8iSIPleC\n+ORrz+QH71kD4GU0DtvUaPSPZJjqMprWRLyEm6vyJYiEFDDYWiWIcWukVko9SPFN9NX1pKUUdEBY\n0Ispp/wCJX3DaYZS4eH0+vkGXUzNNitB8FJzZ1tuXdAvVDLhX6jVC1UHfrkrXldbghMjGWKxwh3Q\nSCbnMqHq+qiYliJeTBrVq5hMCaKwryjwvJhECgPlUlkvDiESfZ4XUzQPq4ltSW65fDEf+uHT7InH\niMfiiOt+WzqSujlcMc1cTOCMg3ZDTcZjdBnS2IaXnXxqcya3McdNcnjMYxCl/QinuYympUSqjaxR\nDKglHivI7upJEBUm62tWjMtI6koQzAQaZoMYTuc4PpQuGexUqwRRygYRxmnyJQhd2N0/P5KpzQah\nXy39MoalCRjJ1CcOwtzVhnVVC4MY9UjqmLOI6V2/UorBVIbO1ugShJYYfDfXyu+d7KakGEwbO2Cj\n1nMYlFJVpy0ZTZipNsCZe3ohTsRjeWM5mMrS3dnC686ezUS3HrupYio1N7W7rMM4VUHJYPBT2Th0\nObFTplutJ0FUUOfBBsqdhAhGQZsqJlOc3Hd8mM4SeuRQG0QUCSJwrfbcK2aDMMP2vdTcefl/arNB\n6D61oTpMn+swobHXW5fRMFXPIIxI6prjIPBVlMm471ev7TS1SBCZCMn6NDrd/gbS/gLXkiguQSil\nmqainN7w6P8/FjMSHsaFVfOn8OGe01m72LEfvuPCBbQl40x0A+iODxkShPHvTAi8v9MmOBKE3v2H\n2WfSuZxXtEhL6OYY6sJMlWVzdT7/88MXc8MKx+OpSTRMlkEUg96tZD0Vg/NpqpjAmXTBCWYiTIKo\nph6ET0/pSGpTeNYLpDnZhtNZ+obTBamjK4WmXe/KJESfq1NtjLVaotyiVVsktStBGHWMq0F+sj5f\nxTTgVj6rxovJlyCiM68Od5c9nPXnR6mUEqu/cC/bDvQ3nRcTuBKEu1NPxGLEYsKnrl/GslkTScaF\nd7iBr50tcRIx4VD/iHNfLH9udgUKMZkSBITbZ7JGIKoO4DPXBa2aqjRZH8CqBVN490ULy15fT1gG\nUQR6AdU7SPEYRGH1qK6oEkQEOvSCb5ZYdOgoIkEYx/QLZdZpOHBihN9teJWeM6ZHoKKw/avPmgHg\n1O0O0KF3T43K5qpRfSS1X46y9lQbfpvJeAylYOurJ7yI3arcXGtQMZlzNWm4aYYxCKWUFznfDBKE\nlnhMxxHPi8l4zz50xen85INrmTGhDXCYwdKZEzjUb3gxGe0GK/XplB2tXnqSQjtEOqc8CV1/7js+\nzN4jg6SzOZ7ecwyo1Ejtf/cyMTSJkskyiCIILq5msr6gyBncgZio3YvJ+dST0Fy4wkxtZtNaJDcZ\nxL2b9zOYyvJfLiweqFcKmvY/PW8eAOedNqXQBuHu6hqVrE+jmopyEJAgRstIHfMXuNd97X7PDbOU\nerIofVrFVEVpVZMh5UkQIWqUw0ZalSYQIEIkCLN+g0/grEltrFnYnXfvqgWTve/BBJPBDZ7+3epu\nysKYZ8YoJ6qZ083fepjL/mkd3/jDdn75nJP6PooEEfzeDLAMogiCNggzWV8wqKi0BFGjF5P4L7FJ\nj6PCKX49mEzOP7+/zwkEmjslv3xlpdATuLM1wfOfv47P3HBW3stmvrRjrZYo13yt6b7vfOoldh5y\n0sBXklI7lAb3U3sLgfM8XjnmPIfqVEzOZzVpzU2bh2eDKCJB7Do04H1vBjdXPw7C+R2LCX2uXaGr\nNVnsNgBWzTcYRIGKKf9eveHTNojvPbCLXE5xz+40h101la7YCL4kpl1rv3u/n0an1DtgVrb0rpf8\nc43GuCw5Wgm8Hbung3Y+leHFpBEUUcEXEcMliMrp0Jd6i4te7YtEt5pH4gGpA+CwK2ZXs3OF/Mms\n7RAxYzw6WxMegxjrNWUs3VxH0jk+8bPnQvuKAi+S2lUxabx42Fl8a5IgPAeKCPfGhPZknKF01nfT\nLGKkNhlEM7i56oXbrBh4wl2US0nx4Oj3NYJG6o9dtYRnXjzKCdcupDd8+p3794d3s3RmFz/akiL1\n2y18+S3nkskaKibXSK3HdSgd7hpbDGGxS83CIKwEUQRB9YyvYir0ajB3L8EXKVzErPxl0/1rMbZc\nRblYqARhMIgBZwfUEaGKWV77IauR2WdXa8KrOVDPmtRhC1jVFeVEeOX4UKCv6tryXDJj+fUp9roJ\nHEfHBhGNOM2UtIqkJR6uYtrhVll0+oxM5qhj0bQOAF455jwbEfE8k0pJ8QCLp3UysS3cNXvV/Mms\n/9vXer8nBCQIgKd2OwkpvSDFXM6zU+pxjMoY9CbSqphOQgS9hvK8mDI5OlzPCChjgwh5s6IsNh6D\nCDVSl7ZB6IlrJrVMZxVtyVjVXkzl0nt0tia8XV0l+tdaMFZeTLEYvHwsn0FUnYvJuD9PgvAYRHQJ\nQkcRa1VnVElJxwt4KpIQFVM6m+MuV48OzbFwLZzaCcDRQZ1TyYl8hnAp3kQsJqx0pYh4wEgdfBf0\nhq8l7jPvB7Y7WYnbW+IopdxIan/8wvCFN51dyb8VUDE1fpxNWAZRBAlPxZRvg9Buri2JmOdfHbZ7\n8WpOhKmBIkwCvbHz9dd+qo2yEoT7Pyyd2ZV3TWcVi1JY+2HHOlsTXkBStWqsSlFuGBNVGg7iMSkQ\n8UcrklpDp4CvJtWGbk9H70YlTTMlPT9akzHPq0rjdxteZd/xYeZ3O7aqZnBzXTStM+93UHIth9UL\nphAT5/8u9TyDNgiAgyccyfv4YJo//dbDgCGBJQrbmju5nXesLawvEwZzaKtVi44VLIMogqCBVz82\nJ1DOyR6p3eHM3UsljzfKHMgGVUxewaAicRAhk+0fblrBD99/IdPcHDO1LNxhpOd7hMQ918haGFEl\nKLdoVWtYjofcWH0ktXif6YzPdV466kgo1STrA+d/r1bF1OVKEHMmOYv/0hldbDvQnydF/OChXZw2\ntYMbVsx2+6iKzFGFjgLXMMv7VqKqe/9li7j9fRfQ2ZooyVQ1swm6s4MTja1dWPe5asiwjUiUCHlz\nw2jaOpsBlkEUgV4kQiOpM04NXK3TrGT3YiLK+6wlmJZAVHSxmtRhXkwdLQkuXTrNSwlSjd47rH2N\nPAmiJeH5pndUkUYiCsotWtVHUof1VaUE4d3vR/KC7zrZXqUtKC5SVaAc+Juehe6OfNWCKaQyOTbv\n6wMcT7dn9hzj7Rcs8GqtN8l6BcC8KVqqcX53tSYqksq7WhNcttSJ/9FTI0wNqt/nGRPbCs5tP+jb\nZZbOdFL5h6mYKtmEhdWk9uMgmgOWQRRBIuABpCejVjEl4+KrmMroP4OIpmLKt0GYcRClalJD4QLZ\n3emkEIjK0Iq1r2GSYQYG1dJPJSjnelmta6YeN3Pxrjqbq2ekljwGoduvLV+ULq0a7V6tLlnkMQjH\nBfSZPY4h9oQbozFncrtX66RUMr96YvPfXc99n7gC8J/vxLbSLq5heNl1M37zqrkF5/QzWTKji+c+\nd513PBGDvUccqeFrb1vJey9eCJCnOpwzyWEqUaTnvHfW2iBODgQ9gEJtEO7ENCdDJc83yhQIMoiM\nJ0EUWayN1oOBYjpffrVqDSim1vKPmW69tUgqlaAco61+UXfuWzZ7gnGsqqbyEsvddN5cJrUnWTzd\nWZhrk+Sc2gWJmHiMv1IcOOEsjppBzJ7UzqT2JDsOOm6tAyNulHcy7jH8Usn86on2lrgXW6SfUzUb\nkWvOmsHSGV385bVnlLxuUofPfBZM8Of2yvmTvfk1xaiFrTPHVvJsQyUIQ1PRDLAMogiKJevLKVwJ\nIsbE9gQdLZXvAs20C5UiG/Biyq90VlqCCHpnTPUkiOoXpnK0myL7WEsQ5Ya9lkhq8NM+O33VJkHE\nY7B4ehfP/c11rHSDtmpRwekF+/XnzKa7s6XM1eE4bWqH9727s8VzLvDSgLTGvWykpQoKNQp6gY4q\nwQOcM28y99x6BTND1EjFMG+CH6g3d7IfaLpoqm881wyiWkGgWrvZWMEGyhWBNjz5XB73t2Ik4zCI\nt66Zz1mzJ1bcZjLuVKiKMnk0g2oNRFIXy+Zq8ozgAqkLobQnazBSl5nAEwxxvxoXzigo6+ZaI4Mw\nkzBW+8Lr20xpRzOeWoz42s7zppWFKpJy+Nl/vZgf3fN4njpwckfSq5cwlNaJBBO0tTSXBGFCP96x\n3ohozOp0JctZE/M2X+Y80wyiWgnAkyCaxArRZPyqeRC0QWjVzYd++DQPbDtESzzGqgVTeFeE7Ita\n/RJlN5rzVEyuRFPWBuEfC0o2emHK5Kp/2cvRPs0o+zj2EsTY2iCCtberQZjUqLOFjoYKzswxVClW\nzJvE9Yvy9fZTOlo4NhSQIFritCV0yuvmWLBMeCqmKiSIatDiPsz3XLKw4NyFbolincImpIRERWg2\nFZOVIIqgWKCcmTI4KnQsQyQbRBEVk1LhNIRFUmtoCWIoFS3iM7/90udNtUxbBdW0akE5aaaWehCQ\nz+BGIw5Cw5MgRoGBBl0/q26nPcnWV08AMOjaINrzbBDVz5mxgn4mpdLtR8G9t14e6uL84H+/kmOD\naV7Z8jRXXnAOVy2bUXDN999zPs/uOcZgKuMeKb/Ch13RZDZqK0EUg15c9GY7aBDVniBRoCWIKF5M\nngQRlmojLJur8T042bXXVdSUACbKSxA+gxjr/D1lvZiqZBBa354vQVTVlBFJ7R/TjLpWCeI1cypX\nb5bD5I4Wjnk2CL9WRXsTq5g8SW+UGMSSGRMKgvEA5k3p4Oy5k2iJC1efNTN0Xne1Oq7kpq2yUoRN\n4yYRIKwEUQxBt9LgAqEDnYohLNBFtxll3cx4cRCFEkSluZg0tMFxuAYGUY72qV2js6OtBGOlYtIS\n1oTWBBNandrb1euUXVpihRJELTaazX93/ahG3U7pSDKQypLK5BhMaxVTwrN9NaORWs/jqV3RvLjG\nEqY7fDXQT7RZVExWgigCT4Lw4iDyX8awKlNQWn1UjQ3CVzHle1WpYu3keTHln18xbxKzJ7Vx67Vn\nVtx/YfOVSxBjjXLDWK2RWktYXW0JvvyWc1nQ3VG1nlvvNmMhKqZaJIj2lnhopuBqMdm1ixwbSjGU\nyiLiqAh1H80oQWivq2l13JSUw+oF3UzrauFjVy1tNCmjAitBFIFngwik+64F1UgQZjQ0+CVQK6ko\nF9xhdrUmeOTTV1feeQjKjcOUjuhBS9VirBKbaSNtV2uSa5fP5PqzZ1Xdlm+k9o91j6KRerQw2VU/\n3vytR7j8jGl0JON5NSyakUHolC713JSUw6SOJE9+9tqKrg3TMuhNzVjb7yqFZRBFkAiomEy94+xJ\nbXz1z1aWvN989Hf+t4vYtr+fnzyx12krAh0f7llCOqt410Wn8bX7thlxECpUF5qfamP0J1m5Rbna\nLLHVYKzyAw15DKL21+M1U+N86IrTWTzdT5jYkojx2defxcWnT6u5/dHCFNfYvefIII/uPEK7uyFp\nZglC6/nrqdYcC5hS+bSuVj752jN5vZsDq9GwDKIICiKpjXO3XL6YCxdPDb0vbNFefVo3q0/r5s6n\nXwJ8NVEl6GxN8D9uOMtbtDwjda6YDcL/PhaZIZvJy2KsJIiBVGUppCvBhBbhtp5lBcc/cNnimtse\nTUzp9CW/7Qf6WdDtBNFpJqnzDjUjmkmCGA185MoljSbBg2UQRVAskhqqy/0C/m4sWLK0EmhhwGQu\noWkvSqTaGA1U4pl098cvrzoJXRSMVQpqbfwcDQZxsmD57Il8711r+OtfbGDf8WFP/TW5o4Wf3rKW\ns0bRY2q0UW0kuUV5NIeiqwmRjOV7DeUxiPbqGIS2QaSy0b2ItEdOnoop5Lq8dN9haUnrgDNnTWCB\nkcbhZMPgKKqYThaICNcsn8kFbsCXaR+5cPHUqjdF9UBbHTYj4xVNxyBE5HoR2Soi20XktkbR4ddz\n1nT55yZWsLMMc1PzDX7RJQhPookQSZ1stsQuJwk8BjGOJAiNVTpP1BinSbHw0Uxq2yCaagURkTjw\nv4DXAcuBt4vI8kbQonfsfiR1ZRJEqYedrMGnXJeZLBdJbfY/a1LlichOViyd0cWXbloxqm1+5a3n\ncvr0Ti9r6HjCKrcsZ3sTeVgVwwcvW8SfnDun0WRUjWaJdSiFZtsmXABsV0rtBBCRO4AbgU31JiRY\nE8BcjEvpprXnUFgRES15VFstKi7Cfzz6Ir/d8CqHB1KEZ3P1jy2Z0VVw/lTDPbc6tQF6e3eOWps3\nnTePm86bN2rtnUw4a/ZEWhKxpnLBLYbPvL4he8dRg06E2Gx1qE1Is5S2AxCRm4HrlVIfcH+/E7hQ\nKfVR45pbgFsAZs6cufqOO+6our/+/n66uoovor/ZlWLl9ARzumKkc4ofbUqRjMPbl7UUfaiZnOLO\nbWneeHqS9kCt2oG04tc709y0NFnSgFyMrl9sT7H3hF+H+JoFSc7sLnyR79qRYuX0OAsmjt5L/mJf\nlvWvDvGGM8LH64GX0szoiIXSUw/09/fz5NFW5k2IkcrCqwM5rlrQeL15uTnWKJSia92eNLO7Yixr\n4LM82casGhweyvHAyxluPD1ZU1qaaui68sorn1JKrSl7oVKqaf6Am4HvGb/fCfxrsetXr16tasG6\ndetqun+sYOmKjmalzdIVHc1K26lEF/CkqmBNbiobBPAyMN/4Pc89ZmFhYWFRZzQbg3gCWCoii0Sk\nBXgbcFeDabKwsLAYl2gqI7VSKiMiHwXuBuLAD5RSGxtMloWFhcW4RFMxCACl1G+A3zSaDgsLC4vx\njmZTMVlYWFhYNAksg7CwsLCwCIVlEBYWFhYWobAMwsLCwsIiFE0VSR0VInIQeLGGJqYBh0aJnNGE\npSs6mpU2S1d0NCttpxJdpymlppe76KRmELVCRJ5UlYSb1xmWruhoVtosXdHRrLSNR7qsisnCwsLC\nIhSWQVhYWFhYhGK8M4jvNJqAIrB0RUez0mbpio5mpW3c0TWubRAWFhYWFsUx3iUICwsLC4sisAzC\nwsLCwiIU45JBiMj1IrJVRLaLyG0NpmW3iKwXkWdF5En3WLeI3CMi29zPKXWi5QcickBENhjHitIi\nIp92x3CriLy2znR9XkRedsftWRG5oQF0zReRdSKySUQ2ishfuMcbOmYl6GqGMWsTkcdF5DmXtr91\njzd6zIrR1fAxc/uKi8gzIvIr93d9xquSqkKn0h9OGvEdwGKgBXgOWN5AenYD0wLH/gm4zf1+G/CP\ndaLlcuA8YEM5WoDl7ti1AovcMY3Xka7PA38Vcm096ZoNnOd+nwC84Pbf0DErQVczjJkAXe73JPAY\nsLYJxqwYXQ0fM7e/W4EfA79yf9dlvMajBHEBsF0ptVMplQLuAG5sME1B3Ajc7n6/HXhTPTpVSt0P\nHKmQlhuBO5RSI0qpXcB2nLGtF13FUE+69imlnna/nwA2A3Np8JiVoKsY6jlmSinV7/5Mun+Kxo9Z\nMbqKoW5jJiLzgNcD3wv0P+bjNR4ZxFxgr/H7JUq/PGMNBdwrIk+JyC3usZlKqX3u91eBmY0hrSQt\nzTCOHxOR510VlBaxG0KXiCwEVuHsPJtmzAJ0QROMmasueRY4ANyjlGqKMStCFzR+zL4KfArIGcfq\nMl7jkUE0Gy5VSq0EXgd8REQuN08qR25sCl/kZqIF+BaOmnAlsA/450YRIiJdwJ3Ax5VSfea5Ro5Z\nCF1NMWZKqaw75+cBF4jI2YHzDRmzInQ1dMxE5A3AAaXUU8WuGcvxGo8M4mVgvvF7nnusIVBKvex+\nHgD+E0cc3C8iswHczwONoq8ELQ0dR6XUfveFzgHfxRej60qXiCRxFuEfKaV+7h5u+JiF0dUsY6ah\nlDoGrAOupwnGLIyuJhizS4A3ishuHHX4VSLyQ+o0XuORQTwBLBWRRSLSArwNuKsRhIhIp4hM0N+B\n64ANLj3vdi97N/CLRtDnohgtdwFvE5FWEVkELAUerxdR+uVw8WaccasrXSIiwPeBzUqprxinGjpm\nxehqkjGbLiKT3e/twLXAFho/ZqF0NXrMlFKfVkrNU0otxFmr/qCUegf1Gq+xsro38x9wA45nxw7g\nMw2kYzGOx8FzwEZNCzAVuA/YBtwLdNeJnp/giNFpHN3l+0vRAnzGHcOtwOvqTNd/AOuB592XYnYD\n6LoUR7R/HnjW/buh0WNWgq5mGLNzgGdcGjYAnys35+s0ZsXoaviYGf314Hsx1WW8bKoNCwsLC4tQ\njEcVk4WFhYVFBbAMwsLCwsIiFJZBWFhYWFiEwjIICwsLC4tQWAZhYWFhYREKyyAsTjmIyGQR+bDx\ne46I/N8x6utNIvI59/t0EXnMzbp52Vj0F4Gu/ykiVzWSBouTH9bN1eKUg5t/6FdKqbPLXDoafT0M\nvFEpdUhE3gZco5T6QMh1caVUdqzpMfo7DfiuUuq6evVpcerBShAWpyK+BJzu5u//sogsFLeWhIi8\nR0T+n5tDf7eIfFREbnV3/Y+KSLd73eki8js3ieIDIrIs2ImInAGMuMxhJU4K5hvdfttFpF9E/llE\nngMuEpHPicgTIrJBRL7jRjwjIr0i8i8i8qSIbBaR80Xk5+Lk+v+C0d87xKlZ8KyI/G83uVxcRP7d\nbXO9iPwlgFLqRWCqiMwa68G2OHVhGYTFqYjbgB1KqZVKqU+GnD8buAk4H/h7YFAptQp4BHiXe813\ngI8ppVYDfwV8M6SdSwCdVvtZ4HPAT91+h4BO4DGl1LlKqQeBf1VKne9KNu3AG4y2UkqpNcC3cdIm\nfMSl8z0iMlVEzgL+DLhEOQnlssCf4ySRm6uUOlsptQL4N6PNp10aLSyqQqLRBFhYNADrlFMn4YSI\nHAd+6R5fD5zjZkG9GPiZu8kHpwBLELOBgyX6yeIkzNO4UkQ+BXQA3TjpVXTfOh/YemCjclM5i8hO\nnORrlwKrgSdcmtpxErT9ElgsIt8Afg383ujvADCnBH0WFiVhGYTFeMSI8T1n/M7hvBMx4Ji7Uy+F\nIWBSifPD2u4gIm04UsgapdReEfk80BZCk0mPSZMAtyulPh3sRETOBV4LfAh4K/A+91SbS6OFRVWw\nKiaLUxEncEptVgXl1E7YJSJvASc7qrsIB7EZWFJhs5oZHHIllJsjknUfcLOIzHBp6haR00RkGhBT\nSt0JfBanNKvGGfjZRy0sIsMyCItTDkqpw8BDruH2y1U28+fA+10D80bCy9LeD6wSQw9VgqZjOPUE\nNgB346SdrxhKqU04DOD3IvI8cA+Oimsu0CtOJbQfAp8Grx7EEuDJKP1YWJiwbq4WFjVARL4G/FIp\ndW+jaTEhIm8GzlNK/XWjabE4eWElCAuL2vBFHKNzsyFBA8uwWpwasBKEhYWFhUUorARhYWFhYREK\nyyAsLCwsLEJhGYSFhYWFRSgsg7CwsLCwCIVlEBYWFhYWofj/ZbCxYMJc7NsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd4a54dc0b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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cj4ExtAkCoRF02zTEzZtmm3msZBmZ78e1SD1krhMJqrVG9GJQICLK5Ou5HOG5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\n5jn/D3yazeJVtdH0AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd4a54dc240>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "TypeError",
"evalue": "unsupported operand type(s) for +: 'int' and 'NoneType'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-8c8f3d539460>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mnd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_spec_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetwork_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moutput_fn_postfix1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Wo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mnd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_spec_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetwork_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moutput_fn_postfix1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'E'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mnd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_spec_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetwork_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moutput_fn_postfix1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'LSPs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/media/phil/Data/projects/babble-rnn/notebooks/network_data.py\u001b[0m in \u001b[0;36mplot_spec_params\u001b[0;34m(network_tag, iteration, params, loc, lsp_param, test_data_fn, test_seed_start)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mparams\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'LSPs'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlsp_param\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 158\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mlsp_param\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mlsp_param\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 159\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mlsp_param\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'"
]
}
],
"source": [
"print(output_fn_postfix1)\n",
"nd.plot_codec_params(network_model,output_fn_postfix1, scale_up='orig')\n",
"nd.plot_spec_params(network_model,output_fn_postfix1, params='Voicing')\n",
"nd.plot_spec_params(network_model,output_fn_postfix1, params='Wo')\n",
"nd.plot_spec_params(network_model,output_fn_postfix1, params='E')\n",
"nd.plot_spec_params(network_model,output_fn_postfix1, params='LSPs')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"nd.plot_audio_waveform(network_model, output_fn_postfix1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Listen to the WAV file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"display(Audio(filename=home + \"/store/c2gen/out/\"+network_model+\"/out-c2cb-\"+str(output_fn_postfix1)+codec_sub+\".wav\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nd.plot_audio_waveform(network_model, output_fn_postfix1_mid)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"display(Audio(filename=home + \"/store/c2gen/out/\"+network_model+\"/out-c2cb-\"+str(output_fn_postfix1_mid)+codec_sub+\".wav\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Later Iterations\n",
"==="
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nd.plot_codec_params(network_model,output_fn_postfix2, scale_up=True)\n",
"nd.plot_spec_params(network_model,output_fn_postfix2, params='Voicing')\n",
"nd.plot_spec_params(network_model,output_fn_postfix2, params='Wo')\n",
"nd.plot_spec_params(network_model,output_fn_postfix2, params='E')\n",
"nd.plot_spec_params(network_model,output_fn_postfix2, params='LSPs')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"nd.plot_audio_waveform(network_model, output_fn_postfix2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Listen to the WAV file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"display(Audio(filename=home + \"/store/c2gen/out/\"+network_model+\"/out-c2cb-\"+str(output_fn_postfix2)+codec_sub+\".wav\"))"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"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
}
| apache-2.0 |
CompPhysics/MachineLearning | doc/Programs/JupyterFiles/Examples/Intro to ML Examples/Faces.ipynb | 2 | 515888 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x576 with 10 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3023, 87, 65)\n",
"62\n",
"Alejandro Toledo 39 \n",
"Alvaro Uribe 35 Amelie Mauresmo 21 Andre Agassi 36 \n",
"Angelina Jolie 20 Ariel Sharon 77 Arnold Schwarzenegger 42 \n",
"Atal Bihari Vajpayee 24 Bill Clinton 29 Carlos Menem 21 \n",
"Colin Powell 236 David Beckham 31 Donald Rumsfeld 121 \n",
"George Robertson 22 George W Bush 530 Gerhard Schroeder 109 \n",
"Gloria Macapagal Arroyo 44 Gray Davis 26 Guillermo Coria 30 \n",
"Hamid Karzai 22 Hans Blix 39 Hugo Chavez 71 \n",
"Igor Ivanov 20 Jack Straw 28 Jacques Chirac 52 \n",
"Jean Chretien 55 Jennifer Aniston 21 Jennifer Capriati 42 \n",
"Jennifer Lopez 21 Jeremy Greenstock 24 Jiang Zemin 20 \n",
"John Ashcroft 53 John Negroponte 31 Jose Maria Aznar 23 \n",
"Juan Carlos Ferrero 28 Junichiro Koizumi 60 Kofi Annan 32 \n",
"Laura Bush 41 Lindsay Davenport 22 Lleyton Hewitt 41 \n",
"Luiz Inacio Lula da Silva 48 Mahmoud Abbas 29 Megawati Sukarnoputri 33 \n",
"Michael Bloomberg 20 Naomi Watts 22 Nestor Kirchner 37 \n",
"Paul Bremer 20 Pete Sampras 22 Recep Tayyip Erdogan 30 \n",
"Ricardo Lagos 27 Roh Moo-hyun 32 Rudolph Giuliani 26 \n",
"Saddam Hussein 23 Serena Williams 52 Silvio Berlusconi 33 \n",
"Tiger Woods 23 Tom Daschle 25 Tom Ridge 33 \n",
"Tony Blair 144 Vicente Fox 32 Vladimir Putin 49 \n",
"Winona Ryder 24 Knn score: 0.23255813953488372\n",
"(1547, 100)\n",
"Knn score: 0.3003875968992248\n"
]
},
{
"data": {
"image/png": 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cDhWM1ROTEEFMgja2o6bayHfvLWHRT5vIyyqibedopt09kpFXuH7+XnvncN785692+0PC/Bl7ZcNJaBVFURSlvg05Sbx54GebfTlVRbywdybR3g/SOTDOTTVTTpfqpnTh1Ue+59t3l5CbWYiUksP70/nvoz8wr4FUF2Ov6s+9z1xBSJh/7b7u/eJ59Ys7CAhWSy0piqIoTTc7daXD/UZp4pcTq1u4NkpzUi1jaC1gHgbbdSsP7Epl7Z+7HR7/3XtLGX/NQDy9tB9f8r6TpOxLIyTcn8ShnfAw6Jl0wyAmTLuI44ez8QvwIiom5Ix/DkVRlPPRpv3H+W3tXnKLSukWH8W0kb1pFRro7mq1uKOlzmfjHy1RM/XPZRd0MPb7t2uZ8/lKTh7JJjQykIk3DeXaey9B76Fnx7pkp+cV5ZdyaN9J2nSM4qXHfmDburrEseGRgTzz5vV07d0GD4Oedp2jATh2OIvd247h5+/N4BGd8fbxPOOfT1EU5Vz30bx1fPp7XS7dTftT+XnVbj5+eArdEqLdWLOWF+kdQl5VscOyKG/7F/69hUmsy91AhbGSroGdGRY+GG+995mupnIK3BaMCSHigG+AKLQlRD6RUr7TUvf/7p0/+fatP2q387KKmPnmIk4eyeIfb92El7frYMnb15MPXppvE4gB5GQV8dz9M/n6z8fx8fWiurqGN56by4rFtXkg8fXz4vHnr2ToqK7N+6EURVHOI0cz8vhsof2iBiXllbzywzJmPnWDG2rlPpNjB7O/6LjDsitiB9tsf3XkW/7KWlG7vSlvC39mLOWf3Z4gyBB0JqupnAJ3jhmrAR6TUnYDBgH3CSG6tcSNS4vK+XnGModly+ZsJTUlk2Hje6L3cPzjie8YRWhkIKv+cNyNWVRQxopFuwCY+ckKm0AMoKy0klee/pkMJ2tiKoqiKLB0azLSyWqfe49mcjKnsGUr5GbjWw3g6rjh6BC1+zyEnrvaTWBAWJfafXsK99kEYhYZFZnMSrWfXKa4n9uCMSllupRym/n7YiAJaJG8D/t3HKOirMpp+Y51yYRHB3HXk5cjhLAp8/Hz4sEXp5KbVURNjfP8Lhkn8jEaTSz81XGS1+pqI7NmrmXn9mPk55We2gdRFEU5j1VU17gsr6xyXX4+uq/jZL4b/DQPdZrCo52v5qch/+SGhEtsjlmXs8Hp+RtyN2OSpjNdTaWJzooxY0KIBLR8PS2yyK6Pn5fLcm9frYty8q3D6NKnDX/M2kROZiHtu8Yw4fpBRMaEUFJUjqeXB1WVjh8Gce0iKC+rpLiw3K5MAtJDx/x5O5g/bwceHjrGXNaL+x8bj6fnWfFHoiiK4hZFpRWUlFUSFRbA4G7xfLFok8PjWoUFkhAd2sK1OztE+4RyZWvnK8NUmJwnHq8yVWGSJnRCJVM4m7j9N78Qwh/4BXhYSlnkoLzZF9rt0jeeiJgQstPsuwn1HjqW/radbesOMWZqf/oN7UTn3vb39Q/0Yczkfvw+y/5BER4ZyPCxPfAw6AmLCCA3u27ApRaI6UFX1+JWU2Ni0fwdCCF45MmJzfIZFUVRziVZecW8MXMZa7YfxmiSRIb6c9sVAxnWoy1r9hyxOVYIuG/yEHQ64eRqF7auAZ3ZnOe4V6ajf3s8dG7/1a/U49bQWAhhQAvEvpNSOuzIPhML7ep0OvyCHOf7MholuzYeZsWCHTxz++d88foip9e554kJjL68j80DIb5DJC99cjueXgZ0Oh1XXjfQ9iQhbAIxa0sW7aIgX3VZKopyYamsquG+V39m5dZDGE3aILGsvBJe+/ovhnWJ557LBxEdEoCnh54+7WN4697JTBioJkA5c3HEEKK9o+z269AxpfVkN9RIaYg7Z1MK4HMgSUr5ZkveO2XvSY4eyAChQxsdKoG6NSmRUvs/MPvTFYy6og9tu7Syu46nl4EnXpnGrQ9cyuH96QSH+dO1XivatFuGUlRQxtyfNlJdZQQXL3LV1UZSj+USHOLXjJ9WURTl7PbXpoMcz3A8oenb37cw5807uOeKwQ7LFXveem+e6foEs1J/YUPuZqplNR392zOl9WR6BLXIPDmlidzZVjkUuBnYLYTYYd73tJRy4Zm+8YnDWdo3luCrPqtgDGDl7zsdBmMWUTEhTpO6CiG486GxTLt1KPv3nGTDumQWzN1uH5SZZwyFRwY05aMoiqKc83anpDkty8wrJie/lKgw9WxsimDPIO5uP507292GURox6AzurpLigtuCMSnlGly2E505rdqEuT6gXoBWWVl92vcMCvZj4LBOzP11i+NPLaBjp2haqUz9iqJcYIL8fZyWeeh1+PmqJNmnSid0arD+OeCC/BPq3LsNnfu4mAxQLxjrN7Rjs9y3rLSSrZuOOC1v2z6yWe6jKIpyLpkwrBs6R70UwMj+HfD3cT0DXlHOdRdkMAbwzAe30LFHa/sCve2PpPeg9iQO79ws91yz8gDSWQZDQKdXM4MUpbkJIex+kzvap7hPm+gQHr9ltF1A1jY2jEdvGuWmWilKy7lg57dGtArm3XkPs3fLEU4eySYmPpyKimp++ng5+3ceJzDElzFXJXLdvaPtEr+eCpNJ8s0Xq1weM2BQBwCys4vw9DQQFOS46f7QkSxm/riebTuP4+1l4NKRXbnx2kH4+arfL4riwHqgXyP2KW409ZLeDOwRz6K1+ygsqaBnxxhGX9QRg4fe3VVTlDPugg3GLLontqV7Ytva7ca2gqWdyOP3X7Zw4lguMXGhTJzSn9bx4U6PP3wok8wM50t3REYHYURy2+2fcvx4LkJAYmJb7r9vDHFxdYkND6Zk8OATP1BeoY1jK6Sc72ZvZPP2o7z/+g14ealBmooCIISIRlvVw0cI0Ze60ZqBgOPcNopbtY4K5q4pQ9xdDUVpcRd8MHYqNq45yAtP/KSlqjCbP2sTT79yNUNGOs59U9v8bkmdUU+P3nG88NK82iIpYfPmIzzy6Hd8/tkdBJnzon0+c01tIGbtYEomS1ckMXFcr9P8dIpy3hgH3Aa0BqzT5xQDT7ujQoqiKI5csGPGTlV1dQ1v/uc3m0BM22/kzRfmUeVk5mXb9pHEtDbPlBTC7uvI8VyHC+Lm5ZXy+8KdgNbVuWmr1QQAnflLL0AHf/y1x/4CinKBklJ+LaUcBdwmpRxl9TXJWZJpRVEUd1DBWBPt2HSEAicLexcXlrNlfYrDMiEE9z00FoPBfvzDlGsGkHIk2+k99+49ab6GNs0b0P7krPOkCcGeA2kcSMkAYNf+kzz56hwm3/ERtz76NbMWbMVoVIvDKhekBUKIG4QQTwshnrV8ubtSiqIoFqqbshFS9qeza+sRfP28EDrX8Wt5WZXTsgGDO/DujNv4ddYmkg9mEBYewGWX92HE6K78sWwvxcUVDs8LDNQG8gshuHhIJ/5aleQwWa3JJPlu9kbGjenB06/9Vht85RaU8u6Xy9m9/yQvPD6psR9bUc4XvwGFwFbA+QrKiqIobqKCMReqqmp49emfWbs8qXafp6cHOr3AZLTvU9TpBb36J7i8ZodO0TzxT/uAaOzYHvzyyxaH54wb26P2+7tuG86KdQcwmiTWNbCEZlt2HiMlLddhK9jy9QfZvf8kPbvEuqwjQEVVNSVllYQE+qJvIABVlLNcaynleHdXQlEUxRkVjLnw7YzlNoEYaAGas1QXE6ckEhEVdEr3uv22i0nal8a+JNtlQa6/fhB9+sTXbreKCiIg0Jv8wnJth7kqliU2K6trOJ6W5/Q+67YedhmMlZRX8u4Pq/hjXRKVVTVEhPhxw/hEbris/yl9LkU5C6wTQvSUUu52d0UURVEcUcGYE0ajiYVztjosk1LSKzGBzPRCMtMKiIwOYtK1A5h6o+OFbMvLq1i/LpnS0kp69mpDQoJ9CgxfXy/++99rmfH5CjZvPYKnQc/Ey3pz9VUX2R1bWVWDrB8PCi0uk1K6XGRKr3OdM+2xN+ey48DJ2u3s/FLe+WElldU13D5poMtzFeUsNQy4TQhxBK2b0vxPRaqpx4qinBVUMOZEZUU1xZbWJwdCQv15fcbt1NQY8XCRlHDN6gO89t/fKS2tG6oyclRXnnzqCpvB/MXFFTz8xPccshrI/+Gny9mblMZzT09GZw6i8gpKKbPM2KwXV0kgPMwf/2BfDh7OdFifEYM7Oa3rln3HbQIxa98t3ML14/rhrfKYKeeey9xdAUVRFFfUYCAnfHw9iYoJdlqe0EFbR9JVIJaeXsCLL/xmE4gBrFiexNdfrbbZ9833a20CMYuVaw6wfJXWVZqbX8LrHyyuS4Fh7prEatPX34tO7SIxGHTUH9U29bK+dExwvv7lzoNpTsuKyyo5dCLHabminK2klMeAOGC0+fsy1LNPUZSziGoZc0IIwdSbhvDhawvtyvwDvBk/ueGVVBYt3El1tdFh2YIF27l9+nD05lQVf61IcngcwFsfLGHJyiQOHMkkN7/UpkFMmv8jBUgPSE7NITlVC5oC/L2ICA0gMsyfiaN7MnqI69UFAv1cL6cU6O/tslxRzkZCiOeARKAz8CVgAL4FhrqzXoqiKBYqGHNh8rUDKS4s5+eZa2tTVsQlhPP481cRGh7Q4PlpaflOy4qLKigrqyQgQEtb4Sirfu2xJRWs23LY4bu8AEyA1GOX7qK4tJIOCZH8759XN1hXgEsHdubdH1dR5SCA7NmhFXFRIbXbaTmFrN93DE+DnhG92hPopwI15ax1FdAX2AYgpUwTQjT8D7gBQog44BsgCu296BMp5Tune13l/GA0mVicepA/jydjlCYubd2BCQldMOjUWpuKPRWMNeCmu0dy1Q2DSN6Xhl+ANx27xjR4zsoVScz6aSMHDqQ7PSYs3B8/qwCmX+82rN3gOGGsltzVxQ0FDvOOAWzfm0pqej5xrUIcllsLCfTlmTvG8p9P/7RJjREe7Mczd4wFtAkCr/24gtkrd2Iy95d6Gzx4dNpwrh7Ru8F7KIobVEkppRBCAggh/JrpujXAY1LKbebgbqsQYomUcl8zXV85R1WbjNy9/FeWnzxUu2/+0SS+O7idry+9Fh8PNfZWsaWCsUbw8/emz4B2jTp2xsfLmPXTxgaPmzLlItIzCvjqu7WsXnuQaqMJdAJpkvbdkA2NbnE9QZK9B9MaFYwBjB/SlV4dY5i/ag85BaV0io/ksqFd8ffRujBnr9zFTyt22JxTUV3DKz8so2PrCHq3bzhYVZQWNksIMQMIFkLcBUwHPj3di0op04F08/fFQogktIXJVTB2gZuVsssmELPYlHWCL5I2c19PtRi6YksFY83oxIk8Zs9qOBAbf1kvRozqwt8fnqnlC7MOpnRCS08h0fbrzUseWbYdcLCkpU3Zfz5eRNLRLB65dVSjPkdMRBD3THU8nGb2yp2O7yPhl1W7VDCmnHWklG8IIcYARWjjxp6VUi5pznsIIRLQukIbfgAozWpfaiZzNuwht7iM7nFRXDW4B6H+vm6t02+H9zotm3t4nwrGFDsqGGtGa9ccdLjYd32XXtqdb35Ybx+IWQhhXgTcQe4KB7tctpwJkAhm/bGNbh2iGTe0q90hJpNkc9Jx8orK6JoQRUKrUKeXS8stOqUyRXEnc/DVrAGYhRDCH/gFeFhKWVSv7G7gboA2bdqcidtf0Gau2MYbc1fWbv+1K4WZK7fx6b1X0zHGPp9jQ3LLysgoKiEuOJBA71MfB1tS7XxZvFIXZcqFSwVjzagxgZjeQ8fBw1ksWtxAMvB61xLm68e1CiEyMoCS0kqSjmQiLQuGO7mEtBorOm/ZbrtgLOloJk9+uIC07MLafSP7deCFuy9zmFOsbXQo+445zmHWrlWY68+kKG4ghJgC/BeIxDzCEi3pa2AzXNuAFoh9J6X8tX65lPIT4BOAxMTERjwhlMY6mVvIm7+tstufX1LOi7OX8vVD1zX6WsUVlfzrz7/480AyNSYTXh56pvTszjOXjMDLo+m/JgdFt2FffpbTMkWpT+XaaUaDh3Ro8JiBgzsw48uVDR5X/6ltaRT77O1beOuFa3nj39PQe+ldB2Ie2JRn55XYHFNeWc1Db/5qE4gBrNiWwhvfLXd43ZsudZzSw+Ch55qRagC/clZ6DZgkpQySUgZKKQP5L7aqAAAgAElEQVSaKRATwOdAkpTyzdOupdIkf2w7UDuJqL4dR9I5mWv7XDueV8Cbi9fw2KyFfLB8A1nFdc/D++bM5/ekA9SYtIlLlTVGfti+i3/9sfSU6nZH14sI8fKx2+/n4cnfeww6pWsq5zcVjDWj+PhwJjnJP6bTCcaO70l0bDAmk/kB0sAMSUtOV8vjZkBiW3x8PAEICvDhkiFdbE6RgNSByQOkAbtuzo7xETbbizfuJ7/Y8SoDi9YnUVRaYbd//IAuPDz1Yvy8PWv3RQT78drdE+kQ2/RuAUVpAZlSSueJ/E7dUOBmYLQQYof5a8IZuI/iQFmV83RA9csX703m8ne/5tPVm1m4+wDvL1vPxHe+Zuuxk+xKz2D9sVSH15i3dz/pRcVNrlusfxCzx93I2LiO6IVAJwQjY9vx07gb6BisnpOKPdVN2cwefGgsnbu0YuGCHeTklBAbG8Kgwe0ZOaobYWH+vPT6AkBL0ooOLUlYfZYyUdfVKHSCtNwifl64jSvH9cFDr+OxOy8hr6CUzbuOadf0wH6cmQCkFgxeP9F2se/jmQVOP0dVjZHMvGKH+cNuGZvI1OG92JGShpdBT58OsXjoTy+ur6iuIauohPAAP3w91bRvpVltEUL8BMxFW5sSAEfdik0hpVxDg3OZlTNlQMc4PluyyWGZl0FPqJ82iL+0sopn5izWZqxbKams4qlf/uTWkc4TeBulZF9mFq0Cm56WrkNwOJ+Mmkq1yYiU4KlX+cUU51Qw1syEEIwf34vx4x2vQdypQxSLl+0zB1sCdNIuIHvs/jHkF5Yzf+kuMvK1pnQJHDuZx9ufL2Pr7uO8/MRk/Hy9ePvZaSSlZLBm6yG++G2Dk0pBv55xzFuzl68Xbia/pBwkBAf6OJ2l6emhJyrU+QPIz9uToT0SGvx5NKTaaOTtRWv5eeNuSiur8PE0cGViNx6bOBxvg/rrqTSLQLQlkMZa7ZPAaQVjinsN7NSGAR3j2JRs36pVUWPk0a/n8/UD17J8/2FKKh0Pmk/NL6SkzPWA+gj/00tLp5K8Ko2hftu1sPGX9uSrH9ZTUm5+QbcEZFbWbz3C8CGdCA7zrw3GrK3elMKWXce5qHc8AF07RFNQWg6/Ob/vxj3HISnVLvDSC4FRSrv9lw3u2iJZ9f/9y1/M3VI3Dby8qpof1u0kt7iMN2++/IzfXzn/SSlvd3cdlDPjP9ePZcKLX2A052fUsvpqX9uPpLHl0AmKKytdXqNDWBiR/n5klZTalXWJjKBXq+gzUXVFsaGCsRYWEODNvXeO5LX3/qzbWW8Q/prNh1i91fHyR3XHpNQGYwBtY8PQCeF0QKul27M+k5T4+XhSWlH3dqjTC37buI/s0jL+NmkwXeOjGvPRmiy9oJj52xznx1y8O5lDmbm0j1IzNJXTI4RoDbxH3VqUq4GHpJQn3FerC1PykSxm/76VlGM5RIb5M3lMbwb3b1xCbUeO5xZgRNYO6aj/jNuXmsmQbglOz/fy0NM/Ppb3r7qcu2f/RkFF3TjZVoEBvDXpslOuG2iZ+IurKgny9Eavcz2Uo7i6gnmpOzlYlEmUTyBXxvUhxjfY5TkmaWJtzk5WZm+nylRN3+DOjI0ehJ/Hqb9IVxrL2F+0mtKaAqJ9OtLWry/CyUQxpfmoYMwNxo7qzqczV5NfUGZX5uiB4kj9fxvR4YFcMqgTS9YfsL+mi+WSALwMHjx35zhenLmU/NJyjEKryeo9R9iQdIzPHr+Gnu1aAVq3YnFZJYG+3qc9TmxPagZGk/PZ/rtTM1QwpjSHL4HvgWnm7ZvM+8a4rUYXoDWbD/HPN36jpkYbl3HwcCZrNh/ijmuHcPs1p5YENSzAnNzVyeMt1N+XDpFhTOjZmYW77Z+NNw/uS7CvN319Y1hx7x0sTDpIamEh7cNCGd+54ymltQCoMNbwxvZV/JS8i+LqSqJ8/Lm9ayL3dB/gMLBJLsrkjnXfkFtZ1zr3ycHVvN5/KmNiujm8h0maeCXpa9bk1K2IsjlvHwvT1/J67wcJ9mz6OLfDJVuZe+IVKk11v5uivTtwbZsX8PUIavL1lMZTsyndwGDQ89A9l6KvP9geavOGmeMhp4YP6Gi376m7xjF+WFf05iBJoAVipgaGLNTUGDmckUd+WYU2AcDSiiag2mTigffnUFRazru/reGSp2Yw+qkZjHnmE2Ys2oDRPBW8rLKKFXsOsXz3IZtWNleCfe2nfjelXFEaKUJK+aWUssb89RUQ0dBJSvMxmSRvfba0NhCz9tXs9WTmnFrC6A7R4fRo47gbMdDHi0t6aemGXp06jntGDCDE/ExpFRTAE+OH8+iYYbXH+3l6Mq13Dx4dPpTJ3bueciAG8MCqeXy2bzPF1VoXaWZ5Ca9uW8Hr2+3zogE8tW2OTSAGWqvaU9vmUFxtP6sdYE3OTptAzOJEeRbfHlvU5DqXG4uZc+Jlm0AMIKMihUXp7zX5ekrTqJYxNxk1rDOtooL4Zf5W9h5I40R6gXmGpVWAZgnG6sVslwztTL+e9okDfbwNPHfvBO69fjip6fn4eBu46+WfqKo2uqxLjZR8u2Srw3sBFJVVMv3t2aSk59buyy8p56Pf15NXXEb72DDenreGUvMgWV8vAw9ePozrL+7j8r7928YSFxZEar18QAARAX4M7Rzv4CxFabJcIcRNwA/m7euBXBfHK80sKSWDzBzHKSKMJsnqTSlcPcH5rEZXXrlxPPd8/Ctp+XUBna+XgTduvRwf88xsg17Pw5cO5cHRQyirqsLPy/OMdb3tyc1kSWqyw7Iv92/lnh4DCfKs60ZMLsokqTDD4fHlxmoWp+1jarz9z2Zl1jandViZvZ37O17TpHrvK1xBlclxqqPk4g2U1uTj59G4NY6VplPBmBt16RjNM49OJDO7iGv+9imyXpedJev+xQPak5peQGCAN5eN7M7E0T1dXjcixJ+IEH8ApozqxY+Lt2sFTpZTKqmqghrhtJ1Ugk0gZu3nNbuoEbYTAMoqq3n1l+W0CQ9maNcEp/XU6QSv3TCBv30+h8Kyurc/f29PXr9xAgY1FVxpHtPRxoy9Zd5eC6hB/S3IZHKUw6eO0ei63JX4iBB+e/JWluxMJjk9h+jgACb270Kgr/24KZ1O4O/tdcr3aozNWY5zlgGU11SzOyeDYTEJtfsKqhwHQBaFTsorTc57ICqMrictOFJc7fz9RGKiRAVjZ5QKxs4CURGBXDKsC0tW2eel7NcjjleevKpR16msquHQyRz8fbxoE639o3no+hH4+3jxw5JtFFdU2XR/SmHpFm3gwi7KjSbpdHLAF39tdhmMAfSMi+aP/5vO/G1JHM3OJy4siEn9uxHk4EGqKKdCSnkMmOTuelzIurSPJjTYlzwH42SF4LQG8YM27vXyRPt1d90h0NP1syvQ0zYY7BIUjY/eQLnRcRLbvmFxjveHdGZr/n6HZf1Cujjc70qkd1unZZ46H0I8WzX5mkrjqWDsLPF/947F09ODxSv2UV1jRK8TXDywI0/cO9bh8TU1RiqravDz1f5hf/fHVj6bu57Scu1tKcjfm3/cPJqxg7pw/fh+zFm3m+LqKi2lWf0B/VKav0SzprDcfvgkhaUVBDWQIiPAx4sbhrru0lSUUyWEaAe8AwxCexVZDzwipTzs1opdQAwGPX+/aTgvf/CH3Rq+V47rQ5uYUPdU7AwYG9cRPw9PSmvsW646BIXRK9w2qAkweHNzu0F8krza7vhB4W3pG+p4Lcvx0YNZmLaWtIocm/1eOgM3xo9vcr07Bw4lJDuG/Ko0u7J+IRPx1KkxvGeSkI1Z3foskZiYKLds2eLuapxRBUVlpGUUEhkeQHiov115YXE57/+4isXrD1BZVUP7uHC6d2zF3JWOFx7/5x1j2ZZyggXrtBQSNrM1rQIyKaW2LbALyExgE7ZLqOvSlDhtGZPAI5Mu5vbRia4/tKK4IITYKqU85b9EQogNwAfUjRm7DnhASjmwOerXWBfC86shG7cf4cf5Wzh0LJuIsAAmj+nNFZf2bNL4LSkl65OO8ee2g1RW1zC4azzj+3fGq5mSREspWXP0GH8eTMYoJaPatSUy0J+Pt2xi/YlU/AwGJnXpyr2JAwn0ctzluejYAR5aPZ8qU9143WBPb7659Bq7YMxyz8+S1zDz8AZyK0vx0Ru4Iq4Xj3cfi5+Hdo8ak5G/MneyOlvLyzgsohv9Qtry4/ElrMzeRpWpmn4hnbm2zVhKqgs5WZ5JpHcYA0J7Y9A1blWTwqpMFqS9xfGyXQB4CC/6hUxgVNR0dEINGzkVjX1+qWDsHFJdY2T6s9+TfDy7dp8JnAZDAB6eOqqk/XgMLaCyPcmyRJPlb4S0HkMmbI9pTAuaFDC8e1vev/PKhg9WFCeaIRjbJaXsVW/fTilli65sf6E/v5qDlJJ/zfyTBZtsh3R0jYvkkwevJsDn9MaDmaTkkQUL+X2/bRoMoQOjh+3Y2B4Rkcyedr3TWZepJYXMSt7JydJiOgeHM61DT0K9fV3ev8ZkJLeylCBPH7z1dQFUpbGax3d8zvZ828bcPsHt+F/fO/AyH5tRkc1L+94jo6Lud0SIIYinut5HW3/H3Z2O5FelU1qTT7hXG7z19o0CSuM19vmluinPIcs3JdsEYrUtVE4CIwlUmUzOAydLa5gVk6VlzElLl6tWMGF9nPkagaf5cFSUZrBICPEk8CPaX89rgYVCiFAAKWWeOyun2MsvKuOPDfvJKyqjW9sohvdtj16nY9nOFLtADCApNYs3flnB41NHnlZANnfvPrtADECaACM2vzH3ZGcx7+B+pnXr4fBacf5BPNZ3eKPvvSnnCItO7qG8pppBEe24LLZ7bZD1y4l1doEYwI6Cw/xyYh03xI8A4K0Dn9kEYgD51YW8tv9j3u//H/SNbN0K8Wylxoi1MBWMnUO273eQMNxVC5WToMpSVL9NVLoIxBpzP5vrmY+7PNFxwkJFaUGWOf731Nt/Hdpf29MbPa40q2Vbknn2k4VUWqXkaR8bxvv/uJrfNzsesA4wd8M+Fu46wMT+XXlw4jCOZufhZfCgW2xko7tB5+61D/QshMn+mbni6BGnwVhT/GfnAn46WtdqOv/ELr45tJ4vh95GkKcPSzPs84lZLM3YwQ3xIzhUcozDpcft640krzqH304s4srWE9AJlV70bKSCsXOIn6+nzbZsxsH2NuPAToFA6zKVOsD88uXv7Ulmkf3amorSkqSUzqeJKTY2rz7A7M9Xc2h/OqERAYyfmsiVNw+pTSTdFLl5JaxZn0yN0cTAxHa0jmk4LUJeUZldIAZw6GQu//1mKaU4nnFoUVlt5JdNe5i7fR815nQa8eHBPDf1Uga0b7ibrsjVOpYORvR4NUP6ndWZyTaBmMWBokze37+cZ3pNoMLoPI1FuTmNRV5VQb0SiUFnRC+0is9Jm8vqnNU80PHvtPNPOO16K81LhcjnkHFDbKduNxiLSZwOapX1vpf6xlzQ5a20a1iuI6Cksop/zV7My3OXn/qFFeU0CSH0QohJQogHhRCPWr7cXa+zzdJ523n27zPZtfkIpcUVpB7O5tPXF/HGUz83+Vo//LyRa277mDc/WMK7H//FTXd9ytsfLqGhMcp/rE+yC8QsVu84TLc4F4t2C3O6Hj21gRjAsZwC7vvyN1Jz6wcr9hJbxzovdPDbckLHTg1esyHzT+xyXpa6E4D+oR2cHpMYqq3G0sY3FmH1EPcQdYGYRU5VLm8ceOeU8pApZ5ZbgzEhxHghxAEhRIp5TIfiQsc2Edw91WoNN4nTJZP8fDx5+9Er6dkhxr47EkuOMdG4rkkLV12UlrFkDur0/bodHG/Eg1BRzpD5wG1AGBBg9aWYGWuMfPnWnw6DpeW/7yR538lGX2vL9qN8/MVKm6WPpIQ5C7Yz/4+dLs/NLSx1WmY0SUZ0b0tksP2Acon2DJJOGqrKq6r5ab3zoMfitv59CXKSFLb+tSd06MTotu0bvGZDSpwsdwRQWlOFlJLr40cQaLAf/B9o8OW6Ntq4tCjvcAaF9bXU1i4QsyiuKWZD7qbTrrfSvNzWTSmE0KNNNx8DnAA2CyHmSSn3uatO54LpVw1iUK8EFq7ZS1FJBSYBSzcdxCRl7cD5AD8vxg3pyjdLtrI9JU17Y7RaWsmy/qUNE7Xdi84y9TsL3U3mGZa1A/gdLOO0dHcK00fWTSjJKCxm4e4DlFRWMaBtawa1c5xLR1GaQev6sykVW4cPZpCb5Xi5IoAtqw/SsZuLViMr8xY6H980b+EOJl3mPKdgl4Qop2WBfl50iY/ky4ev4Z15a/hrRwpGk6k2ebVsoGlhd6r9kkPZJaXsOJGOv5cnA+Jb0zooiO+vu4ZXV6xizdFjSCAxNpa7BiSyNzeLNcePUW0yMjSuDfcPGIyuGZZUSgxLYGWm4+WT+ofHI4QgxieUDxP/zmeHFrMmex8SybDwbtzZfhyxvmG1x9/b4RbSKzI5Vppq94i3llmRddr1VpqXO8eMDQBSLIkXhRA/ApMBFYw1oFv7aLq1r2uuv25cP56asYD0PO1hWlRZyazl5geiTtTmD5ONaAGrHdhvlaUfF+ksJNQFcfULrI43WaXX+GHTTl76fbmWvR/4aMVGBraN48ObJuPr2bh8OIrSBIuEEGOllIvdXZGzlaGBHF0eTcjhlZXtPKhzVQYwql8HElqFcjTdfoLrdWP64e1pIDY8iNemT6SquoYpr8/keHbjWt2rjXXdnyYpefnPFfy4dRfV5i7NmKAAXps8noviW/PltKmUVFVhMpkI9NaSVmeUl3AgP4eiykp2ZGXw4749PD98FJd3anq2e2tT4/vx/ZFNpJfbrtGrF4K/dxpRu53gF8WLvW6ubb10NCnBW+9FpakUnTA5mixfq5WP86BXcQ93dlPGAtaLeJ0w71Oa6Lul22oDMYvaf4ONzCMXEeRHj/hoEqJC8PU2aG+aHmjhupPxZJaugfqE9QFmI7tqE9YOZuTwwoJltYGYxcYjqby9dG2j6qooTbQBmCOEKBdCFAkhioUQRQ2edQFJ6BhFXLsIh2U6nWDYmO6NvlbbhPBTKgPw8NDzwT+mMrRX29pAIsDXi7smD+LOSYNsjvU0ePDGrRMJ9XeduwsACX3iY2o3Z6zZxMzNO2oDMYC0wmKmf/8Lb6xYw5ojxzDodLWB2PKjh/nniqU2A/xzy8t4ePFCtmekN3x/F4I8ffhm2O2Mj+mOh3mmY6+QWD4edBMDI+znngghXM4Oza8qRAjhbAQLQYZABoZedFp1VprfWT+bUghxN3A3QJs2qiurvtzCUpZtPeiwzC59hYPuR4uHp1zMhIHaBIGR//yYUifrpFmuIXQCE7JR4fzEPl3oEK09hH/etsdpfDh3+z7+b/xw9Do1r0RpVm8Cg4Hd8lzKct3C7v/XJJ699xsqy23/7d/w91G0imv8ckVTJ/Vn8bK92pix2jGp2oOnuLSStIwCYqKDnZ4fGRLA249cRXZ+CQUl5cRFBuPt5bjFvEtsJAv/OZ2F2/ZzOCOXX7bsobSq2vY5Z24hutG85JrRZOLbzfZdqVJABSZmbNjMjA2bCfDy5Nkxo7iyRzc+37HV4f2NUvL1zm30jZ7YqJ+NMzG+wfzvomlUGqupNhnxN5z62rwJfnGklBzBZB6fYr36na/el390fhgvvcr/eLZx52+9k4D1XOPW5n02pJSfSCkTpZSJERGO39wuZOm5RXatTM4IMOefqNun1+m4ffxFtYHYrLU7KSxzPqBUCNB5Cmp00ukYDcvYNW+Dnk6xEcRHBZNtTnGRU+x8gG5xRSUV1TWN+iyK0gSpwB4ViLnWe0A73p99H5NuGES3Pm0YNrYHL31yGzfde0mTrtOhXST/eXoy/gFedcMkzA4dzeaRZ2ZRUeE6RQVARIg/HeMinAZiFj6eHnRrHcmY3h15ZspobRyXZSKR+U98+shEYkODACiurCKn1HbBcsssTOsgrriyiv/7fTGbjp8gJc95XuDkvNwGP0tjeekNpxWIAVwRM878ncCEDiMCoxTo8eQ/3f9FvJ9q1DgbubNlbDPQUQjRFi0Iuw64wY31OSf5NPCgsiPMA+6Bfu1jePXOiUSGaLOTVu87wos/L7ObDWmdWd8kgBpZNwYNu5dQ7XQPKBNG9mdls39ZNl+t3cbHt15Jt5hIFu1x3JIXHxaMn5enwzJFOQ2HgRVCiEVAbT+TlPJN91Xp7BTXNoJ7n7nitK8z8KL2eHl7UlJmnx8rI6uQpSuTuHzc6c+p2HjwOC/O+ovjOdq4sRA/H24Z0pfUwiKSM3KIDgrgmsE9Gd+7c+05/l6eBPt4U1Be99IpnYyJNUnJ11u2ExsQQGap45yJrQODTvtz2N/XhAT0p5CgdUBoP+5sezM/n/iNguoiQNDaJ4bpbW8kykc1aJyt3BaMSSlrhBD3A3+ijUr6Qkq51131OVf98Nd2p72PtQPxzS+KJg9s2kK3HUvj6W8W8dlD0wB4a8FqraBedFW7VqXVBAApAL22TIiUVlktLPeoV6GSyiqe+vlPfvzbdXy5dit5peV29b3rYjWOQTkjjpi/PM1fyhmWl1dCbp7zhM8HDmVwOacXjB3LzufBT3+zaU3PLy1n5opt/O/2y7m0d0eH53nodFzXrxcfr7VK7+BiYlNKTi5/Gz6AbU7Ght3Ys/mWOD1cnM17+5eyMvMAEhgR1Zn7u1xCh4DIJl3nkqiLGRExmGNlJ/DUeRLnG9PwSYpbuXXMmJRyIbDQnXU41y3eorUy1c8mYdkOCfBlcI8ECiorWL3viN35W5JP8OqsZdw+dgAp6bbN7fXTWdisTWmJviyLi0vb7Pv1SeBYfiF3fTuX3nHRZBSWkJSuraEW5ufLPSMGMLX/6S8roij1SSn/DSCE8Ddvq2UhzjB/P28MHnqqaxwncA0N9mv0tXKLSvli4Sb+2qZl8x/aoy13ThzIT2t2Oh3W8NWyLU6DMYD7RwwivaiYebuTbF5aHYkJCuSqLt1Izsvlk22bMZp7uz11eh4bPJThbRIa/VlcOVGWz61rP6Owuu5FdVlGEltyj/LDxfcQ59f4cXsAHjoP2rs5035VTQZFFWvR6bwJ8h6JXtf4P/cLzVk/gF9xrbKqpm5MhiXXGIAQDOgSx3sPT8FDr2Po4x84vcbcDXvtHo7Wi33XbpsDsdqy+v931aJubknbk5ZZu+uWIX25snc32keEapMBFOUMEEL0AGYCoebtHOAW1RJ/5vj6ejLq4s4sXm6fqUinE4y/pHEvXoWlFdzx2ixSswpqnzvzN+xj+fYU4ts6D04OpmU7LQMw6PW8duV47r14IK8sXcmyFPsXVYsb+2otX08MuZibe/Vh+dEj6IXgkrbtCfdtxEzORvrm0FqbQMyiqLqcrw6t5V+9Tr/7uKVIKUkteJHM4q/QVlgHnfAnPvQFwv2ucmvdzlYqGDvHDewWz9rd5gdJvenOw/u0x8O8plxZhfO1zSqqakjP11Jj1AVz2L4p1g/EHCWFbWL+w282bKfCVMOS/SnklZXTOjiQ2wf156aL6pJC/nkgmZlbd3A0L5/4kBBuSezDuM7O33gVxYFPgEellMsBhBAjgU+BIa5OUk7PA3eNZtvO4+RYdVdKtEz6m7YfYfJ458lfLWav2MnxrAK7Z05JVRUpJ50PnI8KatwCCwlhIXwwbRJ3/jSHtUfNi2xb3Wf6Rf24tFNdlv1W/gHc0OPM5A/emHPYadkmF2Vno6ySmWQWf26zzyRLOJL7OD6Gjvh5ql6Q+lQwdo6754pBbDmQqrWQWUmIDmXS0O5sP3SS1XuP4ONloKzS8Qwmg4eeuIgg+4CqfuJXCwdBl8Cc0qyJAdlPW3bXtqidKCjihT+Wk19WzgMjBvPJhs28vmJN7bGZJaVsSj3B4yOHcc8gNb5MaTQ/SyAGIKVcIYRQ/SVnWE2NifyiMvs2bwEzvl7FuFHdG5wpuW7Pkdpz6qsorQYnGRquHtr4gKmsqortJ63Gg1lVeH9WTqOvU5+UklVpR1icmoxEMqZ1R0bEtnOatd9X73w4o4+LsrNRVvHXTkqMZJd8h1/oKy1an3OBCsbOcd3bRvPZP6bx6YKNbNh7DB8vA2Mv6swdEwfw3Ld/snRHSoPXuHxAVxZtP3Da61M6G3fhLDmsM1+s38pVvbvx3poNDsvfX7OBa3v3JNjn9KaAKxeMw0KIf6F1VQLchDbD8rQIIb4ALgeypJTqVb+ejduOUGM0OXwmlJRWsmNPKoP6t3N5DQ+93ukzR0ho5e9PdlmZzcLgOp1gwdYkQvx9mHRRN5tzaoxGZqzaxOZjJ4kK9OfBUUNYeeQIZdWOX1TXHz3OycIiYoMCXX/YeowmE/etmssfx+tmjn9/cAejY9szY9QUDDr7wbUTYnuxtzDN4fUmtO7ZpPu7W2XNcedl1c7LLmROgzEhRBzwOlpW/EXA61LKanPZXCnllS1TRaUh3RKieev+yTb7Zv61tVGBWO92rRiX2JmfP97j9BghG9nqJQETCF3dy2W4vy9ZZWXOz3Wwv6y6mv8tX0NFjePBuRU1Naw7eowJXTs7LFeUeqYD/wZ+Rfurudq873R9BbwPfNMM17rgiEa8/V3SryNbkk84LfeQOhY9dwfPfPcHG5K1BV2MSJLSs3n6xz/ZkHKcl68fD8CBjGyu+fRHKq2eK7/tTOKits4XfpFAbmlZk4OxH1N22gRiFstOpjB9xU+UGSvxNXgyqU13rkrohYdOx7UJA1iZdYBNObbj1y4KS+D6hIFNur+7eRvaUV59wGmZYs9Vy9gXwC9oS4ncAawUQlwhpcwF4luicsqpm7vB+djk+MhgRvfuwOjeHeiZ0IpfNzgPxGqZqBug7+xN1fz/v40cSLfYKPy8DFzUtjU3fj6LHSfsp4W7WitzQdJBlxMCdKeQf0e5MEkp84EHz8B1VwkhEpr7ume7E/HGfAMAACAASURBVKl5bNt0GE8vD4Zc3InAIMeD2AcntsPToKeq2n5GZWCANz27Npxu4cqLe/Dpwg3kldgPbAfonqCt0bvl8Mna3IfWvYDztiax41gaiZ3bMGfXXpvljyw2HzkJBuxmgkskBk8dz/y1BB+DgQmdOnF9r154eTTcofTrIUfPVInO08S67Lpga13mUZamJfPhkKl46j34aOAtLEnfy/KM/UgpGRXdlbEx3R22pDUHKSWF1bkYdF74eTRunF1jRAVM52je/9ntFxiI9L+52e5zPnH1typCSvmx+fsHhBA3AauEEJOwSwuqnG3yisuclgX4ePPQ5Itrt+PCnS9NAlZ5xizpK+ovNWK1GeTrzYpDh/lswxZ0OoFBrycqwK/2OjapN1x1aVqlzqidI2re52fwZFhb9T6gNI4QYgkwTUpZYN4OAX6UUo5zfaZizWSSvPPaQhbN3167pNl7nh78/eExXH5lf7vjg4N8uePGYXz01Uqb/VIHBn9PxtzyHkEBPkwc3YM7rhnicPyYl8GDdx+4itte+1Hr8rRi8NBz69j+bDh4nBqTyS4QA+2RcTynkCP5u5EuRjUEGTwpNNVNcpJIpCdUCRP7srWZmVvSTvLFjq30i42hXUgo13brSZS/v8PrFVVV2u0THtLhwt1LTx5kadpBxrXugkGnZ0JsLybEntokgXJjBdvy91JhrKJnUCcivcOcHrs9fy1/ZswmpyodgaBzQG+ujL2dcK/oU7q3tQj/a6mqOUl68Qyk1H4WHrow4kNfwsez02lf/3zkKhgzCCG8pZQVAFLKb4UQGWhJWtXg17NctzZRrN131GFZ9/gom+3E9rF0bBVOcrr9YFUJWnJXzMuFOGiQklLbL3WQV11BXnqF9hQ0Qnl1DUUVlVormKzL2u8oMSxO7iEQSFG3DqaXl57s0lL8VbZ+pXHCLYEYaC1lQoimZdE8RefT2rpzZm1i4bztNvuqqmp49/VFdOgUTZdu9t19108ZQHxcGF/+uI4TafkID0FheSVZudrs7cLicr7/bTMHD2fyznPXOLxvt/goPnpoKv+bvZL9qVkAdIgN595JQwjwrYuwnK2d3ZihsG2DQ4gID2BZ8iGMUuLhpaeauhY9KSRSD6mlRaQe1NaYn7FtMzMmTGZYG/sXw8TI1iQXWj9PJULnvA1jUep+xrXu4rKO2/MOk1KSToRXEMMiuuJRr7VsdfYWPj70A2VGbWUBHYKx0RdzV7tpdj0Juws38d3xd61qJ9lfvIOPDv2bxzq9jq+H4yCzKWKDHyUqYDrFlRvQCW8CvIegE+qZ7YyrYOwzYCBQ+1ojpVwqhJgGvHamK6acntvHXMSG/cfs1q308TRww4i+ACzfc4gvlm9m/8lsgv18CA/wJceqRc0m6auzPGKWZZE8bPc5OExr8XIUzFlfx1nvo2WdJQF55eX8fe48Ft1+C8LZE1hR6piEEG2klMcBhBDxtFDrvpTyE7TUGiQmJp6TPQonj+cyf/ZmFiywX1wbtJex3+ducxiMGf+fvfOOj6pK///73JlJ75UQQhIIvUnv0gQBERVFQext9buWXdeyu+5vi7q7uq59VXR1EXtXVEDpIB2poScBAiGV9DaZcs/vjzszmcncGQIEQZz36xXNnHPPuSchc+a5z3mez2NXWbx8Nwdyi5urgOi8Z3/MPsrW7KMM7KNvsA7s2oEPHptDYXkNFouVj9ft4o/vLqahyUposAkjApuff1Kh+o97HZSRyqOXjqG8voET9Q1c9+nHWJs0Y0wivepWghbb+uDSxay75U5MBk/D6I6eg/nmyF7qrL4lhdyxqfriuAAVTXU8vH0+e2uOudoSgqN4+qIb6RmtlXfOry/khYPzUWn2HqpIviteQ7uQBK5I9awvuqzkC917VVsr2FyxkrFJbaNpZjTEEBs2uU3mutDxGXgjpXxeSrlap327lHLi2V1WgDNlUJcOPHP7NDomNh9Bdu+QxKu/vor05Fi+2ryH++d9zY4jRZitNoqrajlR18DgrA48OXsSf7/+UjA1e7L8ZUMK8C4B0PJ792taNEmDo4ySn7CIlsG+ueXlbC7wHdgbIIAbjwFrhRDvCiHeA9YAfzjHa/pZsHVjHnfPfo0vP9xIk5/i3iXF1brt36/cw+oNboHsfh6efszOP+l62sdHMfe7TXy4eodLqqexyYpqlX7Na+FWNLwlJoPC/eNGsHj/QR769jt++/UizI1WXHaNn9jWsoZ61h7zXnfn6Hg+mDibYclO41IQ7acAeKeoOMx2/d/v3/d85mGIAZxoquHh7fNpcoz5vvgHD0PMncVFnh/jNtXK8UbfIrf5DQfJq93Ih0ce5IX90/lv7s1sPPEhdqmfUBWgbQhIW1zAjO+Xxbi+nckvrcRoUOjgiA2z2VVeWrxOd8yWvAIenDaafy5c5fJUnexxXrr/362AOI7X7nFlXjbbyZT7/VBYU3t6AwP8opBSfieEGAAMczT9Rkp5+gJSDoQQHwJjgQQhRAHwFynlW/5H/XxQVZUX//ENlibHh7Afz1J6pn4B6u9XeSvw+yI05ORHWPmllXy/3TtLT+Dwfuk80DlDLYTqLVodFRLMGzdcxUvrNvDmpq1ec57sIRGgpsk7Pgygb0IKH116PdUWM0g4Wl/J9SvfpcHWwugSkrkHf+CjI1u4q9so7ujarEVc1FjJ+hP7deevsNSxunQPk1IuorTJtwBuSVM5UkrXKYJBGAlWQmlS9ZMiVLWCLwv+4npdbS1mbdnblJhzuKLDn33eJ8CZETDGLnCq6s18u20/a/YeQhGCCX27MCCzPWU19T7HLNp+gB35juzHUzwFdBliLRX6nRu5M3bM+W0rDTHZMlMA6JqQQE2TmYKaGpLDI4hvw9IkAS4sHMbXt2085+y2nO98Y//u45QUukLtEKqK1MnqCwoyMn3GIN056htaGCqqBMV7U5HAN6t3ExsbxuXjfGtq7c4vdiUPuI9FaMZWUlQ4pfX17lENSEUz0pwGmwqM7NKRu8cOZXBGB/Irq3irhSGmTSnAJl3xrnp7oSIEg9r7lsYAiA7SPGJ9glP4dMLNzN23gbUlh6i2NCKFiqJogf3VVjPP7F5GlCmEazMHAFBq1vc4OilqrASgfWgyWyv1M+hTQ5M9wjmEEAyKvZh15d/rXC1psOXozpNTu47Cxn20D+3hd00BTo+AMXYBU17bwE0vfcSx8uY39N6CUjISY08y0tsX5kth3yuuDO9rXPUtW/S33FR9r0Z6ec+GpnXgo727+GzvHprsNoyKwpSsrjwx7hKign3IcgcIEKDVWFpU7BCqBJsdaVBcx41JyVH85tHLSEvXz9rr17MDB/JK3ObQguFbHldKBQpKq/jHG0uorGngpiv0dbViwkM9x7XwrJdV1xMWZOLWSwbRKSWeIZ3TWLTrAO9t2E5+eRUd42O4YfhFzB7aDyEE2wsKeXzJSp/ef4EgPiSUJsVGrc07/uuaHr1IjWy9Bln3mGReGH4lc/f/wPN7V+pe81bOegYldOD9I+vZWn4Yi13BICSK8M7GDFa0j/DJ7UbzXdEarDpHidNSxnm1TU6ZxdHGPI41eGpRjo4fT07tlz7Xf7jux4AxdpY4qTEmhEgG/gG0l1JOEUL0BIZfSO74C5V5K7Z4GGJOjpRVEh8ZRnmdt/yFIgTXjejHt9kHvPpdG1/LWAqnceVvMa30sDk9YC7Dzc2j5mzq2y6Z+Kgw3s/e6RpnU1W+Obif8sYG3rtqZutuFiBAAJ90792BsPBgGuqbvVtClaDaEYrg0SdmMHZibxQdT5eTmdMHsuD7HTQ5tMYEIFXAkVkohfAKVZi/YDPXXNqfMJ1jy6HdOpIcE0FJVZ1PeZxGi5UVu/L41STtVDotPpoe7ZNIjo6gX1oKE3pkIYTg/R938sTiFagKfo8ipSq5e+AQzNh4P3snFeZG4kJCmdOnH/cPGe57oB/2VZf47DtSV8Gcda/RpDqNYQWbBEWomAyecWFBBk0OpH1oEo92v5NXcj+g0qrt+UGKiSvaT2ByymhaEmoI476sJ9hTvYWcuj2EGELoHzOKYEX4NcaMgWzIs0ZrPGNvA/PQgmABDgIfAwFj7DxnebZvBf6YsBAarVavepW/njyc9MRY7pkwjCcXrPAeKPC9cal++pxjnZuuQ+bCaXRpJwH6WUvu1yMgv7aaXZUlusbf+mNH2VVSTN/kM9fKCfDzRggR569fSlnxU63lp6K6vJbv31lD7s58YpOiufSmi+nUO+205goJDeL6Oy7mzReXerQLYNJl/Rh/6clL9ISHBWsGlwMpAIPbkRk4qoc74rOAhkYLuw4U0iElhrCQIOKimsMPjAaFp26dyn1zF1BrbvL5kLe/oJT8sko+27qbt3740dW+Me8YH23axYtzLuepJau1PcTPviWRVJrNPLt2HXcOHsSm2++mpslMVHAIRuX0haeTQ30LrBqEwGy3eHnBVKmgStXjlDcuqFmCYmBcb94Y9AR7anIw25voGZVFpMm3CpUiFHpGD8Ss1pFdvYFvCg/SLbI/cUEZVFiO6IwQdIu6uJU/YYBTpTXGWIKU8hMhxB8ApJQ2IYTvPNwA5w3+PFVhQSY++90NfLRuJ/sKSkmMiuDqYb0ZkqVt3LOH9yMsyMSbq7ZwqKyCyJBgwkNNFNbV+Zyzf0Y7th8v9n3TFoH9LueXcGRVtvC0efwQbn1VZjOqIr21yhyxaNmlJQFjLADAVnyHnUvggqrLcnjPMX5/+TNUn2hObPn69eX8+tkbmHbH+NOac+aNI4mLj+Dz9zZwJK+UpHbRXHbNIK6e0zqPUM7hUiwW7ejMX41aITUPFIpAVeCvb31HRU0DQsCw3hk8fON4OiRpCUj9O6Wy6K+38cj8hWw44LvO4YGiMg9DzEl1o5m/frUMi93NW2dHV4HfXeT67e3bSI+LISMmhqEdTs/AdXJNen/ezd2MqrNLK4rNZ9KpqiooDu9YXFAEIxM9tcmMioF+Mf71ypzYpY23D/+TnLrmE4ZD9buJMcUTLMKwS8+TkZEJNxITlNKquQOcOq0xxuqFEPE44ySFGAb4jyoMcF4wtmcnPlirrw00tndn0uJjeHj6GJ/jrxjYkysG9qTRYiXYaMRiszPkH69gtXunUKvAjkLfhpivIwUPY0tPCFbgZVW6gvl9XBtsODulQwL8vJBSZp7rNfyUvHj/fA9DDEBKyWuPfMCwqf1JaH+yWFF9Jkztx4Sp/U5rbFRkCzkHP9IWQoJdAWkSVNRohoCUsCH7CPc89Skf/v0mIkK1eNCosBDumTzcpzHWPjaKPUWlPu915EQlBOPaQ4SK9jBnkK6HQqchJpFgBIti57GVmpcwPTqGV6ZeTs9E39rBq44f4n/7tpBXXU5qRDQ3dRvAtAwt3qprdBKP97+Mv+1cjNVNY2xIQjq7a/UD6N0JNwbzZN/rMSmnH/a9rXK1hyHmpMpazsDYCSQGBVFiziHcGEffmCl0DL/otO8V4OS05l/yQeBroLMQYh2QCFxzVlcVoE24dfxglu/OpaTK05uVkRTLrJGt31xDg7S4hJAgIwvvu5kZr71PXZN76RDA6GZw6bnkXBub59GkM+1JC+r1sQDnhqjgXY5J59oKs37KdoBfLo4SSF0Al3UgpVxz7lbUthQeKmX/ljzdPrvNzuovNnP1vW1X/am4qIqN6zSjYcSoriS1i9a9LisjiS6dksg55NswciIBTPpv7pKKWhau3ct1E/u72vpltmdC3yyW7/IMxxAC7ps2kuwS33FZrhu22IukBNmyKpNOVZD86ipuWfA5q2++g1CTdxmn9w5s50+bmrMVj9fXsLnkGAerTvDgRVoM18zMAYxL6cp3x/dSb7MwJCGd/vFpzFzzEnl1+r+vSSl9uCgunckp/Yk0hepe01qyqzb47DtQu4OZPd88o/kDnBp+jTEhhIK2eY0BuqH9zR6QUvpW/wtw3pAcE8H7D8zm7ZU/snrPIRRF4ZK+Wdw8diBRYX4KtfkhLT6GPuntWJ/jeCLVO1psaZA5+tUWgbrS+Z/WBPc7gmw99Mp8jC1r8C3bEeCXhxDiDuABoAOwA01vbANwemd35yENtf4fQBpq2u4B5b+vLuezDzeiOqp7vPbSEmbOHk6HzAQ2bT6E0ahw8cXdGDmiK4oieOz+qTz410+oqGpASu9MSidTx/Xmm02+dcn2HCoC+mOzq3y7eS+LtuynzmxhYEYqhdU1VNWb6ZWWzK0TBjGqZyaRB0OYv26b7lzSx54j7BCkKFgcBcX1MrmdnGhoYGHOAa7p2dujvdFm5eltq9zv5tqnXs5ey5G6cnrFJzMmpTPdY5K4ofMQj/G/6T6Z3259H5v0jAYaHJ9B/7gMkkOiCTWceSC9zc/HuE1tXeWAAG2HX2NMSqkKIV6RUvYH9EVMApzXJEVH8MiVY3nkyrGtHmOzq+wpLEERgl7tk72ypfLKKjziK3SPIIVnf0tDTO86v4aZ8H7pKyauV2Kyj54Av1AeAAYDG6WU44QQ3dEyxC8Y0nukEp0Q6XVM6aTv6NbFEZ2Mlcv28Mn7nh4V1S75+L312E0KGLQ3+fLlexk5sgt//ctVdM5I5INX7+D7VXv4cVc+G3YewWL1NDTumTOa6y4fxNLtBzFb9JXeYyJCUVXJQ299y6pdnl7A2IhQPv7dHNKTm49iR3VJZ2inNDYdOtZyKlRnolCLTSQrIZ6ImGC2FhZqDXrhEG4cqaryattYcpRaqzMDVdKiLCTfHNnHN/l7+ZdhJTMy+vD00GkobgbqyKSuvDX8Dubn/cCe6gIiTSE0qQ3sqs5lV7XmBWwXEsO/+t9El8j2vhd3ErpGXsShev2P9W5RA0573gCnR2vSQZYLIa4WgSKAvwgW7trPhGffZNYbH3Ht6x8y8fn/sWKf58bXIdZTV8d13OgLX4aYxyXC5xx6gq9unR6kRUUzJauL/5sF+KVhllKaAYQQwVLK/Wie/gsGU5CR6x+Zrtt30Zge9GsjY+zbr7zFUZ0ods8347p1OSxduhvQsipnTB3AP35/FR//5w5uvnoYwy7K4LJxvXntydnccNVQTEYDk4f71rDadaSYm//1oZchBlBZ18hLX691vd6SV8DHG3Yxe3A/7hozhOSoCIKMBgZlpBIREewzezI0yMiOoqLmBj9llADSY2K82hR3/72ffUtV4Ysj2cw/uMWru09MGv8eeD2Lxz9CSlg4pU2eRl+xuYqHts33W9PyZAyNn0R8kHeiU7ASxvikq0973gCnR2tixn6FFjdmE0KYcep/Stl6pbsAPws2Hz7GI599h+qmxnq8qob7P/6GEVnpxISFML5bZ64d0IetRws9B/vwajlrT/pDuu92LeZxBs/6LD7u9npoageemTCZYENAyziABwVCiBjgK2CpEKISyD/Ha2pzrrj7EkIjgvn4uUUczy0mPDqUSXNGc/OfZ7TZPcpKanx36qg4L1+xl8mT+3q0JcVHctfsUbpT3HvtaA4cLWXf4eZ4L2cWZvaRYq3QuI/9ZFV2HkWVNfzmnW/ZU9A8PjU2itdvvYquKQkA/OHrJXyxQ98j1KdDCjuq3ERqEVqWp84940PDmNbF06a3qnZyqsoRqqIdySoSqXiLtWqBsBIp4aO87dzarfmosqChkpyaYhJDoog2BbO5XD+gv7SpmrVl+xib3Fu3/2SEGsK5u/OTrCj9jOzqDVhVK90j+zM++RqSQ84sW9QfUtppMH9PU9OPKEosEWEzMBpTHX02VPNiVPNSQKKETEAJuQwhvOPyLjRO+qklpfQtiBLggmLeuq0ehphTXd+GZE3uEQC+3rWfEZ06ctfIwfxvw1ZsjtiKIEWhe/skdheVuOaIDA7GKuw02FtRYNaXwebnmMC50mCDgflXXM3Q1LO3gQT4+SKlvMrx7V+FECuBaOC7c7iks8akG0Yz6YbRNNaZCQoNwmA4fS0sPTI6JVFU6H00B+jGgjU26MceHTp2gt0HComJCmV4/06YTNoGcLS4ksTocI6GBIGAukaLdqToR1jWiSolj330vYchBnC8soZ75y1g0aO3YjQo3DNqCCsPHqKywTOOLiMuhtuGDuDjfdku2QsA7GibjZsGYlpUNK9MvdwjeN+uqty57EtWHT/k/IVo8RmqRBpV/VA5CcWNWoJVo83Cn3d+xZLCPa4H1IwIb8+bO85ySKdLpCmGK1Lv4IrUO85oHl9IKbHYizAo4RiVaOz2ckpOzMJi3e26pqrmX8TH/ouIsBlYK+5EWpo9nKp5ISLoY0xx8xDi9OKcfy60RoFfV+XtQspECqBxoLhF7WQfhtD6Q0cZnZXB8t/czg85R1AUwdgumcSFh3GssopN+QWEmUyM69qJF9as539b9INoW4MChJqCqLfqb+odoqN4evylZ2yINVgtvHdwOwvz92Ox2xmb2olbewwiKTTi5IMDnPc4CoWPQvtYXSelvKAjlEMjzs4H19WzhrJpfY4reN+JBFSjt+HXv3+663ubXaWuzszTry9hzebmDMjY6DAe/+00bAJ++9yXWG3NhpAAFDuozhJKfgRa+2W2Z/OhAt2+wsoa1uw7zPjenekYF8Mnt81i7trNrM45jNGgMLlHV+4aNRi7VIkJCaG0vt4t01ILoVDsgsfGjSErPp4RaR094rwAlh3LczPE3BGaZofRKQkkUYyqa/4Qg0JBfRVzD67g+8LdHiPz6yoJ81PdLTPi/I2PLa9fwPHq52myHQEUokPGEaXYPAwxDRvllQ9jshch3AwxJ9KyCXv92xgj7v4pln3OaM15zsNu34cAQ9DEFC+YTKQAGklRERRVuwUA+3kY/SZ7P7eOGMg1Azxd5GmxMaTFNj/NPTB6BNnFJWw+dlz3KFOifwRgMhj47YjhXNGjB4oieGLNSpbk5WJVVdKiorm0cxYTO2cxMCXVa1M8VdYXHeGxjd9zuLb5KXNfZSlfHdrDF1NuJCU8cCL/c0YI8WdgJvCFo2meEOJTKeWT53BZZ4zdbqepwUJY5JlJHJwK/fqn88ifpvPGK8upKNc8OtExYdRZrFhaGGhxceFceeVAqqobmPvOGpav2UeTxab5fNy8XZXVDTz69Fckpsd4GGJOhFvVb6E6yyl5XhNsMjKqTyY/FmnhE66VuIVvHa9slsfsGBfDP6ZPAqCsrp4gg4GokGCmz3+f0rp63RhXVUqKamq5pb9+cPuSo370wRyyGUJIFJOnl6zcUs/M5fNoFN5HwBKBza5gNHhrO3aKSGZofBdKzRV8XrCc7ZUHCFJMXJzYn+mpYwgxnLsavRUNCzlU/oBbi0qNeRkmxeLjY8VOXf2H+DqGUxu/hl+6MSalvNz9tRAiDXjhrK0owDnj2kF92Hms6OQXAvVN+o4FKSVrjxxlRe4hhICJXbJ4//qZ/HA4n0937WZpTi421VmXzpE2ruAVJHtz/4v41ZDmOIqXp1xOncVCg9VCYlg4bZFPkltdzq9Xf8WBqjLd/qKGWl7atZ5/Dp98xvcKcE6ZA/RzC+J/Ck3i4mdpjJkbmpj32Id8//ZK6qsbaJ/Vjmsfms5ld038Se4/4dI+jJnQk/17joMQ9OiZSm5eCf+bt4YffzyMwaAwalRXbr99DBGRIdz10Lsczm/2uru8XUiXQVZvtlB97ISPOzqMMMUx1qrFobpKpwkw2228tmgDCmB36oK1kNs52MLzvyrnEM+tWMeB0hMIoEdKEvtOlPn9VFyWl8cfxugLZUudmLkWVyAM+seVZeZ6jEYI0gmNarIZyIpM4mhDiUuxv19MBn/rO4tCcxkP73iBamuzluTh+uNsKt/NP/vdR5BybmKtCqtf9mo7SWIqqmz0HZIivesoX2icTqRzARAo234BMmNAL/YWlvLB5h1aLK4fqYkhGR282myqyr1ffcuynOZsp3e27uCy7l15fvpULu6UQc8XX0RaHU957k+fLe5j0Kn7llNZzru7t1NQU0OnmFhu6tOfngm+FbD9YVXt3LL8Uwrq/BeTWHLsYMAY+/lTiObVNzteBwPHz91yzoy/XPUvti3d5XpdmFvMC3e/QUOtmZm/u9zPyLbDaDTQu19H1+tu3VJ4+qnrsNnsCCFcsWqLV+z2MMScuAwyx9tcSkB1Vtbw/ZHtlNER0iGX4+ZVt6mqK3Fb1fG2f7l1D3eMG0x6QiwbDh/l/z7+GrvDgJLA3qJSzZjz86lYY27y2TexYxZf5PlQgFIkihEvmQt3VLsAXdtJcEPGOAbEd+RwXQntQmPp5DiefGbffA9DzMn+2iOsKNnC5JQRvm94lrCr9TRa93u1q4BdepQm9SAoqDdYN+v2KUGnV5D958RJozuFEC8LIV5yfP0H+AE4/SCgAOc1f5o2joX33czDl47mVxcPISLYW1wwIjiI20YM8mp/f9tOD0PMycL9B/lsl7ZJJYaFa4lEvpUsABjSwdPY+3hvNjM+f58vDuxlc1EBH+3LZvpn77Eo78Cp/YAOlh3LPakhBlpQboCfPdXAHiHE20KIecBuoMq5r53jtZ0Su9bs9TDE3Pnwn19gMZ/bUDij0eCRNLAj21vjy4kjLV/LkAwSGOyg2EDYpFdmphQOT7oBlyK+wQ7ChtdGIlS0T/4WSAkLt2tGwmtrN7sMsZZr8ld5uaKxkc92t4x50pjUsQuj22fo9EjQOWZsSUxwmG57QnAEl6T0JCkkmtKmSv659xNmrnuKv2S/zw9l2T7n21Cu/3dytlFEMIrQ+1kEjT5S642GjkRGPwlCJ2FBRGKIuLNtF3ke0hrPmHulVRvwoZRy3VlaT4DzgMzEODIT4wCY2rsrzy1fxw+ObMpRndP57YSRZCZ417n7Yrdv9ey3t26jpKGWyODg5oLgoGuR9U9JYUxGhut1TVMTf1u7wvNSATZU7l/+LZuKjjE6LYNRqemEGFvnlj9UU9Gq6yamBTTLLgC+dHw5WXWO1nHGRHojGQAAIABJREFU7Frt+z1WW1HHoV35dB9y/vzNhoX5VoqXgDTikSkpcHi91GZJHOl0d7mVVHNd77BxpNsnmXMOvYe9BoumOr/tWKFOr2O81e0IVIe5W7ZwTW9vOQmDovDmJTN4d/92vsrbS7m5gcigIA7WlTZ7xPycNtzVdSQbKvazpfywqy0lNJrnB81GAvdseZW9Nc21OIsaKwBBuFHBqHgbe6JVpU3aHiGMxIddQVn9h159jRhJDr+NxsYvUdUTgCA0eCzxsU9jMKYh4j/BXvtv1KYVgEQJHoch8kEUY6ef/Of4qWmNMRYjpXzRvUEI8UDLtgAXJt3aJfL6nCux2GxIqQXK+qKq0XkK1ELsUMKBshMcqDzhkstw9ku3yyXQr10y/5482SMmbOXRQzTY3Ep3uMZLbFIyf+925u/dTnRwCH8aNo6Z3U6uu2MQ/jT8NRJCwriv78iTzhXg/EZKOf9cr6GtCI/W9560tr817N1xlB++z6apycqAEV0YPq6Hl0TG5g25fPrhRvJyS0lIjGTq9IuYftUgr2odk8b25PNvfZQkUnAp9rdEUcFmkEiDcCvaLZqrfbjdRkjvoH5f5Y6GdtayrmNCQyir81E2zd/eIOBwZSUNVithOjUpgw1G7ug1mDt6DXa13b7yE1YU5mp1L4VAEd66Y0mhEXSMjOX2brewp7qQ/dVFJIVEMiIxi3JLLdete4Zyi56kiKDRbiRS8faIDo/vo/8znCYWex15tcuos5USF9SJjMgxGHzof3WI/T31ll00WD2PbVOjf0di9H3ImD9htR1BUWIwGppDTRRTFkrcXKSjFJQQJxGpvIBojTF2M9DS8LpFpy3ABUyQ0ftP5UhFJQv3HaTRamVERkcGdmjP8b3V3offjr2tpSHm6qN569t1ooRx7/2PLnHx/GboCKZmdfXU/HFtxN7q1tVNZh5ZvZj0qBhs2Pnfnq0cqi6nQ2QMN3XvzyUds9hdXsxD6xeyv1I/aB8g1GBiZlYf7uo1lA4R+gWQA5z/CCE+kVJeK4TIRufTVUrZV2fYec2Ya4fzxsPvYLN6n6V1HdSZtG6pPsdKKcnNPoa1yUaXfh0xBXm/p1/957d8/UFzuaNFn26hZ/90/j73ZkIdGgtLFu/i3//4xnWSWFvTyCvPLyHvYAm/+8M0j/l6dEnhhmuG8t5nmzzakxOj6Nw9mdXb9IubA83eMGj2iOlFgQtPT5jLk9aCgZmpjOqWAcBVfXvyxnpv5XvpHK+63avFPaOCgwnR2Q998erFM3h25xo+zttBjcWMagNhkCiKtmIhoKyplns3fsrUDj15bsgMesc0/zv+e98CypqqfEqtqVJBlZqR56RHVCbjkgfrDzgNChu2s6zwj1jU5vi0yPIUpqQ+T1SQ99+cUYmmZ7sFVDZ+T415AwYlkviwKwkL0kRyhQgiyNTV5/1+SUaYE59/UUKI2cD1QKYQ4mu3rkigdWc8AS5YXlu/mefXrHNtgK9v3ELvlCTfUYg6m5pHnwNVakZWTkU59y7+htemTmd0h3SMiqIJzAr/lcUl8MSmFeyuLHat7VBNJWuOH+bG7hfx1eHd1Np8xdUIRqWk88qYK4kOurAFBn8hOHPrp/m96mdEXLtY7nvlTl68+3UPra/ohEge/K/v1P9ta/bznz98TNERLZg+Oj6Cmx+dxpQ5zZ7fzWsOeBhiTvZuz+eDuSu5/cHJ2Gx23pq7Qk9sn+8W7uTq64aS0SnRo/2uGy9m+KDOLFm1h9q6Jvr2TGXyuN6s+jHHrzHm4d1yvu39lUVzXKMa8TSmJFyUnsJrt17p8rjfM3ooW48dZ6vbcaUEVBOgNGuLId2yvh33ntm79ynJ6QQbjPxxwHiMBpi7T/v9KgaJTo4Siwr2MiGlK5d31LxaVZZ61pft95+GCExMHsrBusMEKSbGJA7ksvaj2yyT0qaaWV74Jw9DDKDWWsSq4ieZ3vE13XFCGIkLu4y4sMvaZB0XOv7M+/VAEZAAPOvWXgucm8jAAOcFPx47znNrvMMGs4tK/Cvpt0IMvOXxwstbNvLtrBu5re8A3tix5aSbEkj2VJboHjK8u387KPr14mKCQnjnkln0TfCu1Rbg54mU0qnTogBFbtIWocD5q5Z5EqbeMYFeI7qy+K0VlBdVknVRJpNvG0d0gr4e3rHcYv526xtYGh0PIUJQXV7HS498RExCJMMv1RyEy7/Z7vOey77Zzu0PTibnQDEV5T6O94BNG3K9jDGAPj1S6dPD04MyfmhXXnpvFdV1Zq/rB/fuyJajhVhtds3GcqTiSSG8veuOl8HBRi7qlMrWowWYbXaXtlff9GQuG9Cd6gYz4Y6EpLAgE/NuuJofco+wcM8BFh/Iwe40ulogpNBkKwSMTk/nwRGnl6H4zdE9ruNJ4fTu6/D1sd0uY6zW1qjJWUiH+0+HrpGpPNj9+tNaU2s4UreGJlW/DFapeTeVTYeJDc48a/f/peDTGJNS5qPVb7vwc0oDnBLOzMhTwVcMhzuq4vYE6jh22F1WSoPVyh9HjGVFwSFyK8v9TyJwafHodkp9Y6zKYqZjZOBI8gLlU8D9E9TuaGu7c5yfmPSeadz97M2tuvbdZxZhaXCTZJAONXsh+Oy15S5jrKbKt5ZTXbVWOsioo7LvjrOsUWsICTbx9O+u5PfPLaCqtrk0UZf0RMaN7Mb6Q8fcjiYFQjbHh8kW9WolYLbY2Hggnz7p7bh6VB9W7T/EqgOH2XW8hF3HS/jnN6u4adQAhnZJ45XVG9l5vJiwIBPT+/Tg+Sun8rtF32HxUXi7XVgEL0yfyuAO3pI+raXewyPffETpjpRQUN8sPp0SEktCcBQnmmpQpfQ6qjQKA/d1PbuO3wab/4OwRnsFsQSMsTOlNeWQhgEvo2mLBaH5PuoDhcJ/uZxo8CPA5/8U0c+wFkr8jjkUIQgyaB0FtdWuq88Mt4wBx80iTcGEm3xnfgX4WWN0L38kpbQIIX4R/9iFR8pYt1DH4+U4Zzy8r/mYrteAdLZv1D827Okoa5TVtR3tU2MpPO5dE1FRBKMu7ubV7o9+3VL58uU7Wb0ll7KKOrpmJDK4dzpvfL3BwxBzRwDYQLqdwkk3b1l2fjFj+nRi2V7Pn0WVkrd/2Mq8jVtdOmQNFisfbd3FjoIiUqMjOVypX3czxGjyaYhtLTnOGzu3sKusmITQMK7t1ofre/Tz0kocmpjOkuMOKR6przkmBFRZmvdXo2LgpsyxPLf/a6QUmhSbw0MWYQzl5YF30TXKd5xgW5AQ4vvfVBEmYoMu/EzHn4LWVJH9DzAbyAFCgTuAV87mogKc3/Rtp3/CIxBkxsQSbPR+OvZX51cRAmlwBn24f2lerj1lpQBEB4d4ZWm2tMumd+pJVJCvMiASFIlQHI4Bt6zMmVl92FleyDO7VvJs9ir2VBb7XnCAnxtlQojpzhdCiCsA33LvFxBfvr4C1e5D40pKElKadZ0umzmUmLhwr8sUg8KsO8cCIITgvt9NxhTk/R6/8bbRJLU7de9ySJCJS0f24IbLBzOkTwZCCJJjHYVxfMRmCQDVsQW4B/o7+GqLH++9U5vMbf/YX1JGUrjvOrSD0/QNnhVH87ju64/4/kgORfW1ZJ8o4f+tW8aDqxZ5XHeoppwGixVpF0g7qDZFywDVocLSQGFDswbizI4jeLjHlSSHxCARKJiYkNyfD0c8fNYNMYD2Yf1JCtHPUO8WdRmhRm+ZowCnTmuMMaSUuYBBSmmXUs4DApLkv2Bm9e9DbKh+gPtDY0ax8JabmNmnN0EmA1Joatiqj7+0IIOByV2zmo8nPYJ0tZ1yV5lmGM3o2ktrdghAOr+cRJhMPHXxJP4waKy+c85hiLkjBJgMguPmCq5b8Q5z963n1b3rmL7kLf6y9btW/DYC/Ay4G/ijEOKoEOIY8Cjwq3O8pp+EPZv9ZCsCU+Y0n97GJkTwzLw7GTSyiyvQvVO3dvzlpTn0H9bZdd2gIZ149c3buWx6f7r3bM+oMd34x7OzuOGW0W227olDuhGik+3pjlS040o9TbBGi01/jDPA36NR+yo4UaXrdA8zmbh98EDd+Z7csAqbjlW1IHcf20s1r2N+bSXXLHmHH4qd+mECKRVsFoNuIgTAt8c8xVxnpA3j89GPsuDiP/DduD/zeN/ZxAX7Nh7bmkmpT5EZMRbhOL4wilB6x8xkeNIDHtdJaafavI3Kxo3YVe9YwAC+aU1+boPDpb9DCPEvtKD+VhlxAS5MkiIimD/rGv6yZDnbj2sx0ilRkfxm9HAu7aYJTtbZLZix6/6FjU5PR0ro164d4zMzufrLD3XT1Z0iZAmhmnbSff2HsaWogC2lBR6ZUtIRS/HEqImEmYKY3a0faZHR3Lvqa6qaHLEoioqvbGmbUFlW6F3k973crQxJ7MhlHXue6q8owHmElDIPGCaEiHC89q4fcwFQX91AcFgQRjctwPAo30XEI6LDuOJ2zzqLaZ0SeXLuLdTVNGK12IlN0P/Az+iUyG8emdo2C9chPCSIGyYN5M1F+uVxXB4xHx73nunJlOw/5DlGOLIlfYwpqqlr3q8c1xiE4LlpU+iSEO91/aGqCg5V+46nWnYkj/5J7Xlj3waqLI06VwhUm4LB5G3MLS3cz13dRnm0GYRCUkjrPY+qVDlQm0uTvYkukZ0JN56+Bl2IIZoJ7Z+gwVZBo62cSFN7ggyeXtTyhtXsL/8rTXbNCDUq0XSKuZ8OUTec9n1/SbTGGLsRzfi6F/gtkAZcfTYXFeD8p0dyIp/cOIvj1TU0WK10iot1xUiYbVaW5OX6HJsRE8Pfxk0AYNZXH/sOuBcQYQomt7qcKxe8h5SSqOBgT++Z43sVyeNblvHF4d2UN9VTbm6gfWQEKNLHRuh2G8V3DNpnh3f6NMZyakr4X+56tpcfJdIUwvS0fszKHIxJ+eVp5JzPCCGC0fasDMDo9PpIKR8/h8tqM5a9/wMfPr2AgoNFhIQHM+H6Udz+5CzCo8OYcM0Qdm/Ufy9e98ClKHr6CkCEHyPudLDZ7Bh1whd8kV9cyUfLtjcnG+gg7PoG2fDu6bSLiTyl9Ukct3JIYkghkQrYhORXXy+gQ1QUtw4cwC0DBrjG1Fp816kEMDhiM9YWHfF5jaoK3QT0MnMdpeYakkKaQ7OllB5i2C2xqFYqLTVEmyLIqcvjjbx3OGHRjMVgJYhp7S/lmg5nVrs0zBhHmDHOq73OkkN26b2oNCcp2NRqDlY8QZAhkaTwS8/ovr8ETmqMSSnzHangKVLKv7XFTYUQzwCXAxYgD7hVSqkfORngvCY12juPw2KzY/VT07HeUZKkyW5jS1GB3/nrbGae3bpWv1Np3qelFFQ0NfJD0WHX5lzSqDlAesQmER8SxtriQ57HlK4IYd+UN3knK5ww1/Hy/hV8cXS7povmYG91ERvKDvHK0Nl+N80APzkL0OpTbgX8f4L+zFj01gpe/PVbrtfm+iYW/nc5eTuP8PyqvzJx1nC2rtrH2m89g/gHju3B9NvGtJyuzVm3KZd3Pt7A/pxiwsOCmDS+F3fcMJqIcF9xnRrvLfmReledTcd7TAi3mpVasZ/Y4FCmDOvG3oIy9hwrxmyzs/ZgPvJwvrf8hQTRIvDf1UeLODRnqpqDgpoanli5isrGRu4fPpwn1q3ivb07mgfrMDlTEzX1JxDrS3ax1FzFpUufZ2L7nsSFmFhatIt6WxN9Y9O5I2s8Q+KzXNfbpZ338xeyqGgt9bZGghUTYMGoWFz7Y5Nq4fOCb4g1RTMh+WKf6zldjte+52GIuXOsZl7AGGsFrcmmvBz4N1omZaYQ4iLgcSnldP8j/bIU+IOU0iaEeBr4A1ocR4ALgKiQEHolJrkC71syPE0rSWK22XSL9TYjdXXLhPN80r0kilPHTGd321dZytSMrrR0WGmnoNKH3IXWUGau4bndy5nTeTDJoVHk1JRw67r5VFr0M0pXlxxkbWkuo5PPn/qAAeggpTwrca5CiMlo1UgMwJtSyqfOxn30sNvsvPO3T5Fu4q/Ov+P9m/PY/N0Ohk0dwB/fuJ1tq/ezftEOVLvK0Ev7MuSSXj69YmdKfX0TC7/byffL95B3uBSpCBBQ32Dhy2+3s+9AEa88Mwejj1JIADtyjjf/ONLtver8MQX8auowrh/fH5PRyKVPvonZbm/WM/RhIAm7tyyG4xZuF+nvOwBv/biVOmnh7extHjI8Lee7tlsfesZrZX4u69iDF7J/0J0vNEjBRks5DU2d3yZVVpRs9xCH3VF5hAd+fJvnBtzE8ETN2Hs99zMWFzc/sDapVkBgk0bCjFa3WWFR0dKzYozVWbzDPJr7Drb5/S5EWvNu/CswBKgCkFLugDMTFZFSLpFSOiMsNwKnL94S4LzkN8NH6KpUd0tIYFpXLVU6OjiE7nEJvic5meK2Tvalr4uXOlPK3acX2hGlK7tSuJ+IaHOWW2t5/eA6rlj+Bjk1pTy+c6FPQ8zJiuL9fvsD/OSsF0K0baE+QGg1W14BpgA9gdlCiJ8swHDjom1UFFd7NjpV46Vk15p9znUycGwP7vvXbB54dg7DJvU5a4ZYZVU9dz/wDq/+dyV5h0q1Zya7BLt0GVP7c4pZt9H3hzdAZJhngpBeAY8Zo/oQGRbCsuwcKuqaQxH8aRr67HKbXPr51TTabHyYvcttMucgz6+hKWmuMbd1H0LfuBSvuYYmdeTDcTcxJjnL9YAphMRoUBECDIq+Sr9dqszNWQpAhaWaJSXrdddqlwZUKZr3NqDQXIzqK43zDAgxev98zX3t2/x+FyKteUdapZQt3vFnLPTkzm3A4jacL8B5wIROnXhz+pX0b6e9ScNMJmb37sMHV88k2M1t/8BgP2rWJ/vr1Mm89HWdbw+c8BrqNMgMjk0RoNLSwOM7FrOt4uhJFuX03J06q0p38tD2udyy6V88uec9DtQcO615AngxCtgqhDgghNglhMgWQrRFFZEhQK6U8pBDx+wj4Io2mLdVfPGi/20zPOrMi4afKvPfX8+xAu+gdtHieWnrTv/vo6nDe/jt79c5hcQYLbmguKq21etzPcO5twn/BlhLGmzWFi3C66vOLZ4swhTMR5fcwN+HTGFs+85MSO3C00MvY/642fSJS+X1kddzf8/RGA2qyxADUIRvo2lfzXFqrY3k1h7F7mFcSQxCJUixEaTYsEvF5VAUQpMYarS3fZZj+8hZOq0SEzZMsoRtBRdxsPRmaswb2/zeFwqtCeDfI4S4HjAIIboA96OVSvKLEGIZoFdb5jEp5QLHNY+hqb6872eeu4C7ADp27NiK5QY4XxibmcnYzEwsdjsmRdGNo5rSuSvPjp/CQysXe6Z5n8yecT8eEO4NbYMQ3keXW07ko7ji1KRLfBEpnHraAExI6X7K95ub+w0fH13lep1fX8Lq0l083ucWhicEsjnPkClnad5UwN1iLgCGnqV7edBYZ2b3Wm9vrzvjZ4302w9gbrSw7vtsThRXk9kthUFjup2R12ylwxuniypdgoOhof7rJl45ug8b9xxh1XZPaQ4JhIUE8eB1Y11tXdo1e9elW5a13h7iksFwVBhyFRV3D3mwO5x4OuPToqM4bqvRYmL9CFyPSPX8rAoxmpid1Z/ZWf11r+8QHtsiT0Gb3CkRJ4RTfV9iEBKDIvlPzjf0jErxGGNSVAzCcyO1qgaMit2l9bijKpuRCW37ZxobMpgusX8gt/LfSDRjNUTYMAkVu1oGQLV5NdXmtWQlvEZs2KQ2vf+FQGuMsfuAx9ACXz8AvgeePNkgKeUl/vqFELegFfCdIKXvwCEp5RvAGwCDBg1qS49cgJ8Ip4K+L67u3ou4sFDuXPwlNtXNCHI+TXtteG415Nz7VXTjRfrGtuNAXSlW3VIn+uWR9Iw7CSSHRlFmrkZxz8AUEim1z5pLUnoyIrEzp0JBQxmfHF3t1W6Tdv5z8CuGxndH0ZPrDuAXIUSUlLIGrZ7uuVrDWXmYlFLiZ9uk++DOpHRK8jvHnq1HePyet6mpbD52T++SzBNv3U6imxjsqdDUpK/v1ZJLxvj3fBkNCs/833TW7z7Cu9//SH5JJSajkRG907n+kgF0TG4WGh3dI5POyXHklVQ4inx7xPlrOI0vN00ygeaxU1W0GDGHISekQLFIRIjw8KhHBAXx7ylT+HD/Lj4/sMenMXZ55+50ifUTfqHDxPY9+fee76mw1AMSg6I6nPbOzF+BlCohJhvOULvFRVtYXCRJCQ2jwd6A4jDUvBHYVQXFoFl2Nmmj2lrBmrLvyK3bQ5ASwsDYkQyOuxiDaI1JoE9a9C0kR0yjrGE5Zut+Kurm6Vxlp6DqKWJCJwaSnFrgc4cXQrzr+PZOKeVjUsrBjq8/OQvuni6OoNdHgOlSSv8BOAF+EYzr2IktN93DsNQ0N7V+gUBopZKcX87gWvd4Ddflnq/DjCbu7DmYjy6dw7299L0EQpE+Mue92/vEtmdWxkBPQ8zt1r1i2vHc4JmnvMmsLdvt+Bm9KTSXk1dXqNsX4KR84Pj/VuBHx/+3ur0+U46jSf046eBocyGlfENKOUhKOSgx0buA9ukSFhlKvzG+PaazHvF/WtpktvL4PfM9DDGA/JwSnn30k9Ne1+ABfsKJHe8Lk1EhNeXkqu1CCEb2yWTuQzNZ/MxdfP3P2/j9nAkehhhoZZjm3jWDgZ1SPQwtJNoDmopr69D9xHMq+DvftkLbebpGxnPHoIFM7dqVB4YPZ8mttzAoNZW/jh6veb6c87sMNm2POl5f5RJ8bS0hBhMvDZlNQnAEitCPFZMoqF7nqYISs40wQwiKj0Li2liBlGAQBlJDE3n2wB9YUfo1RxvyyK3bw8fH3uB/h58943iyIEMCqZHXEeSn2pjZdogmW/4Z3edCxN/j9kAhRHvgNiFErBAizv3rDO/7HyASWCqE2CGEmHuG8wW4AIgNDeOj6dex8Ya7+c/EacybOoP119+FYkTz4RpxM8T0aN6M2odHse3aB5iQlsXOikK6xiYgFMeu7NiZhePpU2+elgaXSSg82Gs8DXbfygi5tSU02vTTu/3hyxBzovrNOA3gCynlNKFZxmOklJ2klJluX21RUG8L0EUIkekQxp4FfN0G87aK2/8xixAdiYhBk/oy9LIBOiOaWfd9NjWV9bp9OzfkUph/etWibr5hJCEh3keQElyfNlabyrpNvnUIT4d2sZG8fe+1JEQ2x8m1jOTSiwuTeMezOTlYVk5SSDgvTruM+0cMJzlCi1GLDArmgyuu5fMZ13NHv4Fa8XRnWIMC20qLuGHRJ+RV+S+w3ZJ+cWksmfhbksN8K+vb7N6nDKpU6BHZlx5R/vLqtI1uSrtLWHdiEbW2lmHgsLdmO9nVW05pzT7vJvwfQ5+sH0BKFWk/jlR/GapX/oyxucByoDueT5Rn/FQppcySUqZJKS9yfN19JvMFuLBICo9gWufujOvYiVCT6dQiwRxPh0WN1Yz65mXmrHyfG1Z+wAMbvkAooBil9mVwZlE6Y7+ajTRnH2hvkIuTs3ig1xjWlO5nQ5n7h4jnLm6TKg32UzfGRiT08tmXHBxLVuTZrz93oeIIgVh4lua2oYlhfw/sAz6RUvopiti2dB+cxYs/PM4lc0aRnJ5Ap77p3PX0HP76+e8w+JGNACgvqfHbf6JllmYryeqUxMvPzqF3z9Rmf7YCGD1Slampbf3hSm5BGdl5hTRZT34EesMo/ZgsCa7i4EDzkaTApT+mt9H8c+UaJr45jwNlZV59A9u1p6ixViuH1CLVs95q5a3sU/+YNClGv94pX3uhWbVxVQffcVihhiB+1fkWrkub4dfg2lm1qZUr9U9cmO/qDOFB/Qg2+t/TZMMXyBOXIMvGIUuHoVb+Cmnzr0n5c8fnAbGU8iXgJSHEa1LKe37CNQUI4KKgrga/kbJOhASDc6vSSh9VNjWnu1tVVf84UoBi8G3uhRuDaVDrePnAUrdWzXPWHI4iUVVBx/B4EoP1lb/z6opotFvoGplKkOL5tksPT+aK1BEsOO6ZF6MIhXu6XI4hEC92pmwTQgyWUrbNY78bUspFwKKTXniWyOydxiPz/u+Ux3Xq4VuKwGgy0DEr+bTX1KVzMn//69Vcc+tcLD5qRPbpcfIHjN2Hinhy/lJyj2teuuiIEO6YNozZl/j2+t06ZhBHT1Tz5ZbdLsPFqe0s3L7Hvc/ZpDpiytwRkF9Vxe2ff8mKO2/3in/dVlLoOZHb/NtO8ajSSf+4DJYX69v0BsVpqDWnDEmgX0wmA2P7kBWRQW7dEY8xQYqJx3o+QLfITqhS9WvsqVIvrvbUCQvqSbipF/XWlj+HJNzk++ETQDZ+jaz5vfuqoGkl0pYD8d8gFO9i9hcCrVHgDxhiAc4ZqeFRGIXAppve5NgBDaqHsr5Q8BEH5jbGx8uW1Nqa2FVZ4KE/ZmgRZybQYs9mpA3wihfbXZ3P03s/43B9CQAxpnBu7XQJV6d5xrA90HUG3aPS+LZwEyeaqsmKaM+1HcfSN6YtTtN+8QwF5ggh8oF6nDHeUvY9t8s6dwwY1ZVO3VM4tL/Iq++SqwYSE39mRaijo0K5aupFfPyVt3do+ODOdD2JsVdaWcu9z39BXWNzWEB1nZlnP1pFVHgIlw1vjperrjfz7dZ9HC+vJjM5jkcvH8O0Ad257b+fNWdFCpoFX3F72zszK0GL/3KztaRbck9xbR3LcvOY2q2rxzpDjUbvPcRxz9jgUHaXF7O/qox2oZGMSEnX1V5sya2dL2Zt6QGaVE9DNtwYjGKwIpEoLWJavyr4gcLGfPbX5iMQKI5FhRvD+X2Pu+kWqe0jilDoHnUR+2o8KzI46Rnt/3i7tVhshZite1FQUR2/RO2fQVLZ8AUdYn+PQfGu3gIg617Vn9ReAOYFEHZ9m6zxfOP0UycCBPgJqLQ0khWXwP7yMnQtJ0XSKseR9A7Wd4ZiGYWiHTXo4BRa2hRFAAAgAElEQVStcJbIU4R+wL8QUNBY7tFWYq7id9veot5N16fKWs/zBxYQaQpjUrv+buMFk1OGMDllSCt+mACnSKAWSwuEEDzx1u08++gnbF+Xg5QSo8nAxBmDuPv/tY1U2t23jiUiIoTPv9lGVXUDoaEmpkzow923nFwB/vPVuzwMMXfe+W6Lyxj7MbeA+99aQJ25OTzglcUbmHv3VUzs04Ulu5vFZZ1B/S5R2Jb/d/RrWYzSI/MSIL/SM3apuslMWb1+3B1ScsJWy7SFb7uaMiJjeX3sDKptDXyUt51Scy09YtpxQ9ZA0iOaw7B7RqfyypBb+M+BJeyoPIpAMDwxi990n0x5UyX/L3seVulpqJU2VbGoqIKoIG3HsjsWXmNrZO2JbfSIai6fNLXdtRyq20eT6nlUnB6WxYAYP7qPp0CN+QfXKgwt9m1VNlBr3kRM2ESvcVKtBvshr3ZXv2U7ImCMBQjw0/Lege38efNSLYDdgEeIliIEnaJjya1rXaCxopM16XxtU+2OWDPRfJTh2My0QH6tVUr8Gn4/Vhzm3cOr2FddQHp4EvW2Bg9DzJ0PjqzyMMYCnD0c9XUHoIm/SmCdlHLbOV7WOScuKYq/z7uD4mMVnCippkNm4hl7xNxRFMFN1w3n+quHsH33MZDQs1sKwcEnD94+eMw7RstJ3vFy7KqKXVV5aP5ClyHm/Mgvr2/gppc/4VeThiCAZXtzsava+79TXCx5VZW+ox4EqAbpFQMGkBnrKffx6YFsaq36MaLGYEFOjefedKS2kpnfv0e9qHftPRtL8/n40DZeGHYVkUHBJASHkxmZwIC4DP43/C5qrY0oQiHcqCVq1FhrvAwxJ6pUsKkCY4vko1Wlm7mz07Wu1x3CMnmgyxN8dfwd8htyMAoTg+IuZkq7mRiVk//btAbFTzal1u+jNqkIBUIAHzGFysmzcH+uBIyxAOclx2qrmg0xJ44Nsm98O76YciNvH/iRv29b7jVWqiA8Qjt8aYk19ysCl4irdJPpbk4Xdxhk+J6qsLGcV3O+82hzql63JK+uGCllQGvnJ0AI8WdgJvCFo2meEOJTKeVJ9RJ/CbRLi6Nd2pkmyOuTc6iEf764mNzDmnEVGmJi5vRB3D5npN+//aQY30ZhSLCJBev2EBxqoqJOk+ZwxYI5pmy0Wnlh4Toyk2L59NdzqDab+TH/OF9s3+13L2gfGUlBoyO5we3N3iE6iglZnvqB20u9j3idA23oe9prLE0oJoFwxalKzGoT92760GVMDohL46lBV5EWHkukKdRj/Ikm/4kVdlW49iyBtv802s2oUnVpFTbZzXx5fD45dbu115hZe2IxBuxEmSKIMMbQK3oEIYbTr+AQHToBRYSiykavPqOSSETIMN1xQgQhQ6dB42f6/aFXnvaazncCxliA85IvD+/xKemwu6KEGouZGZm9eXHXD9R5yUkIrXiyWwKXvxgypUWMmWuMl9aYQKrCLVGgeQ4hpFtwrVuP1HTSWt4/PjgSIQT59cV8UbCCPTWHiTKFMSFpMJNTRgSC9tuWOUA/pz6iEOIpYAetEK8OcPrU1pl58M+fUl3T/IHcaLbyzicbCA8PYvZVvo/krxzdhy/W7EJvC2i0WHny/WWEBjk+vpwPXjrv8cOllTzy/iJG9+3EW+t+1AweP/I4ZqvN7RxTCybrnBjHq1dMx9QieD8uJNR7Auc6/D1juf1MiiIxGDzFbbZVHOP2de/y7SW/JkjxvGfnSP91HjV9ModQrGP9PaIyPUSjFxZ95DLEnAsKEmY2VyxwtSwumsd1HX9Hl8jTiyEzKFF0iP0bRysexf0HFpjoGPd3v54zEfkw0roHbPtatD+CMF241UgCO36A8xL3TMiWqFJSY2kiLiSM/469hviQlk9wzZubJl2h6m7qzf36fRJN6VxRVIwGO0aDHSEkEcYQj6tMBjtBBlXXAwYCVefe01OHsq/mCL/Z/ixLSjZxvLGUfTVH+E/up/xz39t+1dWdVFur+bZwMW8ffp+lxStosAX0k31QiHb24SSYFuKsAdqexct3exhi7ny6YCt2u++svh4ZyTxy/XiMLSQ63OtINlpsGCycNNk6r7iCt9ZqiQQuMVgfnPDYdzSranD7DnSO9/Yczuzmo/b8yd66bslAzXqG0uPrWH0Fi45lew3tHNGeQXFdvdoBjMLuVQpJAiXmUorNmmfSLu1sqVjtMS5EsXkp9zepDXx09BkabKdfvCIhYhbdkhcQFz6TiOChJETMoXu7hcSETfY7TiixiPjPEDEvQtgcCL8HkbAYEX7Haa/l50DAMxbgvGRwUgfm7duq29cuLIIOEdEADEtOZ/2V97KqMI/KpkaSQsPZWJrPodoKOkbE0CcumYe3fI2vHVL4Ua0GgdFg91DDFo4U+ZsyR1LYWMXh+kIKGn3Ht2izKOB2bHFxYm9uzBjH73f9B7PqHXOy7sROdlQdpH9sN59z7qrazYs5r2JxG//l8a95tPuDpIcHari2oBqtxu5StD+EicBmIcRLAFLK+8/l4i5UDh3x/b44UVFHTW0jsTG+ZQpmjruIQ0UVfLxyh5t6q6fVJQCh6gu6el3o/FaluYalG6oBXffEZ9l76JIcz5AOHejhVkWhb2I7fj/kYp7evMZjd+kcE09eY5m+gSgkwi2mS7hZhy0fCl/ct4IpHXoRbPCM4xoa14MfKw66tUiMikqoQT+WrMZWwys583miz0NY1CbMarPBKZAYfBypWlQzu6p+YFiCb82wkxEefBHhwRed8jghTBAyBRFytsrKnn8EjLEA5yWT0rrSPTaR/ZXeG/r/9R6O0c1CCjIYmJTW/LQ4LjXL4/r1pUf4Mn8nLVSFOLn2vb7XrNZm5t3DazEoqs/sSnce6nEFdmnHbLcwOL4L3aPSqLbWsbfmsM8xG8qzfRpjFtXCq7lveBhi2rrqeC3vTZ7q+7j/Bf3y+NLx5WTVOVrHL4rEBH3NPYCw0CDCdaoHuFNZ28CXa3c7Ai99Xzcgsz3bCopQWykPLQDs2jdSwKD0VNISovls717d622qyuPLV4ICo9PTeXnaNCKDtbXffdFQJmV0YUHuPuqsTQxL6cjywhzycvUNUcXgndEthLchBlBiruGrozu5LnOQW1slr+V943FdqMGKyY9WIsD+2jwKG0tICUkiISiZE5YSx+/C//5VYyv33RmgTQkcUwY4LzEqCu9PnMWVmT1dcROp4VE8OXQSN3U/tTiGpwdfznNDr6JnTAqu6sDCKYkhfB5hBhuE3yNM8BeLphFlCmN66hBmdhzFjZnj6R6V5n+Ac34/x5TbKndQb9c/kjzeWEhene/U8F8iUsr5/r7O9fouVKZe0kcrFaTDlAm9CTL59wVszzmO1eYQIfVja5iCDKiq1L1G4vCCtXD+OLeBMIOR12ZNZ0hGB5/zu+uN/ZCfzx+XLvXo7xQTx28HjeT/DR/PxIwsLKoKuoFjAtWm8ECvi+kYEUuwwUS0yUfcmYOlhZ4G4rLibV6irfaTugU1aqx1CCEYl3S5q031s/8BtAvJaNXcAc6cgDEW4LwlPiSMF0Zfzvbr7mfTNf/HDzPu5oZupy4HIYTg8v/P3pnHV1Feffz7zF2z7wmBbIQ1YRdQQGRXsAqK+161Vm2rtVpra7V2se1ba9++tVq31q24Ky4ogiCisiMgeyDsgUD2fbvbPO8fN9vNnXuTACFAnu/nkw/ceWaeOQO5M2fOc87vpA3lw+l3cGXGSJ83UUGTqnXrO5IkzGxmbEJ7jlPwG5lJaLxy3j2YNf9+clGWcLKD9JIbHx8gHwWodtUEtarGHUD7SKE4hSQnRfGbBy7F3kbK4txRGdz1/fa1xpqOC/a+k5IYxdq9h1tywVr9NCvvCxAu/Jw1kyb4/ZwZRIbYuXjwQJ9oezA+37OHoprA38EpfVp/rwWtHbMxCSncO2QSX37vJ+y48lcsvugezMbJpgB42txgqlz+322nbsKtB55DSrBpVlJDvV0XJsTPYE7vmwg1hQMCl1/bAS+x1l5kRxpXPSpOPmqZUnHaE2axEmYJrlvTEYQQ/Pmc2VzUezB/37mMPZVFzTlhQsrmRUsBOKSTCHPg0u7mhU4ZKDomEXiCRs7uyLyMh7c+i6PNcuOo6P7kVO1iW8V2RsUMYVjUYB8ZgAER/dtO1YxZmMkISw98UoXiFDJ14iDGjEzn69W5VNc2MCI7hexBwSsCmxg7KJW4yFBKq+oM2w3NHDOI9XlHfDe3bbCh0dRyFlzelkpj+6WQHB3B1aOH0T8xDoAahxO3Hkj4ubE6u9Fn8UhJflUVieHG8hsXpw9i9K5NbCz2rRGxaiYeHOXrhMbYQrkkZRgLDm81nGtqL99k/aHRfXn38Ndt9hLUuS1YNQ82U9t7jkBHcFGvSYS1up9NTbyUifEXcazhMFZh47uKpXxbuhiX9N6LMsKGcEXKT0+a7piifZQzpuhRCCGYmjyQ1LBoLlv+rI/0RVu/aXP5YbyB/KZScWjSG2vWHxM0Vmu2XpbwjksB35XvJznEWMMpK7Iv/xh1P/OPLGdn5X4iLGFEWjRyqnayp8a7PPHpsWWMih7CLwbfhaXxxpgRlsbI6OFsrvC/gU9NnESUxbjNiELRHUSE27n0os53nrKYTfzmlgt56PlPcbo9zdEukxD8+qbpXH7+UEb+/B+BJ2iKkglAg/jwUF64fS5ZvRP9dq1xGou3GtqlaaRGRwcc/yJvLw6XC6RXnNqsaUzsncG9wyYwKsHfEf3RoMmsKNxLudM39WBgZCJXpvumZEyIH0L/8N7srWnb91IEzD2bEHcON6bPNbgOK2mhXu20i0NuY0riNRQ3HCbMHEWcLXDvUkXXoJwxRY+kf2Qit/WfwGv7VgbYQ1LpqsCb8tIiBiuRrYq6JBZNb26fYlQVFWIKHtFLDe3FdWkzCDHZ2V9ziCd2Pee3z3cVO1iQv5QrU1uqmu7pfxdv5b3HypI1OHQH4eYwpidO5YqUOR39JzjrEUJ8QpBsIyml+sc6Ddm29yiLVucgBEwc2Y+3H7uJ+Su2caignJSEKK6cNJzM3t6I1sDkBHYfDVy12Ss6AqxQVFWLFPDJ5l2kxEYRYfctHth2rDC4Ua2+05cMGkR8qHHU/N09W3lo1aLmz7qUOHUPUpeGjhhAengcb0++g//krmRF0V6smpmZfbK5vf/5hFl87TQJjb+NvItn9nzM10VbcEkPibZodOpwSv/2UTGWSO4fdFuHdAtDTGGkhQ1udz9F1yA6omd0ujBmzBi5YYN/41mF4ni5Z90bfFO022+7WfNgNqhQaq2abzW5EaL14qYv4eYQPr7gUWwmC9WuOjaW56BLndExg4myRvBF4WreyfuMEmc5AFHmcGrcVYZvt7HWaF4Y8z9+2x0eB9XuGqIskc2Rs7MNIcRGKeWY9vf0O25y41+vAHoBrzd+vh4olFLef5JM7BDq/uXlyLFyduQeIyoyhDHD0320xH74x7fZssc36jM2O5X/e2CuYcL/wo05PPy6b9eLpqR9qQFWf298aJ8kXr/rGqxm73wOt5sL/vVvyuobDBPUpGhZokyOjGDJrbcSavH/rrl1nfPff47COuN8svcuvpGxSYELBQJR73ZS0FBBnC2CyFYJ//VuBzWeBmKtEeytyeMPO56lppXWYKjJzqPZdzMkKnBag6Lr6ej9S0XGFD2ax0dezu2rX2ZfTeu3a4lJM35JEcLbbmRARCIH6456HSfpjYqJxsaWOgKTMPFw9lXYTBYW5H/NqwcW4NBdAJiFiQnxQ1lb5tsesdJdAwhMsnW5uXfJs8pVyqJjS5ieNBVrK6fLZrJhMwWXCOipSCm/BhBC/G+bm+EnQgjlFZ1inC43f35mMctW7WoufEmIDed3D1zKiKwUHnv+Mz9HDODbnYd5fdFGbp9znt/YJaOzeG/1Njbuz2/uK9vU4DtAXjrb8wtZvG0P0REhvLRmA9uOFlDjdCG0RgdOeBNBm6soG33FpPAw3rvuOkNHDCC3oiSgIwaw4uiBTjljbt3DM7mf82Heeuo8TizCxEW9h/Ng1mzCLXZCzDZCGntWDorI4IUxv2N50XqO1hfRyx7PtMTziLAE1nFTnF6oakpFjybGFsYbF9zJ3LRRjWr8EpMWPPFeItlfW4AuBVJKTEJi1rx/mjSJWehMScxmcuIwNpfv5oV985sdMQC39LC6NFCfatFKDU1i09xYNQ9mTefNvHd5cPPD5NUdPmnX30MIE0JkNn0QQvQF1FPqFPP86yv4YuUunwrk4rIafvnnDykqreaL9bkBj53/5eaAY/2SvUuWOq0cMQj6dHt7/RbufOtD1h48TK3T5fW5dNA83oR9pDcdgcbesn1jonl8xgySIwJrp4WYzd5UhlY/vuOdi1w/ufMT3jiwkjqPN5/NJT0szP+OX373puH+4eZQZveewl39ruGyPtOUI3aGoZwxRY8n1GyjoKEMs0nHbNLRtOCSFaLV34xEX4WAlcXbKGwo59OjKwxmkAFaJzWNet/xLZrbb+5yVwVP73m+Q+2SFM3cD3wlhPhKCPE1sBz4WTfb1KNwOFx8usy/vQ9ATZ2DtxZ8iztIe6TKmgb+89k6rv/T61zz+Dye/mgl5dXeJblJWX1bIlitvy9BviI5hcWGw6Ix9VMgEFIgPKBLOFBewY8XfMJXBwILNX9+cA+a1EAXjT8tDpkmBJdm+OZjNXhcLMnfxUeHtnKszrcBeImjmgVH2nYgkWhCZ0NZLssLtqM4u1DLlAoFUNBQ4fM5cLu7VpWUjW6TETo6Sws2sq/mcKtYV8vzIbAkRuM5gjhsBQ2F7KrOJSsycLskRQtSysVCiAFA09Nwl5QG2c6Kk8r2nfl8szoXXdcZOKAXdfWBKxbLA/SwbEICzy5Y3fx5b34JSzbs5pVfXMcFWX2ZMCidVbmHmsebswcCfIfqdHdLg/G26LSMtXLw3LrO31asZEpff33ABfty+J9vv245Od6UBim9OaW/GD2J1IiWCszP83N4dMOnVLoaAG+F6LWZo3ls5Cw0IcitOoZbelqup/k6BCZN59Ft87i0dAwPZV/ZoeR8xemPcsYUCiAzPJEjdWXNnyUCXTY5Xi2SFWZN93Gigi1nri3ZRomzzHefxvJ8XQq/5rxNWDQPWjutXSqcFUHHFX6MBjLw3vNGND4o/9u9Jp2dSCn5y98/Y/GyHT7bLRYNl8n4LSS7Xy/2lpSRm2dcGVnf1L+oFfklVby2ZAM/v3oyT/9gDo+9s5RPN+9qHhce4x6UEwaks+LIIQJh0gRudO+SZxs/J6e4mMKaGpLaaIy9tKMxBbGNvERTsU/v0Ba5mf3VJTyw9gNcrZT0PVLy5r4NpIZG84NB44m1hrWao/lvSMCta5g1nU+PbiA5JJZbM6cHvJaO4NQb2FW1CafuoH/4UKKtCe0fFAQp3ZTVr8DpKSbcOpQIW/YJzddTUC61QgHckHF+s55Ya8yajknzYNY8WEy633Mk2Grh/lr/G36TnplHQp+QJN8xBDenX8aY2OHBZcdBNQPvBEKIecDfgInA2MafTldnKjrGvLfX+DliALpLR3j89w8PszFrcja/v+tiosLtfuMxsaFgNv5CLN+8FwCr2cwfr5tJalxU85gAhNvrlLVuh7Rm3yGi7f7nAe9y4txR2UgLhk9HgbcXblv2VXh7OAZ6OXtm65rmv7+9f5OPI9aaefu+BWBwVB/CzbYA8wl06R344PCaE0pZ2FKxij/tvJM38/7O+0f+xRO7fsJH+f/2a7nUUaocW1h3ZBrbi+4it/RRNh27nK0Ft+PWg3cNcTnWUFt+PzWl36eh+h/onsByJWcrKjKmUABj4zL53fAr+ceuRZQ7vS1Hoi2h1MvqgH5RsCa7keYQdCr8Uli8xwhmJE7gnoHXsal8J9srcwkx2ZmUMIbkkETgInZU7uKlA69R7PC/KZ0TM5LeIUqUsROMAbKlSrTrEtas2cPSz7dTXdNAdnYfXv9gfcB9I+02qnWXt5ckkBQfwe8fmE1kRAiRESF8+OQPWLhyB1v3HiU6IpTrLhrFI68spvig8TJm6/9Rs0njhTuu4ME3FrLzSJHvjq2WG6WEqqoGLCEarjZ5aj+ZNI7z+6fx7g7jnKyRycnEhPj3k0yJiCKnPLADsaeilPyaSj4+tJ35B4zV9gGO1lVy37p3mdF7UHNHECO8ItOSUmc1Tt2NzdR5WZvChsO8k/dPdFo8ZInO2tLPibP24oKE2UGO9sej17G98E5cernP9vKGlewp/R1ZCX8zPK6+6q84ap5u/ux2fImj9jXC497BZBloeMzZiHLGFIpGLu0zipnJw9hcnocARsSk8dDmV1lfusdwf7Nm/PYYb41AF1W429xLNbyVlkLA2rK1JB4O59rU2YyJHeo3x5Cowfxp2G957cDrrC37Fo/0YBImxsWdy60ZN57opfY0tuPVGTvW3YacbTz1j89Z8HFLZfCmjQe9LoRNwyjp0Wo28fbfb2HnngKiIuycMzQNUyudsfBQG9dedA7XXtSiPD9peCbbDxYYnn/KiEyfz2nx0bx17/WM/+2z1DpdXqmKVhGu5kplHZLDIsivqcLT6BjaTCaiQ+yM6t2bm0aM4PUtW/wi1DuLi1iTl8f4NN/I9C1Zo3h49RLjfyTAbjZz69dvsb+6DKHpBG6DKVmcn8PSYzuJsLsDztdEL3vMcTliAGtLl/g4Yq1ZU7qo085YUe1CP0esieLaRfSL/TVWk283Eo8rx8cRa0LqJdRXPkZ4/NudsuFMRjljCkUrLJqZsXEtN/gHBl3OPRtfoMRR5bOfSfjLX9g1C3NTLuCY4xCbykv85paNOR8CcEkX848sYkH+YvqFpzK7z0zOjfVtfRJisnN3/zu43nUNpY5S4m3xRFoCl9YrAhIP7BRCrAeaE/eVAv+JsW3bYR9HrAkBaC4d3ea/nJfSJ4beSdH0TgrcTqgtl5w3mLeWbaK8usHnJL1iI7h15lgAahocvPzlBhZ9t5vqegd1DS5vEn5TNAx8HCtdg7wq3wpGh8fD44uX0zcuhh+ddy5vbtuK3iaY2uBx89NFC7ksO4vMmFguGziYMKuVGwaPZF3BYT4+kGN4Df1io9hV7VX5l7pAGlRhAwghvRXdQuL0SCwBigyaiogGRyaztzqf/hF9jHcMQpkzcNeBMmeRj8B1R2hwHwk4JnHhcBf4OWPO+gUBj3E7V6F7itFMJ5bDdqagnDGFIgipYfHMG38/C/M3sKl8H+tKdyKEbljp+OTIu8iKSuPKVfcFnE+XolU1Jrikzt6a/fxf7nPclnEjF/Wa4ndMlCVS9Zs8MX7X3QacjXy13NjxAG/kyahk+Por/YVbW7M/v5TXPl3PxpzDhNgtTB87kK+2H6CiusEnSGU1mXj8tpkkRIfT4HRz+7Pvk9NqaVKAV1qiSfy1TSJ/IEFYgHnrNzN+UJqfIyaFRJqgxFnHS5u9shN/X7uS1y67kiEJSTw1ZTYpEVH8a+tan+PSIqKp1VsX7wp0XUPzKQbyahxaLS0veR5dwyQ8flE0IXSE8MpcrCrZyurSrZwfP4TfDLm5U1GyBFtvdlcb6x3GW3t3yhEDCLX4V5k224wVu9nfYZSynSradsbPJlQCv0LRDpGWUK7PmMSTo27jouQRho7Y8KhMhkb3xSM97dRBtsXb4FcT8Ebeu7haicMqTg6NSvy7gIjGn5wmdX7F8eNwBF5Ga2zd2oKEUcPTmHBev4DH7D5UxO1/eJNFq3MoKq/h0LFyXl6wjn0HivwqZZxuD+9/5c29+mTjTh9HrLUNmqvVBz8DjTlUVoHD7XttEtni1LWitL6eexYvbE6iN2saLR3KvT951eVUORt8D5QC3aPh8Wh4PN6qHrO5bYGQwOkx4XRrDI5M4dI+53Bb5iTCzBKrye1T2b2qZAf/2f9Z4IsyYFzcTMzC2HmbmHBJp+YCSAi9GKspyXCsV/hcLKYov+0W28SA82mmvmim1E7bcaainDGFohP8YvC1XJx8LhbhfbXWEFyQMIzHh98GeLWIguH/stkqSqa72Fqxs8O2lDqK2Fm1kaP1Bzt8TE9ECHENsB64GrgGWCeEuKp7rTrzGTMmcCQkJNTqjaxI70Nm+uTB/M/vrgg633Pvr/QuL7ZByMZIWxu+2brf++fOwEKsAQUDg7wxpcdGMykjw3fX5l5L/vsfrCjn26P5FNfX8uy2NS1RuFY/NQ6jlywB0vsy5n0hM5ShRZcaoVo403sNYXnRFhy6xK1rzRWVTSw6uh6n3uJEVrqqeO/wR/xh5xM8sespVpas9amSjLclc1P6g0SYW5aMzcLClIS5jIubGfgfKACaZmN40suEWga03kpi2KX0i33E8BizbRom67mG122PfLDT0bkzGbVMqVB0ApvJwkNZ13Jnv0s4Wl9Kgj2aBFvLG1+o2U4vezwFDf45Y6JRzLU1rRX8hYBjDQXAiKA2NHjqeefwc2yvXN9ccZUeOoAb0+8jNoBGkFt3cqh2CzoeUkOHYTf1qFYpjwBjpZRFAEKIBOAL4P1utaob2bFhP/t25BObGMl504dgsQZ/FDTUOykqqCQmLpyISG814QWTBpGV3Zucnb79JE0mjd/+di6JvaIoLK4iPTWOpMTgy+xuj87abYG1v3yEWJvO07h+ZzF1PqYgPI19KA24+dyRDE5I4PKsLD7MyWl0qESzOr9s8jBb+Qll9fUczq/AFeBlzOmUxETYqXK1iZAhEQEKgVpT1FDBQ5tfbbIeicCte3NXm/ro1noaqHHVEWuLpKihmN/vfIIKV0te3NbK7Wws38y9/e9EaxSKHRw5ml9lPc++mu04dQd9w7IIMx9/SkSYdQBj+yykqmEzTk8R4dZs7JbA/TiF0AiPnUdD9d9x1r+H1MsxWUZhj7gHi/3C47bjTEQ5YwrFcRBtDSfaGm44dmP6pfzv7ldp/XCSwtEAACAASURBVBotkJjaSGEIdExtXvn7hqW3e+73Dr/Atsp1PtsO1e3hpf1/4eeDnmy+0Taxo/IrlhY8T73HW4RgEXYmJtzAuPgeExzSmhyxRko5wVUBIcTVeHPRsoBzpZRnROPxqvJafv/Dl9i5oSWaFJMQwW+ev52s0Rl++3vcHl751zIWfrCBulonFouJSRcO4ScPfY+wcDt/ffI6Xp+3mqVLtlFd3cCw4anceNMERo70/h6np8V1yC4hQGjCK8DXQaad0x+AGcMHsHTrXsN9zGYNj9T9omNCAm7A3PItDbVa+Pm0iZyf6bX9N1OmsGjvHho8vkuWAq+yftOcmhAMTUxiQ3GwnrEC3SUYHZdKTmUBdW5no0Cs9C5Z6hIphfezAYWOYsPfWI/U0KQ3zyzGEk5UYz/Ktw/P93HEmlhftpHvKrYyOmZk8zaTMDMwYqTfvidCpL3j8wktlJCoRwmJerTTRQNnE8oZUyg6gVN3kVdbQJg5hOSQeMN9JiWMwa17eCvvM4ocpTQrjAnpbSyueaul2uaeRZojyIoMrqtT4Sxha2VTgrBsXj0BKHQcYXf1FrIiRzXvf7Q+l0/y/4ZspXrpkg0sL3qZKGsSWZEXdOr6z1AWCyE+B95q/HwtsOgE59wOXAG8cILznFL++fC7Po4YQHlxNb/74X94bdVj2EOsPmPP/e9iPnnv2+bPLpeHZZ9tpaSoir8+fyuhoTbuvGsqd9419YTsMmkak0ZlsnyDsVPVNoqVGB3OXbPHA3DRiIEs2JDDql0HffeJDOOlH1+NSYNLn5mH0+Mr4yB0ePKyWZjNJoQQnJ+ZRrjN1jz+aW6unyPWYhDNS6CXDcwiJTKScFsmdpM5wDGSSoeD9cfy+cO5F7K5Mo9P83Y0fncFSbZIHh01g7/lfEZRQ7XPkQMjEznqOBwgzU00d/O4IvUCTJoJj/SwoTxwY/V1pRt9nLHTiZ7qiIFyxhSKDvPBkS95L28pVW6vKOzgyAzuG3A9aWH+AqzTks5jSuJYlhas4uUDbyPx3qAlAqSGSdN9RB2tmpV7B9zlF9VqS7HjGBIdrVEoo+neJSV40Chy5JNFizO2sWyBjyPWmg2lH/cIZ0xK+QshxBV4FfgBXpRSfniCc+bAmfXwKC2sZM0S42bdVWW1rPh0Mxde3ZK/U1FWw+KPjKvttmw4yI4teQwZcfI6Qfz46gvYvDuf8mrfCroJwzMYN7IvSzfm4nC5GZedzsXnDebNr7+jrKaeGSMH8PQP5vDx+p0s+m439U4X4wamccPEkcRHeiNFK391Jw9/sITVe/NweTz0T4zjlxdPYlxmYPuPVPlHlpoQCGxmE9cNHcavzp8EQLQthJ+OmMBfN30T9Dpf3Lmeb+bezX1DJrO5NJ9oawjnJ2Vi1jTGJqTz1oF1rC7ei00zM7PPMBLtIfxm2+sB5zNrJq5OPZ8b0qcB3nZUwRT03bJ9/TLFqUc5YwpFB/js6Epe2v+Rz7ZdVQd5eOszPD/mESIsoX7HaEIjzhZBlNVMrcfbJFkgmBg/niv6XMqXRSsodpTQO6QXUxMvINYa064dMdYEb+5Zm+UMIcAkdUI0X3XwUkdg7Z9SZ+CxswkhRF/gMynlB42fQ4QQGVLKg91r2aml5FhFs/K9EUX5ZT6f9+UW4nIZi4IC5O48GtQZ27w1jwOHSkhMiOC8sf0wt5PblZYUzXO/upoFK3awKecwoSFWZo4bzOxJQzGbNK6b7n3JePqTVVz553nNxy1cn0NidDjv/uomrho/zGdOp9vN0fJqosNCeOaGzsnK9YuNNdwukWhmQWJEOJsKj/H2jm3cMGQ4FpPJTw6jBdEo9QFHair58MA25vYdRnq47znibOHcM3g69wxu6TdZ73ESZrZR6/bvbW/VzMwb9wB9QluWg82amezIweyoMpYeGRHtLzKt6H6UM6ZQtIOUkvePLDMcq3BVs6xwHZen+C/T5Ncf4+m9L/i8iUokK0pWMyA8k+vSgleXSSkpdOSjoZFo7w1ArDURq2bCY/B2KwRUuX0fqNHWZI415BrOH23pFfT8ZxHvARNaffY0bhsb7CAhxBd4lfvb8oiU8uOOnFgIcSdwJ0BaWvf2E01Oj8diNeNyGkdGtm88yBvPfMFFV44hITma6NjgRR7RMcbjZeW1/Pp389mV26Kan5QYyR8fm8uAfsbSB+8t+Y43PttIQUkV4aE2Lp00hLuvPh+7zUJxRQ1Lvs2luq6B8DAbL33u326pqKKGu5/5gLd/5e1OIaXkhS/XM2/VJirqGjBrGtOH9OPRy6YRG+7/4mTEpQMH8b+rV1FUW9u8TSKRZvAIyaGqCg5VwZaiAr7OO8g9Y8/jo707A1dwtprlwbWf8u9d6/jv1OtJDDHOPW0ixGTlngGX8tecD/xaJP14wPd8HLEmrkm9nD/l7MOpO322Z4alMz7OqHpR0d0oZ0yhaIcadx2FDaUBxxce+4bz4oY09pVs4YvCrwIuCSwuWMb0pMkB59xasZ4FR1+n1OnNO0+2p3JFn1vpHZJu6Ig1UeLwrWwbHXMJOVXfYFSTPzq2c+1OzmDMUsrmp5KU0imEsAY7oHG/GSd6Yinli8CLAGPGjOnW3piRMWHMuHIsi95aYzi+ee1+Nq87wDvPL+fhf9zA+BlD6D84mb27/LtIRUSFMGHKYMN5/vzkQh9HDKCwqIpf/+4D3nrlTsxm37LIVz9ex/PvrWr+XFPn4O3FmziYX8rkCQP5yxtf4m7sIamb8auqbGLXkSIKK2pIig7n2S/W8uyyFvFVt67z+bY95JVW8u49N6AZiQW2IcRi4bW5V3LfooXkljZ+/w20xgC+PLSfL/P3NibZe3XDMEljp6xRyiK3sphfrlvIK1OubdeWOSnnkhIax/uHV5NXW0yf0DiuTB3PuXHGOab9wzN5LPshPs7/jJ1Vu7Cb7EyIO485vS/Gqlkod5ZS5Cgg1hpPgs3YQVacWpQzplC0g91kI8Rko97jv0wAUNBQzENb/8YTwx+kdyuHzCtTYUywsTUlX/LukRfb7H+YF/c/wc8GPk6oKYI6T7XhsbHWlvPr0kOMNZkZSXfyddGruKTXfg0z4+KvZFj0dMM5zkKKhRBzpJQLAIQQlwH+2iM9gLt/Oxenw8XyjzehNzXJFgLM5mYRPJfTzZO/eIfXVzzCLx+/god/8l9Kilp+30JCrTzyP1djs/sLhh45Ws6G7w4anru4pJrV6/Yx6fwWB6K+wcXrC40LUdduO8Sq3Dz01r0l2/GhCsuriQy1MW/Vd4bjOUeLWJF7gOjwENYdPEyo1cqs7AHEhxtH+QbFx7P45u+z+dgx8quruGfpp4FPrgvQGsNiEm/81dzW/5YIU8u2b47t41hdFcmh/nISpY4aXtv/DcsLdyKRTEoczM8GzSHR7i+eakTfsHR+NvBHPtsaPPW8vP+fbK5okcXJihzOzel3E2Hp2LyKrkE5YwpFO1g0M9MSx7Lw2EqDUYlJSGrctbx/ZDE/HXBL80iSLZFtGIu4JtmN9cB2V2/l3SP/NhxzSScrSj7nvLgLWV70gd+4WVgYG+sN5mwonc+GsvnUuEsxCxvZkRfQO3Q4mjCRGXYO4RbjfJizlLuBN4QQ/8L7mDwC3BL8kOAIIeYCTwMJwEIhxGYpZeeVMk8xVruFB/9+I9//xSWsXrKN5x9fgFHX6vpaB6uXbmfG3NG8/MFP+Xrpdg7uKyKxVxTTLh5OZJTxUl9hYZXftmbXQ8BHn29m9eYDDMpM5KLJ2ew7UkpNnfFLDuCVumgVxRIysF6rAFITojlQXEZ1Q+A5/7Loaw5UVTR/fuLzr3nk4qlcN2Z4wGNGJiczIK5jMh3NtKq4BAlmHaH5Cj9LoKi+xs8ZK3fUctua58mvb2m8/c6htXxVmMNr4+8mwX58WmCvH3qBLRXf+mzLqdrKv/f/Hw8M+t1xzak4OShnTKHoALf2ncOhumNsr9zXaqvXEWu6uW4s2+FzzPSkySwvXoFH+idBX5g0zfA8H+b/l2Dy4EfqDnDVwMcpdxaxuaLFObSbwrgm5R5irAmsLXmTlcWvNY+5pYOdVV9Q6TrG9Rl/b/9izzKklPuAcUKI8MbPNSdhzg+BE6rI7E4SkqNJG5Bs6Ig1UV1ZB4DNbuGi2aMC7tea1JRYNK1RNwsal/W8XxAJbNh8CGkSLAT++/46HvxxO8KebfXBmsRaDSJk5/TvQ0x4CA3u4NWCB8sqfJ58Ll3n9wuXMTIlmcG9/F+S9peV8ea2rRysqKB3SAT5ddXGy49a2++tYFhcEoPi4pl/aIthY/AQk4W+Ef4vRm8dWu3jiDVR2FDJvAMreSDre0Gv0YhiRyFbK4yjkAdq97C/JpfM8ODSOoquQ7VDUig6QKjZzhMj7mNARDKa8Iq1WjS9Wf0avCXmrUkLTeFH/W4n1NRS4aihMavXdGYaOGPFjmMUNgSvcIwwR2ESZq5L+xk/H/gUV/S5ixvSHuDXWS+SHTUWl97At6XzDY/Nr99BXm1g/aGzFSFEkhDiJeA9KWWNECJbCPGD7raru+k/pLfhUmMTQ0YHbnfUFiklBw+WUF/nYPLEQX6OGDQK2UugsaKzpKyGN+evZ2B6osGMYNIEepv8MCFBuPF7XxnYJ55//dhbEJMcHcH4/oGLJYyahEvgvU3b/bYv3beX770xj5e/28SXB/ZzrKoGzS3wU4sRxvlhj4+/kCfGX8zAKGNNwhv6jyLSavfbvqJoV0D7vwkyFoyC+iN+BQCtOVofTLRW0dWoyJhC0Qlm9TqfF/a/Yzg2Ps4/ejA+7lxGRY9ga+V2HB4H2ZGDibN534Tz6/LYXLEej3STHTWSyKBtSLytlHqHJFHUcJhEeyoJ9j4k2Pv47FXiOIRDDxz4OVK3nbSw01PwsQt5FXgFb1skgFzgHeCl7jLodCAiKpTZN03g/f/490wfO3kwA4cFbmPTmrVr9/Lcs8s4csRbydunTwzJvSI5VmKc1yh0kEKCEGzNyecvj87l8f8soaKVvphJEzxwy1ReWLSO0qo6n+M1HX5xxRSkGUqr6pg1ehD9e/s6O7+/YgY/+M98Dpe1aIVZTBoNJn81/iaKqn2/Nw63m18tXeInFgtgx0xSZBjhVhs7ygv82iOB18FbcnAfIxN689rU6/nVuoWsKNiPxBsRu6H/KH450jhCHkxvUDtObbtoa/Bl1ph2xhVdS7c6Y0KInwN/AxKklD0yoVZxZjEtaTwrSjaws2qfz/ZkewJXpRinDNlNNs6NHd382ak7eePQi2wqb6lqW1r4CcOjxpBo60ORI58mdX0vErvmxiQk60oXsK50AYMixnBV6v3YTb65O3ZT8DJ5uymio5d6NhEvpXxXCPEwgJTSLYQILKDVg7jtwVmERthZ8N9VVJTWEBJmZcblo7n9oY4tg+3KOcpvH5uP290SKsrPL0faTWAKpBnvG9iKjw7jnb/eyidfbyc3r5jE2HBmTxpKeu9YRmWn8sf/LmXLPm9FZ1S4ndsuPrdZcywQfWKjWPDALXy+bQ87jhSSEBnGnFFZ3PLf9zlQ6r/8BzAoydehW5l3iPKGtr0kvTg9Hh47fxpjU/ow7LV/Nl9XE03Xd7i6goPVZbyUs55jtdUMiU7m/F4Z/DDrPGLtgSU2piVls6vqqOHY9F5DAh4XjNTQDNJCM8mr2+83FmtNICsycM6couvpNmdMCJEKXATkdZcNCkVnsWoWfjvkXr4sXMOq0u9w625GxwxhVvIkws3t6xctKVjAkoKPcOj+CcZbKzcwIW4KZc6jaLgbV3gkZnS/fJPd1Rv4OP85rk37uc/2GGsfeodkcbTeX/DRJCwMjgwsp3EWUyuEiKPxGSmEGAcEllfvQWiaxvU/msbVd0ymsqyWiOgQrLbAS5dtee+99T6OWBO6RwZ0xlo7YpHhdjJS4rDZLNx0qb/sW4jNwvRzBjBqQB+y0pOYNCITq6Vjjy2r2czsUVnMHpXVvO0HE8bw6CdL/faNtNu4ZrSvYGyt0xV0/jqXk3CLlaSwcApra3wXABsvPcJmZc6iV6l2tXzft5UVsK+qlOcnXRkwynVt+ni+KNhObrVv1XXfsARuyphoeExHuL3vT3lu718pbCWBE2OJ487MB9rt/qHoWrozMvZ/wENAh8QTFYrTBatmYVbyJGYlT+rUcV8VLeaTo8ZLnE3kVm/BLNzNN3YBhom/ADsrV1Pluo3INpWRM5Mf4N28X1LbSgBWw8TM5AcINUd3yuazhAeABUA/IcQqvBWQPaZLejD25xzl2KFSUjITSB/YeRHg3Fx/DTIAza3jsQZ4uLf6pb7+8rHYAjh/Ly5Yw38+Weujaj930jB+ffOM425DddU5Q6lqcPDCinVUNlZcDkyM549zLiQxwjeqPC41BbOm4db9nU2zpnGoupx/fLuKicnpzN+7w2+ZMtRsYV9dsY8j1sTSI3v4+ug+pvbpb2hnuMXOv8/7IfMPr2d54U50KZmcOJhr0scRYQkxPKYjxNkS+HX2E+RUbaWgIZ84ayLDokdhEipjqbvplv+BRp2ffCnlljOpt5tCcbzoUmdZ4cJ296v3lDZmOXsRQRJudXTKnQV+zlicLY3bM//DzsplFDXsI8wSy9Coi4i2+vfQ7AlIKTcJISYDg/A+MndLKYOHPc5ySgsr+Z97X2dHq6bho84fwC+fuomodpT3WxMbG87RoxV+24WEjMRojlXU4Gil+C80gS4kCXHhXH/ZWK6ePdrvWICVW/fz4gJ/cdoPv9nGoLRErpoyosM2tuX2CaO5YewIcgqKCLNZGZhonFyfGBbOjcOG89oW/6IXXdP52/oWodq4kFAq9Xrc0qtnEWWzM6vvAN47sDlgmdziw7kBnTHwOmTfz5zE9zM799LXHprQGBI1kiFRPS539LSmy5yxYK1EgF/jXaLsyDynTTsRheJ4qXFXUeEqa3c/Tfi29Q4m2S7QiLEaq2fbTGGMiu1cL76zDSHEWOCwlLKgMU9sNHAlcEgI8TspZfv/IWcpf7jrVXK3+lbPfbdqD0/c9zp/nndXh+e5+OIRbN9uXAF807XjGDdhAF+vyqWmtoERQ1PJTI+nutZBTFQopiC9Kj/8xrihedPYiThjAHaLmVGpvdvdr7DWN6lfIpGaRG8TQyirr2daeiaJUaG8v3c7Ve4G3t2zDTB5Kw5M0i/CLQP2sex6Curz0dHpZe/jszyZU7mSdaUfUuzII8qSwDkx32N07CXHHYlUdJwuc8YCtRIRQgwD+gJNUbEUYJMQ4lwppZ8s+enUTkShOF7splCsmg2nQa5YExoa0dYYypwtXwOJQEr/GzlAduR5RFqCV0A5PNV8V/oqe6uX4tLrSA4Zxai475MU0iOaBb8AzAAQQkwC/gLcC4zEe0/pkUuVOzce9HPEmvhu1R4O7j5GxqCORVFnzhrGzp35LFzoGz2aPXsUMy4cihCCS2f6JoYHWpZsTXFF4IrgonLjKs2TzYqDB1m8d6/vRo2AT82v8g6g2zz+1Zq6Buh+avwzUgacLFM7zO7qHbx/+DUKGvIBiLcmclmfGxgZM5ZvSxewpKCl80exI4/PC56n2HGIi3v/5JTb2tM45Rl7UsptUspEKWWGlDIDrxr2OUaOmEJxtmDVrIyJmRBwPNYaz+2Z93FB/CVtRgRuNNq+RPcPH8llKcFvkG7dwWdH7mN7xbs0eMrxSAdH6tby2ZH7KKjbcpxXckZhahX9uhZ4UUo5X0r5GyDw+tBZzuF9RUHHj+wv7vBcLpeH2NgwoqNCEUB8XAQ/+vF0fnb/rBOKpgxIMe5Q0d7YyWJrQQF3fuyfzixFkLQBGaQ9gC58vsOTkvsyPcgSZVdwrP4IL+x9stkRAyhxFvHKgX+yq2oL3xS9YXjcpvLFlDmMKzsVJw+VtadQnCIuT7mBgoaj7K/d3bzNJExcmnwN05O8SwG61Mmr28N3Fd807yPQmBB/GYMjR1HlKqWXPYNeIRntnm9f9ReUOvb4bfdIJxtK/8OloU+flOs6jTEJIcxSSjcwncZ0h0Z67L2vV2rwVli90jquN/W7xz5g3boWmZeSkmqe+9cyIsLtzJx1/FIJ100fxWdrc3C5fRVIhICbZ4457nk7ym+XLcPh8fiFK4QUQYRTAzQGbyTGGkqf8Ajm9h3KzQNHYwrS/aArWF60CJdBqqSOzqJj82nQawMcKdlXs5FYW/vLuorjp9tvSI3RMYXirCfEFMr9gx5jd9V29tXuJtQUxuiY8T4NejWhcW3aT5mYcCm7qjaiCRNDI8/zE3ftCPl16wOOFdRvxq07MGu247qWM4S3gK+FECVAPbACQAjRnx4sbTF8XD8yBvbiYK7/YkTWOen0H9Kx37WtW/J8HLHWvPLyN8y4cGjQvLBgDEhN4G8/mcNf3/yS/GLvf1V8VBj3XHkB44dmHNecHaHa4eCNTVvYerTQu0Hg62DptOo32QYtuDM2ODKRt2Zdf9Js7SyH6w4GHCtsKCAiiDdg1qwn3yCFD93ujCkUPY1BkUMZFBk4Z8sj3Ryo+ZacyiXUusvJq1nD+ISrGBhxXqfOYxKBb6AaZjRh0BfmLEJK+SchxDIgGVgiWzKmNby5Yz0SIQSPvXgbv7/zZQ7lFjZv7z+0Dw8/fXOH59m48UDAseLiavLySunb9/iXFM8f1pcP/3Q7u/OKcHs8ZKUnYTZ33e/sN/sPcu/HC6l1OhEIBKC7pfcp2ZEVV13g9daMdzaSyDiVRFqivK8kBkRb4wjVnFS7/bXXTcLMwIhxXWydQjljCsVpxgeH/0Ju9drmz0fqc3gv73Fm97mf4dHTOzxPZsR09lQtNhzLiJiM1gO0haSUaw225XaHLacTyWlxPLfoQbau3cexvFJS+iYw9NzMTs1htQZPxLdZT/z3S9MEWRnGFcMnk6qGBu79+FM/oVdNF+gu2fKkNBHEMRPgEWBqHTqTzUMX9M442WZ3ivFxU9lZZZwrOiF+Ksn2GOYf/hMe6dtofXrS7YSZowyPU5w8lOSuQnEacaQux8cRa81Xhf9Flx3v4pMSeh79I/wVZMLMiYyN77h8geLsRAjBiPH9mXXteZ12xACmTB0cUJB40KBe9O4Tc4IWnjo+zdkdUHFfSG8/TSGhf2zwfDvcrZ2wFkcMID6k4/ptXcHImLFMTbzYb/vY2IlMjJ/BgIix/CDzKc6JuZjU0GyGRE3m5oy/MDauZ0vknCrO/ldjheIMYn/NpoBj1e5Sih2HSLJ37MEphGByr0dJD7+gWdqid8goBkfPwW7qkUr8ipNInz6x3Hb7JF5+6Ruf7eHhdu67f1Y3WXV8FNUESl4HgSAxPIwbRg7nvPRUrv7wrcATSQEuwCBDoKQh8DlOFVek3MSEuKlsrliPjs6wqHNIDe3bPJ5gT1cyFt2EcsYUitMIc5A8r46Mt0UIQd+IKfSNmHICVvni1qsoqXmXqoY1mLRQ4sIuJ8o+TQlD9kBuvOl8hg1L5bPPtlBRXsuAgcnMuWwUCQmRp9yWosoaXvlqA1/v2I9J05g2tB+3ThlDTHj77YOykxIDjoVaLHx+2/eJsHmLXaalZ/LlIf9m2wi8SfwBVm/7R3W8SrUr6RXSh1khc7vbDEUblDOmUJxGZEVNZHnRfzESLEqyZxJnSzn1RrXC6S5kV9HVONx5zdvK6j4hPuxqMmL/qhyyHsjwEWkMH9F13VHyCsv5/Nvd1DtcnJuVxnlZaX6/Z4WVNdz41FsUVraIxb68fANfbt/H6z+9jqhQe9BzTOufSb/YWPaV+TdluGHU8GZHDODZmbN5cu1K5u3YjNPTmDYgYGJKOlFhVhbm7fKbIzU8igvTTr3Iq+LMQTljCsVpRIw1mUkJN/BNsa8Ao1ULYVbyj7rJqhaOVD7h44g1UVL7HrGhlxAVMuXUG6U4a3n5s/U8+9GqZsHU1xZvYOzgVP7vnssIaaXk/8ryb30csSYOFpfz1qrN3H1h8GpAs6bx6rVX8KtFS1h9MA8JhFjMXD9yOA9Onuizr91s4TcTp/LwhMkU1FZTXFdLQmgYKRFR1LtdmFYJPj24q7nB+dC4JJ6ePAeLdnZXLytODOWMKRSnGRckXk9q2BA2l39OjbucZHt/Rsd+j2irUavXk0ONcw8HK5+jvH4VmrCTGPY9MqLuwtIqt0xKnbK6wM3OS+sWKGfsLEPXdTau2UfOtsNExYQx5aKhRMWcmkT0rfuO8q8PV/lt/3bXYV78ZA23XnwulXUNJMdE8NUOg2XDRr7asb9dZwwgOTKC1669kvzKKopra+kXF+sTEWuLWdNIiYgiJaKl0jDEbOGfk+fw0OjJ7CovpldoOEPjuu57qzh7UM6YQnEakhE2nIyw41cw7ww1zt1sPHY9HtmSYHy46mXK6lcwOvkdzFoYDa6DVDZ8g5SBe2vqegARI8UZSVVFHY/cO4/cnS2tcP7z1BIe+sMVXDBjSJef/+NVOwKOvbXsO+Z98x1uXSc2IhSnKbCGl9bJpfM+UZHEh4Xy7ZF8PLrO2JQUQtuR8WhLSngUKeFKDkLRcZQzplD0cA5UPOPjiDVR69rD0er38bh3UFL7HoEb73mJtE8MOq44s/jXXxf6OGIAToebJx6dT/aIVOK6OEm/rKou4JjLrePRAAFl1XXoJgI+zaYN7dep836Ss4vHv1xOWZ335SLCZuP+iRO45ZxRnZpHoegMSmdMoejhlNZ/E3CsuGYeJbXv0p4jZrcMIC5MVWidLdRU17NyWY7hmMvl4YuFXd9oPjuI2Kts06ZIeEAzUGMd1DuB684f0eFzbj56jAcXLmp2xMDbIukPy5azbK9x66fTgXqPg8N1hdS5VXT6TEVFxhSKHo6GBZ0GwzHdcyTosSYRQWzYHPpE/RyTFtoV5im6gcqKOtzuwALDZcXVXW7DFZOG8fayzVTU+DsYeptceAFIh+TaKcPZcugYmiaYMWwA108cQbi9S+Nr+AAAGKtJREFU4/1X/7vpOzzS+MXjlY2bmN6/c1G2rsalu3hp/8csLVhLg+7EplmYlnQud2TOxW5S/STPJJQzplD0cBLCZnKs5n2DEQkEzhGLsk9jYOLLXWaXovtISIoiKjqUygrjpcL+g3t3uQ1xkWE8//Or+Msby9i817tcGh5qo8rp8LYlaoNJCO6+aBzxkcdfYLC31F/aool9Qca6i6dy32J50Ybmzw7dxaJjq6hwVvPokDu60TJFZ1HOmELRw8mM/inlDWtocOc3b9PQCTfZkdIZ8LgQi9JNOluxWs1cfsM4Xnv2S7+xpN7RTL6o6xP4AQakxPPSL6/lWGkVDU4XdpuF2b9/BbfHP2F/QnbGCTliAClRkewsKgo4djpRUF/K10UbDcfWlG4lr/YYaWHJp9gqxfGinDGFoodSVr+CgpoPcHnKSAy5EISJSsdmBCZ013qkDLwUpQk7CRE3Nn+W0o2uV6BpUQjRucozxenJ9bdPwuPW+fDNtdTWeJexR4zpy/2/mYPVdmr/j5PjWhyh3990Eb+dtwS33uKQpSVE8+h100/4PDeOHMGSPXsDjp1O7KnJQw+Sy7m5Ilc5Y2cQyhlTKHog+8ue5HDVv5s/VzSswaxFM6LXaxyr+CsVrsDLk1ZTbzJin8BuTkNKD5XVT1FV8wq6XoKmxRARdiPRkQ8pp+wMRwjBzXdN5epbzufwwRIio0NJSu7+nqaXjM1idP8UPl2fQ1l1HUPSkrhw1ACslhN/nJ2fkc6vp07mb9+sbFbXN2saPzx3DJcPyT7h+U8m0ZbwoOOvHpiPzaQxs9cFp8gixYmgnDGFoodR48zxccSacOsV7Cn9Pbprg8FRLQxKfBO7JQOAsorfUF37SvOYrpdTWf0Mbs9REmL/dVLtPp0QQjwJzAacwD7gNillRfda1TXYQ6wMyOr6HLHO0Csmgjtmntslc98+ZjRzh2Tz1b4DuKXOpL4ZJIUHd3y6gyFR/Ui2x3OsocRgVKKj88K+dxgQnkFmeOopt0/ROZS0hULRwyiq/SzgWJVjE3qQPDEwYTElAOD2FFJdO89wr9q6D3G5Tl8pgJPAUmColHI4kAs83M32KE4iMSEhzB2azdXDhp6WjhiAJjR+mXUrkZa2eXISs6YjBEgkSwv9uxgoTj+UM6ZQ9DD0ICr6XgSBdMXCrSMwad6bv8O5EXAHmEPS4Fx/vCae9kgpl0gpmy5+LdC9HdwVncaoCOBMY0BEGi+O+Q0moaMJHZPQsWg6WivJtVLHWRmwPetQy5QKRQ8j1j6R/KpXDcdCzH2JNIdT4/wW76Oq5a4uEGTG/bP5syaCt3vRtNOr+qwLuR14p7uNULSPy+Ph+W/W8+7GrRTX1JEZH8sPJozmynOGdrdpx02EJZSMsCQO1xcYjqeH9TnFFimOBxUZUyh6GDEhFxBtH28wotE35gHS4/4Xm6kPGhKBjkDHhIl+8c9is7QEgOy2cZhMxjd6TYshxH7i1W3diRDiCyHEdoOfy1rt8wje8OAbAea4UwixQQixobi4+FSZrgjAL+Yv4l9fr6W4xquftr+kjEcWLOWlVcHzJE93Lu8zw3B7qMnOzF6qTdmZgIqMKRQ9DCEEQxNfIK/yeQpq5uPylBNpG0Fa9N3Ehngrr7KTl1Net4A653YspiTiwq/CYkpqM4+JhNhnKCy5GSlrWm23Ex/7DJqwn9LrOtlIKY2fcI0IIW4FLgWmS2ks2y6lfBF4EWDMmDHBe0opupScY0Us3rnHcOz5b9Zzw9gRhARoCL7+yBH2l5eRHh3NuJRURCebj3c105LGU+2u5b3Di6n1eDsWpIT04if9byTeFtPN1ik6gnLGFIoeiEmz0zfmZ/SN+ZnhuKbZiQu/hjiuCTqP3TaOlF6rqa59G5d7LxZzOuGh12E2G1ffSakjxJkfkBdCzAIeAiZLKQN3tFacNqw9cDjgWLXDwbajhZyb4Zv6d7S6mrsWfMyO4hYh2IFxcfx7zuWkRgVfpgeodDRgNZkIMXe9zMtlfWYwq9ck9tXmEaLZ6KsqKM8olDOmUChOCJMpgejIe4Pu46lfjKf2WaRrO4gYTKFXY4q4DyFCTpGVJ51nABuwtDFKslZKeXf3mqQIRqCoVxOhBuP3LPzExxEDyC0t5a5PPuazm24JONfy/H38ffNKtpUWYBYaM1L788iYaaSGt+/AnQg2k5XsyP5deg5F13Dmv6IqFIrTGk/dB7grfux1xABkOZ7aF3GV3UGA1b3THillfyllqpRyZOOPcsROc2ZmDcBqMmhqCWTGxzK0t+8y/NbCAjYXGCfF7yopYf2RI4ZjK48e5I4v57Ot1HusW+oszsvl2sVvUOloOIErUJzNKGdMoVB0GVLquGv+bjzmXIN0rjzFFil6IjvyC/nF+4twuTx+qi1hVit/nOOfHniksironHmVlYbbn9q6Co/BS8bRumre3rOl40YrehRqmVKhUHQZ0nMQPEcDjuuOVWg21a5F0XXsLSrllpffo87p8m6QgACzSePascO44/yxJEdF+B3XNyZ44nu/WP9xXUo2FBlHzADWFx3mLs7rlP2KnoGKjCkUii6j3ZwwEXpqDFH0WF5auaHFEcOrnCckeNw6mhSGjhhAVkIC41OMk+BH9UpmVLJ/kYomBOEWW0BbIq1ndoWxoutQzphCoThudE8hbucWdN1Y5VuYkhGWMQGOFmghl3adcQoFsP5A4EjVuv2BKywB/vm9S5iQmuaz7dw+fXhu9uyAx1yROSTg2NwgY4qejVqmVCgUnUbXy6mveAhXwxJAB2xYQ68hJOq3COEbGTBH/AJ3+Q+R0jcHxxT+AJo589QZreiRhNusAcci7P5RrKqGBuwWC1aTibjQUF6/8ipyS0s4UF5OWlQ0WQkJQc/3wMgL2FCcz46yQp/ttw4ezaTefY/vIhRnPcoZUygUnaa29DY8ro2ttjhw1s0DXIRGPwmAlC5k9V+g7l3MOECYkSIa3ToOU9gtaNZAETOF4uQxe8Rg/neJcaHIpSMGN/99Yc5unlm1lr0lZdjMJi7JGsQvp04iNjSEgXHxDIyL79D5omx2Prj4Jj47tJuVxw4SarYwJyObsUmqfakiMMoZUygUncLtWNvGEWvBWTcfe8Qv0EyJyOq/Qt08n3EhKzC59yAso06FqYoeQJ3DxStffssnG3OoqXcwul8Kd0w/l2HpvQC4edwoVu09xNo2S5JTBvXlqtHenpQLduzi558sah5zuD18sG0nOwqK+ODWGwJKYgTCZjIzN3OIWpZUdBjljCkUik7hbtILM8SFx7ULIexQF6B3tmcfOJaDPWi3IYWiXdwenR+9+AHfHWip2F2+fR8rcw7ywt1XMKZfCjaLmX/fcgXLcvaybNc+NCG4MHsAUwdlomkCKSVPr1xjOP/u4hIW79rDnCGDDccVipOFcsYUCkWn0EyJ7YwngecgEETg0p0DKGdMcWIs27bHxxFrwuXx8PRnq3jt3msBr4zFzKEDmTl0oN++RTW1HCw3LkABWH/4iHLGFF2OqqZUKBSdwmK/CKHFGY6ZLOdgsgwCLRGviEAAtKTAYwpFB1mZczDg2HcHjlLT4Gh3jlCLBVOQxt+RtsBSFQrFyUI5YwqFolMIYScs5kWE8O2zp5nSCI35p3cfUy+wTQowQQTYv9fVZip6AJYguVyaEGgdaEofYbcxbUC/gOOXDck6LtsUis6gnDGFQtFpzLZziUxaR0jUk9jCf0ZozHNEJH6FyZzevI+I/DOY2yzviAhE9DMILfwUW6w4G7lwxICAYxOzMgi1BW8O3sSj0yeTEhXpt/3+SRMYlNixKkqF4kRQOWMKheK4EFoYtrDrAo+bEiDuI3CuANdO79KlfRZCCzuFVirOZsYNTOPS0YP5dOMun+0x4SE8MLvjbbZ6R0Xy6Q9u5qPtOWzKP0qU3c7coVkMS+51sk1WKAxRzphCoegyhNDANtn7o1CcZIQQ/OmGWVyQncmnG3Korm9gdL8Urjt/JEnRnYu+hlmt3HjOCG48Z0QXWatQBKbbnDEhxL3ATwAPsFBK+VB32aJQKBSKMxMhBBePGsTFowZ1tykKxXHTLc6YEGIqcBkwQkrpEEIEr5VXKBQKhUKhOEvprgT+HwF/kVI6AKSURd1kh0KhUCgUCkW30l3O2EDgAiHEOiHE10KIsd1kh0KhUCgUCkW30mXLlEKILwCjUpRHGs8bC4wDxgLvCiEypZTSYJ47gTsB0tLSuspchUKhUCgUim6hy5wxKWXAXidCiB8BHzQ6X+uFEDoQDxQbzPMi8GLjccVCiENdZHIT8UBJF5+jKzmT7T+TbQdlf1eR3v4uCoVCcebSXdWUHwFTgeVCiIGAlQ48BKSUCV1tmBBig5RyTFefp6s4k+0/k20HZb9CoVAojo/ucsZeBl4WQmwHnMD3jZYoFQqF4mxh48aNNUKI3d1tRxtOt2iosic4yp72Od1s6pDmSrc4Y1JKJ3BTd5xboVAouondp1vk8XSLhip7gqPsaZ/TzSYhxIaO7Kd6U/rzYncbcIKcyfafybaDsl+hUCgUx4FyxtrQWDBwxnIm238m2w7KfoVCoVAcH8oZUygUilPD6ejsnm42KXuCo+xpn9PNpg7ZI1TevD9nQ99MIcTPgb8BCVLK0ymZMShCiCeB2XgLO/YBt0kpK7rXqvYRQswCngJMwH+klH/pZpM6hBAiFfgvkARI4EUp5VPda5VCoVD0LFRkrA1t+mYOwevQnFE0PmAvAvK625bjYCkwVEo5HMgFHu5me9pFCGEC/gVcDGQD1wshsrvXqg7jBn4upczGK8L8kzPIdoVCoTgrUM6YP2dD38z/+//27j3Iq7KO4/j7o5IYatBokwm4CqYpKpKSpJmiOU45kgrV6JgMOiPjXTNltMxLNhjllKlZFmGKZJqkEgaW1wiEwOUmoobkZfCehgok8OmP5/nZj2WvuMvzW/i+ZnY45+w55/n+zu4envM95zxf4CJSpqNTsT3V9uo8OwPoWTKeVhoIPGt7SX5T+HekDn3Ns73M9pw8vRxYBOxcNqpNl6SrJM2TVC9pqqRPFY5njKSnckwTJXUvGU+OaZikhZLWSir2VpykoyUtlvSspFGl4sixjJX0ah4OqjhJvSQ9JOnJ/LM6t3A8XSXNlDQ3x3NFyXgqJG0p6QlJk1paNzpj6+vUdTMlDQFesj23dCztYARwf+kgWmFn4IWq+RfphB0aSXXA/sDjZSPZpI2xva/t/sAk4LLC8dRiJnoBcDzwaKkAajDbPQ44umD7DdVaRn0VMNj2fkB/4GhJBxWMp+Jc0gVui0oN+lpUe9XNLKWF+C8h3aKsWc3Fb/uevM6lpD/48Rszts2VpG2BPwDn2f5P6Xg2VQ2ObTcKZ69tT62anQEMLRVLhe1FAJJKhvFBtjvHUsl2P1kiGNuP5oulmmB7GbAsTy+XVMmolzo+Bt7Js13yV9G/LUk9ga8AVwMXtLT+ZtkZa6+6maU0Fb+kfYBdgbn5RNYTmCNpoO2XN2KIzWru+ANIGg4cAxxRS53gZrwE9Kqa75mXdQqSupA6YuNt3106nk2dpKuBbwJvk8rC1YoRwB2lg6gRjWW7P1colppWKxn1nM2cDfQFbrBdOsP/E9LjQtu1ZuW4Tbm+St1M2lI3sxbYnm/7E7brbNeRTiADaqkj1pL8VuJFwLG23ysdTyvNAnaXtKukjwDfAO4tHFOrKPXafw0ssn1t6Xg2BZL+ImlBI19DAGxfarsXKet7Vul48jobNRPdmphC7auljLrtNfn2f09goKR+pWKRdAzwqu3Zrd1ms8yMtSDqZpZ1PbA18EDO7s2wPbJsSM2zvVrSWcAU0tAWY20vLBxWax0MnAzMl1Sfl11ie3LBmDq1ljK/VcYDk4HvdWA4NZmJbsMxKqVTZ7s3hlrNqNt+S9JDpGfsSr3wcDBwrKQvA12B7SXdZrvJMpAxzlgIIWwkkna3/UyePhv4ou1iz2nlTPS1OY6aeRQDQNLDwIW2W1Xbr53b3or0QsMRpE7YLODEkhdZ+XbgJNvFMj4VOaN+C/Cm7fNqIJ4dgfdzR2wbYCpwje0W32LsaJIOI/0eH9PcenGbMoQQNp7R+XbcPNKLNkWHBCBlorcjZaLrJd1UOB4kHSfpRWAQ8CdJUzZ2DHl4nUq2exHw+8IdsQnAdGAPSS9KOrVULFkloz44/97U5yxQKTsBD+W/q1nAA7XQEWuLyIyFEEIIIRQUmbEQQgghhIKiMxZCCCGEUFB0xkIIIYQQCorOWGhXks6RtEhSm8crklQn6cSOiCvv/1BJcyStllR8pPEQQggBojMW2t8ZwJdsn7QB29YBbe6M5ZGXW+N5YDhwe1vbCCGEEDpKdMZCu8mvxe8G3C/pfEndJI2VNDNXrh+S16uT9FjOUs2R9Pm8i9GkIu31efvhkq6v2v+kPGYLkt6R9GNJc4FBkj6bC7vPljRF0k4N47O91PY8YG0HH4oQQgOS1lQNg1CfzwMHSLquDfvoLumMdojlSkkbNPCspMmSum/gtuM2h6y8pMOqzuuhFWIE/tBubI/Mg0gebvt1ST8AHrQ9Ip+8ZioVCX+VlD1bKWl3YAJwADCKqsHx8sjgTekGPG77W3kk6EeAIbZfk/R1UnHWER31WUMIbbYil6upthRYb1BXSVvlsb4a6k7Kvt+4oUFI2tL2ZRu6ve2S42l1FoeRCnf/vXAcnUZkxkJHOgoYlcvsPEwqC9Eb6ALcLGk+cCew1wbsew2pFAfAHkA/8sCVwHdI5UtCCDUsZ1Am5enLJd0qaRpwq6S9c1a9XtK8fOE2GuiTl41psK86SU9JGp+fW71L0kfz95ZKukbSHGBYdYYqf++KnKWfL2nPvHxbSb/Jy+ZJOqFq/R1aaO8ySbPyAL+/lFJtt2aOQ1+lep1zcxx9lIzJ+5ifLzIrx+wRSfdIWiJptKST8rGaL6lPXm+cpJsk/UPS00r1EpHUtepzPSGpUot5uKS7Jf1Z0jOSflgV31GSpufY7lSqSdnosVOqFDASOD//nL6w4b8hm4/IjIWOJOAE24vXWShdDrwC7Ee6IFjZxParWfeCoWvV9Erba6raWWh7UHsEHULoENvo//VPn7N9XCPr7AUcYnuFpJ8BP7U9XtJHSHVfRwH9GsmwVewBnGp7mqSxpCzaj/L33rA9AD4oA1XtddsD8i3QC4HTgO8Cb9veJ2/Tow3tXW/7yrzdraTan/c1c2zGA6NtT5TUlXTeOx7oTzpP7gDMkvRoXn8/4DPAm8AS4Fe2B0o6FzgbqJQoqgMGAn1II9T3Bc4EbHuf3PGcKunTef3+wP7AKmBx/hmsIF3gHmn7XUkXAxcAVzZ27GyfpvTIyju2K8c+tCAyY6EjTQHOrlwVSto/L/8YsMz2WlJJjcoD+MtJpVkqlgL9JW0hqRfppNKYxcCOkgbldrpI2rtdP0kI4cNaYbt//mqsIwZwr+0VeXo6cEn+z3+XquXNecH2tDx9G3BI1ffuaGa7SqHr2aQODMCRwA2VFWz/uw3tHS7pcaXs/2CgyfORpO2AnW1PzO2stP1e3tcE22tsv0J6FOPAvNks28tsrwL+SarFCDC/Kn5IZZzW5nqoS4A9835vy209BfwLqHTG/mr7bdsrgSeBXYCDSJ3kabkzfUpeXtHYsQttFJ2x0JGuIt2SnCdpYZ6H9LzHKUoP3+8JvJuXzwPW5FT9+cA04DnSSeE6YE5jjdj+LzAUuCbvsx5Y7+FRSQcq1bwbBvwixxRCqB2VcwG2bweOJWVmJksa3IrtG9b3q55/l6atyv+uoW13jNZrL2e2bgSG5qzazayb1W8Pq6qm11bNr2Xd+Js7Hi3tt3IsRKr1WOlI72X71Ea2aeuxC1XiwIV2ZbuuanoFcHoj6zwD7Fu16OK8/H3SVWS1RofIsL1tg/l64NAWYptFPEsWQqcgaTdgie3rJPUmnTPmsm72vKHekgbZnk4aJudvHyKEB0i39M7L8fRoJDvWWHuVjtfr+dmqocBdTTVie7lS8e+v2v6jpK1JdwseA06XdAvwcdL57dukC9jWGpa335X0pvvivN+TgAfz7cneefmAJvYxA7hBUl/bz0rqRsrkPd1Mu8uB7dsQ52YvMmMhhBBq0deABfnWWD/gt7bfIN0uW6AGD/Bni4EzJS0CegA//xDtfx/okduaCxzemvZsv0XKhi0gPaoxqxVtnQycI2ke6Q3ETwITSXcL5gIPAhfZfrmNn+F5YCZwPzAy3368Edgi30K9Axieb3c2yvZrpPEZJ+T4ptNyh/A+4Lh4gL/1ZLeUtQwhhBBqW36Lb5Ltfptie20laRwpviazcqF2RGYshBBCCKGgyIyFEEIIIRQUmbEQQgghhIKiMxZCCCGEUFB0xkIIIYQQCorOWAghhBBCQdEZCyGEEEIoKDpjIYQQQggF/Q/i2GbnEpQtOwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x864 with 15 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn.datasets import fetch_lfw_people\n",
"people=fetch_lfw_people(min_faces_per_person=20, resize=0.7)\n",
"image_shape=people.images[0].shape\n",
"\n",
"fig, axes=plt.subplots(2,5, figsize=(15,8), subplot_kw={'xticks': (),'yticks': ()})\n",
"for target,image, ax in zip(people.target, people.images, axes.ravel()):\n",
" ax.imshow(image)\n",
" ax.set_title(people.target_names[target])\n",
"#plt.subtitle(\"some_faces\")\n",
"plt.show()\n",
"\n",
"print (people.images.shape)\n",
"print (len(people.target_names))\n",
"\n",
"#count how often each target appears\n",
"counts=np.bincount(people.target)\n",
"#prints counts next to target names:\n",
"for i, (count,name) in enumerate(zip(counts, people.target_names)):\n",
" print(\"{0:25} {1:3}\".format(name, count), end=' ')\n",
" if (i+i)%3==0:\n",
" print()\n",
" \n",
"mask=np.zeros(people.target.shape, dtype=np.bool)\n",
"for target in np.unique(people.target):\n",
" mask[np.where(people.target==target)[0][:50]]=1\n",
"X_people=people.data[mask]\n",
"y_people=people.target[mask]\n",
"#scale the grey-scale values between 0-1 instead of 0 and 255 for numerical stability\n",
"X_people=X_people/255\n",
"\n",
"#Use a kneighbor classifier\n",
"import mglearn\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.decomposition import PCA\n",
"#split data into training and test sets\n",
"X_train, X_test, y_train, y_test=train_test_split(X_people, y_people, stratify=y_people, random_state=0)\n",
"#build a KNeighborsClassifier with one neighbor\n",
"knn=KNeighborsClassifier(n_neighbors=1)\n",
"knn.fit(X_train, y_train)\n",
"print (\"Knn score: \", knn.score(X_test, y_test))\n",
"\n",
"mglearn.plots.plot_pca_whitening()\n",
"pca=PCA(n_components=100, whiten=True).fit(X_train)\n",
"X_train_pca=pca.transform(X_train)\n",
"X_test_pca=pca.transform(X_test)\n",
"print(X_train_pca.shape)\n",
"\n",
"knn=KNeighborsClassifier(n_neighbors=1)\n",
"knn.fit(X_train_pca, y_train)\n",
"print (\"Knn score: \", knn.score(X_test_pca, y_test))\n",
"\n",
"pca.components_.shape\n",
"fig, axes= plt.subplots(3,5, figsize=(15,12),subplot_kw={'xticks': (), 'yticks': ()})\n",
"for i, (component, ax) in enumerate(zip(pca.components_, axes.ravel())):\n",
" ax.imshow(component.reshape(image_shape), cmap='viridis')\n",
" ax.set_title(\"%d. component\" % (i+i))"
]
},
{
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| cc0-1.0 |
GoogleCloudPlatform/ml-design-patterns | 07_responsible_ai/heuristic_benchmark.ipynb | 1 | 18283 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Heuristic Benchmark\n",
"\n",
"This notebook demonstrates the Heuristic Benchmark design pattern\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Regression on poorly understood features\n",
"\n",
"Problem: Time interval before a question on Stack Overflow is answered.\n",
"\n",
"Benchmark: Median time to first answer over the entire training dataset, so 2120 seconds."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>time_to_answer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2120.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time_to_answer\n",
"0 2120.0"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%bigquery\n",
"SELECT \n",
" bqutil.fn.median(ARRAY_AGG(TIMESTAMP_DIFF(a.creation_date, q.creation_date, SECOND))) AS time_to_answer\n",
"FROM `bigquery-public-data.stackoverflow.posts_questions` q\n",
"JOIN `bigquery-public-data.stackoverflow.posts_answers` a\n",
"ON q.accepted_answer_id = a.id"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Find the error metric of always predicting that it will take 2120 seconds to get an answer. This the baseline metric against which to report model performance."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean_absolute_error</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>857315.119106</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean_absolute_error\n",
"0 857315.119106"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%bigquery\n",
"WITH benchmark_eval AS (\n",
"SELECT \n",
" 2120 - TIMESTAMP_DIFF(a.creation_date, q.creation_date, SECOND) AS error\n",
"FROM `bigquery-public-data.stackoverflow.posts_questions` q\n",
"JOIN `bigquery-public-data.stackoverflow.posts_answers` a\n",
"ON q.accepted_answer_id = a.id\n",
")\n",
"\n",
"SELECT\n",
" AVG(ABS(error)) AS mean_absolute_error\n",
"FROM\n",
" benchmark_eval"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Classification on poorly understood features\n",
"\n",
"Problem: Whether or not an accepted answer will be edited.\n",
"\n",
"Benchmark: Probability distribution of accepted answers that are edited."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>prob_edited</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.36226</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" prob_edited\n",
"0 0.36226"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%bigquery\n",
"SELECT \n",
" AVG(IF(a.last_edit_date IS NULL, 0, 1)) AS prob_edited\n",
"FROM `bigquery-public-data.stackoverflow.posts_questions` q\n",
"JOIN `bigquery-public-data.stackoverflow.posts_answers` a\n",
"ON q.accepted_answer_id = a.id"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Problem: Country from which a Stack Overflow question will be answered.\n",
"\n",
"Benchmark: Fractions of answers written by people from France, India, and so on."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>from_france</th>\n",
" <th>from_india</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.029717</td>\n",
" <td>0.08415</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" from_france from_india\n",
"0 0.029717 0.08415"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%bigquery\n",
"SELECT \n",
" COUNTIF(ENDS_WITH(u.location, 'France')) / COUNT(u.location) AS from_france,\n",
" COUNTIF(ENDS_WITH(u.location, 'India')) / COUNT(u.location) AS from_india\n",
"FROM `bigquery-public-data.stackoverflow.posts_questions` q\n",
"JOIN `bigquery-public-data.stackoverflow.posts_answers` a\n",
"ON q.accepted_answer_id = a.id\n",
"JOIN `bigquery-public-data.stackoverflow.users` u\n",
"ON u.id = a.owner_user_id"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Regression with one good numeric feature\n",
"\n",
"\n",
"Problem: Predict taxi fare amount given pickup and dropoff locations.\n",
"The distance between the two points is, intuitively, a key feature.\n",
"\n",
"Benchmark: linear regression based on this feature"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>f0_</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.644356</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" f0_\n",
"0 4.644356"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%bigquery\n",
"With trips AS (\n",
"SELECT\n",
" total_amount,\n",
" ST_Distance(ST_GeogPoint(pickup_longitude, pickup_latitude),\n",
" ST_GeogPoint(dropoff_longitude, dropoff_latitude))/1000 AS dist\n",
"FROM `bigquery-public-data.new_york.tlc_yellow_trips_2015`\n",
"WHERE pickup_latitude BETWEEN 35 and 45\n",
"AND dropoff_latitude BETWEEN 35 and 45\n",
"AND pickup_longitude BETWEEN -80 and -70\n",
"AND dropoff_longitude BETWEEN -80 and -70\n",
"AND total_amount IS NOT NULL\n",
")\n",
"\n",
"SELECT AVG(total_amount)/AVG(dist)\n",
"FROM trips"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Regression with one or two important features\n",
"\n",
"Problem: Predict duration of bicycle rental.\n",
" \n",
"Benchmark: Lookup table"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>start_station_name</th>\n",
" <th>is_peak</th>\n",
" <th>predicted_duration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Contact Centre, Southbury House</td>\n",
" <td>False</td>\n",
" <td>7012.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Stewart's Road, Nine Elms</td>\n",
" <td>False</td>\n",
" <td>6401.018182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Speakers' Corner 2, Hyde Park</td>\n",
" <td>True</td>\n",
" <td>4455.441717</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Speakers' Corner 2, Hyde Park</td>\n",
" <td>False</td>\n",
" <td>3785.754375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Speakers' Corner 1, Hyde Park</td>\n",
" <td>True</td>\n",
" <td>3728.008525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Stewart's Road, Nine Elms</td>\n",
" <td>True</td>\n",
" <td>3727.422680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Speakers' Corner 1, Hyde Park</td>\n",
" <td>False</td>\n",
" <td>3702.115147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Black Lion Gate, Kensington Gardens</td>\n",
" <td>True</td>\n",
" <td>3653.733728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Black Lion Gate, Kensington Gardens</td>\n",
" <td>False</td>\n",
" <td>3552.613008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Mechanical Workshop Penton</td>\n",
" <td>True</td>\n",
" <td>3533.424658</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" start_station_name is_peak predicted_duration\n",
"0 Contact Centre, Southbury House False 7012.500000\n",
"1 Stewart's Road, Nine Elms False 6401.018182\n",
"2 Speakers' Corner 2, Hyde Park True 4455.441717\n",
"3 Speakers' Corner 2, Hyde Park False 3785.754375\n",
"4 Speakers' Corner 1, Hyde Park True 3728.008525\n",
"5 Stewart's Road, Nine Elms True 3727.422680\n",
"6 Speakers' Corner 1, Hyde Park False 3702.115147\n",
"7 Black Lion Gate, Kensington Gardens True 3653.733728\n",
"8 Black Lion Gate, Kensington Gardens False 3552.613008\n",
"9 Mechanical Workshop Penton True 3533.424658"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%bigquery\n",
"CREATE TEMPORARY FUNCTION is_peak_hour(start_date TIMESTAMP) aS\n",
"(EXTRACT(DAYOFWEEK FROM start_date) BETWEEN 2 AND 6 -- weekday\n",
" AND (\n",
" EXTRACT(HOUR FROM start_date) BETWEEN 6 AND 10\n",
" OR\n",
" EXTRACT(HOUR FROM start_date) BETWEEN 15 AND 18))\n",
";\n",
"\n",
"SELECT \n",
" start_station_name,\n",
" is_peak_hour(start_date) AS is_peak,\n",
" AVG(duration) AS predicted_duration,\n",
"FROM `bigquery-public-data.london_bicycles.cycle_hire`\n",
"GROUP BY 1, 2\n",
"ORDER BY predicted_duration DESC\n",
"LIMIT 10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, use this benchmark to compute the overall RMSE, so that you can compare with the model."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rmse</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9814.442983</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rmse\n",
"0 9814.442983"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%bigquery\n",
"CREATE TEMPORARY FUNCTION is_peak_hour(start_date TIMESTAMP) aS\n",
"(EXTRACT(DAYOFWEEK FROM start_date) BETWEEN 2 AND 6 -- weekday\n",
" AND (\n",
" EXTRACT(HOUR FROM start_date) BETWEEN 6 AND 10\n",
" OR\n",
" EXTRACT(HOUR FROM start_date) BETWEEN 15 AND 18))\n",
";\n",
"\n",
"WITH benchmark AS (\n",
"SELECT \n",
" start_station_name,\n",
" is_peak_hour(start_date) AS is_peak,\n",
" AVG(duration) AS predicted_duration,\n",
"FROM `bigquery-public-data.london_bicycles.cycle_hire`\n",
"GROUP BY 1, 2\n",
")\n",
"\n",
"SELECT\n",
" SQRT( SUM( (duration - predicted_duration)*(duration - predicted_duration)) / COUNT(duration) ) AS rmse\n",
"FROM `bigquery-public-data.london_bicycles.cycle_hire` c\n",
"JOIN benchmark b\n",
"ON c.start_station_name = b.start_station_name AND is_peak_hour(c.start_date) = b.is_peak"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright 2020 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License"
]
}
],
"metadata": {
"environment": {
"name": "tf2-gpu.2-1.m54",
"type": "gcloud",
"uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-1:m54"
},
"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": "3.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
weixuanfu/tpot | tutorials/cuML_Classification_Example.ipynb | 1 | 5164 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook walks through a basic example of using the GPU-accelerated estimators from [RAPIDS](https://rapids.ai/) cuML and [DMLC/XGBoost](https://github.com/dmlc/xgboost) with TPOT for classification tasks. You must have access to an NVIDIA GPU and have cuML installed in your environment. Running this notebook without cuML will cause TPOT to raise a `ValueError`, indicating you should install cuML."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from tpot import TPOTClassifier\n",
"from sklearn.datasets import make_classification\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"NSAMPLES = 50000\n",
"NFEATURES = 20\n",
"SEED = 12\n",
"\n",
"# For cuML with TPOT, you must use CPU data (such as NumPy arrays)\n",
"X, y = make_classification(\n",
" n_samples=NSAMPLES,\n",
" n_features=NFEATURES,\n",
" n_informative=NFEATURES,\n",
" n_redundant=0,\n",
" class_sep=0.55,\n",
" n_classes=2,\n",
" random_state=SEED,\n",
" \n",
")\n",
"\n",
"X = X.astype(\"float32\")\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=SEED)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that for cuML to work correctly, you must set `n_jobs=1` (the default setting)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Optimization Progress', max=30.0, style=ProgressStyle(des…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Generation 1 - Current best internal CV score: 0.9695733333333334\n",
"Generation 2 - Current best internal CV score: 0.9695733333333334\n",
"Generation 3 - Current best internal CV score: 0.9695733333333334\n",
"Generation 4 - Current best internal CV score: 0.9705333333333334\n",
"Generation 5 - Current best internal CV score: 0.9705333333333334\n",
"Best pipeline: KNeighborsClassifier(input_matrix, n_neighbors=20, weights=uniform)\n",
"0.97704\n"
]
}
],
"source": [
"# TPOT setup\n",
"GENERATIONS = 5\n",
"POP_SIZE = 100\n",
"CV = 5\n",
"\n",
"tpot = TPOTClassifier(\n",
" generations=GENERATIONS,\n",
" population_size=POP_SIZE,\n",
" random_state=SEED,\n",
" config_dict=\"TPOT cuML\",\n",
" n_jobs=1, # cuML requires n_jobs=1, the default\n",
" cv=CV,\n",
" verbosity=2,\n",
")\n",
"\n",
"tpot.fit(X_train, y_train)\n",
"\n",
"preds = tpot.predict(X_test)\n",
"print(accuracy_score(y_test, preds))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"import numpy as np\n",
"import pandas as pd\n",
"from cuml.neighbors import KNeighborsClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# NOTE: Make sure that the outcome column is labeled 'target' in the data file\n",
"tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)\n",
"features = tpot_data.drop('target', axis=1)\n",
"training_features, testing_features, training_target, testing_target = \\\n",
" train_test_split(features, tpot_data['target'], random_state=12)\n",
"\n",
"# Average CV score on the training set was: 0.9705333333333334\n",
"exported_pipeline = KNeighborsClassifier(n_neighbors=20, weights=\"uniform\")\n",
"# Fix random state in exported estimator\n",
"if hasattr(exported_pipeline, 'random_state'):\n",
" setattr(exported_pipeline, 'random_state', 12)\n",
"\n",
"exported_pipeline.fit(training_features, training_target)\n",
"results = exported_pipeline.predict(testing_features)\n",
"\n"
]
}
],
"source": [
"tpot.export('tpot_classification_cuml_pipeline.py')\n",
"print(tpot.export())"
]
}
],
"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": "3.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| lgpl-3.0 |
PhilHarnish/forge | src/puzzle/examples/mim/p3_4.ipynb | 1 | 4907 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scores:\n",
"Beth 1 == (0 + 1 + 0 + 0 + 0 + 0)\n",
"Charles 2 == (1 + 0 + 0 + 0 + 1 + 0)\n",
"David 1 == (0 + 1 + 0 + 0 + 0 + 0)\n",
"Frank 6 == (1 + 1 + 1 + 1 + 1 + 1)\n",
"Jessica 3 == (1 + 1 + 1 + 0 + 0 + 0)\n",
"Karen 3 == (1 + 0 + 1 + 0 + 1 + 0)\n",
"Taylor 5 == (1 + 1 + 1 + 0 + 1 + 1)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Widget Javascript not detected. It may not be installed properly. Did you enable the widgetsnbextension? If not, then run \"jupyter nbextension enable --py --sys-prefix widgetsnbextension\"\n"
]
}
],
"source": [
"import forge\n",
"from puzzle.puzzlepedia import puzzlepedia\n",
"\n",
"puzzle = puzzlepedia.parse(\"\"\"\n",
"import collections\n",
"import itertools\n",
"\n",
"with init:\n",
" all_players = 'BCDFJKT'\n",
" all_matches = []\n",
" # dict mapping player_name to list of (match_name, score_condition) matches.\n",
" player_matches = collections.defaultdict(list)\n",
" for a, b in itertools.combinations(all_players, 2):\n",
" name = '%sv%s' % (a, b)\n",
" all_matches.append(name)\n",
" player_matches[a].append((name, 'win'))\n",
" player_matches[b].append((name, 'lose'))\n",
"\n",
"# Dimensions.\n",
"match in list(all_matches)\n",
"result in {win, lose}\n",
"game in {chess, shogi, xiangqi}\n",
"\n",
"# Setup.\n",
"# Each player played 2 of each type of game.\n",
"for matches in player_matches.values():\n",
" sum(chess[m] for m, first in matches) == 2\n",
" sum(shogi[m] for m, first in matches) == 2\n",
" sum(xiangqi[m] for m, first in matches) == 2\n",
"\n",
"def score(p):\n",
" return sum(match[m].result[score_condition] for m, score_condition in player_matches[p])\n",
"\n",
"def record(p, game):\n",
" return sum(match[m].game[game] and match[m].result[score_condition] for m, score_condition in player_matches[p])\n",
"\n",
"#1: T>D @ chess, F>C @ shogi, J>B @ xiangqi\n",
"chess.DvT == lose\n",
"shogi.CvF == lose\n",
"xiangqi.BvJ == lose\n",
"\n",
"#2: K>B @ chess, F>D @ shogi, T>C @ xiangqi\n",
"chess.BvK == lose\n",
"shogi.DvF == lose\n",
"xiangqi.CvT == lose\n",
"\n",
"#3: C>B @ shogi, T>K @ xiangqi\n",
"shogi.BvC == lose\n",
"xiangqi.KvT == lose\n",
"\n",
"#4: There was a chess game between two undefeated players.\n",
"# HACK: With a little bit of work it is easy to determine this is FvT.\n",
"match.FvT == chess\n",
"# Undefeated until last round implies they won n-1 rounds.\n",
"score('F') >= 5\n",
"score('T') >= 5\n",
"\n",
"#5: Only one player lost both chess games.\n",
"sum(record(p, chess) == 0 for p in all_players) == 1\n",
"\n",
"#6: Jessica's shogi record == Karen's xiangqi record & vice-versa.\n",
"record('J', shogi) == record('K', xiangqi)\n",
"record('K', shogi) == record('J', xiangqi)\n",
"\n",
"#7: Two players tied for last with 1:5.\n",
"sum(score(p) == 1 for p in all_players) == 2\n",
"\n",
"#8a: The winner's...\n",
"sum(score(p) == 6 for p in all_players) == 1\n",
"#8b: ...shogi opponents had a better shogi record than the runner up.\n",
"# At this point Frank and Taylor are always in last round.\n",
"# Frank plays C & D, Taylor plays B & J.\n",
"if record('C', shogi) + record('D', shogi) > record('B', shogi) + record('J', shogi):\n",
" score('F') == 6\n",
"else:\n",
" score('T') == 6\n",
"\n",
"print('Scores:')\n",
"print('Beth', score('B'))\n",
"print('Charles', score('C'))\n",
"print('David', score('D'))\n",
"print('Frank', score('F'))\n",
"print('Jessica', score('J'))\n",
"print('Karen', score('K'))\n",
"print('Taylor', score('T'))\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
},
"widgets": {
"state": {
"93a4132d4b6045afa088efdb1f2665a0": {
"views": [
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}
},
"nbformat": 4,
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| mit |
rahlk/learnPy | Lecture4-Main.ipynb | 2 | 22926 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CSX91: Python Tutorial"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Functions"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"+ Fucntions in Python are created using the keyword `def`\n",
"+ It can return values with `return`\n",
"+ Let's create a simple function:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def foo():\n",
" return 1\n",
"foo()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Q. What happens if there is no `return`?***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Scope"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ In python functions have their own scope (namespace).\n",
"+ Python first looks at the function's namespace first before looking at the global namespace.\n",
"+ Let's use `locals()` and `globals()` to see what happens:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"aString = 'Global var'\n",
"def foo():\n",
" a = 'Local var'\n",
" print locals()\n",
"\n",
"foo()\n",
"print globals()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### 2.1 Variable lifetime"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Variables within functions exist only withing their namespaces. Once the function stops, all the variables inside it gets destroyed. For instance, the following won't work. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def foo():\n",
" x = 10\n",
"\n",
"foo()\n",
"print x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Variable Resolution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Python first looks at the function's namespace first before looking at the global namespace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"aString = 'Global var'\n",
"def foo():\n",
" print aString\n",
"\n",
"foo()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ If you try and reassign a global variable inside a function, like so:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"aString = 'Global var'\n",
"def foo():\n",
" aString = 'Local var'\n",
" print aString\n",
"\n",
"foo()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Q. What would be the value of aString if I print it?***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ As we can see, global variables can be accessed (even changed if they are mutable data types) but not (by default) assigned to.\n",
"+ Global variables are ***very*** dangerous. So, python wants you to be sure of what you're doing.\n",
"+ If you MUST reassign it. Declare it as ```global```. Like so:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"aString = 'Global var'\n",
"def foo():\n",
" global aString # <------ Declared here\n",
" aString = 'Local var'\n",
" print aString\n",
"\n",
"def bar():\n",
" print aString\n",
"\n",
"foo()\n",
"bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Function Arguments: args and kwargs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Python allows us to pass function arguments (duh..) \n",
"+ The arguments are local to the function. For instance:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def foo(x):\n",
" print locals()\n",
"\n",
"foo(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Arguments in functions can be classified as:\n",
" - Args\n",
" - kwargs (keyword args)\n",
"+ When calling a function, args are mandatory. kwargs are optional."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"Args\"\n",
"def foo(x,y):\n",
" print x+y\n",
"\n",
"\"kwargs\"\n",
"def bar(x=5, y=8):\n",
" print x-y\n",
"\n",
"\"Both\"\n",
"def foobar(x,y=100):\n",
" print x*y\n",
"\n",
"\"Calling with args\"\n",
"foo(5,12)\n",
"\n",
"\"Calling with kwargs\"\n",
"bar()\n",
"\n",
"\"Calling both\"\n",
"foobar(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Other ways of calling:\n",
"+ All the following are legit:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"Args\"\n",
"def foo(x,y):\n",
" print x+y\n",
"\n",
"\"kwargs\"\n",
"def bar(x=5, y=8):\n",
" print x-y\n",
"\n",
"\"Both\"\n",
"def foobar(x,y=100):\n",
" print x*y\n",
"\n",
"\"kwargs\"\n",
"bar(5,8) # kwargs as args (default: x=5, y=8)\n",
"bar(5,y=8) # x=5, y=8\n",
"\"Change the order of kwargs if you want\"\n",
"bar(y=8, x=5)\n",
"\n",
"\"args as kwargs will also work\"\n",
"foo(x=5, y=12)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Q. will these two work?***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"Args\"\n",
"def foo(x,y):\n",
" print x+y\n",
"\n",
"\"kwargs\"\n",
"def bar(x=5, y=8):\n",
" print x-y\n",
"\n",
"\"Both\"\n",
"def foobar(x,y=100):\n",
" print x*y\n",
"\n",
"bar(x=9, 7) #1\n",
"foo(x=5, 6) #2\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ **Never call args after kwargs**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Nesting functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ You can nest functions.\n",
"+ Class nesting is somewhat uncommon, but can be done."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def outer():\n",
" x=1\n",
" def inner():\n",
" print x\n",
" inner()\n",
"\n",
"outer()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ All the namespace conventions apply here.\n",
"\n",
"### What would happen if I changed x inside `inner()`?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def outer():\n",
" x = 1\n",
" def inner(): \n",
" x = 2\n",
" print 'Inner x=%d'%(x)\n",
" inner()\n",
" return x\n",
"\n",
"print 'Outer x=%d'%outer()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"***What about global variables?***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = 4\n",
"def outer(): \n",
" global x\n",
" x = 1\n",
" def inner(): \n",
" global x\n",
" x = 2\n",
" print 'Inner x=%d'%(x)\n",
" inner()\n",
" return x\n",
"\n",
"print 'Outer x=%d'%outer()\n",
"print 'Global x=%d'%x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Declare global every time the global x needs changing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Classes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Define classes with the `class` keyword\n",
"+ Here's a simple class"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class foo():\n",
" def __init__(i, arg1): # self can br replaced by anything.\n",
" i.arg1 = arg1\n",
" def bar(i, arg2): # Always use self as the first argument\n",
" print i.arg1, arg2\n",
"\n",
"FOO = foo(7)\n",
"\n",
"FOO.bar(5)\n",
"\n",
"print FOO.arg1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ All arg and kwarg conventions apply here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 6.1 Overriding class methods"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets try:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class foo():\n",
" def __init__(i, num):\n",
" i.num = num\n",
"\n",
"d = foo(2)\n",
"d()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ We know the `__call__` raises an exception. Python lets you redefine it:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class foo():\n",
" def __init__(i, num):\n",
" i.num = num\n",
" def __call__(i):\n",
" return i.num\n",
"d = foo(2)\n",
"d()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ There are many such redefinitions permitted by python. See [Python Docs](https://docs.python.org/2/reference/datamodel.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 6.2 Emulating numeric types"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ A very useful feature in python is the ability to emulate numeric types."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Would this work?***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class foo():\n",
" def __init__(i, num):\n",
" i.num = num\n",
"FOO = foo(5)\n",
"FOO += 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's rewrite this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class foo():\n",
" def __init__(i, num):\n",
" i.num = num\n",
" def __add__(i, new):\n",
" i.num += new\n",
" return i\n",
" def __sub__(i, new):\n",
" i.num -= new\n",
" return i\n",
"\n",
"FOO = foo(5)\n",
"FOO += 1\n",
"print FOO.num\n",
"FOO -= 4\n",
"print FOO.num"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Aside: `__repr__`, `__call__`,`__getitem__`,... are all awesome."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class foo():\n",
" \"Me is foo\"\n",
" def __init__(i, num):\n",
" i.num = num\n",
" def __add__(i, new):\n",
" i.num += new\n",
" return i\n",
" def __sub__(i, new):\n",
" i.num -= new\n",
" return i\n",
" def __repr__(i):\n",
" return i.__doc__\n",
" def __getitem__(i, num):\n",
" print \"Nothing @ %d\"%(num)\n",
"\n",
"FOO = foo(4)\n",
"FOO[2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Functions and Classes are Objects"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Functions and objects are like anything else in python.\n",
"+ All objects inherit from a base class in python.\n",
"+ For instance,"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"issubclass(int, object)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ It follows that the variable `a` here is a class."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = 9\n",
"dir(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ This means: \n",
" - Functions and Classes can be passed as arguments.\n",
" - Functions can return other functions/classes. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from pdb import set_trace # pdb is quite useful\n",
"\n",
"def add(x,y): return x+y\n",
"def sub(x,y): return x-y\n",
"def foo(x,y,func=add): \n",
" set_trace()\n",
" return func(x,y)\n",
"\n",
"foo(7,4,sub)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Closures"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remember this example?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def foo():\n",
" x=1\n",
" \n",
"foo()\n",
"print x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Obviously, this fails. Why? As per variable lifetime rules (see [2.1](http://localhost:8888/notebooks/Lecture4.ipynb#2.1-Variable-lifetime)), `foo()` has ceased execution, `x` is destroyed.\n",
"\n",
"***So how about this?***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def foo():\n",
" x='Outer String'\n",
" def bar():\n",
" print x\n",
" return bar\n",
"\n",
"test = foo()\n",
"\n",
"test()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***This works. But it shouldn't, because `x` is local to `foo()`, when `foo()` has ceased execution, `x` must be destroyed. Right?***\n",
"Turns out, Python supports a feature called **function closure**. This enables nested *inner* functions to keep track of their namespaces."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 8.1 Aside: lambda functions and sorted"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Anonymous functions in python can be defined using the `lambda` keyword.\n",
"+ The following two are the same:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def foo(x,y): return x**y\n",
"\n",
"bar = lambda x,y: x**y # <--- Notice no return statements\n",
"\n",
"print foo(4,2)\n",
"print bar(4,2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Nested `lambda` is permitted (idk why you'd use them, still, worth a mention)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"foo = lambda x: lambda y: x+y\n",
"print foo(3)(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 8.1.1 Sorted"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Python's sorted function can sort based on a `key` argument, `key` is a lambda function that deterimes how the data is sorted."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"student_tuples = [ #(Name, height(cms), weight(kg))\n",
" ('john', 180, 85),\n",
" ('doe', 177, 99),\n",
" ('jane', 169, 69),\n",
"]\n",
"\n",
"# Sort based on height\n",
"print 'Weight: ', sorted(student_tuples, key=lambda stud: stud[1])\n",
"\n",
"# Sort based on Name\n",
"print 'Name: ', sorted(student_tuples, key=lambda stud: stud[0])\n",
"\n",
"# Sort based on BMI\n",
"print 'BMI: ', sorted(student_tuples, key=lambda stud: stud[2]*100/stud[1])\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9. Decorators!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Decorators are callables that take a function as argument, and return a replacement function (with additional functionalities)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def outer(func):\n",
" def inner(*args):\n",
" \"Inner\"\n",
" print 'Decorating...'\n",
" ret = func()\n",
" ret += 1\n",
" return ret\n",
" return inner\n",
"\n",
"def foo():\n",
" \"I'm foo\"\n",
" return 1\n",
"\n",
"print foo()\n",
"\n",
"decorated_foo = outer(foo)\n",
"\n",
"print decorated_foo()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Lets look at memory locations of the functions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def outer(func):\n",
" def inner(*args):\n",
" \"Inner\"\n",
" print 'Decorating...'\n",
" ret = func()\n",
" ret += 1\n",
" return ret\n",
" print inner.__doc__, inner\n",
" return inner\n",
"\n",
"def foo():\n",
" \"I'm foo\"\n",
" return 1\n",
"\n",
"print foo.__name__, foo\n",
"\n",
"decorated_foo = outer(foo)\n",
"\n",
"print decorated_foo.__name__, decorated_foo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ A common practice is to replace the original function with the decorated function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def outer(func):\n",
" def inner():\n",
" \"Inner\"\n",
" print 'Decorating...'\n",
" ret = func()\n",
" ret += 1\n",
" return ret\n",
" return inner\n",
"\n",
"def foo():\n",
" \"I'm foo\"\n",
" return 1\n",
"\n",
"print foo()\n",
"foo = outer(foo)\n",
"\n",
"print foo()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Python uses `@` to represent `foo = outer(foo)`. The above code can be retwritten as follows:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def outer(func):\n",
" def inner():\n",
" \"Inner\"\n",
" print 'Decorating...'\n",
" ret = func()\n",
" ret += 1\n",
" return ret\n",
" return inner\n",
"\n",
"@outer\n",
"def foo():\n",
" \"I'm foo\"\n",
" return 1\n",
"\n",
"print foo()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 9.1 Logging and timing a function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Decorators can be classes, they can take input arguments/keyword args."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Lets build a decorator that logs and times another function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import time\n",
"from pdb import set_trace\n",
"\n",
"def logger(func):\n",
" def inner(*args, **kwargs):\n",
" print \"Arguments were: %s, %s\"%(args, kwargs)\n",
" return func(*args, **kwargs)\n",
" return inner\n",
"\n",
"def timer(func):\n",
" def inner(*args, **kwargs):\n",
" tb=time.time()\n",
" result = func(*args, **kwargs)\n",
" ta=time.time()\n",
" print \"Time taken: %f sec\"%(ta-tb)\n",
" return result\n",
" return inner\n",
"\n",
"@logger\n",
"@timer\n",
"def foo(a=5, b=2):\n",
" return a+b\n",
"\n",
"@logger\n",
"@timer\n",
"def bar(a=10, b=1):\n",
" time.sleep(0.1)\n",
" return a-b\n",
"\n",
"if __name__=='__main__': ## <----- Note\n",
" foo(2,3)\n",
" bar(5,7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# References"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[1] [Understanding Python Decorators in 12 Easy Steps!](http://simeonfranklin.com/blog/2012/jul/1/python-decorators-in-12-steps/)\n",
"\n",
"[2] [Python Docs](https://docs.python.org/2/)"
]
}
],
"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 |
ispmarin/text_norm | src/Search Engine.ipynb | 1 | 4382 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Search using Whoosh\n",
"\n",
"We will use Whoosh, a search engine with Python, to retrieve a few candidates. The search engine is already doing some parsing, but with a more complex problem we can use it for a few fields."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from retrieve.search import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This will be our canonical database for the addresses:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"doc1 = {\n",
" 'street': 'XV de novembro',\n",
" 'number': 123,\n",
" 'complement': 'bloco 22',\n",
" 'cep': '02837-223',\n",
" 'city': 'São Paulo'\n",
"}\n",
"doc2 = {\n",
" 'street': 'XV de piracicaba',\n",
" 'number': 123,\n",
" 'cep': '02833-023',\n",
" 'city': 'São Paulo'\n",
"}\n",
"\n",
"doc3 = {\n",
" 'street': 'Grande marcha de novembro',\n",
" 'number': 123,\n",
" 'complement': 'bloco 22',\n",
" 'cep': '02833-023',\n",
" 'city': 'São Paulo'\n",
"}\n",
"\n",
"doc4 = {\n",
" 'street': 'XV de novembro',\n",
" 'number': 123,\n",
" 'complement': 'bloco 23 A',\n",
" 'cep': '02837-223',\n",
" 'city': 'São Paulo'\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All documents that we have available:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'number': 123, 'city': 'São Paulo', 'cep': '02833-023', 'complement': 'bloco 22', 'street': 'pindamonhangaba'}\n",
"{'number': 123, 'city': 'São Paulo', 'complement': 'bloco 22', 'cep': '02833-023', 'street': 'pindamonhangaba'}\n",
"{'number': 123, 'city': 'São Paulo', 'cep': '02833-023', 'complement': 'bloco 22', 'street': 'pindamonhangaba'}\n",
"{'number': 123, 'city': 'São Paulo', 'complement': 'bloco 22', 'cep': '02837-223', 'street': 'XV de novembro'}\n",
"{'number': 123, 'city': 'São Paulo', 'cep': '02833-023', 'street': 'XV de piracicaba'}\n",
"{'number': 123, 'city': 'São Paulo', 'complement': 'bloco 22', 'cep': '02833-023', 'street': 'Grande marcha de novembro'}\n",
"{'number': 123, 'city': 'São Paulo', 'complement': 'bloco 23 A', 'cep': '02837-223', 'street': 'XV de novembro'}\n"
]
}
],
"source": [
"for doc in all_documents(idx):\n",
" print(doc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What happens when we search for a string, like `novembro`?"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[9, 11, 12]\n",
"[{'number': 123, 'city': 'São Paulo', 'cep': '02837-223', 'complement': 'bloco 22', 'street': 'XV de novembro'}, {'number': 123, 'city': 'São Paulo', 'cep': '02837-223', 'complement': 'bloco 23 A', 'street': 'XV de novembro'}, {'number': 123, 'city': 'São Paulo', 'cep': '02833-023', 'complement': 'bloco 22', 'street': 'Grande marcha de novembro'}]\n"
]
}
],
"source": [
"schema = create_schema()\n",
"idx = create_index(schema, 'indexdir')\n",
"add_documents([doc4], idx)\n",
"results = search('novembro', 'street', idx)\n",
"print(results)"
]
},
{
"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
}
| mit |
aemerick/onezone | testing/Summary_Plots.ipynb | 1 | 43461 | {
"cells": [
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from astropy import units as u\n",
"from onezone.constants import CONST as const"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def SNII_luminosity(x, dt):\n",
" return 1.0E51 * x / dt"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def cumulative_to_rate(x):\n",
" rate = np.zeros(np.shape(x))\n",
" rate[0] = 0.0\n",
" rate[1:] = x[1:] - x[0:-1]\n",
" \n",
" return rate"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('t', 'M_gas', 'M_DM', 'M_star', 'Z_gas', 'Z_star', 'N_star', 'N_SNIa', 'N_SNII', 'Mdot_ej', 'L_FUV', 'Q0', 'Q1', 'L_wind', 'L_Q0', 'L_Q1', 'm_tot', 'Ni', 'C', 'H', 'O', 'N', 'Fe', 'm_metal', 'He')\n"
]
}
],
"source": [
"#\n",
"# Load the summary output file\n",
"#\n",
"\n",
"data = np.genfromtxt('summary_output.txt', names=True)\n",
"\n",
"# all information\n",
"print data.dtype.names"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00 0.00000000e+00 2.00000000e+00\n",
" 5.00000000e+00 9.00000000e+00 1.50000000e+01 2.40000000e+01\n",
" 3.30000000e+01 4.60000000e+01 6.40000000e+01 9.00000000e+01\n",
" 1.18000000e+02 1.51000000e+02 1.88000000e+02 2.25000000e+02\n",
" 2.66000000e+02 3.14000000e+02 3.64000000e+02 4.18000000e+02\n",
" 4.75000000e+02 5.31000000e+02 5.96000000e+02 6.59000000e+02\n",
" 7.30000000e+02 8.03000000e+02 8.80000000e+02 9.62000000e+02\n",
" 1.04600000e+03 1.13300000e+03 1.22100000e+03 1.32300000e+03\n",
" 1.42000000e+03 1.52600000e+03 1.63200000e+03 1.74000000e+03\n",
" 1.84800000e+03 1.95900000e+03 2.07900000e+03 2.19900000e+03\n",
" 2.33200000e+03 2.46500000e+03 2.59500000e+03 2.73300000e+03\n",
" 2.87200000e+03 3.02600000e+03 3.17400000e+03 3.33400000e+03\n",
" 3.49700000e+03 3.66500000e+03 3.82300000e+03 3.98700000e+03\n",
" 4.15900000e+03 4.33000000e+03 4.51500000e+03 4.69000000e+03\n",
" 4.86900000e+03]\n",
"[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 2.\n",
" 3. 4. 6. 9. 9. 13. 18. 26. 28. 33. 37. 37.\n",
" 41. 48. 50. 54. 57. 56. 65. 63. 71. 73. 77. 82.\n",
" 84. 87. 88. 102. 97. 106. 106. 108. 108. 111. 120. 120.\n",
" 133. 133. 130. 138. 139. 154. 148. 160. 163. 168. 158. 164.\n",
" 172. 171. 185. 175. 179.]\n"
]
}
],
"source": [
"nsnII = data['N_SNIa']\n",
"print nsnII\n",
"print cumulative_to_rate(nsnII)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final time = 32.00000 Myr\n",
"M_gas = 1.40982E+06\n",
"M_star = 5.32217E+05\n",
"N_star = 308558\n",
"N_SNII 0\n",
"N_SNIa 4869\n"
]
}
],
"source": [
"print \"Final time = %.5f Myr\"%(data['t'][-1])\n",
"print \"M_gas = %5.5E\"%(data['M_gas'][-1])\n",
"print \"M_star = %5.5E\"%(data['M_star'][-1])\n",
"print \"N_star = %8i\"%(data['N_star'][-1])\n",
"\n",
"print 'N_SNII %4i'%(data['N_SNII'][-1])\n",
"print 'N_SNIa %4i'%(data['N_SNIa'][-1])\n"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f270df78ad0>"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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nfO1096Xua+8cv1NE2gWarzEmOHbt2kWzZs1o1qyZX7GpWbMmn3zyCR9++KEVmzwss4JT\nH/gMaAqUA1ap6ojUnyx8Tx/Ad/G2IcBKZ/LPz4ChACJyLdACqAncA7wk5yZDmgl0UtVqQDURaeLE\nOwGHVbUqngI52blWBDASz6i6BsAo38JmjAmdY8eOMXjwYK6//nqWLVvmjZcuXZpp06axadMmmjRp\nksEVTF6QYcFR1XhVHQDcBCwAWojINhG5N9AvEJFKeArWqz7hB4C5zvZczq23cz+wSFXPqupuIBGo\nLyLlgVKqut45bp7POb7XWgKkPl1sAqxQ1SRVPQqsAO4ONG9jzIVLSUlh3rx5VKtWjcmTJ3vXqBER\nOnfuTGJiIn369KFw4cIuZ2pCIdBh0RF4Wh3V8cyhdjgL3zEVGAj4ti7Kqep+AFX9VUTKOvHLga99\njtvnxM4Ce33ie5146jl7nGsli0iSiJTxjae5ljEmBNavX0+vXr1Yu3atX7xhw4bMmDGDunXrupSZ\ncUtmgwbaAS2Bi4G3gTaq+kugFxeRZsB+Vf1WRCIzODQnh4lleT7y0aNHe7cjIyOJjIzMwXSMyV8O\nHjzI0KFDmT17tl+8QoUKPPPMM7Ru3dqWDQhDcXFxxMXFXdA1MhwW7bzcuQX4wQn5HayqD2V4cZEJ\nQBs8LZTiQCngXTyzTUeq6n6nu+xzVa0pIkM8l9VJzvmfAKOAH1OPceLRwG2q2j31GFVdKyIFgV9U\ntaxzTKSqdnPOiXGusThNjjYs2pgckJycTExMDMOHD+fo0aPeeJEiRejXrx/Dhg2jVKlSLmZoclJ2\nhkVnVnDuyOhkVV0V8BeJ3Ab0V9X7RWQycEhVJ4nIYCBCVYc4gwYW4HnIfzmeWamrqqqKyBqgN7Ae\n+AiYoaqfiEgP4HpV7eEUmeaqGu0MGogH6uB5VhUP1HWe5/jmZQXHmAv03//+l549e/Ltt9/6xZs1\na8bUqVOpWrWqS5mZYMnx93CyUlCyaCIQKyId8bReWjjflyAisXhGtJ0BevhUg57A63iWuF6mqp84\n8dnAfBFJBA4B0c61jojIWDyFRoExaYuNMebC7N+/n8GDBzN37ly/+DXXXMP06dNp1qyZS5mZ3Ciz\nFs57wCzgU1U9m2ZfFaA9sFdVXwtqlkFkLRxjsu7s2bPe7rOkpCRvvHjx4gwbNowBAwZQrFgxFzM0\nwRaMLrXLgf54hiDvBw7iaWFcjWdutRdV9e1sZ5wLWMExJmvS6z578MEHmTp1KlWqVHEpMxNKOV5w\n0lz873hmHDgF7FTVY5mcEhas4BgTmIMHDzJ48GDmzJnjF69atSrPP/+8vbiZzwS14ORVVnCMyVhy\ncjKvvPIKw4YN48iRI9548eLFGT58OP3796do0aIuZmjcEMz1cIwx+dD69evp0aMH8fHxfvHmzZsz\nbdo06z4zWWLrsxpj/uLw4cN069aNBg0a+BWba665hmXLlvHuu+9asTFZluWCIyKlnfdljDF5TEpK\nCq+99hrVq1dn1qxZpHY3Fy1alNGjR7N161buuecel7M04SqgLjURWQU8CBQENgKHReQzVR0YzOSM\nMaGzadMmevTowX//+1+/eNOmTZkxYwbXXHONS5mZvCLQFk4ZVf0deAh4Q1Xr4pmN2RgT5pKSkujb\nty916tTxKzZVqlThvffe48MPP7RiY3JEoAWnkIhcBkQBHwQxH2NMiKgqb775JjVq1GD69OmkpKQA\nULhwYYYNG0ZCQgIPPPCATbRpckygo9TGAV8Aq1V1nYhcDfwveGkZY4Jp+/bt9OzZk88//9wvfscd\nd/DCCy9Qo0YNlzIzeVmmBceZgbmcqnoHCqjqD3gWPjPGhJGTJ08ybtw4pkyZ4l0MDaBixYo899xz\ntGjRwlo0JmgCevFTRNapav0Q5BNy9uKnyS8+/PBDevXqxe7du72xggUL0qdPH0aPHm1LB5gsCdpM\nAyLyHJ7nPYuBE6lxVd2c1SRzGys4Jq/bs2cPffr04d133/WLN2zYkJkzZ3LDDTe4lJkJZ8EsOF+e\nJ6yq+q+sfFluZAXH5FVnzpxh6tSpjBkzhpMnT3rjZcqUYfLkyXTo0IECBezdb5M9NpdaNljBMXnR\nF198QY8ePUhISPCLd+zYkUmTJnHppZe6lJnJK4I6l5qINAGuw7M8AQCqOiErX2aMCa6DBw/Sv39/\n5s+f7xevVasWL730Ev/85z9dysyYAN/DEZGX8Cy21g8oDrQB/h7EvIwxWZCSksLs2bOpUaOGX7G5\n6KKLePbZZ9mwYYMVG+O6QJ/hbFbVG0Rkk6reKCKlgI/sGY4x7ktISKBbt258+aX/o9aoqCiee+45\nKlWq5FJmJi/LTpdaoE8MTzn//ENEygN/ABWz8kXGmJx16tQphg8fzk033eRXbK666io+/vhjYmNj\nrdiYXCXQZzgfi8glwBTgWyAZmBe0rIwxGfr000/p3r0733//vTdWqFAhBgwYwIgRIyhRooSL2Rlz\nfoF2qRVS1bPOdnE8z3F+T42FM+tSM+HkwIED9OvXjwULFvjFb7nlFmbNmkWtWrVcyszkN8HsUluX\nuqGqp1T1sG/MGBNcKSkpvPrqq9SoUcOv2JQuXZqYmBhWr15txcbkehl2qYlIWaACUFxEagGp1exi\nwNrsxoRAQkICXbt2ZfXq1X7xVq1a8dxzz1G+fHmXMjMmazJ7htMM6AhUAl7kXME5BowIYl7G5Hun\nTp1i/PjxTJ482W+izauvvpqXXnqJJk1sSSoTXgJ9htNCVWNDkE/I2TMckxutXLmS7t27891333lj\nhQoVYuDAgYwYMYLixYu7mJ0xQZjaRkSaAltV9Sfn8zDgYeBH4ElV/fEC8s0VrOCY3OTgwYP069eP\nN954wy/esGFDZs2axfXXX+9SZsb4C8aggaeBQ87FU7vXegArgFnZSdIY81eqyuuvv06NGjX8ik3q\noIAvv/zSio0Je5k9w1FVTV2O4CHgVVVdC6wVka7BTc2Y/CExMZGuXbv+ZfXNFi1aMG3aNCpUqOBS\nZsbkrMxaOAVEpIR4lgC8A/jMZ1/RzC4uIkVFZK2IfCMiW0RklBOPEJEVIrJTRJaLSGmfc4aKSKKI\nbBeRu3zidURks4jsEpFpPvEiIrLIOedrEbnCZ1975/idItIu89thTOicPn2aCRMmUKtWLb9iU6VK\nFT766CMWL15sxcbkKZkVnOeBb4C1QKKqrgMQkRuBXzO7uKr+CfxbVWsDNwH3iEh9YAiwUlWr4yli\nQ53rXgu0AGoC9wAvybn1bmcCnVS1GlDNmb0aoBNwWFWrAtOAyc61IoCRwM1AA2CUb2Ezxk1r166l\nbt26PPXUU/z555+AZ/XNAQMGsG3bNpo2bepyhsbkvAwLjqq+AjQGegJ3++z6Dc/znEypaurKT0Xx\ndOEp8AAw14nPBZo72/cDi1T1rKruBhKB+s78baVUdb1z3Dyfc3yvtQS43dluAqxQ1SRVPYrnuZPv\n72BMyB07dozevXtzyy23sHXrVm+8bt26rF+/nmeeeYaSJUu6mKExwZPpXGrOCLWf0sT2BfoFIlIA\n2ABcA7yoqutFpJyq7neu9avzginA5cDXPqfvc2Jngb0+8b1OPPWcPc61kkUkSUTK+MbTXMsYVyxb\ntoxu3bqxZ8+5/yxLlCjB2LFj6d27N4UKBbw8lTFhKej/hatqClBbRC4G3hWR6/C0cvwOy8GvzNIw\nPYDRo0d7tyMjI4mMjMzBdEx+d+DAAfr27cvChQv94k2aNGHmzJlcddVVLmVmTODi4uKIi4u7oGtk\nNrXNFanv4FwoVf1dROLwdGvtT23lON1lB5zD9gGVfU6r5MTSi/ue87OIFAQuVtXDIrIPiExzjv8w\nIIdvwTEmp6gqb7zxBn379uXw4cPe+N/+9jemT59O69atOfeI0pjcLe1fxseMGZPla2Q2aOBdABFZ\nkeUre867NPVBvTPLdGNgO7AUeMw5rD3wvrO9FIh2Rp5dhWdV0XWq+iuQJCL1nUEE7dKc097ZjuLc\nSLrlQGMRKe0MIGjsxIwJut27d3PPPffQrl07v2LTpk0btm/fzqOPPmrFxuQ7mXWpFRSRQUBNEemd\ndqeqzsjk/ArAXOc5TgFgsaouE5E1QKyIdMQza0EL53oJIhILJABngB4+0wD0BF4HigHLVPUTJz4b\nmC8iiXheUo12rnVERMYC8Xi67MY4gweMCZrk5GReeOEFnnrqKU6cOOGNV6lShVmzZtn8ZyZfy2xq\nm5p4Xvh8Ang17X5VDfsJPG1qG5NTEhIS6NixI2vXrvXGRITevXszbtw4LrroIhezMyZn5fhcaj4X\nvk9VP8h2ZrmYFRxzoc6cOcOkSZMYO3Ysp0+f9savu+46Xn31Vf7xj3+4mJ0xwRHMgnMxMBz4lxP6\nAhinqseynGUuYwXHXIiNGzfSsWNHNm3a5I0VLlyYp556iqFDh1KkSBEXszMmeIK54udsPM9U2jk/\np4E5WUvPmLzjjz/+YNiwYdSvX9+v2NSvX5+NGzcyatQoKzbGpBFoC+dbVb0ps1g4shaOyaqvvvqK\nTp06sXPnTm+sWLFijBs3jr59+1KwYEEXszMmNILZwvlDRLwd0c72H1n5ImPC3fHjx+nduze33nqr\nX7G57bbb2LJlC/3797diY0wGAp1poAeeocdF8bzJfxJoG7SsjMllPv30Uzp37syPP55bc7BUqVJM\nnjyZLl26UKBAoH93Myb/CqhLzXuwZ44yVPVwZseGC+tSMxlJSkqif//+zJ492y/etGlTYmJiqFy5\ncjpnGpO3BW2UWl5mBcek5+OPP6ZLly7s3Xtu3tgyZcowffp0mynA5HvBfIZjTL5x5MgROnToQNOm\nTf2KzSOPPEJCQgJt2rSxYmNMNth86Mb4+OCDD+jatSu//PKLN3bZZZfx4osvEhUV5WJmxoS/gFo4\nIvKQiJRytoeISKyIhP2QaGNSHTp0iDZt2nD//ff7FZvo6Gi2bdtmxcaYHBBol9poVT0mIg2BpsAC\nICZ4aRkTOu+88w7XXXcdCxYs8MbKli3L22+/zcKFC7nssstczM6YvCPQgpPs/PNeYJaqvo9nyWhj\nwtZvv/1GdHQ0Dz/8MPv37/fGH330URISEnjooYdczM6YvCfQZzi/iMiLeBZPqyciRbABByaMvf32\n23Tv3p2DBw96YxUqVGDWrFncd999LmZmTN4V6NQ2F+HpStusqjtEpCJwo6p+HOwEg82GRecvv/32\nG0888QSLFy/2i7dv356pU6cSERHhUmbGhJdgzhZ9JfCLqv4pIv8EbgDeUNXfs5NobmIFJ/945513\n6N69OwcOHPDGKlasyCuvvELTpk1dzMyY8BPM93DeA1JE5Bo8s0RXBd7MYn7GuOLQoUO0bt2ahx9+\n2K/YPPbYY2zbts2KjTEhEugznBRVPSMiDwHPq+oMEfkmmIkZkxPef/99unbt6jcowFo1xrgj0BbO\nWRGJwjNh54dOrHBwUjLmwh0+fJi2bdvSvHlzv2JjrRpj3BNoC6cjnhmjJ6vqDyJyFbAweGkZk30f\nfPABXbp04ddff/XGKlSowCuvvEKzZs1czMyY/M0m77RBA3nGkSNH6Nu3L/PmzfOLt23blunTp9sI\nNGNyUHYGDQTUwnEGC4wHrgWKpcZVtVqWMjQmSJYtW0bnzp35+eefvbHy5csza9Ys7r//fhczM8ak\nCvQZzut4RqcJcA8QCyzO6ARjQuHYsWN07tyZZs2a+RWb1q1bs3XrVis2xuQigb6Hs0FV64rIFlWt\n5cTiVbVe0DMMMutSC19xcXF06NCB3bt3e2Nly5YlJiaGBx980L3EjMkHgvkezp8iUgD4XkS6ich9\nQKksZ2hMDjh16hRPPvkk//73v/2KTVRUFNu2bbNiY0wuFWgLpwGQAETgeZZTGpikql8FN73gsxZO\neFm/fj3t2rVjx44d3lhERAQvvvgi0dHRtjCaMSFiS0xngxWc8HDmzBnGjRvH+PHjSU5O9sbvvvtu\nZs+eTcWKFV3Mzpj8J8dHqYnIOxntV1Wbv90EXUJCAm3btmXjxo3eWMmSJXnuuefo3LmztWqMCROZ\nPcOJBK4EvgZeAF5M85MhEakkIp+JyDYR2SIivZ14hIisEJGdIrJcREr7nDNURBJFZLuI3OUTryMi\nm0Vkl4gee6lSAAAV8ElEQVRM84kXEZFFzjlfi8gVPvvaO8fvFJF2gdwQk3ukpKQwdepU6tSp41ds\nbr31VjZv3kyXLl2s2BgTRjLsUhORwkAToBVQE1gKLFTVnQFdXKQ8UF5Vv3WWONgAPAB0AA6p6mQR\nGQxEqOoQEbkWz2qiNwOVgJVAVVVVEVkLPKGq60VkGTBdVZeLSHeglqr2EJGWwIOqGi0iEUA8UAfP\ncO4NQB1VTUqTo3Wp5UK7d+/mscce44svvvDGihQpwvjx43nyyScpWLCgi9kZY3J8lJqqnlHVD1X1\nUaAR8BOwWkR6BHJxVf1VVb91to8D2/EUkgeAuc5hc4Hmzvb9wCJVPauqu4FEoL5TuEqp6nrnuHk+\n5/heawlwu7PdBFihqkmqehRYgWcBOZOLqSpz5szhhhtu8Cs2tWvXZsOGDQwYMMCKjTFhKtOZBpxW\nzj14WjnVgJeAD7L6Rc6aOjcBa4ByqrofPEVJRMo6h12Op/su1T4ndhbY6xPf68RTz9njXCtZRJJE\npIxvPM21TC514MABunTpwvvvv++NFShQgGHDhjFixAiKFCniYnbGmAuV2aCB14DawHI8w6C/zc6X\nON1pS4A+qnpcRNL2YeVkn1aWO/VHjx7t3Y6MjCQyMjIH0zGBeP/99+ncubPfks9Vq1Zl3rx5/OMf\n/3AxM2MMeF60jouLu6BrZPYMJwX4HU9B8D1QAFXVMpl+gUghPEsafKyq053YdiBSVfc73WWfq2pN\nERniXHeSc9wnwCjgx9RjnHg0cJuqdk89RlXXikhBPCuTlnWOiVTVbs45Mc41FqfJz57huOjYsWP0\n7duX1157zS/es2dPJk2aRMmSJV3KzBiTkWDMNFAY+BtwKXCZz0/q50C8BiSkFhvHUuAxZ7s98L5P\nPNoZeXYV8Hdgnar+CiSJSH3xDEtql+ac9s52FPCZs70caCwipZ0BBI2dmMklVq9ezY033uhXbCpW\nrMjy5ct54YUXrNgYk8cE9cVPEWkE/AfYwrlW0jBgHZ4JQCvjab20cB7sIyJDgU7AGTxdcCuceF08\nk4gWA5apah8nXhSYj6fr7xAQ7Qw4QEQeA55yvnecqvrPW4+1cNxw+vRpRo0axaRJk/C99y1btuSl\nl16iTJlMG87GGJfZTAPZYAUntBISEmjTpg3ffHNuhfLSpUszc+ZMWrVq5WJmxpisCObkncZckJSU\nFGbMmEHdunX9is3tt9/Oli1brNgYkw8EusS0Mdm2b98+OnTowKeffuqNFS1alIkTJ9K7d28KFLC/\n9xiTH2Q2LPoI5x+yHPAoNZO/LVmyhC5dunDkyBFv7MYbb+SNN97g+uuvdzEzY0yoZdbCuTQkWZg8\n5/fff6dXr17Mm3dunIaIMGjQIMaMGUPRokVdzM4Y44YMC46qJvt+dt7gL+YT+hlj0li9ejVt27b1\nWxztiiuuYP78+fzrX/9yLzFjjKsC6jwXkWYisgvPlDJrnX9+lvFZJr85c+YMw4cP57bbbvMrNm3a\ntGHz5s1WbIzJ5wIdNDAez+SdK1S1tog0BloELy0TbhITE3n00UdZv369N3bJJZcQExNDy5YtXczM\nGJNbBDo86KyqHgQKiOfFlU+B+kHMy4QJVeXVV1+ldu3afsUmMjKSzZs3W7ExxngF2sJJcibgXA3M\nE5EDwKngpWXCwaFDh+jSpQvvvHNuYdjChQszfvx4+vfvb8OdjTF+ApppQERKASfxtIjaAaWBear6\nW3DTCz6baSB7Vq1aRbt27fj553PjRmrWrMmCBQuoXbu2i5kZY0IhmDMNDFXVZGdBttmq+hzQL+sp\nmnB3+vRpBg0aROPGjf2KTffu3YmPj7diY4xJV6AtnI2qWidNbJOq3hi0zELEWjiB27FjB61bt/ab\nmubSSy9lzpw53HvvvS5mZowJtey0cDKbaaAr0A2oJiIbfXaVAjZkPUUTjlSVl19+mSeffJJTp849\numvSpAmvv/465cuXdzE7Y0y4yGwBtgg86+E8DQzx2XVMVQ8EObeQsBZOxg4dOsTjjz/Oe++9540V\nKVKEyZMn06tXLxsYYEw+FdTlCUTkOuBW5+OXqroti/nlSlZw0ne+gQHXXXcdCxcupFatWi5mZoxx\nW9AGDYhIT+At4ArnJ1ZEemQ9RRMOTp8+zZAhQ/4yMOCJJ55g/fr1VmyMMdkS6KCBzUBDVT3ufL4I\n+K+q3hDk/ILOWjj+vvvuO1q1akV8fLw3ZgMDjDFpBXNYtACnfT6fcWImj1BV5s6dS+3atf2KzV13\n3cXmzZut2BhjLlhmo9QKqepZYD6wVkTednY9CMwNdnImNJKSkujevTsLFy70xgoXLszEiRPp27ev\nDQwwxuSIzEaped+/EZH6wD+dXV+q6vp0Twwj+b1Lbc2aNbRq1cpvdudq1aqxcOFC6tSpk/6Jxph8\nLcffw8Gn20xV1wHrspOYyX2Sk5OZNGkSI0eOJDn53LJHnTp1Yvr06ZQsWdLF7IwxeVFmBecyEUl3\nChtnihsTZvbt20ebNm2Ii4vzxkqXLs0rr7xCVFSUe4kZY/K0zApOQeAibIBAnrF06VI6dOjA4cOH\nvbGGDRvy5ptvUqVKFRczM8bkdQE/w8mr8ssznD/++INBgwbx/PPPe2MFChRg+PDhjBgxgkKFAl2p\nwhhjgvwMx4Sv7du3Ex0dzebNm72xSpUqsWDBAlv22RgTMpmNd70jJFmYoFBVZs+eTb169fyKTfPm\nzdm0aZMVG2NMSGVYcFT1cEb7Te6VlJRE69atefzxxzl58iQARYsW5cUXX+Sdd96hTJkyLmdojMlv\nrOM+D1q3bh3R0dH873//88Zq1qzJ4sWLbR40Y4xrgvoKuYjMFpH9zlxsqbEIEVkhIjtFZLmIlPbZ\nN1REEkVku4jc5ROvIyKbRWSXiEzziRcRkUXOOV+LyBU++9o7x+8UkXbB/D1zi5SUFKZMmUKjRo38\nik3nzp2Jj4+3YmOMcVWw5yyZAzRJExsCrFTV6sBnwFAAEbkWaAHUBO4BXhKR1EELM4FOqloNz2Jw\nqdfsBBxW1arANGCyc60IYCRwM9AAGOVb2PKiAwcO0LRpUwYOHMjZs2cBuPjii1m8eDEvv/wyJUqU\ncDlDY0x+F9SCo6qrgSNpwg9wbh62uUBzZ/t+YJGqnlXV3UAiUF9EygOlfKbSmedzju+1lgC3O9tN\ngBWqmqSqR4EVwN059ovlMqtWreLGG29k+fLl3liDBg349ttvadGihYuZGWPMOW7MylhWVfcDqOqv\nQFknfjmwx+e4fU7scmCvT3yvE/M7R1WTgSQRKZPBtfKUs2fPMnz4cBo3bsyvv/7qjQ8aNIgvv/yS\nq666ysXsjDHGX24YNJCTb13mm/eGfvrpJ1q3bs1XX33ljZUtW5b58+dz1113ZXCmMca4w42Cs19E\nyqnqfqe77IAT3wdU9jmukhNLL+57zs8iUhC4WFUPi8g+IDLNOZ+nl9Do0aO925GRkURGRqZ3aK6w\ndOlSHnvsMY4cOddbeeeddzJ//nzKly/vYmbGmLwqLi7Ob/7FbFHVoP4AVwJbfD5PAgY724OBic72\ntcA3QBHgKuA7zk29swaoj6cFswy424n3AF5ytqPxPAMCiAC+B0r7bF+STn4aLv744w/t06eP4mkV\nKqAFCxbU8ePHa3JystvpGWPyEefPzizVg6C2cETkTTwtjb+JyE/AKGAi8JaIdAR+xDMyDVVNEJFY\nIAHPiqI9nF8KoCfwOlAMWKaqnzjx2cB8EUkEDuEpOqjqEREZC8Q7fzCPUc/ggbD1/fff07JlSzZs\n2OCNVa5cmYULF9KoUSMXMzPGmMBkOHlnfhAOk3fGxsby+OOPc+zYMW/s/vvvZ86cOTZjgDHGFdmZ\nvNPWDs7FTp06Rbdu3WjZsqW32BQuXJhp06bx3nvvWbExxoSV3DBKzZzHzp07adGihd+km1dffTWL\nFy+mXr16LmZmjDHZYy2cXGjBggXUrVvXr9hERUWxceNGKzbGmLBlBScXOXnyJI8//jht2rThxIkT\ngGeG55kzZ7J48WJKl87Ts/MYY/I461LLJXbs2EFUVBRbt271xqpWrUpsbCw33XSTi5kZY0zOsBZO\nLjB//nzq1avnV2xatWrFhg0brNgYY/IMKzguOnnyJJ06daJdu3beLrRixYrx8ssvs2DBAkqVKuVy\nhsYYk3OsS80l5+tCq169OrGxsdxwww0uZmaMMcFhLRwXLFiw4C9daI8++ijx8fFWbIwxeZYVnBA6\ndeoUXbp08RuFVqxYMV555RXmz5/PRRdd5HKGxhgTPNalFiK7du0iKirK792aatWq8dZbb1mrxhiT\nL1gLJwRiY2OpV6+eX7GJjo62LjRjTL5iBSeI/vzzT5544gm/udCKFi1KTEwMb775po1CM8bkK9al\nFiT/+9//iIqK8ltO4JprruGtt96idu3aLmZmjDHusBZOECxdupQ6der4FZtHHnmEDRs2WLExxuRb\nVnBy0JkzZxg0aBAPPPAAR4961nsrXLgw06dPJzY21uZCM8bka9allkP27dtHdHQ0q1ev9sauuOIK\nYmNjadCggYuZGWNM7mAtnBywcuVKateu7VdsmjVrxjfffGPFxhhjHFZwLkBKSgpjx47lrrvu4uDB\ngwAUKFCAp59+mqVLl9qKnMYY48O61LLpt99+o23btnzyySfeWLly5Vi0aBGRkZHuJWaMMbmUFZxs\nWLt2LVFRUezZs8cbu+2221i4cCEVKlRwMTNjjMm9rEstC1SV559/nltvvdWv2AwdOpSVK1dasTHG\nmAxYCydAx48fp3PnzixatMgbu+SSS5g/fz733nuvi5kZY0x4sIITgISEBB5++GF27NjhjdWtW5cl\nS5Zw5ZVXupeYMcaEEetSy8SiRYuoX7++X7Hp2rUrq1evtmJjjDFZYAUnHadPn6ZXr160atXKu3ZN\n8eLFmTdvHjExMRQrVszlDI0xJrxYl9p57NmzhxYtWrBmzRpvrGrVqrz99tvUqlXLxcyMMSZ8WQsn\njZUrV1KnTh2/YvPQQw8RHx9vxcYYYy5Ani84InK3iOwQkV0iMji941JSUpgwYQJNmjTht99+A6Bg\nwYJMmTKFJUuWcPHFF4csZ2OMyYvydMERkQLAC0AT4DqglYjUSHvc0aNHad68OU899RQpKSkAlC9f\nns8++4z+/fsjIiHNOyvi4uLcTuGCWP7usvzdE865Z1eeLjhAfSBRVX9U1TPAIuCBtAfVq1ePDz74\nwPv51ltvZePGjfzrX/8KXabZFO7/0Vr+7rL83RPOuWdXXi84lwN7fD7vdWJ+vv/+e+92v379WLVq\nlc0aYIwxOcxGqTkuuugiXnvtNaKiotxOxRhj8iRRVbdzCBoR+QcwWlXvdj4PAVRVJ/kck3dvgDHG\nBJGqZukBd14vOAWBncAdwC/AOqCVqm53NTFjjMmH8nSXmqomi8gTwAo8z6tmW7Exxhh35OkWjjHG\nmNwjr49Sy1CgL4XmViKyW0Q2icg3IrLO7XwyIyKzRWS/iGz2iUWIyAoR2Skiy0WktJs5ZiSd/EeJ\nyF4R2ej83O1mjukRkUoi8pmIbBORLSLS24mHxf0/T/69nHi43P+iIrLW+X91i4iMcuLhcv/Tyz9L\n9z/ftnCcl0J34Xm+8zOwHohW1R0ZnpiLiMgPQF1VPeJ2LoEQkX8Cx4F5qnqDE5sEHFLVyU7Rj1DV\nIW7mmZ508h8FHFPV51xNLhMiUh4or6rfishFwAY876R1IAzufwb5tyQM7j+AiJRQ1ZPOs+WvgN7A\nw4TB/Yd087+HLNz//NzCCeil0FxOCKN/h6q6GkhbHB8A5jrbc4HmIU0qC9LJHzz/HnI1Vf1VVb91\nto8D24FKhMn9Tyf/1Hfqcv39B1DVk85mUTzPz5Uwuf+Qbv6QhfsfNn9YBUFAL4Xmcgp8KiLrRaSz\n28lkU1lV3Q+eP1SAsi7nkx1PiMi3IvJqbu0S8SUiVwI3AWuAcuF2/33yX+uEwuL+i0gBEfkG+BX4\nVFXXE0b3P538IQv3Pz8XnLygkarWAZoCPZ0un3AXbn28LwFXq+pNeP5HzNVdO0531BKgj9NSSHu/\nc/X9P0/+YXP/VTVFVWvjaVnWF5HrCKP7f578ryWL9z8/F5x9wBU+nys5sbChqr84/zwIvIunmzDc\n7BeRcuDtpz/gcj5ZoqoH9dyD0FeAm93MJyMiUgjPH9bzVfV9Jxw29/98+YfT/U+lqr8DccDdhNH9\nT+Wbf1bvf34uOOuBv4tIFREpAkQDS13OKWAiUsL52x4iUhK4C9jqblYBEfz7fJcCjznb7YH3056Q\ny/jl7/whkeohcve/g9eABFWd7hMLp/v/l/zD5f6LyKWp3U0iUhxojOc5VFjc/3Ty35HV+59vR6mB\nZ1g0MJ1zL4VOdDmlgInIVXhaNYrnAd6C3J6/iLwJRAJ/A/YDo4D3gLeAysCPQAtVPepWjhlJJ/9/\n43mekALsBrqm9snnJiLSCPgPsAXPfzMKDMMz+0Ysufz+Z5B/a8Lj/tfCMyiggPOzWFXHi0gZwuP+\np5f/PLJw//N1wTHGGBM6+blLzRhjTAhZwTHGGBMSVnCMMcaEhBUcY4wxIWEFxxhjTEhYwTHGGBMS\nVnCMyQYRKS0i3X0+VxCR2CB91wMiMtzZHi0iKSJytc/+vk6sThav+2lunnvM5D1WcIzJngigR+oH\nVf1FVVsE6bsGAS+mfhWwGc/MGKkeIYtv2IuIAPOAnjmRoDGBsIJjTPY8DVztLDo1yZkiaQuAiLQX\nkXedhbV+EJGeIvKkc+x/ReQS57irReRjZ7bvL0SkWtovEZGqwB9p1jx6H2cpDaelkwT85nzuICJT\nfc5/XESedfLbISJznTwrAR8ArYJyd4w5Dys4xmTPEOB7Va2jqqmrxfpO23EdnrVN6gPjgePOzN5r\ngHbOMS8DT6jqzcBAYOZ5vqcRsDFN7HdgjzPbcDSetZxSxQL3OYtkgWeBtdnO9t+BF1S1lqrucaZQ\nKSIiEVn5xY3JrkJuJ2BMHvW5s2DVSRE5CnzoxLcAtZwJVxsCbzndWwCFz3OdCsDBNDHFU2Si8Uza\negfQEUBVT4jIKuBeEdkBFFLVBBGpAvzos4ZJqoNARc6/sJwxOcoKjjHB8afPtvp8TsHz/10B4IjT\n6snIKeDi88Q/AqYA61T1+LmaBXhaNMOAHcAcn/iJ81ynmPMdxgSddakZkz3HgFLZPVlVjwH/E5FH\nUmMicsN5Dt0OVD3P+afwDCaYcJ596/DMPtwKWOiz63xLAZfDM8uvMUFnBceYbFDVw8BXIrJZRCZl\ndng68TZAJ2d53q3A/ec55j94pn8/Xw6xqvptOt8RC3ylqknp5SEidYE1qpqSSf7G5AhbnsCYXM4Z\ndfaBqn6WhXM+AJ5T1c8zOGYa8H5GxxiTk6yFY0zuNwEoEciBzgupO4ETARSSLVZsTChZC8cYY0xI\nWAvHGGNMSFjBMcYYExJWcIwxxoSEFRxjjDEhYQXHGGNMSFjBMcYYExL/D2Zp5lD/Q6S2AAAAAElF\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f270e4ce690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#\n",
"# Lets plot cumulative SF\n",
"#\n",
"plt.plot(data['t'], data['M_star'], lw = 3, color ='black')\n",
"plt.xlabel('time (Myr)')\n",
"plt.ylabel('Total Mass of Stars (Msun)')\n",
"\n",
"# constant SFR... not surprising"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nsnII = data['N_SNIa']\n",
"dNsnII_dt = cumulative_to_rate(nsnII)\n",
"\n",
"L_SNII = SNII_luminosity(dNsnII_dt, 0.5 * 1.0E6 * const.yr_to_s)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f270de7d650>"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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1VYmIZCgnNwCWCiUOEZHM5eoGQBERkTpKHCIikpEWE0dTV1aJiMieKZ0jjl+Z\n2Ytm9q8q6CQiIi0mDnc/EfgSMABYZGb3m9nnch6ZiIgUpbSvqorqj38BuA3YQrgv43p3z2qRp6R6\nHHcTxsX6FWHIk3nufn8T6+mqKhGRDOWqHseRwETCzvwZwj0XL5tZf+Cv7p48gGFWRPU4fg7MBTa6\n+2wze9DdL2lieSUOEZEM5epy3CnAy8Cn3f0b7v4ygLuvJtTMaCmottTjuJ0wHEk8IGJNGvGKiEgO\npZM4Zrr7dHffHjeY2TUA7j49jfXbWo9jJSF5QMNhS0REpADSSRypSrV+Od0NuPsCYGNSc109Dnev\nBuJ6HCTU4/gXM7sSeDR6/kvgiXS3KyIiudHkIIdmNhYYBww2s8cTXuoGbGjjdjOtx/GVdN5U9ThE\nRJqXjXoczY2O+zxh5Np9gP9MaP8YeDXlGgU2d+7cQocgIlLUkn9Um2V+BqDJxBGVdV0GjGxFbC1R\nPQ4RkRLVXCGnBdHjx2a2JWH62My2ZLidJutxmFlHQj2Ox1OuKSIiRaW5I44TosdubdlAYj0OM1tO\nfT2ObwJPU1+P4822bEdERPIjnRsADwJWuvtOMxsNHAnc6+6b8hBf2nQDoIhI5nJ1A+CjQI2ZDQHu\nIIxZlXLYDxERaf/SSRy17r4b+CIwxd1/QCgjKyIie6B0Ekd1dE/HZcCTUVtF7kISEZFilk7imEi4\nJPcn7v6emQ0G0hlqRERE2qG0hlWPLpkdGs2+FQ0TUlR0clxEJHOtOTne3J3j8ZuOBu4B3ifcizHA\nzC5z9/mtCTKN7Q0GbgC6u/sYMzuBUEiqA/DJ+DJhEREpjHQux10EjHP3t6L5ocAD7j4ip4GZPezu\nYxLmzwf6uPudTSyvIw4RkQzl6nLcijhpALj722RwcrwN9TiSjUOXAYuIFFw6ieMlM7vLzEZH053A\nSxlsI+N6HPFiCcsPADa5+9YMtisiIjmQTuL4OvB34FvR9PeoLS2tqMfRy8x+DQxLOBK5nJCARESk\nwFo8Oe7uO4FboylbmqvHsYGkxOTuk9N5U9XjEBFpXq7rcQBgZscDk4GBicu7+4Gt2mIOqR6HiEjz\nclqPI8HdwHeARUBNxltITfU4RERKVDqJY7O7P9XG7TRZj4NQZfASYGwbtyEiInmQzsnxP5nZLWY2\n0syOiqd0NxDV43geGGpmy81sorvXAHE9jjeAB1WPQ0SkNKRzA+CfUjS7u5+Sm5BaRzcAiohkrjU3\nAKY1VlWeJEdAAAAOyElEQVQpUOIQEclcVseqMrNL3f0+M/tuqtfdPZuX54qISIlo7uR41+ixTTXH\nRUSkfVFXlYjIHixXw6oPJlwBNYiGNwCel2mAIiJS+tK5j2MW4SbAJ4Da3IbTuB5H1NYFmAfc5O5z\nch2DiIg0LZ3EscPdb8t5JBF3fw/4qpk9nNB8LfBQvmIQEZGmpXMD4C/M7KY23ADYpnocZnYaYUTe\nj2h497mIiBRAOkccRwDjgVOo76ryaD4d04ApwL1xQ0I9jlOB1cBCM3vM3ZcmrBcnidFAF0Ldjm3A\n7DS3KyIiOZBO4rgIONDdd7VmA+6+IBqTKlFdPQ4AM4vrcSw1s17AT4jqcbj7jdEyE4B1rYlBRESy\nJ53E8TqwN7A2i9vNqB5H1H5vclsy1eMQEWleXupxEJLGUjNbCOyMG4vxclzV4xARaV6+6nHclPG7\ntkz1OERESlQ6pWPnZWE7qschItJOtHg5rpl9bGZbommHmdWY2ZZ0N6B6HCIi7UtGY1VZ6Aw7HzjO\n3a/LWVStoLGqREQyl7d6HGb2irsPz3jFHFLiEBHJXK4GObwgYbYMOBrYkWFsIiLSTqRzVdW5Cc93\nA+8DRXcproiI5Edru6q+7e7/nYN4Wk1dVSIimcvnOY7l7n5Ay0vmjxKHiEjmWpM40hkdN+W2Wrle\ny29sNtjM7oqHVU+eFxGRwmpt4sjZT3t3f8/dv9rUvIiIFFaTiSPpxr/E6WOgf7obaGs9DhERKS5N\nJg537+bu3VNM3dw9nauxYtOAMxIbEupxnEGoszHWzA5NWi+5O0xFnEREikBru6rS5u4LgI1JzXX1\nONy9GojrcWBmvczs10T1OJLncx2viIg0L5Mjh2zKtB5Ho/ocqageh4hI8/JVj6NkqB6HiEjzslGP\nI+ddVU1QPQ4RkRKVr8TRZD0OM+tIqMfxeJ5iERGRNsh54lA9DhGR9qVVQ44UIw05IiKSuXwOOSIi\nInsoJQ4REcmIEoeIiGREiUNERDKixCEiIhkpiTvHzWwAcBuwnjDG1c8KHJKIyB6rVI44jgBmRHU5\nhhU6GBGRPVlBEkcranS8AHzVzJ4F/pDXYEVEpIFCHXFkWqNjIvBDdz8NOCefgYqISEMFSRyZ1ugg\nHGVcE9XleC9/kYqISLJiOjneXI2ON4CLWnoD1eMQEWme6nEkUT0OEZHmlXI9jlRUo0NEpAQUMnGo\nRoeISAkq1OW4qtEhIlKiVI9DRGQPpnocIiKSc0ocIiKSESUOERHJiBKHiIhkRIlDREQyUhKJw8xG\nmdl8M/u1mZ1U6HhERPZkJZE4AAc+BioJY1iJiEiBlEQ9Dnef7+5nA9cBP8p3vCIiUq9U6nHENgEd\n8xKhiIikVJDRcd19gZkNTGquq8cBYGZxPY6lZvZFQkLpQUguIiJSIMU0rHpz9ThmAjNbegPV4xAR\naZ7qcSRRPQ4RkeapHoeIiOSd6nGIiEhGVI9DREQyonocIiJ7MNXjEBGRnFPiEBGRjChxiIhIRpQ4\nREQkI0ocIiKSkZK4c9zMzgfOBroBU939mQKHJCKyxyqpy3HNbG/gFne/IsVruhxXRCRDJXM5bqb1\nOBLcCPwyP1GKiEgqJVOPw8x+Csxx98X5DFRERBoqSOJw9wXAxqTmunoc7l4NxPU4MLNvAqcC/2Jm\nV+Y1WBERaaCYTo43V49jCjClpTdQPQ4RkeapHkcS1eMQkVLkDrW1YAZlOe4HykY9jmJKHKrHIZJF\n7lBTk95UW9t8W/LrTc0nP6Z6nrhMU+s195j8vLnXkpdrap2Wlk1n/ZaWi5NDqglC0vje9+CWWwr7\nd5OOQiaOJutxAB8Q6nGMLURgUlri/5DV1bB7d3hMfL57d+PnqebTmWpq0p+Pnyc+pmpLtUzysum8\nnryTdg+/XsvLW56aWi6xPXmZpl6Ln2faVlZWP8XLdOzYuC15ucTnZi0vFy+T2N5SW6p1U713U8uk\nmhKXa8WP/oIqSOKI6nGMBnqb2XLgJnefFp0Ef5pw0v5u1ePIP/ewM9q5s+G0a1fD5/F8/Ly5qbq6\n+efxjj7VfOKUmBSS582goiJMHTo0/Zj8vKm28vKGbfHOKLmtQ4ewc+vSpeEy8WtxW+LyiW2plkmc\nUq3X3PKpdn4i2VZSNwA2pz3eAOgOO3bAtm2wdWt4bGravj1MO3Y0fr5jR9PTzp2NH8vKoLKy8dSx\nY8PniVNyW0VFaKuoqJ9P1Zb4WuLzpqZ4J588n+t+YZH2qjU3ACpxZJl72Mlv2gQbN8LmzWHasqX+\nccsW+Pjj+qmqKvW0bVvYKXbpAl27NnyMp86dm546dWr8GE+VlY0f4+fl5YX+FkUkX1qTOIrp5HhR\nqq2FtWvhww9hzZrwPH5ctw7Wr69/3LAhJIuKCujZE/beO0w9ekD37uGxRw/o1g322Sc8Jk5du8Je\ne4UpThAd9C8kIkVmjz/iqKmBFSvgn/+Ed94Jj8uWwcqVsGoVfPBB2Pn37w99+oSpb1/Yd98w9e5d\nP/XqFRJGZWUOPqCISA6oq6qFz7JtG7z6Krz8MixaFB7ffDMkgCFD4KCDwuOgQbD//mHq10+JQETa\nLyWOFJ9l0yZ47DF4+GGYPx8OOQSOOqp+Ovzw0CUkIrInapeJw8wGAzcA3d19TDPLNUgcDz0E06eH\nZHHqqTBmDJxzTjiXICIiQckMq54Jd3/P3b+a6XorVsC4ceFcxcyZMHZscSeNUh8upZTjL+XYQfEX\nWqnH3xp5SxxtqMHRKt//fkgc3btn6x1zq9T/+Eo5/lKOHRR/oZV6/K2RzyOOjGpwmNl4M7vVzPrF\ni+cxVhERaULeEkemNTjcfbq7fxfYaWa/BoZl84hERERaJ68nx6MBDJ9w9yOj+QuBM9z9ymj+UuBY\nd/9WK967uM/yi4gUqT32zvFMP7iIiLROoa+qUg0OEZESk+/E0WQNDjPrSKjB8XieYxIRkQzk83Lc\n+4HngaFmttzMJrp7DRDX4HgDeFA1OEREils+r6oa5+793b3S3Q9w92lR+1Pufoi7H+zuP830fXN1\nH0gupbqnxcx6mtnTZvaWmf3RzHoUMsammNn+Zvacmb1hZq+Z2bei9lKJv9LM/mZmr0Tx3xS1l0T8\nEC5jN7OXzezxaL5kYgcws/fNbEn0b/Bi1FYSn8HMepjZDDN7M/o/8JkSin1o9J2/HD1uNrNvtSb+\nQp/jaJPm7gMpco3uaQGuA55190OA54BJeY8qPbuB77r7p4CRwDei77wk4nf3ncDJ7j4cGAacZWbH\nUiLxR64B/p4wX0qxA9QCo919uLsfG7WVymf4BTDH3T8JfBpYSonE7u5vR9/5UcAIYCswk9bE7+4l\nOwHHAU8lzF8HXFvouNKMfSDwasL8UqBv9PwTwNJCx5jm55gFnFaK8QNdgJeAY0olfsIFJM8QSi8/\nXop/O8B7QO+ktqL/DEB34J0U7UUfe4qYTwf+3Nr4S/qIA9gPWJEwvzJqK0V93H0NgLt/CPQpcDwt\nMrNBhF/tLxD+8Eoi/qir5xXgQ+AZd19I6cT/X8APgMT7lkol9pgDz5jZQjOLx6Erhc8wGFhnZtOi\n7p47zKwLpRF7souB+6PnGcdf6omjPSvqGxrNbC/gEeAad6+icbxFG7+713roqtofONbMPkUJxG9m\nZwNr3H0xzQ/BU3SxJzneQ3fJ5wldnSdSAt8/4b63o4BfRvFvJfRylELsdcysAjgPmBE1ZRx/qSeO\n9nQfyBoz6wtgZp8A1hY4niaZWQdC0pju7o9FzSUTf8zdtwBzgTMpjfiPB84zs3eBB4BTzGw68GEJ\nxF7H3T+IHj8idHUeS2l8/yuBFe7+UjT/KCGRlELsic4CFrn7umg+4/hLPXGU8n0gyfe0PA58OXp+\nGfBY8gpFZCrwd3f/RUJbScRvZvvEV42YWWfgc8CblED87n69hysSDyT8rT/n7uOBJyjy2GNm1iU6\nWsXMuhL62l+jNL7/NcAKMxsaNZ1KuI2g6GNPMpbwwyOWefyFPkmThZM8ZwJvAf8Arit0PGnGfD+w\nGtgJLAcmAj2BZ6PP8jSwd6HjbCL244EaYDHwCvBy9G/Qq0TiPyKKeTHwKnBD1F4S8Sd8jlHUnxwv\nmdgJ5wniv53X4v+zpfIZCFdSLYw+w/8APUol9ij+LsBHQLeEtozjL/oKgCIiUlxKvatKRETyTIlD\nREQyosQhIiIZUeIQEZGMKHGIiEhGlDhERCQjShyyRzKzXglDTH9gZisThptekKNtDjOzO6PnXzaz\nWjM7JeH1L0RtF2T4vg+Y2UHZjlekKe2m5rhIJtx9AzAcwMx+CFS5+6053uz1wI/iEAg3IF5CGMqa\n6PniTN4wKi3wK+Ba4MrshCnSPB1xiCQNGGhmH0ePo8xsrpnNMrN/mtl/mNm4qBDUEjMbHC23j5k9\nErX/zcw+22gDYZiNI9z99YTmBYRBFsuj4TeGECUOMzvZzGYmrH+amT0ax2dmP49G+D0uep/ToiQi\nknP6QxNpLHE4hSMJv+QPA8YDB7v7Z4C7CWWPIRT3uTVq/xfgrhTveTTwelKbE4Z6OBM4n4Qxgtz9\nT8AhZtY7apoYbROgK/BXD0V5nvcw/MM/CMNhiOScEodI8xa6+1p33wW8QxjLB8I4S4Oi56cBt0dH\nAI8De0V1GhL1I4wRlMiBBwldVBcTBp5LPPqZDlwaDcp4HPCHqH03YZykRB8B/TP+dCKtoHMcIs3b\nmfC8NmG+lvr/PwZ8xt2rm3mf7UCn5EZ3f8nMjiCcY/mnWYNes98RRr7dCcxw99qofYc3HmSuU7QN\nkZzTEYdIY80VSUrlaUId8LCyWaouozeBg5tY/1rghuRGD3UrVkevTWshvqE07goTyQklDpHGmhoy\nuqn2a4CjoxPmrwNfa7Si+1tA9+gkePJrf3T3eU1s4/eE4kFvNRWHmfUBtrl7sRcQknZCw6qL5ImZ\nXQN87O5TM1hnCvCyu09rZplvA5ubW0Ykm3TEIZI/v6HhOZNmmdlLhMJT97Ww6EbgnjbEJZIRHXGI\niEhGdMQhIiIZUeIQEZGMKHGIiEhGlDhERCQjShwiIpKR/w8uITlQ+ZQUswAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f270e0616d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#\n",
"# Lets plot the luminosities\n",
"#\n",
"\n",
"plt.plot(data['t'], data['L_Q0'], label = 'L_H', lw = 3, color = 'red')\n",
"plt.plot(data['t'], data['L_Q1'], label = 'L_He', lw = 3, color = 'red', ls = ':')\n",
"plt.plot(data['t'], data['L_FUV'], label = 'L_FUV', lw = 3, color = 'red', ls = '-.')\n",
"\n",
"plt.plot(data['t'], data['L_wind'], label = 'L_wind', lw = 3, color = 'black', ls = '-')\n",
"\n",
"plt.plot(data['t'], L_SNII, label='L_SNII'\n",
"\n",
"plt.xlabel('Time (Myr)')\n",
"plt.ylabel('Luminosity (erg/s)')\n",
"\n",
"plt.semilogy()\n",
"plt.legend(loc='best')"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"$4.8481368 \\times 10^{-6} \\; \\mathrm{pc}$"
],
"text/plain": [
"<Quantity 4.848136811133344e-06 pc>"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(1.0 * u.AU).to(u.pc)"
]
},
{
"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 |
lmoresi/UoM-VIEPS-Intro-to-Python | Notebooks/Numpy+Scipy/3 - Discussion - The Game of Life.ipynb | 1 | 16362267 | null | mit |
abelfunctions/abelfunctions | notebooks/Riemann Constant Vector Paper.ipynb | 2 | 66545 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Computing the Riemann Constant Vector\n",
"\n",
"This iPython notebook contains the example code presented in the paper [Computing the Riemann Constant Vector](http://www.cswiercz.info/assets/files/rcv.pdf) by Bernard Deconinck, Matthew S. Patterson, and Chris Swierczewski.\n",
"\n",
"First, we construct the Riemann surface, $X$, defined by the plane algebraic curve\n",
"\n",
"$$\n",
"f(x,y) = x^2 y^3 - x^4 + 1.\n",
"$$\n",
"\n",
"We computationally determine its genus as well as a basis $\\{\\tilde{\\omega}_1, \\ldots, \\tilde{\\omega}_g\\}$ of Abelian differentials of the first kind, $\\Omega_X^1$, defined on the surface $X$."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"differentials:\n",
"1/3*x**2*y**2\n",
"x/3*x**2*y**2\n",
"x*y/3*x**2*y**2\n",
"x**2/3*x**2*y**2\n",
"genus: 4\n"
]
}
],
"source": [
"from sympy.abc import x,y\n",
"from abelfunctions import (RiemannSurface, RiemannTheta, Jacobian,\n",
" AbelMap, RiemannConstantVector, puiseux)\n",
"\n",
"f = x**2*y**3 - x**4 + 1\n",
"X = RiemannSurface(f,x,y)\n",
"g = X.genus()\n",
"omega = X.holomorphic_differentials()\n",
"\n",
"print 'differentials:'\n",
"for omegai in omega:\n",
" print omegai\n",
"print 'genus:', g"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we compute a canonical basis of cycles $\\{a_1, \\ldots, a_g, b_1, \\ldots, b_g\\}$ of the first homology group $H_1(X, \\mathbb{Z})$. That is, every cycle on $X$ can be written as a linear combination of these cycles."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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4AdgemE2at2dNiaW7evmQ2vsB5uSIsU66lsQc2mupFlVNzf8+0gISN/dUQNIA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\nNzPrQP8Pv1Lla2dKx24AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1044dc890>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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tSolfhgzLbQPiJLAf8Nayl+YQ5Z0XEd8EXq7y9spjDSfGD5pAnAy2Y+GhpQFe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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1044dc4d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"a = X.a_cycles()\n",
"b = X.b_cycles()\n",
"\n",
"# use 256 points to plot the x- and y-parts of the path\n",
"figx = a[0].plot_x(256, color='blue', linewidth=2, linestyle='dashed')\n",
"figy = a[0].plot_y(256, color='green', linewidth=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using these data we can compute the period matrix of the surface:\n",
"\n",
"$$\n",
"\\tau = [A \\; B] \\in \\mathbb{C}^{g \\times 2g}\n",
"$$\n",
"\n",
"where\n",
"\n",
"$$\n",
"A_{ij} = \\oint_{a_j} \\tilde{\\omega}_i, \\quad \\text{and} \\quad B_{ij} = \\oint_{a_j} \\tilde{\\omega}_i\n",
"$$\n",
"\n",
"If using a *normalized* basis of differentials, $\\{\\omega_1, \\ldots, \\omega_g\\}$, the period matrix is of the form\n",
"\n",
"$$\n",
"\\tau = [I \\; \\Omega] \\in \\mathbb{C}^{g \\times 2g}.\n",
"$$\n",
"\n",
"This is equivalent to setting $\\Omega = A^{-1}B$ and, in fact, since Abelfunctions returns a non-normalized basis this is how the matrix $\\Omega$ is computed. We computationally verify that $\\Omega$ is a \"Riemann matrix\": a symmetric matrix with positive definite imaginary part."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.2800+1.045j 0.2800-0.485j -1.8100+1.045j 0.0000-0.j ]\n",
" [ 0.6625-1.1475j 0.6625+0.3825j -0.6625-1.1475j 0.0000+1.53j ]\n",
" [-0.8347+0.4819j -0.8347+0.4819j 0.8347-1.4457j 0.0000+1.9276j]\n",
" [-1.0450+0.28j -1.0450+1.81j -0.4850+0.28j 0.0000+0.j ]]\n",
"\n",
"[[-0.2800+0.485j 0.2800-1.045j 0.0000-2.09j 0.7650-1.325j ]\n",
" [ 0.6625+0.3825j -0.6625+0.3825j 0.0000-0.765j 0.0000-1.53j ]\n",
" [-0.8347+0.4819j 0.8347-1.4457j 0.0000-0.9638j -1.6694-0.9638j]\n",
" [ 1.0450-1.81j -1.0450-0.28j -0.0000-0.56j 0.7650-1.325j ]]\n",
"\n",
"[[ 0.3934+0.795j -0.7541-0.3691j -0.4426-0.0284j 0.2049+0.2697j]\n",
" [-0.7541-0.3691j 0.2787+0.8518j 0.0984+0.1988j -0.4344-0.1562j]\n",
" [-0.4426-0.0284j 0.0984+0.1988j -0.3770+0.6815j -0.9180+0.4543j]\n",
" [ 0.2049+0.2697j -0.4344-0.1562j -0.9180+0.4543j -1.2787+0.8802j]]\n"
]
}
],
"source": [
"# for brevity, we only print the first four significant digits\n",
"import numpy\n",
"numpy.set_printoptions(precision=4, suppress=True)\n",
"\n",
"tau = X.period_matrix()\n",
"A = tau[:g,:g]\n",
"B = tau[:g,g:]\n",
"Omega = X.riemann_matrix() # returns A**(-1)*B\n",
"\n",
"print A\n",
"print\n",
"print B\n",
"print\n",
"print Omega"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"error: 3.54420172099e-10\n",
"evals: [ 1.4038 1.1654 0.4294 0.21 ]\n"
]
}
],
"source": [
"symmetric_error = numpy.linalg.norm(Omega - Omega.T)\n",
"imag_part_evals = numpy.linalg.eigvals(Omega.imag)\n",
"\n",
"print 'error:', symmetric_error\n",
"print 'evals:', imag_part_evals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Places and Divisors\n",
"\n",
"Places $P \\in X$ are represented using a Puiseux series. If $x$ and $y$ are the affine coordinates of the underlying curve $f$ then we write\n",
"\n",
"$$\n",
"P =\n",
"\\begin{cases}\n",
"x(t) = \\alpha + \\lambda t^e \\\\\n",
"y(t) = \\sum_i \\beta_i t^{n_i}\n",
"\\end{cases}\n",
"$$\n",
"\n",
"In the case when $e = 1$ (an \"unramified place\") and $x \\neq \\infty$ a place is synonymous with a tuple $(\\alpha, \\beta) \\in \\mathbb{C}^2$ such that $f(\\alpha, \\beta) = 0$.\n",
"\n",
"A divisor $\\mathcal{D}$ is a *formal sum* of places, $\\mathcal{D} = \\sum_i n_i P_i$, where each of the $n_i$ are the *multiplicities* of the places $P_i$ in the divisor. Below we compute local representation of several places on the Riemann surface and construct some divisors from them."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(-t**3, -1/t**2 + O(1))]\n"
]
}
],
"source": [
"places_above_zero = X(0)\n",
"print places_above_zero"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Places:\n",
"(2, RootOf(4*_y**3 - 15, 0))\n",
"(2, RootOf(4*_y**3 - 15, 1))\n",
"(2, RootOf(4*_y**3 - 15, 2))\n",
"Puiseux:\n",
"(t + 2, RootOf(4*_y**3 - 15, 0) + O(t**2))\n",
"(t + 2, RootOf(4*_y**3 - 15, 1) + O(t**2))\n",
"(t + 2, RootOf(4*_y**3 - 15, 2) + O(t**2))\n"
]
}
],
"source": [
"print 'Places:'\n",
"places_above_two = X(2)\n",
"for P in places_above_two:\n",
" print P\n",
" \n",
"print 'Puiseux:'\n",
"series_at_two = puiseux(f,x,y,2)\n",
"for p in series_at_two:\n",
" print p "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Divisor: (2, RootOf(4*_y**3 - 15, 0)) + 3(-t**3, -1/t**2 + O(1))\n",
"Degree: 4\n"
]
}
],
"source": [
"P = places_above_zero[0]\n",
"Q = places_above_two[0]\n",
"D = 3*P + Q\n",
"\n",
"print 'Divisor:', D\n",
"print 'Degree:', D.degree"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2, RootOf(4*_y**3 - 15, 0)) + (2, RootOf(4*_y**3 - 15, 2)) + (2, RootOf(4*_y**3 - 15, 1))\n"
]
}
],
"source": [
"D2 = sum(places_above_two)\n",
"print D2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Abel Map\n",
"\n",
"Given a fixed *base place* $P_0 \\in X$, the Abel Map $A : X \\to J(X)$ is defined\n",
"\n",
"$$\n",
"A(P) = \\left( \\int_{P_0}^P \\omega_1, \\ldots, \\int_{P_0}^P \\omega_g \\right)\n",
"$$\n",
"\n",
"where the path of integration from $P_0$ to $P$ is the same for each Abelian differential of the first kind. The Abel map is linear over divisors. That is, if $\\mathcal{D} = \\sum_i n_iP_i$ then\n",
"\n",
"$$\n",
"A(\\mathcal{D}) = \\sum_i n_i A(P_i).\n",
"$$\n",
"\n",
"When we want to change or make the back point explicit we write $A(P_0,\\mathcal{D})$. Below we evaluate the Abel map at the places and divisors constructed above."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-0.5261+0.0864j 0.0669+0.6392j -0.7495+1.1037j -1.5030+1.0356j]\n",
"[-0.3875+0.1157j -0.0290+0.4437j -0.4532+0.7774j -0.9721+0.6732j]\n",
"[ 0.1468-0.0985j 0.8467+0.6989j 0.0996+1.0083j -1.1003+0.8159j]\n",
"6.48145569903e-16\n"
]
}
],
"source": [
"J = Jacobian(X) # reduces vectors modulo lattice ZZ^g + Omega ZZ^g\n",
"z1 = AbelMap(P) # Abel map from P0 to P\n",
"z2 = AbelMap(Q) # Abel map from P0 to Q\n",
"z3 = AbelMap(P,Q) # Abel map from P to Q\n",
"print z1\n",
"print z2\n",
"print z3\n",
"\n",
"# numerically verify that A(P,Q) = A(P0,Q) - A(P0,P)\n",
"print numpy.linalg.norm( J((z2-z1) - z3) )"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.0670-0.1361j 0.9421+0.7429j -0.4887+0.7663j -1.5057+0.6992j]\n",
"1.3608726004e-15\n"
]
}
],
"source": [
"w = AbelMap(D)\n",
"print w\n",
"\n",
"# verify that w = 3*z1 + z2 mod period lattice\n",
"z = J(3*z1 + z2)\n",
"print numpy.linalg.norm(w-z)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Canonical and Valuation Divisors\n",
"\n",
"Let $P = (x(t), y(t))$ be the Puiseux series representation of a place $P \\in X$. Given a meromorphic differential $\\nu$ we can write the \"localization of $\\nu$ at $P$\" as a Laurent series in the local parameter $t$:\n",
"\n",
"$$\n",
"\\nu\\big|_P = (ct^n + \\cdots) dt.\n",
"$$\n",
"\n",
"Then the \"valuation\" of $\\nu$ at $P$ is\n",
"\n",
"$$\n",
"\\text{val}(\\nu,P) = n.\n",
"$$\n",
"\n",
"The **valuation divisor** $(\\nu) \\in \\text{Div}(X)$ of $\\nu$ is the divisor\n",
"\n",
"$$\n",
"(\\nu) = \\sum_{P \\in X} \\text{val}(\\nu,P) P.\n",
"$$\n",
"\n",
"A divisor is **canonical** if and only if it's the valuation divisor of a *holomorphic* differential on $X$. All canonical divisors are of degree $2g-2$. Below, we compute a canonical divisor for each holmomrphic differential basis element $\\tilde{\\omega}_i$ and verify that their degree is $6$."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6 (t**(-3), t**(-2) + O(1))\n",
"Degree: 6\n"
]
}
],
"source": [
"C0 = omega[0].valuation_divisor()\n",
"for place,multiplicity in C0:\n",
" print multiplicity, place\n",
"print 'Degree:', C0.degree"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3 (-t**3, -1/t**2 + O(1))\n",
"3 (t**(-3), t**(-2) + O(1))\n",
"Degree: 6\n"
]
}
],
"source": [
"C1 = omega[1].valuation_divisor()\n",
"for place,multiplicity in C1:\n",
" print multiplicity, place \n",
"print 'Degree:', C1.degree"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 (t**3/4 + 1, t + O(t**3))\n",
"1 (-t**3*RootOf(_x**2 + 1, 0)/4 + RootOf(_x**2 + 1, 0), t + O(t**3))\n",
"1 (-t**3/4 - 1, t + O(t**3))\n",
"1 (t**(-3), t**(-2) + O(1))\n",
"1 (-t**3, -1/t**2 + O(1))\n",
"1 (-t**3*RootOf(_x**2 + 1, 1)/4 + RootOf(_x**2 + 1, 1), t + O(t**3))\n",
"Degree: 6\n"
]
}
],
"source": [
"C2 = omega[2].valuation_divisor()\n",
"for place,multiplicity in C2:\n",
" print multiplicity, place\n",
"print 'Degree:', C2.degree"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6 (-t**3, -1/t**2 + O(1))\n",
"Degree: 6\n"
]
}
],
"source": [
"C3 = omega[3].valuation_divisor()\n",
"for place,multiplicity in C3:\n",
" print multiplicity, place\n",
"print 'Degree:', C3.degree"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Riemann Constant Vector\n",
"\n",
"The Riemann constant vector satisfies the following two theorems:\n",
"\n",
"**Theorem 1:** $\\mathcal{C}$ is a canonical divisor if any only if $K(P_0) \\equiv -A(P_0,\\mathcal{C})$.\n",
"\n",
"**Theorem 2:** $\\theta(W,\\Omega) = 0$ if and only if $W = A(P_0,\\mathcal{D}) + K(P_0)$ where $\\mathcal{D}$ is a degree $g-1$ divisor.\n",
"\n",
"We compute $K$ below and verify that these two theorems are satisfied."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.8488+0.7203j -0.5941-0.1146j -0.7432+0.8913j -0.8189+1.1381j]\n"
]
}
],
"source": [
"K = RiemannConstantVector # alias the RCV function for brevity\n",
"P0 = X.base_place()\n",
"print K(P0)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.+0.j -0.+0.j -0.+0.j 0.+0.j]\n"
]
}
],
"source": [
"z = J(2*K(P0) + AbelMap(C3))\n",
"print z"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.59621137233e-08\n"
]
}
],
"source": [
"W = K(P0)\n",
"v = RiemannTheta.oscillatory_part(W,Omega)\n",
"print abs(v)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9.39107997133e-10\n"
]
}
],
"source": [
"D = sum(places_above_two)\n",
"W = J(AbelMap(D) + K(P0))\n",
"v = RiemannTheta.oscillatory_part(W, Omega)\n",
"print abs(v)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Additional Code\n",
"===============\n",
"\n",
"The following code generates Figure 1 in the paper \"Computing the Riemann Constant Vector\"."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from abelfunctions.riemann_constant_vector import initialize_half_lattice_vectors"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"h = initialize_half_lattice_vectors(X)\n",
"V = 0.5*AbelMap(C3)\n",
"\n",
"oscillatory_parts = [\n",
" RiemannTheta.oscillatory_part(J(hi.T-V),Omega)\n",
" for hi in h\n",
"]\n",
"oscillatory_magnitudes = sorted(map(abs,oscillatory_parts))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/cswiercz/anaconda/lib/python2.7/site-packages/matplotlib/figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
" \"matplotlib is currently using a non-GUI backend, \"\n"
]
},
{
"data": {
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44Bh0pRrxYjWa1dXVZWmMgw1vKRrC4Li8Sw2z/7qMjIwkt/v6+t7M1DBXV1eP6Vt9pc4F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\niIiIlMX/BywGhuIONA4SAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1095384d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.semilogy(range(256), oscillatory_magnitudes,\n",
" linestyle='', marker='d',markerfacecolor='grey',markersize=8)\n",
"ax.axis([-32,256+32,1e-9,10])\n",
"ax.set_xticks([-32] + [32*k for k in range(10)])\n",
"ax.set_xticklabels([''] + [str(32*k) for k in range(9)] + [''])\n",
"ax.grid(True)\n",
"fig.set_figwidth(9)\n",
"fig.set_figheight(4)\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
nkmk/python-snippets | notebook/pandas_merge_join.ipynb | 1 | 17068 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df_ab = pd.DataFrame({'a': ['a_1', 'a_2', 'a_3'], 'b': ['b_1', 'b_2', 'b_3']})\n",
"df_ac = pd.DataFrame({'a': ['a_1', 'a_2', 'a_4'], 'c': ['c_1', 'c_2', 'c_4']})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 a_1 b_1\n",
"1 a_2 b_2\n",
"2 a_3 b_3\n"
]
}
],
"source": [
"print(df_ab)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a c\n",
"0 a_1 c_1\n",
"1 a_2 c_2\n",
"2 a_4 c_4\n"
]
}
],
"source": [
"print(df_ac)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"0 a_1 b_1 c_1\n",
"1 a_2 b_2 c_2\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"0 a_1 b_1 c_1\n",
"1 a_2 b_2 c_2\n"
]
}
],
"source": [
"print(df_ab.merge(df_ac))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"0 a_1 b_1 c_1\n",
"1 a_2 b_2 c_2\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac, on='a'))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a_ c\n",
"0 a_1 c_1\n",
"1 a_2 c_2\n",
"2 a_4 c_4\n"
]
}
],
"source": [
"df_ac_ = df_ac.rename(columns={'a': 'a_'})\n",
"print(df_ac_)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b a_ c\n",
"0 a_1 b_1 a_1 c_1\n",
"1 a_2 b_2 a_2 c_2\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac_, left_on='a', right_on='a_'))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"0 a_1 b_1 c_1\n",
"1 a_2 b_2 c_2\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac_, left_on='a', right_on='a_').drop(columns='a_'))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"0 a_1 b_1 c_1\n",
"1 a_2 b_2 c_2\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac, on='a', how='inner'))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"0 a_1 b_1 c_1\n",
"1 a_2 b_2 c_2\n",
"2 a_3 b_3 NaN\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac, on='a', how='left'))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"0 a_1 b_1 c_1\n",
"1 a_2 b_2 c_2\n",
"2 a_4 NaN c_4\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac, on='a', how='right'))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"0 a_1 b_1 c_1\n",
"1 a_2 b_2 c_2\n",
"2 a_3 b_3 NaN\n",
"3 a_4 NaN c_4\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac, on='a', how='outer'))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c _merge\n",
"0 a_1 b_1 c_1 both\n",
"1 a_2 b_2 c_2 both\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac, on='a', how='inner', indicator=True))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c _merge\n",
"0 a_1 b_1 c_1 both\n",
"1 a_2 b_2 c_2 both\n",
"2 a_3 b_3 NaN left_only\n",
"3 a_4 NaN c_4 right_only\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac, on='a', how='outer', indicator=True))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c indicator\n",
"0 a_1 b_1 c_1 both\n",
"1 a_2 b_2 c_2 both\n",
"2 a_3 b_3 NaN left_only\n",
"3 a_4 NaN c_4 right_only\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac, on='a', how='outer', indicator='indicator'))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 a_1 c_1\n",
"1 a_2 c_2\n",
"2 a_4 c_4\n"
]
}
],
"source": [
"df_ac_b = df_ac.rename(columns={'c': 'b'})\n",
"print(df_ac_b)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b_x b_y\n",
"0 a_1 b_1 c_1\n",
"1 a_2 b_2 c_2\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac_b, on='a'))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b_left b_right\n",
"0 a_1 b_1 c_1\n",
"1 a_2 b_2 c_2\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac_b, on='a', suffixes=['_left', '_right']))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"df_abx = df_ab.assign(x=['x_2', 'x_2', 'x_3'])\n",
"df_acx = df_ac.assign(x=['x_1', 'x_2', 'x_2'])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b x\n",
"0 a_1 b_1 x_2\n",
"1 a_2 b_2 x_2\n",
"2 a_3 b_3 x_3\n"
]
}
],
"source": [
"print(df_abx)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a c x\n",
"0 a_1 c_1 x_1\n",
"1 a_2 c_2 x_2\n",
"2 a_4 c_4 x_2\n"
]
}
],
"source": [
"print(df_acx)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b x c\n",
"0 a_2 b_2 x_2 c_2\n"
]
}
],
"source": [
"print(pd.merge(df_abx, df_acx))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b x c\n",
"0 a_2 b_2 x_2 c_2\n"
]
}
],
"source": [
"print(pd.merge(df_abx, df_acx, on=['a', 'x']))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b x_x c x_y\n",
"0 a_1 b_1 x_2 c_1 x_1\n",
"1 a_2 b_2 x_2 c_2 x_2\n"
]
}
],
"source": [
"print(pd.merge(df_abx, df_acx, on='a'))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a c x_\n",
"0 a_1 c_1 x_1\n",
"1 a_2 c_2 x_2\n",
"2 a_4 c_4 x_2\n"
]
}
],
"source": [
"df_acx_ = df_acx.rename(columns={'x': 'x_'})\n",
"print(df_acx_)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b x c x_\n",
"0 a_2 b_2 x_2 c_2 x_2\n"
]
}
],
"source": [
"print(pd.merge(df_abx, df_acx_, left_on=['a', 'x'], right_on=['a', 'x_']))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b x c\n",
"0 a_2 b_2 x_2 c_2\n"
]
}
],
"source": [
"print(pd.merge(df_abx, df_acx, on=['a', 'x'], how='inner'))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b x c\n",
"0 a_1 b_1 x_2 NaN\n",
"1 a_2 b_2 x_2 c_2\n",
"2 a_3 b_3 x_3 NaN\n"
]
}
],
"source": [
"print(pd.merge(df_abx, df_acx, on=['a', 'x'], how='left'))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b x c\n",
"0 a_2 b_2 x_2 c_2\n",
"1 a_1 NaN x_1 c_1\n",
"2 a_4 NaN x_2 c_4\n"
]
}
],
"source": [
"print(pd.merge(df_abx, df_acx, on=['a', 'x'], how='right'))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b x c\n",
"0 a_1 b_1 x_2 NaN\n",
"1 a_2 b_2 x_2 c_2\n",
"2 a_3 b_3 x_3 NaN\n",
"3 a_1 NaN x_1 c_1\n",
"4 a_4 NaN x_2 c_4\n"
]
}
],
"source": [
"print(pd.merge(df_abx, df_acx, on=['a', 'x'], how='outer'))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b x c\n",
"0 a_1 NaN x_1 c_1\n",
"1 a_1 b_1 x_2 NaN\n",
"2 a_2 b_2 x_2 c_2\n",
"3 a_3 b_3 x_3 NaN\n",
"4 a_4 NaN x_2 c_4\n"
]
}
],
"source": [
"print(pd.merge(df_abx, df_acx, on=['a', 'x'], how='outer', sort=True))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" c\n",
"a \n",
"a_1 c_1\n",
"a_2 c_2\n",
"a_4 c_4\n"
]
}
],
"source": [
"df_ac_i = df_ac.set_index('a')\n",
"print(df_ac_i)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"0 a_1 b_1 c_1\n",
"1 a_2 b_2 c_2\n"
]
}
],
"source": [
"print(pd.merge(df_ab, df_ac_i, left_on='a', right_index=True))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" b\n",
"a \n",
"a_1 b_1\n",
"a_2 b_2\n",
"a_3 b_3\n"
]
}
],
"source": [
"df_ab_i = df_ab.set_index('a')\n",
"print(df_ab_i)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" b c\n",
"a \n",
"a_1 b_1 c_1\n",
"a_2 b_2 c_2\n"
]
}
],
"source": [
"print(pd.merge(df_ab_i, df_ac_i, left_index=True, right_index=True))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" b\n",
"a \n",
"a_1 b_1\n",
"a_2 b_2\n",
"a_3 b_3\n"
]
}
],
"source": [
"print(df_ab_i)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" c\n",
"a \n",
"a_1 c_1\n",
"a_2 c_2\n",
"a_4 c_4\n"
]
}
],
"source": [
"print(df_ac_i)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" b c\n",
"a \n",
"a_1 b_1 c_1\n",
"a_2 b_2 c_2\n",
"a_3 b_3 NaN\n"
]
}
],
"source": [
"print(df_ab_i.join(df_ac_i))"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" b c\n",
"a \n",
"a_1 b_1 c_1\n",
"a_2 b_2 c_2\n"
]
}
],
"source": [
"print(df_ab_i.join(df_ac_i, how='inner'))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 a_1 b_1\n",
"1 a_2 b_2\n",
"2 a_3 b_3\n"
]
}
],
"source": [
"print(df_ab)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b c\n",
"0 a_1 b_1 c_1\n",
"1 a_2 b_2 c_2\n",
"2 a_3 b_3 NaN\n"
]
}
],
"source": [
"print(df_ab.join(df_ac_i, on='a'))"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" d\n",
"a \n",
"a_1 d_1\n",
"a_4 d_4\n",
"a_5 d_5\n"
]
}
],
"source": [
"df_ad_i = pd.DataFrame({'a': ['a_1', 'a_4', 'a_5'], 'd': ['d_1', 'd_4', 'd_5']}).set_index('a')\n",
"print(df_ad_i)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" b c d\n",
"a \n",
"a_1 b_1 c_1 d_1\n",
"a_2 b_2 c_2 NaN\n",
"a_3 b_3 NaN NaN\n"
]
}
],
"source": [
"print(df_ab_i.join([df_ac_i, df_ad_i]))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" c d b\n",
"a \n",
"a_1 c_1 d_1 b_1\n",
"a_2 c_2 NaN b_2\n",
"a_4 c_4 d_4 NaN\n"
]
}
],
"source": [
"print(df_ac_i.join([df_ad_i, df_ab_i]))"
]
}
],
"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 |
buncem/deep-learning | transfer-learning/Transfer_Learning.ipynb | 1 | 1696986 | null | mit |
bosscha/alma-calibrator | notebooks/2mass/01_2MASS_environment.ipynb | 1 | 54613 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('../../src/utils/')\n",
"\n",
"from galenv import *\n",
"\n",
"from astroquery.irsa import Irsa\n",
"Irsa.ROW_LIMIT = 10000\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def plot_cone(coord, theta, res, xSize=7.5, ySize=7.5, title='', show=True, savefig=False, imgname=\"plot.png\"):\n",
" '''Only cone\n",
" coord = astropy coordinates\n",
" theta = Cone angle\n",
" res = result catalog\n",
" '''\n",
" ra = coord.ra.value\n",
" dec = coord.dec.value\n",
"\n",
" fig = plt.figure(figsize=(xSize, ySize)) \n",
" gs = gridspec.GridSpec(1, 1)\n",
" \n",
" ax = plt.subplot(gs[0])\n",
" # ax.axis('equal')\n",
" limangle = 1.5*theta\n",
" ax.set_xlim((ra-limangle, ra+limangle))\n",
" ax.set_ylim((dec-limangle, dec+limangle))\n",
" \n",
" # Central position/object\n",
" ax.plot(ra, dec, 'ro', alpha=0.5)\n",
" \n",
" # Catalog object\n",
" ax.plot(res['ra'], res['dec'], 'k.')\n",
" \n",
" plt.gca().invert_xaxis() # RA from E to W\n",
" ax.set_xlabel('RA (deg)')\n",
" ax.set_ylabel('DEC (deg)')\n",
" plt.title(title)\n",
"\n",
" # Circle\n",
" # it is wrong if I draw a circle around (ra, dec) with radius theta\n",
" # due to small circle in celestial sphere for DEC\n",
" circle = plt.Circle((ra, dec), theta, fc='none', ec='black')\n",
" ax.add_artist(circle)\n",
" \n",
" fig.tight_layout()\n",
"\n",
" if savefig:\n",
" plt.savefig(imgname)\n",
"\n",
" if show:\n",
" plt.show()\n",
"\n",
" plt.close()\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"ga = Galenv()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check using name"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# objlist = ['J0541-0211', 'J1733-3722', 'J1610-3958', 'J1743-0350', 'J2253+1608', \n",
"# 'J1851+0035', 'J0541-0541', 'J0601-7036', 'J1130-1449', 'J1305-4928', \n",
"# 'J0336+3218', 'J0006-0623', 'J1717-3342', 'J1833-210B', 'J0237+2848', \n",
"# 'J0750+1231', 'J1751+0939', 'J0948+0022', 'J1107-4449', 'J1256-0547', \n",
"# 'J1830+0619', 'J1225+1253',\n",
"# 'J0747-3310', 'J1516+1932', 'J0438+3004', 'J2134-0153', 'J2226+0052', \n",
"# 'J0426+2327', 'J1626-2951', 'J1058-8003']\n",
"\n",
"#objlist = ['J2253+1608']\n",
"# typical size (diameter) of galaxy cluster => 2 - 10 Mpc\n",
"#tangential_dist = 0.05 # Mpc \n",
"\n",
"# objlist = ['WISE J161021.87-395858.4', '[HB89] 1741-038', '3C 454.3', 'PKS 0539-057', 'PKS 0601-70', 'SSTSL2 J113006.83-144912.6',\n",
"# 'NGC 4945', '[HB89] 0333+321 ABS01', 'PKS 0003-066', 'PKS 1830-21', '[HB89] 0234+285', '[HB89] 0748+126',\n",
"# '[HB89] 1749+096', 'WISE J094857.31+002225.6', '[HB89] 1104-445', '3C 279', 'WISE J183005.92+061915.7', \n",
"# 'MESSIER 084', '[HB89] 1514+197', 'LQAC 069+030 001', '[HB89] 2131-021', '4C +00.81', 'LQAC 066+023 001',\n",
"# 'PKS 1622-29', 'PKS 1057-79']\n",
"\n",
"# for obj in objlist:\n",
"# objname = 'PKS ' + obj\n",
"\n",
"def search_and_plot(objname, ra, dec, tangential_dist, cat='fp_psc'):\n",
" try:\n",
" print(objname)\n",
" z, v0, _ra, _dec = ga.queryobject_byname(objname)\n",
" print(\"NED (z, v, ra, dec): \", z, v0, _ra, _dec)\n",
"\n",
" obj_coord = coordinates.SkyCoord(ra=ra, dec=dec, unit=(u.deg, u.deg))\n",
"\n",
" dA, theta = ga.calc_dA_theta(z, tangential_dist)\n",
" print(\"From redshift & tangential_dist (dA, theta):\", dA, theta)\n",
" \n",
" result = Irsa.query_region(obj_coord, catalog=cat, spatial=\"Cone\", radius= theta * u.deg)\n",
" \n",
" plot_cone(obj_coord, theta, result, savefig=True, imgname=objname + '.png')\n",
" \n",
" return result\n",
" print(\"----\")\n",
" \n",
" except:\n",
" print(\"error! maybe can not identify from name\") \n",
" print(\"----\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PKS J2253+1608\n",
"NED (z, v, ra, dec): 0.859 257522.0 343.49062 16.14821\n",
"From redshift & tangential_dist (dA, theta): 1629.9307676687627 0.07030455605796143\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 540x540 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = search_and_plot('PKS J2253+1608', 343.49061, 16.148211, 2, 'fp_psc')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"936.0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"0.26*3600"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<i>Table masked=True length=676</i>\n",
"<table id=\"table140028490072640\" class=\"table-striped table-bordered table-condensed\">\n",
"<thead><tr><th>ra</th><th>dec</th><th>clon</th><th>clat</th><th>err_maj</th><th>err_min</th><th>err_ang</th><th>designation</th><th>j_m</th><th>j_cmsig</th><th>j_msigcom</th><th>j_snr</th><th>h_m</th><th>h_cmsig</th><th>h_msigcom</th><th>h_snr</th><th>k_m</th><th>k_cmsig</th><th>k_msigcom</th><th>k_snr</th><th>ph_qual</th><th>rd_flg</th><th>bl_flg</th><th>cc_flg</th><th>ndet</th><th>gal_contam</th><th>mp_flg</th><th>hemis</th><th>xdate</th><th>scan</th><th>glon</th><th>glat</th><th>a</th><th>dist_opt</th><th>phi_opt</th><th>b_m_opt</th><th>vr_m_opt</th><th>nopt_mchs</th><th>ext_key</th><th>dist</th><th>angle</th><th>j_h</th><th>h_k</th><th>j_k</th><th>id</th></tr></thead>\n",
"<thead><tr><th>deg</th><th>deg</th><th></th><th></th><th>arcs</th><th>arcs</th><th>deg</th><th></th><th>mag</th><th>mag</th><th>mag</th><th></th><th>mag</th><th>mag</th><th>mag</th><th></th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>deg</th><th>deg</th><th></th><th>arcs</th><th>deg</th><th>mag</th><th>mag</th><th></th><th></th><th>arcs</th><th>deg</th><th></th><th></th><th></th><th></th></tr></thead>\n",
"<thead><tr><th>float64</th><th>float64</th><th>object</th><th>object</th><th>float64</th><th>float64</th><th>int32</th><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>object</th><th>object</th><th>object</th><th>object</th><th>object</th><th>int32</th><th>int32</th><th>object</th><th>object</th><th>int32</th><th>float64</th><th>float64</th><th>object</th><th>float64</th><th>int32</th><th>float64</th><th>float64</th><th>int32</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>object</th></tr></thead>\n",
"<tr><td>343.491</td><td>16.148</td><td>22h53m57.75s</td><td>16d08m53.63s</td><td>0.07</td><td>0.06</td><td>90</td><td>22535774+1608536</td><td>14.494</td><td>0.027</td><td>0.03</td><td>61.6</td><td>13.855</td><td>0.029</td><td>0.03</td><td>52.7</td><td>13.061</td><td>0.026</td><td>0.027</td><td>55.9</td><td>AAA</td><td>222</td><td>111</td><td>000</td><td>665566</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>73</td><td>86.111</td><td>-38.184</td><td>U</td><td>0.0</td><td>115</td><td>14.8</td><td>14.1</td><td>1</td><td>--</td><td>0.083101</td><td>29.956018</td><td>0.639</td><td>0.794</td><td>1.433</td><td>0</td></tr>\n",
"<tr><td>343.492</td><td>16.152</td><td>22h53m58.16s</td><td>16d09m06.78s</td><td>0.07</td><td>0.06</td><td>90</td><td>22535816+1609067</td><td>11.844</td><td>0.017</td><td>0.021</td><td>707.7</td><td>11.339</td><td>0.021</td><td>0.023</td><td>535.0</td><td>11.239</td><td>0.019</td><td>0.021</td><td>299.1</td><td>AAA</td><td>222</td><td>111</td><td>000</td><td>666666</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>73</td><td>86.115</td><td>-38.182</td><td>U</td><td>0.8</td><td>104</td><td>14.3</td><td>13.0</td><td>1</td><td>--</td><td>14.524088</td><td>24.472708</td><td>0.505</td><td>0.1</td><td>0.605</td><td>1</td></tr>\n",
"<tr><td>343.490</td><td>16.152</td><td>22h53m57.56s</td><td>16d09m08.57s</td><td>0.27</td><td>0.25</td><td>106</td><td>22535755+1609085</td><td>16.164</td><td>0.094</td><td>0.095</td><td>13.2</td><td>16.003</td><td>0.177</td><td>0.177</td><td>7.3</td><td>15.724</td><td>0.237</td><td>0.238</td><td>4.8</td><td>ACD</td><td>222</td><td>111</td><td>ccc</td><td>060606</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>73</td><td>86.113</td><td>-38.18</td><td>U</td><td>1.2</td><td>284</td><td>16.7</td><td>16.1</td><td>1</td><td>--</td><td>15.262256</td><td>349.610108</td><td>0.161</td><td>0.279</td><td>0.44</td><td>2</td></tr>\n",
"<tr><td>343.493</td><td>16.141</td><td>22h53m58.37s</td><td>16d08m27.05s</td><td>0.07</td><td>0.06</td><td>90</td><td>22535836+1608270</td><td>14.8</td><td>0.035</td><td>0.037</td><td>46.5</td><td>14.103</td><td>0.038</td><td>0.039</td><td>42.0</td><td>13.838</td><td>0.04</td><td>0.041</td><td>27.3</td><td>AAA</td><td>222</td><td>111</td><td>000</td><td>666646</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>73</td><td>86.108</td><td>-38.191</td><td>U</td><td>1.0</td><td>172</td><td>18.7</td><td>16.9</td><td>1</td><td>--</td><td>27.969178</td><td>161.413114</td><td>0.697</td><td>0.265</td><td>0.962</td><td>3</td></tr>\n",
"<tr><td>343.497</td><td>16.142</td><td>22h53m59.33s</td><td>16d08m32.80s</td><td>0.08</td><td>0.06</td><td>90</td><td>22535933+1608328</td><td>15.616</td><td>0.066</td><td>0.067</td><td>21.9</td><td>15.064</td><td>0.078</td><td>0.078</td><td>17.3</td><td>14.725</td><td>0.092</td><td>0.092</td><td>12.1</td><td>AAA</td><td>222</td><td>111</td><td>000</td><td>562616</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>73</td><td>86.114</td><td>-38.192</td><td>U</td><td>0.5</td><td>93</td><td>19.7</td><td>17.8</td><td>1</td><td>--</td><td>30.858459</td><td>132.263622</td><td>0.552</td><td>0.339</td><td>0.891</td><td>4</td></tr>\n",
"<tr><td>343.499</td><td>16.143</td><td>22h53m59.74s</td><td>16d08m33.12s</td><td>0.28</td><td>0.26</td><td>133</td><td>22535974+1608331</td><td>16.162</td><td>0.09</td><td>0.091</td><td>13.3</td><td>15.66</td><td>0.12</td><td>0.12</td><td>10.0</td><td>15.675</td><td>0.226</td><td>0.227</td><td>5.0</td><td>ABD</td><td>222</td><td>111</td><td>000</td><td>061606</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>73</td><td>86.116</td><td>-38.193</td><td>U</td><td>0.5</td><td>142</td><td>17.4</td><td>16.7</td><td>1</td><td>--</td><td>35.299853</td><td>125.369099</td><td>0.502</td><td>-0.015</td><td>0.487</td><td>5</td></tr>\n",
"<tr><td>343.501</td><td>16.158</td><td>22h54m00.21s</td><td>16d09m29.41s</td><td>0.22</td><td>0.21</td><td>86</td><td>22540021+1609294</td><td>16.827</td><td>0.154</td><td>0.155</td><td>7.2</td><td>16.046</td><td>0.163</td><td>0.164</td><td>7.0</td><td>15.275</td><td>--</td><td>--</td><td>--</td><td>BCU</td><td>220</td><td>110</td><td>000</td><td>060600</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>73</td><td>86.129</td><td>-38.181</td><td>0</td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>--</td><td>50.450937</td><td>44.711647</td><td>0.781</td><td>--</td><td>--</td><td>6</td></tr>\n",
"<tr><td>343.500</td><td>16.165</td><td>22h53m59.94s</td><td>16d09m54.03s</td><td>0.07</td><td>0.06</td><td>90</td><td>22535994+1609540</td><td>14.215</td><td>0.025</td><td>0.028</td><td>79.7</td><td>13.795</td><td>0.026</td><td>0.027</td><td>55.7</td><td>13.641</td><td>0.046</td><td>0.047</td><td>32.7</td><td>AAA</td><td>222</td><td>111</td><td>000</td><td>666666</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>73</td><td>86.132</td><td>-38.175</td><td>U</td><td>0.7</td><td>77</td><td>16.4</td><td>15.4</td><td>1</td><td>--</td><td>68.238247</td><td>27.605575</td><td>0.42</td><td>0.154</td><td>0.574</td><td>7</td></tr>\n",
"<tr><td>343.511</td><td>16.150</td><td>22h54m02.65s</td><td>16d08m58.85s</td><td>0.11</td><td>0.11</td><td>83</td><td>22540264+1608588</td><td>15.912</td><td>0.071</td><td>0.072</td><td>16.7</td><td>15.491</td><td>0.1</td><td>0.1</td><td>11.7</td><td>15.456</td><td>0.203</td><td>0.203</td><td>6.2</td><td>AAC</td><td>222</td><td>111</td><td>000</td><td>260606</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>73</td><td>86.133</td><td>-38.193</td><td>U</td><td>0.1</td><td>190</td><td>17.4</td><td>16.7</td><td>1</td><td>--</td><td>70.830343</td><td>85.709464</td><td>0.421</td><td>0.035</td><td>0.456</td><td>8</td></tr>\n",
"<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
"<tr><td>343.260</td><td>16.007</td><td>22h53m02.49s</td><td>16d00m26.05s</td><td>0.09</td><td>0.08</td><td>178</td><td>22530248+1600260</td><td>15.696</td><td>0.06</td><td>0.062</td><td>21.1</td><td>15.285</td><td>0.094</td><td>0.095</td><td>12.3</td><td>15.503</td><td>0.164</td><td>0.165</td><td>6.3</td><td>AAC</td><td>222</td><td>111</td><td>000</td><td>361606</td><td>0</td><td>0</td><td>n</td><td>1999-11-01</td><td>49</td><td>85.777</td><td>-38.177</td><td>U</td><td>0.4</td><td>191</td><td>17.0</td><td>16.5</td><td>1</td><td>--</td><td>944.42937</td><td>237.527268</td><td>0.411</td><td>-0.218</td><td>0.193</td><td>666</td></tr>\n",
"<tr><td>343.243</td><td>16.038</td><td>22h52m58.24s</td><td>16d02m17.80s</td><td>0.28</td><td>0.26</td><td>158</td><td>22525823+1602177</td><td>17.06</td><td>0.179</td><td>0.179</td><td>6.0</td><td>16.314</td><td>0.188</td><td>0.188</td><td>4.8</td><td>15.874</td><td>0.241</td><td>0.241</td><td>4.5</td><td>CDD</td><td>222</td><td>111</td><td>000</td><td>060606</td><td>0</td><td>0</td><td>n</td><td>1999-11-01</td><td>49</td><td>85.782</td><td>-38.142</td><td>U</td><td>0.9</td><td>85</td><td>18.8</td><td>18.2</td><td>1</td><td>--</td><td>944.544798</td><td>245.263305</td><td>0.746</td><td>0.44</td><td>1.186</td><td>667</td></tr>\n",
"<tr><td>343.453</td><td>15.888</td><td>22h53m48.73s</td><td>15d53m17.60s</td><td>0.08</td><td>0.08</td><td>90</td><td>22534873+1553175</td><td>15.627</td><td>0.059</td><td>0.06</td><td>21.7</td><td>15.102</td><td>0.074</td><td>0.074</td><td>16.7</td><td>15.13</td><td>0.132</td><td>0.132</td><td>8.3</td><td>AAB</td><td>222</td><td>111</td><td>000</td><td>560406</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>73</td><td>85.889</td><td>-38.38</td><td>U</td><td>0.6</td><td>96</td><td>17.7</td><td>16.5</td><td>1</td><td>--</td><td>944.936468</td><td>187.908775</td><td>0.525</td><td>-0.028</td><td>0.497</td><td>668</td></tr>\n",
"<tr><td>343.751</td><td>16.070</td><td>22h55m00.35s</td><td>16d04m12.21s</td><td>0.08</td><td>0.06</td><td>0</td><td>22550035+1604122</td><td>15.298</td><td>0.044</td><td>0.045</td><td>29.4</td><td>14.627</td><td>0.056</td><td>0.057</td><td>26.8</td><td>14.424</td><td>0.081</td><td>0.082</td><td>15.9</td><td>AAA</td><td>222</td><td>111</td><td>000</td><td>665626</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>75</td><td>86.322</td><td>-38.388</td><td>U</td><td>0.6</td><td>288</td><td>16.7</td><td>16.2</td><td>1</td><td>--</td><td>945.057736</td><td>107.283285</td><td>0.671</td><td>0.203</td><td>0.874</td><td>669</td></tr>\n",
"<tr><td>343.238</td><td>16.248</td><td>22h52m57.10s</td><td>16d14m54.33s</td><td>0.09</td><td>0.08</td><td>0</td><td>22525710+1614543</td><td>15.579</td><td>0.06</td><td>0.061</td><td>23.5</td><td>15.073</td><td>0.094</td><td>0.095</td><td>15.0</td><td>15.237</td><td>0.14</td><td>0.14</td><td>8.1</td><td>AAB</td><td>222</td><td>111</td><td>000</td><td>562606</td><td>0</td><td>0</td><td>n</td><td>1999-11-01</td><td>49</td><td>85.926</td><td>-37.965</td><td>U</td><td>0.6</td><td>184</td><td>17.7</td><td>16.5</td><td>1</td><td>--</td><td>945.11161</td><td>292.475862</td><td>0.506</td><td>-0.164</td><td>0.342</td><td>670</td></tr>\n",
"<tr><td>343.733</td><td>16.270</td><td>22h54m55.99s</td><td>16d16m11.10s</td><td>0.31</td><td>0.24</td><td>90</td><td>22545599+1616110</td><td>16.587</td><td>0.128</td><td>0.128</td><td>9.0</td><td>16.016</td><td>0.15</td><td>0.15</td><td>7.5</td><td>15.704</td><td>0.221</td><td>0.221</td><td>4.9</td><td>BBD</td><td>222</td><td>111</td><td>000</td><td>060506</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>75</td><td>86.444</td><td>-38.211</td><td>U</td><td>0.2</td><td>165</td><td>18.4</td><td>17.4</td><td>1</td><td>--</td><td>946.220436</td><td>62.423538</td><td>0.571</td><td>0.312</td><td>0.883</td><td>671</td></tr>\n",
"<tr><td>343.256</td><td>16.284</td><td>22h53m01.40s</td><td>16d17m01.77s</td><td>0.09</td><td>0.08</td><td>3</td><td>22530140+1617017</td><td>16.119</td><td>0.078</td><td>0.079</td><td>14.3</td><td>15.384</td><td>0.098</td><td>0.098</td><td>11.2</td><td>15.159</td><td>0.127</td><td>0.127</td><td>8.7</td><td>AAB</td><td>222</td><td>111</td><td>000</td><td>060605</td><td>0</td><td>0</td><td>n</td><td>1999-11-01</td><td>49</td><td>85.969</td><td>-37.946</td><td>U</td><td>0.4</td><td>225</td><td>18.9</td><td>18.0</td><td>1</td><td>--</td><td>947.056875</td><td>301.064048</td><td>0.735</td><td>0.225</td><td>0.96</td><td>672</td></tr>\n",
"<tr><td>343.764</td><td>16.168</td><td>22h55m03.38s</td><td>16d10m03.29s</td><td>0.06</td><td>0.06</td><td>90</td><td>22550338+1610032</td><td>14.979</td><td>0.032</td><td>0.034</td><td>38.1</td><td>14.202</td><td>0.032</td><td>0.033</td><td>38.3</td><td>14.078</td><td>0.054</td><td>0.055</td><td>21.9</td><td>AAA</td><td>222</td><td>111</td><td>000</td><td>666646</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>76</td><td>86.403</td><td>-38.313</td><td>U</td><td>0.5</td><td>143</td><td>18.1</td><td>16.5</td><td>1</td><td>--</td><td>948.194755</td><td>85.744511</td><td>0.777</td><td>0.124</td><td>0.901</td><td>673</td></tr>\n",
"<tr><td>343.225</td><td>16.213</td><td>22h52m53.93s</td><td>16d12m47.46s</td><td>0.08</td><td>0.06</td><td>0</td><td>22525393+1612474</td><td>14.804</td><td>0.03</td><td>0.032</td><td>48.0</td><td>14.38</td><td>0.05</td><td>0.051</td><td>28.3</td><td>14.373</td><td>0.068</td><td>0.068</td><td>17.9</td><td>AAA</td><td>222</td><td>111</td><td>000</td><td>663636</td><td>0</td><td>0</td><td>n</td><td>1999-11-01</td><td>49</td><td>85.888</td><td>-37.988</td><td>U</td><td>0.7</td><td>164</td><td>16.8</td><td>15.7</td><td>1</td><td>--</td><td>948.556183</td><td>284.312508</td><td>0.424</td><td>0.007</td><td>0.431</td><td>674</td></tr>\n",
"<tr><td>343.747</td><td>16.054</td><td>22h54m59.20s</td><td>16d03m13.68s</td><td>0.23</td><td>0.2</td><td>4</td><td>22545919+1603136</td><td>16.879</td><td>0.165</td><td>0.166</td><td>6.9</td><td>15.781</td><td>0.134</td><td>0.134</td><td>9.3</td><td>15.274</td><td>0.167</td><td>0.167</td><td>7.3</td><td>CBC</td><td>222</td><td>111</td><td>000</td><td>060615</td><td>0</td><td>0</td><td>n</td><td>1998-10-01</td><td>75</td><td>86.305</td><td>-38.399</td><td>U</td><td>0.2</td><td>185</td><td>19.1</td><td>18.0</td><td>1</td><td>--</td><td>948.615207</td><td>110.959223</td><td>1.098</td><td>0.507</td><td>1.605</td><td>675</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table masked=True length=676>\n",
" ra dec clon clat ... j_h h_k j_k id \n",
" deg deg ... \n",
"float64 float64 object object ... float64 float64 float64 object\n",
"------- ------- ------------ ------------ ... ------- ------- ------- ------\n",
"343.491 16.148 22h53m57.75s 16d08m53.63s ... 0.639 0.794 1.433 0\n",
"343.492 16.152 22h53m58.16s 16d09m06.78s ... 0.505 0.1 0.605 1\n",
"343.490 16.152 22h53m57.56s 16d09m08.57s ... 0.161 0.279 0.44 2\n",
"343.493 16.141 22h53m58.37s 16d08m27.05s ... 0.697 0.265 0.962 3\n",
"343.497 16.142 22h53m59.33s 16d08m32.80s ... 0.552 0.339 0.891 4\n",
"343.499 16.143 22h53m59.74s 16d08m33.12s ... 0.502 -0.015 0.487 5\n",
"343.501 16.158 22h54m00.21s 16d09m29.41s ... 0.781 -- -- 6\n",
"343.500 16.165 22h53m59.94s 16d09m54.03s ... 0.42 0.154 0.574 7\n",
"343.511 16.150 22h54m02.65s 16d08m58.85s ... 0.421 0.035 0.456 8\n",
" ... ... ... ... ... ... ... ... ...\n",
"343.260 16.007 22h53m02.49s 16d00m26.05s ... 0.411 -0.218 0.193 666\n",
"343.243 16.038 22h52m58.24s 16d02m17.80s ... 0.746 0.44 1.186 667\n",
"343.453 15.888 22h53m48.73s 15d53m17.60s ... 0.525 -0.028 0.497 668\n",
"343.751 16.070 22h55m00.35s 16d04m12.21s ... 0.671 0.203 0.874 669\n",
"343.238 16.248 22h52m57.10s 16d14m54.33s ... 0.506 -0.164 0.342 670\n",
"343.733 16.270 22h54m55.99s 16d16m11.10s ... 0.571 0.312 0.883 671\n",
"343.256 16.284 22h53m01.40s 16d17m01.77s ... 0.735 0.225 0.96 672\n",
"343.764 16.168 22h55m03.38s 16d10m03.29s ... 0.777 0.124 0.901 673\n",
"343.225 16.213 22h52m53.93s 16d12m47.46s ... 0.424 0.007 0.431 674\n",
"343.747 16.054 22h54m59.20s 16d03m13.68s ... 1.098 0.507 1.605 675"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 0.5 Mpc is too small?\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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-2.0 |
dharness/seam_carving | demo/Seam Carving Visual Demos.ipynb | 1 | 2363881 | null | mit |
mliu49/RMG-stuff | Thermo/ModifyThermo.ipynb | 1 | 6265 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os.path\n",
"import math\n",
"import rmgpy\n",
"import rmgpy.constants as constants\n",
"from IPython.display import display\n",
"from rmgpy.data.rmg import RMGDatabase\n",
"from rmgpy.thermo.nasa import NASA, NASAPolynomial\n",
"from rmgpy.species import Species\n",
"from rmgpy.chemkin import readThermoEntry, writeThermoEntry\n",
"from rmgpy.thermo.thermoengine import processThermoData\n",
"from rmgpy.data.thermo import findCp0andCpInf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"databasePath = rmgpy.settings['database.directory']\n",
"\n",
"database = RMGDatabase()\n",
"database.load(\n",
" path = databasePath,\n",
" thermoLibraries = [],\n",
" reactionLibraries = [],\n",
" seedMechanisms = [],\n",
" kineticsFamilies = 'none'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = [\n",
" \"\"\"C2HBr T04/04C 2.H 1.BR 1. 0.G 200.000 6000.000 1000. 1\n",
" 6.55399311E+00 3.37962726E-03-1.18362410E-06 1.87797808E-10-1.11059116E-14 2\n",
" 3.17495713E+04-8.20269727E+00 1.10795098E+00 3.21065018E-02-6.02244383E-05 3\n",
" 5.45400888E-08-1.86034151E-11 3.26428366E+04 1.67414085E+01 3.39671249E+04 4\n",
" \"\"\",\n",
" \"\"\"C2HCl T05/08C 2.H 1.CL 1. 0.G 200.000 6000.000 1000. 1\n",
" 6.52865585E+00 3.32425623E-03-1.14637403E-06 1.79972218E-10-1.05639468E-14 2\n",
" 2.51378884E+04-9.16499932E+00 1.25077097E+00 3.10939695E-02-5.78728028E-05 3\n",
" 5.20651866E-08-1.76611780E-11 2.59985454E+04 1.50044210E+01 2.73367422E+04 4\n",
" \"\"\",\n",
" \"\"\"CL J 6/82CL 1 0 0 0G 200.000 6000.000 1000. 1\n",
" 2.94658358E+00-3.85985408E-04 1.36139388E-07-2.17032923E-11 1.28751025E-15 2\n",
" 1.36970327E+04 3.11330136E+00 2.26062480E+00 1.54154399E-03-6.80283622E-07 3\n",
"-1.59972975E-09 1.15416636E-12 1.38552986E+04 6.57020799E+00 1.45891941E+04 4\n",
" \"\"\",\n",
" \"\"\"BR J 6/82BR 1 0 0 0G 200.000 6000.000 1000. 1\n",
" 0.20866945E+01 0.71459733E-03-0.27080691E-06 0.41519029E-10-0.23016335E-14 2\n",
" 0.12857696E+05 0.90837335E+01 0.24820782E+01 0.18570465E-03-0.64313029E-06 3\n",
" 0.84642045E-09-0.30137068E-12 0.12709455E+05 0.68740409E+01 0.13453589E+05 4\n",
" \"\"\",\n",
" \"\"\"I J 6/82I 1 0 0 0G 200.000 6000.000 1000. 1\n",
" 2.61667712E+00-2.66010320E-04 1.86060150E-07-3.81927472E-11 2.52036053E-15 2\n",
" 1.20582790E+04 6.87896653E+00 2.50041683E+00-4.48046831E-06 1.69962536E-08 3\n",
"-2.67708030E-11 1.48927452E-14 1.20947990E+04 7.49816581E+00 1.28402035E+04 4\n",
" \"\"\"\n",
"]\n",
"\n",
"thermo = []\n",
"for entry in data:\n",
" thermo.append(readThermoEntry(entry)[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"base = thermo[0].toThermoData()\n",
"print base\n",
"base = subtractThermoData(base, thermo[3].toThermoData())\n",
"base = addThermoData(base, thermo[4].toThermoData())\n",
"print base"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"spc = Species().fromSMILES('C#CCl')\n",
"display(spc)\n",
"findCp0andCpInf(spc, base)\n",
"spc.thermo = processThermoData(spc, base)\n",
"print writeThermoEntry(spc)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"base2 = thermo[1].toThermoData()\n",
"print base2\n",
"base2 = subtractThermoData(base2, thermo[2].toThermoData())\n",
"base2 = addThermoData(base2, thermo[4].toThermoData())\n",
"print base2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def addThermoData(thermoData1, thermoData2):\n",
" \"\"\"\n",
" Add the thermodynamic data `thermoData2` to the data `thermoData1`,\n",
" and return `thermoData1`.\n",
" \"\"\"\n",
" for i in range(thermoData1.Tdata.value_si.shape[0]):\n",
" thermoData1.Cpdata.value_si[i] += thermoData2.Cpdata.value_si[i]\n",
" thermoData1.H298.value_si += thermoData2.H298.value_si\n",
" thermoData1.S298.value_si += thermoData2.S298.value_si\n",
"\n",
" return thermoData1\n",
"\n",
"def subtractThermoData(thermoData1, thermoData2):\n",
" \"\"\"\n",
" Subtract the thermodynamic data `thermoData2` from the data `thermoData1`,\n",
" and return `thermoData1`.\n",
" \"\"\"\n",
" for i in range(thermoData1.Tdata.value_si.shape[0]):\n",
" thermoData1.Cpdata.value_si[i] -= thermoData2.Cpdata.value_si[i]\n",
" thermoData1.H298.value_si -= thermoData2.H298.value_si\n",
" thermoData1.S298.value_si -= thermoData2.S298.value_si\n",
"\n",
" return thermoData1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:rmg_env]",
"language": "python",
"name": "conda-env-rmg_env-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
MarineLasbleis/GrowYourIC | notebooks/SupplMaterials_S1_S2.ipynb | 1 | 542094 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/marine/ownCloud/Research/Projets/CIDER_IC/GrowYourIC/GrowYourIC/data/CM2008_data.mat\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"# import statements\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt #for figures\n",
"from mpl_toolkits.basemap import Basemap #to render maps\n",
"import math\n",
"import json #to write dict with parameters\n",
"\n",
"from GrowYourIC import positions, geodyn, geodyn_trg, geodyn_static, plot_data, data\n",
"\n",
"plt.rcParams['figure.figsize'] = (8.0, 3.0) #size of figures\n",
"cm = plt.cm.get_cmap('viridis')\n",
"cm2 = plt.cm.get_cmap('winter')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Waszek and Deuss 2011 successfully loaded. 3184 trajectories.\n"
]
}
],
"source": [
"## real data set\n",
"data_set = data.SeismicFromFile(\"../GrowYourIC/data/WD11.dat\")\n",
"residual = data_set.real_residual()\n",
"\n",
"velocity_center = [0., -80]#center of the eastern hemisphere\n",
"r, t, p = data_set.extract_rtp(\"bottom_turning_point\")\n",
"dist = positions.angular_distance_to_point(t, p, *velocity_center)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Figure S1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1188b7438>]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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MN4vJSHVx93lZDB8QXgn/ep6/PqAAeOe/tRSXBMlMdyr5z7zYcFd/wybLx58H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DWYotAmsU3BTWm+vCe9dZlqpqbN3lM1lSFNi7X+XUcR7+WOCNS27xgEnFXG7lpL4Sjz5gpF8f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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11858c358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"\n",
"color = dist\n",
"fig, ax = plt.subplots(figsize=(10, 6))\n",
"sc = ax.scatter(p,t, c=1221*(1-r), linewidth=0, s=10, cmap=plt.cm.get_cmap('winter')) #c=dist>90\n",
"ax.set_xlabel(\"longitude\")\n",
"ax.set_ylabel(\"latitude\")\n",
"\n",
"cbar = fig.colorbar(sc)\n",
"\n",
"#to add histograms to see the actual repartition. Please see http://matplotlib.org/examples/axes_grid/scatter_hist.html\n",
"divider = make_axes_locatable(ax)\n",
"axHistx = divider.append_axes(\"top\", 1.2, pad=0.1, sharex=ax)\n",
"axHisty = divider.append_axes(\"right\", 1.2, pad=0.1, sharey=ax)\n",
"plt.setp(axHistx.get_xticklabels() + axHisty.get_yticklabels(),\n",
" visible=False)\n",
"binwidth = 10.\n",
"limX = (int(180./binwidth) + 1)*binwidth\n",
"limY = (int(90./binwidth) + 1)*binwidth\n",
"binsX = np.arange(-limX, limX + binwidth, binwidth)\n",
"binsY = np.arange(-limY, limY + binwidth, binwidth)\n",
"axHistx.hist(p, bins=binsX)\n",
"axHisty.hist(t, bins=binsY, orientation='horizontal')\n",
"#axHistx.axis[\"bottom\"].major_ticklabels.set_visible(False)\n",
"for tl in axHistx.get_xticklabels():\n",
" tl.set_visible(False)\n",
"axHistx.set_yticks([0, 100, 200, 300])\n",
"#axHisty.axis[\"left\"].major_ticklabels.set_visible(False)\n",
"for tl in axHisty.get_yticklabels():\n",
" tl.set_visible(False)\n",
"axHisty.set_xticks([0, 300, 600])\n",
"ax.set_aspect('equal')\n",
"ax.axis([-180, 180, -90, 90])\n",
"#ax.plot([-180,180],[15,15], 'k')\n",
"#ax.plot([-180,180],[-15,-15],'k')\n",
"ax.plot([-180,180],[0,0],'k--')\n",
"ax.plot([10,10],[-90,90],'k:')\n",
"ax.plot([-170,-170],[-90,90],'k:')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Figure S2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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03+S716CleeiHpuREoeURqkSbJh+Hlv6hqVisSrldTrScRtVoiRdtPv8rfTPrf3/WHGI6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\ndz9/D8WgYMrogUQSOewMMs7qfnH+Gx9dLIT4GZgqpew8g2oXflV0MaX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isBFRZqHt5T4yPXSSCEJDHWi9MWJj7ah0emcuTU2zs918JFX/HZtVcQumkWutsR6QNUYaCokBaf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xBC3NZcO//fkGSBJDdvLTlyzN80zCUXQ3UhkiT9C/gYSeLuuXMZeMstF30MsiyTm5KCV0gI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\nGT84giHRvZFlmY92nGJKUhR+OlfuW5HKqw+Px9tDS1ltC6MG9WVCQ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"text/plain": [
"<matplotlib.figure.Figure at 0x116ad2550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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bHRXg0rcQUDtNpcyq/oFf9VnkHnUB19qOhp8GwMpkmJcG1WY8SM7e/Auv7dkF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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1179bb6a0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Jg2vWU1BVh2GYqEFwKPA/z6xAmODSYGxiOomtJoEMgZCCbUXVLHpqKb+7ZiE1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K/bXYSlh/KkBdCFwGk9ecbxR4NgBzZRgt0fE5BadZNKbyLZzlasmkGc5idFj1Vz+4wgX5TswERy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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x117a904e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m, fig = plot_data.setting_map() \n",
"# cm = plt.cm.get_cmap('seismic')\n",
"x, y = m(p, t)\n",
"sc = m.scatter(x, y, c=residual, s=8, zorder=10, cmap=cm, edgecolors='none')\n",
"plt.title(\"Real residual from Lauren's data set\")\n",
"cbar1 = plt.colorbar(sc)\n",
"cbar1.set_label(\"Residuals\")\n",
"plt.savefig(\"S2-1.pdf\")\n",
"## phi and distance plots\n",
"\n",
"fig, ax = plt.subplots(sharey=True, figsize=(8, 2))\n",
"cm2 = plt.cm.get_cmap('winter')\n",
"sc1 = ax.scatter(p, residual, c=t, s=10,cmap=cm2, linewidth=0)\n",
"ax.set_xlabel(\"longitude\")\n",
"ax.set_ylabel(\"residuals\")\n",
"ax.set_xlim([-180, 180])\n",
"#sc2 = ax[1].scatter(dist, residual, c=\"k\", s=10,cmap=cm2, linewidth=0)\n",
"#ax[1].set_xlabel(\"angular distance to ({}, {})\".format(*velocity_center))\n",
"#ax[1].set_xlim([0, 180])\n",
"#fig.suptitle(\"Dataset: {},\\n geodynamic model: {}\".format(data_set_random.name, geodynModel.name))\n",
"cbar2 = fig.colorbar(sc1)\n",
"cbar2.set_label(\"latitude: abs(theta)\")\n",
"plt.savefig(\"S2-2.pdf\")\n",
"\n",
"fig, ax = plt.subplots(figsize=(8, 2))\n",
"rICB_dim = 1221. #in km\n",
"sc=ax.scatter(p,rICB_dim*(1.-r), c=residual, s=10,cmap=cm, linewidth=0)\n",
"ax.set_ylim(-0,120)\n",
"fig.gca().invert_yaxis()\n",
"ax.set_xlim(-180,180)\n",
"cbar = fig.colorbar(sc)\n",
"cbar.set_label(\"Residuals\")\n",
"ax.set_xlabel(\"longitude\")\n",
"ax.set_ylabel(\"depth (km)\")\n",
"ax.plot([11,11],[10,30], 'k')\n",
"ax.plot([21,21],[30,58], 'k')\n",
"ax.plot([38,38],[58,110], 'k')\n",
"ax.plot([-80,100], [30,30], 'k:')\n",
"ax.plot([-80,100], [58,58], 'k:')\n",
"plt.savefig(\"S2-3.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
kimkipyo/dss_git_kkp | Python 복습/08일차.금_정규표현식, class, 크롤링, 숙제/8일차_1T,3T_정규 표현식_이메일, 핸드폰 번호, Class_.ipynb | 1 | 14087 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1T_정규 표현식 ( Regular Expression ) - 이메일, 핸드폰 번호"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 이메일, 핸드폰 번호 정규표현식"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"중국 중앙은행인 인민은행은 16일 환율을 달러당 6.6305위안으로 고시했다. 지난 15일 고시환율 달러당[email protected] 6.6430위안에 비해 달러 대비 위안 가치가 0.19% 상승했다.\n",
"A new material has been created that 공일공육이삼오삼삼일칠can self-assemble into a swarm, acting as a single-minded unit.\n",
"\n",
"The material, which is made up of a set up spheres, is able공일공-둘둘삼삼-사사오오 to automatically arrange into a pattern when exposed to an electric field.\n",
"\n",
"In the future, this could be used to create armies of robots that act as a relentle010-4567-9201ss, single-minded unit.\n",
"한경닷컴 증권금융팀\n"
]
}
],
"source": [
"with open(\"crawled.txt\", \"r\", encoding='utf8') as f: #crawled.txt는 보기와 같이 임의로 텍스트 파일을 만들었습니다.\n",
" data = f.read()\n",
" print(data)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['010']\n"
]
}
],
"source": [
"import re\n",
"\n",
"with open(\"crawled.txt\", \"r\", encoding='utf8') as f:\n",
" data = f.read()\n",
" \n",
" phonenumber_regex = \"010\" # 1. 정규표현식 (regex)\n",
"# phonenumber_regex = \"\\d{3}[-]?\\d{4}[-]?\\d{4}\"\n",
" # \\d => 숫자가 나온다. => [0-9]\n",
" # \\d{3} => 숫자가 3번 나온다.\n",
" # [-]? => \"-\"가 나올 수도 있고 안 나올 수도 있다.\n",
" phonenumber_pattern = re.compile(phonenumber_regex) # 파이썬에서 정규표현식을 사용할 수 있도록 SRE_Pattern 객체로 변경\n",
" phonenumber_list = phonenumber_pattern.findall(data) # 2. 파이썬에서 정규표현식 함수를 사용한다.\n",
"\n",
"print(phonenumber_list)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['공일공육이삼오삼삼일칠', '공일공-둘둘삼삼-사사오오', '010-4567-9201']\n",
"['[email protected]']\n"
]
}
],
"source": [
"with open(\"crawled.txt\", \"r\", encoding='utf8') as f:\n",
" data = f.read()\n",
" \n",
" phonenumber_regex = \"[0-9영공빵일이둘삼사오육칠팔구]{3}[-]?[0-9영공빵일이둘삼사오육칠팔구]{3,4}[-]?[0-9영공빵일이둘삼사오육칠팔구]{4}\" \n",
" # * => 0~n\n",
" # ? => 0-1\n",
" # + => 1-n\n",
" \n",
" # 이런 데이터를 먼저 전처리를 하고 ( 공 => 0 )( 지금은 이게 더 바람직 )\n",
" \n",
" phonenumber_pattern = re.compile(phonenumber_regex) \n",
" phonenumber_list = phonenumber_pattern.findall(data)\n",
" \n",
" # email_regex = \"[a-zA-Z0-9_]+[@][a-zA-Z0-9_]+[.][a-z]+[.]?[a-z]+$\"\n",
" email_regex = \"[a-zA-Z0-9_]+[@][a-zA-Z0-9.]+\"\n",
" # email_regex = \"\"\n",
" # 정규표현식\n",
" \n",
" # \"다섯\" 이라는 텍스트가 포함이 되는...\n",
" # \"12다섯456\"\n",
" # [a-zA-Z0-9_]*다섯[a-zA-Z0-9_]*\n",
" \n",
" # {} -> 자릿수가 정해저있는 상황 ( 핸드폰 번호 ... )\n",
" email_pattern = re.compile(email_regex)\n",
" email_list = email_pattern.findall(data)\n",
" \n",
" # .com\n",
" # .co.kr\n",
" \n",
"print(phonenumber_list)\n",
"print(email_list)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def preprocess(phonenumber):\n",
" preprocess_dict = {\n",
" \"영\": 0,\n",
" \"공\": 0,\n",
" \"일\": 1,\n",
" \"둘\": 2,\n",
" \"이\": 2,\n",
" \"삼\": 3,\n",
" \"사\": 4,\n",
" \"오\": 5,\n",
" \"육\": 6,\n",
" \"칠\": 7,\n",
" \"팔\": 8,\n",
" \"구\": 9,\n",
" \"-\": \"\",\n",
" }\n",
" \n",
" for key, value in preprocess_dict.items():\n",
" phonenumber = phonenumber.replace(key, str(value))\n",
" return phonenumber"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['01062353317', '01022334455', '01045679201']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[preprocess(phonenumber) for phonenumber in phonenumber_list]"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# 3T_객체 지향 프로그래밍 - 클래스 메쏘드와 인스턴스 메쏘드"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Class - 클래스는 매우 중요합니다.\n",
"* Class => object ( 객체가 생성되는 클래스 ) 에 대해서만 배웠다."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 예제: 피보나치 수열"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Fibonacci():\n",
" \n",
" cache = {1: 0, 2: 1}\n",
" # n=1 => 0\n",
" # n=2 => 1\n",
" # f(n) = f(n-1) + f(n-2)\n",
" \n",
"# def __init__(self):\n",
"# self.cache = {}\n",
"# 기존의 이런 형태가 아니라 다른 형태로 받기 위해서\n",
"\n",
" # 클래스 => 클래스 메쏘드\n",
" # 객체 ( 인스턴스 ) => 인스턴스 메쏘드\n",
"\n",
" @staticmethod # 클래스 메쏘드 \n",
" def calc(n): # self 가 없습니다. self.cache가 아니라 Fibonacci.cache로 바뀜\n",
" \n",
" if n in Fibonacci.cache: \n",
" return Fibonacci.cache[n]\n",
" \n",
" if n <= 0:\n",
" return 0\n",
" \n",
"# if n == 1:\n",
"# Fibonacci.cache[n] = 0\n",
"# return Fibonacci.cache[n]\n",
" \n",
"# if n == 2:\n",
"# Fibonacci.cache[n] = 1\n",
"# return Fibonacci.cache[n]\n",
" \n",
" Fibonacci.cache[n] = Fibonacci.calc(n-1) + Fibonacci.calc(n-2)\n",
" return Fibonacci.cache[n]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4181"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Fibonacci.calc(20)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{1: 0,\n",
" 2: 1,\n",
" 3: 1,\n",
" 4: 2,\n",
" 5: 3,\n",
" 6: 5,\n",
" 7: 8,\n",
" 8: 13,\n",
" 9: 21,\n",
" 10: 34,\n",
" 11: 55,\n",
" 12: 89,\n",
" 13: 144,\n",
" 14: 233,\n",
" 15: 377,\n",
" 16: 610,\n",
" 17: 987,\n",
" 18: 1597,\n",
" 19: 2584,\n",
" 20: 4181}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Fibonacci.cache"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 예제: Factorial"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Factorial():\n",
" \n",
" cache = {\n",
" 1: 1,\n",
" }\n",
" \n",
" @staticmethod\n",
" def run(n):\n",
" \n",
" if n in Factorial.cache:\n",
" return Factorial.cache[n]\n",
" \n",
" Factorial.cache[n] = n * Factorial.run(n-1)\n",
" return Factorial.cache[n]\n",
" \n",
" @staticmethod\n",
" def prettify():\n",
" print(\"\\n\".join([\n",
" \"{n}! == {result}\".format(n=key, result=value)\n",
" for key, value\n",
" in Factorial.cache.items()\n",
" ]))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"24"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Factorial.run(4)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1! == 1\n",
"2! == 2\n",
"3! == 6\n",
"4! == 24\n"
]
}
],
"source": [
"Factorial.prettify()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 예제: 달력"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import functools\n",
"\n",
"\n",
"class Calendar():\n",
" \n",
" __days = {\n",
" 1: 31,\n",
" 2: 28,\n",
" 3: 31,\n",
" 4: 30,\n",
" 5: 31,\n",
" 6: 30,\n",
" 7: 31,\n",
" 8: 31,\n",
" 9: 30,\n",
" 10: 31,\n",
" 11: 30,\n",
" 12: 31,\n",
" }\n",
" \n",
" @staticmethod\n",
" def is_leap(year):\n",
" return\\\n",
" year % 4 == 0\\\n",
" and not year % 100 == 0\\\n",
" or year % 400 == 0\n",
" \n",
" @staticmethod\n",
" def days_in(year):\n",
" days = Calendar.__days.copy()\n",
" \n",
" # days 라는 새로운 변수 => Calendar.__days 를 가리키고 있는 애 \n",
" # 우리가 의도한 바 : 기존의 애를 복사하는 것\n",
" \n",
" days[2] += int(Calendar.is_leap(year)) # True => 1, \n",
" # False => 0\n",
" return days\n",
" \n",
" @staticmethod\n",
" def total_days_in(year):\n",
" return functools.reduce(\n",
" lambda x,y: x+y,\n",
" Calendar.days_in(year).values(),\n",
" )\n",
" \n",
" \n",
" @staticmethod\n",
" def total_days_until(year):\n",
"# total_days = 0\n",
"# for i in range(1900, year):\n",
"# total_days += Calendar.total_days_in(i)\n",
"# return total_days\n",
" return functools.reduce(\n",
" lambda x,y: x+y,\n",
" [\n",
" Calendar.total_days_in(i)\n",
" for i\n",
" in range(1900, year) \n",
" ]\n",
" )\n",
" \n",
" \n",
" # 1년 1월 1일 부터 특정 년도 까지의 day 수 합계를 구하시오.\n",
" # 예, 1년 1월 1일 부터 2년 1월 1일 => 365\n",
" # 1년 1월 1일 부터 3년 1월 1일 => 365 * 2\n",
" # 하는 이유: day 수 합계 % 7 ==> 요일 ( 1년 1월 1일이 월요일 입니다 )"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1826"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Calendar.total_days_until(1905)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{1: 31,\n",
" 2: 29,\n",
" 3: 31,\n",
" 4: 30,\n",
" 5: 31,\n",
" 6: 30,\n",
" 7: 31,\n",
" 8: 31,\n",
" 9: 30,\n",
" 10: 31,\n",
" 11: 30,\n",
" 12: 31}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Calendar.days_in(2016)"
]
}
],
"metadata": {
"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": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
mdeff/ntds_2016 | project/reports/stock_market/Final project Stock market.ipynb | 1 | 1024571 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib inline\n",
"from scipy import stats\n",
"from myutil import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Project : Stock market analysis and prediction\n",
"Jeroen Le Maire --- A network tour of data science\n",
"\n",
"This project aims to check if it is possible to discover ouperforming stocks by machine learning. The data is from Professer Milosevic from the university from Manchester. I got in contact with him because of this project. The data contains 1739 stocks and quarterly data over a period from 2012 to 2015. Furthermore it contains about 20 features for every data point.\n",
"The data was formated in a kind of flash cards, so it took a while to reformat them correctly."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Load the data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0\n",
"11.36385158809826\n",
"22.72770317619652\n",
"34.09155476429478\n",
"45.45540635239304\n",
"56.8192579404913\n",
"68.18310952858955\n",
"79.54696111668781\n",
"90.91081270478608\n",
"100\n"
]
}
],
"source": [
"df = load_data()\n",
"#Shows which percentage of the data is loaded (long load time)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A quick view to check if everything is ok."
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>Date</th>\n",
" <th>CUR_MKT_CAP</th>\n",
" <th>PXnow</th>\n",
" <th>PX1YR</th>\n",
" <th>DIVIDENDY</th>\n",
" <th>BEST_EPS</th>\n",
" <th>EPS_GROWTH</th>\n",
" <th>Sales_growth</th>\n",
" <th>PE</th>\n",
" <th>fiveyrAvPriceEarnings</th>\n",
" <th>Pricebook</th>\n",
" <th>Pricesales</th>\n",
" <th>CURratio</th>\n",
" <th>Quick</th>\n",
" <th>DebtEQ</th>\n",
" <th>Rating</th>\n",
" <th>Prof_margin</th>\n",
" <th>oper_margin</th>\n",
" <th>assetTurnover</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A UN Equity</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>29.2757</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <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>A UN Equity</td>\n",
" <td>NaN</td>\n",
" <td>15472.8</td>\n",
" <td>31.8285</td>\n",
" <td>30.0122</td>\n",
" <td>0.3142</td>\n",
" <td>4.295</td>\n",
" <td>25.8621</td>\n",
" <td>3.3393</td>\n",
" <td>9.4925</td>\n",
" <td>14.8595</td>\n",
" <td>2.3457</td>\n",
" <td>1.6308</td>\n",
" <td>3.2752</td>\n",
" <td>2.6185</td>\n",
" <td>46.0741</td>\n",
" <td>4.875</td>\n",
" <td>14.7144</td>\n",
" <td>17.311</td>\n",
" <td>0.7515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A UN Equity</td>\n",
" <td>29/06/2012</td>\n",
" <td>13635.9347</td>\n",
" <td>28.06</td>\n",
" <td>30.5771</td>\n",
" <td>0.7128</td>\n",
" <td>4.75</td>\n",
" <td>-26.3158</td>\n",
" <td>1.8924</td>\n",
" <td>9.1789</td>\n",
" <td>13.0287</td>\n",
" <td>2.0109</td>\n",
" <td>1.431</td>\n",
" <td>2.0148</td>\n",
" <td>1.3746</td>\n",
" <td>45.434</td>\n",
" <td>4.882</td>\n",
" <td>14.1033</td>\n",
" <td>15.6703</td>\n",
" <td>0.7368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A UN Equity</td>\n",
" <td>28/09/2012</td>\n",
" <td>13397.6378</td>\n",
" <td>27.4951</td>\n",
" <td>36.6482</td>\n",
" <td>1.4548</td>\n",
" <td>4.2</td>\n",
" <td>46.988</td>\n",
" <td>2.2569</td>\n",
" <td>7.9493</td>\n",
" <td>12.1619</td>\n",
" <td>1.8358</td>\n",
" <td>1.3952</td>\n",
" <td>2.4453</td>\n",
" <td>1.7295</td>\n",
" <td>45.5545</td>\n",
" <td>4.895</td>\n",
" <td>24.0521</td>\n",
" <td>15.7329</td>\n",
" <td>0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A UN Equity</td>\n",
" <td>31/12/2012</td>\n",
" <td>14244.4388</td>\n",
" <td>29.2757</td>\n",
" <td>40.8958</td>\n",
" <td>1.3663</td>\n",
" <td>3.98</td>\n",
" <td>-21.2121</td>\n",
" <td>2.7523</td>\n",
" <td>8.9409</td>\n",
" <td>12.4997</td>\n",
" <td>1.8995</td>\n",
" <td>1.4748</td>\n",
" <td>2.5525</td>\n",
" <td>1.8007</td>\n",
" <td>44.1226</td>\n",
" <td>4.882</td>\n",
" <td>10.6548</td>\n",
" <td>12.9167</td>\n",
" <td>0.699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>A UN Equity</td>\n",
" <td>28/03/2013</td>\n",
" <td>14570.2759</td>\n",
" <td>30.0122</td>\n",
" <td>39.9877</td>\n",
" <td>1.7326</td>\n",
" <td>3.7</td>\n",
" <td>-34.2466</td>\n",
" <td>-0.0577</td>\n",
" <td>9.5532</td>\n",
" <td>12.4754</td>\n",
" <td>1.9518</td>\n",
" <td>1.5089</td>\n",
" <td>2.5252</td>\n",
" <td>1.8003</td>\n",
" <td>44.3858</td>\n",
" <td>4.7</td>\n",
" <td>9.5843</td>\n",
" <td>12.2979</td>\n",
" <td>0.6902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>A UN Equity</td>\n",
" <td>28/06/2013</td>\n",
" <td>14729.7956</td>\n",
" <td>30.5771</td>\n",
" <td>41.0746</td>\n",
" <td>1.766</td>\n",
" <td>4.09</td>\n",
" <td>-28.5714</td>\n",
" <td>-4.1207</td>\n",
" <td>10.2251</td>\n",
" <td>12.951</td>\n",
" <td>2.1143</td>\n",
" <td>1.5433</td>\n",
" <td>2.9574</td>\n",
" <td>2.0677</td>\n",
" <td>56.3883</td>\n",
" <td>4.579</td>\n",
" <td>10.1695</td>\n",
" <td>14.2857</td>\n",
" <td>0.6819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>A UN Equity</td>\n",
" <td>30/09/2013</td>\n",
" <td>16953.0896</td>\n",
" <td>36.6482</td>\n",
" <td>40.7456</td>\n",
" <td>1.2552</td>\n",
" <td>4.115</td>\n",
" <td>-47.541</td>\n",
" <td>-2.7731</td>\n",
" <td>15.4391</td>\n",
" <td>14.7228</td>\n",
" <td>2.3087</td>\n",
" <td>1.8393</td>\n",
" <td>3.1105</td>\n",
" <td>2.231</td>\n",
" <td>51.0304</td>\n",
" <td>4.778</td>\n",
" <td>12.2817</td>\n",
" <td>16.5891</td>\n",
" <td>0.6391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>A UN Equity</td>\n",
" <td>31/12/2013</td>\n",
" <td>18976.1282</td>\n",
" <td>40.8958</td>\n",
" <td>40.94</td>\n",
" <td>1.2031</td>\n",
" <td>4.057</td>\n",
" <td>-28.8462</td>\n",
" <td>-40</td>\n",
" <td>18.2048</td>\n",
" <td>17.8189</td>\n",
" <td>2.509</td>\n",
" <td>2.2581</td>\n",
" <td>3.275</td>\n",
" <td>2.3183</td>\n",
" <td>49.4768</td>\n",
" <td>4.667</td>\n",
" <td>19.3452</td>\n",
" <td>12.3016</td>\n",
" <td>0.574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>A UN Equity</td>\n",
" <td>31/03/2014</td>\n",
" <td>18644.8762</td>\n",
" <td>39.9877</td>\n",
" <td>41.55</td>\n",
" <td>1.2604</td>\n",
" <td>3.95</td>\n",
" <td>-75</td>\n",
" <td>-42.9561</td>\n",
" <td>22.7455</td>\n",
" <td>21.2314</td>\n",
" <td>2.3668</td>\n",
" <td>2.4903</td>\n",
" <td>3.128</td>\n",
" <td>2.2414</td>\n",
" <td>47.8415</td>\n",
" <td>4.692</td>\n",
" <td>14.0688</td>\n",
" <td>9.5142</td>\n",
" <td>0.4966</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Date CUR_MKT_CAP PXnow PX1YR DIVIDENDY BEST_EPS \\\n",
"0 A UN Equity NaN NaN NaN 29.2757 NaN NaN \n",
"1 A UN Equity NaN 15472.8 31.8285 30.0122 0.3142 4.295 \n",
"2 A UN Equity 29/06/2012 13635.9347 28.06 30.5771 0.7128 4.75 \n",
"3 A UN Equity 28/09/2012 13397.6378 27.4951 36.6482 1.4548 4.2 \n",
"4 A UN Equity 31/12/2012 14244.4388 29.2757 40.8958 1.3663 3.98 \n",
"5 A UN Equity 28/03/2013 14570.2759 30.0122 39.9877 1.7326 3.7 \n",
"6 A UN Equity 28/06/2013 14729.7956 30.5771 41.0746 1.766 4.09 \n",
"7 A UN Equity 30/09/2013 16953.0896 36.6482 40.7456 1.2552 4.115 \n",
"8 A UN Equity 31/12/2013 18976.1282 40.8958 40.94 1.2031 4.057 \n",
"9 A UN Equity 31/03/2014 18644.8762 39.9877 41.55 1.2604 3.95 \n",
"\n",
" EPS_GROWTH Sales_growth PE fiveyrAvPriceEarnings Pricebook Pricesales \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 25.8621 3.3393 9.4925 14.8595 2.3457 1.6308 \n",
"2 -26.3158 1.8924 9.1789 13.0287 2.0109 1.431 \n",
"3 46.988 2.2569 7.9493 12.1619 1.8358 1.3952 \n",
"4 -21.2121 2.7523 8.9409 12.4997 1.8995 1.4748 \n",
"5 -34.2466 -0.0577 9.5532 12.4754 1.9518 1.5089 \n",
"6 -28.5714 -4.1207 10.2251 12.951 2.1143 1.5433 \n",
"7 -47.541 -2.7731 15.4391 14.7228 2.3087 1.8393 \n",
"8 -28.8462 -40 18.2048 17.8189 2.509 2.2581 \n",
"9 -75 -42.9561 22.7455 21.2314 2.3668 2.4903 \n",
"\n",
" CURratio Quick DebtEQ Rating Prof_margin oper_margin assetTurnover \n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 3.2752 2.6185 46.0741 4.875 14.7144 17.311 0.7515 \n",
"2 2.0148 1.3746 45.434 4.882 14.1033 15.6703 0.7368 \n",
"3 2.4453 1.7295 45.5545 4.895 24.0521 15.7329 0.7 \n",
"4 2.5525 1.8007 44.1226 4.882 10.6548 12.9167 0.699 \n",
"5 2.5252 1.8003 44.3858 4.7 9.5843 12.2979 0.6902 \n",
"6 2.9574 2.0677 56.3883 4.579 10.1695 14.2857 0.6819 \n",
"7 3.1105 2.231 51.0304 4.778 12.2817 16.5891 0.6391 \n",
"8 3.275 2.3183 49.4768 4.667 19.3452 12.3016 0.574 \n",
"9 3.128 2.2414 47.8415 4.692 14.0688 9.5142 0.4966 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[0:10,:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Clean the data \n",
"First the data points are deleted for which it is impossible the calculate the future return. Those can be divided in two groups. The first group is the more recent data. For them the future return is not available yet. The second group are the datapoints for which the date is unknown.\n",
"We add a column that contains the return in the next year.\n",
"A feature called 'breturn' is added. It is a binary column that contains a one if the stock will be performing really good (more than 15 %).\n",
"The incomplete data is noted as -9999. We change this to NaN for easier filtering."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"df=df[df['PX1YR'] != 'empty']\n",
"df=df.dropna(subset=['Date'])\n",
"df['return']=(df['PX1YR'].astype(float)/df['PXnow'].astype(float)-1)\n",
"df['breturn'] = (df['return']>0.15)*1\n",
"df.iloc[0:10,:]\n",
"df=df.replace(to_replace='-9999',value=float('NaN'))\n",
"#df['DIVIDENDY'].fillna(0, inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"The feature types are transformed to floats to ensure correct calculations. The first 2 features are not tranformed because they contain the name and date.\n",
"The intermediary data frame is saved as well.\n",
"With the describe option we get a quick overview of the data. The percentiles are not correct as there are still some incomplete data points. In the next part only the complete data will be used."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df,attributes = columnstofloat(df)\n",
"\n",
"import os.path\n",
"df.to_csv(os.path.join('df.csv'))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Jeroe\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
" RuntimeWarning)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CUR_MKT_CAP</th>\n",
" <th>PXnow</th>\n",
" <th>PX1YR</th>\n",
" <th>DIVIDENDY</th>\n",
" <th>BEST_EPS</th>\n",
" <th>EPS_GROWTH</th>\n",
" <th>Sales_growth</th>\n",
" <th>PE</th>\n",
" <th>fiveyrAvPriceEarnings</th>\n",
" <th>Pricebook</th>\n",
" <th>Pricesales</th>\n",
" <th>CURratio</th>\n",
" <th>Quick</th>\n",
" <th>DebtEQ</th>\n",
" <th>Rating</th>\n",
" <th>Prof_margin</th>\n",
" <th>oper_margin</th>\n",
" <th>assetTurnover</th>\n",
" <th>return</th>\n",
" <th>breturn</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1.650000e+04</td>\n",
" <td>16453.000000</td>\n",
" <td>16982.000000</td>\n",
" <td>9518.000000</td>\n",
" <td>14151.000000</td>\n",
" <td>14334.000000</td>\n",
" <td>15365.000000</td>\n",
" <td>13471.000000</td>\n",
" <td>12666.000000</td>\n",
" <td>14120.000000</td>\n",
" <td>14112.000000</td>\n",
" <td>12265.000000</td>\n",
" <td>12265.000000</td>\n",
" <td>14710.000000</td>\n",
" <td>16514.000000</td>\n",
" <td>14908.000000</td>\n",
" <td>14878.000000</td>\n",
" <td>14422.000000</td>\n",
" <td>17600.000000</td>\n",
" <td>17600.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.703872e+05</td>\n",
" <td>359.456248</td>\n",
" <td>424.790391</td>\n",
" <td>2.007120</td>\n",
" <td>19.581739</td>\n",
" <td>68.616251</td>\n",
" <td>14.045440</td>\n",
" <td>40.186552</td>\n",
" <td>29.694856</td>\n",
" <td>8.574161</td>\n",
" <td>3.302338</td>\n",
" <td>1.988559</td>\n",
" <td>1.304729</td>\n",
" <td>172.121549</td>\n",
" <td>3.766162</td>\n",
" <td>0.091711</td>\n",
" <td>9.257534</td>\n",
" <td>0.772832</td>\n",
" <td>0.168684</td>\n",
" <td>0.493807</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.692068e+05</td>\n",
" <td>1195.496974</td>\n",
" <td>1500.420915</td>\n",
" <td>3.303404</td>\n",
" <td>68.958320</td>\n",
" <td>1451.354254</td>\n",
" <td>139.714338</td>\n",
" <td>205.690963</td>\n",
" <td>98.387869</td>\n",
" <td>211.896594</td>\n",
" <td>47.350949</td>\n",
" <td>2.019026</td>\n",
" <td>1.845391</td>\n",
" <td>835.996715</td>\n",
" <td>0.622679</td>\n",
" <td>542.134746</td>\n",
" <td>159.391942</td>\n",
" <td>0.700797</td>\n",
" <td>0.414809</td>\n",
" <td>0.499976</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>3.998060e+01</td>\n",
" <td>0.036700</td>\n",
" <td>0.043500</td>\n",
" <td>0.000000</td>\n",
" <td>-57.300000</td>\n",
" <td>-32788.888900</td>\n",
" <td>-314.150900</td>\n",
" <td>0.036200</td>\n",
" <td>0.366300</td>\n",
" <td>0.111600</td>\n",
" <td>0.012600</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-60687.471000</td>\n",
" <td>-9791.930800</td>\n",
" <td>-0.087900</td>\n",
" <td>-1.195420</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.145885</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.330797</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2.213614e+07</td>\n",
" <td>43400.000000</td>\n",
" <td>55560.000000</td>\n",
" <td>101.672200</td>\n",
" <td>1440.159000</td>\n",
" <td>100100.000000</td>\n",
" <td>6732.291700</td>\n",
" <td>10582.335700</td>\n",
" <td>2857.352000</td>\n",
" <td>15072.373300</td>\n",
" <td>3802.634200</td>\n",
" <td>69.114900</td>\n",
" <td>69.114900</td>\n",
" <td>34463.636400</td>\n",
" <td>5.000000</td>\n",
" <td>7514.559800</td>\n",
" <td>497.914200</td>\n",
" <td>10.464200</td>\n",
" <td>7.817914</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CUR_MKT_CAP PXnow PX1YR DIVIDENDY BEST_EPS \\\n",
"count 1.650000e+04 16453.000000 16982.000000 9518.000000 14151.000000 \n",
"mean 1.703872e+05 359.456248 424.790391 2.007120 19.581739 \n",
"std 7.692068e+05 1195.496974 1500.420915 3.303404 68.958320 \n",
"min 3.998060e+01 0.036700 0.043500 0.000000 -57.300000 \n",
"25% NaN NaN NaN NaN NaN \n",
"50% NaN NaN NaN NaN NaN \n",
"75% NaN NaN NaN NaN NaN \n",
"max 2.213614e+07 43400.000000 55560.000000 101.672200 1440.159000 \n",
"\n",
" EPS_GROWTH Sales_growth PE fiveyrAvPriceEarnings \\\n",
"count 14334.000000 15365.000000 13471.000000 12666.000000 \n",
"mean 68.616251 14.045440 40.186552 29.694856 \n",
"std 1451.354254 139.714338 205.690963 98.387869 \n",
"min -32788.888900 -314.150900 0.036200 0.366300 \n",
"25% NaN NaN NaN NaN \n",
"50% NaN NaN NaN NaN \n",
"75% NaN NaN NaN NaN \n",
"max 100100.000000 6732.291700 10582.335700 2857.352000 \n",
"\n",
" Pricebook Pricesales CURratio Quick DebtEQ \\\n",
"count 14120.000000 14112.000000 12265.000000 12265.000000 14710.000000 \n",
"mean 8.574161 3.302338 1.988559 1.304729 172.121549 \n",
"std 211.896594 47.350949 2.019026 1.845391 835.996715 \n",
"min 0.111600 0.012600 0.000000 0.000000 0.000000 \n",
"25% NaN NaN NaN NaN NaN \n",
"50% NaN NaN NaN NaN NaN \n",
"75% NaN NaN NaN NaN NaN \n",
"max 15072.373300 3802.634200 69.114900 69.114900 34463.636400 \n",
"\n",
" Rating Prof_margin oper_margin assetTurnover return \\\n",
"count 16514.000000 14908.000000 14878.000000 14422.000000 17600.000000 \n",
"mean 3.766162 0.091711 9.257534 0.772832 0.168684 \n",
"std 0.622679 542.134746 159.391942 0.700797 0.414809 \n",
"min 1.000000 -60687.471000 -9791.930800 -0.087900 -1.195420 \n",
"25% NaN NaN NaN NaN 0.000000 \n",
"50% NaN NaN NaN NaN 0.145885 \n",
"75% NaN NaN NaN NaN 0.330797 \n",
"max 5.000000 7514.559800 497.914200 10.464200 7.817914 \n",
"\n",
" breturn \n",
"count 17600.000000 \n",
"mean 0.493807 \n",
"std 0.499976 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 1.000000 \n",
"max 1.000000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[:,attributes].describe().astype(float)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Let's calculate the percentage of data points that have a positive return after 1 year."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"71.93181818181819"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Percent of companies whos stock price increases\n",
"df[df['return'] > 0].shape[0]/df.shape[0]*100\n",
"#In the last column we see that the average return is 16.86 %"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As the data contained some outliers, we want to remove these. In some cases, the values are impossibly high. In other cases, they just happen very rare, so you can't get conclusions from that. The z-score is used to delete some data points. In this way, the plots are clearer."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = remove_outliers(df)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 3. Basic data analysis and visualisation\n",
"The PE ratio is one of the most important ratio's in fundamental analysis, let's try to find a relation between the PE (price/earnings) ratio and the return of a share. Therefore we split the data into different classes based on their PE value. For each class the average return is calculated. Normally the lower the PE ratio, the higher should be the return. The plot is confirming this. \n",
"Note: The low values fluctuate a lot as there are fewer data points."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bins = [5,6,7,8,9,10,12,14,16,18,20,25,30,35,40,1000]\n",
"labels = [x+0.5 for x in bins[0:15]]\n",
"df['bin']=pd.cut(df['PE'],bins,labels=labels)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" CUR_MKT_CAP\n",
"bin \n",
"5.5 15\n",
"6.5 27\n",
"7.5 49\n",
"8.5 88\n",
"9.5 106\n"
]
},
{
"data": {
"image/png": 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IiIhkiGGPiIiIiIhIhhj2iIiIiIiIZIhhj4iIiIiISIYY9oiIiIiIiGSIYY+IiIiIiEiG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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1ed963fa550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"group = df.groupby('bin').mean()\n",
"counter = df.groupby('bin').count()\n",
"print(counter.iloc[:5,:1])\n",
"corr = df['return'].corr(df['bin'], method='pearson')\n",
"group['return'].plot(grid=True, title='Pearson correlation: {:.4f}'.format(corr), figsize=(15,5));\n",
"df['BEST_EPS']=df['BEST_EPS']/df['PXnow']\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We've seen the correlation between the PE and the return let's check the correlations between the different features and the returns. The next plot shows a dot for every data point. The y-axis shows the return, while the x-axis shows the features. In each plot a stright line is added, it shows the value of the correlation between the features."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x1ed953605f8>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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4NYrERg6mbUNTFJwaHmrL/h4REREd3JWPHx3p3KfWXO3y9BiuzSeg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Fy8NrfsrjNC6tx3mVxo4AfRlmvdDpL/lWFo8bmHIYwkr78zw/d+NBOb/Rk03YDOwwgByzVP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jIu0epFRc+HyFcsFmtOzgRk2PUikMxYZF+iAMI/69e2Ge2aUmlaKDQH+mO21aHCb4dPr8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qG6yXCgW/u4H+IVh6F9zswGMuwFnUTZNIp5exPBtZCzGB0oUGv6yKDraUCLVLnVPl30GKJkZDJo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V9MBTKvIZDVtVFxx1YSGlpLtmZBDsUa45cDwfVcvDy8+N4Ztffip8yLWbSy3JzJkWBvVyQSyM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gf14nu3uPrxwpaOH8ilyGVM8hmLseH8pvdbHJdmlyoEoUKKaANesz95lMcRjeagcagvxdGh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\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4dlvQ/Oi9u0Qilq9rKl8nHLmzWCV319Kx6+mAzcjwZmzAj/Iu2Kv3utWQMW4de2WcO81+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\n469O+vsHt++taC/91e2t5YMMIyIWNTW8YgzkRG1D42qnBu1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Y4ZM4bhw4cTFxdHkSJF+OGHH4iPj1dUjNHR0URERDBp0iT9bUr4zFgjZyuNtfK5JWV0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\n7pErVy6yZMmSJHdHRkaSK1euVNtkzZo17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"text/plain": [
"<matplotlib.figure.Figure at 0x1ed95360a20>"
]
},
"metadata": {},
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],
"source": [
"import seaborn as sns\n",
"x = attributes\n",
"y = ['return']\n",
"sns.pairplot(df, hue=None, hue_order=None, palette=None, x_vars=x, y_vars=y, kind='reg', diag_kind='hist', markers=None, size=5, aspect=1, dropna=True)\n"
]
},
{
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"metadata": {
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"source": [
"## Conclusions and interpretation of some of the plots\n",
"Here, two plots are described and interpreted.\n",
"**Current market capitalization** Some of the smallcaps have higher returns, but some have big losses. The spread of return is a lot higher for small companies. It proves that smallcaps have higher risks. The regression line shows the negative correlation between the return and de market capitalization.\n",
"\n",
"**BEST EPS** The scatter plot is not that clear, but the regression line shows that there is a relation between the best EPS value and the return. Note that I divided the EPS by the price of the share to make it like a ratio. In fact, it is becoming the \"BEST EP\", an inverse of the BEST PE."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"This is an interactive plot that shows the market capitalization, the BEST EP ratio and the returns for every data point. The color of the dot shows the dividend. The higher the dividend, the greener the dot. In fact, these plots are the same as the ones above, but interactive."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force !== \"\") {\n",
" window._bokeh_onload_callbacks = [];\n",
" window._bokeh_is_loading = undefined;\n",
" }\n",
" \n",
" \n",
" \n",
" if (typeof (window._bokeh_timeout) === \"undefined\" || force !== \"\") {\n",
" window._bokeh_timeout = Date.now() + 0;\n",
" window._bokeh_failed_load = false;\n",
" }\n",
" \n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
" \n",
" function display_loaded() {\n",
" if (window.Bokeh !== undefined) {\n",
" Bokeh.$(\"#23ec9120-74cd-4a2a-94b7-5d045b5ddf13\").text(\"BokehJS successfully loaded.\");\n",
" } else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
" \n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"23ec9120-74cd-4a2a-94b7-5d045b5ddf13\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid '23ec9120-74cd-4a2a-94b7-5d045b5ddf13' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
" \n",
" var js_urls = [];\n",
" \n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" Bokeh.$(function() {\n",
" var docs_json = 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923.5729,5011.4107,4599.1711,4777.8462,5132.1556,6898.1685,6731.6332,16160.6433,17534.7008,20755.977,19132.8066,20884.8673,23830.5166,26936.0118,26828.134,29055.3699,27117.5722,16130.406,16870.3832,16654.2391,18248.9372,17924.1981,19191.0594,18964.2115,20901.9546,22445.8823,21818.2774,5475.6612,6611.7961,7842.4129,8651.61,10135.7608,9681.8828,11102.2807,10184.0285,9601.9529,9981.9459,6094.4767,5585.5125,5585.5125,5585.5125,5585.5125,7533.61,7867.4911,32476.4876,30974.0563,32520.4849,33036.8865,34058.6157,35443.1819,38276.0733,38432.5166,38913.4526,37672.0942,16613.5044,19769.6792,26157.6068,25524.5625,26745.4174,32873.6199,28186.0896,95450.8198,106442.2789,105683.8894,115028.7957,105536.1892,115551.0806,125440.5984,133358.5653,133679.584,125316.2815,2552.0471,3100.3618,3156.3521,3123.9053,3880.3311,4240.4105,4685.0224,4774.503,25643.8814,24506.5586,23157.2358,24600.6646,26463.3291,26538.2731,27017.2883,28658.5878,32349.2355,31345.727,7280.7908,7315.014,8407.7123,10429.484,12069.4944,12350.1441,14959.174,30917.6925,27363.9848,30006.4914,28495.7353,36436.1849,9767.9087,9066.603,8734.5077,10619.5484,10041.7423,10421.4909,10546.2228,15678.0165,16132.9725,16048.3666,18681.8384,17278.613,16038.2805,17820.3848,14924.7442,16300.3199,11579.1871,12984.7528,13785.189,13715.4316,15520.0212,15957.6728,15061.0673,15951.4052,14417.1441,14208.2874,14487.3784,9816.8643,11700.5877,12072.0491,11623.2818,14829.2962,16401.1889,17285.2104,17327.8993,15149.4183,12589.8645,12300.0545,13178.4836,13772.247,13290.6367,14569.9392,14634.0269,14091.8578,14465.7484,12041.9153,13480.7184,14172.4951,15774.5467,16103.6056,15377.1918,13992.6886,15859.0807,10034.349,11352.8547,10217.9866,13196.7561,12608.7724,12400.1523,13778.0751,13581.1114,14664.7578,11439.3823,9796.3257,10048.7621,10696.7328,11965.7005,12283.1913,12077.0835,11955.1358,12249.8805,13530.8056,13879.3156,18863.5981,18978.2068,18987.1448,19186.4111,21463.4786,24854.3812,28976.109,31070.3444,28861.0578,31575.2642,40443.22,38570.0,39871.8307,42680.1273,49071.6562,57835.2599,58902.2479,55467.9438,58251.6631,56685.5529,6257.0991,6337.8173,6015.0144,6774.3971,6849.3766,6444.9207,6595.9594,7243.611,7619.0382,7047.2437,18287.6491,20545.0615,23060.5104,22911.4214,23866.7936,24756.1333,28490.7065,26339.2905,4689.7327,4690.3405,5326.3267,6679.6373,6449.0613,7355.9351,7395.3488,13668.7241,15282.976,15859.9343,17463.5806,18218.8543,18706.8123,18594.6111,19735.5883,20618.9702,21414.5845,3745.4138,3944.3768,4324.7433,5449.2241,5453.6214,5769.9176,6312.9556,8492.4972,8869.6509,9137.2911,9449.1555,9353.918,10635.6832,10931.9278,11042.4542,10894.4273,10082.712,10853.5649,10078.8989,91987.33,99679.687,95268.3922,116916.47,118670.8111,127377.93,153399.3167,131834.6052,3617.0113,4179.988,4599.6826,4819.053,5208.4301,5787.8287,6368.5593,6616.3532,6902.5042,7036.5884,8888.7642,10490.282,10532.3116,13301.6018,14869.8191,14305.5843,15934.3839,18308.952,23623.2449,21963.2264,8640.5024,9122.8229,8679.5147,9546.853,9786.5264,11394.3558,12622.2673,11104.9423,11596.6853,10942.1533,40220.3528,40319.388,37420.4577,40772.0624,38410.3356,35987.3287,36248.3536,39016.1105,40455.3864,39097.1563,8993.7256,7860.9614,7682.2501,8975.959,10529.6993,9676.2347,10386.912,7379.1707,7009.3535,7794.8342,9140.9832,9528.0561,9911.8177,9956.9776,9675.7401,10029.0886,9885.4862,16599.4673,15587.2003,17156.9596,19448.6667,19914.8776,20907.6485,21937.3366,23720.8611,25698.623,25912.2789,12524.688,13227.56,11137.5279,11439.3107,12929.8415,15404.9863,18077.8279,18564.4281,19682.5859,17118.6154,15607.1736,18316.1466,18462.8896,18555.0922,18672.0491,8733.891,9809.1066,10811.7546,13316.0631,16127.2888,16957.0724,16771.406,10998.5835,12820.035,12485.0707,12975.4907,12516.1186,14498.2675,12539.2255,12652.3565,13705.2627,13875.6486,8903.2566,11001.9985,11147.5457,12142.5505,11705.3038,13081.8896,12825.5873,12788.0241,13834.4259,16238.3188,16061.3129,12342.9325,20989.4091,21175.5249,20842.5965,24603.641,24392.4529,25557.9555,28434.3996,30811.95,31965.2004,30570.5754,15653.3873,17290.8947,16412.4226,13810.341,15839.7919,16229.211,209074.58,217491.3,191472.8,201487.148,190452.0,179618.02,185644.8,182604.2158,183518.4,182754.64,8880.859,8755.084,9197.0358,10310.0571,10829.4673,13717.3008,13781.9432,9657.6256,8207.5817,9042.2643,7419.4193,7848.2496,6339.5399,6497.6,3895.1771,3842.1861,3629.9496,3871.4967,3740.2556,3594.3084,3746.2797,3739.9969,4025.049,4056.6293,22615.7192,24687.004,25275.594,22628.2239,3460.9018,4107.7245,4138.4656,5021.9315,5598.5553,6138.6549,6741.0968,6279.9345,7100.873,6694.9516,38475.5661,41565.5709,38507.503,43902.9543,44187.6856,40379.3736,39992.1893,38313.4,36723.1704,39721.3497,4961.9088,4771.3989,4185.233,4179.4286,4177.0844,4583.8533,5815.2077,6710.1311,7836.3349,7269.227,8839.7372,9292.8545,9803.1459,11880.3236,11031.3632,12936.7014,12452.3807,32969.9425,34092.563,40332.1352,45324.7029,43134.2104,37221.5867,41001.3575,19066.7198,21505.4243,22974.5066,27354.7362,30477.5466,33221.5402,40238.2465,47647.1806,47074.7186,48601.5011,5967.0608,7505.4177,8726.9946,10844.0239,11767.0353,12884.8693,15500.6961,13053.4368,5984.8218,7010.2826,6223.7955,7245.0216,8207.8181,9378.1254,10829.1646,9797.7603,8364.9742,8468.4852,15098.3282,8046.1928,7105.8513,5940.9473,4623.6211,5572.6515,6315.9104,5712.7655,5928.2828,5748.6738,25655.789,29123.0937,29329.3871,28387.055,32727.554,31893.3135,38196.0804,38060.7559,41039.4801,40118.3511,36956.7619,43014.7116,45288.1934,53862.6302,53898.3013,60545.6057,63077.2508,58317.5558,61967.3646,64292.1879,39197.8553,38676.2146,44344.2307,48062.2662,51120.6896,51582.1937,50911.7238,6970.5886,7352.2342,6986.4564,8117.8503,7238.1159,7744.7398,10335.0917,10940.8669,10688.1812,10048.4612,19096.0255,19517.0792,21034.2079,19767.3731,4925.5069,5837.7353,5098.2076,5395.1385,6273.5968,8424.1502,9221.7991,12206.8693,12678.1545,14737.5393,10557.0711,16582.5579,15339.8255,17479.5534,6235.5492,6568.0401,7354.1704,7978.9421,8087.1633,9460.4868,10398.0954,60690.7365,57222.2222,55405.7594,58635.976,66790.0596,72805.866,75808.8909,81103.8384,80103.6087,83802.6405,56817.6044,56217.2336,59138.3343,66832.8287,72014.611,72053.5782,77375.5305,85396.1124,90355.8928,97299.1282,75747.2813,68595.625,70665.8594,81261.7969,81666.6406,86284.4688,97551.0313,89525.5156,94379.6484,89788.1953,3995.2859,5466.1446,5743.0412,5656.6518,5909.7201,5418.8632,5465.1851,5372.38,4664.2615,4930.1339,4214.7196,5138.6453,4703.2685,5437.5544,7267.6183,9176.2872,10187.169,10655.5344,68834.886,71383.8176,75165.814,85641.6652,85439.8089,98925.8569,104420.8282,107077.3206,105836.6313,96603.9357,112246.8672,118779.2013,123181.0375,141755.9487,136705.5187,132405.4477,132959.3463,6776.0859,6678.347,7684.4946,7856.1502,7313.3858,8044.9628,8213.3294,8697.414,8642.5994,8268.4919,14621.5697,17513.4247,16630.478,18616.1238,21144.614,21879.4101,27448.6037,27249.9518,27110.2527,28465.5737,25021.6966,27618.2042,26563.0869,30510.8794,33009.8755,39873.5309,38973.9534,37243.9193,37279.4192,32551.5108,10362.5669,10053.2,10907.7877,11056.2017,10003.4609,9986.8541,10136.6706,17368.1352,16697.8827,22362.8797,26738.9364,126254.04,129828.93,123492.58,140484.2354,144022.74,133524.4136,140626.431,196994.0595,202626.4169,207220.1444,7062.618,7306.2079,7575.4413,8065.6296,8573.0649,9051.0259,8513.0356,9233.942,8855.2127,8322.525,14952.1087,19827.0796,21693.5384,21696.8307,22734.8525,9119.1044,8681.2399,8478.0831,9822.0286,9386.917,9193.1827,9364.3187,10513.8775,10581.3728,9697.3011,17497.3113,17986.6052,16943.9893,16077.5511,19093.6269,21792.0034,21520.76,18862.5988,14199.3473,13767.0561,4725.0565,6429.2975,7921.6239,9304.1919,9053.4535,11608.219,12312.7118,11580.3597,10823.2004,11359.5921,14870.9342,15651.9559,18265.8651,18835.1389,19332.3311,21052.3829,19570.6294,20815.6518,22168.0671,21907.5652,22068.9102,25530.2155,22167.0514,24840.9682,26359.8336,27773.6614,43482.2092,41363.7063,235900.4796,248074.592,228245.5746,246373.5366,244079.7244,240773.5298,254622.935,246805.8676,241996.0076,246425.5831,8554.2589,9532.1769,10299.1207,9526.3378,8964.2435,9344.9402,8497.5072,15065.4231,17111.3259,16400.8378,16670.5182,18397.5928,17135.4003,19357.8903,16831.7912,8810.2058,7775.9795,8106.5818,9602.3076,9383.8586,9639.5901,10170.7858,12630.108,18960.248,4724.7927,5034.5741,4996.0005,6518.499,5618.3689,8338.9001,9110.2191,10364.0031,12485.7702,11010.8854,13834.0565,13510.1159,13024.317,14502.0347,14091.7614,13737.9332,13904.0475,15128.0291,16178.4843,15363.2525,8859.2703,8759.1001,9367.1216,9983.8103,10451.633,12478.7045,12320.9604,14482.2115,12693.1122,11370.206,400139.4842,422127.663,394611.117,403733.3231,401730.0695,378716.4032,442093.5836,422098.603,432357.6258,401094.2727,6059.6927,5868.8538,6176.7351,6915.7804,6529.0939,6713.8617,6465.0601,10524.9975,11137.0787,12682.7604,14982.6605,13389.8834,14521.4753,15256.212,6383.2929,6739.884,7182.0359,6483.3503,19356.919,18924.2693,23535.4983,25913.481,27184.959,33833.8499,41024.0475,36237.1848,35369.1687,40530.1032,29669.2043,30241.0955,30000.0906,32425.4577,31191.7657,31856.0097,33671.4441,33317.9258,35840.0861,31645.9407,11336.2348,11812.8558,11565.6504,12738.8151,12617.1832,13926.732,15933.7069,15942.2473,17429.7194,17023.1905,18519.0958,2152.8436,2108.0931,2138.0683,2172.9975,2123.338,2104.7989,2242.357,2176.5844,2574.3317,1758.6144,1668.5729,1991.7584,2391.2088,2281.1523,2664.4993,2901.0509,2889.9116,3186.8288,3195.8588,3357.4393,5057.5057,4884.1703,5881.5421,5762.6761,5180.032,5280.4877,4263.9369,4148.7461,4333.396,4310.0096,5199.6898,5537.8822,5574.0176,6173.0781,6414.4739,7320.6733,7211.9223,1499.6624,1257.1678,1275.0223,1326.5902,1893.3401,2373.2777,2126.3638,3455.3919,3776.93,2794.4421,2801.532,3164.4966,3799.748,3931.9478,3327.4805,5319.1366,4714.6651,5544.8252,5547.9622,5471.8352,5121.7007,5159.9011,5282.8867,5687.5492,4608.4042,2688.8959,3007.6093,2258.4226,2739.7207,3027.0798,3841.7718,4590.216,4219.8968,4574.8482,4175.3861,4499.0613,3656.2936,4370.3124,5101.9288,6422.0847,6531.6947,5872.7824,4435.2185,4286.3313,4670.7192,5054.022,5415.5451,5486.6188,5033.7678,7567.175,6793.9119,7997.6305,8079.9344,7127.3997,7025.7036,6962.8287,2266.5848,2657.7557,2911.8391,3400.4932,3363.9827,4181.7778,4920.6337,4206.0744,4492.3032,4256.7307,1693.7762,1965.7418,1977.3096,2356.9839,3369.8205,4012.7583,4676.1716,4104.1954,4306.2726,4171.1128,4861.8598,5465.238,5933.7329,6018.986,5470.7068,5438.5686,5623.6214,6338.0769,5868.1916,6594.8073,7154.0144,7521.7039,7740.093,8474.2783,8125.2746,2837.8327,3303.6097,2664.1932,3525.6089,3112.7251,3408.0228,2840.2849,3057.8189,3150.4012,3181.1692,3334.548,3556.7805,3944.3757,3381.7517,4720.482,5349.9534,4786.775,3797.3386,3682.3059,3985.6136,4465.5088,4331.4739,4386.0497,3956.3135,6999.4926,6891.4864,6205.8492,8961.1644,9108.1977,8933.3382,7469.5061,6351.1079,6347.2647,5476.2204,4476.2553,4111.5747,4200.7143,4958.8642,4604.9186,5733.8693,6068.3126,6436.2323,6132.4882,5737.1293,3301.0142,2826.5992,2975.2715,3119.8071,2911.8116,3147.6269,3071.4051,6040.6945,6538.4955,6562.9802,7351.8579,7326.5954,7346.3591,7533.8677,8113.0394,8852.4751,8640.352,3234.9821,3941.1718,4693.3337,5708.4578,5973.5814,5086.7973,2749.7487,2786.9607,3208.0416,3126.9206,2889.5981,3609.5104,4189.0318,4197.9278,3906.4441,3914.4074,4536.5142,4377.0908,5134.7545,6314.9014,6606.5154,7735.6345,9117.8964,9121.456,9734.7828,8839.1045,10820.2295,10371.7659,11177.3653,12120.0529,11728.7296,11135.1334,12289.3287,3571.6578,3209.3801,3367.9864,3547.4865,3688.2729,3469.0043,3284.7281,4157.0113,4027.1737,4014.9895,4216.4462,3965.2824,4100.6974,3797.4178,4710.1157,4436.1615,5024.713,4976.2152,3937.8899,4617.5573,3939.5505,4695.8742,2210.6926,2176.3801,2539.3078,3182.5853,2698.1902,3451.6307,3312.4726,3819.874,4074.2007,3111.5591,3907.3384,3735.3096,3661.6011,4611.9496,4643.6034,4651.2795,4565.0561,4473.1797,4452.1693,4647.0932,3248.5415,3369.5022,3742.4976,3788.1762,3668.617,3579.8886,2975.6041,4258.0343,4536.8596,4448.0203,4706.0408,4624.8535,4431.7535,4185.3993,2985.2573,3097.5765,3115.4115,4333.4874,5172.7966,6291.1175,5710.4801,6697.8947,6386.0019,7272.6229,8938.1615,9406.8715,7373.1307,6258.8627,3223.8129,4070.358,3966.7147,4530.7786,4894.9858,4231.1736,4560.8472,3294.3901,3801.9388,3739.525,3278.7453,6896.8267,6183.486,3833.2098,4034.1626,4507.2023,5063.5648,4991.5885,5414.599,6036.2119,7103.9646,7032.6884,7152.4277,8423.9762,8720.855,8711.8022,10023.2947,9085.1336,4378.4414,5295.738,5749.8353,6490.2327,8809.7388,9210.3431,7045.5467,2728.1193,3332.3728,2947.1985,3334.6562,3492.9878,7704.8495,7516.4393,7484.9133,8928.996,8525.5826,8320.844,9202.852,9481.0168,9506.9592,9377.6073,2530.5639,2213.7988,2586.2024,3329.8684,2741.8719,2790.3493,2467.1267,3555.0931,3637.1596,3327.0363,3904.2005,3269.7143,12060.0986,13929.274,13596.8263,16134.434,15975.0818,19911.5909,20901.1411,23067.2983,29411.286,24745.1654,2522.6253,2469.6303,2473.6929,2625.4449,2815.3755,2551.6982,2679.8257,2765.1135,3210.4718,2828.8835,2624.8641,2577.2989,2985.3151,3384.1398,3219.5606,3661.1428,3844.1326,3347.2313,4079.9265,3938.5941,4523.9823,4483.9482,5212.2871,5792.5125,6348.0461,5944.3909,6573.7919,6497.1281,7519.9532,6499.5849,5860.316,5302.2514,5815.9905,6149.6058,7100.2667,6438.5957,6326.1602,4760.7051,8530.2361,8490.9029,8843.8918,10080.6481,8735.2433,4298.2013,3863.6637,3989.4859,4601.6386,4580.3644,4532.6023,3953.3451,2290.1014,2634.4844,3220.8038,3465.5317,3581.5392,3910.6535,4177.9721,4375.3986,3728.379,2961.4901,3951.3134,6381.894,7449.2394,7229.6744,7552.3707,6881.69,6090.8155,4892.3568,3398.8715,4388.5447,4495.4704,3914.4139,4165.8492,3701.0451,4119.639,1929.7317,1819.281,2133.0348,2011.6702,2269.5555,2552.1712,2875.7683,2588.6025,2788.4887,6575.5068,7641.3923,8226.1395,8861.0135,9201.336,8870.6913,2525.5872,3101.987,3681.9428,4806.1029,5307.8283,5388.1137,5090.975,5024.1169,4309.4848,3962.2589,4464.9093,5050.0746,5081.9022,5551.1576,5154.2289,2425.0834,2650.8958,2618.8928,2786.6551,2316.4386,2365.9114,2697.4117,2778.3539,4482.2214,4184.3882,3883.9527,8854.31,9882.1405,8425.9072,10197.6961,8768.137,11423.4212,11346.6503,12882.3135,16072.3011,14162.8948,3916.7362,3822.6406,4000.5123,1610.2034,1577.7237,1394.1317,1586.5653,1611.1426,1720.7737,1572.626,4367.6047,4418.8302,3934.6789,3729.5567,4443.3038,5229.6291,6332.3591,5122.3875,4816.0845,5311.7003,5240.2276,5571.1417,6472.3675,6155.994,6049.8576,5621.8074,2968.4723,4375.3954,5486.0071,9551.4226,6104.9107,6573.2077,5096.9839,3408.4343,3957.8226,3711.197,3796.8657,3587.5015,4270.0586,4059.0594,5039.7318,4694.5061,20554.5195,26516.0157,28513.5201,29872.3992,29759.0653,5670.2688,6295.098,5579.485,5821.277,6269.046,6699.7153,7308.2832,6763.1617,5710.2849,5319.9625,3923.9653,4545.1244,4430.5805,4267.8726,3502.4278,3919.3666,4564.6074,4813.6368,5141.6403,5349.3296,4871.6456,4699.1369,5902.5056,6321.7404,6269.3431,6965.0772,6463.2137,6865.3119,6024.7022,5838.1202,6492.1011,5294.9083,6699.6071,7547.6176,7526.4985,7453.3942,3531.9193,3664.7185,4666.8298,4473.4038,4559.7936,4460.7307,3573.9596,3244.8793,3826.5181,3991.6681,3658.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"In the first analysis NaN values don't do any harm as these values are neglected. For further calculations it is important to delete these data as it is influencing the results. Now the data can be summarized and we can do a first check."
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CUR_MKT_CAP</th>\n",
" <th>PXnow</th>\n",
" <th>PX1YR</th>\n",
" <th>DIVIDENDY</th>\n",
" <th>BEST_EPS</th>\n",
" <th>EPS_GROWTH</th>\n",
" <th>Sales_growth</th>\n",
" <th>PE</th>\n",
" <th>fiveyrAvPriceEarnings</th>\n",
" <th>Pricebook</th>\n",
" <th>Pricesales</th>\n",
" <th>CURratio</th>\n",
" <th>Quick</th>\n",
" <th>DebtEQ</th>\n",
" <th>Rating</th>\n",
" <th>Prof_margin</th>\n",
" <th>oper_margin</th>\n",
" <th>assetTurnover</th>\n",
" <th>return</th>\n",
" <th>breturn</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>count</th>\n",
" <td>5549.000000</td>\n",
" <td>5549.000000</td>\n",
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" <th>mean</th>\n",
" <td>24223.183024</td>\n",
" <td>91.816200</td>\n",
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" <td>0.843654</td>\n",
" <td>0.151882</td>\n",
" <td>0.486214</td>\n",
" </tr>\n",
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" <th>std</th>\n",
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" <td>1.752427</td>\n",
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" <td>183.544575</td>\n",
" <td>0.518267</td>\n",
" <td>17.722974</td>\n",
" <td>15.811613</td>\n",
" <td>0.539859</td>\n",
" <td>0.280525</td>\n",
" <td>0.499855</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>969.982300</td>\n",
" <td>1.850000</td>\n",
" <td>2.440000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.030581</td>\n",
" <td>-8520.689700</td>\n",
" <td>-85.940300</td>\n",
" <td>2.103400</td>\n",
" <td>4.806800</td>\n",
" <td>0.363700</td>\n",
" <td>0.128700</td>\n",
" <td>0.146300</td>\n",
" <td>0.034500</td>\n",
" <td>0.000000</td>\n",
" <td>1.444000</td>\n",
" <td>-385.164800</td>\n",
" <td>-404.120900</td>\n",
" <td>0.026000</td>\n",
" <td>-0.837658</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>4888.997400</td>\n",
" <td>34.030000</td>\n",
" <td>36.920000</td>\n",
" <td>0.000000</td>\n",
" <td>0.058837</td>\n",
" <td>-11.111100</td>\n",
" <td>-0.573100</td>\n",
" <td>15.119100</td>\n",
" <td>14.833900</td>\n",
" <td>1.930700</td>\n",
" <td>1.103900</td>\n",
" <td>1.125600</td>\n",
" <td>0.606700</td>\n",
" <td>31.370000</td>\n",
" <td>3.583000</td>\n",
" <td>4.894900</td>\n",
" <td>8.812900</td>\n",
" <td>0.449400</td>\n",
" <td>-0.011496</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>9459.449900</td>\n",
" <td>51.210000</td>\n",
" <td>57.270000</td>\n",
" <td>1.465300</td>\n",
" <td>0.071962</td>\n",
" <td>9.375000</td>\n",
" <td>4.995900</td>\n",
" <td>19.130900</td>\n",
" <td>18.517900</td>\n",
" <td>3.054900</td>\n",
" <td>1.810000</td>\n",
" <td>1.626800</td>\n",
" <td>0.957000</td>\n",
" <td>60.668200</td>\n",
" <td>4.000000</td>\n",
" <td>8.801400</td>\n",
" <td>14.260400</td>\n",
" <td>0.699900</td>\n",
" <td>0.140897</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>21412.941000</td>\n",
" <td>76.070000</td>\n",
" <td>86.390000</td>\n",
" <td>2.625500</td>\n",
" <td>0.090429</td>\n",
" <td>30.952400</td>\n",
" <td>12.223100</td>\n",
" <td>24.751000</td>\n",
" <td>23.888600</td>\n",
" <td>4.687300</td>\n",
" <td>2.943100</td>\n",
" <td>2.371300</td>\n",
" <td>1.487900</td>\n",
" <td>108.876600</td>\n",
" <td>4.316000</td>\n",
" <td>14.436600</td>\n",
" <td>21.895200</td>\n",
" <td>1.090300</td>\n",
" <td>0.303649</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>625493.531500</td>\n",
" <td>4441.500000</td>\n",
" <td>4626.500000</td>\n",
" <td>11.879000</td>\n",
" <td>0.526175</td>\n",
" <td>4422.329000</td>\n",
" <td>248.562900</td>\n",
" <td>639.000800</td>\n",
" <td>321.366100</td>\n",
" <td>569.648300</td>\n",
" <td>65.878800</td>\n",
" <td>7.989400</td>\n",
" <td>6.486300</td>\n",
" <td>2563.768100</td>\n",
" <td>5.000000</td>\n",
" <td>590.006600</td>\n",
" <td>150.704200</td>\n",
" <td>2.866800</td>\n",
" <td>1.402414</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CUR_MKT_CAP PXnow PX1YR DIVIDENDY BEST_EPS \\\n",
"count 5549.000000 5549.000000 5549.000000 5549.000000 5549.000000 \n",
"mean 24223.183024 91.816200 102.887837 1.736713 0.077426 \n",
"std 47042.799294 255.627182 280.646720 1.752427 0.035220 \n",
"min 969.982300 1.850000 2.440000 0.000000 -0.030581 \n",
"25% 4888.997400 34.030000 36.920000 0.000000 0.058837 \n",
"50% 9459.449900 51.210000 57.270000 1.465300 0.071962 \n",
"75% 21412.941000 76.070000 86.390000 2.625500 0.090429 \n",
"max 625493.531500 4441.500000 4626.500000 11.879000 0.526175 \n",
"\n",
" EPS_GROWTH Sales_growth PE fiveyrAvPriceEarnings \\\n",
"count 5549.000000 5549.000000 5549.000000 5549.000000 \n",
"mean 22.588672 7.309742 25.055014 23.384719 \n",
"std 313.096212 18.039976 31.261978 21.701677 \n",
"min -8520.689700 -85.940300 2.103400 4.806800 \n",
"25% -11.111100 -0.573100 15.119100 14.833900 \n",
"50% 9.375000 4.995900 19.130900 18.517900 \n",
"75% 30.952400 12.223100 24.751000 23.888600 \n",
"max 4422.329000 248.562900 639.000800 321.366100 \n",
"\n",
" Pricebook Pricesales CURratio Quick DebtEQ \\\n",
"count 5549.000000 5549.000000 5549.000000 5549.000000 5549.000000 \n",
"mean 4.694617 2.453699 1.940756 1.225409 99.022084 \n",
"std 11.901145 2.492265 1.209221 0.982652 183.544575 \n",
"min 0.363700 0.128700 0.146300 0.034500 0.000000 \n",
"25% 1.930700 1.103900 1.125600 0.606700 31.370000 \n",
"50% 3.054900 1.810000 1.626800 0.957000 60.668200 \n",
"75% 4.687300 2.943100 2.371300 1.487900 108.876600 \n",
"max 569.648300 65.878800 7.989400 6.486300 2563.768100 \n",
"\n",
" Rating Prof_margin oper_margin assetTurnover return \\\n",
"count 5549.000000 5549.000000 5549.000000 5549.000000 5549.000000 \n",
"mean 3.930807 10.325047 15.731832 0.843654 0.151882 \n",
"std 0.518267 17.722974 15.811613 0.539859 0.280525 \n",
"min 1.444000 -385.164800 -404.120900 0.026000 -0.837658 \n",
"25% 3.583000 4.894900 8.812900 0.449400 -0.011496 \n",
"50% 4.000000 8.801400 14.260400 0.699900 0.140897 \n",
"75% 4.316000 14.436600 21.895200 1.090300 0.303649 \n",
"max 5.000000 590.006600 150.704200 2.866800 1.402414 \n",
"\n",
" breturn \n",
"count 5549.000000 \n",
"mean 0.486214 \n",
"std 0.499855 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 1.000000 \n",
"max 1.000000 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df= df.iloc[:,2:].reset_index(drop=True)\n",
"df=df.dropna() #Tot hiervoor konden de enkele NA's geen kwaad, maar voor het NN willen we dat niet\n",
"df.loc[:,attributes].describe().astype(np.float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is possible to find some conclusions with the 'correlation plots'. Although, it is better the have a numerical value. To calculate the correlation coefficients, the data should be normalised. We see that the dividend, market capitalization, sales growth and best EP have the biggest correlation with the return."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DIVIDENDY -0.104225575413\n",
"CUR_MKT_CAP -0.0720714047831\n",
"Pricesales -0.0482633624651\n",
"PXnow -0.0400802070805\n",
"oper_margin -0.0389658987233\n",
"Prof_margin -0.00435273379013\n",
"PE 0.00295107421611\n",
"Pricebook 0.00325980783593\n",
"fiveyrAvPriceEarnings 0.0100711755587\n",
"EPS_GROWTH 0.0117654999064\n",
"DebtEQ 0.021673495901\n",
"CURratio 0.0268171530037\n",
"Quick 0.027724935736\n",
"PX1YR 0.0292110084654\n",
"Sales_growth 0.0447181953908\n",
"Rating 0.0460916895817\n",
"assetTurnover 0.0701406100772\n",
"BEST_EPS 0.18738466211\n"
]
}
],
"source": [
"print_correlation_return(df,attributes)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"### Conclusions:\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The right columns of the data are splitted in training data and test data and the input is separated from the labels. A permutation puts the data in random order."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Split: 550 testing and 4999 training samples\n",
"[ 315 3023 5528 ..., 1024 263 191]\n"
]
}
],
"source": [
"# Training and testing sets.\n",
"x_test,x_train,y_test1,y_test2,y_train1,y_train2 = split_and_permute(df,attributes,550,5000)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data is normalised. Note that the normalization of along the data is out commented. After some classification runs, I noticed that the performance increases if I only normalise along the features."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Normalise along each dimension\n",
"x_test_orig = x_test.copy() # This variable is later used for checking the performance of the classifier\n",
"x_test -= x_test.mean(axis=0)\n",
"x_test /= x_test.std(axis=0)\n",
"x_train -= x_test.mean(axis=0)\n",
"x_train /= x_test.std(axis=0)\n",
"\n",
"\n",
"#Normalise along each data\n",
"#x_test = x_test.div(np.square(x_test).sum(axis=1),axis=0)\n",
"#x_train = x_train.div(np.square(x_train).sum(axis=1),axis=0)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"To make sure everything is right, the data is reindexed. The last part of this notebook contains a neural network. That part requires to be run in the docker container with tensorflow. To make this notebook runnable, the data is stored in files.\n",
"There are two sets of labels, label1 (y_test1 and y_train1) contain the actual value of the return. Label2 is binary. A one means the the stock will increase more than 15%."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_test_orig.reset_index(drop=True)\n",
"x_test.reset_index(drop=True)\n",
"x_train.reset_index(drop=True)\n",
"y_test1.reset_index(drop=True)\n",
"y_test2.reset_index(drop=True)\n",
"y_train1.reset_index(drop=True)\n",
"y_train2.reset_index(drop=True)\n",
"\n",
"\n",
"np.save('x_test',x_test)\n",
"np.save('x_train',x_train)\n",
"np.save('y_test1',y_test1)\n",
"np.save('y_test2',y_test2)\n",
"np.save('y_train1',y_train1)\n",
"np.save('y_train2',y_train2)\n",
"\n",
"#x_test.to_csv(os.path.join('x_test.csv'))\n",
"#x_train.to_csv(os.path.join('x_train.csv'))\n",
"\n",
"#y_test1.to_csv(os.path.join('y_test1.csv'))\n",
"#y_test2.to_csv(os.path.join('y_test2.csv'))\n",
"\n",
"#y_train1.to_csv(os.path.join('y_train1.csv'))\n",
"#y_train2.to_csv(os.path.join('y_train2.csv'))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linear classification\n",
"\n",
"Two different classifiers are tested, the linear SVM and Logistic regression. In the rest of this notebook, the performance of the Logistic classifier is analysed because it performs slightly better and it commonly used for binary classification. (label2)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"336.0\n",
"317.0\n",
"Train accuracy: 54.40 53.76\n",
"Test accuracy: 52.28 52.46\n"
]
}
],
"source": [
"test_pred = classifiers(x_test,x_train,y_test2,y_train2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The classifiers don't reach high accuracies which means that it is hard to predict which stocks will perform better."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"res = np.matrix([test_pred,y_test2])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There were 157 false positives and 104 false_negatives\n"
]
}
],
"source": [
"false_positive =0\n",
"false_negative=0\n",
"for i in range(0,res.shape[1]):\n",
" if (res[0,i] != res[1,i]) and (res[0,i] != 0):\n",
" false_positive+=1\n",
" elif (res[0,i] != res[1,i]) and (res[0,i] == 0):\n",
" false_negative+=1\n",
" \n",
"print('There were ',false_positive, ' false positives and ',false_negative,'false_negatives') \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"matrix([[ 317.],\n",
" [ 264.]])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res.sum(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next bokehplot shows 3 features and the stocks that will have higher returns according to the classifier are marked green. As you can see, most of the data points with high returns are detected. The plot with the Dividend shows that it is the main feature for classification. Almost all data points below a certain treshhold are marked green. "
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"\n",
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" <span id=\"710ee8c0-cd12-4527-90dd-cc691a0b27b2\">Loading BokehJS ...</span>\n",
" </div>"
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{
"data": {
"application/javascript": [
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"\n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
"\n",
" function display_loaded() {\n",
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" }\n",
"\n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
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" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
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" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
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" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
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" s.onerror = function() {\n",
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" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
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"\n",
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" function(Bokeh) {\n",
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" },\n",
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" function(Bokeh) {\n",
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" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.3.min.css\");\n",
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" ];\n",
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" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
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" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
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"As mentioned before, the performance of the classifier isn't really good. Let's imagine that we would have bought the stocks that are detected by the classifier. The first box shows the average return of all the test data. The second box shows the average return of the stocks that were detected by the algorithm. It is outperforming the 'market' with 3 percent. So that seems quiet ok."
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"## Neural network\n",
"To use the techniques seen in the course, I implemented a three-layer neural network. The three layers are fully connected and contain 200,50 and 2 nodes. The performance is similar to the that from the classifier, although it doesn't seem to work properly. I tried a lot of different learning rates, different sizes of networks, adding dropout, adding regularization with different parameters, different loss fonctions, different amount of runs,... but the network never converged nicely. All the code is in the myutilnn.py. "
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"Training data shape: (5000, 17)\n",
"Training label shape: (5000, 2)\n",
"Test data shape: (550, 17)\n",
"Test label shape: (550, 2)\n",
"\n",
"Iteration i= 0 , train accuracy= 0.54 , loss= 46.0\n",
"test accuracy= 0.519999998808\n",
"\n",
"Iteration i= 1000 , train accuracy= 0.44 , loss= 56.0\n",
"test accuracy= 0.509999996424\n",
"\n",
"Iteration i= 2000 , train accuracy= 0.5 , loss= 50.0\n",
"test accuracy= 0.504000002146\n",
"\n",
"Iteration i= 3000 , train accuracy= 0.47 , loss= 53.0\n",
"test accuracy= 0.540000003576\n",
"\n",
"Iteration i= 4000 , train accuracy= 0.47 , loss= 53.0\n",
"test accuracy= 0.512000000477\n",
"\n",
"Iteration i= 5000 , train accuracy= 0.4 , loss= 60.0\n",
"test accuracy= 0.522000002861\n",
"\n",
"Iteration i= 6000 , train accuracy= 0.42 , loss= 58.0\n",
"test accuracy= 0.505999994278\n",
"\n",
"Iteration i= 7000 , train accuracy= 0.48 , loss= 51.6279\n",
"test accuracy= 0.467999994755\n",
"\n",
"Iteration i= 8000 , train accuracy= 0.43 , loss= 57.0\n",
"test accuracy= 0.530000013113\n",
"\n",
"Iteration i= 9000 , train accuracy= 0.53 , loss= 47.0\n",
"test accuracy= 0.537999987602\n",
"\n",
"Iteration i= 10000 , train accuracy= 0.51 , loss= 49.0\n",
"test accuracy= 0.534000003338\n"
]
}
],
"source": [
"from myutilnn import *\n",
"trainnn()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion\n",
"The basic data analysis gave some good insights and shows that there is no 'Golden rule' to find outperforming stocks. The classifiers didn't perform very well, although it was possible to reach higher returns by using them. It is also possile to do some further optimization, adapt the classifiers, do some feature extraction,...\n",
"Concerning the Neural network, it is hard to find if there are errors in the code / data or maybe the setup of the network is wrong.\n",
"Finally, algorithms can help people with investment decisions although some human intervention is usefull."
]
},
{
"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": 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 |
drvinceknight/gt | nbs/exercises/01-Normal-Form-Games.ipynb | 1 | 2835 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Normal Form Games - Exercises\n",
"\n",
"1. Give the definition of a Normal Form Game.\n",
"\n",
"2. Give the definition of a zero sum game. Obtain the full game representations $(A, B)$ for the zero sum games with row play payoff matrix given by:\n",
" 1. $A =\\begin{pmatrix}1 & 3\\\\ -1 & 4\\end{pmatrix}$\n",
" 2. $A =\\begin{pmatrix}1 & -2\\\\ -1 & 2\\end{pmatrix}$\n",
" 3. $A =\\begin{pmatrix}1 & -2 & 4\\\\ 2 & -1 & 2\\\\ 7 & -7 & 6\\end{pmatrix}$\n",
" \n",
"3. Consider the game described as follows:\n",
"\n",
" > An airline loses two suitcases belonging to two different travelers. Both suitcases have the same value. An airline manager tasked to settle the claims of both travelers explains that the airline is liable for a maximum of £5 per suitcase. \n",
"\n",
" > To determine an honest appraised value of the suitcases, the manager separates both travelers and asks them to write down the amount of their value at no less than £2 and no larger than £5 (to the single dollar):\n",
" > - If both write down the same number, that number as the true dollar value of both suitcases and reimburse both travelers that amount. \n",
" > - However, if one writes down a smaller number than the other, this smaller number will be taken as the true dollar value, and both travelers will receive that amount along with a bonus/malus: £2 extra will be paid to the traveler who wrote down the lower value and a £2 deduction will be taken from the person who wrote down the higher amount. \n",
" \n",
" Represent this as a Normal Form Game.\n",
" \n",
"4. For each of the following give a Normal Form Game representation or explain why this is not possible.\n",
"\n",
" 1. Alice, Bob and Celine are childhood friends that would like to communicate online. They have a choice between 3 messaging platforms: facebook messenger, whatsapp or signal. They need to choose without prior communication.\n",
" 2. Alice must choose a number $0 < x < 1$, she receives a utility of $f(x)$ for some function $f:\\mathbb{R}\\to\\mathbb{R}$\n",
" 3. Rock Paper Scissors (official rules: https://www.wrpsa.com/the-official-rules-of-rock-paper-scissors/)."
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:gt]",
"language": "python",
"name": "conda-env-gt-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
gciteam6/xgboost | notebooks/layer0.gru.time_series_validation.ipynb | 1 | 34435 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## GRUで発電量予測\n",
"\n",
"#### やったこと\n",
"+ GRU(Gated Recurrent Unit)で発電量予測"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from os import path, pardir\n",
"import sys\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"PROJECT_ROOT_DIRPATH = path.join(os.getcwd(), pardir)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sys.path.append(PROJECT_ROOT_DIRPATH)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"from sklearn.metrics import mean_absolute_error\n",
"from sklearn.preprocessing import MinMaxScaler"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"KWARGS_READ_CSV = {\n",
" \"sep\": \"\\t\",\n",
" \"header\": 0,\n",
" \"parse_dates\": [0],\n",
" \"index_col\": 0\n",
"}\n",
"KWARGS_RESAMPLING = {\n",
" \"rule\": \"30T\",\n",
" \"axis\": 0,\n",
" \"closed\": \"right\",\n",
" \"label\": \"right\"\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import keras\n",
"from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
"from keras.models import Model\n",
"from keras.layers import (\n",
" BatchNormalization,\n",
" Dense,\n",
" Dropout,\n",
" Input,\n",
" GRU,\n",
" Masking,\n",
" TimeDistributed\n",
")\n",
"from keras.optimizers import Adam\n",
"from keras.regularizers import l1_l2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"epochs = 10000\n",
"validation_split=0.2\n",
"\n",
"gru_params = {\n",
" \"units\": 48,\n",
" \"batch_size\": 256,\n",
" \"activation\": 'sigmoid',\n",
" \"recurrent_activation\": 'sigmoid',\n",
" # \"kernel_regularizer\": {\"l1\": 0.01, \"l2\": 0.005},\n",
" \"dropout\": 0.1,\n",
" \"recurrent_dropout\": 0.1,\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"freq_minute = 10\n",
"validation_year = 2015"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"LOCATIONS = (\n",
" \"ukishima\",\n",
" \"ougishima\",\n",
" \"yonekurayama\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"TRAIN_TEST_FILEPATH_PREFIX = path.join(PROJECT_ROOT_DIRPATH, \"data\", \"processed\", \"dataset\")\n",
"TRAIN_TEST_FILEPATH_EXTENTION = \"tsv\"\n",
"\n",
"interim_serialize_filename_prefix = path.join(\n",
" PROJECT_ROOT_DIRPATH, \"models\", \"gru.interim\", \"fit_model.layer0\"\n",
")\n",
"model_serialize_filename_prefix = path.join(\n",
" PROJECT_ROOT_DIRPATH, \"models\", \"gru\", \"fit_model.layer0\"\n",
")\n",
"predict_serialize_filename_prefix = path.join(\n",
" PROJECT_ROOT_DIRPATH, \"models\", \"gru\", \"predict.layer0\"\n",
")\n",
"os.makedirs(path.dirname(interim_serialize_filename_prefix), exist_ok=True)\n",
"os.makedirs(path.dirname(model_serialize_filename_prefix), exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_dataset(target,\n",
" location,\n",
" prefix=TRAIN_TEST_FILEPATH_PREFIX,\n",
" suffix=TRAIN_TEST_FILEPATH_EXTENTION):\n",
" if target == \"train\":\n",
" filepath = '.'.join([prefix, \"train_X_y.every_10\", location, suffix])\n",
" df = pd.read_csv(filepath, **KWARGS_READ_CSV)\n",
"\n",
" return df.iloc[:, :-1], df.iloc[:, -1]\n",
" elif target ==\"test\":\n",
" filepath = '.'.join([prefix, \"test_X.every_10\", location, suffix])\n",
"\n",
" return pd.read_csv(filepath, **KWARGS_READ_CSV)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def gen_model_name_from_param_dict(param_dict):\n",
" model_name = str()\n",
" for k, v in sorted(gru_params.items()):\n",
" if isinstance(v, dict):\n",
" for k_inner, v_inner in v.items():\n",
" model_name += \"{ko}_{ki}_{v}.\".format(ko=k, ki=k_inner, v=v_inner)\n",
" else:\n",
" model_name += \"{k}_{v}.\".format(k=k, v=v)\n",
"\n",
" return model_name[:-1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def gen_modified_param_dict(gru_params):\n",
" modified_param_dict = dict()\n",
" pt_regularizer = re.compile(\"regularizer\")\n",
" for k, v in gru_params.items():\n",
" if pt_regularizer.search(k):\n",
" modified_param_dict[k] = l1_l2(l1=v['l1'], l2=v['l2'])\n",
" else:\n",
" modified_param_dict[k] = v\n",
"\n",
" return modified_param_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def gen_sequential(input_shape, gru_params):\n",
" inputs = Input(shape=input_shape)\n",
" x = Masking(mask_value=1.)(inputs)\n",
" x = BatchNormalization()(x)\n",
" x = GRU(return_sequences=True, **gru_params)(x)\n",
" predictions = TimeDistributed(Dense(1, activation=\"linear\"))(x)\n",
" model = Model(inputs=inputs, outputs=predictions)\n",
"\n",
" optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
" model.compile(loss=\"mean_squared_error\", optimizer=optimizer, metrics=[\"mae\", ])\n",
"\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def gen_callbacks(target, location):\n",
" serialize_path = '.'.join([interim_serialize_filename_prefix,\n",
" \"ep_{epoch:04d}.mae_{mean_absolute_error:.2f}.val_mae_{val_mean_absolute_error:.2f}\",\n",
" \"{t}.{l}.hdf5\".format(t=target, l=location)])\n",
"\n",
" cp_best = ModelCheckpoint(filepath=serialize_path,\n",
" monitor='val_mean_absolute_error',\n",
" save_best_only=True,\n",
" mode='auto')\n",
" cp_period = ModelCheckpoint(filepath=serialize_path, period=100)\n",
" es = EarlyStopping(monitor='val_mean_absolute_error', min_delta=0, patience=100, mode='auto')\n",
"\n",
" return [cp_best, cp_period, es]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def split_from_index(ndarray, begin_val_index, end_val_index):\n",
" if ndarray.ndim == 2:\n",
" return ndarray[:begin_val_index, :], ndarray[begin_val_index:end_val_index, :]\n",
" elif ndarray.ndim == 3:\n",
" return ndarray[:begin_val_index, :, :], ndarray[begin_val_index:end_val_index, :, :]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class DummyScaler(BaseEstimator, TransformerMixin):\n",
" def __init__(self, copy=True):\n",
" self.copy = copy\n",
"\n",
" def fit(self, X, y=None):\n",
" return self\n",
"\n",
" def transform(self, X):\n",
" return X\n",
"\n",
" def inverse_transform(self, X):\n",
" return X"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 浮島"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 定数の設定"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"location = \"ukishima\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### トレーニングデータ取得"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"df_x, df_y = get_dataset(\"train\", location)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 定数の定義"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len_series = 144\n",
"num_features = df_x.shape[1]\n",
"datetime_index = df_y.index.copy(deep=True)\n",
"is_null_y = df_y.isnull().copy(deep=True)\n",
"\n",
"model_name = gen_model_name_from_param_dict(gru_params)\n",
"modified_param_dict = gen_modified_param_dict(gru_params)\n",
"modified_param_dict[\"name\"] = model_name\n",
"target_val = \"foldout_{y}\".format(y=validation_year)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 系列データ全体でmin-maxスケーリング"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndarray_x = df_x.as_matrix()\n",
"ndarray_y = df_y.as_matrix().reshape(-1, 1)\n",
"is_nan_y_train = np.isnan(ndarray_y.reshape(-1))\n",
"\n",
"ndarray_x[is_nan_y_train, :] = 1.\n",
"ndarray_y[is_nan_y_train, :] = 1.\n",
"\n",
"x_scaler = MinMaxScaler()\n",
"ndarray_x = x_scaler.fit_transform(ndarray_x)\n",
"\n",
"y_scaler = DummyScaler()\n",
"ndarray_y = y_scaler.fit_transform(ndarray_y)\n",
"\n",
"ndarray_x[is_nan_y_train, :] = 1.\n",
"ndarray_y[is_nan_y_train, :] = 1."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### データセットの分割"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndarray_x = ndarray_x.reshape(-1, len_series, num_features).astype('float32')\n",
"ndarray_y = ndarray_y.reshape(-1, len_series, 1)\n",
"\n",
"begin_val_datetime = pd.to_datetime(\"{y}-01-01 00:10:00\".format(y=validation_year))\n",
"end_val_datetime = pd.to_datetime(\"{y}-01-01 00:10:00\".format(y=validation_year+1))\n",
"begin_val_index = int(datetime_index.get_loc(begin_val_datetime) / len_series)\n",
"val_end_index = int(datetime_index.get_loc(end_val_datetime) / len_series)\n",
"\n",
"x_train, x_val = split_from_index(ndarray_x, begin_val_index, val_end_index)\n",
"y_train, y_val = split_from_index(ndarray_y, begin_val_index, val_end_index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 欠損サンプルの除去"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"num_sample_train = x_train.shape[0]\n",
"num_sample_val = x_val.shape[0]\n",
"\n",
"remove_sample_list = [\n",
" i for i in range(0, int(df_y.size/len_series))\n",
" if is_null_y.iloc[np.arange(len_series*i, len_series*(i+1))].sum() == len_series\n",
"]\n",
"remove_samples_train = [\n",
" elem\n",
" for elem in remove_sample_list\n",
" if elem < num_sample_train\n",
"]\n",
"remove_samples_val = [\n",
" elem - num_sample_train\n",
" for elem in remove_sample_list\n",
" if num_sample_train <= elem < num_sample_train + num_sample_val\n",
"]\n",
"\n",
"x_train = np.delete(x_train, remove_samples_train, 0)\n",
"y_train = np.delete(y_train, remove_samples_train, 0)\n",
"x_val = np.delete(x_val, remove_samples_val, 0)\n",
"y_val = np.delete(y_val, remove_samples_val, 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### モデルの宣言"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = gen_sequential((len_series, num_features), modified_param_dict)\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### モデリング for validaton data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"history = model.fit(x_train,\n",
" y_train,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" validation_split=validation_split,\n",
" callbacks=gen_callbacks(target_val, location))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### validation dataに対する結果の出力および保存"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = y_scaler.inverse_transform(model.predict(x_val).reshape(-1, 1))\n",
"y_true = y_scaler.inverse_transform(y_val.reshape(-1, 1))\n",
"\n",
"index = pd.date_range(begin_val_datetime,\n",
" end_val_datetime + pd.Timedelta(-freq_minute, unit='m'),\n",
" freq=pd.offsets.Minute(freq_minute))\n",
"val_index = index.copy(deep=True)\n",
"for i in remove_samples_val:\n",
" val_index = val_index.drop(index[len_series*i:len_series*(i+1)])\n",
"\n",
"pd.DataFrame(\n",
" y_pred, index=val_index, columns=[model_name,]\n",
").to_csv(\n",
" '.'.join([predict_serialize_filename_prefix,\n",
" model_name,\n",
" target_val,\n",
" location,\n",
" \"tsv\"]),\n",
" sep=\"\\t\"\n",
")\n",
"model.save(\n",
" '.'.join([model_serialize_filename_prefix,\n",
" model_name,\n",
" target_val, \n",
" location,\n",
" \"hdf5\"])\n",
")\n",
"\n",
"print(\"MAE convert into every 30 in {y}: \".format(y=validation_year),\n",
" 3 * mean_absolute_error(y_true, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### モデリング for test data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"history = model.fit(x_train,\n",
" y_train,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" validation_data=(x_val, y_val),\n",
" callbacks=gen_callbacks(\"test\", location))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### test dataに対する結果の保存"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_test = get_dataset(\"test\", location)\n",
"x_test = x_scaler.transform(df_test.as_matrix())\n",
"\n",
"y_pred = y_scaler.inverse_transform(\n",
" model.predict(\n",
" x_test.reshape(-1, len_series, num_features).astype('float32')\n",
" ).reshape(-1, 1)\n",
")\n",
"df_y_pred = pd.DataFrame(y_pred, index=df_test.index, columns=[model_name,])\n",
"\n",
"df_y_pred.resample(\n",
" **KWARGS_RESAMPLING\n",
").sum().to_csv(\n",
" '.'.join([predict_serialize_filename_prefix,\n",
" model_name,\n",
" \"test\",\n",
" location,\n",
" \"tsv\"]),\n",
" sep=\"\\t\"\n",
")\n",
"model.save(\n",
" '.'.join([model_serialize_filename_prefix,\n",
" model_name,\n",
" \"test\",\n",
" location,\n",
" \"hdf5\"]),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 扇島"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 定数の設定"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"location = \"ougishima\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### トレーニングデータ取得"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_x, df_y = get_dataset(\"train\", location)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 定数の定義"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len_series = 144\n",
"num_features = df_x.shape[1]\n",
"datetime_index = df_y.index.copy(deep=True)\n",
"is_null_y = df_y.isnull().copy(deep=True)\n",
"\n",
"model_name = gen_model_name_from_param_dict(gru_params)\n",
"modified_param_dict = gen_modified_param_dict(gru_params)\n",
"modified_param_dict[\"name\"] = model_name\n",
"target_val = \"foldout_{y}\".format(y=validation_year)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 系列データ全体でスケーリング"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndarray_x = df_x.as_matrix()\n",
"ndarray_y = df_y.as_matrix().reshape(-1, 1)\n",
"is_nan_y_train = np.isnan(ndarray_y.reshape(-1))\n",
"\n",
"ndarray_x[is_nan_y_train, :] = 1.\n",
"ndarray_y[is_nan_y_train, :] = 1.\n",
"\n",
"x_scaler = MinMaxScaler()\n",
"ndarray_x = x_scaler.fit_transform(ndarray_x)\n",
"\n",
"y_scaler = DummyScaler()\n",
"ndarray_y = y_scaler.fit_transform(ndarray_y)\n",
"\n",
"ndarray_x[is_nan_y_train, :] = 1.\n",
"ndarray_y[is_nan_y_train, :] = 1."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### データセットの分割"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndarray_x = ndarray_x.reshape(-1, len_series, num_features).astype('float32')\n",
"ndarray_y = ndarray_y.reshape(-1, len_series, 1)\n",
"\n",
"begin_val_datetime = pd.to_datetime(\"{y}-01-01 00:10:00\".format(y=validation_year))\n",
"end_val_datetime = pd.to_datetime(\"{y}-01-01 00:10:00\".format(y=validation_year+1))\n",
"begin_val_index = int(datetime_index.get_loc(begin_val_datetime) / len_series)\n",
"val_end_index = int(datetime_index.get_loc(end_val_datetime) / len_series)\n",
"\n",
"x_train, x_val = split_from_index(ndarray_x, begin_val_index, val_end_index)\n",
"y_train, y_val = split_from_index(ndarray_y, begin_val_index, val_end_index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 欠損サンプルの除去"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"num_sample_train = x_train.shape[0]\n",
"num_sample_val = x_val.shape[0]\n",
"\n",
"remove_sample_list = [\n",
" i for i in range(0, int(df_y.size/len_series))\n",
" if is_null_y.iloc[np.arange(len_series*i, len_series*(i+1))].sum() == len_series\n",
"]\n",
"remove_samples_train = [\n",
" elem\n",
" for elem in remove_sample_list\n",
" if elem < num_sample_train\n",
"]\n",
"remove_samples_val = [\n",
" elem - num_sample_train\n",
" for elem in remove_sample_list\n",
" if num_sample_train <= elem < num_sample_train + num_sample_val\n",
"]\n",
"\n",
"x_train = np.delete(x_train, remove_samples_train, 0)\n",
"y_train = np.delete(y_train, remove_samples_train, 0)\n",
"x_val = np.delete(x_val, remove_samples_val, 0)\n",
"y_val = np.delete(y_val, remove_samples_val, 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### モデルの宣言"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = gen_sequential((len_series, num_features), modified_param_dict)\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### モデリング for validaton data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"history = model.fit(x_train,\n",
" y_train,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" validation_split=validation_split,\n",
" callbacks=gen_callbacks(target_val, location))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### validation dataに対する結果の出力および保存"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = y_scaler.inverse_transform(model.predict(x_val).reshape(-1, 1))\n",
"y_true = y_scaler.inverse_transform(y_val.reshape(-1, 1))\n",
"\n",
"index = pd.date_range(begin_val_datetime,\n",
" end_val_datetime + pd.Timedelta(-freq_minute, unit='m'),\n",
" freq=pd.offsets.Minute(freq_minute))\n",
"val_index = index.copy(deep=True)\n",
"for i in remove_samples_val:\n",
" val_index = val_index.drop(index[len_series*i:len_series*(i+1)])\n",
"\n",
"pd.DataFrame(\n",
" y_pred, index=val_index, columns=[model_name,]\n",
").to_csv(\n",
" '.'.join([predict_serialize_filename_prefix,\n",
" model_name,\n",
" target_val,\n",
" location,\n",
" \"tsv\"]),\n",
" sep=\"\\t\"\n",
")\n",
"model.save(\n",
" '.'.join([model_serialize_filename_prefix,\n",
" model_name,\n",
" target_val, \n",
" location,\n",
" \"hdf5\"])\n",
")\n",
"\n",
"print(\"MAE convert into every 30 in {y}: \".format(y=validation_year),\n",
" 3 * mean_absolute_error(y_true, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### モデリング for test data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"history = model.fit(x_train,\n",
" y_train,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" validation_data=(x_val, y_val),\n",
" callbacks=gen_callbacks(\"test\", location))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### test dataに対する結果の保存"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_test = get_dataset(\"test\", location)\n",
"x_test = x_scaler.transform(df_test.as_matrix())\n",
"\n",
"y_pred = y_scaler.inverse_transform(\n",
" model.predict(\n",
" x_test.reshape(-1, len_series, num_features).astype('float32')\n",
" ).reshape(-1, 1)\n",
")\n",
"df_y_pred = pd.DataFrame(y_pred, index=df_test.index, columns=[model_name,])\n",
"\n",
"df_y_pred.resample(\n",
" **KWARGS_RESAMPLING\n",
").sum().to_csv(\n",
" '.'.join([predict_serialize_filename_prefix,\n",
" model_name,\n",
" \"test\",\n",
" location,\n",
" \"tsv\"]),\n",
" sep=\"\\t\"\n",
")\n",
"model.save(\n",
" '.'.join([model_serialize_filename_prefix,\n",
" model_name,\n",
" \"test\",\n",
" location,\n",
" \"hdf5\"]),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 米倉山"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 定数の設定"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"location = \"yonekurayama\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### トレーニングデータ取得"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_x, df_y = get_dataset(\"train\", location)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 定数の定義"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len_series = 144\n",
"num_features = df_x.shape[1]\n",
"datetime_index = df_y.index.copy(deep=True)\n",
"is_null_y = df_y.isnull().copy(deep=True)\n",
"\n",
"model_name = gen_model_name_from_param_dict(gru_params)\n",
"modified_param_dict = gen_modified_param_dict(gru_params)\n",
"modified_param_dict[\"name\"] = model_name\n",
"target_val = \"foldout_{y}\".format(y=validation_year)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 系列データ全体でmin-maxスケーリング"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"ndarray_x = df_x.as_matrix()\n",
"ndarray_y = df_y.as_matrix().reshape(-1, 1)\n",
"is_nan_y_train = np.isnan(ndarray_y.reshape(-1))\n",
"\n",
"ndarray_x[is_nan_y_train, :] = 1.\n",
"ndarray_y[is_nan_y_train, :] = 1.\n",
"\n",
"x_scaler = MinMaxScaler()\n",
"ndarray_x = x_scaler.fit_transform(ndarray_x)\n",
"\n",
"y_scaler = DummyScaler()\n",
"ndarray_y = y_scaler.fit_transform(ndarray_y)\n",
"\n",
"ndarray_x[is_nan_y_train, :] = 1.\n",
"ndarray_y[is_nan_y_train, :] = 1."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### データセットの分割"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndarray_x = ndarray_x.reshape(-1, len_series, num_features).astype('float32')\n",
"ndarray_y = ndarray_y.reshape(-1, len_series, 1)\n",
"\n",
"begin_val_datetime = pd.to_datetime(\"{y}-01-01 00:10:00\".format(y=validation_year))\n",
"end_val_datetime = pd.to_datetime(\"{y}-01-01 00:10:00\".format(y=validation_year+1))\n",
"begin_val_index = int(datetime_index.get_loc(begin_val_datetime) / len_series)\n",
"val_end_index = int(datetime_index.get_loc(end_val_datetime) / len_series)\n",
"\n",
"x_train, x_val = split_from_index(ndarray_x, begin_val_index, val_end_index)\n",
"y_train, y_val = split_from_index(ndarray_y, begin_val_index, val_end_index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 欠損サンプルの除去"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"num_sample_train = x_train.shape[0]\n",
"num_sample_val = x_val.shape[0]\n",
"\n",
"remove_sample_list = [\n",
" i for i in range(0, int(df_y.size/len_series))\n",
" if is_null_y.iloc[np.arange(len_series*i, len_series*(i+1))].sum() == len_series\n",
"]\n",
"remove_samples_train = [\n",
" elem\n",
" for elem in remove_sample_list\n",
" if elem < num_sample_train\n",
"]\n",
"remove_samples_val = [\n",
" elem - num_sample_train\n",
" for elem in remove_sample_list\n",
" if num_sample_train <= elem < num_sample_train + num_sample_val\n",
"]\n",
"\n",
"x_train = np.delete(x_train, remove_samples_train, 0)\n",
"y_train = np.delete(y_train, remove_samples_train, 0)\n",
"x_val = np.delete(x_val, remove_samples_val, 0)\n",
"y_val = np.delete(y_val, remove_samples_val, 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### モデルの宣言"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = gen_sequential((len_series, num_features), modified_param_dict)\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### モデリング for validaton data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"history = model.fit(x_train,\n",
" y_train,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" validation_split=validation_split,\n",
" callbacks=gen_callbacks(target_val, location))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### validation dataに対する結果の出力および保存"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = y_scaler.inverse_transform(model.predict(x_val).reshape(-1, 1))\n",
"y_true = y_scaler.inverse_transform(y_val.reshape(-1, 1))\n",
"\n",
"index = pd.date_range(begin_val_datetime,\n",
" end_val_datetime + pd.Timedelta(-freq_minute, unit='m'),\n",
" freq=pd.offsets.Minute(freq_minute))\n",
"val_index = index.copy(deep=True)\n",
"for i in remove_samples_val:\n",
" val_index = val_index.drop(index[len_series*i:len_series*(i+1)])\n",
"\n",
"pd.DataFrame(\n",
" y_pred, index=val_index, columns=[model_name,]\n",
").to_csv(\n",
" '.'.join([predict_serialize_filename_prefix,\n",
" model_name,\n",
" target_val,\n",
" location,\n",
" \"tsv\"]),\n",
" sep=\"\\t\"\n",
")\n",
"model.save(\n",
" '.'.join([model_serialize_filename_prefix,\n",
" model_name,\n",
" target_val, \n",
" location,\n",
" \"hdf5\"])\n",
")\n",
"\n",
"print(\"MAE convert into every 30 in {y}: \".format(y=validation_year),\n",
" 3 * mean_absolute_error(y_true, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### モデリング for test data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"history = model.fit(x_train,\n",
" y_train,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" validation_data=(x_val, y_val),\n",
" callbacks=gen_callbacks(\"test\", location))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### test dataに対する結果の保存"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_test = get_dataset(\"test\", location)\n",
"x_test = x_scaler.transform(df_test.as_matrix())\n",
"\n",
"y_pred = y_scaler.inverse_transform(\n",
" model.predict(\n",
" x_test.reshape(-1, len_series, num_features).astype('float32')\n",
" ).reshape(-1, 1)\n",
")\n",
"df_y_pred = pd.DataFrame(y_pred, index=df_test.index, columns=[model_name,])\n",
"\n",
"df_y_pred.resample(\n",
" **KWARGS_RESAMPLING\n",
").sum().to_csv(\n",
" '.'.join([predict_serialize_filename_prefix,\n",
" model_name,\n",
" \"test\",\n",
" location,\n",
" \"tsv\"]),\n",
" sep=\"\\t\"\n",
")\n",
"model.save(\n",
" '.'.join([model_serialize_filename_prefix,\n",
" model_name,\n",
" \"test\",\n",
" location,\n",
" \"hdf5\"]),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
emdodds/matching-pursuit | Notebooks/developing matching pursuit.ipynb | 1 | 28128 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def snr(signal, recon):\n",
" \"\"\"Returns signal-noise ratio in dB.\"\"\"\n",
" ratio = np.var(signal)/np.var(signal-recon)\n",
" return 10*np.log10(ratio)\n",
" \n",
"# dynamic compressive gammachirp\n",
"def dcGC(t,f):\n",
" \"\"\"Dynamic compressive gammachirp filter as defined by Irino,\n",
" with parameters from Park as used in Charles, Kressner, & Rozell.\n",
" The log term is regularized to log(t + 0.00001).\n",
" t : time in seconds, greater than 0\n",
" f : characteristic frequency in Hz\n",
" One but not both arguments may be numpy arrays.\n",
" \"\"\"\n",
" ERB = 0.1039*f + 24.7\n",
" return t**3 * np.exp(-2*np.pi*1.14*ERB*t) * np.cos(2*np.pi*f*t + 0.979*np.log(t+0.000001))\n",
"\n",
"# adapted from scipy cookbook\n",
"lowcut = 100\n",
"highcut = 6000\n",
"def butter_bandpass(lowcut, highcut, fs, order=5):\n",
" nyq = 0.5 * fs\n",
" low = lowcut / nyq\n",
" high = highcut / nyq\n",
" b, a = scisig.butter(order, [low, high], btype='band')\n",
" return b, a\n",
"\n",
"def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n",
" b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n",
" y = scisig.lfilter(b, a, data)\n",
" return y\n",
" \n",
"def plot_spikegram( spikes, sample_rate, markerSize = .0001 ):\n",
" \"\"\"adapted from https://github.com/craffel/spikegram-coding/blob/master/plotSpikeGram.py\"\"\"\n",
" nkernels = spikes.shape[0]\n",
" indices = np.transpose(np.nonzero(spikes))\n",
" scalesKernelsAndOffsets = [(spikes[idx[0],idx[1]], idx[0], idx[1]) for idx in indices]\n",
" \n",
" for scale, kernel, offset in scalesKernelsAndOffsets:\n",
" # Put a dot at each spike location. Kernels on y axis. Dot size corresponds to scale\n",
" plt.plot( offset/sample_rate, nkernels-kernel, 'k.', \n",
" markersize=markerSize*np.abs( scale ) )\n",
" plt.title( \"Spikegram\" )\n",
" plt.xlabel( \"Time (s)\" )\n",
" plt.ylabel( \"Kernel\" )\n",
" plt.axis( [0.0, spikes.shape[1]/sample_rate, 0.0, nkernels] )\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class SignalSet:\n",
" \n",
" def __init__(self, sample_rate = 16000, data = '../Data/TIMIT/'):\n",
" self.sample_rate = sample_rate\n",
" if isinstance(data, str):\n",
" self.load_from_folder(data)\n",
" else:\n",
" self.data = data\n",
" self.ndata = len(data) \n",
" \n",
" def load_from_folder(self, folder = '../Data/TIMIT/'):\n",
" min_length = 800 # TODO: should not be hard-coded\n",
" files = os.listdir(folder)\n",
" file = None\n",
" self.data = []\n",
" for ff in files:\n",
" if ff.endswith('.wav'):\n",
" file = os.path.join(folder,ff)\n",
" rate, signal = wavfile.read(file)\n",
" if rate != self.sample_rate:\n",
" raise NotImplementedError('The signal in ' + ff +\n",
" ' does not match the given sample rate.')\n",
" if signal.shape[0] > min_length:\n",
" # bandpass\n",
" signal = signal/signal.std()\n",
" signal = butter_bandpass_filter(signal, lowcut, highcut,\n",
" self.sample_rate, order=5)\n",
" self.data.append(signal)\n",
" self.ndata = len(self.data)\n",
" print(\"Found \", self.ndata, \" files\")\n",
" \n",
" def rand_stim(self):\n",
" \"\"\"Get one random signal.\"\"\"\n",
" which = np.random.randint(low=0, high=self.ndata)\n",
" signal = self.data[which]\n",
" signal /= np.max(signal) # as in Smith & Lewicki\n",
" return signal\n",
" \n",
" def write_sound(self, filename, signal):\n",
" signal /= np.max(signal)\n",
" wavfile.write(filename, self.sample_rate, signal)\n",
" \n",
" def tiled_plot(self, stims):\n",
" \"\"\"Tiled plots of the given signals. Zeroth index is which signal.\n",
" Kind of slow, expect about 10s for 100 plots.\"\"\"\n",
" nstim = stims.shape[0]\n",
" plotrows = int(np.sqrt(nstim))\n",
" plotcols = int(np.ceil(nstim/plotrows))\n",
" f, axes = plt.subplots(plotrows, plotcols, sharex=True, sharey=True)\n",
" for ii in range(nstim):\n",
" axes.flatten()[ii].plot(stims[ii])\n",
" f.subplots_adjust(hspace=0, wspace=0)\n",
" plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False)\n",
" plt.setp([a.get_yticklabels() for a in f.axes[:-1]], visible=False)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class MatchingPursuer:\n",
" \n",
" def __init__(self,\n",
" data = '../Data/TIMIT/',\n",
" data_dim=1,\n",
" nunits = 32,\n",
" filter_time = 0.05,\n",
" learn_rate = 0.01,\n",
" thresh = 0.5,\n",
" normed_thresh = None,\n",
" max_iter = 100,\n",
" min_spike = 0.01,\n",
" mask_epsilon = None,\n",
" sample_rate = 16000,\n",
" paramfile= 'dummy'): \n",
" \n",
" self.thresh = thresh\n",
" self.min_spike = min_spike\n",
" self.sample_rate = sample_rate\n",
" self.nunits = nunits\n",
" self.lfilter = int(filter_time * self.sample_rate)\n",
" self.normed_thresh = normed_thresh or 2/np.sqrt(self.lfilter)\n",
" self.mask_epsilon = mask_epsilon or 0.01*np.sqrt(1/self.lfilter)\n",
" self.max_iter = max_iter\n",
" self.data_dim = data_dim\n",
" \n",
" def initial_filters(self, gammachirp=False):\n",
" \"\"\"If 1D, Return either a set of gammachirp filters or random (normal) filters,\n",
" not normalized. Otherwise return Gaussian noise.\"\"\"\n",
" if self.data_dim==1:\n",
" if gammachirp:\n",
" gammachirps = np.zeros([self.nunits, self.lfilter])\n",
" freqs = np.logspace(np.log10(100), np.log10(6000), self.nunits)\n",
" times = np.linspace(0,self.lfilter/self.sample_rate,self.lfilter)\n",
" for ii in range(self.nunits):\n",
" gammachirps[ii] = dcGC(times, freqs[ii])\n",
" filters= gammachirps \n",
" else:\n",
" filters = tf.random_normal([self.nunits, self.lfilter])\n",
" return tf.expand_dims(filters,2)\n",
" elif self.data_dim>2:\n",
" normal = tf.random_normal([self.nunits, self.lfilter, self.nfreqs])\n",
" return normal"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "None values not supported.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\u001b[0m in \u001b[0;36mapply_op\u001b[1;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[0;32m 489\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_arg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_ref\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 490\u001b[1;33m preferred_dtype=default_dtype)\n\u001b[0m\u001b[0;32m 491\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[1;34m(value, dtype, name, as_ref, preferred_dtype)\u001b[0m\n\u001b[0;32m 668\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 669\u001b[1;33m \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mas_ref\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 670\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py\u001b[0m in \u001b[0;36m_constant_tensor_conversion_function\u001b[1;34m(v, dtype, name, as_ref)\u001b[0m\n\u001b[0;32m 175\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 176\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mconstant\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 177\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py\u001b[0m in \u001b[0;36mconstant\u001b[1;34m(value, dtype, shape, name, verify_shape)\u001b[0m\n\u001b[0;32m 164\u001b[0m tensor_value.tensor.CopyFrom(\n\u001b[1;32m--> 165\u001b[1;33m tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))\n\u001b[0m\u001b[0;32m 166\u001b[0m \u001b[0mdtype_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mattr_value_pb2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAttrValue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtensor_value\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\tensor_util.py\u001b[0m in \u001b[0;36mmake_tensor_proto\u001b[1;34m(values, dtype, shape, verify_shape)\u001b[0m\n\u001b[0;32m 359\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 360\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"None values not supported.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 361\u001b[0m \u001b[1;31m# if dtype is provided, forces numpy array to be the type\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: None values not supported.",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-22-a17894f1867b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[0mcoeffs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros_like\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[0;32m 40\u001b[0m \u001b[0mresid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0midentity\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[1;32m---> 41\u001b[1;33m \u001b[0minf_loop\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhile_loop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwhile_cond\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwhile_body\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mkk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0monespike\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoeffs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mback_prop\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\u001b[0m in \u001b[0;36mwhile_loop\u001b[1;34m(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name)\u001b[0m\n\u001b[0;32m 2634\u001b[0m \u001b[0mcontext\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mWhileContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparallel_iterations\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mback_prop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mswap_memory\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2635\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_to_collection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGraphKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mWHILE_CONTEXT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2636\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBuildLoop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcond\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloop_vars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape_invariants\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2637\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2638\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\u001b[0m in \u001b[0;36mBuildLoop\u001b[1;34m(self, pred, body, loop_vars, shape_invariants)\u001b[0m\n\u001b[0;32m 2467\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEnter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2468\u001b[0m original_body_result, exit_vars = self._BuildLoop(\n\u001b[1;32m-> 2469\u001b[1;33m pred, body, original_loop_vars, loop_vars, shape_invariants)\n\u001b[0m\u001b[0;32m 2470\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2471\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\u001b[0m in \u001b[0;36m_BuildLoop\u001b[1;34m(self, pred, body, original_loop_vars, loop_vars, shape_invariants)\u001b[0m\n\u001b[0;32m 2417\u001b[0m \u001b[0mstructure\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moriginal_loop_vars\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2418\u001b[0m flat_sequence=vars_for_body_with_tensor_arrays)\n\u001b[1;32m-> 2419\u001b[1;33m \u001b[0mbody_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mpacked_vars_for_body\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2420\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_sequence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody_result\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2421\u001b[0m \u001b[0mbody_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mbody_result\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-22-a17894f1867b>\u001b[0m in \u001b[0;36mwhile_body\u001b[1;34m(kk, winning_val, coeffs, resid, error)\u001b[0m\n\u001b[0;32m 18\u001b[0m padding=\"SAME\", name='convolutions')\n\u001b[0;32m 19\u001b[0m \u001b[0mwinning_val\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreduce_max\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconvs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mwinner\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconvs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 21\u001b[0m \u001b[1;31m#coeffs = tf.select(convs == winning_val,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;31m# convs,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\u001b[0m in \u001b[0;36margmax\u001b[1;34m(input, axis, name, dimension)\u001b[0m\n\u001b[0;32m 247\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot specify both 'axis' and 'dimension'\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 248\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdimension\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 249\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mgen_math_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marg_max\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 250\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 251\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\u001b[0m in \u001b[0;36marg_max\u001b[1;34m(input, dimension, name)\u001b[0m\n\u001b[0;32m 166\u001b[0m \"\"\"\n\u001b[0;32m 167\u001b[0m result = _op_def_lib.apply_op(\"ArgMax\", input=input, dimension=dimension,\n\u001b[1;32m--> 168\u001b[1;33m name=name)\n\u001b[0m\u001b[0;32m 169\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\u001b[0m in \u001b[0;36mapply_op\u001b[1;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[0;32m 501\u001b[0m \u001b[1;31m# What type does convert_to_tensor think it has?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 502\u001b[0m observed = ops.convert_to_tensor(values,\n\u001b[1;32m--> 503\u001b[1;33m as_ref=input_arg.is_ref).dtype.name\n\u001b[0m\u001b[0;32m 504\u001b[0m prefix = (\"Input '%s' of '%s' Op has type %s that does not match\" %\n\u001b[0;32m 505\u001b[0m (input_name, op_type_name, observed))\n",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[1;34m(value, dtype, name, as_ref, preferred_dtype)\u001b[0m\n\u001b[0;32m 667\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 668\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 669\u001b[1;33m \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mas_ref\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 670\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 671\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py\u001b[0m in \u001b[0;36m_constant_tensor_conversion_function\u001b[1;34m(v, dtype, name, as_ref)\u001b[0m\n\u001b[0;32m 174\u001b[0m as_ref=False):\n\u001b[0;32m 175\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 176\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mconstant\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 177\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py\u001b[0m in \u001b[0;36mconstant\u001b[1;34m(value, dtype, shape, name, verify_shape)\u001b[0m\n\u001b[0;32m 163\u001b[0m \u001b[0mtensor_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mattr_value_pb2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAttrValue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 164\u001b[0m tensor_value.tensor.CopyFrom(\n\u001b[1;32m--> 165\u001b[1;33m tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))\n\u001b[0m\u001b[0;32m 166\u001b[0m \u001b[0mdtype_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mattr_value_pb2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAttrValue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtensor_value\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 167\u001b[0m const_tensor = g.create_op(\n",
"\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\tensor_util.py\u001b[0m in \u001b[0;36mmake_tensor_proto\u001b[1;34m(values, dtype, shape, verify_shape)\u001b[0m\n\u001b[0;32m 358\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 359\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 360\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"None values not supported.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 361\u001b[0m \u001b[1;31m# if dtype is provided, forces numpy array to be the type\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 362\u001b[0m \u001b[1;31m# provided if possible.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: None values not supported."
]
}
],
"source": [
"g = tf.Graph()\n",
"\n",
"self=MatchingPursuer()\n",
"\n",
"with g.as_default():\n",
" \n",
" x = tf.placeholder(tf.float32, shape = [1,None,self.data_dim,1])\n",
" \n",
" phi = tf.Variable(self.initial_filters())\n",
" phi_for_conv = tf.transpose(phi, [1,2,0])\n",
" phi_for_conv = tf.expand_dims(phi_for_conv,2)\n",
" rev_phi = tf.reverse(phi, dims=[False, True, False])\n",
" \n",
" with tf.variable_scope('inference'):\n",
" def while_body(kk, winning_val, coeffs, resid, error):\n",
" convs = tf.nn.convolution(resid,\n",
" phi_for_conv,\n",
" padding=\"SAME\", name='convolutions')\n",
" winning_val = tf.reduce_max(convs)\n",
" winner = tf.argmax(convs)\n",
" #coeffs = tf.select(convs == winning_val,\n",
" # convs,\n",
" # coeffs)\n",
" update = tf.scatter_nd([winner], [winning_val], tf.shape(coeffs))\n",
" coeffs = coeffs + update\n",
" xhat = tf.convolution(coeffs,\n",
" rev_phi,\n",
" padding=\"SAME\", name='reconstruction')\n",
" resid = x - xhat\n",
" error = tf.mean(tf.square(resid))\n",
" return tf.add(kk, 1), winning_val, resid, error\n",
" def while_cond(kk, winning_val, coeffs, resid, error):\n",
" maxitercheck = kk < self.max_iter\n",
" spikecheck = winning_val > self.min_spike\n",
" return tf.logical_and(maxitercheck, spikecheck)\n",
" kk = tf.constant(0)\n",
" onespike = tf.constant(0.0)\n",
" error = tf.constant(1.0)\n",
" coeffs = tf.zeros_like(x) # WRONG\n",
" resid = tf.identity(x)\n",
" inf_loop = tf.while_loop(while_cond, while_body, [kk, onespike, coeffs, resid, error], back_prop=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
dhpollack/programming_notebooks | coursera-algos-twosum.ipynb | 1 | 8868 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy.stats\n",
"import scipy\n",
"import bisect\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#raw1 = np.loadtxt(\"2sum.txt\", dtype=int)\n",
"#np.savez_compressed(\"2sum.txt.npz\", data=raw1)\n",
"with np.load('2sum.txt.npz') as data:\n",
" raw1 = data['data']\n",
"raw1.shape\n",
"U = raw1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-1.1990247437581012"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.kurtosis(U)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean: 1322010.15214\n",
"Standard Deviation: 57723915587.6\n"
]
}
],
"source": [
"U_mean = scipy.mean(U)\n",
"U_std = scipy.std(U)\n",
"\n",
"print \"Mean:\", U_mean\n",
"print \"Standard Deviation:\", U_std"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"U.sort()"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"set([8195, 5308, 3445])\n"
]
}
],
"source": [
"# this function takes advantage of the fact the numbers are uniformly distributed\n",
"# thus the answer should simple be on the \"opposite\" side of the distribution\n",
"# the function searches the other side of the distribution for a range of possible \n",
"# y indexes for a given x index\n",
"def find_yrange(U, i, i_mag, mag_fac = 100, mag_zoom = 20):\n",
" # U: universe of numbers\n",
" # i: x index\n",
" # i_mag: y index search median\n",
" # mag_fac: number of indices between range checks\n",
" global t_bounds\n",
" x_i = U[i]\n",
" y_indices = [mag_fac*k + i_mag for k in range(-10, 11) if -1*(mag_fac*k + i_mag) > 0 and -1*(mag_fac*k + i_mag) < len(U)]\n",
" y = U[y_indices]\n",
" t = x_i + y\n",
" t_min = t[0]\n",
" t_max = t[-1]\n",
" # check if the max or min is outside of the bounds\n",
" if t_max < t_bounds[0] or t_min > t_bounds[1]:\n",
" return None\n",
" l_bound = y_indices[bisect.bisect_left(t, -t_bounds[0])-1]\n",
" r_bound = y_indices[bisect.bisect_right(t, t_bounds[1])]\n",
" m_bound = np.mean([l_bound, r_bound], dtype=int)\n",
" mag_fac /= mag_zoom\n",
" if mag_fac >= 5:\n",
" l_bound, r_bound = find_yrange(U, i, m_bound, mag_fac)\n",
" return l_bound, r_bound\n",
"\n",
"t_bounds = (-10000,10000)\n",
"t_set = set()\n",
"for i in range(1000,1010):\n",
" bounds = find_yrange(U,i, -1*(i+1))\n",
" if bounds:\n",
" t_i = [U[i] + U[i_inv] for i_inv in range(bounds[0], bounds[1]+1) if U[i] + U[i_inv] > t_bounds[0] and U[i] + U[i_inv] < t_bounds[1]]\n",
" if t_i: t_set.update(t_i)\n",
"print t_set\n",
"# set([8195, 5308, 3445])"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"set([2048, 4097, 6146, 8195, 6145, 8194, 7171, -7637, -2795, 1024, -6613, -2794, 6890, -8104, -8103, -4006, -4005, 92, 2141, 6238, 8287, 8288, -747, 9125, 6891, -5588, -746, 3073, 4096, -9873, -8848, -4751, -2702, -653, -8011, -5962, -5961, -1864, 185, 2234, 2235, 6332, 8381, 2421, 4470, 9591, -3354, 5122, -1492, 2327, -9967, -7918, -7917, -5868, -3819, -1770, 279, 4376, 6425, 8474, 8475, 2328, 3351, -4286, -9127, -9500, -4285, 557, -6427, -4378, -1305, -9874, -7825, -5776, -3727, -3726, 371, 2420, 4469, 6518, 6519, 8568, 558, 3818, 7915, 7916, -2237, -6055, -9780, -7731, -5682, -1585, 464, 2513, 2514, 4563, 8660, 8661, 2607, 7449, -5030, -1211, -188, 3631, 7450, -7080, -5031, -6054, -1957, 1116, -7638, -5589, -3540, -1491, 2606, 4655, 4656, 8753, 8754, -8382, 3166, 6239, 9312, 838, 1395, 5681, 6704, -9594, -7545, -5496, -5495, -1398, 651, 2700, 4749, 8846, -5775, -1956, -6147, -933, 7728, 2887, -8569, -6707, -6706, -3633, -1584, -9501, -7452, -5403, -5402, 1489, 2792, 2793, 4842, 8939, 8940, 4562, -7824, 6611, 9777, 6612, -7544, 1117, 4936, -3541, -1678, 5960, 6983, -9408, -9407, -7358, -5309, -1212, 837, 2886, 4935, 9032, 9033, -1677, 2142, 4189, -7359, 1023, 4190, -5310, -3261, -3260, -9315, -9314, 1861, -5216, -3167, -1118, 931, 2980, 1862, 9126, -3447, 372, 5959, 6984, 8009, -7265, -3446, 1396, 5215, -9222, -7173, -5124, -3075, -3074, -1025, 3072, 5121, 7170, 9219, 9220, 3444, -5217, -1397, -6240, -6986, 650, -3913, -3912, 1209, -9128, -7079, -2982, -2981, -932, 3165, 5214, 7263, 7264, 9313, 5307, 5493, 5308, 7357, -2144, -6985, -2143, 1676, 2699, -9035, -9034, -4937, -2888, -839, 3258, 3259, 7356, 9405, 9406, -4938, 7542, -9779, -9687, -8662, -94, 8567, -4564, -2515, -8942, -8941, -4844, -466, 1302, 1303, 5400, 5401, 9498, 9499, -2889, 930, 3632, -8010, 6705, -7730, -5683, 1955, 5774, 6797, -8849, -6800, -6799, -4750, -2701, -652, 3445, 5494, 7543, 9592, -1863, -840, -7266, -4657, -3634, -6241, 7823, -3168, -1119, -8756, -8755, -4658, -2609, -560, -559, 3538, 2979, 9684, 9685, 186, 6052, 5028, 8101, -4191, 7078, -7451, -6428, 1210, 5029, -2608, -8663, -6614, -4565, -2516, -467, 1582, 1583, 5680, 7729, 9778, 7077, -9966, -6893, 4283, -3820, -1771, 278, -8570, -6521, -6520, -2423, -374, -373, 3724, 5773, 7822, 9871, 9872, 3352, 465, 4284, 4377, 6426, 93, -7172, -3353, -2329, 1490, -8477, -8476, -4379, -2330, -281, 1768, 1769, 5866, 5867, 9964, 9965, -1304, 8380, -9593, -280, 3539, -4472, -4471, -2422, -8383, -6334, 1675, -2236, -187, 3910, 3911, 8008, 4748, 744, 3725, 5586, 6798, 8847, 745, 5587, -6892, -5869, -2050, -8290, -8289, -4192, -95, 1954, 4003, 4004, 6053, 8102, -2049, -1026, 7635, -9686, 3817, 7636, -9221, -4843, -8196, 4841, -5123, -4098, -8197, -6148, -4099, -1, 2047])\n"
]
}
],
"source": [
"t_bounds = (-10000,10000)\n",
"t_set = set()\n",
"for i in range(len(U)):\n",
" bounds = find_yrange(U,i, -1*(i+1))\n",
" if bounds:\n",
" t_i = [U[i] + U[i_inv] for i_inv in range(bounds[0], bounds[1]+1) if U[i] + U[i_inv] > t_bounds[0] and U[i] + U[i_inv] < t_bounds[1]]\n",
" if t_i: t_set.update(t_i)\n",
"print t_set\n"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"427\n"
]
}
],
"source": [
"print len(t_set)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"The goal of this problem is to implement the \"Median Maintenance\" algorithm (covered in the Week 5 lecture on heap applications). The text file contains a list of the integers from 1 to 10000 in unsorted order; you should treat this as a stream of numbers, arriving one by one. Letting xi denote the ith number of the file, the kth median mk is defined as the median of the numbers x1,…,xk. (So, if k is odd, then mk is ((k+1)/2)th smallest number among x1,…,xk; if k is even, then mk is the (k/2)th smallest number among x1,…,xk.)\n",
"\n",
"In the box below you should type the sum of these 10000 medians, modulo 10000 (i.e., only the last 4 digits). That is, you should compute (m1+m2+m3+⋯+m10000)mod10000.\n",
"\n",
"OPTIONAL EXERCISE: Compare the performance achieved by heap-based and search-tree-based implementations of the algorithm.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2 (SageMath)",
"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",
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}
| mit |
markus-antero/Stock | finance.ipynb | 1 | 258390 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from data.finance.LoadDataYahoo import LoadDataFromSQL, LoadDataFromYahoo\n",
"from data.finance.addins.main import (\n",
" Utils,\n",
" testVisualization )\n",
"\n",
"from data.finance.addins.mlToFinance1 import PredictSVMOnQuandl, LinkQuandl\n",
"from data.finance.addins.neuralNetTestModified import NNtrainer, NeuralNetForward\n",
"from data.finance.addins.reloadSandP import LoadYahooFinance, CompanyHistory\n",
"\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loading stocks to shared dataframe, recordset based on query:\n",
" SELECT distinct [GICS_Sector] FROM [finance].[dbo].[SandP500Index] \n",
"loading stocks to shared dataframe, recordset based on query:\n",
" SELECT [symbol], [dateAdded], [CIK], [security] FROM [dbo].[SandP500Index] \n",
"\n",
"statistics loaded\n",
"\n",
"confidence: -0.0468546548935\n"
]
},
{
"data": {
"text/plain": [
"(SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
" kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n",
" -0.046854654893454084)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"Test and upload of required data-sources (MSSQL) and libraries \n",
"'''\n",
"#from data.finance.addins.reloadSandP import main \n",
"pq = PredictSVMOnQuandl()\n",
"pq.populateCompanyData(readClassifier = True, runList = False)\n",
"pq.SupportVectorMachineAsAutomated()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loading stocks to shared dataframe\n",
"Stocks loaded: 0\n",
"Stocks loaded: 10\n",
"Stocks loaded: 20\n",
"Stocks loaded: 30\n",
"Stocks loaded: 40\n",
"Stocks loaded: 50\n",
" symbol Date Open High Low Close Volume \\\n",
"0 AAP 2001-11-29 40.159999 43.399999 40.079999 41.640000 371100.0 \n",
"1 AAP 2001-11-30 41.640000 42.799999 41.640000 42.799999 165300.0 \n",
"2 AAP 2001-12-03 42.700001 42.700001 41.350001 41.350001 127500.0 \n",
"3 AAP 2001-12-04 41.350001 41.350001 39.700001 39.700001 95400.0 \n",
"4 AAP 2001-12-05 40.300000 44.350001 40.300000 44.000001 598200.0 \n",
"\n",
" Adj Close 100ma \n",
"0 13.273657 13.273657 \n",
"1 13.643432 13.458545 \n",
"2 13.181214 13.366101 \n",
"3 12.655240 13.188386 \n",
"4 14.025959 13.355900 \n",
"\n",
"Column labels: Index(['AAP', 'BBBY', 'BBY', 'BWA', 'CBS', 'CCL', 'CMCSA', 'COH', 'DG', 'DHI',\n",
" 'DIS', 'DLPH', 'DRI', 'EXPE', 'F', 'FL', 'FOX', 'FOXA', 'GM', 'GPC',\n",
" 'GPS', 'GRMN', 'GT', 'HAR', 'HAS', 'HBI', 'HD', 'HOG', 'IPG', 'JWN',\n",
" 'KSS', 'LEN', 'LOW', 'M', 'MAR', 'MAT', 'NKE', 'NWL', 'OMC', 'PHM',\n",
" 'PVH', 'RCL', 'RL', 'ROST', 'SBUX', 'SIG', 'SNA', 'SNI', 'SWK', 'TGNA',\n",
" 'TGT', 'TIF', 'TJX', 'TSCO', 'TWX', 'ULTA', 'VFC', 'VIAB', 'WHR',\n",
" 'WYN'],\n",
" dtype='object', name='symbol') \n",
"\n",
"Row labels: Index(['AAP', 'BBBY', 'BBY', 'BWA', 'CBS', 'CCL', 'CMCSA', 'COH', 'DG', 'DHI',\n",
" 'DIS', 'DLPH', 'DRI', 'EXPE', 'F', 'FL', 'FOX', 'FOXA', 'GM', 'GPC',\n",
" 'GPS', 'GRMN', 'GT', 'HAR', 'HAS', 'HBI', 'HD', 'HOG', 'IPG', 'JWN',\n",
" 'KSS', 'LEN', 'LOW', 'M', 'MAR', 'MAT', 'NKE', 'NWL', 'OMC', 'PHM',\n",
" 'PVH', 'RCL', 'RL', 'ROST', 'SBUX', 'SIG', 'SNA', 'SNI', 'SWK', 'TGNA',\n",
" 'TGT', 'TIF', 'TJX', 'TSCO', 'TWX', 'ULTA', 'VFC', 'VIAB', 'WHR',\n",
" 'WYN'],\n",
" dtype='object', name='symbol')\n",
"loading stocks to shared dataframe\n",
"Stocks loaded: 0\n",
"Stocks loaded: 10\n",
"Stocks loaded: 20\n",
"Stocks loaded: 30\n",
" symbol Date Open High Low Close Volume \\\n",
"0 ADM 2000-01-03 11.999999 12.062499 11.8750 11.999999 984600.0 \n",
"1 ADM 2000-01-04 11.812500 12.187499 11.8125 11.875000 1088000.0 \n",
"2 ADM 2000-01-05 11.875000 11.875000 11.6250 11.687500 1087900.0 \n",
"3 ADM 2000-01-06 11.625000 11.875000 11.5625 11.750000 899900.0 \n",
"4 ADM 2000-01-07 11.875000 11.999999 11.8125 11.937500 1186200.0 \n",
"\n",
" Adj Close 100ma \n",
"0 7.855350 7.855350 \n",
"1 7.773524 7.814437 \n",
"2 7.650784 7.759886 \n",
"3 7.691697 7.742839 \n",
"4 7.814437 7.757158 \n",
"\n",
"Column labels: Index(['ADM', 'BF-B', 'CAG', 'CHD', 'CLX', 'COST', 'COTY', 'CPB', 'CVS', 'DPS',\n",
" 'EL', 'GIS', 'HRL', 'HSY', 'K', 'KHC', 'KO', 'KR', 'MDLZ', 'MKC', 'MO',\n",
" 'PEP', 'PG', 'RAI', 'SJM', 'STZ', 'SYY', 'TAP', 'TSN', 'WBA', 'WFM',\n",
" 'WMT'],\n",
" dtype='object', name='symbol') \n",
"\n",
"Row labels: Index(['ADM', 'BF-B', 'CAG', 'CHD', 'CLX', 'COST', 'COTY', 'CPB', 'CVS', 'DPS',\n",
" 'EL', 'GIS', 'HRL', 'HSY', 'K', 'KHC', 'KO', 'KR', 'MDLZ', 'MKC', 'MO',\n",
" 'PEP', 'PG', 'RAI', 'SJM', 'STZ', 'SYY', 'TAP', 'TSN', 'WBA', 'WFM',\n",
" 'WMT'],\n",
" dtype='object', name='symbol')\n",
"loading stocks to shared dataframe, recordset based on query:\n",
" SELECT [symbol], [dateAdded], [CIK], [security] FROM [dbo].[SandP500Index] \n",
"Stocks loaded: 0\n",
"Stocks loaded: 100\n",
"Stocks loaded: 200\n",
"Stocks loaded: 300\n",
"Stocks loaded: 400\n",
"Stocks loaded: 500\n",
"\n",
"statistics loaded\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Markus.Walden\\Documents\\arcgis\\data\\finance\\addins\\main.py:71: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with \n",
"\tDataFrame.rolling(window=12,center=False).mean()\n",
" rolmean = pd.rolling_mean(i, window=12)\n",
"C:\\Users\\Markus.Walden\\Documents\\arcgis\\data\\finance\\addins\\main.py:72: FutureWarning: pd.rolling_std is deprecated for DataFrame and will be removed in a future version, replace with \n",
"\tDataFrame.rolling(window=12,center=False).std()\n",
" rolstd = pd.rolling_std(i, window=12)\n",
"C:\\python\\New folder\\lib\\site-packages\\matplotlib\\axes\\_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
" warnings.warn(\"No labelled objects found. \"\n"
]
},
{
"data": {
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cOaLUvpTJ7t27ER8fj1OnTiEsLAxDhgyBvb09+vXrJ9Mwy8zMRGZmJu7cuYOtW7dW+57P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\nwiwAAAA8izALAAAAzyLMAgAAwLP+AeyiQHcGrfFEAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2760bace748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
},
{
"data": {
"image/png": 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K5EBl890HdPXq1Vi1ahUWL16MY445Bps3b8bKlSvx3nvvYc2aNdJtOjs7cc01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ksRQoweY3/lmC5e7s5Dx08BFQNBDVh2v1hSgNbW5mTI06QC5sJWsEz6ciIwPXSo61nKBugOO+wo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"text/plain": [
"<matplotlib.figure.Figure at 0x2760df6a400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"'''\n",
"The two plots, visualized at bottom part, measure \n",
"- the similarity of specific companies using a heatmap and \n",
"- volatility by measuring (rolling) mean as red and (rolling) standard deviation as black signals. \n",
"'''\n",
"\n",
"#from data.finance.addins.main import main as financeAddinsMain\n",
"testVisualization ()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Adj. Close HL_PCT PCT_change Adj. Volume label\n",
"Date \n",
"2004-08-19 50.322842 3.712563 0.324968 44659000.0 68.752232\n",
"2004-08-20 54.322689 0.710922 7.227007 22834300.0 69.639972\n",
"2004-08-23 54.869377 3.729433 -1.227880 18256100.0 69.078238\n",
"2004-08-24 52.597363 6.417469 -5.726357 15247300.0 67.839414\n",
"2004-08-25 53.164113 1.886792 1.183658 9188600.0 68.912727\n",
"2004-08-26 54.122070 0.037068 2.820391 7094800.0 70.668146\n",
"2004-08-27 53.239345 2.326896 -1.803885 6211700.0 71.219849\n",
"2004-08-30 51.162935 3.411430 -3.106003 5196700.0 72.278116\n",
"2004-08-31 51.343492 1.308977 0.048866 4917800.0 74.810934\n",
"2004-09-01 50.280210 2.713217 -2.385589 9138200.0 74.199045\n",
"Linear\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\python\\New folder\\lib\\site-packages\\sklearn\\preprocessing\\data.py:167: UserWarning: Numerical issues were encountered when centering the data and might not be solved. Dataset may contain too large values. You may need to prescale your features.\n",
" warnings.warn(\"Numerical issues were encountered \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"forecast: \n",
" [ 959.29338243 968.16614648 972.81379578 982.53439819 989.07636073\n",
" 996.7404547 1009.35525457 1012.06430964 1015.08126821 1005.21745978\n",
" 1007.22124831 1014.7868354 1022.38933579 1014.84571938 1020.55266751\n",
" 1023.56132083 985.98830891 978.76024708 988.50215338 985.91037292\n",
" 977.81571562 977.12537519 993.34699829 986.93749547 997.1360472\n",
" 995.49540203 1004.44478835 989.38017353 964.8594157 978.41716956\n",
" 955.10892384 947.04218191 936.92058002]\n",
"\n",
"confidence: 0.974404988515\n"
]
},
{
"data": {
"image/png": 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p8yxugXCYEEJAjBxrP3Zb2qyj4aoORERERKHiOqpbfdItT9We+to1TQfi6hsh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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Adj. Close</th>\n",
" <th>HL_PCT</th>\n",
" <th>PCT_change</th>\n",
" <th>Adj. Volume</th>\n",
" <th>label</th>\n",
" <th>Forecast</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2004-08-19</th>\n",
" <td>50.322842</td>\n",
" <td>3.712563</td>\n",
" <td>0.324968</td>\n",
" <td>44659000.0</td>\n",
" <td>68.752232</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2004-08-20</th>\n",
" <td>54.322689</td>\n",
" <td>0.710922</td>\n",
" <td>7.227007</td>\n",
" <td>22834300.0</td>\n",
" <td>69.639972</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-08-23</th>\n",
" <td>54.869377</td>\n",
" <td>3.729433</td>\n",
" <td>-1.227880</td>\n",
" <td>18256100.0</td>\n",
" <td>69.078238</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2004-08-24</th>\n",
" <td>52.597363</td>\n",
" <td>6.417469</td>\n",
" <td>-5.726357</td>\n",
" <td>15247300.0</td>\n",
" <td>67.839414</td>\n",
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" <tr>\n",
" <th>2004-08-25</th>\n",
" <td>53.164113</td>\n",
" <td>1.886792</td>\n",
" <td>1.183658</td>\n",
" <td>9188600.0</td>\n",
" <td>68.912727</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-08-26</th>\n",
" <td>54.122070</td>\n",
" <td>0.037068</td>\n",
" <td>2.820391</td>\n",
" <td>7094800.0</td>\n",
" <td>70.668146</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2004-08-27</th>\n",
" <td>53.239345</td>\n",
" <td>2.326896</td>\n",
" <td>-1.803885</td>\n",
" <td>6211700.0</td>\n",
" <td>71.219849</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-08-30</th>\n",
" <td>51.162935</td>\n",
" <td>3.411430</td>\n",
" <td>-3.106003</td>\n",
" <td>5196700.0</td>\n",
" <td>72.278116</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2004-08-31</th>\n",
" <td>51.343492</td>\n",
" <td>1.308977</td>\n",
" <td>0.048866</td>\n",
" <td>4917800.0</td>\n",
" <td>74.810934</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-01</th>\n",
" <td>50.280210</td>\n",
" <td>2.713217</td>\n",
" <td>-2.385589</td>\n",
" <td>9138200.0</td>\n",
" <td>74.199045</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-02</th>\n",
" <td>50.912161</td>\n",
" <td>0.847207</td>\n",
" <td>2.442224</td>\n",
" <td>15118600.0</td>\n",
" <td>70.462511</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-03</th>\n",
" <td>50.159839</td>\n",
" <td>1.729827</td>\n",
" <td>-0.931154</td>\n",
" <td>5152400.0</td>\n",
" <td>74.921275</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-07</th>\n",
" <td>50.947269</td>\n",
" <td>0.413467</td>\n",
" <td>0.564301</td>\n",
" <td>5847500.0</td>\n",
" <td>86.481962</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-08</th>\n",
" <td>51.308384</td>\n",
" <td>0.713587</td>\n",
" <td>1.548541</td>\n",
" <td>4985600.0</td>\n",
" <td>93.990139</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-09</th>\n",
" <td>51.313400</td>\n",
" <td>0.390969</td>\n",
" <td>-0.185366</td>\n",
" <td>4061700.0</td>\n",
" <td>91.181468</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-10</th>\n",
" <td>52.828075</td>\n",
" <td>1.167758</td>\n",
" <td>3.804080</td>\n",
" <td>8698800.0</td>\n",
" <td>93.272925</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-13</th>\n",
" <td>53.916435</td>\n",
" <td>0.846512</td>\n",
" <td>0.815905</td>\n",
" <td>7844100.0</td>\n",
" <td>96.949273</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-14</th>\n",
" <td>55.917612</td>\n",
" <td>0.457440</td>\n",
" <td>3.769546</td>\n",
" <td>10828900.0</td>\n",
" <td>95.615155</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-15</th>\n",
" <td>56.173402</td>\n",
" <td>1.991071</td>\n",
" <td>1.302460</td>\n",
" <td>10713000.0</td>\n",
" <td>98.318500</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-16</th>\n",
" <td>57.161452</td>\n",
" <td>1.605686</td>\n",
" <td>1.450952</td>\n",
" <td>9266300.0</td>\n",
" <td>97.736704</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-17</th>\n",
" <td>58.926902</td>\n",
" <td>0.000000</td>\n",
" <td>2.683097</td>\n",
" <td>9472500.0</td>\n",
" <td>96.131750</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-20</th>\n",
" <td>59.864797</td>\n",
" <td>1.876676</td>\n",
" <td>2.060710</td>\n",
" <td>10628700.0</td>\n",
" <td>92.635958</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-21</th>\n",
" <td>59.102444</td>\n",
" <td>2.189409</td>\n",
" <td>-1.963394</td>\n",
" <td>7228700.0</td>\n",
" <td>84.937193</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-22</th>\n",
" <td>59.373280</td>\n",
" <td>1.089711</td>\n",
" <td>0.791826</td>\n",
" <td>7581200.0</td>\n",
" <td>86.542147</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-23</th>\n",
" <td>60.597057</td>\n",
" <td>1.498096</td>\n",
" <td>1.666106</td>\n",
" <td>8535600.0</td>\n",
" <td>84.611187</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-24</th>\n",
" <td>60.100525</td>\n",
" <td>3.563381</td>\n",
" <td>-0.942382</td>\n",
" <td>9123400.0</td>\n",
" <td>84.189886</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-27</th>\n",
" <td>59.313094</td>\n",
" <td>2.215457</td>\n",
" <td>-1.087320</td>\n",
" <td>7066100.0</td>\n",
" <td>91.793357</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-28</th>\n",
" <td>63.626409</td>\n",
" <td>0.425666</td>\n",
" <td>4.713165</td>\n",
" <td>16929000.0</td>\n",
" <td>91.281778</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-29</th>\n",
" <td>65.742942</td>\n",
" <td>3.005798</td>\n",
" <td>3.595985</td>\n",
" <td>30516400.0</td>\n",
" <td>92.721222</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-09-30</th>\n",
" <td>65.000651</td>\n",
" <td>2.083333</td>\n",
" <td>-0.230179</td>\n",
" <td>13758000.0</td>\n",
" <td>86.539640</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-20</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>982.534398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-21</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>989.076361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-22</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>996.740455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-23</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1009.355255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-24</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1012.064310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-25</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1015.081268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-26</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1005.217460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-27</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1007.221248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-28</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1014.786835</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-29</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1022.389336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-30</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1014.845719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-31</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1020.552668</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1023.561321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-02</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>985.988309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-03</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>978.760247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-04</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>988.502153</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-05</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>985.910373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-06</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>977.815716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-07</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>977.125375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-08</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>993.346998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-09</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>986.937495</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-10</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>997.136047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-11</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>995.495402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-12</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1004.444788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-13</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>989.380174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-14</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>964.859416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-15</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>978.417170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-16</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>955.108924</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-17</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>947.042182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-18</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>936.920580</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3241 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Adj. Close HL_PCT PCT_change Adj. Volume label \\\n",
"2004-08-19 50.322842 3.712563 0.324968 44659000.0 68.752232 \n",
"2004-08-20 54.322689 0.710922 7.227007 22834300.0 69.639972 \n",
"2004-08-23 54.869377 3.729433 -1.227880 18256100.0 69.078238 \n",
"2004-08-24 52.597363 6.417469 -5.726357 15247300.0 67.839414 \n",
"2004-08-25 53.164113 1.886792 1.183658 9188600.0 68.912727 \n",
"2004-08-26 54.122070 0.037068 2.820391 7094800.0 70.668146 \n",
"2004-08-27 53.239345 2.326896 -1.803885 6211700.0 71.219849 \n",
"2004-08-30 51.162935 3.411430 -3.106003 5196700.0 72.278116 \n",
"2004-08-31 51.343492 1.308977 0.048866 4917800.0 74.810934 \n",
"2004-09-01 50.280210 2.713217 -2.385589 9138200.0 74.199045 \n",
"2004-09-02 50.912161 0.847207 2.442224 15118600.0 70.462511 \n",
"2004-09-03 50.159839 1.729827 -0.931154 5152400.0 74.921275 \n",
"2004-09-07 50.947269 0.413467 0.564301 5847500.0 86.481962 \n",
"2004-09-08 51.308384 0.713587 1.548541 4985600.0 93.990139 \n",
"2004-09-09 51.313400 0.390969 -0.185366 4061700.0 91.181468 \n",
"2004-09-10 52.828075 1.167758 3.804080 8698800.0 93.272925 \n",
"2004-09-13 53.916435 0.846512 0.815905 7844100.0 96.949273 \n",
"2004-09-14 55.917612 0.457440 3.769546 10828900.0 95.615155 \n",
"2004-09-15 56.173402 1.991071 1.302460 10713000.0 98.318500 \n",
"2004-09-16 57.161452 1.605686 1.450952 9266300.0 97.736704 \n",
"2004-09-17 58.926902 0.000000 2.683097 9472500.0 96.131750 \n",
"2004-09-20 59.864797 1.876676 2.060710 10628700.0 92.635958 \n",
"2004-09-21 59.102444 2.189409 -1.963394 7228700.0 84.937193 \n",
"2004-09-22 59.373280 1.089711 0.791826 7581200.0 86.542147 \n",
"2004-09-23 60.597057 1.498096 1.666106 8535600.0 84.611187 \n",
"2004-09-24 60.100525 3.563381 -0.942382 9123400.0 84.189886 \n",
"2004-09-27 59.313094 2.215457 -1.087320 7066100.0 91.793357 \n",
"2004-09-28 63.626409 0.425666 4.713165 16929000.0 91.281778 \n",
"2004-09-29 65.742942 3.005798 3.595985 30516400.0 92.721222 \n",
"2004-09-30 65.000651 2.083333 -0.230179 13758000.0 86.539640 \n",
"... ... ... ... ... ... \n",
"2017-05-20 NaN NaN NaN NaN NaN \n",
"2017-05-21 NaN NaN NaN NaN NaN \n",
"2017-05-22 NaN NaN NaN NaN NaN \n",
"2017-05-23 NaN NaN NaN NaN NaN \n",
"2017-05-24 NaN NaN NaN NaN NaN \n",
"2017-05-25 NaN NaN NaN NaN NaN \n",
"2017-05-26 NaN NaN NaN NaN NaN \n",
"2017-05-27 NaN NaN NaN NaN NaN \n",
"2017-05-28 NaN NaN NaN NaN NaN \n",
"2017-05-29 NaN NaN NaN NaN NaN \n",
"2017-05-30 NaN NaN NaN NaN NaN \n",
"2017-05-31 NaN NaN NaN NaN NaN \n",
"2017-06-01 NaN NaN NaN NaN NaN \n",
"2017-06-02 NaN NaN NaN NaN NaN \n",
"2017-06-03 NaN NaN NaN NaN NaN \n",
"2017-06-04 NaN NaN NaN NaN NaN \n",
"2017-06-05 NaN NaN NaN NaN NaN \n",
"2017-06-06 NaN NaN NaN NaN NaN \n",
"2017-06-07 NaN NaN NaN NaN NaN \n",
"2017-06-08 NaN NaN NaN NaN NaN \n",
"2017-06-09 NaN NaN NaN NaN NaN \n",
"2017-06-10 NaN NaN NaN NaN NaN \n",
"2017-06-11 NaN NaN NaN NaN NaN \n",
"2017-06-12 NaN NaN NaN NaN NaN \n",
"2017-06-13 NaN NaN NaN NaN NaN \n",
"2017-06-14 NaN NaN NaN NaN NaN \n",
"2017-06-15 NaN NaN NaN NaN NaN \n",
"2017-06-16 NaN NaN NaN NaN NaN \n",
"2017-06-17 NaN NaN NaN NaN NaN \n",
"2017-06-18 NaN NaN NaN NaN NaN \n",
"\n",
" Forecast \n",
"2004-08-19 NaN \n",
"2004-08-20 NaN \n",
"2004-08-23 NaN \n",
"2004-08-24 NaN \n",
"2004-08-25 NaN \n",
"2004-08-26 NaN \n",
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"2004-08-30 NaN \n",
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"2004-09-01 NaN \n",
"2004-09-02 NaN \n",
"2004-09-03 NaN \n",
"2004-09-07 NaN \n",
"2004-09-08 NaN \n",
"2004-09-09 NaN \n",
"2004-09-10 NaN \n",
"2004-09-13 NaN \n",
"2004-09-14 NaN \n",
"2004-09-15 NaN \n",
"2004-09-16 NaN \n",
"2004-09-17 NaN \n",
"2004-09-20 NaN \n",
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"2004-09-24 NaN \n",
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"2004-09-29 NaN \n",
"2004-09-30 NaN \n",
"... ... \n",
"2017-05-20 982.534398 \n",
"2017-05-21 989.076361 \n",
"2017-05-22 996.740455 \n",
"2017-05-23 1009.355255 \n",
"2017-05-24 1012.064310 \n",
"2017-05-25 1015.081268 \n",
"2017-05-26 1005.217460 \n",
"2017-05-27 1007.221248 \n",
"2017-05-28 1014.786835 \n",
"2017-05-29 1022.389336 \n",
"2017-05-30 1014.845719 \n",
"2017-05-31 1020.552668 \n",
"2017-06-01 1023.561321 \n",
"2017-06-02 985.988309 \n",
"2017-06-03 978.760247 \n",
"2017-06-04 988.502153 \n",
"2017-06-05 985.910373 \n",
"2017-06-06 977.815716 \n",
"2017-06-07 977.125375 \n",
"2017-06-08 993.346998 \n",
"2017-06-09 986.937495 \n",
"2017-06-10 997.136047 \n",
"2017-06-11 995.495402 \n",
"2017-06-12 1004.444788 \n",
"2017-06-13 989.380174 \n",
"2017-06-14 964.859416 \n",
"2017-06-15 978.417170 \n",
"2017-06-16 955.108924 \n",
"2017-06-17 947.042182 \n",
"2017-06-18 936.920580 \n",
"\n",
"[3241 rows x 6 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"The prediction and confidence of Alphabet (Google) stock performance based on the adjusted close field \n",
"using model based on linear regression as kernel.\n",
"\n",
"The prediction can also be based around nonlinear or polynomial kernels \n",
"'''\n",
"\n",
"# machine learning to finance - predicting performance\n",
"lq = LinkQuandl()\n",
"df = lq.createQuandlDataFrame(ticker = 'GOOGL', database = 'WIKI')\n",
"X, X_lately, y = lq.produceForecast(df = df)\n",
"forecast_set, clf, accurary = lq.forecastLinear(X = X, X_lately = X_lately, y = y)\n",
"lq.visualizeForecast(clf = clf, accuracy = accurary, forecast_set = forecast_set, df = df)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"def testUtils(): \n",
"#principle coordinate analysis\n",
" x = NeuralNetForward.x/np.amax(NeuralNetForward.x, axis = 0)\n",
" Utils.correlation(x)\n",
" Utils.covariance(x)\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": 1
}
| apache-2.0 |
daviddao/subpop-ladder | subpop_main.ipynb | 1 | 14323 | {
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=> Extracting data from txt\n",
"=> Extracted 2283 labeled objects with 97 features\n",
"=> Extracting data from txt\n",
"=> Extracted 2170942 unlabeled objects with 97 features\n",
"=> Split labeled data into 0.80 training and 0.20 test\n"
]
}
],
"source": [
"import input_data\n",
"subpop = input_data.read_subpop_data()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import input_data\n",
"import math\n",
"import os\n",
"import csv\n",
"from tqdm import tqdm\n",
"\n",
"layer_sizes = [97, 1000, 500, 250, 23]\n",
"\n",
"L = len(layer_sizes) - 1 # number of layers\n",
"\n",
"num_examples = 50000\n",
"num_epochs = 150\n",
"#num_labeled = 23\n",
"\n",
"starter_learning_rate = 0.02\n",
"\n",
"decay_after = 15 # epoch after which to begin learning rate decay\n",
"\n",
"batch_size = 100\n",
"num_iter = (num_examples/batch_size) * num_epochs # number of loop iterations\n",
"\n",
"inputs = tf.placeholder(tf.float32, shape=(None, layer_sizes[0]))\n",
"outputs = tf.placeholder(tf.float32)\n",
"\n",
"bi = lambda inits, size, name: tf.Variable(inits * tf.ones([size]), name=name)\n",
"wi = lambda shape, name: tf.Variable(tf.random_normal(shape, name=name)) / math.sqrt(shape[0])\n",
"\n",
"shapes = zip(layer_sizes[:-1], layer_sizes[1:]) # shapes of linear layers\n",
"\n",
"weights = {'W': [wi(s, \"W\") for s in shapes], # Encoder weights\n",
" 'V': [wi(s[::-1], \"V\") for s in shapes], # Decoder weights\n",
" 'beta': [bi(0.0, layer_sizes[l+1], \"beta\") for l in range(L)], # batch normalization parameter to shift the normalized value\n",
" 'gamma': [bi(1.0, layer_sizes[l+1], \"beta\") for l in range(L)]} # batch normalization parameter to scale the normalized value\n",
"\n",
"noise_std = 0.3 # scaling factor for noise used in corrupted encoder\n",
"\n",
"denoising_cost = [1000.0, 10.0, 0.10, 0.10, 0.10, 0.10, 0.10] # hyperparameters that denote the importance of each layer\n",
"\n",
"join = lambda l, u: tf.concat(0, [l, u])\n",
"labeled = lambda x: tf.slice(x, [0, 0], [batch_size, -1]) if x is not None else x\n",
"unlabeled = lambda x: tf.slice(x, [batch_size, 0], [-1, -1]) if x is not None else x\n",
"split_lu = lambda x: (labeled(x), unlabeled(x))\n",
"\n",
"training = tf.placeholder(tf.bool)\n",
"\n",
"ewma = tf.train.ExponentialMovingAverage(decay=0.99) # to calculate the moving averages of mean and variance\n",
"bn_assigns = [] # this list stores the updates to be made to average mean and variance\n",
"\n",
"def batch_normalization(batch, mean=None, var=None):\n",
" if mean == None or var == None:\n",
" mean, var = tf.nn.moments(batch, axes=[0])\n",
" return (batch - mean) / tf.sqrt(var + tf.constant(1e-10))\n",
"\n",
"# average mean and variance of all layers\n",
"running_mean = [tf.Variable(tf.constant(0.0, shape=[l]), trainable=False) for l in layer_sizes[1:]]\n",
"running_var = [tf.Variable(tf.constant(1.0, shape=[l]), trainable=False) for l in layer_sizes[1:]]\n",
"\n",
"def update_batch_normalization(batch, l):\n",
" \"batch normalize + update average mean and variance of layer l\"\n",
" mean, var = tf.nn.moments(batch, axes=[0])\n",
" assign_mean = running_mean[l-1].assign(mean)\n",
" assign_var = running_var[l-1].assign(var)\n",
" bn_assigns.append(ewma.apply([running_mean[l-1], running_var[l-1]]))\n",
" with tf.control_dependencies([assign_mean, assign_var]):\n",
" return (batch - mean) / tf.sqrt(var + 1e-10)\n",
"\n",
"def encoder(inputs, noise_std):\n",
" h = inputs + tf.random_normal(tf.shape(inputs)) * noise_std # add noise to input\n",
" d = {} # to store the pre-activation, activation, mean and variance for each layer\n",
" # The data for labeled and unlabeled examples are stored separately\n",
" d['labeled'] = {'z': {}, 'm': {}, 'v': {}, 'h': {}}\n",
" d['unlabeled'] = {'z': {}, 'm': {}, 'v': {}, 'h': {}}\n",
" d['labeled']['z'][0], d['unlabeled']['z'][0] = split_lu(h)\n",
" for l in range(1, L+1):\n",
" print \"Layer \", l, \": \", layer_sizes[l-1], \" -> \", layer_sizes[l]\n",
" d['labeled']['h'][l-1], d['unlabeled']['h'][l-1] = split_lu(h)\n",
" z_pre = tf.matmul(h, weights['W'][l-1]) # pre-activation\n",
" z_pre_l, z_pre_u = split_lu(z_pre) # split labeled and unlabeled examples\n",
" if training:\n",
" # Training\n",
" # batch normalization for labeled and unlabeled examples is performed separately\n",
" m, v = tf.nn.moments(z_pre_u, axes=[0])\n",
" if noise_std > 0:\n",
" # Corrupted encoder\n",
" # batch normalization + noise\n",
" z = join(batch_normalization(z_pre_l), batch_normalization(z_pre_u, m, v))\n",
" z += tf.random_normal(tf.shape(z_pre)) * noise_std\n",
" else:\n",
" # Clean encoder\n",
" # batch normalization + update the average mean and variance using batch mean and variance of labeled examples\n",
" z = join(update_batch_normalization(z_pre_l, l), batch_normalization(z_pre_u, m, v))\n",
"\telse:\n",
" # Evaluation\n",
" # obtain average mean and variance and use it to normalize the batch\n",
" \t mean = ewma.average(running_mean[l-1])\n",
" \t var = ewma.average(running_var[l-1])\n",
" z = batch_normalization(z_pre, mean, var)\n",
" # Instead of the above statement, the use of the following 2 statements containing a typo \n",
" # consistently produces a 0.2% higher accuracy for unclear reasons.\n",
" # m_l, v_l = tf.nn.moments(z_pre_l, axes=[0])\n",
" # z = join(batch_normalization(z_pre_l, m_l, mean, var), batch_normalization(z_pre_u, mean, var))\n",
" if l == L:\n",
" # use softmax activation in output layer\n",
" h = tf.nn.softmax(weights['gamma'][l-1] * (z + weights[\"beta\"][l-1]))\n",
" else:\n",
" # use ReLU activation in hidden layers\n",
" h = tf.nn.relu(z + weights[\"beta\"][l-1])\n",
" d['labeled']['z'][l], d['unlabeled']['z'][l] = split_lu(z)\n",
" d['unlabeled']['m'][l], d['unlabeled']['v'][l] = m, v # save mean and variance of unlabeled examples for decoding\n",
" d['labeled']['h'][l], d['unlabeled']['h'][l] = split_lu(h)\n",
" return h, d\n",
"\n",
"print \"=== Corrupted Encoder ===\"\n",
"y_c, corr = encoder(inputs, noise_std)\n",
"\n",
"print \"=== Clean Encoder ===\"\n",
"y, clean = encoder(inputs, 0.0) # 0.0 -> do not add noise\n",
"\n",
"print \"=== Decoder ===\"\n",
"\n",
"def g_gauss(z_c, u, size):\n",
" \"gaussian denoising function proposed in the original paper\"\n",
" wi = lambda inits, name: tf.Variable(inits * tf.ones([size]), name=name)\n",
" a1 = wi(0., 'a1')\n",
" a2 = wi(1., 'a2')\n",
" a3 = wi(0., 'a3')\n",
" a4 = wi(0., 'a4')\n",
" a5 = wi(0., 'a5')\n",
"\n",
" a6 = wi(0., 'a6')\n",
" a7 = wi(1., 'a7')\n",
" a8 = wi(0., 'a8')\n",
" a9 = wi(0., 'a9')\n",
" a10 = wi(0., 'a10')\n",
"\n",
" mu = a1 * tf.sigmoid(a2 * u + a3) + a4 * u + a5\n",
" v = a6 * tf.sigmoid(a7 * u + a8) + a9 * u + a10\n",
"\n",
" z_est = (z_c - mu) * v + mu\n",
" return z_est\n",
"\n",
"# Decoder\n",
"z_est = {}\n",
"d_cost = [] # to store the denoising cost of all layers\n",
"for l in range(L, -1, -1):\n",
" print \"Layer \", l, \": \", layer_sizes[l+1] if l+1 < len(layer_sizes) else None, \" -> \", layer_sizes[l], \", denoising cost: \", denoising_cost[l]\n",
" z, z_c = clean['unlabeled']['z'][l], corr['unlabeled']['z'][l]\n",
" m, v = clean['unlabeled']['m'].get(l, 0), clean['unlabeled']['v'].get(l, 1-1e-10)\n",
" if l == L:\n",
" u = unlabeled(y_c)\n",
" else:\n",
" u = tf.matmul(z_est[l+1], weights['V'][l])\n",
" u = batch_normalization(u)\n",
" z_est[l] = g_gauss(z_c, u, layer_sizes[l])\n",
" z_est_bn = (z_est[l] - m) / v\n",
" # append the cost of this layer to d_cost\n",
" d_cost.append((tf.reduce_mean(tf.reduce_sum(tf.square(z_est_bn - z), 1)) / layer_sizes[l]) * denoising_cost[l])\n",
"\n",
"# calculate total unsupervised cost by adding the denoising cost of all layers\n",
"u_cost = tf.add_n(d_cost)\n",
"\n",
"y_N = labeled(y_c)\n",
"cost = -tf.reduce_mean(tf.reduce_sum(outputs*tf.log(y_N), 1)) # supervised cost\n",
"loss = cost + u_cost # total cost\n",
"\n",
"pred_cost = -tf.reduce_mean(tf.reduce_sum(outputs*tf.log(y), 1)) # cost used for prediction\n",
"\n",
"correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(outputs, 1)) # no of correct predictions\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\")) * tf.constant(100.0)\n",
"\n",
"learning_rate = tf.Variable(starter_learning_rate, trainable=False)\n",
"train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)\n",
"\n",
"# add the updates of batch normalization statistics to train_step\n",
"bn_updates = tf.group(*bn_assigns)\n",
"with tf.control_dependencies([train_step]):\n",
" train_step = tf.group(bn_updates)\n",
"\n",
"print \"=== Loading Data ===\"\n",
"\n",
"saver = tf.train.Saver()\n",
"\n",
"print \"=== Starting Session ===\"\n",
"sess = tf.Session()\n",
"\n",
"i_iter = 0\n",
"\n",
"ckpt = tf.train.get_checkpoint_state('checkpoints/') # get latest checkpoint (if any)\n",
"if ckpt and ckpt.model_checkpoint_path:\n",
" # if checkpoint exists, restore the parameters and set epoch_n and i_iter\n",
" saver.restore(sess, ckpt.model_checkpoint_path)\n",
" epoch_n = int(ckpt.model_checkpoint_path.split('-')[1])\n",
" i_iter = (epoch_n+1) * (num_examples/batch_size)\n",
" print \"Restored Epoch \", epoch_n\n",
"else:\n",
" # no checkpoint exists. create checkpoints directory if it does not exist.\n",
" if not os.path.exists('checkpoints'):\n",
" os.makedirs('checkpoints')\n",
" init = tf.initialize_all_variables()\n",
" sess.run(init)\n",
"\n",
"print \"=== Training ===\"\n",
"print \"Initial Accuracy: \", sess.run(accuracy, feed_dict={inputs: subpop.test.data, outputs: subpop.test.labels, training: False}), \"%\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%prun\n",
"\n",
"for i in tqdm(range(100)):\n",
" data, labels = subpop.train.next_batch(batch_size)\n",
" sess.run(train_step, feed_dict={inputs: data, outputs: labels, training: True})\n",
" if (i > 1) and ((i+1) % (num_iter/num_epochs) == 0):\n",
" epoch_n = i/(num_examples/batch_size)\n",
" if (epoch_n+1) >= decay_after:\n",
" # decay learning rate\n",
" # learning_rate = starter_learning_rate * ((num_epochs - epoch_n) / (num_epochs - decay_after))\n",
" ratio = 1.0 * (num_epochs - (epoch_n+1)) # epoch_n + 1 because learning rate is set for next epoch\n",
" ratio = max(0, ratio / (num_epochs - decay_after))\n",
" sess.run(learning_rate.assign(starter_learning_rate * ratio))\n",
" saver.save(sess, 'checkpoints/model.ckpt', epoch_n)\n",
" print \"Epoch \", epoch_n, \", Accuracy: \", sess.run(accuracy, feed_dict={inputs: subpop.test.data, outputs:subpop.test.labels, training: False}), \"%\"\n",
"\twith open('train_log', 'ab') as train_log:\n",
" # write test accuracy to file \"train_log\"\n",
" train_log_w = csv.writer(train_log)\n",
" log_i = [epoch_n] + sess.run([accuracy], feed_dict={inputs: subpop.test.data, outputs:subpop.test.labels, training: False})\n",
" train_log_w.writerow(log_i)\n",
"\n",
"print \"Final Accuracy: \", sess.run(accuracy, feed_dict={inputs: subpop.test.data, outputs: subpop.test.labels, training: False}), \"%\"\n",
"\n",
"sess.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.data.dtype"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"labels"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data, labels = mnist.train.next_batch(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"labels"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
Ankhee/steam-games-graph | anaconda_notebooks/temp_test.ipynb | 1 | 5494 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.8744840621948242\n"
]
}
],
"source": [
"import numpy as np\n",
"import time\n",
"\n",
"t = time.time()\n",
"\n",
"base = np.asarray(range(15000))\n",
"\n",
"for _ in range(10000):\n",
" result = np.isin(base, np.asarray([10, 100, 200, 300, 400, 500, 1000, 1500, 6756, 11245, 14672,\n",
" 2000, 3532, 5234, 1234, 4763, 5764, 7456, 3673, 11111]))\n",
" \n",
"print(time.time() - t)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"warning len: 0 []\n"
]
}
],
"source": [
"import json\n",
"\n",
"# appids given by steam via http://api.steampowered.com/ISteamApps/GetAppList/v0001/ which is our base\n",
"with open('C:\\\\Users\\\\Admin\\\\Documents\\\\GitHub\\\\GamesGraph\\\\scripts\\\\wip_data\\\\_all_appids.json', \n",
" 'r', encoding='utf8') as f:\n",
" steam_json = json.loads(f.read())\n",
"\n",
"steam_list = []\n",
"\n",
"for app in steam_json['applist']['apps']['app']:\n",
" steam_list.append(int(app['appid']))\n",
"\n",
"# appids from all the users we've scrapped\n",
"# let's see if there are any that were not given in the first place\n",
"\n",
"scraped_list = []\n",
"\n",
"with open('C:\\\\Users\\\\Admin\\\\Documents\\\\GitHub\\\\GamesGraph\\\\scripts\\\\wip_data\\\\_dataframe_merged.csv', 'r') as f:\n",
" for line in f.readlines():\n",
" line = line.replace(',profile,time','')\n",
" line = line.strip('\\n')\n",
" line = line.split(',appid')\n",
" for appid in line:\n",
" if len(appid)>0:\n",
" scraped_list.append(int(appid))\n",
" break # just need the first line\n",
"\n",
"warning = [] \n",
"\n",
"for i in scraped_list:\n",
" if i not in steam_list:\n",
" warning.append(i)\n",
" \n",
"print('warning len:',len(warning),warning)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4717\n"
]
}
],
"source": [
"# test for flukes and redirects\n",
"\n",
"import json\n",
"with open('C:\\\\Users\\\\Admin\\\\Documents\\\\GitHub\\\\GamesGraph\\\\scripts\\\\wip_data\\\\_appids_scraped.json', \n",
" 'r', encoding='utf8') as f:\n",
" j = json.loads(f.read())\n",
"\n",
"redirects = {} \n",
"\n",
"for app in j:\n",
" if app['appid'] != app['requested_appid']:\n",
" #if app['appid'] == str(515850):\n",
" # print('appid:',app['appid'],'req_appid',app['requested_appid'],'title',app['title'])\n",
" if app['appid'] in list(redirects.keys()):\n",
" redirects[app['appid']] += 1\n",
" else:\n",
" redirects[app['appid']] = 1\n",
" \n",
"#sorted(redirects.items(), key=lambda x: x[1], reverse=True)\n",
"\n",
"# let's check if any of these redirect to another redirect // WE'RE GOOD\n",
"\n",
"redirects = {}\n",
"\n",
"for app in j:\n",
" if app['appid'] != app['requested_appid']:\n",
" try:\n",
" redirects[int(app['requested_appid'])] = int(app['appid'])\n",
" except ValueError:\n",
" pass\n",
"print(len(redirects))\n",
"for end in list(redirects.values()):\n",
" if end in list(redirects.keys()):\n",
" print('warning!',end)\n",
" \n",
"with open('C:\\\\Users\\\\Admin\\\\Documents\\\\GitHub\\\\GamesGraph\\\\scripts\\\\wip_data\\\\_redirects_from_to.json', 'w') as f:\n",
" f.write(json.dumps(redirects))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"item={\n",
" 'id': 12455,\n",
" 't':'asdfsdfxcasg2sSS',\n",
" 'pos':[432.234,123.235,125.64],\n",
" 'c':'A1A1A1',\n",
" 's':0.546,\n",
" #'tags':[1,4,78,2],\n",
" #'devs':[1,2],\n",
" #'pubs':[1],\n",
" #'var':{'1':1,'2':2}\n",
" }\n",
"list = []\n",
"while True:\n",
" list.append(item)\n",
" if len(list)>10000:\n",
" break\n",
"\n",
"with open('C:\\\\Users\\\\Admin\\\\Documents\\\\GitHub\\\\GamesGraph\\\\scripts\\\\wip_data\\\\_test_size.json', \n",
" 'w') as f:\n",
" f.write(json.dumps(list)) "
]
},
{
"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
}
| gpl-3.0 |
ComputationalModeling/spring-2017-danielak | past-semesters/fall_2016/day-by-day/day17-analyzing-tweets-with-string-processing/In-Class-Strings.ipynb | 1 | 22971 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Day 17 In-class assignment: Data analysis and Modeling in Social Sciences\n",
"\n",
"# Part 3\n",
"\n",
"\n",
"The first part of this notebook is a copy of a blog post tutorial written by Dr. Neal Caren (University of North Carolina, Chapel Hill). The format was modified to fit into a Jupyter Notebook, ported from python2 to python3, and adjusted to meet the goals of this class. Here is a link to the original tutorial:\n",
"\n",
"http://nealcaren.web.unc.edu/an-introduction-to-text-analysis-with-python-part-3/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Student Names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"// Put the names of everybody in your group here!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning Goals \n",
"\n",
"Natural Language Processing can be tricky to model comparied to known physical processes with mathematical rules. A large part of modeling is trying to understand a model's limitations and determining what can be learned from a model despite its limitations: \n",
"\n",
"* Apply what we have learned from the Pre-class notebooks to build a Twitter \"bag of words\" model on real Twitter data.\n",
"* Introduce you to a method for downloading data from the Internet.\n",
"* Gain practice doing string manipulation.\n",
"* Learn how to make Pie Charts in Python."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This assignment explains how to expand the code written in your pre-class assignemnt so that you can use it to explore the positive and negative sentiment of any set of texts. Specifically, we’ll look at looping over more than one tweet, incorporating a more complete dictionary, and exporting the results. \n",
"\n",
"Earlier, we used a small list of words to measure positive sentiment. While the study in Science used the commercial [LIWC](http://www.liwc.net/) dictionary, an alternate sentiment dictionary is produced by Theresa Wilson, Janyce Wiebe, and Paul Hoffmann at the University of Pittsburgh and is freely [available](http://www.cs.pitt.edu/mpqa/). In both cases, the sentiment dictionaries are used in a fairly straightforward way: the more positive words in the text, the higher the text scores on the positive sentiment scale. While this has some drawbacks, the method is quite popular: the LIWC database has over 1,000 citations in Google Scholar, and the Wilson et al. database has more than 600.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Do the following on your own:\n",
"\n",
"First, load some libraries we will be using in this notebook. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from string import punctuation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Downloading**\n",
"\n",
"Since the Wilson et al. list combines negative and positive polarity words in one list, and includes both words and word stems, Dr. Caren cleaned it up a bit for us. You can download the positive list and the negative list using your browser, but you don’t have to. Python can do that.\n",
"\n",
"First, you need to import one of the modules that Python uses to communicate with the Internet:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import urllib.request"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like many commands, Python won’t return anything unless something went wrong. In this case, the In [*] should change to a number like In [2]. Next, store the web address that you want to access in a string. You don’t have to do this, but it’s the type of thing that makes your code easier to read and allows you to scale up quickly when you want to download thousands of urls."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"url='http://www.unc.edu/~ncaren/haphazard/negative.txt'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also create a string with the name you want the file to have on you hard drive:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"file_name='negative.txt'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To download and save the file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"urllib.request.urlretrieve(url, file_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This will download the file into your current directory. If you want it to go somewhere else, you can put the full path in the file_name string. You didn’t have to enter the url and the file name in the prior lines. Something like the following would have worked exactly the same:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"urllib.request.urlretrieve('http://www.unc.edu/~ncaren/haphazard/negative.txt','negative.txt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the location and filename are both surrounded by quotation marks because you want Python to use this information literally; they aren’t referring to a string object, like in our previous code. This line of code is actually quite readable, and in most circumstances this would be the most efficient thing to do. But there are actually lots of files that we want to get: the negative list, the positive list, and several lists of tweets. And we can download the datasets using a couple of simple loops:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# get negative and positive words\n",
"files=['negative.txt','positive.txt']\n",
"path='http://www.unc.edu/~ncaren/haphazard/'\n",
"for file_name in files:\n",
" urllib.request.urlretrieve(path+file_name,file_name)\n",
" \n",
"# get politician tweets\n",
"files=['BarackObama_tweets.txt','HillaryClinton_tweets.txt',\n",
" 'realDonaldTrump_tweets.txt','mike_pence_tweets.txt',\n",
" 'timkaine_tweets.txt']\n",
"path='https://raw.githubusercontent.com/bwoshea/CMSE201_datasets/master/pres_tweets/'\n",
"for file_name in files:\n",
" urllib.request.urlretrieve(path+file_name,file_name)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first line creates a new list with two items - the names of the two files to be downloaded. The second line creates a string object that stores the url path that they all share. The third line starts a loop over each of the items in the files list using ```file_name``` to reference each item in turn. The fourth line is indented, because it happens once for each item in the list as a result of the loop, and downloads the file. This is the same as the original download line, except the URL is now the combination of two strings, ```path``` and ```file_name```. As noted previously, Python can combine strings with a plus sign, so the result from the first pass through the loop will be http://www.unc.edu/~ncaren/haphazard/negative.txt, which is where the file can be found. Note that this takes advantage of the fact that we don’t mind reusing the original file name. If we wanted to change it, or if there were different paths to each of the files, things would get slightly trickier.\n",
"\n",
"The second set of files, url path, and loop will download collections of tweets from various politicians involved in the current Presidential election: the sitting president (Barack Obama), the two candidates (Hillary Clinton and Donald Trump) and their vice-presidential running mates (Tim Kaine and Mike Pence). **Everybody should pick one of these people to analyze - coordinate with your group members!**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**More fun with lists**\n",
"\n",
"Let’s take a look at the list of Tweets that we just downloaded. First, pick one of the politicians to analyze and open the appropriate file (you're going to have to change some stuff to do so):\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tweets = open(\"CHOOSE_YOUR_FILE_NAME_tweets.txt\").read()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you might have guessed, this line is actually doing double duty. It opens the file and reads it into memory before it is stored in ```tweets```. Since the file has one tweet on each line, we can turn it into a list of tweets by splitting it at the end of line character. The file was originally created on a Mac, so the end of line character is an \\n (think \\n for new line). On a Windows computer, the end of line character is an \\r\\n (think \\r for return and \\n for new line). So if the file was created on a Windows computer, you might need to strip out the extra character with something like windows_file=windows_file.replace('\\r','') before you split the lines, but you don’t need to worry about that here, no matter what operating system you are using. The end of line character comes from the computer that made the file, not the computer you are currently using. To split the tweets into a list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tweets_list = tweets.split('\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"As always, you can check how many items are in the list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"len(tweets_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can print the entire list by typing ```print(tweets_list)```, but it will be very long. A more useful way to look at it is to print just some of the items. Since it’s a list, we can loop through the first few item so they each print on the same line."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for tweet in tweets_list[0:5]:\n",
" print(tweet)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note the new ```[0:5]``` after the ```tweets_list``` but before the : that begins the loop. The first number tells Python where to make the first cut in the list. The potentially counterintuitive part is that this number doesn’t reference an actual item in the list, but rather a position between each item in the list–think about where the comma goes when lists are created or printed. Adding to the confusion, the position at the start of the list is 0. So, in this case, we are telling Python we want to slice our list starting at the beginning and continuing until the fifth comma, which is after the fifth item in the list.\n",
"\n",
"So, if you wanted to just print the second item in the list, you could type:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(tweets_list[1:2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OR"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(tweets_list[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This slices the list from the first comma to the second comma, so the result is the second item in the list. Unless you have a computer science background, this may be confusing as it’s not the common way to think of items in lists.\n",
"\n",
"As a shorthand, you can leave out the first number in the pair if you want to start at the very beginning or leave out the last number if you want to go until the end. So, if you want to print out the first five tweets, you could just type ```print(tweet_list[:5])```. There are several other shortcuts along these lines that are available. We will cover some of them in other tutorials.\n",
"\n",
"Now that we have our tweet list expanded, let’s load up the positive sentiment list and print out the first few entries:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pos_sent = open(\"positive.txt\").read()\n",
"positive_words=pos_sent.split('\\n')\n",
"print(positive_words[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like the tweet list, this file contained each entry on its own line, so it loads exactly the same way. If you typed ```len(positive_words)``` you would find out that this list has 2,230 entries.\n",
"\n",
"**Preprocessing**\n",
"\n",
"In the pre-class assignment, we explored how to preprocess the tweets: remove the punctuation, convert to lower case, and examine whether or not each word was in the positive sentiment list. We can use this exact same code here with our long list. The one alteration is that instead of having just one tweet, we now have a list of 1,365 tweets, so we have to loop over that list."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for tweet in tweets_list:\n",
" positive_counter=0\n",
" tweet_processed=tweet.lower()\n",
" for p in punctuation:\n",
" tweet_processed=tweet_processed.replace(p,'')\n",
" words=tweet_processed.split(' ')\n",
" for word in words:\n",
" if word in positive_words:\n",
" positive_counter=positive_counter+1\n",
" print(positive_counter/len(words))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Do the next part with your partner\n",
"\n",
"If you saw a string of numbers roll past you, it worked! To review, we start by looping over each item of the list. We set up a counter to hold the running total of the number of positive words found in the tweet. Then we make everything lower case and store it in ```tweet_processed```. To strip out the punctuation, we loop over every item of punctuation, swapping out the punctuation mark with nothing. \n",
"\n",
"The cleaned ```tweet``` is then converted to a list of words, split at the white spaces. Finally, we loop through each word in the ```tweet```, and if the word is in our new and expanded list of positive words, we increase the counter by one. After cycling through each of the tweet words, the proportion of positive words is computed and printed. \n",
"\n",
"The major problem with this script is that it is currently useless. It prints the positive sentiment results, but then doesn’t do anything with it. A more practical solution would be to store the results somehow. In a standard statistical package, we would generate a new variable that held our results. We can do something similar here by storing the results in a new list. Before we start the tweet loop, we add the line:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"positive_counts=[]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, instead of printing the proportion, we can append it to the list using the following command:\n",
"\n",
"```\n",
"positive_counts.append(positive_counter/word_count)\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Step 1: make a list of counts.** Copy and paste the above and rewrite it using the above append command."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Put your code here\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next time we run through the loop, it shouldn't produce any output, but it will create a list of the proportions. Lets do a quick check to see how many positive words there are in the entire set of tweets:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"len(positive_counts)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next step is to plot a histogram of the data to see the distribution of positive texts:"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"**Step 2: make a histogram of the positive counts.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Put your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Step 3: Subtract negative values.** Now redo the caluclation in **Step 1** but also subtract negative words (i.e. your measurement can now have a positive or negative value):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Put your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Step 4: Generate positive/negative histogram.** Generate a second histogram using range -5 to 5 and 20 bins."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Put your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another way to model the \"bag of words\" is to evaluate if the tweet has only positive words, only negative words, both positive and negative words or neither positive nor negative words. Rewrite your code to keep track of all four totals."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Step 5: Count \"types\" of tweets.** Rewrite the code from **steps 1 & 3** and determin if each tweet has only positive works, only negative words, both positive and negative words or neither positive nor negative words. Keep total counts the number of each kind of tweet."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"only_positive=0;\n",
"only_negative=0;\n",
"both_pos_and_neg=0;\n",
"neither_pos_nor_neg=0;\n",
"#Put your code here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Step 6:** Check your answer. If everything went as planned, you should be able to add all four totals and it will be equal to the total number of tweets!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Run this code. It should output True. \n",
"print(only_positive)\n",
"print(only_negative)\n",
"print(both_pos_and_neg)\n",
"print(neither_pos_nor_neg)\n",
"only_positive + only_negative + both_pos_and_neg + neither_pos_nor_neg == len(tweets_list)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Step 7: Make a Pie Graph of your results.** Now we are just going to plot the results using matplotlib pie function. If you used the variables above this should just work."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# The slices will be ordered and plotted counter-clockwise.\n",
"labels = 'positive', 'both', 'negative', 'neither'\n",
"sizes = [only_positive, both_pos_and_neg, only_negative, neither_pos_nor_neg]\n",
"colors = ['yellowgreen', 'yellow','red', 'lightcyan']\n",
"explode = (0.1, 0, 0.1, 0) \n",
"\n",
"plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
" autopct='%1.1f%%', shadow=True, startangle=90);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assignment wrapup\n",
"\n",
"Please fill out the form that appears when you run the code below. **You must completely fill this out in order to receive credit for the assignment!**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
"HTML(\n",
"\"\"\"\n",
"<iframe \n",
"\tsrc=\"https://goo.gl/forms/MEOZvOwBcY7CEfEj1?embedded=true\" \n",
"\twidth=\"80%\" \n",
"\theight=\"1200px\" \n",
"\tframeborder=\"0\" \n",
"\tmarginheight=\"0\" \n",
"\tmarginwidth=\"0\">\n",
"\tLoading...\n",
"</iframe>\n",
"\"\"\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Submitting this assignment\n",
"\n",
"Submit this assignment by uploading it to the course Desire2Learn web page. Go to the \"In-Class-Activities\" folder, find the dropbox link for Day 17, and upload it there.\n",
"\n",
"Have a Great Day!"
]
}
],
"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
}
| agpl-3.0 |
tensorflow/docs | site/en/guide/ragged_tensor.ipynb | 2 | 73699 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "nibpbUnTsxTd"
},
"source": [
"##### Copyright 2018 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "tXAbWHtqs1Y2"
},
"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": "HTgMAvQq-PU_"
},
"source": [
"# Ragged tensors\n",
"\n",
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://www.tensorflow.org/guide/ragged_tensor\"><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/guide/ragged_tensor.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/guide/ragged_tensor.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/guide/ragged_tensor.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
" </td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5DP8XNP-6zlu"
},
"source": [
"**API Documentation:** [`tf.RaggedTensor`](https://www.tensorflow.org/api_docs/python/tf/RaggedTensor) [`tf.ragged`](https://www.tensorflow.org/api_docs/python/tf/ragged)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cDIUjj07-rQg"
},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KKvdSorS-pDD"
},
"outputs": [],
"source": [
"!pip install --pre -U tensorflow\n",
"import math\n",
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pxi0m_yf-te5"
},
"source": [
"## Overview\n",
"\n",
"Your data comes in many shapes; your tensors should too. *Ragged tensors* are the TensorFlow equivalent of nested variable-length lists. They make it easy to store and process data with non-uniform shapes, including:\n",
"\n",
"- Variable-length features, such as the set of actors in a movie.\n",
"- Batches of variable-length sequential inputs, such as sentences or video clips.\n",
"- Hierarchical inputs, such as text documents that are subdivided into sections, paragraphs, sentences, and words.\n",
"- Individual fields in structured inputs, such as protocol buffers.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1mhU_qY3_mla"
},
"source": [
"### What you can do with a ragged tensor\n",
"\n",
"Ragged tensors are supported by more than a hundred TensorFlow operations, including math operations (such as `tf.add` and `tf.reduce_mean`), array operations (such as `tf.concat` and `tf.tile`), string manipulation ops (such as `tf.strings.substr`), control flow operations (such as `tf.while_loop` and `tf.map_fn`), and many others:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vGmJGSf_-PVB"
},
"outputs": [],
"source": [
"digits = tf.ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []])\n",
"words = tf.ragged.constant([[\"So\", \"long\"], [\"thanks\", \"for\", \"all\", \"the\", \"fish\"]])\n",
"print(tf.add(digits, 3))\n",
"print(tf.reduce_mean(digits, axis=1))\n",
"print(tf.concat([digits, [[5, 3]]], axis=0))\n",
"print(tf.tile(digits, [1, 2]))\n",
"print(tf.strings.substr(words, 0, 2))\n",
"print(tf.map_fn(tf.math.square, digits))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Pt-5OIc8-PVG"
},
"source": [
"There are also a number of methods and operations that are\n",
"specific to ragged tensors, including factory methods, conversion methods,\n",
"and value-mapping operations.\n",
"For a list of supported ops, see the **`tf.ragged` package\n",
"documentation**."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r8fjGgf3B_6z"
},
"source": [
"Ragged tensors are supported by many TensorFlow APIs, including [Keras](https://www.tensorflow.org/guide/keras), [Datasets](https://www.tensorflow.org/guide/data), [tf.function](https://www.tensorflow.org/guide/function), [SavedModels](https://www.tensorflow.org/guide/saved_model), and [tf.Example](https://www.tensorflow.org/tutorials/load_data/tfrecord). For more information, check the section on **TensorFlow APIs** below."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aTXLjQlcHP8a"
},
"source": [
"As with normal tensors, you can use Python-style indexing to access specific slices of a ragged tensor. For more information, refer to the section on **Indexing** below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "n8YMKXpI-PVH"
},
"outputs": [],
"source": [
"print(digits[0]) # First row"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Awi8i9q5_DuX"
},
"outputs": [],
"source": [
"print(digits[:, :2]) # First two values in each row."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sXgQtTcgHHMR"
},
"outputs": [],
"source": [
"print(digits[:, -2:]) # Last two values in each row."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6FU5T_-8-PVK"
},
"source": [
"And just like normal tensors, you can use Python arithmetic and comparison operators to perform elementwise operations. For more information, check the section on **Overloaded operators** below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2tdUEtb7-PVL"
},
"outputs": [],
"source": [
"print(digits + 3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "X-bxG0nc_Nmf"
},
"outputs": [],
"source": [
"print(digits + tf.ragged.constant([[1, 2, 3, 4], [], [5, 6, 7], [8], []]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2tsw8mN0ESIT"
},
"source": [
"If you need to perform an elementwise transformation to the values of a `RaggedTensor`, you can use `tf.ragged.map_flat_values`, which takes a function plus one or more arguments, and applies the function to transform the `RaggedTensor`'s values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pvt5URbdEt-D"
},
"outputs": [],
"source": [
"times_two_plus_one = lambda x: x * 2 + 1\n",
"print(tf.ragged.map_flat_values(times_two_plus_one, digits))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HNxF6_QKAzkl"
},
"source": [
"Ragged tensors can be converted to nested Python `list`s and NumPy `array`s:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "A5NHb8ViA9dt"
},
"outputs": [],
"source": [
"digits.to_list()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2o1wogVyA6Yp"
},
"outputs": [],
"source": [
"digits.numpy()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7M5RHOgp-PVN"
},
"source": [
"### Constructing a ragged tensor\n",
"\n",
"The simplest way to construct a ragged tensor is using `tf.ragged.constant`, which builds the `RaggedTensor` corresponding to a given nested Python `list` or NumPy `array`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yhgKMozw-PVP"
},
"outputs": [],
"source": [
"sentences = tf.ragged.constant([\n",
" [\"Let's\", \"build\", \"some\", \"ragged\", \"tensors\", \"!\"],\n",
" [\"We\", \"can\", \"use\", \"tf.ragged.constant\", \".\"]])\n",
"print(sentences)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TW1g7eE2ee8M"
},
"outputs": [],
"source": [
"paragraphs = tf.ragged.constant([\n",
" [['I', 'have', 'a', 'cat'], ['His', 'name', 'is', 'Mat']],\n",
" [['Do', 'you', 'want', 'to', 'come', 'visit'], [\"I'm\", 'free', 'tomorrow']],\n",
"])\n",
"print(paragraphs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SPLn5xHn-PVR"
},
"source": [
"Ragged tensors can also be constructed by pairing flat *values* tensors with *row-partitioning* tensors indicating how those values should be divided into rows, using factory classmethods such as `tf.RaggedTensor.from_value_rowids`, `tf.RaggedTensor.from_row_lengths`, and `tf.RaggedTensor.from_row_splits`.\n",
"\n",
"#### `tf.RaggedTensor.from_value_rowids`\n",
"\n",
"If you know which row each value belongs to, then you can build a `RaggedTensor` using a `value_rowids` row-partitioning tensor:\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "SEvcPUcl-PVS"
},
"outputs": [],
"source": [
"print(tf.RaggedTensor.from_value_rowids(\n",
" values=[3, 1, 4, 1, 5, 9, 2],\n",
" value_rowids=[0, 0, 0, 0, 2, 2, 3]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RBQh8sYc-PVV"
},
"source": [
"#### `tf.RaggedTensor.from_row_lengths`\n",
"\n",
"If you know how long each row is, then you can use a `row_lengths` row-partitioning tensor:\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LBY81WXl-PVW"
},
"outputs": [],
"source": [
"print(tf.RaggedTensor.from_row_lengths(\n",
" values=[3, 1, 4, 1, 5, 9, 2],\n",
" row_lengths=[4, 0, 2, 1]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8p5V8_Iu-PVa"
},
"source": [
"#### `tf.RaggedTensor.from_row_splits`\n",
"\n",
"If you know the index where each row starts and ends, then you can use a `row_splits` row-partitioning tensor:\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FwizuqZI-PVb"
},
"outputs": [],
"source": [
"print(tf.RaggedTensor.from_row_splits(\n",
" values=[3, 1, 4, 1, 5, 9, 2],\n",
" row_splits=[0, 4, 4, 6, 7]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E-9imo8DhwuA"
},
"source": [
"See the `tf.RaggedTensor` class documentation for a full list of factory methods.\n",
"\n",
"Note: By default, these factory methods add assertions that the row partition tensor is well-formed and consistent with the number of values. The `validate=False` parameter can be used to skip these checks if you can guarantee that the inputs are well-formed and consistent."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YQAOsT1_-PVg"
},
"source": [
"### What you can store in a ragged tensor\n",
"\n",
"As with normal `Tensor`s, the values in a `RaggedTensor` must all have the same\n",
"type; and the values must all be at the same nesting depth (the *rank* of the\n",
"tensor):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "SqbPBd_w-PVi"
},
"outputs": [],
"source": [
"print(tf.ragged.constant([[\"Hi\"], [\"How\", \"are\", \"you\"]])) # ok: type=string, rank=2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "83ZCSJnQAWAf"
},
"outputs": [],
"source": [
"print(tf.ragged.constant([[[1, 2], [3]], [[4, 5]]])) # ok: type=int32, rank=3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ewA3cISdDfmP"
},
"outputs": [],
"source": [
"try:\n",
" tf.ragged.constant([[\"one\", \"two\"], [3, 4]]) # bad: multiple types\n",
"except ValueError as exception:\n",
" print(exception)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EOWIlVidDl-n"
},
"outputs": [],
"source": [
"try:\n",
" tf.ragged.constant([\"A\", [\"B\", \"C\"]]) # bad: multiple nesting depths\n",
"except ValueError as exception:\n",
" print(exception)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nhHMFhSp-PVq"
},
"source": [
"## Example use case\n",
"\n",
"The following example demonstrates how `RaggedTensor`s can be used to construct and combine unigram and bigram embeddings for a batch of variable-length queries, using special markers for the beginning and end of each sentence. For more details on the ops used in this example, check the `tf.ragged` package documentation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZBs_V7e--PVr"
},
"outputs": [],
"source": [
"queries = tf.ragged.constant([['Who', 'is', 'Dan', 'Smith'],\n",
" ['Pause'],\n",
" ['Will', 'it', 'rain', 'later', 'today']])\n",
"\n",
"# Create an embedding table.\n",
"num_buckets = 1024\n",
"embedding_size = 4\n",
"embedding_table = tf.Variable(\n",
" tf.random.truncated_normal([num_buckets, embedding_size],\n",
" stddev=1.0 / math.sqrt(embedding_size)))\n",
"\n",
"# Look up the embedding for each word.\n",
"word_buckets = tf.strings.to_hash_bucket_fast(queries, num_buckets)\n",
"word_embeddings = tf.nn.embedding_lookup(embedding_table, word_buckets) # ①\n",
"\n",
"# Add markers to the beginning and end of each sentence.\n",
"marker = tf.fill([queries.nrows(), 1], '#')\n",
"padded = tf.concat([marker, queries, marker], axis=1) # ②\n",
"\n",
"# Build word bigrams and look up embeddings.\n",
"bigrams = tf.strings.join([padded[:, :-1], padded[:, 1:]], separator='+') # ③\n",
"\n",
"bigram_buckets = tf.strings.to_hash_bucket_fast(bigrams, num_buckets)\n",
"bigram_embeddings = tf.nn.embedding_lookup(embedding_table, bigram_buckets) # ④\n",
"\n",
"# Find the average embedding for each sentence\n",
"all_embeddings = tf.concat([word_embeddings, bigram_embeddings], axis=1) # ⑤\n",
"avg_embedding = tf.reduce_mean(all_embeddings, axis=1) # ⑥\n",
"print(avg_embedding)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y_lE_LAVcWQH"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "An_k0pX1-PVt"
},
"source": [
"## Ragged and uniform dimensions\n",
"\n",
"A ***ragged dimension*** is a dimension whose slices may have different lengths. For example, the inner (column) dimension of `rt=[[3, 1, 4, 1], [], [5, 9, 2], [6], []]` is ragged, since the column slices (`rt[0, :]`, ..., `rt[4, :]`) have different lengths. Dimensions whose slices all have the same length are called *uniform dimensions*.\n",
"\n",
"The outermost dimension of a ragged tensor is always uniform, since it consists of a single slice (and, therefore, there is no possibility for differing slice lengths). The remaining dimensions may be either ragged or uniform. For example, you may store the word embeddings for each word in a batch of sentences using a ragged tensor with shape `[num_sentences, (num_words), embedding_size]`, where the parentheses around `(num_words)` indicate that the dimension is ragged.\n",
"\n",
"\n",
"\n",
"Ragged tensors may have multiple ragged dimensions. For example, you could store a batch of structured text documents using a tensor with shape `[num_documents, (num_paragraphs), (num_sentences), (num_words)]` (where again parentheses are used to indicate ragged dimensions).\n",
"\n",
"As with `tf.Tensor`, the ***rank*** of a ragged tensor is its total number of dimensions (including both ragged and uniform dimensions). A ***potentially ragged tensor*** is a value that might be either a `tf.Tensor` or a `tf.RaggedTensor`.\n",
"\n",
"When describing the shape of a RaggedTensor, ragged dimensions are conventionally indicated by enclosing them in parentheses. For example, as you saw above, the shape of a 3D RaggedTensor that stores word embeddings for each word in a batch of sentences can be written as `[num_sentences, (num_words), embedding_size]`.\n",
"\n",
"The `RaggedTensor.shape` attribute returns a `tf.TensorShape` for a ragged tensor where ragged dimensions have size `None`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "M2Wzx4JEIvmb"
},
"outputs": [],
"source": [
"tf.ragged.constant([[\"Hi\"], [\"How\", \"are\", \"you\"]]).shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "G9tfJOeFlijE"
},
"source": [
"The method `tf.RaggedTensor.bounding_shape` can be used to find a tight\n",
"bounding shape for a given `RaggedTensor`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5DHaqXHxlWi0"
},
"outputs": [],
"source": [
"print(tf.ragged.constant([[\"Hi\"], [\"How\", \"are\", \"you\"]]).bounding_shape())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "V8e7x95UcLS6"
},
"source": [
"## Ragged vs sparse\n",
"\n",
"A ragged tensor should *not* be thought of as a type of sparse tensor. In particular, sparse tensors are *efficient encodings for `tf.Tensor`* that model the same data in a compact format; but ragged tensor is an *extension to `tf.Tensor`* that models an expanded class of data. This difference is crucial when defining operations:\n",
"\n",
"- Applying an op to a sparse or dense tensor should always give the same result.\n",
"- Applying an op to a ragged or sparse tensor may give different results.\n",
"\n",
"As an illustrative example, consider how array operations such as `concat`, `stack`, and `tile` are defined for ragged vs. sparse tensors. Concatenating ragged tensors joins each row to form a single row with the combined length:\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ush7IGUWLXIn"
},
"outputs": [],
"source": [
"ragged_x = tf.ragged.constant([[\"John\"], [\"a\", \"big\", \"dog\"], [\"my\", \"cat\"]])\n",
"ragged_y = tf.ragged.constant([[\"fell\", \"asleep\"], [\"barked\"], [\"is\", \"fuzzy\"]])\n",
"print(tf.concat([ragged_x, ragged_y], axis=1))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pvQzZG8zMoWa"
},
"source": [
"However, concatenating sparse tensors is equivalent to concatenating the corresponding dense tensors, as illustrated by the following example (where Ø indicates missing values):\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "eTIhGayQL0gI"
},
"outputs": [],
"source": [
"sparse_x = ragged_x.to_sparse()\n",
"sparse_y = ragged_y.to_sparse()\n",
"sparse_result = tf.sparse.concat(sp_inputs=[sparse_x, sparse_y], axis=1)\n",
"print(tf.sparse.to_dense(sparse_result, ''))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Vl8eQN8pMuYx"
},
"source": [
"For another example of why this distinction is important, consider the\n",
"definition of “the mean value of each row” for an op such as `tf.reduce_mean`.\n",
"For a ragged tensor, the mean value for a row is the sum of the\n",
"row’s values divided by the row’s width.\n",
"But for a sparse tensor, the mean value for a row is the sum of the\n",
"row’s values divided by the sparse tensor’s overall width (which is\n",
"greater than or equal to the width of the longest row).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "u4yjxcK7IPXc"
},
"source": [
"## TensorFlow APIs"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VoZGwFQjIYU5"
},
"source": [
"### Keras\n",
"\n",
"[tf.keras](https://www.tensorflow.org/guide/keras) is TensorFlow's high-level API for building and training deep learning models. Ragged tensors may be passed as inputs to a Keras model by setting `ragged=True` on `tf.keras.Input` or `tf.keras.layers.InputLayer`. Ragged tensors may also be passed between Keras layers, and returned by Keras models. The following example shows a toy LSTM model that is trained using ragged tensors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pHls7hQVJlk5"
},
"outputs": [],
"source": [
"# Task: predict whether each sentence is a question or not.\n",
"sentences = tf.constant(\n",
" ['What makes you think she is a witch?',\n",
" 'She turned me into a newt.',\n",
" 'A newt?',\n",
" 'Well, I got better.'])\n",
"is_question = tf.constant([True, False, True, False])\n",
"\n",
"# Preprocess the input strings.\n",
"hash_buckets = 1000\n",
"words = tf.strings.split(sentences, ' ')\n",
"hashed_words = tf.strings.to_hash_bucket_fast(words, hash_buckets)\n",
"\n",
"# Build the Keras model.\n",
"keras_model = tf.keras.Sequential([\n",
" tf.keras.layers.Input(shape=[None], dtype=tf.int64, ragged=True),\n",
" tf.keras.layers.Embedding(hash_buckets, 16),\n",
" tf.keras.layers.LSTM(32, use_bias=False),\n",
" tf.keras.layers.Dense(32),\n",
" tf.keras.layers.Activation(tf.nn.relu),\n",
" tf.keras.layers.Dense(1)\n",
"])\n",
"\n",
"keras_model.compile(loss='binary_crossentropy', optimizer='rmsprop')\n",
"keras_model.fit(hashed_words, is_question, epochs=5)\n",
"print(keras_model.predict(hashed_words))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8B_sdlt6Ij61"
},
"source": [
"### tf.Example\n",
"\n",
"[tf.Example](https://www.tensorflow.org/tutorials/load_data/tfrecord) is a standard [protobuf](https://developers.google.com/protocol-buffers/) encoding for TensorFlow data. Data encoded with `tf.Example`s often includes variable-length features. For example, the following code defines a batch of four `tf.Example` messages with different feature lengths:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xsiglYM7TXGr"
},
"outputs": [],
"source": [
"import google.protobuf.text_format as pbtext\n",
"\n",
"def build_tf_example(s):\n",
" return pbtext.Merge(s, tf.train.Example()).SerializeToString()\n",
"\n",
"example_batch = [\n",
" build_tf_example(r'''\n",
" features {\n",
" feature {key: \"colors\" value {bytes_list {value: [\"red\", \"blue\"]} } }\n",
" feature {key: \"lengths\" value {int64_list {value: [7]} } } }'''),\n",
" build_tf_example(r'''\n",
" features {\n",
" feature {key: \"colors\" value {bytes_list {value: [\"orange\"]} } }\n",
" feature {key: \"lengths\" value {int64_list {value: []} } } }'''),\n",
" build_tf_example(r'''\n",
" features {\n",
" feature {key: \"colors\" value {bytes_list {value: [\"black\", \"yellow\"]} } }\n",
" feature {key: \"lengths\" value {int64_list {value: [1, 3]} } } }'''),\n",
" build_tf_example(r'''\n",
" features {\n",
" feature {key: \"colors\" value {bytes_list {value: [\"green\"]} } }\n",
" feature {key: \"lengths\" value {int64_list {value: [3, 5, 2]} } } }''')]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "szUuXFvtUL2o"
},
"source": [
"You can parse this encoded data using `tf.io.parse_example`, which takes a tensor of serialized strings and a feature specification dictionary, and returns a dictionary mapping feature names to tensors. To read the variable-length features into ragged tensors, you simply use `tf.io.RaggedFeature` in the feature specification dictionary:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xcdaIbYVT4mo"
},
"outputs": [],
"source": [
"feature_specification = {\n",
" 'colors': tf.io.RaggedFeature(tf.string),\n",
" 'lengths': tf.io.RaggedFeature(tf.int64),\n",
"}\n",
"feature_tensors = tf.io.parse_example(example_batch, feature_specification)\n",
"for name, value in feature_tensors.items():\n",
" print(\"{}={}\".format(name, value))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IK9X_8rXVr8h"
},
"source": [
"`tf.io.RaggedFeature` can also be used to read features with multiple ragged dimensions. For details, refer to the [API documentation](https://www.tensorflow.org/api_docs/python/tf/io/RaggedFeature)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UJowRhlxIX0R"
},
"source": [
"### Datasets\n",
"\n",
"[tf.data](https://www.tensorflow.org/guide/data) is an API that enables you to build complex input pipelines from simple, reusable pieces. Its core data structure is `tf.data.Dataset`, which represents a sequence of elements, in which each element consists of one or more components. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fBml1m2G2vO9"
},
"outputs": [],
"source": [
"# Helper function used to print datasets in the examples below.\n",
"def print_dictionary_dataset(dataset):\n",
" for i, element in enumerate(dataset):\n",
" print(\"Element {}:\".format(i))\n",
" for (feature_name, feature_value) in element.items():\n",
" print('{:>14} = {}'.format(feature_name, feature_value))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gEu_H1Sp2jz1"
},
"source": [
"#### Building Datasets with ragged tensors\n",
"\n",
"Datasets can be built from ragged tensors using the same methods that are used to build them from `tf.Tensor`s or NumPy `array`s, such as `Dataset.from_tensor_slices`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BuelF_y2mEq9"
},
"outputs": [],
"source": [
"dataset = tf.data.Dataset.from_tensor_slices(feature_tensors)\n",
"print_dictionary_dataset(dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mC-QNkJc56De"
},
"source": [
"Note: `Dataset.from_generator` does not support ragged tensors yet, but support will be added soon."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K0UKvBLf1VMu"
},
"source": [
"#### Batching and unbatching Datasets with ragged tensors\n",
"\n",
"Datasets with ragged tensors can be batched (which combines *n* consecutive elements into a single elements) using the `Dataset.batch` method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lk62aRz63IZn"
},
"outputs": [],
"source": [
"batched_dataset = dataset.batch(2)\n",
"print_dictionary_dataset(batched_dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NLSGiYEQ5A8N"
},
"source": [
"Conversely, a batched dataset can be transformed into a flat dataset using `Dataset.unbatch`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CxLlaPw_5Je4"
},
"outputs": [],
"source": [
"unbatched_dataset = batched_dataset.unbatch()\n",
"print_dictionary_dataset(unbatched_dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YzpLQFh33q0N"
},
"source": [
"#### Batching Datasets with variable-length non-ragged tensors\n",
"\n",
"If you have a Dataset that contains non-ragged tensors, and tensor lengths vary across elements, then you can batch those non-ragged tensors into ragged tensors by applying the `dense_to_ragged_batch` transformation:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PYnhERwh3_mf"
},
"outputs": [],
"source": [
"non_ragged_dataset = tf.data.Dataset.from_tensor_slices([1, 5, 3, 2, 8])\n",
"non_ragged_dataset = non_ragged_dataset.map(tf.range)\n",
"batched_non_ragged_dataset = non_ragged_dataset.apply(\n",
" tf.data.experimental.dense_to_ragged_batch(2))\n",
"for element in batched_non_ragged_dataset:\n",
" print(element)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nXFPeE-CzJ-s"
},
"source": [
"#### Transforming Datasets with ragged tensors\n",
"\n",
"You can also create or transform ragged tensors in Datasets using `Dataset.map`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ios1GuG-pf9U"
},
"outputs": [],
"source": [
"def transform_lengths(features):\n",
" return {\n",
" 'mean_length': tf.math.reduce_mean(features['lengths']),\n",
" 'length_ranges': tf.ragged.range(features['lengths'])}\n",
"transformed_dataset = dataset.map(transform_lengths)\n",
"print_dictionary_dataset(transformed_dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WD2lWw3fIXrg"
},
"source": [
"### tf.function\n",
"\n",
"[tf.function](https://www.tensorflow.org/guide/function) is a decorator that precomputes TensorFlow graphs for Python functions, which can substantially improve the performance of your TensorFlow code. Ragged tensors can be used transparently with `@tf.function`-decorated functions. For example, the following function works with both ragged and non-ragged tensors:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PfyxgVaj_8tl"
},
"outputs": [],
"source": [
"@tf.function\n",
"def make_palindrome(x, axis):\n",
" return tf.concat([x, tf.reverse(x, [axis])], axis)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vcZdzvEnDEt0"
},
"outputs": [],
"source": [
"make_palindrome(tf.constant([[1, 2], [3, 4], [5, 6]]), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4WfCMIgdDMxj"
},
"outputs": [],
"source": [
"make_palindrome(tf.ragged.constant([[1, 2], [3], [4, 5, 6]]), axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "X2p69YPOBUz8"
},
"source": [
"If you wish to explicitly specify the `input_signature` for the `tf.function`, then you can do so using `tf.RaggedTensorSpec`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "k6-hkhdDBk6G"
},
"outputs": [],
"source": [
"@tf.function(\n",
" input_signature=[tf.RaggedTensorSpec(shape=[None, None], dtype=tf.int32)])\n",
"def max_and_min(rt):\n",
" return (tf.math.reduce_max(rt, axis=-1), tf.math.reduce_min(rt, axis=-1))\n",
"\n",
"max_and_min(tf.ragged.constant([[1, 2], [3], [4, 5, 6]]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fSs-7E0VD85q"
},
"source": [
"#### Concrete functions\n",
"\n",
"[Concrete functions](https://www.tensorflow.org/guide/function#obtaining_concrete_functions) encapsulate individual traced graphs that are built by `tf.function`. Ragged tensors can be used transparently with concrete functions.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yyJeXJ4wFWox"
},
"outputs": [],
"source": [
"@tf.function\n",
"def increment(x):\n",
" return x + 1\n",
"\n",
"rt = tf.ragged.constant([[1, 2], [3], [4, 5, 6]])\n",
"cf = increment.get_concrete_function(rt)\n",
"print(cf(rt))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iYLyPlatIXhh"
},
"source": [
"### SavedModels\n",
"\n",
"A [SavedModel](https://www.tensorflow.org/guide/saved_model) is a serialized TensorFlow program, including both weights and computation. It can be built from a Keras model or from a custom model. In either case, ragged tensors can be used transparently with the functions and methods defined by a SavedModel.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "98VpBSdOgWqL"
},
"source": [
"#### Example: saving a Keras model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "D-Dg9w7Je5pU"
},
"outputs": [],
"source": [
"import tempfile\n",
"\n",
"keras_module_path = tempfile.mkdtemp()\n",
"tf.saved_model.save(keras_model, keras_module_path)\n",
"imported_model = tf.saved_model.load(keras_module_path)\n",
"imported_model(hashed_words)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9-7k-E92gaoR"
},
"source": [
"#### Example: saving a custom model\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Sfem1ESrdGzX"
},
"outputs": [],
"source": [
"class CustomModule(tf.Module):\n",
" def __init__(self, variable_value):\n",
" super(CustomModule, self).__init__()\n",
" self.v = tf.Variable(variable_value)\n",
"\n",
" @tf.function\n",
" def grow(self, x):\n",
" return x * self.v\n",
"\n",
"module = CustomModule(100.0)\n",
"\n",
"# Before saving a custom model, you must ensure that concrete functions are\n",
"# built for each input signature that you will need.\n",
"module.grow.get_concrete_function(tf.RaggedTensorSpec(shape=[None, None],\n",
" dtype=tf.float32))\n",
"\n",
"custom_module_path = tempfile.mkdtemp()\n",
"tf.saved_model.save(module, custom_module_path)\n",
"imported_model = tf.saved_model.load(custom_module_path)\n",
"imported_model.grow(tf.ragged.constant([[1.0, 4.0, 3.0], [2.0]]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SAxis5KBhrBN"
},
"source": [
"Note: SavedModel [signatures](https://www.tensorflow.org/guide/saved_model#specifying_signatures_during_export) are concrete functions. As discussed in the section on Concrete Functions above, ragged tensors are only handled correctly by concrete functions starting with TensorFlow 2.3. If you need to use SavedModel signatures in a previous version of TensorFlow, then it's recommended that you decompose the ragged tensor into its component tensors."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cRcHzS6pcHYC"
},
"source": [
"## Overloaded operators\n",
"\n",
"The `RaggedTensor` class overloads the standard Python arithmetic and comparison operators, making it easy to perform basic elementwise math:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "skScd37P-PVu"
},
"outputs": [],
"source": [
"x = tf.ragged.constant([[1, 2], [3], [4, 5, 6]])\n",
"y = tf.ragged.constant([[1, 1], [2], [3, 3, 3]])\n",
"print(x + y)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XEGgbZHV-PVw"
},
"source": [
"Since the overloaded operators perform elementwise computations, the inputs to all binary operations must have the same shape or be broadcastable to the same shape. In the simplest broadcasting case, a single scalar is combined elementwise with each value in a ragged tensor:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IYybEEWc-PVx"
},
"outputs": [],
"source": [
"x = tf.ragged.constant([[1, 2], [3], [4, 5, 6]])\n",
"print(x + 3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "okGb9dIi-PVz"
},
"source": [
"For a discussion of more advanced cases, check the section on **Broadcasting**.\n",
"\n",
"Ragged tensors overload the same set of operators as normal `Tensor`s: the unary operators `-`, `~`, and `abs()`; and the binary operators `+`, `-`, `*`, `/`, `//`, `%`, `**`, `&`, `|`, `^`, `==`, `<`, `<=`, `>`, and `>=`.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f2anbs6ZnFtl"
},
"source": [
"## Indexing\n",
"\n",
"Ragged tensors support Python-style indexing, including multidimensional indexing and slicing. The following examples demonstrate ragged tensor indexing with a 2D and a 3D ragged tensor."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XuEwmC3t_ITL"
},
"source": [
"### Indexing examples: 2D ragged tensor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MbSRZRDz-PV1"
},
"outputs": [],
"source": [
"queries = tf.ragged.constant(\n",
" [['Who', 'is', 'George', 'Washington'],\n",
" ['What', 'is', 'the', 'weather', 'tomorrow'],\n",
" ['Goodnight']])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2HRs2xhh-vZE"
},
"outputs": [],
"source": [
"print(queries[1]) # A single query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EFfjZV7YA3UH"
},
"outputs": [],
"source": [
"print(queries[1, 2]) # A single word"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VISRPQSdA3xn"
},
"outputs": [],
"source": [
"print(queries[1:]) # Everything but the first row"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "J1PpSyKQBMng"
},
"outputs": [],
"source": [
"print(queries[:, :3]) # The first 3 words of each query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ixrhHmJBeidy"
},
"outputs": [],
"source": [
"print(queries[:, -2:]) # The last 2 words of each query"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cnOP6Vza-PV4"
},
"source": [
"### Indexing examples: 3D ragged tensor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8VbqbKcE-PV6"
},
"outputs": [],
"source": [
"rt = tf.ragged.constant([[[1, 2, 3], [4]],\n",
" [[5], [], [6]],\n",
" [[7]],\n",
" [[8, 9], [10]]])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "f9WPVWf4grVp"
},
"outputs": [],
"source": [
"print(rt[1]) # Second row (2D RaggedTensor)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ad8FGJoABjQH"
},
"outputs": [],
"source": [
"print(rt[3, 0]) # First element of fourth row (1D Tensor)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MPPr-a-bBjFE"
},
"outputs": [],
"source": [
"print(rt[:, 1:3]) # Items 1-3 of each row (3D RaggedTensor)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6SIDeoIUBi4z"
},
"outputs": [],
"source": [
"print(rt[:, -1:]) # Last item of each row (3D RaggedTensor)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_d3nBh1GnWvU"
},
"source": [
"`RaggedTensor`s support multidimensional indexing and slicing with one restriction: indexing into a ragged dimension is not allowed. This case is problematic because the indicated value may exist in some rows but not others. In such cases, it's not obvious whether you should (1) raise an `IndexError`; (2) use a default value; or (3) skip that value and return a tensor with fewer rows than you started with. Following the [guiding principles of Python](https://www.python.org/dev/peps/pep-0020/) (\"In the face of ambiguity, refuse the temptation to guess\"), this operation is currently disallowed."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IsWKETULAJbN"
},
"source": [
"## Tensor type conversion\n",
"\n",
"The `RaggedTensor` class defines methods that can be used to convert\n",
"between `RaggedTensor`s and `tf.Tensor`s or `tf.SparseTensors`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "INnfmZGcBoU_"
},
"outputs": [],
"source": [
"ragged_sentences = tf.ragged.constant([\n",
" ['Hi'], ['Welcome', 'to', 'the', 'fair'], ['Have', 'fun']])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "__iJ4iXtkGOx"
},
"outputs": [],
"source": [
"# RaggedTensor -> Tensor\n",
"print(ragged_sentences.to_tensor(default_value='', shape=[None, 10]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-rfiyYqne8QN"
},
"outputs": [],
"source": [
"# Tensor -> RaggedTensor\n",
"x = [[1, 3, -1, -1], [2, -1, -1, -1], [4, 5, 8, 9]]\n",
"print(tf.RaggedTensor.from_tensor(x, padding=-1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "41WAZLXNnbwH"
},
"outputs": [],
"source": [
"#RaggedTensor -> SparseTensor\n",
"print(ragged_sentences.to_sparse())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "S8MkYo2hfVhj"
},
"outputs": [],
"source": [
"# SparseTensor -> RaggedTensor\n",
"st = tf.SparseTensor(indices=[[0, 0], [2, 0], [2, 1]],\n",
" values=['a', 'b', 'c'],\n",
" dense_shape=[3, 3])\n",
"print(tf.RaggedTensor.from_sparse(st))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qx025sNMkAHH"
},
"source": [
"## Evaluating ragged tensors\n",
"\n",
"To access the values in a ragged tensor, you can:\n",
"\n",
"1. Use `tf.RaggedTensor.to_list` to convert the ragged tensor to a nested Python list.\n",
"2. Use `tf.RaggedTensor.numpy` to convert the ragged tensor to a NumPy array whose values are nested NumPy arrays.\n",
"3. Decompose the ragged tensor into its components, using the `tf.RaggedTensor.values` and `tf.RaggedTensor.row_splits` properties, or row-partitioning methods such as `tf.RaggedTensor.row_lengths` and `tf.RaggedTensor.value_rowids`.\n",
"4. Use Python indexing to select values from the ragged tensor.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uMm1WMkc-PV_"
},
"outputs": [],
"source": [
"rt = tf.ragged.constant([[1, 2], [3, 4, 5], [6], [], [7]])\n",
"print(\"Python list:\", rt.to_list())\n",
"print(\"NumPy array:\", rt.numpy())\n",
"print(\"Values:\", rt.values.numpy())\n",
"print(\"Splits:\", rt.row_splits.numpy())\n",
"print(\"Indexed value:\", rt[1].numpy())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "J87jMZa0M_YW"
},
"source": [
"## Ragged Shapes\n",
"\n",
"The shape of a tensor specifies the size of each axis. For example, the shape of `[[1, 2], [3, 4], [5, 6]]` is `[3, 2]`, since there are 3 rows and 2 columns. TensorFlow has two separate but related ways to describe shapes:\n",
"\n",
"* ***static shape***: Information about axis sizes that is known statically (e.g., while tracing a `tf.function`). May be partially specified.\n",
"\n",
"* ***dynamic shape***: Runtime information about the axis sizes."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IOETE_OLPLZo"
},
"source": [
"### Static shape\n",
"\n",
"A Tensor's static shape contains information about its axis sizes that is known at graph-construction time. For both `tf.Tensor` and `tf.RaggedTensor`, it is available using the `.shape` property, and is encoded using `tf.TensorShape`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "btGDjT4uNgQy"
},
"outputs": [],
"source": [
"x = tf.constant([[1, 2], [3, 4], [5, 6]])\n",
"x.shape # shape of a tf.tensor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "__OgvmrGPEjq"
},
"outputs": [],
"source": [
"rt = tf.ragged.constant([[1], [2, 3], [], [4]])\n",
"rt.shape # shape of a tf.RaggedTensor"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9EWnQd3qPWaw"
},
"source": [
"The static shape of a ragged dimension is always `None` (i.e., unspecified). However, the inverse is not true -- if a `TensorShape` dimension is `None`, then that could indicate that the dimension is ragged, *or* it could indicate that the dimension is uniform but that its size is not statically known."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "75E9YXYMNfne"
},
"source": [
"### Dynamic shape\n",
"\n",
"A tensor's dynamic shape contains information about its axis sizes that is known when the graph is run. It is constructed using the `tf.shape` operation. For `tf.Tensor`, `tf.shape` returns the shape as a 1D integer `Tensor`, where `tf.shape(x)[i]` is the size of axis `i`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kWJ7Cn1EQTD_"
},
"outputs": [],
"source": [
"x = tf.constant([['a', 'b'], ['c', 'd'], ['e', 'f']])\n",
"tf.shape(x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BeZEfxwmRcSv"
},
"source": [
"However, a 1D `Tensor` is not expressive enough to describe the shape of a `tf.RaggedTensor`. Instead, the dynamic shape for ragged tensors is encoded using a dedicated type, `tf.experimental.DynamicRaggedShape`. In the following example, the `DynamicRaggedShape` returned by `tf.shape(rt)` indicates that the ragged tensor has 4 rows, with lengths 1, 3, 0, and 2:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nZc2wqgQQUFU"
},
"outputs": [],
"source": [
"rt = tf.ragged.constant([[1], [2, 3, 4], [], [5, 6]])\n",
"rt_shape = tf.shape(rt)\n",
"print(rt_shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EphU60YvTf98"
},
"source": [
"#### Dynamic shape: operations\n",
"\n",
"`DynamicRaggedShape`s can be used with most TensorFlow ops that expect shapes, including `tf.reshape`, `tf.zeros`, `tf.ones`. `tf.fill`, `tf.broadcast_dynamic_shape`, and `tf.broadcast_to`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pclAODLXT6Gr"
},
"outputs": [],
"source": [
"print(f\"tf.reshape(x, rt_shape) = {tf.reshape(x, rt_shape)}\")\n",
"print(f\"tf.zeros(rt_shape) = {tf.zeros(rt_shape)}\")\n",
"print(f\"tf.ones(rt_shape) = {tf.ones(rt_shape)}\")\n",
"print(f\"tf.fill(rt_shape, 9) = {tf.fill(rt_shape, 'x')}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rNP_3_btRAHj"
},
"source": [
"#### Dynamic shape: indexing and slicing\n",
"\n",
"`DynamicRaggedShape` can be also be indexed to get the sizes of uniform dimensions. For example, we can find the number of rows in a raggedtensor using `tf.shape(rt)[0]` (just as we would for a non-ragged tensor):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MzQvPhsxS6HN"
},
"outputs": [],
"source": [
"rt_shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wvr2iT6zS_e8"
},
"source": [
"However, it is an error to use indexing to try to retrieve the size of a ragged dimension, since it doesn't have a single size. (Since `RaggedTensor` keeps track of which axes are ragged, this error is only thrown during eager execution or when tracing a `tf.function`; it will never be thrown when executing a concrete function.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HgGMk0LeTGik"
},
"outputs": [],
"source": [
"try:\n",
" rt_shape[1]\n",
"except ValueError as e:\n",
" print(\"Got expected ValueError:\", e)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5QUsdawGU0SM"
},
"source": [
"`DynamicRaggedShape`s can also be sliced, as long as the slice either begins with axis `0`, or contains only dense dimensions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "APT72EaBU70t"
},
"outputs": [],
"source": [
"rt_shape[:1]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a-Wl9IrQXcdY"
},
"source": [
"#### Dynamic shape: encoding\n",
"\n",
"`DynamicRaggedShape` is encoded using two fields:\n",
"\n",
"* `inner_shape`: An integer vector giving the shape of a dense `tf.Tensor`.\n",
"* `row_partitions`: A list of `tf.experimental.RowPartition` objects, describing how the outermost dimension of that inner shape should be partitioned to add ragged axes.\n",
"\n",
"For more information about row partitions, see the \"RaggedTensor encoding\" section below, and the API docs for `tf.experimental.RowPartition`."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jfeY9tTcV_zL"
},
"source": [
"#### Dynamic shape: construction\n",
"\n",
"`DynamicRaggedShape` is most often constructed by applying `tf.shape` to a `RaggedTensor`, but it can also be constructed directly:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NSRgD667WwIZ"
},
"outputs": [],
"source": [
"tf.experimental.DynamicRaggedShape(\n",
" row_partitions=[tf.experimental.RowPartition.from_row_lengths([5, 3, 2])],\n",
" inner_shape=[10, 8])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EjzVjs9MXIIA"
},
"source": [
"If the lengths of all rows are known statically, `DynamicRaggedShape.from_lengths` can also be used to construct a dynamic ragged shape. (This is mostly useful for testing and demonstration code, since it's rare for the lengths of ragged dimensions to be known statically).\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gMxCzADUYIjY"
},
"outputs": [],
"source": [
"tf.experimental.DynamicRaggedShape.from_lengths([4, (2, 1, 0, 8), 12])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EdljbNPq-PWS"
},
"source": [
"### Broadcasting\n",
"\n",
"Broadcasting is the process of making tensors with different shapes have compatible shapes for elementwise operations. For more background on broadcasting, refer to:\n",
"\n",
"- [NumPy: Broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n",
"- `tf.broadcast_dynamic_shape`\n",
"- `tf.broadcast_to`\n",
"\n",
"The basic steps for broadcasting two inputs `x` and `y` to have compatible shapes are:\n",
"\n",
"1. If `x` and `y` do not have the same number of dimensions, then add outer dimensions (with size 1) until they do.\n",
"\n",
"2. For each dimension where `x` and `y` have different sizes:\n",
"\n",
"- If `x` or `y` have size `1` in dimension `d`, then repeat its values across dimension `d` to match the other input's size.\n",
"- Otherwise, raise an exception (`x` and `y` are not broadcast compatible).\n",
"\n",
"Where the size of a tensor in a uniform dimension is a single number (the size of slices across that dimension); and the size of a tensor in a ragged dimension is a list of slice lengths (for all slices across that dimension)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-S2hOUWx-PWU"
},
"source": [
"#### Broadcasting examples"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0n095XdR-PWU"
},
"outputs": [],
"source": [
"# x (2D ragged): 2 x (num_rows)\n",
"# y (scalar)\n",
"# result (2D ragged): 2 x (num_rows)\n",
"x = tf.ragged.constant([[1, 2], [3]])\n",
"y = 3\n",
"print(x + y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0SVYk5AP-PWW"
},
"outputs": [],
"source": [
"# x (2d ragged): 3 x (num_rows)\n",
"# y (2d tensor): 3 x 1\n",
"# Result (2d ragged): 3 x (num_rows)\n",
"x = tf.ragged.constant(\n",
" [[10, 87, 12],\n",
" [19, 53],\n",
" [12, 32]])\n",
"y = [[1000], [2000], [3000]]\n",
"print(x + y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MsfBMD80s8Ux"
},
"outputs": [],
"source": [
"# x (3d ragged): 2 x (r1) x 2\n",
"# y (2d ragged): 1 x 1\n",
"# Result (3d ragged): 2 x (r1) x 2\n",
"x = tf.ragged.constant(\n",
" [[[1, 2], [3, 4], [5, 6]],\n",
" [[7, 8]]],\n",
" ragged_rank=1)\n",
"y = tf.constant([[10]])\n",
"print(x + y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rEj5QVfnva0t"
},
"outputs": [],
"source": [
"# x (3d ragged): 2 x (r1) x (r2) x 1\n",
"# y (1d tensor): 3\n",
"# Result (3d ragged): 2 x (r1) x (r2) x 3\n",
"x = tf.ragged.constant(\n",
" [\n",
" [\n",
" [[1], [2]],\n",
" [],\n",
" [[3]],\n",
" [[4]],\n",
" ],\n",
" [\n",
" [[5], [6]],\n",
" [[7]]\n",
" ]\n",
" ],\n",
" ragged_rank=2)\n",
"y = tf.constant([10, 20, 30])\n",
"print(x + y)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uennZ64Aqftb"
},
"source": [
"Here are some examples of shapes that do not broadcast:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UpI0FlfL4Eim"
},
"outputs": [],
"source": [
"# x (2d ragged): 3 x (r1)\n",
"# y (2d tensor): 3 x 4 # trailing dimensions do not match\n",
"x = tf.ragged.constant([[1, 2], [3, 4, 5, 6], [7]])\n",
"y = tf.constant([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\n",
"try:\n",
" x + y\n",
"except tf.errors.InvalidArgumentError as exception:\n",
" print(exception)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qGq1zOT4zMoc"
},
"outputs": [],
"source": [
"# x (2d ragged): 3 x (r1)\n",
"# y (2d ragged): 3 x (r2) # ragged dimensions do not match.\n",
"x = tf.ragged.constant([[1, 2, 3], [4], [5, 6]])\n",
"y = tf.ragged.constant([[10, 20], [30, 40], [50]])\n",
"try:\n",
" x + y\n",
"except tf.errors.InvalidArgumentError as exception:\n",
" print(exception)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CvLae5vMqeji"
},
"outputs": [],
"source": [
"# x (3d ragged): 3 x (r1) x 2\n",
"# y (3d ragged): 3 x (r1) x 3 # trailing dimensions do not match\n",
"x = tf.ragged.constant([[[1, 2], [3, 4], [5, 6]],\n",
" [[7, 8], [9, 10]]])\n",
"y = tf.ragged.constant([[[1, 2, 0], [3, 4, 0], [5, 6, 0]],\n",
" [[7, 8, 0], [9, 10, 0]]])\n",
"try:\n",
" x + y\n",
"except tf.errors.InvalidArgumentError as exception:\n",
" print(exception)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "m0wQkLfV-PWa"
},
"source": [
"## RaggedTensor encoding\n",
"\n",
"Ragged tensors are encoded using the `RaggedTensor` class. Internally, each `RaggedTensor` consists of:\n",
"\n",
"- A `values` tensor, which concatenates the variable-length rows into a flattened list.\n",
"- A `row_partition`, which indicates how those flattened values are divided into rows.\n",
"\n",
"\n",
"\n",
"The `row_partition` can be stored using four different encodings:\n",
"\n",
"- `row_splits` is an integer vector specifying the split points between rows.\n",
"- `value_rowids` is an integer vector specifying the row index for each value.\n",
"- `row_lengths` is an integer vector specifying the length of each row.\n",
"- `uniform_row_length` is an integer scalar specifying a single length for all rows.\n",
"\n",
"\n",
"\n",
"An integer scalar `nrows` can also be included in the `row_partition` encoding to account for empty trailing rows with `value_rowids` or empty rows with `uniform_row_length`.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MrLgMu0gPuo-"
},
"outputs": [],
"source": [
"rt = tf.RaggedTensor.from_row_splits(\n",
" values=[3, 1, 4, 1, 5, 9, 2],\n",
" row_splits=[0, 4, 4, 6, 7])\n",
"print(rt)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wEfZOKwN1Ra_"
},
"source": [
"The choice of which encoding to use for row partitions is managed internally by ragged tensors to improve efficiency in some contexts. In particular, some of the advantages and disadvantages of the different row-partitioning schemes are:\n",
"\n",
"- **Efficient indexing**: The `row_splits` encoding enables constant-time indexing and slicing into ragged tensors.\n",
"- **Efficient concatenation**: The `row_lengths` encoding is more efficient when concatenating ragged tensors, since row lengths do not change when two tensors are concatenated together.\n",
"- **Small encoding size**: The `value_rowids` encoding is more efficient when storing ragged tensors that have a large number of empty rows, since the size of the tensor depends only on the total number of values. On the other hand, the `row_splits` and `row_lengths` encodings are more efficient when storing ragged tensors with longer rows, since they require only one scalar value for each row.\n",
"- **Compatibility**: The `value_rowids` scheme matches the [segmentation](https://www.tensorflow.org/api_docs/python/tf/math#about_segmentation) format used by operations, such as `tf.segment_sum`. The `row_limits` scheme matches the format used by ops such as `tf.sequence_mask`.\n",
"- **Uniform dimensions**: As discussed below, the `uniform_row_length` encoding is used to encode ragged tensors with uniform dimensions."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bpB7xKoUPtU6"
},
"source": [
"### Multiple ragged dimensions\n",
"\n",
"A ragged tensor with multiple ragged dimensions is encoded by using a nested `RaggedTensor` for the `values` tensor. Each nested `RaggedTensor` adds a single ragged dimension.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yy3IGT2a-PWb"
},
"outputs": [],
"source": [
"rt = tf.RaggedTensor.from_row_splits(\n",
" values=tf.RaggedTensor.from_row_splits(\n",
" values=[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
" row_splits=[0, 3, 3, 5, 9, 10]),\n",
" row_splits=[0, 1, 1, 5])\n",
"print(rt)\n",
"print(\"Shape: {}\".format(rt.shape))\n",
"print(\"Number of partitioned dimensions: {}\".format(rt.ragged_rank))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5HqEEDzk-PWc"
},
"source": [
"The factory function `tf.RaggedTensor.from_nested_row_splits` may be used to construct a RaggedTensor with multiple ragged dimensions directly by providing a list of `row_splits` tensors:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "AKYhtFcT-PWd"
},
"outputs": [],
"source": [
"rt = tf.RaggedTensor.from_nested_row_splits(\n",
" flat_values=[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
" nested_row_splits=([0, 1, 1, 5], [0, 3, 3, 5, 9, 10]))\n",
"print(rt)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BqAfbkAC56m0"
},
"source": [
"### Ragged rank and flat values\n",
"\n",
"A ragged tensor's ***ragged rank*** is the number of times that the underlying `values` tensor has been partitioned (i.e. the nesting depth of `RaggedTensor` objects). The innermost `values` tensor is known as its ***flat_values***. In the following example, `conversations` has ragged_rank=3, and its `flat_values` is a 1D `Tensor` with 24 strings:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BXp-Tt2bClem"
},
"outputs": [],
"source": [
"# shape = [batch, (paragraph), (sentence), (word)]\n",
"conversations = tf.ragged.constant(\n",
" [[[[\"I\", \"like\", \"ragged\", \"tensors.\"]],\n",
" [[\"Oh\", \"yeah?\"], [\"What\", \"can\", \"you\", \"use\", \"them\", \"for?\"]],\n",
" [[\"Processing\", \"variable\", \"length\", \"data!\"]]],\n",
" [[[\"I\", \"like\", \"cheese.\"], [\"Do\", \"you?\"]],\n",
" [[\"Yes.\"], [\"I\", \"do.\"]]]])\n",
"conversations.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DZUMrgxXFd5s"
},
"outputs": [],
"source": [
"assert conversations.ragged_rank == len(conversations.nested_row_splits)\n",
"conversations.ragged_rank # Number of partitioned dimensions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xXLSNpS0Fdvp"
},
"outputs": [],
"source": [
"conversations.flat_values.numpy()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uba2EnAY-PWf"
},
"source": [
"### Uniform inner dimensions\n",
"\n",
"Ragged tensors with uniform inner dimensions are encoded by using a\n",
"multidimensional `tf.Tensor` for the flat_values (i.e., the innermost `values`).\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "z2sHwHdy-PWg"
},
"outputs": [],
"source": [
"rt = tf.RaggedTensor.from_row_splits(\n",
" values=[[1, 3], [0, 0], [1, 3], [5, 3], [3, 3], [1, 2]],\n",
" row_splits=[0, 3, 4, 6])\n",
"print(rt)\n",
"print(\"Shape: {}\".format(rt.shape))\n",
"print(\"Number of partitioned dimensions: {}\".format(rt.ragged_rank))\n",
"print(\"Flat values shape: {}\".format(rt.flat_values.shape))\n",
"print(\"Flat values:\\n{}\".format(rt.flat_values))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WoGRKd50x_qz"
},
"source": [
"### Uniform non-inner dimensions\n",
"\n",
"Ragged tensors with uniform non-inner dimensions are encoded by partitioning rows with `uniform_row_length`.\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "70q1aCKwySgS"
},
"outputs": [],
"source": [
"rt = tf.RaggedTensor.from_uniform_row_length(\n",
" values=tf.RaggedTensor.from_row_splits(\n",
" values=[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
" row_splits=[0, 3, 5, 9, 10]),\n",
" uniform_row_length=2)\n",
"print(rt)\n",
"print(\"Shape: {}\".format(rt.shape))\n",
"print(\"Number of partitioned dimensions: {}\".format(rt.ragged_rank))"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "ragged_tensor.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
paulrevere4/udacity-deep-learning | assignment1/1_notmnist.ipynb | 1 | 120343 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5hIbr52I7Z7U"
},
"source": [
"Deep Learning\n",
"=============\n",
"\n",
"Assignment 1\n",
"------------\n",
"\n",
"The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.\n",
"\n",
"This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used with python experiments. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST."
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"collapsed": false,
"id": "apJbCsBHl-2A"
},
"outputs": [],
"source": [
"# These are all the modules we'll be using later. Make sure you can import them\n",
"# before proceeding further.\n",
"from __future__ import print_function\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os\n",
"import sys\n",
"import tarfile\n",
"from IPython.display import display, Image\n",
"from scipy import ndimage\n",
"from sklearn.linear_model import LogisticRegression\n",
"from six.moves.urllib.request import urlretrieve\n",
"from six.moves import cPickle as pickle\n",
"\n",
"# Config the matplotlib backend as plotting inline in IPython\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "jNWGtZaXn-5j"
},
"source": [
"First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labelled examples. Given these sizes, it should be possible to train models quickly on any machine."
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 186058,
"status": "ok",
"timestamp": 1444485672507,
"user": {
"color": "#1FA15D",
"displayName": "Vincent Vanhoucke",
"isAnonymous": false,
"isMe": true,
"permissionId": "05076109866853157986",
"photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
"sessionId": "2a0a5e044bb03b66",
"userId": "102167687554210253930"
},
"user_tz": 420
},
"id": "EYRJ4ICW6-da",
"outputId": "0d0f85df-155f-4a89-8e7e-ee32df36ec8d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found and verified notMNIST_large.tar.gz\n",
"Found and verified notMNIST_small.tar.gz\n"
]
}
],
"source": [
"url = 'http://commondatastorage.googleapis.com/books1000/'\n",
"last_percent_reported = None\n",
"\n",
"def download_progress_hook(count, blockSize, totalSize):\n",
" \"\"\"A hook to report the progress of a download. This is mostly intended for users with\n",
" slow internet connections. Reports every 5% change in download progress.\n",
" \"\"\"\n",
" global last_percent_reported\n",
" percent = int(count * blockSize * 100 / totalSize)\n",
"\n",
" if last_percent_reported != percent:\n",
" if percent % 5 == 0:\n",
" sys.stdout.write(\"%s%%\" % percent)\n",
" sys.stdout.flush()\n",
" else:\n",
" sys.stdout.write(\".\")\n",
" sys.stdout.flush()\n",
" \n",
" last_percent_reported = percent\n",
" \n",
"def maybe_download(filename, expected_bytes, force=False):\n",
" \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
" if force or not os.path.exists(filename):\n",
" print('Attempting to download:', filename) \n",
" filename, _ = urlretrieve(url + filename, filename, reporthook=download_progress_hook)\n",
" print('\\nDownload Complete!')\n",
" statinfo = os.stat(filename)\n",
" if statinfo.st_size == expected_bytes:\n",
" print('Found and verified', filename)\n",
" else:\n",
" raise Exception(\n",
" 'Failed to verify ' + filename + '. Can you get to it with a browser?')\n",
" return filename\n",
"\n",
"train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)\n",
"test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "cC3p0oEyF8QT"
},
"source": [
"Extract the dataset from the compressed .tar.gz file.\n",
"This should give you a set of directories, labelled A through J."
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 186055,
"status": "ok",
"timestamp": 1444485672525,
"user": {
"color": "#1FA15D",
"displayName": "Vincent Vanhoucke",
"isAnonymous": false,
"isMe": true,
"permissionId": "05076109866853157986",
"photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
"sessionId": "2a0a5e044bb03b66",
"userId": "102167687554210253930"
},
"user_tz": 420
},
"id": "H8CBE-WZ8nmj",
"outputId": "ef6c790c-2513-4b09-962e-27c79390c762"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"notMNIST_large already present - Skipping extraction of notMNIST_large.tar.gz.\n",
"['notMNIST_large/A', 'notMNIST_large/B', 'notMNIST_large/C', 'notMNIST_large/D', 'notMNIST_large/E', 'notMNIST_large/F', 'notMNIST_large/G', 'notMNIST_large/H', 'notMNIST_large/I', 'notMNIST_large/J']\n",
"notMNIST_small already present - Skipping extraction of notMNIST_small.tar.gz.\n",
"['notMNIST_small/A', 'notMNIST_small/B', 'notMNIST_small/C', 'notMNIST_small/D', 'notMNIST_small/E', 'notMNIST_small/F', 'notMNIST_small/G', 'notMNIST_small/H', 'notMNIST_small/I', 'notMNIST_small/J']\n"
]
}
],
"source": [
"num_classes = 10\n",
"np.random.seed(133)\n",
"\n",
"def maybe_extract(filename, force=False):\n",
" root = os.path.splitext(os.path.splitext(filename)[0])[0] # remove .tar.gz\n",
" if os.path.isdir(root) and not force:\n",
" # You may override by setting force=True.\n",
" print('%s already present - Skipping extraction of %s.' % (root, filename))\n",
" else:\n",
" print('Extracting data for %s. This may take a while. Please wait.' % root)\n",
" tar = tarfile.open(filename)\n",
" sys.stdout.flush()\n",
" tar.extractall()\n",
" tar.close()\n",
" data_folders = [\n",
" os.path.join(root, d) for d in sorted(os.listdir(root))\n",
" if os.path.isdir(os.path.join(root, d))]\n",
" if len(data_folders) != num_classes:\n",
" raise Exception(\n",
" 'Expected %d folders, one per class. Found %d instead.' % (\n",
" num_classes, len(data_folders)))\n",
" print(data_folders)\n",
" return data_folders\n",
" \n",
"train_folders = maybe_extract(train_filename)\n",
"test_folders = maybe_extract(test_filename)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4riXK3IoHgx6"
},
"source": [
"---\n",
"Problem 1\n",
"---------\n",
"\n",
"Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAB0ElEQVR4nG3SO2uUQRjF8f/MOxuX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\nSUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAB60lEQVR4nG2RTUjUcRCGn9/8d103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\nnPsguSR3d/fYh25sB+Mv+Nr4jWhQvzcAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABhElEQVR4nHWRv2tUURCFv5l7XxQN\nBIVdLYwGIULAHzFgiAjaqEVsLFJZ2SgoESzt7K1S+AcELPMHxMbKxk4LwSykEFdRRDSQNYu+e2cs\n3Ie7b5+n/eacM/eOUCmaQWtudmb64P7Q/7X7qftcBiiYy7nla3MtrYZdbkcARHOxcv9iADd8AONU\nHLCrT847SVSqKDKHIqBWrK2SCZEhOUUEtfbG5UygLo2otTcXyihjDI1i+myhLMYRoMrj66mZJc3z\njxrqACErDydSQx9AT2duNhtRdvTGlDUbhW+6WH1XTc5eR2f5TyM7H+JkEzTHije9+BOvU0fBeUXc\nXhrrFN5v9Y+efAmrnn1E5ru3CoQDAY59dRuBpa8RVQXQj+vkWmwLgkgQ4PA7T6PBfrd6QeDCDy9r\n9MWdxeNHBAhc+uxp2GzmbmXv7F/v9KZbLvO/xVKy5PdQyNpdXnmtQcVzNjMzA8u0B9dR4pWnb/vD\ntckfVGtFM/adOnP6RGtygt97379sdzrd/Ac0SenCAe20tgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import display, Image\n",
"\n",
"display(Image(filename=\"notMNIST_small/A/Q0NXaWxkV29yZHMtQm9sZEl0YWxpYy50dGY=.png\"))\n",
"display(Image(filename=\"notMNIST_small/B/Q2FsaWd1bGEgUmVndWxhci50dGY=.png\"))\n",
"display(Image(filename=\"notMNIST_small/C/QmVlc2tuZWVzQy5vdGY=.png\"))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PBdkjESPK8tw"
},
"source": [
"Now let's load the data in a more manageable format. Since, depending on your computer setup you might not be able to fit it all in memory, we'll load each class into a separate dataset, store them on disk and curate them independently. Later we'll merge them into a single dataset of manageable size.\n",
"\n",
"We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road. \n",
"\n",
"A few images might not be readable, we'll just skip them."
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 30
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 399874,
"status": "ok",
"timestamp": 1444485886378,
"user": {
"color": "#1FA15D",
"displayName": "Vincent Vanhoucke",
"isAnonymous": false,
"isMe": true,
"permissionId": "05076109866853157986",
"photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
"sessionId": "2a0a5e044bb03b66",
"userId": "102167687554210253930"
},
"user_tz": 420
},
"id": "h7q0XhG3MJdf",
"outputId": "92c391bb-86ff-431d-9ada-315568a19e59"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"notMNIST_large/A.pickle already present - Skipping pickling.\n",
"notMNIST_large/B.pickle already present - Skipping pickling.\n",
"notMNIST_large/C.pickle already present - Skipping pickling.\n",
"notMNIST_large/D.pickle already present - Skipping pickling.\n",
"notMNIST_large/E.pickle already present - Skipping pickling.\n",
"notMNIST_large/F.pickle already present - Skipping pickling.\n",
"notMNIST_large/G.pickle already present - Skipping pickling.\n",
"notMNIST_large/H.pickle already present - Skipping pickling.\n",
"notMNIST_large/I.pickle already present - Skipping pickling.\n",
"notMNIST_large/J.pickle already present - Skipping pickling.\n",
"notMNIST_small/A.pickle already present - Skipping pickling.\n",
"notMNIST_small/B.pickle already present - Skipping pickling.\n",
"notMNIST_small/C.pickle already present - Skipping pickling.\n",
"notMNIST_small/D.pickle already present - Skipping pickling.\n",
"notMNIST_small/E.pickle already present - Skipping pickling.\n",
"notMNIST_small/F.pickle already present - Skipping pickling.\n",
"notMNIST_small/G.pickle already present - Skipping pickling.\n",
"notMNIST_small/H.pickle already present - Skipping pickling.\n",
"notMNIST_small/I.pickle already present - Skipping pickling.\n",
"notMNIST_small/J.pickle already present - Skipping pickling.\n"
]
}
],
"source": [
"image_size = 28 # Pixel width and height.\n",
"pixel_depth = 255.0 # Number of levels per pixel.\n",
"\n",
"def load_letter(folder, min_num_images):\n",
" \"\"\"Load the data for a single letter label.\"\"\"\n",
" image_files = os.listdir(folder)\n",
" dataset = np.ndarray(shape=(len(image_files), image_size, image_size),\n",
" dtype=np.float32)\n",
" print(folder)\n",
" num_images = 0\n",
" for image in image_files:\n",
" image_file = os.path.join(folder, image)\n",
" try:\n",
" image_data = (ndimage.imread(image_file).astype(float) - \n",
" pixel_depth / 2) / pixel_depth\n",
" if image_data.shape != (image_size, image_size):\n",
" raise Exception('Unexpected image shape: %s' % str(image_data.shape))\n",
" dataset[num_images, :, :] = image_data\n",
" num_images = num_images + 1\n",
" except IOError as e:\n",
" print('Could not read:', image_file, ':', e, '- it\\'s ok, skipping.')\n",
" \n",
" dataset = dataset[0:num_images, :, :]\n",
" if num_images < min_num_images:\n",
" raise Exception('Many fewer images than expected: %d < %d' %\n",
" (num_images, min_num_images))\n",
" \n",
" print('Full dataset tensor:', dataset.shape)\n",
" print('Mean:', np.mean(dataset))\n",
" print('Standard deviation:', np.std(dataset))\n",
" return dataset\n",
" \n",
"def maybe_pickle(data_folders, min_num_images_per_class, force=False):\n",
" dataset_names = []\n",
" for folder in data_folders:\n",
" set_filename = folder + '.pickle'\n",
" dataset_names.append(set_filename)\n",
" if os.path.exists(set_filename) and not force:\n",
" # You may override by setting force=True.\n",
" print('%s already present - Skipping pickling.' % set_filename)\n",
" else:\n",
" print('Pickling %s.' % set_filename)\n",
" dataset = load_letter(folder, min_num_images_per_class)\n",
" try:\n",
" with open(set_filename, 'wb') as f:\n",
" pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)\n",
" except Exception as e:\n",
" print('Unable to save data to', set_filename, ':', e)\n",
" \n",
" return dataset_names\n",
"\n",
"train_datasets = maybe_pickle(train_folders, 45000)\n",
"test_datasets = maybe_pickle(test_folders, 1800)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "vUdbskYE2d87"
},
"source": [
"---\n",
"Problem 2\n",
"---------\n",
"\n",
"Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10e9c02b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10f7f7438>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x113cf7a58>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10c82d8d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10c6195c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10e85c128>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10cca3908>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10cb0de48>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10c606c18>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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5iZxi+ImcYviJnGL4iZxi+ImcYviJnGL4iZxin79YKc4N3/PkeLO+8YxHzHpeR5n1Pg33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\n8BM5xfATOcXwEznF8BM5xfATOfV/jOQwYSfmxNwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c4a6cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# print(train_datasets)\n",
"# print(\"\")\n",
"# print(test_datasets)\n",
"for letter_index in range(10):\n",
" pickle_file = train_datasets[letter_index] # index 0 should be all As, 1 = all Bs, etc.\n",
" with open(pickle_file, 'rb') as f:\n",
" letter_set = pickle.load(f) # unpickle\n",
" sample_idx = np.random.randint(len(letter_set)) # pick a random image index\n",
" sample_image = letter_set[sample_idx, :, :] # extract a 2D slice\n",
" plt.figure()\n",
" plt.imshow(sample_image) # display it\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "cYznx5jUwzoO"
},
"source": [
"---\n",
"Problem 3\n",
"---------\n",
"Another check: we expect the data to be balanced across classes. Verify that.\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of A samples: 52909\n",
"Number of B samples: 52911\n",
"Number of C samples: 52912\n",
"Number of D samples: 52911\n",
"Number of E samples: 52912\n",
"Number of F samples: 52912\n",
"Number of G samples: 52912\n",
"Number of H samples: 52912\n",
"Number of I samples: 52912\n",
"Number of J samples: 52911\n",
"\n",
"Average length: 52911.40\n",
"Standard Deviation of lengths 0.92\n"
]
}
],
"source": [
"letters = \"ABCDEFGHIJ\"\n",
"lengths = []\n",
"\n",
"for letter_index in range(10):\n",
" pickle_file = train_datasets[letter_index] # index 0 should be all As, 1 = all Bs, etc.\n",
" with open(pickle_file, 'rb') as f:\n",
" letter_set = pickle.load(f) # unpickle\n",
" length = len(letter_set)\n",
" print(\"Number of %s samples: %d\" %(letters[letter_index], length))\n",
" lengths.append(length)\n",
" \n",
"lengths = np.array(lengths)\n",
"\n",
"print()\n",
"print(\"Average length: %.2f\" %np.average(lengths))\n",
"print(\"Standard Deviation of lengths %.2f\" %np.std(lengths))\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "LA7M7K22ynCt"
},
"source": [
"Merge and prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune `train_size` as needed. The labels will be stored into a separate array of integers 0 through 9.\n",
"\n",
"Also create a validation dataset for hyperparameter tuning."
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 411281,
"status": "ok",
"timestamp": 1444485897869,
"user": {
"color": "#1FA15D",
"displayName": "Vincent Vanhoucke",
"isAnonymous": false,
"isMe": true,
"permissionId": "05076109866853157986",
"photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
"sessionId": "2a0a5e044bb03b66",
"userId": "102167687554210253930"
},
"user_tz": 420
},
"id": "s3mWgZLpyuzq",
"outputId": "8af66da6-902d-4719-bedc-7c9fb7ae7948"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training: (2000, 28, 28) (2000,)\n",
"Validation: (100, 28, 28) (100,)\n",
"Testing: (100, 28, 28) (100,)\n"
]
}
],
"source": [
"def make_arrays(nb_rows, img_size):\n",
" if nb_rows:\n",
" dataset = np.ndarray((nb_rows, img_size, img_size), dtype=np.float32)\n",
" labels = np.ndarray(nb_rows, dtype=np.int32)\n",
" else:\n",
" dataset, labels = None, None\n",
" return dataset, labels\n",
"\n",
"def merge_datasets(pickle_files, train_size, valid_size=0):\n",
" num_classes = len(pickle_files)\n",
" valid_dataset, valid_labels = make_arrays(valid_size, image_size)\n",
" train_dataset, train_labels = make_arrays(train_size, image_size)\n",
" vsize_per_class = valid_size // num_classes\n",
" tsize_per_class = train_size // num_classes\n",
" \n",
" start_v, start_t = 0, 0\n",
" end_v, end_t = vsize_per_class, tsize_per_class\n",
" end_l = vsize_per_class+tsize_per_class\n",
" for label, pickle_file in enumerate(pickle_files): \n",
" try:\n",
" with open(pickle_file, 'rb') as f:\n",
" letter_set = pickle.load(f)\n",
" # let's shuffle the letters to have random validation and training set\n",
" np.random.shuffle(letter_set)\n",
" if valid_dataset is not None:\n",
" valid_letter = letter_set[:vsize_per_class, :, :]\n",
" valid_dataset[start_v:end_v, :, :] = valid_letter\n",
" valid_labels[start_v:end_v] = label\n",
" start_v += vsize_per_class\n",
" end_v += vsize_per_class\n",
" \n",
" train_letter = letter_set[vsize_per_class:end_l, :, :]\n",
" train_dataset[start_t:end_t, :, :] = train_letter\n",
" train_labels[start_t:end_t] = label\n",
" start_t += tsize_per_class\n",
" end_t += tsize_per_class\n",
" except Exception as e:\n",
" print('Unable to process data from', pickle_file, ':', e)\n",
" raise\n",
" \n",
" return valid_dataset, valid_labels, train_dataset, train_labels\n",
" \n",
" \n",
"train_size = 2000\n",
"valid_size = 100\n",
"test_size = 100\n",
"\n",
"valid_dataset, valid_labels, train_dataset, train_labels = merge_datasets(\n",
" train_datasets, train_size, valid_size)\n",
"_, _, test_dataset, test_labels = merge_datasets(test_datasets, test_size)\n",
"\n",
"print('Training:', train_dataset.shape, train_labels.shape)\n",
"print('Validation:', valid_dataset.shape, valid_labels.shape)\n",
"print('Testing:', test_dataset.shape, test_labels.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "GPTCnjIcyuKN"
},
"source": [
"Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match."
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"collapsed": true,
"id": "6WZ2l2tN2zOL"
},
"outputs": [],
"source": [
"def randomize(dataset, labels):\n",
" permutation = np.random.permutation(labels.shape[0])\n",
" shuffled_dataset = dataset[permutation,:,:]\n",
" shuffled_labels = labels[permutation]\n",
" return shuffled_dataset, shuffled_labels\n",
"train_dataset, train_labels = randomize(train_dataset, train_labels)\n",
"test_dataset, test_labels = randomize(test_dataset, test_labels)\n",
"valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "puDUTe6t6USl"
},
"source": [
"---\n",
"Problem 4\n",
"---------\n",
"Convince yourself that the data is still good after shuffling!\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10e797da0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10ecdfb00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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mHH6zTDn8Zply+M0y5fCbZcrhN8uUw2+WqWa66N4euACYTNEl95yIOFPSJOASYCpFN91H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\nlMNvlqn/ByzY1Kq+0pPkAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e714e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# check that the labels and images match in each data set, 10 times\n",
"for trial in range(1):\n",
" # check for training randomization\n",
" train_sample_i = np.random.randint(train_dataset.shape[0])\n",
" train_sample_image = train_dataset[train_sample_i, :, :]\n",
" train_sample_label = train_labels[train_sample_i]\n",
" plt.figure()\n",
" plt.title(\"Training set: Should be a '%s'\" %letters[train_sample_label])\n",
" plt.imshow(train_sample_image)\n",
"\n",
" # check for sample randomization\n",
" test_sample_i = np.random.randint(test_dataset.shape[0])\n",
" test_sample_image = test_dataset[test_sample_i, :, :]\n",
" test_sample_label = test_labels[test_sample_i]\n",
" plt.figure()\n",
" plt.title(\"Testing set: Should be a '%s'\" %letters[test_sample_label])\n",
" plt.imshow(test_sample_image)\n",
"\n",
" # check for sample randomization\n",
" valid_sample_i = np.random.randint(valid_dataset.shape[0])\n",
" valid_sample_image = valid_dataset[valid_sample_i, :, :]\n",
" valid_sample_label = valid_labels[valid_sample_i]\n",
" plt.figure()\n",
" plt.title(\"Validation set: Should be a '%s'\" %letters[valid_sample_label])\n",
" plt.imshow(valid_sample_image)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "tIQJaJuwg5Hw"
},
"source": [
"Finally, let's save the data for later reuse:"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"collapsed": true,
"id": "QiR_rETzem6C"
},
"outputs": [],
"source": [
"pickle_file = 'notMNIST.pickle'\n",
"\n",
"try:\n",
" f = open(pickle_file, 'wb')\n",
" save = {\n",
" 'train_dataset': train_dataset,\n",
" 'train_labels': train_labels,\n",
" 'valid_dataset': valid_dataset,\n",
" 'valid_labels': valid_labels,\n",
" 'test_dataset': test_dataset,\n",
" 'test_labels': test_labels,\n",
" }\n",
" pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
" f.close()\n",
"except Exception as e:\n",
" print('Unable to save data to', pickle_file, ':', e)\n",
" raise"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 413065,
"status": "ok",
"timestamp": 1444485899688,
"user": {
"color": "#1FA15D",
"displayName": "Vincent Vanhoucke",
"isAnonymous": false,
"isMe": true,
"permissionId": "05076109866853157986",
"photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
"sessionId": "2a0a5e044bb03b66",
"userId": "102167687554210253930"
},
"user_tz": 420
},
"id": "hQbLjrW_iT39",
"outputId": "b440efc6-5ee1-4cbc-d02d-93db44ebd956",
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Compressed pickle size: 6908489\n"
]
}
],
"source": [
"statinfo = os.stat(pickle_file)\n",
"print('Compressed pickle size:', statinfo.st_size)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gE_cRAQB33lk"
},
"source": [
"---\n",
"Problem 5\n",
"---------\n",
"\n",
"By construction, this dataset might contain a lot of overlapping samples, including training data that's also contained in the validation and test set! Overlap between training and test can skew the results if you expect to use your model in an environment where there is never an overlap, but are actually ok if you expect to see training samples recur when you use it.\n",
"Measure how much overlap there is between training, validation and test samples.\n",
"\n",
"Optional questions:\n",
"- What about near duplicates between datasets? (images that are almost identical)\n",
"- Create a sanitized validation and test set, and compare your accuracy on those in subsequent assignments.\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of unique images in Training Set 1976\n",
"Number of unique images in Testing Set 100\n",
"Number of unique images in Validation Set 98\n",
"\n",
"Number of shared images between Training and Testing sets 1\n",
"Number of shared images between Training and Validation sets 1\n",
"Number of shared images between Testing and Validation sets 0\n"
]
}
],
"source": [
"train_set = set()\n",
"for i in range(train_dataset.shape[0]):\n",
" train_set.add(tuple(list(train_dataset[i, :, :].flatten())))\n",
" \n",
"test_set = set()\n",
"for i in range(test_dataset.shape[0]):\n",
" test_set.add(tuple(list(test_dataset[i, :, :].flatten())))\n",
" \n",
"valid_set = set()\n",
"for i in range(valid_dataset.shape[0]):\n",
" valid_set.add(tuple(list(valid_dataset[i, :, :].flatten())))\n",
"\n",
"print(\"Number of unique images in Training Set %d\" %len(train_set))\n",
"print(\"Number of unique images in Testing Set %d\" %len(test_set))\n",
"print(\"Number of unique images in Validation Set %d\" %len(valid_set))\n",
"print(\"\")\n",
"print(\"Number of shared images between Training and Testing sets %d\" %len(train_set & test_set))\n",
"print(\"Number of shared images between Training and Validation sets %d\" %len(train_set & valid_set))\n",
"print(\"Number of shared images between Testing and Validation sets %d\" %len(test_set & valid_set))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "L8oww1s4JMQx"
},
"source": [
"---\n",
"Problem 6\n",
"---------\n",
"\n",
"Let's get an idea of what an off-the-shelf classifier can give you on this data. It's always good to check that there is something to learn, and that it's a problem that is not so trivial that a canned solution solves it.\n",
"\n",
"Train a simple model on this data using 50, 100, 1000 and 5000 training samples. Hint: you can use the LogisticRegression model from sklearn.linear_model.\n",
"\n",
"Optional question: train an off-the-shelf model on all the data!\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of trained classifier: 78.0000\n"
]
}
],
"source": [
"train_dataset_flat = train_dataset.reshape(train_dataset.shape[0], train_dataset.shape[1]*train_dataset.shape[2])\n",
"test_dataset_flat = test_dataset.reshape(test_dataset.shape[0], test_dataset.shape[1]*test_dataset.shape[2])\n",
"\n",
"logreg = LogisticRegression()\n",
"logreg.fit(train_dataset_flat, train_labels)\n",
"accuracy = logreg.score(test_dataset_flat, test_labels)\n",
"\n",
"print(\"Accuracy of trained classifier: %.4f\" %(accuracy*100.0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"default_view": {},
"name": "1_notmnist.ipynb",
"provenance": [],
"version": "0.3.2",
"views": {}
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
AllenDowney/ModSimPy | examples/wall.ipynb | 1 | 16493 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Thermal behavior of a wall"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Modeling and Simulation in Python*\n",
"\n",
"Copyright 2021 Allen Downey\n",
"\n",
"License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# install Pint if necessary\n",
"\n",
"try:\n",
" import pint\n",
"except ImportError:\n",
" !pip install pint"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# download modsim.py if necessary\n",
"\n",
"from os.path import basename, exists\n",
"\n",
"def download(url):\n",
" filename = basename(url)\n",
" if not exists(filename):\n",
" from urllib.request import urlretrieve\n",
" local, _ = urlretrieve(url, filename)\n",
" print('Downloaded ' + local)\n",
" \n",
"download('https://raw.githubusercontent.com/AllenDowney/' +\n",
" 'ModSimPy/main/modsim.py')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# import functions from modsim\n",
"\n",
"from modsim import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This case study is based on Gori, Marincioni, Biddulph, Elwell, \"Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces\", *Energy and Buildings*, Volume 135, 15 January 2017, Pages 398-409, [which I downloaded here](https://www.sciencedirect.com/science/article/pii/S0378778816313056).\n",
" \n",
"The authors put their paper under a Creative Commons license, and [make their data available here](http://discovery.ucl.ac.uk/1526521). I thank them for their commitment to open, reproducible science, which made this case study possible.\n",
"\n",
"The goal of their paper is to model the thermal behavior of a wall as a step toward understanding the \"performance gap between the expected energy use of buildings and their measured energy use\". The wall they study is identified as the exterior wall of an office building in central London, [not unlike this one](https://www.google.com/maps/@51.5269375,-0.1303666,3a,75y,90h,88.17t/data=!3m6!1e1!3m4!1sAoAXzN0mbGF9acaVEgUdDA!2e0!7i13312!8i6656)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following figure shows the scenario and their model:\n",
"\n",
"\n",
"\n",
"On the interior and exterior surfaces of the wall, they measure temperature and heat flux over a period of three days. They model the wall using two thermal masses connected to the surfaces, and to each other, by thermal resistors.\n",
"\n",
"The primary methodology of the paper is a Bayesian method for inferring the parameters of the system (two thermal masses and three thermal resistances).\n",
"\n",
"The primary result is a comparison of two models: the one shown here with two thermal masses, and a simpler model with only one thermal mass. They find that the two-mass model is able to reproduce the measured fluxes substantially better."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tempting as it is, I will not replicate their method for estimating the parameters. Rather, I will implement their model and run it with their estimated parameters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following cells download and read the data."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"download('https://raw.githubusercontent.com/AllenDowney/' +\n",
" 'ModSim/main/data/DataOWall.csv')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv('DataOWall.csv', \n",
" parse_dates=[0], index_col=0, \n",
" header=0, skiprows=[1,2])\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The index contains Pandas `Timestamp` objects, which is good for dealing with real-world dates and times, but not as good for running the simulations, so I'm going to convert to seconds."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"timestamp_0 = data.index[0]\n",
"timestamp_0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Subtracting the first `Timestamp` yields `Timedelta` objects:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"time_deltas = data.index - timestamp_0\n",
"time_deltas.dtype"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we can convert to seconds and replace the index."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"data.index = time_deltas.days * 86400 + time_deltas.seconds\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The timesteps are all 5 minutes:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"np.all(np.diff(data.index) == 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the measured fluxes."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"data.Q_in.plot(color='C2')\n",
"data.Q_out.plot(color='C0')\n",
"decorate(xlabel='Time (s)',\n",
" ylabel='Heat flux (W/$m^2$)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the measured temperatures."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"data.T_int.plot(color='C2')\n",
"data.T_ext.plot(color='C0')\n",
"decorate(xlabel='Time (s)',\n",
" ylabel='Temperature (degC)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Making the System object\n",
"\n",
"`params` is a sequence with the [estimated parameters from the paper](https://www.sciencedirect.com/science/article/pii/S0378778816313056#tbl0005)."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"R1 = 0.076 # m**2 * K / W,\n",
"R2 = 0.272 # m**2 * K / W,\n",
"R3 = 0.078 # m**2 * K / W,\n",
"C1 = 212900 # J / m**2 / K,\n",
"C2 = 113100 # J / m**2 / K"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"params = R1, R2, R3, C1, C2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll pass `params` to `make_system`, which computes `init`, packs the parameters into `Series` objects, and computes the interpolation functions."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def make_system(params, data):\n",
" \"\"\"Makes a System object for the given conditions.\n",
" \n",
" params: Params object\n",
" \n",
" returns: System object\n",
" \"\"\"\n",
" R1, R2, R3, C1, C2 = params\n",
" \n",
" init = State(T_C1 = 16.11, T_C2 = 15.27)\n",
" \n",
" t_end = data.index[-1]\n",
" \n",
" return System(init=init,\n",
" R=(R1, R2, R3),\n",
" C=(C1, C2),\n",
" T_int_func=interpolate(data.T_int),\n",
" T_ext_func=interpolate(data.T_ext),\n",
" t_end=t_end)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make a `System` object"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"system = make_system(params, data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test the interpolation function:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"system.T_ext_func(0), system.T_ext_func(150), system.T_ext_func(300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementing the model\n",
"\n",
"Next we need a slope function that takes instantaneous values of the two internal temperatures and computes their time rates of change.\n",
"\n",
"The slope function gets called two ways.\n",
"\n",
"* When we call it directly, `state` is a `State` object and the values it contains have units.\n",
"\n",
"* When `run_solve_ivp` calls it, `state` is an array and the values it contains don't have units.\n",
"\n",
"In the second case, we have to apply the units before attempting the computation. `require_units` applies units if necessary:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following function computes the fluxes between the four zones."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"def compute_flux(t, state, system):\n",
" \"\"\"Compute the fluxes between the walls surfaces and the internal masses.\n",
" \n",
" state: State with T_C1 and T_C2\n",
" t: time in seconds\n",
" system: System with interpolated measurements and the R Series\n",
" \n",
" returns: Series of fluxes\n",
" \"\"\" \n",
" # unpack the temperatures\n",
" T_C1, T_C2 = state\n",
" \n",
" # compute a series of temperatures from inside out\n",
" T_int = system.T_int_func(t)\n",
" T_ext = system.T_ext_func(t)\n",
" \n",
" T = [T_int, T_C1, T_C2, T_ext]\n",
" \n",
" # compute differences of adjacent temperatures\n",
" T_diff = np.diff(T)\n",
"\n",
" # compute fluxes between adjacent compartments\n",
" Q = T_diff / system.R\n",
" return Q"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can test it like this."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"compute_flux(0, system.init, system)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's a slope function that computes derivatives of `T_C1` and `T_C2`"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def slope_func(t, state, system):\n",
" \"\"\"Compute derivatives of the state.\n",
" \n",
" state: position, velocity\n",
" t: time\n",
" system: System object\n",
" \n",
" returns: derivatives of y and v\n",
" \"\"\"\n",
" Q = compute_flux(t, state, system)\n",
"\n",
" # compute the net flux in each node\n",
" Q_diff = np.diff(Q)\n",
" \n",
" # compute the rate of change of temperature\n",
" dQdt = Q_diff / system.C\n",
" return dQdt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test the slope function with the initial conditions."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"slopes = slope_func(0, system.init, system)\n",
"slopes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's run the simulation, generating estimates for the time steps in the data."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"results, details = run_solve_ivp(system, slope_func,\n",
" t_eval=data.index)\n",
"details.message"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's what the results look like."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"results.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"def plot_results(results, data):\n",
" data.T_int.plot(color='C2')\n",
" results.T_C1.plot(color='C3')\n",
" results.T_C2.plot(color='C1')\n",
" data.T_ext.plot(color='C0')\n",
" decorate(xlabel='Time (s)',\n",
" ylabel='Temperature (degC)')\n",
" \n",
"plot_results(results, data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These results are similar to what's in the paper:\n",
"\n",
". \n",
"\n",
"To get the estimated fluxes, we have to go through the results and basically do the flux calculation again."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def recompute_fluxes(results, system):\n",
" \"\"\"Compute fluxes between wall surfaces and internal masses.\n",
" \n",
" results: Timeframe with T_C1 and T_C2\n",
" system: System object\n",
" \n",
" returns: Timeframe with Q_in and Q_out\n",
" \"\"\"\n",
" Q_frame = TimeFrame(index=results.index, \n",
" columns=['Q_in', 'Q_out'])\n",
" \n",
" for t, row in results.iterrows():\n",
" Q = compute_flux(t, row, system)\n",
" Q_frame.loc[t] = (-Q[0], -Q[2])\n",
" \n",
" return Q_frame"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"Q_frame = recompute_fluxes(results, system)\n",
"Q_frame.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see how the estimates compare to the data."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"def plot_Q_in(frame, data):\n",
" frame.Q_in.plot(color='gray')\n",
" data.Q_in.plot(color='C2')\n",
" decorate(xlabel='Time (s)',\n",
" ylabel='Heat flux (W/$m^2$)')\n",
" \n",
"plot_Q_in(Q_frame, data)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"def plot_Q_out(frame, data):\n",
" frame.Q_out.plot(color='gray')\n",
" data.Q_out.plot(color='C0')\n",
" decorate(xlabel='Time (s)',\n",
" ylabel='Heat flux (W/$m^2$)')\n",
" \n",
"plot_Q_out(Q_frame, data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These results are also similar to what's in the paper (the bottom row):\n",
"\n",
""
]
},
{
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
cydcowley/Imperial-Visualizations | visuals_maths/Linear Algebra/Python/(discontinued)planes_intersection.ipynb | 1 | 40399 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Planes intersection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Making a plane"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
],
"text/vnd.plotly.v1+html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.offline as py\n",
"import plotly.graph_objs as go\n",
"py.init_notebook_mode(connected=True)\n",
"#numpy for calculations\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"utils.py\"\"\"\n",
"import numpy as np\n",
"def mesh2d(xlim, ylim, n=5):\n",
" \"\"\"Create 2d mesh in sepecifies x and y axes limits, with number of points for every dimension separate.\"\"\"\n",
" if isinstance(n, int):\n",
" xx = np.linspace(xlim[0],xlim[1],n)\n",
" yy = np.linspace(ylim[0],ylim[1],n)\n",
" elif isinstance(n, list):\n",
" xx = np.linspace(xlim[0],xlim[1],n[0])\n",
" yy = np.linspace(ylim[0],ylim[1],n[1])\n",
" else:\n",
" raise Exception(\"Wrong number of points parameter\")\n",
" return np.meshgrid(xx, yy)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"utils.py\"\"\"\n",
"def normalize(v):\n",
" \"\"\"Normalizes a 3d vector v, returns a 3d vector.\"\"\"\n",
" magnitude = np.sqrt(v[0]**2+v[1]**2+v[2]**2)\n",
" if magnitude==0:\n",
" raise ValueError(\"Zero vector cannot be normalized.\")\n",
" else:\n",
" return v/magnitude"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"utils.py\"\"\"\n",
"def jsonify(obj):\n",
" \"\"\"Transforms a graphic object (go) into json data dictionary.\n",
" @author Nick Metelski\n",
" @since 27.07.17\"\"\"\n",
" go_obj = obj.goify()\n",
" json_obj = dict()\n",
" json_obj['name'] = go_obj['name']\n",
" json_obj['x'] = list(go_obj['x'])\n",
" json_obj['y'] = list(go_obj['y'])\n",
" json_obj['z'] = list(go_obj['z'])\n",
" return json_obj"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need classes for Point, Line and Plane for uniform output:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"point.py\"\"\"\n",
"class Point:\n",
" \"\"\"Point class to make returns from intersections more reasonable.\n",
" @author Nick\n",
" @since 25.07.17\"\"\"\n",
" def __init__(self,position):\n",
" self.pos = np.array(position)\n",
" \n",
" def getXYZ(self, layout=None):\n",
" \"\"\"Generate x,y,z data based on the layout box. Returns tuple (x,y,z).\n",
" @author Nick Metelski\n",
" @since 27.07.17\"\"\"\n",
" return np.array([self.pos[0]]), np.array([self.pos[1]]), np.array([self.pos[2]])\n",
" \n",
" def goify(self):\n",
" \"\"\"Transform a point into graphics object (go).\n",
" @author Nick Metelski\n",
" @since 25.07.17\"\"\"\n",
" pt = go.Scatter3d(\n",
" mode=\"markers\",\n",
" x=[self.pos[0]],\n",
" y=[self.pos[1]],\n",
" z=[self.pos[2]]\n",
" )\n",
" return pt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'mode': 'markers', 'x': [1], 'y': [2], 'type': 'scatter3d', 'z': [3]}\n",
"{'z': [3], 'y': [2], 'name': None, 'x': [1]}\n"
]
}
],
"source": [
"pt = Point([1,2,3])\n",
"print(pt.goify())\n",
"print(jsonify(pt))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"line.py\"\"\"\n",
"import plotly.graph_objs as go\n",
"import numpy as np\n",
"class Line:\n",
" \"\"\"Line class for intersections simulation\n",
" @author Nick Metelski\n",
" @since 25.07.17\"\"\"\n",
" \n",
" def __init__(self, vec, offset):\n",
" self.vec = normalize(np.array(vec)) #normalize direction vector\n",
" self.offset = np.array(offset) #cast to numpy array\n",
" #minimize the offset to be minimum distance\n",
" self.offset = self.offset - (np.dot(self.offset,self.vec) * self.vec)\n",
" \n",
" def getXYZ(self, layout=None):\n",
" \"\"\"Generate x,y,z data based on the layout box. Returns tuple (x,y,z).\n",
" @author Nick Metelski\n",
" @since 26.07.17\"\"\"\n",
" #TODO import data from layout\n",
" t = np.linspace(0,1,2) #the parameter\n",
" xx = self.offset[0]+t*self.vec[0] #generate xpoints\n",
" yy = self.offset[1]+t*self.vec[1] #for y points\n",
" zz = self.offset[2]+t*self.vec[2] #for z points\n",
" return xx, yy, zz\n",
"\n",
" def goify(self,layout=None):\n",
" \"\"\"Export the line into graphics object\n",
" @author Nick Metelski\n",
" @since 26.07.17\"\"\"\n",
" xx, yy, zz = self.getXYZ()\n",
" line = go.Scatter3d(\n",
" mode=\"lines\",\n",
" x=list(xx),\n",
" y=list(yy),\n",
" z=list(zz),\n",
" line = dict(\n",
" color = ('rgb(205, 12, 24)'),\n",
" width = 10)\n",
" )\n",
" return line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'line': {'color': 'rgb(205, 12, 24)', 'width': 10}, 'type': 'scatter3d', 'y': [0.14285714285714279, 0.67737962668199159], 'x': [0.5714285714285714, 0.83868981334099579], 'mode': 'lines', 'z': [-0.28571428571428581, 0.51606944002298738]}\n",
"{'z': [-0.28571428571428581, 0.51606944002298738], 'y': [0.14285714285714279, 0.67737962668199159], 'name': None, 'x': [0.5714285714285714, 0.83868981334099579]}\n"
]
}
],
"source": [
"line = Line([1,2,3],[1,1,1])\n",
"print(line.goify())\n",
"print(jsonify(line))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Equation of a plane: $$\\vec{n} \\cdot (\\vec{r} - \\vec{r_0}) = 0$$"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"plane.py\"\"\"\n",
"\"\"\"Plane class for intersections simulation\n",
"@author Nick\n",
"@since 25.07.17\"\"\"\n",
"import plotly.graph_objs as go\n",
"#import .line #import local line module\n",
"import numpy as np\n",
"\n",
"class Plane:\n",
" \"\"\"Planes are defined by their normal and offset vector.\"\"\"\n",
" \n",
" def __init__(self, normal, offset):\n",
" self.normal = normalize(np.array(normal)) #normalize normal vector\n",
" self.offset = np.array(offset)\n",
" self.offset = np.dot(self.offset,self.normal)*self.normal\n",
" \n",
" def getXYZ(self, xlim=[-1,1], ylim=[-1,1], zlim=[-1,1], n=2):\n",
" \"\"\"Generate x,y,z data based on the layout box. Returns tuple (x,y,z).\n",
" @author Nick Metelski\n",
" @since 25.07.17\"\"\"\n",
" if self.normal[2] == 0: #check if z is zero, then we have to generate x or y from other meshes\n",
" if self.normal[1] == 0: #check if z and y is zero, then we have to generate x from yz mesh\n",
" if self.normal[0] == 0: \n",
" return ValueError(\"Normal vector is zero vector.\")\n",
" else:\n",
" #cannot generate z but can y, try generating y for xz mesh\n",
" y, z = mesh2d(ylim, zlim, n)\n",
" x = (np.dot(self.normal,self.offset)-self.normal[1]*y-self.normal[2]*z)/self.normal[0]\n",
" else:\n",
" #cannot generate z but can y, try generating y for xz mesh\n",
" #self.normal[2] = 0.01 # TODO THIS IS VERY CRUDE\n",
" x, z = mesh2d(xlim, zlim, n)\n",
" y = ((np.dot(self.normal,self.offset)\n",
" - self.normal[0]*x\n",
" - self.normal[2]*z)\n",
" / self.normal[1])\n",
" else:\n",
" #try generating z\n",
" x, y = mesh2d(xlim, ylim, n)\n",
" #Generate plane z-values array\n",
" z = (np.dot(self.normal,self.offset)-self.normal[0]*x-self.normal[1]*y)/self.normal[2]\n",
" return [list(i) for i in x],[list(i) for i in y],[list(i) for i in z]\n",
"\n",
" def goify(self, layout=None):\n",
" \"\"\"Export the plane into graphics object.\n",
" @author Nick Metelski\n",
" @since 25.07.17\"\"\"\n",
" xx,yy,zz = self.getXYZ()\n",
" surf = go.Surface(\n",
" x=xx,\n",
" y=yy,\n",
" z=zz\n",
" )\n",
" return surf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'x': [[-1.0, 1.0], [-1.0, 1.0]], 'y': [[-1.0, -1.0], [1.0, 1.0]], 'type': 'surface', 'z': [[3.0000000000000004, 2.333333333333333], [1.6666666666666667, 0.99999999999999989]]}\n"
]
}
],
"source": [
"plane = Plane([1,2,3],[1,1,1])\n",
"print(plane.goify())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Let's try it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate some objects"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"point1 = Point([0.5,0.0,0.2])\n",
"point2 = Point([0.1,-0.4,0.3])\n",
"line1 = Line([2,1,0],[0,0.1,0])\n",
"line2 = Line([2,1,1],[0.2,0.1,0])\n",
"plane1 = Plane([1,1,1],[0,0.1,0])\n",
"plane2 = Plane([2,0,-1],[-0.2,0.1,0.3])\n",
"\n",
"objlist = [point1, point2, line1, line2, plane1, plane2]\n",
"data = [obj.goify() for obj in objlist]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define layout"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"u"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the figure"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"mode": "markers",
"type": "scatter3d",
"x": [
0.5
],
"y": [
0
],
"z": [
0.2
]
},
{
"mode": "markers",
"type": "scatter3d",
"x": [
0.1
],
"y": [
-0.4
],
"z": [
0.3
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},
{
"line": {
"color": "rgb(205, 12, 24)",
"width": 10
},
"mode": "lines",
"type": "scatter3d",
"x": [
-0.04,
0.8544271909999158
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0.08,
0.5272135954999579
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"z": [
0,
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"line": {
"color": "rgb(205, 12, 24)",
"width": 10
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"mode": "lines",
"type": "scatter3d",
"x": [
0.03333333333333327,
0.8498299142610595
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0.016666666666666635,
0.42491495713052974
],
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-0.08333333333333337,
0.3249149571305297
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"type": "surface",
"x": [
[
-1,
1
],
[
-1,
1
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],
"y": [
[
-1,
-1
],
[
1,
1
]
],
"z": [
[
2.0999999999999996,
0.10000000000000002
],
[
0.10000000000000002,
-1.9000000000000001
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"type": "surface",
"x": [
[
-1,
1
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[
-1,
1
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],
"y": [
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-1,
-1
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[
1,
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"z": [
[
-1.3,
2.7
],
[
-1.3,
2.7
]
]
}
],
"layout": {
"font": {
"family": "Verdana"
},
"height": 400,
"plot_bgcolor": "rgb(255, 255, 255)",
"scene": {
"aspectmode": "cube",
"camera": {
"center": {
"x": 0,
"y": 0,
"z": 0
},
"eye": {
"x": 1,
"y": 1,
"z": 1
}
},
"xaxis": {
"autorange": false,
"range": [
-1,
1
],
"zeroline": false
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"yaxis": {
"autorange": false,
"range": [
-1,
1
],
"zeroline": false
},
"zaxis": {
"autorange": false,
"range": [
-1,
1
],
"zeroline": false
}
},
"showlegend": false,
"width": 400
}
},
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig=go.Figure(data=data,layout=layout)\n",
"py.iplot(fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Making intersections"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need points as well as planes and lines."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"algebra.py\"\"\"\n",
"\"\"\"This contains algebra methods to calculate intersections etc.\"\"\"\n",
"def intersection(obj1, obj2):\n",
" \"\"\"Intersection between two algebra 3d objects.\n",
" @author Nick Metelski\n",
" @since 26.07.17\"\"\"\n",
" #plane-plane\n",
" if isinstance(obj1,Plane) and isinstance(obj2,Plane):\n",
" return _plane_plane_intersection(obj1,obj2)\n",
" \n",
" #plane-line\n",
" elif isinstance(obj1,Plane) and isinstance(obj2,Line):\n",
" return _plane_line_intersection(obj1,obj2)\n",
" elif isinstance(obj1,Line) and isinstance(obj2,Plane):\n",
" return _plane_line_intersection(obj2,obj1)\n",
" \n",
" #plane-point\n",
" elif isinstance(obj1,Plane) and isinstance(obj2,Point):\n",
" return _plane_point_intersection(obj1,obj2)\n",
" elif isinstance(obj1,Point) and isinstance(obj2,Plane):\n",
" return _plane_point_intersection(obj2,obj1)\n",
" \n",
" #line-line\n",
" elif isinstance(obj1,Line) and isinstance(obj2,Line):\n",
" return _line_line_intersection(obj1,obj2)\n",
" \n",
" #line-point\n",
" elif isinstance(obj1,Line) and isinstance(obj2,Point):\n",
" return _line_point_intersection(obj1,obj2)\n",
" elif isinstance(obj1,Point) and isinstance(obj2,Line):\n",
" return _line_point_intersection(obj2,obj1)\n",
" \n",
" #point-point\n",
" elif isinstance(obj1,Point) and isinstance(obj2,Point):\n",
" return _point_point_intersection(obj1,obj2)\n",
" \n",
" #wrong params\n",
" else:\n",
" raise TypeError(\"Invalid parameter types - please pass intersections.Point, intersections.Line or intersections.Plane.\")\n",
"\n",
"def _plane_plane_intersection(p1,p2):\n",
" \"\"\"Private function; do not use. Use intersection(obj1,obj2) instead.\n",
" plane-plane intersection submethod. Raises exceptions or returns Line.\n",
" @author Nick Metelski\n",
" @since 26.07.17\"\"\"\n",
" #cross-product\n",
" cross = np.cross(p1.normal,p2.normal)\n",
" if np.all(cross==0):\n",
" #planes are parallel or overlap\n",
" raise ArithmeticError(\"Planes are parallel.\")\n",
" else:\n",
" #sample point: x=0\n",
" mat = [[p1.normal[1],p1.normal[2]],[p2.normal[1], p2.normal[2]]]\n",
" axis = 0 #keep track of which axis we zero out; 0=x, 1=y, 2=z\n",
" #NOTE: linalg.solve can raise np.linalg.LinAlgError exception when x=0 results in singular matrix\n",
" #we have to check some other cases\n",
" #premise: the line has to intersect at least one of the planes: xy, yz or xz.\n",
" if np.linalg.matrix_rank(mat) == 1:\n",
" mat = [[p1.normal[0],p1.normal[2]],[p2.normal[0], p2.normal[2]]]\n",
" axis = 1\n",
" if np.linalg.matrix_rank(mat) == 1:\n",
" mat = [[p1.normal[0],p1.normal[1]],[p1.normal[0], p2.normal[1]]]\n",
" axis = 2 \n",
" rhs = [np.dot(p1.normal,p1.offset),np.dot(p2.normal,p2.offset)]\n",
" sol = np.linalg.solve(mat,rhs)\n",
" if axis == 0:\n",
" return Line(cross, [0,sol[0],sol[1]])\n",
" if axis == 1:\n",
" return Line(cross, [sol[0],0,sol[1]])\n",
" if axis == 2:\n",
" return Line(cross, [sol[0],sol[1],0])\n",
"\n",
"def _plane_line_intersection(plane,line):\n",
" \"\"\"Private function; do not use. Use intersection(obj1,obj2) instead.\n",
" Intersection of a line an a plane. Raises exceptions or returns Point.\n",
" @author Nick Metelski\n",
" @since 26.07.17\"\"\"\n",
" check = np.dot(plane.normal,line.vec)\n",
" if np.all(check==0):\n",
" #plane and line are parallel or overlap\n",
" raise ArithmeticError(\"Plane and line are parallel.\")\n",
" else:\n",
" #there is an explicit formula for parameter of the line for which intersection is met:\n",
" #$$t = \\frac{\\vec{n} \\cdot (\\vec{d_p}-\\vec{d_v})}{\\vec{n} \\cdot \\vec{v}}$$,\n",
" #where n is normal to plane, v is line's direction, rp is plane offset, rv is vector offset\n",
" t = np.dot(plane.normal,(plane.offset-line.offset))/np.dot(plane.normal,line.vec)\n",
" return Point(line.offset+t*line.vec)\n",
" \n",
"def _plane_point_intersection(plane,point):\n",
" raise NotImplementedError(\"Plane-point intersections not implemented.\")\n",
"\n",
"def _line_line_intersection(line1,line2):\n",
" raise NotImplementedError(\"Line-line intersections not implemented.\")\n",
" \n",
"def _line_point_intersection(line,point):\n",
" \"\"\"Private function; do not use.\n",
" Intersection of a line and point. Intersection is a Point.\n",
" Raises exceptions or returns Point.\n",
" @author Nick Metelski\n",
" @since 27.07.17\"\"\"\n",
" offset_diff = point.pos - line.offset #difference in offsets must be parallel to direction vector\n",
" cross = np.cross(line.vec,offset_diff) # this is for checking if there exists an intersection\n",
" if cross == 0:\n",
" return Point(point.pos) #return a point coinciding with the original point passed as param\n",
" else:\n",
" raise ArithmeticError(\"Point does not lie on the line.\")\n",
" \n",
"def _point_point_intersection(point1, point2):\n",
" \"\"\"Private function; do not use. Use intersection(obj1,obj2) instead.\n",
" Intersection of a two points. Intersection is the point if the point are the same position. \n",
" Raises exceptions or returns Point.\n",
" @author Nick Metelski\n",
" @since 27.07.17\"\"\"\n",
" if np.all_equal(point1,point2):\n",
" #just return one of the moints when they coincide\n",
" return Point(point1.pos)\n",
" else:\n",
" raise ArithmeticError(\"Points do not coincide.\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
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{
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]
},
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}
],
"source": [
"plane1 = Plane([1,1,1],[0,0,0])\n",
"plane2 = Plane([1,-1,1],[0,0,0])\n",
"plane3 = Plane([-1,1,0],[0,0.5,0])\n",
"line1 = intersection(plane1,plane2)\n",
"line2 = intersection(plane2,plane3)\n",
"objs = [plane1, plane2, plane3, line1, line2]\n",
"data = [obj.goify() for obj in objs]\n",
"fig=go.Figure(data=data,layout=layout)\n",
"py.iplot(fig)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"ename": "ArithmeticError",
"evalue": "Plane and line are parallel.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mArithmeticError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-16-4a547a988637>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mplane\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPlane\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m.5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mintersec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplane\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mplane\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgoify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgoify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mintersec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgoify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-14-dfc34c4021bf>\u001b[0m in \u001b[0;36mintersection\u001b[0;34m(obj1, obj2)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;31m#plane-line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mPlane\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mLine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_plane_line_intersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mobj2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mLine\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mPlane\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_plane_line_intersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mobj1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-14-dfc34c4021bf>\u001b[0m in \u001b[0;36m_plane_line_intersection\u001b[0;34m(plane, line)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheck\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0;31m#plane and line are parallel or overlap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mArithmeticError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Plane and line are parallel.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 82\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;31m#there is an explicit formula for parameter of the line for which intersection is met:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mArithmeticError\u001b[0m: Plane and line are parallel."
]
}
],
"source": [
"try:\n",
" plane = Plane([1,0,0],[0,0.5,0])\n",
" line = Line([0,1,1],[-.5,0,0.5])\n",
" intersec = intersection(plane,line)\n",
" fig=go.Figure(data=[plane.goify(),line.goify(),intersec.goify()],layout=layout)\n",
" py.iplot(fig)\n",
"except:\n",
" raise"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"help(Point)\n",
"help(_point_point_intersection)\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 |
bollwyvl/K3D-jupyter | examples/text.ipynb | 1 | 819 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from k3d import K3D\n",
"\n",
"position = (0.5, 1.0, 0.0)\n",
"\n",
"plot = K3D()\n",
"plot += K3D.text('K3D Jupyter', position, color=0xff0000, font_face='Arial', font_weight='bold')\n",
"plot.display()"
]
}
],
"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
}
| gpl-2.0 |
JuBra/cobrapy | documentation_builder/deletions.ipynb | 1 | 18595 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simulating Deletions"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas\n",
"from time import time\n",
"\n",
"import cobra.test\n",
"from cobra.flux_analysis import \\\n",
" single_gene_deletion, single_reaction_deletion, \\\n",
" double_gene_deletion, double_reaction_deletion\n",
"\n",
"cobra_model = cobra.test.create_test_model(\"textbook\")\n",
"ecoli_model = cobra.test.create_test_model(\"ecoli\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Single Deletions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform all single gene deletions on a model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"growth_rates, statuses = single_gene_deletion(cobra_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These can also be done for only a subset of genes"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>growth_rates</th>\n",
" <th>status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>b0116</th>\n",
" <td>0.782351</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b0118</th>\n",
" <td>0.873922</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b0351</th>\n",
" <td>0.873922</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b0356</th>\n",
" <td>0.873922</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b0474</th>\n",
" <td>0.873922</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b0726</th>\n",
" <td>0.858307</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b0727</th>\n",
" <td>0.858307</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b1241</th>\n",
" <td>0.873922</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b1276</th>\n",
" <td>0.873922</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b1478</th>\n",
" <td>0.873922</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b1849</th>\n",
" <td>0.873922</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b2296</th>\n",
" <td>0.873922</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b2587</th>\n",
" <td>0.873922</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b3115</th>\n",
" <td>0.873922</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b3732</th>\n",
" <td>0.374230</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b3733</th>\n",
" <td>0.374230</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b3734</th>\n",
" <td>0.374230</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b3735</th>\n",
" <td>0.374230</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b3736</th>\n",
" <td>0.374230</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>s0001</th>\n",
" <td>0.211141</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" growth_rates status\n",
"b0116 0.782351 optimal\n",
"b0118 0.873922 optimal\n",
"b0351 0.873922 optimal\n",
"b0356 0.873922 optimal\n",
"b0474 0.873922 optimal\n",
"b0726 0.858307 optimal\n",
"b0727 0.858307 optimal\n",
"b1241 0.873922 optimal\n",
"b1276 0.873922 optimal\n",
"b1478 0.873922 optimal\n",
"b1849 0.873922 optimal\n",
"b2296 0.873922 optimal\n",
"b2587 0.873922 optimal\n",
"b3115 0.873922 optimal\n",
"b3732 0.374230 optimal\n",
"b3733 0.374230 optimal\n",
"b3734 0.374230 optimal\n",
"b3735 0.374230 optimal\n",
"b3736 0.374230 optimal\n",
"s0001 0.211141 optimal"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gr, st = single_gene_deletion(cobra_model,\n",
" cobra_model.genes[:20])\n",
"pandas.DataFrame.from_dict({\"growth_rates\": gr,\n",
" \"status\": st})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This can also be done for reactions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>growth_rates</th>\n",
" <th>status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ACALD</th>\n",
" <td>8.739215e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ACALDt</th>\n",
" <td>8.739215e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ACKr</th>\n",
" <td>8.739215e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ACONTa</th>\n",
" <td>-3.963237e-27</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ACONTb</th>\n",
" <td>6.162976e-33</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ACt2r</th>\n",
" <td>8.739215e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADK1</th>\n",
" <td>8.739215e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AKGDH</th>\n",
" <td>8.583074e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AKGt2r</th>\n",
" <td>8.739215e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALCD2x</th>\n",
" <td>8.739215e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ATPM</th>\n",
" <td>9.166475e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ATPS4r</th>\n",
" <td>3.742299e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Biomass_Ecoli_core</th>\n",
" <td>0.000000e+00</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CO2t</th>\n",
" <td>4.616696e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CS</th>\n",
" <td>-5.916457e-30</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CYTBD</th>\n",
" <td>2.116629e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D_LACt2</th>\n",
" <td>8.739215e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ENO</th>\n",
" <td>-3.266892e-18</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ETOHt2r</th>\n",
" <td>8.739215e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EX_ac_e</th>\n",
" <td>8.739215e-01</td>\n",
" <td>optimal</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" growth_rates status\n",
"ACALD 8.739215e-01 optimal\n",
"ACALDt 8.739215e-01 optimal\n",
"ACKr 8.739215e-01 optimal\n",
"ACONTa -3.963237e-27 optimal\n",
"ACONTb 6.162976e-33 optimal\n",
"ACt2r 8.739215e-01 optimal\n",
"ADK1 8.739215e-01 optimal\n",
"AKGDH 8.583074e-01 optimal\n",
"AKGt2r 8.739215e-01 optimal\n",
"ALCD2x 8.739215e-01 optimal\n",
"ATPM 9.166475e-01 optimal\n",
"ATPS4r 3.742299e-01 optimal\n",
"Biomass_Ecoli_core 0.000000e+00 optimal\n",
"CO2t 4.616696e-01 optimal\n",
"CS -5.916457e-30 optimal\n",
"CYTBD 2.116629e-01 optimal\n",
"D_LACt2 8.739215e-01 optimal\n",
"ENO -3.266892e-18 optimal\n",
"ETOHt2r 8.739215e-01 optimal\n",
"EX_ac_e 8.739215e-01 optimal"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gr, st = single_reaction_deletion(cobra_model,\n",
" cobra_model.reactions[:20])\n",
"pandas.DataFrame.from_dict({\"growth_rates\": gr,\n",
" \"status\": st})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Double Deletions\n",
"\n",
"Double deletions run in a similar way. Passing in return_frame=True will cause them to format the results as a pandas Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>b2464</th>\n",
" <th>b0008</th>\n",
" <th>b2935</th>\n",
" <th>b2465</th>\n",
" <th>b3919</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>b2464</th>\n",
" <td>0.873922</td>\n",
" <td>0.864759</td>\n",
" <td>0.873922</td>\n",
" <td>0.873922</td>\n",
" <td>0.704037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b0008</th>\n",
" <td>0.864759</td>\n",
" <td>0.873922</td>\n",
" <td>0.873922</td>\n",
" <td>0.873922</td>\n",
" <td>0.704037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b2935</th>\n",
" <td>0.873922</td>\n",
" <td>0.873922</td>\n",
" <td>0.873922</td>\n",
" <td>0.000000</td>\n",
" <td>0.704037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b2465</th>\n",
" <td>0.873922</td>\n",
" <td>0.873922</td>\n",
" <td>0.000000</td>\n",
" <td>0.873922</td>\n",
" <td>0.704037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b3919</th>\n",
" <td>0.704037</td>\n",
" <td>0.704037</td>\n",
" <td>0.704037</td>\n",
" <td>0.704037</td>\n",
" <td>0.704037</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" b2464 b0008 b2935 b2465 b3919\n",
"b2464 0.873922 0.864759 0.873922 0.873922 0.704037\n",
"b0008 0.864759 0.873922 0.873922 0.873922 0.704037\n",
"b2935 0.873922 0.873922 0.873922 0.000000 0.704037\n",
"b2465 0.873922 0.873922 0.000000 0.873922 0.704037\n",
"b3919 0.704037 0.704037 0.704037 0.704037 0.704037"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"double_gene_deletion(cobra_model, cobra_model.genes[-5:],\n",
" return_frame=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, the double deletion function will automatically use multiprocessing, splitting the task over up to 4 cores if they are available. The number of cores can be manually sepcified as well. Setting use of a single core will disable use of the multiprocessing library, which often aids debuggging."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Double gene deletions for 200 genes completed in 11.13 sec with 2 cores\n",
"Double gene deletions for 200 genes completed in 14.14 sec with 1 core\n",
"Speedup of 1.27x\n"
]
}
],
"source": [
"start = time() # start timer()\n",
"double_gene_deletion(ecoli_model, ecoli_model.genes[:200],\n",
" number_of_processes=2)\n",
"t1 = time() - start\n",
"print(\"Double gene deletions for 200 genes completed in \"\n",
" \"%.2f sec with 2 cores\" % t1)\n",
"\n",
"start = time() # start timer()\n",
"double_gene_deletion(ecoli_model, ecoli_model.genes[:200],\n",
" number_of_processes=1)\n",
"t2 = time() - start\n",
"print(\"Double gene deletions for 200 genes completed in \"\n",
" \"%.2f sec with 1 core\" % t2)\n",
"\n",
"print(\"Speedup of %.2fx\" % (t2 / t1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double deletions can also be run for reactions"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>ACKr</th>\n",
" <th>ACONTa</th>\n",
" <th>ACONTb</th>\n",
" <th>ACt2r</th>\n",
" <th>ADK1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ACKr</th>\n",
" <td>0.873922</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.873922</td>\n",
" <td>0.873922</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ACONTa</th>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ACONTb</th>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ACt2r</th>\n",
" <td>0.873922</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.873922</td>\n",
" <td>0.873922</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADK1</th>\n",
" <td>0.873922</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.873922</td>\n",
" <td>0.873922</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ACKr ACONTa ACONTb ACt2r ADK1\n",
"ACKr 0.873922 0 0 0.873922 0.873922\n",
"ACONTa 0.000000 0 0 0.000000 0.000000\n",
"ACONTb 0.000000 0 0 0.000000 0.000000\n",
"ACt2r 0.873922 0 0 0.873922 0.873922\n",
"ADK1 0.873922 0 0 0.873922 0.873922"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"double_reaction_deletion(cobra_model,\n",
" cobra_model.reactions[2:7],\n",
" return_frame=True)"
]
}
],
"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
}
| lgpl-2.1 |
brunorpinho/ufpel-python | modulos/Modulo 2.ipynb | 2 | 1304761 | null | mit |
relopezbriega/mi-python-blog | content/notebooks/DataViz.ipynb | 1 | 401155 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualizaciones de datos con Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Esta notebook fue creada originalmente como un blog post por [Raúl E. López Briega](https://relopezbriega.com.ar/) en [Matemáticas, análisis de datos y python](https://relopezbriega.github.io). El contenido esta bajo la licencia BSD.*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img alt=\"Visualizaciones de datos con Python\" title=\"Visualizaciones de datos con Python\" src=\"https://relopezbriega.github.io/images/DataViz.png\" high=400px width=600px>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introducción\n",
"\n",
"Las visualizaciones son una herramienta fundamental para entender y compartir ideas sobre los datos. La visualización correcta puede ayudar a expresar una idea central, o abrir un espacio para una más profunda investigación; con ella se puede conseguir que todo el mundo hable sobre un [conjunto de datos](https://es.wikipedia.org/wiki/Conjunto_de_datos), o compartir una visión sobre lo que los datos nos quieren decir.\n",
"\n",
"Una buena visualización puede dar a quien la observa un sentido rico y amplio de un [conjunto de datos](https://es.wikipedia.org/wiki/Conjunto_de_datos). Puede comunicar los datos de manera precisa a la vez que expone los lugares en dónde se necesita más información o dónde una hipótesis no se sostiene. Por otra parte, la visualización nos proporciona un lienzo para aplicar nuestras propias ideas, experiencias y conocimientos cuando observamos y analizamos datos, permitiendo realizar múltiples interpretaciones. Si como dice el dicho *\"una imagen vale más que mil palabras\"*, un gráfico interactivo bien elegido entonces podría valer cientos de [pruebas estadísticas](https://es.wikipedia.org/wiki/Contraste_de_hip%C3%B3tesis).\n",
"\n",
"## Librerías para visualizar datos en Python\n",
"\n",
"Como bien sabemos, la comunidad de [Python](https://python.org/) es muy grande, por lo tanto vamos a poder encontrar un gran número de librerías para visualizar datos. Al tener tanta variedad de opciones, a veces se hace realmente difícil determinar cuando utilizar cada una de ellas. En este artículo yo voy a presentar solo cuatro que creo que cubren un gran abanico de casos:\n",
"\n",
"* **[Matplotlib](https://matplotlib.org/gallery.html)**: Que es la más antigua y se convirtió en la librería por defecto para visualizaciones de datos; muchas otras están basadas en ella. Es extremadamente potente, pero con ese poder viene aparejada la complejidad. Se puede hacer prácticamente de todo con [Matplotlib](https://matplotlib.org/gallery.html) pero no siempre es tan fácil de averiguar como hacerlo. Los que siguen el [blog](https://relopezbriega.github.io/) me habrán visto utilizarla en varios artículos.\n",
"\n",
"\n",
"* **[Bokeh](https://bokeh.pydata.org/en/latest/)**: Una de las más jóvenes librerías de visualizaciones, pero no por ello menos potente. [Bokeh](https://bokeh.pydata.org/en/latest/) es una librería para visualizaciones interactivas diseñada para funcionar en los navegadores web modernos. Su objetivo es proporcionar una construcción elegante y concisa de gráficos modernos al estilo de [D3.js](https://d3js.org/), y para ampliar esta capacidad con la interactividad y buen rendimiento sobre grandes volúmenes de datos. [Bokeh](https://bokeh.pydata.org/en/latest/) puede ayudar a cualquier persona a crear en forma rápida y sencilla gráficos interactivos, *dashboards* y aplicaciones de datos. Puede crear tanto gráficos estáticos como gráficos interactivos en el servidor de [Bokeh](https://bokeh.pydata.org/en/latest/docs/user_guide/server.html).\n",
"\n",
"\n",
"* **[Seaborn](https://stanford.edu/~mwaskom/software/seaborn/)**: Si de gráficos estadísticos se trata, [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/) es la librería que deberíamos utilizar, con ella podemos crear gráficos estadísticos informativos y atractivos de forma muy sencilla. Es una de las tantas librerías que se basan en [Matplotlib](https://matplotlib.org/gallery.html) pero nos ofrece varias características interesantes tales como temas, paletas de colores, funciones y herramientas para visualizar [distribuciones](https://relopezbriega.github.io/blog/2016/06/29/distribuciones-de-probabilidad-con-python/) de una o varias [variables aleatorias](https://es.wikipedia.org/wiki/Variable_aleatoria), [regresiones lineales](https://es.wikipedia.org/wiki/Regresi%C3%B3n_lineal), [series de tiempo](https://es.wikipedia.org/wiki/Serie_temporal), entre muchas otras. Con ella podemos construir visualizaciones complejas en forma sencilla.\n",
"\n",
"\n",
"* **[Folium](https://folium.readthedocs.io/en/latest/)**: Si lo que necesitamos es visualizar datos de [geolocalización](https://es.wikipedia.org/wiki/Geolocalizaci%C3%B3n) en mapas interactivos, entonces [Folium](https://folium.readthedocs.io/en/latest/) es una muy buena opción. Esta librería de [Python](https://python.org/) es una herramienta sumamente poderosa para realizar mapas al estilo [leaflet.js](https://leafletjs.com/). El hecho de que los resultados de [Folium](https://folium.readthedocs.io/en/latest/) son interactivos hace que esta librería sea útil para la construcción de *dashboards*.\n",
"\n",
"## ¿Cómo elegir la visualización adecuada?\n",
"\n",
"Una de las primeras preguntas que nos debemos realizar al explorar datos es ¿qué método de visualización es más efectivo?. Para intentar responder esta pregunta podemos utilizar la siguiente guía: \n",
"\n",
"<img alt=\"Visualizaciones de datos con Python\" title=\"Visualizaciones de datos con Python\" src=\"https://relopezbriega.github.io/images/chartchooserincolor.jpg\" high=400px width=600px>\n",
"\n",
"Como podemos ver, la guía se divide en cuatro categorías principales y luego se clasifican los distintos métodos de visualización que mejor representan cada una de esas categorías. Veamos un poco más en detalle cada una de ellas:\n",
"\n",
"* **[Distribuciones](https://relopezbriega.github.io/blog/2016/06/29/distribuciones-de-probabilidad-con-python/)**: En esta categoría intentamos comprender como los datos se distribuyen. Se suelen utilizar en el comienzo de la etapa de exploración de datos, cuando queremos comprender las variables. Aquí también nos vamos a encontrar con variables de dos tipos [cuantitativas](https://relopezbriega.github.io/blog/2016/03/13/analisis-de-datos-cuantitativos-con-python/) y [categóricas](https://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/). Dependiendo del tipo y cantidad de variables, el método de visualización que vamos a utilizar.\n",
"\n",
"* **Comparaciones**: En esta categoría el objetivo es comparar valores a través de diferentes categorías y con el tiempo (tendencia). Los tipos de gráficos más comunes en esta categoría son los [diagramas de barras](https://es.wikipedia.org/wiki/Diagrama_de_barras) para cuando estamos comparando elementos o categorías y los [diagramas de puntos y líneas](https://en.wikipedia.org/wiki/Line_chart) cuando comparamos variables [cuantitativas](https://relopezbriega.github.io/blog/2016/03/13/analisis-de-datos-cuantitativos-con-python/).\n",
"\n",
"* **Relaciones**: Aquí el objetivo es comprender la relación entre dos o más variables. La visualización más utilizada en esta categoría es el [gráfico de dispersión](https://es.wikipedia.org/wiki/Diagrama_de_dispersi%C3%B3n).\n",
"\n",
"* **Composiciones**: En esta categoría el objetivo es comprender como esta compuesta o distribuida una variable; ya sea a través del tiempo o en forma estática. Las visualizaciones más comunes aquí son los [diagramas de barras](https://es.wikipedia.org/wiki/Diagrama_de_barras) y los [gráficos de tortas](https://es.wikipedia.org/wiki/Gr%C3%A1fico_circular).\n",
"\n",
"## Ejemplos en Python\n",
"\n",
"Luego de esta introducción es hora de ensuciarse las manos y ponerse a jugar con algunos ejemplos en el uso de cada una de estas 4 librerías que nos ofrece [Python](https://python.org/) para visualización de datos. Obviamente los ejemplos van a ser sencillos ya que un tutorial exhaustivo sobre cada herramienta requeriría mucho más espacio.\n",
"\n",
"### Matplotlib\n",
"\n",
"Comencemos con [Matplotlib](https://matplotlib.org/gallery.html); como les comentaba, es tal vez la librería más utilizada para gráficos en 2d. El objeto `pyplot` nos proporciona la interfase principal sobre la que podemos crear las visualizaciones de datos con esta librería."
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"source": [
"# <!-- collapse=True -->\n",
"# importando modulos necesarios\n",
"import numpy as np\n",
"import pandas as pd\n",
"from pydataset import data\n",
"import re\n",
"\n",
"# librerías de visualizaciones\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt \n",
"from bokeh.io import output_notebook, show\n",
"from bokeh.charts import Histogram, Scatter\n",
"import folium\n",
"\n",
"# graficos incrustados\n",
"%matplotlib inline\n",
"output_notebook()"
]
},
{
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"source": [
"# Cargamos algunos datasets de ejemplo\n",
"iris = data('iris')\n",
"tips = data('tips')"
]
},
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k0OSR8uVV+vSRvrFGshzYj2PxQgDcg4eilSptyM8NNWSHvF1lhEGsVtyj7gbA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\nJEwhhBCiEv4f6C/UMhADUaIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf07ebf828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Ejemplo matplotlib\n",
"# graficanco funciones seno y coseno\n",
"X = np.linspace(-np.pi, np.pi, 256, endpoint=True)\n",
"C, S = np.cos(X), np.sin(X)\n",
"\n",
"# configurando el tamaño de la figura\n",
"plt.figure(figsize=(8, 6))\n",
"# dibujando las curvas\n",
"plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\", label=\"coseno\")\n",
"plt.plot(X, S, color=\"red\", linewidth=2.5, linestyle=\"-\", label=\"seno\")\n",
"# personalizando los valores de los ejes\n",
"plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],\n",
" [r'$-\\pi$', r'$-\\pi/2$', r'$0$', r'$+\\pi/2$', r'$+\\pi$'])\n",
"\n",
"plt.yticks([-1, 0, +1],\n",
" [r'$-1$', r'$0$', r'$+1$'])\n",
"# agregando la leyenda\n",
"plt.legend(loc='upper left')\n",
"# moviendo los ejes de coordenadas\n",
"ax = plt.gca() # get current axis\n",
"ax.spines['right'].set_color('none')\n",
"ax.spines['top'].set_color('none')\n",
"ax.xaxis.set_ticks_position('bottom')\n",
"ax.spines['bottom'].set_position(('data',0))\n",
"ax.yaxis.set_ticks_position('left')\n",
"ax.spines['left'].set_position(('data',0))\n",
"# mostrando el resultado\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En este primer ejemplo vemos como podemos acceder a la API de [Matplotlib](https://matplotlib.org/gallery.html) desde el objeto `pyplot` e ir dando forma al gráfico. Veamos ahora unos ejemplos con el dataset iris."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sepal.Length</th>\n",
" <th>Sepal.Width</th>\n",
" <th>Petal.Length</th>\n",
" <th>Petal.Width</th>\n",
" <th>Species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n",
"1 5.1 3.5 1.4 0.2 setosa\n",
"2 4.9 3.0 1.4 0.2 setosa\n",
"3 4.7 3.2 1.3 0.2 setosa\n",
"4 4.6 3.1 1.5 0.2 setosa\n",
"5 5.0 3.6 1.4 0.2 setosa"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Ejemplo con iris\n",
"# histograma de Petal.Length\n",
"iris.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# separo en especies\n",
"setosa = iris[iris.Species == 'setosa']\n",
"versicolor = iris[iris.Species == 'versicolor']\n",
"virginica = iris[iris.Species == 'virginica']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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KzJcDM4E7ShUoSZLUamo5B2zrzLx+1ZvMvAl4eeNKkiRJam39HYJc5X8j4j+A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\n/x/6+CDlbwDS7QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf045155c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# crear histograma\n",
"plt.figure(figsize=(10, 8))\n",
"n, bins, patches = plt.hist(setosa['Petal.Length'], 12, \n",
" facecolor='red', label='setosa')\n",
"n, bins, patches = plt.hist(versicolor['Petal.Length'], 12, \n",
" facecolor='green', label='versicolor')\n",
"n, bins, patches = plt.hist(virginica['Petal.Length'], 12, \n",
" facecolor='blue', label='virginica')\n",
"plt.legend(loc='top_right')\n",
"plt.title('Histograma largo del pétalo')\n",
"plt.xlabel('largo del pétalo')\n",
"plt.ylabel('cuenta largo del pétalo')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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eadO24Nlnn+ab3/waM2Z8in333Z/jjvsmEyY08aEPfZgtttiS8847izXXHMuECRMYM2YM\nhx56OKecciKXXno1Bx30ObbYYkteeOF5RowYwZlnnkvEs9x88y849dTTuf32m7n55ptob29n++13\n4NBDD+cXv/gZDzxwL0uXLmX8+Amcfvo5jBpV+fLJW5ySJGnQ3HvvwlJxBjCCxx77f8yb99qA5tx7\n7xnceedtANx5520cfviRHWOtra2cf/4P+PznD+Scc87g5JP/lQsuuJh3v3tyxzbLG54vXvwGu+66\nOxdddBlrr93M7373247x1tZWrr/+Oi655Equuup63n77bRYvXsyiRW1ccMElXHrp1Sxbtoxnn316\nQOdSLgs0SZI0aCZMeBt4p+P9xIl/o7Fx3IDm3GabbXn22adpa2vjiSdmM2bMmI6x9defxMiRIwH4\n+9/n0dIyFYBp07bInGvjjTcBYJ111uWtt97s+Hzu3L/w3ve+l9GjRwPwla8cyZprrsnIkaM45ZQT\nOfPM7/L666+xbNmyAZ1LuSzQJEnSoDnuuB3ZcceraGx8kJaWWzj22NVZc801BzRnQ0MDO+30Mc49\n9wx22GHHLrdLl18dA1hnnfWYM+cVAP7nf/640rmyvPvdk5kzZ05HAXbyyccze/ZjPPjgfZx66ukc\nffSxtLe3U63v7vsdNEmSNGjGjh3LT3/6GVpb5zN16ta0tb01KPPuscfefOYz+3HDDbN4/PE/dHze\nueD6p386ntNPP5U111yT0aNHs/bazd1macjcD2DChAl84Qtf4sgjZzJiRAMf/vAObLbZ5qyxxpp8\n7WuHUSgUWGutZl5/fd6gnE9ffIpzGPOpop7MSTbzks28ZDMvPZmTbNXOy003/ZxddtmV8eMncPnl\nlzB69GgOPviwqh2/XD7FKUmS6sbEiRM5+ugjWWONNRk7diwnnXRqrUPqNws0SZI0LOy44y7suOMu\ntQ5jUPiQgCRJUs5YoEmSJOWMBZokSVLOWKBJkiTljAWaJElSzligSZIk5YwFmiRJUs5YoEmSJOWM\nBZokSVLOWKBJkiTljAWaJElSzligSZIk5YwFmiRJUs6MqvYBU0qjgKuAqcBqwGkRcVun8W8ChwGv\nlT76SkQ8X+04JUmSaqXqBRrwReD1iPhSSqkJmA3c1ml8K+DAiHi8BrFJkoap+fMXcPzx9zJnzjha\nWhZy9tk709Q0YVDmnDu3iUmT5neZsxLHU/2oRYH2M+DnpdcjgLe7jW8FnJBSWh+4IyLOrGZwkqTh\n6fjj7+WWWw4EGpg9uwD8iMsvnzFoc0LXOStxPNWPqhdoEbEYIKXUSLFQO6nbJjcAPwDagJtTSntE\nxJ19zdvc3DjYoQ4L5qUnc5LNvGQzL9mGYl7mzm2iWEgBNDB3btOAz6O3OStxvKGoHs95MNTiChop\npQ2Am4CLIuKn3YYviIi20nZ3AFsAfRZo8+YtGvQ4h7rm5kbz0o05yWZespmXbEM1L5Mmzad4lat4\ntWvSpNYBn0dvc1bieEPNUP1ZqbRyitZaPCSwLvAr4MiIuLfb2DjgqZTSpsASYGfgymrHKEkafs4+\ne2fgR6XvhLVx9tk7Ddqcxe+gtXaZsxLHU/1oKBQKVT1gSun7wKeBZ1lx0/5y4F0RcUVK6QvAUcBS\n4O6IOLWMaQtW6D35m0tP5iSbeclmXrKZl57MSTbzkq25ubGhr21q8R20bwLf7GX8x8CPqxeRJElS\nvrhQrSRJUs5YoEmSJOWMBZokSVLOWKBJkiTljAWaJElSzligSZIk5YwFmiRpwObPX8DMmbPYbbe7\nmTnzJlpbF9QkjhdfnMP06f9OS8sspk+/kJdfnlOTOKSBqkmrJ0nS8JKXxuAHHHArc+eeADSwZEmB\nGTPOYPbsb1Q9DmmgvIImSRqwOXPG0bkxePF99bW2Tu4SR/G9NPRYoEmSBqylZSHFzn0ABVpa2moS\nR1PTq13iaGr6c03ikAbKW5ySpAHLS2PwWbP2ZcaMM2htnUxT05+ZNWufmsQhDZQFmiRpwJqaJtTk\nO2fdbbhhi98507DgLU5JkqScsUCTJEnKGQs0SZKknLFAkyRJyhkLNEmSpJyxQJMkScoZCzRJkqSc\ncR00SdKAzZ+/gOOPv7e0UO1Czj57Z5qaJlRsbLBjzNN+Q8VwP79as0CTJA1Yb83SKzE22DHmab+h\nYrifX615i1OSNGC9NUuvxNhgx5in/YaK4X5+tWaBJkkasN6apVdibLBjzNN+Q8VwP79a8xanJGnA\nemuWXomxwY4xT/sNFcP9/GqtoVAo9L1V/hXmzVtU6xhyp7m5EfPSlTnJZl6ymZds5qUnc5LNvGRr\nbm5s6Gsbb3FKkiTljAWaJElSzligSZIk5YwFmiRJUs5YoEmSJOWMBZokSVLOuA6aJGnYsG/mqqvn\nc88zCzRJ0rBh38xVV8/nnmfe4pQkDRv2zVx19XzueWaBJkkaNuybuerq+dzzzFuckqRhw76Zq66e\nzz3P7MU5jNkDrSdzks28ZDMv2cxLT+Ykm3nJZi9OSZKkIcgCTZIkKWcs0CRJknLGAk2SJClnLNAk\nSZJyxgJNkiQpZyzQJEmScsaFaiVJubSyJt69Nffu71h/4hhKhsM51BsLNElSLq2siXdvzb37O9af\nOIaS4XAO9cZbnJKkXFpZE+/emnv3d6w/cQwlw+Ec6o0FmiQpl1bWxLu35t79HetPHEPJcDiHeuMt\nTklSLq2siXdvzb37O9afOIaS4XAO9cZm6cOYTWp7MifZzEs285LNvPRkTrKZl2w2S5ckSRqCLNAk\nSZJyxgJNkiQpZyzQJEmScsYCTZIkKWeqvsxGSmkUcBUwFVgNOC0ibus0vjfwbeBt4OqIuKLaMUqS\nJNVSLa6gfRF4PSJ2AHYHLlo+UCrezgM+BuwIHJ5Saq5BjJIkSTVTi4Vqfwb8vPR6BMUrZcttBjwf\nEW0AKaXfADsAv6hqhJJUp8ppNj53bhOTJs2veJNy9VSJXPrnk09VL9AiYjFASqmRYqF2UqfhccDC\nTu8XAeOrF50k1bdym40X2wZVtkm5eqpELv3zyaeatHpKKW0A3ARcFBE/7TTURrFIW64RWFDOnM3N\njYMX4DBiXnoyJ9nMS7Z6y8vcuU10bqo9d25TRw4qMTacVOOcKpHLSv/5DMc/62qoxUMC6wK/Ao6M\niHu7DT8DbJRSmgAspnh785xy5rWVRE+22OjJnGQzL9nqMS+TJs2neHWseJVs0qTWjhxUYmy4qNbP\nSiVyWck/n3r8b6gc5RSttbiCdgIwAfh2Suk7FH8qLgfeFRFXpJSOAX5N8Sflioj4aw1ilKS6VE6z\n8eJ30For3qRcPVUil/755JPN0ocxf3PpyZxkMy/ZzEs289KTOclmXrLZLF2SJGkIskCTJEnKGQs0\nSZKknLFAkyRJyhkLNEmSpJyxQJMkScoZCzRJkqScsUCTJJVl/vwFzJw5i623vo2ZM2+itXVFJ74X\nX5zD9On/TkvLLKZPv5CXX56zSnPuttvdPeYc6obzuanyatKLU5I09PTWLP2AA25l7twTgAaWLCkw\nY8YZzJ79jVWac7g16h7O56bK8wqaJKksc+aMo3NT7eL7otbWyV3Giu8HNudQN5zPTZVngSZJKktL\ny0KKV84ACrS0tHWMNTW92mWsqenPA55zqBvO56bK8xanJKksvTVLnzVrX2bMOIPW1sk0Nf2ZWbP2\nWaU5h2Oj7uF8bqo8m6UPYzap7cmcZDMv2cxLNvPSkznJZl6y2SxdkiRpCLJAkyRJyhkLNEmSpJyx\nQJMkScoZCzRJkqScsUCTJEnKGQs0Saozjz72BFPeeyrrTvoPprznX5j9xBMdY0Ohp2ZvMfY3joHu\nl9WftL/s4SlwoVpJqjszPnUzSxedAzSw9P8K7LP/sfzpxWnA0Oip2VuM/Y1jMPbr3p+0v+zhKfAK\nmiTVnTeXbEznHpHF90VDoadmbzH2N45q71ftOTX0WKBJUp0Zs/pzdO4ROWaN5zvGhkJPzd5i7G8c\n1d6v2nNq6PEWpyTVmVtnzWCf/Y/lzSUbM2aN57n1pv06xoZCT83eYuxvHAPdL6s/aX/Zw1NgL85h\nzR5oPZmTbOYlm3nJZl56MifZzEs2e3FKkiQNQRZokiRJOWOBJkmSlDMWaJIkSTljgSZJkpQzFmiS\nJEk5Y4EmSZKUMy5UK0k5Nn/+Ao4//t7SoqULOfvsnWlqmlCbWFrnc/xVxzB32atMGjmZsw87n6YJ\nE2sSizTcWaBJUo7lqXH28Vcdwy2r31TqC/4IXNnA5f90TU1ikYY7b3FKUo7lqXH2nKWvdA6l+F5S\nRVigSVKO5alxdsuYls6h0LL61JrFIg133uKUpBzLU+Pssw87H65sKH4HbdQGnP3l82oWizTcWaBJ\nUo41NU2o2XfOumuaMJHL/+kaG2BLVeAtTkmSpJyxQJMkScqZPm9xppQ+CuwDbAy0Ay8At0TEgxWO\nTZIkqS6ttEBLKU0Hvg+8BjwI3A+8DWwI/GNK6TTgmxHxWDUClSRJqhe9XUH7AnBARPw9Y+zilNI6\nwLcACzRJkqRBtNICLSKO7W3HiHgNOGbQI5IkSapz5XwH7SPAN4Gmzp9HxM6VCkqSJKmelbMO2jXA\nqcCcyoYiSepueYPyOUtfoWVMS9kNyvu7X7XjzIs8NaWXoLwC7S8RcV3FI5Ek9dDRoHwNmF14rOwG\n5f3dr9px5kWemtJLUF6BdmFK6XrgHmDZ8g8t2iSp8uYsfQXWKL1ZhQbl/d2vv6p9vMGWp6b0EpS3\nUO3XgEnAR4CdSv/sWMGYJEkl/W1QXu3G5kO9kXqemtJLUN4VtPUjYrOKRyJJ6mF5g/I5S1+hZfWp\nZTco7+9+1Y4zL/LUlF4CaCgUCr1ukFK6DLgVuCsilvW6ce0UbNzbkw2NezIn2cxLNvOSzbz0ZE6y\nmZdszc2NDX1tU84VtL2Bw4BCSgmKN+kLETFyYOFJkiQpS58FWkSsv/x1SqkhInq/5CZJkqQB6fMh\ngZTSjimlh0pvN0kpvZRS2q7CcUmSJNWtcp7iPA/4CkBEBLAHcEElg5IkSapn5RRoq0fEU8vfRMSz\nwOjKhSRJklTfynlI4NmU0lnAj0rvPws8N9ADp5S2Ac6MiJ26ff5Nig8lvFb66CsR8fxAjydJkjRU\nlFOgfRn4LnAD8DZwPzBzIAdNKR0LHAj8X8bwVsCBEfH4QI4hSZI0VK20QEsprRcRf4uIVuDrvW3T\nj+O+AMxgxVW5zrYCTkgprQ/cERFn9mN+Saq6SjQMv+Wum5l56ZdgLeDvcPVXf8yeu+3d5/H6G8uL\nL7/IAWfsRevo+TS9NZFZJ9/Bhi3vKc5Zaig+d24TkybN79JQ3Gbj0uDq7QramSmlvwDXRkSXW5op\npU0pXllbj+KVsFUSEbNSSi0rGb4B+AHQBtycUtojIu5c1WNIUrVVomH4zEu/BB+jtAIlHHLJF3ht\nt7Y+j9ffWA44Yy/mTvsLNMCSwl+YcdqezL7smeKcnRqKF9sirWgobrNxaXCttECLiINTSnsCl6eU\nNgbmUmyWPhl4ETgnIm6vQEwXREQbQErpDmALoM8Crbm5sQKhDH3mpSdzks28ZFuVvMxd9mrnftvM\nXfbqwPO6Fl3mZK0VMfV2vP7GsmC11i77LVitdcWcc5u6BDN3blNZY/Wi3s63XOalf3r9DlpE3AHc\nkVJqAt4LtAMvl257DoYurQ5SSuOAp0pX6JYAOwNXljORrSR6ssVGT+Ykm3nJtqp5mTRyMhQe6bjA\nNGnUBgPP69+Lc3VctPr7ir/vejtef2OZ8GYTiwuLO/ab8FbTijknze8SzKRJrWWN1QP/G8pmXrKV\nU7SW85AApYLsDwMNKEMBIKX0OeBdEXFFSukE4D5gKXB3RNxVgeNK0qCrRMPwq7/6Yw655AtdvoNW\nzvH6G8usk+9gxml7Fr+D9vZEZp10x4o5Sw3Fi99Ba+3SUNxm49Lg6rNZ+hBhs/QM/ubSkznJZl6y\nmZds5qUnc5LNvGQrp1l6OQvVSpIkqYr6vMWZUmoAjgB2KW1/L/DvEdFe4dgkSZLqUjnfQTsb2Bi4\niuK3Pw8BpgJHVy4sSZKk+lVOgbYbsMXyK2alpS/+iAWaJElSRZTzHbRRdC3kRgHvVCYcSZIklXMF\n7cfAfSk9DqyCAAAfJUlEQVSlG0rvP0dxtX9JkiRVQJ8FWkScnlJ6nOKisSOA00oL2ErSkFWJvpn9\n1Vv/y970dg699tTM0bkPNnuCarhY6TpoKaUdetsxIh6oSET94zpoGVx/pidzkq0e8zLz3IOLvSpL\nK+bvu3T/Hr0qq5WX6Ydv1tH/kgJMeuLdHf0ve9PbOfQ2Zznn3ps8/7zMnDmrS7/QffetTk/QPOek\nlsxLtnLWQevtCtqpvYwVKF5Rk6Qhac7SV2CN0puG0vsaaR09v0v/y9bR88var7dz6G3OPJ37YJsz\nZxydT7z4Xhp6emuWbp8OScNWy5gWZhce67iK1LL61JrF0vTWRJYUVlztanq7vNuNvZ1Db3Pm6dwH\nW0vLQmbPXtETtKWlrdYhSf1SzkK12wPHAmMp/sSPBFoiYmplQ5OkyqlE38z+6q3/ZW96O4dee2rm\n6NwHmz1BNVz02YszpfQscBZwMHAhsDuwKCLytA6a30HL4L3/nsxJNvOSzbxkMy89mZNs5iXbYPXi\nXBIRVwP3Aa3ATOCjAwtNkiRJK1NOgbY0pTQRCOBDEVEA3lXZsCRJkupXOQXaecBPgduAL6WU/gf4\nQ0WjkiRJqmPldBL4L+DGiCiklLYCNgEWVDYsSZKk+rXSAi2ltAHFpzbvBHZPKS3/QttC4JfAppUP\nT5Ikqf70tVDtTsAkoHPXgGXA7ZUMSpIkqZ71tlDtoQAppeMj4qzqhSRJklTfynlI4PsppRNTStem\nlMallL6TUlqt4pFJUsn81vnMPPdgdjttR2Z+7yBaF5TXCqm/Hn3iUaZ8dl1GzBjBlM+uy+w/Pt4x\n9uLLLzL98M1oOXJdps/cjJfnvDTgsd7Or79jkoa23pqlfyQiHkwpXQ7MA/YBtgZ+CDRExIHVC7NP\nLlSbwQUCezIn2fKel4E2915VUz67Lks/sqTjeKs/uAZ/+sn/Ar03Ie/vWG/n19+xSsr7z0stmJNs\n5iXbQBeq/VFK6Sxgq4g4EXg7IhYDBwFbDFKMktSnOUtf6dL4u9LNvd8ct7TL8d4ct7RjrLcm5P0d\n6+38+jsmaWjrrUDbELgJaC/d0lx+qW3tTq8lqeJaxrSs+FunCs29x7St3uV4Y9pW7xhremtil7HO\nTcj7O9bb+fV3TNLQ1ttDAgXg4ZTSBRTXQls/pfR9YAbFJzwlqSqq3dz71pPuYp/TPsGb45Yypm11\nbj3pro6x3pqQ93est/Pr75ikoa3PZukAKaXNKS65MQK4PyKerHRgq8jvoGXw3n9P5iSbeclmXrKZ\nl57MSTbzkm1QmqWnlEYDuwGfoFikbdNp0VpJkiQNsnJaPV0BrAFcRrGg+xLwPuCbFYxLkiSpbpVT\noG0TER1tnVJKtwFPVS4kSZKk+lbOQrWvppQ26vR+XeAvFYpHkiSp7pVzBW008ERK6QGKfTi3B/6a\nUroHICJ2rmB8kiRJdaecAu2Ubu+/V4lAJEmSVNRngRYR91cjEEmSJBWVcwVNkoad+a3zOf6qY4qL\nvI5p4ezDzqdpwsQuY3OXvcqkkZO7jPV3zkrsV+05JVWPBZqkunT8VccUG42vAbMLj8GVDR2NxjvG\nGoDCI13G+jtnJfar9pySqmelBVpKaYfedoyIBwY/HEmqjjlLXymu8AjZTchXMtbfOSuxX7XnlFQ9\nvV1B663fZgHw6U1JQ1bLmJbilaUGMpuQr2ysv3NWYr9qzympenprlr5TNQORpGoqpwn53GWvMmnU\nBmU3Ie9v8/JKND23kbo0tPXZLD2l1EKx3dNU4CPAfwCHRsQrlQ5uFdgsPYNNansyJ9nMSzbzks28\n9GROspmXbIPSLB24FDgH+D/gf4EbgOsGFpokSZJWppwCbe2I+DVARBQi4nJgXGXDkiRJql/lFGhL\nUkqTKT4YQEppe+DNikYlSZJUx8pZB+0Y4HbgvSml2cBE4NMVjUqSJKmOldPq6ZGU0j8AmwAjgWcj\n4q2KRyZJklSneluo9mpKtzUzxoiIQysWlSRJUh3r7Tto9wH3A43AJOAe4NdAUx/7SZIkaQB6W6j2\nWoCU0teAbSOivfT+Z8B/Vyc8SZXU36bgQ0U5DdFtJi4pj8p5SGA8xQcDXi+9XxcYW7GIJFVNf5uC\nDxVlNUS3mbikHCqnQDsNeDKl9BDFhwS2Ab5R0agkVcVwb6hdiYboklQNfX6XLCJ+BGwF/AS4Htgi\nIm6qdGCSKq9lTMuKR4GGYUPt3s5vuJ+7pKGtnCtoRMRfgV9UOBZJVdbfpuBDRTkN0W0mLimP+myW\nPkTYLD2DTWp7MifZzEs285LNvPRkTrKZl2yD1SxdkiRJVdTnLc6U0prAvwA7l7a/Fzg5It6obGiS\nJEn1qZwraBcBawKHAgcBo4EfVjIoSZKkelbOQwJbRcS0Tu+/nlJ6ulIBSZIk1btyrqCNSClNWP6m\n9HpZ5UKSJEmqb+VcQTsPeCSldCvF9cb3Bs4Y6IFTStsAZ0bETt0+3xv4NvA2cHVEXDHQY0mSJA0l\n5SxUezUwA3gJeBnYPyKuGshBU0rHApcDY7p9PopiQfgxYEfg8JRS80COJam6Xnz5RaYfvhktR67L\n9Jmb8fKclyq63/zW+cw892B2O21HZn7vIFoXzB9I+F3m3Pqft+4xZyWOJ0nd9VmgpZRGAxsAbcBC\nYIuU0pcGeNwXKBZ93W0GPB8RbRHxNvAbYIcBHktSFR1wxl7MnfYXlrxvCXOn/4UZp+1Z0f2W99Sc\n3fQYt6wxi+OuPGYg4XeZ85HGR3rMWYnjSVJ35dzi/DmwPvAMXRqjcF1/DxoRs1JKLRlD4ygWgcst\notisvU/NzY39DWdYMy89mZNsg5WXBau1Fr8MAdBQfF/O3P3db+6yV7vsN3fZqwM+l97mrMTxhqJ6\nPOe+mJNs5qV/yinQNo2ITSseSVEbxSJtuUZgQTk7ulJxT67g3JM5yTaYeZnwZhOLC4uLRUwBJrzV\nVNbc/d1v0sjJUHikY79JozYY8Ln0NmcljjfU+N9RT+Ykm3nJVk7RWk6B9mJKaUpE/GngIfXQvdXB\nM8BGpSdFF1O8vXlOBY4rqUJmnXwHM07bk9bR82l6eyKzTrqjovtVoqdmbz1K7eEpqRpW2oszpXQv\nxVuZ61D8DtoTFJfXaAAKEbHzQA5cusV5Q0Rsl1L6HPCuiLgipbQncErpOFdGRDmL4tqLM4O/ufRk\nTrKZl2zmJZt56cmcZDMv2crpxdnbFbR/GbxQeoqIOcB2pdc3dPr8DqC8X50lSZKGoZU+xRkR90fE\n/RSfuNyj9PpPwJeBZ6sUnyRJUt0pp5PA9RTXQAOYCzwI/KhiEUmSJNW5cgq0iRFxKUBEvBkRlwNr\nVzYsSZKk+lVOgbYkpbT78jcppV2ANyoXkiRJUn0rZ5mNI4DrU0o/ovhk5Z+AAysalSRJUh3rs0CL\niNnA+1NKawFvR0Rb5cOSJEmqX30WaCmlLYATgYlAQ0oJgIGugyZJkqRs5dzivA64FHiKFb04JUmS\nVCHlFGiLI+KiikciSZIkoLwC7VcppW8AvwKWLv+wQr05JUmS6l45BdryJzaPYcUtzjHAuysSkSRJ\nUp3rcx20iNgwIjYENqH4sMAcYEKlA5MkSapX5TzFuSHwFeBgoAk4Dfh0ZcOSJEmqXyst0FJKMygu\nUrslMIvirc7LI+JfqxSbJElSXertCtovgJ8D20bECwAppfaqRCVJklTHeivQPkDxtuZvUkqvADf0\nsb0kSZIGwUofEoiIpyLinyk+rXkGsCOwbkrpjpTSHlWKT5Ikqe6U04vzHeAW4JaUUjPF76KdAdxZ\n4dgkSZLq0irdsoyIecB5pX8kSZJUAX2ugyZJkqTqskCTJEnKGQs0SZKknLFAkyRJyhkLNEmSpJyx\nQJMkScoZCzRJkqScsUCTJEnKGQs0SZKknLFAkyRJyhkLNEmSpJyxQJMkScoZCzRJkqScsUCTJEnK\nGQs0SZKknLFAkyRJyhkLNEmSpJyxQJMkScoZCzRJkqScsUCTJEnKGQs0SZKknLFAkyRJyhkLNEmS\npJyxQJMkScoZCzRJkqScsUBTTSycP5/bZx7Mg7vtyO0zD2Jh6/xahyRJUm6MqnUAqk8PHn8MB99y\nEw1AYfZjXEMDe11+Ta3DkiQpF7yCppoYP+cVGkqvG0rvJUlSkQWaamJhSwuF0usCsLBlag2jkSQp\nX7zFqZr4yNnncw0NjJ/zCgtbpvKRs8+rdUiSJOWGBZpqYnzTRL9zJknSSniLU5IkKWcs0CRJknLG\nAk2SJClnLNAkSZJyxgJNkiQpZyzQJEmScqbqy2yklBqAi4FpwFLgsIh4qdP494EPA4tKH+0bEYt6\nTCRJkjRM1WIdtP2AMRGxXUppG+C80mfLbQV8PCLsnl2nFs6fz4PHH1NaxLaFj5x9PuObJtY6LEmS\nqqYWBdr2wF0AEfFwSumDywdKV9c2Bi5LKa0HXBkRV9cgRtWQjdQlSfWuFgXaOGBhp/fLUkojIqId\neBdwIcWraqOAe1NKj0TEU31N2tzcWJFgh7qhmJe1577apZH62nNfHdTzGIo5qQbzks28ZDMvPZmT\nbOalf2pRoLUBnf+0lhdnAIuBCyNiKUBK6R6K31Xrs0CbN8+vqXXX3Nw4JPPy+qTJFHikeAUNeH3S\nBoN2HkM1J5VmXrKZl2zmpSdzks28ZCunaK1FgfYQsBdwY0rpQ8AfO41tAvw0pTS9FNv2wDVVj1A1\nZSN1SVK9q0WBNgvYNaX0UOn9ISmlo4HnI+L2lNJ1wMPAW8C1EfFMDWJUDdlIXZJU76peoEVEAfhq\nt4+f6zR+LnBuVYOSJEnKEReqlSRJyhkLNEmSpJyxQJMkScoZCzRJkqScsUCTJEnKGQs0SZKknLFA\nU1kWzp/P7TMP5sHdduT2mQexsLXvXvZPP/ooF0xZl9vWHc8FU9bl6dmPlzVff441kP0kScqbWixU\nqyGoPw3M/3PGHnx36ZLiPkuX8O19PsHmf/rfPufrb7N0m6xLkoYLr6CpLOPnvNKlgfn4Oa/0uc97\n3lzaZZ/3vLm0rPn6c6yB7CdJUt5YoKksC1taKJReF4CFLVP73OelMat32eelMauXNV9/jjWQ/SRJ\nyhtvcaos/Wlgvuutd/HtfT7Be95cyktjVmfXW+8qa77+Nku3ybokabhoKBQKfW+Vf4V58xbVOobc\naW5uxLx0ZU6ymZds5iWbeenJnGQzL9mamxsb+trGW5ySJEk5Y4EmSZKUMxZokiRJOWOBJkmSlDMW\naJIkSTljgSZJkpQzFmiSJEk5Y4Gmsrz64otcNn0z7mxZl8umb8arL7/UMbaypug2PZckqX/sJKCy\n/PKAvThx7l+KjciX/IXTZ+zJ4bOfAVbeFN2m55Ik9Y9X0FSWya3zuzQin9zpqtbKmqLb9FySpP6x\nQFNZ/tw0sUsj8j83TewYW1lTdJueS5LUP97iVFl2n3UHp8/Yk8mt8/lz00R2n3VHx9jKmqLb9FyS\npP6xWfowZpPansxJNvOSzbxkMy89mZNs5iWbzdIlSZKGIAs0SZKknLFAkyRJyhkLNEmSpJyxQJMk\nScoZCzRJkqScsUAbhpb3srxt661XqZdlb/02H77nHs5fbwK3rTOO89abwMMP3N8xdu353+PcdcZx\n2zrj+N4647jqwvMB+Nlll3T5/CdXXtaxz9233Nwxdu4647j7jtt6xG8PT0lSvXIdtGHo9pkHr+hl\nCVyz7/5l9bK8bPpmK/ptAqdPendHv83z15vAae3tHWMnjRjB0X9bAMC564zjDOgYOwH4p9faVvp5\nb/sMJP5y9nNNnmzmJZt5yWZeejIn2cxLNtdBq1P97WXZW7/NjUrF2fKxjdrbO8Y2KX22fGyTPj7v\na8wenpKkemeBNgz1t5dlb/02nx8xosvYCyNW/OhE6bPlY8/18Tml1ysbs4enJKne2YtzGFrey3Lt\nua/y+qQNyu5l2Vu/zQ//7BZO+vS+bNTezgsjRrDdz27pGJt48qmc8G+nsAnFQqvp5FMBmHzG9zjh\nhH/u+PzdZ3yvY5/pV/+YEw75QsfY9Kt/3CN+e3hKkuqV30Ebxrz335M5yWZespmXbOalJ3OSzbxk\n8ztokiRJQ5AFmiRJUs5YoEmSJOWMBZokSVLOWKBJkiTljAWaJElSzligSZIk5YwFWp2pREPxlTVZ\n7635uiRJWjk7CdSZB48/ZkVD8dmPcQ0NZTUi780vD9hrRZP1JX/h9Bl7cvjsZ1b6uSRJ6p1X0OpM\nJRqKr6zJem/N1yVJ0spZoNWZSjQUX1mT9d6ar0uSpJXzFmedqURD8ZU1We+t+bokSVo5m6UPYzap\n7cmcZDMv2cxLNvPSkznJZl6y2SxdkiRpCLJAkyRJyhkLNEmSpJyxQJMkScoZCzRJkqScqfoyGyml\nBuBiYBqwFDgsIl7qND4TOBx4GzgtIlybQZIk1ZVaXEHbDxgTEdsBJwAdC3GllNYFvgFsC3wCOCOl\nNLoGMUqSJNVMLQq07YG7ACLiYeCDnca2Bn4TEcsiog14HvhA9UOUJEmqnVoUaOOAhZ3eL0spjVjJ\n2P8B46sVmCRJUh7UotVTG9DY6f2IiGjvNDau01gjsKCcSZubG/veqA6Zl57MSTbzks28ZDMvPZmT\nbOalf2pRoD0E7AXcmFL6EPDHTmO/B/4tpbQasAawKfBUOZPaSqInW2z0ZE6ymZds5iWbeenJnGQz\nL9nKKVprUaDNAnZNKT1Uen9ISulo4PmIuD2ldCHwG6ABODEi3qpBjJIkSTVT9QItIgrAV7t9/Fyn\n8SuBK6salCRJUo64UK0kSVLOWKBJkiTljAWaJElSzligSZIk5YwFmiRJUs5YoEmSJOWMBZokSVLO\nWKBJkiTljAWaJElSzligSZIk5YwFmiRJUs5YoEmSJOWMBZokSVLOWKBJkiTljAWaJElSzligSZIk\n5YwFmiRJUs5YoEmSJOWMBZokSVLOWKBJkiTljAWaJElSzligSZIk5YwFmiRJUs5YoEmSJOWMBZok\nSVLOWKBJkiTljAWaJElSzligSZIk5YwFmiRJUs5YoEmSJOWMBZokSVLOWKBJkiTljAWaJElSzlig\nSZIk5YwFmiRJUs5YoEmSJOWMBZokSVLOWKBJkiTljAWaJElSzligSZIk5YwFmiRJUs5YoEmSJOWM\nBZokSVLOWKBJkiTljAWaJElSzligSZIk5YwFmiRJUs5YoEmSJOWMBZokSVLOWKBJkiTljAWaJElS\nzligSZIk5YwFmiRJUs5YoEmSJOWMBZokSVLOWKBJkiTlzKhqHzCltDpwPbAO0AYcFBF/77bNzcBa\nwNvAkojYs9pxSpIk1UrVCzTgq8CTEfGvKaXPAN8Gvtltm40j4n3VD02SJKn2anGLc3vgrtLrXwIf\n6zyYUloHmJBSujWl9EBKyatnkiSprlT0ClpK6VDgaKBQ+qgB+BuwsPR+ETCu226rAd8DLqB4m/Oh\nlNLDEfF6JWOVJEnKi4oWaBFxFXBV589SSr8AGktvG4EF3Xb7G3BpRLQD81JKjwMJ6K1Aa2hubuxl\nuH6Zl57MSTbzks28ZDMvPZmTbOalf2pxi/MhYI/S6z2AB7uNfwz4OUBKaSzwPuCZqkUnSZJUYw2F\nQqHvrQZRSmkN4FpgfeBN4PMR8VpK6Szg5xHxh5TSecC2wDvAWRFxW1WDlCRJqqGqF2iSJEnqnQvV\nSpIk5YwFmiRJUs5YoEmSJOWMBZokSVLO1KLV06BIKTUAFwPTgKXAYRHxUm2jyo+U0jbAmRGxU61j\nyYOU0iiKa/JNpbgY8mk+HQwppRHA5RTXGmwHjoiIp2sbVT6Uupr8AfhYRDxX63jyIKX0KCsWGn85\nIr5cy3jyIqX0LWAfYDRwcURcXeOQai6ldBBwMMWF6teg+P/q9SKirZZx1VLp/0PXUvz/0DJgZm9/\ntwzlK2j7AWMiYjvgBOC8GseTGymlYyn+T3dMrWPJkS8Cr0fEDsDuwEU1jicv9gYKEbE9xb64p9c4\nnlwo/UX6Q2BxrWPJi5TSGICI2Ln0j8UZkFL6KLBt6f9FOwIb1DaifIiIayNip4jYGXgU+EY9F2cl\newAjI+LDwHfp4+/boVygdfT0jIiHgQ/WNpxceQGYUesgcuZnFAsQKP7cv13DWHIjIm4BDi+9nQq0\n1i6aXPkecAkwt9aB5Mg04F0ppV+llP6rdJVe8HHgqZTSzcCtwO01jidXUkofBDaPiCtrHUsOPAeM\nKt0BHA+81dvGQ7lAG8eKS+0Ay0q3a+peRMyiePlUJRGxOCLeSCk1UuxUcVKtY8qLiGhPKV1Dsf/t\nj2scTs2llA4GXouI/6TYP1hFi4FzIuLjwFeBH/t3LgBrA1sBn6SYl/+obTi5cwJwaq2DyIn/AzYE\nngUuBS7sbeOh/B9XGyt6egKMKPXvlDKllDYA7gGujYif1jqePImIg4FNgCtK3T7q2SHArimle4Hp\nwHWl76PVu+coFfAR8Tzwd4odYerd34FfRcSy0veJlqaU1q51UHmQUhoPbBIR99c6lpw4GrgrIhLF\nK9LXpZRWW9nGQ7lA6+jpmVL6EPDH2oaTS/72X5JSWhf4FXBcRFxb63jyIqX0xdIXnKH4sM07FB8W\nqFsR8dHSd2d2AmYDX4qI12odVw4cCpwLkFKaRPEX5L/WNKJ8+A3wCejIy5oUizbBDsDdtQ4iR+az\n4s7fAooPao5c2cZD9ilOYBbF33IfKr0/pJbB5JR9vFY4AZgAfDul9B2Kudk9It6sbVg1dxNwdUrp\nfop/HxxlTrrwv6EVrqT4s/IgxSL+UO9aQETckVL6SErp9xR/Kf5aRPhzU5QAV1dY4fvAVSmlByg+\n8XtCRCxZ2cb24pQkScqZoXyLU5IkaViyQJMkScoZCzRJkqScsUCTJEnKGQs0SZKknLFAkyRJyhkL\nNEmDJqX00dIK/DWXUjqltOZdb9u8nFKasorzjkspvZ5S2qjTZ/eUsd8qHSulNCKl9IuU0uqrEl/G\nPNemlFzxXxpiLNAkDbahtLhif2LdBfjXiHih02c7VuBYX6XYFmbpKu7X3VkUF8iUNIQM5U4CkoaI\nlNJI4BLgfcC6QAD7A+sBdwGvA0sotm+7FNgOmEuxqPnXiHggpXQi8AVgGfBrim27Ct2OcywwE5hH\nsZXKw6XPP0GxYfMo4GVgZkS0ktEOLaV0UCm2icA6wO0R8U+lseOBTwEjU0qTIuJbKaULSmO/i4ht\nU0pfB75IseVPO/CZiIjlx0opNVAsmHYpjV8fEWdnpO0bwD+U9pkCXF2K543SObYBN1Ncqf3/AX8A\n7gMOptg1Y0YUPZ1SakkpbRgRL2f9+UjKH6+gSaqG7YA3I+LDwMYUi5c9SmObAJ+PiN2AI4A1ImIz\niu3bPgiQUtod2AvYovTPxqVtO6SUtqJYnEwDdgUmlz5fGzgD2C0itqJY3GUVRJ19EJhBsaD8UEpp\nv5TSx4GtKBZNWwKTU0qfj4ijAErFWSOwD/DRiPgAcAvwtW5zHwFMjoj3A9sAB5TOr/O5TAMWRMSi\n0kcXAz+PiP9HsdA8qfT5B4BTI2KTUlwtEbEd8BPg8E5TPlTKn6QhwgJNUsVFxIPAJSmlrwEXABsB\nY0vDr0XEq6XXuwI/Lu3zJ1Y0Wt4ZuCEi3ir1f7yK4hWoznYE7oyIJRGxGPh56fNtgCnAvSmlx4Ej\ngff2EfKtEfF6RCyjWOzsAnwM2Bp4FHiMYrG2ebfzXETxKt/nUkqnA3t3Os/ldgauKW2/pHS+3c9l\nY+DPnd5/FLi+tM8vI+Kzpc//GhFPll7/mRX5mgM0ddp/TmlOSUOEtzglVVxKaR+KV37Op1hcrc2K\n24udmwW/Q9dfHJdv0/2XyQZ6/v1V6LbdMmA1YCTwYETsV4plNaCxj5CXdXo9Ani79O/vR8T3S/OM\nL33eIaU0meJtxn8H7gT+BkzvFF+559LeLYa3uh1nM2Bx98+77dPZ26U5JQ0RXkGTNNh6fK+L4hWi\nn0bEdcBrwA4UC6fu2/8n8FmAlNIkilfFCsA9FK9KrZ5SGkXx9mf3p0XvBvZKKTWWnnycUfr8YWDb\nlNLyK0inAOf0cQ67d5rnc8AvS8c7MKX0rlIMNwOfLG2/rPQ9u38Ano+IC4BHgN0zzvMe4KDSU5pr\nUrzi1v1cXgSmdnr/QKe87Erxe3qd5+zLhsALfW4lKTe8gvb/27lj1qiCKAzDb9KbRv/D14hapAzY\nWNmrhYWwqcRYJYUE/A82AUUrLQQ7BTGQRrCwsTCCwunsJClEbNRqLeYiKus2WS4TeZ92Z3fObS4f\nc86spEVbS/KVFh6mtNbcDvA4ySXgB/CaFhrgz9uN94FzSd4Bn4CPwLeqejXMZb2hBZ5d2inVL1W1\nn+TOsObz8F2q6iDJBHiSZJnWCrw6Y+/fHdJOwE4BD6tqDyDJGVrgWwZeDIET4BnwltZOvZ7kPfB9\nWHv6r73u0ebu9mnv4EdV9XTGs5xMcmJom94EHiS5QbsksD6j/nm3RM8Dl+d8LqkzS9PpcboRL+l/\nluQisFRVz5Os0Ga9Vqvqy4g1XKMN+U/G2vMfdWwA06raOeLvnAW2q+rKYiqTNAZbnJJ68gG4NQzz\nvwRujxnOOnMXuHDUP6oFtoDNBdQjaUSeoEmSJHXGEzRJkqTOGNAkSZI6Y0CTJEnqjAFNkiSpMwY0\nSZKkzvwECOT6lhhaVtwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf044b0f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Ejemplo diagrama de dispersion entre Petal.Length y Petal.Width\n",
"plt.figure(figsize=(10, 8))\n",
"plt.scatter(setosa['Petal.Length'], setosa['Petal.Width'], \n",
" c='red', label='setosa')\n",
"plt.scatter(versicolor['Petal.Length'], versicolor['Petal.Width'], \n",
" c='green', label='versicolor')\n",
"plt.scatter(virginica['Petal.Length'], virginica['Petal.Width'], \n",
" c='blue', label='virginica')\n",
"plt.title('Tamaño del pétalo')\n",
"plt.xlabel('Largo del pétalo (cm)')\n",
"plt.ylabel('Ancho del pétalo (cm)')\n",
"plt.legend(loc='top_left')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bokeh\n",
"\n",
"[Bokeh](https://bokeh.pydata.org/en/latest/) además de generar unos hermosos gráficos interactivos nos permite realizar gráficos complejos en forma muy sencilla. La interfase de alto nivel con la que vamos a trabajar principalmente para generar visualizaciones con esta librería es `bokeh.charts`. Repitamos los ejemplos que realizamos anteriormente sobre el dataset iris y veamos que sencillo que es realizarlos con [Bokeh](https://bokeh.pydata.org/en/latest/)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
" <div class=\"bk-root\">\n",
" <div class=\"plotdiv\" id=\"c195cf80-93de-4663-b5d3-f106a1051aec\"></div>\n",
" </div>\n",
"<script type=\"text/javascript\">\n",
" \n",
" (function(global) {\n",
" function now() {\n",
" return new Date();\n",
" }\n",
" \n",
" var force = \"\";\n",
" \n",
" if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force !== \"\") {\n",
" window._bokeh_onload_callbacks = [];\n",
" window._bokeh_is_loading = undefined;\n",
" }\n",
" \n",
" \n",
" \n",
" if (typeof (window._bokeh_timeout) === \"undefined\" || force !== \"\") {\n",
" window._bokeh_timeout = Date.now() + 0;\n",
" window._bokeh_failed_load = false;\n",
" }\n",
" \n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
" \n",
" function display_loaded() {\n",
" if (window.Bokeh !== undefined) {\n",
" Bokeh.$(\"#c195cf80-93de-4663-b5d3-f106a1051aec\").text(\"BokehJS successfully loaded.\");\n",
" } else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
" \n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"c195cf80-93de-4663-b5d3-f106a1051aec\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'c195cf80-93de-4663-b5d3-f106a1051aec' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
" \n",
" var js_urls = [];\n",
" \n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" Bokeh.$(function() {\n",
" var docs_json = {\"9a7ef9a1-1421-4082-99ad-6e5085ca7300\":{\"roots\":{\"references\":[{\"attributes\":{\"callback\":null,\"column_names\":[\"color\",\"label\",\"x\",\"line_color\",\"width\",\"height\",\"line_alpha\",\"fill_alpha\",\"y\"],\"data\":{\"chart_index\":[\"(6.7, 6.9]\"],\"color\":[\"#407ee7\"],\"fill_alpha\":[0.6],\"height\":[1.0],\"label\":[\"(6.7, 6.9]\"],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.1999999999999993],\"x\":[\"6.800000000000001\"],\"y\":[0.5]}},\"id\":\"f0919d08-3312-4eb1-9203-d799f245e6ac\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"axis_label\":\"Count( Petal.Length 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"source": [
"# Ejemplo de histograma de Petal.Length\n",
"# solo 2 lineas de código\n",
"hist = Histogram(iris, values=\"Petal.Length\", color=\"Species\",\n",
" legend=\"top_right\", bins=12)\n",
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" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (!force) {\n",
" var cell = $(\"#ab950219-9790-4c01-8a06-67fa0b5f89e7\").parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Ejemplo diagrama de dispersion entre Petal.Length y Petal.Width\n",
"# solo 2 lineas de código\n",
"disp = Scatter(iris, x='Petal.Length', y='Petal.Width', color='Species', \n",
" legend='top_left', marker='Species', title=\"Tamaño del petalo\")\n",
"show(disp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Seaborn\n",
"\n",
"[Seaborn](https://stanford.edu/~mwaskom/software/seaborn/) tiene su énfasis en los gráficos estadísticos. Nos permite realizar fácilmente gráficos de regresión y de las principales distribuciones; pero donde realmente brilla [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/) es en su capacidad de visualizar muchas características diferentes a la vez. Veamos algunos ejemplos"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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PeA5Aa30M2Dm5QWvdD2zVWkeBamA0HUWK3PfWlQGC41HqKhw4nG7sDtec/xQUOoz+33mb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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf044110f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Ejemplo gráfico de distribuciones\n",
"x = np.random.normal(size=100)\n",
"dist= sns.distplot(x)"
]
},
{
"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>total_bill</th>\n",
" <th>tip</th>\n",
" <th>sex</th>\n",
" <th>smoker</th>\n",
" <th>day</th>\n",
" <th>time</th>\n",
" <th>size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>16.99</td>\n",
" <td>1.01</td>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.34</td>\n",
" <td>1.66</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>21.01</td>\n",
" <td>3.50</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>23.68</td>\n",
" <td>3.31</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>24.59</td>\n",
" <td>3.61</td>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill tip sex smoker day time size\n",
"1 16.99 1.01 Female No Sun Dinner 2\n",
"2 10.34 1.66 Male No Sun Dinner 3\n",
"3 21.01 3.50 Male No Sun Dinner 3\n",
"4 23.68 3.31 Male No Sun Dinner 2\n",
"5 24.59 3.61 Female No Sun Dinner 4"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Ejemplo gráfico regresión con tips dataset\n",
"tips.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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0ohEN27WxU3lyBY/ZVZk65UXnzwxw6lBt1URL6wVrSkBE15r2FLCZI0MJXr06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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf044b9160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"reg = sns.regplot(x=\"total_bill\", y=\"tip\", data=tips)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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lXIu4+NzuQNLtmq7OjNsd146ebke6xZqeBKufixSBEPP4ffXsWL8VX6WP50f2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\ndzYr+eGv265Rzi+21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ffVduKWnddheG4Une52bPUY97LYKkG56HetzSeP6OdsbPVsweuz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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf043a6898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Ejemplo pairplot con datase iris\n",
"g = sns.pairplot(iris, hue=\"Species\", diag_kind=\"hist\") "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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W5rYumsPF3Xbcpj/eSGMnpMsBHE2cXNLtYBERESkr2eQCRn2O8ZADmEqNQBERESkrg3IB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\nRURERMqQGoEiIiIiZUiNQBEREZEy9P8BQZB48+nmYGIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf03dfe7b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Ejemplo FacetGrid con iris\n",
"g = sns.FacetGrid(iris, col=\"Species\")\n",
"g = g.map(plt.scatter, \"Petal.Length\", \"Petal.Width\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Folium\n",
"\n",
"Por último, veamos un ejemplo de como utilizar [Folium](https://folium.readthedocs.io/en/latest/). Ya que yo soy adepto al uso de la bicicleta para moverme por la ciudad, y muchas veces se hace difícil encontrar una bicicletería en donde poder encontrar repuestos o reparar la bicicleta; en este ejemplo vamos a crear un mapa interactivo del barrio de <a href=\"https://es.wikipedia.org/wiki/Palermo_(Buenos_Aires)\">Palermo</a> en donde vamos a marcar la ubicación de los negocios de bicicleterías. Esta información la podemos extraer del padrón que ofrece el [gobierno de la Ciudad de Buenos Aires](https://www.buenosaires.gob.ar/) en su [portal de datos](https://data.buenosaires.gob.ar/). "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
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},
"outputs": [
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" WKT ID \\\n",
"0 POINT (-58.466041249451614 -34.557060215984805) 52 \n",
"1 POINT (-58.41279876038783 -34.591915372813645) 32 \n",
"2 POINT (-58.425646989945932 -34.580365554062418) 30 \n",
"3 POINT (-58.437608880680997 -34.6045094278806) 107 \n",
"4 POINT (-58.439598908303168 -34.58547499220991) 118 \n",
"\n",
" NOMBRE DIRECCION TELEFONO \\\n",
"0 11 A FONDO CONGRESO 2757 45421835 \n",
"1 AMERICAN BIKE AV. CNEL. DIAZ 1664 48220889 \n",
"2 ANDINO BIKES GUEMES 4818 47753677 \n",
"3 BABE BIKES WARNES 10 48549862 \n",
"4 BELGRAVIA TAILOR MADE BICYLES BONPLAND 1459 1544291001 \n",
"\n",
" EMAIL WEB MECANICA_S \\\n",
"0 [email protected] https://WWW.11AFONDO.COM NO \n",
"1 [email protected] https://WWW.AMERICANBIKE.COM.AR/ SI \n",
"2 [email protected] https://WWW.ANDINOBIKE.COM.AR/ NO \n",
"3 [email protected] NaN NO \n",
"4 [email protected] WWW.FACEBOOK.COM/BELGRAVIABIKES NO \n",
"\n",
" HORARIO_DE \\\n",
"0 LUN A VIE DE 10 A 14 Y DE 16 A 20/SAB DE 10 A 14 \n",
"1 LUN A VIER DE 10 A 14 Y DE 15 A 20.30 / SAB DE 10 A 14 Y DE 15 A 20 \n",
"2 LUN A VIER DE 9 A 19 / SAB DE 10:00 - 17:00 \n",
"3 NaN \n",
"4 NaN \n",
"\n",
" CALLE ALTURA DIRECCION_ BARRIO COMUNA \n",
"0 CONGRESO AV. 2757 CONGRESO AV. 2757 NUÑEZ COMUNA 13 \n",
"1 DIAZ, CNEL. AV. 1664 DIAZ, CNEL. AV. 1664 PALERMO COMUNA 14 \n",
"2 GUEMES 4818 GUEMES 4818 PALERMO COMUNA 14 \n",
"3 WARNES 10 WARNES AV. 10 VILLA CRESPO COMUNA 15 \n",
"4 BONPLAND 1459 BONPLAND 1459 PALERMO COMUNA 14 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# dataset de bicicleterías de Ciudad de Buenos Aires\n",
"# descargado desde https://data.buenosaires.gob.ar/dataset/bicicleterias\n",
"bici = pd.read_csv('data/bicicleterias.csv', sep=';')\n",
"bici.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# corregimos el campo de coordenadas del dataset.\n",
"def coord(c):\n",
" coor = re.findall(r'-?\\d+\\.\\d{7}', c)\n",
" coords = [float(s) for s in coor]\n",
" return coords[::-1]\n",
"\n",
"bici['WKT'] = bici['WKT'].apply(coord)\n",
"\n",
"# filtramos solo las bicicleterías de palermo\n",
"bici_palermo = bici[bici.BARRIO == 'PALERMO'][['WKT', 'NOMBRE']]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# creamos el mapa con folium\n",
"mapa = folium.Map(location=[-34.588889, -58.430556], zoom_start=13)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# agregamos los markers con el nombre de cada bicicletería.\n",
"for index, row in bici_palermo.iterrows():\n",
" mapa.simple_marker(row['WKT'], \n",
" popup=row['NOMBRE'], marker_color='red',\n",
" marker_icon='info-sign')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
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                .addTo(map_e01b9b9ef72d47fcbde9f91917546943);
            
        
            

                var icon_efcb30a76ab5454c878e77e311e56a5e = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'red',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_8031bd00034e4b71a9879888e2786044.setIcon(icon_efcb30a76ab5454c878e77e311e56a5e);
            
        
            
            var popup_def617985d974f4baedf473824b21174 = L.popup({maxWidth: '300'});

            
                var html_fdd8e53a12f44df58e7161b4744a9f2e = $('         <div id="html_fdd8e53a12f44df58e7161b4744a9f2e"                 style="width: 100.0%; height: 100.0%;">                 TU HOGAR SUSTENTABLE</div>                 ')[0];
                popup_def617985d974f4baedf473824b21174.setContent(html_fdd8e53a12f44df58e7161b4744a9f2e);
            

            marker_8031bd00034e4b71a9879888e2786044.bindPopup(popup_def617985d974f4baedf473824b21174);

            
        
        
            

            var marker_0840be4ce59a4a44953004d1b99c6c4c = L.marker(
                [-34.5947856,-58.4227541],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(map_e01b9b9ef72d47fcbde9f91917546943);
            
        
            

                var icon_6c80c6fcfe3b4eb1ae9e11f45eb09e06 = L.AwesomeMarkers.icon({
                    icon: 'info-sign',
                    iconColor: 'white',
                    markerColor: 'red',
                    prefix: 'glyphicon',
                    extraClasses: 'fa-rotate-0'
                    });
                marker_0840be4ce59a4a44953004d1b99c6c4c.setIcon(icon_6c80c6fcfe3b4eb1ae9e11f45eb09e06);
            
        
            
            var popup_835fc9ba08e4492fac37a7547903d5c1 = L.popup({maxWidth: '300'});

            
                var html_972c14d7f35046dc8a592f9a2afdfd4f = $('         <div id="html_972c14d7f35046dc8a592f9a2afdfd4f"                 style="width: 100.0%; height: 100.0%;">                 VIDA SANA BIKE</div>                 ')[0];
                popup_835fc9ba08e4492fac37a7547903d5c1.setContent(html_972c14d7f35046dc8a592f9a2afdfd4f);
            

            marker_0840be4ce59a4a44953004d1b99c6c4c.bindPopup(popup_835fc9ba08e4492fac37a7547903d5c1);

            
        
        
        
        </script>
        \" style=\"position:absolute;width:100%;height:100%;left:0;top:0;\"></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7fdf0435dcf8>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# visualizamos el mapa con los markers\n",
"mapa"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Aquí concluye este artículo, ya no hay excusas para graficar sus datos, como vimos [Python](https://python.org/) cuenta con herramientas que son fáciles de usar y muy poderosas. A divertirse!\n",
"\n",
"Saludos!\n",
"\n",
"*Este post fue escrito utilizando IPython notebook. Pueden descargar este [notebook](https://github.com/relopezbriega/relopezbriega.github.io/blob/master/downloads/DataViz.ipynb) o ver su version estática en [nbviewer](https://nbviewer.ipython.org/github/relopezbriega/relopezbriega.github.io/blob/master/downloads/DataViz.ipynb).*"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
vivarose/using_trackpy | backgrounding_color_avi_movie_examples.ipynb | 1 | 6039902 | null | mit |
PySCeS/PyscesToolbox | documentation/notebooks/Thermokin.ipynb | 1 | 6433164 | null | bsd-3-clause |
wcmckee/wcmckee | Untitled0.ipynb | 1 | 6102 | {
"metadata": {
"name": "",
"signature": "sha256:ace2fc785ad9e5698a90fe9ca257d1dc4842f4bdb9ab0cd1baafbaa71ca213e0"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ImportError",
"evalue": "No module named pandas",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-4-fa44af558781>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mImportError\u001b[0m: No module named pandas"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"randn = np.random.randn"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"randn()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"-1.2995164622081463"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.arange(15).reshape(3,5)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"array([[ 0, 1, 2, 3, 4],\n",
" [ 5, 6, 7, 8, 9],\n",
" [10, 11, 12, 13, 14]])"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"opind = open('/home/wcmckee/visignsys/index.meta')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"doubme = opind.read()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(doubme)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"\"['17:47', 'ESW', 'William Mckee', '12-Oct-2014', 'it was a good day']['17:59', '12-Oct-2014']['17:59', 'fun day', '12-Oct-2014']\""
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for dou in doubme:\n",
" print dou"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[\n",
"'\n",
"1\n",
"7\n",
":\n",
"4\n",
"7\n",
"'\n",
",\n",
" \n",
"'\n",
"E\n",
"S\n",
"W\n",
"'\n",
",\n",
" \n",
"'\n",
"W\n",
"i\n",
"l\n",
"l\n",
"i\n",
"a\n",
"m\n",
" \n",
"M\n",
"c\n",
"k\n",
"e\n",
"e\n",
"'\n",
",\n",
" \n",
"'\n",
"1\n",
"2\n",
"-\n",
"O\n",
"c\n",
"t\n",
"-\n",
"2\n",
"0\n",
"1\n",
"4\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"'\n",
",\n",
" \n",
"'\n",
"i\n",
"t\n",
" \n",
"w\n",
"a\n",
"s\n",
" \n",
"a\n",
" \n",
"g\n",
"o\n",
"o\n",
"d\n",
" \n",
"d\n",
"a\n",
"y\n",
"'\n",
"]\n",
"[\n",
"'\n",
"1\n",
"7\n",
":\n",
"5\n",
"9\n",
"'\n",
",\n",
" \n",
"'\n",
"1\n",
"2\n",
"-\n",
"O\n",
"c\n",
"t\n",
"-\n",
"2\n",
"0\n",
"1\n",
"4\n",
"'\n",
"]\n",
"[\n",
"'\n",
"1\n",
"7\n",
":\n",
"5\n",
"9\n",
"'\n",
",\n",
" \n",
"'\n",
"f\n",
"u\n",
"n\n",
" \n",
"d\n",
"a\n",
"y\n",
"'\n",
",\n",
" \n",
"'\n",
"1\n",
"2\n",
"-\n",
"O\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"c\n",
"t\n",
"-\n",
"2\n",
"0\n",
"1\n",
"4\n",
"'\n",
"]\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
tarashor/vibrations | py/notebooks/.ipynb_checkpoints/MatricesForOrthogonalCoordinatesPlane-checkpoint.ipynb | 1 | 280402 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Matrix generation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Init symbols for *sympy*"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sympy import *\n",
"from geom_util import *\n",
"from sympy.vector import CoordSys3D\n",
"N = CoordSys3D('N')\n",
"alpha1, alpha2, alpha3 = symbols(\"alpha_1 alpha_2 alpha_3\", real = True, positive=True)\n",
"init_printing()\n",
"\n",
"%matplotlib inline\n",
"\n",
"%reload_ext autoreload\n",
"%autoreload 2\n",
"%aimport geom_util"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Lame params"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAJMAAABLCAMAAABDVkVlAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$\\left[\\begin{matrix}H_{1,1} & 0 & H_{1,3}\\\\0 & 0 & 0\\\\0 & 0 & 0\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡H_{1,1} 0 H_{1,3}⎤\n",
"⎢ ⎥\n",
"⎢ 0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎣ 0 0 0 ⎦"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"H1=symbols('H1')\n",
"H2=S(1)\n",
"H3=S(1)\n",
"\n",
"H=[H1, H2, H3]\n",
"DIM=3\n",
"dH = zeros(DIM,DIM)\n",
"for i in range(DIM):\n",
" for j in range(DIM):\n",
" if (i == 0 and j != 1):\n",
" dH[i,j]=Symbol('H_{{{},{}}}'.format(i+1,j+1))\n",
"\n",
"dH\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Metric tensor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"${\\displaystyle \\hat{G}=\\sum_{i,j} g^{ij}\\vec{R}_i\\vec{R}_j}$"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"G_up = getMetricTensorUpLame(H1, H2, H3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"${\\displaystyle \\hat{G}=\\sum_{i,j} g_{ij}\\vec{R}^i\\vec{R}^j}$"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"G_down = getMetricTensorDownLame(H1, H2, H3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Christoffel symbols"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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"text/latex": [
"$$\\left[\\begin{matrix}\\left[\\begin{matrix}\\frac{H_{1,1}}{H_{1}} & 0 & - H_{1} H_{1,3}\\\\0 & 0 & 0\\\\\\frac{H_{1,3}}{H_{1}} & 0 & 0\\end{matrix}\\right] & \\left[\\begin{matrix}0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\end{matrix}\\right] & \\left[\\begin{matrix}\\frac{H_{1,3}}{H_{1}} & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\end{matrix}\\right]\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡⎡H_{1,1} ⎤ ⎤\n",
"⎢⎢─────── 0 -H₁⋅H_{1,3}⎥ ⎡H_{1,3} ⎤⎥\n",
"⎢⎢ H₁ ⎥ ⎡0 0 0⎤ ⎢─────── 0 0⎥⎥\n",
"⎢⎢ ⎥ ⎢ ⎥ ⎢ H₁ ⎥⎥\n",
"⎢⎢ 0 0 0 ⎥ ⎢0 0 0⎥ ⎢ ⎥⎥\n",
"⎢⎢ ⎥ ⎢ ⎥ ⎢ 0 0 0⎥⎥\n",
"⎢⎢H_{1,3} ⎥ ⎣0 0 0⎦ ⎢ ⎥⎥\n",
"⎢⎢─────── 0 0 ⎥ ⎣ 0 0 0⎦⎥\n",
"⎣⎣ H₁ ⎦ ⎦"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DIM=3\n",
"\n",
"G_down_diff = MutableDenseNDimArray.zeros(DIM, DIM, DIM)\n",
"for i in range(DIM):\n",
" for j in range(DIM):\n",
" for k in range(DIM):\n",
" \n",
" G_down_diff[i,i,k]=2*H[i]*dH[i,k]\n",
" \n",
"\n",
"GK = getChristoffelSymbols2(G_up, G_down_diff, (alpha1, alpha2, alpha3))\n",
"GK"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gradient of vector"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$ \n",
"\\left( \n",
"\\begin{array}{c} \n",
"\\nabla_1 u_1 \\\\ \\nabla_2 u_1 \\\\ \\nabla_3 u_1 \\\\\n",
"\\nabla_1 u_2 \\\\ \\nabla_2 u_2 \\\\ \\nabla_3 u_2 \\\\\n",
"\\nabla_1 u_3 \\\\ \\nabla_2 u_3 \\\\ \\nabla_3 u_3 \\\\\n",
"\\end{array} \n",
"\\right)\n",
"= \n",
"B \\cdot\n",
"\\left( \n",
"\\begin{array}{c} \n",
"u_1 \\\\\n",
"\\frac { \\partial u_1 } { \\partial \\alpha_1} \\\\\n",
"\\frac { \\partial u_1 } { \\partial \\alpha_2} \\\\\n",
"\\frac { \\partial u_1 } { \\partial \\alpha_3} \\\\\n",
"u_2 \\\\\n",
"\\frac { \\partial u_2 } { \\partial \\alpha_1} \\\\\n",
"\\frac { \\partial u_2 } { \\partial \\alpha_2} \\\\\n",
"\\frac { \\partial u_2 } { \\partial \\alpha_3} \\\\\n",
"u_3 \\\\\n",
"\\frac { \\partial u_3 } { \\partial \\alpha_1} \\\\\n",
"\\frac { \\partial u_3 } { \\partial \\alpha_2} \\\\\n",
"\\frac { \\partial u_3 } { \\partial \\alpha_3} \\\\\n",
"\\end{array} \n",
"\\right) \n",
"= B \\cdot D \\cdot\n",
"\\left( \n",
"\\begin{array}{c} \n",
"u^1 \\\\\n",
"\\frac { \\partial u^1 } { \\partial \\alpha_1} \\\\\n",
"\\frac { \\partial u^1 } { \\partial \\alpha_2} \\\\\n",
"\\frac { \\partial u^1 } { \\partial \\alpha_3} \\\\\n",
"u^2 \\\\\n",
"\\frac { \\partial u^2 } { \\partial \\alpha_1} \\\\\n",
"\\frac { \\partial u^2 } { \\partial \\alpha_2} \\\\\n",
"\\frac { \\partial u^2 } { \\partial \\alpha_3} \\\\\n",
"u^3 \\\\\n",
"\\frac { \\partial u^3 } { \\partial \\alpha_1} \\\\\n",
"\\frac { \\partial u^3 } { \\partial \\alpha_2} \\\\\n",
"\\frac { \\partial u^3 } { \\partial \\alpha_3} \\\\\n",
"\\end{array} \n",
"\\right) \n",
"$"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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HxJMTfMMYqqs/yYCVem3J/62725h+6yf1n0iz9UEkJf+X7tS27ckL3hxvfSyH7/Pp0G/U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\n",
"text/latex": [
"$$\\left[\\begin{array}{cccccccccccc}- \\frac{H_{1,1}}{H_{1}} & 1 & 0 & 0 & 0 & 0 & 0 & 0 & H_{1} H_{1,3} & 0 & 0 & 0\\\\0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\- \\frac{H_{1,3}}{H_{1}} & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0\\\\- \\frac{H_{1,3}}{H_{1}} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1\\end{array}\\right]$$"
],
"text/plain": [
"⎡-H_{1,1} ⎤\n",
"⎢───────── 1 0 0 0 0 0 0 H₁⋅H_{1,3} 0 0 0⎥\n",
"⎢ H₁ ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 1 0 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢-H_{1,3} ⎥\n",
"⎢───────── 0 0 1 0 0 0 0 0 0 0 0⎥\n",
"⎢ H₁ ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 1 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 1 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 0 1 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢-H_{1,3} ⎥\n",
"⎢───────── 0 0 0 0 0 0 0 0 1 0 0⎥\n",
"⎢ H₁ ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 0 0 0 0 1 0⎥\n",
"⎢ ⎥\n",
"⎣ 0 0 0 0 0 0 0 0 0 0 0 1⎦"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def row_index_to_i_j_grad(i_row):\n",
" return i_row // 3, i_row % 3\n",
" \n",
"\n",
"B = zeros(9, 12)\n",
"B[0,1] = S(1)\n",
"B[1,2] = S(1)\n",
"\n",
"B[2,3] = S(1)\n",
"\n",
"B[3,5] = S(1)\n",
"B[4,6] = S(1)\n",
"B[5,7] = S(1)\n",
"\n",
"B[6,9] = S(1)\n",
"B[7,10] = S(1)\n",
"B[8,11] = S(1)\n",
"\n",
"for row_index in range(9):\n",
" i,j=row_index_to_i_j_grad(row_index)\n",
" B[row_index, 0] = -GK[i,j,0]\n",
" B[row_index, 4] = -GK[i,j,1]\n",
" B[row_index, 8] = -GK[i,j,2]\n",
"\n",
"B"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Physical coordinates"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$u_i=u_{[i]} H_i$"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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S8D0t3axDQN2pqc0n1gYmEJo2IJJIMTQVB9YAPDRtQCSRYmgqDqwBeGjagEgixdBUHFgD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\n+HmvjmEQUifwcVbyufs/Lw/1bXI82cIAAAAASUVORK5CYII=\n",
"text/latex": [
"$$\\left[\\begin{array}{cccccccccccc}H_{1} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\H_{1,1} & H_{1} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & H_{1} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\H_{1,3} & 0 & 0 & H_{1} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1\\end{array}\\right]$$"
],
"text/plain": [
"⎡ H₁ 0 0 0 0 0 0 0 0 0 0 0⎤\n",
"⎢ ⎥\n",
"⎢H_{1,1} H₁ 0 0 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 H₁ 0 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢H_{1,3} 0 0 H₁ 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 1 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 1 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 1 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 0 1 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 0 0 1 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 0 0 0 1 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 0 0 0 0 1 0⎥\n",
"⎢ ⎥\n",
"⎣ 0 0 0 0 0 0 0 0 0 0 0 1⎦"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"P=zeros(12,12)\n",
"P[0,0]=H[0]\n",
"P[1,0]=dH[0,0]\n",
"P[1,1]=H[0]\n",
"P[2,0]=dH[0,1]\n",
"P[2,2]=H[0]\n",
"P[3,0]=dH[0,2]\n",
"P[3,3]=H[0]\n",
"\n",
"P[4,4]=H[1]\n",
"P[5,4]=dH[1,0]\n",
"P[5,5]=H[1]\n",
"P[6,4]=dH[1,1]\n",
"P[6,6]=H[1]\n",
"P[7,4]=dH[1,2]\n",
"P[7,7]=H[1]\n",
"\n",
"P[8,8]=H[2]\n",
"P[9,8]=dH[2,0]\n",
"P[9,9]=H[2]\n",
"P[10,8]=dH[2,1]\n",
"P[10,10]=H[2]\n",
"P[11,8]=dH[2,2]\n",
"P[11,11]=H[2]\n",
"P=simplify(P)\n",
"P"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/latex": [
"$$\\left[\\begin{array}{cccccccccccc}0 & \\frac{1}{H_{1}} & 0 & 0 & 0 & 0 & 0 & 0 & \\frac{H_{1,3}}{H_{1}} & 0 & 0 & 0\\\\0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & \\frac{1}{H_{1}} & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0\\\\- \\frac{H_{1,3}}{H_{1}} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & \\frac{1}{H_{1}} & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1\\end{array}\\right]$$"
],
"text/plain": [
"⎡ 1 H_{1,3} ⎤\n",
"⎢ 0 ── 0 0 0 0 0 0 ─────── 0 0 0⎥\n",
"⎢ H₁ H₁ ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 1 0 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 1 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 1 ⎥\n",
"⎢ 0 0 0 0 0 ── 0 0 0 0 0 0⎥\n",
"⎢ H₁ ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 1 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 0 1 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢-H_{1,3} 1 ⎥\n",
"⎢───────── 0 0 0 0 0 0 0 0 ── 0 0⎥\n",
"⎢ H₁ H₁ ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 0 0 0 0 1 0⎥\n",
"⎢ ⎥\n",
"⎣ 0 0 0 0 0 0 0 0 0 0 0 1⎦"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"B_P = zeros(9,9)\n",
"\n",
"for i in range(3):\n",
" for j in range(3):\n",
" \n",
" row_index = i*3+j\n",
" \n",
" B_P[row_index, row_index] = 1/(H[i]*H[j])\n",
" \n",
"\n",
"\n",
"Grad_U_P = simplify(B_P*B*P)\n",
"Grad_U_P"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Strain tensor\n",
"\n",
"$ \n",
"\\left( \n",
"\\begin{array}{c} \n",
"\\varepsilon_{11} \\\\ \n",
"\\varepsilon_{22} \\\\ \n",
"\\varepsilon_{33} \\\\ \n",
"2\\varepsilon_{12} \\\\ \n",
"2\\varepsilon_{13} \\\\ \n",
"2\\varepsilon_{23} \\\\ \n",
"\\end{array} \n",
"\\right)\n",
"= \n",
"\\left(E + E_{NL} \\left( \\nabla \\vec{u} \\right) \\right) \\cdot \n",
"\\left( \n",
"\\begin{array}{c} \n",
"\\nabla_1 u_1 \\\\ \\nabla_2 u_1 \\\\ \\nabla_3 u_1 \\\\\n",
"\\nabla_1 u_2 \\\\ \\nabla_2 u_2 \\\\ \\nabla_3 u_2 \\\\\n",
"\\nabla_1 u_3 \\\\ \\nabla_2 u_3 \\\\ \\nabla_3 u_3 \\\\\n",
"\\end{array} \n",
"\\right)$"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAARMAAACWCAMAAADKfZxVAAAAOVBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$\\left[\\begin{matrix}1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1\\\\0 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡1 0 0 0 0 0 0 0 0⎤\n",
"⎢ ⎥\n",
"⎢0 0 0 0 1 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0 0 0 1⎥\n",
"⎢ ⎥\n",
"⎢0 1 0 1 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 1 0 0 0 1 0 0⎥\n",
"⎢ ⎥\n",
"⎣0 0 0 0 0 1 0 1 0⎦"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"E=zeros(6,9)\n",
"E[0,0]=1\n",
"E[1,4]=1\n",
"E[2,8]=1\n",
"E[3,1]=1\n",
"E[3,3]=1\n",
"E[4,2]=1\n",
"E[4,6]=1\n",
"E[5,5]=1\n",
"E[5,7]=1\n",
"E"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAf8AAACjCAMAAACzFOe0AAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$\\left[\\begin{array}{cccccccccccc}0 & \\frac{1}{H_{1}} & 0 & 0 & 0 & 0 & 0 & 0 & \\frac{H_{1,3}}{H_{1}} & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1\\\\0 & 0 & 1 & 0 & 0 & \\frac{1}{H_{1}} & 0 & 0 & 0 & 0 & 0 & 0\\\\- \\frac{H_{1,3}}{H_{1}} & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & \\frac{1}{H_{1}} & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0\\end{array}\\right]$$"
],
"text/plain": [
"⎡ 1 H_{1,3} ⎤\n",
"⎢ 0 ── 0 0 0 0 0 0 ─────── 0 0 0⎥\n",
"⎢ H₁ H₁ ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 1 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 0 0 0 0 0 1⎥\n",
"⎢ ⎥\n",
"⎢ 1 ⎥\n",
"⎢ 0 0 1 0 0 ── 0 0 0 0 0 0⎥\n",
"⎢ H₁ ⎥\n",
"⎢ ⎥\n",
"⎢-H_{1,3} 1 ⎥\n",
"⎢───────── 0 0 1 0 0 0 0 0 ── 0 0⎥\n",
"⎢ H₁ H₁ ⎥\n",
"⎢ ⎥\n",
"⎣ 0 0 0 0 0 0 0 1 0 0 1 0⎦"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"StrainL=simplify(E*Grad_U_P)\n",
"StrainL"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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vAAAAAElFTkSuQmCC\n",
"text/latex": [
"$$\\left[\\begin{array}{cccccccccccc}- \\frac{H_{1,3}}{H_{1}} \\left(\\frac{u_{3,1}}{2} - \\frac{H_{1,3} u_{1}}{2 H_{1}}\\right) - \\frac{H_{1,1}}{H_{1}^{3}} \\left(\\frac{H_{1} H_{1,3}}{2} u_{3} + \\frac{u_{1,1}}{2} - \\frac{H_{1,1} u_{1}}{2 H_{1}}\\right) & \\frac{1}{H_{1}^{2}} \\left(\\frac{H_{1} H_{1,3}}{2} u_{3} + \\frac{u_{1,1}}{2} - \\frac{H_{1,1} u_{1}}{2 H_{1}}\\right) & 0 & 0 & 0 & 0 & 0 & 0 & \\frac{H_{1,3}}{H_{1}} \\left(\\frac{H_{1} H_{1,3}}{2} u_{3} + \\frac{u_{1,1}}{2} - \\frac{H_{1,1} u_{1}}{2 H_{1}}\\right) & \\frac{u_{3,1}}{2} - \\frac{H_{1,3} u_{1}}{2 H_{1}} & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\- \\frac{H_{1,3}}{H_{1}^{3}} \\left(\\frac{u_{1,3}}{2} - \\frac{H_{1,3} u_{1}}{2 H_{1}}\\right) & 0 & 0 & \\frac{1}{H_{1}^{2}} \\left(\\frac{u_{1,3}}{2} - \\frac{H_{1,3} u_{1}}{2 H_{1}}\\right) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & \\frac{u_{3,3}}{2}\\\\0 & 0 & \\frac{2}{H_{1}^{2}} \\left(\\frac{H_{1} H_{1,3}}{2} u_{3} + \\frac{u_{1,1}}{2} - \\frac{H_{1,1} u_{1}}{2 H_{1}}\\right) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & u_{3,1} - \\frac{H_{1,3} u_{1}}{H_{1}} & 0\\\\- \\frac{H_{1,3} u_{3,3}}{H_{1}} - \\frac{2 H_{1,1}}{H_{1}^{3}} \\left(\\frac{u_{1,3}}{2} - \\frac{H_{1,3} u_{1}}{2 H_{1}}\\right) & \\frac{2}{H_{1}^{2}} \\left(\\frac{u_{1,3}}{2} - \\frac{H_{1,3} u_{1}}{2 H_{1}}\\right) & 0 & 0 & 0 & 0 & 0 & 0 & \\frac{2 H_{1,3}}{H_{1}} \\left(\\frac{u_{1,3}}{2} - \\frac{H_{1,3} u_{1}}{2 H_{1}}\\right) & u_{3,3} & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\end{array}\\right]$$"
],
"text/plain": [
"⎡ ⎛u_{3,1} H_{1,3}⋅u₁⎞ ⎛H₁⋅H_{1,3}⋅u₃ u_{1,1} H_{1,1}\n",
"⎢ H_{1,3}⋅⎜─────── - ──────────⎟ H_{1,1}⋅⎜───────────── + ─────── - ───────\n",
"⎢ ⎝ 2 2⋅H₁ ⎠ ⎝ 2 2 2⋅H₁\n",
"⎢- ────────────────────────────── - ──────────────────────────────────────────\n",
"⎢ H₁ 3 \n",
"⎢ H₁ \n",
"⎢ \n",
"⎢ 0 \n",
"⎢ \n",
"⎢ ⎛u_{1,3} H_{1,3}⋅u₁⎞ \n",
"⎢ -H_{1,3}⋅⎜─────── - ──────────⎟ \n",
"⎢ ⎝ 2 2⋅H₁ ⎠ \n",
"⎢ ──────────────────────────────── \n",
"⎢ 3 \n",
"⎢ H₁ \n",
"⎢ \n",
"⎢ \n",
"⎢ \n",
"⎢ \n",
"⎢ 0 \n",
"⎢ \n",
"⎢ \n",
"⎢ \n",
"⎢ ⎛u_{1,3} H_{1,3}⋅u₁⎞ \n",
"⎢ 2⋅H_{1,1}⋅⎜─────── - ──────────⎟ \n",
"⎢ H_{1,3}⋅u_{3,3} ⎝ 2 2⋅H₁ ⎠ \n",
"⎢ - ─────────────── - ──────────────────────────────── \n",
"⎢ H₁ 3 \n",
"⎢ H₁ \n",
"⎢ \n",
"⎣ 0 \n",
"\n",
"⋅u₁⎞ H₁⋅H_{1,3}⋅u₃ u_{1,1} H_{1,1}⋅u₁ \n",
"───⎟ ───────────── + ─────── - ────────── \n",
" ⎠ 2 2 2⋅H₁ \n",
"──── ──────────────────────────────────── 0 \n",
" 2 \n",
" H₁ \n",
" \n",
" 0 0 \n",
" \n",
" \n",
" \n",
" \n",
" 0 0 \n",
" \n",
" \n",
" \n",
" ⎛H₁⋅H_{1,3}⋅u₃ u_{1,1} H_{1,\n",
" 2⋅⎜───────────── + ─────── - ─────\n",
" ⎝ 2 2 2⋅\n",
" 0 ──────────────────────────────────\n",
" 2 \n",
" H₁ \n",
" \n",
" ⎛u_{1,3} H_{1,3}⋅u₁⎞ \n",
" 2⋅⎜─────── - ──────────⎟ \n",
" ⎝ 2 2⋅H₁ ⎠ \n",
" ──────────────────────── 0 \n",
" 2 \n",
" H₁ \n",
" \n",
" 0 0 \n",
"\n",
" ⎛H₁⋅H_{1,3}⋅u₃ u_{1,1} H\n",
" H_{1,3}⋅⎜───────────── + ─────── - ─\n",
" ⎝ 2 2 \n",
" 0 0 0 0 0 ────────────────────────────────────\n",
" H₁ \n",
" \n",
" \n",
" 0 0 0 0 0 0 \n",
" \n",
" u_{1,3} H_{1,3}⋅u₁ \n",
" ─────── - ────────── \n",
" 2 2⋅H₁ \n",
" ──────────────────── 0 0 0 0 0 \n",
" 2 \n",
" H₁ \n",
" \n",
"1}⋅u₁⎞ \n",
"─────⎟ \n",
"H₁ ⎠ \n",
"────── 0 0 0 0 0 0 \n",
" \n",
" \n",
" \n",
" ⎛u_{1,3} H_{1,3}⋅\n",
" 2⋅H_{1,3}⋅⎜─────── - ────────\n",
" ⎝ 2 2⋅H₁ \n",
" 0 0 0 0 0 ─────────────────────────────\n",
" H₁ \n",
" \n",
" \n",
" 0 0 0 0 0 0 \n",
"\n",
"_{1,1}⋅u₁⎞ ⎤\n",
"─────────⎟ ⎥\n",
" 2⋅H₁ ⎠ u_{3,1} H_{1,3}⋅u₁ ⎥\n",
"────────── ─────── - ────────── 0 0 ⎥\n",
" 2 2⋅H₁ ⎥\n",
" ⎥\n",
" ⎥\n",
" 0 0 0 ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" u_{3,3}⎥\n",
" 0 0 ───────⎥\n",
" 2 ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" H_{1,3}⋅u₁ ⎥\n",
" 0 u_{3,1} - ────────── 0 ⎥\n",
" H₁ ⎥\n",
" ⎥\n",
" ⎥\n",
"u₁⎞ ⎥\n",
"──⎟ ⎥\n",
" ⎠ ⎥\n",
"─── u_{3,3} 0 0 ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" 0 0 0 ⎦"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def E_NonLinear(grad_u):\n",
" N = 3\n",
"\n",
" du = zeros(N, N)\n",
"\n",
" # print(\"===Deformations===\")\n",
"\n",
" for i in range(N):\n",
" for j in range(N):\n",
" index = i*N+j\n",
" du[j,i] = grad_u[index]\n",
"\n",
" # print(\"========\")\n",
" \n",
" I = eye(3)\n",
"\n",
" a_values = S(1)/S(2) * du * G_up\n",
"\n",
"\n",
" E_NL = zeros(6,9)\n",
" E_NL[0,0] = a_values[0,0]\n",
" E_NL[0,3] = a_values[0,1]\n",
" E_NL[0,6] = a_values[0,2]\n",
"\n",
" E_NL[1,1] = a_values[1,0]\n",
" E_NL[1,4] = a_values[1,1]\n",
" E_NL[1,7] = a_values[1,2]\n",
"\n",
" E_NL[2,2] = a_values[2,0]\n",
" E_NL[2,5] = a_values[2,1]\n",
" E_NL[2,8] = a_values[2,2]\n",
"\n",
" E_NL[3,1] = 2*a_values[0,0]\n",
" E_NL[3,4] = 2*a_values[0,1]\n",
" E_NL[3,7] = 2*a_values[0,2]\n",
"\n",
" E_NL[4,0] = 2*a_values[2,0]\n",
" E_NL[4,3] = 2*a_values[2,1]\n",
" E_NL[4,6] = 2*a_values[2,2]\n",
"\n",
" E_NL[5,2] = 2*a_values[1,0]\n",
" E_NL[5,5] = 2*a_values[1,1]\n",
" E_NL[5,8] = 2*a_values[1,2]\n",
"\n",
"\n",
" return E_NL\n",
"\n",
"\n",
"%aimport geom_util\n",
"u=getUHat3DPlane(alpha1, alpha2, alpha3)\n",
"\n",
"\n",
"# u=getUHatU3Main(alpha1, alpha2, alpha3)\n",
"\n",
"gradu=B*u\n",
"\n",
"\n",
"E_NL = E_NonLinear(gradu)*B\n",
"\n",
"E_NL\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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cBge+BQFwWcST2OtFAGZwWQBk7PUiAbIClwVgRv6yAMh4d1UEZOz1IgIz+mUBmLHXiwDI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\nq/8D0QiuNkhvNwAAAAAASUVORK5CYII=\n",
"text/latex": [
"$$\\left[\\begin{matrix}\\frac{1}{2 H_{1}^{4}} \\left(H_{1}^{2} \\left(H_{1,3} \\operatorname{u_{1}}{\\left (\\alpha_{1},\\alpha_{2},\\alpha_{3} \\right )} - \\frac{\\partial}{\\partial \\alpha_{1}} \\operatorname{u_{3}}{\\left (\\alpha_{1},\\alpha_{2},\\alpha_{3} \\right )}\\right)^{2} + H_{1,3}^{2} \\operatorname{u_{3}}^{2}{\\left (\\alpha_{1},\\alpha_{2},\\alpha_{3} \\right )}\\right)\\\\0\\\\0\\\\0\\\\0\\\\0\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡ 2 \n",
"⎢ 2 ⎛ ∂ ⎞ 2 2 \n",
"⎢H₁ ⋅⎜H_{1,3}⋅u₁(α₁, α₂, α₃) - ───(u₃(α₁, α₂, α₃))⎟ + H_{1,3} ⋅u₃ (α₁, α₂, α₃\n",
"⎢ ⎝ ∂α₁ ⎠ \n",
"⎢─────────────────────────────────────────────────────────────────────────────\n",
"⎢ 4 \n",
"⎢ 2⋅H₁ \n",
"⎢ \n",
"⎢ 0 \n",
"⎢ \n",
"⎢ 0 \n",
"⎢ \n",
"⎢ 0 \n",
"⎢ \n",
"⎢ 0 \n",
"⎢ \n",
"⎣ 0 \n",
"\n",
" ⎤\n",
" ⎥\n",
")⎥\n",
" ⎥\n",
"─⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎦"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%aimport geom_util\n",
"\n",
"\n",
"u=getUHatU3MainPlane(alpha1, alpha2, alpha3)\n",
"\n",
"gradup=Grad_U_P*u\n",
"\n",
"# e=E*gradup\n",
"# e\n",
"\n",
"\n",
"E_NLp = E_NonLinear(gradup)*gradup\n",
"\n",
"\n",
"simplify(E_NLp)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"w"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Virtual work"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVIAAACWCAMAAAC7BfDLAAAAOVBMVEX///8AAAAAAAAAAAAAAAAA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\no+TYPYz3qy8kPwRuDuSx+gtrmo7bWXPIBQAAAABJRU5ErkJggg==\n",
"text/latex": [
"$$\\left[\\begin{matrix}\\lambda + 2 \\mu & \\lambda & \\lambda & 0 & 0 & 0\\\\\\lambda & \\lambda + 2 \\mu & \\lambda & 0 & 0 & 0\\\\\\lambda & \\lambda & \\lambda + 2 \\mu & 0 & 0 & 0\\\\0 & 0 & 0 & \\mu & 0 & 0\\\\0 & 0 & 0 & 0 & \\mu & 0\\\\0 & 0 & 0 & 0 & 0 & \\mu\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡λ + 2⋅μ λ λ 0 0 0⎤\n",
"⎢ ⎥\n",
"⎢ λ λ + 2⋅μ λ 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ λ λ λ + 2⋅μ 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 μ 0 0⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 μ 0⎥\n",
"⎢ ⎥\n",
"⎣ 0 0 0 0 0 μ⎦"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%aimport geom_util\n",
"C_tensor = getIsotropicStiffnessTensor()\n",
"C = convertStiffnessTensorToMatrix(C_tensor)\n",
"C"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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apty73+wS5XyUhQucDGo5bXE2b0jk1MmVxfOqBglaFyp3P47jVWxDKr9vfbgE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\nNKG3GZD1PP4jQKBTuTZv9BJ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zeqgvHSA0j72+7QiyIR0sfbbTFIwuKlOxWNVZhcNr6B6u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\nJjPwkQy4b1h9pIHfbNT/AR2Z0Pt6YATYAAAAAElFTkSuQmCC\n",
"text/latex": [
"$$\\left[\\begin{array}{cccccccccccc}\\frac{H_{1,3}^{2} \\mu}{H_{1}} & 0 & 0 & - H_{1,3} \\mu & 0 & 0 & 0 & 0 & 0 & - \\frac{H_{1,3} \\mu}{H_{1}} & 0 & 0\\\\0 & \\frac{1}{H_{1}} \\left(\\lambda + 2 \\mu\\right) & 0 & 0 & 0 & 0 & \\lambda & 0 & \\frac{H_{1,3}}{H_{1}} \\left(\\lambda + 2 \\mu\\right) & 0 & 0 & \\lambda\\\\0 & 0 & H_{1} \\mu & 0 & 0 & \\mu & 0 & 0 & 0 & 0 & 0 & 0\\\\- H_{1,3} \\mu & 0 & 0 & H_{1} \\mu & 0 & 0 & 0 & 0 & 0 & \\mu & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & \\mu & 0 & 0 & \\frac{\\mu}{H_{1}} & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & \\lambda & 0 & 0 & 0 & 0 & H_{1} \\left(\\lambda + 2 \\mu\\right) & 0 & H_{1,3} \\lambda & 0 & 0 & H_{1} \\lambda\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & H_{1} \\mu & 0 & 0 & H_{1} \\mu & 0\\\\0 & \\frac{H_{1,3}}{H_{1}} \\left(\\lambda + 2 \\mu\\right) & 0 & 0 & 0 & 0 & H_{1,3} \\lambda & 0 & \\frac{H_{1,3}^{2}}{H_{1}} \\left(\\lambda + 2 \\mu\\right) & 0 & 0 & H_{1,3} \\lambda\\\\- \\frac{H_{1,3} \\mu}{H_{1}} & 0 & 0 & \\mu & 0 & 0 & 0 & 0 & 0 & \\frac{\\mu}{H_{1}} & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & H_{1} \\mu & 0 & 0 & H_{1} \\mu & 0\\\\0 & \\lambda & 0 & 0 & 0 & 0 & H_{1} \\lambda & 0 & H_{1,3} \\lambda & 0 & 0 & H_{1} \\left(\\lambda + 2 \\mu\\right)\\end{array}\\right]$$"
],
"text/plain": [
"⎡ 2 \n",
"⎢H_{1,3} ⋅μ \n",
"⎢────────── 0 0 -H_{1,3}⋅μ 0 0 0 0 \n",
"⎢ H₁ \n",
"⎢ \n",
"⎢ λ + 2⋅μ \n",
"⎢ 0 ─────── 0 0 0 0 λ 0 \n",
"⎢ H₁ \n",
"⎢ \n",
"⎢ 0 0 H₁⋅μ 0 0 μ 0 0 \n",
"⎢ \n",
"⎢-H_{1,3}⋅μ 0 0 H₁⋅μ 0 0 0 0 \n",
"⎢ \n",
"⎢ 0 0 0 0 0 0 0 0 \n",
"⎢ \n",
"⎢ μ \n",
"⎢ 0 0 μ 0 0 ── 0 0 \n",
"⎢ H₁ \n",
"⎢ \n",
"⎢ 0 λ 0 0 0 0 H₁⋅(λ + 2⋅μ) 0 \n",
"⎢ \n",
"⎢ 0 0 0 0 0 0 0 H₁⋅μ \n",
"⎢ \n",
"⎢ \n",
"⎢ H_{1,3}⋅(λ + 2⋅μ) \n",
"⎢ 0 ───────────────── 0 0 0 0 H_{1,3}⋅λ 0 \n",
"⎢ H₁ \n",
"⎢ \n",
"⎢-H_{1,3}⋅μ \n",
"⎢─────────── 0 0 μ 0 0 0 0 \n",
"⎢ H₁ \n",
"⎢ \n",
"⎢ 0 0 0 0 0 0 0 H₁⋅μ \n",
"⎢ \n",
"⎣ 0 λ 0 0 0 0 H₁⋅λ 0 \n",
"\n",
" ⎤\n",
" -H_{1,3}⋅μ ⎥\n",
" 0 ─────────── 0 0 ⎥\n",
" H₁ ⎥\n",
" ⎥\n",
"H_{1,3}⋅(λ + 2⋅μ) ⎥\n",
"───────────────── 0 0 λ ⎥\n",
" H₁ ⎥\n",
" ⎥\n",
" 0 0 0 0 ⎥\n",
" ⎥\n",
" 0 μ 0 0 ⎥\n",
" ⎥\n",
" 0 0 0 0 ⎥\n",
" ⎥\n",
" ⎥\n",
" 0 0 0 0 ⎥\n",
" ⎥\n",
" ⎥\n",
" H_{1,3}⋅λ 0 0 H₁⋅λ ⎥\n",
" ⎥\n",
" 0 0 H₁⋅μ 0 ⎥\n",
" ⎥\n",
" 2 ⎥\n",
"H_{1,3} ⋅(λ + 2⋅μ) ⎥\n",
"────────────────── 0 0 H_{1,3}⋅λ ⎥\n",
" H₁ ⎥\n",
" ⎥\n",
" μ ⎥\n",
" 0 ── 0 0 ⎥\n",
" H₁ ⎥\n",
" ⎥\n",
" 0 0 H₁⋅μ 0 ⎥\n",
" ⎥\n",
" H_{1,3}⋅λ 0 0 H₁⋅(λ + 2⋅μ)⎦"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"StrainL.T*C*StrainL*H1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tymoshenko theory\n",
"\n",
"$u_1 \\left( \\alpha_1, \\alpha_2, \\alpha_3 \\right)=u\\left( \\alpha_1 \\right)+\\alpha_3\\gamma \\left( \\alpha_1 \\right) $\n",
"\n",
"$u_2 \\left( \\alpha_1, \\alpha_2, \\alpha_3 \\right)=0 $\n",
"\n",
"$u_3 \\left( \\alpha_1, \\alpha_2, \\alpha_3 \\right)=w\\left( \\alpha_1 \\right) $\n",
"\n",
"$ \\left( \n",
"\\begin{array}{c} \n",
"u_1 \\\\\n",
"\\frac { \\partial u_1 } { \\partial \\alpha_1} \\\\\n",
"\\frac { \\partial u_1 } { \\partial \\alpha_2} \\\\\n",
"\\frac { \\partial u_1 } { \\partial \\alpha_3} \\\\\n",
"u_2 \\\\\n",
"\\frac { \\partial u_2 } { \\partial \\alpha_1} \\\\\n",
"\\frac { \\partial u_2 } { \\partial \\alpha_2} \\\\\n",
"\\frac { \\partial u_2 } { \\partial \\alpha_3} \\\\\n",
"u_3 \\\\\n",
"\\frac { \\partial u_3 } { \\partial \\alpha_1} \\\\\n",
"\\frac { \\partial u_3 } { \\partial \\alpha_2} \\\\\n",
"\\frac { \\partial u_3 } { \\partial \\alpha_3} \\\\\n",
"\\end{array} \n",
"\\right) = T \\cdot \n",
"\\left( \n",
"\\begin{array}{c} \n",
"u \\\\\n",
"\\frac { \\partial u } { \\partial \\alpha_1} \\\\\n",
"\\gamma \\\\\n",
"\\frac { \\partial \\gamma } { \\partial \\alpha_1} \\\\\n",
"w \\\\\n",
"\\frac { \\partial w } { \\partial \\alpha_1} \\\\\n",
"\\end{array} \n",
"\\right) $"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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"text/latex": [
"$$\\left[\\begin{matrix}1 & 0 & \\alpha_{3} & 0 & 0 & 0\\\\0 & 1 & 0 & \\alpha_{3} & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 1 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 1 & 0\\\\0 & 0 & 0 & 0 & 0 & 1\\\\0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡1 0 α₃ 0 0 0⎤\n",
"⎢ ⎥\n",
"⎢0 1 0 α₃ 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 1 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 1 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 1⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎣0 0 0 0 0 0⎦"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"T=zeros(12,6)\n",
"T[0,0]=1\n",
"T[0,2]=alpha3\n",
"T[1,1]=1\n",
"T[1,3]=alpha3\n",
"T[3,2]=1\n",
"\n",
"T[8,4]=1\n",
"T[9,5]=1\n",
"T"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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"text/latex": [
"$$\\left[\\begin{matrix}0 & \\frac{1}{H_{1}} & 0 & \\frac{\\alpha_{3}}{H_{1}} & \\frac{H_{1,3}}{H_{1}} & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0\\\\- \\frac{H_{1,3}}{H_{1}} & 0 & \\frac{1}{H_{1}} \\left(H_{1} - H_{1,3} \\alpha_{3}\\right) & 0 & 0 & \\frac{1}{H_{1}}\\\\0 & 0 & 0 & 0 & 0 & 0\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡ 1 α₃ H_{1,3} ⎤\n",
"⎢ 0 ── 0 ── ─────── 0 ⎥\n",
"⎢ H₁ H₁ H₁ ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢ 0 0 0 0 0 0 ⎥\n",
"⎢ ⎥\n",
"⎢-H_{1,3} H₁ - H_{1,3}⋅α₃ 1 ⎥\n",
"⎢───────── 0 ─────────────── 0 0 ──⎥\n",
"⎢ H₁ H₁ H₁⎥\n",
"⎢ ⎥\n",
"⎣ 0 0 0 0 0 0 ⎦"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D_p_T = StrainL*T\n",
"simplify(D_p_T)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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SnuW6l+emkBnINuLHsev7l+KKbLB2IxxKTGkCtrwrdL7DJhZZaDm+fg2B2qEJ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\n",
"text/latex": [
"$$\\left[\\begin{matrix}\\frac{H_{1,3}}{H_{1}} w{\\left (\\alpha_{1} \\right )} + \\frac{1}{H_{1}} \\left(\\alpha_{3} \\frac{d}{d \\alpha_{1}} \\theta{\\left (\\alpha_{1} \\right )} + \\frac{d}{d \\alpha_{1}} u{\\left (\\alpha_{1} \\right )}\\right) + \\frac{1}{2 H_{1}^{4}} \\left(H_{1}^{2} \\left(H_{1,3} \\left(\\alpha_{3} \\theta{\\left (\\alpha_{1} \\right )} + u{\\left (\\alpha_{1} \\right )}\\right) - \\frac{d}{d \\alpha_{1}} w{\\left (\\alpha_{1} \\right )}\\right)^{2} + \\left(H_{1,3} w{\\left (\\alpha_{1} \\right )} + \\alpha_{3} \\frac{d}{d \\alpha_{1}} \\theta{\\left (\\alpha_{1} \\right )} + \\frac{d}{d \\alpha_{1}} u{\\left (\\alpha_{1} \\right )}\\right)^{2}\\right)\\\\0\\\\\\frac{\\theta^{2}{\\left (\\alpha_{1} \\right )}}{2 H_{1}^{2}}\\\\0\\\\\\theta{\\left (\\alpha_{1} \\right )} - \\frac{H_{1,3}}{H_{1}} \\left(\\alpha_{3} \\theta{\\left (\\alpha_{1} \\right )} + u{\\left (\\alpha_{1} \\right )}\\right) + \\frac{1}{H_{1}} \\frac{d}{d \\alpha_{1}} w{\\left (\\alpha_{1} \\right )} + \\frac{1}{H_{1}^{3}} \\left(H_{1,3} w{\\left (\\alpha_{1} \\right )} + \\alpha_{3} \\frac{d}{d \\alpha_{1}} \\theta{\\left (\\alpha_{1} \\right )} + \\frac{d}{d \\alpha_{1}} u{\\left (\\alpha_{1} \\right )}\\right) \\theta{\\left (\\alpha_{1} \\right )}\\\\0\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡ \n",
"⎢ d d 2 ⎛ \n",
"⎢ α₃⋅───(θ(α₁)) + ───(u(α₁)) H₁ ⋅⎜H_{1,3}⋅(α₃⋅θ(α₁) + u(α₁)) \n",
"⎢H_{1,3}⋅w(α₁) dα₁ dα₁ ⎝ \n",
"⎢───────────── + ────────────────────────── + ────────────────────────────────\n",
"⎢ H₁ H₁ \n",
"⎢ \n",
"⎢ \n",
"⎢ 0 \n",
"⎢ \n",
"⎢ 2 \n",
"⎢ θ (α₁) \n",
"⎢ ────── \n",
"⎢ 2 \n",
"⎢ 2⋅H₁ \n",
"⎢ \n",
"⎢ 0 \n",
"⎢ \n",
"⎢ d ⎛ \n",
"⎢ ───(w(α₁)) ⎜H_{1,3}\n",
"⎢ H_{1,3}⋅(α₃⋅θ(α₁) + u(α₁)) dα₁ ⎝ \n",
"⎢ θ(α₁) - ────────────────────────── + ────────── + ────────\n",
"⎢ H₁ H₁ \n",
"⎢ \n",
"⎢ \n",
"⎣ 0 \n",
"\n",
" 2 2⎤\n",
" d ⎞ ⎛ d d ⎞ ⎥\n",
"- ───(w(α₁))⎟ + ⎜H_{1,3}⋅w(α₁) + α₃⋅───(θ(α₁)) + ───(u(α₁))⎟ ⎥\n",
" dα₁ ⎠ ⎝ dα₁ dα₁ ⎠ ⎥\n",
"──────────────────────────────────────────────────────────────⎥\n",
" 4 ⎥\n",
" 2⋅H₁ ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" d d ⎞ ⎥\n",
"⋅w(α₁) + α₃⋅───(θ(α₁)) + ───(u(α₁))⎟⋅θ(α₁) ⎥\n",
" dα₁ dα₁ ⎠ ⎥\n",
"────────────────────────────────────────── ⎥\n",
" 3 ⎥\n",
" H₁ ⎥\n",
" ⎥\n",
" ⎦"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u = Function(\"u\")\n",
"t = Function(\"theta\")\n",
"w = Function(\"w\")\n",
"\n",
"u1=u(alpha1)+alpha3*t(alpha1)\n",
"u3=w(alpha1)\n",
"\n",
"gu = zeros(12,1) \n",
"gu[0] = u1\n",
"gu[1] = u1.diff(alpha1)\n",
"gu[3] = u1.diff(alpha3)\n",
"\n",
"\n",
"gu[8] = u3\n",
"gu[9] = u3.diff(alpha1)\n",
"\n",
"\n",
"\n",
"gradup=Grad_U_P*gu\n",
"\n",
"\n",
"\n",
"# E_NLp = E_NonLinear(gradup)*gradup\n",
"\n",
"\n",
"# simplify(E_NLp)\n",
"# gradup=Grad_U_P*gu\n",
"\n",
"# o20=(K*u(alpha1)-w(alpha1).diff(alpha1)+t(alpha1))/2\n",
"# o21=K*t(alpha1)\n",
"# O=1/2*o20*o20+alpha3*o20*o21-alpha3*K/2*o20*o20\n",
"# O=expand(O)\n",
"# O=collect(O,alpha3)\n",
"# simplify(O)\n",
"\n",
"StrainNL = E_NonLinear(gradup)*gradup\n",
"StrainL*gu+simplify(StrainNL)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Square theory\n",
"\n",
"$u^1 \\left( \\alpha_1, \\alpha_2, \\alpha_3 \\right)=u_{10}\\left( \\alpha_1 \\right)p_0\\left( \\alpha_3 \\right)+u_{11}\\left( \\alpha_1 \\right)p_1\\left( \\alpha_3 \\right)+u_{12}\\left( \\alpha_1 \\right)p_2\\left( \\alpha_3 \\right) $\n",
"\n",
"$u^2 \\left( \\alpha_1, \\alpha_2, \\alpha_3 \\right)=0 $\n",
"\n",
"$u^3 \\left( \\alpha_1, \\alpha_2, \\alpha_3 \\right)=u_{30}\\left( \\alpha_1 \\right)p_0\\left( \\alpha_3 \\right)+u_{31}\\left( \\alpha_1 \\right)p_1\\left( \\alpha_3 \\right)+u_{32}\\left( \\alpha_1 \\right)p_2\\left( \\alpha_3 \\right) $\n",
"\n",
"$ \\left( \n",
"\\begin{array}{c} \n",
"u^1 \\\\\n",
"\\frac { \\partial u^1 } { \\partial \\alpha_1} \\\\\n",
"\\frac { \\partial u^1 } { \\partial \\alpha_2} \\\\\n",
"\\frac { \\partial u^1 } { \\partial \\alpha_3} \\\\\n",
"u^2 \\\\\n",
"\\frac { \\partial u^2 } { \\partial \\alpha_1} \\\\\n",
"\\frac { \\partial u^2 } { \\partial \\alpha_2} \\\\\n",
"\\frac { \\partial u^2 } { \\partial \\alpha_3} \\\\\n",
"u^3 \\\\\n",
"\\frac { \\partial u^3 } { \\partial \\alpha_1} \\\\\n",
"\\frac { \\partial u^3 } { \\partial \\alpha_2} \\\\\n",
"\\frac { \\partial u^3 } { \\partial \\alpha_3} \\\\\n",
"\\end{array} \n",
"\\right) = L \\cdot \n",
"\\left( \n",
"\\begin{array}{c} \n",
"u_{10} \\\\\n",
"\\frac { \\partial u_{10} } { \\partial \\alpha_1} \\\\\n",
"u_{11} \\\\\n",
"\\frac { \\partial u_{11} } { \\partial \\alpha_1} \\\\\n",
"u_{12} \\\\\n",
"\\frac { \\partial u_{12} } { \\partial \\alpha_1} \\\\\n",
"u_{30} \\\\\n",
"\\frac { \\partial u_{30} } { \\partial \\alpha_1} \\\\\n",
"u_{31} \\\\\n",
"\\frac { \\partial u_{31} } { \\partial \\alpha_1} \\\\\n",
"u_{32} \\\\\n",
"\\frac { \\partial u_{32} } { \\partial \\alpha_1} \\\\\n",
"\\end{array} \n",
"\\right) $"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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AdBhTHNpFdHxJDq8r2NQY655tZ1ONGrWi8dAgdHx1lQIREg1EhzHFoV1Ex5fk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\n92TO/Q13wB1wB/AOtJdyV/wf8Xl1kAflcnIAAAAASUVORK5CYII=\n",
"text/latex": [
"$$\\left[\\begin{array}{cccccccccccc}- \\frac{\\alpha_{3}}{h} + 0.5 & 0 & \\frac{\\alpha_{3}}{h} + 0.5 & 0 & - \\frac{4 \\alpha_{3}^{2}}{h^{2}} + 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & - \\frac{\\alpha_{3}}{h} + 0.5 & 0 & \\frac{\\alpha_{3}}{h} + 0.5 & 0 & - \\frac{4 \\alpha_{3}^{2}}{h^{2}} + 1 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\- \\frac{1}{h} & 0 & \\frac{1}{h} & 0 & - \\frac{8 \\alpha_{3}}{h^{2}} & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & - \\frac{\\alpha_{3}}{h} + 0.5 & 0 & \\frac{\\alpha_{3}}{h} + 0.5 & 0 & - \\frac{4 \\alpha_{3}^{2}}{h^{2}} + 1 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & - \\frac{\\alpha_{3}}{h} + 0.5 & 0 & \\frac{\\alpha_{3}}{h} + 0.5 & 0 & - \\frac{4 \\alpha_{3}^{2}}{h^{2}} + 1\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & - \\frac{1}{h} & 0 & \\frac{1}{h} & 0 & - \\frac{8 \\alpha_{3}}{h^{2}} & 0\\end{array}\\right]$$"
],
"text/plain": [
"⎡ 2 \n",
"⎢ α₃ α₃ 4⋅α₃ \n",
"⎢- ── + 0.5 0 ── + 0.5 0 - ───── + 1 0 0 \n",
"⎢ h h 2 \n",
"⎢ h \n",
"⎢ \n",
"⎢ 2 \n",
"⎢ α₃ α₃ 4⋅α₃ \n",
"⎢ 0 - ── + 0.5 0 ── + 0.5 0 - ───── + 1 0 \n",
"⎢ h h 2 \n",
"⎢ h \n",
"⎢ \n",
"⎢ 0 0 0 0 0 0 0 \n",
"⎢ \n",
"⎢ -1 1 -8⋅α₃ \n",
"⎢ ─── 0 ─ 0 ────── 0 0 \n",
"⎢ h h 2 \n",
"⎢ h \n",
"⎢ \n",
"⎢ 0 0 0 0 0 0 0 \n",
"⎢ \n",
"⎢ 0 0 0 0 0 0 0 \n",
"⎢ \n",
"⎢ 0 0 0 0 0 0 0 \n",
"⎢ \n",
"⎢ 0 0 0 0 0 0 0 \n",
"⎢ \n",
"⎢ \n",
"⎢ α₃ \n",
"⎢ 0 0 0 0 0 0 - ── + \n",
"⎢ h \n",
"⎢ \n",
"⎢ \n",
"⎢ \n",
"⎢ \n",
"⎢ 0 0 0 0 0 0 0 \n",
"⎢ \n",
"⎢ \n",
"⎢ \n",
"⎢ 0 0 0 0 0 0 0 \n",
"⎢ \n",
"⎢ -1 \n",
"⎢ 0 0 0 0 0 0 ─── \n",
"⎢ h \n",
"⎣ \n",
"\n",
" ⎤\n",
" ⎥\n",
" 0 0 0 0 0 ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" 0 0 0 0 0 ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" 0 0 0 0 0 ⎥\n",
" ⎥\n",
" ⎥\n",
" 0 0 0 0 0 ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" 0 0 0 0 0 ⎥\n",
" ⎥\n",
" 0 0 0 0 0 ⎥\n",
" ⎥\n",
" 0 0 0 0 0 ⎥\n",
" ⎥\n",
" 0 0 0 0 0 ⎥\n",
" ⎥\n",
" 2 ⎥\n",
" α₃ 4⋅α₃ ⎥\n",
"0.5 0 ── + 0.5 0 - ───── + 1 0 ⎥\n",
" h 2 ⎥\n",
" h ⎥\n",
" ⎥\n",
" 2 ⎥\n",
" α₃ α₃ 4⋅α₃ ⎥\n",
" - ── + 0.5 0 ── + 0.5 0 - ───── + 1⎥\n",
" h h 2 ⎥\n",
" h ⎥\n",
" ⎥\n",
" 0 0 0 0 0 ⎥\n",
" ⎥\n",
" 1 -8⋅α₃ ⎥\n",
" 0 ─ 0 ────── 0 ⎥\n",
" h 2 ⎥\n",
" h ⎦"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L=zeros(12,12)\n",
"h=Symbol('h')\n",
"p0=1/2-alpha3/h\n",
"p1=1/2+alpha3/h\n",
"p2=1-(2*alpha3/h)**2\n",
"\n",
"L[0,0]=p0\n",
"L[0,2]=p1\n",
"L[0,4]=p2\n",
"\n",
"L[1,1]=p0\n",
"L[1,3]=p1\n",
"L[1,5]=p2\n",
"\n",
"L[3,0]=p0.diff(alpha3)\n",
"L[3,2]=p1.diff(alpha3)\n",
"L[3,4]=p2.diff(alpha3)\n",
"\n",
"L[8,6]=p0\n",
"L[8,8]=p1\n",
"L[8,10]=p2\n",
"\n",
"L[9,7]=p0\n",
"L[9,9]=p1\n",
"L[9,11]=p2\n",
"\n",
"L[11,6]=p0.diff(alpha3)\n",
"L[11,8]=p1.diff(alpha3)\n",
"L[11,10]=p2.diff(alpha3)\n",
"\n",
"L"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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GIasVGGesHNeFqbRnNKJAyUXbTa4t9syb8HCSmot0f1Y8wDoJ89hMCBkf4Jsh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\nxf/0Ki6+VN3EvAAAAABJRU5ErkJggg==\n",
"text/latex": [
"$$\\left[\\begin{array}{cccccccccccc}0 & \\frac{1}{H_{1} h} \\left(- \\alpha_{3} + 0.5 h\\right) & 0 & \\frac{\\alpha_{3} + 0.5 h}{H_{1} h} & 0 & \\frac{1}{H_{1} h^{2}} \\left(- 4 \\alpha_{3}^{2} + h^{2}\\right) & - \\frac{H_{1,3}}{H_{1} h} \\left(\\alpha_{3} - 0.5 h\\right) & 0 & \\frac{H_{1,3}}{H_{1} h} \\left(\\alpha_{3} + 0.5 h\\right) & 0 & \\frac{H_{1,3}}{H_{1} h^{2}} \\left(- 4 \\alpha_{3}^{2} + h^{2}\\right) & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & - \\frac{1}{h} & 0 & \\frac{1}{h} & 0 & - \\frac{8 \\alpha_{3}}{h^{2}} & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\\frac{1}{H_{1} h} \\left(- H_{1} + H_{1,3} \\left(\\alpha_{3} - 0.5 h\\right)\\right) & 0 & \\frac{1}{H_{1} h} \\left(H_{1} - H_{1,3} \\left(\\alpha_{3} + 0.5 h\\right)\\right) & 0 & \\frac{1}{H_{1} h^{2}} \\left(- 8 H_{1} \\alpha_{3} + H_{1,3} \\left(4 \\alpha_{3}^{2} - h^{2}\\right)\\right) & 0 & 0 & \\frac{1}{H_{1} h} \\left(- \\alpha_{3} + 0.5 h\\right) & 0 & \\frac{\\alpha_{3} + 0.5 h}{H_{1} h} & 0 & \\frac{1}{H_{1} h^{2}} \\left(- 4 \\alpha_{3}^{2} + h^{2}\\right)\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\end{array}\\right]$$"
],
"text/plain": [
"⎡ \n",
"⎢ -α₃ + 0.5⋅h α₃ + 0.5⋅\n",
"⎢ 0 ─────────── 0 ─────────\n",
"⎢ H₁⋅h H₁⋅h \n",
"⎢ \n",
"⎢ \n",
"⎢ 0 0 0 0 \n",
"⎢ \n",
"⎢ \n",
"⎢ 0 0 0 0 \n",
"⎢ \n",
"⎢ \n",
"⎢ \n",
"⎢ 0 0 0 0 \n",
"⎢ \n",
"⎢ \n",
"⎢-H₁ + H_{1,3}⋅(α₃ - 0.5⋅h) H₁ - H_{1,3}⋅(α₃ + 0.5⋅h) \n",
"⎢────────────────────────── 0 ───────────────────────── 0 \n",
"⎢ H₁⋅h H₁⋅h \n",
"⎢ \n",
"⎢ \n",
"⎣ 0 0 0 0 \n",
"\n",
" 2 2 \n",
"h - 4⋅α₃ + h -H_{1,3}⋅(α₃ - 0.5⋅h) \n",
"─ 0 ──────────── ────────────────────── \n",
" 2 H₁⋅h \n",
" H₁⋅h \n",
" \n",
" 0 0 0 \n",
" \n",
" -1 \n",
" 0 0 ─── \n",
" h \n",
" \n",
" \n",
" 0 0 0 \n",
" \n",
" ⎛ 2 2⎞ \n",
" -8⋅H₁⋅α₃ + H_{1,3}⋅⎝4⋅α₃ - h ⎠ -α₃ \n",
" ─────────────────────────────── 0 0 ────\n",
" 2 \n",
" H₁⋅h \n",
" \n",
" 0 0 0 \n",
"\n",
" ⎛ 2 2⎞ \n",
" H_{1,3}⋅(α₃ + 0.5⋅h) H_{1,3}⋅⎝- 4⋅α₃ + h ⎠ \n",
" 0 ──────────────────── 0 ────────────────────── 0 \n",
" H₁⋅h 2 \n",
" H₁⋅h \n",
" \n",
" 0 0 0 0 0 \n",
" \n",
" 1 -8⋅α₃ \n",
" 0 ─ 0 ────── 0 \n",
" h 2 \n",
" h \n",
" \n",
" 0 0 0 0 0 \n",
" \n",
" 2 \n",
"+ 0.5⋅h α₃ + 0.5⋅h - 4⋅α₃ + h\n",
"─────── 0 ────────── 0 ───────────\n",
"H₁⋅h H₁⋅h 2 \n",
" H₁⋅h \n",
" \n",
" 0 0 0 0 0 \n",
"\n",
" ⎤\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
"2⎥\n",
" ⎥\n",
"─⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" ⎦"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D_p_L = StrainL*L\n",
"simplify(D_p_L)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAKsAAAAPBAMAAABpSyLSAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYII=\n",
"text/latex": [
"$$0.166666666666667$$"
],
"text/plain": [
"0.166666666666667"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h = 0.5\n",
"exp=(0.5-alpha3/h)*(1-(2*alpha3/h)**2)#/(1+alpha3*0.8)\n",
"p02=integrate(exp, (alpha3, -h/2, h/2))\n",
"integral = expand(simplify(p02))\n",
"integral"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mass matrix"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAErCAMAAADt3NTnAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$\\left[\\begin{array}{cccccccccccc}\\rho & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & \\rho & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & \\rho & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\end{array}\\right]$$"
],
"text/plain": [
"⎡ρ 0 0 0 0 0 0 0 0 0 0 0⎤\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 ρ 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0 0 0 ρ 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎢0 0 0 0 0 0 0 0 0 0 0 0⎥\n",
"⎢ ⎥\n",
"⎣0 0 0 0 0 0 0 0 0 0 0 0⎦"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rho=Symbol('rho')\n",
"B_h=zeros(3,12)\n",
"B_h[0,0]=1\n",
"B_h[1,4]=1\n",
"B_h[2,8]=1\n",
"M=simplify(rho*P.T*B_h.T*G_up*B_h*P)\n",
"M"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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iDUuus2Rrvz+WuG66Eu5X0zzBDw8XAXyJu3cr6AqhrjKUgBI4igA64vaMd5/7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\n",
"text/latex": [
"$$\\left[\\begin{array}{cccccccccccc}0.125 \\rho + \\frac{0.0104166666666667 \\rho}{h^{2}} & 0 & 0.125 \\rho - \\frac{0.0104166666666667 \\rho}{h^{2}} & 0 & 0.25 \\rho - \\frac{0.0208333333333333 \\rho}{h^{2}} & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0.125 \\rho - \\frac{0.0104166666666667 \\rho}{h^{2}} & 0 & 0.125 \\rho + \\frac{0.0104166666666667 \\rho}{h^{2}} & 0 & 0.25 \\rho - \\frac{0.0208333333333333 \\rho}{h^{2}} & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0.25 \\rho - \\frac{0.0208333333333333 \\rho}{h^{2}} & 0 & 0.25 \\rho - \\frac{0.0208333333333333 \\rho}{h^{2}} & 0 & 0.5 \\rho - \\frac{0.0833333333333333 \\rho}{h^{2}} + \\frac{0.00625 \\rho}{h^{4}} & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0.125 \\rho + \\frac{0.0104166666666667 \\rho}{h^{2}} & 0 & 0.125 \\rho - \\frac{0.0104166666666667 \\rho}{h^{2}} & 0 & 0.25 \\rho - \\frac{0.0208333333333333 \\rho}{h^{2}} & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0.125 \\rho - \\frac{0.0104166666666667 \\rho}{h^{2}} & 0 & 0.125 \\rho + \\frac{0.0104166666666667 \\rho}{h^{2}} & 0 & 0.25 \\rho - \\frac{0.0208333333333333 \\rho}{h^{2}} & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0.25 \\rho - \\frac{0.0208333333333333 \\rho}{h^{2}} & 0 & 0.25 \\rho - \\frac{0.0208333333333333 \\rho}{h^{2}} & 0 & 0.5 \\rho - \\frac{0.0833333333333333 \\rho}{h^{2}} + \\frac{0.00625 \\rho}{h^{4}} & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\end{array}\\right]$$"
],
"text/plain": [
"⎡ 0.0104166666666667⋅ρ 0.0104166666666667⋅ρ \n",
"⎢0.125⋅ρ + ──────────────────── 0 0.125⋅ρ - ──────────────────── 0 0.\n",
"⎢ 2 2 \n",
"⎢ h h \n",
"⎢ \n",
"⎢ 0 0 0 0 \n",
"⎢ \n",
"⎢ 0.0104166666666667⋅ρ 0.0104166666666667⋅ρ \n",
"⎢0.125⋅ρ - ──────────────────── 0 0.125⋅ρ + ──────────────────── 0 0.\n",
"⎢ 2 2 \n",
"⎢ h h \n",
"⎢ \n",
"⎢ 0 0 0 0 \n",
"⎢ \n",
"⎢ 0.0208333333333333⋅ρ 0.0208333333333333⋅ρ \n",
"⎢0.25⋅ρ - ──────────────────── 0 0.25⋅ρ - ──────────────────── 0 0.5⋅ρ -\n",
"⎢ 2 2 \n",
"⎢ h h \n",
"⎢ \n",
"⎢ 0 0 0 0 \n",
"⎢ \n",
"⎢ \n",
"⎢ 0 0 0 0 \n",
"⎢ \n",
"⎢ \n",
"⎢ \n",
"⎢ 0 0 0 0 \n",
"⎢ \n",
"⎢ \n",
"⎢ 0 0 0 0 \n",
"⎢ \n",
"⎢ \n",
"⎢ \n",
"⎢ 0 0 0 0 \n",
"⎢ \n",
"⎢ \n",
"⎢ 0 0 0 0 \n",
"⎢ \n",
"⎢ \n",
"⎢ \n",
"⎣ 0 0 0 0 \n",
"\n",
" 0.0208333333333333⋅ρ \n",
"25⋅ρ - ──────────────────── 0 0 0 \n",
" 2 \n",
" h \n",
" \n",
" 0 0 0 0 \n",
" \n",
" 0.0208333333333333⋅ρ \n",
"25⋅ρ - ──────────────────── 0 0 0 \n",
" 2 \n",
" h \n",
" \n",
" 0 0 0 0 \n",
" \n",
" 0.0833333333333333⋅ρ 0.00625⋅ρ \n",
" ──────────────────── + ───────── 0 0 0 \n",
" 2 4 \n",
" h h \n",
" \n",
" 0 0 0 0 \n",
" \n",
" 0.0104166666666667⋅ρ \n",
" 0 0 0.125⋅ρ + ──────────────────── 0 0.125\n",
" 2 \n",
" h \n",
" \n",
" 0 0 0 0 \n",
" \n",
" 0.0104166666666667⋅ρ \n",
" 0 0 0.125⋅ρ - ──────────────────── 0 0.125\n",
" 2 \n",
" h \n",
" \n",
" 0 0 0 0 \n",
" \n",
" 0.0208333333333333⋅ρ \n",
" 0 0 0.25⋅ρ - ──────────────────── 0 0.25⋅\n",
" 2 \n",
" h \n",
" \n",
" 0 0 0 0 \n",
"\n",
" ⎤\n",
" 0 0 0 0⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" 0 0 0 0⎥\n",
" ⎥\n",
" ⎥\n",
" 0 0 0 0⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" 0 0 0 0⎥\n",
" ⎥\n",
" ⎥\n",
" 0 0 0 0⎥\n",
" ⎥\n",
" ⎥\n",
" ⎥\n",
" 0 0 0 0⎥\n",
" ⎥\n",
" 0.0104166666666667⋅ρ 0.0208333333333333⋅ρ ⎥\n",
"⋅ρ - ──────────────────── 0 0.25⋅ρ - ──────────────────── 0⎥\n",
" 2 2 ⎥\n",
" h h ⎥\n",
" ⎥\n",
" 0 0 0 0⎥\n",
" ⎥\n",
" 0.0104166666666667⋅ρ 0.0208333333333333⋅ρ ⎥\n",
"⋅ρ + ──────────────────── 0 0.25⋅ρ - ──────────────────── 0⎥\n",
" 2 2 ⎥\n",
" h h ⎥\n",
" ⎥\n",
" 0 0 0 0⎥\n",
" ⎥\n",
" 0.0208333333333333⋅ρ 0.0833333333333333⋅ρ 0.00625⋅ρ ⎥\n",
"ρ - ──────────────────── 0 0.5⋅ρ - ──────────────────── + ───────── 0⎥\n",
" 2 2 4 ⎥\n",
" h h h ⎥\n",
" ⎥\n",
" 0 0 0 0⎦"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"M_p = L.T*M*L\n",
"integrate(M_p, (alpha3, -h/2, h/2))"
]
}
],
"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 |
nipunsadvilkar/ProbabilityForHackers | Introduction to Probability.ipynb | 1 | 131220 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Probability* is \n",
">*How **likely** something is to happen.*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start with obligatory example of coin-toss<br>\n",
"Here is our virtual coin so that everyone can see it:<br>\n",
"https://nipunsadvilkar.github.io/coin-flip/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe src=\"https://nipunsadvilkar.github.io/coin-flip/\" width=\"100%\" height=\"700px\" scrolling=\"no\" style=\"margin-top: -70px;\" frameborder=\"0\"></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"HTML('<iframe src=\"https://nipunsadvilkar.github.io/coin-flip/\" width=\"100%\" height=\"700px\" scrolling=\"no\" style=\"margin-top: -70px;\" frameborder=\"0\"></iframe>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Seems like it will take endless time click everyone and record it to prove as we increase n then probability of Heads is going to be: $$\\frac{1}{2}$$ \n",
"<br>and same for Tails.<br> Lets try with a different way - another virtual coin flipping and comparing it with theoretical probability of Heads and Tails $$\\frac{1}{2}$$\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe src=\"http://localhost/Seeing-Theory/basic-probability/index.html#first\" width=\"100%\" height=\"700px\" scrolling=\"no\" style=\"margin-top: -70px;\" frameborder=\"0\"></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"HTML('<iframe src=\"http://localhost/Seeing-Theory/basic-probability/index.html#first\" width=\"100%\" height=\"700px\" scrolling=\"no\" style=\"margin-top: -70px;\" frameborder=\"0\"></iframe>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Still taking lot of time to get 0.5 probability. Lets try it with Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simulating coin-toss experiment with Python"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from utils import comp_prob_inference\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'heads'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comp_prob_inference.flip_fair_coin()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"flips = comp_prob_inference.flip_fair_coins(100)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEXCAYAAACTRp41AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQd0vdx8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1a611236a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"comp_prob_inference.plot_discrete_histogram(flips)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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P4BOqw/BBjBUiyb8Angf8FN2scgGuBL4E3AfMAEcBL/bLosUkOaKqHu6vIXw/8NWq+nKS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\naQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1a5edc67b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"comp_prob_inference.plot_discrete_histogram(flips, frequency=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# For n=100000"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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AJPkJ4DnAD9CtKhfgcuBzwL3AMuBI4EV+WDSbJEdU1UP9MYRvB75YVZ9P8n7gw1V1ZZLz\n6PaMdwHrgPuBN4IhPIQFsRraJEmSmT/oSX4XeDzwi/2/V1bVuiQ/2q+1TJKXACf5IdFMSZZU1YPT\nxw76EL6EbiGsZwCXAZuS/ElVXdM/5xhgFfBZf6aG4xTEPNJ/UKp/fET/71LgQeANVXVLVT0L+J4k\na6tqb5JTk1wPvAz4q2bFa96ZnretqgeTLE3y40lO7je/mG6xrDOr6h+AvwYu6Z/3M8BHgVuq6jUN\nSp8YBvA80n9QHpPkcuBXk5xBF74r6Obnpl0HXNGf+XAs8L6qekY/T6wJl2R5kieO/DJ/EfAPwJOB\nK5Ns6PeGf5nutmHQ3Q7srCTfC3wAOL2qLmtQ/kQxgBuaeeJ7ktPo9jz+H91ynv8HeCLwbuB/JVnV\nD70HeBzw5Kr6UFW94/BVrQXgGODyJGckeT3wdOB8upD9BrqfpeVV9TZgX5Kfr6p7gI3AF6pqd1V9\nqVn1E8SDcI1MHxCZ0fdddAc99gHvpDuodm9V/WCSy+gWtX8q8BHgsqr67GEuWwtAkscA/0gXxOdW\n1d8kORd4LfAautt7faWqLkiyFri8qp7YruLJ5UG4RvoDISvpDoB8GvhMVW1OspzuRqW/D/wF3alA\nL62qjUlOojvQ9tF2lWsB2Au8h+4mB5/q+74HeHdV3ZjkBOD3klxVVX9B93OmBpyCOExmmW54KnAN\n3V1BbgTekWQ18B3Av1bVHwGPAv4/cH6SY6vqLsNXB1JVD1TVLwHvA36n794DfEeSnwWeCfwK8Jk2\nFWqaUxADS/IDwHFVdXXfflpVfSLJC4Av0005XALsqKoNSR5Ld0T6RuBs4E3A5qr6cpNvQAtWkscD\nH6Q74+GxwEV054+/qar+tmVt6hjAA+vn3i4FzqU76vxE4A/ozsFcD/wl8Jaq+liSo4CH6A6wPRu4\nrar+vkXdWhyS/Djd3O9XgddUlacqziMG8MD6czHfBpxGdyHFx4DX0V29dhFwQVV9MMmxwFuBj3pW\ng+ZSklOBf5y+aEfzhwF8GCR5Ct06Dqf30w9n0Z0a9N10fxruAp4EXF9Vr2tXqaTDyQA+TJL8BrC8\nqs7rr257OXAisBV4ALirqna3rFHS4eVZEIfPrwMrkzynqvbR7RH/X+Cfq+rvDV9p8rgHfBgleTnw\nqqpa3boWSe15Icbh9QfA9HKA5SpT0mRzD1iSGnEOWJIaMYAlqREDWJIaMYAlqREDWJIaMYC1aCT5\ncJLnzOi7KMlvP8xz7hu+Mml2BrAWk2voVpgbtb7vl+YdA1iLyXXA85IcCZBkBfAtwKeS/FWSTya5\nNck5M5+Y5JlJ/nyk/bYkF/SPT0nykSQ3J7mhX2dXesQMYC0aVfVFujU2zu671gPX0q2F+/yqOhk4\nk+6GlRnnNfs7T/8m8MKqOoXuXn1vnOvaNZm8FFmLzfQ0xPv7f19KdxeIX0lyOt2C98vpFr3/1zFe\n7wl0y4Z+qM/sJXSLKEmPmAGsxeb9wJuTnAwcVVU391MJy4BTquqBJHfS3W9v1D7++1+E09sDbK+q\npw9btiaRUxBaVKrqPuDDdFMF0wffjgE+14fvmcBJszz1LmB1kq/v707y/X3/DmBZkqdDNyWRxFu4\na064B6zF6Bq6OwJPnxHxR8AHktwKTAH/NPMJVXV3kmuBTwP/Qn8796ram+SFwFuTHEP3mbkS2D74\nd6FFz9XQJKkRpyAkqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqZH/BPIMUq5v/cEuAAAAAElF\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1a5edd2ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# TODO: make this plot more beautiful(with grids and color) and should be able to show value on hover\n",
"flips = comp_prob_inference.flip_fair_coins(100000)\n",
"comp_prob_inference.plot_discrete_histogram(flips, frequency=True)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n = 100000\n",
"heads_so_far = 0\n",
"fraction_of_heads = []\n",
"for i in range(n):\n",
" if comp_prob_inference.flip_fair_coin() == 'heads':\n",
" heads_so_far += 1\n",
" fraction_of_heads.append(heads_so_far / (i+1))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f1a5edc6748>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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4Gj4F+Dog4DjgtlS+AHgw/Z2fhuenaT9M8yq9942t/txNqss/Bv4N+Goavw44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\nJ3UzM7OccFI3MzPLif8BwrZRFgXEyugAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1a60eb1160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 4))\n",
"plt.plot(range(1, n+1), fraction_of_heads)\n",
"plt.xlabel('Number of flips')\n",
"plt.ylabel('Fraction of heads')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Exercise 1:\n",
"\n",
"### Do the same simulation for die experiment. You should get stable line at 1/6 when number of throws is higher(~n=10K). "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ZWckOQ0Q6AOWFrqOlc4M6ECJxtmzZkuwQWkVqaip9+vRJdhgiIh1SZ8wNygvS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\ncSAJ69Gwgt0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1a5e4d7080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.core.pylabtools import figsize\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"figsize(11, 9)\n",
"\n",
"import scipy.stats as stats\n",
"\n",
"dist = stats.beta\n",
"n_trials = [0, 1, 2, 3, 4, 5, 8, 15, 50, 500]\n",
"data = stats.bernoulli.rvs(0.5, size=n_trials[-1])\n",
"x = np.linspace(0, 1, 100)\n",
"\n",
"# For the already prepared, I'm using Binomial's conj. prior.\n",
"for k, N in enumerate(n_trials):\n",
" sx = plt.subplot(len(n_trials)/2, 2, k+1)\n",
" plt.xlabel(\"$p$, probability of heads\") \\\n",
" if k in [0, len(n_trials)-1] else None\n",
" plt.setp(sx.get_yticklabels(), visible=False)\n",
" heads = data[:N].sum()\n",
" y = dist.pdf(x, 1 + heads, 1 + N - heads)\n",
" plt.plot(x, y, label=\"observe %d tosses,\\n %d heads\" % (N, heads))\n",
" plt.fill_between(x, 0, y, color=\"#348ABD\", alpha=0.4)\n",
" plt.vlines(0.5, 0, 4, color=\"k\", linestyles=\"--\", lw=1)\n",
"\n",
" leg = plt.legend()\n",
" leg.get_frame().set_alpha(0.4)\n",
" plt.autoscale(tight=True)\n",
"\n",
"\n",
"plt.suptitle(\"Bayesian updating of posterior probabilities\",\n",
" y=1.02,\n",
" fontsize=14)\n",
"\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What does 0 and 1 actually mean?\n",
"\n",
"<img src=\"media/probability-line.png\">\n",
"\n",
"source: https://www.mathsisfun.com/data/probability.html"
]
},
{
"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 |
NahsiN/SafeWalk | folium_choropleth.ipynb | 1 | 12514 | {
"cells": [
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import folium\n",
"import psycopg2\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dbname = 'routing_db_crime_2'\n",
"username = 'nishan'\n",
"password = 'vikaspuri'\n",
"con = psycopg2.connect(database=dbname, user=username, password=password)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"query = \"\"\"\n",
" SELECT *, ST_AsGeoJSON(the_geom) AS geojson_geometry FROM ways WHERE\n",
"\t ST_DWithin(the_geom, ST_GeomFromText('POINT(-73.947494 40.687179)', 4326), 0.05)\n",
" \"\"\"\n",
"df = pd.read_sql(query, con)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['gid', 'class_id', 'length', 'length_m', 'name', 'source', 'target',\n",
" 'x1', 'y1', 'x2',\n",
" ...\n",
" 'cost_crime_offense_class_indirect_bodily_harm_hour_15',\n",
" 'cost_crime_offense_class_indirect_bodily_harm_hour_16',\n",
" 'cost_crime_offense_class_indirect_bodily_harm_hour_17',\n",
" 'cost_crime_offense_class_indirect_bodily_harm_hour_18',\n",
" 'cost_crime_offense_class_indirect_bodily_harm_hour_19',\n",
" 'cost_crime_offense_class_indirect_bodily_harm_hour_20',\n",
" 'cost_crime_offense_class_indirect_bodily_harm_hour_21',\n",
" 'cost_crime_offense_class_indirect_bodily_harm_hour_22',\n",
" 'cost_crime_offense_class_indirect_bodily_harm_hour_23',\n",
" 'geojson_geometry'],\n",
" dtype='object', length=105)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_tmp = df.loc[:,['gid', 'cost_crime0']]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# normalize between 0 and 1\n",
"df_tmp.cost_crime0 = df_tmp.cost_crime0/df_tmp.cost_crime0.max()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# (40.687179, -73.947494)\n",
"mymap = folium.Map(location=[40.687179, -73.947494], tiles='https://api.mapbox.com/styles/v1/mapbox/streets-v9/tiles/256/\\{z\\}/\\{x\\}/\\{y\\}?access_token=pk.eyJ1IjoibmFoc2luIiwiYSI6ImNpdDdwdDV0bzA5dHkyeW13ZTh4enl0c3MifQ.iOW2JTxp_HkABm9wuTuPqA', attr='My Data Attribution')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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style=\"position:absolute;width:100%;height:100%;left:0;top:0;\"></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f70f6230400>"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mymap"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/nishan/.local/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: 'threshold_scale' default behavior has changed. Now you get a linear scale between the 'min' and the 'max' of your data. To get former behavior, use folium.utilities.split_six.\n",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"mymap.choropleth(geo_path='roads.json', data=df_tmp, columns=['gid', 'cost_crime0'], key_on='feature.id', fill_color='YlOrRd', fill_opacity=1.0, line_color='red', line_opacity=1.0, line_weight=2)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'{\"type\":\"LineString\",\"coordinates\":[[-73.9147568,40.6843469],[-73.9174143,40.6840406]]}'"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.geojson_geometry[0]"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<folium.features.PolyLine at 0x7f70f61d5208>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"folium.PolyLine([[-73.9147568,40.6843469],[-73.9174143,40.6840406]],color='#0060ff', weight=10, opacity=1.0).add_to(mymap)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"folium.PolyLine?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mymap.save('test.html')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
bdestombe/flopy-1 | examples/Notebooks/flopy3_multi-component_SSM.ipynb | 2 | 9465 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# FloPy\n",
"\n",
"## Using FloPy to simplify the use of the MT3DMS ```SSM``` package\n",
"\n",
"A multi-component transport demonstration"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.6.0 |Anaconda 4.3.0 (x86_64)| (default, Dec 23 2016, 13:19:00) \n",
"[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]\n",
"numpy version: 1.11.3\n",
"flopy version: 3.2.6\n"
]
}
],
"source": [
"import os\n",
"import sys\n",
"import numpy as np\n",
"import flopy\n",
"\n",
"print(sys.version)\n",
"print('numpy version: {}'.format(np.__version__))\n",
"print('flopy version: {}'.format(flopy.__version__))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we will create a simple model structure"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nlay, nrow, ncol = 10, 10, 10\n",
"perlen = np.zeros((10), dtype=np.float) + 10\n",
"nper = len(perlen)\n",
"\n",
"ibound = np.ones((nlay,nrow,ncol), dtype=np.int)\n",
"\n",
"botm = np.arange(-1,-11,-1)\n",
"top = 0."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create the ```MODFLOW``` packages"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model_ws = 'data'\n",
"modelname = 'ssmex'\n",
"mf = flopy.modflow.Modflow(modelname, model_ws=model_ws)\n",
"dis = flopy.modflow.ModflowDis(mf, nlay=nlay, nrow=nrow, ncol=ncol, \n",
" perlen=perlen, nper=nper, botm=botm, top=top, \n",
" steady=False)\n",
"bas = flopy.modflow.ModflowBas(mf, ibound=ibound, strt=top)\n",
"lpf = flopy.modflow.ModflowLpf(mf, hk=100, vka=100, ss=0.00001, sy=0.1)\n",
"oc = flopy.modflow.ModflowOc(mf)\n",
"pcg = flopy.modflow.ModflowPcg(mf)\n",
"rch = flopy.modflow.ModflowRch(mf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll track the cell locations for the ```SSM``` data using the ```MODFLOW``` boundary conditions.\n",
"\n",
"\n",
"Get a dictionary (```dict```) that has the ```SSM``` ```itype``` for each of the boundary types."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'CHD': 1, 'BAS6': 1, 'PBC': 1, 'WEL': 2, 'DRN': 3, 'RIV': 4, 'GHB': 5, 'MAS': 15, 'CC': -1}\n",
"[('k', '<i8'), ('i', '<i8'), ('j', '<i8'), ('css', '<f4'), ('itype', '<i8')]\n"
]
}
],
"source": [
"itype = flopy.mt3d.Mt3dSsm.itype_dict()\n",
"print(itype)\n",
"print(flopy.mt3d.Mt3dSsm.get_default_dtype())\n",
"ssm_data = {}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add a general head boundary (```ghb```). The general head boundary head (```bhead```) is 0.1 for the first 5 stress periods with a component 1 (comp_1) concentration of 1.0 and a component 2 (comp_2) concentration of 100.0. Then ```bhead``` is increased to 0.25 and comp_1 concentration is reduced to 0.5 and comp_2 concentration is increased to 200.0"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('k', '<i8'), ('i', '<i8'), ('j', '<i8'), ('bhead', '<f4'), ('cond', '<f4')]\n"
]
}
],
"source": [
"ghb_data = {}\n",
"print(flopy.modflow.ModflowGhb.get_default_dtype())\n",
"ghb_data[0] = [(4, 4, 4, 0.1, 1.5)]\n",
"ssm_data[0] = [(4, 4, 4, 1.0, itype['GHB'], 1.0, 100.0)]\n",
"ghb_data[5] = [(4, 4, 4, 0.25, 1.5)]\n",
"ssm_data[5] = [(4, 4, 4, 0.5, itype['GHB'], 0.5, 200.0)]\n",
"\n",
"for k in range(nlay):\n",
" for i in range(nrow):\n",
" ghb_data[0].append((k, i, 0, 0.0, 100.0))\n",
" ssm_data[0].append((k, i, 0, 0.0, itype['GHB'], 0.0, 0.0))\n",
" \n",
"ghb_data[5] = [(4, 4, 4, 0.25, 1.5)]\n",
"ssm_data[5] = [(4, 4, 4, 0.5, itype['GHB'], 0.5, 200.0)]\n",
"for k in range(nlay):\n",
" for i in range(nrow):\n",
" ghb_data[5].append((k, i, 0, -0.5, 100.0))\n",
" ssm_data[5].append((k, i, 0, 0.0, itype['GHB'], 0.0, 0.0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add an injection ```well```. The injection rate (```flux```) is 10.0 with a comp_1 concentration of 10.0 and a comp_2 concentration of 0.0 for all stress periods. WARNING: since we changed the ```SSM``` data in stress period 6, we need to add the well to the ssm_data for stress period 6."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('k', '<i8'), ('i', '<i8'), ('j', '<i8'), ('flux', '<f4')]\n"
]
}
],
"source": [
"wel_data = {}\n",
"print(flopy.modflow.ModflowWel.get_default_dtype())\n",
"wel_data[0] = [(0, 4, 8, 10.0)]\n",
"ssm_data[0].append((0, 4, 8, 10.0, itype['WEL'], 10.0, 0.0))\n",
"ssm_data[5].append((0, 4, 8, 10.0, itype['WEL'], 10.0, 0.0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add the ```GHB``` and ```WEL``` packages to the ```mf``` ```MODFLOW``` object instance."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ghb = flopy.modflow.ModflowGhb(mf, stress_period_data=ghb_data)\n",
"wel = flopy.modflow.ModflowWel(mf, stress_period_data=wel_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create the ```MT3DMS``` packages"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"User specified FTL file does not exist in model directory\n",
"MT3D will not work without a linker file\n"
]
}
],
"source": [
"mt = flopy.mt3d.Mt3dms(modflowmodel=mf, modelname=modelname, model_ws=model_ws)\n",
"btn = flopy.mt3d.Mt3dBtn(mt, sconc=0, ncomp=2, sconc2=50.0)\n",
"adv = flopy.mt3d.Mt3dAdv(mt)\n",
"ssm = flopy.mt3d.Mt3dSsm(mt, stress_period_data=ssm_data)\n",
"gcg = flopy.mt3d.Mt3dGcg(mt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's verify that ```stress_period_data``` has the right ```dtype```"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('k', '<i8'), ('i', '<i8'), ('j', '<i8'), ('css', '<f4'), ('itype', '<i8'), ('cssm(01)', '<f4'), ('cssm(02)', '<f4')]\n"
]
}
],
"source": [
"print(ssm.stress_period_data.dtype)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create the ```SEAWAT``` packages"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"swt = flopy.seawat.Seawat(modflowmodel=mf, mt3dmodel=mt, \n",
" modelname=modelname, namefile_ext='nam_swt', model_ws=model_ws)\n",
"vdf = flopy.seawat.SeawatVdf(swt, mtdnconc=0, iwtable=0, indense=-1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mf.write_input()\n",
"mt.write_input()\n",
"swt.write_input()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And finally, modify the ```vdf``` package to fix ```indense```."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fname = modelname + '.vdf'\n",
"f = open(os.path.join(model_ws, fname),'r')\n",
"lines = f.readlines()\n",
"f.close()\n",
"f = open(os.path.join(model_ws, fname),'w')\n",
"for line in lines:\n",
" f.write(line)\n",
"for kper in range(nper):\n",
" f.write(\"-1\\n\")\n",
"f.close() \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
clazaro/elastica | 2DRodFF_7.ipynb | 1 | 180280 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"© C. Lázaro, Universidad Politécnica de Valencia, 2015"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Form finding of planar flexible rods (7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Refinement of 2DRodFF_6"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import scipy.special as sp\n",
"import scipy.optimize"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 Form-finding tool for a single rod"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def omegaDeltaomega(muA, muB, flag, k):\n",
" if (abs(muA)<1E-8 and abs(muB)<1E-8):\n",
" omega_0 = -.5*np.pi\n",
" Deltaomega_0 = np.pi\n",
" else:\n",
" if (muA >= 0):\n",
" omega_0 = np.arccos(np.pi*muA/2./k) # OK\n",
" Deltaomega_0 = np.arccos(np.pi*muB/2./k) - omega_0 # OK\n",
" omega_1 = -np.arccos(np.pi*muA/2./k) # OK\n",
" Deltaomega_1 = np.arccos(np.pi*muB/2./k) - omega_1 # OK\n",
" else:\n",
" omega_1 = np.arccos(np.pi*muA/2./k) - 2*np.pi\n",
" omega_0 = -np.arccos(np.pi*muA/2./k) # OK\n",
" if (muB >= 0):\n",
" Deltaomega_1 = -np.arccos(np.pi*muB/2./k) - omega_1\n",
" Deltaomega_0 = -np.arccos(np.pi*muB/2./k) - omega_0 # OK\n",
" else:\n",
" Deltaomega_1 = -np.arccos(np.pi*muB/2./k) - omega_1\n",
" Deltaomega_0 = -np.arccos(np.pi*muB/2./k) - omega_0 # OK\n",
" \n",
" if (flag == 0):\n",
" #print('omega_0 = {0:.5f}, Deltaomega_0 = {1:.5f}'.format(omega_0, Deltaomega_0))\n",
" return(omega_0, Deltaomega_0)\n",
" else:\n",
" #print('omega_1 = {0:.5f}, Deltaomega_1 = {1:.5f}'.format(omega_1, Deltaomega_1))\n",
" return(omega_1, Deltaomega_1)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def lmbdk(muA, muB, flag, k):\n",
" wA, Deltaw = omegaDeltaomega(muA, muB, flag, k)\n",
" Deltaxi = (2*(sp.ellipeinc(wA+Deltaw,k**2) - sp.ellipeinc(wA,k**2)) - (sp.ellipkinc(wA+Deltaw,k**2) - sp.ellipkinc(wA,k**2)))/np.pi\n",
" Deltaeta = -2*k*(np.cos(wA+Deltaw) - np.cos(wA))/np.pi\n",
" lmbd = np.sqrt(Deltaxi**2 + Deltaeta**2)\n",
" return(lmbd)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class F:\n",
" def __init__(self, muA, muB, lmbdAB, flag):\n",
" self.muA = muA\n",
" self.muB = muB\n",
" self.lmbdAB = lmbdAB\n",
" self.flag = flag\n",
" \n",
" def __call__(self, k):\n",
" lmbd = lmbdk(self.muA, self.muB, self.flag, k)\n",
" return(self.lmbdAB - lmbd)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class Elastica:\n",
" def __init__(self, gammaA=None, gammaB=None, R=None, MA=None, MB=None, EI=None):\n",
" self.gammaA = gammaA\n",
" self.gammaB = gammaB\n",
" self.beta = np.angle(gammaB - gammaA)\n",
" \n",
" self.R = R\n",
" self.MA = MA\n",
" self.MB = MB\n",
" self.EI = EI\n",
" \n",
" self.dAB = np.absolute(gammaB - gammaA) # Distance between rod end-sections\n",
" self.S = (MA - MB)/self.dAB # Shear force S (perpendicular to R)\n",
" self.P = - np.sqrt(R**2 + self.S**2) # Invariant compressive force\n",
" self.lcrit = np.pi*np.sqrt(EI/abs(self.P)) # Critical length\n",
" \n",
" self.lmbdAB = self.dAB/self.lcrit # Normalized distance\n",
" self.muA = MA/abs(self.P)/self.lcrit # Adimensional moments\n",
" self.muB = MB/abs(self.P)/self.lcrit\n",
" \n",
" self.flag = 0\n",
" \n",
" self.k = None # k parameter, to be computed\n",
" self.HmodP = None # H/|P| = -cos(theta_0)\n",
" \n",
" self.alpha = None\n",
" self.phiA = None\n",
" self.phiB = None\n",
" \n",
" def data(self):\n",
" print(' dAB = {0:8.4f} lcrit = {1:8.4f} m'.format(self.dAB, self.lcrit))\n",
" print(' R = {0:8.4f} kN S = {1:8.4f} kN'.format(self.R, self.S))\n",
" #print(' P = {0:8.4f} kN'.format(self.P))\n",
" #print('lambdaAB = {1:8.4f}'.format(self.lcrit, self.lmbdAB))\n",
" print(' MA = {0:8.4f} MB = {1:8.4f}'.format(self.MA, self.MB))\n",
" print(' muA = {0:8.4f} muB = {1:8.4f}'.format(self.muA, self.muB))\n",
" \n",
" def compute_k(self):\n",
" muA = self.muA\n",
" muB = self.muB\n",
" lmbdAB = self.lmbdAB\n",
" flag = self.flag\n",
" \n",
" if (abs(muA)<1E-8 and abs(muB)< 1E-8):\n",
" kmin = 0.\n",
" else:\n",
" kmin = max(np.pi*abs(muA)/2., np.pi*abs(muB)/2.) # Lowest possible value for k\n",
" print('kmin = {0:.5f}'.format(kmin))\n",
" \n",
" rng_k = np.arange(kmin, 1., 0.05)\n",
" rng_f = []\n",
" flag = 0\n",
" print('flag = {}'.format(flag))\n",
" i = 0\n",
" for k in rng_k:\n",
" # print(i)\n",
" lmbd = lmbdk(muA, muB, flag, k)\n",
" f = lmbdAB - lmbd\n",
" rng_f.append(f)\n",
" # print('k = {0:.5f}, lmbd = {1:.5f}, f = {2:.5f},'.format(k, lmbd, f))\n",
" i += 1\n",
" rng_signf = np.sign(np.asarray(rng_f))\n",
" rng_index = rng_signf[:-1] + rng_signf[1:]\n",
" rng_index = rng_index.tolist()\n",
" #print(rng_index)\n",
" \n",
" if 0. in rng_index:\n",
" index00 = rng_index.index(0.)\n",
" index01 = len(rng_index) - (rng_index[::-1]).index(0.) - 1\n",
" #print(index00, index01)\n",
" else:\n",
" rng_f = []\n",
" flag = 1\n",
" print('flag = {}'.format(flag))\n",
" i = 0\n",
" for k in rng_k:\n",
" #print(i)\n",
" lmbd = lmbdk(muA, muB, flag, k)\n",
" f = lmbdAB - lmbd\n",
" rng_f.append(f)\n",
" print('k = {0:.5f}, lmbd = {1:.5f}, f = {2:.5f},'.format(k, lmbd, f))\n",
" i += 1\n",
" rng_signf = np.sign(np.asarray(rng_f))\n",
" rng_index = rng_signf[:-1] + rng_signf[1:]\n",
" rng_index = rng_index.tolist()\n",
" #print(rng_index)\n",
" \n",
" index00 = rng_index.index(0.)\n",
" index01 = len(rng_index) - (rng_index[::-1]).index(0.) - 1\n",
" #print(index00, index01) \n",
" \n",
" HmodP00 = -np.cos(2*np.arcsin(rng_k[index00]))\n",
" HmodP01 = -np.cos(2*np.arcsin(rng_k[index01]))\n",
" #print('H/|P|00 = {0:.5f}, H/|P|01 = {1:.5f}'.format(HmodP00, HmodP01))\n",
" if (index00==index01):\n",
" index0 = index00\n",
" else:\n",
" if (HmodP00<=HmodP01):\n",
" index0 = index00\n",
" else:\n",
" index0 = index01\n",
" #print(index0)\n",
" \n",
" ka = rng_k[index0]\n",
" kb = rng_k[index0+1]\n",
" #print('ka = {0:.5f}, kb = {1:.5f}'.format(ka, kb))\n",
" \n",
" f = F(muA, muB, lmbdAB, flag)\n",
" self.k = scipy.optimize.brentq(f, ka, kb) # Solve for k\n",
" self.HmodP = -np.cos(2*np.arcsin(self.k))\n",
" self.flag = flag\n",
" print('k = {0:.5f}, H/|P| = {1:.5f}'.format(self.k, self.HmodP))\n",
" print\n",
" \n",
" def compute_Config(self):\n",
" gammaA = self.gammaA\n",
" beta = self.beta\n",
" muA = self.muA\n",
" muB = self.muB\n",
" lcrit = self.lcrit\n",
" flag = self.flag\n",
" try:\n",
" k = float(self.k)\n",
" except ValueError:\n",
" raise ValueError('Method .compute_k has to be executed first')\n",
" \n",
" nVertex = 101\n",
" (wA, Deltaw) = omegaDeltaomega(muA, muB, flag, k)\n",
" if Deltaw<0:\n",
" wA = -wA\n",
" Deltaw = -Deltaw\n",
" rng_w = np.linspace(wA, wA+Deltaw, nVertex)\n",
" \n",
" rng_zeta = []\n",
" rng_xi = []\n",
" rng_eta = []\n",
" for w in rng_w:\n",
" zeta = (sp.ellipkinc(w,k**2) + sp.ellipk(k**2))/np.pi\n",
" xi = 2*(sp.ellipeinc(w,k**2) + sp.ellipe(k**2))/np.pi - zeta\n",
" eta = -2*k*np.cos(w)/np.pi\n",
" rng_zeta.append(zeta)\n",
" rng_xi.append(xi)\n",
" rng_eta.append(eta)\n",
" rng_xi = np.asarray(rng_xi)\n",
" rng_eta = np.asarray(rng_eta)\n",
"\n",
" alpha = np.arctan2(rng_eta[-1]-rng_eta[0], rng_xi[-1]-rng_xi[0])\n",
" print('beta = {0:.5f} alpha = {1:.5f}'.format(beta, alpha))\n",
" rng_x = gammaA.real + np.cos(beta - alpha)*lcrit*(rng_xi - rng_xi[0]) - np.sin(beta - alpha)*lcrit*(rng_eta - rng_eta[0])\n",
" rng_y = gammaA.imag + np.sin(beta - alpha)*lcrit*(rng_xi - rng_xi[0]) + np.cos(beta - alpha)*lcrit*(rng_eta - rng_eta[0])\n",
" \n",
" phiA = 2*np.arcsin(k*np.sin(wA)) + beta - alpha\n",
" phiB = 2*np.arcsin(k*np.sin(wA + Deltaw)) + beta - alpha\n",
" print('phiA = {0:.5f} phiB = {1:.5f}'.format(phiA, phiB))\n",
" \n",
" self.alpha = alpha\n",
" self.phiA = phiA\n",
" self.phiB = phiB\n",
" \n",
" #return(rng_xi, rng_eta)\n",
" return(rng_x, rng_y)\n",
" \n",
" def plot(self):\n",
" fig = plt.figure(figsize=(9,9))\n",
" ax = fig.gca(aspect='equal')\n",
" \n",
" (x, y) = self.compute_Config()\n",
" ax.plot(x, y, color ='b')\n",
" ax.grid()\n",
" ax.set_xlabel(r'$x$')\n",
" ax.set_ylabel(r'$y$')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"test_element = Elastica(gammaA=complex(0.0, 0.0), gammaB=complex(5.2318, 0.), R=-26.1588, MA=-16.2602, MB=-84.6883, EI=500.)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" dAB = 5.2318 lcrit = 12.9897 m\n",
" R = -26.1588 kN S = 13.0793 kN\n",
" MA = -16.2602 MB = -84.6883\n",
" muA = -0.0428 muB = -0.2229\n"
]
}
],
"source": [
"test_element.data()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"kmin = 0.35016\n",
"flag = 0\n",
"k = 0.35323, H/|P| = -0.75045\n",
"\n"
]
}
],
"source": [
"test_element.compute_k()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"beta = 0.00000 alpha = 0.46364\n",
"phiA = 0.24462 phiB = -0.37069\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x994cf60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"test_element.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2 Complex force densities"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The 2D formfinding equations in complex form are"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\\mathbf{D}_l = \\mathbf{C}_l^{\\mathsf{T}} \\,\\mathbf{P}\\,\\mathbf{C}_l$$\n",
"$$\\mathbf{D}_f = \\mathbf{C}_l^{\\mathsf{T}} \\,\\mathbf{P}\\,\\mathbf{C}_f $$\n",
"$$\\mathbf{\\gamma}_l = \\mathbf{D}_l^{-1} \\bigl( \\mathbf{\\phi}_l - \\mathbf{D}_f \\,\\mathbf{\\gamma}_f \\bigr)$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Where the diagonal elements of the force density matrix are the complex force densities\n",
"$$q_{IJ} = r_{IJ} + i \\,s_{IJ}$$\n",
"$r_{IJ} = R_{IJ}/d_{IJ}$ is the tension (compression) tension density and $s_{IJ} = S_{IJ}/d_{IJ}$ the transverse force density. \n",
"$\\mathbf{C}_l$, $\\mathbf{C}_f$ are the connectivity matrixes."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class StructuralElement:\n",
" def __init__(self, startNode, endNode, complexq):\n",
" self.startNode = startNode\n",
" self.endNode = endNode\n",
" self.complexq = complexq\n",
" \n",
" self.gammaA = None\n",
" self.gammaB = None\n",
" self.dAB = None\n",
" self.R = None\n",
" self.S = None\n",
" self.DeltaM = None\n",
" self.MA = None\n",
" self.MB = None\n",
" \n",
" self.EI = 500.\n",
" \n",
" def setActiveParams(self, gammaA, gammaB):\n",
" self.gammaA = gammaA\n",
" self.gammaB = gammaB\n",
" self.dAB = np.absolute(gammaB - gammaA)\n",
" \n",
" self.R = self.complexq.real*self.dAB\n",
" self.S = self.complexq.imag*self.dAB\n",
" self.DeltaM = - self.S*self.dAB\n",
" \n",
" def setEndMoments(self, node, M):\n",
" try:\n",
" DeltaM = float(self.DeltaM)\n",
" except ValueError:\n",
" raise ValueError('Method .setActiveParams has to be executed first')\n",
" \n",
" if node=='start':\n",
" self.MA = M\n",
" self.MB = self.MA + self.DeltaM\n",
" else:\n",
" self.MB = M\n",
" self.MA = self.MB - self.DeltaM"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fixed nodes\n",
"[-5.+0.j 5.+0.j 0.+5.j 0.-5.j]\n",
"\n",
"Connectivity matrix\n",
"[[-1. 0.]\n",
" [ 1. -1.]\n",
" [ 0. 1.]\n",
" [ 1. 0.]\n",
" [ 0. 1.]]\n",
"[[ 1. 0. 0. 0.]\n",
" [ 0. 0. 0. 0.]\n",
" [ 0. -1. 0. 0.]\n",
" [ 0. 0. -1. 0.]\n",
" [ 0. 0. 0. -1.]]\n",
"\n",
"Complex force densities\n",
"[[-5.0+4.7895j 0.0+0.j 0.0+0.j 0.0+0.j 0.0+0.j ]\n",
" [ 0.0+0.j -7.5-6.6428j 0.0+0.j 0.0+0.j 0.0+0.j ]\n",
" [ 0.0+0.j 0.0+0.j -5.0+4.7895j 0.0+0.j 0.0+0.j ]\n",
" [ 0.0+0.j 0.0+0.j 0.0+0.j 7.5+0.j 0.0+0.j ]\n",
" [ 0.0+0.j 0.0+0.j 0.0+0.j 0.0+0.j 7.5+0.j ]]\n",
"\n",
"External forces\n",
"[ 0.+0.j 0.+0.j]\n",
"\n"
]
}
],
"source": [
"# Input data\n",
"\n",
"# Total number of nodes\n",
"NN = 6 \n",
"\n",
"# Nodes\n",
"indexFreeNode = [1, 2]\n",
"indexFixedNode = [0, 3, 4, 5]\n",
"gammaF = np.array([\n",
"complex(-5.0, 0.),\n",
"complex(5.0, 0.),\n",
"complex(0., 5.0), \n",
"complex(0., -5.0)\n",
"])\n",
"NF = len(gammaF) # Number of fixed nodes\n",
"print('Fixed nodes')\n",
"print(gammaF)\n",
"print\n",
"\n",
"# Structural Members\n",
"NABR = 1 # Number of active bending rods\n",
"rng_activeBendingRod = [None]*NABR\n",
"# Active Members\n",
"rng_activeBendingRod[0] = []\n",
"rng_activeBendingRod[0].append(StructuralElement(0, 1, complex(-5., 4.7895)))\n",
"rng_activeBendingRod[0].append(StructuralElement(1, 2, complex(-7.5, -6.6428)))\n",
"rng_activeBendingRod[0].append(StructuralElement(2, 3, complex(-5., 4.7895)))\n",
"# Tension members\n",
"rng_axialMember = []\n",
"rng_axialMember.append(StructuralElement(1, 4, 7.5))\n",
"rng_axialMember.append(StructuralElement(2, 5, 7.5))\n",
"\n",
"# Number of members\n",
"MMA = 0\n",
"for activeBendingRod in rng_activeBendingRod:\n",
" MMA += len(activeBendingRod) # Bending active members\n",
"MMT = len(rng_axialMember) # Tension active members\n",
"MM = MMA + MMT\n",
"\n",
"# Connectivity matrix\n",
"C = np.zeros((MM, NN))\n",
"i = 0\n",
"for activeBendingRod in rng_activeBendingRod:\n",
" for activeElement in activeBendingRod:\n",
" C[i, activeElement.startNode] = 1\n",
" C[i, activeElement.endNode] = -1\n",
" i += 1\n",
"for axialMember in rng_axialMember:\n",
" C[i, axialMember.startNode] = 1\n",
" C[i, axialMember.endNode] = -1\n",
" i += 1\n",
"\n",
"CL = C[:, indexFreeNode]\n",
"CF = C[:, indexFixedNode]\n",
"print('Connectivity matrix')\n",
"print(CL)\n",
"print(CF)\n",
"print\n",
"\n",
"# Force densities in kN/m corresponding to every element\n",
"q = []\n",
"for activeBendingRod in rng_activeBendingRod:\n",
" for activeElement in activeBendingRod:\n",
" q.append(activeElement.complexq)\n",
"for axialMember in rng_axialMember:\n",
" q.append(axialMember.complexq)\n",
"\n",
"qQ = np.diagflat(q)\n",
"print('Complex force densities')\n",
"print(qQ)\n",
"print\n",
"\n",
"# External forces\n",
"fL = np.array([\n",
"complex(0., 0.),\n",
"complex(0., 0.)\n",
"])\n",
"print('External forces')\n",
"print(fL)\n",
"print"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Free node coordinates\n",
"[-1.87206777+0.188222j 1.87206777-0.188222j]\n",
"\n"
]
}
],
"source": [
"# Solution of the force density equations\n",
"\n",
"gammaL = np.zeros(NN - NF)\n",
"DL = np.zeros((NN - NF, NN - NF))\n",
"DF = np.zeros((NN - NF, NF))\n",
"\n",
"DL = np.dot(np.transpose(CL), np.dot(qQ, CL))\n",
"DF = np.dot(np.transpose(CL), np.dot(qQ, CF))\n",
"\n",
"gammaL = np.linalg.solve(DL, fL - np.dot(DF, gammaF))\n",
"print('Free node coordinates')\n",
"print(gammaL)\n",
"print"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-5.00000000+0.j -1.87206777+0.188222j 1.87206777-0.188222j\n",
" 5.00000000+0.j 0.00000000+5.j 0.00000000-5.j ]\n"
]
}
],
"source": [
"rng_gamma = np.empty(len(indexFreeNode)+len(indexFixedNode), dtype=complex)\n",
"rng_gamma[indexFreeNode] = gammaL\n",
"rng_gamma[indexFixedNode] = gammaF\n",
"print(rng_gamma)"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xf590b70>"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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BAMYRKADAOAIFABhHoAAA4wgUAGAcgQIAjCNQAIBxBAoAMI5AAQDGESgAwDgCBQAYR6AA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xf140470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(9,9))\n",
"\n",
"ax = fig.gca(aspect='equal')\n",
"ax.scatter(gammaF.real, gammaF.imag, color='b')\n",
"ax.scatter(gammaL.real, gammaL.imag, color='r')\n",
"ax.set_xlabel('$x$')\n",
"ax.set_ylabel('$y$')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3 Structural form-finding (1)"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"totalDeltaM = [0.]*NABR\n",
"i = 0\n",
"for activeBendingRod in rng_activeBendingRod:\n",
" for activeElement in activeBendingRod:\n",
" startNode = activeElement.startNode\n",
" endNode = activeElement.endNode\n",
" activeElement.setActiveParams(rng_gamma[startNode], rng_gamma[endNode])\n",
" totalDeltaM[i] += activeElement.DeltaM\n",
" i += 1"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total DeltaM = 0.00387\n"
]
}
],
"source": [
"for i in range(NABR):\n",
" print('Total DeltaM = {:.5f}'.format(totalDeltaM[i]))"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"stateM = [-0.5*M for M in totalDeltaM]\n",
"#stateM = [0]\n",
"i = 0 # Hay que revisar el código para el caso en que los nodos no sean correlativos\n",
"for activeBendingRod in rng_activeBendingRod:\n",
" for activeElement in activeBendingRod:\n",
" activeElement.setEndMoments('start', stateM[i])\n",
" stateM[i] = activeElement.MB\n",
" i += 1 "
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(-0.0019340606818829542, -47.031890833445075)\n",
"(-47.031890833445075, 47.031890833445075)\n",
"(47.031890833445075, 0.0019340606818829542)\n"
]
}
],
"source": [
"for activeBendingRod in rng_activeBendingRod:\n",
" for activeElement in activeBendingRod:\n",
" print(activeElement.MA, activeElement.MB) "
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rng_elastica = [None]*NABR\n",
"i = 0\n",
"for activeBendingRod in rng_activeBendingRod:\n",
" rng_elastica[i] = []\n",
" for activeElement in activeBendingRod:\n",
" startNode = activeElement.startNode\n",
" endNode = activeElement.endNode\n",
" R = activeElement.R\n",
" MA = activeElement.MA\n",
" MB = activeElement.MB\n",
" EI = activeElement.EI\n",
" rng_elastica[i].append(Elastica(rng_gamma[startNode], rng_gamma[endNode], R, MA, MB, EI))\n",
" i += 1"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 3.1336 lcrit = 15.0814 m\n",
" R = -15.6680 kN S = 15.0083 kN\n",
" MA = -0.0019 MB = -47.0319\n",
" muA = -0.0000 muB = -0.1437\n",
"kmin = 0.22578\n",
"flag = 0\n",
"k = 0.39627, H/|P| = -0.68594\n",
"\n",
"Element 1\n",
" dAB = 3.7630 lcrit = 11.4409 m\n",
" R = -28.2226 kN S = -24.9969 kN\n",
" MA = -47.0319 MB = 47.0319\n",
" muA = -0.1090 muB = 0.1090\n",
"kmin = 0.17128\n",
"flag = 0\n",
"k = 0.36863, H/|P| = -0.72822\n",
"\n",
"Element 2\n",
" dAB = 3.1336 lcrit = 15.0814 m\n",
" R = -15.6680 kN S = 15.0083 kN\n",
" MA = 47.0319 MB = 0.0019\n",
" muA = 0.1437 muB = 0.0000\n",
"kmin = 0.22578\n",
"flag = 0\n",
"k = 0.39627, H/|P| = -0.68594\n",
"\n",
"\n"
]
}
],
"source": [
"i = 0\n",
"for elasticaRod in rng_elastica:\n",
" j = 0\n",
" print('Elastica Rod {}'.format(i))\n",
" print('***************')\n",
" for element in elasticaRod:\n",
" print('Element {}'.format(j))\n",
" element.data()\n",
" element.compute_k()\n",
" j += 1\n",
" print\n",
" i += 1"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"beta = 0.06010 alpha = 0.76390\n",
"phiA = 0.11111 phiB = -0.04037\n",
"beta = -0.10021 alpha = -0.72486\n",
"phiA = -0.04038 phiB = -0.04038\n",
"beta = 0.06010 alpha = 0.76390\n",
"phiA = -0.04037 phiB = 0.11111\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10447ac8>"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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OIgkRzJJcJG5VNVVMGTXFdwwJjApmjELt7YSaG9rOvmjTIra9t43RQ0fHHyhL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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10445ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(5,5))\n",
"ax = fig.gca(aspect='equal')\n",
"font = {'size' : 16}\n",
"\n",
"ax.scatter(gammaF.real, gammaF.imag, color='b')\n",
"ax.scatter(gammaL.real, gammaL.imag, color='r')\n",
"\n",
"for elasticaRod in rng_elastica:\n",
" for element in elasticaRod:\n",
" (x, y) = element.compute_Config()\n",
" ax.plot(x, y, color ='b')\n",
"\n",
"for axialMember in rng_axialMember:\n",
" startNode = axialMember.startNode\n",
" endNode = axialMember.endNode\n",
" complexq = axialMember.complexq\n",
" if abs(complexq) > 1E-8:\n",
" x = rng_gamma[[startNode, endNode]].real\n",
" y = rng_gamma[[startNode, endNode]].imag\n",
" if complexq > 0:\n",
" ax.plot(x, y, color ='g')\n",
" else:\n",
" ax.plot(x, y, color ='r')\n",
"\n",
"ax.grid()\n",
"ax.set_xlabel(r'$x$', fontdict=font)\n",
"ax.set_ylabel(r'$y$', fontdict=font)\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"phi0A = 0.11111 phi0B = -0.04037\n",
"phi0A = -0.04038 phi0B = -0.04038\n",
"phi0A = -0.04037 phi0B = 0.11111\n"
]
}
],
"source": [
"i = 0\n",
"for elasticaRod in rng_elastica:\n",
" for element in elasticaRod:\n",
" print('phi{0}A = {1:.5f} phi{2}B = {3:.5f}'.format(i, element.phiA, i, element.phiB))"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.0038681213637659084]\n"
]
}
],
"source": [
"print(totalDeltaM)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4 Structural form-finding (2)"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def setForceDensityMat(rng_activeBendingRod):\n",
" q = []\n",
" for activeBendingRod in rng_activeBendingRod:\n",
" for activeElement in activeBendingRod:\n",
" q.append(activeElement.complexq)\n",
" for axialMember in rng_axialMember:\n",
" q.append(axialMember.complexq)\n",
"\n",
" qQ = np.diagflat(q)\n",
" #print('Complex force density matrix')\n",
" #print(qQ)\n",
" #print\n",
" return(qQ)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def computeGamma(indexFreeNode, indexFixedNode, gammaF, CL, CF, qQ):\n",
" NL = len(indexFreeNode)\n",
" NF = len(indexFixedNode)\n",
" \n",
" DL = np.zeros((NL, NL))\n",
" DF = np.zeros((NL, NF))\n",
" gammaL = np.zeros(NL)\n",
" \n",
" DL = np.dot(np.transpose(CL), np.dot(qQ, CL))\n",
" DF = np.dot(np.transpose(CL), np.dot(qQ, CF))\n",
" gammaL = np.linalg.solve(DL, fL - np.dot(DF, gammaF))\n",
" \n",
" rng_gamma = np.empty(len(indexFreeNode)+len(indexFixedNode), dtype=complex)\n",
" rng_gamma[indexFreeNode] = gammaL\n",
" rng_gamma[indexFixedNode] = gammaF\n",
" \n",
" return(gammaL, rng_gamma)"
]
},
{
"cell_type": "code",
"execution_count": 258,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class G:\n",
" def __init__(self, indexFreeNode, indexFixedNode, gammaF, CL, CF, rng_activeBendingRod):\n",
" self.indexFreeNode = indexFreeNode\n",
" self.indexFixedNode = indexFixedNode\n",
" self.gammaF = gammaF\n",
" self.gammaL = None\n",
" self.rng_gamma = None\n",
" self.CL = CL\n",
" self.CF = CF\n",
" self.rng_activeBendingRod = rng_activeBendingRod\n",
" self.rng_elastica = None\n",
" \n",
" def __call__(self, x): \n",
" indexFreeNode = self.indexFreeNode \n",
" indexFixedNode = self.indexFixedNode \n",
" gammaF = self.gammaF \n",
" CL = self.CL \n",
" CF = self.CF\n",
" rng_activeBendingRod = self.rng_activeBendingRod\n",
" NABR = len(rng_activeBendingRod)\n",
" \n",
" # Update shear densities in each element\n",
" i = 0\n",
" for activeBendingRod in rng_activeBendingRod:\n",
" v = x[i]\n",
" #v = np.insert(v, 0, activeBendingRod[0].complexq.imag)\n",
" NELS = len(activeBendingRod)\n",
" v = np.append(v, activeBendingRod[NELS-1].complexq.imag)\n",
" print('v = {}'.format(v))\n",
" k = 0\n",
" for activeElement in activeBendingRod:\n",
" complexq = activeElement.complexq\n",
" #if k==1:\n",
" # complexq = complexq.real + 1j*v[0]\n",
" #activeElement.complexq = complexq\n",
" activeElement.complexq = complexq.real + 1j*v[k]\n",
" print(activeElement.complexq)\n",
" k += 1\n",
" i += 1\n",
" \n",
" # Compute new nodal coordinates\n",
" qQ = setForceDensityMat(rng_activeBendingRod)\n",
" (gammaL, rng_gamma) = computeGamma(indexFreeNode, indexFixedNode, gammaF, CL, CF, qQ)\n",
" self.gammaL = gammaL\n",
" self.rng_gamma = rng_gamma\n",
" \n",
" # Compute R, S, DeltaM \n",
" rng_totalDeltaM = []\n",
" rng_stateM = []\n",
" i = 0\n",
" for activeBendingRod in rng_activeBendingRod:\n",
" totalDeltaM = 0.\n",
" for activeElement in activeBendingRod:\n",
" startNode = activeElement.startNode \n",
" endNode = activeElement.endNode\n",
" activeElement.setActiveParams(rng_gamma[startNode], rng_gamma[endNode])\n",
" totalDeltaM += activeElement.DeltaM # Compute Sum DeltaM\n",
" \n",
" rng_totalDeltaM.append(totalDeltaM)\n",
" i += 1\n",
" \n",
" # Compute start M\n",
" i = 0\n",
" rng_elastica = [None]*NABR\n",
" for activeBendingRod in rng_activeBendingRod:\n",
" rng_elastica[i] = []\n",
" stateM = 0. # Set M at the start node\n",
" #stateM = -0.5*rng_totalDeltaM[i] \n",
" for activeElement in activeBendingRod:\n",
" startNode = activeElement.startNode\n",
" endNode = activeElement.endNode\n",
" activeElement.setActiveParams(rng_gamma[startNode], rng_gamma[endNode])\n",
" \n",
" activeElement.setEndMoments('start', stateM) # Compute element end moments\n",
" stateM = activeElement.MB\n",
" \n",
" R = activeElement.R # Define elastica elements from parameters\n",
" MA = activeElement.MA\n",
" MB = activeElement.MB\n",
" EI = activeElement.EI\n",
" rng_elastica[i].append(Elastica(rng_gamma[startNode], rng_gamma[endNode], R, MA, MB, EI))\n",
" \n",
" rng_stateM.append(stateM)\n",
" i += 1\n",
" self.rng_activeBendingRod = rng_activeBendingRod\n",
" \n",
" # Compute k and configuration of elastica elems.\n",
" rng_diffRotation = []*NABR\n",
" i = 0\n",
" for elasticaRod in rng_elastica:\n",
" j = 0\n",
" print('Elastica Rod {}'.format(i))\n",
" print('***************')\n",
" sttRotation = []\n",
" endRotation = []\n",
" for element in elasticaRod: \n",
" print('Element {}'.format(j))\n",
" element.data()\n",
" element.compute_k()\n",
" element.compute_Config()\n",
" sttRotation.append(element.phiA)\n",
" endRotation.append(element.phiB)\n",
" j += 1\n",
" print\n",
" \n",
" sttRotation = np.array(sttRotation) # Compute nodal rotation differences\n",
" endRotation = np.array(endRotation)\n",
" print(sttRotation)\n",
" print(endRotation)\n",
" diffRotation = np.empty(len(elasticaRod)-1)\n",
" diffRotation[:] = endRotation[:-1] - sttRotation[1:]\n",
" #phiStart = 0.\n",
" #diffRotation = np.append(phiStart - sttRotation[0], diffRotation)\n",
" #diffRotation = np.append(phiStart - sttRotation[0], diffRotation[0])\n",
" rng_diffRotation.append(diffRotation)\n",
" i += 1\n",
" self.rng_elastica = rng_elastica\n",
" \n",
" # Define and compute residuals for root search\n",
" rng_residual = [] \n",
" for i in range(NABR):\n",
" #totalDeltaM = rng_totalDeltaM[i]\n",
" #stateM = rng_stateM[i]\n",
" diffRotation = rng_diffRotation[i]\n",
" residual = diffRotation\n",
" #residual = diffRotation[0]\n",
" #residual = np.hstack((totalDeltaM, diffRotation))\n",
" rng_residual.append(residual)\n",
" \n",
" rng_residual = np.array(rng_residual).flatten()\n",
" print(x, rng_residual)\n",
" print\n",
" return(rng_residual)\n",
" \n",
" def plot(self):\n",
" gammaF = self.gammaF\n",
" gammaL = self.gammaL\n",
" rng_gamma = self.rng_gamma\n",
" rng_activeBendingRod = self.rng_activeBendingRod\n",
" rng_elastica = self.rng_elastica\n",
" \n",
" fig = plt.figure(figsize=(9,9))\n",
" ax = fig.gca(aspect='equal')\n",
" ax.set_ylim(-4., 8.)\n",
" ax.scatter(gammaF.real, gammaF.imag, color='b')\n",
" ax.scatter(gammaL.real, gammaL.imag, color='r')\n",
"\n",
" for elasticaRod in rng_elastica:\n",
" for element in elasticaRod:\n",
" (x, y) = element.compute_Config()\n",
" ax.plot(x, y, color ='b')\n",
"\n",
" for axialMember in rng_axialMember:\n",
" startNode = axialMember.startNode\n",
" endNode = axialMember.endNode\n",
" complexq = axialMember.complexq\n",
" if abs(complexq) > 1E-8:\n",
" x = rng_gamma[[startNode, endNode]].real\n",
" y = rng_gamma[[startNode, endNode]].imag\n",
" if complexq > 0:\n",
" ax.plot(x, y, color ='g')\n",
" else:\n",
" ax.plot(x, y, color ='r')\n",
"\n",
" ax.grid()\n",
" ax.set_xlabel(r'$x$')\n",
" ax.set_ylabel(r'$y$')\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 259,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fixed nodes\n",
"[ 0.0+0.j 25.0+1.5j -1.5+7.5j]\n",
"\n",
"Connectivity matrix\n",
"[[-1. 0. 0.]\n",
" [ 1. -1. 0.]\n",
" [ 0. 1. -1.]\n",
" [ 0. 0. 1.]\n",
" [ 1. 0. 0.]\n",
" [ 0. 1. 0.]\n",
" [ 0. 0. 1.]]\n",
"[[ 1. 0. 0.]\n",
" [ 0. 0. 0.]\n",
" [ 0. 0. 0.]\n",
" [ 0. -1. 0.]\n",
" [ 0. 0. -1.]\n",
" [ 0. 0. -1.]\n",
" [ 0. 0. -1.]]\n",
"\n",
"External forces\n",
"[ 0.+0.j 0.+0.j 0.+0.j]\n",
"\n"
]
}
],
"source": [
"# Input data\n",
"\n",
"# Nodes\n",
"indexFreeNode = [1, 2, 3]\n",
"indexFixedNode = [0, 4, 5]\n",
"gammaF = np.array([\n",
"complex( 0. , 0. ),\n",
"complex(25. , 1.5),\n",
"complex(-1.5, 7.5)\n",
"])\n",
"print('Fixed nodes')\n",
"print(gammaF)\n",
"print\n",
"\n",
"# Structural Members\n",
"NABR = 1 # Number of active bending rods\n",
"rng_activeBendingRod = [None]*NABR\n",
"# Active Members\n",
"rng_activeBendingRod[0] = []\n",
"rng_activeBendingRod[0].append(StructuralElement(0, 1, complex(-2.5, 0.5)))\n",
"rng_activeBendingRod[0].append(StructuralElement(1, 2, complex(-2.0, 0.5)))\n",
"rng_activeBendingRod[0].append(StructuralElement(2, 3, complex(-1.5, 0.5)))\n",
"rng_activeBendingRod[0].append(StructuralElement(3, 4, complex(-1.0, 0.26285)))\n",
"# Tension members\n",
"rng_axialMember = []\n",
"rng_axialMember.append(StructuralElement(1, 5, 0.08))\n",
"rng_axialMember.append(StructuralElement(2, 5, 0.09))\n",
"rng_axialMember.append(StructuralElement(3, 5, 0.10))\n",
"\n",
"# Number of members\n",
"MMA = 0\n",
"for activeBendingRod in rng_activeBendingRod:\n",
" MMA += len(activeBendingRod) # Bending active members\n",
"MMT = len(rng_axialMember) # Tension active members\n",
"MM = MMA + MMT\n",
"\n",
"# Connectivity matrix\n",
"NN = len(indexFreeNode) + len(indexFixedNode)\n",
"C = np.zeros((MM, NN))\n",
"i = 0\n",
"for activeBendingRod in rng_activeBendingRod:\n",
" for activeElement in activeBendingRod:\n",
" C[i, activeElement.startNode] = 1\n",
" C[i, activeElement.endNode] = -1\n",
" i += 1\n",
"for axialMember in rng_axialMember:\n",
" C[i, axialMember.startNode] = 1\n",
" C[i, axialMember.endNode] = -1\n",
" i += 1\n",
"\n",
"CL = C[:, indexFreeNode]\n",
"CF = C[:, indexFixedNode]\n",
"print('Connectivity matrix')\n",
"print(CL)\n",
"print(CF)\n",
"print\n",
"\n",
"# Force densities in kN/m corresponding to every element\n",
"qQ = setForceDensityMat(rng_activeBendingRod)\n",
"\n",
"# External forces\n",
"fL = np.array([\n",
"complex(0., 0.),\n",
"complex(0., 0.),\n",
"complex(0., 0.)\n",
"])\n",
"print('External forces')\n",
"print(fL)\n",
"print"
]
},
{
"cell_type": "code",
"execution_count": 260,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 4.77412526-0.34980858j 10.38206817-0.26462256j 16.88248047+0.61232596j]\n"
]
}
],
"source": [
"(gammaL, rng_gamma) = computeGamma(indexFreeNode, indexFixedNode, gammaF, CL, CF, qQ)\n",
"print gammaL"
]
},
{
"cell_type": "code",
"execution_count": 261,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x133e3668>"
]
},
"execution_count": 261,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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7VZ0C7GW0eeT2qvpG08IkdcMRFUktvZDRrrPfYPR99K/AXU0rktQVg4qkZqrqi1X1v8Cv\nAnNVNVdVD7euS1I/DCqSmknyK0mOB15QVd8c70QrSfu5RkVSS5uBe4DPJtkC3Nu4HkmdSVW1rkGS\nJGlBTv1IkqRuGVQkSVK3DCqSJKlbBhVJktQtg4okSeqWQUWSJHXLoCJJkrplUJEkSd36f5rvmsMr\nk7O3AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x131b46d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(9,9))\n",
"\n",
"ax = fig.gca(aspect='equal')\n",
"ax.scatter(gammaF.real, gammaF.imag, color='b')\n",
"ax.scatter(gammaL.real, gammaL.imag, color='r')\n",
"ax.set_xlabel('$x$')\n",
"ax.set_ylabel('$y$')"
]
},
{
"cell_type": "code",
"execution_count": 262,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"g = G(indexFreeNode, indexFixedNode, gammaF, CL, CF, rng_activeBendingRod)"
]
},
{
"cell_type": "code",
"execution_count": 263,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"v = [-0.67561189 -0.31634141 0.12285089 0.26285 ]\n",
"(-2.5-0.67561189j)\n",
"(-2-0.31634141j)\n",
"(-1.5+0.12285089j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.7236 lcrit = 20.0852 m\n",
" R = -11.8089 kN S = -3.1913 kN\n",
" MA = 0.0000 MB = 15.0743\n",
" muA = 0.0000 muB = 0.0614\n",
"kmin = 0.09638\n",
"flag = 0\n",
"k = 0.14411, H/|P| = -0.95846\n",
"\n",
"beta = -0.36429 alpha = -0.26394\n",
"phiA = -0.38958 phiB = -0.31505\n",
"Element 1\n",
" dAB = 5.7822 lcrit = 20.5300 m\n",
" R = -11.5644 kN S = -1.8292 kN\n",
" MA = 15.0743 MB = 25.6509\n",
" muA = 0.0627 muB = 0.1067\n",
"kmin = 0.16763\n",
"flag = 0\n",
"k = 0.16793, H/|P| = -0.94360\n",
"\n",
"beta = -0.19881 alpha = -0.15687\n",
"phiA = -0.31478 phiB = -0.06208\n",
"Element 2\n",
" dAB = 7.0860 lcrit = 21.5111 m\n",
" R = -10.6290 kN S = 0.8705 kN\n",
" MA = 25.6509 MB = 19.4825\n",
" muA = 0.1118 muB = 0.0849\n",
"kmin = 0.17564\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.17564, lmbd = 0.22546, f = 0.10395,\n",
"k = 0.22564, lmbd = 0.50741, f = -0.17800,\n",
"k = 0.27564, lmbd = 0.60091, f = -0.27150,\n",
"k = 0.32564, lmbd = 0.65061, f = -0.32120,\n",
"k = 0.37564, lmbd = 0.67619, f = -0.34678,\n",
"k = 0.42564, lmbd = 0.68551, f = -0.35610,\n",
"k = 0.47564, lmbd = 0.68236, f = -0.35295,\n",
"k = 0.52564, lmbd = 0.66865, f = -0.33924,\n",
"k = 0.57564, lmbd = 0.64524, f = -0.31583,\n",
"k = 0.62564, lmbd = 0.61229, f = -0.28288,\n",
"k = 0.67564, lmbd = 0.56935, f = -0.23994,\n",
"k = 0.72564, lmbd = 0.51527, f = -0.18586,\n",
"k = 0.77564, lmbd = 0.44800, f = -0.11859,\n",
"k = 0.82564, lmbd = 0.36385, f = -0.03444,\n",
"k = 0.87564, lmbd = 0.25592, f = 0.07349,\n",
"k = 0.92564, lmbd = 0.10961, f = 0.21980,\n",
"k = 0.97564, lmbd = 0.13719, f = 0.19222,\n",
"k = 0.18298, H/|P| = -0.93303\n",
"\n",
"beta = 0.12283 alpha = 0.08172\n",
"phiA = -0.06159 phiB = 0.29228\n",
"Element 3\n",
" dAB = 8.6099 lcrit = 23.5441 m\n",
" R = -8.6099 kN S = 2.2631 kN\n",
" MA = 19.4825 MB = -0.0026\n",
" muA = 0.0930 muB = -0.0000\n",
"kmin = 0.14601\n",
"flag = 0\n",
"k = 0.16064, H/|P| = -0.94839\n",
"\n",
"beta = 0.41314 alpha = 0.25704\n",
"phiA = 0.29018 phiB = 0.47878\n",
"\n",
"[-0.38957981 -0.31478376 -0.0615872 0.29017847]\n",
"[-0.31504982 -0.06208014 0.29227563 0.47878371]\n",
"([[-0.67561189, -0.31634141, 0.12285089]], array([-0.00026606, -0.00049294, 0.00209716]))\n",
"\n"
]
},
{
"data": {
"text/plain": [
"array([-0.00026606, -0.00049294, 0.00209716])"
]
},
"execution_count": 263,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g([[-0.67561189, -0.31634141, 0.12285089]])"
]
},
{
"cell_type": "code",
"execution_count": 264,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"beta = -0.36429 alpha = -0.26394\n",
"phiA = -0.38958 phiB = -0.31505\n",
"beta = -0.19881 alpha = -0.15687\n",
"phiA = -0.31478 phiB = -0.06208\n",
"beta = 0.12283 alpha = 0.08172\n",
"phiA = -0.06159 phiB = 0.29228\n",
"beta = 0.41314 alpha = 0.25704\n",
"phiA = 0.29018 phiB = 0.47878\n"
]
},
{
"data": {
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EKxo+HMLD/14PD//7Kot4PdIGEq9t7pG5DN40mH3v7bNsO+HwtcPUmlWL3mV6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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c459e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g.plot()"
]
},
{
"cell_type": "code",
"execution_count": 265,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"v = [-0.64511629 -0.30982594 0.10766722 0.26285 ]\n",
"(-2.5-0.64511629j)\n",
"(-2-0.30982594j)\n",
"(-1.5+0.10766722j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.7305 lcrit = 20.1008 m\n",
" R = -11.8262 kN S = -3.0517 kN\n",
" MA = 0.0000 MB = 14.4359\n",
" muA = 0.0000 muB = 0.0588\n",
"kmin = 0.09237\n",
"flag = 0\n",
"k = 0.13796, H/|P| = -0.96194\n",
"\n",
"beta = -0.35201 alpha = -0.25254\n",
"phiA = -0.37627 phiB = -0.30478\n",
"Element 1\n",
" dAB = 5.7751 lcrit = 20.5479 m\n",
" R = -11.5501 kN S = -1.7893 kN\n",
" MA = 14.4359 MB = 24.7691\n",
" muA = 0.0601 muB = 0.1031\n",
"kmin = 0.16200\n",
"flag = 0\n",
"k = 0.16238, H/|P| = -0.94726\n",
"\n",
"beta = -0.19498 alpha = -0.15369\n",
"phiA = -0.30628 phiB = -0.06343\n",
"Element 2\n",
" dAB = 7.0768 lcrit = 21.5334 m\n",
" R = -10.6152 kN S = 0.7619 kN\n",
" MA = 24.7691 MB = 19.3770\n",
" muA = 0.1081 muB = 0.0846\n",
"kmin = 0.16977\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.16977, lmbd = 0.21406, f = 0.11458,\n",
"k = 0.21977, lmbd = 0.50559, f = -0.17695,\n",
"k = 0.26977, lmbd = 0.60182, f = -0.27318,\n",
"k = 0.31977, lmbd = 0.65283, f = -0.32419,\n",
"k = 0.36977, lmbd = 0.67919, f = -0.35055,\n",
"k = 0.41977, lmbd = 0.68907, f = -0.36043,\n",
"k = 0.46977, lmbd = 0.68639, f = -0.35775,\n",
"k = 0.51977, lmbd = 0.67312, f = -0.34448,\n",
"k = 0.56977, lmbd = 0.65018, f = -0.32154,\n",
"k = 0.61977, lmbd = 0.61776, f = -0.28912,\n",
"k = 0.66977, lmbd = 0.57545, f = -0.24680,\n",
"k = 0.71977, lmbd = 0.52217, f = -0.19353,\n",
"k = 0.76977, lmbd = 0.45596, f = -0.12732,\n",
"k = 0.81977, lmbd = 0.37332, f = -0.04468,\n",
"k = 0.86977, lmbd = 0.26776, f = 0.06088,\n",
"k = 0.91977, lmbd = 0.12556, f = 0.20308,\n",
"k = 0.96977, lmbd = 0.10524, f = 0.22340,\n",
"k = 0.17809, H/|P| = -0.93657\n",
"\n",
"beta = 0.11301 alpha = 0.07166\n",
"phiA = -0.06624 phiB = 0.27919\n",
"Element 3\n",
" dAB = 8.5867 lcrit = 23.5759 m\n",
" R = -8.5867 kN S = 2.2570 kN\n",
" MA = 19.3770 MB = -0.0032\n",
" muA = 0.0926 muB = -0.0000\n",
"kmin = 0.14541\n",
"flag = 0\n",
"k = 0.16033, H/|P| = -0.94859\n",
"\n",
"beta = 0.41370 alpha = 0.25704\n",
"phiA = 0.29184 phiB = 0.47872\n",
"\n",
"[-0.37626929 -0.30628353 -0.06623746 0.29184005]\n",
"[-0.30477874 -0.06343494 0.27919453 0.47871674]\n",
"(array([[-0.64511629, -0.30982594, 0.10766722]]), array([ 0.0015048 , 0.00280253, -0.01264553]))\n",
"\n",
"v = [-0.58741476 -0.20236283 -0.3772268 0.26285 ]\n",
"(-2.5-0.587414760119j)\n",
"(-2-0.202362825171j)\n",
"(-1.5-0.377226801824j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.7670 lcrit = 20.0773 m\n",
" R = -11.9176 kN S = -2.8002 kN\n",
" MA = 0.0000 MB = 13.3489\n",
" muA = 0.0000 muB = 0.0543\n",
"kmin = 0.08531\n",
"flag = 0\n",
"k = 0.12637, H/|P| = -0.96806\n",
"\n",
"beta = -0.26370 alpha = -0.23078\n",
"phiA = -0.28634 phiB = -0.21965\n",
"Element 1\n",
" dAB = 5.8457 lcrit = 20.4925 m\n",
" R = -11.6914 kN S = -1.1830 kN\n",
" MA = 13.3489 MB = 20.2640\n",
" muA = 0.0554 muB = 0.0841\n",
"kmin = 0.13218\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.13218, lmbd = 0.27112, f = 0.01414,\n",
"k = 0.18218, lmbd = 0.57641, f = -0.29115,\n",
"k = 0.23218, lmbd = 0.66807, f = -0.38281,\n",
"k = 0.28218, lmbd = 0.71389, f = -0.42863,\n",
"k = 0.33218, lmbd = 0.73573, f = -0.45047,\n",
"k = 0.38218, lmbd = 0.74192, f = -0.45666,\n",
"k = 0.43218, lmbd = 0.73635, f = -0.45109,\n",
"k = 0.48218, lmbd = 0.72094, f = -0.43568,\n",
"k = 0.53218, lmbd = 0.69657, f = -0.41131,\n",
"k = 0.58218, lmbd = 0.66345, f = -0.37819,\n",
"k = 0.63218, lmbd = 0.62124, f = -0.33598,\n",
"k = 0.68218, lmbd = 0.56906, f = -0.28380,\n",
"k = 0.73218, lmbd = 0.50530, f = -0.22004,\n",
"k = 0.78218, lmbd = 0.42726, f = -0.14200,\n",
"k = 0.83218, lmbd = 0.33020, f = -0.04494,\n",
"k = 0.88218, lmbd = 0.20504, f = 0.08022,\n",
"k = 0.93218, lmbd = 0.03855, f = 0.24671,\n",
"k = 0.98218, lmbd = 0.29851, f = -0.01325,\n",
"k = 0.13231, H/|P| = -0.96499\n",
"\n",
"beta = -0.07561 alpha = -0.10084\n",
"phiA = -0.17434 phiB = 0.03682\n",
"Element 2\n",
" dAB = 6.9389 lcrit = 21.4430 m\n",
" R = -10.4084 kN S = -2.6175 kN\n",
" MA = 20.2640 MB = 38.4270\n",
" muA = 0.0881 muB = 0.1670\n",
"kmin = 0.26228\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.26228, lmbd = 0.32352, f = 0.00008,\n",
"k = 0.31228, lmbd = 0.52278, f = -0.19918,\n",
"k = 0.36228, lmbd = 0.58786, f = -0.26426,\n",
"k = 0.41228, lmbd = 0.62162, f = -0.29802,\n",
"k = 0.46228, lmbd = 0.63641, f = -0.31281,\n",
"k = 0.51228, lmbd = 0.63723, f = -0.31363,\n",
"k = 0.56228, lmbd = 0.62648, f = -0.30288,\n",
"k = 0.61228, lmbd = 0.60523, f = -0.28164,\n",
"k = 0.66228, lmbd = 0.57373, f = -0.25013,\n",
"k = 0.71228, lmbd = 0.53148, f = -0.20788,\n",
"k = 0.76228, lmbd = 0.47722, f = -0.15362,\n",
"k = 0.81228, lmbd = 0.40857, f = -0.08497,\n",
"k = 0.86228, lmbd = 0.32123, f = 0.00237,\n",
"k = 0.91228, lmbd = 0.20759, f = 0.11601,\n",
"k = 0.96228, lmbd = 0.08027, f = 0.24333,\n",
"k = 0.26228, H/|P| = -0.86241\n",
"\n",
"beta = -0.14155 alpha = -0.24638\n",
"phiA = -0.34465 phiB = 0.10496\n",
"Element 3\n",
" dAB = 8.7527 lcrit = 23.3513 m\n",
" R = -8.7527 kN S = 2.3006 kN\n",
" MA = 38.4270 MB = 18.2903\n",
" muA = 0.1818 muB = 0.0865\n",
"kmin = 0.28563\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.28563, lmbd = 0.34261, f = 0.03222,\n",
"k = 0.33563, lmbd = 0.52739, f = -0.15257,\n",
"k = 0.38563, lmbd = 0.58649, f = -0.21166,\n",
"k = 0.43563, lmbd = 0.61628, f = -0.24145,\n",
"k = 0.48563, lmbd = 0.62802, f = -0.25319,\n",
"k = 0.53563, lmbd = 0.62622, f = -0.25140,\n",
"k = 0.58563, lmbd = 0.61300, f = -0.23818,\n",
"k = 0.63563, lmbd = 0.58923, f = -0.21440,\n",
"k = 0.68563, lmbd = 0.55495, f = -0.18013,\n",
"k = 0.73563, lmbd = 0.50949, f = -0.13467,\n",
"k = 0.78563, lmbd = 0.45127, f = -0.07645,\n",
"k = 0.83563, lmbd = 0.37741, f = -0.00258,\n",
"k = 0.88563, lmbd = 0.28281, f = 0.09201,\n",
"k = 0.93563, lmbd = 0.16070, f = 0.21413,\n",
"k = 0.98563, lmbd = 0.13819, f = 0.23663,\n",
"k = 0.28715, H/|P| = -0.83510\n",
"\n",
"beta = 0.49568 alpha = 0.25704\n",
"phiA = 0.17965 phiB = 0.75005\n",
"\n",
"[-0.28634045 -0.17433856 -0.34464774 0.17964586]\n",
"[-0.2196515 0.03682358 0.10496121 0.75004595]\n",
"(array([[-0.58741476, -0.20236283, -0.3772268 ]]), array([-0.04531293, 0.38147132, -0.07468465]))\n",
"\n",
"v = [-0.64508426 -0.30976629 0.10739806 0.26285 ]\n",
"(-2.5-0.645084260025j)\n",
"(-2-0.309766287494j)\n",
"(-1.5+0.107398056542j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.7304 lcrit = 20.1009 m\n",
" R = -11.8261 kN S = -3.0515 kN\n",
" MA = 0.0000 MB = 14.4350\n",
" muA = 0.0000 muB = 0.0588\n",
"kmin = 0.09236\n",
"flag = 0\n",
"k = 0.13795, H/|P| = -0.96194\n",
"\n",
"beta = -0.35196 alpha = -0.25253\n",
"phiA = -0.37622 phiB = -0.30473\n",
"Element 1\n",
" dAB = 5.7750 lcrit = 20.5480 m\n",
" R = -11.5501 kN S = -1.7889 kN\n",
" MA = 14.4350 MB = 24.7660\n",
" muA = 0.0601 muB = 0.1031\n",
"kmin = 0.16199\n",
"flag = 0\n",
"k = 0.16236, H/|P| = -0.94728\n",
"\n",
"beta = -0.19491 alpha = -0.15366\n",
"phiA = -0.30621 phiB = -0.06338\n",
"Element 2\n",
" dAB = 7.0768 lcrit = 21.5335 m\n",
" R = -10.6152 kN S = 0.7600 kN\n",
" MA = 24.7660 MB = 19.3874\n",
" muA = 0.1081 muB = 0.0846\n",
"kmin = 0.16975\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.16975, lmbd = 0.21380, f = 0.11484,\n",
"k = 0.21975, lmbd = 0.50545, f = -0.17681,\n",
"k = 0.26975, lmbd = 0.60173, f = -0.27309,\n",
"k = 0.31975, lmbd = 0.65277, f = -0.32412,\n",
"k = 0.36975, lmbd = 0.67915, f = -0.35050,\n",
"k = 0.41975, lmbd = 0.68904, f = -0.36040,\n",
"k = 0.46975, lmbd = 0.68637, f = -0.35772,\n",
"k = 0.51975, lmbd = 0.67311, f = -0.34447,\n",
"k = 0.56975, lmbd = 0.65017, f = -0.32153,\n",
"k = 0.61975, lmbd = 0.61776, f = -0.28912,\n",
"k = 0.66975, lmbd = 0.57546, f = -0.24682,\n",
"k = 0.71975, lmbd = 0.52219, f = -0.19355,\n",
"k = 0.76975, lmbd = 0.45599, f = -0.12735,\n",
"k = 0.81975, lmbd = 0.37337, f = -0.04472,\n",
"k = 0.86975, lmbd = 0.26783, f = 0.06082,\n",
"k = 0.91975, lmbd = 0.12565, f = 0.20299,\n",
"k = 0.96975, lmbd = 0.10505, f = 0.22359,\n",
"k = 0.17810, H/|P| = -0.93656\n",
"\n",
"beta = 0.11287 alpha = 0.07148\n",
"phiA = -0.06639 phiB = 0.27910\n",
"Element 3\n",
" dAB = 8.5866 lcrit = 23.5760 m\n",
" R = -8.5866 kN S = 2.2570 kN\n",
" MA = 19.3874 MB = 0.0074\n",
" muA = 0.0926 muB = 0.0000\n",
"kmin = 0.14549\n",
"flag = 0\n",
"k = 0.16038, H/|P| = -0.94856\n",
"\n",
"beta = 0.41375 alpha = 0.25704\n",
"phiA = 0.29179 phiB = 0.47887\n",
"\n",
"[-0.37621812 -0.30620513 -0.06639226 0.29178988]\n",
"[-0.30473305 -0.06338334 0.27909937 0.47886805]\n",
"(array([[-0.64508426, -0.30976629, 0.10739806]]), array([ 0.00147209, 0.00300893, -0.01269051]))\n",
"\n",
"v = [-0.58741476 -0.20236283 -0.3772268 0.26285 ]\n",
"(-2.5-0.587414760119j)\n",
"(-2-0.202362825171j)\n",
"(-1.5-0.377226801824j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.7670 lcrit = 20.0773 m\n",
" R = -11.9176 kN S = -2.8002 kN\n",
" MA = 0.0000 MB = 13.3489\n",
" muA = 0.0000 muB = 0.0543\n",
"kmin = 0.08531\n",
"flag = 0\n",
"k = 0.12637, H/|P| = -0.96806\n",
"\n",
"beta = -0.26370 alpha = -0.23078\n",
"phiA = -0.28634 phiB = -0.21965\n",
"Element 1\n",
" dAB = 5.8457 lcrit = 20.4925 m\n",
" R = -11.6914 kN S = -1.1830 kN\n",
" MA = 13.3489 MB = 20.2640\n",
" muA = 0.0554 muB = 0.0841\n",
"kmin = 0.13218\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.13218, lmbd = 0.27112, f = 0.01414,\n",
"k = 0.18218, lmbd = 0.57641, f = -0.29115,\n",
"k = 0.23218, lmbd = 0.66807, f = -0.38281,\n",
"k = 0.28218, lmbd = 0.71389, f = -0.42863,\n",
"k = 0.33218, lmbd = 0.73573, f = -0.45047,\n",
"k = 0.38218, lmbd = 0.74192, f = -0.45666,\n",
"k = 0.43218, lmbd = 0.73635, f = -0.45109,\n",
"k = 0.48218, lmbd = 0.72094, f = -0.43568,\n",
"k = 0.53218, lmbd = 0.69657, f = -0.41131,\n",
"k = 0.58218, lmbd = 0.66345, f = -0.37819,\n",
"k = 0.63218, lmbd = 0.62124, f = -0.33598,\n",
"k = 0.68218, lmbd = 0.56906, f = -0.28380,\n",
"k = 0.73218, lmbd = 0.50530, f = -0.22004,\n",
"k = 0.78218, lmbd = 0.42726, f = -0.14200,\n",
"k = 0.83218, lmbd = 0.33020, f = -0.04494,\n",
"k = 0.88218, lmbd = 0.20504, f = 0.08022,\n",
"k = 0.93218, lmbd = 0.03855, f = 0.24671,\n",
"k = 0.98218, lmbd = 0.29851, f = -0.01325,\n",
"k = 0.13231, H/|P| = -0.96499\n",
"\n",
"beta = -0.07561 alpha = -0.10084\n",
"phiA = -0.17434 phiB = 0.03682\n",
"Element 2\n",
" dAB = 6.9389 lcrit = 21.4430 m\n",
" R = -10.4084 kN S = -2.6175 kN\n",
" MA = 20.2640 MB = 38.4270\n",
" muA = 0.0881 muB = 0.1670\n",
"kmin = 0.26228\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.26228, lmbd = 0.32352, f = 0.00008,\n",
"k = 0.31228, lmbd = 0.52278, f = -0.19918,\n",
"k = 0.36228, lmbd = 0.58786, f = -0.26426,\n",
"k = 0.41228, lmbd = 0.62162, f = -0.29802,\n",
"k = 0.46228, lmbd = 0.63641, f = -0.31281,\n",
"k = 0.51228, lmbd = 0.63723, f = -0.31363,\n",
"k = 0.56228, lmbd = 0.62648, f = -0.30288,\n",
"k = 0.61228, lmbd = 0.60523, f = -0.28164,\n",
"k = 0.66228, lmbd = 0.57373, f = -0.25013,\n",
"k = 0.71228, lmbd = 0.53148, f = -0.20788,\n",
"k = 0.76228, lmbd = 0.47722, f = -0.15362,\n",
"k = 0.81228, lmbd = 0.40857, f = -0.08497,\n",
"k = 0.86228, lmbd = 0.32123, f = 0.00237,\n",
"k = 0.91228, lmbd = 0.20759, f = 0.11601,\n",
"k = 0.96228, lmbd = 0.08027, f = 0.24333,\n",
"k = 0.26228, H/|P| = -0.86241\n",
"\n",
"beta = -0.14155 alpha = -0.24638\n",
"phiA = -0.34465 phiB = 0.10496\n",
"Element 3\n",
" dAB = 8.7527 lcrit = 23.3513 m\n",
" R = -8.7527 kN S = 2.3006 kN\n",
" MA = 38.4270 MB = 18.2903\n",
" muA = 0.1818 muB = 0.0865\n",
"kmin = 0.28563\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.28563, lmbd = 0.34261, f = 0.03222,\n",
"k = 0.33563, lmbd = 0.52739, f = -0.15257,\n",
"k = 0.38563, lmbd = 0.58649, f = -0.21166,\n",
"k = 0.43563, lmbd = 0.61628, f = -0.24145,\n",
"k = 0.48563, lmbd = 0.62802, f = -0.25319,\n",
"k = 0.53563, lmbd = 0.62622, f = -0.25140,\n",
"k = 0.58563, lmbd = 0.61300, f = -0.23818,\n",
"k = 0.63563, lmbd = 0.58923, f = -0.21440,\n",
"k = 0.68563, lmbd = 0.55495, f = -0.18013,\n",
"k = 0.73563, lmbd = 0.50949, f = -0.13467,\n",
"k = 0.78563, lmbd = 0.45127, f = -0.07645,\n",
"k = 0.83563, lmbd = 0.37741, f = -0.00258,\n",
"k = 0.88563, lmbd = 0.28281, f = 0.09201,\n",
"k = 0.93563, lmbd = 0.16070, f = 0.21413,\n",
"k = 0.98563, lmbd = 0.13819, f = 0.23663,\n",
"k = 0.28715, H/|P| = -0.83510\n",
"\n",
"beta = 0.49568 alpha = 0.25704\n",
"phiA = 0.17965 phiB = 0.75005\n",
"\n",
"[-0.28634045 -0.17433856 -0.34464774 0.17964586]\n",
"[-0.2196515 0.03682358 0.10496121 0.75004595]\n",
"(array([[-0.58741476, -0.20236283, -0.3772268 ]]), array([-0.04531293, 0.38147132, -0.07468465]))\n",
"\n",
"v = [-0.42100387 -1.60330899 -0.10294885 0.26285 ]\n",
"(-2.5-0.421003872612j)\n",
"(-2-1.60330899304j)\n",
"(-1.5-0.102948851147j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 5.1014 lcrit = 19.5337 m\n",
" R = -12.7535 kN S = -2.1477 kN\n",
" MA = 0.0000 MB = 10.9563\n",
" muA = 0.0000 muB = 0.0434\n",
"kmin = 0.06812\n",
"flag = 0\n",
"k = 0.09337, H/|P| = -0.98256\n",
"\n",
"beta = -0.18014 alpha = -0.16684\n",
"phiA = -0.20032 phiB = -0.14111\n",
"Element 1\n",
" dAB = 4.8455 lcrit = 19.9326 m\n",
" R = -9.6910 kN S = -7.7688 kN\n",
" MA = 10.9563 MB = 48.6002\n",
" muA = 0.0443 muB = 0.1963\n",
"kmin = 0.30836\n",
"flag = 0\n",
"k = 0.39358, H/|P| = -0.69019\n",
"\n",
"beta = -0.63539 alpha = -0.67575\n",
"phiA = -0.75525 phiB = -0.45384\n",
"Element 2\n",
" dAB = 7.6970 lcrit = 20.6499 m\n",
" R = -11.5455 kN S = -0.7924 kN\n",
" MA = 48.6002 MB = 54.6992\n",
" muA = 0.2034 muB = 0.2289\n",
"kmin = 0.35954\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.35954, lmbd = 0.15177, f = 0.22097,\n",
"k = 0.40954, lmbd = 0.36419, f = 0.00855,\n",
"k = 0.45954, lmbd = 0.44483, f = -0.07209,\n",
"k = 0.50954, lmbd = 0.49095, f = -0.11821,\n",
"k = 0.55954, lmbd = 0.51582, f = -0.14309,\n",
"k = 0.60954, lmbd = 0.52494, f = -0.15221,\n",
"k = 0.65954, lmbd = 0.52094, f = -0.14820,\n",
"k = 0.70954, lmbd = 0.50497, f = -0.13223,\n",
"k = 0.75954, lmbd = 0.47719, f = -0.10445,\n",
"k = 0.80954, lmbd = 0.43681, f = -0.06408,\n",
"k = 0.85954, lmbd = 0.38184, f = -0.00910,\n",
"k = 0.90954, lmbd = 0.30817, f = 0.06457,\n",
"k = 0.95954, lmbd = 0.20697, f = 0.16577,\n",
"k = 0.41362, H/|P| = -0.65783\n",
"\n",
"beta = 0.06295 alpha = -0.06853\n",
"phiA = -0.40025 phiB = 0.54335\n",
"Element 3\n",
" dAB = 9.6771 lcrit = 22.2080 m\n",
" R = -9.6771 kN S = 2.5436 kN\n",
" MA = 54.6992 MB = 30.0843\n",
" muA = 0.2462 muB = 0.1354\n",
"kmin = 0.38667\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.38667, lmbd = 0.31529, f = 0.12046,\n",
"k = 0.43667, lmbd = 0.47157, f = -0.03582,\n",
"k = 0.48667, lmbd = 0.52238, f = -0.08664,\n",
"k = 0.53667, lmbd = 0.54728, f = -0.11153,\n",
"k = 0.58667, lmbd = 0.55526, f = -0.11951,\n",
"k = 0.63667, lmbd = 0.54985, f = -0.11410,\n",
"k = 0.68667, lmbd = 0.53252, f = -0.09677,\n",
"k = 0.73667, lmbd = 0.50362, f = -0.06787,\n",
"k = 0.78667, lmbd = 0.46253, f = -0.02679,\n",
"k = 0.83667, lmbd = 0.40757, f = 0.02818,\n",
"k = 0.88667, lmbd = 0.33542, f = 0.10033,\n",
"k = 0.93667, lmbd = 0.24007, f = 0.19568,\n",
"k = 0.98667, lmbd = 0.12308, f = 0.31267,\n",
"k = 0.41549, H/|P| = -0.65474\n",
"\n",
"beta = 0.51967 alpha = 0.25704\n",
"phiA = -0.04265 phiB = 0.99261\n",
"\n",
"[-0.20032403 -0.75525285 -0.40024844 -0.04264734]\n",
"[-0.14110708 -0.45383561 0.54334689 0.99260529]\n",
"(array([[-0.42100387, -1.60330899, -0.10294885]]), array([ 0.61414576, -0.05358717, 0.58599422]))\n",
"\n",
"v = [-0.56980016 -0.35065304 -0.34819447 0.26285 ]\n",
"(-2.5-0.56980016069j)\n",
"(-2-0.350653035852j)\n",
"(-1.5-0.348194469231j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.7797 lcrit = 20.0663 m\n",
" R = -11.9492 kN S = -2.7235 kN\n",
" MA = 0.0000 MB = 13.0173\n",
" muA = 0.0000 muB = 0.0529\n",
"kmin = 0.08315\n",
"flag = 0\n",
"k = 0.12281, H/|P| = -0.96984\n",
"\n",
"beta = -0.24965 alpha = -0.22409\n",
"phiA = -0.27179 phiB = -0.20656\n",
"Element 1\n",
" dAB = 5.7953 lcrit = 20.4784 m\n",
" R = -11.5905 kN S = -2.0321 kN\n",
" MA = 13.0173 MB = 24.7940\n",
" muA = 0.0540 muB = 0.1029\n",
"kmin = 0.16162\n",
"flag = 0\n",
"k = 0.16314, H/|P| = -0.94677\n",
"\n",
"beta = -0.14115 alpha = -0.17356\n",
"phiA = -0.24716 phiB = -0.01199\n",
"Element 2\n",
" dAB = 6.9902 lcrit = 21.4114 m\n",
" R = -10.4853 kN S = -2.4340 kN\n",
" MA = 24.7940 MB = 41.8078\n",
" muA = 0.1076 muB = 0.1814\n",
"kmin = 0.28494\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.28494, lmbd = 0.29819, f = 0.02828,\n",
"k = 0.33494, lmbd = 0.49389, f = -0.16742,\n",
"k = 0.38494, lmbd = 0.55989, f = -0.23342,\n",
"k = 0.43494, lmbd = 0.59485, f = -0.26838,\n",
"k = 0.48494, lmbd = 0.61075, f = -0.28428,\n",
"k = 0.53494, lmbd = 0.61249, f = -0.28602,\n",
"k = 0.58494, lmbd = 0.60241, f = -0.27594,\n",
"k = 0.63494, lmbd = 0.58154, f = -0.25507,\n",
"k = 0.68494, lmbd = 0.55005, f = -0.22358,\n",
"k = 0.73494, lmbd = 0.50736, f = -0.18089,\n",
"k = 0.78494, lmbd = 0.45202, f = -0.12555,\n",
"k = 0.83494, lmbd = 0.38127, f = -0.05480,\n",
"k = 0.88494, lmbd = 0.28999, f = 0.03648,\n",
"k = 0.93494, lmbd = 0.16903, f = 0.15744,\n",
"k = 0.98494, lmbd = 0.09151, f = 0.23496,\n",
"k = 0.28607, H/|P| = -0.83632\n",
"\n",
"beta = -0.11301 alpha = -0.22809\n",
"phiA = -0.35078 phiB = 0.16594\n",
"Element 3\n",
" dAB = 8.7989 lcrit = 23.2899 m\n",
" R = -8.7989 kN S = 2.3128 kN\n",
" MA = 41.8078 MB = 21.4580\n",
" muA = 0.1973 muB = 0.1013\n",
"kmin = 0.30994\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.30994, lmbd = 0.32894, f = 0.04885,\n",
"k = 0.35994, lmbd = 0.50743, f = -0.12963,\n",
"k = 0.40994, lmbd = 0.56546, f = -0.18767,\n",
"k = 0.45994, lmbd = 0.59492, f = -0.21712,\n",
"k = 0.50994, lmbd = 0.60651, f = -0.22871,\n",
"k = 0.55994, lmbd = 0.60452, f = -0.22672,\n",
"k = 0.60994, lmbd = 0.59095, f = -0.21315,\n",
"k = 0.65994, lmbd = 0.56657, f = -0.18877,\n",
"k = 0.70994, lmbd = 0.53132, f = -0.15352,\n",
"k = 0.75994, lmbd = 0.48434, f = -0.10655,\n",
"k = 0.80994, lmbd = 0.42377, f = -0.04598,\n",
"k = 0.85994, lmbd = 0.34618, f = 0.03162,\n",
"k = 0.90994, lmbd = 0.24548, f = 0.13232,\n",
"k = 0.95994, lmbd = 0.11938, f = 0.25841,\n",
"k = 0.31367, H/|P| = -0.80322\n",
"\n",
"beta = 0.50858 alpha = 0.25704\n",
"phiA = 0.15499 phiB = 0.79904\n",
"\n",
"[-0.2717928 -0.24716432 -0.35078391 0.15498842]\n",
"[-0.20656348 -0.01199158 0.16594432 0.79904159]\n",
"(array([[-0.56980016, -0.35065304, -0.34819447]]), array([ 0.04060084, 0.33879233, 0.0109559 ]))\n",
"\n",
"v = [ 0.08143918 -0.03509599 0.24842585 0.26285 ]\n",
"(-2.5+0.0814391834143j)\n",
"(-2-0.0350959912486j)\n",
"(-1.5+0.248425851985j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.8150 lcrit = 20.2419 m\n",
" R = -12.0376 kN S = 0.3921 kN\n",
" MA = 0.0000 MB = -1.8881\n",
" muA = 0.0000 muB = -0.0077\n",
"kmin = 0.01217\n",
"flag = 0\n",
"k = 0.01790, H/|P| = -0.99936\n",
"\n",
"beta = -0.14040 alpha = 0.03256\n",
"phiA = -0.13716 phiB = -0.14670\n",
"Element 1\n",
" dAB = 5.7247 lcrit = 20.7592 m\n",
" R = -11.4493 kN S = -0.2009 kN\n",
" MA = -1.8881 MB = -0.7380\n",
" muA = -0.0079 muB = -0.0031\n",
"kmin = 0.01248\n",
"flag = 0\n",
"k = 0.01317, H/|P| = -0.99965\n",
"\n",
"beta = -0.14178 alpha = -0.01755\n",
"phiA = -0.13264 phiB = -0.14869\n",
"Element 2\n",
" dAB = 6.7789 lcrit = 21.8813 m\n",
" R = -10.1683 kN S = 1.6840 kN\n",
" MA = -0.7380 MB = -12.1538\n",
" muA = -0.0033 muB = -0.0539\n",
"kmin = 0.08465\n",
"flag = 0\n",
"k = 0.09922, H/|P| = -0.98031\n",
"\n",
"beta = 0.10486 alpha = 0.16413\n",
"phiA = 0.13922 phiB = 0.04427\n",
"Element 3\n",
" dAB = 8.1471 lcrit = 24.2036 m\n",
" R = -8.1471 kN S = 2.1415 kN\n",
" MA = -12.1538 MB = -29.6005\n",
" muA = -0.0596 muB = -0.1452\n",
"kmin = 0.22805\n",
"flag = 0\n",
"k = 0.22911, H/|P| = -0.89502\n",
"\n",
"beta = 0.28278 alpha = 0.25704\n",
"phiA = 0.44705 phiB = 0.06971\n",
"\n",
"[-0.13716466 -0.13264326 0.13922196 0.44704756]\n",
"[-0.14670399 -0.14869345 0.04427193 0.06970904]\n",
"(array([[ 0.08143918, -0.03509599, 0.24842585]]), array([-0.01406073, -0.28791541, -0.40277562]))\n",
"\n",
"v = [-0.41510276 -0.27569464 -0.20647144 0.26285 ]\n",
"(-2.5-0.415102761125j)\n",
"(-2-0.275694643585j)\n",
"(-1.5-0.206471440585j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.7862 lcrit = 20.1705 m\n",
" R = -11.9655 kN S = -1.9868 kN\n",
" MA = 0.0000 MB = 9.5090\n",
" muA = 0.0000 muB = 0.0389\n",
"kmin = 0.06105\n",
"flag = 0\n",
"k = 0.09025, H/|P| = -0.98371\n",
"\n",
"beta = -0.22449 alpha = -0.16454\n",
"phiA = -0.24069 phiB = -0.19297\n",
"Element 1\n",
" dAB = 5.7528 lcrit = 20.6129 m\n",
" R = -11.5055 kN S = -1.5860 kN\n",
" MA = 9.5090 MB = 18.6329\n",
" muA = 0.0397 muB = 0.0778\n",
"kmin = 0.12226\n",
"flag = 0\n",
"k = 0.12400, H/|P| = -0.96925\n",
"\n",
"beta = -0.14055 alpha = -0.13698\n",
"phiA = -0.21830 phiB = -0.04504\n",
"Element 2\n",
" dAB = 6.9855 lcrit = 21.5999 m\n",
" R = -10.4783 kN S = -1.4423 kN\n",
" MA = 18.6329 MB = 28.7083\n",
" muA = 0.0816 muB = 0.1257\n",
"kmin = 0.19738\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.19738, lmbd = 0.27526, f = 0.04815,\n",
"k = 0.24738, lmbd = 0.52347, f = -0.20007,\n",
"k = 0.29738, lmbd = 0.60549, f = -0.28208,\n",
"k = 0.34738, lmbd = 0.64897, f = -0.32556,\n",
"k = 0.39738, lmbd = 0.67048, f = -0.34707,\n",
"k = 0.44738, lmbd = 0.67673, f = -0.35333,\n",
"k = 0.49738, lmbd = 0.67097, f = -0.34757,\n",
"k = 0.54738, lmbd = 0.65482, f = -0.33142,\n",
"k = 0.59738, lmbd = 0.62894, f = -0.30553,\n",
"k = 0.64738, lmbd = 0.59330, f = -0.26990,\n",
"k = 0.69738, lmbd = 0.54727, f = -0.22386,\n",
"k = 0.74738, lmbd = 0.48943, f = -0.16603,\n",
"k = 0.79738, lmbd = 0.41728, f = -0.09387,\n",
"k = 0.84738, lmbd = 0.32627, f = -0.00287,\n",
"k = 0.89738, lmbd = 0.20777, f = 0.11564,\n",
"k = 0.94738, lmbd = 0.05350, f = 0.26990,\n",
"k = 0.99738, lmbd = 0.31792, f = 0.00549,\n",
"k = 0.19947, H/|P| = -0.92043\n",
"\n",
"beta = -0.06089 alpha = -0.13679\n",
"phiA = -0.23108 phiB = 0.13342\n",
"Element 3\n",
" dAB = 8.5539 lcrit = 23.6210 m\n",
" R = -8.5539 kN S = 2.2484 kN\n",
" MA = 28.7083 MB = 9.4757\n",
" muA = 0.1374 muB = 0.0454\n",
"kmin = 0.21585\n",
"flag = 0\n",
"k = 0.21700, H/|P| = -0.90582\n",
"\n",
"beta = 0.45986 alpha = 0.25704\n",
"phiA = 0.24741 phiB = 0.61569\n",
"\n",
"[-0.24068871 -0.21830405 -0.23108402 0.24740971]\n",
"[-0.1929703 -0.04503689 0.13341667 0.61569419]\n",
"(array([[-0.41510276, -0.27569464, -0.20647144]]), array([ 0.02533375, 0.18604713, -0.11399303]))\n",
"\n",
"v = [-1.15566018 -0.14139043 0.1041118 0.26285 ]\n",
"(-2.5-1.15566018263j)\n",
"(-2-0.141390427958j)\n",
"(-1.5+0.104111800449j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.4859 lcrit = 19.9855 m\n",
" R = -11.2146 kN S = -5.1841 kN\n",
" MA = 0.0000 MB = 23.2553\n",
" muA = 0.0000 muB = 0.0942\n",
"kmin = 0.14794\n",
"flag = 0\n",
"k = 0.23271, H/|P| = -0.89169\n",
"\n",
"beta = -0.53184 alpha = -0.43301\n",
"phiA = -0.56856 phiB = -0.46006\n",
"Element 1\n",
" dAB = 5.9219 lcrit = 20.3868 m\n",
" R = -11.8438 kN S = -0.8373 kN\n",
" MA = 23.2553 MB = 28.2137\n",
" muA = 0.0961 muB = 0.1166\n",
"kmin = 0.18309\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.18309, lmbd = 0.19161, f = 0.09887,\n",
"k = 0.23309, lmbd = 0.48144, f = -0.19097,\n",
"k = 0.28309, lmbd = 0.57944, f = -0.28896,\n",
"k = 0.33309, lmbd = 0.63217, f = -0.34169,\n",
"k = 0.38309, lmbd = 0.66001, f = -0.36953,\n",
"k = 0.43309, lmbd = 0.67111, f = -0.38064,\n",
"k = 0.48309, lmbd = 0.66940, f = -0.37892,\n",
"k = 0.53309, lmbd = 0.65686, f = -0.36638,\n",
"k = 0.58309, lmbd = 0.63442, f = -0.34394,\n",
"k = 0.63309, lmbd = 0.60226, f = -0.31178,\n",
"k = 0.68309, lmbd = 0.55993, f = -0.26946,\n",
"k = 0.73309, lmbd = 0.50629, f = -0.21581,\n",
"k = 0.78309, lmbd = 0.43919, f = -0.14871,\n",
"k = 0.83309, lmbd = 0.35483, f = -0.06435,\n",
"k = 0.88309, lmbd = 0.24587, f = 0.04460,\n",
"k = 0.93309, lmbd = 0.09620, f = 0.19427,\n",
"k = 0.98309, lmbd = 0.16081, f = 0.12967,\n",
"k = 0.18968, H/|P| = -0.92804\n",
"\n",
"beta = -0.10741 alpha = -0.07058\n",
"phiA = -0.26715 phiB = 0.06235\n",
"Element 2\n",
" dAB = 7.2252 lcrit = 21.3128 m\n",
" R = -10.8379 kN S = 0.7522 kN\n",
" MA = 28.2137 MB = 22.7786\n",
" muA = 0.1219 muB = 0.0984\n",
"kmin = 0.19140\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.19140, lmbd = 0.20091, f = 0.13810,\n",
"k = 0.24140, lmbd = 0.48023, f = -0.14122,\n",
"k = 0.29140, lmbd = 0.57549, f = -0.23648,\n",
"k = 0.34140, lmbd = 0.62701, f = -0.28800,\n",
"k = 0.39140, lmbd = 0.65418, f = -0.31517,\n",
"k = 0.44140, lmbd = 0.66482, f = -0.32581,\n",
"k = 0.49140, lmbd = 0.66271, f = -0.32371,\n",
"k = 0.54140, lmbd = 0.64978, f = -0.31077,\n",
"k = 0.59140, lmbd = 0.62689, f = -0.28788,\n",
"k = 0.64140, lmbd = 0.59418, f = -0.25517,\n",
"k = 0.69140, lmbd = 0.55114, f = -0.21213,\n",
"k = 0.74140, lmbd = 0.49654, f = -0.15754,\n",
"k = 0.79140, lmbd = 0.42811, f = -0.08911,\n",
"k = 0.84140, lmbd = 0.34174, f = -0.00273,\n",
"k = 0.89140, lmbd = 0.22943, f = 0.10957,\n",
"k = 0.94140, lmbd = 0.07367, f = 0.26534,\n",
"k = 0.99140, lmbd = 0.21409, f = 0.12492,\n",
"k = 0.20430, H/|P| = -0.91652\n",
"\n",
"beta = 0.11531 alpha = 0.06930\n",
"phiA = -0.09699 phiB = 0.31409\n",
"Element 3\n",
" dAB = 8.8266 lcrit = 23.2533 m\n",
" R = -8.8266 kN S = 2.3201 kN\n",
" MA = 22.7786 MB = 2.3001\n",
" muA = 0.1073 muB = 0.0108\n",
"kmin = 0.16860\n",
"flag = 0\n",
"k = 0.17588, H/|P| = -0.93814\n",
"\n",
"beta = 0.41743 alpha = 0.25704\n",
"phiA = 0.26055 phiB = 0.51231\n",
"\n",
"[-0.56856119 -0.26715454 -0.09699452 0.26055446]\n",
"[-0.46005773 0.06235338 0.31409338 0.51230621]\n",
"(array([[-1.15566018, -0.14139043, 0.1041118 ]]), array([-0.19290319, 0.1593479 , 0.05353892]))\n",
"\n",
"v = [-0.68798967 -0.22620508 -0.09202508 0.26285 ]\n",
"(-2.5-0.687989665859j)\n",
"(-2-0.226205082572j)\n",
"(-1.5-0.0920250832233j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.6921 lcrit = 20.1398 m\n",
" R = -11.7303 kN S = -3.2281 kN\n",
" MA = 0.0000 MB = 15.1468\n",
" muA = 0.0000 muB = 0.0618\n",
"kmin = 0.09710\n",
"flag = 0\n",
"k = 0.14635, H/|P| = -0.95716\n",
"\n",
"beta = -0.34071 alpha = -0.26855\n",
"phiA = -0.36591 phiB = -0.29159\n",
"Element 1\n",
" dAB = 5.7940 lcrit = 20.5707 m\n",
" R = -11.5880 kN S = -1.3106 kN\n",
" MA = 15.1468 MB = 22.7406\n",
" muA = 0.0631 muB = 0.0948\n",
"kmin = 0.14890\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.14890, lmbd = 0.26802, f = 0.01364,\n",
"k = 0.19890, lmbd = 0.55741, f = -0.27575,\n",
"k = 0.24890, lmbd = 0.64752, f = -0.36585,\n",
"k = 0.29890, lmbd = 0.69359, f = -0.41192,\n",
"k = 0.34890, lmbd = 0.71601, f = -0.43435,\n",
"k = 0.39890, lmbd = 0.72276, f = -0.44110,\n",
"k = 0.44890, lmbd = 0.71758, f = -0.43592,\n",
"k = 0.49890, lmbd = 0.70236, f = -0.42069,\n",
"k = 0.54890, lmbd = 0.67793, f = -0.39627,\n",
"k = 0.59890, lmbd = 0.64447, f = -0.36281,\n",
"k = 0.64890, lmbd = 0.60160, f = -0.31993,\n",
"k = 0.69890, lmbd = 0.54831, f = -0.26665,\n",
"k = 0.74890, lmbd = 0.48284, f = -0.20118,\n",
"k = 0.79890, lmbd = 0.40211, f = -0.12045,\n",
"k = 0.84890, lmbd = 0.30066, f = -0.01899,\n",
"k = 0.89890, lmbd = 0.16766, f = 0.11400,\n",
"k = 0.94890, lmbd = 0.04571, f = 0.23595,\n",
"k = 0.99890, lmbd = 0.49244, f = -0.21078,\n",
"k = 0.14904, H/|P| = -0.95558\n",
"\n",
"beta = -0.12563 alpha = -0.11262\n",
"phiA = -0.23596 phiB = -0.00038\n",
"Element 2\n",
" dAB = 7.0771 lcrit = 21.5404 m\n",
" R = -10.6157 kN S = -0.6513 kN\n",
" MA = 22.7406 MB = 27.3498\n",
" muA = 0.0993 muB = 0.1194\n",
"kmin = 0.18752\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.18752, lmbd = 0.18751, f = 0.14104,\n",
"k = 0.23752, lmbd = 0.47556, f = -0.14701,\n",
"k = 0.28752, lmbd = 0.57357, f = -0.24502,\n",
"k = 0.33752, lmbd = 0.62653, f = -0.29797,\n",
"k = 0.38752, lmbd = 0.65462, f = -0.32607,\n",
"k = 0.43752, lmbd = 0.66593, f = -0.33738,\n",
"k = 0.48752, lmbd = 0.66439, f = -0.33584,\n",
"k = 0.53752, lmbd = 0.65197, f = -0.32342,\n",
"k = 0.58752, lmbd = 0.62958, f = -0.30103,\n",
"k = 0.63752, lmbd = 0.59740, f = -0.26885,\n",
"k = 0.68752, lmbd = 0.55496, f = -0.22641,\n",
"k = 0.73752, lmbd = 0.50109, f = -0.17254,\n",
"k = 0.78752, lmbd = 0.43358, f = -0.10503,\n",
"k = 0.83752, lmbd = 0.34849, f = -0.01994,\n",
"k = 0.88752, lmbd = 0.23819, f = 0.09036,\n",
"k = 0.93752, lmbd = 0.08565, f = 0.24290,\n",
"k = 0.98752, lmbd = 0.18364, f = 0.14491,\n",
"k = 0.20026, H/|P| = -0.91979\n",
"\n",
"beta = 0.00739 alpha = -0.06127\n",
"phiA = -0.18333 phiB = 0.20932\n",
"Element 3\n",
" dAB = 8.6080 lcrit = 23.5467 m\n",
" R = -8.6080 kN S = 2.2626 kN\n",
" MA = 27.3498 MB = 7.8731\n",
" muA = 0.1305 muB = 0.0376\n",
"kmin = 0.20499\n",
"flag = 0\n",
"k = 0.20696, H/|P| = -0.91433\n",
"\n",
"beta = 0.44966 alpha = 0.25704\n",
"phiA = 0.24966 phiB = 0.59202\n",
"\n",
"[-0.36591385 -0.23596103 -0.18332676 0.24966092]\n",
"[ -2.91593428e-01 -3.83639506e-04 2.09316392e-01 5.92023535e-01]\n",
"(array([[-0.68798967, -0.22620508, -0.09202508]]), array([-0.0556324 , 0.18294312, -0.04034453]))\n",
"\n",
"v = [-0.70914966 -0.29915161 0.12079699 0.26285 ]\n",
"(-2.5-0.709149658255j)\n",
"(-2-0.299151610245j)\n",
"(-1.5+0.120796993273j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.7089 lcrit = 20.0818 m\n",
" R = -11.7722 kN S = -3.3393 kN\n",
" MA = 0.0000 MB = 15.7245\n",
" muA = 0.0000 muB = 0.0640\n",
"kmin = 0.10051\n",
"flag = 0\n",
"k = 0.15076, H/|P| = -0.95454\n",
"\n",
"beta = -0.37667 alpha = -0.27640\n",
"phiA = -0.40295 phiB = -0.32548\n",
"Element 1\n",
" dAB = 5.7931 lcrit = 20.5241 m\n",
" R = -11.5861 kN S = -1.7330 kN\n",
" MA = 15.7245 MB = 25.7639\n",
" muA = 0.0654 muB = 0.1072\n",
"kmin = 0.16832\n",
"flag = 0\n",
"k = 0.16838, H/|P| = -0.94329\n",
"\n",
"beta = -0.19001 alpha = -0.14848\n",
"phiA = -0.30916 phiB = -0.05110\n",
"Element 2\n",
" dAB = 7.0924 lcrit = 21.5026 m\n",
" R = -10.6386 kN S = 0.8567 kN\n",
" MA = 25.7639 MB = 19.6876\n",
" muA = 0.1123 muB = 0.0858\n",
"kmin = 0.17634\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.17634, lmbd = 0.22318, f = 0.10666,\n",
"k = 0.22634, lmbd = 0.50545, f = -0.17561,\n",
"k = 0.27634, lmbd = 0.59923, f = -0.26939,\n",
"k = 0.32634, lmbd = 0.64913, f = -0.31929,\n",
"k = 0.37634, lmbd = 0.67487, f = -0.34503,\n",
"k = 0.42634, lmbd = 0.68432, f = -0.35448,\n",
"k = 0.47634, lmbd = 0.68127, f = -0.35143,\n",
"k = 0.52634, lmbd = 0.66763, f = -0.33779,\n",
"k = 0.57634, lmbd = 0.64429, f = -0.31445,\n",
"k = 0.62634, lmbd = 0.61139, f = -0.28155,\n",
"k = 0.67634, lmbd = 0.56847, f = -0.23864,\n",
"k = 0.72634, lmbd = 0.51442, f = -0.18458,\n",
"k = 0.77634, lmbd = 0.44713, f = -0.11729,\n",
"k = 0.82634, lmbd = 0.36293, f = -0.03309,\n",
"k = 0.87634, lmbd = 0.25487, f = 0.07497,\n",
"k = 0.92634, lmbd = 0.10821, f = 0.22162,\n",
"k = 0.97634, lmbd = 0.13968, f = 0.19016,\n",
"k = 0.18405, H/|P| = -0.93225\n",
"\n",
"beta = 0.12187 alpha = 0.08036\n",
"phiA = -0.06392 phiB = 0.29289\n",
"Element 3\n",
" dAB = 8.6193 lcrit = 23.5313 m\n",
" R = -8.6193 kN S = 2.2656 kN\n",
" MA = 19.6876 MB = 0.1599\n",
" muA = 0.0939 muB = 0.0008\n",
"kmin = 0.14746\n",
"flag = 0\n",
"k = 0.16155, H/|P| = -0.94780\n",
"\n",
"beta = 0.41357 alpha = 0.25704\n",
"phiA = 0.28858 phiB = 0.48105\n",
"\n",
"[-0.40295224 -0.30916217 -0.06392194 0.28857952]\n",
"[-0.32548098 -0.05110498 0.2928942 0.48104813]\n",
"(array([[-0.70914966, -0.29915161, 0.12079699]]), array([-0.01631881, 0.01281696, 0.00431468]))\n",
"\n",
"v = [-0.67520063 -0.31414199 0.12112496 0.26285 ]\n",
"(-2.5-0.675200626429j)\n",
"(-2-0.314141986997j)\n",
"(-1.5+0.121124958929j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.7231 lcrit = 20.0866 m\n",
" R = -11.8078 kN S = -3.1890 kN\n",
" MA = 0.0000 MB = 15.0622\n",
" muA = 0.0000 muB = 0.0613\n",
"kmin = 0.09630\n",
"flag = 0\n",
"k = 0.14402, H/|P| = -0.95852\n",
"\n",
"beta = -0.36405 alpha = -0.26379\n",
"phiA = -0.38931 phiB = -0.31485\n",
"Element 1\n",
" dAB = 5.7824 lcrit = 20.5315 m\n",
" R = -11.5648 kN S = -1.8165 kN\n",
" MA = 15.0622 MB = 25.5658\n",
" muA = 0.0627 muB = 0.1064\n",
"kmin = 0.16708\n",
"flag = 0\n",
"k = 0.16736, H/|P| = -0.94398\n",
"\n",
"beta = -0.19764 alpha = -0.15580\n",
"phiA = -0.31339 phiB = -0.06129\n",
"Element 2\n",
" dAB = 7.0855 lcrit = 21.5129 m\n",
" R = -10.6282 kN S = 0.8582 kN\n",
" MA = 25.5658 MB = 19.4848\n",
" muA = 0.1115 muB = 0.0849\n",
"kmin = 0.17507\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.17507, lmbd = 0.22417, f = 0.10519,\n",
"k = 0.22507, lmbd = 0.50710, f = -0.17774,\n",
"k = 0.27507, lmbd = 0.60090, f = -0.27154,\n",
"k = 0.32507, lmbd = 0.65075, f = -0.32139,\n",
"k = 0.37507, lmbd = 0.67642, f = -0.34706,\n",
"k = 0.42507, lmbd = 0.68581, f = -0.35645,\n",
"k = 0.47507, lmbd = 0.68271, f = -0.35335,\n",
"k = 0.52507, lmbd = 0.66905, f = -0.33969,\n",
"k = 0.57507, lmbd = 0.64569, f = -0.31633,\n",
"k = 0.62507, lmbd = 0.61280, f = -0.28344,\n",
"k = 0.67507, lmbd = 0.56993, f = -0.24057,\n",
"k = 0.72507, lmbd = 0.51594, f = -0.18658,\n",
"k = 0.77507, lmbd = 0.44878, f = -0.11942,\n",
"k = 0.82507, lmbd = 0.36479, f = -0.03543,\n",
"k = 0.87507, lmbd = 0.25710, f = 0.07226,\n",
"k = 0.92507, lmbd = 0.11121, f = 0.21815,\n",
"k = 0.97507, lmbd = 0.13384, f = 0.19552,\n",
"k = 0.18254, H/|P| = -0.93336\n",
"\n",
"beta = 0.12174 alpha = 0.08058\n",
"phiA = -0.06223 phiB = 0.29094\n",
"Element 3\n",
" dAB = 8.6081 lcrit = 23.5466 m\n",
" R = -8.6081 kN S = 2.2626 kN\n",
" MA = 19.4848 MB = 0.0078\n",
" muA = 0.0930 muB = 0.0000\n",
"kmin = 0.14604\n",
"flag = 0\n",
"k = 0.16067, H/|P| = -0.94837\n",
"\n",
"beta = 0.41322 alpha = 0.25704\n",
"phiA = 0.29024 phiB = 0.47892\n",
"\n",
"[-0.38931435 -0.31338993 -0.06222955 0.29024463]\n",
"[-0.31485315 -0.06128556 0.29094377 0.47891984]\n",
"(array([[-0.67520063, -0.31414199, 0.12112496]]), array([-0.00146322, 0.00094399, 0.00069914]))\n",
"\n",
"v = [-0.67127963 -0.31542524 0.12066485 0.26285 ]\n",
"(-2.5-0.671279632012j)\n",
"(-2-0.315425239385j)\n",
"(-1.5+0.120664854317j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.7246 lcrit = 20.0875 m\n",
" R = -11.8114 kN S = -3.1715 kN\n",
" MA = 0.0000 MB = 14.9839\n",
" muA = 0.0000 muB = 0.0610\n",
"kmin = 0.09581\n",
"flag = 0\n",
"k = 0.14324, H/|P| = -0.95897\n",
"\n",
"beta = -0.36255 alpha = -0.26232\n",
"phiA = -0.38769 phiB = -0.31359\n",
"Element 1\n",
" dAB = 5.7812 lcrit = 20.5326 m\n",
" R = -11.5623 kN S = -1.8235 kN\n",
" MA = 14.9839 MB = 25.5261\n",
" muA = 0.0623 muB = 0.1062\n",
"kmin = 0.16683\n",
"flag = 0\n",
"k = 0.16714, H/|P| = -0.94413\n",
"\n",
"beta = -0.19826 alpha = -0.15642\n",
"phiA = -0.31357 phiB = -0.06227\n",
"Element 2\n",
" dAB = 7.0847 lcrit = 21.5144 m\n",
" R = -10.6270 kN S = 0.8549 kN\n",
" MA = 25.5261 MB = 19.4696\n",
" muA = 0.1113 muB = 0.0849\n",
"kmin = 0.17481\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.17481, lmbd = 0.22388, f = 0.10542,\n",
"k = 0.22481, lmbd = 0.50714, f = -0.17784,\n",
"k = 0.27481, lmbd = 0.60103, f = -0.27173,\n",
"k = 0.32481, lmbd = 0.65091, f = -0.32161,\n",
"k = 0.37481, lmbd = 0.67660, f = -0.34730,\n",
"k = 0.42481, lmbd = 0.68601, f = -0.35671,\n",
"k = 0.47481, lmbd = 0.68292, f = -0.35362,\n",
"k = 0.52481, lmbd = 0.66927, f = -0.33997,\n",
"k = 0.57481, lmbd = 0.64593, f = -0.31663,\n",
"k = 0.62481, lmbd = 0.61306, f = -0.28376,\n",
"k = 0.67481, lmbd = 0.57021, f = -0.24091,\n",
"k = 0.72481, lmbd = 0.51625, f = -0.18695,\n",
"k = 0.77481, lmbd = 0.44913, f = -0.11983,\n",
"k = 0.82481, lmbd = 0.36520, f = -0.03590,\n",
"k = 0.87481, lmbd = 0.25761, f = 0.07169,\n",
"k = 0.92481, lmbd = 0.11189, f = 0.21741,\n",
"k = 0.97481, lmbd = 0.13245, f = 0.19685,\n",
"k = 0.18229, H/|P| = -0.93354\n",
"\n",
"beta = 0.12143 alpha = 0.08027\n",
"phiA = -0.06226 phiB = 0.29042\n",
"Element 3\n",
" dAB = 8.6065 lcrit = 23.5487 m\n",
" R = -8.6065 kN S = 2.2622 kN\n",
" MA = 19.4696 MB = -0.0003\n",
" muA = 0.0929 muB = -0.0000\n",
"kmin = 0.14594\n",
"flag = 0\n",
"k = 0.16061, H/|P| = -0.94841\n",
"\n",
"beta = 0.41323 alpha = 0.25704\n",
"phiA = 0.29041 phiB = 0.47881\n",
"\n",
"[-0.3876875 -0.31357415 -0.0622649 0.29040621]\n",
"[-0.31358706 -0.06226998 0.29041906 0.4788069 ]\n",
"(array([[-0.67127963, -0.31542524, 0.12066485]]), array([ -1.29067629e-05, -5.07810850e-06, 1.28527629e-05]))\n",
"\n",
"v = [-0.6712316 -0.31543261 0.12063918 0.26285 ]\n",
"(-2.5-0.671231602674j)\n",
"(-2-0.315432606012j)\n",
"(-1.5+0.120639178125j)\n",
"(-1+0.26285j)\n",
"Elastica Rod 0\n",
"***************\n",
"Element 0\n",
" dAB = 4.7246 lcrit = 20.0875 m\n",
" R = -11.8114 kN S = -3.1713 kN\n",
" MA = 0.0000 MB = 14.9830\n",
" muA = 0.0000 muB = 0.0610\n",
"kmin = 0.09580\n",
"flag = 0\n",
"k = 0.14323, H/|P| = -0.95897\n",
"\n",
"beta = -0.36253 alpha = -0.26231\n",
"phiA = -0.38766 phiB = -0.31357\n",
"Element 1\n",
" dAB = 5.7812 lcrit = 20.5326 m\n",
" R = -11.5623 kN S = -1.8236 kN\n",
" MA = 14.9830 MB = 25.5253\n",
" muA = 0.0623 muB = 0.1062\n",
"kmin = 0.16683\n",
"flag = 0\n",
"k = 0.16714, H/|P| = -0.94413\n",
"\n",
"beta = -0.19826 alpha = -0.15643\n",
"phiA = -0.31357 phiB = -0.06228\n",
"Element 2\n",
" dAB = 7.0847 lcrit = 21.5144 m\n",
" R = -10.6270 kN S = 0.8547 kN\n",
" MA = 25.5253 MB = 19.4701\n",
" muA = 0.1113 muB = 0.0849\n",
"kmin = 0.17480\n",
"flag = 0\n",
"flag = 1\n",
"k = 0.17480, lmbd = 0.22386, f = 0.10544,\n",
"k = 0.22480, lmbd = 0.50713, f = -0.17783,\n",
"k = 0.27480, lmbd = 0.60102, f = -0.27172,\n",
"k = 0.32480, lmbd = 0.65091, f = -0.32161,\n",
"k = 0.37480, lmbd = 0.67660, f = -0.34730,\n",
"k = 0.42480, lmbd = 0.68601, f = -0.35671,\n",
"k = 0.47480, lmbd = 0.68292, f = -0.35363,\n",
"k = 0.52480, lmbd = 0.66928, f = -0.33998,\n",
"k = 0.57480, lmbd = 0.64594, f = -0.31664,\n",
"k = 0.62480, lmbd = 0.61306, f = -0.28376,\n",
"k = 0.67480, lmbd = 0.57021, f = -0.24091,\n",
"k = 0.72480, lmbd = 0.51626, f = -0.18696,\n",
"k = 0.77480, lmbd = 0.44914, f = -0.11984,\n",
"k = 0.82480, lmbd = 0.36521, f = -0.03591,\n",
"k = 0.87480, lmbd = 0.25762, f = 0.07168,\n",
"k = 0.92480, lmbd = 0.11190, f = 0.21740,\n",
"k = 0.97480, lmbd = 0.13242, f = 0.19688,\n",
"k = 0.18229, H/|P| = -0.93354\n",
"\n",
"beta = 0.12142 alpha = 0.08025\n",
"phiA = -0.06228 phiB = 0.29040\n",
"Element 3\n",
" dAB = 8.6065 lcrit = 23.5488 m\n",
" R = -8.6065 kN S = 2.2622 kN\n",
" MA = 19.4701 MB = 0.0003\n",
" muA = 0.0929 muB = 0.0000\n",
"kmin = 0.14594\n",
"flag = 0\n",
"k = 0.16061, H/|P| = -0.94841\n",
"\n",
"beta = 0.41323 alpha = 0.25704\n",
"phiA = 0.29040 phiB = 0.47882\n",
"\n",
"[-0.38766495 -0.31356889 -0.06227643 0.29040492]\n",
"[-0.31356916 -0.06227674 0.29040464 0.47881569]\n",
"(array([[-0.6712316 , -0.31543261, 0.12063918]]), array([ -2.71160760e-07, -3.03648175e-07, -2.78490747e-07]))\n",
"\n"
]
}
],
"source": [
"x0 = [[-0.64511629, -0.30982594, 0.10766722]]\n",
"sol = scipy.optimize.broyden1(g, x0)"
]
},
{
"cell_type": "code",
"execution_count": 266,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"beta = -0.36253 alpha = -0.26231\n",
"phiA = -0.38766 phiB = -0.31357\n",
"beta = -0.19826 alpha = -0.15643\n",
"phiA = -0.31357 phiB = -0.06228\n",
"beta = 0.12142 alpha = 0.08025\n",
"phiA = -0.06228 phiB = 0.29040\n",
"beta = 0.41323 alpha = 0.25704\n",
"phiA = 0.29040 phiB = 0.47882\n"
]
},
{
"data": {
"image/png": 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3otLkShy7csxOUcZPRo+MDHx7IMc+PkaaZGkoOa4k3VZ1c9hkdkIIIaxHhoESobaL2/Ka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\nSX7sS4oVIYQQQlia9KwIIYQQIsFIz4o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"text/plain": [
"<matplotlib.figure.Figure at 0x13181be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g.plot()"
]
},
{
"cell_type": "code",
"execution_count": 266,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 266,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 266,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
joelagnel/lisa | ipynb/tests/Frequency_Invariance_Test.ipynb | 1 | 118738 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frequency Invariant Load Tracking Test\n",
"\n",
"FreqInvarianceTest is a LisaTest class for automated testing of frequency invariant load tracking. This notebook uses the methods it provides to perform the same analysis as the automated test and plot some results.\n",
"\n",
"The test class runs the same workload at a selection of frequencies, each entry in `t.experiments` represents a run at a different frequency."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import json\n",
"\n",
"from trace import Trace\n",
"from trappy.plotter import plot_trace\n",
"from trappy.stats.grammar import Parser\n",
"from trappy import ILinePlot"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-13 18:36:59,553 INFO : root : Using LISA logging configuration:\n",
"2017-02-13 18:36:59,554 INFO : root : /home/brendan/sources/lisa/logging.conf\n"
]
}
],
"source": [
"import logging\n",
"from conf import LisaLogging\n",
"LisaLogging.setup()\n",
"logging.getLogger('Analysis').setLevel(logging.ERROR)\n",
"logging.getLogger('Trace').setLevel(logging.ERROR)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run test workload\n",
"\n",
"There's currently no way to pass a `TestEnv` or configuration to automated test classes. Instead the target information comes from the `target.config` file (in the root of the LISA source tree), so you'll need to edit that to configure LISA to connect to your target."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Goal\n",
" ====\n",
" Basic check for frequency invariant load tracking\n",
"\n",
" Detailed Description\n",
" ====================\n",
" This test runs the same workload on the most capable CPU on the system at a\n",
" cross section of available frequencies. The trace is then examined to find\n",
" the average activation length of the workload, which is combined with the\n",
" known period to estimate an expected mean value for util_avg for each\n",
" frequency. The util_avg value is extracted from scheduler trace events and\n",
" its mean is compared with the expected value (ignoring the first 300ms so\n",
" that the signal can stabilize). The test fails if the observed mean is\n",
" beyond a certain error margin from the expected one. load_avg is then\n",
" similarly compared with the expected util_avg mean, under the assumption\n",
" that load_avg should equal util_avg when system load is light.\n",
"\n",
" Expected Behaviour\n",
" ==================\n",
" Load tracking signals are scaled so that the workload results in roughly the\n",
" same util & load values regardless of frequency.\n",
" \n"
]
}
],
"source": [
"from tests.eas.load_tracking import FreqInvarianceTest\n",
"\n",
"t = FreqInvarianceTest()\n",
"print t.__doc__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To run automated tests from within a notebook we instantiate the test class and call `runExperiments` on it."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-13 18:36:59,608 INFO : LisaTest : Setup tests execution engine...\n",
"2017-02-13 18:36:59,609 INFO : TestEnv : Using base path: /home/brejac01/sources/lisa\n",
"2017-02-13 18:36:59,610 INFO : TestEnv : Loading default (file) target configuration\n",
"2017-02-13 18:36:59,611 INFO : TestEnv : Loading target configuration [/home/brejac01/sources/lisa/target.config]...\n",
"2017-02-13 18:36:59,613 INFO : TestEnv : Loading custom (inline) test configuration\n",
"2017-02-13 18:36:59,614 INFO : TestEnv : Devlib modules to load: ['bl', u'cpuidle', 'cgroups', 'cpufreq']\n",
"2017-02-13 18:36:59,615 INFO : TestEnv : Connecting linux target:\n",
"2017-02-13 18:36:59,616 INFO : TestEnv : username : brendan\n",
"2017-02-13 18:36:59,617 INFO : TestEnv : host : 192.168.2.2\n",
"2017-02-13 18:36:59,618 INFO : TestEnv : password : password\n",
"2017-02-13 18:36:59,618 INFO : TestEnv : Connection settings:\n",
"2017-02-13 18:36:59,619 INFO : TestEnv : {'username': u'brendan', 'host': u'192.168.2.2', 'password': u'password'}\n",
"2017-02-13 18:37:07,367 INFO : TestEnv : Initializing target workdir:\n",
"2017-02-13 18:37:07,369 INFO : TestEnv : /home/brendan/devlib-target\n",
"2017-02-13 18:37:11,034 INFO : CGroups : Available controllers:\n",
"2017-02-13 18:37:12,775 INFO : CGroups : cpuset : /home/brendan/devlib-target/cgroups/devlib_cgh2\n",
"2017-02-13 18:37:14,512 INFO : CGroups : cpu : /home/brendan/devlib-target/cgroups/devlib_cgh3\n",
"2017-02-13 18:37:16,249 INFO : CGroups : cpuacct : /home/brendan/devlib-target/cgroups/devlib_cgh3\n",
"2017-02-13 18:37:17,988 INFO : CGroups : blkio : /home/brendan/devlib-target/cgroups/devlib_cgh4\n",
"2017-02-13 18:37:19,726 INFO : CGroups : memory : /home/brendan/devlib-target/cgroups/devlib_cgh5\n",
"2017-02-13 18:37:21,465 INFO : CGroups : devices : /home/brendan/devlib-target/cgroups/devlib_cgh6\n",
"2017-02-13 18:37:23,205 INFO : CGroups : perf_event : /home/brendan/devlib-target/cgroups/devlib_cgh7\n",
"2017-02-13 18:37:24,944 INFO : CGroups : hugetlb : /home/brendan/devlib-target/cgroups/devlib_cgh8\n",
"2017-02-13 18:37:26,682 INFO : CGroups : pids : /home/brendan/devlib-target/cgroups/devlib_cgh9\n",
"2017-02-13 18:37:29,594 INFO : TestEnv : Topology:\n",
"2017-02-13 18:37:29,596 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n",
"2017-02-13 18:37:32,227 INFO : TestEnv : Loading default EM:\n",
"2017-02-13 18:37:32,229 INFO : TestEnv : /home/brejac01/sources/lisa/libs/utils/platforms/juno.json\n",
"2017-02-13 18:37:33,996 WARNING : LinuxTarget : Event [sched_load_avg_task] not available for tracing\n",
"2017-02-13 18:37:33,999 WARNING : LinuxTarget : Event [sched_load_avg_cpu] not available for tracing\n",
"2017-02-13 18:37:34,001 INFO : TestEnv : Enabled tracepoints:\n",
"2017-02-13 18:37:34,002 INFO : TestEnv : sched_switch\n",
"2017-02-13 18:37:34,003 INFO : TestEnv : sched_load_avg_task\n",
"2017-02-13 18:37:34,004 INFO : TestEnv : sched_load_avg_cpu\n",
"2017-02-13 18:37:34,006 INFO : TestEnv : sched_pelt_se\n",
"2017-02-13 18:37:34,007 WARNING : TestEnv : Using configuration provided RTApp calibration\n",
"2017-02-13 18:37:34,008 INFO : TestEnv : Using RT-App calibration values:\n",
"2017-02-13 18:37:34,010 INFO : TestEnv : {\"0\": 354, \"1\": 138, \"2\": 138, \"3\": 363, \"4\": 355, \"5\": 357}\n",
"2017-02-13 18:37:34,011 INFO : EnergyMeter : HWMON module not enabled\n",
"2017-02-13 18:37:34,012 WARNING : EnergyMeter : Energy sampling disabled by configuration\n",
"2017-02-13 18:37:34,013 INFO : TestEnv : Set results folder to:\n",
"2017-02-13 18:37:34,015 INFO : TestEnv : /home/brejac01/sources/lisa/results/20170213_183734\n",
"2017-02-13 18:37:34,016 INFO : TestEnv : Experiment results available also in:\n",
"2017-02-13 18:37:34,017 INFO : TestEnv : /home/brejac01/sources/lisa/results_latest\n",
"2017-02-13 18:37:34,018 INFO : Executor : Loading custom (inline) test configuration\n",
"2017-02-13 18:37:34,019 INFO : Executor : \n",
"2017-02-13 18:37:34,020 INFO : Executor : ################################################################################\n",
"2017-02-13 18:37:34,022 INFO : Executor : Experiments configuration\n",
"2017-02-13 18:37:34,023 INFO : Executor : ################################################################################\n",
"2017-02-13 18:37:34,024 INFO : Executor : Configured to run:\n",
"2017-02-13 18:37:34,025 INFO : Executor : 1 target configurations:\n",
"2017-02-13 18:37:34,026 INFO : Executor : freq_450000\n",
"2017-02-13 18:37:34,027 INFO : Executor : 1 workloads (1 iterations each)\n",
"2017-02-13 18:37:34,028 INFO : Executor : fie_10pct\n",
"2017-02-13 18:37:34,029 INFO : Executor : Total: 1 experiments\n",
"2017-02-13 18:37:34,030 INFO : Executor : Results will be collected under:\n",
"2017-02-13 18:37:34,031 INFO : Executor : /home/brejac01/sources/lisa/results/20170213_183734\n",
"2017-02-13 18:37:34,032 INFO : Executor : rt-app workloads found, installing tool on target\n",
"2017-02-13 18:37:34,033 INFO : LisaTest : Experiments execution...\n",
"2017-02-13 18:37:34,034 INFO : Executor : \n",
"2017-02-13 18:37:34,035 INFO : Executor : ################################################################################\n",
"2017-02-13 18:37:34,036 INFO : Executor : Experiments execution\n",
"2017-02-13 18:37:34,037 INFO : Executor : ################################################################################\n",
"2017-02-13 18:37:34,038 INFO : Executor : \n",
"2017-02-13 18:37:34,039 INFO : Executor : ================================================================================\n",
"2017-02-13 18:37:34,040 INFO : Executor : configuring target for [freq_450000] experiments\n",
"2017-02-13 18:37:35,760 INFO : Executor : Configuring all CPUs to use [userspace] cpufreq governor\n",
"2017-02-13 18:37:36,666 INFO : Executor : CPUFreq - CPU frequencies: {1: 450000}\n",
"2017-02-13 18:37:38,683 INFO : Workload : Setup new workload fie_10pct\n",
"2017-02-13 18:37:38,685 INFO : Workload : Workload duration defined by longest task\n",
"2017-02-13 18:37:38,686 INFO : Workload : Default policy: SCHED_OTHER\n",
"2017-02-13 18:37:38,687 INFO : Workload : ------------------------\n",
"2017-02-13 18:37:38,688 INFO : Workload : task [fie_test0], sched: using default policy\n",
"2017-02-13 18:37:38,689 INFO : Workload : | calibration CPU: 1\n",
"2017-02-13 18:37:38,691 INFO : Workload : | loops count: 1\n",
"2017-02-13 18:37:38,692 INFO : Workload : + phase_000001: duration 1.000000 [s] (62 loops)\n",
"2017-02-13 18:37:38,693 INFO : Workload : | period 16000 [us], duty_cycle 10 %\n",
"2017-02-13 18:37:38,694 INFO : Workload : | run_time 1600 [us], sleep_time 14400 [us]\n",
"2017-02-13 18:37:39,836 INFO : Executor : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
"2017-02-13 18:37:39,838 INFO : Executor : Experiment 0/1, [freq_450000:fie_10pct] 1/1\n",
"2017-02-13 18:37:40,255 WARNING : Executor : No freezer cgroup controller on target. Not freezing userspace\n",
"2017-02-13 18:37:40,257 WARNING : Executor : FTrace events collection enabled\n",
"2017-02-13 18:37:46,898 INFO : Workload : Workload execution START:\n",
"2017-02-13 18:37:46,899 INFO : Workload : /home/brendan/devlib-target/bin/taskset 0x2 /home/brendan/devlib-target/bin/rt-app /home/brendan/devlib-target/run_dir/fie_10pct_00.json 2>&1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-13 18:37:54,459 INFO : Executor : Collected FTrace binary trace:\n",
"2017-02-13 18:37:54,460 INFO : Executor : <res_dir>/rtapp:freq_450000:fie_10pct/1/trace.dat\n",
"2017-02-13 18:37:54,461 INFO : Executor : Collected FTrace function profiling:\n",
"2017-02-13 18:37:54,463 INFO : Executor : <res_dir>/rtapp:freq_450000:fie_10pct/1/trace_stat.json\n",
"2017-02-13 18:37:54,463 INFO : Executor : --------------------------------------------------------------------------------\n",
"2017-02-13 18:37:54,464 INFO : Executor : \n",
"2017-02-13 18:37:54,465 INFO : Executor : ################################################################################\n",
"2017-02-13 18:37:54,467 INFO : Executor : Experiments execution completed\n",
"2017-02-13 18:37:54,467 INFO : Executor : ################################################################################\n",
"2017-02-13 18:37:54,468 INFO : Executor : Results available in:\n",
"2017-02-13 18:37:54,469 INFO : Executor : /home/brejac01/sources/lisa/results/20170213_183734\n"
]
}
],
"source": [
"t.runExperiments()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Show variance in util_avg and load_avg\n",
"We want to see the same util_avg and load_avg values regardless of frequencies - the bar charts below should have bars all with roughly the same height."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# Get the frequency an experiment was run at\n",
"def experiment_freq(exp):\n",
" [cpu] = exp.wload.cpus\n",
" freq = exp.conf['cpufreq']['freqs'][cpu]\n",
" return freq\n",
"freqs = [experiment_freq(e) for e in t.experiments]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_signal_against_freq(signal):\n",
" means = [t.get_signal_mean(e, signal) for e in t.experiments]\n",
" limits = (0 , max(means) * 1.15)\n",
" pd.DataFrame(means, index=freqs, columns=['Mean ' + signal]).plot(kind='bar', ylim=limits)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot of variation of util_avg value with frequency:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['sched_switch', 'sched_pelt_se']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t.get_trace(t.experiments[0]).available_events"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd3f57dba10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_signal_against_freq('util_avg')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### And the same thing for load_avg:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd3f52c9150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_signal_against_freq('load_avg')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Examine trace from workload execution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot task residency and `sched_util` and `sched_load` for the workload task, along with the expected mean value for util_avg. Note that assuming the system was under little load, so that the task was RUNNING whenever it was RUNNABLE, `load_avg` and `util_avg` should be the same. \n",
"\n",
"Call `examine_experiment` with different experiment indexes to get plots for runs at different frequencies."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"signals = ['util_avg', 'load_avg']\n",
"def examine_experiment(idx):\n",
" experiment = t.experiments[idx]\n",
" \n",
" [freq] = experiment.conf['cpufreq']['freqs'].values()\n",
" print \"Experiment ran at frequency {}\".format(freq)\n",
" events = t.te.test_conf[\"ftrace\"][\"events\"]\n",
" \n",
" # todo add get_trace method\n",
" tasks = experiment.wload.tasks.keys()\n",
" #trace = Trace(t.te.platform, experiment.out_dir, events, tasks)\n",
" print 'Trace plot:'\n",
" plot_trace(t.get_trace(experiment).ftrace)\n",
" \n",
" # Get observed signal\n",
" signal_df = t.get_sched_task_signals(experiment, signals)\n",
" # Get expected average value for util_avg signal\n",
" expected_util_avg_mean = t.get_expected_util_avg(experiment)\n",
" \n",
" # Plot task util_avg signal with expected mean value\n",
" util_avg_mean = pd.Series([expected_util_avg_mean], name='expected_util_avg', index=[signal_df.index[0]])\n",
" df = pd.concat([signal_df, util_avg_mean], axis=1).ffill()\n",
" ILinePlot(df, column=signals + ['expected_util_avg'], drawstyle='steps-post',\n",
" title='Scheduler task signals').view()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Experiment 0: 450000Hz\n"
]
}
],
"source": [
"for i , f in enumerate(freqs):\n",
" print \"Experiment {}:{:10d}Hz\".format(i, f)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Experiment ran at frequency 450000\n",
"Trace plot:\n"
]
},
{
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" EventPlot.generate('fig_de98d94c094f48cc85343707f81b5c45', '/nbextensions/', {\"lanes\": [{\"id\": 0, \"label\": \"CPU :0\"}, {\"id\": 1, \"label\": \"CPU :1\"}, {\"id\": 2, \"label\": \"CPU :2\"}, {\"id\": 3, \"label\": \"CPU :3\"}, {\"id\": 4, \"label\": \"CPU :4\"}, {\"id\": 5, \"label\": \"CPU :5\"}], \"colorMap\": null, \"keys\": [\"sshd-3943\", \"sshd-3946\", \"sshd-3948\", \"sshd-3945\", \"bash-3919\", \"bash-3957\", \"bash-3922\", \"bash-3949\", \"fie_test0-3941\", \"bash-3954\", \"bash-3927\", \"sh-3925\", \"sh-3952\", \"sudo-3954\", \"sudo-3921\", \"sudo-3957\", \"sudo-3956\", \"sudo-3959\", \"sudo-3951\", \"sudo-3922\", \"bash-3940\", \"sudo-3919\", \"sudo-3949\", \"shutils-3932\", \"kworker/u12:2-3923\", \"kworker/u12:1-3950\", \"kworker/u12:1-3947\", \"kworker/u12:1-3944\", \"kworker/u12:1-3955\", \"kworker/u12:1-3958\", \"kworker/u12:2-3920\", \"shutils-3953\", \"shutils-3926\", \"sudo-3924\", \"shutils-3937\", \"shutils-3929\", \"shutils-3931\", \"busybox-3928\", \"shutils-3939\", \"shutils-3933\", \"shutils-3935\", \"busybox-3930\", \"systemd-journal-1675\", \"sudo-3915\", \"busybox-3934\", \"shutils-3930\", \"busybox-3936\", \"shutils-3936\", \"busybox-3938\", \"shutils-3928\", \"shutils-3938\", \"shutils-3934\", \"sh-3960\", \"sh-3917\", \"sshd-1747\", \"shutils-3927\", \"sh-3924\", \"systemd-1\", \"scp-3945\", \"scp-3948\", \"shutils-3952\", \"sh-3951\", \"sshd-3752\", \"shutils-3925\", \"ksoftirqd/0-3\", \"bash-3754\", \"in:imuxsock-1761\", \"rs:main Q:Reg-1763\", \"rt-app-3940\", \"jbd2/sda1-8-990\", \"kworker/1:0-3513\", \"true-3938\", \"ksoftirqd/2-23\", \"rt-app-3941\", \"kworker/u12:1-3942\", \"kworker/2:1-2987\", \"kworker/u12:2-3314\", \"kthreadd-2\", \"ksoftirqd/1-17\", \"kworker/u12:0-3216\", \"usb-storage-975\", \"kworker/3:2-1646\", \"rcu_preempt-7\", \"kworker/0:2-1645\", \"rcu_sched-8\", \"khungtaskd-320\", \"kworker/4:1-969\", \"kworker/5:2-1697\", \"kworker/1:1H-989\", \"kthreadd-3942\", \"watchdog/2-21\", \"watchdog/1-15\", \"watchdog/0-12\", \"watchdog/4-33\", \"watchdog/5-39\", \"watchdog/3-27\"], \"stride\": false, \"showSummary\": true, \"xDomain\": [6.6000000060739694e-05, 5.9025510000001304], \"data\": {\"watchdog/4-33\": {\"4\": [[2.6286530000002131, 2.6286620000000767]]}, \"kworker/1:0-3513\": {\"1\": [[0.36080000000038126, 0.36083300000018426], [0.95277600000008533, 0.95296000000007552], [1.1768650000003618, 1.1769090000002507], [1.4647730000001502, 1.4648310000002311], [1.4657990000000609, 1.4658850000000712], [1.9768579999999929, 1.9770490000000791], [2.4166840000002594, 2.4167290000000321], [3.0006780000003346, 3.0008670000001985], [3.4808760000000802, 3.4809390000000349], [3.4813740000004145, 3.4814530000003288], [4.0246540000002824, 4.0248430000001463], [4.260827000000063, 4.2608750000003965], [5.0488240000004225, 5.0490110000000641], [5.1767060000001948, 5.1767400000003363], [5.4967710000000807, 5.4968310000003839], [5.4976760000004106, 5.4977630000003046]]}, \"true-3938\": {\"5\": 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"<!-- TRAPPY_PUBLISH_SOURCE_LIB = \"http://dygraphs.com/extras/synchronizer.js\" -->\n",
"<!-- TRAPPY_PUBLISH_SOURCE_LIB = \"https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.8.3/underscore-min.js\" -->\n",
"\n",
" <script>\n",
" /* TRAPPY_PUBLISH_IMPORT = \"plotter/js/ILinePlot.js\" */\n",
" /* TRAPPY_PUBLISH_REMOVE_START */\n",
" var ilp_req = require.config( {\n",
"\n",
" paths: {\n",
" \"dygraph-sync\": '/nbextensions/plotter_scripts/ILinePlot/synchronizer',\n",
" \"dygraph\": '/nbextensions/plotter_scripts/ILinePlot/dygraph-combined',\n",
" \"ILinePlot\": '/nbextensions/plotter_scripts/ILinePlot/ILinePlot',\n",
" \"underscore\": '/nbextensions/plotter_scripts/ILinePlot/underscore-min',\n",
" },\n",
"\n",
" shim: {\n",
" \"dygraph-sync\": [\"dygraph\"],\n",
" \"ILinePlot\": {\n",
"\n",
" \"deps\": [\"dygraph-sync\", \"dygraph\", \"underscore\"],\n",
" \"exports\": \"ILinePlot\"\n",
" }\n",
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" });\n",
" /* TRAPPY_PUBLISH_REMOVE_STOP */\n",
" ilp_req([\"require\", \"ILinePlot\"], function() { /* TRAPPY_PUBLISH_REMOVE_LINE */\n",
" ILinePlot.generate(fig_f340545ad6424a77a2dbb85b4915b323_data);\n",
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"examine_experiment(0)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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}
},
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}
| apache-2.0 |
vincent-noel/libSigNetSim | notebooks/BIOMD0000000139.ipynb | 1 | 990 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"from libsignetsim import CombineArchive"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"archive = CombineArchive()\n",
"archive.readArchive(\"combine_archives/BIOMD0000000139.sedx\")\n",
"sedml_doc = archive.runMasterSedml()\n",
"sedml_doc.showFigures()"
]
}
],
"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": 2
}
| gpl-3.0 |
OSGeo-live/CesiumWidget | GSOC/notebooks/Projects/FIONA/fiona_mapnik_example0.ipynb | 1 | 4172 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import os\n",
"\n",
"from IPython.core.display import Image\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import fiona\n",
"import mapnik\n",
"import shapely.geometry\n",
"\n",
"\n",
"## Fiona, mapnik demo\n",
"## Live 8.5 * darkblue-b\n",
"##\n",
"## based on UoLPythonGroup/data-hack-0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"BASE_FOLDER = '/home/user/data/north_carolina/shape'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"c_shapefile = os.path.join(BASE_FOLDER, 'nc_state.shp')\n",
"f = fiona.open(c_shapefile)\n",
"shps = list(f)\n",
"print 'f: ',type(f)\n",
"print f.schema"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"type(f[2])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Use the Shapely geometry classes\n",
"## instantiate a Polygon from the Fiona collection "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import shapely.geometry\n",
"\n",
"geo = shapely.geometry.shape(f[0]['geometry'])\n",
"geo"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## plt.plot(*geo.xy) ## hmm not implemented"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Mapnik\n",
"## load the Mapnik python interfaces\n",
"## read the shapefile directly with Mapnik libs\n",
"## use the IPython Image interface as the drawing target"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def show_mapnik(m):\n",
" \"\"\"Returns an IPython Image of the rendered map.\"\"\"\n",
" im = mapnik.Image(m.width, m.height)\n",
" mapnik.render(m, im)\n",
" return Image(data=im.tostring('png32'))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import mapnik\n",
"\n",
"m = mapnik.Map(600, 600)\n",
"\n",
"layer = mapnik.Layer('contour')\n",
"layer.datasource = mapnik.Shapefile(file=c_shapefile)\n",
"\n",
"style = mapnik.Style()\n",
"rule = mapnik.Rule()\n",
"\n",
"line_symbolizer = mapnik.LineSymbolizer(mapnik.Color('green'),0.4)\n",
"rule.symbols.append(line_symbolizer)\n",
"\n",
"m.layers.append(layer)\n",
"style.rules.append(rule)\n",
"m.append_style('My Style', style)\n",
"layer.styles.append('My Style')\n",
"\n",
"m.layers.append(layer)\n",
"m.zoom_all()\n",
"show_mapnik(m)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.0"
},
"latex_envs": {
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
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| apache-2.0 |
RyanAlberts/Springbaord-Capstone-Project | Instacart_Train_Faster_v31.ipynb | 2 | 1278448 | null | mit |
brian-rose/ClimateModeling_courseware | Lectures/Lecture04 -- Intro to CLIMLAB.ipynb | 1 | 28817 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# [ATM 623: Climate Modeling](../index.ipynb)\n",
"\n",
"[Brian E. J. Rose](http://www.atmos.albany.edu/facstaff/brose/index.html), University at Albany\n",
"\n",
"# Lecture 4: Building simple climate models using `climlab`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Warning: content out of date and not maintained\n",
"\n",
"You really should be looking at [The Climate Laboratory book](https://brian-rose.github.io/ClimateLaboratoryBook) by Brian Rose, where all the same content (and more!) is kept up to date.\n",
"\n",
"***Here you are likely to find broken links and broken code.***"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"### About these notes:\n",
"\n",
"This document uses the interactive [`Jupyter notebook`](https://jupyter.org) format. The notes can be accessed in several different ways:\n",
"\n",
"- The interactive notebooks are hosted on `github` at https://github.com/brian-rose/ClimateModeling_courseware\n",
"- The latest versions can be viewed as static web pages [rendered on nbviewer](http://nbviewer.ipython.org/github/brian-rose/ClimateModeling_courseware/blob/master/index.ipynb)\n",
"- A complete snapshot of the notes as of May 2017 (end of spring semester) are [available on Brian's website](http://www.atmos.albany.edu/facstaff/brose/classes/ATM623_Spring2017/Notes/index.html).\n",
"\n",
"[Also here is a legacy version from 2015](http://www.atmos.albany.edu/facstaff/brose/classes/ATM623_Spring2015/Notes/index.html).\n",
"\n",
"Many of these notes make use of the `climlab` package, available at https://github.com/brian-rose/climlab"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Ensure compatibility with Python 2 and 3\n",
"from __future__ import print_function, division"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Contents\n",
"\n",
"1. [Introducing `climlab`](#section1)\n",
"2. [Using `climlab` to implement the zero-dimensional energy balance model](#section2)\n",
"3. [Run the zero-dimensional EBM out to equilibrium](#section3)\n",
"4. [A climate change scenario in the EBM](#section4)\n",
"5. [Further `climlab` resources](#section5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"____________\n",
"<a id='section1'></a>\n",
"\n",
"## 1. Introducing `climlab`\n",
"____________\n",
"\n",
"`climlab` is a python package for process-oriented climate modeling.\n",
"\n",
"It is based on a very general concept of a model as a collection of individual, \n",
"interacting processes. `climlab` defines a base class called `Process`, which\n",
"can contain an arbitrarily complex tree of sub-processes (each also some \n",
"sub-class of `Process`). Every climate process (radiative, dynamical, \n",
"physical, turbulent, convective, chemical, etc.) can be simulated as a stand-alone\n",
"process model given appropriate input, or as a sub-process of a more complex model. \n",
"New classes of model can easily be defined and run interactively by putting together an\n",
"appropriate collection of sub-processes.\n",
"\n",
"`climlab` is an open-source community project. The latest code can always be found on `github`:\n",
"\n",
"https://github.com/brian-rose/climlab\n",
"\n",
"You can install `climlab` by doing\n",
"\n",
"```\n",
"conda install -c conda-forge climlab\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import climlab"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"____________\n",
"<a id='section2'></a>\n",
"\n",
"## 2. Using `climlab` to implement the zero-dimensional energy balance model\n",
"____________\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recall that we have worked with a zero-dimensional Energy Balance Model\n",
"\n",
"$$ C \\frac{dT_s}{dt} = (1-\\alpha) Q - \\tau \\sigma T_s^4 $$ "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we are going to implement this exact model using `climlab`.\n",
"\n",
"Yes, we have already written code to implement this model, but we are going to repeat this effort here as a way of learning how to use `climlab`.\n",
"\n",
"There are tools within `climlab` to implement much more complicated models, but the basic interface will be the same."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AttrDict({'Ts': Field([[32.]])})"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a zero-dimensional domain with a single surface temperature\n",
"state = climlab.surface_state(num_lat=1, # a single point\n",
" water_depth = 100., # 100 meters slab of water (sets the heat capacity)\n",
" )\n",
"state"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we have created a dictionary called `state` with a single item called `Ts`:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Field([[32.]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state['Ts']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This dictionary holds the state variables for our model -- which is this case is a single number! It is a **temperature in degrees Celsius**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For convenience, we can access the same data as an attribute (which lets us use tab-autocomplete when doing interactive work):"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Field([[32.]])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state.Ts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is also possible to see this `state` dictionary as an `xarray.Dataset` object:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (depth: 1, depth_bounds: 2, lat: 1, lat_bounds: 2)\n",
"Coordinates:\n",
" * depth (depth) float64 50.0\n",
" * lat (lat) float64 0.0\n",
" * depth_bounds (depth_bounds) float64 0.0 100.0\n",
" * lat_bounds (lat_bounds) float64 -90.0 90.0\n",
"Data variables:\n",
" Ts (depth, lat) float64 32.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"climlab.to_xarray(state)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"climlab Process of type <class 'climlab.radiation.boltzmann.Boltzmann'>. \n",
"State variables and domain shapes: \n",
" Ts: (1, 1) \n",
"The subprocess tree: \n",
"OutgoingLongwave: <class 'climlab.radiation.boltzmann.Boltzmann'>\n",
"\n"
]
}
],
"source": [
"# create the longwave radiation process\n",
"olr = climlab.radiation.Boltzmann(name='OutgoingLongwave',\n",
" state=state, \n",
" tau = 0.612,\n",
" eps = 1.,\n",
" timestep = 60*60*24*30.)\n",
"# Look at what we just created\n",
"print(olr)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"climlab Process of type <class 'climlab.radiation.absorbed_shorwave.SimpleAbsorbedShortwave'>. \n",
"State variables and domain shapes: \n",
" Ts: (1, 1) \n",
"The subprocess tree: \n",
"AbsorbedShortwave: <class 'climlab.radiation.absorbed_shorwave.SimpleAbsorbedShortwave'>\n",
"\n"
]
}
],
"source": [
"# create the shortwave radiation process\n",
"asr = climlab.radiation.SimpleAbsorbedShortwave(name='AbsorbedShortwave',\n",
" state=state, \n",
" insolation=341.3, \n",
" albedo=0.299,\n",
" timestep = 60*60*24*30.)\n",
"# Look at what we just created\n",
"print(asr)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"climlab Process of type <class 'climlab.process.time_dependent_process.TimeDependentProcess'>. \n",
"State variables and domain shapes: \n",
" Ts: (1, 1) \n",
"The subprocess tree: \n",
"EnergyBalanceModel: <class 'climlab.process.time_dependent_process.TimeDependentProcess'>\n",
" OutgoingLongwave: <class 'climlab.radiation.boltzmann.Boltzmann'>\n",
" AbsorbedShortwave: <class 'climlab.radiation.absorbed_shorwave.SimpleAbsorbedShortwave'>\n",
"\n"
]
}
],
"source": [
"# couple them together into a single model\n",
"ebm = olr + asr\n",
"# Give the parent process name\n",
"ebm.name = 'EnergyBalanceModel'\n",
"# Examine the model object\n",
"print(ebm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The object called `ebm` here is the entire model -- including its current state (the temperature `Ts`) as well as all the methods needed to integrated forward in time!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The current model state, accessed two ways:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AttrDict({'Ts': Field([[32.]])})"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebm.state"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Field([[32.]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebm.Ts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is some internal information about the timestep of the model:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2592000.0\n",
"0\n"
]
}
],
"source": [
"print(ebm.time['timestep'])\n",
"print(ebm.time['steps'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This says the timestep is 2592000 seconds (30 days!), and the model has taken 0 steps forward so far."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To take a single step forward:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"ebm.step_forward()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Field([[31.61786227]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebm.Ts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model got colder!\n",
"\n",
"To see why, let's look at some useful diagnostics computed by this model:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'OLR': Field([[300.896072]]), 'ASR': 239.25130000000004}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebm.diagnostics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is another dictionary, now with two items. They should make sense to you.\n",
"\n",
"Just like the `state` variables, we can access these `diagnostics` variables as attributes:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Field([[300.896072]])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebm.OLR"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"239.25130000000004"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebm.ASR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So why did the model get colder in the first timestep?\n",
"\n",
"What do you think will happen next?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"____________\n",
"<a id='section3'></a>\n",
"\n",
"## 3. Run the zero-dimensional EBM out to equilibrium\n",
"____________"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at how the model adjusts toward its equilibrium temperature.\n",
"\n",
"Exercise:\n",
"\n",
"- Using a `for` loop, take 500 steps forward with this model\n",
"- Store the current temperature at each step in an array\n",
"- Make a graph of the temperature as a function of time"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"____________\n",
"<a id='section4'></a>\n",
"\n",
"## 4. A climate change scenario \n",
"____________"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Suppose we want to investigate the effects of a small decrease in the transmissitivity of the atmosphere `tau`. \n",
"\n",
"Previously we used the zero-dimensional model to investigate a **hypothetical climate change scenario** in which:\n",
"- the transmissitivity of the atmosphere `tau` decreases to 0.57\n",
"- the planetary albedo increases to 0.32\n",
"\n",
"How would we do that using `climlab`?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recall that the model is comprised of two sub-components:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OutgoingLongwave\n",
"climlab Process of type <class 'climlab.radiation.boltzmann.Boltzmann'>. \n",
"State variables and domain shapes: \n",
" Ts: (1, 1) \n",
"The subprocess tree: \n",
"OutgoingLongwave: <class 'climlab.radiation.boltzmann.Boltzmann'>\n",
"\n",
"AbsorbedShortwave\n",
"climlab Process of type <class 'climlab.radiation.absorbed_shorwave.SimpleAbsorbedShortwave'>. \n",
"State variables and domain shapes: \n",
" Ts: (1, 1) \n",
"The subprocess tree: \n",
"AbsorbedShortwave: <class 'climlab.radiation.absorbed_shorwave.SimpleAbsorbedShortwave'>\n",
"\n"
]
}
],
"source": [
"for name, process in ebm.subprocess.items():\n",
" print(name)\n",
" print(process)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The parameter `tau` is a property of the `OutgoingLongwave` subprocess:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.612"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebm.subprocess['OutgoingLongwave'].tau"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and the parameter `albedo` is a property of the `AbsorbedShortwave` subprocess:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.299"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebm.subprocess['AbsorbedShortwave'].albedo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's make an exact clone of our model and then change these two parameters:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"climlab Process of type <class 'climlab.process.time_dependent_process.TimeDependentProcess'>. \n",
"State variables and domain shapes: \n",
" Ts: (1, 1) \n",
"The subprocess tree: \n",
"EnergyBalanceModel: <class 'climlab.process.time_dependent_process.TimeDependentProcess'>\n",
" OutgoingLongwave: <class 'climlab.radiation.boltzmann.Boltzmann'>\n",
" AbsorbedShortwave: <class 'climlab.radiation.absorbed_shorwave.SimpleAbsorbedShortwave'>\n",
"\n"
]
}
],
"source": [
"ebm2 = climlab.process_like(ebm)\n",
"print(ebm2)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"ebm2.subprocess['OutgoingLongwave'].tau = 0.57\n",
"ebm2.subprocess['AbsorbedShortwave'].albedo = 0.32"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now our model is out of equilibrium and the climate will change!\n",
"\n",
"To see this without actually taking a step forward:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Field([[-46.76117229]])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Computes diagnostics based on current state but does not change the state\n",
"ebm2.compute_diagnostics()\n",
"ebm2.ASR - ebm2.OLR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Shoud the model warm up or cool down?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well, we can find out:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Field([[31.61786227]])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebm2.Ts"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"ebm2.step_forward()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Field([[31.32798841]])"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebm2.Ts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Automatic timestepping"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Often we want to integrate a model forward in time to equilibrium without needing to store information about the transient state.\n",
"\n",
"`climlab` offers convenience methods to do this easily:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"ebm3 = climlab.process_like(ebm2)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Integrating for 608 steps, 18262.11 days, or 50 years.\n",
"Total elapsed time is 50.10373938170343 years.\n"
]
}
],
"source": [
"ebm3.integrate_years(50)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Field([[17.94837835]])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# What is the current temperature?\n",
"ebm3.Ts"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Field([[-0.00021699]])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# How close are we to energy balance?\n",
"ebm3.ASR - ebm3.OLR"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"# We should be able to accomplish the exact same thing with explicit timestepping\n",
"for n in range(608):\n",
" ebm2.step_forward()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Field([[17.94837835]])"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebm2.Ts"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Field([[-0.00021699]])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ebm2.ASR - ebm2.OLR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"____________\n",
"<a id='section5'></a>\n",
"\n",
"## 5. Further `climlab` resources\n",
"____________"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will be using `climlab` extensively throughout this course. Lots of examples of more advanced usage are found here in the course notes. Here are some links to other resources:\n",
"\n",
"- The documentation is hosted at <https://climlab.readthedocs.io/en/latest/>\n",
"- Source code (for both software and docs) are at <https://github.com/brian-rose/climlab>\n",
"- [A video of a talk I gave about `climlab` at the 2018 AMS Python symposium](https://ams.confex.com/ams/98Annual/videogateway.cgi/id/44948?recordingid=44948) (January 2018)\n",
"- [Slides from a talk and demonstration that I gave in Febrary 2018](https://livealbany-my.sharepoint.com/:f:/g/personal/brose_albany_edu/EuA2afxy5-hNkzNhHgkp_HYBYcJumR3l6ukRVIEl4W3MmA?e=sbXN0d) (The Apple Keynote version contains some animations that will not show up in the pdf version)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"____________\n",
"## Version information\n",
"____________\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading extensions from ~/.ipython/extensions is deprecated. We recommend managing extensions like any other Python packages, in site-packages.\n"
]
},
{
"data": {
"application/json": {
"Software versions": [
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"module": "Python",
"version": "3.7.3 64bit [Clang 4.0.1 (tags/RELEASE_401/final)]"
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},
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"module": "OS",
"version": "Darwin 17.7.0 x86_64 i386 64bit"
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"module": "numpy",
"version": "1.16.4"
},
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},
"text/html": [
"<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.7.3 64bit [Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>IPython</td><td>7.6.0</td></tr><tr><td>OS</td><td>Darwin 17.7.0 x86_64 i386 64bit</td></tr><tr><td>numpy</td><td>1.16.4</td></tr><tr><td>matplotlib</td><td>3.1.1</td></tr><tr><td>climlab</td><td>0.7.5</td></tr><tr><td colspan='2'>Wed Jul 03 14:49:48 2019 EDT</td></tr></table>"
],
"text/latex": [
"\\begin{tabular}{|l|l|}\\hline\n",
"{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
"Python & 3.7.3 64bit [Clang 4.0.1 (tags/RELEASE\\_401/final)] \\\\ \\hline\n",
"IPython & 7.6.0 \\\\ \\hline\n",
"OS & Darwin 17.7.0 x86\\_64 i386 64bit \\\\ \\hline\n",
"numpy & 1.16.4 \\\\ \\hline\n",
"matplotlib & 3.1.1 \\\\ \\hline\n",
"climlab & 0.7.5 \\\\ \\hline\n",
"\\hline \\multicolumn{2}{|l|}{Wed Jul 03 14:49:48 2019 EDT} \\\\ \\hline\n",
"\\end{tabular}\n"
],
"text/plain": [
"Software versions\n",
"Python 3.7.3 64bit [Clang 4.0.1 (tags/RELEASE_401/final)]\n",
"IPython 7.6.0\n",
"OS Darwin 17.7.0 x86_64 i386 64bit\n",
"numpy 1.16.4\n",
"matplotlib 3.1.1\n",
"climlab 0.7.5\n",
"Wed Jul 03 14:49:48 2019 EDT"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%load_ext version_information\n",
"%version_information numpy, matplotlib, climlab"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"____________\n",
"\n",
"## Credits\n",
"\n",
"The author of this notebook is [Brian E. J. Rose](http://www.atmos.albany.edu/facstaff/brose/index.html), University at Albany.\n",
"\n",
"It was developed in support of [ATM 623: Climate Modeling](http://www.atmos.albany.edu/facstaff/brose/classes/ATM623_Spring2015/), a graduate-level course in the [Department of Atmospheric and Envionmental Sciences](http://www.albany.edu/atmos/index.php)\n",
"\n",
"Development of these notes and the [climlab software](https://github.com/brian-rose/climlab) is partially supported by the National Science Foundation under award AGS-1455071 to Brian Rose. Any opinions, findings, conclusions or recommendations expressed here are mine and do not necessarily reflect the views of the National Science Foundation.\n",
"____________"
]
}
],
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"name": "python3"
},
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},
"file_extension": ".py",
"mimetype": "text/x-python",
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},
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}
| mit |
eneskemalergin/MachineLearning_Beyond | 00-Others/StanfordOnlineCourse/Week5/backprob_neural_net_implementation.ipynb | 1 | 40871 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"clear ; close all; clc"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%% Initialization\n",
"\n",
"%% Setup the parameters you will use for this exercise\n",
"input_layer_size = 400; % 20x20 Input Images of Digits\n",
"hidden_layer_size = 25; % 25 hidden units\n",
"num_labels = 10; % 10 labels, from 1 to 10\n",
" % (note that we have mapped \"0\" to label 10)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading and Visualizing Data ...\n"
]
},
{
"data": {
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upsOAd07n142CpEyrchQmTQUD930ONJ6SHFEfGgvjjJrkilXDnJsxgSD5IemZ0QhsX8dp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\n4FIAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"% Load Training Data\n",
"fprintf('Loading and Visualizing Data ...\\n')\n",
"\n",
"load('./data/ex4data1.mat');\n",
"m = size(X, 1);\n",
"\n",
"% Randomly select 100 data points to display\n",
"sel = randperm(size(X, 1));\n",
"sel = sel(1:100);\n",
"\n",
"displayData(X(sel, :));"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Loading Saved Neural Network Parameters ...\n"
]
}
],
"source": [
"% Load pre-initialized weights for neural network\n",
"\n",
"fprintf('\\nLoading Saved Neural Network Parameters ...\\n')\n",
"\n",
"% Load the weights into variables Theta1 and Theta2\n",
"load('./data/ex4weights.mat');\n",
"\n",
"% Unroll parameters\n",
"nn_params = [Theta1(:) ; Theta2(:)];\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Feedforward Using Neural Network ...\n",
"Cost at parameters (loaded from ex4weights): 0.287629\n",
"Cost at parameters (loaded from ex4weights): 0.287629\n"
]
}
],
"source": [
"% Compute Cost\n",
"fprintf('\\nFeedforward Using Neural Network ...\\n')\n",
"\n",
"% Weight regularization parameter (we set this to 0 here).\n",
"lambda = 0;\n",
"\n",
"J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, num_labels, X, y, lambda);\n",
"\n",
"fprintf(['Cost at parameters (loaded from ex4weights): %f'], J);"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Checking Cost Function (w/ Regularization) ... \n",
"Cost at parameters (loaded from ex4weights): 0.383770 \n",
"Cost at parameters (loaded from ex4weights): 0.383770 \n"
]
}
],
"source": [
"% Implement Regularization\n",
"printf('\\nChecking Cost Function (w/ Regularization) ... \\n')\n",
"\n",
"% Weight regularization parameter (we set this to 1 here).\n",
"lambda = 1;\n",
"\n",
"J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, ...\n",
" num_labels, X, y, lambda);\n",
"\n",
"fprintf(['Cost at parameters (loaded from ex4weights): %f '], J);"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Evaluating sigmoid gradient...\n",
"Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\n",
" \n",
"0.196612 0.235004 0.250000 0.235004 0.196612 \n",
"\n",
"\n",
" 0.196612 0.235004 0.250000 0.235004 0.196612 \n"
]
}
],
"source": [
"fprintf('\\nEvaluating sigmoid gradient...\\n')\n",
"\n",
"g = sigmoidGradient([-1 -0.5 0 0.5 1]);\n",
"fprintf('Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\\n ');\n",
"fprintf('%f ', g);\n",
"fprintf('\\n\\n');"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Initializing Neural Network Parameters ...\n"
]
}
],
"source": [
"fprintf('\\nInitializing Neural Network Parameters ...\\n')\n",
"\n",
"initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size);\n",
"initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels);\n",
"\n",
"% Unroll parameters\n",
"initial_nn_params = [initial_Theta1(:) ; initial_Theta2(:)];"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Checking Backpropagation... \n",
" -9.2783e-03 -9.2783e-03\n",
" 8.8991e-03 8.8991e-03\n",
" -8.3601e-03 -8.3601e-03\n",
" 7.6281e-03 7.6281e-03\n",
" -6.7480e-03 -6.7480e-03\n",
" -3.0498e-06 -3.0498e-06\n",
" 1.4287e-05 1.4287e-05\n",
" -2.5938e-05 -2.5938e-05\n",
" 3.6988e-05 3.6988e-05\n",
" -4.6876e-05 -4.6876e-05\n",
" -1.7506e-04 -1.7506e-04\n",
" 2.3315e-04 2.3315e-04\n",
" -2.8747e-04 -2.8747e-04\n",
" 3.3532e-04 3.3532e-04\n",
" -3.7622e-04 -3.7622e-04\n",
" -9.6266e-05 -9.6266e-05\n",
" 1.1798e-04 1.1798e-04\n",
" -1.3715e-04 -1.3715e-04\n",
" 1.5325e-04 1.5325e-04\n",
" -1.6656e-04 -1.6656e-04\n",
" 3.1454e-01 3.1454e-01\n",
" 1.1106e-01 1.1106e-01\n",
" 9.7401e-02 9.7401e-02\n",
" 1.6409e-01 1.6409e-01\n",
" 5.7574e-02 5.7574e-02\n",
" 5.0458e-02 5.0458e-02\n",
" 1.6457e-01 1.6457e-01\n",
" 5.7787e-02 5.7787e-02\n",
" 5.0753e-02 5.0753e-02\n",
" 1.5834e-01 1.5834e-01\n",
" 5.5924e-02 5.5924e-02\n",
" 4.9162e-02 4.9162e-02\n",
" 1.5113e-01 1.5113e-01\n",
" 5.3697e-02 5.3697e-02\n",
" 4.7146e-02 4.7146e-02\n",
" 1.4957e-01 1.4957e-01\n",
" 5.3154e-02 5.3154e-02\n",
" 4.6560e-02 4.6560e-02\n",
"The above two columns you get should be very similar.\n",
"(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
"\n",
"If your backpropagation implementation is correct, then \n",
"the relative difference will be small (less than 1e-9). \n",
"\n",
"Relative Difference: 2.48422e-11\n"
]
}
],
"source": [
"fprintf('\\nChecking Backpropagation... \\n');\n",
"\n",
"% Check gradients by running checkNNGradients\n",
"checkNNGradients;"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Checking Backpropagation (w/ Regularization) ... \n",
" -9.2783e-03 -9.2783e-03\n",
" 8.8991e-03 8.8991e-03\n",
" -8.3601e-03 -8.3601e-03\n",
" 7.6281e-03 7.6281e-03\n",
" -6.7480e-03 -6.7480e-03\n",
" -1.6768e-02 -1.6768e-02\n",
" 3.9433e-02 3.9433e-02\n",
" 5.9336e-02 5.9336e-02\n",
" 2.4764e-02 2.4764e-02\n",
" -3.2688e-02 -3.2688e-02\n",
" -6.0174e-02 -6.0174e-02\n",
" -3.1961e-02 -3.1961e-02\n",
" 2.4923e-02 2.4923e-02\n",
" 5.9772e-02 5.9772e-02\n",
" 3.8641e-02 3.8641e-02\n",
" -1.7370e-02 -1.7370e-02\n",
" -5.7566e-02 -5.7566e-02\n",
" -4.5196e-02 -4.5196e-02\n",
" 9.1459e-03 9.1459e-03\n",
" 5.4610e-02 5.4610e-02\n",
" 3.1454e-01 3.1454e-01\n",
" 1.1106e-01 1.1106e-01\n",
" 9.7401e-02 9.7401e-02\n",
" 1.1868e-01 1.1868e-01\n",
" 3.8193e-05 3.8193e-05\n",
" 3.3693e-02 3.3693e-02\n",
" 2.0399e-01 2.0399e-01\n",
" 1.1715e-01 1.1715e-01\n",
" 7.5480e-02 7.5480e-02\n",
" 1.2570e-01 1.2570e-01\n",
" -4.0759e-03 -4.0759e-03\n",
" 1.6968e-02 1.6968e-02\n",
" 1.7634e-01 1.7634e-01\n",
" 1.1313e-01 1.1313e-01\n",
" 8.6163e-02 8.6163e-02\n",
" 1.3229e-01 1.3229e-01\n",
" -4.5296e-03 -4.5296e-03\n",
" 1.5005e-03 1.5005e-03\n",
"The above two columns you get should be very similar.\n",
"(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
"\n",
"If your backpropagation implementation is correct, then \n",
"the relative difference will be small (less than 1e-9). \n",
"\n",
"Relative Difference: 2.4094e-11\n",
"\n",
"\n",
"Cost at (fixed) debugging parameters (w/ lambda = 3.000000): 0.576051 \n",
"(for lambda = 3, this value should be about 0.576051)\n",
"\n"
]
}
],
"source": [
"fprintf('\\nChecking Backpropagation (w/ Regularization) ... \\n')\n",
"\n",
"% Check gradients by running checkNNGradients\n",
"lambda = 3;\n",
"checkNNGradients(lambda);\n",
"\n",
"% Also output the costFunction debugging values\n",
"debug_J = nnCostFunction(nn_params, input_layer_size, ...\n",
" hidden_layer_size, num_labels, X, y, lambda);\n",
"\n",
"fprintf(['\\n\\nCost at (fixed) debugging parameters (w/ lambda = %f): %f ' ...\n",
" '\\n(for lambda = 3, this value should be about 0.576051)\\n\\n'], lambda, debug_J);"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training Neural Network... \n",
"Iteration 50 | Cost: 4.631687e-01\n"
]
}
],
"source": [
"fprintf('\\nTraining Neural Network... \\n')\n",
"\n",
"% After you have completed the assignment, change the MaxIter to a larger\n",
"% value to see how more training helps.\n",
"options = optimset('MaxIter', 50);\n",
"\n",
"% You should also try different values of lambda\n",
"lambda = 1;\n",
"\n",
"% Create \"short hand\" for the cost function to be minimized\n",
"costFunction = @(p) nnCostFunction(p, ...\n",
" input_layer_size, ...\n",
" hidden_layer_size, ...\n",
" num_labels, X, y, lambda);\n",
"\n",
"% Now, costFunction is a function that takes in only one argument (the\n",
"% neural network parameters)\n",
"[nn_params, cost] = fmincg(costFunction, initial_nn_params, options);\n",
"\n",
"% Obtain Theta1 and Theta2 back from nn_params\n",
"Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ...\n",
" hidden_layer_size, (input_layer_size + 1));\n",
"\n",
"Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ...\n",
" num_labels, (hidden_layer_size + 1));"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Visualizing Neural Network... \n"
]
},
{
"data": {
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ckmzbYHmlg2V9/ALH/wyfB8F4y0A7fbyFmClHYTwjtQV2SrI5VbRLu3W5P01skZDMYrGp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\nT2f9Zz//Dw+E2kSAzRLyAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fprintf('\\nVisualizing Neural Network... \\n')\n",
"\n",
"displayData(Theta1(:, 2:end));"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training Set Accuracy: 96.420000\n"
]
}
],
"source": [
"pred = predict(Theta1, Theta2, X);\n",
"\n",
"fprintf('\\nTraining Set Accuracy: %f\\n', mean(double(pred == y)) * 100);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Octave",
"language": "octave",
"name": "octave"
},
"language_info": {
"file_extension": ".m",
"help_links": [
{
"text": "GNU Octave",
"url": "https://www.gnu.org/software/octave/support.html"
},
{
"text": "Octave Kernel",
"url": "https://github.com/Calysto/octave_kernel"
},
{
"text": "MetaKernel Magics",
"url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md"
}
],
"mimetype": "text/x-octave",
"name": "octave",
"version": "4.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
yorkerlin/shogun | doc/ipython-notebooks/neuralnets/autoencoders.ipynb | 6 | 21223 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deep Autoencoders"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### by Khaled Nasr as a part of a <a href=\"https://www.google-melange.com/gsoc/project/details/google/gsoc2014/khalednasr92/5657382461898752\">GSoC 2014 project</a> mentored by Theofanis Karaletsos and Sergey Lisitsyn "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook illustrates how to train and evaluate a deep autoencoder using Shogun. We'll look at both regular fully-connected autoencoders and convolutional autoencoders."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A (single layer) [autoencoder](http://deeplearning.net/tutorial/dA.html#autoencoders) is a neural network that has three layers: an input layer, a hidden (encoding) layer, and a decoding layer. The network is trained to reconstruct its inputs, which forces the hidden layer to try to learn good representations of the inputs.\n",
"\n",
"In order to encourage the hidden layer to learn good input representations, certain variations on the simple autoencoder exist. Shogun currently supports two of them: Denoising Autoencoders [1] and Contractive Autoencoders [2]. In this notebook we'll focus on denoising autoencoders. \n",
"\n",
"For denoising autoencoders, each time a new training example is introduced to the network, it's randomly corrupted in some mannar, and the target is set to the original example. The autoencoder will try to recover the orignal data from it's noisy version, which is why it's called a denoising autoencoder. This process will force the hidden layer to learn a good representation of the input, one which is not affected by the corruption process.\n",
"\n",
"A deep autoencoder is an autoencoder with multiple hidden layers. Training such autoencoders directly is usually difficult, however, they can be pre-trained as a stack of single layer autoencoders. That is, we train the first hidden layer to reconstruct the input data, and then train the second hidden layer to reconstruct the states of the first hidden layer, and so on. After pre-training, we can train the entire deep autoencoder to fine-tune all the parameters together. We can also use the autoencoder to initialize a regular neural network and train it in a supervised manner.\n",
"\n",
"In this notebook we'll apply deep autoencoders to the USPS dataset for handwritten digits. We'll start by loading the data and dividing it into a training set and a test set:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%pylab inline\n",
"%matplotlib inline\n",
"from scipy.io import loadmat\n",
"from modshogun import RealFeatures, MulticlassLabels, Math\n",
"\n",
"# load the dataset\n",
"dataset = loadmat('../../../data/multiclass/usps.mat')\n",
"\n",
"Xall = dataset['data']\n",
"# the usps dataset has the digits labeled from 1 to 10 \n",
"# we'll subtract 1 to make them in the 0-9 range instead\n",
"Yall = np.array(dataset['label'].squeeze(), dtype=np.double)-1 \n",
"\n",
"# 4000 examples for training\n",
"Xtrain = RealFeatures(Xall[:,0:4000])\n",
"Ytrain = MulticlassLabels(Yall[0:4000])\n",
"\n",
"# the rest for testing\n",
"Xtest = RealFeatures(Xall[:,4000:-1])\n",
"Ytest = MulticlassLabels(Yall[4000:-1])\n",
"\n",
"# initialize the random number generator with a fixed seed, for repeatability\n",
"Math.init_random(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating the autoencoder"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similar to regular neural networks in Shogun, we create a [deep autoencoder](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CDeepAutoencoder.html) using an array of [NeuralLayer](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CNeuralLayer.html)-based classes, which can be created using the utility class [NeuralLayers](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CNeuralLayers.html). However, for deep autoencoders there's a restriction that the layer sizes in the network have to be symmetric, that is, the first layer has to have the same size as the last layer, the second layer has to have the same size as the second-to-last layer, and so on. This restriction is necessary for pre-training to work. More details on that can found in the following section.\n",
"\n",
"We'll create a 5-layer deep autoencoder with following layer sizes: 256->512->128->512->256. We'll use [rectified linear neurons](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CNeuralRectifiedLinearLayer.html) for the hidden layers and [linear neurons](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CNeuralLinearLayer.html) for the output layer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from modshogun import NeuralLayers, DeepAutoencoder\n",
"\n",
"layers = NeuralLayers()\n",
"layers = layers.input(256).rectified_linear(512).rectified_linear(128).rectified_linear(512).linear(256).done()\n",
"\n",
"ae = DeepAutoencoder(layers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pre-training"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can pre-train the network. To illustrate exactly what's going to happen, we'll give the layers some labels: L1 for the input layer, L2 for the first hidden layer, and so on up to L5 for the output layer.\n",
"\n",
"In pre-training, an autoencoder will formed for each encoding layer (layers up to the middle layer in the network). So here we'll have two autoencoders: L1->L2->L5, and L2->L3->L4. The first autoencoder will be trained on the raw data and used to initialize the weights and biases of layers L2 and L5 in the deep autoencoder. After the first autoencoder is trained, we use it to transform the raw data into the states of L2. These states will then be used to train the second autoencoder, which will be used to initialize the weights and biases of layers L3 and L4 in the deep autoencoder.\n",
"\n",
"The operations described above are performed by the the [pre_train()](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CDeepAutoencoder.html#acf6896cb166afbba063fd1257cb8bc97) function. Pre-training parameters for each autoencoder can be controlled using the [pt_* public attributes](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CDeepAutoencoder.html#a6389a6f19b8854c64e1b6be5aa0c1fc4) of [DeepAutoencoder](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CDeepAutoencoder.html). Each of those attributes is an [SGVector](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1SGVector.html) whose length is the number of autoencoders in the deep autoencoder (2 in our case). It can be used to set the parameters for each autoencoder indiviually. [SGVector's set_const()](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1SGVector.html#a8bce01a1fc41a734d9b5cf1533fd7a2a) method can also be used to assign the same parameter value for all autoencoders.\n",
"\n",
"Different noise types can be used to corrupt the inputs in a denoising autoencoder. Shogun currently supports 2 [noise types](http://www.shogun-toolbox.org/doc/en/latest/namespaceshogun.html#af95cf5d3778127a87c8a67516405d863): dropout noise, where a random portion of the inputs is set to zero at each iteration in training, and gaussian noise, where the inputs are corrupted with random gaussian noise. The noise type and strength can be controlled using [pt_noise_type](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CDeepAutoencoder.html#af6e5d2ade5cb270cc50565d590f929ae) and [pt_noise_parameter](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CDeepAutoencoder.html#adbdff6c07fa7dd70aaf547e192365075). Here, we'll use dropout noise."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from modshogun import AENT_DROPOUT, NNOM_GRADIENT_DESCENT\n",
"\n",
"ae.pt_noise_type.set_const(AENT_DROPOUT) # use dropout noise\n",
"ae.pt_noise_parameter.set_const(0.5) # each input has a 50% chance of being set to zero\n",
"\n",
"ae.pt_optimization_method.set_const(NNOM_GRADIENT_DESCENT) # train using gradient descent\n",
"ae.pt_gd_learning_rate.set_const(0.01)\n",
"ae.pt_gd_mini_batch_size.set_const(128)\n",
"\n",
"ae.pt_max_num_epochs.set_const(50)\n",
"ae.pt_epsilon.set_const(0.0) # disable automatic convergence testing\n",
"\n",
"# uncomment this line to allow the training progress to be printed on the console\n",
"#from modshogun import MSG_INFO; ae.io.set_loglevel(MSG_INFO)\n",
"\n",
"# start pre-training. this might take some time\n",
"ae.pre_train(Xtrain)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fine-tuning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After pre-training, we can train the autoencoder as a whole to fine-tune the parameters. Training the whole autoencoder is performed using the [train()](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CAutoencoder.html#ace3eb6cc545affcbfa31d754ffd087dc) function. Training parameters are controlled through the [public attributes](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CDeepAutoencoder.html#pub-attribs), same as a regular neural network."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ae.set_noise_type(AENT_DROPOUT) # same noise type we used for pre-training\n",
"ae.set_noise_parameter(0.5)\n",
"\n",
"ae.set_max_num_epochs(50)\n",
"ae.set_optimization_method(NNOM_GRADIENT_DESCENT)\n",
"ae.set_gd_mini_batch_size(128)\n",
"ae.set_gd_learning_rate(0.0001)\n",
"ae.set_epsilon(0.0)\n",
"\n",
"# start fine-tuning. this might take some time\n",
"_ = ae.train(Xtrain)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can evaluate the autoencoder that we trained. We'll start by providing it with corrupted inputs and looking at how it will reconstruct them. The function [reconstruct()](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CDeepAutoencoder.html#ae8c2d565cf2ea809103d0557c57689c7) is used to obtain the reconstructions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# get a 50-example subset of the test set\n",
"subset = Xtest[:,0:50].copy()\n",
"\n",
"# corrupt the first 25 examples with multiplicative noise\n",
"subset[:,0:25] *= (random.random((256,25))>0.5)\n",
"\n",
"# corrupt the other 25 examples with additive noise \n",
"subset[:,25:50] += random.random((256,25))\n",
"\n",
"# obtain the reconstructions\n",
"reconstructed_subset = ae.reconstruct(RealFeatures(subset))\n",
"\n",
"# plot the corrupted data and the reconstructions\n",
"figure(figsize=(10,10))\n",
"for i in range(50):\n",
" ax1=subplot(10,10,i*2+1)\n",
" ax1.imshow(subset[:,i].reshape((16,16)), interpolation='nearest', cmap = cm.Greys_r)\n",
" ax1.set_xticks([])\n",
" ax1.set_yticks([])\n",
"\n",
" ax2=subplot(10,10,i*2+2)\n",
" ax2.imshow(reconstructed_subset[:,i].reshape((16,16)), interpolation='nearest', cmap = cm.Greys_r)\n",
" ax2.set_xticks([])\n",
" ax2.set_yticks([])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The figure shows the corrupted examples and their reconstructions. The top half of the figure shows the ones corrupted with multiplicative noise, the bottom half shows the ones corrupted with additive noise. We can see that the autoencoders can provide decent reconstructions despite the heavy noise.\n",
"\n",
"Next we'll look at the weights that the first hidden layer has learned. To obtain the weights, we can call the [get_layer_parameters()]() function, which will return a vector containing both the weights and the biases of the layer. The biases are stored first in the array followed by the weights matrix in column-major format."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# obtain the weights matrix of the first hidden layer\n",
"# the 512 is the number of biases in the layer (512 neurons)\n",
"# the transpose is because numpy stores matrices in row-major format, and Shogun stores \n",
"# them in column major format\n",
"w1 = ae.get_layer_parameters(1)[512:].reshape(256,512).T\n",
"\n",
"# visualize the weights between the first 100 neurons in the hidden layer \n",
"# and the neurons in the input layer\n",
"figure(figsize=(10,10))\n",
"for i in range(100):\n",
"\tax1=subplot(10,10,i+1)\n",
"\tax1.imshow(w1[i,:].reshape((16,16)), interpolation='nearest', cmap = cm.Greys_r)\n",
"\tax1.set_xticks([])\n",
"\tax1.set_yticks([])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we can use the autoencoder to initialize a supervised neural network. The network will have all the layer of the autoencoder up to (and including) the middle layer. We'll also add a softmax output layer. So, the network will look like: L1->L2->L3->Softmax. The network is obtained by calling [convert_to_neural_network()](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CDeepAutoencoder.html#a8c179cd9a503b2fa78b9bfe10ae473e5):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from modshogun import NeuralSoftmaxLayer\n",
"\n",
"nn = ae.convert_to_neural_network(NeuralSoftmaxLayer(10))\n",
"\n",
"nn.set_max_num_epochs(50)\n",
"\n",
"nn.set_labels(Ytrain)\n",
"_ = nn.train(Xtrain)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we'll evaluate the accuracy on the test set:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from modshogun import MulticlassAccuracy\n",
"\n",
"predictions = nn.apply_multiclass(Xtest)\n",
"accuracy = MulticlassAccuracy().evaluate(predictions, Ytest) * 100\n",
"\n",
"print \"Classification accuracy on the test set =\", accuracy, \"%\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convolutional Autoencoders"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convolutional autoencoders [3] are the adaptation of autoencoders to images (or other spacially-structured data). They are built with convolutional layers where each layer consists of a number of feature maps. Each feature map is produced by convolving a small filter with the layer's inputs, adding a bias, and then applying some non-linear activation function. Additionally, a max-pooling operation can be performed on each feature map by dividing it into small non-overlapping regions and taking the maximum over each region. In this section we'll pre-train a [convolutional network](http://deeplearning.net/tutorial/lenet.html) as a stacked autoencoder and use it for classification.\n",
"\n",
"In Shogun, convolutional autoencoders are constructed and trained just like regular autoencoders. Except that we build the autoencoder using [CNeuralConvolutionalLayer](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CNeuralConvolutionalLayer.html) objects:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from modshogun import DynamicObjectArray, NeuralInputLayer, NeuralConvolutionalLayer, CMAF_RECTIFIED_LINEAR\n",
"\n",
"conv_layers = DynamicObjectArray()\n",
"# 16x16 single channel images\n",
"conv_layers.append_element(NeuralInputLayer(16,16,1)) \n",
"\n",
"# the first encoding layer: 5 feature maps, filters with radius 2 (5x5 filters)\n",
"# and max-pooling in a 2x2 region: its output will be 10 8x8 feature maps\n",
"conv_layers.append_element(NeuralConvolutionalLayer(CMAF_RECTIFIED_LINEAR, 5, 2, 2, 2, 2)) \n",
"\n",
"# the second encoding layer: 15 feature maps, filters with radius 2 (5x5 filters)\n",
"# and max-pooling in a 2x2 region: its output will be 20 4x4 feature maps\n",
"conv_layers.append_element(NeuralConvolutionalLayer(CMAF_RECTIFIED_LINEAR, 15, 2, 2, 2, 2))\n",
"\n",
"# the first decoding layer: same structure as the first encoding layer\n",
"conv_layers.append_element(NeuralConvolutionalLayer(CMAF_RECTIFIED_LINEAR, 5, 2, 2))\n",
"\n",
"# the second decoding layer: same structure as the input layer\n",
"conv_layers.append_element(NeuralConvolutionalLayer(CMAF_RECTIFIED_LINEAR, 1, 2, 2))\n",
"\n",
"conv_ae = DeepAutoencoder(conv_layers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll pre-train the autoencoder:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"conv_ae.pt_noise_type.set_const(AENT_DROPOUT) # use dropout noise\n",
"conv_ae.pt_noise_parameter.set_const(0.3) # each input has a 30% chance of being set to zero\n",
"\n",
"conv_ae.pt_optimization_method.set_const(NNOM_GRADIENT_DESCENT) # train using gradient descent\n",
"conv_ae.pt_gd_learning_rate.set_const(0.002)\n",
"conv_ae.pt_gd_mini_batch_size.set_const(100)\n",
"\n",
"conv_ae.pt_max_num_epochs[0] = 30 # max number of epochs for pre-training the first encoding layer\n",
"conv_ae.pt_max_num_epochs[1] = 10 # max number of epochs for pre-training the second encoding layer\n",
"conv_ae.pt_epsilon.set_const(0.0) # disable automatic convergence testing\n",
"\n",
"# start pre-training. this might take some time\n",
"conv_ae.pre_train(Xtrain)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then convert the autoencoder to a regular neural network for classification:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"conv_nn = ae.convert_to_neural_network(NeuralSoftmaxLayer(10))\n",
"\n",
"# train the network\n",
"conv_nn.set_epsilon(0.0)\n",
"conv_nn.set_max_num_epochs(50)\n",
"conv_nn.set_labels(Ytrain)\n",
"\n",
"# start training. this might take some time\n",
"_ = conv_nn.train(Xtrain)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And evaluate it on the test set:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"predictions = conv_nn.apply_multiclass(Xtest)\n",
"accuracy = MulticlassAccuracy().evaluate(predictions, Ytest) * 100\n",
"\n",
"print \"Classification accuracy on the test set =\", accuracy, \"%\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- [1] [Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion, Vincent, 2010](http://jmlr.org/papers/volume11/vincent10a/vincent10a.pdf)\n",
"- [2] [Contractive Auto-Encoders: Explicit Invariance During Feature Extraction, Rifai, 2011](http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf)\n",
"- [3] [Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction, J. Masci, 2011](http://www.idsia.ch/~ciresan/data/icann2011.pdf)"
]
}
],
"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
}
| gpl-3.0 |
minrk/wurlitzer | Demo.ipynb | 1 | 5851 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Capturing C-level stdout/stderr with `wurlitzer`\n",
"\n",
"Sometimes in Python you are calling some C code.\n",
"Sometimes that C code makes calls to `printf`,\n",
"or otherwise writes to the stdout/stderr of the process."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import ctypes\n",
"libc = ctypes.CDLL(None)\n",
"\n",
"try:\n",
" c_stderr_p = ctypes.c_void_p.in_dll(libc, 'stderr')\n",
"except ValueError:\n",
" # libc.stdout is has a funny name on OS X\n",
" c_stderr_p = ctypes.c_void_p.in_dll(libc, '__stderrp')\n",
"\n",
"\n",
"def printf(msg):\n",
" \"\"\"Call C printf\"\"\"\n",
" libc.printf((msg + '\\n').encode('utf8'))\n",
"\n",
"def printf_err(msg):\n",
" \"\"\"Cal C fprintf on stderr\"\"\"\n",
" libc.fprintf(c_stderr_p, (msg + '\\n').encode('utf8'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"IPython forwards the Python-level `sys.stdout` and `sys.stderr`,\n",
"but it leaves the process-level file descriptors that C code will write to untouched.\n",
"That means that in a context like this notebook, these functions will print to the terminal, because they are not captured:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"printf(\"Hello?\")\n",
"printf_err(\"Stderr? Anybody?\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With wurlitzer, we can capture these C-level functions:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from wurlitzer import pipes, sys_pipes, STDOUT, PIPE"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with pipes() as (stdout, stderr):\n",
" printf(\"Hello, stdout!\")\n",
" printf_err(\"Hello, stderr!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and redisplay them if we like:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello, stdout!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Hello, stderr!\n"
]
}
],
"source": [
"import sys\n",
"sys.stdout.write(stdout.read())\n",
"sys.stderr.write(stderr.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some tools, such as the IPython kernel for Jupyter,\n",
"capture the Python-level `sys.stdout` and `sys.stderr` and forward them somewhere.\n",
"In the case of Jupyter, this is over a network socket, so that it ends up in the browser.\n",
"\n",
"If we know that's going on, we can easily hook up the C outputs to the Python-forwarded ones with a single call:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello from C, 0!\n",
"Hello from C, 1!\n",
"Hello from C, 2!\n",
"Hello from C, 3!\n",
"Hello from C, 4!\n"
]
}
],
"source": [
"import time\n",
"\n",
"with sys_pipes():\n",
" for i in range(5):\n",
" time.sleep(1)\n",
" printf(\"Hello from C, %i!\" % i)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also capture the pipes to any writeable streams, such as a `StringIO` object:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello, stdout!\n",
"Hello, stderr!\n",
"\n"
]
}
],
"source": [
"import io\n",
"\n",
"stdout = io.StringIO()\n",
"with pipes(stdout=stdout, stderr=STDOUT):\n",
" printf(\"Hello, stdout!\")\n",
" printf_err(\"Hello, stderr!\")\n",
"\n",
"print(stdout.getvalue())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## IPython extension\n",
"\n",
"You can also enable wurlitzer as an IPython extension,\n",
"so that it always forwards C-level output during execution:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%load_ext wurlitzer"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello from C, 0!\n",
"Hello from C, 1!\n",
"Hello from C, 2!\n",
"Hello from C, 3!\n",
"Hello from C, 4!\n"
]
}
],
"source": [
"for i in range(5):\n",
" time.sleep(1)\n",
" printf(\"Hello from C, %i!\" % i)"
]
}
],
"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": 1
}
| mit |
nottia/BuildingMachineLearningSystemsWithPython | ch03/Clustering1.ipynb | 2 | 22608 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3章 クラスタリング：関連のある文書を見つける\n",
"\n",
"この章でやりたいことは，ある新しい文書に対して類似する文書はどれかを素早く見つけ出すこと． \n",
"そのために，教師なし学習であるクラスタリングを行う． \n",
"SciKitライブラリにある手法を使えば実現出来る．"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3.1 文書の関連性を推測する"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.1.1 やってはいけないこと\n",
"\n",
"テキストデータの類似度はレーベンシュタイン距離（編集距離）で求めることが出来る． \n",
"例えば，「machine」と「mchiene」という単語の距離は２． \n",
"また，文章内の単語を最小単位として，文章動詞の編集距離を計算することも出来る． \n",
"「How to format my hard disk」と「Hard disk format problems」の距離は５． \n",
"（how, to, format, myを削除して，format, problemsを追加する） \n",
"しかし，この方法は効率が悪い．"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.1.2 どうやるべきか\n",
"\n",
"単語の出現回数を数えるというbag-of-wordsを特徴量に使う． \n",
"これにより文書をベクトル化出来るので，距離が計算出来るしクラスタリングも出来る． "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3.2 前処理：共通する単語の出現回数を類似度として計測する"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.2.1 テキストデータをbag-of-wordに変換する"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"単語の出現回数を数えてベクトル表記する．\n",
"ScikitのCountVectorizerを使う．"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"vectorizer = CountVectorizer(min_df=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"上記min_dfパラメタの値より出現回数の小さい単語は無視される． \n",
"他にどんなパラメタがあるかは下記の通り．"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',\n",
" dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n",
" lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
" ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
" strip_accents=None, token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
" tokenizer=None, vocabulary=None)\n"
]
}
],
"source": [
"print(vectorizer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"試しにベクトル化してみる．"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'disk', u'format', u'hard', u'how', u'my', u'problems', u'to']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"content = [\"How to format my hard disk\", \" Hard disk format problems \"]\n",
"X = vectorizer.fit_transform(content)\n",
"vectorizer.get_feature_names()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 1]\n",
" [1 1]\n",
" [1 1]\n",
" [1 0]\n",
" [1 0]\n",
" [0 1]\n",
" [1 0]]\n"
]
}
],
"source": [
"print(X.toarray().transpose())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.2.2 単語を数える"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['This is a toy post about machine learning. Actually, it contains not much interesting stuff.',\n",
" 'Imaging databases provide storage capabilities.',\n",
" 'Most imaging databases save images permanently.\\n',\n",
" 'Imaging databases store data.',\n",
" 'Imaging databases store data. Imaging databases store data. Imaging databases store data.']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"TOY_DIR = \"/Users/Atsushi/Desktop/BuildingMachineLearningSystemsWithPython/ch03/data/toy\"\n",
"posts = [open(os.path.join(TOY_DIR, f)).read() for f in os.listdir(TOY_DIR)]\n",
"posts"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#samples: 5, #features: 25\n"
]
}
],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"vectorizer = CountVectorizer(min_df=1)\n",
"\n",
"X_train = vectorizer.fit_transform(posts)\n",
"num_samples, num_features = X_train.shape\n",
"print(\"#samples: %d, #features: %d\" % (num_samples, num_features))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[u'about', u'actually', u'capabilities', u'contains', u'data', u'databases', u'images', u'imaging', u'interesting', u'is', u'it', u'learning', u'machine', u'most', u'much', u'not', u'permanently', u'post', u'provide', u'save', u'storage', u'store', u'stuff', u'this', u'toy']\n"
]
}
],
"source": [
"print(vectorizer.get_feature_names())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"新しい文書のベクトル化は以下のようにする． \n",
"大抵が疎なベクトルなので，出現した単語のみの情報だけをデータに格納する．"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0, 4)\t1\n",
" (0, 5)\t1\n"
]
}
],
"source": [
"new_post = \"imaging databases\"\n",
"new_post_vec = vectorizer.transform([new_post])\n",
"print(new_post_vec)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"toarray()メソッドを用いることで，特徴ベクトルの全ての要素を表示することができる．"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
]
}
],
"source": [
"print(new_post_vec.toarray())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"新しい文書と他の既存の文書の類似度（ユークリッド距離）を計算する．"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import scipy as sp\n",
"def dist_raw(v1, v2):\n",
" delta = v1 - v2\n",
" return sp.linalg.norm(delta.toarray())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dist = dist_raw"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ※"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== Post 0 with dist=1.41: This is a toy post about machine learning. Actually, it contains not much interesting stuff.\n",
"=== Post 1 with dist=0.86: Imaging databases provide storage capabilities.\n",
"=== Post 2 with dist=0.63: Most imaging databases save images permanently.\n",
"\n",
"=== Post 3 with dist=0.77: Imaging databases store data.\n",
"=== Post 4 with dist=0.77: Imaging databases store data. Imaging databases store data. Imaging databases store data.\n",
"Best post is 2 with dist=0.63\n"
]
}
],
"source": [
"import sys\n",
"best_dist = sys.maxsize\n",
"best_i = None\n",
"\n",
"for i in range(0, num_samples):\n",
" post = posts[i]\n",
" if post == new_post:\n",
" continue\n",
" post_vec = X_train.getrow(i)\n",
" d = dist(post_vec, new_post_vec)\n",
" print(\"=== Post %i with dist=%.2f: %s\" % (i, d, post))\n",
" if d < best_dist:\n",
" best_dist = d\n",
" best_i = i\n",
"print(\"Best post is %i with dist=%.2f\" % (best_i, best_dist)) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"文書3と文書4を見比べる． \n",
"文書4は文書3を3回繰り返しただけなので，新しい文書に対しての類似度は，その二つの文書で同じであるべきでしょう．（ほんと？）"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]]\n",
"[[0 0 0 0 3 3 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0]]\n"
]
}
],
"source": [
"print(X_train.getrow(3).toarray())\n",
"print(X_train.getrow(4).toarray())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"単語の出現頻度だけを特徴量として用いるのは単純すぎる． \n",
"特徴ベクトルを単位長さにするために，正規化する必要がある．"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.2.3 単語の出現回数ベクトルを正規化する"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"dist_raw関数を拡張して，単語の出現頻度からなるベクトルではなく，正規化したベクトルを返すようにする．"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def dist_norm(v1, v2):\n",
" v1_normalized = v1 / sp.linalg.norm(v1.toarray())\n",
" v2_normalized = v2 / sp.linalg.norm(v2.toarray())\n",
" delta = v1_normalized - v2_normalized\n",
" return sp.linalg.norm(delta.toarray())"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dist = dist_norm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"上記スクリプト（※）でもう一度類似度を計算する． \n",
"今度は文書3と文書4は同じ類似度になっている．"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.2.4 重要度の低い単語を取り除く"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"文書2に含まれるmostのような単語は分野に関係なく様々な文章で登場する． \n",
"そのような単語は，imagesのような特定の分野で登場しやすい単語と比べて情報を持たず，文章の分類に貢献しない． \n",
"そのため，ストップワード（stop word）として処理の対象外とすべき． "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"vectorizer = CountVectorizer(min_df=1, stop_words='english')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"stop_words='english'とすることで，318個の単語をストップワードとして登録出来る． \n",
"どのような単語がストップワードとして登録されるかを見るには以下のようにする．"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['a', 'about', 'above', 'across', 'after', 'afterwards', 'again', 'against', 'all', 'almost', 'alone', 'along', 'already', 'also', 'although', 'always', 'am', 'among', 'amongst', 'amoungst']\n"
]
}
],
"source": [
"print(sorted(vectorizer.get_stop_words())[0:20])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.2.5 ステミング（stemming）"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"今は，意味的には同じ単語が語形変化により異なる単語としてカウントされている．（imaging, imagesなど） \n",
"これらは同じ単語としてカウントすべき． \n",
"Scikitには標準ではステミングを行う機能は含まれていない． \n",
"[Natural Language Tooklit（NLTK）](http://nltk.org/install.html)を用いることで出来る．"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NLTKには異なる種類のステマーがあるが，例えば英語の場合，SnowballStemmerを用いる．"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"graphic\n",
"imag\n",
"imag\n",
"imagin\n",
"imagin\n"
]
}
],
"source": [
"import nltk.stem\n",
"s= nltk.stem.SnowballStemmer('english')\n",
"print(s.stem(\"graphics\"))\n",
"print(s.stem(\"imaging\"))\n",
"print(s.stem(\"image\"))\n",
"print(s.stem(\"imagination\"))\n",
"print(s.stem(\"imagine\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"上記の例でわかるように，ステミングの結果は必ずしも正しい英単語になるとは限らない． \n",
"動詞については，次のような結果になる．"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"buy\n",
"buy\n",
"bought\n"
]
}
],
"source": [
"print(s.stem(\"buys\"))\n",
"print(s.stem(\"buying\"))\n",
"print(s.stem(\"bought\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### NLTKのステマーを用いて，ベクトル化を拡張する\n",
"文書をCountVectorizerに入力する前に，ステミングを行う必要がある． \n",
"ここでは，次のようにbuild_analyzerを上書きすることで対応する．"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import nltk.stem\n",
"english_stemmer = nltk.stem.SnowballStemmer('english')\n",
"\n",
"class StemmedCountVectorizer(CountVectorizer):\n",
" def build_analyzer(self):\n",
" analyzer = super(StemmedCountVectorizer, self).build_analyzer()\n",
" return lambda doc: (english_stemmer.stem(w) for w in analyzer(doc))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"結果を見ると，imagesとimagingが同じ単語としてカウントされている．"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[u'actual', u'capabl', u'contain', u'data', u'databas', u'imag', u'interest', u'learn', u'machin', u'perman', u'post', u'provid', u'save', u'storag', u'store', u'stuff', u'toy']\n"
]
}
],
"source": [
"vectorizer = StemmedCountVectorizer(min_df=1, stop_words='english')\n",
"X_train = vectorizer.fit_transform(posts)\n",
"print(vectorizer.get_feature_names())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"再び上記スクリプト（※）で類似度を計算してみる．"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.2.6 TF-IDFを用いる"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"これまで考えてきた特徴量は，単語の出現頻度を数えるだけの単純なものだった． \n",
"しかし，例えばsubjectのような，どの文書にも存在するような単語の影響を考えると今までのやり方は良くない．\n",
"\n",
"そこで，「ある単語に対して，対象の文書中で出現した回数をカウントするのに加えて，その単語が他の文書でどれだけ出現するかをカウントし，その回数で割る」方法で解決する． \n",
"それによって，特定の文書だけで現れやすい単語，つまり，他の文書ではあまり現れない単語の特徴量の値は大きくなる． \n",
"これを，TF-IDF（term frequency - inverse document frequency）という． "
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import scipy as sp\n",
"\n",
"def tfidf(t, d, D):\n",
" tf = float(d.count(t)) / sum(d.count(w) for w in set(d))\n",
" idf = sp.log(float(len(D)) / (len([doc for doc in D if t in doc])))\n",
" return tf * idf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"簡単な例を以下に示す．"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0\n",
"0.270310072072\n",
"0.0\n",
"0.135155036036\n",
"0.366204096223\n"
]
}
],
"source": [
"a, abb, abc = [\"a\"], [\"a\", \"b\", \"b\"], [\"a\", \"b\", \"c\"]\n",
"D = [a, abb, abc]\n",
"\n",
"print(tfidf(\"a\", a, D))\n",
"print(tfidf(\"b\", abb, D))\n",
"print(tfidf(\"a\", abc, D))\n",
"print(tfidf(\"b\", abc, D))\n",
"print(tfidf(\"c\", abc, D))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"TF-IDFはTfidfVectorizer（CountVectorizerを継承したクラス）の中に含まれているため，これまで使用してきたステミングの機能を一から実装し直す必要はない．"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"\n",
"class StemmedTfidfVectorizer(TfidfVectorizer):\n",
" def build_analyzer(self):\n",
" analyzer = super(TfidfVectorizer, self).build_analyzer()\n",
" return lambda doc: (english_stemmer.stem(w) for w in analyzer(doc))\n",
"\n",
"vectorizer = StemmedTfidfVectorizer(min_df=10, max_df=0.5, stop_words='english', decode_error='ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"charset_errorと書くとエラーになるので注意 → decode_error"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.2.7 ここまでやってきたこと"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"１．テキストデータをトークン化する．\n",
"\n",
"２．頻出しすぎる単語は，関連する文書を見つけるために役立たないため，取り除く．\n",
"\n",
"３．滅多に使われない単語は，新しい文書でも使われる可能性が低いため，取り除く．\n",
"\n",
"４．残った単語について，その出現回数をカウントする．\n",
"\n",
"５．文書全体の状況を考慮するため，単語の出現回数からTF-IDFを計算する．"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ただし，以下のような欠点がある．\n",
"\n",
"●単語の関連性について考慮していない．「Car hits wall」と「Wall hits car」が同じ特徴ベクトルになる．\n",
"\n",
"●否定的な意味を捉えることが出来ない．「I will eat ice cream」と「I will not eat ice cream」は意味的に逆だが，似た特徴ベクトルになる．しかし，２つの単語のペア（バイグラム）や３つ（トリグラム）をカウントすることで解決可能．\n",
"\n",
"●タイプミスに対応出来ない．databaseとdatabasのように明らかに同じ意味だとわかる単語も別々に扱われる．"
]
}
],
"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 |
dmyersturnbull/pynd-pubs-ms | growth.ipynb | 1 | 111327 | {
"cells": [
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pickle\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import pandas\n",
"import math\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pandas.read_csv('growth_data.csv', header=0) # , index_col=0\n",
"\n",
"df['time'] = [float(t.replace(':', '.')) for t in df['time']]\n",
"df['OD600'] = [math.log(od) / math.log(2) for od in df['OD600']]\n",
"#df['time'] = [t - 13.39 for t in df['time']]\n",
"time = 'incubation time (hr)'\n",
"od600 = 'log2 (OD600)'\n",
"conc = u'concentration (\\u03BCL)'\n",
"df.columns = [time, od600, conc]"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"collapsed": false
},
"outputs": [
{
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OFzWPqbxwWGH91c5YJjul4oFbNLXrKmYHgTMggAEAAKzjvVcSVeRci0YbbS5uqLJ4n+on\nHtdqW/mMZvZep9n9NyqTLfSzPGBoEMAAAADWoNHGqbxPVF9+VJXFB+RdqzM+MXOZSvO3Kzexu4/V\nAcOHAAYAANAWRzX5uC4FGZbSSWrVXlL5yGHF4XJnLFvYrdL8bZqcvbx/hQFDjAAGAADGnvdOcViW\ndzGzXpLi8KTKC3epVX2uMxZk8prdf6Nm9l6nIKCtPHCuCGAAAGCsJXFTSVxVINrLOxeqeuyzqi19\nXvJJZ3xq1xtVPHCzsrnpPlYHjAYCGAAAGEveeyVhWc5HCjTewct7r2b5KZWP3iMX1zrj+akL0rby\nUwf6WB0wWghgAABg7LgkVByWFQTB2IevqHFUJxcOK2oc6YxlcjMqHbhFk6U3DPysoO90ZASGAwEM\nAACMjbS9fFXONcf+Xq8krqty9F41Tj6xOhhkNbP3zZrd/xZlMgPeVt47KVNQLr9Lb/2ynyeFYWgQ\nwAAAwFhIklBxa3ns28t7n6i29Iiqxx6Qd2FnfGL2CpXmb1OusKuP1W2D9wqyBWVy08pkeCuL4cN3\nLQAAGHlxVFPYaEjyA7+krpua1RdUXrhTSXiiM5Yr7FHpgjs0MfO6PlZ2Zl5OmcyksvmZsQ7QGH4E\nMAAAMLLSTZUr8j5REMz2u5y+icMTKi/cqVb1hc5YkJlQce5GTe+5dqDbynt5ZTKTyuVnxjo8Y3QQ\nwAAAwEhKN1VuSMH4tpd3SajqsQdVW3pIkmuPBprefbVm596ubG6qn+VtynsvBYEy2UnlctNj+/ph\nNBHAAADASHEuURJV2psqj+cbd++9GiefVOXoPXJJozNemL5IpfnblZ+c62N1m/M+7aWRyU0pS/DC\niCKAAQCAkZHEjfY+VuM76xU2jqh85LCi5tHOWCY3q9L8rZosHhrIr8tK8MrmppXJTQ1kjcBOIYAB\nAICh571THJblXTS2DRqSqKbK4j1qnLSrg0FWs/veotl9NyjI5PtX3CYIXhhHBDAAADDUkqSpJKoq\nUDCW4cu7RLWlz6t67EF5H3fGJ4sHVZy/Vbl8qY/VbaxzjxdLDTGGCGAAAGAoee+VhGU5HynQ+L2B\n996rVX0+bSsflTvjuYl9Ks3foYmZi/tY3cbS4MU9XhhvBDAAADB0XBIqDssKgmAsw1fUWlJ54U6F\ntZc6Y0F2UsW5t2t699UDORPo5QlegAhgAABgyMRhRc41BzJkdJtLWqos3q/68iOSfHs00PSea1Wc\nu0mZ7GQ/y9sQ+3gBpyKAAQCAoZBuqlyW927swpf3To0TT6iyeK9c0uyMF6YvabeV39fH6jbm5ZTJ\nTBG8gHUIYAAAYOAlUV0uro/lpsph/VWdPHJYcetYZyybL6k0f5smZq8YuK8HwQvYGgEMAAAMrLS9\n/El5l4zdm/kkqqh89B41y093xoIgp9n9b9XM3jcryAzW27g0eE0ql58du9cKOBuD9ZMLAADQdmp7\n+fF5Q+9drOrxz6l6/LPS2rbypTeodOAWZfOzfazudN47ZbIEL2C7CGAAAGCgeO+VRGU5FyrQ+Nzr\n5b1Xs/KsKkfvVhJVOuP5yTmV5u9QYfrCPlZ3utXgNTN29+QB54MABgAABoZLIsVRWYE0VuErah5L\n28rXX+mMZbJTKh64WVO73jhYM0veK8hOELyAc0QAAwAAAyGOavJxY7DCRpe5uKnKsftUX35Mq23l\nM5rZe51m979NmexEP8s7lXcKspPKEryA80IAAwAAfeVc0m4vPz6NNrx3qi8/psriffKu1RmfmLlU\npfnblZvY08fq1vFOQWZCmcKMMplsv6sBhh4BDAAA9M04tpdv1V5WeeGw4tZSZyxb2KXSgds1Wby8\nf4Wt471TJlNoBy/eMgI7hZ8mAADQc84lSqKKvIvGZjlbHJZVOXq3mpVnO2NBJq/Z/W/TzN7rFQSD\nMbvk5RQEBeUIXkBX8FMFAAB6KokbSuKqAmXGInw5F6l27LOqLn1O8klnfGrXVSoeuFnZ3Ewfq1uV\nBq+8crldymR5iwh0Cz9dAACgJ9JNlSvyLhyL4OW9V7P8tMpH75GLq53x/OS8ShfcocLUfB+rW7Ua\nvErKZPP9LgcYeQQwAADQdUncbM96BWMRvqLGok4uHFbUeK0zlslOq3jgFk3tMgNxv5uXV6Cscvmi\nMtlCv8sBxsZIBTBjzEWSnpD0QWvtL/W7HgAAxp33XklYlvORAvU/dHRbEtdVWbxPjROPrw4GGc3s\nfbNm9711IIKO905BkFMuPzMQ9QDjZmQCmDFmVtKfSCpqdSMNAADQJ0nSVBK1Z71GPHx5n6i29Iiq\nxx6Qd2FnfGL2cpXmb1OusLuP1aXS4JVVtlBSdpD2FwPGzEgEMGPMZUrD1w39rgUAgHE3brNereqL\nOrlwp5JwuTOWK+xRaf52Tcxe2sfKUqvBq6hsdrLf5ew47/m7O4bL0C/CNsZ8v6RHJL1J0qf7XA4A\nAGMtSZqKWsflfTzy4SsOT2jppb/S0kuf7ISvIFNQaf527b/yG/sevlaCSTZfVH5y78iFL++96rVQ\nS8dq+sj7Pzna32wYKaMwA/Y+Sc9Jeq8kI+kd/S0HAIDxM06zXi4JVT3+oGrHH5LkOuPTu6/W7NzN\nyuam+lec0tciCAJl87PK5kYrdK1o1EM1G5EkDURDE+BsjEIA+w5Jn7LWemPMVf0uBgCAceOSluKo\nqkAa6fDlvVfjpFXl6D1ySb0znp+6ULsuuEP5ybk+VjcewavViNSoR2kHx3bwYgkihs3QBzBr7d/1\nuwYAAMbR6qxXqGD472rYUthYUPnIYUXNhc5YJjer0oFbNVk61NdZmLEIXs128Go/11EO+hh9Qx/A\nAABA77kkVBxV2rNeoxu+kqimyuK9apx8cnUwyGp23w2a2fcWZTL927i401wjPzOywSsKE9VroZLE\nKZMJWG6IkUAAAwAA2+a9VxKV5dxoz3p5l6i2/JCqxx6Ud1FnfLJ4UMUDtypXKPWvNjkFyilbmBm5\nxhor4jhRoxYqjhIFmYwyGYIXRsfYBbA9e6aVy2X7XcYp5uaK/S4BXcJrO9p4fUcXr+3GkqipMKwo\n0ISk4dxHau/emS0/771XdflZHXnh0wqbJzrjE9NzuvCKd2hm92XdLnGL2pwymbxyE7PKjugGys55\nVcpNBU4q7Jre3mMSd+aDgAEydgFsebl+5oN6aG6uqMXFSr/LQBfw2o42Xt/RxWt7unTWqyLnWkM9\n67V374yWlmqbfj5uLau8cKdatRc7Y0F2QsX9b9f0nmvUchm1tnh813inIFNQJj+jTCYn1VqSWr2v\no4u892rUQjWb8VnPdjlHEw4Ml7ELYAAAYPtcEioOy+3GB8Mbvrbikpaqxx5QbekRrbaVDzS951oV\n525Spl/L/LxTkJlQpjCjTGawVu/sFO+9mvVIzUakIBOw1BBjgQAGAABOc8qsVzCawct7p8aJJ1VZ\nvFcuaXTGC9MXqzR/h/KT+/pVmIJMvh28RvetWqsZqV6LJHkFBC+MkdH9qQYAAOdkHDochvXXdHLh\nsOLmYmcsmy+qeOA2TRav7Eu3PS+nTFAY+eAVhrEatUjOufbXmfCF8TJqP92+/R8AADhLo3Kv11aS\nqKry0bvVLD/dGQuCnGb2v1Wze9+soA/Bx7fv8crlZpTJjtpbs1VxnKheTVvKBwEt5TG+Ruqn3Fr7\nu5J+t991AAAwbEZ91su7WIsv3aPFl+6R93FnfLL0epUO3KJsvg+dL9v3eGUL0yM945UkTo1aS2HI\nXl6ANGIBDAAAnJ101qsq55qjGby8V7PyBVWO3qUkWu1umZvYr10X3KHC9EV9qMkpk51UJjc9ss01\npPRrX6u2FLUSGmwAaxDAAAAYUy6JlUTldAncCDbaiJrHVV44rLD+Smcsk51Uce5mTe1+Y8+fs5dX\nJjOpXH5mpGeB0pbykVrNtLMhDTaAUxHAAAAYQ3FYlU+a0gjei+OSpiqL96u+/KhWbw3PaO+FNyhf\nvEGZbI83kfZeQXb0g5ckNeqhmo1IkghewCYIYAAAjBHnYiXhyqzXaL1B9t6pvvyYKsfuk09WNyqe\nmLlUxfnbNH/h67bciLkLBSnI5pXNF0dyhnGtVjNSox7Jez9y31fATiOAAQAwJpKoLhfXpCAzcm+S\nW7VXVF44rLh1vDOWzZdUmr9dE7OX9/T5ejkFQUHZEW8nL0lRmKheSzsb0mAD2J7R/q0AAADkvVMc\nnpR3ycjNxMRRWZWFu9WsPNsZCzJ5ze57m2b2Xq+gh00u0lnFnHL5ojLZQs+u2w9xnKhRCxVHiYJM\nhgYbwFkggAEAMMKSpKkkqirQaM1OeBepevyzqh7/nOSTzvjULqPi3C3K5md6V8tK8CqMfvByzqte\nbSmKknQvr8xoBXqgFwhgAACMoFHdVDltK/+Mygt3y8XVznh+8oBKF9yhwtQFPazFKQiyyhZKyva6\nsUePpZ0NQzWbMUsNgfNEAAMAYMSk7eVPShqtTZWj5qLKC3cqrL/aGctkp1Q8cKumdpmehYLV4FVU\nNjvZk2v2i/dezUakZj1iLy9ghxDAAAAYIXFUk48b0gjNULi4ocrifaqfeFxr28rP7Ltes/ve1rNl\nf+MUvKS0s2G9FknytJQHdhABDACAEXBqo43ReLPsfaL68qOqLD4g79a0lZ+9XKX525Qr7O5RHeMX\nvJr1SK7TUn40vp+AQUEAAwBgyLmkpTiqjFSjjVb1pbStfLjcGcsWdqs0f7smZy/rSQ3jFrxoKQ/0\nBgEMAIAhNYqNNuLwpMoLd6lVfa4zFmQKmt1/o2b2vklB0P228hsFr2b1BdWXH1fYeE2SVJi6UNN7\nru5ZGOwmWsoDvUUAAwBgCDkXKwnL7bAw/OHLuVDVY59Rbenzkned8andV6s493Zlc9Ndr2GzGa/K\n4v2qHX/olGPD2ssKay9rZt/1Ks7d1PXauiFJnBq1lsKwPeNFS3mgJwhgAAAMmSSqy8V1KRj+ZWLe\nezXKT6ly9B65uNYZz09dqF3ztys/daAHNWy+1LBZfeG08LVW7fhDyk9doMnZS7td5o7p7OUVJnQ2\nBPqAAAYAwJDw3rcbbUQjMesVNhZUXjisqLHQGcvkZlQ6cKsmS6/verjczj1e9eXHz3ie+vJjQxHA\nTtvLi+AF9AUBDACAIeCSsN1oQ0MfvpK4psrRe9U4+eTqYJDV7N4bNLP/Lcpk8l29/tk011i55+t8\nj+kn770a9UitBnt5AYOAAAYAwICLw6qcawx9ow3vE9WWHlb12APyLuqMTxSvVOnAbcoVSl2+vpMU\njE1XQ0lq1EM1G+nXmhkvYDAQwAAAGFDOJUrCkyPRaKNZfV7lhbuUhCc6Y7mJvSrN366Jmdd19dor\nM16FiV3KT55dM4/C1IUKay+f8ZhB02pGatQiefmhv08QGDUEMAAABlASN+Si2tA32ohby2lb+doL\nnbEgM6Hi3E2a3nNtV4Pl+qWG2fykpOiMj1tres/VZwxg03uuOY8qd1YYxmrUotW9vNhEGRg4BDAA\nAAaI915JWJbz0VAHL5e0VD32oGpLD0taaSsfaHr3NSrO3aRMbqpr1/beKwgyO7LUcHL2Ms3su37T\nTogz+64fiAYcUZioUU83UQ4C7vMCBhkBDACAAeGSUHFYVhAM78yF916Nk0+ocvReuaTRGS9MX6TS\n/B3KT+7v6rUlKZufUXYHA15x7iblpy5QffmxdRsxX9P38LV+E+VhDu3AuCCAAQAwAOKoJh83hvoN\ndFg/kraVbx7tjGVzsyrO36bJ4sGuPjcvr0xuStncdFeuMzl7ad/D1lpsogwMLwIYAAB9dGqjjeEM\nX0lUU+Xo3WqUn1odDHKa3XeDZvfdoKCLbeW9d8pkp5TLzwzt1+9sOOdVr7UUtdhEGRhWBDAAAPok\niZtK4qoCDWejDe9i1ZYeUvXYg/I+7oxPFg+pNH+rsvliNy+uIDuhXH526DtEbsfKJsqtZqyATZSB\noUYAAwCgx7z3SqKynIuG8l4v771a1efStvJRuTOem9in0vwdmpi5uIsXdwoyBWUKs8pkst27zoDw\n3qtZj9Rspk1ZCF7A8COAAQDQQy6JlUQnJWkow1fUWlJ54U6FtZc6Y0F2UsW5t2t699Vdm43y3imT\nKShTmFEjQSBzAAAgAElEQVQmMx5vX5r1SI1GJLGXFzBSxuM3GAAAAyBttFGXhnDJnEuaqiw+oPry\nI5J8ezTQ9J43qTh3ozLn2e59M15OQVBQLj+jTHY83ra0mpEa9ajdTj+QhjCoA9jcePwmAwCgj7x3\nisOT8i4ZuvuVvHeqn3hc1cX75JJmZ7wwc4lK87crP7GvO9eVUxDklcvtGpvgddomysx6ASNpPH6j\nAQDQJy5pKY4qQ9loI6y/qpNHDituHeuMZfMlleZv08TsFV15Pr59j1cuNz4zXnGUqF5jE+Vz4ZzT\nq88dO/OBwAAZj99sAAD0WNpooyLnWgo0XLNeSVRR+ejdapaf6YwFQU6z+9+qmb1vVtCNe7C8V5DJ\nKztG93itbKIcRcx4na1GtaWnH3lFzz69rGYz0RVLn5f0j/tdFrAt4/EbDgCAHnIuVhKW23t7DU/4\n8i5W9fjnVD3+WWlNW/mp0htUPHCLsvnZLlw0DV7j1FxjZRPlKGLG62wtvlbW04+8qpdeLMu5flcD\nnJvx+E0HAECPJHFDLqpJwfDMaHjv1aw8q8rCXUriamc8P3lApfk7VJi+YOev2W6uMU4zXt571auh\nWq2YGa+zkCROLz69qKceXdDSUvOUz00UMrr8slmF9/WpOOAcjMdvPAAAusx73260EQ3VrFfUPJa2\nla+/0hnLZKdUPHCLpnZdteMhYRyba6SbKEdqNSMFGWa8tqtRC/X0I6/q2aeOq9lMTvncnt0FHTq4\nS5dcPCPJ6fMEMAyR8fjNBwBAF7kkVByW041yhyR8ubipyuJ9qp94TKtt5TOa2XudZvffqEy2cFbn\na9VfUbP8rKLmUUnp7Nlk6aAmptNNmVeDV/Gszz2s2ET53BxbqMg+9IpeeqEs71fHg0C65OIZHbxy\nl/btnej8cSBJWIuI4XJOAcwY8zpJl0g60D7HkqSnrLWvbPlAAABGTBxW5VxjaIKX90715UdVWbxf\n3rU64xOzl6l04HblJnaf9TlrSw+rceLJU8ai+pH0v91Gs3tvUC4/PsFLklqNSPU6myhv15bLDCey\nuvLyoq68oqSpKeYOMPy2/V1sjHm7pG+R9H9IukKn7wrojTEvSfpLSb9jrf3cThUJAMCgcS5pN9oY\nnr29WrWXVV44rLi11BnLFnarNH+7JmcvO7dz1l85LXxJ6eyPAqlx4mlNlQ4pP7nnnOseJmyifHYa\n9VBPP7zZMsMJHTpY0iUXzyqb5euI0XHGAGaMuUXSz0m6vT3kJT0t6TlJJyVlJM1JukjS6yV9l6Tv\nMsb8laQPWWs/04W6AQDomyRuysVVaUj29orDsspH71Kr8oXOWJDJa3b/jZrZe52CIHvO526Wnz3l\n3yvBS0FWmUwaTOvLj2ly9tJzvsYwYBPls5MuM3xVL71wcsNlhocO7tLePRN8HTGSNg1gxpgZSf9R\n0r+SdELSr0r6pKTD1trmJo/ZpzSofZ2kb5D0LmPMxyT9m80eAwDAsPDeKwnLcj5SMAQzG85Fqh37\njKpLn5f86uzC1K43qnjgZmVz0+d9jZV7vjYKXivCxmvnfZ1BFceJ6tVQSZwoyGRosLGFJHF64Zlj\nevqRIxsvM7yiqCsvZ5khRt9W3+Gfk1SS9H2Sftta29riWEmStfa4pD+X9OfGmPcpDW8/IulLJV11\n/uUCANAfLgkVR5U0Ywx4+PLeq1l+WuWjd8vFtc54fuqCtK381IEdvZbkNwxeo+y0TZTH6LmfrUY9\n1FOPvKZnnzymVotlht1kjDlkrX3mzEee33mNMc9LWrbW3rDT1zqLmn5J0py19p+d5eMul/QFSb9r\nrf3WMxz7l5IetNZ+6Fzr3MhWAeyTSpcQVs7lxNbaZUk/b4z5HUkfPJdzAADQb957JVFFzrUUaPDf\nZEeNRZ1cOKxozaxTJjeTtpUvvWHHlnSlM16BCtMXKW4sbHlsYerCHbnmIHDOq15tKQoTWspvwXuv\n40dr7W6GGy0znNWhgyXt2zvZvyJHjDHmZyV9vaRDPTjv+ySdcXKmW4wxN0n6dklvPI/T+DMfoh+U\n9KAx5g+stU+cx7VOsWkAs9a+fycuYK1dkvT9O3EuAAB66ZT28gMevpK4rsrifWqceHx1MMhoZu+b\nNbv/rcpkdq4DoZdTJjulbH5Gs/uu14mX/3bL46f3XLNj1+6X0zZRJnhtKEmcXnzmuOwjr2l5s2WG\nV5Q0Nckywy74cqkrv6hOO6+19s+7cJ2z8TFJ/8Va+0I3L2KtfdwY8+dKb8X6hzt1Xr77AQBYZ/Ve\nr3DgOxx6n6i29Iiqxx6Qd2FnfGL2CpXmb1OusGsHL+YUZCeUyxc7M2mTs5dpZt/1qh1/aMOHzOy7\nfqgbcLCJ8vY06qGefvSInnli8fRlhnsm9PqDJV18EcsMcf6MMe+Q9BZJ/7JHl/xNSZ82xtywU13e\ntxXA2msl3ynpWknzkmaVJuGqpNckPSLpf1hrn9uJogAA6JckaipqHVegwZ/1alZfUHnhTiXhic5Y\nrrBHpfk7NDH7uh27TrqJckHZwowymdPfOhTnblJ+6gLVlx/rNNwoTF2o6T3XDHX4atRDNRtsoryZ\n1WWGr+qlF06ctszwdZfM6uCV/VtmaIx5k9LbYP6BpElJT0j6OWvtn6w5pijpx5Q2j7tI0qLSLZV+\n3Fq7sOa45yXdI+n32+e8Rmk38D+U9MPW2vqaY6ck/VtJ/1TSxUrfK/+hpJ+01lbXHPcNkt4v6U2S\nIkl3Sfrg2g7ixpiPS/pHkr5E0ke12pX8f0r6gLX22fZxbs1jnNLbiD6ypu6XJX2npIakr7XWHjbG\n3CrpA5JukbRXUlnS3ZL+nbX24W2c95R7wIwxhyR9WOmWVSWlHdP/q6R/b62N28dcrvT+q+9Wmife\nq3Rv4Rck/Zq19pd1Zu+T9LS19pS/+mx2X5ox5s8kvdtae66/0P8/SUfb1/2WczzHKbYMYO0Nl39N\n0ldu41zOGPOnkr5n7TcsAADDwHunOKwoDAsD32QjDk+ovHCXWtXnO2NBpqDi3E2a3nPtebWVXyvd\nyyqjXH7XGTdRnpy9dKjD1lqtZqR6jU2UN5MkTi88e1xPPbz5MsODV5Q02cdlhsaYGyX9vaSa0uVq\nr0l6j6Q/Msa8x1r7CWNMSdJhpRMMH5d0v9Jg9V5J7zTGvN1ae6R9Si/pVknvkvQb7f/eLel7JOWV\nhhsZY3JKw9HNkn5P0p1KA9b7lc7afFn7uB+S9DOS/k7pfUa7JH2bpDuNMe+01v79mqczqzQE/J2k\nfyPpaknfq7TB3dXtY94j6ackTSu99efhNXW/S9Ir7RoOSrrfGPNF7TofbtdRlfQ2Sd8q6RZjzOXW\n2toW510598rX+7r219IrzQ4vKQ2OPynpDmPMu6y1bs1jf1Bp39RfVxr8vlPSLxpjFqy1f6BNGGMm\n2l/Dj29yyGb3dW3nfq8NWWu9MebTSr+OO2KrNvQXSLpP0gWSrKS/aH98TWl6lqQpSRcqvQHu3Urb\nz7/VGHMLIQwAMCySuKEkrrVnvSb6Xc6mXBKqevwzqi19XvKr72Wmd1+j2bm3K5ub2rFreUnZ/MyO\nnnPQtZqRmvVIjk2UN1SvhXrm8QU98/jR05YZ7t0zoUMHd+mSi2cGZZnmL0gKJb3VWvuS1JlNekjS\nj0v6hNLZnzdJeq+19rdWHmiM+Vul73t/XmkAkdJvhtdJeseacPQ7xpjHJP1ztQOY0hmSmyX9a2vt\nL60553FJP2aMuU1pGPopSR+31n7bmmN+RdLnlYa7td3DZyR9dG0nPmPMpKT3tkPife1A+QFJzlr7\n+2seG7Qf//XW2sfWPP79SreZ+qJ20Fp5Pick/ZCkmyT9ry3Ou96vKZ1lvMFau3Ij6m8YY/6DpB9Q\nGux+Z83xRUmvbzftU3sS5xVJ/0LSpgFM0tslTSh9HXvpEUn/1Bhzzdqv47na6k8TH1Eavn5G6VTk\nlsmxneR/Qmnb+Q9p9RsRAICB5FyiJKrIu2hH7vVq1V9Rs/xsZ2+s/OQBTZYOamL64vM6r/dejZNW\nlaP3yCWdlU4qTF2o0gV3KD85d17nP/ViTkF2Srn8zNjM/kRhonotZBPlDaTLDKuyDx/RS88vb7jM\n8NCVJe0doG6Gxpg5pcvq/u+V8CVJ1tqWMeZdkuL20NdKenVt+Gof95fGmAclfbUxJljzHnhh3cyU\nlM4IvdEYM9MOMl+ldKLiY+uO+6ikP1E6mfFdSm/l+WNjzP51x/2FpPcZY66y1j65Zvy/rTvu8+2P\n6x+/kcUNQsPXSNq9JnzJGDMtaSVZz2zjvCuPOyDpNkl/sCZ8rfiw0qV7X6dTA9jfrYQvSbLWHjHG\nHJV0pl9mB9sfd7zV/hms7Dh/UFJXA9i7JD1mrf3R7ZyoPa34o8aYd6s9vQoAwKCKo5p83JCCYEfC\nV23pYTVOPHnKWFQ/oqh+RPHuqzSz97pzOm/YOKLykcOdUCdJmdysSvO3arJ4aOfCgncKsgVl88WB\nbzyyU9hEeXNJ4vTis0tpN8PjjVM+NzmR1ZVXlnTl5cW+LjPcwmVKZ37s+k+s3DPVdoWkezc5x2NK\nl+TNKb3/R5I2Wt210op9Zd3vFZKet9ZG665bUXv5njHm9e3hT25ybd9+Dmt/oay/9vrrbuW0uq21\nzhhziTHmg0qXYF7e/m/lh/9sfglc3v54Wpt2a23FGPPymmM2rUnpczrT81kJaCfPor6d+MFeud6O\n/LVrq5+aPUpvBjxbVtJXnFs5AAB0l3OxkrAi75MdCy+t+iunha+1GieeVG5yvyamL9r2OZOopsri\nPWqcXPMeMshqdt9bNLvvBgWZ/PmUvMp7BZm8MoVZZTI7c+/YoIvjRM16qDBkE+X16rVQTz++oGeH\nY5nhZlbe357pvp+tnsTKD8Pava7cRgducO34DMesfMN9k9KmHxt5eN2/t3PtzSTrB4wx3yPplyU9\nr/Reub+R9DlJb1C6nPBsnOmbIavT9ww71+ez8rjNfmg3ull1J/bgWPl+OJ/XoWOrAPacpJuMMfn1\nKX4z7anLW5Su4QQAYKCks151Kcjs6DKzZvnZbRzzzLYCmHeJaksPqXr8QXm3+n+/k8WDKs7fqly+\ndF61dq5zhs6Go8g5r2q5ySbK63jvdWyhqqcePaKXnttkmeHBkvbuGZxlhmewsjfUG9Z/whjzHqUd\nBd+v9L2uWbfMcMXVkirW2rOZaVm59m3GmNxK57/2dS9UGnj+c/u6knTUWvu/1tX3FqWzLHV1Sfv+\nsZ+V9BlJt1lrwzWfe/s5nHLl+Vy9/hPtRicXa3XJ5PlaaYqy2dLLjWaotrNM80xWzvHalkdt01Z/\n8vk9SZcqXZ96xrZGxphLJP2p0qYcW92kBwBAT3nvFLWW5eKG1IXldWuXB57rMd57NSvPafELv6/K\n4j2d8JWb2Ke9l3619lzy5TsSvrycFGSVy+9RfmLXWIQv771qlZaWFquKY0dL+bYkdvrCU4v6H3/y\nqD71F0/oxS+shq/JyayufuMevevLL9VNbzswTOFL1trXlIaLr2s3lZMkGWMKShtMvKN9/9HK+9Zv\nX/v49n1ib1F6P9bZ+gulHQO/bd34tyq9D6op6c+Uzs79qDGmM+1sjNkl6Y+UNgg5l5mWRNtbOjjV\nrvGZdeFrT7tO6dRJmi3Pa609qrTV/dcaY9bvuv5jSmfIdmrj5ufbHzfLJnPGmJtX/mGMOSjp+h24\n7sr1nt/qoO3a6rfuLyr9C8FXSvoKY8xDkh5VmvzqSr9xppQ26rha0luVTs/dqTRVAwDQd0ncVBJX\n0w6HA9pcIWotqbJwl1q1FztjQXZCxbmbNb376h25Jyud8corlyspk92h5YsDznuvRj1Sq5Fuokzw\nSqXdDI/qmccXTl9muHdCrz+4SxdfNPDLDM/k+5S2Wf+MMeZjSjv+fbPS7oJf0z7m59r/+9fbMz8P\nKH1P+39JelVpq/Sz9VtKuyL+ersV/oOS3qx00+A/WpnxMsb8rNLGdfcYY/670vfV36703q9vtdY2\nNzr5GRyRdJ0x5gck3WmtvX+jg6y1y8aYuyR9gzHmmNLZqUuVhsal9mG7z/K836O0Vf5d7a/3y5K+\nVNJXS/q00pm/nXCf0rb1tyltYb+RPzXGfFTp1/R97Y8bfTPfaoz5jU3O8VFr7dNr/n2LpJfWNUY5\nZ5sGMGtto/0XgO9V2j7yhvZ/m3lB6RfiF7a7ZBEAgG7x3iuJynIu6vq+XvnJA4rqR854zHouaal6\n7AHVlh7R6h+8A03vuVbFuZuUyZ7/rIOXV6CscvniGffyGiUrmyhLInhpdZmhffSIXl63zDCTkV53\n8awOHtylvXsGdxuGzfgN7vSy1t7Tbvn+YaXt5gOlQePLrLWfbh9Tbm9G/ONKg9j/qXSi4TclfcRa\nu/b+rK32l+p8zlobGmPe0T7nP2mf83mlHcL/w5rjftQY84TS4PITSu+ReljS+621f73Z+c8w/hOS\nDintYP6fle5rtlnd/0Rpm/2vl/SvJD2utAP6JyQtK91MeSU0nfG81trPtUPsh5QGyVmlnQp/WGmY\n2c4+XGc8xlobtbcJ+KJNDnlY0l8rDbc5pTNvFaWher3Xa4Nlqu06fl/S05JkjMkrDWDrO1Ges8Bv\n9F27jjEmo7RDyjVKp2pnlE5H1pT+heCRnUqE3ba4WDnnjdi6YW6uqMXFSr/LQBfw2o42Xt/B5pJQ\ncVQ5p9i1d++MlpZqZz5wjVb9FVWObN23qnjB7Z17wLx3apx4QpXFe+WS1T90F6YvUWn+duUn9519\n4et47xQEOWXzM2MVvFrNSI1alAbPdTOe5/LaDrskTjdNto8e0Yn13Qwnszp4RUlXXFHS5MRwNWDx\n3st7r1wuq1zOa+GnPpi57c//eKDe46E7jDH/UOmm1Gv3ZJMx5nlJS9bat+zw9b5a6RYCb7bWrm+O\nck62tfC73WL+YZ3ekQUAgIGy0l6+l8sNJ6YvVrz7qk07IU7tvqoTvsL6qzp55LDi1rHO57P5kkoH\nbtNE8YrzrrsTvAqzymaHbzbjXIWtNHitbKLc7VnPQVevtrsZPnF6N8N9e9NuhsO4zNB5p1w2o3w+\nq4mJrIIgkHOnNfnDCLPW/k9jzP1Kl3X+fQ8u+W2S/nqnwpe0zQAmScaYNyqdBZtXOq2YkVRVOlU7\nNDNgAIDR5L1THJblXdyXe71m9l6n3OR+NcvPrNuI+ZAmpi9SElVUPnqPmuXV2wqCIKeZ/W/V7N43\nKzjPZhhp8MoqWyiNVfCKwkSNeqg4ZhPl1WWGC3r5uaXTlxleMqtDV+7SniFbZui8UzabUT6f0eRE\nfqxfY3R8QNKnjDEfWXev1o4yxtygdH/jm3byvFv+tjfG5JTexPi9Sm8K3OrY55Xu8v3r7RkzAAB6\nIkmaSqL+N9qYmL7otFbz3sWqHHtQtWOfkfer2wNNll6v0oFblc3Pntc1xzV4xXGiRi1UHLGJ8lbL\nDKcm002Tr7h8uJYZOrcauiYm8mP9+uJ01trDxpj/JOknJX1je7gbS1B/WtLP7eTsl7RFAGu36vwb\nSV+s9H6vB5XuyP2apJWf7iml94S9UWmDjl+R9E5jzNeubWsJAEA3pI02KnKupWBb3Zd7J20r/wVV\njt6lJFq9XzA3Oadd83eoMH3heZ5/JXgVld2BZh3DIkmcGrUWmyhLqlVbeubxo3r2iUW1Wqfu/TuM\nywyd88pkAhUKhC6cmbX2e9f9+4ouXOOdO31OaesZsB9UGr4+JelfWmtf2upE7b3CflvSV0j6fkn/\nfodqBADgNM7FSsJyO4gM1pvwqHlc5YXDCuuvdMYy2SkV527W1O6rzqte770UBMrmi8rmxid4OedV\nr7UUtcZ7E2XvvRaPpJsmv/z8Bt0Mh2yZoW/fs5fOdGWUzQ7WzzLQDVsFsPco7fv/1dbaM+7Gba19\n0RjzNUpbNr5HBDAAQJckcUMuqknBYN3z4+KmKsfuV335Ua2uhsloZu+bNLv/RmXOc4mgl1MmN61s\nbnqgnnc3ee/VqIVqNuP2jNd4PO/14tjphWeO66nHFnTi+Klvy4ZtmWHaKEVp6CrklMsRujBetgpg\nl0r65HbC1wprbc0Yc7ekrkzXAQDGm/deSXgy3dtrgGa9vHeqLz+myrH75JNWZ3xi5lKV5m9XbmLP\neV7AKchOKJcvjlfwWrOJ8rjOeNWqLT3dXmYYrl9muG9Sh64sDcUyw07oymU0MZFVLjf4QRHolq0C\n2GuSrjyHc75B6Q7VAADsGJeEisNy2mJ8gMJXq/ayygt3Km4d74xl87tUmr9dE7OXnVdg8nIKgryy\nhV3KnGeXxGEy7psop8sMK3rq0YUNlhkGet0lMzp0cJf27B7sZYbpJuBSLpdRvpBVIU/oAqStA9jf\nSfp2Y8y/tdb+9JlOZIwJlO6gfa2kj+9MeQAASHFYlXONgQpecVhW5ejdalae7YwFmbxm979NM3uu\nV5A59zeb6X0xGeXyu8ZvE+V61LkvaNx0lhk+ekQnltZ1M5xqb5p8eUkTA7zMML1HUcplMyoUsioU\nultrI26o3qrr3mundVtXrwTsnK0C2IeVNtT4SWPMt0j6pKRHlc6M1ZUubp+SdIGkayT9Y0lXKb1v\n7IPdKxkAMC6cS9qNNpKBCV/ORaod+6yqS5+T/OoGsFO7rlJx7mZl8zPndX4vKZufUTY3dZ6VDo8w\njNWoRUqS8dzLq1Zt6enHjurZJ48qXLdp8v59kzp0sKSLLhzcZYa+PUWXy/UmdMUuViNuqJWE8pK8\ni7p6PWCnbRrArLWvGmNukfQxSV8p6V9v43x/I+m7rbUv71B9AIAxlcRNJXFFgTID8Ybce69m+WmV\nj94jF1c74/nJeZUuuEOFqfnzvYCC7KRy+ZmBeL69sLKJcpI4BcF43eflvdfR19Jlhq+8cPoyw0tf\nN6tDV5a0e0CXGXrv5ZXe01UoZJXPd//ntBE31ExaipM43X4gCBSoO5s/Ad205YLydpB6tzHGSPpH\nSme6LpQ0o3RvsJqkVyU9IulvrLXPd7VaAMDI894pDivyLhyYWa+ouaiTRw4rarzWGctkp1U8cIum\ndpnzvs8rExSUKcwqcx7LFofJ+k2UxyVwSulzf+GZ47KPLujkhssMd+mKy4sDuczQey/vvXK5bHum\nq/uv3frZriAY773fMBq2dUevtdZKsl2uBQAwhprVF1Rfflxh4zU5lyg/sU9Tuw5pYvrifpemJG6o\nsnivGiceXx0MMprZe71m973tvO7PWmmwkcvtUiY7Hg02xnkT5Vql3c1wCJcZOu+UzWY0kc9qYiLb\nk8C82WwXMAq29RvfGJOx1rp1Y7OSbpa0X9JRSQ9Yays7XyIAYFRVFu9X7fhDcs5JcpL3ihsLqjQW\nFO++SjN7r+tLXd4nqi8/qsri/fIu7IxPzF6u0vxtyhV2n8e5nYIgp1y+ODYNNpxL9/IKW/FYtZTf\n1jLDgyXt3jV4ywydS0NXvpDRRCHfk9eM2a7hYIy5SNITkj5orf2lDT7/z5XeuvR6ScuS/p/2sbVt\nnn9a0o9I+iZJF0l6TtKvWWs/tjPPoP+2DGDGmMsl/ZKkiyW9rT2WVdrt8PuVNuFY0TTG/BdJP2it\npQ09AGBLzeoL7fCVpPtcBYG05i/rjRNPKje5XxPTF/W0ruryczr2hU8pDpc7Y9nCHu2av10Ts5ee\n83lXOhtmC0Vls5M7UerAW9lEudVMg9e4tJSP40TPP51umjxMywyd88pkAhUKGU1M9CZ0Sec32+WW\nlhXZp7paH1a1J2D+RFJRG9x+Z4z5EUk/JekhSb8s6TqlYexmY8wXW2u37JjSzhl/qHRP4b9SGt6+\nQtKvGmOusNZ+YAefTt9sGsCMMYck3Stpr6Tn13zq45K+Wek9YHdLekFpJ8S3SfoOpV/g27abcgEA\n46l6/BE5F0lemy5papaf6VkAi8MTKi/cpVb1+c5YkCmoOHeTpvdcqyA4tzfLaVvuQNn8rLK58Qle\nzXqkZjNqz2KMR/CqVlp6+rEFfcEunrbMcG7/pA5duUsXXjg9UDOAKy3/8/mMJiYyymZ7M+N0PrNd\nvlZX9PiTih99XO6VV9PBa6e7VywkScaYy5SGrxu2+PxHlOaDf2CtTdrjH5b0Y0pzwq+d4TLfqDR8\n/by19ofaj/+g0kZ/P2CM+V1r7aM78HT6aqsZsI8oDV+/LOmHJckY8yVKw9cLkt5trX1k5WBjzD5J\nvynpa5VOG/67LtUMABhycVRTWH9FgQJt9afuqHm067W4JFT1+GdUW/q85FdX20/tvlrFubfrf7P3\n5uFxZWed/+duVaVSlWTLlmTLtlbbx+2l00nI1gsECBAGenp4mulmGQYY8gt0whCgmRl2krAMMAQS\nBmgCM2ESJoHukBmaEHh+EPiFdDprJ2635eXIluTd2mwttdddzu+PW6WtqrSUVFKVfD/96FH73nNv\nnaq6pTrf+77v9zXM6hd2CoVuNmFu0Jq+kcimbTIZG7g3enmtlmbYcyjGQJ2lGXpKoWn4oitkYppb\nl+aXdbJk3Oy6o13KtnHkJZzB87jDoyx5oQNqjhDiJ/G1QQT4Z+Cbygx7O2AAv1EUXwV+A3gX8DZW\nF2DvBOzCMQBIKR0hxC/iC7sfYW3O7HXNSgLsLcAl4KeklMWr/DsLv39ssfgCkFLeEUL8O+Ay8ASB\nAAsICAgIWIbnOrj2XKEOim31j1ZKkZmVJCa/gOek57dH4weI7nkIq6l9Ayf30IwwphW/J0QIlGui\nvLOft2O7XLl8h6HBcWanl6YZRptM+vtb6OupnzRDVfiwWaZOOGxgmls3r2qjXcrzcEev4gyex5FD\nkF+WvWaamEcPo993FOSnazP5LebRp5/vBd4KtOD31v3UJ9/32J1tnZTPu/BrsX4UEJQXYF+P/1f9\nM4s3SilzQogvAt8qhIhX8owQQoSB1wNfk1LOLtv9FSBTeIyGZyUB1gp8dpH4Aj/fE+DFcgdIKbNC\niO8bEJYAACAASURBVJeAb92k+QUEBAQE7ACUUrh2CuVm/TvemoYV6cBOj614nBXpqMl88plx5sZe\nwM6Oz2/TzWZaOh6kq/cBpqfTKxxdGd/ZMIQRakbX7w1nw3utiXJyLsel8+MMX5zEztd3mmFRdJmG\nTihsELK2VgxmnSxZN4ft2muOdiml8MbGfdF17gIquayiRdMwersxT57APHYELRzGsfMN79X96NPP\nh4FfoXQN/Y5Hn37+Q59832N/sg3TWszbgU9LKZUQ4liFMQPAuJSy3B/QK4XfR4GvVji+Bz+CNrx8\nh5TSFUJcLxzf8Kz07XADOLVsWzHnsg+/91c5juI7ngQEBAQEBOC5eVw7sSgy4hNpGVhVgEVaDm/q\nXFw75dvKz15c2KgZxNpeTfPe16DrVlUComiwYVqt94yzoWO7pFP3RhNlpRTjt+YKaYYzS/YZhkb3\nwfpJM1SF1Dyz0CA5FNpa0VVttMubnsE5dwH77DnUnbsl+/V9HZgnj2OeuA897scD8rZDLpUnk83j\nJdo2+6lsNe8GvqXMdhN4+6NPP5/65Pse++jWTmkBKeU/rmHYHsqIpwLFiFbrKscDzFTYPwscKefO\n3misJMCeB35SCPGbUsqfLWz7GPDLwG8LIb6jjDX9fwaOAR+uyWwDAgICAhoKJ59cEvVaTDh6AGfX\nMTIzF8se27Tr2KYZcCjPJTV9huTUSyhvIY0pEu8n3vEQZqiluvMWFrv3ksFGsYmybe/8iJdjL3Iz\nLJNmONDfQl9vfMtFznKU8mNdVkF0WdbWN7euxslQpTM4Fy5inz2Pd+NmyX6ttQXz5HGsk8fR2/cC\n4DouybRN3vGjjxpLzFMbkkeffr6f8uJrMT/86NPPf/yT73ssv8q47cQCchX2Fbev9IfSWjZ2pXNU\nl6ZQJ6wkwH4d+G7gPwsh3gT8HvBl4Hvw7SFfFkL8KX5I8QDwb4FvBJL4RXoBAQEBAfconufi5udQ\nyl1xIdjcdj9mZC/ZucvzhhtWpINIy+FNEV9KKXLJq8yNfw7XXigpMMNttHQ+Qrj5YPXnLhhsGGZ0\nR4uQIsUmyra98yNeq6YZDrSyf9/2phkqpVBKYZpGIdK19aKrmmiXsm3cS8PYg+dxL4+AtyyQEYlg\nHhdYJ0+gHzqApmm+q2bWIWe7uJ5//e2wq+/b1jBmF37/3c/WeC4bIQNUSgEohodXckkv3uVY6Ryq\nQopjQ1FRgBVMNb4B+DP8grdHWFoufRK/R9hibgHfK6Uc3eyJBgQEBAQ0Bq6TwbNTZaNe5QhHu2pi\nNe/kppkb/xy51LX5bZoeJt7+BqK7T6BpVTq/KQ/NiGBasXtCeCmlSCVz5HPujo54rZpmeCjG4f5W\nWlu3N8XUUx6moWNZBuGwseXvh1KKrJtdV7RLeR7u1et+XddFCbllQRzDwDh6GOvkcYyBPjTTX57a\njksu75K33fnH2KHX30ppedWM2y6mqTzH4vbl5hrLj188ttw5klXMq+5YsUK4IKTeLIR4BHgceA1w\nGP8FCOOH/yaBV4C/B/6ikrNJQEBAQMDORimFm5/F8+zqxc0m4Lk5klNfIXX3LFC8u64R3X2C+N43\noFeZKqjw0LUQeiiGrteHs10t8Zso2+SyNpq+cyNeju0yWkgznFueZhgtpBn2bG+aoac8DEPHsnQi\n4erqFDdK3s2TcbLkCym8q0W7lFJ44xMLZhqJ0nWzb6ZxHPOYQIv4ARLlKdIZh7zt4Pkt9Haq6FrM\n7TWOW7lodvsZAh4RQoSllMvTCPvwewhfWuH4K0C+MHYJhQbNh1jwo2ho1mTRJKV8AXihxnMJCAgI\nCGhQPDePk5/zF2XbJL58W/kLJCa+iOcuLKRD0QO0dD6MFdlb5Xk9NM0sGGzsfGdDpRTZjE02s7Ob\nKCfnsgydm2BElkkzbI9wpOBmuF2Lf89bEF3hsLUtAthPMcyS8/KFz8HqaY7e7JwvugbP401OlezX\nO9oxT53wzTRa4vPbc3mHvO1hOwtpyztfd83zKeApVjfHe2lrplM1LwBvxs+cmzftEEJE8NMnz0kp\nK6YgFvp9fQl4vRAiJqVcrNpfDzQBX6jFxLeanf9NEhAQEBBQU5x8Es/LbGvUK5++zdz4C9jZyflt\nhhUn3vEgkfjABpwNNYxQHMO4Nww2chmbdHrnNlFWSjF2Y9ZPM7xWmmboN01upbVle9IMi9ecZelE\nItsjujzlkS2IrvkUQ1jx860yWd9MY/A83rUbJfu1lrgf6Tp5HKNjob+e67hk8t4yQ42dd92txiff\n99jUo08//2H8JsPl8ID3f/J9j9V79+mPAT8PvFsI8S9SymKu6c/jt7Jai5X+R/DLnt4DPA0ghLCA\nX8UvhfrTzZ70drAmASaE2Af8EPDN+Dbze/FfhLv46Yf/BHxUSjlRm2kGBAQEBNQbSnk4+VmU526b\n+HLtJHMTnyc7tyirRTOJ7XkNsT2vRquyF5dCYZhRDCu6STOtb3Z6E2XbdrlyaYrhC5NM31lavx+N\nmhzub6F3m9IMF4uucFjHMLbns5R1cuTcLHnXnq/fXDHF0HGWmmm4S6OIhMMFM43j6N2H5oWVUopc\nzt3JhhpV8cn3PfbMo08/nwJ+kKU1UDfwxddntmVi60BKKYUQvwP8F+C0EOJvgRPAvwI+xzLxJIT4\nSXxzkd9b1Hj5z4AfBn5KCHEK+Bp+Y+r7gf8mpTy3JU+mxqz6zSSE+CXg5yhvGxkFDuK/sL8qhHiv\nlPK3N3eKAQEBAQH1hufmcew5NLbHlEF5Dsm7L5Oa+ipKOfPbIy1HaOl4E4YVX+HolU6s0I0wVjh8\nT9yJ3+lNlBNzWS6dG2dETtVVmqGnFJqGL7pCBqa5PfVljmeTdrLk3UKgYg11Xb6ZxjmcC0OQW1bm\nYxgYh/t9M40jA/NmGnBPGWpUzSff99hHHn36+Wfx0/Va8WvDXqrDyJdiqTHfPFLKnys0TH4H8BP4\nz+F3gfdIKe1lw98FdAMfomDOIaX0hBBvxY+APQE8DFwG3gn88eY/le1BK/YwKYcQ4o+AHwMc4BPA\n3+L3Gp/Bt4hsAx4AvpOFzt3vl1L+dA3nvCEmJxN1dRG3t8eZnAx8S3YiwXu7s7mX31/HTuG5aTS2\n/k69UopcYpS5iRdx7bn57WZ4L637HiFUpZvivMGGFaOzc9eOf28dxyWdzOM67pqa5DYSSinGbvpu\nhreWpRmapu9mONC/9WmGCoWG3yDZCumErO0RXZ7yCj27bDzPWdP7705M4pw9hzN4AZUo/Wzo3Qex\nTp7AvE+gNS3cr1eeIpN1yTkOStWupstxHE5/6m/0//T7H6irNV5AQCUqRsCEEG/BF1+3ge+WUlYq\nevsc8AdCiDcAfwW8Swjx8RXGBwQEBAQ0IEopnPzMtqUc2tk7zI2/QD690LBVNyLE299I0677qpqT\nn/6lFww2ttdefCsoaaK8g8SXbbtcGZry3Qxnskv2NRfcDO+/v4NsZuv62BZvcpuFBsnb5aSolCLn\n5si6OWzXXqjrWuH99+YSC2YaE5Ml+/X2vX5d14n70HctdQ3P5R1yeRfbVRTL2IJgV0DAAiulIL6t\n8Pt71yKmpJRfEkJ8D74DytvZIS4lAQEBAQHFlMPEtqQNeW6WxOSXSU8PspD1ohHdfYp4++vQqzTI\nUHjoZhTTat60udYrnqdIJ3PYeXfHWconZhelGdpL0ww72ps4PNDC/n1+mmE4bJDNVDjRKkzeyXD9\nRpKZGV/A7doV4tDBGO17mpaMKzZItixfcFnW1jdILpL3bLJOhrxrr6lRsspmcS4M4Qyex716rWS/\nFo/Nm2noHe1Lnlc5Q40ddJkFBGwqKwmwNwAXpZRr7rgtpXxRCHEJeNOGZxYQEBAQsO0opXDtBJ6X\n2/KUQ6U80jPnSUx+CeUuRDRCzYd8W/lwW3XnLaQbmqH4tjo3bgVKKdLJPLmcU4h47YwV8Upphoah\n0dMd53B/Cy2blGZ4aXiGa9eW9rGavptj+m6O7m6/QbNSyk8v3KYGyUXKphiuYHShHAd3eBT77Dnc\nS8NlzDRCmMeE72DYc2iJgFtsqOF4HnpgqBEQsCZWEmDt+O6G60UC31TddAICAgIC6oUlUa8tFl+5\n1E3mxl/Ayd2Z32ZYLbR0Pkw41lulrXyxn1d8x6cbKqXIpG1ymZ3VRNnOu4xemmJocJzE7LI0w2aT\ngb7NdzOcvJMpEV9FlFJcu5akfW8T3Ydi22oq4TdK9qNdq6UYKqXwrt/APnse54KE7NLXEl33zTRO\nncA43I9mWUt2zxtq5F3QtUK0a2dcYwEBW8FKAqwJqKYKOVE4NiAgICCgAdnOqJdjz5EY/zzZxPD8\nNk0zie39OprbHkDT17+wvpf6ec0Lr6xvNrZTIl6J2SxD58YZXUOa4WZz/cZS8VW8njQNdN1PL7x1\nK0VPd5XOmxtgIdqVx1N+beaKdV2TU77oOnceNTtXsl8/dBDr5HHM4wKtaVlqpeeRyXpLDDV2yvUV\nELDVrCTANPzGb+tFsdMaiAQEBATcI3huHtf2771tpfhSnk3yzmmSd74GamGB3dQqiLe/CaPKOi2l\n7o1+XkopshmbbKYgvHZANGJx0+Rb12eX7KtFmmElZmbyZUXXYqbu5iocXRvKRrsqpNN6iQTOuQu+\nmcZYabtWbU8b1qkTfl3XMjMNpRR52w0MNQICNpnqOlSuTmADGhAQENBA+FGvJJ6X3VrhpRTZxGXm\nxj+P5yxEGqxIBy37HiHUtK+68+Kh602YVvOOECOV2InCy867jBbcDMumGfa30Ntd+6bJS0WXhr7N\njpF+tCtL1s2tGu1SuRzOxUs4g+dwR6+W7NdizZgn7vNF177OkusmMNQICKgtqwmwZiFE9zrOp+E3\nZw4ICAgIaBA818a15+Yt2bcKOzvJ3PjnyKdvzW/TjSbiHW+iqfVY1XVeuhHCtHa2wcaC8HIAtSOE\nVzHNcERO4thLE3A6Opo43F+7NMMii3t1hcN+g+SO9iYmJ7MrHre3LVyzOeU9m4ydIe/mV4x2KdfF\nHR71reOHLoPjLB0QsjDFUcxTJzB6u0vEm1KKbM5vlBwYagQE1JbVBNh3Af9mnefU2IIImBDCBP4j\n8P8Avfj9yv4M+E0ppbPCoQEBAQEBFKNeKd9hUNO2bBHvORkSk18iPXOeha8Lnea2VxHb+3VVGWQo\n5aHpVqGfV62SO7af8sKrcZfJSiluF9IMb5dJM+ztiTPQ30JLvHZphkt6dYWNkgbJ/X0tqwqwvr6W\nTZ9TxsmQ9fK4ru9kWC7apZTCu3ELZ/A89vmLkFnmsa/rGAN9vnX80cMlZhqw1FCjWNMVGGoEBNSW\ntXxLVfMp3IpP7h/ii68XgL8GHgbeC7wK+Ldb8PgBAQEBDYvnOoWol7dlwkspl/T0IInJr6C8hZqZ\ncKyXlo6HMMO7qjin7waw0w02yqcaNu4iebU0w8P9rfT2xLGs2kUxPc/DNI1Cg+TKvbr2dUY5fLiF\ny5dLTSsADh9uYV/n5iT/OJ5N2smQc/OAVrFvlzd1B3vwPM7gBdTMTMl+/WAX5snjWPcdQ2sunVtg\nqBEQsL1UFGBSyrrN3RBCPIgvvj4upXxy0fb/Bfx7IcR3SCk/tV3zCwgICKhnnHxyy6NeudR15sZe\nwMlPz28zQrto6XyYSKynqnPeC42Ud1qN19xMhkvnJhgZKk0z7Oxo4vBAK/s6m2r2PD1XoelgWTqR\nsLXmxzlxXxt72iKMjs7NG27sbQvT17dx8aWUIuv6tV1OMdpVJsXQS6Zwzl/AOXse7/ZYyX6tbbfv\nYHjyOHrb7rKPExhqBATUB42ap/HOwu/3LNv+c8APAG8DAgEWEBAQsIjtiHo5+Vnmxl8klxyd36bp\nIWJ7X0dz2yk0rQpbeTx0PYRptTS8IKmEUops2iabbXzhpZTi9vVZhs6Vphma5oKbYbxGaYae52EY\nOlZIp60tgq5XY/DsR8I2K9IF4HgOmUK0S0HZaJfK53HkJZyz53FHr4BaWuGhNUcXzDT27yt7nQSG\nGgEB9ce6BZgQoh3owv8M35BSTm36rFbn64FJKeX5xRullLeFEJcK+wMCAgICCmx11Mvz8iSnvkbq\n7stLbeV3HSfe/gYMc/0LWd8kxNjRdV4lfbwaWHjZeZeRoUkunRsnMbvUpr252eTwQCu93bVJMyw6\nGIZCOuGwNd+Iuh4aUmcLToZ20UJ+mdmFcl3c0Ss4ZwtmGra99ASWhSmO+GYafT0Va8NyOZfcTjfU\nUAqUQpk78+9BwM5lTVesEOIgvuHF9wIHlu27DXwM+EMpZanX6SYjhAgX5vDFCkOuAEeFEHuklHdq\nPZ+AgICAemaro15KKTJzQyQmvoDnpOa3W037aO18BKupo7rzAobVjGE2rTq2EdlJwmtuJuM3TR6a\nKk0z7GzicH9t0gw9pdC0YnqhiWHUTyWFpzxSdpqcl6dYdLVYOCml8G7d9h0Mz11EpdNLT6BpGP19\nmKcKZhqh8tHCxYYa6Foh2tW411JZPA8MAyyLrOFxxb7NtdR1LvQe2u6Z3RMIIX4V+IUKu5+VUn7v\nKse34Xs2fCfQDlwAfltK+dymTrTOWVWACSG+H/gDYHF3vruAA7ThR8N+BvhRIcRPSCk/XIuJLqKt\n8Lu06tSnmN/QCgQCLCAg4J6k2NdLubkti3rlMxPMjb+AnVmoT9HNZlo6HiTScqS6OSgPzYhgWrGG\nFiWV2CnCq5hmKAfHGbtRmmbY2+27GW52mqEquGhai2zj64mskyPrZrE9e6Gua9F77N2d9h0Mz55D\nTZcx0+ja7zsYnjiG3ly+1lF5ikzW3bmGGsUnZZpgWaRMl9H0VUZmRriZvjF/DcDAtk7zHuJVQA74\nr2X2Da50oBCiGfjHwjmeA64B3w38pRCiXUr5h5s817plRQEmhPjXwEfw0w0/hm/z/lkppV3YbwGv\nAX6k8PMhIURKSvlXNZxz0UO1Utv54vada4cVEBAQsAKem8e1E/NpWLXGddIkJr5IZvbCwkbNINb2\nAM17X4OuV2Erj0LTTIxQK7q+89KLlFJkUja5nF1o9tuYC+Z83mFU+m6GybmlX8uxQpphzyanGS6x\njQ8ZNW/IvF78hskZMm6uEHleaqrhpVI45y/iDJ7Hu3m75Hht9y7fwfDUcfS2tpL9RXJ5Z2caahTr\n3AqCi1CIWTKMJIYZmR1hPFPGgGRnJljWK/cD56SU763i2HcBrwbeKaV8BkAI8WvAF4DfEkI8J6Wc\n3Lyp1i8Vv9WEEB344ssBvktK+XfLxxSE2JeALwkh/hL4W+B/CCH+pYYvYLHJRaVv9GI3xFS5nbt3\nR+vuDll7e3y7pxBQI4L3dmdTb++vUgo7N4frOGha7Z0BPc/l7u2vMnX983hufn57vO0I+/q+kVCk\nelv5UCiOYW3ffbRavbfKU6SSObIZm1gsTCxWuwa+tWT6ToqzX73JxcEx7Ly7ZN/BAzFOHG/jwIHN\ni1oqpUCBaemEQybhcGXb+NVoa6vNZyPn5Ek7aRzHJhzRCbOQLuvl82TPSdJfO0t26DJ4S8009OYo\nTQ+cIPqa+wkdOlDxudmORzZnk8176JZJNFTa16vR8N9bD82wIGRCOAIhi6nsHeTdIYbGLjGRnig5\nztAMelt7EG1H6Y338IGbn96G2d9bCCFagG7gn6s8xTuAMeCPixuklEkhxK/jB3q+D/jARufZCKx0\nW/EdQAvwU+XE13KklP8shPhJ/Bf1B4Hf2ZwpljCLXw7QWmF/a2H/bLmd09Ppcpu3jfb2OJOTie2e\nRkANCN7bnU29vb+em8fJz21ZJCWbvMLc+Iu4+YW0KTPcRkvnw4SbD5FMA+my98EqoxSa2YRhRtHS\nNmCvekgtqMV7Ox/xytoNmx6mlOLW9VmGBscYu7G0J5ZpFpom9y2kGc7Nrty8eC14ysM0dCzLTzF0\nHY+047C8RGqttLU1c/fuOq/LVeaXcbJk3TyecpZEupTn4Y5e9eu6Lg6VmmmYZsFM4zhGXy+aYZAB\nMnNLX7dyhhoNz6I6LkIhCIVQmsZ4epyR8WFGEyPM2qXLOFOz6In10B/vp6e5l1ChabuddkvGNipP\nPPtUL/BW/DX4GPCp5558pl5Kau4v/H5lvQcKIQbwy5Y+LqVUy3Z/pvD76wkEGI/i13p9cB3n+5/A\nbwDfRY0EmJQyL4S4CvRVGNKH75BYqUYsICAgYMfg13rN4Xn5sr2DNhsnN8Pc+OfIpRY8lzQ9TLz9\n9UR3n6xqDgoPXQthhONb8hy2El945cllHTRda0jxlc87jMgpLpVLM4z5TZM3M83QUx6G7tvGr6dX\n11ZiezZpO0PeXRDUmqb7Zhq3xxbMNFLLxJ6mYfT1+nVd4jBauHIEdN5Qw3bnnRIbVnx5HgWHFP8n\nEgHDwFUut9O3GJkYYTQ5QsopFcdhPUxfvI++2ACHmg9h7sCUZIAnnn0qDPwK8K3Ldr3jiWef+tBz\nTz7zJ9swreUUBViHEOIfga/DD3r8E/ALUsqhFY4tFukNL98hpRwTQuSAo5s52Xpmpav4CPBlKeWa\nb2FJKV0hxJfx8ztryQvADwghjkgpLxU3CiG68Of9NzV+/ICAgIBtx3WzuHYKDdCorXDx3DzJqa+Q\nuvsKUHS204gWbOX1KtwJ/Ro1vWArX5seUNvFThBes9MZLhXdDJ2lbob7Ov2myZ0dm+Nm6HkKw9AK\nkS6rLuzil6OUIlNomOwWGyYX5ulNz/hmGoPnUXfulhyr79+3YKYRi1V+jHKGGo0oupbXcYXD/m/8\n/mfXU9cYTQ4zmhgl55WW9EfNKP2xAfrj/eyPdmFU0S+wAXk38C1ltpvA25949qnUc08+89GtnVIJ\nRQH2M8Dz+EGaVwGPA28RQrxZSnmmwrF7Cr8rBUjmqJzdtuNYSYCFqc5FMMWCU2Gt+Ah+w+XfEEI8\nIaVUQgiNBUeWerhLEBAQEFATlPJw8wk8Zde8+FwpRWb2AomJL+K5mfntoWgXLZ2PYEX2VndePAyz\nGcPavMa29UCjCy+lFLeuzTA0OM7YzTJpht1xBgZaicc2XntUNInxRZdeV7bxi3E8h7STIefmAG2+\nYbJKp3HOS+zBc3g3bpUcp7W2Yp46jnXyOPrePaUnXkTDG2oUBdfitMJF0b28m+fq3BAjiRGuJq/i\nqNL04harlf54P/3xATojnY0pPKvkiWef6qe8+FrMDz/x7FMff+7JZ/KrjKslDn67px+SUn62uFEI\n8X3A/wY+BLy2wrFrMdG7Zwz0VhJgE1Tn6dlTOLZmSCn/SQjxLPAk8AUhxGeAB4GH8XNLV61ZCwgI\nCGhEXCeD66TQCv/Vknx6zLeVzy78SdfNGC2dDxKJH65qgVRMNzRDOyvdUClFOpknn2tM4ZXPO4xc\nnOLS+QpphgOt9BxaOc1w8k6G6zeSzMz468Ndu0IcOhijfc9CdLTebeMXk3EyZN0cjuv676mmo2wb\ne+gyzuB53OFRP7VuMU0RzPuOYZ06gX6wa8XPiOu4ZPIeecevX/JTDGv4hDYbzwNdXyq4FvU2yzgZ\nRpOjjCSGuZG+jqe8klPsCe+hPz5Af6yftvCee0p0LePb1jBmF/BG4LOrDawVUsofr7D9Y0KItwNf\nL4Q4WiEVcS0meptXoFnnrCTAvgh81/I0v5UoFNi9Fvg/mzG5VfgB4BzwQ/i2lleBXwJ+ewseOyAg\nIGBL8TwHN59ALSv0rwWunSIx8Xkyc4u+QzWD2J7XENvzajR9/dGPnZpu2OjCa3bab5p8ZYNphpeG\nZ7h2Lblk2/TdHNN3cxw61MyRgV2+bXzYIGTVr+hyPIeMkyHn5lEU0/8UzsgiM438sgCEaWIeHcA8\neQJjoA/NqPz8yhlqNMwVo5T/U6zjWpRWWCRhJxhNjDCSGOZ25vaiHl0LdDbtoz/mR7paQ/dMxtlq\nrPWFqOcX7DS+iUYvUE6ATRd+V3oOLUBpX4YdykoC7KP4OZ1/KIT4tjKOJUsQQhj4uaAa8L82bYYV\nkFI6wK8VfgICAgIagmzyKunp8+Qz/vdMqGk/0d3HicR6Kh7j5JMoNwPL+gltNspzSN09Q3LqJZRy\n5rdH4odp6XwQw6rWml2hm02YVu2t8bcKzyumGtroht5QwsvzFLevzyAHxxkvl2bYE+dwfyuxNaYZ\nTt7JlIivYq8uTdO4cSPFwQMx9u+r3/d/ecNkBXjjEzhnz+Gcu4BKlt6YN3p7/Lqu+46uaKYBDWyo\nUYxyFZwKCYdLciOnc9OMJH3nwolsaQKUhsaB6AH64v30x/pptirXwN3DrFV4lDZB2yIK6/xXAYaU\n8itlhhRD3ZW8I4qirMRETwixHz8CJjc6z0ahogCTUv61EOKzwFuAvxZCvF1KOV5ubMH84kPANwGf\nlFJ+qiazDQgICGhgEpNfJnVnaX1yPnWDfOoGzXteRbz99Uv2LW2oXEPhpRS55KhvK28vLMjN8B5a\nOh8h3HyguvMW3Q13ULrhcuGl12ndUjnyOYdhOcmlcxOkEkvTDOMxi8MDLXSvkmZYjus3FsRXsa5L\n17UlDaavXEnUnQDzlEe6kGZYdLxQswnswfN+k+Sp0jJ4vbMD89RxzOP3obesfEOiIQ01lptnRCL+\n/y8ZopjKTfmNkRMjTOdLTUcMzeBQczf98X56Y71EjPWb9NxjfAp4ipUDIzeAl7ZmOmWx8Hv/zgkh\n2qWU8yHzgg/Dg/i9Q14ud7CU8poQ4hrwiBBCWxbYeXPh9xdqMvM6ZDUvz+/GzzV9FLgihPg0/ps/\njv8i7wXehN+vIAScAf5dzWYbEBAQ0KBkk1dLxNdiUnfOYDXtIxLrXmotT/UNZ9eCnbvL3PjnyKeu\nz2/TjAjx9jcQ3XW8Olv5wiJ8J6UbzguvnIOuaw0lvGanMwwNjjN6aQq3TJrhkcOtdLRX72Y4Pe2L\nOU0DXS9/vU7drVR3v/Xk3TwZp2ghr6MymYKZxnm86zdKxmstcT/SdfI4Rkf7qudvOEON5T25w/WV\nlwAAIABJREFUykS5POUxnhnzRVdyhIRd2ifP0i16Y730xwbojnVj6Tvjs78VPPfkM1NPPPvUh4Ef\nqTDEA97/3JPPrJiNVkuklFkhxN8CjwE/i992qsjTwEngw1LKuXLHF/hz4BeAHwf+O4AQIl7Yli7s\nvydYUYBJKaeEEG8Afg+/ufJ3FH6W4wDvB35OSlk/f2UDAgIC6oT09Pk1jDmHFWlfZLJRu0W+52ZJ\nTH6F9PRZmK/T0IjuPkW8/XXoRpVmVGpnpRsudzWsR3v0cnhewc3wXLk0Q53enti60gxLzq8UmgYh\ny8AwNFD1/br4DZMzOKkss/kkuB7upWHswfO4l4ZLzTQiYcz7BNbJE+jdB1cVpw1lqFGMci3rybUc\nV7ncTN1gJDnCaGKUjFva/TpiROiL9dMf7+dg9BCGXr/1ffXOc08+88wTzz6Vwl9vL66TuoEvvj6z\nLRNbytP4ka5fE0K8Gb8h82uBb8D3Zfjp4kAhxLsBJaV8z6Ljfxt4AviAEOIbgBH8cqde4D9KKeul\n4XTNWbWbnZQyAbyt8EJ+B/AGoAPQgZvA14Dn7qUXLSAgIGC9FGu+KuF5HtnUNWJ2qqYRL6U80jPn\nSU5+Cc9dSNUPNR+kpfNhrPDKdtkVz4tC0yyMUAx9ByzCfOFlk8vaDWWusWKaYdxioD9Ob3cLplld\nZBOt1MGwfW8Tk5Mrtwzd27ZyjVStyHs2GTtN3rX9FMPL4+S+eBrnooTcMjMNw8A4MoB18jjG4X40\nc+UlUsMYahTNMwxjaS1XGWzP5nrqGiOJEa4kR8l7pY7nMTM2X8+1P9qFvkPSi+uB55585iNPPPvU\ns/huh634tWEvbWfkazFSyhEhxNcB78V3bvwGfIH4O8CvFjRDkV/Gv7v3nkXHJ4QQj+BHzx7Fz6C7\ngB/AeW5rnkV9sOZ24lLKG/gmGx+s3XQCAgIC7i2UUijlglJoGDUVX/n0LWbHXsDJTc1vM6wWWjof\nIhzrq85WvlDcYlpxdGN7FtmbiVKKVCLLzN10od9TXS6pS5i9m2bo3Dijl+6UpBnu3+e7GVabZugp\nD9MwCIcNQqFScd3f17KqAOvra1n341aLUoqMkyHj5vE8B29yyncwHLxAKlGaOmf0HMI8ecI304is\nHvltCEONYtGZafqCKxJZYhG/mJyb40ryCiOJYa6nruEsMuApsiu0a94uvj3SUf+1bA3Mc08+kwP+\nZbvnUQkp5XXgh9cwruwFJ6WcAN622fNqNCoKsEKB3eRmPIgQoqPwggcEBATck4Sa9pNPLa0v8TwP\nlOeXW2gaVqSjJo/t2gnmJj5Pdu7y/DZNM4ntfS3NbQ+g6Wu+F7cU5aEbTRhW845YkGXTNplMHr1N\na4jnM59mODjO+K2laYaWpdPbHWNgoJVYc7VtAzRCIZ1w2Fox9XJfZ5TDh1u4fLl86cfhwy3s66x9\nw23bs30LecfGS8zhnruIc/Yc3uRUyVi9o92v6zpxH3rr6uJQeYr05Su4Fy7CpL80MtrbUUePog5U\nZ1Kz6XjegnlGOOwLrwqknRSjiVFGksPcTN3Eo7RHV3uknb7YAP3xftrCbbWceUDAPcdK37pDQoj3\nAv+9YPm+boQQYfx80f9CffcuCAgICKgp0d3H5wWY53mAC2qpK1qk5fCmPqbyHJJ3vkbyzmlYdFe7\nqeUo8Y43YVRpB62Uh6ZbGKFW9GrFWx2RzztkUjae5y1x7qtX8jmH4YuTXDo/TiqxNEWspZBm2FNF\nmuHSFENzXcefuK+NPW0RRkfn5g039raF6eurrfhSSpFxs+TcHHYqiSsvYZ89j3fteslYLR6j+bX3\n4x45itG5tpsdRUMN92unMS5eXHq+8XG08XHUsWN4DzywKc9nXawjygUwl59jJOk7F45VSInuaury\n0wvj/cStrYtaBgTca6z0zflLwH8DfkII8QF8Z5PpFcbPI4Toxg8v/gi+8PqZjU40ICAgoJGJxHpo\n2n2S1J2X/XRDTWNxwUjTrmOEo12b8lhKKbKJYRLjL+I6CxbhVqSDls5HCEX3bejchhXHMKs06agj\nHMclk8rj2C5aBfe+emLmbpqhwXGuXLqD625emqGfYuiLrnIphmtlX2d0SyJdsCjalcvgXB7BOXfB\nN9Nw3aUDwyHM+4TfJLnnELt2RZmZzax47uWGGvrNmyXiazHaxYtoHR2ors35/FakaJ5RdCxcJcql\nlGI6f5eRQmPkqVyZSCA6B5sP0hfvpy/WT9TcmvcvIOBeZ6U+YH8ghPgH4I+A3wV+SwjxOXxb+jPA\nFWAWMPDt6LvwnVEewXdE0YF/AH5cSnm55AECAgIC7hF8W/kU0ZYjGFaM7Nxl7ELDUivSQaTl8KaJ\nLzs75dvKp2/Ob9ONJuIdb6Sp9b7qRYZSaEYI04rXvVBZDaUUqWQOO+cWDDbq10SgmGYoB8eYuLW0\nfsmyfDfDgf71pxmuJ8WwXvCdDLNk3Rz2lSs45y7gXJCQXWa+rOsLZhpHBlY104ClhhpuMRJa2KcN\nDa14bHFMTQSY5/lRrlBowbFwhetVKcVEdoKRxDCjyRFm8jMlY0zNpDvWQ3+sn55YL+EGrt30PMXt\nqSzXxkodGgMC6pnVbOiHgLcIIb4VP43wGws/RRa8ixfw8IXXr0op75mGagEBAQHlcJ0sru1HoTRN\nIxzt2jSxtRjPyZCY/DLpmXMs/GnWaW67n9jer6vaIEMpD00zMUJxdKOx0w0bydkwl3UYkRXSDFtC\nDPTF6emOrytNUCmFwk8xjEQWXAzrnWLfruzYbV90DV5AzZXWm+mHDmKdOo55n0BrWlvj33KGGstv\nMGiTq5fDaxObVOa+OMpVdCu0VhbXnvK4nb5V6NE1SmpR1LtIWA/TE+ulPz7AoeZDWHp17QfqgbmU\nzZVbaa7cTHPtdoac7UeDzTopxQsIWAtr+jaVUv4D8A+F1MJvwbed7Aba8Ttj3wWGgBeBTxUcEwMC\nAgLuWTzPxbUTKM+uqpnxWlHKIz09SGLyyyhvIRIQbu6mpfNhzPDu6s+NwrBiGObaFrP1ilKKTLog\nvOrc2XClNMOu/VEG+mN0tK/P9EQpha5rhMIGkXBtnTY3i6KTYWr6Dva5QZzBC3jjpSJH27sH69QJ\n30xj19pKzZWnyGRd8o6DVyij2tbXxPP8qNbiRsirRGUdz+FG+vq8XXzWLXWhjBrR+XqurugBDK0x\nBPdyHNfjxnh2XnTdmS21xg8IaDTWdTtTSnkN+J+Fn4CAgICAMjh2CuVk/DvqNRRfudR15sY/h5O7\nO7/NCLXS0vEwkXhv1edVeOh6uOHTDZVSZDM22YwNbM0ie+L2HNeG7zI9lQJg995mugfa6Nhf2dDA\n8xQ3r04zNDjOxO1yaYZxBvpixGJrj2JuxFBjO3E8h2RymvT5gui6cq1kjBZr9h0MTx5H71y7JXou\n7zA9l2M6kZ0/Zi2HqvZ2tPHxlcd0rMPBtNiXq9gIeQ1RLvAjgVdTVxlNjHA1dQXbs0vGtFgthR5d\nA+xr2teQn1+lFNMJmys301y5leb6WAbHLW2DpWmwb0+Y3gPNHNwX5v8OXtqG2QYEVEdj55MEBAQE\n1BGe6+Dac4W0vdotfJz8HHMTL5JLjMxv03SL2N7X0dx2P1qVd7r9uiAd09rd8OmGvqW8DagtW4QO\nnR1j5NJSo4OpiSRTE0n6j+zl6Kml5ie5rO9mePn8OKnksjTD1kKa4aH1pxnqhkYkZBIK1b+xSJFM\nNkli6AK5V87iXh4BZ5n5ciiEeewo5snjGL3da67bW26oYYSsdb8m6ujR1QXY0aMrn2RxlCsc9n/W\nMI+Mk+FKcpSRxAg30tdxlVsypi3cRn9sgP74AHvCexrmPV9M3va4dtsXXFdupZlNljffjjUZ9HRF\n6TsQpXt/lKaw/7fOXm6+EhBQ5zT2N2xAQEBAHVA02VButhD1qs0CyPNsUlNfJXn3ZVi0EGtqPUa8\n440YZnPV51YoDDOKYTW2C1omnSeXcVDzwmtrFqMTt+dKxNdiRi5NsXtvlPb9LUzfSXPpXGmaoabB\n/q4YA73NdLRH13wdLY52RSImhtEY0S7Xc5kbvUTqzMs4Fy5CZlkana5jDPRhnjqBeWQAbQ1RIljZ\nUKMa1IEDqGPH0Co4Iapjx0oNOJZHuSIR3y5+DSTtJKNJ37nwVvoWitLoT2ekk/74AH3xfnaFdq37\nOW03Sikmp/PzaYU3JzN4pa3I0HU42NFEb1eU3gNR9u4KNaTADAhYTiDAAgICAjaA5+b9Wi9Vu0iL\nUors3CXmJj6P56Tmt1tNnb6tfFNn9efGQ9dDmFZLwy5sFlINHYoRr40tudfPteG7K+5XSnHu9C3c\nl26WpBmGQga9vS3090TXnWao6xqRiEEo1Bi1XQCp8ZskTn+V3NlB1GwZM42DBxbMNKJrvyGwFkON\navEeeACtowNtaGjecEN1dPiNmIviy3UXzDOKtVxrfPyZ/IzvXJgYYTxbGm3T0OiKdvmiK9ZPrMoe\nfttJJutydVGUK5UpH7XaFbfmo1yHOpsIWY1xQyEgYD0EAiwgICCgCpTycPKzeF4ejdqletmZSWbH\nX8Be1DhVN6PEOx6kqeVo1Y+7kG7Yim5U7iVUzyilyKZtstnFNV7bI0KKNV/L8TxFPuuQyzrM3Fna\nf6p1V4SB/jiHupqwrLV9HauCS55lNVZtlz03y+zLXyX9yhm8sTICY08b1snjvplG29qNY5SnyORc\n8nbtDTVUV9fSSFc5x8I1RrmUUtzJTfk9upLD3M2VCnhd0zkUPUR/fIDeWB9NDWaGU7SILwqusalc\n2XGmqdG9rxDl6oqyu6Ux/x4FBKyHQIAFBAQErBPXzZJLZ1Ceg0ZtFsCukyYx+SUyM+cXNmo6zW0P\nENv7WnS9+kWKwsMwmxs23XCxqyFss4NdBRzHI5exyeeW3uXXNOg6GGegN8bethC6vrZ6vWK0Kxw2\nCDeIk6GXy5E49wqJl7+GM3plQbAU0JqbMU8c88009q/PMCKXd8jbHrbjrstQY8N43kIj5HVGuZRS\njGXGGEn6ka45uzT6Z+kWPc299Mf76W7uIdRgN0cSaWfePOPq7TS5fJm8QmDvrtB8WuGBjiZMo/6v\n54CAzSQQYAEBAQFrxI96JVDKBmqTAqSUS+ruWZJTX0F5C8YM4VgfLZ0PYYbWZrVd9tx46FoIMxSv\nqTtjrZjv45WrP+G1e28zk+MJ7LxLLuPgOEsXnpoGrbvCvPF17TRH124E4SlFqIGiXcp1yVy+xOzp\nl8hdvFhqpmFZC2YafT3raoK93FBjM1MMK1IUjYtruYy1m9y4yuVW+iYjiRFGEyOk3dKGwREjQm+s\nj/74AAejBzH1xlmaOa7i5kRmXnRNzZS3iI+EdLr3R+ejXPHmxnmOAQG1IPgEBAQEBKwB107juik/\n3bBGaW7Z5FXmxj+Hm5+Z32aGdtPS+Qjh2KGqz9vo6Ya+8MqTyzp+A+U6El4AuayN5ylmp7Mob2mU\nxzD8qJVlwatOtRFrXv31L5pqhEMGkUj9R7uUUuRuXCdx+qukB8+i0stEhqb5Zhonj2MePYwWWvs1\nuNmGGmuiGOVaXMu1DmzP5nrqOiOJYa4mr5DzSlPvms1m+mP99McH2B/tQm+gGyLTcwvmGdfGMzhO\nqUkIwP694UKUq5l9e8LoNeq/56GgjFFJQG0QQuwD3g18B9CB3wv408AvSylH13D8C8BDFXY/JaX8\n4CZNta5ZVYAJIUzgQWA/cAP4gpSyfEzZH/8w0C+l/MimzTIgICBgm/BNNlIo5dYsauTkZ5gbf5Fc\n8sr8Nk0PEW9/PdHdJ6u2lYdiM+XmhmymrJQincyTzxWEV501UJ6eSjF0bpyrl+/gLutT5Ndo6Rg6\n6LpOd3eM9j0rvweeUhi6RiRiEg7Xf9Nce2qS5JmXSb58Gne6TA1T137fwfC4QG9en0On47hk817B\nUKPG0S5VKB4zTT/K1dS0aiPk5eTcHFeTVxhJDHMtdQ1Hldqot1qt9Md9u/iOyNr7l203edvj+liG\n0VtprtxMVbSIb24y5iNcPfujNEVqcw17hTYflm5h6iYhLYRmGTg3j9Tk8QIWKIivLwMHgX8APgYc\nA74P+HYhxBullJdXOc39wEXgL8vs+8omTreuWVGACSEeAf4c6F60+aYQ4mellB+tcNiPAd8LBAIs\nICCgYVHKw80n8FTtTDY8N0/yzkuk7pwBFu5rRXedINb+ho2JJuWhGY3ZTLmehZfnKW5cmWbo3DiT\ny90Mwwb79sfAdUkm86Bp7N4V4uDBlcWX5ylMU6cpYhCy6lt4uckEqbOvkHz5NPmbN0oHhMNo4RA0\nNWHs34fR2b5m8VU22lWrt36DUS6AtJMu2MWPcDN1A4/Se9N7w3sLoquf3aG2hvgsLrGIv5Xm5kRl\ni/gD7U3zjoXtuzffIl4phUJh6iaGbmJqJhEjjL7sppRbboIBteDd+OLrp6WU7y9uFEJ8P75eeB/w\nWKWDhRC9QBz4Oynle2s60zqnogATQhwD/h6IAi8Dl4DXAv3AR4QQD0op31nh8Pr/CxMQEBBQhpKe\nXjUw2VBKkZmVJCa+gLeoJiTUtJ+WfY9gRdo3cG4PTbcwQq3oDVRLAmVSDetIeOWyNpcvTHL5/ATp\n1NI6l11tTRw+3MqBzhCmsbaUwcVuhvXeu8vL50mfP0fqzGkyw5dZvhrXolG0vW2oZBosc/75uzdv\n4d68hXn/SUKvf23F89fSPn6exVGupqaqolwAc/m5edF1O3Or7Jh9Tfvpj/fTHxugJdSy0ZlvCWu1\niG+NmfNphd37Nt8i3lMeuqZj6gaGZmHpFiE96P1VR3wXMLFYfAFIKT8qhHgP8K2rHH9/4fcrtZhc\nI7HSt/Mv4ouvn5dS/iaAEEIHfgj4APCUECIspXxbmWODZNyAgICGw3WyuHYSqF26Uz4zxtzYC9jZ\nifltuhmjpfNBIvHDG7KVBzBCcQwjsilz3SrqWXjdnUoxNDjO1eE7eIvSDDUNDnS3crgvzu5WHWON\nYrdR3AyV65IZvkzqzMukL5xD5ZeZK5gmpjiCeeo4mCb5T38GLVS+UbLzyiDGvk6M7oML5y8b7drE\n12KxRbxl+RGuUAh9VxRmSo0wKp9GMZ2fLphoDDOZmywZo6NzoPkA/bEB+uJ9RDfQEH2r8DzF2J0c\nV26muHIrze1KFvGGxqGCRXzfgSi74ms3kFl1DsoX8qtFt+41Xnzs8V7grUALMAZ86qHnP3FnWyfF\nvAb4daC80wrkgJAQwpJS2hXGBAKswErfGN8MDBXFF0Ch9utDQogz+Lmf/0EIMS2l/E81nmdAQEBA\nzfBcp9BM2alZnZdrp0hMfoHMrFzYqBnE9rya2J7XoOnlF69rQnnoRhOG1Vy3C/pyzLsaZu26El6u\n63Ft5C5Dg2NMjiWX7AuFDfoHdtPfE6UprK/5evE8D8vyTTVMsz4XmEop8jdvkDxzmtQrr+Cllj53\nNA2jrxfz1HFMcWTeTCP39/+46rmd8xcxug/iuS7pXI2cDJdbxIdCVUW5lFJMZCcKka5hZhaZ4hQx\nNZNDzd30xwfoifUQaYCbHsm0w5VbaUZvprl2O022gkX8nqJFfFeUg50RzE2KzgbRrZV58bHHw8Cv\nUBpFeseLjz3+oYee/8SfbMO05ilogN8vt6+QNXcMGF5BfIEvwBTwiBDiQ8BRYBr4K+BXpJSlvRl2\nKCsJsD3A58vtkFJ+VQjxVuCfgZ8WQlyXUpZ9UwICAgLqFaU8XDuxqJlyDdINPZfU9BmSUy+hvIXv\npUh8gHjng5hW9SlKCoWmmQ2ZbpjL2qRTNqDqRnhlMzbDFycZuThJMrE0IlBMMzy4L4Shrz3NUNPA\nsgyamjYvcrDZ2HfukDpzmuQrL+NMTZXs1/fv80XX8WPosdL2C+54aWPlkse4NUY+lSfveOjaJtUp\neJ4vsBZbxFchuMAXB7cztxlJ+D26kk6yZExIDxXs4vs51NyNtZGbJluA4ypuTfjmGVdvpZmcLh+4\nCFs6PV1+LVdvV5SW5o0/ryC6VRXvBr6lzHYTePuLjz2eeuj5T1TyX9g2CpGxP8D/WK8mEu8vjHsv\n8HHgM8CbgXcB3yyEeEhKmah49A5ipW/saaCn0k4p5VeEEE8CfwO8TwhxW0r58c2eYEBAQEAtcOwU\nnpsu2MrXps4rV7SVt2fnt5vhPb6tfPOBDZ0bwAjFGi7d0M67pFN5XNcr2FJvvyjZ9DRDFBoa4YhB\npE7TDN1UktTZs6TOnCZ3/VrJfm1XK+bJ41injqPv2VPVYyilUKpoL+P57/lGXgql/J+i4AqH/d9V\n4nouN9LXGUmMcCU5SsbNlIxpMqL0xf0eXQeiBzDqXEDMJGyu3EwzeivF9bEMdgWL+H17fIv4nq4o\nXe2RDVvEL0S3TAzNDKJb6+TFxx7vp7z4WswPv/jY4x9/6PlPVEoB3HKEEBrwQeCb8B0M37/K2Gng\nNPCdUsrbi7b/EfCj+CL06drOuj5Y6dvk88BjQohHpZSfLDdASvl3Qoh3As8Af154ERX18I0aEBAQ\nUAbfVj4x3xurFji5aebGP0cutbCw1Yww8fY3Et11fGOP26Dphp7nkU7myOd94VWrnkDrmc+N0Wnk\n4DhT40ujHZEmk96+XYU0Q23NbQCUUuiGRlPYJBSqv4W6l8+TvnjBN9O4NFRipkFTE9bxY5gnj6Mf\n7Frz9WV0duLe9A0pFArlUdKZSXV0VDnpRVGucNj/2cB1n3fzDM9dZiQ5zJXkFWyvNFsqbsXpj/l2\n8Z1NnXXdo8u2Pa6PZxgtNEKeSZTP/opGFlnEd0WJbsAiPohu1YRvW8OYXcAbgc/WeC5rotCm6k+B\nHwSGgceklOV7FABSSgW8qdx2IcTPAP8e+B4CAcZvAY8C/0cI8b+Bf5ZS/vnyQVLKDwohOvFV618A\nswQmHAEBAXWG5zq4TtKv86I2zXw9N0dy6iuk7p5lwVZeI7r7JPH216NvIFrlC0YDI9xY6YbFOq9s\n1q4L4ZXN2AxfmOTShXEyqaWL1WKa4fGjraSS+TVfI57ysMz6rO9Snkd2ZJjky6dJnx8sb6Zx9DDm\nyeMYA31oxvrnb544hn3jph+gqjSPo0fXOOHNjXIBZN0sV5KjjCRGuJG6XrZHV1uojb643xh5b3hv\n2ff+VvoWlxOXmMz6RhztkXYOx4/QFe3a0PzWg1KKqZmFRsg3JzK45SziNejqiNDb1bxhi/jFfbcM\nzSSshzD1+k2pbVBaN3lcTRFCRPFTCL8dGALeIqUcq/Z8UsqUEGIIuF8IEZJS1k2Ur1ZU/BaXUn5R\nCPGDwB/jq9tT+B7/5ca+VwgxA/wOvkIPBFhAQEBd4Hkunp306680Da0GAXqlPDIzF0hMfglvURpT\nKHqAls5HsCLVpXDNnx+FYUYxrOhGp7ql+HVevpDZbuF1d3KhabLnLUsz7NnF4f44bXEdXTewLBNN\nW6mOvJAGqkHIMohErG1/fotRSpG/fYvUy6dJvXIGN7mspELTMHq7MU+ewDx2BK2KHlgAruOSyXvk\nd7Wji2PoFy+Wn8+xY6iuFUTKJke5AFJ2ktHkKCOJYW6mb6LKLEs6Ih30xwfoi/WzO7x7xfO9cvcV\n5OyFJdvG02OMp8cQrfdxf9v9FY7cOJmcy7WiRfzNNMkKFvEtzSa9B/woV/e+KOHQ+iN3Hr4ALka3\nLM0kHES3toLbaxxXtcjZLIQQu/HbVL0e+BrwVillafFo6XFx4ARwR0p5qcyQJvw7lyv/8d0hrHgb\nteDr//8Cj7NKWqGU8veFEP8f8EusLZQaEBAQUDP8fl4JlJsDTd/wgq4S+fQtZsdewMktfP8YVpyW\njocJx/s2dJdY4aFrIcxQvGbpkrWgWOflFezFtwvP87g+Os1QmTTDUNhkQOyhvydKxFh7OqqnFLqm\nEYnUn428ffcuqVdeJnXmZezJiZL9+r4OX3SdOIYej1f1GEop8rZLNu/iugX7eEA98ABeRwfa0BDa\nhP/YqqMDdfRoefG1uBFywSJ+o8zmZxlJDDOSHGE8U7pO1dA41HKQ7kgvffF+4tbaXoNb6Vsl4msx\ncvYCeyN7Ny0S5nmK8bu5Qi1XmrGp7Lyr/mIWW8T3dkXZ3bL+qJRfu2UUnAmD6NY28ingKVZel98A\nXtqa6ZRHCBEB/hZffH0G+NdSylLHmvK8HvhH4JMsa9YshNiP32f4dCFVccezah5LQdV+cC0nk1Ke\nBZ7Y6KQCAgICqqXYSNnzMr65Ro2Ei52bY/rmP5GdW7iRp2kmzXtfS6ztAbQNpAmqQtNY02pFNza+\nMN0qXNcjk1qo89quRVw2Y3P5wgSXz0+QSS+9mbp7T5TD9+3lUEcIzXMKc1x9np7yMA2DpohByKqf\naICbTpEaPEvqzMvkrl4p2a+1tvhmGiePo7fvrfpx/GbJXqFZcnn7eNXVVTnStTi1MBTyHQurSHdc\nekrFndydebv4O7nSVkm6pnMoeoj++AC9sT727d3D7Dr6gAFcTpS7Wb+U4cSlDQmwZNrh6m3fIv7q\nrRUs4ltD81GuAx0RLHPtf9+UUiiC6FY98tDzn5h68bHHPwz8SIUhHvD+h57/xHaLk9/Ar+P6PPDt\nUsryDeTK8wIwAfwrIcQjUsoXAIQQIXwXRRP4w02eb93SOIUEAQEBAavg2mlcN+MvDmvgbAigPIfk\nndOMydNLbeVbjtDS8SCGVWrTvb4HUOhmE4YZbZi70PVS53V3MoUcHOPa8N2SNMNDfW0MiDb2NINy\nHDTlrSrOi26TlqUTiYQwNqkf0kbxbJuMvEDyzMtkhiS4y1LSIhHM48IXXYcObqi5dybrYtsOrlKF\nZsnrmejmpxYqpRjPjjGSGGEkMcLcIofRIqZm0RProT/eT09zL6EN3sQo1nytxMQaxizGdRW3JhfM\nMypZxIcsnZ79Tb7o2h+lJbb2ejiv4MZpFaJblh4KnAnrmIee/8QzLz72eAq/7GdxrdedFGcVAAAg\nAElEQVQNfPH1mW2ZWAEhxD7gnYV/XgR+TghRbuh/lVLmhBDvBpSU8j0AUsq8EOLH8GvHPi2EeA64\ni+/+eAz4Cynlh2v8NOqGNQswIcQoq9d2KfzczVl8R5T/G1jTBwQE1BrXzeLZqYJRRW0WF0opsokR\nEhMv4toLNTVmpJ3WzkcIRfdv7Px4aFoIIxRD1xvjjrRSimzaF17bVefleR7XR6YZOleaZhiOmBy+\nr4O+vhYimo1yHXBX7/emCuKtGhv5zJURUmfPkr91E4BQ1wGaT52iqbd/nc9s+Zw8sqMjpM6cJnVu\nEJVbduPZMDCODGCdOuGbaZjV31/N5R3ytoftuPPPfU2vQTHKZZoLqYUbNNAAcJXLrfQtRhMjjCSH\nSTulEaywHi6YaPRzMHoIsw6NamYS9nwd17WxdEWL+M6CRXxvV5T97RGMNX6uXDx0dCzdwNQtQloY\ny6jvXmUBS3no+U985MXHHn8W3+2wFb827KU6iHyBPycLf63/HyqMUcDvAjnglwv/fk9xp5Tyr4UQ\nbwZ+Ed/ozwIk8ONSyj+q2czrkPX8hXKBONBe+LcHTOJnIuyFktvNrwO+Rwjxb6SU37/RiQYEBAQs\nx7eUT/nOhppeM/FlZ+8wN/4C+fTN+W2GFSW25w007Tq2ofqsomhstHTDTDpPNuNHALfjjnombTN8\nsUKa4d4oR050cnB/BD2fQ3lZ0FYXXp6nME2NWDyEppVPAVuJ2S+8SPL015Zsy12/Ru76NWKvfg2t\nb3poXedTSpEfu03qjF/X5SbmSsYYPd0YJ49j3SfQItWZaUAxxdAl7/jPu1yKYYVJ+lEt01yIclXZ\nDHkxjudwPXVtvkdXzivNdGo2m+mL9TMQH2B/tKtmdvHtkXbG0yt7H3RE2ku2FS3ii6JregWL+J6u\nKH1dUXr2NxFtWtvSzCsILlM3MXWTsBbBNOpPeAasj4ee/0QO+JftnsdypJR/Telaf6XxZcdKKV/E\nd0+8p1nPJ/Vh4MX/n703jZIsTe/6fneLPXJfKrfKrbJuVdfeM0ijWSSQpZEwzDEYGws42KxCCLB8\nPB58QNYgmlUez0GyQUj6IAvJYFuyOaDxcMwgdMb0rJrprspauvpWVa5VlVm5L7He7X394UZERmZE\n7pFLdd/fl+yOe+PeNyIjK+7/Ps/z/xMIr/8O+JeWZRWgMpT3HxOoXgf4UQLl/nPAnzBN8ysfprJi\nSEjIySKEVxJeThCkfEIXXsIvkln6PfJrD9lqAFBJtt1gYOwH2Nis70Z2YF7DdkO74FLIu0HY8Bms\neWUxy5NHC7u2GV6+1kVLUgYVomIxcL7c4/Oxvc1QR9NUohGN3CHXVZierBFf1WTvvku0t5fY4PC+\nx/LW18mO3yM3fhd3caFmu9rdhXbtCsb1a6hNRzPTgC0XQ9fzKzrqQL/Rcmthucp1RBfFndi+zWxu\nhsnMBDPZWTxZK1iajWZG0qOMpEfoinWfymfwUnpsXwE2mh4LZtI2nIp5xsuF+hbxigK9nTGGS7Nc\nXW3RA70OgUBBQVcMDM0gqkRDwRUS8ppymL/cvwf0AR+xLOtR9QbLsooEeWF3gYfAX7cs6ydM0/wj\nwEuCUmUowEJCQo6FED7Cy1WcDU9szksK8muPyCx/OzhXiWjyIunuT2BE29D0GBz6Mr10/JK7ofoa\ntRvaxZLwKs8CnYCd/274ftnN8BUri9vf83Kb4chYGzHFQdhFsEufjT2WKKVEVRUi0cO3GdYj9+DB\nAfa5v6sA8wt58g8fkh2/iz09VbNdaWpCvxaEJGvdRww1Zvtcl1dydIQDjGYJ0fDWQoC8l9/K6Mo/\nr4T8VtMR7ahkdLVF2k5d+PcmejGbr9Z1QhSeRpt9lQf3dL40N0M2Xz+HNp3UGe5NMNSX4OKFONED\nBHUH+VsqhqKjawZRNXouWytDQkIOz2H+kj8DfHWn+KrGsqwp0zS/CvxR4Ccsy8qbpvlNgnbEkJCQ\nkArF7Az5tfdwCkH8SSTeQ6L1DWKpwZp9A+GVRfpOyVL+5MwQ7NxLNhfexqtyVNOMJpq6P0k0NXQ8\nW3kZ2J2/Tu2GdsGlUKgSXqd48VvIu0w8XuTpe4uVdscybR0Jxq5309+fRrULCCeLPECboZQC/QRC\nk8szX3thv9y+j3BdCk8scuN3yVvv15hpKNEo+lUT7foVtMHBY733juthO9vnutS9jrfTtTAeb0hr\nIUDGzTCZmWQqM8F8Yb5uRteF+AVGUqMMp0dojpx99uzNtpt0xDp4tvmUF0tZCmtNeOtt5DaivJAA\n29tDNXW7RXxb8/7W7luCy8DQdKJaDC10KAwJ+UByGAEWJ2gv3A9JMCtWJgckD7OokJCQDzaZpd8j\ntzK+7TEn9wIn94Jk+y3Snd8DlEOUc0hhn7jw8txNMgvfoJiZqDymqAap9o+SbLuFcsxK1esWplzI\nO9gFr9JqeJrCa2Uxy5OHC8xO7mwzVBgYacW83k1LWkMWCsjsBlLR9p/vkpKIoRKPR880NFkKQXF6\nKpjrevQAWSxu30HTMC6Nol67gn750rHMNOq2GO4numC7a2GDRNeavVbK6Jqo6yqootKb6AuCkdPD\nJPXzc9mQK3jBHNecyszcAAW7/nxgW7NREVz93fF9LeJ9KSr5W4ZqEAst4UNCPjQc5l/2x8APmqY5\nbFlWbX8EYJrmReD3A0+qHr5E0IYYEhISQjE7UyO+qsmtjKNFO4lE209FeAnhklt5l+zKXZBbFYh4\n8xXSnR9DM455ISglihZBN9Lnfs5LSkmx4FIseMDptRouzm8y82yFVy83KeZdPG/7BW40pnPpjS4u\nmR1EcBFFG5EVJeOV3S9Ygzw1iEaCitdJvv+R3j7s57O7r8V1UZC8+OLP4W/UWqcbg4Oo166iXRlD\nTcSPvA4pJbbtY7s+flUQ9r4vvYFW8eV1LNlLTGYmmMpMsuas1eyjKRoXk4MMp0cYSg0R02LHPm8j\n8IVkbrFYEl15FlfrRx1FDIWLPYF5xlDv3hbxUkpEKYNLVwKXwlBwhYR8eDmMAPvHwK8Bv2ua5k8B\n/8ayLA/ANE0D+BHg54EEpeDmkt//LeBXG7jmkJCQ15j82nu7bpNSIqVPbuUuRs8PnKjwklJS3HzG\n5uI3EN6WfbkR66LpwqeIxC8c8/gCRdHRImnU12BQ3i665HMuVMw1TkcsPnrnJU8eL2IX3EoBpkwi\nGeHm7+unf7AZxS4gCptBmyHs+dmQpfyjWEwjFjud9z5540aNAJO+jyjkEfk8eLWzQXpXF8b1N+Cq\nidrSdCyBWHExdMWuQclbCyu90eV5rlhjhI+QgleF+UpGV9bL1OwTUSMMpoYYSY1wMTWIoZ4Pm/SN\n7HaLeMet7/rd1RatmGfsZRG/M/Q4ohhEtOiJOTV+mBFCUmd0MCTkXHPgbybLsn7dNM2PAH8N+FeA\nbZrmKwJLym6gPNDwq8Avlaphv0iQBfCPGrrqkJCQ15byzFc1gfASIIM79p59uFDTw+IWl9h89fa2\ntahagnTX9xFvNo9dKZFSohnpklHH+cbzfPJZB88Tpda80xFey4tZHnznBa9e1lqrG9FAOGmqEhhr\nbKyiHqDNUEqJqinEozqRA5gcNJL40AipO2+Seee7yGIBkc8jndqufS3dRPTGNdRrVxAd7ajHCHc+\ncFBydT5XudIVacwMoi99XuZeBJWu7BQFv1CzT1yLM5waZjg9Sn+iH+0cGM+4nuBFySJ+6mWetc36\nFvHxqBq0FfYlGeyJk9zFIr5acOmKjqFFiKqRE3No/bDiC4GKgqarGJqCrqlEDBVD1/jSF/+T85CV\nFRJyIA51a9CyrJ8yTfO3gf8a+I+A8rS8DfwO8AuWZX0ZwDTNFPCbpcd2Ne4ICQn5cCOEDwiQJ58n\n5XsFskvfIr9eXYVTSbbfItX+0eMbY0iBosXRjeRr0W6Yy9g4jo+qnk6Isu8Lnk+u8uThAitL290M\nFSVoNYzGdFQFkAIpBc9nNui4VZuxVI2QAuMEjDUOivQ8Ck8t7OlpvKXFWjMNwyBx/Qba9at4/RdQ\nVBWUo3t41jPUqPm8VedzRSJBlatB81yucJnNzjCZnWQmO40jaoVmSk8zUnIuvBC/cOaVHyklqxsu\nj6ZyPH62zouFIr6ovV4vW8SXZ7m62+tbxFeHHgczXBEiauTc/92/Lvi+QFFA01R0TUXXFHQ1iIjQ\nj3HTIiTkvHDo3gzLsv498O9N01SAttIxli3L8nfs9x7wYw1ZZUhIyAeGSLwHJ/cCIQTgbwmvqusW\nI3Z0m+16SOmTX3tIZun3kFUXi9HUEE3dn0CPtBzv+MhSu2Ez6mtgE13IOxTzLsopCa9C3uHZe4s8\ne7xU42ao6SrRmE4kqqEIWRJeQQuhgsLa+u7eT0IIIhGNWCyCdsoXZVII7NlZsuN3yT98gCjkt++g\nqsQvm0Rv3ECOXsRTQajqkUXXgQw1hABNCwRXub2wQRT9ItPZaaYyE8zmZvFlbQZeS6SV0VJGV0e0\n88zFiO34zM4XmCq1FmZ2s4hP6AyV2gov9sSJ7aielue3NEVDV4P5rTCDqzEIIYOOAU3F0EOhFfLh\n4Uj/epimGQE+ClwgqH4tmKZ5rzwTFhISErIbsaZLFDNTdYVX9T6Nws7OsrnwNbwqEwAt0kJT9yfr\nWt4fBlm6EtaMNJrWuIvdk6KYdykWS5bypyC8lktuhs/ruBleHG1jfSWHqqogfKTvUf4w7GX8UTbW\niBgasZhx6o6GzuIiufG75Mbv4a3XGktELw6SuHUb5colnKiGW7IWP8oqK4Yanl+qCOww1Ki2ijeM\nwCpea1wFMOflmCrNc83lXyKoHbTpjHUxkhphOD1CW7StYec+ClJKFldtpl4G5hlzS8WauUIILOL7\nu2MM9SUY7k3WWMSXBZdRmt8ylMAS/qyreK8zUkqECLL39FJVy9AUDEMjoqtnLtZDQk6bQwmwktnG\nW8BfpdZaft00zV8BfsayrPrN1CEhIR9ahO/guzn0SAuJlqsU1t+vu1+85QrRRO+xz+c5G2wufA07\nO115TFEjpDp+H8m2G3u65x2EYN4ohnbO2w3rOhue4Hp9XzA7scqTRwus7mgzjMUNLl3t5NIbXUR0\neOftKVZW8pVqVz1aW4K20LKxRjTWmODkw+BlNsndHyc3fq9u3pfR2Uny1h30a2/gNcVxfDdoM4Qj\nzQA5rofjChzXB0XZbqghRNBKWM7naqBVPMCGs1Ga55rkVeFVzXYFhZ54D8PpUUbSw6SNpsq2ufwc\nzzJPKzbznbFOLqXH6G3A3/Nu5Ase0/MFpl/mmJ4vUCjWVuYAWpsCi/hrl1tpS6nbLOKDdkK25rd2\naSecnt/k/sQyc8tBtbO3I8HN0Q6GepoI2UKIoKVc08tCS0XXFWKGfqYRECEh54kDCzDTNDXgtwnc\nDn3g28AUoAEjwJvAXydwPfyDDV9pSEjIa0lZeEnplWzDVZJtN9FjHRQ3n+EWF4Gg7TDWdOnY4ksI\nh+zyO+RW71FtjRVveYN05/ei6cfL4Sq7G0bjbeiFWsOB84KUkkLOwbaDxoSTdjbM5xyePV7k2eNF\n7ML2Zoi2ziTm9W76h1vBLiILm/i+T39fgtWVvd/Dvt4kqgrRUzbWEMUi+fcekR2/S3Fygp2lFC2V\nJnnzFrGbN/E6W3CkF1S7pF8RX4fB83yKjsDxym25pd9ZtYFGWXAZjXMOlFKy6qwGGV2ZSVbs5Zp9\nVFT6kwOMpEcYSg2TqPM3dH/1PtbG422PLeRfsZB/hdl8lZttNxuyXl9I5peKFfOM3SziDX27RXxz\nOnjPmlsSrK1lAaXSThhRIhja3u/pNx7M8+7T7e/N7GKO2cUcb4518PEbPQ15fa8TQkpkuaqll4SW\nqhKNBMLrPN+YCgk5aw5TAftxAvH1HeDHdmaBmaY5CvzvwI+YpvnnLMsKredDQj7EVIQXflDf2FEJ\niCZ6G1LpKiOlpLD5hMziNxDe1jyOEe+hufuTGPHjz5VJJJqRQtPj59Za3vcFxbyLY3so6slWu6SU\nVaHJa0F7YAlVVRgYaePy9W7a2uKIQg6xtgJSKVXhNDrb41y8mGJ2Nlv32MPDTQwPNaHvE2jbsNfj\neRSePWHj8UPWx+8jd1jHK5EIiTeuk7x1G3mxFwePnO+hyLLIPdw6pZAU7B0uhsEWQNmqcsViDcnm\nqpxXShaLCyW7+Ak23NpcMl3RGUwNMpIe5WJykOgeLbZz+bka8VWNtfGYjljHkSthmyWL+Km5PLPz\nhcBqvw6drZGSRXyS3s4YmqaU3AlBVVQ0RSNlJFFjh8vfmp7frBFf1bz7dJnejuQHuhIWzOwStA/q\nWy2E0bCqFRJyJA5zBfFngCzwhy3LqvGItixrwjTNPwRMAH+OMPsrJORDSY3wOgVbc6ewwObC27iF\nhcpjqp6kqevjxJrGjm8rj0BVI+jG8bKaAOaerzPxeJHlhUB0dHSnGL3aRe/A8YxAHMfHLrh4rh8I\nrxO8KNq3zfCNLi5d7SSiSWS+gLu6HNjIo9YU4cZGW2hpifLiRZbVNRtFUehoi3LpUjM9F44Zgn0A\npJTYz2eDua4H94PMrmpUlfjY5aDFcGwUW/XJ+R6IYK07q13z2Vc8WZtkuRBcsHfEO7jcOkJP6kLl\nfI4bZHZ51XNdZWeNcpWrgQYaEDhFzuXnmMpMMJmdJOflavaJqlGGUsOMpEcYSF5EP6ChzLPM0333\nmcg8PbAAcz3By7JF/Fye1Y36Uw2xqMpQT6JioJGM6wiCmwA6peqWZpTaCYPfU8JIUFRqX/te3J/Y\nXXyVeTCx8oEQYNtmtXZUtYwzcBgNOZ+YptkLPAY+b1nWLxzwOR8Bfgb4FJACngO/Bfwdy7LyO/b9\nDeBP7XKon7Ms628cde3nhcMIsDeA360nvspYlrVkmuZXgU8ed2EhISGvF1vCy0NBPRXh5Xs5Movf\norBRNU+maKTa7pDseBP1mCGvUkoURUU3mo9vUQ88+O4LrIfb52oW5jZZmNvEvH6BGx/tP/T67IKH\nXXQR5QrKCQqvvdoM27uSXL7eTf9Qqc0wv4kvfBRF27fa0N4Wo7M9TiymEomcznyXu7REtmymsbZa\nsz06cJHkrTvEr1/DiWoUfYeCLKJIddf3+P7iI95fe7LtsYX8Agv5BcaaxxhrMoMWQ0pByVIG81tl\n0dWgbK4ynvB4kXvOZHaS6ewURb9Ys09CTzCSGmU4PUJvohftCLOR5ZmvvVjcYx8pJaubLjOltsIX\nCwU8v75FfE9H2TwjQVdbFJTgc2+oBrqql9oJG/s+lme+9uLl8uFE3XlAyJIDoRq4DxqlylYsoqE1\ncK4w5INFKWbqXwJp4EDZa6Zp/gHg/wUE8H8Dc8APAP898IOmaX6/ZVnV/cS3gFfAL9U53NeOvvrz\nw2EE2GH+Gs+/HVhISEhDqBVeJ//FLaVPbnWc7PJ3kWLr7ngsPUK66xPokQbciZYSVQ8yvRrB3PP1\nGvFVjfXwFR3dKXoOUAkTQlLMb5/vOinRIqVkeSHLk0cLPK/TZnix1GbY2hpDFPKI1dWqNe19MS+E\nRNdV4jGNiHHyd9f9bIbc/XGy4/dwXr6o2a63d5C6dZve7/8464pOwSuw7jso/v6GGvPZVzXiS1Ia\n4RKSx6tPSKhNdEU6KA21nYjocnyHmdwMk5kJZnMzuKK2etRkNDFSsovvjl04k1kd2xHMvgrcCqdf\n5tnM1TdRTiU0hnqTgUX8hRjRqIqm6uhKyQ5eix5JNH6YkFIipERVlMDuXQt+RrTghocazmqFHBDT\nNAcJxNedQz71F0s/P2VZ1nerjvfLwF8EfhL4R6XHDOAK8NuWZb117EWfUw4jwB4DP2CaZptlWbW3\nCwHTNNuA7y/tGxIS8gFG+B6+l0VK99SEF0AxM83m4tfwna25FT3aRlP3p4gmD1dBqsdWplcaVW3c\nhd3E48V993n2eGlPAeZ5fjDfVQpPPhU3w4evWN1RAYjFDcbe6GL0aicRVSALBby1fMlkZf81CSlK\nNvL6ied3CdsOzDTu36P47GmNmYaaTJG8eZPUrTvovb0U/SKbSZWN9c3g9RywEvBkbbLy32XfDCFl\nUOkSEqkoTDov6Oq61HDRVfAKTGenmMxM8Dz/HCFrZ6Tao+0Mp4Jg5PZoe0M/O52xThbyu99cAOiM\ndrKwEphnTM/lmVssUicHGU2Fvu44w70JLvYlaG82iKg6mmpgqAbRqnbC06K3I8Hs4t4Vrr6Ok2+X\nPQh1c7XCqlZIAzBN878hcEKPAb8L/OABn/cGYAL/V7X4KvEWgQD7UUoCDLhKoE/uN2DZ55bDCLBf\nA/5n4F+bpvljlmVt8+I1TXOAwISjCfiNhq0wJCTkXCGEh3BzCOmcqvDy7LXAVj43W3lMUaOkO7+H\nROv1hlyUSSnRIik0LXbsY+2kPPO19z6Zuo/bxcBGXviBw95JDr3nc+XQ5EXsYr02wwv0DzaDY5fa\nDEXF3XIvTjO/S/o+hWdPyY3fI//4EdLdXgVSDKNiphEfvYSLT8Er4BTXUFSVKPFDf56W88sIwVaF\nsCS+XF3DTaj4hkZe3WyY+Mq4mSCjKzvJfH4OWacTqDvWXal0NR8zbHwvLqXH6gow39EprqcprDWz\nsNHOd+zaqiNAa9pgsDfBYF+cwe4E8Wik0k6oq8aZVOiquTnasa8AuzHafkqr2aJijKFrGCWhFTFU\nIvrpxjSENJa3PvulIQJB0kTQhvflz3/xMytnuqiAnyJwP/9LBILqQAIM2CBwSX9YZ5tT+pmqeqxs\nmRoKsBK/BPwxgp7NSdM0vwVMl7YNAx8rHe8/sFVqDAkJ+YAgpcB3M0jfAeX0hJfwbbLL3yG3+gAq\nQbAKiZZrpDu/F1VvgFiSEkWLoBvpc3PhIqWkmHexi16pKldr+NDIcy0vlEKTp+q0GY62cflaqc0w\nn0OsrVYEyr7CqyQMTjq/S0qJ8+I52fF75B6MI3I7LphVlfilMZI3b5O4+gZEDApegVV3EyH9Q1W7\nqs9p2z626+MJEbxvUuJrKm5Ew41oDXUvXHfWKs6Fi8XaiqqCQl+ij5H0KEOpYVJGqs5RGk9vohez\n+Srvrz/G2UxSWGumuNaEk01QL/rA0BUuXogz2BtnpC9Ne3McQw3Cjs9jO+FQTxNvjnXs6oT45tjJ\nZ4EJIVBQ0ErGGIamEI1ooTHGB4i3PvulKPC3gE/v2PSTb332S7/6+S9+5lfOYFnV/DjwO5ZlSdM0\nrxz0SaWCzf+0y+Y/Wvr5qOqxsgC7Yprm10v/nwe+DPy0ZVnzh1v2+eTAAsyyLM80zT8I/H3gJwhc\nTD5VtUsB+MfA3wyDmENCPjhIKfHdLNK3S+FEp2QJLiWFjcdkFr+F8LeyoiKJXpq6P4UR62jIORRF\nRYs0n7itfEd3ioW5zX32SZfaDD1cJ7CRR+HEDE18TzAzucKThwus7WgzjCcCN8PRK11EFA9ZLFa1\nGe7/GSgHJ8djOtHoyV0kuivL5MbvkR2/i7dSe5M40j9A6tZtkjduoqXSOL5DxivgFDJHDkt2vcDF\nsBKULAQtsXZe+as4UQ25S3WvK9Z5qPNIKVm2lysZXWtObfe/pmgMJC+WMrqGiGnxQ53juGzm3NIc\nVyev5hO4u3z7d7ZGGOxNMNqXYvBCmpgRPZN2wqPy8Rs99HYkeTCxUjHc6OtIcmO0veHia3tlKxBc\noQvhh4KfBX64zuM68ONvffZLuc9/8TP//HSXtIVlWf+ukcczTbOboAVRAtXisizAfobAsOMbBEWe\nPwP8sGmaH9vZhfc6cqgrDsuyisB/a5rmTwMfBXoJbm/NAd+xLOv8ppKGhIQcirLwEqJYsg8/vcqQ\nk58PbOWrnNM0PUW6+xPE0qONqaI02GRjP0avdu0qwGTJjaxnIM3megFV3d1prxHs1WbY0Z3i8rVu\n+gaboVhA5jcO3GYIwWtRNYX4CQYn+7ksuQf3yd67i/Piec12va2d5K3bpG7dxujoREhB3itQLK6W\ngrSPUO0SkkLRx/E8hASFoMWwnNPVn7rFzMLbex5jND2273mEFCwUXgWiKztJxq1tSzVUg6HUEMOp\nUQZTFzHUxs6U7YXnC14sFCvmGSsbTt39YhG1IrguDTTTnkyei3bC4zDU09RwseULgVqpbCklsRVW\ntj5svPXZL41QX3xV82ff+uyXfuvzX/xM/T+61wjTNJsJKlpdwC/smA3LA0+AP2pZ1uOq5/xN4O8S\njEP9sVNc7olwpFu+JaG19zdNSEjIa0nQaphD+MXgQvWUWg0BfDfL5uI3KW5WuckpOqn2N0m130E5\nYC7RXpyUycZ+9A60YF6/sM0JUQiBFMGahi910NF9cm1MB2ozvN5Na8vh2wzLx9c0lVhMwzgBR0Ph\nOOQfv0du/C6FZ09BbDeaUBNJkjdukrx9h2j/AIqiUPRscvYGru8eudplOx624+P6ElUBhEAxjCAc\nuSoguTcStOHtFkhsNl/dNQfLlz4vcy+YzE4ylZmi4Nfanse0WMlEY4T+xADaKX12pZSsZVymXwbm\nGc9f7W4Rf6E9xmhfissXWxnqakHXQhFRppyvpWlbxhhlsaWfsBFNyGvBjxxgnxaCStB/OOG1nCim\naXYSWNLfAb4EfLZ6u2VZ/+kuT/0HwF8A/rBpmomd2WGvG7tezZim+ac4oL9/PSzL+hdHfW5ISMjp\nUxZe5VbD02wNksIjt3qP7PI7SLlVkYk1XaKp6+NoRrox5zlBk42DcOOj/bR3JbEeLrD8KgMotHcl\nGRxto/OEZkh8TzAzscKTR7u3GV662oWBhygWDtVmCEHFxtADR0Ndb+xnRvo+xckJsuN3yb/3COls\nv/GrGAaJK2+QvH2H+KUxFE3Dlz5ZN0fRt4MK1RFm51xPkM27W5ldJQtvolGIx0Gv/9V5s+0mHbEO\nJjJPK7lXXbFORtNjNeLLFS6zuVmmMkFGlyNqb2qn9BTD6RFGUiP0JHpRT+lv0lum3RkAACAASURB\nVHEFs/P5imPhRra+RXwyrjHan+ZyfwtmfztL6wXuTyzzO9+eB+bp7Uhwc/Tk56POG9Viq+xEaOgq\nMUM/UfOZkNea5gbvdy4xTXMU+LfACPCvgT9uWVatbWsdSrNn48AQ0E9QJXtt2et28nGcDCUQCrCQ\nkNeAncLrNFsNpZTY2Sk2F76O72615+nRDpoufIroLhWDI5zozE02yrNd8WSE2987cOLryGcdnr63\nwMT7S/XbDK930z/YjCwUENkNfHnwNkPYEl6peKShVvJSSpy5l+TG75K9P47I7nCPVBRiI5dI3b5N\n4o3rqNEgdrLo2RTtLK5wD2yHv/O8RTuY63KExPV8FCEqLYbEDibaexO9u1a6in6Rmew0k5lJnudm\n8WStsGmJtATOhakROmNdp/J5lVKytOYw9TLP9FxuV4t4VYWBriSXB5q5crGdC22Jyvq+8WC+xqRi\ndjHH7GKON8c6+PiNnhN/HWdBPdv3UGyFHIGDGkvsnfdwjjFN8zaB+OokcFb/CzvFl2maMeAGULQs\n60Gdw5SHXGtT5V8z9hJgv36M4x65chYSEnI6COEjvLLwOt0ZLwDXXmVz4W2c3JY1tarFSHd+jHjL\n1QbZyoutdsMTNtmof35JseDi2D6+76OqKsXpaXIP7uPMBzPEkZ4+kjduEh8ebsj5ttoMV7dFXqmq\nwuCldi5f66alJYLI5/BXV1AUrVQpOqDwEoJIRCMejzb0AtNdXSmZadzDW16q2R7p7SN56zbJm7fQ\n00FFRUhBzs1R8O2t2a7DGmq4gYuh4/jB3J0QwWclrgbVrkNWz14u5bBm11hYC0ai29sUkp0brPKC\nl7mXCGpv9nZGOxku2cW3RdsOdb6jki/6zMznmSlVuXIFv+5+LWmDy/0tXLnYxnBvE9E67aXT85u7\nOgQCvPt0md6O5GtfCfN9EYQZl+a1Qtv3kAbyZeAvs/d1+QtgZ47Wa4FpmpeArwDtwBcty/rcLrv2\nAd8GHgC3dhwjAbwJLFqWNVvnua8Vu/6iLcv6M6e4jpCQkFNCCB/h5pCiLLxOd/5A+EUyS98hv/aA\nrXs1ConWG6Q7fx9qg9oDJRLNSKHpp+sKB4FIKeRcHNsLXAwVBVVV2fjm18m+++62fe3ns9jPZ0m9\n+SbN3/eJI52v0mb4cIG1ldo2w7E3uhm92hm0GRYKeGu5klg5+IyOlLIkvBpnpODncuQePiA3fhd7\ndqZmu97SGoiu23eIdHZVHnd8J8jtOuJslxSCfNHH8Xxk2VBDLbUYxmKonc2wfvjxgrtPlng0s4bQ\nCrjJRbzEAmuRdagTIdUT72UkPcJwaoSmyMkLEyEk88tbQcivlu26+xm6wnBPGvNiG5f7W2hv3v/v\n8f7E7uKrzIOJlddGgJVbCFVVQS/ZvuuqSiwazmuFnAyf/+Jnlt/67Jf+GfDnd9lFAD//+S9+5rUr\ncJimqRLkBHcAP7+H+MKyrIlSm+Et0zT/ZHmcyTRNBfiHpWP87VNY9olz+reEQ0JCzoRygLIUzpkI\nLykF+fX3yCx9G+lvdQ9EkgM0dX8So0F3/qUUqFr0TNoNXcenWHDx3KCaUu1kWJiaqhFf1WTffZdo\nTx+xoaEDny+XtXn23uKubYbm9W76LjYji3lEdh2/ZLt/ULFSDk+ORjRiscbc5ReuS+H998iO36Pw\nxKo104gnAjONW7eJXhysnFNIQcErUPSdI+V2+Z6P7Uhc38fzRWCooSgo0VKL4TECkqWUvP9qjnfX\nHuB2LyAidQK1pUJXpJer7WMMp0ZI6Ikjn++gZPJexTxjZj6P7dQftehujXF5oJWxgRaGLqQPLTLm\nlvcXq2Xr9vOIL4LKVllsGbpKLKKhnVDuXkhIPT7/xc/807c++6Uc8F+xfdbrBYH4+uqZLOyQmKb5\ns4C0LKsslP4I8BHABnKl7TuZtyzrl0v//RPA7wK/YZrmHwNmCGKvPgL8fwRxWK89oQALCfmAE1S8\nskjhnmqOVzV27iWbC2/j2Vs5TZrRRFP3J4imhhtyYR9kemnop5DptRO76FIseAjfR9nFQj734P6+\nx8k9GN9XgEkpWXqV5cmjBV7sbDPUFAZH27l8vZvmtI7MF/DWVlAVLcgSO+D73OjwZCkExalJcvfu\nknvvIdLeUX3RdRJXrpK6dYf42GWUKpOL41S7grwugef5+FJW/DzVaKRS7Trya5KSxeIik5kJprKT\nrDvrtePxQkUvdmAUutELHbS1NnNttP/I59wPz5e8XCxURNfy+m4W8RqX+pu53N/C2EALzcnTs7E/\na4QvkEIGYksPK1sh54vPf/Ezv/7WZ7/0fxK4HTYTzIZ99xxWviS7jxt9vrStLMDKmcER4Kd3ec49\n4JcBLMv6tmma30OQi/YHgBQwCfwPwBc+KFnDSrUV8YeBpaXMuXrBnZ1plpbq3C0Nee05699tYK6R\nQfrOmYguAM/dJLPwDYqZicpjiqKT6vgoybbbKA2y0pZINC2BZpx8VaFMR0eK57OrFAseIPcVKXO/\n/ItIr/6cTRlF1+j9Sz9Zd5vnCWaeBW6G6zvbDJNBm+GI2YEhXWShgBT+oVoMYSs8ORbTjh2eLKXE\nmZ8jN36P3P1x/MyODDRFITY8ErQYXruBWiWGKrldVbNdB8X3fAqOCBwMZUlzSgmGsSW69vldNbck\n2KjTgiikYD4/V7KLnyTrZWufLHSMQid6oRu92I4it95HTVH4Ez+0fxbYYVjbdCqZXLMLBTyvjkU8\n0N+VYqy/mcsDLfR1ptAaOL/321+bZHZx7wrXYFeKz3zy+HOOh0GI4BpRL1W1DE2lr7el7u825PWn\nszMdDuKFvDaEFbCQkA8Y5QDlLVfD0xdfUrhkV+6SXXkX5JboiDebpDu/D61B4ccSgapG0I2mU2s3\nlFJSyLusyhzFgls678mdO5e1efreIhOPl4KZsio6L6S5fL2b3r40lNoMBQqKohx6vktVFeKx44cn\ne2trZO/fIzd+F3dxsWZ7pKeH5K07gZlG0/aSUdGzKfpFXOFVfp8HzSCz7cBMwyu1kylCBHbxZev4\nI7aT+cLnef45U5lJprKTFP1a8624lsDfaEfPd6LZbSeanee4guevCkzN5Zl+mdvVIj4VN7g80MxY\nfwtj/c0kYsaJrenmaMe+AuzGaPuJnR9ASLmtsmVoKhFDI6Jvd8SMnEBGXUhISMhhCQVYSMgHhEB4\n5RCiEFwAnoErl5SSYuYZmwvfQFRVB4xYF00XPkUkfqFh51EUFd1oRtVOp31KSkkh52AXvaDFMBk9\nlOiL9PRhP9/buCna21c519KrDE8eLvBiem33NsOkiiwWERtrR3IADOyzFaIx/VgXpn4+T/7hA7Lj\nd7Fnpmu2a80tpG7dJnnrDpHu7m3bPOFR8ArYfqldTlEO/L4GLYaBdTyKsj2vK5GAIwYB277Ns82n\nTGYmmclN44rajpcmo6mU0TVKd7ybr747x5y9d2Wlu/XwhjBSSpbXyxbxeV4uFnaOzQGgqQoXu9Nc\nHgiqXNUW8SfNUE8Tb4517OqE+OZYY7PAdlq/G7pKVA8EV+hGGBIS8joQCrCQkNccKSW+l0f4BRSU\nE737vhducYnNha/h5Ocqj6lanHTX9xFvvtKwCyOJQNUT6A2qou2H5/nYBQ/H9mqMNQ5D8sbNfQVY\n7I2bTLy/VLfNMJGMcOmNLkbNdnTfQdoF/MzRrNeFlIHRwDHCk4XrUrDeJzd+l/wTC/zt7ZVqLEbi\n+k1St24THRzaZpghpaTgF7F9G8/3gm0HFV2uj+0K3LKDoVKqP+r6ofK6dlL0C0xnppnMTvA89xxf\n1raLtkXbGEkFdvHt0Y5tn2nzYitzq3sLMPNi64HWUrD9ij38XhbxrekoY/3NmAMtjPQ2Ez1m9fI4\nfPxGD70dSR5MrFQMN/o6ktwYbT+W+JJlsVWe2dJUoqH1e0hIyGtOKMBCQl5TtoRXMQgaPqOLEeEV\nyCx9m/z6e2zN5Kok226R6vhowypUEoGqRNAj6YZkhO2H4/jYBRfP84OWvmPOzMSHh0m9+WZdJ8Qi\nEZZ7b/Ktr63i2NszsDp70lx+o4ueC3EUx8bPriNL7YWHF17l8GT9SOHJUgiK01Pkxu+Se/QQWdzR\njqdpJK5cJXnrNonLV7aZaUDZUKOI47sVe/79nAyllDiuj+sJHE9szXUJgaJpW9WuI7QYZt0sU9lJ\nJjOTzOVfVsxHqumKdTOSHmEkPUJLZHcB1deZ5NpgK49m1upuvzbYSl9n/ZsGQkherdhMv8wFFvEr\nNvXGsw1NZaS3ibGBFi4PNNPeFDtXImSop+lYYqt6ZkuvtBGGYiskJOSDRyjAQkJeQ3yvgO8Gd5kV\n5eDudo1ESp/82iMyS78XZIqViKYGaer6JHq0pUHnOb12QykldsHDtr0gdFU9eDvcQWj+vk8Q7ekj\n92Cc4txLNkgxHx9ksRiHJYCg0qFpQWjy6OU2WhIgbAeZy4GioB7SWKP8uoyISjx2tPBk59U82fF7\n5Mbv4W9u1Gwvm2kkrt1Ai29vs/OER9G3txlqHETM2o6H4wpc1wdVQQGUcskrcnTr+HVnnanMJJOZ\nCRaKCzXbFRQGmvoZjA8znBohZaQOfOw7lzvpak1sC2Lubo1jXqwVX9m8V6lwzczlKe5iEd/VGudy\nfwuXB1oYvJDGOGLF8rxRPbMVKYmtaETD0MMZrZCQkA8+oQALCXmN8P1ikOV1hhUvADv3nM1Xb+M5\nW3f7tUgLTd2fJJYabNyJpETV4yfebiiEoJh3sUsmF0Fw8sm8v3r/AOvFBE/sBdZXC1BVREokI4ya\n7QwPJYkgkMJGOtqRKn7HzfDy1tfJPRgne+8u7sKrmu1G9wVSt++QvHELvWW72C6LLtt3EcI7sH38\nNgdDgtbCiothWXRFo4d6HVJKVuwVJjMTTGYnWLVXa/ZRFZWBxEVG0iMMpYa50NF+ZKe8vs5k3UqX\n70teLm1ZxC+t7WER3xfMcY31N9OcOtzrPa+Us7a2ZrY0opGwshUSEvLhJBRgISGvAcJ3AmfDcgDt\nGV20eM4Gmwtfx85OVR5T1EjJVv7moW3Pd6PcbqhFT7bd0C662EUP3xPBfNcJvq+5TMnN8P1FHHv7\nTE9nd4pLl5ro6YyiIFFkUA05yvtZtpKPxzWi0cP9E+8XCuQfPSA3fo/i9BQ7++C0pmaSt26TunWb\nyIWebdt2FV0HaDEsFH1c18MrmWgowYbAOr4sug7xu5FS8qrwisnsBFOZSTbdzZp9DNVgMDnESHqE\ni8lBIidQXV3PuEy/zDM1l+P5qwLuLhbxvZ1JLg+0cLm/hf6uxlrEnwVCSJBlR0INQ1OJx8Jg45CQ\nkJAyoQALCTnHBMIrh5TekcwWGrYO4ZBdfofc6j2QW61S8earpLs+hqY3Jn+rXNk7yXbDiqmGs1Xt\nOu58125IKVmcD9wMX85sdzPUNIWBwSYuDaVobjKqfrdHW4uUElVTiEcPZyUvPY/8k/fJjd8jb70P\n3nZbcyUWI3ntOslbd4gNDW8TVI5wsb0ijvAPJbqgqsWwNGMHBPYx1S6Gh7hg96XPXP4lk5kgoyvv\n11awYlqModQwI+kR+hMD6GpjvwJdV/B8oVBxLFzP1M8LTcUNxvqbGRto4VJfM6n4yVnEnzRSSoSo\nciTUVKKRcG4rJCQkZC9CARYScg4RvofvZZHSReHshJeUksLmEzKL30R4Wzk/RvwCTd2fIhLvauTJ\nUPU4mt54+2wpJY7tYRc9PK/xs1078Tyf6adBaPLGamHbtkhMQe/YJJ+cZToiyWU6uKSP0Jc8mkW/\nKM3RxGIaxgGt5KUQ2DPTZMfvkX94H1HPTOOySfL2HeKXr6AaWwKh6Nk4wsYRXhD2XArTPojoCmzj\nd7YYKiDEVkjyIVoMPeHxPDfLZGaC6ew0dtUsYpmknmQkPcpIaoSeRC9qA/+WpJSsrDulTK7AIt6v\nM8qlKgoXL6Qqs1wX2hOBXf5rSMUoQ9cqs1uxaFjdCgkJCTkMoQALCTlHCOGVKl5OILzOyFIewCks\nsrnwNm5ha/5H1ZM0dX0fsabLDbSVlyiKgRZJoaqNHcCvV+06qdkugGzG5umjBSatpZo2w47OGHrb\nOnOatVXkErCQX2Ahv4DZNsatjusHPlfF0TB1cEdDZ2GB3PhdsuP38DfWa7ZHB4dI3bpD4voNtMRW\nVTNwLyzgCBeFLdMX5QC/L9/zsR2J43mIUoVTgUB0HSEo2fZtZrLTTGYnmc3O4MnaIOJmozkQXelR\numJdDRXbRdtnZr7A9FzgWJjN17eIb0lFgrbCgRZGepuIRV6/r1tRChzTdQ1DU7bCjcMw45CQkJBj\n8fp9I4SEfAARwke4WaRwg0DZMxRevpcns/gtChuPtx5UNJJtt0l1vImqNshWvuRopxtNDW83LM92\nnUa1a682Q1VTuNifZHSkibyxztdfWrsex1p9Ske8Y89KWNlYI2JoxGLGgcSkt7lB7v44ufG7OPPz\nNduNzi6St++QvHkLo7Wt8riQgnwpIFlUZg8P9rksV7o8z8cvhyNTcjFU1cBQIx4PBNgByHt5prNT\nTGYmeJF7gaC2zNQR7azYxbdG2hr2OxdCsrBql2a58rxaLta1iNc1JbCIL1W5OprPl0X8flRXtgxt\nq5UwdCUMCQkJaTyhAAsJOUOkFIG5hm+DcvAw2pNZi09u9T7Z5e8ixZZDWzQ1TFP3J9AjzQ08mWBx\n0WXKyrC8MANAR3eK0atd9A4czb7edX2c4ulVu1zH59njRZ48XGBjbXubYSKuMzKcYniomWg0uIC9\n/2ISAMcTFBwfzytXF1TikaCda2J9sq4AEyURE4tpRKP7z9aIYpHcew/J3btHcWqi1kwjnSZ58xbJ\nW3eI9PRuO17RK1L0bVzfPbB74a5ZXbDVandI6/iMu8lkJsjomi/M1d3nQrwnEF2pUZoiR8+fqjl3\nzuXRxCZTL/PMzOcp2vUt4jtb4lweCBwLhy40vTYW8aIUbqzrVXNbYWUrJCQk5NQIBVhIyBkgpcR3\ncwhRCKpdZzTjVaaYnWFz4Wv4zlZbmh5to6n7k0STAw07j5QCRTV4/2EW6+H2wOGFuU0W5jYxr1/g\nxkf7D3Q8z/Wxix6O4wcmFCdc7YKtNsMpa7liW1+msyPGpZFmenoSNeJvqbhMvuiR39Ga6LoC1xUk\nohqL6vK2beU2w3h0/4tj6XkUnj4hO36PwvvvIXeaaUSjJN64RurWHWIjo9vNNHyHol8KSIagCnvU\nma7yf1S7GMZi+x4LYNVeLZloTLC0I5AaQEWlL9nHSGqU4fQwCb0x8QS+L5lbKjA9l2fq5e4W8VFD\nY7SvCXOghbGBFlpeA4t4IQNHQlVVqyzgg1bC16lCFxISEvJBIhRgISGniJQS38sj/AIKZ9tqCODZ\n62wufA07N1N5TFGjpDu/h0Tr9Yaaf0gp0SJpFuaKNeKrGuvhKzq6U/TsUgkriy7X8YPKkKoQZFGf\nbJvhwlyGJw9fMTe7vr3NUFW4OJDk0mgzLc27X5A7rqgRX9XkbZ+IEVRahJREDJVYLLLnfJeUEnt2\nhtz4PXIP7iMKO5z/VJX4ZZPUrdvEzauoVdUnR7gUvSKOcCsByQepwFbbxvvVM10QzHVp2oFdDKWU\nLBUXmXxxl6nCDGuRWtdAXdEZSF5kJD3KYGqQmHYwMbcfGxk3MM+YyzM7n69rEQ/Q15FkbKCFywPN\nDHSlzrXZhJBBZUtTtypbEV0lEtFeW9OPkJCQ84Vpmjrw14C/CAwB88D/CvxDy7Jqh3Jrn98GvAX8\nYaATeAz8j5Zl/eZJrfk8EgqwkJBTwnfz+H4BzjhEGQJ7++zyd8it3ofKPI1CouUN0p3fi6rHG3cy\nKVC0IExZURQmHs/u+5Rnj5e2CbBq0RVU0VRQOPGLSs+tcjPc0WaYTOqMDDUxNJiutBnuhSzG2Za6\nXLMDiEIcw1CJx/euTjhLi4HoGr+Ht1YbLBwduBjMdV2/iZbcqhJ5wqXgFbGFixTi4C2GImgxdLzt\ntvGKomy1N5ZdDPdpMRRSMF+YZzITZHRlvWywoeppEQ+Gl5XALv7OD2Gox7dpdz3BiyqL+LXN+hbx\nyZjOtdEOhrpSXOo/vxbx9cSWoStEI3ootkJCQk6Sf0Igvt4G/hXwSQJBdQv4z/d6ommaSeDflfb9\nTWAW+M+A/8M0zU7Lsv7JCa77XBEKsJCQE8b3CvhuYOGuKMoZz3lJChvvB7by/pagiCR6aer+JEas\ns4HnEiiKjhZtRq3KW1peyO773OWFTMXBcKfoOg1L/uymzdP3Fph4fwnX2RGa3BHj0mgzV660k9nc\nQ1DtwN9sg8TaLlslQnPx880kEvX/WfYymyUzjXs4cy9rtusdnaRu3SZ56zZGW3vlcbdS6fIQwj9Q\nVtfWTJcsGWkEH9uKbTwE1a7qFsM9Pte+8HmRf85kZpLp7BQFv1CzT9yBkWWF0SWF/nUFTSrADKJj\nDjk4uOux93oNqxsuU3M5pl/mebFQxBd1gpAVuNidrrQV9rQn6GhPsbqaq3PUs6HiRqip6LqKrqlE\ntLCyFRIScrqYpvlxAvH1W5Zl/RdVj/8a8F+apvmHLMv68h6H+CngDvBXLMv6p6Xn/l3gm8DPmab5\nm5Zl7d4i8wEiFGAhISeE7xZxiyuVcOGzxim8YvPV27jFxcpjqp6iqfvjxNKXGrpGiUQzUmiHrKQJ\nIUAEFZfMeuFURVfQZrhZcjPcbtGuaQoXB1JcGmmmuTko1RzW4MPwmonYXTjR8vsvQZH4qgeaT7TQ\nS8Rr2/YcYdvk33tEdvwuxYlnNWYaaipFqmym0dtX+R2WA5JrKl17iK6Kc6Hv44vtrYWVl1pyrqxY\nx+/hYugKh9nsLJPZIKPLFbUVp7SrMfpKMLKs0rMBap0QauXhwwMLsKLjMztfqDgWZvP1u2GakgaX\nB1owB1oZ7TtfFvFSSoSU6NpWC2E0ooVuhCEhIeeBv1L6+bd3PP43gD8N/AVgLwH2k8Ar4JfKD1iW\nlTVN8+8B/wL4k8AvNGy155jz860TEvIBQfgOvpvFsYNZlbMWX76bI7P0TQobVRboikaq/U1S7XdQ\nGtDeVUYiUJUIeiS9q2jq6E6xMLe59ZxSK5UUMtAXikJHR/JAJhCNwHN9pp6u8OThKzbXt1e0Egmd\n0ZEmhgfTRCLHuwDuao0zv9KH5qVxogu40RXQJIbbTCTfg+G00tuVRPo+hWdPyY3fJf/4PaS7Xbgo\nkch2Mw0tWFdgpGEHM10HFF2+51MsmWhU3yioqaoc0FCj6BdLdvGTPM/N4svambfWSGspo2uE7t/4\nVyj+3n8fylx9B8RgWZKFFbtinjG/i0W8pioM9SQxB9q4PNBKZ8v5sYgXQqCgVBwJI4ZKLGwjDAn5\n0PLOVz43BPwo0EQgVr78kU9/YeVMF7XF9wNLlmW9V/2gZVnzpmk+LW2vi2mao0AvQfVs57/UX606\nfijAQkJCDk4gvHJI/MBg44ydDaXwya3eC2zlq8JqY+lR0t0fRzcaZ9sNwcWwHmneN9Nr9GoXr15u\nlETXVh5YUOoK9hkcbdvzGI0gu1nkycMgNNl1t9uMd3bGGCu5GTbqQv3yQAtzK3l0rwldNEFhbGuj\nlLTklrnpvM/zn/vfEPkd7W+qSvzSGMlbd0hcfaNipmH7DraTw/ZdQFY+c3u2FwpBwRa4rodXndFV\nT3QpSiC49jDUyHk5pkp28S/zL5DUKqDOWFfFLr412lp5XKlT8dqPXMFjZi6ocM3M5SnsYhHf1hRh\nbKCJKwPtDPc2ETkHFaSyI6Gmbc1txcLqVkhICPDOVz4XBf4W8Okdm37yna987lc/8ukv/MoZLKuC\naZpRoA/41i67TAOXTdNstyyrnmAcLf2c2LnBsqxXpmnawOVGrPV1IBRgISHHRPgevpdFShcF9UgX\nlY1ESomdnQ5s5d2tSpMebaep+1NEk32NPiGKFkE30vuKFcfxaWqOMTDSxuSDOUSxgHQDy2/FiKDF\nE4xe76Wzp7HicGupkvmZVZ48WmD+5fZZtEqb4WgzzU2NC4YWUmLoKleGWtksOrz7dMtqPlncpG91\nmr7VKZJ2sJ5qORHpHyBVNtNIpRBSBBld9gau8JAl0aWUBewu+J6P7Uhc38cTstJSWLfKUq52xeNB\nq2EdNpwNJjMTTGYnWSi8qtmuoNAT72EkPcpweoS0ka57HNnTg/Lixa7rBvB6enlZZZ6xuGrX3S9i\nKAz2pDAHWrh6sZ3WdGPcEo+DFBKJxDCCnLegnTCsboWEhNTlZ4EfrvO4Dvz4O1/5XO4jn/7CPz/d\nJW2jfGd0fZftG6WfzUA9AVYeTt7t+Zul534oCAVYSMgREcJDuDmEdErC6+ztqV17lc2Fr+Hknlce\nU7Qo6c6PkWh5o+G28oqioUXSqNru/5T4vsAuuDj2lm18T3YSsf6UJaWNrBK49KXsVTqLz+jOFoDa\nMOIjr1MI7M0sM89Wefp0jUxme0tfIqFzaSRwMzxum2E1QgoihkYsples5D9+vYfehOTlN79Lctai\nJVf7HaW3t5O6dScw02jvwPEd8r6Da6/j+16Q0VUyc9lL7JfbCwMTjer2wjo771PtklKyaq8wmQ0q\nXSv2cs0hVEWlPzEQiK7UMPEDzP/J69frCrANJcaU1s6U2s7MRhfOv601HQHoaosy2pfiykAbI72t\nZ24RL4REAXRDJaprRCMqUSP8mg0JCdmbd77yuRHqi69q/uw7X/ncb33k01+oH1R48pTnFerfBdt6\nfLe7Xwd5/tnfOTslwm+GkJBDIoSPcHNI4ZQugs9eeAnfLtnKP2CbrXzrddKd34PaoOykMhKJpifQ\njET97VJSLLi4jo/nicCwomQbX5iaIvvuuzQDzTJT89zsu+8S7ekjNjR0tLVJibBtpOOQWc3xbGKd\nmdlcTZthV2ecS6NN9FxoXJth0E5JSXgZFaMO4Tjk33tEbvwuTDyjT2xfK0xB4QAAIABJREFUi5pM\nkrxxi9TtO+i9vdiiJLoKK5UqF+zdWgjguj62uyW6dm0v3FrwrtUuKSULxYWKXfyGu1HzdF0xGEwN\nMpIeYTA5RGSf9tOa0w8NIW/fxrt3n+dqayC6tHZW1aqA5aoxsnhUZbA3waX+Jq4OtNOaakwQ81Ep\nzy4auhbEB0S1UHCFhIQchR85wD4twMeA/3DCa9mNsn3tbv/Ql79EdrOQPcjzz4/97AkTflOEhBwQ\nKQW+m0X6NhwwtPY01lRYf0xm6VsIf8tAIpLoL9nKt+/x7COcD4GqRtCNppqLeiklju1hF70t0UWt\nW2Duwf19z5N7MH4oASZcL2hn9FyE47K4VGRiMsP8q+3BxJqmMHgxxehIY9sMy3NP0ZhGLBpkeEnf\nJ//kGbnxe+QfP0I6229aKoZB4uo1krduow0P4uCRFT7CXtuqVO5T5ZJCYjs+ri9wPVF+CrBHRtoe\n1S5f+szn55jMTDKVnSTn1X4XRtUow+lhRlKj9CcH0NXDf42ULeKn5/JMZy/yItWBV2eUS1GgpyPG\nUF+Cy/3NDHa1ENV3D7s+DXxfBDbwukYkopKI6ufG0CMkJOS15aCtd2fZorcByD3W0FzaXnu3LmCt\nar96NBGEOn8oCAVYSMg+BMIrVxJeSiC+zgFOfo6NV2/jVbWDaUaapq5PEk0PN9ZWXgZVGN2oNdmw\ni0F7oef6Jct4ZU+Ldme+fjvZtmPWybnauR5RLCIdB+G64Pv4QmFmNsOziU0y2e1thsmSm2Gj2wwl\nQZUpFtWJRrVAhL58QXb8Lrn79xG5HZlnikLs0hjJm7dQLo/i6Qp54SH93FaVa4/Pl5QSzxe4biC4\nPCGqqlz7Lba+k6EnPJ7nnjOVnWAqM4UtartDknqS4dQII+kRehK9aMrh30O7bBFfMtDI5OpbxKcS\nOkO9cYb6koz1NtOSSGIcsrLWSIQQKIpCRNeIGirxmH7mrY4hISEfOA4qPGqHbk8Jy7Ic0zRngOFd\ndhkmcEjcbcbrSdV+2zBNs4egAmbt3PZBJRRgISG7IKXEd7MIvxhcFJ+Tu9y+m2Fz8ZsUN59WHlMU\nnWTHR0i13UY5QkViLyQCVU+gG1vtXuWQZMfxSudXUA6Zi3Xodfg+frGIdOySNXtgPpHLujyb3GB6\nJoPnbXfgO4k2Qwg+G6oKibhOJKLhrqyw/vW7ZMfv4a3UzkdF+vqJ3biO9sYV/ESMgvBQVRHcK9yn\nyuV6Pq4rcYWP7wlA2b/KtbXQutUux3eYyU0zmZlkJjuDJ2szupqM5sC5MD1Kd6z70O+flJLF1S2L\n+Lml3Sziob87zlBvgqG+JBdaEsSNxKHbGRuFFBIhJLqqYBgaiWjoUhgSEnLifBn4y+x9Xf4C+O7p\nLGdX3gb+tGmaY5ZlVS5CTNPsBcaA397tiZZlzZqmOQt8yjRNZYcV/e8v/fzmCaz5XBIKsJCQHQTC\nK4cQhcBc45xUvKTwyK7cJbvyLlTbyjeN0dT1cTQj1djz7cj0Ks91ObaP7/uoqnokURPp6cN+Prvn\nPtHewKkxaC3MB5Uuz0dVyxfCKguLBZ5NbPJqoV6bYZpLI000NbDNEILPhqYpRGM6SdVl/d3fY2X8\nbt3Xo7W2Er1+He36VWRbMz4SoaiAQN2lgrJV4ZJ4wsfzZZDXXHqbD/x+15ntKngFpjanmMxM8CL/\nHCFre/7ao+0Mp0YYTY/SFm0/9O83X/QrFvHTc3kKxdocMIDWJiMQXL1x+i7ESRpRonqMqBo99Xa+\nilOhrmHowRxXf3eapfPxZx8SEvIh4COf/sLyO1/53D8D/vwuuwjg5z/y6S/UuY11qvw6QeDy3zdN\n849bliVN01SAf1Davp9V/m8APw38VeB/ATBNM116LF/a/qFAkfVuSX6AWVrKnKsX3NmZZmmp1ogg\n5PTZKbyOS1tbktXV48+TSikpZibILH4D3936rBixTpq6P0Uk0XPsc9Q7px5Jo2pRHNvFLvq4joeq\nHf99KUxNsfpv/p/dToyUgtYf/CEi3ReCQOEqAey6gpnnGSbqtRkmS22GFxvbZgiBu51hqERUH+eZ\nRW78LoWnT2CHmYYSjxO5dhX12hWU3h5Ube91SClxXB/Pk3hC4PvbK1xHoqralXEzpYyuCeYL83Uz\nurrjFxhJBZWu5sjhxguEkMwtFYNZrrk8Cyv1za0MXfn/2XuTH0nSND/v+Wxx8yX2jH1fcqltpjkz\nPQtnSEEEqQMPog46UCdJf4B4ISBAR+mig3QTIF0EQUcBOgjQRRAEiiA0bHJ6pqfZU1NdWZGVERmZ\nGXtExuLhm9m36WC+hnssmbFkZNb3DLKrx93czNwj0ut7+n2/38v8VJ6l6TwLUzkGB0JCERL5EZF/\nv0ORG7O4wtAn4/tko+6kQve9/PnifrafL2Nj/Q+jTeUG/PX/81/+p8B/Ruc+qU1S+fpXH+WmzvHs\n2bP/DfinwF+SDlD+U+DvkQ5Y/qdtx/3XgF1dXf1v2h7rJ63iPQH+D2Ad+I+BReCfra6u/k/38iYe\nAJ+NgD179uy/AP4HYGh1dbV40XFOwBznaYlX7VZneN2GgMnaYRorX2ntifL8HP3jf0Ju8MvbX7ha\ni/Cz4OVIaoo41vV2u9u9zum//QWlX/+6KVzY9NpYS+6rrxj4vZ93HF+6rM1wPMfj5dtvM4Q0Sj7w\nBN7uBpVv/4bK9991hWkQBARPV/C+/pLw8QriEulqCJdUtiMe/kZ3bS0EQVrtymY5jo9ZL62xfrbO\nQW2/63CBYCY/y3L/Mkt9SxTes3JaLMmmcL3eqZLI3oOQx4YzLM3kWZzOMzmWTYMrREgURGTuudJl\njE07McM0JCWbuTw4w30vf764n+3ny+cgYNAcyPwnpBK2A/zqAVS+mjx79iwA/ivgPycdzPyatHL1\n362ursq24wypgPnnXj8O/LfAfwgUgOfAf7+6uvq/38sbeCB8FgL27Nmzfw/4v0k38A07AXNcB2st\nWlUwunonw5M/RMDiyha14hpJdRcjSxhdbXvWozDyO/SN/iGef7tJcGl8uoc2OWRCR4rhbWONQVer\n1NbXqH7/W+L9vTTkYGKC3NNnRNNzzXtK2wxP2d2rdpzD9wWL8/2s3EGbIYA2Bu94D/38WyrffYs+\n6/47Gj1Zwn7xBeEXTxEXDCzuEC6t0eYWhKt1cogibDbLoTlhvV7pOk6Ouw71hc98YZ6l/hUW+xbJ\nvsdYAqUNm3s1NrbKvNqucHTavV8MIJtJI+KXZvIsTOUp5NOqUipdWaJb/p29CmMMnvCIMuk+rihz\n/Y579738+eJ+tp8vn4uAOX4afPJ7wJ49e/afAP8L6fC2ByVXjodJu3il+2sexnd2+ehbKsfPMaaG\nUWXaf52DzDDDs/+YIBq+9esqqZAyQpsQhLkyxfBDaEhXGqChEMIjmpwmmpzuOlZKk6YZrhcp9Wgz\nfLw8yOJCP2F4u5t0rLWY4jHmxXfEv/0WedCjejQxTvg7XxN8/QUjs+OcnFa7ztEQLq016pxwXRma\ncfVNgudhogy7nLJe/oFX++ucqe4FZcbLsNC3yHLfMvN984Te9UTVWstxUTbDMzb3quletHMIAZOj\nWZam8yzO5JkYidLWSQGhyJAN0vbC+8Ka9B7DMI2Iz7nwDIfD4XA8UD5ZAXv27Nko8D8D/xHwinRc\n5+OPelOOB01LvGppq51IhwM/BOLKFuWjb9GqBLYtuED4+H4BIQK0rhJwOwKmjSGpSpTysF4B3/dv\nPV3fap1Kl0ya0gUXx6yflSRrF7QZTozneLwyyORE7taFWVfLqNXvkT98i3z7uut5MThA8M1XhN98\nhTc22vGcNYZEGqROhUsbW081TLmxcDUvZNGhz5Y9Yj1+y6ujdaq62nVYzs+x2LfEcv8ys/k5fO96\nAhInhje7lWZrYbF0QUR8zmdxJs/idIGFqRzZevS+xRJ6Idl7DNIw1oKp7+UKPbLh+1W5HA6Hw+H4\nWHzK/7b6BvgnwP8K/HPg/wRWPuodOR4k1lqMqqJ1tU28Hoh5ASopcrr9L9GyvXNW4Pl5PL8lHLXi\nS6J8d8XoulhriWNFEiuMBj/ThxfebvueThJsXOtKLbxIuqy17O5VWVvvbjMMgjTNcGV5gIH+W04z\nlJJkbRX5/Fvk+o9gzqX1ZbMEXz1LpWtuNh2sbAxxrFDaoqxBGiiexR2SdaviYS1SGN6YA9blFq+P\nX5OYpOuwvqC/Hhe/zGRuCu8aJp1GxCdsbJfZ2K6wvV/DXBARPzOeY3EmDdB4NJRJP4u6dPkiaFa6\nbvLe35Re89uT79itpiNuJnOTfD30DfN9Cx3HaWPwhUcUuiHIDofD4fh0+ZQF7CXwu6urq78FePbs\n2Ue+HcdDo3fF6+Es1oyRlA9/Teno33VUvYSXxQ8KXdIiewQqXIW1FpkoZGLSQcmexfOyBFHhVhau\n1hhMLcbGMUYl9c85lS7vkupLs81wrUipfD9thtYY1NsN4t/+Bvnjc0jOJfb5Pv7Tx6l0LS8iEdSk\nRVcSlLb1YdSt6pbwxO1VuNqoqRqv7T7rcos31U207Y5yH84Ms1Sf0TUWjV3rZ1mpaV7vVNjYSqtc\nlQsi4of604j4pZk8cxO55s8glS4IhE8miMj52VsZ0fCXB7/k26PfdDy2Vd5kq7zJ7wz/jD8c/aN0\nCHLGJxcFBLeQxOlwOBwOx8fkkxWw1dXVTdJoToejg4cuXtZaasUfKe7/W4wqNR8XIsAL+vC88MbX\nSKVLp9Il6vN+fR/h9+HdcFCzNQZdqaSthYkE4dXFxLuypfPsLGFtvcjGm/tpM7TWovd3qX3/75DP\nv4NyqesYf2Ee/+svUSuP0WFITRlMRXXKFne7V7CsK7xKtllXW2zHexi6kwXHsuMs9y2z1L/MSDRy\n5TmNsewc1iPityrsXhIRPzeZY2m6wOJMnqH+1u+ftRaDJRQhmSBza9LV4E3pdZd8WVqf9/en3/L1\nxCLTQ8u3dk2Hw+FwOD42D0rAnj17tgHMX3HY/7i6uvrP7v5uHJ8aPcM1HpB4AcjqAad7f46s7jQf\n84I8QWYEo2qXLvLD7Pil59ZKk8QaKRVpqKEAL7WvtJ3xwwMR0oHI1bS1UCu8epVLXGOPUaPN8OX6\nKXsXtBk+Xh6g/5baDI01xCeHyOd/i/7hO3j3rusYb3wMvvwC8/QptVwBY21azVKp+NxFZes8p7rE\nq/gt63KLXXnY9bxAMJWbYql/heX+JfrDgSvPeVZW9X1cZV5vV4kviYhfrIdnTI/lCPzW+21ocShC\nojC60z1dvz35rnnNhnT5nqA9A+bXB9/yeNgJmMPhcDg+Hx6UgJEOZRu94phf3seNOD4dzs/xEjyc\ncI0GWlU4O/gl1ZPvWw8Kj8LI36Hv0R8g4wPOdn9x6TmyA90ZMy3pSud1CS+VTiFIq39ehAjev93Q\nWouJ09ZCK2XHQOSGfF2FlIaNN2esrZ1SKneGOvQVAlZWBlmcv3mbobWWxEiSyhn6x+eYH75HbPUo\njvf3wRdfoJ8+Ix4eQXh3EJZxxX0e6VPWk01exZsc6pOuYzw8ZgtzLPcvs9i3RD7IX3pOpQ1bezVe\n1cMz3p107xGDVkT8Yv1PX77zqz8dQyCac7ruI71Qa8NedRchBIF38fDpt2dbvZ9wOBwOh+MT5UEJ\n2Orq6j+/62sMD+cJHlg08dhY/8e+hU8Saw0yLmFUDUQAvN9A2ftgeCjLu51fc/j232B0qwWsf+Qx\nE4v/gCjXSDUcJuOfcbL3Xc/zDE18w6OpJ0AqXbWqQkoNvk+h0L2YFsLHD/vx/Ov/FdeJxMQ1TBxj\nEgUCRD7gfb8mTk5ivn9+xMuXJ0jVWYGZnenjq69GmJ3p++CqirWWRCdIrUiSGsmLVeR3v0WvrYHW\nne4dRfhfPkN8/RXewhyed7v7h/oHclfe6058yIvya16UX3OsukcUhl7I8tASz0aesjK0Qja4eF6W\ntZZ3Jwkv35zx4+szNjZLSNUjIh6YmczzZKGfx/N9zEzku0YL2Prg67DeWpgNbhakcRXWWoyFKPDI\nZgP6siH57RBpLr9m6Pkf5TvSfS9/vrifrcPh+Ng8KAG7D46PKx/7FjpwQyHfn7TiVapXvB7uhvyM\n2Gfz5b9Atw3GDTLDDEz8PaK+ecpVKFdbg5pF9AX50QFqxZfNwI0wO0524DEmmGB785hEaqypV7p6\nYS3Cz+P5GSCu/7ng0EaARpJgpARrPnh/T7PNcO2Uvf3uNsPG0ORGm2HxtMZWeZeXx+sc1NL2u7Hs\nKI+Hl5kpTHadX1tNrBO0UUgtYXsTsfoD9scfEefCNKznYRcXMV98gV1cRAb1r7nSxZ/Fh9A/kOOs\n2B0Fb6xhRx6wnmyynmxSNt3HRF7EYt8iS/0rzBXmCOv7/uKSJqbzOyqRhje7VTa20sTC0wsi4gs5\nv1nhWpjKk8u2/oem9vs01hB4IZEfkfPzkAiqGKrc/nejMRYhIAr9+kDkAGEMSSXhqJIwkZ1ko9gd\n/d/O3ODsvX9Huu/lzxf3s/18cWLt+JT4yQmY49PFWpO2GuoaQngPVr5UckJx7xfEpY3mY8LL0D/2\nR+SHv2mmBPYiyk83o+aNNSRVTSIVtThuSldP+bIW4WUQweWVpY6YeK0ReK2wiQ+QLykNG6/PWFvv\n1WYYsrIy0LPN8G8Ov2P16MeOx/Yqe+xV9ng28oSfjX6DNEla6TIaYw3i6B32+ffw4gdEKQ3TaH+n\nZno6la7HjyF7fwOAAZTVbMpd1uNNNpItara7FTDv5+vJhctM52fwL/g9sNZycJw0wzO2DqqYHlu5\nPA9mx3Ms1BMLR+sR8b0wGHwCoiBDzs9fK6r+Q7A2TYsM/HQ2Vy4KiMKLf9//YOJnVwrY74//7LZv\n0+FwOByOj4oTMMeDpyFeVsf1/U0PU7yMTii9+xXld38DbSl2+aGv6Bv7E/zg8nY1qEtXLd3TZbS5\nXLogDdsQAhH04XndIRZGKkwSQ6PKBe+9l6sXxUaa4esztO5sgZucSNMMJ8Z7pxlulXe75AvSaonB\n8NvD50R+xKPoEea0CD+u4v/4A/7Ru66tfWZ4BFsP02Dg6pCK2yQ2kpfxG9bjTV7LbaTtrkwNhAOp\ndPWtMJmbvFCQqo2I+PpernK1d0T8YF/A4kyBpek8c5M5MpfsnzNYPAQZLyIX5AhumH554XXOVbly\nUXDtPXWPh5b448mf88vdX/V8/o8nf87joaXbvF2Hw+FwOD46n5uAPbDoBcdNMEZjVEO8vAeXaNjA\nWkv1dJWz/X+L0a02rvzALPlHf0qYHbv09cZaklq6p8tqA1dIV+vC4Pk5PL8ldtaYNDwjibFSYbVu\nJhXeVFyvbDNc6Gdl6eo0w5fH6wDUEkVNGZRKxcX3BVHokzWW4l//kpHdMuHOVrd05QuYJ0+wX30J\no6P3+ntRNTEbyRbr8Sab7/Z6zugaiUZY7lthuX+FR9GjntJljGX3XY1XWxVeb1fYOezdHhkEgvmJ\ndBDy4nSe4YHLP9uGCmdESDbIkfFvd4B1A20MgeeRCX3ykU+U+fB/lfz7c3/GbP80v97/m2bgxlz/\nDL8//jMnXw6Hw+H4LPmcBMzSWn84PmGMURhZwZqGeD3MihdAUt2luPvnHUOSvaCPgfE/ZXrxZxfu\nObTWEscKJTVGtaSLq6QrfTHCC+vthl6rrVAqrGzN5YLrxcRfRaPN8OX6KeXzbYZ9IY+XB1i4Is3Q\nWou0Eqklu+VdSjVJTbYGMHvGMnFQZmmvyMy7Mr7p/KtswxC19Bj75TPE3Gzaf3dPlHSFV/X9XNvy\nANvja2Y8O85K/2OW+pcZygz1PM9ZRTWHIL/ZqVBLekfEjw61IuJnxjsj4nuRJhhCKDJk7yjBMA3Q\nsIS+TxT65LM+4S2GGT0eWnKy5XA4HI6fDJ+NgK2urv6Dj30PjpuRilcZa5IHL15aljk7+Auqpz+0\nHhQ+fY9+j8Kj38fzwq7KR7t0adVqL7yWdNVfLxDgFTCxAVlsC8+4/lyu63KTNkOAxCQkWqKtJlES\npdJqX1XqVL6MZaxYY3GvyPz+GdG5xEQrPNTsPOrpF4jHi/hR5t5K3Ce6yHq8xXryln111PW8QDDf\nN8N8YZmlvmX6wu4ETqUt2/vVNCJ+q8LhBRHxUcZjYaoVEd9fuPpr2VqLxRJ6Lem67QRDYwxCpFXJ\nKPTJZa/fWuhwOBwOh+NiPhsBc3y6GK3QKhUv8cDFyxpN+fhvKB3+CmtaFZxs/wr9439KkOnch2St\nRSaKJNFoadK3JsTV7YXn0HEMWiBUgNGnbeEZAm6wl+s81lp2diusrRcvbjNcHqS/L+y+x3pSodSS\nSpJgTNqqpuvVrMY77ntnWdk7ZHGvSF+te9/U/mCW7fFJHv/JPyQcyBH4d//7YK3lnT5hPU4rXUf6\ntOsYH4+57AzLg09YHFhi4tEjTk86K5zHxaS5j+vNbhXVIyIeYHI0agrX1Gi2KyL+IpoJhkFEzs/e\n6n5Iay3WWMKMT8a//SqXw+FwOByOFCdgjo9GKl4lrJV1oXjA4mUtcWmD4t4v0LK1OA+iEQYm/j5R\nYbbjWJkozopVzk6qqXkIgbiilQzg6KDE9ptTTo+rYAz9/QETY3lGhofx6zO9bhKecRGXtRn296Vp\nhgtz3W2GiU6oyBpVKZFKYUwaDNI+C1sAolImXP+RzNoq//DdYdf1T/MZNib62RgfoNTnEZXn+Hq4\ncOvvsx1rLbvqkPV4k1fJJkVT7jomJGAhO8Ny/wrzQ4+79lQl0vB2r9psLTw5k13nAMhnfZbq+7jm\np/Lks9f/GRpr8EVA5GfIBbebYGisRVjIhD7Z6P0CNG6TF29P+Mvne7zZS9Mt5yf6+KMvJ3g617ud\n0+FwOByOTxknYI57x2iZVrwa4vVA4+QbqPiY4t6/Ji6/aT4m/Ij+0T8mP/x1UxxlIpGJQUkNArJR\n5trthVjL+vM9tt6c1AfkpuJyeOCzt5swP1/mycrtL0aLxXqb4ZtebYZ5Hq8MdLQZKqspVavESlJT\nCcbYVC7bXtdcvycJ4et1wrVVgp0thO08fzXj83q8n1cTAxz3R1hfY3xJUJ3AV4O3/l4hrdJtyX3W\nk0024i0qttZ1TFZELGWmWe5bZnZoBT9sSZe1lsOThI2tCm/3d3m9XeodES9gZrwVnjE2fHFEfC8a\nCYaRF5G95QTDRmphtild3dXM++Rf/Ootv/hut+Oxte0ia9tF/uybSf7Rz+c+0p05HA6Hw3E3OAFz\n3BtGJ2hV+WTEy+iY0uFfUT76W1qx8oL88Df0j/0Rnp9FSo2ME5RSaSS8J66/p0tpTFzDKsW7gzKb\nr0/TapG1aOMjZZZGHenNmxJDQxFjj66Osr/yuvU2w5frRfYvaDN8vDxIoRCgtKYUJ1RlTC1JkFbh\ntQlXl1QYTbD5lnBtlfDNK4TuTAm0Qcj+yAw/9k+zO5RF5U5Q0SnWl3iyQFSZw08GGR2Kbvw+G0ir\neJvssp5s8jrZIrbdVaqCl2M5nGU5v8DUwDxeLt98rhpr3uxUmomFpQsi4gf6ApamW1WuyyLie9EI\n08iIDLkgR3iLCYbGGDzhpcOQsz5R+DC++l+8PemSr3Z+8d0u8xP9rhLmcDgcjs+Kh/FvYcdnjdFJ\nOscL9UmIl7WG6skPnB38BUa3BCWTn2Fg4u/jBUPENY2U1TQYwxP1+WRXnNjYdC+XlPWZXLZZPdvb\nr6ZSYyGREcZ0VyU2N0s3ErAk0enQ5FfFnm2Gy8v9TE7mMMJSlmWOjhQand5nXbn8Xm/SWvyDPcKX\nq4SvXuLFnVWlNExjDrnyDDm/SKmk2V8/RBhLWO0jrM52nXJp6mYVsNgkbCTbvEo2eZPsoOiWpiG/\nn+XMLEuZWcbzk4hCAYIAYyw7B7V6eEaZ3XcxtsdWrjAQzE7kmnu5hge6g1eug7GW0AvIBtlbDdPo\nlK7o0oHIH4u/fL535TF/9XzfCZjD4XA4PiucgDnuDK3jNE4eTRoX8bDFCyCp7HC69+eo2kHzMT/s\np/Do7yLCeao1g7VxXbp6VIDOYaSsz+SS6WBlRBqeIQTtY+uKZwla+0gZdTzezvEFKXpXUSwmvFw/\n5fWbUleb4fh4lum5HLl+0EZxWDuhXbja//M83ukx4doLwrUX+GfFrufV2ARy5Sly6Qk2l8PaND1+\nZiJDWfXz41b3awCezAwwMfL+olkxtTQuPt5kS+5hesTFj/rDLEezLIfTDGdGELkc5HKUqpqNjXQf\n1+vtiyPiHzUi4qfzfPV0hEqpu4XxOhgMPj6RH93qvi5jDH5jPtcDla52Gnu+LuP13tk93InD4XA4\nHPeHEzDHraN1LRUvqxHCQ1ywgH9IaFmiuP9vqBV/bD0oArIDP8PLfY3GB22uli5l0EkNpKImy+hi\n3Dy+1yLbWosQgjjOYfTtJtrt7FZ4uVZk/6C7zXBsIsP4dEiYs1hipLlauABEtUK4/iPh2guCw/2u\n5/XAUCpdK08wA0P1ewHfgzAj8IP0PX65OMLIQI5XO6ccnqZDiEcHI5amBt9Lvoq6xKtki/X4LTuq\nO9wDYCoYZTmaYykzw4BXgEwGncny9rQhXYccHF8QER96zE+19nINFFqVyTB4v5+XqYtt5EXk/ByB\nf/OvX2stxlgyoU8m9OnLBfj3OCPN4XA4HA7H++MEzHFrdInXB/6v+nFli1pxrTncOMyOkx1YIcrP\n3ObtAmCNonT0G8qHf421rbY8P7dM2Pdz/B7znTpPYDFxgpUJVimsNoj6AliIy1vSUvnK4Hl5BgcT\njo/iSy81PHT1nqBKLHn1qsjGRonqub1KubzH+HTI8IRXj3ZvxMNfIcgyIXz9KpWu7bddYRomm0Mu\nP0GuPEWPjjdTOKwF3+8Ur3YmRnLvXemy1nKsi6zXK12H+rjrGA/BTDjBcpS2F+ZFBEJwokN+c2jY\n2KnwZvcQeUFE/MSjKN3LNfN+EfEX3W9jX1c2yHWlKH4IDyW58Da9HtyaAAAgAElEQVSYn+hjbbt3\nJbTBwkT/Pd2Nw+FwOBz3gxMwx43RqoZR5WY15yZx8uWjb6me/NDxmKzsIiu7qKEvKIz87k1vF0gX\nxrWzdc72f4GWrRYnEYwQDf4JfjR54WubbYVKY5UCRFO0xDWqD9ZSF9Q8Xn1w8txs35UCNjubyqCy\nBqV0fc6WxVjL6WnC5tsSuzu1NJmwjcERn/GZkIEh7/r7i4wh2GoL01Cde8ZsECAXlpErT1HTc2lv\nYdv7CwJBkAH/FmZ4WWvZV0esJ2lc/InubkkL8JnPTLGcmWUhM03kZZCJ5u0RbBxpNvZiji+JiG+0\nFS5Mv19E/EU05nVlgyzZW9jXZa3FWksUBmQjn3wU3Prg5Y/BH305caWA/eGX4/d0Nw6Hw+Fw3A9O\nwBwfjFZVjKq0idfNFoRxZatLvtqpnvxAkB0lyk/f6Dqy9o7i3p+TVLZaD3pZMv1/QJB/0iWQVmmM\nTCBRGC3BtsIz3lc2088qi+dlOx4fe5Rjfr6PN28698RYLNbC9HSBMOdxdFbFWIsnBNbC4UGNt29L\nnBx3yoXvw+hkwNh0SDZ3zXu0Fv9wP5Wu9Zd4tc7WRSsEamYubTGcX4Jz8ewIQRgIwujmvwvGGnbk\nQV26tiiZStcxkQhZyMywnJllLjNJgM+7M83fbitevSuz9U6iL4iInx7PNqVrfCS6FZlpbzHMB3l8\n72Yi1xiMHGU+L+lq5+ncEH/2zeSFSYh/9s2kC+BwOBwOx2eHEzDHe2GtxagqWqUL4tsQrwa14to1\njnn5wQKmVJXi7l8Qnz2HZkCDICh8Rab/7yC8evR5e1qhUmDa2grTTWDvfe1eVa/zPFkZYmAgw5u3\nZ5yeJlgLff0h4xNZhoezGG2xWGpJzM52lZ2tmKTWWe3K5gXj0yGPJgL8awx+BvCKJ60wjeJp1/Nq\ndDyVruUn2LZ49vR9pfIdZjzCzM1+F7TVvJV7vIpT6arZ7opgTmTrrYUzzITjSCV4c6j4lwcxG4eK\nUq13W+FAIWju45qfzBNlbneYcaZe7Yr87NUvuITGDLhM6BOFPvncp9teeF3+0c/nmJ/o56+e7zcD\nNxYm+vnDL8edfDkcDofjs8QJmONaWGvRqoLRtXoF6PYXhY09Xzc9ph1rLbVaQvX4e+LiX0Pbot6P\nZsgM/DGeP4hJEow8wyrVjO+GehzFDUMNLqp6AUitkcqgtEVpjTjeYe7wJTOHB6kiJiPQv0Slf4yz\nM8neluRoX3UN/x165DM+HdJ/zTZDUa0SvqqHaRx0R4Hr/oF6mMYzzGCvRbAFBJmMRxh9+OeTGMkb\nucN6vMlruY20quuYAa/AUjTLcmaWcX+Eg6Ll1Y7iF4cVdo51j6xDCPw0In5pJm0rHPnAiPiLMKSJ\nltkgR97P3ajtthkXH/pkI++jD0b+GDydG3Ky5XA4HI6fDE7AHJdirUXLMsbU6lHyfFAF6D6x1pLE\nCik1SXkLefZLjGqFNQi/n7DwBwjGMBWDlkf1OV4XpxV+6H1Aq+qljEZKgzIWbSxaa9r3jyXf/Qb9\n8oemUFgs9mCP4yPF3oClKDsFrtFmOD4dEl2nzVDJNExj/QXB5pvuMI0o2wrTGJu44OecilcYeYTv\nOWi4Qc3EbCRbrCebvE120XT3CY74gyxnZlmOZsmqAV4fan59INk4LFGTvatcI4NhcxDyzETuvVMK\nr6LRZpnxMuT9HGP5IY5q5Q86lzaGwEtndBWyEWHwsOPiHQ6Hw+Fw3B5OwBw9sda0idf9RMmH2XFk\npfdekPZjemGtRSYKmRi00hhdIjn7K3Rto+2ogCD8EuEtQuyBSKst1wnOeB+MtSilqck85dhDa4k2\naeWtvVKirUEhMVqjdrfxXz6nEQGvCNgPJ9kPp4m9HLRt8XqvNkNjCHY20xbDjXWE6twrZv0AOb+U\nhmnMzsGF+5bSqmeQ+TDxKulKGhefvGVbHmB71K3GgxGWM3MshDNUink23kr+r0PFQbH3HKhM6LEw\n1RqEPNB3+5Uja9O2z7CtxfBDK2ntg5H7ck66HA6Hw+H4qeIEzNGBMToN1tBxWhW6x+HJ2YGVKwUs\nO/C44/+XiUQmBiU1CLBWI0vfIkt/C7Ri2D1vjiD8GiHef8DvRRhrMSZtH2ykERqj0cZDeHmCJESq\ndFCvECCNQhmNMQpl06pPYynvvdoABBWvwG44zbtgAiPaFujWMuSfMf71+NVthtbivztIpWv9BV61\nR5jG1GzaYri43BGm0eNkHyxep/qM9XiT9WSTPfWu63mBYCocYzkzy6ie4uBdhleHkn99qJC694De\niUdRU7imxrL4N4iIvwxjLaEXkAkicn72g1sMjUkDU6LMpzEY2eFwOBwOx93jBMwBgDEqneFlYhDe\nR2kzjPIzqKEvONn+d6hKBRrR50FAkM8zNP17RPnplnSpumAJgRWgK+vExb8C20rME2KYIPxdPG/k\ng++rS7SwGJ0Kl0A0P6q0ppPH8zIYDFVVo6LKKKOxGNonbrV/utZajk8Fu9mfcRZ07oPxrWRM7jIh\nt8kSo4f/8YX3Kc6KZNZeEK6t4p+edD2vH42RNMI08oUr3nUrXCO4pnhZa3mnT1mP37KebHKkuwM9\nPDzmwkkWwhmC0gTbmx6/OlQclzVQ7To+F3mpcM0UWJzKkc/d3VeWsZbAC4j8DLkb7Otqr3Tls4GT\nLofD4XA4HB04AfuJk4pXGWuSunjdX8WrFz/+qy3C/QOiWZ9wKF24yv0axc0KW4+2+eI/eJwmCtYr\nHyZJ0LV9ZPXXWNNeZYkIgq/x/Plrt4xZa9HGoLVF2/RPL9Fq0EqnsyjjIW2AsRJlqun+rUwOZept\njj1aOJW0HOxKDrYVSfbrjudyusyE3GJU7eE39kj1eB+iViN89ZJwbZVgv7t6aPr6U+laeYoZup6E\nCgGZjId/DfGy1rKn3tUHI7+laLr3RAUELGSmGDMzyKNHvDkQ/ItjhTbdgRtCwPRImArXbB8Tj24n\nIv4ijDX4IiDyI3JB7oP3/3VKl6t0ORwOh8PhuBgnYD9RjFHouniJByBeABt//R388C0SkEcajYcW\nARYfhA8nP7I1Pc3kyiwkEi1LaPUDxmy0nUXg+4/xg2cI0XtPkLUWYyxKm07RMmnIwvmutl4x4Mpo\nlJVoY5A2A1hEW5jEZXvmKiXD/naPNENrGdbvmJBbDOiTrjPYR4/qF1eEb16lCYabbxC2M8TCRBFy\n6TFy5Rl6fPJa1Uxr07DHTEZcKV7aGrblfnMwcsXUuo6JRIb5YJp8dYqzgxE2Dgzf1SztbaEN+rOC\nxbGQpZk8cwtDZKO7/VoyGDx8Ii+Vrg+d12WMQZBGxrs9XQ6Hw+FwOK6LE7CfGI2Kl6mL103is2+b\nw7/+NQKPUjhIMRolCfoAiFSJvviIvDyj+JvfMD41gtav0Oo57ekUnjeBH/wOntfffEzXAzG06RSt\ndH5Z5/W9C/cTWVR935axCm0tYLE2wJK7VjyJtZaTd5q9LUnptFOY/ADGBhIm3/6aqMfsq/oJ8Ppy\nRH/+/xJurCHk+TANHzm3WA/TWEgjEq91X+mhYUbgX5IaqKzibbLLerLJRrJNbJOuYwpejgk7jX82\nycHOAH97kg6RPi9dvgezIwGLowFL0zlGxvoQUXSt+/1Q0uxGyHhpe2Hgf1hghzEG3/PIhD4TIwWy\nd7QHzeFwOBwOx+eLE7CfCA9ZvLTSJLFGH59xkp+hlBnreD4O+omDfpL4gInoHTL5l1jbSsYTog8v\n+B1gjERZtJHpvq16++B5sbpYtBo0hEtjrEbb9v1bFmEFmhxwteR0tBnGncl/ubxgfCZkZDzA9wt4\nwQysr7fdhsVTCUG1RKBivL2Nc3cJemqGZOVZGqaRub7EXEe8YpPwOtnmVbLF62Qb1at6JfoYktPI\no3F2t/v4bdPLOt/rcMFjaSyVrtlHIWEhB/n8jWesXUUappEmGGY/cEhyu3S1V7oyrs3Q4XA4HA7H\nB+AE7DOntcdL1mddPQzx0lqT1DRSaozW2CSm4me75KtBlE2Y/ibh0ZjXlC9LgOYJSi1gpECgOqta\nonf7YC+UkW3CZRsvr/+ztdfL2gBDBFfUvSolw+arEntbMbbX0OSZkP7BzjRD88UXiJERvBerhDtv\nCaol/HOx8QB65FEqXctPsIW+a72/BleJV8XU0hld8Sabcg/TY0bXAENkK5OUdsfZP8yx3+OzCH1Y\nGA1ZHA1YHAsYzAoIQ8jlIPthInRdDAYfnyjIfvCQ5PY9XYVcRMa1FzocDofD4bglnIB9phit0Kp9\nj9fHb5VqSZdC12JQEqsUVhuE53HaN8r59b7nG2YXjpieO8bzUjGyFrSdQ9mnQFr1eb9OsMsqXL3U\nyoLlyqqXtZaTQ83edu82w9HJkPHpgCjbLQQirhG8WiOztkqwt9P1vCn0tcI0hh+9z5ut31tdvCKB\n73de/0yX0/1c8SY76rDnjK5+PYI4neR4e5S9Sr7nNcYHfBbHAhZHQ6aHfXxB+nsXRal4BXf3dZMO\nSYaMF5H/wBZDFxnvcDgcDofjPnAC9pmRilcJa2U6QPkjV7wa0pVUq6hajNAaq+szsIRIAzGEQElF\nLewjjBviYhmbOGNh5YBM1Gp9Oy3myBZ+H8vgte/BYpFGNmXLdAnXZfZmsDa8tOolpeVwR3Kw06PN\nsJAOTU7bDM+9XimCzddkXq4SbL5GmE5pS/yQd2Nz2KdfUlhZ+CCJthb8QBBm6BCvY1VkPUnj4g/U\ncdfrhBXk5Sjx4Thne2NUZXfVKhuKpnAtjgUUovr5jYHAb1W77jLFEINPQC6MyPn5905M7JQuFxnv\ncDgcDofj7nEC9plgdIJWlZZ43eMA5fNorYkrMUmpgk4UaAUIjLUoZVBts7SsSSsXQghM6CE1DOcq\nLD3Zp3+wla4X1wLWNsbZOxvk7/7+5fLVKVy6GSPf4HLhajuPtRjyXFT1qpQ0+1uKdweqZ5vh/OMC\nYUZ1SoG1+LvbZNZW0zCNpDPMQguP3YEJNofn2B2YwHg+FGFhu8jKzHtIp4WgLl6e72GtZV8dsR6n\nyYXHutj1GmE9wsoYlb1x5PE4Fd1ZRRLA1LDP4mjI0ljA+KDfavG0Nv0TRal0ZS4b7nwzblrtctLl\ncDgcDofjY+IE7BPnoYiX1prqyRmymqBrdfkxpLJlLbqt6tWOaOsdzPcp5hf3mRprDRE2WrC5OcLG\nzhhK+GQK3e1xtyVcHWe0PoYs56te1lqODzX7W5JSsUea4WTIWL3NsNAXUi6l1Tvv6F0qXes/4pVL\nXVesjk7yPDfJ9uA0MuiWl9d7JYb6sjwavDxow5KKVyYSWCy76pD16ibrySYlU+k63jMB9nSc2uE4\n5nQUTOdXQl9WNCtcC6MB2fMR9Y38+lw9VOOOq10BAVGYrQ9Kvv613HBkh8PhcDgcDwUnYJ8oRifp\nHC/URxEvay26VqV2VqVWrlGrJljhYYxBmzT0u319fPli2eCLDf7w2Us8ryU1+0f9vHwzSS3OND1o\naqwPZTQahbEGbTSWmwpX2/vCYmwEdFZVGm2G+9sKmVyzzfCsSOZvvyOz9gL/+B3n0UMjJI/TMI3f\n7Ma8K14QQV9n8+Csp4ClFSFBGAi8jGFL7fGqtMmrZItqj1h7oTLIo3H08QSm+Ahs63enPSJ+cSzk\nUZ/X+2dnbStU4w4j5BvvLfIy5Pw8gX/9ryw3HNnhcDgcDsdDxAnYJ4bWMUZWsGgE4l7FSycJSalM\n6bRCXEtIVEtE0kV67yrXZXjsE3jf44lWdaZUifjx9STHxc6Ev8KIZWAwS1VXOxTrJsLVwmKtV285\nbJ2vUtLsbSmO9lV9plWLoVGfiemQvvY0wzgmfL1G5uUL/N2trr9gJl9ArjwlWX6KGXnUrBgdn6XJ\njjo8Q0XHmKAKgKdyBPEwvuzn+KyzXdHadJ4ZoWaHPV7JLV6Xt0lsd3IiSQ51NI4+msCUhjve43DB\na+7lmnsUEJ7fq3aebPbOI+QNlgCfbJgj62ev/TuljSHw6umFWTcc2eFwOBwOx8PDCdgngta1VLys\nTud43Yp0XI6SkmqxjKzFVMtVktigDc0qz/sGHrQjKBF4z/HFQfMxa0OkfcJmMeJMVUGkxuNnDaNj\nERPD198D9T5YDMZmaCQqGpMOTb6wzXAqZGyqLc1Qa4K3r8msvyB4u4HQnfOybJhBLq6QPH6Knpi+\nUFxkbh+VPex4zIQlkrBEUBslqk007zhBsiV2eGO2eFPaRfeY0WUqfejjCfTxOLYyQEO6Qh/mRwOW\n6q2Fg/krJMWYe4mQ76h2BXkC73pfT9oYMr5PJuOTj3wnXQ6Hw+FwOB40TsAeOFrVMKpNvO4o1VAb\nQxwrZKWKjGNkNSapSawQaF2PeRcC/8ZrW0kgXuKLDYRoxcrHZpqyXMAQMDokGB3qv+mFrkF71ctH\nJpbD3YvaDD3GpwMejQd4vmiGaYRrq2RerSGSzlY/63nYxSWqC49Rc4tXRrDnBmqUvcMLn1fZQ/qi\nPKt6jTd2i2213zMu3pQG6tI1ga21KohjAz6LowFLY/WI+Kty++sydB8R8sYaAi8kF2SJrlHtstZi\nrSUT+GQzPvlseI3h2g6Hw+FwOBwPAydgD5RUvMrNNrPbFC9tDInUSG1JqjV0rYZJJEIrlLZobUmL\nOOk+rttZ21p8sYkvVvFEq5Uu0YOU1QraprJwX8vodK9XBsjU2wzjnm2Gw6M+421tht7xO8K1F2TW\nXvQM01ATU8iVp8ilx+QfDaFKta5jeuH3nUJ3Rgba18RZRZxNOMh8y4/nugutBXM2nO7nOp7AJjmg\nHhE/le7j6oiIvwpjUtm64wj5D6l2aWOJAo9cNiQfBTeqwDocDofD4XB8LJyAPTC0rKB1FZridbNF\nptKaRKbR70obZJxgajU8Y0AmWATaGJQyaVo86Zr7tta2yigs78j5qwReS1i0jSjLZRIzyv1pFzSq\nXsrkOHln2N+qdrUZBgGMToWMTwVksh6iUib8LpUu/6i7SqWHhpv7umz/wAfdVSzKZMKAWEp0YIiz\nkjiboEPTdaw1AlMcRR9NoE/GQWVaEfELPSLir4MxrWrXHUbIv2+1yzT2dEUB/TlX6XI4HA6Hw/Hp\n4wTsAWCtxagqWqUlEPEBBmStRWqDVAatDdJYjDIYYxAyBqmwUoI1eJ6XHiMNWqcln3qH4Y1RRqKs\nQRsFVCmEG2T9/bb79KioOap6lovma90VFkscZzjYhYPtWs82w4mZgJGxAE9LwteraZjGzmaXIppc\nHrn8hGTlGebR6I0+PGstVT+h3F+lFNRQfg/p0j76ZAxzPI4+HQMdphHxk5dExF994fSfjQj5OwrV\naFRxM15ELshdWe1qzIbLhgH5nEsvdDgcDofD8XnhBOwjYq1FJmVkLY0ov261y1iLlBpp0vlaUhuM\nShetnicwSQJxjNUSpMb66cLamIagaYxJWwtv5lwWZRTKarTRGAxpJqEh52+SD94gREsmYj1GWS7V\n52vdJ4ZS0Wd32+doX/ZuM5wJ6StYwu23hP/fC8I3r3qEaYTIhRWSlafoqZkbCYuxhl1zwBuzxWu9\nRbXQ3apoVYg+HkMfT2JOHyEQzI1kWHyS7uW6MCL+KhqzuwqFVL7uCGMNoRc2kwwvvaWmdPnkooBs\n5L6aHA6Hw+FwfJ64Vc5HwFqLVhWMrqGS/KWLaGMtcaJQJt2bpXQ6Zyvdm5W+zmoDSRWbSLRUWFoD\njq0nkIlCa4vRrULNh3RypfO3NMZqjDUY26qeNf6Z8Q4pBOv4Xq3tdQVKcgVlh97/ojfAGDg6MOxu\n+5SLFtqSAltthj65033C374gfPUjXnwuTEN4qNn5dF/X/CIEnfPB3gdlNTtmj9dmizd6m4Sk6xib\nRM3kQnM2QiaMGeo/oW/hBX+6sMJMYeSDr4+1nfu77oB0AhxkvIh8kMf3Lq5etUtXNvLJRR/+2Toc\nDofD4XB8KjgBu0estWhZxpgaWLr2eLWHYyidthIakxYr2o/zBNgkwSQJVknSmEKv7cm0QqaUaSUY\n8n5dcgaLMhJTr2zpc7J1/r/7okwhWCPjn7TOYQMqapGanuI+93nJBPZ3DHvbHjLxoC0tMN+XphmO\nZkpEr74j81cv8ErFrnOo8clmmIbNfniVKLGSTbPDa73Fpt5Bix5x8bUc+ngSfTSBqPZRKBQp9J/Q\nP/WaTJQK4VL/CjOF8Q+7CWvTfV35fBonfwc0q13B5dUuYy0CJ10Oh8PhcDh+ujgBuwfaxUvU/09q\njVQG76zG0VkNrdIGvvZ4cNEW+26UhriWCpeSWNrkrS5fSrWki3qK+PUqXY1WQoNFo4yB9Aqte7ng\nlQJJPnhN1t9uCp61UNPTVNQClvtbYJfPYHfL8m4/3WvWzvCYz8SIZvjgBZlfrhK8O+h6vR4YQj5+\nilx+ihn48JljNRvzVm+zcbzNttrFNtow2z5EU+lHH4+jjyZ5FAywNJZh8ZuAMHfMVvUdR/ERACPR\nGPOFBcaz7ylf97C/q6G1WS8id0m1y1iLsBBlfHJOuhwOh8PhcPzEcQJ2h1hrkfEZtbiMNvW0Qd0K\nvvA8QZg1GGMRnuiIpLDWYuMEkjiVLqOhscAVXnMt3wjTMMpi63u6BFxRcGrfu6Uw9cax9lbCqytW\nlqy/Qz7YwBOq+Wiih+qx8oVrfEI3xxg4PoTdLUgLWa37DkIYG/eYUlv0bXxP8BebiHMbwEwuh1x6\nglx5ih4d/+AwjbKt8Ept8TLZ5MQ75NyHmV6rNIg+miQ4m2BhYJClsYDFZyF92XY5Gmcq/4GVLujc\n33VHMfLXqXYZa8FaojAgG/kuNt7hcDgcDoejjhOwW0QqTZxoEqWRSQklq1grEB4dkeAXRWkbpVpV\nLqmw7S2KbdWFXgmGXBqo0RKuNBb+vHC938I4ECf0hWsEXrl1TyZLWS2TmEfcR7th2mYIe9vpf28n\nXxBM5U6Z3P+O6F+vI7TqeN4GAXJhBbnyFDU9+8HVoRNd5EW8xYbZpBLUWy/bLNpagSmOYI4nGNFT\nrIz0s7QcMPG+EfHXwZi0zTCXS+Pkb5n2alc+zOOJ7mpXY0Cyky6Hw+FwOByOi3EC9gEYa5GqPl+r\nHv2utMFag7BVsAkCgXfFwt4ai65VMKfFNLGwseELwPM6NKZduqy9PMHQYtG3KFwNPGoUwldEfqt9\nL42Vn6/Hyt9NjHk7pWIqXWmbYedzI/2S2coaI6u/wa91pgpaIVAzjTCNpQ/aC2WtZU8d8311kx2x\nhQxL6Vtue9vWeJjTUfziBEvRHEvDEYu/G5LL3MFnUx9mTBSlFa87aDNMq12Z5tyu7ltw0uVwOBwO\nh8PxPjgBu4CGZMn6EGPd+KMb6X+iuV/LGA2mgjAxQnhcVgEysh4RryQojRnIpfIFXQto0yZdpk26\nzq9vW8KlUEbfmnC10PVY+bcdsfI1PU5FLmG4/YpLO2maIextQems87kgsEyKA+a2f0X+5X7Xa9XY\nRCtMI5d/72trY1irHPAi2eQo2MFkqnBuTrHVPuZknMF4isVwipXRLJNLHkMjfZwVq+99zSsxppVm\neAcx8gaLhyDysvVqV7fYGWPIBD65bOiky+FwOBwOh+M9cAIGGGOpSVWvZNl0VpYx9SpW58JSCIEv\nGuKlwFTAJKl49VioWmOxcRUSiVUyHUrbXuU6t3A1zWHKtjmrCzrDNO5euFpXyniHFMJ1fNGKZ5em\nj7J8jLIDt3Sd3iQJ7G+nrYbn2wwLfpXZ4gumdr/Ft53JgnpgMJWu5aeYwfePvi8nmt+e7fDGbFHK\n7kKYdP1NsTLEK04wYaZ5mp9kdjJDPvIII3F3MtJIM8zl0n/eMsZaQi+k74JqlzGGwE+DNAq58Pbb\nKB0Oh8PhcDh+AvzkBEwbQy3RzbZBqdIQDM/rXDj7l7RzGaNAV9JWw3PiZa3FJgk0I+JVW3hG78W5\nMWlsvNYWa7pnddl6JLy2Gm0NxjYGHtdPewd7rnxRqsfKn7bu04aU1RKxnuAu93mVimmoxtFBZ5uh\nwDKq95jf+TWDtf2OOzDZHHLpcRqmMTbxXuETxlq2zxJ+KG+z620jC/uIguo6zsZZ8tVJ5sQMXw6M\nMzTtI4QgCAVh5o7EqxGqkc2m4nXLbYYGg49P5KdJhuerXdoYAs8jyvgUshFhcPFcL4fD4XA4HA7H\n1fzkBGzvqNIxfyuNer/ewjkVrzJYlb6+vlg1sk24lDoXEd97wWqtJUk0ZSGplFVzXS1Ep3DdbYWr\nmzRWfoOsv9MWKy+o6hmqah57R78yjTbD3a00Tr6d0CbMnP7A7PFzIt1q6bN+gFxYSsM0ZuYu/Kx7\nUYoNr06qrMXbHGd2oP8AMZy2V3bMOosLDCdTPA5nedr/iGCwMVdMEIaCMLqjfW+NUI1s9taHJhvS\nWVyRF5HzcwR+5344bQyh55Fx0uVwOBwOh8Nx6/zkBOyqYIxenBcvow1W1rBJXbja2wqFd6EeWduq\ndDUGJKevtUgj0Uaj7P0KV9vdkfW3yQevz8XKj1BSyxj7/vunrsNlbYb9yRFzx98xcbaBRypHVgjU\n9Cxy5RlyYQnC67XiaWPZOdWsn5Z4q3eo9e3g9R8hBm3XpxvGg0yYab7KzTLd0cJoEQKCjEcY3nGo\nRi6X7vO6tVOnYwoyIkM2iLpaDE2z0hWQz/pOuhwOh8PhcDjuiJ+cgL0PxiSgq1iTBmcgVRoVb65u\nK2yQSpdBa9OSLizKSozRmERSljXuX7hahN4xhWCNwKs0H1MmR1mtIM3InVzzwjZDaxgvbTB3+pyB\n2kHzk1CPxpCPn6VhGvnrzRg7rRreHCk2zors+9swtIc3cYIQHWnxYCEvR5j3ZvkqP8NAtq/jPNaC\n70MYevh3IV7toRq3PLsrbTEMyIUROT/f8buqtSEMfKLQd9LlcDgcDofDcU84AeuB1jG2egK1SjoA\nuX0fF1zZ6tYuXUaDxaCswliNthpjbVOyQvx7F64GnqhSCNHYl7UAACAASURBVNaJ/HfNx4z1qaoF\nqnqa246Vv7TNUFWZLa4yc7rabDM0fQPEK0+RK08xQ8NXnl9qy9aJ5vWR5G3lhGphF394D+/RGV0/\nMSsY0mM8DmdZiWbI5XpFrIPvC8IIfP+OxKtR7brFUI1GtSsSGfJBvqPFUBtL6Kd7uvpywaV7HR0O\nh8PhcDgct48TsDomjjHVIlaegYzB86/cx9WOtRatDEoZlLJIK7F0Cxfcf4WrG00+eEPO30SItPxk\nLcR6grJawp7PWb8hl7UZDtQOmD15zkQpbTO0UUT85Cvkyhfo8clLq0HWWo7KhjfHmjfHkj11hBja\nwx/dw8tVOD/py7M+43aCJ5lZ5vxpMqL3LDBLXbwydyBejTbDbBby+VsN1bio2tVIL4wyPv258MJB\n4A6Hw+FwOByOu+cnK2BGSqjVsCrB1MpYEoTQaaqhf72PpSFdUmlqUmKMQmOwPDThamCJvH3y4St8\n0TIhaQYoyxWU7b/Vq7XaDC3Wtn0eVqdthifPGYwPsb6PWpqntvIEObN86edfk5bNE8XbI8WbY0kt\nOsIf2cNb2CcT1bqOD2zInDfNYjDDjDdJIC6WaWshCATBXYjXHYVqXFTtMibd25aNXJCGw+FwOBwO\nx0PiJydg+vgdSIU1BisUEIMw9ZlG11t0S6moyoREpsEZCB6ocLXwxRl94RqhV2w+pm2GilwiNuPc\nVqx8q83QUj5r7WoDyKgqM/U2w4yuoqenqK38GWphHp0ZhO5GQYy17J8Z3h6n0rVfkojBd/jDe/jT\n+0Sh7HpNZLMs+tMs+LNMemM9Bwm30xCvMAPeLYqXbWxuu4NQjV7VLmss1lqyYUA+FxCFTrocDofD\n4XA4Hho/OQFDKYyNQSRgTT1O/hL5sOnQ46pKkFKTKF1/XbpQv7Ohu7eEIKEQbBD5u+di5WepqHl6\nSc+HkMRpi+H+lkEqj3ahG6gdMHfynPHSBvbREOoPvqayvIQt5LA2xBB1HF+ODW+PdSpdx4rYKPyh\nA7zxPaKnBwhfd12/jwIL/gwL/gxj4tG1fi53JV6NUA3R1weZwq2FavSqdjUkLwo8spFPLurdVulw\nOBwOh8PheBj85ATMmCJYe6F4WWvSSHgUSaLqsfGNQ+u1rSsqKg8D0xYr3xKWWD+irJYxNnfjK1gL\npTPYe6M5eufVp0vVxdRqJkobzJ48pz+solZWqK78E+zwUP21tn4PPtpYdouKN0epdL0rGwgS/KF9\n/KU9soPvEJ7puv6wGGTeS6VrWAxeX4Yt+IEgk73l4cnnQjW8Qh5k5erXXXXac9WuBk66HA6Hw+Fw\nOD49fnIClgpUsxSENgqJwliDMhqtFVoKjIHGwN0HXuTqIvSO6rHyraHFyuTrsfJXpwlehTHwblez\n/1pRSiLaq2gZVWHmdJXp6iv8xUnkz/+QysR4m+warA04qWbS8IyjmK0TjTJAWMMf3iMzt5fO6Orx\nuY+KkbTS5c0w4L3nnrW7EK87CtWw1oKAjBeR93P4XvpXNRv6TrocDofD4XA4PmF+cgKW6BiNQhuD\nsWllyBiLkmnivLXtwvVpmZcnqvQFa2T8o+ZjxgZU1AI1PcVNY+WTquHgZZm9owhJhnbxGqgdMFv8\ngUcjGv0Hy6jZ30X5reelNmwew+vjgLfHmtNqWhkSURl/Yo9oeA+v77TrmgLBpDfGvDfDvD9DQbx/\n5a7Ranir4mUMhGFrdtct0V7tirwcnhBEoU/OSZfD4XA4HA7HZ8FPTsD+//buO07SrK73+OeJlTpM\n6slhA+zZXQTJsMAGJXuVJKBeFVARA0EwIQq4cAV8CSKw4l7WBIjey73ILnARBMVldwEByWE57E6e\nnZme0BM6TKd6zv3jPNVdXVPdXZ2qu6e/79cLavupp6pO19MzU9/+nfM7w1XfKS/LMqpjUK1OFjFg\nUffAbZuAcUrxIUrRA1Payg9XtzE0vmdBbeVd5hh6oJ/eg2OcGl+PCyarToGrsrn/ANuTE5Sv2MT4\nZY9jNN/PyreIdxzsg0N9jqPnIHMAYwSlfuIdvX6PrvLARa8ZErI93MKecAe7ou0Ug8L8xr7Ya7xq\nPyiL3FSjvtpVCkvEYUwhVegSERERuRStuQA2NpKRZb6AsZpDl+coRL1U4gOEU9rKdzMwdiVV1zH/\nZz4/wBnbx/HznfTH+bTF/H1Kx4fYPnqQzdsgfPRuXMeVjJO3iD/pONTng9fgxJAcYcdZ4vV56Cpe\nuOj1YmJ2htvYE+1gZ7iVZJo9uloau4MogqQQLE47+bypxkS1a5F+YCarXUUKQYFCElMqxpQL8Ypv\n7iIiIiIi87PmAlg170ex2j/fxsF5KslekrB/4ljVFRgcu4LRbBPzmj45MkL1/gc4eTTjaLiDsXj3\nlJ+QrtFTbOs4z7qHrIdN15A5x/F+OHjAceiMo/e8XzUH+Nb+XX2+Xfz6EwTpyEUvVyBld7Sd3eFO\ntoWbZ9yjqzW+uUqhGBDFixS86ppqLIb6alchKFJKC5QKEZWiNkgWERERWQvWXABb7QJGqCT7KUYn\nJo45F+Zt5Xcx57by4+OEhx/gwr6THB3eyMnKZbh0MrwErkpPcJrNuyIql29kcGwjP+iDg/dmHO6D\n4fG65wqrhF2niDb0Eq87CfHFe3SVKbEn2sHucAdbwk2z7tHVqiCAOA1JkgU+3xI11chcRhzEJFFK\nR1KhkMZ0lGJtkCwiIiKyxiiArRoZpegIpfhwQ1v5TQyOXUHGHBpBOEd4vJfovn2cPhVwpPJg+ot7\noG7GYupG2LLuAhse1MHp8R6+1ec49HXHqcYlW9EY0bqTJBt6CbtP4cKL9+jqCjrYE+5kd7SDTcH6\nRZteNzHVMF2EitcSNNVwzuFwJKRU0gqVYkGhS0RERGSNUwBb8Rxp3lY+Cocnjo5nlbyt/LqWnyns\nO0O8dy/V/cc4muzmga5HMLZxalfBzuQCXdsjziQp3ziTcOSbMFZ1U58oHiFaf4JSTy9Z5TSu1vij\n7pQNwbqJSte6oGtR1zS5vJ18krKwNV5L1FQjcxlRENOVdtDR2UWlFJMqdImIiIgICmArWhQMUYn3\nkkZnJo75tvKX5W3lZw81weAg8d79RHv3MjCUsLf7Gk5ufiSubupfQEapo8rZUsTXBwqcPQhT4xQE\n6QWKm3opbOplpHAGAmisdW0ONrIn2snucAedYWX+3/h06oLXgroa1ppqFIs+eC1COMxwOAeVqMS6\ncidd5ZTt29Zx8mT/7A8WERERkTVDAWwFChinHB+kGB1taCu/PW8rP0uHwNFR4gMHie/fB8dPcKLj\nco50P4n+DRunvk6YcT6F/cMwMhBBw/TCsDhA97ZegnW9XEjOA1DfSiMgYFvYw55wJ7uiHZSDxdsP\nq96i7eO1yE01nHNUXUYpKtJdrrChUtH0QhERERGZkQJYg7GT+yhxjGLZN5AYHkq4wDaSniva8OqO\nQnQ8bys/2cBitLqOwfErqboZqkrVKtGRB4j37iM+dIgRihzsNjxw2Y2MRVOD0VDgOOYcZzJww/X3\nOCrrztO5tZfRjl4uhINMuRuICNkZ9rA72sHOcBeFYHG6AzazKFMNl6Cpxni1SiFK6CyX2dTRSSHR\nXl0iIiIi0hoFsDru2NfY2D11yli5MkaZQ5w7doZg26OW7LXj4BwdyV7icLIMVc0KDI5fyWi2kabT\nDZ0j7D1Bsncf8f4DMDLC2eJmjmy6npMde6ZMM8ycoy+AXhxDdbMLo8DRs+0MhY299Bd7uRAMc67h\nZVJidkWbuSzawo5gC2FQZs7dFudgUfbxWuSmGtWsShhGdBXK9HR0UirMb3NoEREREVnbFMByYyf3\nXRS+6nV393P65L5Fr4QFbpjOxFKITk4ccy5kaHw3F6o7gYsDSHD2LMn9+4j37iMcGKAaRBzruJzD\nm69hoDB1muEojhM4TgZQ6xjfXc7YtPU0rOulLz7BOUYveo0SKXuiLeyJtrIt3EgYQOZSYCmDxwL3\n8VrkphrOgSOjo1BiQ6WD7tISrGsTERERkTVFASxX4lhL54yzWAGs6tvKu8MEUTZxdLjaw9DYFWQN\nQScYGiLeu594716i033+3LjMkQ2P5Gj3VRdNM+zPg9cZII5g5/pxOjefYqSjl+Oc5CjjNOoISlwW\nbWVPuIXN4XrCIAAczgVkrsjSVb188Jr3Pl6L2FTDAVm1SrlQYF25zIZK16LtVSYiIiIiogCWq635\nmu2cxm2w5s6RhqeoJPuIgsmWFuNZBwNjVzLuuidPHR0lPniI+P69RMeOEziHA84UN3Nk3bWcqOyG\n+mmGOE4DJ3BUOmD3hlEetPEkZwu9HM1O0ctk0KtZF3T40BVtYWNDu3hHtqRVL+d88ErSkCSdZ8Ur\nTRelqcZ4tUoax3SXKvR0dJLEWtclIiIiIotPAaxR4Ji6q1XgV1+5hbcqj4KBvK385CorR8LA2GWM\nVLfie7tXiR44Snz/XuJDhwmqvtl7NYjo7bycQ+uuZbCwYcrzjuLoCx3lDbBr0wg7u3s5FvTyw6wP\nh6Mxd20KuidC17qwg4s5nAvJWLq1Xg7y4DXHzoaL2FSjmjnCADqLJXq6OiknpdkfJCIiIiKyAApg\nueGhhFLHKI37X0EeyAIYHkxhHsuAAsYoxwcoRscmZsc5FzBc3U6WPJiR8SrhiZO+g+G+/YQjk5Wx\n4bjC4e6rOdJlyKKpVZ6RyFHcANt3DNJV7uVQ1st/ZWf9t+DqXx+2hBsmphd2hNMHDYfLq15L093Q\nAUkckBTmGLxqTTVq0wwX8PpZltGRFlhfqbCu3Lmom0SLiIiIiMxEASx31q2nxPEZznCcdRtoVi+a\n6THF6Bjl+ABhMLnmarS6nsHxK8nOjFE69E1K9/6QaGCg7lFwtriF+zY8nPOlrQ3TAh2FbtiwZ4DT\nHcc4WO3lO66fxiVdISE7wo3sibayO9pMKZhtGmF91WvxA4lzECcB6XyC1yLs3TWWZZTimO5yhZ6O\nLqJQ+3WJiIiISPspgOWOFiPCswFb1jVWwLzeswFHKyFXtfh8SXiGSryXOByaOFbNigwO7SL7QR/B\nff9Ope/0lMdUg4h9667lcPe1uNhXeWpRJUocndsvcHbzEe4Lj9Lvhi4KXTGRbxcfbmFn1EMatLaO\nyVe9CjDbBs/zMK9NlBdpmmG1mhFHIV2lMj0dXRSSpduzTERERESkFQpguYGxAb6fVOjrG2ZnOk5H\nPtVwYBCOjMYcLxQJx2ZvwREGw1TifRSiUxPHMhcydKzC4BdP0dl7L2HDNMe+dAPf3/QohkrbiBo6\n7qVdY1zYeox9XfczFObbItc9vEDC7mgLl0Vb2B5uIg7mUtlxOBeRUWSxq17zCl6LsHfXeJYRB9BR\nLLOxo4OOQnlezyMiIiIishQUwBqccBs42ddNeMxXoLLCBVzlHDA08wOpUo4PU4oOEwSTCan/gCO4\n8xDx4Ch1/Q0ZCWK+t/5H6O2+iiQqERBMtrsIHNVNZziy+Yf0V/oueqUyBfZEW7ks2sLWcMO82qQv\nVdWrFrySFMJWNlFehL27qs4R4SgVCmyoVOgqdqh1vIiIiIisSApguY6kg8FTMWF/95Tj4XAJhksE\nneeobLp47yzfVv5k3lZ+ckPjsd5RsrtOkpyYbKiREbC3soPDPQ/DFTfBeDil1UWWjnJq8wFO9xyk\nmkxti98VlCdCV0+wbgGNI2prvUo02+R5vpyDKA9eUavBq37vrjmqZo44DEmTiHXlEuuKncSRfpxF\nREREZGXTJ9Zcd3UrF/qn3wss7O+me31Dtcj1Uwz30pGenzw0OM7Yl/rI7OR0xePFjTyw1TC84XLG\nBmNcNZiyfmuws4/TWw5wfn0v1FXPNgSdE+3i1wcL79bnq14Ji7mvl3MQRZAUgtmDl8u/t0LBr+2a\nY7WrmjmSKCSKYXOlQkdSoZTMb6qiiIiIiMhyUADLVc+XSEIYy5qHsCRMGD9X4kzBcezsKNvLe7ly\ny6nJtvJVR/Wb5xj/2hkYc5xPOzi140pGdz2YwZEKZ08HMJnTyIIqZzcdpW/LAYbL/RPHN4fruCz0\noasrnEfP+6YcuCCvei1O9785B68w9JWuchnmECQz54iCgCQJ6SildKQlKklFUwxFREREZFVSAMtd\nGMpIg5Qoihl3Y1QzvwGyCyLOjxfouxBz/sw4Nw3fyxOvOUtUmAwR1f2DjH/hNMODEed3XkVkruBs\nuJnjRwMuHJ36OqPpBfo2H+RMz2GqyRghAdvDjVwWbWV3tIVKsNgVnQznkrzRxsL54BWQFFqYauic\nbx0/xxbytWVhaRJRSAM6CiUqcZlCvHiVOxERERGR5aAA1iB0ISPVIqfGQk6Ohpwbc+y+0Mv1nfu4\n8jFjROsTah0Ds75RRr94hkHXQ/CYG7jQs5MTxwJOHHRk41OrPIOdp/NphieIgoAdYQ+XRVu4qmMn\nbnhpvhfnXL6v18KrXrXmGvFsa7zm2ULe5f+XJiHFNKSQJpTjoqpdIiIiInJJUQADLow5TmURDwzA\nqdGQ0Qy2jPTxyP59PCQ8TPd1FaLLK9Q6BrqRKiM/qDIc7mHs+hs4N5Rw6MgYQwcCAgImAlpQ5eym\nBzi95SBZ+QK7o808Ono4O8MeksC/9cUw5QKj04xsvjKcixenvXxdc40ZuxrWphlWKnNqqpE5v66r\nWIhJk4BiXKAclShqbZeIiIiIXILWZADLnKP3PBzqcxw8Ayf6ASK6x/p5dP9+HtK/j430Ez9qPdHD\newgiH2Kcc4ydLDIQX83wlZ3s7x3kzLcyggsBkE5EndH0An1bDjK0qZddxQ08NLqabeHGi/b4Wgq+\n0UaRhbaXb3kfryybnGZYaG2KYNU5kryDYakQEUcRpbigapeIiIiIXPLWXAD79PczDp+BkbwLYbE6\nzCMGDvKQ/n3sHD4JQGg6SK7bRVCZfHsuDJYYiB7M3miU40dGCE+UiaqdU+pLA52nGdz6AJs3xjw2\n3sqW0BAusHNh62rt5csspOrV0j5e8+hmWL+uq5SGRFFEMU4pJ2UKUevrw0REREREVrM1F8DuPwlx\nNs7Vg0d4yMA+rhh8gMivQCLYXCC5fiPh1snpb6NjCfdf6Oa7ZyOyYyN0nN1MUhdwsrDK0MYTdG0f\n4Ue71rMxeOiC28XPla96pcD8g0xL+3jVdzMslWZd31Vb11VI/BTDOAqIo5hSVKSSlNv+PomIiIiI\nLLc1F8B+ovcLXD14iLS+3Xw5Ir5uA/HVnROHMgffHY752uEOuo7vonKhc8rzVNNh0m0DPGhbkY2F\nbe0afoNa1avIfBtttNROvtbNsFhsaZph1TnSfF1XIQl9T464QDkuk6raJSIiIiJr2JoLYA/r3zvx\n3y6E8Em7SK9NCaLJDZDvG3Z8e/82kgcuZ3N16lqqsPsC27eHbO8pEix6y/jWLXRT5YnglQZE8QyV\nrBa7GU40P0xiKqUYhyOJfCfDUlxStUtEREREhDUYwACqG9bjHrWT9IpR0niMfLIcp0cDvrV3B0OH\nLqdYv44qzNiw2bFjR0S5o/UOf0vD4RawqfKs+3j5RWB+imFx9oDpnCOJI8rFiCSOCIBCXKAjrhBH\na/LHS0RERERkWmvuE3LvTz+J8qZT9MSDE8eGqwH37tvBsf2X41wwEb3SgmPrjoCerSHxwpoKLgqH\nw7kEN4+q16xrvLJssqlGMvM3m+F/cNI0r3Y5SKJY1S4RERERkVmsuQC2c8sDRHlAyBwcPLKF++67\ngrGxydDRtQ627ID1GwNWRpZwMM+qV0tdDVuYZujw1a5iElEsxCRxCDiKqnaJiIiIiLRszX1qroWv\nU33dfP/eK+kf6AB89ti0xQevcmU5RziVr3rFOOa23mzG4JVlk9MMZ9k0ubZnVzGNKBVjP+UwSijG\nRSqqdomIiIiIzMmaC2BDFwrca6/geO8mIKBQhC3boWcrK2Ka4aR5Vr3yqYZNN1BucdPkDL+TWCGJ\nKBcioigkwHcyrKjaJSIiIiIyb2vuk/Tn73k0WRbVTTNkhUwznDSftV61ilfT4OVcS5smVx2kUUCl\nEFNMIxyONEz92q5kuZuPiIiIiIisfmsugG3aEq24aYaT5l71cuTBq9AQvCb6ws+8vqt2WiGJqJRi\nggDCIPJru5IKYTBz+3kREREREWndqgtgxpinAK8FHgMUgb3AB4F3WGursz3+8quWdnzzNdeqlwOS\nOCBpFrzC0E8zLJenLe9lmSNNIkqFiDSJAEchKlCOSxTi+e0tJiIiIiIiM1tVAcwY8wv4sHUO+Ahw\nHng68DbgOuA5yze6eXIOHC1XvaYNXrXGGuXytPt3Zc4RB8FE+/jMOVI11BARERERaZtVE8CMMSXg\n3cBZ4BHW2oP58Rj4GPAsY8xzrbW3L+Mw58T5VVZUSVs4d4bglaY+eKUXP4/L/6+QRBQLEUkcEAQh\nxahAJS6roYaIiIiISButpgU+PwasB/6mFr4ArLXjwFvzL5+xHAObu7zq5UoQTt9e3jnn13glAeVK\nSFoMJ8OXcz5wbdgA69ZdFL6qzhEGAZVCwqZ1RTrLMZVCge60my3lHroLXQpfIiIiIiJttpo+ge8D\nXgd8vsl9o/ltR/uGMz+trPVyzhEEAUkakqTB1NA1Q2ONiWpXOtk+PgwiSnGRSlJSQw0RERERkWW2\nagKYtfYHwA+mufu5+e332jSceZi9w6FzjoAmwWuWjZMz54jDkGIaUypGOAfFOKWclClEs09vFBER\nERGR9lg1AWw6xphrgN8ChoEPLPNwmpqt6lWreKVpSNwYvAoFH7oaphhmDsK8fXypEBGGAVEYq9ol\nIiIiIrKCLXsAM8YcAHbPctp7rbWvbPLYncC/ACXgNdbaBxZ9gAsyc9XLOQdAmoYkhbB2MN+Yq3DR\nNMPJhhohxUJMmkRkWZZXuyqqdomIiIiIrHDLHsCAjwKbZjnny40HjDEPAj4L7AFutda+ewnGNm8z\nV70c4CteHV0x/efHJrsZFotT2sg7wOV7dhXSiGLqg1wQBJTiojZLFhERERFZRYJaFWY1McY8Bvgk\nPrjdaq19eauP/fxnPu2CcAn3u3IOCHGUIGysevnqVpqGpIVwogIWFAsEHR0E0eT5WTUjjSMKhYhy\nMSEMA1yWEUcxHUmFSqG8dN+DiIiIyOqizUxl1VgJFbA5McY8FbgdP+3wT6y1b5zL44eHx5ZkXFBf\n9YqBsfx//h7we3glScjIhYyRMd9Uo3vbRs6dHYL+kYlmGr6LYUzgMkaHM4aHRinGKZWkTBilDF2o\nMkT/kn0fsjh6ejo5eVLX6VKl63vp0rW9dOnaXrp6ejqXewgiLVtVAcwY83jgDqAAvNpae8syDynn\ncE3XetWCV0iShH6aYZJMaarhsowAKCQxpWJIlFfBnMsICCnFJTXVEBERERG5RKyaAGaM6QA+jK98\nrZjw5cjIXApT1nrlwSsNSZLgor27arM+C0nEhq4Sad2UyCzLKMQplbhMIZ5+rzAREREREVl9Vk0A\nA14G7AL6gPXGmJubnHOvtfbD7RlOrepVplb1mrKBcsxFe3dlmaMQhxTTiELq3/o0jXy1KwjVVENE\nRERE5BK3mgLY9fjS0nrgj6c55w58lWxJNVa9ahsop2lIkuCnF5bLkKZ+XVcQ+HVdxXhyjy98tSsJ\nY7rTbkpJsfmLiYiIiIjIJWPVBDBr7XOXewy+6hVOrXpBPtUQgkIBKhVcHPv9uuKIUjEiiSfXhTVW\nu3oq3Zwc0oJgEREREZG1YNUEsOXmcHnVK2+eASRxQJJCUCxCpUI1jEiigGISUypGDdWuKmlcoByV\nVO0SEREREVmjFMBmVV/1CnDOL+1KCwFBqURWrhBGIYUkplyKCMO69VvO7/tVjAp0ljq0tktERERE\nZI1TAJuBr3oVgMQHrwjSYuDXd5UrpGlEsRCTJlM3XM5cRhqmlBNVu0REREREZJICWFMO5yIyijgX\nEOdTDV2lQtxZoVhMKKZTpxjiHA4m1nZFYTTts4uIiIiIyNqkANbAOUdGAecSogiiQkjS1UGxq/Pi\nKYbUV7uKlJLSMo1aRERERERWAwWwCRnOxVRdkTCApBJS3tBNqbvjoimG9dWuSlwmjvQ2ioiIiIjI\n7JQc8FWvqisSBDHlrpTSxnVUustTpxiSV7uilHKkapeIiIiIiMzdGg9gGdVqTBgW6NxUoXNTF0lp\narBq3LdLnQxFRERERGS+1mQAc4DLMtK4yLrN6yn1rCOMp74VWZZRiFMqcZlCXFiegYqIiIiIyCVl\nzQWwIMhIkwJdmzaSblxPUNdUI8syojD2a7uSkqpdIiIiIiKyqNZcANu4Yyvp+o0Twcs5BziKcYFy\noUwapcs7QBERERERuWStuQBW2NgD+GpXEiW+fXxcuqjhhoiIiIiIyGJbcwHMOUcpLlApVNQ+XkRE\nRERE2mrNJZCtlc3LPQQREREREVmj1GVCRERERESkTRTARERERERE2kQBTEREREREpE0UwERERERE\nRNpEAUxERERERKRNFMBERERERETaRAFMRERERESkTRTARERERERE2kQBTEREREREpE3i5R5Au337\n39/O4EDMeLqT65/5guUejoiIiIiIrCFrrgIWho7OrjHWF/dz50feu9zDERERERGRNWTNBbB6mzYO\ncvenP7LcwxARERERkTViTQcwgHj48HIPQURERERE1og1H8AqnePLPQQREREREVkj1nwAExERERER\naZc1H8AG+9dcI0gREREREVkmaz6AjRd3LfcQRERERERkjVjTAezU6QrXP+P5yz0MERERERFZI9bc\n/LssCxjsjxkv7uKm5yt8iYiIiIhI+6y5APawJ//ecg9BRERERETWqDU9BVFERERERKSdFMBERERE\nRETaRAFMRERERESkTRTARERERERE2kQBTEREREREpE0UwERERERERNpEAUxERERERKRNFMBERERE\nRETaRAFMRERERESkTRTARERERERE2kQBTEREREREsp42VwAAFrNJREFUpE0UwERERERERNpEAUxE\nRERERKRNFMBERERERETaRAFMRERERESkTRTARERERERE2kQBTEREREREpE0UwERERERERNpEAUxE\nRERERKRNFMBERERERETaRAFMRERERESkTRTARERERERE2kQBTEREREREpE0UwERERERERNpEAUxE\nRERERKRNFMBERERERETaRAFMRERERESkTRTARERERERE2kQBTEREREREpE0UwERERERERNpEAUxE\nRERERKRNFMBERERERETaRAFMRERERESkTRTARERERERE2kQBTEREREREpE0UwERERERERNpEAUxE\nRERERKRNFMBERERERETaRAFMRERERESkTRTARERERERE2kQBTEREREREpE0UwERERERERNpEAUxE\nRERERKRNFMBERERERETaRAFMRERERESkTRTARERERERE2kQBTEREREREpE0UwERERERERNpEAUxE\nRERERKRNFMBERERERETaRAFMRERERESkTRTARERERERE2kQBTEREREREpE0UwERERERERNpEAUxE\nRERERKRNFMBERERERETaRAFMRERERESkTeLlHsBcGWOeANwMPBI//i8Db7XWfn45xyUiIiIiIjKb\nVVUBM8Y8HbgbH74+DPwD8HDgc8aYFy7n2ERERERERGazagKYMSYE/hY4CzzcWvtya+0r8QHsDPAX\nxphoOccoIiIiIiIyk1UTwIArgPPAB6y1R2oHrbXHgLuAbcCeZRqbiIiIiIjIrFbNGjBr7f3AtY3H\n88rYVcA4cLrd4xIREREREWnVqglgjYwxCWCA1+OD2S3W2nPLOyoREREREZHprdoABuwDduT//RHg\nNcs4FhERERERkVktewAzxhwAds9y2nvzhhv17gBGgKcCzwfuMMa8wFo7suiDFBERERERWQTLHsCA\njwKbZjnny40HaoEs73z4QeDngFcC71jsAYqIiIiIiCyGwDm33GNYMGPMduAIcI+19oblHo+IiIiI\niEgzK6EC1hJjzDbg8cA3rbX7G+4+hu+COFslTUREREREZNmspn3Afhz4Z+DlTe67Fh8m97Z1RCIi\nIiIiInOwmgLYJ4EB4FeNMQ+qHTTGVIBb8i//bjkGJiIiIiIi0opVtQbMGPMi4O/xQezDwCjwE8Bl\nwK3W2mbVMRERERERkRVhVQUwAGPMk4E/BB6Lr+B9B9+m/h+WdWAiIiIiIiKzWHUBTEREREREZLVa\nNV0QV7u8Vf69wButte+e5dxXAO8Bfsla+4F2jE/mb7Zra4x5BvAHwCPxm4d/LT/3K20dqMzZTNfW\nGFMGbgZeAGwHTgGfAP7IWnu6zUOVFhljtuKv238DNgN9wL/hr/H+hnNfBLwGeDBwBvg/+XmD7Ryz\ntKbVa2uM6QTeADwP2AX0A3cDN1trv9XmYUuL5vJnt+Fx+kwlK85qasKxahljOvAbTncCM5YcjTF7\ngLfl56k8ucLNdm2NMb8K/AtwJfA3wMeBG4C7jTGPa+NQZY5murb5BvCfAX4XOAG8Gz8d+mXAF40x\nXe0drbQi/wD3Ffx1+h7wrvzr/w58taHB0+uA9+dfvgf4Fj6MfcYYk7Rx2NKCVq9t/ouTu/F/do/j\n/+x+Fv+h/kvGmCe0f/Qym7n82W14nD5TyYqkCtgSy//wfxR4RIsPuQ2oLN2IZLHMdm2NMbvx/7h/\nH7jBWtuXH38f8EXgT4Efa89oZS5a+HP708ATgI9aa59f97i3AK8DXg28eanHKXN2M7AT+G1r7btq\nB40xPw/8A/DnwLPz6/9m/J/TG6211fy8N+ErJy8D3tveocssbqaFawu8CngY8G5r7WvqzrsB+Hfg\nVuBH2zdsadHNtHZ9G+kzlaxIqoAtIWPMq/G/FX8o8LkWzv8l4Kn4iomsYC1e218BisCrauELIJ96\n+Gf4qYiywrR4bR+Z376/4fht+a2qmyvTc4ET9R/gAKy1/wjsA55mjAnwASsC3loLX7m3AueBl7Zp\nvNK6Vq/t84AMH6Trz7sL+DzwUGPMtvYMWeZg1uvb+AB9ppKVTBWwpfVbwH7g1wCD30y6qfwv/Hfi\nP9B9C99eX1auVq7tM4E+a+1FH+KttX+4tMOTBWjl2vbmt5c1HN+Z355ckpHJvBljQuAt+O1LmhkB\nUiDBTxN2wJ31J1hrR4wx/4n/MN9pre1fuhFLq+Z4bW8FNltrB6Y5D6Bj0Qcp89bq9TXGJNbasfwx\n+kwlK5oC2NJ6GfBv1lpnjLl6lnP/ChgGfht4yVIPTBZsxmub/6b1WuCbeSOHt+H/ASgB9wCv1WLv\nFauVP7f/CPw+8EZjzF7gLuBq4H34DwOanrbCWGsz/Fqui+TX+Wpgr7V21BhzJdBrrR1qcvqB/PYq\nVMVeEeZybfF7iTY7bxNwPX6f0QNLM1KZjzlc37G6u/SZSlY0TUFcQtbaz1prZ130aYz5GfK56dba\ns0s/MlmoFq5tN1DGB66v4Pet+xDwSeDJwD3GmEct+UBlzlr5c2utPQFch1/E/0l8F7WvAtuAp1hr\nv7rkA5VFkf92/S+BgMkppBuB6f4uPpffdi/x0GSBprm203k7vvL1wYYP8rJCTXd99ZlKVgMFsGWW\n/9btFuDj1tr/u9zjkUVTW/T7CHwTjodba19jrf0Z/BqECrN/IJAVyhizHvgA8CP4dWLvAP4fsA64\nzRizaxmHJy3KK9Xvw08z/Sq+sxr4qWoj0zysdry4tKOThZjh2jY79/XAi/GVrz9qx/hkYaa7vvpM\nJauFAtjyezd+bvpvLvdAZFFl+a0DfsdaO/Fhzlr7CfzakkdM1zpXVrxb8NOVft9a+xRr7e9ba58F\nPB+4BtA//CucMSYG/g7fLGcv8Gxr7Xh+9wX838vNFPJb7QW2Qs1ybRvPfTO+4+Up4L9Za881O09W\njlmurz5TyaqgALaMjDE/Cfwc8AfW2qNNTgnaPCRZPLV/xMestd9pcn9t/dcVbRqPLJL8H/8XAvut\nte+ov89aezvwKeCxxphrlmN8Mrt8L6iP4asePwR+zFp7vO6UM0w/xbB2XB/UV6AWrm3tvMgY8zfA\n6/FNdZ5srb23rYOVOZvp+uozlawmCmDLq7Z/0F8ZY7La//CdewD+Pj92wzKNT+YpX7x/DIjyTXsb\n1TZybbbIX1a2HnwDIzvN/d/PbzUNcQXKp49+Dt+l9OvAk6y1RxpO+yGwxRhTaHw8cDlQBe5b0oHK\nnLV4bcmv6+3AL+M7nj5pml+UyQrSwvXVZypZNdQFcXndjt+/otF1wNOBO4BvAgfbOShZNHcBPwPc\nhN/gs96jgDEmP6zL6tGHb4d81TT3Pzi/vei37rK8jDFF/Fq9x+KnAT9rmnbkd+P/3N4AfLbh8Y8H\nvmet1RTEFaTVa5uvHfon4CeB7wJPa1Yhk5Wlxeurz1SyaiiALSNr7cfwpfQp8o1gnw7cYa39YNsH\nJovlNnwA+zNjzI21fyzyDk2PAz5av0GzrA75XlB3AC80xrzCWvuXtfuMMU8Ffgr4vrX228s2SJnO\nW/Efxr4IPLN+bWaDfwL+ELjZGPP5vH05+bFO1EBnJWr12r4Sv6nvfcBN+jt41Zj1+uozlawmCmAi\nS8Ra+x/GmPcArwK+a4z5KH6j3ufhqyOvWc7xyYK8Bh+i32OMeRbwDeBBwHPwLelfvIxjkyaMMVuB\nl+df/gB4nTGm2alvs9ZaY8w7gNcC3zDG/D/gIfi9/O4B/roNQ5YWtXhtHfAXwBvyr78DvGqan4Fb\nrbW9ze6Q9pvD9f3TGYK3yIqiANY+Lv/fYp8ry2/a62WtfbUx5hvAK4BfB87jN/F9vbX2cPuGKPPU\n9Npaa48ZYx4DvBG/38xNwGl85eRN1tr72zlIacnj8WsvHX7tTzMOv15kxFr7OmPMYXw3tVfh13S+\nE399tU/UytLqtb0Dv8ebw/8i7HnTnPdRfGMOWRlavb5/QfPtI/SZSlacwDn9TIqIiIiIiLSDuiCK\niIiIiIi0iQKYiIiIiIhImyiAiYiIiIiItIkCmIiIiIiISJsogImIiIiIiLSJApiIiIiIiEibKICJ\niIiIiIi0iQKYiIiIiIhImyiAiUhbGGNuNsZkxpg/Wu6xTCcf37ElfP7QGPNyY8w7G46vqPfGGNNt\njHmPMebnG47fmY/zics1trkwxsTGmC8ZY/5X3bHa9/CERXj+sjHmkDHmbQt9LhERWTsUwESkXVzd\n/1aypRzfzwK3AOuavOZKem/eDrwCiBqOr7RxzuYPgYcBv9twfFHGb60dAn4P+L3VEkpFRGT5KYCJ\nSLv8JXAN8D+XeyDLaLq/c1faezPdOF+EH+fX2ziWeTHGXIkPYO+y1j7QcHewWK9jrf0w/v14nzGm\nMbCKiIhcJF7uAYjI2mCtPQ2cXu5xrBBTAsAKfm8ax3l4uQYyD/8jv31XG17r7cCHgRcDf9eG1xMR\nkVUscG61zCQRkdXMGHMz8EbgDdbatzQc+yn8L4Rei58yNgbcCbzRWvudJs/1GOB3gOuBbuAA8H+B\nP7fWDuTnvAT/YfhvrbW/2vD4ncAh4KC19vK64xlwHHgs8A7g6fhpeF8B3m6t/dcmY7k6H8uPAdvz\nw4eAjwFvtdaey8+7E7ih4eFvsta+qdl7U/f8vwD8GvCj+Xt0H/C/8JWd4brzbgI+B/w58H7gLfnr\nFYFvAH9mrf1Y4/ibfD9Zk8M3WWvvqvserrfWfqHh++oEXgn8CrArfw9usdbeYozZALwNeDZQBr4F\nvM5ae0+T138G8NvAY4BC/v1+EHiPtXZstvHnz3EZsBe43Vr7/Ib7auO9CXgU8FLgSuAM8Kl8XL11\n59+MvzYvAH4SeCFwAXintfat+TkJcBQ4aa29tpUxiojI2qUpiCLSbs1+6/NS4Hb8h/hP4T8MPxu4\n2xizp/5EY8wvAl/AfxA+kJ/fBfwx8FljTKGF15vpvg7gS8Az8SHwq/gP658yxvxGw1huxE8/+xXg\nFPAJ4D+By/Frgz5rjKlVkT4DfDH/733Ah/BBpOl48oYd/4QPH4/Mv+dP4UPeW4F7jDHdTcb/o8CX\n8eHi88B3geuA240xz5v2nZj0j/jwQj7eDwG9dfdPtwbs/wBvzh/7eeAK4N3GmNfnz/OcfFz3AU8E\nPmeMeWj9Exhj3gD8Cz5Yfzv/7634CtOnjTFpC+MH+EV89W6mwPk+fMjuAz6ND7cvAb5gjCk3Of8t\n+BD2r8Ax4Hu1O/Jg+BngamPM41oco4iIrFEKYCKyEjwb+HVr7UOttS8ADL6a0wX8eu0kY8wu4Fag\nCjzDWvvE/PwH48PJ44DfWuBYOoB+4Gpr7XOttU8GngaMAO9sCITvxVdpnm2tfby19mestT+OXyd1\nBng0PvyQV0tuzR93l7X2RdbaO2YYxyvwTTsscI219pnW2p/Gh7tP4kNZszVjT8FPh7syH/9jgT/J\n7/u92b55a+0v4gMUwG35OG3dKdOtn3oC8Ghr7TOstU8HXp0ffzN+euVV1tpnW2sfBfwTPvD8cu3B\nxpinAG8CDgKPtNbemFevLgc+jq8w/vFs4889DR8S75rhnB3Ajdba6621zwGuxlftrsCHxUZXAE+0\n1j7PWvvQfEz1au/Z01oco4iIrFEKYCKyEtxjrb2t9kVeUfjr/Mv6KV0vwk9hu8Va+5m680fw0wD3\nAZsXOBYHvNpaO9GO3lr7OeCv8GHrlwGMMR346thfW2s/Uf8E1tp9+AAJfjpezVyaP7wmH8tLrLWH\n6p57APh54BzwwjyU1rsAvMpaO1p37L357VJOj7vNWvvtuq//d37rgD+oTcXMfSS/vbLuWK1T4Sut\ntffWDuadBl+K/75ePlsVLK+APg44b609OMOpf1k/BTJfh/f+/MuHNjn/bmvtt+rOb6wC1u67cabx\niYiIqAmHiKwEX25yrDbtrVJ37Kb89hM0sNb+AHjQIoylrz7c1fk4PhQ9KX+9AeCX6k/Ipxvuxlen\nauGi1Wlz9c+zC9gDHLbWXvTeWGvPG2M+ha+Q3YCfNljzfWvtYMNDTuS3FZbOfzZ8Xd9U5JsN99XC\nWBEg7x54Az6s3dn4xNbaU8aYb+CrbI+g+c9LzTb8v20zhS+YnA5a70h+27hNAFw8XbTRgfy2MRCL\niIhMoQAmIivB2SbHxvPb+kr9NvyH9KXsxndgmuO1D+fb6w8aY24AfpXJ0FULXLUKyXxantdeY7qx\n1N+3peH4Re+ltdblzTUWrf16E31NXhOgaq3tbzi3sXq0kTyMAefzx01nJzMHsJ789twM58DMP3PN\n2sn3NTlWr/Z6C63AiojIJU4BTERWglbbsS7W31kz7dc0PMN94Ds0AmCMuRXfoXAc32nwH/DNGb6I\nX7v2knmOr5WgVPseRhqOL1dr25Y6FE6j9r0MAR+d5dzjs9yfNDzndJp1e1zI+bVfFOjfVRERmZH+\noRCR1eQ4cBV+mteBxjuNMS8FevM1WbUPzM3+nms2xaxm+zTHL8tvD+evdSM+fO0Dnm6t3Vt/sjHm\ntTO8xmyO5reXz3DOFflt7wznrBan8SE2Al7cZH3VXNQqVZsWPKq5qVXeZquUiYjIGqcmHCKymtSa\nJjyz8Y5876fb8C3aAQby261NnufxM7zGZcaYBzc5/tP57Z35ba3d+IebhK8yvtU6TP17tqVgkTfd\nOAjsNMZcNNa8/fzT8N0gZ+r0N19traLlDUO+hG9y8pTG+40xiTHmHmPMfxhjds/ydPvw70uz676U\nasH9h21+XRERWWUUwERkNflb/JS7Vxljrq8dzAPPX+Vffii/rXXk+3FjzI/UnWuAN8zwGgHw/vo9\ntowxzwFehm8t/3f54VpnwqfV7z2WP+4fmVwLVFvbBJPTG2eqwNX8RX77/vrW93n3xQ/h90z7Z2vt\niWYPXqC5jHOxvCu//Z8N1yvO73sC0FXfEbKZPMz9F9BV/zxtcF1+26y5h4iIyARNQRSRVcNau98Y\n85v4FvX/YYz5An7K1+PwFY878Zv2Yq293xjzCeCngK8YY/4dX2G5Efg3fLOMZmut9uK7Kd5vjLkb\nH6SegA9+L8rblYPvxLgXv+HxXmPMV/At8p8EDOJD0i/gG4fU1KojzzLGfAz4uLX2b6f5dm/JX/eF\nwL3GmLvwa6Suxzet+DrwG9M8dqFq4/zjPOi+o1k3xsVkrb3dGPMu/P5hXzPGfA0/5fTR+MYbJ/Bd\nH1vxCfzPxA34jajbofYLgYs6dIqIiNRTBUxE2sVx8dS2Zsfq77uItfbv8SHqX4CH4Kcj9uM38X2m\ntba+WcLPAm8BjuGntl2B35T4Ofhpas1e4xh++uB/5o95CP5D9XXW2k/WjWMQ3xb/A/gGFM/EB7fb\ngB8B3pOf+pN1j/kO8Dp8mHgqk9MUL3ofrLXOWvuz+Fb3X8eHsafgpyb+Tj6eM7O9X3O4v95f4wNk\nDDydyX2x5noN5zQWa+1vA8/Fb2p8df7aA/j38hHW2vtafP7aNXneNK89l5+5Wb8/Y0wn/tp821r7\nXy2OUURE1qjAueVqmCUiIrI0jDHvw2/gfFXjGr0leK3fwG92/QJr7T8v5WuJiMjqpwqYiIhcit6C\nr4K9YilfJN98+zeB7yh8iYhIKxTARETkkpM363gz8OvGmJna+S/Ui/HTJV+2hK8hIiKXEAUwERG5\nVP0p8LX8dtHl3Tf/BHjbUjcpERGRS4fWgImIiIiIiLSJKmAiIiIiIiJtogAmIiIiIiLSJgpgIiIi\nIiIibaIAJiIiIiIi0iYKYCIiIiIiIm2iACYiIiIiItIm/x8rcmjMqm3YWgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f7d3f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.lmplot(time, od600, data=df, hue=conc, fit_reg=True, size=10, scatter_kws={\"s\": 100})\n",
"sns.set(font_scale=2)\n",
"sns.plt.xlim(min(df[time]),)\n",
"plt.show()\n",
"#plt.savefig(\"conc.png\")"
]
},
{
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |