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GoogleCloudPlatform/training-data-analyst	courses/machine_learning/deepdive2/how_google_does_ml/bigquery/solution/analyze_with_bigquery_solution.ipynb	2	86989	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyze a large dataset with Google BigQuery\n", "\n", "Learning Objectives\n", "\n", "1. Access an ecommerce dataset\n", "1. Look at the dataset metadata\n", "1. Remove duplicate entries\n", "1. Write and execute queries\n", "\n", "\n", "## Introduction \n", "BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.\n", "\n", "We have a publicly available ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store loaded into a table in BigQuery. In this lab, you use a copy of that dataset. Sample scenarios are provided, from which you look at the data and ways to remove duplicate information. The lab then steps you through further analysis the data.\n", "\n", "BigQuery can be accessed by its own browser-based interface, Google Data Studio, and many third party tools. In this lab you will use the BigQuery directly in notebook cells using the iPython magic command `%%bigquery`.\n", "\n", "The steps you will follow in the lab are analogous to what you would do to prepare data for use in advanced ML operations. You will follow the notebook to experiment with the BigQuery queries provided to analyze the data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set up the notebook environment\n", "\n", "__VERY IMPORTANT__: In the cell below you must replace the text `<YOUR PROJECT>` with you GCP project id." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "import pandas as pd\n", "\n", "PROJECT = \"<YOUR PROJECT>\" #TODO Replace with your project id\n", "\n", "os.environ[\"PROJECT\"] = PROJECT\n", "\n", "pd.options.display.max_columns = 50" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore eCommerce data and identify duplicate records\n", "\n", "Scenario: You were provided with Google Analytics logs for an eCommerce website in a BigQuery dataset. The data analyst team created a new BigQuery table of all the raw eCommerce visitor session data. This data tracks user interactions, location, device types, time on page, and details of any transaction. Your ultimate plan is to use this data in an ML capacity to create a model that delivers highly accurate predictions of user behavior to support tailored marketing campaigns.\n", "\n", "First, a few notes on BigQuery within a python notebook context. Any cell that starts with `%%bigquery` (the BigQuery Magic) will be interpreted as a SQL query that is executed on BigQuery, and the result is printed to our notebook.\n", "\n", "BigQuery supports [two flavors](https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#comparison_of_legacy_and_standard_sql) of SQL syntax: legacy SQL and standard SQL. The preferred is standard SQL because it complies with the official SQL:2011 standard. To instruct BigQuery to interpret our syntax as such we start the query with `#standardSQL`.\n", "\n", "Our first query is accessing the BigQuery Information Schema which stores all object-related metadata. In this case we want to see metadata details for the \"all_sessions_raw\" table. \n", "\n", "Tip: To run the current cell you can click the cell and hit shift enter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO 2" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>table_name</th>\n", " <th>column_name</th>\n", " <th>ordinal_position</th>\n", " <th>is_nullable</th>\n", " <th>data_type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>all_sessions_raw</td>\n", " <td>fullVisitorId</td>\n", " <td>1</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>all_sessions_raw</td>\n", " <td>channelGrouping</td>\n", " <td>2</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>all_sessions_raw</td>\n", " <td>time</td>\n", " <td>3</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>all_sessions_raw</td>\n", " <td>country</td>\n", " <td>4</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>all_sessions_raw</td>\n", " <td>city</td>\n", " <td>5</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>5</td>\n", " <td>all_sessions_raw</td>\n", " <td>totalTransactionRevenue</td>\n", " <td>6</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>6</td>\n", " <td>all_sessions_raw</td>\n", " <td>transactions</td>\n", " <td>7</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>7</td>\n", " <td>all_sessions_raw</td>\n", " <td>timeOnSite</td>\n", " <td>8</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>8</td>\n", " <td>all_sessions_raw</td>\n", " <td>pageviews</td>\n", " <td>9</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>9</td>\n", " <td>all_sessions_raw</td>\n", " <td>sessionQualityDim</td>\n", " <td>10</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>10</td>\n", " <td>all_sessions_raw</td>\n", " <td>date</td>\n", " <td>11</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>11</td>\n", " <td>all_sessions_raw</td>\n", " <td>visitId</td>\n", " <td>12</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>12</td>\n", " <td>all_sessions_raw</td>\n", " <td>type</td>\n", " <td>13</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>13</td>\n", " <td>all_sessions_raw</td>\n", " <td>productRefundAmount</td>\n", " <td>14</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>14</td>\n", " <td>all_sessions_raw</td>\n", " <td>productQuantity</td>\n", " <td>15</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>15</td>\n", " <td>all_sessions_raw</td>\n", " <td>productPrice</td>\n", " <td>16</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>16</td>\n", " <td>all_sessions_raw</td>\n", " <td>productRevenue</td>\n", " <td>17</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>17</td>\n", " <td>all_sessions_raw</td>\n", " <td>productSKU</td>\n", " <td>18</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>18</td>\n", " <td>all_sessions_raw</td>\n", " <td>v2ProductName</td>\n", " <td>19</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>19</td>\n", " <td>all_sessions_raw</td>\n", " <td>v2ProductCategory</td>\n", " <td>20</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>20</td>\n", " <td>all_sessions_raw</td>\n", " <td>productVariant</td>\n", " <td>21</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>21</td>\n", " <td>all_sessions_raw</td>\n", " <td>currencyCode</td>\n", " <td>22</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>22</td>\n", " <td>all_sessions_raw</td>\n", " <td>itemQuantity</td>\n", " <td>23</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>23</td>\n", " <td>all_sessions_raw</td>\n", " <td>itemRevenue</td>\n", " <td>24</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>24</td>\n", " <td>all_sessions_raw</td>\n", " <td>transactionRevenue</td>\n", " <td>25</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>25</td>\n", " <td>all_sessions_raw</td>\n", " <td>transactionId</td>\n", " <td>26</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>26</td>\n", " <td>all_sessions_raw</td>\n", " <td>pageTitle</td>\n", " <td>27</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>27</td>\n", " <td>all_sessions_raw</td>\n", " <td>searchKeyword</td>\n", " <td>28</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>28</td>\n", " <td>all_sessions_raw</td>\n", " <td>pagePathLevel1</td>\n", " <td>29</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>29</td>\n", " <td>all_sessions_raw</td>\n", " <td>eCommerceAction_type</td>\n", " <td>30</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " <tr>\n", " <td>30</td>\n", " <td>all_sessions_raw</td>\n", " <td>eCommerceAction_step</td>\n", " <td>31</td>\n", " <td>YES</td>\n", " <td>INT64</td>\n", " </tr>\n", " <tr>\n", " <td>31</td>\n", " <td>all_sessions_raw</td>\n", " <td>eCommerceAction_option</td>\n", " <td>32</td>\n", " <td>YES</td>\n", " <td>STRING</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " table_name column_name ordinal_position is_nullable \\\n", "0 all_sessions_raw fullVisitorId 1 YES \n", "1 all_sessions_raw channelGrouping 2 YES \n", "2 all_sessions_raw time 3 YES \n", "3 all_sessions_raw country 4 YES \n", "4 all_sessions_raw city 5 YES \n", "5 all_sessions_raw totalTransactionRevenue 6 YES \n", "6 all_sessions_raw transactions 7 YES \n", "7 all_sessions_raw timeOnSite 8 YES \n", "8 all_sessions_raw pageviews 9 YES \n", "9 all_sessions_raw sessionQualityDim 10 YES \n", "10 all_sessions_raw date 11 YES \n", "11 all_sessions_raw visitId 12 YES \n", "12 all_sessions_raw type 13 YES \n", "13 all_sessions_raw productRefundAmount 14 YES \n", "14 all_sessions_raw productQuantity 15 YES \n", "15 all_sessions_raw productPrice 16 YES \n", "16 all_sessions_raw productRevenue 17 YES \n", "17 all_sessions_raw productSKU 18 YES \n", "18 all_sessions_raw v2ProductName 19 YES \n", "19 all_sessions_raw v2ProductCategory 20 YES \n", "20 all_sessions_raw productVariant 21 YES \n", "21 all_sessions_raw currencyCode 22 YES \n", "22 all_sessions_raw itemQuantity 23 YES \n", "23 all_sessions_raw itemRevenue 24 YES \n", "24 all_sessions_raw transactionRevenue 25 YES \n", "25 all_sessions_raw transactionId 26 YES \n", "26 all_sessions_raw pageTitle 27 YES \n", "27 all_sessions_raw searchKeyword 28 YES \n", "28 all_sessions_raw pagePathLevel1 29 YES \n", "29 all_sessions_raw eCommerceAction_type 30 YES \n", "30 all_sessions_raw eCommerceAction_step 31 YES \n", "31 all_sessions_raw eCommerceAction_option 32 YES \n", "\n", " data_type \n", "0 STRING \n", "1 STRING \n", "2 INT64 \n", "3 STRING \n", "4 STRING \n", "5 INT64 \n", "6 INT64 \n", "7 INT64 \n", "8 INT64 \n", "9 INT64 \n", "10 STRING \n", "11 INT64 \n", "12 STRING \n", "13 INT64 \n", "14 INT64 \n", "15 INT64 \n", "16 INT64 \n", "17 STRING \n", "18 STRING \n", "19 STRING \n", "20 STRING \n", "21 STRING \n", "22 INT64 \n", "23 INT64 \n", "24 INT64 \n", "25 STRING \n", "26 STRING \n", "27 STRING \n", "28 STRING \n", "29 STRING \n", "30 INT64 \n", "31 STRING " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery --project $PROJECT\n", "#standardsql\n", "SELECT * \n", "EXCEPT \n", " (table_catalog, table_schema, is_generated, generation_expression, is_stored, \n", " is_updatable, is_hidden, is_system_defined, is_partitioning_column, clustering_ordinal_position)\n", "FROM `data-to-insights.ecommerce.INFORMATION_SCHEMA.COLUMNS`\n", "WHERE table_name=\"all_sessions_raw\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next examine how many rows are in the table." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO 1" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>f0_</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>21552195</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " f0_\n", "0 21552195" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery --project $PROJECT \n", "#standardSQL\n", "SELECT count()\n", "FROM `data-to-insights.ecommerce.all_sessions_raw`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now take a quick at few rows of data in the table." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>fullVisitorId</th>\n", " <th>channelGrouping</th>\n", " <th>time</th>\n", " <th>country</th>\n", " <th>city</th>\n", " <th>totalTransactionRevenue</th>\n", " <th>transactions</th>\n", " <th>timeOnSite</th>\n", " <th>pageviews</th>\n", " <th>sessionQualityDim</th>\n", " <th>date</th>\n", " <th>visitId</th>\n", " <th>type</th>\n", " <th>productRefundAmount</th>\n", " <th>productQuantity</th>\n", " <th>productPrice</th>\n", " <th>productRevenue</th>\n", " <th>productSKU</th>\n", " <th>v2ProductName</th>\n", " <th>v2ProductCategory</th>\n", " <th>productVariant</th>\n", " <th>currencyCode</th>\n", " <th>itemQuantity</th>\n", " <th>itemRevenue</th>\n", " <th>transactionRevenue</th>\n", " <th>transactionId</th>\n", " <th>pageTitle</th>\n", " <th>searchKeyword</th>\n", " <th>pagePathLevel1</th>\n", " <th>eCommerceAction_type</th>\n", " <th>eCommerceAction_step</th>\n", " <th>eCommerceAction_option</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>140505387240227138</td>\n", " <td>Direct</td>\n", " <td>0</td>\n", " <td>Ghana</td>\n", " <td>not available in demo dataset</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>1</td>\n", " <td>None</td>\n", " <td>20161007</td>\n", " <td>1475872666</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>9990000</td>\n", " <td>None</td>\n", " <td>GGOEGDHG014499</td>\n", " <td>Google Infuser-Top Water Bottle</td>\n", " <td>Home/Drinkware/</td>\n", " <td>(not set)</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Drinkware \| Google Merchandise Store</td>\n", " <td>None</td>\n", " <td>/store.html</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>2161300302561027053</td>\n", " <td>Organic Search</td>\n", " <td>0</td>\n", " <td>India</td>\n", " <td>not available in demo dataset</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>1</td>\n", " <td>None</td>\n", " <td>20170521</td>\n", " <td>1495355218</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>99990000</td>\n", " <td>None</td>\n", " <td>GGOEGBRJ037299</td>\n", " <td>Google Alpine Style Backpack</td>\n", " <td>Home/Bags/</td>\n", " <td>(not set)</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Bags \| Google Merchandise Store</td>\n", " <td>None</td>\n", " <td>/google+redesign/</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>5809923035957342173</td>\n", " <td>Direct</td>\n", " <td>0</td>\n", " <td>United States</td>\n", " <td>not available in demo dataset</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>1</td>\n", " <td>None</td>\n", " <td>20161008</td>\n", " <td>1475972048</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>99990000</td>\n", " <td>None</td>\n", " <td>GGOEGBRA037499</td>\n", " <td>Waterproof Backpack</td>\n", " <td>Home/Bags/</td>\n", " <td>(not set)</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Bags</td>\n", " <td>None</td>\n", " <td>/google+redesign/</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>8178337623496064877</td>\n", " <td>Referral</td>\n", " <td>0</td>\n", " <td>United States</td>\n", " <td>Mountain View</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>1</td>\n", " <td>None</td>\n", " <td>20160909</td>\n", " <td>1473454898</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>10990000</td>\n", " <td>None</td>\n", " <td>GGOEGCLB020832</td>\n", " <td>Softsided Travel Pouch Set</td>\n", " <td>Home/Bags/</td>\n", " <td>(not set)</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Bags</td>\n", " <td>None</td>\n", " <td>/google+redesign/</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>9730509990517739822</td>\n", " <td>Direct</td>\n", " <td>0</td>\n", " <td>Canada</td>\n", " <td>Toronto</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>1</td>\n", " <td>None</td>\n", " <td>20160928</td>\n", " <td>1475105477</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>3500000</td>\n", " <td>None</td>\n", " <td>GGOEGBJR018199</td>\n", " <td>Reusable Shopping Bag</td>\n", " <td>Home/Bags/</td>\n", " <td>(not set)</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Bags</td>\n", " <td>None</td>\n", " <td>/google+redesign/</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>5</td>\n", " <td>9977935485234401557</td>\n", " <td>Referral</td>\n", " <td>0</td>\n", " <td>United Kingdom</td>\n", " <td>London</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>1</td>\n", " <td>None</td>\n", " <td>20160811</td>\n", " <td>1470905998</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>4990000</td>\n", " <td>None</td>\n", " <td>GGOEGOAA017199</td>\n", " <td>Rubber Grip Ballpoint Pen 4 Pack</td>\n", " <td>Home/Office/</td>\n", " <td>(not set)</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Office</td>\n", " <td>None</td>\n", " <td>/google+redesign/</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>6</td>\n", " <td>0064667731979082203</td>\n", " <td>Organic Search</td>\n", " <td>0</td>\n", " <td>United States</td>\n", " <td>New York</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>1</td>\n", " <td>None</td>\n", " <td>20161012</td>\n", " <td>1476318035</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>17990000</td>\n", " <td>None</td>\n", " <td>GGOEGOAB016099</td>\n", " <td>Leather and Metal Ballpoint Pen</td>\n", " <td>Home/Office/</td>\n", " <td>(not set)</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Office \| Google Merchandise Store</td>\n", " <td>None</td>\n", " <td>/google+redesign/</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " fullVisitorId channelGrouping time country \\\n", "0 140505387240227138 Direct 0 Ghana \n", "1 2161300302561027053 Organic Search 0 India \n", "2 5809923035957342173 Direct 0 United States \n", "3 8178337623496064877 Referral 0 United States \n", "4 9730509990517739822 Direct 0 Canada \n", "5 9977935485234401557 Referral 0 United Kingdom \n", "6 0064667731979082203 Organic Search 0 United States \n", "\n", " city totalTransactionRevenue transactions \\\n", "0 not available in demo dataset None None \n", "1 not available in demo dataset None None \n", "2 not available in demo dataset None None \n", "3 Mountain View None None \n", "4 Toronto None None \n", "5 London None None \n", "6 New York None None \n", "\n", " timeOnSite pageviews sessionQualityDim date visitId type \\\n", "0 None 1 None 20161007 1475872666 PAGE \n", "1 None 1 None 20170521 1495355218 PAGE \n", "2 None 1 None 20161008 1475972048 PAGE \n", "3 None 1 None 20160909 1473454898 PAGE \n", "4 None 1 None 20160928 1475105477 PAGE \n", "5 None 1 None 20160811 1470905998 PAGE \n", "6 None 1 None 20161012 1476318035 PAGE \n", "\n", " productRefundAmount productQuantity productPrice productRevenue \\\n", "0 None None 9990000 None \n", "1 None None 99990000 None \n", "2 None None 99990000 None \n", "3 None None 10990000 None \n", "4 None None 3500000 None \n", "5 None None 4990000 None \n", "6 None None 17990000 None \n", "\n", " productSKU v2ProductName v2ProductCategory \\\n", "0 GGOEGDHG014499 Google Infuser-Top Water Bottle Home/Drinkware/ \n", "1 GGOEGBRJ037299 Google Alpine Style Backpack Home/Bags/ \n", "2 GGOEGBRA037499 Waterproof Backpack Home/Bags/ \n", "3 GGOEGCLB020832 Softsided Travel Pouch Set Home/Bags/ \n", "4 GGOEGBJR018199 Reusable Shopping Bag Home/Bags/ \n", "5 GGOEGOAA017199 Rubber Grip Ballpoint Pen 4 Pack Home/Office/ \n", "6 GGOEGOAB016099 Leather and Metal Ballpoint Pen Home/Office/ \n", "\n", " productVariant currencyCode itemQuantity itemRevenue transactionRevenue \\\n", "0 (not set) USD None None None \n", "1 (not set) USD None None None \n", "2 (not set) USD None None None \n", "3 (not set) USD None None None \n", "4 (not set) USD None None None \n", "5 (not set) USD None None None \n", "6 (not set) USD None None None \n", "\n", " transactionId pageTitle searchKeyword \\\n", "0 None Drinkware \| Google Merchandise Store None \n", "1 None Bags \| Google Merchandise Store None \n", "2 None Bags None \n", "3 None Bags None \n", "4 None Bags None \n", "5 None Office None \n", "6 None Office \| Google Merchandise Store None \n", "\n", " pagePathLevel1 eCommerceAction_type eCommerceAction_step \\\n", "0 /store.html 0 1 \n", "1 /google+redesign/ 0 1 \n", "2 /google+redesign/ 0 1 \n", "3 /google+redesign/ 0 1 \n", "4 /google+redesign/ 0 1 \n", "5 /google+redesign/ 0 1 \n", "6 /google+redesign/ 0 1 \n", "\n", " eCommerceAction_option \n", "0 None \n", "1 None \n", "2 None \n", "3 None \n", "4 None \n", "5 None \n", "6 None " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery --project $PROJECT \n", "#standardSQL\n", "SELECT \n", "FROM `data-to-insights.ecommerce.all_sessions_raw`\n", "LIMIT 7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Identify duplicate rows\n", "\n", "Seeing a sample amount of data may give you greater intuition for what is included in the dataset. But since the table is quite large, a preview is not likely to render meaningful results. As you scan and scroll through the sample rows you see there is no singular field that uniquely identifies a row, so you need advanced logic to identify duplicate rows.\n", "\n", "The query below uses the SQL GROUP BY function on every field and counts (COUNT) where there are rows that have the same values across every field.\n", "\n", "If every field is unique, the COUNT will return 1 as there are no other groupings of rows with the exact same value for all fields.\n", "If there is a row with the same values for all fields, they will be grouped together and the COUNT will be greater than 1. The last part of the query is an aggregation filter using HAVING to only show the results that have a COUNT of duplicates greater than 1.\n", "Run the following query to find duplicate records across all columns." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO 3" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>num_duplicate_rows</th>\n", " <th>fullVisitorId</th>\n", " <th>channelGrouping</th>\n", " <th>time</th>\n", " <th>country</th>\n", " <th>city</th>\n", " <th>totalTransactionRevenue</th>\n", " <th>transactions</th>\n", " <th>timeOnSite</th>\n", " <th>pageviews</th>\n", " <th>sessionQualityDim</th>\n", " <th>date</th>\n", " <th>visitId</th>\n", " <th>type</th>\n", " <th>productRefundAmount</th>\n", " <th>productQuantity</th>\n", " <th>productPrice</th>\n", " <th>productRevenue</th>\n", " <th>productSKU</th>\n", " <th>v2ProductName</th>\n", " <th>v2ProductCategory</th>\n", " <th>productVariant</th>\n", " <th>currencyCode</th>\n", " <th>itemQuantity</th>\n", " <th>itemRevenue</th>\n", " <th>transactionRevenue</th>\n", " <th>transactionId</th>\n", " <th>pageTitle</th>\n", " <th>searchKeyword</th>\n", " <th>pagePathLevel1</th>\n", " <th>eCommerceAction_type</th>\n", " <th>eCommerceAction_step</th>\n", " <th>eCommerceAction_option</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>4890832126611809362</td>\n", " <td>Organic Search</td>\n", " <td>0</td>\n", " <td>Sri Lanka</td>\n", " <td>not available in demo dataset</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>NaN</td>\n", " <td>20170610</td>\n", " <td>1497087233</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>20990000</td>\n", " <td>NaN</td>\n", " <td>GGOEGAAX0356</td>\n", " <td>YouTube Men's Vintage Tank</td>\n", " <td>Home/Shop by Brand/YouTube/</td>\n", " <td>(not set)</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>YouTube \| Shop by Brand \| Google Merchandise S...</td>\n", " <td>None</td>\n", " <td>/google+redesign/</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>477997596462135678</td>\n", " <td>Direct</td>\n", " <td>653506</td>\n", " <td>United States</td>\n", " <td>San Jose</td>\n", " <td>246000000.0</td>\n", " <td>1.0</td>\n", " <td>931.0</td>\n", " <td>36</td>\n", " <td>NaN</td>\n", " <td>20170118</td>\n", " <td>1484796890</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>119000000</td>\n", " <td>NaN</td>\n", " <td>GGOENEBQ078999</td>\n", " <td>Nest® Cam Outdoor Security Camera - USA</td>\n", " <td>Nest-USA</td>\n", " <td>Single Option Only</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>ORD201701182177</td>\n", " <td>Checkout Confirmation</td>\n", " <td>None</td>\n", " <td>/ordercompleted.html</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>4890832126611809362</td>\n", " <td>Organic Search</td>\n", " <td>0</td>\n", " <td>Sri Lanka</td>\n", " <td>not available in demo dataset</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>NaN</td>\n", " <td>20170610</td>\n", " <td>1497087233</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>24990000</td>\n", " <td>NaN</td>\n", " <td>GGOEYHPA003610</td>\n", " <td>YouTube Wool Heather Cap Heather/Black</td>\n", " <td>Home/Shop by Brand/YouTube/</td>\n", " <td>(not set)</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>YouTube \| Shop by Brand \| Google Merchandise S...</td>\n", " <td>None</td>\n", " <td>/google+redesign/</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>3211801082901013750</td>\n", " <td>Referral</td>\n", " <td>417832</td>\n", " <td>United States</td>\n", " <td>New York</td>\n", " <td>409000000.0</td>\n", " <td>1.0</td>\n", " <td>418.0</td>\n", " <td>23</td>\n", " <td>NaN</td>\n", " <td>20170601</td>\n", " <td>1496375475</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>1.0</td>\n", " <td>100000000</td>\n", " <td>102250000.0</td>\n", " <td>GGOEGGCX056199</td>\n", " <td>Gift Card- $100.00</td>\n", " <td>Gift Cards</td>\n", " <td>Single Option Only</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>409000000.0</td>\n", " <td>ORD201706013038</td>\n", " <td>Checkout Confirmation</td>\n", " <td>None</td>\n", " <td>/ordercompleted.html</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>8306703617933176158</td>\n", " <td>Organic Search</td>\n", " <td>559533</td>\n", " <td>Canada</td>\n", " <td>not available in demo dataset</td>\n", " <td>151000000.0</td>\n", " <td>1.0</td>\n", " <td>567.0</td>\n", " <td>18</td>\n", " <td>NaN</td>\n", " <td>20170511</td>\n", " <td>1494505602</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>149000000</td>\n", " <td>NaN</td>\n", " <td>GGOENEBB081499</td>\n", " <td>Nest® Cam Indoor Security Camera - CA</td>\n", " <td>Nest-Canada</td>\n", " <td>Single Option Only</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>ORD201705112202</td>\n", " <td>Checkout Confirmation</td>\n", " <td>None</td>\n", " <td>/ordercompleted.html</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <td>610</td>\n", " <td>2</td>\n", " <td>6706553303219862080</td>\n", " <td>Organic Search</td>\n", " <td>78319</td>\n", " <td>Australia</td>\n", " <td>Melbourne</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>88.0</td>\n", " <td>8</td>\n", " <td>NaN</td>\n", " <td>20170301</td>\n", " <td>1488431352</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>4990000</td>\n", " <td>NaN</td>\n", " <td>GGOEYDHJ056099</td>\n", " <td>22 oz YouTube Bottle Infuser</td>\n", " <td>Home/Shop by Brand/YouTube/</td>\n", " <td>(not set)</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>YouTube \| Shop by Brand \| Google Merchandise S...</td>\n", " <td>None</td>\n", " <td>/google+redesign/</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>611</td>\n", " <td>4</td>\n", " <td>9758235511148216157</td>\n", " <td>Direct</td>\n", " <td>682296</td>\n", " <td>United States</td>\n", " <td>Sunnyvale</td>\n", " <td>134000000.0</td>\n", " <td>2.0</td>\n", " <td>705.0</td>\n", " <td>22</td>\n", " <td>NaN</td>\n", " <td>20170615</td>\n", " <td>1497575168</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>25000000</td>\n", " <td>NaN</td>\n", " <td>GGOEGGCX056299</td>\n", " <td>Gift Card - $25.00</td>\n", " <td>Gift Cards</td>\n", " <td>Single Option Only</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>Payment Method</td>\n", " <td>None</td>\n", " <td>/payment.html</td>\n", " <td>5</td>\n", " <td>2</td>\n", " <td>Payment</td>\n", " </tr>\n", " <tr>\n", " <td>612</td>\n", " <td>3</td>\n", " <td>1915538933685278364</td>\n", " <td>Referral</td>\n", " <td>487234</td>\n", " <td>United States</td>\n", " <td>San Jose</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>638.0</td>\n", " <td>22</td>\n", " <td>NaN</td>\n", " <td>20161221</td>\n", " <td>1482356339</td>\n", " <td>PAGE</td>\n", " <td>None</td>\n", " <td>1.0</td>\n", " <td>100000000</td>\n", " <td>NaN</td>\n", " <td>GGOEGGCX056199</td>\n", " <td>Gift Card- $100.00</td>\n", " <td>Gift Cards</td>\n", " <td>Single Option Only</td>\n", " <td>USD</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>Checkout Your Information</td>\n", " <td>None</td>\n", " <td>/yourinfo.html</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>Billing and Shipping</td>\n", " </tr>\n", " <tr>\n", " <td>613</td>\n", " <td>2</td>\n", " <td>8368489856222393707</td>\n", " <td>Direct</td>\n", " <td>331079</td>\n", " <td>United States</td>\n", " <td>Mountain View</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>423.0</td>\n", " <td>13</td>\n", " <td>NaN</td>\n", " <td>20170216</td>\n", " <td>1487286356</td>\n", " <td>EVENT</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>16990000</td>\n", " <td>NaN</td>\n", " <td>GGOEGAAX0104</td>\n", " <td>Google Men's 100% Cotton Short Sleeve Hero Tee...</td>\n", " <td>Home/Apparel/Men's/</td>\n", " <td>(not set)</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>Men's Apparel \| Google Merchandise Store</td>\n", " <td>None</td>\n", " <td>/google+redesign/</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <td>614</td>\n", " <td>2</td>\n", " <td>917551604501805376</td>\n", " <td>Direct</td>\n", " <td>86855</td>\n", " <td>Russia</td>\n", " <td>not available in demo dataset</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>145.0</td>\n", " <td>53</td>\n", " <td>NaN</td>\n", " <td>20170428</td>\n", " <td>1493445465</td>\n", " <td>EVENT</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>24990000</td>\n", " <td>NaN</td>\n", " <td>GGOEAHPA004110</td>\n", " <td>Android Wool Heather Cap Heather/Black</td>\n", " <td>Home/Shop by Brand/Android/</td>\n", " <td>(not set)</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>Android \| Shop by Brand \| Google Merchandise S...</td>\n", " <td>None</td>\n", " <td>/store.html</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>615 rows × 33 columns</p>\n", "</div>" ], "text/plain": [ " num_duplicate_rows fullVisitorId channelGrouping time \\\n", "0 2 4890832126611809362 Organic Search 0 \n", "1 2 477997596462135678 Direct 653506 \n", "2 2 4890832126611809362 Organic Search 0 \n", "3 4 3211801082901013750 Referral 417832 \n", "4 2 8306703617933176158 Organic Search 559533 \n", ".. ... ... ... ... \n", "610 2 6706553303219862080 Organic Search 78319 \n", "611 4 9758235511148216157 Direct 682296 \n", "612 3 1915538933685278364 Referral 487234 \n", "613 2 8368489856222393707 Direct 331079 \n", "614 2 917551604501805376 Direct 86855 \n", "\n", " country city totalTransactionRevenue \\\n", "0 Sri Lanka not available in demo dataset NaN \n", "1 United States San Jose 246000000.0 \n", "2 Sri Lanka not available in demo dataset NaN \n", "3 United States New York 409000000.0 \n", "4 Canada not available in demo dataset 151000000.0 \n", ".. ... ... ... \n", "610 Australia Melbourne NaN \n", "611 United States Sunnyvale 134000000.0 \n", "612 United States San Jose NaN \n", "613 United States Mountain View NaN \n", "614 Russia not available in demo dataset NaN \n", "\n", " transactions timeOnSite pageviews sessionQualityDim date \\\n", "0 NaN NaN 2 NaN 20170610 \n", "1 1.0 931.0 36 NaN 20170118 \n", "2 NaN NaN 2 NaN 20170610 \n", "3 1.0 418.0 23 NaN 20170601 \n", "4 1.0 567.0 18 NaN 20170511 \n", ".. ... ... ... ... ... \n", "610 NaN 88.0 8 NaN 20170301 \n", "611 2.0 705.0 22 NaN 20170615 \n", "612 NaN 638.0 22 NaN 20161221 \n", "613 NaN 423.0 13 NaN 20170216 \n", "614 NaN 145.0 53 NaN 20170428 \n", "\n", " visitId type productRefundAmount productQuantity productPrice \\\n", "0 1497087233 PAGE None NaN 20990000 \n", "1 1484796890 PAGE None NaN 119000000 \n", "2 1497087233 PAGE None NaN 24990000 \n", "3 1496375475 PAGE None 1.0 100000000 \n", "4 1494505602 PAGE None NaN 149000000 \n", ".. ... ... ... ... ... \n", "610 1488431352 PAGE None NaN 4990000 \n", "611 1497575168 PAGE None NaN 25000000 \n", "612 1482356339 PAGE None 1.0 100000000 \n", "613 1487286356 EVENT None NaN 16990000 \n", "614 1493445465 EVENT None NaN 24990000 \n", "\n", " productRevenue productSKU \\\n", "0 NaN GGOEGAAX0356 \n", "1 NaN GGOENEBQ078999 \n", "2 NaN GGOEYHPA003610 \n", "3 102250000.0 GGOEGGCX056199 \n", "4 NaN GGOENEBB081499 \n", ".. ... ... \n", "610 NaN GGOEYDHJ056099 \n", "611 NaN GGOEGGCX056299 \n", "612 NaN GGOEGGCX056199 \n", "613 NaN GGOEGAAX0104 \n", "614 NaN GGOEAHPA004110 \n", "\n", " v2ProductName \\\n", "0 YouTube Men's Vintage Tank \n", "1 Nest® Cam Outdoor Security Camera - USA \n", "2 YouTube Wool Heather Cap Heather/Black \n", "3 Gift Card- $100.00 \n", "4 Nest® Cam Indoor Security Camera - CA \n", ".. ... \n", "610 22 oz YouTube Bottle Infuser \n", "611 Gift Card - $25.00 \n", "612 Gift Card- $100.00 \n", "613 Google Men's 100% Cotton Short Sleeve Hero Tee... \n", "614 Android Wool Heather Cap Heather/Black \n", "\n", " v2ProductCategory productVariant currencyCode \\\n", "0 Home/Shop by Brand/YouTube/ (not set) USD \n", "1 Nest-USA Single Option Only USD \n", "2 Home/Shop by Brand/YouTube/ (not set) USD \n", "3 Gift Cards Single Option Only USD \n", "4 Nest-Canada Single Option Only USD \n", ".. ... ... ... \n", "610 Home/Shop by Brand/YouTube/ (not set) None \n", "611 Gift Cards Single Option Only USD \n", "612 Gift Cards Single Option Only USD \n", "613 Home/Apparel/Men's/ (not set) None \n", "614 Home/Shop by Brand/Android/ (not set) None \n", "\n", " itemQuantity itemRevenue transactionRevenue transactionId \\\n", "0 None None NaN None \n", "1 None None NaN ORD201701182177 \n", "2 None None NaN None \n", "3 None None 409000000.0 ORD201706013038 \n", "4 None None NaN ORD201705112202 \n", ".. ... ... ... ... \n", "610 None None NaN None \n", "611 None None NaN None \n", "612 None None NaN None \n", "613 None None NaN None \n", "614 None None NaN None \n", "\n", " pageTitle searchKeyword \\\n", "0 YouTube \| Shop by Brand \| Google Merchandise S... None \n", "1 Checkout Confirmation None \n", "2 YouTube \| Shop by Brand \| Google Merchandise S... None \n", "3 Checkout Confirmation None \n", "4 Checkout Confirmation None \n", ".. ... ... \n", "610 YouTube \| Shop by Brand \| Google Merchandise S... None \n", "611 Payment Method None \n", "612 Checkout Your Information None \n", "613 Men's Apparel \| Google Merchandise Store None \n", "614 Android \| Shop by Brand \| Google Merchandise S... None \n", "\n", " pagePathLevel1 eCommerceAction_type eCommerceAction_step \\\n", "0 /google+redesign/ 0 1 \n", "1 /ordercompleted.html 6 1 \n", "2 /google+redesign/ 0 1 \n", "3 /ordercompleted.html 6 1 \n", "4 /ordercompleted.html 6 1 \n", ".. ... ... ... \n", "610 /google+redesign/ 2 1 \n", "611 /payment.html 5 2 \n", "612 /yourinfo.html 5 1 \n", "613 /google+redesign/ 1 1 \n", "614 /store.html 1 1 \n", "\n", " eCommerceAction_option \n", "0 None \n", "1 None \n", "2 None \n", "3 None \n", "4 None \n", ".. ... \n", "610 None \n", "611 Payment \n", "612 Billing and Shipping \n", "613 None \n", "614 None \n", "\n", "[615 rows x 33 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery --project $PROJECT \n", "#standardSQL\n", "SELECT count() AS num_duplicate_rows, \n", " \n", "FROM `data-to-insights.ecommerce.all_sessions_raw` \n", "GROUP BY fullvisitorid, \n", " channelgrouping, \n", " time, \n", " country, \n", " city, \n", " totaltransactionrevenue, \n", " transactions, \n", " timeonsite, \n", " pageviews, \n", " sessionqualitydim, \n", " date, \n", " visitid, \n", " type, \n", " productrefundamount, \n", " productquantity, \n", " productprice, \n", " productrevenue, \n", " productsku, \n", " v2productname, \n", " v2productcategory, \n", " productvariant, \n", " currencycode, \n", " itemquantity, \n", " itemrevenue, \n", " transactionrevenue, \n", " transactionid, \n", " pagetitle, \n", " searchkeyword, \n", " pagepathlevel1, \n", " ecommerceaction_type, \n", " ecommerceaction_step, \n", " ecommerceaction_option \n", "HAVING num_duplicate_rows > 1; " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see there are quite a few \"duplicate\" records (615) when analyzed with these parameters.\n", "\n", "In your own datasets, even if you have a unique key, it is still beneficial to confirm the uniqueness of the rows with COUNT, GROUP BY, and HAVING before you begin your analysis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyze the new all_sessions table\n", "\n", "In this section you use a deduplicated table called all_sessions.\n", "\n", "Scenario: Your data analyst team has provided you with a relevant query, and your schema experts have identified the key fields that must be unique for each record per your schema.\n", "\n", "Run the query to confirm that no duplicates exist, this time against the \"all_sessions\" table:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>fullvisitorid</th>\n", " <th>visitid</th>\n", " <th>date</th>\n", " <th>time</th>\n", " <th>v2productname</th>\n", " <th>productsku</th>\n", " <th>type</th>\n", " <th>ecommerceaction_type</th>\n", " <th>ecommerceaction_step</th>\n", " <th>ecommerceaction_option</th>\n", " <th>transactionrevenue</th>\n", " <th>transactionid</th>\n", " <th>row_count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [fullvisitorid, visitid, date, time, v2productname, productsku, type, ecommerceaction_type, ecommerceaction_step, ecommerceaction_option, transactionrevenue, transactionid, row_count]\n", "Index: []" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery --project $PROJECT\n", "#standardSQL\n", "SELECT fullvisitorid, # the unique visitor ID \n", " visitid, # a visitor can have multiple visits \n", " date, # session date stored as string YYYYMMDD \n", " time, # time of the individual site hit (can be 0 or more) \n", " v2productname, # not unique since a product can have variants like Color \n", " productsku, # unique for each product \n", " type, # visit and/or event trigger \n", " ecommerceaction_type, # maps to ‘add to cart', ‘completed checkout' \n", " ecommerceaction_step, \n", " ecommerceaction_option, \n", " transactionrevenue, # revenue of the order \n", " transactionid, # unique identifier for revenue bearing transaction \n", " count() AS row_count \n", "FROM `data-to-insights.ecommerce.all_sessions` \n", "GROUP BY 1, \n", " 2, \n", " 3, \n", " 4, \n", " 5, \n", " 6, \n", " 7, \n", " 8, \n", " 9, \n", " 10, \n", " 11, \n", " 12 \n", "HAVING row_count > 1 # find duplicates \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The query returns zero records indicating no duplicates exist." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Write basic SQL against the eCommerce data (TODO 4)\n", "\n", "In this section, you query for insights on the ecommerce dataset.\n", "\n", "A good first path of analysis is to find the total unique visitors\n", "The query below determines the total views by counting product_views and the number of unique visitors by counting fullVisitorID." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>product_views</th>\n", " <th>unique_visitors</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>21493109</td>\n", " <td>389934</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " product_views unique_visitors\n", "0 21493109 389934" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery --project $PROJECT\n", "#standardSQL\n", "SELECT count() AS product_views, \n", " count(DISTINCT fullvisitorid) AS unique_visitors \n", "FROM `data-to-insights.ecommerce.all_sessions`; " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next query shows total unique visitors(fullVisitorID) by the referring site (channelGrouping):" ] }, { "cell_type": "code", "execution_count": 23, "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>unique_visitors</th>\n", " <th>channelgrouping</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>38101</td>\n", " <td>Social</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>57308</td>\n", " <td>Referral</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>11865</td>\n", " <td>Paid Search</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>211993</td>\n", " <td>Organic Search</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>3067</td>\n", " <td>Display</td>\n", " </tr>\n", " <tr>\n", " <td>5</td>\n", " <td>75688</td>\n", " <td>Direct</td>\n", " </tr>\n", " <tr>\n", " <td>6</td>\n", " <td>5966</td>\n", " <td>Affiliates</td>\n", " </tr>\n", " <tr>\n", " <td>7</td>\n", " <td>62</td>\n", " <td>(Other)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " unique_visitors channelgrouping\n", "0 38101 Social\n", "1 57308 Referral\n", "2 11865 Paid Search\n", "3 211993 Organic Search\n", "4 3067 Display\n", "5 75688 Direct\n", "6 5966 Affiliates\n", "7 62 (Other)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery --project $PROJECT\n", "#standardSQL\n", "SELECT count(DISTINCT fullvisitorid) AS unique_visitors, \n", " channelgrouping \n", "FROM `data-to-insights.ecommerce.all_sessions` \n", "GROUP BY 2 \n", "ORDER BY 2 DESC;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To find deeper insights in the data, the next query lists the five products with the most views (product_views) from unique visitors. The query counts number of times a product (v2ProductName) was viewed (product_views), puts the list in descending order, and lists the top 5 entries:" ] }, { "cell_type": "code", "execution_count": 24, "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>product_views</th>\n", " <th>ProductName</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>316482</td>\n", " <td>Google Men's 100% Cotton Short Sleeve Hero Tee...</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>221558</td>\n", " <td>22 oz YouTube Bottle Infuser</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>210700</td>\n", " <td>YouTube Men's Short Sleeve Hero Tee Black</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>202205</td>\n", " <td>Google Men's 100% Cotton Short Sleeve Hero Tee...</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>200789</td>\n", " <td>YouTube Custom Decals</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " product_views ProductName\n", "0 316482 Google Men's 100% Cotton Short Sleeve Hero Tee...\n", "1 221558 22 oz YouTube Bottle Infuser\n", "2 210700 YouTube Men's Short Sleeve Hero Tee Black\n", "3 202205 Google Men's 100% Cotton Short Sleeve Hero Tee...\n", "4 200789 YouTube Custom Decals" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery --project $PROJECT\n", "#standardSQL\n", "SELECT count() AS product_views, \n", " ( v2productname ) AS ProductName \n", "FROM `data-to-insights.ecommerce.all_sessions` \n", "WHERE type = 'PAGE' \n", "GROUP BY v2productname \n", "ORDER BY product_views DESC \n", "LIMIT 5;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now expand your previous query to include the total number of distinct products ordered and the total number of total units ordered (productQuantity):" ] }, { "cell_type": "code", "execution_count": 25, "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>product_views</th>\n", " <th>orders</th>\n", " <th>quantity_product_ordered</th>\n", " <th>v2productname</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>316482</td>\n", " <td>3158</td>\n", " <td>6352</td>\n", " <td>Google Men's 100% Cotton Short Sleeve Hero Tee...</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>221558</td>\n", " <td>508</td>\n", " <td>4769</td>\n", " <td>22 oz YouTube Bottle Infuser</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>210700</td>\n", " <td>949</td>\n", " <td>1114</td>\n", " <td>YouTube Men's Short Sleeve Hero Tee Black</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>202205</td>\n", " <td>2713</td>\n", " <td>8072</td>\n", " <td>Google Men's 100% Cotton Short Sleeve Hero Tee...</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>200789</td>\n", " <td>1703</td>\n", " <td>11336</td>\n", " <td>YouTube Custom Decals</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " product_views orders quantity_product_ordered \\\n", "0 316482 3158 6352 \n", "1 221558 508 4769 \n", "2 210700 949 1114 \n", "3 202205 2713 8072 \n", "4 200789 1703 11336 \n", "\n", " v2productname \n", "0 Google Men's 100% Cotton Short Sleeve Hero Tee... \n", "1 22 oz YouTube Bottle Infuser \n", "2 YouTube Men's Short Sleeve Hero Tee Black \n", "3 Google Men's 100% Cotton Short Sleeve Hero Tee... \n", "4 YouTube Custom Decals " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery --project $PROJECT\n", "#standardSQL\n", "SELECT count() AS product_views, \n", " count(productquantity) AS orders, \n", " sum(productquantity) AS quantity_product_ordered, \n", " v2productname \n", "FROM `data-to-insights.ecommerce.all_sessions` \n", "WHERE type = 'PAGE' \n", "GROUP BY v2productname \n", "ORDER BY product_views DESC \n", "LIMIT 5; " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lastly, expand the query to include the average amount of product per order (total number of units ordered/total number of orders, or `SUM(productQuantity)/COUNT(productQuantity)`)." ] }, { "cell_type": "code", "execution_count": 26, "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>product_views</th>\n", " <th>orders</th>\n", " <th>quantity_product_ordered</th>\n", " <th>avg_per_order</th>\n", " <th>productName</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>316482</td>\n", " <td>3158</td>\n", " <td>6352</td>\n", " <td>2.011400</td>\n", " <td>Google Men's 100% Cotton Short Sleeve Hero Tee...</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>221558</td>\n", " <td>508</td>\n", " <td>4769</td>\n", " <td>9.387795</td>\n", " <td>22 oz YouTube Bottle Infuser</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>210700</td>\n", " <td>949</td>\n", " <td>1114</td>\n", " <td>1.173867</td>\n", " <td>YouTube Men's Short Sleeve Hero Tee Black</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>202205</td>\n", " <td>2713</td>\n", " <td>8072</td>\n", " <td>2.975304</td>\n", " <td>Google Men's 100% Cotton Short Sleeve Hero Tee...</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>200789</td>\n", " <td>1703</td>\n", " <td>11336</td>\n", " <td>6.656489</td>\n", " <td>YouTube Custom Decals</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " product_views orders quantity_product_ordered avg_per_order \\\n", "0 316482 3158 6352 2.011400 \n", "1 221558 508 4769 9.387795 \n", "2 210700 949 1114 1.173867 \n", "3 202205 2713 8072 2.975304 \n", "4 200789 1703 11336 6.656489 \n", "\n", " productName \n", "0 Google Men's 100% Cotton Short Sleeve Hero Tee... \n", "1 22 oz YouTube Bottle Infuser \n", "2 YouTube Men's Short Sleeve Hero Tee Black \n", "3 Google Men's 100% Cotton Short Sleeve Hero Tee... \n", "4 YouTube Custom Decals " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery --project $PROJECT\n", "#standardSQL\n", "SELECT count() AS product_views, \n", " count(productquantity) AS orders, \n", " sum(productquantity) AS quantity_product_ordered, \n", " sum(productquantity) / Count(productquantity) AS avg_per_order, \n", " v2productname AS productName \n", "FROM `data-to-insights.ecommerce.all_sessions` \n", "WHERE type = 'PAGE' \n", "GROUP BY v2productname \n", "ORDER BY product_views DESC \n", "LIMIT 5; " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that among these top 5 products by product views that the 22 oz YouTube Bottle Infuser had the highest avg_per_order with 9.38 units per order." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You have completed this lab exercise. In this situation the \"all_sessions\" was provided to you with the deduplicated records. In the course of your own future analysis you may have to create this on your own using BigQuery and the `create table DATASET.TABLE2 as select from DATASET.TABLE1` syntax." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2019 Google Inc.\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", "http://www.apache.org/licenses/LICENSE-2.0\n", "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 4 }	apache-2.0
luisborges/LearnDataSci	HomeWork/HW1.ipynb	38	38443	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "source": [ "# Homework 1. Which of two things is larger?\n", "\n", "Due: Thursday, September 19, 11:59 PM\n", "\n", "<a href=https://raw.github.com/cs109/content/master/HW1.ipynb download=HW1.ipynb> Download this assignment</a>\n", "\n", "---" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "Useful libraries for this assignment\n", "\n", "* [numpy](http://docs.scipy.org/doc/numpy-dev/user/index.html), for arrays\n", "* [pandas](http://pandas.pydata.org/), for data frames\n", "* [matplotlib](http://matplotlib.org/), for plotting\n", "* [requests](http://docs.python-requests.org/en/latest/), for downloading web content\n", "* [pattern](http://www.clips.ua.ac.be/pages/pattern), for parsing html and xml pages\n", "* [fnmatch](http://docs.python.org/2/library/fnmatch.html) (optional), for Unix-style string matching" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 1, "input": [ "# special IPython command to prepare the notebook for matplotlib\n", "%matplotlib inline \n", "\n", "from fnmatch import fnmatch\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import requests\n", "from pattern import web\n", "\n", "\n", "# set some nicer defaults for matplotlib\n", "from matplotlib import rcParams\n", "\n", "#these colors come from colorbrewer2.org. Each is an RGB triplet\n", "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n", " (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n", " (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n", " (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n", " (0.4, 0.6509803921568628, 0.11764705882352941),\n", " (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n", " (0.6509803921568628, 0.4627450980392157, 0.11372549019607843),\n", " (0.4, 0.4, 0.4)]\n", "\n", "rcParams['figure.figsize'] = (10, 6)\n", "rcParams['figure.dpi'] = 150\n", "rcParams['axes.color_cycle'] = dark2_colors\n", "rcParams['lines.linewidth'] = 2\n", "rcParams['axes.grid'] = True\n", "rcParams['axes.facecolor'] = '#eeeeee'\n", "rcParams['font.size'] = 14\n", "rcParams['patch.edgecolor'] = 'none'" ], "metadata": {} }, { "source": [ "## Introduction\n", "\n", "This was the [XKCD comic](http://xkcd.com/1131/) after the 2012 Presidential election:\n", "\n", "<img src=\"http://imgs.xkcd.com/comics/math.png\">" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "The comic refers to the fact that Nate Silver's statistical model (which is based mostly on combining information from pre-election polls) correctly predicted the outcome of the 2012 presidential race in all 50 states. \n", "\n", "Polling data isn't a perfect predictor for the future, and some polls are more accurate than others. This means that election forecastors must consider prediction uncertainty when building models.\n", "\n", "In this first assignment, you will perform a simple analysis of polling data about the upcoming <a href=\"http://en.wikipedia.org/wiki/Governor_(United_States)\">Governor races</a>. The assignment has three main parts:\n", "\n", "First you will build some tools to download historical polling data from the web, and parse it into a more convenient format. \n", "\n", "Next you will use these tools to aggregate and visualize several past Governor races\n", "\n", "Finally you will run a bootstrap analysis to estimate the probable outcome of current Governor races, given the level of precision of historical polls.\n", "\n", "---" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "\n", "\n", "## Part 1: Collect and Clean\n", "\n", "The [Real Clear Politics](http://www.realclearpolitics.com) website archives many political polls. In addition, they combine related polls to form an \"RCP average\" estimate of public opinion over time. For example, the chart on [this page](http://www.realclearpolitics.com/epolls/2012/president/us/general_election_romney_vs_obama-1171.html) shows historical polling data for the Obama-Romney presidential race. The chart is an average of the polling data table below the chart.\n", "\n", "The data used to generate plots like this are stored as XML pages, with URLs like:\n", "\n", "http://charts.realclearpolitics.com/charts/[id].xml\n", "\n", "Here, [id] is a unique integer, found at the end of the URL of the page that displays the graph. The id for the Obama-Romney race is 1171:\n", "\n", "http://charts.realclearpolitics.com/charts/1171.xml\n", "\n", "Opening this page in Google Chrome or Firefox will show you the XML content in an easy-to-read format. Notice that XML tags are nested inside each other, hierarchically (the jargony term for this is the \"Document Object Model\", or \"DOM\"). The first step of webscraping is almost always exploring the HTML/XML source in a browser, and getting a sense of this hierarchy.\n", "\n", "---\n", "\n", "#### Problem 0\n", "\n", "The above XML page includes 5 distinct tags (one, for example, is `chart`). List these tags, and depict how they nest inside each other using an indented list. For example:\n", "\n", "* Page\n", " * Section\n", " * Paragraph\n", " * Conclusion" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "Your Answer Here" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "---\n", "#### Problem 1\n", "\n", "We want to download and work with poll data like this. Like most programming tasks, we will break this into many smaller, easier pieces\n", "\n", "Fill in the code for the `get_poll_xml` function, that finds and downloads an XML page discussed above\n", "\n", "Hint \n", "\n", "`requests.get(\"http://www.google.com\").text` downloads the text from Google's homepage" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 2, "input": [ "\"\"\"\n", "Function\n", "--------\n", "get_poll_xml\n", "\n", "Given a poll_id, return the XML data as a text string\n", "\n", "Inputs\n", "------\n", "poll_id : int\n", " The ID of the poll to fetch\n", "\n", "Returns\n", "-------\n", "xml : str\n", " The text of the XML page for that poll_id\n", "\n", "Example\n", "-------\n", ">>> get_poll_xml(1044)\n", "u'<?xml version=\"1.0\" encoding=\"UTF-8\"?><chart><series><value xid=\\'0\\'>1/27/2009</value>\n", "...etc...\n", "\"\"\" \n", "#your code here \n" ], "metadata": {} }, { "source": [ "Here are some other functions we'll use later. `plot_colors` contains hints about parsing XML data." ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 3, "input": [ "# \"r\"egular \"e\"xpressions is kind of a mini-language to\n", "# do pattern matching on text\n", "import re\n", "\n", "def _strip(s):\n", " \"\"\"This function removes non-letter characters from a word\n", " \n", " for example _strip('Hi there!') == 'Hi there'\n", " \"\"\"\n", " return re.sub(r'[\\W_]+', '', s)\n", "\n", "def plot_colors(xml):\n", " \"\"\"\n", " Given an XML document like the link above, returns a python dictionary\n", " that maps a graph title to a graph color.\n", " \n", " Both the title and color are parsed from attributes of the <graph> tag:\n", " <graph title=\"the title\", color=\"#ff0000\"> -> {'the title': '#ff0000'}\n", " \n", " These colors are in \"hex string\" format. This page explains them:\n", " http://coding.smashingmagazine.com/2012/10/04/the-code-side-of-color/\n", " \n", " Example\n", " -------\n", " >>> plot_colors(get_poll_xml(1044))\n", " {u'Approve': u'#000000', u'Disapprove': u'#FF0000'}\n", " \"\"\"\n", " dom = web.Element(xml)\n", " result = {}\n", " for graph in dom.by_tag('graph'):\n", " title = _strip(graph.attributes['title'])\n", " result[title] = graph.attributes['color']\n", " return result" ], "metadata": {} }, { "source": [ "---\n", "\n", "#### Problem 2\n", "\n", "Even though `get_poll_xml` pulls data from the web into Python, it does so as a block of text. This still isn't very useful. Use the `web` module in `pattern` to parse this text, and extract data into a pandas DataFrame.\n", "\n", "Hints\n", "\n", "* You might want create python lists for each column in the XML. Then, to turn these lists into a DataFrame, run\n", "\n", "`pd.DataFrame({'column_label_1': list_1, 'column_label_2':list_2, ...})`\n", "\n", "* use the pandas function `pd.to_datetime` to convert strings into dates" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 4, "input": [ "\"\"\"\n", " Function\n", " ---------\n", " rcp_poll_data\n", "\n", " Extract poll information from an XML string, and convert to a DataFrame\n", "\n", " Parameters\n", " ----------\n", " xml : str\n", " A string, containing the XML data from a page like \n", " get_poll_xml(1044)\n", " \n", " Returns\n", " -------\n", " A pandas DataFrame with the following columns:\n", " date: The date for each entry\n", " title_n: The data value for the gid=n graph (take the column name from the `title` tag)\n", " \n", " This DataFrame should be sorted by date\n", " \n", " Example\n", " -------\n", " Consider the following simple xml page:\n", " \n", " <chart>\n", " <series>\n", " <value xid=\"0\">1/27/2009</value>\n", " <value xid=\"1\">1/28/2009</value>\n", " </series>\n", " <graphs>\n", " <graph gid=\"1\" color=\"#000000\" balloon_color=\"#000000\" title=\"Approve\">\n", " <value xid=\"0\">63.3</value>\n", " <value xid=\"1\">63.3</value>\n", " </graph>\n", " <graph gid=\"2\" color=\"#FF0000\" balloon_color=\"#FF0000\" title=\"Disapprove\">\n", " <value xid=\"0\">20.0</value>\n", " <value xid=\"1\">20.0</value>\n", " </graph>\n", " </graphs>\n", " </chart>\n", " \n", " Given this string, rcp_poll_data should return\n", " result = pd.DataFrame({'date': pd.to_datetime(['1/27/2009', '1/28/2009']), \n", " 'Approve': [63.3, 63.3], 'Disapprove': [20.0, 20.0]})\n", "\"\"\"\n", "#your code here\n" ], "metadata": {} }, { "source": [ "The output from `rcp_poll_data` is much more useful for analysis. For example, we can plot with it:" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 5, "input": [ "def poll_plot(poll_id):\n", " \"\"\"\n", " Make a plot of an RCP Poll over time\n", " \n", " Parameters\n", " ----------\n", " poll_id : int\n", " An RCP poll identifier\n", " \"\"\"\n", "\n", " # hey, you wrote two of these functions. Thanks for that!\n", " xml = get_poll_xml(poll_id)\n", " data = rcp_poll_data(xml)\n", " colors = plot_colors(xml)\n", "\n", " #remove characters like apostrophes\n", " data = data.rename(columns = {c: _strip(c) for c in data.columns})\n", "\n", " #normalize poll numbers so they add to 100% \n", " norm = data[colors.keys()].sum(axis=1) / 100 \n", " for c in colors.keys():\n", " data[c] /= norm\n", " \n", " for label, color in colors.items():\n", " plt.plot(data.date, data[label], color=color, label=label) \n", " \n", " plt.xticks(rotation=70)\n", " plt.legend(loc='best')\n", " plt.xlabel(\"Date\")\n", " plt.ylabel(\"Normalized Poll Percentage\")" ], "metadata": {} }, { "source": [ "If you've done everything right so far, the following code should reproduce the graph on [this page](http://www.realclearpolitics.com/epolls/other/president_obama_job_approval-1044.html)" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 6, "input": [ "poll_plot(1044)\n", "plt.title(\"Obama Job Approval\")" ], "metadata": {} }, { "source": [ "---\n", "\n", "## Part 2: Aggregate and Visualize\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "#### Problem 3\n", "\n", "Unfortunately, these data don't have any error bars. If a candidate leads by 10% in the RCP average, is she a shoo-in to win? Or is this number too close to call? Does a 10% poll lead mean more 1 day before a race than it does 1 week before? Without error estimates, these questions are impossible to answer.\n", "\n", "To get a sense of how accurate the RCP polls are, you will gather data from many previous Governor races, where the outcome is known.\n", "\n", "This url has links to many governer races. \n", "\n", "http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html\n", "\n", "Notice that each link to a governor race has the following URL pattern:\n", "\n", "http://www.realclearpolitics.com/epolls/[YEAR]/governor/[STATE]/[TITLE]-[ID].html\n", "\n", "\n", "Write a function that scans html for links to URLs like this\n", "\n", "Hint The [fnmatch](http://docs.python.org/2/library/fnmatch.html) function is useful for simple string matching tasks." ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 7, "input": [ "\"\"\"\n", " Function\n", " --------\n", " find_governor_races\n", "\n", " Find and return links to RCP races on a page like\n", " http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html\n", " \n", " Parameters\n", " ----------\n", " html : str\n", " The HTML content of a page to scan\n", " \n", " Returns\n", " -------\n", " A list of urls for Governer race pages\n", " \n", " Example\n", " -------\n", " For a page like\n", " \n", " <html>\n", " <body>\n", " <a href=\"http://www.realclearpolitics.com/epolls/2010/governor/ma/massachusetts_governor_baker_vs_patrick_vs_cahill-1154.html\"></a>\n", " <a href=\"http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html\"></a>\n", " </body>\n", " </html>\n", " \n", " find_governor_races would return\n", " ['http://www.realclearpolitics.com/epolls/2010/governor/ma/massachusetts_governor_baker_vs_patrick_vs_cahill-1154.html',\n", " 'http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html']\n", "\"\"\"\n", "#your code here\n" ], "metadata": {} }, { "source": [ "#### Problem 4\n", "\n", "At this point, you have functions to find a collection of governor races, download historical polling data from each one,\n", "parse them into a numerical DataFrame, and plot this data.\n", "\n", "The main question we have about these data are how accurately they predict election outcomes. To answer this question, we\n", "need to grab the election outcome data.\n", "\n", "Write a function that looks up and returns the election result on a page like [this one](http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html). \n", "\n", "Remember to look at the HTML source!\n", "\n", "You can do this by selection `view->developer->view source` in Chrome, or `Tools -> web developer -> page source` in Firefox. Altenatively, you can right-click on a part of the page, and select \"inspect element\"" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 8, "input": [ "\"\"\"\n", " Function\n", " --------\n", " race_result\n", "\n", " Return the actual voting results on a race page\n", " \n", " Parameters\n", " ----------\n", " url : string\n", " The website to search through\n", " \n", " Returns\n", " -------\n", " A dictionary whose keys are candidate names,\n", " and whose values is the percentage of votes they received.\n", " \n", " If necessary, normalize these numbers so that they add up to 100%.\n", " \n", " Example\n", " --------\n", " >>> url = 'http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html'\n", " >>> race_result(url)\n", " {'Brown': 56.0126582278481, 'Whitman': 43.9873417721519}\n", "\"\"\"\n", "#your code here\n" ], "metadata": {} }, { "source": [ "Here are some more utility functions that take advantage of what you've done so far." ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 9, "input": [ "def id_from_url(url):\n", " \"\"\"Given a URL, look up the RCP identifier number\"\"\"\n", " return url.split('-')[-1].split('.html')[0]\n", "\n", "\n", "def plot_race(url):\n", " \"\"\"Make a plot summarizing a senate race\n", " \n", " Overplots the actual race results as dashed horizontal lines\n", " \"\"\"\n", " #hey, thanks again for these functions!\n", " id = id_from_url(url)\n", " xml = get_poll_xml(id) \n", " colors = plot_colors(xml)\n", "\n", " if len(colors) == 0:\n", " return\n", " \n", " #really, you shouldn't have\n", " result = race_result(url)\n", " \n", " poll_plot(id)\n", " plt.xlabel(\"Date\")\n", " plt.ylabel(\"Polling Percentage\")\n", " for r in result:\n", " plt.axhline(result[r], color=colors[_strip(r)], alpha=0.6, ls='--')\n" ], "metadata": {} }, { "source": [ "Now that this is done, we can easily visualize many historical Governer races. The solid line plots the poll history, the dotted line reports the actual result.\n", "\n", "If this code block fails, you probably have a bug in one of your functions." ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 10, "input": [ "page = requests.get('http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html').text.encode('ascii', 'ignore')\n", "\n", "for race in find_governor_races(page):\n", " plot_race(race)\n", " plt.show()" ], "metadata": {} }, { "source": [ "Briefly summarize these graphs -- how accurate is the typical poll a day before the election? How often does a prediction one month before the election mispredict the actual winner?" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "Your summary here" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "---\n", "\n", "## Part 3: Analysis\n", "\n", "#### Problem 5\n", "\n", "You are (finally!) in a position to do some quantitative analysis.\n", "\n", "We have provided an `error_data` function that builds upon the functions you have written. It computes a new DataFrame with information about polling errors.\n", "\n", "Use `error_data`, `find_governer_races`, and `pd.concat` to construct a Data Frame summarizing the forecast errors\n", "from all the Governor races\n", "\n", "Hint \n", "\n", "It's best to set `ignore_index=True` in `pd.concat`" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 11, "input": [ "def party_from_color(color):\n", " if color in ['#0000CC', '#3B5998']:\n", " return 'democrat'\n", " if color in ['#FF0000', '#D30015']:\n", " return 'republican'\n", " return 'other'\n", "\n", "\n", "def error_data(url):\n", " \"\"\"\n", " Given a Governor race URL, download the poll data and race result,\n", " and construct a DataFrame with the following columns:\n", " \n", " candidate: Name of the candidate\n", " forecast_length: Number of days before the election\n", " percentage: The percent of poll votes a candidate has.\n", " Normalized to that the canddidate percentages add to 100%\n", " error: Difference between percentage and actual race reulst\n", " party: Political party of the candidate\n", " \n", " The data are resampled as necessary, to provide one data point per day\n", " \"\"\"\n", " \n", " id = id_from_url(url)\n", " xml = get_poll_xml(id)\n", " \n", " colors = plot_colors(xml)\n", " if len(colors) == 0:\n", " return pd.DataFrame()\n", " \n", " df = rcp_poll_data(xml)\n", " result = race_result(url)\n", " \n", " #remove non-letter characters from columns\n", " df = df.rename(columns={c: _strip(c) for c in df.columns})\n", " for k, v in result.items():\n", " result[_strip(k)] = v \n", " \n", " candidates = [c for c in df.columns if c is not 'date']\n", " \n", " #turn into a timeseries...\n", " df.index = df.date\n", " \n", " #...so that we can resample at regular, daily intervals\n", " df = df.resample('D')\n", " df = df.dropna()\n", " \n", " #compute forecast length in days\n", " #(assuming that last forecast happens on the day of the election, for simplicity)\n", " forecast_length = (df.date.max() - df.date).values\n", " forecast_length = forecast_length / np.timedelta64(1, 'D') # convert to number of days\n", " \n", " #compute forecast error\n", " errors = {}\n", " normalized = {}\n", " poll_lead = {}\n", " \n", " for c in candidates:\n", " #turn raw percentage into percentage of poll votes\n", " corr = df[c].values / df[candidates].sum(axis=1).values * 100.\n", " err = corr - result[_strip(c)]\n", " \n", " normalized[c] = corr\n", " errors[c] = err\n", " \n", " n = forecast_length.size\n", " \n", " result = {}\n", " result['percentage'] = np.hstack(normalized[c] for c in candidates)\n", " result['error'] = np.hstack(errors[c] for c in candidates)\n", " result['candidate'] = np.hstack(np.repeat(c, n) for c in candidates)\n", " result['party'] = np.hstack(np.repeat(party_from_color(colors[_strip(c)]), n) for c in candidates)\n", " result['forecast_length'] = np.hstack(forecast_length for _ in candidates)\n", " \n", " result = pd.DataFrame(result)\n", " return result" ], "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 12, "input": [ "\"\"\"\n", "function\n", "---------\n", "all_error_data\n", "\n", "Calls error_data on all races from find_governer_races(page),\n", "and concatenates into a single DataFrame\n", "\n", "Parameters\n", "-----------\n", "None\n", "\n", "Examples\n", "--------\n", "df = all_error_data()\n", "\"\"\"\n", "#your code here\n" ], "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 13, "input": [ "errors = all_error_data()" ], "metadata": {} }, { "source": [ "Here's a histogram of the error of every polling measurement in the data" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 14, "input": [ "errors.error.hist(bins=50)\n", "plt.xlabel(\"Polling Error\")\n", "plt.ylabel('N')" ], "metadata": {} }, { "source": [ "### Problem 6\n", "\n", "Compute the standard deviation of the polling errors. How much uncertainty is there in the typical RCP poll?" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 15, "input": [ "#your code here\n" ], "metadata": {} }, { "source": [ "### Problem 7\n", "\n", "Repeat this calculation for the data where `errors.forecast_length < 7` (i.e. the polls within a week of an election). How much more/less accurate are they? How about the data where `errors.forecast_length > 30`? \n", "\n", "Comment on this in 1 or 2 sentences. Does this make sense?" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 16, "input": [ "#your code here\n" ], "metadata": {} }, { "source": [ "### Problem 8\n", "\n", "Bootstrap resampling is a general purpose way to use empirical data like the `errors` DataFrame to estimate uncertainties. For example, consider the [Viriginia Governor Race](http://www.realclearpolitics.com/epolls/2013/governor/va/virginia_governor_cuccinelli_vs_mcauliffe-3033.html). If we wanted to estimate how likey it is that McAuliffe will win given the current RCP data, the approch would be:\n", "\n", "1. Pick a large number N of experiments to run (say N=1000).\n", "2. For each experiment, randomly select a value from `errors.error`. We are assuming that these numbers represent a reasonable error distribution for the current poll data.\n", "3. Assume that the error on McAullife's current polling score is given by this number (and, by extension, the error on Cuccinelli's poll score is the opposite). Calculate who actually wins the election in this simulation.\n", "4. Repeat N times, and calculate the percentage of simulations where either candidate wins.\n", "\n", "Bootstrapping isn't foolproof: it makes the assumption that the previous Governor race errors are representative of the Virginia race, and it does a bad job at estimating very rare events (with only ~30 races in the errors DataFrame, it would be hard to accurately predict probabilities for 1-in-a-million scenarios). Nevertheless, it's a versatile technique.\n", "\n", "Use bootstrap resampling to estimate how likely it is that each candidate could win the following races.\n", "\n", " * [Virginia Governor](http://www.realclearpolitics.com/epolls/2013/governor/va/virginia_governor_cuccinelli_vs_mcauliffe-3033.html)\n", " * [New Jersey Governor](http://www.realclearpolitics.com/epolls/2013/governor/nj/new_jersey_governor_christie_vs_buono-3411.html)\n", " \n", "Summarize your results in a paragraph. What conclusions do you draw from the bootstrap analysis, and what assumptions did you make in reaching this conclusion. What are some limitations of this analysis?\n", " " ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "language": "python", "outputs": [], "collapsed": false, "prompt_number": 17, "input": [ "#your code here\n" ], "metadata": {} }, { "source": [ "Your summary here" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "## Parting Thoughts\n", "\n", "For comparison, most of the predictions in Nate Silver's [presidental forecast](http://fivethirtyeight.blogs.nytimes.com/fivethirtyeights-2012-forecast/) had confidences of >95%. This is more precise than what we can estimate from the RCP poll alone. His approach, however, is the same basic idea (albeit he used many more polls, and carefully calibrated each based on demographic and other information). Homework 2 will dive into some of his techniques further.\n", "\n", "\n", "## How to submit\n", "\n", "To submit your homework, create a folder named lastname_firstinitial_hw0 and place this notebook file in the folder. If your notebook requires any additional data files to run (it shouldn't), add them to this directory as well. Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. If we cannot access your work because these directions are not followed correctly, we will not grade your work." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "---\n", "css tweaks in this cell\n", "<style>\n", "div.text_cell_render {\n", " line-height: 150%;\n", " font-size: 110%;\n", " width: 800px;\n", " margin-left:50px;\n", " margin-right:auto;\n", " }\n", "</style>" ], "cell_type": "markdown", "metadata": {} } ], "metadata": {} } ] }	gpl-2.0
gschivley/Index-variability	Notebooks/archive/Explore method for allocating state-level data to NERC regions.ipynb	1	879014	{ "cells": [ { "cell_type": "code", "execution_count": 74, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:46:03.602776Z", "start_time": "2017-08-08T16:46:03.376758Z" }, "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import os\n", "import glob\n", "import numpy as np\n", "import geopandas as gpd\n", "from shapely.geometry import Point\n", "from geopandas import GeoDataFrame\n", "sns.set(style='whitegrid')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import facility data and NERC labels" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:39:43.440142Z", "start_time": "2017-08-08T19:39:34.762652Z" }, "collapsed": true }, "outputs": [], "source": [ "path = os.path.join('Data storage', 'Facility gen fuels and CO2 2017-05-25.zip')\n", "facility_df = pd.read_csv(path, parse_dates=['datetime'])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:52:17.840902Z", "start_time": "2017-08-08T16:52:17.804900Z" } }, "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>f</th>\n", " <th>fuel</th>\n", " <th>month</th>\n", " <th>plant id</th>\n", " <th>total fuel (mmbtu)</th>\n", " <th>year</th>\n", " <th>generation (MWh)</th>\n", " <th>elec fuel (mmbtu)</th>\n", " <th>geography</th>\n", " <th>last_updated</th>\n", " <th>lat</th>\n", " <th>lon</th>\n", " <th>prime mover</th>\n", " <th>datetime</th>\n", " <th>quarter</th>\n", " <th>all fuel fossil CO2 (kg)</th>\n", " <th>elec fuel fossil CO2 (kg)</th>\n", " <th>all fuel total CO2 (kg)</th>\n", " <th>elec fuel total CO2 (kg)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>M</td>\n", " <td>NG</td>\n", " <td>3</td>\n", " <td>10275</td>\n", " <td>0.0</td>\n", " <td>2017</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>USA-FL</td>\n", " <td>2017-05-24T14:26:30-04:00</td>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>ALL</td>\n", " <td>2017-03-01</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>M</td>\n", " <td>NG</td>\n", " <td>2</td>\n", " <td>10275</td>\n", " <td>0.0</td>\n", " <td>2017</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>USA-FL</td>\n", " <td>2017-05-24T14:26:30-04:00</td>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>ALL</td>\n", " <td>2017-02-01</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>M</td>\n", " <td>NG</td>\n", " <td>1</td>\n", " <td>10275</td>\n", " <td>0.0</td>\n", " <td>2017</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>USA-FL</td>\n", " <td>2017-05-24T14:26:30-04:00</td>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>ALL</td>\n", " <td>2017-01-01</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>M</td>\n", " <td>NG</td>\n", " <td>12</td>\n", " <td>10275</td>\n", " <td>0.0</td>\n", " <td>2016</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>USA-FL</td>\n", " <td>2017-05-24T14:26:30-04:00</td>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>ALL</td>\n", " <td>2016-12-01</td>\n", " <td>4</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>M</td>\n", " <td>NG</td>\n", " <td>11</td>\n", " <td>10275</td>\n", " <td>0.0</td>\n", " <td>2016</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>USA-FL</td>\n", " <td>2017-05-24T14:26:30-04:00</td>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>ALL</td>\n", " <td>2016-11-01</td>\n", " <td>4</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " f fuel month plant id total fuel (mmbtu) year generation (MWh) \\\n", "0 M NG 3 10275 0.0 2017 0.0 \n", "1 M NG 2 10275 0.0 2017 0.0 \n", "2 M NG 1 10275 0.0 2017 0.0 \n", "3 M NG 12 10275 0.0 2016 0.0 \n", "4 M NG 11 10275 0.0 2016 0.0 \n", "\n", " elec fuel (mmbtu) geography last_updated lat lon \\\n", "0 0.0 USA-FL 2017-05-24T14:26:30-04:00 27.9114 -81.6006 \n", "1 0.0 USA-FL 2017-05-24T14:26:30-04:00 27.9114 -81.6006 \n", "2 0.0 USA-FL 2017-05-24T14:26:30-04:00 27.9114 -81.6006 \n", "3 0.0 USA-FL 2017-05-24T14:26:30-04:00 27.9114 -81.6006 \n", "4 0.0 USA-FL 2017-05-24T14:26:30-04:00 27.9114 -81.6006 \n", "\n", " prime mover datetime quarter all fuel fossil CO2 (kg) \\\n", "0 ALL 2017-03-01 1 0.0 \n", "1 ALL 2017-02-01 1 0.0 \n", "2 ALL 2017-01-01 1 0.0 \n", "3 ALL 2016-12-01 4 0.0 \n", "4 ALL 2016-11-01 4 0.0 \n", "\n", " elec fuel fossil CO2 (kg) all fuel total CO2 (kg) \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "\n", " elec fuel total CO2 (kg) \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "facility_df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:39:43.916170Z", "start_time": "2017-08-08T19:39:43.443143Z" }, "collapsed": true }, "outputs": [], "source": [ "facility_df.dropna(inplace=True, subset=['lat', 'lon'])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T18:47:29.919203Z", "start_time": "2017-08-08T18:47:29.555183Z" }, "collapsed": true }, "outputs": [], "source": [ "cols = ['lat', 'lon', 'plant id', 'year']\n", "small_facility = facility_df.loc[:, cols].drop_duplicates()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T18:47:32.042329Z", "start_time": "2017-08-08T18:47:29.922204Z" }, "collapsed": true }, "outputs": [], "source": [ "geometry = [Point(xy) for xy in zip(small_facility.lon, small_facility.lat)]\n", "# small_facility = small_facility.drop(['lon', 'lat'], axis=1)\n", "crs = {'init': 'epsg:4326'}\n", "geo_df = GeoDataFrame(small_facility, crs=crs, geometry=geometry)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read NERC shapefile and merge with `geo_df`" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:46:05.950909Z", "start_time": "2017-08-08T16:46:05.280871Z" }, "collapsed": true }, "outputs": [], "source": [ "path = os.path.join('Data storage', 'NERC_Regions_EIA', 'NercRegions_201610.shp')\n", "regions = gpd.read_file(path)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:48:45.036910Z", "start_time": "2017-08-08T16:48:26.845859Z" }, "collapsed": true }, "outputs": [], "source": [ "facility_nerc = gpd.sjoin(geo_df, regions, how='inner', op='within')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:49:03.540936Z", "start_time": "2017-08-08T16:49:03.518930Z" } }, "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>lat</th>\n", " <th>lon</th>\n", " <th>plant id</th>\n", " <th>year</th>\n", " <th>geometry</th>\n", " <th>index_right</th>\n", " <th>NERC</th>\n", " <th>NERC_Label</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>10275</td>\n", " <td>2017</td>\n", " <td>POINT (-81.6006 27.9114)</td>\n", " <td>1</td>\n", " <td>FRCC</td>\n", " <td>Florida Reliability Coordinating Council (FRCC)</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>10275</td>\n", " <td>2016</td>\n", " <td>POINT (-81.6006 27.9114)</td>\n", " <td>1</td>\n", " <td>FRCC</td>\n", " <td>Florida Reliability Coordinating Council (FRCC)</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>10275</td>\n", " <td>2015</td>\n", " <td>POINT (-81.6006 27.9114)</td>\n", " <td>1</td>\n", " <td>FRCC</td>\n", " <td>Florida Reliability Coordinating Council (FRCC)</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>10275</td>\n", " <td>2014</td>\n", " <td>POINT (-81.6006 27.9114)</td>\n", " <td>1</td>\n", " <td>FRCC</td>\n", " <td>Florida Reliability Coordinating Council (FRCC)</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>10275</td>\n", " <td>2013</td>\n", " <td>POINT (-81.6006 27.9114)</td>\n", " <td>1</td>\n", " <td>FRCC</td>\n", " <td>Florida Reliability Coordinating Council (FRCC)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " lat lon plant id year geometry index_right \\\n", "0 27.9114 -81.6006 10275 2017 POINT (-81.6006 27.9114) 1 \n", "3 27.9114 -81.6006 10275 2016 POINT (-81.6006 27.9114) 1 \n", "15 27.9114 -81.6006 10275 2015 POINT (-81.6006 27.9114) 1 \n", "27 27.9114 -81.6006 10275 2014 POINT (-81.6006 27.9114) 1 \n", "39 27.9114 -81.6006 10275 2013 POINT (-81.6006 27.9114) 1 \n", "\n", " NERC NERC_Label \n", "0 FRCC Florida Reliability Coordinating Council (FRCC) \n", "3 FRCC Florida Reliability Coordinating Council (FRCC) \n", "15 FRCC Florida Reliability Coordinating Council (FRCC) \n", "27 FRCC Florida Reliability Coordinating Council (FRCC) \n", "39 FRCC Florida Reliability Coordinating Council (FRCC) " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "facility_nerc.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Merge NERC labels into the facility df" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:39:53.186693Z", "start_time": "2017-08-08T19:39:51.180580Z" }, "collapsed": true }, "outputs": [], "source": [ "cols = ['plant id', 'year', 'NERC']\n", "facility_df = facility_df.merge(facility_nerc.loc[:, cols],\n", " on=['plant id', 'year'], how='left')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:39:53.834730Z", "start_time": "2017-08-08T19:39:53.797728Z" } }, "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>f</th>\n", " <th>fuel</th>\n", " <th>month</th>\n", " <th>plant id</th>\n", " <th>total fuel (mmbtu)</th>\n", " <th>year</th>\n", " <th>generation (MWh)</th>\n", " <th>elec fuel (mmbtu)</th>\n", " <th>geography</th>\n", " <th>last_updated</th>\n", " <th>lat</th>\n", " <th>lon</th>\n", " <th>prime mover</th>\n", " <th>datetime</th>\n", " <th>quarter</th>\n", " <th>all fuel fossil CO2 (kg)</th>\n", " <th>elec fuel fossil CO2 (kg)</th>\n", " <th>all fuel total CO2 (kg)</th>\n", " <th>elec fuel total CO2 (kg)</th>\n", " <th>NERC</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>M</td>\n", " <td>NG</td>\n", " <td>3</td>\n", " <td>10275</td>\n", " <td>0.0</td>\n", " <td>2017</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>USA-FL</td>\n", " <td>2017-05-24T14:26:30-04:00</td>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>ALL</td>\n", " <td>2017-03-01</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>FRCC</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>M</td>\n", " <td>NG</td>\n", " <td>2</td>\n", " <td>10275</td>\n", " <td>0.0</td>\n", " <td>2017</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>USA-FL</td>\n", " <td>2017-05-24T14:26:30-04:00</td>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>ALL</td>\n", " <td>2017-02-01</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>FRCC</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>M</td>\n", " <td>NG</td>\n", " <td>1</td>\n", " <td>10275</td>\n", " <td>0.0</td>\n", " <td>2017</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>USA-FL</td>\n", " <td>2017-05-24T14:26:30-04:00</td>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>ALL</td>\n", " <td>2017-01-01</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>FRCC</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>M</td>\n", " <td>NG</td>\n", " <td>12</td>\n", " <td>10275</td>\n", " <td>0.0</td>\n", " <td>2016</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>USA-FL</td>\n", " <td>2017-05-24T14:26:30-04:00</td>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>ALL</td>\n", " <td>2016-12-01</td>\n", " <td>4</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>FRCC</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>M</td>\n", " <td>NG</td>\n", " <td>11</td>\n", " <td>10275</td>\n", " <td>0.0</td>\n", " <td>2016</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>USA-FL</td>\n", " <td>2017-05-24T14:26:30-04:00</td>\n", " <td>27.9114</td>\n", " <td>-81.6006</td>\n", " <td>ALL</td>\n", " <td>2016-11-01</td>\n", " <td>4</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>FRCC</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " f fuel month plant id total fuel (mmbtu) year generation (MWh) \\\n", "0 M NG 3 10275 0.0 2017 0.0 \n", "1 M NG 2 10275 0.0 2017 0.0 \n", "2 M NG 1 10275 0.0 2017 0.0 \n", "3 M NG 12 10275 0.0 2016 0.0 \n", "4 M NG 11 10275 0.0 2016 0.0 \n", "\n", " elec fuel (mmbtu) geography last_updated lat lon \\\n", "0 0.0 USA-FL 2017-05-24T14:26:30-04:00 27.9114 -81.6006 \n", "1 0.0 USA-FL 2017-05-24T14:26:30-04:00 27.9114 -81.6006 \n", "2 0.0 USA-FL 2017-05-24T14:26:30-04:00 27.9114 -81.6006 \n", "3 0.0 USA-FL 2017-05-24T14:26:30-04:00 27.9114 -81.6006 \n", "4 0.0 USA-FL 2017-05-24T14:26:30-04:00 27.9114 -81.6006 \n", "\n", " prime mover datetime quarter all fuel fossil CO2 (kg) \\\n", "0 ALL 2017-03-01 1 0.0 \n", "1 ALL 2017-02-01 1 0.0 \n", "2 ALL 2017-01-01 1 0.0 \n", "3 ALL 2016-12-01 4 0.0 \n", "4 ALL 2016-11-01 4 0.0 \n", "\n", " elec fuel fossil CO2 (kg) all fuel total CO2 (kg) \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "\n", " elec fuel total CO2 (kg) NERC \n", "0 0.0 FRCC \n", "1 0.0 FRCC \n", "2 0.0 FRCC \n", "3 0.0 FRCC \n", "4 0.0 FRCC " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "facility_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Filter out data older than 2014 to reduce size" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:40:02.461222Z", "start_time": "2017-08-08T19:40:00.995134Z" }, "collapsed": true }, "outputs": [], "source": [ "facility_df['state'] = facility_df['geography'].str[-2:]\n", "keep_cols = ['fuel', 'year', 'month', 'datetime', 'state', 'plant id', 'NERC',\n", " 'generation (MWh)', 'total fuel (mmbtu)', 'elec fuel (mmbtu)']\n", "facility_df = facility_df.loc[facility_df['year'] >= 2014, keep_cols]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:40:03.232260Z", "start_time": "2017-08-08T19:40:03.075251Z" }, "collapsed": true }, "outputs": [], "source": [ "facility_fuel_cats = {'COW': ['SUB', 'BIT', 'LIG', 'WC', 'SC', 'RC', 'SGC'],\n", " 'NG': ['NG'],\n", " 'PEL': ['DFO', 'RFO', 'KER', 'JF',\n", " 'PG', 'WO', 'SGP'],\n", " 'PC': ['PC'],\n", " 'HYC': ['WAT'],\n", " 'HPS': [],\n", " 'GEO': ['GEO'],\n", " 'NUC': ['NUC'],\n", " 'OOG': ['BFG', 'OG', 'LFG'],\n", " 'OTH': ['OTH', 'MSN', 'MSW', 'PUR', 'TDF', 'WH'],\n", " 'SUN': ['SUN'],\n", " 'DPV': [],\n", " 'WAS': ['OBL', 'OBS', 'OBG', 'MSB', 'SLW'],\n", " 'WND': ['WND'],\n", " 'WWW': ['WDL', 'WDS', 'AB', 'BLQ']\n", " }" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:40:04.378330Z", "start_time": "2017-08-08T19:40:03.756290Z" }, "collapsed": true }, "outputs": [], "source": [ "for category in facility_fuel_cats.keys():\n", " fuels = facility_fuel_cats[category]\n", " facility_df.loc[facility_df['fuel'].isin(fuels),\n", " 'fuel category'] = category" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:40:04.514333Z", "start_time": "2017-08-08T19:40:04.487331Z" } }, "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>fuel</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>datetime</th>\n", " <th>state</th>\n", " <th>plant id</th>\n", " <th>NERC</th>\n", " <th>generation (MWh)</th>\n", " <th>total fuel (mmbtu)</th>\n", " <th>elec fuel (mmbtu)</th>\n", " <th>fuel category</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>NG</td>\n", " <td>2017</td>\n", " <td>3</td>\n", " <td>2017-03-01</td>\n", " <td>FL</td>\n", " <td>10275</td>\n", " <td>FRCC</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>NG</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>NG</td>\n", " <td>2017</td>\n", " <td>2</td>\n", " <td>2017-02-01</td>\n", " <td>FL</td>\n", " <td>10275</td>\n", " <td>FRCC</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>NG</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>NG</td>\n", " <td>2017</td>\n", " <td>1</td>\n", " <td>2017-01-01</td>\n", " <td>FL</td>\n", " <td>10275</td>\n", " <td>FRCC</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>NG</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>NG</td>\n", " <td>2016</td>\n", " <td>12</td>\n", " <td>2016-12-01</td>\n", " <td>FL</td>\n", " <td>10275</td>\n", " <td>FRCC</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>NG</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>NG</td>\n", " <td>2016</td>\n", " <td>11</td>\n", " <td>2016-11-01</td>\n", " <td>FL</td>\n", " <td>10275</td>\n", " <td>FRCC</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>NG</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " fuel year month datetime state plant id NERC generation (MWh) \\\n", "0 NG 2017 3 2017-03-01 FL 10275 FRCC 0.0 \n", "1 NG 2017 2 2017-02-01 FL 10275 FRCC 0.0 \n", "2 NG 2017 1 2017-01-01 FL 10275 FRCC 0.0 \n", "3 NG 2016 12 2016-12-01 FL 10275 FRCC 0.0 \n", "4 NG 2016 11 2016-11-01 FL 10275 FRCC 0.0 \n", "\n", " total fuel (mmbtu) elec fuel (mmbtu) fuel category \n", "0 0.0 0.0 NG \n", "1 0.0 0.0 NG \n", "2 0.0 0.0 NG \n", "3 0.0 0.0 NG \n", "4 0.0 0.0 NG " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "facility_df.head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:40:12.389777Z", "start_time": "2017-08-08T19:40:12.379777Z" } }, "outputs": [ { "data": { "text/plain": [ "fuel object\n", "year int64\n", "month int64\n", "datetime datetime64[ns]\n", "state object\n", "plant id int64\n", "NERC object\n", "generation (MWh) float64\n", "total fuel (mmbtu) float64\n", "elec fuel (mmbtu) float64\n", "fuel category object\n", "dtype: object" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "facility_df.dtypes" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T18:48:02.434039Z", "start_time": "2017-08-08T18:48:02.403037Z" } }, "outputs": [ { "data": { "text/plain": [ "array(['HI', 'FL', 'VA', 'MI', 'ME', 'MN', 'CA', 'AK', 'NY', 'MD', 'WI',\n", " 'NH', 'PA', 'OR', 'MA', 'IL', 'DC', 'RI', 'TX', 'CT', 'WA'], dtype=object)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "facility_df.loc[facility_df['NERC'].isnull(), 'state'].unique()" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Import state-level generation data" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:54:51.321569Z", "start_time": "2017-08-08T16:54:51.311569Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "folder = os.path.join('Data storage', 'Derived data', 'state gen data')\n", "states = [\"AL\", \"AK\", \"AZ\", \"AR\", \"CA\", \"CO\", \"CT\", \"DE\", \n", " \"FL\", \"GA\", \"HI\", \"ID\", \"IL\", \"IN\", \"IA\", \"KS\", \n", " \"KY\", \"LA\", \"ME\", \"MD\", \"MA\", \"MI\", \"MN\", \"MS\", \n", " \"MO\", \"MT\", \"NE\", \"NV\", \"NH\", \"NJ\", \"NM\", \"NY\", \n", " \"NC\", \"ND\", \"OH\", \"OK\", \"OR\", \"PA\", \"RI\", \"SC\", \n", " \"SD\", \"TN\", \"TX\", \"UT\", \"VT\", \"VA\", \"WA\", \"WV\", \"WI\", \"WY\"]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:55:04.590342Z", "start_time": "2017-08-08T16:55:03.431253Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "state_list = []\n", "for state in states:\n", " path = os.path.join(folder, '{} fuels gen.csv'.format(state))\n", " df = pd.read_csv(path, parse_dates=['datetime'])\n", " state_list.append(df)\n", "state_df = pd.concat(state_list)\n", "state_df.reset_index(inplace=True, drop=True)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:55:05.447367Z", "start_time": "2017-08-08T16:55:05.439366Z" }, "hidden": true }, "outputs": [ { "data": { "text/plain": [ "end int64\n", "f object\n", "geography object\n", "last_updated object\n", "sector int64\n", "series_id object\n", "start int64\n", "type object\n", "units object\n", "year int64\n", "month int64\n", "generation (MWh) float64\n", "datetime datetime64[ns]\n", "quarter int64\n", "total fuel (mmbtu) float64\n", "elec fuel (mmbtu) float64\n", "all fuel CO2 (kg) float64\n", "elec fuel CO2 (kg) float64\n", "dtype: object" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "state_df.dtypes" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:55:25.906527Z", "start_time": "2017-08-08T16:55:25.784515Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "state_df['state'] = state_df['geography'].str[-2:]\n", "keep_cols = ['state', 'type', 'year', 'datetime', 'generation (MWh)',\n", " 'elec fuel (mmbtu)']\n", "\n", "fuel_cats = facility_fuel_cats.keys()\n", "state_df = state_df.loc[(state_df['year'] >= 2014) &\n", " (state_df['type'].isin(fuel_cats)), keep_cols]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:55:29.381719Z", "start_time": "2017-08-08T16:55:29.373718Z" }, "hidden": true }, "outputs": [ { "data": { "text/plain": [ "array(['COW', 'HYC', 'NUC', 'NG', 'PEL', 'DPV', 'OTH', 'OOG', 'WWW', 'SUN',\n", " 'WAS', 'WND', 'HPS', 'PC', 'GEO'], dtype=object)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "state_df['type'].unique()" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Total generation and fuel consumption for each fuel category" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "### Annual" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:56:13.777231Z", "start_time": "2017-08-08T16:56:13.601215Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "annual_facility = facility_df.groupby(['year', 'state', 'fuel category']).sum()\n", "# annual_facility.reset_index(inplace=True)\n", "annual_facility.drop('plant id', axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:56:15.795339Z", "start_time": "2017-08-08T16:56:15.780338Z" }, "hidden": true }, "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></th>\n", " <th></th>\n", " <th>generation (MWh)</th>\n", " <th>elec fuel (mmbtu)</th>\n", " </tr>\n", " <tr>\n", " <th>year</th>\n", " <th>state</th>\n", " <th>fuel category</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"5\" valign=\"top\">2014</th>\n", " <th rowspan=\"5\" valign=\"top\">AK</th>\n", " <th>COW</th>\n", " <td>558292.181</td>\n", " <td>7216953.0</td>\n", " </tr>\n", " <tr>\n", " <th>HYC</th>\n", " <td>1538738.000</td>\n", " <td>14633403.0</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>3288022.319</td>\n", " <td>32828304.0</td>\n", " </tr>\n", " <tr>\n", " <th>OOG</th>\n", " <td>56165.769</td>\n", " <td>546450.0</td>\n", " </tr>\n", " <tr>\n", " <th>PEL</th>\n", " <td>445621.447</td>\n", " <td>6927101.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " generation (MWh) elec fuel (mmbtu)\n", "year state fuel category \n", "2014 AK COW 558292.181 7216953.0\n", " HYC 1538738.000 14633403.0\n", " NG 3288022.319 32828304.0\n", " OOG 56165.769 546450.0\n", " PEL 445621.447 6927101.0" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "annual_facility.head()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:56:29.680123Z", "start_time": "2017-08-08T16:56:29.666122Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "annual_state = state_df.groupby(['year', 'state', 'type']).sum()\n", "# annual_state.reset_index(inplace=True)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:56:34.046370Z", "start_time": "2017-08-08T16:56:34.022368Z" }, "hidden": true }, "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></th>\n", " <th></th>\n", " <th>generation (MWh)</th>\n", " <th>elec fuel (mmbtu)</th>\n", " </tr>\n", " <tr>\n", " <th>year</th>\n", " <th>state</th>\n", " <th>type</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"25\" valign=\"top\">2014</th>\n", " <th rowspan=\"9\" valign=\"top\">AK</th>\n", " <th>COW</th>\n", " <td>558292.17</td>\n", " <td>7216950.0</td>\n", " </tr>\n", " <tr>\n", " <th>HYC</th>\n", " <td>1538738.00</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>3288022.33</td>\n", " <td>32828310.0</td>\n", " </tr>\n", " <tr>\n", " <th>OOG</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>OTH</th>\n", " <td>-2312.99</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>PEL</th>\n", " <td>445621.46</td>\n", " <td>6927090.0</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>62511.68</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>WND</th>\n", " <td>151957.00</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>WWW</th>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"10\" valign=\"top\">AL</th>\n", " <th>COW</th>\n", " <td>47301626.28</td>\n", " <td>488993810.0</td>\n", " </tr>\n", " <tr>\n", " <th>DPV</th>\n", " <td>3101.38</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>HYC</th>\n", " <td>9466872.01</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>48270074.40</td>\n", " <td>362215370.0</td>\n", " </tr>\n", " <tr>\n", " <th>NUC</th>\n", " <td>41243689.00</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>OOG</th>\n", " <td>180403.48</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>OTH</th>\n", " <td>140.51</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>PEL</th>\n", " <td>98100.01</td>\n", " <td>1199180.0</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>46936.84</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>WWW</th>\n", " <td>2732084.23</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">AR</th>\n", " <th>COW</th>\n", " <td>33220754.79</td>\n", " <td>334098580.0</td>\n", " </tr>\n", " <tr>\n", " <th>DPV</th>\n", " <td>4853.48</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>HPS</th>\n", " <td>67070.00</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>HYC</th>\n", " <td>2639776.01</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>9613708.03</td>\n", " <td>70429870.0</td>\n", " </tr>\n", " <tr>\n", " <th>NUC</th>\n", " <td>14478259.00</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " generation (MWh) elec fuel (mmbtu)\n", "year state type \n", "2014 AK COW 558292.17 7216950.0\n", " HYC 1538738.00 NaN\n", " NG 3288022.33 32828310.0\n", " OOG NaN NaN\n", " OTH -2312.99 NaN\n", " PEL 445621.46 6927090.0\n", " WAS 62511.68 NaN\n", " WND 151957.00 NaN\n", " WWW 0.00 NaN\n", " AL COW 47301626.28 488993810.0\n", " DPV 3101.38 NaN\n", " HYC 9466872.01 NaN\n", " NG 48270074.40 362215370.0\n", " NUC 41243689.00 NaN\n", " OOG 180403.48 NaN\n", " OTH 140.51 NaN\n", " PEL 98100.01 1199180.0\n", " WAS 46936.84 NaN\n", " WWW 2732084.23 NaN\n", " AR COW 33220754.79 334098580.0\n", " DPV 4853.48 NaN\n", " HPS 67070.00 NaN\n", " HYC 2639776.01 NaN\n", " NG 9613708.03 70429870.0\n", " NUC 14478259.00 NaN" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "annual_state.head(n=25)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "It's interesting that the facility data has fuel consumption for solar generation and the state data doesn't. Looking at a 923 data file, it's clear that the fuel consumption is just based on a conversion efficiency of 36.6% across all facilities." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:57:05.735159Z", "start_time": "2017-08-08T16:57:05.717158Z" }, "hidden": true }, "outputs": [ { "data": { "text/plain": [ "generation (MWh) 19030396.62\n", "elec fuel (mmbtu) NaN\n", "Name: (2016, CA, SUN), dtype: float64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "annual_state.loc[2016, 'CA', 'SUN']" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:57:06.679212Z", "start_time": "2017-08-08T16:57:06.669212Z" }, "hidden": true }, "outputs": [ { "data": { "text/plain": [ "generation (MWh) 14354970.0\n", "elec fuel (mmbtu) 133773953.0\n", "Name: (2016, CA, SUN), dtype: float64" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "annual_facility.loc[2016, 'CA', 'SUN']" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "How much generation from large sources (Hydro, wind, coal, natural gas, and nuclear) is missed by monthly 923 data? " ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T16:59:00.892661Z", "start_time": "2017-08-08T16:59:00.853659Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HYC has an error of 24.60%\n", "WND has an error of 3.35%\n", "COW has an error of 1.15%\n", "NG has an error of 5.21%\n", "NUC has an error of 0.00%\n", "SUN has an error of 37.42%\n" ] } ], "source": [ "for fuel in ['HYC', 'WND', 'COW', 'NG', 'NUC', 'SUN']:\n", " state_total = annual_state.loc[2016, :, fuel]['generation (MWh)'].sum()\n", " facility_total = annual_facility.loc[2016, :, fuel]['generation (MWh)'].sum()\n", " \n", " error = (state_total - facility_total) / state_total\n", " print('{} has an error of {:.2f}%'.format(fuel, error * 100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2015 generation and fuel consumption from annual vs monthly reporting plants\n", "The goal here is to figure out how much of generation and fuel consumption from facilities that only report annually is in each NERC region (by state)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T18:06:18.811693Z", "start_time": "2017-08-08T18:05:36.522281Z" }, "collapsed": true }, "outputs": [], "source": [ "path = os.path.join('Data storage', 'EIA923_Schedules_2_3_4_5_M_12_2015_Final.xlsx')\n", "frequency = pd.read_excel(path, sheetname='Page 6 Plant Frame', header=4)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T17:56:22.758012Z", "start_time": "2017-08-08T17:56:22.738010Z" } }, "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>YEAR</th>\n", " <th>Plant Id</th>\n", " <th>Plant State</th>\n", " <th>Sector Number</th>\n", " <th>NAICS Code</th>\n", " <th>Plant Name</th>\n", " <th>Combined Heat And\n", "Power Status</th>\n", " <th>Reporting\n", "Frequency</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2015</td>\n", " <td>2</td>\n", " <td>AL</td>\n", " <td>1</td>\n", " <td>22</td>\n", " <td>Bankhead Dam</td>\n", " <td>N</td>\n", " <td>A</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2015</td>\n", " <td>3</td>\n", " <td>AL</td>\n", " <td>1</td>\n", " <td>22</td>\n", " <td>Barry</td>\n", " <td>N</td>\n", " <td>M</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2015</td>\n", " <td>4</td>\n", " <td>AL</td>\n", " <td>1</td>\n", " <td>22</td>\n", " <td>Walter Bouldin Dam</td>\n", " <td>N</td>\n", " <td>M</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2015</td>\n", " <td>7</td>\n", " <td>AL</td>\n", " <td>1</td>\n", " <td>22</td>\n", " <td>Gadsden</td>\n", " <td>Y</td>\n", " <td>A</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2015</td>\n", " <td>8</td>\n", " <td>AL</td>\n", " <td>1</td>\n", " <td>22</td>\n", " <td>Gorgas</td>\n", " <td>N</td>\n", " <td>M</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " YEAR Plant Id Plant State Sector Number NAICS Code Plant Name \\\n", "0 2015 2 AL 1 22 Bankhead Dam \n", "1 2015 3 AL 1 22 Barry \n", "2 2015 4 AL 1 22 Walter Bouldin Dam \n", "3 2015 7 AL 1 22 Gadsden \n", "4 2015 8 AL 1 22 Gorgas \n", "\n", " Combined Heat And\\nPower Status Reporting\\nFrequency \n", "0 N A \n", "1 N M \n", "2 N M \n", "3 Y A \n", "4 N M " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frequency.head()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T18:06:23.326924Z", "start_time": "2017-08-08T18:06:23.320923Z" }, "collapsed": true }, "outputs": [], "source": [ "frequency.rename(columns={'Plant Id': 'plant id',\n", " 'Plant State': 'state',\n", " 'YEAR': 'year',\n", " 'Reporting\\nFrequency': 'Reporting Frequency'}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T18:48:29.635576Z", "start_time": "2017-08-08T18:48:29.618575Z" } }, "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>year</th>\n", " <th>plant id</th>\n", " <th>state</th>\n", " <th>Sector Number</th>\n", " <th>NAICS Code</th>\n", " <th>Plant Name</th>\n", " <th>Combined Heat And\n", "Power Status</th>\n", " <th>Reporting Frequency</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2015</td>\n", " <td>2</td>\n", " <td>AL</td>\n", " <td>1</td>\n", " <td>22</td>\n", " <td>Bankhead Dam</td>\n", " <td>N</td>\n", " <td>A</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2015</td>\n", " <td>3</td>\n", " <td>AL</td>\n", " <td>1</td>\n", " <td>22</td>\n", " <td>Barry</td>\n", " <td>N</td>\n", " <td>M</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2015</td>\n", " <td>4</td>\n", " <td>AL</td>\n", " <td>1</td>\n", " <td>22</td>\n", " <td>Walter Bouldin Dam</td>\n", " <td>N</td>\n", " <td>M</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2015</td>\n", " <td>7</td>\n", " <td>AL</td>\n", " <td>1</td>\n", " <td>22</td>\n", " <td>Gadsden</td>\n", " <td>Y</td>\n", " <td>A</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2015</td>\n", " <td>8</td>\n", " <td>AL</td>\n", " <td>1</td>\n", " <td>22</td>\n", " <td>Gorgas</td>\n", " <td>N</td>\n", " <td>M</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " year plant id state Sector Number NAICS Code Plant Name \\\n", "0 2015 2 AL 1 22 Bankhead Dam \n", "1 2015 3 AL 1 22 Barry \n", "2 2015 4 AL 1 22 Walter Bouldin Dam \n", "3 2015 7 AL 1 22 Gadsden \n", "4 2015 8 AL 1 22 Gorgas \n", "\n", " Combined Heat And\\nPower Status Reporting Frequency \n", "0 N A \n", "1 N M \n", "2 N M \n", "3 Y A \n", "4 N M " ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frequency.head()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T18:06:26.345094Z", "start_time": "2017-08-08T18:06:26.338094Z" } }, "outputs": [ { "data": { "text/plain": [ "year int64\n", "plant id int64\n", "state object\n", "Sector Number int64\n", "NAICS Code int64\n", "Plant Name object\n", "Combined Heat And\\nPower Status object\n", "Reporting Frequency object\n", "dtype: object" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frequency.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a dataframe with generation, fuel consumption, and reporting frequency of facilities in 2015 " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:40:27.236621Z", "start_time": "2017-08-08T19:40:27.052605Z" }, "collapsed": true }, "outputs": [], "source": [ "freq_cols = ['year', 'plant id', 'Reporting Frequency']\n", "df = pd.merge(facility_df, frequency.loc[:, freq_cols], on=['year', 'plant id'])" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:40:28.030661Z", "start_time": "2017-08-08T19:40:28.005659Z" } }, "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>fuel</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>datetime</th>\n", " <th>state</th>\n", " <th>plant id</th>\n", " <th>NERC</th>\n", " <th>generation (MWh)</th>\n", " <th>total fuel (mmbtu)</th>\n", " <th>elec fuel (mmbtu)</th>\n", " <th>fuel category</th>\n", " <th>Reporting Frequency</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>NG</td>\n", " <td>2015</td>\n", " <td>12</td>\n", " <td>2015-12-01</td>\n", " <td>FL</td>\n", " <td>10275</td>\n", " <td>FRCC</td>\n", " <td>4344.509</td>\n", " <td>55210.0</td>\n", " <td>22133.0</td>\n", " <td>NG</td>\n", " <td>A</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>NG</td>\n", " <td>2015</td>\n", " <td>11</td>\n", " <td>2015-11-01</td>\n", " <td>FL</td>\n", " <td>10275</td>\n", " <td>FRCC</td>\n", " <td>4304.052</td>\n", " <td>54695.0</td>\n", " <td>21927.0</td>\n", " <td>NG</td>\n", " <td>A</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>NG</td>\n", " <td>2015</td>\n", " <td>10</td>\n", " <td>2015-10-01</td>\n", " <td>FL</td>\n", " <td>10275</td>\n", " <td>FRCC</td>\n", " <td>4810.546</td>\n", " <td>61133.0</td>\n", " <td>24507.0</td>\n", " <td>NG</td>\n", " <td>A</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>NG</td>\n", " <td>2015</td>\n", " <td>9</td>\n", " <td>2015-09-01</td>\n", " <td>FL</td>\n", " <td>10275</td>\n", " <td>FRCC</td>\n", " <td>5058.453</td>\n", " <td>64282.0</td>\n", " <td>25770.0</td>\n", " <td>NG</td>\n", " <td>A</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>NG</td>\n", " <td>2015</td>\n", " <td>8</td>\n", " <td>2015-08-01</td>\n", " <td>FL</td>\n", " <td>10275</td>\n", " <td>FRCC</td>\n", " <td>5404.571</td>\n", " <td>68680.0</td>\n", " <td>27533.0</td>\n", " <td>NG</td>\n", " <td>A</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " fuel year month datetime state plant id NERC generation (MWh) \\\n", "0 NG 2015 12 2015-12-01 FL 10275 FRCC 4344.509 \n", "1 NG 2015 11 2015-11-01 FL 10275 FRCC 4304.052 \n", "2 NG 2015 10 2015-10-01 FL 10275 FRCC 4810.546 \n", "3 NG 2015 9 2015-09-01 FL 10275 FRCC 5058.453 \n", "4 NG 2015 8 2015-08-01 FL 10275 FRCC 5404.571 \n", "\n", " total fuel (mmbtu) elec fuel (mmbtu) fuel category Reporting Frequency \n", "0 55210.0 22133.0 NG A \n", "1 54695.0 21927.0 NG A \n", "2 61133.0 24507.0 NG A \n", "3 64282.0 25770.0 NG A \n", "4 68680.0 27533.0 NG A " ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T18:33:18.341118Z", "start_time": "2017-08-08T18:33:06.228439Z" } }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x16fb2e198>" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AqHU+r5b83XffeU5JlqTGjRvr+++/l9Vq5arJAAAAAIA6wefK7YABAzRu3DgN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\nQqNHj9bAgQP1xRdfyN/fX61bt66w7c+Fzf/5n/+p8i+vnpo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dzgFaOd471GQc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\nE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"text/plain": [ "<matplotlib.figure.Figure at 0x17f445748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.factorplot(x='fuel category', y='generation (MWh)', hue='Reporting Frequency',\n", " col='NERC', col_wrap=3, data=df, estimator=np.sum, ci=0, kind='bar',\n", " palette='tab10')\n", "\n", "g.set_xticklabels(rotation=30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Number of NERC regions in a state" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T18:29:45.685110Z", "start_time": "2017-08-08T18:29:45.660109Z" } }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[df['state'] == 'TX', 'NERC'].nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fraction of generation/consumption from Annual reporting facilities in each NERC region of a state\n", "This is development of a method that will be used to approximate the fraction of EIA-estimated generation and consumption within each state that gets apportioned to each NERC regions (when there is more than one). The idea is to take data from the most recent \"final\" EIA-923 and use the annual reporting facilities to approximate the divisions for more recent data. I still need to figure out if it's better to do the calculation by month within a year or just for the year as a whole.\n", "\n", "Determining if it's better to do month-by-month vs a single value for the whole year will depend on if the share of generation/consumption from Annual reporting facilities in each NERC changes much over the course of the year. There is the potential for error either way, and maybe even differences by state. Annual is certainly simpler.\n", "\n", "While looking at data for Texas, I've discovered that generation from Annual reporting facilities can be negative. Need to figure out how (if?) to deal with this...\n", "\n", "#### Conclusion\n", "While there can be variation of % generation in each NERC within a state over the course of 2015, most fuel categories across most states are quite stable. And when fuels do a have a wide spread over the year, they also tend to not be a large fraction of total generation within the NERC region. Given these observations, I'm going to stick with a split calculated as the average over an entire year." ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:36:45.097072Z", "start_time": "2017-08-08T19:36:45.042069Z" }, "collapsed": true }, "outputs": [], "source": [ "def annual(df, state):\n", " \"\"\"Return the percent of gen & consumption by fuel type in each NERC region\n", " for a state\"\"\"\n", " a = df.loc[(df.state == state) & \n", " (df['Reporting Frequency'] == 'A')].copy()\n", " a.drop(['plant id', 'year'], axis=1, inplace=True)\n", " a = a.groupby(['NERC', 'fuel category']).sum()\n", " \n", " fuels = set(a.index.get_level_values('fuel category'))\n", " \n", " temp_list = []\n", " for fuel in fuels:\n", " temp = (a.xs(fuel, level='fuel category')\n", " / a.xs(fuel, level='fuel category').sum())\n", " temp['fuel category'] = fuel\n", " temp_list.append(temp)\n", " \n", " result = pd.concat(temp_list)\n", " result.reset_index(inplace=True)\n", " result['state'] = state\n", " \n", " rename_cols = {'generation (MWh)': '% generation',\n", " 'total fuel (mmbtu)': '% total fuel',\n", " 'elec fuel (mmbtu)': '% elec fuel'}\n", " \n", " result.rename(columns=rename_cols, inplace=True)\n", " \n", " return result" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:56:34.974260Z", "start_time": "2017-08-08T19:56:34.901256Z" }, "collapsed": true }, "outputs": [], "source": [ "def annual_month(df, state):\n", " \"\"\"Return the percent of gen & consumption by fuel type and month in each \n", " NERC region for a state\"\"\"\n", " a = df.loc[(df.state == state) & \n", " (df['Reporting Frequency'] == 'A')].copy()\n", " a.drop(['plant id', 'year'], axis=1, inplace=True)\n", " a = a.groupby(['NERC', 'fuel category', 'month']).sum()\n", " \n", " fuels = set(a.index.get_level_values('fuel category'))\n", " \n", " temp_list = []\n", " for fuel in fuels:\n", " for month in range(1, 13):\n", " temp = (a.xs(fuel, level='fuel category')\n", " .xs(month, level='month')\n", " / a.xs(fuel, level='fuel category')\n", " .xs(month, level='month')\n", " .sum())\n", " temp['fuel category'] = fuel\n", " temp['month'] = month\n", " temp_list.append(temp)\n", " \n", " result = pd.concat(temp_list)\n", " result.reset_index(inplace=True)\n", " result['state'] = state\n", " \n", " rename_cols = {'generation (MWh)': '% generation',\n", " 'total fuel (mmbtu)': '% total fuel',\n", " 'elec fuel (mmbtu)': '% elec fuel'}\n", " \n", " result.rename(columns=rename_cols, inplace=True)\n", " \n", " return result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the percent of generation, total fuel consumption, and electric fuel consumption from facilities that report annually to EIA-923" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:36:51.258425Z", "start_time": "2017-08-08T19:36:49.361313Z" }, "collapsed": true }, "outputs": [], "source": [ "df_list = []\n", "for state in states:\n", " num_nerc = df.loc[df.state == state, 'NERC'].nunique()\n", " if num_nerc > 1:\n", " df_list.append(annual(df, state))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:56:46.356903Z", "start_time": "2017-08-08T19:56:36.828365Z" }, "collapsed": true }, "outputs": [], "source": [ "df_list = []\n", "for state in states:\n", " num_nerc = df.loc[df.state == state, 'NERC'].nunique()\n", " if num_nerc > 1:\n", " df_list.append(annual_month(df, state))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:57:01.368750Z", "start_time": "2017-08-08T19:57:01.343749Z" }, "collapsed": true }, "outputs": [], "source": [ "fuel_by_nerc_month = pd.concat(df_list).reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:36:53.553550Z", "start_time": "2017-08-08T19:36:53.537549Z" }, "collapsed": true }, "outputs": [], "source": [ "fuel_by_nerc = pd.concat(df_list).reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:36:54.848622Z", "start_time": "2017-08-08T19:36:54.828627Z" } }, "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>NERC</th>\n", " <th>month</th>\n", " <th>% generation</th>\n", " <th>% total fuel</th>\n", " <th>% elec fuel</th>\n", " <th>fuel category</th>\n", " <th>state</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-</td>\n", " <td>0.333333</td>\n", " <td>0.000066</td>\n", " <td>0.034382</td>\n", " <td>0.000065</td>\n", " <td>WWW</td>\n", " <td>AR</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>SERC</td>\n", " <td>0.666667</td>\n", " <td>0.999934</td>\n", " <td>0.965618</td>\n", " <td>0.999935</td>\n", " <td>WWW</td>\n", " <td>AR</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>SERC</td>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>PC</td>\n", " <td>AR</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>-</td>\n", " <td>0.276596</td>\n", " <td>0.002770</td>\n", " <td>0.001223</td>\n", " <td>0.002772</td>\n", " <td>NG</td>\n", " <td>AR</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>SERC</td>\n", " <td>0.446809</td>\n", " <td>0.219980</td>\n", " <td>0.636033</td>\n", " <td>0.175285</td>\n", " <td>NG</td>\n", " <td>AR</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NERC month % generation % total fuel % elec fuel fuel category state\n", "0 - 0.333333 0.000066 0.034382 0.000065 WWW AR\n", "1 SERC 0.666667 0.999934 0.965618 0.999935 WWW AR\n", "2 SERC 1.000000 NaN NaN NaN PC AR\n", "3 - 0.276596 0.002770 0.001223 0.002772 NG AR\n", "4 SERC 0.446809 0.219980 0.636033 0.175285 NG AR" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fuel_by_nerc.head()" ] }, { "cell_type": "code", "execution_count": 214, "metadata": { "ExecuteTime": { "end_time": "2017-08-08T19:57:16.667614Z", "start_time": "2017-08-08T19:57:16.646613Z" } }, "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>NERC</th>\n", " <th>% generation</th>\n", " <th>% total fuel</th>\n", " <th>% elec fuel</th>\n", " <th>fuel category</th>\n", " <th>month</th>\n", " <th>state</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2704</th>\n", " <td>RFC</td>\n", " <td>0.249526</td>\n", " <td>0.382119</td>\n", " <td>0.513470</td>\n", " <td>NG</td>\n", " <td>10</td>\n", " <td>WI</td>\n", " </tr>\n", " <tr>\n", " <th>2705</th>\n", " <td>MRO</td>\n", " <td>0.946647</td>\n", " <td>0.666793</td>\n", " <td>0.665857</td>\n", " <td>NG</td>\n", " <td>11</td>\n", " <td>WI</td>\n", " </tr>\n", " <tr>\n", " <th>2706</th>\n", " <td>RFC</td>\n", " <td>0.053353</td>\n", " <td>0.333207</td>\n", " <td>0.334143</td>\n", " <td>NG</td>\n", " <td>11</td>\n", " <td>WI</td>\n", " </tr>\n", " <tr>\n", " <th>2707</th>\n", " <td>MRO</td>\n", " <td>0.923901</td>\n", " <td>0.658481</td>\n", " <td>0.718664</td>\n", " <td>NG</td>\n", " <td>12</td>\n", " <td>WI</td>\n", " </tr>\n", " <tr>\n", " <th>2708</th>\n", " <td>RFC</td>\n", " <td>0.076099</td>\n", " <td>0.341519</td>\n", " <td>0.281336</td>\n", " <td>NG</td>\n", " <td>12</td>\n", " <td>WI</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NERC % generation % total fuel % elec fuel fuel category month state\n", "2704 RFC 0.249526 0.382119 0.513470 NG 10 WI\n", "2705 MRO 0.946647 0.666793 0.665857 NG 11 WI\n", "2706 RFC 0.053353 0.333207 0.334143 NG 11 WI\n", "2707 MRO 0.923901 0.658481 0.718664 NG 12 WI\n", "2708 RFC 0.076099 0.341519 0.281336 NG 12 WI" ] }, "execution_count": 214, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fuel_by_nerc_month.tail()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "st" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "split_states = []\n", "for state in states:\n", " if df.loc[df.state == state, 'NERC'].nunique() > 1:\n", " split_states.append(state)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['AR',\n", " 'FL',\n", " 'IL',\n", " 'IA',\n", " 'KS',\n", " 'KY',\n", " 'LA',\n", " 'MI',\n", " 'MO',\n", " 'NE',\n", " 'NM',\n", " 'NC',\n", " 'OK',\n", " 'SD',\n", " 'TX',\n", " 'VA',\n", " 'WI']" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "split_states" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "cols = ['state', 'NERC', 'fuel category']\n", "a = fuel_by_nerc_month.groupby(cols).std()\n", "a.drop('month', axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 63, "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></th>\n", " <th>% generation</th>\n", " <th>% total fuel</th>\n", " <th>% elec fuel</th>\n", " </tr>\n", " <tr>\n", " <th>NERC</th>\n", " <th>fuel category</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"5\" valign=\"top\">-</th>\n", " <th>HYC</th>\n", " <td>4.928033e-09</td>\n", " <td>4.608873e-07</td>\n", " <td>4.608873e-07</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>3.047858e-04</td>\n", " <td>4.821993e-04</td>\n", " <td>2.272056e-04</td>\n", " </tr>\n", " <tr>\n", " <th>PEL</th>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>SUN</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>WWW</th>\n", " <td>2.242653e-05</td>\n", " <td>6.797735e-03</td>\n", " <td>1.781255e-05</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"7\" valign=\"top\">SERC</th>\n", " <th>HYC</th>\n", " <td>6.275202e-09</td>\n", " <td>8.077144e-07</td>\n", " <td>8.077144e-07</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>8.582103e-02</td>\n", " <td>1.433441e-01</td>\n", " <td>6.745018e-02</td>\n", " </tr>\n", " <tr>\n", " <th>OOG</th>\n", " <td>3.920610e-08</td>\n", " <td>4.330886e-06</td>\n", " <td>4.330886e-06</td>\n", " </tr>\n", " <tr>\n", " <th>PC</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>PEL</th>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>WWW</th>\n", " <td>2.242653e-05</td>\n", " <td>6.797735e-03</td>\n", " <td>1.781255e-05</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">SPP</th>\n", " <th>HYC</th>\n", " <td>5.370029e-09</td>\n", " <td>6.464797e-07</td>\n", " <td>6.464797e-07</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>8.551625e-02</td>\n", " <td>1.428619e-01</td>\n", " <td>6.722309e-02</td>\n", " </tr>\n", " <tr>\n", " <th>OOG</th>\n", " <td>3.920610e-08</td>\n", " <td>4.330886e-06</td>\n", " <td>4.330886e-06</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " % generation % total fuel % elec fuel\n", "NERC fuel category \n", "- HYC 4.928033e-09 4.608873e-07 4.608873e-07\n", " NG 3.047858e-04 4.821993e-04 2.272056e-04\n", " PEL 0.000000e+00 0.000000e+00 0.000000e+00\n", " SUN NaN NaN NaN\n", " WWW 2.242653e-05 6.797735e-03 1.781255e-05\n", "SERC HYC 6.275202e-09 8.077144e-07 8.077144e-07\n", " NG 8.582103e-02 1.433441e-01 6.745018e-02\n", " OOG 3.920610e-08 4.330886e-06 4.330886e-06\n", " PC NaN NaN NaN\n", " PEL 0.000000e+00 0.000000e+00 0.000000e+00\n", " WAS 0.000000e+00 0.000000e+00 0.000000e+00\n", " WWW 2.242653e-05 6.797735e-03 1.781255e-05\n", "SPP HYC 5.370029e-09 6.464797e-07 6.464797e-07\n", " NG 8.551625e-02 1.428619e-01 6.722309e-02\n", " OOG 3.920610e-08 4.330886e-06 4.330886e-06" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.xs('AR', level='state')" ] }, { "cell_type": "code", "execution_count": 70, "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></th>\n", " <th></th>\n", " <th>% generation</th>\n", " <th>% total fuel</th>\n", " <th>% elec fuel</th>\n", " </tr>\n", " <tr>\n", " <th>state</th>\n", " <th>NERC</th>\n", " <th>fuel category</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">AR</th>\n", " <th>SERC</th>\n", " <th>NG</th>\n", " <td>NaN</td>\n", " <td>0.143344</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>SPP</th>\n", " <th>NG</th>\n", " <td>NaN</td>\n", " <td>0.142862</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">FL</th>\n", " <th>FRCC</th>\n", " <th>COW</th>\n", " <td>0.102601</td>\n", " <td>NaN</td>\n", " <td>0.134697</td>\n", " </tr>\n", " <tr>\n", " <th>SERC</th>\n", " <th>COW</th>\n", " <td>0.102601</td>\n", " <td>NaN</td>\n", " <td>0.134697</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"5\" valign=\"top\">IA</th>\n", " <th>-</th>\n", " <th>NG</th>\n", " <td>0.303138</td>\n", " <td>0.264098</td>\n", " <td>0.300394</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">MRO</th>\n", " <th>COW</th>\n", " <td>0.132677</td>\n", " <td>NaN</td>\n", " <td>0.140241</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>0.211916</td>\n", " <td>0.213591</td>\n", " <td>0.214703</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">SERC</th>\n", " <th>COW</th>\n", " <td>0.153661</td>\n", " <td>NaN</td>\n", " <td>0.152410</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>0.121395</td>\n", " <td>NaN</td>\n", " <td>0.113601</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">MI</th>\n", " <th>MRO</th>\n", " <th>PEL</th>\n", " <td>0.579756</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>RFC</th>\n", " <th>PEL</th>\n", " <td>0.579756</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">TX</th>\n", " <th rowspan=\"2\" valign=\"top\">SPP</th>\n", " <th>WND</th>\n", " <td>0.214635</td>\n", " <td>0.214635</td>\n", " <td>0.214635</td>\n", " </tr>\n", " <tr>\n", " <th>WWW</th>\n", " <td>5.272077</td>\n", " <td>NaN</td>\n", " <td>0.124454</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">TRE</th>\n", " <th>WND</th>\n", " <td>0.214635</td>\n", " <td>0.214635</td>\n", " <td>0.214635</td>\n", " </tr>\n", " <tr>\n", " <th>WWW</th>\n", " <td>5.272077</td>\n", " <td>NaN</td>\n", " <td>0.124454</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">VA</th>\n", " <th rowspan=\"3\" valign=\"top\">RFC</th>\n", " <th>COW</th>\n", " <td>0.169581</td>\n", " <td>NaN</td>\n", " <td>0.185219</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.116589</td>\n", " </tr>\n", " <tr>\n", " <th>PEL</th>\n", " <td>NaN</td>\n", " <td>0.115835</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">SERC</th>\n", " <th>COW</th>\n", " <td>0.169581</td>\n", " <td>NaN</td>\n", " <td>0.185219</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.116589</td>\n", " </tr>\n", " <tr>\n", " <th>PEL</th>\n", " <td>NaN</td>\n", " <td>0.115835</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">WI</th>\n", " <th rowspan=\"2\" valign=\"top\">MRO</th>\n", " <th>NG</th>\n", " <td>0.159994</td>\n", " <td>NaN</td>\n", " <td>0.143071</td>\n", " </tr>\n", " <tr>\n", " <th>PEL</th>\n", " <td>0.187468</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">RFC</th>\n", " <th>NG</th>\n", " <td>0.159994</td>\n", " <td>NaN</td>\n", " <td>0.143071</td>\n", " </tr>\n", " <tr>\n", " <th>PEL</th>\n", " <td>0.187468</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " % generation % total fuel % elec fuel\n", "state NERC fuel category \n", "AR SERC NG NaN 0.143344 NaN\n", " SPP NG NaN 0.142862 NaN\n", "FL FRCC COW 0.102601 NaN 0.134697\n", " SERC COW 0.102601 NaN 0.134697\n", "IA - NG 0.303138 0.264098 0.300394\n", " MRO COW 0.132677 NaN 0.140241\n", " NG 0.211916 0.213591 0.214703\n", " SERC COW 0.153661 NaN 0.152410\n", " NG 0.121395 NaN 0.113601\n", "MI MRO PEL 0.579756 NaN NaN\n", " RFC PEL 0.579756 NaN NaN\n", "TX SPP WND 0.214635 0.214635 0.214635\n", " WWW 5.272077 NaN 0.124454\n", " TRE WND 0.214635 0.214635 0.214635\n", " WWW 5.272077 NaN 0.124454\n", "VA RFC COW 0.169581 NaN 0.185219\n", " NG NaN NaN 0.116589\n", " PEL NaN 0.115835 NaN\n", " SERC COW 0.169581 NaN 0.185219\n", " NG NaN NaN 0.116589\n", " PEL NaN 0.115835 NaN\n", "WI MRO NG 0.159994 NaN 0.143071\n", " PEL 0.187468 NaN NaN\n", " RFC NG 0.159994 NaN 0.143071\n", " PEL 0.187468 NaN NaN" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[a > .1].dropna(how='all')" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "ExecuteTime": { "end_time": "2017-08-09T15:00:10.701750Z", "start_time": "2017-08-09T15:00:08.473624Z" }, "scrolled": 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71VRl7qqdb5FefuYbzbzuFEX3bPrb9wHB3dVv5DWqripWVUW+HA5/+QdHmX6MG+P0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\nNXZKipZ/sl2StGHtIQ0YEqvEZu7Gb8nuHcc1//W1qq6qrd+WnhWjK64bKC8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CEe4ubSn4THUefs7c0eCYzrY+mOU3SVKu1qjD2tRfoRG19Y5RCUo8GHA7bGTSjvGvmbuR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\nDRejtsKgrYSjVzcMnni7pOX7TRk16/l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s5SXNKM5v1C8vWRMxrs/zJdVt+MPrp/D3D84OSjCwtZLj0U2xePn/LkVUuNuo5goOc0VM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\nXX26ttDOlGyVlFod+0OC/PTwhL7qH92yLlIGAAA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emNvb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\nMrjUosOpokacK2uB2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eH3ffLG9WZs4no/ti+uL2if14oM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\nXvJxxR07aZCiY9svILBYzJp1yYjjTfu0ccMFw92PdxVUaMWmAp+e32q1K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"text/plain": [ "<matplotlib.figure.Figure at 0x1778bdc88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fuels = ['NG', 'HYC', 'COW', 'GEO', 'WND', 'SUN']\n", "\n", "sns.factorplot(x='month', y='% generation', hue='fuel category', col='NERC',\n", " row='state',\n", " data=fuel_by_nerc_month.loc[(fuel_by_nerc_month['fuel category'].isin(fuels)) &\n", " (fuel_by_nerc_month['NERC'] != '-')],\n", " n_boot=1)\n", "path = os.path.join('Figures', 'SI', 'Annual facility seasonal gen variation.pdf')\n", "# plt.savefig(path, bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 222, "metadata": { "ExecuteTime": { "end_time": "2017-08-09T14:57:45.549559Z", "start_time": "2017-08-09T14:57:45.511552Z" } }, "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>NERC</th>\n", " <th>% generation</th>\n", " <th>% total fuel</th>\n", " <th>% elec fuel</th>\n", " <th>fuel category</th>\n", " <th>month</th>\n", " <th>state</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2265</th>\n", " <td>SPP</td>\n", " <td>4.484362</td>\n", " <td>0.538867</td>\n", " <td>0.562231</td>\n", " <td>WWW</td>\n", " <td>1</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2266</th>\n", " <td>TRE</td>\n", " <td>-3.484362</td>\n", " <td>0.461133</td>\n", " <td>0.437769</td>\n", " <td>WWW</td>\n", " <td>1</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2267</th>\n", " <td>SPP</td>\n", " <td>4.132260</td>\n", " <td>0.473945</td>\n", " <td>0.564103</td>\n", " <td>WWW</td>\n", " <td>2</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2268</th>\n", " <td>TRE</td>\n", " <td>-3.132260</td>\n", " <td>0.526055</td>\n", " <td>0.435897</td>\n", " <td>WWW</td>\n", " <td>2</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2269</th>\n", " <td>SPP</td>\n", " <td>5.280268</td>\n", " <td>0.519683</td>\n", " <td>0.550018</td>\n", " <td>WWW</td>\n", " <td>3</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2270</th>\n", " <td>TRE</td>\n", " <td>-4.280268</td>\n", " <td>0.480317</td>\n", " <td>0.449982</td>\n", " <td>WWW</td>\n", " <td>3</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2271</th>\n", " <td>SPP</td>\n", " <td>2.961378</td>\n", " <td>0.697669</td>\n", " <td>0.725501</td>\n", " <td>WWW</td>\n", " <td>4</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2272</th>\n", " <td>TRE</td>\n", " <td>-1.961378</td>\n", " <td>0.302331</td>\n", " <td>0.274499</td>\n", " <td>WWW</td>\n", " <td>4</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2273</th>\n", " <td>SPP</td>\n", " <td>-5.065479</td>\n", " <td>0.586754</td>\n", " <td>0.959101</td>\n", " <td>WWW</td>\n", " <td>5</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2274</th>\n", " <td>TRE</td>\n", " <td>6.065479</td>\n", " <td>0.413246</td>\n", " <td>0.040899</td>\n", " <td>WWW</td>\n", " <td>5</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2275</th>\n", " <td>SPP</td>\n", " <td>3.538606</td>\n", " <td>0.417402</td>\n", " <td>0.656162</td>\n", " <td>WWW</td>\n", " <td>6</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2276</th>\n", " <td>TRE</td>\n", " <td>-2.538606</td>\n", " <td>0.582598</td>\n", " <td>0.343838</td>\n", " <td>WWW</td>\n", " <td>6</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2277</th>\n", " <td>SPP</td>\n", " <td>5.823124</td>\n", " <td>0.481409</td>\n", " <td>0.577494</td>\n", " <td>WWW</td>\n", " <td>7</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2278</th>\n", " <td>TRE</td>\n", " <td>-4.823124</td>\n", " <td>0.518591</td>\n", " <td>0.422506</td>\n", " <td>WWW</td>\n", " <td>7</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2279</th>\n", " <td>SPP</td>\n", " <td>2.823845</td>\n", " <td>0.542277</td>\n", " <td>0.564253</td>\n", " <td>WWW</td>\n", " <td>8</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2280</th>\n", " <td>TRE</td>\n", " <td>-1.823845</td>\n", " <td>0.457723</td>\n", " <td>0.435747</td>\n", " <td>WWW</td>\n", " <td>8</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2281</th>\n", " <td>SPP</td>\n", " <td>4.395278</td>\n", " <td>0.587122</td>\n", " <td>0.573002</td>\n", " <td>WWW</td>\n", " <td>9</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2282</th>\n", " <td>TRE</td>\n", " <td>-3.395278</td>\n", " <td>0.412878</td>\n", " <td>0.426998</td>\n", " <td>WWW</td>\n", " <td>9</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2283</th>\n", " <td>SPP</td>\n", " <td>-12.152599</td>\n", " <td>0.618150</td>\n", " <td>0.499494</td>\n", " <td>WWW</td>\n", " <td>10</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2284</th>\n", " <td>TRE</td>\n", " <td>13.152599</td>\n", " <td>0.381850</td>\n", " <td>0.500506</td>\n", " <td>WWW</td>\n", " <td>10</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2285</th>\n", " <td>SPP</td>\n", " <td>4.745620</td>\n", " <td>0.507283</td>\n", " <td>0.534694</td>\n", " <td>WWW</td>\n", " <td>11</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2286</th>\n", " <td>TRE</td>\n", " <td>-3.745620</td>\n", " <td>0.492717</td>\n", " <td>0.465306</td>\n", " <td>WWW</td>\n", " <td>11</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2287</th>\n", " <td>SPP</td>\n", " <td>3.483764</td>\n", " <td>0.473996</td>\n", " <td>0.558542</td>\n", " <td>WWW</td>\n", " <td>12</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>2288</th>\n", " <td>TRE</td>\n", " <td>-2.483764</td>\n", " <td>0.526004</td>\n", " <td>0.441458</td>\n", " <td>WWW</td>\n", " <td>12</td>\n", " <td>TX</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NERC % generation % total fuel % elec fuel fuel category month state\n", "2265 SPP 4.484362 0.538867 0.562231 WWW 1 TX\n", "2266 TRE -3.484362 0.461133 0.437769 WWW 1 TX\n", "2267 SPP 4.132260 0.473945 0.564103 WWW 2 TX\n", "2268 TRE -3.132260 0.526055 0.435897 WWW 2 TX\n", "2269 SPP 5.280268 0.519683 0.550018 WWW 3 TX\n", "2270 TRE -4.280268 0.480317 0.449982 WWW 3 TX\n", "2271 SPP 2.961378 0.697669 0.725501 WWW 4 TX\n", "2272 TRE -1.961378 0.302331 0.274499 WWW 4 TX\n", "2273 SPP -5.065479 0.586754 0.959101 WWW 5 TX\n", "2274 TRE 6.065479 0.413246 0.040899 WWW 5 TX\n", "2275 SPP 3.538606 0.417402 0.656162 WWW 6 TX\n", "2276 TRE -2.538606 0.582598 0.343838 WWW 6 TX\n", "2277 SPP 5.823124 0.481409 0.577494 WWW 7 TX\n", "2278 TRE -4.823124 0.518591 0.422506 WWW 7 TX\n", "2279 SPP 2.823845 0.542277 0.564253 WWW 8 TX\n", "2280 TRE -1.823845 0.457723 0.435747 WWW 8 TX\n", "2281 SPP 4.395278 0.587122 0.573002 WWW 9 TX\n", "2282 TRE -3.395278 0.412878 0.426998 WWW 9 TX\n", "2283 SPP -12.152599 0.618150 0.499494 WWW 10 TX\n", "2284 TRE 13.152599 0.381850 0.500506 WWW 10 TX\n", "2285 SPP 4.745620 0.507283 0.534694 WWW 11 TX\n", "2286 TRE -3.745620 0.492717 0.465306 WWW 11 TX\n", "2287 SPP 3.483764 0.473996 0.558542 WWW 12 TX\n", "2288 TRE -2.483764 0.526004 0.441458 WWW 12 TX" ] }, "execution_count": 222, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fuel_by_nerc_month.loc[(fuel_by_nerc_month.state=='TX') &\n", " (fuel_by_nerc_month['fuel category'] == 'WWW')]" ] }, { "cell_type": "code", "execution_count": 232, "metadata": { "ExecuteTime": { "end_time": "2017-08-09T19:44:25.935800Z", "start_time": "2017-08-09T19:44:25.854795Z" } }, "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></th>\n", " <th></th>\n", " <th>year</th>\n", " <th>plant id</th>\n", " <th>generation (MWh)</th>\n", " <th>total fuel (mmbtu)</th>\n", " <th>elec fuel (mmbtu)</th>\n", " </tr>\n", " <tr>\n", " <th>NERC</th>\n", " <th>month</th>\n", " <th>fuel category</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"12\" valign=\"top\">SPP</th>\n", " <th>1</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>158335</td>\n", " <td>24477.085</td>\n", " <td>1031862.0</td>\n", " <td>270598.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>158335</td>\n", " <td>23591.727</td>\n", " <td>1002454.0</td>\n", " <td>260114.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>158335</td>\n", " <td>23338.641</td>\n", " <td>1055755.0</td>\n", " <td>258287.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>158335</td>\n", " <td>23981.906</td>\n", " <td>1079494.0</td>\n", " <td>265683.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>158335</td>\n", " <td>22927.767</td>\n", " <td>1020067.0</td>\n", " <td>253919.0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>158335</td>\n", " <td>19081.413</td>\n", " <td>824972.0</td>\n", " <td>210964.0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>158335</td>\n", " <td>22039.664</td>\n", " <td>994750.0</td>\n", " <td>243501.0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>158335</td>\n", " <td>22143.524</td>\n", " <td>1014658.0</td>\n", " <td>243921.0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>158335</td>\n", " <td>22219.108</td>\n", " <td>1071510.0</td>\n", " <td>245327.0</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>158335</td>\n", " <td>15374.204</td>\n", " <td>712726.0</td>\n", " <td>169390.0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>158335</td>\n", " <td>22856.243</td>\n", " <td>1012581.0</td>\n", " <td>253042.0</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>158335</td>\n", " <td>21888.202</td>\n", " <td>964300.0</td>\n", " <td>241652.0</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"12\" valign=\"top\">TRE</th>\n", " <th>1</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>75188</td>\n", " <td>-19018.764</td>\n", " <td>883010.0</td>\n", " <td>210695.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>75188</td>\n", " <td>-17882.569</td>\n", " <td>1112673.0</td>\n", " <td>200997.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>75188</td>\n", " <td>-18918.668</td>\n", " <td>975783.0</td>\n", " <td>211310.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>75188</td>\n", " <td>-15883.681</td>\n", " <td>467792.0</td>\n", " <td>100523.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>75188</td>\n", " <td>-27454.045</td>\n", " <td>718424.0</td>\n", " <td>10828.0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>75188</td>\n", " <td>-13689.060</td>\n", " <td>1151472.0</td>\n", " <td>110548.0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>75188</td>\n", " <td>-18254.812</td>\n", " <td>1071581.0</td>\n", " <td>178150.0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>75188</td>\n", " <td>-14301.901</td>\n", " <td>856449.0</td>\n", " <td>188369.0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>75188</td>\n", " <td>-17163.885</td>\n", " <td>753512.0</td>\n", " <td>182816.0</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>75188</td>\n", " <td>-16639.300</td>\n", " <td>440272.0</td>\n", " <td>169733.0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>75188</td>\n", " <td>-18039.961</td>\n", " <td>983507.0</td>\n", " <td>220204.0</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <th>WWW</th>\n", " <td>6045</td>\n", " <td>75188</td>\n", " <td>-15605.285</td>\n", " <td>1070104.0</td>\n", " <td>190996.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " year plant id generation (MWh) \\\n", "NERC month fuel category \n", "SPP 1 WWW 6045 158335 24477.085 \n", " 2 WWW 6045 158335 23591.727 \n", " 3 WWW 6045 158335 23338.641 \n", " 4 WWW 6045 158335 23981.906 \n", " 5 WWW 6045 158335 22927.767 \n", " 6 WWW 6045 158335 19081.413 \n", " 7 WWW 6045 158335 22039.664 \n", " 8 WWW 6045 158335 22143.524 \n", " 9 WWW 6045 158335 22219.108 \n", " 10 WWW 6045 158335 15374.204 \n", " 11 WWW 6045 158335 22856.243 \n", " 12 WWW 6045 158335 21888.202 \n", "TRE 1 WWW 6045 75188 -19018.764 \n", " 2 WWW 6045 75188 -17882.569 \n", " 3 WWW 6045 75188 -18918.668 \n", " 4 WWW 6045 75188 -15883.681 \n", " 5 WWW 6045 75188 -27454.045 \n", " 6 WWW 6045 75188 -13689.060 \n", " 7 WWW 6045 75188 -18254.812 \n", " 8 WWW 6045 75188 -14301.901 \n", " 9 WWW 6045 75188 -17163.885 \n", " 10 WWW 6045 75188 -16639.300 \n", " 11 WWW 6045 75188 -18039.961 \n", " 12 WWW 6045 75188 -15605.285 \n", "\n", " total fuel (mmbtu) elec fuel (mmbtu) \n", "NERC month fuel category \n", "SPP 1 WWW 1031862.0 270598.0 \n", " 2 WWW 1002454.0 260114.0 \n", " 3 WWW 1055755.0 258287.0 \n", " 4 WWW 1079494.0 265683.0 \n", " 5 WWW 1020067.0 253919.0 \n", " 6 WWW 824972.0 210964.0 \n", " 7 WWW 994750.0 243501.0 \n", " 8 WWW 1014658.0 243921.0 \n", " 9 WWW 1071510.0 245327.0 \n", " 10 WWW 712726.0 169390.0 \n", " 11 WWW 1012581.0 253042.0 \n", " 12 WWW 964300.0 241652.0 \n", "TRE 1 WWW 883010.0 210695.0 \n", " 2 WWW 1112673.0 200997.0 \n", " 3 WWW 975783.0 211310.0 \n", " 4 WWW 467792.0 100523.0 \n", " 5 WWW 718424.0 10828.0 \n", " 6 WWW 1151472.0 110548.0 \n", " 7 WWW 1071581.0 178150.0 \n", " 8 WWW 856449.0 188369.0 \n", " 9 WWW 753512.0 182816.0 \n", " 10 WWW 440272.0 169733.0 \n", " 11 WWW 983507.0 220204.0 \n", " 12 WWW 1070104.0 190996.0 " ] }, "execution_count": 232, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[(df.state == 'TX') &\n", " (df['fuel category'] == 'WWW') &\n", " (df['Reporting Frequency'] == 'A')].groupby(['NERC', 'month', 'fuel category']).sum()" ] }, { "cell_type": "code", "execution_count": 230, "metadata": { "ExecuteTime": { "end_time": "2017-08-09T19:44:01.909443Z", "start_time": "2017-08-09T19:44:01.839439Z" } }, "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></th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>plant id</th>\n", " <th>generation (MWh)</th>\n", " <th>total fuel (mmbtu)</th>\n", " <th>elec fuel (mmbtu)</th>\n", " </tr>\n", " <tr>\n", " <th>NERC</th>\n", " <th>fuel category</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>SPP</th>\n", " <th>WWW</th>\n", " <td>72540</td>\n", " <td>234</td>\n", " <td>1900020</td>\n", " <td>263919.484</td>\n", " <td>11785129.0</td>\n", " <td>2916398.0</td>\n", " </tr>\n", " <tr>\n", " <th>TRE</th>\n", " <th>WWW</th>\n", " <td>72540</td>\n", " <td>234</td>\n", " <td>902256</td>\n", " <td>-212851.931</td>\n", " <td>10484579.0</td>\n", " <td>1975169.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " year month plant id generation (MWh) \\\n", "NERC fuel category \n", "SPP WWW 72540 234 1900020 263919.484 \n", "TRE WWW 72540 234 902256 -212851.931 \n", "\n", " total fuel (mmbtu) elec fuel (mmbtu) \n", "NERC fuel category \n", "SPP WWW 11785129.0 2916398.0 \n", "TRE WWW 10484579.0 1975169.0 " ] }, "execution_count": 230, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[(df.state == 'TX') &\n", " (df['fuel category'] == 'WWW') &\n", " (df['Reporting Frequency'] == 'A')].groupby(['NERC', 'fuel category']).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### States that include more than one NERC region" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "ExecuteTime": { "end_time": "2017-07-21T15:40:07.367990", "start_time": "2017-07-21T15:40:07.341486" }, "collapsed": true }, "outputs": [], "source": [ "NERC_states = ['WY', 'SD', 'NE', 'OK', 'TX', 'NM', 'LA', 'AR',\n", " 'MO', 'MN', 'IL', 'KY', 'VA', 'FL']" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "ExecuteTime": { "end_time": "2017-07-21T15:50:04.437271", "start_time": "2017-07-21T15:50:04.318767" }, "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "error_list = []\n", "for state in NERC_states:\n", " error = (annual_state.loc[2016, state]\n", " - annual_facility.loc[2016, state]) / annual_state.loc[2016, state]\n", " error['state'] = state\n", " \n", " for col in ['generation (MWh)']:#, 'elec fuel (mmbtu)']:\n", " if error.loc[error[col] > 0.05, col].any():\n", " error_list.append(error.loc[error[col] > 0.05])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dataframe below shows all states with more than one NERC region where facility generation is at least 5% below EIA's state-level estimate in 2016. " ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "ExecuteTime": { "end_time": "2017-07-21T15:50:11.212518", "start_time": "2017-07-21T15:50:11.159172" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>generation (MWh)</th>\n", " <th>elec fuel (mmbtu)</th>\n", " <th>state</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>COW</th>\n", " <td>0.057014</td>\n", " <td>0.058988</td>\n", " <td>WY</td>\n", " </tr>\n", " <tr>\n", " <th>HYC</th>\n", " <td>0.095835</td>\n", " <td>NaN</td>\n", " <td>WY</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>0.413092</td>\n", " <td>0.508736</td>\n", " <td>WY</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>0.212550</td>\n", " <td>0.233675</td>\n", " <td>SD</td>\n", " </tr>\n", " <tr>\n", " <th>HYC</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>NE</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>0.141895</td>\n", " <td>0.122942</td>\n", " <td>NE</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>NE</td>\n", " </tr>\n", " <tr>\n", " <th>HYC</th>\n", " <td>0.327894</td>\n", " <td>NaN</td>\n", " <td>OK</td>\n", " </tr>\n", " <tr>\n", " <th>OTH</th>\n", " <td>0.359396</td>\n", " <td>NaN</td>\n", " <td>OK</td>\n", " </tr>\n", " <tr>\n", " <th>PEL</th>\n", " <td>0.098354</td>\n", " <td>0.089782</td>\n", " <td>OK</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>OK</td>\n", " </tr>\n", " <tr>\n", " <th>WWW</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>OK</td>\n", " </tr>\n", " <tr>\n", " <th>HYC</th>\n", " <td>0.688630</td>\n", " <td>NaN</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>0.053121</td>\n", " <td>0.062912</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>OOG</th>\n", " <td>0.132470</td>\n", " <td>NaN</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>OTH</th>\n", " <td>0.533980</td>\n", " <td>NaN</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>SUN</th>\n", " <td>0.247843</td>\n", " <td>NaN</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>WWW</th>\n", " <td>0.443587</td>\n", " <td>NaN</td>\n", " <td>TX</td>\n", " </tr>\n", " <tr>\n", " <th>GEO</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>NM</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>0.090467</td>\n", " <td>0.085517</td>\n", " <td>NM</td>\n", " </tr>\n", " <tr>\n", " <th>SUN</th>\n", " <td>0.638877</td>\n", " <td>NaN</td>\n", " <td>NM</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>NM</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>0.055065</td>\n", " <td>0.050793</td>\n", " <td>LA</td>\n", " </tr>\n", " <tr>\n", " <th>OOG</th>\n", " <td>0.100225</td>\n", " <td>NaN</td>\n", " <td>LA</td>\n", " </tr>\n", " <tr>\n", " <th>OTH</th>\n", " <td>0.580154</td>\n", " <td>NaN</td>\n", " <td>LA</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>0.930467</td>\n", " <td>NaN</td>\n", " <td>LA</td>\n", " </tr>\n", " <tr>\n", " <th>WWW</th>\n", " <td>0.516165</td>\n", " <td>NaN</td>\n", " <td>LA</td>\n", " </tr>\n", " <tr>\n", " <th>HYC</th>\n", " <td>0.202653</td>\n", " <td>NaN</td>\n", " <td>AR</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>AR</td>\n", " </tr>\n", " <tr>\n", " <th>WWW</th>\n", " <td>0.086687</td>\n", " <td>NaN</td>\n", " <td>AR</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>0.089319</td>\n", " <td>0.123133</td>\n", " <td>MO</td>\n", " </tr>\n", " <tr>\n", " <th>SUN</th>\n", " <td>0.860146</td>\n", " <td>NaN</td>\n", " <td>MO</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>MO</td>\n", " </tr>\n", " <tr>\n", " <th>HYC</th>\n", " <td>0.942665</td>\n", " <td>NaN</td>\n", " <td>MN</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>0.064268</td>\n", " <td>0.084587</td>\n", " <td>MN</td>\n", " </tr>\n", " <tr>\n", " <th>OTH</th>\n", " <td>0.555342</td>\n", " <td>NaN</td>\n", " <td>MN</td>\n", " </tr>\n", " <tr>\n", " <th>SUN</th>\n", " <td>0.794821</td>\n", " <td>NaN</td>\n", " <td>MN</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>0.711827</td>\n", " <td>NaN</td>\n", " <td>MN</td>\n", " </tr>\n", " <tr>\n", " <th>WND</th>\n", " <td>0.112260</td>\n", " <td>NaN</td>\n", " <td>MN</td>\n", " </tr>\n", " <tr>\n", " <th>WWW</th>\n", " <td>0.596931</td>\n", " <td>NaN</td>\n", " <td>MN</td>\n", " </tr>\n", " <tr>\n", " <th>NG</th>\n", " <td>0.060489</td>\n", " <td>0.059321</td>\n", " <td>IL</td>\n", " </tr>\n", " <tr>\n", " <th>OOG</th>\n", " <td>0.961357</td>\n", " <td>NaN</td>\n", " <td>IL</td>\n", " </tr>\n", " <tr>\n", " <th>OTH</th>\n", " <td>0.987482</td>\n", " <td>NaN</td>\n", " <td>IL</td>\n", " </tr>\n", " <tr>\n", " <th>PEL</th>\n", " <td>0.052267</td>\n", " <td>0.059331</td>\n", " <td>IL</td>\n", " </tr>\n", " <tr>\n", " <th>SUN</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>IL</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>IL</td>\n", " </tr>\n", " <tr>\n", " <th>HYC</th>\n", " <td>0.128468</td>\n", " <td>NaN</td>\n", " <td>KY</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>KY</td>\n", " </tr>\n", " <tr>\n", " <th>HYC</th>\n", " <td>1.139389</td>\n", " <td>NaN</td>\n", " <td>VA</td>\n", " </tr>\n", " <tr>\n", " <th>OTH</th>\n", " <td>0.164274</td>\n", " <td>NaN</td>\n", " <td>VA</td>\n", " </tr>\n", " <tr>\n", " <th>PEL</th>\n", " <td>0.055163</td>\n", " <td>0.055660</td>\n", " <td>VA</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>0.642315</td>\n", " <td>NaN</td>\n", " <td>VA</td>\n", " </tr>\n", " <tr>\n", " <th>WWW</th>\n", " <td>0.095183</td>\n", " <td>NaN</td>\n", " <td>VA</td>\n", " </tr>\n", " <tr>\n", " <th>HYC</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>FL</td>\n", " </tr>\n", " <tr>\n", " <th>OTH</th>\n", " <td>0.318424</td>\n", " <td>NaN</td>\n", " <td>FL</td>\n", " </tr>\n", " <tr>\n", " <th>SUN</th>\n", " <td>0.317072</td>\n", " <td>NaN</td>\n", " <td>FL</td>\n", " </tr>\n", " <tr>\n", " <th>WAS</th>\n", " <td>0.585390</td>\n", " <td>NaN</td>\n", " <td>FL</td>\n", " </tr>\n", " <tr>\n", " <th>WWW</th>\n", " <td>0.466676</td>\n", " <td>NaN</td>\n", " <td>FL</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " generation (MWh) elec fuel (mmbtu) state\n", "COW 0.057014 0.058988 WY\n", "HYC 0.095835 NaN WY\n", "NG 0.413092 0.508736 WY\n", "NG 0.212550 0.233675 SD\n", "HYC 1.000000 NaN NE\n", "NG 0.141895 0.122942 NE\n", "WAS 1.000000 NaN NE\n", "HYC 0.327894 NaN OK\n", "OTH 0.359396 NaN OK\n", "PEL 0.098354 0.089782 OK\n", "WAS 1.000000 NaN OK\n", "WWW 1.000000 NaN OK\n", "HYC 0.688630 NaN TX\n", "NG 0.053121 0.062912 TX\n", "OOG 0.132470 NaN TX\n", "OTH 0.533980 NaN TX\n", "SUN 0.247843 NaN TX\n", "WAS 1.000000 NaN TX\n", "WWW 0.443587 NaN TX\n", "GEO 1.000000 NaN NM\n", "NG 0.090467 0.085517 NM\n", "SUN 0.638877 NaN NM\n", "WAS 1.000000 NaN NM\n", "NG 0.055065 0.050793 LA\n", "OOG 0.100225 NaN LA\n", "OTH 0.580154 NaN LA\n", "WAS 0.930467 NaN LA\n", "WWW 0.516165 NaN LA\n", "HYC 0.202653 NaN AR\n", "WAS 1.000000 NaN AR\n", "WWW 0.086687 NaN AR\n", "NG 0.089319 0.123133 MO\n", "SUN 0.860146 NaN MO\n", "WAS 1.000000 NaN MO\n", "HYC 0.942665 NaN MN\n", "NG 0.064268 0.084587 MN\n", "OTH 0.555342 NaN MN\n", "SUN 0.794821 NaN MN\n", "WAS 0.711827 NaN MN\n", "WND 0.112260 NaN MN\n", "WWW 0.596931 NaN MN\n", "NG 0.060489 0.059321 IL\n", "OOG 0.961357 NaN IL\n", "OTH 0.987482 NaN IL\n", "PEL 0.052267 0.059331 IL\n", "SUN 1.000000 NaN IL\n", "WAS 1.000000 NaN IL\n", "HYC 0.128468 NaN KY\n", "WAS 1.000000 NaN KY\n", "HYC 1.139389 NaN VA\n", "OTH 0.164274 NaN VA\n", "PEL 0.055163 0.055660 VA\n", "WAS 0.642315 NaN VA\n", "WWW 0.095183 NaN VA\n", "HYC 1.000000 NaN FL\n", "OTH 0.318424 NaN FL\n", "SUN 0.317072 NaN FL\n", "WAS 0.585390 NaN FL\n", "WWW 0.466676 NaN FL" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.concat(error_list)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-3-clause
bosscha/alma-calibrator	notebooks/2mass/2MASS_test03_object_in_the_list_extended.ipynb	1	622949	{ "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 = 5000\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 = 1.5theta\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": [ { "name": "stdout", "output_type": "stream", "text": [ "WISE J161021.87-395858.4\n", "0.518 155293.0 242.59116 -39.98287\n", "1321.2981017765974 0.3252243727364208\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "[HB89] 1741-038\n", "1.054 315981.0 265.99523 -3.83462\n", "1717.70727627639 0.2501697188357098\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "3C 454.3\n", "0.859 257522.0 343.49062 16.14821\n", "1629.9307676687627 0.2636420852173554\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "PKS 0539-057\n", "0.839 251526.0 85.40868 -5.69706\n", "1618.0605034352159 0.26557619164166346\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "PKS 0601-70\n", "2.409 722200.0 90.29717 -70.60239\n", "1719.5229114516012 0.2499055659487283\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "SSTSL2 J113006.83-144912.6\n", "0.95 284803.0 172.52828 -14.82011\n", "1676.7426585476921 0.2562816328179527\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "NGC 4945\n", "0.001878 563.0 196.36449 -49.46821\n", "8.292149410020395 51.822311092083964\n", "error! maybe can not identify from name\n", "----\n", "[HB89] 0333+321 ABS01\n", "0.9532 285762.0 54.12516 32.30815\n", "1678.189933316063 0.2560606149621004\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "PKS 0003-066\n", "0.346676 103931.0 1.55789 -6.39315\n", "1044.0300496957655 0.41159576438756634\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "PKS 1830-21\n", "2.507 751580.0 278.4162 -21.06105\n", "1705.1650629156973 0.25200982338526984\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: NoResultsWarning: Query returned no results, so the table will be empty [astroquery.irsa.core]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "[HB89] 0234+285\n", "1.213 363648.0 39.46836 28.8025\n", "1759.843219104895 0.2441799028931021\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "[HB89] 0748+126\n", "0.889 266516.0 117.71686 12.51801\n", "1646.623423400248 0.26096941185298866\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "[HB89] 1749+096\n", "0.322 96533.0 267.88674 9.6502\n", "994.3201278022433 0.43217303394826034\n" ] }, { "data": { "image/png": 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hQ4eg1Wrh6emJP//8E15eXqojEVmFEAL9+/fHpk2bMHr0aAwbNozXAyGzsuSy0UUADABqCSFOCSH6AvgGwMtCiBQAL2ffhxBCK4SYCwBSyosAvgSwK/s2LvsxIpNs2LABer0eY8aMwYwZM7h1NjklX19f7Ny5EwcPHkRQUBCuXr2qOhI5CCFlrtMT7I5Wq5W8UA49TlhYGMaPH49ly5ahRYsWquMQKZeeno4hQ4Zgy5Yt3FKe8iSE2C2l1D7pOFuclElkNhkZGXjvvfcwa9YsbN26lWWCKFuhQoUwc+ZMDBgwAM2aNcPWrVtVRyI7x0JBDuvy5cvo0KEDjh07hm3btqFatWqqIxHZFCEEhgwZgvDwcHTu3Bm//PKL6khkx1goyCGdOnUKzZo1g4+PD37//Xdei4MoDwEBAdi8eTM+//xzjBs3Do5yKpysi4WCHE5ycjJatGiBPn36YPr06dxfgigf6tati23btmHFihUYNmwYsrKyVEciO8NCQQ5lz5490Ov1GD16NEaMGKE6DpFdcXd3R2xsLHbv3o3Q0FBkZGSojkR2hIWCHMaWLVvQvn17fP/99+jbt6/qOERWYzAYMGHCBBgMBpO/1zPPPIONGzfi7NmzeO2113jFUso3FgpyCGvXrkWXLl2wcOFCvPbaa6rjEFmNwWCAv78/Ro8eDX9/f7OUimLFimHVqlUoWrQoAgICuFcF5QsLBdm9xYsXo0+fPvj999/x8ssvq45DZFUxMTEwGo3IzMyE0WhETEyMWb6vRqPBwoULUbt2bbRp0wYXLlwwy/clx8VCQXZt8eLFeP/99/HHH3/gpZdeUh2HyOr0ej00Gg1cXV2h0Wig1+vN9r1dXV0xc+ZMtGnTBm3btsXFi9y0mB6P09/Jbi1duhTvv/8+Nm7ciHr16qmOQ6SETqdDVFQUYmJioNfrodPpzPr9hRCYOHEisrKy0LZtW0RFReHZZ58162uQY+DW22SXVq5ciXfffRcbNmxAgwYNVMchcnhSSgwfPhxxcXGIiori3i5OhFtvk8OKjIzEO++8g3Xr1rFMEFmJEALffvstdDodOnTogOvXr6uORDaGhYLsSlRUFN566y2sXr0aDRs2VB2HyKkIITB9+nT4+PigU6dOuHXrlupIZENYKMhu7Ny5EyEhIVi+fDmaNm2qOg6RU3JxccHs2bPh7u6Orl27cvMrysFCQXbh6NGj6NSpE8LDw+Hn56c6DpFTc3V1xU8//YT09HS8++67vPYHAWChIDtw7tw5BAQE4IsvvkBgYKDqOESEu5c/X7ZsGeLj4/H111+rjkM2gMtGyabdvHkTQUFB6NatGwYOHKg6DhHdp2TJkli7di2aNWuG5557Dr1791YdiRRioSCblZmZiZCQENSqVQtffvml6jhElItKlSohMjISer0eHh4e3K3WifGUB9kkKSUGDx6MmzdvYs6cORBCqI5ERI/h4+OD5cuXo2fPnti7d6/qOKQICwXZpGnTpmHr1q1Yvnw5NBqN6jhE9AQtW7bEf//7XwQFBSEtLU11HFKApzzI5kRHR2PixInYsWMHd+MjsiNvvPEGDh8+jDfeeAPR0dH8ZcDJcISCbMqJEyfQo0cP/Prrr6hSpYrqOET0lD777DOUK1cOw4YNUx2FrIyFgmzGzZs30blzZ3z88cdo06aN6jhEVAAuLi6YP38+oqOjMW/ePNVxyIp4yoNsgpQSAwYMQJ06dfibDZGdK1WqFCIiIuDn54cXXniBO9s6CY5QkE2YMWMGDh48iB9//JErOogcQO3atTF37ly8/vrrOH36tOo4ZAUcoSDltmzZggkTJmD79u0oVqyY6jhEZCavvvoqEhIS0LVrV0RHR8PNjW85jowjFKTUpUuX8OabbyI8PBxeXl6q4xCRmY0ZMwaFCxfG+PHjVUchC2OhIGWklOjfvz86d+6MDh06qI5DRBZwb5LmrFmz8Oeff6qOQxbEQkHKzJ07F0ePHsU333yjOgoRWVClSpUwZ84cvPnmm7h8+bLqOGQhLBSkxOHDh/Hpp59i0aJFKFKkiOo4RGRhQUFBCAoKwsCBA3m5cwfFQkFWd+fOHYSEhGD8+PHw8fFRHYeIrGTy5Mk4dOgQfvrpJ9VRyAJYKMjqRo4cCW9vb/Tv3191FCKyoiJFimDx4sX46KOPkJycrDoOmRkLBVnVli1bsGTJEl5BlMhJ1a1bF2PGjEFoaCiysrJUxyEzYqEgq7l16xb69euHsLAwlClTRnUcIlLkvffeg4uLC/773/+qjkJmxEJBVjNu3Dg0aNAAnTt3Vh2lwAwGAyZMmACDwaA6CpHdcnFxwdy5czF27Fj8/fffquOQmXDbMrKKhIQEhIeHY9++faqjFJjBYIC/vz+MRiM0Gg2ioqKg0+lUxyKyS7Vq1cKIESMwcOBArF+/nqdAHQBHKMji0tPT0bdvX0yZMgUVK1ZUHafAYmJiYDQakZmZCaPRiJiYGNWRiOzaiBEjcP78efz888+qo5AZsFCQxU2ePBnu7u548803VUcxiV6vh0ajgaurKzQaDfR6vepIRHbNzc0N8+bNw0cffcQLiDkA4SgbjGi1WhkfH686Bj0kOTkZzZs3x+7du+Hp6ak6jskMBgNiYmKg1+t5uoPITEaNGoWUlBQsXbpUdRTKhRBit5RS+8TjWCjIkjp27IjWrVtjxIgRqqMQkY26desW6tSpg/DwcLRu3Vp1HHpIfgsFT3mQxURGRuLo0aMYMmSI6ihEZMOKFi2KKVOmYOjQocjIyFAdhwqIhYIswmg0YtiwYZg6dSo0Gs0Dz3HpJRE9rEuXLihXrhxmz56tOgoVEJeNkkVMnz4dNWrUeOSy5Fx6SUS5EUJg+vTp8Pf3R/fu3VG2bFnVkegpcYSCzO706dOYOHEipk6d+shzXHpJRI9Tr149dO3aFWPGjFEdhQqAhYLMbuTIkejTpw9q1qz5yHNceklEeRk3bhyWL1+OxMRE1VHoKfGUB5nVnj17sH79eiQlJeX6vE6nQ1RUFJdeElGuypQpg88//xzDhw/HH3/8oToOPQUuGyWz6tixI1555RUMHjxYdRQislPp6enw8fHBnDlzuIzUBnDZKFndtm3bcODAAQwYMEB1FCKyY4UKFcIXX3yBUaNGwVF+6XUGLBRkFlJKjBo1CmPGjEHhwoVVxyEiOxcSEoIrV64gMjJSdRTKJxYKMouoqCj8+++/6N27t+ooROQAXF1d8eWXX+Kzzz5DVlaW6jiUDywUZDIpJT799FOMGzcObm6c50tE5tG5c2e4urpixYoVqqNQPrBQkMlWr16NO3fuoGvXrqqjEJEDEUJg/PjxGDNmDDIzM1XHoSdgoSCTSCkxduxYjBs3Di4u/HEiIvNq164dypUrhyVLlqiOQk/AdwAySVRUFO7cuYOgoCDVUYjIAQkhMHLkSEyaNIkrPmwcCwWZZNKkSfjwww85OkFEFhMQEIDMzExs2rRJdRTKA98FqMD27t2LQ4cOoUePHqqjEJEDE0Lgww8/xOTJk1VHoTywUFCBTZ48GcOGDXvk8uRERObWvXt3HDlyBAkJCaqj0GOwUFCB/P3339iwYQN3xSQiq9BoNBg2bBhHKWwYCwUVyNSpU9GvXz+UKlVKdRQichL9+/fHpk2bcPz4cdVRKBcsFPTULl++jF9++QVDhgxRHYWInEipUqXQr18/TJs2TXUUygULBT21X375Be3atYOHh4fqKETkZN555x0sWLAAN2/eVB2FHsJCQU9FSonZs2dj4MCBqqMQkROqUqUKXnrpJSxdulR1FHoICwU9lW3btiE9PR16vV51FCJyUgMHDsTs2bNVx6CHsFDQU7k3OiGEUB2FiJxUhw4dcOrUKSQmJqqOQvdhoaB8u3DhAlavXq30EuUGgwETJkyAwWBQloHoYfy5tC43Nzf069ePoxQ2hteapnybP38+goKCULZsWSWvbzAY4O/vD6PRCI1Gg6ioKOh0OiVZiO7hz6Ua/fr1Q7169TBp0iSUKFFCdRyCohEKIcRQIcQBIcRBIcSwXJ4vLYT4XQiRmH1MqIqc9P+klPjxxx+VTsaMiYmB0WhEZmYmjEYjYmJilGUhuoc/l2pUrlwZfn5+vAqpDbF6oRBCvACgP4AXATQAECiEqPHQYe8BOCSlbABAD+BbIQT3d1YoISEB6enpaN68ubIMer0eGo0Grq6u0Gg0nBhKNoE/l+q89dZbWLBggeoYlE3FKQ8fANullDcBQAgRC6AzgEn3HSMBlBR3Z/6VAHARQIa1g9L/W7RoEUJCQpROxtTpdIiKikJMTAz0er1dDSsbDAa7zE1PZs8/l/YuICAAffr0wb///ovKlSurjuP0hLWvLy+E8AGwCoAOwC0AUQDipZSD7zumJIDVAGoDKAmgm5RybS7fawCAAQDg6enZ+MSJE5b/AzihrKwseHp6YuPGjahTp47qOHaH59iJLKdPnz6oV68e3n//fdVRHJYQYreUUvuk46x+ykNKeRjARACbAKwHkIhHRx/aA9gLwAOAL4AwIcQjF42QUv4opdRKKbXly5e3bHAntmXLFpQrV45looB4jp3Icnr06IFff/1VdQyCokmZUsp5UspGUko/3D2dkfLQIaEAVsq7jgI4jrujFaTAr7/+ipCQENUx7BbPsRNZTuvWrfHPP/8gJeXhtxGyNlWrPCpkf/QE0AXAoocOOQnAP/uYigBqAThmzYx0l9FoxIoVK9C9e3fVUezWvXPsX375JU93EJmZq6srunbtisWLF6uO4vRU7UOxQghRFkA6gPeklJeEEG8DgJTyBwBfAvhJCLEfgADwsZTyvKKsTm3jxo2oXbs2qlSpojqKXdPpdCwSRBbSo0cPhIaG4rPPPuMuvgopKRRSypa5PPbDfZ+nAmhn1VCUqxUrVqBr166qYxARPVbTpk1x8+ZNHD58mHO9FOLW2/RYWVlZiIyMRGBgoOooRESPJYRAYGAg1q59ZDEgWRELBT1WfHw8ypUrh2rVqqmOQkSUp8DAQKxZs0Z1DKfGQkGPtWbNGo5OEJFdaN26Nfbu3YuLFy+qjuK0WCjosVgoiMheFClSBHq9Hhs2bFAdxWmxUFCu/v33X5w4cYIrE4hImae9LHzHjh152kMhXr6cchUZGYn27dvDzY0/IkRkfQXZsr5jx44YOXIkMjIy+G+XAhyhoFytXbsWHTt2VB2DiJxUQbasr1y5MqpUqZLvEQ0yLxYKekRmZiZiY2Ph7++vOgoROamCblnv7+/P6+UowkJBj9i/fz8qVqwId3d31VGIyEkVdMv6Vq1aITY21sLpKDc8yUSPiI2NhZ+fn+oYROTkCrJlfYsWLRASEpIz94KshyMU9IjY2Fi0atVKdQwioqf2zDPPoHr16oiPj1cdxemwUNADpJSIi4vjCAUR2a1WrVohLi5OdQynw0JBDzh06BBKlSqF559/XnUUIqIC8fPz4zwKBVgo6AE83UFE9s7Pzw/btm1DRkaG6ihOhYWCHrB161a0bPnI1eWJiOxGuXLlULlyZezfv191FKfCQkEPSEhIgFarVR2DiMgkWq0WCQkJqmM4FRYKynH9+nWcOHECPj4+qqMQEZmkUaNG2LNnj+oYToWFgnIkJibihRdeQKFChVRHISIySaNGjThCYWUsFJQjISEBDRs2VB2DiMhkvr6+2LdvHzIzM1VHcRosFJQjISEBjRo1Uh2DiMhkpUqVQqVKlZCUlKQ6itNgoaAce/bsYaEgIofB0x7WxUJBAIDbt28jOTkZ9erVUx2FiMgsWCisi4WCAABJSUnw8vJCkSJFVEchIjKL+vXr48CBA6pjOA0WCgIApKSkoGbNmqpjEBGZTY0aNZCSkqI6htNgoSAALBRE5Hi8vLyQlpaGO3fuqI7iFFgoCMDdQlGjRg3VMYiIzMbNzQ1VqlTBsWPHVEdxCiwUBABITk5moSAih1OjRg0kJyerjuEU3PJ6UgjxHIDuAFoC8ABwC8ABAGsBrJNSZlk8IVkFRyiIyBFxHoX1PHaEQgjxPwDhAIwAJgIIAfAugD8AvALgTyGEnzVCkmVdvXoVN27cgIeHh+ooRERmVbNmTRYKK8lrhOJbKWVu620OAFgphNAA8LRMLLKmlJT7gzcpAAAgAElEQVQUVK9eHUII1VGIiMyqRo0aWLp0qeoYTuGxIxSPKRP3P2+UUh41fySytpMnT6JKlSqqYxARmV2VKlWQnJyMCRMmwGAwqI7j0PKcQwEAQoj9AORDD18BEA/gKynlBUsEI+s5ffo0KlWqpDoGEZHZnTx5EqmpqRg9ejQ0Gg2ioqKg0+lUx3JI+VnlsQ53J2H2zL79DiAOwGkAP1ksGVlNWloaCwUROaSdO3cCADIzM2E0GhETE6M2kAN74ggFgOZSyub33d8vhNgqpWwuhHjTUsHIetLS0qDValXHICIyO71eDyEEXFxcoNFooNfrVUdyWPkZoSghhGh6744Q4kUAJbLvZlgkFVkVRyiIyFHpdDrUr18foaGhPN1hYfkZoegHIFwIca9EXAPQTwhRHMAEiyUjq+EcCiJyZLVq1YK/vz/LhIU9sVBIKXcBqCeEKA1ASCkv3/c01+I4gLS0NLi7u6uOQURkEe7u7khLS1Mdw+E98ZSHEKKiEGIegMVSystCiDpCiL5WyEZWkJWVhXPnzqFixYqqoxARWUSlSpVYKKwgP3MofgKwAXe33gaAZADDLBWIrOvatWsoUqQINBqN6ihERBZRpkwZXLp0SXUMh5efQlFOSrkUQBYASCkzAGRaNBVZzfXr11GiRIknH0hEZKdKlCiB69evq47h8PJTKG4IIcoie3MrIcRLuLuxFTmAa9euoWTJkgX6WoPBwN3niMjmlSxZkoXCCvKzyuMDAKsBeAshtgIoD+B1i6YiqynoCIXBYIC/vz+MRiN3nyMim1aiRAlcu3ZNdQyH98QRCillAoBWAJoBGAigrpRyn6WDkXUUtFDExMTAaDRy9zkisnk85WEdjx2hEEJ0ecxTNYUQkFKutFAmsqKCnvLQ6/XQaDQ5IxTcfY6IbBVPeVhHXqc8grI/VsDd0Yno7PutAcQAYKFwAAUdodDpdIiKikJMTAz0ej1PdxCRzeIIhXU8tlBIKUMBQAixBkAdKWVa9v1KAP5rnXhkaaas8tDpdCwSRGTzSpYsyTkUVpCfVR5e98pEtjMAalooD1mZ0WhE4cKFVccgsmtc8WTb7p2eJcvKzyqPGCHEBgCLcHfpaHcAmy2aiqwmKysLLi756ZVEZrBvH7ByJXDyJODpCXTpAtSvrzqVSbjiyfa5uLggKytLdQyHl59VHoMA/ACgAQBfAD9KKQdbOhhZBwsFWc2+fcCUKcClS8Bzz939OGXK3cftGFc82T4WCuvIa5WHkFJKAJBS/gbgt7yOIfuUlZUFIYTqGOQMVq4Enn327g34/48rV9r1KAVXPNk+FxcXZGZyg2dLy+uUx2YhxAoAq6SUJ+89KITQAGgBoDfunvr4yaIJyaJYJshqTp68OzJxv9Kl7z5ux7jiyfZJKTkSawV5FYpXAPQBsEgIURXAZQBFcfc0yUYAU6WUey0fkSyJQ4FkNZ6ed09z3BuZAIArV+4+bue44sm28dSudeS1bPQ2gJkAZgohCgEoB+CWlPKytcKR5bFQkNV06XJ3zgRwd2TiypW7BaNvX7W5yOGxUFhHvv6GpZTpUso0lgnHw3OLZDX16wMjRtwdoTh16u7HESPsev4E2QfOFbOO/CwbJQdWpEgR3L59W3UMchb167NAkNXdunULxYoVUx3D4XEMyMlxS1oicnSm7AhM+ffYQiGEqC6EaJ7L4y2FEN6WjUXWwi1picjRFfQiiPR08hqhmAYgt3eaW9nPkQPgCAUROTqOUFhHXoXCS0r5yBZ2Usp4AF4WS0RWxREKInJ0165dY6GwgrwKRZE8nitq7iCkBkcoiMjRcYTCOvIqFLuEEP0fflAI0RfAbstFImsqUaIERyiIyKFxDoV15LVsdBiA34QQPfH/BUILQAOgs6WDkXXwlAcROTqe8rCOvHbKPAOgmRCiNYAXsh9eK6WMtkoysopixYpBCMEhQSJyWOfPn0f58uVVx3B4eS0bbQMAUsrNANZIKb+/VyaEEF2slI8sTAgBd3d3nD59WnUUIiKLSEtLg7u7u+oYDi+vORRT7vt8xUPPfWaBLKRIpUqVkJaWpjoGEZFFpKWloVKlSqpjOLy8CoV4zOe53X8qQoihQogDQoiDQohhjzlGL4TYm31MrCmvR3mrVKkSRyiIyGGdPn2ahcIK8pqUKR/zeW73800I8QKA/gBeBGAEsF4IsVZKmXLfMc/g7pVOX5FSnhRCVCjo69GTcYSCiBwZRyisI69CUU0IsRp3RyPufY7s+1VNeE0fANullDcBIHv0oTOASfcd0wPASinlSQCQUp414fXoCdzd3VkoiMghSSlx5swZVKxYUXUUh5dXoeh03+dTHnru4ftP4wCA8UKIsri7jXcHAPEPHVMTQCEhRAyAkgCmSynnm/CalIdKlSohLi5OdQwiIrO7cOECihcvjiJF8tqrkcwhr2WjOfMWhBDlsx87Z+oLSikPCyEmAtgE4DqARAAZueRqDMAfd3flNAghtkspk+8/SAgxAMAAAPD09DQ1mtPiKQ8iclQ83WE9eS0bFUKIz4UQ5wEcAZAshDgnhBhj6otKKedJKRtJKf0AXASQ8tAhpwCsl1LekFKeBxAHoEEu3+dHKaVWSqnlGuOCq1q1Ko4dO6Y6BhGR2R07dgzVqlVTHcMp5LXKYxiAFgCaSCnLSimfBdAUQHMhxPumvOi9SZZCCE8AXQAseuiQVQBaCiHchBDFsl/3sCmvSY9XrVo1nDx5Eunp6aqjEBGZVXJyMmrUqKE6hlPIq1D0AhAipTx+7wEp5TEAb2Y/Z4oVQohDAH4H8J6U8pIQ4m0hxNvZr3MYwHoA+wDsBDBXSnnAxNekx9BoNKhcuTKOHz/+5IOJyC4YDAZMmDABBoNBdRSlUlJSWCisJK9JmYWyTzc8QEp5TghRyJQXlVK2zOWxHx66PxnAZFNeh/KvRo0aSElJQc2aNVVHISITGQwG+Pv7w2g0QqPRICoqCjqdTnUsJVJSUtC1a1fVMZxCXiMUxgI+R3aoZs2aSEl5eCoLEdmjmJgYGI1GZGZmwmg0IiYmRnUkZfiLkvXkNULRQAhxNZfHBQCuv3EwNWrUwJEjR1THICIz0Ov10Gg0OSMUer1edSQlbty4gYsXL+K5555THcUp5LVs1NWaQUitGjVq4Pfff1cdg4jMQKfTISoqCjExMdDr9U57uuPo0aOoVq0aXFzyGownc8lrhIKcSM2aNZGUlKQ6BhGZiU6nc9oicU9SUhJPd1gRaxsBuLsXxeXLl3HhwgXVUYiIzGLv3r1o0OCRLYzIQlgoCADg4uKChg0bYs+ePaqjEBGZRUJCAho1aqQ6htNgoaAcDRs2REJCguoYREQmk1KyUFgZCwXlaNSoEUcoiMghpKamQggBDw8P1VGcBgsF5WjUqBFHKIjIIdwbnRBCqI7iNFgoKEft2rVx6tQpXL2a2/YjRET2IyEhAQ0bNlQdw6mwUFAONzc31KtXD4mJiaqjEBGZhPMnrI+Fgh6g1WqxY8cO1TGIiApMSomdO3eicePGqqM4FRYKekDLli0RFxenOgYRUYGlpKTAzc0NXl5eqqM4FRYKekCrVq3w559/IisrS3UUIqICiYuLQ6tWrTgh08pYKOgB7u7uKF++PPbv3686ChFRgcTGxsLPz091DKfDQkGP8PPzQ2xsrOoYRERPTUqJ2NhYtGrVSnUUp8NCQY9o1aoV51EQkV06ceIEjEYjLwqmAAsFPeJeoZBSqo5CRPRU7o1OcP6E9bFQ0COef/55FC9eHIcPH1YdhYjoqXD+hDosFJSrl19+GRs2bHjkcYPBgAkTJsBgMChIRUT0eFJKbNiwAW3btlUdxSmxUFCuAgMDsWbNmgceMxgM8Pf3x+jRo+Hv789SQUQ2Ze/evShevDhq1aqlOopTYqGgXPn7+2PXrl0PXNcjJiYGRqMRmZmZMBqNiImJUReQiOgha9euRWBgoOoYTouFgnJVvHhxNG/eHBs3bsx5TK/XQ6PRwNXVFRqNBnq9Xl1AIqKHrFmzhoVCIRYKeqyHT3vodDpERUXhyy+/RFRUFHQ6ncJ0RET/78yZM0hKSkKLFi1UR3FabqoDkO3q2LEjxo4di8zMTLi6ugK4WypYJIjI1qxbtw5t27aFRqNRHcVpcYSCHsvLywsVKlTArl27VEchIsoTT3eox0JBeQoKCsKqVatUxyAieqxbt24hKioKAQEBqqM4NRYKylPXrl2xZMkS7ppJRDYrMjISjRs3RoUKFVRHcWosFJQnX19fFC5cGDt27FAdhYgoV4sWLUJISIjqGE6PhYLyJIRASEgIfv31V9VRiIgeceXKFWzatAldunRRHcXpsVDQE4WEhGDp0qXIyMhQHYWI6AERERFo3bo1nn32WdVRnB4LBT1RjRo18Pzzz3NnTCKyOTzdYTtYKChfevTowdMeRGRTzp49i+3btyMoKEh1FAILBeVTt27dEBERgdu3b6uOQkQEAFi2bBkCAwNRrFgx1VEILBSUTx4eHmjSpAlWrFihOgoREaSUmDt3Lnr37q06CmVjoaB8GzhwIGbPnq06BhFRztWQ/f39VUehbCwUlG9BQUE4evQoDh48qDoKETm5H374AQMGDICLC9/GbAX/S1C+FSpUCH379sWPP/6oOgoRObHLly/jt99+Q2hoqOoodB8WCnoq/fr1w8KFC3Hz5k3VUYjISf3yyy9o3749t9q2MSwU9FSqVKmCpk2bYunSpaqjEJETklJi9uzZGDhwoOoo9BAWCnpqb7/9NidnEpESW7duRXp6OvR6veoo9BAWCnpqHTp0QFpaGnbu3Kk6ChE5mRkzZuCdd96BEEJ1FHoICwU9NVdXV3zwwQeYPHmy6ihE5ESOHj2KzZs3o1+/fqqjUC5YKKhA+vbti5iYGBw9elR1FCJyEt999x0GDhyIEiVKqI5CuWChoAIpXrw43nnnHXz77beqoxCREzh79iwWLVqEwYMHq45Cj8FCQQU2aNAgLFmyBGfOnFEdhYgcXFhYGLp164aKFSuqjkKPwUJBBVahQgV0794dYWFhqqMQkQO7fv06Zs2aheHDh6uOQnlgoSCTfPDBB/jhhx9w/fp11VGIyEGFh4ejVatWqFGjhuoolAcWCjJJ9erV0aZNG8ycOVN1FCJyQLdv38aUKVPw0UcfqY5CT8BCQSb7/PPPMWXKFFy5ckV1FCJyMLNnz4avry9efPFF1VHoCVgoyGR16tTBK6+8gqlTp6qOQkQO5Pr165gwYQK+/PJL1VEoH1goyCy++OILfP/99zh//rzqKETkIGbMmAG9Xo8GDRqojkL5wEJBZlGtWjV07doVEydOVB2FiBzA5cuXMXXqVIwdO1Z1FMonFgoym88++wzz5s1Damqq6ihEZOemTJmCoKAg1KpVS3UUyicWCjKbypUrIzQ0FOPHj1cdhYjs2NmzZzFr1iyMGTNGdRR6CiwUZFaffPIJli5diiNHjqiOQkR26osvvkDPnj3h5eWlOgo9BTfVAcixlC9fHiNHjsT777+PyMhIXmKYiJ5KYmIiVqxYgcOHD6uOQk+JIxRkdoMGDcLx48exdu1a1VGIyI5IKTF06FCMHTsWZcqUUR2HnhILBZmdRqPBtGnTMGzYMNy5c0d1HCKyE8uWLcPly5fRv39/1VGoAFgoyCJeeeUV1KlTB9OmTVMdhYjswM2bN/Hhhx9ixowZcHV1VR2HCoCFgizmu+++w+TJk5GWlqY6ChHZuEmTJkGn08HPz091FCogFgqymOrVq6Nfv374+OOPVUchIht24sQJhIWFYfLkyaqjkAlYKMiiRo0ahdjYWPzxxx+qoxCRDZJS4u2338YHH3yA559/XnUcMgELBVlUyZIl8cMPP2DAgAG4ceOG6jhEZGMWLFiAtLQ0fPjhh6qjkIlYKMjiAgIC0KJFC4waNUp1FCKyIWfOnMGIESMQHh6OQoUKqY5DJmKhIKuYOnUqlixZAoPBoDoKEdmIwYMHIzQ0FI0aNVIdhcxASaEQQgwVQhwQQhwUQgzL47gmQohMIcTr1sxH5le2bFnMmDEDffv25d4URITffvsNiYmJ+Pzzz1VHITOxeqEQQrwAoD+AFwE0ABAohKiRy3GuACYC2GDdhGQpr7/+OmrVqoWvvvpKdRQiUujSpUsYNGgQ5s6di6JFi6qOQ2aiYoTCB8B2KeVNKWUGgFgAnXM5bjCAFQDOWjMcWY4QAjNnzsTs2bOxe/du1XGISJGhQ4ciODgYLVu2VB2FzEhFoTgAwE8IUVYIUQxABwAPrBUSQlTG3ZLxQ17fSAgxQAgRL4SIP3funMUCk/lUqlQJM2bMQEhICK5fv646DhFZ2a+//oqdO3di0qRJqqOQmVm9UEgpD+PuqYxNANYDSASQ8dBh0wB8LKXMfML3+lFKqZVSasuXL2+RvGR+3bt3R/PmzTFkyBDVUYjIio4fP45hw4Zh0aJFKF68uOo4ZGZKJmVKKedJKRtJKf0AXASQ8tAhWgCLhRB/A3gdwEwhRLCVY5IFff/99/jzzz+xZMkS1VGIyArS09PRo0cPjBw5Eg0bNlQdhyzATcWLCiEqSCnPCiE8AXQBoLv/eSll1fuO/QnAGillhHVTkiWVKFECixYtQkBAAJo2bQovLy/VkYjIgsaNG4fSpUtj6NChqqOQhSgpFABWCCHKAkgH8J6U8pIQ4m0AkFLmOW+CHEfjxo3x0UcfoWfPnoiNjYWbm6ofRyKypNjYWMydOxd79+6Fiwu3P3JUQkqpOoNZaLVaGR8frzoGPaWsrCy88soreOmllzBu3DjVcYjIzC5cuICGDRti9uzZCAgIUB2HCkAIsVtKqX3ScayKpJSLiwvmz5+P8PBwrFmzRnUcIjKjjIwMdO/eHd27d2eZcAIsFKScu7s7li1bhj59+iA5OVl1HCIyk08//RQA8PXXXytOQtbAQkE2QafT4auvvkJwcDCuXbumOg4RmWjJkiVYvnw5Fi9ezPlRToKFgmzGgAED0LJlS/Tu3RtZWVmq4xBRASUmJmLQoEFYuXIlypYtqzoOWQkLBdmUGTNmIC0tDRMmTFAdhYgK4OLFi+jSpQumT58OX19f1XHIijgORTalcOHCWLFiBZo0aQJfX1907NhRdSQiyqeMjAyEhIQgODgYPXr0UB2HrIwjFGRzPDw8sHz5coSGhmLPnj2q4xBRPkgpMWjQIEgpMXHiRNVxSAEWCrJJOp0Os2bNQlBQEE6cOKE6DhE9wTfffIPt27dj+fLlnITppPhfnWzWa6+9hlOnTiEgIABbt27Fs88+qzoSEeViwYIFmD17NrZt24ZSpUqpjkOKcISCbNrQoUMREBCA4OBg3L59W3UcInpIVFQUhg8fjrVr18LDw0N1HFKIhYJs3uTJk+Hu7s7lpEQ2Zt++fQgJCcHSpUtRt25d1XFIMRYKsnkuLi74+eefkZaWhg8//BCOcv0ZInv2zz//IDAwEN9//z1atWqlOg7ZABYKsgtFihRBREQENm3ahK+++kp1HCKndvr0afj7++ODDz5At27dVMchG8FJmWQ3ypQpg02bNsHPzw/FihXD8OHDVUcicjrnz59H27Zt0atXLwwbNkx1HLIhLBRkVypWrIioqCj4+fmhSJEieO+991RHInIaly5dQvv27REUFIRRo0apjkM2hoWC7M5zzz2HqKgo6PV6uLm5YeDAgaojETm8y5cvo127dvDz88PXX38NIYTqSGRjWCjILlWtWhXR0dFo3bo1XFxc0L9/f9WRiBzWlStX0L59ezRr1gzfffcdywTlioWC7Ja3tzeio6PRpk0bZGZm4u2331YdicjhXLx4EQEBAXjxxRcxbdo0lgl6LBYKsmvVq1fH5s2b8fLLL+PSpUv45JNP+A8ekZmkpqaiffv2aN++PSZPnsz/tyhPXDZKds/b2xt//vknFi5ciI8++oj7VBCZwV9//YWWLVuiR48eLBOULywU5BA8PDwQFxeHLVu2oH///sjMzFQdichu7d+/H61atcKHH36IkSNHskxQvrBQkMMoU6YM/vjjD5w4cQLdunXDnTt3VEcisjvbtm1D27Zt8e2333JeEj0VFgpyKCVKlMCaNWsAAIGBgbh27ZriRET2Y/369ejUqRN++ukn7oBJT42FghxO4cKFsWTJEnh7e6N58+Y4ceKE6khENm/mzJl46623EBERgYCAANVxyA6xUJBDcnV1xaxZs9CnTx/odDoYDAbVkYhsUkZGBgYPHoywsDBs3boVzZs3Vx2J7BQLBTksIQSGDRuGuXPnolOnTli4cKHqSEQ25cqVKwgMDERycjIMBgO8vb1VRyI7xkJBDq9Dhw6Ijo7GZ599hjFjxiArK0t1JCLljh07Bp1Oh+rVq2Pt2rUoXbq06khk51goyCm88MIL2LFjB6KiotC9e3fcuHFDdSQiZeLi4tCsWTO89957CAsLg5sb9zgk07FQkNOoUKECoqKiULx4cbz44os4fPiw6khEViWlxOTJk/HGG29g/vz5vFovmRULBTmVIkWKIDw8HMOHD4efnx/nVZDTuHjxIjp16oQVK1Zg165daNeunepI5GBYKMjpCCHQp08f/PHHHxg7diwGDhyI27dvq45FZDG7du1C48aN4e3tjbi4OHh6eqqORA6IhYKcVoMGDRAfH49Lly5Bp9Phr7/+Uh2JyKyklAgLC0OHDh0wZcoUTJ06FRqNRnUsclAsFOTUSpUqhSVLlqBv377Q6XRYvHix6khEZnHhwgV07doV8+bNg8FgwGuvvaY6Ejk4FgpyekIIDBo0CJGRkRg7diy6deuGCxcuqI5FVGCRkZGoX78+KleujG3btqF69eqqI5ETYKEgyqbVapGQkAAPDw/Ur18fa9euVR2J6Klcu3YNAwYMwLvvvosFCxZg2rRpKFq0qOpY5CRYKIjuU7RoUUydOhULFy7EoEGD0K9fP1y9elV1LKIniouLQ4MGDZCZmYl9+/ahdevWqiORk2GhIMqFXq9HYmIigLuTN2NiYtQGInqMW7duYcSIEejevTumT5+OefPmoVSpUqpjkRNioSB6jFKlSmHu3Ln4/vvv8Z///AehoaE4d+6c6lhEOSIjI1G3bl2cOnUK+/btQ1BQkOpI5MRYKIieIDAwEIcOHcKzzz6LunXrYs6cObweCCl16tQpvP766xgyZAhmzpyJxYsXo1y5cqpjkZNjoSDKh5IlS+K7777Dxo0bER4ejhYtWuScEiGyloyMDHz33Xfw9fVF3bp1sX//frzyyiuqYxEBYKEgeiq+vr7YunUr3nrrLbz88sv44IMPcO3aNdWxyAkYDAZotVpERkZi27ZtGDt2LFdwkE1hoSB6Si4uLhgwYAAOHDiAS5cuoUaNGpg5cybS09NVRyMHdPToUXTt2hVvvPEGPv74Y2zatAk1a9ZUHYvoESwURAVUoUIF/O9//8O6devw22+/oW7duli5ciWklKqjkQM4d+4chg4dipdeegm+vr5ITk5GSEgIhBCqoxHlioWCyEQNGzbEpk2bEBYWhrFjx6J58+bYunWr6lhkp27evIkJEybAx8cHUkocPnwYn376KYoVK6Y6GlGeWCiIzKRdu3ZISEjA22+/jZCQEHTp0gX79+9XHYvshNFoxJw5c1CrVi3s2bMH27dvx4wZM1C+fHnV0YjyhYWCyIxcXV3Rq1cvJCUloVmzZmjXrh2Cg4MRHx+vOhrZqFu3biEsLAzVq1fH8uXLsWzZMixdupTX3yC7w0JBZAFFixbFiBEjcOzYMfj7+6Nz584ICAjgqRDKcf36dXz77bfw9vbGpk2bsHz5cmzYsAEvvfSS6mhEBcJCQWRBRYsWxeDBg3H06FF06dIF//nPf9C6dWv88ccfnLzppC5fvozx48ejWrVq2LlzJ9atW4dVq1bhxRdfVB2NyCQsFERWULhwYfTv3x/Jycno06cPBg8eDF9fX8yZMwc3b95UHY+s4NChQ3jnnXdQtWpVJCUlITY2FkuWLEGDBg1URyMyCxYKIityc3PDf/7zHxw8eBCTJ0/G6tWr4enpiREjRuD48eOq45GZZWZmIiIiAv7+/vD390eFChVw8OBBzJ8/Hz4+PqrjEZkVCwWRAi4uLmjXrh1+//137Ny5EwDQpEkTvPrqq9i0aROvFWLnLly4gEmTJsHb2xsTJ05Enz59cOLECYwdOxYeHh6q4xFZBAsFkWLVqlXDlClTcOLECQQGBmLEiBGoVq0aRo8ejeTkZNXxKJ+MRiMiIiLQpUsXVKtWDQcPHsTy5cthMBjQs2dPaDQa1RGJLIqFgshGFC9eHAMGDMDevXsRERGBmzdvws/PDzqdDrNmzcLFixdVR6SHSCmxc+dODBo0CJUrV8bUqVPRsWNHnDx5Ej///DO0Wq3qiERWIxxlprlWq5Vc60+OJiMjA5s2bcL8+fOxbt06+Pv7o1u3bggICEDJkiVVx3NK93avXLlyJRYsWIDMzEz06tULb775JqpWrao6HpHZCSF2Symf2I5ZKIjsxJUrV7Bs2TKsWLECW7duRYsWLRAcHIxXX30V7u7uquM5tKysLGzfvh0RERGIiIjArVu30KlTJ/Ts2RMvvfQSr69BDo2FgsiBXblyBevXr0dERATWr18PHx8fBAcHIygoCLVr1+YbnBncuHEDMTExWLVqFVavXo0KFSqgU6dOCA4ORqNGjfh3TE6DhYLISRiNRsTExCAiIgJr165Feno62rRpk3Pz8vJSHdEu3LlzBzt27EB0dDSio6ORkJCAxo0bo1OnTujUqRO8vb1VRyRSgoWCyAlJKXH8+PGcN8Xo6GgUK1YMbdq0QevWraHT6VC1alX+do27V/Xcu3cv4uLiEB0dDYPBAB8fn5wi1rx5cxQvXlx1TCLlWCiIKGcC4b1ysXPnTty6dQtarRZNmjTJ+ejh4eHQJcNoNGL//v3YtWsX4uPjsQeYVfIAAAwaSURBVGvXLqSkpMDHxwctW7ZEmzZt4Ofnh2eeeUZ1VCKbw0JBRLlKTU3F7t27H3hzdXNzQ6NGjVC7dm3UqlUr51axYkW7KhpGoxFHjx5FUlJSzu3gwYM4cOAAvL29c0qUVqtF/fr1UaRIEdWRiWweCwUR5YuUEidPnsTevXtz3oSPHDmCpKQkZGRkoGbNmqhVqxaqVq0KDw+PB24VK1aEq6ur1bLeuHEDqampSE1Nxb///pvzMSUlBUlJSfjnn3/g6emZU4hq164NHx8f+Pr68vQFUQGxUBCRyS5cuJBTME6ePJnzZn7vdvHiRZQvXx6VKlVC6dKlUbJkyUdupUqVQvHixeHq6goXFxe4uLhACIGsrKycm9FoxLVr1x64Xb16Nefz8+fPIzU1Ff/X3v0HbVbWdRx/f5JFWKk1F0nYBRZ2BXNoBVypJkBBR0eGsdBKkHFoJmwiK5HMmSbHappmKp2csdGIpBmnVPwBlKlhOCOKP1bbCNYlCBDJyA1WoTUWil332x/nPDM3j8+v3es+9/082/s1c2bv5/y47uv6zjP3fp5zzn2uJ598knXr1n1fsNm0aROnnnoqGzdu9ImU0pgZKCQNbu/evTz00EPs3LmT3bt3zxsI9uzZ85QAsX///qcEjFWrVs0ZRGZer127lnXr1rFmzZoVdQlGOhQsNVAcNonOSDo0rVq1ivXr17N+/fppd0XSlDmXhyRJamagkCRJzaYSKJK8KcmOJHcmuXKO7Zcm2d4vX0rygmn0U5IkLc3EA0WS04A3AGcBLwAuTPLcWbt9A3hxVW0Gfh+4ZrK9lCRJB2IaZyh+FNhaVY9X1T7gc8BFoztU1Zeq6tH+x62Ad3xJkrSMTSNQ7ADOTbI2yWrgAuD4Bfb/ReDv59qQ5JeSbEuybdeuXQN0VZIkLcXEvzZaVXcl+SPgZuAx4A5g31z7JjmPLlCcPU9b19BfDtmyZcuh8UANSZJWoKnclFlV11bVmVV1LvAIcO/sfZJsBt4H/HRVfWfSfZQkSUs3lQdbJTmmqh5OcgLwauAnZ20/AbgBeH1V3TONPkqSpKWb1pMyr0+yFtgLvLGqHk3yywBVdTXwdmAt8N7+Mbv7lvLYT0mSNB1TCRRVdc4c664eeX05cPlEOyVJkg6aT8qUJEnNDBSSJKmZgUKSJDUzUEiSpGYGCkmS1MxAIUmSmhkoJElSMwOFJElqZqCQJEnNDBSSJKmZgUKSJDUzUEiSpGYGCkmS1MxAIUmSmhkoJElSMwOFJElqZqCQJEnNDBSSJKmZgUKSJDUzUEiSpGYGCkmS1MxAIUmSmhkoJElSMwOFJElqZqCQJEnNDBSSJKmZgUKSJDUzUEiSpGYGCkmS1MxAIUmSmhkoJElSMwOFJElqZqCQJEnNDBSSJKmZgUKSJDUzUEiSpGYGCkmS1MxAIUmSmhkoJElSMwOFJElqZqCQJEnNDBSSJKmZgUKSJDUzUEiSpGYGCkmS1MxAIUmSmhkoJElSMwOFJElqZqCQJEnNDBSSJKmZgUKSJDUzUEiSpGYGCkmS1MxAIUmSmhkoJElSMwOFJElqZqCQJEnNDBSSJKmZgUKSJDUzUEiSpGYGCkmS1MxAIUmSmhkoJElSMwOFJElqZqCQJEnNDBSSJKmZgUKSJDUzUEiSpGapqmn3YSyS7AL+bdr9GIOjgW9PuxPLmPVZnDVanDVanDVa2P+n+pxYVc9ebKdDJlAcKpJsq6ot0+7HcmV9FmeNFmeNFmeNFmZ9vp+XPCRJUjMDhSRJamagWH6umXYHljnrszhrtDhrtDhrtDDrM4v3UEiSpGaeoZAkSc0MFJIkqZmBYiBJjk/y2SR3JbkzyZtGtv1akn/t1/9xv25Vkvcn+Vp/zG8t0v6fJnls6HEMaagaJflAf+yOJH+ZZNWkxjRuA9bopCRfSXJvkg8nOXxSYxqng6jPpUluH1n2Jzl9jnZPT7K132dbkrMmOa5xGqpG8x2/Eg1Zo37/tySpJEdPYjxTU1UuAyzAscCZ/esfBO4Bng+cB3wGeHq/7Zj+39cB1/WvVwMPABvmaXsL8FfAY9Me53KsEXABkH75EHDFtMe6DGv0EeDi/vXVK7VGB1qfWcf+GHD/PO3+A/DKkd+nW6Y91mVYo0WPXynLUDXqtx8PfJruwYtHT3usQy6eoRhIVe2sqtv61/8N3AWsA64A/rCq/rff9vDMIcAzkhwGHAk8CXx3drtJnga8A3jr4IMY2FA1qqpPVQ/4KrB+8MEMZIgaJQlwPvCxftX7gZ8ZeCiDOIj6jLqELnDO2TTwQ/3rNcC3xtnvSRqwRks5fkUYsEYA76L7vD7kvwFhoJiAJBuAM4CvAKcA5/Snmz+X5EX9bh8D9gA7gW8C76yqR+Zo7leBj1fVzsE7PkFjrtFMm6uA1wM3Ddj1iRljjdYC/1VV+/qfH6T78FzRllifUa9l/v8IrgTekeTfgXcCC16CXCnGXKOlHL/ijLNGSV4F/EdV3TFQd5eVw6bdgUNdkqOA64Erq+q7/V+OPwz8BPAi4CNJTgbOAr4HHNdvvzXJZ6rq/pG2jgN+DnjJZEcxrHHWaJb3Ap+vqlsHH8TAxlyjzPEWK/qvp6XWpz9rRZIfBx6vqh3zNHkF8Oaquj7JzwPXAi8bfCADGqBGCx6/Eo2zRklWA78NvHxiA5gyz1AMqP8L+XrgA1V1Q7/6QeCG/oz8V4H9dJPMvA64qar29qfVvkh3r8SoM4BNwH1JHgBWJ7lvAkMZzAA1mmn3d4BnA1cNPYahDVCjbwPP7D8sobsktGJP6R9gfWZczMKnqS8DZtr6KF1QW7EGqtFix68oA9RoI3AScEf/eb0euC3Jc4bo/3JgoBhIf536WuCuqvqTkU1/Q3f9miSnAIfTfcB/Ezg/nWfQJeK7R9usqk9W1XOqakNVbaBLxpuGH80whqhRf8zlwCuAS6pq/7CjGNZAv0cFfBb42X7VZcDfDjmOoRxEfUjyA3Rn+q5boOlvAS/uX58P3Dvenk/OgDWa9/iVZogaVdXXquqYkc/rB+lu/PzPwQYybbUM7gw9FBfgbLrTyNuB2/vlArpfyL8GdgC3Aef3+x9F95fQncC/AL850tangOPmeI+V/i2PQWoE7AO+PtLm26c91mVYo5Ppbli9r9//6dMe6yTq0x/zEmDrHG29D9gy0u4/AXfQXUt/4bTHugxrNO/xK20Zqkaz1j/AIf4tDx+9LUmSmnnJQ5IkNTNQSJKkZgYKSZLUzEAhSZKaGSgkSVIzA4WkRSX5Xj+r4o4kf5fkmbO2vznJ/yRZs0Abxyb5xDzbbkky50PKltC3C5P83sEcK2l8DBSSluKJqjq9qk4DHgHeOGv7JcA/Ahct0MZVwF8M0LdPAq/qH3UsaUoMFJIO1JcZmUwsyUa6B2q9jS5YzOc19BO1JTkyyXVJtif5MN3MqDPtvTzJl5PcluSj/fwKJLkgyd1JvpDk3TNnO6p7mM4twIXjHaakA2GgkLRkSZ4GvBT4+MjqmembbwVOTXLMHMedBDxa/TTQdJNvPV5Vm4E/AF7Y73c0XTB5WVWdCWwDrkpyBPDnwCur6my6eVpGbQPOGc8oJR0MA4WkpTgyye3Ad4BnATePbLsYuK66eVNuoJvfYLZjgV0jP59L90hjqmo73SOPoZt75PnAF/v3uww4EXgecH9VfaPfb/aETA/TzbAqaUqcvlzSUjxRVaf3N11+gu4eincn2Qw8F7i5m1+Jw4H7gffMPh44Yta6uZ77H+DmqnrKpZMkZyzSvyP695A0JZ6hkLRkVbUb+HXgLf10z5cAv1v9jIpVdRywLsmJsw69B9gw8vPngUsBkpwGbO7XbwV+KsmmftvqfpbHu4GTk8y08dpZ7Z9CN4GTpCkxUEg6IFX1z3SzcF7cLzfO2uXGfv3oMXuAr88EBeDPgKOSbAfeSjfzKVW1C/gF4EP9tq3A86rqCeBXgJuSfAF4CNg98hbn0X3bQ9KUONuopIlIchHdNOBvO8jjj6qqx9JdW3kPcG9VvSvJjwAfrKqXjrO/kg6MZygkTURV3Qg80NDEG/obNe8E1tB96wPgBOA32nonqZVnKCRJUjPPUEiSpGYGCkmS1MxAIUmSmhkoJElSMwOFJElq9n9mPxo8weRQJQAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "WISE J094857.31+002225.6\n", "0.585102 175409.0 147.23883 0.37377\n", "1403.290777152521 0.30622188454774696\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "[HB89] 1104-445\n", "1.598 479068.0 166.78622 -44.81877\n", "1793.3027653878596 0.2396239802012333\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "3C 279\n", "0.5362 160749.0 194.04653 -5.78931\n", "1344.817110605312 0.3195366440234375\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "WISE J183005.92+061915.7\n", "0.745 223345.0 277.52475 6.3211\n", "1553.4923551806653 0.27661439395892157\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "MESSIER 084\n", "0.003392 1017.0 186.2656 12.88698\n", "14.949233862918131 28.745175190150867\n", "error! maybe can not identify from name\n", "----\n", "[HB89] 1514+197\n", "1.07 320778.0 229.23665 19.53694\n", "1722.9801220862478 0.24940412303062362\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "LQAC 069+030 001\n", "1.454 435898.0 69.52062 30.07931\n", "1789.4637633558145 0.24013805428630675\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "[HB89] 2131-021\n", "1.285 385233.0 323.54296 -1.88812\n", "1772.3854802599205 0.2424519672126287\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "4C +00.81\n", "2.249877 674496.0 336.6939 0.86981\n", "1741.4070377375724 0.24676502221238603\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "LQAC 066+023 001\n", "0.55 164886.0 66.73223 23.46101\n", "1361.9988946301735 0.3155056498520873\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "PKS 1622-29\n", "0.815 244331.0 246.52509 -29.85749\n", "1602.9924999664522 0.2680725869628901\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n", "PKS 1057-79\n", "0.581 174179.0 164.68046 -80.06504\n", "1398.6384248097584 0.3072404838345317\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x540 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----\n" ] } ], "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", "\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", "# typical size (diameter) of galaxy cluster => 2 - 10 Mpc\n", "tangential_dist = 7.5 # Mpc \n", "\n", "\n", "for obj in objlist:\n", " objname = obj #'PKS ' + obj\n", " try:\n", " print(objname)\n", " z, v0, ra, dec = ga.queryobject_byname(objname)\n", " print(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(dA, theta)\n", " \n", " result = Irsa.query_region(objname, catalog=\"fp_xsc\", spatial=\"Cone\", radius= theta * u.deg)\n", " \n", " plot_cone(obj_coord, theta, result, savefig=True, imgname=objname + '.extended.png')\n", " \n", " print(\"----\")\n", " \n", " except:\n", " print(\"error! maybe can not identify from name\") \n", " print(\"----\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-2.0
biocore/scikit-bio-presentations	sloan-2015/Toward self-documenting, customizable microbiome analysis and metaanalysis with scikit-bio, QIIME 2 and Qiita..ipynb	1	10078	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "\n", "# Toward self-documenting, customizable microbiome analysis and metaanalysis with scikit-bio, QIIME 2 and Qiita.\n", "\n", "Greg Caporaso and Rob Knight\n", "\n", "[Caporaso Lab](http://caporasolab.us), Northern Arizona University\n", "\n", "[Knight Lab](http://https://knightlab.ucsd.edu/), University of California San Diego" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# QIIME" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Cited 2969 times (as of 16 July 2015) since publication." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Nearly monthly workshops, somewhere in the world." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* The next generation of QIIME: scikit-bio, QIIME 2 and Qiita.\n", " * Simplified installation.\n", " * Browser-based interface will replace/supplement the command line interface.\n", " * Workflow transparency. \n", " * Plugin framework, so other developers can build on QIIME." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## scikit-bio\n", "\n", "Low-level, a bioinformatics library for data scientists, students, educators, and bioinformatics software developers.\n", "\n", "Free, Sloan-funded companion text, An Introduction to Applied Bioinformatics available at [readIAB.org](http://www.readIAB.org).\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "## QIIME 2\n", "\n", "A stable and well-documented API and command line interface.\n", "\n", " \n", " \n", "\n", "### scikit-bio\n", "\n", "Low-level, a bioinformatics library for data scientists, students, educators, and bioinformatics software developers.\n", "\n", "Free, Sloan-funded companion text, An Introduction to Applied Bioinformatics available at [readIAB.org](http://www.readIAB.org).\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Qiita\n", "\n", "End-user, browser-based environment for microbiome analysis and meta-analysis.\n", "\n", " \n", " \n", "\n", "\n", "### QIIME 2\n", "\n", "A stable and well-documented API and command line interface.\n", "\n", " \n", " \n", "\n", "### scikit-bio\n", "\n", "Low-level, a bioinformatics library for data scientists, students, educators, and bioinformatics software developers.\n", "\n", "Free, Sloan-funded companion text, An Introduction to Applied Bioinformatics available at [readIAB.org" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# A scikit is a python scientific computing library that cleanly integrates with others." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "scikit-bio is built on top of scipy, numpy, pandas, etc." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### This enables:\n", "* beautiful visualizations with seaborn\n", "* powerful machine learning and optimization with scikit-learn\n", "* fast sequence collapsing with marisa-trie and related packages\n", "* statistical modeling with Stats.Models\n", "* whatever comes next..." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "## scikit-bio 0.4.0, our first beta release, is now available\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Developing your bioinformatics software with scikit-bio gets you a lot of functionality for free.\n", "\n", "See Jai Rideout and Evan Bolyen's SciPy 2015 talk for more detail and cool demos http://bit.ly/skbio-scipy2015 \n", "\n", "(We'll tweet the link with ``#microbenet``.) " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Example: building a better taxonomy classifier in under 100 lines of code" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from skbio import DNA\n", "import numpy as np\n", "import skbio\n", "\n", "## Load Greengenes and slice all sequences to the region \n", "## amplified by 515F/806R. \n", "\n", "aligned_seqs_fp = 'data/gg_13_8_otus/rep_set_aligned/82_otus.fasta'\n", "taxonomy_fp = 'data/gg_13_8_otus/taxonomy/82_otu_taxonomy.txt'\n", "\n", "\n", "fwd_primer = DNA(\"GTGCCAGCMGCCGCGGTAA\",\n", " metadata={'label':'fwd-primer'})\n", "rev_primer = DNA(\"GGACTACHVGGGTWTCTAAT\",\n", " metadata={'label':'rev-primer'}).reverse_complement()\n", "\n", "def seq_to_regex(seq):\n", " result = []\n", " for base in str(seq):\n", " if base in DNA.degenerate_chars:\n", " result.append('[{0}]'.format(\n", " ''.join(DNA.degenerate_map[base])))\n", " else:\n", " result.append(base)\n", "\n", " return ''.join(result)\n", "\n", "regex = '({0}.*{1})'.format(seq_to_regex(fwd_primer),\n", " seq_to_regex(rev_primer))\n", "\n", "starts = []\n", "stops = []\n", "for seq in skbio.io.read(aligned_seqs_fp, format='fasta', \n", " constructor=DNA):\n", " for match in seq.find_with_regex(regex, ignore=seq.gaps()):\n", " starts.append(match.start)\n", " stops.append(match.stop)\n", " \n", "locus = slice(int(np.median(starts)), int(np.median(stops)))\n", "\n", "## Get all kmers for all sequences.\n", "kmer_counts = []\n", "seq_ids = []\n", "for seq in skbio.io.read(aligned_seqs_fp, format='fasta',\n", " constructor=DNA):\n", " seq_ids.append(seq.metadata['id'])\n", " sliced_seq = seq[locus].degap()\n", " kmer_counts.append(sliced_seq.kmer_frequencies(8))\n", " \n", " \n", "## Load the training/test data, perform feature selection, and train \n", "## and test the classifier using cross-validation.\n", "from sklearn.feature_extraction import DictVectorizer\n", "from sklearn.feature_selection import SelectPercentile\n", "from sklearn.svm import SVC\n", "from sklearn.cross_validation import train_test_split\n", "\n", "X = DictVectorizer().fit_transform(kmer_counts)\n", "\n", "taxonomy_level = 3 # class\n", "id_to_taxon = {}\n", "with open(taxonomy_fp) as f:\n", " for line in f:\n", " id_, taxon = line.strip().split('\\t')\n", " id_to_taxon[id_] = '; '.join(taxon.split('; ')[:taxonomy_level])\n", "\n", "y = [id_to_taxon[seq_id] for seq_id in seq_ids]\n", "\n", "X = SelectPercentile().fit_transform(X, y)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y,\n", " random_state=0)\n", "y_pred = SVC(C=10, kernel='linear', degree=3,\n", " gamma=0.001).fit(X_train, y_train).predict(X_test)\n", "\n", "## Define a function to use for plotting. \n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n", " plt.figure()\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", " plt.ylabel('Known taxonomy')\n", " plt.xlabel('Predicted taxonomy')\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Support Vector Machine genus classifier\n", "\n", "![](./svc-genus.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Naive Bayes genus classifier\n", "\n", "![](./naive-bayes-genus.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# QIIME 2" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Self-documenting analyses simplify bioinformatics methods reporting, and therefore reproducibility and transparancy." ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
juditacs/snippets	misc/sparse_matrix.ipynb	1	3451	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from scipy.sparse import coo_matrix, csr_matrix" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "86614" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open(\"f1.txt\") as f:\n", " i = []\n", " j = []\n", " for line in f:\n", " fd = line.strip().split()\n", " i.append(int(fd[0]))\n", " j.append(int(fd[1]))\n", " j.append(int(fd[0]))\n", " i.append(int(fd[1]))\n", "\n", "N = 626892\n", "F1 = coo_matrix((np.arange(len(i)), (i, j)), shape=(N, N))\n", "\n", "F1.nnz" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "481466" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open(\"f2.txt\") as f:\n", " i = []\n", " j = []\n", " for line in f:\n", " fd = line.strip().split()\n", " i.append(int(fd[0]))\n", " j.append(int(fd[1]))\n", " j.append(int(fd[0]))\n", " i.append(int(fd[1]))\n", "\n", "N = 626892\n", "F2 = coo_matrix((np.arange(len(i)), (i, j)), shape=(N, N))\n", "F2.nnz" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(83944, 481466)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "F1 = F1.tocsr()\n", "F2 = F2.tocsr()\n", "F1.nnz, F2.nnz" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n", "Wall time: 19.1 µs\n" ] }, { "data": { "text/plain": [ "83943" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time\n", "\n", "S = F1 + F1\n", "S.nnz" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n", "Wall time: 6.44 µs\n" ] }, { "data": { "text/plain": [ "1534169693" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time\n", "\n", "S = (F1+F2) * (F1+F2)\n", "S.nnz" ] } ], "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 }	lgpl-3.0
ericmjl/bokeh	examples/howto/Range update callback.ipynb	1	3214	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "from bokeh.models import ColumnDataSource, CustomJS, Rect\n", "from bokeh.plotting import output_notebook, figure, show\n", "from bokeh.layouts import row\n", "\n", "output_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "N = 20\n", "img = np.empty((N, N), dtype=np.uint32)\n", "view = img.view(dtype=np.uint8).reshape((N, N, 4))\n", "for i in range(N):\n", " for j in range(N):\n", " view[i, j, 0] = int(i/N255)\n", " view[i, j, 1] = 158\n", " view[i, j, 2] = int(j/N255)\n", " view[i, j, 3] = 255" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "source = ColumnDataSource({'x':[], 'y':[], 'width':[], 'height':[]})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "JSCODE = \"\"\"\n", "var data = source.data;\n", "var start = cb_obj.start;\n", "var end = cb_obj.end;\n", "data[%r] = [start + (end - start) / 2];\n", "data[%r] = [end - start];\n", "\n", "// this is needed because we modified .data \"in place\"\n", "source.change.emit();\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p1 = figure(title='Box Zoom Here', plot_width=400, plot_height=400,\n", " x_range=(0,10), y_range=(0,10), tools ='box_zoom,wheel_zoom,pan,reset')\n", "p1.image_rgba(image=[img], x=[0], y=[0], dw=[10], dh=[10])\n", "\n", "xcb = CustomJS(args=dict(source=source), code=JSCODE % ('x', 'width'))\n", "ycb = CustomJS(args=dict(source=source), code=JSCODE % ('y', 'height'))\n", "\n", "p1.x_range.js_on_change('start', xcb)\n", "p1.x_range.js_on_change('end', xcb)\n", "p1.y_range.js_on_change('start', ycb)\n", "p1.y_range.js_on_change('end', ycb)\n", "\n", "p2 = figure(title='See Zoom Window Here', plot_width=400, plot_height=400, \n", " x_range=(0,10), y_range=(0,10), tools=\"\")\n", "p2.image_rgba(image=[img], x=[0], y=[0], dw=[10], dh=[10])\n", "p2.rect('x', 'y', 'width', 'height', fill_alpha=0, line_color='black', source=source)\n", "\n", "show(row(p1, p2))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.1" } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-3-clause
nick5435/thermo-bridge	notebooks/Derivatives-Contour-Master.ipynb	1	383936	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-04-27T14:36:29.411252Z", "start_time": "2017-04-27T14:36:27.465187Z" }, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "import ThermoPyle as TP\n", "from ThermoPyle import \n", "import matplotlib\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt, mpld3\n", "\n", "%matplotlib notebook\n", "mpld3.enable_notebook()" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2017-03-29T15:02:59.917098Z", "start_time": "2017-03-29T09:39:22.040171-05:00" }, "run_control": { "frozen": false, "read_only": false }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 0\n", " Generating Initial Fluid Object.\n", 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Iteration: 0 hours, 0 minutes, 10.187 seconds\n", " Time Elapsed: 0 hours, 20 minutes, 18.502 seconds\n", "\n", "Iteration 109 of 124:\n", " Calculating Column d(S)/d(U)\|P\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 10.067 seconds\n", " Time Elapsed: 0 hours, 20 minutes, 28.569 seconds\n", "\n", "Iteration 110 of 124:\n", " Calculating Column d(U)/d(T)\|S\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 10.685 seconds\n", " Time Elapsed: 0 hours, 20 minutes, 39.254 seconds\n", "\n", "Iteration 111 of 124:\n", " Calculating Column d(T)/d(G)\|D\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 10.301 seconds\n", " Time Elapsed: 0 hours, 20 minutes, 49.555 seconds\n", "\n", "Iteration 112 of 124:\n", " Calculating Column d(P)/d(S)\|T\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 10.041 seconds\n", " Time Elapsed: 0 hours, 20 minutes, 59.596 seconds\n", "\n", "Iteration 113 of 124:\n", " Calculating Column d(P)/d(S)\|D\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 10.367 seconds\n", " Time Elapsed: 0 hours, 21 minutes, 9.963 seconds\n", "\n", "Iteration 114 of 124:\n", " Calculating Column d(P)/d(S)\|G\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 10.862 seconds\n", " Time Elapsed: 0 hours, 21 minutes, 20.825 seconds\n", "\n", "Iteration 115 of 124:\n", " Calculating Column d(S)/d(T)\|G\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 21.831 seconds\n", " Time Elapsed: 0 hours, 21 minutes, 42.656 seconds\n", "\n", "Iteration 116 of 124:\n", " Calculating Column d(P)/d(G)\|D\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 14.476 seconds\n", " Time Elapsed: 0 hours, 21 minutes, 57.131 seconds\n", "\n", "Iteration 117 of 124:\n", " Calculating Column d(G)/d(U)\|D\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 11.404 seconds\n", " Time Elapsed: 0 hours, 22 minutes, 8.535 seconds\n", "\n", "Iteration 118 of 124:\n", " Calculating Column d(U)/d(S)\|P\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 11.474 seconds\n", " Time Elapsed: 0 hours, 22 minutes, 20.009 seconds\n", "\n", "Iteration 119 of 124:\n", " Calculating Column d(D)/d(T)\|S\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 11.678 seconds\n", " Time Elapsed: 0 hours, 22 minutes, 31.687 seconds\n", "\n", "Iteration 120 of 124:\n", " Calculating Column d(U)/d(G)\|D\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 11.576 seconds\n", " Time Elapsed: 0 hours, 22 minutes, 43.263 seconds\n", "\n", "Iteration 121 of 124:\n", " Calculating Column d(S)/d(U)\|G\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 12.64 seconds\n", " Time Elapsed: 0 hours, 22 minutes, 55.903 seconds\n", "\n", "Iteration 122 of 124:\n", " Calculating Column d(D)/d(S)\|T\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 15.181 seconds\n", " Time Elapsed: 0 hours, 23 minutes, 11.083 seconds\n", "\n", "Iteration 123 of 124:\n", " Calculating Column d(U)/d(P)\|S\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 14.199 seconds\n", " Time Elapsed: 0 hours, 23 minutes, 25.283 seconds\n", "\n", "Iteration 124 of 124:\n", " Calculating Column d(G)/d(P)\|S\n", "======================================================\n", " Success!\n", " This Iteration: 0 hours, 0 minutes, 12.283 seconds\n", " Time Elapsed: 0 hours, 23 minutes, 37.566 seconds\n", "\n", "Total running time: 0 hours, 23 minutes, 37.876 seconds.\n", "\n" ] } ], "source": [ "%run ../scripts/der_CSV_gen.py --no-write" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "ExecuteTime": { "end_time": "2017-03-29T15:03:10.700379Z", "start_time": "2017-03-29T10:03:10.685377-05:00" } }, "outputs": [ { "data": { "text/plain": [ "11.293975878982417" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "times = np.array(itertimes)\n", "times[:,2].mean()" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "ExecuteTime": { "end_time": "2017-03-29T15:26:06.678933Z", "start_time": "2017-03-29T10:26:06.635914-05:00" }, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "Water = myfluid.copy()" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "ExecuteTime": { "end_time": "2017-03-29T15:26:08.485906Z", "start_time": "2017-03-29T10:26:08.095906-05:00" }, "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "T\n", "P\n", "U\n", "d(P)/d(G)\|S\n", "d(U)/d(T)\|G\n", "d(T)/d(D)\|G\n", "d(U)/d(D)\|S\n", "d(P)/d(D)\|G\n", "d(G)/d(T)\|S\n", "d(G)/d(U)\|P\n", "d(U)/d(P)\|G\n", "d(G)/d(T)\|D\n", "d(G)/d(D)\|U\n", "d(T)/d(S)\|P\n", "d(D)/d(T)\|G\n", "d(T)/d(D)\|P\n", "d(S)/d(D)\|P\n", "d(G)/d(S)\|P\n", "d(P)/d(T)\|D\n", "d(T)/d(G)\|P\n", "d(T)/d(D)\|S\n", "d(P)/d(G)\|U\n", "d(U)/d(D)\|T\n", "d(G)/d(T)\|U\n", "d(G)/d(U)\|T\n", "d(D)/d(P)\|T\n", "d(G)/d(P)\|T\n", "d(D)/d(U)\|P\n", "d(T)/d(S)\|D\n", "d(T)/d(S)\|G\n", "d(S)/d(U)\|T\n", "d(U)/d(S)\|T\n", "d(U)/d(G)\|P\n", "d(D)/d(P)\|U\n", "d(D)/d(U)\|G\n", "d(P)/d(D)\|T\n", "d(P)/d(U)\|S\n", "d(D)/d(T)\|U\n", "d(T)/d(U)\|D\n", "d(G)/d(S)\|T\n", "d(T)/d(U)\|G\n", "d(S)/d(G)\|U\n", "d(U)/d(P)\|T\n", "d(S)/d(T)\|P\n", "S\n", "d(G)/d(D)\|T\n", "d(U)/d(T)\|D\n", "d(S)/d(T)\|D\n", "d(T)/d(D)\|U\n", "d(S)/d(P)\|U\n", "D\n", "d(T)/d(P)\|S\n", "d(D)/d(U)\|T\n", "d(P)/d(S)\|U\n", "d(D)/d(T)\|P\n", "d(D)/d(S)\|U\n", "d(D)/d(S)\|P\n", "d(G)/d(S)\|D\n", "PHASE\n", "d(D)/d(P)\|S\n", "d(S)/d(G)\|D\n", "d(T)/d(G)\|U\n", "d(U)/d(S)\|D\n", "d(S)/d(P)\|G\n", "d(P)/d(T)\|U\n", "d(T)/d(P)\|G\n", "d(T)/d(P)\|U\n", "d(U)/d(G)\|S\n", "d(T)/d(S)\|U\n", "d(G)/d(D)\|P\n", "d(T)/d(U)\|S\n", "d(G)/d(D)\|S\n", "d(D)/d(S)\|G\n", "d(S)/d(P)\|T\n", "d(T)/d(G)\|S\n", "d(G)/d(S)\|U\n", "G\n", "d(T)/d(P)\|D\n", "d(U)/d(D)\|G\n", "d(D)/d(P)\|G\n", "d(S)/d(U)\|D\n", "d(P)/d(G)\|T\n", "d(U)/d(T)\|P\n", "d(P)/d(D)\|U\n", "d(P)/d(U)\|G\n", "d(T)/d(U)\|P\n", "d(S)/d(G)\|P\n", "d(D)/d(G)\|P\n", "d(D)/d(U)\|S\n", "d(U)/d(D)\|P\n", "d(P)/d(U)\|T\n", "d(S)/d(P)\|D\n", "d(U)/d(P)\|D\n", "d(G)/d(T)\|P\n", "d(S)/d(D)\|T\n", "d(U)/d(S)\|G\n", "d(G)/d(P)\|U\n", "d(D)/d(G)\|T\n", "d(D)/d(G)\|U\n", "d(G)/d(U)\|S\n", "d(S)/d(D)\|U\n", "d(P)/d(T)\|S\n", "d(S)/d(G)\|T\n", "d(P)/d(D)\|S\n", "d(P)/d(U)\|D\n", "d(P)/d(T)\|G\n", "d(S)/d(D)\|G\n", "d(S)/d(T)\|U\n", "d(G)/d(P)\|D\n", "d(U)/d(G)\|T\n", "d(D)/d(G)\|S\n", "d(S)/d(U)\|P\n", "d(U)/d(T)\|S\n", "d(T)/d(G)\|D\n", "d(P)/d(S)\|T\n", "d(P)/d(S)\|D\n", "d(P)/d(S)\|G\n", "d(S)/d(T)\|G\n", "d(P)/d(G)\|D\n", "d(G)/d(U)\|D\n", "d(U)/d(S)\|P\n", "d(D)/d(T)\|S\n", "d(U)/d(G)\|D\n", "d(S)/d(U)\|G\n", "d(D)/d(S)\|T\n", "d(U)/d(P)\|S\n", "d(G)/d(P)\|S\n" ] } ], "source": [ "Water.clean()\n", "Water.refresh()\n", "print(\"\\n\".join(Water.vars))" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "ExecuteTime": { "end_time": "2017-03-29T15:26:01.777944Z", "start_time": "2017-03-29T10:26:01.735947-05:00" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>T</th>\n", " <th>P</th>\n", " <th>U</th>\n", " <th>d(P)/d(G)\|S</th>\n", " <th>d(U)/d(T)\|G</th>\n", " <th>d(T)/d(D)\|G</th>\n", " <th>d(U)/d(D)\|S</th>\n", " <th>d(P)/d(D)\|G</th>\n", " <th>d(G)/d(T)\|S</th>\n", " <th>d(G)/d(U)\|P</th>\n", " <th>...</th>\n", " <th>d(P)/d(G)\|D</th>\n", " <th>d(G)/d(U)\|D</th>\n", " <th>d(U)/d(S)\|P</th>\n", " <th>d(D)/d(T)\|S</th>\n", " <th>d(U)/d(G)\|D</th>\n", " <th>d(S)/d(U)\|G</th>\n", " <th>d(D)/d(S)\|T</th>\n", " <th>d(U)/d(P)\|S</th>\n", " <th>d(G)/d(P)\|S</th>\n", " <th>V</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "<p>0 rows × 128 columns</p>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [T, P, U, d(P)/d(G)\|S, d(U)/d(T)\|G, d(T)/d(D)\|G, d(U)/d(D)\|S, d(P)/d(D)\|G, d(G)/d(T)\|S, d(G)/d(U)\|P, d(U)/d(P)\|G, d(G)/d(T)\|D, d(G)/d(D)\|U, d(T)/d(S)\|P, d(D)/d(T)\|G, d(T)/d(D)\|P, d(S)/d(D)\|P, d(G)/d(S)\|P, d(P)/d(T)\|D, d(T)/d(G)\|P, d(T)/d(D)\|S, d(P)/d(G)\|U, d(U)/d(D)\|T, d(G)/d(T)\|U, d(G)/d(U)\|T, d(D)/d(P)\|T, d(G)/d(P)\|T, d(D)/d(U)\|P, d(T)/d(S)\|D, d(T)/d(S)\|G, d(S)/d(U)\|T, d(U)/d(S)\|T, d(U)/d(G)\|P, d(D)/d(P)\|U, d(D)/d(U)\|G, d(P)/d(D)\|T, d(P)/d(U)\|S, d(D)/d(T)\|U, d(T)/d(U)\|D, d(G)/d(S)\|T, d(T)/d(U)\|G, d(S)/d(G)\|U, d(U)/d(P)\|T, d(S)/d(T)\|P, S, d(G)/d(D)\|T, d(U)/d(T)\|D, d(S)/d(T)\|D, d(T)/d(D)\|U, d(S)/d(P)\|U, D, d(T)/d(P)\|S, d(D)/d(U)\|T, d(P)/d(S)\|U, d(D)/d(T)\|P, d(D)/d(S)\|U, d(D)/d(S)\|P, d(G)/d(S)\|D, PHASE, d(D)/d(P)\|S, d(S)/d(G)\|D, d(T)/d(G)\|U, d(U)/d(S)\|D, d(S)/d(P)\|G, d(P)/d(T)\|U, d(T)/d(P)\|G, d(T)/d(P)\|U, d(U)/d(G)\|S, d(T)/d(S)\|U, d(G)/d(D)\|P, d(T)/d(U)\|S, d(G)/d(D)\|S, d(D)/d(S)\|G, d(S)/d(P)\|T, d(T)/d(G)\|S, d(G)/d(S)\|U, G, d(T)/d(P)\|D, d(U)/d(D)\|G, d(D)/d(P)\|G, d(S)/d(U)\|D, d(P)/d(G)\|T, d(U)/d(T)\|P, d(P)/d(D)\|U, d(P)/d(U)\|G, d(T)/d(U)\|P, d(S)/d(G)\|P, d(D)/d(G)\|P, d(D)/d(U)\|S, d(U)/d(D)\|P, d(P)/d(U)\|T, d(S)/d(P)\|D, d(U)/d(P)\|D, d(G)/d(T)\|P, d(S)/d(D)\|T, d(U)/d(S)\|G, d(G)/d(P)\|U, d(D)/d(G)\|T, d(D)/d(G)\|U, d(G)/d(U)\|S, ...]\n", "Index: []\n", "\n", "[0 rows x 128 columns]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Water.data[\"V\"] = Water.M / Water.data[\"D\"]\n", "Water.vars.append(\"V\")\n", "Water.refresh()\n", "Water.data" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2017-03-28T23:34:08.885419Z", "start_time": "2017-03-28T18:34:08.875409-05:00" }, "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/plain": [ "'1.0221E+07'" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"{:.4E}\".format((lambda x,y: xy)(Water.data.shape))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2017-03-28T23:38:18.683278Z", "start_time": "2017-03-28T18:38:17.894292-05:00" }, "run_control": { "frozen": false, "read_only": false }, "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/ Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio \|\|\n", "\tthis.context.webkitBackingStorePixelRatio \|\|\n", "\tthis.context.mozBackingStorePixelRatio \|\|\n", "\tthis.context.msBackingStorePixelRatio \|\|\n", "\tthis.context.oBackingStorePixelRatio \|\|\n", "\tthis.context.backingStorePixelRatio \|\| 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var 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\" width=\"639.999974568686\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Water.xvar = \"P\"\n", "Water.yvar = \"T\"\n", "Water.zvar = \"D\"\n", "Water.refresh()\n", "Water.clean()\n", "TP.fluid_contour_plot(Water, \"d(U)/d(S)\|T\")" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "ExecuteTime": { "end_time": "2017-03-29T15:28:33.017082Z", "start_time": "2017-03-29T10:28:32.235089-05:00" }, "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio \|\|\n", "\tthis.context.webkitBackingStorePixelRatio \|\|\n", "\tthis.context.mozBackingStorePixelRatio \|\|\n", "\tthis.context.msBackingStorePixelRatio \|\|\n", "\tthis.context.oBackingStorePixelRatio \|\|\n", "\tthis.context.backingStorePixelRatio \|\| 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" 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DawesLab/LabNotebooks	Superoperators.ipynb	1	12344	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## A study of superoperators in QuTiP\n", "### and some notes about computer implementations in general\n", "\n", "Useful references: \n", " - https://en.wikipedia.org/wiki/Superoperator\n", " - http://qutip.org/docs/latest/apidoc/functions.html?highlight=spre#module-qutip.superoperator" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from qutip import " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = False\\begin{equation}\\left(\\begin{array}{{11}c}1.0 & 2.0\\\\3.0 & 4.0\\\\\\end{array}\\right)\\end{equation}" ], "text/plain": [ "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = False\n", "Qobj data =\n", "[[1. 2.]\n", " [3. 4.]]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# prototype density matrix (i.e. nonsense)\n", "rho = Qobj([[1,2],[3,4]])\n", "rho" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[[2], [2]], [1]], shape = (4, 1), type = operator-ket\\begin{equation}\\left(\\begin{array}{{11}c}1.0\\\\3.0\\\\2.0\\\\4.0\\\\\\end{array}\\right)\\end{equation}" ], "text/plain": [ "Quantum object: dims = [[[2], [2]], [1]], shape = (4, 1), type = operator-ket\n", "Qobj data =\n", "[[1.]\n", " [3.]\n", " [2.]\n", " [4.]]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rho_v = operator_to_vector(rho)\n", "rho_v" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is where I got confused. By hand I write this as 1,2,3,4 in order from top to bottom, based on the notation:\n", "$$\\rho = \\sum_{i,j}\\rho_{ij}\|i\\rangle\\langle j\| \\rightarrow \|\\rho\\rangle\\rangle = \\sum_{i,j}\\rho_{ij}\|i\\rangle \\otimes \|j\\rangle$$\n", "\n", "I'll let $$\|1\\rangle = \\begin{pmatrix}1\\\\0\\end{pmatrix}$$ and $$\|2\\rangle = \\begin{pmatrix}0\\\\1\\end{pmatrix}$$ so $i=j=1,2$ in the sums.\n", "The outer products are:\n", "$$\|1\\rangle\\langle 1\| = \\begin{pmatrix}1&0\\\\0&0\\end{pmatrix}$$\n", "$$\|1\\rangle\\langle 2\| = \\begin{pmatrix}0&1\\\\0&0\\end{pmatrix}$$\n", "$$\|2\\rangle\\langle 1\| = \\begin{pmatrix}0&0\\\\1&0\\end{pmatrix}$$\n", "$$\|2\\rangle\\langle 2\| = \\begin{pmatrix}0&0\\\\0&1\\end{pmatrix}$$\n", "And the tensor products are:\n", "$$\|1\\rangle \\otimes \|1\\rangle = \\begin{pmatrix}1\\\\0\\\\0\\\\0\\end{pmatrix}$$\n", "$$\|1\\rangle \\otimes \|2\\rangle = \\begin{pmatrix}0\\\\1\\\\0\\\\0\\end{pmatrix}$$\n", "$$\|2\\rangle \\otimes \|1\\rangle = \\begin{pmatrix}0\\\\0\\\\1\\\\0\\end{pmatrix}$$\n", "$$\|2\\rangle \\otimes \|2\\rangle = \\begin{pmatrix}0\\\\0\\\\0\\\\1\\end{pmatrix}$$\n", "\n", "I see the code for `operator_to_vector` takes the transpose, but I'm not sure why that is? Maybe to align to the underlying Fortran-style column-major data in the underlying implementation?\n", "\n", "So, the bottom line is: Either trust and use the QuTiP functions or roll your own. Mixing and matching builds you a world that will be ripe with transpose errors :-)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\\begin{equation}\\left(\\begin{array}{{11}c}1.0\\\\0.0\\\\\\end{array}\\right)\\end{equation}" ], "text/plain": [ "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\n", "Qobj data =\n", "[[1.]\n", " [0.]]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "one = Qobj([[1],[0]])\n", "one" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\\begin{equation}\\left(\\begin{array}{{11}c}0.0\\\\1.0\\\\\\end{array}\\right)\\end{equation}" ], "text/plain": [ "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\n", "Qobj data =\n", "[[0.]\n", " [1.]]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "two = Qobj([[0],[1]])\n", "two" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rho == 1oneone.dag() + 2onetwo.dag() + 3twoone.dag() + 4twotwo.dag()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So this is consistent with my prototype $\\rho$" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[2, 2], [1, 1]], shape = (4, 1), type = ket\\begin{equation}\\left(\\begin{array}{{11}c}1.0\\\\0.0\\\\0.0\\\\0.0\\\\\\end{array}\\right)\\end{equation}" ], "text/plain": [ "Quantum object: dims = [[2, 2], [1, 1]], shape = (4, 1), type = ket\n", "Qobj data =\n", "[[1.]\n", " [0.]\n", " [0.]\n", " [0.]]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor(one,one)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[2, 2], [1, 1]], shape = (4, 1), type = ket\\begin{equation}\\left(\\begin{array}{{11}c}0.0\\\\1.0\\\\0.0\\\\0.0\\\\\\end{array}\\right)\\end{equation}" ], "text/plain": [ "Quantum object: dims = [[2, 2], [1, 1]], shape = (4, 1), type = ket\n", "Qobj data =\n", "[[0.]\n", " [1.]\n", " [0.]\n", " [0.]]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor(one,two)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[2, 2], [1, 1]], shape = (4, 1), type = ket\\begin{equation}\\left(\\begin{array}{{11}c}0.0\\\\0.0\\\\1.0\\\\0.0\\\\\\end{array}\\right)\\end{equation}" ], "text/plain": [ "Quantum object: dims = [[2, 2], [1, 1]], shape = (4, 1), type = ket\n", "Qobj data =\n", "[[0.]\n", " [0.]\n", " [1.]\n", " [0.]]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor(two,one)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[2, 2], [1, 1]], shape = (4, 1), type = ket\\begin{equation}\\left(\\begin{array}{{11}c}0.0\\\\0.0\\\\0.0\\\\1.0\\\\\\end{array}\\right)\\end{equation}" ], "text/plain": [ "Quantum object: dims = [[2, 2], [1, 1]], shape = (4, 1), type = ket\n", "Qobj data =\n", "[[0.]\n", " [0.]\n", " [0.]\n", " [1.]]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor(two,two)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are also consistent with \"by hand\" notation.\n", "So everything individually works as it would \"by hand\" but the operator_to_vector takes a transpose first... presumably to align the numpy arrays?\n", "\n", "Ok, so knowing that, it's a bit hard to compare to by-hand results, but we can check a few. First, the left-multiplication should be the same as `spre`. Defined as $\\mathcal{L}(A) = A\\otimes I$:" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[[2], [2]], [[2], [2]]], shape = (4, 4), type = super, isherm = False\\begin{equation}\\left(\\begin{array}{{11}c}0.0 & 1.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation}" ], "text/plain": [ "Quantum object: dims = [[[2], [2]], [[2], [2]]], shape = (4, 4), type = super, isherm = False\n", "Qobj data =\n", "[[0. 1. 0. 0.]\n", " [0. 0. 0. 0.]\n", " [0. 0. 0. 1.]\n", " [0. 0. 0. 0.]]" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spre(destroy(2))" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation}\\left(\\begin{array}{{11}c}0.0 & 0.0 & 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 & 0.0\\\\\\end{array}\\right)\\end{equation}" ], "text/plain": [ "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n", "Qobj data =\n", "[[0. 0. 1. 0.]\n", " [0. 0. 0. 1.]\n", " [0. 0. 0. 0.]\n", " [0. 0. 0. 0.]]" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor(destroy(2),identity(2))" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation}\\left(\\begin{array}{{11}c}0.0 & 1.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation}" ], "text/plain": [ "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n", "Qobj data =\n", "[[0. 1. 0. 0.]\n", " [0. 0. 0. 0.]\n", " [0. 0. 0. 1.]\n", " [0. 0. 0. 0.]]" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tensor(identity(2),destroy(2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But this is instead showing that left-multiplication is $\\mathcal{L}(A) = I\\otimes A$, doesn't match the expected definition.\n", "\n", "I think this is generally known, as we're mixing notation with a particular (matrix) representation. MATLAB does a similar thing (I suspect for the same Fortran-roots reason): https://physics.stackexchange.com/questions/163546/finding-the-matrix-representation-of-a-superoperator" ] }, { "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
gautam1858/tensorflow	tensorflow/lite/g3doc/examples/style_transfer/overview.ipynb	12	20987	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "g_nWetWWd_ns" }, "source": [ "##### Copyright 2019 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "2pHVBk_seED1" }, "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": "M7vSdG6sAIQn" }, "source": [ "# Artistic Style Transfer with TensorFlow Lite" ] }, { "cell_type": "markdown", "metadata": { "id": "fwc5GKHBASdc" }, "source": [ "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/lite/examples/style_transfer/overview\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/examples/style_transfer/overview.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/examples/style_transfer/overview.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/tensorflow/tensorflow/lite/g3doc/examples/style_transfer/overview.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca href=\"https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/hub_logo_32px.png\" /\u003eSee TF Hub model\u003c/a\u003e\n", " \u003c/td\u003e\n", "\u003c/table\u003e" ] }, { "cell_type": "markdown", "metadata": { "id": "31O0iaROAw8z" }, "source": [ "One of the most exciting developments in deep learning to come out recently is [artistic style transfer](https://arxiv.org/abs/1508.06576), or the ability to create a new image, known as a [pastiche](https://en.wikipedia.org/wiki/Pastiche), based on two input images: one representing the artistic style and one representing the content.\n", "\n", "![Style transfer example](https://storage.googleapis.com/download.tensorflow.org/models/tflite/arbitrary_style_transfer/formula.png)\n", "\n", "Using this technique, we can generate beautiful new artworks in a range of styles.\n", "\n", "![Style transfer example](https://storage.googleapis.com/download.tensorflow.org/models/tflite/arbitrary_style_transfer/table.png)\n", "\n", "If you are new to TensorFlow Lite and are working with Android, we\n", "recommend exploring the following example applications that can help you get\n", "started.\n", "\n", "\u003ca class=\"button button-primary\" href=\"https://github.com/tensorflow/examples/tree/master/lite/examples/style_transfer/android\"\u003eAndroid\n", "example\u003c/a\u003e \u003ca class=\"button button-primary\" href=\"https://github.com/tensorflow/examples/tree/master/lite/examples/style_transfer/ios\"\u003eiOS\n", "example\u003c/a\u003e\n", "\n", "If you are using a platform other than Android or iOS, or you are already\n", "familiar with the\n", "\u003ca href=\"https://www.tensorflow.org/api_docs/python/tf/lite\"\u003eTensorFlow Lite\n", "APIs\u003c/a\u003e, you can follow this tutorial to learn how to apply style transfer on any pair of content and style image with a pre-trained TensorFlow Lite model. You can use the model to add style transfer to your own mobile applications.\n", "\n", "The model is open-sourced on [GitHub](https://github.com/tensorflow/magenta/tree/master/magenta/models/arbitrary_image_stylization#train-a-model-on-a-large-dataset-with-data-augmentation-to-run-on-mobile). You can retrain the model with different parameters (e.g. increase content layers' weights to make the output image look more like the content image)." ] }, { "cell_type": "markdown", "metadata": { "id": "ak0S4gkOCSxs" }, "source": [ "## Understand the model architecture" ] }, { "cell_type": "markdown", "metadata": { "id": "oee6G_bBCgAM" }, "source": [ "![Model Architecture](https://storage.googleapis.com/download.tensorflow.org/models/tflite/arbitrary_style_transfer/architecture.png)\n", "\n", "This Artistic Style Transfer model consists of two submodels:\n", "1. Style Prediciton Model: A MobilenetV2-based neural network that takes an input style image to a 100-dimension style bottleneck vector.\n", "1. Style Transform Model: A neural network that takes apply a style bottleneck vector to a content image and creates a stylized image.\n", "\n", "If your app only needs to support a fixed set of style images, you can compute their style bottleneck vectors in advance, and exclude the Style Prediction Model from your app's binary." ] }, { "cell_type": "markdown", "metadata": { "id": "a7ZETsRVNMo7" }, "source": [ "## Setup" ] }, { "cell_type": "markdown", "metadata": { "id": "3n8oObKZN4c8" }, "source": [ "Import dependencies." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xz62Lb1oNm97" }, "outputs": [], "source": [ "import tensorflow as tf\n", "print(tf.__version__)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1Ua5FpcJNrIj" }, "outputs": [], "source": [ "import IPython.display as display\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "mpl.rcParams['figure.figsize'] = (12,12)\n", "mpl.rcParams['axes.grid'] = False\n", "\n", "import numpy as np\n", "import time\n", "import functools" ] }, { "cell_type": "markdown", "metadata": { "id": "1b988wrrQnVF" }, "source": [ "Download the content and style images, and the pre-trained TensorFlow Lite models." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "16g57cIMQnen" }, "outputs": [], "source": [ "content_path = tf.keras.utils.get_file('belfry.jpg','https://storage.googleapis.com/khanhlvg-public.appspot.com/arbitrary-style-transfer/belfry-2611573_1280.jpg')\n", "style_path = tf.keras.utils.get_file('style23.jpg','https://storage.googleapis.com/khanhlvg-public.appspot.com/arbitrary-style-transfer/style23.jpg')\n", "\n", "style_predict_path = tf.keras.utils.get_file('style_predict.tflite', 'https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/int8/prediction/1?lite-format=tflite')\n", "style_transform_path = tf.keras.utils.get_file('style_transform.tflite', 'https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/int8/transfer/1?lite-format=tflite')" ] }, { "cell_type": "markdown", "metadata": { "id": "MQZXL7kON-gM" }, "source": [ "## Pre-process the inputs\n", "\n", "* The content image and the style image must be RGB images with pixel values being float32 numbers between [0..1].\n", "* The style image size must be (1, 256, 256, 3). We central crop the image and resize it.\n", "* The content image must be (1, 384, 384, 3). We central crop the image and resize it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Cg0Vi-rXRUFl" }, "outputs": [], "source": [ "# Function to load an image from a file, and add a batch dimension.\n", "def load_img(path_to_img):\n", " img = tf.io.read_file(path_to_img)\n", " img = tf.io.decode_image(img, channels=3)\n", " img = tf.image.convert_image_dtype(img, tf.float32)\n", " img = img[tf.newaxis, :]\n", "\n", " return img\n", "\n", "# Function to pre-process by resizing an central cropping it.\n", "def preprocess_image(image, target_dim):\n", " # Resize the image so that the shorter dimension becomes 256px.\n", " shape = tf.cast(tf.shape(image)[1:-1], tf.float32)\n", " short_dim = min(shape)\n", " scale = target_dim / short_dim\n", " new_shape = tf.cast(shape * scale, tf.int32)\n", " image = tf.image.resize(image, new_shape)\n", "\n", " # Central crop the image.\n", " image = tf.image.resize_with_crop_or_pad(image, target_dim, target_dim)\n", "\n", " return image\n", "\n", "# Load the input images.\n", "content_image = load_img(content_path)\n", "style_image = load_img(style_path)\n", "\n", "# Preprocess the input images.\n", "preprocessed_content_image = preprocess_image(content_image, 384)\n", "preprocessed_style_image = preprocess_image(style_image, 256)\n", "\n", "print('Style Image Shape:', preprocessed_style_image.shape)\n", "print('Content Image Shape:', preprocessed_content_image.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "xE4Yt8nArTeR" }, "source": [ "## Visualize the inputs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ncPA4esJRcEu" }, "outputs": [], "source": [ "def imshow(image, title=None):\n", " if len(image.shape) \u003e 3:\n", " image = tf.squeeze(image, axis=0)\n", "\n", " plt.imshow(image)\n", " if title:\n", " plt.title(title)\n", "\n", "plt.subplot(1, 2, 1)\n", "imshow(preprocessed_content_image, 'Content Image')\n", "\n", "plt.subplot(1, 2, 2)\n", "imshow(preprocessed_style_image, 'Style Image')" ] }, { "cell_type": "markdown", "metadata": { "id": "CJ7R-CHbjC3s" }, "source": [ "## Run style transfer with TensorFlow Lite" ] }, { "cell_type": "markdown", "metadata": { "id": "euu00ldHjKwD" }, "source": [ "### Style prediction" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "o3zd9cTFRiS_" }, "outputs": [], "source": [ "# Function to run style prediction on preprocessed style image.\n", "def run_style_predict(preprocessed_style_image):\n", " # Load the model.\n", " interpreter = tf.lite.Interpreter(model_path=style_predict_path)\n", "\n", " # Set model input.\n", " interpreter.allocate_tensors()\n", " input_details = interpreter.get_input_details()\n", " interpreter.set_tensor(input_details[0][\"index\"], preprocessed_style_image)\n", "\n", " # Calculate style bottleneck.\n", " interpreter.invoke()\n", " style_bottleneck = interpreter.tensor(\n", " interpreter.get_output_details()[0][\"index\"]\n", " )()\n", "\n", " return style_bottleneck\n", "\n", "# Calculate style bottleneck for the preprocessed style image.\n", "style_bottleneck = run_style_predict(preprocessed_style_image)\n", "print('Style Bottleneck Shape:', style_bottleneck.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "00t8S2PekIyW" }, "source": [ "### Style transform" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cZp5bCj8SX1w" }, "outputs": [], "source": [ "# Run style transform on preprocessed style image\n", "def run_style_transform(style_bottleneck, preprocessed_content_image):\n", " # Load the model.\n", " interpreter = tf.lite.Interpreter(model_path=style_transform_path)\n", "\n", " # Set model input.\n", " input_details = interpreter.get_input_details()\n", " interpreter.allocate_tensors()\n", "\n", " # Set model inputs.\n", " interpreter.set_tensor(input_details[0][\"index\"], preprocessed_content_image)\n", " interpreter.set_tensor(input_details[1][\"index\"], style_bottleneck)\n", " interpreter.invoke()\n", "\n", " # Transform content image.\n", " stylized_image = interpreter.tensor(\n", " interpreter.get_output_details()[0][\"index\"]\n", " )()\n", "\n", " return stylized_image\n", "\n", "# Stylize the content image using the style bottleneck.\n", "stylized_image = run_style_transform(style_bottleneck, preprocessed_content_image)\n", "\n", "# Visualize the output.\n", "imshow(stylized_image, 'Stylized Image')" ] }, { "cell_type": "markdown", "metadata": { "id": "vv_71Td-QtrW" }, "source": [ "### Style blending\n", "\n", "We can blend the style of content image into the stylized output, which in turn making the output look more like the content image." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eJcAURXQQtJ7" }, "outputs": [], "source": [ "# Calculate style bottleneck of the content image.\n", "style_bottleneck_content = run_style_predict(\n", " preprocess_image(content_image, 256)\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4S3yg2MgkmRD" }, "outputs": [], "source": [ "# Define content blending ratio between [0..1].\n", "# 0.0: 0% style extracts from content image.\n", "# 1.0: 100% style extracted from content image.\n", "content_blending_ratio = 0.5 #@param {type:\"slider\", min:0, max:1, step:0.01}\n", "\n", "# Blend the style bottleneck of style image and content image\n", "style_bottleneck_blended = content_blending_ratio * style_bottleneck_content \\\n", " + (1 - content_blending_ratio) * style_bottleneck\n", "\n", "# Stylize the content image using the style bottleneck.\n", "stylized_image_blended = run_style_transform(style_bottleneck_blended,\n", " preprocessed_content_image)\n", "\n", "# Visualize the output.\n", "imshow(stylized_image_blended, 'Blended Stylized Image')" ] }, { "cell_type": "markdown", "metadata": { "id": "9k9jGIep8p1c" }, "source": [ "## Performance Benchmarks\n", "\n", "Performance benchmark numbers are generated with the tool [described here](https://www.tensorflow.org/lite/performance/benchmarks).\n", "\u003ctable \u003e\u003cthead\u003e\u003ctr\u003e\u003cth\u003eModel name\u003c/th\u003e \u003cth\u003eModel size\u003c/th\u003e \u003cth\u003eDevice \u003c/th\u003e \u003cth\u003eNNAPI\u003c/th\u003e \u003cth\u003eCPU\u003c/th\u003e \u003cth\u003eGPU\u003c/th\u003e\u003c/tr\u003e \u003c/thead\u003e \n", "\u003ctr\u003e \u003ctd rowspan = 3\u003e \u003ca href=\"https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/int8/prediction/1?lite-format=tflite\"\u003eStyle prediction model (int8)\u003c/a\u003e \u003c/td\u003e \n", "\u003ctd rowspan = 3\u003e2.8 Mb\u003c/td\u003e\n", "\u003ctd\u003ePixel 3 (Android 10) \u003c/td\u003e \u003ctd\u003e142ms\u003c/td\u003e\u003ctd\u003e14ms\u003c/td\u003e\u003ctd\u003e\u003c/td\u003e\u003c/tr\u003e\n", "\u003ctr\u003e\u003ctd\u003ePixel 4 (Android 10) \u003c/td\u003e \u003ctd\u003e5.2ms\u003c/td\u003e\u003ctd\u003e6.7ms\u003c/td\u003e\u003ctd\u003e\u003c/td\u003e\u003c/tr\u003e\n", "\u003ctr\u003e\u003ctd\u003eiPhone XS (iOS 12.4.1) \u003c/td\u003e \u003ctd\u003e\u003c/td\u003e\u003ctd\u003e10.7ms*\u003c/td\u003e\u003ctd\u003e\u003c/td\u003e\u003c/tr\u003e\n", "\u003ctr\u003e \u003ctd rowspan = 3\u003e \u003ca href=\"https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/int8/transfer/1?lite-format=tflite\"\u003eStyle transform model (int8)\u003c/a\u003e \u003c/td\u003e \n", "\u003ctd rowspan = 3\u003e0.2 Mb\u003c/td\u003e\n", "\u003ctd\u003ePixel 3 (Android 10) \u003c/td\u003e \u003ctd\u003e\u003c/td\u003e\u003ctd\u003e540ms\u003c/td\u003e\u003ctd\u003e\u003c/td\u003e\u003c/tr\u003e\n", "\u003ctr\u003e\u003ctd\u003ePixel 4 (Android 10) \u003c/td\u003e \u003ctd\u003e\u003c/td\u003e\u003ctd\u003e405ms\u003c/td\u003e\u003ctd\u003e\u003c/td\u003e\u003c/tr\u003e\n", "\u003ctr\u003e\u003ctd\u003eiPhone XS (iOS 12.4.1) \u003c/td\u003e \u003ctd\u003e\u003c/td\u003e\u003ctd\u003e251ms\u003c/td\u003e\u003ctd\u003e\u003c/td\u003e\u003c/tr\u003e\n", "\n", "\u003ctr\u003e \u003ctd rowspan = 2\u003e \u003ca href=\"https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/fp16/prediction/1?lite-format=tflite\"\u003eStyle prediction model (float16)\u003c/a\u003e \u003c/td\u003e \n", "\u003ctd rowspan = 2\u003e4.7 Mb\u003c/td\u003e\n", "\u003ctd\u003ePixel 3 (Android 10) \u003c/td\u003e \u003ctd\u003e86ms\u003c/td\u003e\u003ctd\u003e28ms\u003c/td\u003e\u003ctd\u003e9.1ms\u003c/td\u003e\u003c/tr\u003e\n", "\u003ctr\u003e\u003ctd\u003ePixel 4 (Android 10) \u003c/td\u003e\u003ctd\u003e32ms\u003c/td\u003e\u003ctd\u003e12ms\u003c/td\u003e\u003ctd\u003e10ms\u003c/td\u003e\u003c/tr\u003e\n", "\n", "\u003ctr\u003e \u003ctd rowspan = 2\u003e \u003ca href=\"https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/fp16/transfer/1?lite-format=tflite\"\u003eStyle transfer model (float16)\u003c/a\u003e \u003c/td\u003e \n", "\u003ctd rowspan = 2\u003e0.4 Mb\u003c/td\u003e\n", "\u003ctd\u003ePixel 3 (Android 10) \u003c/td\u003e \u003ctd\u003e1095ms\u003c/td\u003e\u003ctd\u003e545ms\u003c/td\u003e\u003ctd\u003e42ms\u003c/td\u003e\u003c/tr\u003e\n", "\u003ctr\u003e\u003ctd\u003ePixel 4 (Android 10) \u003c/td\u003e\u003ctd\u003e603ms\u003c/td\u003e\u003ctd\u003e377ms\u003c/td\u003e\u003ctd\u003e42ms\u003c/td\u003e\u003c/tr\u003e\n", "\n", "\u003c/table\u003e\n", "\n", "\u0026ast; 4 threads used. \u003cbr/\u003e\n", "\u0026ast;\u0026ast; 2 threads on iPhone for the best performance.*\n" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "overview.ipynb", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
fabianvf/cos-notebooks	Mendeley API tests.ipynb	1	5177	{ "cells": [ { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from mendeley import Mendeley\n", "\n", "from local import secret, client_id" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "url = 'https://api.mendeley.com:443/documents'" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mendeley = Mendeley(client_id, secret)\n", "session = mendeley.start_client_credentials_flow().authenticate()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "doi = '10.2108/zsj.12.655'" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"Two Japanese wildcats, the Tsushima cat and the Iriomote cat, show the same Mitochondrial DNA lineage as the leopard cat Felis bengalensis\" has 92 readers.\n" ] } ], "source": [ "doc = session.catalog.by_identifier(doi=doi, view='stats')\n", "print '\"%s\" has %s readers.' % (doc.title, doc.reader_count)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "person = doc.authors[0]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['first_name', 'last_name']" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "person.fields()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "advanced = session.catalog.advanced_search(title='*', view='all', min_year=2015, max_year=2015)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [], "source": [ "all_results = advanced.list()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "556914" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_results.count" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "one_doc = all_results.items[0]" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'The Suffocation Model: Why Marriage in America Is Becoming an All-or-Nothing Institution'" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "one_doc.title" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2015" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "one_doc.year" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a_person = one_doc.authors[0]" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['first_name', 'last_name']" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a_person.fields()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'http://www.mendeley.com/research/suffocation-model-marriage-america-becoming-allornothing-institution'" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "one_doc.link" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
ellisonbg/talk-2013-pydata-nyc	Useful in Multiple Contexts.ipynb	1	45805	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Attribute: Useful in Multiple Contexts" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import display, Image, HTML\n", "from talktools import website, nbviewer" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scientific computing and data science are complex activities that involve a wide range of contexts:\n", "\n", "1. Individual, interactive exploration\n", "2. Debugging, testing\n", "3. Production runs\n", "4. Parallel computing\n", "5. Collaboration\n", "6. Publication\n", "7. Presentation\n", "8. Teaching/Learning\n", "\n", "There are a large number of software tools that we use across these different contexts:\n", "\n", "Python Ruby Perl C C++ Fortran Numba Cython MPI Hadoop Excel LaTeX Powerpoint Word\n", "Keynote Vim Emacs Make JavaScript Matlab Mathematica\n", "\n", "This places a massive cognitive burden on users. This burden has nothing to do with the challenging technical problems users are trying to solve. This burden pulls them away from solving their actual problems.\n", "\n", "We are working really hard to make sure that IPython is useful in the following contexts." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Interactive exploration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First and foremost, IPython is an interactive environment for writing and running code. We provide features to make this as pleasant as possible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tab completion:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "math." ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interactive help:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "math.cos?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inline plotting:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "plot(rand(50))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "[<matplotlib.lines.Line2D at 0x10e79b4d0>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Mpu61nUTxS9imPm9ejpm6aFLPRaZuGGQmQZm6LpROFu9DMpj8JvUgpu42eSkM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\ndJBR+t1XTMzUa2qCmbrTXRp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"text": [ "<matplotlib.figure.Figure at 0x10e764190>" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "ls" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Close to Data.ipynb \u001b[34mdata\u001b[m\u001b[m/ load_style.py\r\n", "Introduction.ipynb frontmatter.py load_style.pyc\r\n", "LICENSE frontmatter.pyc nbimport.py\r\n", "Multi-lingual.ipynb \u001b[34mimages\u001b[m\u001b[m/ talk.css\r\n", "Open.ipynb ipythonproject.py talktools.py\r\n", "README.md ipythonproject.pyc talktools.pyc\r\n", "Useful in Multiple Contexts.ipynb \u001b[34mipythonteam\u001b[m\u001b[m/\r\n" ] } ], "prompt_number": 6 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Publishing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IPython Notebook contain everything related to a computation and its results: code, narrative text, equations, plots, images, videos, HTML, JavaScript. We have developed tools for \"publishing\" these Notebook documents in different contexts:\n", "\n", "* nbconvert: converts Notebooks to different static formats: HTML, LaTeX, PDF, slideshows.\n", "* [nbviewer](http://nbviewer.ipython.org): provides a static HTML view of any Notebook on the internet." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's generate a static PDF of this talk's introduction:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!ipython nbconvert --to latex --post pdf \"Introduction.ipynb\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[NbConvertApp] Using existing profile dir: u'/Users/bgranger/.ipython/profile_default'\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[NbConvertApp] Converting notebook Introduction.ipynb to latex\r\n", "[NbConvertApp] Support files will be in Introduction_files/\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[NbConvertApp] Loaded template latex_article.tplx\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[NbConvertApp] Making directory Introduction_files\r\n", "[NbConvertApp] Writing 21039 bytes to Introduction.tex\r\n", "[NbConvertApp] Building PDF\r\n", "[NbConvertApp] Running pdflatex 3 times: ['pdflatex', 'Introduction.tex']\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "libpng warning: iCCP: known incorrect sRGB profile\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "libpng warning: iCCP: known incorrect sRGB profile\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "libpng warning: iCCP: known incorrect sRGB profile\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "libpng warning: iCCP: known incorrect sRGB profile\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "libpng warning: iCCP: known incorrect sRGB profile\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "libpng warning: iCCP: known incorrect sRGB profile\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[NbConvertApp] Running bibtex 1 time: ['bibtex', 'Introduction']\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[NbConvertApp] WARNING \| bibtex had problems, most likely because there were no citations\r\n", "[NbConvertApp] Removing temporary LaTeX files\r\n", "[NbConvertApp] PDF successfully created\r\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is the nbviewer website:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "website('nbviewer.ipython.org')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<iframe src=\"http://nbviewer.ipython.org\" width=\"800\" height=\"450\">\n", "</iframe>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "<IPython.core.display.HTML at 0x10e77ee50>" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also maintain a [gallery of interesting Notebooks](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks) that contains a curated list\n", "of IPython Notebooks on various topics." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Examples of Notebook based publication" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cam Davidson-Pilon has written an entire book on Bayesian Statistics as a set of IPython Notebook that are hosted on GitHub and viewed on http://nbviewer.ipython.org." ] }, { "cell_type": "code", "collapsed": false, "input": [ "website('http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<iframe src=\"http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/\" width=\"800\" height=\"450\">\n", "</iframe>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "<IPython.core.display.HTML at 0x10b8b9650>" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Matthew Russell has written an O'Reilly published book that includes IPython Notebooks for all examples." ] }, { "cell_type": "code", "collapsed": false, "input": [ "website('http://shop.oreilly.com/product/0636920030195.do')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<iframe src=\"http://shop.oreilly.com/product/0636920030195.do\" width=\"800\" height=\"450\">\n", "</iframe>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "<IPython.core.display.HTML at 0x10b8b9c50>" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Jose Unpingco has written a series of blog posts on Signal Processing using the IPython Notebook. These blog posts were the basis of a full length book [Python for Signal Processing](http://www.springer.com/engineering/signals/book/978-3-319-01341-1), Springer (2013)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "website('http://python-for-signal-processing.blogspot.com/')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<iframe src=\"http://python-for-signal-processing.blogspot.com/\" width=\"800\" height=\"450\">\n", "</iframe>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "<IPython.core.display.HTML at 0x10e77ecd0>" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Jake Vanderplas and others publish technical blogs that are authored as IPython Notebooks." ] }, { "cell_type": "code", "collapsed": false, "input": [ "website('http://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<iframe src=\"http://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/\" width=\"800\" height=\"450\">\n", "</iframe>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "<IPython.core.display.HTML at 0x10e77edd0>" ] } ], "prompt_number": 12 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Presenting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The IPython Notebook, is being used extensively for presentations on technical topics across a wide range of fields. The Notebook has a cell toolbar for adding slide related metadata to cells. However, we are working on improving this usage case:\n", "\n", "1. Prototype live presentation mode.\n", "2. Use nbconvert to export to [reveal.js](http://lab.hakim.se/reveal-js/#/) based slideshow." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Parallel Computing" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Teaching" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The IPython Notebook is being used for lecture materials and student work in a number of university and high school courses on scientific computing and data science. Most of these courses are being developed publicly on GitHub. Here is a short list:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%file data/courses.csv\n", "\"Course\",\"University\",\"Instructor\"\n", "\"Data Science (CS 109)\",\"Harvard University\",\"Pfister and Blitzstein\"\n", "\"Practical Data Science\",\"NYU\",\"Josh Attenberg\"\n", "\"Scientific Computing (ASTR 599)\",\"University of Washington\",\"Jake Vanderplas\"\n", "\"Working with Open Data\",\"UC Berkeley\",\"Raymond Yee\"\n", "\"Computational Physics\",\"Cal Poly\",\"Jennifer Klay\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Overwriting data/courses.csv\n" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "df = pandas.read_csv('data/courses.csv'); df" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Course</th>\n", " <th>University</th>\n", " <th>Instructor</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Data Science (CS 109)</td>\n", " <td> Harvard University</td>\n", " <td> Pfister and Blitzstein</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Practical Data Science</td>\n", " <td> NYU</td>\n", " <td> Josh Attenberg</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Scientific Computing (ASTR 599)</td>\n", " <td> University of Washington</td>\n", " <td> Jake Vanderplas</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Working with Open Data</td>\n", " <td> UC Berkeley</td>\n", " <td> Raymond Yee</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Computational Physics</td>\n", " <td> Cal Poly</td>\n", " <td> Jennifer Klay</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ " Course University Instructor\n", "0 Data Science (CS 109) Harvard University Pfister and Blitzstein\n", "1 Practical Data Science NYU Josh Attenberg\n", "2 Scientific Computing (ASTR 599) University of Washington Jake Vanderplas\n", "3 Working with Open Data UC Berkeley Raymond Yee\n", "4 Computational Physics Cal Poly Jennifer Klay" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a teaching context it is also important to be able to includes video content. The IPython display architecture makes this simple for YouTube videos." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('Jsiy4TxgIME')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " <iframe\n", " width=\"400\"\n", " height=300\"\n", " src=\"http://www.youtube.com/embed/Jsiy4TxgIME\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "<IPython.lib.display.YouTubeVideo at 0x10f142a50>" ] } ], "prompt_number": 24 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Styling" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext load_style" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "%load_style talk.css" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<style>\n", "\n", ".rendered_html {\n", " font-family: \"proxima-nova\", helvetica;\n", " font-size: 150%;\n", " line-height: 1.5;\n", "}\n", "\n", ".rendered_html h1 {\n", " margin: 0.25em 0em 0.5em;\n", " color: #015C9C;\n", " text-align: center;\n", " line-height: 1.2; \n", " page-break-before: always;\n", "}\n", "\n", ".rendered_html h2 {\n", " margin: 1.1em 0em 0.5em;\n", " color: #26465D;\n", " line-height: 1.2;\n", "}\n", "\n", ".rendered_html h3 {\n", " margin: 1.1em 0em 0.5em;\n", " color: #002845;\n", " line-height: 1.2;\n", "}\n", "\n", ".rendered_html li {\n", " line-height: 1.5; \n", "}\n", "\n", "/.prompt {\n", " font-size: 120%; \n", "}/\n", "\n", ".CodeMirror-lines {\n", " font-size: 120%; \n", "}\n", "\n", "/.output_area {\n", " font-size: 120%; \n", "}/\n", "\n", "#notebook {\n", " background-image: url('files/images/witewall_3.png');\n", "}\n", "\n", "h1.bigtitle {\n", " margin: 4cm 1cm 4cm 1cm;\n", " font-size: 300%;\n", "}\n", "\n", "h3.point {\n", " font-size: 200%;\n", " text-align: center;\n", " margin: 2em 0em 2em 0em;\n", " #26465D\n", "}\n", "\n", ".logo {\n", " margin: 20px 0 20px 0;\n", "}\n", "\n", "a.anchor-link {\n", " display: none;\n", "}\n", "\n", "h1.title { \n", " font-size: 250%;\n", "}\n", "\n", "</style>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x10f142dd0>" ] } ], "prompt_number": 26 } ], "metadata": {} } ] }	mit
davidparks21/qso_lya_detection_pipeline	docs/nb/Testing_with_SDSS.ipynb	1	5368	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Testing with the SDSS DR5 dataset of JXP" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# imports\n", "from pyigm.surveys.dlasurvey import DLASurvey" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load DR5" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SDSS-DR5: Loading DLA file /Users/xavier/local/Python/pyigm/pyigm/data/DLA/SDSS_DR5/dr5_alldla.fits.gz\n", "SDSS-DR5: Loading QSOs file /Users/xavier/local/Python/pyigm/pyigm/data/DLA/SDSS_DR5/dr5_dlagz_s2n4.fits\n", "SDSS-DR5: Performing stats (~60s)\n", "SDSS-DR5: Loaded\n" ] } ], "source": [ "sdssdr5 = DLASurvey.load_SDSS_DR5()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<QTable length=5>\n", "<table id=\"table4356969488\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>PLATE</th><th>FIB</th><th>RA</th><th>DEC</th><th>FLG_BAL</th><th>IQSO</th><th>MAG</th><th>S2N</th><th>Z_START</th><th>Z_END</th><th>ZEM</th><th>DX</th></tr></thead>\n", "<thead><tr><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n", "<thead><tr><th>int32</th><th>int32</th><th>float64</th><th>float64</th><th>int16</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>266</td><td>5</td><td>146.93861</td><td>-0.68701194</td><td>1</td><td>0</td><td>19.341999054</td><td>4.94595003128</td><td>2.39664643878</td><td>2.74649000168</td><td>2.8287498951</td><td>1.17426266257</td></tr>\n", "<tr><td>266</td><td>92</td><td>146.22601</td><td>-0.72509875</td><td>0</td><td>4</td><td>19.0820007324</td><td>8.54980564117</td><td>2.20000004768</td><td>2.25759506226</td><td>2.29049992561</td><td>0.184012498912</td></tr>\n", "<tr><td>270</td><td>254</td><td>152.23239</td><td>-0.97123272</td><td>0</td><td>9</td><td>19.0230007172</td><td>7.49763822556</td><td>2.30636157714</td><td>3.0556242466</td><td>3.09659004211</td><td>2.56581152274</td></tr>\n", "<tr><td>271</td><td>391</td><td>154.14992</td><td>0.14750838</td><td>0</td><td>16</td><td>18.0650005341</td><td>18.982629776</td><td>2.20000004768</td><td>2.25551605225</td><td>2.28839993477</td><td>0.177634457341</td></tr>\n", "<tr><td>271</td><td>166</td><td>154.45375</td><td>-0.52347949</td><td>0</td><td>17</td><td>18.9650001526</td><td>10.8409719467</td><td>2.20000004768</td><td>2.25027894974</td><td>2.28310990334</td><td>0.158514320617</td></tr>\n", "</table>" ], "text/plain": [ "<QTable length=5>\n", "PLATE FIB RA DEC ... Z_END ZEM DX \n", " deg deg ... \n", "int32 int32 float64 float64 ... float64 float64 float64 \n", "----- ----- --------- ----------- ... ------------- ------------- --------------\n", " 266 5 146.93861 -0.68701194 ... 2.74649000168 2.8287498951 1.17426266257\n", " 266 92 146.22601 -0.72509875 ... 2.25759506226 2.29049992561 0.184012498912\n", " 270 254 152.23239 -0.97123272 ... 3.0556242466 3.09659004211 2.56581152274\n", " 271 391 154.14992 0.14750838 ... 2.25551605225 2.28839993477 0.177634457341\n", " 271 166 154.45375 -0.52347949 ... 2.25027894974 2.28310990334 0.158514320617" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdssdr5.sightlines[0:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate the list for Dave" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "out_fil ='../../data/dr5_test_set.csv'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tbl = sdssdr5.sightlines[['PLATE','FIB','RA','DEC']]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tbl.write(out_fil)" ] }, { "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
SATHVIKRAJU/Inferential_Statistics	Hospital_readmit.ipynb	1	101034	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hospital Readmissions Data Analysis and Recommendations for Reduction\n", "\n", "### Background\n", "In October 2012, the US government's Center for Medicare and Medicaid Services (CMS) began reducing Medicare payments for Inpatient Prospective Payment System hospitals with excess readmissions. Excess readmissions are measured by a ratio, by dividing a hospital’s number of “predicted” 30-day readmissions for heart attack, heart failure, and pneumonia by the number that would be “expected,” based on an average hospital with similar patients. A ratio greater than 1 indicates excess readmissions.\n", "\n", "### Exercise Directions\n", "\n", "In this exercise, you will:\n", "+ critique a preliminary analysis of readmissions data and recommendations (provided below) for reducing the readmissions rate\n", "+ construct a statistically sound analysis and make recommendations of your own \n", "\n", "More instructions provided below. Include your work in this notebook and submit to your Github account. \n", "\n", "### Resources\n", "+ Data source: https://data.medicare.gov/Hospital-Compare/Hospital-Readmission-Reduction/9n3s-kdb3\n", "+ More information: http://www.cms.gov/Medicare/medicare-fee-for-service-payment/acuteinpatientPPS/readmissions-reduction-program.html\n", "+ Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n", "**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import bokeh.plotting as bkp\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable" ] }, { "cell_type": "code", "execution_count": 2, "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>Hospital Name</th>\n", " <th>Provider Number</th>\n", " <th>State</th>\n", " <th>Measure Name</th>\n", " <th>Number of Discharges</th>\n", " <th>Footnote</th>\n", " <th>Excess Readmission Ratio</th>\n", " <th>Predicted Readmission Rate</th>\n", " <th>Expected Readmission Rate</th>\n", " <th>Number of Readmissions</th>\n", " <th>Start Date</th>\n", " <th>End Date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>FROEDTERT MEMORIAL LUTHERAN HOSPITAL</td>\n", " <td>520177</td>\n", " <td>WI</td>\n", " <td>READM-30-HIP-KNEE-HRRP</td>\n", " <td>242</td>\n", " <td>NaN</td>\n", " <td>1.9095</td>\n", " <td>10.8</td>\n", " <td>5.6</td>\n", " <td>38.0</td>\n", " <td>07/01/2010</td>\n", " <td>06/30/2013</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>PROVIDENCE HOSPITAL</td>\n", " <td>90006</td>\n", " <td>DC</td>\n", " <td>READM-30-HIP-KNEE-HRRP</td>\n", " <td>247</td>\n", " <td>NaN</td>\n", " <td>1.7521</td>\n", " <td>9.2</td>\n", " <td>5.3</td>\n", " <td>33.0</td>\n", " <td>07/01/2010</td>\n", " <td>06/30/2013</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>BEAUFORT COUNTY MEMORIAL HOSPITAL</td>\n", " <td>420067</td>\n", " <td>SC</td>\n", " <td>READM-30-HIP-KNEE-HRRP</td>\n", " <td>586</td>\n", " <td>NaN</td>\n", " <td>1.5836</td>\n", " <td>7.6</td>\n", " <td>4.8</td>\n", " <td>53.0</td>\n", " <td>07/01/2010</td>\n", " <td>06/30/2013</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>ADVOCATE CHRIST HOSPITAL & MEDICAL CENTER</td>\n", " <td>140208</td>\n", " <td>IL</td>\n", " <td>READM-30-HIP-KNEE-HRRP</td>\n", " <td>965</td>\n", " <td>NaN</td>\n", " <td>1.5760</td>\n", " <td>9.0</td>\n", " <td>5.7</td>\n", " <td>95.0</td>\n", " <td>07/01/2010</td>\n", " <td>06/30/2013</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>BRAZOSPORT REGIONAL HEALTH SYSTEM</td>\n", " <td>450072</td>\n", " <td>TX</td>\n", " <td>READM-30-HIP-KNEE-HRRP</td>\n", " <td>149</td>\n", " <td>NaN</td>\n", " <td>1.5308</td>\n", " <td>8.2</td>\n", " <td>5.4</td>\n", " <td>20.0</td>\n", " <td>07/01/2010</td>\n", " <td>06/30/2013</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Hospital Name Provider Number State \\\n", "0 FROEDTERT MEMORIAL LUTHERAN HOSPITAL 520177 WI \n", "1 PROVIDENCE HOSPITAL 90006 DC \n", "2 BEAUFORT COUNTY MEMORIAL HOSPITAL 420067 SC \n", "3 ADVOCATE CHRIST HOSPITAL & MEDICAL CENTER 140208 IL \n", "4 BRAZOSPORT REGIONAL HEALTH SYSTEM 450072 TX \n", "\n", " Measure Name Number of Discharges Footnote \\\n", "0 READM-30-HIP-KNEE-HRRP 242 NaN \n", "1 READM-30-HIP-KNEE-HRRP 247 NaN \n", "2 READM-30-HIP-KNEE-HRRP 586 NaN \n", "3 READM-30-HIP-KNEE-HRRP 965 NaN \n", "4 READM-30-HIP-KNEE-HRRP 149 NaN \n", "\n", " Excess Readmission Ratio Predicted Readmission Rate \\\n", "0 1.9095 10.8 \n", "1 1.7521 9.2 \n", "2 1.5836 7.6 \n", "3 1.5760 9.0 \n", "4 1.5308 8.2 \n", "\n", " Expected Readmission Rate Number of Readmissions Start Date End Date \n", "0 5.6 38.0 07/01/2010 06/30/2013 \n", "1 5.3 33.0 07/01/2010 06/30/2013 \n", "2 4.8 53.0 07/01/2010 06/30/2013 \n", "3 5.7 95.0 07/01/2010 06/30/2013 \n", "4 5.4 20.0 07/01/2010 06/30/2013 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# read in readmissions data provided\n", "hospital_read_df = pd.read_csv('data/cms_hospital_readmissions.csv')\n", "hospital_read_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Preliminary Analysis" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Sathvik\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:477: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self.obj[item] = s\n" ] } ], "source": [ "# deal with missing and inconvenient portions of data \n", "clean_hospital_read_df = hospital_read_df[hospital_read_df['Number of Discharges'] != 'Not Available']\n", "clean_hospital_read_df.loc[:,'Number of Discharges'] = clean_hospital_read_df['Number of Discharges'].astype(int)\n", "clean_hospital_read_df = clean_hospital_read_df.sort_values('Number of Discharges')\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0NHMuROSjwN3AHhEZEJFfFJHXi8jrAaLw/v8OPAjcC7zXGPNQ56ehrEUK6TjV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\nVQLthG6y7qXFBnOrusPz1FAWFFxZatAKQm6uNDk5k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"text/plain": [ "<matplotlib.figure.Figure at 0x1cee6759588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# generate a scatterplot for number of discharges vs. excess rate of readmissions\n", "# lists work better with matplotlib scatterplot function\n", "x = [a for a in clean_hospital_read_df['Number of Discharges'][81:-3]]\n", "y = list(clean_hospital_read_df['Excess Readmission Ratio'][81:-3])\n", "\n", "fig, ax = plt.subplots(figsize=(8,5))\n", "ax.scatter(x, y,alpha=0.2)\n", "\n", "#ax.plot(x,y)\n", "\n", "ax.fill_between([0,350], 1.15, 2, facecolor='red', alpha = .15, interpolate=True)\n", "ax.fill_between([800,2500], .5, .95, facecolor='green', alpha = .15, interpolate=True)\n", "\n", "ax.set_xlim([0, max(x)])\n", "ax.set_xlabel('Number of discharges', fontsize=12)\n", "ax.set_ylabel('Excess rate of readmissions', fontsize=12)\n", "ax.set_title('Scatterplot of number of discharges vs. excess rate of readmissions', fontsize=14)\n", "\n", "ax.grid(True)\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Preliminary Report\n", "\n", "Read the following results/report. While you are reading it, think about if the conclusions are correct, incorrect, misleading or unfounded. Think about what you would change or what additional analyses you would perform.\n", "\n", "A. Initial observations based on the plot above\n", "+ Overall, rate of readmissions is trending down with increasing number of discharges\n", "+ With lower number of discharges, there is a greater incidence of excess rate of readmissions (area shaded red)\n", "+ With higher number of discharges, there is a greater incidence of lower rates of readmissions (area shaded green) \n", "\n", "B. Statistics\n", "+ In hospitals/facilities with number of discharges < 100, mean excess readmission rate is 1.023 and 63% have excess readmission rate greater than 1 \n", "+ In hospitals/facilities with number of discharges > 1000, mean excess readmission rate is 0.978 and 44% have excess readmission rate greater than 1 \n", "\n", "C. Conclusions\n", "+ There is a significant correlation between hospital capacity (number of discharges) and readmission rates. \n", "+ Smaller hospitals/facilities may be lacking necessary resources to ensure quality care and prevent complications that lead to readmissions.\n", "\n", "D. Regulatory policy recommendations\n", "+ Hospitals/facilties with small capacity (< 300) should be required to demonstrate upgraded resource allocation for quality care to continue operation.\n", "+ Directives and incentives should be provided for consolidation of hospitals and facilities to have a smaller number of them with higher capacity and number of discharges." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Exercise\n", "\n", "Include your work on the following in this notebook and submit to your Github account**. \n", "\n", "A. Do you agree with the above analysis and recommendations? Why or why not?\n", " \n", "B. Provide support for your arguments and your own recommendations with a statistically sound analysis:\n", "\n", " 1. Setup an appropriate hypothesis test.\n", " 2. Compute and report the observed significance value (or p-value).\n", " 3. Report statistical significance for $\\alpha$ = .01. \n", " 4. Discuss statistical significance and practical significance. Do they differ here? How does this change your recommendation to the client?\n", " 5. Look at the scatterplot above. \n", " - What are the advantages and disadvantages of using this plot to convey information?\n", " - Construct another plot that conveys the same information in a more direct manner.\n", "\n", "\n", "\n", "You can compose in notebook cells using Markdown: \n", "+ In the control panel at the top, choose Cell > Cell Type > Markdown\n", "+ Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Solution: " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The report conclusion says there is a strong coorelation between the Hospital capacity and readmission rates.We have not tested if there is really a strong coorelation between them and hence need to set up a hypothesis test. \n", "\n", "Null hypothesis:\n", "There is no statistical difference between the hospital capacity and the readmission rates.\n", "\n", "Alternate hypothesis:\n", "There is a statistical difference between the hospital capacity and the readmission rates.\n", "\n", "We can compare small number of hospitals(hospital_capacity<100) and large number of hospitals(hospital_capacity>1000) to fit our hypothesis and since the number of samples are different for the two of them we can perform ANOVA. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hospital_cap_small= clean_hospital_read_df[clean_hospital_read_df['Number of Discharges'].astype(int)<100]\n", "hospital_cap_small = hospital_cap_small[hospital_cap_small['Number of Discharges'].astype(int) != 0]\n", "hospital_small=sorted(hospital_cap_small['Excess Readmission Ratio'])\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hospital_cap_big= clean_hospital_read_df[clean_hospital_read_df['Number of Discharges'].astype(int)>1000]\n", "hospital_cap_big = hospital_cap_big[hospital_cap_big['Number of Discharges'].astype(int) != 0]\n", "hospital_big=sorted(hospital_cap_big['Excess Readmission Ratio'])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "F_onewayResult(statistic=101.21608280273044, pvalue=3.7787539233400004e-23)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy import stats\n", "statistic = stats.f_oneway(hospital_small,hospital_big)\n", "statistic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Statistical significance\n", "\n", "With alpha=0.01 the p-value is way too less than alpha and hence we can say that the null hypotheis can be rejected and the difference is not due to randomness. There is a significant difference between the two groups but we should also consider various other factors that might influence this difference. \n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
joonasfo/python	Assignment_06.ipynb	1	79519	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Assignment: 06 Applications\n", "Introduction to Numerical Problem Solving, Spring 2017 \n", "4.3.2017, Joonas Forsberg<br />\n", "Helsinki Metropolia University of Applied Sciences\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Initial import statements\n", "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import sympy as sy\n", "from matplotlib.pyplot import \n", "from numpy import \n", "from numpy.linalg import \n", "from sympy import " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 01\n", "\n", "<b>Note: I assumed there is a mistake in the assignment and instead I copied the equations from the book. $µ_4$ is not defined but it was used in the equations. The results with the given expressions made no sense or I was just not able to calculate them accordingly.</b>\n", "\n", "Four blocks of different masses $m_i$ are connected by ropes of negligble mass. Three of the blocks lie on a inclined plane, the coefficients of friction between the blocks and the plane being $µ_i$. The equations of motion for the blocks can be shown to be:<br />\n", "\n", "$$T_1 + m_1a = m_1g(sin θ − µ_1 cos θ)$$\n", "$$−T_1 + T_2 + m_2a = m_2g(sin θ − µ_2 cos θ)$$\n", "$$−T_2 + T_3 + m_3a = m_3g(sin θ − µ_3 cos θ)$$\n", "$$−T_3 + m_4a = -m_4g$$\n", "\n", "where $T_i$ denotes the tensile forces in the ropes and a is the acceleration of the system. \n", "\n", "(a) Determine $a$ and $T_i$, when $θ = 45^{\\circ}$ and $g = 9.81 m/s^2$, $m = [10.0, 4.0, 5.0, 6.0]$ kg, and $µ = [0.25, 0.30, 0.20]$.\n", "\n", "(b) What the angle should be in order that the system is in balance? Try a couple of different values for angle and find out what are the values for $a$ and $T_i$. Based on these values make a graph (x-axis = angle = θ, y-axis = acceleration = a) and based on the graph estimate the angle giving the acceleration $a = 0.0 m/s^2$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before I actually start to create the code, it might be a good idea to convert the equations to matrix form:\n", "\n", "$$\n", "\\begin{bmatrix}\n", "1.0 & 0.0 & 0.0 \\\\\n", "-1.0 & 1.0 & 0.0 \\\\\n", "0.0 & -1.0 & 1.0\n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", "T_1 \\\\\n", "T_2 \\\\\n", "T_3 \n", "\\end{bmatrix}\n", "=\n", "\\begin{bmatrix}\n", "m_1g(sin θ - µ_1 cos θ) - m_1a\\\\\n", "m_2g(sin θ - µ_2 cos θ) - m_2a\\\\\n", "m_3g(sin θ - µ_3 cos θ) - m_3a\n", "\\end{bmatrix}\n", "$$\n", "\n", "...but from that point forward I have no idea how to continue with matrices, so I decided to use the sympy.solve() function to find out the equations for t1, t2 and t3 to be used when calculating a. Once I have a numerical value for a, I can use it to find out the values for t1, t2 and t3 using basic matrix calculations." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Value of a is: 1.61340242340770\n", "Values for t are:\n", "t1 = [35.8913571917233]\n", "t2 = [39.1801420232784]\n", "t3 = [50.7929878829616]\n" ] } ], "source": [ "# The value for gravity doesn't change as long as we're in on earth, so it can be defined \"globally\"\n", "gravity = 9.81\n", "\n", "# Define characteristics of our application\n", "mass = np.array(([10.0, 4.0, 5.0, 6.0]))\n", "friction = np.array(([0.25, 0.30, 0.20]))\n", " \n", "# Calculate value for acceleration\n", "def calculate_acc(theta):\n", "\n", " a = Symbol('a')\n", " \n", " t1 = Symbol('t1')\n", " temp = sy.solve([t1 + (mass[0] * a) - (mass[0] * gravity * (sin(theta) - friction[0] * (cos(theta))))], [t1, a])\n", " t1 = temp[t1]\n", "\n", " t2 = Symbol('t2')\n", " temp = sy.solve([-t1 + t2 + (mass[1] * a) - (mass[1] * gravity * (sin(theta) - friction[1] * (cos(theta))))], [t2, a])\n", " t2 = temp[t2]\n", "\n", " t3 = Symbol('t3')\n", " temp = sy.solve([-t2 + t3 + (mass[2] * a) - (mass[2] * gravity * (sin(theta) - friction[2] * (cos(theta))))], [t3, a])\n", " t3 = temp[t3]\n", "\n", " acc = np.array(zeros(1)) # Create array as the result sympy solve is in list format\n", " acc = sy.solve(-(t3) + mass[3]a + mass[3]gravity, a)\n", " return acc[0]\n", "\n", "a = calculate_acc(np.deg2rad(45.0))\n", "\n", "def calculate_t(theta):\n", " A = np.array(([1.0, 0.0, 0.0],\n", " [-1.0, 1.0, 0.0],\n", " [0.0, -1.0, 1.0]))\n", " b = np.array(([mass[0] * gravity * (sin(theta) - friction[0] * (cos(theta))) - (mass[0]a)],\n", " [mass[1] gravity * (sin(theta) - friction[1] * (cos(theta))) - (mass[0]a)],\n", " [mass[2] gravity * (sin(theta) - friction[2] * (cos(theta))) - (mass[0]a)]))\n", "\n", " t = dot(inv(A), b)\n", " \n", " return t\n", "\n", "t = calculate_t(np.deg2rad(45.0))\n", "\n", "print(\"Value of a is:\", a)\n", "print(\"Values for t are:\\nt1 = {}\\nt2 = {}\\nt3 = {}\".format(t[0], t[1], t[2]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have values for a, t1, t2 and t3 with the angle of 45$^{\\circ}$, we can start to iterate to find out the angle in which is the application is in balance. The application is in balance when acceleration equals 0.0 (e.g. it doesn't move one way or another)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Value of a at break point is: 0.0324279236377120\n", "Values for t are:\n", "t1 = [29.1789278625799]\n", "t2 = [38.9741692218080]\n", "t3 = [55.5036963398813]\n" ] }, { "data": { "application/javascript": [ "/ Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "iterations = 100\n", "step = -1\n", "\n", "theta = np.deg2rad(45.0)\n", "\n", "# Create empty arrays to store values into\n", "x = []\n", "y = []\n", "\n", "for i in range(iterations):\n", " \n", " aOld = a.copy\n", " \n", " # Increment theta by step\n", " theta = degrees(theta)\n", " theta += step\n", " theta = deg2rad(theta)\n", " \n", " # Perform calculations with new theta\n", " aOld = calculate_acc(theta)\n", " t = calculate_t(theta)\n", " \n", " x.append(degrees(theta))\n", " y.append(aOld)\n", " \n", " # Break if we go under 0.0 \n", " if aOld <= 0.0:\n", " break\n", " a = aOld\n", "\n", "print(\"Value of a at break point is: {}\".format(a))\n", "print(\"Values for t are:\\nt1 = {}\\nt2 = {}\\nt3 = {}\".format(t[0], t[1], t[2]))\n", " \n", "# Create plot\n", "figure('Problem 1')\n", "\n", "plt.plot(x, y)\n", "plt.ylabel(\"Acceleration (m/s^2)\")\n", "plt.xlabel(\"Angle (degrees)\")\n", "\n", "plt.grid()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the graph we can see to get acceleration of $0.0 m/s^2$ the angle must be roughly 31.7457 degrees. We can confirm this by using the functions previously created." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Value of a is: 4.28378074389000e-6\n", "Values for t are:\n", "t1 = [35.8913571917233]\n", "t2 = [39.1801420232784]\n", "t3 = [50.7929878829616]\n" ] } ], "source": [ "angle = deg2rad(31.7457)\n", "a = calculate_acc(angle)\n", "calculate_t(angle)\n", "\n", "print(\"Value of a is:\", a)\n", "print(\"Values for t are:\\nt1 = {}\\nt2 = {}\\nt3 = {}\".format(t[0], t[1], t[2]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So as we can see, the value of a is close to zero at the given angle." ] } ], "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 }	mit
Ledoux/ShareYourSystem	Pythonlogy/ShareYourSystem/Standards/Classors/Tester/PreReadme.ipynb	1	8667	{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n<!--\nFrozenIsBool False\n-->\n\n#Tester\n\n##Doc\n----\n\n\n> \n> The Tester helps for defining asserts between \n> attested Strs stored in the Attests Folder and\n> new calls of the attest functions. Thanks here \n> to a wrap of the unitest python module :\n> https://docs.python.org/2/library/unittest.html\n> \n> \n\n----\n\n<small>\nView the Tester notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Tester.ipynb)\n</small>\n\n", "cell_type": "markdown", "prompt_number": 0, "metadata": { "slideshow": { "slide_type": "slide" } } }, { "source": "\n<!---\nFrozenIsBool True\n-->\n\n##Example\n\nThe Incrementer from the previous Attester Module is now tested from its corresponding attesting function\nattest_increment. Here a test_increment method is implicitely defined in a unittest class and when we call\nthe test method, a unittest.run is done.", "cell_type": "markdown", "prompt_number": 1, "metadata": { "slideshow": { "slide_type": "subslide" } } }, { "cell_type": "code", "prompt_number": 2, "language": "python", "input": [ "#ImportModules\n", "import ShareYourSystem as SYS\n", "\n", "#Attest the module\n", "SYS.DecrementerClass(\n", " ).setAttest(\n", " SYS.TesterClass.DeriveClassor.AttestingFolderPathStr\n", " )\n", "SYS.Decrementer.test()\n", "\n", "\n", "\n" ], "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "###########################################\n", "\n", "AttestStr is :\n", "-1\n", "\n", "TestStr is :\n", " -1\n", "\n" ] } ], "collapsed": false, "metadata": { "slideshow": { "slide_type": "-" } } }, { "source": "\n<!--\nFrozenIsBool False\n-->\n\n##Code\n\n----\n\n<ClassDocStr>\n\n<small>\nView the Tester sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Standards/Classors/Tester\" target=\"_blank\">Github</a>\n</small>\n\n----\n\n```python\n# -- coding: utf-8 --\n\"\"\"\n\n\n<DefineSource>\n@Date : Fri Nov 14 13:20:38 2014 \\n\n@Author : Erwan Ledoux \\n\\n\n</DefineSource>\n\n\nThe Tester helps for defining asserts between \nattested Strs stored in the Attests Folder and\nnew calls of the attest functions. Thanks here \nto a wrap of the unitest python module :\nhttps://docs.python.org/2/library/unittest.html\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Standards.Classors.Attester\"\nDecorationModuleStr=BaseModuleStr\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\nimport collections\nimport copy\nimport os\nimport sys\nimport unittest\nimport ShareYourSystem as SYS\nfrom ShareYourSystem.Standards.Interfacers import Printer\nAttester=BaseModule\n#</ImportSpecificModules>\n\n#<DefineLocals>\nAttestingStr='attest'\n#</DefineLocals>\n\n#<DefineFunctions>\ndef setTestFunctionWithFolderPathStrAndAttestUnboundMethod(\n\t_FolderPathStr,_AttestUnboundMethod):\n\n\t#Definition\n\tAttestUnboundMethodStr=_AttestUnboundMethod.__name__\n\tTestUnboundMethodStr='at'.join(AttestUnboundMethodStr.split('at')[1:])\n\tTestModule=sys.modules[_AttestUnboundMethod.__module__]\n\n\t#Debug\n\t'''\n\tprint('l 50 Tester')\n\tprint('_AttestUnboundMethod is ',_AttestUnboundMethod)\n\tprint('_AttestUnboundMethod.__module__ is ',_AttestUnboundMethod.__module__)\n\tprint('')\n\t'''\n\n\t#Define\n\tdef test(_InstanceVariable):\n\n\t\t#Debug\n\t\t'''\n\t\tprint('Tester l 62')\n\t\tprint('_FolderPathStr is '+_FolderPathStr)\n\t\tprint('AttestUnboundMethodStr is '+AttestUnboundMethodStr)\n\t\tprint('')\n\t\t'''\n\t\t\n\t\t#Get the AssertedStr\n\t\tFile=open(_FolderPathStr+AttestUnboundMethodStr+'.txt','r')\n\t\tAttestStr=File.read()\n\t\tFile.close()\n\n\t\t#Call the attest function to get the TestVariable\n\t\tTestVariable=_AttestUnboundMethod(\n\t\t\t\t\t\tgetattr(\n\t\t\t\t\t\t\tSYS,\n\t\t\t\t\t\t\tSYS.getClassStrWithNameStr(\n\t\t\t\t\t\t\t\tTestModule.__name__.split('.')[-1]\n\t\t\t\t\t\t\t\tif '.' in TestModule.__name__ else TestModule\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t)()\n\t\t\t\t\t)\n\n\t\t#Bind with TestStr setting\n\t\tPrinter.RepresentingIdBool=False\n\t\tTestStr=Printer.getPrintStr(TestVariable)\n\t\tPrinter.RepresentingIdBool=True\n\n\t\t#Represent maybe\n\t\tif TestModule.TestingPrintIsBool:\n\n\t\t\t#debug\n\t\t\tprint(\"\\n###########################################\")\n\t\t\tprint(\"\")\n\t\t\tprint('AttestStr is :')\n\t\t\tprint(AttestStr)\n\t\t\tprint(\"\")\n\n\t\t\t#debug\n\t\t\tprint('TestStr is :')\n\t\t\tprint(TestStr)\n\t\t\tprint(\"\")\n\n\t\t#Assert\n\t\t#print(\"a\",AssertingStr)\n\t\t#print(\"b\",_InstanceVariable.TestedPointer.TestStr)\n\n\t\t#assert\n\t\t_InstanceVariable.assertEqual(\n\t\t\t\t#1,1\n\t\t\t\tAttestStr,\n\t\t\t\tTestStr\n\t\t)\n\n\t#Copy a form of the test function and name it differently\n\ttest.__name__=TestUnboundMethodStr\n\n\t#Debug\n\t'''\n\tprint('l 96 Tester')\n\tprint('TestModule is',TestModule)\n\tprint('')\n\t'''\n\t\n\t#Append in the Test OrderedDict\n\tTestModule.TestedOrderedDict[test.__name__]=test\n#</DefineFunctions>\n\n#<DefineClass>\n@DecorationClass()\nclass TesterClass(BaseClass):\n\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t\t\t\t\t\t]\n\n\tdef default_init(self,_KwargVariablesDict):\n\n\t\t#Call the parent init method\n\t\tBaseClass.__init__(self,_KwargVariablesDict)\n\n\tdef __call__(self,_Class):\n\n\t\t#debug\n\t\t'''\n\t\tprint('Tester l.146 __call__ method')\n\t\tprint('_Class is ',_Class)\n\t\tprint('')\n\t\t'''\n\t\t\n\t\t#Call the parent init method\n\t\tBaseClass.__call__(self,_Class)\n\n\t\t#Represent\n\t\tself.test()\n\n\t\t#Return\n\t\treturn _Class\n\n\tdef do_test(self):\n\t\t\n\t\t#Init in the classed Module\n\t\tif hasattr(self.Module,'TestingPrintIsBool')==False:\n\t\t\tself.Module.TestingPrintIsBool=True\n\t\tself.Module.TestedOrderedDict=collections.OrderedDict()\n\n\t\t#Debug\n\t\t'''\n\t\tprint('Tester l 160')\n\t\tprint('self.AttestingFolderPathStr is '+self.AttestingFolderPathStr)\n\t\tprint('')\n\t\t'''\n\n\t\t#set the tests for each asserting function\n\t\tmap(\n\t\t\t\tlambda __AttestedUnboundMethod:\n\t\t\t\tsetTestFunctionWithFolderPathStrAndAttestUnboundMethod(\n\t\t\t\t\tself.AttestingFolderPathStr,\n\t\t\t\t\t__AttestedUnboundMethod\n\t\t\t\t),\n\t\t\t\tself.AttestedUnboundMethodsList\n\t\t\t)\n\t\t\t\n\t\t#Definition the TestClass\n\t\tclass TestClass(unittest.TestCase):\t\t\t\t\n\n\t\t\t#Bind with the Tested object\n\t\t\tTestedPointer=self\n\n\t\t\t#Bound the setUp function\n\t\t\t#locals().__setitem__(setUp.__name__,setUp)\n\t\t\t\n\t\t\t#Bound each testing function\n\t\t\tfor __InstanceVariableedKeyStr,__InstanceVariableedMethod in self.Module.TestedOrderedDict.items():\n\t\t\t\tlocals().__setitem__(__InstanceVariableedKeyStr,__InstanceVariableedMethod)\n\n\t\t\ttry:\n\t\t\t\tdel TestedKeyStr,TestedMethod\n\t\t\texcept:\n\t\t\t\tpass\n\n\t\t#Give a name\n\t\tTestClass.__name__=SYS.getClassStrWithNameStr(self.NameStr+'Test')\n\n\t\t#set to the module of the classing class\n\t\tself.Module.TestedClass=TestClass\n\n\t\t#Definition\n\t\tdef test():\n\n\t\t\t#Call to the unittest runner\n\t\t\tTestLoader=unittest.TestLoader().loadTestsFromTestCase(\n\t\t\t\tself.Module.TestedClass\n\t\t\t)\n\t\t\tunittest.TextTestRunner(verbosity=2).run(TestLoader)\n\n\t\t#Link to the module of the classing class\n\t\tself.Module.test=test\n#</DefineClass>\n\n#Set\nTesterClass.DeriveClassor.AttestingFolderPathStr=Attester.AttesterClass.DeriveClassor.AttestingFolderPathStr\n\n```\n\n", "cell_type": "markdown", "prompt_number": 3, "metadata": { "slideshow": { "slide_type": "subslide" } } } ] } ], "metadata": { "name": "", "signature": "" }, "nbformat_minor": 0 }	mit
vharavu/unsupClust	customer_segments.ipynb	1	865944	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning Engineer Nanodegree\n", "## Unsupervised Learning\n", "## Project: Creating Customer Segments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Welcome to the third project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with 'Implementation' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `'TODO'` statement. Please be sure to read the instructions carefully!\n", "\n", "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide. \n", "\n", ">Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting Started\n", "\n", "In this project, you will analyze a dataset containing data on various customers' annual spending amounts (reported in monetary units) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.\n", "\n", "The dataset for this project can be found on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers). For the purposes of this project, the features `'Channel'` and `'Region'` will be excluded in the analysis — with focus instead on the six product categories recorded for customers.\n", "\n", "Run the code block below to load the wholesale customers dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wholesale customers dataset has 440 samples with 6 features each.\n" ] } ], "source": [ "# Import libraries necessary for this project\n", "import numpy as np\n", "import pandas as pd\n", "from IPython.display import display # Allows the use of display() for DataFrames\n", "\n", "# Import supplementary visualizations code visuals.py\n", "import visuals as vs\n", "\n", "# Pretty display for notebooks\n", "%matplotlib inline\n", "\n", "# Load the wholesale customers dataset\n", "try:\n", " data = pd.read_csv(\"customers.csv\")\n", " data.drop(['Region', 'Channel'], axis = 1, inplace = True)\n", " print \"Wholesale customers dataset has {} samples with {} features each.\".format(data.shape)\n", "except:\n", " print\"Dataset could not be loaded. Is the dataset missing?\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Exploration\n", "In this section, you will begin exploring the data through visualizations and code to understand how each feature is related to the others. You will observe a statistical description of the dataset, consider the relevance of each feature, and select a few sample data points from the dataset which you will track through the course of this project.\n", "\n", "Run the code block below to observe a statistical description of the dataset. Note that the dataset is composed of six important product categories: 'Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', and 'Delicatessen'. Consider what each category represents in terms of products you could purchase." ] }, { "cell_type": "code", "execution_count": 2, "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>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Detergents_Paper</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>12000.297727</td>\n", " <td>5796.265909</td>\n", " <td>7951.277273</td>\n", " <td>3071.931818</td>\n", " <td>2881.493182</td>\n", " <td>1524.870455</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>12647.328865</td>\n", " <td>7380.377175</td>\n", " <td>9503.162829</td>\n", " <td>4854.673333</td>\n", " <td>4767.854448</td>\n", " <td>2820.105937</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>3.000000</td>\n", " <td>55.000000</td>\n", " <td>3.000000</td>\n", " <td>25.000000</td>\n", " <td>3.000000</td>\n", " <td>3.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>3127.750000</td>\n", " <td>1533.000000</td>\n", " <td>2153.000000</td>\n", " <td>742.250000</td>\n", " <td>256.750000</td>\n", " <td>408.250000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>8504.000000</td>\n", " <td>3627.000000</td>\n", " <td>4755.500000</td>\n", " <td>1526.000000</td>\n", " <td>816.500000</td>\n", " <td>965.500000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>16933.750000</td>\n", " <td>7190.250000</td>\n", " <td>10655.750000</td>\n", " <td>3554.250000</td>\n", " <td>3922.000000</td>\n", " <td>1820.250000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>112151.000000</td>\n", " <td>73498.000000</td>\n", " <td>92780.000000</td>\n", " <td>60869.000000</td>\n", " <td>40827.000000</td>\n", " <td>47943.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen \\\n", "count 440.000000 440.000000 440.000000 440.000000 \n", "mean 12000.297727 5796.265909 7951.277273 3071.931818 \n", "std 12647.328865 7380.377175 9503.162829 4854.673333 \n", "min 3.000000 55.000000 3.000000 25.000000 \n", "25% 3127.750000 1533.000000 2153.000000 742.250000 \n", "50% 8504.000000 3627.000000 4755.500000 1526.000000 \n", "75% 16933.750000 7190.250000 10655.750000 3554.250000 \n", "max 112151.000000 73498.000000 92780.000000 60869.000000 \n", "\n", " Detergents_Paper Delicatessen \n", "count 440.000000 440.000000 \n", "mean 2881.493182 1524.870455 \n", "std 4767.854448 2820.105937 \n", "min 3.000000 3.000000 \n", "25% 256.750000 408.250000 \n", "50% 816.500000 965.500000 \n", "75% 3922.000000 1820.250000 \n", "max 40827.000000 47943.000000 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display a description of the dataset\n", "display(data.describe())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Selecting Samples\n", "To get a better understanding of the customers and how their data will transform through the analysis, it would be best to select a few sample data points and explore them in more detail. In the code block below, add three* indices of your choice to the `indices` list which will represent the customers to track. It is suggested to try different sets of samples until you obtain customers that vary significantly from one another." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Int64Index([0, 3, 11, 58, 91, 138, 177, 191, 225, 247, 323, 426], dtype='int64')\n", "Int64Index([ 42, 100, 137, 159, 164, 173, 175, 187, 188, 218, 221, 244, 253,\n", " 268, 304, 354, 382],\n", " dtype='int64')\n", "Int64Index([12, 30, 39, 68, 85, 100, 196, 211, 382], dtype='int64')\n", "Chosen samples of wholesale customers dataset:\n" ] }, { "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>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Detergents_Paper</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>13146</td>\n", " <td>1124</td>\n", " <td>4523</td>\n", " <td>1420</td>\n", " <td>549</td>\n", " <td>497</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>3087</td>\n", " <td>8080</td>\n", " <td>8282</td>\n", " <td>661</td>\n", " <td>721</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>56159</td>\n", " <td>555</td>\n", " <td>902</td>\n", " <td>10002</td>\n", " <td>212</td>\n", " <td>2916</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Detergents_Paper Delicatessen\n", "0 13146 1124 4523 1420 549 497\n", "1 3087 8080 8282 661 721 36\n", "2 56159 555 902 10002 212 2916" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Detergents_Paper</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>12000.297727</td>\n", " <td>5796.265909</td>\n", " <td>7951.277273</td>\n", " <td>3071.931818</td>\n", " <td>2881.493182</td>\n", " <td>1524.870455</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>12647.328865</td>\n", " <td>7380.377175</td>\n", " <td>9503.162829</td>\n", " <td>4854.673333</td>\n", " <td>4767.854448</td>\n", " <td>2820.105937</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>3.000000</td>\n", " <td>55.000000</td>\n", " <td>3.000000</td>\n", " <td>25.000000</td>\n", " <td>3.000000</td>\n", " <td>3.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>3127.750000</td>\n", " <td>1533.000000</td>\n", " <td>2153.000000</td>\n", " <td>742.250000</td>\n", " <td>256.750000</td>\n", " <td>408.250000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>8504.000000</td>\n", " <td>3627.000000</td>\n", " <td>4755.500000</td>\n", " <td>1526.000000</td>\n", " <td>816.500000</td>\n", " <td>965.500000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>16933.750000</td>\n", " <td>7190.250000</td>\n", " <td>10655.750000</td>\n", " <td>3554.250000</td>\n", " <td>3922.000000</td>\n", " <td>1820.250000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>112151.000000</td>\n", " <td>73498.000000</td>\n", " <td>92780.000000</td>\n", " <td>60869.000000</td>\n", " <td>40827.000000</td>\n", " <td>47943.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen \\\n", "count 440.000000 440.000000 440.000000 440.000000 \n", "mean 12000.297727 5796.265909 7951.277273 3071.931818 \n", "std 12647.328865 7380.377175 9503.162829 4854.673333 \n", "min 3.000000 55.000000 3.000000 25.000000 \n", "25% 3127.750000 1533.000000 2153.000000 742.250000 \n", "50% 8504.000000 3627.000000 4755.500000 1526.000000 \n", "75% 16933.750000 7190.250000 10655.750000 3554.250000 \n", "max 112151.000000 73498.000000 92780.000000 60869.000000 \n", "\n", " Detergents_Paper Delicatessen \n", "count 440.000000 440.000000 \n", "mean 2881.493182 1524.870455 \n", "std 4767.854448 2820.105937 \n", "min 3.000000 3.000000 \n", "25% 256.750000 408.250000 \n", "50% 816.500000 965.500000 \n", "75% 3922.000000 1820.250000 \n", "max 40827.000000 47943.000000 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Comparison with Mean--\n" ] }, { "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>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Detergents_Paper</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1146.0</td>\n", " <td>-4672.0</td>\n", " <td>-3428.0</td>\n", " <td>-1652.0</td>\n", " <td>-2332.0</td>\n", " <td>-1028.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-8913.0</td>\n", " <td>2284.0</td>\n", " <td>331.0</td>\n", " <td>-2411.0</td>\n", " <td>-2160.0</td>\n", " <td>-1489.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>44159.0</td>\n", " <td>-5241.0</td>\n", " <td>-7049.0</td>\n", " <td>6930.0</td>\n", " <td>-2669.0</td>\n", " <td>1391.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Detergents_Paper Delicatessen\n", "0 1146.0 -4672.0 -3428.0 -1652.0 -2332.0 -1028.0\n", "1 -8913.0 2284.0 331.0 -2411.0 -2160.0 -1489.0\n", "2 44159.0 -5241.0 -7049.0 6930.0 -2669.0 1391.0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Comparison with Median--\n" ] }, { "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>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Detergents_Paper</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>4642.0</td>\n", " <td>-2503.0</td>\n", " <td>-233.0</td>\n", " <td>-106.0</td>\n", " <td>-267.0</td>\n", " <td>-469.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-5417.0</td>\n", " <td>4453.0</td>\n", " <td>3526.0</td>\n", " <td>-865.0</td>\n", " <td>-95.0</td>\n", " <td>-930.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>47655.0</td>\n", " <td>-3072.0</td>\n", " <td>-3854.0</td>\n", " <td>8476.0</td>\n", " <td>-604.0</td>\n", " <td>1950.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Detergents_Paper Delicatessen\n", "0 4642.0 -2503.0 -233.0 -106.0 -267.0 -469.0\n", "1 -5417.0 4453.0 3526.0 -865.0 -95.0 -930.0\n", "2 47655.0 -3072.0 -3854.0 8476.0 -604.0 1950.0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Comparison with 75th percentile--\n" ] }, { "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>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Detergents_Paper</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-3788.0</td>\n", " <td>-6066.0</td>\n", " <td>-6133.0</td>\n", " <td>-2134.0</td>\n", " <td>-3373.0</td>\n", " <td>-1323.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-13847.0</td>\n", " <td>890.0</td>\n", " <td>-2374.0</td>\n", " <td>-2893.0</td>\n", " <td>-3201.0</td>\n", " <td>-1784.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>39225.0</td>\n", " <td>-6635.0</td>\n", " <td>-9754.0</td>\n", " <td>6448.0</td>\n", " <td>-3710.0</td>\n", " <td>1096.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Detergents_Paper Delicatessen\n", "0 -3788.0 -6066.0 -6133.0 -2134.0 -3373.0 -1323.0\n", "1 -13847.0 890.0 -2374.0 -2893.0 -3201.0 -1784.0\n", "2 39225.0 -6635.0 -9754.0 6448.0 -3710.0 1096.0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# TODO: Select three indices of your choice you wish to sample from the dataset\n", "indices = []\n", "print data[(data['Fresh'] >= 12647) & (data['Fresh'] <= 1.112647)].index\n", "print data[(data['Milk'] >= 7380) & (data['Milk'] <= 1.17380)].index\n", "print data[(data['Delicatessen'] >= 2820) & (data['Delicatessen'] <= 1.12820)].index\n", "#indices = [181, 85, 183]\n", "indices = [11, 137, 39]\n", "# Create a DataFrame of the chosen samples\n", "samples = pd.DataFrame(data.loc[indices], columns = data.keys()).reset_index(drop = True)\n", "#samples = pd.DataFrame(data.loc[indices], columns = data.keys())\n", "print \"Chosen samples of wholesale customers dataset:\"\n", "display(samples)\n", "\n", "display(data.describe()) # just to confirm nothing changed with the original\n", "print \"\\nComparison with Mean--\"\n", "display(samples - np.round(data.mean()))\n", "print \"\\nComparison with Median--\"\n", "display(samples - np.round(data.median()))\n", "print \"\\nComparison with 75th percentile--\"\n", "display(samples - np.round(data.quantile(q=0.75)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 1\n", "Consider the total purchase cost of each product category and the statistical description of the dataset above for your sample customers. \n", "What kind of establishment (customer) could each of the three samples you've chosen represent?* \n", "Hint: Examples of establishments include places like markets, cafes, and retailers, among many others. Avoid using names for establishments, such as saying \"McDonalds\" when describing a sample customer as a restaurant." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "\n", "See display statements for comparison of samples spendings vis-a-vis full data stats.\n", "\n", "Sample0 spends the median amount on Fresh and closer to the 25% on other items. It could be a small cafe.\n", "\n", "Sample1 spends near the median on Milk and above 50% on Groceries. This could be a retailer.\n", "\n", "Sample2 spends near median of Deli and above 75% on Milk. This could be a restaurant.\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Feature Relevance\n", "One interesting thought to consider is if one (or more) of the six product categories is actually relevant for understanding customer purchasing. That is to say, is it possible to determine whether customers purchasing some amount of one category of products will necessarily purchase some proportional amount of another category of products? We can make this determination quite easily by training a supervised regression learner on a subset of the data with one feature removed, and then score how well that model can predict the removed feature.\n", "\n", "In the code block below, you will need to implement the following:\n", " - Assign `new_data` a copy of the data by removing a feature of your choice using the `DataFrame.drop` function.\n", " - Use `sklearn.cross_validation.train_test_split` to split the dataset into training and testing sets.\n", " - Use the removed feature as your target label. Set a `test_size` of `0.25` and set a `random_state`.\n", " - Import a decision tree regressor, set a `random_state`, and fit the learner to the training data.\n", " - Report the prediction score of the testing set using the regressor's `score` function." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Fresh Milk Grocery Frozen Delicatessen\n", "0 12669 9656 7561 214 1338\n", "1 7057 9810 9568 1762 1776\n", "2 6353 8808 7684 2405 7844\n", "3 13265 1196 4221 6404 1788\n", "4 22615 5410 7198 3915 5185\n", " Detergents_Paper\n", "0 2674\n", "1 3293\n", "2 3516\n", "3 507\n", "4 1777\n" ] }, { "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>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>330.000000</td>\n", " <td>330.000000</td>\n", " <td>330.000000</td>\n", " <td>330.000000</td>\n", " <td>330.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>12341.412121</td>\n", " <td>5756.266667</td>\n", " <td>7894.872727</td>\n", " <td>3188.218182</td>\n", " <td>1555.769697</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>13040.056452</td>\n", " <td>7771.397189</td>\n", " <td>9780.044382</td>\n", " <td>5281.659636</td>\n", " <td>3111.264011</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>3.000000</td>\n", " <td>112.000000</td>\n", " <td>3.000000</td>\n", " <td>25.000000</td>\n", " <td>3.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>3390.250000</td>\n", " <td>1546.500000</td>\n", " <td>2125.000000</td>\n", " <td>749.750000</td>\n", " <td>405.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>8873.000000</td>\n", " <td>3363.500000</td>\n", " <td>4592.500000</td>\n", " <td>1519.500000</td>\n", " <td>910.500000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>16882.500000</td>\n", " <td>6795.250000</td>\n", " <td>10483.000000</td>\n", " <td>3718.750000</td>\n", " <td>1774.750000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>112151.000000</td>\n", " <td>73498.000000</td>\n", " <td>92780.000000</td>\n", " <td>60869.000000</td>\n", " <td>47943.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Delicatessen\n", "count 330.000000 330.000000 330.000000 330.000000 330.000000\n", "mean 12341.412121 5756.266667 7894.872727 3188.218182 1555.769697\n", "std 13040.056452 7771.397189 9780.044382 5281.659636 3111.264011\n", "min 3.000000 112.000000 3.000000 25.000000 3.000000\n", "25% 3390.250000 1546.500000 2125.000000 749.750000 405.000000\n", "50% 8873.000000 3363.500000 4592.500000 1519.500000 910.500000\n", "75% 16882.500000 6795.250000 10483.000000 3718.750000 1774.750000\n", "max 112151.000000 73498.000000 92780.000000 60869.000000 47943.000000" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>110.000000</td>\n", " <td>110.000000</td>\n", " <td>110.000000</td>\n", " <td>110.000000</td>\n", " <td>110.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>10976.954545</td>\n", " <td>5916.263636</td>\n", " <td>8120.490909</td>\n", " <td>2723.072727</td>\n", " <td>1432.172727</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>11382.607223</td>\n", " <td>6088.322109</td>\n", " <td>8659.367732</td>\n", " <td>3249.103387</td>\n", " <td>1673.861496</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>9.000000</td>\n", " <td>55.000000</td>\n", " <td>137.000000</td>\n", " <td>47.000000</td>\n", " <td>3.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>3044.000000</td>\n", " <td>1541.250000</td>\n", " <td>2412.250000</td>\n", " <td>675.750000</td>\n", " <td>456.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>7211.500000</td>\n", " <td>4335.500000</td>\n", " <td>5196.500000</td>\n", " <td>1555.000000</td>\n", " <td>1092.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>16973.000000</td>\n", " <td>8073.250000</td>\n", " <td>10885.250000</td>\n", " <td>3251.000000</td>\n", " <td>1946.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>56082.000000</td>\n", " <td>38369.000000</td>\n", " <td>59598.000000</td>\n", " <td>18028.000000</td>\n", " <td>14351.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Delicatessen\n", "count 110.000000 110.000000 110.000000 110.000000 110.000000\n", "mean 10976.954545 5916.263636 8120.490909 2723.072727 1432.172727\n", "std 11382.607223 6088.322109 8659.367732 3249.103387 1673.861496\n", "min 9.000000 55.000000 137.000000 47.000000 3.000000\n", "25% 3044.000000 1541.250000 2412.250000 675.750000 456.000000\n", "50% 7211.500000 4335.500000 5196.500000 1555.000000 1092.000000\n", "75% 16973.000000 8073.250000 10885.250000 3251.000000 1946.000000\n", "max 56082.000000 38369.000000 59598.000000 18028.000000 14351.000000" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "0.738270823177\n" ] }, { "data": { "text/plain": [ "[('Fresh', 0.033475403746747838),\n", " ('Milk', 0.028395485164218175),\n", " ('Grocery', 0.88680582252753892),\n", " ('Frozen', 0.027463411730545215),\n", " ('Delicatessen', 0.023859876830949835)]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.tree import DecisionTreeRegressor\n", "# TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature\n", "new_data = data.copy()\n", "droppedFeat = 'Detergents_Paper'\n", "new_data.drop([droppedFeat], axis = 1, inplace = True)\n", "labels = data.loc[:,[droppedFeat]]\n", "#labels = data[droppedFeat]\n", "print new_data.head()\n", "print labels.head()\n", "\n", "# TODO: Split the data into training and testing sets using the given feature as the target\n", "X_train, X_test, y_train, y_test = train_test_split(new_data, labels, test_size=0.25, random_state=3)\n", "display(X_train.describe())\n", "display(X_test.describe())\n", "\n", "# TODO: Create a decision tree regressor and fit it to the training set\n", "regressor = DecisionTreeRegressor(random_state=0)\n", "regressor.fit(X_train, y_train)\n", "\n", "# TODO: Report the score of the prediction using the testing set\n", "score = regressor.score(X_test, y_test)\n", "print score\n", "zip(new_data, regressor.feature_importances_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 2\n", "Which feature did you attempt to predict? What was the reported prediction score? Is this feature necessary for identifying customers' spending habits? \n", "Hint: The coefficient of determination, `R^2`, is scored between 0 and 1, with 1 being a perfect fit. A negative `R^2` implies the model fails to fit the data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "\n", "Delicatessen was the label I tried to predict. The R^2 score was negative, implying that the model is unable to predict. Hence this feature is necessary; removing it will be the total opposite of information gain; we will lose information.\n", "\n", "Detergents_Paper was also selected as label to predict. R^2 score was 0.75 implying the regressor was quite good. So this feature can probably be projected onto a latent feature.\n", "\n", "On reviewing the feature_importances of the regressor it can be seen that Grocery having a score of 0.88 can be used the best to derive Detergent_Paper." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize Feature Distributions\n", "To get a better understanding of the dataset, we can construct a scatter matrix of each of the six product features present in the data. If you found that the feature you attempted to predict above is relevant for identifying a specific customer, then the scatter matrix below may not show any correlation between that feature and the others. Conversely, if you believe that feature is not relevant for identifying a specific customer, the scatter matrix might show a correlation between that feature and another feature in the data. Run the code block below to produce a scatter matrix." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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kU6F7+jW4HmzpiBH2GxwczgBQNh3KlkNcguBZEiEfrbEAxaq9qrOMOq7H0bE8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\nPbw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xUhNbKYrpGHFTMdKdwfF9/vyNGRpegO+HxCyTd+7twzDg/FyDFy+XmKg4PL67m8e2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\nL+DQRIFdVSnmtt3YpmIQxsyu9dFac9dU6bKifQXX4qnjKyiVFsq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CGBSw1g95+sQq/TDi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P/Qoq68cGWeStNlMJfgwdE8NdvDEGEol+BLF6bpy4ROSNP1yaxSyMQwhCMDGSbL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\n/a8ffZD/4dfOUr8t4s4kXDDJpSwO9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"text/plain": [ "<matplotlib.figure.Figure at 0x64dcbe0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Produce a scatter matrix for each pair of features in the data\n", "#pd.plotting.scatter_matrix(data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');\n", "\n", "axes = pd.plotting.scatter_matrix(data, alpha = 0.3, figsize = (16,10), diagonal = 'kde')\n", "corr = data.corr().as_matrix()\n", "for i, j in zip(np.triu_indices_from(axes, k=1)):\n", " axes[i, j].annotate(\"%.3f\" %corr[i,j], (0.8, 0.8), xycoords='axes fraction', ha='center', va='center')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3\n", "Are there any pairs of features which exhibit some degree of correlation? Does this confirm or deny your suspicions about the relevance of the feature you attempted to predict? How is the data for those features distributed?* \n", "Hint: Is the data normally distributed? Where do most of the data points lie? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "\n", "Milk and Grocery is one pair that show some correlation. So does Detergents_Paper and Grocery.\n", "\n", "The matrix for Delicatessen - almost no correlation with all others. The data points are all concentrated along the X-axis. This confirms the inability of the regressor to predict Delicatessen as a function of the other features.\n", "\n", "In the scatter matrix, I have plotted the correlation also. The following pairs are highly correlated (>0.50)\n", "\n", "Milk-Grocery; Milk-D_P; Grocery-D_P; \n", "Fresh, Frozen and Deli aren't well correlated with any other features. \n", "\n", "Regarding data distribution of each feature -- Below, the boxplots for the data are plotted. It can be seen that for all features, the median is closer to the first quartile and the tail of the distribution is longer on the right side than left. Hence it can be concluded that the data is skewed right. The histograms in the diagonal of the scatter plot above also infers the same conclusion." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xfc77128>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xa4a0908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "color = dict(boxes='Green', whiskers='DarkOrange', medians='Black', caps='Yellow')\n", "data.plot.box(color=color, sym='r+', vert=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Preprocessing\n", "In this section, you will preprocess the data to create a better representation of customers by performing a scaling on the data and detecting (and optionally removing) outliers. Preprocessing data is often times a critical step in assuring that results you obtain from your analysis are significant and meaningful." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Feature Scaling\n", "If data is not normally distributed, especially if the mean and median vary significantly (indicating a large skew), it is most [often appropriate](http://econbrowser.com/archives/2014/02/use-of-logarithms-in-economics) to apply a non-linear scaling — particularly for financial data. One way to achieve this scaling is by using a [Box-Cox test](http://scipy.github.io/devdocs/generated/scipy.stats.boxcox.html), which calculates the best power transformation of the data that reduces skewness. A simpler approach which can work in most cases would be applying the natural logarithm.\n", "\n", "In the code block below, you will need to implement the following:\n", " - Assign a copy of the data to `log_data` after applying logarithmic scaling. Use the `np.log` function for this.\n", " - Assign a copy of the sample data to `log_samples` after applying logarithmic scaling. Again, use `np.log`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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zgPFxWy4e9V1VAa6Ofh4At0iJCYLw94G/D7C0tPTT/zzmIfKHr68iCQJ/76V7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9/Zrg+i7WQuBIoIZPtUJXisKALT9pgst5goNjkzXaVpewAczMf5c6eH+cDxPp49nA837Zvg4eEs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VGH0AqajKr/6FFzk/muGv/9rb/J+/e/2ZCIw3y3Ld4rXbRV67XbzPr6VLl63m5mKNN++U+Mbt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MR05eomFScpLTuknNNGC2NIFKec3RGdPjdMMFSZKEnZarqYmnLdZ3htN+l2pwZ+nIwpikP/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3RQBUqDmYmsJ0scH4Uo2elLlu87Ncny9fXMb3Bfs7Y4x2xpkqhB8PYKgyA5ko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rrAm7a48H8dsWh7c1s3/LOI6rGEwV6E5GaI74+anru3l6MEUqV+byrQnaogG++9wIAdsilStz\ny771ERpxbXeSyzrj5EoO7bEgqXyZwckpArZFayxAwCdcvjUxbSFqV1t0WhhYJODjuaEMT/RPsqc9\nRnfrhefCAbuayK7l8C9WkNzXEUMpxWAqTzzk83b3bTqbQiQjAV0wsinE8FSB1miAIzuaKDmKTL7E\n+Yk8+bKudRQN2OzfosOVfLbeDZjMlnCUIld0uGFXa9XpraXWXtB9zk17WimUXfIl7QiCTnifz/lx\nXUWu5BAJ2A3f568WtZLuflvYkSywpSnE4+cmcRxFYI5FjrloivjJlx22NUc4M5bHZ9u0x0Lsbo+w\ntSlMyG/xUN8ErldvbyCVQynIl9yqozw8VaC7VU/9ciWHyWyJna1RdrZGuLY7SYvnKI+mi7RGA9N2\nbPy2xf4tcfZvidMU8vPY2Qn2dsQoecXH9rRHCfps/JbwzWeGGEzlKZYdQNjaFCYa9M06h3Fd5RUE\n95kdonn4wLdOkgj5+Mnrd9bbFH7g8g7e/bVn+cZTQ/zE0R31NmfJLMX5ySmlXBEpi0gCGAKWKzB+\nu4jcDXxGKfWeZX7GirEWk7mz41keOzuJzxLa4yH2dMZmTZoHHV99ZkyHquztiJEvauGB/oncgs5P\nU9jP8/e187BXv6d/UtcECfqs6gCUjAbw2UK2WKY7GSbihalEgnpr3XEV5yfznBvLMZlPEfDplSSF\non88x862KNtawliik4FT+fKC4VDibcWvJaPpAidHMiSjgSWpP0WDPp6/rw2lpjtyylsBLzou+zri\nBHzWRfVKnr+vXSfOWtoBHc9qCdzTY1lP1Us8KdQAV+1spuzqCWhHIkDJUUQCvjlXyYem8kx4uULX\ndie57+Qo49kS21rCtMeC1dAcCxjNFNnWHMZxFYe7mniob5yetsg0p6giRZ4plBlLF8kUyt5uX5RH\nz07S7eUCaAUtBxEaSr58M3LF9ib2b4lzZjzLd0+MMpop0NMa5cCWBE0RPx3xEDf22Nz5yDme6k/R\nGg1w0542tifDnB7LcHokQ3MkQHsshOvmCfmj+O24J2ZxYSKuF0/0DmPJcQn6dCLznvYY5yfzFEsO\nzwxMEQ/5aQ4HcFCMZ/Ruaf9kjrFMkXPjecqOy817G1/2uJZ46IL61NU7mtmZDGvpf7T4x3x1wvon\ntKDJ+UkdfnRuPHeRM3F2Iofrwrnx3KzOj2VpxbUrtjWR8u7vlrCfA1sTiAi5Ypl7T47iurC1OcSh\nribOjGUZTherOYXZosO58Ryuq3jp4S1cvbOFouPy6JkJQn6bLYkQPtta9OpxpSxD2K9r4+SKDttb\n5h+LHj49zkS2xJam0IZS/lwsrqs4dj5Foexy+dY4HYkQHQkdchj1+7CD2pGei3zJ4dxEjmQkcFER\n6XDAxwsu08nubYkg+7ckqk7DzXvbcJXiob5x7js5SiLk5/pdLVo0B6nKeYPu+wtlh6Df4si25ur3\nPHJmgslsiZDfnnPh4sodzRzaliDos71dYEXZVYT8Nsf6U5QdXSOwOewnlS/R1RwmOofS41MDKc5P\n5PHZws172+acG21mTg6n+cqxAX75tj1LrlW4GhzcmmBbc5ivHhvY8M7PgyLSDPwTWvUtDdy/jO88\nD1wGFIDPebWCHqs8KSJvAN4AsHNn/bzbsuNydlyHadV2FsthMJXn6fNTZItlXFextzNOLDD3pX9m\nYIqyoyWS7zjQQSpXZixbXHRcsm1JNUn3ueE0mUKZZDTAtd0tWl7ZEhIhXZwzErSrtQ6aIwGeODep\nd6iG9PtsW0gEtSzyVMHhwdPjbG0JVzunw9sudnpOjWToG82wrTk8rVr4WnNiKM1UvsxktlTNpVks\nIsLMxcqhqQJ93qpnwLZmPbfacKVTIxl6RzJM5orVOOzxbLG649fdGiUcsJnIlLAtoT0RrBbFm0m+\n5PDgqTFOjmSIh/z80BVbq+Ga6XyZe06MMJouVL8nV9Srvw/06nCVV1+3Y846RxEvAVYE9nREAaE5\nou0olF064iFu2K1Xbxuh093sBHwWE9ki2ZJeJIkGfPh96Wr4TO94lr6xLOfG8+xuj5Au6JpiE14Y\nrKOUrtszR9J6ruhwf++YlxMQruYRXNeTZGSqwKNnxokF/VzhiWTsSIaJBXwkYwFSOX2vnR3Psbcj\nyni2VBV0WY+Os4jQFgtx854gZ8d1cci57oHhqUI1QVwpsH0yLZyswo6WCGfGsmybx3kou0qHv5XK\nZAs6mXyn50R958QIT/RPsqs1SkvJz0i6wDMD2jnriIfYvzXOswNTDEzmsC3hueEM+7bEOT+R5/un\nJ7At2N4SXnBXezZsLxR3IVxXMemFDo9likv+no3ASLrAwKTe8e8bzVYdnWcGp0jl9bXJlZw529MT\n57TCaJ+VmaYw2pkI4W7TixNBn8VT56c4dj7FVdt1gdqKQzuWKVZVZAM+m1v3Tf/dCmWHx89NEvTZ\ntET80wRWSt78oeS4uiDvjMGwWHa5/9QY+ZLDwa4EXc1hHj49wVi6yNZmvUP5+LlJOhNBOhMh2uMh\n9nXE5uwDKrtSZUefl9n8uZh/uvskftvi55+3q96mALpvfPGhTj7+vdPrsn9fiuDBL3t/vl9Evgwk\nap2WJXxOAe34ICJfAA4Dj9U8/0HggwDXXXdd3dIqjw+lOTeuZR5v2J28pF2Linxmc1iH2C2005SM\nBhhKFWiJ6rjwK3csPcn8cFcTZyeyWmlFhIlsyRtQBVuEsN8mW9QdbyV2HS6ozuxqixLxqo/v8VRm\nxtIlYkEf2YLj5SjNTt9ohrKjOD2WravzU8mBiQZ9Sw4vmA1dA0kX+FzMjV4JR6rUNwn4bLpnKN11\nxEM8N5TRu33jWV5wWfusISK2JUzmy+RLLmG/S7ZY5sj2JqYKZXwiHB/S+WO2JWxridDTGmEiV6JQ\n0oPYSLowp/MjIlw1o40NpfKMZ0vs9NqqqcPQWOxpj1FyFLmizj1J1uw+Rvw+9nbEyRUdupNRIp7K\nkm1btEYD2PbFjn0tqXypOvnpG80iCC6KiWxR57W16HDO2/d3TFuRrp0U6/AVi3wpzflJ3ZbWS+jb\nbJwcSdM74i18dFsXrcQD08IP989Q86plb0dsQWGdq3Y267CyRJChVIFIwCbosxic0gsc25sjhAM2\nB7YkKLsXhsmKeuiR7c2MZ0v0T+bo8PKXekczpAu6nkzvaGZZzs9isSzhss44A179us1ILOTD54Wg\nt9TcnzGvLp7fZ807LlWiDkQuLgtcyQV65MwExbLLWLpIKl+aFoVwTXcLftuiJeKftb1Z3kJo2VEX\n7bQc3t7E+Yk87fHgrONRulCuOiyj6SJbEiHG0sXq41ROl1fw2TLnmFbLgS0JekczVXEHw3SGpvL8\n50Pn+PHrtl/yYvxK8qKDnXz4O73cfXyYlx7eWm9zlsRSBA9eCXxDKTWplOoVkWYReYVS6rNL+UIR\niSulpryHNwMNKZ9duVdFuOR45fZ4kCM7mnBd5pyA1nLFtiZyHVppa7k0Rfw0RZpoiwU5OZyhIxGs\ndnCWJRzdlSSdL18U772vI0ZTWEuu1u5CNIX9PD0whXIVA6kcfp9MK/hXS1dzmNOj2UWd62qyrzPO\ntpYwQZ+97CTL4SktU72jJUIi5OfG3a2UvFyJhehp1YXkAj5r3oTAC21tbhv9tsUd+zu49+QozRE/\nbbEg0aCPDvTq3HiuRNlxOdTVVC0imYwE6EyEyJWci5yuhaiEaBgak+ZIgBt3t3K0J0mx7E4rHNrj\nLVzsa4+iELq9yeehrgQDk3maI/55w0raYkHa40GKjsvutiinRjJezbEQZUfni+xuj1V3B2cjEvBx\nqKuJ8UyJfMmZ19laH1w4gbnOJRnVoawlx72oIPVSSYR0zl3Gc1YqYWod8SBDsQDNEf+0e/26nhaK\nZbd6z1qWcPuBDiayRUJ+LX5wxbYEZ8ezBG0tkrLa7EhGNl1+YL7kcHosS7MXgnrz3jYcLxSswr6O\nGO0xra46X9jh4a4mBlP6fp1LAGVrU4ixTIFowHfRDlKthP5s+G2L63clmcyVLsrdSoT8JLbMfX83\nh/1V1cCdrREsS9jboUNiu1sjnB7TCwWLnTtFg7q/MMzOR77TS8l1+aVbl5tpsjpc35OkKeznK08O\nblznB3i7UurOygOl1ISnALck5we4VUT+BL37c49S6ntLfP+asK8jTjTgIxKwVyTUZymqOCIXHIvh\nqQL5kkNXc3hZKludidC0xOUKfnv66mXJcZnKl2kO+2d9fXNEh83dfXyYM2M5JnNlrt+VnPU7L+uM\ns68j1hBJrnM5aIuhUHZ47OwESmlBgmu7k0v6PBGhaxGhilft0Ku8yViAiWyJcMCedfWrsynEK67e\ndtFxv21dtHMDegJ0xfblDSiuqzg3kSPktxtqpckwHduSaY4P6DCXbLHMqJeDc3Ikw2Wdcfy2tajJ\nqG1N322u3SHoaYvS3RpZ9L19Tbcu5ijIuq4GvrstSshvEfTZ01bXZ7Jc1aN8ySFXdKp9cqHsVMOP\nXaV3bp8fDeC3La6eJexsLptqj3c1R/jFW3bPGsZkWBmOnU8xli5yZgxu3quVAWc2eRGZdedwJgHf\nwvdrZyJER83uzJQXTrfYnfpIwLesMbIiZV1LT1u0uttZiV5prVE1HJjM65DbppBpf0tgKl/iX+/r\n46WHtjRcfS2fbfHig518+YmBdde/L6XVz7b0sOS7Rin1RbRUdkNjW7KkVavxTJFC2aUzMfs28UK4\nruLMeHZazYHJXIlHz0wAOjb4smWGkLmumncHSynFA6fGyBYd2uPBaROfvtEMo5kiu9uixEN+bMvC\ndV0sgWcHp7BE2NMeveiz13PnVnJczoxlCfmtalhAbeX1lSbkt9nZGuG54TSnhjPYtnDT7tYldyS5\nosNIukB7LEBojgGt7LicHMnguIqiV5PhwJb4RSuLzw2nq/lNR3clF7XTZVgbxjJFxr0cwJltZDJb\n4sG+MTLFMsWSS3MkUN3lGU0XGEjl6WoKL2ryNReVe/vcRI7BVJ72mC6s2eLtGNfe+0GfzeBUAcdR\nZIrlai2pRiZfchjNTFe6sqzZ83cuBaUUE7kSzw5O0TuSoTUapLs1wr7OOPedHOXMaI5UvsTu9ihh\nn68aPn2pLKdv7h3JkC877G6L1V1it5EJ1ERXFMsu5yZytEQC80q9F8suJ0fShHz2nKGS81H5PUc9\nVU7QYgQrvWilFQgLBHzWtPMZyxTpHc3QHgtW50yVMa3CUCrPE+cmcV2F66pNtyN4KXzsvtNM5cu8\n+bY99TZlVl559TY+9dBZvnZssK61h5bKUgUP3g38Pbpy16+ihQ82PZO5Eg/1jQOQLUbZvQRlsQp9\nY1meG0oDWhJzpeonjKQL1d2LG3e3TstVKZZdjg9NYYtWfgOphlmAngQcH9Q2lR3F0Z4Wjva0MJkr\n8czAFPefGqMzESQSsBe1w7FeeGZgqpqoemR7E45S1doHK0HJcTk+mMZvC3tq6h5kCzqG2nEU+aKD\nqxRh/9wysTNXcL/x9CDPDE4RC/q4YVcrRcdl/5b4tPDFM+M5To9mGZzK47csr8Cpf/7BaAMVNFzv\nFMsuj5wZx3VhIlvi2m69C1B2XI4PpRlNF3FcRcTvY2dLkK7mUHUS9JgnrzuaLnLz3raLwuUW2hEY\nSuXpHc3SEQ+yMxnh6fMplILvnRylLRZkeKrAVTubOdqTXBdqTZW6bjNLATxyZoJ0vjyv0lWFfMnh\n2PkUtggHuxKLPu9i2eXB3jGe6J8k4rcZSBWIBX26vo+reHYwzWS2RCRgc3BrgrZ4cMmhu8vd4RnP\nFDkznq1GDYykC5zwxiZgVoU6g+byrQlaYwHiIT/PDEwxninSZ2W4ZW/7nE7jyZE0Z8d0fnE85Ju2\n0zqaLnBuIseWptCCc4Js0aHsuCjwxvMLn7MSu329oxfmKEe9RYxnh6Y4OZwmGQ0wltZiPnPdA2Ne\nu8qXdR28uUL5DBfIlxz++Z6T3LqvrWELjN+4u5WuphCfefjshnV+fhX4A+CT6ADorwK/shpGrTec\nmoTT2r+Xgl3TMVX+bgr7uXJHM7miM68y0HwMTxXoH88zkMqTLzm89PDWavhc32iG8xN6kt/ZFEJ5\nFadBO3SCVgLLFMsMpfJ8+ckBOuJBDnc1MZQqkC069E/k19VW52Kwq4mmOml1sWEBE9kiQZ99URjS\nTPpGs/RPVAY7fzU3am9HTH9n0EfvWJaRqQJt8eCsIW0nhtL0jmSmyciOpovVSfHxwSmaIwF6RzLT\nOs2QXw84Yb+NJdoRm63N7m6PEfTZhAK6oKKhMbiwg6uonQufHc9paWOl8wvaYkH2zZDTD/lsMk6Z\ngM/ieydHvdpmUfZ2xHh6IMXZsRwtUT/7OuOzqg4eH0qTKzqkciW2t4SJh/ykciVCPtsTVHEZTBUY\nSReqCdkVdbDxTLHuOYC1TGSLPHxaL1hds7NlWnhY5X4oV+QU5+HpgRT94zo8dGAyv+gV7al8iWzR\nIeSzKZR1blWtOmZPa4ThQIFtzZGqyttSePzsJIOpPLvao0uS+QcdupUrOgxP6eKFQZ9VFXqp5KEq\npRjPloh6xVQNGtuSaW0fZlcOrSXkq+wuQtC7vhW1zif6U5TKuuZOx4H57594yMdwuoiL4lrvcypO\ndqHscmR70yWJXJSdC/dD2XU5PZZlMlsiU3AI+XX9uNriphPZIgGfLnrakQiRjAVwUYQDNulCed7w\nUYPmkw+cYSRd5C237623KXNiWcKPXr2ND377JMNThXUTJr8UtbcM8LZVtGXdkowGuLwrQaHkLFvZ\nZkcyjN8n+CxrWgd1qQ1pW0uY+06OauUZSyiUnepkPuLtAlmWll2uTHj6J3Ic6/eqQe9o5tRohvtP\njlFwXAQ9Ye5pixKwLbYnw+uievtSuKwzTjykE0gX6/j0jmQ4MZTGtoQbds+fGxT11LdEmOYohQN2\n1ZH5xtODgF6FnY3zk9p5GpjMc3BrAssS7ri8g3tPjNIW13HWZWe6yhBolaCQJwDhuC4P9I5zYiiN\nq9S0HUvbkmlhC4bGwG9bXNfdUq2fUqGq6ObF4s92T17b3cJErkjQZ/HAKT3xn8jq9tU/kWMqX+Lx\ncxOMZ0oc2dF00UpzSyRArpijyUvAvra7RYeydbdwYjjNk/2TjEwVeOp8itZosLrSPbNQbyMwmStV\npeInc9NVso5s14nm7QustJ+byHFqJEvvSJrLOuNLWiRojgRojQWIBGx2JiN0zlgxv2lPG+OZ4qz5\nlwuhC9DqRa3+idySnZ9o0Eeu6FQXSOIhP9fvaqVQcqpj07Hzui5L0G/xvD1ty8pH3egsVmCkpy1K\nPOQj6Nf5xfmSw30nR3FcRbZY9vJyFnYwp/Jltnp9QrpQpgOYyBXJFvUO52CqcEnOz+72GLYlBP02\nrbEgmYLDaLrIvo4Yh7YlSIT81d2l06NZHRZvwQ27dMTJNTtbeOp8iljQN2dJB8MFimWXD3zrOa7r\nbpkzv7pReNU12/jHbz7HZx4+yxtf0JjheTNZcGYnIn+jlHqriPwXswTAKKV+ZFUsW2cstgbPXIhc\nWDFaLtlimeODaaJBm70degUxEfLziqu28ezgFPmSU627UbE5FvTht6crt+ktc12rougV5myLBzk3\nniMZDRAN+riuu4XCtqYFdznWI/Yy4vsz3jVzXEW+5DLfotbWpjCRgHZG55LM3r8lwbnx3Jw7fjuT\nEU6NZNjaFK6Gw2xvifDqo9rusuNWC87NpCUaIFss8/jZFIOTeRJeiEbXEmshGepDbRHOCoupxRTw\nWVWHpqctwni2VJ0Y70xGeSQ9Xk3YzxYcmJFieLArwZZEiNF0gfFMkZZooDqJuS6axLakuvtYctxp\nYT5DqTznvCLNjaAiuLUpzHi2VP27ltmu72xkC2XiQR8HtiY4sq1pSRM625KLhAscV3FmLEvAZ9Hl\n9c0LcX4yV91xqvx2tiVsm85wxAAAIABJREFUT4YZmMzTvQxVtyPbmpjIlYiHfNXJbCw4XU2sMqEu\nlFxKjottmX5jJosVGIHpoiL5klPdfexujbIjGSERWrgtbGkKMZYp4np1vEAvWNiWMDSV52DXpZWd\nsC2ZtkC2szVSVZGd6fxWxkPX1fnK0aAu3n3z3ovDSM+MZRlJF9jVFjW7QTX82/2n6Z/M8+c/dkXD\n51Dv7Yhz/a4k/3pfH6+/dfe6WAxZzLL2v3r//9VqGmK4dJ4byjA8VWB4ClqjwWpSc0s0QFs8yKnh\nDE/1pwjYVnVHabYV2e7WKMWyIuATOhNBXaPGtrhxd2u1M5+5a7HZ2dMeQykdJriYnbCFVsIXkint\nbo1eVDm+Fp9tMV80ysnhDHmvBtC5iSy72mM8cW5yXSSkG2ZnKbWYKosjFx7H2NUW5cRQGoVi+xxO\n98mRNBPZEmcmstMKL4LeMT1lZ2gK+y9y6p/sT+G4OsG/EZyfgG92hcSl0N0apeTofnIlch4rRZEr\n9i2kHOe6imP9Ou8qXShz67726nMHtiSWnZtjWbJgH7Z/S5y+kSzJmKnLstI0RwLs6YiRKZTZ2xFb\n9PX129ZFNQFtERA9Hzg9lqOreWV38+eybVdbFMfVIW7zteNC2akW6C2UXW7c3bqi9q1XpvIl3vf1\n49y4O8kLLmtf+A0NwC88r4c3f/xh7npqkJcc2lJvcxZkQedHKfWQ9/+3Vt+clWNoKk+h5LKtObzs\nGi/rjUTYx2AKfPbF8re1OUW+Ba6H37Y42HVh4IwFfcuWTN4shPz2RdKfy6HkuPRP5IiH/KsaTpgI\n+RmYzLOtJYzjhrBENs19Ypgd2xL2b5l7dXgiW2RoKo8lQsi2Lyq8GA365rwH4iEfE9nSolaw1wNl\nx+X8ZI7ORHDFioXWrpbai1jptbyd43S+vOZhRImQ34wJq8hKShpbIjioFVuNr5RBCPqsORcyFjse\n+i2LSEAXXDehcBf4p2+fZDRT5F9+8PKG3/Wp8KKDnWxrDvPh75zaGM6PiDzO7HpPAiil1JEVt+oS\nmcgWeezMJKBXExaqpr1R6G6N0hINEPRZFyWhdrdGCPqti+r7rBXrtbbEWttdUZoTgZv2tF5SnaL5\n2NkaoSWqY9HLrmIiu7z8AsPaUc97KF9yePj0uK4KL8J1PcklqTVdvbOFdL5MfIM4P0+vwn3a0xoh\n5LcILKGPvq67hUzB2TDXdT3SyGObZQlHe1q0YEJiZZz03tEMJ4f1DuU13dYlLdJVCq5r58e0YdBC\nVB+8+yQvP7L1op28RsZnW/zsTd2880tP8/3T47PWI2skFtPaXr7qVhhWjLlWT1Yip2i5jKYLPHZ2\nkqDP4tqelnWjDjQ0pWsThP0+rutpWRfyvUuhNkxqJQr5GlYHx1U81DdOulDi4Namuqqm+SwdMrvU\n9mJbYlQDF2CpffSZMZ1U3hwJcPU6miRtJEbSBR4/O0nQb3Fdd7IhayBFAj4iycbt3/22RVO48a5b\nPVBK8b/vfBy/ZfH7LztYb3OWzE/f2M0Hvn2S99x1nP/3uuvrbc68LCbsra/yt4h0A/uUUneJSHgx\n768HzZEAR3Y0VcPe1isDk3lS+RI7k5F1HVf92NlJzo5naY8HmcyW6Eisj3MZnCzgupAplEnlSosO\nb8kVHU6OpEnnS3Q1R5ZU0O2AV5dnKRLbho1NOq/bH+gE90txfopll77RDOGAPU3UI1ssc2ZMC5rM\npjAZ8ttcs1PX+NpINb2Wy6Xep2OZIsNTBbqaQ4vO1To9mqXoOPS0RvHZFgOpPEppRch8jYqnYe0Y\nTOW1KlvBYSJXXLH6fEul7Lj0jmYI+ux5xxulFH2jWcquYldbdFmhcD2tUfy2RdB3abs+hov5xP2n\n+c6JUf7kFYcbqjTAYokFfbzx+bt555ee5sHesYbOIV60uy0ivwR8GviAd2g78NnVMGol6IiH2JGM\nrNs8hkyhzBPnJjk9mq0mBK5HRtMFUrkS45kSuaJTl5C75bKtJUzQb9ES9S9JheapgRQP9o5zz4lR\nnjg3yWi6sOj3+myLna0RM6gYqujChwECPmvJKoQzeW44Td9olqfPT1VlrkELEpwZy/LY2QlKzuz1\nbZojAbq9ic9m51LuU9dVPHpmgjNjWR4/N7mo9wxPFXh2cIrekSy9ozrkaEdLBL/PojMRqtbfMawt\n25r1GNEc8ZOso1KZFsvQc4WRecabwVShWiPu9Fh2Wd9lWcKOZKQhhEs2Ek+cm+SP/+sYt+5r46ev\n31lvc5bNz9zUTVsswDu/9DRKNW6F9KUsFf0KcD3wPQCl1HER6VgVqwzYlmBbguOqhtxKXyx+n0Us\n5ONgV4K9HbF1NXFKRgPTFJQWS8C28NniiQjoa2AwLBdrFlnk5VK5/0SYlrMT8P722RZWg+YvbBT0\ntdd9e3CRfUOg5req/IZbmkLrcnV4I9EcWd4YsdLU3tfzjbG1cwm/be7zRuHMWJbXf/RBWqMB/uY1\nV63bRXvQYZa//ZL9/O5/Ps6d3z/Hj12zvd4mzcpSnJ+CUqpYSewTER+zCyEYlkjJcS/qsEJ+m6O7\nkmQKZdpXSE2oHiRCfq7rTlJwnLqFBKw1l29N0BYLki+XaQkHp+VhOa5CYF13bob1y572KImQj1DA\nnpa3c6grwUi6SCLsw7aEsuNuGgVA11UoWLPaFCLC0Z4kE9kSrbHF7RY0Rfwc7dlc/ahhYSpzh562\nKJGgTdC25y2jkIwGuLa7hbKrLrmAumFleHogxS9+5EFyJYd/f8ONK6YeWU9efe0OPnH/Gf78i0/z\nwoOdDanktxTn51si8r+BsIi8CPhl4L9Wx6zNw6NnJhieKrCtJczlW6fXZZhZWG69ohOdG6/xrxa2\nJbOuyI6mCzx6dgKfZXG0J2nqJBnWHBGZNVzFZ1vVNjs8VeDxcxP4bd1O13O+4UJkCmUe7BvHdRVX\n7Whes7DckN9mS9PSrutm60cN8/NQ3zjjmSI9bRH2dsQX7RSvp9DzjUz/RI5PPXiWf/zWCZrCfj7+\n+hsumgOuVyxL+JMfPcQr/+G7/NHnnuTdr7mq3iZdxFJm1m8DfhF4HHgj8EXgQ6th1GZBKcXwlI7P\nHZoqcPnWOhtkWFVGM0VcF4quy0SuSDhgEscNjcfwlBb6KLguqVxpQzs/49kipbLOcRrNFMzE0LAu\nKDku4xmdszeYKlxUtNjQeAxN5bn3uVHuOznKvc+N0juqc65edsVW3v7DBzdcDtWR7c285fa9vPfr\nx7ntQAc/cmVXvU2axqKdH6WUKyKfBT6rlBpeRZs2DSLC7vYo5yfz7JxFoaXkuDzZn0IpxcGuxLqR\niF4tekcyDE0V6Gldn8mWXc1hxjJF/PbC1dsNG4/To1kGUnm6WyMNXVNpRzLMZK5E0L/x1Zw64iEe\n6B0jlStzqGtjrLoa5qbkuBzrT+EoxcGtiXXr2Ptti+7WCENThRUtiGpYWXpHMvznw2e566khnjqf\nAiAe9HHD7iSvvbGbW/e1z1tYer3zq3fs5e7jw/zupx9jV2u0oQojL6bIqQBvB96CLmwqIuIAf6uU\nescq27fh2d0eY3f77EVYBybzjHg7Q/0TeXa1RXFdxZP9KTLFMpdvTcwb37uRKDsuJ4bSABwfSq9L\n5ycW9HHj7tY1/c5i2eXxc5MopTi8rWndDvbrHddVPDuoVRufHZxqKOdnaCrPiaE0bbEgl3XGiYf8\n3LRnbdtpvSiUHeJBP/Ggn/7JPFsbUMb75HCagVSentaokRm/RAYm89Voi3MTOfbMMfauB/Z1xtnX\nGadYdnmob9z08Q2CUor7T43xoXtOcddTgwhwXU+S333pAW7e28qhrqY1yy+sNz7b4v0/cy2v/Pvv\n8rqPPsAn33DjnPPdtWYxUjNvBW4GjiqlWpVSSeAG4GYR+fVVtW6Tkwj7sS2tGNbsOTnj2SKDqTzp\nfJkzy5SqXI/UFklsqaOk6HpjMJVnPFNkIlvi3ESu3uZsWixLaG7Q9ntyOEO24HB6NEu+5NTbnDUl\n7LeruXeNuMvluqr6+zw3nK63OeuepsiFMbXR7sPlUtvH95s+vm6UHJfPPXKOH/m77/CaD97Hg71j\nvOX2vdz3ez/Af7zxJt582x6ObG/eNI5PhY54iI/8wlFcV/Hj77+Xh/rG6m0SsLiwt58FXqSUGqkc\nUEqdFJHXAl8F3rNaxm12msJ+bt7bhkJVQ95iIR8hv02h7CxaKWgjICJcu7PFFPNbIs0RPz5bUIq6\n1qEwwDVe+220mizt8SDpfJlE2D9NUnkz4LMtbtzdSrHsNqQAiWUJyViAsXTRhMquAInQxWPqeqc5\n4se2BVRjOvAbGcdVPNk/yX8/fp7PP9LP+ck8u9ui/OkrDvOqa7Y3ZJ9SD/Z1xvn0m5/Hz/3L/bz6\n/ffyS7fu5s237VlS/cSVZjGzSH+t41NBKTUsIpsj5qqOzKzxE/TZPG9PK45S66pmzkpgWWIcnyUS\nD/m5dV87SqlpdV0Ma0+jtt897TG2t4QJ2BayCWv82JY09CTl6h3NFB13w0zW6816rps3G/GQn+eb\nPh7QIWer0YdlCmUGU3n6xrL0jWToHc1yYijNI2cmSBfK+Czhln1t/MmPHuaOAx2bokTAUtnVFuUL\nv3YLf/qFY3zg2yf52H19vOTwFl56aAvX70quuSO0mJG4uMznDKuEZQkW5uYyLA69zW7ai2FuzMS6\ncRER8/sY5sX08Zp3felp/vmeUwR8FmG/TSLsJxHyef/7SYR93v86/DFXdMiX9L9cySFXcquP04Uy\nI+kCI1NFcjPCgaMBm562KK+4uovrupO84LJ2oxS5CBIhP//3x6/kF2/ZzYfuPslXnhzgMw+fA3QN\nuiPbm9m/Jc7+zjiXbYnT1RRatQW5xTg/V4pIapbjAjRO1q7BYDAYDAaDYVNy055WbEsoll2yJYep\nfJlUrkQqr/OhUt7jgidvD1QdpbDfJuS3CHl5gNGAj507I7TFgrTFgnTEg3S3RuhujdIWC2zKXfKV\nYv+WOH/56iv5s1dewcOnx3mob5yH+8a57+Qod37/XPV1Yb9NRyJIeyxIPOTDZ1v4bcHyrr0Cgra1\nrDpCCzo/Simz5LQMzoxl6Z/IsSMZWXWFnuGpAqdGMrTGAtPUaxxXcaw/RdFxuHxrYs1DblxXcex8\ninxJf390AxRs3Ug8MzDFRFZv3pZdhasUiZCfQ12JecMnckWHY+dT+G3h4Nb5X2tYPzw3nGY0XWR3\ne3TO/JKhVJ5TIxmCPouio0hG/YuuMTKSLnByOEMyGmBvR2Mo/iyFvtEMA5N5etqiq6LWV9tfHtia\nWPEC10u9b0fTBZ5bx7/XSjKRLfLsYJrmiJ/LOtenNPGJoTRjGX1/54rOsucnmUKZp86nCPltDm5N\nNFSI1237O7htf8eCr8uXHBxXEfLbm06AoJEI+HTOZa0K7mSuxLODUzwzMMWpkQzDUwWGpvKMpIuU\nHBfHVTiuqm50LjeH1sxGVwGltKytUlqWebWdnxNDaTIFvaKxvSVcDZEYniowmMoDcGYst+Z68qOZ\nIgOT+vv7RrMcNHU0GoZUvsSZsSxT+RJD6QIRv02h7NLTGmVwqsC2edrsmfFstcBee7zA1iYjv7ve\nyZccTg1nAKqy17NxfChNrujw0OAUe9qjpHIltjVHFpUzc2IoTTp/oZ9aT5K8rqs4PqjV1lZLqnws\ne6G/7B3JcHjbytbEOLvE+/bEULq6cr7efq+V5rnhjN5ByJXY2hQiHlpf6c75kkPvyIX7O1Moo5Ru\ny0udn/SNZpnIloASHYkgHfH1FwC0mdtyo9MU9nO0J8nRnuSqfo9Zsl0FRKQa/7kWClsV1bdYyIff\nuvCTJsI+fLYgAi2Rte+s4yEffi+51KjQNBYVid+Q36Y1EiAa9BEP+bBtIRGaf02kJRJABO+162sS\nYJidgG0R8373+e7VijxwV3MY27KIBn0EF5lA3hq90E+tN1W5Wqny1erLYsEL/eVqKHk2L/G+rR1X\n1tvvtdJUfvNIwG44tcbFUHt/t0YD1flJa3TpCoLJqG5Hfp9l+n/DukWUUvW2YU5EZBjoq7cdl0gb\ncJFa3gZltc71GuDhVf6O5dAotjSKHbD6ttS2hfVKI/1ea8FqnG8920Gj/36byb7VaAeNcP3qbUO9\nv3+pNmyEcWEmjfAb1JPlnH+3Uqp9MS9saOdnIyAiDyqlrqu3HWvBWpxrI13PRrGlUeyAxrKlUdls\n12ijnW+jn4+x79JoBPvqbUO9v79RbKgn5vxX9/w39162wWAwGAwGg8Fg2DQY58dgMBgMBoPBYDBs\nCozzs/p8sN4GrCFrca6NdD0bxZZGsQMay5ZGZbNdo412vo1+Psa+S6MR7Ku3DfX+fmgMG+qJOf9V\nxOT8GAwGg8FgMBgMhk2B2fkxGAwGg8FgMBgMmwLj/BgMBoPBYDAYDIZNgXF+DAaDwWAwGAwGw6bA\nOD+rhIgcFpGfFJGj9bZlIyEiv1Kn793q/S8i8goR+T3v9/WtsR1+EflhEXme9/i1IvIrItK8lnYY\nlofpF9YnIhIVke0iEqu3LYaNiWljBjBjxFphBA9WEBH5slLqpSLyVuAHgP8GbgbOKaXeVl/rVh4R\nuRa4EWgBJoD7lFIPruDn3w1UGqh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"text/plain": [ "<matplotlib.figure.Figure at 0xff66be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy import stats\n", "# TODO: Scale the data using the natural logarithm\n", "log_data = np.log(data)\n", "\n", "# TODO: Scale the sample data using the natural logarithm\n", "log_samples = np.log(samples)\n", "\n", "# Produce a scatter matrix for each pair of newly-transformed features\n", "pd.plotting.scatter_matrix(log_data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Observation\n", "After applying a natural logarithm scaling to the data, the distribution of each feature should appear much more normal. For any pairs of features you may have identified earlier as being correlated, observe here whether that correlation is still present (and whether it is now stronger or weaker than before).\n", "\n", "Run the code below to see how the sample data has changed after having the natural logarithm applied to it." ] }, { "cell_type": "code", "execution_count": 8, "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>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Detergents_Paper</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>9.483873</td>\n", " <td>7.024649</td>\n", " <td>8.416931</td>\n", " <td>7.258412</td>\n", " <td>6.308098</td>\n", " <td>6.208590</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>8.034955</td>\n", " <td>8.997147</td>\n", " <td>9.021840</td>\n", " <td>6.493754</td>\n", " <td>6.580639</td>\n", " <td>3.583519</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>10.935942</td>\n", " <td>6.318968</td>\n", " <td>6.804615</td>\n", " <td>9.210540</td>\n", " <td>5.356586</td>\n", " <td>7.977968</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Detergents_Paper Delicatessen\n", "0 9.483873 7.024649 8.416931 7.258412 6.308098 6.208590\n", "1 8.034955 8.997147 9.021840 6.493754 6.580639 3.583519\n", "2 10.935942 6.318968 6.804615 9.210540 5.356586 7.977968" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display the log-transformed sample data\n", "display(log_samples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Outlier Detection\n", "Detecting outliers in the data is extremely important in the data preprocessing step of any analysis. The presence of outliers can often skew results which take into consideration these data points. There are many \"rules of thumb\" for what constitutes an outlier in a dataset. Here, we will use [Tukey's Method for identfying outliers](http://datapigtechnologies.com/blog/index.php/highlighting-outliers-in-your-data-with-the-tukey-method/): An outlier step is calculated as 1.5 times the interquartile range (IQR). A data point with a feature that is beyond an outlier step outside of the IQR for that feature is considered abnormal.\n", "\n", "In the code block below, you will need to implement the following:\n", " - Assign the value of the 25th percentile for the given feature to `Q1`. Use `np.percentile` for this.\n", " - Assign the value of the 75th percentile for the given feature to `Q3`. Again, use `np.percentile`.\n", " - Assign the calculation of an outlier step for the given feature to `step`.\n", " - Optionally remove data points from the dataset by adding indices to the `outliers` list.\n", "\n", "NOTE: If you choose to remove any outliers, ensure that the sample data does not contain any of these points! \n", "Once you have performed this implementation, the dataset will be stored in the variable `good_data`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q1=8.04805870221\n", "Q3=9.73706394795\n", "step=2.53350786861\n", "Data points considered outliers for the feature 'Fresh':\n" ] }, { "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>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Detergents_Paper</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>65</th>\n", " <td>4.442651</td>\n", " <td>9.950323</td>\n", " <td>10.732651</td>\n", " <td>3.583519</td>\n", " <td>10.095388</td>\n", " <td>7.260523</td>\n", " </tr>\n", " <tr>\n", " <th>66</th>\n", " <td>2.197225</td>\n", " <td>7.335634</td>\n", " <td>8.911530</td>\n", " <td>5.164786</td>\n", " <td>8.151333</td>\n", " <td>3.295837</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td>5.389072</td>\n", " <td>9.163249</td>\n", " <td>9.575192</td>\n", " <td>5.645447</td>\n", " <td>8.964184</td>\n", " <td>5.049856</td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td>1.098612</td>\n", " <td>7.979339</td>\n", " <td>8.740657</td>\n", " <td>6.086775</td>\n", " <td>5.407172</td>\n", " <td>6.563856</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td>3.135494</td>\n", " <td>7.869402</td>\n", " <td>9.001839</td>\n", " <td>4.976734</td>\n", " <td>8.262043</td>\n", " <td>5.379897</td>\n", " </tr>\n", " <tr>\n", " <th>128</th>\n", " <td>4.941642</td>\n", " <td>9.087834</td>\n", " <td>8.248791</td>\n", " <td>4.955827</td>\n", " <td>6.967909</td>\n", " <td>1.098612</td>\n", " </tr>\n", " <tr>\n", " <th>171</th>\n", " <td>5.298317</td>\n", " <td>10.160530</td>\n", " <td>9.894245</td>\n", " <td>6.478510</td>\n", " <td>9.079434</td>\n", " <td>8.740337</td>\n", " </tr>\n", " <tr>\n", " <th>193</th>\n", " <td>5.192957</td>\n", " <td>8.156223</td>\n", " <td>9.917982</td>\n", " <td>6.865891</td>\n", " <td>8.633731</td>\n", " <td>6.501290</td>\n", " </tr>\n", " <tr>\n", " <th>218</th>\n", " <td>2.890372</td>\n", " <td>8.923191</td>\n", " <td>9.629380</td>\n", " <td>7.158514</td>\n", " <td>8.475746</td>\n", " <td>8.759669</td>\n", " </tr>\n", " <tr>\n", " <th>304</th>\n", " <td>5.081404</td>\n", " <td>8.917311</td>\n", " <td>10.117510</td>\n", " <td>6.424869</td>\n", " <td>9.374413</td>\n", " <td>7.787382</td>\n", " </tr>\n", " <tr>\n", " <th>305</th>\n", " <td>5.493061</td>\n", " <td>9.468001</td>\n", " <td>9.088399</td>\n", " <td>6.683361</td>\n", " <td>8.271037</td>\n", " <td>5.351858</td>\n", " </tr>\n", " <tr>\n", " <th>338</th>\n", " <td>1.098612</td>\n", " <td>5.808142</td>\n", " <td>8.856661</td>\n", " <td>9.655090</td>\n", " <td>2.708050</td>\n", " <td>6.309918</td>\n", " </tr>\n", " <tr>\n", " <th>353</th>\n", " <td>4.762174</td>\n", " <td>8.742574</td>\n", " <td>9.961898</td>\n", " <td>5.429346</td>\n", " <td>9.069007</td>\n", " <td>7.013016</td>\n", " </tr>\n", " <tr>\n", " <th>355</th>\n", " <td>5.247024</td>\n", " <td>6.588926</td>\n", " <td>7.606885</td>\n", " <td>5.501258</td>\n", " <td>5.214936</td>\n", " <td>4.844187</td>\n", " </tr>\n", " <tr>\n", " <th>357</th>\n", " <td>3.610918</td>\n", " <td>7.150701</td>\n", " <td>10.011086</td>\n", " <td>4.919981</td>\n", " <td>8.816853</td>\n", " <td>4.700480</td>\n", " </tr>\n", " <tr>\n", " <th>412</th>\n", " <td>4.574711</td>\n", " <td>8.190077</td>\n", " <td>9.425452</td>\n", " <td>4.584967</td>\n", " <td>7.996317</td>\n", " <td>4.127134</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Detergents_Paper Delicatessen\n", "65 4.442651 9.950323 10.732651 3.583519 10.095388 7.260523\n", "66 2.197225 7.335634 8.911530 5.164786 8.151333 3.295837\n", "81 5.389072 9.163249 9.575192 5.645447 8.964184 5.049856\n", "95 1.098612 7.979339 8.740657 6.086775 5.407172 6.563856\n", "96 3.135494 7.869402 9.001839 4.976734 8.262043 5.379897\n", "128 4.941642 9.087834 8.248791 4.955827 6.967909 1.098612\n", "171 5.298317 10.160530 9.894245 6.478510 9.079434 8.740337\n", "193 5.192957 8.156223 9.917982 6.865891 8.633731 6.501290\n", "218 2.890372 8.923191 9.629380 7.158514 8.475746 8.759669\n", "304 5.081404 8.917311 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" <th>343</th>\n", " <td>7.431892</td>\n", " <td>8.848509</td>\n", " <td>10.177932</td>\n", " <td>7.283448</td>\n", " <td>9.646593</td>\n", " <td>3.610918</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Detergents_Paper \\\n", "66 2.197225 7.335634 8.911530 5.164786 8.151333 \n", "109 7.248504 9.724899 10.274568 6.511745 6.728629 \n", "128 4.941642 9.087834 8.248791 4.955827 6.967909 \n", "137 8.034955 8.997147 9.021840 6.493754 6.580639 \n", "142 10.519646 8.875147 9.018332 8.004700 2.995732 \n", "154 6.432940 4.007333 4.919981 4.317488 1.945910 \n", "183 10.514529 10.690808 9.911952 10.505999 5.476464 \n", "184 5.789960 6.822197 8.457443 4.304065 5.811141 \n", "187 7.798933 8.987447 9.192075 8.743372 8.148735 \n", "203 6.368187 6.529419 7.703459 6.150603 6.860664 \n", "233 6.871091 8.513988 8.106515 6.842683 6.013715 \n", "285 10.602965 6.461468 8.188689 6.948897 6.077642 \n", "289 10.663966 5.655992 6.154858 7.235619 3.465736 \n", "343 7.431892 8.848509 10.177932 7.283448 9.646593 \n", "\n", " Delicatessen \n", "66 3.295837 \n", "109 1.098612 \n", "128 1.098612 \n", "137 3.583519 \n", "142 1.098612 \n", "154 2.079442 \n", "183 10.777768 \n", "184 2.397895 \n", "187 1.098612 \n", "203 2.890372 \n", "233 1.945910 \n", "285 2.890372 \n", "289 3.091042 \n", "343 3.610918 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[[65, 66, 81, 95, 96, 128, 171, 193, 218, 304, 305, 338, 353, 355, 357, 412], [86, 98, 154, 356], [75, 154], [38, 57, 65, 145, 175, 264, 325, 420, 429, 439], [75, 161], [66, 109, 128, 137, 142, 154, 183, 184, 187, 203, 233, 285, 289, 343]]\n", "Number of possible outliers 48\n", "[(154, 3), (128, 2), (65, 2), (66, 2), (75, 2), (193, 1), (264, 1), (137, 1), (142, 1), (145, 1), (412, 1), (285, 1), (161, 1), (420, 1), (38, 1), (171, 1), (429, 1), (175, 1), (304, 1), (305, 1), (439, 1), (184, 1), (57, 1), (187, 1), (203, 1), (325, 1), (289, 1), (81, 1), (338, 1), (86, 1), (343, 1), (218, 1), (95, 1), (96, 1), (353, 1), (98, 1), (355, 1), (356, 1), (357, 1), (233, 1), (109, 1), (183, 1)]\n" ] }, { "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>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Detergents_Paper</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " <td>440.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>8.730544</td>\n", " <td>8.121047</td>\n", " <td>8.441169</td>\n", " <td>7.301396</td>\n", " <td>6.785972</td>\n", " <td>6.665133</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>1.480071</td>\n", " <td>1.081365</td>\n", " <td>1.116172</td>\n", " <td>1.284540</td>\n", " <td>1.721020</td>\n", " <td>1.310832</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.098612</td>\n", " <td>4.007333</td>\n", " <td>1.098612</td>\n", " <td>3.218876</td>\n", " <td>1.098612</td>\n", " <td>1.098612</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>8.048059</td>\n", " <td>7.334981</td>\n", " <td>7.674616</td>\n", " <td>6.609678</td>\n", " <td>5.548101</td>\n", " <td>6.011875</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>9.048286</td>\n", " <td>8.196159</td>\n", " <td>8.467057</td>\n", " <td>7.330388</td>\n", " <td>6.705018</td>\n", " <td>6.872645</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>9.737064</td>\n", " <td>8.880480</td>\n", " <td>9.273854</td>\n", " <td>8.175896</td>\n", " <td>8.274341</td>\n", " <td>7.506728</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>11.627601</td>\n", " <td>11.205013</td>\n", " <td>11.437986</td>\n", " <td>11.016479</td>\n", " <td>10.617099</td>\n", " <td>10.777768</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Detergents_Paper \\\n", "count 440.000000 440.000000 440.000000 440.000000 440.000000 \n", "mean 8.730544 8.121047 8.441169 7.301396 6.785972 \n", "std 1.480071 1.081365 1.116172 1.284540 1.721020 \n", "min 1.098612 4.007333 1.098612 3.218876 1.098612 \n", "25% 8.048059 7.334981 7.674616 6.609678 5.548101 \n", "50% 9.048286 8.196159 8.467057 7.330388 6.705018 \n", "75% 9.737064 8.880480 9.273854 8.175896 8.274341 \n", "max 11.627601 11.205013 11.437986 11.016479 10.617099 \n", "\n", " Delicatessen \n", "count 440.000000 \n", "mean 6.665133 \n", "std 1.310832 \n", "min 1.098612 \n", "25% 6.011875 \n", "50% 6.872645 \n", "75% 7.506728 \n", "max 10.777768 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# For each feature find the data points with extreme high or low values\n", "import collections\n", "dupCnt = collections.Counter()\n", "outlierIndices = []\n", "concatFrList = []\n", "for feature in log_data.keys():\n", " \n", " # TODO: Calculate Q1 (25th percentile of the data) for the given feature\n", " Q1 = np.percentile(log_data[feature], 25)\n", " print \"Q1={}\".format(Q1)\n", " \n", " # TODO: Calculate Q3 (75th percentile of the data) for the given feature\n", " Q3 = np.percentile(log_data[feature], 75)\n", " print \"Q3={}\".format(Q3)\n", " \n", " # TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)\n", " step = (Q3 - Q1)1.5\n", " print \"step={}\".format(step)\n", " \n", " # Display the outliers\n", " print \"Data points considered outliers for the feature '{}':\".format(feature)\n", " display(log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))])\n", " concatFrList.append(list(log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))].index.values))\n", "\n", "print concatFrList\n", "tempList = []\n", "for featList in concatFrList:\n", " for x in featList:\n", " tempList.append(x)\n", "print \"Number of possible outliers\", len(tempList)\n", "print collections.Counter(tempList).most_common()\n", "# OPTIONAL: Select the indices for data points you wish to remove\n", "outliers = []\n", "\n", "# Remove the outliers, if any were specified\n", "good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)\n", "display(good_data.describe()) # just to confirm nothing has changed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 4\n", "Are there any data points considered outliers for more than one feature based on the definition above? Should these data points be removed from the dataset? If any data points were added to the `outliers` list to be removed, explain why.* " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "\n", "I used the collections.Counter feature and here are the data points that are considered outliers for more than one feature-- #154 (3x), #128, #65, #66 and #75 (2x). There are several more for one feature only. Since there are six features, I wasn't comfortable to remove all these; So I plan to increase step size by 1.9x instead of 1.5x of IQR and arrive at list of 'outliers'.\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q1=8.04805870221\n", "Q3=9.73706394795\n", "step=3.2091099669\n", "Data points considered outliers for the feature 'Fresh':\n" ] }, { "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>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Detergents_Paper</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>65</th>\n", " <td>4.442651</td>\n", " <td>9.950323</td>\n", " <td>10.732651</td>\n", " <td>3.583519</td>\n", " <td>10.095388</td>\n", " <td>7.260523</td>\n", " </tr>\n", " <tr>\n", " <th>66</th>\n", " <td>2.197225</td>\n", " <td>7.335634</td>\n", " <td>8.911530</td>\n", " <td>5.164786</td>\n", " <td>8.151333</td>\n", " <td>3.295837</td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td>1.098612</td>\n", " <td>7.979339</td>\n", " <td>8.740657</td>\n", " <td>6.086775</td>\n", " <td>5.407172</td>\n", " <td>6.563856</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td>3.135494</td>\n", " <td>7.869402</td>\n", " <td>9.001839</td>\n", " <td>4.976734</td>\n", " <td>8.262043</td>\n", " <td>5.379897</td>\n", " </tr>\n", " <tr>\n", " <th>218</th>\n", " <td>2.890372</td>\n", " <td>8.923191</td>\n", " <td>9.629380</td>\n", " <td>7.158514</td>\n", " <td>8.475746</td>\n", " <td>8.759669</td>\n", " </tr>\n", " <tr>\n", " <th>338</th>\n", " <td>1.098612</td>\n", " <td>5.808142</td>\n", " <td>8.856661</td>\n", " <td>9.655090</td>\n", " <td>2.708050</td>\n", " <td>6.309918</td>\n", " </tr>\n", " <tr>\n", " <th>353</th>\n", " <td>4.762174</td>\n", " <td>8.742574</td>\n", " <td>9.961898</td>\n", " <td>5.429346</td>\n", " <td>9.069007</td>\n", " <td>7.013016</td>\n", " </tr>\n", " <tr>\n", " <th>357</th>\n", " <td>3.610918</td>\n", " <td>7.150701</td>\n", " <td>10.011086</td>\n", " <td>4.919981</td>\n", " <td>8.816853</td>\n", " <td>4.700480</td>\n", " </tr>\n", " <tr>\n", " <th>412</th>\n", " <td>4.574711</td>\n", " <td>8.190077</td>\n", " <td>9.425452</td>\n", " <td>4.584967</td>\n", " <td>7.996317</td>\n", " <td>4.127134</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Detergents_Paper Delicatessen\n", "65 4.442651 9.950323 10.732651 3.583519 10.095388 7.260523\n", "66 2.197225 7.335634 8.911530 5.164786 8.151333 3.295837\n", "95 1.098612 7.979339 8.740657 6.086775 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"</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Detergents_Paper</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Fresh, Milk, Grocery, Frozen, Detergents_Paper, Delicatessen]\n", "Index: []" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Q1=6.01187465693\n", "Q3=7.50672842655\n", "step=2.84022216227\n", "Data points considered outliers for the feature 'Delicatessen':\n" ] }, { "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", 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<td>4.317488</td>\n", " <td>1.945910</td>\n", " <td>2.079442</td>\n", " </tr>\n", " <tr>\n", " <th>183</th>\n", " <td>10.514529</td>\n", " <td>10.690808</td>\n", " <td>9.911952</td>\n", " <td>10.505999</td>\n", " <td>5.476464</td>\n", " <td>10.777768</td>\n", " </tr>\n", " <tr>\n", " <th>184</th>\n", " <td>5.789960</td>\n", " <td>6.822197</td>\n", " <td>8.457443</td>\n", " <td>4.304065</td>\n", " <td>5.811141</td>\n", " <td>2.397895</td>\n", " </tr>\n", " <tr>\n", " <th>187</th>\n", " <td>7.798933</td>\n", " <td>8.987447</td>\n", " <td>9.192075</td>\n", " <td>8.743372</td>\n", " <td>8.148735</td>\n", " <td>1.098612</td>\n", " </tr>\n", " <tr>\n", " <th>203</th>\n", " <td>6.368187</td>\n", " <td>6.529419</td>\n", " <td>7.703459</td>\n", " <td>6.150603</td>\n", " <td>6.860664</td>\n", " <td>2.890372</td>\n", " </tr>\n", " <tr>\n", " <th>233</th>\n", " <td>6.871091</td>\n", " <td>8.513988</td>\n", " <td>8.106515</td>\n", " <td>6.842683</td>\n", " <td>6.013715</td>\n", " <td>1.945910</td>\n", " </tr>\n", " <tr>\n", " <th>285</th>\n", " <td>10.602965</td>\n", " <td>6.461468</td>\n", " <td>8.188689</td>\n", " <td>6.948897</td>\n", " <td>6.077642</td>\n", " <td>2.890372</td>\n", " </tr>\n", " <tr>\n", " <th>289</th>\n", " <td>10.663966</td>\n", " <td>5.655992</td>\n", " <td>6.154858</td>\n", " <td>7.235619</td>\n", " <td>3.465736</td>\n", " <td>3.091042</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Detergents_Paper \\\n", "109 7.248504 9.724899 10.274568 6.511745 6.728629 \n", "128 4.941642 9.087834 8.248791 4.955827 6.967909 \n", "142 10.519646 8.875147 9.018332 8.004700 2.995732 \n", "154 6.432940 4.007333 4.919981 4.317488 1.945910 \n", "183 10.514529 10.690808 9.911952 10.505999 5.476464 \n", "184 5.789960 6.822197 8.457443 4.304065 5.811141 \n", "187 7.798933 8.987447 9.192075 8.743372 8.148735 \n", "203 6.368187 6.529419 7.703459 6.150603 6.860664 \n", "233 6.871091 8.513988 8.106515 6.842683 6.013715 \n", "285 10.602965 6.461468 8.188689 6.948897 6.077642 \n", "289 10.663966 5.655992 6.154858 7.235619 3.465736 \n", "\n", " Delicatessen \n", "109 1.098612 \n", "128 1.098612 \n", "142 1.098612 \n", "154 2.079442 \n", "183 10.777768 \n", "184 2.397895 \n", "187 1.098612 \n", "203 2.890372 \n", "233 1.945910 \n", "285 2.890372 \n", "289 3.091042 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[[65, 66, 95, 96, 218, 338, 353, 357, 412], [154], [75], [38, 65, 420], [], [109, 128, 142, 154, 183, 184, 187, 203, 233, 285, 289]]\n", "[(154, 2), (65, 2), (128, 1), (142, 1), (412, 1), (285, 1), (289, 1), (420, 1), (38, 1), (183, 1), (184, 1), (187, 1), (66, 1), (75, 1), (203, 1), (338, 1), (218, 1), (95, 1), (96, 1), (353, 1), (357, 1), (233, 1), (109, 1)]\n", "Number of outliers 25\n" ] }, { "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>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Detergents_Paper</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>417.000000</td>\n", " <td>417.000000</td>\n", " <td>417.000000</td>\n", " <td>417.000000</td>\n", " <td>417.000000</td>\n", " <td>417.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>8.870789</td>\n", " <td>8.131142</td>\n", " <td>8.437300</td>\n", " <td>7.365533</td>\n", " <td>6.804749</td>\n", " <td>6.783186</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>1.196590</td>\n", " <td>1.047681</td>\n", " <td>1.042657</td>\n", " <td>1.205377</td>\n", " <td>1.668218</td>\n", " <td>1.069507</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>5.081404</td>\n", " <td>4.718499</td>\n", " <td>5.384495</td>\n", " <td>3.637586</td>\n", " <td>1.098612</td>\n", " <td>3.583519</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>8.149891</td>\n", " <td>7.378384</td>\n", " <td>7.662938</td>\n", " <td>6.683361</td>\n", " <td>5.572154</td>\n", " <td>6.098074</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>9.071997</td>\n", " <td>8.198089</td>\n", " <td>8.446127</td>\n", " <td>7.406103</td>\n", " <td>6.698268</td>\n", " <td>6.902743</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>9.737197</td>\n", " <td>8.868976</td>\n", " <td>9.256365</td>\n", " <td>8.182000</td>\n", " <td>8.271037</td>\n", " <td>7.516433</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>11.627601</td>\n", " <td>11.205013</td>\n", " <td>11.437986</td>\n", " <td>11.016479</td>\n", " <td>10.617099</td>\n", " <td>9.712509</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Detergents_Paper \\\n", "count 417.000000 417.000000 417.000000 417.000000 417.000000 \n", "mean 8.870789 8.131142 8.437300 7.365533 6.804749 \n", "std 1.196590 1.047681 1.042657 1.205377 1.668218 \n", "min 5.081404 4.718499 5.384495 3.637586 1.098612 \n", "25% 8.149891 7.378384 7.662938 6.683361 5.572154 \n", "50% 9.071997 8.198089 8.446127 7.406103 6.698268 \n", "75% 9.737197 8.868976 9.256365 8.182000 8.271037 \n", "max 11.627601 11.205013 11.437986 11.016479 10.617099 \n", "\n", " Delicatessen \n", "count 417.000000 \n", "mean 6.783186 \n", "std 1.069507 \n", "min 3.583519 \n", "25% 6.098074 \n", "50% 6.902743 \n", "75% 7.516433 \n", "max 9.712509 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# For each feature find the data points with extreme high or low values\n", "import collections\n", "dupCnt = collections.Counter()\n", "outlierIndices = []\n", "concatFrList = []\n", "for feature in log_data.keys():\n", " \n", " # TODO: Calculate Q1 (25th percentile of the data) for the given feature\n", " Q1 = np.percentile(log_data[feature], 25)\n", " print \"Q1={}\".format(Q1)\n", " \n", " # TODO: Calculate Q3 (75th percentile of the data) for the given feature\n", " Q3 = np.percentile(log_data[feature], 75)\n", " print \"Q3={}\".format(Q3)\n", " \n", " # TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)\n", " step = (Q3 - Q1)1.9\n", " print \"step={}\".format(step)\n", " \n", " # Display the outliers\n", " print \"Data points considered outliers for the feature '{}':\".format(feature)\n", " display(log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))])\n", " concatFrList.append(list(log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))].index.values))\n", "\n", "print concatFrList\n", "tempList = []\n", "for featList in concatFrList:\n", " for x in featList:\n", " tempList.append(x)\n", " if x in indices:\n", " print \"ERROR ERROR: outlier is in sample list! Index:\", x\n", "print collections.Counter(tempList).most_common()\n", "# OPTIONAL: Select the indices for data points you wish to remove\n", "outliers = tempList\n", "print \"Number of outliers\", len(outliers)\n", "\n", "# Remove the outliers, if any were specified\n", "good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)\n", "display(good_data.describe()) # just to confirm reduced size" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature Transformation\n", "In this section you will use principal component analysis (PCA) to draw conclusions about the underlying structure of the wholesale customer data. Since using PCA on a dataset calculates the dimensions which best maximize variance, we will find which compound combinations of features best describe customers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: PCA\n", "\n", "Now that the data has been scaled to a more normal distribution and has had any necessary outliers removed, we can now apply PCA to the `good_data` to discover which dimensions about the data best maximize the variance of features involved. In addition to finding these dimensions, PCA will also report the explained variance ratio* of each dimension — how much variance within the data is explained by that dimension alone. Note that a component (dimension) from PCA can be considered a new \"feature\" of the space, however it is a composition of the original features present in the data.\n", "\n", "In the code block below, you will need to implement the following:\n", " - Import `sklearn.decomposition.PCA` and assign the results of fitting PCA in six dimensions with `good_data` to `pca`.\n", " - Apply a PCA transformation of `log_samples` using `pca.transform`, and assign the results to `pca_samples`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cumsum for explained variance [ 0.49563859 0.72976652 0.83341134 0.93287023 0.97862931 1. ]\n" ] }, { "data": { "image/png": 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B1atXs3jxYn7zm9/Qo0cPZs6cycqVK9lrr70YNWoUl156KVdccQX33XcfAJMm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\nHxFnAg8CNcBNKaUXI+JioC6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"text/plain": [ "<matplotlib.figure.Figure at 0x13302518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.decomposition import PCA\n", "# TODO: Apply PCA by fitting the good data with the same number of dimensions as features\n", "pca = PCA(n_components=6).fit(good_data)\n", "\n", "# TODO: Transform log_samples using the PCA fit above\n", "pca_samples = pca.transform(log_samples)\n", "\n", "print \"cumsum for explained variance\", np.cumsum(pca.explained_variance_ratio_)\n", "\n", "# Generate PCA results plot\n", "pca_results = vs.pca_results(good_data, pca)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 5\n", "How much variance in the data is explained *in total* by the first and second principal component? What about the first four principal components? Using the visualization provided above, discuss what the first four dimensions best represent in terms of customer spending. \n", "Hint: A positive increase in a specific dimension corresponds with an increase of the positive-weighted features and a decrease of the negative-weighted features. The rate of increase or decrease is based on the individual feature weights." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "0.7298 is the variance explained by the first two PCs put together. \n", "0.9329 is the variance explained by the first four PCs put together. \n", "And it is 1.0 for all six PCs put together, obviously!\n", "\n", "D1 -- The weights of Milk, Grocery and Det_paper are high. This indicates two things - 1. these three are highly correlated with each other; 2. The variance of these three are correlated with this Dimension 1. We can conclude that D1 represents customer spending on Milk, Grocery and Det_paper. Supermarkets or retaiers would be an example of these customers.\n", "\n", "D2 represents customer spending on the remaining three namely Fresh, Frozen and Deli. Similar logic that was applied to D1 can be applied to D2, for the other three features. Restaurants would be an example of these customers.\n", "\n", "D3 -- in this dimension, Fresh vs Frozen (or Deli) have opposite signs - this D3 captures customers who, if they spend more on Frozen and/or Deli, they would spend less on Fresh. Alternatively, if a customer spends more on Fresh, they would spend less on Frozen/Deli. So this D3 captures customers different than D2. These could be specific type of restaurants that offer 'organic' recipes rather than processed food recipes.\n", "\n", "D4 -- we see negative correlation betw Deli and Det_paper/Frozen. This dimension could represent hotels that need cleaning supplies but not necessarily deli prepared food.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Observation\n", "Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it in six dimensions. Observe the numerical value for the first four dimensions of the sample points. Consider if this is consistent with your initial interpretation of the sample points." ] }, { "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>Dimension 1</th>\n", " <th>Dimension 2</th>\n", " <th>Dimension 3</th>\n", " <th>Dimension 4</th>\n", " <th>Dimension 5</th>\n", " <th>Dimension 6</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.9813</td>\n", " <td>-0.1174</td>\n", " <td>0.5896</td>\n", " <td>0.5905</td>\n", " <td>-0.3542</td>\n", " <td>0.6248</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-0.2363</td>\n", " <td>-2.3131</td>\n", " <td>0.1766</td>\n", " <td>1.7859</td>\n", " <td>2.0475</td>\n", " <td>0.1399</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.9366</td>\n", " <td>2.5313</td>\n", " <td>0.1723</td>\n", " <td>0.0664</td>\n", " <td>-1.4339</td>\n", " <td>-0.1497</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Dimension 1 Dimension 2 Dimension 3 Dimension 4 Dimension 5 \\\n", "0 0.9813 -0.1174 0.5896 0.5905 -0.3542 \n", "1 -0.2363 -2.3131 0.1766 1.7859 2.0475 \n", "2 2.9366 2.5313 0.1723 0.0664 -1.4339 \n", "\n", " Dimension 6 \n", "0 0.6248 \n", "1 0.1399 \n", "2 -0.1497 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display sample log-data after having a PCA transformation applied\n", "display(pd.DataFrame(np.round(pca_samples, 4), columns = pca_results.index.values))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Dimensionality Reduction\n", "When using principal component analysis, one of the main goals is to reduce the dimensionality of the data — in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being explained. Because of this, the cumulative explained variance ratio is extremely important for knowing how many dimensions are necessary for the problem. Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards.\n", "\n", "In the code block below, you will need to implement the following:\n", " - Assign the results of fitting PCA in two dimensions with `good_data` to `pca`.\n", " - Apply a PCA transformation of `good_data` using `pca.transform`, and assign the results to `reduced_data`.\n", " - Apply a PCA transformation of `log_samples` using `pca.transform`, and assign the results to `pca_samples`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: Apply PCA by fitting the good data with only two dimensions\n", "pca = PCA(n_components=2).fit(good_data)\n", "\n", "# TODO: Transform the good data using the PCA fit above\n", "reduced_data = pca.transform(good_data)\n", "\n", "# TODO: Transform log_samples using the PCA fit above\n", "pca_samples = pca.transform(log_samples)\n", "\n", "# Create a DataFrame for the reduced data\n", "reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Observation\n", "Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions. Observe how the values for the first two dimensions remains unchanged when compared to a PCA transformation in six dimensions." ] }, { "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>Dimension 1</th>\n", " <th>Dimension 2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.9813</td>\n", " <td>-0.1174</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-0.2363</td>\n", " <td>-2.3131</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.9366</td>\n", " <td>2.5313</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Dimension 1 Dimension 2\n", "0 0.9813 -0.1174\n", "1 -0.2363 -2.3131\n", "2 2.9366 2.5313" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display sample log-data after applying PCA transformation in two dimensions\n", "display(pd.DataFrame(np.round(pca_samples, 4), columns = ['Dimension 1', 'Dimension 2']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing a Biplot\n", "A biplot is a scatterplot where each data point is represented by its scores along the principal components. The axes are the principal components (in this case `Dimension 1` and `Dimension 2`). In addition, the biplot shows the projection of the original features along the components. A biplot can help us interpret the reduced dimensions of the data, and discover relationships between the principal components and original features.\n", "\n", "Run the code cell below to produce a biplot of the reduced-dimension data." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x13302470>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7PmSyz7n4BmRaB3d9XAN1rmUAGmitk4J5HiIif7EljoiISiX7KJypkPTZTEj67COQ0ULn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\n7fcBWmnwcVc4t0h6StIzDf48J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"text/plain": [ "<matplotlib.figure.Figure at 0xff66780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a biplot\n", "vs.biplot(good_data, reduced_data, pca)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Observation\n", "\n", "Once we have the original feature projections (in red), it is easier to interpret the relative position of each data point in the scatterplot. For instance, a point the lower right corner of the figure will likely correspond to a customer that spends a lot on `'Milk'`, `'Grocery'` and `'Detergents_Paper'`, but not so much on the other product categories. \n", "\n", "From the biplot, which of the original features are most strongly correlated with the first component? What about those that are associated with the second component? Do these observations agree with the pca_results plot you obtained earlier?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "My observation: With the 1st component labeled as Dimension1, Milk, Det_p and Grocery, which are \"along\" the x-axis are strongly correlated.\n", "Wth Dimension2, Fresh, Frozen and Deli are strongly correlated. And Yes, the biplot does agree with the vs.pca_results bar chart." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clustering\n", "\n", "In this section, you will choose to use either a K-Means clustering algorithm or a Gaussian Mixture Model clustering algorithm to identify the various customer segments hidden in the data. You will then recover specific data points from the clusters to understand their significance by transforming them back into their original dimension and scale. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 6\n", "What are the advantages to using a K-Means clustering algorithm? What are the advantages to using a Gaussian Mixture Model clustering algorithm? Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "Advantage of using K-Means - faster than GMM.\n", "\n", "With GMM - soft assignment of datapoints to clusters is the biggest advantage. GMM works with probabilities of a sample belonging to each cluster. As the model iterates, these probabbilities are refined. GMM works better than KMeans on non-linear data. KMeans uses Euclidian distance which can cause unequal weighting of underlying factors, GMM uses weighted distance.\n", "\n", "GMM works well if the data points are distributed in a Gaussian manner. This is as per the doc in sklearn. Below I have plotted the histogram of good_data. It can be seen that all features (except to some extent Det_Paper) exhibit a normal distribution.\n", "Hence I have chosen to go with GMM even though it is slower than KMeans. Given our size of data, GMM should be ok.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Creating Clusters\n", "Depending on the problem, the number of clusters that you expect to be in the data may already be known. When the number of clusters is not known a priori, there is no guarantee that a given number of clusters best segments the data, since it is unclear what structure exists in the data — if any. However, we can quantify the \"goodness\" of a clustering by calculating each data point's silhouette coefficient. The [silhouette coefficient](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html) for a data point measures how similar it is to its assigned cluster from -1 (dissimilar) to 1 (similar). Calculating the mean silhouette coefficient provides for a simple scoring method of a given clustering.\n", "\n", "In the code block below, you will need to implement the following:\n", " - Fit a clustering algorithm to the `reduced_data` and assign it to `clusterer`.\n", " - Predict the cluster for each data point in `reduced_data` using `clusterer.predict` and assign them to `preds`.\n", " - Find the cluster centers using the algorithm's respective attribute and assign them to `centers`.\n", " - Predict the cluster for each sample data point in `pca_samples` and assign them `sample_preds`.\n", " - Import `sklearn.metrics.silhouette_score` and calculate the silhouette score of `reduced_data` against `preds`.\n", " - Assign the silhouette score to `score` and print the result." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KMeans num clusters= 14 0.359466142909\n", "GMM num clusters= 14 0.339565599485\n", "KMeans num clusters= 13 0.363886536115\n", "GMM num clusters= 13 0.27179478225\n", "KMeans num clusters= 12 0.351387998876\n", "GMM num clusters= 12 0.307593765034\n", "KMeans num clusters= 11 0.358874273796\n", "GMM num clusters= 11 0.274152535966\n", "KMeans num clusters= 10 0.366627766606\n", "GMM num clusters= 10 0.306826901179\n", "KMeans num clusters= 9 0.378377894975\n", "GMM num clusters= 9 0.335113209215\n", "KMeans num clusters= 8 0.361307627778\n", "GMM num clusters= 8 0.286831351597\n", "KMeans num clusters= 7 0.371114152363\n", "GMM num clusters= 7 0.325637423149\n", "KMeans num clusters= 6 0.357580643416\n", "GMM num clusters= 6 0.240358456265\n", "KMeans num clusters= 5 0.344771887334\n", "GMM num clusters= 5 0.273169963762\n", "KMeans num clusters= 4 0.324788066196\n", "GMM num clusters= 4 0.278252147953\n", "KMeans num clusters= 3 0.34770946801\n", "GMM num clusters= 3 0.299534700889\n", "KMeans num clusters= 2 0.441693704182\n", "GMM num clusters= 2 0.438679049598\n", "Final choice is KMeans n_components = 2 score = 0.441693704182\n" ] } ], "source": [ "from sklearn.cluster import KMeans\n", "from sklearn.metrics import silhouette_score\n", "from sklearn.mixture import GaussianMixture\n", "clustererChoice = \"GMM\"\n", "for i in range(14, 1, -1):\n", " \n", " # TODO: Apply your clustering algorithm of choice to the reduced data \n", " clusterer = KMeans(n_clusters=i, n_init=10, random_state=1).fit(reduced_data)\n", "\n", " # TODO: Predict the cluster for each data point\n", " preds = clusterer.predict(reduced_data)\n", " #print preds\n", "\n", " # TODO: Find the cluster centers\n", " centers = clusterer.cluster_centers_\n", " \n", " # TODO: Predict the cluster for each transformed sample data point\n", " sample_preds = clusterer.predict(pca_samples)\n", " \n", " # TODO: Calculate the mean silhouette coefficient for the number of clusters chosen\n", " score = silhouette_score(reduced_data, preds)\n", " print \"KMeans num clusters=\", i, score\n", "\n", " GMMclusterer = GaussianMixture(n_components=i, n_init=10, random_state=1).fit(reduced_data)\n", " GMMpreds = GMMclusterer.predict(reduced_data)\n", " GMMcenters = GMMclusterer.means_\n", " GMMsample_preds = GMMclusterer.predict(pca_samples)\n", " GMMscore = silhouette_score(reduced_data, GMMpreds)\n", " print \"GMM num clusters=\", i, GMMscore\n", "\n", "#Final choice\n", "clusterer = KMeans(n_clusters=2, n_init=10, random_state=1).fit(reduced_data)\n", "preds = clusterer.predict(reduced_data)\n", "centers = clusterer.cluster_centers_\n", "sample_preds = clusterer.predict(pca_samples)\n", "score = silhouette_score(reduced_data, preds)\n", "print \"Final choice is KMeans n_components = 2\", \"score =\", score\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 7\n", "Report the silhouette score for several cluster numbers you tried. Of these, which number of clusters has the best silhouette score? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "Best silhoutte score is for KMeans 2 clusters. score = 0.4417\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cluster Visualization\n", "Once you've chosen the optimal number of clusters for your clustering algorithm using the scoring metric above, you can now visualize the results by executing the code block below. Note that, for experimentation purposes, you are welcome to adjust the number of clusters for your clustering algorithm to see various visualizations. The final visualization provided should, however, correspond with the optimal number of clusters. " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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0004lZdMUCImIiIiIVImCoOKVeu0UCImIiIiINKmXX36Z0047jT333JMDDjiA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"text/plain": [ "<matplotlib.figure.Figure at 0x1385fa20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display the results of the clustering from implementation\n", "vs.cluster_results(reduced_data, preds, centers, pca_samples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Data Recovery\n", "Each cluster present in the visualization above has a central point. These centers (or means) are not specifically data points from the data, but rather the averages of all the data points predicted in the respective clusters. For the problem of creating customer segments, a cluster's center point corresponds to the average customer of that segment. Since the data is currently reduced in dimension and scaled by a logarithm, we can recover the representative customer spending from these data points by applying the inverse transformations.\n", "\n", "In the code block below, you will need to implement the following:\n", " - Apply the inverse transform to `centers` using `pca.inverse_transform` and assign the new centers to `log_centers`.\n", " - Apply the inverse function of `np.log` to `log_centers` using `np.exp` and assign the true centers to `true_centers`.\n" ] }, { "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>Fresh</th>\n", " <th>Milk</th>\n", " <th>Grocery</th>\n", " <th>Frozen</th>\n", " <th>Detergents_Paper</th>\n", " <th>Delicatessen</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Segment 0</th>\n", " <td>9142.0</td>\n", " <td>1898.0</td>\n", " <td>2428.0</td>\n", " <td>2098.0</td>\n", " <td>300.0</td>\n", " <td>758.0</td>\n", " </tr>\n", " <tr>\n", " <th>Segment 1</th>\n", " <td>4899.0</td>\n", " <td>8131.0</td>\n", " <td>12080.0</td>\n", " <td>1034.0</td>\n", " <td>4687.0</td>\n", " <td>1109.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fresh Milk Grocery Frozen Detergents_Paper Delicatessen\n", "Segment 0 9142.0 1898.0 2428.0 2098.0 300.0 758.0\n", "Segment 1 4899.0 8131.0 12080.0 1034.0 4687.0 1109.0" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1354d400>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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4cCxwS0ppSkRMAM4CxmdfP0gpfSkihgM/Ar4ZEQdk4w4EvgDMjIgvp5TW1+uT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\nb65Ts+1RyRZYDuxT7X0n4K3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"text/plain": [ "<matplotlib.figure.Figure at 0x1386ccc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# TODO: Inverse transform the centers\n", "log_centers = pca.inverse_transform(centers)\n", "\n", "# TODO: Exponentiate the centers\n", "true_centers = np.exp(log_centers)\n", "\n", "# Display the true centers\n", "segments = ['Segment {}'.format(i) for i in range(0,len(centers))]\n", "true_centers = pd.DataFrame(np.round(true_centers), columns = data.keys())\n", "true_centers.index = segments\n", "display(true_centers)\n", "true_centers=true_centers.append(data.describe().loc['mean'])\n", "true_centers=true_centers.append(data.describe().loc['std'])\n", "true_centers=true_centers.append(data.describe().loc['50%'])\n", "\n", "true_centers.plot(kind = 'bar', figsize = (16, 6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 8\n", "Consider the total purchase cost of each product category for the representative data points above, and reference the statistical description of the dataset at the beginning of this project. What set of establishments could each of the customer segments represent? \n", "Hint: A customer who is assigned to `'Cluster X'` should best identify with the establishments represented by the feature set of `'Segment X'`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "\n", "Sg0_Fresh ~= Fresh_50% <---\n", "\n", "Sg0_Milk << Milk_Mean < Milk_Median\n", "\n", "Sg0_Grocery << Groc_Mean ~= Groc_Median\n", "\n", "Sg0_Froz ~=Froz_50% <---\n", "\n", "Sg0_Dp << Dp_Mean < Dp_Med\n", "\n", "Sg0_Deli ~= Deli_50% <---\n", "\n", "\n", "Sg1_Fresh << Fresh_Mean < Fresh_Median\n", "\n", "Sg1_Milk > Milk_Mean ~= Milk_Median <---\n", "\n", "Sg1_Grocery >> Groc_Mean ~= Groc_Median <---\n", "\n", "Sg1_Froz << Froz_Mean < Froz_Med\n", "\n", "Sg1_Dp >> Dp_Mean < Dp_Med <---\n", "\n", "Sg1_Deli ~= Deli_Mean and less than Deli_Med\n", "\n", "Customers assigned to Segment0 are characterized by focused/combined spending on Fresh, Frozen and Deli. (Eg, restaurants).\n", "\n", "Customers assigned to Segment1 are characterized by focused/combined spending on Grocery, Milk and Det_paper. (Eg Retailers)\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 9\n", "For each sample point, which customer segment from *Question 8* best represents it? Are the predictions for each sample point consistent with this?\n", "\n", "Run the code block below to find which cluster each sample point is predicted to be." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample point 0 predicted to be in Cluster 0\n", "The distance between sample point 0 and center of cluster 0:\n", "Fresh 4004.0\n", "Milk -774.0\n", "Grocery 2095.0\n", "Frozen -678.0\n", "Detergents_Paper 249.0\n", "Delicatessen -261.0\n", "dtype: float64\n", "Sample point 1 predicted to be in Cluster 1\n", "The distance between sample point 1 and center of cluster 1:\n", "Fresh -1812.0\n", "Milk -51.0\n", "Grocery -3798.0\n", "Frozen -373.0\n", "Detergents_Paper -3966.0\n", "Delicatessen -1073.0\n", "dtype: float64\n", "Sample point 2 predicted to be in Cluster 0\n", "The distance between sample point 2 and center of cluster 0:\n", "Fresh 47017.0\n", "Milk -1343.0\n", "Grocery -1526.0\n", "Frozen 7904.0\n", "Detergents_Paper -88.0\n", "Delicatessen 2158.0\n", "dtype: float64\n" ] } ], "source": [ "# Display the predictions\n", "for i, pred in enumerate(sample_preds):\n", " print \"Sample point\", i, \"predicted to be in Cluster\", pred\n", " print 'The distance between sample point {} and center of cluster {}:'.format(i, pred)\n", " print (samples.iloc[i] - true_centers.iloc[pred])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "\n", "Sample0 - Quite close to the cluster0 center for Frozen and Deli; Close to the median Fresh spending, and higher than the cluster0 center for Fresh; so to predict as cluster0 is reasonable.\n", "\n", "Sample1 - Very close to cluster center for Milk. Quite far from Deli; these two justify cluster1 prediction.\n", "\n", "Sample2 - Seems too far from cluster0 center for Milk. Milk Groc and Det_pap are quite close to cluster0 center. This could have been classified as cluster1; borderline perhaps.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this final section, you will investigate ways that you can make use of the clustered data. First, you will consider how the different groups of customers, the *customer segments, may be affected differently by a specific delivery scheme. Next, you will consider how giving a label to each customer (which segment* that customer belongs to) can provide for additional features about the customer data. Finally, you will compare the *customer segments* to a hidden variable present in the data, to see whether the clustering identified certain relationships." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Question 10\n", "Companies will often run [A/B tests](https://en.wikipedia.org/wiki/A/B_testing) when making small changes to their products or services to determine whether making that change will affect its customers positively or negatively. The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively. How can the wholesale distributor use the customer segments to determine which customers, if any, would react positively to the change in delivery service? \n", "Hint: Can we assume the change affects all customers equally? How can we determine which group of customers it affects the most?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "The wholesale distributor can construct an A/B test as follows-\n", "1. Use the customer segments as follows - pick a subset of customers, say 10 from a segment. Construct 5 pairs using these 10; where the pair are very close to each other in the segments. This should be possible using sklearn APIs. Each pair could be at a deterministic distance from the centers. For each pair, apply the 5-day delivery service to one and 3-day service delivery to the other.\n", "2. Figure out a data metric - say it should be a product rating on a 5-star scale. \n", "3. Run the experiment.\n", "4. Analyze results - did the 3-day deliv service result in lower star rating than the 5-day? If no, then the cost savings on the 3-day service is justifiable.\n", "5. Repeat the above for the other segment too, because the change in deliv service may not effect all customers equally.\n", "\n", "To create the pairs of customers for experiments, one could perhaps cluster the unlabeled data into 3 clusters instead of two, and run the A/B test.\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 11\n", "Additional structure is derived from originally unlabeled data when using clustering techniques. Since each customer has a *customer segment* it best identifies with (depending on the clustering algorithm applied), we can consider 'customer segment' as an engineered feature for the data. Assume the wholesale distributor recently acquired ten new customers and each provided estimates for anticipated annual spending of each product category. Knowing these estimates, the wholesale distributor wants to classify each new customer to a *customer segment* to determine the most appropriate delivery service. \n", "How can the wholesale distributor label the new customers using only their estimated product spending and the *customer segment* data? \n", "Hint: A supervised learner could be used to train on the original customers. What would be the target variable?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "A DecisionTreeClassifier can be built using the customers.csv with one added column namely \"Segment\" which takes the value 0 or 1. This \"Segment\" is the targer variable.\n", "Then, when the new customers come in, the DecisionTreeClassifier model can be run to predict whether they are retail type or they are restaurant type." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualizing Underlying Distributions\n", "\n", "At the beginning of this project, it was discussed that the `'Channel'` and `'Region'` features would be excluded from the dataset so that the customer product categories were emphasized in the analysis. By reintroducing the `'Channel'` feature to the dataset, an interesting structure emerges when considering the same PCA dimensionality reduction applied earlier to the original dataset.\n", "\n", "Run the code block below to see how each data point is labeled either `'HoReCa'` (Hotel/Restaurant/Cafe) or `'Retail'` the reduced space. In addition, you will find the sample points are circled in the plot, which will identify their labeling." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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BKY9VMkOBkIiIiIgUnc6dO7Ny5UrWrl1Ljx49eOSRR6K+PpFAaOnSpUmN6dCh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"text/plain": [ "<matplotlib.figure.Figure at 0x1385f860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display the clustering results based on 'Channel' data\n", "vs.channel_results(reduced_data, outliers, pca_samples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 12\n", "How well does the clustering algorithm and number of clusters you've chosen compare to this underlying distribution of Hotel/Restaurant/Cafe customers to Retailer customers? Are there customer segments that would be classified as purely 'Retailers' or 'Hotels/Restaurants/Cafes' by this distribution? Would you consider these classifications as consistent with your previous definition of the customer segments?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "From the viz for \"underlying distributions\" it can be seen that there is a left-right distribution of retail-horeca. This is quite similar to the what the clustering model has inferred. There are a bunch of data points that are actually horeca but predicted to be retailers by the clustering algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
diegocavalca/Studies	phd-thesis/Hipótese - Gráfico de Recorrência.ipynb	1	383130	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Identificação de Cargas através de Gráfico de Recorrência" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* `Artigo:` ...\n", "* `URL`: ...\n", "* `Source-code`: ...\n", "* `Estratégia proposta`: converter série-temporal em GRÁFICO DE RECORRÊNCIA, extrair características com DNN (VG16) e classificação supervisionada." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Carregando ambiente e parâmetros" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:11:09.200652Z", "start_time": "2019-10-01T15:11:04.863526Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.style.use('ggplot')\n", "plt.rc('text', usetex=False)\n", "from matplotlib.image import imsave\n", "import pandas as pd\n", "import pickle as cPickle\n", "import os, sys, cv2\n", "from math import \n", "from pprint import pprint\n", "from tqdm import tqdm_notebook\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "from PIL import Image\n", "from glob import glob\n", "from IPython.display import display\n", "\n", "from tensorflow.keras.applications.vgg16 import VGG16\n", "from tensorflow.keras.preprocessing import image as keras_image\n", "from tensorflow.keras.applications.vgg16 import preprocess_input\n", "\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score\n", "\n", "from pyts.image import RecurrencePlot\n", "\n", "REDD_RESOURCES_PATH = 'datasets/REDD'\n", "BENCHMARKING2_RESOURCES_PATH = 'benchmarkings/Imaging-NILM-time-series/'\n", "\n", "HYPOTHESIS_RESOURCES_PATH = 'datasets/hipotese1-recurrenceplot-vggembedding/'\n", "\n", "sys.path.append(os.path.join(BENCHMARKING2_RESOURCES_PATH, ''))\n", "\n", "from serie2QMlib import " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pré-processamento dos dados " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T14:46:30.511876Z", "start_time": "2019-10-01T14:46:30.503898Z" } }, "outputs": [], "source": [ "def standardize(serie):\n", " dev = np.sqrt(np.var(serie))\n", " mean = np.mean(serie)\n", " return [(each - mean) / dev for each in serie]\n", "\n", "# Rescale data into [0,1]\n", "def rescale(serie):\n", " maxval = max(serie)\n", " minval = min(serie)\n", " gap = float(maxval - minval)\n", " return [(each - minval) / gap for each in serie]\n", "\n", "#modified from https://stackoverflow.com/questions/33650371/recurrence-plot-in-python\n", "from sklearn.metrics import pairwise\n", "def recurrence_plot(s, eps=None, steps=None, scaling = False):\n", " if type(s) == list:\n", " s = np.array(s)[:, None]\n", " if scaling:\n", " s = rescale(s)\n", " if eps==None: eps=0.1\n", " if steps==None: steps=10\n", " d = pairwise.pairwise_distances(s)\n", " d = np.floor(d / eps)\n", " d[d > steps] = steps\n", " #Z = squareform(d)\n", " return d" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2019-09-10T14:33:17.434313Z", "start_time": "2019-09-10T14:33:17.430325Z" } }, "source": [ "## Parâmetros gerais dos dados utilizados na modelagem (treino e teste)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T14:46:30.526837Z", "start_time": "2019-10-01T14:46:30.514872Z" } }, "outputs": [], "source": [ "#################################\n", "###Define the parameters here####\n", "#################################\n", "\n", "#datafiles = ['dish washer1-1'] # Data file name (TODO: alterar aqui)\n", "#trains = [250] # Number of training instances (because we assume training and test data are mixed in one file)\n", "size = [32] # PAA size\n", "#GAF_type = 'GADF' # GAF type: GASF, GADF\n", "#save_PAA = True # Save the GAF with or without dimension reduction by PAA: True, False\n", "#rescale_type = 'Zero' # Rescale the data into [0,1] or [-1,1]: Zero, Minusone\n", "\n", "directory = os.path.join(HYPOTHESIS_RESOURCES_PATH, '') #the directory will be created if it does not already exist. Here the images will be stored\n", "if not os.path.exists(directory):\n", " os.makedirs(directory)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gerando dados" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A fim de normalizar os benchmarkings, serão utilizados os dados das séries do `bechmarking 1` para o processo de Extração de Características (conversão `serie2image` - benchmarking 2)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extração de Características" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T14:46:46.376217Z", "start_time": "2019-10-01T14:46:46.366270Z" } }, "outputs": [], "source": [ "def serie2RP(serie, scaling = False, s = 32):\n", " \"\"\"\n", " Customized function to perform Series to Image conversion.\n", " \n", " Args:\n", " serie : original input data (time-serie chunk of appliance/main data - REDD - benchmarking 1)\n", " s : Size of output paaimage originated from serie [ INFO: PAA = (32, 32) / noPAA = (50, 50) ]\n", " \"\"\"\n", " rp = None\n", " rpmatrix = None\n", "\n", " std_data = serie\n", " if scaling:\n", " std_data = rescale(std_data)\n", " paalistcos = recurrence_plot(std_data)#, s, None)\n", " # paalistcos = rescale(paa(each[1:],s,None))\n", " \n", " # paalistcos = rescaleminus(paa(each[1:],s,None))\n", "\n", " ################raw###################\n", " datacos = np.array(std_data)\n", " #print(datacos)\n", " datasin = np.sqrt(1 - np.array(std_data) 2)\n", " #print(datasin)\n", "\n", " paalistcos = np.array(paalistcos)\n", " paalistsin = np.sqrt(1 - paalistcos 2)\n", "\n", " datacos = np.matrix(datacos)\n", " datasin = np.matrix(datasin)\n", "\n", " paalistcos = np.matrix(paalistcos)\n", " paalistsin = np.matrix(paalistsin)\n", " if GAF_type == 'GASF':\n", " paamatrix = paalistcos.T * paalistcos - paalistsin.T * paalistsin\n", " matrix = np.array(datacos.T * datacos - datasin.T * datasin)\n", " elif GAF_type == 'GADF':\n", " paamatrix = paalistsin.T * paalistcos - paalistcos.T * paalistsin\n", " matrix = np.array(datasin.T * datacos - datacos.T * datasin)\n", " else:\n", " sys.exit('Unknown GAF type!')\n", " \n", " #label = np.asarray(label)\n", " image = matrix\n", " paaimage = np.array(paamatrix)\n", " matmatrix = np.asarray(matmatrix)\n", " fullmatrix = np.asarray(fullmatrix)\n", " #\n", " # maximage = maxsample(image, s)\n", " # maxmatrix = np.asarray(np.asarray([each.flatten() for each in maximage]))\n", " \n", " if save_PAA == False:\n", " finalmatrix = matmatrix\n", " else:\n", " finalmatrix = fullmatrix\n", "\n", " # uncomment below if needed data in pickled form\n", " # pickledata(finalmatrix, label, train, datafilename)\n", "\n", " #gengramImgs(image, paaimage, label, directory)\n", " \n", " return image, paaimage, matmatrix, fullmatrix, finalmatrix" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:14:17.997074Z", "start_time": "2019-10-01T15:14:17.993047Z" } }, "outputs": [], "source": [ "# Reading power dataset (benchmark 1)\n", "BENCHMARKING1_RESOURCES_PATH = \"benchmarkings/cs446 project-electric-load-identification-using-machine-learning/\"\n", "\n", "size_paa = 32\n", "size_without_paa = 30\n", "\n", "# devices to be used in training and testing\n", "use_idx = np.array([3,4,6,7,10,11,13,17,19])\n", "\n", "label_columns_idx = [\"APLIANCE_{}\".format(i) for i in use_idx]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conjunto de Treino" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T14:48:51.390342Z", "start_time": "2019-10-01T14:48:50.958500Z" } }, "outputs": [ { "data": { "image/png": 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bySttI2Xoz7Zez0GgvBUkbXoUeg9Cqs37WumRIiIiIlKFFLRFLR+o7e/c0SX/7elhaNoDh46VZ4CObmhsLPNK25i3opdq9b5WrzYRERERqUIK2qKWT4ns7oWlRezycmXnUyZ2ZBhuvR2TSJTl/KauDg70l22lzc5d84K03oNeMRVTp/RIEREREalKCtqilltdM1093tc7cF+bzczBhfOYqPuzbWB6D5dvpS0XDJreQ16AmEypeqSIiIiIVCUFbRGz+SAtH7TtxBTJM8+AtZiBO8o7Tu9BmJ7ALi5EfmqbDwbzBU+Srd7qm4iIiIhIlVHQFrX8nrauXu/PnbjSNvI0JBJwy0BZxylUkBy/EP3J06PQ2ATtXd7XqVaV/BcRERGRqqSgLWqZWTAG09md+3oHBm2nh+HoIKaxqbwD9eYrSEafImnTo9DTv9ZjLtmm9EgRERERqUoK2qI2P+cVtkh6FQnL2Ry6EuziArz4fPlK/a/X3Qt1dYX9Z5FKj2F6DhW+NMlWFSIRERERkaqkoC1qmTlobvECN9h56ZFnn4PV1bIXIQEwiQbo7sOmX4z0vHbhOsxMFlbygFx65Cw2m410LBERERGRsBS0RcxmZqElBXubwZgdlx5pTw975fFvPRHPgL0Ho19pu+TtkTN9ayttJFvBZndekC0iIiIiNS9wky3HcQ4Bfwj0AFngk67rPug4TjvwGeAocA5wXNe9HH6qNWI+Ay1Jb69Uc3LnBW0jw3D4GGZvcyzjmd5D2Ke+h11ZieycN1WOBEi1eX/OXiuktopI+Uzd3Vv0Ofs+9Z0YZuLpPHY08LGJuWU6v5X2dUwprz9KcV5LgJU3vSLW8S7dVeY91hv0/863Ax1nZu8l8dXv+z4u7usZtyv9jbGOl+kzsY11gOA/O5tq9v2zT5ybDjxeEH5/94X17Ad7Yh0P4v1dvZ2iQdtWwRnwW3iB2SxwHvig4zhfBv4x0A4sAC8B/gy4pxyTr0qZWUznAe/vzS07Kmizy8tw9jnMPW+Pb9Deg7C6ymp6DPYmoznnxVGor1+r8AmYVCsWtK9NRERERKpOKemRK8Cvu657AngN8IDjOCfxgrEB13XvBE4BFugH7gNGXdf9sdzzX+c4ztFyTL4qZea89EiAlhR2fgcVIjk/AstLsexny8uX/V8ZOxfZOW16DLp6MYl19yzyq2uzKvsvIiIiItWl6Eqb67ppIJ37+6zjOM8A/a7rfmnd054H+oDHgTag3nGcBHAVLzDcFcsXNpstpEcC3p87aaVt5JT3lxiDNnq8YiErY+cgqmbe46M3pkaCV/IfsHNXiS9pQkRERESkOF972nIrZi/DC87yjyWBfw/8N9d1rzmOswxk8AK9ZmDBdd2ZTc71KeBnAFzXpbOzM+hrKEgkEpGcJ6hsZpZJm6Wl6wAtnZ1c2d/BysxUWeZUidd6+dwIq4duofOWW2Mdd7K7l+z55yN5vXZ5mYmJcVrufjPJdeezrSkmgJbsKi0VfA/lVfq9HKfd9Fphd73e7V7rwx+5r/jxH3xP1FPaWlPwPTWHbz3Ag5//kK9jVpINgccLItZribcXJ07LLfHWVWv82XcHOu7wiX4eeuKjvo+L+3rGbbUx3tul2Yb4xmvIBK9KfeRIB5946H5fx5il6GoAVKOF7ph/dwb7p14WJQdtueDss8Cvua57LfdYA/B3wCjwz3NPvQo04K283QY86TjOMdd1z64/n+u67wPel/vSTk1NhXkdAHR2dhLFeYKyk+MAZKjj+tQU2UQjdvZKWeYU92u12VWyp36AefU9sV9je3SQxVM/ZHJyEmPC/aK1F1+E7Crz+zpZ2Pg6mvaSuZTmegXfQ3mVfi/HaTe9Vthdr3er19rX18f9H/l00ePjLJ6RCFGI5MHPf4hffe/HfB2jQiTRqpVCJA898VEeeNVv+j5upxcimdvJhUi+txj42E88dD8feOBhX8fsibkQSdziLkTS+WS8NxS+9/C/2PJ7Jd2aygVnnwX+2HXdz+UeM8DXgCTwatd1be7pM8CK67rLwE8BzwKvDDz7WpIrF29uSI/M7IzeX6PnYOE6DMTQVHujwSGyl6cgFxSHkqscaTamR0KuV9uuyOQVERERkRpSSvVIA/wB8Izrur+37lv/ArgbrwjJtx3HAfgw8BfAP3EcZwQYAxrxAredL5MrOpJvrN2c9Hp/LVz3KknWMDvyNACmAkGbGRzC4rUbMN3h7k4Xyv339N/8zWQrVoVIRERERKTKlJIeeTdeGuOPHMf5Qe6xDwO/gheULece+67ruo85jvN14GTuvwPAH7iu+1S0065ONl90ZF31SMAL5mo+aDsFnQcw7RXYi9NzENO6D04Pw90/Ee5c6THo6MY07bn5e6k2uLp7WgqKiIiISG0opXrkN2HTgnqPbfH8OeDekPOqTYWgzVtpMy1Jr/fXfKZiU4qCtRZOD2N+rDJZrsYYGk68hMWR4dDnsulRr/fbZuMkU9gL50OPISLFldKQ9fxvvC6GmXiu9wRPY1/obvC9z+L2j8fbkDbOawnh9vEEEXSPWVAXAl7PpZ5koGPjvp5xi7tB876z52IbK+79iHHvl516uS3+pAjd/vEItsrUqHjLLe10+fTIlnXpkesfr1XjY95er8EK7GfLaRh6KUyOYy8H32Brs1m4dGHz/WzgrbTNKT1SRERERKqLgrYozWegsRHTkKuClEuPtDXeq82e9la4KrGfLa/x5Eu9uYRZbZuegKWlm3u05SVbYWkJu7iz72iKiIiISG1R0BalzCw0p9a+bsntY5uv7aCNkWFo2w8hi4CEkTh6HPbs9eYSVKFy5ObpkSRbvT+12iYiIiIiVURBW4RsZm4tNRJ2RHqktRZ7ehgzMBS6R1oYpr4ejp8orPoFYdNj3l+2WGkzqTbvL6ogKSIiIiJVREFblOZvDNpMYxM0Nq4VKKlF0xNweaqi+9nyzMAQXHwROxuwl1p6FFJtmJbU5t8vrLSpV5uIiIiIVA8FbVHamB4J3mpbDadHru1nO1nhmXj92gA4cyrQ8XZ8DPoOb/2E3Epb4KBQRERERKQMFLRFKTOHadnQj60lha3h9EhGhr0ec31HKj0TODIADY2BipFYayE9uvV+NtBKm4iIiIhUJQVtUZqfW2uondeSrOn0SDtyCgaGMHWVf6uYhgY4dluwfW1XL3vVPXu2qBwJXnBaX689bSIiIiJSVYo215bS2OUlWFpcKz6S15yEqUuVmVRI9uplr6/ZG36y0lMpMAMnsY8+gl2Yx+xpLv3AYpUj8Zp4k2zVSptIlYizofBcf2PgYxPvhs4nK1eoqRRxN2dOfPX7sY4XdwPjoNezIZMNdGzc1zN2x47GOlyc75cwPzsze6/v4ztjvpYQczPvmJuHx934fTuVXz7ZKfKraS03Bm2mllfacmmIpgqKkOSZgSGwWTjzrK/j7Pj2lSMLkq3a0yYiIiIiVUVBW1TyxUZuSo9M1WzJf3t6GBqb4NCxSk9lza23Q329/31t6VGvz9u+9u2fl2xVnzYRERERqSoK2qKSW00zLZukRy4tYpeXKzCpcOzIMNx6OyZRPVm0pmkPHL7Vd9Bm02PQe6horzmj9EgRERERqTIK2qKSX03buKctH8TVWNl/m5mDC+erKjUyzwwOwQunvX2EpUqPYoqlRoJX9l/pkSIiIiJSRRS0RcRulx4JNRe0ceYZsBYzcEelZ3ITMzAEKyvwwumSnm/n57zqkduV+89LtcL8HHZ1NeQsRURERESioaAtKvliIxtW2kz+6xrb12ZHnob6BNwyUOmp3Oz4STCm9NL/aa8ISUkrbclWsLbmfl4iIiIisnMpaItKZhaMgb0bytDn0yNrrIKkPT0MtwxgGpsqPZWbmJYk9B8peV+bzZX7L22lrc37UymSIiIiIlIlFLRFZX4OmpM3N6HOpUfaGgra7OICvPi8l4ZYpczAEDz/bGlpjOkxSDRA54Hi5022en9RMRIRERERqRLVUxaw1mXmoLnl5sfz6ZHzNZRud/Y5WF2tyiIkBQND8LVH4cWzRVM4bXoUevoxdfXFz5vKB20q+y9STqU0SN33qe/EMBNPmIa0ibll3w1Y424QG+e1hPibXV+6K96skP7f+Xag44I0S4b4r2fcroRobh9Epm/7StJROkDwn51NNfv+2SfOTQceL4i4m08/+8GeWMeLu3n4drTSFhE7P3dzERLw0iWNqan0SHt6GEwd3Hqi0lPZkhk4CeT23hVTauVIgKSXHlmpBtt2cZHslz6PXVysyPhSu2w2S/Zrj2K1SiwiIrLjKGiLSmZubf/aOqauDva21FbQdm4E+g5hNu7PqyJmXzt09xUtRmKXFmF6AnpK2M8GkMwF3hVaabNf/UvsI/8viz94vCLjSw07N4L99H/F/u23Kj0TERERiZiCtqhkZtcqRW7UkqypoI30KKbvcKVnUZQZHIKRU9hsdusnjV/wqkGWuNJmEg1ekF2BlTa7uIj98p8DkJ0cj318qW2FwjxK7RUREdlxFLRFZav0SICWFLZG9rTZxUWYmSw5yKmogSHvuuerQ24iXznS9Pl4PclURQqR2G98EWavgjGsKmgTnwqrzqp8KiIisuMoaIuAzWYhk9k0PRLwipHUykrbpTGvqXYp5fErrLCvbbsUyfExb39ed1/pJ061YWfjXa2wy8vYL34OBu+AA30K2sQXm12FkVPeF9rTJiIisuMoaIvCwjzY7E2NtfNMDaVH2lwj6ppYaes8APs7YZt+bfbiKHT1YBoaSj9vqi32D77221+BKzPUvfNeaO9W0Cb+XHgRrmcAYr/hICIiIuWnoC0K+YBsm/TImin5f3EU6nyuTFWIMQYzMIQ9PYy1dvMnpUdLa6q9/rzJVKwpZnZlBfuFP4VbBuHESzEdXaxOKGiT0hX2sx08qpU2ERGRHUhBWxTmvaDNbJsemdm+YEaVsOOj0NXrb2WqkgaH4OoMTN7cJ8SursJEuvRy/3nJNpi7unUgGDH7xNdheoK6d9yLMQY6urHXrqjsv5Tu9DC0d2GOHNeeNhERkR1IzbWjkMmtom1XPdJmYeH65g24q0l6zPfKVCWZwSEsYEdOYTauDk6mYXXF/+tJtcLKCixehz3lbXtgs6veKtvBo3DnXd6DHV3en5cnS29VILuWtRY7Mow58RJIthZuOBgTX/PYIEpp9nzlfa+NYSaeMM12l/Y3cv5ef9kJQZszBxXntYT4G+4eORfrcEwFvJ4rHS2BfhZxX8+4Jb56LtbxOo8djXW8oMzSCnt8NsteONpRptlsLu7G9kf/Mt4b2omvfj/W8Xh4629ppS0CNuPtJdk2PRLWgrsqZVdWYOJiTRQhKeg56H1Q3awYSW5/nun12b4g12A7lhWLJ78D42OYdzheTz/AtOeCtunJ8o8vte/SRbh2xVt1TrWt3XAQERGRHUNBWxTywdgW6ZGmJbe6Np+JaUIBTY7D6ir01EARkhxjDAycXNvTs06+3D+9/f7OmWz1/lLmvUHWWrKPPgI9/ZhXrLvz2tHtfX96oqzjy86Qf++bgTu8GxigFEkREZEdRkFbFIoEbTTXxkob6RcBnz3NqoAZHILJcezM1I3fSI/C/k6M3xTHVP6Db5mr8D31tzD2AubtP4epq197fF8H1NVrpU1KMzLsrbD19GNS8dxwEBERkXgpaIvCfAYaGzENjZt/P5ceaau87H+h3H+N7aMyA3cA3LTaZoPuz0t56ZG2jKsV3irbZ6CjG/Oqe274nqmvp66jE2a00ibF2dPDMHDSW3VO5VN7VfZfRERkJ1HQFoXM7Npq2mYK6ZHVHbSRHoX2TsyevZWeiT+HjsKevXDmVOEhm816e8WC9JuLIz3y2afghdOYt/0sJnFzPaD6rh6lR0pRdnoSpicwA0PeA7n3bjlvOIiIiEj8FLRFwGbmtk6NhLWqklWeHmnTYzW1ny3P1NXD8RPeikPe5WlYXAjWJHzPXkgkyrpakX3UhbZ2zN1v3vT79V09So+Uogr72QZzQZvSI0VERHYkBW1RmN8+aDONTdDYuNaEuwqtrUzVVmpknhkYgosvrq0w5IqQBHk9xphc6fTyfPC1Z07Bcz/CvPW9W6bU1nf1wJVpr9ecyCOCosIAACAASURBVFZGTsHeZq9lBEBT+W84iIiISPwUtEWhWHokeKtt1ZweeXkKlhahxoqQ5BVWGnIpkmuVIwO+nmQbtkxBW/bRRyDZivl7b93yOXVdPZDNwhV//Vlkd7Ejw3D8ZKGQjXfDoU0rbSIiIjuMmmtHYT6zVtZ/Ky0pbDWnR17MrUzVYHokAEcGoKERe3oY87LXeCttyRQmX5jBr1R5Vtrs+efh6e9jfvofYpr2bPm8+u4e7y/Tk4UWACLr2WtXID2Kee0bb/xGqrVsNxyidOE3Xlf0OUceuRjDTDz7zp4LfGzjz77bd7PsUl5/lOK8lgBTd/fGO97Lbazj3f7xYM2uE3PLgRplx30945bx2Zw+rOs92djG6nzSBD52Jdng+2efvLAUeLwg/P7uC+vsp18a63hNd8X7u3o7oYI2x3H+O/BTwITrunfkHvsI8E+A/IacD7uu+1iYcapeZnbrxtp5LcnqTo8MuzJVYaahAW4ZLOzxCbs/zyRbsefPRDW9guxjLuxtwbzxnds+r77zAAB2ZgLDUOTzkB0gt6pcKEKSl2xVeqSIiMgOE3al7X8A/xn4ww2P/yfXdf9DyHPXBLu87KUVNm9TiAS8709dimdSQYyPeSl7+UIGNcgMDmEffQS7MA/jo5iXh7g7kmqLvEGxvfAiPPkdzDsdTPP2K7P1XetW2kQ2YUdOQUMjHD1+w+Mm1abKoyIiIjtMqD1trut+HZiJaC61Kb9PbbvqkYCphZW2Gi1CkmcGhsBmsT94HOZmw72eZCtcz2BXViKbn/3CI9C0B/Pmdxd9rmna4wWO+vAtW7Cnh+HYbZhEw43fSLZGfsNBREREKqtce9r+meM4vwj8LfDrrute3vgEx3HeD7wfwHXdMk0jBvl9asXSI6u4EIm1Fi6OYl55d6WnEs6tt0N9PfZvvgiE3J+3vnT6vvbQU7MTaewT38C85d2lr2a2d3l9uEQ2sPMZGH0B807n5m+m1m44bNYDsFq4D9xX9DmN98W4N2Mx+FiHT/Tz0BMf9XXMUk+R7IyIxXot8fbixDpec6zDsedNy4GOO3zrAR78/Id8Hxf39YxbtiH4vq9g48W3BzJR/D7tlm7p6+DhjxT/Xble/VK8+zvN7L2xjrd4LN5/7Ob11VOzsej/0R3HOYSX/tgDZIFPuq77oOM4vwu8C7BAv+M4+1zXvQL8PvBnuT/fBfy04zh9rusurD+v67qfBD6Z+zLed1iUcqtnplh6ZEsKFhewy8ve/qtqMnvFCyhrdD9bnmnaA4dvXWuyHeL1mFSb96aMKmj7q89CfT3mLT9d+kEd3XDxfOixZQd6/lmw2bWqqeslc8V3Inrvlovz0KeLPifO4hkrIQqRPPTER3ngVb/p6xgVIol4vNgLkYwHOu7Bz3+IX33vx3wft+MLkfTFG7TVSiGShz9yH/d/pPjvyvXiLkSS+Or3Yx0v9kIkP4w3SDz17/75lt8rJXxcwVstOwG8BnjAcZyTwJeBO4C3A4vAb+WeP40X5H0AuAu4BAS7JVULSkyPLHy/Glfb0mMAmBoP2mBdUYamPdDeGfxEydxqWAQFHezMJPbbX8W8/i0YHx+iTUcXzEx6K6Ei69iRp6G+Ho7ddtP3Ciu5cypGIiIislMUDdpc1027rvtk7u+zwDNAv+u6X3JdN7/hZx7IbyD6eeAp13V/CLwX+KHruju2Q7D1kx4JVRm0rVWOrO09bbCuX1vPQa9nVVC51YooSqfbL34esJi3/Yy/Azu6YWlJlQDlJnbkFBw5vnnbiPxKm/a1iYiI7Bi+Njw4jnMUeBnweO7rPwF+HDgAvN1xnF/GW2E77jjOLLAKfDzC+W7KzkyS/Y//mqn6elZXS4wPG5uo+5XfxHSHTDnIFxcpkh5pWlJeul0Evdrsqb/jyhNfx/7iBzF1EeTapsegaS/sD7EyVS2OnwRjwq8apnJBeMigzWZmsd/4EuY1b8T47LdmOrq898zMJLTuCzWPwnxGXyD7uYep+8BvbtsnLi7Zx/+Ga2Nn4Wd/qdJTqRl2aRFeGMH8xBYbJXKrxHbuGvEmHImIiEi5lBy0OY6TBD4L/JrrutcAXNf9Bcdx/iXwSuBnXNe1juPsBx7AS42cB77iOM5fu677lQ3nu6EQSWdn8IBhNVHH3G1DGGNKSiWziwssPvENkuOj7D35Y4HHBZizWTLG0Hno8LYB1HL/QWaA1vo6mkK8VoBrzz7F9W99hQ7nl0gcPhbqXACXpy+RPXSEjq6u0Ocql0QiUdp7pLOT+V/+NRoGh2gIcZ3t/n1MAM2ryyRDnGdx7HmuLC+x7yffTaOP8yQSCfbdOsgMkFpeYE/I90ze3Ff+gszTT9I6PkrTK14byTnDuPKj73H98a/Tdd8/pa6G2034UfJ7eQtLTz/J5dUV2l7x2k1/l2QTdUwCSZulOaL3TVDbvdaWi8V/V4fZZ+ZX4tjR4Ac3Nfo+vpTXH6U4ryVA8mhHrONl+ppiHS/w9VxcCnRs3Nczfo0xjxdfcYnkhcXAx9YvWd971Pacmw48XiBhfncGkL0U7w3nuH9Xb6ekoM1xnAa8gO2PXdf93LrH78drrv1m13Xzr2oM+BvXdadyz3kMeDlwQ9C2sRDJ1NRUmNcBv/hBOjs7KeU8dnkJnvgGs+fPkgk5bnbqEjQnmZ7ZvvOBXfIySa+mL1IXcszVCy8CMPPEt6hrDv9Bd/X8WcyJO0u6dpVS6s8WgFe/0fsz7OtpSTE/Mc5CiPNkn/WafV9NtmF8nKezs5Mrdd7/xK698DxzA+FuLhTm8/xz3ny+/x3qjgxEcs4wVnP7Kaef+CbmJa+q8Gzi4eu9vIns974NxnCtu3/T95RdXQVjmBu/wHyF/01v9Vr7+voqMBsREZHaVfRWg+M4BvgD4BnXdX9v3eNvA34DeLfruvPrDvkicKfjOM2O4ySAe4BT0U47HNPQCG37o+mBlZmDIo2SgbU9b/Ph0yML8x4ZDn0qO5+BK9M1XzmyLKLod5Ue81JnUwHSG5tbvLTVmejK/ttckGQjeO9EItfSwJ6ukvnUADsyDP1Hvd6PmzD19d57LoL9mCIiIlIdSllpuxt4H/Ajx3F+kHvsw8DHgCbgy47jAHzXdd0PuK572XGc3wO+h1fK/zHXdR+NfuohtXdhI/gwbOfnihchAdjbDMZE02B7Zu2DrrU2XMGN8XzlyNovQhK5VCs2ZBEQO+41LQ/yMzLGQEcXNqIG2za7CpcuQF0dvDCCXVrENMabUnTDfBauF/Z4Vk0QWeXsygo8/yzm9W/Z/okpNdgWERHZSYoGba7rfhM23c/+2DbH/BHwRyHmVXamoxv74vPhT5SZK17uH7z9bntbQgdtdn4Ors9T39PP6vgFmLoEXT3Bz5dbeSFMI+qdKtkKk8F68RSkx8Kl/XV0R7MiDN6q1vISvOw18HffhRdOw23RpF0Gng947+XzZ7AL1zF79lZuPrXgxedhaREzcHL75yXD33AQERGR6lE9bb7jlu+BlQ3ZYDEzW7yxdl5LMvxKW+6D7p573gpEkFaWHoVEIlTgt1OZVFuoFDM7e80r1x9iFdN0dBV+5qHlWjvUveEnwZjKpyTOeMHonnveCtksnH22svOpAYUVyYFNmmqvlwz33hUREZHqsouDtm5YWYFrV8Kdp9T0SICWFDbsnrbcqkvTy1/njRsyrcyOj8GBfm8fjNwomYK5a8GbW+eCJNN7OPgc2rthfg67MF/8uUUU+vEdux36j1Y8JTGf9rnnDW+Bujqv95hsy54e9v69tu3f9nkm1aqgTUREZAfZtUGbac/1zAqRemazWchkSkqPBLziAGHTI/MpZd09cPxE+A/eF1/E9Gg/26aSbbC6CtczgQ634xE0Le/ItWGIYrUtPQqt+zAtSa8J+fPPenukKmVmEuoT1PcegkPHKr/yV+VsNgtnTq01kN9OsjXcDQcRERGpKrs2aMt/GLYzIUpiL1wHmy3aWDvPRJEeOTMJDY2Ytv3eh7eJNPZKsJ4cdnkJpiZUOXIrqTbvz6AFHdJj0NgE7cH73xUackdRNCc9VvhZm8EhWFr09khVyvQk7O/A1NV58zn7HHZ5uXLzqXYXz8N8xmsgX0wq3A0HERERqS4lN9fecfIfpGdCFHnIVb7zkx4ZuuT/9AS0d2GMwQzcgQXsyCnMXW/wf65LF7ygU5UjN2WSrVjw0swO+O8rZdOj0NO/bdP1ovI3F6YnNq0GVPJcrPWKorz6Hu+BXCELOzKMOXZbiDOHmNP0hJemjBdE2i//OZwbKcxNbpRfiSx5pQ28Gw6l7rmN2b5Pfafoc668L74G8Jm+4P/ClvY3cv5ef78j+n/n24HHCyLOawnQ+a10rOMdORfrcEwFvJ4rHS2BfhZxX8+4Jb56LtbxOmNuCB2UWVrx3Sx7IeZG7JfuircK9dG/DN6sPIjEV78f63g8vPW3du1Km2lu8ao5hqnMN++tmm3VL+kmzUnIZEIVP7Ezk2spc4ePQdOewPva8pUjjVbaNpfKf/ANWIUvPYYJW5WzdT/UJ8KnR1697K265AJ007ofDvRXdh/Z9CQmf/Mkt3pkTz9duflUu9PD0N5ZCHS3Y/LvXe1rExER2RF2bdAG5HpghfgwnE919FM90ma9tMqgpicKKXOmvh5uPRF8L1B61OsdF2AVaVfIrVbYAB987cJ1L6WxL1zQZurqvA/qYcv+F4qirM3HDA7ByHD4CqoB2JVluDqzttKWbIW+w9gzKkayGWst9swpzMBQaT3/kiFvOIiIiEhV2eVBW7geWDYftJWcHpkL7jLBUiTt8pJX7XLdHikzcBIunMcGOWd6DDoPVLTBclULs6ctyqblETSCL1SOXD+fgSFvj9TF86HOHcjlabB2bdWYXBB55hns6mr886l2E2lvtbSU1EgovHeD3HAQERGR6rOrgzbT3hWuwENhT5uPQiTgfVAOIr8quC49qrC/JUCam02PqgjJNkzTHmhsDJRiVmhaHsH1NVE02E6Pwd5maGtfO29+X1slqjbmXo9Zn+o3MOStQo+9EP98qlw+bdQU68+WlwxZREdERESqyq4O2ujohuvz2PmAFR3zx5W8py23IhdwpS1fNMWsW53glkFIJHyX/rerq3DpQjQrQTtZsjVYill6FOrroas3/Bw6uuDqZS+lMKB8gH5Dal1Ht5d6WYGgrZCWvH6lLReQqPT/JkaGvfdiie05TFNT7oaD0iNFRER2gl0dtJmwPbAyc9DYiGloLO35ueDOBiz7bzdbaWtohFsG/ReUmLrkNRfXStv2km3B9rSlx6CrF5OIoEBrR7eXShimPcX42E0BuleBdAh75lT8/bzyK4f71wVt+zugq6fiTb+rkT09DIMl7mfLS7ZppU1ERGSH2NVBWyH4CZp6lpldWz0rRSE9MuDK3vQEmLobUtwAzMAdcP6MV/yiVPnCFGqsvb1ck2Lf0qORtVIoVFgMmMprM3PefqjNAvTBIe97EzGXk56ZhLZ2TEPDDQ+bgVxxFDWFLrAzk14BolJTI/OSrdrTJiIiskPs8qAt3wMr4Ifh+bnSUyNhrcpk0PTI6UnY337T6o0ZOAnZLJx9tuRTRbnnaiczKf/pkXZlGSbTmN7D0Uwid3MhcKXTQoB+88/aDNzhnTvmUvs3tK5Yb3AI5mYLcxYKq+i+g7ZUwBsOIiIiUnV2b3NtgNQ+SDQEL0aS8Re0mcYmaGhcaxXgk52ZhPZNejQdvx1MHfb0MObky0o7WfpF2Nfu9auTraXa/H/wvZT2guio9gu2d3qtGQKuCG9aOTKvp997jSPD8IafDDFJn6YnMIdvvelhMzDkNYw/PYzpiyjorXWnh2HPXjh01NdhJtWGvXSxPHOKwNlPv7Toc9q/EMNEcg58L3jD1oZM1vfxpbz+KMV5LQGe/WBPrOMdv3Ms1vH4H/EOF/f1jFvdgX2xjnfsQIjtBj6deSr4Z4GF7gbfP/u94/Gux4T53RnEa3/viVjH+8ypV8Q63nZ29UqbMcYrnx9XeiR4QV6I9EizyeqE2dMMh4/52gtk02NaZStFshUWrmOXfRQBGb+5J1oYJtEAbfsLhWh8Gx/zbhZ03hzwG2Ng4GSsTbZtNuvdKNlspa2rB/a1B24YvxPZkWE4fhJTV+/vwGSr9rSJiIjsELs6aAO8BttBV9rmM5gWnytVLalAPdVsdhWuTN9QhGQ9MzgEZ0+XFFxYa73CFNrPVlzKf5PiwspWT3908+joDp7Gmx6DA/1bfug3A0MwdSl0L7iSXbviFcHZ5L1cKI5yWvvaAOzsVUiPrrX28CPZCovXvf6OIiIiUtN2fdAWqgdWZrb0xtp5zS3B+rRdmYHV1c1XJ8gFbSvLcG6k+LkuT3v9sLTSVpRJ5oI2PymS6THo6Pb6vEU1jzArwhdf3La1Qz4giG21Ld+jbbNUX/D6tV2Z9iqc7naF/Wwn/R9buOGg1TYREZFat+uDNjq64NoV33ej7fIyLC2uFRcpVUsqWCGS3CrLlh90j+cbJZdQUCKfvtenoK2ofJNiH/2u7MUyNC1v74LLU15qoQ92cdFLRdxuPgePeo23Y+qPVljR2+4GBKj0P7lr0NAIRwZ8H2sK710FbSIiIrVOQVs+CPKbeua3sXaOaUkGKkRi86ssW6VHJluh73BJH3TXKkcqPbKolPfB15a4WmGzZWpa3tHtpRReu+zvuEtjYO32K2119XDrifiCpCLvZXoPeTc31GTb68927LabWiOUJOX/hoOIiIhUp10ftJn8B0e/RR7yq2W+0yMDFiLJf9Bt33x1AnIrFM8/i11d3f5cF0e9eaTirdZUk/ymR05PwvJS5CttQRvBl9rawQwOQXrU20NVbjOT0NyC2du8+Vzq6nLFUXZ30GYvT8PoC/5L/efl3rul3nAQERGR6rXrg7bAvdpygZcJkh65uOCvGiF4H3STrZimpq2fMzDk7VUbe2HbU9lxr/GzMcbfHHajlhavoXmpwUy+J1o5VtrAf7GQ9CjU1UF337ZPKwQGMexrs9NbtK64YT4nYSKNvTJT9vlUK/ulz4MB87o3BTtBKsB+TBEREalKCtr2dXgfyv0WecgES48sPN/napudntg6nSwn/8HbFksrS49FVo5+pzN19d7PrMQPvmVrWl5YafP3PrXpUejqLZ5ed/Q4NDTGs7o1PbHlfra8QtPvGFsRVBN77Qr263+FefWPY7oC9mdqTvq74SAiIiJVa3c31wZMIgH72/2nnYVJjwQvaGvbX/px05NQpHCI2d8BXT1e0PaW92z6HDt3zfsQp6CtdKm20lPM0i9C6z6M3/dFEWZPs/fe8bsinB4rae+iSTTAsduKB/xRmJnEDN6x/XMOH4OmPTDyNNz1+vLPqcrY//UXsLyMecfPBT6HqauDZKpqV9rav7C36HP2feo7MczEkzh2NPCxZmmFPeemfR3T/oXewOMFEee1BEheiLch7YXxw7GO1/+pbwc6LvHB9wT6WcR9PeM211/890GULvTF9345GqL5dNPbLEf/0l9mlt/fRbXmT752d6zjdT4Zc1ba39/6W1ppA2gP0Kstv1LmMz2y8GHeRwVJa633QbdIShnkVtvODG9dZTC3EqSVNh9Srf5W2sp1bTu61grSlDKXlRWYuFhyqqYZGILRF7DX54POsPic5ufg+nzxVeP6XHGUXViMxGbmsF97FPOKu8P3Uky2ak+biIjIDqCgjVwZ/SDpkcZ4pdL9yKdH+qkgOTfrtRcoklIGwOCQ9/zxsU2/bdMven9R5cjSJVtLSjGz1uZST8t0bTu6vb2NpZoc93r79ZQWRJrBIbBZeP6ZgBMsQb51RQnvZTNwEi6cD9SMvpbZr/1PWLiOece94U+WalX1SBERkR1AQRt4wdCVaa9ce6kys9Cc9FKQ/MitzFk/QVuusqUpsjoBJexrS49BY9O2VSjlRibZVtpK29XLcD1TcpDkex65BtvW2tIOSPvsx3fsNqivL+/qVrFy/+vk+7VxpoxBZJWxC9ex/+sv4SWvwhy6JfwJk21qri0iIrIDKGgD7wPk6ir4qVSXyUBzi/+x8umR8z5WDwofdEsItLp6YF87bFFQwqZHoafff7C5myVbYW62eGPrclWOzOvo8qqDzmdKerrNzYcSU+xM0x44crysxUgKVVpLeS/fMgiJxK5KkbR/81eQmaUuilU2cv0bq3RPm4iIiJROn9wJ1gPLzs/6L0ICXjqlMb7SI9c+6JawOmEMZmAIe3p48xWZ9BimTCtBO1aq1UsbLFLxsxAklbqy5VNhpbXUVN70KLR3YvaUvsHbDJyEF0awS8E3Tm9rZgIaGkvqEWgaGuGWwV3Tr80uLXpl/k+8BHPstmhOmirxhoOIiIhUNQVtsNYDy8++tsyc/3L/5Cq67W3xt6dtegKa9pZe9GRgCK5Mw9SlGx62C9e9PVFlCip2rFIbbKfHvKC8rb0882j31wjepsd8p2qagTtgdQVeGPE7u9JMT0J7V8k9As3AEJw/4713dzj7zS/DtSvUvfPnoztpqq2kGw4iIiJS3RS0wdr+Lp9Bm+/G2nktSf8rbe2dpX/Qze0FummF4tIF7/sqQuKLSbV5fymyN8hLPS1j0/JCI/ipok+12SyMByiKcvwEGIMdeTrIDIvPa2aytNTIHDMwBNksnH2uLPOpFnZlGfvFz3nXP7+XLwr5Gw7a1yYiIlLTFLSR28uTbPVXmS9oeiRAc9JLryzVTPHG2jfoPeTNbcNeIHtxdO37UrpUiStt42VuWp5qg8bG0lbaLk95FUd9rqqaliT0Hy3fPrLpiZIK6hQcvx1M3Y5PkbTf+RrMTFH3TifSoN+U+t4VERGRqrbrm2sXdHSXnB5ps1mvEEmA9EjAC6h8pUdOYm4ZLPnppq4OBk7e/EE3PQr19dAVb1PXmpdbrbCzV9nq47Sdn/OqR5ZxFdMY4/UULOV9mgvQg+xfNIND2G9+Gbuy4jWfj4hdXoJrV6C9s/S57GmGw8d2dDESu7qK/avPwpHjMPTyaE9eWGmrvrL/Uy8vXgU1zobCiZgb0pby+qMUd3PmS3c1xTre4kvK119yMytvCnY9bao50LFxX8+4Xe+Jd99t3YH4Uu4v4bM11DrLLXW+f/Yt/fF+xuv8VjrW8Y7fuXlLq3I5Q/Vkp4X6ROY4zn8HfgqYcF33jtxj7cBngKPAOcBxXfdyuGnGoL0Txi+U9tyF694+kYDpkaYlid2w32wrduG6117Az+oEXkEJ+4PHsVdmMPu8PVY2PQZdvZF+EN8VSvngW2hafri8c2nvLqlgTqEoSoCVPzNwEvvV/wmjZ70KjlHJz7uEJvE3zGdwCPu1x7DLy5iGhujmUyXs334TJtLU/cpvRZ9am/RSe+3ctS1vOIiIiEj1C5se+T+At2147DeBr7iuOwB8Jfd11TMd3aX3wMo3+w2aHtmSLL3kfz5l02dfNTNwBwB25NTag+OjKkISgGlsgqY9XtPyLdiL8TQtNx1dpe29HB+DZOtaepwfxXr9BeWj3+B6ZmAIVpbhXJmKo1SQzWaxjz0CfYfhpa+OfoBU9a60iYiISOlCBW2u634d2Njc7D3Aw7m/Pwz8dJgxYtPR5e0B2uaDeUGuEpsJmh7ZnIJMprQy3LnVCb8fdDl8zAs0cgUl7MoyTKRV7j+oZCvMbfPBd3wMEg3Q6fPn5FdHN8xeLVqS36ZHAweQpm0/HOiPfB+Zrx5t6x0/6R2/E/e1/eBxuPgi5h33lqV3omlo9CrPlvJ7TURERKpWOQqRHHBdNw2Q+7PMn2KjYfyUU8/vRwtTPdJmvTTLIgr7l/yuTtTXw60n1lZLLqW9KnyqHBlMqg27TTEHr7x+P6auvrzzyK+4blM0x1oLF0dDFUUxg0MwMhxtf6/pCTB1sK/D31xSrdB3eMcFbdZaso89At29mFe+vnwDpYrccBAREZGqV7HNTY7jvB94P4DrupWaxpr1jYuPHN/2qTYftIVJjwQvzbK5ZfvnzkxAfQLa9vsexgwOYf/sj7CZWS81Espb3XAnS7YW2dM26qtYTFCmowsL3gpszxYB+OwVbzU4zM96YAi+8SW4eB4O3hL8POtNT8L+9kB7Ks3gEPa7f41dXfVuSOwEw0/C+TOYX/xn5X1NyVZsFaZH/vnf/wdFn9P0tviKdZillcDHHr71AA9+/kO+jlnojnd/ZpzXErwCCnGyzfEWsmh8WbDjjhzp4BMP3e/7uLivZ9yyDfG+P02M45nXB//Z3drdjvvAfb6OqVuO91om5pZjHY8j8Q63cLJ69tKXI2i75DhOr+u6acdxeoFNl65c1/0k8Mncl/G+wzZT6IE1WXzDfr5RbcD0SNOS9F7wfKb4k6cnYX9HoNQpM3DSG2fk1Fphip5+3+cRb7WnsG9tA7u46AX7r3tz+SeSbwQ/s837tFAUJcRKW+69Y0dOYSIK2uzMpO8iJAXHT8JffwHGXih6U6UWWGvJPup6/Rdf+8byDpZq86p2Vpn3/H9/XPQ5R/8yvg8De0JUj3zw8x/iV9/7MV/HPPvBnsDjBRHntYSdXz3y8P8T7EbLJx66nw888HDxJ26g6pHRqjuwENtYTT8MXj3SfeA+nIc+7euYlovxfqSOu3rkyn8LfoMtiDNPxZuh9sKv/vqW3yvHrZu/APK3ke4H/rwMY0SvJeXtASulyEOhEEmIPW3rz7MNO+2zR9t6twxCIuEVI0mPQUe315NO/Eu2bt3r6tIYWBtP0/J9HVBXt+37dK1yZIj5dHR7FVWjLEYyPeEVUgnAlKs4SqWcHoYzz2De+jOYRHnv4pliq8QiIiJS9cKW/P8T4MeBTsdxxoD/E/go4DqO88vAi8C9YScZh7UeWCU02M7MQWOjt8k/iFywZzNzxVf1picxJ14SaBjT0Ai3DHp7gVaW1VQ7jFQbLC1iFxcxTTfe8bS5la04rq+pr/cCt+3e6gmE3AAAIABJREFUp+kxr/jE/tL7od00jjGYgSHss09hrQ1dit5mV+HKdOAbEKa9E7p6vPfyW94Tai7VIPvoZ6B1H+b1byn/YKltbjiIiIhITQgVtLmu+wtbfCuGPLEy6OgurRDJ/NzaalkQ+RW6+e0bbNuVZbg6E3ylDa/0v/2rP4W6esztdwY+z66X79U2dw2aNqwWpUe9AhvdffHMpaMLu8371KZHvaIoYXt+DQ7B438DE2k4EPK1XZmB1VX/lSPXMQND2KeeiCSIrKTl08PwzA8xP/ePvHYS5Zbc+oZDJf3CG79V9Dl/wt0xzMSzdzz4e3xpfyPn7/V3/C+88RuBxwsizmsJcPzOzdPJy+W3jj4W63i/fOn9gY5b3Gc49y7/q+txX8+43dVxPtbxfiIVX9bGvzvwjsDHNrYu0f+T/n72Zy8Fv2EbRKYvps8+OU+f+C+xjvevOn4s1vFg6/TIokGb4ziHgD8EeoAs8EnXdR90HOd3gXcBS8DzwC+5rntl3XGHgVPAR1zX/Q+h5h8T09GFfeF00efZzGzw1EhYqzpZLD3y8jRYG/KD7knsY9lc5UittAVlUq3e/sC5qzf9PLym5T2xNX42Hd039t/bKD0aeHX2hnEG7sjtaxvGhA3a8q0rfPYbvMHgEHz7K16Q3FfmJuZlNPenD0NLCnPPxhaXZZLM3WCauwpNNVHMV0RERDYoZU/bCvDrruueAF4DPOA4zkngy8AdruveCZwGfmvDcf8J+EKUky279i7IzGKLleLPzIUK2kxjEzQ0Fl1py5d1992jbb3jt3urQBDPnqudKtnm/bnZ3qB0zE3L27vh8hR2dfWmb9n5jLeqFUWA3tPvpYVGsI8saOuK9cxArl9bDe9rs6MvsPS9b2Le/C7MnuCb0/0wqdx7VymSIiIiNavoSluu11q+79qs4zjPAP2u635p3dO+C/xc/gvHcX4aOAuUUB6xiuQ/UM5Mbn8nf34OOkNW/mpJrvV728LaB90QK217mr1G2+fPaKUtjFx6pJ27dsM+RLuyAhMXMS99VXxz6ejyVk6vzNz83hjPV44MH6AbY2DgZDT90fLv5TArbV290NaO/e7XyK6UXgnPDAxhjtwafNwI2ccewextxrzpp+IbNJ/aq2IkIiIiNcvXnjbHcY4CLwMe3/Ctfwx8JvecFuA3gLcA//s257qhT1tnZ/gc3EQiEeo8S8cGuAy0Li/StM15Jq/P09jeQVuIsaZSbSSWl9i3zTnmrmfIAJ0Dt99U9MTPa828/s0sGOg4ElG/rQoI+7MNK7unkUmgJbtKy7p5rIydY3p1ldTASfZGOL/tXu/iLbdyBWhbXaRxw3OuP/U414D9J+4kEcF85l/6Kmaf/A77WaW+80Dg81ybn2WhdR9d/TcHk35+ttde/fe4/qU/wz7/bMljm/ZOOj7xp8ELB0UkO59h8snvkHz3z9Ny5Ghs464sHWUaSGIjfY+WotL/bkVERHaKkoM2x3GSwGeBX3Nd99q6x/8lXgplvunO/wX8J9d15xzH2fJ8G/u0TU1N+Zz6zTo7OwlzHlvvfai7+sIZ6o4MbPm87OxVFhMNocZa3bOX1Ssz254jO3oO2tqZvnpzWpOf12rveQfmx98Zar6VFvZnG5a1FurqyFxKc33dPOypHwEwl9xHJsL5bfd6bcJr23Dl7Ah13TcGQdnTz0AiweVEEyaC+dj+owBMP/5N6l59T+DzrF4chf2bvyZf7+Wf+yXq3rH175WbnHmG7H/+bSb/wqUurj1kW7BPfx+yqyRe+upY38t2xUujnU1fiPQ9WoqtfrZ9MW9cFxERqXUl9WlzHKcBL2D7Y9d1P7fu8fuBnwL+geu6+W5+rwb+veM454BfAz7sOM4/i3TW5bJvP9TXb1tB0i4vw9LiWjGRoFpSRQuReM2Iw9+lruVKe9XCGOOlmW1IMVvriRZj0/J8iuEmZf9tehS6+7zWAFE4eBT2NkPYFMnpyVBpvnnGGExLsuT/uPOVXtuLL/ypl8paQXbkFNTX03jbHfEOvLfF+72m9EgREZGaVTRocxzHAH8APOO67u+te/xteGmQ73Zddz7/uOu6b3Bd96jrukeB/xv4t67r/ufIZ14Gpq7e6221XQ+sfPGQMNUjAdNcfE+b14xY1d6qRqoNO7th1XN8DPZ3xlZUAvDKtqfaCoVqbpAexUS4d9HU1cOtJ0IV/7DWeu/l9vjfy8YY6t758zA9gX3i67GPv549PQyHb8Xs2RvruIUbDipEIiIiUrNKSY+8G3gf8CPHcX6Qe+zDwMeAJuDLuTTI77qu+4GyzDJOHd1rBUA2k18dawnRpw28oG+b6pE2m/U+lL/sNeHGkehs8sHXpscqU+Clveum96ldXoKpCXj1j0c6lBkcwn7u+9jZq2uVCP2Ym/VWpyNYaQvkzlfCwVuwX3gE+5p7vEA0ZnZpEc6dxrzpXbGPDUCy9eYbDiIiIlIzSqke+U1gs/y6op0sXdf9SIA5VZRp78I+88Otn5ALtEwU6ZGLC9jl5c37e127AisroUqkS7RMshV74Vzha5vNeitbb/jJ+CfT0Q0XNzTcvHQBbBYibu1gBoa8HnUjp+Dlr/V/gly6caVWjb3VtnvJ/td/D09+B175+vgn8cIIrKxgBofiHxuqcqXtM6deUfQ5nU/Gl9qdvLAY+NiGTJYD3/N3/GdeUvz1RynOawlwhnhbzPw7gjcwDiLo9Uy8O9ixcV/PuJ09EG/Bou8dOBLbWGeeCv6zWzjZ4Pv4veMl7XyKjN/ffWH9q4l4m12X8v+qKP3bO7f+Xrw/2VrQ0Q1XZ7BblRTPRJMeWdgTt9VqW24VpRIpZbKF1IYPvpenvBWkCqy0mY4umJnwUg9zbDpf7j/i+Rw9Dg2NwUv/R1HuP6yXvxZ6+sk++sgN1ywudmQYjIHjJ2MfG3K92ua0p01ERKRWKWjbqL0TrIXL05t+20aZHglbBm02v1+pUillcrNkG2TmsNlcU+tcEZKKNC3v6IalpRuDyIujXmBwINrKfCbRAMdu8wppBGCnK/9eNnX1mLffC2MvwFN/G/v4dmQY+o94xVEqIdkKSo8UERGpWQraNjDrG2xvJh9khUyPNPmgb6sKkoWgTSttVSPV6gX0udXW/MpWxVbaYG0VC7wgsvMAprEp+vEGhuDFs9jr88WfvNH0BDTtCX+jIyTzqr8HnQfIPvqZWFfb7MoKPP8sZqAyq2yA996dn8OurlZuDiIiIhKYgraNckHSlsVIMnPeasbekNUC80HfVhUkpyeguQUTdhyJTrLV+zNfOj09CsnWYMU5wsqnza6rdGrHy1cUxQwOefvlnn/G97F2ehLauyreesIkEpi3/Sy8cBqefSq+gUfPwuICDMRc6n+9ZP6Gw/ZtRkRERKQ6KWjbKN8Xbauy/5lZaE5i6kJeulyalN0iaMt/0JXqUQjOcmlmNj0aedGPkuVW2vI3F+zqKly6UL5UzWO3QX19sNL/M5NVs2JsXvdm2NdO9lE3tjHz16yyK225926VFSMRERGR0iho28A0NELb/hvTztbLZKC5JfxA+VSx+S3ufE9PVM0HXcnJr7TNXfPS69Jj0Rf9KFVzEpr2rqXRTl3yqo2Wa6WtaQ8cvjVYMZKZibV0zgozDQ2Yt74XnvsR9kywPXp+2ZFhr+H5vvZYxtuMKawSK2gTERGpRQraNtPetVYIZAM7PxvN3py9zV6a5VbpkTOTGK20VZeU98HXzl71UiQzsxVbaTPGQEfXWpGPtFf+3/SUbz5mcAjOjXg9x0pkFxe8Pm1VdAPCvOGtkGoj++gjZR/LZrMwcqpypf7zUvkbDqogKSIiUosUtG3CdHRvs9I2F77cP3jplXtbNg3a7PwcXJ+vqg+6wg0rbeTL6/dUaKUNvPdHrgdaHEVRzMAd3mreCyOlH1QN5f43ME17MD/xbnj6+9jzZ8o72MUXveJFlUyNBK/yKajBtoiISI0q2lx7V+rogh98F5vN3rx3LTOH6TwQzTgtyc1X2nKrJ9WSUiYek2jwVkjnrnn72QD6Khe0mY4u7NnnvC/So7CvHRNF6u5Wjp8AY7AjT2NuK7GoRuG9XF03IMwb34n94ufIPvYI9b/yW2Ub5/9v796D46zOPI9/j6621JIvkq8C29jIwZfJcAmEQC7EbAgwEMICp5LsZl0zW8umipDMztRUMpmZCrWVrSU1u7NLUq6k2JkUTDaZ5DDBk7BACAXJJiHMwEISgmyQbGOMLyBbso0k33Q5+8fbLcmi2+p+1e/b79v9+1Sp1LfT57yXvpw+5zxPbjqp6a7wSFsmOzsgQSNtzb+dPchS5zMHY2hJYGzP3tBlzdAdNDz9Qkllmi+/KnR9YcS5LwMrYq0t7uTTFz1zKFS5huFROkOVjXd/xm1kZbxB13ZFOCtlprkktg+TjD1zIN5k16W+981V3Mmui/msKqtPFL5LI235dCwNRhTePvbO+8o1PRKgJRNMt5wpNzqRsC+6Qjbf1fGgk9Q8HxZ1Vq4ti5fCyBD+1MlgpC3i9XWmNQNdq0sKRuITei6b+S2YLTfBi8/iD+yLrqK+HcE5Uq4fekIKfnBoDaaqioiISOqo05bH5FqyGVMk/cREEIikXAlyW9vyT49MQDJiKSDTjs+NtC3vqmwY++m52g7tj3Q9W47p3gR7Xg1yjxVjsB/q62HBwmgbFoK59mZonod/PJq1bd57fG8PpntTxdMdAMG6tqHkjLSJiIhI8dRpyycXTn3wyNm3nzoZ5KqaY2LtHFNoeuTgYWhsgrbkfdGteW0LsiNtFYwcmZWbcuh374TTJ2NJ8m3Wbwpyjr2xp7gCA4dhUSemrj7ahoVgMu2YD92Af+4X+P4Ipo4dPgTHByu/ni0n0x4E0REREZHUUactn1zi4sEZwUhyiWnLNT2yNZM/5P9AfyKSEcs7mbb24PgcG6hcjrac3I8LL78IEF2Otumya7OKnSLpE5SjLR9z3ceD/HM/frjszz2Zn63SkSNz2hYoT5uIiEhKqdOWh2lpDdZ/zIwgeWIkuL9c0yNb2mBkJJh2OY0fPDyV5FuSJdM+dR5UMAgJAO2LoL4Bdv42uB5De8yCRbCsq/h8bQPJTl1hFizCfOAj+F89XTDNR2i9PcH5UuER2RyTaVOeNhERkZRSp62Q6TmwcnIjbWWaHklrazDd8tTJs28f6E9ctD3JalswdbmS4f7Jpo1Y3BmcPy2Z2KbTmu6N0LfjHT82zOTHxuDYYKJH2gDMR28DPP6J7WV9Xr9rB3RvTM6IeWbBVGJ4ERERSRV12grJk6vN59aflW16ZPZ5RqamSPrRM0HUSgUhSaZcrraGBliyvLJtgan8ZyvOi69z0L0pyD12cJaoi0ePBD9KJPxcNh1LMFd+GP+Ln+CPHy3Lc/rBI3D4zeRMjYTgB4ex0WD9o4iIiKSKOm0FmMVLgoAg053IddrKGIgEJqfbAZN5rSbX1UmimGySYpauxNRXPrhGbkQ2zqAouY7IrFMkB5OZoy0fc8PtMDaGf/KfyvJ8icnPNl3uBwdNkRQREUkdddoK6VgKJ0/gT0yL7jgZiKRca9oyZz8vTAY/ScMX3ZrUlv3iW+kgJDkdUyNt8dW5NJiWOUswkqkcbckeaQMwy1ZiLv8A/mc/xpcjWEdfD8ybD+ddMPfnKhOTO3cVjERERCR1GirdgKQyHUvwEIx8TXauhqGpCdPYVJ5KctMjp3UMlaMt4bKjFWbFqgo3JGtypC2+9hhjMBduwr/6Et77wtMyc+fyonScy+bG2/HP/V/8U/8Hc8un5vRcvm8HXLghEaOxkyZH2sKF/fdv7mfif3+DOvtHmFXr5tyc1Q/Nnmbh1JqOOddTrOGrV4QuO9bRyrFPv6+kMsVsfznFuS8BFn772Vjryxy4LNb6wu5P39QQqmzc+zNunWvXxFpfnK+HhqdfCF/27ltKPvYNMe/LIyW+983V4sdjrY7OZ+J9r+a/Fr5LI22F5Ea6pq9rOzEcRHwsl+yInZ+eq22gH0wdLIz3A1aK1LEUc9W1mPe8v9ItAcBsvARz+Qege0O8Fa/fBMePQv+hwo8Z6IcFizCNjfG1aw5M12q45Er804/gT54I/Tx+6G04uC9ZUyNhMohO2JFE/+rL8OrvghFEERERiZU6bYVkAzxMjyDpR4bKNzUS8k+PHDgMixZjGjQImkSmvp66P/w8pisZI21mUQd1d/4ZZl5LvPUWsa4tSF2RjlG2nLo/sHBiBP+zx8I/ya4dQILys+W0zXFNW28PLFgMS8KPSImIiEg46rQV0rYAGhrPDkYyMlzWTptpaobGprOnR6bwi67UoOXnBdPtzrWubeBw6tZmmtUXwuZL8U/+EH/6dKjn8L09wXvH6u4yt26OmucHUU9DjLR57/F9PZgkpTAQERGpIeq0FRDkwFqSZ3pkGUfaIOgEzpgeaRQ5UhLOGAPrNxUcafMTE8EPHilcm1l3o4Wh4/hfPBGqvO/rgbXvSty0UGNMkKstzJq2gf4ghUPSRg9FRERqhDpt59KxJBj5yhkZngrTXy4tmWDaJeAnxuHYQCq/6ErtMd0b4chbZ79GcoaOBznBUjbSBtntWr8Z/8TD+NHRksr6Uydg357kTY3MybSHWtPmexOYwkBERKSGqNN2DmZmgu0Tw+VLrJ3TmpnK03ZsEMbHU/lFV2qP6d4MZCMlzpR93aR11LjuDywcG8Q/+1RpBXe9An4iuZ2btvZwIf/7eoJZBiuTsZZTRESk1qjTdi4dS+DtY/jRM8Ev7qdPRTA9sm0qEMlALhmxRtokBc5fE0QSzDNFMvWpKzb8PlywHv/4D/Dj40UX8309UF8P6y6KsHHhmbZw0yN9bw90bwymjYuIiEjs9Al8LrlRgoHDU8FCyjw90rRMrWmbSkacztEJqS2mrh4u3Dg5de4sg+k+l40xwWjbkbfwz/286HK+twdWrcM0z4uwdXOQKX2kzR8/Cv0Hkzt6KCIiUgMiiytvrd0LDAHjwJhz7j1R1RUV07E0SLA92A+LOoMbI5keme0Q5jptih4pKWG6N+JffgE/dDwYxckZ6IeWVsz8eFMRlNW7L4fz1uAfewj/3g8GndRz8KNnYG8vZsvNMTUwhLb2IKXB2FjxaUWyI6nlXqd3pIhk1nEmFJ5Lct+G4VE6nzlHzsI8itn+coo7OfPYlniTXb91eXOs9XV99VehypmhO0IlW457f8btWFdTrPWNrIwvCu4ywh8739ZS8rFv2DsQur4wSn3vm6tX7l4ea32QnDQ3UY+0fdg5d3EaO2zA5NQuP3B4cgqjiWJ65OlTwfTLwcOQaU/ur/QiM0x+kZ+xrs0PHJ4aqU4pYwzmRgtv7ocXi/jC+1ovjI0lNwgJBCNtUNJom+/tgeZ5cP7aiBolIiIis9H0yHNZ2AGmLhg1GIlmeuTkGrkTw8H0yJROJ5MatbobGpveGYxkoD+969mmMZe9D5Z3MfHoQ3jvz/lY39sDxsCFG2NqXekmR0NL6bT19cC6i4ofmRMREZGyi7LT5oGfWGtfsNbeGWE9kTENDbBwMQwcxk922iKYHgnBFMmBw7C4s7zPLxIh09gIa9/1znxtg0cwVTDN19TVY264Hfa/Bi/9v3M+1vf1QNfq8qcFKadMttNWZDASPzIMB14P0iCIiIhIxUT50+nVzrmD1tqlwJPW2lecc5Mr+rMduTsBnHMRNmOOsrnazIlshMcyT480LZlg3dzwEAwexmy+tKzPLxI1070J/6jDnzyBmd+CPzECJ0eqZtTYXPEh/I/+gYlHv0/du98TJKmewY+Pw+5XMFdtqUALS5CdHumHhyhqRceuneD9ZHqHcnrwnk/N+piGu28pe70FNYdfU7Nq3TLu2/65ksqMZeJNvh7rviRYixOn0dZ4Jw413faxUOVWbehi23P3llwu7v0Zt/Gm+NaYAUw0xldf48hE6LKrV3fwzW1bSypjzoyFri8NTi2N+b0z3Es9EpF12pxzB7P/+62124ErgJ9Pu/9+4P7s1XPPO6ogs3gpfvfOYHqkMVDuwArZkTvffwjOnK6KKWVSW0z3RryfgN07YfNlk5EjqyV1hWlowFx/G/4734BXXgrSAcy0b0+QEiSCzk1ZteXWtBU50tb3MjQ0wAXdZW/K1nu+O+tj4gye0TCHQCT3bf8cn7/1ayWVUSCS8kpLIJJtz93LXVd8seRy1R6IZLiaA5E8fzp02W9u28pn7nqwpDLzYg5EEre4A5F0vhjvDwrPP/gnBe+L5Kcpa22rtbYtdxm4Dng5iroi17EEjg0Ea0BaMuXPU5SbSvXGHiC9yYilhq27COrrp0L/T+Zoq55z2Vx9LSxczMSj+WcF+L7g7S3x0whz07uLnR7Z2wNr1mOa4v1CLCIiImeLaj7BMuCX1trfAs8BjzrnfhxRXdHqWArj4/gDr0NLa/mfP9tp8/t2T9UnkiKmeR6sWjcZjGQq32B1jLQBmMYmzHW3wqu/w+/a8Y77fW8PLF2JWbi4Aq0rnqmvDzpuRQQi8adPwb7dyY6GKSIiUiMimR7pnNsD5JlDlD6mY0kwd/ON12D5eeWvYH5LMO3yjdeC61X0RVdqh1m/Cf/UI/gzp4ORtsYmaFtY6WaVlfngR/GPPcTEow9R//kvT97uJyagbwfmkisr2LoSZNphqIjokXtehfHx5I8eioiI1ADFcJ5NbuTr1Mnyh/sniE7H/NYgemTzvPJHpxSJgenehH9iO7zWF4T7X7wkb8CONDPN8zAfuQW//dv413djVq8L7ji4L3j9pmVEqq0dX8xIW29PkPJk3YZImlFMQtbXv3BVJHXnc3J5+GABp5Y2lrzO4qKvx5uQNs59CXNbxxNG2DVmYR0IuT/PLM+EKhv3/oxb3AmaF+7ZG1tdca9HjHu97JFL4w1LcdHX34y1viRRnrbZTAtbXvbE2jm5zmAVftGVGnHhRjAG3/cyfvBw1Y4Ym2tuhPmtTDw2tbYtNy3UdKek05ZZUNSaNt/XA6vWYsodfElERERKpk7bLEzzPMhkR7+iGgXLdQa1nk1SyrRmoGt1MDozeLgqcrTlY1paMdfeBC8+iz+wL7ixrwcWdULnsso2rkimrX3WNW1+dBT2vJqejqiIiEiVU6etGLmIjlElzc12Bqv1i67UBtO9CXa/AsePVu1IG4C59mZonod//CG89/jeHkz3pvSMkmeCTpv355jS8nofjJ7RejYREZGEmHVNm7X2fODvgeXABHC/c+4+a+1fAzcDZ4DdwB86545Zaz8C3As0Ze/7M+fc01FtQCw6lsC+3WVPrJ1jWrMJtqv4i67UgO5N8NNHg8tVnLrCZNoxH7oB/+QPMVd+GI4PQpo6N5l2GB8PEqAXeE/LTflM1XaJiIhUsWJG2saAP3XObQCuBO6y1m4EngQ2O+feDfQCf559/BHgZufc7wFbgW+Xv9nxMrlpi1FNj2zV9EhJv+mjMqbKz2Vz3cehvp6JB+4LrqclCAlA24Lg/zmmSPreHlhxPib3WBEREamoWUfanHOHgEPZy0PW2p1Al3PuJ9Me9s/A7dnH/Hra7T3APGtts3MuvaGPsiNgJqrpkS3Z6ZEaaZMUMwsXw9KV0H+w6keNzYJFmA98BP/Tx4KRqxXnV7pJRTNt7cHI/tDbwfGawU+Mw64dmPd+KPa2iYiISH4lrWmz1q4BLgH+ZcZdfwQ8nqfIbcCvU91hA0wuwECmPZoK2rLP25GOQAYihZj1m6C+HhZ2VLopkTMfvS3Y1u6N6VnPBlPvY4UiSL6xN0hxoiAkIiIiiVF0njZrbQb4AfDHzrm3p93+FwRTKL8z4/GbgK8C1xV4vjuBOwGcc3R2dpbc+JkaGhrK8jwz+Wuu51R9PfOuuBpTV/7YLRM33c6Z1WuZ1/2uostEta1Jpe1Nh/GtdzF2zUdpXl58zqq0biudnZz+0l9Tv2wlDSW0v9LbOz4xyhEgg2d+nnaMPPsUw0DHe99P/RzbWeltFRERqRZFddqstY0EHbbvOOcennb7VuAm4FrnnJ92+3nAduDfOed253tO59z9wP3Zq/7IkSPhtmCazs5OyvE8ef3e5YwMDkbz3ADdmxkuoe2RbmsCaXvTog5Wr2eoVs7lVRcG/1O0vX50HIChQwcYydOO8d88B53LOEp9SduVT6FtXblyJb1fWThr+cWPx5e0ddnzo6HLNl/vWfNIaeWL2f5yinNfAuy9uTHW+i78i/Nira/1gXD7s27U03qw9LJx78+41S2L9/Wwdll858vel8Ifu9MLTcnHfv6b8c78WPPImVjru+ThvN2KyHx/R7zJ0c9l1mEja60B/g7Y6Zz7m2m3Xw98AfiYc+7EtNsXAo8Cf+6ce6b8TRYRkbBM8zxoasobiMR7D9kUBiIiIpIcxYy0XQ18GvidtfY32du+BHwNaAaetNYC/LNz7jPAZ4ELgb+y1v5V9vHXOef6y9pyEREJJ7Mg/5q2N/cHnbk0RcMUERGpAcVEj/wlkG+s9bECj/8K8JU5tktERKKSacfnG2nr7QHQSJuIiEjClD+qhoiIJFtbe/48bb09sGARLF0Rf5tERESkIHXaRERqjMm0v2N6pPce3xesZ0tVCgMREZEaoE6biEitaVvwzpG2gX44ekTr2URERBJInTYRkVqTaYdTJ/GjU2Hqp9azbaxUq0RERKQAddpERGpNW3vwf/oUyb4eaGmFlasr0yYREREpqKjk2iIiUj1MZgEegimSizsB8H07oHsTpi6e3/LW/+WxWR/z+h0tMbQkcOTSeBPgXvSXA6HrCyPOfQnxJ9zlP+2PtbqRL6wKVW6i0TCysvQ1o7Hvz5jN2zv7+0E5je2J73xZs2VZ6LLN13vWPDI6+wOnGe5qCl1fGHEnfp/3r9fFWt964j03+UThuzTSJiJSazLZkbbhYKTNHz8Kbx1QqH8REZGEUqdNRKTWtC0AwA9lg5FjgXxWAAAIq0lEQVT0ZdezKQiJiIhIIqnTJiJSa3Jr2rIRJH1vDzQ1w/lrK9goERERKUSdNhGRWtOSAVM31Wnr64F1F2EatMxZREQkidRpExGpMaauDjJtMHQcPzIMB17X1EgREZEEU6dNRKQWZdqDNW27doL3mO7NlW6RiIiIFKBOm4hILWprh+G38X0vQ30DXNBd6RaJiIhIAeq0iYjUosyCYHpkbw9c0I1paq50i0RERKQArToXEalBJtOOPzYAZ05jrrs19vqPXL1i1sd0ffVXMbQk0LB2Teiy87aMctHX3yypTDHbX05x7kuAsS2XxVrfgS9cFWt9Yfdn020fC1U27v0Zt7hfDyN3rIytrmXPn46tLoDOZw7FXF+s1fHK3ctjra/zRRNrfeeiTpuISC1qa4eTJwDlZxMREUk6TY8UEalFmWyuNlMH6zZUti0iIiJyTuq0iYjUorYFwf/zL8DMb6lsW0REROSc1GkTEalBpi0YadPUSBERkeRTp01EpBZ1LgdjMJsurXRLREREZBYKRCIiUoPM0hXU/bcHMe0LK90UERERmYVG2kREapQ6bCIiIumgTpuIiIiIiEiCRTY90lp7PXAfUA/8rXPu3qjqEhERERERqVaRjLRZa+uBbcANwEbgk9bajVHUJSIiIiIiUs2imh55BbDLObfHOXcG+B5wS0R1iYiIiIiIVK2oOm1dwBvTru/P3iYiIiIiIiIliGpNm8lzm59+xVp7J3AngHMuomaIiEgSPXjPp2Z9TMPdMU7QaG4KXXTVumXct/1zJZUZyzSGri+MWPcl4NtaYq1vtDXeuGpNt30sVLlVG7rY9lzpS/zj3p9xG2/K97UxOhON8dXXODIRuuzq1R18c9vWksqYM2Oh60uDU0tjfu8M91KPRFSdtv3A+dOunwccnP4A59z9wP3Zq2d16EREpLptvee7sz5m4befjaElgYa1a0KXvW/75/j8rV8rqcyRq1eEri+MOPclwNiWy2Kt763Lm2Otr+urvwpVbttz93LXFV8suVzc+zNuw13hfzQJY2RlfJ22Zc+fDl32m9u28pm7HiypzLy9A6HrS4NX7l4ea32dL8b7g8LzD/5Jwfui6rQ9D3Rbay8ADgCfAGb/WVVERERERETOEsl8AufcGPBZ4AlgZ3CT64miLhERERERkWoWWZ4259xjwGNRPb+IiIiIiEgtMN4nYjlZIhohIiIiIiJSQXkX0sUbbqkwE+bPWvvCua5X818tbWu5tjdN+yxNba3GbY2yTUncXm2rtlN/8fzpXNFfKX86X2r2L6+kdNpEREREREQkD3XaREREREREEiztnbb7Z7lezWppW6E825umfZamts5VErc1yjYlcXujUivbWivbKXOnc0VKofNFJiUlEImIiIiIiIjkkfaRNhERERERkaoWWZ62qFlrrwfuA+qBnwNXZy//rXPu3kq2LWrW2r3AEDAOjDnn3lPZFpWXtfZbwE1Av3Nuc/a2xcD3gTXAXsA6544W+Xx7Sej+KrCt9wD/ATicfdiXsnkPU89aez7w98ByYAK43zl331yObxnaVNbzbdrzFtrWe6jC42utnUfwXtxM8Nnyj865L1trLwC+BywGXgQ+7Zw7U7mWzk2tvWYlvCS+30ky1drnhYSTypE2a209sA24AdgM/BvgbmAj8Elr7cYKNi8uH3bOXZykDkgZPQBcP+O2LwJPOee6gaey10uR1P31AO/cVoD/kW3vxVX2Bj0G/KlzbgNwJXBX9vU61+M7Fw9Q/vMNCm8rVOfxPQ1scc79PnAxcL219krgqwTb2w0cBf59BdtYDg9QW69ZCS+J73eSTLX2eSEhpLLTBlwB7HLO7QEuAV4HLsv+evs94JZKNk7mxjn3c2Bwxs23AA9mLz8IfDzWRkWkwLZWLefcIefci9nLQ8BOoIsKHt+ozrdzbGtVcs5559xw9mpj9s8DW4B/zN6e+tdurb1mJbwkvt9JMtXa54WEk9ZOWxfwxrTLB5k6ufdT/Se6B35irX3BWntnpRsTk2XOuUMQvLkBS0som8b99Vlr7UvW2m9ZaxdVujFRsNauIfjR5V+Y2/GNQlnbM2NboUqPr7W23lr7G6AfeBLYDRxzzo1lH1LN789VeUylPBL+ficJUiufF1K6tHbaTJ7L08NgVntIzKudc5cSTA+9y1r7wUo3KOHStr++AawjmGJ2CPjvlW1O+VlrM8APgD92zr1d6fZEKc+2Vu3xdc6NO+cuBs4jmBGxIc/DqvH9uWqPqcxdLb3fydzU0ueFlC6tnbb9wPnTLq8kGG2D4MvCwXyFqoVz7mD2fz+wneDLUbV7y1q7AiD7v7/YgmnbX865t7JffieA/0XC21sqa20jwYfSd5xzD2dvDn18I1KW9uTb1mo/vgDOuWPAzwjWZiy01uaCXlXl+3MtHFMJJyXvd5IAtfp5IcVLa6fteaA7G5XsN8Bq4AVrbRPwCeBHlWxclKy1rdbattxl4Drg5cq2KhY/ArZmL28FflhMoTTur9yHedatJLy9pbDWGuDvgJ3Oub+Zdleo4xuhOben0LZW6/G11i6x1i7MXp4P/CuCdRk/BW7PPiwJx7bsqvWYytyk6P1OKqzWPi8knNQm17bW3gj8T4Iw/78E3pe9/C3n3H+pZNuiZK1dSzBaBEFY7e9W2/Zaa/8BuAboBN4Cvgz8E+CAVcA+4A7n3KzBAJK+vwps6zUEUyE8QTjo/5hb/5B21tr3A78AfkcQ1hjgSwRz90s+vmVqU9nOtxnPW2hbP0kVHl9r7bsJgirUE/wg6Jxz/zn7GsyF/P818G+dc6cr19K5qbXXrISXxPc7SaZa+7yQcFLbaRMREREREakFaZ0eKSIiIiIiUhPUaRMREREREUkwddpEREREREQSTJ02ERERERGRBFOnTUREREREJMHUaRMREREREUkwddpEREREREQSTJ02ERERERGRBPv/l/liYNgSFvoAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 1080x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Train...\n", "train_power_chunks = np.load( os.path.join(BENCHMARKING1_RESOURCES_PATH, 'datasets/train_power_chunks.npy') )\n", "train_labels_binary = np.load( os.path.join(BENCHMARKING1_RESOURCES_PATH, 'datasets/train_labels_binary.npy') )\n", "\n", "# Testando função de conversão serie -> RP\n", "# serie = list(np.sin(np.linspace(0,24,1000)))\n", "serie = train_power_chunks[22, :].tolist() \n", "image = RecurrencePlot().fit_transform([serie])[0]\n", "\n", "# Visualizing Serie/RP\n", "fig = plt.figure(figsize=(15,5))\n", "fig.tight_layout() # Or equivalently, \"plt.tight_layout()\"\n", "ax = fig.add_subplot(1, 1, 1)\n", "ax.imshow(image, origin=\"lower\", aspect=\"auto\")\n", "ax = fig.add_subplot(1, 2, 1)\n", "plt.plot(list(range(0,len(serie))), serie);" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T14:59:46.844570Z", "start_time": "2019-10-01T14:58:40.364410Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing dataset (Series to Images)...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f814a775669e49dd8804b2ec85d243dd", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=4000), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Saving processed data...\n" ] }, { "data": { "text/plain": [ "<Figure size 32.4x32.4 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(\"Processing train dataset (Series to Images)...\")\n", "\n", "image_size_px = 32\n", "image_size_inch = round(image_size_px / 71, 2) # Convert px to inch\n", "\n", "fig = plt.figure(frameon=False)\n", "fig.set_size_inches(image_size_inch, image_size_inch) # 0.45inch = 32px\n", "\n", "rp_series_train = []\n", "\n", "# Serie -> RP\n", "X_rp = RecurrencePlot().fit_transform(train_power_chunks)\n", "\n", "#for idx, row in tqdm_notebook(df_power_chunks.iterrows(), total = df_power_chunks.shape[0]):\n", "for idx, power_chunk in tqdm_notebook(enumerate(train_power_chunks), total = train_power_chunks.shape[0]):\n", "\n", " serie = power_chunk\n", " image = X_rp[idx]\n", " labels = train_labels_binary[idx, :].astype('str').tolist()\n", " labels_str = ''.join(labels)\n", " \n", " # Persist image data files (PAA - noPAA)\n", " np.save(\n", " os.path.join( \n", " HYPOTHESIS_RESOURCES_PATH, \n", " \"{}_{}_train.npy\".format(idx, labels_str) \n", " ), \n", " image\n", " )\n", " # x is the array you want to save \n", " imsave(\n", " os.path.join( \n", " HYPOTHESIS_RESOURCES_PATH, \n", " \"{}_{}_train_color.png\".format(idx, labels_str) \n", " ), \n", " arr=image\n", " )\n", "# ax = plt.Axes(fig, [0., 0., 1., 1.])\n", "# ax.set_axis_off()\n", "# fig.add_axes(ax)\n", "# ax.imshow(image, aspect='auto')\n", "# fig.savefig(os.path.join( HYPOTHESIS_RESOURCES_PATH, \"{}_{}_train_color.png\".format(idx, labels_str) ))\n", " Image.fromarray(image255).convert('RGB').resize((image_size_px, image_size_px)).save(os.path.join( HYPOTHESIS_RESOURCES_PATH, \"{}_{}_train_black.png\".format(idx, labels_str) ))\n", " rp_series_train.append( list([idx]) + list(image.flatten()) + list(labels) )\n", " \n", "# VIsualizing some results...\n", "plt.figure(figsize=(8,6));\n", "\n", "ax1 = plt.subplot(121);\n", "plt.title(\"RP - RGB\");\n", "color_rp = plt.imread(os.path.join( HYPOTHESIS_RESOURCES_PATH, \"{}_{}_train_color.png\".format(idx, labels_str) ))\n", "plt.imshow(color_rp, origin=\"lower\");\n", "\n", "divider = make_axes_locatable(ax1);\n", "cax = divider.append_axes(\"right\", size=\"2.5%\", pad=0.2);\n", "plt.colorbar(cax=cax);\n", "\n", "ax2 = plt.subplot(122);\n", "black_rp = plt.imread(os.path.join( HYPOTHESIS_RESOURCES_PATH, \"{}_{}_train_black.png\".format(idx, labels_str) ))\n", "plt.title(\"RP - BlackWhite\");\n", "plt.imshow(black_rp, origin=\"lower\");\n", "\n", "print('Saving processed data...')\n", "df_rp_train = pd.DataFrame(\n", " data = rp_series_train,\n", " columns = list([\"IDX\"]) + [\"DIMESION_{}\".format(d) for d in range(len(image.flatten()))] + list(label_columns_idx)\n", ")\n", "df_rp_train.to_csv(os.path.join( HYPOTHESIS_RESOURCES_PATH, \"df_rp_train.csv\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conjunto de Teste" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:00:30.609351Z", "start_time": "2019-10-01T15:00:30.249339Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Test...\n", "test_power_chunks = np.load( os.path.join(BENCHMARKING1_RESOURCES_PATH, 'datasets/test_power_chunks.npy') )\n", "test_labels_binary = np.load( os.path.join(BENCHMARKING1_RESOURCES_PATH, 'datasets/test_labels_binary.npy') )\n", "\n", "# Testando função de conversão serie -> RP\n", "# serie = list(np.sin(np.linspace(0,24,1000)))\n", "serie = test_power_chunks[22, :].tolist() \n", "image = RecurrencePlot().fit_transform([serie])[0]\n", "\n", "# Visualizing Serie/RP\n", "fig = plt.figure(figsize=(15,5))\n", "fig.tight_layout() # Or equivalently, \"plt.tight_layout()\"\n", "ax = fig.add_subplot(1, 1, 1)\n", "ax.imshow(image, origin=\"lower\", aspect=\"auto\")\n", "ax = fig.add_subplot(1, 2, 1)\n", "plt.plot(list(range(0,len(serie))), serie);" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:02:57.918018Z", "start_time": "2019-10-01T15:01:55.207512Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing test dataset (Series to Images)...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f531d684f3f54a3e9407d3d5d50ab6c1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=4000), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Saving processed data...\n" ] }, { "data": { "text/plain": [ "<Figure size 32.4x32.4 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(\"Processing test dataset (Series to Images)...\")\n", "\n", "image_size_px = 32\n", "image_size_inch = round(image_size_px / 71, 2) # Convert px to inch\n", "\n", "fig = plt.figure(frameon=False)\n", "fig.set_size_inches(image_size_inch, image_size_inch) # 0.45inch = 32px\n", "\n", "rp_series_test = []\n", "\n", "# Serie -> RP\n", "X_rp = RecurrencePlot().fit_transform(test_power_chunks)\n", "\n", "#for idx, row in tqdm_notebook(df_power_chunks.iterrows(), total = df_power_chunks.shape[0]):\n", "for idx, power_chunk in tqdm_notebook(enumerate(test_power_chunks), total = test_power_chunks.shape[0]):\n", "\n", " serie = power_chunk\n", " image = X_rp[idx]\n", " labels = test_labels_binary[idx, :].astype('str').tolist()\n", " labels_str = ''.join(labels)\n", " \n", " # Persist image data files (PAA - noPAA)\n", " np.save(\n", " os.path.join( \n", " HYPOTHESIS_RESOURCES_PATH, \n", " \"{}_{}_test.npy\".format(idx, labels_str) \n", " ), \n", " image\n", " )\n", " # x is the array you want to save \n", " imsave(\n", " os.path.join( \n", " HYPOTHESIS_RESOURCES_PATH, \n", " \"{}_{}_test_color.png\".format(idx, labels_str) \n", " ), \n", " arr=image\n", " )\n", "# ax = plt.Axes(fig, [0., 0., 1., 1.])\n", "# ax.set_axis_off()\n", "# fig.add_axes(ax)\n", "# ax.imshow(image, aspect='auto')\n", "# fig.savefig(os.path.join( HYPOTHESIS_RESOURCES_PATH, \"{}_{}_test_color.png\".format(idx, labels_str) ))\n", " Image.fromarray(image255).convert('RGB').resize((image_size_px, image_size_px)).save(os.path.join( HYPOTHESIS_RESOURCES_PATH, \"{}_{}_test_black.png\".format(idx, labels_str) ))\n", " rp_series_test.append( list([idx]) + list(image.flatten()) + list(labels) )\n", " \n", "# VIsualizing some results...\n", "plt.figure(figsize=(8,6));\n", "\n", "ax1 = plt.subplot(121);\n", "plt.title(\"RP - RGB\");\n", "color_rp = plt.imread(os.path.join( HYPOTHESIS_RESOURCES_PATH, \"{}_{}_test_color.png\".format(idx, labels_str) ))\n", "plt.imshow(color_rp, origin=\"lower\");\n", "\n", "divider = make_axes_locatable(ax1);\n", "cax = divider.append_axes(\"right\", size=\"2.5%\", pad=0.2);\n", "plt.colorbar(cax=cax);\n", "\n", "ax2 = plt.subplot(122);\n", "black_rp = plt.imread(os.path.join( HYPOTHESIS_RESOURCES_PATH, \"{}_{}_test_black.png\".format(idx, labels_str) ))\n", "plt.title(\"RP - BlackWhite\");\n", "plt.imshow(black_rp, origin=\"lower\");\n", "\n", "print('Saving processed data...')\n", "df_rp_test = pd.DataFrame(\n", " data = rp_series_test,\n", " columns = list([\"IDX\"]) + [\"DIMESION_{}\".format(d) for d in range(len(image.flatten()))] + list(label_columns_idx)\n", ")\n", "df_rp_test.to_csv(os.path.join( HYPOTHESIS_RESOURCES_PATH, \"df_rp_test.csv\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Modelagem" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:11:52.562919Z", "start_time": "2019-10-01T15:11:52.552940Z" } }, "outputs": [], "source": [ "def metrics(test, predicted):\n", " ##CLASSIFICATION METRICS\n", "\n", " acc = accuracy_score(test, predicted)\n", " prec = precision_score(test, predicted)\n", " rec = recall_score(test, predicted) \n", " f1 = f1_score(test, predicted)\n", " f1m = f1_score(test, predicted, average='macro')\n", " \n", "\n", " # print('f1:',f1)\n", " # print('acc: ',acc)\n", " # print('recall: ',rec)\n", " # print('precision: ',prec)\n", "\n", " # # to copy paste print\n", " #print(\"{:.4}\\t{:.4}\\t{:.4}\\t{:.4}\\t{:.4}\".format(acc, prec, rec, f1, f1m))\n", "\n", " # ##REGRESSION METRICS\n", " # mae = mean_absolute_error(test_Y,pred)\n", " # print('mae: ',mae)\n", " # E_pred = sum(pred)\n", " # E_ground = sum(test_Y)\n", " # rete = abs(E_pred-E_ground)/float(max(E_ground,E_pred))\n", " # print('relative error total energy: ',rete)\n", " return acc, prec, rec, f1, f1m\n", "\n", "\n", "def plot_predicted_and_ground_truth(test, predicted):\n", " #import matplotlib.pyplot as plt\n", " plt.plot(predicted.flatten(), label = 'pred')\n", " plt.plot(test.flatten(), label= 'Y')\n", " plt.show()\n", " return\n", "\n", "def embedding_images(images, model):\n", " \n", " # Feature extraction process with VGG16\n", " vgg16_feature_list = [] # Attributes array (vgg16 embedding)\n", " y = [] # Extract labels from name of image path[]\n", "\n", " for path in tqdm_notebook(images):\n", "\n", " img = keras_image.load_img(path, target_size=(100, 100))\n", " x = keras_image.img_to_array(img)\n", " x = np.expand_dims(x, axis=0)\n", " x = preprocess_input(x)\n", "\n", " # \"Extracting\" features...\n", " vgg16_feature = vgg16_model.predict(x)\n", " vgg16_feature_np = np.array(vgg16_feature)\n", " vgg16_feature_list.append(vgg16_feature_np.flatten())\n", "\n", " # Image (chuncked serie) \n", " file_name = path.split(\"\\\\\")[-1].split(\".\")[0]\n", " image_labels = [int(l) for l in list(file_name.split(\"_\")[1])]\n", " y.append(image_labels)\n", "\n", " X = np.array(vgg16_feature_list)\n", " \n", " return X, y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Benchmarking (replicando estudo)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:11:16.690168Z", "start_time": "2019-10-01T15:11:14.120742Z" } }, "outputs": [], "source": [ "# Building dnn model (feature extraction)\n", "vgg16_model = VGG16(\n", " include_top=False, \n", " weights='imagenet', \n", " input_tensor=None, \n", " input_shape=(100, 100, 3), \n", " pooling='avg',\n", " classes=1000\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Embedding das imagens de Treino" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:12:30.597391Z", "start_time": "2019-10-01T15:11:54.619924Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "115962a7891d406c8eacea66cffc64cb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=4000), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# Colored recurrence plots generated\n", "images = sorted(glob( \n", " os.path.join(\n", " HYPOTHESIS_RESOURCES_PATH, \n", " \"_train_color.png\"\n", " ) \n", "))\n", "X_train, y_train = embedding_images(images, vgg16_model)\n", "\n", "# Data persistence\n", "np.save( os.path.join(HYPOTHESIS_RESOURCES_PATH, 'X_train.npy'), X_train)\n", "np.save( os.path.join(HYPOTHESIS_RESOURCES_PATH, 'y_train.npy'), y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Embedding* das imagens de Teste" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:13:34.506620Z", "start_time": "2019-10-01T15:12:58.655738Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c79e5e91732c40f1970b3d42afd6b19f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=4000), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "images = sorted(glob( \n", " os.path.join(\n", " HYPOTHESIS_RESOURCES_PATH, \n", " \"_test_color.png\"\n", " ) \n", "))\n", "X_test, y_test = embedding_images(images, vgg16_model)\n", "\n", "# Data persistence\n", "np.save( os.path.join(HYPOTHESIS_RESOURCES_PATH, 'X_test.npy'), X_test)\n", "np.save( os.path.join(HYPOTHESIS_RESOURCES_PATH, 'y_test.npy'), y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Treinando Classificador Supervisionado" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:14:08.052400Z", "start_time": "2019-10-01T15:14:06.712969Z" } }, "outputs": [ { "data": { "text/plain": [ "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=15,\n", " max_features=None, max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, presort=False,\n", " random_state=None, splitter='best')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Training supervised classifier\n", "clf = DecisionTreeClassifier(max_depth=15)\n", "\n", "# Train classifier\n", "clf.fit(X_train, y_train)\n", "\n", "# Save classifier for future use\n", "#joblib.dump(clf, 'Tree'+'-'+device+'-redd-all.joblib')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Avaliando Classificador" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:14:31.465950Z", "start_time": "2019-10-01T15:14:25.632538Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RESULT ANALYSIS\n", "\n", "\n", "ON/OFF State Charts\n", "-------------------------------------------------------------------------------------------------------------------\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-------------------------------------------------------------------------------------------------------------------\n", "\n", "FINAL PERFORMANCE BY APPLIANCE (LABEL):\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Appliance</th>\n", " <th>Accuracy</th>\n", " <th>Precision</th>\n", " <th>Recall</th>\n", " <th>F1-score</th>\n", " <th>F1-macro</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>APLIANCE_3</td>\n", " <td>54.55</td>\n", " <td>54.71</td>\n", " <td>57.80</td>\n", " <td>56.21</td>\n", " <td>54.48</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>APLIANCE_4</td>\n", " <td>60.55</td>\n", " <td>53.30</td>\n", " <td>44.43</td>\n", " <td>48.47</td>\n", " <td>58.25</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>APLIANCE_6</td>\n", " <td>97.58</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>49.39</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>APLIANCE_7</td>\n", " <td>95.40</td>\n", " <td>1.21</td>\n", " <td>8.70</td>\n", " <td>2.13</td>\n", " <td>49.89</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>APLIANCE_10</td>\n", " <td>98.45</td>\n", " <td>62.86</td>\n", " <td>55.00</td>\n", " <td>58.67</td>\n", " <td>78.94</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>APLIANCE_11</td>\n", " <td>96.22</td>\n", " <td>40.18</td>\n", " <td>34.88</td>\n", " <td>37.34</td>\n", " <td>67.70</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>APLIANCE_13</td>\n", " <td>98.68</td>\n", " <td>9.52</td>\n", " <td>21.05</td>\n", " <td>13.11</td>\n", " <td>56.22</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>APLIANCE_17</td>\n", " <td>97.25</td>\n", " <td>3.90</td>\n", " <td>7.69</td>\n", " <td>5.17</td>\n", " <td>51.89</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>APLIANCE_19</td>\n", " <td>88.15</td>\n", " <td>26.74</td>\n", " <td>5.30</td>\n", " <td>8.85</td>\n", " <td>51.25</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Appliance Accuracy Precision Recall F1-score F1-macro\n", "0 APLIANCE_3 54.55 54.71 57.80 56.21 54.48\n", "1 APLIANCE_4 60.55 53.30 44.43 48.47 58.25\n", "2 APLIANCE_6 97.58 0.00 0.00 0.00 49.39\n", "3 APLIANCE_7 95.40 1.21 8.70 2.13 49.89\n", "4 APLIANCE_10 98.45 62.86 55.00 58.67 78.94\n", "5 APLIANCE_11 96.22 40.18 34.88 37.34 67.70\n", "6 APLIANCE_13 98.68 9.52 21.05 13.11 56.22\n", "7 APLIANCE_17 97.25 3.90 7.69 5.17 51.89\n", "8 APLIANCE_19 88.15 26.74 5.30 8.85 51.25" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "OVERALL AVERAGE PERFORMANCE:\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Metric</th>\n", " <th>Result (%)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Accuracy</td>\n", " <td>87.43</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Precision</td>\n", " <td>28.05</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Recall</td>\n", " <td>26.09</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>F1-score</td>\n", " <td>25.55</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>F1-macro</td>\n", " <td>57.56</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Metric Result (%)\n", "0 Accuracy 87.43\n", "1 Precision 28.05\n", "2 Recall 26.09\n", "3 F1-score 25.55\n", "4 F1-macro 57.56" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Predict test data\n", "y_pred = clf.predict(X_test)\n", "\n", "# Print metrics\n", "final_performance = []\n", "y_test = np.array(y_test)\n", "y_pred = np.array(y_pred)\n", "\n", "print(\"\")\n", "print(\"RESULT ANALYSIS\\n\\n\")\n", "print(\"ON/OFF State Charts\")\n", "print(\"-\" 115)\n", "for i in range(y_test.shape[1]):\n", " \n", " fig = plt.figure(figsize=(15, 2))\n", " plt.title(\"Appliance #{}\".format( label_columns_idx[i]))\n", " plt.plot(y_test[:, i].flatten(), label = \"True Y\")\n", " plt.plot( y_pred[:, i].flatten(), label = \"Predicted Y\")\n", " plt.xlabel('Sample')\n", " plt.xticks(range(0, y_test.shape[0], 50))\n", " plt.xlim(0, y_test.shape[0])\n", " plt.ylabel('Status')\n", " plt.yticks([0, 1])\n", " plt.ylim(0,1)\n", " plt.legend()\n", " plt.show()\n", " \n", " acc, prec, rec, f1, f1m = metrics(y_test[:, i], y_pred[:, i])\n", " final_performance.append([\n", " label_columns_idx[i], \n", " round(acc100, 2), \n", " round(prec100, 2), \n", " round(rec100, 2), \n", " round(f1100, 2), \n", " round(f1m100, 2)\n", " ])\n", "\n", "print(\"-\" 115)\n", "print(\"\")\n", "print(\"FINAL PERFORMANCE BY APPLIANCE (LABEL):\")\n", "df_metrics = pd.DataFrame(\n", " data = final_performance,\n", " columns = [\"Appliance\", \"Accuracy\", \"Precision\", \"Recall\", \"F1-score\", \"F1-macro\"]\n", ")\n", "display(df_metrics)\n", "\n", "print(\"\")\n", "print(\"OVERALL AVERAGE PERFORMANCE:\")\n", "final_performance = np.mean(np.array(final_performance)[:, 1:].astype(float), axis = 0)\n", "display(pd.DataFrame(\n", " data = {\n", " \"Metric\": [\"Accuracy\", \"Precision\", \"Recall\", \"F1-score\", \"F1-macro\"],\n", " \"Result (%)\": [round(p, 2) for p in final_performance]\n", " }\n", "))\n", "# print(\"-----------------\")\n", "# print(\"Accuracy : {0:.2f}%\".format( final_performance[0] ))\n", "# print(\"Precision : {0:.2f}%\".format( final_performance[1] ))\n", "# print(\"Recall : {0:.2f}%\".format( final_performance[2] ))\n", "# print(\"F1-score : {0:.2f}%\".format( final_performance[3] ))\n", "# print(\"F1-macro : {0:.2f}%\".format( final_performance[4] ))\n", "# print(\"-----------------\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusões" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A adoção do gráfico de recorrência para o processo de classificação de série temporal, no contexto de NILM, apresentou desempenho superior ao método GAF (benchmarking 2).\n", "\n", "Agora, o próximo passo é estruturar um pipeline único de comparação das 3 abordagens, que inclui desde o pré-processamento até a análise de resultados." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	cc0-1.0
bbglab/adventofcode	2018/ferran/day18/settlers_of_the_north_pole.ipynb	1	5599	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Part 1" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from itertools import product" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def parse(fn):\n", " acres = []\n", " with open(fn) as f:\n", " for line in f.readlines():\n", " acres.append(list(line.rstrip()))\n", " acres = np.array(acres)\n", " return acres" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def change(acres):\n", " n, m = acres.shape\n", " index = {'.': 0, '\|': 1, '#': 2} \n", " counter = np.zeros((n, m, 3))\n", " for i in range(n):\n", " for j in range(m):\n", " for s, t in product([-1, 0, 1], repeat=2):\n", " if (s2 + t2 != 0) and (0 <= i + s <= n-1) and (0 <= j + t <= m-1):\n", " counter[i + s, j + t, index[acres[i, j]]] += 1\n", " new = np.empty((n, m), dtype=object)\n", " for i in range(n):\n", " for j in range(m):\n", " a = acres[i, j]\n", " if a == '.':\n", " if counter[i, j, 1] >= 3:\n", " new[i, j] = '\|'\n", " else:\n", " new[i, j] = '.'\n", " if a == '\|':\n", " if counter[i, j, 2] >= 3:\n", " new[i, j] = '#'\n", " else:\n", " new[i, j] = '\|'\n", " if a == '#':\n", " if (counter[i, j, 1] >= 1) and (counter[i, j, 2] >= 1):\n", " new[i, j] = '#'\n", " else:\n", " new[i, j] = '.'\n", " return new\n", "\n", "def resource_value(acres):\n", " wooded = 0\n", " lumber = 0\n", " for i in range(acres.shape[0]):\n", " for j in range(acres.shape[1]):\n", " wooded += (acres[i, j] == '\|')\n", " lumber += (acres[i, j] == '#')\n", " return wooded * lumber\n", " \n", "def evolve(acres, time):\n", " for _ in range(time):\n", " acres = change(acres)\n", " return acres" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Test" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1147" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "acres = parse('input_test.txt')\n", "acres = evolve(acres, 10)\n", "resource_value(acres)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "644640" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "acres = parse('input.txt')\n", "acres = evolve(acres, 10)\n", "resource_value(acres)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 2" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Check whether some periodicity is reached soon enough that we can see it.\n", "\n", "def get_hash(arr):\n", " n, m = arr.shape\n", " return ''.join(arr.reshape(1, n * m).tolist()[0])\n", "\n", "def find_period(acres, time):\n", " \n", " t = 0\n", " visited = [get_hash(acres)]\n", " res = [resource_value(acres)]\n", " while t < time:\n", " \n", " t += 1\n", " acres = change(acres)\n", " h = get_hash(acres)\n", " \n", " if h not in visited:\n", " visited.append(h)\n", " res.append(resource_value(acres))\n", " else:\n", " i = visited.index(h)\n", " period = t - i\n", " return res[i + ((time - i) % period)]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "191080" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "acres = parse('input.txt')\n", "find_period(acres, 1000000000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
gabrielhuang/musicalign	genmidi.ipynb	1	16487	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "midifile = 'data/chopin-fantaisie.mid'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "device 0 ('ALSA', 'Midi Through Port-0', 0, 1, 0)\n", "device 1 ('ALSA', 'Midi Through Port-0', 1, 0, 0)\n", "device 2 ('ALSA', 'TiMidity port 0', 0, 1, 0)\n", "device 3 ('ALSA', 'TiMidity port 1', 0, 1, 0)\n", "device 4 ('ALSA', 'TiMidity port 2', 0, 1, 0)\n", "device 5 ('ALSA', 'TiMidity port 3', 0, 1, 0)\n" ] } ], "source": [ "import time\n", "import copy\n", "import subprocess\n", "from abc import abstractmethod\n", "\n", "import numpy as np\n", "import midi # Midi file parser\n", "\n", "from midipattern import MidiPattern\n", "from distorter import *\n", "from align import align_frame_to_frame, read_align, write_align" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "MidiPattern.MIDI_DEVICE = 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Init Pygame and Audio\n", "--------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Midi Pattern\n", "--------" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pattern = MidiPattern(midi.read_midifile(midifile))\n", "simple = pattern.simplified(bpm=160)\n", "simple.stamp_time('t0')\n", "midi.write_midifile(\"generated/simple.mid\", simple)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'t0': 203.24999999999994}, {'t0': 203.25468749999993}, {'t0': 203.34843749999993}, {'t0': 203.34843749999993}, {'t0': 203.43749999999994}, {'t0': 203.44218749999993}, {'t0': 203.53124999999994}, {'t0': 203.53124999999994}, {'t0': 203.62499999999994}, {'t0': 203.62499999999994}, {'t0': 203.71874999999994}, {'t0': 203.71874999999994}, {'t0': 203.81249999999994}, {'t0': 203.81249999999994}, {'t0': 203.90624999999994}, {'t0': 203.90624999999994}, {'t0': 203.99999999999994}, {'t0': 203.99999999999994}, {'t0': 203.99999999999994}, {'t0': 203.99999999999994}, {'t0': 204.04687499999994}, {'t0': 204.09374999999994}, {'t0': 204.14062499999994}, {'t0': 204.18749999999994}, {'t0': 204.28124999999994}, {'t0': 205.49999999999994}, {'t0': 205.49999999999994}, {'t0': 205.49999999999994}, {'t0': 205.49999999999994}, {'t0': 205.49999999999994}, {'t0': 205.49999999999994}, {'t0': 205.54687499999994}, {'t0': 205.59374999999994}, {'t0': 205.64062499999994}, {'t0': 206.99999999999994}, {'t0': 206.99999999999994}, {'t0': 206.99999999999994}, {'t0': 206.99999999999994}, {'t0': 206.99999999999994}, {'t0': 209.52109374999995}]\n" ] } ], "source": [ "print simple.attributes[0][-40:]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Was playing note 40 time 4.44444444444\n" ] } ], "source": [ "pattern[0]\n", "pattern.play(180)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Was playing note 5 time 3.0\n" ] } ], "source": [ "simple.play()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Distorter\n", "--------" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "VelocityNoiseDistorter(sigma=7.63)\n", "Was playing note 5 time 2.66666666667\n" ] } ], "source": [ "distorter = VelocityNoiseDistorter(sigma=20.)\n", "distorter.randomize()\n", "print distorter\n", "dist_pattern = distorter.distort(simple)\n", "midi.write_midifile('generated/velocity-noise.mid', dist_pattern)\n", "dist_pattern.play(bpm=180)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'t': 206.99999999999994, 't0': 206.99999999999994}, {'t': 206.99999999999994, 't0': 206.99999999999994}, {'t': 206.99999999999994, 't0': 206.99999999999994}, {'t': 209.52109374999995, 't0': 209.52109374999995}]\n" ] } ], "source": [ "print dist_pattern.attributes[0][-4:]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "VelocityWalkDistorter(sigma=19.24, min=1.18, max=1.23)\n", "Was playing note 5 time 2.66666666667\n" ] } ], "source": [ "distorter = VelocityWalkDistorter(sigma=0.1)\n", "distorter.randomize()\n", "print distorter\n", "dist_pattern = distorter.distort(simple)\n", "midi.write_midifile('generated/velocity-walk.mid', dist_pattern)\n", "dist_pattern.play(bpm=180)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ProgramDistorter(ticks=0.00)\n", "Was playing note 19 time 2.66666666667\n" ] } ], "source": [ "distorter = ProgramDistorter()\n", "distorter.randomize()\n", "# for some reason GM 1- 3 makes no sound in pygame?\n", "print distorter\n", "dist_pattern = distorter.distort(simple)\n", "midi.write_midifile('generated/program.mid', dist_pattern)\n", "dist_pattern.play(bpm=180)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TempoDistorter(sigma=0.83, min=0.99, max=1.21)\n", "time warp [{'t': 200.28854166666386, 't0': 206.99999999999994}, {'t': 200.28932291666385, 't0': 206.99999999999994}, {'t': 200.29010416666384, 't0': 206.99999999999994}, {'t': 200.38932291666384, 't0': 209.52109374999995}]\n", "Was playing note 14 time 0.360416666667\n" ] } ], "source": [ "distorter = TempoDistorter(sigma=0, min=0.5, max=2.)\n", "distorter.randomize()\n", "print distorter\n", "dist_pattern = distorter.distort(simple)\n", "print 'time warp', dist_pattern.attributes[0][-4:]\n", "midi.write_midifile('generated/tempo.mid', dist_pattern)\n", "dist_pattern.play(bpm=180)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TimeNoiseDistorter(sigma=0.08)\n", "time warp [{'t': 206.99999999999977, 't0': 206.99999999999994}, {'t': 206.99999999999977, 't0': 206.99999999999994}, {'t': 206.99999999999977, 't0': 206.99999999999994}, {'t': 209.52109374999978, 't0': 209.52109374999995}]\n", "Was playing note 5 time 2.62916666667\n" ] } ], "source": [ "distorter = TimeNoiseDistorter()\n", "distorter.randomize()\n", "print distorter\n", "dist_pattern = distorter.distort(simple)\n", "print 'time warp', dist_pattern.attributes[0][-4:]\n", "midi.write_midifile('generated/time.mid', dist_pattern)\n", "dist_pattern.play(bpm=180)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Individual Note Times to Global Alignment\n", "-------" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0,\n", " 1,\n", " 3,\n", " 3,\n", " 4,\n", " 5,\n", " 6,\n", " 7,\n", " 8,\n", " 9,\n", " 10,\n", " 11,\n", " 12,\n", " 13,\n", " 14,\n", " 15,\n", " 16,\n", " 17,\n", " 18,\n", " 19,\n", " 20,\n", " 21,\n", " 22,\n", " 23,\n", " 24,\n", " 25,\n", " 26,\n", " 27,\n", " 28,\n", " 29,\n", " 30,\n", " 31,\n", " 32,\n", " 33,\n", " 34,\n", " 35,\n", " 36,\n", " 37,\n", " 38,\n", " 39,\n", " 40,\n", " 41,\n", " 42,\n", " 43,\n", " 44,\n", " 45,\n", " 46,\n", " 47,\n", " 48,\n", " 49,\n", " 50,\n", " 51,\n", " 52,\n", " 53,\n", " 54,\n", " 55,\n", " 56,\n", " 57,\n", " 58,\n", " 59,\n", " 60,\n", " 61,\n", " 62,\n", " 63,\n", " 64,\n", " 65,\n", " 66,\n", " 67,\n", " 68,\n", " 69,\n", " 70,\n", " 71,\n", " 72,\n", " 73,\n", " 74,\n", " 75,\n", " 76,\n", " 77,\n", " 78,\n", " 79,\n", " 80,\n", " 81,\n", " 82,\n", " 83,\n", " 84,\n", " 85,\n", " 86,\n", " 87,\n", " 88,\n", " 89,\n", " 90,\n", " 91,\n", " 92,\n", " 93,\n", " 94,\n", " 95,\n", " 96,\n", " 97,\n", " 98,\n", " 99,\n", " 100,\n", " 101,\n", " 102,\n", " 103,\n", " 104,\n", " 105,\n", " 106,\n", " 107,\n", " 108,\n", " 109,\n", " 110,\n", " 111,\n", " 112,\n", " 113,\n", " 114,\n", " 115,\n", " 116,\n", " 117,\n", " 118,\n", " 119,\n", " 120,\n", " 121,\n", " 122,\n", " 123,\n", " 124,\n", " 125,\n", " 126,\n", " 127,\n", " 128,\n", " 129,\n", " 130,\n", " 131,\n", " 132,\n", " 133,\n", " 134,\n", " 135,\n", " 136,\n", " 137,\n", " 138,\n", " 139,\n", " 140,\n", " 141,\n", " 142,\n", " 143,\n", " 144,\n", " 145,\n", " 146,\n", " 147,\n", " 148,\n", " 149,\n", " 150,\n", " 151,\n", " 152,\n", " 153,\n", " 154,\n", " 155,\n", " 156,\n", " 157,\n", " 158,\n", " 159,\n", " 160,\n", " 161,\n", " 162,\n", " 163,\n", " 164,\n", " 165,\n", " 166,\n", " 167,\n", " 168,\n", " 169,\n", " 170,\n", " 171,\n", " 172,\n", " 173,\n", " 174,\n", " 175,\n", " 176,\n", " 177,\n", " 178,\n", " 179,\n", " 180,\n", " 181,\n", " 182,\n", " 183,\n", " 184,\n", " 185,\n", " 186,\n", " 187,\n", " 188,\n", " 189,\n", " 190,\n", " 191,\n", " 192,\n", " 193,\n", " 194,\n", " 195,\n", " 196,\n", " 197,\n", " 198,\n", " 199,\n", " 200,\n", " 201,\n", " 202,\n", " 203,\n", " 204,\n", " 205,\n", " 206,\n", " 207,\n", " 208,\n", " 209]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stride = 1.\n", "align = align_frame_to_frame(dist_pattern, stride)\n", "align" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "True 1.0 1.0\n" ] } ], "source": [ "write_align('generated/align.txt', align, stride)\n", "align2, stride2 = read_align('generated/align.txt')\n", "print align2 == align\n", "print int(stride2) == int(stride), stride2, stride" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Actual Generation\n", "----" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83, 84, 86, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 106, 107, 108, 109, 110, 111, 112, 114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 207]\n", "Was playing note 77 time 3.18072916667\n" ] } ], "source": [ "dist_pattern = random_distort(simple)\n", "align = align_frame_to_frame(dist_pattern, stride=1.)\n", "print align\n", "dist_pattern.play()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "generated/sample-0.wav\n", "Done generating generated/sample-0\n", "generated/sample-1.wav\n", "Done generating generated/sample-1\n", "generated/sample-2.wav\n", "Done generating generated/sample-2\n", "generated/sample-3.wav\n", "Done generating generated/sample-3\n", "generated/sample-4.wav\n", "Done generating generated/sample-4\n", "generated/sample-5.wav\n", "Done generating generated/sample-5\n", "generated/sample-6.wav\n", "Done generating generated/sample-6\n", "generated/sample-7.wav\n", "Done generating generated/sample-7\n", "generated/sample-8.wav\n", "Done generating generated/sample-8\n", "generated/sample-9.wav\n", "Done generating generated/sample-9\n" ] } ], "source": [ "num_samples = 10\n", "stride = 0.1\n", "for i in xrange(num_samples):\n", " base_name = 'generated/sample-{}'.format(i)\n", " align_name = '{}.txt'.format(base_name)\n", " midi_name = '{}.mid'.format(base_name)\n", " wav_name = '{}.wav'.format(base_name)\n", " distorted = random_distort(simple)\n", " align = align_frame_to_frame(distorted, stride)\n", " write_align(align_name, align, stride)\n", " midi.write_midifile(midi_name, distorted)\n", " # Convert to wav using timidity\n", " print wav_name\n", " subprocess.check_call(['timidity', '-Ow', midi_name, '-o', wav_name])\n", " print 'Done generating {}'.format(base_name)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
weleen/mxnet	example/notebooks/basic/advanced_img_io.ipynb	1	755983	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fast Image Processing with MXNet\n", "\n", "Previous tutorials have shown two ways of preprocessing images:\n", "- `mx.io.ImageRecordIter` is fast but inflexible. It is great for simple tasks like image recognition but won't work for more complex tasks like detection and segmentation.\n", "- `mx.recordio.unpack_img` (or `cv2.imread`, `skimage`, etc) + `numpy` is flexible but slow. The root of the problem is python's Global Interpreter Lock (GIL). GIL is a complicated topic but the gist is python doesn't really support multi-threading; even if you spawn multiple threads execution will still be serialized. You can workaround it with multi-processing and message passing, but it's hard to program and introduces overhead.\n", "\n", "To solve this issue, MXNet provides `mx.image` package. It stores images in [mx.nd.NDArray](./ndarray.ipynb) format and leverages MXNet's [dependency engine](http://mxnet.io/architecture/note_engine.html) to automatically parallelize image processing and circumvent GIL.\n", "\n", "Please read [Intro to NDArray](./ndarray.ipynb) first if you are not familar with it.\n", "\n", "Setup environment:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "from __future__ import print_function\n", "import os\n", "import time\n", "# set the number of threads you want to use before importing mxnet\n", "os.environ['MXNET_CPU_WORKER_NTHREADS'] = '4'\n", "import mxnet as mx\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import cv2" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# download example images\n", "os.system('wget http://data.mxnet.io/data/test_images.tar.gz')\n", "os.system('tar -xf test_images.tar.gz')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Image Loading\n", "First we load images with mx.image.imdecode. The interface is very similar to opencv. But everything runs in parallel.\n", "\n", "We also compare performance agains opencv. You can restart the kernel and change MXNET_CPU_WORKER_NTHREADS to see the performance improvement as more threads are used." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "263.867350651 images decoded per second with opencv\n" ] }, { "data": { "image/png": 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Dy1LpPYxhzxBgVB7atvH69Stunz4L47md0NKQuiYajComcyFs6pDprGgNpLCo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\nlr5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wOPLjP/HTmKyg6xpu3r3DL54szUnKDO88292OTz/9DlVRsCwQxQLsnuyMWhRxLrPB\n1XqFWzzr1RqhssUB3q5pm5ab22sO93s++vA1k7U8ffmCeVoo8hKPPESHvmOcZe7upolVXTNNM3Vd\ng4ro+544FQdYlecMk8WhyIucrmmZRxkXTG4hK3JJf+hQbjifqOqSNM24vbllu92yuAWvZNI7T9Lk\nGvqBJE3wGtIkwXYdP/T1rzPamfvDkd12y7lpHqHawyAmgyhSNP1ArDWr9Vq8Zl6hEDvBNC80TSt1\n+GAyiGOh5vXNHe9v3hCbhLreslpvMGmCMTlZgDJFJqE9tzTHe3COrCpZbXZEStF2liRLiWJojsdw\nc1BUZR1q9dIu7AeL0nA8Hyjzgkgb5tmTmoSubUlMzC98lWewv/X7v4OPItrznu50IDKGxOSsypL3\n1+/ZbC/wSGZtu9kQJ4Kba5r2BydJ71kmiwBaI0yc0g8NRSag7WGQTbnkPyeUjnDLRBLDaGVWqI2h\nbweZ587it1rmRRQxiycOUI1pHNCx4nw+U1Y159OB23fvuLy85OLiadDXADju7/fSaplmNqsNJhFF\n9fvbPd/99BOWeebJ06c8f/aSxo4U9SqowB1FmZMYQ3Ns8FHEm3dvefHs+cPXjQhNH04+guAzRFpz\n7hryNBMKvhKwsx1lXr1arcB78qLgEOAkOhbGQVVVABwOB66ePKHtO66vbyiKEhPAOyBV1cPhQFnK\nOMXNM2km44aul7zohx9+yPEgG+uHE/9oeyk/OC9JijyXE7dfmOY52AA0caw5nc7Sn48NDk+WZbIF\nnsVvpVTE8XigzPPH9IIo1guZMVvL0AsAJgnJBrfMGBPzm7/xG7x69ZxXr1+H3n7Fv/6//pBf/E/+\nCX/yR3/Ih69f872//h6vX3+Nm3dv+eij1/z3/93/wH/2X/zn/Mkf/xuMMXzw4ddYryUVAjFtf+Jw\nOFBVNUVeorznh7/xDe7u9+RlxdiPmNhwbs5s12tubm6o61oigIc9UfCdETvau1vs6chq+wSTrxn9\nzAcffMjd/T3t+URd1z8wCJiEKdw83Gx5/8UX7J4+JSsqHIo0NUzD+Ij6W7wL82VZZBI4rUKC07Js\nxFOv6sCSkDGHHDwmDqeTkO9Cy6zve5I0ZrPZcG4a9gd5kH3w6jXjg1TQTo9wF2MSzueGTz/5C+x0\n5tWr1xzuOz58/RFt0/Bv/vgP+PLNZ/zsz/0cebElLzIudxeSJ3eeth9YpglnJ778/LsUZcrF5ZZP\nv/ddynJFUVZMduLcNnz96z9CllcsXlGWNX3fkuc5p/sDsZHngB0n1pt1KPAMRApMmnC/P1EUFfO0\noOOIX/j5X/rqPmC/+X//PpODwXbC4nQzQ9+TZ7noLeZZIMVxLFm6tgUU+Ogx5iHNK8c8WfCKJCvp\nbE8SxWhjmNxMrLSoQuYFFSvcNDL2jXT5k4y8LJknOfWK0mJmmi1FWdE0LVoLD+D923ckScJqtSZL\nU7q2xU4DRVmRZQnt8Q4dp/R24unVE9rmxP3dHaf7E4kxrFY1dllY73ZkZc35fGayI3lWopOUeZoo\n0pRzcxLqT56Ja34O2VQ/Y+KEJMmZloV5GsOooCY2hnboHquD3s2kmWHs5XqutWYaRykjLO4xhiLL\ngBm3SOZzGHriOMGGDKhJRAXjFicg67rEBvvAAwci1jFumqViOg3EicH7GK80aZYx2V7UNEFd/qDx\nEZSeOOrjOBZoR5Y/lkjyXKhQVVnT9T2b7Ya2aR7VJ1FsiHQspCxFACg/AGgsaRrT9cIdHYaGZbF4\nt/Bnf/ptPn79WmDRaHkILDNJmrPbrPmLb/0xx+Md9arm9Uc/jIs07aklzwuMSSUNMS9YK9JFHUWy\nNY8jpq4NLjZPc25Zr7cy/pknMW94mCeLczMRMgvOy5pIw+effcaqrsjyjMU5lNb07cDLlx9we3fL\nvEwU5YoszcXk6mShGytoD/esdzuiWGJKsY7o21bq4MY8iiurqsQOgrScZ4cPmVoX/F3GaKydSUwS\nJJaew/GA906UK5NYOo7HQ0hVSCFntdo8FgEEXtQRx5q264LZWWax1g4s8yTw7GWiqEr6pmFV5djJ\nMjnQOsaYHOcWmubIZrNhcTJGun1/TV1lEC2Sma5r9nd7Dsc99WpDlpcsbibLc/JiRVFtsHYMnj5h\ngjglxuS+kZ+Jw/6aclWzOMfFxSUmLTifO+rVin/wH/3Dr+4D9je/+dukWcHiJ/qhxc0Rm9VGLJbI\nr3mn5Hobx0Qo4iSRhgew4AO3YObcnEhMRlGumN1ErhPQYBcrpySliSIRJGZaQDBN16OjBI/DOQFU\n2H7AWkue54xDT6QUaZ7JtTyMIvIsxyvHZAWDNgwN8zLw+Wd/RWZyVpsd24sd7z5/S1nV1JstJjVi\nHDAp8zTSD7KlzbKSZfayiJjEG+SBKBbizzT2gMbPi1DGtMZOM+vNhsVKdKrrOqpVzewWDAKLEbjJ\niHMLWVHhl5ksSZkmKWR4PHlW0A8NPlJMo7ASrJWZ5+l0EnvusshybhhkXgp4pUTgZ6dQoOhEgjjP\nRMgCT8cxPmy6lZLGlV8ceVEQxZpuHJhGK009BUrFVFUtbaxZZjbibhKFeKx1sPdCEaJ9DkmUJHGI\nkjmPJ5IAvw+/ZxixdqSqCrq+4XzquLi4wHtPG8oNaagHW2u5ub5ms65FNV4UQEzXDSgl6Ql5aLRk\nSYYHeSl1Hcf9LYnRNM2Z3ZMLfBRhp5nRLlxdPUNrWUx5PzNNk5Cl7MB2vSUxGePQoNMUFkm9WDWj\nrCZNM7784guePHsa0i6JhOoj4ajqWBCOOrzAyrxksAOS3laPEbq27VDRIlBsr2maY1DxzLJo8z6Y\nCpLwoE0C2k8Egi6cZCMVcTwdyfMCgCwLBx0bqq1Z/gjmzvPssTUVBb6yRBaXQNWKwsjkTJoa5sXL\nCymSEcP19TuunjwRtmukiVWEjhR27EEp9nd3XN+8w3nHs2cvWEKSxSN/TlauSDKBbeuQzhAThsS2\n/vxbf8JkG54926GTjGGYeXL1nHK9wjtP0wz80j/6Co8I/tdf+WWqskJrT5YlzM5TZCmnwz6YIT1Z\nIezGxVqOxwN9N4CH3W5HtVoTKU3vZ1gWVtWa07FltD2bqoBI0TQn2rbj8vIJRVVzur9HzSP7+1uq\nzQUmLVB47m6+oG+PxEnJZnshV7F54XC6px8Xrp5eUZQlTdNwPp94ursgy3LeX1+z2+0YbUfXnllV\na1QU8Xvf/B1+8j/4D/EoTueGq6srokhx2O/Ji5z784nnT69IkizkVdec2jNj37Ndr7FTy36/Z54d\nq/pCANhpIvJHLQ/ROI4F1BI64A/fS8miEq7PNnjDNEPX41FC/JpnxtHi/RxQb2ADlpBIwNTb7VbK\nCtOEVlHIwy4Sy3LCue2HgSROUeHDssxW4DbjQFaULE4Yu1prUh2HyJoir0pOp4MAcOIYvGIIdLCh\n76SqGTblsiRxpJmchJqmJTI6zNYci53ou5YnT56K66uSamfbNZzPZ6Hja02SpCKdDMqS42n/aBlW\nKnr89+E9s3MUZUHX9Y98YjtN5EUhSQfvaPsRjZKs8vs3vPnyCzbbmk++8wk/+qM/jvKeut7w/NVr\nvv3pJzjvubgUNm9iJKq2v7uTZVWW4rQiT1KO9/c8f/0S7w2b1RqtNfvDPZvdjtPhEKzGhjwvBKqd\nmkdbatt0aOMpi5LjUeDieVlwe/2ezXaFcz+gk/W9ELzW67X0/wN4pq5r2rYleiBPTTNpnlAU8r2Q\nFIPDWoGQj+NAkWUURREepnB7e0e12tB3ncCK7IRJ4seWZJbmnNqG9Xr9CMeOIk1sUoa+E3xk1/Jg\nex6tCEPb5kySGNmLxIYiJCzapqPvu0dP3KvXr1E65nyQtl2WpvS9gGSatg034hNv3nzGNA0cTtf8\n7b/1o9TVjs+//wVXT59hkox/+kv//Kv7gP3ff/c3ZD6qFW3XEbmFrj1yuL+nrles1lt0JFVTY0zQ\nipR452m7ligSM0EcZHh2XtDaUJQZ8yh+nsUtzPMUYkmitG6bE3aZKYoqLHc0fX8OcBcDPkKpmKzI\n8cykWfWY5yyKFK0c8yQz2sVJeDwJrSu3yK9XdYV3PjRwBE5hx568KEEpkjTl5voGFlht1hLV0RGn\n+z1D11FUlcxJA58hTbLHxECkdVi+R0QhPyineoVXksJYwqkiNoKKk69pjTZCA1sWh1eRkIVwLLPM\nM5umoQxNHhdIYCbW4l1S6pF6FOkYpbVAerRGB9VHmef0XStUslnQe7Ex4BDr6iLXUq0NaBciQpJv\nXRaIlTjQ9vs9VVnhnCJOJecZRSpYgUex1yoB4qSJoW3OTIuIKPM8Z7Ky/ES5AHaR+W1zPsp/bxxj\njKFtGvKiwA4CWh6GLvTdPWkqCEe3zNSrHeNgcYpHlXaqPff7Pc9evWJ2oLVhaDrO3YnD/S1/+Zff\n4tnVFT/103+fzz77ktXmktjEGCP4xDyr6JqGrmvIs5Tj+UQa9NWTm0lMQXM6U5QFq/VaWAptyzRZ\nzqczURRxebljtCJb7PsRtzj6oZfkhfLYoUfrmKIuGMaRqlyHGvAit7Rg8nXBMiBK9Ro7WkAxhbRM\nFMtNIs9yxml6fFE6JzaDcRweLc9N06G1Js/lxZ2ahLGXuvZDiaZtO+IkIQqQJR9ILUmaywvOWpJw\nwpaFrMIE5vBmvaFpzj+4NUXyctSxETfYMNCPPXlR0JwDi2CeGHuJqG1Wa8kauwnPwjjIC25xjqKU\n0dlgJ7Ky5Of//t8wcPtv6n9KKf/7/89v4wWVwvF4xFnLPI2M40hZlCRJSpqIoTPJSw7HTooJZc5q\ntcbaCYcicsIC8JHGoSiKTEypzrPMM1557DzRN2fiKKGoS9CGzMT07RmtFYMdmOeIPBekmoljbvc3\nlFWF8oEjqcC5kcP9LdvNDq9iVus1cZJy/f49T3cXDOOIUxDpmDnYSb/84nOKMtB8jCFLpYCQZvL2\ndcjGuChyhr6XB44WZoFUI3OWeQnSREkkRLF+nIvJ4mLGuZk0Kx6vvjoAUoxOGcdRoMbei23VeVBa\n+KOxYeh6lHckacLhdKasKhYnfIXz6RxeECN5XpDnReAwyCkoKzJcYErUVR2iUhME0V0UWl6TtWSJ\njFmUjmVBNvYBVj6LUaDvZP5dFHgX2AzBIOEBFcWyzR5HUDKCkEVZ9HjaUV7C48orokjjvaQu2vaE\nUorVess8e87n86Nw8wF+riIlWMTEYKeR4+Ge6+t3rFdbrq6ekxdVeEFobu7uGPozkV8kLjhPxDpn\nu1tRVoWMpKKYm/fX1Ku1NAa7I3MQI46Dpa5q9ne3Yr4I22sisRfjFWVZsloLJ6FtW1nALiNaSY76\ndD6RZgU4JXXRRIsJt2vxbiLW4J3nzfU1P/xDfztwGyZik6ICqnNxi4yAgPV6Tdu2InL0sN6saYKY\n1IVadJrnTNMc5ppyLU9SAyhMLDP7cbAMY0tZVyQm4Xw64kJG936/DzJLR1nUwrQImqJIibsrDg/v\nKJJkiPOLjO4KAXQ/vBgf9OEykiiYF/cIqJ+XmSo01YSlLNwS2w2Selks02i53+9ZlxXH5h47W3wE\nVb3i8skL/vHPfoVHBP/j//Tf8uzZM8ZuoCpXEMPQD+A9XTfw5svPuXqyYVlmhnFhe/mKOJINemwM\nu90uNLwSikJC3ONo2e9vwxtcobxnta1Z3EIc52gV0dteSDtthzER8zxgIo1SCf0wU9fiivJI26bI\nBQadlTl3N2+wY0fXdLx89bEoOsoUlMM2DdO8oLMMkxSPldLEpI+81ElNDE3P2E+UqxVpWTG0DTHS\nhiFStH1HUWbCnTWiG3Hz8ogpVAFQ0w8D49AzjgPb7ZokTcRLP0+UZcZgLW/fvqEsNyQmpl5VzPPM\n+dxQlDVltWKcRQUyDaLI6YeOOE1xAE5msgpk4z9Z0tBAMyZ55EdMs8y5daTRkZyY264hKUrsOFKV\nkkGM4wgTWAVzuI6fzjJ/a89n6rIKHArFgkTpDoc9WkdkWYmdZ8bJUuYF0zBSrtbSRMoy7DBRVjmJ\njvnyy8/ZbjZMS48xGUmcc3d3g7MDcZKJGsdanjx9Qts2bNZbKUkMA4fTnjg2bLe7cN0VaEhdZTTt\ngEMzhE38Ms94N5FkhnFa2O6ewLRg+yN9f2K1vSAraprmQJZVXL+5oSpzTGro+kFOY9sty+Sod1vc\nMrPdbmn7AeXh7uaaKNasNhvGUZgV09DBMnNz/ZbIaOqqJstKvNOsViu+8+m30Uzkec796RhEgobd\nxRV5VmPHnsPxjjwrKaoaY4wsr8Io6BSC+fLQgsirIOxUYmqONFEeY8fpUb1ijCGLDU1zwi0L0zTw\ntQ8/5ObuQJxKzjpSUmjIs4wIxe3tNZvNBrsIN9IFrKCJxZVndIQPotDFeaJIcX88UK9WkmeNInSs\nxWhBWNYhD1uNIsvTsCRP0CaR1Eoouix+oekblHekxhBHmvv9mVNzEH15XqEj0Sf90ld5RPDrv/XL\n5Knh/m7P/u6OvKipVxvyAJiep4llsYzjwKpeYUwiQFwitElQKLRSEor3XuAq08TiZ9IokaVPZtjf\n3uKWWbK1kcaricU5tJeGUpamdF0vXMxAyNeJ/L7dbovSMoNzOEElhnbKPA107Ymxa+VDOlnKomBx\noHRKmmYS/M5SpnnBmASlwtZcJyjk1JeF2eAyS/1ysCFLGGp7IMYG5xb84mm6lqquiCIl4fBpZp4s\nSZqEl0bMaAeGYZClgVegNcrJD3Fre/ziWa1WOEfIEAs9fllmdJLIog3Jxp6P94J5C5Dx8/lMXdVy\nmygKAVdHChuUHnhPhMZOA2glZKg0o+s6kqDR8UoIaXme07YNRfhaLcGFhBIo8rJAlSe0rShnUEqS\nD9PEsniJ1CwTaaw5HY+UpYxgvPrBiXaZBVwukHP5AM/LQlmWTNOEn2fs2Av2LknwKLIso20bTKwF\neZnEnE8N6/UGrSVOtiwLfd883rbsMlFkoYufaJy1vH/7ObDw6vVH2FnGWEWRy6mukWSCd57319d4\nP+GWiWEYePn8NTpJGEexdygku5lXBW5ecIugIlUUoYF5HGn7jihwiW+ub6hWpYTsU4NJMtq2Dcus\nidWqom8HJmtZrVYsXhbFeSaJlFgnZIXMK3WfYB3/AAAgAElEQVQUERF8a0nCtIjexfsflBTOpzPt\n+Yi1PYmRzHi93uKWhU8//Sv+7k/8JD6Kac8dh/fv+NM/+j/56Btf46Mf+nGSPIfIMFqH85NwWUdx\ncM3zzG63lfFgIZ+TNMxTozhGqUgqxgpGO1JkEgcUpOiCj+U2VVclTdNR5CXTIqdloqCg6kfyLMVN\nwjhpzkeGsWfxC//8n/2XX90H7P/xe7/K8XggzaSmFy2Kqlpxc3tLVcvD0iQxbhnZ31xjh55pcHz8\njW/QDBKRmQZLFvJ6znmqqgyxE884DhApaS4h0Zk8lQfb+/dv2e4uuL29kTdwWpLnJYQFktZRuLoP\n0hVPU6ZJOJVyLXH4yBMbzen+QF1WKLdQlAX7+3s2myeMs1g3k9CWejh9PsC9kzDPTCLN6XQiy3KU\nicPQxD921pdlJtYRWkVMPviL3EyMeywaeCek+Dw1OBR2nsA5YiWh9SgWf/1oLSZNKKqSyU6PeEgV\nSbtn6DqhaAFD10t0rMgfgTxSFhAMo3OS5ogiEc6ZRBaDbp4Ze0tZ5jjlUETh75eRZRKDa7oO5aEo\nC6wd+fLLL3ny5Mmj2aEsS7F8Oo8PKMqiKJisxdqRJE6IAqPCuUVOtGVBFEk+en84kWUpcWywkyV9\nNJb6R0WPDRrnPDFoHTN7iT3pSDNZeYAkSYIKmLyHK/xqtUIpxf54jx1atIoo0owvv/g+l5cvuHz1\nlO999j3s+cznn36H9bYiL2tWu2dsL67owxInMymnpqEfe9KipK5LTscDdVkTqTiA14WCFkURdVXJ\nCMo5ySeH1EeEyEFk2Kw5nxvR6zz87M0zGI1CbnTD0EtcanJkWcon3/k2L189C9+fnGly6DihHQbW\n6w3zbMOoTV5QuZFZaKw1wzhQr1Ysi5xox2FknGdQmu58AD+h8dze3FCtd1xevuR43POtP/lDfuKn\nfoJ5cJKTrmp2F1cMoeHYtR192+G94/JSUhlxpHjzxee4eeLq6hmdnR4/i2mWkoef04efa6UVSVlg\nxwETCSh/cR4b8rmLd7IjcR4/yyFlHEc5GWcGbWJ+/j/+J1/dB+y/+OX/GR8pzq0E1NvDSa6ZcVCQ\naE0Sx+z3e4wRopMxmu1mQ2FSzl3PpCJ8gDMsIQqUJvLBU0oxjSMmjdntdvRdxyeffJu27dldblmv\n16RpirWWfpjkdBLFmCTm3bt3DNay3e7ws7i41itpMS3LwvFwIi3yRy6qmyYipRiGnjRNqVYr+m4g\nijWLc49LgDwvHremxsjJuB8HsQIgkOC6rrk73FME++3iF2mWeRhD1GW2gvJ79/YdFxcXbHdbur7n\ndDqw3V7glQbnUG7m7nAgimKyh358GFfEWqMjTRRsDw/er9gYRjtwsd3RtA0ozzjKLSE2CalJaFqZ\nX8ZxxDTJbK1re/KqlEXcojidDuSV/NA/2EPzPOd8PrPZrsF5zm3z2HBy3gvQJwDGl8VT1hXTZIlj\nw9j1j3qd+/0dKtLEacKqrhm64ZE3+vBwwSmZvSlJVpgsZQ5/R8I/N88zfl7IkgLiGDtbmTfbkWka\nxahwPkvuNjTJ7DDKUnOeyHKDHUe+8+1v8/r1B8RxzLv3N7z+8EOWaaEqC7wbZKEY59wfzqxXJe3p\nnqEf2G4vuDke8HYhSWKGaeJ4anj27DnTNPHs2TP6YAF4yJheX7/jT7/1b/nRH/33+PztO55ePuU7\nf/pn/Ow//AeQxCyLl59T74kjkXqqxIhq/XCkripQctgYxzHoiGbqsuRwOpPnOSqKiRN5+G43a549\nv5KZtUlpm0Gy1pHCe3nI39y8I0sSLi8uw0vbYJeZsshROIZ+IK9LdGQY+4GqKoT9GjgYsY45nI6U\nqy1ZlorPLkQzH6J4cRzjkDqvjjSJkRd3YlLKsqQduoBXROJZi9DaijTjeHfDar3GOYiDxUBywTKW\nmgcZ+6w2a8ZpEqTk/ZH/9J99hUcEv/yr/wKlI6q6Jopjpr5DK9lWEmlQMA5DYMaKP0fHcPf2LamO\nKVYrMCkErug8zfJQwaO8hNGdm9isKu7v7gQdqDTGpGSZCZDuoAReYJ5niqIkzzO6rqcoJVT/wOHs\nzmfSLCOOHrKTMjfVRuhDkRLgtAmV3Qc4cVoUjw8Ra61I/YyoQ+ZlJk4SOVXNTnQYQB5aQdZa6YkH\nEM2y+EDcEkKTihQ4wddNs0VrRVWVeBRdI/PJKI5JkOu1jxXzNHF/d8fTp095//49SimyrBDmbiqV\n177rifCkWYILuUEXZmHKy8nITpZhlKVklmWURUX3EN1pe5A9GlUlzZi2bUmScCp3kuGdp4n1aiMj\nm1hLxjYScIjzjjmc4HUUE4dMpfdBcunF0Ou9eJ9GK9lK59wP/hvtJKfk0Ne3y8Q0zTJLDAuUsihp\nTieiWAs8fZ7I8kw6/V1H0zQUeRbwlpJosaElVVQFXS/wnWFsKfNMKPpFxe3dXhZG04Bnoa42whnw\ni6ikjVS1H8SI0zwxTzNZlojbLIoYhpHEyPfgdDxg2zPVdstqu+FwPAXxpiYvC/aHe3abLXFsGEZL\nFBtubu/Y7XZEiJ0DD6fjMbjsHOfmTBxprJXdR1kU5FnOuWlwwDCOsiR78wYTKy52W548fUYUJwyz\noygqWVoGS7MC5mUON0BRxSyzoDynacFNM7Zv2B/eURQZJs5JEuEVL4sQ82KtycoSa2eyVAo4p9OB\noihxHmbnA9diwi/ysNVJEma1EX3XS5QrgtykTNbil5lz25CXFVEstwMZA7WkJg5/B4TSlwnAyMQx\nv/DzX+El16/+5r8i0hqTpjJzHcQOifMkeYFDMpSxUizzIrnOcWSyA/3QUZUrlDKYImMJJ0jvZCse\nRxEex83NO4yWD20/z2R5TaQNY3emOR/Y7i4xicSadPhge49clRaxFejEiLyP6BFJZ+3EMk3YyT5m\nQLMkYRg6Zmvp2pb1ekecJMyAiR/YAJ4kFhOmdyJN9Cj6cQjkKIi0DkAU0choEzOOwZbrPeMwgl9I\nc2m7RBAiKB6wnO6P4CEtCja7C5pzix8EinNqjzx7/pzFeXbbNW3f4x28fSewbpNmssWNNTpAReTB\nFUvcZhqw4xSgHWsiE8uoZpqEmB/iNMoTtOtaliQBKr0sy4NIAoc8GJdFmmE6jmmHnjwRZc1oJZFR\n5jICWkIkZ1lm2q7j4vJS0HTayPzYLcyzeJ2WaWa0YnYYBstorTw0w5Z7CKesNMuYJosdRzwOHcXY\nITR/5oVIi7WhKPMApJlZlhmTJCG3mYiHzSviQPZ64C4kJmEerWAc84z31+8pq5y3b96wzAs/+uM/\nRtP2OK+oCsm03r6/ZrdZyRU+klHAOFqKvBLVjdGkWUGa5AzDiF8Wzs0JrUXiWa+3uFm4HNrEbDZb\nvFc0pyMeifAZ8xDBaxlGS5LmUhToW/quQ0eSovn+97/Phx9/DecjvI+Y7cC3/u0fERvNdrPj6fMX\nvHzxAc0gjAOTpOgoxuGZZgn3Kx1jh4EkWIszkzB2ZxZmTGIwOkFh6Lqec3MA57jYrjmcGnaXT1EB\nNC4yTlnuRqE0MHtHnmXMiwC8lSifmWYLTgRCAq+RtuHsHdoYdEi5OLeQpWn4engmK9Cou/2eNMtI\n0oR/8Pd+7qv7gP313/w1Ih2RpMIKnaYJAjmqG3rsZLlYrQWQ4hxVvaEbLcfjUYbrSip6d/s9VZkT\nR4r3b9+EoHTHZl1jEsPbd29pTmdevPyQNM/I85wkiWnbjr4fZHPbnsnSlCRN6HuLdzA5R12vGe3I\nNI8UeU57PJMmAiuOooi8LDk3Z/Isl+oqjraRk9rx1LDZ7ogTmStO1vL86hnntsUkEp1SXtTkUWzI\nkkQWVoswLmc7UmaZdOudo6wrWdwZw+l0oswLtNYMwxjGBg19eyDVMYmO2e8PlKuayCQMg5wq7NCy\n3W7IsozbmxvuD/coHfP8xQeUleDnRMwncz87WYyWvCFAtSoZ7MIyyHy8HQeK4PCanaPIMvq+p2kk\nRO69Z7KywHtydSVep6Bhn9xCezpTVxVeBWNubOjb7nFGlhY5+9s7Yq0pixL377aNiiyUIIK/KbiY\nbD+gFORF/khzAhkTVEXF/f6eqlqRZObx/18W9zhrniZRRW+3W+7v7/HePbaRsixjvV1hrQ2alYh5\ndhRJhvezZE1r2QM055aiLBgG0UY/tKqMMY8tNBV+jvyyMHQ9x+OBp0+fcH9/j0lTtruN3CaU5ng4\nBtMyJEay14uTmer5dKCqKtKs5N/86b/l6dUVL5+9YOwl5zrP02NTy1pL3/e4pacoaqZZTBxNc8Kk\nMcNgcZPn+u3n7C4v2V5cERvD+XTi2bMrbm+vZUQyjSF3XoayS8LN7R1ROFEPw4COIjKT0FvL7Bba\n44n7+2uyxFCkOfW6RscJJs749nf+AmsHuubE6w++xuR8ePhPvHj5LHwPHpT3oWiTpbx5+5aqlHLE\nar2WpfIwUpQ54yA3RnlZy6KuqipZkgHd0IfPf0sSyjpRHEvBJDFfbR7sr/zGv6SuamkePdT+xnDq\ncY5lmZiGhrubd6zqmvOhZbW9xGQ5aVhoZLFBR2IeWGbL4XCPUoqqzNFecTyfKNc76krCxf0odKDU\nSENlHCVRAB479kyzEwVLbEjTXMDUanmMoyjnMfph0TWjoogkPGCWacIvM7HROK9oWylG9HYkTRLG\noac7n9luL9Bpip2lg88yy4+MB4/HhWWCQkA2SSK0KDtNtH0n89bmzDLNiH1WB9+Q5Xi84fO//iu+\n/P73+Pd/7MfQ2pAWBavdMyYcQ9/QnI4sduB+f89ud8nFkydARJZX0mBzTlieeOq6lk27949beYfC\nhGZXZ0dSI3Liw+FAlqVyDY5imkBZmieZIQ/jIMuaouLcttRVJV76h/na4hjtSFWUDOHETqSI4+Qx\ncF6VeVjiJDgFeEWkIqms9j1pkmLtiFby4JuXUQwXkXBkm7bFLZ55Xoi0mHLtPIuF2E4ogta7kw3/\nQ9THuWASmEaG4WEmWrDdXTDPMs9brVb0tpdtfyaQE9F1S7VXR7H8rD9YNpDDRN/3RD4mihyxkQfI\nZBdJYHhJrCjn8YsHJaaCrjtjx4GqXklcahxkwejAx0pSJ9NMEqdy+wnULbxE+ru2lT8rS6mq9SMI\nZVpCm08JqnAO1gQ3h1r4NGG0BjxpksiL+vaWaZp49eqDfyc6OJKXBXGkg+hRoWJJpXTnBrfMxDrm\n5uaN3MQWGUOlaUI3tBiT4hcoi4rz6YRDFtiyv5ByTNcN6Eiz2azJC0E7xjrme3/916RJGub3UhH3\nyjP7wNQY+0fLh2R6BTLedy3zJF+raZ6I44xf/Llf/Oo+YH////097vf3xNqAJ+DYJs7NmXmyzE5I\n/ZHyvH/7lqeXTymKirYfiRLDul7Rnc+Uecq76/ePC5DUGA77txgUJsm4fPaSu/sTVVERp4I7s6NU\nbrOsQoVW0rzID9EUQMGxMfTdSJzox8aI0TEm+kFU6+E0mWVC+jKJDizK8HAKTaemkZOvcxNtN7De\nbFmWB86qZxwGNus1i3eo6KEhE0kkqs6x/UgUxRSlXGGGvqOs1pgkfRyxRF5xffMet4xcv/8SraDM\nCk7Hhr/1Yz+BdY6qyhm6LlDlexKTSSMqAhUp7u7uWW834jHzhMVOFFisyIc8NLskUygvp0hFAgIx\nYgzNs0KMs1EkYHMX4jVdT5EVsjwjekwRPMxm52XCLY4sy3GeR5apvHRn0lSWS957UHJVTIwBItI0\nwdqRWEUM44hW4tEiIPji2DAvcygfSMa5aVuKqgQkP6zDmOmhPThN8nKt6hWLm6XAoAgtPbFg2GkO\np2oLkQ+WYGmkjYNYVJ1zmEQ4x1IgmR4BNwowccJoe/q+I88LoigOiRa5qUXO03cdeZFzPJ2I44ii\nyGgbMd5utltQEKkYHSna80G+L7GhqDdSNw521lhrJjs8iiyXBdrTCTv2tEPHy5cv+PLLt6g4JktT\naXbZkThO0ElCEkl6oG870iQmDkZoO89EWtO1rXAJykIswm3D559/xuZiR1GuMIls83s7UuVS2+77\ngdgYIhUJg0IrVCTjpSRJSOIkcF6HYN11mCTG2pnz+cz9Yc/l5Ybrt295/vwlk3O8v7sRI8TdHat6\nJY1JL1HLul4zLZ4lwG4eGmYyfjpT1zWeiJ/7ez//1X3A/vY3f0VC1URs1zvcMjHNLugaRsZp4N27\na+mIlyVRpDifTrx6+YpxnAD5ImdZzt1e6D5lWXJ7fcM0tox9gzGa73/+hp/8yZ/ieDyGzewVt/d7\noUZZx+WTZ4AosiWrKhDooR9ZnMwHZZYnnFUTC4n/4XqntZZoSd8yDD0qUmR5GTKUNoj6OgnqJymL\ng7KSPy9SGqmqznRDx6ouydOMN2+/lOG/UtzevqPMJTvZ9iO7i0tp7wCKiPvDnjw31OUarWO6oacb\nOuqypO9a8JosL1BxJCaBKBa1MeoHkI80kdD9ZkM/jKRFTntucM6xqVdESnFzc0MURSSFcFvjQLGP\nwza6LuSEcTqfhOEaxyRJipukZTbOExolczPvWJwjNQlt0wAyMnj4WiqviLXMH7OiwjnBD/ZD94g3\nnKeFaZofPxxKQZYYQSoWpQDqiQJcRGDLGpFDDcPAdruh63uGSTbPz66ecbi7AwXTbCnLgq4bWK8E\nXzmOA87NnJqW7WbHMs8URcE4iubZzhPb7Y7z6fxoMI2N5s2bN6zX68eXsMBXBKFngiutbyV9kmbp\nozW273tZuiSZvJyX5f+j7s1idc3y8r7fmt7xm/Zw9qmqrqYbbCDBbct2Q8BMnoiFYytRrpB8ESFC\n5JvcJOQi9o0jJYoUS4kUodxElhLs2MpALKxgbDA4QEKIM1rCzgWmu6u76lSdae9vf8M7ryEX/7W/\nRohYSgigrpvuqnP2OXt437X+w/P8nryky2yH/Kzu93uZ7TuptI1WBD/x0YdfAm25e/fTWOtwxmKd\noTudSSmIiiHzeLU2TIu09D7PxK/WK0JcuH88MC2Bu7t3OJ67HDO0UDrL49u3fPzRhzx/73nOqWvY\nrHc0dc0Xv/hPcrbZPf/TL/0Sn/2Gr+flJx/zrd/2bShtee/9r6c7j5SllYX2NNJ3AykKM3Z/eOS9\n994jJgkwnMYZbRTd6cjx8AAknr/7KYqyEVD98Z71ak1RN9iiYPGB3fWWTz56gQqKV29ecvfsli99\n+St8/lu/A5S+6Gqfwh9DEFfgixcfcvf8lu/73j/7tXvA/md/5S/z/mc+S1SKt2/3mAQ3N7fizFnE\nR6+UJQaV6VdHlsVL9dbUpJg4nY5MU+T5u+9mJcFCXVfM80AKnhAj3TBS1zUfv/gQawzvvfMuS5DE\nU+dqTicR7g/9GWedtMkp0tQtIQbmzCwNeflDTlht6oa2XTHPE2jN0HfY/BDP84i1MnM7HSSSxgfP\nkjWewzBSVoJKPO4fxf+uE/cP9yzTwGZ1xRykda3KAqMgRKEzoQ3tao1KSr7GsPD65QuasqQfA3fv\nyRLLWYc2iiV46loMAVVZivtNiVSpaVtBy2mNMkaq0hCZotC7NuuNxDUPg2yetWaz3TBOo0C3tWOO\nM6sqa1SznOjcndFGKm6dFMfTgaZtKH5dwmxUCT8tQtMyRpaKi6gEyrJGYYTrmlMsZr/gSktd1wxd\njytklhtCwGTTxTD0Yi0OgdN4pqkanC0IYWacxgxPl4q8cgV9LwaGoip52D9iM5c2pYTVBufk8vB+\nERZCIR/jyuKyBLNZHRJDlKjvohSJkdaiRBh7ySNLEjve9WdWqxXGGF69FJldjF8NigxROLlhXthd\nX8n80AcKYzn1J+E7WEnXdUYOp6qqUMbQDyPzMnN+vCfMI+vtFa5ZAQpnxHYegufq+kpA8Cnx8vVr\n6qYhAs5atpuWL/3qF4ljR1FZdFlx9/5nKKqWcV7ojkdR2IwT929fc3O9oyprxnnGVoUs5vqB/f6R\npq1pakfXd8zBU1qH8rDZXdHUDcdTzxzEzq6toipq/BQISSy8VSVAIZUhaYVxDMMZYzVFaSBpylJG\nP/Pcs163jMMixoyUqDOk3ijNPA0UrkCXFU1V0HVzpoT10rUEuLq+ZhpG/Dzy8OYlP/ADP/S1e8D+\n5X//3+L26paoNPvTmT/8h/4Qx+P5YjAYxoH1ak1dbQFFWRUoA0uYqJ1l/+aeoqioVpKh5JyVh3Oe\nZCu4jJRlDcqInCnJDCssC8FH+mHk5voGm11S4yBayvVmQwL8InMik/F6VVkJPSlXpSTF6SSH5ziL\ntrWqquyhhqd8eWcdTVMLu7Z2jOMiyQ1BosZNlv9opSTjqywYp6c8rvwCZyjKsixoI5Irgji/zt2Z\nsqw5HPZY7VDG4IqCpqrFcIC8uKAy2NpetJzzNOEKx9D3JBJN08rhVZUUVSWi7KJg7PvLYrHIoZHz\nLNrQOSxYJQSu7dXVBUlnC9GIutxSFkUpgJWuk++f0fhZAhersmKJsqSaJjnAxJNhSEGMH9YY5uCZ\nx5G6lDl5n5MlBBok4XcJkbc9LTnmccLmbCofPFVdcT6fqYqKaZry3G9g1a4vUqwYI1Yb+l6SFoax\np6mbzGOQud2S56soxAlYys9elCBygZ1zyN/TzD7GKNHS48TxcGC9XosjbBpwrqBt14S8VEsxSuyL\nF5aC0YYpX+bWymxzWUTRsdrIRa+VRmlxOJ7PJ6q6IWlRepSu4NXLT4TKtsiWPwZPdzpytdvSnc+c\njg80TUV/Gvg//tf/haatsK7i+fuf5v2v+/qMF02SEmwsr16+ZLWqoXRYZUlKMQwnbnYbUrAM4yxW\n6PNJjDZalDQ6BD788pe5evaMerXFOGEZzNNM0Im2rOSy07KPUSiiD/h5oSgti59JBApbcO56iarf\nrAgxYZTYa421LNMgXNuUmL2nbls0mtPpwG59zRIC43ymriuKsiJG6W78JPHl3/VHfptHBEqp94G/\nCjxHlDX/aUrpR5VSfwn414DX+bf+xZTS380f8xeAHwI8/5TY7h/7a/+JWBOVolw1zMeesii5u7sj\npsQwjXTjQAiJdbNht9vy8sWHbDYt//gf/Qrb9ZayaNBFzfPndzzuH6iqQiK9TYFyhmkOkhSQImVd\ncepP2WKrmadFuJZKPNZ+XigyzqwoLVM/sNtKC/V4OtGsWnRSLP1ASMLRnKeJczdwd3fHOE/0fc9m\ns+F0OhC8Z7PeCCQjLnzy4iuYAJ/9vd/MaVzQVjPNvehijYTLWVtwPp+pN1uUUhl0EmjLmhg9wzCw\n3z/w/Pkdrz7+mLppKeuaqm6wrpAIcYRrqxBdX7Nacz6Lu+dpIea9l1wsrTMEJlI3NefTmbAEqqaR\nsL4Ml7HZ8FGWJRiLs8JfCEGkS85a4SVEoYld7Xay6bcWFUUus2QlQlXJ9l87I5XpNLPb7jgeT1RV\ni3OGJYqMxsdA6Up0EhJT349YJwStEBJTdjbVK8lQKotaDqtCXkjnZNlT1zXjODL0vbjOnMsLuIWu\nO9OuVzw+PrLd7mSME0IGiNScu5Mc4Eqg1W3bAnKoPsW1Kyui95urK06nE87YPJOXS98aWYTGIDPD\noihoGrHqQkIZcry1xpUlh8OBoiyyOiEJwyEJvMgWTmJ+jEEnWBbP8XRgvWqYhp7T8cCzu3dwRc04\nzaCgO3c4a0SqVpYkpXh7f0/tHFM/Mk0disBHLz5AG0PTrnn/6z5DiIFpEj7y4XjmanvDkrGWKsJ+\n/5YUIkXpePniBUXpWO+2/NoX/gmf+fTX8elv+Gb6fmDV1oydLOKCmvCjPBvnJdCu1qSoRPsdE4sf\nOT8eKa3l8fER6ww2R76fjo8oBc/eueUrH3zANE38nm/8RkJIHPYPrNcrSVnoOvb7PbvdNTc3N7K8\nrmqwVgwxdZUxlRZtyePJIGaUcWaapdP87m/77t/2A/Yd4J2U0j9USq2A/x34l4AfAE4ppf/oN/z+\nfxb4G8C3Ae8DPwt8Y/oNf5Fkcv0kzlmYZkpt+OT+Dc5a1us1x/NAUZZcbXdoLULmhOJ4OHE6dxRV\nyWa3Y1kCYVko3Fe3wGVZMAwjq90V0yxMg9IZ0Ilh7AmLZ7Pe4lyZEYey9JjGCefERbJataQY0THh\nUyQqecGnfmC32XI8PbJqa4rCZZmUYAOVsWw2myz9ESCI956wDExThzUOHzW7mzuKqmQYO4rCQISp\nlyrPWCeLncKJphLFMoxUVXlJO22amqETeVFRyqHsrCEkSTE1STFOE9ubK8YMxrbW5phmWQZ1Z/HR\nWyNwb58idbNiOAsJvyokrmWa58uSryxLyUZLSaj4mT3r8wtvCwE0P8FtgvfYPKs+HA5c3VwTQpRL\npBUyUsrKDJCKqq4rjqcTbbvGe09dFRyPB1RWmhgrFYm1YkGe55myLjB5/LAsYjBpCuFKPPFE67pC\no5lmAafUdQ2I3lYjn6MysvCYxinbRme00Rf/+8Pbe7a7HUVZEEKkLMrLPPVwOlCV5cVSTJLK+3Q+\nstteXwTwwzDIyw6i3U4JW5h8IRU8Hg9cXV+TomeehP5vtMrA9cQ4zWw2VzLq0QZj5e96uH+L1QrS\nwuwT4+S5vX2WZ4ynrA4Q88A4TbR1Lc9W8JCBKXMI1E1JPwizVSstJpZMuDNGkke26zUffvgVdtst\nx+ORjz/6kLF75AsffoHv/7P/Ik21Zeo6Rh9JUeKG0IrHN2/Yf/KCP/D5z/N2f+D5e++TSJfFZdd1\n1EWdf+4V3djzlY++wrZtOB6PbDcbYgqMwyBW3Xlid31FCJGrq1uhlE0T1zc3fPLxR1grxhilFH4O\nrNdbipXEhmtt0Vas6YmAQiKY5lmg3WjNn/gjv8OJBkqpnwB+FPhu4JxS+g9/w6//20BKKf0H+d//\nDvDvpJT+wW/4felv/+xPEZaZx/t7Nm1DsW4knTIlknI4Z9nfv4IUWa/WTMOCKWvWux2LT4i0agQl\n8gujRKRPStnHHinLFUXpOBzuqauSeXOR2ZAAACAASURBVBoI80JCoZ3MslD2whoQmZXNW/QEQba/\nKUnMRD90MqOLgi48HvaA4ub2TtQQWqrDzXrDOE2UdS0xIX4khllUBbokacOSIrqw9KcjlSuw2uaD\nSX/1MHGih12vN3R9d5kFn89n2nYl8qRpwjqbQcZyEJRWDv5ms0ElUSuQEmUlmuPufKaqBGs3ZRlZ\nNwwkbWhK4QVUZc1+v6dpaol1doUslYxCRAUqR5BrjNYX6PMwTZkvIxXq0PUEP8vMu3Dirc881idO\nq7UFQz/R1BUpxdwCJpq6oT8dMw3N4KwcxNM0582+jBUUkiTqCoszlsV75mmkrqtsNRazhpDBxLI7\nzZL4EGOkKkqZl3dnUopstxJW6HOCsVbmgjZcgpgJIMmcfg5M0yDSo6JkGsfLQksrLllh5/NZlmLL\nTFnV9MNAXZYXapT3gmQsKzGspLCgtGNePFppNFA2Fc5amUsqhLWBJvkkIZReIOrGWKwpGPpRLpec\nPOAz9lFrLfPkoc+d2ELMcUMxBWIK9OOEy8u04KO4tKInLJ5lmdhs1zmQ1ONKx5vXr7HW8alPfYqx\n63l8eOD27o77t2+4ffYMlGUYRh7evuJ6t6MbJvwSadqST16+4Bu/6ZtIyVJWNeezAIREIRJlLhrE\nnDBPE845mkrcdT5G6rrlcDiyeyKP5dgcay3H4yNGKTEILeLMtM6xXq0Z50XgUdmq3dQ1U3YLKqX4\nvu/9HdTBKqU+C/w88DngR4AfBA7A/wb8SErpoJT6UeCXU0p/I3/MXwF+KqX0N3/Dn5V+7n/8OWLe\n7A5dT2JBoxiHibbdYJ1lWkZ0ikJIb1aU1Yp58diyoKoLQfwlLmSkYeghJspSvvkxyew1eWnTrTOU\nRcHD/pGyqEhaWpNlmoR9UDpSlPbw+PiYsYeGeZlyDlfPdrtlWYZLhPbj/kBT16w3gt+7ublmnkaG\n84nj+cSzuztJQ3g8MI0D53Hi9u5dfFJ5ay6e+LjMlKWEEQ79mIXaiqIoOHcdKYlusxt6rm9uKKxl\n6AeGYWCz2VyibsZR9KZPAYWSrTRBEoA2KWKLmoeHRzbbHUkr+qHnertjmQR/6FxJ3bR58eEYR0kI\nPZ0eMcYRY+TZs2f0g2g9tVG0qxXn00kcNQnKqsYj1sm4LBRW0/e9uPeqihSksiCmnDYqc0YfAnPW\nY5ZlgR9l9FIUBeM4cX19fdnST9OEMgo/zdR55vzq1Ss2mw3OalxT59SBmWEaudrd5kUXDINcLOMw\n8vC459nzu8uhGCOEEPF+FPi1ySYEv4hT0BYEP7MkaFdtlnZ5ovfUTcOQzQ11VTGNA36Wz/Xm5jYb\nORZmP0v6rnM5DsfIYhdFij4fXKLFXvzC4ieqspE02dyNWK0prAMfMsi9ICLc33GS7qKwBZ5IyFX7\nPM8SUTTP+JAYhpGmLrBagjlD9JdnSRvD6Xxmt7siLp7By8c+VZBEz/F4vPxMTqcTfvHs93tub27k\nsCfx+vVrgchXDT5I4oJWok0exp5nz67YPz7y8HBgs71BJcSy3p3JekGaqub+4YH1Vhi1TVHk2HPZ\nSThrxfprLW2Oai9riYwR67dA74dx4f7+LdbJyOT99z+FNg1LCBS5IymLknnxfP+f/B0KPczjgZ8H\n/t2U0t9SSj0D3qaUklLq30PGCD/8/+aA/Zlf+GmSThTGolOUID+/iCsqRIZ+wGrRvrVNI6zTkNhs\nd1Kh+AVrNOWlTZfNeZGjp4uypht6QJFipCodrz75hKJwbDc70SRak1tE8bIvCVniANEvl3bPWsM0\njgTvefnyFXfvvENd56pgCWzWG+Z54ng88fbta+qqZLOq6buOc9fhyhK/BKqqZnN1jS0FelKWJZvN\nTlrWomCaR3wIKG1wRlB5KtO3ViupGGxZCPFIy60MYktQShFCoijshdOav9ekuLB/eKDvem5vb7Cu\nBGTx5YonYbUmLhMPDw9My8K7z9/PefRrXr56hbMG67RkkiVZwBVZ22qdlVYzJbGp9gPr7QZXVXLJ\nkSAFTscj7WrF7GVeCmC1pu/OUrlNM9oYWcpl+yIpZhZCpChKipy84DKbtmlrTocjbdPw+s1rtrtr\nikJ89+LK6SiMIRJJSeURgsSInLsujy0kTlyj0AbRZZoC4wzWFigtrbwPCypB35+p6hqTk3PD4hmW\nCZupblXbUFiZYYcUmMZR4miUuLb84lEaSFE6jBBQxmBzBNDpcMRZLTK3EKQA0ArtystlU5RCnWqb\nlqE/Yazmzf0bnCtp2w1lWWdsnycZw9B1XF1dybIoBtpGZGghJbGPeolIP52PuaOTd1FSCjqBGfmZ\npm5QSWzsbVZshLAwjSN104i4v5KFESmJUSBF0Zk7hzGGwsniq2lWmS8RWG02OQssMziUBEoeDo/c\nvfOcw+MhpwFLOsOTnC2kwDD0nE8nNus1bdsyLTPz7GUpWcm7Ngwdw+nIsJxZrTYcDgdxgJ3PtLVY\nj7/lc5+jbFYkbRn9wp/6nt8Bo4FSygI/CfydlNJ//Jv8+meA/y6l9Ad+kxHB3wX+0m82IvjBH/5X\nxDEUAt/+7d/K5z73ObTWrNYth8MjrnCUhUB9fd7arlppZ0JIQuu3lpie/kwJ3AshMI8SVdy2EgYY\nYyTl9FJhdp6ZppGyrgX6kRI+Jsp2jZ8n6kq4B0/kK/lH/qI6R4qIPAi6c4/WliXP5LIkn5evX2KU\nom4aVu2Gum2yXjJcKhUxLMghKG2iyNOmccktqWUaBcCcUhI5izHMc0RrsNZkfa7N8Jknm6swAWRz\nHbBWcTqcxCJaWE6nM84VeB9l5n08UtcVb9++YbVqWa03aCVVorjYZHxRNbJ5V/mrnKaZplmhtKIf\nh5xoG2jrmq474dHEIDM4jciwVqs1ATJwRNJuhR8gBLSkNCFFQgw0dYPVEjXjvacsK7pehOybzYZ5\nksjluhL04/HUsbu6EXdOxkRWVcnx8IjRirKqMphH0iHKsr6wJ7q+o8ojlBgi2uiMLYTRe6pKEid2\nmzVDd8QYy7Hr2e2uAE03jsRlviQkmKwTNs4Ss8Z3yc5BHzx1VWYXkSGEWVxYMVJWAgdCi1vqmA/b\nZfG0653Mta2l788UZcHpcKQsBdRzHjrqskIpyTlLKcnSMIHT0qE97ve0rXQbm60sI4tC5tfjKEwM\nnRUeJDGgKCNM2H4aL5rcGGJ2ty34ZcIWkvLrihJjLITAMAzZ0CAhkUapPC6RrylGjzVOZqjXt/Tj\nmCPEoSicwF68J+UEY6I4CuUA1hIVNculbPPBHaNYgo+nM4csKavKCkXki1/4AqfznhQjm806842P\nxDDSHQ/86q9+wKv7M7d3zzGu5L/4sb/+O3LA/lWkWv03f91/eyel9DL//38D+LaU0p9TSn0L8NeB\nbwc+Bfw9/h+WXD/5M39TWk+lOe4fWe2uaCtpca3T9PlG3DwF0YUgmVc+4IxkVG13O4Z5ltZNa2SE\nGyldLRVdkuF9jJGybfCzCLU1ovkbssxFKakWrJNANb9MNFWRD1gNyPJEbvaJ4GfmeRTpzrxQNS3W\nZLxgCGJ51IZpmfOMTQTlVoGy5vJQWyXIt/O5o6xLUJLRdT6KrOVJ1vPkp/delA/z7FExskQRuztn\nGfqeJbd/xhju7+9pmoYm04HKUlo+VxhZ1mxFUtX3kuH0JNhfluUi/6rKkjpXKefuhHXSIVhrBUFX\nVRxPZ1CKSJLZo3USUa2yiqFeQRCCVMzbeWU0y7TQrATk4qyV73VMEvB3cyO0KS+x1OMwXCDV682a\nZZHqfFXXojnWmhQj4+JZrdd4Hy8pqCF6gbVMI6UtGBeJpxY3leP+zZ7Neo0PgaJyLJOnrhrePjyw\n210JCN2ZrJkU2lb0E8sysFpvKcuKvpM4oFXb4qOXJaeXKlkpxZB1xE+Kh5SSaFtLR9f3xGWmzjzW\nFx99zHuf+hQPb19T5Z+l1qIu6OZZAO3BU1YlXXfGaANJCZmrqnBGLo+qqun7HhR4L7K5LluUl1k6\nFusc3bmnbZo8/zeCzywKUvBM08ThfGS33ZJCzPI5OSSNlpl4WYq9erVe88UPviSfg7UoHwBFVdf0\n+ZImz8x9WDgfD1gnYKN3332f+/sHitLlZJJR5I/jSNM0hMzImOc5d6gJNBnaIvuRZQmkFPDLzPl0\n4O7uDlPVdPl7kBJs1xus1rx+/Yq+P/HixUfUdcmLjz9kVZesqko6gmFm1a75oT//I7/tKoLvAn4R\n+BWkhEvAXwT+HPAHEenWB8CfTym9yh/zF4B/FVj4p8i0/t7/8FMkJcF9aQkUbY0fZY4VYqSsS9Eo\nns8yN3SOpDTDuaeuWrFXpsSsZ/wShL9ZCpqufAKitCshN6WEtoZ5mvKB5OizRrHre0nMnGdiity/\nfcvtzY7j4RGlNFdXuxzOJ+Ql6wzzID9YlKJqGpYlcNgfuLq6kpA/reiHQahU1hKmhdI4fPSSjGAt\nzlru37zl+ub2IofCaAG6GHvRnT5xEETLKjldVZYYKasx+VAkJVhm0clmXN80SRBj3VSirFiv2e/v\n2a13zBkAAuIDDyFwOBy4vr6WeI/1ChW5XG4SwTyJlGVeaGoxFzxt9X0MoAzjKHNFozSm0AxdR+Ec\n8zRitCwivQ8s04LSMC8z2+2OlCQ1wvtAWVSy4EgC5LBWFlkoqaZ8kLgP85ShZTQKmEOk63vqusU6\nJ+aHEGjXLVM/oHzAOMPhuOf22Q0JyRWrXUXygXnqGacT2oF2FdY07O/vqVzJsASWAPdvXlE6zYsX\nX8IVBZ+8eMn19TNu3nmHeUp89/d+tyDxlkz/WsSF5r0nxSS83WkSs0Xe3lsrRDCfJVByaOTMtXFi\nCZ7VZk1b1bx+9Zq+F7CJsiabEwRzWdU1/XkiJs/p8Mj11TVlLSAilJKUhHESIX5chMGwLNSulHj0\nsmCeg4yDjKLrjgyLPDdLP2Gd+yrmr3zKektURcXiZflX1TWn44nKlWil6IaOdrViGHqRu2mTZYPS\nYdRNyzL6bIde8ujJXYoVkqIsK0xZcDqd2W43NE3NPMv4o+sOmea20NQtH33lixRO07YtxtWUdU3f\nj1kWJ897jDHzhyW3juB5++YVQ3fk9atPuL25xSf44R/81792jQZ//5d+hmVaqOuWKWc/AZdlhjWK\nGD1OG/F8+wXrCpRRzONCaR1L/kZVVQkocUiVcvs6Yy+H08X77eWAfDwdKa3GJhi6HoyjvbrOdtYK\n72cSEWsKIMkcLc99XE5clWWXOL2Syk1z9HSngc3umjlI4qYxOovCZ1xVUBi5iY1xxCRUIGMti5eg\nxugFsPG0HY8hSRAeOT0gQQoBlIwIlBbXyuI9pdEEn6v2lKhqIflLiF7IcisjKaFR/u6h71GQM7WE\nw6udhNypRBbwS1yIz58vSDtZFk6WNSGyWV8Jo1Ypib4ZB+kogqfIh10MM0qDDxkGrbU4sjK4JeW5\nWkzxMoZwRa5uk0SRowTePXRnASq3Nbao+PjFC652WwnRmybqskIebyWLomWmrZ+q+eKy7Bn6gfG8\nZ+yPxBTY3lzRjyOrzTVDP7HbXdE9PjJME8oUaCNprh+++JDtZss8Cebui1/4Iu+++658LzZXWYqV\nBD2Z1R9JgTUuX4hyGHVdR1FKBJL3IkGq65rFz3n+L6yMZZ553O+pqorNZiuoxgzTUUpjrEIhAHWx\nfTvmUcwySsOyTFmzLEaRfugoXEWKiqZ2nM4nyrrKW3SoS7HeukK6rxSEMjZOgwCRnLjJhCMB09jL\nrmKaSEqxyhlrAP00XowaRS0hnssk76LOxcRTEu1wHgA5BCXYs8KHJLbh0hF9YOjHfDFJYsm0eJSx\n8mcSOR8eIMmYqGw2GFejgbppOXYnmpW424KXUM558SitqKuSoe/FKKISf/w7//hv6YC1/18/8P+P\nf8IkqY5Gabq+Z7Pd5PjhrF1cFpqmQcXE6Xhgd7XDL3JYVKXDKEtYFqHlJDELlNZSFYb9/khyhTh7\nkjzgMl9SDONAW5dE73n1+lWOG1ZMY8fYT5z8kWfvPOPt/RvWu50kbMaAKx1NWUgY4LLw9v6eaZwz\nArECFYgxcHUj2kC0keTLzK3sY2AZRtrdDlKirgTpNvuZac5OIALayOGWIrL8UJLWGTJGT+fZbZWT\nOff3Dzjn2O22LN5fIleUgnkcCHkGmpL877zI4VxaLU6jzfoCcxn7gbZpud8/sNls5L8Nk5CihkHM\nAlVGLSpFPyxEEk29Ep2mUszTlOeEGmc0Y9Y4OueYU6SqCmYfWDWt5J9lmpb8fGy25Cpclp71vTwj\nklAhqD2V56RFWXI+9czhwHa7yXCcNU0j4yCd42GaqkSpKACh0qGtwSSLMlZITMvA7e4KHwPjOLPZ\nPuOjFx/x2c9+lsf9I9pYNtdbsSA7x+HYcXf3aTkoFoUrWr7pmz8nSzFXEBepKGMMF1lVSol5WVhv\n1heZ2zxNlxHS6XzCWUuzXonkzonhwXtPWuRrefdT7wvjVMnMcZk86+1OAClWkTAUWnM8j6KowaJJ\npEWhlaWuHdM0orQkOlQ59PD4eMg2aXneqkqUAnVTX5ZqcfaZxiWMicfHx0u1DZBUJq4V9mLCSLkA\nurq6koVljLRlySl0lG2J1RKCKZK4mbdvT5TWURQl0yJkuvtHmRlL8CaXROG6bfDTcNGbl0rTDwOu\nqRnHGRVnIp5qvSHGWXCKb15R1TVDN5CQ4qWpm4wZ7VEk5mViXa143D/+ls+439UK9md+9m8xjOIE\n2m63LD5QlFKJylyzAa3R0VM6y/ksmfbTONB1sgmsmxVEhQ8TwyBgjrIq6HpxtAhQo6AoCu7fPuCD\npygs+IUYE+vt9gKYATImMTFPM9PcsVnvMtFfoCx934ssRmW/ui0oa7GcGg1LCAzDzM3VLbOX1vB4\nPBAWcX65urrc6ikm2a7m4LlhmghBNubDuaOopLJzrkSlhHGG/nSmzIDwFILAKbIraRxHIondZsvj\n4x6tVcbhTRmZ5xkGWdw1eeamtaaspVWLUWDgwziA+mo1aY27SHDKPFNMGh4fDzx//px5Ed3lNI40\n2eI4jP3lUNVG4UMUp5yz+SV0F0vq04JPowgxYY04pp6qvrZdcToecM6J0iGK5M6VJcELenBaZna7\nHcHL5yzULEkmhUSMgbIoL8aDZZ4pSllI7vd7lNY0K0nWqOsVp9MJpURnvWpatJV8MOHiqsuo42mR\npbVQspIPhBiJiFUzBBmFlFUFJBYfqPMiVGvFHDyBiItyYM7LxLzMVE3F2I+5w+guNC5XPc1KtTyT\nSWR0/dgxLzN1tULFSND5gMdiUMQw5xFTzoMrBKe4jELwKlxFXTfSLhvNOI7sdjuWnEk3TiPTMkvu\n1SIR4tdXMmbyfmG1WouDzYkt/HQ6opXj1HVsd9vLOGpVNzzeP2QVRMnheOD6+oYlfBXaoxMcDkfa\nzRo08vlZkWRaa3JsU8ouv4JpEhlc02Q3o/AVGYeDZJs9oVCHjqZd4wqBpKcE87zgfWC1W6NBLuWU\nc0Kt5Y9+59cwTesnfurHqeqawsoDF/IQPSUhA2kluLYn2ro2WjgFxjAOZ7CWTWZZ7q5WWWAsou2q\nKS/5WSkL7TfrLf3QSewJcD6dadoN1hbS7owD2hlUzDqAKJk+T63e6XRivdngrGEYJ4w2VFUNSg7e\n0+FA1bR5y+9zVIrMGSWyo2TyOSssc0Yf9/uL+2cJIog3xlBYg3YOpTXL5NFaYfL3TisBh0DKcS1y\n6MnirKTvzuisn13ypl5aMJnnTsOCdY5m1RKTEN+DDwQfGMeJ1WrN7AUIo41UGClJ6ODhcGRZZpp1\nK/g/L3EuhZN4FWvFJz/NMyqPE4o8q5M0WHnRh7HPMzGfv9cxw8NlqSGxMpGnVN2+61ivWoahY7Vq\nJbjPLxgt8POydKikUcpQZtjMar3ifD6h8kVRlZVcBlpCMMexZxh6NtstrqhRyqCMJZHyhSMXISnR\nNGuJbylrtJYD++lgXZZF7Kf50hOjDCidY35mn+fUUZ4NL+OwQluMUkzTmMlbBdZohnHI7ArZ0JcZ\nku0Kh8tdj9D6PUWeZ2sricVGGTk4/ET5tAxVSXYY1l7iuJumoes6qXwVaCWz3GWWGei8LFR1wzJN\nlPk5jApikniiuCyytS8c576jKCvOXc/VbsfY51mrFcNHJNG2LY/7PWVR4KzFasPbN29pVzXKWKJS\n8pwME8P5TEqJsq5ESdI2Uqm3NW/fviXGKF2XtrlLUUzDkN1iOZbJGpSCumg4Dz1NU+J04v5hT1U1\naG2IPAWlrjmeD0J5WyRgNKVEP4786T/5L3ztHrA/98s/J/zLspGWSkkezuFw4PR4IHrRl6IUm+2O\nfuwwRUVtHV/58APe/7rPCH4ues7dMdvcAuvVhuN5zzzP7LbbDB+RbCfhgKpcNdmL7i6ihJw0DLmq\n0pg8H5rHidPpiHWW27s7TqczyyRg6JhELjWOI86aLFnSzEHAzdMwoU3euhotK0GhmAhIpKooK7Gk\nhrw0A5mxmqJAG0PhSnmAnKPPuVbLMqNzjLVzIimz1lJkATokyqoixIRSiePhIIxNNFUhzqxu6lFW\ncX488PrlK957732stZLiME1C/1pEniRwm4QrKs7nI4GI0gajJcrb5CgXsfP6bL8VXuuyZKh5SjJq\ncRZtNN25o2kaQF1a0zlneo3zQp2RjCHK4tMaRUxSiY7jKC+gl4qsrgRtWJYN59MZ42ROO00DVV1h\ntEElSd4ty5LufOJ0fuRT773LEhIkzeHUkUKgqhvqNuMglWYcJJxPaYFQKwXaKLRyzMtXCV0pKWKK\nOGMJixelSuEEHL8sebnnUUZl84PHFZZxnmjWEoMeQ7yEMsZsSVZATHIBLUGYDnVVE5Yg4BSjMba8\nRAc5axmXEW0lanyzbvCLVNwpBcpa0JTBP70L0rkZY6hz0rEQuyq64SwFS1Xy+HBPu2pZfGDTtLx+\n/ZrVanU5fIdhQGtDU0rHlUj0Q09RyqhjmiZ2my0+ihLIzzNay9IP41DaiZa2kAiXvuvQTmBLKSSG\nLlO0Cic/By3zWBmhGbQxnM9yEVd1TT+MctEpRXc+EeaJq+tnJHkUSdJGyCW7avIzVRNSlM9xmvnn\n/+if+to9YP/2z/+ktMlzYFW1RCPkdFTEIQsQpQ1oTVwEKPLw8Jb1ZoO1JTEGTsd7mrYmhIhSBav1\nSiAh3tP3Z5Z5pirrS9KldQLErvNyQStNWdasN+IOKYpCQtJ8ZJhGqrq5bPBDeKLMJ3wOvwPRp4bg\n5QDse+pWKgexplZZSzrIrewqNtvtxS0jOs2KGBJRkbF4WmJYUCxeBPDWGrTSHE8H2rpBawjZ2khS\nzIsg2YoMIY5IBPd6vbmYEfpxksNPKYpsBz6ejsToub6+5tyP4ntXcDqeuLm7ywjIwONhz9V2R0gC\nawkx0A0DdVVJgJ+ShZzEHssYwJVFbs1Goo9SyRsBc6cEzmn2+z2rVQuIyD4pWQyOg8SJS9KBaJqr\nqiF6qea9j/mFOrNqW6bxjLEOhWLIrrNh6IUpkVROvR3ZP97LxVo4nBOEnfeB9eYKlS3TPo+q9o8P\nVE2bbZkrzicBXUtOVFYH1IW0+yj8vGBL2cYvSwZtB7FNT/MEJIo8GpHKX/SwMSiZDZuSX/k//zGf\nvHjFbneDLj3f8Ht/D7e3t8QAtzc3DFNHPwiqcV7IFLCaeRjRWjqU9WYl36PFM80SmkiSKJRpmSjL\niqHrqdtVlo/Js9zUNYeMzvQ+0qxX9H1m9QYvHY3SEmqZTTer9Rq/eJHEGYvLXI8hU8gi0v0E72lX\na7qhpyjEHtx3HYmAsxqjLUkZDIppHvAxYJUTXWseVxljMFY4uSF4/OxZrzcXLsb9wwNVWdOuWl69\nfoktCkpX5xBJn7GVIcu5kjgoCWKyyR3f8Xhkvd3IMtBYvvvbv+dr94D9hV/+7wW3tni2mx39cMI6\nIymyXYfG8PbxkWd3d8QYGbsuL3YQ14sRqUkIi2zGlWEcRtGgJgNqyVg3SwhwOh1pGmn3YwxstlvG\ncWKeJWqirit8TJe4X+espJOWJYfTKUtUJOabKHPbYRjyJtfjnM1SJHFgPYmu53kWLV+QgMKYIspo\njseTyEG8B6MvVVxY5kuk9FN733Xd5c+4aCiLApVVBSH4rL6QA0P0pQG/CNHfaMPixcsdfciVYs4K\nWxYhuCtpjaxO+ZBW+CAjkqdYlsK4i9Nnzh83TZKwGoMwPG2W4iwRAW33PavcicTgOZ+OOGOp6oK+\nHy+LqJQSReWyekIWXEorol9o1y3aOMZeDpKnGavWwm1AiU7ZuUI0xWWBRuZ65FSAL3/5K9zc3LLd\nyoy6rss8ZzYsiyxJrXMyJ61rQowsmagfslZ6msb8NY9oK6L2JV+2cZGxgtKacZ5YrRpZmkzyeZ2O\nPX0/0fXn/PlFXnz4Fb7ln/l9mLTil/7BTzNMb8S92C30ufWPyrCqt6zaHc/u3uczn/00u+s1y9xT\nlRKnU9R1TsfgsskXiluUi6CSMZQPMr/VSjNMI/M8c3NzQ4qR0+lEDLI3KMpaIrFzHFKzkpTX4D0x\nRXkWrDz3fglUdZm7NCPqG60Z55EQUu4YehmTILlr1soh3dY1wS8cj9KB1kXJ/vRA1TTEmGgLcW1p\nrTHWYq3h4eE+ozUbjHHMUzbWZBTm+Xzm7p07YoJ5XCgKR900jOMkgZ5O5J11XTEMfYZuyw7mqVh6\nShz+Y9/1W1MR/O5GxvzPf59lXiAzAwyBh8MRo93FDVPkhY7kFiW22y1TJjg5a9jv34LWGKWpqwa/\nyIGrlCPEUXz1tqZwDUXtLqLtvu+YpgFjHYUreJpw2rLIs1BDdzrRlCUx0/ddUUoliUhIjJF4FFsU\nvy6o0LPbrJinURJxnVQ487KwTAttI7Y9VxY0TcPQi8xlzDM2ay3WWpLP871c5briKe5Etucmz5qm\naWK72TDNs8wmZ8kseni4p6nlrAH8SwAAIABJREFU66qbFoWi68744CkLUR88ZUbd3t5lfKATitCT\n/VZLq+6K8pIdlXzAGHUBn8QEwT9Btjup0osiXwhrpmlA6YQry4ssqjsf6I5niXxOot0UCItnnj3a\naJpmhVaaaR7IcxWsKbIdOKCyTbjI8OlpmsQp9CQh63vevn7J7e0149BzOh159733OB0Ffq2NFuvn\nNF2oXTqTw6yzBC/kpSW7imJasshdTB/r1YbZz9RNg4YsVyrBOMZBGLfLNDH2Ix9//IIXH3+Zrn/k\n+mbL8+fv8Pz5u3z5i1/AGvjgS1/merfi05/5BnRZSRqrjvgAb+/fcnV9jdaOu7v3+Ef/8FcAQ/SB\nj770Ad/xnd/ON//+38exP0MS8Ms8DRSlZJQVVrosifYBsl24aRrmWS63J4XG8XjMIv5IShqlEqvV\nir4feHh8pG1WYrQZO6qmZppmjuczV7vry/xfrOUJv3j6cciLxyhpFsaSsj7bh4BCnkGB8tQkpVmG\ngcKVtJsVj497mrLkfD7mS1CMPjHDbDabHSGknIAgC7DD4Uhd1xijePHxS25vn+FTTju2QtlrmoZ+\n6POewhEWfwmEHMchQ4gSh+Oef/nP/MDX7gH74z/+n/P83feYfaAbJ8a+ExRcUZD8U3bQIoerc9RN\ny8PDnvV6RWElu8doOZyfFg5h8Vhb4ErL03JEK0fTruj6LkeLyAw2kS55SgqxBrqqJCZQKJzReC/t\nvcrzs7IQHetTxYUWzJ3o6ORw7E9HVutWxPlPlKQQaNtWYCtK5RjjnnW7YhjHvPyQzXjhKobzibKQ\njPiu6zMvYKYsHOfTmbpuSCmQAnlxIc6WohLOJURimKnKhmma6LuOm1th1ppciZWlqBQSsmDESB5Z\nW1WkIAA3YwzjIIf3PA6UZY7eNpqEhqSwxjL7iRD8pepX5IQHJ1vf/cOe9XaNJ2C0pS5KQtb3ooQV\nUTphSJy7jqJq0AmMRn4PSezIs2yyRdsryyifovjPJyHUayWt+NB1OGNYlumyuFFJk5LwFcZJtMjy\nZ3kgYYzO1XjMWmxhUVzvnvF4fGCYBrquo6oaHt4+cDocCBEeHh7pholxXHg83oNagJmmKWnalros\nee+99+Tl1Y5VuyGGBa0jCdhtn+FKWSahDMN5ZL2t0GHhfDpStStcvZIKXiUKJ2qKjz95w+v7Rz7/\n+W/L8BWFc0aKAi9mmHmZcU40okVRkBSMs5hByrJkf38v7kUfcsS4hFZWlXRoUkw4OZj6M8fDnu1u\nK8YFK92hc7KI2+6uef3yJXVTYa3NNnKDcXL4PpG8QoxYoyCpjOwsJBrGSYpFCALOv72+5tyfpSNU\n5ALKc319xevXDzm5OGSUo2XVrtjv91RVQdNu6LqzEOSsYCIL68R92J3E5RkTyUeqsiZEqfS9Xzgc\nHimrgu//E1/DkTE//TP/LX0/s95e0axXTOMEydOdDpIj//I1ZVVxe3PDNC+yVMk6SZ3TNY/HE0pb\nVqvVJf101dS8ebinLIsMlXZEMmIvu2aKsuBwPFI1rWyfjSL4WSyFWkuOupcDA5WEd6CUtF1eYkye\nrLhPzNSybPP2W6y4yshGm5Twy0h37LCFtM/KiLUzZSDGZrcTrWS2x+p86Kya5uLSCsEz5dt+HHNl\n6xw+pxakFFDK4AqZT8+5nRWwsGaeFxJQN02eUQ6ynJFoPdCidOhOEq4ndPcpAzFOtE0tN/vhkLOk\nFE27YgkxM1dHnLU5vlyhVUIpGbHM48Q4jbiylAcgygKJTLmXy2bN0J9/3XIOgZAXoneeJoltripZ\nmmgjPM+ILNmGYaRtVix+vihFYozCDR1HtDUSUWNU3n4P1M2KeRQpUuEk3poUefniYz744Nc4PL5h\nmk7M4wmfZjwCGZrnyDIuFDl2qKhaqqri7uY5S4hsdlfcXN2gteH9r/s0n3z8EU1Ts263LIs8M9po\nkhKZVV2VvH1zz+3tHfMkI4eH/QOnw1vubm+IyhKTzD+XRebcyxyp6jpHi8PdszvGYaLKyMd5ntnt\nthfwededZS6cEuvthtPxLGoPrVi1bd7+y/zcR0lZdkUBCl6/ecOz22dMw8D1zTNCWOi6U4bRRPmZ\nBaFePY2xFIqqKBmGEWOLHAEjrf84jxijMVpCJo2Vi9vnhGV5DjtZ7EWflQ6QcmKJ9z6rASTBQSku\nS0GjhVEwzh5FRCtDUzf4GJjmSeSEbUtClpDLvFBUVU5plj3BNI34xfOnv+/PfO0esD/38z+FxjAt\nC9M8s91s8F70pD5KdamR2crYDyilOHcdWot9dZpniqIUAEgnnFYRp/ci2zElLJLSOviJ6IX3SJY5\nxSQ/lLbdsCwjVekkniIlwcnN81dpWi7/WhbSO21z9ScSIG3s5WWZxpHHx0eurm7Ej+9nUgis1xvm\nXK0uyyw226piHidCFB9/UrBkfaqgGzu0EjH0kiEf9w/3EnqoTa4YJYq4607cXD2TmJns2/bLJDPk\nR2FlojSRRIwyl9VaY51hmma6oWe7vYJkWLw4p4wVnaef5nyhiOTIuoKLwVu5DK3xkGCzETlVCgLy\nKTKjtiiKvG2XDkHlwMcnulWKkaoSWtR6sxbrpQ8iUVP64iBbMhSmrGqRoRUWrTTjOKGTytV1zA4m\ndRHri0wp4ozKP8cZZQwhMwlSSsxTzy/+wi9yOj3w+z/3TewPL5lGyYLThWWcPbfP7nCuZLe6kVGG\nrrIUSJZwRS2Iy6asUUlobaKwqFBozv3A1fUVp/OZ7WbD2I8sXhZfbdPk6HpH29aMU09V1pyOHcss\n/IPt7gox5iVCDAzjxK9+8Yt86+f/Oax1zOOZGANVLdrbGJJU8DnWyFhLdz5T17XwF8qSwoocLyye\nw+GALRzr9TpfXPLOnc6iPX/79g273VZkUZCldjKvb1frSy5b9IqwLDkhpBSIe1nR9wNFKR1ZCIJ/\nVPHJai0/m6ZpBDZupZI+nh5xxtKdTyw5yr5w9cXyWtU10zTmQzJJpl67AkSHTJLdR8ja7KoQtYSP\nkWEcadcrkpdnJqmUv0/maztV9r/68b/Gu8/foSgKTt2Zx8cDm81aqrIQMa7I0ArZOg5DT7Naf3Wj\naORgWKZRgBPBs16vsqRKE4Nmu95yGs4Mo0RU11WF0lLRGGdzrLLDas009ex2O+7v7y9c1bquub+/\n5+bm5hJDfTweqZ6MCZmoX9f15RB5crY0zYr9wyPPnz8DZONrtOJ0OlFVFYV1kjh6taMuK0BJplEv\nEGTrLCFMBD9yPnWURcN6s2GcJdQtKZVtuBHy4fTixcdsNlsOh0cBZVcO/dTq5xf36UV40qbGGJkz\nNMNoh3El8kTKyzeOA8EHisJhywIfyAL6icJpVM4I0zpbaq2FIC/BNI40q5b942N+efRlRFNkK3NV\nCeB7WYTQHzOP90kPG+Xt4Hg8SpuZXxLvA3XTCJhc6zwqUajsKCoyTavre9brFSkmfFKQIqWzAofJ\nVup5npn9zIdf/oDtbkXXndlud3mMYNlsZdliyyrHvyhMStiiFKtsXgahtRgI5gWrDYUt+PjFJ6zW\nzcWVZ5xwKGQ5KfP+gFzqRmUuxOIxTnHuJ9qqZf/mNT/5E/81q11Ju97xa1/4Mqv1mhgi1zfXvPup\n9/iDf/hbSUlRWgHE7Pf37HY7lDJS6efInmmcqeqaECQyPsZI2VT4IMxYqw3H80kcTvl5rLN8SRtD\nP2ZgzBIorJPEXb9c0gB0DosUvGjWEmt5BmNOEJnmiaLIGWoxiiHE2ItWF7jo1/004wqdTSfSKczT\nLEyF7IizWaaFgvPxJAvsaWS/37MsC7vra5wx2eorTOH+nDtKY+iHIaNBFdpY2vWaxS+/Zavs7+oB\n+1/+Nz/G0HcU+bbcXd0yj5O0GwhvNMUo1rgkGeZtXQnYJQaKqs5b5JhfruyQ0koqzOTp+g5jKoqi\nwmjFOA4CPWnbSws/9iJS7oeO4L3c6IVsZ59egCeZ1piXKWWWbj1RqELwHA6SNGsLl8Eos8RbW5fJ\n6z6DP0qGXj6HmOSl834hLF5mcCh8kIG+NYY3r1/inBMDwOxpVi3B54MnJdE0hoBzBU1VZXVDlxcZ\nAmMRt5PwGJ7Sd58qQlLCaKlUy6omRnUBuzxV8bJck88vRJmbkjzRLzLWSGCd4PfatiH5JYdPyoIQ\nLfO6p1j0eZ7RUayu1lm6rmMaR9ar1eWAEimXu8xon2JeJOVVtuFPVZQ2iqf4aWMEiFO44hJBrZRi\nWfylmtNWXfSm4zBK6J5KGZwtMTYQSUocdKXO9Hzv85zWSAWcQc8ht7amcOJCrGqsfsJFCjISBenp\nYul7cZc1AtROQaQQMuaR9joEz+wDWhm+9Kv/F854bFVgXElVtXzwla9wtduhleJT73+G+/0jRSGH\n4na7kQBBL2kIqCRb/7w8FUi6udDQiqqUtnocsEreu6KUePWqqBiHgaSU7BRCpGlquv4MMQqPOdPC\nrq5umBcZRS1LyJ2KZl5knm2MFaaxM9nMIRv7shAWcPM0Esujo2WaIUR8mKlL0chvt1sSiRgWrLPs\nH/aiBfczNo8LY3wKnFxjC3GHCjujoM+mBGP0xSK+LMKfldSHiLEFVVXxPd/xW4uM+V1lEdiq4t3r\nq5zrJEg3aYsHlDbsDwee3d5eXD5lWRLyN+KJ/jROEzbHKccoYuolRlxpOe/PhDmRnPAKQm4nVusV\nYVloypJlGAnLwpixgNYYXr1+nb37skVdQuDtmzdsNxuauqasKnxua5d5oTvLVrVuVyhtmX2kKBXK\nOpz9v6l7sx5bt/2s7ze6t51NVa21++Nz7LgJJjgQKcQJiMaWDcJRrpLwAVBukovchlxESe5CvkWE\ncoFFQAnCDtiAAYGIkLAiRdiAHTf77L3XXk1VzTnffnS5+I859zHgCHOwj1xXW2vXWtXMd47xb57n\n9ziS96iUMdrhsy/g4YqQM0pZUGVJMy83mVOrK5Zl47Mvv+Dh7lg2m5qHhxcyJjGKXdtxPp1478VL\nQkpkrQnrjPfrrapOZFSRTwXvuT/e3aq2ELaSRSWPgVTAn3I8CvRmf7zHFIlTDAJUrsvl41PJ0qqc\nuJKaBr8Fkk+cHt9Sl64DYNfvmJYRDeQgr5lzsoRMOXA6SXyN6Xu0FgupOK1Wur5nXSTdIeevWKCS\nD1aYqJUj+yxhfLX427dtYRoHcaul8rykUHCUMvs01w16q1CFq2tMxW4n7rB5GMnloldGmMEKyCHh\nk1T0kg0mTra6rnn79ku5FGJgKWi9tmAD14JYXGNmXmb8NhLCzOYjleuwVhINvnj1OQ8Pd4QtMs0D\nbdtzfHjBdDmJWSLB89MT7794j3GSdIunp2eOuztCVGQlSx/RgNfEEvj3xedf8OLhQSrWugKjGJYJ\n2wj8JcckF2BKaKW5TAOHuzu2bS0SOCfzX6MZLmcyMF7OtC9fUllLZR3LMjEOE9O6cH9/L9UzCaI4\nukL0hBRQSZW2Xoqnqm4Y54WkwJROLmWRE1bW4Ure2scff8z5/FRSB0Rp9PDwQhi7QbqmbQ2ghMNc\nlXj1nGWbu6ZEU2h610o5BC+z664FrWkqd6Osfbsf39EK9q/89F+i73ucFYRaUhIc2Pc9T2ehBhEk\n98h7T4giwlfGoCtpTQyK2lkRH3svRJyu5fWXX7Lrd3TdruQnydJJK4UukI23b99ijOGDjz7EWs3z\n8yOVdWS0ZHZpQeBlJFdJKUUqf75MM6fnZ+qqYrfbsfrAFsUSKDpXR1NJPPB11immM/FRGy1V+7rN\nWK1Yl0WCAMtDV9VXt5kBNNu6se97Xn3xmqqqePHyHl9aeqWk4jVVRVqLf32eMVbx/Hxivz/IksEH\nnKvYdT3TOqGtJgMqgt9WsZVqXYTbHfvdnSyuKnfb6i7LSt02cukpzRo2Tk+PJWlV03U9CeEa1IXG\nf9XbXqv6uq4hR5mDfwtsXMj4G8uy0rRdUV0E1lUC6sZx5uHhRZHSGB6f3rHb7RjHhcOuZxxHjsfj\nrRXXVqOUFrZn17Nta4G1yCEcil1ZGc0wyPjBKHER2boW8I7KUokmiXzpSlCjyhm/BWISx1aIK29f\nv+WjTz4qM99I5WpCKBHcGRG858AwD9wdDyzDwnQZsNrSHXc8ny4CgSaz60UO9vbta+qqISFR1Ie7\nA8s8QgZra3KCqim/50bstZlMXVfifrNW5u4lVSLHIJjOvkVbAWQrpdlCIKVMWJdbmnAGMJLdlUJE\nGSWMgLajaTogY41iWUaenp45HA4obZi3jV3fczqdUCnz8uEF0zKC1uSkyFiMc2xpw0S5wG1lWDeP\naxpYPakssWOU8VVtNOuyiLTSWLrdTiJznIy4KuuIOaOtXJpNJc/Y6XyW5OoMsVzQGi0uwJxIKTEO\nA3Utlf+6TPJ8x8Rxt+eH/+Af+t07Ivg/fvovoktgYVO3+KTo+75EE1shDWUhSeUkDp+6rli2QEhJ\nHCopQQo3BUF1zXw34kyZlwVrBWosD8RXh6zWmrZteT49F+I8RC8bTVc3ABL74bfbTPAaEx1D5LDf\n47187abvmeZRiFgpMc2CZ5PWQ2y519a4aTu0Eq/0ui1wDVoM8WYw8H4V11OQ8EFZBMWiU4Tz+cTd\n/b0cWFVDyhLmFoNEzMhCSQ7uEPLNXRWKImG3O5TcpkUsrFHE1iGGMhIoKa858/wsHvLgvfB1rWEa\nLqKRnCcxf5AIvqQ8WE3TtlLlx8x+LwsryQr7KknVGFNisHfEKDItyAzzTNd35JQKPU1splUl0SXG\napQSeLf8vlIZkUjXMRUv/LatoIz8+6HkTHUdClVa0XDjCEzTWDoYx7yssj3XWjbXMZIRdF7KWeat\nUSJT6qri8fEtXVtGFYVNIDPM3W+4XFIK7A97vvzyFYfDgRwFbHM+ndnfHwQS7gVEXVlDLKqXtutI\nSbOsQoZrmwprLD4kKIJ4g6bpWlwrz8rldJY5aZAFXoKytddsy0JfWL6iV+5BiVZ72dbC2TVgrXRf\nGcbhwv54BCCpfJu3r/NCipHDvi+FgRhQfAg3BQ0Zqf7L+MsYxxYSWZUooELDSjGjssI2osVFCcjH\nWovRsE4Tzgq2dPFenpEyj18X2SfMg7yO27bKzHa3Exa0EbC3MaZoZuV1Fl6zYRymmzMzhEDTNmij\n+SN/8A9/Wwes/tc+If8NfFRtjSt++6u3XHiVlnev3+A3z34nm0ytFFrJttyvMzkEdE4l2ngtlW9H\nBGwtVP3VC6fVx0DdNhyPx9tSJyOWz9PpRNu0hdqfGae5BN+J9XFZBMSx2+0AMAqaYghYlgUfPbau\nSDliFEyXM8s0UlmxDLZtJ0AYKNyAGq0d6yb+/JzBllZTDBdySHXdHqMdbdsJtd4aqpKT9PbdWw6H\nI8MgNsZ5mRgGkeAYLRlSubhYchJTQFucPnXTYqzjfH5CqYQxsCwXICJFdmbbFkLw+OiJKfDhhx+U\nw8cK3zWHgvjbJPAvi1tJTBICe9Fa3S6EYRiYJ/G3L+siW34lc9zrAx9DLHHJcNjvC6inxH8j7r6c\nIvf3B1IKXC4DKSYe3z1CiqScOF9OpAQvX74vb5YEbSXV9i3BNifWdRbHW5SOZ5knCfJbZESjFCL9\nQTOOM/M6M0+zpABowzxOnJ5PDJcLb968oa5rHp9OTPPM6XTidBIA9DgNcvk2NcPlSZ7VbaNtdzw9\nndnCxvPpiUzg8fFJtNkJdrsjh/2dzNTbnsPhiELz4sVLdt0eayq0MuwPR6yTEYSzDmJmnWeWaeLh\n4UFYu04WfcPlLAueeblJ9rQWKPXlcmYaLzTOcizpIdpo4W3seqw1HO/usNYwTiPjMDFPEwZFW9c8\nPNzx9PTM09Pzzc11TR1ISpG0xACtRX72+vVrVJYLclsWkdOFgMpJFlE+0FhHZSuRKW6edV5uO4+u\n7Xhx/0D2gVoZdEjkIPuNru1oarkod/sD8yZhpilD1/ei9Y2BdfOAJiXFuoQbJe5qIHKu4vn5/G2f\ncd/RCvanf/avCgkqZ86nM1sM3N/dS+YVEDYZxKcYZDBuZXZ2dQ6FmCQWYxCws7idMr7EIFdVRSbj\nqgqjxL0lt64wJO/v7hmH4YYqbDsJwQNF23Zik60qUqErXQ9VlcE4URRIfHZ/0/454wqZaykHvcCi\nr1Khvt0xlMjspquY5hFS+ippdNtkiF+YDOMwYLRmGmdxYBEl9G3ZcFVF0wnrQGuNKySueVloWnFe\nqWxYvbAXXr9+jdaCAtyWlbfv3vFdX/su3nvxAmsVmcTj42OJPvbYqsIUOEoIYhBQJnM6n8ub2hKS\nyGDO5zP39w+lehDodrfbFTDOJrCeabzJvuR35pnnBefk9ZGfX+yVxpqSZbZnGUdiDJwvJ2IOdG1L\nVYnsJ2yCu1xKp9J2e169esXd3RFnHJfLhfuHe+Z5LJjGFlBoIwfuvCzsd/sitZPDfp4XclZUVV0c\nPl8ZS1IKRSlRloJ1zeZlEThOEyonxulC27ZFWbJwOZ/Y7/qygLO4TlISKmdI0bOME8o03N0/oLXo\nk7MPKJu5xs1obcgoIb9Zg9aGqqm5DCMGWOeV/eFA09WgFJ+/esVHH33MUubXD3dHWXQuvqQKwDhN\naFOccXADg3u/Ym3F/njg6UmgSQ8PDySkunM46sbx+PgOyDyfHnm4F6WMqyuWWbq8/rAnxsjlIkmv\nbV0LrD7Lz+VTKuQsyT+TQ04L10KB36RoGsYLXd/f4pCenp6omhqfRSd+tZLPy8z9/u7Gk647sTtv\n3mO0pSlckHWbyUkq6eA9Titc49hCYFvlbAGIIfAnfuR3IPTwt+NDKZX/xt/+66zF7rnb7SAHyLos\nMOoyd403yZRzFeMw4LcF7xecrUlZsz8eqCt3s9It8yoLseuCpvwbIYgrrGkqSEg10TSsi8S1XDfG\n794+8nD/gspVssn1st2UGy6zzDOucgVE/FW0i1K6yIRUcXgZeXGRVAChSYmnOqWEqaRt3Ob1ttFP\nSkTWcRWNoC+tTtf1JbzQls2nYt0irpINP0VJMfvAF6++wKfEOMw8vnvmP/z3fz//8B/+A77+jU/4\n4vNfBzJtu6Nrd3ztk6+z6/qiKXbl3xet6tUDrgrKLqVEzIktBA59XzindWnjDDEKnWkcBw6HoyQ9\nZEkstU5C+JS2RZ2w0PXCNr1GgxulSbGAUFxV1GeyLa6cY1lGAY2ss1TohUOqstD667pDYcXW7CzT\nOGGdZvMbCpEjyXNQrM9O5EwpXdtXCvtBldcTbstXZ29SwGWeBBRdng3rRPomeL9ZTB7bwsP9vVym\nVc1wlhRcba38WdtQNxXayGx0Pq/ooi+WVh6enp84n098/MknRQJYQ9JFKhfp6pZ8JT/FRNvIuOuw\n75iXlc3Hm2okxkBKYiTxm9jJrdHoAsxWCMjaWVMqWLGO+020uTFGxLinWMaJEITfoY1BFe6whB/G\nW2fYtK0UB9NE07eSjrus7PZ7zsNF5GpOVDh+EXCQtRafPDnmknZSGAvWlG5AZtM+SEpG7SrmaZRx\nQyUzaVe+L0FAOrR1hJBYl5V1W+n3DdsasNqR4kZbV0TkZ/YxoJVwdpXK/NH/6I/97j1gf+Zv/7Ui\nIZENn9IQfaAtfNR5XmQ2lATEcYMa58x85T8iMz2lxKPvV7nlqrYt9COpSnLhCdRFf3rdRC/zTFs3\nJROrJLvmSFO1rNOGrkxB70nGlkbT1DVokdXM80RTEk1FKiKedtAYJy6RdZrZ7/dscSN5IWwp62QD\nboQO7/2Gq8SrvRWFwzWnS9pVVZJ1V3JKoDR9v2NZheWQc8CZAlyJYheuK4Ftz8sZkiTZrpvMF11V\nMQ1jkU1pcoTdFdwSPTHB4f6OeZlJWdp1Zx1zsVtqbfGrMGNTTjIyiEG27Urjiq1VW+Q1JqN0gW0b\nQ/KeaVmEeFSQjY/v3lFX7qZfFXdVRQhSpV5jfMgRrRR12/H09MR+v5eqxTiqSsT8WisyihAC67JQ\nVZKaEFPi+flZRPRG5GtVVTNcLnIgWCtLzpzJOYlSIISCoAxoK1pOlXKxd8YyM/eQFUaLZG3dFkiR\ncbzQ9r18j9PMbrdjWURNYOQsI4aM0bL4RKmyWE2My8ju7iAR3zFhUYzTxt3hBafTmV/7lV+l3+34\n7u//bkIum/C2IsdMLiaWeZo43N3x/CzweQUlkVWJjEpLYRDCJpjFHLFK1AwxJ4xyzNNEXcviFKVF\n4qhkMaq0FcA1IsXTKFQR+qts8EGel+kyyCUdPDF4MAbnanzIxdpsZJyXMl1t8JsYBeZ5JqaAKTpr\nfwOxfEX4SiFKllsM1JVoZLumKQuyFVMSeZ2raJpeOMKVE+rXMHLc9wUSVUtH5jeuQKif+PHfxVbZ\nv/5zP4XVhmVaePHwgudpkBtplgrFaE3TSpiasZJIuW1bofMnfJSUTmUcbeMIWxDPcoaIbCFF2iWz\nsy2shJgl8bI83dMwokD0fE0lGMEc0cbhcCLi15LDBaC0FceQESvnui7l9l+om6sNVETjtrKkBE3d\nUzuJMk5wG7r7GLhcLjzcHW+ZYZfLhZcPL0q0scxYD4cD0zSjFJjS0nXdrlTXAdfUjNMFYuL8fOGD\nD96XmWyBoByOOyCxTLMQ65WwW43WsrhbPApu2sjXX37B/csXpCS+fWcdd0eJwZFAuupmqmiahnEo\nAI11oaoatFEslwtGiW3YOdmCo/RteeiXuRwkWYhZKWGUwdQ1yW/FmbWwTDNVU90q9xg2rJVOAi0J\nCDnLxdw0Lfu9xMZ8+umnfPTRRwXgsdz+vo/htqys61owiko0pyFKzHtM8hqtvmSObSsqJ86XC03b\nMc8zL+8fZJlWyXOplOLp6QmtNceXL2QjPU20Tc1uf+Dt27copQr135aOJBVIimNbzhgtz8snn3wX\n58sFpQzD+Exd1zjVsa7w+rMv+Nt/92f4fb/ve/nk4w948+bCP/r5/5s/+af+BN/47u/l7emZrNIN\nWKNzaf2dI+XI49t3Jf4y77mWAAAgAElEQVRn4cUHhVK3rfStJBO/fv2GQ78TQ4fKclkpLQea9yzr\nChqsdWgjzIbopahICipjRY1T1/LMlTTkvt2hlMznc5KD/Xi85/PPXhWba+DFey+wtiKZiq5t+ean\n3+S47whxI6TCyyjIxxijgIdqx9PzM8f94eayNMqQk2iJb4qWuiPmLFHopmILAWUkPfrKLQ5BGBXT\ndCGTuQwD/9l/8p//7j1g/8pf/Umsc9i6xtU1YRHZzDTPxIKJC8GjjClLC2k3E9zgLlf50zwNPNzd\nsa0Cvs5K8VwOhNYJPEYbS9t2zOuKvpKwSpuhyITgSSmgnMGnyK7ak4Lk+uSUWKYFtKXu+hvBhxRE\nu2okSHCLgdoInPpyEUap2DAVVisyWoTwUTiUXd/j1xmlNCmIbvR0OlE1tYwl8ldx4TJ5EHfL5XKh\n7hqmeePu/gFUYjhd2Pf7wr61N91oVVdsq6D/QEF50EWILv/2tm23hNisEsoIxKNtW4wSbXJdyc/h\nnJNgPDKr33jx4r0iwQoln16zjic+/7VfoW4c3sOHH3+DmKHqGqZZBN+VNmSdiWnDGScHvVFs60JT\nyEzTOIohpGAfjdYolbhczigjgYkpQd8KMLnbiaRvXWfausNVFY+PT3Rdh7aCJbRWCwFrKYdsMTHE\nmOgK31QrqYJjkqTXbVvo+55hGLi7u2OaZkjphpRsmkYcfm1LVMjYYNswKJ6eHrl/uC/CemH7gvxM\nMQYyiW2bys+mC9eixtUdfg7oYPilX/xlXn32iqfpie6+4g/8Bz9ERrHMK13T8As//08wIXM4dPzA\nH/ghooKu61nGSebHWgmrtTgPj4cDc4gY59DKMM+S/GuMxhVylewD7gp5bi2z+RXrLFvIpAhWZdFe\nkwg501U1yzqTAKtLYKMykozQtHi/FtWAKA0OxwcpFNYZMmhjqeoOV8mSOhaCnFEavy1oBX7b2B8O\nnC8jrm2EmxFE593WNe/evuGLLz5jXkZevPce1tZUppIdDAFSYt02vvjyDb/nB3+QpmmoS6LFssxF\nThdR2vBjf+S3eQarlKqR2O4KMSb8xZzz/6iUugf+AvANJLb7T+ecT+Xv/LfAnwEC/z+x3X/hL/95\n7vYHSXHUGoVFGRHo5/xV2mpVVeSUMcUbv5RcpevDPY4jtbMs60yOHmM1MSYJSlOGtsydUhSZjfii\nLTFds7EoL24ZJeRAylAVjak2lBywmbbtxEa3Ch/UGM3zs8i8DndHfIawblRGFhpKl7gWK24WYiIj\nrhpjxaKXEXXBtq7C2axqnk5PtK3MKPte0jiD/wrqsi4rVdNQNx0hJXxY6etecINZnGH1tdUmY7SM\nGHKRLa3bclu+9a08+M5KNdAdOqqqldBF78uDIouIaZDK09U1ddOiSozPPEv7G0OQtIWcIUfG6cK7\nt0/smiOH+zuqthIkpJdZV107iehIQeKpN7Fxrtsi87JuX9w6J1JMHA57mrpmXiYkYEAzTfLmn6eZ\ntu9KhPOGyloqXAX9ficGD2WlYipGketyMcaIa2oBSXux+6pi7yTnsuiUbXNd1wzDIEkOxhBiFA2m\n0qzLyr7MGLURh5IEOBo5lCkR3Ved8BUjWDb3AITM5fnCfAl88fpL/ukv/1M+/PiB59Nbfujf/f2c\np5HDYUfreow2vHr9Off3dzy9feZXf+X/5Xv+re/lB37w93K6yNzXOSNmA2dLpR5KsIYtapqWeRqL\nswm2ZWVbV7HHhsC+hF/GdFUhgNWOX//1T/n881/n448+5Bvf//1oI2MhlRM+B4iKfb+7mURSTtjK\nUTvLeBkkXl5ptnWl3/UCUspJdMUhEsk0bSf8WVOxLRNGS3c4zSvH+wcJVUwR0Dw9P3LY7di2mctw\nwtWW0zhDEKQhhc9wfLhHK0O6otLVFYWpRYqXhOtrXfU7k2iglOpyzpMSOsffA/5r4D8F3uWc/2el\n1H8D3Oec/6xS6vcC/yvwB4GvAT8LfH/+576QUir/tZ/938VmimRL6aQJKnL38AJjZREVyyHqtw0t\no0exkBaYybZtWONkfhs8u13L69evOO7vWJaNumqJKZI14lApm+JEpG0atsUTQpI5aBBC0O7QU9cN\n67wScqK2jhA38cyHzDxNOGcY54m2EQ3h/f0967ISlKJrWtZp5jJesFrmm23fgcmoBOsm2LQvvviC\ntu3ZHe+wRmAj14tj2zapOkrkuMypM7rYKYV5KdlXKYtDRieFdZq+34tPW6ty+YiOuG1bhnGiahpJ\nRj2d2fXCrjVW5rdd2zL5mRQyoG8Q47vDXua/8ZqWSuHgBrSVy2FXDjdX2tOQMiFtvHv9DrUptIIv\n333JJ9/1Ce+//wHDONG1rUR96yiHHgaUaD+zUjyfzhgNu92Bpha52zxPWKMYp0vJ+UqcTifee+89\nYpRAxRg9VpesNb/R9tLaG124E/N040fcHY/SkluDKgi7yjlxHvlY2vhGLKwl7XYcxSba7XrWLWDr\nihgTrRX+r5DzxUacUyq7g4SrHFobxnGg7yVpQ6FxRjqLnDR/86d+jl/4R/8PX/uBj7h7eccP/tAP\nEsJGjIH2cC+flxP7VpIwlhBwbY2Kmae3b9ntdhjXkIFd3zOPA40TffayTrJkmkeSD8W+7ACReJ0u\nAw939zhr0UqikB4fHyWdosDdD3d7UogMRdVxvL9DU7NsgeNOOA6ucVgneuEvX33J8e5ACGIkMNrw\n+PoN+4d7LLaQv1bhcxSMpyANA8sySdu+JNrK4qxinBZShrptGJ6e6PuWfrfjm9/8TC6E3QFTVWxF\nTbQtM3VT8fDeS37101/j4fC+nB3rzMOD0MYulwtd16OUoq0qlk0Qlz/+x//k79yIQCnVIdXsfwn8\neeCP5Zy/VEp9CPxczvn3KKX+LJBzzn+u/J2fBv6HnPP/9c8fsH/lp36Sygl/dVplqyzWIi3sgMJS\nvXrlJYxMYkmcE4lF5WrICR+FvEMOUsk0Yj3c/ELXiv4tkQh+k5bbSRWhlOQsWWMkUTZv0o76tbTI\nPQoYx5G2aUoo4E7ab+KNqxpDJpbYl8PxyOXpGWs1xljquuEyDlSVxWnD5XKi3+9k2YDkeK2rvKBX\nOlcIknpwdarITV2sjOUNbF1dICkru75nGkes0fg10bQVxiouw0TbSIDkPM3UTVskZUK+n8ZB5pFt\nIzyGabjpjhOKyslDfzq9Y11X9juZ/V4fm5QV++MBpaRKGocLSin2hwMhBnKCL798g1GWh4cjmQgx\ncJln7l68IPlEzhFjhRWw7w/My0KIUmFemRBXyM7hcMAYzbqMSLx0yzhMWOfKnFrTd51Euyi5hFVR\nQKiy8CSKfx4lY4LgPXUjFuhpmiAnzG05KqYIZw0++GIcKAkGKQrg2m/lDR2kMynjBUlkKKkLKEgS\niCgpvGI/NkaoaXUrm+1l3fj1T7/J3YuXrNvAw3HHOFzouj3KuHLIlVEPMjc/HA6s63pbijpX4aNH\nF3pVSpl13TClQ7tmzl0XxsrosgOoaJqKx7fvaJqatq6hJHzMk6T8Uqy059OJu6MoRZyrmOeBfTGM\nhCgzYG0MflnpmpZhGrF1xTjNPBzvZJzSt+SYZXSzriTv2e33xJR4++Y1dSUhlNZVONOgNcVQI0yE\n09MzKW6onFk2z3vvf4D3G8M43rCYriqmDO/lsPWemCIvHvbMw8g0i9Z39ZH9ocdvkWWaub97YPYr\nP/qHf/S332iglNJKqZ8HXgE/k3P+h8AHOecvkdP0FfB++fRPgE+/5a9/Vv7sX/jYtih5WxraXQ/G\nEhNUrkErGfgrvnJBhSiSlGvapLaGSMKjBNCsjci29ne0bXv7OufzGaWkFWq7Hf3ugKs7rKslfVJn\nxvFCiFuRlYzEQrIHitBdQNZi7RUXi9GOuuowSlE58b8bDdGv9H1704+GEArbQBxHTVOQdEa28ds6\ns8wj0zhKJlkS6Agpscwz83AhhvCVUsIH5mUmbAvrPKBz4Nd+5ZeY54GmrjBGWvsvX71iKxbccRhE\nErZOzOOFaTxzuZxAgavkoN2WhdrVVE5g2NEHlnnkcn7kcn6iKrQjax3OVdRNJ5rdmAje8/z8WFw3\nmsv5zNO7R5SC917cUznNm7dvyEqhnONwOOKMoa4bjHH4mLFVyy/+s1/m1ZevSTHfyFl1yeZqmoa3\nb9/ewg1zUqXaUazLjLWGftfLgRoBMtM0QopoJTNBUw6ma8BgKplomcQwnEX6UwtQyG9BTNM5yWy/\nSO0kAUMRoxg1lEoiktdG2A9lFt+U4ERyJoaNdV0YhjPOibFmGM+kFJmWhdN5ZJkXnIWvfe0lfQsv\nj3c44ySaXlvBa6LIWosE0RqavpOkYmvYHY9UbSv66LqVLs97Uag4c3vttJb/vkKMVEaYDMWc0vcH\nKtdyGWc2L8u/uutZtoBzwtHdH48M00RbN8Tg6ZqeqpLDvGtbsaqWcdhYRnoCw5eLKufM8/MJjLqp\nRJSGYbwwDE+0XS3QFV1RuQbbNJyniRCkjQ0h0fU9xlXYpuHDr32NkBO2rvnwo4+w1rDfd6gUMSqT\nwkbX1HRNg1Ga56cT2xa4PxzZtz2NNaQt8kv/7JdIObOFDX4Lxedv9vGvBHvJOSfg31NKHYC/rJT6\nd5Ba8zd82m/1i//Fn/zLKKPRVvPDf+iH+b7v+16s1sQQyDqVTSaQE0rrm199HmUgX1dSma1BaEVG\nQd+0BfQxi1aw62gbuam99zecYEpJbvim5nwumr2CkUuJ2wJtnRdQqUjCTPGBixbvfB447PYkJUaI\n/f6BkCLLsmHRZKRN7LqOebyw23cs08rT0xMP9/e8e/eOw+GO4XIi58zd/f0NZnI6nbi7v5PqqnYM\nw5nD4Y6nx3fc392xbzuenqTSyBk++OAlMWZO50dUBp1FS7k/NAzDJPlfL18SgmeaRuqmpXIV4zjR\n9y0ffvhAjJHz+SwVW2VYx5XdQWJf+v2eyzCzbJ6+7+n7nmmeGYaBrnAa7u+ORRtc0VQNm1/48vPP\naI1lXlYe3n+fyjmauma8DHz5+SuU1nT7HVVdMYwj3/jub0hLbg1bgdZYbQpisefjjz/GGss4Tmwh\n0nWNeM6fTzy8fO9WnTtteX5+5u54ZBxGmRWX7CxhCIs91lqDMYrz0zPH4528yadJfgfaMK8Lft3o\ne4m5FvOK2KyNtVzGgcvlxMPDezhbQzmwSJl5nIhewjC7psUbi9WK5GU8ZY0mKsWaAoduh99W0YWX\nZWPC03QH7l+8zy/+wj+hbVv2d2JXlQSHtbAqIEaB4xhjCrnNM82jSPKsI+bEOq8FlF5mypsvLqqI\nqxuWoqxYy7L5cDgyTYPQ0JLobJdl4etf/zqXy4V5WRimkaYSWMzhcOQXf/EXOR6PhRmicXXN3d2R\n56cnyFn4u2UW6rJo1Z+HC52T3K6qrnDaUbmaynUSVrlMMu5oJWBzPJ94+fI9lmWmdmJ2ydHTtjXL\nOJO0oVKK2hrWHNiWxHi50FRSmPVNi2stlXH4deN8fhY4TkgMl4H/7Sf/klT2v/Uj7V/4+C2rCJRS\n/x0wAf8F8Me/ZUTwt3LOP/gvGRH8n8B//y8bEfzs3/nrQhhSqWQDCThZsnsEV0aWZUjw4SYK1/or\nL7tGY+pKtu1FwK6Vko3sFS+njTAtu6YcHo6cYF2uYWqaHJNky5siqA8ldtk5YooSKaIy1wC9nL9q\n/bQ2TOMoioHKkVPi+fGJ/U6g2MYYYpJW3odI27RcTheOhyMJVVB2Elm9P+zxmwittbFUbUMocSyC\nOiyMznkqdPtNql2V5XeWI35dOD1f+Pr3fA+YhN8i2yZ8BqUUh92O8/mZkDLOCcZRuJ254AETzuqS\n+RXoup6YElXdFm2nHDRGi74150TYNp6enzgejrK4KAqJbVn48pvf5Ovf+B50XREjvH39pnBKhY2a\nSNiSkBBDou8lLDCTGS4XyAJURgnwQ5x2AltGJU6nZ/b7nagwlC5M2VwOT9ExZ+D5+fnWFrdthzay\nYIlRdI+2cFT1VbGgJJfKWsul5FXFlOi6jmEYWEvb2fddOeDETSbOQDlgXTGQxPIai2FDpILeL2Sg\nquW5zNGLpjMnQsz0Xc2yyPMb07eEXFLkbagSDySBlOu2FdmipnwaPgrLApVJId2ss/Oy4JzBZAo0\nXS4xWzlAlnLbJioTU/YdTmmGceRQlngvX7zkV3/tV3i4u6Pv93z22We8995LpmmU11BpQhIeb9d1\n5Xv03B2PrOsm9ugsMCCVEo2rOY0XalsLIzYnjFG0bc2n3/yc4MXlGEKUGX+U0M15WURbjqKuZKwx\nLyOffvbrfN+//XtvYKIrplDAL7LwdkrRNhbvI+M8UHc9KSlIGXLix3/0J37bVQQvAZ9zPimlWuCv\nAf8T8MeAx5zzn/tNllw/jIwGfobfZMn1N//e3ypzRbH+LQW43DayfHDWkpP/lgNHy7wmBKrS7ojj\nRlI0tZI3u3GOsMq2s6prog9ym5eZaPCeaZ447I5UBRKsUCSlSUqJbCsnjFasq7wJrLGE6EGJLa9y\n0t6qYioQeXUCJeFxYds4FDF7SoKn00bR9XtiCEwlZVPE02IJXNYFW1nZdictMRo5oS3kFMolU5fk\nUFlMhCBLJb9tVM4RtpV1mVHKcry/5zJeygGs6Hd7nKs4PZ/YtlG4u14gMSlGjocj4ziSs0hjRGJj\nqaoOyojGf4tcyfuVD957yTgMvH37WhgCKRc7pyOQOJ3OHA47sU7u9uL9LgYSVwnYuypa0xQz0yyH\nlDH6tlhZt4UY0m2euCzCNchJZn3OauZF3FUxikNrXgRCIj93R9i8tPZFqjaOE1VT4dcFpYUjG0uu\nmrZVmbtaYgqkKMm8GW7SsaZu2EIoia7C0GgqsXqi5FnIUeJqVu8lMjqlohwAZzXrOgtY2xjmZWLf\ntUJfs4a6apmniW0N9Psd2loikeTloL0CTsTS2xS+how8skrl+BXdr3N1kRJa5kn02l3bci5jn1Dm\nrwm58LxfZLHsPc4IyDqHyGeff85HH310S7oQJqzEujw9PrHf7Zi3ucQdCUMiFaj5Bx9+yFqA79u2\nykzYypzWGE1lRCqmrRhvVDnTvvj8Mw6HPfXuQNiWYlpBjEcliflyETj65lf8upJTJKZM3dU0/Z7k\nI2HbSKlQ1ozFB3GJ+W3DGYTypSMxR5Yl0PcH1mXmT/3Yt2c0+FcZEXwE/C9KKY3MbP9CzvmnlFL/\nAPhJpdSfAX4N+NMAOed/rJT6SeAfAx74r/75w/X6kQp6EGvxMaF1AzkQ/Ior9k/ZFrbEmEgxs/mV\nvmsL7JiiXd2+ShLQthDKPSYJDcs5i0sNFslBMkqygMZpQJfZ1DzPuLoihoxPGasVi5/ZtsDx/gEU\nWA3zOKGVout6SWkNgbX4l4XSY6mco+865mmi7yzTNJJSJmfLPJbolBueL1M3BVTd9YQcCvi6xm/S\nXo7ThdP5HZ98/DXmWdwzVSVVhaQBRAkC9KI8CFlJ/E5MtHVLVVnGeWGepd01RuPanrqqyGyswbM/\nHkt085mUAg8PD6yrp+sq1m2ladobvCaUQEqjay7DgF9n2rbh8emJjz/6umzJl4lM4oMPPry55KZR\nZnE+iM7z+fTMe++95Onx8ZYivNvvfkPy77CJiUFpSdjNmRtI3FqLQjFcRqrqeuFGTucnqrqm7xqq\nuiaVDbkX/yP7/ZGUEufzuSzeEvOyoZ2j7/csy4pCF4lWQhs5qKpGDrLj8VjYuCLBujrtnDbooj9W\nSXLexnHCKkNtDd/85ivef/8jTGFVnE7PPDw8EGPicLwj+42+32GdkYO127PbfzXeapoaj7+xkK2z\nJXZ94ZrPZizMq7ghl3ml3+2FPFcuJKXlPROiuwHHJTZILvuQryoRcTRZXdG4Bo/iG9/4LpR1vHnz\nhr5p+eLV53z88YdMy8Ld/R0Z2Dd3YgwIARUS/X7HNE5sqy8VZiVoxZ0jZWi7lqfHR9acb3S2GsMX\nrz6jbVqODy847A9sMZG1JRalDUCKmYRidzgwb56u7aSSVYrLMKCMZbzMtJVDZVFE7HY7rDXUJclg\nnWeGaaPpGlIB9Oz296zLjA/bv+65evv4jhoN/tbf/7vSnRR8mPcbioSxhXivNMa4W5pAzhmtZRQg\nCDiRMbm6gSwpAyF4coriQlHScgqw17CugvzzYWGeR6noqoZxuFCXFquqWgFSpMiyTTjXoKwR2VPR\nOvptwVYOqySqQqKtqxvK8Koy0MpIMqk1GPOVDVJ0dhKfvK4rTVPd5ml1SYXd1g2ljcQZ50iMHqXB\nGGmlQZxuMcqbT+lE2whFK0Vo2453b94yTxPdrsO5GhQEv5FyYn//QPIbcZ7YvGeLgbZtca7GVY7T\n+Ywxir6XkQVQgCMJhRLilvcsyySb+74jBmmz27YnpA2lReKklKVtupvu9OoCU1oxjdMtNaJpugJU\nEWGyMYphuAiPNiSMFt2ksZaqrkgxl5FBJqcs4xIyu31PypmcAm3d8O7dI9YJt9evkpSQEMPGw4sH\npnHAR4mbsVaWSXXTkIJU8jEE0JamFfjLVmzR0zQIxUobQMTwVwPMNM1Cumpr1nmismKjNrVA0LUS\nu7cxgvBrKwmUXJdJFkI5M0/y/5XWtzl0Lt+36FudxKUPA01dknaVQOD7pmXdxPcv+ltEepYSS5SI\n9m2cUFlcitYKh+J0OpUuUOMaxzzOqIIR3baVEBMff+1rfPbNb0pM/DKjtZUOL6fC+mho6prpcsYH\nz2F/4Pn5mYeHFzhXcblcAFEnTPPCy5cvcdZJV1E3smBC1EJtJ7lhbd3dlAAhxJLgrPFrGYtoLcqM\nnMg63+DulXXkmDhfznT7PQDRb+VMkd1NXTLj2ra5WaSXZaZtd/zIH/0dlGn9m/xQSuWf+fs/h7M1\nMcmbyqaN648SS+5TU3LQU6Gs25JldN3MK1VcXSj8umCNEOh9CCgrrMkco8R7FyKStQZMZFs9YCR2\nelnKfK3mfBaNIiqTkug313UtLb1QjaL3kufeNdL61U0Bo6hC5Gk5n88CQDGyAa+bGqsM58tZZoP2\nmpDLb5AF1XWNDwEfRTeZi/NFQuw0oUSNb2V8oZQmxmIhDYFY5rcSPx0k6K/MFY+HA/MyyWa5bHSr\nuiIFOfycq5jmhS144a2W+Jt5mnh4+YJl9beKNIYgsJdNQuXatmUcFzKaw2HPul2Y54nd7sg0Lt8C\nzCnhkdtaRgoyc08FK5hzBgUxyHy5KkuUaZrp+r0c0ssijqK1RJEUf3qGMu/2OKdxRnS8xlS0Xce6\nrHIItI3ohKeJ5D39bidLnWUmQ/ndRa7hiE3T8nx+pmkk1y3EiEL4pRT5YPIiJwtJEitsVbOuMzpF\npnHkeLzjPC1YKxDrp6fH20xRZ1WAORI9LiqNhnkSORoZSdV1EvPit01kd618P0abWyz9+XSiclVx\nAFZoo8vFnlm3Ddc0XOaJF8c7xstZlshoqqri3btH2rah3/VgJNDQaVecVytGV8XcM+FqVzoZUVds\nOVE5yYATN6G5EdSM0nKAVjLnziSaumbdVqwT40nYgig4YsTVlci9rNC3coQUNoyzxCSBl9M0yQWE\nOBYLA4mQA37b6JuWZZ447GROm42A63WWOXosUe1d1zEvs2jc5xlrFF3f4H3gx37k2xsRfEd5sMpH\ndAywbtiUMLZGIVtRcqapZNhdWVcI8hs5J9Z1kZntspIRpUHKEuERSztUOUf2Gzl4qkpzHh/pejmQ\nh3GQ/HhrIHtIRSNIZF5mjBX77fF45LA/4CpL17UcdjuaqmbX9fT9HqUtrqnpuha/rlxO5xJjM/Pu\n3Tu6ruPu7k42ptbi11WAH/1OXE8lL8o6qWCvABpZMliJvU6pRBYrtkXeVNoasHJhmLKkiyFyOUsi\nQlXVQl4qyQyucfgoG/TLcBa+bb8THGPdoLTDVTXjMLCMI2GVhF2QSvJyOqGU4vHdO6ZhEixi4RhI\ndhWcTk+8evU5KQV2O5HItfUeVzqQa5Xw6aefMs/zzcevdJb8Kit64XVdRc1RVfTXJIYEGUXX9yzL\nwjRNRfDvqOqauq1k5nl9sLLMlHNSVHWHdY0gK43FVJb7Fw9iRXWO/f5A0/c0bcfbx3c3atiNzYBi\nv9uxTCOHfkffdkUDXeZ2ZR5+GQZcJbB3Z51ojIczOmdiSLi6JmtNVwhTxhhRLSgxc7jKsPn5dtFu\na+D1F18yXgZ5ncvlrrVwVStXk5LgC6dl5TJc8N7fgiOFLiZAk7dv3rCtMvfMpYtqjWW8XLDW0bQ9\n67oVSLVA1dd1IwVIIYs0Sht8SPgY2R/3dLsdxlW0XYura2FGtJLuHENgmEeWdaXtOrquR1vL4XCk\n2/X0+x2uqjmdLpyGmZBgK7wESeDNaF1JxNIWISnqWnjNpHzr6K6qIm1MybaDZZ4xWeNMhbEV2jkW\n/5Xd/nDYS1xTSsQkGNJt24heDnNlDM1uz7JF2rb/9s+47yjs5e/8DfnlBA8Kmqor9lclN62zjNMi\n2rWrKFpJdaNLJZtjQuvMPEtU9zov5Jwg5/JmAJ/kwfObF9PBFonZS4vhV9kYKs3+eC/JAcUBAiJ+\nrxvL+fxcUhAMxlTUbYN1NcN4RpNYxomu727oOq0dKQrUxVipkLWCZV24u3shUhwjQBNlLM4a0dKW\nlmdeZnGkGF2E4gtNI7No19QYbRguF2GyhhIJrSAnz7JsGCMLsr7vGeeBrrSQ0yTf57ZuJVMqFTBG\nZl1myDK6cHUty5y2lSp0fxCYTdLM88CVJA8CS56XBbThcDgUenwuOlEZg4hCwZBy5s2bN2hlcHVF\n33VfzTtR1LUDZZjnRaDPwdMYUTacLxce7u9FBudcibdJzNuKUZq2aQUAYgxb0VZKRE1DXVeFrGRv\nhpVYYkSqsiS01hbORcIqLZHp2hA2UX9co6edkypumhbJAys20CstyxadtDKaZZM0jKauZP6tHcoo\nSY7QWrqnbS1VcBhtEmcAACAASURBVKKylmmYqKu6RG1vVLVEpF/NOOuy4rSMB8ZpkLA/8xVnd1tm\nUaWUJZzVpoRO1tRNXV5riYaZx0l+psLMuFzO9Ls9MQa6ruVyGcX8Yg2mdIZ1MX9cn4F5EnKYcjLr\nzEqUOnHzN2Sg1fLaT+PI/nBgXTeUlgt+msSCnlMi+cAaxc6eCtwlRjEzBD+TcsI1HevqiT5iLSzr\nNTI9lUSJRNVKZ+XqCqu10LhUoattMjKxZcG2LIssQEvyrq0qmeEqxU/82H/8u7eCTSkStpW6rkq7\nMMsmHnHOKKBrarRRuNriw0pIBa6sMiYniJ51Gkkl9llr9RvAK4/Pj7cNq6sqMjBvM7aqCSlxOQ88\nPz2TMszzIgP6uJIJoALWpduCoesbtrCircyNtdalFQtUbY9PWRIJguQ1NU1bZkaeqnKczxdpwacB\nbTSv37yWykRJtbTMkltvjaHv+tIyRuGDGov34eZEenoUXWEqNszNbzcDQr/rcZWVDKNtoXZClLrC\nxWOI4nCx8ubIZDbvqeqGppEolXleON4dxQdeS6KqD4FpHoqLKZVqW0YUxlZ0XY9zFcNlwBWnlNJG\nCEpZUg80cH935IP33+f+7o6cBAu3rqIMCFFShLuuk5l05YgkVh9oi9g9wS1qZFslbbRpGuZ5Ypwm\nHp8eb0uf3W53AwIpLUureVlurFtnrcQEGUMMHh1F8wmS7rqus+RbleWKXEbyelXXlASlMNpILlmp\nbpOCZQlUxaIagqAOtbNoJ4wN4yoSAjhxdYPWjhCh2+9JWhGKASdnGYNV1kqckjaShTVPKBRt02G0\nhII+vnu8xf0sBXSujUYbx/HwQI6C7rPlmaCoRSTmxXO4uyOUfiCmTH840PQCzZHMK0eKinXZClJS\n+AJVI1Cguq4lHcTLWK/rOpy1LPNCjomu64sVWWLTY/BopWibuozpApUz5BSIyQsvVyVQksqrjZGu\n1jnqSpQYfdehlXSk1hraTizIdS2LvXmeGadJOh+lsIWBgDbM80pdNQKBmpcbm0JpxfHu7ts+476j\nB+w8jaicJK8nRqxWED3zcCEHjzMaq6/zTqH4xBSojCLMC+vlxDqcUSS2ZWYaBp6fn4kp0TQ18zQI\nGScGlmlmXRb8JjCOdV3RRh6MDz76hJcv3xcARDC0zZEUNTEojKlvs0nvg0Rth0DKYoWMMQpHddtQ\nSbzktsBqQpBYjHm6cDo9lwXPCiqhCHzt448lh8tV3B2O2KqStAHvi0VT3TKdRBucbw+ANVrkTd7T\n7XoyQpPPSvH49E784E5mk2GLZKXpD8diQZaRQFaartsxXGZiymhreTpduHvxkocXLzHOoDTYqvAb\nSjrtFrzEyxSylzKWruTVP59PwvUMgaqubgkG4zBQ1RXPz4/M08i6zpKpBsXyKlAPU9WEJJ7/HENJ\nHK0Ff1d4DDFGlmW5WWnnSdQJVVWXr2lxlcPHgI8bWYljcFpXcQdqRQTGYSCsG04pwrownJ9ZloF1\nHiAFhstJfofWykweeYPLnFaWIQqZ12Ygoghklm1DKUNVNYAtrip3W3SuS0DrSliotiZkTSipuHXT\nEhM0XUck41Mk5ISpHPO63rboAvhuZFaZsiyttk004LbCx8wHH3wIKNr+IBffMDCMl7Is1riqpq4k\n0j5niV3fUiSQUNbiE4zzwrpsXM4D3idCEA3p7rAvMkJ5/Z6Hs2TlFWmXghtbQCktOmfERCPSrRWr\nDU4bVBLz0NWrlIJwAmLaGMYTwa+s6yzLzyyz/2k8A4nj4SjBj06qznVdWZYZU8hzCiUjw7pCOc2W\nAso4jsd7QHHYH0QnPy/CfL5G2ORYEhu+vY/v6Ijg5/7uz+L9Vt64VxF/kggPLJvfWP2KUiLf8V60\nbOuyYJVingb6rmP1HqUNfdnehyhie2P0TVPrQ8DZqgSatXKLW1cMC2WuVVW0bVu4pwicw1mcrRnG\ny23ja12FtRXb5oEkbXwU4tS6iRsmJCT0cNs4nZ5kPlzmTNZqYopM48LLl+IwnhdpiZVCrLe2Zgvr\nzS4qfvwiWo+BbRMJzjUdwftVVBdWWh6jvoo0yVndKFPTOGFLC22tKbE4dXE5iXHBe8+8SFpD18lG\n3hhDKFtcgOFypmt7CRcMkXWZb8s5rQ1N3QkQ2RhRaKwLIa5opTidzzzcP1BVHavfbi33sizFndUR\n/Ub014iQ0w1u0zaNVFfl866tqg+B59MzD/f3XC4Dd/dSfRhjeDo9YwrYPGzyXKSc0VmVZOJMIlKV\nC0prw9vXX0owITL3vFwu9P0OY8u8PGz01xmdUoSURPOc5XkgK4IX6ljMAWKAEEWrXJQG1hiWbWPz\nQhBLIWK0Egt4cfRVpetKCnzw7Bpppa9VaM7yd64hf85WRCJG6ZI04fA+UlWKnE2Jg5HfdVXJTmJb\nV1xVY61ELGWTmS+i9EhBLnOjNFuIJBTebxwOkuqqS0WYVCbHTApS/7ZtQyjLa6W0jDxy5jKcaRup\nas/DWcIVy/OVFfKMp8TqNzbvaduGaRwlgttVzPMiKgyt5ftNsogzRtG0oqiQ5A8jo4Wizkg5imux\n292W5M4Z1mmma2VGH3Iiei/zdS2ZfN8ucPs7XMEOkKTNqOuK3X5H3TSMy8QaAyFFFEkCBGMgho1p\nGGmaFlVZ+uMdphKog0Lx9O5JmKJZSxIn8PbpHc/nk9zOwVN3jUBI4FushuIMGoYzn3/xGVXt/j/q\n3i3E1nVP7/q9x+84RlXNwzrsvbuDEg9RCa0XgoKQQEfFCxVByYV4IYoggrcqSFAU8cJceuWNImhE\nCBjtRDtibBA18RAwRkEv0un03nPNNWfVOH3H9+TF/62xdscN6bhsNj1gwVxVs6pmjfGN93vf5/88\nvwcpAZTE2Mvps3BplSYjYQSQtFjcg+T8S0YZsEaR404JO7friZQi3reM4xHrDcYqnl+e6ZpWWj/D\nJpFNZ2uO35CS9AZR/Y9yM/D3Kbs2lm7oiUl0vxgj+7pBSczTwrbs9cZhSUUuFqEHbXffY44Bq8Vu\n9eopjTHVyuyGw2HkeHiD8y0hJNZlqTUp5bvyRGc5Xy9oozg+HFmWGe/FEmeMQqlC0zS1OidhbIdr\nBt5/+UNCNpxvExlDSJl5FpfBOIodyFqL8y1KGY5vHujGgW7o+Y3f/A2mSTTgdV05nU6cz2dyzrx9\n+45lWXl8fORyuRLCVptEnUB9ECQluVBCEE6lUpiuxzU958vMvibOpyvjeKyNGmKFe7XeSdWPk9CE\nQnaYtcrHe48qSpB9qqBNIcWN1irhvVrYwkwhoo1oh13rGDpxyvimIjQL9wFeKUKai9tO5zzb7Qop\nUHKEEoVuVuH0SmmK0ijVVFi6nDKMBZT8u7SSsEHfP+B8g1KGfjiQk8ha27ZJ62zfiR5ZdfAYd2Lc\nWTfxHL921A1dD2RUihyGjra26yoF+7JCSlil2ZaFl+dnAeDnxLQsmIqELFUiA0kUmsYTQ2RsO+Ie\n6IaB+TaxzDNt46u7Q0BQuUSsU9XyVbu4Kv+YnAn7wvV2AQpt02CUprGWxgu2UhtFUYXL+ZmSdihZ\nrvOY7t2A3+fx22IR/E49xsOREFa01Tgn0JdQCuPDI6fnF1on2ldB8uWajCKR886+BcY6VdZA2Fe6\noeN1R7ltK33X0TgJMWwp1k4g0Wq7vieUyGvNcggBPwy4PVMyGGXZ98j1cuHxzRtSEkCFUZDWHXqp\nNtYGxq7H25YYd4KSN+31MpNS5O2bt3WXt5OyxPMejo/EOgDLJdK0jsbLkOpyO6GUUJAKgXVdasFf\nYV2rgdtodNG0Q1OhJlQtMPP05sCybNIrhBy1RSvNQBQfcdi51Ohn23YoDfsux96uIvBiyGgVYU84\nLVLJvGw8Pr6RbnqrSCkwjmK9mucFtKXrD8SQWTbxil4uF5yxDOMoR3/rKGSs13S99GtprUlKjqOS\nTOqE90oh1+FN1lLT/sX7LzFGi8ywSzW0sQZlZVeutOI23VBadoAhBNpGYrWaQsmiZzrfsiyr7LrX\nDW81KQaSs4zHA2GPFCSmuoWNtmu53GacceQsO+NU5LVwxhHXgE4SRnHGih+7SMX1dV7o+4PU3hTo\nm46MYo0rJmTmeebpizfEZcc7y7avONMSdtH+Wy/kuJIitvXMa8T7RuSmnMmNJLt0MUzTSjd2guXM\nhZIFzxjDjm3FmtaoQmM10zZTilwfh2HgdhE0pFGGFDa6ViyCscAWIv1hZDACBM8x4oxlWudq2N/u\nC/ThKGnFkiM5ADpBiQxDi1EIfazyKRrf0jXCODi9vND2Ld4qfOPwrQRF1nXhzZu34huuu/vOSXpy\nWxeGrmefJ7rmwLpt9+QlCvZtoW9bkYFyllh8jFilQCmag7wuru3BeXBAVuy3G/x/37jeHz9XieDX\n/vs/I/rosnI8Hglx5zZP+MZh691Mm+qvvN7kCLnvHB8fsEZE+q5tIadqwhav4DpvuEbuHbITsrim\nYbnNOPOdTLBuKw8PD6zLineWPYVafwG5ZAEEuwanHVtNy7RNjzZWDpUhkssuXldlcNay7Tufnp/5\n8ouv8VVSCCnXI0vgervgfYuzYs3ZtgXnPBTROadlouvGehde2Pa1XizmfixqmoY9Rm63qRK7Io13\nWKMwSn7vPVQUntI8f/6MtVoaW3Pm6eltjfhSTf2yg3itNH91B0hn1c50u9bacsX1eqMgwwttdJUv\nxKM8HA7Ms8QsRZsUtiu5yHFWCZgj1lbgfdvuA8DXMElKcppRWWLAr4mlVwO/fF8lXWFZesSWKon0\nXcvtesMYXUkGogO+DplyzqLp1x1Q3/ecz2cBXReETVv7ql5hP7kOSVzjcU3DNK9yrEUah7dNJtJt\n9Z0Kum/CeSdIQGsxxrGt4mZ43QVL6EJKL1UpYDTz5UrXdqA1rhM0JkVOAdNNJCpjDesWhA8Rg8TB\nfcO+S0PAHiMoRes8zlpsRTbuQdgD0zRxOAxcL1dMbactBVLMHA6D8Cf2VYZ1uRDizpdffc3lNslC\n1DjGYeT8+ZltXXl8ekOKkaRF77RKMd2utZcOKSbNQr46XV4kRehlnvHKX/306RMPDw+EGEkl4m1T\no9+eZV04PjwwTzMhS9jocDhIBdS2o7QSOleUm06MiaW6LXLOtN4LW7kOa6XFIxJqk4erqbpufKXv\nyWah8fK++vv/4D/4uzdo8Gv/w38jO7IMzlmRC5QciZZZGkStsfVJECtHCPIG2DaZmh/GQTq8kLpu\nhcYaJ4uqF+BvjBtFF8K2s62S1mqaThagmgaJYcN6x7yutN7LC+IkXdJ3PefrhcfHJyjSLWWdDFw+\nffpQyUY7yzwxHp4YD0e0hvU2UVCcL9KTFWNg3YSx0NakTUyRrushq++CB1oK55wzMl13AnN+HRbk\n8mqsfs1la3JOlBTpqh1r3zfxmjqped73ja5v77YUo93dTvSq8Xrvcb4h7BvGKoSYGOtsqUCRwrxx\nHCt1S+rNM5Icy6XcFx95zQRSY6ymZIVzrfQiqVx9pKKfgqrgllpgWXGNr6EE68Rs/lpPbpwsCvsu\nso1vZJCSY6x8AkdOspu6t/CWUhGFklhLKTMvC1RehSrcgyxhFwuRtYZ5WWWxNXXiX50TbduyTgLc\niSncm2y18azTjaYVDukaQg1TuFoPI8WIQM37y9G4hCiVLqU23FqDMur+umslr4VVmmXbBJS+L7Xf\nTTCfUoUt9TvkIoEUK8/F9Xa9D0a3fRNngRLehXcNnz9/5gc/+EGdtAttNiWZ3KOU2CKp6aq2YZ9X\nQXEmwWseno6UlFHIzawCxYQlfLkIQL2Uyu8Vz3CplLzpJv5tlGKLO4duIG3i+V7Whf4wooCm79DG\n8vLpE4+Ho+jOgr6TVtsYQenvriWjsUhhqUgQ4KxlXmas8yjF/YbqvLhzSio0Xcd1WWj6jl/+e3/5\nd68Gu+0yudu2iefnb9nDxvl0wmiJXnrfcDpfyCicdVwvl3os2LHOM/QDczXeayM5cuebGi9VdyN+\niol1WjDG0Q8D42FgXcW2cbvdKthaAB/D+MBweGDsjxgtVTXLOjMMtXnSGnKOLNPM5XxmGAa6bqDr\ner748utKeUpYo8lItn8YqscvybGq5BqayAprGpzxdeKcmZdJmKI16uu96EYppmqxkobNrmnINeVU\nilSJlywgmvP5pUYAPd7LkRMt6ZfrbWZdBe4B38kjkq4RLoJxjhjEgA0aYzylaAoa62QXlzJSV5My\nSsmC5+tFG+LO58+f0VrM3a/e5tebY7mHRgSyElNGaYPWVlpak3gnUYqQE6kCQ0opdG0PRfRF8bc2\nAjXZN+FFeH8ftL2mwkou9F1fO6ckFno6nQSN1zQ439z/fs6pshAMt9tNggxthzKmUrK477alVbbc\n47u2aUSy0pp5XrktK0qZGo2Wm8Nri2/O0pIrMV+FsUZ6rJyT2YMCSi0b3BdC2FkWqZ12phYHKlMX\nYIU1Csg0zkOW+vQ9bKyr8HL7oZOqnpSqe6AjF8Eevu4KRVqRhSnmxMv5xLJuxJD49M23dxSltMnC\num+4xtH0HT/+jb/My/MnLpcLuShiLvi2Zdl3hsOBojW+79n2iG97mrZnj6mWaDbstZqocS1h3eT6\nMZr3X36Bc048zinz8u1nHg4P/OQnPyHGjVfoeogRUzdjwvKQCvBlX4U5DQyHA3tKHB4ea2VUph96\nlFZsa6brBjISNX778MBo/fde436uO9hf+dU/IUeeu6VI32OU2hiUMWjvKTFCShgN67bL9NNovHW8\nvLxwPAzM64Z30oJwPV9omxa06G6fP37ihz/6EUlR898GayqZvpEJ9jIvQlqqySRrNKkkms7X+hTu\nOXrBzQmFq+s9qELYMzFklBIYyfV6xXopF4whoOvORVyUEIJ4UbWxWCU4uhBrM60ydcdYmCe5qF+t\nX0LKX8Rao+SI/1paqAr1+CnH/a7rBAZSm3i3PdxhKdfrjWHoiVF6vtZtZl03uv4gySsnGrS1Foyu\nz5mufVdiaBdGwXe7gxhltyox04LWVuxcGrZlwTcD2tm64EDTCNe1adqKa9zJOWDrztUYg29b4r6j\njWG63Xg4PLBsK23XM90mvLNiWHceilikjJL4ZMiRb775SN/1PFRze1vBOt57bsssibG2lyYDhF8w\nz6tAWEpm2Tb6/nBnyd4hM9W0/p1TQ9wrvg5G274jJLhdrzSNnEy6fiTsEuU1ppDTzrquDMOBIhlf\nudGVIlP5bPCNYl0mvG0oRZN0qgMtscKRFdfrhXGUVJ38J5CVl7pZcUYm9v0oVUiqKJyyfHr5RONb\nmkaOydYZvLfi2hgGXuvXtVb34s2cE3tYiXug7Qf2LO2uvjESFVYG6xpA1eGSSEXGN+jaepFykvYR\n5M8lS8mkpA8VKidijnRjL3Hw9HptyQ3TOyedZxS6/gDKVBlmwTlDCDspSwGnrj/fWrkZymlGTkep\ntttu20YKmRBW3r15IoXI6XSia1t++XvStH6uC+x/8at/HFurK+pHSXmvBPYWV/mlp+dntJEK7wL0\n3QFdMWshbLw8f8T7lrYdCSHW9ssZbQq32yz1Mc6gKz/TaM9lvmGNwXnJU2sKGIkR7tsuR+WcWNcZ\nVy+wV47ougbaxrOFvaZagsgG2pFyxNVSO+fkc3vVb733dO13oGThEKi6U1FgJe3StA0p7NxuV5nK\nG1sRhZ6M3PXleA++qXzPeRLgTSu5fq0syyKWJOekcNEYw76l2sO1g1Z3y4sco+RNJNRFwfeVXKOI\ndfhBHQ5M88RhGGUB6QaslwHKvs3V1iNRZu9tbRhI1U6VK6ZQE9JPly6GyksFivB4nXO1wl28r10F\ne4cQGMdRWLoxMd2ksrwb+jrY2IhBaGu+bekPD1IVkhJeadH5vbQkbNtaLXQZo6TBtxtG9hDuOrUx\nFrImphoYuV3FpZITYduktkhJxQ5akbcN7ywpZWke0Lr2cpm7VS+mVEMmSlJjFUCSc6Zt2+rTFlO9\noQhSsA5sjRZ7nVKFkCKpFIZhrEZ5LWi+lO9Q9Gbo2Ne97oZ3yBnnbZ0LFFIIwnZoHK2VzYtcKwJd\nUVYgRPu2k0KiqRar8TCSEdmo6zpyEubCMAyglKAYvec2zXdOq9EwzRNKC1v28HBEK4UuoCjVneDB\nGtmtW4czRurpnSclATaByIS+EaeJ1a83PRl0aqPuNjJjDLpUeWCeabpOartVDTR5S1g2VEmkmMl5\nl41VgT/0B34X27RKlLvxK7glBKlR8a4XYlNYWKYrOYuAbU2D1Z5tWdiXmW2ZMRXwIXHCIlFLcs35\nKx4fH+si4+6aHsC7d++w3lbgSOR6PbNMN7799hu0Knhr8NbQWJnwHw/He4LpcJD21raRON7QHWh9\nXy0vR67TTCbx/PwRpUtthh1Ec4sbhVRTawlV7TraKlSJNFaji/Arx3GUdgXrKcaxU9DO453HKGlb\nWKaFGBKN7+oOQHTMZZnZ4471BrQc7ZumBYTShBZ0oxD+Pc55xuFIjgnvLDntnE4vAu/Qmq7r7hqy\nt4bWOdZtoR17jBco+b6vd01XvIkOlK7VPlC0xjaeZVvYw4zOid5bwrqgS0blhM5FKr29vw81xb41\n3lNT1ljmaZFFy2jGYWA8DggRRMA4RYmnMYXMPi/CBFiFwlZKYRiGe9pr32O9GRm6fmALAaq7JKdS\n02aBnCIpRx6OB2JtAN532YXmamjXiOdYVeSiNxZiovMNcd8Fk6cgx4BC0nAvLy+kEiXAYeQma15T\nW1YLNk9LdZIzhhR3tFGsmyT/nDFcXl4wFPbqE0UVbtNVblg501jLOk+42hE3jgPbupFzoOsbmsbT\ntVJtvs6LBHP2DWU0KQTCugq4XBeu6ywhg5S43Sbarr+D0UsWitztciOnzHK9MTQNOicgoZ1FGbFR\nyrBL2A0xyY1nGEdc21CQ18g3Xlp7K9ozZ6rMIta/7+YIictFmBlaSc28sxZfK4xCTlzXBdu1zPMN\nSDjzeroU/XoPMvxq+4F+OKDc95cIfq42rRgCuuvw3vP8/IypvfWS7Q5Sd+wtIXppfU2J9rVLXmWU\nyVyuNw6HA/seGPqGlLIcNUvkdDoz9CON95yen1HomoBR7Lddiv66luvlBOZVKA/M08T5chYq1HhA\n1bK0LUUaJVXbEv9bePn0mfiU0FZ6plKB4+MjKgfM4Viz8O6eOgLZJamqK+eU8K3nNl0wSt9jpesW\n+eLwJSkV0V5TYdlXtj0SVCEGwQUq3QqEWwm5KOVA41u00XSuqToq9egpQweKxHH39N1gZd92tDYo\nNOez+HePDyPLJg0CxShC2OiGsQLPHVsKtYBSyjVykh6xxnmSLmyrHO1DSFVrjVgrvAFViujUOWOc\nuWfWFUAuXE5nHt88VfDJRtgDx+NRBihZyPyv2Epn5LjZdx3Xq0zbrbZMcWHsWuaK0DNVR217CZ+8\nJvGenp6EGLauONfUokKD9WLhSpXGZqxlnhfaVuq2c5L6nH48kFIiBrH/pPpv1Fqz7QGsJlSCkzgN\nHMM43AMHx+ORqOqwMe01iCJ/T2uF9Fw1EnboO7TibuYPUYaQbdfI4qYlxPIayni14+3bzmEY6wnO\ncr1MaCWUt2maaJqO622ibxoOg5RwNuNICIm26ZinG4kF7R3tYSAuW22fOLBtgabvWbedbhjYU8J3\n3R21+GoXtFoGTnI9Rx6fHmUjtW2V26vICJWssZbz84m262q5qYQMur6tEPenyibOgvtcV5rWEtNG\nZz3OyKlhSZLqTDmT4o7pOkJtRrZKsYWI3lNlPjQMw8jlcubbX//LHCve8Ps8fq472K7rUHVg0DQN\njW9w1nEYRqzxtQESumGoIbpCKgXzmuU2VtCBCfp+rPluSyyiCb59+7YeRZZqwpYLMUZpory8vEjb\npW94enyDsQ1tOzCMR47HB4ahk5bK6xXjhGkKAphZ15V+7PjBD7+iaURrXNeFsC1cT2cZPhhD23aV\nBCRs1LC9aoSgiuhlOUa6tmM8ymIec2YYe/ZN+unP5xPLciPvodrOPDknHo4j1krb6rzKsO/Nmzey\nK9s2KApnZbebYzWwWyd4P2vlGJezoPeUIifhnxYkThrzTiaRkaSN806cBNtGSEKactpgtMJSaIwW\nqEnYuV0vpBggp8qMiHhjaJtGPgaEmsKSmhPxsa7ryocPH8TGtUveXdUgSCnlp/Lk8sbtu64eKx3z\nsrCHwHg4UoqwbF9Zs0op1n27Sw6vmuKrlvp67ciAUaQiBTXHr3Des2wrw+GAMrb+e4SEdrlcoN4w\nSgos8ySuDq3wncRQcyn3770H+b1ikl2/UmLzUrlglaakiAFyEUatUrbm+lus1UzTVdCcVupwlmWp\nE//6OlmpTb/dJpEpYpKk2Kuu7S2KTMpB0IA14bjME/u+8823H9lCEK92tR62g5SKHsYDeUs458kK\n9n2t9j6x4qGMuF2s8JWLVhJRNlqiy+tWZbGCLhD3TaKsXjT0ZZoxWhF38QSnGGWQqFWVPGaaxhO2\nRWYaJQucp8pyMUZut5to5d7jrWPfdgqyxsgswIslbxMJIsUoCUvnWGss/fjwIKbm7/n4uS6wKUU+\nffrETz5I+d0eYy26kzjf4fCIdQ3zvPNaNJdiIO6xdmZpjocjD8eH+mYw+LaRVs1u4DatzNOKUo5S\nDGOtfHbekuKOVYbWeBrfcLvNpCgT4bbrxF8XAnvVgZbbhMqFbVnJIYp2ZDS3OhzRdUoetpXjQQYO\ny7IxT0vttpLf6/Pnz8zLwjLNzLeJ6+XCay13qhUnSltClS6cM0DGtxLl01qTtkAOAu3+9Okz07Rg\nrbgoQggVpiE2oZLBWXOfrqYkYQep3q4VzqWQYrhbsh6fHunHkbbvKBSZcKeEs078iV4m3Tln4rZD\nSndPqzUSfWy8wzeGbV04n04VgByJ207cdkoSDKXRAltRWYp32r7leDzepYamMghedWthw+q6MCdp\njQWm60RKG2YPVAAAIABJREFUhcPDIzEX2kYWZFWga1q8lbJFmfDPNDUnL9phqd5d0euEPSwlQH3X\n4p3j5fSC0oaYxFYG0lZAlZ0ulwv7upJzpO/aCrDZ5HlTmrTtd917PB5QVRqToW5huS0YFCUmdAFd\n5JgfYxTnzFZ72VLg4fFIP7TEWlr4OuR89fwaJbVLzjmeHp/EH74HQsrsYefDh5/w+fNnur4hl0Qu\nCWPg4WHEesfbr77Edi3bHoRR4S2hTvz3JdBWpmvMCWMNlCjXY0jiv9WGXG1/SsnCuMwLzko4x1lb\nTyUbKQjxjCLSzzgMUnJYMiln9hTQ3lKAmCJD30sCryROnz9LTHjdKFmSi1pbUiz03cC2Rm7nK6oi\nTzMF13qcVsRtEzJ9loQo1YqotaX1DX3bE5b4vde4n6tEsIcd3zoe3ryhAJ0XQEvIG844wraBUjw8\nHCBDTJl5Ez9nSRJDdM6w7ZHrbcKuVujszlUOQSsLjBd7TImRFGXq/jJJUiPmzOH4UPkCllKisDCz\nwjlP17RkpRjagVwUvpG75RYT+7bS2pYcCqbzpBzwtcIlxB1jqPl0AZZYK7FcW2n0KSVMlJZTrXWt\nF1csy8K7N29JObKFHa1Ec2qMZp8n9j3QdT3GaH7ww68pWKZ5YV1nGRDtK6ooktYY77leLjhvKMXU\nY+Uq7AQUKsMWAm3dzYUQxAFhDCHVLHwJLJMcnxtv2PelBgQ8W5xZzwvOOZpGUjGyyMsAL2VhHFyv\nF/HhVr/vlgJqDXIKUTK9D9sKpaNpG2mWrS0RhcLx8UEsasZIk/CyYqymGXrB3Q3SQLpW7+uySYQY\nC+t1ldOMUmhnMMjNeJpm+Zp1hV3Sa76x5F126KoUpsuNrOD4cCTEDFlI+Mu+SEtDDIRl4enpSXbc\nJckgSRu0LkCuU+1ESQUDvHz7kZAyjw9vmecby75SNIRNesf2EOj7UehSe3WRuBalCtPtyu12Edi2\nb2maFt/IDvBWEXvaeCKFYjRoLXwFq4mxYLTm3bu3cjO0jvlyo+16yNA0nlh14RIDrfOgwVjLsu3S\nz6Vgi2KpapuOFAUJCKJ3Xy4X2rZhXWeOx6N4sGsQJsUow+F9vzsvCoXOORpnud1WijF0TVd96Jm8\n1xuxsZwqY3mrermvGyVjNSlLA8i2zeScmbdZAkWjJ6aIWgvOW5Z1Fhuo81Ayvu3w2pFV4vJywjYN\ntjqaht/tEoHvBpzrMGhs0cKxzIVlWnh+lsioNYZlWhA2a6LvB6xtwBic90y3idvtxrovuMbhW09J\ngZjCHZYxTythkzx9qb/2ODzw+PRUsXqFnCMgpu59l5I6b8Xgr2t+f99XdOMoTuGqlcl6S1JwvV7w\nra8pIV0vAImdeu9lCqxNTQkh5PqUWJatms2/ow+9f/8FWSmsa3k6vqXzYuzWGkreUVpM88+fX1jm\nhdv5TGPFCaGzEKict6zrJGCcpsU4GRycL2eA+9HReTka7Xsk58pYsBbjxIButGi0AouRHTjIrlDV\nyWzXtz+V1ZfnylhDqki/rvP0fUMhY1oHWjrPWt/inZUbkVXVukNN3hRyKljj7o6Lbd8F9FISaGqQ\nofBaK7SuK6V6Zo2V3btori23eWKPmZgLRRliLHeIs7Ue6xqMFZ5p4zzzPJNKEebF8QBK2k2Vjjin\nK5tX5IdXbVi6rQTbqEqhseIAQcniJeyHmWEYeTg+sqUIRgtGMUtNjfEO4yyn0wvbOvPm7RuUlutL\nKQ3G0rQdX379Nd1hZHx8kOho1+Hqv3+PgVyZyDnuGAWfPnyQWLORdtVlWbnWxVAVJTDrn/rdZUcp\nZaI5irTgjJFkYbWppSgFk7frFUE2glIZa2DoPefTC6fPnwjzKq4Ho6WeqDbKuqYRK1XlHWy7DO32\nbZO0VQgCsw87+77SDb2kCIcOpeDtuzfSBqKUbCzCxrunN6zTXBGWDY/HEa3h8e0TpRT6pq1N0Rkq\nqS+GjZdvP4tsoRRKadZ1q9a97/f4uS6wFIPWjnWPvFxvWOOIsdB2I91wJMTI+XRFac28LrRdJ3fX\nLLG8EALj4UDXtRwOB3JOrOvCsiycK4VfANaFUqB7eMPh8YmiJU2079LrlerQRgYkiq5vSDlwOj3z\n+fMnfvzNB37yk5/gfUPvWw5tR4qCZlv2FW0N/dCDls6wDx8+QC58+/FbyX57j1YwXS9s88Q2zZQ9\nEvdA3/Y0vpUhT9WVcypcz1em20xRqvaLFaGOJalpcd7xcHys1KSdbZ2ZbjfmeRMebcy03SA6YxHS\nkULIYSkWGt/RtaLL+cajrcE3EmE0Vi76fjygtcNZT9jD3XMrQwqRCKyxgNCstrDfKWRaG8L+CuXI\nSBq0NuZaw7osnE4vkuJpxf0QovghZQD4HdwmpSQT/Zzpu/auvVnr6vTf3nGOscIZjNY0rb/ruEXp\n+ylF2nj3uyYp9DMJO4QQCTFJ+aB1GO/v6Ml13Sjlu9jta0w5pCjAkpxAy3F6HA8i/RQZwkzX6z3E\nAIpcMm3jsE6eE5BE2fU6sayiyx7HkZeXZ17LND8/P3N+ORFCBgzjcAA0bSe+65JzbTy2DH3P5eVE\n3CO384kv3r0F5OaVAde2ONthlOV6u+CcrlLFROMb2rbl20+f6HwrDpNuZF4WCdE4LXVBjSduG9Zo\nbpczXd/gva1Bl8zxeKia6BXvpPZH2iJKxYgmFPDy/Mzz508YJYGRtm0JYRfY0baRq49c0nYGVRR9\n33G5nOqxXjEOA9Y5TpcTQ99htcIp+PjhA+fzi4Q6stC+iOLddc4IjL8kju+ecL1gDa/nE7rAYfhd\n3mjwn//Xf5JSEl3fM00TDiXHv+qrk84cULVo7zWzvswL2zLhGuGngryBrXV3r+TldGbbI1988cXd\nEK6dluqVbeMwPpByrL49eTNIZQt3wX3bFpquASotvhtZ11V2K0Ze5C0ElBGvJylzu1ywWtG1nrUa\n+3NJ0qYZA9frjXE4kFLm8PjA58uJzneMgyTASix8+vSM0pbxYZQdNjJYM1osWKEUrpdzBRxLf5Iw\nUR2n84XHpwdS3cG3vq2LRmEYW1JOpFgYh0HcDErCD9ZKZj1VH6bWcgxXwLau9EPHts30vXxdipGw\nie4Xq8VlGEZKSczTjZySsHNzrrHgnRhlodRG+qeEHmbx1vPp+ZnD4XhPer0eecdxlNcvC4jbewl7\nDMPAPK/yZkwyJIwVul6K/JzGO9p2IIRcU0xBkIDVUaG07Iy7Kuv8lmhulJhoyuVu7BcyWsP1dBZ2\nRtgwVjPNM9q8JsHKPRmXws4y33j/7i0lgzaOEAOpCIxkDxumJpku5xeO4xGtDc/Pz1KrYiwv59Nd\nW71NE9Y5wWBu8h65Vk+vqtStrpVd4fbqVNGabZcwx/Ui7AplNK5psFqL17kkti3ck2ltK55W44xw\nOnzHtMx4p4lID1nrWy7nM13r2dZJhsXOMgy1WDAlmqars4VcgeVOmqKjQIuOx0eePz/z8Hjk+eW5\nYivlOnSV0yARbMTTWhujS6k2x1J4enovVTOrxGZj3DHIa9x0PfOy47xwGYzSTOsCZKbrDessvuvY\n9moV22eOj29Y5xVdCp+en/nD//g/+TsbNFBKNcCvAR7RbP/TUsq/ppT6I8A/C3ysf/VfKaX8qfo1\n/zLwTwMR+BdLKf/Vz1pgf+W//dP1zlQoJVaLkiyU6zKTc8F7eVG0gnWRxs3L6czQiadTa00smULB\nGkmaiN4lyavhdSHRmWW6YYzB2IbaO4n4+2XA8VpmmGur5rKs0p2+BbEw1Vz2a+9RjLtMiVEC6SiJ\n6+VM4xrO5xO6+ipLkfLC2/WCUgbnLCUVDg8Hpm3GItLBNx++wRrLcXygPx7Z407XyVF8W1autytd\n4ylouq4R2o8Slum+R7q2pyiNNrJD29cNhdCHnG05PhyY5gslC/N0mWca76W0sevvNp9t3+l74ZPO\nk0QtrVUYJaV5KFUBNxrnGrQ1MoTIBV2Pw9syi4lbUVMz9XuvVd9U1IYAUGjBBlZmq1bIdNs5YQZM\nAk9pmpYYQ42qSt48VF02pYg1rh7xqA3FYH3LFpMc/1WBInYx7xwhxDpIk/pr37RI3RB38M0rbS1n\nYT/I677VBo5dZA8j5DJ5L9XgTM5SKZ/kpr3OG10/kFEUpaThNcjkP+XEtk6oyp41Vmxc27bf00c5\nCzTcVNtb5xvmVapSQgyS4ksZlKbxli1Fwd2ESMxyOtJJdrDG18iwMZSUmedJAERe/KjUxOC2bxLH\nbvs6zEuCyVylW2663fjyi3dM1zNLzowPB/qmY51mrHGcTucaZoGh7eQ9VrIkC8e+zlgMv/njv8LX\nX/+QnBPLssgA0hr2bcF6z2W6yvDMtfVa2jFaCwLTiD6dS2LbZsKyYDR0/SBsEmPJKeOs4AlTKRQN\nhHqztwJmHxqP8xa0EWtdFubwP/QP/MPfa4H9aw65SimbUuoPllJmJVDN/04p9Sfrp/9oKeWP/lUL\n5+8D/gng9wE/Av60UupvKj9jJTfGolGSFkKSJTkmSpLUSikV6RdlWHC7XaX3ymqxD5VCWqTFtOtF\nnzkcRtZlZd8jh8OhErk0nz49M47ik421nsR7mWp++vajxEP9gO9aXO2O7zq5AytrCbvsCJu+YdoX\nxmbANVJPvO+R2/XKti20rWcNO+PxKFanlGnqDqnre0COYrYTOLPVkkgrwMPTE2DohgNaS9leTsIz\n2NeFw6Enhp2u8cQ9cLmc8U3LdbrVIQ5cLyfaTrqjYpLkzPE4Mk+B88sLkCpnStE2Dl+RkMuyMI4H\nsfTU1E7bOIaxZ9tWtm2lcQZFoet7lDYoZZnnVTyt1omOqiUfX4qhFEEaivzhMV6OiI33XK5XHh8F\nU9f3jdThGNklbmGX3XclSrVdJyWWpdwhL687xmWVgYdzHoW0LoC09IY9oEsWqSAEDMIhyEW0diHX\n5zrwqdq7cRgjHu3XyfLrRFtTaBoPyt273JR5bcnVdXclO3DrPDFHULJBGMYBhWKdF7RzmE7qqHOI\n7EFupNu20XU91osOqoxFG1NbdBca71EFkpJipRijsI2zpMWUfuU8BNlkAH3TkNZE07TM5wsxRVrX\ns6878z5V+53o8ymlyohAvLhJUmphW35ql7cz9oPcYNaZ6+1GyplxGKAozi8nXj59ZhgGmRUkxXh4\n4OM33/L4+CjIzvp8zetKjIG379+jjGLbQgU8KbZtqeAbKQBtuh6t5YRQYuCbT59o246373piiCLP\nWVj2lb4b2WOhH0Y+f/ok5ZIgmE1j6NueH3/6MY+Pj7i2Id4CxjnA3ivBQwpEvv/p/relwZZSXtXe\nBlmUX3/yz1rZ/xHgPy6lxFLKXwL+L+Dv/pnfN2UR0UtGWQ0pUtLGNL3gtcJq2OYLcZ9ZphuuglaO\nDw8cjo/4pkM7d38za6X58M03oArjUM3k2442lqc3b0B5lLYcH54YBplU3q4XjNE8PDzQtA5tpME0\nxUIKkbiv5BTp+56nN09sIXN8fMI6T85wmzZA8/j0huODQIy7duD48ICuPUmAaLZROAvTtOBcg7Md\nt9uKUhrvxF7WDz0xiR/0drlKakfLK6WMuAKWbWPZFw6PA7YxfPnVD+7HXaWTxC+15ct370nbxvnl\nxKdvf8L19nLHH3qjpKxxvcmxyipi3CklYshYDcs6s25SNBdToe1HrPecTqdaEiiXj7QKyO+Gorb+\nSvw2pcTlciHGwIcP33C9Tig079+9B4SLYI1nGA8UJHiQcpa2Uu8YjwMpF2HJ1uiqVLTIIvPw9EZI\nT9rQdj3GOgF9OF8hQZKkUlH00FJibQBYyCUAUvlOKthqAwvbhqYIE6HGYxtvJXK8zpTKbygly1Cs\nkTp1rSSZlFJgmi6CHXTNfccdY+Th4SgBiyiNDc5a+raBOr3XRghqr0yCGGMNvVCTV9yfh66TXaG1\nlrgHwr5RYiBWV4hRUprolPAFbNPgGs/5fGILAlqyjadoU2veDcN4YNs3nl+e+fz8zBo2bvXkp7Ui\n7hvzfOP5+RNffPEeVKbtB6y2zKcLfeP5+gdfkSg0nefx8XhPwBlnJV7etNymGde0PL154jD2aODx\nMGK04q/8+q+TipQPpqwxyrFuO+fzWTi7GN6/+5K+b/n200em5YQwoxXvv/qa49s3KOe4zTPDOGKs\neIVvt4mcC9fLVSBNY0fYV+EEY7gtszBovRf72/8PSa7f1gKrlNJKqf8V+AD8ainlz9VP/QtKqT+v\nlPr3lFIP9WM/BH7jp778N+vH/l+PabrKxD9spD2wzjeulxfCuvHxmx8T960ucLLDUAr6bsBoy+nl\nQika43r6w0hS4LqWx8dHmkaSYVLvXN+0MVGKxvueGBK328Lz588yWexH0IasBEqyzhPWGrFIWYdR\nSqbTy87YdaQ90PVC8PFeOqBCCMzzTNh31nXjcp3wjexcU4yM44h3Yj/68osvSVGOzNLxJcmlxnlK\nitJFZiVl80pLevf+CygOo1symraX2mRXI6WvOpcMZyyfPn0ml8LhYcRaw49+9AOenh7p2hZV74vT\nLNi2EKNMblPgcj5xu105n8+kmIgxMh4ODOOBEDPbHgW0Mk/M83zPoYMM8fa1ZvtTug+fvvzqK5xr\nePPmLX0/VHB4rk6BV1LWKzRGCVUraxmCKnuPOVsrrQ9aSxwy58LldkUpjTaaaZnvMd3XloqSC3GX\n2hOVEyGsKCVgcaUUxmj2bUNpeVbivuGcld1tzmhU9eRCyvn+86me2VIBQrqa4501UFN7GioTwdC2\nwtGY54WSEvPtBimyLhPn5xdilF1mihkQr+7rEK5t5DWzxjDfpuo5loTeuq5smwDBUQrrHa5pxBta\nCiHKjv4wDBSl5fVrO5HjkKCL94593/jm4weeXz4z9D3Hw4F3795TUBwfn2i6jqYTkLcdet588Y7z\nWYZoVotEM44dl+lGzIlf+IUfsm+Z6SbvieHQY43icRyFD+AcRcup4fOnZy7nM9uycDtf+fKr93Rt\nh3WetmnZlo0SAk7DNt/Iaa+pRTgeHvDeYZWGVFjnhRLF+XPoJRh0vV5xbcPj27dgLcZoSbftEdD3\nQe0r1vB6vdJ2Qvz6vo/flg+2lJKBv1MpdQT+uFLqbwP+XeBfL6UUpdS/Afw7wD/z1/PD/9h/+B9h\ntVSL/O1/x9/K3/I3/42MvXAevZfhVt92XKdbHRxYpmnicvoxFM1XP/wabTUaLRSsKBUz8/VG1/X4\npudWubJi2pcKiGVZsE7x7t07coLnlxPON2wpMHiP8449LFLrqyzt2LEsO93Qi7ieE8syczgcaJqG\nZV6qXuyl3bJteT6dRL9ylqaRmCNFM44j5/NVGAwhcLvdsMZzPD4y3W7cbsKOVUjSynmRNbZ1x9Y+\nKIpmuk10vWeeBA5stMVagWG8XE/EHPnJNz+WhFwjmvO+1eFLyVhnaGqtTkyRYeil6nyQRd01Dq0d\nbefZdknT7FtAGfH7WuerDc7yWo/9+Ph4h+KUkqTGY13Ru6ZtpFPLle92oTmBFDta6VDToHWh6wZi\nlKDBOr/Wx/h7YKOUzHgY2UISi1SlegF3PRbAKNFPXeNJVd9zTVMHUQmURhmFNokYAk3fCAzcWMEO\nGoNxVnRe6ykkQpYj+SuUnCzV5DkEYoh0fUvIhbRLnUnTNNXul+gHmcSLTt3cOQtd14lHc9lru4To\nybmGT3IWuLtr2zqc3LBaYCc5JlwnFe1yDUe81fXacYRVGLQ5JPac8K6R5FLjMU7jrSVuG9N8RZtC\n30vaqe972XQUaUrOSaQyiTwneutYlo3r5UIqGnJknm90Y8e+wf/9v/8fvPvqC0E5ugYTBRq0rFOF\n/Si6pmOZZ44PT+LIWWf6w0gmVQ5vxhpxKzhnhLtbEBmvKby8PHM8PpIzrMsCaAyKbRZf9jTPtL6R\nYISzhJQoMXI4Hrldr4Q11DSjwufMOB74X/7nP8//9Gf/HIXvyHff5/HX7SJQSv2rwPTT2qtS6vcA\nf6KU8vuVUv8SUEop/3b93J8C/kgp5X/8q75P+dU/81+ybgsl7jJ9N/oOiM5kQBE2MSUbZ/FNJ8Ty\n+cbDw4F13QhJ8fz5hR/94GuULtymhett5s3jIyg59mqtUCXjfUfaV4GU9MPdg9qPI30/SD9PklqV\n6XYl7EnE/0a8fofDgXWfMd7SGEEIvrw8Y13DOA6gEsu0ojCs+8bj0xtpWJgmucs6KxwD51iWicfj\nU53iZ7ZtvVP+RV9saNqWnBIfv/lI1w7ipW39b8G7aa2JcavAbGmMXbYV3zZoMmELMlioU3WlCuu2\n4n2LorDvAd80pFh+S5wyxkgKWVi7WoZO4ziyzLNMhJ3EIxWqLn4LbesIW2C6XOtCk6qXWZ6rvVb1\nqLpz0hW593J64bEa9buuETtaFLCHxBfXmo+Xgrpt30FbSl1AS06M/UBTWbDp1TVSIGtQSmxFKI1t\nZLi177GmwoSjKjXPMiR1RqKm1mhiyqAt1jfM05XGWakeCuKVPl8ujA8jVLlpaGv6rZLgcsqULIMs\n650gMZX4hOMuevr1diEXKjFsFFB10woovg65jDKUlMCaOnST/HzjhZ1QFMQgPAZhzUbariWGSCqy\nE1+mmXEccU6GaR++/chxPBC3VX7XHOm7kWmaRY9PEvcNIfL86TPeGbq2oWktFMXL6YRve46PT4R5\nJofAZblyfHrk+nKiUBjHozwXWhi727LcaV3bthFyou96UghCcQO6vuN0OlFKwhrFMHSsW+B8vvLw\ndGQYRqZpxWolGxclHuI9RlrXouqgcnw48vGbD/R9y8vLZ7766kc0XkpNX9ZJetSUQZckNxPAtw3z\nuqNQPBwf+AN/z9/3O0vTUkq9ez3+K6U64A8B/6dS6quf+mv/GPAX6p//M+APK6W8UupvAH4v8Gd/\n1vfOKZH3AEXx9t07iWaqIhdEBTCnAhhDQUk0zxj6fqjVJJbDOPJ7fvGHQGa63VAofvEXf5FmHMjA\n8fiIM5ZcxG+XYqRxDipx6P2X7zkcRkAmjSkVLtcF6weytjSHgevtyrt377icT5US79li4Hw507YN\njdNs68xWBy7GGB4fH6EkrIa2bRDNLt1L2bTW+EaSJes6V+9frIZvTY5weTlJUkUXKIHxMKIVd9Se\n1qpGHcUTGnZxQMjnBZLddj22aXFth+86Abw0nsY7UEh/2bqhtK4Ljxw9tdZ0Y4OxclR21nK7XnHW\nShWJtbRtQ9f3rOvK5XJhWTb2PaKtRxuPdx3WeqlJXzeJSO6rJJtS4nQ6k0vh3fv3hH2nqU0EsbaJ\neu9ou5aua0lF2J3n20V8p7Uy22rD0Pes68zpfELVWO22rexJXALbtlJKgpIotV7k2HfoknFKcGzO\nSw49hEjMmZATW5Q3Xkh7rWcRBwla0nh73OhG8Wyqknnz+EiModrfpPLGWM0aNvFJ10CAtYatHu3P\n14sMmYq6O0xEx14qH7dIKCRnQkx3XnLbdrRtJxYkJw0Gfd9Ke23JQojKoAqYIljHvutYl4XL5SIE\nsPr99n3ndpvQSjgY2lrhaoSdbZWpfN93jONAKYlvP37kdDrJYLjxMhhTsIYgDp1isE3P09svyWj2\nXU4Ae06YviOWwhYCznsBaZdMSuL0yQS29Yb3MB46+vFIyAajPW/ffcHh8EgpCmsdsWTefvEe4x2+\na1HOkHVCe432ig8ffhPjIKSd8XDg228/8OGb3yTmyLvHJx6PDwLPsY6+l4qZsAeshqFtJFn4PR+/\nHYnga+DfV9JPooE/Vkr5FaXUf6CU+iWkTe8vAf8cQCnlLyql/hPgLwIB+Od/loMAYJkWrLFYZ6Ti\nuVL2VWU4guLpzTusrUONAinsXC9Xunaoxm+xnpClPG4YDizThLKKxlnSLi9kMw48Pj5y+vSRHCXj\nPh5GsV6RuV7PTDfF49ObuqPLvP+i53I5MY4iDczLLEDsIm9s5YSnID1bgjW0ziAUe2kwAEUqhW0P\ntKbl4eFB+AbOMc+zRFfDjtGGZugwxhJC5Hz6xB52QtzR2tIPg1CekjAZjDbM800K5irVqu9GnPds\ntRzRKMO67ZRcsM4JP9Z3lBx4eX7GOEvbiKZrrWMcxcO4LBMh7PTDyB62ymmVmullXcUyZS2plBro\nKBwOx2prMhyfngj7zlLjil3b1Vy9EKJC5eA632Cdlx57o/BObjha6bpTlrjyVmlYWkl3lveeGDNU\n9m5KCeUMJRe2sAsMyCisb0il0BqLKok9bizLdwxfYxQhRbE/WUPvHGmPOGvJWXCaGLnoc4qE17bb\niu3rfH1TbpsEX/a9kqwEoeesnC7evBXpJJMqPjDR1KRb1/Vinao35n2XhJ9rGnzt9dr3SAiRcTiw\nR2l2uN1u96+JMcpJKEkTQtwrUPx6pe97ClDqzcE37s5ffvPmzb39oe97ilLSQlwKvmmlkJKC1Zqu\nFWdDIfP45h2qyDDYalMbJwJoy/EwsIfAMB6IpeC7nhwTewrVjSMnib5ppKFEy3vo6elRThG6ZVpm\nsUpShDuQEQ7Ctgl1zljapkdpy77tNE4SnQ9PjyIjrQveWA6HIyknYkz03Yh1Ax8//oQ2Js63K4du\npB96aSrxzR11+Fq/vlcc5Pd5/DV3sKWU/62U8neVUn6plPL7Syn/Zv34P1X//5dKKf9oKeWbn/qa\nf6uU8ntLKb/vZ3lgXx8h7qSSWfdAiNI4qo3jcr1WOMpO65s6xImEbaFtPA8PBwqiv/l67E0l8aNf\n+BG5JK7XEx9+46+gstzCSylcXp45n0/YtiEquFzlhdq2jev1KkftJEzJZbmxLBdePn7Lep0oRUkb\n7OOD3OlSQmWFKprD8ZGHN2+xvmFdd24VADJNE0obPr+c6IaRt++/pCCTYWtdTRRx1+HE97gJNGRf\n6YZWft7Q43zDvK7iEWwa2kbQe4fDw713ynuBoizLcreXbVHsJ40X8EvcA854tPYcHp9ouwG05XB8\nQFu96sm+AAAePElEQVRLSMJhWNcN6xzTNLH+FHKwIKg5pTXfPn/mcjkLUasyY1U95sa4Vy1UduzS\nVf8KsxEClWtajHvdCfcY67lNYgdq2l7abpUs9qpo5spCGIcjeygobdFKIpYppQotN8zrJouEkhv1\n/9PeucZImp31/Xcu77Wqurqnd3a9N3vXsR1CcIKSGBKbKCaJzIYoOJ8CUoQcLGwLFEGQIITkQ6QQ\nJKMksjEQbGQHY1sEKUjBNrbAAZIogBeWeNe78S4b3y+zs3Ppnqmue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xHNGpvedLnrK58B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\n0sm3JDTjAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f9c186a8210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# opencv\n", "N = 1000\n", "tic = time.time()\n", "for i in range(N):\n", " img = cv2.imread('test_images/ILSVRC2012_val_00000001.JPEG', flags=1)\n", " img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", "print(N/(time.time()-tic), 'images decoded per second with opencv')\n", "plt.imshow(img); plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "951.311962346 images decoded per second with mx.image\n" ] }, { "data": { "image/png": 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RcBIXmx2GYba5bsIB2XRuReMdzvU4AJxGLODWL0LcSMz1jITG9xhQhjRr7D38DSeMWyRm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\nlr5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wOPLjP/HTmKyg6xpu3r3DL54szUnKDO88292OTz/9DlVRsCwQxQLsnuyMWhRxLrPB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\nJKMksjEQbGQHY1sEKUjBNrbAAZIogBeWeNe78S4b3y+zs3Ppnqmue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xHNGpvedLnrK58B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\n0sm3JDTjAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f9ca42d8e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# mx.image\n", "tic = time.time()\n", "for i in range(N):\n", " img = mx.image.imdecode(open('test_images/ILSVRC2012_val_00000001.JPEG','rb').read())\n", "mx.nd.waitall()\n", "print(N/(time.time()-tic), 'images decoded per second with mx.image')\n", "plt.imshow(img.asnumpy()); plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Image Transformations\n", "\n", "Once images are loaded as NDArray, you can then use `mx.nd.*` operators to transform them. mx.image provides utility functions for some typical transformations:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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v8+PU3h0ppqF7qjwL8NZaxW2gvphgeHYmt/3cRF3Alzva7YrtdvWFLvO7koU5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\nWiPG1HL9xQ8q2/qJAFoFoCBj/Y3h4F4HpKR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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9ca090bad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# resize to w x h\n", "tmp = mx.image.imresize(img, 100, 70)\n", "plt.imshow(tmp.asnumpy()); plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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69iGA3DoUfl/xEIM/y5dQFQZKd0qMafojGwjEHa5KqQZLfGAPM0WhcCrgHdwXpojEFnUy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\ne5fXXn2N29+9zcXLl7h46aI++yKLPf3g8WgqaRznEQ03JXYW1mm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Yb4XvXoOrSZNI6AaG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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f9c2c1416d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# resize shorter edge to size while preserving aspect ratio\n", "tmp = mx.image.resize_short(img, 100)\n", "plt.imshow(tmp.asnumpy()); plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(267, 141, 150, 200)\n" ] }, { "data": { "image/png": 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WV69e4qzhRz/8IYN+zj/5p/8E6fkcHh0TRQmr5ZrlYtG1OVJ2mkrWY71as98X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S8oun7UA7NkDC4HJiWRb2hrUFnspYuqVSG7CHJafd/D0MvYMYsVVxlLRJZTZNbRZUyS/+hn/2Gb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\nVhs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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9c18492910>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# crop a random w x h region from image\n", "tmp, coord = mx.image.random_crop(img, (150, 200))\n", "print(coord)\n", "plt.imshow(tmp.asnumpy()); plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Other utility functions include `fixed_crop`, `center_crop`, `color_normalize`, and `random_size_crop`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ImageIter\n", "\n", "Given the above functionalities, it's easy to write a custom data iterator. As an example, we provide `mx.image.ImageIter`, which is similar to Torch's [resnet](https://github.com/facebook/fb.resnet.torch/blob/master/dataloader.lua) image loading pipeline. For more details, please see `mxnet/python/mxnet/image.py`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
sharnett/logistic-regression-stuff	batch regularized lr with weights in python.ipynb	1	22161	{ "metadata": { "name": "", "signature": "sha256:922a22cb3af191cc2bce3b1c1c42799b3839b98c3c47539d74b31b67ce7f4515" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Use binomial instead of bernoulli\n", "\n", "$l_i(\\theta) = - y_i \\log(p_i) - (1-y_i) \\log(1-p_i) $, $y_i \\in {0,1}$\n", "\n", "becomes\n", "\n", "$l_i(\\theta) = - k_i \\log(p_i) - (n_i-k_i) \\log(1-p_i) $\n", "\n", "where $p_i = f(x_i^T \\theta + \\theta_0)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$ r = n \\odot f(X^T \\theta + \\theta_0) - k $\n", "\n", "$ \\nabla_0 l = \\frac{\\sum r}{\\sum n} $\n", "\n", "$ \\nabla l = \\frac{X^T r + \\lambda \\theta}{\\sum n} $" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.optimize import fmin_l_bfgs_b\n", "\n", "ALPHA = 1.0\n", "FACTR = 1e10\n", "\n", "def logistic(x):\n", " return 1/(1+np.exp(-x))\n", " \n", "def log_loss(yp_orig, n, k, eps=1e-15):\n", " yp = yp_orig.copy()\n", " yp[yp < eps] = eps\n", " yp[yp > 1 - eps] = 1 - eps\n", " x = -k * np.log(yp) - (n - k) * np.log(1 - yp)\n", " return x.sum()/n.sum()\n", " \n", "def lbfgs_func(x, X, n, k, alpha=ALPHA):\n", " intercept, theta = x[0], x[1:]\n", " yp = logistic(X.dot(theta) + intercept)\n", " penalty = alpha/2(thetatheta).sum()/n.sum()\n", " obj = log_loss(yp, n, k) + penalty\n", " \n", " r = nlogistic(X.dot(theta)+intercept) - k\n", " grad_intercept = r.sum()/n.sum()\n", " grad_coefs = (X.T.dot(r) + alphatheta)/n.sum()\n", " grad = np.hstack([grad_intercept, grad_coefs])\n", " \n", " return obj, grad\n", "\n", "#x0 = np.zeros(X.shape[1]+1)\n", "#x_opt, _, info = fmin_l_bfgs_b(lbfgs_func, x0, factr=FACTR, args=(X, n, k))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fake some data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import numpy as np\n", "import scipy.sparse\n", "from scipy.stats import norm as norm_dist, uniform" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 86 }, { "cell_type": "code", "collapsed": false, "input": [ "n_features = 25\n", "n_datapoints = 1e6\n", "sigma = 2\n", "intercept = 1.14\n", "theta = norm_dist.rvs(0, sigma, size=(n_features, 1))\n", "X = np.ceil(scipy.sparse.rand(n_datapoints, n_features, .3)) # binary features\n", "p = logistic(X.dot(theta) + intercept)\n", "y = uniform.rvs(size=(n_datapoints, 1)) < p" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 212 }, { "cell_type": "code", "collapsed": false, "input": [ "n = np.ones_like(y)[:, 0].astype(float)\n", "k = y[:, 0].astype(float)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 213 }, { "cell_type": "code", "collapsed": false, "input": [ "%%time\n", "factr = 1e7\n", "alpha = sigma-2\n", "\n", "x0 = np.zeros(X.shape[1]+1)\n", "x_opt, _, info = fmin_l_bfgs_b(lbfgs_func, x0, factr=factr, args=(X, n, k, alpha))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CPU times: user 4.77 s, sys: 627 ms, total: 5.39 s\n", "Wall time: 5.48 s\n" ] } ], "prompt_number": 214 }, { "cell_type": "code", "collapsed": false, "input": [ "%precision 2\n", "x_opt[0], x_opt[1:]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 215, "text": [ "(1.15, array([-0.73, -3.81, -1.01, -3.41, 2.47, -1.54, -1.89, 1.08, 0.06,\n", " -1.19, -0.49, 1.05, 1.65, -2.41, -1.91, 3.27, 0.74, -3.69,\n", " 2.96, -2.42, 1.12, -3.48, 0.57, -1.38, 0.34]))" ] } ], "prompt_number": 215 }, { "cell_type": "code", "collapsed": false, "input": [ "%precision 2\n", "intercept, theta[:, 0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 216, "text": [ "(1.14, array([-0.74, -3.81, -1.01, -3.41, 2.46, -1.54, -1.87, 1.09, 0.05,\n", " -1.18, -0.5 , 1.05, 1.66, -2.42, -1.91, 3.27, 0.73, -3.68,\n", " 2.96, -2.42, 1.12, -3.47, 0.56, -1.37, 0.34]))" ] } ], "prompt_number": 216 }, { "cell_type": "code", "collapsed": false, "input": [ "%%time\n", "df = pd.DataFrame(X.todense())\n", "cols = list(df.columns)\n", "df['y'] = y.astype(float)\n", "summarized = df.groupby(cols).aggregate([np.sum, len]).reset_index()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CPU times: user 9.82 s, sys: 1.32 s, total: 11.1 s\n", "Wall time: 11.9 s\n" ] } ], "prompt_number": 217 }, { "cell_type": "code", "collapsed": false, "input": [ "X = summarized.values[:, :-2]\n", "n = summarized.values[:, -1]\n", "k = summarized.values[:, -2]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 218 }, { "cell_type": "code", "collapsed": false, "input": [ "%%time\n", "factr = 1e7\n", "alpha = sigma-2\n", "\n", "x0 = np.zeros(X.shape[1]+1)\n", "x_opt, _, info = fmin_l_bfgs_b(lbfgs_func, x0, factr=factr, args=(X, n, k, alpha))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CPU times: user 3.14 s, sys: 459 ms, total: 3.6 s\n", "Wall time: 2.89 s\n" ] } ], "prompt_number": 219 }, { "cell_type": "code", "collapsed": false, "input": [ "%precision 2\n", "x_opt[0], x_opt[1:]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 206, "text": [ "(1.14, array([-1.21, 0.28, -0.73, -2.34, 3.74]))" ] } ], "prompt_number": 206 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Old stuff" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Searching around the internet, I couldn't find an expression for the gradient of regularized logistic regression with sample weights. Here it is:\n", "\n", "$$r = f(X^T \\theta + \\theta_0) - y$$\n", "\n", "$$\\nabla_0 l = \\frac{r^T w}{\\sum w} $$\n", "\n", "$$\\nabla l = \\frac{X^T (w \\odot r) + \\lambda \\theta}{ \\sum w} $$\n", "\n", "where $\\odot$ indicates element-wise multiplication. The loss function here is\n", "\n", "$l(\\theta) = -w^T \\left(y \\odot \\log(p) + (1-y) \\odot \\log(1-p)\\right) / \\sum w $\n", "\n", "with $p = f(X^T \\theta + \\theta_0)$\n", "\n", "i.e. the $w$-weighted average of the usual log-loss.\n", "\n", "Note that I'm not regularizing the intercept term, $\\theta_0$.\n", "\n", "<hr>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scikit-learn's LogisticRegression classifier (which uses LIBLINEAR) doesn't support weighted samples. SGDClassifier does support weighted samples, but it can be tricky to tune. For my application, solving the optimization problem without scikit-learn using L-BFGS worked best." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#No regularization, no weights\n", "\n", "In other words, all samples have weight $1.0$. $M$ training examples and $N$ features,\n", "one of which is the intercept.\n", "\n", "$\\theta$ - real vector of $N$ coefficients\n", "\n", "$y$ - response variable, binary vector of $M$ failures or successes\n", "\n", "$X$ - $M$x$N$ design matrix. Element $(i, j)$ of $X$ is the value of the $j$th feature for training example $i$. One of the $N$ columns is a dummy column of ones for the intercept.\n", "\n", "$f(x) = \\frac{1}{1+e^{-x}}$ - logistic function\n", "\n", "$l(\\theta)$ - loss function. This is what we're trying to minimize when we're considering different coefficients $\\theta$.\n", "\n", "$p = f(X^T \\theta)$ - real vector of $M$ predictions.\n", "\n", "And finally\n", "\n", "$l_i(\\theta) = - y_i \\log(p_i) + (1-y_i) \\log(1-p_i) $\n", "\n", "$l(\\theta) = \\sum_i l_i(\\theta)/M$ is the average log-loss\n", "\n", "$r = f(X^T \\theta) - y$ - residual, real vector of $M$ elements\n", "\n", "$\\nabla l = X^T r $ - gradient of loss function, real vector of $N$ elements" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Standard regularization\n", "\n", "No regularization on the intercept. $M$ training examples and $N$ features.\n", "\n", "$\\theta \\in \\mathbb{R}^N$ - coefficients\n", "\n", "$\\theta_0 \\in \\mathbb{R}$ - intercept\n", "\n", "$y \\in \\{0, 1\\}^M$ - response variable ($M x 1$ vector)\n", "\n", "$w \\in (0, \\infty)^N $ - per example weight ($N x 1$ vector)\n", "\n", "$X \\in \\{0, 1\\}^{MxN}$ - $M x N$ design matrix\n", "\n", "$f(x) = \\frac{1}{1+e^{-x}}$ - logistic function\n", "\n", "$l(\\theta)$ - loss function\n", "\n", "$l(\\theta) = -w^T \\left(y \\odot \\log(p) + (1-y) \\odot \\log(1-p)\\right) / \\sum w $\n", "\n", "where $p = f(X^T \\theta + \\theta_0)$\n", "\n", "aka average of the usual log-loss, weighted by $w$\n", "\n", "$$r = f(X^T \\theta + \\theta_0) - y$$\n", "\n", "$$\\nabla_0 l = \\frac{r^T w}{\\sum w} $$\n", "\n", "$$\\nabla l = \\frac{X^T (w \\odot r) + \\lambda \\theta}{ \\sum w} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Code" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from scipy.optimize import fmin_l_bfgs_b" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "def logistic(x):\n", " return 1/(1+np.exp(-x))\n", "\n", "REG = 1.0" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Old method" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def old_method(X, y, w, reg=REG):\n", " def log_loss(yp_orig, yt, w, eps=1e-15):\n", " yp = yp_orig.copy()\n", " yp[yp < eps] = eps\n", " yp[yp > 1 - eps] = 1 - eps\n", " x = -w * (yt * np.log(yp) + (1 - yt) * np.log(1 - yp))\n", " return x.sum()/w.sum()\n", "\n", " def lbfgs_func(x, X, y, w, reg):\n", " m, n = X.shape\n", " intercept, theta = x[0], x[1:].reshape(n, 1)\n", " yp = logistic(X.dot(theta) + intercept)[:, 0]\n", " penalty = reg/2(thetatheta).sum()/w.sum()\n", " obj = log_loss(yp, y, w) + penalty\n", "\n", " y, w = y.reshape((m, 1)), w.reshape((m, 1))\n", " r = logistic(X.dot(theta)+intercept) - y\n", " grad_intercept = np.average(r, weights=w)\n", " grad_coefs = (X.T.dot(wr) + regtheta)/w.sum()\n", " grad = np.hstack([grad_intercept, grad_coefs[:, 0]])\n", "\n", " return obj, grad\n", "\n", " def lr(X, y, w, reg, factr=1e10):\n", " x0 = np.zeros(X.shape[1]+1) # a coefficient for each feature, plus one for the intercept\n", " x_opt, _, info = fmin_l_bfgs_b(lbfgs_func, x0, factr=factr, args=(X, y, w, reg))\n", " assert info['warnflag'] == 0, 'l-BFGS did not converge'\n", " return x_opt\n", " \n", " x = lr(X, y, w, reg)\n", " intercept, theta = x[0], x[1:]\n", " y_pred = logistic(X.dot(theta) + intercept)\n", " return log_loss(y_pred, y, w)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## New method: use n and k instead of y and w" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def new_method(X, n, k, reg=REG):\n", " def log_loss(yp_orig, n, k, eps=1e-15):\n", " yp = yp_orig.copy()\n", " yp[yp < eps] = eps\n", " yp[yp > 1 - eps] = 1 - eps\n", " x = -k * np.log(yp) - (n - k) * np.log(1 - yp)\n", " return x.sum()/n.sum()\n", "\n", " def lbfgs_func(x, X, n, k, reg):\n", " intercept, theta = x[0], x[1:]\n", " yp = logistic(X.dot(theta) + intercept)\n", " penalty = reg/2(thetatheta).sum()/n.sum()\n", " obj = log_loss(yp, n, k) + penalty\n", "\n", " r = nlogistic(X.dot(theta)+intercept) - k\n", " grad_intercept = r.sum()/n.sum()\n", " grad_coefs = (X.T.dot(r) + regtheta)/n.sum()\n", " grad = np.hstack([grad_intercept, grad_coefs])\n", "\n", " return obj, grad\n", "\n", " def lr(X, n, k, reg, factr=1e10):\n", " x0 = np.zeros(X.shape[1]+1) # a coefficient for each feature, plus one for the intercept\n", " x_opt, _, info = fmin_l_bfgs_b(lbfgs_func, x0, factr=factr, args=(X, n, k, reg))\n", " assert info['warnflag'] == 0, 'l-BFGS did not converge'\n", " return x_opt\n", " \n", " x = lr(X, n, k, reg)\n", " intercept, theta = x[0], x[1:]\n", " y_pred = logistic(X.dot(theta) + intercept)\n", " return log_loss(y_pred, n, k)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "def log_loss(yp_orig, n, k, eps=1e-15):\n", " yp = yp_orig.copy()\n", " yp[yp < eps] = eps\n", " yp[yp > 1 - eps] = 1 - eps\n", " x = -k * np.log(yp) - (n - k) * np.log(1 - yp)\n", " return x.sum()/n.sum()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "x = np.zeros(X.shape[1]+1)\n", "k, n = f.raw_data.leads.values, f.raw_data.clicks.values" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "M, N = X.shape\n", "intercept, theta = x[0], x[1:].reshape(N, 1)\n", "yp = logistic(X.dot(theta) + intercept)[:, 0]\n", "penalty = reg/2(thetatheta).sum()/n.sum()\n", "obj = log_loss(yp, n, k) + penalty\n", "print(obj)\n", "n, k = n.reshape((M, 1)), k.reshape((M, 1))\n", "r = nlogistic(X.dot(theta)+intercept) - k\n", "grad_intercept = r.sum()/n.sum()\n", "grad_coefs = (X.T.dot(r) + regtheta)/n.sum()\n", "grad = np.hstack([grad_intercept, grad_coefs[:, 0]])\n", "grad" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.69314718056\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "array([ 3.94585197e-01, 1.26462011e-04, 1.75182761e-05, ...,\n", " 1.00857694e-04, 8.20019783e-04, 3.01097803e-06])" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "intercept, theta = x[0], x[1:]\n", "yp = logistic(X.dot(theta) + intercept)\n", "penalty = reg/2(thetatheta).sum()/n.sum()\n", "obj = log_loss(yp, n, k) + penalty\n", "print(obj)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.69314718056\n" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "r = nlogistic(X.dot(theta)+intercept) - k\n", "grad_intercept = r.sum()/n.sum()\n", "grad_coefs = (X.T.dot(r) + regtheta)/n.sum()\n", "grad = np.hstack([grad_intercept, grad_coefs])\n", "grad" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "array([ 3.94585197e-01, 1.26462011e-04, 1.75182761e-05, ...,\n", " 1.00857694e-04, 8.20019783e-04, 3.01097803e-06])" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Do things" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import scipy.sparse\n", "from sys import path\n", "path.insert(0, '/home/seanharnett/Dropbox/cvr_redux/cvr-logistic-regression/cvrlr')\n", "from assemble_features import Features, get_feature_categories" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "data_file = '/home/seanharnett/Documents/vw/db_data_20141003.csv'\n", "data = pd.read_csv(data_file, nrows=100000)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "f = Features()\n", "f.raw_data = data\n", "f.feature_categories = {0: [],\n", " 2: ['adconfiguration'],\n", " 3: ['provider'],\n", " 4: ['segment'],\n", " 7: ['target'],\n", " 11: ['provider', 'segment'],\n", " 13: ['provider', 'servicegroup']}\n", "f.encoded_data, f.reverse_encodings = f.encode_feature_dataframe()\n", "f.matrix, f.breaks = f.sparsify()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "reg = 15" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "def assemble_response_variable(leads, clicks):\n", " \"\"\"\n", " Creates the left-hand-side 'y' vector that we're trying to predict from the\n", " design matrix X, as well as the vector of weights for each example.\n", " \"\"\"\n", " successes = leads\n", " failures = clicks - leads\n", " sample_weight = np.hstack([failures, successes])\n", " y = np.hstack([np.zeros_like(failures), np.ones_like(successes)])\n", " return y, sample_weight" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "%%time\n", "y, w = assemble_response_variable(f.raw_data.leads, f.raw_data.clicks)\n", "X = scipy.sparse.vstack([f.matrix, f.matrix])\n", "print(old_method(X, y, w, reg))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.286539087713\n", "CPU times: user 2.93 s, sys: 357 ms, total: 3.29 s\n", "Wall time: 3.29 s\n" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "%%time\n", "X = f.matrix\n", "k, n = f.raw_data.leads.values, f.raw_data.clicks.values\n", "print(new_method(X, n, k, reg))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.286539087686\n", "CPU times: user 1.46 s, sys: 87.8 ms, total: 1.55 s\n", "Wall time: 1.55 s\n" ] } ], "prompt_number": 35 } ], "metadata": {} } ] }	mit
zipfian/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers	Chapter4_TheGreatestTheoremNeverTold/LawOfLargeNumbers.ipynb	1	851400	{ "metadata": { "name": "LawOfLargeNumbers" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Chapter 4\n", "______\n", "\n", "##The greatest theorem never told\n", "\n", "\n", "This chapter focuses on an idea that is always bouncing around our minds, but is rarely made explicit outside books devoted to statistics. In fact, we've been used this simple idea in every example thus far. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###The Law of Large Numbers\n", "\n", "Let $Z_i$ be $N$ independent samples from some probability distribution. According to the Law of Large numbers, so long as the expected value $E[Z]$ is finite, the following holds,\n", "\n", "$$\\frac{1}{N} \\sum_{i=1}^N Z_i \\rightarrow E[ Z ], \\;\\;\\; N \\rightarrow \\infty.$$\n", "\n", "In words:\n", "\n", "> The average of a sequence of random variables from the same distribution converges to the expected value of that distribution.\n", "\n", "This may seem like a boring result, but it will be the most useful tool you use." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Intuition \n", "\n", "If the above Law is somewhat surprising, it can be made more clear be examining a simple example. \n", "\n", "Consider a random variable $Z$ that can take only two values, $c_1$ and $c_2$. Suppose we have a large number of samples of $Z$, denoting a specific sample $Z_i$. The Law says that we can approximate the expected value of $Z$ by averaging over all samples. Consider the average:\n", "\n", "\n", "$$ \\frac{1}{N} \\sum_{i=1}^N \\;Z_i $$\n", "\n", "\n", "By construction, $Z_i$ can only take on $c_1$ or $c_2$, hence we can partition the sum over these two values:\n", "\n", "\\begin{align}\n", "\\frac{1}{N} \\sum_{i=1}^N \\;Z_i\n", "& =\\frac{1}{N} \\big( \\sum_{ Z_i = c_1}c_1 + \\sum_{Z_i=c_2}c_2 \\big) \\\\\\\\[5pt]\n", "& = c_1 \\sum_{ Z_i = c_1}\\frac{1}{N} + c_2 \\sum_{ Z_i = c_2}\\frac{1}{N} \\\\\\\\[5pt]\n", "& = c_1 \\times \\text{ (approximate frequency of $c_1$) } \\\\\\\\ \n", "& \\;\\;\\;\\;\\;\\;\\;\\;\\; + c_2 \\times \\text{ (approximate frequency of $c_2$) } \\\\\\\\[5pt]\n", "& \\approx c_1 \\times P(Z = c_1) + c_2 \\times P(Z = c_2 ) \\\\\\\\[5pt]\n", "& = E[Z]\n", "\\end{align}\n", "\n", "\n", "Equality holds in the limit, but we can get closer and closer by using more and more samples in the average. This Law holds for almost any distribution, minus some important cases we will encounter later.\n", "\n", "##### Example\n", "____\n", "\n", "\n", "Below is a diagram of the Law of Large numbers in action for three different sequences of Poisson random variables. \n", "\n", " We sample `sample_size = 100000` Poisson random variables with parameter $\\lambda = 4.5$. (Recall the expected value of a Poisson random variable is equal to it's parameter.) We calculate the average for the first $n$ samples, for $n=1$ to `sample_size`. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "\n", "figsize( 12.5, 5 )\n", "import pymc as mc\n", "\n", "sample_size = 100000\n", "expected_value = lambda_ = 4.5\n", "poi = mc.rpoisson\n", "N_samples = range(1,sample_size,100)\n", "\n", "for k in range(3):\n", "\n", " samples = poi( lambda_, size = sample_size ) \n", " \n", " partial_average = [ samples[:i].mean() for i in N_samples ]\n", " \n", " plt.plot( N_samples, partial_average, lw=1.5,label=\"average \\\n", "of $n$ samples; seq. %d\"%k)\n", " \n", "\n", "plt.plot( N_samples, expected_valuenp.ones_like( partial_average), \n", " ls = \"--\", label = \"true expected value\", c = \"k\" )\n", "\n", "plt.ylim( 4.35, 4.65) \n", "plt.title( \"Convergence of the average of \\n random variables to its \\\n", "expected value\" )\n", "plt.ylabel( \"average of $n$ samples\" )\n", "plt.xlabel( \"# of samples, $n$\")\n", "plt.legend();" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", "For more information, type 'help(pylab)'.\n" ] }, { "output_type": "display_data", "png": 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+PDVu3JgaNGhAAQEBtGTJEpJIJGKZsLAwjYHMREQnTpwgiURC165dE9dt3ryZ\nmjdvTjKZjDw9PWnPnj1adf7xxx/k5eVFcrmcLCwsKCAgQJztRVddX375JTk5OWms+/rrrysdhExE\ndOfOHTIxMSGZTKYxKJeIKCMjg3r16kX169cna2trCgsLo6CgIHrjjTfEMroGKJdff/z4cWrXrh3V\nq1eP3NzcaMeOHVoDWgVBoPDwcPrPf/5DpqamZGNjQ99//73GfsvPbqRWq2nBggXk5uZGJiYm1KRJ\nE/Lx8aHt27cTEVF0dDT5+vpS06ZNqV69etSiRQutmW/KS01NJS8vLzIzMyOJRELHjx8nIqKkpCR6\n5513yMzMjMzMzKhfv37iAF9dRo8eLc5uRFQy288777xDDRs2JEEQ6Oeff36iGIlKBlO3b9+e6tWr\nR40aNSJPT0/68ccfiYhowoQJ1Lx5c3r48KFY/sSJE2RsbEyRkZFE9L9B1d988w1ZW1uTqakp+fv7\n0927dzXq2b17N3Xp0oVMTU2pQYMG1L59e/rvf/+rUSY8PJxatmxJJiYmpFAoaPDgweJnZ86coY4d\nO5JcLtf4Tly7do0CAgKoadOmJJPJyMHBgQIDA+nq1avitlX5zugycOBAkkgkFBERofXZZ599RgqF\ngurXr099+vShTZs2acR27NgxkkgkGgOVT58+TR4eHiSXy6l9+/Z04sQJjYHLREQ5OTn0/vvvk62t\nLZmYmJCtrS0NGjSIzp07V2m8jDHDEIieQcfRJ6RWq9GpUycolUrs27dP6/OoqChMnjwZKpUKTZo0\nQVRUFADA0dERDRo0gFQqhbGxMf7888/nHDljjLG6avTo0bh+/ToOHz5s6FAYY8xgDDomITw8HK1b\nt8bDhw+1Pvvnn38QEhKCQ4cOQalU4s6dO+JngiAgKipKYzYNxhhjjDHG2LNhsDEJmZmZiIyMxNix\nYyvst7tx40b4+flBqVQCAJo0aaLxuQEfgDDGGKvDBEHQ6FPPGGMvI4MlCZMnT8bChQvF6dPKS05O\nxt27d/HGG2+gU6dOWLdunfiZIAjo0aMHOnXqhBUrVjyvkBljjL0E1qxZg19//dXQYTDGmEEZpLvR\n/v37YWVlhQ4dOojjDMpTqVSIjY3FkSNHkJ+fjy5dusDT0xOurq6IiYmBjY0Nbt++jbfeegtubm7o\n3r378z0IxhhjjDHG6iiDJAm///479u7di8jISDx+/BgPHjzAyJEjsXbtWrGMnZ0dmjRpArlcDrlc\nDi8vL8ToSXATAAAgAElEQVTFxcHV1RU2NjYAgKZNm2LgwIH4888/tZKEn35cg+au9s/1uBhjjDHG\nGKtpubm5GDBgQI3WYdDZjQDg+PHj+Oabb7RmN7p06RJCQ0Nx6NAhFBQU4LXXXsOWLVvg6OgItVoN\nc3Nz5OXloWfPnpg9ezZ69uypsf2RI0dwdPstHHNoil5trODbvBE+3p+Mr3s3R0fbBs/zENkL4Ouv\nvxbnUWdMH24rrDq4vbCq4rbCqiM2NhZvvvlmjdZRK964XDpAbPny5QCA8ePHw83NDW+//Tbatm0L\niUSC4OBgtG7dGqmpqRg0aBAAoKioCAEBAVoJQnkSAZBKSupQF+styl5S6enphg6BvSC4rbDq4PbC\nqorbCqttDJ4keHt7w9vbG0BJclDW1KlTMXXqVI11zs7OOHfuXJX3LxBBgIB/cwSoeVYkxhhjjDHG\n9DLY7EbPi4B/nyT8+7SimJMEVoHhw4cbOgT2guC2wqqD2wurKm4rrLap80kCAIC7G7FKdOvWzdAh\nsBcEtxVWHdxeWFVxW2G1jcG7G9U0gUoyIbG7UTE/SWDaYmJi+D/QrEq4rbDqqGvtJTc3F/fv3+eX\nzdWA+/fvo2HDhoYOg9USRISGDRvCzMzMYDHU/SQBBEEQxCcJ3N2IMcYYq76cnBwAgI2NDScJNaB0\nenfGgJIk4e7duygoKIClpaVBYngpuhuVHZPAA5dZRerSL32sZnFbYdVRl9pL6c0KJwiM1TxBEGBp\naYmCggKDxVDnkwSBSk60ROAxCYwxxhhjjFVF3U8S/v1H+u+R8pMEVpGYmBhDh8BeENxWWHVwe2GM\nvahejiSh7BSoPHCZMcYYY4wxvep8kgAiSAQBktIpUDlHYBWoS/2GWc3itsKqg9sLY+xFVeeTBLG7\nEU+ByhhjjLE6JDk5GV5eXrC3t8eKFSsMHc4z165dOxw/ftzQYby06nySAPw7uxFPgcr04H7DrKq4\nrbDq4PbCalJERAS8vLyQnp6O4OBgQ4fzzAmCUOdm07p37x4CAwNhZ2eHdu3aYceOHYYOSae6/54E\nAiAIPAUqY4wxxp5KUVERjIxqz61TZmYmXn31VUOHwaph2rRpkMlkSEpKwvnz5zF06FC4u7vDzc3N\n0KFpqfNPEgRQyRuXJTwFKtON+w2zquK2wqqD28vztWjRInh4eMDe3h5dunTBgQMHAADh4eEYPXq0\nRtkZM2ZgxowZAIAbN25g5MiRaNGiBTp06ICffvpJLNeuXTtERESgW7dusLe3h1qt1llPqbi4OHh7\ne8Pe3h7vvfcexowZg7lz51ZaV3lJSUno168fnJyc0LVrVxw8eFD8bMCAAYiJicEnn3wCe3t7pKam\nPtW5Ky88PBzu7u6wt7fHa6+9hujoaAC6zzFQcq4WL14snquJEyfi1q1bePfdd+Hg4ICBAwfi/v37\nGuUXLVqELl26wNnZGaGhoTrfC6DvvOmKFSi5KZ82bVq1jrGy+s6fPw8fHx/Y29sjKCgIQUFB4vXV\nJy8vD/v378fMmTNhamoKT09PvPPOO9i6dWul2xpC7UmHa4gAoItDQ/ybI3B3I8YYY6wGLDuViZSc\nR0+1j+aWcrzfRfnE2zs5OSEyMhIKhQK7du3ChAkTcPbsWfj5+WHhwoXIzc2FmZkZ1Go19u7di3Xr\n1qG4uBjDhw9Hnz59sHr1aly/fh0DBw6Ei4sLfH19AQA7d+7E1q1bYWlpCalUWmE9Z86cgUKhQGFh\nIQIDAxEaGoqgoCD88ssvGDt2LD788EMQUaV1lVKpVBg+fDgCAwOxa9cunDp1CgEBATh69ChcXFyw\nZ88e9O/fH4MHD8aIESOe6ryXl5ycjJUrV+Lo0aNQKBTIzMxEUVGR3nNsZWUFQRCwf/9+7N69GyqV\nCj4+PoiPj8eSJUvg6uqKIUOGYPny5Zg+fbpY1/bt27Fjxw6Ymppi2LBh+OabbzBr1iyNePRdIzs7\nO52xAsDChQurfYz66uvWrRtGjBiBDz74AMHBwThw4ACCg4Px0UcfVXpeU1JSYGRkBGdnZ3Gdu7s7\nTp48WfWL8xzV+ScJoztaw6mxvGSGI4EHLrOKcb9hVlXcVlh1cHt5vgYMGACFQgEAGDhwIJydnREb\nGwulUom2bduKv3pHR0dDLpfDw8MDsbGxyMnJwdSpU2FkZAQHBwcEBgZi586dAEr6xY8bNw42NjaQ\nyWR66wGAM2fOQK1WY9y4cZBKpejbty86duwIADh79qzeuso6c+YM8vPzMWnSJBgZGaF79+7o1auX\nVh92quKPn3FxcVi1ahXmzp2LAwcOYO/evQgNDa2wrFQqRWFhIS5dugSVSgWlUglHR8dKjx0Axo0b\nhyZNmsDa2hqenp7o3Lkz2rRpA5lMhj59+iA+Pl4sKwgCxo4dCxsbG1hYWODjjz+u8Fzou0ZGRkY6\nY9VH3zHqq6/0+k6YMAFSqRT9+/dHhw4dqnQN8vLyYG5urrHOzMwMubm5Vdr+eavzTxII//vySASB\np0BljDHGasDTPAF4VjZv3oxly5YhPT0dQMlNWU5ODgDA398fO3bswJAhQ7B9+3b4+/sDADIyMpCd\nnQ0nJydxP2q1Gl27dhWXbW1tK63n7t27AEq6qVhbW2uUt7W1BREhMzOz0rpK3bhxQ6teOzs73Lhx\nQ2NdVQf23r59G66uroiKisKsWbNARAgLC6uwrLOzM+bNm4f58+fj0qVL8PX1xZdffolmzZrpPccA\n0LRpU/FvuVyusSyTybRuiMseo1KpRHZ2tlY8+q6Rk5OTzlj10XeM+urLzs7Wur52dnZVStbq16+P\nhw8faqx78OABzMzMKt3WEOr8kwShzDWT8pMEpgP3G2ZVxW2FVQe3l+cnIyMDkydPxoIFC5Camoq0\ntDS0atVKvHnr378/Tp48iaysLERGRopJglKphIODA9LS0sR/0tPTsXnzZnHfZW/EK6unWbNmWjfy\nmZmZEAQBtra2ldZVytraGtevX9e4+czIyICNjc0TnZ8ePXogKioKgwcPBgD8+eefen8B9/PzQ2Rk\nJOLi4iAIAubMmYPMzExMmjRJ57FXpLKb5+vXr4t/Z2ZmVnhzX9k1qijWqtC1nb7rpFAotK5vRkZG\nlZK15s2bo6ioSGP8SEJCAlq1alWleJ+3Op8koMyTBKlE4DEJjDHGWB2Ul5cHQRBgaWmJ4uJibNiw\nARcvXhQ/b9KkCV5//XWEhITA0dERrq6uAAAPDw+YmZkhIiICjx49glqtRmJiIv7+++8nqqdz586Q\nSqVYsWIFioqKEBkZKe6rOnV16tQJcrkcERERUKlUiImJwaFDhzBo0CCNclXtbgQAJ06cgLe3NwBg\ny5YtGDlyJH777TetcleuXEF0dDQKCgogk8kgk8kgkUiQl5cHiUSi89iri4iwatUqZGVl4d69e/ju\nu++0jg/Qf950xVoqJCQEISEhVT7Gyup79dVXIZVKsXz5cqhUKuzbt09nWymvfv366Nu3L7766ivk\n5+fjjz/+wMGDB8XErbap80lC2e+ORBB4diNWIe43zKqK2wqrDm4vz4+bmxtCQkLQq1cvuLm54eLF\ni/D09NQo4+/vj+joaPj5+YnrJBIJNm3ahPj4eHTs2BGurq6YPHmyVreQqtZjYmKCtWvXYv369XB2\ndsa2bdvQs2dPmJiYVKsuY2NjbNy4Eb/99htcXV0xffp0/Pjjj3BxcdEoV9XuRvn5+WjYsCEaNGgA\nADA1NcWdO3fQqFEjrbKFhYX44osv4OrqilatWuHu3bv4/PPP0bJly0rPcXll4yv/3gNBEODv7w8/\nPz907NgRzs7OmDJlitY+9J03XbGWysrKqjBGfdtJpVKd9RkbG2Pt2rXYtGkTmjdvjt27d6Nv374a\nydrgwYOxaNGiCs/HN998g8ePH6Nly5YYP348vv32W7Rs2VLvOTQUgaqTgr5Ajhw5gqPbb6FVbzf0\n6e4IAHh3fTy6O1ngw9ftDBscq3ViYmK4WwCrEm4rrDrqUnvJysp64q4uL7sePXogKCgIw4YNM3Qo\ntUr79u3FF8LVhMLCQnh7eyMmJgZSqbRG6gBKnlbY2Nhozcr0LOj63sXGxuLNN9985vWVVeefJKDc\nmATubsQqUlf+J85qHrcVVh3cXl5Ov//+O27evImioiJs2rQJly5dqvEbOqbNxMQEp06dqtEEoS6r\n87Mble1vVNLdiJMExhhjjNWc5ORkjBkzBvn5+XB0dMSaNWtgZWVl6LBYDapqt68XSd1PEsoNXOYp\nUFlF6lKXAFazuK2w6uD28nIaNWoURo0aZegwar1z584ZOoRnYunSpYYOoUa8XN2NJEAxP0lgjDHG\nGGNMr5cgSSj/MjVOEpg2/qWPVRW3FVYd3F4YYy+qlyBJ+N+fUp4ClTHGGGOMsUrV/SShDKkE/CSB\nVYjnMmdVxW2FVQe3F8bYi6rOJwlUrNndiMckMMYYY4wxpl+dTxLKKpndiJMEpo37DbOq4rbCqoPb\nC2PsRVXnk4SyTxKamBoj458C1NGXTDPGGGOMMfZM1PkkoSwPZQPczC1E5v0CQ4fCahnuN8yqitsK\nqw5uL4yxF1XdTxLKPDXoYGMOAIjPzjVUNIwxxhhjjNV6dT9JKKNBPSkAoKCI50FlmrjfMKsqbius\nOri9sJqUnJwMLy8v2NvbY8WKFYYO55lr164djh8/bugwXlp1PkkoOyZBKggAADXPcMQYY4yxF1xE\nRAS8vLyQnp6O4OBgQ4fzzAmCAOHfe7e6YsWKFfD19YW1tTVCQkIMHY5edT5JKNu0pJJ/kwTOEVg5\n3G+YVRW3FVYd3F7qlqKiIkOHoCEzMxMtW7Y0dBisGqytrTF16lQEBAQYOpRK1fkkoexMRv/mCPwk\ngTHGGKuDFi1aBA8PD9jb26NLly44cOAAACA8PByjR4/WKDtjxgzMmDEDAHDjxg2MHDkSLVq0QIcO\nHfDTTz+J5dq1a4eIiAh069YN9vb2UKvVOuspFRcXB29vb9jb2+O9997DmDFjMHfu3ErrKi8pKQn9\n+vWDk5MTunbtioMHD4qfDRgwADExMfjkk09gb2+P1NTUpzp35YWHh8Pd3R329vZ47bXXEB0dDUD3\nOQZKztXixYvFczVx4kTcunUL7777LhwcHDBw4EDcv39fo/yiRYvQpUsXODs7IzQ0FAUFFU8uo++8\n6YoVAKZNm4Zp06ZV6xgrq+/8+fPw8fGBvb09goKCEBQUJF7fyvTt2xfvvPMOGjVqVKXyhmRk6ABq\nXJl84H9PEjhJYJq43zCrKm4rrDpepvZy8bNFeHAh+an20aCNK1r9d9ITb+/k5ITIyEgoFArs2rUL\nEyZMwNmzZ+Hn54eFCxciNzcXZmZmUKvV2Lt3L9atW4fi4mIMHz4cffr0werVq3H9+nUMHDgQLi4u\n8PX1BQDs3LkTW7duhaWlJaRSaYX1nDlzBgqFAoWFhQgMDERoaCiCgoLwyy+/YOzYsfjwww9BRJXW\nVUqlUmH48OEIDAzErl27cOrUKQQEBODo0aNwcXHBnj170L9/fwwePBgjRox4qvNeXnJyMlauXImj\nR49CoVAgMzNTfIqi6xxbWVlBEATs378fu3fvhkqlgo+PD+Lj47FkyRK4urpiyJAhWL58OaZPny7W\ntX37duzYsQOmpqYYNmwYvvnmG8yaNUsjHn3XyM7OTmesALBw4cJqH6O++rp164YRI0bggw8+QHBw\nMA4cOIDg4GB89NFHz/Qa1AYvwZOE//0tEQQI4CcJjDHGWF00YMAAKBQKAMDAgQPh7OyM2NhYKJVK\ntG3bVvzVOzo6GnK5HB4eHoiNjUVOTg6mTp0KIyMjODg4IDAwEDt37gRQ0i9+3LhxsLGxgUwm01sP\nAJw5cwZqtRrjxo2DVCpF37590bFjRwDA2bNn9dZV1pkzZ5Cfn49JkybByMgI3bt3R69evbBjxw6N\nclV991NcXBxWrVqFuXPn4sCBA9i7dy9CQ0MrLCuVSlFYWIhLly5BpVJBqVTC0dGx0mMHgHHjxqFJ\nkyawtraGp6cnOnfujDZt2kAmk6FPnz6Ij48XywqCgLFjx8LGxgYWFhb4+OOPKzwX+q6RkZGRzlj1\n0XeM+uorvb4TJkyAVCpF//790aFDhypdgxeNQZ8kqNVqdOrUCUqlEvv27dP6PCoqCpMnT4ZKpUKT\nJk0QFRUFADh48CAmTZoEtVqNsWPH4pNPPtFdSbkvT8lbl5/lUbC6ICYm5qX6xY89OW4rrDpepvby\nNE8AnpXNmzdj2bJlSE9PBwDk5eUhJycHAODv748dO3ZgyJAh2L59O/z9/QEAGRkZyM7OhpOTk7gf\ntVqNrl27isu2traV1nP37l0AJd1UrK2tNcrb2tqCiJCZmVlpXaVu3LihVa+dnR1u3Lihsa6qA3tv\n374NV1dXREVFYdasWSAihIWFVVjW2dkZ8+bNw/z583Hp0iX4+vriyy+/RLNmzfSeYwBo2rSp+Ldc\nLtdYlslkyM3VnIa+7DEqlUpkZ2drxaPvGjk5OemMVR99x6ivvuzsbK3ra2dnVydf1GvQJwnh4eFo\n3bp1hQ38n3/+QUhICPbt24cLFy5g+/btAEouUmhoKA4ePIjExERs2rQJFy9e1F1J+SRB4CcJjDHG\nWF2TkZGByZMnY8GCBUhNTUVaWhpatWol3rz1798fJ0+eRFZWFiIjI8UkQalUwsHBAWlpaeI/6enp\n2Lx5s7jvsvcpldXTrFkzrRv5zMxMCIIAW1vbSusqZW1tjevXr2vcfGZkZMDGxuaJzk+PHj0QFRWF\nwYMHAwD+/PNPvb+A+/n5ITIyEnFxcRAEAXPmzEFmZiYmTZqk89grUtnN8/Xr18W/MzMzK7y5r+wa\nVRRrVejaTt91UigUWtc3IyOjzs3CBBgwScjMzERkZCTGjh1bYQPauHEj/Pz8oFQqAQBNmjQBUNKo\nXVxc4OjoCGNjYwwdOhR79uzRWU/5XUslAorrYLbHns7L8ksfe3rcVlh1cHt5fvLy8iAIAiwtLVFc\nXIwNGzZo/IjYpEkTvP766wgJCYGjoyNcXV0BAB4eHjAzM0NERAQePXoEtVqNxMRE/P33309UT+fO\nnSGVSrFixQoUFRUhMjJS3Fd16urUqRPkcjkiIiKgUqkQExODQ4cOYdCgQRrlqvML9okTJ+Dt7Q0A\n2LJlC0aOHInffvtNq9yVK1cQHR2NgoICyGQyyGQySCQS5OXlQSKR6Dz26iIirFq1CllZWbh37x6+\n++47reMD9J83XbGWCgkJqXCqUX3b6avv1VdfhVQqxfLly6FSqbBv3z6dbaUiarUajx8/hlqtRnFx\nMQoKCqBWq5/g7NU8gyUJkydPxsKFCzUuZFnJycm4e/cu3njjDXTq1Anr1q0DUJJx2tnZieWUSqVG\nFqqlou5G/C41xhhjrE5xc3NDSEgIevXqBTc3N1y8eBGenp4aZfz9/REdHQ0/Pz9xnUQiwaZNmxAf\nH4+OHTvC1dUVkydPxsOHD5+oHhMTE6xduxbr16+Hs7Mztm3bhp49e8LExKRadRkbG2Pjxo347bff\n4OrqiunTp+PHH3+Ei4uLRrmq/oKdn5+Phg0bokGDBgAAU1NT3Llzp8JZdgoLC/HFF1/A1dUVrVq1\nwt27d/H555+jZcuWlZ7j8srGV/69B4IgwN/fH35+fujYsSOcnZ0xZcoUrX3oO2+6Yi2VlZVVYYz6\ntpNKpTrrMzY2xtq1a7Fp0yY0b94cu3fvRt++fTWStcGDB2PRokUVno+FCxfC1tYW4eHh2Lp1K2xs\nbPDtt9/qPYeGIpABOlHt378fv/zyC5YuXYqoqCh8++23WmMSQkNDERsbiyNHjiA/P1+cZuv8+fM4\nePCg+GbB9evX4/Tp01i8eLHG9keOHMHkD+bAyc0Fbm5N0bBhQ7zyyisIv9oQ3Z0s0JGuAfjfrzyl\nc1nz8su5vGzZMrzyyiu1Jh5err3LZee9rw3x8HLtXq5L7cXZ2fmJu7q87Hr06IGgoCAMGzbM0KHU\nKu3btxdfCFcTCgsL4e3tjZiYGEil0hqpAyh5WmFjY6M1K9OzcPHiRXHMR0xMjDgWZOzYsXjzzTef\neX1lGSRJmDlzJtatWwcjIyM8fvwYDx48gJ+fH9auXSuWmT9/Ph49eiQOqhk7dizefvttKJVKhIWF\niXMFf/XVV5BIJFqDl48cOYKj22+heWc7DBzoLq4fujEer9k1xOTu9lWKNfthAa7ceYRuThZPedSs\nNnuZBheyp8NthVVHXWovWVlZnCRU0e+//47mzZvD0tIS27Ztw7Rp0xAbGwsrKytDh1ar1HSS8LzU\nZJKg63sXGxtb40mCQbobzZs3DxkZGUhLS8PmzZvh6+urkSAA/3tJiFqtRn5+Pk6fPo3WrVujU6dO\nSE5OxtWrV1FYWIgtW7agf//+uivTGrhcvTEJc35LwxdH0vDgcVHlhdkLq678T5zVPG4rrDq4vbyc\nkpOT4e3tDWdnZyxbtgxr1qzhBKGOq4sDl2vFy9RKT+zy5csBAOPHj4ebmxvefvtttG3bFhKJBMHB\nwWjdujUAYMmSJejVqxfUajWCgoLQqlUrnfum4orGJFQ9SXisKhnAkHAzD10cGlbruBhjjDH28hk1\nahRGjRpl6DBqvXPnzhk6hGdi6dKlhg6hRhg8SfD29hZH2o8fP17js6lTp2Lq1Kla2/Tu3Ru9e/eu\n0v61kgSheu9JUDaU4fqDAlzIzuUkoQ6rS10CWM3itsKqg9sLY+xFVeffuFysfronCYX/bh9z9R+e\nOpUxxhhjjL0U6n6SUKw532l1X6aWV1gyd+2Nh4WIy8qtpDR7UfEvfayquK2w6uD2whh7UdX5JIHK\nPUmQSASo/30iUJoA6JNbqEYX+4aQCMD5bE4SGGOMMcZY3VfnkwTtJwkCigk4k/kAA9eeR/y/N/5Z\nDwpwJ69Qa/u8QjUs6xvDrmE9JN/Jfy4xs+ev7FzmjOnDbYVVB7cXxtiLqu4nCVpjEkq6G6XkPAJQ\nMtYAAEZvTcTwTQkaZYkIuQVFMDORwrWJHMl38qv1+nPGGGOMMcZeRHU+SaAKniSoiWBpagwAyLpf\noHPbvzIfQE2AmYkUblb1ce9REa7987hG42WGwf2GWVVxW2HVwe2FMfaiqvNJQsWzG0Ecl3A64wF6\nrvxbazsiwqeHUgEA9WVSdHeygJFEwNyjV9Fz5d/IyVPVfPCMMcYYY4wZwEuXJEiEkilQVZW8LCG3\nzKBmiSCgkdwY7W3McO1eyZOEyzw+oU7hfsOsqritsOrg9sIYe1HV+SRBq7uRpOQpgkpdrGOLErdy\n/zeI2b6hrOTfFvXEddWZRpUxxhhj7FlLTk6Gl5cX7O3tsWLFCkOH88y1a9cOx48fN3QYL606nyRo\ndTf690lCYSVPEm7llnQnWviOC9ybmQEAbBvIxM+zHxZgc1w2Cor0JxvsxcD9hllVcVth1cHthdWk\niIgIeHl5IT09HcHBwYYO55kTBAGCIBg6jGemsLAQEydORLt27WBvbw9vb2/89ttvhg5LJyNDB1DT\ntKZAlQgorsaThLJPD2wb/i9J+OnPLABAI7kxerWwfFbhMsYYY6yWKioqgpFR7bl1yszMxKuvvmro\nMFgVFRUVQalU4sCBA1Aqlfj1118xZswYnDx5EnZ2doYOT8vL9yTh34HLlT1JuJlbCGOpAAv5//5j\nYF3mSYJY7qH2uxXYi4f7DbOq4rbCqoPby/O1aNEieHh4wN7eHl26dMGBAwcAAOHh4Rg9erRG2Rkz\nZmDGjBkAgBs3bmDkyJFo0aIFOnTogJ9++kks165dO0RERKBbt26wt7eHWq3WWU+puLg4eHt7w97e\nHu+99x7GjBmDuXPnVlpXeUlJSejXrx+cnJzQtWtXHDx4UPxswIABiImJwSeffAJ7e3ukpqY+1bkr\nLzw8HO7u7rC3t8drr72G6OhoALrPMVByrhYvXiyeq4kTJ+LWrVt499134eDggIEDB+L+/fsa5Rct\nWoQuXbrA2dkZoaGhKCioeNZJfedNV6wAMG3aNEybNq1ax1hZfefPn4ePjw/s7e0RFBSEoKAg8frq\nY2pqik8++QRKpRIA0LNnTzg4OCAuLq7SbQ2h9qTDNaRYXX4K1KqNScj45zFszGUaj7kUZibo3dIS\nvyTliOtS7z56tgEzxhhjL6Cj+y/i1o0HT7UPK+sG8O3b6om3d3JyQmRkJBQKBXbt2oUJEybg7Nmz\n8PPzw8KFC5GbmwszMzOo1Wrs3bsX69atQ3FxMYYPH44+ffpg9erVuH79OgYOHAgXFxf4+voCAHbu\n3ImtW7fC0tISUqm0wnrOnDkDhUKBwsJCBAYGIjQ0FEFBQfjll18wduxYfPjhhyCiSusqpVKpMHz4\ncAQGBmLXrl04deoUAgICcPToUbi4uGDPnj3o378/Bg8ejBEjRjzVeS8vOTkZK1euxNGjR6FQKJCZ\nmYmioiK959jKygqCIGD//v3YvXs3VCoVfHx8EB8fjyVLlsDV1RVDhgzB8uXLMX36dLGu7du3Y8eO\nHTA1NcWwYcPwzTffYNasWRrx6LtGdnZ2OmMFgIULF1b7GPXV161bN4wYMQIffPABgoODceDAAQQH\nB+Ojjz6q9nm+desWUlJS4ObmVu1tn4eX8klCMekfk0BEuHgrD25WphrrJYKAyd3txWUHi3qcJNQR\n3G+YVRW3FVYd3F6erwEDBkChUAAABg4cCGdnZ8TGxkKpVKJt27bir97R0dGQy+Xw8PBAbGwscnJy\nMHXqVBgZGcHBwQGBgYHYuXMngJJ+8ePGjYONjQ1kMpneegDgzJkzUKvVGDduHKRSKfr27YuOHTsC\nAM6ePau3rrLOnDmD/Px8TJo0CUZGRujevTt69eqFHTt2aJSr6kte4+LisGrVKsydOxcHDhzA3r17\nERoaWmFZqVSKwsJCXLp0CSqVCkqlEo6OjpUeOwCMGzcOTZo0gbW1NTw9PdG5c2e0adMGMpkMffr0\nQcL+35kAACAASURBVHx8vFhWEASMHTsWNjY2sLCwwMcff1zhudB3jYyMjHTGqo++Y9RXX+n1nTBh\nAqRSKfr3748OHTpU6RqUpVKpMH78eAwbNgwuLi7V3v55qPtPEip6mVoxdE6BSkTIvF+ABwVqtLaq\nX2EZhZkJbuYWwtelEdacuYEHj4tgJpNCUocG1zDGGGPV8TRPAJ6VzZs3Y9myZUhPTwcA5OXlISen\n5Om/v78/duzYgSFDhmD79u3w9/cHAGRkZCA7OxtOTk7iftRqNbp27Sou29raVlrP3bt3AZR0U7G2\nttYob2trW3J/kZlZaV2lbty4oVWvnZ0dbty4obGuqgN7b9++DVdXV0RFRWHWrFkgIoSFhVVY1tnZ\nGfPmzcP8+fNx6dIl+Pr64ssvv0SzZs30nmMAaNq0qfi3XC7XWJbJZMjNzdWoq+wxKpVKZGdna8Wj\n7xo5OTnpjFUffceor77s7Gyt62tnZ1flZA0ouTedMGECZDIZFixYUOXtnreX7kmCRJzdqOLuRoVq\nwtyjVyEVgHY25hWWCe/fAqv8W4lJxA+nMjF0wwU85pmOXljcb5hVFbcVVh3cXp6fjIwMTJ48GQsW\nLEBqairS0tLQqlUr8eatf//+OHnyJLKyshAZGSkmCUqlEg4ODkhLSxP/SU9Px+bNm8V9l70Rr6ye\nZs2aad3IZ2ZmQhAE2NraVlpXKWtra1y/fl3j5jMjIwM2NjZPdH569OiBqKgoDB48GADw559/6v0F\n3M/PD5GRkYiLi4MgCJgzZw4yMzMxadIkncdekcpunq9fvy7+nZmZWeHNfWXXqKJYq0LXdvquk0Kh\n0Lq+GRkZVU7WiAgTJ05ETk4Ofv75Z0il0iptZwgvQZKg4z0JOt5zkFugRurdRxjWvhlsKhioDACN\nTY1hZ1EPLZqaQiIAR1Pu4Z/HRUjjrkeMMcaYQeTl5UEQBFhaWqK4uBgbNmzAxYsXxc+bNGmC119/\nHSEhIXB0dISrqysAwMPDA2ZmZoiIiMCjR4+gVquRmJiIv//++4nq6dy5M6RSKVasWIGioiJERkaK\n+6pOXZ06dYJcLkdERARUKhViYmJw6NAhDBo0SKNcdX7BPnHiBLy9vQEAW7ZswciRIyucgvPKlSuI\njo5GQUEBZDIZZDIZJBIJ8vLyIJFIdB57dRERVq1ahaysLNy7dw/fffed1vEB+s+brlhLhYSEICQk\npMrHWFl9r776KqRSKZYvXw6VSoV9+/bpbCsVmTJlCpKTk7Fhwwax+1pt9RIkCaTxBSqZ3Uj3wOX7\nj0sGrZSd1UgXubEUDmWmSE3J4SThRcX9hllVcVth1cHt5flxc3NDSEgIevXqBTc3N1y8eBGenp4a\nZfz9/REdHQ0/Pz9xnUQiwaZNmxAfH4+OHTvC1dUV/5+98w6Tqjob+O9On9mZ2d4LuwtLb4uCilEE\nBHtBsUc0xthNovmMxvp9Rj/9LAkGjSXGFhN7CTaUjhQDUqS3ZXuv0+u99/tjdgeWXWAXdtkFzu95\neNhb5px37rz33vOet5x77rkHl8t1WP0YDAbeeecd3n33XfLz8/noo4+YPn06BoOhW33p9Xr+9a9/\nsWDBAgoKCvj973/PK6+80iF+vasz2F6vl9jYWOx2OxCptNPQ0EB8fHyHc4PBII8//jgFBQUMGzaM\npqYmHn30UYYMGXLIa7w/+8q3/7oHkiQxc+ZMLr/8csaNG0d+fj6/+93vOrRxsOt2IFnbqKqq6lTG\ng31Oq9UesD+9Xs8777zDe++9x8CBA/n888+58MIL2401r7zySmbPnt2hz/Lyct5++222bNnCsGHD\nyMnJIScnp0OeSX9BUrtjgh5DLFy4kEUf1wFw7x+no9FG7KGXV1Xw3a4mhiRbWFe596bUayVCssoz\n5w/i91/v5r5JOUwrOPT6B/+7qJgle1oAuHBoEr/+Wf+rcysQCAQCwZFSVVV12KEuJzpnn302v/zl\nL7nmmmv6WpR+xdixY6MLwvUGwWCQSZMmsXz58l4N67nzzjvJyMjoUJWpJzjQfbdu3TqmTp3a4/3t\ny3HvSQCQ5c48CSoW/d6vf29r1SJnIOJJMOu6pkwD4s3Rv4uavD0hrqAPEHHDgq4idEXQHYS+nJis\nXLmS2tpawuEw7733Htu3b+/1AZ2gIwaDgVWrVvXruP/+zAlhJOxb4ahtnYSgrBBj2Ks0hlZPg9Mv\nA2DSd+3SJMfoo3/vafIj75Pr0OILcesn20SugkAgEAgEJxC7du1i0qRJ5Ofn8/LLL/Pmm2+SkpLS\n12IJepGuhn0dSxz3JVAB5PDegbtmH0+C1aCl3hMCwKCN/LhOf5snoWtGwpDkyFoKY9Kt/FTtptIZ\nIKc1T2FlqYPiZj//2lDDQ1PyDtaMoI8RccOCriJ0RdAdhL6cmNxwww3ccMMNfS1Gv2fDhg19LUKP\n8NJLL/W1CL3CCeFJkOV9PQkSikrEk2Dc60nQtxoJjtZwo656EgbEm/n456O47dRInd99k5cbvaHo\nvpJm4U0QCAQCgUAgEBwbnHhGgiZiDATCCtZOwo0+21wPRCoXdRW7SUdOnAmdRmJP4968hD2tBkOF\nI8Atn2w//C8g6HVE3LCgqwhdEXQHoS8CgeBY5YQwEpR2RkLkf/9+RkKbJ6GNroYb7f28hgHxJor2\nyT8obvYfhrQCgUAgEAgEAkHfckIYCe2qG7UmlvhDCpZOPAltdDXcaF8GJpij4UaKqlLnDlKYYY0e\nf2llBd9sb+h2u4LeR8QNC7qK0BVBdxD6IhAIjlW6NRJetGgRe/bsAaC6uppZs2bxi1/8gpqaml4R\nrqfoLNwopKjtDAPDfp4EYzc9CQADE800+8KsKnVQ3OQjrKj8LDeO308aAMC/t9bz9zVVh/MVBAKB\nQCAQCASCo0a3RsJ33HEHOl2kINK9995LOBxGkiRuueWWXhGup1D2S1xuY98Qo/1LV2kOo5TVxAFx\nADw2fw+3f7YDgFSbgXS7IXqOOyi3K5Mq6B+IuGFBVxG6IugOQl8EAsGxSrdKoFZVVZGTk0MoFOLb\nb7+ltLQUo9FIenp6b8l3ZCgKaDQdFlNro22dhJw4E+k2AzeclM7ba6sPu7tUm4Hrx6WxcHcTVc4g\nAClWA3GmyGU26TT4wwr1niBpNuNh9yMQCAQCgUAgEPQm3fIk2O12ampqWLZsGSNGjMBms6GqKqFQ\nqLfkOyIkNWIcqOo+6yTs4yCwGrS8f+1I5lwyGEmSuK4w7Yj7vH5cOm9dOSK6nWo1EGfW84+rRvDY\n2ZG1EmpdQVp8IT7fUo83KB9xn4IjR8QNC7qK0BVBdxD6IjhWKCsrIzExsd0CtD3B8uXLGTlyZI+2\nKTg6dMtIuPvuu5kwYQLXXnstd9xxBwArVqxg2LBhvSLcEdNmHOwT3bNvHoLVoCXBou9WudOucnKW\nDdhbSjUSdhTxHmyqcXPrp9v566oKPthY2+N9CwQCgUBwojFmzBiWLVvW12L0GU8//TS33XZbX4sh\nOI7olpFw//33M3/+fFauXMk111wDQFZWFq+//nqvCHfEqBFreF9PQqxpb4RVjKGjcfDQlFzuPSPn\niLv+n2n5fDZrdLt9yTF6NBK8s64GX0gh3WZg7tYGfCGZnQ2R9RWCsoJHeBeOOiJuWNBVhK4IuoPQ\nl6OHJEnt3vf7Ew6Hj6I0AsGxT7dL+JSWlvLkk09y4YUXAuB0Oqmvr+9xwXoCibZwo737DmUkTMqP\n59whiUfct16r6dC+XqthUKIFiHgafnFyBp6gzKwPtnLX5zvYWe/lgW92c/37W1hT7uQvK8oP+sAT\nCAQCgUAAt912GxUVFVx77bXk5OQwZ86caPjMu+++y+jRo5kxYwYrVqzoEPoyZswYli5dCkQmFWfP\nns1JJ53EoEGDuOmmm2hpaTlgv99++y1nnnkmeXl5nHvuuWzduhWATz/9lMLCQlwuFwDz589n2LBh\nNDU1AZCYmMhrr73GuHHjKCgo4LHHHmv3vn/33Xc59dRTyc/PZ+bMmVRUVESPbdu2jRkzZjBw4ECG\nDh3Kn//8ZxYuXMjs2bP57LPPyMnJYdKkSUBkjHb33XczfPhwRowYwZNPPhkNJ1IUhUceeYSCggLG\njRvHd999d8Dv+cILL3DjjTe22/fAAw/wwAMPAPDPf/6TU089lZycHMaNG8dbb711wLYSExMpKSmJ\nbt955508+eSTh7ymgqNPt4yEOXPmcPvtt1NQUBB16ZlMJh5++OFeEe6I6SQnYV8jwWrs+TCjQ5HR\nWulocLKFoSkRg8Hhj8xu3PXvHWyu8eAOyjz0bRFfbmtgU437qMt4IiLihgVdReiKoDucaPqSkJDQ\n6b+unn+4vPLKK2RlZfHee+9RVlbG3XffHT22atUq/vOf//DRRx91OvEmSVK0wuGrr77KN998w5df\nfsm2bduIi4vjvvvu67TPjRs38utf/5rZs2ezZ88ebrzxRq699lpCoRCXXXYZEyZM4IEHHqCpqYnf\n/va3/OUvf2n3Hb/++msWL17M4sWL+eabb3j33Xej+2fPns0//vEPdu/ezWmnncbNN98MgMvl4rLL\nLmPatGls27aNH3/8kTPPPJOpU6dyzz33cNlll1FWVhY1eu68804MBgNr165l6dKlLF68mHfeeQeA\nt99+m++++46lS5eyaNEi5s6d26HSYxuXX345CxYswO2OjElkWWbu3LlcccUVAKSkpPDBBx9QVlbG\niy++yMMPP8zGjRu7/Pu19XugaxoMBrvclqDn6JaR8Oc//5kFCxbwhz/8Aa02MsAeNmwY27dv7xXh\njpS2xGVln5KjdtNew6AzT0Jvc2Z+PACFGTZSrYYOx6cVJGDbx3iZt6PxqMkmEAgEAsHxxv3334/Z\nbMZkMh3y3LfeeouHHnqI9PR09Ho9v//975k7d26nybxvv/02N9xwA+PGjUOSJK6++mqMRiNr1qwB\n4Nlnn+X777/n4osv5txzz2XatGntPv/rX/+a2NhYsrKyuO222/j0008BePPNN/ntb39LQUEBGo2G\ne+65h82bN1NRUcF3331HWload9xxBwaDAavVykknnQREJkT3NYLq6upYsGABTz75JGazmaSkJG6/\n/XY+++wzAD7//HNuv/12MjIyiIuL45577jlg9EJWVhajR4/mq6++AmDZsmWYzeZo39OmTWPAgMia\nUBMnTmTy5MmsWrXqkNe7q9f0xx9/7HZbgiOnWyVQ3W432dnZ7fYFg0GMxn5azrPtpt5H5/dNUu4L\nI+FnuXF8/PNR2Fs9Gg9NycWs1xCUVXwhmWkFiTy1uITFRc0ArK9yo6rqAa17Qc+wfPnyE27GT3B4\nCF0RdIcTTV/awml66/zDITMzs8vnlpeXc/3116PR7J1D1el01NXVkZaW1uHcDz74gL/97W/RfeFw\nOLrArN1u5+KLL+bll1+Ozt4fSK6srCyqq6uj7T744IM88sgj7c6vqqqisrIyOhjvyncJhULtisso\nikJWVhYANTU1HWQ4GDNnzuSTTz7hqquu4uOPP2bmzJnRY/Pnz+eZZ55hz549KIqCz+dj+PDhXZJz\nf5kPdk0FR5duGQlnnHEGTz/9dLvwojlz5jB58uQeF6xn6BhutC/7Vjo6mtj3CXma1OpZ2Jd0W8TD\nkGDR0egNUeUMkhnbTw0xgUAgEAj6AQeaTNt3v8ViwefzRbdlWaaxca/HPisrizlz5jBhwoRD9peV\nlcW9997Lvffe2+nxTZs28a9//YuZM2dy//3389FHH7U7XlFRwZAhQ6J/t605lZWVxX333cfll1/e\noc3y8vKoJ2B/9jVsIGKEGI1GioqKOhwDSEtLo7Kysp08B+Piiy/mkUceoaqqiq+//jqawxAIBLjx\nxht55ZVXOP/889FqtVx//fUHHHtZLBa8Xm90u7a2NmqsHOqaCo4u3c5J+OyzzxgwYABut5vBgwfz\nwQcf8Pzzzx9W57IsU1hYyEUXXdTh2JIlS4iNjaWwsJDCwkL++Mc/Ro/l5uYyevRoCgsLD3oj710n\n4bDE6zMuG5nCpLw4HpycC8BGkZfQ65xIM32CI0PoiqA7CH05eiQnJ1NcXHzQcwYNGkQgEGD+/PmE\nQiGee+45AoFA9PiNN97IE088ER0wNzQ08M0333Ta1qxZs3jzzTdZu3Ytqqri8Xj47rvvcLvd+P1+\nbr31Vh599FHmzJlDdXU1b7zxRrvPv/jiizgcDioqKnj11VeZMWMGAL/4xS/405/+FA3ldjqdfP75\n5wCcc8451NbW8sorrxAIBHC5XKxduxaI5AWUlZVFB+dpaWlMnjyZhx56CJfLhaIoFBcXs3LlSgAu\nvfRSXn31VaqqqmhpaeGFF1446LVLSkri9NNP58477yQ3N5eCggIgElESDAZJTExEo9Ewf/58Fi9e\nfMB2Ro4cyccff4wsyyxYsKBdWNLBrqng6NMtIyEjI4M1a9bw4Ycf8s9//pN33nmHNWvWHPaKyy+8\n8ALDhw8/oPU/adIk1q9fz/r169u53SRJYsmSJaxfv57Vq1cfuINOEpePBewmHQ9NzWNkmhWbUcv2\nOk9fiyQQCAQCQb/mnnvu4fnnnycvL4+XXnoJ6OhdsNvtPPvss/zmN79h5MiRxMTEtAu5ue222zj3\n3HO5/PLLycnJ4ZxzzmHdunWd9jd27Fhmz57N/fffT35+PuPHj+f9998H4PHHHyc7O5sbb7wRg8HA\nq6++ypNPPtnOiDn//POZPHkyZ511Fueccw4///nPAbjgggv4zW9+w80338yAAQM4/fTTWbRoEQBW\nq5VPPvmEb7/9lmHDhjFhwgRWrFgBwCWXXALAwIEDmTJlCgB//etfCYVCnHbaaeTn5/OLX/yC2trI\n+kyzZs1iypQpnHnmmUyZMoWLLrrokKHNM2fOZNmyZe28HDabjaeffpqbbrqJ/Px8Pv30U84777x2\nn9u33aeeeop58+aRl5fHJ598wgUXXNClayo4+kjqIUbQCxcu7FI8fJtCdpWKigpuvPFGHnroIf70\npz/xxRdftDu+ZMkSnn/++Q77AfLy8vjxxx9JTDxwqdKFCxfy/Vu7CFljueiasQwZtTeWcEOVC2cg\nzJl5HUN9+hsPzttNoyfE/0zPJznGgFYjchN6gxMtblhw+AhdEXSH40lfqqqqyMjI6GsxjgsSExNZ\nu3Ytubm5fS2KoJ9zoPtu3bp1TJ06tVf7PmROwi9/+csuGQmHcvHtzz333MOzzz6L0+ns9LgkSaxc\nuZIxY8aQmZnJc889F02CkSSJs88+G61Wy6233sqvfvWrzjtp8yQo7e2gsRm2bsnalwxLieEf62qY\n9cFWzh2cyL1nHvlCbwKBQCAQCAQCwcE4pJGw74IXPcWXX35JSkoKhYWFLFmypNNzxo0bR3l5ORaL\nhW+++YZLL72UnTt3ArBixQrS09Opr69n2rRpDB06lDPOOKNjI52suHysMSwlJvr3vJ2NNHpDPHFO\nvqh21MMcLzN9gt5H6IqgOwh9EXSGeIcLjgW6Vd2op1i5ciVz587l66+/xu/343Q6mTVrVrsSYTbb\n3tn+8847jzvuuIOmpiYSEhKiORDJycnMmDGD1atXd2okfL7yHWyJWVR7v2fQkCxGjRoVfWAvX74c\noN9vjxl/KgDOog0ArGEsX21vJLZhG5Ik9bl8Yltsi22xLbZPjO38/HwEPUNDQ0NfiyA4RnA4HOzZ\nsweI3ItlZWUA0QX2epND5iTsSyAQ4IknnuC9996LxkhdffXVPPzww11apKQzli5dynPPPdch96C2\ntpaUlBQkSWL16tVceeWVlJSU4PV6kWUZm82Gx+Nh+vTpPPbYY0yfPr3d5xcuXMjy17cSjEvivJmj\nGDGu63WS+xvTX18PwDPnD+LxBcW4gzLPnj+IMcdQ2FR/53iKGxb0LkJXBN3heNIXkZMgEBx9+nVO\nwr7cfvvt7Ny5kzlz5pCTk0NZWRlPPvkklZWVvPnmm4ctxL7LoQPceuutfPzxx7z88svodDosFks0\nu72mpobLLrsMiCywcd1113UwEKLtHqPVjfbn8en5KKrK2AwbL88YyvUfbGFdpUsYCQKBQCAQCASC\nXqFbnoSEhASKioqIj99bFaipqYmBAwfS3NzcKwIeLgsXLmTFa5sIJKRyzmUjGXVyVl+L1GP8Zu4O\n6j0hHP4w/zMtn5Oz7H0tkkAgEAiOc9oWHUtISBAx9QJBL6OqanRF8s6qefY7T0J6ejper7edkeDz\n+fqt+1E6xIrLxyrjMu38c31kifI3f6wSRoJAIBAIep3ExETcbjdVVVXCSBAIehlVVYmNjcVqtfaZ\nDN0yEq6//nrOO+887rrrLrKzsykrK+Ovf/0rs2bNii70Ad1fM6HXUDovgXqsc/aghKiRUNrsxxeS\nMeu1fSzVsc3xFDcs6F2Ergi6w/GmL1artU8HLcczx5uuCI59umUkvPLKK0Bktbw2VFXllVdeiR6D\n7q+Z0FtI0RKofSxID5MZa2TmqBQavSEWFzWzucbD+GzhTRAIBAKBQCAQ9AzdMhJ6Y82Eo8HxFm4E\ncMspmQTCCstLWlhX6RRGwhEiZm8EXUXoiqA7CH0RdBWhK4L+hqavBehVotWN+liOXsKo0zA6zcqK\nUgfK8folBQKBQCAQCARHnW4ZCS0tLTz++OPMmDGDadOmRf8dqARpX3O8lEA9GFMHJVDjCvJTlbuv\nRTmmaVssSCA4FEJXBN1B6IugqwhdEfQ3uhVudMUVV6AoCjNmzGi3eFq/rXIQzUk4fo2E03NjSfpR\nz/8tKeHNK4eLBGaBQCAQCAQCwRHTLSNh9erV1NXVYTQae0uenuU4DzcCMOu13HFqFo8vLKak2c+w\nlJi+FumYRMSCCrqK0BVBdxD6IugqQlcE/Y1uhRtNnDiR7du395YsPc6JEG4EkB0XMdqqnIE+lkQg\nEAgEAoFAcDzQLU/CW2+9xXnnncdpp51GampqdPAtSRKPPvporwh4RBynJVD3J80WMRKqW40Ehz/M\not1NnDskUYQfdRFRn1rQVYSuCLqD0BdBVxG6IuhvdMtIePDBB6msrKS2than09lbMvUY+3sSZF+A\nZafMZMRzD5Ay/fS+FK1HMeo0JFn0VLmCqKrKb+bupMoZQFZh5qiUvhZPIBAIBAKBQHCM0S0j4cMP\nP2THjh1kZGT0ljw9S5uREHEo4CuvJlDXyPbHXjiujASAdLuRXQ1ettZ6omFHy4tbhJHQRcTsjaCr\nCF0RdAehL4KuInRF0N/oVk5CXl4eer2+t2Tpcfb3JKjhcOR/WekzmXqLswsSKG32c8+Xu9BKMGNk\nMlvrPDj94b4WTSAQCAQCgUBwjNEtI2HWrFlccsklvPfeeyxatKjdv/5JeyMh7PZGtmW5zyTqLc4b\nksi4TBsAhZk2JubEArC93tOXYh0ziPrUgq4idEXQHYS+CLqK0BVBf6Nb4UYvvvgikiTx4IMPdjhW\nXFzcY0L1GCpI0t7E5bArMmBWlePPkwAwNNnCukoX4zLtDE62oJFga62HCdmxfS2aQCAQCAQCgeAY\noltGQklJSS+J0XtIkrTXk9BqJKAcn+WOrhidigpcMDRS1WhwkoXFRc1cPTYNk65bTqMTDhELKugq\nQlcE3UHoi6CrCF0R9De6ZSQA1NbWsnr1ahoaGtqtP3DTTTf1qGA9RcST0BZu1OpJOA7DjQBiDFp+\ncfLepPKbxmfw+69388ySEs7Kj+eUnFiMwlgQCAQCgUAgEByCbo0YP//8cwYOHMijjz7KLbfcwpw5\nc7j11lv5xz/+0VvyHSFqqychsnW8hxvtz9gMG6cPiGV5iYMnFpXw0aa6vhap3yJiQQVdReiKoDsI\nfRF0FaErgv5Gt4yEhx56iDfeeIP169djtVpZv349r732GuPGjest+Y6cfT0JrtbE5fDx6UnojDsn\nZnHDSenoNBLFTb6+FkcgEAgEAoFAcAzQLSOhvLycK6+8MrqtqiqzZs3inXfe6XHBegoJ8JXXAHvD\njcIuzwnjTUiKMXBdYRonZ9koa/H3tTj9FhELKugqQlcE3UHoi6CrCF0R9De6ZSSkpKRQUxMZcOfm\n5rJq1SqKiopQ+vGAW/Z4qflqSeTv1hKoqGq0HOqJQk6ciUpHAPk4TdoWCAQCgUAgEPQc3TISbr75\n5mjM3D333MPkyZMZM2YMt99+e68I1xNIqoqKhCrLe6sbAWGHqw+lOvrkxJkIKyrlDuFN6AwRCyro\nKkJXBN1B6IugqwhdEfQ3ulXd6IEHHoj+PWvWLM466yw8Hg/Dhg3rccF6DDWyWELY5YmGGwEowVAf\nCnX0Kcy0oZXgm+2NjMu04fCHmT44sa/FEggEAoFAIBD0Q7plJCxatIjc3Fzy8/Oprq7m4YcfRqvV\n8tRTT5GWltZbMh4ZrUZCyOFu50k40YyE5BgDk/Lj+WxLPZ9tqQdgVJqVdLuxjyXrH4hYUEFXEboi\n6A5CXwRdReiKoL/RrXCjO+64A50uYlfce++9hMNhJEnilltu6RXhjhRJVZEAVZIIuyJGgqSPyH+i\nGQkAJ2fZ223/7qtdNHp77zoEwgruQLjX2hcIBIITnaCs8FOViw1VLlaXO3jtP5Xc//UulhQ197Vo\nAoHgGKdbnoSqqipycnIIhUJ8++23lJaWYjQaSU9P7y35jhhJAiQNYaeHsNuLITGOQE3DCWkkjEyL\nif49Jt3KT9VulhQ1c/molB7vq84d5Ofvb2FIsoU5lwzp8fZ7muXLl4tZHEGX6K6u+EIyP1W7WbS7\nCWdARlZUXIEw5w9N4qJhSUiS1IvSCvqaA+mLqqpsqHJT7wkyONlCqtWAWa/tUpvBsMLCVn36Zkcj\nVc5A9JgExFt0/O/iEiocfnLjzZj0GmRFxaDVMDrdilYjEZQVNJKEBGg1Qgf7A+I9JOhvdMtIsNvt\n1NTUsGXLFkaMGIHNZiMQCBAK9d8BtyS19ySYs9IiRkIgCEDlh98QMzCbuJNG9rGkvU+q1YBFl6/F\nFwAAIABJREFUr2HKoATunpjFzHc3UdTkwxeSMeu1KKrKT9VuxqRbCYQVttd5Kcy0HVZff11VAcCO\nei9lzX7qPEHyEswkWvQ9+ZUEgn5NabOPh74tos4dwmbUkmI1oNNIaDUSL66s4IOfagnJKsNSYzgr\nP55KZ4BKh59Ei55rxqYRY+h80BgIK/xQ5iDdbmRgglkM8vo5Vc4AlY4AVqMWjQQLdjWzodpFaXP7\nQhI2o5b8BDMj06wUN/mwGLRYDVry4k24gzJWgxZfWOGrbQ2UOyKGQYpVz8NTcok16dBqJHLiTFgM\nWh79roh31tV0kCUr1kiiRc/mGjeKCmprv1MGxnNKTmz0Oe0KhFFV8IUUVpc78IYU8hJMjM+y95hh\nW+0KEGvUYTmAnvcEqqriCsjYTbqoYdbgDRJr0hFWVAYnWdBIErXuIFmxRmzGbg2LBILjmm7dDXff\nfTcTJkwgEAgwe/ZsAFasWNG/E5cB2JuTYEiMA/aGG2369R8BOLdmZZ9Jd7SQJInPbxiDqkZWos6K\nNbJgVxPrKpy8f90o3l5bzXsbanlwci7LiptZXuLg9lMzmTFyr6fhn+tryI41cmZ+/AH7qXT4WVnq\n4KRMG2srXdz8yTYAzsyL4+Gped2W2xOUDzhY6g5BWeGllRWY9RpuOzWr3TExeyM4GFXOACFZISSr\njD751EOer6oqf1tdxZfbGjDrNfz3tDxOzrJj0EYiPBVVZcGuJr7Y1hAxzqtcrCp1AJBk0dPgDbGx\n2k28Rc+uBi9mnQZfSEFRVUx6LZ6gjMMfCeWzGbVk2iMDv6xYIzNGpuAOymTHGoWX4igQkhUWFzVj\nMWgZm25ldbkTX1jBqNVQ2uyj1JvO6o+2sm/1ab1WYmhyDLecksmYdCsVjgD17iA1riDrqpz8c30N\nRq1EWFFbZ/3bl67OtBt54px8Mu0mUm0Rw3N/Hj07nx8rnJh0GtwBmWSrnrKWAEuKmnH4w8wYmYJR\np0ECttd7+PfWBv69tQGAvHgTpS1+OquYHW/WEW/WYdRpuHZsGiNSYwgpKnEmHYoKZS1+1lW6SLUa\nGJxsIcVqwOkP80OZA1kFf0hmdbmTaleQKmcAg1ZiRGpkYqow08aQZAuuQJhqZxC9VsKo07Cl1sOw\nZAsXDEvCrNdS7QrwnzInaTYDQ5MtxJk7Tj45/WE217r5++oqyh0B9BoJSaLDtdyfpBg9Fw9P4opR\nqUfd+BbvIUF/o1tGwv3338+ll16KVqtl0KBBAGRlZfH666/3inA9gSRJIEkEautBVdG3GglqP/Z+\n9DZtAwd764xJky9MrSvIJ5vqAPhkcx076iPrSHy4sY5LRyQjSRJ17iBvr60G4P9MOlz+MCPSrB28\nAz9WRMrL3n5aFn9cUExGrJHNNW5WlzsJyQp6rYawovLqD5UoqkpBkoXpgxPQdDKg2VDl4vdf7+au\niVlcPDz5iL73P9fX8M2ORgBOy4llTMbheUkExzeBsMKqUgcba9wkx+ipcASYv6spelyvkbhqTCqX\nj0rp1Hgta/Hz7Y5GPt5Ux8lZNu4+PZt0W/sCARpJYvrgxGiFsWZfiCZviMxYEyadhnk7GnlpVQXV\nriCj0qy4AmGKGn0MS4nBqJOQJInpBQm4gzIbq93UuoNUOAKsKnPwwcbIfTw8JYbhqTH4QjJ17hA7\nG7wMSjTz+PR8NJJEpSNAZqzxgAOhGleA9ZWRezk3wczgJEu/81ioqoo/HHmmBMJKp79HnTvIsj3N\nbKr1oAHsJh0XDE1icLKly/0EZYVgWGFZcQsOf5h6d4itdW6CskogrFDvOfD7RKeROG9IIlMHJeAN\nyXiDCiPTYkiKMUTPKUjaK4uiqvhDCma9BlmNfMcGTwi7SYcrEMag1RBv1h3SADTpNPwsN67dvhGp\nVs4b0rGqnaKqlDb7qfcE2V7nZV2li/OGJKLTSCgqTB4YT4xBy8ZqN5tq3LiDMtXOAI98t4c2lYg1\n6Wjxhdl/CG7SRX6bffcnxegZkRrDWflxuAIy2+o8hBSVf67v6Ploa/v74ha+2NZAjEFLSbOfcKsF\nY9BGvCdN3hAj06zoNBKVzkD0HZZg0XHN2FRafGFcgTCFGTZGplmpdQfRSFDnDhGSFUx6LS2+EJtq\n3Lyxppo31lRTmGElw26ktNlPut3IgDgTQ1NiiDfryLBH7p1mb4had5AhyRZhlAuOOyRVVY/L1bUW\nLlzIj88tJZg1AGPJbk7P11D21qfk/HImZX//mLGvPUHaxVOYlzYRgLN3zUdnizl4o8cZ/7uomCV7\nWgA4Iy+O74tbSDDraPJFZih/OT6Dv6+p4sVLh+AJytz/9e4ObWTFGvnVhEwW7W5iVLqVFl+YxUXN\nqKi8deWIqNdiZWkL/z2/mP+elseE7Fg+2ljLmz9WR9vJtBt5+rxBpNoiL05XINLOpho3S1tl1Gsk\nBiWZef7Cwe1mzhRVZVO1m5Fp1gMOYhYXNfPMkhImZMeytc7DuEwbf5icGz3e1VhQVyDM/y0p5aRM\nWzsPi+DYZWuth7+vqcKk0+Dwhylq9CKrYNZHZu8B8hPMXDUmBQmJ979awB7LIFKtBq4em4pBGxlw\n+8IKm2vc7GrwATB1UDz3TRrQqfHbFQJhBY0E+lbvQ9u9dDBWlTpYX+XCZtTyyaY6vK3yx5p0JMfo\n2d3oY0C8CXdAptEbYkiyhesK0zgl205IUal0BNjT5OOTTXXsbvS1a3tAnIlhKTGMz7ZzSradSmeA\nsKISZ9aRHGNAVlQ0EkdloNTsCzFvRyPLS1rY1eBDAiwGLS9eMoTM2L0G2fY6Dw99W4QrIJNqNdDk\nDRFSVAxaiekFiVw0PImsWCPuoExJkx9XMExyjIEMuxGdRuLr7Q3sqPeyqtRBaL9p9ZFpMcTotfhC\nCpeMSMYTlNnZ4OXUHDvZcSZcfhlFVWnYuf64nCH2hWTeWBN5hus00OgNEWfWk2Yz8LPcOEqafZS3\nBKhzB7EatYzPsmMzajHptMSadZ16PzxBmZJmH4GwwvBUK05/GKc/zMBEMxuq3PxtdSU2o46CJDMT\nB8ThDcn8p8xJpdOPUathfZUr6iU/JdvO6HQbQ5MtGHTdqtHC8pIW1lW4WFPhxBUIkxtvptIZiHrv\nADLsRoanxrC0qJmQopIda2RISgzZsUa213nJjTdhNmiYOCCuy149kZMg6A7r1q1j6tSpvdrHcW8k\nhLIGYCgtYpyxnvrvljPovpvZ/ezrjH7xUdIuOZvvss8E4JR/v0z8KWP6WOqjS5M3xMpSBx9vqqXK\nGcSk0zD7osHc9tl2BiaaeercgVzzr83kJ5qpdASw6LVcMTqFoSkx/GbuTgCMWonAfu5bCbj79Gwu\nHJYU3ecLyVz2zkZkNWJYtPgiD/6rxqRS6w7ywvJyrhqTyi/HZwDw2n8q+bjVszF5YDyJFn10+7kL\nBqHXavixwkmdO8h3O5tQgatGp/DLCZkdvmeLL8TP399CitXA/547kPc21PLNjkZmjEwmN97M+Cwb\n29evPujD2ReSeeWHSlaXO6MVoc7Ii2NUmpXBSRaGp55YBuaxzoqSFuasKAcinjSjViIxRk9yjIFh\nKTGMTIvhpEw7gbBCjStIUoweuynieVu+fDkJBYX87+Ji6twdZ5CvGpPKtEEJ5MSbjup32h9fKJIk\nrahEZf98Sz2rSltQVBiabGHxnmbq3CGyY43UuIOEWu/lDLuRC4cmMiE7Fr1OYn2li4831eHwh3EF\n5Hb9WPQaJg+MZ1lxC5Py4vn1z7Kjxxq9IT7dVMfINCulLT7Cssp1hWmHHDApqkpZi58fK1yElYih\n0+iJzATXeYJsqfGgAmk2A5Pz46l1B1lU1IzdqOW6wjRyE8x4gjJ//r4Mq0HL49PzyYkzUesO0uwL\n8+mmOlZ2MvDvjLZn3JBkC7PGpTM8NYYaV4D8BLMY+PUzgrKCxF7Duqdx+sNsrfPQ7A3xxbYGSlv8\nTMqPpyDRzPISBxUOP82+MAZt+xCxGIMWi17DgHgTKVYDEpAYY6C02Ud2rAmTXoM/pKBWbCZlSCHN\nvjAhRWVQopmsWCPZcabDnmwQHL8cDSPh+M7QUdXoOgn+qlqAdjkJsm9v0pinuKLLRoK3tJJgo4PY\nsUNR/EG0lr4dDBwuCRY9Fw5LwukP89baai4flUJ+opm3rxyOxaAl1qTj0hHJfLK5Hotew58vGhyd\n6f+/8wdh0EgYdBq21no4bUAsYUXFZozMrKVYDe36Muu12Iw6WvxhKloT7i4enhwty/pDqYOvtjWQ\nZjNw7uBESpr3zmLeeFI66XYjUwfFc/tnO/ivrzp6NAA+2FhHWFG5akxquxjVL7c3EpRVHjs7jzSb\nkfOHJrJgVxOfba7f59MxjGrZxc0TMshsXTuibWAlKyrPLi1leYmDk7Ns/NeZOby4soLvi1v4vjji\n5bh0RDIFSWYmZMcSazrwbTV3az0lzX6mFSSwtsLJFaNTMR5kluunKhdmvbZboRGCg/P+TzW8saaa\nvHgTaTYjq8sdPHJ2xMO1PxaDlvxEc7t9bQO+Vy8bRlmLn5CsMDAxEopT7w6SHdc/ngedVcq5dEQy\nl47YG7Z3w8kZfLWtgaXFzYzPtjMk2UJWrInceFO7gVb6UCPnD01CVlRWlztZWdqCTiORaNHzxbYG\nvt3ZRFhR+XJ7JKbdatTyQ5mDktbE3I9aDXyIzBZrNRKlzX52NXoZm27D2ppXEWvSYdBp+GxzPZtq\n3O1kN+k0WAwa7EYdPx+Xxpl5cQyI3/vbXD02lT8uKOblHyqj+5Jj9Dx9/iDSbUYC/jCheg/pcSYe\nnJJLgyfI43N30NDs55ShSQzKsJMUoycQVmj0hvAGZU7KspOik/j6w40kBrXk6iQ0YZmBiV2/H/c1\nEPy+ELKsEArK6HQarPb2uiKHFTQaCY87gKPZR8muBlRFJRiQMZh0BPwhQkGZ9KxYBhQkYYs1oW39\nnXZtqaVkVwP5Q5PJK0hCVaGqrIWdW2owmfWkZ8eRN/j4rqZl6CXjoA27ScepOZHnxHmt90Ob93rG\nyBRUVaXeEyLBoqfC4Scsq2yu9VDh8OMNKWyocrG1NhJaFZJVEi16lu1p2ScUKwnqyzvte0y6FVdA\nRqeRou+Mtjles15LIKyQEx8JV7QatLiDMhl2Ixl2A0kxBkw6TYf3skBwKI5vT8KzSwhn5qCrLGXQ\npkUEG5oZ+7cn2PCrhxn+9H+Rev4kFo++CICBv7uJgvtu7lLbbSFKA393E0XPv8FZ6/+NKX3vi1cJ\nhan9agmpF5yFRt//7bCworKu0slJmfYO4TqBsMLKUgeDkyzt3PiHw/oqF9/vaWF4agwrSlr4w+Tc\nqBu4rMXP88tK2VbnZWyGlW21Hgozbdx4Uka7Qdr019cDcMHQRNJtRpJi9IxOt6LXanhxRTlLWwft\nz10wCJ1Gw/s/1fBDmZPxWXaePHdgtB1ZUfnb6koSzHoqnYForkIbyTF6Xp85DLNey9/XVPHBT7Xc\ndmoml7WGGFU5A5S1+BmSZOGvqyqi/ebGm7iuMI0fK5ycmRfPgHgTn2+p56JhSRh0Gq751+Z2/diN\nWgYmWhicZKbFH2Z3ow+jVsOYDCuj06z8YV4RAE+ck0+SxRCpxiEMhsMiEFZYX+Xif+bv4bQBsdw3\naQBmvZawonYa+iDoGnLrbHxIUXl8wZ5oTtLQZAtlLX5mjExhbLqVZKuBZ5aUsrXOg1aCVJuRVKuB\nHfUeFBX8YaVduz8vTOOkTBsZsUY0koRJpzmoQQ3Q6AmxvKgJz856NKEwJ52UiUEjsX1jDbu31eHz\nRKrapWbaiUuwsGNTJAZeq9OQmmFn6Og0rHYTzhYfFSXNaLUayoubCPhCyK0zw1qtxNhTc0jPiqO5\n0YvBqMVsMZCVF489ztxBpmAwzJplxdRUOKgoaSYUlKPtjD8zn9RMO0ajjt3b6vjpP2WoKihtHo5W\ntdTrtYRDMhqNhKTREA5F2tDpNaRnx2E06di9da8hZrbo0Wg1eFyBdrKkZ8cyekI2A4emoNVqMB5k\nQkPQewTCCk2+EGlWAwFZxROUMWgjHrtkqyEanlTlDLCzwUtxk4/1lZFQKoc/TKJFh0GrQVEjORkh\nRUVVobjJF03O1mmkaN5GGwVJZm44Kb1HK1QJ+o5jItxo3rx5JCQkMGHChJ6SqUfYayRko6ssZ8Cy\nz1BlmQmfvsTqy+5k6OO/IeWcn7HslCsAyLjyfEb/5eFDtiv7AszPm9xuX9b1lzDy2fuj27XzlrH+\nxgfIufEyhj15D2GPD73d2rNf8DhEVVX+taGWTzbVkRNn4s6JWe0S+gA2VrvRSjAireP19AZl/m9p\nabRKjF4jodFIKIrKU+cNYnR657+Bqqq8/9VCvnanU+sORve3zbyUtURm/u+bNKDTz9e5g/zXV7uI\nNemiyXL7MzDRTEGihW93NqLTRB7q47PsxJq07GzwUdbij8qcZjNEyxuaWgdF+w6gnjl/EGNF0nW3\nKG7y8d/z91DdGjr00qVDiO+kIkpXEOEjB0ZVVXwhBZ1WwqDVtJtphUgYkS+koG893rZPAspbIjkO\nRU1e4s36Dos/HgpFVvC4g3z1wU9UlDSjax1Yt5GVF8+Y8dl43EHWrijB5fAzfGwGg4anUFbUxO5t\ntbj3WW/AFmdCkVXiEsycfckI/N4QHneAPTvq2bq+qlMZ7HEm8gYnk5AcQ2y8mQ3/KWflyhVkJA8h\nISmGxBQrTQ0esnLjcTr8FO+ob/f5oaPTMZn1JKbEoDdoGTQ8FaMpkqQcDIbRaTUgSTTUuKgobaal\nwUtFaTN+X4hBQ1M4fVoBpbsb2LyuEgkYXphJdn4CeoOWNcuKWblwrxdWo5GwxppISIohNcOOxWqg\nqd4DEtjjzFisBvIHJxNjO/TkkLPFhyXGgK6L6zwIOqenni3+sIJBK7GrwRtNrG/0RvJ4KhwBbEYt\ndqOOswsSaPaFiNFH8kTqWkMOR6bFYNJpsRm1DIiPhDpVOgNk2Y0YdZp293RYUalrfW+urXAiSRJN\n3hCekEx2rIkz8uLY0+jDbtKiqhEvY1oXdEpwaPptuNFNN93E0qVLGT9+PL/61a8oKirqd0ZCG1Jr\nuJEqR14W+oSIq1AJBJF9e18IvvLqTj+/P82rf+qwz7OrtN12qCkySC1761P0CXEU/ekNhjx2F5lX\nnIch6cClQ090JEniusI0ritMO+A5BxroQyQ05H+m5bNgVxPPLC0lpKh8dO1IbEbtQeM5JUkiO87E\nK9OHotVEZiyXFTezpdZDcZOPgqR4bj2lY65DGylWA29fORxFhUe/28NP1S6evaCARk+IcoefQFjh\nXxtqKWr0cemIZK4YncInm+q4rjANmzFSq3vu1npGplpJtuqJM+mYu7WBkmYf14xNw27Ssb7SRVBW\nmLOinN9/vZsx6Vb+68wB0fCv/al0+LEadQcNfToWqHMH+fP3ZexpilT2mVaQwMg0a4fvtbnGjTck\nc3KWHY0k0eAJsrXOgz+ksLykhR/KnOi1Erecksm5gxOwilrovYIkSe1q3u/vmdRIUocKRG33ZlsO\nx/7hXfsTDsm4nQEczV5qq1zs2FRNwBfG2eKLzsBPu3QEwwszWLOsGI1WYtzEARgMe3/zwtNyCIdk\njKaIoTh4ZBqTLxxKS6OXxjo36dlxWG1GpE48TENHpzP5gqG4HH6sdhOKrOB1B6koaaZkVwMbf6xA\nbZXDajeSkRPH1T+fQFZeQrt2VFWlptIJqkooJGO1RwbsB2Jf+VMy7KRkdG5EDR6ZxuCRHZ+hE6cO\nYvyZeWzbUIXfF8LrDuJs8dHU4KFkWQOoYDTp0GgkfK15V0aTjuz8BDJy4qivdiFpJApPzWHnllrK\nihrRtxoFFaXNxCdaGH9GHi6Hn/oaF4WnDiAzNx7dft4fWVYoavXqmFsrPKVlxaLVavB5g5gtBsr2\nNGK1m0hOs2HqxJhXVZWWJi/L5u1Eq41U+7JYDaSk24mNN9PU4Gk1fIxY7XuThp0tPswxBhxNPuxx\nJvQG7XE5o942uTQkub0+XTI8mfm7mihq9LK93svba6vRayMTabIayb/RaTV8sa3hgG1b9BpGpllJ\niTHQ4A3yU7U7WuBhfxn8YYW/rOgYPjUo0Uy8Wc+4TFtraKOERa8lO850SG+h4OhyWJ6ETz75hMsu\nu4xVq1bxzjvvEBMTw/PPP98b8h02bZ4EOT0TTW01AxZ+CMDkTV+yeNSFDLrvZpKnnMqq825GZ7ei\ns1s568dPD9luyavvs/2xv0S37aMGE3K4mbT64+i+otlvsevp1wDQmk3R3IfMqy9g1OyHouepioKk\nETdETxMMK9zz5U4uHZHMtIKO5f56m/3DV8KKys/f20xAVnnryuFHNHD/fEs9X21voLTZ3y7Ru41a\nV5BHvyuiuNlPdqyR12cOO2ZfgiFZ4Tdzd1LlDBDfGhbWxoXDkrhrYhav/FDJpho3Ra2VeEakxnBK\njp25Wxpo8O5NKh6WYuGBybkdSpEK+j/BQBhDq1G3cuFu/rOkKBr6A5CQHIMiq+QNTiI+OQatVsPo\n8Vl9pveqqtJQ66a2yklBqyegv+N2+tFoNJhj9JGZ4AYPLY1eVi3aTXV5ZNJLq9OAqiLLKpIE6dlx\nyLJCS6OXYWMyKNpRh6ul/eJwMTYjRqMOk0VPWmYsGp3Enm31NDV4uiSXVqdh4tRBJKZY8XmCNNV7\nKC1qxOXwR0PHzBY9Wp0GnyfYTi/aSEiOISMnDr8v1C4kCyA+ycJpkweRlGrFYjVgMOowGHWEQ/Jx\n7xVpK3ubYNFHB/R2oxZFjSyCGpIjuTkOf5iArKDXaPAEZVp8YTbWuHH6w1iNWsakWxmUZEFVI89f\nm1FLnFmPVoLt9V5+KHVQkGyJLNqq01LpDPBjRaQASFt+YhtGrUSK1UBhpg1/SGFDtQtvMFLlrSDJ\nQm68icxYE1qNRIsvhKxGQo878wr7QjIGrYYGT4ifql0YdZHVxr1BhViTFqNOE827Citq1KNi1mtw\nBWRCihopNUxk8kNuPccTlEmOMWAxaGn2hlhV5kCnkbCbdOTEmYgz6fCFFeLNOjSSFL1O+05UqmrE\nKDvSMNd+60nQaiPW98SJE5k4cWJPy9Tz7PPj6O1WJK0WJRhE9kcUNKZgAI7121BlGUl78AeDt7gC\nXayNtIsmEz9hDO4deyh57YN2A/5A3d6a6vsmRzt+2h79u3n1RtbdeD+D/3Ar2ddf2iNfUxDBoNPw\n0qVD+6z//W98nUbiwSm5yCpHPLPflnj6yLdFfLWtAbtRy8lZdjZUuRibYWPejkaKW5NFyx0BVpQ6\nOtRK76+oqsrKUgdFjT5+KHPQ7AvT6A3x8NRcTh8QR507yEcb6yhr8fPltgYWFzXjCcoMiDdx84QM\n1lY4WV/lZkuth1iTjifOySfWpCMpxoDNoO12GUTB0aOipJmSnfXkD02mrspFRUkzgUAYVVUp2dlA\n4ak56FpDZgpGpJKTn0BiqpW4BEuneQB9iSRJJKfZSE47dkIC90+gTkiKISEphvwhyYRDMpWlLaRk\n2PC6g5QVNTKgIKmD1+PM4GCa6z0oioreoKW63MHmtRX4fWEkSWLjj+WoKiSlWLnkukLSsmLxeoIo\nisqOTdXodFp0+shgf8CgJCQJVi8r5vtvd7brJ3NAHLkFiSSn2cjOSyA9u7UYiaxQX+PC7QoQl2Ch\nsrSZgD9M8Y76SAK4CiPGZWCPM2OOMeBq8bN1QxVff7Qx2rakkYiNM9PS5MUcYyA23ozNbiJ7YAKJ\nyVYURWHbT9W4HX5yBiUyfGxGv9O/rqKRJPIS9sreNoOvleixan3DUmIYltKxravGpKKoKhWOACXN\nvqiRsrkmkuT9xdYGrEYthRk24s16grLCT9VuNtd6COyXu/TBhhoGJ8eQFKNnYIKZkKKyttLJ5hpP\n6wCfThcFNOsj640YtRq8ISUaamzSaQiGZbSyiiJJqBowtRqMbSWl9RqJVJuBKmeg07bb2h8QZ2J7\nvReLXkOy1UBevImS5kgFLE/rCupj0q14QjIVjkjFtEGtIdZt64skmHUMTYlhR72XXQ1ettR6SI7R\nc9bAeI5GXMpheRIefvhhtmzZwvXXX8/UqVOJje1YFaSvafMkKGkZ0FBP7vz3ADinegUL8qeSfeNl\nJJ5xMmuvvZe0i6dSM3chU7bNwxB/8DjYNVf/llCTk4nfvQFA6d8/ZttDf2Lyxi8I1DViSk9hy/3P\n0rJ6I4G6SDJs6vmTMGWmUvq3D5m86UuMyQksn3w97m1FIElML12CxnB48dGCnuFYizMvafbxl+Xl\nbK7tOCM3eWA8vzszh9/O3UmNK8ic/WrH90d8IZknF5WwutwZ3XdGXhxTBsZz+n5GjtMf5q5/70Aj\nRV5Cv580AEmS8ARlttd5GJFmxaCVeq1k4LGmKwfD4wrgdPiJSzCj1WrQ6bVo9jNyFUXtsO9ABPwh\nIJILpG8NK1Jkhe2bakjNsGO1m9j0Yzl+X5i8wUnUVTlxOwP8Z+medu3YYk3IskI4pGC1GWlu9KCq\nkVCf868YhaaXq9j0JMeTvhwJqqJCN9fRUFUVR7MPZ7OPUFAmb0hyl3WxK8iyQnODh8Y6D35fiJZG\nL80NHhKSY/D7Qjhb/DiavDQ37s01Mxi1xMZbqK9xodNrGTMhi/whyeQMTESSJFqavMhhhYriJop3\nNkRC4CSw2oxkDognPslCYoqtg4cpFJRZtHAx48aewq6ttVjtRnILkrDZTbQ0e9FqNegNkST5vqK+\nxkVTvSf6W8YnWohNsGA06di6oYqgP0xCspXGOjced4BQILJWiFanobbSgdGoo6XJR3p2LCkZdgYO\nTcZmNxEOK1SWNlNd7iAUkknOjiM5yULZ7kYC/jABf4hwSMHnC+ELyOhNWuJizQSBrdUuaixGqmRo\ndgfRKiqZsUbGF0QWA5QVlUn58TT5Qmyp9ZBuM9DsDVG5q4GQw0fYGyKsqNhVFXxBZF+yep9FAAAg\nAElEQVQYSSOhtuYzaW1GVJ0GJaRgy4nDmmLDFZKpdwdJHxDPlEEJqIAjEKbGFYwufLitzkNJk49R\nrSHSta4gRU0+4kw60mxGEi16nIEwP1W5sBgi1d0qnYEO3pV9seg15MabqfcECcoqfxge6p+ehIyM\nDKZMmcL8+fN55plniIuLY968ed1uR5ZlTj75ZLKysvjiiy/aHVuyZAmXXHIJ+fn5AFx++eU8/HAk\nsXjevHn89re/RZZlbr75Zu6///4ObUdQQVWingSN0YAkSUgGPUowiNLqSTCmR+r5hx3OTo0Ef20D\nWqMBfZwdX0kl9jF7Z6lNmZFqN65tRay97ncRL0UgSMLp45B9fsIuD4mTJmDJy6L0bx/i3lGMxmjA\nva2ImIJcPLtK8JVXEzMw56DXyl9VhyE54ZioliTofXLjzfzposEs29PMvJ2NXD8unUW7m2jxh7nz\ntCwMWg2PnJ3HXZ/v4OklJfxxej42o67frZjbxvsballd7uTmCRk0eUNcMDTpgKVE7SYd71w1osP+\nGIOWk7qZ7HqismdHPUu/2UFjXfsyo22VeoKByMtep9dQVdbCiMJMUjPt+LwhinfWE/CFMBh1DB6V\nxsChKXhcAVYu2k3Jzkgss9mi56TTc9m5uYa66ki1I61Og06nIeAPgwQ/LC5q1/f4M/NISrFiNOkY\nODTyXFVVNVqlJxxWiI0/NmdtBXSa33HIz0gScQkW4hJ6p6KbVqshKdVGUuqBvT5t4WN+XwhVUUnL\nisVg1NHS6OXLD35i3cpS1q4oxR5nwmI1UlPhiH42PtGCTq9FRaWypJmNayoA0Bu0DB+bQYzNiNcT\nZOv6SoIBmdLKrWz6Ptyuf61Og9w2ey5BQmIM4bBMSoadnIGJ1FU5MZn1pKTbSU63RUK8TLpoadxD\nEfCHaKr34POGSM+OJRiQcTn8aLUSjmYfLoef2HgzVeUtrF1RGs23aUNv0GKLNUWS3vdB0kgYDFpU\nVSUcVkhKseL3hTBb9OzaUsvmtZUs+mIbOp0GWVbaqtWj0Ugdwsa0WgmDUYfJrEfSSAQDYYpdNbRN\ncae0/otSDs4d1SSn2cjKjiNokLBKcKpBYseP5ZRvbq1qBlgtekIhGUVWscebyR6agqKoJKZYCfpD\nkSpnrb9BxdZaXFsj5fTNgH9nHXO/kTFbDGh1kWphSclWDEYdF2TY0MbaMbTm+YSsOki3EN9aKMDl\n8GM06Qj4I/lUzmYfDgPIicbIs9YfBlnBr5GoQyJc1kxmghmTxUDAb6C8wsnR4LBGnKeeeip1dXU8\n9dRTAHi9nVd0ORQvvPACw4cPx+VydXp80qRJzJ07t90+WZa56667WLBgAZmZmYwfP56LL76YYcOG\nddqGpKqorXXk2mbrNQZ9u3USTGmR8qWhZifktv+8qij/z955x0dRp3/8Pdt7Nr33QgihFwEBKSoo\nWPGUU9Q7z37qnadnvTvbeZbz9Dz9qWc7K3ZFQUSUJr1DCBASAum97ibZvvP7Y5INSxJMFDTqvF8v\nXuzOfmfmu7PfzHyf7/M8n4c1I8/FlJXKpBWv4iivIea8bstNHx8NQPXirxG9PrTRETgraxH9IhMW\nP0fpS+8TNWdqwN/VXlSCoJJW2KLnnsbhf5dQs3Q1qTdc2qc3wdNiY82Y80m+5mKGPvTHXtvIfD9+\nqit909JCmZYmOR2PdevGmrXcMDGBx9eWcvHb+QjAgpHR/PaYPIZj8fpFaZXE0rv3od3tY9WhJvJq\n2rgwN4qhUUaaOzx801k34uzscGZmdCdp+jrdvzsq7QyPNjEi1oRFp6LG7sLp9dPc4eWDvXXMTA/l\n4hHR3/qdHR1uigvqSc2MwGjWYmtx0FDbhiiKqNVKktJPbh7KiR4rToeHkqIGElKk3/HY0I9vw+32\nUlnSTGJqWGBCXV3eQpvNFVCwaW3qIG1IJIcO1LF7cxmtzQ60OhVxSVaGjoqj/HAT1eUthIRKBcLU\nGiVOh4fWJmkFd/eWssD5QsMNWEL1VJW1UFHSzKqlB6RQEZWCuCQrbrcXl8PL+q+KAvtMnJ5GY107\nXq+PyadnEhZh4MAe6SEeFmnE4/b1GrYhdN67+6OwM1j5qd5bZCS6wseOxRpuYOGNk/B6fOzfXUXp\noUba21xMnJFOeKQRg0lLUnpYwHPi8/klr0RDBwf2VLFnWzldBRIyh0UTkxDCbGMuPq+fhNRQSg81\nYmuR/v7Co0wIgoCjw01NRSs6vZryI00c2l+HSqVAhG5DAmninj0iFpVKgVqrJDTCSEtDBwaTBnOI\nDkEh0NzQTvGBeqrKW3pM/Hu/DlJS/MQZ6Z0TeT+NtW0cPliPo8PNmMnJpGdHUVUm3Uei4yWZVVEU\nQQw2EkVRlM5fUE9rkwOdQU1iaiixiVYUSkUgsT0lKwKjSRvwSh6Nz+unvc2FSq3k4N4abC0OTGYt\ndpuTtlYnao2K5oZ28raVs3Njt7iMWqNk+LgE4pKs5IyOCxhT3s4K98fzUrbbXTTVtwfqnZQUNaBQ\nCIH7aUebW1Ib6/Cwe4u3z+NotErcbh8MMI6n6KjXUbFmyDj5iybfyUgYM2ZM0HuDYeBWfkVFBcuW\nLePee+/lySef7LVNb5FQW7duJSMjg5SUFAAWLFjAp59+2qeRgCgiCr0YCS5PICehq8aBp7WnsdK8\nVYpXbCs8QsPqzXiVKkKOKrpmzskgdNJoKt9ZCkDmndey95aHEAQBy7DMQKKyKIooTQYO/v15kq68\nAIDos07j8L9fp+iR/wKQ/ocre5zfa2/nyHOLAKhdtlY2EmQGxMwMyc2KCFvKbbyzp5asSEOPEB5R\nFDsriXr5vKCBHZV2hkYZmJ4WSnq4nt1VbWwuayU3xsSqQ03YOqvurj3cQm60kQN17XQt/uTVtLG0\noIGHZ6ejVytZtLuGN3dKKzef5NejEODKsbG8n1dHe6dmfLRJw42TEo77XRwdbgr31rDq8wJ8Xj+R\nMWbGnJrMys8OBEldhkYYmDF3KDs2lFBfY0ehEAJxy7MvzEWnV9Nmc7J9fQlpQyKJTbT2+hA6mfi8\nfor211Jd3kJlaUvQ6mNUnIXUrAisYQZyx8YfNzyjsrSZ1Z8XUFPR2kOZ5lhWfy7lRMUmhpCaFcm0\ns7ICijmjJ/btyRRFEafDQ22ljYhoE0azpBbjcno4UthAXbWNsuImzv7VcMIiJdd6u91FcUEdQ4bH\nBoUeHc2oU7rPqZdLf8j8RFGplYwYn8iI8YnHbadUKgiLNBEWaSJ9aBRn/cqP3yfidnsxmnoawcfz\nboAUvmVrdWAwalEqpWTzumo7jnY3VWUtHNxbg6Jz1d3fhxEQGWtm/NQUYuJD0OnVVJQ0Y7JoMRg1\nuFxeIqLNaLRKHO1uwqNMATWwLqJiLQwdFbzoNGR4sLKWIAiBeh9Hb+u6Fr1x7DF6Q6lSBBYWjnf/\n8vn8NNTYA0aKxarvVTHrWBWu3jCatUELFtkjYnttJ4oitVU2nB0eBAHUGhVanQpRhPLDjTQ3diD6\nJVWzyBgzoRFGLFY9IaF6PG4fDbV21BolKrUyEA6XlRuNRqvC6fCgVCkIDTewa9eub+3z9+VHi125\n9dZb+ec//4nN1rvLRBAENm7cyMiRI4mPj+eJJ54gJyeHyspKEhO7/xgTEhLYsmVLr8e468uHgzdU\nQxNS2JHo6fYkaGMiudRdABee1eMYG295IPC66JlFHFh4JwaHlaFhYT3aAlRMHMXo/z2CJTcraHt4\n+FGrm//eKf0/YzKLNFLokrNKUl0QfT5Ev0hEdJDzTOJIAU09twIQ1kd/mpp630NuH9z+2LjhH7s/\nJ6v9glc28tDKI2RHSioU985KxenxkRbfcwV/O3Dg8ZVB2w41OoizaFn1h+lB7booKKvh8TWl5NW0\n8dDKI6SE6vk4v47tdwTHTW7t/P/PH+1kSLie2dkRQdKYffX/nusXEZdkJXdsPKuWHODLj/IJizRy\n+0PnBTf8u/Tfq//5WpqQ75NcxDUVrYRHmbj2tjN7Pf7R11MURdrtLrR6NdHRkd/avou6ahvZw1J6\nbf/2i2upKmumuaHb+yoI8I8XLu21/b8fXEpSWhiJaWGkZ0fh9/nZsvYIcy8a32v7N55bRVxSKBHR\nJsxWHYjQ0tTB2IlD+t1/6N940+rUZI+IJXtELGFhYdzSS5mZwTb+f4z269ev59xzzx00/TmZ7UVR\nxO90Exnf++SpqakJT6sdv9uDs6IGfWIsmojQQdP/H7v9Z5991qvnqT/HPzps6oxzem9fWVGDrcWJ\n3+fHZNEF5GG/7fih4cFe6p/K9WxqakKpVBAdH9Lv9ieiP0HzvWPaR0T3NIyOd/xjvVjmEB1hYWF9\nnvtE86MYCUuXLiUqKorRo0ezZs2aXtuMGTOG8vJyDAYDX3zxBeeffz6FhYW9th0oCrWKnZUlGPUi\nFkAX28uEvJPqj1cAsN/fjmdfPspRF7B/d981FXTR4XRox/LX+17ljPNymH3W6f3q086KIzSvX495\n0dc0bdj5re0/efAJdHFRnHX1Ff06/vr164H+u75/ae337t07qPpzsto/PDudj/LrWPr1GmrsbrYc\nlSjcG8+dP4T1JS0s+WoNo+PMLJg7i4wIA2HX994+yqThiXmZ3P+/z1i1rontqSPJjTEGjIJjMX5z\niBKPj2dV1aRkhnPxr+f1ueoFkDM6jmmzs9idt53M8ZAYM4yMnGhuf6j39ucvlLye69atk+KKG/WU\nFjf23hhY+u4e1Bole/ftoK7aRpQ1s18Sll3X39Mazo4NpX2227ezEoVSQBvWRFikgQt/dTYADz/f\ne3tBgO3rS/jovWUYTBpSE4cFFfw6lnkLRnX3p1oaD6HH0d4/tv9TpkyhevHXA2p/PERRRBAE1q9f\nj8/hZELO8ECIZm/Y9x+idvk6tu7LI/aCMxiflcPeP/y9z/aHnngFldVMfnsTCo2aGefMRRv17eFm\ng+Xv8afevuazVejio8hrqcdRVon+5aV0FJf12X77r2+l8Zvt7PNI951h2hAMyX2HP+78zZ0otBoO\n4ECfEMOZV16GIal3A+S79P+X1l5v0KA3aH7Q/nQJuPSGt60dlck4aK7PYG9/9Osbb7yRq6++ul/7\nfx++Vd3o2Wef5aabbgLg0KFDZGRkfO+T3nPPPbz55puoVCqcTic2m4358+fzxhtv9LlPamoqO3bs\noLCwkPvvvz+QKP3II4+gUCh6JC+vXLmS7Y+vhvBwvA4XactexzQklSlr32bjmb9FGxWOZUQ2xU++\nyhklq/kqZQaZd11L+h9/EzjG8hhJ3jX0lJE0b9mDYvKp5GXPwmTRcv1d3VWXfU4XX6VI7+fUbOTr\nz/aze3MZ0+ZkMWFaWlC/3I0tHHl+Eel/vBKVyYiztoHNZ1+DOSeDkc/fz9cZZ0jnnDiS5s3dhdv0\nibE4yquZ9OWrFPztaZq37MGQmsC0Te9/h1/g50HVxyvIu/H+oG3Rc6cz6sWHvlXK9ueA6BfJ31VJ\neKSJ2MSQAevCf5Jfx5IDDVS2unjwzDROSTqxKmVev4jH50ffKR/XUGtn/VdFFBfUB2JgU9JDMYca\nKS6oo6Or2rUARpOWdrtLclN33qFmnZtzXLdyf+mauHbR0ebms0W7qKm0BcKWDEYNSemSvGLRvloq\nS5sDSXJanYqLr55Am80ZSKh1O710tLvZubGUYWPiaG7oCMTmtjY7AufS6lTccM/Mfrm2u/B5/eza\nXEpdtR2X00v28JgeLv4TgbOmniPPLaL0xfcASRpaZTLSums/SVddROqNl6JP6BkG4He5cTe2IPp8\nOKvrKXz4eWz7ivC1daBPjiN0/HAUWg21y9biabaReOUFZN9/C+2HSrDvL8a2vwhbXiGtu/fjP6rA\npcpiwtvWgUKjwu+UxoZCpyFixkQc5dXY84t69AVAE25lwifPYcpKOeHX6KdMR2kVe//wd9yNLfjd\nbhylVWjCrZiGpqOJCMWUlUrkzImYh2X2KZBRt2IDpS+/T+vuA3htRyW9KxTg96OLjybmnJm07t4f\n9PwSNGpEtwd9Uhwx82agDgtBnxhLy7Y8OkoqcVbXYz9QDH4prl4bFY4+JR5XTYOU49dZDBWFAkNy\nHJbcLEInjSZ86lg0oSFykdITjK/DSfXir2k7eBjTkFQMaYmYMpLxe7342h0Y0hJ7PG9Ev5+65euw\n5RehMhuw5R3E02LHll+Iu76PVW9BwDp+OJGzJhE2eQz6hBj8bjftRaUotBqat+zB3dCMoFXjqmmg\nrbAEpUGHISkOr70dU1Yq1vG5qEMsWMcPD4xbv9uD3+NBZfxx4xidNfU4SqvwtnXQVngEbWQYkWec\nisoi5Zj4XW5c9U0oDXrai0owZadJz0afD5XZiKu+CV18NH6HC0GpQKENVrb6IeokfKuRYLFYAiFB\nR78+Uaxdu5Ynnniih7pRbW0tUVFRCILA1q1bufjiiykpKcHr9TJkyBBWrlxJXFwcEyZM4J133umR\nkxAwEsLC8Lo8pH3+GpYRQ5i84n9snnctSoMeS24Wpf/7kDOPrOar1JkkXnE+2Q/cAkgWbteEfchf\nf48mIhR7Wg5LPynAYtVx7R3Tg87XZVDMqdnIlx/ns3d7BZNnZTB51rcbVTsuuw1XfRNpN1/O7mt6\n+uzHvfsUSr2OLefdELRdExHKzPzP+zzuzivvQGUxM+KZv35rH35InDX11H3xDYm/ufA7Fzzyuz2s\nGXM++sRYcv91F9t//Sf0yXG0bM0j+4FbSLluAQDN2/dy+Ok3GPK332PKTDmB3+LHZ+/2Cr78OB+A\noaNimXvxyG/Zoyd+UaSpw0OE8cTJ6rldklpDRLQZr70dn9OF0hrCi4+vxe8XGT4+gcgQFbtfWkro\nysXEnzsTXUoCeeVebAmZZI2Mx97qxODrwPD5x7RMmE5kdhIjZ+Wc1AJZoihKyX+CEKji2kW73UVl\naTN6o4Zl7+dhb3X2eozsETGcffFIfF4/m1YfYus3R0hICcXn9RMdH0LG0ChSMiNOSv+97Q6aNuzE\nlldA++FyPM022g+VIqiUZN55LTHnzQr6Tn6Xm4pFS2jauAtXQzPNW/aA30/iFeeTfO0lGFMTEH1+\n9tx4P7VLVwPSYoXX3kbImFzSbrmclu35HH76dbz2bmUTbXQEMefMQGU20bx1D47yGrz2NqzjhqMO\nMVP14XIUem2QQaCNiSDmnJmEjMxGlxCDoFBQ+tL7GDNTSLjsHJR6Ha27D1D14XKqP16BaWg6CQvm\nkvTb+bibW2nZno/f7cbT2MqhJ/+H0qBj9MsPEzKqj1y1nzFdj3RnVR3aiFC89nbKXv+E8tc/wWtv\nx5CWiMpsIOK0CdgPHKZp407cDc2B/Q2pCdLExC3ltog+HwqNpAzYtHEnSpOB6LNOI/rsaeiT4nCU\nVWHbW4RCoyLxygsDCoEdZdW0HyqlrfAIzqo69EmxJP92fp8LOKLfT8vOffg6nIRNHo1CJU34vG3t\nIChwlFVRu2wt9v2HaFy3PchI0SfGknDpPFJ/vxCFRo0oinSUVKK2mNCE96wTI4oijtJKNJFhgICg\nUOAor0YXH4XKaMDv9uBtd3yrJHqfv4Hfj6OiFl1MxE9C3rytqIT6rzfSXlSKOtxK/VcbaCs4HDD+\njkWfFIcuLhJNRBiIIs1b8xAUCly1PSs1m4dlkvvUPeDzScqSTheiz0/rngO4G1uo/2oj9n29G/wo\nFCi0akS3F31iDMaMZDytdhxl1WjCrbQVlSB6pARhbVQ4urgovG3ttBeXIygUJF55AbpY6d6iDg1B\nqddR/enXIIIpOw1fewdeWxseWxtKvY6IGafgabGj1GlRh4UEEq/t+4roKK3CWVmLJjIUfWIsIcOH\nIKhV0jgUBGx7C1HoNHiabCD6qfpoBTVLV/e8fgqpKKHKYsLncCK6e88h60JtNeNpsYNCIeXPCgLW\n0TlYxw+naVzmjy+BmpaWxm233UZOTg4ej4dXX301aCWu6/VVV131nTvRdaz//ldK4L3uuuv48MMP\nef7551GpVBgMBt59912pwyoVzz77LLNnz8bn8/G73/2ub2UjRETR34u6kUYqpuZwotRJSSjqUEtQ\n4rKrVnKRCSolcRefhTYyLKDwoeilSnLq7y9DoZWO1dokxRu3NPVP9UkbHUH9yk3svuYvCEolw/55\nJ363G21MBFGzpwauz7j3n2b7xX8g8vTJhJ06hoMPPIu7saXXm2D74XLqvpTcUum3/gZj2vGTqk4G\n9as3Y88vJH7BPNoKS/A7nJiGprPnhvto2ZqHMSOZ8KnjvtOxG9Zsxd3QTO5T92DOyWDGHkkFa9sl\nf6Dgvv/gqm8i7NQx7LzyTkS3B2d1HZM+fwmFVkPr7gPs/ePDRJ89nbaDh8n6y43sqiwZ9CokXXr1\nPp+fLWsOs3HlIWISQoiOs7Bnazmnzspk365KYuJDSB/adwjd0SgE4YQaCI4ONy//cw0ul5+5s2Ip\nue9JVDWVsPBKOtyRzJ5gwfHiM5Rv3UOY14d5WCZVHy4HUcQExI8dRu7cu6jdu51Dj7+EA2DFOmo1\naioeuY3Ey3qP7T4RCILQZ5VVo1lLVq60ip403IurMYr4lFAsVj0mi5bwTnm/LnUghUbJtNlDmDg9\nPVAt+EQg+nw4yqtxVtVT++U3WEcPk9z5okjJi+/hrOyU50uOQ221EDI6h/ZDpey5/m80rtuGUq/D\n02LHWVNP08Zd4PejDrOi0Kgwpicy+uV/YBqS2n1NlEpGv/ww9gPFHPm/t/A5XCi0Guq/3sjW828E\nIHLWJMJnnIK3xY6rronU31+KITm+z+8Qe+GZVL6/DMuwTELGDEOp02BITUQTFuzJCp0wIuh95MyJ\nRM6cSM4//iStxHXeh3XREcTMnR5oZ50wgh2/vpVNZ13NiOfuI+6C3vNPfij6UyfB7/EiqJQDMoJd\ndY0U//t1mtbvIHb+mbQXl+Oqradl6178bo9UHFStAr+I6PNhHZdL5l3XET5lbI9jeVrteFrbaFy3\njcr3luF3e3A3ttBRXEbopNH4XW78bjcJl59H5h3XoI3sjqG2DMsk+qzTehzTkBSLISmWyJkT+/V9\nBIWC0HHDe2xXmaRwOfPQdMxD04HOSXh5DQ1rt+JpsdG4ditFj71EyX/fxZiVSuvuA4HJlzk3k9Tf\nX0bMvJl42zqoXbqK8jc/xZZ3sMe5FFoN1rG52PcX4WmxEzI6B4VWQ3tRCWFTxmIZPgRjRhKtuw+g\nNOjRhFrwtjtw1TTQUVKBu6kVQaHAll+Ir60DU1YqETNOwTwsk+izpwW+i9/jxWtrQ1CrcJRW4vf4\nMA1JYcOmTZx6ykS89nZa8wrw2trRxUUROnFkwGg6kThrG9j358epXyHNFdRhVjwtNnRxUYx5/TEi\nZk6i/VAp9v2H8LTYEVRKfA4nLdv24mm2Yd9XhN/tIXT8cASNmug5U4k8fTJ+jw9BIaAyG0EQeh3X\nXX/fWXddh7uhmaYte3DXNeJzurCOG47f7cGUlYLaYsLncqO29Izl9zld2PYW4iivpu7LdXhtbWjC\nrcScezodJRWUv/4Jos9H4cMvDPjaKLQawk4di6O8mvaikp6f67WoTEbJSyIIcMx6u8piIvX6XxN+\n2niUBj36pFhcVXXULFuLoBDoKKlEE2bFnJOOs6oOQ2oizpp6lDotot9P24Fi9MnxdBwuw5CaiN/l\npu3gYQS1mpad+2jauBPrm32HYp4ovtWTcPDgQR5//HFKS0tZs2YNU6dO7bXd6tWrT0oHvysrV65k\nx+OrwGrF7YX0pa8SNnkMEz5+lm0L/ojX1o5pSCqNa7cyfedi1s+4HENyHGNeewyAxg072Tb/JsZ/\n+B/Cp0gT2Q1fF7FpVTGh4QZ+d9u0Xs/r9/l5/tE1ONrdxCaGMG/BKCxW3XFv/nk3P0TVB18A0orI\nads+6rNtW1EJ+vgYmrflsf2SPzL+w2d6vekXPvICh5+Wwrcy77me9Fv6l7twomjemsfWi24+rpUc\nMmYY4xb9C7V14Ks1eTc/RP1X65mRtzRopaajpIJ9dz1B4xopCt6ck0Hy1ReT/6d/oE+MxZCWgC4u\nOqBGBVJYhe8vVzF99hkD7sdAEUWRNcsKCI8ykTs2od+FgdrbXLz13CZGnZJE4d4aaqtsZOVGc/p5\nw/B5/bz0xFpEvxi4T116/UTikr5fpWVHh5t9O6vQ6lQ0N7ZTVdpC6pBIYhOkyZw13IDL6eXg3hqq\ny1vIzIlmz9Yy6muCtfet1cXYIhLQtjSQtuQVNKEW4n51FvG/moNl+BB8DhfetnbK3/yUQ4+/FNjP\nMiIbU3Yartp6PM12bHkFJF93CcbURBKvvEBawRFFlHodPpcblcmAu6GZHQtvJ2r2VBKvOD9oMgOS\nnHDtsm9wNzYhen3EnH8GxtTjqyody4ksjuWqb8KWd5CWHfvQRoZinTCCirckg1eh0yJ6POhT4lHq\ndWjCrRQ+/EKvDywAXUIMGX+6CmNWctBkS/T52PuHhyVj7CgMaYmk3Xw58ZecHXjICb0sgPRG10Nd\nnxBDyMgfr7p5X3hsbey47DZatu0l8oxTSbn2EhrXbad52170CTHY9hQw7J93dK4mOtDF9W5Uiz4f\nglKJs6aeirc+wzwsg6g50wY0mT/eeLEXHKbs1Q+pWLQEY0Yy1rHDCJ82gZhzZwado+6rDdj3H0L0\n+tDFRVH14XJaduTjd3kQ1CpEtwdNuBVdfAwho7JR6LRowkLw2toRNCriLpw94PArv9vTrxo+gwFR\nFGlYtZnqT76iragE6+gcTENS8bY7KH9zMY7SKsk74nLjbmhGE24l9WapoKnaakFQKVGZjbibW2la\nvxNTdirG9GTqvliLz+lGnxBDy859eDsXEgWlsjsECkAQ0MVFoYuLQvT5sQzPwpAUR/nbn+Gqrsfn\ncKLQawmfMg7R66V52158bT0XEff728lR9Mwh0kSEEn7aeLRRESgNOlJv+HXA4PxNiiEAACAASURB\nVBgo7mYbbQeKqVm6OvAcTL1pIQmXnYMuJhK/14ugHJjBOljxdThxVNXStG47freH9sPlGJLiCJ82\njo6yatQWE6rOf87KGlq256O2WvC02HDVNNCwZgva6Ahi588mZMQQdPHReJpaaS8uo2njTtoOHiFs\n8mh8DhfmYZkoDdJ9WvT5MQ9N+86/UX/wtNrZW1z044cbHc3MmTNZtWrVyezPCSNgJFhCcIsK0pe8\nQsSMUxj3zlPsuOIOnFW1GDOSseUdZNrG99h8znUotBpGvfAgmohQqj5ZQd4N9zPlm0WYslJoqLXz\n2tMbACle+cZ7Z/Y4p8ftI39HBSuXHMBk0QYSDOctGNlDKmvfrkrCo0zExIdQsWgp+X/6ByBZ8rP2\nL/vW7+eqb2L18HlkP/gHwqeNp+bTlaTf+hvJ1erzsXb8fEzZ6XQcKcc8NJ3Rrz7yfS9pv2k/UsGG\nGQvRxUaReec1lLz0PokLz0P0+Sh79SPaj5QTe+4sqj76kqSrLmLog38Y0PFFUWT1iHMIO3UMo154\nsNc21Yu/onnrXjJuuwpNuJXSVz+iYfVm6r+SfsPw6RMY9ujtOCpq2L7gVsKnjmfsW//8TrkMPp8f\ngWB95ZqKVsxWHYbOVXpBEKiuaOWdFzYHEnOt4QbmXJhLfEpowK25Z0s5jXVtZI+MJT5ZirN1Ojx8\ns/xgoBAPSJJv0+dmBzSeC/ZUs/S9PVJSrFaF3qjhN7ecGuiT6Bep7IyRN4f0rcPfWNeG3qhhy9rD\n5G+vkApf9QOVWhmI5zeXHSTS0YAvLh5HcibNbX7i44yM0DbiO1RM6g2X9jkp6yipoGbpGqxjhxE2\naXRgu9/lZtvFf5BCYoDQSaNp2ZEfZIRqo8IRRTEQ/6oyGxn54kNEzphIy859VL3/BS078rHt7RZA\nUOp1jF30L0JG5SColb2u1jWs3Yp93yEsI4YQespIfB0OfB1O1KEWqj74AtPQdEJGDaW9qBRtTCQq\no57mrXsIPWUUjooaapasQqnV4G5qIXT8CDz2NtoOHsHX1kHlB8sDE4/eENSqgDu9q78p1y9AHW4l\n4ZK52A8Uo40KQ6HRoIkI7TO0wdfhpPqzlVjHDkMdYkah06LQqAOe1J8jvg4npa+8z+Fn3gqEBGij\nw/E02/C73AgadSAuGKQQCmN6IuahGWiiwmjevJu65etQ6DT4XZ7ASmHcRXNIvOJ8rOOH9zmRat19\ngNY9BSjUarzt7bTuOoDf5Q5M9I0ZyXSUVtJxuBwEgZjzZuGsqMF+4DC+9g6MGUmEnjKS9sMVtBUU\nSzV8jkITEUr03OmkXLcAQ0o8rrpGtJFhv4hcrIEi+v3Uf7WBikVLEH1+EhaeS/jUcd8pXr2jtAr7\ngUOETx2Hr8MpGRwRoZI3v48Vc1EUadmeT9UHX9C8eQ8KvRbz0HQsuVn4HA60UREotGo6yqrxttrx\nOV1ooyMInzIOtdVMW8Fhqj/5iuYte3DVN4Eook+OI+6iOSg7x2ZXnLs+Phq/x4ujvBpXTT0Na7dh\nzy9ElxCD2mKiafNuyYMiighqFXHzZ5N28+U/CUNQpieDIifhp0rASDCbcQlqMj57mchZkxj79r/Y\nfd1fseUXYcpMxlFWzamr3mDbgj8GVp9zHr0dn8PJwQeeZVbhCtQWE6s/PxCkWjL/N2NJzeqWRXQ5\nvTzzoKQKEh5lYtqcLD55Q1IpmjgjnSlnZAbaut1e/nO/1Pb2f8xBFEVcNQ2sGX0e8b+ex/Cn7unX\nd1w1fB7WscOoW74OgLFvPUHk6ZPZfc1fqFmyitH/e4SaJatp3ryb6TsXf4+reXw8tjZql64h9oIz\naN6ym/I3FtOwegtTN70XKFR3NF0haruuvpemjbuYsfvToMmN6Bd7VOf0uz0UP/U/GtfvwGtvp63g\nMLlP3kPCpfMG1NcdV9xB/Yr1jHj2b8RdNAeAsjcWs/+Ox8l57M+BGha9Ifr9eFrsaMJCEH0+apas\nQpOVzqdfVmINM2AO0aFSK5gwLY3nH1mNWqNEb1ATnxyKz+enMF8KBUlMC2PkhETWLCugzeZCjQ+D\nyk/y6NQgQyA2MQSTRReQ78zKjZaqPyoVXH3bNMpf/YCaz1aS9Nv5xM2fTZvNic/np7bSxmeLdqNT\niUxSV+LevIXySfMo69CiUikYMz6OvN21pGVHMnpiEpWlzShVSg4X1HGksDumNC7OxLR5OThKKyle\nuhFF8SE6DhThDpEm480TZ9FhDid3TDyTT01g8+//QVt+IcP/tJC0q+cjKBS9FtL5roiiiLuhmd3X\n3EvbwSPEzZ+N196OKsSMLiYS214pUS7hsnPQRIRy4N6nsO8rwpiRREdpVWCynXrz5cTMnY42Kpyt\nF92Ms6oWdYgFlcXIuHeeQp8QI61MrtxEycvvB+4LIBkiCAKeFhvqEPNxlTu6Yk+PdUMfjXVcLmmd\n9VGa1u9AnxRH2OTRKI0G9PFRoFDgbmjG1+GkrfAIxvSkHyV08KeMp8VGw9ptmLJSAuEq7qZWDj74\nLEqdlurPVqGNCMU8LIPWXfvpKKkM7Jt45QUoDXqUOi1xv5pD+RuLKXnhHQDCTxuPJsyKz+lCnxSL\nLjYKR1k1KpOBI//3dtBKsy4+GqVeizEzBUGhwF5wGFNGEqGTRhN5+mRMGcmAdI8pe+0TapeupnXX\nfjRRYURMPwVDcjzW8cMxJMfhrKzFkJ7Ua/iFzM8bb7uD6sVfUfLCO7QX9VRRU+g0iD5/4F4nqJSY\nczJx1dTjbmrBOjaX8GnjCRk1FMvwrH4pgckMXn4II2HAQW6FhYW88847VFZWkpCQwIIFC8jKyvr2\nHX8sRBG6JiidVr7SoJdWAztdgAAqQ3flusPPvkXM3OlSzJnZSMGeakoPSZOBLg/BR6/t4KyLhjNs\njBR722brTmQMjTCQktGdnOhyBofcVJW2BL0vK24kKi6EaVs+QBvdvV+73YVSpei18AeAOSc9YCAA\n1K1YT+ikUdR8voak384nas40HJW1VH+8gtbdB05oEl9HSQWCUsmh1z6l5NWPUTvaAt4QgLQ/Xtmr\ngQDdOSgJv55H7dLV1Hy2MjBhz99RwYrF+xg1IYkZ87IDbSvf+5zip17DMjyLtsISBI2aiOkTBtzv\nMa8/huj2BKkEJF5+Hl/89zX0z75F1JlTsO0txN3YTPyCuRz65yvY9xWSftvvKPjbv2nZlk/KDb+m\nacNOWnbtp3re5TRFpQaVpN+3qwqQPEtqtYIDe7olc4dV7yBFaaTyyRVkR4TRmjKUhnZoSR9J3rYK\nYkMEZp4Szu56DdWFVdhbnETHW9AbNZx5QS4qlYL26gYK7vkX5W98AiC5SEPMRJ4uJc+bTRos9jps\n5ig2NOpRxkzA0aEltGQfzthktm7yo3J1cDDPy/7OvgJovU50LU34tAYiSvMJfXUVhf8Nw9NqR6lW\no9RrGXnTpcRecAbVi7+m4MFnpdXZr2LZFxuJd/MeJvzffcTNnx38W58gr7UgCGgjwxj/4TMA3xqj\nO3bRvzjyf2/TtH4H5pwM4i+Zi2VEVlAoTs6jt7Pj0j/hqm3AVdvA7qvvJXreDOq/2kDzlj1oYyLI\nuvd64i+ZS8PqLey743EKVB4mjByOoBDIvOtaGjfsQFAoUYeYUFstNG/LQ2nQ07R+B7r4aEb/71EE\nhYA6xEzjhp2oQ0zo4mMwJMUGrfxGnXFqr9+jK2TqeFKRMn2jtlqIPS/4QaoJCwkUusy481pUBh0K\nrQa/x4uzskYKt1CpAoU2u8i+/2ai5kyl5rNVlL+5GJXJgEKnpf6rDYheXyDR0zQklTFvPoGnqYWt\nBfuYvuCifvVVUChIvmo+yVfN77ONPLH7+fJtoYwqo57Ey84l8bJzA6o4nhYbdV+uR2XUY8svRPT5\nCZ0wAm10OOah6YFwXtHv73c4oYxMFwMyEpYsWcJll13GvHnzSE5OpqCggHHjxvHmm29y3nnnffsB\nfgz8flAFz1JURj2+dgd+pzvgblceZSQ4K2qoWLQEy/AhCILA0vekEIeQMD0jxiey7kspXKH0UGPA\nSAjINyIVu1CqpNXed1/agq3ZQXubi/27qhg3JYWyw92rj5WlzXzw6naUKgW33Hc6Pq+fjlYnthYH\n7/x3C9YwA1ff3nv+gyE5nka2kXjlBXiaWqn9fA3R82aA30/krEkIgkD8RXM48p832TTnd99p5b0v\nvpl4MX6FgoKFdyL+6mZy3ngkMBc0ZaVivPA88raVM3xcQp8u+YgZp2AaksrhZ9+iPXskxYu/oVYV\nit8nsnNTKWFmBdmnpKFWwpHnFmEZmc2k5a/gd7rxtXd8J9k7QRAQjpEREwSBxMvPxfmP11kzunsc\nOytrKX7yVQDqV25C9PlBFDny7FtoYyPR/OoimsypRO76Bp9WR5SvFd+0GZQX1zA82cToK8+g6Oo/\nc9hjQeVow3ooDwEoRZK41YRZURzYQ9rEUZSu/4DqpOGEbF7Onqc60FjNxLXYifvVWYy4u1udyud0\nsXv+jTjKq7FOGMGY1x5j20U3k3fzg4xb9CTm4VkU/uMFkj5YhPGv97LXGEtohIFQnx3z7gLaD29H\nnHUGniUfIgJNmaOxlBSgdDlQt9vIuusa1FYt1RXNqM6cQv2K9UTPnc6wx/4cdL1Trl/AwQefDeQE\nNG/eg2XEkCAD4WTR3wQ+XXTEt4ayRUwbz/Rdn6LUaalfuZE9199H6+4D6OKjyXnkNhIWnheQ1Yu/\n5Gwiz5yCfu9uJk7r/ptMuPScXo/tbrYFJp9dHJ1gKzM4OFrBRqFWYUg5fo5K2MRRhE0cRead16DQ\nalDqtPi9Xtx1TWijw+koqUSfGCt5R5Ni0bcdx9skI/Md6Vo8UBn1AaMy/pKz+2wvGwgy34UBGQl3\n3303n376KTNmdNcIWLNmDTfddNOgNRIEUQyoG/XmSehalVEa9UH7ee3tDHv8jqBtBqOG3LHxuF1e\nSg81BvTPd24qZdWSA4F25hDpWNZwA9FxFmwtTpa9n0fpoUaS08Opq+qOQd6y9jAgaaGXFTfyxQd7\n6WjvNjhamjqorWztUTEQIPnaS9DGRJJ28+U0rNlCzZJVFD32IkDAa6C2Whj3/tPsue6v7L/7CZQG\nHbHn96/Am9/lRlCretxcnFV1+FQaCi69DVEleTlGb1yMwe/kQKWX6DgL7/xPqsNrsep7lXwURZFV\nSwuonrOQ5rJ6XIv2ACGAnzHZZg6u3cfXK+DrFUeIr8wn9EgFo176O4IgoNRrUepPbCz12ddfRevk\nU6lbvo66L9dh31fEoSdeIWR0DqETRlD68geMee1RIk+fjLOqDl1cFIvf2oW2qI5UVyWC3UX7oTJU\n2zaRHWLG22pn59NPApB79mkMf+ZJBEGBz+HEUVGDZXhW0HXNrKmn/uuN6K6dwqEnXqF15z4Aqj74\ngrBJo4n/9Vxadx1g5+W3425sYdx7/ybiNMmTkvvk3Ww6+xo2nXU1+uQ4HKVVxF18NsNvPJupRxto\nN3YnZu9tr6by3c/J1rSQ8d+7qf3iGyJOm0DUmdIqVtKVF0hhcNX1veYPCAoFU9ZKIRXGjGRK/vsu\n4acN3LMzGOh62MaefwbamEj8Lnfg2h6LJtTC1Gm9G+29tZX5+aIO6a6EqlCpAn8nx8Z3D3bVNJnB\ngzxWZAYbAzISKisre6gbnXrqqVRUVPSxx4+MKEqeBOFYI0GH6PXhtbWhT4rr3KbvsbspMzmo+qvB\nqMFo0jL1zCzsrXns31XFV5/uY8+W8qD9LEclhlqsesqPNAd01W2tThrr2sgcFk1tZSuHC+oDbXdt\nKqOj3U1kjJn6mm5D4s3/28TVt03DGh6caGXKSCbjT78FpFV5XVwUrTv2Yc7JCFr1NWenMfq1x9gy\n71rybnmIyFmT8Hu85N30IGk3LwwkiLrqm9h/5z+JPH0yot/Pvjv+SdSZpwYUn7qo+2oDzvAYRJUa\nS80RbDGp7Nxeg8GsYfu6EgB0ejVOh4fdW8p6NRLqqu3s2lyGSq1ErxAx522kYYQULtN+3wNkxERQ\nLU6kLiaL+pAE0iaPIfrsnjJ735XK0mbabC5UKgVVZS3Ep4QSl5lGU7OaxPkXUP/PZ6hbvIL4S88h\n8dJ5JF27gP1H2ileewSNVoXF1kDxwQYmzshkyt9fB6DggWdpP1TKsMfvoHnLHvZc/zcAhtx/SyBJ\nrkv94Fh0MZEkLpQM7YjTxtOyaz/6xFj2XPc38v/0D6o/+xrbngK8bR0k/XZ+0CQ2ZNRQpqx+k/qv\nN1Ly0nskX3cJQx84/gr6kPtuJmr2FKLOnIKgVAYUvI5G6FTs6IujpTLTbr78uOf7qRA2cdSP3QUZ\nGRkZGZlBwYCMhJEjR/LEE09w1113AdJq8JNPPsmoUYP4wSqKAeOgy1boMgjcjS0o9dKEXmXsaSQI\nSiXOju5V/aPVayxWqf2xBgKAVt99WcMijbhd3eoktZWt2FudjJqYxLgpKXz8+g4iY8xUlDRz+GA9\nZquOK285la3fHOGb5d06zvt2VRKfHNpnISaFSkXazZez/+5/kXZLzwmbMTWB0a8+wpbzbqB+5SZs\n+YU0rNpEcXk76ogwrIW7iZo9ldpla6ldtjawX92KDQEFB5Ak+4oe/S/iOGnCfskjl7JxXRn7dlYG\nnW/egpEcPljPnq3luN1eNBrpmqxdfhCVShG4Jtfcfho6tRRXv7lMwLV1O2kXn0HG7b9DExbCtuX5\nfLMB0p575IQpdxzaX8vit3YFbStdtJ/MtBGBfunjp6C4dgIHi1Qo/r466DfsQqtTMfbU5MD77Ptu\nCryOPf90dPHRNG/ehSEptse+x0NQKgNx8+M//A9Fj77Ikf97G2NGMpOWv9KrBr1pSCqmIamk/v6y\nfp1DE2rpVdtc5ts5kRKoMj9/5PEi01/ksSIz2BiQkfD8889zzjnn8PTTT5OYmEh5eTkGg6FHteRB\nhehHPNaT0GkQeO3tgbCVY8ONunB2dCcd+7zdlfNM5u5wl8TUMMqPdJcdj4zpdkMnpgXrtG9aVQxA\nRJSJ+ORQbvrrLNwuL888uBKAmM6wognTUhk+Lp7Xnt5Au90V2G/hjZOISegZegSQ+JsLsY4fjiW3\n90Ry67hctLGRbHvxc2rCUoiKTaJ86vkADNu0hopFS7COH07azZdTcN9/SLj0HAoffp5VuXOZsv4d\nHCWV7Pn9Ayh1WrQzZ2Co7iAkOoQZc7Mxh+hobe5gSG4MMQmSKo8gwM6NpZQVN2E0SZVqmxu7taHj\nk0Mxdl7H8CljmQtw6ZigPg87LZv1m2rI31nJjLOzaaxrQ6EUCA03Uphfg9/fqZS0qZTTzhpCbOLx\nawNsWXuYdV8WEhZpRKEQiE8OJTzKxEfvHwFEZs/Pxef1c3BvDWq1Ep1BzYHdVYFQs8zcGGorW6mv\ntjNyQiJ6Q9+FyELHDyd0fM/iQANBoVIx5C83kn7rb1HqtXJcqYyMjIyMjMwPwoCMhKFDh3LgwAE2\nb95MVVUVcXFxTJw4EbV6EJcd9/tB6JxYHRVu1EWXulGXd0ETGYa7vglBJa1aO442EnxHGQkWab/T\nz8th1ClJPHGPVKjo9n/MCTp9eFS3TF1XUb7IGDMJqWGd2wS0uu7rd7QBoDdouOHuGYEibgD2Vmef\nRoIgCH0aCCCtUI964UHe/t8unGEx2JKGBD6L/N2ltH25iqx7rids0miizpyCKIp4mm0cee5tdl99\nL20FhzFlpTLmrSd4/5NiouOkmGudXh0k8dpFQkoYBqOGFZ/kBxK7BQGmzRmCVqciIye6z752YTRp\nyciJYtfGUg7mVQdqT8y9ZASfv5cX1Pbt5zeTPSKW0ZOSMFm0hIQGh2c5OtxsWnWIjKFRzPv1KFSq\n7gn3mMk34PP5A3UHRp3SHVd85vnDgirxxvZx/U8mvXm6ZH4c5JU+mYEgjxeZ/iKPFZnBxoAlUNVq\ndZ9VlwcjgigGPAldq7BHy512qRt1TcI0YSFEn31aQJLT6eg2EsZNSQm8Th8axYJrTyE+WVq5Pv28\nnF6r5wqCwDmdE9L6Wju1FTbmXjIiaNJ5NGlDesqGJqaGsQnJSGhp6lmlsb/UV9upVoThDI1GX1uO\naUQ2SVaRHQc70F54PuMevimovSAIDPnb70GAI//3NmqrmUnLX8GrUNJQt4es3Jjjnk+pUjDnouF8\n/PoOAK64aTI6gzoQqtVfZp2Tg1qjoqqsObDt8/fyUCgEZp0zlMa6dgwmDTs3lVKQV01BXjVqjZKz\nfzWCzGHdhkje1nK8Hj+nnpEZZCAE+qvsfZW+r99KRkZGRkZGRubnyoCNhJ8cfj/HCrUfHVrUlZMQ\nSFxWKBj22J8Dn3eFG/3uT1MJjegusS0IAgkp3cnBR688H8uQ4dJkOn1o30mggkJA9ItERPcskBOb\nZMUcosPe6gwYCYX5NVis+j69CsciiiKvP7Ohq/Occ8MMksZlIPpF9j28isqSZoaP7V36L/WGSyl7\n/RPiLpqDV6Hk5Se+ARHiko4f2gOS0XPlLafi6HATFffd1F6MZi1nXTQcURTxef202VzU19ixhhuC\nQrtGT0pi2Qd7iYo1c3BvDZ++vYtTTktj6uwsfF4/uzaXkZwRHrRPF3IsqEx/kceKzECQx4tMf5HH\nisxg45dhJPRQN+o2EhS64JyEY2O+HZ2JyzrDyQ2puvq2qfh8Yq81BdRqJdfdOZ0XH1/Dni3luBwe\nCvJq0OnV3PRXqUiQx+3D7/cHhS514ff5+fiNHUHbEsdKlUcFhUB8SigVJc099utCExHK1PXvoLZa\nOLCvNhCCFZvYPwOlt0n5d0EQBFRqJdZwQw+lJwCtTs0Fl0s5DZNmpPPVp/vZsvYwiWlhdLS7abO5\nOPOC3BPSFxkZGRkZGRmZnzM/byOhUwJVFAQ8BjM1Omklv8t7cPTrgHfhmDm60+EBgV4n3yeSY+Pn\neyMpPZz8HZUU5NUE+rZpVTE6vYqCvGoaatu47s7paLTdP6ujw03RvlpKihoZPy2V+mo7aUMig4yR\nhJRQig/UseiFzYw8JZFho3uq5+hiImlt7mDt8kIMJg2X/37ySb8m3weFUsGsc4ZSUdLE4rd2odOr\nCIswktqHOpS8eiPTX+SxIjMQ5PEi01/ksSIz2BiwkbBixQreffdd6urqWLp0Kdu3b8dmszFz5syT\n0b/vjyh5Eqomn43dMoQJdW3ojd0T8q6chK4qrj0Kh3V40OnUveYb/NDMnDeUqWdm0WZzUl/bxvIP\n97Lh66KgNv954Gt+84cpRESbaLM5eeHRNYAUGjRtdlavnorkDKmgXFVZC1VlLT2MhKb6NtZ8cTBQ\n02HqmZmYj6oFMVhRqZXMmS/lRCgUAnMuykUYBL+jjIyMjIyMjMxgZ0B6is888ww33HADmZmZfPPN\nNwDodDr+8pe/nJTOnRBEEZRKlC6pmFlJUQMqQ09PAmJX0bTgSaSjw3PSQ436i0arwmjWEh0fQmZO\nVEA+tIuupOey4kYAjhQ1BD4761fDezUQoGc4kCiKQe/XLi8MGAhnXjCMU6anf78v8gMSnxzKTX+Z\nxbV3TCcuKbTPduvXr/8BeyXzU0YeKzIDQR4vMv1FHisyg40BGQlPPfUUX3/9NXfffTfKzsJWQ4cO\npaCg4KR07kQgiCKCWk3yWRMBKD/c1CMnYd/OSt75shbLmGHkPHpb0P5OhwedfnAYCUej1am5+vZp\nnHp6p/SoABdeORaTRUt1RQuF+TV8+VE+Or2a2x6eTWi4sc9jCYLA6efmBN4fq6BUV20jKzeGi68e\n32dy82BG9h7IyMjIyMjIyAyMARkJbW1tJCYmBm1zu91otdo+9hgEdK6Ka+MlhSGX0xuojQCgNGj5\n6tP9tNlcjHzvWaxjhgXt7uhwDxpPwrGo1UrSsiXvga4zPyA20UpJYQPrV0hhSOOnpfbpQTiaUROT\n+O0fpXjILq8BSEaSvcVJdLyFpLTwn+2EW44Flekv8liRGQjyeJHpL/JYkRlsDMhImDp1Ko8++mjQ\ntmeeeYYZM2ac0E6dSAS/H7+gwO+XjAWfzy8VHRuZDYBCo0HfaQS0Njt67O90eAKfD0YiokykZIZz\n/sLRAEw4LQ2VRklTQzsz5mZzymlp/T5WeJSJ6DgL+TsqEf0i5Yeb2LbuCHDiFIpkZGRkZGRkZGQG\nPwPOSfjkk09ITk6mra2NrKws3nvvPf71r3+drP59bxQeFz5Bic8rVUvu+n/8+0+T9ZcbCRmdEzAC\nbL0ZCR2DM9yoC6VKwUW/HR+o4BybEMJv/ziFeQtGHrd2Q1+MnpxMfY2dtcsP8t7LW9my5jAqtbLf\n9Rh+qsixoDL9RR4rMgNBHi8y/UUeKzKDjQGpG8XFxbF9+3a2bt1KWVkZiYmJTJgwAYViQLbGD4aA\niNLjAsDl8ALg7TQS1CFm0m5aCBAwAlpbgo0Ev8+Py+kd1EZCb2g0KrJHxH6nfYeNjuPA7iq2ry8B\npAJlI8YnYjBqTmAPZWRkZGRkZGRkBjMDMhL++te/BuLbRVFk7969LFu2DI1GQ2JiInPmzCE6Ovqk\ndPS7onBLRoLDIRUA8/n8Pdp0ifkc60lwOiXDQm/45UyQBUFg+tnZvP/yVk47O5vcMT1rJvwckWNB\nZfqLPFZkBoI8XmT6izxWZAYbAzISCgsLWbx4MRMmTCAxMZGysjK2bdvGvHnzWLJkCTfeeCMffvgh\nZ5111snq74Dp8iQ4Oysnd4UbHY3bLRkDx6r6ODsNi8GauHyyiIwxc8M9MwdFbQgZGRkZGRkZGZkf\nngHFCYmiyLvvvsu6detYtGgR69ev5/3330epVLJlyxaee+457r777pPV14EjdnsSnB2dnoTejASX\nZCQ0NbQHbe8yLH5q4UYngl+agSDHgsr0F3msyAwEebzI9Bd5rMgMNgZkJCxfvpxzzz03aNvcuXP5\n4osvALjssssoLi4+cb07ASjdUhE1R0ff4UZulw8Ae4sz4FU4ep/BrG4koTwqoQAAIABJREFUIyMj\nIyMjIyMjc6IZkJGQnp7Oc889F7TthRdeICMjA4CGhgaMxr6Ldv0YKLrCjRzH9ySYrVLl5eaG7pCj\nX2q40S8RORZUpr/IY0VmIMjjRaa/yGNFZrAxoJyEV155hQsuuIDHHnuM+Ph4KisrUSqVfPzxx4CU\ns/DQQw+dlI5+V5Sd4UZdeH1+RFHsTsD2i3jcPlIyI7C3OGmqbyM6zgJ0hyj9khKXZWRkZGRkZGRk\nZAbkSRgzZgxFRUUsWrSIW2+9lbfffpuioiLGjh0LwLRp07jmmmtOSke/G2IgJ+GoTfj9Im6Xl/Ur\nCgOqR+FRJgDabN3tHR0eEECrHZAtJfMTRI4Flekv8liRGQjyeJHpL/JYkRlsDHj2q9FomDZt2sno\ny0lB4XUDItCdiOvz+tmy9jBb1hymuVEKLzJbtCgUQsB7AFK4kU6nRviFJfHKyMjIyMjIyMj8shmw\nkVBTU8PWrVtpbGxE7CowAFx11VUntGMnCgFQ4ceLMrDN5/Pj90t9P7i3BgC1RoXOoMbRqWgEkrqR\nnLT8y0COBZXpL/JYkRkI8niR6S/yWJEZbAzISFi8eDELFy4kMzOT/Px8cnNzyc/PZ8qUKYPWSIBe\njASvH5UqONIqPMqI3qAJKBpBpydBNhJkZGRkZGRkZGR+YQwoJ+Hee+/l1VdfZdeuXZhMJnbt2sWL\nL77ImDFjTlb/vh+djg4lYtBmr9ePWtNtNEydnUV0fAj6YzwJjg7PL7JGwi8RORZUpr/IY0VmIMjj\nRaa/yGNFZrAxICOhvLyciy++OPBeFEWuuOIK3njjjRPesROJQgg2Eo71JBiMknqR3qDB0X6UJ6HD\nIysbycjIyMjIyMjI/OIYkJEQFRVFTY0Uw5+SksKmTZsoLi7G7+9Ze2AwoTjGkyDlJHS/7/IW6Azq\nQG0ER4cbu82JKUT7g/VT5sdDjgWV6S/yWJEZCPJ4kekv8liRGWwMyEi4+uqrA+6wW2+9lZkzZzJy\n5EhuuOGGk9K5749kHHSJE3WWRsDn9QdVXu7KO9AbpXAjURQp3FuD3ycyJDfmB+2xjIyMjIyMjIyM\nzI/NgIyEP//5z1x00UUAXHHFFRw8eJAdO3bw97///aR07kTRFW7UlYfg8/qDKi93eRL0Bg1+n4jb\n5aOyrAWTRUtUZ2E1mZ83ciyoTH+Rx4rMQJDHi0x/kceKzGCj3+pGXq8Xs9lMS0sLWq0UgpOcnHzS\nOnYi6bKE1BoVbpcPn8+P1+sLfN4lc2o0S9+rzebE7fSiN2gClZllZGRkZGRkZGRkfin025OgUqnI\nzMykoaHhZPbnpKDsnOer1ZInwXusJ6EzOdli1QNga3HgcnnRyJWWfzHIsaAy/UUeKzIDQR4vMv1F\nHisyg40BzYIXLlzIOeecwy233EJiYmLQKvvMmTMHfHKfz8e4ceNISEhgyZIlvbbZtm0bkyZN4r33\n3mP+/PmAlDRtsVhQKpWo1Wq2bt163PMcG27UVN+Oz9udzNyldBQS2mkkNDtwO70Bz4KMjIyMjIyM\njIzML4kBGQnPPfccAA888ECPz44cOTLgkz/99NPk5ORgt9t7/dzn83HnnXcyZ86coO2CILBmzRrC\nwsL6dZ6uxOUuI2HtFwdJzYoAYOjI2EA7o1mLQinQ2ulJCI0wDvQryfxEWb9+vbyKI9Mv5LEiMxDk\n8SLTX+SxIjPYGJCRUFJScsJOXFFRwbJly7j33v9v796Dq6jv/4+/TjiRa4AEEhAC5G4I4ZJwb+Gr\nEgnYHyA3FbEwiiClgz9k/Dr0q/xGnY4koLZSRIYyyq0t2NYZkAxQlBREQQETbAOKCiEJCZcvJDFN\nuCSEz+8PyJFDbnswyVlPno8Zx+ye3ex7ndfIebPv3X1Rv/vd72rcZsWKFZo6daoOHTpU7TNjTA17\n1Oz2JkGSyv5zVR0CW+v/PNr/h+38HHI6W+jg3hsNT6/ITpaPAQAAAPgKj55uJEm7du3SrFmzNG7c\nOEnS4cOHlZ6e7vGBFy5cqNdee01+fjWXkJ+fr61bt7oer3rraJPD4dADDzygQYMGac2aNbUew3Gz\nkWhx8xBV9yRI0qWycrVwVj9224AfXp7GPQnNB397A6vICjxBXmAVWYHdeNQkrFixQvPmzVN0dLQ+\n/vhjSVKrVq20ePFijw6alpamkJAQJSQk1HpF4Nlnn1VqaqocDoeMMW7bffrpp8rMzNSOHTu0cuVK\n7du3r87jVd2T0KbdDw3A5UsVNTYJE3+Z6PqZJgEAAADNkUffgn//+99r9+7dCg8P17JlyyRJvXv3\n1tdff+3RQffv368PPvhA27dv15UrV1RSUqKZM2dqw4YNrm2++OILTZs2TZJ04cIF7dixQ/7+/pow\nYYLuvvvGfQTBwcGaNGmSDh48qJEjR1Y7zuZj29Xr2v+q8sAHKlMHtQwapicWjNe65Z/qZE6Wii+1\nlfRzST88n3jEiBHqFNJOGZkHFfRNqYaPiqz2Ocu+t7xq1Sr17dvXNvWwbN/lW59lbod6WLb3Mnlh\n2epy1Tq71MOyvZarfs7NzZV04wXHjc1hPBjuDwkJUUFBgZxOpwIDA1VUVKTLly8rIiJCZ86cuaMC\n9u7dq9dff73WpxtJ0pNPPqnx48dr8uTJunTpkiorKxUQEKCysjIlJyfrpZdeUnJysts+u3fvVuar\nO9X90zRV/N//1vHSNho8MlwJw3vqj8v2SpJCwwI17emh1Y73p7cP6Ozp7zV2al/FJ3a/o/PCT8sn\nn3DDGKwhK/AEeYFVZAWeyMjIUFJSUqMew6Nxo5EjRyo1NdVt3YoVK3T//ff/qCKq7jdYvXq1Vq9e\nXee2Z8+e1ciRIzVgwAANHTpU48aNq9Yg3K7FLe9Du3WEqKZxI+mHG5zvuuVGZ/g2/scMq8gKPEFe\nYBVZgd0469/kBytWrND48eO1Zs0alZaWKiYmRgEBAUpLS7vjAu69917de++9kqS5c+fWuM3atWtd\nP0dEROjIkSMeHaPq3mhjjHuT0KLmJqGqOeBtywAAAGiOPLqS0K1bNx06dEh//etf9ec//1kbNmzQ\noUOHXPcI2JXfzWegGmPk5+dwXSmo7UpCwM03L6P5uHXmD6gLWYEnyAusIiuwG4+uJCxYsECPP/64\nhg4dqqFDq8/y28+N2y2qLgiY6zf+fVdLpyrKK2u9kvBfY2LUvmNrRfYOaYoiAQAAAFvx+D0JEydO\nVFRUlF566SUdP368MWpqeI4friRI0l0tb1xJcPrXMm7U0qkh/xXuugIB38csKKwiK/AEeYFVZAV2\n41GTsHz5cuXl5WnVqlXKzc3VsGHDNHDgQL3xxhuNVV+D8LvZJFx3NQk3LqDUdiUBAAAAaM48/pbc\nokULjR49WmvXrlVWVpaCgoL0/PPPN0ZtDeaHcaPbmoRa7klA88MsKKwiK/AEeYFVZAV24/G35NLS\nUm3cuFG/+MUvFB0dLX9/f7eXoNnKzTdAuJqEm8vt2reUJFWUV3qhKAAAAMDePLpx+eGHH9b27duV\nmJio6dOna/369QoODm6s2hqM4+YzUKvuSRiZHKOC3GL1jAzyZlmwEWZBYRVZgSfIC6wiK7Abj5qE\nQYMG6Y033lDPnj0bq55G4bh5veT6zXGj9h1ba85/3+vFigAAAAD78qhJWLRokc6dO6dt27bpwoUL\nrr+Zl6RZs2Y1eHE/XtUjUB23LgLVfPLJJ/wtDiwhK/AEeYFVZAV241GTsGXLFv3yl79UdHS0srKy\nFB8fr6ysLI0YMcKmTcINfrc9AhUAAABA7Ty6cfnFF1/Uu+++q8zMTLVr106ZmZn64x//qMTExMaq\nr0FUvUA5LLqzdwuBbfG3N7CKrMAT5AVWkRXYjUdNQl5enh555BHXsjFGM2fOtO/TjW4KbOun+f8v\nSX0Su3u7FAAAAMD2PGoSQkJCdPbsWUlSWFiYDhw4oBMnTuj69euNUtyP5aiaLnJIrVr7e7UW2BvP\np4ZVZAWeIC+wiqzAbjxqEmbPnu0K8cKFCzVq1Cj1799f8+bNa5TiGkrVI1ABAAAA1M9hfsTdvDk5\nOSorK1NcXFxD1tQgdu/erSO/3a5uB7ar36qX1W1SsrdLAgAAAH60jIwMJSUlNeoxPHq60e169erV\nUHU0ktsegQoAAACgXs1jDsfRPE4Td45ZUFhFVuAJ8gKryArspll8e3b4cSUBAAAAsKpZNAnixmXU\ng+dTwyqyAk+QF1hFVmA3zeLbM/ckAAAAANY1iyZBjBuhHsyCwiqyAk+QF1hFVmA3zaJJ4D0JAAAA\ngHW+/e3Z3PLKZaAOzILCKrICT5AXWEVWYDe+3STcxNONAAAAAOuaRZPA041QH2ZBYRVZgSfIC6wi\nK7CbZvHtmSsJAAAAgHXNokkQj0BFPZgFhVVkBZ4gL7CKrMBumkWTwNONAAAAAOuax7dnriSgHsyC\nwiqyAk+QF1hFVmA3vt0kVD0ClSYBAAAAsMy3m4SbuHEZ9WEWFFaRFXiCvMAqsgK7aSZNQrM4TQAA\nAKBBNItvzw5/p7dLgM0xCwqryAo8QV5gFVmB3fh0k1A1ZORo0cKrdQAAAAA/JT7dJFTx40oC6sEs\nKKwiK/AEeYFVZAV20yyaBK4kAAAAANY1jyaBKwmoB7OgsIqswBPkBVaRFdiNjzcJN96TwJUEAAAA\nwDqvNgmVlZVKSEjQ+PHja93m0KFDcjqdev/9913rdu7cqdjYWEVHR2vp0qX1HsfhpElA3ZgFhVVk\nBZ4gL7CKrMBuvNokLF++XHFxcXLU8kbkyspKLVq0SGPHjnVbN3/+fO3cuVPHjh3Tpk2b9NVXX9V5\nHG5cBgAAAKzzWpNw+vRpbd++XbNnz5YxpsZtVqxYoalTpyo4ONi17uDBg4qKilJYWJj8/f01bdo0\nbd26teaD3Py1jBuhPsyCwiqyAk+QF1hFVmA3XmsSFi5cqNdee01+tbwNOT8/X1u3btW8efMkyXW1\nIT8/Xz169HBtFxoaqvz8/DqPxY3LAAAAgHVe+faclpamkJAQJSQkaM+ePTVu8+yzzyo1NVUOh0PG\nGNfVhtpGk2qy6fgu9bz2v8pY/qYCO3dS3759XTN/VR07yyxX+eSTT2xTD8v2XR4xYoSt6mHZ3svk\nhWWWWW6I5aqfc3NzJUmzZ89WY3OY2mZ9GtELL7ygjRs3yul06sqVKyopKdGUKVO0YcMG1zYRERGu\nxuDChQtq06aN1qxZo5CQEL388svauXOnJCklJUV+fn5atGiR2zF2796tL1/+QHcf3KXknD3ya3lX\n050gAAAA0EgyMjKUlJTUqMfwyrjRkiVLlJeXp+zsbG3evFmjRo1yaxAk6eTJk8rOzlZ2dramTp2q\nVatWacKECRo0aJC+/fZbnTp1SuXl5Xrvvfc0YcKEOo/HuBHqc2unDtSFrMAT5AVWkRXYjS2+PVeN\nEK1evVqSNHfu3Fq3dTqdeuuttzRmzBhVVlbqqaeeUu/evev+/bXc9wAAAACgOq+MGzWFW8eNxp7d\n7+1yAAAAgAbhs+NGTccn+x8AAACgUfl4kwBYwyworCIr8AR5gVVkBXZDkwAAAADADU0CoB+eRwzU\nh6zAE+QFVpEV2I1vNwnckgAAAAB4zLebBMAiZkFhFVmBJ8gLrCIrsBuaBAAAAABufLpJcDBvBIuY\nBYVVZAWeIC+wiqzAbny6SQAAAADgOZoEQMyCwjqyAk+QF1hFVmA3NAkAAAAA3Ph2k8AtCbCIWVBY\nRVbgCfICq8gK7Ma3mwQAAAAAHqNJAMQsKKwjK/AEeYFVZAV24+NNAvNGAAAAgKd8vEkArGEWFFaR\nFXiCvMAqsgK7oUkAAAAA4IYmARCzoLCOrMAT5AVWkRXYDU0CAAAAADc0CYCYBYV1ZAWeIC+wiqzA\nbmgSAAAAALjx7SbB8AhUWMMsKKwiK/AEeYFVZAV249tNAgAAAACP0SQAYhYU1pEVeIK8wCqyAruh\nSQAAAADgxuebhJ/tXu/tEvATwCworCIr8AR5gVVkBXbj001Cy5BOat8n2ttlAAAAAD8pPt0kAFYx\nCwqryAo8QV5gFVmB3fh4k8AjUAEAAABP+XiTAFjDLCisIivwBHmBVWQFdkOTAAAAAMANTQIgZkFh\nHVmBJ8gLrCIrsBufbhIc3JIAAAAAeMynmwTAKmZBYRVZgSfIC6wiK7AbmgQAAAAAbmgSADELCuvI\nCjxBXmAVWYHd0CQAAAAAcOPVJqGyslIJCQkaP358tc+2bt2q/v37KyEhQQMHDlR6errrs7CwMPXr\n108JCQkaMmRIU5YMH8UsKKwiK/AEeYFVZAV249UmYfny5YqLi5PD4aj22QMPPKAvv/xSmZmZWrdu\nnZ5++mnXZw6HQ3v27FFmZqYOHjzYlCXDR/373//2dgn4iSAr8AR5gVVkBXbjtSbh9OnT2r59u2bP\nni1jqj+rtG3btq6fS0tL1blzZ7fPa9qnOp6BCmu+//57b5eAnwiyAk+QF1hFVmA3XmsSFi5cqNde\ne01+frWXsGXLFvXu3VsPPvig/vCHP7jWOxwOPfDAAxo0aJDWrFnTFOUCAAAAzYZXmoS0tDSFhIQo\nISGhzisCEydO1FdffaVt27ZpxowZrvWffvqpMjMztWPHDq1cuVL79u1rirLhw3Jzc71dAn4iyAo8\nQV5gFVmB3TiMtbmdBvXCCy9o48aNcjqdunLlikpKSjRlyhRt2LCh1n0iIyN18OBBderUyW39K6+8\nonbt2um5555zW79161a1a9euUeoHAAAAvKW0tFQPPfRQox7DK03Crfbu3avXX39d27Ztc1t/4sQJ\nRUREyOFwKCMjQw8//LBOnDihS5cuqbKyUgEBASorK1NycrJeeuklJScne+kMAAAAAN/i9HYBklxP\nN1q9erUkae7cuXr//fe1YcMG+fv7q127dtq8ebMk6ezZs5o8ebIk6dq1a3r88cdpEAAAAIAG5PUr\nCQAAAADsxSffuLxz507FxsYqOjpaS5cu9XY5aCJ5eXm6//771adPH8XHx7ueiFVYWKjRo0crJiZG\nycnJKi4udu2TkpKi6OhoxcbGateuXa71X3zxhfr27avo6GgtWLDAtf7q1at69NFHFR0drWHDhikn\nJ6fpThAN7vYXOpIV1Ka4uFhTp05V7969FRcXp88//5y8oEYpKSnq06eP+vbtq+nTp+vq1atkBS6z\nZs1Sly5d1LdvX9e6psrH+vXrFRMTo5iYmDrvA3YxPubatWsmMjLSZGdnm/LyctO/f39z7Ngxb5eF\nJnDmzBmTmZlpjDHmP//5j4mJiTHHjh0zzz//vFm6dKkxxpjU1FSzaNEiY4wxR48eNf379zfl5eUm\nOzvbREZGmuvXrxtjjBk8eLD5/PPPjTHGPPjgg2bHj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} ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the above plot, it is clear that when the sample size is small, there is greater variation in the average (compare how jagged and jumpy* the average is initially, then smooths out). All three paths approach the value 4.5, but just flirt with it as $N$ gets large. Mathematicians and statistician have another name for flirting: convergence. \n", "\n", "Another very relevant question we can ask is how quickly am I converging to the expected value? Let's plot something new. For a specific $N$, let's do the above trials thousands of times and compute how far away we are from the true expected value, on average. But wait — compute on average? This simply the law of large numbers again! For example, we are interested in, for a specific $N$, the quantity:\n", "\n", "$$D(N) = \\sqrt{ \\;E\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\;\\right)^2 \\;\\;\\right] \\;\\;}$$\n", "\n", "The above formulae is interpretable as a distance away from the true value (on average), for some $N$. (We take the square root so the dimensions of the above quantity and our random variables are the same). As the above is an expected value, it can be approximated using the law of large numbers: instead of averaging $Z_i$, we calculate the following multiple times and average them:\n", "\n", "$$ Y_k = \\left( \\;\\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\; \\right)^2 $$\n", "\n", "By computing the above many, $N_y$, times (remember, it is random), and averaging them:\n", "\n", "$$ \\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k \\rightarrow E[ Y_k ] = E\\;\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i - 4.5 \\;\\right)^2 \\right]$$\n", "\n", "Finally, taking the square root:\n", "\n", "$$ \\sqrt{\\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k} \\approx D(N) $$ " ] }, { "cell_type": "code", "collapsed": false, "input": [ "figsize( 12.5, 4)\n", "\n", "N_Y = 250 #use this many to approximate D(N)\n", "N_array = np.arange( 1000, 50000, 2500 ) #use this many samples in the approx. to the variance.\n", "D_N_results = np.zeros( len( N_array ) )\n", "\n", "lambda_ = 4.5 \n", "expected_value = lambda_ #for X ~ Poi(lambda) , E[ X ] = lambda\n", "\n", "def D_N( n ):\n", " \"\"\"\n", " This function approx. D_n, the average variance of using n samples.\n", " \"\"\"\n", " Z = poi( lambda_, size = (n, N_Y) )\n", " average_Z = Z.mean(axis=0)\n", " return np.sqrt( ( (average_Z - expected_value)*2 ).mean() )\n", " \n", " \n", "for i,n in enumerate(N_array):\n", " D_N_results[i] = D_N(n)\n", "\n", "\n", "plt.xlabel( \"$N$\" )\n", "plt.ylabel( \"expected squared-distance from true value\" )\n", "plt.plot(N_array, D_N_results, lw = 3, \n", " label=\"expected distance between\\n\\\n", "expected value and \\naverage of $N$ random variables.\")\n", "plt.plot( N_array, np.sqrt(expected_value)/np.sqrt(N_array), lw = 2, ls = \"--\", \n", " label = r\"$\\frac{\\sqrt{\\lambda}}{\\sqrt{N}}$\" )\n", "plt.legend()\n", "plt.title( \"How 'fast' is the sample average converging? \" );" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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alcWLFxMYGMjt27eZMmUKjRo1YuDAgcrxW7ZsISgoiJiYGBo0aMDo0aOB3JeR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Sq1tbVISkqC1WpFY2MjamtrAQAajSbAIyMiop/BRoCIiFR59OgREhISEBISgq1bt6KgoADPnj2D\n0WgM9NCIiOgnDAj0AIiI6O/g9XqVvzdu3Ijo6GjExsZix44dARwVERH9LJ4RICKiH+rq6kJoaKjy\nOjw8HMuXL8ft27d96kRE9PdgI0BERN9VXV2N7Oxs2O12vHnzRqlv374dqampARwZERH9Ci4oRkRE\nREQUhHhGgIiIiIgoCLERICIiIiIKQmwEiIiIiIiCEBsBIiIiIqIgxEaAiIiIiCgIsREgIiIiIgpC\nbASIiIiIiIIQGwEiIiIioiDERoCIiIiIKAj9F2L6xXorYvIvAAAAAElFTkSuQmCC\n" } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the expected distance between our sample average and the actual expected value shrinks as $N$ grows large. But also notice that the rate* of convergence decreases, that is, we need only 10 000 additional samples to move from 0.020 to 0.015, a difference of 0.005, but 20 000 more samples to again decrease from 0.015 to 0.010, again only a 0.005 decrease.\n", "\n", "\n", "It turns out we can measure this rate of convergence. Above I have plotted a second line, the function $\\sqrt{\\lambda}/\\sqrt{N}$. This was not chosen arbitrarily. In most cases, given a sequence of random variable distributed like $Z$, the rate of converge to $E[Z]$ of the Law of Large Numbers is \n", "\n", "$$ \\frac{ \\sqrt{ \\; Var(Z) \\; } }{\\sqrt{N} }$$\n", "\n", "This is useful to know: for a given large $N$, we know (on average) how far away we are from the estimate. On the other hand, in a Bayesian setting, this can seem like a useless result: Bayesian analysis is OK with uncertainty so what's the statistical point of adding extra precise digits? Though drawing samples can be so computationally cheap that having a larger $N$ is fine too. \n", "\n", "### How do we compute $Var(Z)$ though?\n", "\n", "The variance is simply another expected value that can be approximated! Consider the following, once we have the expected value (by using the Law of Large Numbers to estimate it, denote it $\\mu$), we can estimate the variance:\n", "\n", "$$ \\frac{1}{N}\\sum_{i=1}^N \\;(Z_i - \\mu)^2 \\rightarrow E[ \\;( Z - \\mu)^2 \\;] = Var( Z )$$\n", "\n", "### Expected values and probabilities \n", "There is an even less explicit relationship between expected value and estimating probabilities. Define the indicator function\n", "\n", "$$\\mathbb{1}_A(x) = \n", "\\begin{cases} 1 & x \\in A \\\\\\\\\n", " 0 & else\n", "\\end{cases}\n", "$$\n", "Then, by the law of large numbers, if we have many samples $X_i$, we can estimate the probability of an event $A$, denoted $P(A)$, by:\n", "\n", "$$ \\frac{1}{N} \\sum_{i=1}^N \\mathbb{1}_A(X_i) \\rightarrow E[\\mathbb{1}_A(X)] = P(A) $$\n", "\n", "Again, this is fairly obvious after a moments thought: the indicator function is only 1 if the event occurs, so we are summing only the times the event occurs and dividing by the total number of trials (consider how we usually approximate probabilities using frequencies). For example, suppose we wish to estimate the probability that a $Z \\sim Exp(.5)$ is greater than 10, and we have many samples from a $Exp(.5)$ distribution. \n", "\n", "\n", "$$ P( Z > 10 ) = \\sum_{i=1}^N \\mathbb{1}_{z > 10 }(Z_i) $$\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pymc as mc\n", "\n", "N = 10000\n", "print np.mean( [ mc.rexponential( 0.5 )>10 for i in range(N) ] )" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.0058\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What does this all have to do with Bayesian statistics? \n", "\n", "\n", "Point estimates, to be introduced in the next chapter, in Bayesian inference are computed using expected values. In more analytical Bayesian inference, we would have been required to evaluate complicated expected values represented as multi-dimensional integrals. No longer. If we can sample from the posterior distribution directly, we simply need to evaluate averages. Much easier. If accuracy is a priority, plots like the ones above show how fast you are converging. And if further accuracy is desired, just take more samples from the posterior. \n", "\n", "When is enough enough? When can you stop drawing samples from the posterior? That is the practitioners decision, and also dependent on the variance of the samples (recall from above a high variance means the average will converge slower). \n", "\n", "We also should understand when the Law of Large Numbers fails. As the name implies, and comparing the graphs above for small $N$, the Law is only true for large sample sizes. Without this, the asymptotic result is not reliable. Knowing in what situations the Law fails can give use confidence in how unconfident we should be. The next section deals with this issue." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Disorder of Small Numbers\n", "\n", "The Law of Large Numbers is only valid as $N$ gets infinitely large: never truly attainable. While the law is a powerful tool, it is foolhardy to apply it liberally. Our next example illustrates this.\n", "\n", "\n", "##### Example: Aggregated geographic data\n", "\n", "\n", "Often data comes in aggregated form. For instance, data may be grouped by state, county, or city level. Of course, the population numbers vary per geographic area. If the data is an average of some characteristic of each the geographic areas, we must be conscious of the Law of Large Numbers and how it can fail for areas with small populations.\n", "\n", "We will observe this on a toy dataset. Suppose there are five thousand counties in our dataset. Furthermore, population number in each state are uniformly distributed between 100 and 1500. The way the population numbers are generated is irrelevant to the discussion, so we do not justify this. We are interested in measuring the average height of individuals per county. Unbeknownst to the us, height does not vary across county, and each individual, regardless of the county he or she is currently living in, has the same distribution of what their height may be:\n", "\n", "$$ \\text{height} \\sim \\text{Normal}(150, 15 ) $$\n", "\n", "We aggregate the individuals at the county level, so we only have data for the average in the county. What might our dataset look like?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "figsize( 12.5, 4) \n", "std_height = 15\n", "mean_height = 150\n", "\n", "n_counties = 5000\n", "pop_generator = mc.rdiscrete_uniform\n", "norm = mc.rnormal\n", "\n", "#generate some artificial population numbers\n", "population = pop_generator(100, 1500, size = n_counties )\n", "\n", "average_across_county = np.zeros( n_counties )\n", "for i in range( n_counties ):\n", " #generate some individuals and take the mean\n", " average_across_county[i] = norm(mean_height, 1./std_height*2,\n", " size=population[i] ).mean()\n", " \n", "#located the counties with the apparently most extreme average heights.\n", "i_min = np.argmin( average_across_county )\n", "i_max = np.argmax( average_across_county )\n", "\n", "#plot population size vs. recorded average\n", "plt.scatter( population, average_across_county, alpha = 0.5, c=\"#7A68A6\")\n", "plt.scatter( [ population[i_min], population[i_max] ], \n", " [average_across_county[i_min], average_across_county[i_max] ],\n", " s = 60, marker = \"o\", facecolors = \"none\",\n", " edgecolors = \"#A60628\", linewidths = 1.5, \n", " label=\"extreme heights\")\n", "\n", "plt.xlim( 100, 1500 )\n", "plt.title( \"Average height vs. County Population\")\n", "plt.xlabel(\"County Population\")\n", "plt.ylabel(\"Average height in county\")\n", "plt.plot( [100, 1500], [150, 150], color = \"k\", label = \"true expected \\\n", "height\", ls=\"--\" )\n", "plt.legend(scatterpoints = 1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 20, "text": [ "<matplotlib.legend.Legend at 0xc5cb030>" ] }, { "output_type": "display_data", "png": 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lGI1GHnroIZ5//nnefPNNMjMzef/99x2Of/vttykuLub06dMsXbqU0aNHAzBl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cgPdVOTM+QPrOVsQxSknL0SuU2WZbQ65T/IHk+SFDL5+qIqyNi91fig8Vo3NO4+7jEZrFCQxoZp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\n8n47mS8gTbdEkZQNm5WUd7+QLoppawweF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lGzDsx0vObuaorjmtnloEq+YHP0pMpqGRBs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\nYk67X5J29mLD8GbJ+9/d2UZXbi5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XZjpecTdYsHevxnQsxUQFRfjFlo1l6VRqNr3dCvWm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FdHFCZYLejU3GiZ2DuqImSBCbsHFdRbFr78jDj0/jqGv6ZR2/7NGqGafHKac2+QNtyajvUq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} ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What do we observe? Without accounting for population sizes* we run the risk of making an enormous inference error: if we ignored population size, we would say that the county with the shortest and tallest individuals have been correctly circled. But this inference is wrong for the following reason. These two counties do not necessarily have the most extreme heights. The error results from the calculated average of smaller populations not being a good reflection of the true expected value of the population (which in truth should be $\\mu =150$). The sample size/population size/$N$, whatever you wish to call it, is simply too small to invoke the Law of Large Numbers effectively. \n", "\n", "We provide more damning evidence against this inference. Recall the population numbers were uniformly distributed over 100 to 1500. Our intuition should tell us that the counties with the most extreme population heights should also be uniformly spread over 100 to 4000, and certainly independent of the county's population. Not so. Below are the population sizes of the counties with the most extreme heights." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Population sizes of 10 'shortest' counties: \"\n", "print population[ np.argsort( average_across_county )[:10] ]\n", "print\n", "print \"Population sizes of 10 'tallest' counties: \"\n", "print population[ np.argsort( -average_across_county )[:10] ]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Population sizes of 10 'shortest' counties: \n", "[181 168 229 110 156 123 222 154 498 375]\n", "\n", "Population sizes of 10 'tallest' counties: \n", "[105 114 111 236 373 244 183 278 234 268]\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not at all uniform over 100 to 1500. This is an absolute failure of the Law of Large Numbers. \n", "\n", "##### Example: Kaggle's U.S. Census Return Rate Challenge\n", "\n", "Below is data from the 2010 US census, which partitions populations beyond counties to the level of block groups (which are aggregates of city blocks or equivalents). The dataset is from a Kaggle machine learning competition some colleagues and I participated in. The objective was to predict the census letter mail-back rate of a group block, measured between 0 and 100, using census variables (median income, number of females in the block-group, number of trailer parks, average number of children etc.). Below we plot the census mail-back rate versus block group population:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "figsize( 12.5, 6.5 )\n", "data = np.genfromtxt( \"./data/census_data.csv\", skip_header=1, \n", " delimiter= \",\")\n", "plt.scatter( data[:,1], data[:,0], alpha = 0.5, c=\"#7A68A6\")\n", "plt.title(\"Census mail-back rate vs Population\")\n", "plt.ylabel(\"Mail-back rate\")\n", "plt.xlabel(\"population of block-group\")\n", "plt.xlim(-100, 15e3 )\n", "plt.ylim( -5, 105)\n", "\n", "i_min = np.argmin( data[:,0] )\n", "i_max = np.argmax( data[:,0] )\n", " \n", "plt.scatter( [ data[i_min,1], data[i_max, 1] ], \n", " [ data[i_min,0], data[i_max,0] ],\n", " s = 60, marker = \"o\", facecolors = \"none\",\n", " edgecolors = \"#A60628\", linewidths = 1.5, \n", " label=\"most extreme points\")\n", "\n", "plt.legend(scatterpoints = 1);" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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6kc1tv99PS0vmfnr33Xfp7OwE4PLLLx/1eg7oSqft7e2ce+652afEadOm8dZb\nb1FcXExvby+nnXYa9fX12Vz2ba8rzjrrLG677TZOOumkEefbttLp5pvvofPPL+D6xW2s7u9h7smf\nw+4049QnqV90Nbbqck5c/sCIY+OxJP3dARKJFHmFdtIpDZ83gtlioKjUhcGoIxSIMdAbQFUUCsuc\nmM0G+nuDhIMxHE4zBSVOVFXBOxjGOxTCZNJTVO7CaBz9ucc7GGLYEyYSSmKy6CkqceLKtX7q2IZD\ncQZ6AgghKCxxYnfun1cuO4pGkwx0+0mlNPKL7GzYtPZT9c6Eg5l6AxSUOrE7TPurqoeEkH/rPaZm\n7jGrbffXv2rVKtkjNo5kvMefjPn4OhLi3dvbu1cN9qZf/oHWex/ntI3/xOh2jvhMiyd485hzKT7n\nNI7+9Y37pX5z5szhvvvuyzZev/Wtb1FdXc0NN9wAwBNPPMELL7zA8uXLufvuu6mrq+ORRx4BMj3b\ns2bN4uGHHx4xm0kwGOR73/seer2ehx56iKVLl9LW1sayZct2WYfVq1ezZMkSVq9evcvP0+k0Z599\nNlOnTuWVV17hjTfeyDaYv/Od71BSUsJNN9006jX99re/pb29nd/+9re7PP95553HhRdeyMUXXwzA\nnXfeSU9PD/fffz8Ab731Ftdffz0ffvghy5cv56mnnuL555/fo/ied955fOYzn+EnP/kJAA0NDcyf\nP5/e3l5+/etfjxnP7a9jxxh2dnZy7LHHMjg4iKqqHHvssdx7770jHkK26erq4sILL+Sqq67KXuM2\no92fB81Kp+eddx6PPfYYAI899hj/8R//kd3/l7/8hUQiQVtbG01NTZx44uipF9Xf+TqmwlyCt91B\nXuNaygOd6P+1gvpFV5P0Bai95aqdjjGZDVRMyaNmehHuPBv5RQ5qphdRXpWLwagDwO40M/moQqpq\nC7DaTKg6lZJyFzXTiygqc6GqmV7/3AIbNdOLmDQ5b8zGeqasnSnTipj1mXJqZxbvsrEu9iGP2WY3\nUV1bwOSjCg9YYx3AYjFQWZPPlGmFuNyf/kHD5jBRfVQB1UcVHHGNdQC7y8zkaZ/cY5IkSdLEKz7n\nNEQ6Tccfn93psy1/+Scpf5Dic06bgJrBwoULef3111m5ciXJZJIHHngAk8nEiSeeSHNzMytXriQe\nj2MymTCbzeh0mTZNUVERnZ2do6bFHH/88djtdu677z6i0SjpdJrNmzfz0UcfAfCb3/wGnU7H/fff\nz3e+8x0TCk0yAAAgAElEQVS+/e1vZycEKSgooL29fcx6X3DBBbz22mu8+eabpNNpYrEYq1atoqen\nJ1tm+7qN1X+8YMECmpubefbZZ0kmkySTSdauXTtisOj2hBA8++yzNDQ0EIlEuOuuu1i4cCGKoowZ\nz721YxxWrVpFXV0d6XQau92OwWDI/h6f1gFrsF900UXMnTuXhoYGJk2axKOPPsqNN97I66+/Tm1t\nLW+++Wa2R33GjBlceOGFzJgxg7PPPpsHH3xwzHQZc1E+J/19GfmnnUTePz/go8tupOnOZdiqJ3HS\n8gfJOXbGgbqs/SqV0mhrHGTtvzto2Nh3SORzH+69MgcjGfPxJeM9/mTMx5eM984cM2oo+Y8zaPn1\nI2y68Vf4120msKmJhp89yOabfkPu3OPIm3/CAa3Dju2ebdtTp05l2bJl3HDDDUydOpUVK1bw9NNP\no9frSSQS/OxnP6O2tpbp06fj8Xi45ZZbgExDH2DKlCl84Qtf2On7VFXlmWeeYcOGDRx33HFMnTqV\n7373uwSDQdatW8dDDz3EQw89hKIoXHvttSiKwr333gvAxRdfTENDA9XV1SxevHiX11NWVsaTTz7J\nPffcQ21tLccccwwPPPDAiIb59te8q1TpbdsOh4Pnn3+e5cuXM3PmTKZPn87PfvYzksldt5u25bBf\nffXVTJ8+nUQikc3mGCuee/O7AFx33XXcfffdVFdX88ADDzAwMMBll11GVVUVJ598MqeccsqYaTt7\n44CmxOxv21JitpcYGibaM4AxLwdLWdFOx2hpDVW3d88lyUSKcCiB3qD7VL3AQhMMDYSIhOLYnSZy\nC+wjfuj+7gAt9QPZ7fJKN+WTc1EARZXLKkuSJEnSvtjblBjIpL5s/sm9bHn6H4hkKrNTVSk57wvM\n/NUN6B22A1BT6UDYMd3mYLMvKTETNuh0f/mgftMuewtSKY2uVg+egTA2h5Gq2gIsFsOo59HSGoP9\nQcLBBJFwnJA/M1/6lOmFFBTv2wjfwf4gzXWZBrmiQO0shbwC+3Z1/GQqRkVVCEfifPR/HehUlaqp\n+eTkjUxB8XnC+IejmMx6Coqd6PTjv1DtkZD7eLCRMR9fMt7jT8Z8fMl475pqMjJz6Q+ouf5yvP/+\nCDSNnBOO2WVnoHTwO4T6o/fIId9gH413IERvV2bRgUQ8hcXqp2pq/qjl+3sCtDUOEQklGB4KM3la\nAfFYip4u3z432MPBTxZJEgLCoQR5nww2JifPSt8WP/FYCovFQP8WP0ZT5qGipX6A2SdVoN/aKPf7\notSv78vO251MaUyqyt2nekmSJEmStGumglxKztt1L6d06BgrtfpQdMg32EfrJdhxQZ7dLSwU3Do/\ntqKCponsKpRGw94NFkinNBRVQVUVrHbjJ/s1DQVB0B/F4bIAmYGjM44rIxqKowmIhD+Znz2d1hCa\nxrZhBtFwYsQiO0Ffpr7hYJyhgSCqopBf4hzzLcL+IHtlxp+M+fiS8R5/MubjS8ZbOtz9/e9/n+gq\n7HeHfIN9NO48K33dBmKRJDqdSkGRfczyDqeZob4QZosBV54Vq9WIqlOYNCVvl+U1TTDYGyASTmB3\nmskvstPXHaC73YuqU5lcm09BsRMFhWAgRjQUp7vdx5a2YSYfVUBRmQvIzMJisRhIpzUKSxwM9AYB\nKJmUg2G7GWisNiOqqmQb7Y4cM8lEisaNfUQjmUEXAV+UabNL0e1lzr4kSZIkSZJ08DrkG+yj5eJZ\nbEZmHltKJJzAaNJjs+968KjXE2aoN4CiKlRMySWd0rC7zOTkWlHVXS/utG12l8aNfZjNBix2I4lk\nms6moa0ro6ZpbRhk9kkVFJZmcs37uwPZ43u7/NkG+zY6nUp1bQH5xQ40TeDMGTlVozPHwrRjikfk\nsEcjiWxjHSAUSJBMpNFZDlyDXeY+jj8Z8/El4z3+ZMzH15EQ78Mtf1k6vOzL/XnIN9jHYjIbMJlH\nTxHxDAT5+IMuBntDmCx6JlW7ceZYiMdSCDF6/lNvl4/+LX5C/jihQJwC1UE0GGf7+KfTAk0T6HRk\n8tAVQGT+bjDp8Q6GcLmtIwaO6vQqPk+Y7g4fJoue6tp88go+yZ/PybORk/fJKHWjWY/VbiQSSpBM\nplF1Cn3dfvIKbNm0m92JRpN0t3kJBeO4ciwUljlHfbg50m17qxIOxbE5TBQWO+VsPpIkSQep0Vaj\nlKSJtK8Pk7qf/vSnP92/VTlw2tradpoGp6KiYp/P19Puo6t9mHRKIyfXQkeTB01AKBAHRWAy69Hr\nVRRFIZ3W6O/x09XmxdMfxGw14vOEERqYLQYmTXaTSqYZ6g/h90bIL7RnGtQ2E6pOwWTUk0gmSWsC\nvyfCsCdCMpHC5jCCAFWnMtAX4N9vtuL3RhkejKBpgpJJOdkFm3ak06k43WYMeh1pTUNVFHyeCD5P\nlNwCGzqdSjKR3ulNQTScoLtjmOGhMEP9ATat7WagJ4hnMIxep+DOt406FeanifehbrA3SEv9IKFA\nnOGhCBarEds4LP50JMd8Ish4jz8Z8/F1JMTbZDLR29uLw+E47AYfSoc2r9eLzWbDaDTu9Flvby+T\nJ0/e5XGHdQ/77ugNKjm5Vvq7A6RSGjqDit6g4vdGCPiibPywhynTCqiqzaOtaYiBniABXxTd1gGl\ntTOLicdTFE9yMdgbJBSMo9MrlE/Oo71piFg0QSyS3DpbjaC0wkVHyzDptIZ3KMxgX4BgIEbIH2PG\nnFJi4STp7QbLBv3xzCqoY4x7tdpMGCbp6O32o2mZhwwBhMNx2hoGCfpjOFxmJk8rwGQ2kE5pNG8e\nIOiPkU5rDHsimK1G0uk4yUSKRDJNLJrEvpeDbY8EkUhi5HY4DuzbDEKSJEnSgWM0GikqKqKvrw84\n/GYMkQ5NQghMJhN2+9jjKnflkG+wf5pcvKJJOSSTaewuE1abCavdiJYWeAbCFJc78XvD1G/oJR5L\n0tcTwGjU4R0IE40ksFiNuHKtaELQ2zGMTqejedMAoWAMmzPAUUcXE48laGscwmjU4xkM0dPlR6dT\nCPriaJpGNJLA7jTR1TJMKBjnpNMm48qz4PdEQYHyyhz0u2k4p9Ma6bQgt9DGYHeQzk4vFpsBZ46Z\nYU8EVacQCScYGghSVpFLMpkmEspMN6mqCmgCm81IYDgKZHqRzRYjNTMKdzl4dSJzH8PBGP09ATRN\nUFDkwJVr3f1B+5HdYUJR2JouBXbn+KQOHQn5pgcTGe/xJ2M+vo6UeBuNRkpLSye6GkdMvA8mh2PM\nD/kG+97wecIMDYQwGHQUlbmwWAxMnVmMEAJFUfB5I3gGghgMKv09QYwmHeFgnP7eAKlEGpNZT8AX\nxWDMpKB0tXqJx1OZXm6Dit6oI50W6HQK3sEQ8XiKwHCU8qpcBrae4+jjy0jE0ugMKk63hf6eIDl5\nFvzeCIlYipNPncJgfzBTx3LXmNcTDsVpqR8gGk5isRpIJFJYbAYsVgMBbwS9XiWRTNPT4SMUjOFw\nWrA7TLjcVrxDYRRFYdKUXNz5NlAzKTaZB5YQZVXu7CqvqWQanzeKJjTisSThUIyBnmCm4VzswJmz\nZ/nyn4bfG+Gj9zsZ7Alid5nwDoQ5+jNlWG3jl2+fV2hHURUioQRWm5HcArnqnSRJkiRJB94Rk8Pu\nHw5Tt66XcDBB0B8jEU+RX5RJZ9j2qsxsMZDjtpKIpxnauuqpxWbAbM70kJdWuEjEUtgcJvR6FUFm\nPvRYJElppRvPQBCjSY/FYsBsMRCPpYiGk6hqJkfdbDWioGC2GRBpwUBvEIvNiM8bYfrsUlxuK+48\nG0P9Ibq7fAQ8UcwWAxbryDynSDiOzxuht9NPYDiG0AT+4ShWuwmTOVN+W959R9MQBqMOhEIinqK0\nwo3DZSYeS5FKpTGZDRQUOwiHkmhpgapT0etVSipy0Ot1pNMarQ2DtGweoH59L05bAV5PmGg4ScAX\nw+eJkFtoH/VNQCqlkUikUFV1n19JCiFoaRykr9NPMpEmHkuh16vkFtiy1zoeFEXBajPicluw2sbv\ne4+EfNODiYz3+JMxH18y3uNLxnv8HaoxP6Jz2IUQdHcMM9AToLt9mNx8GxabkaA/jqaJnQZ0qjqV\nKdMLM+kvnjAdW3PXq2oL6Gz1UjEll7YmD06XBVUHfV0+rA4TzhwjU6cXEgzEKZnkYt37XYSDcSw2\nIzl5VuKxrakoKlTX5tPe5KF0kou0JtDpVMxWPa5cK92dw3gGQugVhUQiRX+PD6vdiKKA0WTI9Kpv\nHiAWTQKCSDiO0ajHZNLjcJmIRZP4PBGEBrmFNhwuM+FQglgkDCp4BkNoaY325iFC/jhaWmOoP8Ss\nz5TTu8WP0DTKKnMxb51dJx5N4ukPEfBFQYN4PImiCBw5FpKJNIlEmmQihXkXCzaFAjGaN/cTj6bI\nLbBRVVuAYR9z45OJFDn5NqKdPtJpjVQqzZa2YfzeKFVT80c8MKSSaVIpDaNJP+qAXUmSJEmSpEPF\nIT/f0apVq0ZsZ2ZqCTLYFySZSBMOxOls9SLI5BxrmsBg1JFXaNupMRcKxhnoCRD0x7C7TCTiKYor\ncnDlWTDbDNjsJho39JNMpEml0qRTaWafOIlZx5WRTGioW3uk2xqHKCl3UV7lxp1nxeYyU1rpomZm\nIUVlLgb6gtjsRqLRFK0Ng5nvDsTpbvcSDSfobPGweX0v7U1DhAJx/vXPela93kzvFh+e/jCDfUF6\nOnwEhmNYbSaSiTRmm56eDh+t9YN4B8LkFdlJJTVc7ky6TSyWwmo1MtATyKTOWI2YLHqcuRY0TUNR\nYOqMQmZ9ZhI5uRZikcxUkTq9is6gQwhwF1p5618r6WwdprtjGKvdiN1pIhyMZ1eG3V5Pl49IKEk6\nLRjsCzE8FN6n31hRFMorc7HaDVTV5jNlegFFZTlEI0kGeoMMez45bygQY+OaLax7v5Omusxvdajb\n8R6fCOGt/zb83shEV+WAOxjifaSRMR9fMt7jS8Z7/B2OMT+setg1TdDeNJRdLTSv0EZxuQsEpJIa\nOblWerf4MZl1ON2FI44N+qPUreslndJQVYWaGUVMqs7FMxCi6LgynG4LsXCSZGqY/CI7NocJi9VA\nb+cw3qEI+cUOcvKszDy+jI4mD8FADL1Bh81pJBZOULemh7xiG2gw2BfEbDFQWummsiaX3k4fXa1e\nerf4KCh0ggJi62Qxdet6cboshINxPnynHVeuhebN/ZRX5uIbjuAdCqPTq+QX2dEbVIonudjS6iUc\njGN1GDFpBorLXKTSgo7mIfKKbETDCTqahwj4YpgtBmpnFtHR4iUZT1FZk4fPG2F4KILZoqdmZjFT\njspHUQT93QF0OpVYNIl/OLMa67AnSigQxzsUoXZmEUIIUkktM1uNJnb6fbxDIZLxNM4cCxbbzlMa\njSa/yIHNbiKd1ujt9DHYH8p+Jj6ZWIfeLX4i4cxiUp7+EO5cK4Wlzr25jaQdhAIx6j7uIZXY+mA3\nsyibTiZJkiRJ0oF3WOWwx6NJWhuGsgsYRSNJSia5UHUqsUiCjmYPLrcVUIhHE5RMyskeO9QXZNgT\nQadT0RlUIuEEqaRGbqGd8ko3JpOBSDhOYYkDnyeCyaIn5I8Tj2V6oY0mPaWT3CTjKdz5NtKJND2d\nPpIJDbNZTyScwO4w0bRpAE0TRMMJEAKr3ZjJdY8kGerLDECNhZMM9gVxOC0kt/Zc6/QKAjAYVHze\nKMlUmknVuaRSaXQ6HSazjtb6ITyDYcqr3eTkmEglBQO9Adz5NiKhOK5cK8Vlrswg1EAMq82Iaevi\nSzq9iqYJwoE4vuEoiqKQTGpoaUE4FEcAiVgKNW3HYNBhMOrIL7SjAAajjng0hdlqoGnTAD2dPiKR\nJIWlTgLeKOm0ICfPismsp7lukOGhCD5PGHeedbez4GzPYNRhNOkzU296ItnzllbmZGe08QyEiIQ+\nmX4xJ9+K3Wke7ZSHhInOxRvqD+Ed/OQthqIq5BXu/ZRUh4qJjveRSMZ8fMl4jy8Z7/F3qMb8iMlh\n1+l1GEx64tFMD6vRqMNo1FE1JQ+L2UDIH2PYG8U7EMKdb6WgxEleoQ2LxYjRosdo1BEOx7HajPR0\nDuPKsW5tAMYJDEdxuEykUhoms450UkNn0GUWPkLB5jRhNCvoVJVwKEZ7i4dwMIEmwGjSY3VkBoTa\nnCYSsRQCcOVZMRgUyqvdGIx6dDqVeDRFRU0eOr1KXoENnUFHX5cPVVUorcihq9VDUamDkkk59Pdk\nerz1Rh3DnszsNQFfjOa6QY4+rpSGjf0AdLUNM3VGEVaHiVQys5CS3qAjndLIzbdhd5rQG3SZvH6h\nkYhnUmbSaY1EMkUkGMdgyCwC5c63ITTBpMl59PcG8Q1lUiSqavMY6A0SDsUJBTLTL+pUhZnHlxEN\nJzBbDTTXDQCQSKTwDoYwmvWUTsrBbDVsfQjYs9vR5bYy64RykkkNs8WQWUl2q+IyF4HhKIl4mpw8\nK7n5ciaXT8toGvlQZR5j9WBJkiRJkva/Q76HfdWqVdknKZ1Oxe7INKrNFgNVNfnYHGYURcFiMxLw\nx2jZPJCd1rFhfR9CQDyWxGIz0tniJR5P4x0M4xuKYLEZiITihANx4rEUbU1DuPNsdDZ76en04cgx\nYXea2dLhQ2gCVVHY0ukjHkniyk7ZaGOgJ4BOr5JMpCitzAEBOXkWnC4Lfm+mNzseS1NY6sRo0qPT\nZQa/tjUMkUylqT6qEJ1Bpa/Lh9Ntxeow09niobfTj2cghNlswOG2MNATIByKo9Mp6HQqiXgKVZeZ\nPrJkkpNQIEbAFyMcSoAChUV2YrEUw54IzXX92OwmdKqytac9hqIobGn1MjQQwTccxp1vpaFlA9Nn\n1KCQeUtgd5kRCMwmPVa7icG+IIHhGKmkhtVhIh5L0N7koaPFi9VmJJnIjDFIxNLYnWYaN/YTiyQZ\n6Algd2YeavaEXp/pbReaoKdzmC3tw8SiSXIL7BSWOMgvdlJU5sJg1GXy8RPpzGw5h6Dt7/GJYLYa\n0Rt0aJpGXoGNku3eaByOJjreRyIZ8/El4z2+ZLzH36Ea8yOmhx3AmWPZ5bzgOr1KcZmTyil5aEKj\nq82DQZ9p8PV2+dHrVRw5Flo2D1BQbMfvi2ZTX1Az6TUKCrFokmAght1pYngoQiqeJh5NojlNdLYM\nZ44djlFY6mTq0YVEgkkcLjOqAp6BMHaniaqaPPzDETZ91E1xuYuuj3oRmsBo1DFtdgk+T5TeLT5K\nq3IY7A3x4Tvt2F0mbPZM73xuvpVELEU6raFtndJx+pwSOps86PUqeUV2zBY9sbiBeDiJgmCwJ4gm\nIBFLkldkZ6gviSbAOxjG4co81AQDUeJxPSXlLkKqQjyWYrA/hNlqwIKBcCiBxaJneChMJJygt9OP\nzqCjYnIuyVSmMdfZ5gWguNyJqio01Q3i35pqFNhaz1g0iclsYKg/yPBgmNKKHNKpNN2dw0ydYdqr\nxqBnMERXqxchwOfJzD1fMikH49bp2fu6/TRt6iceS1I5JY/J03a9INTeEJrAOxQmEU/jzDFjc4zf\nXPBjiUYSdHcMk4inMw8t+ynPfNvbndKKnN0X3k+EJohGEpnpUHcxA5EkSZIkHUkO+R72vXmCMlkM\nhAKxrQ08QXVtAdrWgZGFpQ5CoQSRYBy9XkdeoR13npX8Egc6nY6AL4rJpEfTMikj6ZSGwaDD5jRn\nUlw0QV6hjWAghqoo5OZncradbjM+b4RwMIHRpMdkMjDsjRCPpzML8NiNJGIp4vEUDmdmIKvZlpnH\nPRJMYHeaMVn1oAEKuHKt5ORaiUZThAIx9HqVqqn5mes5qgCHy4xOp+IusFFSnoPeoJKMp2nePMDw\nUISaGYV0d/gYHopgNOkJ+qOYrQaSiTQOlwVVUVBUBU9/CFWnMjwURksLDAYduQU2VOEgr8BOT4cP\ni82IXq+i06sUlToJBeI4XSYcThOegTCqTsE7ECIcTKA3qJjMOnJybQR8UVIpjXg88yYiHIwz2BfC\nPxzFaNBlFnLaDS2t0dPlo71xCE0TGE16tHRmTEDO1hVQ47Ekm9Z207clQDiYoL87gCvXmpk5ZziK\ndzBMKpXeaZ77beKxJPFoCp1OHTGjUG+XPzO1ZiSB3xvNDO41HbhG5Z7e4y2bBxjqDxGLJhkeiuDK\ntezxG4uDiaYJOpo9tGweYKA3iNmSeXszXg7FXplDnYz5+JLxHl8y3uPvUI35EdXDPiYhUPQKs04o\nR1UV2hoGycm1UVrpJr/QkZm1RAjqPupB00RmIaVcC1aHkeHBEBWT89C0zKqoQhMoChSVOYhGErjz\nbbhzLXz4Tgc1Mwpp3NRPOJTAbDEwY04JQhOkUhqRSIz8YjfxWBKDUcVgUAn5VVy5FgxGHTq9incg\ngn84gtGoxz8cpWZ6IQ0beykpz6G8yk0kGGfyUfkUFNu3njeNlobGDX0gwJFjpnlTH2VVecSiqcxM\nMrpMwzqVTKMAVnvmWqfOLEZRoLDEjrvAjqcvjN6gYrUbiUSSVNbmYzToUBQY6g9itRoxGHQkkxpC\nZKbKdDjNDA+Fycm1IoSCzWnGMBgCBOVVuWz6qJtUMk1xeQ4bP+wm4I+ipQW1s4rIL7LzwdvtRMJx\n8grtrF+zhXg8szBSKqWh6jIDes0WPTluCxabiVgsM0C3vclDNJIg4ItSXpmLqiq4tnu7oiiQTKRJ\np7ZOI6NAwJ95c1K/vnfrbwm1RxeRVziyN9o/HKFpUz+JeJrcfBuTpxdg3Jpj7x0MYbIY6Gz1kIil\n0DSNo48v36sBtJ9GOqXhGQyRTmVmPto2204kHM+W0TRB4hCd0jLoj9Hb5QMy19rR4iG3wC7n1Jck\nSZKOWId8IurezLUZiSQIeKL4PFF83iglFTnMPK6Uqpo8VJ2Kw2khJ9dKeVUurlwL/uEIFruJLW3D\nTJqcj284isFowJFjYuZnyqioycMzEKZqaj7uXAuewTCuPAuRSAJPfwidTsHuMNHSMEhXu5f+7gB2\nm4WPP+iks8WLqqqUV+Uy49gyZp9Qjj3HTGNdPw5XpjfR5jJRWZNHKBijrNJNSYWL4cFMD6pvKIzJ\nbEBVFRRgeCjM8FCYvu4AjRv6cOfZaa0fpHiSC1euhdJKF0VlTlxuC44cC+m0ht8XIxyMIzSNojIX\njRv6aG8eoqfTR26BDYNeh0GvUD7FTX+3n3AwQfdgA/09AWbMKaG8OodJk3MpKc+hoMSJqqq01g9S\n/3EPLnfmDYPZpmf2iZOompqP0ZiZEjKd0jK57H2ZXnxFyeSkaynBQE+AgC/GR//uJBFLsWH1Ft5/\nq4X/+1cLjRv7qVvfzZpV7XS2eBnsC+AZCBGPJlFUmDanBJfbwmBfIDtDT/W0AgwmHXqDSsWUzIJQ\nAX8s+2ZFCAj4YjvdK71dfhLxTIPXOxTG5/lk/nGHy4ynP0g8mhk87B0KM9AbyCyMdQDseI93tnpo\nrhugrXGI+g29xGOZQdYFJZ9MX2ndum7A4WC8m+mH4/y9BzsZ8/El4z2+ZLzH3+EY8yOih32gJ0BH\ni4doNIHJrKevO0BugQ2b3YQjx4KyXc9dTq6V/u4AJpMem82EXqfgcJoZ6A0QjSQJ+uNMn11EIqHR\n0zFMbr6dvi1+LDYj7jwr8Wgqs/KoRU86paHTKaSSEI+k0BtUUmmNsko3PZ0+8gpt9HT4EUKg6lXy\nC+w0buzH7jSTTgsSsTTDQ8PYnSaMRgP9PQEmVblprh9ksCdIKqUx7Zhi9AZla++5htGoQ6c3YHWY\nmFTtJpVI48yxkkqlsdlNeIciW6d6tGMy6zIPFJsHUXUqoKDXq8QiSYb6QjhcJuLRFI3r+5gyvQij\nUce6jVswmnQITRCPpTGZ0kR1cVJJLTMQ12XGZs8syhSLJOnt8hEKJImGE0ydWYjVYSSVSqOgUFDs\nIDgcpfboIlobBlF0CsXlLkK+KEJAJJwgHkuSTKbR61W62rzEokmKSp3YXSaC/jjxaBKL1YiqqhgM\nOrq7fHS1ZPLojUYdM44r5ZQFNfi9MYwmHcXlLvze6Ij7Y1c50ooCJrOeZCKNoigo2z3alkxy0dPl\nI7b1uxPxzBSeHc0eKqbkUVbpzpZNJFJ0tw8T9Mdw59sorfh0AzbTaQ3PwCdz0EfDSSLhBCazgbIK\nN1abiVQihSvXesBzvxOJFN6BMJoQ5Obb9tv3OV1myqrc9Hb50OkyKV/j2bsejyYJBePYbMYR/22Q\nJEmSpIly2OewB/xRGjb00dcdwDcUwWgxUFjioLDUSVVN/k5pDBarEUeuGVeulfJqN6pOIRrJ5ASr\namb2lZJyFxvX9CDIzN/u6Q+jqgrxeJqcXDNpTeDOs2Ew6pgyrYD+7sD/Z++9uuQ6rzTN53gXJ3xE\nRnoDTy9T1aWq6jXds2atuZhfOndzNxezaqZ7FduoVDK0sJlA+gxvzonjzVx8gSQpghIpURBB5XtF\nEECajRMZ+9vfu5+XIi9Y36mTxBkgsbnbwHENTo4mXJ0vGPV91rer3H17javzBa1uhVrdxFsIzOST\nT/rMpyFpWlCtmUyGAWVZCvJKVuKu0IyGqbG5W2c2EZjHwE8YDXw0TWF46VFv2izmERtbVZIoI4mF\nh7taNwmXCbqhUKmZ1FsOQZAQRxn1poNhC+xlu9mjWre4PFsQeDFpmuFUDKpNE9sRS7FZlvP88RDd\n1Fa3AMKasphF/Pyf9zAMlXbPJU0yhlcezY7D2kaVdq9CluTEiZhct7sVxgMfy9YZ9X3sisHl6Zw0\nzelsVEmTjFrdptoQvPqd/RZnLyYURSksNanw5XfXq7S6FepNG1VVsBxd2I8Uie56lbWNGlGQMLj0\niIIU09LQDIWjR0MGlwssR+PieMZ4sMSwVFRdJUtyvHm0IhBpKKpCkZd484j2mnv9XF2czjh/MSOJ\ncxbTEMvSvvOS6pefcVmWmE9CohW6VFFk1nfq6LqKJEnYjk6lar7SnlMWJZcnM46ejvDnEXbF+JNt\nPBJl5p4AACAASURBVEVRcvhQMPdn4wBvHtPsON8LPUaSJGoNi07PZX2r9lo5+hcnM4KZOBxTQrVh\nIUk3TftfWm+q3/RN1U29X69u6v369abW/G/aw54mOWUpJqYgItbdqkm1Zr4S81eWJYtxxNnxhErV\nwJ9HtLsOEiIwRjdV5pOAtQ13xS6PQJKoVA3Ono/Zu32A58f4M0GSWXoxbs2ks+by8KNL1rdrDC49\nhlce+3fbKIpEuEwoAUqJw0dDvHlM6Mfs3G7R6lbon88BqNYsJkOfRsvGMBWioMStGSRxju3qOK5O\nkcPV2RzDVMmzcuWFVyjygt5WFbdusL5V4+NfnSNJJXff7ZHnJZ/99oIiF4uzuq4wGnjEUYZd0Tk/\nnvD2TzdZzCLGQ58sKVj6CU5FR9PFjcVosERRJRFaJEk4rsmzzwaUpQg3anUqxHGKLIkaDy49/EWE\n4xrkecnHvzrBMDXqTZvNvTqmqVIUJT//5z0WswjLNhgNFgLlCMRhQrVuMe77xFHKgw82sFbN6mQ4\nJgpT2msuhvn1f2NZlljfrrO+XcdfxDz86ILLsznttQpxlDGbBPS2qxiWxu7tNo8/viSOM2rNkPkk\n4PZbHUZ9n7XNKpquMhktyVJhn5Ek6SsejiTKfu95/Oqv/xTt3etgns7EwaXnfmvry2S05MWzMQDL\nRYyiyOzf6/xJX0OaZMy+dFPhLyLiMP3e0JmSJL12OkwcpZw9n1wHr50fT2l1Kz8YCtCNbnSjG93o\nb1c/eg97xTVwXJ1qzUI1ZLrrVRptm3rrCxLJYhZy/mLKuO/hzQIefnRBWYjl08ko4Ff/+oLjZ2Oe\nft6n4poM+j79Sw9JlgjD1QLkkxF33lnH9xM0RRbLkqsgI28eE4apaPZnIdNxQFkK33mrU6Fat7j9\noMtkHDDu+yy9GM1UybKC7nqF7YMmnXWXctVJdHout99a44Nf7OAvYnwvIfJTTo+m/Opfn68WXmOs\nikalYjAdLzk/nlFr2siqzGS4RNFkNEMliTLmkwAJYauJghS7ojM49zh5NmF04bG2VSfNcmRF5t9+\n+UuSOCdLcxpth8NHQ5I44+jREG8WUUogURJFKcqKIGO7Olv7DXpbNaI4w61bVOsm9ZZNd6NKGCQU\nWUm1bhIs49WOQcTZ8ZzJcIlpaxzca7G502D3dosHH6zTaDlQlOzcbrF/tyOQkocjwiBGliWyrGAx\nCymLrz8TeV4wuFhwcTrl6aeXnL2YMp+EfPTLE/x5xMe/PmMxjbAsDUkSTHy3ZlIWJVGY8eLZhJPD\nCY8/7rOYhezeaqJqMqoms3+n/ZVgoUbLubZzqJq8Str9855xy9LYv9vh7ts9UYdvqTT96hJquJrS\n/ylSNQXL+eL71E0VzXgzOfcvJcsSSBKffPZrgK9ZoW70l9OP0W/6Q9ZNvV+vbur9+vVjrPmPfsJu\nmBr33l3Hmwvcou3qmKZ27U31FhHPHg7E78uCntLpVZiOA8Jlgu0Y+F6CWzPo9FyWngg60jSFJM5Q\nZAm3blJxDWTg8mTG/t02eZozHC55/++36W5UiCMR3KOqMo2WjWmpzMYB999fx1tE1Js2aZJh2jpK\nklEUJbu3m0RhRhJlIg1Vlak1bR59ekm8TLn77hpJnGPaGkgS1brF0aMhiirT7roEXsLR0xFlUdLd\ncBleLVjfrKMoEutbddI0p9a0UXWF2WQs0kY1hTjKQBINZpoWaJrM4WdD6m0bSUbgFw2FWtOi3rRQ\nFBm3LmwL7bZDve3gzUIuToQVJI0zjh6PGA18nIrOuz/bRDMUerUqQZBQb1r87J92KcoSTVe4OJmh\naTLToc/5iwm37neRZYm8KPAXMZqmkMU5tZbN2kYVWVX49X97znwSIcmwtdukt1kjTXKSJMdbRJw8\nG5MmORu7daIwpX++IEtzLk5nZGlBHIr9BABDV1n6CXff7XF5NhMJt2khrEJNmyhI0FY+/sHFgkpF\nZ/ugRavjoBtffUk1Ow5v/3SDKEyxHeNr09qiKBkPxCKxu/r4fylV6yaGqRJHGZIEnT+D064oMrcf\ndOmfLciLgu5G9Y1ESH5Zmq5ycK/Nx5+Kw9f2QQvbuZmu3+hGN7rRjf76+lF62POswFuItE3dUFE1\nBadiYFcEkvDLntTZaMngYkEUpJw9n3KxCgPKkoyrswXtXgV/EdPbqgnbRNdlPgupVHQURWY+jYCC\nPCvxFhHzSUAYpOzf6ZCmOYcPh0jIpGlOUQpvu7+IBZKv7SCrMqoqY9gKRQ66qdDuVrj9VpfFNODh\n7y6JI8F9377V4pNfnREuUxodG0mSV0FOMZQgybC528StGczGAZIk7DaapghSiizT26mhqDJPP+uT\nZQVFUbB/t4VhaNiOgWGJMCl/ITzJuqFimCqLRYQ/j7j/4DZIJQ/e30A3VSRKvHlMFCT0tmvkWcnZ\niwnzSYimq7R7FZrdCs+fjFjbdNE1hSwvOTuaICsyTtWgs+byu3875fPfXjKfhhzcaxMFGc8eDtna\naxBFGd4kxLBUTFPDW8Rs7NQIg4ydgxaDiwXPn4zI84Ikzq8n+4oqsXPQ4nB1IEvTnDBI8GYRp88n\nxGFGs+0wnwSoukLFNdF0FbdusrZZxakYHD8doyhiobdSNWh2HK7O5pi2hqYp2BUDCZnZOKC15n6t\nYQdxaHQqxit/7+p8weHDAfNJwNkqdKosuEY1ftMz/lJJkjE4nzOfhCiafI2efJU0XaXRsnFrJr2t\nOs3Ot5/Of+PHazs0O5Xv1KyXZfmD9YXbFYN3P7hHb7P2Fz083eirelP9pm+qbur9enVT79evN7Xm\nf1Me9jwrOHoyZHjpIUmwd7fD+lbtG/+8qissvZg8L0jTnKUXs5xHNDsVNnfrTEdL3vnZJnZFo71e\nYXTpY5oabt0kXKbsHDRAkhhd+sgK5FlJFKYsvZirszmzSchymdBs27R7LsdPR6iaLBp3P8ayNQYX\ni1XC6ZwyL3n05AqnovPOzzYxTJXpKGD/bofLkznzqfANz0YRcZDhL2LBbh/5HNzvkMYZtbolrDuX\nC9prLkmSoeoKuqXy6b+f01yr0N2oUmQli2lE4GfMpkvOn8/Zud1CRkzkkzgXfnlbxVnopElOve3Q\n7Do8fzwkClO2D5o8+Mk6gR9z9nyKrMiM+kuSOKPWsFBUGcNSWd+pMRuHmKaCYSoUq7TQasPixTPh\nOZcVidkkYD6NsCs6B/faGKaKoiqcDX3OT2d011162zWefT6gt1VHkiVUTUY3FebjlLwosCs6rmuQ\nFyX+IiKJM+TVn1MkicHIJ4kzpsMlisJ1+qmuK5RIdHsVups1Aj++DmVqdSuYtrYK8tFYLhNaHQfH\nNcmznCwrxCHvPCKJc2oNi1rj64m7AFGYrjj8CpOhR1kKO8a4v8R2dPrncw7udzFMDcvRvrEJL8uS\n509GjPuCGjO48nj7Jxt/sHm2HP0rh4FwmZDEGZajv/JA8X0qywrOnk8YD33cmsnurdYPciqvad9Q\n76LE8yIkoOKaNwSZG93oRje60WvTG+/Q/PDDDylXTG0A34sZXnqAYGyfv5h8EZzze0qSjNGVx8Zu\nHd3UyDMRNW85ukgsVSRu3+/guIZIC/10wOGjIZOBz+jKx3HFJDtNs+tJvL1axJyOl5i2TsU1UBWZ\nds8lSzORqikJ8oyuKXR7Lh/8ww6jviC5jPpLLFsjTQuOHg1556ebHDxorybLopHN84KlL5Zag2Ui\n8JGqQllKSJJMtWHhzyMUVaG74fLg/XWCRcSzzwYsZhGGIZCTZ8dTKlWT42djZEnhzttdHFcnikW6\n5/ZBg5KSwEvp9Fw66y6fP/4ts3FIHOdMRgEf/fKU42djTFPHtHRqDRvdVFEU0ag3Ow6NlkO4TCnL\nkqKAs+cz8kzYWyQJiqJgPo1wKgayLNHuVhhd+URBRpYWBH6CJMk4roGsyBR5QZJkjAc+oysPbxYh\nyzK9nSoP3uuh6wpLPyEOM0I/ZX27RolYxk3SHEpB+9ncayApMqMrj8U0oLVWYfdWk85GFVWVsR39\negr9ksijKDKmrVOtmfhezOHDAdNxQBxlfPRvp7x4OuLyZMqjjy7xFl/w3YuiZD4LGVwuePzJJR/+\nP0/59X8/4fnjMWfPJxw+HNBoO6i6imao/O6Xp3z2m3Mef3LF//f//pdXPr9ZVnwFURkFKVH47Zda\nZ+MlH//7GZ/99oKHH138WZ72b6Nx3+fiZEYcZoyufPrni7/o5/tT9SrvY1mUnB5PePLJFZ/++pzT\nF5PrnZIb/fn6MfpNf8i6qffr1U29X79+jDV/oyfsSz/mxdMRlnTM2maVzZ0GiiwhSVyTHhRVfuUk\nLEkyHn18xeOPL1FkmXvvrWGZKqat8+LpUCRFxjlhlGFKEsNLj6IEy9ZIXnqj5xGeF9HZrJLGOTu3\nWqRfaspPn09J4oy1jZpAGa47tLuuWFTt+1DCw99dsrZVZToK6K6Lpl43VJZegqYrHB+O2TpoMup7\ndNddRlce7W5FeM810cS6VQO3ZnH4eV9MhI81shW7fDr0qdZNqg2bervCchFhWhpuzWR7v0FeFJy/\nmNLdqOJ7EWsb4jbi+HCMJEucHE5YLmK2Dxps7jaQVzXNM5GYKikS1ZrJ0eMhSZIRRSnddZdwmeDW\nLKIwZTELcasG3jxANxSSJMd2DRGglBZIisSt+23yrMCpNvG8iJPDCUVZYNpNKlWTi+MJpqOTJuIw\n4S8SNE1lOlkym4bIsoQ3jWh1HJ4/HqHqCpalc+tBB1VVCJcJ9abN5emMKMyueeYP3l9nOg6ot2xe\nPB0hy2LH4OB+RyAy9+pkWUGe5TgVjWCpEQUpSZTR7DiYhkYUpcz8gDwrSOKMW/e75FnB8NJjOlji\n1Az8RcT5ixl5UdA/WyArML7yCIKERtMmDBLqbYuyKJlNA14+st4sYuknr3z+VVXGrRpMV6FOuq5g\nmN/+JT248q4Ps0svYT4OsP7AbdSfqyz76tJr8j0Qc16XRgOPz399TpaX9DaqXJ7N6a5XXzvJ5kY3\nutGNbvS3qTe6YT97PuFg5x3iKOPkcIJTMag1LNo9l5PDMaats3+n88rQlcU0xJuFGKZKuEw5P57R\naFloukSJxHS8ZOnFdDdcll4MEoJEciSmpvv323jTiErVZD4JSeOcYd+j3rTIs5JWx6a7XsWwVOIw\nZTL0SZOMs+cTdu+0oITJyGdjp47vxWiqTODFvPOzTZ5+NsB2NJodm/k0YnjhcXEywzAU7r+/gWkq\nZGnBs8/7bO7W6fZcTo4mRFGG7ejEK3uJYarMphH6uaCtBH7C3p0WvhdjGCqjvkeaFNirybaqKpir\nMCXHMZiPA7I0F4uRwyWtrsN6+x7Ntk22wmW2ewKFmMQZYZBgOTpOV1huFtOA+VRw8CuuweZekzjM\n2L9bZTGLKLKcIisI/YRWt0KjZTEdBwwv/Wsv/eDSY22jyt1315nPAjprLqO+x9KL0XWFNMlF0M0i\nplI1yLOS/Xsd8qzAMFUkJPK8EIeMoiBJchazkDwrkCSJoiyRJBheetiOjmmpnK9CkWzXQJElFisb\n0tJLuP22sM+EfsLZiylWRScIk2vffBQJW46kSBw/G2GYGllWUFktm8qSxGwc0OjY2K7OfCYsU0Ve\nYJga1ZqJriv43hepqb/4xS9e+fxLkgia0i0VRZaEbec7NJCq+lWqi6z8ZS0e9abNpTkniTIURXy9\nP0T98z//8/V/F0VJkRW8eDpmMY8pi5IXyzF33ukiv/H3kz8cfbnmN/rL66ber1c39X79+jHW/I1u\n2EUI0RfKsoL+pce4718vjH3TtbWiyEiSRL1l02hJOBWN7lZNNPqWwB1u7jUwLY2r0zm2q9Puuvzi\nP92ilGAy8JlNAjRN4fJkztqmi6YpKKrM3q0ms2lIWRYcPhyQpTnv/HwLw9I4uNclCBJePB3xzk83\nmE8j8rwgzwtUXUHTVe6+2+PF0xGDC4+t3QaSIgkrTJZjWSr9iwXeLGTnVpvL0ymdXgXbFU33fBqy\nf7dD/1zQT5odh2iZIkngVA3iFYJx906L9e0Ghika98BL2LvdYjJacnI0wTBUHnywQf9igWmrUILj\nmjz6uE//csHWXoO/+497zKZiAffk+QQKmAyWtLoVFlORVnpxPEOSJeyKsA/dfXuN+SxkdOWxtlnj\n/GRGmuQEfoKmy8wmEWVRUm/a6IaKosmEYcpssqTbcxn1fRRVWZFmVIq8ZHjlMZ+G+IuYtc0qRVFy\ndT5H0xTaay6SJFEWJSWCjDLu+/iLmJ3bTebTkFbHYTJcglTy5NMrll7MrB2wfdBAUX4PVVhCq1Mh\nreVEYcp0HNDbrHFxPBWLum1HTMzDjDTJr//SS/66LEtsHzQI/IS8LFcEmUz44hcxe7dbtNdcjh4P\nyPOCVqfyFQTplzW4XHD0SNwG1RomG7sNolAkn1qW9hWv+svXhz+Prm9FeptVgmVCuBQHpmbnjzfQ\nk9Hy+qDb6VVR1G/ftTquwTs/2bhOZv2h880HlwtOj8QisKYrNNr2tQWp03PRjb/udD3PCmRF+sEu\n8N7or6fFLGQ6WqJqCp119w8uo9/oRjd6M/RGU2JkSea//td/Za2zTqVqsrlbZ3glpq95VgiLhWtQ\nrVtiUpaX19N209RW1plS2FMkickowJ9F6JaGLEvXYUe7t9tQwulzkUpqmCq9jRpJklGpmmiajKxI\nTEdLNncbnJ/MMG3BQHeqBtsHDZbzhMvTOYePh6RJzt69FnlWMpsE+F5MrWlhmhrLIKbICxotR4Tx\nSCAhEYUpeV7gzWOKosCydaIgpbfTIE9LNFViY7dOq+NQrRm01io0WjayLK9uGzQMXaXaEImkYZBQ\nqRpUqiZ5nmNZuqDD+Am2rWNYGk5Fp73u4ri6sMOoEo+ffMTO9o6w3pgqv/lvx2RpQbVh02w79LZq\nKLIMkljWDJcJmi48842WzWS0JAoydm438aYhgxXP3nJ0TFvj2ecDNvea5JlIdJ2NA4qixHENzo/n\neIuIPC3QDGVFcJFQNHH4UlTRiL54MmI+DYnCDN1UGfc9pmOxCLuxW6csodl28BcRuqGi6SKkKc8K\nvLnYEyjLEk0V6MosFbcJL+ugagqKItPsVHAqOr4XE4c5uqliWCp5UdJdd68XhF/65f15TJGX3P9g\ng/27Hbb3Gixm4vtJk5wkymh1KtQaFqatMxst8eYRv/71L7n34PbXGrNnD/sksTgUxFGGZqgcPR7S\nP18w6nu4VRNjNXHPs4KjRwOOD8cMrzxkWaLVden0XNY2RBLsq26ivqz5NOTxx5csZhHTcYCqyVTr\nr16s/SYJfrtOkuT4Xnx9s/ND0ocffki7ucajj6/IUvFzZOnH1Ovi32X/boedg9YfrddfSkVRcno0\n4fDRgMloScV9NYHoTdKHH374xlIdfmgKljGf/+6C+SRkPgnJsvxrh/Gber9e3dT79etNrfmPlhLT\nWXfZv9fm/vvr129aFddgeOlhmCplKSLq59OQ54+HZFnO5k6D9R1BF9k+aJHlYvlRliXhQU4y+peL\n60jyat0iz3LyLEfXFZI0Z3CxwDQ14ijj+NkEWZZ49+ebyIpMEmd4c8EKr1RNpoMl9n6TKMrQdAXL\n1pmOluzeboomU5FZegn1VsHg0hONrq5h2hrzaSjY8Y5OrWkhleD5MXkGF1czln5Mlufs31vjYpXM\nOhuHnD6fYNk6997r0T+fsb4tEH5pkiMrEv3zBfv3OqiKxGwacvZihqopbO81iAJBuFFVGVmWiMKE\nrf0m5y+mGIaCrMqcnUzRNJU8F41pURQsvYhIFpaQ2291AZAl2LndYjLwkSSJ9lqFpw8HzEYh27t1\nmt0K4+FSTILLElmW2b/bQVEkkWaaZLTWXIJljCRLxHF6jajM8xLTUikpWUxDZFksg1oVHd1UsRwd\nRZNZTEKKsmQ+CYnjjGrdZG2jyme/Oce0dWxb5+FvL2i0bTb3GwTLhCwtyNKCWtNma6+OqqkkSU7F\n1b82VX3JyR/1Be3FtDVMU6PZcdA0hTBMcasmkiQsSkUupv87+00ANncbvHg6Yjz0aXUrnL6YEEcp\n/YsF/fMFJXB2OmUxj6j9XnOs6ypLvvC3x2F6nayapQXjkU91RapZ+rE4mAKUcH4siDu6oaFq365h\nDpcJxZcWvBeziM3db/1yvdZk6PPksz5FXmI7GvffW8e09T/+F1+jirz8yvdq2Rr7D7pIgFuzvtPN\nwh9SlhV4M7GDUa1b34o8MxsvOXsxBSCJc85eTLj37vof+Vs3+ltRFKRk6Reghfkk/EGjVG90oxt9\nO73RE3aAW7f2sWz9+g3Uqeiomszl2ZxgKZrPcNWE5nnJfBpQb1rXOLnAT64noaatYjmC6lJrOuiG\njGXrjPq++Byagm3rDC48NnbqnBxOaHYcFFVmNglEA+gYzMZLNvdXi6IbVY4eDqhUDcYDH7du0loT\nvu8iL8nSjO0DYU0RtglpNXWUGV56yIpMo+vQaNlcnS/Yu9Xh9GhMmhZ01lwCP4Gi5Op8QRxlK+68\niu3oTIZLuhvCqrP0Yh5/csV8Gq4OIQVZlnN1Kvzti2lItWHi1kzk1deg6SobW3WOHo2oVA2KEpTS\nZWu3QZEXFFmOoirXS6X7d9u4dZPDhwP8Rcz2QYtRX0yJw0DsCbz9k3WytMRyNJ4/HlFrOUgl3Hlr\njcefXjG49JhNQjo9l1rD4upsRv9iQRJl9DaqhEGGYWnYjk4UpswmITu3WrR6FVptm0rVQFFliqJk\nMYtY365xeTonSTLKssStmtRbFrqu4qwOeeOhL5j6k4C9223qLRtzReoxLZ12zxWEmFdMguM4Y3jl\nXdOBZEXi4F6HVrdCpSqCkGxHWIYWM2F/kiWJo0cDnj0cMB0vcVwdfxYzvPSJ4wyronFxMmMxjUii\njHq1w9ZeE7vy1abWcnQCPwYkNvfq6KYI43qpRtuhVrcEqtSP6J/N8RYx3jwizwqicHWIdL5ds5zn\nJeO+f73QvbZR/c4TdoCTwzGBn5ClOZPhkhKwXwNW8ttqZ2cHRZNJk/x6f2Vrr0lvo4Zl69/bZP3l\nrcfJ4YThlcDQfpskXN+LhYVrJU1T6G5Uv5ev6a+lN3ES9kNVSclo4FPk4oXaXvu63e2m3q9XN/V+\n/XpTa/6jnbB/WVGYMrhciGVCWUwfdV00GZPR8nq6WZZc/yADMaUPlwnj4RJZkel2HLKsYDZe0tus\ncXW+IFgmwl6SF8hI3HrQIUkyTFsjDETD32g5uFVhv6lUN5mOAsZ9D1aMbVVX6G3VMG1N2HXygpOj\nMe/8bJOLkxnNtkMUZcSriWxn3WXpi8n/5k4dJNi/08b3Qw7ud7g6X6BqMmWpEASC6Q0SbtUgXvmY\nVVWis17j2ad9sjzn3b/b5uJ4iqrJ1JomeVZeoxeztEBRRBBTe73C4MKnLEvOjmcslzFZltNo29gV\nnaUnGs/17SaBHxOFGo2WQ1mWTIcBB/e6jK58Ql/UzZtH2I6yajQC2j0Hw9SYTUOGfZF+unXQBMSU\nsSxLll6Mpstigh3nzJKAjZ0GD97vMZ0EZEl+/e8yGS7Js4IHH6xz+nzK6dEEy9bYu9vm+HDE7be6\nnB5OUDSZw0dDuusuaxsuZy9mlEB33eXqfIEkiQVVUTsFSYLLszlrm69O8SyLksH5gsCLiSNxkPjJ\nL3ZodSpfm2bpqym2qimMBz5hILCTL5+FwBfTa38eIwHNTgVvFq/86RaG+fXDQsU1eOdnW5RFiayI\nBjPwEqbjgGrDZG3dZTYOePzJlVi81RRKEuI4pdVxSOKMw0dD3JoIjPpjqjUs7r3XYzENMS2Ndu9P\nS0rV9BWHf7gkjjJxmPz0SuwlvGav7Wy8ZDoO0HTR9L70+iqKzP7dNp21CpIs4dbM7/1zB8sv3Xog\nnrXeVu2P1qDWMHHr5gpnKtHb/suRfW705sl2DB68t850KALhOr0f5nL3jW50o++mN55z8OGHH1KW\nJcfPRlydzjl9PmU2CklT4e2VZUmwtFdjwdZaBaf6xZuvrqvcfmuNzZ0aZQlPPulz/mJKFKSM+z5h\nkJKnBc+fjDAtjUrDEMuGo3A1aS7prFVorzmMBkskSeLybMHgwmN9p4FTNSglicCL6V8syNKCq7MF\ncZSzuVvn4njOuL/k0ceX1OsWb/1kg1bXIVw1o5quMJsGHD4c4FQMQj8jjjIaLYuN7Rr33lsnChLS\ntBDIRlujUjOpNSzuv7/Bx786IQgSrs4XPProko3dOu21Cldnc+IwFX54S8OpGZi2zvZ+gzQpuHVf\n0HUW04Bb97sYlo4sKwynT9ncbbB7p4Vd0fC9mN52jSwrGFz6ZFnB7/7HMaqmcH4y4+psznS8ZDJe\n0mgJK8F8HHB2NObdn22yc7vJ7u0WymoZU1JA1WV6my5lCUsvpr5CWFaqBpqhcPRoyNW5sCVlaUEU\npEzHSy5Xn69SM5lNQubjANPSsF2NSsMkWCa8+7NNRlc+v/vlGY6r01lz0XSF3TtttvYbLP0YiS+a\nbVWVkZVXv0yyLMdbRJi2Tq1pY1oamqG+0tbQ2xG2JKeiUWt88fwVZUnFNTFtDVWTaa45dDdcHNfg\n1oMut9/qcDl+IlChr5AkSddfn6Yr3HrQ5af/uMvdt3vohsZk5ItQsETcpnTWXAxD5fnTEeEyxZtH\nZPmrcwpepUbLYfd2m7XNGso31OWPaWOnTr1loWoy2wdNyqIkClLi6PViHn0v5tEnV1yezjk5nFwn\nzb7k9yqKTK1pC6vKX8BOoKxsZy+lagryl9AzaSoCuX5fuqFx790eb/1kg3d/vkW7+6cdnH5I+jEy\nk/+acmsWO7dbbOzUX3kAvKn369VNvV+/fow1/1FM2LPVsuDzJyMAppbKrbe6xGGG4xrs3mkLPGEu\nlhfVV/hPZ5OQIi+u3yDLEnw/ordZ4zga026KJurydMberRbPHg4oSoPuRpXB1YKTI2GPOTua6fqD\nkgAAIABJREFUcHo0QVYkkiRn51aD++/3kErQzxdcnc2wHYP17SplURIGC7JULCwePR3h1i0ef9rn\nP/wvt0jijGrT4fJkglUxGFx5JHGGU3Xw5iHPHo/Yv91i705b+N1liXHfR9VkFvNQ2C0qJv2zOZIk\n/OjhMsO0VGp1m7MXU9Y2XJCkVXqjzvMnY+aTgP7ZjO5qstw/n+NUTPbvNDm5FAuOvc0qYZixfdDk\n4njKfBoRhSmNjoNdMcR0fJ6QJjmNpo23IqjkeQEIr/2o72M5wsL0m/9xQlmUPHh/Hd1UV6mpEu/9\n/RbzaUit3qHesgUdZ7/J0o+vLSjj4ZJa3bp+DixHJ44y6i0LyzE4O5ohKxJ33uqK4J4oI1gmHD+b\nsHsLigIiLxaYxM0q9ZbNeBAgy7Cx2+DieEqeF7TXXCxHX3nmJaqrNNPxQNgTTFvDsl79krIsjY3d\nOoefD7ArBvWWzWIW0l2vUq2bbO42UBSZ9pqD5RjkOVwez1B1ne5G9Vv7zEEcUsuVbz9Li2uspWYo\nlKW4ZTJNjfk0oNWtMB0uWRoxuqGKPQdZot60v/ckzzhKV/sdMg/eWxdBYQP/Gnv5XRjy34eiIPnK\nbdtiGr3yz5VFSZpmqJr6vS6a2o44lJ09n6AoErt32tfWvsuzOWfPJ0iysFg1218lBem6it78Ufz4\nvtGNbnSjG30LvfE/8V+yNosv4RuzFWFk+yetLyZjf4RPXWvZeEcTehtVjo/GlEVJd71Ks+Og6wLX\nePhoQLhMWcwitvYamJZBlkSCklK3aLRshlfiijvPCoy6ilMRaaS1moVhaXTWq2iaQhKnwiNtq9Rb\nNllesHe3zXwuPNlpkvHs8z7ePGLnoIlpqLx4MhJhTkFCb6tGEmVcns1or1VJ4gxVlVBUlcvTOcEy\nYbno8x/+0wGLeYQa52xsV5mMfTb36iwXC2bjAEWVGfcF93xrr0GW5iLBdRoSLlM2d+uoukKWFPzP\n//oCo9zk4nhOXpSMr3yKUlg2Aj/Fm0X484jepkgKNUyNSlWg+zrrLqUk8dlvz6m4JvW2jaGp2DWD\nT//9HBBLjaO+R1YUDC88eps1zo9nbO3UyYuSf//X56i6SqNpY7sG80lId6PK+3+/xeGjIcMrn2bb\nodV2qFREmNR//5dD0jRHN4RnvdqwuDqdY1oq/iLm6sIjDlOqdZOdWy0mQ59wmXHv/R61msXDjy6Z\nTQIkCebTAF3XrnceNncbwrdfMwmXCZIscXEyY22j9kpf+GTgc3U+Jwoy1rdr3P7HDm7VxjAVpuOQ\npRcxn0Zkecnzx0PyTDzT+9tvf+fXxfnJlJNnE3RDQVVlVF3hnZ9uYtk6tqNDCUma4y8iTg7HZGnO\ncpmiqmJvY2uvwc6t1nf+vN+kOEp5/PEVvifSbde368RJhqYpOK7Yf3jddpiXh8WXC3r1tvCPf5nf\nm8QpR49HLKYhTtXk4H4H63sMS+r0XNprX7VQLb2YF0+G17sCR48HVGs73+nQ9ucoS3Pm01B46pv2\nn3yT8l30Y2Qm/5B1U+/Xq5t6v379GGv+RjfsZVEyHS1ZzEOabYdpO6DMS2xXp1q3v/ImWBYCoZhl\nBbWG+RXax3wWousKu7eaLBYxP/unXfK8ZLmIeLZaoLQdHVVTKMqEMsmp1kzOTmboungTTeKM6Sig\n0XaYjkWD19uo8vzpiPHAZ+92m0rN4OrJnEbXZnO3wee/O6fTc5FKCVWXsSyN8xcT8lwsRxq2ilOt\n43sRtZZNGKQ4roEkC691HOWomky4TDAMjXrbwp/H11PSlySbtQ2XOMxI05xur0qR50iKRLtXASTS\ntMCpCrxho21z/GyMriq4NRPL0Vj6CbIs4c8jSsSb+HQUUAKUwoqhaDKb+3UaLYf1rRqTkY/tGpwe\nTcizAtPSqLcldu90kJGucYuaqeDWRcNrOTqmpTMdiyXfohCUn7woRWLqPEICVEVi6cdU6xaLaYBb\nM8VSbF7SaDtEQUK9Lb7GJMnJs4KiEIvHW/sN5tOANC4EmaQQjPT5JCDbqeMvYsIwY9z3kSVI0wy7\nItJNywJG/QW1hk2WFYwHHhs7Nepte2V3ykGS8Bcxb32wgazIq/CnBd4iYnTlAcIPfXI4prPusrVr\n0T+f8/zxEICpHmBXdMIgpVIxVhSRV09+v0lFUXJ1ugDEtF2SoVY3Wdus4taEFeXT35xTFCVpkosg\nqCDj+OmYVtdBNwTrf32njrZqEr15SBhmFHmxsopY34nt7M3j6zCooih5+tkVjbbYnZhPQtL029ty\nvi85FYMH768zm4RoumD2/76Gff96wXM+CRhdLtg++P4OMsDX7DYvb0FeSuzevDpP4vtWnhccPh4y\nXnnr17dq7N1pf6vblmL1WtI05RttZDe60Y1udKM/TW90w352OuX/+j//b7qNO9iuzp23uzgV0bzV\nW1+lLZydTLk8niEpolm8/aCL4xiM+h5PPx9QZAWLeXjtsd7cb2KYCotpyNJP2dpvcvxkRBrn6IbA\n9dm2znjgc3CvQ1mWrG3WOHs+5sH769gVnWzVLHbWXCRZoihgc7eOUzU5etzHMFW8WYikyBRxiaZm\n9DZquHUTRZVpth0GFx7NjkOtbtLqOiRJjr7imkdhQjhKcWum4ES3LGptG3doEixi1ndqPP1sgCSB\nv4jJ0pyf/tMuz5+M8RYh2/tNQb2QIIoyVFVmcCFsKoosmk1Fljl/MaW3WaPdq/Dp57+ht/V3uDVT\nkD7KnGbbwdAVJFWmSEuCICVNCoKl8CWniUhLVTWRBrr0E06fTyjLkiwtuPPWGk8+vsLpGNTbFrIq\n8fSzPlmac/utNR5+dMmtex2qNZMoTFF1BVVV8ObRFyE8jkFRFJSUmJZGnuZU6yb1psV0FIjgp4rO\n57+55IN/2MJydE6PJpweTTEtne6Gy+XpjKUnMIlpkvHb/3HC+fEMbxaxud9gOlrS7Dg8/WzA9kHz\n+hk7PZzw5JM+iirRWqtg6IpYSrZ0xkOf48Mxi2nE8eEYy9bI84Kdg+Z1cFAQfIFmzPOC42djll7M\noFhwcL/D4fGn/OQX356fKMsShqWiqCKldTGLAAnfS3j7JxskSUa1YZHGokaPP7nErYk9gTwvSVe1\nezlZnYyWPPusT54VnB1Paa9V6K673H2n962n4pomr3IPxK91U2BBX6osXk9D+vtyaxZu7aukmw8/\n/PCLm7v8q19X/hq+TrtiXNONJAm295qv7fYhXO3uvNTgcsH6bh3zFUvXX1YcpRw9GrKYRTiuzq23\n1r7TTcSXa36jv7xu6v16dVPv168fY83f2Ia9KEqmgyWzSUi3AYGX0D9f8Iv/3P0aHq7IC4YXC8FQ\nfzHHrhgoksS9d3uM+j4SJUhwdTZnfbsOkoQswdnRlPXtOhfHUwI/ZnOvwXpeEgYx44HP7q02hqnS\nP5+jqArPPh+wd7vF4GKBWzPQdMHvVlSFWt0iLwryFBEj33G5OJvSWavy/MkIVRMHhYMHXY4eDpFl\nSJKc1lqF8+MZsiqzvlVjPFzS3aiy9GMsW0dColozMC0NRZU4eTpm71YLp6qzXMQEXsIySKi3bMqi\nYD4JGF56uHWTk8MJ7/3dJlsHTUxLYz4JmK/Qg4oisbFT5/mTIYoqvMYbOw2Gixrbe3V0U2My9Gmv\nVXj2cMDWXoPHn/bRNBnb0anWTVRNHHgUVca0VBoth8U8olI38RYhnY0qeVoQhwkf/NMOsiTY3vNp\nyK37XdIk4+psxu7tFrNpyO231piOA9yaARIMLzx0XRWT2yKi2rBotR3yLBP/30/ZvdOm04vRDIXL\n0xmzcchv//spa1sujbbDzq0WRVFiWSp2Rae1JnjwUZAy6vti96EoVhP2kiwprj3gSZyTJhmT4RLD\nUlnMQiafDpDf6zG48NAtlavTOWGQ4i8iDFN4oBVFpbOyWwG4VZMraS4oL7OEPC1otG2ytMCtWXTy\nKmUpEl1nkwDL0elt1q6n36/S/t0OTz+74ujxEFVTiIKUg3sdojCjf74gXQUuZVlOb7vO0o95/++3\nmY4CWm2Hg3uda7/2bLxEUWUuTqZEYYrvCb974CfUvqWPutqw2LvboX8+x7Q0qnWLk8MxIGwhbvXr\nFJY4Srk6m4vXQadyXa8/Ry8XcDVd+VZWj1bXYdT3CJcphqm+cgr/fUuWJXZvt4VVRpapfMdE2KIo\nVxY5+TvbaFRNRlXl610eVVe/VZ1GfZ/pCim6mEUMLxfsfM83ETe60Y1u9LesN5bDLkmSsJ6kFYqi\nRJKg3ROM8N9fDJNkERDUP5ujqgr9iwVhkKAbKo8/vuToyYill9DqOuR5ycXxjDvvrvHkkz6yIt48\n87xgMYvw5iEV1+D2gx7PHvavqS+LaUijZZMkGaGfUHFNXjwb8c5PN9FUBdPR6Z8vmM9CDh8OaHRs\nmu0KYZiQxjnhMiHPxTT+5PkYWZHJ0hzbMZhPAmxHNKn+IhIT0JoJkkSrV2E2ElPUKEjJioIsEcmc\nL56NcaoG3Q0XbxazsVvH9xIkSYRrGLoCksTH/3ZGsbIMmZZKHGXYjs7e7Ta+l7D0Y9KV/ebnf/82\nSHB6NEVRJYZ9jzQRjWWW5XR6LtOJYL0Pr/xrD2ylaqGvbiyqDYt2r8Lp0ZQiL0iSHFWRkWQJ09J4\n8XREsExWNByb8+OZqFGYUqubPPt8gK6r7N5q4c1DxoMAy9FodSvMZxEXJ3NKQFUVzl5M2Nipc3U6\nI1xmRGFGtWHizWPyNKfRcsTtR16Q5gXjoS9QnIrEdBxSFCWBn9Ds2mRpTmvNxTAU8qKkWrdorQl0\nYp4V+Ito1UxXGfU9omVKWZYMLjwqVYM4Ft9Pb7vOvXfXMC3hc7crBqalkqU59ZbDaOiTRBmNjsPB\nvS537uzz/MmIp5/3V2msIkjq94OUvizdUJkMl0ShsPKUJbTXXTZ268xGwXVCap6V3H7QZTEJGVzN\nMUyNatMmS3JMW9jAfD9mukpdXUwjNE1B1WR2b4klyf7ZnIvTGWmUYVcEpzxJstU+QIphqciyjFs1\n6W3VaK+5uDWTZteh26vS3ai+Mojo+WNBAwr8hMlwSa1lY6wO40mckWY5qipwoSdHYyajJZquvBLB\nCQL9+vSzPieHYxbTELdhvfLQ82V+r6artDoVWl2H9e268P+/BomgLe07s+nTJOfw0YAXT0dMRj6V\nmvWdPoaqKlgVnThKRarrnfa3+p7ns5DFarcDoFITGQTfVm8qM/lN1d9KvWeTJSdHk2sUrab/dVKV\n/1bq/UPSm1rzHy2HfXu/SVnC1dmMas3k7ru9b0wg3NprMBsv+fy3F9iOjiRJPPrkaoXVM0iSnHrT\nIokL7ry9RhJn9LbqXJzMaHUFHSSJMmaTgCJf0mjbtFZhFMMrj6WXIEkSjbZNZ72KokrEYYaiKZi2\nRpbl16mb+/c6SJKEpiu4NYuLkzmAQOVpMs12BVmRmE8DVFXi1oMuTkUEL5WlCHtSFJlbDzqcPZ8R\nRgnhMuXqfM7993os/YTpszGGpTEdLPFnEd3NKnGUsbFT49NfnVFt2nR6LocPBxR5iTePMC2Renn3\n7TXsis4nvz5D1RR6G1UuTmfUGibzeUiRFiLoxlSZjgK6Ww7nx1Pm04D5JGTnoEGe5ZRlSRik1Bsm\nFydzNF1eNcc5cVSQROnKhuOJQ0LFoOIa1zz1WsPCdnT6Fx5LX7DOX04b/XlEq1tBURR2bzWouCaX\nJzPms4hb9ztcHM9Y36mjGxq//m/HbO41UDWFessiTQuSKGPpJ5iWsK8Ey4TTFxPsisF46FHkDgf3\n2gyuREhWvWmiaipXZwtm05AkFvxw01KRZcQNRlleh2KpuiJuVPKSrf0GlqNxcL+DYao02xUsR8eb\nh5wfz5hNAhpNm8uTGf1Lj83dBrIisXerRa1h0b9Y8OzzAbNxwBDYv9ch9JNXPudfVrPtMGk7GJZg\n8u/ebqHrKvv3BZkkTTLWt+vXaMhqzWZwuSAKUrobLnGcce/dddY2qiwmIVlWUG+KBeneZhXHNYT/\n/qmgM437PrIi0exWePrZgPlETFzXt2rs3W1/zattO394cvzS8w6rqXGUQRX6F3OOHg6RZImtgybD\nywXhMgVgMQ159+dbr7SQjK485hPRVC5mYqdge5U4+4ekG+oPJtTpj2kyWl5bWgI/5ep0xu231r7T\nx2i2na9Raf6YWh2Hcd8n8BNMW3stNxE3utEfUrCMefyJGHIAhEHCgw82bhJfb/TG6s14F/oGOa7B\n2DvkP/8ff9yn5FZNdg9aDC480iQnDFIaLZuLqwVuzUIzVPISFFWiUrMoc4neVpVGx+bk6Zh2zyVJ\ncjZ2xJRteOnjezGLacjB/a6YelcNppOQweGEg/td/uP/fo+lJ3zWl6dz4iglSQsuTmbs3+swuPAo\nyoJ77/bon8+ZjZfEUcre3RaXpzMevL+Oosq8eDrGm4UkSY5l61yezsjzgul4SZHn14mVmq6gaQr1\npi2+Rz9FlsGp6gRewmwSYDsav/hfD0iTgsvzOfWmzdJLWMwiuhtV1jdrZEXBR//zlDBIUBUZ29Z5\n/++3uTiZ8uLsIc3KAbIs0Vl3+cf/7TYnhwIFub5dJ89z2t0KaV7Q7LicPh+jagr33+9xcTIjiXOB\nXQxTkMTho8gLGm2Hy5MZtqMzOPeo1S3iMKW9VqEoCrHcasgksbC5jK48LEujvVah3rL55NfnGIZK\nnuUkScbWQRNNlzk9ClEUmcuTGe21Cp2ew4vDifD9ezGLeUwcpTz5tM9iFlIUcP+9HllWoMgynbUK\nJTC8WtJZqzC8Eg0tEowHS3wv5vRwwuZenQ/+YYdx36dat6g3LQ4fDSnLUuwx9Kp0vhQ0lCYZz5+M\nePzJFaqqXFs/JEmQZnZvt8jygquzOf/yL/+FO3vvUBQFi2lEmmTUGtaXPlbOdCwWIxst53qK1Nus\noekKcfz/s/ceP5KlZ3rv73gT3qc3lVVZtrvJppVEaWZEjnChK1zMZqCdZjGANoKghRaavRajpf4A\nAboDzcUdERAGAiToSjQzJJszTTbbVlWXzar0kRnenBPHn3MXX1R2V/sWyWZXM59dICMiT355zPu9\n72NiiiXzjKudLxhcef6dxODxSDiCJHFKlgp7yiTJcCYBWZah6yrLGxW8WYhhqqL7O58O+F701HXm\nexH+LDwr1gG6p1NWNiufmodda+Q5dIeA4Lzn8jqjvssrP3nMbBoK+1BDxp2EZ9SPJ5qJD/pd79Vt\nfhhv/lnmPr5XnJp+RmJVO2dw7ctLhPO0Zd1QCfyILOPsnPkoPMtr/izit2G9oyA5K9YBXCckSTJU\n9bMv2H8b1vvzhi/imj/TBfuHIYoEt1g3NFRVJstEMEupZnH1S4vcfatNEsksrJTOPKvLtRyVeo6M\nhOko4PDxkKX1iijOZDFaGw08WksFVFVmca1M5MdMpj5ZmlJv5rh785QkSqg28owHHv2Ow3Tss7ha\nYuYEVOo5Aj/GsjXcsU8QxqiKwmwScLQnHGeeCEorNZs7b7TRTY1R32VxrYwsS+SKOgsrRRqLJaIg\nRjc1Qj9Bzclce2ERZBkpzVA1Bc8NUVSF+kKRn//1IzRd4e5bJ3z5m2v0ug6ToU9jQRS8uqGSJCn3\nbp5w/cXlua1lgTjKyIBR3yNOxNo6k4Ctqw0OHg04ORizdbXJuDEj8ENAYjL2sXIab/58H00XXfjN\n7TpWTkc3Ukplk3sHQza3GyiKTBSKyYWgXAj/+IvXWjjTAM8NuHC5gTMJkBXp7H8Z+DGv/s0eF640\n6J06mJbg2pZrOeIwwZdiCsUchbKJezihWDZxJgFL6zKyJIoYu6Az7IlCVzPUubNFSuDHrF9sUarZ\n9E+mnBxPCP2Y8chj/WKNO2+2SZOM5Y0yzsgny2Ay9nEm/lzgKaY2V55bpH04xJ/FBEHE7oMerhNg\nWhpWTmfmBqRJhqQxDw8S6adxLCYYJ4djeqcu92+d0ChukS8Z1FsFFpYFjSRJUtoHI/Ye9uchPDLD\nnsvmpTqnxxOcaUi5YrG4Uv5I//BS2WLrSpP2wQjDUrFs/cxr/0mhVapYrGxU6Z5MsWyN5XlnulCy\nkOQxWZohyWKzq2nCBvXJw9I4W9tPh+WNCmZOIw5TSlUL09bZ3emTRNlZqJY7EfoMZyK68aWqjf4h\nlJhaK0e/K7rAdl6fuyR9sVCt5+hXXcaDGbqhsLD82aWginRp8Ujpnkx5fK9LmmUsr1dY2aicdzbP\n8ZnCymnYeZ3ZfBpZa+Y/MIPlHOd4ViBln5Vf2K8AP/jBD3jxxRc/8j2uE/Dg7VM8J6RYNrlwpUnv\nZMqDO6eQweUbCyBJnByNcScBYZhw4XKdDAl/FuK5ITv3OpimRu/UobFQIPAivFlIrVWkfzqlvpAn\njlK67Smt5SKNxQL9jsPh4wGBn7CyWaHbnlJv5cnIkCSJ1mKBnbs9dENhaa2C70UomkK5auKMA06P\nJuiGSvtwxPrFKoqicLw/olC2GA9EwI0/C1lYLjHoz5iOPKqNHPmSiSJLaLqKbins3evT77ogQblm\nsbRSIc1Sbv7iCFkWNJwXvr7KyeGYQsnkzhttMjLWtqqUqjahF1OqW5DC3bfauNOQfNEECa5/eZHu\nyZTjvRGSLDNzQzRNIYpivvE7W0RxTBJnBHMHnZ//5PHcUjFjfauGpiuUqhZxllLIm7T3xxTLJlmW\nie5HnNLvuqRJype/sUaaZeiWSu9kynQsRKXFosndmycMezMMQ6WxWDgLw3KnAbNZyMpGhcCL0OaC\nud7JFEkWE5lBb4ad05lNA+oLBey8Tr5g8NYvjuadWZmNS3WxmVNkOkcT3vz5PjM3ws5pfPP3tlBV\nmSBMCLyI8cBjOqfh9DoOhqmJQlXK2NxusL8j0jMlSQge/VlEHKfUmjkUReJofzQvSG1GgxlREFGo\n2BRLJrqusLczgAzqC3n6HYdy1ebKC4ssr1fotEV67cnRhCRJWd2sEAYJl260OHw0PLsmLl1v0lgo\nfqJrbOYGjAceiiZTa+Q/keBwNJidFcFPeMuDnkv7YIyqSizPE4HbhyOQJJZWS+9zZvkopEmK50Vo\nmsKD2yd4s4jjvRFhEHPjqytc2K4z7M/IMkRuwpy+4s/dd8x3pcRGYUwwd3r6MFvKKIw53h/hOiJt\neGG59CsPkfp1Io5TfC9C1+Wn7Gs/K0RRwhsv7xOFQichSfDc11Y/tXj2HOf4ZeHPQoZ9D0WVqDbO\nC/ZzfP7x2muv8e1vf/sDf/aF6rBHYczugx6d4wmWrTMeCp7q7dePGA98VFX4T/+971zCtHWO9wZk\nGbzxs32aiyWOD4YUiiZZAu2DMYap4nsBS2tVdEPlzptt4efenzHozTBNleODEfmiyXTsc+FKk0f3\nukiSiF/3/Qjm3VfDVHnhG6sEXogzCRkPPVRNZjb12brWwsxrxEGCaWsgQaFiUvFECJCd0wmCiPpC\ngTSD9v6IfFHw7tMko3M0IY6FBSKyhKxIzNyQUS9jcaWCbqhzIV6GrEgoqkxzuYgEXLrRwLQ0vFnC\n4NTBcXwkWRKJobaOJMmEYYyuKxzujcjnDbautubizIzRwMPOa4RBxP3bp8iy6PRLskS9VaBzPAEy\nqo0cO3c65IsGziTg8ds9ShWTIFA5ORyxebnJztsdCkWDLMtQdJn7bxyTpbC0WqZctTnaH8Ky4Fxf\n+9ISg76DOafFCHFlRhgmvP3aMa3lElE0o1SxOTmaYOV0yvXcXJQMaT3H/uMB/VOHctXixleXGXZn\nSPOJS2upRO90iqIKcWfox8RRyrDn0lopMT0Vgk534nPxWgPD1DAsDd+N6PccJCRK1Ry+F+G5IZW6\nxf3bHSbDGZqmUG1sEsUptUaeLBMcbbF5EV10WZaJ40RMXQyVftc50z0cPBpQqeUIvBhZkece+SHT\nsbDunAy8p66LwIs/8TVk54yP5Za/F+Wq/T6B4bt50GEQ8dYrh2dC15kTcOMrKx/pcvMEUZTw6F6X\nQcdB0xWW18vs7QwoVEwarQKb23V0U6P1nk5y+2DE3tyFZu2CiGgHISL9OGrOyeGYo70RAKP+7EM9\n2j+vUNVP7yzz64TIa3hm+kLn+ALBtHUW7c9GKP5BmI49nEkoXNI+pS7kHOd4L5757eZLL70EiC7c\no3tdRr0Zw+6MYdclTZ+MziPKdRvdUAj8mGF/Rq2RI01FZ7dQsoiiGEmSSLMUzVTIFwysnE6+ZHP/\n1inO1MewVEpVMfKPgpgMyFIIw5gshe6Jw+alOrm8ThDE1JsFvFmIokh0jqc8utejfTjh3q1TTo8n\njPoeQRDjuyGFgkm3PeXezRNuv3bM8e6I5fUKQRDTO5ngOSH7D/touky+aKLpKrIkiVAiWzikHD4e\ncOlak1zBYO1CjRe+sYqqydy7ecLyeonVC9X5aBoOHvUZdGf4s5je6YzpxCcIEyzbYNT30A0ZzVDR\ndIXQj0VoVJzy1q1XQYKNecfctFW+9M11Dh8PIQPL1giChLdfP6JYMvnaP9jka/9gA98P2b6xQAaE\nvuDc9zsusixRqeUwTZUrX1rAsjVaS0Ue3DqltVRiOvHwgwjdVJkMffodl+ZSAWfs4znC9u/x/R5Z\nmnHweIDnBlQagsddrtgEfkRrRaTL+m5EGMY4k5CZGxB6MZYlQor2dwZE88Ta619expx7SO8/7DPo\nzdi4VKfayOF7MYePhyiKhGGoVOp56q0iFy432bxUx3UDJCRWL1SQsoze6RRnEiBJYgIgyxJpKsS4\nT7rgvhdxcjAmTTJmToRuKFTrIigrXzI57NxFVRVKFeupYrNQNtE0hWLFIlcwqDZsVjaqIsBpXh8p\nikThI9xkPgtEoXACeoLAi0mi5CM+8Q7Ggxn9UyG2DoOEk6MJYRAjS5KYbozfHyrlexH7O33SJCNN\nMvZ3+njux4t0n+Cnf/s3T70Ogk++4TkHaJrC+lYNZb6ZXN14J3Pgw/DkPn6OXy+cic/IwmO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MmktVikezJlea1CuW7jOiHHeyPyBUPQQeZhOYoqU6pYBH5Mv+OALBwaTg7GyDKYOY3xwGPY8xj2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hEHLqQhVFk5hYLgc7XayBy+l7qwz6NlZ/wtxSjv2dHqIoMJl4LK/lcV2fQiWFIMYT5GRaRxRB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} ], "prompt_number": 89 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above is a classic phenomenon in statistics. I say classic referring to the \"shape\" of the scatter plot above. It follows a classic triangular form, that tightens as we increase the sample size (as the Law of Large Numbers becomes more exact). \n", "\n", "I am perhaps overstressing the point and maybe I should have titled the book \"You don't have big data problems!\", but here again is an example of the trouble with small datasets, not big ones. Simply, small datasets cannot be processed using the Law of Large Numbers. Compare with applying the Law without hassle to big datasets (ex. big data). I mentioned earlier that paradoxically big data prediction problems are solved by relatively simple algorithms. The paradox is partially resolved by understanding that the Law of Large Numbers creates solutions that are stable, i.e. adding or subtracting a few data points will not affect the solution much. On the other hand, adding or removing data points to a small dataset can create very different results. \n", "\n", "For further reading on the hidden dangers of the Law of Large Numbers, I would highly recommend the excellent manuscript [The Most Dangerous Equation](http://nsm.uh.edu/~dgraur/niv/TheMostDangerousEquation.pdf). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Example: How to order Reddit comments\n", "\n", "You may have disagreed with the original statement that the Law of Large numbers is known to everyone, but only implicitly in our subconscious decision making. Consider ratings on online products: how often do you trust an average 5-star rating if there is only 1 reviewer? 2 reviewers? 3 reviewers? We implicitly understand that with such few reviewers that the average rating is not a good reflection of the true value of the product.\n", "\n", "This has created flaws in how we sort items, and more generally, how we compare items. Many people have realized that sorting online search results by their rating, whether the objects be books, videos, or online comments, return poor results. Often the seemingly top videos or comments have perfect ratings only from a few enthusiastic fans, and truly more quality videos or comments are hidden in later pages with falsely-substandard ratings of around 4.8. How can we correct this?\n", "\n", "Consider the popular site Reddit (I purposefully did not link to the website as you would never come back). The site hosts links to stories or images, and a very popular part of the site are the comments associated with each link. Redditors can vote up or down on each comment (called upvotes and downvotes). Reddit, by default, will sort comments by Top, that is, the best comments.\n", "\n", "<img src=\"http://i.imgur.com/QjtUF3x.png\" />\n", "\n", "\n", "How would you determine which comments are the best? There are a number of ways to achieve this:\n", "\n", "1. Popularity: A comment is considered good if it has many upvotes. A problem with this model is that a comment with hundreds of upvotes, but thousands of downvotes. While being very popular, the comment is likely more controversial than best.\n", "2. Difference: Using the difference of upvotes and downvotes. This solves the above problem, but fails when we consider the temporal nature of comments. Comments can be posted many hours after the original link submission. The difference method will bias the Top comments to be the oldest comments, which have accumulated more upvotes than newer comments, but are not necessarily the best.\n", "3. Time adjusted: Consider using Difference divided by the age of the comment. This creates a rate, something like difference per second, or per minute. An immediate counter example is, if we use per second, a 1 second old comment with 1 upvote would be better than a 100 second old comment with 99 upvotes. One can avoid this by only considering at least t second old comments. But what is a good t value? Does this mean no comment younger than t is good? We end up comparing unstable quantities with stable quantities (young vs. old comments).\n", "3. Ratio: Rank comments by the ratio of upvotes to total number of votes (upvotes plus downvotes). This solves the temporal issue, such that new comments who score well can be considered Top just as likely as older comments, provided they have many upvotes to total votes. The problem here is that a comment with a single upvote (ratio = 1.0) will beat a comment with 999 upvotes and 1 downvote (ratio = 0.999), but clearly the latter comment is more likely to be better.\n", "\n", "I used the phrase more likely for good reason. It is possible that the former comment, with a single upvote, is in fact a better comment than the later with 999 upvotes. The hesitation to agree with this is because we have not seen the other 999 potential votes the former comment might get. Perhaps it will achieve an additional 999 upvotes and 0 downvotes and be considered better than the latter, though not likely.\n", "\n", "What we really want is an estimate of the true upvote ratio. Note that the true upvote ratio is not the same as the observed upvote ratio: the true upvote ratio is hidden, and we only observe upvotes vs. downvotes (one can think of the true upvote ratio as \"what is the underlying probability someone gives this comment a upvote, versus a downvote\"). So the 999 upvote/1 downvote comment probably has a true upvote ratio close to 1, which we can assert with confidence thanks to the Law of Large Numbers, but on the other hand we are much less certain about the true upvote ratio of the comment with only a single upvote. Sounds like a Bayesian problem to me.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One way to determine a prior on the upvote ratio is that look at the historical distribution of upvote ratios. This can be accomplished by scrapping Reddit's comments and determining a distribution. There are a few problems with this technique though:\n", "\n", "1. Skewed data: The vast majority of comments have very few votes, hence there will be many comments with ratios near the extremes (see the \"triangular plot\" in the above Kaggle dataset), effectively skewing our distribution to the extremes. One could try to only use comments with votes greater than some threshold. Again, problems are encountered. There is a tradeoff between number of comments available to use and a higher threshold with associated ratio precision. \n", "2. Biased data: Reddit is composed of different subpages, called subreddits. Two examples are r/aww, which posts pics of cute animals, and r/politics. It is very likely that the user behaviour towards comments of these two subreddits are very different: visitors are likely friend and affectionate in the former, and would therefore upvote comments more, compared to the latter, where comments are likely to be controversial and disagreed upon. Therefore not all comments are the same. \n", "\n", "\n", "In light of these, I think it is better to use a `Uniform` prior.\n", "\n", "\n", "With our prior in place, we can find the posterior of the true upvote ratio. The Python script `comments_for_top_reddit_pic.py` will scrape the comments from the current top picture on Reddit. Below is the picture, and some comments:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import Image\n", "#adding a number to the end of the %run call with get the ith top photo.\n", "%run top_pic_comments.py 2\n", "\n", "Image(top_post_url)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Version 2.1.4 of praw is outdated. Version 2.1.5 was released 4 days ago.\n", "Title of submission: \n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "My friend is 7'5 ft tall, and he finally found a chair big enough for him.\n", "http://i.imgur.com/M7LjpG6.jpg\n" ] }, { "jpeg": 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EsIo91YYc4xSI5SHRu4TeOcXUo/Pw3tcB1ATwwY5B6m3fa1lKNFJ2VXiPTo8UOyseNr2O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w3kApOwjT2aSd+Bcb2aXH5Ybtc0OIDyf1Uh8mhDyTDgBrwQ1wLzd/usC3I8r0b8gDptLzRVv4dh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\n6+yPbY5bRT7Q1pvuUZBA4pUFDIZfPQpxo2g8glGHA/UAhsjJ9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"output_type": "pyout", "prompt_number": 2, "text": [ "<IPython.core.display.Image at 0x10bdb1b50>" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "contents: an array of the text from all comments on the pic\n", "votes: a 2d numpy array of upvotes, downvotes for each comment.\n", "\"\"\"\n", "n_comments = len(contents )\n", "comments = np.random.randint( n_comments, size=4)\n", "print \"Some Comments (out of %d total) \\n-----------\"%n_comments\n", "for i in comments:\n", " print '\"' + contents[i] + '\"'\n", " print\"upvotes/downvotes: \",votes[i,:]\n", " print" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Some Comments (out of 39 total) \n", "-----------\n", "\"Weta could have saved a fortune by hiring this guy in The Hobbit, for the forced perspective scenes\"\n", "upvotes/downvotes: [50 14]\n", "\n", "\"Hodor? \"\n", "upvotes/downvotes: [54 16]\n", "\n", "\"Please provide a dollar bill or ruler for scale. He might be 2'7\" and be sitting in doll furniture. \"\n", "upvotes/downvotes: [657 126]\n", "\n", "\"The lack of replies for the OP is disturbing - I am starting to think this guy may not be his friend after all. Or he could have posted and just gone away from the computer without checking up on his post. Which made front page. But would any redditor really do that?\n", "\n", "Who is this /u/Triforcetrilogy? A guy with over 10k link karma, but only 200 comment karma. Who posts and doesn't talk?\n", "\n", "I am not sure what my point is, I guess I am just genuinely curious.\"\n", "upvotes/downvotes: [10 3]\n", "\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ " For a given true upvote ratio $p$ and $N$ votes, the number of upvotes will look like a Binomial random variable with parameters $p$ and $N$. (This is because of the equivalence between upvote ratio and probability of upvoting versus downvoting, out of $N$ possible votes/trials). We create a function that performs Bayesian inference on $p$, for a particular comment's upvote/downvote pair." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pymc as mc\n", "\n", "def posterior_upvote_ratio( upvotes, downvotes, samples = 20000):\n", " \"\"\"\n", " This function accepts the number of upvotes and downvotes a particular comment recieved, \n", " and the number of posterior samples to return to the user. Assumes a uniform prior.\n", " \"\"\"\n", " N = upvotes + downvotes\n", " upvote_ratio = mc.Uniform( \"upvote_ratio\", 0, 1 )\n", " observations = mc.Binomial( \"obs\", N, upvote_ratio, value = upvotes, observed = True)\n", " #do the fitting; first do a MAP as it is cheap and useful.\n", " map_ = mc.MAP([upvote_ratio, observations ]).fit()\n", " mcmc = mc.MCMC([upvote_ratio, observations ])\n", " mcmc.sample(samples, samples/4)\n", " return mcmc.trace(\"upvote_ratio\")[:]\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below are the resulting posterior distributions." ] }, { "cell_type": "code", "collapsed": false, "input": [ "figsize( 11., 8)\n", "posteriors = []\n", "colours = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\", \"#CF4457\"]\n", "for i in range(len(comments)):\n", " j = comments[i]\n", " posteriors.append( posterior_upvote_ratio( votes[j, 0], votes[j,1] ) )\n", " plt.hist( posteriors[i], bins = 18, normed = True, alpha = .9, \n", " histtype=\"step\",color = colours[i%5], lw = 3,\n", " label = '(%d up:%d down)\\n%s...'%(votes[j, 0], votes[j,1], contents[j][:50]) )\n", " plt.hist( posteriors[i], bins = 18, normed = True, alpha = .2, \n", " histtype=\"stepfilled\",color = colours[i], lw = 3, )\n", " \n", "plt.legend(loc=\"upper left\")\n", "plt.xlim( 0, 1)\n", "plt.title(\"Posterior distributions of upvote ratios on different comments\");" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[**************100%***************] 20000 of 20000 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "output_type": "display_data", "png": 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0dI1A0cjIiP9dS0tLUNdVv9O3hAlpeTRurfWgtibKUKAox9raGklJSXWmqxqz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\npQW/+MUvU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} ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some distributions are very tight, others have very long tails (relatively speaking), expressing our uncertainty with what the true upvote ratio might be.\n", "\n", "### Sorting!\n", "\n", "We have been ignoring the goal of this exercise: how do we sort the comments from best to worst? Of course, we cannot sort distributions, we must sort scalar numbers. There are many ways to distill a distribution down to a scalar: expressing the distribution through its expected value, or mean, is one way. Choosing the mean bad choice though. This is because the mean does not take into account the uncertainty of distributions.\n", "\n", "I suggest using the 95% least plausible value, defined as the value such that there is only a 5% chance the true parameter is lower (think of the lower bound on the 95% credible region). Below are the posterior distributions with the 95% least-plausible value plotted:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "N = posteriors[0].shape[0]\n", "lower_limits = []\n", "\n", "for i in range(len(comments)):\n", " j = comments[i]\n", " plt.hist( posteriors[i], bins = 20, normed = True, alpha = .9, \n", " histtype=\"step\",color = colours[i], lw = 3,\n", " label = '(%d up:%d down)\\n%s...'%(votes[j, 0], votes[j,1], contents[j][:50]) )\n", " plt.hist( posteriors[i], bins = 20, normed = True, alpha = .2, \n", " histtype=\"stepfilled\",color = colours[i], lw = 3, )\n", " v = np.sort( posteriors[i] )[ int(0.05N) ]\n", " #plt.vlines( v, 0, 15 , color = \"k\", alpha = 1, linewidths=3 )\n", " plt.vlines( v, 0, 10 , color = colours[i], linestyles = \"--\", linewidths=3 )\n", " lower_limits.append(v)\n", " plt.legend(loc=\"upper left\")\n", "\n", "plt.legend(loc=\"upper left\")\n", "plt.title(\"Posterior distributions of upvote ratios on different comments\");\n", "order = argsort( -np.array( lower_limits ) )\n", "print order, lower_limits" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[2 0 1 3] [0.68661513199565671, 0.67591601037476801, 0.81609140121125456, 0.53945522050538297]\n" ] }, { "output_type": "display_data", "png": 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j6Ojo8GlWVlb85/z8fEE5HMfBwsICeXl5AKovGUdFReHixYvw8PCAh4cHoqOj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\n8cSsvr6erq4ubrrpJ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} ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The best comments, according to our procedure, are the comments that are most-likely to score a high percentage of upvotes. Visually those are the comments with the 95% least plausible value close to 1.\n", "\n", "Why is sorting based on this quantity a good idea? By ordering by the 95% least plausible value, we are being the most conservative with what we think is best. That is, even in the worst case scenario, when we have severely overestimated the upvote ratio, we can be sure the best comments are still on top. Under this ordering, we impose the following very natural properties:\n", "\n", "1. given two comments with the same observed upvote ratio, we will assign the comment with more votes as better (since we are more confident it has a higher ratio).\n", "2. given two comments with the same number of votes, we still assign the comment with more upvotes as better.\n", "\n", "### But this is too slow for real-time!\n", "\n", "I agree, computing the posterior of every comment takes a long time, and by the time you have computed it, likely the data has changed. I delay the mathematics to the appendix, but I suggest using the following formula to compute the lower bound very fast.\n", "\n", "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n", "\n", "where \n", "\\begin{align}\n", "& a = 1 + u \\\\\\\\\n", "& b = 1 + d \\\\\\\\\n", "\\end{align}\n", "\n", "$u$ is the number of upvotes, and $d$ is the number of downvotes. The formula is a shortcut in Bayesian inference, which will be further explained in Chapter 6 when we discuss priors in more detail.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def intervals(u,d):\n", " a = 1. + u\n", " b = 1. + d\n", " mu = a/(a+b)\n", " std_err = 1.65np.sqrt( (ab)/( (a+b)*2(a+b+1.) ) )\n", " return ( mu, std_err )\n", "\n", "print \"Approximate lower bounds:\"\n", "posterior_mean, std_err = intervals(votes[:,0],votes[:,1])\n", "lb = posterior_mean - std_err\n", "print lb\n", "print\n", "print \"Top 40 Sorted according to approximate lower bounds:\"\n", "print\n", "order = np.argsort( -lb )\n", "ordered_contents = []\n", "for i in order[:40]:\n", " ordered_contents.append( contents[i] )\n", " print votes[i,0], votes[i,1], contents[i]\n", " print \"-------------\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Approximate lower bounds:\n", "[ 0.83557827 0.81654362 0.81502525 0.81295936 0.68825128 0.72597113\n", " 0.77427338 0.81305042 0.74613833 0.85444435 0.79445943 0.82407077\n", " 0.73404509 0.70913094 0.63630795 0.65082982 0.70597731 0.75075188\n", " 0.68187341 0.54368293 0.56085485 0.60091591 0.55091897 0.60091591\n", " 0.53055613 0.60091591 0.56085485 0.5543662 0.5997376 0.51184301\n", " 0.51184301 0.45074913 0.53055613 0.3726793 0.43047887 0.43047887\n", " 0.43047887 0.43047887 0.43047887]\n", "\n", "Top 40 Sorted according to approximate lower bounds:\n", "\n", "84 8 Why no NBA? \n", "-------------\n", "1000 172 i want to see more pics of this giant friend of yours\n", "-------------\n", "55 6 OP won't deliver :(\n", "-------------\n", "657 126 Please provide a dollar bill or ruler for scale. He might be 2'7\" and be sitting in doll furniture. \n", "-------------\n", "371 68 For the love of God, post pics of him holding comically small things.\n", "-------------\n", "400 75 I'm just gonna say it. I want to see his penis.\n", "-------------\n", "85 12 AMA request, your friend\n", "-------------\n", "56 8 Does your friend have marfans syndrome, by any chance? I do, is why I ask. \n", "-------------\n", "36 5 That would make him one of the top 5 tallest people in the New World. He's tall, alright, but I'm calling shenanigans.\n", "-------------\n", "8 0 I expected a picture of him sitting on Lincoln's lap at the Lincoln memorial.\n", "-------------\n", "73 16 You should post one of you two next to each other\n", "-------------\n", "17 2 I'm 4'10\"...I wish we could stand next to each other...and have him hold really small things and I could hold really large things. Damn that's tall.\n", "-------------\n", "39 8 This guy should do an AMA i bet being 7'5 is not very fun, but i bet it has its perks\n", "-------------\n", "58 15 I think your friend should lift weights. Do you know how awesome he would look with huge muscles?\n", "-------------\n", "29 6 \"7 foot 5 foot tall\"...\n", "-------------\n", "50 14 Weta could have saved a fortune by hiring this guy in The Hobbit, for the forced perspective scenes\n", "-------------\n", "54 16 Hodor? \n", "-------------\n", "28 8 7'5ft is like saying $2.50 dollars \n", "-------------\n", "24 7 Serious question: how does someone who is not 7 foot 5 even get into that chair? I see no step, no tiny ladder. \n", "-------------\n", "4 0 So...you want me to write the theme tune, sing the theme tune...\n", "-------------\n", "4 0 I'm glad he's finally able to experience dangling his legs.\n", "-------------\n", "4 0 He looks 7'4\"\n", "-------------\n", "12 3 I call bullshit, that guy is not 7'5. Just because O.P put it in the title does not make it true.\n", "-------------\n", "6 1 Bad Luck Brian: \n", "\n", "Finally finds a chair not small enough for him.\n", "\n", "Too Big\n", "-------------\n", "6 1 How big is his penis?\n", "-------------\n", "21 9 Your friend is cute, more pictures in general!\n", "-------------\n", "10 3 The lack of replies for the OP is disturbing - I am starting to think this guy may not be his friend after all. Or he could have posted and just gone away from the computer without checking up on his post. Which made front page. But would any redditor really do that?\n", "\n", "Who is this /u/Triforcetrilogy? A guy with over 10k link karma, but only 200 comment karma. Who posts and doesn't talk?\n", "\n", "I am not sure what my point is, I guess I am just genuinely curious.\n", "-------------\n", "25 12 hey, has he tried basketball?\n", "\n", "^^^hahahaha ^^^haha ^^^ha ^^^hahaha ^^^ha\n", "-------------\n", "3 0 jebus - i thought i had it bad, but that guy is 6\" taller than i am!\n", "-------------\n", "3 0 I have a friend that's 7'2\", where can I buy this comically large chair?\n", "-------------\n", "5 1 Dear god. Put him in power armor and he would be almost the same height of a space marine, a rather short space marine. \n", "-------------\n", "5 1 Pretty sure this guy isn't 7ft 5. I looked up the tallest ppl in the world, got a list of everyone over 7ft with pictures. The Internet is fact. Gotta believe it.\n", "-------------\n", "4 1 Where is the photo analyst guy with height estimates when you need him?\n", "-------------\n", "2 0 I see red carts and Texas stars, is this at a HEB?\n", "-------------\n", "2 0 proof?\n", "-------------\n", "2 0 This guy is tall enough to make eight figures on an NBA bench. \n", "-------------\n", "2 0 He looks so satisfied \n", "-------------\n", "2 0 7'5 ft?\n", "-------------\n", "3 1 Those feet.\n", "-------------\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can view the ordering visually by plotting the posterior mean and bounds, and sorting by the lower bound. In the plot below, notice that the left error-bar is sorted (as we suggested this is the best way to determine an ordering), so the means, indicated by dots, do not follow any strong pattern. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "r_order = order[::-1][:40]\n", "plt.errorbar( posterior_mean[r_order], np.arange( len(r_order) ), \n", " xerr=std_err[r_order],xuplims=True, capsize=0, fmt=\"o\",\n", " color = \"#7A68A6\")\n", "plt.xlim( 0.3, 1)\n", "plt.yticks( np.arange( len(r_order)-1,-1,-1 ), map( lambda x: x[:30], ordered_contents) );" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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Va9euYWdnp7QFYPTo0Xz44Yf069fPoHNQ2HPm9uWXX2JmZkZGRgYdO3bk5MmT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9KqVStsbGzYtm1bhc9769YtPvnkEz788MNSq0H5+PjQpk0bzM3NmTZtmnTNvn3/zkhasmdl3759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\nyG9EeBRrV27FrF4PzBv2wKxeD9a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} ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the graphic above, you can see why sorting by mean would be sub-optimal." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extension to Starred rating systems\n", "\n", "The above procedure works well for upvote-downvotes schemes, but what about systems that use star ratings, e.g. 5 star rating systems. Similar problems apply with simply taking the average: an item with two perfect ratings would beat an item with thousands of perfect ratings, but a single sub-perfect rating. \n", "\n", "\n", "We can consider the upvote-downvote problem above as binary: 0 is a downvote, 1 if an upvote. A $N$-star rating system can be seen as a more continuous version of above, and we can set $n$ stars rewarded is equivalent to rewarding $\\frac{n}{N}$. For example, in a 5-star system, a 2 star rating corresponds to 0.4. A perfect rating is a 1. We can use the same formula as before, but with $a,b$ defined differently:\n", "\n", "\n", "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n", "\n", "where \n", "\n", "\\begin{align}\n", "& a = 1 + S \\\\\\\\\n", "& b = 1 + N - S \\\\\\\\\n", "\\end{align}\n", "\n", "where $N$ is the number of users who rated, and $S$ is the sum of all the ratings, under the equivilance scheme mentioned above. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Example: Counting Github stars\n", "\n", "What is the average number of stars a Github repository has? How would you calculate this? There are over 6 million respositories, so there is more than enough data to invoke the Law of Large numbers. Let's start pulling some data. TODO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conclusion\n", "\n", "While the Law of Large Numbers is cool, it is only true so much as its name implies: with large sample sizes only. We have seen how our inference can be affected by not considering how the data is shaped. \n", "\n", "1. By (cheaply) drawing many samples from the posterior distributions, we can ensure that the Law of Large Number applies as we approximate expected values (which we will do in the next chapter).\n", "\n", "2. Bayesian inference understands that with small sample sizes, we can observe wild randomness. Our posterior distribution will reflect this by being more spread rather than tightly concentrated. Thus, our inference should be correctable.\n", "\n", "3. There are major implications of not considering the sample size, and trying to sort objects that are unstable leads to pathological orderings. The method provided above solves this problem.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Appendix\n", "\n", "##### Derivation of sorting comments formula\n", "\n", "Basically what we are doing is using a Beta prior (with parameters $a=1, b=1$, which is a uniform distribution), and using a Binomial likelihood with observations $u, N = u+d$. This means our posterior is a Beta distribution with parameters $a' = 1 + u, b' = 1 + (N - u) = 1+d$. We then need to find the value, $x$, such that 0.05 probability is less than $x$. This is usually done by inverting the CDF ([Cumulative Distribution Function](http://en.wikipedia.org/wiki/Cumulative_Distribution_Function)), but the CDF of the beta, for integer parameters, is known but is a large sum [3]. \n", "\n", "We instead using a Normal approximation. The mean of the Beta is $\\mu = a'/(a'+b')$ and the variance is \n", "\n", "$$\\sigma^2 = \\frac{a'b'}{ (a' + b')^2(a'+b'+1) }$$\n", "\n", "Hence we solve the following equation for $x$ and have an approximate lower bound. \n", "\n", "$$ 0.05 = \\Phi\\left( \\frac{(x - \\mu)}{\\sigma}\\right) $$ \n", "\n", "$\\Phi$ being the [cumulative distribution for the normal distribution](http://en.wikipedia.org/wiki/Normal_distribution#Cumulative_distribution)\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Exercises\n", "\n", "1\\. How would you estimate the quantity $E\\left[ \\cos{X} \\right]$, where $X \\sim \\text{Exp}(4)$? What about $E\\left[ \\cos{X} \| X \\lt 1\\right]$, i.e. the expected value given we know $X$ is less than 1? Would you need more samples than the original samples size to be equally accurate?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## Enter code here\n", "import scipy.stats as stats\n", "exp = stats.expon( scale=4 )\n", "N = 1e5\n", "X = exp.rvs( N )\n", "## ..." ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\\. The following table was located in the paper \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression\" [2]. The table ranks football field-goal kickers by their percent of non-misses. What mistake have the researchers made?\n", "\n", "-----\n", "\n", "#### Kicker Careers Ranked by Make Percentage\n", "<table><tbody><tr><th>Rank </th><th>Kicker </th><th>Make % </th><th>Number of Kicks</th></tr><tr><td>1 </td><td>Garrett Hartley </td><td>87.7 </td><td>57</td></tr><tr><td>2</td><td> Matt Stover </td><td>86.8 </td><td>335</td></tr><tr><td>3 </td><td>Robbie Gould </td><td>86.2 </td><td>224</td></tr><tr><td>4 </td><td>Rob Bironas </td><td>86.1 </td><td>223</td></tr><tr><td>5</td><td> Shayne Graham </td><td>85.4 </td><td>254</td></tr><tr><td>\u2026 </td><td>\u2026 </td><td>\u2026</td><td> </td></tr><tr><td>51</td><td> Dave Rayner </td><td>72.2 </td><td>90</td></tr><tr><td>52</td><td> Nick Novak </td><td>71.9 </td><td>64</td></tr><tr><td>53 </td><td>Tim Seder </td><td>71.0 </td><td>62</td></tr><tr><td>54 </td><td>Jose Cortez </td><td>70.7</td><td> 75</td></tr><tr><td>55 </td><td>Wade Richey </td><td>66.1</td><td> 56</td></tr></tbody></table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In August 2013, [a popular post](http://bpodgursky.wordpress.com/2013/08/21/average-income-per-programming-language/) on the average income per programmer of different languages was trending. Here's the summary chart: (reproduced without permission, cause when you lie with stats, you gunna get the hammer). What do you notice about the extremes?\n", "\n", "------\n", "\n", "#### Average household income by programming language\n", "\n", "<table >\n", "<tr ><th></th></tr>\n", " <tr><td>Language</td><td>Average Household Income ($)</td><td>Data Points</td></tr>\n", " <tr><td>Puppet</td><td>87,589.29</td><td>112</td></tr>\n", " <tr><td>Haskell</td><td>89,973.82</td><td>191</td></tr>\n", " <tr><td>PHP</td><td>94,031.19</td><td>978</td></tr>\n", " <tr><td>CoffeeScript</td><td>94,890.80</td><td>435</td></tr>\n", " <tr><td>VimL</td><td>94,967.11</td><td>532</td></tr>\n", " <tr><td>Shell</td><td>96,930.54</td><td>979</td></tr>\n", " <tr><td>Lua</td><td>96,930.69</td><td>101</td></tr>\n", " <tr><td>Erlang</td><td>97,306.55</td><td>168</td></tr>\n", " <tr><td>Clojure</td><td>97,500.00</td><td>269</td></tr>\n", " <tr><td>Python</td><td>97,578.87</td><td>2314</td></tr>\n", " <tr><td>JavaScript</td><td>97,598.75</td><td>3443</td></tr>\n", " <tr><td>Emacs Lisp</td><td>97,774.65</td><td>355</td></tr>\n", " <tr><td>C#</td><td>97,823.31</td><td>665</td></tr>\n", " <tr><td>Ruby</td><td>98,238.74</td><td>3242</td></tr>\n", " <tr><td>C++</td><td>99,147.93</td><td>845</td></tr>\n", " <tr><td>CSS</td><td>99,881.40</td><td>527</td></tr>\n", " <tr><td>Perl</td><td>100,295.45</td><td>990</td></tr>\n", " <tr><td>C</td><td>100,766.51</td><td>2120</td></tr>\n", " <tr><td>Go</td><td>101,158.01</td><td>231</td></tr>\n", " <tr><td>Scala</td><td>101,460.91</td><td>243</td></tr>\n", " <tr><td>ColdFusion</td><td>101,536.70</td><td>109</td></tr>\n", " <tr><td>Objective-C</td><td>101,801.60</td><td>562</td></tr>\n", " <tr><td>Groovy</td><td>102,650.86</td><td>116</td></tr>\n", " <tr><td>Java</td><td>103,179.39</td><td>1402</td></tr>\n", " <tr><td>XSLT</td><td>106,199.19</td><td>123</td></tr>\n", " <tr><td>ActionScript</td><td>108,119.47</td><td>113</td></tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### References\n", "\n", "1. Wainer, Howard. The Most Dangerous Equation. American Scientist, Volume 95.\n", "2. Clarck, Torin K., Aaron W. Johnson, and Alexander J. Stimpson. \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression.\" (2013): n. page. Web. 20 Feb. 2013.\n", "3. http://en.wikipedia.org/wiki/Beta_function#Incomplete_beta_function" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open(\"../styles/custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<style>\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", " }\n", " div.cell{\n", " width:800px;\n", " margin-left:16% !important;\n", " margin-right:auto;\n", " }\n", " h1 {\n", " font-family: Helvetica, serif;\n", " }\n", " h4{\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", " div.text_cell_render{\n", " font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 145%;\n", " font-size: 130%;\n", " width:800px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " }\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n", " }\n", " .prompt{\n", " display: None;\n", " }\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-size: 22pt;\n", " color: #4057A1;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " \n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " } \n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\$\",\"\\\$\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>" ], "output_type": "pyout", "prompt_number": 32, "text": [ "<IPython.core.display.HTML at 0x1155d1210>" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<style>\n", " img{\n", " max-width:800px}\n", "</style>" ] } ], "metadata": {} } ] }	mit
spa-networks/hpa	solver/example_solver.ipynb	1	112743	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import solver\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generalized PA equation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This solver implements the generalized PA equation\n", "\n", " $$N_k(t+1) = N_k(t) + B\\delta_{k,1} + \\frac{G}{(B+G)t + K_0} [(k-1)N_{k-1}(t) - k N_k(t)],$$\n", " \n", "where $N_k(t)$ counts the number of elements with $k$ shares of the total resource $K(t)=t + K_0$ at time $t$, where $B$ is the birth rate and $G$ is the growth rate.\n", "\n", "This equation works for abritrary $B,G,K_0$.\n", "So we can test the equation right away, starting from the condition $N_0(t=0)=0$ and $N_1(t=0)=1$." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "state = np.array([0, 1])\n", "birth_rate = 2\n", "growth_rate = 1\n", "for t in range(1, 10):\n", " state = solver.update_preferential_attachment(state, birth_rate, growth_rate, t)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot the (unormalized) mean field solution after 10 time steps." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns # for style, comment if unavailable\n", "%matplotlib inline\n", "sns.set_style('ticks')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f33418d79e8>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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B7ki9dxLaLnei+Ga/xLFqHT7YdxIf7DsJJwcZZk7yxpxoX8TG+MKeo5xonLHKtZJuXURv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\nv2YOenr12PD2N6g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"text/plain": [ "<matplotlib.figure.Figure at 0x7f33780dd240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.loglog(state)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And a few more iterations will allows us to converge nicely" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f33418f07b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for t in range(10, 100):\n", " state = solver.update_preferential_attachment(state, birth_rate, growth_rate, t)\n", "\n", "plt.loglog(state);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Automated rate computation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, the interesting part of the `solver` module is all the birth / growth computations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, if we have a $d=3$ HPA structure with the parameters used in Fig. 4 of Hébert-Dufresne et al. PRE 92, 062809 (2016), $\\vec{p} = (0, 0.0005, 0.185, 0.385, 0)$ and $\\vec{q} = (0.8, 0.6, 0.5, 1, 0)$, then the structural / nodal rates of growh / birth are immediately given by the module:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Structural birth rate\n", " [ 0.00000000e+00 5.00000000e-04 1.85407500e-01 4.99025612e-01\n", " 1.00000000e+00]\n", "Structural growth rate\n", " [ 5.00000000e-04 1.84907500e-01 3.13618112e-01 5.00974388e-01\n", " 0.00000000e+00]\n", "Nodal birth rate\n", " [ 0.37428703 0.37428703 0.37428703 0.37428703 0.37428703]\n", "Nodal growth rate\n", " [ 0. 0.09357176 0.40426884 0.62571297 0.62571297]\n" ] } ], "source": [ "p = np.array([0, 0.0005, 0.185, 0.385, 1])\n", "q = np.array([0.80, 0.60, 0.50, 1, 0])\n", "\n", "\n", "sb = solver.get_sb(p)\n", "sg = solver.get_sg(p)\n", "q_prime = solver.get_q_prime(q, sb, sg)\n", "r = solver.get_r(q_prime, p)\n", "ng = solver.get_ng(r)\n", "nb = solver.get_nb(r, q)\n", "\n", "print(\"Structural birth rate\\n\", sb)\n", "print(\"Structural growth rate\\n\", sg)\n", "print(\"Nodal birth rate\\n\", nb)\n", "print(\"Nodal growth rate\\n\", ng)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Complete HPA solutions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above rates can be We can solve the generalized PA equations for level $k=1,2,3$ separately and re-obtain Fig.4." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "init = np.array([0,1])\n", "n = [init.copy() for i in range(3)]\n", "s = [init.copy() for i in range(3)]\n", "t_max = 1000\n", "\n", "for t in range(1, t_max):\n", " for k in [1, 2, 3]:\n", " s[k - 1] = solver.update_preferential_attachment(s[k - 1], sb[k], sg[k], t)\n", " n[k - 1] = solver.update_preferential_attachment(n[k - 1], nb[k], ng[k], t)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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S1B2+ILQprXIOEg0NjXteq1QqJBIJ06ZNA+CLL76gpKSENWvW4OPjw5gxYx56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\nlkZAQMA1l9D0ERcXR2ZmJpmZmaxatYrJkyczduxYY4UrTGT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"text/plain": [ "<matplotlib.figure.Figure at 0x7f33417dbb38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,3))\n", "\n", "plt.subplot(1,2,1)\n", "for k in [1,2,3]:\n", " plt.loglog(n[k - 1] / sum(n[k - 1]), label=\"$k=\" + str(k) + \"$\")\n", "plt.xlabel('$m$')\n", "plt.ylabel('$\\\\tilde{N}_{k,m}$')\n", "plt.ylim(1e-6,1)\n", "plt.legend(loc=1)\n", "\n", "plt.subplot(1,2,2)\n", "for k in [1,2,3]:\n", " plt.loglog(s[k - 1] / sum(s[k - 1]), label=\"$k=\" + str(k) + \"$\")\n", "plt.xlabel('$n$')\n", "plt.ylabel('$\\\\tilde{S}_{k,n}$')\n", "plt.ylim(1e-6,1)\n", "plt.legend(loc=3)\n", "\n", "plt.tight_layout(pad=2)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Et voilà!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For ease of use, we also provide wrapper functions that perform all the computations at once.\n", "The first is a function that computes all the birth / growth rates." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Structural birth rate\n", " [ 0.00000000e+00 5.00000000e-04 1.85407500e-01 4.99025612e-01\n", " 1.00000000e+00]\n", "Structural growth rate\n", " [ 5.00000000e-04 1.84907500e-01 3.13618112e-01 5.00974388e-01\n", " 0.00000000e+00]\n", "Nodal birth rate\n", " [ 0.37428703 0.37428703 0.37428703 0.37428703 0.37428703]\n", "Nodal growth rate\n", " [ 0. 0.09357176 0.40426884 0.62571297 0.62571297]\n" ] } ], "source": [ "(sb, sg, ng, nb) = solver.get_all_rates(p, q)\n", "print(\"Structural birth rate\\n\", sb)\n", "print(\"Structural growth rate\\n\", sg)\n", "print(\"Nodal birth rate\\n\", nb)\n", "print(\"Nodal growth rate\\n\", ng)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the second iterates depth $d$ system of equations for the user." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n, s = solver.solve_hpa(p, q, 100)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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jZstcd1u8Xa3FbA/jSCS3IyT6ft1YS0cv//deNul5dSgVMr63dj7r4uaO2brr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\nQgghhBDCakhxK4QQQgghrMb/BzDOz23uvgdDAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f334115db00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,3))\n", "\n", "plt.subplot(1,2,1)\n", "for k in [1,2,3]:\n", " plt.loglog(n[k - 1] / sum(n[k - 1]), label=\"$k=\" + str(k) + \"$\")\n", "plt.xlabel('$m$')\n", "plt.ylabel('$\\\\tilde{N}_{k,m}$')\n", "plt.ylim(1e-6,1)\n", "plt.legend(loc=1)\n", "\n", "plt.subplot(1,2,2)\n", "for k in [1,2,3]:\n", " plt.loglog(s[k - 1] / sum(s[k - 1]), label=\"$k=\" + str(k) + \"$\")\n", "plt.xlabel('$n$')\n", "plt.ylabel('$\\\\tilde{S}_{k,n}$')\n", "plt.ylim(1e-6,1)\n", "plt.legend(loc=3)\n", "\n", "plt.tight_layout(pad=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This would be what the system looks like after 100 iterations instead of 1000." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
csaladenes/csaladenes.github.io	test/vallas/religion-en.ipynb	3	7525	{ "metadata": { "name": "", "signature": "sha256:90479418330e6250388c22816c2c2ddeb0de27463b48bc3b8f38a15fbf2c0f7a" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np, pandas as pd\n", "from pygeocoder import Geocoder\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "df=pd.read_excel('http://www.recensamantromania.ro/wp-content/uploads/2013/07/sR_TAB_13.xls')\n", "megye=[]\n", "for i in df.index[6:3434]:\n", " try:\n", " if np.isnan(df.ix[int(i)-1,u'13. POPULATIA STABILA DUPA RELIGIE - JUDETE, MUNICIPII, ORASE, COMUNE']) and\\\n", " np.isnan(df.ix[int(i)+1,u'13. POPULATIA STABILA DUPA RELIGIE - JUDETE, MUNICIPII, ORASE, COMUNE']):\n", " megye.append([i,df.ix[i,u'13. POPULATIA STABILA DUPA RELIGIE - JUDETE, MUNICIPII, ORASE, COMUNE']])\n", " except:\n", " pass" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 82 }, { "cell_type": "code", "collapsed": false, "input": [ "#run once only!\n", "df=df.drop([u'Unnamed: 1',u'Unnamed: 24'],axis=1)\n", "df.columns=[u'Falu',u'Ortodox',u'Katolikus',u'Reform\u00e1tus',u'P\u00fcnk\u00f6sdista',u'G\u00f6r\u00f6g katolikus',u'Baptista',u'Adventista',u'Muzulm\u00e1n',u'Unit\u00e1rius',u'Jehova tan\u00faja',u'Luther\u00e1nus evang\u00e9likus',u'\u00d3katolikus',u'Luther\u00e1nus',u'Szerb ortodox',u'Evang\u00e9likus',u'K\u00e1lvinista',u'Zsid\u00f3',u'\u00d6rm\u00e9ny',u'M\u00e1s',u'Nem vall\u00e1sos',u'Ateista',u'N/A']\n", "df=df.drop(u'N/A',axis=1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 83 }, { "cell_type": "code", "collapsed": false, "input": [ "#run once only!\n", "df=df.loc[df.index[7:]]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 84 }, { "cell_type": "code", "collapsed": false, "input": [ "data={}\n", "data2={}\n", "data3={}\n", "ez=0\n", "for i in df.index:\n", " try:\n", " if megye[ez][0]<i: ez+=1\n", " if not (megye[ez][1]==df.ix[int(i),u'Falu']):\n", " if df.ix[int(i),u'Falu'] not in [u' A. MUNICIPII SI ORASE',u' B. COMUNE',np.NaN,'NaN']:\n", " if megye[ez-1][1][2:] not in data: data[megye[ez-1][1][2:]]={}\n", " data[megye[ez-1][1][2:]][df.ix[int(i),u'Falu'][3:]]={}\n", " data3[df.ix[int(i),u'Falu'][2:]]={}\n", " for j in df.columns[1:]:\n", " if df.ix[int(i),j] not in [u'',u'-',np.NaN,'NaN']:\n", " data[megye[ez-1][1][2:]][df.ix[int(i),u'Falu'][3:]][j]=df.ix[int(i),j] \n", " data3[df.ix[int(i),u'Falu'][2:]][j]=df.ix[int(i),j] \n", " else: \n", " if df.ix[int(i),u'Falu'] not in [u' A. MUNICIPII SI ORASE',u' B. COMUNE',np.NaN,'NaN']:\n", " data2[df.ix[int(i),u'Falu'][2:]]={}\n", " for j in df.columns[1:]:\n", " if df.ix[int(i),j] not in [u'',u'-',np.NaN,'NaN']:\n", " data2[df.ix[int(i),u'Falu'][2:]][j]=df.ix[int(i),j] \n", " except: pass" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "#add Bucharest to main dataset\n", "data['MUNICIPIUL BUCURESTI']={}\n", "data['MUNICIPIUL BUCURESTI']['MUNICIPIUL BUCURESTI']=data2['MUNICIPIUL BUCURESTI']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "#save religion data\n", "import json\n", "file('data.json','w').write(json.dumps(data))\n", "file('data2.json','w').write(json.dumps(data2))\n", "file('data3.json','w').write(json.dumps(data3))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "#county name converter\n", "cc={}\n", "for i in pd.read_csv('ro.csv').T.iteritems():\n", " if ' '+i[1][11].upper() not in cc: cc[' '+i[1][11].upper()]=i[1][9].upper()\n", "\n", "#fix db\n", "cc['Bicazu ']='BACAU'\n", "cc['Municipiul Brasov']='BRASOV'\n", "cc['Oras intorsura ']='COVASNA'\n", "cc['Sanmihaiu de ']='MURES'\n", "cc['Municipiul Resita CS']='CARAS-SEVERIN'\n", "\n", "\n", "#hungarian settlement names, where applicable\n", "hun3={}\n", "dh=pd.read_csv('magyar.csv',sep='\|').dropna(axis=0)\n", "for i in dh.T.iteritems():\n", " try:\n", " m=cc[i[1][1][str.find(i[1][1],',')+1:]] #county\n", " if m not in hun3: hun3[m]={}\n", " f=i[1][1][:str.find(i[1][1],',')].upper() #comune\n", " if (i[1][2]):\n", " if (i[1][0].upper()[:-1]==f): # village\n", " hun3[m][f]=i[1][2]\n", " if ('MUNICIPIUL '+i[1][0].upper()[:-1]==f): # city\n", " hun3[m][f]=i[1][2]\n", " if ('ORAS '+i[1][0].upper()[:-1]==f): # town\n", " hun3[m][f]=repr(i[1][2])\n", " except: pass\n", " \n", "file('hun2.json','w').write(json.dumps(hun3))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 163 }, { "cell_type": "code", "collapsed": false, "input": [ "#parse country for settlement coordinates\n", "coords={}\n", "de=pd.read_csv('ro.csv')\n", "for i in de.T.iteritems():\n", " if i[1][9].upper() not in coords: coords[i[1][9].upper()]={}\n", " if i[1][5]!='V':\n", " coords[i[1][9].upper()][i[1][8].upper()]=[i[1][0],i[1][1]]\n", " coords[i[1][9].upper()][i[1][2].upper()]=[i[1][0],i[1][1]]\n", " if i[1][11].upper() not in cc: cc[i[1][11].upper()]=i[1][9].upper()\n", "\n", "#fix db\n", "coords['MURES']['ORAS SANGEORGIU DE PADURE']=[Geocoder.geocode('SANGEORGIU DE PADURE').coordinates[1],Geocoder.geocode('SANGEORGIU DE PADURE').coordinates[0]]\n", "coords['MURES']['RICIU']=[Geocoder.geocode('RICIU, MURES, ROMANIA').coordinates[1],Geocoder.geocode('RICIU, MURES, ROMANIA').coordinates[0]] \n", "coords['MUNICIPIUL BUCURESTI']={'MUNICIPIUL BUCURESTI':coords['BUCURESTI']['MUNICIPIUL BUCURESTI']}\n", "coords['HARGHITA']['RICIU']=[Geocoder.geocode('RICIU, MURES, ROMANIA').coordinates[1],Geocoder.geocode('RICIU, MURES, ROMANIA').coordinates[0]] \n", "import json\n", "file('coords2.json','w').write(json.dumps(coords))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 88 } ], "metadata": {} } ] }	mit
chandupatilgithub/ml_lab_ecsc_306	labwork/lab1/lab1_assign_2.ipynb	5	1578	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Write a program using tensorflow to calculate : \n", " $$y=mx+c$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Part 1\n", "\n", "1. Read 2 arrays x,y containing floating point values\n", "2. Calculate mean of x & y\n", "3. Calculate variance for x\n", " $$variance(x)=sum((x-mean(x))^2)$$\n", "4. Calculate covariance of x & y\n", " $$covariance = sum((x(i) - mean(x)) * (y(i) - mean(y)))$$\n", "5. Calculate value of m\n", " $$m = covariance(x,y)/variance(x)$$\n", "6. Calculate value of c\n", " $$c = mean(y) -m* mean(x)$$\n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Part 2\n", "\n", "1. Plot graph for actual values against predicted value\n", "2. Calculate root mean square error." ] } ], "metadata": { "celltoolbar": "Slideshow", "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
molgor/spystats	notebooks/.ipynb_checkpoints/Analysis of spatial models using systematic and random samples-checkpoint.ipynb	1	767995	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load Biospytial modules and etc.\n", "%matplotlib inline\n", "import sys\n", "sys.path.append('/apps')\n", "import django\n", "django.setup()\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "## Use the ggplot style\n", "plt.style.use('ggplot')\n", "sys.path.append('..')\n", "import tools\n", "import geopandas as gpd\n", "from HEC_runs.fit_fia_logbiomass_logspp_GLS import prepareDataFrame, createVariogram, buildSpatialStructure,calculateGLS, bundleToGLS, fitLinearLogLogModel" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "new_data['residuals1'] = results.resid" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/envs/biospytial/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2821: DtypeWarning: Columns (24) have mixed types. Specify dtype option on import or set low_memory=False.\n", " if self.run_code(code, result):\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Reprojecting to Alberts equal area\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Removing possible duplicates. \n", " This avoids problems of Non Positive semidefinite\n" ] } ], "source": [ "new_data = prepareDataFrame(\"/RawDataCSV/idiv_share/plotsClimateData_11092017.csv\")\n", "## En Hec\n", "#new_data = prepareDataFrame(\"/home/hpc/28/escamill/csv_data/idiv/plotsClimateData_11092017.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Subseting the data\n", "\n", "Three different methods for subsetting the data.\n", "1. Using a systematic selection by index modulus\n", "2. Using a random uniform selection by indices.\n", "2. A geographic subselection (Clip)\n", "\n", "### Systematic selection" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def systSelection(dataframe,k):\n", " n = len(dataframe)\n", " idxs = range(0,n,k)\n", " systematic_sample = dataframe.iloc[idxs]\n", " return systematic_sample\n", "##################\n", "k = 10 # The k-th element to take as a sample" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "systematic_sample = systSelection(new_data,k)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HSvSMlmirieAHATt7cyc8/6hn+4t8a8vI2K0Svh9QcjwOpR3amqPUxsPUxsMk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\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4d7KLi8sFzWiqVBHdALmiwbWdvpf1IrN/qvpimiuZqKJNR/jnH/+1Ja5gWjaTBYtmv8Sm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yBVgNcHR3MLTI+4NI5ZddHRl8UwDFpGfjHtG3Z45YBDxG8R8Y92fJT5PeZXFJdlBC2D/7szO24G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\nEREREZEIUuMtIiIiIiIiEkH/AOLH9f+8ws63AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fccdf529f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax= systematic_sample.plot(column='logBiomass',figsize=(16,10),cmap=plt.cm.Blues,edgecolors='') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Random (Uniform) selection\n" ] }, { "cell_type": "code", "execution_count": 234, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def randomSelection(dataframe,p):\n", " n = len(dataframe)\n", " idxs = np.random.choice(n,p,replace=False)\n", " random_sample = dataframe.iloc[idxs]\n", " return random_sample\n", "#################\n", "n = len(new_data)\n", "p = 3000 # The amount of samples taken (let's do it without replacement)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "random_sample = randomSelection(n,p)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Gq4vblR2XhTGPK8dMFdjck2fNwQwBy+D958XoiPlO9xRvabFYjFQq9Wa/DHkb\nUZuS8aT2JOPteJtK5F0Kjkdb2KzO7/7VwTR7B/LMaw4ybPgYKnqUHI9jw0UcDxLDeQYzRWzLxPM8\nGnzwhfdMeZVnlLe7jo6ON/w51eMtcoYs0yCXq9SzDgRsfLbJtKZT9zI3h21+64JKLe1YxEcqVRu8\n/+idU3j3+Y3kii4LJ0dIZkrV0A3w2Mu9XHnnc3zo0sn8ybVTJ+gdnd68pgD/87JTXw0O+00WNAdI\n5Mp86dkEibxDyDaI2gb+gIXtVg5sTtElX6oMS68PWUw+xdD918oD0oUy2WIZMGgOW7RHQwwz2rNt\nWyb/uq6LhqDJokkRjqVLfOfloepog3/ZNMhnr2o5aWEWEREReWtqDJps7C3yi8MF/JaBVSzwgy0J\nAH62e5j3n19PS9zProzHyCkI9bEgRcejUHIwDINkSX2O8ubQ+EuRM1ByXB7bPkih4FAoOKRSBW5Y\nUE/gdQxhntcWZsnUKLZpcKrslyu53P9cN0/tHnwdr3ziPH4gQyJf6dbOlT2iYT/ntYQI+S1WTItx\n3qQwoZBNQ8zHX7x76useZj7WVdMjTIrauB6YhscHFtRx07wYxpgBPIWSQzJTYnNnBoCejFMzxD9Z\ncMnp4CsiInLO6Mo4vNRfpuxBtuzx9MHa+dwPbRvkG890V6f7HecbU+o1psVW5U2iHm+RM7D+YIrO\nodESVZ4Hl0yNvuJ90oUyX/r5IQ4MFFgxPcofvmvqaXtXG6N+/uT62dz9s30AROMhYrEg+YJDX6p0\n2ufY3JNnY1eOeMDixrlRIm/gXOYTJ6nYlsE/rpwGwH0b+/jPg5VhhvmyxyPbB7lszJDv1yvqt/ib\nq9o4mipRH7BoCFUOoh+cG+KbLw4ylHPYeSRJsewyvbEyX35GnY+QbZAbKRM3NW6Tdiy6hg1ClkdH\neHSxNhEREXnrOX4MPy4UsCtlVUYcPzcZShdpGKkaYwBuuRLELQNuW9rwhrxWkRMpeIucgYe21PY6\nWwZMip+61vRx//zoIX7ycj8A24+maIr6ufWS9pp9Nu7qIZMvcen5k7j9+jnMn9XItzcNEokG8TyP\n3mNJrphz6rJi+waLfPflZHWhs0TO4VPL3riDybUzI2ztzTNUcAlaBjfMGQ3WqULtleYTt8/EI1sT\nbDiSYtnUKDcvaqr527quAkdSZRY2+ZhZP3rlui5g0hHz8cKBJI7nMbMjzqTGED/dPcyvDmeI+EwW\ntwSJBiwKlQRtAAAgAElEQVSWTY7Sma1cqBguGTiey/SoesBFRETeqiZHLCI+g8zIiLXWmJ9DfZWR\nbZ4Hx5euOtQzjGnEqYtWwvdHlzbSGjKpD5o15V1F3kgK3iJnoOx6BIM2xWIlQF45t462Vwne+3pz\ntdt92Zrtz35nPd99bAcAF85t4f6/up5dSY9ItNJDaxgGV1/QyqS6U8+NPpws1ZTWOJQ8fc/4RGgO\n2/zVlS30pMs0hS1iYw5k7z6vjsd2DZEvexjAjeef3QWBb67r5nubBwBYfzDNsWSR379iEgAP78mw\nPVEGYM9QmWzZY0X76GfUX4QFMxur2w/uGGZX7+hnP1xw+Kff7OBQurZ7O1M2OLlYiYiIiLxVBG2D\n984McnC4jN8y+EF/csxfR4/hlmWSzpdoGAnew0WPi9rOzQVV5e1Dc7xFzsCqpU34bZNQyEd7Q5BP\nXt7+qve5bHZtT/Vls0a3hzOFaugGeHFPH89u7SLmr/1KtkRsHNcjV3I50fQ6H2Ojo+F5FMon7zeR\nQrbJjHp/TegGmNMc4qsfmMWfXjWJL713OtfNr6/5u+d5/Gx7gn97ppvNnbXzswAe25Ws2V6zZ3R7\nz1C55m8v9BRrtidFal/LkaFCzfZAtnLxJHrC8TfqU+gWERF5qwvZBgsafcyus/nkihaWTgrRErFZ\n1F5ZWyYc8hEM2ESCPjzPw3VdpsXUyy1vPvV4i5yBK2fFmdkYoGu4xLyWIPWhV//q/P41U2iM+Dg8\nVOaiKSGuPX90uLRtmdiWQdkZDXvBgM37Z8fpSpU4OFRiZoOPBa1B/nptHwXHY0lbgI9fUFedJz6r\nwU9HCPYMlnBcjyPpAg9usfjIhc3j/wG8BpPr/EyuO/WogG+t7+GBTZVh+D94qZ87b5rBRWPmzMeD\nFoO50YAdG7Mwm98yKI1ZJS3iq+25XjkrTMjO0ZVx6M+5hAIW2eLoUPfQyP6dqSKdKYj5LXJlly1H\nswzkHeY2+LhxdgRLE75FRETeslIFh29tHuJYHs6fHOPGeXE6B/PsSRSYVe/H5/fx37uSpAsO//JC\ngT9c1siM+lcerSgykazPfvazn32zX8RbjWpPyqnEgzaT6/wEz3ABM8MwWDQ5ynuWTmZyXe2VVp9t\n0VIf4umXO3E9+NA753HbdecTsE2unB7hxnkxlk4K86+bhiiMhPOejENb2GLSmNrTj+9JcqA/T34k\nWDaEbC6f/sqLvr0VfP2Xx0iOrIjuUQnWK6aPzhFfPi3KE7uHKDoe0YDJP948g3iwcrFjatRi20AJ\n16uE7o/Nj+CzRkOybRrMbfAxs87m5b4S0aCPXNGh7LjUBU3+/IpW6oIWjx0ukCm7JAtl9g/k6M6U\nyZc9jqYdfKbBjLq37pC0QCBAsVh89R1FztCrtSnPg86swcsDDkczLq1BA1sXp+Q09Bsl42kw55B1\nDHyUMQyDbX0FNvUUWHMgza6BIrNawkxqDHM06+H327x3bpSZDQF+tCvJgcECjutRKLn0ZB0unxrG\n9TwyZQ/LRCVFf43FYuO36O+ZUo+3yGtQcjxSJY+Y38D3Gk8+P/zO81h56SwKJYemeLDmb+miy90b\nhsiXXYwxB4XBfO0iZZdMjfLysdyY7chrei1vtMn1fg4NFmq2x5oU9/Pg7yw45X1ty+DD86N0RMxX\nPPFvCFq8Z0aQZ7oKLJ0c413TgsysO/VPXsmpHaI/kDv7xeBE3s66c/BsV56+TGUtib0Jkw+fF8Jv\n6aRVRCbOo3tT/NfWITxgeUeIizrC/HBPZTG1bL6MZRpMbQxV9x/Iezy8O0PEgu19+ertHjCQLZMq\nuvzicJFUySNiG1w33U/cr5m38sZQ8BY5S4N5lx/uy5EpeUR8Bu+bHaIheGY/2l97vp9tfQVs0+Cj\ni+u4eHKEaOjkntWf78vQly3huh72yIltqezglWsD4XXz6qgLWuwbyLOgNcRFk98awbvsejy0O8Pu\nRJGWsMWq+VEaxtTN/NNrJuN6RzmYKNDeEMIKBxnMOzX7nMrzPUW2DFSGoDcGDVbOCL7ihY+FzX4W\nNp96WNlFLT6e762EiGjAolCuPK4BLDjNfUR+XSVLVEM3wHDRpTPlMKtepxEiMv5KLvTl3GroBtjY\nlSM75jTIZ5sUSg6u59X0XL/UkyeZK9dMSwOY2eDnhd4SqZEV0TNlj029Za6ZomO+vDF0xBQ5S893\nF/E8g/nNYUqOy/PdRa6bEXzV+z28I8mW3kovb9Hx+PcXh7h4TFAuux4bDw5TcmHd0UovtmkaOK7L\nwFCOTLZM+4X1Jz3updOiXDrtrTW8fF1Xnm39lWGGXWmHR/Zm+dii0SE9DWGbv7puKt/YnCJb9tjc\nV2J/sswfLI2fNkg7rlcN3QD9OZdfHckyq87H9NcwZ2thk4+pMYtE3qU9HGTnQInuTJnZ9T7mNuog\nLDJWva9SoWDsaawq8ojIRCg68PIgpEon1xpJFDxiIR+u65EzykRsgy2Hk1wwtQ7TNOgarIRuz/Oq\nNb0BfJbJwZTLlp4EbXUBJjcEMTBwPC2sKm8cBW+Rs2QaBiumjC5yVnL8wCsPTX76SI6nDteWF/OA\n7uEi332hn85kkc5EnmS2hFt2ueD8tup+hmHguS6rljSyqD083m9nQqQKtUO3U8WTV1vvy7pky6MH\nvOGix1DepSV86rN5w6jUT3c8cD2PrUeGeSZX6YG7YW6MVYvOvoZ53G9Wh5gtbQsApy7dJvJmyZVc\nutNlWsIW0cCbl3TbwwaXdgR47lgB14MLmnxMiekUQkTGX28eCi74LZMLp8TZdCSJ63mEbBOfVTlm\nm6ZBNGhzqCfNwHCBvd1pLNMgErD5wPIOth9L8VLnMNNaoriuh2UaGIaBbZt0JwvUh23qgz4WNel3\nTN44am0iZ2lWQ4i8N9or67NMHNfBOs1oc9fzWHs4RyhgkS+NBvR4wOTOJ7rYN5DH8zwMwyAY9OE6\nLql0gdhI7UnHdfnsu6cwr/nVe9XfKha3Bnihu8DxXF0JtbUagyZ+q3JlGyBsG8QDpx+ybxoGV3b4\n+WVXkUS6RDI3Ouz1p3tS3HReHaEzXPhOZKLtGypzYLhMQ8BkaasP6zUs4NObKXPn0z0kcg5hn8Gf\nXdHKnMaJuTi0eyDPoaSHbZrMiFo0B8on7bOs1Ue9v3IhbW7DW3fxQRE5t41dOmLZtDoGM0V292ZI\nOw653jQz22L4bBMDA8usHPfLjkfZ8ZjW6CMasEnly7ge4FUqyRznjQw/d1y4eZafuIbuyBtIwVvk\nLMX9kK8tDY0BZIsOn/vpIV48kua8tjCfXTmdhnDl5NQ0oFgok+gZYue2Q+SyBa5c2EZ3oB7XBdse\nPSiYlsmefQPMm9uCZRkUSy4Hh4rnVPCeErP55NI4+4fKNIdN5jacPHQ76je5dX6UX3bmMQ24ZmqQ\nwKss1DS7zmZ6zGJLj8G2o6O3m0blv9PJl12+vyPFkeESM+p8fHBBXItCyYQ5kCzzs4Oji/qkSx5X\nTzn7wPzT3cMkRhb6y5Y8Htqe5C/e0Tpur/O4vrzHruP17h2HfSkI2xZhq3Ykzy878zzXXZlCsu5Y\nkY8uiBDTokQiMs7aQjBQqKwtYRmwv7+ymJoBWKbBcK5IPOTHAOa2R0jnSmQLZerDNleeVymp2hoL\nsLcvS08yR0s8iGlWSriWXY9IwGJmvU+hW95wCt4iZ6kl6DJccsm7leDWGnQwTfj2um6e3pMEYOOh\nFPes7eJ/3zAd0zCY6S/z/af3kB0cwh1ZxOupLd00tJdoaGvGND3MkeTouh6zJkUpu5UDhAHnZN3J\ntohNW+Tkn5ijQwXuXddDtuRyy+ImfmvB2ZVzsE2Dpe1BLp0SZn1nFgO49YIGAvbpA8DP92V4eWR+\n/WC+QDyQYeXcyrz4fNkjXXJpCJg1tbvLrqdySfKadKZrA2tnqsxbeRpD5oTO7aLjUnJtOCF4b+od\nLQ+VKXnsGSxzUdu599skIm9tpgGLGiDneGRLEA9YJAsu7Y1hfJY5Mn/bA8MgWXD5v1Z00DVcxANC\nvkqYXtQRI11w2NWbYTBTJBby4eHRGLYI2iZTwzq+yxtPwVvkNZgdK+O6YI7Jer2pUs0+vanRk9Sf\nbTiK644cKMbIp/MU6ooUixaBkfmbF3aE+Kv3TOeBrYMk8y5XTo+cU73dr8T1PD7zyCG6hyuf1dau\nLN/40GymNZxdKDEMg0+taOaDC8v4LYP4q8x9HVsebGZjiFAoSGfWoFwq8f3dGYouNAVNYpZLpuSQ\nd0wG8i7NIZOPLoy96mrrImM1nVDloDn02trP9XNjbD6WYzBfGWp+y4K68Xh5J2nwwyFGFzEK2xbB\nkdBdcj2e7SowmK/MkcyXXTKFcqU0T84GFLxFZPw5nse2QY90Ga48r5lnDySr87sNw8DxwB7Jzt3p\nUrX86p7+HPGgRcHxKBvQXh+k5FR+3Ypll4IHyYLLf2wZoj3aRJ3f5Kc7kxQdj9+cF6c5omk0MnEU\nvEVeI/OEDtbfmN/AEzsHOV694rrzGwH472397BqpJWkHApSyWQCCPovP3LKAx3YM4RgmRsiPZVtc\nPLeRuqDF7y9vfsPeyxslXXCqoRsqJ/UHE3lM2yJTcpketwnaJvmyy3DBoTls15QIAdgzUOCpg2mi\nfpOb59cRPoN53YtaAuwcKDK1PsiKaZXwciwHAxk4vu7bQN7lcL6E43oER467/TmXRw9k+fBZ9srL\nr7fzm3xkyx4HkpU53ldOfm293W1RH3e+exLd6RItYXvCFler8xtc0hFiXyKPbZpMDXsEzMoP2ROH\n82wbU00gX3IolCtfmicPZrig2U97VKcSIjK+nu8uUTAqvy2tsQDz2yMcGSqetF/YNjg0kKVnuDKq\nbXJDiIZAkLagwTsnx7AMj0f3Zyg5Hjv6Ry/Cux50Dpf48qZ+9g5U7rtm7zBfuXnam7qQpby96Wgp\ncoLDgwX+c2Mf4HHrshamN55Zb/M75tRxz4fn8lJnZY73JTPjHEsV+c7GHuJ1YQr5Er5QCMu2iQRM\nvvuHyzhvch0fecc0vvRsH7sHKgeUR/dnmFzn59Ip58YK5mcjFrCY2RjgQKJykAvaJhlsvr0tTbHk\nYONyzZQg39mcIF/2mN3o5zNXtVcXTesaLnHn0z3V2pz7EgX++ur2V33eSyaHOJZxKJzwkxc+xfyu\nE9fAypVUakTO3vI2P8vHYRh2yGcy8yxHhLwWLREfQTcP1FYg6ErXbo+9EOZ4cGS4RGvEfsU1FkRE\nzsYznTk29JRYOnm0VOrUuiCHBwsYhoHPhIsnBYn6TWI++NrzA9X9jg7m+N0lcabFR3uuf3dJPfsT\nBQ4M5EmXPWzLxG8aRGyjGroBBrJl9vbnWTqm1KvIeFLwFgGG82XuXddD93CR3b050iPlsDYeTvPt\nj84746ufS6ZEWTKlcqAoOR53Pt4FmLRPqqNQLJPPlSDkp1hyOJgoct7kyv36MrVzKXtPnHT5NmEY\nBl+4eQb3b+wjW3S46YJGfnSoSDpXYm/nEK4HLx8En9/GNA32JYo8vi/FTfMrvdS7B/LV0A2ws7+A\nM1Im5FQO9OfoThZY2BHFwSB7QoiO+0ZLlBmeR+9QlnzRIRLw0doQwjYMLu44d4b59xegNwc+E6ZH\nQeteyals7S/xfE8RyzC4crKfkG3QU8rTYHonLTo4KWIyOKY8YNRnkB45TzUMOFa0OJwPUe+HOjN3\n0oUrEZGz4XkeT+waonO4RHPEZkp9EM/z8Jse5zf7mF3vZ3FLgMjIAe6x/emTHqPuhIPfxqNZVq/r\nxfUqC7QtmxLhnTOj/HBvjnlT6kgMF+gfriz02hLVUHOZOAreIsAXHz/KxiMn/3gP5Rw6hwrMbzv7\n3uc9fTn29uWIxwIEAjazZ7cwPJznaOcghumSKYyG7SXtQdYerKzaaRmwqPXcCXtnqyFs88dXTapu\n/7yzRHciWx2i73pQLjs0xAK0xgNkXKNabm1qXWUV0+PxeXLMd9rQ/chLffz9IwdwPWiL+7l95Vx2\nDxbx9Rk0hX3U+eGiNouZ0RjJgsvmzhSb9lZGHeQLDjPrbH57WTOTzpFhtKkS7B6GymkF5B2PC86+\ntLm8zQ3mXX7ZdXy4psczx0oEfCaQJmrDtVP8NdUFfmNakJBdIJF3mV1vMytu8+DuNAN5lyn1QUJ+\ni6OpHJHGGDnPh88tUnK9mikgA9ky/71rmKLj8e5ZUWafoiRaZTFJCNpK7iK/zj73k4P8bGsCgIOd\nSS5bMomrpoW55YI2UqnUSftHAxZ+26Q4MgUmGrCpO2Fdlp/vGa6eY3hAb7rEs8dK2D6bJp9NUzyI\n1QW3XlBH0Geyen0fAzmHS6eEuWFufELfr/x6OTfOKEUm2J6+XM22YYDnVYZGd9S9tuGix3vJh1MF\nBnZ1EmmoI5stYVoWAdNkRkuER3YM0Ry2+NDCOB0xm/6sw4XtQWadovzW29VNs8JsOzRUc5vPMlkx\nqwHbMskBz3aXuGKSn9mNAX5/RRNr9qcJ+UwWtQXZ2JXlokmh6hDYbMnl2c4s3986hGGa4Lj0DBc5\n2DnEFbOa6EyVCQGXtAawDKgLmNQFTH46XLs4XiJTOmdCN0C6DMdD9+i2SK1suXbUh9+ubTPHsi4z\nYqMnrT7L4JqpoxcCXc9jStyHbXs0hOzqbQCHh0p8/olDlFyPa2fF+L0VzeRLLl96to++bOVC45ae\nPPPqbHb15WiL+rhieoTGeJAdQ5DIl5kVt7hhZhBDXeciv3YSmVI1dAOkMkWaTIerp4VOe5+5DX46\n6oOkCw6GYTCvKcCewRKz6ytrxPRlHbpOGFXYn3OJFyqj2wqOi+fBeR0xrp4V5wu/6mXfYOXi5MM7\nh2mP+rho0umfX+RsnDtnlSITaNGkMM8cqFxJNYCF7WFiQYuPrWglHjz7r4njQSwc4CPLW/nu+i6S\ngxmMwJhec8Pg7x4/ijMSlK6dG+dPx/QC/zqZ0+Djb97ZzuceP8pgzqEtanPzkha6i6M9ZgeHy1wx\nqXIx4oppUZZ3hPn8L3t5cPswAMsmhfiDFU2UXI+7n0/QlS5T1xxjYSTAlm3deF5lPvlVU4J0DpfI\nlFwcD8ZeE5/XEuTxvcOj2+fYSvIxGww8vJE2FdNoOTmF1rBJQ8BgsFAJyyfm21eanuB5Hv++eYgX\nuyuLRTZHfVw2q562SBADl39Zf7Q6FeSJ/SkM22J7f5HkmKoCBcfjpd48F0ytpz7io9szafRHee/5\nUdKFMj/e1cuORJnpcQvPg6jmS4j82gjYJrZpUB4zpeyaGZFXLO3ZHLb4vSVxXu4tsH/YpSvr8sDO\nDG1BeO/cCP+5M0dbfYhc0aFYdvHbJrGQj0zBoT7sxzJNMkWXi9orI3G6T7hq3Z0uAQreMj4UvEWA\nP792Mu0b+uhLl7hmTh1XzIqztzdLruicdT1nx4MdQwZZx2DapEbS3dsoOx6lQhFfoBIeI0Gbkjta\nu/uJPcN86vI2gq9Qi/rtbEZDgG/cMoPBrENj2KYz49B9dHT10qiv9vPfNVDgaGr04PjCsRwbO7MY\nJuw4lsGyDCIhH+GQn3DIx6zGAB9c3srjBzI8ur8ypL89avFHyxqqn/m7ZscplF02d2WZVu9n1eKm\nN+Cdj5+oD+bXQW/ew2fCtLff2nwyDnymwftnh9g9VMYyIB4w2dBXpuTC7LhJR+T061n0Z51q6Abo\nT5eY5POYHnZwnDw9Y0ooBnwmOxMlDKNSk/f4ebQBzJoUp+CZ9KQdQj6Xbb3DtEcDxIM+lrTHeb57\niEf2Oxzuy5AtlJle7+ePL2mmKaxTFpG3s0jA4n9dP50v/vwQJcfjfRe2MOSabOnNc3ksRtHxKLoQ\ntmsXemyP2BhtBht60xSKDjsOJSiWXB7banLetHoiQR9TmiLkiuXqaJrj09RMw+D8Bourp1bC9eK2\nIOs6K9VnbBMWtpxbF+HlrU1HMREg5LP45OWjq2P/y1NH+fazxwBYPiPGV1bNxbbOLBQni5B1Kj/o\nvYNZeoZyGKZFMjFMMBwkGLA5lijiD9h0TGvBskz8loHv13xZYL9l0harfMYz4zaJvMvepEPENnhH\nR+3Q++gJq5F7nseXftWDWypTHKnXmS86tDWE+D8fm8+skd7rxw9kqvt3px229BZY0TF6Jfv68+q5\n/rz6CXuPE63BX/lvvDmuh+Nx0sJbcm4K2AYXNI8OiZgcMYlEY2QzJ69zceL9xq6xABDxW1i2jW1b\nXDcnxqN7KyOH2qI+LMvENAxM0yBXrPR614X9Nb+luZJHweeRL7vEgULZoT/n0pvMM5StTP/YM1Dg\n75/q4Z+u6ziri6Aicu658YImfvP8BrpTZb7+4hAP76r8puxOemCZOB40BQwW1xvc/0Ifw3mH6xc0\nMLe1crW5qz9DsVSZ710su3T2pjlvWgMhv4XjepQdF9syaIpWDpa5YpklM4Lkyi4h2+TjSxpoCVts\n6S3QHrWJq7SYjCMFb5ETZApONXQDbDyY4rkDw1wx58wC2fFs0jecJ10oEw35yORL1DfW4Q/6yGUr\nSwIXC2WSg2laW+v4kyvbq1dfs0WHvb052uJ+2uK/PnO9T7Ss1c+y1lP/bVaDn5vmxXhkdwrX8yiW\nXBzHpeSMRoJcrsTHr2pnTstosLYNyBQdHNfDMCBXck/18DLGkbTDxv4yrgfToybLmm3Nv32bMQyj\n+vvTn3N4aHeGwbzLgiY/K2ePrp8QD1hcN6+RR/ck8Dy4YkYDdbE4jl35nfrIxdNZPqWfXMllT9Jl\nZ6ISnIOmTVPEz2hRgdr247dMNh8b5uBQnrLjYhhGzXcZKgu0PbY/zfVzYgBsT5Q4mHKI+wxWtNUu\nCCci5zafZbJzsER+zJoUzx9JsWx6pcLJQMHjf/+8k8OJAoYBu4bKzG8PM6UhxH6v9rejOtrGgN9d\nWsesOpvvbx9m51AJz/Voi5i8nDQh6RDzOVzaYvNiT4G+rENvzmWgOMxNc6PMa7BretlFXgsFb5ET\nmCcMjQTOqpelzg8v7enhm2sP4nkwe2Yr+VKZvFH5uoUiQQZ6hijki7z7vHr+8sbZ1TrVA+kSn/zu\nTo4OFfBbBn//vllcOKOOH+4cZndfjlkNfj50QUPNisG/rt47vw4Lj/98ubIwm3nC/0etMR8XnrAg\nyuwGHxu6KkPUPQ9e7M5x1XTV6zwdz/N4YSR0AxxKu0yOuEwKqwfg7erH+7L0ZCsXpF7qK9IRtVje\nProK+RXTYiyaVI9HJTBb5uhvkWv6WdgWwgCe70nWPO6MuMXhtEPBARMPdyR8WwZ0DuVI5ivzL23T\nIBr00RDxkUgVqr3rPtukf2SBtgPDZZ7rqYT6HqDgFrl2ypnXOi+7kCwZlcUVfd5J89zPdnqRiIy/\n+AnrO/hHpoVVvpkezQ1hDicK1MeDRMJ+jgyXOTKc4vp5MX74cpFUwcU0oLEuQLZYZm69zex6H13p\nMsGAj5n1JjnXwAM2HEqSyjssaI8Qs6Av62AaML89StBnsb6nzNGMy7VTz/x3RuT/Z+/NA+yq67v/\n19nuufs2+5ZkJttkTyABwhYBWRRcoFaxWos+tW61ldZa+7RPi7W/R23r+lhstdpFVAQVBQRU9i0Q\nQhbInslkktm3u29n//1xbu7MzQ4EC+S8/sq5c+52cr/f81nfn+PhOd4eHkcR8En86RVdfP2hQWwH\nruhNsK77pY2T+NHTgxwJug6myjTG/HX1mT6/QkNA5GOXzak53QA/3TLBcKaaEbccbn10mFXLbF4Y\nzOEA+ycrDGV0/s/lb3whNst2uPfFFON5nQ0LYyw+zki3qxfG2DulsWWkTGvUx5JkiGcPF4j5Zf7k\n4pZjzj9aqCmvexnvk2HjahbMxvQu2Ruagl7/H57XbSqWRNmWkQSHqKTjoGI4Ij7RIuCbXZXj1HLZ\n8xMK46VZIxObVa5bIJPRbDTD4otPTNDdEKIzGeTFca02Csi0HQqaQVkzaYiqVHSr2icusLrVbRmZ\nrtT/CFOV0/9Rmjb05WV02/2kZV1HNzTWtqoMZ3W+uWmKbMXi/M4gf7S24YQZrqcOFxnKGSxuVFnd\n6gkveXicada1+zmY0dk6ViEekLh8foLd0xXCfhkQWN2dIFPQKdQLlpPSHC5alGDzwRyOLAECU9kK\nV3VGSVcs/ntngSPFbrIIDSGF1pifvFbkUFojV7FojfmRBfArM0HmwYLNwazBg4crlExIqCLX9QRI\n+r1EiMfp4zneHh7HYe3cCH98WSddCZVLFr30YchHVz0mgzKjs8ZZfOyyLt51TnP1BjLD0UaeA4zm\n9bqeyt2Tldpc6zcyX3tkmLtfcMeK3LlliltvXMDC5noDV5EE/vziFizbqZXKfnT9sQ73Ec7vCPDU\nYKnWB35x10y2+/6dKW5/fpKgT+JTl7XXHP28ZnHPniwV0+HN8yPMiZ895f+SILAwKrE/V+3P9Qm0\nBT0j443MyiYfjw+5AmqKCAsSKinTDwjYjsMDfSmypUnWdgZZ0x7CtMCS/AiOjWwVMW0B3Ra5fG6Q\nqCoyWbJYmFDobagKSyoiD/eX0QybG1d34JNF0hWT/VPF2mcwLQdFElFkCZ/sGr6rmxVWNLuOd1+6\nXnW42e+ufct2SGs2flkgfIKqoIIp1JxugIDPx5NDeXZNGfRP5MlULGRJYHfK4PsvZnnvstgx2gb3\n78/zi73uBIRHBop8+Jwk57Z7zreHx5lEFARuXBbjxmUxypbMlx4dYX5HuO6cNXMi7BwpUrAhFlSI\nBhTGKw6TBZ1M2cSyDVJZdz/bkw7SX6qgSCIlzUC3bBRZJBF0S8jDqowiS+7IRUEgr5v4ijqJkLt3\nSQLcP1BBq5py0xWb+w6Wef8Sr2rO4/TxHG8Pj6PYdDDLzbfvw7BcZ+5L71rApS/R+f7zq+by+XsP\nYtoOyzsj9I/mMSUZURJY0hzgpgvbj/u83z23mUf2pjkwWSagiHz8sk4eGNLrzumM+t7wTjfA4/tn\nSrAdEmIAACAASURBVFV1y+GZgfwxjvcRpNMsC50T8/FXFzWxd1qjJSyzuMEtG9s/UeYffzNUC3D8\n1S8G+MmHlyAAX3x8nIGM+3+wcbDIl65uJxk4e7bOlQ0y7SER3XZo9oteCe4bnA1dflpDEumKxfy4\ngt+nkq+KRd61fZjH9k8B8Ot9Wf7minZWtYFsu4atZolMaH4cBAQczmkV8UvHZqPnxHz4ZLFWOrq2\nI0b/dLFWXSFj4/f7EEUB23bQTJuALDGYN3l2RKNgup/nyDb4y71ZNh3OM78lTFZ3s+4dEYWEX6In\nIpBUZ36z8lE/X8OyAYG86ZDXbSRRoDkaQBQF9mcsfrgrz00r6iueXpyoHHPsOd4eHmeOyaJJtmIx\nJ64gCQL/vmWa0ZLO5IE058yLEQso4DhcvzTGTavi/GRPgZFZy1IQBabSZUoVN0jXlgygiQqa4aDK\nIgGfSDqjgwYhRcCwqWubAVf7YiSrEVVlgqrIgpjM/iyMzxo3lvOq5jxeImeP9ejhcZrctWWyJuxj\n2Q4/e37iJTve1yxvYN28KLmKyfbhCv8wkAHcMOkLVXG14xELyvzHTUsYSms0hBViAZnORoPvb4fR\nrEZHVObj5zW97O/2eqIjrpIpl2rH7bGTZ5qHcjoV06En4TupAEpLWKYlXL/1DWe1uqqCVMmkqNnY\nUHO6AUqGTX9KI9lx8q3zycNFBjIG85M+1ne+/ud6NXqldGcVi5MK4KqeV6wZw3LXaL72bwfYNlJi\nVdvM7ztvyrVicweBvgzcvm2UhqDMB1YlaAjKbBkscPuWScSKxbahDEtbo3TE/Fy/vJWJXI6VTSp3\n9xXJVOeMi6KAIgnIEvzXjjy2A+0xN2B2pJ3HJ0uUbMhWy+QdYCCro1kKJUtmfRO1qRGmZbJzvERL\nWCXmV9gz7WbaA4rIhQuSTJdMJgtG7Tv1pY1jer5bwzL9ab3u2MPD48zw5KEC392SIqBIdEYVzusM\nsnO8gCAIWA48P5ClLerjmp4w20dK7J/WCAXq7QNVFklE/fhlnZ64wvy2KNMzy7oW9AOYKug4iAR8\nEv7qxBTHcTCq7S9XzVHImAKHCg5RVWaiaNb2noVxb+17vDS8X4zHWYlu2vRNlokHZNrj9WIZsaOy\nmUcfny4NYYWGsMJkqf7x5lMolftkkZ5ZStzzEwq3vOkE8t6/RbYO5vn7ewbIVix+Z00jn7is81V9\nv7+5posv/WaI8ZzBFb1xrjjJmK+f7spwT7X0c1mznz9b33TaWXCAFe0hon6JXMUNjqzsCBHxS9iO\nQ0AWKFeVVSUB2iLKyV6Kh/oLDJYF4pEgL6Y0dKvIBk/AzeN1il+yCNllpioi7VEfk4WZwGFErQ/I\nOIjIkoDjuEHLwZzBaMFktGDyneen+eCqBP/nl4fQLQefT+JHW4cRhBG6EgHaku6ed21UqZaCz4TC\nVFnk/n05GqIBJFEgWzZr+3JRt1B9bv/5bATgYKrC8tYQmiWgiJDXLf5p4xQ5zTWoO+J+GsK+2jxz\nBOEY4cqYemyVx+8ujWHajtvj3aByZU99+auHh8fL544dGc7pinFRdxJBEBjP1RtRjgPnt6pkKia3\nbU8DEPFrzG+N1M6RBTinLcCG7ibawjJTJYt7+mdS4iVtpvXPsECWoFydeOJXxNq/28IywyWHrZM6\nPkmkOSyzIOknq1kUKiZx2ct4e7w0PMfb46yjpFt88sd97B0vIwrwF1d28baVDbW/f2RDB/vGi+wY\nLrKoJcgnLu96Re93xbJmPnhRO3dtnSAZUvj7dy54pV/hf4S/+flB0iW3xOoHmyaIxIJcvzxOxPfq\nZEPb4ypf/935pzyvbNg1pxtg50SFnRMVVr4EwaOGkMK/vGcB9+1MEfJJ3LDa/T3cuzPN2HSZYFBB\nEOC8rhAdpwicZG2J7urc8ERQYSJTPu3P4eHxWiOr2dy2O0dGcwiEVdZ0WOweK5OvWHx34wSm6fDu\n1Q1otoQlSojVjLdp2zzcN1l7nbGCyeGMVtNXiMf9tZaZwXQZRZFIhH3olkNElShV1Y8EIOgTMWwH\ny7aRRIm8ZlHSLSRRQBAEZFGgqJmUdYuAzw2Y5coGjWGVomZh4yNvCGwaLtScboDxnEZD2OfOJ69+\nloBPoiWiYJoWIUXk7QuODZoFFJEPrUm+Ktfbw+Ns5oh+zRGnG6AlGqQ76edgynWcr1sUwZAUHtib\nrj0vXzEZTZdIRvzYtkNJt1i3KEpfUWBX1mBeWOSt3X4GsiYhRWDbqE5JE5kbU+hLG7Uwn27aXNQV\npKBZSCJc1Bng5wcqOEDFtBnM6HREFRzLZtdInqgQYF3Hb/kiebyu8Rxvj7OO3+xOs3fcdYZsB/7l\nsZE6xzsRUvjeB5dhWjay9MqcSttx+PHzo0Qifv7zD1fQGX99jqIoaVbN6T5CX0rnoSGdt85VjxEf\n+m0iCK5xPjvfNbtVK6tZPFUVi7q4009UPf4orK6EykcurleL3z5SxLYdCgW3rHQqd/JsN0BAqX/9\neNDbZj1evzw+VGaqZCEKApoo0JwM8XRfDlkSCQRkbt+e4m3LEliiwuz53LplM13Ua8bzytYA8xv8\nRFTXcT5ap8KybeZFJUKKwNyYq4ehmw4+WUCyHKI+kfcuS4Ls52CmSKpsoJkO2WqVStSvcGi6jCKJ\nWLZD0CfRGHZniD8zYtAVC1C264NmouDuG5LoCscdaVHpbVC5ouvkLSseHh5nnp+8mCZXNo7ZH35/\nZYKxskNQMCnbAr85VEFVJCjP2CUl3cZX7elOBGUKlsBoTidVstg/BaubFK6Y4wbFz29T2Tde5OYf\n9xGOBZjX5bYTtkUVxssO5aqOxKND9eK2DvCb3dOY1Rmbc+Ontgk8PGbjWYQeZx1HG1Mnqkg+ldNt\n2Q6jWY14QCbsl3lhvMKBtM7cmMI5bQFsx+GP7ujn8LTr9N2xbYrPvKmNnkY/ncnXlxDP7Zsn6o5D\nfpk5LWHKlttX2RQQ2DRSZsuYRkwVuW5h+CVlwjXT5skDOUQBLp4fRXkJAQ+/LPKe5XF+vCODA6zr\nCLK0yb256pbDv23N1sYN7ZjU+NS6xGkHCnoa/GwcKMwcVzPZJ6KkW2zpT3Px0pnWgO6IZ7x7vD6x\nbIenBvJMV4NuTVE/lk9BlkUSszLWD/UXWNspgG8msPjMoXTday1tVEmGFL58fTc/3TbFhOYwWnbX\nZdwvcX1vlNXNrnDktd0BHhi00C2HeYkACxvCPLF3BEn2899bhijpFp1JPx1xd5/NazY+WSTokyho\n7vixxrDCRE5j8yFXpDHsk3j3ynYWJAv0pTR8ksAHV8UJqDLTZZvmoIhhu/eDeRHJc7o9PP4H2DpS\nIl82eaY/xQU9blVJVLFpC4ksbouQz+d5ohpIb4r6sR3QDZMLO4PoiPRl3L1KlQQ00yY1a6ThtkmD\n1c0KDX43OP7xH+yloNlMFw0Gx/I0NQQpNoXoneu+r18WsAShLrDv2A5LmkMUNINzWlUu7PLayDxe\nGp7j7XFWYVg2xUKFlrDMeMFEEuFPLutgIGeyK2Xik2Bds4+YenLHr6Rb/MmP9rFjpEhAEbnp0k6e\nm57Z4Au6zc7hAoOpmZ4i3XT4zJ37KBUqfPqt8/nAJa9uj/SZZKoqNiRLAucvaWJRV4yQX0YWIKwI\n7Evp/HTvjIOa020+subEPdmzMSybT991kN3VKoRzu0J84e3z6gxfx3HI6O7NL+HjmGj4NQujnN8Z\nRLMcWkIygiDw/GiZFyY0JksmluUwPFFgr2khVDT+7NLW0+oBv3FNI7rpsHOsxOLmAO8/9+TCdhN5\ng99sHydV0GlNBDg4XuCjFzRBXGG6ZPKNp8bZMVRAlQTevbqBd86qtPDweK2xeaRcc7oBpvIVVjXK\nzG0MkJ81O3fTcImQT2DbRJZV7TEyZYNH+6bq1unDAyXmNkZpiwf4xIYOhgoWIzkNvwjLm/08cbjE\nX2xPoUoC718Z5+K59Wu0KeLnu5sOk9Pcz7N/vEhElZElkflJP8mgjO0EKBo2ogAVw2b/xExvaEG3\n6Jsu8LFzk5QMi4AsoFYFlubHXq0r6OHhcTqYtsN/bUuTMiES8vHw3gl2DGe5bmmc1QsizL7lL04q\nbB7XMWyB1niAi9pVLupQMWyH50YqlE2H1c0+dmSdY99o1kOFWW0ntu2gigJdDQFEwa2GVBWxZoek\nizq2A9mywZyowl9e1PhqXQqPNzie4+1xVvFn39/BQzvdcTjNcT/f/fBqElE/dx+s1PbjjKbxrgUn\nz0j/YtskO0ZcNdyyYfODjaMsWjST5dw0WOTR3SlEUcCyZnZ6y7JxHPjq/f28d307ivz6UIu+ammS\n+3akMCyHAyN5zu2J0xQQWJFUCMgCI/n6MvSRgnmCVzqW/ZOVmtMN8PxgkaGMzpzETPZsR8ZhvOxe\nxyY/rEyIxzjfiVkieJtGyty+c6bve3iiQLlqsD/Rn2N5a4C39J46MPDcwSxjkwVWN/m58bxm5FNk\nylujPpojCs8fSANpYgGJuclOcprNPzw2wd7BHKZpkwdufXKMZEjmUs/q93iNYtj1hqttO9imxVWL\novx098z6shyHlpDClqEUW4bcDHNAEZm19aHIMhlDImuI7JrMk9XdMV5LkzITJYu797mK6RXT4btb\n0/zFpTHiYVcxXTdt8qZRc7qPUDZskorIZFEnVTZIBCQCVVXigE86ppop7nPwSeCTjt9u8mrwWF+W\nW58ax7QdPrCuiXcs93rDPTyO5sH+As8Mu4EyWRZpjKpc0BHgzT0h7tw+zZ6JMqu64rytN0RzUOID\nS0MczJrEVJGFCbfcWxEFLuycsd0uDTjkyjL70u6+sbxBoSEws/abIgqTeTepcO6iBq5Z24EgCEgC\nTBf0WuNMxbBIFWck0Ufys+TRPTxeIp7j7XHWUNTMmtMNMJGp8Ll7+nnzOe04/pkS4rzhYFgOykmc\nLMM6KpLq1B+HFIGKZhGNqpTLJrZdzcKU6mdyv15Y3RXm2+9fxJbDBbob/ZzfXa/i2x1XalFigPkv\noe8pqkp1pVyiAOFZZepFY8bpBpisQN6Ak2mc7ZmqH9lmWvXKo7PHBZ2IzQez/PH3d9W+0+HpMv/7\nbceKvdmOQ06zifhE/IrI197Vw22bJrBsuHFtE7GAzDOjGvmKiXXU59gzXvYcb4/XLOe2BfjZ7iz5\n6qxa27K5aG6EpwdyVComPp+EZdnsOFwm3xvnfSsTPNSfJ6pK3Lgizl17cuyf1mgKq2xY4FZ3OAhI\nogS4r7knbaLG6/faiukQlcoYFZuyCWHJwLChOexjoqq3oEjuaKGi4S5QTbfwiQ4PbhsjWzKY1xxi\nz4FpuuYkURQJQzfZ0HVyYcQzTb5i8eVHR2v9oN9+epw1HaG6oKKHx9lESbf50QspJosmy1sC+BQR\nvyzy1FC9cnlr1Mfvn9PIj7dO8d+bXbvt2UMFHu8L8LmrO2kISHVO9PEQBIG39QSYqJabNwfrz7/t\nQ8v47M/6mC4aXLWmrRbMtxy4oN1P1Cfw+IhOxC8jCDNm3qIGb/16vHw8x9vjrMGvSEQDMrlZYhz9\nKZ2fbB7n+ovmcKSWqdEvntTpBrhuZSN3b5tiKKMhCfCRDZ00tSQBgWxZY6pQobc7wWS6QhkHURRI\nCiZDuG9z81t6XjfZ7iMsagmyqOX4M6nnxhRuWhFj23iFmCpy+bzT73vqTKh85OJWvrtxHFGAT1zS\nRjI047gfryL8VC3abWGZbeMzzveS1iAvDLml8IoksH7uqcf/bDyQYXbC76m+zDHnpMomX356ivGi\nSWNQ4s/WN9EeU/nMlfVK+D5JoCGqMjJZxDRnnO8lJ7ieHh6vBQKKyBeuaOXXfXlKhs2l88K0RRS+\nN1Yml68Pbm0dr/DRcxJcszBae+wzF7kKwweKPkzHXbS241A2Z+rUHdth06SNKoto1bWxssVPyB8A\nQSBquu/TERJZ0h4hki5j2m6GfdYobRwH7ts8ynTOPX8yq1HIVRidOIwkuarl2tXtKL+9ZDcF3ao5\n3eAGF7NlEzzH2+Ms5dZnJ9k8XEIQoD9n1kq5ZUkkpEpEAz5My2ZJg2sDzK6GA7dC7taNE3xmgyuE\nqpk2d+zIMJY3WNsR5LKeCEdztMMNcOfODE8dLtLSEeevzkmwJyegz7rfSyI0B0R+p0fFcmCgXeH5\n0TJRVeLybq+v2+Pl4zneHmcNkijw//5gBX9z5x7Gsxq+UACfqlAomwwOpbliRROKKLCq8dTZ2mRI\n4b8+tJQ9Y0Wawj4KUpCy5d5AQqrMvmkNxSfT3hJmMQHe0RtldUeIvrEiYb9MR/LkIl2/bUq6RVaz\nTzmjGuDu7VPsnyhzzpwwly1O1B5f3OBjccPLyyi9a3UjN6xqQODY/u2ALNATEejPu3fFeWGBkHJy\nz/vyeSGKhsOBtE5nVOH6xRGePZRnz2SF+Y1+ehpOff17moJHHR/bfvCLPTnGi24gZ6pk8ZOdGT5+\n3rG9XysbFXa3hxEFgYMjOUzLoSkRYFGr53h7vLZRZZG39dZXZayfG+G5wWLtWJYE4ieYFiCKAkmp\nwneeS1HULXyqjOqXaQwr+ERXnBFgXmOQQsVkQ6fK+vmtoFSdUzkAlQy9CRlVEkglZZoDIjsmdSYn\n9JoauSKL5Er1lSySLGLoYFkOqk8keIp945VgOw5bxjQKhs3yRpXGoDuWbHVHkG3VEtrupMqi5teX\nsKaHx5lk70SZSsXAr8o1pztX0NA1i5ZEgFiDgigIvDhV4byUhv+ofUUUBQ6kZoJ+33t+micOuXvR\n1tEyQUXk/K4QtuPwy74Ce6Y02sIy71oSxV9NdmweKfFAn9vaUjBsvvjkNA5uln1VZ4TWoMScsHuu\nIAjIAixI+liQ/O1WzHi8MfEcb4+zinXzE/zqs+u57bkJvrdxHHA38rGiyY82jXHBnDDrmk+vBy+k\nSpw7183ubE3V/y0y62bhVyS+8vgYEVXkTy9pe8043Y7jMFmy+PqmFKmqgFJQEfj8ZS0EleNn47//\nzBj/+vgoAD/bOsXfXedw1dIz07N4MhXhnohIZ9A10E9HkVwSBd65uD7yPVy0eHK4wpPDFTYOlfn4\neY0kTlKqdu2qJkYzFR7enaIjofLZa3uOOUc7quXgiQM5VNPkf13YOvO+eZN0xeJNnSoHpjUWVRVT\nfSKv2gx0D4+Xi2k7SEJ9AMyyHR48VCarOaxoUrh6SZzJksndO9I4AlyyKMEFnUF2TGnsmDIpGg6L\nkjKXdPhxHIfP/3qQvilXaFIUBdqbwwimyqVzg3x71xSm5TCnKURrIsCqthCSMisjLEogymAbdEcl\n1IJDVrO5oM2HaTvsnDaQRQEHaEoEGJl0jXABSIZ8jJR0RFHgM2+d96oqld+1r8jWapXNk4MVPnZO\njIaAxOeu6eKxA26w7dL50Zqgm4fH2UhZM7Ftp9Z2lS9oTKXcrHauqGNaNr1zE0RDPn62M83utEHA\nL6MbFqIooCgiS2YFr/ZP11fe7J/W6G3ys2m4xIMH3b1gKG9iWA7ZislAxiA6SzxXFsVam9tYTmd8\n1zRvXRhGaoni4fFq4DneHmcV4wWD6ZLJ9asaEIGdYyVSBkxWe4B+sStDV8zHhuOUK43nDXTLpjPm\nRj0fGygwkjdZ3RogHgyQ0mZKKUeq5Y7lisEz1cxQXrP4wsPD/Pd7F7wq322qYjNRton5BDpCJ6+n\nLBoOj4/qPH+4wPB0CVWVEUWRkuFw+44MH1pzfGd6Y3/umOMz5XifilcyK7yo29y/P1873jet8aVn\n0lwyJ8i180+cdf6DSzq5/oJO/BKE5GPf/8qeMC+MV9AtB9t28Psknh7TWXKoyIVzQ2weq3BvXwkH\nCMoC1/QEeXZUQwCu6QmeMMDh4fHbxnEcfr6vwLYJHb8s8K7FYRYkFJ4arvDoYJkjHRK7pjWMTIGQ\nIvCmpQ1kLBHbJ/KrwxWCijuSa7pis3FEJ6GKdATFmtMNrkAbls07eyN86eER0tWg367DGboTPsKy\n49aNH3GSHQcc9803j2n85pD7WiFF4A+WhelNyNy+t4QoCiyam2BeQwDDsFnSGaW7KUTfWJ5re/w0\nn0wU4hWimTaP9uWwHIdY2EcFkf0pnYaOAIok8uZFpzfhwcPjjY5pzWgy5Is6mm7V/T1VbWEplE0q\nJQtBEAiH3fJzy7Rp9ot87PwmbMdBt2Bhg8rYLDHX4ZzBJ+4dQgACfrkmtrhjokK24r5XQbeRBAHr\nKG0ecNtB7usrsLLFz5yYl+H2OPN4jrfHWcOThwr866YpbAdawjK3XN7G761r5qN3DdSdN3ocxco7\nX5jmB1umAbhgbpiepgB373Gd0Pv35fj0xU34RRlJlmgPi0y3q6QrMoYm85PJmZLMbNnt+ZNPY5TV\n0fSnNO7bn0MWBW5YEqM5PFMWPlG2eWLUqEVuVyQdFsdPvLx3pE0OTpa455F9aLqFqsqsXtGOrMhU\nzOOM4Kgyr8HP9qFi3TFUs+dlG1kUSPpPz5k0LIcDKY2wT6wFM468Vl/OJqM7JFWBnsixCuYvFVGg\nTsANXLt+y4TO6mYfHZFjr1XFctiagqquFIujDq2B+s8xP6ny8XMTfOYXh+jpihGsivTde7DE0hY/\nG4dn1PJLpkNOt/nkuZ6YmsdrjxfGSmydcDNOubLFt7ekeGdvlEcHNWbJEiAIAi+OlihrJh1NJst7\nGjBtyGs2tuNW+wRkG8uBA1mTxQk/8YBEpuwavZIAn76okbawXKu0AXdtLoxW17qWA1/YXaRGCRz3\nuc+MzmS3iobDjimduWGRwakCfp9Mc9TPsrkJOmMzGfOLuiM0R1+9TLdlO3zuwRFGqqMjMwWNea1R\nYv7fYjO5h8frhHVzQjx2wA2Cl8sG1/bG+PGWGdHbeMhHpqAxOFnEr87cl2VJxNAtVjb7SVVsftZX\npmw6dMSCvHWhxFjRoDOqcG91OoIDlCpmbSSYelTgvj3mpzWmki4ZHE5XOJqScWI7yMPjleA53h5n\nDXfumBHLGi+YfO/pURpVWJj0MV1yS50kEc7tqM+AFnWLH1adboBnDhUYK81YopbtcPP3tnF4NIcg\nCPzeVYu45dp5CIJAqmTy0L5MLatzcXfkZTndmbLJF58Yp1x1ivdOVfinqztqrzVUsGoOnuM4HMpb\nJ3W8LRse3zpSizZrmsmhw2l6F7XwjsXHZvuP8MeXdaBbDvvHS6yZE+F957VgOw4PDGgcqg72PadZ\n4fzWYyPFqaLB390zwP6JMmu6whBUGci46kjvXZng7UvcrNC+rM3+nHt9x8sOAtATfflG7BMH83zv\nuSkcAQRJwgEiAaUWCT9RnGG0PON0AxwuQutx2jOXtwY5tzOE5p8JhJg2jBbMY2726nGy5h4erwW2\njxWxHYdMSa+p996xI0NDRAVmfreOM1MmGg/Xi4TppoOlOEiigCwIjBQdnhs3+dzVXXznmXHKhs3v\nrmpgQWMAx3FoifoYz7l7gE8SZtS+bQMqaRzH4cH+AvtTBguSKkd3Zpi2w0TZAsumUDYwTRvTtIir\nEhG/RMwn0B159dacbjk8sC/L3skZw10zbHrjEktept6Fh8cbFdtx+PiFrXQn/UwXDS7ujtLb7Cer\nw9ahAomIjzU9SaJ+t43PsGye7c8wltWwLJuoT+S9a5v4adXpBhguWFzaGeL9rSovjpdrjvcR1rX5\nmRf3IQH/sS1de7y3NUx3g2vrKVaZB/tyHMwYCALMiSnMT3jr1+PVwXO8Pc4aZrfWpacL/Mc2tzFb\nlgQ+9paFBAIK53WFWNhY34PtOPWZUoCYX2KqmsHRihUCsRDNjsjkeJYfP7Sf91/UycKEQjIo8683\nLuWBF0eJqBKXLXh5fUODOaN2owFXyCtVNmmuqn8Hqg5drmIxlNGwHahUTN6+0FXfLJsOe6qzLHsT\nMgtjEs5RZVaOA+0RmY5qSeYLY2V++GIa23b43eVx1nWECPok/uatc+ueN5S3ak43wJYJg1WNCv6j\nnMyvPzTElsOusvjj+7NEYyrxuOvJ3rEjzXWLY4iiQFqv/1wpzeHY7urTI10y+ebT47WMnShYXLui\nkcHq5JIFcZmuyPGd+kzZZPYWWdQsTrRl/u01nfx/T6UomUf60KE1LHPdghA/2JmnYDh0x2TOb3tt\n9Pd7eBzNtqECuu3UTUbULYd0QSeT15jTGsEBBkZy6NUFVazUVwfJEmRKJr5Z0uEHcyYX9gb54nX1\n+4Zhw+UrWtg5mMWwHBa1hukrOLyYqdAcEDm3SeFXfXl++IJrLD99KE9vW5jORID940UGJwpsP2DT\nmgzRnAgiWQaqJHBDb4jeplfftLEdhy8/PcGeyfpsmQAcnirxl7/Mc2l3hGuXJo7/Ah4eZxF9GZPH\nhjUsB5Y1hLhhxUzQbkNvksZm1zbySUJt4osiiZw7L8Zvdk6R1y1yFgxkdLT66nT60zrntar0Nqos\na/azZ7KC5cDlPWE+sHJm/cX9EgMZHdWvEgm6tocqOixPqqxubuTFiQqGBStb1FNOtvHweLl4jrfH\nWcMHVif52sZJNNOhnJuZGWlaDoOjWf7+dxYf93lhVeI9q5L8eLvrqK/tDPHx9U38x9YUwwUTJ+Y6\nU7btsKdvioG+kdpYHIDmiI/rV7yyPuiOqOvIVkyHkE/ivWs6yNp+9JJNW8AgJNpMFXQmCmYtq79l\nQmdJo4/umMz9hzRyVYf2YM7i7d0qf/bmOXzqtjxl3UKWRebPS9YEybYMFvjqs1O1PstbN03xT1er\nNAaP3TKOVwV+vFvWxFEl/NasQIIkCLUnJXwCU5WZvyXUl38DzGlWXZms7cBFHX6CqozlQGdEOqHg\n0o+3TrJqTpz2RICybnH388OsTHTQcJxroEgin1yb4FcHi0wVTWRJ4PHDJa7uCfHp8+Lolpft9nht\no0gCE0V3jc5ur24PCuzqLzI6VYRqEFJRREAgHPShmW6LSUCG8xskpjXon+lGIagIPDxiIAlwr2LM\ndgAAIABJREFUToNUExRURGgMSJzT7RrGmmkzWnQXa0az8IkCL4zXCydNF3Q6EgH2D2Vre+zAWI7e\nOQniQZVPX3D8fdayHe7ZnWEop3NuR4j1c049TvBUjBVM9k5pCIKA3y+jaSZBRSSmijXF9z0TFRrD\nCuefgffz8Hi9YtlOzekG2JkyGc0bXNMdJOwTWZyQ6M+ZlM1jx4fKokAi7OPGtR34RIGibbOu1eLX\nA2UEQcAwbe7flaUnKpE2JTqao3Q1R/Fjsi9l8E/PpHjHojCLkj6WNftZ1uzHcWBKs8npNhuHSjwx\nYLMgoXD1PP+rKsDo4QGe4+1xFrGqLci/XNdFTrP4x7tLPJidyVQ0Rk5eVvTeNY1c2hOlYto0BGUm\nCwYfW9fAt7ZkGMq7mWRRFGhuDNEQ7GTBGS5TSgZk/uLiZn65N8eargbaYgG3h8mSmNYcfLLJ4bSG\nItX3Q5dNh6zu1JxugJzukNEc2hIq//CeJWBZtCUDzEn4CftEdNPmCw8N44/MZGdN251ZfTzHuz0k\nsiAm0Zd1w9DntyrHdTKvWprkhWHXIJUEWNEVZqRsIwnwwXMaaje8hTHXMM/oDg3VHu+Xy5Qh8s51\nHeimzXMHUoRlmJc4vWh2rmzyo6cHCakSFcPGsh3yFeu4jjdAQ1DivDY//7olgwMcSBtMly0+tCqO\n6u20Hq9xPnBuM195fAjNopb1ViX40DlJtg9kmawKGPn9MoGAgiwKRMMqummjA5YFi5ujKKJDZMKg\nP2cRVgQkWeJIK/cTYxbXdIIoijw3XObuF9M4CFy+KIkqwmw3O6vbhAP14w2jAYWKbtUFNh0HdNPG\ndk7cjvJfW6Z5YF8WgMcPFlAuFVjbefJZvBXT5rGBApYNl8wN1U2qAJjIamTSZSRFJBJW8ck+vnBl\nG5+553DdeQdTGufPCTNdNPjFzjQ+SeRty+LE/N6m4HF2YDkwewBIumTQXzLZMlrh/csjLG1UeXu3\n27vtE2HjhIldjcSP5wzWdERpDM7YVEnFYvfQGKoska8YmJbD1gmdQMDNottAxhTJVXvFbtuZ56/X\nJ2t2iSBAkx8eOVRmsFqtt21CpzEgsq61vn3Gw+NM4+38HmcVQZ9I0CfymevmM5HT2Dta5Pz5cf5w\nw5xTPrcj5mPPRJmP3NlPUbdJBmVWd9er1RqmzUcu6TimzPpMsKjBz6IL/QyVFEqzSq0sR6AtJDM/\nKjFQcNU6ARzLYmFCBgQkYebGJwmwfaTIt5+dwMEdvbW6y+aja90bTq5ikS2ZSH4LpVoyGlPFEyp8\nCoLAlXP9rK3YyCcZkXX9mkZaY4o7A7wrwrL2IBNFk4AsEp0lRCQKAovjr1yYaLRosSfjjiDx+yQu\n7W3knd2+0y4hu35ZnFufrFRLzGF+g0pX/OQBlYGsUdeWcDBzrFCfh8drkWUtIT5xboLP3HMIv9+H\nIEJ3RCIZVPj++xex8WCeTSMlto+VCfkkFrYEmXbbsxEF6G0J8fi4iQAsjctc2K6yI2UyUJhZETZQ\nMsG0LP5t8xQCAh9Y10nGMCjPLk0B2kMS/rlRSobNSFYj4pdZ0BJCcKA1ojBWraBRZBFVEbh2wYmn\nE+wYL9UdvzhWPqnjbdkO//jEBAfS7hd8pD/Pu5fFaAkrzEmo7B4rcfMdfejVTdVusPjjN3XQGlZY\n1KiycaCAJAlIosCK1gD7pyr8xT2HqLbG89TBPN+4ft4ZK2fN6za/PlQhrdksiCts6PBxf1+eF8Yr\ntIUV3rM8hiwK7J6sIIkCSxrVVyxY6XH2Ylg2FcMmcprBI58k0JuQ2ZM2qRh2TfPGBn60K8/fXexD\nlQTaqtNYLmx2uGNfGUkScQSB1mh9i5aJgG05pDW99lgiIDO76WP2z1u3HJ46XODyo6bVZDX7pMce\nHq8GnuPt8YZk/7TGL/a6quPX90aZn1SxHYevPDzMI/uzNIUVbnlXLwuajqOWdRJ+uGWKYjWKmiqZ\nPLFrisaGEAG/TK6gUyzpPDNp09tq0hZ+dZZXVLEoWW6pJzhEFdcx/F9rEtz14jTPDhWJB2Q+tLaB\nQLVX6rJOH89PuE7h2maFLz86iQOEAwoNUT/TGnz/xRx/vDZB2XYI+mXS6TKBgIIqi/zlFV34TzF/\nNnEaaubre2Ks75lR9W4JKyc5+5VROaoPzBGElzRD98oFUbpiCo8eyNEW8XHN4hjSKYTxuqL132dO\n9NX7fh4eZ4KpgsF4wWCJHOBnO9MgiaSyZWzbIZ2e+b2v746wvnvGcLVsm//akWesaDEnrqJWR+M5\nwOYJg5/typCq2Jw3LzarfNNhz5SOJUms7Iqi6w57x/MkEyqyNDOOcWnSx8K4xEjJ4fwetxTdtGxa\nVIeOsMh1c7u4e2eanGazqCXIue2BE1aiAMyNqwxlZ4Jg805RkTRZMjmQ1mtCcvvHKnx+pIAAfGR9\nM4cmyzWnG0A2La7oifDw3gyP7Epj2g5Rv8TNV3WyrDXIp+85XHO6AYayOmN5na74mcmuPTBQYX9K\nx3Jc4dDBdIXHDroiU30pHd2ySZctdk+5NQUXdAb5yNqG476WZTvctyfDcFZnbWeItV1emfzZgOM4\n/HJ3hr0TZXqbAyfUJtjYn+OWXx6ibNhsWBjjlmvnnvK+CHBph0pSFbhzd734mWHDV56e5CNrG2qT\nAGJ+CQsBq7rGshWT5Kzql3RR5w/Pa+AH29JUDJtV7UGumx/kgcM6haoaebEyMzFBNy1u35FhTVuA\nRGBmn+hNKjw57K4JUYDFCe9+7fHq4zneHm848prFNzZN1cZiff3ZKb5wRRtPHMjyqz0ZAIazOv/0\n0DD/duMrm6mdLehMZ2cKJONRlYrl8NRwhXctfnUMlqhiIws6mi0SkGz80owBeP2KBq5fcexzOsMS\nneGZLLIqi0iiQFM8UMt8DBUsshWLLaMac9ujTGcq2I7D4rbQKQ1Ew3J4ckQjq9n0JhV6k6d3A8uU\nTX6xI41lO7x9WYLGM+iIt4VEgjK1MtfuqHhcA2Hz4TyDaZ1zukLMTdZH1nubAvS+hODM4gYf710a\nYcu4RlwVuW6hZ7R6vHbZdLjA/31wGMNy8MmD+Hwyy+clURV3dE9qOn/C50qiyIdWukG0AzmLPdkZ\nz/LgdJmRnLvwXhwusLA5gCyK7BkvYrSFCCgCi1vDVAyL+7aMcXWiFaDqfAtEAyGKpkF70CIkO5Qs\niCtiTUQSRD6wtomi4WDYDlHfyQ3/D69rxCcJjOQM1nQEuWz+yUUuIz4JnyRQ0m003XJnj+MGFX64\ndZobltSPBGypClL+25OjmNVzcxWLkWrG3DxKyFIWqXMAXilDebNW0WQ7MFiojzr2pTQmijOPPTNU\n4neWxGgMuZ+hbNjsmNSI+ASe7s9x9y73PvmrvVn+5s0dnHuKsnyP1z93vZjie5smAXikL4dpO7xj\n+bGaCV/89SBlw13rj+3P8vDeDFcuOT0BweWNPoyFIX66t1CbJlLRTXaldf57e4pPnt8EuJovs6v0\nRvIammHR2xjg8f4MO8YKyCJ84oImljf7eehgkS8/M03EJ3JFT5iuqMJoTuBfNpewHYdi2U06bBkp\nc8X8meDhJZ1+kn6RVMWmJyYfd6yoh8eZxvuVebzhmCpZdbOoy6bDdNmslTcd4ejj0+F95zSyd6JM\nUXd7veMyHE7NON6+6ogqw3bYMqHTFpKInHg618smKDsEsU594gm4YXmCrz4xxsBoDp8i0ZIMIksi\nBcMh5BORJJHm6qiN03GG7ztYZnfKvZ57UiY3LhaYFzv59mJYNp+99zCD1ZFij/fnuPV3ugn6zsz8\nW78k8JY5KoMFyx1VFD422/2TbVP821NjgCt+9uXre1jc/NKqII5mbXuAte2v7DU8fvuM53W+8/Q4\nuYrF25YnuGT+G3/e+m2bJzGq1q1uOqzoiREN+WiJuBMZmB9nV9pk56EsRc3izYtjdB4nCNcVEtmZ\nNpFEEcdxalVBAFNFA3HcJm9APCgTmKV47lckEGDXYI6lXa4zPCcawCdJDJcdRgsaqxsk2gKuY23a\nDtmKRcwvcSBnsXXK3XNaAyKXtivHCCPplsO9+3JMFE3WdIb5WOeJy9Fno8rwe6sbeXwgz76xIro+\ns9f6JIF3rGpk30SZx/dn6IirfPZqt1XJrvevKZk2tuPQFA+QswQyOQ3Hcfjo+mbC6kvf5yzb4ZH9\nWYq6xSXzoySD7t4clAUKM1W3RI7aQ7sTKhPFmXJ7AfBVgxiHsgY/3F1EkUUMy2bf4RllPAfYNlL0\nHO+zgBdH69sxto+Ujut4l/R6u6NkvLTy7DWtfhY1+PjOljR7pipUqq83Way3x9qDIocKruBEumSS\nymukixV2jLm/T9OG3xwooEgC9/a501JSFZu79+b564sbCTeoNKjQn56pdPnhi2nWtAdIzgp6LWv0\nxoZ5/HbxHG+PNxytYZm4XyJTrTVOBiRaQjKXzI/y4y2TNaPwmtMc8/L4wTy7Jyq8cDhPsWLwjiUJ\nzuuO0hqR+ZPb9wOuIaP4JAzLRtMM9k3DcNFBFATeI6t0vEb0OizH4cnBCvfuzdcM7rJmMpkucV53\nnJaQRFMwyMGMzp4pnYgq0uAX2DlRZtlJHNLBvHXUsXlKx3ska9ScboDJgslASmNp6+kZx6eDXxZY\neJJ55g/smpnrqZkOj+zLvGLH2+P1yd/ed5iBahDtxdEi34j6WPgSW1FebxxdARJQZVRZcJ3uKgcL\nDndsT1GomPxyd5pb39VD01HBOJ8ksLZB4leHNTTLIeiTyJaropMCNAUlKkUHzXQwbQe5+r6yAJf1\nRPnJ9ikumtfA/KYQsugGyHTbomTBi2mLi1tkxgoGX9k4Raps0RySWNQedR13YKxsM1K066p6AG57\nIUvGEpAlhXsPFPHLAqtbZ/5PDdthX8bCsh16YhJhRaRk2Nx7sEIyrHLRwgaWdUS545lBKrqNIgp8\nZH0Lkijw2avn1BzuI/R2RZjcncJx3CDsgZzJ5x4cYdoUCfoVgn73uq2fG0a3HHxH9XhbtsNwwcQv\nieSLGvmKxfKOELLkXpMvPTjEo31uC9WPt0xx67vnEw/IXDXPzw93F7Ed9150VXeQ81sVNg2XyJZN\nEj54c0+IB/uLCMCNy+NEVYkDKY0vPjmJaTv4FZF5TWEaE0HSxWztM51K18LjjcHRYen5DfVGi+M4\n7JiocOXyBu7dNgVAR9zHmxa+9ABlSBG5sjvE9pEikijgVyQKJvymv8CVPW6V2LpWlf59RcZzWk1n\nZbrkztk+UkASUASmy/W2R6piYTvuHvMHq5P83SNjtb/plsNk0axzvD08ftuckV/ft771LbZs2UIs\nFuOf//mfASgUCnzta19jcnKS5uZmbr75ZoJB16C+6667eOSRR5AkiZtuuolVq1YB0N/fz6233oph\nGKxZs4abbroJANM0+eY3v0l/fz+RSISbb76ZxsZGAB599FHuuusuAG644QY2bNgAwMTEBF//+tcp\nFAp0d3fzyU9+Ekk6M5k0j9c2AUXkMxc28esDeQQBrpofQZVFOuMq33r3Ap49lKc5onBh96lnav9k\nR5qf7nTL7hwHUnmTbz81Rm9rkDs2jbG7LkrsEG+LkCoZ+DSLTEmkIxlk62iZjnmvXu+QYdlsGy6R\n020WN/kRBIHBnMHcmEJbpP59f7Qrz2DBrut/Ajdj8uHVsZpB/OE1CZ4ZLHDrpmkOTMJ9e3N87LxG\n1ncdP/PRGpLoy5h1x6ciGZIJKGKtbM0nCTS/ij3fxyMRlDmU1uqOAR7el+E7T48hCPDRi9q4dMEb\nP/t5NmNaTs3pBjdz2T9VecM73h88r4m///UwZcMmGVKYzlWI+I9d40cc9IJm8+JoicuPY2y3h2W6\noxa7pg0ifgVFElEFh+vmB/jWcyl8fh+m7bBnvMScuIqhm6TTRW5a28hbFkUoGA6iKKBbNhsHM4wW\nNGJ+iXkJ1wG4a3eOVNXIniha+KfLxEMqNhDyiTgcu3dURNeZBpjTEGTjYKnmeDuOw0/2Fdk/peFX\nROYm/ZybFNg5pZGMzLSIxIMKC9tjTOQrfOWadiInGVHQEvcztyuGZdn4fO7MYE2zaG2cuaYC8PCI\ngSVYJFSBi1tchfiK5XD7rjwDWZO+gWn2HZwGYHVXhFt/fwkO1JxucIWg7tyRYV1XiNWtAT68MsJI\nwaQ1JNEakslpIv++aZJsxeKFEehJ+vh/b21HEgQC1Wty+45MrTS+YthM5zVUReKCOWHGCwbrukJc\n+TIcK4/XF/fvSvHY/iyyLCKKAqvag7xnTWPdOf/63BRPHnKzzRcsbeAt3WHWzo2wa6zErtEiy9pC\nnDfv9Mv7ljX7uWFRiAcOVRBFAcOGu/flGSmYLAg4+GWR9/eG+OLT5dpzbMedxZ0uWzQGJW5Y4uqu\n+OVCrcpxRbNaq3zpjPloDcuMVScyxP0SnVEvkOTxP8sZcbwvu+wy3vKWt/DNb36z9tjPf/5zVqxY\nwTve8Q5+/vOfc9ddd/G+972PoaEhNm7cyFe/+lWmp6f5/Oc/zze+8Q0EQeDf//3f+ehHP8qCBQv4\nwhe+wLZt21i9ejUPP/ww4XCYb3zjGzz99NPcdtttfOpTn6JQKPDTn/6UL33pSziOw2c/+1nWrVtH\nMBjkBz/4Addddx3r16/nO9/5Dg8//DBXXnnlmfi6Hq9xBlIaj/bniKoS1y2N45NEdo2V+NHzk0ii\nwB+c10x3g//ULwRsHp5Vnled16rrFodTFQamK3XnKorEvKYglyxIokgiQ+kyg1mD8AlUvk+H0ZJD\nWneIKAKdQY5Roi1oFp+44wB2QEUUBQQg4JOQJBFZgI+uTdLbqKJbDg8drjBZcQCBeFhlYtY4tbJh\ns3eywopZ2aBnh0o1hW4HeOpw8YSO93U9AR4ZrNR6vBeehkhJRJX426s6+I9Nk1i2w/vXNp3RHu/T\n4VNvaufvHxhkKKNx/rwI169qYCKv848PDdXEkL74myFWtIdqTrnHGw9ZEljaGmDXmGvkKZLAkjNY\nefFaZWV7iP9873xSJZOF7Umy+Ry/PFBGNyx81WzygdE82dJMuWbbSQzXpUmZ/RkT0wa/LNIWFIir\nEgPTZeY2S6iKRL5i8fDOKfoG3YBmtqDz11d3EQ8AlLhrf5G9U24lTK5i0RYUAblOzAwgHPDVekAL\nms1AWmMkC0sbVSKqhGU7tUyxq8LsMJzS+fbTY/zRha3sT+k8vD9Ty54VKiYbDxgkVIHzFwRrzwWo\nmBY+RSaonDygeNWCCDsnK+iWiACUyyaGaVMo6YSr45DmNqhYgvvaac3hjj155jWGQRBpjgU5nM3V\nnG6AbYN5Ht+X5oolSSKqW0kgSQJzOqK8MG3wwnSGy+YZ3NAbpTk48/kOTGtkZylM9qd0NNOhYdY5\n5lG18ZbtcF6bytsX1TtdJ2JPymBP2kKVYUOHj+BLEK/0eO3w7IBbqm1WJws4s6pSANJls+Z0Awzn\nTZriKhsP5vi/vxqsPf7X13RxZe/pVRIC3LdtErmhPrDzi+dG6TswiSyJXHFOG7HGKJOzxrh0xxT+\n/IJGGkNSzcH+1HlJtoxWCPlELuqcsWF8ksD/vrSF+/blsBy4ZkGE0Cuwxzw8zgRnxJLs7e1lcnKy\n7rHNmzdzyy23APCmN72JW265hfe9731s3ryZCy+8EEmSaG5upq2tjb6+PpqamiiXyyxY4IpdXXrp\npTz33HOsXr2a5557jne/+90AXHDBBXzve98DYPv27axcubKWSV+5ciXbtm3jwgsvZMeOHfzpn/4p\nABs2bODOO+/0HO+zgNGczl/dP1iLfu6ZqPCx9c381T0DlKol5rvHSvzn+xfVov7gZj8mSxaKJJCY\nNdqqJSxzaFY5tGXZ+GWBc7rCWJbNztESiuIaeWG/xAXdCZSqwdaZCBCQRa7oiWJVZm5ap/1dSg57\nc+73mKg47J+okMsU2bA4TqLa23f39ilyFkSrN0kH0EyboCRiOvDUYIneRpWnRjT6c+5oLXCIBH0s\n7oyTL+lkSjoV0+FrT03wnRvm1G5mR4v/NARObHT6ZYG3dL/07ODK9hBffeeZ7R/ULYe9abckrTeh\n1BkQAAdzJgezFhGfwJqm/5+99w6T6y7vvj+nn+ltm7Zq1btkFdtylYVtMNi4YlNSKA4EeCAxPCSE\nPOSlpD+hh7yQhJK8VNtUOwYD7say3CRZxepbtL1OL6e+f5zZ2Z3dteQiG8nM57p8WTM7Mzs7c875\n/b53+d7qHIO9ibxV5UBsOi7dk6Wa8H6N8+mr2vne06OkizZXrYzRHjtD+kNeYYKaRFCTUGURXZa4\ncXkQ13VJGi6CAMt9fo7362RLDtetjbOy8fnP8wa/RFxxOTRpYdkug2kXw7SI6RI9I1kCmozjuvQP\nTZu2HZw16mumCRh4bubgbZoPjZcwbBe/LMwJQt7XU6BgOtxzLMtHz0sQ1iRaAiLdaZuJ8sY9Fvax\nbzJPzrA5NFZipufZaNbAthxG0haulGRzh1cBtG8gw3jOZHFcQxIFnh3McyJlENclxvMWu8ZMCrbA\nxiadG1cE+ez2Jh7tynJ4JM8jY957H08WUWUBXVdIhKqPK01TUSQJRRSR/AJtMa2qnBaoOL6/7fwF\nfO3+E0TDelU/+87+PBe1+vhZ2RTt2lVRGoNylUFVSBUJadWi45plYf7tyXFs1xMpb1sdZHPzC7se\n96QtnhgpVzmZcFdXiVuWvrYrRF6rtMc1OA7hsEbQr2DIMrsH86xu8KFIAqpUbXYGXmDtgcPe8SYI\nEApp3HEgRSKssbH5hQUtbRfymRLh8jnhui49vRPIksh5WzogoJEsOYRUkUzJpmQ57Ow3Gcqa/M22\nxsrrNAZkrloyv5FpzCfzjvVze9Vr1Phd8YrtJFOpFNGoN+M4Go2SSnk9QxMTEyxbtqzyuHg8zsTE\nBJIkkUhMj7dIJBJMTExUnjP1M1EU8fv9ZLPZqvtnvlYmkyEYDCKWe8USiQSTk9O9nDVeu+wbKlQZ\nqz3dn6NnskjecPBpMq0NQRAEnhspsrHFWxwc1+W7+9LsHTUQgDcsDnBZh/ezd2+qw7RH6UuZBGRo\nbQpzxcoY7XEd24V4zMfU+NkbNySqxDzAec0afkUkU50cf0FMGtXZiJ6Uxdd/2cO3dwzyX+9chV+T\nyJsuzizH3Jn4y++nP2NyYqKIIon4y6WSQZ9C0KcwUXblyZkOhu1WZpDftDrKaM7i8HiJJXGVm9dE\n5/8lZxCW43LnkTwjhXKQZdzixqW+yia1L2tz34npQErWdLmstXojvCih0xnX6CqXHvt9Cvd0e2XH\np6pecFyXB49lmChYnN8epPV5Zp+DFyAwHZeAUovAnwmEdZk/vXDB7/ptnBEIgkBM886ZaJ3OF6/v\nfMHP7UmZFM3pa9LuEZOPX9rId/d4o3+OjBYqY4IA6mdl0DsiMoMzxHdH2Lte1Yc0PnB+I7mSRc9k\nieMZC031fua4LrqqoKtQMCy+vSeF4LrYhomjqMzsYI2HdSRBYMEsB2PXdcmUW3CKNvxi/2iltxTg\nysVB7j2c4j+fHKs8vlj05pZ3NEfY0V+gM6qw60SG/3nOEyS+ckAjHNBobwwylinRN1lkZVMAQRAw\nLIc6XaUl4E2XcFyX4UyJ1UsbOHBkBMeFS5bFuHhpjF2jJs+OGjTU+/H5FIqmjSwJqLJESBX55L39\njJUNQ586keOLb27nQxc08OP9STRJ4I82JlCl6mtNZ0xFwOvDtxyXLz02SmckySe2LUA/xXWpe5av\nR+mle33WeBXZ3Z/jR3vG0WWRd53XQHNE5ZJlUbrSFkMl77zM2PDFx0dJKAKfubKFgCrxzo0Jvv2M\nF6R507IwSxIaTeVzNxr1EQio5B34wmOj/PWljayoP3lV4WjWpCAp5AomtusS8CnIApimTVNDiGDA\nW5dFQcBwqJyb4F1jUkWH2EmSATVqnKm8aimc2dHpl4N7EqHxYh5T47XHgllzkxtDCp1xnYgus6Qz\nUSmfvLe3xPIGnYAicmzSZO+oJ8Zc4JfHcmxt0TnQn+Vf7u2hZDq85+IW3riuuvzukeOZiugGb/TK\nB1ujGIL3HmTB5fBoiYIj8TzB2JMSUgRGitPH8cCElxkaShncd3iSp8YtRvM2iaiPQslCVWUCikBj\nSGW04NAWVnjT0iCO67KrP0fJctEViaAuV7IpluNUyg3PbwtUzer2KyIfvbDhxb/x3yEjBaciugH6\nczbJkkO8XMUwNCubNpib68iqyiKfvXohn/j1ACBQH/ORt6E3bbHqFA6o/75zlF8f8fowf7Jvkls2\n1tMSVjmnSavKUD05UOCHB7zyt/Oadd66utZHWeO1QVwXSc5QYZbt0BpR+atLvZFh//DQEE+aDoZh\nI0ki79hSfY25osOHTxYYydl0hGWe7svy/Wcn8CkSQVWiYDr0pQ0EQSDsk4noMqI8vZXxqTLdyek2\nmcGeCVYvnr52BxUBXRHZ3Ozn2uUWvz2RI2e6lcx6LKhVgpELIhoRVWRFncoPn52kd6JY2csIgoAs\nixiGTTJdpLEuwFjerIhu8MYsdi4Ikgjr5ed4c9OfG8xiFi26xgt8+NLFldcUBYEtTQFed8NCDKOV\nvOHQEFaZLDr0ZmwEScA3Y56xZbs0BUW2d/j5XPd0//dY3mIgbbC1PcjW9udffD7/2Cgzr4CqInFo\nrMS9R9Ncu/LkgdYGn0hPZvrZ0unb4tV4hRhIGfzNPb0YtoskCewfLnDl2jqeGS4BEorkYJbPA0kU\n6UmWuP9YhmtWRtm+KMRFHQFsxwtMPT5YQtYVQkEV07QpFk10XcEFDo2VTim8d/RmyZX9XfJFC8uw\nWL0gwMalCXrGC1WPVUSqMu4KLo8cT7N2gZ/O+HTgvGA6fGfPBP0Zk/VNPq5bETmt2qNGjdPBKya8\no9EoyWSy8v9IxNtYxuNxxsbGKo8bHx8nHo8Tj8cZHx+fc//Uc6ZuO45DoVAgGAwSj8fwTMMMAAAg\nAElEQVTZv39/1XPWrFlDKBQin8/jOA6iKFa91mz2799f9Ro333wzoVdi/lONV4WtoRAfzAvctW+M\nqE/mz7a10Rr38enrl/HD/dMbooLlkkejKaSTG0tXvYYLSKqfj92xi3TZmffTdx1ny9IGFjVMl+E1\nx/PA9LGct1zu2DdJXUDhyqUxfnEkiWkDvQXeuCzKFS/AoKtkORwdzRPzK6xsVJGUEqN5i1/tGeYX\nT/VXHtdbFCt9T5IsQhF6T6S4eXMjf/GGxZi2Uyl5z5ZsStaUgY5NKm8S9MmIwFtWxbmoxU9Albh0\ncXTeOddnE/WyhUCekuVgWA5+TSIRCREsj9dpM4vsHpt27G0KqfOe776Ay+LmbMUBXwDaEiFCoWrh\nXbIcREFAKe86H+0+XvlZ0XK5+1CaaFCjO+vyzo2ewLAcl9ufG6lsInYOFLmgM87qxhfeU6yq87/v\nGjVeKi/mmBrLGhwYzNIe83F8ssj/PDdOUJN47/nNvGfLAv72wT7SJW8Tf93KOJHwtJHlxy/38Y2d\ng4xmDS5ZHOWCJTFU2RtDZtgumixydcR7/H89NcRvT+RxHJdkOlMJGOqqRCziI12wyBQsFsT9leud\n47p09acolGx0TUJAoLsvyZrOGIILzQGZtKPSEtG4cInK06MmedNAU2QWNWmMpj3RHfIpGA6MFl3G\n+kocGcyiKBLSDIU5FeB3cRkaz/Ld8QwCLi7Tj5m6pjYGZOKqjiQKXLcqQdGw+OhPjzCRM2mOTJdo\nxwIqjUFPtPQkS/zTjkGyhsOCiIYqV2f4BAH+4apFFE0H/46RSjuVXxVZ1BQjpJ98i5cs9Vfddl0X\nUQBHUE55LJwbgoyd5thkEUUSeMPiSNX1sXaNOvPo65/w3PTLPjCmIJZFt4csiViOg+uCWc4q+HSt\n6ntMFi2+9dQoyYLFY/snKvcXi1b5/BAJ+L3nHBnNc2Akz+KEjzVN1S0MDREDGEMUBRRZ4MjBIfbt\nNVFlkSu3LkSWBEq2dyZdu6qOgCLwk/3jpHIljg7m+MYTRWRRYHlcYzJjsG1Fgowg8HC5F/3YhEFj\nJMDrl9fKzGucnNtvv73y79WrV7N69epX9PedNuHtum5VlnnTpk08+OCDXHfddTz44INs3rwZgM2b\nN/PlL3+Zq6++momJCYaGhliyZAmCIOD3+zl69CiLFy/m4Ycf5qqrrqo856GHHmLp0qXs2LGDNWvW\nALB+/Xp+8IMfVET23r17ecc73gF4H97jjz/OBRdcwEMPPVT5/bOZ70POZDLzPrbGmc9AymAkmePy\nxUHesCKKIllkMhmWRASCikC2XALpkwX8lPjlgRQ/OZRFEKarMl630M/tT5wATUG2XBzbO7a/+uuj\nhEMa2ZLDNWtiXNYZYM+yCI8eT6PIEvVl59qxnMk9B8exZpQ3Pt2X4fzGk5fu5QybT/6qnxMpE1GA\n951Xz/bFYZpVKLZrPLBHIlu0uWVLIz6tWiAL5c3dj54Z5r1b65FEganqdtd1iWgiqZK3kKYLJh0h\nkbeviRDSXJZHvIhxPpd9eR9+mb60ydNDRfyKyKXt/jkjc15JFGBhAO4+mMEFgqrI6GQat9yf3ajA\nBQsUutI2IUXgvEbxec/3P1wV5K6jOQwbLm33ERJKZDLTm5QHeos8PlhCFOCKDp2NjRp1fpkTqelS\n9imDpidOZLl+ideXadjuHFOjZCZHxv/CazVDoVDtOlXjtPJCj6neiRK3/aSLTMlGVUTCM0Tjp355\nnM+/sZWPnRfjsf4ig3mXnGExNJGqtFQIwK0boxiWw4d+3svn7+9FlgQSYY2S7bK+SecDW+p4tDvD\nw8e8oKhp2VU9z0XDxnVdBEHAp0r0jWbobAwjSwJH+zzRDVAs2eBCT87AkkRs28G0HO59box/vqqV\nZ4ZLHOierDw+GlLx+VS0WWXWLqCrMkXDqqwVImCaDq1RDVcUGM2aOOXzWhIBQWBrR4CNHX4agjKr\nE+qMwKYBPvje2xaTKjmkbRtRlFAEG80pMvU13PlskmxZTA+mSiyMyOiySNFycF0XJ13AKOQQgU9c\ntoDv7/aE0Ns3xBHMAhmTk7K6QeOpgULls83nTYJ+hQtb1Rd0LJxbL3Buxfm/+vpYu0adeST8Epos\nIkoiIZ+CNs8s+c6IwnPDefJFm86YxgUtWtX3uH/MoGc0x0S6NOe5AqApEj87MIHsWHzzmfHKiLv3\nbUlwUcd09cWWBQoXdAR5dsxgeDBJseAdrIbl8Ojuft51w3pKpk2q6CC4JmviGlvf2MmHbz9QqWax\nHJen+zJk0iX2nEizvCMMynRFyOHhNBc0v7qGrTXOLkKhUMVD7NXitAjvL33pSxw4cIBMJsP73/9+\nbr75Zq677jq+8IUv8MADD1BfX89tt90GQGtrK1u3buW2225DlmVuvfXWiuB5z3vew1e/+tXKOLEN\nGzYAsH37dr7yla/w4Q9/mFAoVDFNCwaD3HjjjXz84x9HEARuuukmAgFP/LzjHe/gi1/8Ij/84Q9Z\nuHAh27dvPx1/ao0zmJGsyUd/3k2mLDD3Dub4xOWtAGiSwDvXhnigt4jrulzS7iOgiNx7PIdL2cjG\ndXndQj+qY/G9XeMICDjltKQgCPxi7zjhqNeL98ixFBFF4P992zI+elkz39idZP+otxAVDZth0yYR\nmi61iuin7uN96HiGEylv8XFc+N7uCbYv9jI/53VGuOfDGyqbzb60wbPDRXKmg+O4TCY9mR3UpDlZ\n64d6cqTLG9CgKnJui5/rVoSqSp9PF6N5i6/tSlKuIKM3bfKe9a9ub/izg9NlplnD4b7jWW6Z0Z++\nKq6wKn7qxbgtrPCBjfO/98GczeOD3vftuPCr7iIr4gofubiRLz82wmjWRFa9HnrwPvepz1uVBC5p\n9/Nwr9c60BqSWVH3+2HkVePs5679E2TKQlUQq69rQ1nPVG2k4PDsuPeY0YJDxnC4aVl1RcfXdo4w\nlvMqiny6TKl8rd0zVOQbT49z35EUiiyia/Kca5WuSJzfHiWsK7TFfHzl4eN0xGQefG6cgfFqszYX\nl8ZypdLUuKSsYbN/uEAyZ1dEN0AyY3Bue5Dj6bktKGLZ8KxUsmkOq3zlhoUULYewJvGO7x+riG7w\nyusvXxpm36TNvmfGkET40y11nNfqvY+C6fDjvRP0pw2Opy0sVyCsS9x6TpyGhul1o2S7yJKA63qO\n48PDaX674wSqKuM4DpblYFhLUWWRlQ0+PnNly6m+viretSHORGGUw6NFbNshFFT54Ja6V32sY41X\nh8aQwvu2LeJnB0ZorfdEcLpgUCov2Ns6/NywIsxkwSJVtGkJq5VqrimePJ5i12Gv0m/qfAKvTSHk\nVxBFEcuBh7uzTJ0SLnDXoTT3HsuhSgJvXROhZ6zAb/ZP0LQgBMzai5RvaoqEYrosmDGeNDrL9HXm\neafjMjN9sKahZvZX48zjtAjvKSE8m09+8pPz3n/99ddz/fXXz7l/0aJFfO5zn5tzv6IofOQjH5n3\ntbZt28a2bdvm3N/Q0MDf//3fn+Rd13itsWcgVxHdADu6s1gzxmI0BGRuWVnd7yaXXb6niOkSe0+c\n2oFcEAQGk0U+9qNjfPudK3jzsiDDWYuxgo0ueUEARRbxq15J95uXnNopdrZgnq/qeypI1RpW+cxl\njfSlDe7aPU53ySKsS/z169uqHj+UNfn+3hQuXllixnCI6eLsZe60cTxpVkQ3wJEJE8d1XxGR/3zM\ndjGffft0MHu0kQtYDrTHNP7lTd538MtjOR7rL+CXBd66qnpm/PXLQ6xr0ChaLkvj6qtaFVCjxsth\npg+EadpVpdWr6nVkSWC0UF29MVqYK2TTxen7ZvdhPjfqBRJNy8F1LfyaxMKGAAMTBXRF5A/P7WBR\n/fS1vCmkkSvZpIs2miZjml7ViShAOOJDliWmfoUoer3ZDUGFhlmTChRR4E82J/jGrkkG8i5GudxW\nFAWWtkWZSBVwHJfmqMZT/XkuLGfwZGfu33fHEyPEYjrhsI7twL/tHGVFnU5El/jnBwbYNTAdIIiE\nNVJF+OH+JO9U47SHFa8dyhamy8tdlysXRrlrxwkMwwtYdNb7UF/GCC9NFvnExY3s7MsznDNZ26DT\nOcPNP5m3uHPPOAFN5JYNiYphbY2zk3S+SH8yR11kOrgT9qksTIBrmmTSRQ6NKLTFNBbG5pcHu3un\ns9+W5VAf1pB0GZ8mIwgCruuiSJ554eHx6az4UNZCU7x186tPjnO0J4ntut5rNIaYGMtQKJhIosAl\nm9orz7usTadthhHiu7fUsXeoQKpggeuSz01XmL1tUz0ZV2AgbbKuycfmltf+SMgaZx+1+Tg1XhP0\nJUtM5Kyq++oC8ilF140rQnx79yQ9/UkSusiyaBTb8HHnrlFM00KSRezy5isRVrHKuzfXddm4shHd\nr/L+u/uxXZfmoMxHzo1zImXwzd1JCiWb8UyJy5fGCGun3rBsWxTika4sh8aKKKLAuzeffJaqJApM\nlmDb6jo+vK0ZnyJiOS4/OZylJ2XREpIIy1Q2nIrkjeD58aEMOcvl6qWnv/9u9ka23i+9INHdnTT4\n1fEcggBXLQ7SGn7pGZebVoX5wo4xDMcbgXbl4pfgbHcKWoMSrSGJvrKz76qEMuc7fsPiAG9Y/PwB\nl8Wxkxu11ahxJvKWcxI83Zfl6GiRqC7xvs0Jjk2aBFSRq1d4PhYLAhIiVIy7WoJzS1pvXBNj94BX\nnVIybHwz+pGTxWpztnULoqxqieCXHUq2Q2t4+rwqmDbXro4ykTXQFa/iRxAELMvmXRvjjBgCj/RU\nB1PXNfpYUa9zcDhPVBNJlhwUUeC2S5vQZZGuiRJZ0yXkU0CAgmEjqhKuJGILLt3JEp+6e4wLFwbx\nSQLHBrJEYz6kcmtJqZxFTyaLNNb5KRgOlgN9aYOwprN3KI8sCVjlAJ5lOsiSyGDW4stPTlKniziu\ny3jRIexTvayiILC5I8w/3Lyc23cOEvbJfPSqRS/ru7Rdl7zpsqXFhyhUi5RU0eJdPzhWCTLedzjN\nf751ceXnr3ZAtcZLZ/dQgXuPpDk0VsJ2oTVRvS49eixF72geXZe5+0gGRRT48IUNnD+PMV99UOHQ\nyLT5mSUIhPTp9XpZQuOqpSE6IgqTBZuDYyUaAjLjMwJtedPFdb2Almk5SJLAkhULyOcN3rV1AROu\nZ9K2sV7hnPrqvUAioNDWGCRhuYCLT5WIy3DThgSXLD3zJ6/UqFET3jXOeh7ryvAPv+nDckBXREK6\nSESXuPW8xlM+Ny477H/qKF1jRURJ4t39Sf7j1nVMjqaJ1UdQNbdcbu7SEJQpugLDaYNoUCMU0hmY\nnM5adCcNvvNskra4Tn05ohxzVEoOTBZtYrrEWMFmNG/TFpLnjKbSZJFPX9HMUNYkrEmE5um/mqJk\nuXxjT7qymO0bM3jX2hAP9BZ4asiLMp9IGZRMG1kSy/4L05ukvSPFV0R4d0YVblge5PH+IgFF4Lpl\npxa9WcPha89MUigbwHUnTT55cV1VZg28jd5/7Z7k6cECCZ/Mn2yKzyvQd3RnGZ4sIooC6bRLf8pg\nxWkuOZNEgbetCNCVspAE6Iy89i6ljutiO8wpNazx+01Yl/nqTYtIFmxCmoQsCVwy6zENfomrF+kc\nnLTwywJbmuYGmVY2+vjHN7Zy76EUqq6ScUSODGVwXJBFCdf1vDiuX9/MwrJQ0ASbsXyBo8kcCwI6\nrusyki+ytUHhi105WuoC3kiuyQKGZXPliji6IrKp2cfXnhynZLs0BGTed24dhu3wyf/pJVmwcGwX\nSYL2qMrnd4wxVG6OzhYtQn4FRRLxSS5jUz3ckkhd3Md9B5PIeGZUyckCqirjum5FUAuCQCyoQ7aE\nbTs0hxR2DZdoawqDIJAvmgyP5yuGbZoiYdsOPcnpILJtl0iEdXyygCzC69fW8/q19S/7exwv2Nx5\npEDWdInrIm9Z5iM4o7f9rn2TVZU9gxmTwZRBxnL5r2dT5EyHC1v9vGVWNU+NM4vupMG/7hzDctxK\nL/9IqkBTzIciiRTzJU6M5ZFlEbm85pqOy78/MTqv8P7gxU1kihbdEyXWNAcYMKvXh3es9WbIA/zF\nxd4eLGvYfOahEZLl/cqimMqlTQm+tXMU23aYGvfXGPNxxeIgmizi4lWgzMeimMrBCQNJlFjYGiWh\ni1xUE901zhJee7vFGr933L57rDLWq2g6rKrT2HksxQeOpti+PMqnrlnIbw4l2TuQY2Wjnzet8Vwu\nu0ey3PCPD5PKe5ssWdc5Ogy/3DNSWaAEQUCSp8fHfPsPlvOZu44zaEgUzblmWBMFi/TYdOmTKAoc\nmyzxgwM2K+t1nhkxEQQBgRI3L/PRHKw+BSVRoCV88kyo47rs7MtxZDhHwKeiqRK9aYtUyWE4O71h\nM6zp9ze7lHN2Zvp0cl6zj/OaX7jQHc1bFdENkDUdJgo2zaFq4f3b3jw7+71I+3DO4r/3TPKJi+eO\nO9tZbhWY6v16si932oU3eCXsS2OvzV7IJ/py/PtT4xi2y5VLQvzB+mpn2LGCzUjeodEvkqjNUv29\nQxAEYqe4hrSHZdrDJ3/MkoTOkgt09o2bPD5k4lNl8oZ33fJpMm9fV0djbDo7V3Il1tcpPDxocHQy\niyRCwDG464hD2YMMURCoC+tgmxRtl11DeWI+if/7+mYmCzZ1fonfHM3QmyyRNR2KBatyrbhjb4qD\nyWpHMl10+eP1EXYNFaqME6cI6TKlrFnu/7bKIlpAADpbvQoAXZV4+5o4MZ/MPz8+USlD8usKGzqj\nLG/w88SJLJRne8/EtL32oGuX+E9rhvnnR3PsOJqkZNrEgxotQZErO6avkxF97nnt10T+9ZkJMuUP\n+5ETeZbEFc5pqvXSnqn0JA1mdUZhWA5mocQHz43zy8Mmu+aZvju7nWqKREDh/17XWbn9QFeWOw54\nk0KuXR6uiO6ZBFWJv7ywnkd786iSwGWd3ujSrZ0RuieLHBgzEAV488ooAfXU68m1y4IceWJ6Ss14\n0aErabI8Uasiq3HmUxPeNc56ZmdGHz+WolhuNL7/UJKAfoJ7D3oLw//sm6RgOtx0Th13PtZbEd0A\ntmGgairLF4Qw8kUcJ1TpaTMNi49etwRdEfn7G5bwV78ZQlTmLhALQgqTJliz1qyxvM1Tw2alj9sF\nHuor8bYVL+4UTBYs/p/7h5go2kiigGmX8GkyiZCKJnlZiSmm+q2m8CsiIVWgKShzyxk0N7oxIBNU\nRLLl7yyizS/m0qXqQEeqOL8LeGNQrmSsAJpCp08cu65LpuQQ0sSTzgdNFizu2D1OyXK4ZnWcjvjZ\nY55mOS5fL4tugHuPZjhngY/V5eDFk8MGv+0v4eL10F6/xEd7qLaU1HjprIjJ9GVtCvUBukazWLbL\n5mYfm5s1TlSP9MUFXteiMpI1+etfDyCIEgFNwrAcwgEVSRIRBLh2RYTd4yJBX4jHBjI06iXetCzM\n13aO8sBxr081HNIoFiyMstjvTZloslgVVH394hAbmnwsCMrsHymSKjnYtsPYeJ5EWONPtjbwT/f2\nYlsOPp/CmqVxdFWmYDqo5TVifaPO0qiC67rYsyYa6KrCFQt9LPAL/Pxw1ruuz/j5mgad/7Xp9F+v\nn+hKUSz/3eOZEgeG8lXC+5o1cX5zJMXRMa+C6o0ro4Q0qeKyPsXj/cWa8D6D6YyqSAIV7xUBaAkr\nvH9LgqaIyiWL4af7JimZ3nE91S5x89oXNobrss4gl3QEcKn2U/nJ/kl+fSRNWJf40/PqWRjTuHZF\ndXXEojqdRXU625e+uL8ppEpVc70BfEqtMqvG2UFtt1TjrOfWrY2871t76R9IoioisYbqBePwSPXO\n7cmeDDedU+f1781AEgX+z3XLOG9pjC/98Vq+dO9xLEWno8HP/37dIjrrpjcXN6wM863dEwR1GcNy\nsGwHQRAYN7yRHZNFG8t2K66eKxIq3dnqcu+X4vn104MpJsqC03ZcUARMw+LmFXEOT5hMGtMrkSqJ\nGK6N44Iqi8QDCh/feubNtPQrIh/aEuc3XVkEQeCKzgDaPIZBm5p9/Pp4lmI5qnFR+/z90x+8oJGv\nPjbMYMbkvLYgly85PaWQozmTT/+qn76UQXNY4VNXts7r/ms7Lh+/q4fuCW/D+tDRNF+/ZTHxV7DK\n4HRiOe6cbEe+vGvLWS67R82KHaHjwu4Roya8a7wsZFHgDR0621s1FDFYCWq5LoRkm4zlCdiIYjPV\nofP4iRxFyyWgOBzs8YS0T5N44+Zmon6FUVsgIMGxiQILE34ODXmjyXYPznA9FwQWtYYp5E0GxvI4\nssDChJ/jozkKhs2ihMbmFu860xhU+NvtTXzz2TTHxopElySIR3z0lRxWr2hALI8Zs4B4QCGqCiBA\nMW/y7V938c1fwdXrElywOMFD5YkGUV3mws4oz6XgwZ4CSxMa6xt1fKLLE/0FYj6Ra5a9MqXczqwA\nQEyduyB96fpO0kULXRYrJm7NIZn+jFdZJQAlZ/7MaI0zg/aoynUrwtxZzkoDLIyptEa87HBLRGX7\nsgh9WW982PltPmI+mYWxFx4slkSBdNHm9mcnyBk2nXGN7+/xRtuN5S0+e/8AyxcEaQ4q3LQqPCdZ\n8mLRZIG3rAjyo0NZLAe2tftofxm+MDVqvJrUdks1zmp6xwt8+/5u+gdT2LZDwXbQswUUvyeSW6Iq\nmztCdE2MV57TmfD6r//osk4ePjDC44fGCPgU1p/TSY8pkinZXLa6nstWP38f3ap6DRyXZMFEkUQU\nWWJlcxDbFbCBkC6yuV6mYLk0Rf1sSAj8oqvAwUkLUfDKEF/Xrj/v6z8fxVmpdMd1aQwpLIur7Byo\nDjDIEoR1FdPxNoRbFrz43/dqoasS2xaFiarevPX5aAoq/PXFDewfLZLwyaxtnP/vSfhl/ubyFzdW\n54Xwg13j9JVLTQfSJrfdfYKN7SHWNmgMZS2W12mc1xpgMm9VRDdApmRzZLTAeR2nv6f+lUCXRS5d\nGOShbm8wS0tIqYxlKdlUxsdM8Xx9eDVqvFhmu/tbjkv3eAYHkRUJhbryWMZk0cZBQJVFeoanjdMW\n1geI+r0NuOG4pDNFTiQNjk8WKu01Mb9n+jRF54IQDRGdPUe9HvDu8Ry6KrEgqlMf1rnjaJF1dTIX\nNmvoskhIk6krzy4fTRfpLs8fFvACnIIgsH8gC7j81UUNvOdbxysB2Lv2jHF0IE1/zubtly1lSWOw\nkiVsj/l4pi+FgUTCJ3LBwhBr6145MXHFkhB37vPKdXVZ4HWL5vfjCOvV28QrFgX5wYE0rguSJNRM\nIs8CSrMCqVNTAwzL4X/deRwtEgBE9k+YxIMyjUWHdMlmXZOfkazJQNqkM67N234wxd/fP8CRsov5\nb7uzuMJ0i1um5NA1adCdNHFx+cN1sZf9N21o1FnXoJU9IWprUI2zh5rwrnHWMpYp8QdffYaJnIms\nyIiiiGmYTE5k+ObbV2IjsKk9iK54cyX3DXo93u/e6hl++FSZ7952IT/YNcqPDqQoCQJP9eX5pjTG\nn13oPSZXstjTl8Wny6xq9FcysSdSJumSgwDEg2q5tNBFkbzFRhAEnks53LomSCIaJpPJ8KZFftZm\nvOctjSrzZnVPxZWLQ+zsy2M53gCfiF/lzcs9QbemXuOREwUmygYml3cG2Nykc2TSJKKJLIufmRuk\nwbzD/qS3MRBxOSchEtPmX0jrAzLbAqffpfyFkDdn9166HBo3ODBaxHZc7u/KYtgu57cGiPmkyuZe\nEYVKduFs4T0b42xu9lOwHDY0+fCVTZciimegd3DMoWS5+BWBi1vOrr+txplPV8qiK2VxcLxEb8oT\ntg/3ivzZ5ihHJk1+eiSH40Jbws/wWL5Svq3NMqycOaLRdV0mCjbrOyIUbMgbNi0xnda4D8N2OXdJ\njEe7Upg2mLZNrmRTshwMy2EkLbO2TiGsimxr07njcI6C6ZIuTBeFu3gVIJZl45RbfG5/ZoSZCWGj\nZLCrxxM9gm1XCYaS5eC4nrHVRMnlgb4SfllgcfSV2abdtCZGZ0xjNGexfoGPBS+wJWdjk47puByZ\nNGn0S1zWUe2GPpQx2DeYZ+kCgbMk1viaI2s47BouIYsCm5o02metP20R77t+pidDxoKZue0Hu3Ik\nyyO6zm/1s6M7i+m4hDWJv319Cy3zrGWG7VREN3gl4LosVMrbVUWqiPC+tDXn+S8VURBeUuVgjRq/\nS6RPfepTn/pdv4kzjUwmc+oH1fid89sjk/z0qaHKbUEUkCWBTcvreM+2No4nDXaeyKFIIteujXPN\n2gRbO8Nz5mX/tjdH1+S0aY4qCVy+JMzhwSw3fPEpfrRzgLueGea5DPSbEo8PGKgiZGyBhoiOJHkj\ns6ZcQ5Vyj5QgCKiSQGfcj2F4rx/RJBr8px5z9nzEfDIXdQTwqxJL6ny8ZWWIZXVa5X2f06jRGpLZ\n2uJjY5OOLou0hOQz2gDrUMphql17ao/a4Ds9q6nruvzmaJpfH02TNewXVT43m5hf4tGuTKWvLBTU\nKv1wUxttRRQ4vy3AxtYgAymDmF/m/Rc1sarp9M4T1TStckydTlzXZd+4xeGkRWtYYVW9VuVqLgoC\nrQGRpqDC8pjC9lbtZZcN1jgzeKWOqRdLT9riJ8cKDOcd7LJJmWG7lGyXjrDMvV15puweXEHgnAU6\nx0cLOC6sa/JRF/VVxO5k3qpUCWVKFvd150mXbFa1hFjcGCQR0hjLlDg2nGM4a2LaDjPbgdIFk1Te\nZCJr4FdEbw63JrKhQWV5XOGRniwzc4mmZSNJAn5VRhJFdh8cZnV7hKGkJ0oUHMzyBaRnOEtnY4hf\n7Rrg6eMTDOUtdFUiEVQrIiWqCVUzjE83zWGFJQltzgSNH+1P8rnfDnPfsQztUZWGQLUobw0prGvQ\nWBxTqwzfuieK3PazHh7tyvCL/WPE/TJL6s7cSqvXIkXL5d93p9k/ZnJ00qQrZcGg2BgAACAASURB\nVPGmJQH8ikjRhkTEhyV41XhxFX55YIJoZLqNrmhaFVf+nokSVvlkKtne/mZji5+79k7wq+cmkQSB\n5qiGJAo81pOt+LBIAnzk4iZaIiqtUY1xw60c0+e1+FlZ99LW4TPlGlXjtUMo9OpHB2sZ7xpnLW1x\nHVGgssmqrw+ybHkTgiDwsV8NMZEu4rpwz6EkxckcG1uD/PXVi9CUaqGwpTXA/ccymJaDadqogsoz\nvWk++sPDWLKCrIJlWPT0p+ho9Xqk9ycdWuI+xrIm2NOZUEkQUCQBRRQwHbeyaJ1O6vwyN6yc32zH\np4isqT87jLxSBYuBtInlSsD0xu8FmJq+YO56Lsl3dnu9Zvcfy2A5vOSe79WNfr58XQcPd2X5VVeu\nYrw3s1dyQXmTvKhO5x+u6XiZ7/7V55EBg92jXhZv96jJDUt8lRnM6ZJD1nRp8IssDNbEdo1Xhq5Z\nGTFNFsmWbK/CR5eYHZLb2BbivVvqMB0XnyKSt1yOpSwGsxamKhCSJYZzFkM5TxTkDIexjEFzVCNX\nsnimO1URz7Lo9WXjQjmmNmVAzr7hIpcuDBLVJXyyyIKAwGjXANG2JkRJRJEE/Np0NlCSYEFLnEkE\nwhGdQt5ElhT8QZ2F7QkM0+Jf/+cgpXJa0O9TuPqSRVWmjc2BVz9guvNEjh/t90rQC6bNPz00TFxw\n6GwJc25HiM1N6vO2l/zmcIpC+e9xgbv3T/KGFbUxT68m/RmrUvUGcKI88eT1S8LsT7rkTBdc2DlY\n4q0rAryuM8iDPSkiYZ3miML4DBNTWaza3qBIAl9/dIjvPjECwJ3PjPG5GxexqN7HwphC0bSJ+GRu\nXBPj3LYg57Z5z9swUOD2PeP0TRZ5+IjJxkaNjllB8IHxHP94+y5SOYO3X7aUS9e30pexCGsiDf4z\nN3FQo8aLpSa8a5y1+HSFJW1RuocyqJLIws76yqZFEASv/LtkAQIFR+BHj59gcCzLV9+1rmIUA7Cx\nJcCtmxJ8/oF+bAd2HEvzyMFJ8qaLKIpoPg3Hri4znkoC+lWx4qAOsKZepT/vYDoQVERWJ2qGH/Nx\ncKTAp+7tI286RHSJW85rQfepxFXoDJ6+2rE9g9V9788O5V+W2VpTSOXmdXE2tQY4OGbQGJDYM1Sg\nK2mwvE7jmuVnjlv8S+F4alr0uEB32qIlKHFwwuTe7iIOENdFblnmR5drNX41Tj8JfVa5uABhVWTz\nAo0fH8qSMhxEBFygJShxTpOGLHnVTuCZGz7SV6xkxVuCEppQHQDtGS+yry+NIlKVsfaqltzKNAhv\nHZEQRYHBgsM/7pjgTUsCXNzmZzRdZM++PsQD/QRCPi7ctnbO3xIK6wyN5cjnvXVI0DTWrWxAEASS\nqUJFdAPkCybrogKBgELacFkSlek4xTi204nrunxz9ySP9+Xx6xKG6WDZLrIs0tkeR5ZFnhk1GSs6\nXLNofhfz2ZnzoFYL0L3aRDRxKnYEgCKCXxZxXJe8WX0e5EyHP7+infcaXtuDLAr82xOjPNabI6JJ\n3LwmwX8/PU66ZNMaUbhudZQP/fBo5fmOCz/bO8HxrEWh7HafLtlzRHWxZHF01FuLeyYNvvDIEF98\nc3Vg+j1feJDD/Z4B3I7nhnnrzRehRrxs5FWdPjY1nh0JhRo1TkVNeNc4a/nLn3QxVhIIxspC6iQ6\nIJfJY5ZMHto/wl9+dx9f+ON1VT/PFu2qyK40q3xWkiXOWdlYuT2V5PSrEoIA41mTc5tUrl7iZ7xg\nkzZcmgISvpo4mZcf7h6v9EynijbHBpJ8+KKmk47oeim0R1X2Dk+L79m9bi+VxXGNxeURYZtbTm8Z\n+e+SqCaSNuyq2wCP9JeYOj0mig77x002NdZ6u2u8dFzX5RddBfaPmUR1keuX+qnzSaxJKGQML2sd\n00Re167hk0W++Wya4bx3bDq4XNCs84ZF/jmtQ/1Zm5mTB/uzNpe3+/ne/jS26/1eWRaJhXRyRRPM\n6gy7J7yn/22YNromUzJtdEXinmM5trb4iAc1mqI6Q8kimVSeHQ88y/Vv3sRoYXohkcujzaZoSPgr\n1zifriCKQqViRlclFkQ01jZMC4yM4SAKAoFXYVTSofESj/d5buuCIKAqIpZto0sC8oz18ETG62Gf\nKjEvmA7HUhZ+2Wvp2jdUYFd/jgVhjQ9c2PSKv+8a1YQ0EVV0Kdje8esTXFJFi57xImOpAompsnLX\nYUnUSwz4VYne8SIFw+JPt9Tz/nPrK+fVBR1BUkWbeLlFri2m0T2jn/tw0sScUfVVslyOjxerxniO\n5avPsbFc9e2SaXO4P4UoiTi2g+249AylWFoW3o/0FdnUqPFQV4qHulL4ZYHrlgVpDNQkTI2zj9pR\nW+OspHeyxECqutdnZCxPS1OovJlx8MsCgiNwoj/NxNh03/4D+0fnvF5L9PlFREAV+cItq0kqPiaL\nDrYDUb/E4GSRBr9En+EQ1CR68wJHJk2WxhQStbGmJ2W2vhYQTrvoBnjb+jim43JsvMTKBp3rVr98\nN9XXMle0a9x3okSy5LA4IrMq7i0RsytLa+GkGi+X3SMGTw971/ChnM3Pj+Z599oQgiBwQbPGBc3V\nGa7Z86MRmCO6AWK6l/EzHYdkzkQWIOHT2bpA5d5jOXRtetujKRJNPoGC5bIkofFQd64iuqeY3S3k\nut5/g1mLbdvX0T2Uxp6Y4E+vWMK44mNHfwHHcRFFAct2OHZoEMd2WbykiaaGIK7r9bvquswlm1o5\ncGwcSRTYuqaxYnoF8NigweGkF0HYUCezof70Vk85LkxZm8RUsGZ/vILAlkaVBw4mWd0xfd2MakKV\n6P72gRypkvchbW5U+exVbZi2Qzwaqfnl/A44PmkwnrcwLJuJTAnXhT/ryzA8nKVUsulsj+LTZbJ5\ng+AFcSzH5X//rJvejIVjO4Rsk//445VIole9oMkiDeXWoif786xdGCm3a5g0xTSGDBerZM9o12BO\nxvvctgB3PjtRCbZfuqi6r3aiYLN4wyocScbIFxjr6qG+YbpFQREFjidN7tw/PRLtO/vTfPTcM288\nao0ap6ImvGuclRwZK6IoIuaMUr3GqErBtHFst+JyKyBy2bII3+mdFtsLG+ZmKM9pDXLFiig7ujLk\nTQdJEolEfKxv9vPn25ppiek8PWYhStOldEXbi/6Looxf9TYyD/WVWBr7/SwvnyhYFCyXBUG5ynBn\nPt5xTh2HR4ukizZ1AZm3rH9lFlBVFrl1y/OPhatRTUARefM8ZaSXtmrc01XEdqHBL7LmFRxzVOPs\n5rkJkydHTATg/CaVpc/jyp2eJaQzs4X1LLYs0Lj7mJeRVUTY0DB/6WmjX+J17Rr/3+7Jykb/izvH\nuXFFiJLtMNPqy7Yd9o7kUWQR1wXLtBDE6mqnqRJ2TZGQRIErO/2kSzYfv7ffE+W6D729lYtWNtCX\ns3lmuFTJrB86PIpZriCJhb3grgvguiwIq/g0Px1NngiJ62KlwmSs4FREN8DuMYtlURn/acp8uy4c\nTgtkLO/1grJLnU+iISAzUs5GXrEoyM2royyNqewdyxCN+VkQVtjWOv0JHklaFdEN8MyIwfZ2rWIw\nWuOVYzBt8IUHBxnKmlzcGeLW8xtIlRzMcgQlV7Smg0iCQDisMzqao6cvRV19gIXlMXAPHUvTW57L\nLkoiSUfmF3tGuX5zdbXCzw+muL08fk4SZf7oojr+4/GRitmoLAkENIkPba2f43zeHFb5pze2sfNE\nloRf5uKFQZ7tzzGeM3nwcIpn+3NYgoQIqH4fb75yA6s7ovSkLVQJ3tDpY3zGCECAiYJTVXlRo8bZ\nQk141zgrWZzQCfplCiUHx3Fpi2uYknc42zNSFC6wYXGcZv9SfrRzgIaIxmdvXlX1Wrbj8s+PDnE4\nZeNIIko5n6fpIhcsidIS8zYa6+ISu8dtjqcsJgoWGcNBlQT8M/YYr4CX2lnBg905fnwwjQssT6i8\nf1N83mzUFIvrdP79pk5GshZNIQVdqW3UzmSWRBVuXSORt1xiuohU2+zUmIe04bBjyMR2XPKGza97\nbFqDwXlbblbEVXYMlCojh9bUnbx1YWuLjwa/zGjBZnFUof55DJce7iuyo79QNf4vb7oEVYnrV4R4\noDuPIIpEVJE9wwWCPoUlLRFEUWBlyM9zPZNYjkNLVOWyRWG2tAXAdXER0GQBGfibX/dTMmxEUUAU\nBYoWHJkosbLBx4c2RfniYyM81z3J8a6x6feQKxGJekFfF2gKKqRM5sWZnXbHK68/XbUmBZuK6AYY\nzjt85+AkeQt8qsSqOo2bV3sZx6vX1XH187zObJ8HTRJqQuhV4gsPDrK/3Eb1032TpE2XpwYKuMCy\nep1U+WtwHBfHcXHLOWlRgHOa/bz3/AZg7phMURTm3cf8tidLqeSJeVWV+M2RNDPHg+uyyDs3JVjZ\nOH/rVUtE5YZIHNd1+btf9fHwsTSGYVdVmAjl2d8hv8YfrAyQNV10yTOsjWoiuixUphQsjyu1Y63G\nWUlNeNc4K2kKyvgKGVIlEUUUuGldE3cczs3rIi4At27v5NbtnfO+Vl/a5NC4gSSKc8oMj48Xueu5\nJOuafHTENFZHRR7sLs7oIXSJaQKTJW+u9tbm3z8DENtx+emhdKXU7NC4wd6RIhuaTl5v71clFsZr\nbqVnC35FxF9LdNc4CQXLm+TQM17AKGfe7usSuHppcM5jGwMS714b4uikSUQTWT1LeB8cK3H3Ua9U\n+ZqlIZYnNHb25Xiyv4AqC1yzNMD+oSKqJHDd6igxn8zxlMnDfd40i5kTL2QBGgIyqxt03rTMM0B8\n8kSOXT0pmuJ+xHKQUJZFmuJ+ugbT3LQqxsWL5o6a+bv7BxhIe4p5Zn/2SMGhIW/TFJT52EX1fN51\nONEzjmF6i8WmBoXWZp3elElnVGV7p4+fHitQsr016pwZpeT1PhG/4JB3vYDk0ohI8DQGJ70k/rSQ\nH0wXKVheCbwsCBydNJ83mzict7m3p0jedFmTkFlfr7Bn1ESTeF7TtRqnn+7xAqWSiarKiKLAkwPT\nXiaHR4tcvzLMA8ezDKe9fgJRFAkGVT54YSNvXl9XeezFnSFu3zPuuZ0DmmVx1bp6jk+UeLgnS1CV\neNOyMCPJIvm8d9wXiyar6qpNSvOmw9efHOfuQ2k+c3kz/vLx6routx9I80R/HlWEtpDEjt5slZfC\nFK4LPkXg2nVxT4Cr08dfwidx2wXNPHp8goAisLW1dqzVODupCe8aZyX/es8hnny2r3L7bydTfOoP\nN/P9vRM44nQfniBA/BQGHH5FrOQRfD6ZXM5bXHyKyGMn8jzeX0ARBT59RTNL63TeuiLIgyeKuK7L\ntjYfzUGJkbyDXxEqpYK9KYPulEkOBUmepM0vsDL6+yMyf08T/zVq/F5Tp4u4ll0R3QCP9uW5YlEQ\nbZ7LX4NfmndUULpk843dSYyysP3P3Une0BngkV6v1Lxou/zXnhTJjGfy9OxQnn95YxuZkvd7BUEg\nFtTIFExaghJXLg5SP2sd2NIW4OqVEQ7PakN2XJdti0Nc2Dk3WAAwlKlOU/s1mbqoj58fyfHL43k+\nsCnKQz15CqLCZZcsxShZrEoo8xqNvW25n8GcQ0QTqPdNfw47e7N87/FBYkEVx3HJtfq5sLlxzvNf\nKpoEHQGX3px3e2FI5MkZP49o4vNmE+/pKpAyvO/lqRGTNy/ycWWHN9rzlfDpqDGXbz3Sz7GeSQBk\nRaKxae6kjh/sGueChcGK8AavzW6m6AaI+mQ+dXkL//bIILgut12zmJTh8HcPD2OUU9qHR4uMpqeP\ne9eFLc0+Ej6Jp07kSJYclPI+atJw+cdHR7m4I8AVi4I80pvjsT4vEDeUN+mdhEhEJ50uYZpGRXwr\nosCfXNTExYvDNEc0fv7UAJ+94wCO6/KX163g5gvaaA6rXLU4cJo/zRo1Xl1qwrvGWUnvWL7qdr5g\nsLxO5xvXdfDNp8d5sDsLeBuI9Y36fC9RoT4gc8vqKHccSOHTFRRZYmuLj2f6c6TKGznTcXm0O8vS\nOp22kMwfrApioOLluS2ag9P9Rw91Z/nOs0lcQFdEtq9I0ONKJHSHBv21V1ItiQLXLg/zk3Kp+dK4\nytqGk3/mNWrUeO0hiQIbGxSOjhen7xMEhosC7YEXHo6bLDoV0Q1g2C4Hx0pVj5nZytKXMpkoWCyK\nKgSUIjnTRZFFLloY5Ialz79Rf9fmeu4+kuHpUQtZEjEsG0Fwue2S5ud9zvntQX68zxM9ogCrmgOM\n5J3K+9zRl+fQeAlRgFBQwxcPkAX6Mhatoel2qMd7s+RNh/PagoT1adF9aLzEz47maWsMMpEukSuY\nHBjKz3kfL5XhrFkpda/XvM/42KTA6nqV7qRJSBV52+rqsYiW43Is7WA5LoUZ9cWO4/JId4ajAYmL\nOoKnrQe9xvPjOC5ff/BE5bZl2ly/Ioyjqfy6XCFiWd4ouOKsMnJdEfnI3SfwKyLv3lLHorhGzrD5\n2I+OMVwOKP35eJH3XtZaEd0AB8aKaDPKvAEaQyqvXxHDPs/lPT/uoWS7+H0yqiwxVrD5ycE0MV3i\nsf6i144xY1rGlLlgqWSxOKHRGtG4em2cDa1esGssXeIv/nsPZvk9/J/v72PrsgSrQnMrUGrUONuo\nCe8aZyVvubCde548UZm3unBhPQ1BGUkUaAuITIzncFxIqxL/8cQYt1188rEmb1weYVOzjyf68yyM\naUQ0kfuOpisliADhGTNJi+gYgldWXnJdAuSQ8RaWe45kKhnfounQPV5g1YIgRrU3yGuKyxYGWNug\nUbBcmsvfQ40aNX7/2Niks2OgxImUgSTA5rbIHFf8U9EUkEj4pIqhUp1fYmubj70jxUpW1ZyRVY/q\nElFdRpEE3r0mxHPjBj5ZYG39qUfebV8Y4Kn+UU5kLWzH5Q/XR0/6+LdviLMgpDCQNtjYEuCxgRIj\n+emsYl/GqhjHTeQM4gj4VInnJsyK8P7KYyP8tscLDv/0QJL/n733Dm/jvPK272noAAGwV5HqVO+S\ni+QiO+52nNhOb05xypfsJm+yu968m93vS9vNetM3vW/ibJrtOI4dx7HlIluWLKt3iqTYO9E7Zub7\nYyCAIKlO2aI093XpujTAYDAgBvM8v+ec8zv/fmMdbqtEJK3x411BshooskSl30H/cJTZZVOzkPk/\nu0Z5MifOrp/l5j3L/fytPcqjR4xzcVtE3r/cS6m9eGr4Un+WkZyJWrXbQlcojarptPSFeTVhGHM9\n0xrhXzdWYZcvncyu14Pdg8kJNdh1XivXLyzlQF+co8PJfAnEtbNLKHMpbOuM4XPIdIXTHC8v+PKm\nPr535ww6RpJ50Q3QF0oj6hT1Ai+1y7SHCwtfXofEslpjQUsSBT6ytpzvbh1CHmdM2DPmuOPnBKvq\nnIQ9Mh6bxHvXVVLlKfxWR6PpvOgGY6FqOFK88GZiMl0xhbfJtGR9cwU/+vsr+eWWHsp8Tj6+sRGH\nItIfTvO1Z3vzA1MymeW51jDvX11eFFUYT2cwxef+2kM0Z5h25wIv2ayGLBt9WDVNZ02Di9/uD7K3\nP8HiWh/zK93M8DtAEMjqcl54W8cZzsi5ASelXtwJ2GWOqbmdHBpO0TKaot6jnLJO3OTiIJzSeLoj\nTjKrs67GRpPXLCafrkiiwHuXlLAnoCMKIh6LQKXt1Pe+aDyFKAo4bBasssjfrfbzXGccQYCrGhx4\nrBLr65I81RpB03ViiQyCIDCr1MpH11Wg5NzHS6wi62oMobqrN85fW0I4FZG3LSvFP8k9yqGI/PP6\nCjqCaTxWiUrXye9jgiBw7exCam+5S2FwV4iRhEpjiUI0Pa5HcVbFbpHyNdqJjJYX3QCD0Sz7+hNc\nNsNFMKFOaOs1u8rFhy87984MfZFMXnQDPNUaodFnYdOxWP6xSFrj1b4kb5hZSLNPqXpedBsIrK2y\nEkpk2N5a+Kxd4QwPHU2xod5GvfPiy+y6ENB0nd8fijJvTjkHjwyi67Cs0cM1842uIJ/dWMP3tgww\nFM1y1Uw3VzQZ/962XOVXu0fpi6lkVeMCCyZVEhnNcNdXRBK56LjTIrK63onFIvF0WwSXRaTBLdMx\nFM8HIsbPZdbWO1ld6+CXe4O83GPUmgvAvFIrDovE40ejOG0KqqYj6DqNXgvPHxwlkVug2t8X52fv\nmpsX5yUuK0sX1NAzEmdkKERzjZsFdRPT6U1MpiOm8DaZtlzdXM7VzYUJiabr/GH38ITV4EAgwe93\nDHLv5dX5x7KqjiQak6ijA3E+9bsWRmIZPCV2cFnZ3Z/AZ5cI5CIuc8ttPNUWZXNHFE2HrvAgfzk0\nyIcub6S50o1IYbb0ziU+vr1tmHhGp8JtYXa54fJ5eDRLo0s0o8EnYfdAgh+8GsivtN+zQOPqRrOm\n62LnZ/vCDMSM39qh0TQfX+Gl7ASu1SYXPh6LwGUVAhkNLKJ+yoj3f/5sE9/73RYkUeCzH7yO99y+\nmhKbxO1z3YwmVLb0JLFJAvcs9nGgP8bBQSOVfV6ZlS/dVD/pMTtGk3zx6R5U3XBqbg+keOCWhkn3\ntUgC9SUWohkjnVo+g3t0uVPmn68oJa3qWCSB72wfoSecRcyNLzZZYJ5PZnWVJf9eY4UOQEluUbjC\nJWORhKI038VVduxTYKzWF51oof6b/SHsFinfWxzIm2IdJ6NqoGsgFB6f75MRfRK/Eov7f1tlkUMh\n/bQWWkzOHE03/t7VlR7K/E6yqsb7lvvyLe/8Dpl/3lhb9Jp4RuMLz/YzFFexWCQkVSCVVplXbsNl\nlcAq8ZU3zeSHm/sQBPjQ+hpK7DIbGl1saDQWYLZ1RpAFnaxuvM+quon+B6Io8PbFXuyKwGhC5bI6\nJ1VOiefbw3hElYZSG1c3+Znls/BiW5hNewuO/93BNMFEllKnQiSlcv8T3agOB1UOB4vmlPO1NzZi\nVczxwOTiwBTeJhcNP3ppgId3jwKGqRoUWmk8uH2YplIb62aW8JMt/fxx7yh2ReQfrqvlS4+2MphL\niRoeiqJYJOyKyH/c0sBfj4SwSgK3LvDy5c1DRaJe02FL+yhLKywoFCY1c0utfPWGGo6GsnQlRFr7\nozyxqx9V03nQJvHAHTOocp86BfJSZGdfssiYbUd/whTeFzmprJ4X3WBMLHuiWVN4T3MkAaTT+Ar3\nt/bzvd9tAYyU0i/88G/csmEBZV4nkbTGN18J5Ht8HxhO8cUb6+kJpdF0nXrv5F0k0qrGF5/qIZIz\nyqwsdxLRRD7yWA83zXFx+7ziGuauSJZHjiZIa+Czirxlnh3nGYpdiySwuTPG7tw9TBLgihlObpvr\npmSMs5wkCnzyykr+e8sg8YzG7c1eFlQamT1WSeC+5V5+vDtIIqtT6ZS5Yea53/8ePhTi8ZYIkijk\n221aZBFJEkmrOg5FJJHVWVJh5bLaQpbR39qj/OlI1PDuKLPRXOlgTolESa7s6iNryvnR9hFSqk6p\nx0pnMMVCu8xFntz1uiGLAlc12Hm2M4GiSDT4LCwsP3kZQutoiqF44f4qSSK3Nbu4e7E//9iKBjff\nffvk9dP//UIfj+4z5lVNpTZuWODj1mYvfZEMfzkaRRDgptkuKl0Kf9gX4KH9Rq/vTFqlJ5zhWNAo\nw2gdSfGGWYZg74xkEQTyxmrVHgVvrrzh8GCCkXghk2I4qTOuVN3EZFpjCm+TaUkqq9Mfy1JiFclq\nhqHNjoFk/mau65DJZHHYFNYtqUaRJf7Wm+WXh3rIZDUkWSKl6Xz5qZ686D6OBZ13Li+l0q3wrpUF\nB9CZPqO2bSzhRAI7ScYjiwLhSIqHXhmiYziBIAoIgkAwqfL9lwb41xsmj9Jc6pSOE1tldlN8XexY\nZcPReSiXXSIJUO0yv/dLhXii+J6qaTrJlHFPbg+m86Ib4GggQzyjUVty8oXLF1rDdAaMmlC7XcZu\nNaY6OvB4S5SrZzjx2ArTn+e7Uxx/m0BKY8dghvW1p24NORLNMBBO0Vhmx2GR+MvRgr+HqkN7WOWP\n7Wnm+yTWVBbOeXmNgx+9uXHSYzb5LPx/V5UTz+g4FeGcncITGY3HW4wUc4siomk6FllElgoLC59Y\n48cuCXSF0gzFslS5FQKJbL72G6BlOMmb5jqpdRd+m6tqHfylI5X/zKGkyqH+GDNtVip853TaJifg\n5tkuFpRZiWc1ZvssWKSTXx+lDrmoXtsmC9yzxI9NPvXCUm8onRfdAO0jSZZU2clo8F9bhgnnzGcP\nDCX51LrSvOgGeOFYtMgjR9XhsQMB3rOqnM09SeprPIwGE4iCwEevrslnAla4lKJWgE6LaETmTUwu\nEkzhbTLtiKQ1frwnQjCloakawXgaHVAcVhpqRDp6wgCk0yrLm6tQcmYvikXGbbcQjKUoKTFWiXVd\nZ65N5EhnCACnVeLrdzZR55s46Xr7Yi+arvNSZwxVN4xoPrF28tq7Lb1JvvRkV8EgRNWRJCP1MJy6\niF3WzpEbZrkZiascGU1R51F4c3PJqV9kMu1572I3fz1m1HhfVmOjYor8AkwufJbNr2Xt4ga27u0E\n4LarFlBXaRic+W1SkWhwKALWUwgNoCgzyTJJ2D2Y0vCMCRSOD9Bq4xsMT8K2thCf/l0LyYxGdYmF\nH7xnAVZJBMZEF3Oi+VBAZZ5Xy0eKT4UoCLgsU1OS1BdJF/0NRVFgwwwnW7oT6MD1M53IAnz2qV6C\nSRVJNNzej4ymJxwrPS6ULUBR5BJgf0+EjXVmRtdUc2g4RVsgzUyfhfllJ14Uymo6PeEMbquI3y5T\n41a4d4Wfhw+GsEgC71zqOy3RfWIE+qPZvOgGGI2rPHIgOGFPWYDjRui6rtM+ZNR/64DToeB0GF4e\nZW4LyazG9r4kogAfvqySh/aOYpUEPnRZBS+2hhiMZLhhiYzPvLRMpjnm7MZk2vFKX5Jg7qaf1fSi\nSZPDrjC7wkaZVUJUFRhnqCYI5FPtjG0BbFbed0U18bTGG1dUUOebPHXLy3A63AAAIABJREFUKou8\nf0Up719Rmn+sLZjmh7tGSGZ1rpnhYH2Dg1hGZ/dAqsiVEwyXWl3XuXtJ6fhDm+SwSALvXWaGSi41\nvDaJe+abrWIuRRRZ4meffxsv7mxHUSSuWNaYf67Oo/Cm+W42dcSwSiJvnu8+LY+M9TM9/Hl/gEOD\nCaLxFH6vLT9OOBUh7y5+nPleke5whmAsTYlVYnnFqdO7v/tsd75dU18ozW+29fP2FZV8e9sosYyG\nxy5T7S2MJYHU6QvvqSCranzt+X5e7o5jtUo4bTIIAqFggp4eja9cV4emGzXdv9o1SjBpLBjMrHKz\nN6iDqGCVs6RyRdwNHoUZJcWmh5IocEOTgyfaYoBALJml3iMx/xTpzyZnxis9cX6y0/A+EYB7l/tY\nXeuYsF8qq/HVLcO0BzOIArxriZcrGpxF9dqnYiSepSuYpsFroabEwhsX+3lkrxH1vnG+l1llNqJp\nFYciEM8YpXzReIpnWo0Mk+MZGmvrnQhZlec7YoiiYXRbX1OCQxG5ebaLx48a2RRLKw0zzW++EqA3\naqSYN5YofOdNjUiiwLc2dfO/24cA+NmWfn74zrnUn2COZmIyHTCFt8m0Q6Mw8RLHTcJcisgX7pqV\nn5wdHEnz+8NGhFrTNCKJDOOnbaIg8OFr6hHHpfQNxDUOBVUEARb6JErH9eDWdJ2f7g4RzxhTukeO\nRJlRolDqkPG5LDisEvFcdNsii5T5HciS0b/SxMRkctKqzs4BI311WYXlHKMzJtMBiyJxzZrZkz53\neZ0dj0Xg2c44Tx+LcsdcN+WTZER0hNI83R5DFgVunu3iP+9opHU4gccmI0kCjxwKE8toNJQo7B1K\nsTRXVz0az/DdlwYIxjP5yO1jDp23Ly+b8B5jEWQRv99BMpklHk+DALP8Vv7zDVUkMhp/ak+RzY02\n8bTGgeEMjZ5T3/sjaY2RhEqFQ5pgdHa6ZFSNj/zPQQJSruVlSiWdVhkdjhGLpenohOsX+lmaM8k6\n7gjvtsv4XYVoao3PRtdIHE2Hexa4J4yRAOtqbMz3KxwaTmGXYGGl/YzM6UxOzSu9ifzCkZ7bnkx4\nb+tJ0B40yjQ0HX53IMQVDafvEXB4KMHnn+4lmdWxKwL/srGWj1xZza0L/ehAQy4T0GWR+OAKHw8d\nCBNJZQkXjPHRdZ3PX1/D3DIbiYxGJNnD/r4Eq+uc3Lu2EoBb53pYU+sgrerUumWOhTJ50Q1wLJRh\nOK5S6ZJ5fEyqeySp8nxLiHesMYW3yfTFVAAm047V1Ra29yXZ2zpKJqOybJafYDJLmVPm7gWeoohI\nc6mFj62QCCQ1HBJ0huz47SK/3RNg74CR1vSeFf4JE4pEVmfrYDZvErNlIMsN9QrKmGOnsnpedB8n\nkFRpKFFo9ivcvqaW3e1BBHSCGWORQNPh0cMRs02WickkqJrOT/aE6Y4Yk7BX+5Lct7zEnMhfwvRF\nM/x4dzCfPt4XyfJ/ryzLR9Y0XeehIzGea4/khXPraJp/2VDO/MqCOFlcaeOnuwIcHE7xZGuU9y7z\n4bRI/PZgBFmR0PWC18efDgZPKrxbR1MoPjd1XkNoxAIx3ramCoCMBtsGMgSTKvGs4eie1QDXqUX0\nsWCGn+wJkVJ1XBaBDy/3TrrIcCqe3DvC3p4odQ0FEW34nhTS4HtDqbzwvnmuhx29cUaSxS5WgiAg\nCQLXNTlpOEldvdcmsa5uohA0mRp84zL3xm9PFX88ECSZyw1PZHQePRDg0xuqqR9Xejcaz/KNF/oZ\njhn3aXnM4qjbJvFMe4zNnXHuaC7hczdO3kmgwlm4rt0WsagcQhKM2m6AMpdCOFm4bstcxVkXmqaj\n6oXFIxOTCx1TeJtMO0osEsdaBznaYdRy9w6E+Z/3L6CpbPKB32+T8OcGqmq3cdO+/6oqeiMZHIo4\naW/XWEYvcmbNaJDMgjJm7mFXRGpcMoG0MbkSMFrLACwplWlyO3nTHAcvdqd5eP9I/nXm+GBiUkxW\n0zk4miWQVOnJRT5KHQrzK13sC4jMcOlMYrtgcgnQF80W1WwPJ1RSqo5NNm6ku4fS7B5MFdUZjyRU\ngkmVsjH39h19iaLjbu9NMJjUc+3GikXxiSLNx6IasSzs6E7kxwdBEFjY5KM816nit4dj9MUKAlYA\nXBaBtdWnLk7927FYvkdyNK3zXGeCu86wBOMXW/r4zqZuEET6+0JUVRs+GelUlnTaEDA2m8yVswr+\nGS6rxJeur2EkkWX7YJbDQeM3uKbSwqolrlMaeJmcX94438NoQs3XeN8xf/Ke1mtq7bzYFaMtYKSa\n37PwzDxSlHELnOO3j/N8eyQvugEsIkjo9AxGsVZ7ebk7Dhh16V++vvqUC6dlDpl7mt08djSKKAjc\nOc+NKye8P3fLDP7tsQ4GI2luXVrJG5oLpWgvd8X4/rZh0qrOjXM9vGuZ/0RvYWJywWAKb5Npx9a2\nIDtzohsgo+o8ezR6QuE9GaIoUHeSFXyPRcAmwfGFVqcMjuKFVrZ0xeiJZHDbC0+81JPkrnkuklmN\nV/uNOu+1M0rY2hmiN5LFJgs0eC08fjTK5XV2vOdp5drk/KBqutmHfYrRdZ1HWxP05sRKlduKVYRF\nVW4USUQF2qM6DlnDNLe99GjwKEW9revcclH5QTStI0vFETOvTSxq4QUUiXAw0mVrSx1kNZ0D/VFi\nyQzReAabLPCJK6ry+2m6MQ4cDmmEckHx6jI37v4EkdwA4c7VbqdUvUh0A1xZa2VVleW0TOHGZ3Kf\nqaF5KJHlO5u683+HbFajr2uUKxZWkLI6sVtlNF2nuc5DRtX51B/aODqUYEW9i/tvqKfcqXBTk8Jl\nKQ1JMCKRZ8vewTg9gRS1Lum0UuxNToxdEfnYmlN7w1hlkU9fXk5fJIPLIuE7QVeQnz2+m8c2t1BT\n7uZz966nrMSYO62pd7KrL04srVHhlHnL0snf0y4XX5geq0Q6kgBRRBgzPg7GshMWwE7EujrHpFkT\ncyoc/OreZgDcbjeRiOHQn1F1vrttOO+l88SRMKtqHDRXmGnoJhc25t3QZFoxFEnxqV8fQteLb/C6\nzcZISqfUOqZ9xTmIJIsksKFaoTWsIgBzSqS8Q+1xXuiITUhRj2c0NF3n5/si9Mc07BaJ3SMj3N7s\npcIm8sv9YfaNZGAkw56hFJ9c7cNq1rBe8KiazkMtMQ6MZHApAm+Z75pg0GRydkTSel50gyGeZpY6\nUca0O9IRSGmYwvsSpMwh87GVPl7sjmOTRd7QVKhZDSSyeGSQdI10JoskilR7LHxohW9C6ukb53sI\npVTaRg3jqNlVJfmWWl67jAWNjTPsLKtxYFeMCy2pwr4ApDSKegnLosDSGhcvtoVQJIGWQIb/2jLM\nfSv9eK0CwZQhBgRgjk8+LdENcEOTk+6I4RvitYpc03Bm6dvqOLNRgC++aTZer4Nf7gtTX21ESueU\nWfn2c73s6DIMrp5tCdHgt3LvZcaCg/cMTODSqo4sUjQW7hpKs23AWKXYP5plYz3MKjHvl1PNaNxY\nzHdYCjdGWRSoP0lQ4alX2vj8TzcDsLNlgFA0xc//5XZe6Yrx9c0D+evnikb3Cecm1872sLUrxq7e\nOA5F5J4lfr7/yjCVlfbcvMvIBPHbpQkLYGMZiGZ4Itfq7uY5birGpZGfiLZAmv/dH8ZlV4inVFK5\nEopoxuwYY3LhY94JTaYN393UxS9e6iOjgqZlkWQJAYFrl1fTWOkimoFSKxwLpPj3TX0Mx7KsqnPy\nfzZUnVX9j1MRWFJ64p9IiU2iLZTEYZXy9YYrq2xE0zo9ERWPXc4//mx3mlubrATGtOAIJDX6Yyoz\nSkzhfaGzazDNgRFjIhnN6Dx6NMZHl5utzqYCmywgi7k62Bzjf6+KqOMwRfclS6PXQqO3WEy80hPn\n+9uNiFcsmiadu4CSMRm3ZWLKqU0WuW+lEcEbTsLhQtIUB7pCbDsaYNvRAE1+K1++pQGHItIdM0T3\nZAyE0iyptHIkYKTcHgtl+MvRCPfMc/FMZ4qUqrOy0kLFGVy4dR6Ff1jnJ5TU8NulM07x9jsV3r6m\nkge3DQCwutHNhrk+JFEgmtbYO5Si3CFx6xwXnzk4VPTawUhmskOeEE3XefhInL3DaaySwN3znMz2\nGcKpI1IsgDojqim8pxBV0/mPZ3p56VgEWRT45FXVXNboZntPHFEw+qsfT+/WdZ2H9wXY359gVpmV\nofbhomMd7DC2HzsURJKNPu9+j40t/Wm2DQxy7zIfS6uKo8iKJPK562oJJ1Xsish3tg0hjlkoTadV\nltQ4eO+K0hPOvRIZjS+/MJh3098zkOBL11Vjk0UiKZU9A0ncVpEllcV+OFlN5wc7A8QzOpIk4rIL\nZFWNGrfM4grTO8fkwse8E5pMC3Z1RvjJ5l7AWEkVRRE1o+J3W7l6cRUC5Ps7fu/lQYZy9UevdMf4\ny+EQty3wTvk5vWWRl+H4MN3hBPVeG29Z4GGWz0JG07HLQl50QyEF0ioJ+Ro+RQSfzRTd04FEVj/p\ntsnZY5EEbphh47nuFCkV6ktsOBSj9d5IwnCaXlUuIIvmb8WkwG/3B8lqhghJj1m1GY5l6Qikaa48\n8STcKYMIaBjCZPORggdH+2iKZ4+GuLnZVxQ9lgWBQCKD0yLRNhxnT0+Eep8FxvTJiGU0/DaJu+ae\nXqR6W3eMlpEUs0utrK0zIvl2WcR+GkZsJ+Lvrmug0iHy748c4onNA6RDEf77QytZ32C0uzzO9fN9\n7OszanFFAa6de2Zj5IHhDHuHjV7fKVXnkZYYn15jHMNnFRmIF74T7xT1JDcx2NoR5aVjRqQ4q+l8\n64U+nu2I0ZLrvb640sZnrqxAFAQeOxDk59sNcb29O8Zqn+FOf7xX/RWL69jRG6ctnEXJZXocf07V\n4Y9HwhOE93E8uVK58f3dl9U4+KerqiZ7SZ7+aCYvugFGEyr90Sx+u8QXnh9kNGE8d91MF29dVLg2\nExmtyNRWEARuby7h1rkebGfZBcDE5LXEFN4m04JAvHg1XhAE3ryqgjesrMXpkKiyC3hyg3tkXIgi\nlMxyeDiFTRaY4T21wc3p4rVJfHZD5YTHFVHgbQucPHw0mW995rEI6AjMKbPRH81iFXRumOnEY+bO\nTgsWlSls6U0SzwnuNdWm09dU0lQi01QiM5IS6IoZ9bz9sRSjCWMBbcQtU2Y3J1WXIqGkylA8S41b\nmdT0TBSEovpuRRQYimWoTih47ZNPcewyNHuhIwogoEgC2TEObo8ciaFYLVzR4GQ0BVnd6Fn9/KEh\nesKFsai53MaOgRSabiykXlF/+qnhz7ZH+NGruVZJLRFiKzSunTk1vey//edDVNWVsmpGGamMyk82\n93DfVfVF+9yxtJRKj8LRoSTL6pwsqjn9tlMAyXFia+z2uioLgiTTH0lR45RYWn56KcQmp0dKLZ7j\npFWdIyOp/GL/3oEk/ZEsNR6FQ0PJon2TVhc/uv8WntjSSk25i/vuWMH3dwSK9slkNSyyMTc5nSWT\nW+eVcHAwSUo1gg73LPaddP/uYIrO0SQ2Wci7qDsUgTKHxI6+ZF50A2xqj3LPwpJ8KYPLIjLLp9Aa\nMH6HHqvITXM82E3RbTJNMIW3ybRgdaOHBr+NzlFjELlhYSmfvW3yvq83zSvhp7kVXoci0hbOsvll\nY/v6mS7evOD8pwg3llj46DKFXYNpJMWCT87y2yPxnDuvQK1bYV7p1C0CmJxfvDaJ+5Z6aA1lUUUL\nGgJHIzDLpZ+xAZLJiSm16pRadR5uTRJK6+i6Tncgyd7uDC6LyLuXlDDLZ/5uLhWOjKT4xsvDpFSd\nEqvIP1xRTmWuDvQtC718b/swWQRmVjoQVY1ERmMwluW/nuvHZRH5wk31NPmNRbJkVqc3quKxCpTZ\nJYZTUt7N/LZlNTz0ajdZDRw2GZdD4S9tMVZU25jjFtgzlMZjEfjImlK+/tIQowmVdfVO3rnEx8Zo\nlu5whkavhQrn6S+k7uhNTNieKuEtO+w0NFUAYLWKbA9q3DfJfuuaPKxrmtwh+1Q0lyps7hYJ5ha6\nL6spREUtksDNc7x5IyyTqWXdDDczS0dpG0kBcMsCH5t7kvnFJ1EAu2IMTPPLbWxuL3wP8yrsXLW8\njKuWz8g/Nt7k9bg1jiLCnSdwUB9Lc7mNr9xYS084TUOJBd8JFrwAXmgN8+W/daFq4HMqLGrwYFVE\n7mwuwWWR8m7mx3EoYpF/gCAIfHiFj81dCVKqxrpax4TXmJhcyJjC22Ra4LLJ/PTehTx7eBSHReLa\n5hO3jbi12UuT30p/JIMiCzy4r1DM91RbFFGRuWWmY0papIwkDTO1MptYlFoORlr52morbrebZ1qG\ni1ridEaymEwvPFYRxWIlnTW+55EUuGQdsyX71LOx3sIrAxk6gul8tkskrfHgvhD/sr78dT47k9eK\nPx0O50tzQimNv7ZGeddSI5q2qtbBA6W1hJIq1W7D+fz//Wt3PiIdTWs8sm+UT26oJprW+MWBGOG0\njgDc0GhDshSi00vrvTgsAs+2hwyH9Ny9PJDI8sfWBNFcauvScgvfvLUeTdfzYqDOo1DnUfjD/iCP\nt4SxSiLvX+lnZc3Jo99VLgUoiO+qKTRrXD2/gtDYBwSBH7w8QDyjs77JzcpcWnsiq2ORmGAcejo4\nFZEPLXXTGsziUgSavGZU+7XCroh85bYZHOhP4LaKzCm3U38kzG/2BhAFeNcyf1783rrAS1bT2T+Q\nYFaplXvGOZWnVZ219S66wmnaR9PM9Fv48KoyElkdj1XEfZpZeWUO+bTcy/93xxDHA/aBWIZyBe67\noiL//PIqGxtmOHmhI4bTIvLBlRPnelZZZGPTmWVomJhcKJjC22Ta4LHL3L6scIMejKTZ3RWl3mdl\nfnXxTXhhpZ2FlXYOjkuzAmgJZtnSl+KquuK6paym0xU2XEKrXaf+aWztT9MSMlKi6lwiV9VYJojv\n41SNi4SM3zaZHmTGGS1ltbFJriZny2BcpSeSpdolUeWU8VhENtZb2YzK/gEjzVcQhLwAMrk0OFV7\nLa9NKorWje8XfNzYac9whnDauHZ04KXeFOsbi4XxQCSNqukouVZJmqbz+NEo0WwhmrZnKM1tsxxI\nolEju607TiJrGKE9mnNry2oa33tlhO/caj+pqeebF5YQSav5Gu+7F06dD8ndq6v47vZAvmY3kczy\nZIsxFr7YEeVfr6/hcBiOhVWsEtw+y07DWQh/hyKyuNzMQDlTdF3n4EACTYeFVfYTzhtOhk0WWVFX\nmPfcPNfDjXOMjInxEeI7F/u5c/HEYySyGr/YH2M4qYHFxgfWeFlbbT2r8zldxjulx9Mq33+xD7dV\n4k1Ly7ApIu9e6uMdi71m606TixJTeJtMSzpGknzwFwcJJ412X/98cyO3LS2bsN/8Mitra+1s7TEi\nC+UeK6IgEBpXB55RdX68J0x3LhK9cYada2acOGIRy2h50Q3QHdUYTmqUn6BvZr1b5o2z7OweyuBU\nBK5tMHtNTkcqbDrdcWMyIAo6fqspBM+V1mCGBw9EUXUjxfGt813M9RvRswWlFv40xi0XQeDoaJrZ\nfnOyfynwxvklHAsOEc/olNolbpp98lTsd60so2U4SSChUuVW8tG9cW2HkQTIpJLIFhuCICALGpuO\njOC0yUjScTdo6AhlsFllMlkdRRLwOeS8GPjutmFe7jbMybzjooJpVSet6icV3lZZ5L7VE8esqWBN\ng5tNLWFe7oqhazp2W2Gqp+mwvS/NiGqcc0qFpzqSvH+R67ycy6VGPK3x41dH6AylWVxp4x1L/RME\n5H8+08umo8ZCzRVNbj57fe2UiN3x7U1PxZ6hDMNJjXRWYzCY4CdDMf7qkfnkurLTjnSfKR++oor/\n++cOQkmVGT4Lz7aEiOdWtHf3xviP25sA8n8zVdMZiWcpsUlMTSGGicnriym8TaYFWzujbO+OUeNR\nuH2Bjz/tGSacc8TUgV9v659UeAuCwPuW+5lXnuCF3nT+Zj7XV5wWd2g0nRfdAM90JFhfb58QQRl7\n3PGcatBbWGZhYZkpGKYztQ5wyBopFUoscIJ1FpMz4NX+FMd9mTQdXulP5YW3a5LJn+kof+kw02fh\n36+rZjShUu6QTthX+DgNPiv/dVsDjxwIYpELtaHLKiwcCWTpjqpYROgNxPlWexABeNtiL9fPcnP9\nbA8vdMbQx1xeDlkgECuYqV1WY9SLJzJaXnQDBFMqfrtEMGkIiLV1DpyvQ91pKKWxuTdNIqtz67IK\n7l1rRP2/vnmAvf0JNE0HdHwOmZFI4YOmzfbHU8Yvdo2wpSsGQE84g88uc9v8gq9MVyCVF90AL7ZH\naB1JMbvs/C3GH3cpHztHCSezvNgWBtlCMJpCzdXCdYezPN4S4S2Lpr4TDMDcCju/fPdcwgmV7V0R\n/uuZ3vxzO7pipFUNS26xNZJUuf/xTo6NpnBbRf7jznk0mOtDJtMcU3ibXPC82h3jP57ty28Px7IT\nJuTOU6zOXl5np8Il0x9TqXPJNI7rKTq+xk0SCgYjk+GQjR7fe0YMsT6nRKLUbA12SWB6e00t9nHh\nyLHbFklgbY2Nrb1GmmyVU2KuGe2+pHAo4qRu5pORVXW+9Fw/3SFDLG/pjHLPIh+/2jWKpuvcvaQU\nSRZ4sNtou6QDDx8Mcf0sNx9cU06VW+axliiyLOK3GyUPgWQhO6o9YLxOkYQiR2aAe1cY/bftisjK\nmtfH+OHJjiSjKeOcXuhNc3uTjXK7xCfXV3H/Yx20DhmZXy09YTylHiK50o01Vef2m0pkNLZ3RnFY\nRFbWX9rKaKzrPUDfuP7o8iRZECfLjDhXnjgU5MdbB9GB964q5+ZmL1vaw/zwpQF6w2mWz6/IC/Pj\npNTzu7gpCQJPd8TZ1Z+mtspN/1AUVdWp8ih50Q3ww5cHODZqGMhFUhpff6aDr97ecF7PzcTkfGMK\nb5MLknRW48Hdo3QE00U9WgH29Mf5yk31bG0PsbMzSplL4dNvmHGCIxWY7VWYfQIDmPmlCs2lCgdH\nMogC3DbHecoI9pIyhdleGU3XcZmtLExMzoprGuz0xVR6oyqVTonrZhSLljfNc7OwzEpK1ZnnV7CO\nzxs2MckxEMvkRTfASFzle9sKZk4/3T7EO1cUm0uNFT23Nfu4rdkwb4skVT7+p05cLhuiaGQ5DcSy\nZDWdYDzDvBKZPYNJZEXi5rklLK06/VZi5wNd1wmkigVTIKVR45JIZ7W86AZ46lCAB97oxWZTcFvE\nc/IcSWQ0PvXIsbxAuqnZyyc2VJ/18aY7y6sdtOb6aQMsqy6+n1V7LLxtRRm/3mF0WrlrqZ8ZPiOT\nIphU+eGro3SFM8wttfKBFT5sp8jyOBlD0Qzfe2kg70Lyo62DPN8S5JVjBZfzHQcG8JbYKPU50AGb\nLHBN4/k1LnuuI8YLnUbGiMthoanag0PL8PENNUX77eiOFm0PhNOYmEx3TOFtckHyP7tG+WtLIR1L\nFIVcmhzM8FqRJZFrVtTRMDNDlVOi1n9uaVqiIPD2BW6CKQ1J4LT7aztkgdPrdGliYjIZLovIh5Z6\nyGr6CUs7zNZ7JqeDzyZjVwQSuUiuLEJ6TAMJTYd6j4XmMisHh1MoosC7lkzec3ggkqZvOM4cty1f\nWhTJ6DzZEuGHL/ZyfD14ea2Du6bQGO1sEQSBerdEZ8TIG5cFqMkJ6snil4oIc3zn7kS+ozuaF90A\nTxwM8oF1FZdsPe6bFnrx2SU6g2kWVdkndbd/9+pybl3oA13H7yx8B384GKYtaCwc7R9K8URLlDub\nz67dG0BHIFX03evAq53FYlYHwpEUn76qGqtFotFrwXeea6iG48W1DQ2lNu6/sn7Cfi5FIpBQEQQB\nXdeZX/n6Lm6ZmEwFpvA2uSBpGzOQA8wqtYGuUeOx8P7V5bzQnWDfsLH62R7K8nhrnHuazz7FbTSR\n5YmWCC92RBiOZGiusPGPG6pwWMwiXhOT14ITiW4Tk9PFYRH5hw1V/HLnKKqm8+ZFXh49EOLwsFGq\noEgiP98f5s3z3Ny73G+ksY+pxR6IZvjtviDxrMY1jU7KnAqyKBSJl+dbQ4xNwtrZEyeWUnngmR4O\nDyZYWO3gU9fUYn8dsqCub7CyZyhDQtWZ65Xx5cqfKlwKb1zs55G9owBcNdvDvIqpSYd3jhsjLZKA\nIk3+2eNZnXgWXArYzmN69euFqumEUiobGl1FhmqtA1H+9GoffpeFt15ej0UW8U/SeiuULBakodS5\nFd+L6KiqhpT7PlRVg3Fp5fOqHLhKHPzuUIQrGhwsrz7/ZRJLKm280BnP/66WVk4eOPnAZZX82186\nyWQ1vHaJT25sBMyot8n0xhTeJhckCypsHB0piO+b55Vw1czCGvp4V/JQelyfp0noCqb5xosDDMez\nXNno4v2ryhAEgXhG4983DzGaUAERp13h4GCSh/YHeefy0lMe18TExMTkwmBBhZ0v3VCb315a7eDh\nA0Gebo/hsCmAwMNHonz+KntR7biu6zzw0hBDMSNEfmQ4xd9trGP7UJbDg0ZarE0RqbTJ7B/zfgLw\n45cHeLHdSN997miYcpfChy6vOt8fdQKKKLCycvLskI9cWc1NC3xkVZ1ZZbYpaxm1rNbJbQt9/Gl/\nAIsk8KmrayatWe6LaxwJGdlrkgCLfTpu5eIR30OxLN/YOsxwXKXULvH368ood8p0jcS5+2tbiSSM\n6+qV1gDfet+ySY9xWb2DllyauigYJn3nwtwKB25ZJJA03ruhxMJtayv41nO9qJrOe9dV0hKDgdw1\n/5ejUZq8lvMuvueXWfnEGj8Hh1NUuWSaPDK9wRQ1XmvRfqsaXPzs7XMYiGRo9FupKrESiZjC22R6\nYwpvkwuSty4xohEdwTRLquxFohtgUbmF3QNpjsvtJafRS/SbLw3QETRu2k8eCTOn1MZVM910BNM5\n0W0gSSKCAFHT6tXExMRkWmOVRVbWudg6UKj91nSj5ZdjTKZ1PKPO9+GtAAAgAElEQVTnRTeAqkNK\ngwa/Ha9DIZXR8DpkrqmW2dEVJZgbM+5Z5udgX6F+GqD/AqlFTWU1nmyLMZJQWVJhZeV5ElQfvbKK\ne9dWoEjCpL2XN3UmeWUgjQA0+axUui30xcFdMvFY05XHjoTzKdQjCZU/HQlz73I/Lx0eyYtugKf2\nDKJpOuIkf6fL6hyU2iW6wxlm+S3MKDm3EhuXVeKbdzXx6N5RFEngzqWleGwyNy/yo+s6iiTysT/3\nFr0mkDw/855UVuPQUBK3VWKm38qcUuPfz7b086kX+wG4ZZGf+28sNk8rcymUuc69JMLE5ELBFN4m\nFyQZVaezL0L7UIIqRYdZxXVOc3wW7l3qpj2YpcopnVYN6Gg8O+l2qUNGEsi3NNJ1HUUUuHbWqWur\nBqIZfr0nQCytceMcDytrzRokExMTkwuJJq9Ck1ehPVc/u7Lahtc2rjOGRaShRKEzZ85mlQTm+BTa\nYgIggw2q7QJem8Qv3zGHrkCKEruExybzxIEA27sKtbPrZ10YivJ3ByPsHjQyxw4Mp3EoIs1l1lO8\n6uywjUutj6eyHOmPIVpkXhkouMi3BVKUOpUJnUSmO+lxTuDHt+tLi+cENT7bpKL7OHNLrcwtnbrv\nqMJt4QPjsi+Msh7jHNbW2dmcMzqzywKLKs7OL2d/T4T9PVEW1rpYWFscKElkNP7lqR46cl0B3rrE\nz5sX+xiNZfhRTnQD/HnfKLctKWVRzfk1dzMxeT0xhbfJBcnXnurkkZ1DALzcFsZplbhtaXnRPg0e\nhQbP6a+Ebmhy89ihEGAMMKvrjZt7hVPmAyv8PHo4jKrrLCm3sqHRRY3n1GL+Ky8M0Jfr/31oKMmX\n3lBD/TmuUpuYmJiYTB2yKPDRlT4ODadQJOGELek+fUUFfzwUIpnRuXamiyqXQoVTZzChIwpQbisI\npnpfQRzdtMCH1yFzeCDOomonqy6QZsMdoeJWVsdCmfMmvMfSPZrkgz/ZTl8wyczaEtatKu46YhN1\n6l0Xl/C+fpaLA0MpUqqOVRK4fqZxDVw+r5TP3D6XX2/uwu9S+OJbF73OZ1rMO5d4me23EEpqrKi2\nUeE8c1mw6eAIn/nNIVQNJBG++rZmNszz55/f2hmlLeezIIoCv9s3yhsXetEmcf1TJ3vQxOQiwhTe\nJhckB3pjRdv7e2IThHcgpbM3oJLWoMElMNdzciO096wsY3apjZF4lpV1DmrHCOvVtQ5Wn2G0OpHR\n8qIbjIh5ZzBtCm8TExOTCwxZnBjNUzWdY6E0mqYz02fFY5V411J/0T6iIFDlOLVIvKzRzWWNF5aX\nd0OJQnCw4JUy4wwWqs+FnzzfRV/QEFrHesMsi6ewOQzB3+yXWVchTlmN+YXCTJ+Vf726gp5wllKH\nSPWY9OgPbWziQxubXsezOzGiIHB5/blFmB95dSDfsk/V4JEdA0XC+8nDIdRcBoCq6thkCVEw0sjv\nWl7G73cardWumOVhca0Z7Ta5uDGFt8kFyZI6F0cG4vntpfUTIwi7RlWOe6y1RXR8Fo1y28mdZK9o\nPLNIREbVaQ9lsMsC9eMmLXZFpNFn4VgufcoiCczyn/9ogomJiYnJuZFRdR7YMkxvNIuu6wSDCapc\nMnPK7bx3VRnWc+iffKFwd7Mbt0VkNKGyuMLKgvKTj0+bj4Z4+ViERr+VNy0vQzxLcTzWOFvTdfqO\nDfB/7piHLAjM8EgXneg+jlMRefRggH0DSbw2ic+sr2TmJTAn8I+rwR7bIi2e0TgwUOyBcM1Md/4a\n+PuNddy8yE9a1VlQ7chfc8msxo92jNIymqKxxMIHV/ov2RZ1JhcXpvA2uSD5++vrcdsk2ocTrJtZ\nwk2Ly4qe13WdPR0hXjkygt0icd2KalLec+vlPZ6MqvO9nUG6c1Htaxrs3DirWLj/0/pKHjoQJJ7R\nuG6Wmyq3aQJiYmJiciFxeCTFkZE0vZE0u/sSOC0iV85w0hs17u2CIFBSYmdn6witOVfp+9ZVvJ6n\nPCXYZJE3zjs9ufJia4j7/3gsvz0czfDhDTVn9b7vubKWzS1BhsIpXFaJ+65pYLb34h8bn2wJs2/A\niPQHkyo/eXWEL1x/dn/Dk5HVdB54qosXjoap91n53C0N1JS8dgI/EMvQNpykodRGuUvh49fN4Nhw\ngr3dEZbUefjYxkJpgVUSsCsiiUyh88zacaUYcyfpz/3o4TC7c3/LA8Mpfn8gxMfXe8/TJzIxee0w\nhbfJBYkiiXz46roTPt8ymOC3zx3L1wgNhZLc8YEFU3oOB0fSedENsKkzzlAgwZ2L/bisRlq7xyZx\nw1wPI3G1KHXdxMTExOT1Z+9Akh/sCOR7Bmd0GI6r/K01CmJxVDuT0dB1nbbR1MQDTVNCKY2EqlNq\nE1FOYuq1NdcOLb99LMKHN5zdezaWO/jr/evZe2yQOr+9KAJ6MTNWXE62PVU8smuYx/cHADjQH+cr\nf+3m63fPOi/vNZ6WwTgf/81RIkkVuyLy1btmsaTOxU8/sARd1ydkM0iiwGeuruYbL/STyGjcsdDH\noqpTl/WNd1c/X27rJiavNabwNpmWtAzEi4w5BgJJRF3nuFPnVDDOpBVNg9/sGmF7V5Sv3tGIJAps\n7Ynzq70hdMBrFfnUujJ89pPXmpuYmJiYvDbsGkgy1q5JFkWyqko0rVHhFImrRgZVT38EQTCi34ur\nzm8f49eKlmCWbQMZdMBjEbihwYp1kh7bAE1lxRljjaXnlkHmcSgsqT91Z5DpTlbT+Z+tAxzqjzOz\nwoHLIhJNawjATXNP//Mnsxo/3zbEUCTNVbM9eWf8tpEksYzG4jFidShabJo3fvt88uC2QSI5EZzI\naPxi6wAP1BkR7BOVEKysc/KLt82aVJifiLW1Dl7tTeR/u2vNjjEmFwmm8DaZliyocWKRhHzLjgXV\nDixTXJM3r9TC0goruwdTRp3asGH41jqSYiSWpcKt8GRrND8wBFMaL3XHuWWOWYn0epLVdNojGhkN\n6pwiHsvFWU9oYmJyasrGLYTquQLk1bV27ltZyhf/2s2LbSEUATbM87Gywc1tCy6OlNbdw5n8+BRO\n67SFVJr9k0/77lhaymAkw9b2MDNKbXxqY+1rd6LTmF+8PMDPXh4AYEt7hPddXsm8GheVToVZp9EW\nbCSe5fm2CH8+GKStz2hJ98zhIF+6bQabOqLs7DXqo8scMt++vR5RFLl6rpc/7BzOz3/e0OwDIJjI\n8s3NA3QGUyyvdXLfuopc67ACGVXjf3cM0xlIsXaGm+vmndm1roxbuDlZFsV4zqS2f1mVnU9fXk7r\naIoZXgsLyqe2lNDE5PXCFN4m05KmMjtfu2cOj+wawm2X+cCV1VP+HqIg8PaFHlZVJvnHRzvI5ELs\nLquIJzeZGz8IWU4QTTB57dg+rDKcNL6rzqjGhmoZh2x+LyYmlyLXzXQxklA5PJKiwinT5FXw2SQu\nqzeMnD53Yz1Q/3qf5nnBMKoqxPtPNjyJgsB966u5b/3Uj6XTlYyqTxjjx7OvL4bFImGzGdPpLcei\nvG9d1Ulfc5xgIss/Pt5FIGFEkJ1OhVjMiF6/2BZm50Ch5GE4nuXLm/p427JSmqscfPdtc9h2LEKd\nz8JVcwzx/IOtQ+zqM0xpnz4apsqt8ObFxS7933uxn0f3jgLwbEsIiySwYfbp9Z2PpzVURcYii6Sz\nGqVOmQ+ex+tlqnuam5hcCJjC22TasmKGmxUzzn90ucqlUOpSGIikkQSBe9dUYMtF1+9uLuGHO0eJ\nZ3SavAobGsx0qNcTVdfzohsgq8NIUseR6xmbyGj84OVB2kdTLK1x8O6VZUhnsGJvYmIyvVAkgXcu\nuTgi2GfK6kqFzb1pVB0q7CI1DoEfvDpCRzDDnFIr71jsPaWwvJjRdZ2HD4XZ05+gyi3zziU+XBaJ\nSErlm1uHaQ9mqHXLfGJtGaWOyafLM8vsHAlk8tHcvphKTyhN7Wm0Fd3bn8iLbgCLRc4L73qfla0D\nxV4Dr3TF2NkT58s31TOnws6ciuKSiMFxKee/2RsgnNF534rS/GN7eopbtb7SG6MlBg5F5M65Tmyy\nSEuu5/acceUHD+4eZf9wmooqN9msxm0LvMwsuzjKMkxMXitM4W1iMgkZVWckkcVnk/jj/gCjSRVF\nMaLczx+L8oZcetZsv4UvXF1JPKPhsV58vUmnG5IgYJdgzFyGsZ1OfrxtkE2tYQCOBVJ47RJ3LvJj\nYmJicj44NJxiV3+CcofMNU3Os27RdTbUuyTePMtGWtVxKgK/2htkR58hqka64/hsEnfMv/jrsE/E\n8x0x/nzEGA+6whl0HT6yuoxHD4dpDxoitieS5Q8HQ3xoZUG8arrOU8cSHAtncZY4EIRiY7p42jDp\n03ROurDrHyfmBaCsxMrVszzcs6KcHQNJ2nLtSjVNR9eNucmLxyLMyaVeP9MR5+WeJE6LwKJqR5Ex\noCSJPN0WZUmVnZU1RlBgTrmdY7l9nHaZUayMhgwT2Y5wBms6xXNtxue5ZpaHv1tfiN4PxgpdABRF\nYjRhGp6ZmJwppvA2MRlDPKPRFkjx810BRhMqJVaRRldxjWA6W+xUqkgCJZJpqHahsKZcZl9AJa3p\nNLolfNZC7X9XMF207/htExMTk6niyEiKr788nDcCHYhlefvi1zb6bpGEfAnUUKxYKA3Fs5O95JKh\nJ1IcIe7NbcfHuJHrms4jz7XyjQd30FBm51vvW0Z/VublvhSZjEp3fwSPTSKcMxxbWuOgL5Ti/j93\nks5q3LW0lHevLp/0/RdW2nnrUj+PHQzitIjcu7qcJdWO/Pd1eb2TfT0xRFEoWtQvc8pGtP5wlG39\nhogOpyFlE/nU+kq+/fIQgigiScbYF0kVvvePb6jGpoh0BlJUVbgJjLkEhqIZWrsLiwibWsPcuchH\ng89I915X72RPf6En97p652n+pU1MTI5jCm8TkxytgTQ/3BkgEM+QzZmWhFIaKZdMiU0ilFSRRYG7\nlpgR0gsZt0XgssrJb20rap0cHkoWbZuYmJicD/YPJou6b+wdSMLi1+98llfbODxSiIguqzpzwypN\n0xDFqTUyfa3ZP5gkldVoLrPyTFvBIHVRLnV7wwwXr/YlyGrQ3ztKS4fRuutof4z7f72Pu25cSHtX\ngJ6hKDa7Qjqt4bWI/D/X1LKizsk7f3k0b3z2m10jrG5w0Vw5eUr23Uv83H2COYUiCWRzC/2KIiGK\nAhvneLhpvpdNHXFe6kkgS4XvIpjUuLzRTWswy1OthoAutUssry6UwNktEp+4yugt/mJ3gsfbjJpw\nTdfRxl6sOcZG7K+d5cZjE2kdSTGv3MayarO0zsTkTDGFt4lJjj+1REhkJw48Flnk67c30DaSotqj\nUOU2+3VPV+5Z6qfEJnEskGJJtYPLG00HehMTk/NDlUsZt/36TrmubnThtkh0htLM9ltZXHn6wjue\n1rjvt62MxLPIIty/sY4V9U4e3DVCdyjNilont8y/8Gvpf7JjhOeOGXXOM30WPrK6lANDSapdCtfO\ndKHpOoORNFfVO/A7ZLZkohw5XHh922Ccrz10ED0XgU4kspRXuBiJpHh07ygLqux50X2cyFn2oN44\nt4Tnj4bZ2RND1DX+6Zo6LsuNWa0BIzV+bIuuxRUWjo2m6BpNUOMQWV7r5OZ5JZTYJs/Iu6LOztFA\nhr0DCQaCCTQdylwKw7la8RKLyM6uKLUlhYWBVbVOVpkL1iYmZ40pvE1McgiCQKlTISoJjESNFGSb\nLHDLXA8lNpnltebPZbojCAI3ToPJoYmJyfTnsnoHQ/EsO/sSlDnkC8LkbWWNnZU1Z26I9cCmHkZy\nqelZDR54tpfrmn08fdSokd7dl8ChiFwz6/WvGdd0nWMRjaSq51pKFlKuj4tugLZAGlkUeNfSgrD8\n75cG+FuL8Zl8domPrKri4a3dxHPp2rLLkRfdAJmMSiajIksih4ZTHBxIsmGWm+dzEecZPguLa84u\nMmyRRL50awPDsSxOi4jDUhDQtW6ZQyNpsqqOKOgsKLNy+2wnH3u4g3DuXLuDKW48RS/x9yz28Nn+\naD4zI6Ebny0WSxONwtef7SWWVnnLisnT5U1MTM4MU0mYmABDCY3yEgdlGIN2z2icjQ125pRa8dok\nRpMqQ3GNSqeEN1czPBDL0hpIU+WUmekzo+AXA9s6ozy8dxSbInLvmnLqvWYrExMTE4OH9ozy8N5R\nHBaRT6yvYmHVqQXV7fM83D7v9Rej50pwnJFWWtU4OpwseuzoSPKCEN6vDmVpjxgp2oeDKtfVWXAr\nAookIAkwNiBtH9NqUtN1nsktJAAEEiohVeDbH1zBP/7+KIpFxuawEgzEi95PEASsNgWbTSaaVvnM\nNTVc2RQlmdHwOGR++OoILovIXQu8eE4QfX7xWIRHDwSxKyLvW1XGjFxdtSAIlI/LnAC4YaYTVYfO\nUIZGr8KNM530hTN50Q2QzOr0hTP47BOn+rqu0x/JYJPFCRF64/nC/3d0RU3hbWIyRZjC28QEaAll\n83VeoiCwotbF6loryazO/x6K0h3N1VmJcM9cB2lV5zuvBjjus3Z3s5t1tWZbjelMdzDNl5/uyX+n\nx0ZT/OiemWa7MRMTEw4OJPjpK0MABJMqX/xbL798x6zX1KX89eSupX6+8FRPfntlnYtav42OMQaV\n88svjDGwM1owR8to0B/XcJdI2GSR9y7387Odo6g6XD/LxdwxLbNEQaDEJhW1+PLZZRp9DkrL3HnB\n7nRaiEZTiKJAWZkTX4ktP24MJzREQeCKJjedwTT/tqk//7ruUIbPXTOxx3dnIMXXNw/ko86ff6aX\nOxf7OTScos5j4U3NJRPavkmiwG1zXEWPlbtkyp0yQzn3cY9Not5rMRYUWiMMRTOsqXfR5LfwlWf7\neLkjiijAdXO99EczaDrYJIH/n73zDo+jvvP/a2Zne9EW9S5Zcu8FjMFgML2EThJKyAWSS8IlhFzy\nSy7lUiCV48hBII30SiC0hF5smo2xMe5F7up1tb3PzO+PlVdaNUuyZMlmXs/Dg1faMjOanfm+P+X9\n6U5kB1kq3aP3AtDQ0BgcTXhraACGfuLK1BMFf+5ghCMBOSO+kgpsbk8SS6Toa27+TlNUE94nOQ2+\neNbftDOcIhiXcQ6SLdDQ0Phg0dkzX1kQ0mOaIimFeErFrJ+6wrszKmdmeB/vqMsVVQ6+d6mOl/f6\nqM41cd0CD0lZxW4UafQnWFRsZWXV5HpmdIRT1HXFicZVDIbeLHHfqV1nV9pYXmYhpaRnV/fnS+cU\n8ZO3WgmnVKrcJoocBjxWPZ8/u4hfrGtDBVZVukhJEvWR3rRwIikTSyqk+hiUHexOZGXXD3QnkBV1\nQDB3V0ccvV5HIimjqhBOqjyxO5D5nayo3DjfNey+y4rKb9a1EgsnyDVJzC6ycN08D3ajjkc2dvBi\nz9i0f+72c9NCF+8cCQGgqPBKnY+fXFlBd0ymIsfAHzd1sKbOh6rCmVV2PnFGwfAHXkNDY8RoK0oN\nDWCuW6IjquCNq9j1Aoty01+NlshAUxSDDqR+iy3bIDdwjZOLmlwTFr2YGSVT6TYOaUqjoaExMp7Z\n2snDbzSjEwXuOq+U82cNLyCmKvOKLLgsEgmhd7TT4zu6uWWR5xivnBzeaIqzpSMdLKjO0XFZpem4\nxfeiUiuLSnuNtfQ6gY8smBr73xRIcu+6DmI9BqkLSmyUuExU2XWUWAdex5sDSXJMOvKs2cvg2QVm\nqvIs7OmM0xBMcc/rbdyzupALZji5YEZvj/4vNnVRH+kttRdEAatB5IKa3uBDlcuQVdpe5TSgEwVe\nPRxhR2cCl0mkwKLjxSNRCtwWEkmZNm8El0VPvI9gP3yMsZeKqvKn9zp5fEsX8Z5sdXWOnlJnugVu\nQ31vX3tSUdnXx9ke0mXlBTY95T2tVZ9bWcTnVhYN+5kaGhpjQxPeGhqkM9yXVBhJKir6PtHoCofE\n1vYEgpAuQ7MbBFYUG5FEgfpAijpvggKrjqtm2IZ5dw1ILw7agklsBh32KSho82x6vn9pGc/u9mGS\nBG5Y6DnuhaqGxgeZxu44977ckCmhvef5IyypsOGyDOxZneo4zRIfmuviH7v8mZ+tORicksI7lFQy\nohvgoF+mOaxQYpt6193x4p3GSEZ0A3QEYnxmUc6A54UTCt9/o42mYBKdALct9rCivDeYEE0q7Ons\nFabhpEJdV5zlluzl8tJiC1taY4RjKeKJFDkmHXeemU+Jo9fvpcJp4PPL81hzKITNIHL9HCdb2uK8\nVp+ehd0ekdnT1SuqDXodZ1bnMK/AxF93+DI/r/UM7TUiKyp/2R2mPqXnnKWl7DropbE1yMGe/vv2\nYAKXWYevj7P6giILHcEkO9vS2/HhhR6MkpY80NA4EWjCW0OjD/p+JWAXV5qx6wW6ojLVTj0L8gwZ\nMfbJRc6sUR4aQ5OQFb75bD3vN4bR6wS+cn4JZ08buCiabKblmvj8yoE9eBoaGqOnO5LMmmOdlFX8\nUfmkFN4AxTnZJppTtSJmMAl1qltV2AzZe20dogrtzSMhmoLpoISswmM7fVnC2yQJuEw6unuEqkA6\nG9yfJcVmGv1W/rS5C0gbsT2ysZP7Ly/Pet7CIjMLi3rb0DrasrPN/f8sKypsnF5iRhIFdnXEKMvR\nc2lt2rAumFB4/lAUX1yh1qlnVZmR3V0JDgfSPd2CIDCzyk1ja5DTKuz87p02fr+hHVEUqCiwYDLo\nWFFh49xpDlZW2dnfGcOsFzNGbiMhnFBoCSbJt0k4jFPz/NfQmMpowltDYxgkUeCcsqF7tzXRPTLW\n1Pl5vzFd7paUVR58o2VKCm8NDY3xY3qBhdp8M/va05m1OUUWSkexyJ9qnFFmZVd7jDcOhXCaddyx\nfGo6PVv0IqcXGtjQms6mznJJFA1Sbn0qcW6VjX3eODvb4+RadHx03uCj2/oHIPrfwgVB4Isr8vjj\n1m4iSYWLauxUDTG1pL83TFsoNeA5/YPz01163miIZgJS05x69nTKJBWY7jawpChtZLaqysaqquxK\nuhcORTkcSAcENrYlSCZl/v5eOxVlve0bOgG+cF4Jq6bncP2v0wPIFUXlUEuY715RwcrqtIiXRIGZ\n+aPzpWkOJPn+G6344woWvcCXzyygZphsvIaGxkAmXHjfcccdWCwWBEFAp9Pxgx/8gFAoxE9+8hM6\nOjrIz8/nrrvuwmJJj+V48sknWbNmDTqdjo9//OMsWLAAgIMHD/Lwww+TTCZZtGgRH//4xwFIpVL8\n9Kc/5eDBg9jtdu666y5yc3MBWLt2LU8++SQA11xzDeecc85E766GhsYgJPuNK+n/WEND49TDKIl8\ndFkBP3qlHlUVCKQUvOEk+faTc/yiIAjcvjSX25fmTvamHJPTCw3MdkvIKpkRmMMRTch0hpIU5RiQ\ndNnP98dSvHkohFESWFXtGOCwPRUw6AT+47TcQc3L+rKywsb6hgiHfAkkEW6cN9BzoNxp4OvnHNtQ\nbEGRGZMkZErcl5dZs35/ICCz168gAvPcOkqsIhU5ej4xz8GurgRuk8jpxSbiKTvRlIrLNLwJXndc\nyXr8xsEAB1uC5OSYcTpMgMqFVRZOLzLSEUwiigKiKKCqKrKs8sv3vDSHFeoaA7yxP0ChQ8+3Ly1n\n2gjd6J+t8+Pv2YZIUuXJ3T6+fJZmvKahMRomXHgLgsC3vvUtbLbeyN1TTz3FvHnzuPLKK3nqqad4\n8sknuemmm2hsbGT9+vXcf//9dHV1cffdd/PAAw8gCAKPPPIIn/70p6mpqeEHP/gBW7ZsYeHChbz2\n2mvYbDYeeOAB1q1bx5/+9Ce+8IUvEAqF+Mc//sGPfvQjVFXlq1/9KsuWLcsIfA2NkaCoKmsOhdnU\nHMVhFLl+Tg65Fq1QZLScW5vD0zu8HPHGEYBbT8uf7E3S0NA4ATy2pROdLp1t7QrLPLuzm39bri3W\nTwR2w8j6dnc2hbjzb3sJRGWqck08fPMsPD3l1eGEzH+90Eh7TzZ3Q0OYb5xXPGHbfLwca/yjWS/y\n9XMKaAslsRt1x1Uu7TJLXDvXzUFvjFn5Ji6s7a3iCiRUdvvSIlUBtnbJ5JkEDDqBKqeeKmf6+AZi\nKVoCSUqdhmNW0NW69GzsqWIQBfD6YygqbN7Tjs1i4KKZOZxelA4kHO6OY+yzbwJgMkr8Y1sXHZ3p\nOeSNvgT3vtLEzz9aM6L97b99WsWfhsbomXA3BVVVUdXs7NamTZsy2edVq1axcePGzM9XrFiBTqcj\nPz+foqIi9u/fj8/nIxqNUlOTvjicffbZmdds3Lgx817Lly9nx44dAGzdupX58+djsViwWq3Mnz+f\nLVu2TPTuapxgVFVlb0uIIz03kvEkpag8/J6PR3f6OdCd4P3WGA9u6Br3z/kgYDXqePDaan58ZSW/\n+Mg0rp4/9QyJNDQ0xp/+2dGpmC39oPPAqw0EemZXH+qM8ecNLZnf7W6PZUQ3wJbmCP7YwJLq4+Gd\n1gR/2BPh8f1ROqPKsV9wnEiiQInDMCLRvelIkFt/v5ebfrOHNXt7Dc9iKYVvvdrMozu62dgc5YA3\nkSX6E0r2ulcBUv0Kverao9z+twPc9dRhPvXoAV47FOLFQxH2dScZjFWlRkoNMg2tATbvbiPPJCCQ\ndiVXUzJXzOnN3q8/HMzeZ306my6nso+vLzryv+UVMxy4zeljZjOIXDtbaxfT0BgtJyTjfc899yCK\nIueffz6rV6/G7/fjdKb7b5xOJ35/2iXU6/Uyffr0zGvdbjderxedTofH07tQ93g8eL3ezGuO/k4U\nRSwWC6FQKOvnfd9L49RBUVTu+tNOXtuVFsOfWV3BZy+oHLf339uV5Ig/+wbYEkqRlFVt8TgGTHqR\nhSXWYz9RQ0PjlOEzK4v472ePEE4o1OaZuEoLuk05knK2GEv2UYguc7Y4NesFzOM4PvNQIMVOb1r8\n+RMqa5riXF8zut7jiSIUl/na04eJ9oyY/O5z9cwoNFOcY0KK430AACAASURBVGRXe4yGPuuDt+vD\nfGyxnBHzLoOAQw+BnqfkmQT6HUr+/F4HoYSCThQoLnbyZkv6ye+0xLlhhpVZnuyWjO5IikfWNnL0\nr9PijfHVC0uRRIF5JdasFo4iR7Yh3NH2gcvnuPnHxlbCifQ+XTbXPeLjUWDT86MLi2kPp8i1SIPO\nQdfQ0BieCRfed999Ny6Xi0AgwD333ENx8cASpfEsV+mfXdc4ddl40JcR3QA/e/UIN51ZQs44Oubq\n+/W6VTn1muieQnRHUsSSMkU5msGLhsZUZH6xlb/eOgNfTCbfpj9mKbDG6JEVlUhKxSwJSGM4vret\nLOErj+0jIau4rXo+vKy3FWCax8StS3J5YocXkyTy76fnYdCNn+CKJNVhH08m3nAqI7ohXQXXFkhS\nnGMc4KKu1wmY+qwNdKLAigKJlogKqBRZhIFr3Z7HBblWzMbs5fheb3KA8N7XEaPv0REECMZlrls0\n0OTv6vkeWgNJ3msMU+k2cuOSPNxWiVyLxIU1NjbVhyhyGDit0j7gtcNhkkTKc05OjwYNjanAhAtv\nlytd+uJwOFi2bBn79+/H6XTi8/ky/8/JSZeruN1uOjs7M6/t6urC7Xbjdrvp6uoa8POjrzn6WFEU\notEoNpsNt9vNzp07s14zd+7cAdu3c+fOrOfdcMMN2O2juxBpTA4WS7+xHALY7TbsluO/KYTjKZp9\nYfIsEoqqEo3L1OSa+NyKYuyDlKclZZXNrWG6YzK5FolFBZbMAtNgMGjn1ATw93eb+eHzB5AVuHBO\nLj+6bibiB2RRr51TGuPNRJ5TdkBzdZgYAnGZ5/b5CCUUrHqRy2pzcJpGt7S7eJGdeZW5NHRFmVFk\nx2XNDl7feJqdG08rG9V7jvR8mqWX2dLVlTEom51nHvZ1kYRMOC6TdwIM+qZbrMwotLK3NT2Ro9hp\nZHF1HjaTxBK7nesXpPjHtg4MOpE7zy7F4xpYev3C/lZ+v6EFnQi3nFaMThLJMUmsrnHy72dXUPf4\nXiSd0JM06r1/HWwOYpqfj77PfO3ltWYk8QiCKHD2/ALyXWZSBhFFbyZnkL/51y5zDLpfM+12ZpaN\nj0FgIqUQSMi4TTpEcWIz4Np9T2Mi+Pvf/57595w5c5gzZ86Eft6ECu94PI6qqphMJmKxGNu2beO6\n665jyZIlrF27lquuuoq1a9eydOlSAJYuXcoDDzzA5ZdfjtfrpbW1lZqaGgRBwGKxsH//fqZNm8Yb\nb7zBJZdcknnN66+/Tm1tLevXr8+I6wULFvC3v/2NSCSCoihs376dm266acA2DnaQg8HggOdpTD3m\nFBpYPSeXV3emgzWfWV2BKMcJBuPHeOXwxFMKdzy6n/0dMQCWVjr41kWluM0SJCIEEwNfs9sn0xhO\nLxzC/gSinKQ2Jy3Q7Xa7dk6NM4mUkhHdAC/t7OTCmc0srxp8oXGqoZ1TGuONdk6dnLzTmiDUUzYc\nTiq8U+/nzKLRi1KHBHMK9KDECAZjx71dIz2fROBDlUaOBGUskkCVY+g12No6H/c8X09CVjlrmoPv\nXlE5pgz/aLj/2iqe3NJJUla5coEHNRmlZww418y08aHpVkQBREEYsN1HuuP8en0zAAoCT+72IvZk\nuXe2BLh9sZtffbiaTc1RXqqPAQqiINDc6mfN63sJtHn59g2zs97zoRuq+evOIPmudDl+IKHwyoFu\nLiw3TehxGIzd3iSvNcSAdODgskoDVc6Jqz7TrlEa443dbueGG244oZ85ocLb7/dz7733pg0dZJmV\nK1eyYMECpk2bxv3338+aNWvIy8vjrrvuAqC0tJQzzjiDu+66C0mSuP322zOlObfddhsPPfRQZpzY\nwoULATjvvPN48MEH+fznP4/dbufOO+8EwGazce211/LVr34VQRC47rrrsFq1/tJTCVEUuP/m2exr\nC2PW6yjzjE9f2J7WSEZ0A2w6HCASl9PCewgiqeEfa4wvigpKPw8ebUSZhobGB41+Hl6cjJdBu0Fk\nrufY2dIfv9xIomcH3zoQ4PU6H6tnDhwHdjx4oykeereTBn+SWXkmPrPMw8eGceEfTvgf7aMGMOh1\nGdEN8F5zlNsXg82oY1WVjWkeI/evaWRXQ4DGhi5UVWXDvoFmrtMLLJyT1FHn611kxPu7tp0A/HGZ\np+pCCIKAzahDEASe3h/lC0u1ti8NjeGYUOGdn5/PvffeO+DnNpuNb37zm4O+5uqrr+bqq68e8PPq\n6mruu+++AT/X6/V88YtfHPS9Vq1axapVq0a30RonFYIgML3QduwnjgKnRUo7hfY8lsT0jWU48kwC\nXfHem18wEAHP5JREpRR1wrMAk41JL3LL8gL+8E4bAPNLrCyv0krQNDQ0PljMckm0RBIkFZBEmO06\nNcddqqpKop8jdyw1/g7of93u47AvndLe0R7j2boA1852jum9anON1OYa2dcZR+kXKe4/lvS53d3U\nB2VsTivTHWbqdjQwp2xw1/CZLokD/hSymi5On+UeP1+bkRBKKDy4sTsz0zua0JHvMDIBfw4NjVOO\nU/MKraFxHFS4TdxxTjGPrGtFEgW+cG4xzmGy3QDlNpG/vt2IL6FwqDnI3vpu/vSZRcwpPXFiMJxU\neLE+TndcJdckcmG5EbN06grwT51VxLnTcwjHFeYUWwYY4Y2VpmCSlAJlDikrQzFeqKpKMJE2QtKM\n+jQ0NI4Ht0nksgoj/oSCwyBiOcHX/PZQkl9t8ZFUVC6vsbG02DIhnyMIArcuL+BXb7cCUOUxsap2\nbIK4P1taIjy2vRtBJ6KIOmwmiVDPyLRAfOxqUq8TufuiUjbUh5BEAW9c5a2GCA6jyK0LsjP1G+rD\nmX+LosjKhcV859raQd+30Krj2hozrREZt1Ek3zL2WeRjYb83kRHdkO67lxWFhu4om1uMLC6aGq70\nGhpTEU14a2gMwnWLcrlu0ejMR55cX084LmcebzzoO6HCe2Nbku6erHtnTGFzR3JMvX4nE7X547vI\ne6ouyPqmdJvB7FwDt8x1jKv4Tsgqf9gR4LA/hV6ECypMrCjTWmA0NCaTTS1RNjTFsBtErpxuI8d0\nYoXM8WKWBMzSid9mWZb50XpvpjrsLzuDOM0SNa6Jue/ccnoBp1Xa8UdTzCuxYtaPbJ/bg0nufqmR\nQ94Yi0qsfO2CUkIJhUhSwSIJ3PdmG3q9jkJ3ukzaakrPvI7Gk6woG/oe88SeAG/Wh7HqRW6d76TW\nM7DM2iiJnF3d6z1y+Yz0v/0xmXebIuRZdFS5jBTYJQJ91g83ryjFahx6ie40iqw9GODFfQGsBpF/\nX5ZLrefE9HkbBwnu1LWEiKUU3jwS1oS3hsYwaMJbQ2OUbG8Isqs5xNxSG3NKeoX19EIr7x8JZB7X\nFp5YQRXv1+wXn2LNfoqqEk+p4zoDdjzxxeSM6AbY1ZlgX7fCNKeEJI7tWDb6Ezy3x4deFLhqrovd\n3iSH/elMSlKBp+rC7GiJ8KnTBo6D0dDQmHgO+hL8bWcwIx67YzJ3njby2cYfZOoDMv2vjG/VhydM\neAPMKBh9sPXn61rZ35m+tm9qCHPfm60c7Nn2MoeehKxit2aL+FyrnltPd1M2xOisHe0x1hxOZ6n9\ncYWHNnlZ4JZAVXEYdVw+x4VRGvxe1xFOcffrrQTiCgJwywIXd55ZyMPr2+iMpDir0s6ZxxjztbM9\nymM7fEA6K3//2+08/KHyURyVoXmnKcpLB0PodQLXzXRQ6+431qw7mfU4JSuZsn+7cWre3zU0pgqa\n8NbQGIJgXOaX73Vx2JdkhsfI7UvcvF3Xzf97dA+KCjoR7r9xFiunpxdp/3PjbH7wzH7aA3E+tLiQ\nM6ef2MXbLJeeplAcFdAJ6T6wqUK9P8lvtvoIJ1WmufR8YoETwxQrsx6sLT4oG2mJG8g1xDHr5IFP\nGIb1DSEeers9Mwf2/eYIVy/MFtiCIPDqgSBXzHJSZD+xfXoaGhrQHExlicemoOaMOVIKrQMzzlXO\nqVdl5YtmX7sP9gkYNASS5Jh1xJPZz5mVaxhSdAMEE9kl6ClF5fEtvWZoW5oi3H3p4CPY3qoPZUrY\nVeCJ3X7Oq7bz3QtLR7hH4I1kb28grpCU1eNuX2oJpfj7rkDm+Pxmq4/vnJ2Xdb+OJpWsmeRmvUgo\nBlUuA9fOGrwvXUNDI83UWZlraEwx/r7Tx66O9Giy91uj/HNvgNc3t2VcZGUFnt7czsrpbsJxmWd2\ndFNe7OTTF7jGFJU/XsrtOq6qNtEVU8g1i7imUOT5ib1Bwsn0gTvQnWRdY5RVFb3HyBtJsb4+hEUv\ncnaVPTMD/UQiAudVmHntSBSA2fk2Cmzp0sFgSj8q4f3YTh//3OPPiG6ApkCSCrsOm14glFRRVRVv\nz9ieKRaD0ND4wFDl1KMTet3Aa9xTTzhOVcwGHedXWXn1UBgVmObSc07F1GuduXimk91t6eu6JAqI\nQrb7+w3z3DT54/iTYDFKFNslzq8cfj/m5BlxGkV8PQI6HM3OAm9uChNPKYNmvcP9RHum7H0U1WBz\nC0w4jGJGwJ9WahkXzxBfLLuKIZZSiSQVDLreIMvpxWa2tcdJKumA9Ufm5DAvz5AlxjU0NAZHE94a\nGkPg7Rcl747KeGz9s5ICt/1+D43+BHElncF8aXc3j9xUS8kEzrMcCrdJxG3qvXmva4rxbmsciyRw\nxTQLRbbJ+cr3H3cS72N/6o+l+K8XGjPHe3NzhP9cWXjCtk1RVf5vfQdbWtMiuNJl4OZFRZiMvYGB\nurYIzza1c8vpBSNyjF9zKDQgeOAw6ihx6LlzqZNfvedlY0OIRErhipk55A84rzQ0ph7BhIJOYFQC\nYapTYtfzyUVONrXEsBlELqg68UHTk5lLa2xcWjO+k0XGmwtnOilyGDjkjTGvyMIBf5K/bPOhArPz\njKyqsiGJo/NjcRh1fHlFLk/s8vNynY9AKJH1e49VGrLUfG6+mVcOhjKPRSE9DjOoyHRHUxTZ9cc0\nC3WZJb67uph3GsLYDCJnV47P36AiR4/LJNIdS9+jq5x6HP2C+JVOPXcuc1EfSFFk01GiVWtpaIwY\nTXhraAzB8lILezrTGW+BdES5bKadI51RdjaFmFVsZUNDpHeOqgB6vY5YCrY3RyZFePflsD/Jyz3Z\nW38cHt0T5gtLJ6cM7JwKC0/sSfdRWvUCS4t7zVe2t0azghzv1A+dKZgIXjkQzIhugMPdCbY0dnNW\njYmUKtIdTvDzNQdo7I6iqHDbimMHBWwGkbisYrXoicVTlDr0/McZ+Zl9+vzyXFrn5CAKaKJb46Tg\n8d0B3qiPIABXz7SzagpmNsdKrdswoI9V49RiXrGFeT2O61UeE4sKzUSSCiUO/ZgNNB1GHR9f5KbJ\nG2V9sFd463UCn16ez1Pvd1DqMrK00pH1urkFJmbmGqnrSq8vzqm0caQ7zg/XNBNLqZTlGLj7olIc\ng5j8bWmN8tqBIMUOPR+Z5+KKmeN7T7foRe5c5ubd5ih6ncAZpZZBj0++VSLfqkkIDY3Ron1rNDSG\nYGWFDZdZot6XoNZjzDiW/u6T8wF47L0OfvJqY+8L1PR/ggiV7skV3UAmYn0Uf0JBUdUJGZF1LJaX\nmCm1S3RF5Z4Ieu+CwtVvVJvdKI6qZC6RUnjzcAhZhbMqbSPKxrVHVVqiKkYdbGwMD/h9cyhBoTHK\nva828dIuL6me6MrOlsiItumTSzz8bGMnQQHOrXZxWa2dZ/eFeOVwhPOrbVS7DBRqWQKNk4QjvgRv\n1KfPfRV4ck+QZUVmrIZTJ/Ot8cHCY5HwkHYX39AcxaATWFFqGZP3SIXTwHrg6K3VKMI3ntifmXKy\neJqT715eSV5PkFUSBb50Zj67O2IYdAIzck385z/rifVUhjX0mHJ+ZKEn63Peawpz//pOADa3RNnX\nFeebq8a/OizHpOOC6qldxaChcbKiCW8NjWGYm29ibv7gIzpmFw0sSXTYDMwoy8HtmHzhXe3UY5KE\nzM18pnvskf3xoNShp9QxUGzOKTDz4fku/rXHj9Ug8tnl+SPeTkVR+d7aVvZ0pDPWL+8P8L0LijEM\nky33JVR2+FQEIV3JUOKxsbfLm+lPEwQ4vdSKIECl25AR3UAmY3Isaj1G/vfiEhRVRVHh22vb6erJ\n6u/qiPOtc/JxmU+ukUUaH1wS/SYmqEBSGZvTv4bGVCGcUPjfDV2ZIPW2thifW+YeVa+yP5ri+V1e\nVFXNvK7UquNwU28V17YjAe5+rYWfXFGWubdJosC8gt7KL0XN/j7J6sDv1wv7g1mP9/VkzDU0NE4e\nNOGtoTFG5hRb+cSKQv7ybjspVWXONA+15U4A9vtS5FsmV1jlGEVun2dne0cCs15gacHkBwOG4rp5\nbq6bN3oX+JZQMiO6Aep9CQ52J5iZN/Q800Ai3VN3dJFUlW9jbiDOtuYQRkngpgVulpemy2ivX5SL\nosL2pjAzCszctCx/VNsnCgLd0VRGdEN6zFtzMKkJb42ThmpnuhR7nzddTru8xIzzJJt1rXHqEU8p\nBOMKbotu2GBtXFaJJhUcRjHreYd8iazKsP3dSYIJJasi61hsbQrTFU5f3wVBRVXTAdq36rozzxF1\nIi3BJMG4gsUgohMYsL03LvLwP6+3klRUCmwSl8xwDvgsd797hn4STEg1NDSOD014a2gcB7edVcRt\nZxXx6N4IzeFecWUzTI0bosesY1W5+dhPPEmxG3RIIhz1ahMEcJp0RJIKezrjOIwiNf3K/h2DtHJe\nO9/DrQtycJl0WdlyQRD4yJI8PrJk7HO2c0w63GZdpo/dqBMoPknLzJOySl1HFKtBpNI9dHBD49RC\nJwp8domLfd4Ekihozt+nIN5oinpfkhKHnryToHe3rjPGfW93EE4qVDgNfO3sfKyGgYJ5f3eSR/cE\nSchQ7pC4ZY49U07uMusQIOPibZYEzKP0FnFbe6/lqpr29/jwsgLerw/xzkE/kiTi9lgosut5+VCI\nN+vTvdM3z81hYWHvNXRZmY2Hrq6gM5yiwmXEPEjL1CeXetjvTdAWSiGJcMfpngHPGS1N/jhPb/ei\nEwWuX+jBbTk5700aGicLum9/+9vfnuyNmGoEg8FjP0lDow8lNh0tYRlZUZnl0bO8sHe0htFoJJFI\nHOMdNMaCURIpsuvZ3R5F0gl8bLGHareR77/ZzutHwrzdkO5LnZHbK75NOgF/Avq2wFfaINesO+YY\ns+ZAksO+BDaDbsS9gKIgMD/fRCihkG+R+OhcJ8WDlNyPhhN1TqmqSlxOl0UmZYWv/quev2zu5Nld\nPgQB5hWdOgZbH3SOdU6JgkCuRRqQdZuqbGmNsqczgd0gnlIu7BPBE9u7ePDdLt5pjLDmUJAat/G4\nxfdEX6MeWN9BWzg9c90fk9GLArMHaQv7484goZ5Rlv64glUvUuaQkBWVhKySa9HRGkqRYxS5eV7O\nqA3D8u3pcXQHOmM4zRJfOb+UMpeRS+Z5WD7dhWQxMiPfzMWznPxrX9pPRFZhZ2ec1ZVWREEgllRo\nCiRwmiWKHIYhPU5EQeCiGgfXzHZy1SwnRfbjC4AFYik+/49DbGkKs7styoYjQS6d7ZqUcZ4jQVtL\naYw3dvvophmMB1M/rKmhcRJwoDVEtCPEgiILK8pP/Bf5g8yKChsrKnqNYNYeDtER6a0+ePFAkA/N\nyHaVneeCxgjEZPAYwTWCCoU3j4T4zWYvKulKgm+cUzDAGG4o8qwSn1jkGtkOjYG3j4T4+/ZuRAFu\nWeRh8Qh70YejKyrz6N4w/rhKoVWHIRnPzMIF+Mt7nVy/wHPMsTcnA+GkQmMwhdOoo8B6cghLjaF5\ncneAl3rGNf2rTuArZ+WRZ9GWO4OxryPKo9t9mM3pYGBKgef2BQYVsYPxvbUt7GyPIwLXz3Vy5eyB\nJdITQX+Pgef3BbAZRC6qzb7Wp7I9RkkpKsG4zP+s66ApmMJmEPn86blUu4YXsUlFZUdnEkVVme0x\nYJZ67xkfXZLHRwepippXaGFeYfpa/OTeAKqqcvBQF93dUcxmPS1LchB1InevaaU7KuMwinx9VSEV\nrhPTFnawK443kso8bvQlaA8mJ30ii4bGqczJv2LS0Jhk1u7p5j/+vJdfvtHEXY/u45ktHYM+rzuu\n8HJjgn8eSbC1K4U6iHnKiURRVfZ2xdnbFR9g7HIy079UsO8C6SiiIFBuFZjuEPAYRxbdf2aPP1OS\n2BWVeePIQDf0yaA9lORnGzpoD6doDaX4v3XthOLysV84DL64wmsN8UymqDUss8ObGvC80ZgQTVV8\nMZmfbwnwtz1hfrE1wJZ2zbDoZOetht7vZjipsqUlNsyzP9gc6oqj9BOxIy0Q+NcePzt7vi8K8OgO\nH4nU8V17joWiqjyyoZ2GzijRSCKz7bGUyp+2dbPfm/39XVVu5uhVymUUWVRg5MUDIZqC6etZKKHw\n2E7fsJ+pqiqP1UV4+UiMV+vj/Hl3mIQ8+D0znJD5+TvtfPeVJl7e5wegMZDkzfoILa0B2tpDJJIy\n/kCM+144zJO7/HgjKbzeCLsPdPP5R/fTETwxWd1ihz6rcstu1OE+CdoMNDROZjThraExBnzRFF3h\nJAAv7+qi7y345V3eQV+zqSNFOJUuMzsUVGiOKIM+b6Ts6Exw77t+frjBx4aW0YkFVVX57VY/P9/s\n4+ebffx2q3/SAwHjxbISM0uK0n3tRp3ArQvGJ9Pcv/xvLGNnJgJvVKbvGjAhqwSOQ3gfCcqsaU4h\n6SWq3Cb0OgFRgJp8K+fPzyevx7H/jGoH0hQtSRwNm9sTmQCDCrzZqIm0kx2HUUcyJdMdiOELxNBx\nalzbJoJZBWYSsSTJHsEsqCo3zB3ZNXN3x8DvSqM/Oa7b159X6vz8c5ePQEwmJavEYunPOxoD7OqT\nwQ3FZV6q89HuDWMnxe0L7NgMYtakCkhns2OySHvcRHvcRFzJXhr74yqNwd5rqjem0BIe/Br70Pp2\nXtkfYFtrlF9s6ODdhhAtoRQqAvF4dvCy2RdHVVVCoTihYBxZVujwx/nB8/VjPj7D4Q0lWL/PS6sv\n/XfLtxv474vLmFVgZn6xhXsuK8es1yp+NDQmEi20paExSh59v5M/bupEBT40x0VhTnZZVuFg7l1A\nfy0UO47EQCSp8Mz+SEZwvXQ4SnWORN4IndRbQil2dPSK9R0dcZpDKUpOUtOvvoiCwKeXeogkFQw6\nYdzE4c0L3Dz4TgfRlEqN28C5VVNjzmmly0ChTaI1lF7UlTv1vHwgSDihsKrKxuz80Znr7fP3npiS\nKOA06ci16rEZdeAxM7PYwWu72vj0GQXjuh+ThUGcmgEVjbHR4E/SEUrQ0RXhqL56Ymsn51TZpmzv\n6mRS5jLy/csqeG6XF5Nex81Lc3GN0GDrwho777f0tp8IQLVnYk0XW4LZwl4vChnR7TTpmJnb+/kP\nbOhkR2t6+zY3R3iuTs8Nc12cW2nj3aYIgbiCJMLcXCP/7+nDAFy9uJTpBTaKTBGOni4mSUASoGcy\nJwJg0w9+Lh3oN+LrQFec82tz0IuQ67HS2hbkaIz7/NkeLp7t5MXtnVmvafaPf9XNvtYQt/7sfXyR\nJGa9yM9uW8Bp01wsK7ezrF97nC+awmrQDdlrrqGhMXY04a2hMQq6wsmM6AZ4Zmc3P768jCZfnC31\nQWYWWfmP1WWDvrbcJnIwmM5yG0QoNI+94CSWUulf6RZJjTyrM5i4ONUEx3gbKs3OM/GTS0oIJdJ/\nw6ZgimK7NGoX3PHGJIl8Z3Uxaw4F0QkCm5ojvNQz73VDY5h7zi+mLGfkJjzpQEXvuRRLyNj6OJgb\nJJHPrijCM8nj8saL04qM1HUnaAjKmCWBS6uPvz9eY/L483YfoZhM36Tmke4E3kiKPNvJH1icCOYU\nWZhTNPrzfkGRhU8sdvH4Tj96UeArZ098MO70chvP7OzO9G5fND2HMo+JaFLhzHIrOX3G3DUFsjPM\n+71p0V5gk/juuQXU+5OYdHDX4wcJ91zXD3Xu5cfXL8AlwdH4g0kSuGKamZeOxEjIKotyJTxDmAzO\nyjfx1uFQ5nGNJ21U99klLtY1GrGiUN8epsRl4obTCskxS3z9ghK+/NiBzD191SCjxI6X373RgC+S\n3v9oUuEXrx7mtGnZlQ2xpMJ/P1/PtuYINqPIty8uY65moKmhMa5owltDYxQkZXVA0aIoivzw2ppj\nvna+R8JjUojJKoVmEesQEfOR4DKJVOVIHPKnFxYFFh3FozCFyrVIXDLNygsH0r2QF1VbTznzoWhC\nZu0eLwZJZNVM97hku4ySyF5vgt9u8SGr6b/DF0734JrEmcbrG8JsaIyQa9Fx9awc/ra9d35sSoF9\nXfFRCe8FHh2vNibQiSK+aJKDXRFq88zo+pioeUynTpeSQSfwb3PthJMqZknQsqInGd6ozPvtccyS\nwLIiE+GkjNTP8M9h0hGIpXjo9WYSsspHluQxv0QTFOPB+TU5nF+Tc8I+b2a+me9fWsamhjBFdj3n\n1jgyXhOt/jgv7fBT4TExo8hKictId6Q3Qz6roDeAaDPomJ2nY29bNCO6IX3fONAR5tkdXr60sjeQ\n4DbA+m3NtAaSvCCCcn4p500fKJA/szwfVHjjYIBYXOZnb7Yw7apKKp0G6toiHOpOgF5PfUjmwTdb\n+caFpSyvyuHhG6ez7mCAcreRS+aOfUxYNKkMOorM0O87YRgkYPzcrm62NacngYTiCg++0covPjxt\nzNuioaExkFNrpa2hMcEUOgxcMD2Hl+vSpimnlVuZMUL3V4AS6/gIFkEQ+OhMKzu7ksiKypzcoUeQ\nDMWF1TbOLDOzqSlGTFbpCKfGZX5rZyRFNKVSbJMmTcTEkwqf/O1OdrekAwurZrr4nw/PGNIMLCkr\nrDsYRBBgxTF6l5/fH8pkJrpjCm/VR7hi+uid7A91xWgOJJmZb8YzxuO+oy3Kzzd2ZR53RWTKnQaO\n+NLmPAJQ4RzdyBmXUcTrj7LXm8j0QsrxOHkuMykFAVifDwAAIABJREFUSq0COVNkTv14IQgCtlNs\nn8aDhkCK99vSonZlmQnTJFZ3qKo64PsbiCv8fIufcE+P/j5vkoun2fnjNh9up4lQOIFREvn6eUV8\n/ZkjtIfSImxLU5jf3VxL/nGOY9KYHGbkmZmRl91Cs789wid/t4tQXEYU4Fsfqub2RW5+pxPxRlJM\nzzXyodqB1+kyl4E8m0RHT6uO1Sjxr91tBBOpLBH7/K5uWgPp80dW4Pcb2gcV3kZJ5EhHlFCPB0yj\nL8HjW7r41IpC6ruzS8jrfb0mavNKbcwrHb59qSmQZFdHjGK7njn91h3hhMy3Xmhkd1uUApue71xS\nSlkfd/JPra5g3T4vDV1RPDYDd15cPeD9o/0s4KPJ4/Oh0dDQGIgmvDU0RskXzinioplOZEVldqEZ\ncYzOzg3eGK2BBDMLLdhNo/8q6kSB+XnHt3B8fFeQjc3pHrhXD4X4yopcPGPIfMuKSldMYUtrjJcO\nhlGBaU49n1rsnBQDrm2NwYzohrTz/D93+/jQ7IGmQSlF5UtPHGJLY/r5p1fa+eFVlUP+Xfvvzlh2\n77V9fv7vzVYUNe0k++PLyykdpUAG2O/tXbjVFNiw2I1Ms6m4zGGSssrqajvT3KMfDXPtTDuPbPHR\nEkoxw2NgdaUF4ySX1GucWDojMr/bHuDo2rs+kOK2BY7hXzQB7OiI87edfhKyynmVVi6e1itODvmT\nGdENUNed5COz7eRaJN6uD5Nrlbig2oY/msqIbkiX1B7uimvC+xTi6fc7CMXldBDNYeLPmzs5f46H\nL5/uIqWme8EHw2LQcd/V1Tyyvo2NTWHMVgPBRAqHUcTYZyJG/yCyNEygW+5nVCr3BDCXlNl4ant3\npmpuadnIqy4OeuP84M32jJv6TfNdXFjTG0h4fKs3M+6xLZTkl+vaufvS3ra3IqeJf37pdFp9MfIc\nRsyGgVVaF85w8tzObroiKQTgw4tzR7x9GhoaI0MT3hoaY2BWwegMq/rz0i4vd//rMLIKBQ4Dv7x5\nOnkneBGoqirv9THGiSRVdnfGOat8dJeFhKzy7OEYnVGFPa29YveAL8mOjjgLCybWbGcwcvrN1xYE\n+Nf+ELMLLNR4soXovvZoRnQDbDgcpN4bp3IIk6CrZtj55WYfcVmlyCZxTsWxF0/xlMLTewN0hlMs\nK7HwxHZvpgc1GJd5uc7Hv52WP8q9hGnu9DmTazdQ5ExvryoIlOU7+Mj0sZ+jHovEV1bkDppl1Phg\nUB9I0TfhdSSQIqWowwbSFFXlQHcyHXhz6dEd57mTUlT+uN2fERsvHgwz02OgsidI5erX8mDVCxhE\nmJFrZEZu7/dcEiWKHAZaAulAldUgUp174q9LGhOHzahDEKC03IXJpEcFfvh6G99YVTik6D5KcU7a\n3ful/QGeqwtg0Yt8YrEnK/h6xVw3b+73U9cRw6wX+exZRUO+38eW5fOdF+qJp1RyrRJXz0+Xji8q\nsfLti0t5tz5ESY6BldUOQgkZ2yAiuD/rGyJZI8xePxzKEt7hRLZbaygx0L3VIImU5w7dy59n0/Pw\n9dXs6smaa98RDY3xRxPeGhqTwG/ebsmUK7cFEjy9tZPbzyo+odsgCAIuk46uaO8N2jWEYcxw7Pen\n6IoNbuw2WZJteqGVm88s5s/rmhEEgcpKN5KkY3tHbIDwthmz91kUwGIYOrtb6zby3XPyCMQVPBZd\nRoikFJUntnTS4ktwdm0Oi8p6M3O/ed/Lu03pIMem5iiWfkdmsJ68kTCvwMynlnp4vz177mtsFEZ7\nw6GJ7g8uBVYdfW32cs3isKJbVVX+tivEnh4Dq1qXnpvm2MZcEQQQl9UB85KDffpxyx16LptmYV1j\nDKMkcFWtddBzVq8Tue+aKv7wbjvxlMINi3LJ1YzWRkV3NEVbOEWpQz8ioXiiuemMQt6pDxI39f5d\n93TGaQklKRli0kh/LqxxcGHN4FUdNqOOB6+fRmsggdMiYR3mGCwtt/HbG2tpDSaocpuw9rnHLC2z\nsbTMxl+3+/jaa20IwFUzHTiMIvu64tS4jawoTwdzZUWlNZjEZhTJ6RdkyunnK3LRTCev7QsQTSqI\nAlw5wpFw/ckxS5xROfrWKQ0NjZGhCW8NjUlg4EzoySnjvX2Riz9t9xFMKJxZZmFO3ugj3Ef3RBAE\nChwG2nqyStPdeubmpUXuv+qCvFEfxqIXuXmekxr3xGf3P7e6jP1JCUXtFZAzcweWXZe5jPz7WYU8\n8nYrgiBwxzlFxyxBNevFAWL5J6818cy2dL/1U1s7eeCGGub1GDjVdfUKYxVYUGYjEEvRHZWZVWDm\nQ3PcY97PM8utLCwy8/TBeMbZfpZbu7RrHB8ldolrZlh5tyU+Irf3joicEd0A+7qTtIZlim1jPxet\nepEF+Ua2tqd7Y/MsOmpc2d/NFSVmVpQMX93R4Ivz/VeaaPYnOL3CrmXyRsnujhj3r+sgLqs4jCJf\nO7uAokkePflec4Rn9/gxSiI3LXBRmmPgh9fU8MXnmjLBIp3IuAYJdKJAiXPgPeTtQwHagklOK7dR\n2vP7XJt+yODOYV+C14+kq6xU4LGdPuI9/dUvHwgSSSqcXWnlntda2N0eQyfCv5+Wx5JiM1tbo+Ra\nJG5ekC2sp3lM/PSaSva0RylzGpmmneMaGlMSbXWmoTEJfGF1GV994gDhhMKsIgvXLs6blO0oy9Hz\nX2eN/bObAkm2tkRQZQFBJ5FrM3BehRmPUaQiR0IUBPZ0xnnxYHq8SjQl8/CmLn5wXsGE9wxLosid\np+fyu63p0TOrK63MGmIxcuOyfK5fnIuAMGzv3nCsPxjI/FtW4d3DwYzwrnTq2dLaW1mwrMzKJ5Z4\niCaVARn3sWDVi1xVbaIhlB6JVW6fehkpjZOPBflGFuSPzCNArxOyMuQAxlF+l1KywvNb2okkUlw4\nvwCXVc/H5uewpS1OPKUwv8A0puqQh95qpaHHyGrd4SD/3Gnimvljd46eDCJJhTcaosRklWWFJkrs\nJ2759vQeP/GeyoNAXOGFfUH+bfHYg4VjYd2hAIe74iwqteKwSPxkXTspOd0K0+BP8MBlpRTY9Hxi\niYe/bPOiEwQ+tsg9IDM83vx+Ywd/35IOuP5lcxf/e2UF5a7hvzPJflUcKSX78dbWKCIqu9tjQNrM\n7TebOjmz3EpLo5/d4SStTX4e+EhtVltVocNA4Qiz+xoaGpODJrw1NCaQWEohEU6i79cru6TCzjN3\nzMMXTZFvNyAIAoo6NqOuyeJwd4IHN/UaxVQ79Zw7zcFun8zhsEJnIsXSPAl/PLvXLKnA8wdCXDVj\n4o2apnuMfP+8whE9V3+cVQcVHiMdfQycyvuYmt2+2MNjO310RtI93gsL0xm68RDdR7HoBWa4tEu6\nxuTgMuk4v9LMK4fTLRXnVpiHnHU8FF/84w5e3t4BwG/W1vP4F07DbpZYXHh82TtfVB728cnAH3cG\naQ6lt3tnR4LPLM7BfYLGGPZvFzh6qUwpKg3+JFaDSP44TMQYiie2dvHzt1sB+MNGuOW0fELhJKmU\nkr6vqnruW99JrkXHdbNzWD1t5KXSf9/ezcbGMAU2Pbcvy8U5yDHd3hxmS1OYabkmVlRl37de6Zlw\nAmkX8LcPBY8pvKe5DczJM7KzI13JUebQc7iPw3mRXY/cz1A8llJ59WAIi9NMhzfI5rogv3rdwpcu\nrhjxvmpoaEw+2ipNQ2OC2Nwc4afvpMvz5heY+M+zCrJ6JM0GHUa9joaInnBKQC8KFJpk7PrUJG71\nyHn5cDgru3XQl6TI17ugPRJSqLCrzMo1ohfJGDWJgoA3duqNKfnaReXc92ojzb4Eq6bncMGs3lJA\nq0Hk44tObIZI49RCVVV+v7mLd+pDFNj0/McZ+eRNsT7llWVmTi82oTL6bLcvksyIboD6zigb9ndz\n/rzjrwa6aKaTX29oB8AkCawaoo93qhJLqRnRDZBQoCmYOmHC+/o5Tup97QQTCvlWicumO4inFO5b\n38khXwIB+Og8J+dWDj8Oa6y8VufL/FtR4Z3DIVI9pdmqqhKLpdjbFWdvV9qN/8tnDjxnVFVlb0cM\nUYDpPaPIXj8U5Imd6fdu8CdJKR189ZzsQO27R4J889kjGTPMO1YWcVWfagm3RcIb6b1nj2Q0pCgI\n3HGah31dCfQ6KHUY+O3mLt5tCGKVRObmGqnNM/HyPn967ncfBEHAbNYT9IX519ZWLpify4JibSa9\nhsbJgia8NTQmiF+/15Upz9vWFuOtIyFWVfVG4mVBT1NMojuRBFQSCrTGdFil1EmR+Tb1W1jrhOwy\nU0gvkhxGHVfOcPDU3vScbFEQmJc3+hFXE8GRoMy2LhkVmOvWUe0Y+0I216bnB1dWjd/GaWj04bUD\nQZ7dk86udUVkHnqnnW+fX3LCtyOeUtjeGsWsF5kzyHQHwxhbNSwGHRaDjkgfN2bPOE16uHqem0qX\nkeZAggXFlkwf7njw9pEQz+z2YZJEPr7YQ9UYxvcdC6Mu7eDe3ROwFAXIt5y4dpIql4H/ubgYb1Qm\n1yJh0Am83RDmUE+WVgX+scs/YcI7366nriOWedy/3UDpM77rkC9bqEJadP94bQvrj6RbnlbXOvjc\nmYU0BZJZz2vu9xhg7X4/fSvB1+73Zwnv/1xVxI9ea6Y9mGTlNDvnT88Z0T6JgpBx3veGkzy9rpG2\n7nS1yK5DPn79sZncc1EJh7wJ/rC5k7rO3hngiqIyb3EVkl7HD99s55aFbi6dMbLPHYx4SuHP27o5\n4k8yI9fIDXMmZwyohsYHAU14a2hMEP3deAc81lmJpaJZP0sqKuqATsmph6qqNPnjJFIyep2IClw5\n3YbTpmOfP71wzjMJ5JvTN+9zKqx4LBL1/iTlOb2ma5NJLKXyfqecOdJbu2TyzSI2/egWHKG4TDih\nkG+TTogLuKwK+GOQUCSMQorRfKSqqrzfnsAbU5jh0lPm0G4BJwt951ADtAVPfGVMPKXwzRebOOBN\ni4BLZ+Zw27Lx8acwSCL33TKXbzy6i3Bc5pOrK1lUOXYx0Z9FpVYWMb6ZwQZ/gp+ub88Isx++3srD\nV5YPmPl8vAiCwC1z7Lx4KEIspbK82ETBBJZ2D4ZJEim29wre/ns4kZe+O1YWEYzJHPbGWVxm4/pF\nHr72XCPRnjIqSx8n89pBjDsPdMUzohvg1X0BLpzuxG2Rsu62i4oHGggW9Ksq6f+43GXkoWsHD7h6\nI0ke3dRBjkni6oUedrWE8Vj1VOb2BqzWHw7ynecO09Hduxbo7o6wrSlMhcvNjDwTd55ZwE/ebqPZ\nn6SpxYcoCEj63sDLC/sCxyW8H9vp5+2GCACNgSR2g8gVM3LY1xFld1uUmlwTswuHN1fU0NAYGdqq\nS0PjOOiOpLAYxEGNwq6ZncOftnYDUGSTMiNCoPdGb9briCR7MzxmSUUnTG3RDdAdk9mbcepOb7/H\nLDHXI1FmE0kpkGsSsnoD5+YZJ1xwt4eS7GqPUmw3ML3Hod0XSfHWfj92k46za3My4jihDAxvJGQV\nRiG83zgQ4P43WkkpKktKrXzjgpIJyxS0h5L8bH07HRGFFdW5TC+w8dSWFix6lc+ekY/TdOzL+ctH\nYmxoSYumDS1xPjbHRtkwJk17u+L8dUfaWOn8Khurq7SSxsliWZmVZ3b76Kmw5YwRzI8fb7Y0RzKi\nG+C5PX5uXOgZ8zi8/qyanctb3zl7XN7rRNASSGZlQ30xmXBSwTGO3g1H8Zh13Dh7csY8qarKT19v\n4bW9Pgocer5xcTnLii283RCmritdal5s0fF+U5hFJeN/Xnqseu69Klvc/t9VFWxrjlCcY0AWBNY3\nRHCadFw5c2AbwWCBkJ+9140iCOTlGHEZBPwJhY64QlckRVJW2NWadgf/6JI8mv0JNjeGmJZr5jMr\nh57f3RdvJMn1v9ydCbj/6q0WIrEEogBfvqiCaxbnA/DrDe0kBxn/WJLTG0DIt+n5/kWlALy2o427\nX6jPeq5tmPGXI6E5mB3UW3M4TCKh8Jv1LchKusLiq6tLOLtm/AJhGhofVDThraExBpKywndebGRz\nYxizXuS/zi9haVl2md0l03OYnW8mLhgosyhZi1MBkJQYHqsJFZWuUIIX97TRFkoww2Pk3xa6Jtz1\n+3gIJZQBeXlnz5xRt3FytrveF+ebLzdnsiCfXJbLaaVWbvvDHlr86SDBhfNzuWV5IdNcBuz6dHCg\ns2cGucsgkGMcnWh+eF1bxpH2vcYwbx0MTlj/6L1rW9jflRY9j21uRNIJmeP/uaeO8PsbqhDF4Y/9\n3j7jnhQ1PfJpKOGdUlR+vcWXmQn+dF2QGpeeCqfmmjsZ1HhM3H1hCZsaIxTYJFZVn3gRZup3TZJE\nPtAlqbW5RmwGkVDPbPEajxH7cYqgqciLu308/n4nAN5Iiu+90MDPPlrDf56Rxy/f7eCVfQE6vCrv\n1Yf4+nlF4yq+d7SEeXJrF2a9yK2nF2R8DYocBoocBhKySkckxccWuLAOceyr3EYum+Xk2d3pfu6a\nfAuJo0FhUcQnAzodjcEUd7/ZgdcXpTuURAC+cHYh/3Vh2Yi3tzWYQCcIPPZeR1aVW6pnrKWiqvzi\n9aaM8FZUFUmvQ5LETN/6WTPcLC4bvGz/9++0E4rKhEJxbDYjgqpy25Ljc+ifV2Bib1dvQC2pCPx9\na1fG4E1R4YU9Pk14a2iMA5rw1tAYA6/U+dncmJ7DGU0q/O/aZm5YmEttnok5fUqyKpwG7HY7wWBw\nwHvolRiimqTEJPLKrg4OeNM9bO+1RMm3Slwza+re5N5rjWM36wnFkqhAWY6B0kkeY7L2YDAjugFe\nqAsgJ1IZ0V1b7cYrGvi/d70sKzJS7jLREEzhMOiYm2+g3KZDJwjs6ohzyJegPEfPvPyh3ZRVVR0w\nFiapTFy1QqM/u3ex7ydFUyqHuxNUe4Z3f3abRHxxJevxUMRTakZ0H8UXV9A8dCePGo+JmmP8jSeS\nBcUWVtc4eHV/AEmEzyzPRz/Gnu5TAZdZ4rsXFPPK/gAmSeTymTknpN3kRNMWyL72tAXTj0VBYGtD\nmPb2MLKsIEkibx8OZoT3Ow1h6rriVLsMnFVx7P5vVVV56JUjPLmplTyHkTsvruJ7LzdlrkO7WqM8\ncmNNppIqEJf56aZuOiIyRp3AbQudg5aaA3zy9HyumutCJwg8VRdiS1tvz3gqJZNMyhgMehTA4TCj\nN0i0e6P8c1c359WO7F780Lo2Xt2fHitZM0gbj91hJJGQ6VMljkWEaDSFwWzEroPvXFbBmbXZM7o3\nNYV5fm8AsyTQmVCxWAwEfFFisRSCIPDb9W18+5LyMY/CvKTWgaLCv/YF0YnpgK7RKCGEExxtn3dZ\nNLmgoTEeaN8kDY0xEEtmu3L7ojK/XN+GKMBXVpewsnpkWU+dKgMy3n7jbfo/nmpIooBRn3ZlByjJ\nmXx35f6juWxGEWfPjFOrRU+Oo1ew7OhKcTB0dOGVwvX/2Xvv8LjOOu/7c8r0qt5ly5J7jR3bcYnT\nOyEJBJIAoZeXpWQL7dklPCxtd3lYYNnAsktfEnpCSCO9OU7sOO5FsuWm3kfTZ87MKe8fZzzSqNiy\nLTtOcj7XxUVGnjkanTlzn/v7K9+fU2CWX2ZbT4p7947M4373fJ01tRP3tgmCwHtXlPKLV00n5oZi\nB+tmnlkWMqZo/GjrEEfDGZqKHXzywpJ8FufCWg8vHTP7FCcqnSyfIHNtGAY7+xQiisbiMgc3Nrp5\n5HCSo8MKhzvC3L15mG/eOocldeOvV49dLBh5U+wUaSqyst1vdf5mTTl3Li/BLgnndVXOuaLGb+cD\ny0tf77dxVlnf6Oe+rf0oOQF8+dxg/t+GImm0XGpUVXVau82A9NOtw9yzZTD/vGRW5+oTVAM90Bzh\n8UMxlIxARpJp7o7zzUeOkJZG1pz2YYVoWsuv6xvbkwwkzXulohk80hrj71ZPnv0t85j3qRtmezkW\nyRBO64QHImzb1YWuG5SW+Vi8uBZRFLDJEpqmT7nzqHUwnRfdAIeiKnVFdjpyruSSJCCKIk6nyAdW\nmb4I7aE0u3MBfEEQyOhQ6i9sx+qIZPjeS/1oBoiiQFF5gCIgnc4SjZpr86vtce78wxEuaQrwsRUl\np+UxcE2Tj72DGY6EFDRdx+6Qqa/y0dUbY3aZi49cVHHKx7SwsBiPJbwtLE6Dy2YHeHjfML1jeqN0\nA546EM4Lb8OYWgZ0VY0rX+olABdWj3cLPp+4bIab1lCG3oSG1y7wtqaz42Z7KtwwN0BzX4pdvSnK\nPDIfvbCMuqCd2y4s59H9oYLnjs0MdMbMzdvufqXg57v7lUmFN8A7FhezosZDJK0xt9x5xkLkT/sj\ntIbMjVrLoMKDLRHeu8TMfnx2fQWzShwMJTWuWVjBQ7t7ePFIDFEQuHN5CV77+OX8/pYYL7abpjmP\nH47z+YtKqJczfPvx5vxzPve7Fp78/KoJ389HlgV5tTuFohqsqHJOWspp8dbCdxZ6mMfySluM/pjK\n8lo3ddPoQv5WRzcMHmyO0DygMDNo512Lgid1om8sc/Fftzex6UiUCp+dq+ePCO8Kj0zXqOW1KDe7\n/bWOwiqvHT2pSYX3gUGFx1pjpNNZdE1nVmMZO7e3MxhRKC5z5Eu2qwN2/KNGqI0dSjnVgqMKj8zd\n68sIp1Wu/WYLeu6FgwMx+vui2D0OOrui6LrBa8kM29pjrKg/cVBVn+Be/4PbmvjV9kEe3z1Y0Aa0\ntM48lj13vxAE8pUSkVShaWJHOMPxwipxlKB2Om0kEhm03D9mVINN7QlmFdm5qtFHKKXhtolT9l+Q\nRYFb5vr49qaRSgANgf98VxMNVsDVwmLasIS3hcVpEHTJ/OAdDezvS/LswSgvHhmJdBe5ZPb1p/jR\nlkHiGY0b5pfw7pOY4myY4aXIKdEWyTKnxMGckvN7o+m1i/ztqiKiio7XLp4XfZ4OWeSfLq8mqxkF\n5a93XVHLpy+r4S8HYzzflkQAmoI22uMj27bjfc4lrkJBMfbxRMyYxvFBEaVwKxkd9dgmidyyyJwF\n7vN5qPNU8Kk1J85CbOkaccpNZg129ysMjykbHYpn0HWjYFN3HFkUWHuCwIOFxdngtzsG+d1OU839\nZofAv91Qz8yzMKbrrcRTzcP8ZFMvimpg9zpwu20cHFJAIB/cOxGNZS4ay8YHhG+7sJzdXQlU3cAh\nC9yyzMz+1wQKP69q3+RVUVFFo6NtiLajZvVQcakXQYCLF5ZRUuzhaH+SuqCNj6ypLDDsvLjOzc7e\nNKG0jk2EG04QAO6KZnikJYKAwE0LAlR4bZS4ZLJq4ZqrZFUSYT0vxjOqwS9e7i0Q3h3hDPfvG8YA\nbpwXQACKXRIX1XvY3G5msK+bG6DUY6Mq6MTndZBImkH68oA5oxtMs0KHQyKb1fPC+0cbe1k1cyRA\n0VjiwCEJ+dGkxzEMA103iITiqFkVVXHjqitiKKnxby/10zygYJcEPrWqlGVVUwvkV3hkJIG80JcE\n8L9Oni0WFm9WLOFtYXGaeB0Sq+p9zK9wE1VU9vWmmFvu4sOry/nik93EcoY7f9k3xJwiiWVVJxYw\niytcLJ5gLu75iigIBJ0ShgEZ3bw528WxOYiJiSkamm7kSwank4l6TiVR4B3z/FzV4EEUBNw2gU1d\nCp1xlTqfzNpqc5N4TaOXiKKbPd5+G2+bfW4z+evr3ezrT2NgOsmuqTsz0et3iPlSTICAU2TBnCKK\nPTZCCXMjeP3S8glFt4XFiWiLZEmqBrMCNhzy9F4/z44q2U2rBpuOxU4qvI8MZ+iOZWkqtlPpnVzk\nZTWDlkEFhyyc9wHO6aI7rPDNx9vzZllCMkt9XQBJEmkfNfdaUXVEwQzyTZX1TQF+8f65HBpIMb/K\nTV2RKSpvW1rGUCyV7/F+16LgpMeY4ZfzohsgNBjnmktm4y72cnAoSVaWmV3to2qMj0jQKfGFNSX0\nJjSCTpHAJJUY8YzG157tJZI218KtnQmiw0mymsHlSyp4YkcvAHOrvQRKvfQPFo75HH0+Ulmdb74w\n+lhJojEFEbhrQyW3LCpGFoX8PPdrGn3s7kvTHTavuS9cXJEX2Xt7U9hsEuooL432kIKmG/ly8Qqv\njS9fXslTrTF64lmOhk3TN7cs0B5PkYiZ77W/N4LDLmGbF6A51x6U0Qx+tTPEsqqaSc/9aAJOiY8s\nL+b3e8MYwLsWBCixerstLKYV6xtlYXGG+BwS37phxHJK0428y+1xIsr53bN9uhgGDKsO0rq50XWJ\nWYpsyglf8+DeEL96bRADuGF+kI+tLj8H7xRUzeDHL/Ww+WiMuiIH/3h1LetrC4W1QxK4c/H0mtrp\nhsHG9iQ9MZUFZQ6WVExujnVhtZvgeolj4QyziuzMKjozYfCBJUF+tTtMRNG5qMbFBRVOBEHgvv9v\nKU/tG8LvtvG2JW/u/lSL6eepY0k2dpglqWVuiY8t9Y1zPD8Tilwy/fGRktviSTb/R4YVXulIEk5r\n7Ow1A1Z2SeBza0sn/O5kNYPvbRnkWNgMOl1c7+aOEwjCNwO6YdAWzhSUYRuGeZ+SJJhXap6n+7YP\ncv+eYUQBPrq6jGvnFp6XoXiG7zx+jJ5whmsWlXDHRSNjtWaVuZg1Jhtul8Upu227JyiHNhDwOG3U\nlrg52h9nV2+K2xaPZOYHYxnSWZ3aYiczAiOvP9wb58X9/cwo83D5YrMiqKU/nRfKALGMzmBSQ1V1\nhpNwz0eW8uKhKLuGMkRiGbxeO6lUFlXVcdlFPjFqhFh/Qi04loFZAq5pBj/dMsBv75xd8HeUuGX+\n+dIKBpIqxS654G9tLHXwwuHCoNWyWg/37Rgikla5oinAggoXs0uczM6ZKqayZnDEIYt85CfDjGqj\nJ5PK8GhrYYn/WAPQk7Gqxs2qGqvKycLibGEJbwuLaUYSBS5t8PLcUdMIq9RjY1nlm/NGphpiXnQD\npHQbPj2DLE58sw+n1LzoBni0OcxljX6aSk8J+tlPAAAgAElEQVTNqVlR9VPup35w9xBP5MbJRHqS\nfP/5br5148xTOsbp8MjBGE8eMcsPX+pI8vHlRScU303FDpqmqay2PmDj7ovLxv1ckGWSPj8hDX7d\nkuQdTW4Cp1BS2DKUIZrRmV1ko8h59vt9Lc4fdMNgU+dIH+hAUqNlKMuyiunLHn92fSX/7/luemNZ\n1s70cfWc8cGw7liW770ySFYnP9IPzCzfS+3JCYV3y5CSF91gmnO9fY7/De1d0DyUYSClMysgUzvG\nYDGW0bl3f5yhtM5lK2vZsqcPJaMxd0aAqhIXc4tt3DQ/wJGhNH/aMwyYZcY/2TLA2hm+gn7qu/98\nmFePRADY2xWnKujg0nnF0/I3bD4SxeFyoKTMoK3X66S60vzM7bJIMpmlM6Xw4Z/v5WhfArssMpRU\nQRC4ZlEJ37ilCUEQONAV5dbvbCKZC3T/7dvm8Jnr5/C/W/oL2ml0Xc8bwukGDCmwc0gFRDAgk9Go\nq/WDYfDPV1Qxq3hkvTYMA79DzLcB6bqR77M2DIMdPUlsosDCcmc+s+2QxQmnfty0sIh0VmfzsRhJ\nRaMs6CCi6Dy4z/wsXjoa5+82VLDxWALDMHjHoqKCqQarGovYciicf+z0OgumXQjALac5HSWZ0Xis\nOYyqGVwzL2i5mltYTBPWN8nC4ixwZZOPMq+Mzy6xYU4Zspo++YveLJyg6jSrG4UbA0Hg5Y44GjB3\nCuI7ltb4x4eOsbsrQV2RnX+9uYHaKRov9ccLjfD6xxjjnS3258r+dF3HbpN49EiSqArra16fsVDh\ntMpPd0fwusyNYCRjsLlH4ZqZU2tzeKYtxcac8HJKaT621DelXniL0+O4QeP5MqZKAGxiYc/pycy5\nTpXaoJ3/uHnmCZ9zKJTh+HAJgcLxet5JhLRjzPuUBHMW+RuVTV1pnm03v4sbO+G98z3MDNjQDYNt\nvQqv9SoMpHTTpdsmcdXyClo7IoQiKURZJKPDlSmd5JgpHbphBjdh5Ht9qC9Z8JzWvuS0Ce993XGc\nbic2hw0M8PsceZHcNxAnFErRk86QjI8qARdAlmWe2DsETjt3bajmkW3dedENcP8rnXzm+jm0DiQR\nRBGXy4ZhgAs9PyarNmifUFSuqPFwdZOvQHTf80o/LxwxM8oNxQ6q/DYO9SaJY7YG1ZS5+fdNZsn8\nunoPn1x14moiURC444JSblhQxP95spu2nMmn3S6RyWhkdYMfbR7Ij1Nr7k/zHzfW5wMiH7u0HrdN\nZG9njKMp8BQf70M3WFPr5oa5/tMa86npBv/4aDsH+s1r64mWMPfc2oDHbq3zFhZnyhv4lmNhcX7y\nn1tDfO/VYZ5tT/NyV5oil42DoQz/tSPCj3dEOBI+N4LvXGATdTzSSI+gV8owlFLpTmhoE7i8lnls\nXDHbNI4RRQGP28YTh+J8/bleNuZGZZ2I32ztZ3eXmT3uGM5wz/PdU36vG5r8BSZwl0+QRTsbVPlk\nlKzGUCRN92CCPe1hnjicYHvfiUvyT8Zj+0J84c9H+d6zXcRPoZXhhaNxsmOenj2FasRto953WjPY\nP/TmuZ7PN45GVX7Xmua+g2l2D54f51kQBG6e4+F4xeySMjvzSs79OMFqn5yP8YmjBPTcEgfXNU1s\nZjmnxMHF9Wb1kSTAHYuCZ20kmqIZ9Ke0vGg6G+wbHFl7dQOac9/Fp4+leORwkt6E6aVx3CjsSFuI\nl3d00nKwj1dePcpQJE17NMvcMhfzykcE5rqZXsrG9MmvnjWyXooCrGyY2sjMqbC01vy8JElCkiUW\nVnu4qMrO9Q1Omnzm56PrY/xDRp3W/QMK336pH9VmZ9mauSxfN4+qulLKcgZvCyvdqKpOLKaQTCh8\n6coa7rqsmr/ZUMUPb2/C5bThHiUqr5jt52/XlLGgbOScHBhI50U3wNGQwp3LiplRZMdjF6kN2umJ\njbRHbGpPmFn5KdA6pBAeVb4u5XrKRYGC6yeR1QsmqWR0g7jTRUldKdcsGzHadNtEbpwXOC3RDWaQ\n+rjoBuiNZWkdeAslDywsziJWxtvCYhrpjmU5NDyyGRpIaTx+MMQzR+IcN0/97f4Yf7cyOGFf2xuR\ngJzBI5mGL6/1Zzga08ioOqmkwoUVdlbUeAqe/5l1lVzZFOAvLRH25G7uBvDskRgXzzyxmVlsjMCM\npacuOBdVefj+OxvY1hGnLuhgQ9O5Ed7vWhDg5aPR/D5R1QwGoyn6kqdvpPfiwRDffqoz/ziUyPL1\nKZbN2yWB3nAKv9uGJArousHy8qlv0Lw2geQope6d6qBbi1Mioxls6snme3N3DqrUeCVKnK//urGw\n1M7c4iKymjHlcUWni2YYiIzP+DcVO7hzaZCNbQm8donbFgUIOqWTTli4Y1GQm+b6kUWwn4KJ2KkQ\nVnT+2pYmrYFdhKvrHZSdhaqQgEOkL6kXPAY4GCqcXGAYBrIgcKhtOP8zTTPo64tS5a3CJgl87eoa\ntnclkUWBCybo8b377bOYUeKkJ6KwZEaA3RGDJ7YM45RFbp3jocZ3+tvJS+cV8ZUbG7j31T4GUhpd\nGYGn9g/y+UuqmF1urpN2u410ciToJ+Q+Z4/HjsdjpzeepVcTkGXzPNfMLOdvVpg94V+5to5fbukn\nnFK5el4Ri6s9LK4270v7BxUeOpykqtxLIpVlVpGd9y8v4huPHqM3muHK+cXcvKwUbYJZZY/uH+bF\nnBhPhjPIsojXa4p9UZh6JUiZRy6o2pAEmF/moKHMxVOt0Xx23i4JVPtHAiL/9EwP4bT5+QshuKrJ\nh98usqrGTbnH/DyUrM4zB8zP/Yq5RTim8H31OyVcNpFUrhJCFBgXiLGwsDg9LOFtYTGNTHRzjioa\noyeWKJrZe/dGFN6xjM6BYRWHBAuKbXnnVVkwSKkGR2MaWVXnheYB4mmNZw+YZfcfW1nYZzy/wsWu\n/nReeIPpqHoyblhUzFPNw6RVA1GAm5dNzbwHYGtbjMf2DRN0Sbxt4fSUSE4Ft02kxmdjeFT2Q0Bg\npv/0l9993YUGOs19qUmeOZ7LZvnY0plkb3uYgEvmPYuDVHumLgpume3hjwcSRBSdxWV2lp6CaLeY\nOqo+fi7x2JFCryeyKJzVMYKGYbA3bNCT1JEFWFoiUeIo/H1r6zysrfNMcoTJmcram8zqpLI6foc0\n4aSEE7F7KMvxmGBGh52DWa6qm37hff0sN5nWJAMpjaagjdVVpugrdUsMpEZuOhdWOlhb46R5p52e\n8Miau26mlyqvuQ7ZJJHV9ZMHPu2yyEcvqWVbd5Kf7RymPCAiCAJpTecXe2N8ec3JR5KdiEvnFfGL\n3WGCuSRzc3+ab740xO2LAnxsbQVbjsXwzfZT45WoL3FR4rPz/VcGkR0ygiBQ6ZVpDxVWEflzItjv\nlPnsJdWksjqbOpI8dTjOuno3bptIe0RF1czSc7/HjiZKfO2RNjYfNZ31t7fHKfPZWDPLz4oaN9u6\nzJL7G+YFSKQLM9rH+8YB7lxaNG7evaYbPHYwSk8si1eC5p4EHrvEhy8q5+MrS3moOYxDFvng8mJm\nlzj59a4QbqcNJauBAbVFDry5Y2ZUPS+6RVEg6LazayCLLEJjsYNyj8z9+8L85PlOYrkJFg/tGuKe\n22cjn+R69tgl/umqGn78ch9ZzeDOC8uoCVjrvIXFdGAJb4s3BbphcDScRRIFZgZev8hsXcBOjU+m\nK1dy5reLXD+3iOb+NOGcGUupS6T4DdgTm8zq/Kk1RTJX+nYsqnHjrJGsrSSYvZZ9EYV4btdpl0V2\nDWR4tSfNqqrCnuab5gU4Nqywrz9NfcDO+5adXAzPr3TzszvnsLc7SUOJg7kVUzOtOzSQ4p8ebsub\nMB0ZSvMftzZO6bXTwXuWFfMvz/UQy+j4nBLvX1bM/JLT38gsrS0s81w8yai6nmiGP+0y5yHfurSY\nKr8dpyzylcsqGUpq+BziKbtRV3llPrvi3FQLvJVx2wTqvCIduXnzRQ6BctcbL1h3uvSmDFqHVaKK\nhgCkVZ3r66d3898dyTCUyNJU5irI3D9zJMYvt4fyY/3es6SIa2dPvbR6rKwRTmR8cQb47CJ3Lhwv\nlm9s8gAJBlM6c4psXDnThSgIfPWWOfzDb/fTMZTm8gUl3HVF3Sn/zmeOxHHZ5IIKBM0w7w9nEkwW\nEBCFwmCTATx+NMWXVpfxnpWF0y8UVWfBsSQHBxR8TpG7Vpfyuz3DbO00hXFtwMb8UeXzWc3gu5sH\n6Yya9+bNXUm+tK6MnpjCcMKsEJAlgUVlPjaN6Wf/xWtDtEY1PrehgqOhDDZJoC5g56H9w+PeczRq\nBjbq/OP3IfftCvF4awxV1QmHR4KlhwbT/PSOJjbkKr6OhhSeORTFZxcRRQGXw9yqj24HsMti/ne7\nbFK+J17V4ZljSQYTWf68L5wX3QB7uhIcGUwx5wT3Td0w+P32Qfb1JLmk0ccdy8vyAXYLC4szxxLe\nFm94dMPgf/dEaQmZN5gVlQ7eNW/iHr9zwRfWlLKjN4WiGVxY5cRrl/noUj+bu9MIAqypdmJ7A97I\nuhJaXnQDtMc0MpqRL6ezSwLLy2T6IuZjp01i0cwgNknk5d4s3Qmdm5tGbvgum8iXNlRiGMYpGUfV\nBh1TNlQ7TnNvssD5eF9PEt0wEM+RYVVTiZMf3jyD4aRKmcd20ozDyVjbVMTd19XzQmuYSr+dD62p\nHPecZEbjiw+35/sMX+uI8+N3zcJlExEFgTLPqS//sYzOCx1pbCJcMcOJLL51hODrwSU1dtpjGpoB\n9d6Tl1G/mQgpOtFca4kB9CcK15sz5ekDYb79dCe6kTNze+csAi6ZtKrzqx2hfNmvbsDv9oa5sNpN\n6RS/M0tKbXQndJKqgUOCC8rOfjB4c1uMB/eEcNlEPrK6nNvnj78HNpS7eeCuCwt+Fs/oPHwwRkTR\nWVXjZHnliVtgPDaRRFjB57bl121ZnFoVwYlw20XuXF7Kr7aZ87GCXgdelw0d8/Mf+6k/sC/Mvj5T\n5A4lVR5sjvD36yrY0plAUQ1W1XkKgoq9cTUvugG6YyodkQyb2hL5n6maQWNQYk6Vhy2HI/mfL5oR\n5LmjceoCNq6Y5UM3DP5tYx97+9K43HZKXSLtQ+mC+5gkQGcoTXNPnNkVHmaWutiTe7+qWtiv3j6s\nkMxouO0S27uSfGdjL5phHuOqOQGG0zrVPnmcQ/l1jT4ePRih0LIUOmJZ9vYlkcZ8V0QBeiMK1UE7\nXsfE1/Ifdgzyiy39ALzaHgcD3rfy3Iz8tLB4K2AJb4s3PB1RNS+6Abb1Klwxw/26ZpUvGLN58TtE\nrm54Y48U843ZWDklGLvXKrLBpXVOpKyXI1Ed26geyuOOrWM5F27Nc8pdBZmJ2eWucya6j+OURapO\n0+xmIq6YG+SKuZPPIO6KZArMfQYTKl2RzCmPbjtOUtX54Y4ox2Mv+4ay/O1yH6Ilvs8aonBmLQmv\nN5puEFF0fHbxlMu1vRMs31kdpstY+eeb+/LrQWc4w+PNw9y2vMw0IxtT0W8YkBojlk5EwC7yjkYn\nnbEsmzuSPNma4cpZ3tMKdk2F9mGFf3m6i+OdCO3Dnfz0tlkIgsCOrgQvHo5S4rFx69LicRUuv9gV\npjXXE948qBBwSDQWTb5O3bY4SNfLAwzFFEp9DoqcIrfNO7E3x1S5cX6Qi+o8/L4lznDWvF4urXNO\nuFYPJsy1TRQFREFgIKEiigJrJimX9ztEZJF825ckmO1NwhhLfKckctHCCgYyAql0lqoyL5LTDJwM\nJTWymsGhkMLenIiWZZFwFlbP9PFqm2kQOrvcxR92DPLIli4UVccmCXzvPfOoC9joimaRx3wGs0oc\neXO3Zw5H85+jZsBgPMvnN4wPrD59OMr9+81RYmI6S23AQSitI2IGXSVRwOOysXBeGf2DCUKhFGgq\n//D7g5T77fzP++dTU1R4LzAMg2f3DZCMpXB5zXFo+0+hjcnCwuLkvHHv6BYWOcZmgYQJfvZ6sPFg\niAM9CTYsrGJO6Rv/q1bpkVhfbWfnQBabKHBZnaNANB8YUvjZzjBZHZyywKoZXnqSIzuaE+27+5Ma\nD7YmiWd1lpTZuXLGxFmXtkgWzYAZARnpFITz3Ao3//f6eh7dGyLgkvn4uvEbmTcbFT7TbTeRMXea\nHrtIxRkY5OzqzzDaoDmpGrSEMiw4TSFv8eYmktb47x1hBpIaPrvIR5cFqT4FA64ar0TQrhLOmBdd\nvVfEM8rIL6XqPNeWIp7RWV7pIKWoPNQcJuiU+cTK0nHiZixj16Pj5bQeu8QVs7w8c8QUUQKwuMJJ\nzQSlwyfCMOAnr4UYTJoBxx29Kb56acVZ8fZoH1YY3f7fF8+SyOh0RjJ89YnOUQEGhS9dUVPw2rbI\nSNDayD0+kfAudct868qqaa0+GE08o9PkE/C5bMwudVLunjjSsnaGh609KTxOs+w9aQjEFG1cX/Vx\nAk6Jd8718ZtdIURR4P0rSilxy7xncRH37h5GN8zP+YIqF+2JJHMbRlqfhhNm73Spz849u+LoBtSX\nemgfHMmWN4eyrGkMEEmpHB3OsLUrnBvJZpa5/3ZzD3ffPJutXSkEm0hRkZN0WmVxhYu/u6Qqf5yx\n798/iffJk4dGfD6SWYNaN3xsWTG/3BniqAKyaJapO2wSDXVBqn0SW1uGAOiPZrh3cw9fvK6h4Jif\n+/Uent1qTgpxuh1U1Zex4CQVEBYWFqfGG18NWLzlqfHJrKtxsqkrjQBc0+DG73h9s3B/eLWHbz18\nGIAfPdPGD963gPVzzp2h19liaZmdpWUTb8qea0vm5+qmVYOMouGRJRKqgQBcWT+5QHvgYCJvBvRK\nt0K1V2LBmB7oPzTH2JsbqdQYlLlzke+UstYXNwa4uPGN2ZusG+Y5HFsdkNV0BmIZSr127GOEht8p\n8c/X1nHfNnOu7PtWlOEbs4l78VicRw5EcMkiH7igmFnFk5fwB8bMRtYNg0f2h1kwQTbG4s3LQFLj\nwdYEsazOkjLHpEGyZ9uSDOREZyyj89fDcT6ybPIKjbHIosANMx20xTQkEWb4Cq/d3+yLcTRiZj1f\n7UzSMUoEtQyk+c+315/w+J+6uIqvP9GBohrMLXdx/YIRc7APLS/hojoPzQNpagN2VlSdeoVMf0LN\ni26AcFqnJ5al8QTfsdNlTq5H/bgLdWOJacK1pydZkL3f2W32Lr/WFiOR0Vg100dD0MaBITPjLQAN\nwakFGM6G6N7eleBfnu1GM8yy6C9cWkW5e+IM9ooaD5X+KPHchIVE1uCVziRXN46U2D+8N8S9W/tx\nyCIfXVPBL18bJJSrAnpk7yBKJkiNz8a/X2Mar1V4ZURBYFm5nebQyESBeq/Ie+ZV8HBbJv+zUr+T\nUFwhPspgbU9fCi0XARHHBP89DomAU8LtEM2su10C3SAaz/CjF7r52LoqKgN2bl9SREckw+EhhcYS\nB7cvmXjfMHZWvd8hU+ySuKrRx0+2DzP2cpXshffTsddz93CKv2wdGc+ZTipcOsPNHcsLjVEtLCzO\nDEt4W7wpuHG2l0vq3YjC+BvS68Fjuwby/60b8MTewTeF8D4R9jEbDYcs8J55HhKqjs8mnrCkPJIp\nLOOMKiOZApsk0Dyo5EU3wOGwSntUfV2N9M4Ve4ZUDkVNZ+cVZTLVHvP67gyl+eT/7qcnkqEyYOe/\n3r+AuuLC4Mb8ChffuH5iAdIWzvA/WwfzVZbfeamPe26sm1RgLCi180BzFF2WMAzoHkrgZOrltxZv\nDu4/mKAvJyg3daUnDJIBBZ4KANkJJj6cDJsk0BQcv00xDIO2yIjgGU4Uuln3J1R0XR/XBqHpBr/a\nGWJHb5oKj8w9725CFqA6YB9nIDW/zMn8stOv5ih2SbhHjd5zSAKl7rOz5Sr32fjW9XU81hzGbRd5\n91Jz2kPDGJHfUOzgu0938uddZuazsczJd29t5JljSSKKxqoaFw3Bs+9endEMXutJoxsGKyqdRNIq\n33+hl9bBNKphIIoCOgJPHYywqm7yMnaXTSSeHQlujK50OzKY5gfPd4+sb892jwx7B3b1pNjda5aL\nf/yicq6fNxIUmhW08eFFXjpiGlUeiTq/jKIZ6McKx7S9b1kxP97cn38sCRBwS/QMKzhcdpxZnXRS\nob7EyWeunIFDFplf6mRXb4qu7hjJVJau3Gv39SS570PzCLpkvnl1DbpujBPvo/nw8hK+s6mfwYTK\nkkoX18w2Aw6Ly514bCJJ1SgQ3+savOw9IBFXNCoDdt53UVXB8Vx2CUkUCiaz3LSkxDJWs7CYZizh\nbfGm4fXOco+mMlC44an0T3+W43zjhtleOmJZwmmdco/EskonP9uXIK1BtUfkplmuSfs8F5Xa2d5n\nbmrsEtgNnffe20o4pbGyzkNDtX+cCdtog7rhtMZjh+Kksjrr693MLbbzxKEYLYMKdQEbN80LTHv7\ngW4YtCcMUipUuASKR406UnWD5p4kLrtIU9npl+oNpHQORU1xqxrw2oDK29xmsOG/n++kJ2Kes95I\nhi8+cAh3kZdit8Rn1lZQd5LxL33xbIElT0TRSWZ1vCdoor15jodvPNfDcaujzmiaA33JKbvLW7zx\nCSuFXg3HRxqN5eI6N3v7FZKqgU2Ey2ee+tivyRAEgSqfRFfs+PQECRgR4oZhcOdvj7C+wccn1pTn\ng0nPH4uzqcPM+h4NZ/hTc4TPrzs7xlFum8hnVpXw5xZzDvONc/35kYkZzaAvrhJwivgnKY0+VWaX\nubhrzFpT4pIwsioqApIA183x808PteX//fBAmj1dcd61YOqVCGeKphv8ZGeY9pzR2daeNL0D8YJe\nYkEw/zdZmfVx3r0gwE93DKNoBrOKbKyrG1mHBsasb2lVz/e3m8cfWa8f2jecF966YfDYgShHhhXm\nlzlZVWW62TskgWVlNnYOmAHgIofAmhoXQ4uCPLAvjAC8d2kJalbj357oAMBmt/HulZXcdVk1giDw\nWleSHT0p1KyGx20jmRoJJncOK/zwlT4+m2uDOpHoBqgN2Pn+9bWoujHu3ja7xMHOvnS+UmpVtYvb\nFvq5bvYyesNmIMA1Zp0v8tj58jvn8Y37W9B0g49eMZNF9W/MCjELi/MZS3hbWJwFvnB9A8OJDAd6\nE6ydU8pHLql9vd/SWafCI/PldaV0xlRCisHLvSOzbLsTOruHsqyYZObz9Q0uar0ysYzOvBIb//Dg\nMcIp88VbOxIYsoxht0NOfBc7RcrcEvGMjtcu8t/bh+lLmM9vGcqwvtbJwwfMOax7+tOoOty2aHo3\nl0+3KTx/1DTCmVvh5h2zPQTtAqpm8Pk/H2V7h9kj+r5V5XzsFHrKRwcYxhQCoBnkezmVMWZPneEM\nFS6NSFrjB5v6+H/Xn3hU0OwSBz67SCz3S+aUOE4ougEWVbgIDSWRbRLZrI6iqOzttoT3W4mFo4Nk\nIswpmrjqpNIr87mLSuiOZyl3yxRNs9nlexf4efxIgnjW4LZ5Xu7fPUjLgIJhGGQyGooBjx+IMLvM\nyZWzTQERShUGDYZTExs+TheNxQ4+t7awVDemaHx38xB9CRWbKPCx5UUsKj87Pgm/3tLHcGwkS/vQ\n7kHsskBmlFmDZ7rc6qbIYErLi26A3oRGaEzwJpvVWVjt5r0XlJ7wWAvLnXzr8goSWZ1il1RQrbOw\nyk2lz0ZvzBS3i6vcrGv08+C+YQyD/LoHpqM6wPbuJA8fiNI6pKDrBps7khgGXJ0bJeeTDQai5nSS\n7pBOc7nMzfOD9IUVHm0O86OXepg5xgtgW0c8v56HUio2ScTtkHHaZQZD5vHBNGnb1J7EoJ+7TiEY\nNFFA+b2L/BS7REIpnQsqnVxQaV5fAZdMwDX5tv/ODTO49aJaVE3H53rzV5NZWLweWMLbwuIsUOy1\n898fWgyAz+cjFoud5BVvDkJpnb+2Kag644qQs9rkpaaCILA0J8pTWZ1IunBD7JLA5hQZTuv4bLCk\nVOazf+1C1WF1jSsvusEUpodCGQzDQMloaLrBa53xaRXeiqrz14PhfDntzo4Y4ZjCnIBEiUPMi26A\ne1/t5z0XluE5SWbrtc4EP9rcTzpr8PYFQW5fWky5S8Bng9zekXqvmM/0v39tFVsOR0hkNOyySCA4\nIn6PO/6eiCKXzFcvr+K5ozGcsjjlOcWNxQ729ZhZQwGYU2GZ77yVeNssNzW5INn8Ejulk5hfAfgc\nInMdZ6fax+8QefeokVmLrzRNwz70+8OkRy01Q6O+CxdWu3j6SCzvbL2m7twGjHpiWb73cj+9cRWH\nTQJZ5MGW6LQJ73hG5749YXriKvNKHYxt9JVFkf9zTR3/8kQHGdXgpqUlXDjj3I7e9NgK3cUFYFml\nm8fDZruAYRjE4wpzgwGKp1Ca77KJBTPYj+N1SPznuxp5smUYhyxy3YIiHLLIO5aUoKg633q2m13d\nSYJOiU+uqeC1riQ/3GqW4NtkEVXV0XSD5oE0V8/2k8xoPH4wVhC02NGbxikYPNpsuovrBrT0pwve\nx4xR5f5LK1080GLuBex2iYb6ID19cTTdwOu1oygqWzviwJlVYThkkZvnTn3u/GjMTPjrNxHGwuLN\njiW8LSwspo3WsFqwoTq+RXHLAgtKphZBl0WB0oCTniFT3ImiwIZGH6vrPMQzOi5J4K7Hu/O/Z0tX\nivoiB8O5rIkswtwSB9vaY2RyZkNHBtK81pngwtrpKXdNZI2CHlYD0zF4U2uSd48R+KJw8rLBrGbw\n/U19KLlN3f17h1lW5WJeuYtLqm30JnVkUaDSNXKcRbU+/vippRzuT+J2yXznpX7SudevmzG18T6V\nPht3TGLeMxlfv3EmP97YQyiZ5YaFxSyunr4SYovzH0EQWF5x/rbOXDrLzwN7hwFzusJFo74LDUUO\nvryhgr39Zo/38upzJ7wNw+BfX+xjIBcIUDUVn9vGqXe+T84f90fYO2AK2E0dSTbMCLCtLcZgPEvQ\nJfPhtZXMqXBzyewAqaw+bmzaucBrF3p50aUAACAASURBVLl9vp+HWuOkVANJFonIIvUBGwd6k2Sz\nGppm8OyBMB9ZN9KH3B1WuPvhY7SFFFY3+PnK9fU8sXeQJ/cOURV08Jkr6vGPyeYWuWVum8AczCGL\n/PPVtaSyOk5ZQBAEfr49VPAcMdfv3FDkIKPq/N39R0jLNkqCI4HGoFNCGRNQttlFrplbwq7OONUB\nB5ctKGFvf5pF5U4qvDbW17vZ1GGW1XvcdqorvAzHzAy7rhtEExmGEllKPFbG2cLizYglvC0sLKaN\n0eN+BKDMJbCq0kGlR8ItT63H2iYJfHJNGb/cHiKd1dnQ4GVtbjZrwCGR0QzGjtS9tN5Fd1wjlNbw\n2kR8TgmXJJAZaaFjT29y2oR30GluFNtzo3hEAdoHzECBxylx+ZwAzx6MIArwqQ3VE2ZkRpPR9Lzo\nPk4k10trEwXqJhpqDJT77ZTnZoN/61o7WzsSlLhlNjRMz1zdsYSTKi8eirByhpcr5hWdF2P7LCxG\n84GVZTSWOumPZ7mwzkN9sDBIUBewn9T/4GyQyOh50X0cwTBOOzM5EaNd1FVNpyua4Sfvm0M0pVEV\nsOerbg72p/nKY+3EFI3F1W6+ccOMk65R08nicgcuu8jvD5hrpgGokkx6lEN4pb/wM/ruM120DpjZ\n5D29Kb7xVDd/3dqZ//fBeJbv3T43/ziZ1XmlO0NaM1haZqN2glF2o//msaPu/A6R0oDM3q4YA5E0\nbeEMs+vceOwiSUVjZtDGNbM8yKLA0mo3u3KO8VfNCfKhVWU8diDCq70K9x8wM9wb6t3cvijIHYuC\n+B0SbZEsTcV2ShwGX3tixE1cN+DQYNoS3hYWb1Is4W1hYTFtLCq10ZfUORJRCdhFrpnhpMh56hu6\nFVUull5XjaobeUOc49glgStneXkqN2e3zm9jdY2bsKLz3c1DZHTY0afgdUoFJetjHX7PBFEQ+MzK\nYl7qSPL84RgH+sxePbsk0FTq4oI6Px9cU4HPIVE8hQ2Uxy6xpt7DK+3mSKQqn41Fp1jCfbYFRSyt\n8onftuYN3V48FOWbb5951n6fxZuPVFbnhbYEqm6wvt5D8CTmWafL+oYzL5+OKjq7+tPYJYEVlc4z\nDjJ5HRIzgjbawmawzi4JfGF9GbOKpm9dWlrhpD2aJZ3RiCYyvBJVaO5N8fWrqgtaXe7Z2EMsF9jb\n053kl1v6+OT6qskOe1YYGzytq/LT6BF4+UiUuiIHX7i60KMilMydN5vI5ctr2HdksODf93eZ9wPd\nMAglVB4+miKn0zkwnKVcT/PH1wZwyCL/cGUNqxsKAx5XNXoZSKq0DmWo8dtwGBoP7DErJwzdYO3i\nSnxuc331ODVumePGkbs3ffXqWvb0JLFJArNLnXzpiU4Gkjo+z8h6vLE9yfIKJ4pmcMVMD8OKRqlb\nRhYFStwyQ7kxZzZJoC546tfEcFLlxy/30hfLsqHRzzuWlJzyMSwsLM4+lvC2sLCYNiRB4OoZ09Ov\nKIvCpJvd2xYFKfLY6IlrzClxIIsCB4cyBWZkNqedK5qcdEczrKz1cOms6cssAThlkSsbvFxS7+GR\n5jDDKZV5VV5e6NXQDQ2PTeCWxqkL4bvWVXBRfYJUVmd1neekpkeqIZI2bIgYuITMuLmt0832jnhe\ndANsPBQhltbGzQa3sJgI3TD43uZBjuWE56b2JHdfUo57gkxrTNE4MqRQ5bdR7j21zF80reGUhXFz\n7U+FZFbnxzvChHNjDZsHM3xwyZk7PH9pQyV/3h8mldW5qsk/raIb4OpGL0UuiZ+9OjLiKpzWeLI1\nyvsuGBFix+d9g2nq9eThOD3JLv52fcWU+qqng8agTJVHoifnz7Gm2sEVF03uw3HDomL+47lu/G47\nDrtERXFh9dLSeh+prM4/PnyMvT1JZElg6ewyiv1OQrEMD27ryZf13/1wG3/55AJcNnPtah9W+PKj\n7fTHs8yrcPGFdWV865mu/LFdTjkvugEcNglt1Kg6SRRYVuPhmdYID+4dpjOSxTbm+hMF+MozPQC4\nHRKaYWbVP7+unK9fV8cvXjVbhd65pJgKvx3NMMeTTZXvPNfNji4zcHtwII2gG9yy7MTmdBYWFuce\nS3hbWFi8IQinNV7pSOCSBSr8TtqTAogy+4Y1ELLjTJ6KnBLDqSyyJDCr5Oz1pNokgVsWFQHwx4Op\nfN9kImuwezDLuuqp/W5JFFg7xd7sjAbDupvjY72yhkhASp/4RWfI2NJHt31iUyMLi4kYSmp50Q0w\nlNJoC2fGzcrujmb48hNdRNIaNlHg85dUsmIKLSKabvCd57rZeDSGQxb43KXVrJl5epnvY5FsXnQD\nHBzOkszqEwYJToWAU+KDy89uJnJltYu/uOUCx3b7GAV32wWlfO/5bkRRwO93IAgCrcMZvvVCL9+4\nsoqtvQqaDiurnHjsZ+c7bhMF7lzgoT2q4pCECUvBR/POC8qoL3ZyLJQmLEBNuZfLV9bT1h1hVa2L\nT15Wx6P7QuzNGT+qmkHLsRBrl1QTjqUpL3Xj9zlIKypdvXHiaT0vvH+8qZf+uHlttvSl+MPOQWaX\nOtnba/ZiZ7IauqYRimVxOST8bhvBMeNLnzwQ5gcbewFwuWSyqk5KUXHaJZyyyGDOXV6WhPxkiqii\n841nu7EpWW5dXsplc4sYUCQOxWXAoMyhUWwvNBrtGk7z4PZ+nHaJ21dV5isZjoUK1/8fPtfB2lk+\nKt4Co0wtLN5IWMLbwsLivCemaHz1uR6Gcj2M9UUOGipGMth9SZ01jU5unO1lc1cKj00kkdWpqw6i\nG3Dvnghf9NvPejZHHLNHfepwDI+os6xyep2/zVbIkc20YshoOgiCgWGYIn66aYupLJoVpDeigJLl\ni1fVIZ9KSsbiLY3PIeKUhbwBoChA8QSjjf56IJJvEcnqBn/aE5qS8H75WIyNR81+WkU1+I+NPact\nvP1jxKZTEsaJ1/OZ9y8v4d9e6CWe0ZkZtHP93MJs/XULimjuSfBka7RgnnVXNMt/bQ/TmzSDDlt7\n0ty1suis/e02UaAxePKKhoym8+ShGLGMzrqGAC67xLb+LLMCxVx4WSUluXamsSMWRaDeJ3Gsx6C6\n0rwWvB47fpdMqXfk2kuOmduYVHQ+sa4SURQ4PJimscTBX/f00hMxK4s+enE1AUfhtXU82wyQyWh4\nXTJ+Gd67JMDSKhcfvr993N9lGAbDaY1EQuVrj7VTU+zGcB6/rwn0pyX+Z0sPn1hRTJFbJpTI8uGf\n72MokasaaR3mZx9aiCAILKp08dKxOIZhHjcaV9jVHuPqRYXC+zcb23lmbz+zyj38/Y1zxs3ztrCw\nOLtYwtvCwuK8p2VQyYtuMEsD60p1ZMnccJW7zf+/fKaHy2d6aB5U2DZkbqYkARbWBuiMZs+68F5b\nZefhI2k0A5IZla7hND/dpnDPDTXT+nvGJt6yusGT7UmeaI2gG3B9k5drm6ZvTNCmtjj37jRdfx1O\nGysbA+N6JC0sToRTFvnkhcX8fl8EVTe4cY6fCu/476NtTNDINkXRN7p8GiCdNTAMo0BYTpVav43r\nGz280J7EIQncMsd7TowEdcPgV1v62dYRp6HEySfXV+I+DWE0p9TJj26qJ6polLjkCacqfHx9FQcG\n0kRGnaMyj0xPQss/HkxpdMWyNATP3DsiqxlEFI2gU0IWBV7rSdMZyzIzYGNZxeTtSQeHFH6xfYju\nmNkD/fzROF+7vJJrZ45/zTXzi3hwT4hwUkUAPnpROdfP8/BC63DB84p8Dl7tSrG7L0Wt38ZNi4s4\n+KxZreS2maPHbJLABy40HdF/+1p/vs3GMOChHQO8b8VIGXdKNRDlkc9J0ww2zPDyiTUV+Z99aEUJ\nP982hKYbOGzm+chkdSRJxO934vHYOTyQZlbdyLoqCAKdUZV/fLqH/3p7HXs6Y3nRDbC7M85gPMNQ\nSmcgA36/E03T6ekcRs2o1JcUnqNHtvVw9+/3medx3wCRZJZv37lk0nNvYWEx/VjC28LiHBFRdHoT\nGuVukaLzuC82nFJ5tCVsCri5QUo8r/8y4RjjiO62CaytstOd1Cl2iKwoL8yaeB0SoyeJi6JAle/s\nu8RWeSRm+wx+vzdCRtVzPYUGumEgTlEA9MZVHjmcQNEMLq51saR8fKmgywaRtILLZkPTDbqiSf56\nMJIvc3+0Nc7uIQ1FN7N1H1zkoewMgg5t4cwJH1tYTIX5ZU6+eumJPSBuWhBkW1eSzkgGn0Pk/cun\n1qe6dqaPB3aH6MwJpHctLT4t0X2c9bUu1tee2xn1f9kd4rfbTdOwgzlnsH+4/PSCdg5Z5EBngpeO\nxSn1yNyxtLjAN8LrkPjv25vY0h7nhWNxXDaRm+YH+Z9dEcLRNIdae5BEAXXhQuDMhPfhkMJ3Xxkg\nrRpUB+ysq/fwXJtZxv1KV5qsbrCyavy5fuhAjM096bzoBkirBs0DChUT9P53JAyWzaskEldw2iVK\ncn3gC8sdHAyPBG7LPRI/fm0o//jaJh/3vHMW7WGFeeWucY7qYyuIjgdhumNZBhIqm3uzZO1Oako9\nxFMZLqp188GVhWPMrmzyc1G9h4xmYBcFnj0c4ze7RwICkiQiyeAUNeJZAVkS2dMdJZzKUh10si+k\n4ve5EAXy63zQLfODVwY4OJTJB1ckSaS60s/7rqpjXlVh69KOo4UBiJ3HwuPOoYWFxdnl9d9RW1i8\nBeiKqdzXHCejgyzAbfM8NATOv3EhGU3nK0910R01o+ovt8X57tvqz1kvb1o1eLZTYSClUeWRuKzW\ngabDrpDBjFI3XaEUdkngM6tLWVRmZ7JYfbVHImBXiWTMHUqFU6DsHAUQVla7eLw1Rn+u7PHqRt+U\nRbduGPxyT5RorvTxD80xyj0SlRO8d6+YoSO3Ic1qWsFMXo9T5niLaloz+H1Lgk8vPzVzKN0w6Ipm\n8dpFFpQ7ebglkv+3heXTY6Bn8eZHNwwOhLJkNIN5xfZxQbSxBFwy//62OoYSKkGXlHeOPhleh8R3\nb5rBnp4kfqfM/AoXGc2gLZLBZ5eonCC7PhGGYbC9J0Uyq3NBlYtQSufevRGiis7qGhe3zJ28kkRR\ndVqHFHwOiRmnkSU+MlTYp3sspJzyMY6zuzfJD14eMVkbTKh86dLxzuWr672srh8RaNfWO/jSfQdI\nxNNEh+N8+kdRHv7yZZOa1Wm6QTStEXBJE65zqmbwLy/0ksitaUcyGtKYnpyWocw44Z3M6mwbyGCT\nJWRJQB01L3uyz7ItqmKTRUpzs7bboiqrqxx8enUZf9gbpnlQwS0LZDOFY932D6S5bVGQWaUTr2tv\nW1TMMy1hWvpSOGSBT19azSsdSX6zN4KBOeWizO9gZpWZrb5lgWfC69Y7KvCx6UhkXEVG0GvnD/sH\nUTRQVY1DvXFmlblZWO3jgZYI6azOOy5vor03SiapcPG8Eh45GBtX0TC/xsuNF1QwliUzg0Bb/vHy\nWUUT/r0WFhZnD0t4W1icA7b0KnnHbdWALT3KeSm8e6LZvOgGGEiotIcV5padeuYnperEshC0g12a\n2uZ5c2+GzriZmTgW1djen6XKI5JQDWqL3dQWu4GTjwaTRIHrZjjoTuhIAlR7zp0JmNcu8X8vqWDf\nQBqfQ2TeJJu5iUirRl50g5mzH0xqEwrvShd4ZR1FB68s0lLhYHefuVEfu+lLaeNefkIymsG3N/bR\nMqggCfDhFSX8/bpytnYlqfTKvH3e5O7DFhaj+dOBOLv6zSx0hVviExcEcJykfFwWBSpOo0LFbZdY\nPcMUxsmszve3DNETVzF0A5+homs6K+q8hHWRI+EMDUE7714YIK1CkdPsQf/59hAb2xMYhoHfIeGw\nSagICILAi+1JZgVtLJ2gNDqV1fn68720R8z187ZFQW6cd2rBrgvrvTzRMpKFXF538t72yTg0WCja\nW4dObr6YyOj8bk+YmfPMUV7dbf0cOthF11CShorxxo+dYYW7H+9gIK5SX2Tnm9fVUzSmsqY3ns2L\nbsMwyGZ1VE0DRkRoxQTVOEnV4LiPRXnAxVAsjQi8c0GAeWUTr6nlbomDw2rB4+MsKnPwp12DaLnl\n1SaLuFzmNVY/5l58oD/FfTuGMAx4zwUlzK9w8cPbm+iJZAi6JHxOma8+34emGxiAhkEslaXI60AS\nQFM1vv10L9GUytsWF3PRzPFtOVkdFEXFZpMQBFhS4eLlngy5KW/IskSJ18aMYictfYn8OLW4AsXl\nAfwOCa/bDEbouoEgmKXpNlHgpvnj1+eBpMrejIMVy2cw0BdhQ1OQ/3PTnAnPo4WFxdnDEt4WFucA\n+9i+xXPQL3g6FLtlXDaBVNa8odslgbIpzKEeS1dCpzliHkMAlpfoFDlOLn7jY/o041kdn01GgPwo\nGLs4vsd5ImRRoN73+pT0u+0iK2vcp/46m0itT6Yzl8l2ygI2Ef7UEsMuCVxW78Y36jx6bWBuhwU+\nekERe/oUOhM6SUOkZWBko72g+NQ+wy2dCVpyG3fNgF/vDPHTm+tZOQWTKwuL4ySzel50A/QlNX76\n2hAfv7Bkyr3bp4OmGzxzNE5P3Pwe9Q0lOZQw30dnyiDgMwN3O3tTbO9IYLdJzKn2cetsV15064Y5\nSYG0hiwJBNx2BEEgougT/s6tXcm86Ab4494wa2pdlHonznyrusGWHoVY1mBhiY06n8wlTaZQ394R\nZ2aJk5sWF5/2OZhT6ihYN+eeIADYFVboDGcIZw3CmZHMclVdGcOd/ZQFJg50/nLrAAO5c9w+nOF3\nOwb55LrKgucEnRJ2SSCpaEQiaXTd4EhW5dplFYQUnZlBG5fPHL9WFjtFqr0S3XENuyxSHXTxqeWB\nE1YuXVRlJ6MZdMY0qrwS62tG3vfGo7G86AbTAG1msYN55S5uWzQiVGOKxlee6MybADb3Jfn5bY34\nnRJ1o8a/KZrBqCIjMqpGsVPkkjon//pEGwf6zVL6Lcdi/ODWRuZXFf6N715azLef60FRVAJOiQ+v\nLOXXLcmC57z/ghJESeLAQKrg55qmk9Elgl4HM4rstA1n0HWDa+f4uWFeYMIy/KePJokqOg0NZTQ0\nlDGn3IHTMlazsDjnWMLbwuIcsKHWSUdMZSitE3SIXFZ/fpbq+hwSX7ykint3DKEbcPvS4tMyJGuN\njmxJDKAlYrCm/OSvawrIdOc2yALQGJApcopcWmtne3/W7K1Ts3zx6T5kSeCOhQGWT9Ab+EbmQ0v8\nvNieQtEM5pfa+d3+OEquzPJYROXTKybOoomCQH2Rg5Bh4AaWSCLRtEajX2Btzaldb4Zx4scWFlOh\npS+JrhsFpbCb22L4ZHj/irMzY7g3ovCdl/o5MpjG47IR9JsjpI5z3JDRMAx6BxKkcynGroE4M/21\nOOWRwONxVM1A0w0CTpFFZROL0LHma5ph8MVHO/jPW2ZOaJD28JEU+4dMob6jP8MHF3qp8kj/P3vv\nHSbXXd/7v06dXrfvaotWWrWVLFmSJdtyL7hiGwym44BjIBBCbhLgwv2FEAJJSEgIIZCQ4NC5xBi4\ntjHGxjZusi0XWdXqdbW9TG+n/v44o9mdLdJKVjHivJ7Hz+NZnZk558yZM9/3p7w/XD4/UhHgr4el\njX4+sa6BZw9lqPUrvHP59CL+uQNpvvjIEQzLJhZUUPwTj8/mvz62lqC3Wsg9tCPJq3159o9WZ9UL\nxtSgRNAj8ReXNPC5hw5hlfthhjI66VSeP72seXw/+kpsH9XxygK3dPqIeEQ+sCzMpqESmweKbB0s\n8MVnh7ljSYR1rdMHNUVB4IrW6e91kzPxNrCi3sM7z68+L30prSK6wclMv3Aww5smVfnMi6m80j8e\n3Ix5JD68PIRp2RXRDU7g8k/u3cM/v20ey1rGqwYubA/xjbd66E/rzK/1EPbKrG0yebbXOacRj8ii\nuIIqCbSGFfZOaDtQykZudX6ZL72phQNjJSI++Zg+Juakm7jl3tRdXM4KrvB2cTlNjOYN+jI6iyUv\nYY/IR5aHyOk2fkWYdc/v2WBpo5+/v+HEs7WngkVxhYAiMFKwaAxINAWcBcbCmMLCmMLhlM4/Pu+U\nYhqGzQ+2JllS58E7yz7Q3wV8ssh1nU5meceIVhHdAEN5k7xuM9OyfMJ6kbBXJuyVWdNw4tfa2jl+\nHt+fYd+YhgC86zy3F9DlxEnmDQ70puhoiSAKMJYuUdBM9idOrHc5p1n8ZFuC4ZzB6mY/18ybvs/6\nP397mP+7aRRbkLBtGKVAseQn4JNJlucoZ/MaAb9CsWRWRDdAoWQymCzx4dU1/OfLo2QntHwIwA3z\ng1zY4ifumz5LuHaOn8f3edg1WsK2bfJ5nZRucmCsRHfj1PvpvgkzzS3bMURUMVnXFmDecVppZsvF\n7UEubp9aIj6Rn2wcwSgL4kRWpzvuY7ho4ZEEPnpxA6uaq/f9iX1p/vtlxwDOsqyK2ZdXFvD7VX6+\nM81VHQFCEz6iZY0+5sZUthTGAyClCTerbSMaGwbKn49u8+OdOf5oeQhVEmj0i/xg0BGytg33vpZi\nRcOJzxe/dUmUX+1Iki6Z2LaTNW6LTT3PUa9UFSyyLBtZmCpSr5obZNNAsTKT+8KyEZ8kCixs8LGr\nss82+ZLJvzx+hHvev6jqNZrCKk0TjNyuaPUxN6KQ0y3mRmR85d+1j66M8uShPEcyOmMlG0sQ6a5R\nWFarIAgCi+qPH3y+ot3PnoRO0bDxygJXtJ2d33gXl993XOHt4nIa2DlS5KvPj6CZNgF1jE9eXEd7\nVCWovnEF96mkMySwe0LWe0F49sfdGpJpncG/KDdp3qphOQs47zl6J6sPSMjCuKCOekR8ioBu2hxO\nGwQUgZoJQiCmgl+GfHl92+ibmombDR5Z5C+vaORQ0nGWrj+JdgMXl5VzgogvDLJt7zDeCVnTpQ0n\nVqVyz8ZRNvY7QmbnSImIV6pq5ehJafzrhhESRQF/wEt2wsilRLLIz+5ezANbRzmS0riwPURD1MPD\nO1MMDOeq3mfzUJ4X+3KsaPQzL6pw7/YkAvDe5TGumHvs8XySKPDHa2q48yf7sbHLVSL2jGZytT6J\n3uy48N/YlyNfMnjmUI7PX9lIS/jMfOcm99svjMn83doGVGn6APEz+9PY5WypIAh01Hi5fVmchw/k\n2JEw2JEw2D5c4ovXVfc1v3NVHTsGD6ObNiGPxFvOq6n82/5UtdlZcYInRcGYnKkFzbI50aYXjyzy\n1Te38R/PDzKc1bmkM8zF08x5bwirLIypbCtnrSOqyAXtU3u0O2Mqn15Xx2vDRRqCMisax6/pL725\nnU/+/AB7hwuYhoVtQ0mfvkVhMu1hmf1jJb73ygg+WeQt3THCXomr576+Np+BVIl9R1Jk0jnUQpZn\nzXreefm81+X87+LicuKco8tVF5ezy692Z9DKofCcZvHI3gwfWl1znGedO7QFRWKqRUqHWo8zw9e2\nbQzbcXU/2R/7eXGV5qBMX7mv8Lx6D5E38Gi210uNT+KW+X5+vjNDUTcJqxK6YfP1DQP0pnUnE9fp\nY02TU14piwKramCk5JznmteROJNF4ZRl3lzOTUqmSMESUQSbgDzVwS/ql/nXt3by1L4UPWkdG4H5\nNR5umsb86VhMN85uovD+3uYEyZKFIAj4/UqV8G6OqES8Eu+7oLrXZUmdl70DuYpzeF3chyhJSLbF\nxv4C8+Mq99zWdkL7WTBsiiUDuZypNAyL1AzOhm+d7+fhgwUymsX2AUd0A+iWza6R4hkT3h9e18hn\nf3mIvAmRsIdn+kpYr4zykTVTWwH2jxbZ0l/db7ysOUBT1EPOyCEIjkv3SMFkOKczMd66pj3EPe/u\noidRoqvOS3xCMK81JLE3aVR+F5QJz5sXU+mMKuwvVwjE/QrDBZvYSXRrxfwyn7n6+OPZ/v7WDh7b\nmSCvW1y1IEpomt+YomFx/+40u0Y0mkMyTUGZ+oCMIAjE/Qp/c3M7H/7hLkY1G1kSaIh7+eff9vKO\n82tpic58Xx3J6fz1Y72VgMNrQwW+fEPriR/sJL72zAC9vWMc3nEAgI1bexhKFfnErUtf92u7uLjM\nHld4u7icBiYbB51OI6E3KiFVJFSuossZNq+OWhRMCMqwskY8rrPxRF4bKvDtl0YpGhY3Loxw1dwA\nqiRM6y58rvHzV4d5tdcx3dkBCKJIr+4sBG3gsUOFivAGRzA3nltt7y5vQIqmyGDJw1HnacPWiCjG\nlO1qgwq3Lz+xfu5cyeTFQxkCHok17SEW1XpZ3zOenV5YWy1cJpaFR8Iexsby2BbMrfXyhZvbp32P\nrb1ZLmsP4PXJ6JKMNGnyQmYGE7VjEffLhD0iybLYVkSB0YJBybCmTBrwyZDuG2H/UB4rUF0O3nwa\nRfczh7JsGyzSGlG4oSvM/DofP3z/Aj72YA/5cm/7+sM5Vrf4WTPJTLEnpTG56PqmRRG+/uJoJXDg\nUyWCHomYT8YsVgdMmiMqzZGpZnM794+xfmuCztYohaJBg2zC+U42WhYF/mBFjO9sywIQ8so806fR\nEhQJnKYxl7IkcH13de/3w1tH+MZvjyCKAp++vp1RS2TniHN8vRmD//PYAHpJ45OXN7G00U9tSOX7\nH1zMpp4s31w/yNb+Alv7C7x0OMs975o/bd8/wN7RUlWW/0BCI1syCXpOPsBs2TZ5zSKTSFf9/bdb\n+lzh7eJyhjmnhfemTZv47ne/i23bXHnlldx2221ne5dcfk+4fUmEAwmNRNGkMaRyy8KppWq/T+xN\nW5WRVlkDDmRsFkVnJ7wN0+afnhkiVy7V+9GmMb70pmY6z/FsbEazMC2bvglOyYAzVkYZX4S9kf0C\nXM5d8qbEUdENkDPkaYX3iZIrmXzs3n0cKmeib1kW52OXNxP3SwznDFY1+1k2qVT9io4gP9vhzJn3\nygKfva6VK7uiU+YbH+XpPUk++4v9WLZzBAs6Y4QjPizLxjAsVElg7Zxj98DuT+ps6CvglUWu6fAT\nUkW8isjfvbmD/3xugCMpjYIt+PAuKQAAIABJREFU8O2XR3l8X4YvXNtSJb7/6Vf7+dFzvSiqhCQl\nuHZtG6oqc0VH8IRGEM4W3bR55lCO720aA+CFI5DTLd6xNIYsClU91+CMR5vMwjovXlmomI8trPPy\nXE+eodz45y7YNh9eGcOvSGSOP8EMgF0DOQ71pTnU5wjDZXOqAxGG7ZTy50smqmzhVSSKJpypDpj9\nw3k+/+CByuO/+Oke7r6+s2obURTIlCx+sj3FiqxIRrfAtunyK6Qm1M6P5gx6UxpdE0Z05nWL+/fk\nGM6b1PsEZyRZ+eOoC8j4T7CffTKiIHDzkhjf7hmq+vu8pt/vdYmLy9ngnBXelmVxzz338LnPfY5Y\nLMZnPvMZLrjgAlpajl9m5OLyemkOKXz52iZSRZPW+iiFXPZs79JZZbLZrTHVq2ZG8oZVEd3gZHlH\n88Y5LbyfOVLg8UPOqnVRW4TB1xwzI0mAGxaG2ZwQ2JcoIQpw49wzk95Ol0yeOFRAt2wuavEy5yTm\nLLucO0w2nJLFE88QT8dLh7McGitV+ojv3zLKH13axO1LZi5Pv35+iPaIwnDeYEmdl9rjTGL45RZn\nagM495OIbXL3qhp2jFnkNIOWiBdVlbBsmy1DGgXDoidR5MkDWQqaSUdERlM82OXAw5GMzsdXOQaE\nXfU+/uamdt7/0wMcze0eSGhs7s+zpnVcUG7Yl6Sjs45guCz4c0X+9rr5J3fSjsOWwSLf3ZxEM228\nqkS+aJDJlvjRhjybD2X49NXN3LggzIO7HOHbEJRZNc04xMaQyl+/aQ6/2Z0i6JF469IYD+3JVG1T\nH5CYG51+hNpMXDA3wi83j1Q9nsjB0SKvHkxi4wjwK7qixDwzBxxHixb9OYuwempGSt77crVgtWyI\nCDaK6Lie27ZNrqDTFPPS3RZDt532Ks2w2J21CHpEsuUKipBHojFUfX7u35NjWzl7PpiHm5bE2T2U\nx6eIzkixkwiu6qbFt54dYOdAnqXNAf5wXSPLm3x8+yGFfUfGWDQnyl+9e+VJnhEXF5eT5ZwV3nv3\n7qWpqYm6ujoA1q1bx0svveQKb5czhiIJ1AbkkzK3Opco6BabDyY4nDbpbg1TE1RpDcz+nIQ9Essa\nvGwddIRo3CdR1Ewe2ZlkVWuA2nPM+CunWxXRDZBH4v1rG8gVdNa2B+lu9HNRV5BDQyl8inDayi0n\nYtk2/70lzVDeydy8NqLxidVRoudwf73LsQnJBpolUjQlZNEirujHf9IsCKqOH4RpWpUxdv/zyhB3\nXth4zOc9vy/FfZsc8fb2FbV84MKGKduUdIvPPHCADfurS24bQgoeVaU9Pn5fSmg2Tx/MsGlII18y\nSOUdYSTKEnsSBl7Foj7miNP+rEnJtCvtM5IooIgCmmlhWY7RWnFS9HFOU4iRsjQXRYFB8/R9l364\nNVnxHFEkkWJRRy8HMzf35fnOhmH+9PImljf5SZdMltZ7CcxQCt1V66VrQkb+6s4gL/XmSRRNFFHg\ntkURXjic5eW+UWIeeNuy2JQy+8nctLwOG3hxf4oFDX7efVFT1b8/ui9bKXE3LZtSocT2Xufx0pZg\nlWfIUN7i8SMaR8/2Ct2mO37yS91vP9PHL14dRhDGxyrKksjVCyJcPM8Jaty3ZZRCyaCrsbo0XxIF\nbFvkSze384OXhhGA919QP6Vn/Oh99SivDpVI5Eyu7vTRGFLYP1bkkX1ZvIrMVZ1B2sPHP57vPD/I\nTzc634etfXm8isgfXNjANz+89qTPhYuLy+vnnBXeY2Nj1NSMm1nF43H27t17FvfIxeX3k79+5Ajb\ny6NVdvWl+adbOojMsKibiU9e1sAT+7IUDYvhTIkvP9EHQNgr8dVb22kInViG5Y2MNU01wNr2IB2R\n8QCDKAjU+s+c6M3pdtXisGTa9GWNExbelm3zi50Ztg+XqAvIvLs7fE6b453LCALUerTjb3iCrG4P\nsbYjyPq94+L4288OcMfKOnwz9cUOF/jJxvGM6f/dOMLl88N01lZXg/xi8wgvH8oiikJljvH5rUH+\n6PIWJtckKYLN5iHn+CzbJh5QKGgmBd1ClgSMCUK6zi9VeVYoksBH1tbx1acH0Mvbfeu5IRbX+agL\nOt/jG1c08P1NicpzZiqLf73Ytl0R3UdRRYH8hMej5VLxxXUnXuJe65f5wlWN9KZ1avwSPQmNrzw1\nUBHKIzmdT1zSSMmw+M6rCXaPlpgbU7lrZRz/hKDhzcvruHl53bTv4VOqz81TWwb51q8yyLLIwpYQ\nf3BxE1d2ORURh7ImE0McB9Pm6xLej+5Ls2xpI4IgkM2UKBV0FsytwRREmkISTSGFNc1eXunLo6oK\nvRMm5Zm2zYo6hcV1Kn87g9cAwMK4Urm/2rZNtmRiA08cyPHUoTyFol5xRd81WuJja2ppPU4mf89w\ndZ3/nqHCDFu6uLicSc6d4bcuLi5vOLIlkx1DBc7rjHHl+U0s7axhx9AsG/8moEoi1y8Ic9uSKE/s\nHl+Qp4smzx7IHOOZv3uEVJE1TeNl9F0xmbZZZDhOJ35FIOoZ/7mQBWgInPg+re8psP5IgWTJYs+Y\nxk93pI//JJffO247b6oZ27EmIRx1Jp/IgWn+liuX+wqCgCRLtNX6+Ld3LSDql2n22dR4bDyiTUy1\naQ84170iCVzcGeHSrjhXL66lKeLBMCxuWRxlQVzhvHoPH1g2tVd2TWuAgE8mHvUSDKikiya/3j7G\n//npTj75f1+j3iPQWg6mCcDbl8Zme3o4NFbk0w8e5DMPHqQ/fezghyAIXNs5XuLeEJD40No6Jur8\nK7uO3es7lDN4vifPgcT07+VXRLpqPMR9Mq8NFaoM2LaVg67/b2eaDeXM+Mb+AvdtTx77ICfwrmWx\nSqAxrtjsPJJBViSCYR99WZO/ffQIPy6XgwcmjW/zKycf0EgWTcLxQOXaC4Y8nLeogVBAJV/ul0oU\nTb67PcuTfQavDmksiYnUeQXqfQJXt6isqDt+UPhNc/28eX6ACxo9jGZKlHQTSRQRBAHLBlWVK24K\nA8lC1Ri6mTh/kjHeytZjz3OfiVTRZP3hLDuGT/x328XFZSrnbMY7Ho8zMjIeAR8bGyMej0/Zbvv2\n7Wzfvr3y+I477iAUOva8TheXE0FV1d/ba8ofsFnQEqYu6mSeAj6FQVM+6fNh2/aUjHBDNHjOnd93\nLA9xcYeGYdm0R9UpPX5n45r644u8PLAjgWZaXDUvQkfdsc2npiNjVi/eEiX7nPvsfld5I92nrl4W\n5LJtSZ7ePYYgwCeu6aAuHplx+6uW+Pjqk33olXJqgau6mwh5q5c4t6+RuX/rKMm84ZT9XtxadczL\nJ+nPu1YrPLQ7ibdsZiiJAstaQty5oo6VrccWq/e9PICnPLtcUSRM0+I7TxxgOO0EBNbvTvDLz1xG\nqmQR8cm0z3I+1lhO42P37cAo3wg//D/7eOiPV+JXZ17OvXNliNXtBdJFk8X1PnyKRHtdmG19WRY2\nBFjZNvOxHBgr8uXnBigZTkf7B1c3cFnnzJ9Fd4vJfVvHM/kL6537c1KrFtpjJQgGg+R1a8ay9qOo\nXouv3BxCFEVG0kWue7UfRZWrgjHrD2T58JXzWB20yVkZDqc1Yl6Zq+aGCB7j9UuGhWZYU64VgLRV\nmhLwsWyblpBCV0MURRL4xd5hRgtOQGc4b9KTsXnXNIGj43Ft+SNIFy1+u6/6XAmCgNcjUSiZeGSJ\n5pifUOjY3h4fujJIJOhnW2+GFa0h3ra6acZtLdtmz1iJjGbREJBpDTvBgtGczud+u4+xvFMR8a7z\n63nH8voZX+d080a6R7mcO9x7772V/+/u7qa7u/u0vt85K7znz5/PwMAAw8PDxGIx1q9fzyc+8Ykp\n2013kjOZcyuD5nJ2CYVCv9fX1KWdYfZnx4v/TMs66fOxfbCAIVDpt5MkgWV10jl5fqMiIEIuOzXL\ndDauKT/wzkVHxbZ5Uu+/MCryhDBeTr+0Vj0nP7vfRd5o96kvvrmVAyN1+BSR5qinsm/7kwbbR3WC\nqsC6Zg+qJCAC//mOeXz96X4APn55E4JeIDOp7TyqwH+/dwEbe7K8NpDntf4Uj26xuWju+GJ+31iJ\nnpTGghoPzWGVpqDExG+gKgl0RYXjnquDo/mqx/UBiR3p8Sx8pmiw4+Aw6xbEAZ3M5J2dgd+8NlYR\n3QCaafPMzkEunnvsQECD6vxnFPNkitARho6wkwU91rE8viuJWXYVNy2bR3ePcX7dzMWS59VJ3L2m\njhePFKjxCdy5Kk4mk2FFvcoLh8e36wiL3PU/OxktmMyPq3zykgYC07h3P747xb881Y9h2dywOMqf\nXNbEnRc385ONw1Xb1QflynFcUCtwQXnknF3Kkyo6ffbSpHL+R3ck+PKjPeimzc1L43zqTdXzsoOC\nzbJ6L1vLVVrNIYVbuwIsrVUp5rMUgeykkWnZgva6vkfvWxZiSY3Ez3akGS6Xn3tkkbpwAN0wuWF+\niDavMav3uGlxkJsWH/8z3p2yOFK+XHszOqViEcky+f6m0YroBnhw+wg3dp69WZVvtHuUy+8+oVCI\nO+6444y+5zkrvEVR5K677uKLX/witm1z1VVXMWfOnLO9Wy4uv3dc2OLl8J58xdlc0krc9eM+bOCu\nCxtY1zn7kSa27Xy3vV7HgEkQhGOWoLq8sZgXU/nY6hg7RzTqAhKrm9yB4y7TIwoC8+qqr4++rMlP\nd+fH+4cLFm9f4ASDWqIe/v6Wjsq2j+xIsKU3R1e9j1uXxREEgcGszgO70rw2UGBPfxbDsHhsV4q/\nuamN1a1Bnjuc5ZsbRrBxZnB/+tI6+kaL5EyB9voAmm4ymCxA5/Gz0911HnaOjAvt25fXsGvnAAMp\n529+VWJeQ2Cmp89IZ83U926PnZ4JD6Zl05OzifidDGhBMxCw+ejPD5AumtywKMp7VlZnd23bZk2L\nn1tXNFMqjM9eXzvHj18R2DOq0RlTeXhPmtHyjMm9YxoP7U5xx6Rye820KqIb4OEdSS7tDPPxa9q4\nfXUD390wyLaBPO1xL39yeXPleSN5g53DRRqDCvtHCvznC0NYts37VtXxlmVxMkWTv/n1YV4+nHWi\nuNj8ctsYl3eF2ZUyGcsbXN8VZlGdl4+sivPkwSyqJLKuLYBS7uUfzZQYSJZYVa+yL6Fj2KCIzu/d\nibJzpMTLfQXiPolrO4OsbglwXqOfpw9leXh/HlV2ys5VRWZRTfVnbVo2/WmNkFciMk3WfiaePZxj\n+1CR5pBCfby6DH0wb/Gt5/tJT5pl7z8DRp4uLuc656zwBlixYgVf+9rXzvZuuLj8XlPvl3j3wgBH\nsgaSZfGpnx+umP186dEevv++BVOcyQ3L5rFDRQ5lDBr8Etd1+PDJAksavFwwJ8BLR3IIgsBbu6OE\nXXOu3ynmRtUTHjfkcm5h2Tb/b2eaXaMl2iIKb18SRZWOH0DryRhV/cM96eq54Zpp8+KRHFt6svy8\n7HD+yM4kOc3kjvPr+Mf1wxWxVxP3MzySw7JsNh3Jsbo1yGP7MthA0CvTEvfz5aeGSOacjKawdZB8\nwaQt5oHVU9vWJnNDVxi/InIopbOgxsOFc/zMves8vv6bg2iGxQcva6UxcuKCeVGDn9uXx7l/qzOP\n+10ra2mJvn7h3ZvRuX93Bs20uWZugKV1XvYldTL6+Bn3qTK7+1MMl7Pz924eY3GDj5UtTgAhWzT4\n+A93sOVIlohf4QOXzeGKRTFay/u3rMFXmcH+89eqy6kL+lRXScO0q7L7znaOGGyOevjsdW1TjyOt\n88WnBsjrNpZlk04XK9fMd18a5oK2ID/dOOyI7jLRkIdkpsSPX8uQLIvNVweK/MXFtfxse5JdI87o\nRmybKzpDPPXaMH98zyaKusXcej//9qHVaIJIU1Cmxndiv0cHEhr/9tL4eLuBrMEHz4+hSgKrmv08\n0VPtVTCU1fjZ5hEkUeDWJVG+/swAW/vzyKLAn1/RxOXzZ24DOMqGI3l+uCVZOc5bulW83vF78lhO\nmyK6g6rI3atqcHFxeX2c08LbxcXlzPH4wRwbeouEVJE7loSqzLfiXpG4V2XfSKHKYVc3bUZzxhTh\n/eKAxrZRZ3GX0QzUniI3zvUhCgKfvKyBgwkNVRJoibgCzsXld41H92V5eG+5dDyhIQoC71o685zu\nozQEqkVN/YTHRcPiY/cfJlUwyOWqy3839uS4elGsIrrBcRGXZRFNM5lXHo8VVEVM06K1JkCmoFdE\nN4AlCGiGhVeefYXN5R3VmcSOOj//9O4lU7Z7pa/Az3emEYC3LQmzovHYlSB3X9TI3Rcde7zaiWBY\nNv/xSoK05oit/96U5DPraqeMwhSAsVx1SfxRR3SAn2wYYMsRR9Cm8jrfePww921P8ZdvamH1JHOv\nGxaE+fcXR7Bs8MkCV86dav7lVyXe3B3jwe1Oz/j8Wi8r5xy7SuDZw1nyZRFv23ZVoMbGMfw8kqy+\nPgI+Gc2wSBTNqgqqn25PsadctWDZ8INNY1zRGeIrD+ymWA4AHBjK8/DLvXzixpObwb5rtFTlW7Jj\nQpVEzCtxbYefxw46VR4XNqr8y9P9ZMqi+MXDWYbL5nqGZfPv6wenCO/BtIZPEQn7xn+P904yHtx8\nJM1bz6slZ0CNRyCdtQl6nR76bFEnrIp8/aY5bnWZi8spwBXeLi4ur5sdIyV+tdcpKxwtmHx/S4pP\nXjQ1Ot4a9dAe83AoUSo/Vqctk0xOiranJjwWBIG58dNTWuni4nJ6sG2bR7aP0Z/SGKpOVNOTmt1Y\nso6wzI1zvWwd0fHLAvOjMofTOn5F5IHtCVIF54UlSazMqQaYV+sl7pOIeiWSRUd8i0BbVGHVnChX\ndjli5S2LIzy7J4UkTV9SG1BF7l536gQvQKJg8p1NCY7GI+95NcGXrlIJe85cJU9WsyqiG8C0YTBn\n0F3rYVmdytZhDQG4vtNPxArx5D4naBLxSpzfMm6yWNCmum1bwP1bE1OE90WtAZpDCv0Znfk1Hmr9\n0y9HP3pJI5d0hshrFitaAniPU+4cmPDvoiggSQJm+eT6FJF5NV4u6ozwas94xntOfZB8aarLemhS\nz/lRGT9RzAsCFL1+vr89S0tQ4so27xQzzGPREqoOOjeHqs/Dle1+Lmz2YgP7R4sV0W3bNhnNqpov\nPrE6wLJt/uqBgzy+M4Ekwiff1MYty522gI6oyjOHxz0I2iIKCyMipmXz050Zdo4ZNMedAIdpmrxz\nYcAV3S4upwhXeLu4uLxuRvLVC66RwvTjTlRZ5J/fOpcHt41h23Dz0vi0C6kFUZnXRsczK13R6W9V\nPVmT/rxFUBFYGJWQzvLiIFU0OZzSaA4p1MywkHRxORc56rkwmZ1DBb778gi9yRI9gzlKJQNZEmhv\nj+HzOaJjpvnR+5M6vz6Qp6DbrKiTuXpukPPqVBbGFL67PcO2kRKaYWHZMJwYn1Ps8UjYZffpBXU+\n7rqoAdMWWNEaZvtADtuG+TGFJ3YleHhvlowOH7+4HstyxMtwskBtxEs0qJIsmxte0xXhD1bXETrF\nrS2pksnEMduGBemSdUaFd9jjlEn3Z53AhV8WaA0rCILA2xYGuabdRJEEAorIBY2NnNfkJ100WTc3\nVKlW+tnWMZ7sLVUJ3UjMj6qKyDO0EbRHVdpn0XZyXvPse+GvnRdix3CJbUNFfLJIhnIbN3Bes5+C\nbjGa11mzqJZM0SQW8pAt6MiTXNIB/uSiOr742wH2jjml5u9Z7rQY/MWbF/Dx/3ZKzS9e3Y7m83Mo\nbXAobSCJcEXr7L0rljV4efuSMC/1FYh5Je7onloq7iv/RjaHVVQRRpIlDMNCkUXaaz30pQ1EAf5g\nzfgc9Bf2p3l8p1MpYFrwlUd7uGFpHKXcq14wLLYPlWgKydy2yHnPlweKbBosEfCOBwMkSUJV3HYu\nF5dThbsydHFxed0srFFRJaFSRr6sbuaMdNgr857V1SNJejMGjx7MY1g2l7X6WBhXeVuXQE/GoM4v\nsShenRUwLZtvvjBMfX24sljK6zar66u3O5McTJT44pOD5HULjyzwqUsbZhQULi7nCsmiyX+8PEZP\nWkfQNKKlPJ+4YR51YQ9F3eJLj/eRLWdTozEfI8NZDNNmXkCko8VPW1jhqnKZsWnZFA1nvFTJsPmf\nnVmOJmKf7tM4MDTEH66tZ3dCZ7jgCG7LBsO0SGTGy2cFQWBBc5CEZrMtbfLArjRLm4IIkkRXfYBd\ngzm2DBZRVYmSZvLsoSxXzw8zN+6hxi+ztzdFIlNCkUTCYQ/YUBTEUy66wXHKnih654RlGoOzX5rl\nNZNUySTmk/HKJ2d+JQoCH1sV47GDOXTT5tI2P5EJwj864bglUeDqrmpxuHe0yI83jYEk0jwnTknT\nUWQJ1eMcxw1Lpm8jGMob9GYMmoIyjYFTsxz1yCKfvKQevdwf/o9P9rGlP09HzMOHLqzn84/0sGuo\niADEQiojyQKj6RLhUHUAQBKc1/qrq5o4nNIIeUTqykGGK7rr+NIfrOQ7Lw1T11zd7z8wixnbk7my\nI8iVHcefsx33yyyv8/LIiBNk0g2LOp/M/7qihahPrmq90s3q3njTsjEtOKqhr+kMcU1n9WiujGZh\nMzWI5juB9goXF5dj4wpvFxeX1019QObjq6NsGiwRUkUunjP7iL9u2vxwe4a84SwU7t2R5Y9XReiI\nyHREpr9FPbk3zVDBpmHC4mCwYE277ZniV7vT5MvlrSXD5oEdKVd4u5zz/GxHisNppzrFVlU29qa4\n899e4qHPrCNZNCuiGxxBLEkipmmyek6A+jo//VmDzUMagmnwpd/0ktMs1rYH+cgljUx4KqIg8Js9\nGd6zspbJCbjRVJGSZiIIztQDjyJSsEVU2UYzLB7anaajxrkn7R7Mkcw7++tRZSzLRjcsTNvGr4hc\ne1496/elwIbOphCHx/KkCwa96dmN+zpRVEngzy+uZf3hPKIA61r9U3qrZ+Jv7t/D7qKMz+9kQv9i\nXR0dJ2lcGPJIvGXh7CdMTCRdLt9XJJHmWj/DySKG6Xx4rRGFFc3+Kc/Zl9D43tY0hu2I3Pd2h1lY\nc+o8OxRJQJEEPnft+DQbzbAc0V0+vWMZDVmECzpCvG91DV95YYzybleMxGRJoHOa1qZf7c/hC3op\naCbhCYfXFp79snqkYLK+1xlVdkmLd1bGbJOrB9JFk+5GP6VJQvvCzjBLmwNs63NawN53UcNxy/SX\n13t4pqdASTfxKBKiIHBZq5emEwgEubi4HBv32+Ti4nJKaA4pNIdOPOOc1a2K6AYwbBgrWMSOkV1K\nFw3GstUGMZFpZsCeLKZls3nUYKxoUecTWVYjH7dvb/Ji+SSTT6eNI1mTTaMGtg0ramVag275oMvr\nZ6KwBlBVmW39WZJ5nbqAQkdM5WCi7AyOTVARuGZZPeGYn4f2OX2mGwc1xhI5cuXX2nAoy5rWDIpg\no9vO96qkmeSLOqIAC2MK3TUK20Y0RMHJ0IEjugFKukUyUyIe8ZYzfTa1HpgTFHlRr85IiqLA+U1+\nltY7wryEyLKO8bFWQY9MumCwrOHEg2g5zaQnqdEQUoj5Zl5uBRSRN807fsZzIs/tHuOFviItc5yM\nq2bBz15L8ecX1x3nmaeexfU+5kRUVL8XjyoR9CoksyXWNnu5fWkMdZqe+Q39RY7e9k0bXugrnJTw\n/s3eDM8ezhL3ybxvRYz4Mc6zKovUhFRK5ffVdRNdN9k+VGTXsMZ/3dLKgzsT9GVNDNue8XVgfCb4\nWKaEbdt0N/pZ1eRjTePsjiFVNPne9iyF8kk4kDL4o+VhPMfJLt+4JMZvdiTQTBsBuGZxjH97Jclw\nwaIxIPHe7hAhVcQji/zbu7r4/otD7BgqICgyec3Erzr3/bG8wTP70wRUkQV1Pr7yZD+9aZ2VrQGu\n7YpS4xNZWOM5oX51FxeX4+MKbxcXlzNGwbAYLVrEPGLFBCfsEan3SwyV+8QDikDjcUThpZ1h7ts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/2YumXzjecHuag9eNK9rS7nJo/tTnEoMbW/craXiW7afG3DCLtHndfweyQCPhV/SGBN63iQ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\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B+O9e3sTVc+PkSh4rW6InrKx9rjlTgl7O8WgMwahTLiYXd6tnXwyMlXBcrzJKfSpcz2dL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\nkQj6P0TZB8Cj6AuFAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fccdf487ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax= random_sample.plot(column='logBiomass',figsize=(16,10),cmap=plt.cm.Blues,edgecolors='') \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Geographic subselection" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def subselectDataFrameByCoordinates(dataframe,namecolumnx,namecolumny,minx,maxx,miny,maxy):\n", " \"\"\"\n", " Returns a subselection by coordinates using the dataframe/\n", " \"\"\"\n", " minx = float(minx)\n", " maxx = float(maxx)\n", " miny = float(miny)\n", " maxy = float(maxy)\n", " section = dataframe[lambda x: (x[namecolumnx] > minx) & (x[namecolumnx] < maxx) & (x[namecolumny] > miny) & (x[namecolumny] < maxy) ]\n", " return section" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fccdf058290>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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pOUSQogqJ4O9+ST8DPlaiuv/++3n99dcRQpBIJPjOd75DWVnpVEFQ4TfwL6Hmycl2nLDg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e7gaGc8O2K/Jxyijw/GV/MhYwant61gzGlHYNz2EyiV+P6Y+VxbrGJ6IiCX/RZSBIRKEdNLUeXk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"text/plain": [ "<matplotlib.figure.Figure at 0x7fccde794110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# COnsider the the following subregion\n", "minx = -100\n", "maxx = -85\n", "miny = 30\n", "maxy = 35\n", "\n", "section = subselectDataFrameByCoordinates(new_data,'LON','LAT',minx,maxx,miny,maxy)\n", "\n", "#section = new_data[lambda x: (x.LON > minx) & (x.LON < maxx) & (x.LAT > miny) & (x.LAT < maxy) ]\n", "section.plot(column='logBiomass')\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Analysis with the empirical variogram\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Reading the empirical Variogram file\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Instantiating a Variogram object with the values calculated before\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Dropping possible Nans\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Instantiating Matern Model...\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:fitting Whittle Model with the empirical variogram\n", "../tools.py:549: RuntimeWarning: divide by zero encountered in power\n", " g_h = ((sill - nugget)(1 - np.exp(-(halpha / range_aalpha)))) + nuggetIh\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Model fitted\n" ] } ], "source": [ "\n", "gvg,tt = createVariogram(\"/apps/external_plugins/spystats/HEC_runs/results/logbiomas_logsppn_res.csv\",new_data)\n", "#For HEC\n", "#gvg,tt = createVariogram(\"/home/hpc/28/escamill/spystats/HEC_runs/results/logbiomas_logsppn_res.csv\",new_data)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fccdf238890>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FqpXQ6erNnzdtHbRaLR559DHMnj0bPGf+uzZ8J3fu3ovlSz+Ci0IBhcIce5FI\nhJ69YxHQrpNw8MPMYUJmXgF+/Hk7DAaD+aXXg5lMeCi2L0YFhwJgwmfaaGIouFyD0qo6yOQKMAZU\nVdaivugSJG5qtIkymOPOASLO/Jk+cPgoNqz7CpIrLS0SiRRiqRQGiQKtO1mX53ngm83f4YP35sFo\nMoFdOfAymRiGDR+G2bPfNu9jYK6PiTH8uPVnrFj+MeRyOWQuLpDJZHCRK9Av/mF4tw6HCezK5w0w\nmgCVjIdG4dyp4L6+e/qH247c6So4BalUBp2u/k5XwylcLxYNSeJmvjQNBw8mdnPzNwfuyg4RMO/k\nTFdVrLk+Fw0HbQ0HHAwAGBMSxY3EpmE5Vx8YmP/nYGQMpisHACZmu3034nbEgrvyj6N73IY48Twn\nbFvDlIaD0KvLmo9HrtrWKweT11tddIAKHb1dHN6OO3H3dOdOo4Q4mVtJMDe7k2xOjAGGFj5WZTAf\n/Tu8x26C6cpyjMKSnRcT/nG8vJEBRnOwmqdSd4n7utcfIYQQ50dnVHeh6vIyVFwqhr5eC522Djpt\nHfQ6HfzbtINfSNtmWy9jTGiuudqZrKM4+/cxaGtroK2tRn1dLeprqxHd7xFE9uwPAEKTDMcBP61Z\nhl+//QoGnR4iiQRSmQukLnI8MvxFxAxKtGmu+O279cjYuw0GvR5GvR5Gg/n18HPj0HNwoqW56Mp8\nezZ/jWNpu8CLRBCJxeb/RWLEDP4/RDzY16b+uUcPoehsHqQyOVxV7jCZjKirqUJQu0j4t7a9eH7w\n5y24kPc3PHz84dnw8g2A0t3TbnxOHfsTF/NOorriMjieg4tCCZlcgfYP9ILa17YZpfTCOdRUll/Z\nToPwf3D7CLipbXuNlhScQW11JQx6HQy6euh19TDodGgdEQ0Pja9N+ZN//Y6KS8XgeRF4kQgcz4Pj\neLSO6AJ3Lx+b8hdOn0R9TQ3EUinEUhkkUinEEhlc3T0gkcpsytvTcIXBXnxqqyrMvWqlMkgkUvB2\netJej8loBDgOPG977F2QcwLlJYXQaetQr62Dvr4OJhNDVO94ePkF2pQ/vn8XaqsroXRXQ+nhCVd3\nNdQaX4AX2a1/eUkhqisuQ1tbDTBA6uICqYsCXn6BkMjsN6mZTCYY9ToYDHrhc91YPKsuX4LRoAca\nrttyHDiOg0LlAbG9zhRX9g0mo9F8PctoAGMMmsAQyOSK6wfTCVGiugn12jqUXSyAWCKFJihEGC/i\nAKlYhB1ADE3yAAAgAElEQVQbv8DO/34JuasKLq5KuFz5P7rvYET1jrdZ3vH9u3A0bSdMRqN5J19X\ng/raGvQcnIh+ic9CdKWNWiLiIeU5HPtlJ3b/8A1cXOSQK+Tmi+cyGdp4uyLCpwv0JhN0Rga90YR6\nA8M3q5di1+avIZZc2clIZRBLpOiT8Cx6DEqwqU/a/zbg4C+bYdDrYTIaoK+vR11NFR597gU8MWwc\nXCQ8pCIeMhEPiYhDZWYlzleVwE+lhNInAEqVEiqlClFduqBtqFq4iCy6kqziZ00FZr4GuUwGg8GA\nmtpa1NTWQa5UQqVyF9rYjczc9h7x9OMoG/AgpFJzDzipRAKJVAIvD08o3d3NO0EGsCvt/5H/7x+4\n2K+HeSdgMPci0xsMaNshHCEhaqEd33ilE4w2W48LJWdQVluLC1ot9Ho9VCo3PBgRhh7B7hDzgETE\nQ8xzEPNAYG00DsmNKCgoQN7vx1B44TyKLp7HMxOnIzrucfAcIBXxUEhFUEh45Jbkw3T5PILVXuDA\nUF1TjpqyAgT2fABt/ZQwmgCDyQSdwbwz3/3lN/j76F9XenVJr3QykKBzaADCfdtcdR3H3Hnnt6//\nh8y/DkIik0EmlUEqk0Hm4oKeUe0REaACz3NW19byd19AeW4mDAYjjCYjjAYDGBi6dwxFB79Q4XMg\nlUpRXy/BoU2/4tifB1F/pVOErt6cDMdMm4OImD5CRxCTyXyg8Nmcqcj+83fwYjGYyQRdvflAasI7\nH6FbnzhIeA5iEQ8Jz4GB4aPFs3B4/27odTrodfXgRSJIpDKMmbkQHbv3tvl8rn3/dfx9+A8Y9HoY\n9PUw6PRgzIRJ761A1159IeIAscjca1ci4nBo0z6cOXUScrkCCldzZxORSIQILxlaB7sDsL7eczGt\nGhdPHUXepVKUl5Wh/HIZKi+X4aV5yxDWJQYM5ut6Ip6DVMRj87qPcT4/F66uSvA8h7raOmjravHi\nm+8hqEMn6E0MeqP5s8bAkDJlFHKP/gmxRAqxRGL+G0skSH5vGUIiOl+1pea/csr77+PUiaOWXr5g\nAGN49d1laBvZxfzZv7INIp7Dxg8+xJmTWeZegmKx0KFo4sx30TYo8pprWxzcZc6fBqgzhQNOHv4D\ne775EpVlpagqK0F9XS18AoLw2FPP4tnhoyCX8JBLRJBLOMhFPHQ6LS6VXcbl8kqUV1aiorIS5RWV\nCAppg3YdIwFc6Q145aLoyewTyPk7G2IRD4WrK9xUKrgplQgMDICPRgMRD4iv2tncKJ1Oh8qqatTV\n16NOa37Va7VQazTQ+PhBKnOBVlt35QIth6LCiyi7VAqZRAKxRAKFXA4PDze4K5UQi+7t1uJbeeQ4\nYww6E4REdbd/tW4kFhzHCQcANTW1qNXWQafTgReJoZC7wFUuh0wqcSgmOp0ONXVaSGUySKVSAFdd\noWHA5cuXUV9fD5mLDC5SKVxkMkgl5h1yc8VcqVSiqqoKenOOgJgz96C8EcYrXSONRiMkYlGz1rc5\n3YnOFC2aqDIyMrBmzRowxhAXF4fExESr6enp6UhNTQXHcRCJRBg5ciTCw8MBALW1tfjkk09w7tw5\ncByHCRMmoF27dqiursbixYtRUlICHx8fJCcnQ6Fw7PS2IVFVXb6ErEP7oNfVo+8T/w/erhJ4u0oh\nFZm7kZdcLEBB/ikE+Poi0M8Xfr4aSO6hHfat7JzvNRQLC4qFBcXC4p5OVCaTCZMnT8asWbPg6emJ\nGTNm4NVXX0VgoKWNuL6+HjKZuY327NmzSElJQUpKCgDg448/RkREBOLi4mA0GlFfXw+FQoF169ZB\npVIhISEBW7ZsQU1NDZ577jmH6jR4+ARkHdyH0gtn8UDPPnhsyBP4Z8ITUEo4u23R9yr6ElpQLCwo\nFhYUC4s7kaha7LQgNzcX/v7+0Gg0EIvF6N27Nw4dOmRVpiFJAYBWqxWSRW1tLbKzsxEXFwcAEIlE\nwllTeno6+vXrBwDo37+/zTKb4sbr8eabb+LIkaPYuPYzPP90AlRS/r5KUoQQ4uxa7CpaWVkZvLy8\nhGG1Wo3c3FybcgcPHsT69etRWVmJ6dOnAzDfIkalUmH58uU4c+YMQkNDMWrUKEilUlRUVMDDwwMA\n4OHhgYqKCofrtOS9d25xqwghhDQ3p7vQEhMTg5SUFEydOhUbNmwAYG42PH36NAYPHoz58+dDJpNh\ny5YtduensyFCCLm3tNgZlVqtRmlpqTBcVlYGtVrdaPnw8HAUFxejuroaarUaXl5eaNvW/Buhnj17\nConKw8MD5eXlwv/u7u52l5eZmYnMzExhOCkpye4NLO9HDTfbJRSLq1EsLCgW1jZu3Ci8j4yMRGRk\nZLOur8USVVhYGAoLC1FSUgJPT0+kpaVh8uTJVmUKCwvh5+cHwHwHZoPBAKVSCQDw8vLChQsXEBAQ\ngGPHjiEoKAgA0K1bN+zevRuJiYnYvXs3unfvbnf99oJJF0fN6EKxBcXCgmJhQbGwUKlUSEpKatF1\ntlii4nkeY8aMwdy5c8EYw4ABAxAUFITt27eD4zgMHDgQBw4cwN69e4Xb6ycnJwvzjxo1CkuXLhVu\n///SSy8BABITE5GSkoJff/0VGo3Gah5CCCF3v/v6B78XLly401VwCnS0aEGxsKBYWFAsLO7p7umE\nEELIzaBERQghxKlRoiKEEOLUKFERQghxapSoCCGEODVKVIQQQpwaJSpCCCFOjRIVIYQQp0aJihBC\niFOjREUIIcSpUaIihBDi1ChREUIIcWqUqAghhDg1SlSEEEKcGiUqQgghTo0SFSGEEKdGiYoQQohT\no0RFCCHEqVGiIoQQ4tTELbmyjIwMrFmzBowxxMXFITEx0Wp6eno6UlNTwXEcRCIRRo4cifDwcADA\nyy+/DIVCIUx77733AACbNm3Czp074e7uDgB49tlnER0d3ZKbRQghpBm1WKIymUxYvXo1Zs2aBU9P\nT8yYMQM9evRAYGCgUCYqKgrdu3cHAJw9exYpKSlISUkBAHAch7feegtKpdJm2UOGDMGQIUNaZkMI\nIYS0qBZr+svNzYW/vz80Gg3EYjF69+6NQ4cOWZWRyWTCe61WC47jhGHGGBhjdpfd2HhCCCF3vxY7\noyorK4OXl5cwrFarkZuba1Pu4MGDWL9+PSorKzF9+nRhPMdxmDt3LnieR3x8PAYOHChM27ZtG/bu\n3Yu2bdtixIgRUCgUzbsxhBBCWkyLXqNyRExMDGJiYpCdnY0NGzZg5syZAIA5c+bA09MTlZWVmDNn\nDoKCghAeHo7BgwfjqaeeAsdx2LBhA9auXYsJEybYLDczMxOZmZnCcFJSElQqVYttlzOTSqUUiyso\nFhYUCwuKhbWNGzcK7yMjIxEZGdms62uxRKVWq1FaWioMl5WVQa1WN1o+PDwcxcXFqK6uhlKphKen\nJwDAzc0NMTExyM3NRXh4ONzc3IR54uPjMX/+fLvLsxfMqqqqW9mke4ZKpaJYXEGxsKBYWFAsLFQq\nFZKSklp0nS12jSosLAyFhYUoKSmBwWBAWlqa0HGiQWFhofA+Ly8PBoMBSqUS9fX10Gq1AMzXro4e\nPYrg4GAAQHl5uTDPgQMHhPGEEELuDS12RsXzPMaMGYO5c+eCMYYBAwYgKCgI27dvB8dxGDhwIA4c\nOIC9e/dCLBZDKpUiOTkZAFBRUYEFCxaA4zgYjUbExsaiS5cuAIB169YhPz8fHMdBo9Fg3LhxLbVJ\nhBBCWgDH7uMucxcuXLjTVXAK1KxhQbGwoFhYUCwsAgICWnyddGcKQgghTo0SFSGEEKdGiYoQQohT\no0RFCCHEqVGiIoQQ4tQoURFCCHFqlKgIIYQ4NUpUhBBCnBolKkIIIU6NEhUhhBCnRomKEEKIU6NE\nRQghxKlRoiKEEOLUbihRGQwGZGVlYf/+/QDMz4ZqeE4UIYQQ0hwcfh7V2bNnMX/+fEgkEly6dAkP\nPfQQTpw4gT179gjPjSKEEEJuN4fPqFatWoVnnnkGixcvhlhszm8RERHIzs5utsoRQgghDieqgoIC\nxMbGWo1zcXGBTqe77ZUihBBCGjicqDQaDfLy8qzG5ebmws/P77ZXihBCCGng8DWqZ555Bu+//z4G\nDRoEg8GAzZs3Y/v27Rg/fnxz1o8QQsh9jmOMMUcLnz59Gjt37kRJSQm8vLwwcOBAhIaGOryyjIwM\nrFmzBowxxMXFITEx0Wp6eno6UlNTwXEcRCIRRo4cifDwcADAyy+/DIVCIUx77733AADV1dVYvHgx\nSkpK4OPjg+TkZCgUCofqc+HCBYfrfi9TqVSoqqq609VwChQLC4qFBcXCIiAgoMXX6fAZFQC0adMG\nY8eOvakVmUwmrF69GrNmzYKnpydmzJiBHj16IDAwUCgTFRWF7t27AzD3MkxJSUFKSgoAgOM4vPXW\nW1AqlVbL3bJlC6KiopCQkIAtW7Zg8+bNeO65526qjoQQQpyPw9eoFi5ciKysLKtxWVlZ+PDDDx2a\nPzc3F/7+/tBoNBCLxejduzcOHTpkVUYmkwnvtVotOI4ThhljsHfyl56ejn79+gEA+vfvb7NMQggh\ndzeHE9WJEyfQoUMHq3Ht27dHZmamQ/OXlZXBy8tLGFar1SgrK7Mpd/DgQSQnJ2P+/PmYMGGCMJ7j\nOMydOxczZszAjh07hPEVFRXw8PAAAHh4eKCiosLRTSKEEHIXcLjpTyKRQKvVWl3/0Wq1EIlEt7VC\nMTExiImJQXZ2NjZs2ICZM2cCAObMmQNPT09UVlZizpw5CAoKEq5fXe3qs7CrZWZmWiXVpKQkqFSq\n21r3u5VUKqVYXEGxsKBYWFAsrG3cuFF4HxkZicjIyGZdn8OJqkuXLvj0008xbtw4KBQK1NbWYvXq\n1YiOjnZofrVajdLSUmG4rKwMarW60fLh4eEoLi5GdXU1lEolPD09AQBubm6IiYlBbm4uwsPD4eHh\ngfLycuF/d3d3u8uzF0y6OGpGF4otKBYWFAsLioWFSqVCUlJSi67T4aa/ESNGoK6uDqNHj8bYsWMx\nevRo1NbW4vnnn3do/rCwMBQWFqKkpAQGgwFpaWlCx4kGhYWFwvu8vDwYDAYolUrU19cL9xTUarU4\nevQogoODAQDdunXD7t27AQC7d++2WSYhhJC72w11TweA8vJylJaWwtvbW7g25KiMjAx88cUXYIxh\nwIABSExMxPbt28FxHAYOHIjvvvsOe/fuhVgshlQqxfDhw9G+fXsUFxdjwYIF4DgORqMRsbGxQtf2\n6upqpKSkoLS0FBqNBsnJyXB1dXWoPtQ93YyOFi0oFhYUCwuKhcWd6J5+w4mqoqLC5o7pvr6+t7VS\nLYUSlRl9CS0oFhYUCwuKhYVT/44qIyMDK1asQHl5uc201NTU21opQgghpIHDiWr16tX45z//if79\n+0MqlTZnnQghhBCBw4mquroagwYNarT7NyGEENIcHO71N2DAAPz666/NWRdCCCHEhsNnVDk5Odi6\ndSu+++47m95+b7/99m2vGCGEEALcQKIaMGAABgwY0Jx1IYQQQmw4nKj69+/fjNUghBBC7Luhx3yU\nl5cjNzcXVVVVVncypzMtQgghzcXhRHXw4EEsXboU/v7+OHfuHIKDg3Hu3DmEh4dToiKEENJsHE5U\nqampeOmll9CrVy+MGjUKH3zwAX799VecO3euOetHCCHkPudw9/TS0lL06tXLaly/fv2wd+/e214p\nQgghpIHDicrNzU24fZJGo8HJkydRVFQEk8nUbJUjhBBCHG76i4+PR3Z2Nnr27InHH38cb7/9NjiO\nw5AhQ5qzfoQQQu5zN3z39AalpaXQarUICgq63XVqMXT3dDO6M7QFxcKCYmFBsbBw6runX8vb2/t2\n1oMQQgixq8lElZycjJSUFADAhAkTGi23YsWK21srQggh5IomE9X48eOF95MmTWr2yhBCCCHXajJR\nhYeHAwBMJhN27dqF8ePHQyKRtEjFCCGEEMDB7uk8z+Po0aP0LCpCCCEtzuHOFI8//jg2btyIpKQk\niMU31wcjIyMDa9asAWMMcXFxSExMtJqenp6O1NRUcBwHkUiEkSNHCmd1gPnMbsaMGVCr1Zg2bRoA\nYNOmTdi5cyfc3d0BAM8++yyio6Nvqn6EEEKcj8MZZ9u2bSgvL8ePP/4INzc3q2mOdKYwmUxYvXo1\nZs2aBU9PT8yYMQM9evRAYGCgUCYqKgrdu3cHAJw9exYpKSlCZw4A+OmnnxAYGIi6ujqrZQ8ZMoR+\nz0UIIfcohxPVrXamyM3Nhb+/PzQaDQCgd+/eOHTokFWikslkwnutVmvV1Hjp0iUcPnwYTz75JH74\n4QerZd/kT8EIIYTcBRxOVBEREbe0orKyMnh5eQnDarUaubm5NuUOHjyI9evXo7KyEtOnTxfGr127\nFsOHD0dtba3NPNu2bcPevXvRtm1bjBgxAgqF4pbqSgghxHnc0MWm/Px8ZGVl2TyP6plnnrltFYqJ\niUFMTAyys7OxYcMGzJw5E3/99Rfc3d3RunVrZGZmWq178ODBeOqpp8BxHDZs2IC1a9fa/c1XZmYm\nMjMzheGkpCSoVKrbVu+7mVQqpVhcQbGwoFhYUCysbdy4UXgfGRmJyMjIZl2fw4lqx44dWLt2LTp3\n7oyMjAxER0fj6NGjwjWl61Gr1SgtLRWGy8rKoFarGy0fHh6O4uJiVFdXIzs7G+np6Th8+DB0Oh3q\n6uqwbNkyTJw40ep6WXx8PObPn293efaCSbdEMaPbw1hQLCwoFhYUCwuVSoWkpKQWXafDieq7777D\n66+/jo4dO2LUqFGYOnUqDh8+jLS0NIfmDwsLQ2FhIUpKSuDp6Ym0tDRMnjzZqkxhYSH8/PwAAHl5\neTAYDFAqlRg6dCiGDh0KADhx4gS+//57TJw4EYD5qcMeHh4AgAMHDiA4ONjRTSKEEHIXcDhRVVZW\nomPHjgAAjuNgMpnQtWtXfPTRRw7Nz/M8xowZg7lz54IxhgEDBiAoKAjbt28Hx3EYOHAgDhw4gL17\n90IsFkMqlSI5Ofm6y123bh3y8/PBcRw0Gg3GjRvn6CYRQgi5CzicqNRqNYqLi+Hj4wN/f3+kp6dD\npVLd0G+qoqOjsWTJEqtxgwYNEt4nJCQgISGhyWVERERYdexoOLMihBByb3I4yyQkJOD8+fPw8fHB\nU089hUWLFsFgMGDUqFHNWT9CCCH3uZt+HpXBYIDBYICLi8vtrlOLoedRmdGFYguKhQXFwoJiYXEn\nnkfl8KPo16xZY/W7J7FYfFcnKUIIIXcHh5v+GGNYsGABZDIZ+vTpgz59+tyRzErufefOnsXGjz+C\n6fIl8J5eSHr5FQS3anWnq0UIuUNuqOnPZDLh+PHj+O2333Do0CH4+PggNjb2rr3PHjX9mTVHs8bN\nJptzZ8/i88kvYooHB4VYhFqDEYvKGUYv+aRFklVTsbjfEig1d1lQLCzuxAnKTV+jKisrw/Lly3Hs\n2DGkpqbe7nq1CEpUZrf7S+hIsrl2p//0iy8juFUrLHp9GsYVZ0EhFgnLqzUY8ak6DK99uMTuvFcn\njFtJJufOnsXmT5ejvrjI7nLvZAK9E2jnbEGxsHD6RKXVanHw4EGkpaXhxIkTiIiIQO/evdG3b9/m\nrGOzoURldru/hB++NhnjynJtk41PR7w2fyHOHvoDX7z1Oqb4yi07/dyLGPX2PKRu2Igp7JLtMnMu\n4rV//gMFIeFY8/nnmOLJ2yQMADedTK6XiD6c9i/7CfTKNt2LaOdsQbGwuBOJyuFrVIsWLcLhw4cR\nGhqK3r174+WXX7Z53Ae5t1zv7MRquocaTz82GEGnjsP4534o2vlbLUshFsFUbk5AGxcvFJJUw7Qp\nYf5Y+cUX4ANaoba42CYh8ABw9BA2ffMdprTxtZ7Xw4hPPzb/8Lwh0VhNW5qC1xak2N2mp1+ahGDe\nhNQ3ptmf9+OP8Nr8hTBdvmRVJ2GbLp67LbEkhDTO4UTVcGdyb2/v5qwPcRI2ZxjFxVg0+UXhDMNm\nekkxFr35K55vrQEPhlqD0TbZ+Jjvnm8yGuzu9JnUFc+8/AoWTX4RUzyMVmc2o5Z/Aa7oLEwfvG8/\nYZRfAhjsTjMe2gfj8ndxYcgw220aMxTP+ylhOncJivYB9pcLgPf0sptAubOnYPzkfZx/5BlsWvFx\no82RTcWSENI0h7unJyQkUJK6j2z8eImdMwwOG5emXJn+ke30dv7YZFIiKWUlFpWbkxUAIdkkvfwK\nAEAUEiZMa1BrMIL38EJwq1YYveQTfOrTEYt4L3zq0xGjl3yCVhGdwMc9BlHnmEbn5T297E8DB07m\nYr/OwR7YVFQFXuPX6HIBIOnlV2y36Xwlnm7th4LqOnyR/DLGFWdhCruEccVZ+HzyiziTugamNR8h\ndeJo+7H82LHbjxFyv2vyjCo5OVl4wq69R2c0cOQJv+TuwUxGGI8cgiLYw2q8QiyC6bL5DKOxpjAm\ndUOrrt3Myebjj2AqvwTexwuj37acYSQ1ctY0+m1zIgtu1arR6z7Xm9fu2dhn/wGn8YZp6hT7dW7T\nAf/vzbevWyd72xTkpsSit2fZbTZcue4rJPu5wlRRDoVv42dr1CxISNOaTFTjx48X3t/qE36Jc2qs\npxuvdGuk+c78hObGmsIamveaSjaN7fQd2Tlfb96mpjVV54blfvHpctSXFNmtU2PbZKqpsd8cqXQH\nN2wcRP9JRa3O/nodaRakREbudzfdPf1ecL/3+muqpxurrsIXr//Lbu86u9eo7oLu2o7U+WZ6d12v\nR2BT69348Ud2511Z74IpL09AgUSOL96bd0fiTD3dLCgWFk7dPd1oNCItLQ2nT5+GVqu1mnb1mdfd\n5H5IVPaOxoO8PMEO/YZFCxdivMalyR3sxoazE48mev01Mt0ZXa/ON7NDuqHfjV2z3gXjRtnvjn/y\nAl5rH4CUcobxStMd6RZPO2cLioWFU3dPX7p0Kc6ePYvo6Gi4u7s3Z53IbWK3WWnUM3g+0BNBMjFM\nVRVQ+LtazXP1tZOmmu8cme6MmqPOjjRlNrbeRpsjA0PAPfgQTDv2QSHmrOZRiEUwlRaBmYzgeNG1\niyTknuNwosrIyMCKFSsgl8ubsz7kNrLby62VGitPFyM54VHwRjfUGi43ep2JOO5mE2CjnUOWpIBv\n1QqinIuotdM0yF26CNPMl1AQ3Qf/PfgXTBXldP2K3LMc7p4eHByM6urq5qwLuc0a7ZnXtgNEye/g\nmdnvNtmNnDS/xrrjW/WQvPZvdNmEp1v5ouBMPtYsScG40pNW3eLPnT0rLP/c2bP4cNq/sGDcKHw4\n7V9W0wi5Wzh8jaqoqAiffvopunTpYtP0169fv2apXHO7V65RWV2HUqrwdFISWvXs49Btf4RefyVF\nd811pubkjNci7F3fCgoMxIfjR2G88dJNdeJw5G/sjLG4UygWFk59jWr37t3Izs5GTU0NpFKpMJ7j\nuLs2Ud0LbHZGl4uxaPoUjFqz8bq/OQLMR/RvLPmYvoROrLFmRcaLoOAauUsHGmn6veq2UITcLRxO\nVD/99BPmz5+PoKCgm15ZRkYG1qxZA8YY4uLikJiYaDU9PT0dqamp4DgOIpEII0eORHh4uDDdZDJh\nxowZUKvVmDZtGgCguroaixcvRklJCXx8fJCcnAyFQnHTdbzbbFw033Zn1EZjvr/dh0tu+vdKxPld\n77dsjd6fsNy2lyEhzszhROXh4XFLt1AymUxYvXo1Zs2aBU9PT8yYMQM9evRAYGCgUCYqKgrdu3cH\nAJw9exYpKSnCnTEAc7IMDAxEXV2dMG7Lli2IiopCQkICtmzZgs2bN+O555676Xo6o8Z+8MkYg/FE\nBhQh1p0fFGIRTNWVMBqNUHt54fl/z4BWq0VdXR0qq6pw5MgRGAwGmEwmGAwGSKVSVFdXw2AwwGg0\nwmg0Wr1njKGhhfjq91cPXzu9seFrl+FsP+OTyWSor6+/09VwCNP4YfTePegv5yAV8dAZTdhdx/Bg\nRH989tlnyCgqxSdVRZDwll6DehPDYa4Cq1atAsdxTSz97opFc6NYWMyaNavF1+lwonr88cexdOlS\nJCQk2Fyj8vX1ve78ubm58Pf3h0ZjvrNB7969cejQIatEJZPJhPdardbqi3Tp0iUcPnwYTz75JH74\n4QdhfHp6OmbPng0A6N+/P2bPnn1PJaqGpr3xShMqDCacKTyF6UOTEP7wY9AbDNhfVocjRTmoMZpQ\noTegQm9EjdGIWhOwmM6c7gv7rx1evFh4v7uRefZc+c4QcqOcOlGtXr0agDkxXMuRByeWlZXBy8ty\n5K9Wq5Gbm2tT7uDBg1i/fj0qKysxffp0YfzatWsxfPhw1NbWWpWvqKiAh4f5nnQeHh6oqKhwbIOc\nVH19PY4fP46jR48iJycHu7Zthba8DJ/WG6zK7V65Unif1ciyOI6Di4uLzUssFkMsFkMkEkEkEgkH\nCA3DV08TiURWBwwcx1m9rh139bC9eRobdhYSiQR6vf5OV+OWNcS0qqoKOUcywOq14GQuCPPTQFmQ\nh2qVJ7IuFqOrDBDzPAwmEw7XAxH9BkClUgG4d2JxO1As7iyHE1VLPcU3JiYGMTExyM7OxoYNGzBz\n5kz89ddfcHd3R+vWrZGZmdlkc1FjO73MzExkZmYKw0lJScIX8k4yGAw4cOAAfvnlF/z22284cuQI\ndDqdTTkZz8HfRQo/Fwn8XKQoUHjgydFj4enpCZ1OhwPbfoRYWwtXbx8MmzwF7Tt0gFQqdSgJSKVS\nu+u8H90PsTCVFuHdWTMxxyfb5vrWF64yvHGluf3ChQv4Yv48GMpKIVZ7Y1jyVIS0bn2Han1n3Q+f\nixuxceNG4X1kZCQiIyObdX0OJ6oGpaWlKCsrQ/v27W9oPrVajdLSUmG4rKwMarW60fLh4eEoLi5G\ndXU1srOzkZ6ejsOHD0On06Gurg7Lli3DxIkT4eHhgfLycuH/xu6aYS+Yd6qnm8lkQlpaGjZt2oTt\n20eKpTIAACAASURBVLejsrLSarqX0hU9/b3QTWzC0YoaTGzrhzClHPyVpNPQBXnkyJHCPE899ZTV\nMnQ6ncNfLOp6a3FfxEKmgO5ymd2OFto/9qD87VdRoFRj7Xc/INnHfIut2vMXsWjscKe+l2Nzui8+\nFw5SqVRISkpq0XU6nKhKS0uxZMkS5OfnAwC++uor/PHHH8jIyMCLL7543fnDwsJQWFiIkpISeHp6\nIi0tDZMnT7YqU1hYCD8/PwBAXl4eDAYDlEolhg4diqFDhwIATpw4ge+//x4TJ04EAHTr1g27d+9G\nYmIidu/eLXTGcDbnzp7FlykLceTIURw9dx5VVzVhhrRqBTddHV4JUKGXlxvEHLAo5yIeaROIR57o\niTU7dmOK3NRoF3NCblSjPQYNeiDrCDblXEByI09Spq7tpKU5nKg+/fRTdO3aFW+//TbGjBkDAOjc\nuTO+/PJLh+bneR5jxozB3LlzwRjDgAEDEBQUhO3bt4PjOAwcOBAHDhzA3r17IRaLIZVKkZycfN3l\nJiYmIiUlBb/++is0Go1D87S07KwsvDZ8KHKKS1FjNAEAVBIxnn44HqOmv4nNK5fb/DB3Sjt/fOrV\nFq9NfgOj/284dTEnt1Vjv7EbtXQ1eKMWpnfebrJr+/UePUKPJiG3k8OJKjc3F9OnTwfPW+66pFAo\nbDo3NCU6OhpLliyxGjdo0CDhfUJCAhISEppcRkREBCIiIoRhpVKJmTNnOlyHlmQymbBhwwa8NfNN\n1GrNXVsf8lLhxVBfdPdwxaqyIoSGhjb+e5cqc8eQu/Hmr8S5Xe9GuqJ2EXbvMdjoM7QmjsWoN99G\ncGAAzlXX4YvpUxp9xhYlMXKjHE5U7u7uKCwstLp9RkFBAT2evhHnz5/HlClT8NtvvwEAunm44s2O\nQejmqRTKMFfzncuv98NNQprD9Z6knJL8EpLdbO9qYveOF17Ayn9PQnK7AGxkHvbviDF3Fp4a8jjW\nfLYKU7wkjT4o8m5Eybd5OZyonnjiCcyfPx+JiYkwmUz47bffsHnzZpu7SxDgxx9/xGuvvYaqqip4\neXnhwfD2+FBRC1eJJdy1BiN4P/NvyBy51REhLSm4VStM/HQtPv3gPZszrkZbAKQyIKAVWG4hFArb\n6caCfGxaNB9TrnPty1l3+o3Vy+4Z5ivjMWrJJ2gVEtLkvM64Pc7ohp7we+jQIezYsQMlJSXw9vbG\noEGD0KNHj+asX7O6nTelPXf2LFKXLcH+gwdxICcPADB48GB88MEHqKutvekH67UE6tFkQbGwaCwW\n17vZcWPTVxr/f3t3Hld1lT9+/HUXFhFk10GvpIlJorhCiJO7OTmuaTROk1mapkK5ZGqOOpVTYym4\n4pZb4zfX0iZ/ZS655C4lpiKODCmgIsimgAiX+/n9QXyAWMRiuXjfz8djHnP5LOeec7DPm/P5nM95\n22NKSWZqY/tSZYZqXZm2av0DF9L9PRfX3/NcLTUlhaVjXipZr+t3eCV0Odv//WnZ7b12m8m9/0iC\nrQMb9n7HlMYOZpMN+/csWGyWGX5jY2PR6/V4/lL5jIwMNmzYQHx8PE888QQjR47E1ta2Ripb1aoq\nUMXHxfHJG+NITojny5tpaAH/po0J27qj9F9UZpgNVy7ORaQvipTXF5UJJuXt37Z8yW8LcjgSNH4i\n6z/852+6uJZZp8RsXgl5g6ZNmxKfmsb6BR8xxc26zLLDXvkrY8goXW+dKyatruIszVduMK7YKPLX\nba7OkU15ZS98eypjk0u/R1eZzNG1Eah0//hHxWuphIWF8fjjj6vLJC1ZsoTbt2/Tq1cvLly4QFxc\nHB07dqyJula5qrogrZr3Llcjf2R3Yjp2Oi1rOrUg2NOZf0fFEtj3GaDgGV9g32foOnAIgX2fMass\nyTY2NvIy4y+kL4qU1xeOjo60CHyaT6NiOX4ffnT15G9z3lcvrhXtb9rKm/Bdu+lsrWCl1aoB4W9z\n3sfR0ZFj2zfTTZtT4vustFqOx1wlav8eQjzs1YurlVZLZ2uFT4v9dxYfF8cnH7zPse2bOXn0KE1b\neePo6MgnH7xPcP7tkufa6fn0/31DQMyPrP18FyFNHMst+9iG1XRzsCpdrzwdWvdG+N5NwqrYRLNs\nYz5nH29H4Jtvc+zUabrbaUufex8823Vk3ZuvE5x/m27aHHzvJhG+azctAp9WrxHltaky+0qV/X+b\naf7fSM6fPEZ3twZl1qnrwIof59TGQgkPfEZ1/fp1nnzySQCysrI4e/YsCxcupHHjxnTu3JnZs2cz\nZsyYaq+ouTKZTOw5dJjLt9Jx0GvZ6NeSzr9MmJBVqsWj6kEzUcvb/6DZhuVOLHJtiOlOWtnPxtJT\nUDLvELf0QzYcPMaUJg5Fz4pee4lX1/y7/Odq9g2gTUdMCRnllg1g1dyL7PSrpev1hyYVPGN+C42n\nJ7rmT5Q7g7K8VCyrQsYwZewYrrcNKP3865fJJ0C5+5p6epZddpMGrPr+ONpf6lBXJnA9MMNvfn4+\nen1BPLty5QpOTk7q0M/NzY2srKzqraGZ+/DDD7l8IxE7nZbP/J9Qg1S2MR+tk3n+0oWoTYVBbNqq\n9Uydv6DEba4yMxqnK7ywaCW6Tl3V7YXU/84SrrL9231qkIJfLsyN7dm2fElBACzjXJ1PR3Rv/gOd\nr1/5ZQMv/f29crNh/6Yszb+cW34qllSUsyfLCWQato4ZwdbRfylz37blS4AKMnw3foyg8PV1Krv3\nAwNV06ZNOXHiBADHjh2jbdu26r7U1FSLyv30a5s2bSI8PBydBnq4NaClfcGzOnP/pQthriq66Fd0\nwadxU0x/MJQ7KqrwXCoOJgCPNWtWYTCqKPhW1KZyA2jL1mj7Dy8/kN2/jyk3t8JRYHlla5s2x7N1\n2wrbY24eOJkiOjqa+fPnAwWrS7z//vvqiGr37t1cuXLFLFeDqIzfM5ni/PnzDBo0iNzcXBa0fYzA\nfs+y42Y6poxUs5ss8SAygaCI9EURc+yLiiYlPWg24oMmNFW0v7r64kETU8ptk2Nz0MDY9J8rbO9v\nndlXEbOc9Qdw7949bt68iYeHB/Xq1VO337hxA1tb2woXlzVnDxuoCv8h30tK5LPjZ8jIzOSll17i\nwxHDwLezWaWreBjmeEGqLdIXRepaX1TXhRmqty8qCpAVtQmolddezDZQPaoeJlAV/wfzzsU4vrie\niqudLV98vQevli2rsZbVr65dkKqT9EWRutgX1fUaSG32xYMCWU2/9iKBqoY9TKAqHIKfTstk5JkY\nbLQadnVpxb7mHer8Onx18YJUXaQvikhfFJG+KFIbgeqh81FZKlNaCiZg5vk4AN56ojE+jvX5Vqag\nC/FQ7O3t69xtcp1OZxaJVmuSoihkZmbWdjUACVSVpnV2Zf73Z7mek0vbBnaMbtbIrN87EMJcaTQa\nGZ3UAeYUmB84PV0UCOjciU+vJaMB5rd9jFyTSaagCyFEDZARVSWtC1+OAvg0cmOvc1O0TpLAUAgh\naoIEqko4+Z8v2PPfn6mn07Jx23Y8vJ6o7SoJIYTFkFt/lTD/ww8BGNf7aQlSQghRwyRQPcDJkyc5\nHXcDR1sbxr33YW1XRwhRBw0fPpwtW7aUue/69eu0atWKyrwplJCQgMFgwGQyVXUVzVqN3vqLjIxk\nw4YNKIpCz549S2UHjoiIYOvWrWg0GnQ6HS+//DLe3t7k5eUxd+5cjEYj+fn5BAQE8PzzzwOwfft2\nDhw4oC5tP2LECNq3b19ldV6ypGCBx9ETJtKg6WNVVq4QQgA0adKEy5cvV/r4uja1vyrUWKAymUys\nXbuWOXPm4OzszMyZM/Hz86NJkybqMW3btqVz584AxMXFERYWRlhYGFZWVsydOxcbGxtMJhOzZ8+m\nQ4cOeHl5ATBgwAAGDBhQ5XWOjIzk8OHD1K9fn1dffbXKyxdCCPFgNXbrLyYmBg8PD9zd3dHr9XTt\n2pUzZ86UOMbGxkb9nJOTU+Ivh8J9eXl55OeXXBG4uhbXWL58OQCjRo3C2dm5Wr5DCGFebt26xWuv\nvYavry+BgYGsW7cOgNDQUF5//XXefPNNWrVqRe/evTl//jwA4eHhjB07tkQ5c+bMYc6cOerP8fHx\nDBkyhFatWvHiiy+SlpYGlL6dFx8fz7Bhw/D29mbEiBHMmjWLkJAQtRxFUfj888/x9/fH19dXvevz\nKKuxQJWamoqra9HLsS4uLqSmppY67vTp00yePJn58+czfvx4dbvJZOLtt99m7Nix+Pr6qqMpgD17\n9jBt2jRWrlxJdnZ2ldT3+vXrfPvtt+j1ekaPHl0lZQohzJuiKIwaNYo2bdpw9uxZtm7dytq1azly\n5AgA+/btY+jQoURHR9OnTx/eeecdAAYPHszBgwfV64/JZGL37t0899xzatm7du1i0aJF/PTTT9y/\nf5+VK1eq+4r/UT5x4kQ6duzIhQsXmDJlCp9//nmp231nzpzh6NGjbNmyhUWLFhETE1NtfWIOzG56\nur+/P/7+/kRHR7NlyxZmz54NFKQY+eijj8jOzubjjz9W/wrp168fw4cPR6PRsGXLFjZu3FgiwBW6\nePEiFy9eVH8OCgqq8M3rzaEfkZ+fz7Bhw0oExUeRtbW1Wb2FXpukL4pUV1/odLoHHrNw4UJCQ0NL\nbZ8yZQpTp06t1PHlHVuRyMhIUlNTefPNN4GCfHwjRoxg165dGAwG/P396dGjB1AwQWLt2rVAwXOm\ntm3b8s033zBs2DCOHj1KvXr1Sjwvf+GFF2jWrBkAAwcOZP/+/aW+//r16/z0009s27YNvV6Pn58f\nffv2LXGMRqNh6tSpWFtb07p1a1q3bk1UVFSVX6cqWjZq27Zt6mcfHx98fHyq9Lt/rcYClYuLC7dv\n31Z/Tk1NrTA9iLe3N0lJSWRmZmJvb69ut7Ozw8fHh8jISAwGAw0aNFD39e7dW82d9WtldWZ5y7jc\nS0pk4xe7APjbkEGP/HIvsuBmEemLItXVF5UJflOnTn2oIPOwx5cnISGBxMRE9VqhKAomkwl/f38M\nBgPu7u7qsfXq1eP+/fuYTCa0Wi2DBw9m165dDBs2jF27djF06NASZTds2LDEuWVlR7916xZOTk7Y\n2tqq2xo3bszNmzdLHFe8Hra2tlV2J6m4/Pz8Mn//Dg4OBAUFVfn3VaTGbv15eXmRmJhIcnIyRqOR\nY8eOqRMnCiUmJqqfY2NjMRqN2Nvbc+fOHfUXkZuby/nz59UVfNPT09VzTp06RdOmTX9zHePj4lg4\n/S0mDBtMaq6Rlm7O+PXs/ZvLE0LULY0bN8bT01O9AxMVFUV0dDSffvrpA88dOHAgJ06c4ObNm+zZ\ns6fUrObKaNSoEenp6eTk5Kjbfk+C10dFjY2otFoto0ePZt68eSiKQq9evTAYDOzbtw+NRkOfPn04\ndeoUR44cQa/XY21trWYOTk9PZ/ny5ZhMJhRFITAwkI4dOwIF6eCvXr2KRqPB3d291APNyiqeb+pv\nvzzk9LCzJSE+XpZJEsJCdOjQAXt7e8LDw3n11VexsrIiJiamROAorvhELhcXF7p06cKUKVPw9PR8\nqFtxheU0adIEX19fQkNDmTZtGufOnWP//v0lbv9ZYmamGn1G1b59exYvXlxiW/FfwODBgxk8eHCp\n8zw9Pcu9pRccHFwlddu2fAlTnDTcup9HRFoWdjotYV5u/N/yJXU+35QQonK0Wi0bN27k3XffpUuX\nLuTm5tKiRQvefvvtMo//9SSHIUOGMGnSJP7+979XeFxF5SxbtoxJkybRtm1b2rdvz6BBg0q84Pvr\nsizhvSpJnPiLj8e+whQlhQX/vc6SmESGN3EltF0zQrWuTFu1vhZrWf3kuUwR6Ysi1fmMSvq48saP\nH0/Lli2ZMmVKjX5veb+n2kicKEso/ULr7EpmnpHPEwqmzA83uBbkm3KSfFNCiJpz7tw5rl27hqIo\nHDx4kL1799KvX7/arlatMrvp6bUlaOIbTHvlRa7n5GKoZ41vg3qEpiu8+q7kmxJC1JykpCTGjBlD\neno6Hh4e/Otf/6r26d/mTgLVL5p6ekKLJyE6liYGA580as2r70m+KSFEzerbt2+pd6csnQSqXxRO\nmQeYv2YdLVu2rOUaCSGEAHlGpTp58iTp6em0aNFCgpQQQpgRCVS/2PPllwA8++yztVwTIYQQxUmg\nAkx5eXy783MAnunerZZrI4QQojgJVMD5r7/kxr37NLSzpUNAl9qujhBCiGIkUAF7vigYTfXr2A6t\nVrpECFG1KkpFLx7M4q/KiqKw58yPAPxpyHMPOFoIIURNs/hAFXvie/6bkUkDKz1dhg6v7eoIIYT4\nFYsPVHsPHACgV+snsCmWA0YIYZmqKxV9IUVRWLRoEU899RTt27dn0qRJZGZmAjBp0iRWr14NFKQ9\nMhgMbNy4EYCrV69a7AoVFh+o9kREAvCncVWzCrsQou6qzlT0hbZu3cqOHTv4/PPPOXHiBFlZWcya\nNQuALl26cOLECaDg3c7HHnuMU6dOAQX59gICAqq9D8yRRQeqpKQkfvjhB2xsbOjZWxIkCmEuFi5c\nSJMmTUr9b+HChZU+vrxjK1I8Fb1OpyuRih5QU9FrNBqGDx/OpUuXgJKp6IEyU9EX2rlzJ2PHjsVg\nMFCvXj1mzJjBl19+iclkIiAggNOnTwMFgWrChAmcOXMGgBMnTlhsoLLoJZT27duHoij88Y9/LJHu\nXghRux7FVPSFbt26hcFgUH82GAwYjUaSk5N57LHHsLOz48KFC5w+fZrJkyezefNm/ve//3Hy5EnG\njBnzu9tYF1l0oNqzZw8gq1EIIQoUpqL//vvvS+0LDQ2t8NyBAwfy/vvvq6no//Of/5R5XKNGjUhI\nSFB/TkhIwMrKSg2CAQEB7N69m7y8PBo1akRAQADbt2/nzp078ozKEh09ehSNRiMrFQshgJKp6HNy\ncsjPz+fy5cucO3euzON/Syr6IUOGsGbNGuLj48nKymL+/PkMGjRIfYczICCADRs2qLf5unTpwoYN\nG/Dz87OIbL5lsehAlZubi59vW9zc3Gq7KkIIM1CYiv7ixYt06dIFX19fpk2bVm5G4rJS0R89erTU\nbb/ix/3lL39h2LBhPPfccwQGBlKvXj3ef/99dX9AQABZWVlqoPL39ycnJ4cuXSx31ZwaTUUfGRnJ\nhg0bUBSFnj17MmTIkBL7IyIi2Lp1KxqNBp1Ox8svv4y3tzd5eXnMnTsXo9FIfn4+AQEBPP/88wBk\nZmayaNEikpOTadiwIZMnT8bOzq5S9dFoNMx+YSivhy6r8rbWJZIavIj0RRFJRW/ZzCkVfY09ozKZ\nTKxdu5Y5c+bg7OzMzJkz8fPzo0mTJuoxbdu2pXPnzgDExcURFhZGWFgYVlZWzJ07FxsbG0wmE7Nn\nz6ZDhw54eXmxa9cu2rZtqz7I3LlzJy+++GKl6/Wnv75U5W0VQghRdWrs1l9MTAweHh64u7uj1+vp\n2rWrOu2ykI2Njfo5JyenxHC5cF9eXh75+fnq9oiICLp37w5Ajx49SpVZETdba3QNPX5Te4QQQtSM\nGhtRpaam4urqqv7s4uJCTExMqeNOnz7N5s2buXPnDjNmzFC3m0wmZsyYwa1bt+jXr5/6oDIjIwMn\nJycAnJycyMjIqHSdXmjszLo3X+fVxSsl5bwQQpgps5ue7u/vj7+/P9HR0WzZsoXZs2cDBQ85P/ro\nI7Kzs/n4449JSEgo8S5CofJmxVy8eJGLFy+qPwcFBTHM4EZjWw3rV4cza/Hy6mlQHWBtbY2Dg0Nt\nV8MsSF8Uqa6+0Ol0VV6mqHo6na7c3/+2bdvUzz4+PtU+bb7GApWLiwu3b99Wf05NTcXFxaXc4729\nvUlKSiIzM7PEy7h2dnb4+PgQGRmJwWDAycmJ9PR09f8dHR3LLK+szvSyL1jb737yLYt+uCsPt4tI\nXxSpzskUwvzl5+eX+ft3cHAgKCioRutSY8+ovLy8SExMJDk5GaPRyLFjx9SJE4USExPVz7GxsRiN\nRuzt7blz5466hlZubi7nz59XZ5506tSJQ4cOAXDo0KFSZT5ItjEfrZPrgw8UQghRK2psRKXVahk9\nejTz5s1DURR69eqFwWBg3759aDQa+vTpw6lTpzhy5Ah6vR5ra2smT54MQHp6OsuXL8dkMqEoCoGB\ngXTs2BEoeG8hLCyMgwcP4u7urp5TGdnGfELTFV59941qabMQQojfr0bfozI3U1/6K0ET37D4iRRy\nu6uI9EUReY/KspnTe1QWvTLF1PkLLD5ICSGqx+nTp9VXZ8TvY9GBSgghqou/vz+HDx+u7Wo8Esxu\neroQwnLFx8WxbfkSTGkpaJ1df9Ot+aoo4/fKz8+vtmn41Vm2uZIRlRDCLMTHxbHuzdcZm3SJKUoK\nY5Muse7N14mPi6uxMipKKb9161Z69OhBq1at6Nq1K5s2bVKPOXHiBJ07dyY8PJwOHTowZcoUdVuh\nmJgYhg8fTuvWrenduzd79+5V96Wlpalrmw4YMICPPvqoxMK2BoOBDRs28Mc//pGnn35arZefnx/e\n3t70799fTbgIBSlJxo0bR0hICK1ataJPnz7ExsaybNky2rVrh7+/v5q1uC6QQCWEMAvbli9hipMG\nO33BaMFOr2OKk4atI58j/7VB5L82qNxzC/dvHflcmWVsW76kUnUoL6X80KFDcXd359NPP+Xy5cuE\nhobyj3/8gwsXLqjnJicnk5GRwenTp/noo4+AogUIjEYjL7/8Mj179uSnn37ivffeIyQkhNjYWADe\neecd7O3tOXfuHGFhYWzfvr3U4gV79+7l66+/5uDBg0BBSpL9+/cTFRXFkCFDGDduHLm5uerx+/fv\n5/nnn+fSpUv4+Pjw4osvoigKP/74I5MmTWL69OmV6hNzIIFKCGEWTGkpaoApZKfXYXqIeckmhbLL\nSE+p1PnlpZTv0KEDvXr1wvOXW4hPPfUU3bt3LzGK0el0vPXWW1hZWZVYtxTghx9+4N69e0ycOFFd\n67RPnz5qCvpvvvmGt956CxsbG1q2bKlmhyguJCSEBg0aqGUPHToUR0dHtFotY8eOJTc3l//973/q\n8U899RTdunVDq9UyYMAAUlNTCQ4ORqfTMXjwYBISEurM7Et5RiWEMAtaZ1eyk5JKBJpsYz66gB7o\n5i+o8FzdmoJsurrpb5GddKlUGdqGlX+pv7yU8t999x1hYWHExsaiKAo5OTk8+eST6nkuLi5YWVmV\nWeatW7dKTes2GAzcvHmTlJQUjEYjHh5FC2SXNQW8+H6AlStXsmXLFpKSkoCClEepqanq/uJ59mxt\nbXFxcVFHaba2tiiKQlZWVp1YKURGVEIIsxA08Q1C0xWyjQXZEQpfyA+aWPkX8quijIEDB3LixAk1\npfzQoUPJzc1l7NixTJgwgfPnzxMVFUXPnj1LZPitKPvuH/7wB27cuFFi2/Xr1/Hw8MDV1RW9Xs/N\nmzfVfb8+9tflnz59mhUrVrB69WqioqKIiorCwcGBR/W1WAlUQgiz0NTTk1cXr2R1wycJ1bqyuuGT\nD53ZoCrK+HVK+RYtWpCXl0deXh4uLi5otVq+++67h5p63qFDB+rVq0d4eDhGo5Hjx4+zf/9+Bg8e\njFarpX///oSGhnLv3j1iYmLYsWNHheVlZmai1+txdnYmNzeXsLAwMjMzK12fukZu/QkhzEZTT0+m\nPuA2X02UMWTIECZNmsTf//53AOrXr897773HuHHjyMvLo0+fPvTr16/S5VlZWbFhwwZmzpzJ0qVL\n8fDwYMmSJTz++OMAzJs3j0mTJtGxY0datGjB0KFDOXfunHr+r0drPXr0oEePHjz99NPUr1+f1157\n7aFXjKhoBGhuLHoJpbKG15ZIlrQpIn1RRJZQqj0ffPABycnJhIWF1VodZAklIYQQqpiYGC5dugTA\n2bNn2bx5M88++2wt18p8yK0/IYSoZVlZWUyYMIGkpCTc3d0ZP348zzzzTG1Xy2xIoBJCiFrWrl07\njh07VtvVMFty608IIYRZk0AlhBDCrEmgEkIIYdbkGZUQokYpilInlu0pTqfTkZ+fX9vVqFHm9OZS\njQaqyMhINmzYgKIo9OzZkyFDhpTYHxERwdatW9FoNOh0OnXZ+5SUFJYtW0ZGRgYajYbevXvTv39/\nALZv386BAwdwdHQEYMSIEbRv374mmyWEeAh1cQUFeferdtVYoDKZTKxdu5Y5c+bg7OzMzJkz8fPz\no0mTJuoxbdu2VfO3xMXFERYWRlhYmBq0mjVrRk5ODtOnT6ddu3bquQMGDGDAgAE11RQhhBA1qMae\nUcXExODh4YG7u7u6zP2ZM2dKHFN8afycnBx1iQ8nJyeaNWsGFKz626RJkxKrBJvTEFUIIUTVqrER\nVWpqKq6uRUvtu7i4EBMTU+q406dPs3nzZu7cucOMGTNK7U9KSuLatWu0bNlS3bZnzx6OHDlCixYt\nGDlyJHZ2dtXTCCGEEDXO7Gb9+fv7ExYWxrRp09iyZUuJfTk5OYSGhjJq1ChsbW0B6NevH8uWLePj\njz/GycmJjRs31ka1hRBCVJMaG1G5uLhw+/Zt9efU1FRcXFzKPd7b25ukpCQyMzOxt7cnPz+fhQsX\n0q1bN/z8/NTjGjRooH7u3bs38+fPL7O8ixcvcvHiRfXnoKCgWllc0VzVtVlY1Un6ooj0RRHpiyLb\ntm1TP/v4+ODj41Ot31djIyovLy8SExNJTk7GaDRy7NgxdeJEocTERPVzbGwsRqMRe3t7AFasWIHB\nYFBn+xVKT09XP586dYqmTZuW+f0+Pj4EBQWp/yve0ZZO+qKI9EUR6Ysi0hdFtm3bVuJaWt1BCmpw\nRKXVahk9ejTz5s1DURR69eqFwWBg3759aDQa+vTpw6lTpzhy5Ah6vR5ra2smT54MQHR0NN9//z2e\nnp68/fbbaDQadRr6pk2buHr1KhqNBnd3d8aOHVtTTRJCCFEDavQ9qvbt27N48eIS2/r27at+nd9g\nVAAADNdJREFUHjx4MIMHDy51nre3N1u3bi2zzODg4KqtpBBCCLNidpMpakpNDFfrCumLItIXRaQv\nikhfFKmNvrDoDL9CCCHMn8WOqIQQQtQNEqiEEEKYNYtbPf1BC+PWJeUt1puZmcmiRYtITk6mYcOG\nTJ48WV2tY+fOnRw8eBCdTseoUaNo164dUPA6QHh4OHl5eXTo0IFRo0YBYDQaWbZsGbGxsTg4ODB5\n8mTc3NwAOHToEDt37gTgueeeo3v37jXfCcWYTCZmzpyJi4sL06dPt9h+yM7OZuXKlcTHx6PRaBg/\nfjweHh4W2Re7d+/m4MGDaDQaPD09mTBhAjk5ORbTFytWrODHH3/E0dGRBQsWANT6fxdJSUksXryY\nzMxMmjdvTkhICDqdruKGKBYkPz9fCQ4OVpKSkpS8vDzlrbfeUhISEmq7Wr9ZWlqa8vPPPyuKoij3\n7t1T3njjDSUhIUH597//rezatUtRFEXZuXOnsmnTJkVRFCU+Pl6ZNm2aYjQalVu3binBwcGKyWRS\nFEVRZs6cqVy5ckVRFEX54IMPlLNnzyqKoijffvutsmbNGkVRFOXYsWNKWFiYoiiKcvfuXSU4OFjJ\nyspSMjMz1c+16auvvlIWL16s/Otf/1IURbHYfli2bJny3XffKYqiKEajUcnKyrLIvkhJSVEmTpyo\n5OXlKYqiKKGhocrBgwctqi8uXbqk/Pzzz8rUqVPVbbXd/tDQUOX48eOKoijK6tWrlb179z6wHRZ1\n668yC+PWJWUt1puSkkJERIT610uPHj3UNkZERBAYGIhOp6Nhw4Z4eHgQExNDeno69+7dw8vLC4Bu\n3bqp55w5c0YtKyAggAsXLgBw7tw5fH19sbOzo379+vj6+hIZGVmTzS8hJSWFs2fP0rt3b3WbJfZD\ndnY20dHR9OzZEyjIo2RnZ2eRfQEFo+ycnBzy8/PJzc3FxcXFovrC29ub+vXrl9hW2+2/cOECTz31\nFADdu3fn9OnTD2yHRd36q+zCuHVR4WK9TzzxBBkZGTg5OQEFwSwjIwMoaP8TTzyhnuPi4kJqaio6\nna5Ev7i6uqqr0xfvM61Wi52dHZmZmWX2ZfEV7Wvaxo0beemll8jOzla3WWI/JCUl4eDgQHh4ONeu\nXePxxx9n1KhRFtkXLi4uDBgwgAkTJmBjY4Ovry++vr4W2RfF1Wb77969i729PVqtVi0rLS3tgXW2\nqBHVo6qsxXqLK0yXUhUUM3ybofAefLNmzSqs36PeD1Awgvj555/p168f8+fPx8bGhl27dpU6zhL6\nIisri4iICMLDw1m1ahX379/n+++/L3WcJfRFRWq6/b+ljywqUD3swrh1QVmL9To5OalrIKanp6vZ\nj3/d/pSUFFxcXHBxcSElJaXU9sJzCveZTCbu3buHvb19uWXVhujoaCIiIggODmbx4sVcuHCBpUuX\nWlw/QEE9XV1dadGiBVBwO+bnn3+2yL44f/48DRs2VP+C9/f35/LlyxbZF8XVZvsdHBzIzs7GZDKV\nKqsiFhWoKrMwbl1T1mK9nTp14tChQ0DBzJvCNnbu3Jnjx49jNBpJSkoiMTERLy8vnJycsLOzIyYm\nBkVROHLkiBr0OnfuzOHDhwE4ceIEbdq0AaBdu3acP3+e7OxsMjMzOX/+vDpDqKb99a9/ZcWKFSxb\ntoxJkybRpk0bQkJCLK4foOAi5Orqyo0bN4CCi7XBYLDIvnBzc+PKlSvk5uaiKIrF9oWiKCVGMbXd\nfh8fH06ePAnA4cOHK3UNtriVKSIjI1m/fr26MG5dnp4eHR3N3Llz8fT0RKPRqIv1enl5ERYWxu3b\nt3F3d2fy5MnqA9WdO3fy3XffodfrS00/Xb58uTr99JVXXgEgLy+PpUuXcvXqVRwcHHjzzTdp2LAh\nUPCP/IsvvkCj0ZjFVGSAqKgovvrqK3V6uiX2w9WrV1m1ahVGo5FGjRoxYcIETCaTRfbF9u3bOX78\nODqdjmbNmvH666+Tk5NjMX2xePFioqKiuHv3Lo6OjgQFBeHn51er7U9KSmLRokVkZWXRrFkzQkJC\n0Osrni5hcYFKCCFE3WJRt/6EEELUPRKohBBCmDUJVEIIIcyaBCohhBBmTQKVEEIIsyaBSgghhFmT\nQCUeSWvWrOGLL76o7Wr8JtHR0UyePLm2qyGE2ZD3qESdM3HiRDIyMtDpdGi1WgwGA926daNPnz4P\nvW7ZxIkTGT9+vPpGvSgSFRXF0qVLWbFiRW1XRVg4i1o9XTw6ZsyYQZs2bbh37x5RUVGsX7+eK1eu\nMGHChNqu2iPj9/4NazKZ1FWyhfg9JFCJOq1evXp06tQJR0dHZs2axaBBgzAYDISHh+Pq6soLL7zA\n3bt3Wb58OZcvX0aj0dC0aVPeffddli1bxu3bt5k/fz5arZZhw4YxaNAgQkNDiY6OJi8vj8cee4wx\nY8ZgMBgACA8Px8bGhuTkZC5duoTBYCixbEx8fDwbN24kNjYWvV5P//79GTJkCIqi8OWXX3LgwAGy\ns7Np27Ytr732WqlcQVB6JDNx4kT+9Kc/ceTIEW7fvk27du0IDg4uc9mZQ4cOceDAAby8vDh06BD2\n9vaEhIRw48YNtm7ditFo5G9/+5u6nI3RaOSzzz7j5MmTGI1G/Pz8GDVqFCaTiQ8//BCj0cjIkSPR\naDQsXrwYR0fHctuRnJxMcHAw48aNY8eOHTRs2JBZs2axYsUKzp07h8lkwsPDgxkzZtCgQYPq+ich\nHkHy5454JHh5eeHq6sqlS5dK7fvqq69wc3Nj7dq1fPLJJ4wYMQKA4OBg3NzcmD59Ohs3bmTQoEEA\ndOzYkaVLl7JmzRqaN2/OkiVLSpR3/PhxgoKCWL9+PY0aNWLz5s1AQbqVefPm0aFDB1avXs2SJUvU\nW4rffPMNERERvPfee6xatYr69evzySefVLp9J0+eZNasWSxbtoxr166pi4qWJSYmhmbNmrFu3Tq6\ndu3KokWLiI2NZenSpYSEhLBu3Tru378PwKZNm0hMTGTBggUsWbKEtLQ0duzYgY2NDe+88w7Ozs58\n+umnbNy4EScnp0q149KlSyxatIhZs2Zx+PBhcnJyWLlyJevWreO1117D2tq60u0WAiRQiUeIs7Mz\nmZmZpbbr9XrS0tJISkpCq9Xi7e1dYTk9evTAxsYGvV7P8OHDuXbtGvfu3VP3+/v78/jjj6PVann6\n6ae5evUqUJAh1cnJiT//+c/o9XpsbW3VrKj79u3jL3/5C87Ozmq5J0+eVNMdPMizzz6Lk5MT9evX\np1OnTup3lqVhw4Z0794djUZDYGAgKSkpDB8+HL1ej6+vL3q9nsTERAAOHDjAqFGjsLOzw9bWliFD\nhnDs2LFyy65MO4KCgrC2tsbKygqdTsfdu3e5efMmGo2G5s2bl5kzTYiKyK0/8chITU3F3t6+1PZB\ngwaxbds2/vnPfwLQu3fvclfNN5lMbN68mZMnT3L37l11csbdu3epV68egJodFcDGxoacnBz1+xs1\nalRmubdv32bBggUlJnvo9XoyMjJwdnZ+YNt+/Z2F+YQedGzh6KX4rTZra2tycnK4c+cOubm5TJ8+\nXd3365QQD9OOQsUzu3bv3p2UlBQWLVpEdnY2Tz/9NCNGjJBnV+KhSKASj4SYmBjS0tJ48sknS+2z\ntbVl5MiRjBw5koSEBN599128vLxo06ZNqVmCR48e5YcffmDu3Lm4ubmRnZ3NK6+8UqmJBa6uruWO\nRtzc3Bg/fnyJVN+1zcHBAWtra0JDQysVLKHidiQnJ5faptVqGT58OMOHD+f27dt88MEHNG7cmJ49\ne/7u+gvLIX/WiDrt3r17/PDDDyxevJhu3bqpkx6K+/HHH9VbXba2tmi1WvUvekdHR27duqUem5OT\ng5WVFfXr1ycnJ4fPPvus0nXp1KkT6enpfP311xiNRnJycoiJiQGgT58+bN68Wc16eufOHSIiIn5z\nu6uCRqOhd+/ebNiwgTt37gAFo8Jz584BBSOzzMxMsrOz1XMeth0XL14kLi4Ok8mEra0tOp2uSlOf\nC8sgIypRJxXO1Ct8j2rgwIH07du3zGNv3rzJ2rVruXv3LvXr16dfv360bt0agKFDh7Ju3To2bdrE\nsGHD6Nu3L5GRkbz++uvY29vzwgsvsG/fvkrVydbWltmzZ7N+/Xq2b9+OtbU1/fv3x8vLS83APG/e\nPNLS0nB0dCQwMLBy2U2r8cL+4osvsmPHDmbNmsXdu3dxcXHhmWeeoV27djRu3JiuXbsSEhKiJl58\n2Hakp6ezZs0aUlNTsbW1JTAwkG7dulVbe8SjSV74FUIIYdbk1p8QQgizJoFKCCGEWZNAJYQQwqxJ\noBJCCGHWJFAJIYQwaxKohBBCmDUJVEIIIcyaBCohhBBmTQKVEEIIs/b/AUhwJ7xbcjAxAAAAAElF\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fccd94551d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "xx = np.linspace(0,1000000,1000)\n", "\n", "gvg.plot(refresh=False)\n", "plt.plot(xx,gvg.model.f(xx),lw=2.0,c='k')\n", "plt.title(\"Empirical Variogram with fitted Whittle Model\")" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "< Whittle Variogram : sill 0.340274656891, range 41061.6971399, nugget 0.329817414704, alpha1.12113685018 >" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gvg.model" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2min 38s, sys: 1.31 s, total: 2min 39s\n", "Wall time: 1min 48s\n" ] } ], "source": [ "%time n_obs,rsq,params,pvals,conf_int = bundleToGLS(systematic_sample,gvg.model)" ] }, { "cell_type": "code", "execution_count": 235, "metadata": { "collapsed": false }, "outputs": [], "source": [ "samples = map(lambda i : systSelection(new_data,i), range(20,2,-1))\n", "samples = map(lambda i : randomSelection(new_data,3000),range(100))" ] }, { "cell_type": "code", "execution_count": 236, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fccd801c750>]" ] }, "execution_count": 236, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fccd8632490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(map(lambda s : s.shape[0],samples))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Analysis and Results for the systematic sample" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### read csv files\n", "conf_ints = pd.read_csv(\"/outputs/gls_confidence_int.csv\")\n", "params = pd.read_csv(\"/outputs/params_gls.csv\")\n", "params2 = pd.read_csv(\"/outputs/params2_gls.csv\")\n", "\n", "pvals = pd.read_csv(\"/outputs/pvalues_gls.csv\")\n", "pnobs = pd.read_csv(\"/outputs/n_obs.csv\")\n", "prsqs = pd.read_csv(\"/outputs/rsqs.csv\")" ] }, { "cell_type": "code", "execution_count": 57, "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>Unnamed: 0</th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>10</th>\n", " <th>11</th>\n", " <th>12</th>\n", " <th>13</th>\n", " <th>14</th>\n", " <th>15</th>\n", " <th>16</th>\n", " <th>17</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Intercept</td>\n", " <td>8.458627</td>\n", " <td>8.501959</td>\n", " <td>8.444683</td>\n", " <td>8.504319</td>\n", " <td>8.455233</td>\n", " <td>8.487719</td>\n", " <td>8.514224</td>\n", " <td>8.489710</td>\n", " <td>8.430563</td>\n", " <td>8.492380</td>\n", " <td>8.511993</td>\n", " <td>8.459749</td>\n", " <td>8.439471</td>\n", " <td>8.496132</td>\n", " <td>8.430853</td>\n", " <td>8.464045</td>\n", " <td>8.477898</td>\n", " <td>8.447797</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>logSppN</td>\n", " <td>0.388850</td>\n", " <td>0.374571</td>\n", " <td>0.405324</td>\n", " <td>0.363968</td>\n", " <td>0.394236</td>\n", " <td>0.385002</td>\n", " <td>0.369316</td>\n", " <td>0.380915</td>\n", " <td>0.419709</td>\n", " <td>0.376678</td>\n", " <td>0.361736</td>\n", " <td>0.395713</td>\n", " <td>0.407464</td>\n", " <td>0.379930</td>\n", " <td>0.415325</td>\n", " <td>0.392312</td>\n", " <td>0.388304</td>\n", " <td>0.404760</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Unnamed: 0 0 1 2 3 4 5 \\\n", "0 Intercept 8.458627 8.501959 8.444683 8.504319 8.455233 8.487719 \n", "1 logSppN 0.388850 0.374571 0.405324 0.363968 0.394236 0.385002 \n", "\n", " 6 7 8 9 10 11 12 \\\n", "0 8.514224 8.489710 8.430563 8.492380 8.511993 8.459749 8.439471 \n", "1 0.369316 0.380915 0.419709 0.376678 0.361736 0.395713 0.407464 \n", "\n", " 13 14 15 16 17 \n", "0 8.496132 8.430853 8.464045 8.477898 8.447797 \n", "1 0.379930 0.415325 0.392312 0.388304 0.404760 " ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "params" ] }, { "cell_type": "code", "execution_count": 58, "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>Unnamed: 0</th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>0.1</th>\n", " <th>1.1</th>\n", " <th>0.2</th>\n", " <th>1.2</th>\n", " <th>0.3</th>\n", " <th>1.3</th>\n", " <th>0.4</th>\n", " <th>...</th>\n", " <th>0.13</th>\n", " <th>1.13</th>\n", " <th>0.14</th>\n", " <th>1.14</th>\n", " <th>0.15</th>\n", " <th>1.15</th>\n", " <th>0.16</th>\n", " <th>1.16</th>\n", " <th>0.17</th>\n", " <th>1.17</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Intercept</td>\n", " <td>8.382200</td>\n", " <td>8.535054</td>\n", " <td>8.424685</td>\n", " <td>8.579233</td>\n", " <td>8.371694</td>\n", " <td>8.517673</td>\n", " <td>8.430691</td>\n", " <td>8.577947</td>\n", " <td>8.386613</td>\n", " <td>...</td>\n", " <td>8.449598</td>\n", " <td>8.542666</td>\n", " <td>8.387293</td>\n", " <td>8.474413</td>\n", " <td>8.424108</td>\n", " <td>8.503982</td>\n", " <td>8.442086</td>\n", " <td>8.513711</td>\n", " <td>8.416168</td>\n", " <td>8.479427</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>logSppN</td>\n", " <td>0.341435</td>\n", " <td>0.436265</td>\n", " <td>0.326552</td>\n", " <td>0.422590</td>\n", " <td>0.359728</td>\n", " <td>0.450920</td>\n", " <td>0.318351</td>\n", " <td>0.409585</td>\n", " <td>0.351409</td>\n", " <td>...</td>\n", " <td>0.351194</td>\n", " <td>0.408666</td>\n", " <td>0.388426</td>\n", " <td>0.442224</td>\n", " <td>0.367768</td>\n", " <td>0.416855</td>\n", " <td>0.366302</td>\n", " <td>0.410307</td>\n", " <td>0.385440</td>\n", " <td>0.424081</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2 rows × 37 columns</p>\n", "</div>" ], "text/plain": [ " Unnamed: 0 0 1 0.1 1.1 0.2 1.2 \\\n", "0 Intercept 8.382200 8.535054 8.424685 8.579233 8.371694 8.517673 \n", "1 logSppN 0.341435 0.436265 0.326552 0.422590 0.359728 0.450920 \n", "\n", " 0.3 1.3 0.4 ... 0.13 1.13 0.14 \\\n", "0 8.430691 8.577947 8.386613 ... 8.449598 8.542666 8.387293 \n", "1 0.318351 0.409585 0.351409 ... 0.351194 0.408666 0.388426 \n", "\n", " 1.14 0.15 1.15 0.16 1.16 0.17 1.17 \n", "0 8.474413 8.424108 8.503982 8.442086 8.513711 8.416168 8.479427 \n", "1 0.442224 0.367768 0.416855 0.366302 0.410307 0.385440 0.424081 \n", "\n", "[2 rows x 37 columns]" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conf_ints" ] }, { "cell_type": "code", "execution_count": 59, "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>Unnamed: 0</th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>10</th>\n", " <th>11</th>\n", " <th>12</th>\n", " <th>13</th>\n", " <th>14</th>\n", " <th>15</th>\n", " <th>16</th>\n", " <th>17</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Intercept</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>logSppN</td>\n", " <td>1.439457e-54</td>\n", " <td>5.777970e-50</td>\n", " <td>1.444574e-63</td>\n", " <td>2.304296e-52</td>\n", " <td>3.139409e-68</td>\n", " <td>2.182079e-69</td>\n", " <td>2.336946e-70</td>\n", " <td>5.199794e-82</td>\n", " <td>3.306206e-99</td>\n", " <td>3.711292e-88</td>\n", " <td>1.160717e-91</td>\n", " <td>8.572717e-116</td>\n", " <td>2.894346e-137</td>\n", " <td>1.655089e-139</td>\n", " <td>1.054292e-187</td>\n", " <td>1.977681e-202</td>\n", " <td>1.153619e-246</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Unnamed: 0 0 1 2 3 \\\n", "0 Intercept 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", "1 logSppN 1.439457e-54 5.777970e-50 1.444574e-63 2.304296e-52 \n", "\n", " 4 5 6 7 8 \\\n", "0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", "1 3.139409e-68 2.182079e-69 2.336946e-70 5.199794e-82 3.306206e-99 \n", "\n", " 9 10 11 12 13 \\\n", "0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", "1 3.711292e-88 1.160717e-91 8.572717e-116 2.894346e-137 1.655089e-139 \n", "\n", " 14 15 16 17 \n", "0 0.000000e+00 0.000000e+00 0.000000e+00 0.0 \n", "1 1.054292e-187 1.977681e-202 1.153619e-246 0.0 " ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pvals" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fccd90985d0>" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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B4uTkJL6+vhIZGVnhvN95ApJIyena06ZNk8DAQHFycpKQkBD58ssvLe+Hh4eX+sG+IpmZ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\nUyIiIjuxmBIREdmJxZSIiMhO/w/DuugBBboL6gAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fccd9098590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(pnobs.n_obs,prsqs.rsq)\n", "plt.title(\"$R^2$ statistic for GLS on logBiomass ~ logSppn using Sp.autocor\")\n", "plt.xlabel(\"Number of observations\")" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tt = params.transpose()" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tt.columns = tt.iloc[0]" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tt = tt.drop(tt.index[0])" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fccd81a20d0>" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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UlJSgpKQkaF17ezvuv/9+5Obm4vrrr8eUKVNUEXhU028Hy58Ifuxr1S7Bvz4EMIHScwmC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\nnwAc3D/sGrm9B9VWEES0pE5Uj4gezwxu7nKmRJ1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"text/plain": [ "<matplotlib.figure.Figure at 0x7fccd8227390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(pnobs.n_obs,tt.Intercept)\n", "plt.title(\"Intercept parameter\")" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fccd8101dd0>" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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sYjFcbaipHtK6xyCMQ3lHdoRwslCBPA1lCOM0lEG91VCnTwJpmRD93Smd1dVQ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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fccd8238ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(pnobs.n_obs,tt.logSppN)\n", "plt.title(\"logSppn parameter\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Test for analysis" ] }, { "cell_type": "code", "execution_count": 237, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n", "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n" ] } ], "source": [ "ccs = map(lambda s : bundleToGLS(s,gvg.model),samples)" ] }, { "cell_type": "code", "execution_count": 207, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n" ] } ], "source": [ "#bundleToGLS(samples[22],gvg.model)\n", "covMat = buildSpatialStructure(samples[8],gvg.model)" ] }, { "cell_type": "code", "execution_count": 208, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n" ] }, { "ename": "LinAlgError", "evalue": "SVD did not converge", "output_type": "error", "traceback": [ "\u001b[0;31m\u001b[0m", "\u001b[0;31mLinAlgError\u001b[0mTraceback (most recent call last)", "\u001b[0;32m<ipython-input-208-b2bd56754290>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#np.linalg.pinv(covMat)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcalculateGLS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcovMat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;31m#tt = covMat.flatten()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/apps/external_plugins/spystats/HEC_runs/fit_fia_logbiomass_logspp_GLS.py\u001b[0m in \u001b[0;36mcalculateGLS\u001b[0;34m(geodataframe, CovMat)\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0mStupid\u001b[0m \u001b[0mwrapper\u001b[0m \u001b[0mfunction\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcalculating\u001b[0m \u001b[0mspatial\u001b[0m \u001b[0mcovariance\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \"\"\"\n\u001b[0;32m--> 137\u001b[0;31m \u001b[0msecvg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVariogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeodataframe\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'logBiomass'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtheoretical_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 138\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Calculating Distance Matrix\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0mCovMat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msecvg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculateCovarianceMatrix\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/envs/biospytial/lib/python2.7/site-packages/statsmodels/base/model.pyc\u001b[0m in \u001b[0;36mfrom_formula\u001b[0;34m(cls, formula, data, subset, args, kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m kwargs.update({'missing_idx': missing_idx,\n\u001b[1;32m 149\u001b[0m 'missing': missing})\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\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[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformula\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformula\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/envs/biospytial/lib/python2.7/site-packages/statsmodels/regression/linear_model.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, exog, sigma, missing, hasconst, kwargs)\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[0;31m#TODO: add options igls, for iterative fgls if sigma is None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;31m#TODO: default if sigma is none should be two-step GLS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m \u001b[0msigma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcholsigmainv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_sigma\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msigma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\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 441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 442\u001b[0m super(GLS, self).__init__(endog, exog, missing=missing,\n", "\u001b[0;32m/opt/conda/envs/biospytial/lib/python2.7/site-packages/statsmodels/regression/linear_model.pyc\u001b[0m in \u001b[0;36m_get_sigma\u001b[0;34m(sigma, nobs)\u001b[0m\n\u001b[1;32m 77\u001b[0m raise ValueError(\"Sigma must be a scalar, 1d of length %s or a 2d \"\n\u001b[1;32m 78\u001b[0m \"array of shape %s x %s\" % (nobs, nobs, nobs))\n\u001b[0;32m---> 79\u001b[0;31m \u001b[0mcholsigmainv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcholesky\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msigma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msigma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcholsigmainv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/envs/biospytial/lib/python2.7/site-packages/numpy/linalg/linalg.pyc\u001b[0m in \u001b[0;36mpinv\u001b[0;34m(a, rcond)\u001b[0m\n\u001b[1;32m 1615\u001b[0m \u001b[0m_assertNoEmpty2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1616\u001b[0m \u001b[0ma\u001b[0m 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"\u001b[0;32m/opt/conda/envs/biospytial/lib/python2.7/site-packages/numpy/linalg/linalg.pyc\u001b[0m in \u001b[0;36msvd\u001b[0;34m(a, full_matrices, compute_uv)\u001b[0m\n\u001b[1;32m 1357\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1358\u001b[0m \u001b[0msignature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'D->DdD'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'd->ddd'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1359\u001b[0;31m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgufunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1360\u001b[0m \u001b[0mu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1361\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_realType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/envs/biospytial/lib/python2.7/site-packages/numpy/linalg/linalg.pyc\u001b[0m in \u001b[0;36m_raise_linalgerror_svd_nonconvergence\u001b[0;34m(err, flag)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_raise_linalgerror_svd_nonconvergence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"SVD did not converge\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_linalg_error_extobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mLinAlgError\u001b[0m: SVD did not converge" ] } ], "source": [ "#np.linalg.pinv(covMat)\n", "calculateGLS(samples[8],covMat)\n", "#tt = covMat.flatten()" ] }, { "cell_type": "code", "execution_count": 225, "metadata": { "collapsed": true }, "outputs": [], "source": [ "secvg = tools.Variogram(samples[8],'logBiomass',model=gvg.model)" ] }, { "cell_type": "code", "execution_count": 226, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DM = secvg.distance_coordinates" ] }, { "cell_type": "code", "execution_count": 227, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dm = DM.flatten()" ] }, { "cell_type": "code", "execution_count": 228, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dm.sort()" ] }, { "cell_type": "code", "execution_count": 229, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pdm = pd.DataFrame(dm)" ] }, { "cell_type": "code", "execution_count": 230, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/envs/biospytial/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(....) is deprecated, use sort_index(.....)\n", " if __name__ == '__main__':\n" ] } ], "source": [ "xxx = pdm.loc[pdm[0] > 0].sort()" ] }, { "cell_type": "code", "execution_count": 231, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(8996772, 1)" ] }, "execution_count": 231, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xxx.shape" ] }, { "cell_type": "code", "execution_count": 232, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-220" ] }, "execution_count": 232, "metadata": {}, "output_type": "execute_result" } ], "source": [ "8996780 + 3000 - (3000 * 3000)" ] }, { "cell_type": "code", "execution_count": 190, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(9000000, 1)" ] }, "execution_count": 190, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdm.shape" ] }, { "cell_type": "code", "execution_count": 194, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dd = samples[22].drop_duplicates(subset=['newLon','newLat'])" ] }, { "cell_type": "code", "execution_count": 198, "metadata": { "collapsed": false }, "outputs": [], "source": [ "secvg2 = tools.Variogram(dd,'logBiomass',model=gvg.model)" ] }, { "cell_type": "code", "execution_count": 199, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Calculating Distance Matrix\n" ] } ], "source": [ "covMat = buildSpatialStructure(dd,gvg.model)" ] }, { "cell_type": "code", "execution_count": 200, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:HEC_runs.fit_fia_logbiomass_logspp_GLS:Fitting linear model using GLS\n" ] }, { "data": { "text/plain": [ "(<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7fccd8132510>,\n", " <class 'statsmodels.iolib.summary.Summary'>\n", " \"\"\"\n", " GLS Regression Results \n", " ==============================================================================\n", " Dep. Variable: logBiomass R-squared: 0.544\n", " Model: GLS Adj. R-squared: 0.543\n", " Method: Least Squares F-statistic: 3440.\n", " Date: Thu, 18 Jan 2018 Prob (F-statistic): 0.00\n", " Time: 19:57:44 Log-Likelihood: -2917.3\n", " No. Observations: 2891 AIC: 5839.\n", " Df Residuals: 2889 BIC: 5850.\n", " Df Model: 1 \n", " Covariance Type: nonrobust \n", " ==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", " ------------------------------------------------------------------------------\n", " Intercept 8.4682 0.032 263.667 0.000 8.405 8.531\n", " logSppN 0.3972 0.020 19.837 0.000 0.358 0.436\n", " ==============================================================================\n", " Omnibus: 83.032 Durbin-Watson: 1.996\n", " Prob(Omnibus): 0.000 Jarque-Bera (JB): 105.565\n", " Skew: -0.338 Prob(JB): 1.19e-23\n", " Kurtosis: 3.647 Cond. No. 5.07\n", " ==============================================================================\n", " \n", " Warnings:\n", " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", " \"\"\")" ] }, "execution_count": 200, "metadata": {}, "output_type": "execute_result" } ], "source": [ "calculateGLS(dd,covMat)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 197, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(3000, 46)" ] }, "execution_count": 197, "metadata": {}, "output_type": "execute_result" } ], "source": [ "samples[22].shape" ] }, { "cell_type": "code", "execution_count": 181, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "'numpy.ndarray' object is not callable", "output_type": "error", "traceback": [ "\u001b[0;31m\u001b[0m", "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)", "\u001b[0;32m<ipython-input-181-7653dae2ed47>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgvg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxxx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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[0;31mTypeError\u001b[0m: 'numpy.ndarray' object is not callable" ] } ], "source": [ "gvg.model.corr_f(xxx.values())" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.32981741, 0.32981741, 0.32981741, ..., 0.48093745,\n", " 0.4972302 , 0.51122074])" ] }, "execution_count": 166, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kk" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.99882585])" ] }, "execution_count": 139, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gvg.model.corr_f([100])" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.99991112])" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gvg.model.corr_f([10])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Django Shell-Plus", "language": "python", "name": "django_extensions" }, "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-2-clause
zhongyuanzhou/FCH808.github.io	Data Visualization/Project/wrangle/Scrape Nobel Prize Winners.ipynb	2	70409	{ "cells": [ { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import requests\n", "import json\n", "import prettytable\n", "import csv\n", "import codecs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from bs4 import BeautifulSoup\n", "import requests" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [], "source": [ "url = 'http://www.nobelprize.org/nobel_prizes/lists/universities.html'\n", "r = requests.get(url)\n", "soup = BeautifulSoup(r.text, from_encoding=r.encoding)\n", "place_acquired = soup.find_all(name=\"div\", attrs={\"class\": \"by_year\"})" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "ename": "UnicodeDecodeError", "evalue": "'ascii' codec can't decode byte 0xc3 in position 8957: ordinal not in range(128)", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-113-3e8953726478>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplace_acquired\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;31m#soup = unicode(soup)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m#soup = soup.encode('ascii', 'ignore')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\Anaconda\\lib\\site-packages\\IPython\\core\\displayhook.pyc\u001b[0m in 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"\u001b[1;32mc:\\Anaconda\\lib\\site-packages\\IPython\\kernel\\zmq\\displayhook.pyc\u001b[0m in \u001b[0;36mfinish_displayhook\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'content'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'data'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpub_socket\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[0mchunks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 210\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 212\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0miterencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_one_shot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'ascii' codec can't decode byte 0xc3 in position 8957: ordinal not in range(128)" ] } ], "source": [ "place_acquired\n", "#soup = unicode(soup)\n", "#soup = soup.encode('ascii', 'ignore')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def grab_field_and_number(string):\n", " '''\n", " >>>grab_field_and_number(\"The Nobel Prize in Physics 2000\")\n", " ('2000', 'Physics')\n", " \n", " >>>grab_field_and_number(\"The Prize in Economic Sciences 2010\")\n", " ('2010', 'Economic Sciences')\n", " \n", " >>>grab_field_and_number(\"The Nobel Prize in Physiology or Medicine 2000\")\n", " ('2000', 'Physiology or Medicine')\n", " \n", " >>>grab_field_and_number(\"The Nobel in Peace Prize 2010\")\n", " ('2010', 'Peace')\n", " '''\n", " \n", " if \"Economic\" in string:\n", " temp_string = string.split()\n", " year = temp_string.pop()\n", " field = temp_string[-2] + \" \" + temp_string[-1]\n", " elif \"Physiology or Medicine\" in string:\n", " temp_string = string.split()\n", " year = temp_string.pop()\n", " field = temp_string[-3] + \" \" + temp_string[-2] + \" \" + temp_string[-1]\n", " elif \"Peace\" in string:\n", " temp_string = string.split()\n", " year = temp_string.pop()\n", " field = temp_string[-2]\n", " else:\n", " temp_string = string.split()\n", " year = temp_string.pop()\n", " field = temp_string[-1]\n", " return year, field\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('2010', 'Peace')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grab_field_and_number(\"The Nobel in Peace Prize 2010\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#grab_field_and_number(\"The Nobel Prize in Physics 2000\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#grab_field_and_number(\"The Prize in Economic Sciences 2010\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#grab_field_and_number(\"The Nobel Prize in Physiology or Medicine 2000\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def grab_inst_country_citystate(string):\n", " '''\n", " >>>grab_inst_citystate_country(\"Edinburgh University, Edinburgh, United Kingdom\")\n", " ('Edinburgh University', ' United Kingdom', ' Edinburgh', '', '')\n", " \n", " >>>grab_inst_country_citystate(\"Fred Hutchinson Cancer Research Center, Seattle, WA, USA\")\n", " ('Fred Hutchinson Cancer Research Center', ' USA', ' WA', ' Seattle', '')\n", " \n", " >>>grab_inst_country_citystate(\"Columbia University Division, Cardio-Pulmonary Laboratory, Bellevue Hospital, New York, NY, USA\")\n", " ('Columbia University Division',\n", " ' USA',\n", " ' NY',\n", " ' New York',\n", " ' Cardio-Pulmonary Laboratory, Bellevue Hospital')\n", " '''\n", " pieces = string.split(\",\")\n", " institution = pieces[0]\n", " country = pieces[-1]\n", " city_state = pieces[1:-1]\n", " city, state, extra_loc = grab_city_state(city_state)\n", " return institution, country, city, state, extra_loc\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#grab_inst_citystate_country(\"Edinburgh University, Edinburgh, United Kingdom\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#grab_inst_country_citystate(\"Fred Hutchinson Cancer Research Center, Seattle, WA, USA\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#grab_inst_country_citystate(\"Columbia University Division, Cardio-Pulmonary Laboratory, Bellevue Hospital, New York, NY, USA\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def grab_city_state(a_list):\n", " '''\n", " >>>grab_city_state([\"Cardio-Pulmonary Laboratory\", \"Bellevue Hospital\", \"New York\", \"NY\"])\n", " ('NY', 'New York', 'Cardio-Pulmonary Laboratory, Bellevue Hospital')\n", " \n", " >>>grab_city_state([\"Bellevue Hospital\", \"New York\", \"NY\"])\n", " ('NY', 'New York', 'Bellevue Hospital')\n", " \n", " >>>grab_city_state(['New York', 'NY'])\n", " grab_city_state(['New York', 'NY'])\n", " \n", " >>>grab_city_state(['New York'])\n", " ('New York', '', '') \n", " '''\n", " city = a_list.pop()\n", " state = \"\" \n", " other = \"\"\n", " if len(a_list) >= 1:\n", " state = a_list.pop()\n", " other = \", \".join(a_list)\n", " return city, state, other" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#grab_city_state([\"Cardio-Pulmonary Laboratory\", \"Bellevue Hospital\", \"New York\", \"NY\"])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#grab_city_state([\"Bellevue Hospital\", \"New York\", \"NY\"])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#grab_city_state(['New York', 'NY'])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#grab_city_state(['New York'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def separate_old_country_names(country):\n", " old = \"\"\n", " new = \"\"\n", " if \" (now \" in country:\n", " old_and_new = country.split(' (now ')\n", " old = old_and_new[0]\n", " new = old_and_new[1][:-1]\n", " else:\n", " old = country\n", " new = country\n", " return old, new\n", " " ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def find_country_acq(bs4_html):\n", " all_names = [[\"name\", \"institution\",\n", " \"old_country_name_acquired\",\"current_country_name_acquired\",\n", " \"city\",\"state\",\"year\",\"field\"]]\n", " place_acq = \"\"\n", " for i in bs4_html:\n", " #pprint.pprint(i) \n", " #print \"\"80\n", " #print i\n", " if i.find_all('h3'):\n", " #print \"i.TEXT: \", i.text\n", " place_acq = i.h3.text\n", " if i.find_all('a'):\n", " #print \"\"\n", " #print \"i.a.TEXT: \", i.a.text\n", " #print \"i.h6.TEXT: \", i.h6.text\n", " #print \"PLACE_ACQ: \", place_acq\n", " #print \"field_year: \", field_year\n", " field_year = i.a.text\n", " name = i.h6.text\n", " year, field = grab_field_and_number(field_year)\n", " institution, country, city, state, extra_loc = grab_inst_country_citystate(place_acq)\n", " \n", " old_country_name, new_country_name = separate_old_country_names(country)\n", " \n", " all_names.append([name.encode('utf-8'),\n", " institution.encode('utf-8'),\n", " old_country_name.encode('utf-8'),\n", " new_country_name.encode('utf-8'),\n", " city.encode('utf-8'), \n", " state.encode('utf-8'),\n", " year.encode('utf-8'),\n", " field.encode('utf-8')])\n", " \n", " #print \"\"\n", " #print \"\"80\n", " return all_names" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "698" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(find_country_acq(place_acquired))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "url = 'http://www.nobelprize.org/nobel_prizes/lists/countries.html'\n", "r = requests.get(url)\n", "soup = BeautifulSoup(r.text)\n", "birth_html = soup.find_all(name=\"div\", attrs={\"class\": \"by_year\"})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def find_country_birth(bs4_html):\n", " all_names = [[\"name\",\"birth_country_old_name\",\n", " \"birth_country_current_name\",\n", " \"year\",\"field\"]]\n", " place_acq = \"\"\n", " for i in bs4_html:\n", " # Only place acquired entries have an 'h3' sub-class\n", " if i.find_all('h3'):\n", " place_acq = i.h3.text\n", " # Only field_year/name entries have an 'h6' sub-class.\n", " if i.find_all('h6'):\n", " field_year = i.a.text\n", " name = i.h6.text\n", " year, field = grab_field_and_number(field_year)\n", " old_country_name, new_country_name = separate_old_country_names(place_acq)\n", " \n", " all_names.append([name.encode('utf-8'), \n", " old_country_name.encode('utf-8'),\n", " new_country_name.encode('utf-8'),\n", " year.encode('utf-8'),\n", " field.encode('utf-8')])\n", " \n", " return all_names" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "865" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(find_country_birth(birth_html))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "url = 'http://www.nobelprize.org/nobel_prizes/lists/age.html'\n", "r = requests.get(url)\n", "soup = BeautifulSoup(r.text)\n", "age_html = soup.find_all(name=\"div\", attrs={\"class\": \"large-12 columns\"})" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def find_age(bs4_html):\n", " all_names = [[\"name\", \"age\"]]\n", " place_acq = \"\"\n", " for i in age_html[6].find_all(['h3', 'h6']):\n", " \n", " if \"Age\" in i.string:\n", " age = i.string.split()[-1]\n", " if \"Age\" not in i.string:\n", " name = i.string\n", " all_names.append([name.encode('utf-8'), age.encode('utf-8')])\n", " return all_names" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "865" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(find_age(age_html))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nobel_ages = find_age(age_html)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open('nobel_ages.csv', 'wb') as f:\n", " writer = csv.writer(f)\n", " writer.writerows(nobel_ages)" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [], "source": [ "country_acquired = find_country_acq(place_acquired)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#country_acquired" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>institution</th>\n", " <th>old_country_name_acquired</th>\n", " <th>current_country_name_acquired</th>\n", " <th>city</th>\n", " <th>state</th>\n", " <th>year</th>\n", " <th>field</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Zhores I. Alferov</td>\n", " <td> A.F. Ioffe Physico-Technical Institute</td>\n", " <td> Russia</td>\n", " <td> Russia</td>\n", " <td> St. Petersburg</td>\n", " <td> </td>\n", " <td> 2000</td>\n", " <td> Physics</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Jens C. Skou</td>\n", " <td> Aarhus University</td>\n", " <td> Denmark</td>\n", " <td> Denmark</td>\n", " <td> Aarhus</td>\n", " <td> </td>\n", " <td> 1997</td>\n", " <td> Chemistry</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Dale T. Mortensen</td>\n", " <td> Aarhus University</td>\n", " <td> Denmark</td>\n", " <td> Denmark</td>\n", " <td> Aarhus</td>\n", " <td> </td>\n", " <td> 2010</td>\n", " <td> Economic Sciences</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Lev Landau</td>\n", " <td> Academy of Sciences</td>\n", " <td> USSR</td>\n", " <td> USSR</td>\n", " <td> Moscow</td>\n", " <td> </td>\n", " <td> 1962</td>\n", " <td> Physics</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Pyotr Kapitsa</td>\n", " <td> Academy of Sciences</td>\n", " <td> USSR</td>\n", " <td> USSR</td>\n", " <td> Moscow</td>\n", " <td> </td>\n", " <td> 1978</td>\n", " <td> Physics</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name institution \\\n", "0 Zhores I. Alferov A.F. Ioffe Physico-Technical Institute \n", "1 Jens C. Skou Aarhus University \n", "2 Dale T. Mortensen Aarhus University \n", "3 Lev Landau Academy of Sciences \n", "4 Pyotr Kapitsa Academy of Sciences \n", "\n", " old_country_name_acquired current_country_name_acquired city \\\n", "0 Russia Russia St. Petersburg \n", "1 Denmark Denmark Aarhus \n", "2 Denmark Denmark Aarhus \n", "3 USSR USSR Moscow \n", "4 USSR USSR Moscow \n", "\n", " state year field \n", "0 2000 Physics \n", "1 1997 Chemistry \n", "2 2010 Economic Sciences \n", "3 1962 Physics \n", "4 1978 Physics " ] }, "execution_count": 120, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "headers = country_acquired.pop(0)\n", "df = pd.DataFrame(country_acquired, columns=headers)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "#country_birth" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "ename": "UnicodeDecodeError", "evalue": "'ascii' codec can't decode byte 0xc3 in position 171: ordinal not in range(128)", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-44-85da9c4b1a68>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mheaders\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcountry_birth\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcountry_birth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32mc:\\Anaconda\\lib\\site-packages\\IPython\\core\\displayhook.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, result)\u001b[0m\n\u001b[0;32m 236\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_format_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformat_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmd_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 237\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformat_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 238\u001b[1;33m 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message\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\Anaconda\\lib\\site-packages\\IPython\\kernel\\zmq\\session.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(obj)\u001b[0m\n\u001b[0;32m 83\u001b[0m \u001b[1;31m# disallow nan, because it's not actually valid JSON\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 84\u001b[0m json_packer = lambda obj: jsonapi.dumps(obj, default=date_default,\n\u001b[1;32m---> 85\u001b[1;33m \u001b[0mensure_ascii\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_nan\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 86\u001b[0m )\n\u001b[0;32m 87\u001b[0m \u001b[0mjson_unpacker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m:\u001b[0m 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\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 210\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 212\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0miterencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_one_shot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'ascii' codec can't decode byte 0xc3 in position 171: ordinal not in range(128)" ] } ], "source": [ "import pandas as pd\n", "\n", "country_birth = find_country_birth(birth_html)\n", "headers = country_birth.pop(0)\n", "df = pd.DataFrame(country_birth, columns=headers)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'DataFrame' object has no attribute 'birth_country_new_name'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-48-a7a78aa7a2c0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcountries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbirth_country_new_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32mc:\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 1841\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\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 1842\u001b[0m raise AttributeError(\"'%s' object has no attribute '%s'\" %\n\u001b[1;32m-> 1843\u001b[1;33m (type(self).__name__, name))\n\u001b[0m\u001b[0;32m 1844\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1845\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'birth_country_new_name'" ] } ], "source": [ "countries = list(set(df.birth_country_new_name))" ] }, { "cell_type": "code", "execution_count": 238, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#google_api_key = \"AIzaSyDAJxRxTE-ZC5M7qGN5Bg_FXwgc5e_TqdU\"" ] }, { "cell_type": "code", "execution_count": 239, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def lookup_lat_lon(city=\"\", state=\"\", country=\"\", key=\"\"):\n", " return \"https://maps.googleapis.com/maps/api/geocode/json?\"+\"address=\"+country+\"&key=\"+key" ] }, { "cell_type": "code", "execution_count": 288, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"https://maps.googleapis.com/maps/api/geocode/json?address=People's Republic of China&key=AIzaSyDAJxRxTE-ZC5M7qGN5Bg_FXwgc5e_TqdU\"" ] }, "execution_count": 288, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lookup_lat_lon(country=countries[38], key=google_api_key)" ] }, { "cell_type": "code", "execution_count": 289, "metadata": { "collapsed": false }, "outputs": [], "source": [ "url2 = lookup_lat_lon(country=countries[38], key=google_api_key)" ] }, { "cell_type": "code", "execution_count": 290, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r2 = requests.get(url2)" ] }, { "cell_type": "code", "execution_count": 291, "metadata": { "collapsed": false }, "outputs": [], "source": [ "country_json = r2.json()" ] }, { "cell_type": "code", "execution_count": 292, "metadata": { "collapsed": false }, "outputs": [], "source": [ "birth_lat = country_json['results'][0]['geometry']['location']['lat']\n", "birth_lon = country_json['results'][0]['geometry']['location']['lng']\n", "birth_country_long_name = country_json['results'][0]['address_components'][0]['long_name']\n", "birth_country_short_name = country_json['results'][0]['address_components'][0]['short_name']" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'birth_lat' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-45-0d2c4c7e0c91>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mbirth_lat\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mbirth_lon\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m#birth_country_long_name\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'birth_lat' is not defined" ] } ], "source": [ "print birth_lat\n", "print birth_lon\n", "#birth_country_long_name" ] }, { "cell_type": "code", "execution_count": 295, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#country_json" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_long_lat(country_list, birth_countries=True):\n", " \n", " output = [['birth_lat', \n", " 'birth_lon', \n", " 'birth_country_current_name',\n", " 'birth_country_short_name']]\n", " if birth_countries == False:\n", " output = [['acquired_lat', \n", " 'acquired_lon', \n", " 'current_country_name_acquired',\n", " 'acquired_country_short_name']]\n", " # https://console.developers.google.com\n", " # https://developers.google.com/maps/documentation/geocoding/?csw=1\n", " google_api_key = \"AIzaSyDAJxRxTE-ZC5M7qGN5Bg_FXwgc5e_TqdU\"\n", " \n", " for each_country in country_list:\n", " url = lookup_lat_lon(country=each_country, key=google_api_key)\n", " r = requests.get(url)\n", " country_json = r.json()\n", " lat = country_json['results'][0]['geometry']['location']['lat']\n", " lon = country_json['results'][0]['geometry']['location']['lng']\n", " #country_long_name = country_json['results'][0]['address_components'][0]['long_name']\n", " country_long_name = each_country\n", " country_short_name = country_json['results'][0]['address_components'][0]['short_name']\n", " \n", " output.append([lat,\n", " lon,\n", " country_long_name,\n", " country_short_name])\n", " return output" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'countries' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-47-a641c1a16df4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Get the lat/lon from the Google API!\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mlat_lon_birth_countries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_long_lat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcountries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbirth_countries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'countries' is not defined" ] } ], "source": [ "# Get the lat/lon from the Google API!\n", "lat_lon_birth_countries = get_long_lat(countries, birth_countries=True)" ] }, { "cell_type": "code", "execution_count": 308, "metadata": { "collapsed": false }, "outputs": [], "source": [ "headers = lat_lon_birth_countries.pop(0)\n", "birth_countries_df = pd.DataFrame(lat_lon_birth_countries, columns=headers)" ] }, { "cell_type": "code", "execution_count": 313, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>birth_lat</th>\n", " <th>birth_lon</th>\n", " <th>birth_country_current_name</th>\n", " <th>birth_country_short_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-14.235004</td>\n", " <td> -51.925280</td>\n", " <td> Brazil</td>\n", " <td> BR</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 56.130366</td>\n", " <td>-106.346771</td>\n", " <td> Canada</td>\n", " <td> CA</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-18.766947</td>\n", " <td> 46.869107</td>\n", " <td> Madagascar</td>\n", " <td> MG</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 41.608635</td>\n", " <td> 21.745275</td>\n", " <td> Republic of Macedonia</td>\n", " <td> MK</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 41.871940</td>\n", " <td> 12.567380</td>\n", " <td> Italy</td>\n", " <td> IT</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " birth_lat birth_lon birth_country_current_name birth_country_short_name\n", "0 -14.235004 -51.925280 Brazil BR\n", "1 56.130366 -106.346771 Canada CA\n", "2 -18.766947 46.869107 Madagascar MG\n", "3 41.608635 21.745275 Republic of Macedonia MK\n", "4 41.871940 12.567380 Italy IT" ] }, "execution_count": 313, "metadata": {}, "output_type": "execute_result" } ], "source": [ "birth_countries_df.head()" ] }, { "cell_type": "code", "execution_count": 397, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>birth_country_old_name</th>\n", " <th>birth_country_current_name</th>\n", " <th>year</th>\n", " <th>field</th>\n", " <th>birth_lat</th>\n", " <th>birth_lon</th>\n", " <th>birth_country_short_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>859</th>\n", " <td> Robert J. Shiller</td>\n", " <td> USA</td>\n", " <td> USA</td>\n", " <td> 2013</td>\n", " <td> Economic Sciences</td>\n", " <td> 37.090240</td>\n", " <td> -95.712891</td>\n", " <td> US</td>\n", " </tr>\n", " <tr>\n", " <th>860</th>\n", " <td> Lars Peter Hansen</td>\n", " <td> USA</td>\n", " <td> USA</td>\n", " <td> 2013</td>\n", " <td> Economic Sciences</td>\n", " <td> 37.090240</td>\n", " <td> -95.712891</td>\n", " <td> US</td>\n", " </tr>\n", " <tr>\n", " <th>861</th>\n", " <td> Baruj Benacerraf</td>\n", " <td> Venezuela</td>\n", " <td> Venezuela</td>\n", " <td> 1980</td>\n", " <td> Physiology or Medicine</td>\n", " <td> 6.423750</td>\n", " <td> -66.589730</td>\n", " <td> VE</td>\n", " </tr>\n", " <tr>\n", " <th>862</th>\n", " <td> Le Duc Tho </td>\n", " <td> Vietnam</td>\n", " <td> Vietnam</td>\n", " <td> 1973</td>\n", " <td> Peace</td>\n", " <td> 14.058324</td>\n", " <td> 108.277199</td>\n", " <td> VN</td>\n", " </tr>\n", " <tr>\n", " <th>863</th>\n", " <td> Tawakkol Karman</td>\n", " <td> Yemen</td>\n", " <td> Yemen</td>\n", " <td> 2011</td>\n", " <td> Peace</td>\n", " <td> 15.552727</td>\n", " <td> 48.516388</td>\n", " <td> YE</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name birth_country_old_name birth_country_current_name \\\n", "859 Robert J. Shiller USA USA \n", "860 Lars Peter Hansen USA USA \n", "861 Baruj Benacerraf Venezuela Venezuela \n", "862 Le Duc Tho Vietnam Vietnam \n", "863 Tawakkol Karman Yemen Yemen \n", "\n", " year field birth_lat birth_lon \\\n", "859 2013 Economic Sciences 37.090240 -95.712891 \n", "860 2013 Economic Sciences 37.090240 -95.712891 \n", "861 1980 Physiology or Medicine 6.423750 -66.589730 \n", "862 1973 Peace 14.058324 108.277199 \n", "863 2011 Peace 15.552727 48.516388 \n", "\n", " birth_country_short_name \n", "859 US \n", "860 US \n", "861 VE \n", "862 VN \n", "863 YE " ] }, "execution_count": 397, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.merge(df, birth_countries_df)\n", "df.tail()" ] }, { "cell_type": "code", "execution_count": 398, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# df.to_csv('data/temp.csv')" ] }, { "cell_type": "code", "execution_count": 319, "metadata": { "collapsed": true }, "outputs": [], "source": [ "headers = nobel_ages.pop(0)\n", "nobel_ages_df = pd.DataFrame(nobel_ages, columns=headers)" ] }, { "cell_type": "code", "execution_count": 384, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#pd.merge(df, nobel_ages_df).tail(20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since 4 people won Nobel Prizes twice (!) at different ages, these dataframes can't just be merged on the 'name' column. Instead, we can sort/reorder each dataframe by the names and year/age, resetting the index to get them aligned.\n", "\n", "Now, we can merge(or join()) them in pandas on the indices of each dataframe. Now, we can see Marie Curie was age 36 when recieving the nobel when recieving the Nobel Prize in 1903 in Physics, then 44 in 1911 when recieving the Nobel Prize in Chemistry." ] }, { "cell_type": "code", "execution_count": 399, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>birth_country_old_name</th>\n", " <th>birth_country_current_name</th>\n", " <th>year</th>\n", " <th>field</th>\n", " <th>birth_lat</th>\n", " <th>birth_lon</th>\n", " <th>birth_country_short_name</th>\n", " <th>age</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>521</th>\n", " <td> Marie Curie</td>\n", " <td> Russian Empire</td>\n", " <td> Poland</td>\n", " <td> 1903</td>\n", " <td> Physics</td>\n", " <td> 51.919438</td>\n", " <td> 19.145136</td>\n", " <td> PL</td>\n", " <td> 36</td>\n", " </tr>\n", " <tr>\n", " <th>522</th>\n", " <td> Marie Curie</td>\n", " <td> Russian Empire</td>\n", " <td> Poland</td>\n", " <td> 1911</td>\n", " <td> Chemistry</td>\n", " <td> 51.919438</td>\n", " <td> 19.145136</td>\n", " <td> PL</td>\n", " <td> 44</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name birth_country_old_name birth_country_current_name year \\\n", "521 Marie Curie Russian Empire Poland 1903 \n", "522 Marie Curie Russian Empire Poland 1911 \n", "\n", " field birth_lat birth_lon birth_country_short_name age \n", "521 Physics 51.919438 19.145136 PL 36 \n", "522 Chemistry 51.919438 19.145136 PL 44 " ] }, "execution_count": 399, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted1 = df.sort(columns=['name', 'year']).reset_index(drop=True)\n", "sorted2 = nobel_ages_df.sort(columns=['name', 'age']).reset_index(drop=True)\n", "merged = pd.merge(sorted1, sorted2, left_index=True, right_index=True, how='outer', on='name')\n", "merged[merged.name==\"Marie Curie\"]" ] }, { "cell_type": "code", "execution_count": 401, "metadata": { "collapsed": false }, "outputs": [], "source": [ "merged.to_csv('data/temp.csv', encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": 402, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>birth_country_old_name</th>\n", " <th>birth_country_current_name</th>\n", " <th>year</th>\n", " <th>field</th>\n", " <th>birth_lat</th>\n", " <th>birth_lon</th>\n", " <th>birth_country_short_name</th>\n", " <th>age</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> A. Michael Spence</td>\n", " <td> USA</td>\n", " <td> USA</td>\n", " <td> 2001</td>\n", " <td> Economic Sciences</td>\n", " <td> 37.090240</td>\n", " <td>-95.712891</td>\n", " <td> US</td>\n", " <td> 58</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Aage N. Bohr</td>\n", " <td> Denmark</td>\n", " <td> Denmark</td>\n", " <td> 1975</td>\n", " <td> Physics</td>\n", " <td> 56.263920</td>\n", " <td> 9.501785</td>\n", " <td> DK</td>\n", " <td> 53</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Aaron Ciechanover</td>\n", " <td> British Protectorate of Palestine</td>\n", " <td> Israel</td>\n", " <td> 2004</td>\n", " <td> Chemistry</td>\n", " <td> 31.046051</td>\n", " <td> 34.851612</td>\n", " <td> IL</td>\n", " <td> 57</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Aaron Klug</td>\n", " <td> Lithuania</td>\n", " <td> Lithuania</td>\n", " <td> 1982</td>\n", " <td> Chemistry</td>\n", " <td> 55.169438</td>\n", " <td> 23.881275</td>\n", " <td> LT</td>\n", " <td> 56</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Abdus Salam</td>\n", " <td> India</td>\n", " <td> Pakistan</td>\n", " <td> 1979</td>\n", " <td> Physics</td>\n", " <td> 30.375321</td>\n", " <td> 69.345116</td>\n", " <td> PK</td>\n", " <td> 53</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name birth_country_old_name \\\n", "0 A. Michael Spence USA \n", "1 Aage N. Bohr Denmark \n", "2 Aaron Ciechanover British Protectorate of Palestine \n", "3 Aaron Klug Lithuania \n", "4 Abdus Salam India \n", "\n", " birth_country_current_name year field birth_lat birth_lon \\\n", "0 USA 2001 Economic Sciences 37.090240 -95.712891 \n", "1 Denmark 1975 Physics 56.263920 9.501785 \n", "2 Israel 2004 Chemistry 31.046051 34.851612 \n", "3 Lithuania 1982 Chemistry 55.169438 23.881275 \n", "4 Pakistan 1979 Physics 30.375321 69.345116 \n", "\n", " birth_country_short_name age \n", "0 US 58 \n", "1 DK 53 \n", "2 IL 57 \n", "3 LT 56 \n", "4 PK 53 " ] }, "execution_count": 402, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged.head()" ] }, { "cell_type": "code", "execution_count": 403, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[[0, -1, -2, -3], [1, 0, -1, -2], [2, 1, 0, -1]]" ] }, "execution_count": 403, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 404, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[[0, 1, 2], [-1, 0, 1], [-2, -1, 0], [-3, -2, -1]]" ] }, "execution_count": 404, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 405, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Zen of Python, by Tim Peters\n", "\n", "Beautiful is better than ugly.\n", "Explicit is better than implicit.\n", "Simple is better than complex.\n", "Complex is better than complicated.\n", "Flat is better than nested.\n", "Sparse is better than dense.\n", "Readability counts.\n", "Special cases aren't special enough to break the rules.\n", "Although practicality beats purity.\n", "Errors should never pass silently.\n", "Unless explicitly silenced.\n", "In the face of ambiguity, refuse the temptation to guess.\n", "There should be one-- and preferably only one --obvious way to do it.\n", "Although that way may not be obvious at first unless you're Dutch.\n", "Now is better than never.\n", "Although never is often better than right now.\n", "If the implementation is hard to explain, it's a bad idea.\n", "If the implementation is easy to explain, it may be a good idea.\n", "Namespaces are one honking great idea -- let's do more of those!\n" ] } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 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
Lab41/pelops	pelops/analysis/makeFeatureFiles-TEST.ipynb	3	6350	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pelops.datasets.veri import VeriDataset\n", "from pelops.analysis.unsorted.recompute.extract_feats_from_chips import extract_feats_from_chips\n", "import pelops.utils as utils\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-body_type.model.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-body_type.weights.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',\n", " set_type=utils.SetType.TEST.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/image_body_type_TEST',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-color.model.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-color.weights.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',\n", " set_type=utils.SetType.TEST.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/image_color_type_TEST',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-color_body_type.model2.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-color_body_type.weights2.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',\n", " set_type=utils.SetType.TEST.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/image_color_body_type_TEST',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-make_model.model.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-make_model.weights.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',\n", " set_type=utils.SetType.TEST.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/image_make_model_type_TEST',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet.model.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet.weights.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',set_type=utils.SetType.TEST.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/resnet50_TEST',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/compcars_resenet-make_model.model.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/compcars_resenet-make_model.weights.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',set_type=utils.SetType.TEST.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/compcars_make_model_TEST',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/compcars_resenet-colors.model.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/compcars_resenet-colors.weights.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',set_type=utils.SetType.TEST.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/compcars_color_TEST',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
celiasmith/syde556	SPA.ipynb	3	20425	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Symbols and Symbol-like representation in neurons\n", "\n", "- We've seen how to represent vectors in neurons\n", " - And how to compute functions on those vectors\n", " - And dynamical systems\n", "- But how can we do anything like human language?\n", " - How could we represent the fact that \"the number after 8 is 9\"\n", " - Or \"dogs chase cats\"\n", " - Or \"Anne knows that Bill thinks that Charlie likes Dave\"\n", "- Does the NEF help us at all with this?\n", " - Or is this just too hard a problem yet?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Traditional Cognitive Science\n", "\n", "- Lots of theories that work with structured information like this\n", "- Pretty much all of them use some representation framework like this:\n", " - `after(eight, nine)`\n", " - `chase(dogs, cats)`\n", " - `knows(Anne, thinks(Bill, likes(Charlie, Dave)))`\n", "- Or perhaps\n", " - `[number:eight next:nine]`\n", " - `[subject:dogs action:chase object:cats]`\n", " - `[subject:Anne action:knows object:[subject:Bill action:thinks object:[subject:Charlie action:likes object:Dave]]]`\n", "- Cognitive models manipulate these sorts of representations\n", " - mental arithmetic\n", " - driving a car\n", " - using a GUI\n", " - parsing language\n", " - etc etc\n", "- Seems to match well to behavioural data, so something like this should be right\n", "- So how can we do this in neurons?\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Possible solutions\n", "\n", "- Oscilations\n", " - \"red square and blue circle\"\n", " - Different patterns of activity for RED, SQUARE, BLUE, and CIRCLE\n", " - Have the patterns for RED and SQUARE happen, then BLUE and CIRCLE, then back to RED and SQUARE\n", " - More complex structures possible too:\n", " - E.g. the LISA architecture\n", " \n", "<img src=\"files/lecture8/lisa.png\">\n", "\n", "- Problems\n", " - What controls this oscillation?\n", " - How is it controlled?\n", " - How do we deal with the exponentional explosion of nodes needed?\n", " \n", "- Implementing Symbol Systems in Neurons\n", " - Build a general-purpose symbol-binding system\n", " - Lots of temporary pools of neurons\n", " - Ways to temporarily associate them with particular concepts\n", " - Ways to temporarily associate pools together\n", " - Neural Blackboard Architecture\n", " \n", "<img src=\"files/lecture8/nba.png\">\n", "\n", "- Problems\n", " - Very particular structure (doesn't seem to match biology)\n", " - Uses a very large number of neurons (~500 million) to be flexible enough for simple sentences\n", " - And that's just to represent the sentence, never mind controlling and manipulating it\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vector Symbolic Architectures\n", "\n", "- There is an alternate approach\n", "- Something that's similar to the symbolic approach, but much more tied to biology\n", " - Most of the same capabilities as the classic symbol systems\n", " - But not all\n", "- Based on vectors and functions on those vectors\n", " - There is a vector for each concept\n", " - Build up structures by doing math on those vectors\n", " \n", "- Example\n", " - blue square and red circle\n", " - can't just do BLUE+SQUARE+RED+CIRCLE\n", " - need some other operation as well\n", " - requirements\n", " - input 2 vectors, get a new vector as output\n", " - reversible (given the output and one of the input vectors, generate the other input vector)\n", " - output vector is highly dissimilar to either input vector\n", " - unlike addition, where the output is highly similar\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Lots of different options\n", " - for binary vectors, XOR works pretty good\n", " - for continuous vectors we use circular convolution\n", "- Why?\n", " - Extensively studied (Plate, 1997: Holographic Reduced Representations)\n", " - Easy to approximately invert (circular correlation)\n", "- `BLUE` $\\circledast$ `SQUARE + RED` $\\circledast$ `CIRCLE`\n", "\n", "<img src=\"files/lecture8/bind.png\">\n", "\n", "<img src=\"files/lecture8/unbind.png\">\n", "\n", "- Lots of nice properties\n", " - Can store complex structures\n", " - `[number:eight next:nine]`\n", " - `NUMBER` $\\circledast$ `EIGHT + NEXT` $\\circledast$ `NINE`\n", " - `[subject:Anne action:knows object:[subject:Bill action:thinks object:[subject:Charlie action:likes object:Dave]]]` \n", " - `SUBJ` $\\circledast$ `ANNE + ACT` $\\circledast$ `KNOWS + OBJ` $\\circledast$ `(SUBJ` $\\circledast$ `BILL + ACT` $\\circledast$ `THINKS + OBJ` $\\circledast$ `(SUBJ` $\\circledast$ `CHARLIE + ACT` $\\circledast$ `LIKES + OBJ` $\\circledast$ `DAVE))`\n", " - But gracefully degrades!\n", " - as representation gets more complex, the accuracy of breaking it apart decreases\n", " - Keeps similarity information\n", " - if `RED` is similar to `PINK` then `RED` $\\circledast$ `CIRCLE` is similar to `PINK` $\\circledast$ `CIRCLE`\n", " \n", "- But rather complicated\n", " - Seems like a weird operation for neurons to do" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Circular convolution in the NEF\n", "\n", "- Or is it?\n", "- Circular convolution is a whole bunch ($D^2$) of multiplies\n", "- But it can also be written as a fourier transform, an elementwise multiply, and another fourier transform\n", "- The discrete fourier transform is just a linear operation\n", "- So that's just $D$ pairwise multiplies\n", "- In fact, circular convolution turns out to be exactly what the NEF shows neurons are good at" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Building a memory in Nengo\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import nengo\n", "import nengo.spa as spa\n", "\n", "D=64\n", "\n", "model = spa.SPA(label='Binding')\n", "with model:\n", " model.a = spa.Buffer(D)\n", " model.b = spa.Buffer(D)\n", " model.c = spa.Buffer(D)\n", " model.q = spa.Buffer(D)\n", " model.r = spa.Buffer(D)\n", " model.cortical = spa.Cortical(spa.Actions(\n", " 'c = ab',\n", " 'c = c',\n", " 'r = c~q'), synapse=0.1)\n", "\n", " \n", " nengo.Probe(model.a.state.output)\n", " nengo.Probe(model.b.state.output)\n", " nengo.Probe(model.c.state.output)\n", " nengo.Probe(model.q.state.output)\n", " nengo.Probe(model.r.state.output)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "- How does this work so well?\n", " - Exploiting the features of high-dimensional space\n", "\n", "<img src=\"files/spa/vector_packing.png\">\n", "\n", "- Memory capacity increases with dimensionality\n", " - Also dependent on the number of different possible items in memory (vocabulary size)\n", "- 512 dimensions is suffienct to store ~8 pairs, with a vocabulary size of 100,000 terms\n", " - Note that this is what's needed for storing simple sentences" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Symbol-like manipulation\n", "\n", "- Can do a lot of standard symbol stuff\n", "- Have to explicitly bind and unbind to manipulate the data\n", "- Less accuracy for more complex structures\n", "- But we can also do more with these representations\n", "\n", "### Raven's Progressive Matrices\n", "\n", "- An IQ test that's generally considered to be the best at measuring general-purpose \"fluid\" intelligence\n", " - nonverbal (so it's not measuring language skills, and fairly unbiased across cultures, hopefully)\n", " - fill in the blank\n", " - given eight possible answers; pick one\n", " \n", "<img src=\"files/lecture8/ravens.png\">\n", "\n", "\n", "- This is not an actual question on the test\n", " - The test is copyrighted\n", " - They don't want the test to leak out, since it's been the same set of 60 questions since 1936\n", " - But they do look like that\n", "\n", "- How can we model people doing this task?\n", "- A fair number of different attempts\n", " - None neural\n", " - Generally use the approach of building in a large set of different types of patterns to look for, and then trying them all in turn\n", " - Which seems wrong for a test that's supposed to be about flexible, fluid intelligence\n", " \n", "- Does this vector approach offer an alternative?\n", "\n", "- First we need to represent the different patterns as a vector\n", " - This is a hard image interpretation problem\n", " - Still ongoing work here\n", " - So we'll skip it and start with things in vector form\n", " \n", "<img src=\"files/lecture8/ravens2.png\"> \n", " \n", "- How do we represent a picture?\n", " - `SHAPE` $\\circledast$ `ARROW + NUMBER` $\\circledast$ `ONE + DIRECTION` $\\circledast$ `UP`\n", " - can do variations like this for all the pictures\n", " - fairly standard with most assumptions about how people represent complex scenes\n", " - but that part is not being modelled (yet!)\n", " \n", "- We have shown that it's possible to build these sorts of representations up directly from visual stimuli\n", " - With a very simple vision system that can only recognize a few different shapes\n", " - And where items have to be shown sequentially as it has no way of moving its eyes\n", " \n", "<iframe width=\"640\" height=\"390\" src=\"//www.youtube.com/embed/U_Q6Xjz9QHg\" frameborder=\"0\" allowfullscreen></iframe>\n", "\n", "\n", "- The memory of the list is built up by using a basal ganglia action selection system to control feeding values into an integrator\n", " - The thought bubble shows how close the decoded values are to the ideal\n", " - Notice the forgetting! \n", " \n", "- The same system can be used to do a version of the Raven's Matrices task\n", "\n", "<iframe width=\"640\" height=\"390\" src=\"//www.youtube.com/embed/Q_LRvnwnYp8\" frameborder=\"0\" allowfullscreen></iframe>\n", "\n", "- `S1 = ONE` $\\circledast$ `P1`\n", "- `S2 = ONE` $\\circledast$ `P1 + ONE` $\\circledast$ `P2`\n", "- `S3 = ONE` $\\circledast$ `P1 + ONE` $\\circledast$ `P2 + ONE` $\\circledast$ `P3`\n", "- `S4 = FOUR` $\\circledast$ `P1`\n", "- `S5 = FOUR` $\\circledast$ `P1 + FOUR` $\\circledast$ `P2`\n", "- `S6 = FOUR` $\\circledast$ `P1 + FOUR` $\\circledast$ `P2 + FOUR` $\\circledast$ `P3`\n", "- `S7 = FIVE` $\\circledast$ `P1`\n", "- `S8 = FIVE` $\\circledast$ `P1 + FIVE` $\\circledast$ `P2`\n", "\n", "- what is `S9`?\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Let's figure out what the transformation is\n", "- `T1 = S2` $\\circledast$ `S1'`\n", "- `T2 = S3` $\\circledast$ `S2'`\n", "- `T3 = S5` $\\circledast$ `S4'`\n", "- `T4 = S6` $\\circledast$ `S5'`\n", "- `T5 = S8` $\\circledast$ `S7'`\n", "\n", "- `T = (T1 + T2 + T3 + T4 + T5)/5`\n", "- `S9 = S8` $\\circledast$ `T`\n", "\n", "- `S9 = FIVE` $\\circledast$ `P1 + FIVE` $\\circledast$ `P2 + FIVE` $\\circledast$ `P3`\n", "\n", "- This becomes a novel way of manipulating structured information\n", " - Exploiting the fact that it is a vector underneath\n", " - [A spiking neural model applied to the study of human performance and cognitive decline on Raven's Advanced Progressive Matrices](http://www.sciencedirect.com/science/article/pii/S0160289613001542)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Things to note\n", " - Memory slowly decays\n", " - If you push in a new pair for too long, it can wipe out the old pair(s)\n", " - Note that this relies on the saturation behaviour of NEF networks\n", " - Kind of like implicit normalization\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cognitive Control\n", "\n", "- How do we control these systems?\n", " - Lots of components\n", " - Each component computes some particular function\n", " - Need to selectively route information between components\n", "- Standard cortex-basal ganglia-thalamus loop\n", "\n", "<img src=\"files/spa/spa_loop.png\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- compute functions of cortical state to determine utility of actions\n", "- basal ganglia select the action with the highest utility\n", " - using [Gurney, Prescott, and Redgrave, 2001](http://neuroinformatics.usc.edu/mediawiki/images/3/37/Gurney_etal_01_A_computational_model_of_action_selection_in_the_basal_ganglia_-_II.pdf) model, converted to spiking neurons\n", "- thalamus has routing connections between cortical areas\n", " - if action is not selected, routing neurons are inhibited\n", "\n", "- good timing data\n", "\n", "<img src=\"files/lecture7/gpr-latency.png\">\n", "\n", "- [Dynamic Behaviour of a Spiking Model of Action Selection in the Basal Ganglia](http://compneuro.uwaterloo.ca/files/publications/stewart.2010.pdf)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example: Simple Association" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import nengo\n", "import nengo.spa as spa\n", "\n", "model = spa.SPA(label=\"SPA1\")\n", "with model:\n", " model.state = spa.Buffer(16)\n", " model.motor = spa.Buffer(16)\n", " actions = spa.Actions(\n", " 'dot(state, DOG) --> motor=BARK',\n", " 'dot(state, CAT) --> motor=MEOW',\n", " 'dot(state, RAT) --> motor=SQUEAK',\n", " 'dot(state, COW) --> motor=MOO',\n", " ) \n", " model.bg = spa.BasalGanglia(actions)\n", " model.thalamus = spa.Thalamus(model.bg)\n", " \n", " nengo.Probe(model.state.state.output)\n", " nengo.Probe(model.motor.state.output)\n", " nengo.Probe(model.bg.input)\n", " nengo.Probe(model.thalamus.actions.output) " ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example: Sequence" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import nengo\n", "import nengo.spa as spa\n", "\n", "model = spa.SPA(label=\"SPA2\")\n", "with model:\n", " model.state = spa.Buffer(16)\n", " actions = spa.Actions(\n", " 'dot(state, A) --> state=B',\n", " 'dot(state, B) --> state=C',\n", " 'dot(state, C) --> state=D',\n", " 'dot(state, D) --> state=E',\n", " 'dot(state, E) --> state=A',\n", " ) \n", " model.bg = spa.BasalGanglia(actions)\n", " model.thalamus = spa.Thalamus(model.bg)\n", " \n", " nengo.Probe(model.state.state.output)\n", " nengo.Probe(model.bg.input)\n", " nengo.Probe(model.thalamus.actions.output) " ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example: Input" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import nengo\n", "import nengo.spa as spa\n", "\n", "model = spa.SPA(label=\"SPA1\")\n", "with model:\n", " model.state = spa.Buffer(16)\n", " actions = spa.Actions(\n", " 'dot(state, A) --> state=B',\n", " 'dot(state, B) --> state=C',\n", " 'dot(state, C) --> state=D',\n", " 'dot(state, D) --> state=E',\n", " 'dot(state, E) --> state=A',\n", " ) \n", " model.bg = spa.BasalGanglia(actions)\n", " model.thalamus = spa.Thalamus(model.bg)\n", " \n", " def state_in(t):\n", " if t<0.1:\n", " return 'C'\n", " else:\n", " return '0'\n", " model.input = spa.Input(state=state_in)\n", " \n", " nengo.Probe(model.state.state.output)\n", " nengo.Probe(model.bg.input)\n", " nengo.Probe(model.thalamus.actions.output)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example: Routing" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import nengo\n", "import nengo.spa as spa\n", "\n", "model = spa.SPA(label=\"SPA1\")\n", "with model:\n", " model.vision = spa.Buffer(16)\n", " model.state = spa.Buffer(16)\n", " actions = spa.Actions(\n", " 'dot(vision, A+B+C+D+E) --> state=vision',\n", " 'dot(state, A) --> state=B',\n", " 'dot(state, B) --> state=C',\n", " 'dot(state, C) --> state=D',\n", " 'dot(state, D) --> state=E',\n", " 'dot(state, E) --> state=A',\n", " ) \n", " model.bg = spa.BasalGanglia(actions)\n", " model.thalamus = spa.Thalamus(model.bg)\n", " \n", " def vision_in(t):\n", " if t<0.1:\n", " return 'C'\n", " else:\n", " return '0'\n", " model.input = spa.Input(vision=vision_in)\n", " \n", " nengo.Probe(model.state.state.output)\n", " nengo.Probe(model.vision.state.output)\n", " nengo.Probe(model.bg.input)\n", " nengo.Probe(model.thalamus.actions.output)\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spaun\n", "\n", "- This process is the basis for building Spaun\n", "\n", "<img src=\"files/spa/spaun_anatomy.png\">\n", "\n", "<img src=\"files/spa/spaun_function.png\">\n" ] } ], "metadata": {} } ] }	gpl-2.0
FranciscoBraga/AprendendoPython	for/For.ipynb	1	5512	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Utilizando for" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "3\n", "4\n" ] } ], "source": [ "#criando uma tupla e imprimindo cada um dos valores\n", "tupla=(2,3,4)\n", "for i in tupla:\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "leite\n", "carne\n", "detergente\n" ] } ], "source": [ "#imprimando valores de uma lista\n", "listaMercado = [\"leite\",\"carne\",\"detergente\"]\n", "for mercado in listaMercado:\n", " print(mercado)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n" ] } ], "source": [ "#imprindo os valores no intervalo entre o e 5\n", "for contador in range(0,5):\n", " print(contador)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 este número é impar\n", "2 este número é par\n", "3 este número é impar\n", "4 este número é par\n", "5 este número é impar\n", "6 este número é par\n", "7 este número é impar\n", "8 este número é par\n", "9 este número é impar\n", "10 este número é par\n" ] } ], "source": [ "#imprimindo numeros pares e impares\n", "listas= [1,2,3,4,5,6,7,8,9,10]\n", "for valor in listas:\n", " if valor % 2 == 0:\n", " print(str(valor) + \" este número é par\")\n", " else:\n", " print(str(valor )+ \" este número é impar\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loops Aninhados" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n", "0\n", "1\n", "2\n", "3\n", "4\n", "0\n", "1\n", "2\n", "3\n", "4\n", "0\n", "1\n", "2\n", "3\n", "4\n", "0\n", "1\n", "2\n", "3\n", "4\n" ] } ], "source": [ "#loops \n", "for i in range(0,5):\n", " for a in range(0,5):\n", " print(a)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "6\n", "12\n", "20\n", "30\n", "30\n" ] } ], "source": [ "#operando valores\n", "listaB=[1,2,3,4,5]\n", "soma=0\n", "for i in listaB:\n", " double_i = i *2\n", " soma += double_i\n", " print(soma)\n", "\n", "print(soma)\n", " " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3]\n", "[4, 5, 6]\n", "[7, 8, 9]\n" ] } ], "source": [ "listas1 =[[1,2,3],[4,5,6],[7,8,9]]\n", "for valor in listas1:\n", " print(valor)\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "#contando os itens de uma lista\n", "qtaLista=[1,2,3,4,5,6,7,8,9,0]\n", "count=0\n", "for valor in qtaLista:\n", " count= count+1\n", "print(count)\n", " " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "k3\n", "k2\n", "k1\n" ] } ], "source": [ "#listando as chaves de um dicionario\n", "dict ={'k1':'Python','k2':'R','k3':'Scala'}\n", "for item in dict:\n", " print(item)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('k3', 'Scala')\n", "('k2', 'R')\n", "('k1', 'Python')\n" ] } ], "source": [ "#imprimindo valor e chave\n", "\n", "for k,v in dict.items():\n", " print(k,v)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
JasonWayne/course-notes	ml-zb/part2_nnpackage.ipynb	1	223052	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 神经网络包使用\n", "\n", "Torch 中的神经网络模块，包含大部分的神经网络功能，支持构造各种复杂的网络结构。\n", "这个实验中，主要使用Linear包配合各种激活函数、Loss Function以及优化算法等构造基础的神经网络。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "local _ = require('nn') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 构造一个简单2维点阵分类的应用\n", "\n", "\n", "### 1. 构造样本" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "trainSamples = {}\n", "trainSamples.x = {}\n", "trainSamples.y = {}\n", "\n", "for i = 1, 128 do\n", " -- 构造随机坐标，-2.5, +2.5\n", " local x = torch.rand(2)\n", " x = (x - 0.5) * 3\n", " local y = torch.Tensor(1)\n", "\n", " -- 对训练样本进行分类\n", " y[1] = 1\n", " if ( (math.cos(x[1] - 0.5) - 0.5)< x[2] ) then\n", " y[1] = 0\n", " end\n", "\n", " trainSamples.x[#trainSamples.x+1] = x\n", " trainSamples.y[#trainSamples.y+1] = y\n", "end\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. 编写一个现实数据的程序" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Plot = require('itorch.Plot');\n", "\n", "showData = function(samples, lables)\n", "\n", " local l0_x = {}\n", " local l0_y = {}\n", " local l1_x = {}\n", " local l1_y = {}\n", " for i=1, #samples do\n", " if ( lables[i][1] == 0) then\n", " table.insert(l0_x, samples[i][1])\n", " table.insert(l0_y, samples[i][2]) \n", " else\n", " table.insert(l1_x, samples[i][1])\n", " table.insert(l1_y, samples[i][2]) \n", " end\n", " end\n", "\n", " plot = Plot():circle(l0_x, l0_y, 'red', 'hi'):circle(l1_x, l1_y, 'blue', 'bye'):draw()\n", "\n", " plot:title('样本分布'):redraw()\n", " \n", " return plot\n", "end" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { 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" Bokeh.load_models(all_models);\n", " var model = Bokeh.Collections(modeltype).get(modelid);\n", " $(\"#f58a716c-0919-4ed1-ce81-4532f5591c71\").html(''); // clear any previous plot in window_id\n", " var view = new model.default_view({model: model, el: \"#f58a716c-0919-4ed1-ce81-4532f5591c71\"});\n", " });\n", " }\n", "});\n", "</script>\n", "<div class=\"plotdiv\" id=\"5cff97a7-73c3-432a-c325-b8d3f3c8984a\"></div>\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "local _ = showData(trainSamples.x, trainSamples.y )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. 构造网络\n", "\n", "单隐层结构，\n", "\n", "![单隐层的神经网络](./images/simplenn.png)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "nn.Sequential {\n", " [input -> (1) -> (2) -> (3) -> (4) -> output]\n", " (1): nn.Linear(2 -> 5)\n", " (2): nn.ReLU\n", " (3): nn.Linear(5 -> 1)\n", " (4): nn.Sigmoid\n", "}\n", "{\n", " gradInput : DoubleTensor - empty\n", " modules : \n", " {\n", " 1 : \n", " nn.Linear(2 -> 5)\n", " {\n", " gradBias : DoubleTensor - size: 5\n" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ " weight : DoubleTensor - size: 5x2\n", " gradWeight : DoubleTensor - size: 5x2\n", " gradInput : DoubleTensor - empty\n", " bias : DoubleTensor - size: 5\n", " output : DoubleTensor - empty\n", " }\n", " 2 : \n", " nn.ReLU\n", " {\n", " inplace : false\n", " threshold : 0\n", " val : 0\n", " output : DoubleTensor - empty\n", " gradInput : DoubleTensor - empty\n", " }\n", " 3 : \n", " nn.Linear(5 -> 1)\n", " {\n", " gradBias : DoubleTensor - size: 1\n", " weight : DoubleTensor - size: 1x5\n", " gradWeight : DoubleTensor - size: 1x5\n", " gradInput : DoubleTensor - empty\n", " bias : DoubleTensor - size: 1\n", " output : DoubleTensor - empty\n", " }\n", " 4 : \n", " nn.Sigmoid\n", " {\n", " gradInput : DoubleTensor - empty\n", " output : DoubleTensor - empty\n", " }\n", " }\n", " output : DoubleTensor - empty\n", "}\n" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = nn.Sequential()\n", "model:add ( nn.Linear(2,5))\n", "model:add ( nn.ReLU() )\n", "model:add ( nn.Linear(5,1))\n", "model:add ( nn.Sigmoid() )\n", "print(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. 构造Loss Function\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "nn.BCECriterion\n", "{\n", " gradInput : DoubleTensor - empty\n", " sizeAverage : true\n", " output : 0\n", "}\n" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "criterion = nn.BCECriterion()\n", "print(criterion)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. 构造训练函数" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "-- 纪录模型的参数\n", "parameters,gradParameters = model:getParameters()\n", "tindex = 1\n", "doTrain = function(x)\n", " -- get new parameters\n", " if x ~= parameters then\n", " parameters:copy(x)\n", " end\n", " -- reset gradients\n", " gradParameters:zero()\n", " \n", " local yout = model:forward(trainSamples.x[tindex])\n", " local f = criterion:forward(yout, trainSamples.y[tindex])\n", " \n", " local dyout = criterion:backward(yout, trainSamples.y[tindex])\n", " model:backward(trainSamples.x[tindex], dyout)\n", " \n", " return f, gradParameters\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. 执行训练" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script type=\"text/javascript\">\n", "$(function() {\n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\"){\n", " window._bokeh_onload_callbacks = [];\n", " }\n", " function load_lib(url, callback){\n", " 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" Bokeh.load_models(all_models);\n", " var model = Bokeh.Collections(modeltype).get(modelid);\n", " $(\"#fa53e8bb-63ea-4b35-cbbb-060d9521a3d0\").html(''); // clear any previous plot in window_id\n", " var view = new model.default_view({model: model, el: \"#fa53e8bb-63ea-4b35-cbbb-060d9521a3d0\"});\n", " });\n", " }\n", "});\n", "</script>\n", "<div class=\"plotdiv\" id=\"5a5b9abf-c699-4165-c113-4182642f97d5\"></div>\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "require('optim')\n", "state = {\n", " learningRate = 0.1,\n", " momentum = 0.5\n", "}\n", "\n", "local errRecord = { --纪录每次训练的error输出\n", " seq = {},\n", " value = {}\n", "}\n", "\n", "model:training()\n", "for ep = 1,100 do\n", " local errSum = 0.0\n", " for i=1, #trainSamples.x do\n", " tindex = i\n", " local _, err = optim.sgd(doTrain, parameters, state)\n", " errSum = errSum + err[1]\n", " end\n", " errSum = errSum / #trainSamples.x\n", " \n", " errRecord.value[ep] = errSum\n", " errRecord.seq[ep] = ep\n", "end\n", "\n", "Plot = require 'itorch.Plot'\n", "local plot = Plot()\n", "plot:line(errRecord.seq, errRecord.value,'black', 'yolo'):draw()\n", "plot:title(\"训练过程\"):redraw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7. 验证效果" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script type=\"text/javascript\">\n", "$(function() {\n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\"){\n", " window._bokeh_onload_callbacks = [];\n", " }\n", " function load_lib(url, callback){\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading){\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", new Date());\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", new Date());\n", " window._bokeh_is_loading = true;\n", " var s = document.createElement('script');\n", " s.src 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" Bokeh.load_models(all_models);\n", " var model = Bokeh.Collections(modeltype).get(modelid);\n", " $(\"#8e06e78b-290c-40ab-c0e2-f20a21239b49\").html(''); // clear any previous plot in window_id\n", " var view = new model.default_view({model: model, el: \"#8e06e78b-290c-40ab-c0e2-f20a21239b49\"});\n", " });\n", " }\n", "});\n", "</script>\n", "<div class=\"plotdiv\" id=\"946b547e-c9ec-47d7-c70c-f8dba3f7bb6e\"></div>\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "local marginLineX = {}\n", "local marginLineY = {}\n", "local singleSample = torch.Tensor(2)\n", "\n", "for x1=-1.5, 1.5, 0.03 do\n", " for x2=-1.5, 1.5, 0.03 do\n", " singleSample[1] = x1\n", " singleSample[2] = x2\n", " \n", " local y = model:forward(singleSample)\n", " if ( torch.abs(y[1] - 0.5) < 0.1) then\n", " marginLineX[#marginLineX+1] = x1\n", " marginLineY[#marginLineY+1] = x2 \n", " end\n", " end\n", "end\n", "\n", "\n", "plot = showData(trainSamples.x, trainSamples.y )\n", "plot:line(marginLineX,marginLineY,'black', 'yolo'):redraw()" ] }, { "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": "iTorch", "language": "lua", "name": "itorch" }, "language_info": { "name": "lua", "version": "20100" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
post2web/nbloader	tutorial/nbloader_tutorial.ipynb	2	8556	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Use Jupyter Notebooks as \"Objects\"\n", "\n", "\n", "Jupyter Notebooks are great for data exploration, visualizing, documenting, prototyping and interacting with the code, but when it comes to creating a program out of a notebook or using the notebook in porduction they fall short. I often get myself copying cells from a notebook into a script so that I can run the code with command line arguments. There is no easy way to run a notebook and return a result from its execution, can't passing arguments to a notebook or running individual code cells programmatically. Have you ever wrapped a code cell to a function just so you want to call it in a loop with different parameters?\n", "\n", "I wrote a small utility tool ```nbloader``` that enables code reusing from jupyter notebooks. With it, you can import a notebook as an object, pass variables to its namespace, run code cells and pull out variables from its namespace.\n", "\n", "This tutorial will show you how to make your notebooks reusable with ```nbloader```.\n", "<!-- TEASER_END -->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Install nbloader with pip\n", "\n", "```shell\n", "pip install nbloader --upgrade\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load a Notebook" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from nbloader import Notebook\n", "\n", "loaded_notebook = Notebook('test.ipynb')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above command loads a notebook as an object. This can be done inside a jupyter notebook or a regular python script." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run all cells" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I am inside loaded_notebook!\n" ] }, { "data": { "text/plain": [ "<nbloader.Notebook at 0x1094388d0>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loaded_notebook.run_all()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After ```loaded_notebook.run_all()``` is called:\n", "- The notebook is initialized with empty starting namespace.\n", "- All cells of the loaded notebook are executed one after another by the order they are the file.\n", "- All print statement or any other stdout from the loaded notebook will output.\n", "- All warnings or errors will be raised unless caught.\n", "- All variables from the loaded notebook's namespace will be accessible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Here are the contents of loaded_notebook.ipynb\n", "\n", "<img src=\"loaded_notebook.png\" width=\"400\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### This is how you access the namespace of the loaded notebook:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loaded_notebook.ns['a']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The notebooks namespace is just a dict so if you try to get something that's not there will get an error." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'b'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-4-0892bc683411>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mloaded_notebook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mKeyError\u001b[0m: 'b'" ] } ], "source": [ "loaded_notebook.ns['b']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Run individual cells if they are tagged. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7\n", "8\n" ] } ], "source": [ "loaded_notebook.run_tag('add_one')\n", "print(loaded_notebook.ns['a'])\n", "loaded_notebook.run_tag('add_one')\n", "print(loaded_notebook.ns['a'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If a cell has a comment on its first line it will become a tag. Cells can also be taged by the jupyter notebook tags." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### This is how you access the notebook's namespace:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "loaded_notebook.ns['a'] = 0\n", "loaded_notebook.run_tag('add_one')\n", "print(loaded_notebook.ns['a'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The notebook namespace is what you would normally get with ```globals()``` when running the notebook the normal way with jupyter and since the namespace is just a dic, there is no performance penalty when passing large objects to the notebook. All the code from its cells is compiled and can be called in a loop with the speed of a regular function." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example workflows:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create a notebook to parse one file and then call it in a loop when changing its namespace with a new value for ```filename```.\n", "\n", "<table style=\"width: 100%;\"><tr>\n", " <td style=\"text-align: center;\">\n", " <img src=\"looper.png\" width=\"400\"></td>\n", " <td style=\"text-align: center;\">\n", " <img src=\"parser.png\" width=\"400\"></td>\n", "</tr></table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create a notebook with a model and then optimize it with different parameters.\n", "\n", "<table style=\"width: 100%;\"><tr>\n", " <td style=\"text-align: center;\">\n", " <img src=\"optimizer.png\" width=\"400\"></td>\n", " <td style=\"text-align: center;\">\n", " <img src=\"model.png\" width=\"400\"></td>\n", "</tr></table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Added some ```magic_tags``` to make it act more like an a objects\n", "- if a cell has a tag name ```__init__``` will be run at initialization and when restarted.\n", "- if a cell has a tag name ```__del__``` will be run when deleted (or not)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Warning] on using best practices!\n", "\n", "You may be tempted to load the current notebook and then loop a cell from it. I don't think this is a good practice." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## I'd love to hear your comments!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
nick-youngblut/SIPSim	ipynb/bac_genome/fullCyc/trimDataset/.ipynb_checkpoints/rep3-checkpoint.ipynb	1	703565	{ "cells": [ { "cell_type": "markdown", "metadata": { "code_folding": [] }, "source": [ "# Goal\n", "\n", "* Simulating a fullCyc control gradient\n", " * Not simulating incorporation (all 0% isotope incorp.)\n", " * Don't know how much true incorporatation for emperical data\n", "* Using parameters inferred from TRIMMED emperical data (fullCyc seq data), or if not available, default SIPSim parameters\n", "* Determining whether simulated taxa show similar distribution to the emperical data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Input parameters\n", " * phyloseq.bulk file \n", " * taxon mapping file\n", " * list of genomes\n", " * fragments simulated for all genomes\n", " * bulk community richness\n", "\n", "\n", "## workflow\n", "\n", "* Creating a community file from OTU abundances in bulk soil samples\n", " * phyloseq.bulk --> OTU table --> filter to sample --> community table format\n", "* Fragment simulation\n", " * simulated_fragments --> parse out fragments for target OTUs \n", " * simulated_fragments --> parse out fragments from random genomes to obtain richness of interest\n", " * combine fragment python objects\n", "* Convert fragment lists to kde object\n", "* Add diffusion\n", "* Make incorp config file\n", "* Add isotope incorporation\n", "* Calculating BD shift from isotope incorp\n", "* Simulating gradient fractions\n", "* Simulating OTU table\n", "* Simulating PCR\n", "* Subsampling from the OTU table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Init" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "import glob\n", "import re\n", "import nestly" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The rpy2.ipython extension is already loaded. To reload it, use:\n", " %reload_ext rpy2.ipython\n", "The pushnote extension is already loaded. To reload it, use:\n", " %reload_ext pushnote\n" ] } ], "source": [ "%load_ext rpy2.ipython\n", "%load_ext pushnote" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R\n", "library(ggplot2)\n", "library(dplyr)\n", "library(tidyr)\n", "library(gridExtra)\n", "library(phyloseq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### BD min/max" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Min BD: 1.67323 \n", "Max BD: 1.7744 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "## min G+C cutoff\n", "min_GC = 13.5\n", "## max G+C cutoff\n", "max_GC = 80\n", "## max G+C shift\n", "max_13C_shift_in_BD = 0.036\n", "\n", "\n", "min_BD = min_GC/100.0 * 0.098 + 1.66 \n", "max_BD = max_GC/100.0 * 0.098 + 1.66 \n", "\n", "max_BD = max_BD + max_13C_shift_in_BD\n", "\n", "cat('Min BD:', min_BD, '\\n')\n", "cat('Max BD:', max_BD, '\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Nestly\n", "\n", "* assuming fragments already simulated" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "workDir = '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/'\n", "buildDir = os.path.join(workDir, 'rep3')\n", "R_dir = '/home/nick/notebook/SIPSim/lib/R/'\n", "\n", "fragFile= '/home/nick/notebook/SIPSim/dev/bac_genome1147/validation/ampFrags.pkl'\n", "\n", "nreps = 3" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# building tree structure\n", "nest = nestly.Nest()\n", "\n", "# varying params\n", "nest.add('rep', [x + 1 for x in xrange(nreps)])\n", "\n", "\n", "## set params\n", "nest.add('abs', ['1e9'], create_dir=False)\n", "nest.add('percIncorp', [0], create_dir=False)\n", "nest.add('percTaxa', [0], create_dir=False)\n", "nest.add('np', [2], create_dir=False)\n", "nest.add('subsample_dist', ['lognormal'], create_dir=False)\n", "nest.add('subsample_mean', [9.432], create_dir=False)\n", "nest.add('subsample_scale', [0.5], create_dir=False)\n", "nest.add('subsample_min', [10000], create_dir=False)\n", "nest.add('subsample_max', [30000], create_dir=False)\n", "\n", "### input/output files\n", "nest.add('buildDir', [buildDir], create_dir=False)\n", "nest.add('R_dir', [R_dir], create_dir=False)\n", "nest.add('fragFile', [fragFile], create_dir=False)\n", "nest.add('bandwidth', [0.6], create_dir=False)\n", "nest.add('comm_params', ['mean:-7.6836085,sigma:0.9082843'], create_dir=False)\n", "\n", "# building directory tree\n", "nest.build(buildDir)\n", "\n", "# bash file to run\n", "bashFile = os.path.join(buildDir, 'SIPSimRun.sh')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing /home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/SIPSimRun.sh\n" ] } ], "source": [ "%%writefile $bashFile\n", "#!/bin/bash\n", "\n", "export PATH={R_dir}:$PATH\n", "\n", "echo '#-- SIPSim pipeline --#'\n", "\n", "echo '# converting fragments to KDE'\n", "SIPSim fragment_KDE \\\n", " {fragFile} \\\n", " > ampFrags_KDE.pkl\n", " \n", "echo '# making a community file'\n", "SIPSim KDE_info \\\n", " -t ampFrags_KDE.pkl \\\n", " > taxon_names.txt\n", "SIPSim communities \\\n", " --abund_dist_p {comm_params} \\\n", " taxon_names.txt \\\n", " > comm.txt\n", " \n", "echo '# adding diffusion' \n", "SIPSim diffusion \\\n", " ampFrags_KDE.pkl \\\n", " --bw {bandwidth} \\\n", " --np {np} \\\n", " > ampFrags_KDE_dif.pkl \n", "\n", "echo '# adding DBL contamination'\n", "SIPSim DBL \\\n", " ampFrags_KDE_dif.pkl \\\n", " --bw {bandwidth} \\\n", " --np {np} \\\n", " > ampFrags_KDE_dif_DBL.pkl\n", " \n", "echo '# making incorp file'\n", "SIPSim incorpConfigExample \\\n", " --percTaxa {percTaxa} \\\n", " --percIncorpUnif {percIncorp} \\\n", " > {percTaxa}_{percIncorp}.config\n", "\n", "echo '# adding isotope incorporation to BD distribution'\n", "SIPSim isotope_incorp \\\n", " ampFrags_KDE_dif_DBL.pkl \\\n", " {percTaxa}_{percIncorp}.config \\\n", " --comm comm.txt \\\n", " --bw {bandwidth} \\\n", " --np {np} \\\n", " > ampFrags_KDE_dif_DBL_inc.pkl\n", "\n", "echo '# simulating gradient fractions'\n", "SIPSim gradient_fractions \\\n", " comm.txt \\\n", " > fracs.txt \n", "\n", "echo '# simulating an OTU table'\n", "SIPSim OTU_table \\\n", " ampFrags_KDE_dif_DBL_inc.pkl \\\n", " comm.txt \\\n", " fracs.txt \\\n", " --abs {abs} \\\n", " --np {np} \\\n", " > OTU_abs{abs}.txt\n", " \n", "#-- w/ PCR simulation --#\n", "echo '# simulating PCR'\n", "SIPSim OTU_PCR \\\n", " OTU_abs{abs}.txt \\\n", " > OTU_abs{abs}_PCR.txt \n", " \n", "echo '# subsampling from the OTU table (simulating sequencing of the DNA pool)'\n", "SIPSim OTU_subsample \\\n", " --dist {subsample_dist} \\\n", " --dist_params mean:{subsample_mean},sigma:{subsample_scale} \\\n", " --min_size {subsample_min} \\\n", " --max_size {subsample_max} \\\n", " OTU_abs{abs}_PCR.txt \\\n", " > OTU_abs{abs}_PCR_sub.txt\n", " \n", "echo '# making a wide-formatted table'\n", "SIPSim OTU_wideLong -w \\\n", " OTU_abs{abs}_PCR_sub.txt \\\n", " > OTU_abs{abs}_PCR_sub_w.txt\n", " \n", "echo '# making metadata (phyloseq: sample_data)'\n", "SIPSim OTU_sampleData \\\n", " OTU_abs{abs}_PCR_sub.txt \\\n", " > OTU_abs{abs}_PCR_sub_meta.txt\n", " \n", "\n", "#-- w/out PCR simulation --# \n", "echo '# subsampling from the OTU table (simulating sequencing of the DNA pool)'\n", "SIPSim OTU_subsample \\\n", " --dist {subsample_dist} \\\n", " --dist_params mean:{subsample_mean},sigma:{subsample_scale} \\\n", " --min_size {subsample_min} \\\n", " --max_size {subsample_max} \\\n", " OTU_abs{abs}.txt \\\n", " > OTU_abs{abs}_sub.txt\n", " \n", "echo '# making a wide-formatted table'\n", "SIPSim OTU_wideLong -w \\\n", " OTU_abs{abs}_sub.txt \\\n", " > OTU_abs{abs}_sub_w.txt\n", " \n", "echo '# making metadata (phyloseq: sample_data)'\n", "SIPSim OTU_sampleData \\\n", " OTU_abs{abs}_sub.txt \\\n", " > OTU_abs{abs}_sub_meta.txt " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2016-02-17 19:08:18,886 * INFO * Template: ./SIPSimRun.sh\r\n", "2016-02-17 19:08:18,888 * INFO * [7389] Started ./SIPSimRun.sh in rep3/3\r\n", "2016-02-17 19:08:18,890 * INFO * [7390] Started ./SIPSimRun.sh in rep3/2\r\n", "2016-02-17 19:08:18,891 * INFO * [7391] Started ./SIPSimRun.sh in rep3/1\r\n" ] } ], "source": [ "!chmod 777 $bashFile\n", "!cd $workDir; \\\n", " nestrun --template-file $bashFile -d rep3 --log-file log.txt -j 3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%pushnote SIPsim rep3 complete" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# BD min/max\n", "\n", "* what is the min/max BD that we care about?" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Min BD: 1.67323 \n", "Max BD: 1.7744 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "## min G+C cutoff\n", "min_GC = 13.5\n", "## max G+C cutoff\n", "max_GC = 80\n", "## max G+C shift\n", "max_13C_shift_in_BD = 0.036\n", "\n", "\n", "min_BD = min_GC/100.0 * 0.098 + 1.66 \n", "max_BD = max_GC/100.0 * 0.098 + 1.66 \n", "\n", "max_BD = max_BD + max_13C_shift_in_BD\n", "\n", "cat('Min BD:', min_BD, '\\n')\n", "cat('Max BD:', max_BD, '\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading non-PCR subsampled OTU tables" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/3/OTU_abs1e9_sub.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/2/OTU_abs1e9_sub.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/1/OTU_abs1e9_sub.txt']" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "OTU_files = !find $buildDir -name \"OTU_abs1e9_sub.txt\"\n", "OTU_files" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " library fraction taxon BD_min BD_mid BD_max count\n", "1 1 -inf-1.660 Acaryochloris_marina_MBIC11017 -Inf 1.659 1.659 9\n", "2 1 1.660-1.662 Acaryochloris_marina_MBIC11017 1.660 1.661 1.662 30\n", "3 1 1.662-1.665 Acaryochloris_marina_MBIC11017 1.662 1.663 1.665 3\n", "4 1 1.665-1.670 Acaryochloris_marina_MBIC11017 1.665 1.667 1.670 15\n", "5 1 1.670-1.675 Acaryochloris_marina_MBIC11017 1.670 1.672 1.675 3\n", "6 1 1.675-1.679 Acaryochloris_marina_MBIC11017 1.675 1.677 1.679 13\n", " rel_abund SIM_rep\n", "1 0.0008172160 3\n", "2 0.0012000480 3\n", "3 0.0002663116 3\n", "4 0.0010466820 3\n", "5 0.0002258186 3\n", "6 0.0004679457 3\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i OTU_files\n", "# loading files\n", "\n", "df.SIM = list()\n", "for (x in OTU_files){\n", " SIM_rep = gsub('/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/', '', x)\n", " SIM_rep = gsub('/OTU_abs1e9_sub.txt', '', SIM_rep)\n", " df.SIM[[SIM_rep]] = read.delim(x, sep='\\t') \n", " }\n", "df.SIM = do.call('rbind', df.SIM)\n", "df.SIM$SIM_rep = gsub('\\\\.[0-9]+$', '', rownames(df.SIM))\n", "rownames(df.SIM) = 1:nrow(df.SIM)\n", "df.SIM %>% head(n=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# BD range where an OTU is detected \n", "\n", "* Do the simulated OTU BD distributions span the same BD range of the emperical data?" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/3/comm.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/2/comm.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/1/comm.txt']" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "comm_files = !find $buildDir -name \"comm.txt\"\n", "comm_files" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " library taxon_name bulk_abund rank SIM_rep\n", "1 1 Weeksella_virosa_DSM_16922 0.014432778 1 3\n", "2 1 Aquifex_aeolicus_VF5 0.010412296 2 3\n", "3 1 Campylobacter_jejuni_subsp_jejuni_M1 0.009460668 3 3\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i comm_files\n", "\n", "df.SIM.comm = list()\n", "for (x in comm_files){\n", " SIM_rep = gsub('/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/', '', x)\n", " SIM_rep = gsub('/comm.txt', '', SIM_rep)\n", " df.SIM.comm[[SIM_rep]] = read.delim(x, sep='\\t') \n", " }\n", "\n", "df.SIM.comm = do.call(rbind, df.SIM.comm)\n", "df.SIM.comm$SIM_rep = gsub('\\\\.[0-9]+$', '', rownames(df.SIM.comm))\n", "rownames(df.SIM.comm) = 1:nrow(df.SIM.comm)\n", "df.SIM.comm = df.SIM.comm %>%\n", " rename('bulk_abund' = rel_abund_perc) %>%\n", " mutate(bulk_abund = bulk_abund / 100)\n", "df.SIM.comm %>% head(n=3)" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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gIPHon/ggX09PvnSTBway0teVAoKCskUCwQrfDSlV2NRK33C0BEkgvm7+Uj4C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\nSUVORK5CYII=\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 800 -h 400\n", "# Plotting the pre-fractionation abundances of each taxon\n", "\n", "df.SIM.comm.s = df.SIM.comm %>%\n", " group_by(taxon_name) %>%\n", " summarize(median_rank = median(rank),\n", " mean_abund = mean(bulk_abund),\n", " sd_abund = sd(bulk_abund))\n", "\n", "df.SIM.comm.s$taxon_name = reorder(df.SIM.comm.s$taxon_name, -df.SIM.comm.s$mean_abund)\n", "\n", "ggplot(df.SIM.comm.s, aes(taxon_name, mean_abund, \n", " ymin=mean_abund-sd_abund,\n", " ymax=mean_abund+sd_abund)) +\n", " geom_linerange(alpha=0.4) +\n", " geom_point(alpha=0.6, size=1.2) +\n", " scale_y_log10() +\n", " labs(x='taxon', y='Relative abundance', title='Pre-fractionation abundance') +\n", " theme_bw() +\n", " theme(\n", " text = element_text(size=16),\n", " axis.text.x = element_blank()\n", " )" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " library fraction taxon BD_min BD_mid BD_max count\n", "1 1 1.675-1.679 Acaryochloris_marina_MBIC11017 1.675 1.677 1.679 13\n", "2 1 1.679-1.682 Acaryochloris_marina_MBIC11017 1.679 1.680 1.682 4\n", "3 1 1.682-1.690 Acaryochloris_marina_MBIC11017 1.682 1.686 1.690 0\n", " rel_abund SIM_rep bulk_abund rank\n", "1 0.0004679457 3 0.0005748212 578\n", "2 0.0002244165 3 0.0005748212 578\n", "3 0.0000000000 3 0.0005748212 578\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "\n", "## joining SIP & comm (pre-fractionation)\n", "df.SIM.j = inner_join(df.SIM, df.SIM.comm, c('library' = 'library',\n", " 'taxon' = 'taxon_name',\n", " 'SIM_rep' = 'SIM_rep')) %>%\n", " filter(BD_mid >= min_BD, \n", " BD_mid <= max_BD)\n", " \n", "df.SIM.j %>% head(n=3)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " SIM_rep taxon mean_bulk_abund min_BD max_BD\n", "1 1 Acaryochloris_marina_MBIC11017 0.0047250748 1.678 1.772\n", "2 1 Acetobacterium_woodii_DSM_1030 0.0013668524 1.678 1.772\n", "3 1 Acetobacter_pasteurianus_IFO_3283-03 0.0006067577 1.678 1.772\n", " BD_range BD_range_perc\n", "1 0.094 100\n", "2 0.094 100\n", "3 0.094 100\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "# calculating BD range\n", "df.SIM.j.f = df.SIM.j %>%\n", " filter(count > 0) %>%\n", " group_by(SIM_rep) %>%\n", " mutate(max_BD_range = max(BD_mid) - min(BD_mid)) %>%\n", " ungroup() %>%\n", " group_by(SIM_rep, taxon) %>%\n", " summarize(mean_bulk_abund = mean(bulk_abund),\n", " min_BD = min(BD_mid),\n", " max_BD = max(BD_mid),\n", " BD_range = max_BD - min_BD,\n", " BD_range_perc = BD_range / first(max_BD_range) * 100) %>%\n", " ungroup() \n", " \n", "df.SIM.j.f %>% head(n=3) %>% as.data.frame" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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wBtBgzA1AoiDlnHyieysiwqEziR1hg7svAGMDUs5JbCTlvyIc4hP/kUDhoeIZ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\nVBn5uW5ubrbb7doKf/rTn9LT03t7e+O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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -h 300 -w 550\n", "## plotting\n", "ggplot(df.SIM.j.f, aes(mean_bulk_abund, BD_range_perc, color=SIM_rep)) +\n", " geom_point(alpha=0.5, shape='O') +\n", " scale_x_log10() +\n", " scale_y_continuous() +\n", " labs(x='Pre-fractionation abundance', y='% of total BD range') +\n", " #geom_vline(xintercept=0.001, linetype='dashed', alpha=0.5) +\n", " theme_bw() +\n", " theme(\n", " text = element_text(size=16),\n", " panel.grid = element_blank(),\n", " legend.position = 'none'\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Assessing diversity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Asigning zeros" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Below detection level abundances converted to: 3.342134e-06 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "# giving value to missing abundances\n", "min.pos.val = df.SIM.j %>%\n", " filter(rel_abund > 0) %>%\n", " group_by() %>%\n", " mutate(min_abund = min(rel_abund)) %>%\n", " ungroup() %>%\n", " filter(rel_abund == min_abund)\n", "\n", "min.pos.val = min.pos.val[1,'rel_abund'] %>% as.numeric\n", "imp.val = min.pos.val / 10\n", "\n", "\n", "# convert numbers\n", "df.SIM.j[df.SIM.j$rel_abund == 0, 'abundance'] = imp.val\n", "\n", "# another closure operation\n", "df.SIM.j = df.SIM.j %>%\n", " group_by(SIM_rep, fraction) %>%\n", " mutate(rel_abund = rel_abund / sum(rel_abund))\n", "\n", "\n", "# status\n", "cat('Below detection level abundances converted to: ', imp.val, '\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting Shannon diversity for each" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R\n", "shannon_index_long = function(df, abundance_col, ...){\n", " # calculating shannon diversity index from a 'long' formated table\n", " ## community_col = name of column defining communities\n", " ## abundance_col = name of column defining taxon abundances\n", " df = df %>% as.data.frame\n", " cmd = paste0(abundance_col, '/sum(', abundance_col, ')')\n", " df.s = df %>%\n", " group_by_(...) %>%\n", " mutate_(REL_abundance = cmd) %>%\n", " mutate(pi__ln_pi = REL_abundance * log(REL_abundance),\n", " shannon = -sum(pi__ln_pi, na.rm=TRUE)) %>%\n", " ungroup() %>% \n", " dplyr::select(-REL_abundance, -pi__ln_pi) %>%\n", " distinct_(...) \n", " return(df.s)\n", "}" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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9xKsU6vfBXKuwPYZofdqrtI7p8euF1RHV24IBF4AgKcIY6YeVQ0YxqC5KdUE+\n6M7Yd0rx785Vy/f0m/DS1jYvIgTpowuyXmEVytKDGhFV2O4ZFxc+2l49HAy4AQRJEcZI356jXlIo\nnzC2UjvFKtROsYqGq4vhk3urJ0kVOeoZ2B6eID+foC1vVl+t/tpNBVYUtVc5cqd6Uf6P/IjqRjDg\nChAkRWgThW++8KOr1eKJF/xzyQhtKM8smPfWtDzt/aanc/NVc2o6PrxpyZDcZ+rbBenwrH7/zg05\n1yxeNr6/+mq9kFe0claP19sFaek9YenSUcMiqhvBgCtAkBShXzr0m6x9a2jdoPyf7dKGc8vc07qc\nuUTbW5qrX4UvPrHb+RvvK9jTLsjtXebozdeNKBgw+VP11Qq9cmbn055jEQeZDT/p2uOKnRHVzWDA\nDSCIL9itbExcKS3Bsh0I4gOaAzcNde2OBq4GAxDEB3yjnPK1P4MBCOIHXP05VA9/WzULgSAAxAGC\nABAHCAJAHCAIAHH4fyMhkSZS1y3TAAAAAElFTkSuQmCC\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 800 -h 300\n", "# calculating shannon\n", "df.SIM.shan = shannon_index_long(df.SIM.j, 'count', 'library', 'fraction') %>%\n", " filter(BD_mid >= min_BD, \n", " BD_mid <= max_BD) \n", "\n", "df.SIM.shan.s = df.SIM.shan %>%\n", " group_by(BD_bin = ntile(BD_mid, 24)) %>%\n", " summarize(mean_BD = mean(BD_mid),\n", " mean_shannon = mean(shannon),\n", " sd_shannon = sd(shannon))\n", "\n", "# plotting\n", "p = ggplot(df.SIM.shan.s, aes(mean_BD, mean_shannon, \n", " ymin=mean_shannon-sd_shannon,\n", " ymax=mean_shannon+sd_shannon)) +\n", " geom_pointrange() +\n", " labs(x='Buoyant density', y='Shannon index') +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none'\n", " )\n", "p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting variance\n" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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lbIzjuHvvvdfaFCZ4KEFglQYu2g0jWCSTcXHxrLSES8tww90BPENBo3Z24l32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\nQHeNYvq2eIci2+3Z8kW/owVVrPB/8rz/HZqtJbJnXO6oMcw+lK08lA0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Qm8EW\nLISQHdTvDiuKYm5u7rBhw8aMGdP8IdnZ2Uql0tvb25bz+/v7Wx9IpVIAsEZX9U+vcurUqZkzZzZM\nmTFjhi3vghBC9oLLNCCE7ImiqICAgKVLl3p6epaVlXl6ejaV86+//urWrdvNWJ6KZa++s9lrMQiE\nELIRtmAhhOyvrKzMuqZoUxlSU1M/+uijZ5555ma8e1xc3KpVqxqmrF69+ma8EUIINQVbsBBCdrB9\n+/b6GXwVFRWffPLJpEmT1Gr1tRkqKytPnjz5zTffDB06dPLkyTejMK+//vqgQYP0ev3kyZNZlt24\ncePu3btvxhshhFBTcJA7QuhGXdXN5+vre8cddyxevNjNze3aDFKptHv37hMnTnzuueds7LlrOEb+\nqiVDGz5tmG3Pnj0vv/zy6dOn3d3dR44cOXfu3MTERLzdIYTaDAZYCCGEEEJ2hmOwEEIIIYTsDAMs\nhJCDrVmzhmqaTqdzdAERQqjFsIsQIYQQQsjOsAULIYQQQsjOMMBCCCGEELIzDLAQQgghhOwMAyyE\nEEIIITv7f70u4RLWM4jSAAAAAElFTkSuQmCC\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 800 -h 350\n", "df.SIM.j.var = df.SIM.j %>%\n", " group_by(SIM_rep, fraction) %>%\n", " mutate(variance = var(rel_abund)) %>%\n", " ungroup() %>%\n", " distinct(SIM_rep, fraction) %>%\n", " select(SIM_rep, fraction, variance, BD_mid)\n", "\n", "ggplot(df.SIM.j.var, aes(BD_mid, variance, color=SIM_rep)) +\n", " geom_point() +\n", " geom_line() +\n", " theme_bw() +\n", " theme(\n", " text = element_text(size=16)\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Notes\n", "\n", "* spikes at low & high G+C\n", " * absence of taxa or presence of taxa at those locations?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting absolute abundance distributions " ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/3/OTU_abs1e9.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/2/OTU_abs1e9.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/1/OTU_abs1e9.txt']" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "OTU_files = !find $buildDir -name \"OTU_abs1e9.txt\"\n", "OTU_files" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " library taxon fraction BD_min BD_mid BD_max count\n", "1 1 Acaryochloris_marina_MBIC11017 -inf-1.660 -Inf 1.659 1.659 42\n", "2 1 Acaryochloris_marina_MBIC11017 1.660-1.662 1.660 1.661 1.662 14\n", "3 1 Acaryochloris_marina_MBIC11017 1.662-1.665 1.662 1.663 1.665 8\n", " rel_abund SIM_rep\n", "1 0.0005861583 3\n", "2 0.0010715653 3\n", "3 0.0003780540 3\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i OTU_files\n", "# loading files\n", "\n", "df.abs = list()\n", "for (x in OTU_files){\n", " SIM_rep = gsub('/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/rep3/', '', x)\n", " SIM_rep = gsub('/OTU_abs1e9.txt', '', SIM_rep)\n", " df.abs[[SIM_rep]] = read.delim(x, sep='\\t') \n", " }\n", "df.abs = do.call('rbind', df.abs)\n", "df.abs$SIM_rep = gsub('\\\\.[0-9]+$', '', rownames(df.abs))\n", "rownames(df.abs) = 1:nrow(df.abs)\n", "df.abs %>% head(n=3)" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ZEWXVOohoAP9c3ZA37xRDzBU0Pg6RPyeiE+aJ6U2MSDBiRJLTFPjLQLRJWL5k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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 800 \n", "\n", "ggplot(df.abs, aes(BD_mid, count, fill=taxon)) +\n", " geom_area(stat='identity', position='dodge', alpha=0.5) +\n", " labs(x='Buoyant density', y='Subsampled community\\n(absolute abundance)') +\n", " facet_grid(SIM_rep ~ .) +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none',\n", " axis.title.y = element_text(vjust=1), \n", " axis.title.x = element_blank()\n", " )" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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uWBmsXC4DwJEjRzqdDgA88cQTiqLo+pUmNbwRxtjWdrx6A60ftWlnRjG4QlKb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J6azy2kT/p4X4ZyqFJmXoKnVG5HOg8liMddaEdub8X2hVl6uXGj0pRyPIRLG09qbqk1OZhyhVtK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H35aHJ8N38sieB7D+1faR5vBbHC6NFHJp87M8tpWh/W5Z6utcQXLGVTY84sNhbMgdLtrAyH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\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u17x1ho/E/v3TQd/vbvqtn+lfy1iy51TPJlmzrE2E0FyczHtEvBDRtu81sQQ4zIN1yCGH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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 800 \n", "\n", "p1 = ggplot(df.abs %>% filter(BD_mid < 1.7), aes(BD_mid, count, fill=taxon, color=taxon)) +\n", " labs(x='Buoyant density', y='Subsampled community\\n(absolute abundance)') +\n", " facet_grid(SIM_rep ~ .) +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none',\n", " axis.title.y = element_text(vjust=1), \n", " axis.title.x = element_blank()\n", " )\n", "\n", "p2 = p1 + geom_line(alpha=0.25) + scale_y_log10()\n", "p1 = p1 + geom_area(stat='identity', position='dodge', alpha=0.5) \n", "\n", "grid.arrange(p1, p2, ncol=2)" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ztcfzkSFlBZY6XbGrSqvo+XMtf5ZVDLbS24Ibp7qSDvZnq/MQFyZHu2ImhfyB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YweeGYohPRQvj6wPUU31/hNQgSeHZ6SwHp6z3b/+I//+L73vW9jY+PP//zPb7zxxptvvnm5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\nJ9a9bZXViYmJmZmZkZGRRCLx8Y9/fHZ2drWihVFKxjQJJkJeHHO41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1+/d+9eAECz2RQE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\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xp7ffrKk7iP1NTyeiYOvRZ23nz8fDr2azn2s8M56/7sm2CCMRAlpQBegMyhmIAEQEIgRC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\nnQwSGfrdi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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 800 \n", "\n", "p1 = ggplot(df.abs %>% filter(BD_mid > 1.72), aes(BD_mid, count, fill=taxon, color=taxon)) +\n", " labs(x='Buoyant density', y='Subsampled community\\n(absolute abundance)') +\n", " facet_grid(SIM_rep ~ .) +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none',\n", " axis.title.y = element_text(vjust=1), \n", " axis.title.x = element_blank()\n", " )\n", "\n", "\n", "p2 = p1 + geom_line(alpha=0.25) + scale_y_log10()\n", "p1 = p1 + geom_area(stat='identity', position='dodge', alpha=0.5) \n", "\n", "grid.arrange(p1, p2, ncol=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusions\n", "\n", "* DBL is a bit too permissive\n", " * low abundant taxa are spread out a bit more than emperical\n", "* Variance spiking:\n", " * abundance distributions are too tight\n", " * emperical data variance suggests some extra unevenness in heavy fractions\n", " * some taxon DNA seems to be 'smeared' out into the heavy fractions\n", " * possible fixes:\n", " * more abundant, high G+C genomes\n", " * more diffusion\n", " * more 'smearing' into the heavy fractions\n", " * TODO:\n", " * determine what's changing in emperical data between Days 1,3,6 & 14,30,48" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "hide_input": true, "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
TUW-GEO/rt1	doc/examples/example_fitting.ipynb	1	500433	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How to use the rtfits-module" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "from rt1.rtfits import Fits\n", "import rt1.volume as rt_V\n", "import rt1.surface as rt_SRF" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generation of a Dataset\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# define Volume- and Surface properties used for the generation of the dataset\n", "def set_V_SRF_orig(tau, omega, N):\n", " V = rt_V.Rayleigh(omega = omega, tau = tau)\n", " SRF = rt_SRF.LinCombSRF([[0.5, rt_SRF.Isotropic()], \n", " [0.5, rt_SRF.CosineLobe(i=8, ncoefs=10)]], \n", " NormBRDF=N)\n", " return V, SRF\n", "\n", "fit = Fits(sig0=True, dB=False, verbose=0, set_V_SRF = set_V_SRF_orig, int_Q=True,\n", " defdict=dict(tau= [False, 'auxiliary'], \n", " omega=[False, 'auxiliary'], \n", " N= [False, 'auxiliary']))\n", "\n", "\n", "# define parameters for the simulated dataset\n", "index = pd.date_range('1.1.2018', '1.5.2018')\n", "inc = np.deg2rad(np.arange(1,90,1))\n", "# print fit-results\n", "simulation_param = pd.DataFrame(dict(tau = [.2, .5, .3, .56, .32],\n", " omega = [.1, .1, .54, .23, .62],\n", " N = [.4, .6, .8, .32, .24]), \n", " index=index)\n", "\n", "\n", "# evaluate the model for the given set of parameters\n", "data_sim = fit.calc(param=dict(), fixed_param=simulation_param, inc=inc, return_components=False)\n", "\n", "# select only parts of the incidence-angle range and add noise\n", "slices = [slice(4, 75 ,2), slice(9, 45), slice(14, 61), slice(31, 76), slice(20, 55)]\n", "dataset = pd.concat([pd.DataFrame({'sig':data_sim[i][sl], 'inc':inc[sl], 'date':index[i]}).set_index('date')\n", " for i, sl in enumerate(slices)])\n", "\n", "# add some random noise\n", "max_noise = dataset.sig.max()/20.\n", "noise = np.random.uniform(low=0., high=max_noise, size=len(dataset))\n", "dataset['sig'] = dataset['sig'] + noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fit a different model to the data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----------------------------------------------------------------------------\n", "# SCATTERING FUNCTIONS \n", " Volume: Rayleigh \| Surface: HenyeyGreenstein\n", "\n", "# Interaction-contribution? True\n", "\n", "----------------------------- FITTED PARAMETERS -----------------------------\n", " NAME \| START \| VARIABILITY \| BOUNDS \| INTERPOLATION \|\n", " tau \| 0.2 \| D \| 0.0-1.0 \| False \|\n", " omega \| 0.2 \| D \| 0.0-1.0 \| False \|\n", " N \| 0.2 \| D \| 0.0-1.0 \| False \|\n", "\n", "--------- FIXED PARAMETERS ----------\|---------- AUXILIARY DATASETS ---------\n", "-----------------------------------------------------------------------------\n", "# LSQ PARAMETERS \n", " verbose = 0 xtol = 0.001\n", " ftol = 1e-05 max_nfev = 100\n", "-----------------------------------------------------------------------------\n", "Done! (`xtol` termination condition is satisfied.) \n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x720 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# define Model to be fitted to the dataset\n", "# NOTE this is not the same specification as in the generation of the dataset! (so parameters WILL BE DIFFERENT !)\n", "def set_V_SRF(omega, tau, N):\n", " V = rt_V.Rayleigh(omega = omega, tau = tau)\n", " SRF = rt_SRF.HenyeyGreenstein(t=0.35, ncoefs=10, NormBRDF = N)\n", " return V, SRF\n", "\n", "defdict = dict(tau = [True, .2, 'D', ([0.], [1.])],\n", " omega = [True, .2, 'D', ([0.], [1.])],\n", " N = [True, .2, 'D', ([0.], [1.])])\n", "\n", "fit = Fits(dataset=dataset, verbose=0, \n", " sig0=True, dB=False, \n", " set_V_SRF = set_V_SRF,\n", " defdict=defdict, int_Q=True,\n", " lsq_kwargs=dict(verbose=0, \n", " ftol=1e-5, gtol=1e-5, xtol=1e-3,\n", " max_nfev=100))\n", "fit.model_definition\n", "fit.performfit(print_progress=True)\n", "\n", "# plot the obtained results\n", "fig = fit.plot.results(legend=True, legend_fmt='%d.%m.%Y')\n", "# overplot true-values\n", "_ = simulation_param.plot(ax=fig.axes[1], ls='--', marker='.', ms=10, lw=0.5)\n", "_ = fig.axes[1].legend(ncol=2)\n", "\n", "## plot the residuals\n", "#_ = fit.plot.fit_errors()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Re-fit the same model that has been used to generate the data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----------------------------------------------------------------------------\n", "# SCATTERING FUNCTIONS \n", " Volume: Rayleigh \| Surface: LinCombSRF\n", "\n", "# Interaction-contribution? True\n", "\n", "----------------------------- FITTED PARAMETERS -----------------------------\n", " NAME \| START \| VARIABILITY \| BOUNDS \| INTERPOLATION \|\n", " tau \| 0.2 \| D \| 0.0-1.0 \| False \|\n", " omega \| 0.2 \| D \| 0.0-1.0 \| False \|\n", " N \| 0.2 \| D \| 0.0-1.0 \| False \|\n", "\n", "--------- FIXED PARAMETERS ----------\|---------- AUXILIARY DATASETS ---------\n", "-----------------------------------------------------------------------------\n", "# LSQ PARAMETERS \n", " verbose = 0 xtol = 0.001\n", " ftol = 1e-05 max_nfev = 300\n", " gtol = 1e-05 x_scale = jac\n", "-----------------------------------------------------------------------------\n", "Done! (`xtol` termination condition is satisfied.) \n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x720 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x720 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fit = Fits(dataset=dataset, verbose=0, \n", " sig0=True, dB=False, \n", " set_V_SRF = set_V_SRF_orig,\n", " defdict=defdict, int_Q=True,\n", " lsq_kwargs=dict(verbose=0, \n", " ftol=1e-5, gtol=1e-5, xtol=1e-3,\n", " max_nfev=300,\n", " x_scale='jac'))\n", "fit.model_definition\n", "fit.performfit(print_progress=True)\n", "\n", "fig = fit.plot.results(legend=True, legend_fmt='%d.%m.%Y')\n", "# overplot true-values\n", "_ = simulation_param.plot(ax=fig.axes[1], ls='--', marker='.', ms=10, lw=0.5)\n", "_ = fig.axes[1].legend(ncol=2)\n", "\n", "## print residuals\n", "_ = fit.plot.fit_errors()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
idaholab/mastodon	examples/ex08/risk_calcs.ipynb	1	40968	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demonstration of the MASTODON FTA Python module" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis for the 2006 assessment" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Risk of collapse for the 2006 assessment is:\t[5.266270696624276e-05]\n" ] } ], "source": [ "from mastodonutils import FTA\n", "quant2006 = FTA.Quantification(\"gnf_2006\", \n", " logic='logic.csv', \n", " basic_events='bas_events_2006.csv',\n", " analysis='Fragility', \n", " hazard='seismic_hazard_2006.csv', \n", " IM=[0.04, 0.11, 0.21, 0.42, 0.64, 0.87, 1.09, 1.52, 3.26, 4.34, 6.51], \n", " lite=True,\n", " nbins=10,\n", " write_output=True)\n", "print 'Risk of collapse for the 2006 assessment is:\\t',quant2006.toprisk_2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis for the 2015 assessment" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Risk of collapse for the 2015 assessment is:\t[5.195393493062263e-06]\n" ] } ], "source": [ "from mastodonutils import FTA\n", "quant2015 = FTA.Quantification(\"gnf_2015\", \n", " logic='logic.csv', \n", " basic_events='bas_events_2015.csv',\n", " analysis='Fragility', \n", " hazard='seismic_hazard_2015.csv', \n", " IM=[0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.7, 2.0, 3.0, 5.0, 10.0], \n", " lite=True,\n", " nbins=10,\n", " write_output=True)\n", "print 'Risk of collapse for the 2015 assessment is:\\t',quant2015.toprisk_2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting the collapse frequencies of each bin" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([1.e-29, 1.e-26, 1.e-23, 1.e-20, 1.e-17, 1.e-14, 1.e-11, 1.e-08,\n", " 1.e-05, 1.e-02, 1.e+01]), <a list of 11 Text yticklabel objects>)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib\n", "matplotlib.rc('font',family='Times New Roman')\n", "%matplotlib inline\n", "plt.figure(figsize=(10,8))\n", "plt.plot(quant2006.toprisk_2_info[0],quant2006.toprisk_2_info[4],color='blue',label='2006',marker='o',\n", " markerfacecolor='blue',markersize=8,linewidth=2)\n", "plt.plot(quant2015.toprisk_2_info[0],quant2015.toprisk_2_info[4],color='red',label='2015',marker='o',\n", " markerfacecolor='red',markersize=8,linewidth=2)\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.xlabel('Sa (10Hz)',fontname='Times New Roman',fontsize=18)\n", "plt.ylabel('Collapse frequency',fontname='Times New Roman',fontsize=18)\n", "plt.legend(loc='best',fontsize=18)\n", "plt.grid(b=True,which='both')\n", "plt.xticks(fontname='Times New Roman', fontsize=16)\n", "plt.yticks(fontname='Times New Roman', fontsize=16)\n", "# plt.savefig('collapse_frequencies.png',dpi=300)" ] } ], "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": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 2 }	lgpl-2.1
initialkommit/kookmin	W12_pandas_basic.ipynb	1	301451	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 국민대, 파이썬, 데이터" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# W12 pandas Primer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Introduction to pandas\n", "2. Data Structures\n", "3. A Tour of pandas\n", "4. Plotting Basics with pandas" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Introduction to pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A. History" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 2008년, AQR 투자 운용 회사(Capital Management)에 다니던 Wes McKinney가 개발 시작\n", "- 2009년, 오픈 소스로 공개\n", "- 2016.05, 0.18.1 버전" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## B. Definition" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- https://github.com/pydata/pandas\n", "- http://pandas.pydata.org\n", "- pandas: Panel Data의 약자\n", " - 계량 경제학에서 주로 쓰이는 용어\n", " - 횡단면 데이터(개별 단위의 데이터를 한 시점에 모은 것) + 시계열 데이터(특정 개별 주체의 데이터를 여러 시점에서 모은 것)\n", " - 예) 개별 기업의 주식 가격을 모은 것이 횡단면 데이터, 한 기업의 주가를 여러 기간에 걸쳐 기록하면 시계열 데이터이다.\n", " - pandas라는 이름을 Panel Data에서 가져왔다는 것에서 알 수 있듯, 금융 회사에 다니고 있었던 Wes McKinney는 금융 데이터를 분석하기에 적합하고 통합적인 기능을 제공하는 도구를 원함\n", "- Official documentation에는 모두 소문자(pandas)로 사용하고 있으므로 이 문서에도 pandas로 사용" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## C. Installation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```bash\n", "$ pip install pandas\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## D. Coding Convention" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "import pandas as pd\n", "```" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# NumPy and pandas\n", "import numpy as np\n", "import pandas as pd\n", "from pandas import DataFrame, Series\n", "\n", "# Set pandas options\n", "pd.set_option('display.notebook_repr_html', True) # 기본값입니다. False로 하면 DataFrmae의 border=0이 됩니다.\n", "pd.set_option('display.max_columns', 10) # 10 이상 넘어가면 '...' 으로 표시됩니다.\n", "pd.set_option('display.max_rows', 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Data Structures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A. What is Data Structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- pandas를 공부하기 전에 먼저 Data Structure라는 것을 간단히 살펴보도록 하겠습니다. pandas의 중요한 객체인 Series, DataFrame이 바로 Data Structure이기 때문입니다.\n", "- 컴퓨터 공학을 전공하셨거나 관심이 있는 분은 '자료구조' 라는 책을 본적이 있을 겁니다.\n", "- Data Structure가 그 '자료구조'입니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Data Structure\n", " - Data의\n", " - 효과적인 운영을 위해\n", " - Data를 저장하고 (Storing or Collecting)\n", " - 관리/정렬/구조화/정렬/준비하는 (Organising)\n", " - 그릇\n", "- Data를 저장할 수 있는 그 어떤 것도 Data Structure가 될 수 있습니다.\n", " - Integer, Float, Boolean, Char etc.\n", " - 이것을 Primitive Data Structure라고 부릅니다.\n", " - 그 외에 Non-Primitive Data Structure가 있습니다." ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<img src=\"http://4.bp.blogspot.com/-TPH8lzMOP_s/VpASXNKdeNI/AAAAAAAAIAA/zsH9Ixjx0G0/s1600/data%2Bstructure.jpg\"/>" ], "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(url='http://4.bp.blogspot.com/-TPH8lzMOP_s/VpASXNKdeNI/AAAAAAAAIAA/zsH9Ixjx0G0/s1600/data%2Bstructure.jpg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "위 그림에서 Data Stucture의 분류 체계를 알 수 있습니다. 우리는 모든 요소들에 대해 보지는 않겠지만 데이터를 담는 그릇의 종류에는 여러가지가 있음을 알 수 있습니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## B. Why Data Structure is important in pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- pandas는 Data Manipulation을 돕는 고수준의 Data Structure를 제공하기 때문입니다.\n", "- 하지만 pandas 자체만으로는 Data Science Toolkit이라고 말할 수 없습니다. 데이터로부터 특정 결론을 도출하기 위해서는 NumPy, matplotlib 등의 패키지를 추가적으로 이용해야 합니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## C. Data Structures with Languages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1) Javscript" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- String\n", "- Array\n", "- Stacks etc." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2) Python Data Structures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- List/Tuple\n", "- Dictionary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3) NumPy Data Structures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 수치 계산(Numeriacal Computing)을 하는데 있어 매우 중요한 역할을 하는 패키지입니다.\n", "- numpy.ndarray\n", "- 아래 그림을 보면 알 수 있듯 numpy의 array는 데이터를 담는 그릇의 역할을 맡고 있습니다.\n", "- 다차원 배열이라 indexing과 slicing이 다소 헷갈리는 부분이 있는데, 아래 그림을 보며 학습해보면 좋겠습니다." ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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NVmdYu6ph/jQ2vneT7vPawXAXpg2TcADAl7rDvcDKbEIIIYQQwhoyJgSAK9p5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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='images/numpy_indexing.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4) pandas Data Structures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dimensions\|Name\|Path\|Description\n", "--\|--\|--\n", "1\|Series\|pandas.Series\|1D labeled homogeneously-typed array\n", "2\|DataFrame\|pandas.DataFrame\|General 2D labeled, size-mutable tabular structure with potentially homogeneouly-typed columns\n", "3\|Panel\|pandas.Panel\|General 3D labeled, also size-mutable array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pandas의 Data Structure는 세 가지가 있습니다. 거의 대부분 이용할 것은 Series와 DataFrame입니다. 각각 1차원과 2차원 객체입니다. 이 객체를 통해 데이터를 수집하고 Organizing할 수 있는 것입니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "자료 구조(Data Structure)를 다루는 건 쉽지 않습니다. 다만 여기서는 pandas에서 주로 사용하게 될 Data Structure인 Series와 DataFrame을 공부하기 전에 개념을 얇게라도 알고 갈 필요는 있습니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. A Tour of pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pandas에서 대부분의 시간을 할애할 대상은 Series, DataFrame입니다. Series와 DataFrame에 대해 감을 익혀보도록 하겠습니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1) Series" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Creation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pandas의 가장 기본이 되는 Data Structure입니다. NumPy Array와 흡사하지만 NumPy의 Array와는 다르게 index가 있다는 것이 특징입니다. List/Tuple/np.array로 쉽게 Series 객체를 만들 수 있습니다." ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 a\n", "1 b\n", "2 c\n", "3 d\n", "dtype: object" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = Series(['a', 'b', 'c', 'd']) # List\n", "a" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 a\n", "1 b\n", "2 c\n", "3 d\n", "dtype: object" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = Series(('a', 'b', 'c', 'd')) # Tuple\n", "a" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 a\n", "1 b\n", "2 c\n", "3 d\n", "dtype: object" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = Series(np.array(['a', 'b', 'c', 'd'])) # np.array\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "다만 NumPy의 array를 이용할 경우에는 다차원 배열로 만들면 안됩니다. Series 객체는 1 Dimension만 가능합니다." ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "ename": "Exception", "evalue": "Data must be 1-dimensional", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-80-7d6f2c2bc3be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSeries\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;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'b'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'd'\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 2\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/initialkommit/.virtualenvs/kookmin/lib/python3.5/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m data = _sanitize_array(data, index, dtype, copy,\n\u001b[0;32m--> 226\u001b[0;31m raise_cast_failure=True)\n\u001b[0m\u001b[1;32m 227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSingleBlockManager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/initialkommit/.virtualenvs/kookmin/lib/python3.5/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_sanitize_array\u001b[0;34m(data, index, dtype, copy, raise_cast_failure)\u001b[0m\n\u001b[1;32m 2969\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0msubarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2970\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2971\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Data must be 1-dimensional'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2972\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2973\u001b[0m \u001b[0msubarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_tuplesafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mException\u001b[0m: Data must be 1-dimensional" ] } ], "source": [ "a = Series(np.array([['a', 'b'], ['c', 'd']]))\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Series 객체를 출력해보면 두 개의 컬럼이 나타납니다. 각 컬럼의 내용은 아래와 같습니다. pandas는 Index Label을 0부터 시작해 자동으로 부여해주고 있습니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "첫 번쨰 컬럼\|두 번째 컬럼\n", "--\|--\n", "Index Label\|Series 객체의 Value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Lookup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Series 객체의 Value에 접근하는 방법은 아래처럼 Indexing을 하면 Value을 가져올 수 있습니다." ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'c'" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "두 개의 값을 한 꺼번에 가져오기 위해 List or Tuple 형태로 만들어 Index 번호를 가지고 해당 Value를 가져올 수 있습니다." ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1 b\n", "3 d\n", "dtype: object" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[[1, 3]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이번에는 Slicing을 해볼까요?!" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1 b\n", "2 c\n", "dtype: object" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[1:3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### User-defined index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pandas에서 자동으로 만들어주는 index 말고 직접 index label을 지정해줄수도 있습니다." ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "a 1\n", "b 2\n", "c 3\n", "d 4\n", "dtype: int64" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b = Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n", "b" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b['b']" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "b 2\n", "d 4\n", "dtype: int64" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b[['b', 'd']]" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "b 2\n", "c 3\n", "d 4\n", "dtype: int64" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b['b': 'd']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### zero-based index도 가능" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "b 2\n", "d 4\n", "dtype: int64" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b[[1, 3]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "즉, zero-based index로 원하는 값을 가져올수도 있고 특정 Index Label로 가져올수도 있습니다. 아래처럼 Index 만을 가져올수 있으며 이 떄는 Index 객체를 반환해줍니다." ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['a', 'b', 'c', 'd'], dtype='object')" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Series로 Time Series 다루기" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pandas로 데이터를 다룰 때 많은 경우 time series를 다루게 됩니다. 위에서 배운 것을 토대로 index에 날짜를 넣어보도록 하겠습니다. 날짜의 경우 일일이 입력하지 않고 pandas의 date_range() 함수를 이용해보겠습니다." ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2016-05-01', '2016-05-02', '2016-05-03', '2016-05-04',\n", " '2016-05-05', '2016-05-06', '2016-05-07'],\n", " dtype='datetime64[ns]', freq='D')" ] }, "execution_count": 177, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dates = pd.date_range('2016-05-01', '2016-05-07')\n", "dates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DatetimeIndex은 date/time을 나타내는 pandas Index 객체입니다. 이를 만들기 위해 date_range()라는 함수를 사용했습니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Series 객체를 통해 위에서 만든 Datetime 데이터가 있는 Index에 Value를 추가해보도록 하겠습니다." ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-01 80\n", "2016-05-02 92\n", "2016-05-03 82\n", "2016-05-04 85\n", "2016-05-05 97\n", "2016-05-06 84\n", "2016-05-07 78\n", "Freq: D, dtype: int64" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp1 = Series([80, 92, 82, 85, 97, 84, 78], index=dates)\n", "tmp1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Freq: D라고 각 날짜간의 간격이 Daily라는 정보도 함께 표현해주며 각 Index에 해당하는 Value의 Data Type이 integer라는 것도 보여주고 있습니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이번에는 위 일주일 간의 데이터의 평균을 구해보겠습니다. 지금은 의미가 없는 숫자 데이터이지만 매일 특정 내용으로 위와 같은 값이 들어올 때 한 주의 평균을 구하는 것은 기본적인 데이터 분석일 것이라 생각됩니다. NumPy에서 제공하는 통계 함수는 pandas.Series에 적용할 수 있습니다. 아래와 같이 평균값을 내보도록 하겠습니다." ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "85.428571428571431" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp1.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NumPy의 mean() 함수를 이용해서 아래와 같이 직접 평균값을 구할수도 있습니다." ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "85.428571428571431" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(tmp1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "소수점 이하 자리수가 뭔가 불편해 보입니다." ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "85.0" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.around(tmp1.mean(), decimals=0) # around or round: 반올림, ceil: 올림, floor: 내림" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "90.0" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.around(tmp1.mean(), decimals=-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Arithematic Operation(+, -, /, , ...)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Series간 수치 연산이 가능합니다. 이번에는 새로운 Series 객체를 만들고 두 Series 간 연산을 해보도록 하겠습니다." ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-01 86\n", "2016-05-02 79\n", "2016-05-03 89\n", "2016-05-04 98\n", "2016-05-05 85\n", "2016-05-06 85\n", "2016-05-07 75\n", "Freq: D, dtype: int64" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp2 = Series(np.random.randint(60, 100, size=7), index=dates)\n", "tmp2" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-01 -6\n", "2016-05-02 13\n", "2016-05-03 -7\n", "2016-05-04 -13\n", "2016-05-05 12\n", "2016-05-06 -1\n", "2016-05-07 3\n", "Freq: D, dtype: int64" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff_tmps = tmp1 - tmp2\n", "diff_tmps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Lookup" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-7" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff_tmps['2016-05-03']" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-7" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff_tmps['2016/05/03']" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-7" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff_tmps['2016.05.03']" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-7" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff_tmps['05.03.2016']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "위에가 가능한 이유가 Index Label에 들어가 있는 값은 String이 아니라 DatetimeIndex 객체로 만들어진 데이터이기 때문입니다. 아래를 보면 Dtype이 datetime인 것을 확인할 수가 있습니다." ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2016-05-01', '2016-05-02', '2016-05-03', '2016-05-04',\n", " '2016-05-05', '2016-05-06', '2016-05-07'],\n", " dtype='datetime64[ns]', freq='D')" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff_tmps.index" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-13" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff_tmps[3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2) DataFrame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Series 객체가 index label이 있는 Single Array였다면 DataFrame은 Series 객체가 두 개 이상있는 객체라고 생각하시면 됩니다. 가까이로는 엑셀의 표 혹은 데이터베이스의 Table과 유사합니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Creation" ] }, { "cell_type": "code", "execution_count": 104, "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>Math</th>\n", " <th>Philosophy</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-05-01</th>\n", " <td>80</td>\n", " <td>86</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-02</th>\n", " <td>92</td>\n", " <td>79</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-03</th>\n", " <td>82</td>\n", " <td>89</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-04</th>\n", " <td>85</td>\n", " <td>98</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-05</th>\n", " <td>97</td>\n", " <td>85</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-06</th>\n", " <td>84</td>\n", " <td>85</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-07</th>\n", " <td>78</td>\n", " <td>75</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Math Philosophy\n", "2016-05-01 80 86\n", "2016-05-02 92 79\n", "2016-05-03 82 89\n", "2016-05-04 85 98\n", "2016-05-05 97 85\n", "2016-05-06 84 85\n", "2016-05-07 78 75" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz = DataFrame({\n", " 'Math': tmp1,\n", " 'Philosophy': tmp2\n", " })\n", "quiz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "보는바와 같이 테이블 모양으로 데이터를 보여줍니다. 엑셀처럼 컬럼을 가지고 있습니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Column Lookup 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Series 객체와 마찬가지로 원하는 값을 찾을 수 있습니다. 여기서는 컬럼의 이름 값을 기준으로 데이터를 가져올 수 있습니다." ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-01 80\n", "2016-05-02 92\n", "2016-05-03 82\n", "2016-05-04 85\n", "2016-05-05 97\n", "2016-05-06 84\n", "2016-05-07 78\n", "Freq: D, Name: Math, dtype: int64" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz['Math']" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(quiz['Math'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "컬럼의 순서를 바꿔 데이터를 가져올수도 있습니다." ] }, { "cell_type": "code", "execution_count": 107, "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>Philosophy</th>\n", " <th>Math</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-05-01</th>\n", " <td>86</td>\n", " <td>80</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-02</th>\n", " <td>79</td>\n", " <td>92</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-03</th>\n", " <td>89</td>\n", " <td>82</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-04</th>\n", " <td>98</td>\n", " <td>85</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-05</th>\n", " <td>85</td>\n", " <td>97</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-06</th>\n", " <td>85</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-07</th>\n", " <td>75</td>\n", " <td>78</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Philosophy Math\n", "2016-05-01 86 80\n", "2016-05-02 79 92\n", "2016-05-03 89 82\n", "2016-05-04 98 85\n", "2016-05-05 85 97\n", "2016-05-06 85 84\n", "2016-05-07 75 78" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz[['Philosophy', 'Math']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "여기서 문제입니다. 특정 데이터를 찾을 때 Series 객체와 DataFrame 객체는 미묘하게 차이가 있습니다. 무엇일까요?" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-01 80\n", "2016-05-02 92\n", "2016-05-03 82\n", "2016-05-04 85\n", "2016-05-05 97\n", "2016-05-06 84\n", "2016-05-07 78\n", "Freq: D, dtype: int64" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp1" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-02 92\n", "2016-05-04 85\n", "dtype: int64" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp1[[1, 3]]" ] }, { "cell_type": "code", "execution_count": 110, "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>Math</th>\n", " <th>Philosophy</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-05-01</th>\n", " <td>80</td>\n", " <td>86</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-02</th>\n", " <td>92</td>\n", " <td>79</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-03</th>\n", " <td>82</td>\n", " <td>89</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-04</th>\n", " <td>85</td>\n", " <td>98</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-05</th>\n", " <td>97</td>\n", " <td>85</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-06</th>\n", " <td>84</td>\n", " <td>85</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-07</th>\n", " <td>78</td>\n", " <td>75</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Math Philosophy\n", "2016-05-01 80 86\n", "2016-05-02 92 79\n", "2016-05-03 82 89\n", "2016-05-04 85 98\n", "2016-05-05 97 85\n", "2016-05-06 84 85\n", "2016-05-07 78 75" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-01 80\n", "2016-05-02 92\n", "2016-05-03 82\n", "2016-05-04 85\n", "2016-05-05 97\n", "2016-05-06 84\n", "2016-05-07 78\n", "Freq: D, Name: Math, dtype: int64" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz['Math']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "똑같이 '[]'에 기준되는 값을 넣었지만 반환되는 값은 Series의 경우 row에 해당하며 DataFrame의 경우 Column에 해당됩니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "그렇다면 DataFrame의 row를 어떻게 가져올 수 있을까요? 우리가 이미 위에서 확인했지만 `quiz['Math']`의 반환값의 type은 Series 객체이기 때문에 아래와 같이 하면 됩니다." ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-02 92\n", "2016-05-04 85\n", "Name: Math, dtype: int64" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz['Math'][[1, 3]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Column Lookup 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DataFrame은 위와 같이 `quiz['컬럼이름']`으로 컬럼의 값을 찾을 수도 있지만 다른 방법도 제공을 합니다. 아래와 같이 속성처럼 사용할 수 있습니다." ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-01 80\n", "2016-05-02 92\n", "2016-05-03 82\n", "2016-05-04 85\n", "2016-05-05 97\n", "2016-05-06 84\n", "2016-05-07 78\n", "Freq: D, Name: Math, dtype: int64" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz.Math" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DataFrame의 컬럼과 인덱스 값도 알 수 있습니다. `quiz.columns`의 결과값은 아무리 Column이라고 해도 Index 객체로 반환됩니다. 즉 컬럼도 색인된 하나의 객체이기 Index 객체를 사용한 것입니다." ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['Math', 'Philosophy'], dtype='object')" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz.columns" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2016-05-01', '2016-05-02', '2016-05-03', '2016-05-04',\n", " '2016-05-05', '2016-05-06', '2016-05-07'],\n", " dtype='datetime64[ns]', freq='D')" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Arithematic Operation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DataFrame 객체 간 연산도 가능합니다." ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-01 -6\n", "2016-05-02 13\n", "2016-05-03 -7\n", "2016-05-04 -13\n", "2016-05-05 12\n", "2016-05-06 -1\n", "2016-05-07 3\n", "Freq: D, dtype: int64" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff = quiz.Math - quiz.Philosophy\n", "diff" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이번에는 각 날짜마다 Math와 Philosophy의 평균을 구해보도록 하겠습니다. axis는 축을 나타내며 default는 0입니다. 0=column 1=row로 생각하면 됩니다." ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-01 83.0\n", "2016-05-02 85.5\n", "2016-05-03 85.5\n", "2016-05-04 91.5\n", "2016-05-05 91.0\n", "2016-05-06 84.5\n", "2016-05-07 76.5\n", "Freq: D, dtype: float64" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "avg = np.mean(quiz, axis=1)\n", "avg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Adding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DataFrame 객체에 새로운 컬럼을 추가하는 것은 Series 객체를 하나 붙인다고 생각하면 됩니다." ] }, { "cell_type": "code", "execution_count": 118, "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>Math</th>\n", " <th>Philosophy</th>\n", " <th>avg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-05-01</th>\n", " <td>80</td>\n", " <td>86</td>\n", " <td>83.0</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-02</th>\n", " <td>92</td>\n", " <td>79</td>\n", " <td>85.5</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-03</th>\n", " <td>82</td>\n", " <td>89</td>\n", " <td>85.5</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-04</th>\n", " <td>85</td>\n", " <td>98</td>\n", " <td>91.5</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-05</th>\n", " <td>97</td>\n", " <td>85</td>\n", " <td>91.0</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-06</th>\n", " <td>84</td>\n", " <td>85</td>\n", " <td>84.5</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-07</th>\n", " <td>78</td>\n", " <td>75</td>\n", " <td>76.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Math Philosophy avg\n", "2016-05-01 80 86 83.0\n", "2016-05-02 92 79 85.5\n", "2016-05-03 82 89 85.5\n", "2016-05-04 85 98 91.5\n", "2016-05-05 97 85 91.0\n", "2016-05-06 84 85 84.5\n", "2016-05-07 78 75 76.5" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz['avg'] = avg\n", "quiz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Row Lookup - loc(), iloc(), ix()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "위에서 DataFrame 객체의 Row 값을 가져오기 위해 컬럼을 선택하고 Slicing해서 가져오는 방법을 택했습니다. 아래와 같이요." ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-01 80\n", "2016-05-02 92\n", "2016-05-03 82\n", "2016-05-04 85\n", "2016-05-05 97\n", "2016-05-06 84\n", "2016-05-07 78\n", "Freq: D, Name: Math, dtype: int64" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz['Math'][:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "위 방법은 특정 컬럼을 선택하고 그 컬럼의 모든 값을 가져오도록 하였습니다. 하지만 pandas는 하나의 Row에 해당하는 모든 값을 가져올 수 있도록 하는 함수도 제공을 하고 있습니다." ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Math 80.0\n", "Philosophy 86.0\n", "avg 83.0\n", "Name: 2016-05-01 00:00:00, dtype: float64" ] }, "execution_count": 120, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz.loc['2016-05-01']" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Math 80.0\n", "Philosophy 86.0\n", "avg 83.0\n", "Name: 2016-05-01 00:00:00, dtype: float64" ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz.iloc[0]" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Math 80.0\n", "Philosophy 86.0\n", "avg 83.0\n", "Name: 2016-05-01 00:00:00, dtype: float64" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz.ix[0]" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(quiz.iloc[0])" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['Math', 'Philosophy', 'avg'], dtype='object')" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz.iloc[0].index" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 80., 86., 83.])" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz.iloc[0].values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "loc()와 iloc()의 차이점이 보이시나요? loc()* 함수는 Index Label 값을 기준으로 반환해주며 iloc()는 Index 값을 기준으로 값을 반환해줍니다. 두 함수의 공통점은 Name으로 해당 Index Label Value를 보여주고 있다는 것입니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "근데 좀 이상합니다. 하나의 Row를 선택했는데 Row 형태로 안보이고 수직으로 정렬되서 데이터를 보여주고 있습니다. 즉 DataFrame 객체의 Row를 Series로 반환해주고 있습니다. 이것을 Pivoting 했다고 표현합니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "그렇다면 특정 Row를 선택하면 온전한 DataFrame 형태로 보고 싶을 때는 어떻게 할까요? 온전한 DataFrame 형태 즉, Table 형태로 보려면 아래와 같이 하면 됩니다. Filter로 생각하면 됩니다. 이 떄 반환값의 Type은 당연히 DataFrame 객체입니다." ] }, { "cell_type": "code", "execution_count": 126, "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>Math</th>\n", " <th>Philosophy</th>\n", " <th>avg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-05-03</th>\n", " <td>82</td>\n", " <td>89</td>\n", " <td>85.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Math Philosophy avg\n", "2016-05-03 82 89 85.5" ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz[quiz.index == '2016-05-03']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "loc(), iloc() 함수의 경우 한 개의 Row만 선택해 가져올 때는 Series로 변환되지만 여러개를 가져올 때는 DataFrame 객체로 반환됩니다." ] }, { "cell_type": "code", "execution_count": 127, "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>Math</th>\n", " <th>Philosophy</th>\n", " <th>avg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-05-01</th>\n", " <td>80</td>\n", " <td>86</td>\n", " <td>83.0</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-03</th>\n", " <td>82</td>\n", " <td>89</td>\n", " <td>85.5</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-04</th>\n", " <td>85</td>\n", " <td>98</td>\n", " <td>91.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Math Philosophy avg\n", "2016-05-01 80 86 83.0\n", "2016-05-03 82 89 85.5\n", "2016-05-04 85 98 91.5" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz.iloc[[0, 2, 3]]" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-01 80\n", "2016-05-03 82\n", "2016-05-04 85\n", "Name: Math, dtype: int64" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz.iloc[[0, 2, 3]].Math" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Filter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "그렇다면 'Math'과목에서 특정 점수 이상을 받은 날은 어떻게 알 수 있을까요?" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2016-05-01 False\n", "2016-05-02 True\n", "2016-05-03 False\n", "2016-05-04 False\n", "2016-05-05 True\n", "2016-05-06 False\n", "2016-05-07 False\n", "Freq: D, Name: Math, dtype: bool" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz.Math > 90" ] }, { "cell_type": "code", "execution_count": 130, "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>Math</th>\n", " <th>Philosophy</th>\n", " <th>avg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-05-02</th>\n", " <td>92</td>\n", " <td>79</td>\n", " <td>85.5</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-05</th>\n", " <td>97</td>\n", " <td>85</td>\n", " <td>91.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Math Philosophy avg\n", "2016-05-02 92 79 85.5\n", "2016-05-05 97 85 91.0" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz[quiz.Math > 90]" ] }, { "cell_type": "code", "execution_count": 131, "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>Math</th>\n", " <th>Philosophy</th>\n", " <th>avg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-05-01</th>\n", " <td>80</td>\n", " <td>86</td>\n", " <td>83.0</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-02</th>\n", " <td>92</td>\n", " <td>79</td>\n", " <td>85.5</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-03</th>\n", " <td>82</td>\n", " <td>89</td>\n", " <td>85.5</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-04</th>\n", " <td>85</td>\n", " <td>98</td>\n", " <td>91.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Math Philosophy avg\n", "2016-05-01 80 86 83.0\n", "2016-05-02 92 79 85.5\n", "2016-05-03 82 89 85.5\n", "2016-05-04 85 98 91.5" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz[quiz.index < '2016-05-05']" ] }, { "cell_type": "code", "execution_count": 132, "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>Math</th>\n", " <th>Philosophy</th>\n", " <th>avg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-05-02</th>\n", " <td>92</td>\n", " <td>79</td>\n", " <td>85.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Math Philosophy avg\n", "2016-05-02 92 79 85.5" ] }, "execution_count": 132, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quiz[(quiz.index < '2016-05-05') & (quiz.Math > 90)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Plotting Basics with pandas" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# matplotlib plotting functions\n", "import matplotlib.pyplot as plt\n", "\n", "# We want our plots inline (jupyter notebook)\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "우리가 이번에 살펴볼 패키지는 많이 사용되고 있는 시각화 패키지인 matplotlib입니다. pandas data structure를 받아 시각화 시켜줄 수 있는 훌륭한 도구입니다. 하지만 먼저 pandas의 DataFrame에서도 시각화 시킬 수 있는 방법부터 보도록 하겠습니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A. pandas.DataFrame.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "위에서 만든 DataFrame인 quiz를 plotting 시켜보면 아래와 같습니다. matplotlib를 이용하지 않은 것처럼 보이지만 사실 DataFrame의 plot 함수 자체가 matplotlib를 이용하고 있는 것이기에 가능한 것입니다." ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x109ec6588>" ] }, "execution_count": 157, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HODlnAWE/vK/Teev70fw1qr9xXYZCoaDHgDYc2ZeMpOMEae3vTcSqzzj37ldk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\nHZtwH/RAixnF36g+NfX1NaKDCzUaiR3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+fhFNDe2cqSdyQQ/P3gOA9y/nIXjLGlzc+jaM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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x109e4c3c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quiz.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "차트의 Title과 X축 라벨, Y축 라벨을 설정해보도록 하겠습니다." ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1099e7208>" ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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MM8+QnJzMgAEDbrutJEl3PBkolcqb7p3cikqlYtCgQWzYsIG1a9cyYcIEbT+KbEqLK9m2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\n+eWXjBkzRuUqbWdtfPHx8ZSWlpKYmKh2mTZpbGz79u0jKiq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"text/plain": [ "<matplotlib.figure.Figure at 0x1099219e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quiz.plot(title='Score Trend')\n", "plt.xlabel('Date')\n", "plt.ylabel('Score')" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1098a9eb8>" ] }, "execution_count": 171, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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9F1GYGhMeo/nFsvWZnYMF/p2diT+UAUBmSTUH04oZF2yY5aePhLqy+VQ+FbVK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\nY8w5aibJOG4IMgSJrXS321ETMVHCzMSITmgTg7Y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"text/plain": [ "<matplotlib.figure.Figure at 0x10988cd68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quiz.plot(title='Score Trend')" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x10a744e80>" ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10a755940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.xlabel('Date')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## B. matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이번에는 matplotlib.pyplot의 plot() 함수를 이용해 그려보도록 하겠습니다." ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10ac82ac8>,\n", " <matplotlib.lines.Line2D at 0x10acaa080>,\n", " <matplotlib.lines.Line2D at 0x10acaa2e8>]" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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atAlicXvh0fT0dLz33ntYv349MjIy2BKNcUR1rbikQgO2rrlL2sjdnArkZpZh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\nwQSbJnvhiz+LcDm/mlE5uAprRunEiRPYtGkT3nn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"text/plain": [ "<matplotlib.figure.Figure at 0x10a901278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.title('Score Trend')\n", "plt.xlabel('Date')\n", "plt.ylabel('Score')\n", "plt.plot(quiz)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
CNS-OIST/STEPS_Example	user_manual/source/API_2/Interface_Tutorial_5_Efield.ipynb	1	220392	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulating membrane potential\n", "\n", "<div class=\"admonition note\">\n", "Topics: Channels, Complexes, Voltage-dependant reactions.\n", "</div>\n", "\n", "This chapter introduces the concept of simulating the electric potential across a membrane in STEPS using a method that calculates electric potentials on tetrahedral meshes called 'E-Field' (see Hepburn I. et al. (2013) Efficient calculation of the quasi-static electrical potential on a tetrahedral mesh. Front Comput Neurosci. DOI: 10.3389/fncom.2013.00129).\n", "We'll be introduced to new objects that represent phenomena linked to the membrane potential simulation, such as voltage-dependent channel transitions and currents across the membrane. We will look at an example based on a very widely-used model in computational neuroscience, the classical Hodgkin-Huxley model of the action-potential, in molecular form. To demonstrate some useful techniques for spatial simulations we will model action potential propagation in a simple mesh. As with previous chapters, we will briefly introduce the model, then go through Python code used to run the model in STEPS, with thorough descriptions where necessary.\n", "\n", "We will start with spatial stochastic simulation in solvers `'Tetexact'`, then discuss what modifications are necessary to run the equivalent spatial deterministic solution in solver `'TetODE'`.\n", "\n", "## Markov gating scheme\n", "\n", "While many readers may not be familiar with conversion of the classical Hodgkin-Huxley (HH) model to a Markov gating scheme we will only give a brief description here, though there are many sources a reader may consult for a more detailed description (for example Hille B. Gating Mechanisms: Kinetic Thinking. In Ion Channels of Excitable Membranes, 3rd ed. Sinauer Associates, Sunderland, MA: 2001:583-589).\n", "In brief, conductances are converted to a population of individual channels (each with single-channel conductance of typically 20pS), and each individual channel may exist in one of a number of states with rates described of possible first-order transitions to other states. Certain assumptions, such as that the the rate constants do not depend on the history of the system (a Markov process), and with the simplification that states with the same number of 'open' and 'closed' gates behave\n", "identically regardless of specific configuration, lead to gating schemes as shown in the figure below for the HH potassium and sodium channels respectively.\n", "\n", "<img src=\"images/channels_states.png\"/>\n", "\n", "Let us first focus on the top row of the figure. In this representation the potassium channel is described by 4 gates which may be in open or closed configuration. State n3, for example, means that any 3 of the 4 gates are in open state. Where all 4 gates are open (state n4, grey) the channel may conduct a current; all other states are non-conducting states. The sodium channel is represented by 8 possible states- the m3h1 state is the conducting state.\n", "\n", "If we reuse the same type of graphical notation that we used in the [previous chapter](Interface_Tutorial_4_Complexes.ipynb), it becomes apparent that the states of these gating schemes map to the states of complexes with `NoOrdering`.\n", "\n", "Below the markov chains, we represented the full reaction networks when considering that the channels are made up of 4 subunits: 4 identical subunits that can be in closed ('c', light blue) or open ('o', dark blue) states for the K+ channel; 3 identical subunits that can be in closed (c, light orange) or open ('o', dark orange) states as well as one subunit that can be in inactivated ('i', light green) or activated ('a', dark green) states.\n", "The markov chains from the top row are resulting from the grouping of equivalent channel substates. For the K+ channel, state n0 in the markov chain corresponds to only one channel state in which all subunits are in closed state; there are thus 4 ways to go to state n1 in which one of the subunits becomes open, and this is why the markov transition from n0 to n1 is done with a $4a_n$ rate.\n", "The full reaction networks directly result from the application of the reactions over single subunits represented in the bottom row of the figure. K+ subunits can switch between closed and open states with respective rates $a_n$ and $b_n$. The same type of reactions apply to Na+ 'm' and 'h' subunits.\n", "\n", "The transition rates ($a_n$, $b_n$ for the potassium channel - $a_m$, $b_m$, $a_h$, $b_h$ for the sodium channel) should be very familiar to anyone well-acquainted with the HH model:\n", "\n", "\\begin{equation}\n", "a_n = \\frac{0.01\\times(10-(V+65))}{\\exp\\left(\\frac{10-(V+65)}{10}\\right)-1}\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "b_n = 0.125\\exp\\left(\\frac{-(V+65)}{80}\\right)\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "a_m = \\frac{0.1\\times(25-(V+65))}{\\exp\\left(\\frac{25-(V+65)}{10}\\right)-1}\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "b_m = 4\\exp\\left(\\frac{-(V+65)}{18}\\right)\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "a_h = 0.07\\exp\\left(\\frac{-(V+65)}{20}\\right)\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "b_h = \\frac{1}{\\exp\\left(\\frac{30-(V+65)}{10}\\right)+1}\n", "\\end{equation}\n", "\n", "Where V is the potential across the membrane (in millivolts). Modelled as a stochastic process where each state is discretely populated, these functions form the basis of the propensity functions for each possible transition at any given voltage (here units are per millisecond). Voltage continuously changes during simulation, yet over a short period of time the change is small enough so that the transition rates may be considered constant and stochastic algorithms applied. The transition rates must then be updated when the voltage change becomes large enough to merit a reevaluation of these functions.\n", "\n", "## Parameters and HH rate functions\n", "\n", "We first import the required modules:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import steps.interface\n", "\n", "from steps.model import \n", "from steps.geom import \n", "from steps.sim import \n", "from steps.saving import \n", "from steps.rng import \n", "\n", "import numpy as np\n", "import math" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we define some parameters for the simulation, which are intended to remain constant throughout the script. We start with the potassium channel and define the single-channel conductance, channel density and reversal potential, keeping to a conductance to 0.036 S/cm2." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Potassium conductance = 0.036 S/cm2\n", "\n", "# Potassium single-channel conductance\n", "K_G = 20.0e-12 # Siemens\n", "\n", "# Potassium channel density\n", "K_ro = 18.0e12 # per square meter\n", "\n", "# Potassium reversal potential\n", "K_rev = -77e-3 # volts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first thing to note is that, as usual in STEPS, units are s.i., which means in the above example the single channel conductance is given in Siemens and the reversal potential for the ohmic current is in volts.\n", "\n", "Similarly, we define parameters for the sodium channel, also choosing a single-channel conductance of 20pS:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Sodium conductance = 0.120 S/cm2\n", "\n", "# Sodium single-channel conductance\n", "Na_G = 20.0e-12 # Siemens\n", "\n", "# Sodium channel density\n", "Na_ro = 60.0e12 # per square meter\n", "\n", "# Sodium reversal potential\n", "Na_rev = 50e-3 # volts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The HH model also includes a leak conductance, which may also be discretised. The overall conductance is\n", "small compared to maximal potassium and sodium conductances, but we choose a similar channel density to give\n", "a good spatial spread of the conductance, which means a fairly low single-channel conductance:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Leak single-channel conductance\n", "L_G = 0.3e-12 # Siemens\n", "\n", "# Leak density\n", "L_ro = 10.0e12 # per square meter\n", "\n", "# Leak reveral potential\n", "leak_rev = -54.4e-3 # volts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next parameters require a little explanation. Taking the potassium conductance as an example, the\n", "potassium density will convert to a discrete number of channels that will give (approximately) our intended\n", "maximal conductance of 0.036 S/$cm^2$. In the molecular sense, this means that if all potassium channels\n", "are in the 'open' conducting state then we will reach the maximal conductance. However, in fact\n", "each individual channel can be in any one of 5 states (including the conducting state) (see figure above) and the sum of populations of each state should\n", "be equal to the total number of channels. For example, if the surface of the mesh is 100 square microns\n", "by the above density we expect to have a total of 1800 potassium channels in the simulation but at some time\n", "we might have e.g. 400 in the n0 state, 700 in the n1 state, 500 in the n2 state, 150 in the n3 state\n", "and 50 in the conducting n4 state, and the total at any time will be equal to 1800.\n", "\n", "So we intend to initialise our populations of channel states to some starting value. The details of how to\n", "calculate the initial condition will not be given here, but the factors used here are steady-state approximations for\n", "the HH model at an initial potential of -65mV. We then give a table of fractional channel state populations (which\n", "add up to a value of 1). For each channel state the factor multiplied by the channel density and the surface area\n", "of the mesh will give our initial population of channels in that state:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# A table of potassium channel population factors: \n", "# n0, n1, n2, n3, n4\n", "K_facs = [ 0.21768, 0.40513, 0.28093, 0.08647, 0.00979 ]\n", "\n", "# A table of sodium channel population factors\n", "# m0h0, m1h0, m2h0, m3h0, m0h1, m1h1, m2h1, m3h1:\n", "Na_facs = [[0.34412, 0.05733, 0.00327, 6.0e-05],\n", " [0.50558, 0.08504, 0.00449, 0.00010]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now define some more important parameters for our simulation. The first is temperature assumed for\n", "the gating kinetics, which we will give in units of degrees celsius but is not directly used in simulation\n", "(as we will see). The second is a current clamp that we intend for one end of the mesh. The third is a\n", "voltage-range for simulation. These parameters will all be discussed in more detail later:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Temperature for gating kinetics\n", "celsius = 20.0\n", "\n", "# Current injection\n", "Iclamp = 50.0e-12 #\tamps\n", "\n", "# Voltage range for gating kinetics in Volts\n", "Vrange = [-100.0e-3, 50e-3, 1e-4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we will declare a function that computes the HH transition rates in a standardized form. Each of the equations shown above can be modified to fit the following generic form (see Nelson ME (2005) Electrophysiological Models In: Databasing the Brain: From Data to Knowledge. (S. Koslow and S. Subramaniam, eds.) Wiley, New York, pp. 285–301):\n", "\n", "\\begin{equation}\n", "\\frac{A + B \\times V}{C + H \\times \\exp\\left(\\frac{V + D}{F}\\right)}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We thus implement the following function:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def HHRateFunction(A, B, C, D, F, H, V):\n", " num = A + B V\n", " denom = C + H * math.exp((V + D) / F)\n", " if num == denom == 0:\n", " return F * B / (H * math.exp((V + D) / F))\n", " else:\n", " return num / denom" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that, under certain conditions, both the numerator and the denominator can go to zero for some specific membrane potential. In this case, we use [L'Hôpital's rule](https://en.wikipedia.org/wiki/L'H%C3%B4pital's_rule) to return a meaningful value.\n", "\n", "Finally we set some simulation control parameters, the 'time-step' at which we will record data and the total time of the simulation. So we will run for 4ms and save data every 0.1ms:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# The simulation dt\n", "DT_sim = 1.0e-4 # seconds\n", "\n", "# The time until which the simulation should be run\n", "ENDT = 4.0e-3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model declaration\n", "\n", "We move on to the biochemical model description. This is quite different from previous chapters, with new objects to look at, which are important building blocks of any simulation that includes voltage-dependent processes in STEPS.\n", "\n", "To make our potassium, sodium and leak channels we need to use a new class: the [steps.model.Channel](API_model.rst#steps.API_2.model.Channel) class. This class inherits from [steps.model.Complex](API_model.rst#steps.API_2.model.Complex) so channels behave in the same way as complexes (see [previous chapter](Interface_Tutorial_4_Complexes.ipynb)); the main difference lies in `Channel`s being able to conduct currents across membranes.\n", "\n", "### Channel declaration\n", "\n", "As we saw with the first figure, both potassium and sodium channels are described with a markov chain and, while we could describe each state explicitely, we will instead take advantage of the fact that the `Channel` class inherits from `Complex`. We will thus create potassium channels with 4 identical subunits being in either open or closed state, and sodium channels with 3 identical m subunits and one h subunit each of which can also only be in two states. As we saw in the [multi-state complexes chapter](Interface_Tutorial_4_Complexes.ipynb), these declarations will yield the states that are represented in the top row of the first figure.\n", "\n", "We then proceed to declaring the `Channel`s, their associated `SubUnit`s and `SubUnitState`s:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "model = Model()\n", "\n", "r = ReactionManager()\n", "\n", "with model:\n", " ssys = SurfaceSystem.Create()\n", "\n", " # Potassium channel\n", " Ko, Kc = SubUnitState.Create()\n", " KSU = SubUnit.Create([Ko, Kc])\n", " VGKC = Channel.Create([KSU]4)\n", "\n", " # Sodium channel\n", " Na_mo, Na_mc, Na_hi, Na_ha = SubUnitState.Create()\n", " NamSU, NahSU = SubUnit.Create(\n", " [Na_mo, Na_mc],\n", " [Na_hi, Na_ha]\n", " )\n", " VGNaC = Channel.Create([NamSU, NamSU, NamSU, NahSU])\n", "\n", " # Leak channel\n", " lsus = SubUnitState.Create()\n", " Leak = Channel.Create([lsus])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The leak channel can only be in a single state but still needs to have an associated `SubUnitState`. It is however possible to skip declaring a `SubUnit` by giving the `SubUnitState` directly to the `Channel` constructor. Note that we declared a surface system since voltage dependent reactions are going to happen on the cell membrane. \n", "\n", "### Voltage dependent reactions declaration\n", "\n", "We move on to describing the transitions between channel states. Instead of declaring all the reactions from the markov chain, we only declare the subunit reactions from the bottom row of the first figure. STEPS will automatically compute the required channel states and apply the required coefficients for the reaction rates.\n", "\n", "All these reactions are voltage-dependent, the declaration of the rection themselves are the same as for normal reactions but instead of setting the `K` rate constant property to a specific number, we will set it to a `VDepRate` object (see [steps.model.VDepRate](API_model.rst#steps.API_2.model.VDepRate)).\n", "\n", "Note however that, since the voltage is always defined with respect to a membrane, STEPS does not support voltage-dependent reactions for volume systems, only for surface systems.\n", "\n", "We introduce temperature dependence and use the previously defined `celsius` variable to find `thi` at 20 degrees celsius:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "thi = math.pow(3.0, ((celsius-6.3)/10.0))\n", "\n", "_a_n = VDepRate(lambda V: thi 1e3 * HHRateFunction(-0.55, -0.01, -1, 55, -10, 1, V1e3), vrange=Vrange)\n", "_b_n = VDepRate(lambda V: thi 1e3 * HHRateFunction(1, 0, 0, 65, 80, 8, V1e3), vrange=Vrange)\n", "\n", "_a_m = VDepRate(lambda V: thi 1e3 * HHRateFunction(-4, -0.1, -1, 40, -10, 1, V1e3), vrange=Vrange)\n", "_b_m = VDepRate(lambda V: thi 1e3 * HHRateFunction(1, 0, 0, 65, 18, 0.25, V1e3), vrange=Vrange)\n", "\n", "_a_h = VDepRate(lambda V: thi 1e3 * HHRateFunction(1, 0, 0, 65, 20, 1 / 0.07, V1e3), vrange=Vrange)\n", "_b_h = VDepRate(lambda V: thi 1e3 * HHRateFunction(1, 0, 1, 35, -10, 1, V1e3), vrange=Vrange)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first declare voltage dependent rates with the `VDepRate` class. Its constructor takes a function as first parameter and an optional voltage range (with the `vrange` parameter). The function must take a voltage in Volts as a parameter and return a reaction rate constant in S.I. units. Here the rates correspond to the classical HH rates. \n", "`HHRateFunction` expects a voltage to be given in units of millivolts, and will return the transition rate in unit of /ms, so we apply the required conversions to get rate constants in S.I. units.\n", "\n", "Note that any callable function with one argument would be valid here but we chose to use lambda expressions for concision.\n", "\n", "The `vrange` argument is the voltage-range in which to evaluate the rate-function. It should be given as a Python list of length 3 with, in order: the minimum voltage, the maximum voltage, and the voltage-step. We should choose the voltage range to cover what we expect from the simulation, but not by too much since a smaller range gives faster performance, and the voltage-step should be chosen to give only a small error from linear interpolation between voltage-points. It is a very important point because if, during a simulation, the membrane potential goes outside the voltage range for any voltage-dependent reaction located in that membrane, the simulation will fail.\n", "In our example we choose a voltage range of -100mV to +50mV, and tell STEPS to evaluate the voltage every 0.1mV (`Vrange` was declared earlier, with other parameters).\n", "\n", "We then declare all reactions with:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "with model:\n", " with ssys:\n", " with VGKC[...]:\n", " Kc.s <r[1]> Ko.s\n", " r[1].K = _a_n, _b_n\n", "\n", " with VGNaC[...]:\n", " Na_hi.s <r[1]> Na_ha.s\n", " r[1].K = _a_h, _b_h\n", " \n", " Na_mc.s <r[1]> Na_mo.s\n", " r[1].K = _a_m, _b_m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that if we decided to declare n0, n1, etc. states explicitely, we could have declared reactions in the following way:\n", "\n", "```python\n", "...\n", "n0.s <r[1]> n1.s\n", "r[1].K = 4_a_n, _b_n\n", "...\n", "```\n", "We would not need to declare a new `VDepRate` object, we can simply directly multiply the existing one with the appropriate coefficient.\n", "\n", "Since we are declaring reactions between `SubUnitState`s, we first need to specify the `Channel` to which these reactions apply and the state in which this channel must be for the reaction to apply. Since our reactions always apply regardless of the channel state we use e.g.:\n", "```python\n", "with VGKC[...]:\n", " Kc.s <r[1]> Ko.s\n", " r[1].K = _a_n, _b_n\n", "```\n", "Since the reaction happens on the membrane, we need to specify the Channel position by adding the `.s` after the `SubUnitState`. If the state of the channel affected the voltage dependency of the reaction rates, we could have written:\n", "```python\n", "with VGKC[Ko, Ko, ...]:\n", " Kc.s <r[1]> Ko.s\n", " r[1].K = _a_n_2, _b_n_2\n", "```\n", "With this code, we only declare reactions for channels that have at least two subunits in the open state, and we use a different reaction rate.\n", "\n", "### Current declaration\n", "\n", "The final part of our model specification is to add currents. Presently in STEPS we have the choice of two types of current that have quite different behaviour: Ohmic currents- which are represented by [steps.model.OhmicCurr](API_model.rst#steps.API_2.model.OhmicCurr) objects- and currents based on the GHK flux equation- represented by [steps.model.GHKCurr](API_model.rst#steps.API_2.model.GHKCurr) objects. Since the Hodgkin-Huxley model utilises ohmic currents we only need to concern ourselves with those objects here.\n", "\n", "The assumption made in STEPS is that Ohmic current objects are used to model currents of ions that play no other important role in the system other than in membrane excitability, and so it is not necessary to add, in this example, ions of sodium and potassium diffusing both extra- and intra-cellularly. Because of the relatively large concentration of these ions simulating diffusion would be incredibly slowing to simulations with no perceptible benefit to accuracy. It is due to these arguments that an Ohmic current in STEPS will not result in transport of ions between compartments. The GHK current objects are able to model ion transport and so should always be used when modelling currents of important signalling ions, a good example of which for many systems is calcium.\n", "\n", "Because STEPS is primarily a discrete simulator the Current objects in STEPS are based on single-channel currents. A [steps.model.OhmicCurr](API_model.rst#steps.API_2.model.OhmicCurr) is linked to one or several `ComplexState` of the `Channel` and will result in an Ohmic current through every single Channel in that specific state located in the Membrane (which we will create later) at any given time. Therefore, to create an ohmic current in STEPS we need to pass information as to which Channel state the current will be applied to, as well as its single-channel conductance to this current, along with the reversal potential. As usual in STEPS all units are based on s.i. units, and so the single-channel conductance unit is Siemens and reversal potential unit is volts.\n", "\n", "The [steps.model.OhmicCurr](API_model.rst#steps.API_2.model.OhmicCurr) objects need to be created inside a `with ssys:` block and their constructor expects 3 arguments: a reference to a `ComplexState` or `ComplexSelector` specifying the state(s) to which this current applies, a single-channel conductance, and a reversal potential. At the top of our script we already defined conductance and reversal potential for all of our channels in this simulation, i.e. the potassium single-channel conductance ``K_G = 20.0e-12`` Siemens and reversal potential ``K_rev = -77e-3`` volts, the sodium single-channel conductance ``Na_G = 20.0e-12`` Siemens and reversal potential ``Na_rev = 50e-3`` volts, the leak single-channel conductance ``L_G = 0.3e-12`` Siemens and reversal potential ``leak_rev = -54.4e-3`` volts, so we use these values when creating the Ohmic current objects. The conducting states of the potassium, sodium and leak currents respectively are `VGKC[Ko, Ko, Ko, Ko]` (which corresponds to `K_n4` on the markov chain), `VGNaC[Na_mo, Na_mo, Na_mo, Na_ha]` (which corresponds to `Na_m3h1`) and `Leak[lsus]` (which is the only state of the leak channel):" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "with model:\n", " with ssys:\n", " VGKC_I = OhmicCurr.Create(VGKC[Ko, Ko, Ko, Ko], K_G, K_rev)\n", " VGNaC_I = OhmicCurr.Create(VGNaC[Na_mo, Na_mo, Na_mo, Na_ha], Na_G, Na_rev)\n", " Leak_I = OhmicCurr.Create(Leak[lsus], L_G, leak_rev)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As this guide uses jupyter notebook, we need to rewrite both context managers `with model:` and `with ssys:`, but in a normal python script, we would declare the currents in the same `with ssys:` block as the one in which we declared the reactions. Note that you can also group them in a single context manager `with model, ssys:`.\n", "\n", "`VGKC[Ko, Ko, Ko, Ko]` reprensents the K+ channel state in which all 4 subunits are in the open state. Note that it is possible to give a partially specified channel state as first parameter of the current constructor. For example, if we wanted to consider that the K+ channel is open if at least 3 subunits are in the open state, we could have written:\n", "```python\n", "VGKC_I = OhmicCurr.Create(VGKC[Ko, Ko, Ko, :], K_G, K_rev)\n", "```\n", "Note that, since all subunits are identical for the K+ channel, the position of the `:` does not matter as `Channel`s do not take `SubUnit` order into account by default, see the [documentation](API_model.rst#steps.API_2.model.Complex) for details.\n", "\n", "#### State dependent conductances\n", "\n", "Instead of giving a constant conductance, as we did in the main example, we could give a conductance that depends on the channel state:\n", "```python\n", "...\n", "K_conds = [0, 0.05K_G, 0.1K_G, 0.5K_G, K_G]\n", "K_depG = CompDepCond(lambda s: K_conds[s.Count(Ko)], [VGKC])\n", "\n", "VGKC_I = OhmicCurr.Create(VGKC[Ko, ...], K_depG, K_rev)\n", "```\n", "We declare a `Complex` dependent conductance with the`CompDepCond` constructor. The first parameter is a function that takes a `ComplexState` as argument and outputs a conductance; the second argument is a list of complexes (channels here) that specifies on which complexes will the conductance depend. Here we only depend on the state of the K+ channel. \n", "\n", "In the lambda function, we call the `Count` method on the channel state `s` in order to count the number of subunits that are in open state `Ko`, we then use this number to find the corresponding conductance in a small list that we declared just before: 1 open subunit corresponds to 5% of the maximum conductance, 2 open to 10%, 3 open to 50% and 4 open to the maximum conductance. \n", "\n", "The current is then created by specifying that it applies to all K+ channel states that have at least one open subunit. We could have applied it to all states but it would result in null currents since the conductance associated to all subunits being closed is 0. Note that when the first parameter of the current constructor corresponds to more than one channel state, several subcurrents are created and associated to the same current name (`'VGKC_I'` here). When saving data, these subcurrents can be accessed by specifying the state of the channel they correspond to, as we will see later.\n", "\n", "#### GHK currents\n", "\n", "Although we do not use GHK currents in the main example, we will quickly go over how to declare them. Assuming we want to declare a GHK Na+ current, we would have to write:\n", "```python\n", "with mdl:\n", " Na = Species.Create()\n", " Na.valence = 1\n", " \n", " with ssys:\n", " VGNaC_I = GHKCurr.Create(VGNaC[Na_mo, Na_mo, Na_mo, Na_ha], Na, VGNaC_P)\n", "```\n", "The current is created with the `GHKCurr` class that takes the conducting state(s) as first parameter, like for `OhmicCurr`, but then takes the ion for which the current is defined followed by the permeability of the channel (`VGNaC_P` here).\n", "Note that this permeability can be obtained with the `GHKCurr.PInfo` [class method](API_model.rst#steps.API_2.model.GHKCurr.PInfo).\n", "\n", "Like for conductances, it is possible to define state-dependent permeabilities:\n", "```python\n", "Na_perm = [0, 0.05VGNaC_P, 0.5VGNaC_P, VGNaC_P]\n", "Na_depP = CompDepP(lambda s: Na_perm[s.Count(Na_mo)], [VGNaC])\n", "VGNaC_I = GHKCurr.Create(VGNaC[Na_mo, ..., Na_ha], Na, Na_depP)\n", "```\n", "More details can be found in the `GHKCurr` [documentation](API_model.rst#steps.API_2.model.GHKCurr).\n", "\n", "## Geometry declaration\n", "\n", "Coming back to our main example, with the model completed we move on to geometry specification. To simulate action potential propagation we will demonstrate the rather unusual case of using a long cuboid mesh whereas other simulators may typically assume cylindrical geometry. This is partly to demonstrate that the only restriction on geometry used for the membrane potential calculation in STEPS is that it can be represented by a tetrahedral mesh. Since tetrahedral meshes are capable of representing real cellular geometry with high accuracy this opens up many interesting applications, yet for this example we will stick with a rather basic shape. As in previous sections we will import a mesh in Abaqus format, which represents a cuboid of length 1000µm in the z-axis, and a diameter of 0.44µm (which is an equivalent cylindrical diamter of 0.5µm) in the x and y axes:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "mesh = TetMesh.LoadAbaqus('meshes/axon.inp', scale=1e-6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then compute some element lists that will be used later:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "with mesh:\n", " facetris = TriList([tri for tri in mesh.tris if tri.center.z == mesh.bbox.min.z])\n", " injverts = facetris.verts\n", "\n", " memb_tris = mesh.surface - facetris\n", "\n", " # The points along (z) axis at which to record potential\n", " pot_pos = np.arange(mesh.bbox.min.z, mesh.bbox.max.z, 10e-6)\n", " pot_tet = TetList(mesh.tets[0, 0, z] for z in pot_pos)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first create a list (`facetris`) of the triangles that are on one of the ends of the axon (that is oriented along the z-axis) and extract the corresponding vertices with `facetris.verts`. We will use these vertices to inject current in the axon. The triangles in the membrane are all triangles in the mesh surface except the ones that are on the injection face. Finally, we declare a list of regularly spaced tetrahedrons along the axon that we will record potential from.\n", "\n", "We then declare the needed compartment, patch, and membrane for potential computation:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "with mesh:\n", " cyto = Compartment.Create(mesh.tets)\n", " patch = Patch.Create(memb_tris, cyto, None, ssys)\n", "\n", " # Create the membrane across which the potential will be solved\n", " membrane = Membrane.Create([patch])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last line creates a new and very important object for the membrane potential calculation, the 'membrane' itself. The membrane class, [steps.geom.Membrane](API_geom.rst#steps.API_2.geom.Membrane), simply consists of one or more patch objects which must together form one continuous surface, although the membrane may be 'open' or 'closed' ('closed' means all member triangles are directly connected to 3 other membrane triangles and so form a closed surface, and 'open' means some triangles have fewer than 3 neighbours and so the surface contains holes). Any channels that exist in the patch(es) that comprise(s) the membrane are available to conduct a current (specified by [steps.model.OhmicCurr](API_model.rst#steps.API_2.model.OhmicCurr) or [steps.model.GHKCurr](API_model.rst#steps.API_2.model.GHKCurr) objects). The INNER compartment(s) to the membrane patches will comprise the 'conduction volume' representing the intracellular region. The potential at all vertices in the membrane and conduction volume will be calculated and will vary with any channel, capacitive or externally applied currents, relative to the (earthed) extracellular region.\n", "\n", "Where the extracellular space is included in simulations the membrane may be comprised of internal mesh triangles, but for this relatively simple model the membrane is formed from triangles on the surface of the mesh and is comprised of only one patch. This patch contains an inner compartment consisting of all tetrahedrons in the mesh, which will form the conduction volume. \n", "\n", "The [steps.geom.Membrane](API_geom.rst#steps.API_2.geom.Membrane) constructor optionally takes an argument named `opt_method`. This allows the choice of a method for optimization of the ordering of vertices in the membrane and conduction volume, which is essential to produce an efficient calculation, as discussed in Hepburn I. et al. (2013) Efficient calculation of the quasi-static electrical potential on a tetrahedral mesh. Front Comput Neurosci. DOI: 10.3389/fncom.2013.00129. Two methods are presently available: 1) a fast ordering of vertices by their position along the principle axis, which is suitable if one axis is much longer than an other (as is the case here) and 2) a slower breadth-first tree iteration, which produces a similar result to method (1) in cable-like structures but offers a significant improvement to simulation efficiency in complex geometries. Although the initial search for (2) can be slow it is possible to save an optimisation in a file for a specific membrane with `Simulation` method [steps.sim.Simulation.saveMembOpt](API_sim.rst#steps.API_2.sim.Simulation.saveMembOpt), and this optimisation file can then be supplied as the `opt_file_name` argument to the membrane constructor, so each optimisation for any given membrane need only be found once. However, since this example uses a cable-like mesh we can use the faster principle-axis ordering method, though method (2) is recommended when working with complex, realistic geometries.\n", "\n", "There is also an optional boolean argument `verify`, which defaults to False, but if True will verify that the membrane is a suitable surface for the potential calculation- although this verification can take rather a long time for larger meshes, so should only be used when one is not confident in the suitability of the membrane.\n", "\n", "All membrane construction parameters are described ind etails in the [documentation](API_geom.rst#steps.API_2.geom.Membrane).\n", "\n", "## Simulation and data saving\n", "\n", "### Result selectors\n", "\n", "We will use the `'Tetexact'` solver and save the K+ and Na+ currents across the membrane as well as the potential at the regularly spaced tetrahedrons we mentioned before:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model checking:\n", "No errors were found\n" ] } ], "source": [ "rng = RNG('mt19937', 512, 1234)\n", "\n", "sim = Simulation('Tetexact', model, mesh, rng, True)\n", "\n", "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n", "\n", "rs = ResultSelector(sim)\n", "\n", "NaCurrs = rs.TRIS(memb_tris).VGNaC_I.I\n", "KCurrs = rs.TRIS(memb_tris).VGKC_I.I\n", "CellPot = rs.TETS(pot_tet).V\n", "\n", "NaCurrs.metaData['trizpos'] = [tri.center.z for tri in memb_tris]\n", "KCurrs.metaData['trizpos'] = [tri.center.z for tri in memb_tris]\n", "\n", "NaCurrs.metaData['triarea'] = [tri.Area for tri in memb_tris]\n", "KCurrs.metaData['triarea'] = [tri.Area for tri in memb_tris]\n", "\n", "CellPot.metaData['tetzpos'] = pot_pos\n", "\n", "NaCurrs.toFile('NaCurrs.dat')\n", "KCurrs.toFile('KCurrs.dat')\n", "CellPot.toFile('CellPot.dat')\n", "\n", "sim.toSave(NaCurrs, KCurrs, CellPot, dt=DT_sim)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we pass an additional boolean flag to the `Simulation` constructor, it specifies whether membrane potential calculations should be performed. This flag defaults to `False` so we could omit it in previous chapters, but since we do need membrane potential computations, we need to set it to `True`.\n", "\n", "Since the `VGNaC_I` and `VGKC_I` currents are both associated to a single channel state, the `NaCurrs` and `KCurrs` result selectors will save the current corresponding to this state for each triangle in `memb_tris`. If the currents were associated to several channel states, these same result selectors would save the sum of each subcurrent for each triangle. If one wanted to access a specific subcurrent one would write:\n", "```python\n", "rs.TRIS(memb_tris).VGKC_I[Ko, Ko, Ko, Kc].I\n", "```\n", "This result selector would only save the cubcurrent associated to K+ channels with 3 open subunits and one closed. In the same way, it is also possible to save the summed currents associated to several channel states:\n", "```python\n", "rs.TRIS(memb_tris).VGKC_I[Ko, Ko, ...].I\n", "```\n", "This would save the summed currents through all K+ channels that have at least two subunits in the open state. Finally, if one wants to save separately each subcurrent, one can write e.g.:\n", "```python\n", "rs.patch.LIST(VGKC_I).I\n", "```\n", "Iterating over the `VGKC_I` current object returns the sub currents it is composed of.\n", "\n", "Since we want to plot spatial current density profiles, we need to save the z position and area of membrane triangles as metadata. In the same way, we need to save the z position of the tetrahedrons from which we will record potential.\n", "Finally, all our result selectors will be saved to files in order to separate data analysis from the simulation script.\n", "\n", "### Initial state\n", "\n", "#### Setting channel counts\n", "\n", "We should first pause to look at how to specify conductance in STEPS models. Conductance in STEPS comes from [steps.model.OhmicCurr](API_model.rst#steps.API_2.model.OhmicCurr) objects, which provide a single-channel conductance that will be applied to any Channel State molecule to which that conductance is mapped. For example, recall in this model that we created an Ohmic Current called `VGKC_I` to represent the potassium current in the simulation, which will apply to Channel State `VGKC[Ko, Ko, Ko, Ko]`, with a single-channel conductance of 20 pS and reversal potential of -77mV.\n", "\n", "The overall potassium conductance in the simulation at any time will be equal to the number of `VGKC[Ko, Ko, Ko, Ko]` Channel States in existence multiplied by the single-channel conductance, with a maximum conductance equal to the highest possible number of `VGKC[Ko, Ko, Ko, Ko]` Channel States (the total number of potassium channels).\n", "\n", "Other simulators may use different methods from STEPS to specify conductance, and many modellers may be more comfortable working with conductance per unit area, so some care should be taken with the conversion for STEPS models. This typically involves multiplying conductance per unit area by the membrane area to find overall conductance, then injecting the correct amount of channels into the membrane in STEPS to represent this conductance, depending on the single-channel conductance. Since the conducting channels are discrete in STEPS there may be a small discrepancy from the continuous value.\n", "\n", "Recall we have specified potassium channel density, ``K_ro``, as 18 per square micron and sodium channel density, ``Na_ro``, as 60 per square micron, previously in our script.\n", "When multiplied by single-channel conductances it gives maximum potassium conductance of 0.036 Siemens per square cm and sodium conductance of 0.120 Siemens per square cm. So when injecting our channels in STEPS we simply need to multiply these densities by the surface area of the membrane to find the number to inject.\n", "\n", "We thus start a new run and initialize the counts of our channel states with:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "sim.newRun()\n", "\n", "# Inject channels\n", "surfarea = sim.patch.Area\n", "\n", "for state in VGNaC:\n", " prop = Na_facs[state.Count(Na_ha)][state.Count(Na_mo)]\n", " sim.patch.VGNaC[state].Count = Na_ro * surfarea * prop\n", "\n", "for state in VGKC:\n", " prop = K_facs[state.Count(Ko)]\n", " sim.patch.VGKC[state].Count = K_ro * surfarea * prop\n", "\n", "sim.patch.Leak[lsus].Count = L_ro * surfarea" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we set the count of channels by using the `Na_facs` and `K_facs` arrays that contain the fractions of channels that should be in each state. `K_facs` is sorted such that the first element corresponds to the state in which all subunits are in the closed state and the last element corresponds to all subunits in open state. `Na_facs` contains two arrays, the first one corresponding to the `Na_hSU` subunit in state `Na_hi` and the second one corresponds to state `Na_ha`. The two arrays are organized like `K_facs` but for the `Na_mSU` subunit.\n", "\n", "We iterate over all states of e.g. the K+ channel and retrieve its corresponding fraction with:\n", "```python\n", "for state in VGKC:\n", " prop = K_facs[state.Count(Ko)]\n", "```\n", "where `state.Count(Ko)` returns the number of subunits in state `Ko` that are in the channel `state`.\n", "\n", "#### Specifying membrane and EField parameters\n", "\n", "The next few lines of code set some important new simulation variables, all to do with the membranes potential calculation:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Set dt for membrane potential calculation to 0.01ms\n", "sim.EfieldDT = 1.0e-5\n", "\n", "# Initialize potential to -65mV\n", "sim.membrane.Potential = -65e-3\n", "\n", "# Set capacitance of the membrane to 1 uF/cm^2 = 0.01 F/m^2\n", "sim.membrane.Capac = 1.0e-2\n", "\n", "# Set resistivity of the conduction volume to 100 ohm.cm = 1 ohm.meter\n", "sim.membrane.VolRes = 1.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first line sets the time-step period for the potential calculation, specified in seconds. This tells STEPS how often to perform the 'E-Field' calculation to evaluate potential, and update any voltage-dependent processes in the simulation. The optimal value for this time-step will vary for different simulations, so some things should be kept in mind when making the choice. Firstly, the time-step should be short enough that the voltage change occurring during each time-step is small and voltage can be assumed constant during each time-step for any voltage-dependent processes in the model. A large time-step may result in loss of accuracy. Secondly, the shorter the time-step the slower the simulation will be. Thirdly, the time-step must be shorter or equal to the simulation time-step (this is 0.1ms in our model) so that at least one membrane potential calculation can be carried out per simulation time-step. As a rough guide 0.01ms is usually highly accurate, and it is not recommended to exceed 0.1ms. So for this simulation we choose a calculation time-step of 0.01ms (which happens to be the default value).\n", "\n", "The remaining lines respectively set the initial membrane potential (in Volts), the membrane capacity (in Farad per square meter), and the bulk resistivity of the volume enclosed by the membrane (in ohm meter).\n", "\n", "#### Current injection\n", "\n", "The last condition to set is something that will remain unchanged throughout our simulation in this example, which is a constant current injection at one end of the long cubic geometry. This will have an effect of inducing action potentials at the depolarised end, which will then propagate, and a constant current at the correct level will ensure a train of action potentials. In STEPS it is possible to inject current to any vertex in the conduction volume or any membrane triangle by setting their `IClamp` property with a simulation path: `sim.VERT(vert).IClamp = ...` or `sim.TRI(tri).IClamp = ...` (where current will be shared equally between its 3 nodes). Here, we have already found the vertices at one end of the geometry, the minimum z end, and stored them in the `injverts` `VertList`. We now wish to set the current clamp for each of these vertices as a share of the 50pA current we have already defined in variable `Iclamp`. Note: STEPS maintains the convention that the effect of a positive applied current is to make potential more positive, which is the opposite signing convention to channel currents.:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# Set the current clamp\n", "sim.VERTS(injverts).IClamp = Iclamp/len(injverts)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then run the simulation with:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Run the simulation\n", "sim.run(ENDT)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulation with `TetODE`\n", "\n", "Another option for spatial simulations is to use the deterministic solver `'TetODE'`. TetODE shares many similarities with Tetexact in terms of model and geometry construction operating on the same tetrahedral meshes, but solutions are deterministic. TetODE uses CVODE (http://computation.llnl.gov/casc/sundials/description/description.html) for solutions. Although solutions are therefore very different between solver Tetexact and TetODE, in terms of simulation construction there are only a few implementation differences.\n", "Therefore, we can use almost the exact same code as already introduced to run a deterministic simulation, with a few changes highlighted below.\n", "\n", "Nothing needs to change for the model and geometry descriptions, and we can go on to creating the TetODE solver simulation. As a deterministic solver, TetODE does not require a random number generator so that does not need to be created and can be omitted from the simulation construction step. To avoid ambiguity, the `calcMembPot` argument now needs to be given as a keyword argument while we could give it as a positional argument with Tetexact.\n", "\n", "We thus create the simulation with:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model checking:\n", "No errors were found\n" ] } ], "source": [ "sim = Simulation('TetODE', model, mesh, calcMembPot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is unfortunately not possible to record information about the spatial currents in TetODE (see [available simulation paths per solver](API_sim.rst#simulation-paths)), we thus remove anything to do with recording the Na+ and K+ currents, which only leaves electrical potential:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jules/.local/lib/python3.8/site-packages/steps/API_2/sim.py:1217: UserWarning: Cannot reset a TetODE solver, a new run was started but the solver was not reset.\n", " warnings.warn(\n" ] } ], "source": [ "rs = ResultSelector(sim)\n", "\n", "CellPot = rs.TETS(pot_tet).V\n", "\n", "CellPot.metaData['tetzpos'] = pot_pos\n", "\n", "CellPot.toFile('CellPotODE.dat')\n", "\n", "sim.toSave(CellPot)\n", "\n", "sim.newRun()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that, this time, we do not supply a `dt` argument to `sim.toSave(...)`. Instead, for reasons we will explain later, we will manually tell STEPS when the data should be saved.\n", "The call to `newRun()` displays a warning because `TetODE` does not reset the simulation upon a call to `newRun()`. This is not an issue as we will only run one simulation and the solver state is initialized upon creation of the simulation.\n", "\n", "Setting the initial conditions is done in the same way as for Tetexact:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# Inject channels\n", "surfarea = sim.patch.Area\n", "\n", "for state in VGNaC:\n", " prop = Na_facs[state.Count(Na_ha)][state.Count(Na_mo)]\n", " sim.patch.VGNaC[state].Count = Na_ro * surfarea * prop\n", "\n", "for state in VGKC:\n", " prop = K_facs[state.Count(Ko)]\n", " sim.patch.VGKC[state].Count = K_ro * surfarea * prop\n", "\n", "sim.patch.Leak[lsus].Count = L_ro * surfarea\n", "\n", "# Initialize potential to -65mV\n", "sim.membrane.Potential = -65e-3\n", "\n", "# Set capacitance of the membrane to 1 uF/cm^2 = 0.01 F/m^2\n", "sim.membrane.Capac = 1.0e-2\n", "\n", "# Set resistivity of the conduction volume to 100 ohm.cm = 1 ohm.meter\n", "sim.membrane.VolRes = 1.0\n", "\n", "# Set the current clamp\n", "sim.VERTS(injverts).IClamp = Iclamp/len(injverts)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is one important additions to the TetODE solver, the [steps.sim.Simulation.setTolerances](API_sim.rst#steps.API_2.sim.Simulation.setTolerances) method.\n", "To understand what this function does requires a little background on how CVODE works. Although there will only be a brief explanation here, thorough descriptions\n", "are available in CVODE documentation (http://computation.llnl.gov/casc/sundials/documentation/cv_guide.pdf).\n", "\n", "Solving STEPS models in CVODE requires supplying information of all the variables in a STEPS simulation at any time as a state vector to the CVODE solver. The variables in STEPS\n", "are the molecular species, which have unique populations in individual mesh elements (tetrahedrons and triangles) meaning that the state vector can be rather large (number_volume_specsnumber_tetrahedrons + number_surface_specsnumber_triangles). STEPS must also supply a function that describes the rate of change of each of these variables with time depending on other variables in the system. CVODE then finds approximate solutions (here STEPS choses the recommended Adams-Moulton formulas with functional iteration) when the system advances in time.\n", "\n", "To do this it takes a number of 'steps', each time estimating the local error and comparing to tolerance conditions: if the test fails, step size is reduced, and this is repeated until tolerance conditions are met. This means that there is a tradeoff between accuracy and simulation speed- with a high tolerance, steps sizes will be large and few steps will have to be taken to advance the simulation some amount of time though accuracy will be low, with low tolerance, steps sizes will be small so a large number of steps will be taken to advance the simulation although accuracy will be high. Therefore, the tolerance is an important consideration both for accuracy and efficiency.\n", "\n", "STEPS users can control the tolerances with the [steps.sim.Simulation.setTolerances](API_sim.rst#steps.API_2.sim.Simulation.setTolerances) method. Two different types of tolerance are specified: relative tolerance and absolute tolerance, and in STEPS both are scalars. Relative tolerance controls relative errors so that e.g. $10^{-3}$ means that errors are controlled to 0.1% (and it is not recommended to go any higher than that). Absolute tolerances can be useful when any components of the vector approach very small numbers when relative error control becomes meaningless. The absolute values in the internal state vectors within TetODE are the (fractional) number of molecules per tetrahedron or triangle, so if a user specifies an absolute tolerance of $10^{-3}$ it means that populations within tetrahedrons and triangles will be accurate to within 1/1000th of a molecule! In TetODE only one value each for absolute tolerance and relative tolerance can be specified, and will be applied to all species in all locations in the system. The default value for both absolute tolerance and relative tolerance is $10^{-3}$.\n", "\n", "We set tolerances with a call to [steps.sim.Simulation.setTolerances](API_sim.rst#steps.API_2.sim.Simulation.setTolerances):" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "sim.setTolerances(1e-3, 1e-4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first function argument is absolute tolerance, the second is relative tolerance. In this example we set an absolute tolerance of $10^{-3}$ and relative tolerance of $10^{-4}$.\n", "\n", "Note that the `setEfieldDT` method is not supported in TetODE: rather the E-Field time-step is implicitly taken as the simulation time step. E.g. one E-Field calculation will be performed every time the STEPS simulation\n", "is advanced with a call to [steps.sim.Simulation.run](API_sim.rst#steps.API_2.sim.Simulation.run). Therefore, in this model, to achieve an E-Field calculation time-step of 0.01ms we need to split the simulation time ourselves with e.g.:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# Run the simulation\n", "EFDt = 1e-5\n", "for i in range(int(ENDT // EFDt) + 1):\n", " sim.run(i * EFDt)\n", " if i % 10 == 0:\n", " CellPot.save()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Tetexact simulations, we saved data every 0.1 ms. To avoid potential issues when mixing automatic data saving and manual control of simulation steps, we explicitely save the data from `CellPot` every 10 iterations of the loop, i.e. every 0.1 ms. This is done by calling `CellPot.save()`.\n", "\n", "Before proceeding to result plotting, we reset the jupyter notebook so that the remaining code will be executed as if it was run in a separate script:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "%reset -f" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting the results\n", "\n", "We first import the required modules and define a function to plot the potential along the axon at a given time index `tidx`:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "import steps.interface\n", "\n", "from steps.saving import \n", "\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "\n", "def plotPotential(CellPot, tidx):\n", " plt.plot(\n", " CellPot.metaData['tetzpos'] 1e6, \n", " CellPot.data[0, tidx, :] * 1e3, \n", " label=f'{CellPot.time[0, tidx]1e3} ms'\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first argument will be the loaded `ResultSelector`. We then define another function to plot both K+ and Na+ current densities through the membrane along the axon:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "def plotCurrents(NaCurrs, KCurrs, tidx, nbins=100):\n", " for results, currName in zip([NaCurrs, KCurrs], ['Na', 'K']):\n", " data = results.data[0, tidx, :] 1e12\n", " pos = results.metaData['trizpos'] * 1e6\n", " areas = results.metaData['triarea'] * 1e12\n", " bins = np.histogram_bin_edges(pos, nbins)\n", " dig = np.digitize(pos, bins)\n", " # Ignore empty bins\n", " with np.errstate(invalid='ignore'):\n", " meanData = np.bincount(dig, weights=data) / np.bincount(dig, weights=areas)\n", " meanPos = np.bincount(dig, weights=pos) / np.bincount(dig)\n", " plt.plot(meanPos, meanData, label=f'{currName} {results.time[0, tidx]*1e3} ms')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we recorded currents for each membrane triangle, we first need to bin the currents along the axon.\n", "In order to simplify the code, we rely heavily on the numpy functions [histogram_bin_edges](https://numpy.org/doc/stable/reference/generated/numpy.histogram_bin_edges.html), [digitize](https://numpy.org/doc/stable/reference/generated/numpy.digitize.html), and [bincount](https://numpy.org/doc/stable/reference/generated/numpy.bincount.html).\n", "We first create bin edges for the triangle positions with:\n", "```python\n", "bins = np.histogram_bin_edges(pos, nbins)\n", "```\n", "We then assign each triangle to its bin with:\n", "```python\n", "dig = np.digitize(pos, bins)\n", "```\n", "Finally, we compute current densities and average bin position with:\n", "```python\n", "meanData = np.bincount(dig, weights=data) / np.bincount(dig, weights=areas)\n", "meanPos = np.bincount(dig, weights=pos) / np.bincount(dig)\n", "```\n", "Note that `np.bincount(dig)` returns the number of triangles in each bin while `np.bincount(dig, weights=areas)` returns the summed areas of triangles in each bin.\n", "\n", "### Tetexact simulation\n", "\n", "Finally, we load the corresponding result selector from file and plot the potential along the axon at different time steps:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x504 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "CellPot = ResultSelector.FromFile('CellPot.dat')\n", "\n", "plt.figure(figsize=(10, 7))\n", "plotPotential(CellPot, 10)\n", "plotPotential(CellPot, 20)\n", "plotPotential(CellPot, 30)\n", "plt.xlabel('Z-axis (um)')\n", "plt.ylabel('Membrane potential (mV)')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then plot the Na+ and K+ current densities along the axon for the same time steps:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x504 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "NaCurrs = ResultSelector.FromFile('NaCurrs.dat')\n", "KCurrs = ResultSelector.FromFile('KCurrs.dat')\n", "\n", "plt.figure(figsize=(10, 7))\n", "plotCurrents(NaCurrs, KCurrs, 10)\n", "plotCurrents(NaCurrs, KCurrs, 20)\n", "plotCurrents(NaCurrs, KCurrs, 30)\n", "plt.xlabel('Z-axis (um)')\n", "plt.ylabel('Current (pA/um^2)')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### TetODE Simulation\n", "\n", "We now plot the potential along the axon for the `'TetODE'` simulation." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x504 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "CellPotODE = ResultSelector.FromFile('CellPotODE.dat')\n", "\n", "plt.figure(figsize=(10, 7))\n", "plotPotential(CellPotODE, 10)\n", "plotPotential(CellPotODE, 20)\n", "plotPotential(CellPotODE, 30)\n", "plt.xlabel('Z-axis (um)')\n", "plt.ylabel('Membrane potential (mV)')\n", "plt.legend()\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }	gpl-2.0
sroecker/feinstaub	exploration.ipynb	1	133880	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_sz = pd.read_csv('data/sz-daily-2015.csv')\n", "df_sz.index = pd.to_datetime(df_sz.date)\n", "df_sz.drop('date', axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mean_temp</th>\n", " <th>max_temp</th>\n", " <th>min_temp</th>\n", " <th>humidity</th>\n", " <th>mean_wspeed</th>\n", " <th>max_wspeed</th>\n", " <th>wdegree</th>\n", " <th>pressure</th>\n", " <th>rainfall</th>\n", " <th>radiation</th>\n", " <th>radiation_balance</th>\n", " <th>uva</th>\n", " <th>uvb</th>\n", " <th>no</th>\n", " <th>no2</th>\n", " <th>ozone</th>\n", " <th>PM10</th>\n", " <th>PM2_5</th>\n", " </tr>\n", " <tr>\n", " <th>date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-01-01</th>\n", " <td>1.9</td>\n", " <td>3.3</td>\n", " <td>0.4</td>\n", " <td>89.4</td>\n", " <td>1.4</td>\n", " <td>5.4</td>\n", " <td>190.3</td>\n", " <td>1003.0</td>\n", " <td>0.00</td>\n", " <td>10.8</td>\n", " <td>-3.9</td>\n", " <td>2.23</td>\n", " <td>0.036</td>\n", " <td>21</td>\n", " <td>27</td>\n", " <td>13.0</td>\n", " <td>96.0</td>\n", " <td>87.0</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-02</th>\n", " <td>3.2</td>\n", " <td>6.0</td>\n", " <td>-0.9</td>\n", " <td>80.1</td>\n", " <td>1.8</td>\n", " <td>8.5</td>\n", " <td>234.6</td>\n", " <td>999.4</td>\n", " <td>1.00</td>\n", " <td>8.0</td>\n", " <td>-36.4</td>\n", " <td>2.21</td>\n", " <td>0.038</td>\n", " <td>10</td>\n", " <td>36</td>\n", " <td>16.0</td>\n", " <td>11.0</td>\n", " <td>9.0</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-03</th>\n", " <td>3.9</td>\n", " <td>11.2</td>\n", " <td>0.8</td>\n", " <td>88.3</td>\n", " <td>2.0</td>\n", " <td>15.3</td>\n", " <td>228.4</td>\n", " <td>992.8</td>\n", " <td>11.97</td>\n", " <td>11.4</td>\n", " <td>-24.7</td>\n", " <td>3.00</td>\n", " <td>0.061</td>\n", " <td>18</td>\n", " <td>35</td>\n", " <td>12.0</td>\n", " <td>9.0</td>\n", " <td>8.0</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-04</th>\n", " <td>3.6</td>\n", " <td>5.4</td>\n", " <td>2.1</td>\n", " <td>72.9</td>\n", " <td>2.1</td>\n", " <td>10.2</td>\n", " <td>272.6</td>\n", " <td>998.5</td>\n", " <td>0.00</td>\n", " <td>33.6</td>\n", " <td>-14.1</td>\n", " <td>4.01</td>\n", " <td>0.079</td>\n", " <td>4</td>\n", " <td>17</td>\n", " <td>31.0</td>\n", " <td>8.0</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-05</th>\n", " <td>2.7</td>\n", " <td>8.2</td>\n", " <td>-0.1</td>\n", " <td>66.6</td>\n", " <td>1.5</td>\n", " <td>4.5</td>\n", " <td>208.3</td>\n", " <td>1001.0</td>\n", " <td>0.00</td>\n", " <td>61.1</td>\n", " <td>-35.8</td>\n", " <td>5.17</td>\n", " <td>0.103</td>\n", " <td>52</td>\n", " <td>50</td>\n", " <td>10.0</td>\n", " <td>16.0</td>\n", " <td>11.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mean_temp max_temp min_temp humidity mean_wspeed max_wspeed \\\n", "date \n", "2015-01-01 1.9 3.3 0.4 89.4 1.4 5.4 \n", "2015-01-02 3.2 6.0 -0.9 80.1 1.8 8.5 \n", "2015-01-03 3.9 11.2 0.8 88.3 2.0 15.3 \n", "2015-01-04 3.6 5.4 2.1 72.9 2.1 10.2 \n", "2015-01-05 2.7 8.2 -0.1 66.6 1.5 4.5 \n", "\n", " wdegree pressure rainfall radiation radiation_balance uva \\\n", "date \n", "2015-01-01 190.3 1003.0 0.00 10.8 -3.9 2.23 \n", "2015-01-02 234.6 999.4 1.00 8.0 -36.4 2.21 \n", "2015-01-03 228.4 992.8 11.97 11.4 -24.7 3.00 \n", "2015-01-04 272.6 998.5 0.00 33.6 -14.1 4.01 \n", "2015-01-05 208.3 1001.0 0.00 61.1 -35.8 5.17 \n", "\n", " uvb no no2 ozone PM10 PM2_5 \n", "date \n", "2015-01-01 0.036 21 27 13.0 96.0 87.0 \n", "2015-01-02 0.038 10 36 16.0 11.0 9.0 \n", "2015-01-03 0.061 18 35 12.0 9.0 8.0 \n", "2015-01-04 0.079 4 17 31.0 8.0 5.0 \n", "2015-01-05 0.103 52 50 10.0 16.0 11.0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_sz.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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IvGoMRETOB3YqpZYOcv+rRORVEXnVHnQ1hlU1svmC6yKqC/qJinZPNVp6GYx3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\njO/Ok1NW3Up0tQpmVniMxiHdtqYRjScpDjo0y04zV5n7zspnKpfeGke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DyeSj4azbtanj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"text/plain": [ "<matplotlib.figure.Figure at 0x7f730a174048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_sz.PM10.plot()\n", "df_sz.PM2_5.plot()\n", "plt.axhline(y=50, color='r', label='Grenzwert')\n", "plt.xlabel('Datum')\n", "plt.ylabel('Feinstaub $\\mu g / m^3$')\n", "plt.legend()\n", "_ = plt.title('Schwabenzentrum')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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CrdzhPw18pPB4+XysC5K2Bn4E7GD7rfLnm2XAnX5mVeAXpJqzqzNPk+cwkibPw8DmttcH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\nsDhwEMnpVLL9E/Bl4P78eH5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"text/plain": [ "<matplotlib.figure.Figure at 0x7f7307fda518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corr = df_sz.corr()\n", "\n", "# Generate a mask for the upper triangle\n", "mask = np.zeros_like(corr, dtype=np.bool)\n", "mask[np.triu_indices_from(mask)] = True\n", "\n", "# Generate a custom diverging colormap\n", "#cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", "cmap = sns.color_palette(\"RdBu_r\", 12)\n", "\n", "# Draw the heatmap with the mask and correct aspect ratio\n", "#sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n", "# square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n", "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n", " square=True, linewidths=.5, cbar_kws={\"shrink\": .5});" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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qfMYF51hPBMd6IkZQ5YRwPAkhYLVKfDYet6KiOejDHz+2HJNG1+rpgJl53D6P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\ng4h+rl/NnjUJRlEgIjcRfZeI1ujRx8dMTzcQ0V+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"text/plain": [ "<matplotlib.figure.Figure at 0x7f7307ed2048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pm_ratio = df_sz.PM10 / df_sz.PM2_5\n", "pm_ratio.plot();" ] }, { "cell_type": "code", "execution_count": 7, "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>min</th>\n", " <th>mean</th>\n", " <th>median</th>\n", " <th>max</th>\n", " </tr>\n", " <tr>\n", " <th>date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.0</td>\n", " <td>15.692157</td>\n", " <td>14.0</td>\n", " <td>51.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1.0</td>\n", " <td>16.917308</td>\n", " <td>14.0</td>\n", " <td>49.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.0</td>\n", " <td>16.873077</td>\n", " <td>14.5</td>\n", " <td>42.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.0</td>\n", " <td>18.939216</td>\n", " <td>15.9</td>\n", " <td>96.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.0</td>\n", " <td>17.948000</td>\n", " <td>16.5</td>\n", " <td>51.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1.0</td>\n", " <td>15.905882</td>\n", " <td>14.0</td>\n", " <td>49.0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>0.0</td>\n", " <td>14.590000</td>\n", " <td>13.0</td>\n", " <td>37.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " min mean median max\n", "date \n", "0 0.0 15.692157 14.0 51.0\n", "1 1.0 16.917308 14.0 49.0\n", "2 0.0 16.873077 14.5 42.0\n", "3 0.0 18.939216 15.9 96.0\n", "4 0.0 17.948000 16.5 51.0\n", "5 1.0 15.905882 14.0 49.0\n", "6 0.0 14.590000 13.0 37.0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tage = ['Mo', 'Di', 'Mi', 'Do', 'Fr', 'Sa', 'So']\n", "df_sz.PM10.groupby(df_sz.index.dayofweek).agg(['min','mean', 'median', 'max'])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f7307dd7e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.boxplot(x=df_sz.index.weekday_name, y='PM10', data=df_sz)\n", "ax.set_xticklabels(tage)\n", "plt.xlabel('Wochentag');" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "df_nt = pd.read_csv('data/neckartor-daily-2016.csv')\n", "df_nt.index = pd.to_datetime(df_nt.date)\n", "df_nt.drop('date', axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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tPAZhZmaZnCDMzCyTu5isZVo50OtBXrP8OUFYywwODrLmkTUwqwUX25N8W7NpTfPX2t78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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f7307c937f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.boxplot(x=df_nt.index.dayofweek, y='PM10', data=df_nt)\n", "ax.set_xticklabels(tage);\n", "plt.xlabel('Wochentag');" ] } ], "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 }	gpl-3.0
bMzi/ML_in_Finance	0103_Plotting.ipynb	1	1002879	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting with Matplotlib\n", "## Introduction\n", "\n", "Most certainly you are familiar with the frase \"A pictures is worth a thousand words\". Good graphics are tremendously helpful in visualizing, understanding and analyzing data. Matplotlib is the standard plotting tool in Python. As the name hints, it was developped with MATLAB's plotting syntax in mind and people familiar with MATLAB will recognize the many similarities. \n", "\n", "This notebook aims at providing a basic introduction to Matplotlib, its syntax and how it is applied to data. As before, examples will lead the way. \n", "\n", "Important side note: Matplotlib is introduced as it is still understood as the backbone of plotting in Python. However, it must be said that Matplotlib has quite a few shortcomings. This has caused programmers to create seperate plotting packages that make good on the deficiencies of Matplotlib. An excellent overview can be found in [here](https://www.youtube.com/watch?v=0g2Zb2xVeCU). Yet all these well intended efforts by different programmers have caused somewhat of an atomization of plotting packages. Nowadays, the many packages & functions cause quite some confusion among Python programmers, especially for people new to programming. Which package should I use for my task? My advise is this: get going with Matplotlib. Then check the following alternatives to Matplotlib:\n", "* [Seaborn](http://seaborn.pydata.org/index.html)\n", "* [Altair](https://altair-viz.github.io/gallery/index.html#bar-charts)\n", "* [Bokeh](http://bokeh.pydata.org/en/latest/docs/gallery.html)\n", "* [Plotly](https://plot.ly/python/)\n", "\n", "## Imports\n", "Convention is to `import matplotlib.pyplot as plt`. Some people prefer to load `pylab` as it imports NumPy into its namespace and thus saves the user from a second import line. However, polluting your namespace makes it more difficult for others to understand your code and thus it is recommended to follow convention of separately importing `matplotlib.pyplot` and `numpy`. \n", "\n", "## Styles\n", "Matplotlib's standard style is aesthetically not all that pleasing and looks old-fashioned in the context of today's data visualization context. The developing community of Matplotlib addressed this by introducing custom styles. In this notebook we will be using the 'seaborn-whitegrid' style. For an overview of available styles see [Matplotlib's documentation](https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html). \n", "\n", "## Display Plots\n", "If you are running Python within Jupyter notebooks, you should add `%matplotlib inline` and end your plot commands with a semicolon (;). If you wish to make use of matplotlib's interactive features like zooming and panning you might consider using `%matplotlib notebook` instead. In case you run your Python code from a IDE/script, be sure to add `plt.show()` at the very end of your script." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Standard imports\n", "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "plt.style.use('seaborn-whitegrid')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matplotlib's Two Interfaces\n", "\n", "What might be confusing to some is that matplotlib is built to handle two different plotting interfaces: one which follows the MATLAB-style and a more powerful object-oriented interface. Two examples will show the differences.\n", "\n", "### MATLAB-Style Interface" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# x-axis range\n", "x = np.linspace(0, 10, 100)\n", "\n", "# Create first of two panels\n", "plt.subplot(2,1,1) # (row, col, panel no.)\n", "plt.plot(x, np.sin(x))\n", "\n", "# Create second of two panels\n", "plt.subplot(2,1,2)\n", "plt.plot(x, np.cos(x));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to change the figure and axes you could use the `plt.gcf()` (get current figure) and `plt.gca()` (get current axes) routines. \n", "\n", "\n", "### Object-Oriented Interface\n", "And here's the same plot using this style of plotting:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create grid of plots\n", "# ax will be array of two Axes objects\n", "fig, ax = plt.subplots(2)\n", "ax[0].plot(x, np.sin(x))\n", "ax[1].plot(x, np.cos(x));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the object-oriented style you directly work on the `figure` and `axes` objects. Sometimes, if you are asked to draw complicated figures, this approach might be advisable as it provides easier control over the `figure` and `axes` objects. But in general, these two interface styles provide the same functionalities - the differences are of technical nature. Chosing one approach over the other boils down to preference. \n", "\n", "In this notebook we will primarily use the MATLAB style but you should be aware of both, especially when searching the web for an answer to a specific question you are facing in plotting a figure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Line Plots\n", "### Simple Plot\n", "\n", "The most basic plot is the visualization of some function $f(x)$. For simplicity and as a toy example, let the function be the periodic $\\sin(x)$. We wish to plot $f(x)$ for interval $x \\in [0, 10]$." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0, 10, 100)\n", "\n", "plt.plot(x, np.sin(x));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we wish to add a second function to the plot, e.g. the cosine function, we can call the plot function multiple times." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, np.sin(x))\n", "plt.plot(x, np.cos(x));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Line Colors and Styles\n", "\n", "The `plt.plot()` function takes additional arguments which can be used to adjust the plot style. The `color` keyword takes a string argument and can be specified in a variety of ways:\n", "* Specify color by name (red, green, blue, cyan, magenta, yellow, black; [see here](https://matplotlib.org/examples/color/named_colors.html))\n", "* Short color code (r, g, b, c, m, y, k)\n", "* Grayscale between 0 and 1\n", "* Hex code (RRGGBB from 00 to FF)\n", "* RGB tuple, values $\\in [0, 1]$\n", "* all HTML color names ([see here](https://www.w3schools.com/colors/colors_names.asp))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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ipKQET09PYmJiSEhIICgoSFrcq0Ula4WiFlHIcYxMZy8fUUU59bmV23mbFoyQK1SyV8HGraLUcey06OS4bySMGiQ6PGRiPwGly6H8e8AhREqhfxFiJYlCJafTSU5ODmazGZvNhq+vL40bNyY+Ph5vmX9zqCYqWSsU1xkNjVNsYScGjrMeT7xozVhSmUYCneQGv1gIX2yA5RvFxGGThvDyFLF0VrZQqWK7sN7ZDgqhUshACBkOPvXlxUUMnZjNZnJzc3G5XISEhJCYmEhkZCSeEod2/iwqWSsU14kqKtjHQoykU0A2gUTTk1fpxOMEEys3+IEjsHQtbNkuujy6d4bxQyGljWShkkVsA7esBmc+eMVC+GMQPAB08koOmqZRXFyMyWSiqKgIDw8PoqOjSUhIICRE4oLdGkQla4XiGlPCWTKYyW7mY6OEOFIYwae0YjReSDzNOhzwzQ7hjj50DAIDYMwQGD0I9HIf3FF1VgywlG8BzQ5+7SBikliXJVGo5HK5yM3NxWw2Y7Va8fb2pmHDhsTHx9c5r5FK1grFNUBD4yw/sD3hDcx8A3jQgpF0JY363Cp3yrC4FL7aCF9ugIIiaJAAzz0Gg3uLhC0LzQ3WDOp7LwLTUfDwEYtmQ0aAb6K8uAgHi9lsJicnB6fTSVBQEM2bNyc6OlpqqaPKVcXhi4fx0Xxq/GurZK1QSMSBjYMsxYiBXPbiExjCrfyFzkwmjAZygx87DZ+thq+3iY0st3SAV6aKX2XWZt1WKPta9Ec7L+DrEQr1HoCQwaCTN7SjaRqlpaWYTCYKCgoAiIqKQq/XExISIrX17mLFReZmzWVW5ixyynPYMHADLanZsXeVrBUKCZSRQyaz2cUcrFwkilbcyTx8jqfQpnlHeYFdLtiWIUoduw+Kh4TD+olyRyO9vLgAjgsiQZd9fUmo1BLC7+fE+Sha1GstLazb7SYvLw+z2fwfQiXZhsN9ufswGA0sObAEu8tO/yb9+WjYRzRw1PwbsUrWCkUNYiIDIwYOsQw3LpoyhK6k0Zg+eOBBtiZBagRiC/iqzWKIJScf4qLhyQdEog6W2CusaWDbc0moZAR0ENRTlDr8ml36j+Tcs91uvzJl6HA4CAgIoGnTpsTExEgVKjndTlYfXY3BaGDb2W0EeAfwUIeHmNJlCi2ihGkvW4K8SiVrheJP4sJBNsvZyfuY2IkPwXRmMl2YQgRyZfecPi9cHeu+A5sdOraGpx+CHl1AYsLCbRNCpdKV4DgDnmEQNh5ChkgXKlksFkwmExcvXkTTNCIiIkhISJAuVCquLOaD3R8wM3MmZ0vP0iisEe/2e5cHOzxIPX+JY/eXUMlaoagmFVy8JFSaRRlmwkliAAbacz9+SGwHc7uFSGnpGiFW8vGG/j3ELsNmch/c4cyH0tVQth7cZeCTBFHPQmAv8Kz5h2qXcbvdXLx4EbPZfM2FStkXs0k3pvPp/k+xOqz0bNiTf/f/N0ObDUXnee1Wg6lkrVBcJbnsx4iB/SzGhZ1E+nEnc0liIJ5IfHBXYRVK0s/XwrkLEBkOj98Ddw2AehJte5oG9kNiyrBiu3gt8LZLpY7WUvuyq6qqrkwZVlVV4e/vT1JSErGxsVKFSm7NzcYTGzEYDWw6uQlfnS/j24xnWuo02se2lxb3t1DJWqH4A7hxcZQ1GDFwhu/xwp/23E8q04iu4af+/4EpVyTo1VtEwm7dFN54Bm6/VbJQqUoIlUpXQNVx8AyG0FEQOkxsB5dIeXk5JpOJvLw8NE2jXr16NGvWjPDwcKmljjJ7GZ/s+4TpGdM5VniMuKA4Xu/9Oo+lPEZUYJS0uH8ElawVit+gkpJLQqUZlHCaUBrQj3/SgYcI4L+vtPvTaBpk7heljh8yRatdv24wdgi0bvb7n/9ncBaBZY2QKrlKwLsBRKZB0O3ShUoFBQWYzeYrQqXY2Fj0ej2BgYHS4gKcKj7FjIwZfLjnQyx2C10SurB45GJGtRyFj05eeedqUMlaofgVCjh6Saj0MQ4qaEB37uBfNGMYOpk/Nja7ECp9tgZOnoWwEHjwbhg1EKIk2vZA6EhLV4jTNE4ISBWlDv+O0t3Rl6cMLwuVEhMTiYuLkypU0jSN7858h8FoYM3RNeg8dYxqOYq01DS66rtKi1tdVLJWKC7hxs1JvsZIOifYiA4fWjOOrqQRRwe5wfMK4Mv1sPxrKC2Dpo3h1WniwaGvxJOd5hR16NLlQvTv4S86OkKHgbfcvmyr1YrJZCI3Nxe3201oaOg1ESpVOipZtH8R6RnpHMw/SGRAJC91f4nHOz1OQojcze9/hj+UrPft28e7777LwoULOXv2LC+88AIeHh4kJyfz17/+tVabqhSK38NOOfv4BCPTKeQoQcTSm7+TwqMEESMvsKbB/iOi1PHtDtCAnqmiq6Njq2sgVFovOjtcF8ErHiKegOD+QvYvCU3TKCoqwmQyUVxcfEWopNfrCQ4OlhYXwGQxMTNjJvN2z6Oosoh2Me1YMHQB49qMw89L7vBMTfC7yXr+/PmsXr0af39Rq3r77bd58sknSU1N5dVXX+Wbb76hX79+0i9UoahpijlNBjPYzQfYsRBPZ0ayiJbcjRcST7NVDtj8o0jS2ScgOBDGD4O7B0G8xDcHEEtmS1eIHmmtSpQ4QqZCQBepQiWn00leXh4mk4nKykp8fHxo1KgR8fHx+PjI+7PWNI2fTD9hMBr46vBXaGgMazaMtNQ0ejTscd23v1wNv5usGzRowPTp03nuuecAOHToEF26dAGgR48ebN++XSVrRZ1BQ+MM32MknaOsxgNPWjKKVKahp6tcoVJhMZHLN8O2t6GwRIx/v/A4DOoNAfIe3KG5xHRh6Qqw7QUPXwjqB6HDwaeRvLhAZWUlZWVl/PTTT7hcLoKDg2nRogVRUVFS/0Zud9pZdmgZ6Rnp7LqwizC/MJ7q+hSTu0ymUVgjaXFByA1l8LvJun///phMpisfa5p25d0oMDCQsrIyOVemUNQgDio5wBKMpJPHfvyJoBsv0JlJhCC5TnnkpBAqbfqBKIcTbuskujpS20sWKlWAZSNYVoEzB3RREP4QBA8CnbyhHU3TKCkpwWQyUVhYCFw7oVJeeR5zds1h9q7Z5FXk0TyyObMGzWJiu4kE+cgbu3e5YO1aMBjgu+9gwwZvWrSo2RhX/YDx5++GFRUVvynuru58vM1mkzJbX5tR9ywHq1ceJ+p9xsmwZVR5lRBqa0rnotdpYBmMl+aHGQtmLDUf2OUiOOsQ4V9vJ+DYGdy+PpT06kJujxQ8LguVjh6t+biAj0c+9XTbCPU0ovOwY3UnUuR6gDJ7W7DqIMcMmGs8rqZp2Gw2rFYrTqcTDw8PAgIC0Ol0eHp6XvF4yOBQ0SEWHl/IhvMbcLgd9IjrwRspb3BLzC14enhy/uR5KXHLyjxZvjyMxYvrYTL5EBvr4C9/KSI6uqzG/9++6mTdsmVLjEYjqampbNu2ja5d/3uLS4tqvrVkZ2dX+3PrKuqeaw4NDRM72YmBbL7CjYtmDKUraTTy64VHvAfE13hYQWkZrNgEX6wTHR4JMfDUQ3gO60t4UCB5sr7PmgaVWaLUUZkBeENQLwgdToBvU2QOZNtstiuJ2Ol0EhgYSJMmTYiOjkan00n7PjvdTlZkr8BgNLD9/HaCfIJ4LOUxpqZOpWlE0xqP93OOHYP0dPj4Y6iogG7d4L33YMQIb7y8YsjOLqr2PWdlZf3q61edrJ9//nleeeUV3nvvPRITE+nfv3+1LkihqGmcVHGYL9iJgQtk4ksoqUyjC1OoR2O5wU+eEw8M138nls92bisE/906SRYqVYrtK6UrwXEOdPWg3r0QPAS85MqFfu6O1jSNyMhIEhISCAsLk1rqKKosYn7WfGZmzuS85TyNwxrz3h3v8WCHBwn1k+nLhk2bRKljwwbw8YGxY2HaNEhJkRb2Cn8oWev1epYtWwZA48aNWbRokdSLUiiuhnLyyWIumcymnBwiaMogZtCO+/BFoh7U7YYfd4kknbFP9EMP7CXq0UmN5MUFcOSKPYZlG8BdDr5NIep5oSf1kDdIclmoZDKZKCsrQ6fTkZCQQEJCwpWOMVkcyj9EujGdhfsXUumspE/jPswYNIPByYOlCpUqKuDTT8VJ+sgRiImB116Dxx8Xv79WqKEYRZ0lh70YMXCAJbioIokBpLKAJtwhV6hUboU1W4Svw5QL0REweSKM6C8mDmWhaWA7IAZYrD+J1wK7Q+gIIfqXLFS6XOq4LFRKTk4mJiZGulBp3bF1GIwGvjn9DX5efkxoM4FpqdNoE9NGWlyAM2dg5kz44AMoKRGn508/hdGj4Xqsb1TJWlGncOHkKKvYiYFz/IA3gXTkYbowlSiayw1+/gJ8vk4k6opKaNscJk2EPreAxISFuwoqvhWljqqTQqgUNhpC7pQuVCorK8NsNl8RKoWHh5OQkCBdqGSxW/hoz0dMz5jOyeKT6EP0vH372zzS8REiAuSN3WsabNsmSh2rVon3v5EjIS0Nbr1V7pzS76GStaJOYKWIPXxIBjMo5RxhNOIO3qUDD+FPmLzAmgbGveIU/eMuUX/u101MGbZKlhcXwFkAlrVgWQfuEvBuBJFPiaWznvIm7txuN4WFhZhMJkpLS/H09CQuLo6EhATpQqUTRSeYbpzOR3s/oqyqjFvr38pbt7/FiOYj8NbJK+9UVsJnn4lSx759EB4Ozz0HkyZB/frSwl4VKlkrajX5HMZIOvtZiAMrjejFAAw04048kfjgrtIG67+Hz9fAqfMQHgoPjxHu6EiJtj0AW7bo6qjYBrgh4BYxwOLXXrpQ6bI72m634+fnR5MmTYiNjZUuVNpyagvpGemsO7YOL08vxrQeQ1pqGp3iO0mLC3DhAsyaBXPnQkEBtG4N8+fDPfeA5BL8VaOStaLW4cbNCTawEwOn2IwOX9pyD6lMI5Z2coPnXhSn6FWbxV7DZonwWhrc0UNsZJGF5hTJuXQF2I+AR4BI0CHDwDtOXlzEvMRld7Tb7SYsLIykpCQiIyOlljqsDisL9y0kPSOdwxcPEx0YzSs9XuGJzk8QGxQrLS6A0ShKHV98IQZa7rxTlDp6976+pY7fQiVrRa3BThl7+Rgj0yniOMHE04c3SOFRApEoftc02HtYaEm/3wkeQO9bRKmjXQvJQqUSUeawrAFXIXgnQMRkCL4DPOV1R/+aUCkmJga9Xk9QkMQOGuBsyVlmZs7kg90fUGwrpmNcRz4Z/gljWo3B10vek7uqKvjyS5GkMzIgJASmTBH/NGkiLWyNoZK14rpTxEmMTGcPC6iiDD1d6c3facld6JB4mq1ywKZtsHStGAkPCYKJI4RQKVbyVhD7yUuljm9Bc4B/CoQ+Bf6dwUNeJ4vT6bzijr4sVGrcuDFxcXHShUo/nvsRg9HAiiMr8MCDES1GkJaaxm31b5N6gr94UZQ5Zs2CnBxITha16fvvB8mivxpFJWvFdUFDIy/gJ/bwPMdYiyc6WjGaVNLQ00Vu8IIi+HIDLN8IRaWQWB9enCSESn4Se7I0F8Ge++DCB2DbDx5+ENT/klCpoby4CKHSZXe0y+UiJCSERo0aSRcq2Zw2VpxewZfbvmRP7h7q+dXj2VufZVLnSTQIbSAtLsDevSIpL1kCdjvccYdowxswQK6SRRYqWSuuKVVY2c8ijKRzseEhAoiiBy/TiccJkTYDfolDx2Hpati8XRQqu3USpY4u7SSXOsqgbCNYVqP3zgVnNIQ/AsEDQSfvaPd/hUoeHh6/ECrJJKcsh9m7ZjNn1xwuWi/SMqolcwbPYULbCQT6yOsocblg9Wp4/33RghcQAA88AFOnQkvJqzJlo5K14ppQynkymMlu5lNJEbG0p8uFN+kX/zTeSBS/O53wzQ7x0HD/EQj0FyuyxgyG+pLfHKrOgWUllG0GzQZ+bTBVDkbf+G6p7miXy0VeXh5ms5mKigq8vb1p2LAh8fHx+Eqe5sg0Z2IwGlh2aBlOt5PBTQczPG44D/Z8UGqpo7gYPvwQZsyAs2ehYUP417/goYegntyp+2uGStYKaWhonGM7RgxkswLQaM5wUkmjId05UnoE73hJibrEIsocX26A/EKoHwd/eQSG3A5BErVGmhsqd10SKu0So9+BfUSpwzeJsuxsaYnaZrNhNpvJycnB6XQSFBREs2bNrgiVZOFwOVievRyD0cBPpp8I9glmUudJTOkyhaTwJLKzs6Ul6iNHRKnjk0/AaoUePYRQaehQuXNK14Mb7HYUtQEndg7yOUbSySELP8K4hafpzCTq0Uhu8BNnRFfHxq1CqNSlHfzPJLgtRbI7uhLKNomTtMMEunCodz+EDAZdmLSwmqZhsVgwmUxcvHgRgMjISPR6PaGhoVJPswXWAuZlzWNW5izMZWaSwpMwDDBwf/v7CfGVV2Zxu2HjRtHVsWmTECqNHy9a79q3lxb2uqOStaLGKCOXXcxhF3OoII9IWjCY2bRjIj5InHxzueCHTCFU2nVACJUG94ExQ6CJ3IdYOHKE3N+yATQr+DaH6P8Rzg7JQqX8/HxMJhPl5eV4eXlRv3594uPjpQuV9uftJ92YzuIDi7E5bfRN7MucIXMYlDwIT4mdLGVl4gQ9fbpQlMbFweuvw6OPQrTcqftagUrWij/NBbLYiYGDLMWNg2QG05U0Eukrd01WWTms2iLc0eY8iImEqffB8DsgVGJPlqaJ9VilKy8JlTwhsIcQKvnJdZLb7fYrQiWHw0FAQADJycnExsZKLXW43C7WHFuDwWjg+zPf4+/lz71t72Va6jRaRbeSFhfg1ClRi/7wQ7BYoHNnWLwYRo0Sp+qbBZWsFdXChZMjrGAnBs6zHR+C6MTjdGEKkcgVv3PGJB4Yrv1WjIW3bwlT74deXcFLpjvaLhbNWlaKxbOeIRA2FkKGglekvLgIoZLJZCI/P/+KUEmv11OvXj2ppY4SWwkL9ixgRsYMTpecpkFoA/7Z95881PEhwv3ljd1rmliPZTDAmjVCyXL33cId/Rv7Tm5oVLJWXBVWCsliPpnMxIKJejRhAO/TnvvxQ574Hbcbdu4RpY4du8HbC/r3EK13zSWPnzkvCne0ZT24LeCTCJFPXxIqyeuucLvdFBQUYDKZsFgs6HQ64uPjSUhIICBA5u4XOFpwlHRjOp/s+4QKRwXdG3TnX/3+xbDmw/DylJc2KivFqTk9HQ4cgMhIePFFeOIJSJC8KrO2o5K14g+Rx8FLQqVFOKmkMbczmFkkM0iuUMlaCeu+E0n6rBki6sFj42Fkf/F7WWga2H8uVOKSUGkE+LWVLlS6XOr4uVApLi5Oujt608lNGIwGNp7YiI/Oh3Gtx5GWmkaHuA7S4gKYTMIdPW8eFBVB27ai7DF+PPhJ7OysS6hkrfivuHFxjHUYMXCab/HCj7ZMJJVpxNBabvALebBsHazcDOUV0DIJ/v6U0JNKNMChOaB8K1hWgP0YeAZC6MhLQiW5cqHy8vIr7ujLQqXk5GQiIiKkljrKq8r5dN+npBvTOVp4lNigWP7W6288lvIYMUHyVqFoGvz0kyh1fPWV+HjoUHjySdGCV1uFStcLlawV/4GNUvbwERnMoJiThKDndt4mhUcIQJ74HU2DrINCS7o1QwiV+twqSh1tm8v96XUWQ9la4Y92FYF3fYicBkF9wVNed4WmaVfc0SUlJXh6ehITE0NCQoJ0odLp4tPMyJjBh3s+pNReSqf4TiwcsZDRrUbjo5P35K6qCpYtE6WOzEwIDRUJesoUaNRIWtg6T7WStcPh4IUXXsBsNuPp6cnrr79Ok7qgrVL8JgUcI4Pp7OVjqiinPrfRl7dpzgh0Mt/X7VWiL3rpGjh+RnRy3DcSRg0SHR4ysR8XpY7y7wGHECmF/kWIlSQLlS67o202G76+vjRu3Jj4+Hjp7uitZ7diMBpYfXQ1HngwquUopqVO4xb9LVJP8Hl5Qqg0ezbk5kKzZqL0ce+9IPl96YagWj+BW7duxel0snTpUrZv387777/P9OnTa/raFNcADY2TbMaIgeOsxxNvWjOWVKaRgFzxu1exBWYtEpOGJRZo0hBengIDekoXKlGxXZQ6bAeFUClkIIQMBx+5a0GcTifHjx//hVApMTGRyMhIqUKlSkclnx38DIPRwP68/UT4R/D8bc8zqfMk9CF6aXEBDh/24513xCaWqioYOFAMsPTrVzeFSteLaiXrxo0b43K5cLvdVxryFXWLKirYx0KMpFNANoFE05O/0onHCUZubZYDR2DpWpK2/AhuDXp0gXF3QkobyUIli9gGblkNznzwioXwxyB4AOjkHe00TaO4uBiTyURRUREeHh5ER0ej1+sJluzoNFvMzMqcxdysuRRWFtI6ujXz75zPPW3uwd9bXnnH6YSVK0U9+scfGxMYCI88IoRKzZpJC3tD46Fpmna1n5STk8OkSZOwWq0UFxczZ84cOnbs+Iv/Jisrq9rtRTabDb+b7BHwtbrnCi8zJ8I/42TYlzh0FupVtiS5eCINLAPRaRInDJxOQjIOEP71dvxPncfl70tht45YBnTHES2xDg74eOQQrttKqGcmnh4OKtzJFLl6Uu5uDRK3oGuaRmVlJVarFZfLhaenJ97e3gQHB0sdYAHYV7iPhccWssm0CZfmond8byYkTyA1OlVuX3aJJ199FcaSJeHk5Hij11cxenQ+Y8ZUEBzslha3tvFnfp6tVispKSn/8Xq1jsQff/wx3bp145lnniEnJ4f77ruPNWvW/IfRq0WL6k1zZWdnV/tz6yoy71lD4yw/YMTAEVYCHrRgJF1Jo77/rXj4eyDNTlpUAsu/FkKlgiJokADPPYZuSB8Kz56R933W3GDNEKWOyt3g4SMeFoYMJ9A3Uebw+68KlfR6PdHR0Rw9elTaPVe5qvjy8JcYjAYyzBmE+IYwNXUqU7pMIbFeopSYlzl0SDwwXLhQ9Er37i1q00OG+HDsWJn6eb4KsrKyfvX1aiXrkJCQKw9BQkNDcTqduFyual2YQh4ObBxkKUYM5LIXf8K5jefozCRCkbyy+egp8cDw621iI8stHeCVqeJXqUKlCij7GkpXgfMC6CKh3oMQMgh08oZ2NE2jtLQUk8lEQUEBwC/c0TJPs/kV+czdNZfZu2aTU55D04imTB84nfva3Uewr7wyi9sN69eLUseWLeDrCxMmiCnDtm2lhb1pqVayvv/++3nxxRcZP348DoeDp556SvpEleKPY+ECu5jNLuZi5SJRtOJO5tGGe/BB4vfJ6YJtRpGkdx8SDwmH9hWtd43kPsTCYRaujrJNl4RKLSH8AQjsBh7ynqm4XC7y8/Mxm82/EColJCRIL2vtzd2LwWjgswOfYXfZ6d+kPx8O/ZD+Sf2lCpUsFvj4YyFUOnFCTBa++aYQKkVKbt65manW/8WBgYEYDIaavhbFn8REBkYMHGIZblw0ZQhdSaMxfeQKlSzlsHITfLEecvIhLhrSHoBh/cReQ1loGlTuEaUOqxHQQVBPCBkBfnKfYv2aUKlp06bExMRIrUc73U5WHVmFwWjgh3M/EOAdwEMdHmJKlym0iJJbajhxQiTojz4SBrxbboE33oCRI+XOKSkEqo2jjuPCwWG+wogBEzvxIZjOTCaVqYQjuff99Hlxil73Hdjs0LE1PPUg9EiVLFSyCaFS6UpwnAHPMAgbDyF3gpfch5U/d0drmkZERAR6vZ6wsDC5m1Aqi/lg9wfMyJzBudJzNAprxLv93uWhjg8R5hcmLa6mwTffiFLHunVC6D9mjCh1dO4sLaziV1DJuo5SQQFZzCWTWZRxgXCSGUg67bkfXyS2g7ndQqS0dI0QK/l4/69QqZnch1g480UtumwDuMvAJwminoXAXuApr5PF7XZz8eJFzGbzL4RKer1eujs6+2I26cZ0Pt3/KVaHlZ4Ne/J+//cZ2mwoOk95b4hWKyxaJB4aHjokfNGvvAKPPy480oprj0rWdYxc9mPEwH4W48JOE+7gTuaTxAA8JbahUWEVStLP18K5CxAVDk9MEEKlehJte5oG9kNQulwMsgAE3nap1NFaal92VVXVlVJHVVUV/v7+JCUlERsbK12otOH4BtIz0tl0chO+Ol/GtxnPtNRptI9tLy0uwLlzYqpw/nyx17BDB1GfHjtWPEBUXD9Usq4DuHFxlNXsxMBZtuJNAB14gFSmEYXklihTrkjQq7eIhN26KbzxDPS9Te6SO61KjICXroCqE+AZDKGjIHQYeMldC1JeXo7JZCIvLw9N06hXrx7NmjUjPDxcaqmjzF7Gx3s/ZnrGdI4XHSc+OJ43er/BoymPEhUYJS2upsH27aLUsWKF+HjECDFl2K2bEirVFlSyrsVUUsIePiSDGZRwhlAa0I9/0pGH8UeyHjRzvyh1/JApWu36dYOxQ6C15PEzZxFY1gipkqsEvBtCZBoE3S5dqHTZHV1aWoqnpyexsbHo9XoCA2V2ZcOp4lNMN05nwd4FWOwWUhNSWTJyCXe1vEuqUMluh6VLRalj926xBfyZZ2DyZGggeRua4upRyboWUsBRjKSzl09wUEEDunMH79KMYXKFSjY7bPgelq6Fk2dFeePBu+GugSB5yhD70UtCpa2ACwK6iFKHf0fp7ujc3NxfCJUSExOJi4uTLlT67sx3GIwG1hxdg85Tx+hWo5nWZRqp+lRpcUFIlGbPhjlzID8fWrYUv58wASS/Lyn+BCpZ1xI03BxnAzsxcJKv0eFDG8aTyjTikCt+J/cifLkeVmyC0jJo2hhenSYeHPrKXHLn+t9Sh/0weARAyBAIHQ7ecteCWK1WTCYTubm5uN1uQkNDadKkCREREXKFSs5K5mfNJz0jnYP5B4kKiOKl7i/xROcniA+WNUYq2LVLlDo+/1y4OwYPFqWO229XpY66gErW1xk75ezjE35I/H+UcZogYunN30nhMYKQWJvVNNiXLerR3+4ADeiZKoRKHVrJFypZ1pHk8xXkl4JXPERMguA7hOxfEpqmUVRUhNlsviJUuuyOli1UOl96nlmZs5idOZvSqlLaxbRjwdAFjGszDj8vecMzDgcsXy6S9E8/QXCwWJE1dSokJUkLq5CAStbXiWJOk8EMdvMhdkoJd7dhJItoyd14IfE0W+WATT+IJJ19AoIDYfwwuHsQxMvbCiJin75U6vgGtCrsWjO8Y/8iSh6S3dF5eXmYTCYqKyvx8fGhUaNGxMfH4yNxPbamaew4vwOD0cDy7OVoaNwefzsv9X2JHg17SH1YWVAgOjpmzgSzGZo0gfffhwcegJAQaWEVElHJ+hqioXGG79mJgaOsxhMdLRlFKmmUnwmVK7spKBbe6K82QGEJNK4PLzwBg3uDv8SxaM0lpgtLV4BtL3j4QlA/CB3O+ZOVtAiUd8+VlZVXhEoul4vg4GBatGhBVFSU1FKH3Wln2aFlGIwGsnKyCPML46muTzG5y2Qqcypp0UjePR84IB4YLloENhv07Svq0YMGKXd0XUcl62uAg0oOsAQj6eSxH38i6M7/0JlJhCBqs9lkywmefQI+WwObfwCHE27rJEodqe3lljrcFWDZAJZV4MwFXRSEPwTBg0B3+WhX8/esaRolJSWYTCYKCwvx8PAgKiqKhIQEQkMl9oMDueW5V4RKeRV5NI9szqxBs7i33b0E+ojyTnZOzd+zywVr14pSx3ffgb+/2L4ybRq0alXj4RTXCZWsJVKKiUxmkcU8KikkmjYM5QPaMB5vJE6+OV3w/U8iSe/LhgB/GDkAxgyBBnIfYlFlEq6Osk2g2cTgSvjDl4RK8ibuLguVTCYTFRUVeHt706BBAxISEv5D3VvTZF3IwmA08Pmhz6lyVTEoeRDTukyjX5N+UoVKpaWwYAHMmAGnTkH9+vDOO/DwwxAeLi2s4jqhknUNo6FhYic7MXCYL9Fw04yhdCWNRvSSK1QqLRMdHV+sg7wCSIiFpx8S5rsgiT1ZmgaVu0SpozIT8IagXqKrw7epvLgIodLlUofD4SAwMJBmzZoRHR0tXai0InsFBqOB7ee3E+QTxGMpjzGlyxSaRsi956NHhVDp44+hokIMrrzzDgwfLndOSXF9Ud/aGsJJFYf5gp0YuEAmvoTSlTS6MIV6NJYb/OQ5McCy/juxfLZzW3juMejWCWRuJHFXQvkWkaQd50EXDvXuheAh4CVvaEfTtCtCpYKCAjRNIzIykoSEBOlCpUJrIR/s/oCZmTM5bzlPYr1E/t3/3zzQ/gFC/eSVWdxu2LRJ1KM3bAAfHzECnpYG/2dJk+IGRSXrP0k5eexiLruYTTm5RNCMQcykHffii0Q9qMsF27NEks7YJ/qhB/YSU4ZJjeTFBXDkilp02QZRm/ZtClHPQ1APsZFFEpeFSiaTibKyMnQ6HQkJCSQkJEgXKh3MP0i6MZ1F+xdR6aykT+M+zBg0g8HJg6UKlcrL4dNPxUn6yBGIiYHXXhNCpRjJzTuK2oVK1tUkhz3sxMBBPsNFFUkMIJU0mnCHXKFSuRVWb4Zl64S3IyYSptwLw++AMIk9WZoGtv3iFG39SbwW2B1CRwjR/zUWKiUnJxMTEyNdqLTu2DoMRgPfnP4GPy8/JrSZwLTUabSJaSMtLsCZM6IW/cEHojadkiKS9ujRSqh0s6KS9VXgwskRVmLEwDl+xJtAOvIwXZhKFM3lBj93QfRGr/kGrJXQtjlMvhd6d5VbqHRXQfm34qFh1SkhVAobfckdLVeoVFZWhslkIj8/H03TCA8PJyEhQbpQyWK38NGej5ieMZ2TxSdJCE7grT5v8UjKI0QGyFuFommwbZvo6li1Srz/3XWXKHXccouaMrzZUcn6D2CliN18QCYzKeUcYTTiDt6lAw/hT5i8wJoGxr2i1LE9S9Sf+3UTrXctk+XFBXAWCKGSZR24S8G7EUQ+BUF9wFNeX7bb7aawsPAXQqW4uDj0er301XHHC48zPWM6H+39iPKqcm6tfytv3f4WI5qPwFsnzxNis8Fnn4kkvW8fRETA88/DpEmgl7wNTVF3qHaynjt3Lt9++y0Oh4Nx48Zx99131+R11QryOYyRdPazEAdWGtGLARhoxp14IvHBnc0utq98vgZOnYfwUHh4jBAqRUq07QHYskWpo2Ib4IaArqLU4ddeulApJycHs9mM3W7Hz8+PJk2aEBsbK12otOXUFgxGA+uPr8fL04uxrccyLXUaneI7SYsLYrJw9myYO1dMHLZuLaYO77lH9EorFD+nWsnaaDSyZ88ePvvsMyorK1mwYEFNX9d1w42bE5eESqfYjA5f2jKBVKYSSzu5wXMvilr0yk1ir2HzJvDak3BHd7GRRRaaAyp+uCRUOiKESqHDIWQYeMtdC1JRUXHFHe12uwkLCyMpKYnIyEippY6KqgoW7l9IujGd7IJsogOjebXnqzze6XFig2KlxQXYuVOcor/8UjwnvvNOUero3VuVOhT/nWol6x9//JGmTZsyefJkysvLee6552r6uq45dsrYy8cYmU4Rxwkmnj68SQqPEojElc2aBnsPkzBvMWQdBg+g9y1iTVa7FpKFSiWizGFZDa4iYbqLmHxJqCSv5KBpGoWFhRQXF5OXl3dFqKTX6wkKkthBA5wtOcvMzJnM3z2fElsJHeM68snwTxjTagy+XvKe3FVVieT8j3804sAB4eeYOlW4o5tIXpWpuDHw0DRNu9pPevnll7lw4QJz5szBZDLxxBNPsHHjxl+chLKysqpdY7TZbPj5SfRV/Ixy73McC1/M6dDlOHUVRFjb0bR4InpLPzyRd5r1qHIQsnMf4Zu243f2As4Af0r6pFJ8e1eckksdvh4mwnVbCfHMwtPDSbm7OUWunlS4W4DETha3243NZsNqteJyufDw8CAwMBB/f3+prg5N08gqyGLR8UVsMW/BAw/6JvRlQvIEOkZ2lHqCLyrSsWxZGEuX1iM/35sGDWxMmFDCiBGlBAa6pcWtTVzLn+fawp+5Z6vVSkpKyn+8Xq2TdVhYGImJifj4+JCYmIivry9FRUVERPxSUF9dMVF2drZUqZGGxmm+ZScGjrEWT3S0YjSppKEP6AIBgCydckERfLkBvtoIxaWQWB9enMSJxDiat28n7wyvucC645JQ6QB4+EHQAAgdTpBPQ5kd4VRWVl5xR7tcLkJCQkhISKCwsJCWLVtKi2tz2lh6cCnpxnT25O4h3D+cZ299lkmdJ9EgVO4qlH37RKljyRKxkaV/f1HqaNDgNK1atQDkllpqE7J/nmsjf+aes7KyfvX1aiXrlJQUPv30Ux544AHy8/OprKwkLCysWhd2LanCygEWYySdfA4SQBQ9eJlOPE4Ikp0ZB4+JB4abt4tCZbfOYoClSzvw8EDLliRycpVB2UZR6nDmglcMhD8KwQNAJ8/hrGkaxcXFmM3mXwiV9Ho9IZccnUVFRVJi55TlMHvXbObsmsNF60VaRbVi3pB53NP2HgK85ZV3XC5YvVok6a1bISAAHnxQlDsu/9zK+jYrbnyqlax79+5NZmYmo0aNQtM0Xn31Vakehj9LKefJYCa7mU8lRcTSnmF8RGvG4o3Ev545nfDNDtF6d+AoBPrD3QNh9GCoL1uodA4sK6Fs8yWhUluIeAwCbpEuVLrsjrZarXh7e9OwYUPi4+OlC5UyzZkYjAaWHVqG0+1kSNMhpKWm0adxH6mljuJi+PBDMcRy9iw0bAj/+hc89JDYa6hQ1ATVbt2r7Q8VNTTOs4OdGMhmOaDRnBF0JY0GdJMrVCqxCHf0lxsgvxDqx8FfHoEht0OQxF5hzS1ESqUroDILPLwhsM8loZLctSA2m+2KUMnpdBIUFHRNhEoOl4Ovsr/CYDSw07STYJ9gJnWexJQuU0gKl3vP2dnC1fHpp2C1Qo8e8N57MHSoEiopap4b7n8pJ3YO8jlG0skhCz/CuIWn6cJkwmgoN/jx00JLunGr2MiS2h5enAS3psg1v7utQklqWQUO0yWh0v0QMhh0YdLCappGaWkpZrOZixcvAhAZGYleryc0NFTuJhRrAfOy5jErcxbmMjNJ4UkYBhi4v/39hPjKG7t3u2HjRlHq2LRJjH6PGyfq0e3bSwurUNw4ybqMXHYxh13MoYI8ImnBYGbTjon4IFEP6nLBD5kiSWcdEEKlIbeLenSi3IdYOHJEgrZsAM0Kvs0h+n+Es8NDXieL2+2+4o4uLy/Hy8uL+vXrk5CQIP2p//68/aQb01l8YDE2p42+iX2ZO2QuA5MHSnVHl5XBJ58IodKxYxAXB6+/Do89BlFR0sIqFFeo88nazC6MGDjI57hxkMxgupJGIn3lljrKymHVFuGONucJodLU+4RQKVTi8lVNE+uxSleAdSfgCUE9IWQ4+Ml94m63268IlRwOBwEBASQnJxMbGyu11OFyu1hzbA0Go4Hvz3yPv5c/97W7j6ldptIqWu4qlFOnRC36ww/BYoHUVNHhcdddQlOqUFwr6mSyduEgmxUYMXCeHfgQRCceJ5WpRCDZmXHGJIRKa7+FSht0aAlT74deXcFLpjvaLhbNWlaKxbOeoRA2HkKGgJfEoR3AYrFgNpt/IVTS6/XUq1dPaqmjxFbCgj0LmJExg9Mlp2kQ2oB/9v0nD3V8iHB/eatQNE2sx0pPF90dOh3cfbdYk9W1q7SwCsVvUqeStZVCsphPJjOxYKIeifTn33TgAfyQuF/P7Yaf9ojWux27wdsLBvQUa7KaSx4/c14UbXeW9eC2gE8iRD0jHhx6ynVHFxQUYDKZsFgs6HQ64uPjSUhIkC5UOlpwlHRjOp/s+4QKRwXdG3TnX/3+xbDmw/DylPe/bGWlWDSbng4HD0JkJLz4IjzxBCTI6rtXKP4gdSJZ53HwilDJiY3G3M5gZpHMILlCJWulOEF/vhbOmiGiHjw+Hkb0F7+XhaaB/fAlodIP4rWAWy4JldpKFypdLnVcFiolJSURGxsr3R296eQmDEYDG09sxEfnw7jW40hLTaNDXAdpcQFMJpg5E+bNg6IiaNdO7DYcNw5ussE7RS2m1iZrNy6OsQ4j6ZzmG7zwpy0TSWUaMbSWG9ycK4RKq7ZAeYXQkb7+NPS9DSQa4NAcUL5VuKPtx8AzEEJHXhIqyZ14Ky8vx2w2/0KolJycTEREhNRSR3lVOZ/s/YTpGdM5WniU2KBY/tbrbzyW8hgxQfJWoWga/PST6Or46ivx8bBhoqujRw8lVFLUPmpdsq7CytF6n7KJZRRzihD09OUfdORhAoj4/S9QXTQNsg6KAZZtGUKo1Oc24Y5u00zuT6+zmEjdBjhnvCRUqg+R0yCoL3jKc2VeFiqZTCZKSkrw9PQkJiaGhIQE6UKl08WneWfvO6xctZJSeymd4zuzcMRCRrcajY9OXnmnqgqWLRNJetcuCA2FJ58UQqXGkldlKhR/hlqXrLOYy97Yf1Cf2+jLP2jOCHQyL9NmF33Rn6+F42dEJ8d9I2HUINHhIRP7cVHqKP+eKC8H+HSG0L+AfwpIbENzOp1X3NE2mw1fX18SExOJi4uT7o7eenYrBqOB1UdX44knd7W8i7TUNLrqu0o9weflCW/07NmQmwvNm8OsWTBxIkh+X1IoaoRal6w78gi6Ey3okjRAbqD8QvhiPaz4WkwcJjWEl6eIB4d+EseiNRdUbBelDttBIVQKGcTJgtY0SewlLy7C5mU2m38hVEpMTCQyMlKq9c7mtLHkwBIMRgP78/YT4R/BC7e9QL+wfvRK6SUtLsDu3eIUvXSpOFUPHChKHf36yZ1TUihqmlqXrH0JItghcdLwwBExwPLNDtHl0TNVDLCktJHsjrZA2XooXQ2ui+AVC+GPQ8gA8Ayk6qIcw89loZLJZKKoqAgPDw+io6PR6/UEB0vsBwfMFjOzMmcxb/c8CqwFtIluwwd3fsD4NuPx9/YnW5LVyOmEFStEkt6+HQID4dFHhVCpaVMpIRUK6dS6ZC0FhwO2bIela+HQMQgKFAl69GBIkKyqrDoNpaugfAtodrEeK3IKBKRKFyrl5uZiNpuvCJUaNWpEXFycVKGSpmnsNO3EYDTwVfZXuNwuhjYbSlpqGr0a9ZLsjhZrsWbOhPPnRQ36vffggQegDkghFYrf5MZO1kUll4RKG4VHukECPPcYDOkDARKX3GlusBpFqaNyD3j4QNDtovXOR+5TrF8TKjVv3pzo6GippY4qVxVfHPoCg9FA5oVMQnxDmNplKlO6TCGxXqK0uACHDone6IULRa90795iLHzIEDHQolDcCNyYyfrISfHAcONWcDjhlg7wylTxq1ShUgWUfS1O0s4LoIuEeg9CyCDQyRvauSxUMplMFBQUAPzCHS3zNJtfkc/cXXOZvWs2OeU5NI1oyoyBM7iv/X0E+ch7cud2w/r1otSxZYvoh54wQUwZtmkjLaxCcd24cZK10wXbjKL1bvch8ZBwWD+xy7CRXm5shxlKVwrznWYF35YQ/gAEdgMPeX/ELpeL/Px8zGbzFaFSgwYNiI+Ply5U2pu7F4PRwJIDS6hyVTEgaQALUhdwR5M7pAqVLBb46CNxcj55UkwWvvUWPPKImDhUKG5U6n6ytpSLbeDL1ont4PHR8OQDIlEHS+zJ0jRR4rCsECUPdJeESiPAr5m8uPy6UKlp06bExMRIFSo53U5WH12NwWhg29ltBHoH8nCHh5maOpXmkc2lxQU4cUIk6I8+Ega8W2+FN9+EkSPlzikpFLWFupusT58Xp+h134le6ZQ2QvDfvbPcQqW7UgiVSleC46zwRYfdc0moJHFoByFUMplMXLx4EU3TiIiIQK/XExYWJvfBXWURH+7+kBmZMzhXeo5GYY14t9+7PNTxIcL8wqTF1TRR4khPh3XrhNB/zBhR6ujcWVpYhaJWUreStdsNO7JEV8fOPeDjLfqix94JTSWPnznyhFCpbAO4y8AnCaKehaBe4gGiJNxuNxcvXsRkMlFWVoZOpyMhIYGEhAT8/SU+JAUOXzxMujGdhfsXYnVY6dmwJ+/3f5+hzYai85T3hmi1ioeF6elw+DBER8Mrr8DjjwuPtEJxM1I3knWFFdZ8C8vWwrkLEBUOT0yAkf2hnkTbnqaJwRXLCjHIAhB4m/B1+LaS2pddVVV1ZcqwqqoKf3//ayZU2nB8Awajgc2nNuOr82V8m/FMS51G+9j20uICnDsn2u7mzxd7DTt2FML/MWPERhaF4mbmT/3UFxYWMnLkSBYsWECTJhJUoaYc0dWxegtUVELrpvDGM0KoJHPJnVYF5d+LUfCqE+AZDKGjIHQYeEXLi4sQKplMJvLy8tA0jXr16tGsWTPCw8OlljrK7GV8vPdjpmdM53jRceKD43m99+s8lvIYUYHyVqFoGvz4o+jqWLFCvP+NGCGmDG+7TQmVFIrLVDvjORwOXn311ZrvOtA0Ag6dgHlfwY+ZotWu7yWhUmu5D+5wFoJlLZStBVcJeDeEyDTRIy1ZqGSz2dizZw+lpaV4enoSFxdHQkICgYESV5IBJ4tOMiNjBgv2LsBit9BV35W/9/47d7W4C2+dvCd3djusXBnKF1/Anj1iC/hf/iKESg0kb0NTKOoi1U7W77zzDmPHjmXevHk1eT3w1UYa/mO+KG88eDeMGghRch/cYTsqSh3lWwGXmC4MGQH+HaS7oy9PGV5rodK3p7/FYDSw9thadJ46RrcaTVpqGl0SukiLC5CTI2RKc+dCfn48LVuK30+YAJJ3GigUdRoPTdO0q/2k5cuXk5uby6RJk5g4cSKvvfbaf5RBsrKyqrVRxKuwBN3R01R1ao3mI7Mny0Ww5z7Cdd8T4HkGl+ZLqasrRa4eOJC7AdXpdGK1WqmsrATA29sbb29vgoKCpJY6Kp2VrD27lkUnFnG89DjhvuGMaTKGMU3GEO0vt7xz4IAfCxeGs3FjCC4X9OxZzpgxefTo4bipSh02m016D3xtQ93z1WG1WklJSfmP16uVrO+55x48PDzw8PAgOzubRo0aMXv2bKJ+tuY5KyvrVwP+EbKzs2nRQtLyV1epWJFlWQ2uAvCKh9DhEHyHkP1LQtM0ioqKMJlMFBcX4+HhccUdHRwcLPWez5eeZ2bmTObvnk9RZRHtY9uTlprG2NZj8fOS90PkcMDy5aIe/dNPEBwsPB1TpkBysuTvcy1F3fPNwZ+55/+WO6tVBlm8ePGV318+Wf88UddKqk5D6XIo/1Y8QPTvCCFpENBFujs6Ly8Pk8lEZWUlPj4+NGrUiPj4eHwkrsfWNI0d53dgMBpYnr0cDY3hzYeTlppG9wbdpZ7gCwrEiqxZs8BshiZN4P33RaIOCZEWVqG4oakbrXvVRXOJ6cLSFWDbCx6+ENRPnKR9GkkNXVlZeUWo5HK5CA4OpkWLFkRFRUkVKtmddpYdWobBaCArJ4swvzCevuVpJneeTMMwiepZ4MABcYpevBhsNujbF+bMgUGDlDtaofiz/OlkvXDhwpq4jprFXQGWjWBZBc4c0EVB+MMQPBB08o52mqZRUlKC2WymoKAADw8PoqKiSEhIIDRUYj84kFuee0WolFeRR4vIFswZPIcJbScQ6COvvONywZo1YoDlu+/A3x/uu0+4o1u1khZWobjpuLFO1lXnwbLyklDJBn5tRJIOvE26OzovLw+z2UxFRcU1FSplXcjCYDSw9OBSHG4Hg5MHk5aaRt/EvlJLHSUlYgP4jBlw+jTUrw/vvAMPPwzh4dLCKhQ3LXU/WWtuqMwSpY7KTMBbjICHjgDfZKmhbTbbFaGS0+kkMDCQZs2aER0dLV2otDx7OQajgR3ndxDkE8TjnR5nSpcpNI2Quwrl6FEhVPr4Y6iogG7d4J//hOHD5c4pKRQ3O3X3x8tdCWWbxUnacR504VDvXggeAl71pIXVNO0XQiWAyMhIEhISpAuVCq2FzN89n5mZMzFZTCTWS+Tf/f/NA+0fINRPXpnF7YbNm0U9esMG8PGBsWPFlGHHjtLCKhSKn1H3krUjV9SiyzaI2rRvU4h6XuhJPeT1Zbvd7ivu6MtCJb1ef02ESgfzD5JuTGfR/kVUOivp07gPMwfNZHDyYKlCpfJy+PRTcZI+cgRiY+Fvf4PHHoOYGGlhFQrFr1A3krWmgW2/KHVYfxKvBXa/VOpoKV2odLnUcVmolJycTExMjFShksvtYv3x9RiMBr45/Q1+Xn5MaDOBaanTaBMjdxXKmTOiFv3hh6I23amTsOCNHi1O1QqF4tpTu5O1u0r0RVtWQNUp8AyBsNEQMhS85PZ1l5WVYTKZyM/PR9M0wsPDSUhIkC5UKrWV8tHej5ieMZ1TxafQh+h5q89bPJLyCJEB8lahaBps2yZKHatWife/u+4SpY5bblFCJYXielMrk7UXpVD0EVjWgbtULJmNfBqC+oCnPFem2+2moKAAs9n8C6GSXq+v1uj81XCm7AyzN8zmo70fUV5Vzm31b+Mft/+D4c2HSxUq2WywZIlovdu3T3RyPP88TJoEesnb0BQKxR+n9iXr8m0k+bwJJRoE3CJKHX7tpAuVLruj7XY7fn5+NGnShNjYWOlCpc2nNpNuTGfd8XV4e3oztvVY0lLTSImv3qj+H8VsFhOG8+aJicPWrYVH+p57RK+0QqGoXdS+ZO0dT4GrP1GNx4G33LUgFRUVV9zRbrebsLAwkpKSiIyMlFrqqKiqYOH+haQb08kuyCY6MJpJLSfxysBXiA2KlRYXYOdOUer48ksx0DJ0qFiT1bu3KnUoFLWZ2pesfZMocA0kSlKi1jSNwsJCzGbzL4RKer2eoCCJC3aBsyVnrwiVSmwldIzryCfDP2FMqzGcOn5KWqKuqhLJ2WCAjAzh55g6VQiVEhOlhFQoFDVM7UvWknA6nVfc0ZeFSo0bNyYuLk66UOmHcz9gMBpYeWQlHngwssVI0lLTuLX+rVJP8BcvClf0rFnCI52cLNrw7rtPGPAUCkXd4YZP1larFbPZTG5uLi6Xi5CQEBo3bkxkZKRUoZLNaWPpwaUYjAb25u4l3D+cZ299lsmdJ1M/tL60uAB794oHhkuWiI0s/fuLNrz+/ZVQSaGoq9yQyVrTNIqLizGbzRQWFl4RKun1ekIkOzovlF1gzq45zNk1h4vWi7SKasW8IfO4p+09BHjL6yhxOmH1alHq2LZNbF156CFR6rjJVMIKxQ3JDZWsLwuVTCYTVqsVb29vGjZsSHx8PL6S12NnmDMwGA0sO7QMl9vFkKZDSEtNo0/jPlJLHcXF4tQ8YwacPQuNGsG778KDD4q9hgqF4sbghkjWNpvtijva6XQSFBRE8+bNiYqKkipUcrgcfJX9FQajgZ2mnQT7BDO582SmdJlCUniStLgA2dmi1PHpp2C1Qs+e8O9/i+4OibesUCiuE3U2WWuaRmlpKWaz+RdCJb1eT2hoqNwHdxUXmb97PrMyZ2EuM5MUnoRhgIH7299PiK+8MovbDRs3ilLHpk3g6wvjx4vWu/btpYVVKBS1gDqXrC8LlUwmE+Xl5Xh5eVG/fn0SEhKku6P35+3HsNPA4gOLsbvs9Evsx9whcxmYPBBPiavBysqEknT6dDh+HOLj4Y034NFHobZvU1MoFDVDnUnWdrv9ilDJ4XAQEBBA06ZNiYmJkVrqcLldrDm2BoPRwPdnvifAO4AH2j/A1NSptIxqKS0uwKlTIkEvWAAWC6Smig6PUaNA4mClQqGohVQrWTscDl588UXMZjNVVVU88cQT3H777TV9bQC/cEdfFirp9Xrq1asndxOKrYQPd3/IjMwZnCk5Q4PQBvyz7z95uOPD1POX6csW67EMBrEuS6eDu+8WQqXUVGlhFQpFLadayXr16tWEhYXxr3/9i+LiYkaMGFGjyVrTtCulDovFgk6nIz4+noSEBOlCpSMFR5hunM4n+z6hwlFB9wbdebffuwxrPgwvT3l/EbHZPPjgA/HQ8MABiIyEl16CJ54QZQ+FQnFzU63sM2DAAPr373/l45osQ5SUlFBQUEB+fj5+fn4kJSURGxsr1R3t1tx8feJr0jPS2XhiIz46H8a3Gc+0LtPoENdBWlwAk0lMGM6alURpKbRrJ8oe48aB5BK8QqGoQ3homqZV95PLy8t54oknGD16NHfeeecv/l1WVla1TsF2u53y8nKCgoLw8fGRK1RyVLDqzCoWn1jM6bLTRPpFMq7JOEY3GU2EX4S0uJoGe/f6s3BhOJs3B6Np0KtXKffdV0qnTtabRqhks9mkPxSubah7vjn4M/dstVpJSfkV66ZWTS5cuKCNGDFC++KLL3713+/atau6X1o7fPhwtT/3j3Cq6JT29MantdC3QzVeQ+s8r7O2aN8ize60S41rs2nap59qWqdOmgaaFhamaX/5i6adPi3/nmsj6p5vDtQ9Xx3/LXdWq7ZQUFDAgw8+yKuvvsott9xSrXePa42maWw9uxWD0cDqo6vx9PBkVMtRpKWm0VXfVWrs3FwhVJo9G/LyoHlzUfq4914IDBT/TXa21EtQKBR1nGol6zlz5mCxWJg1axazZs0CYP78+bXyrzqVjkqWHFhCekY6+/P2E+EfwQu3vcATnZ9AHyJ3FUpWlujq+PxzoSkdNEgMsPTrp4RKCoXi6qhWsn755Zd5+eWXa/paahSzxcyszFnMzZpLYWUhbaLb8MGdHzC+zXj8veWtQnE6YeVKeP992L5dnJwfeUT4o5s1kxZWoVDc4NSZoZg/yk7TTgxGA18e/hKX28XQZkNJS02jV6NeUh9WFhWJtVgzZ8L589C4Mbz3nhAqhYZKC6tQKG4SbohkXeWq4otDX5CekU6GOYNQ31DSUtOY3Hkyjes1lhr74EExZbhwIVRWivVY06fDkCFKqKRQKGqOOp2s8yvymbtrLrN3zSanPIdmEc2YOWgm97a7lyAfeSu6XC5Yv17Uo7/5RvRDT5gg6tFt2kgLq1AobmLqZLLek7MHg9HAZwc/o8pVxYCkASxIXcAdTe6QKlSyWMTAyvTpwtuh18Pbb8PDD4uJQ4VCoZBFnUnWTreTlUdWYjAa+PHcjwR6B/Jwh4eZmjqV5pHNpcY+flwk6I8+gvJyuPVWeOstGDlSCZUUCsW1odYn66LKIj7Y/QEzM2dyrvQcjcIa8W6/d3mo40OE+YVJi6tpsGWLKHWsXw9eXjBmjCh1dO4sLaxCoVD8KrU2WR++eJh0Yzqf7vuUSmclvRr14v3+7zO02VB0nvKe3FVUwKJFQqh0+DBER8Mrr8Djj0NcnLSwCoVC8ZvUumR9ougED299mB15O/DV+XJPm3uYljqNdrHtpMY9e1a03X3wgdhr2KGDEP6PHSs2sigUCsX1pNYl6x/O/oC5wsybfd7kkY6PEBUobxWKpsGPP4pSx4oV4OEBI0YId/Rtt3HTCJUUCkXtp9Yl6wc6PEBXv660aNFCWgy7HZYuFUl6zx6xBfzZZ2HSJGjQQFpYhUKhqDa1LlnLJCcH5swR/+TnQ6tWQrA0YQJI3mmgUCgUf4qbIllnZopT9LJlwt0xZIjo6rj9dlXqUCgUdYMbNlk7HLB8uUjSP/0EwcGizDFlCiQlXe+rUygUiqvjhkvWBQUwb57wRZvNIjEbDHD//RAScr2vTqFQKKrHDZOs9+8XvdGLF4PNJpzRc+fCwIHKHa1QKOo+dTpZu1ywZo04OX//Pfj7w333CXd0q1bX++oUCoWi5qiTybqkRAiVZsyA06ehfn145x0hVAoPv95Xp1AoFDVPnUrWR4+KUscnn4ix8G7d4J//hOHDhbtDoVAoblSqleLcbjevvfYaR48excfHhzfeeIOGDRvW9LVdigWbNokkvWED+PjAuHGi9a5jRykhFQqFotZRrUdvW7Zsoaqqis8//5xnnnmGf/zjHzV9XZSXC1dHy5biIeGePfC3v8G5c8LZoRK1QqG4majWyTorK4vu3bsD0L59ew4ePFhjF1RUBO+8E83KlVBaCp06CQve3XeLU7VCoVDcjFQrWZeXlxMU9L9rs3Q6HU6nE6//UzjOzs6+6q/9ySfhLFoUzR13lDJxYjHt2lXi4QEnT1bnSusONputWn9edRl1zzcH6p5rhmol66CgICoqKq587Ha7/yNRA9WSMb35JowadZROnZoBN89a8OzsbKnyqtqIuuebA3XPV0dWVtavvl6tmnXHjh3Ztm0bAHv37qVp06bVuqhfQ6eDwEB3jX09hUKhuBGo1sm6X79+bN++nbFjx6JpGm+99VZNX5dCoVAofka1krWnpyd///vfa/paFAqFQvFfUNYMhUKhqAOoZK1QKBR1AJWsFQqFog6gkrVCoVDUAVSyVigUijqAh6Zpmowv/N8auxUKhULx26SkpPzHa9KStUKhUChqDlUGUSgUijqAStYKhUJRB6hVydrtdvPqq68yZswYJk6cyNmzZ6/3JUnH4XDw7LPPMn78eEaNGsU333xzvS/pmlBYWEjPnj05eaPrFC8xd+5cxowZw8iRI/niiy+u9+VIx+Fw8MwzzzB27FjGjx9/w3+f9+3bx8SJEwE4e/Ys48aNY/z48fz1r3/F7a4Z11GtStbXYqlBbWP16tWEhYWxZMkS5s+fz+uvv369L0k6DoeDV199FT8/v+t9KdcEo9HInj17+Oyzz1i4cCG5ubnX+5Kks3XrVpxOJ0uXLmXy5Mm8//771/uSpDF//nxefvll7HY7AG+//TZPPvkkS5YsQdO0GjuA1apkLXOpQW1lwIABpKWlXflYp9Ndx6u5NrzzzjuMHTuW6Ojo630p14Qff/yRpk2bMnnyZB5//HF69ep1vS9JOo0bN8blcuF2uykvL/9VhfKNQoMGDZg+ffqVjw8dOkSXLl0A6NGjBzt27KiROLXqT/CPLjW4kQgMDATEvU+bNo0nn3zy+l6QZJYvX054eDjdu3dn3rx51/tyrgnFxcVcuHCBOXPmYDKZeOKJJ9i4cSMeHh7X+9KkERAQgNlsZuDAgRQXFzNnzpzrfUnS6N+/PyaT6crHmqZd+d4GBgZSVlZWI3Fq1cn6jy41uNHIycnh3nvvZdiwYdx5553X+3Kk8tVXX7Fjxw4mTpxIdnY2zz//PBcvXrzelyWVsLAwunXrho+PD4mJifj6+lJUVHS9L0sqH3/8Md26dePrr79m1apVvPDCC1fKBDc6np7/m1YrKioICQmpma9bI1+lhpC51KC2UlBQwIMPPsizzz7LqFGjrvflSGfx4sUsWrSIhQsX0qJFC9555x2ioqKu92VJJSUlhR9++AFN08jLy6OyspKwsLDrfVlSCQkJITg4GIDQ0FCcTicul+s6X9W1oWXLlhiNRgC2bdtGp06dauTr1qpj68241GDOnDlYLBZmzZrFrFmzAPHA4mZ5+HYz0Lt3bzIzMxk1ahSapvHqq6/e8M8m7r//fl588UXGjx+Pw+HgqaeeIiAg4Hpf1jXh+eef55VXXuG9994jMTGR/v3718jXVROMCoVCUQeoVWUQhUKhUPw6KlkrFApFHUAla4VCoagDqGStUCgUdQCVrBUKhaIOoJK1QqFQ1AFUslYoFIo6gErWCoVCUQf4/+FsLlfziKXYAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, x + 0, color='blue')\n", "plt.plot(x, x + 1, color='g')\n", "plt.plot(x, x + 2, color='0.75')\n", "plt.plot(x, x + 3, color='#FFDD44')\n", "plt.plot(x, x + 4, color=(1.0,0.2,0.3))\n", "plt.plot(x, x + 5, color='chartreuse');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Matplotlib will automatically cycle through a set of default colors if no color is specified. By defining a plot style such as `plt.style.use('seaborn-whitegrid')` the default colors are taken from the 'seaborn-whitegrid'-style file.\n", "\n", "To adjust the linestyle, specify the respective style either with the code or the string. Available are: \n", "\n", "\| Code \| keyword \|\n", "\|:-------------:\|---------------------\|\n", "\| '-' \| 'solid' (default) \|\n", "\| '--' \| 'dashed' \|\n", "\| '-.' \| 'dashdot' \|\n", "\| ':' \| 'dotted' \|\n", "\n", "If you wish to keep your code concise you can combine the linestyle code with the color in a single string." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, x + 0, '-g') # solid green\n", "plt.plot(x, x + 1, '--c') # dashed cyan\n", "plt.plot(x, x + 2, linestyle='-.') # dashdot\n", "plt.plot(x, x + 3, ls='dotted'); # dotted red" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many more arguments to further adjust your plot to your liking. A toy example is given below, where the linewidth is manually set and circled markers are added (`marker='o'`). Regarding available markers a limited overview is shown in section 'Scatterplots' below. The full list of available markers [can be found here](https://matplotlib.org/stable/api/markers_api.html). For a furhter description on adjustable properties [see the Matplotlib documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D). " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Random walk data\n", "np.random.seed(1234)\n", "y = np.hstack([0, np.random.standard_normal(10).cumsum()])\n", "x = range(len(y))\n", "\n", "plt.plot(x, y, color='m', linewidth=2.0, \n", " marker='o', markeredgecolor='b', markersize=20,\n", " markerfacecolor='g', fillstyle='bottom');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Axes, Grids and Labels\n", "\n", "Per default, matplotlib sets the axis based on the plotted data. However, sometimes it is desirable to have better control of the axis and not let matplotlib's default settings rule the axis. Adjusting axis limits is done with `plt.xlim()` and `plt.ylim()` methods. The two input values to these methods will set limits to the axis. Alternatively, the `plt.axis()` method allows for further control. For example `plt.axis('tight')` will automatically tighten the bounds around the current plot (which is sometimes desireable when you want to use your plot in a report/article)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By setting the style to `plt.style.use('seaborn-whitegrid')` each plot automatically adds a grid. Python's default style does not come with a grid. In case you wish to add/adjust a grid to the default style, you do this with the `plt.grid()` method. Details on the valid arguments for the `plt.grid()` [can be found here](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.grid.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Title, axis and labels are added with `plt.xlabel()`, `plt.ylabel()`, `plt.title()`. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 4)) # Adjusting figure size (width, height)\n", "plt.plot(x, y)\n", "plt.title(\"Random Walk with $X \\sim N(0, 1)$\")\n", "plt.xlabel('step')\n", "plt.ylabel('value')\n", "plt.xlim(-1, 11)\n", "plt.ylim(-2, 2);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot a Legend\n", "Adding a legend helps a visualization. Simple legends are added with the `plt.legend()` command, which will automatically place the legend where it fits best. If you wish to manually specify the location of the legend, you can use the argument `loc='string'` where `'string'` is a location like 'upper right', 'upper left', 'bottom left', 'bottom right' ([see here for all options](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html)).\n", "\n", "Other available options are: \n", "* Turning on/off the frame of the legend is done with `frameon=True / frameon=False`. \n", "* `ncol=n` specifies the number of columns in the legend (with `n` and integer). \n", "* `framealpha=n` changes the transparency of the frame (with `n` $\\in [0, 1]$)\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import needed for generating normal probability density function (pdf) data\n", "import scipy.stats as sps\n", "\n", "# Create data for the two normal pdf & cdf\n", "x = np.linspace(-4, 4, 100)\n", "y1 = sps.norm.pdf(x)\n", "y2 = sps.norm.cdf(x, scale=0.5)\n", "\n", "# Plot data including (TeX) label\n", "plt.figure(figsize=(12, 8))\n", "plt.plot(x, y1, label='Normal pdf with \\n $X \\sim N(0, 1)$')\n", "plt.plot(x, y2, label='Normal pdf with' + '\\n' +\n", " r'$X \\sim N\\left(0, \\frac{1}{2}\\right)$')\n", "plt.legend(loc='upper left', frameon=True, framealpha=0.5,\n", " edgecolor='black', facecolor='lightgray', \n", " fontsize=12, labelspacing=1);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### TeX and LaTeX\n", "\n", "As is shown in the legend of above plot we can use TeX notation to typeset mathematical expressions. The good thing is, you do not need to have TeX installed - Matplotlib ships its own TeX expression parser. Any text element in your plot can contain math text. All math text needs to be surrounded by dollar signs ($), as in TeX. For simple math expressions this is enough. As soon as the math gets a bit complicated it is often best to use raw strings, which means you should preceed the quoting sign with an 'r'. If you have line breaks, concatenate the parts as in above figure. \n", "\n", "If you are looking for some basic examples, check Matplotlibs tutorial [Writing mathematical expressions](https://matplotlib.org/stable/tutorials/text/mathtext.html). If you strive for perfection and wish to render your math in full LaTeX you can do that. See [here](https://matplotlib.org/stable/tutorials/text/usetex.html) for details. (You might also consider exporting your figures in `.pgf` file format. By including a `.pgf` file to your LaTeX document the rendering is done directly within your document.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Text and Annotation\n", "In some cases it might be helpful to annotate the figure. This is done with the `plt.text()` method, which takes as arguments a $x/y$ position, a string (`s='text'`), and optional keywords specifying color, size, style, alignment, etc. See the [function documentation](https://matplotlib.org/stable/api/text_api.html) for details. A further option is to use `plt.annotate()`. This function creates some text and combines it with an arrow. A rather extensive example is provided below. Matplotlib has many more examples in [its documentation](https://matplotlib.org/stable/tutorials/text/annotations.html).\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import Polygon functions\n", "from matplotlib.patches import Polygon\n", "\n", "# Create data for a normal pdf's\n", "mu = 4\n", "sig = 0.75\n", "x = np.linspace(0, 8, 100)\n", "y = sps.norm.pdf(x, loc=mu, scale=sig)\n", "\n", "# Draw standard normal\n", "fig, ax = plt.subplots(figsize=(12, 6))\n", "ax.plot(x, y, linewidth=2)\n", "ax.set_xlim(0, 8)\n", "\n", "# Add title\n", "plt.title(r'Extensive Example with Normal Distribution where $X \\sim N(4, \\dfrac{3}{4})$')\n", "\n", "# Calculate polygon coordinates\n", "# and add polygon shape to plot\n", "a, b = 0, 3.5 # Integral limits\n", "ix = np.linspace(a, b)\n", "iy = sps.norm.pdf(ix, loc=mu, scale=sig)\n", "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n", "poly = Polygon(verts, facecolor='0.8', edgecolor='0.65')\n", "ax.add_patch(poly)\n", "\n", "# Add normal distribution function as text\n", "plt.text(x=6.5, y=0.45, s=r'$f(x \| \\mu, \\sigma^2) = \\frac{1}{\\sqrt{2 \\pi \\sigma^2}}' +\n", " r'\\; \\exp\\left(- \\frac{(x - \\mu)^2}{2\\sigma^2}\\right)$', size=16,\n", " horizontalalignment='center');\n", "\n", "# Calculate area under curve\n", "pr = sps.norm.cdf(b, loc=mu, scale=sig)\n", "\n", "# Add annotation with area under curve\n", "t = '$\\Pr(0 \\leq x \\leq 3.5) = $ {0:1.3f}'.format(pr)\n", "plt.annotate(t, xy=(3, 0.1), xytext=(0.75, 0.2), size=14,\n", " arrowprops=dict(arrowstyle='->', \n", " connectionstyle='angle3, angleA=0,angleB=120'));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scatter Plots\n", "\n", "Above we discussed the `plt.plot()` method to draw line plots. The same function is capable of drawing simple scatter plots too. It is highly efficient, especially for larger data sets (> 2'000 data points), but its efficiency comes at the cost of flexibility. The additional `plt.scattter()` function is more flexible and therefore provides a powerful alternative for smaller to medium sized scatter plots. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generate std. normal rv\n", "rv = np.random.normal(size=(200, 2))\n", "\n", "# Draw scatter plot\n", "plt.scatter(rv[:, 0], rv[:, 1]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The standard marker is a circle. The following figure shows other markers. The complete list [can be found here](https://matplotlib.org/stable/api/markers_api.html)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd', 'p']:\n", " plt.plot(np.random.rand(5), np.random.rand(5), marker,\n", " label=\"marker='{0}'\".format(marker))\n", "plt.legend(fontsize=12)\n", "plt.xlim(0, 1.6);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Histogram\n", "\n", "Histograms are often a great first step in understanding the distribution of a given dataset. To plot a histogram Matplotlib offers the `plt.hist()` command. Let's show a simple example:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# RV from a gamma distribution\n", "gamma = np.random.gamma(size=5000, shape=3, scale=2)\n", "\n", "plt.hist(gamma, bins=50, density=True, \n", " alpha=0.5, edgecolor='blue');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As shown in the above example, the figure can again be adjusted to the user's liking. Available arguments are listed [here](https://matplotlib.org/devdocs/api/_as_gen/matplotlib.pyplot.hist.html). `alpha=0.5` adjusts the density of the bar color. With `density=True` the counts will be normalized to form a probability density. These two arguments are helpful if one wishes to combine two histograms as shown in below example where the histogram displays a standard normal pdf combined with its cdf." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# RV from a normal distribution\n", "norm = np.random.normal(size=5000)\n", "\n", "plt.hist(norm, bins=50, histtype='stepfilled', alpha=0.3, density=True, cumulative=True)\n", "plt.hist(norm, bins=50, histtype='stepfilled', alpha=0.3, density=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is an example one could use if one wishes to add a Kernel Density Estimation (KDE) to a histogram. Further information regarding the problem of bin-size when using histograms, and KDE estimation you can find [here](https://mglerner.github.io/posts/histograms-and-kernel-density-estimation-kde-2.html?p=28) or [here](http://www.mvstat.net/tduong/research/seminars/seminar-2001-05/). " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kde = sps.gaussian_kde(norm)\n", "xx = np.linspace(-4, 4, 1000)\n", "\n", "fig, ax = plt.subplots(figsize=(8, 6))\n", "ax.hist(norm, bins=50, histtype='stepfilled', alpha=0.3, density=True)\n", "ax.plot(xx, kde(xx));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bar Charts\n", "\n", "Python offers the `plt.bar()` functionality to plot bar charts. It is shown below in two simple example. Before we explain the code we have to keep in mind two things: First, using bar charts feels awkardly 'old-fashioned'. It is hardly used anymore today and there are better ways to represent your results. Second, Matplotlib's bar charts options are limited and coding it is cumbersome. Simply put, other tools such as R (e.g. its barplot-function or the excellent ggplot2 package) provide much more (and better) functionalities than Matplotlib (see e.g. [here](http://www.r-graph-gallery.com/portfolio/barplot/)). " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1152x360 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Dividend return data for Nestle, Novartis 2012-2016\n", "NESN = (3.61, 3.43, 3.30, 3.01, 3.01)\n", "NOVN = (4.22, 3.91, 3.04, 2.84, 3.09)\n", "\n", "ind = np.arange(len(NESN)) # the x locations for the groups\n", "width = 0.35 # the width of the bars\n", "\n", "# Setup figure, axes: two plots arranged horizontally\n", "fig, ax = plt.subplots(1, 2, figsize=(16, 5))\n", "\n", "# Plot left [0] bar chart\n", "rects1 = ax[0].bar(ind, NESN, width)\n", "rects2 = ax[0].bar(ind + width, NOVN, width)\n", "\n", "# Add text for title, legend and axes ticks\n", "ax[0].set_title('Dividend Return (in %)')\n", "ax[0].set_xticks(ind + width / 2)\n", "ax[0].set_xticklabels(('2012', '2013', '2014', '2015', '2016'))\n", "ax[0].legend((rects1[0], rects2[0]), ('NESN', 'NOVN'))\n", "\n", "# Plot right [1] bar chart\n", "rects3 = ax[1].bar(ind, NESN, width, align='center')\n", "rects4 = ax[1].bar(ind, NOVN, width, bottom=NESN) # bottom used to stack bars\n", "\n", "# Add text for title, legend and axes ticks\n", "ax[1].set_title('Dividend Return (in %) [STACKED]')\n", "ax[1].set_xticks(ind)\n", "ax[1].set_xticklabels(('2012', '2013', '2014', '2015', '2016'))\n", "ax[1].legend((rects3[1], rects4[1]), ('NESN', 'NOVN'));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are other options such as the plotting functionalities provided through [Plotly](https://plot.ly/python/bar-charts/), [Bokeh](http://bokeh.pydata.org/en/latest/docs/gallery.html) or [Altair](https://altair-viz.github.io/), which are much more accessible in plotting barplots than Matplotlib." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pie Chart\n", "\n", "What is written above about matplotlib's bar chart functionalities is true for pie charts as well, although to a lesser extent. Compared to bar charts, the coding of pie charts is less cumbersome and the available options are ok. Nevertheless, pie chart feel awkwardly old fashioned and personally I do not encourage students to use them. [Waffle charts](http://jonathansoma.com/lede/foundations-2018/matplotlib/creating-waffle-charts-in-pandas/), as an equivalent comparable, provide a much better overview. And here again R (combined with the 'ggplo2' and 'waffle' package) simply outdo Matplotlib. Still, for reference I provide below an example of a pie chart code [(based on matplotlib's example)](https://matplotlib.org/stable/gallery/pie_and_polar_charts/pie_features.html)." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Pie chart with slices ordered/plotted counter-clockwise:\n", "labels = ['Electrification Products', 'Robotics and Motion', \n", " 'Industrial Automation', 'Power Grids']\n", "sizes = [1528, 1195, 824, 1021]\n", "explode = (0, 0.1, 0, 0) # only \"explode\" 2nd slice (i.e. 'Robotics')\n", "\n", "plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n", " shadow=True, startangle=90)\n", "plt.axis('equal'); # Equal aspect ratio ensures that pie is drawn as a circle." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Subplots\n", "\n", "Sometimes it is more expressive to plot mutliple figures aligned on columns or rows. Matplotlib's two main routines for doing this are `plt.subplot()` and `plt.subplots()` (note the `s` at the end of the second command). The former serves well for simple plots with a limited number of subplots:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 6 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in range(1, 7):\n", " plt.subplot(2, 3, i)\n", " plt.text(0.5, 0.5, str((2, 3, i)),\n", " fontsize=18, ha='center')\n", "\n", "# Adjust amount of height (hspace) and width (wspace)\n", "# reserved for white space between subplots\n", "plt.subplots_adjust(hspace=0.3, wspace=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `plt.subplots()` function provides better control over elements and axes. Rather than creating subplot by subplot, `plt.subplots()` creates a full grid of subplots in a single line. Accessing them works similar to a NumPy array. Sharing x-axis and y-axis is easily done with the `sharex=` and `sharey=` command, respectively. Below is an example on how to create a 2x3 grid of subplots, where all plots in the same row share a common y-axis scale and all axes in the same column have their x-axis scale in common. We will use the object-oriented approach." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 6 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "markers = ['.', '+', 'x', '1', '2', '3']\n", "\n", "# Draw two rows of 3 plots each with shared axes\n", "fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')\n", "\n", "# Add main title above subplots\n", "fig.suptitle('Main Title', fontsize=18)\n", "\n", "# Access each plot and add text\n", "for i in range(2):\n", " for j in range(3):\n", " # Create some random data\n", " data = np.random.uniform(size=(30, 2))\n", " \n", " # Scatterplot with text label\n", " ax[i, j].scatter(data[:, 0], data[:, 1], marker=markers[2j+i])\n", " ax[i, j].text(0.5, 0.5, str((i, j)), fontsize=18, \n", " ha='center', weight='bold');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An alternative approach emerging is the `plt.subplot_mosaic` function. It offers a suprisingly simple way of arranging plots." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 792x360 with 5 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure_mosaic = \"\"\"\n", "AABC\n", "AADE\n", "\"\"\"\n", "\n", "fig, axes = plt.subplot_mosaic(mosaic=figure_mosaic, figsize=(11, 5))\n", "axes[\"B\"].plot(x, y1)\n", "axes[\"C\"].plot(x, y2)\n", "axes[\"D\"].scatter(x, y1)\n", "axes[\"E\"].hist(y1, bins=50, density=True, alpha=0.5, edgecolor='blue')\n", "\n", "axes[\"A\"].text(0.1, 0.9, \"A\", color='blue')\n", "axes[\"A\"].text(0.5, 0.5, \"Easy, not!?\", ha=\"center\", va=\"center\", size=38)\n", "axes[\"B\"].text(1, 0.35, \"B\", color='blue')\n", "axes[\"C\"].text(1, 0.9, \"C\", color='blue')\n", "axes[\"D\"].text(1, 0.35, \"D\", color='blue')\n", "axes[\"E\"].text(0.35, 35, \"E\", color='blue');\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving Figures\n", "\n", "Matplotlib has the ability to save figures in a wide variety of formats. You can save your figure using the `savefig()` command. The file format is inferred from the extension of the given filename and the file will be stored - if not specified otherwise - into your current working directory (use `!cd` (Windows) or `!pwd` to check the path of your current working directory). For example `plt.savefig('myFigure.png)` will save the figure as a png to your currend working directory or `plt.savefig('../myFigure.pdf)` and would save the pdf in the parent folder. Alternatively you could provide the absolute path as in `plt.savefig('C:/Users/Name/ParentFolder/Folder/Graphics.tiff)`.\n", "\n", "Here are the supported file formats:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Encapsulated Postscript': ['eps'],\n", " 'Joint Photographic Experts Group': ['jpeg', 'jpg'],\n", " 'Portable Document Format': ['pdf'],\n", " 'PGF code for LaTeX': ['pgf'],\n", " 'Portable Network Graphics': ['png'],\n", " 'Postscript': ['ps'],\n", " 'Raw RGBA bitmap': ['raw', 'rgba'],\n", " 'Scalable Vector Graphics': ['svg', 'svgz'],\n", " 'Tagged Image File Format': ['tif', 'tiff']}" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.gcf().canvas.get_supported_filetypes_grouped()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Beyond Matplotlib\n", "\n", "Matplotlib offers the functionality to cover the majority of plotting tasks. However (and as mentioned in the introduction), it has to be said that for more complex tasks, matplotlib does come short compared to other approaches, namely in R (and its seminal 'ggplot2' package by Hadley Wickham) for static figures or R/Shiny and Java Script ('D3.js') for interactive data visualization. \n", "\n", "Python's community has recognizes these shortcomings and as a consequences several additional plotting packages have surfaces in the past couple of years. Among the most popular is 'Seaborn', a package based on Matplotlib but with additional features to serve a more statistically oriented audience. It provides complex yet easy accessible plotting options for static data visualization. For further inspiration see the [gallery on the package's website](http://seaborn.pydata.org/examples/index.html#) - especially the `distplot()` and `jointplot()` might be of interest.\n", "\n", "Another package which is more and more used is 'Bokeh'. It is a Python library focusing on interactive visualization (in the style of D3.js) for modern web browsers. With its extensive documentation and the additional packages to serve not only the Python but also the R and Julia community, Bokeh is well set to become the major source for interactive, java-script-like plotting in Python. We will not make use of the Bokeh package in this course but you might want to have a look at the [gallery](http://bokeh.pydata.org/en/latest/docs/gallery.html) to get some idea of the package's capabilities.\n", "\n", "Finally, there are two more packages worth mentioning: [Altair](https://altair-viz.github.io/gallery/index.html), a declarative statistical visualization library similar to ggplot in R and [Plotly](https://plot.ly/python/).\n", "\n", "A fantastic set of graph examples that combines the best of matplotlib, seaborn and plotly is [The Python Graph Gallery](https://www.python-graph-gallery.com/). If you are looking for inspiration including corresponding code examples, you do not want to miss this website." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples\n", "## Line Plot: Share Price Chart\n", "\n", "Having discussed the basics, below are some real world examples. The first is a line plot displaying (normalized) share price data for Swiss industrial companies: ABB (CH0012221716), Bucher (CH0002432174), Burckard Compression (CH0025536027), OC Oerlikon (CH0000816824), Georg Fisher (CH0001752309) and Schindler (CH0024638212). The data is downloaded from Quandl. For that we use the \"pandas_datareader\" package. \n", "\n", "Note that this package is not part of Anaconda's standard distribution.* To install open the Anaconda Navigator, click on environments, then select \"All\" from the dropdown menu and search for \"pandas_datareader\". When found flag the package and click \"Apply\" (bottom right). Alternatively, open a Shell and type in \"pip install pandas-datareader\". For details see [the package's documentation](https://pandas-datareader.readthedocs.io/en/latest/). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Important: please set up your own Quandl account and insert your personal api_key to get the data!" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "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>ABB</th>\n", " <th>BUCN</th>\n", " <th>BCHN</th>\n", " <th>OERL</th>\n", " <th>FI-N</th>\n", " <th>SCHN</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2022-01-11</th>\n", " <td>34.24</td>\n", " <td>454.6</td>\n", " <td>431.0</td>\n", " <td>9.460</td>\n", " <td>1442.0</td>\n", " <td>237.4</td>\n", " </tr>\n", " <tr>\n", " <th>2022-01-10</th>\n", " <td>33.80</td>\n", " <td>448.2</td>\n", " <td>425.5</td>\n", " <td>9.495</td>\n", " <td>1410.0</td>\n", " <td>231.4</td>\n", " </tr>\n", " <tr>\n", " <th>2022-01-07</th>\n", " <td>35.01</td>\n", " <td>460.4</td>\n", " <td>429.5</td>\n", " <td>9.600</td>\n", " <td>1458.0</td>\n", " <td>239.4</td>\n", " </tr>\n", " <tr>\n", " <th>2022-01-06</th>\n", " <td>35.16</td>\n", " <td>458.4</td>\n", " <td>424.5</td>\n", " <td>9.600</td>\n", " <td>1486.0</td>\n", " <td>239.4</td>\n", " </tr>\n", " <tr>\n", " <th>2022-01-05</th>\n", " <td>35.79</td>\n", " <td>467.0</td>\n", " <td>427.0</td>\n", " <td>9.775</td>\n", " <td>1485.0</td>\n", " <td>242.8</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ABB BUCN BCHN OERL FI-N SCHN\n", "Date \n", "2022-01-11 34.24 454.6 431.0 9.460 1442.0 237.4\n", "2022-01-10 33.80 448.2 425.5 9.495 1410.0 231.4\n", "2022-01-07 35.01 460.4 429.5 9.600 1458.0 239.4\n", "2022-01-06 35.16 458.4 424.5 9.600 1486.0 239.4\n", "2022-01-05 35.79 467.0 427.0 9.775 1485.0 242.8" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load package to download share prices from web\n", "import pandas_datareader.data as web\n", "import pandas as pd\n", "\n", "# List with stock isin numbers\n", "isin = ['SIX/CH0012221716CHF', 'SIX/CH0002432174CHF', 'SIX/CH0025536027CHF',\n", " 'SIX/CH0000816824CHF', 'SIX/CH0001752309CHF', 'SIX/CH0024638212CHF']\n", "\n", "# Download closing prices of defined shares\n", "data = pd.DataFrame()\n", "for tick in isin:\n", " data[tick] = web.DataReader(tick, data_source='quandl', \n", " start='2021-01-12', end='2022-01-11',\n", " api_key='H494hBc5iXyUgiF4oX5y')['Price']\n", "\n", "\n", "# Rename column names from ISIN to Ticker\n", "data.columns = ['ABB', 'BUCN', 'BCHN', 'OERL', 'FI-N', 'SCHN']\n", "\n", "# Show data extract\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot normalized share prices\n", "plt.figure(figsize = (16, 8))\n", "plt.plot(data / data.iloc[-1] * 100) # Normalize prices to 100 and plot \n", "plt.axhline(y=100, color='gray', linestyle=':') # Add dotted line at 100%\n", "plt.legend(data.columns, fontsize=14)\n", "plt.xlabel('Date', fontsize=14)\n", "plt.ylabel('Price (normalized)', fontsize=14)\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12)\n", "plt.title('One-Year Returns of Swiss Industrial Companies', fontsize=16, weight='bold');" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Calculate the log-returns\n", "rets = np.log(data / data.shift(periods=-1))\n", "\n", "# Plot daily returns in histogram\n", "plt.hist(rets)\n", "plt.legend(data.columns, loc='upper left');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scatter Plot: Portfolio Simulation and Sharpe Ratios\n", "\n", "Using the loaded share data we simulate 5'000 possible portfolio combinations (weights per share) and calculate the corresponding portfolio return and volatility. We know that, given an investment into $n$ assets, a portfolio's (expected) return is given by\n", "\n", "$$\\mathbb{E}[r_p] = \\sum_{i} w_i * \\mathbb{E}[r_i] = \\mathbf{w^T R} $$\n", "where $w_i \\in [0,1]$ with $\\sum_i w_i = 1$ is the proportion invested in asset $i$, $\\mathbb{E}[r_i]$ asset $i$'s expected return, $\\mathbf{w, R}$ are $n \\times 1$ vectors with weights and historical returns, respectively. Note that we assume that historical returns are the best estimators for the expected return. \n", "\n", "In the same context, risk is defined as the the portfolio's return variance:\n", "\n", "$$\\sigma_p^2 = \\sum_i \\sum_j w_i w_j \\sigma_i \\sigma_j \\rho_{ij} = \\mathbf{w^T \\Sigma w}$$\n", "\n", "where $\\sigma$ is the (sample) standard deviation of the periodic returns on an asset, $\\rho_{ij}$ is the correlation coefficient between returns on asset $i$ and $j$ and $\\mathbf{\\Sigma}$ the corresponding matrix notation of the variance-covariance matrix.\n", "\n", "Having generated 5000 possible portfolio combinations we use a scatter plot to visualize the resulting portfolios. We apply a color scheme to indicate the Sharpe ratio of each portfolio, where the Sharpe ratio is defined as \n", "\n", "$$SR_p = \\frac{\\mathbb{E}[r_p] - r_f}{\\sigma_p} $$" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "n = 5000 # Number of simulations\n", "pfrets = [] # Portfolio returns\n", "pfvols = [] # Portfolio volatilities\n", "rf = 0 # Risk-free rate\n", "\n", "# Simulate n times 6 random weights and calculate corresponding\n", "# (annual) portfolio return and volatility given the random weights\n", "for p in range(n):\n", " w = np.random.random(rets.shape[1])\n", " w = w / np.sum(w)\n", " pfrets.append(np.sum(rets.mean() * w) * 252 - rf)\n", " pfvols.append(np.sqrt(np.dot(w.T, np.dot(rets.cov() * 252, w))))\n", "\n", "# Convert results to an np.array\n", "pfrets = np.array(pfrets)\n", "pfvols = np.array(pfvols)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x576 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Scatterplot of the 5000 simulated portfolios\n", "plt.figure(figsize = (16, 8))\n", "\n", "# Color (c) of dot based on Sharpe ratio,\n", "# Color map (cmap) set to 'viridis' style \n", "plt.scatter(pfvols, pfrets, c=((pfrets-rf) / pfvols), \n", " marker='o', cmap='viridis')\n", "\n", "# Add colorbar, labels and change tick size\n", "plt.colorbar().set_label(label='Sharpe ratio', size=16)\n", "plt.xlabel('Expected Volatility ($\\sigma$)', fontsize=16)\n", "plt.ylabel('Expected Return ($r_p$)', fontsize=16)\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Histogram: Share Returns\n", "\n", "Literature often assumes returns to be normally distributed. In a hypothetical example, we plot the annual returns of the 5'000 simulated portfolios in a histogram and combine it with a kernel density estimate and a normal distribution (given mean, sd of data at hand)." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Ben Zimmermann\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import Seaborn package\n", "import seaborn as sns\n", "\n", "# Plot histogram with kde and normal fit given data\n", "sns.distplot(pfrets, bins=50, norm_hist=True, fit=sps.norm,\n", " hist_kws={'label':'frequency', 'alpha':0.8, 'edgecolor':'k'},\n", " kde_kws={'label':'kde', 'color':'b'},\n", " fit_kws={'label':'Normal pdf', 'alpha':0.5, 'color':'r'})\n", "plt.legend(fontsize=16);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scatter Plot\n", "\n", "It is often said that central government debt is positively correlated with GDP growth. We could investigate this (on a superficial scale, of course) by plotting the data against each other in a scatter plot. In below chart we use data downloaded from the world bank for Switzerland, the United Kingdom, and the USA. \n", "\n", "In order to download the data, we need to know the indicators in the World Bank's database. The best way to find these is to go to the [World Bank's data catalogue](http://data.worldbank.org/indicator) and use their search engine. Once you've found what you were looking for, you will find the indicator in the url. For GDP growth it is \"NY.GDP.MKTP.KD.ZG\" and \"GC.DOD.TOTL.GD.ZS\" for central government debt (total, in % of GDP). " ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# Import worldbank functions as wb\n", "from pandas_datareader import wb\n", "\n", "# Set parameter and download data\n", "ctry = ['US', 'GBR', 'CH']\n", "ind = ['NY.GDP.MKTP.KD.ZG', 'GC.DOD.TOTL.GD.ZS']\n", "df = wb.download(indicator=ind, country=ctry, start=1990, end=2018)" ] }, { "cell_type": "code", "execution_count": 30, "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>Country</th>\n", " <th>Year</th>\n", " <th>GDP growth</th>\n", " <th>Debt</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Switzerland</td>\n", " <td>2018</td>\n", " <td>2.916905</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Switzerland</td>\n", " <td>2017</td>\n", " <td>1.584820</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Switzerland</td>\n", " <td>2016</td>\n", " <td>2.045186</td>\n", " <td>19.045740</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Switzerland</td>\n", " <td>2015</td>\n", " <td>1.657769</td>\n", " <td>20.223846</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Switzerland</td>\n", " <td>2014</td>\n", " <td>2.446845</td>\n", " <td>20.327055</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Country Year GDP growth Debt\n", "0 Switzerland 2018 2.916905 NaN\n", "1 Switzerland 2017 1.584820 NaN\n", "2 Switzerland 2016 2.045186 19.045740\n", "3 Switzerland 2015 1.657769 20.223846\n", "4 Switzerland 2014 2.446845 20.327055" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Drop (multi)index and format columns\n", "df.reset_index(inplace=True)\n", "df.columns = ['Country', 'Year', 'GDP growth', 'Debt']\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have the data in a proper format, we use Seaborn's `lmplot()` function to plot debt against gdp growth. The `lmplot()` adds a linear regression to the data, incl. confidence bands (here set to 95%; based on 100 bootstrap resamples)." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 827.5x720 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Draw scatterplot incl. linear regression\n", "# ci=95 and n_boot=100 are default values, thus\n", "# these are just shown for better understanding\n", "fig = sns.lmplot(x='GDP growth', y='Debt', hue='Country', data=df,\n", " truncate=True, height=10, ci=95, n_boot=100)\n", "\n", "# Add more informative axis labels\n", "fig.set_axis_labels('GDP Growth (in %)', 'Central Gvmt. Debt (total, in % of GDP)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Further Resources\n", "\n", "In writing this notebook, many ressources were consulted. For internet ressources the links are provided within the textflow above and will therefore not be listed again. Beyond these links, the following ressources are recommended as further reading on the discussed topic:\n", "\n", "* Vanderplas, Jake, 2016, Python Data Science Handbook (O'Reilly Media, Sebastopol, CA).\n", "* Hilpisch, Yves, 2019, Python for Finance (O'Rilly Media, Sebastopol, CA).\n", "* McKinney, Wes, 2013, Python for Data Analysis (O'Rilly Media, Sebastopol, CA).\n", "* Sheppard, Kevin, 2017, Introduction to Python for Econometrics, Statistics and Data Analysis from Website https://www.kevinsheppard.com/images/b/b3/Python_introduction-2016.pdf, 07/07/2017." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
seninp/saxpy	jupyter/tinkah.ipynb	1	4808	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def idx2letter(idx):\n", " \"\"\"Convert a numerical index to a char.\"\"\"\n", " if 0 <= idx < 20:\n", " return chr(97 + idx)\n", " else:\n", " raise ValueError('A wrong idx value supplied.')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'a'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx2letter(0)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'h'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx2letter(7)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'t'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx2letter(19)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "A wrong idx value supplied.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-461b39d3f8af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0midx2letter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-3-d27bfc837d52>\u001b[0m in \u001b[0;36midx2letter\u001b[0;34m(idx)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mchr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m97\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'A wrong idx value supplied.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: A wrong idx value supplied." ] } ], "source": [ "idx2letter(-1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "A wrong idx value supplied.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-8-d6456bfe4a1b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0midx2letter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-3-d27bfc837d52>\u001b[0m in \u001b[0;36midx2letter\u001b[0;34m(idx)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mchr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m97\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'A wrong idx value supplied.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: A wrong idx value supplied." ] } ], "source": [ "idx2letter(20)" ] } ], "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-2.0
ELC/Training-Python	projects/matplotlib/.ipynb_checkpoints/Matplotlib Tutorial Part 06 - Pie Charts-checkpoint.ipynb	1	80205	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Matplotlib Tutorial Part 06 - Pie Charts" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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Bmd9wz10TOk01iurQ5NfMbKqZTQcGUYRZXEn45scjcqigJxopWAMjiJO8W/MI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\nssvM7O9IpWYycOCBjBmjNQtEQlm4EMaPj8jnbya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"text/plain": [ "<matplotlib.figure.Figure at 0x268ace546a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(9,9))\n", "\n", "# Values\n", "slices = [7,2,2,13]\n", "\n", "# Labels\n", "activities = ['sleeping','eating','working','playing']\n", "\n", "plt.pie(slices, labels=activities,)\n", "\n", "plt.title('Pie Chart')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Pie Chart Customized" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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smfQBp7JxY4La2uGce67f65pERCTjlgu/Eg4GAovMbIbXteQjhYm9N5F0eiSr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"text/plain": [ "<matplotlib.figure.Figure at 0x268ad311898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(9,9))\n", "\n", "# Values\n", "slices = [7,2,2,13]\n", "\n", "# Labels\n", "activities = ['sleeping','eating','working','playing']\n", "\n", "# Colours\n", "cols = ['c','m','r','pink']\n", "\n", "plt.pie(slices,\n", " labels=activities,\n", " colors=cols, # Colours\n", " startangle=90, # Angle\n", " shadow=True, # Shadow\n", " explode=(0.05,0.05,0.05,0.05), # Separation\n", " autopct=\"%1.1f%%\") # Show %\n", "\n", "plt.title('Pie Chart')\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
robertoalotufo/ia898	2S2018/Ex04 Filtragem de Imagens.ipynb	1	150482	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ex04 - Filtragem de Imagens" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " convolução discreta é uma ferramenta utilizada para construir qualquer filtro linear ou de deslocamento. \n", "\n", "Filtros suavizantes: são aqueles que removem a nitidez da imagem fazendo uma média, aritmética ou ponderada, na janela do núcleo. O filtro ponderado suavizante mais comum é o da gaussiana. Filtros suavizantes são muito utilizados para eliminar ruído de uma imagem.\n", "\n", "Filtros aguçantes: são aqueles que realçam as bordas da imagem. Um dos filtros aguçantes mais comum é o Sobel (https://en.wikipedia.org/wiki/Sobel_operator).\n", "\n", "Veja mais no notebook sobre [Filtragem no domínio espacial](07 Filtragem no domínio espacial.ipynb)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f538a3a1190>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CYPH4JBPKE2Jyz+h/zhJhZV6Fgc6HZMECgG2S/PTQlOfwckavWnBdnCoTtFN3mCxQWCTh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"text/plain": [ "<matplotlib.figure.Figure at 0x7f538c6ff390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "f = mpimg.imread('../data/retina.tif') \n", "plt.imshow(f,cmap='gray')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercício 1\n", "\n", "Entenda o uso da função numpy.convolve e experimente usá-la para entender o filtro da média. Sugestões:\n", "- comece com uma imagem numérica pequena e depois use a imagem da retina\n", "- faça experimentos variando o tamanho da máscara do filtro e compare as imagens resultantes\n", "- varie também o modos da função, explicando as diferenças encontradas\n", "- compare os resultados obtidos pela função numpy.convolve com a filtragem usando a função conv da toolbox ia898" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercício 2\n", "\n", "Explore o filtro Gaussiano. Sugestões:\n", "- teste a propriedade de decomposição das máscaras da convolução, ou seja, crie um filtro gaussiano bi-dimensional (3,3) a partir de 2 filtros unidimensionais (3,1) e (1,3)\n", "- Faça experimentos de filtragem variando os parâmetros do filtro (tamanho da máscara do filtro, a média e o desvio padrão) \n", "- adicione ruído a uma imagem e depois tente remover o ruído com filtragem" ] } ], "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
torgebo/deep_learning_workshop	3-convnet/convolutional-network.ipynb	1	55499	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"This area sets up the Jupyter environment.\n", "Please do not modify anything in this cell.\n", "\"\"\"\n", "import os\n", "import sys\n", "import time\n", "\n", "# Add project to PYTHONPATH for future use\n", "sys.path.insert(1, os.path.join(sys.path[0], '..'))\n", "\n", "# Import miscellaneous modules\n", "from IPython.core.display import display, HTML\n", "\n", "# Set CSS styling\n", "with open('../admin/custom.css', 'r') as f:\n", " style = \"\"\"<style>\\n{}\\n</style>\"\"\".format(f.read())\n", " display(HTML(style))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Convolutional Neural Networks\n", "\n", "<div class=\"alert alert-warning\">\n", "In this notebook we will become familiar with a type of layer for artificial neural networks called convolutional layers. The data we will attempt to model using these types of networks will be images.\n", "</div>\n", "\n", "\n", "## Images\n", "\n", "For a computer an image is a matrix of data, where each pixel is represented by one or more values:\n", "\n", "\n", "### Matrix with one value per pixel = greyscale images\n", "\n", "<img src=\"./resources/lincoln_pixel_values.png\" alt=\"Grayscale Image\" width=\"700\">[image_source](http://openframeworks.cc/ofBook/chapters/image_processing_computer_vision.html)\n", "\n", "### Matrix with three values per pixel = color images\n", " <img src=\"./resources/color_images.png\" alt=\"Decomposition of a color image\" width=\"400\">\n", "[image_source](https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# The MNIST Dataset\n", "\n", "We had a brief look at this dataset in the previous notebook, and here we will go through it again with much more detail. As before, the MNIST database (Modified National Institute of Standards and Technology database) is a multiclass classification problem where we are tasked with classifying a digit ($0-9$) based on a $28\\times 28$ greyscale image:\n", "\n", ">The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.\n", "\n", ">It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.\n", "\n", "[source](http://yann.lecun.com/exdb/mnist/)\n", "\n", "In the following example we will load data from MNIST.\n", "\n", "* input $\\rightarrow$ 70000 samples of vectors\n", " * Each vector has 784 dimensions\n", " * Here presented as $28\\times 28$ matrices $\\rightarrow$ Greyscale images\n", "* target $\\rightarrow$ 70000 integers indicating a digit from 0 to 9\n", "\n", "<div class=\"alert alert-info\">\n", "<strong>In the following snippet of code we will:</strong>\n", "<ul>\n", " <li>Load the MNIST dataset</li>\n", " <li>Plot the 5th sample of the training set</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Plots will be displaying plots within the notebook\n", "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "from matplotlib.ticker import MaxNLocator\n", "\n", "# NumPy is a package for manipulating N-dimensional array objects \n", "import numpy as np\n", "\n", "# Pandas is a data analysis package\n", "import pandas as pd\n", "\n", "#Library To test/verify some tasks\n", "import problem_unittests as tests # Used to test ouw anwsers\n", "\n", "# Mnist wrapper\n", "from keras.datasets import mnist\n", "\n", "\n", "# Code to load the data\n", "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", "\n", "# Print data shape\n", "print('Shape of x_train {}'.format(x_train.shape))\n", "print('Shape of y_train {}'.format(y_train.shape))\n", "print('Shape of x_test {}'.format(x_train.shape))\n", "print('Shape of y_test {}'.format(y_train.shape))\n", "\n", "\n", "# Code to plot the 5th training sample.\n", "fig,ax1 = plt.subplots(1,1, figsize=(7, 7))\n", "\n", "ax1.imshow(x_train[5], cmap='gray')\n", "title = 'Target = {}'.format(y_train[5])\n", "ax1.set_title(title)\n", "ax1.grid(which='Major')\n", "ax1.xaxis.set_major_locator(MaxNLocator(28))\n", "ax1.yaxis.set_major_locator(MaxNLocator(28))\n", "fig.canvas.draw()\n", "time.sleep(0.1)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Data Pre-Processing\n", "\n", "Before we start classifying digits we need to pre-process the data.\n", "\n", "Your first task is to create a function that normalises 8-bit images from [0,255] to [0,1]:\n", "\n", "\n", "### Task I: Implement an Image Normalisation Function\n", "<div class=\"alert alert-success\">\n", "Task: Implement a function that normalises the images to the interval [0,1].\n", "<ul>\n", " <li>Inputs are integers in the interval [0,255]</li>\n", " <li>Outputs should be floats in the interval [0,1]</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def normalise_images(images):\n", " \"\"\"Normalise input images.\n", " \"\"\"\n", " # Normalise image here\n", "\n", " return images\n", "\n", "### Do not modify the following lines ###\n", "tests.test_normalize_images(normalise_images)\n", "\n", "# Normalize the data for future use\n", "x_train = normalise_images(x_train)\n", "x_test = normalise_images(x_test)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Task II: Expand the Dimension of the Input\n", "\n", "When we loaded the MNIST dataset each digit was represented by a matrix of size $(28, 28)$. However, the artificial neural network we will be building uses the concept of colour channels and feature maps even for greyscale images. This means that we have to transform $(28, 28)$ to $(28, 28, 1)$.\n", "\n", "<div class=\"alert alert-success\">\n", "Task: Write a piece of code that add a new dimestion to `x_train` and `x_test`.\n", "<ul>\n", " <li>The shape of `x_train` should be $(60000, 28, 28, 1)$</li>\n", " <li>The shape of `x_test` should be $(10000, 28, 28, 1)$</li>\n", "</ul>\n", "Take a look at [numpy.expand_dims()](https://docs.scipy.org/doc/numpy/reference/generated/numpy.expand_dims.html) for how you might do this.\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Write your code here\n", "x_train = None\n", "x_test = None\n", "\n", "\n", "### Do not modify the following lines ###\n", "print('Shape of x_train {}'.format(x_train.shape))\n", "print('Shape of y_train {}'.format(y_train.shape))\n", "print('Shape of x_test {}'.format(x_test.shape))\n", "print('Shape of y_test {}'.format(y_test.shape))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Target Pre-Processing\n", "\n", "To classify our digits we need to use one-hot encoding to represent the target outputs. One-hot enconding is a robust yet simple solution to represent multi-categorical targets. \n", "\n", "This enconding is an ideal representation to train a model using gradient descent algorithm with the [softmax function](http://www.cs.toronto.edu/~guerzhoy/321/lec/W04/onehot.pdf) we discussed in a previous notebook.\n", "\n", "### Example of one-hot encoding\n", "\n", "Here's an example of how a one-hot encoding scheme looks like:\n", "\n", "<img src=\"./resources/one_hot.png\" alt=\"One-Hot encoding\" width=\"650\">\n", "\n", "The core idea is that you transform multi-categorical data to a combination of several single class(es). By doing this we can, for each example, see whether it belongs to any class, where 1 indicates that it does and 0 otherwise.\n", "\n", "\n", "### Task III: Implement a Function for One-Hot Encoding\n", "\n", "<div class=\"alert alert-success\">\n", "Task: Implement a function that one-hot encodes a vector of numbers to a matrix of $K$ classes:\n", "<ul>\n", " <li>The first argument is vector with $N$ samples (dimensions)</li>\n", " <li>The second argument is a number $K$ signifying the number of classes</li>\n", " <li>For each sample of the vector you will create an array with $K$ dimensions</li>\n", " <li>The one-hot encoded matrix should have zeros on all positions expect on the position indicated by the current sample in the input vector</li>\n", "</ul>\n", "</div>\n", "\n", "* Try to implement this function by yourself. If you have doubts ask for help\n", "* If you are running out of time use `keras.utils.to_categorical(vector, number_classes)` like we did in the previous notebook and come back to this task later" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def one_hot(vector, number_classes):\n", " \"\"\"Return a one-hot encoded matrix given the argument vector.\n", " \"\"\"\n", " # Where we will store our one-hots\n", " one_hot = []\n", "\n", " # One-hot encode `vector` here\n", "\n", "\n", " # Transform list to numpy array and return it\n", " return np.array(one_hot)\n", "\n", "\n", "### Do not modify the following line ###\n", "tests.test_one_hot(one_hot)\n", "\n", "# One-hot encode the MNIST target values\n", "y_train = one_hot (y_train, 10)\n", "y_test = one_hot(y_test, 10)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now that we have both added an extra dimension to the input data as well as one-hot encoded the target values, let's take a look at the shapes of the data matrices." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "print('Shape of x_train {}'.format(x_train.shape))\n", "print('Shape of y_train {}'.format(y_train.shape))\n", "print('Shape of x_test {}'.format(x_train.shape))\n", "print('Shape of y_test {}'.format(y_train.shape))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Build an Artificial Neural Network with Convolutions and Max-Pooling\n", "\n", "\n", "## Convolutions\n", "\n", "If you have the task of recognising cats in an image, you might want to recognise / classify the animal regardless of its position. To do that we rely on a statistical fact: natural images are stationary [source](http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution).\n", "\n", "So, if we calculate a statistic for some location in the input image, then that statistic might also be valuable to calculate at some other location. One can exploit this property to define small networks that learn features that can be applied on different parts of an image.\n", "\n", "Convolutional neural networks employs these aspects to create very efficient neural networks.\n", "\n", "In case you want to watch a short video (has captions) walking through the concepts behind convolutional networks, take a look at the following YouTube link:\n", "\n", "* [Udacity - Convolutional Networks](https://www.youtube.com/watch?v=jajksuQW4mc)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Sliding windows\n", "\n", "A sliding window defines a small region of interest in an image.\n", "\n", "The region of interested is used to scan the whole image as shown in the following animation:\n", "\n", "<img src=\"./resources/sliding_window_example.gif\" alt=\"Sliding window on a greyscale image\" width=\"200\">\n", "\n", "\n", "If we use the sliding window to define what is the input seen by a small neural network, we have a so called convolution.\n", "\n", "\n", "Assuming we have a color image, and a small neural network with $k$ outputs: for every possible position of the sliding window we will have $k$ outputs.\n", "\n", "\n", "<img src=\"./resources/conv.png\" alt=\"Output of a convolution at for a given sliding window placement\" width=\"300\">\n", "\n", "\n", "After the sliding window has scanned the whole image you have 3 dimensional matrix that can be investigated further.\n", "\n", "\n", "<img src=\"./resources/conv2.png\" alt=\"Output of a convolution at for a given sliding window placement\" width=\"300\">" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "### Convolution Example\n", "\n", "\n", "Let's assume that we have an image of $5 \\times 5=25$ pixels:\n", "\n", "$$\n", "\\begin{equation}\n", "\\begin{array}{\|c\|c\|c\|c\|c\|}\n", " \\hline\n", " 1 & 1 & 1 & 0 & 0 \\\\ \n", " \\hline\n", " 0 & 1 & 1 & 1 & 0\\\\\n", " \\hline\n", " 0 & 0 & 1 & 1 & 1\\\\\n", " \\hline\n", " 0 & 0 & 1 & 1 & 0\\\\\n", " \\hline\n", " 0 & 1 & 1 & 0 & 0\\\\\n", " \\hline\n", "\\end{array}\n", "\\end{equation}\n", "$$\n", "\n", "Assume that we define a small neural network that has $3 \\times 3$ weights and a single output.\n", "The weight matrix is\n", "\n", "$$\n", "\\begin{equation}\n", "\\begin{array}{\|c\|c\|c\|}\n", " \\hline\n", " 1 & 0 & 1 \\\\ \n", " \\hline\n", " 0 & 1 & 0\\\\\n", " \\hline\n", " 1 & 0 & 1\\\\\n", " \\hline\n", "\\end{array}\n", "\\end{equation}\n", "$$\n", "\n", "By feed-forwarding the network using a $3 \\times 3$ sliding window we get the following convolved features (also know as feature map):\n", "\n", "$$\n", "\\begin{equation}\n", "\\begin{array}{\|c\|c\|c\|}\n", " \\hline\n", " 4 & 3 & 4 \\\\ \n", " \\hline\n", " 2 & 4 & 3\\\\\n", " \\hline\n", " 2 & 3 & 4\\\\\n", " \\hline\n", "\\end{array}\n", "\\end{equation}\n", "$$\n", " \n", "\n", "<img src=\"./resources/Convolution_schematic.gif\" alt=\"Sliding window\" width=\"500\">\n", "[source](http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution)\n", "\n", "The number of so-called feature maps produced will depend on the number of outputs of the neural network. In this case we have just one feature map.\n", "\n", "\n", "#### Stride and Padding\n", "\n", "We can use padding and strides to achive different shapes on the feature maps.\n", "Below are four animations that showcases the convolution operation on an input matrix using different paddings and strides:\n", "\n", "<table style=\"width:100%\">\n", " <tr>\n", " <td><img src=\"./resources/no_padding_no_strides.gif\"></td>\n", " <td><img src=\"./resources/no_padding_strides.gif\"></td>\n", " <td><img src=\"./resources/same_padding_no_strides.gif\"></td>\n", " <td><img src=\"./resources/padding_strides.gif\"></td>\n", " </tr>\n", " <tr>\n", " <td>Valid padding, stride=1</td>\n", " <td>Valid padding, stride=2</td>\n", " <td>Same Padding, stride =1</td>\n", " <td>Padding =1 , stride =2</td>\n", " </tr>\n", "</table>\n", "[source](https://github.com/vdumoulin/conv_arithmetic)\n", "[Paper](https://arxiv.org/abs/1603.07285)\n", "\n", "\n", "### Computing the Size of the Convolutions\n", "\n", "To compute the size of the feature map resulting from a convolution we need to know the input size, the size of the kernel (filter), the stride, and the padding:\n", "\n", "$$\n", "\\begin{equation}\n", "output = \\frac{1}{stride} (input - kernel + 2 padding) + 1\n", "\\end{equation}\n", "$$\n", "\n", "The height can be calculated like this:\n", "\n", "$$\n", "\\begin{equation}\n", "height_{new} = \\frac{1}{stride} (height_{input} - height_{kernel} + 2 * padding)+1\n", "\\end{equation}\n", "$$\n", "\n", "The width can be calculated like this:\n", "\n", "$$\n", "\\begin{equation}\n", "widht_{new} = \\frac{1}{stride} (widht_{input} - widht_{kernel} + 2 * padding)+1\n", "\\end{equation}\n", "$$\n", "\n", "#### Example :\n", "\n", "Let us assume that we have an image that is $5 \\times 5$. If we pad this image with a single pixel and then convolve it with a $3 \\times 3$ kernel using a stride of $2$, we get the following feature map:\n", "\n", "<img src=\"./resources/odd.gif\" alt=\"Examples math\" width=\"300\">\n", "\n", "We can compute the output size using the equations above:\n", "\n", "$$\n", "\\begin{equation}\n", "\\begin{aligned}\n", "height_{new} &= \\frac{1}{stride} (height_{input} - height_{kernel} + 2 * padding) + 1 \\\\\n", "&= \\frac{1}{2} (5 - 3 + 2 * 1) + 1 \\\\\n", "&= \\frac{1}{2}(4)+1 \\\\\n", "&= 3\n", "\\end{aligned}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Pooling\n", "\n", "It has become common practice to use pooling layer between convolutional layers.\n", "\n", "Sucessful convolutional neural networks like [alexnet](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks), [VGG16, VGG19](https://arxiv.org/abs/1409.1556) employed this technique.\n", "\n", "Pooling layers also depend on sliding windows, but instead of using the window as inputs for neurons, the sliding input data goes through a `max`, `mean`, or some other operator.\n", "\n", "### A Max-Pooling Example\n", "\n", "<img src=\"./resources/pooling.gif\" alt=\"Max Pooling\" width=\"300\">\n", "\n", "Max-pooling has several advantages:\n", " If you have images where the same class has similar images with small shifts in the pixel position, max pooling will mitigate small translations\n", "* It introduces zero parameters to the model since the max and mean operators are fixed functions that do not depend on weights\n", "* It reduces the amount of data that need to be processed in the next layer, while assuring some pixel translation invariance.\n", "* They are normally used with zero padding (aka valid padding).\n", "* They follow the same dimensional maths as convolutions\n", "\n", "[You can read more about pooling operators here](http://cs231n.github.io/convolutional-networks/#pool)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Implementing a Convolutional Network\n", "\n", "We will implement the following convolution network:\n", "\n", "<img src=\"./resources/mnist_net.png\" alt=\"CNN\" width=\"1280\">\n", "\n", "The components of this network can be seen below:\n", "\n", "Define an input:\n", "\n", "* `input_x = Input(shape=sample_shape)`\n", "* `sample_shape` is an input parameter\n", " \n", "Generate 32 kernel maps using a convolutional layer:\n", "\n", "* The convolution uses a $3 \\times 3$ kernel, stride 1, valid padding, and [ReLU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks) activation\n", "* Use [Conv2D](https://keras.io/layers/convolutional/) from Keras\n", "* `output_layer = Conv2D(PARAMETERS)(input_layer)`\n", " \n", "Generate 64 kernel maps using a convolutional layer:\n", "\n", "* The convolution uses a $3 \\times 3$ kernel, stride 1, valid padding, and [ReLU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks) activation\n", "* Use [Conv2D](https://keras.io/layers/convolutional/)\n", "* `output_layer = Conv2D(PARAMETERS)(input_layer)`\n", "\n", "Reduce the feature maps using max-pooling:\n", "\n", "* The max-pooling should us a $2 \\times 2$ kernel, stride 1, and valid padding\n", "* Use [MaxPooling2D](https://keras.io/layers/pooling/#maxpooling2d)\n", "* `output_layer = MaxPooling2D(PARAMETERS)(input_layer)`\n", "\n", "Flatten the feature map:\n", "\n", "* [Flatten](https://keras.io/layers/core/#flatten)\n", "\n", "Fully-connected, i.e. `Dense`, to 128 dimensions:\n", "\n", "* [ReLU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks) activation\n", "* [Dense](https://keras.io/layers/core/#dense)\n", "\n", "Fully-connected, i.e. `Dense`, to $K$ classes (argument) dimensions:\n", "\n", "* [Softmax](https://en.wikipedia.org/wiki/Softmax_function) activation\n", "* [Dense](https://keras.io/layers/core/#dense)\n", "\n", "\n", "### Task IV: Implement a Convolutional Neural Network Model\n", "\n", "It is time to implement our first convolutional neural network.\n", "\n", "<div class=\"alert alert-success\">\n", "<strong>Task:</strong> Create a function `net_1()` that implements the network specified above.\n", "Make sure to refer back to earlier notebooks if you are unsure about what to do.\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Import Keras library\n", "import keras\n", "from keras.models import Model\n", "from keras.layers import \n", "\n", "\n", "def net_1(sample_shape, nb_classes):\n", " # Define the network input to have `sample_shape´ shape\n", " input_x = None\n", " \n", " # Create network internals here\n", " x = None\n", " \n", " # Dense `nb_classes`\n", " probabilities = Dense(nb_classes, activation='softmax')(x)\n", " \n", " # Define the output\n", " model = Model(inputs=input_x, outputs=probabilities)\n", "\n", " return model" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "<div class=\"alert alert-info\">\n", " <strong>In the following code snippet we will:</strong>\n", "<ul>\n", " <li>Create the network using the function you just made</li>\n", " <li>Display a summary of the network</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Shape of sample\n", "sample_shape = x_train[0].shape \n", "\n", "# Construct net\n", "model = net_1(sample_shape, 10)\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Task V: Define Hyperparameters and Train the Network\n", "\n", "We need to define hyperparameters so our network can learn.\n", "\n", "<div class=\"alert alert-success\">\n", "<strong>Task:</strong> Tune in the hyper-parameters until your `loss` and `val_loss` are both converging to low numbers:\n", "<ul>\n", " <li>Batch size</li>\n", " <li>Number of training epochs</li>\n", "</ul>\n", "</div>\n", "\n", "Keep in mind that training these kinds of networks will take longer than the ones we have looked at so far." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Define hyperparameters\n", "batch_size = None\n", "epochs = None\n", "\n", "### Do not* modify the following lines ###\n", "\n", "# There is no learning rate because we are using the recommended\n", "# values for the Adadelta optimiser more information here:\n", "# https://keras.io/optimizers/\n", "\n", "# We need to compile our model\n", "model.compile(loss='categorical_crossentropy',\n", " optimizer='Adadelta',\n", " metrics=['accuracy'])\n", "\n", "# Train\n", "logs = model.fit(x_train, y_train,\n", " batch_size=batch_size,\n", " epochs=epochs,\n", " verbose=2,\n", " validation_split=0.1)\n", "\n", "# Plot our losses and accuracy\n", "fig, ax = plt.subplots(1,1)\n", "\n", "pd.DataFrame(logs.history).plot(ax=ax)\n", "ax.grid(linestyle='dotted')\n", "ax.legend()\n", "\n", "plt.show()\n", "\n", "# Assess performance\n", "print('='80) \n", "print('Assesing Test dataset...')\n", "print('='80) \n", "\n", "score = model.evaluate(x_test, y_test, verbose=0)\n", "print('Test loss:', score[0])\n", "print('Test accuracy:', score[1])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Should We Use Max-Pooling?\n", "\n", "There is a recent discussion if max-pooling is a good solution for reducing the amount of data between layers of a network. Some recent approaches show that a similar, and sometimes better performance, can be achieved by using convolutions with strides larger than 1.\n", "\n", "\n", "> Getting rid of pooling. Many people dislike the pooling operation and think that we can get away without it. For example, [Striving for Simplicity: The All Convolutional Net](http://arxiv.org/abs/1412.6806) proposes to discard the pooling layer in favor of architecture that only consists of repeated CONV layers. To reduce the size of the representation they suggest using larger stride in CONV layer once in a while. Discarding pooling layers has also been found to be important in training good generative models, such as variational autoencoders (VAEs) or generative adversarial networks (GANs). It seems likely that future architectures will feature very few to no pooling layers.\n", "\n", "[source](http://cs231n.github.io/convolutional-networks/#pool)\n", "\n", "\n", "### Task VI: Implement a Convolutional Network Without Max-Pooling\n", "\n", "Implement a convolutional neural network without pooling layers:\n", "\n", "<img src=\"./resources/mnist_net2.png\" alt=\"CNN\" width=\"1280\">\n", "\n", "<div class=\"alert alert-success\">\n", "<strong>Task:</strong> Replicate the network we made before (`net_1()`), but this time:\n", "<ul>\n", " <li>Remove max pooling and add stride(s) of 2 to the second convolution block (see [Conv2D](https://keras.io/layers/convolutional/))</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def net_2(sample_shape, nb_classes):\n", " # Define the network input to have `sample_shape` shape\n", " input_x = None\n", " \n", " # Create network internals here\n", " x = None\n", "\n", " # Dense number_classes\n", " probabilities = Dense(nb_classes, activation='softmax')(x)\n", "\n", " # Define the output\n", " model = Model(inputs=input_x, outputs=probabilities)\n", "\n", " return model" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "<div class=\"alert alert-info\">\n", " <strong>In the following code snippet we will:</strong>\n", "<ul>\n", " <li>Create the network using the function you just made</li>\n", " <li>Display a summary of the network</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Shape of sample\n", "sample_shape = x_train[0].shape \n", "\n", "# Construct net\n", "model = net_2(sample_shape, 10)\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Task VII: Define Hyperparameters and Train the New Network\n", "\n", "As before, we need to define some hyperparameters and train the network. Feel free to reuse the hyperparameters you found before.\n", "\n", "<div class=\"alert alert-success\">\n", "<strong>Task:</strong> Tune in the hyper-parameters until your `loss` and `val_loss` are both converging to low numbers:\n", "<ul>\n", " <li>Batch size</li>\n", " <li>Number of training epochs</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Define hyperparameters\n", "batch_size = None\n", "epochs = None\n", "\n", "### Do not modify the following lines ###\n", "\n", "# As always we need to compile our model\n", "model.compile(loss='categorical_crossentropy',\n", " optimizer='Adadelta',\n", " metrics=['accuracy'])\n", "\n", "# Train\n", "LOGS = model.fit(x_train, y_train,\n", " batch_size=batch_size,\n", " epochs=epochs,\n", " verbose=2,\n", " validation_split = 0.1,)\n", "\n", "# Plot our losses and accuracy\n", "fig, ax = plt.subplots(1,1)\n", "\n", "pd.DataFrame(logs.history).plot(ax=ax)\n", "ax.grid(linestyle='dotted')\n", "ax.legend()\n", "fig.canvas.draw()\n", "\n", "\n", "# Assess performance\n", "print('='80) \n", "print('Assesing Test dataset...')\n", "print('='80) \n", "\n", "score = model.evaluate(x_test, y_test, verbose=0)\n", "print('Test loss:', score[0])\n", "print('Test accuracy:', score[1])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# CIFAR\n", "\n", "The following explanantion of Cifar 10 comes from [official cifar page](https://www.cs.toronto.edu/~kriz/cifar.html):\n", "\n", "The CIFAR-10 and CIFAR-100 are labeled subsets of the <a href=\"http://people.csail.mit.edu/torralba/tinyimages/\">80 million tiny images</a> dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.\n", "\n", "\n", "## The CIFAR10 Dataset\n", "\n", "The CIFAR-10 dataset consists of 60000 $32 \\times 32$ colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.\n", "\n", "The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.</li>\n", "\n", "Here are the classes in the dataset, as well as 10 random images from each:\n", "<table>\n", " <tr>\n", " <td class=\"cifar-class-name\">airplane</td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane1.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane2.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane3.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane4.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane5.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane6.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane7.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane8.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane9.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane10.png\" class=\"cifar-sample\" /></td>\n", " </tr>\n", " <tr>\n", " <td class=\"cifar-class-name\">automobile</td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/automobile1.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/automobile2.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/automobile3.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/automobile4.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/automobile5.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/automobile6.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/automobile7.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/automobile8.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/automobile9.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/automobile10.png\" class=\"cifar-sample\" /></td>\n", " </tr>\n", " <tr>\n", " <td class=\"cifar-class-name\">bird</td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/bird1.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/bird2.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/bird3.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/bird4.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/bird5.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/bird6.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/bird7.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/bird8.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/bird9.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/bird10.png\" class=\"cifar-sample\" /></td>\n", " </tr>\n", " <tr>\n", " <td class=\"cifar-class-name\">cat</td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/cat1.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/cat2.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/cat3.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/cat4.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/cat5.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/cat6.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/cat7.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/cat8.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/cat9.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/cat10.png\" class=\"cifar-sample\" /></td>\n", " </tr>\n", " <tr>\n", " <td class=\"cifar-class-name\">deer</td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/deer1.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/deer2.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/deer3.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/deer4.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/deer5.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/deer6.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/deer7.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/deer8.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/deer9.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/deer10.png\" class=\"cifar-sample\" /></td>\n", " </tr>\n", " <tr>\n", " <td class=\"cifar-class-name\">dog</td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog1.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog2.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog3.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog4.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog5.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog6.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog7.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog8.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog9.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/dog10.png\" class=\"cifar-sample\" /></td>\n", " </tr>\n", " <tr>\n", " <td class=\"cifar-class-name\">frog</td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/frog1.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/frog2.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/frog3.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/frog4.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/frog5.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/frog6.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/frog7.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/frog8.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/frog9.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/frog10.png\" class=\"cifar-sample\" /></td>\n", " </tr>\n", " <tr>\n", " <td class=\"cifar-class-name\">horse</td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/horse1.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/horse2.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/horse3.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/horse4.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/horse5.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/horse6.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/horse7.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/horse8.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/horse9.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/horse10.png\" class=\"cifar-sample\" /></td>\n", " </tr>\n", " <tr>\n", " <td class=\"cifar-class-name\">ship</td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/ship1.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/ship2.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/ship3.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/ship4.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/ship5.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/ship6.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/ship7.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/ship8.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/ship9.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/ship10.png\" class=\"cifar-sample\" /></td>\n", " </tr>\n", " <tr>\n", " <td class=\"cifar-class-name\">truck</td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/truck1.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/truck2.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/truck3.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/truck4.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/truck5.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/truck6.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/truck7.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/truck8.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/truck9.png\" class=\"cifar-sample\" /></td>\n", " <td><img src=\"https://www.cs.toronto.edu/~kriz/cifar-10-sample/truck10.png\" class=\"cifar-sample\" /></td>\n", " </tr>\n", "</table>\n", "<br/>\n", "The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. \"Automobile\" includes sedans, SUVs, things of that sort. \"Truck\" includes only big trucks. Neither includes pickup trucks." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "In the following example we will load data from CIFAR10.\n", "\n", "* input $\\rightarrow$ 60000 samples of 3072 dimensional vectors.\n", " * Here presented as $32 \\times 32 \\times 3$ matrices $\\rightarrow$ Colour images\n", "* target $\\rightarrow$ 60000 scalars indicating a class from 0 to 9\n", "\n", "<div class=\"alert alert-info\">\n", " <strong>In the following code we will:</strong>\n", "<ul>\n", " <li>Load the CIFAR10 dataset</li>\n", " <li>Plot the 5th sample of training set</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from keras.datasets import cifar10\n", "\n", "# The data, shuffled and split between train and test sets:\n", "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n", "\n", "print('x_train shape:', x_train.shape)\n", "print(x_train.shape[0], 'train samples')\n", "print(x_test.shape[0], 'test samples')\n", "\n", "target_2_class = {0:'airplane',\n", " 1:'automobile',\n", " 2:'bird',\n", " 3:'cat',\n", " 4:'deer',\n", " 5:'dog',\n", " 6:'frog',\n", " 7:'horse',\n", " 8:'ship',\n", " 9:'truck'}\n", "\n", "# Code to plot the 5th training sample.\n", "fig,ax1 = plt.subplots(1,1, figsize=(7,7))\n", "ax1.imshow(x_train[5])\n", "target = y_train[5][0]\n", "title = 'Target is {} - Class {}'.format(target_2_class[target],target )\n", "ax1.set_title(title)\n", "ax1.grid(which='Major')\n", "ax1.xaxis.set_major_locator(MaxNLocator(32))\n", "ax1.yaxis.set_major_locator(MaxNLocator(32))\n", "fig.canvas.draw()\n", "time.sleep(0.1)\n", "\n", "print('Shape of x_train {}'.format(x_train.shape))\n", "print('Shape of y_train {}'.format(y_train.shape))\n", "print('Shape of x_test {}'.format(x_train.shape))\n", "print('Shape of y_test {}'.format(y_train.shape))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Task VIII: One-Hot Encode the Target Values\n", "\n", "<div class=\"alert alert-success\">\n", "<strong>Task:</strong> Use the `one_hot()` function you created earlier to encode:\n", "<ul>\n", " <li>`y_test`</li>\n", " <li>`y_train`</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "y_train = None\n", "y_test = None\n", "\n", "### Do not modify the following line ###\n", "# Print data sizes\n", "print('Shape of x_train {}'.format(x_train.shape))\n", "print('Shape of y_train {}'.format(y_train.shape))\n", "print('Shape of x_test {}'.format(x_train.shape))\n", "print('Shape of y_test {}'.format(y_train.shape))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Task IX: Normalise the Images\n", "\n", "<div class=\"alert alert-success\">\n", "<strong>Task:</strong> Use the `normalise_images()` function you created earlier to normalise the images in:\n", "<ul>\n", " <li>`x_test`</li>\n", " <li>`x_train`</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "x_train = None\n", "x_test = None" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Task X: Create Your Convolutional Neural Network for CIFAR10\n", "\n", "<div class=\"alert alert-success\">\n", " <strong>Task:</strong> Create a neural network model to train on CIFAR using what we have learned so far.\n", "<ul>\n", " <li>Create a new network using either `net_1()` or `net_2()`</li>\n", " <li>Display a summary of the network</li>\n", " <li>Compile the model using eiter `Adadelta`, `Adagrad`, or `Adam` as your optimiser</li>\n", "</ul>\n", "Some of the code is filled in already so alter what you need.\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Shape of samples\n", "sample_shape = x_train[0].shape \n", "\n", "# Construct net\n", "model = None\n", "model.summary()\n", "\n", "# We need to compile our model network:\n", "model.compile(loss='categorical_crossentropy',\n", " optimizer='Adam',\n", " metrics=['accuracy'])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Task XI: Train Your Model\n", "\n", "<div class=\"alert alert-success\">\n", " <strong>Task:</strong> : Train the model created in the previous cell on CIFAR10.\n", " <ul>\n", " <li>Train the network using `epochs = 30`</li>\n", " <li>Train the network using a `batch_size = 128`</li>\n", " <li>Use `validation_split = 0.2` when calling `fit()`</li>\n", " <li>Plot the losses and accuracy</li>\n", " <li>Assess the performance on the test set</li>\n", "</ul>\n", "We recommend that you do not copy-paste from the cells above, but rather re-write the code yourself. You can always take a look at earlier cells if you are unsure about what to do.\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Build the code within this cell\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Topics to Think About\n", "\n", "* Which model performs better?\n", "* Which optimiser performs better?\n", "* Is there any evidence of overfitting?\n", "* How can we improve the performance even further?" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# If You Have Time\n", "\n", "\n", "* Create a new network net_3():\n", " * Add more convolutional layers\n", " * Try adding [dropout](https://keras.io/layers/core/#dropout) after `Flatten` layer\n", " * You can this as reference : https://github.com/fchollet/keras/blob/master/examples/cifar10_cnn.py\n", " * Try adding [batch normalization](https://keras.io/layers/normalization/).\n", " * https://stackoverflow.com/questions/34716454/where-do-i-call-the-batchnormalization-function-in-keras\n", " * https://www.quora.com/How-do-I-apply-Batch-Normalization-to-the-convolutional-layer-of-a-CNN\n", "* Take a look at this [article](http://sebastianruder.com/optimizing-gradient-descent/) to learn more about optimisation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
jakob-bauer/partialflow	MNIST-example.ipynb	1	13841	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import tensorflow as tf\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# MNIST Example with Partial Graph Evaluation\n", "\n", "This example illustrates the use of `partialflow` for training a neural network with heavy memory consumption on a GPU with limited memory resources. To keep things simple, we will train a convolutional network on MNIST and use a very large batch size to make the training process memory-intensive.\n", "\n", "First we prepare the MNIST dataset and build a tensorflow input queue with a batch size of 7500:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# load MNIST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n", "\n", "train_images = np.reshape(mnist.train.images, [-1, 28, 28, 1])\n", "train_labels = mnist.train.labels\n", "\n", "test_images = np.reshape(mnist.test.images, [-1, 28, 28, 1])\n", "test_labels = mnist.test.labels\n", "\n", "# training input queue with large batch size\n", "batch_size = 7500\n", "image, label = tf.train.slice_input_producer([train_images, train_labels])\n", "image_batch, label_batch = tf.train.batch([image, label], batch_size=batch_size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Construct Residual Network\n", "\n", "`partialflow` allows us to split a `tensorflow` graph into several sections which can then be trained separately to lower the memory consumption. This means that the training graph of each section on its own has to fit into GPU memory, whereas the full network's training graph may not.\n", "\n", "The graph sections are managed by a `GraphSectionManager` that orchestrates the data flow between the graph sections during training:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from partialflow import GraphSectionManager\n", "\n", "sm = GraphSectionManager()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now use the `GraphSectionManager` to create new sections in which we define our network. `partialflow` automatically analyzes the `tensorflow` graph and keeps track of tensors flowing across section borders and variables defined in sections.\n", "\n", "In the following, we define our CNN in four sections. This is mainly done for illustrative purposes since two sections might already suffice, depending on your GPU memory. We added some `tf.Print` statements to make `tensorflow` log forward passes for each section." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from BasicNets import BatchnormNet\n", "\n", "# flag for batch normalization layers\n", "is_training = tf.placeholder(name='is_training', shape=[], dtype=tf.bool)\n", "net = BatchnormNet(is_training, image_batch)\n", "\n", "# first network section with initial convolution and three residual blocks\n", "with sm.new_section() as sec0:\n", " with tf.variable_scope('initial_conv'):\n", " stream = net.add_conv(net._inputs, n_filters=16)\n", " stream = tf.Print(stream, [stream], 'Forward pass over section 0')\n", " stream = net.add_bn(stream)\n", " stream = tf.nn.relu(stream)\n", " \n", " with tf.variable_scope('scale0'):\n", " for i in range(3):\n", " with tf.variable_scope('block_%d' % i):\n", " stream = net.res_block(stream)\n", "\n", " \n", "# second network section strided convolution to decrease the input resolution\n", "with sm.new_section() as sec1:\n", " with tf.variable_scope('scale1'):\n", " stream = tf.Print(stream, [stream], 'Forward pass over section 1')\n", " stream = net.res_block(stream, filters_factor=2, first_stride=2)\n", " for i in range(2):\n", " with tf.variable_scope('block_%d' % i):\n", " stream = net.res_block(stream)\n", "\n", "# third network section\n", "with sm.new_section() as sec2:\n", " with tf.variable_scope('scale2'):\n", " stream = tf.Print(stream, [stream], 'Forward pass over section 2')\n", " stream = net.res_block(stream, filters_factor=2, first_stride=2)\n", " for i in range(4):\n", " with tf.variable_scope('block_%d' % i):\n", " stream = net.res_block(stream)\n", " \n", "# fourth network section with final pooling and cross-entropy loss\n", "with sm.new_section() as sec3:\n", " with tf.variable_scope('final_pool'):\n", " stream = tf.Print(stream, [stream], 'Forward pass over section 3')\n", " # global average pooling over image dimensions\n", " stream = tf.reduce_mean(stream, axis=2)\n", " stream = tf.reduce_mean(stream, axis=1)\n", " \n", " # final conv for classification\n", " stream = net.add_fc(stream, out_dims=10)\n", " \n", " with tf.variable_scope('loss'):\n", " loss = tf.nn.softmax_cross_entropy_with_logits(stream, label_batch)\n", " loss = tf.reduce_mean(loss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the loss is defined inside a graph section. This is necessary to ensure that the image and label batches are cached and reused during forward and backward passes over the network. If the loss were defined outside a section, the input queues might be evaluated multiple times which leads to incorrect gradients being propagated." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add training operations and prepare training\n", "\n", "In order to construct the training graph for our network, we ask the `GraphSectionManager` to create training operations for each section. This can be done automatically as shown here, or by handing it a list of (possibly preprocessed) gradients as returned by `opt.compute_gradients`.\n", "\n", "The `verbose` parameter lets the manager add `tf.Print` statements into the gradient computation in order to log backward passes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "opt = tf.train.AdamOptimizer(learning_rate=0.0001)\n", "\n", "sm.add_training_ops(opt, loss, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES), verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, the `GraphSectionManager` needs to analyze the data flows in forward and backward passes across the graph sections:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sm.prepare_training()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point we may perform some sanity checks to vaildate that the right tensors are cached and fed into training runs of different sections. For example, we expect the backward pass of section `sec2` to depend on some output of `sec1` as well as gradients computed in `sec3`:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{<tf.Tensor 'gradients/graph_section_3/final_pool/Mean_grad/truediv:0' shape=(7500, 7, 7, 64) dtype=float32>,\n", " <tf.Tensor 'graph_section_1/scale1/block_1/add:0' shape=(7500, 14, 14, 32) dtype=float32>,\n", " <tf.Tensor 'is_training:0' shape=() dtype=bool>}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sec2.get_tensors_to_feed()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consequently, the corresponding gradient tensors have to be cached during the backward pass of `sec3`:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{<tf.Tensor 'gradients/graph_section_3/final_pool/Mean_grad/truediv:0' shape=(7500, 7, 7, 64) dtype=float32>}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sec3.get_tensors_to_cache()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run Forward and Backward Passes\n", "\n", "We can now open a new session and initialize our graph:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sess = tf.Session()\n", "init = tf.global_variables_initializer()\n", "sess.run(init)\n", "_ = tf.train.start_queue_runners(sess=sess)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual, a simple forward pass ignoring the sections can be performed using `session.run`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "11.650328" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sess.run(loss, feed_dict={is_training: True})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This should log a single forward pass for each section into the console running this notebook:\n", "\n", "```\n", "Forward pass over section 0[[[[0 0 0]]]...]\n", "Forward pass over section 1[[[[0.25482464 0.54249996 0.15713426]]]...]\n", "Forward pass over section 2[[[[1.3474643 0.62452459 0.14982516]]]...]\n", "Forward pass over section 3[[[[0.52292633 -0.39113081 0.74775648]]]...]\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training\n", "\n", "Since intermediate results need to be cached for the backward pass, training operations need to be run using `GraphSectionManager`. The `run_full_cycle` method will run a forward pass, cache intermediate results as needed, and perform a backward pass over the training operations. \n", "\n", "Forward passes are not performed section-wise, because `tensorflow` optimizes memory consumption by dropping intermediate results anyway. Hence the full forward pass graph is assumed to fit into GPU memory. In contrast, backward passes are performed section-wise. `run_full_cycle` takes care of evaluating the graph elements in `fetches` during the right phases of this procedure.\n", "\n", "The following should log a full forward pass followed by interleaved forward and backward passes for each section. Note that the `basic_feed` parameter is used analogous to `feed_dict` in `session.run`:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "11.725137" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sm.run_full_cycle(sess, fetches=loss, basic_feed={is_training:True})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... which should log something like\n", "\n", "```\n", "Forward pass over section 0[[[[0 0 0]]]...]\n", "Forward pass over section 1[[[[0.25558376 0.54339874 0.15626775]]]...]\n", "Forward pass over section 2[[[[1.3982055 0.59655607 0.18760961]]]...]\n", "Forward pass over section 3[[[[1.2530568 -1.3083258 -0.73674989]]]...]\n", "Running backward pass on section 3[-0.099787384 -0.11186664 -0.0903545...]\n", "Forward pass over section 2[[[[1.3982055 0.59655607 0.18760961]]]...]\n", "Running backward pass on section 2[[[[-0.55458224 0.38391361 -0.357202]]]...]\n", "Forward pass over section 1[[[[0.25558376 0.54339874 0.15626775]]]...]\n", "Running backward pass on section 1[[[[-0.0038170468 0.001159993 0.0018510161]]]...]\n", "Forward pass over section 0[[[[0 0 0]]]...]\n", "Running backward pass on section 0[-0.098705873 0.026503615 -0.0099251084...]\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
zhouqifanbdh/liupengyuan.github.io	chapter2/homework/localization/3-29/201611680038.ipynb	27	13130	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7851481634599485\n", "0.7853731633975086\n", "3.1414926535900345\n" ] } ], "source": [ "def computer_sum(n):\n", " i=1\n", " total_n=0\n", " \n", " while i<=n:\n", " total_n=total_n+1/(2i-1)-1/(2i+1)\n", " i=i+2\n", " return total_n\n", "\n", "print(computer_sum(1000))\n", "print(computer_sum(10000))\n", "print(computer_sum(10000)*4)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "能不能告诉我的生日，如果你是1月13号生日，就输入1.13～1.13\n", "Capricorn\n" ] } ], "source": [ "def xingzuo(n):\n", " if 3.21<=n<=4.20:\n", " return('Aries')\n", " elif 4.21<=n<=5.21:\n", " return('Taurus')\n", " elif 5.22<=n<=6.21:\n", " return('Gemini')\n", " elif 6.22<=n<=7.22:\n", " return('Cancer')\n", " elif 7.23<=n<=8.23:\n", " return('Leo')\n", " elif 8.24<=n<=9.23:\n", " return('Virgo')\n", " elif 9.24<=n<=10.23:\n", " return('Libra')\n", " elif 10.24<=n<=11.22:\n", " return('Scorpio')\n", " elif 11.23<=n<=12.21:\n", " return('Sagittarius')\n", " elif 12.22<=n<=1.31 or 1.01<=n<=1.20:\n", " return('Capricorn')\n", " elif 1.21<=n<=2.19:\n", " return('Aquarius')\n", " else:\n", " return('Pisces')\n", " \n", "n=float(input('能不能告诉我的生日，如果你是1月13号生日，就输入1.13～'))\n", "\n", "print(xingzuo(n))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入一个名词fish\n", "在词尾加es哟\n" ] } ], "source": [ "def fushu(n):\n", " if n.endswith('x') or n.endswith('sh') or n.endswith('ch') or n.endswith('x'):\n", " return('在词尾加es哟')\n", " else:\n", " return('直接加s就可以啦～～')\n", " \n", "\n", "n=str(input('请输入一个名词'))\n", "print(fushu(n))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入m：2\n", "请输入n：6\n", "请输入k：4\n", "8\n" ] } ], "source": [ "def sum():\n", " m=int(input('请输入m：'))\n", " n=int(input('请输入n：'))\n", " k=int(input('请输入k：'))\n", " \n", " total=m\n", " \n", " while m<n:\n", " m=m+k\n", " total=total+m\n", " print(total)\n", "sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ " \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, 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null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
Diyago/Machine-Learning-scripts	classification/Kaggle Home Credit Default Risk/lightGBM Param otimiz learn rate 0796 .ipynb	1	56565	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-08-08T11:48:53.261653Z", "start_time": "2018-08-08T11:48:46.611619Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import gc\n", "import time\n", "from contextlib import contextmanager\n", "from lightgbm import LGBMClassifier\n", "from sklearn.metrics import roc_auc_score, roc_curve\n", "from sklearn.model_selection import KFold, StratifiedKFold\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import warnings\n", "warnings.simplefilter(action='ignore', category=FutureWarning)\n", "\n", "@contextmanager\n", "def timer(title):\n", " t0 = time.time()\n", " yield\n", " print(\"{} - done in {:.0f}s\".format(title, time.time() - t0))\n", "\n", "# One-hot encoding for categorical columns with get_dummies\n", "def one_hot_encoder(df, nan_as_category = True):\n", " original_columns = list(df.columns)\n", " categorical_columns = [col for col in df.columns if df[col].dtype == 'object']\n", " df = pd.get_dummies(df, columns= categorical_columns, dummy_na= nan_as_category)\n", " new_columns = [c for c in df.columns if c not in original_columns]\n", " return df, new_columns\n", "\n", "# Preprocess application_train.csv and application_test.csv\n", "def application_train_test(num_rows = None, nan_as_category = False):\n", " # Read data and merge\n", " df = pd.read_csv(r'.\\input\\application_train.csv', nrows= num_rows)\n", " test_df = pd.read_csv(r'.\\input\\application_test.csv', nrows= num_rows)\n", " print(\"Train samples: {}, test samples: {}\".format(len(df), len(test_df)))\n", " df = df.append(test_df).reset_index()\n", " # Optional: Remove 4 applications with XNA CODE_GENDER (train set)\n", " df = df[df['CODE_GENDER'] != 'XNA']\n", " \n", " docs = [_f for _f in df.columns if 'FLAG_DOC' in _f]\n", " live = [_f for _f in df.columns if ('FLAG_' in _f) & ('FLAG_DOC' not in _f) & ('_FLAG_' not in _f)]\n", " \n", " # NaN values for DAYS_EMPLOYED: 365.243 -> nan\n", " df['DAYS_EMPLOYED'].replace(365243, np.nan, inplace= True)\n", "\n", " inc_by_org = df[['AMT_INCOME_TOTAL', 'ORGANIZATION_TYPE']].groupby('ORGANIZATION_TYPE').median()['AMT_INCOME_TOTAL']\n", "\n", " df['NEW_CREDIT_TO_ANNUITY_RATIO'] = df['AMT_CREDIT'] / df['AMT_ANNUITY']\n", " df['NEW_CREDIT_TO_GOODS_RATIO'] = df['AMT_CREDIT'] / df['AMT_GOODS_PRICE']\n", " df['NEW_DOC_IND_KURT'] = df[docs].kurtosis(axis=1)\n", " df['NEW_LIVE_IND_SUM'] = df[live].sum(axis=1)\n", " df['NEW_INC_PER_CHLD'] = df['AMT_INCOME_TOTAL'] / (1 + df['CNT_CHILDREN'])\n", " df['NEW_INC_BY_ORG'] = df['ORGANIZATION_TYPE'].map(inc_by_org)\n", " df['NEW_EMPLOY_TO_BIRTH_RATIO'] = df['DAYS_EMPLOYED'] / df['DAYS_BIRTH']\n", " df['NEW_ANNUITY_TO_INCOME_RATIO'] = df['AMT_ANNUITY'] / (1 + df['AMT_INCOME_TOTAL'])\n", " df['NEW_SOURCES_PROD'] = df['EXT_SOURCE_1'] * df['EXT_SOURCE_2'] * df['EXT_SOURCE_3']\n", " df['NEW_EXT_SOURCES_MEAN'] = df[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis=1)\n", " df['NEW_SCORES_STD'] = df[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].std(axis=1)\n", " df['NEW_SCORES_STD'] = df['NEW_SCORES_STD'].fillna(df['NEW_SCORES_STD'].mean())\n", " df['NEW_CAR_TO_BIRTH_RATIO'] = df['OWN_CAR_AGE'] / df['DAYS_BIRTH']\n", " df['NEW_CAR_TO_EMPLOY_RATIO'] = df['OWN_CAR_AGE'] / df['DAYS_EMPLOYED']\n", " df['NEW_PHONE_TO_BIRTH_RATIO'] = df['DAYS_LAST_PHONE_CHANGE'] / df['DAYS_BIRTH']\n", " df['NEW_PHONE_TO_BIRTH_RATIO_EMPLOYER'] = df['DAYS_LAST_PHONE_CHANGE'] / df['DAYS_EMPLOYED']\n", " df['NEW_CREDIT_TO_INCOME_RATIO'] = df['AMT_CREDIT'] / df['AMT_INCOME_TOTAL']\n", " \n", " # Categorical features with Binary encode (0 or 1; two categories)\n", " for bin_feature in ['CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY']:\n", " df[bin_feature], uniques = pd.factorize(df[bin_feature])\n", " # Categorical features with One-Hot encode\n", " df, cat_cols = one_hot_encoder(df, nan_as_category)\n", " dropcolum=['FLAG_DOCUMENT_2','FLAG_DOCUMENT_4',\n", " 'FLAG_DOCUMENT_5','FLAG_DOCUMENT_6','FLAG_DOCUMENT_7',\n", " 'FLAG_DOCUMENT_8','FLAG_DOCUMENT_9','FLAG_DOCUMENT_10', \n", " 'FLAG_DOCUMENT_11','FLAG_DOCUMENT_12','FLAG_DOCUMENT_13',\n", " 'FLAG_DOCUMENT_14','FLAG_DOCUMENT_15','FLAG_DOCUMENT_16',\n", " 'FLAG_DOCUMENT_17','FLAG_DOCUMENT_18','FLAG_DOCUMENT_19',\n", " 'FLAG_DOCUMENT_20','FLAG_DOCUMENT_21']\n", " df= df.drop(dropcolum,axis=1)\n", " del test_df\n", " gc.collect()\n", " return df\n", "\n", "# Preprocess bureau.csv and bureau_balance.csv\n", "def bureau_and_balance(num_rows = None, nan_as_category = True):\n", " bureau = pd.read_csv('./input/bureau.csv', nrows = num_rows)\n", " bb = pd.read_csv('./input/bureau_balance.csv', nrows = num_rows)\n", " bb, bb_cat = one_hot_encoder(bb, nan_as_category)\n", " bureau, bureau_cat = one_hot_encoder(bureau, nan_as_category)\n", " \n", " # Bureau balance: Perform aggregations and merge with bureau.csv\n", " bb_aggregations = {'MONTHS_BALANCE': ['min', 'max', 'size']}\n", " for col in bb_cat:\n", " bb_aggregations[col] = ['mean']\n", " bb_agg = bb.groupby('SK_ID_BUREAU').agg(bb_aggregations)\n", " bb_agg.columns = pd.Index([e[0] + \"_\" + e[1].upper() for e in bb_agg.columns.tolist()])\n", " bureau = pd.merge(bureau,bb_agg, how='left', left_index=True, right_index=True)\n", " #bureau = bureau.join(bb_agg, how='left', on='SK_ID_BUREAU')\n", " bureau.drop(['SK_ID_BUREAU'], axis=1, inplace= True)\n", " del bb, bb_agg\n", " gc.collect()\n", " \n", " # Bureau and bureau_balance numeric features\n", " num_aggregations = {\n", " 'DAYS_CREDIT': [ 'mean', 'var'],\n", " 'DAYS_CREDIT_ENDDATE': [ 'mean'],\n", " 'DAYS_CREDIT_UPDATE': ['mean'],\n", " 'CREDIT_DAY_OVERDUE': ['mean'],\n", " 'AMT_CREDIT_MAX_OVERDUE': ['mean'],\n", " 'AMT_CREDIT_SUM': [ 'mean', 'sum'],\n", " 'AMT_CREDIT_SUM_DEBT': [ 'mean', 'sum'],\n", " 'AMT_CREDIT_SUM_OVERDUE': ['mean'],\n", " 'AMT_CREDIT_SUM_LIMIT': ['mean', 'sum'],\n", " 'AMT_ANNUITY': ['max', 'mean'],\n", " 'CNT_CREDIT_PROLONG': ['sum'],\n", " 'MONTHS_BALANCE_MIN': ['min'],\n", " 'MONTHS_BALANCE_MAX': ['max'],\n", " 'MONTHS_BALANCE_SIZE': ['mean', 'sum']\n", " }\n", " # Bureau and bureau_balance categorical features\n", " cat_aggregations = {}\n", " for cat in bureau_cat: cat_aggregations[cat] = ['mean']\n", " for cat in bb_cat: cat_aggregations[cat + \"_MEAN\"] = ['mean']\n", " \n", " bureau_agg = bureau.groupby('SK_ID_CURR').agg({num_aggregations, cat_aggregations})\n", " bureau_agg.columns = pd.Index(['BURO_' + e[0] + \"_\" + e[1].upper() for e in bureau_agg.columns.tolist()])\n", " # Bureau: Active credits - using only numerical aggregations\n", " active = bureau[bureau['CREDIT_ACTIVE_Active'] == 1]\n", " active_agg = active.groupby('SK_ID_CURR').agg(num_aggregations)\n", " active_agg.columns = pd.Index(['ACTIVE_' + e[0] + \"_\" + e[1].upper() for e in active_agg.columns.tolist()])\n", " bureau_agg = pd.merge(bureau_agg,active_agg, how='left', left_index=True, right_index=True)\n", " del active, active_agg\n", " gc.collect()\n", " # Bureau: Closed credits - using only numerical aggregations\n", " closed = bureau[bureau['CREDIT_ACTIVE_Closed'] == 1]\n", " closed_agg = closed.groupby('SK_ID_CURR').agg(num_aggregations)\n", " closed_agg.columns = pd.Index(['CLOSED_' + e[0] + \"_\" + e[1].upper() for e in closed_agg.columns.tolist()])\n", " bureau_agg = pd.merge(bureau_agg,closed_agg, how='left', left_index=True, right_index=True)\n", " #bureau_agg = bureau_agg.join(closed_agg, how='left', on='SK_ID_CURR')\n", " del closed, closed_agg, bureau\n", " gc.collect()\n", " return bureau_agg\n", "\n", "# Preprocess previous_applications.csv\n", "def previous_applications(num_rows = None, nan_as_category = True):\n", " prev = pd.read_csv('./input/previous_application.csv', nrows = num_rows)\n", " prev, cat_cols = one_hot_encoder(prev, nan_as_category= True)\n", " # Days 365.243 values -> nan\n", " prev['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace= True)\n", " prev['DAYS_FIRST_DUE'].replace(365243, np.nan, inplace= True)\n", " prev['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace= True)\n", " prev['DAYS_LAST_DUE'].replace(365243, np.nan, inplace= True)\n", " prev['DAYS_TERMINATION'].replace(365243, np.nan, inplace= True)\n", " # Add feature: value ask / value received percentage\n", " prev['APP_CREDIT_PERC'] = prev['AMT_APPLICATION'] / prev['AMT_CREDIT']\n", " # Previous applications numeric features\n", " num_aggregations = {\n", " 'AMT_ANNUITY': [ 'max', 'mean'],\n", " 'AMT_APPLICATION': [ 'max','mean'],\n", " 'AMT_CREDIT': [ 'max', 'mean'],\n", " 'APP_CREDIT_PERC': [ 'max', 'mean'],\n", " 'AMT_DOWN_PAYMENT': [ 'max', 'mean'],\n", " 'AMT_GOODS_PRICE': [ 'max', 'mean'],\n", " 'HOUR_APPR_PROCESS_START': [ 'max', 'mean'],\n", " 'RATE_DOWN_PAYMENT': [ 'max', 'mean'],\n", " 'DAYS_DECISION': [ 'max', 'mean'],\n", " 'CNT_PAYMENT': ['mean', 'sum'],\n", " }\n", " # Previous applications categorical features\n", " cat_aggregations = {}\n", " for cat in cat_cols:\n", " cat_aggregations[cat] = ['mean']\n", " \n", " prev_agg = prev.groupby('SK_ID_CURR').agg({num_aggregations, cat_aggregations})\n", " prev_agg.columns = pd.Index(['PREV_' + e[0] + \"_\" + e[1].upper() for e in prev_agg.columns.tolist()])\n", " # Previous Applications: Approved Applications - only numerical features\n", " approved = prev[prev['NAME_CONTRACT_STATUS_Approved'] == 1]\n", " approved_agg = approved.groupby('SK_ID_CURR').agg(num_aggregations)\n", " approved_agg.columns = pd.Index(['APPROVED_' + e[0] + \"_\" + e[1].upper() for e in approved_agg.columns.tolist()])\n", " prev_agg = pd.merge(prev_agg,approved_agg, how='left', left_index=True, right_index=True)\n", " #prev_agg = prev_agg.join(approved_agg, how='left', on='SK_ID_CURR')\n", " # Previous Applications: Refused Applications - only numerical features\n", " refused = prev[prev['NAME_CONTRACT_STATUS_Refused'] == 1]\n", " refused_agg = refused.groupby('SK_ID_CURR').agg(num_aggregations)\n", " refused_agg.columns = pd.Index(['REFUSED_' + e[0] + \"_\" + e[1].upper() for e in refused_agg.columns.tolist()])\n", " prev_agg = pd.merge(prev_agg,refused_agg, how='left', left_index=True, right_index=True)\n", " #prev_agg = prev_agg.join(refused_agg, how='left', on='SK_ID_CURR')\n", " del refused, refused_agg, approved, approved_agg, prev\n", " gc.collect()\n", " return prev_agg\n", "\n", "# Preprocess POS_CASH_balance.csv\n", "def pos_cash(num_rows = None, nan_as_category = True):\n", " pos = pd.read_csv('./input/POS_CASH_balance.csv', nrows = num_rows)\n", " pos, cat_cols = one_hot_encoder(pos, nan_as_category= True)\n", " # Features\n", " aggregations = {\n", " 'MONTHS_BALANCE': ['max', 'mean', 'size'],\n", " 'SK_DPD': ['max', 'mean'],\n", " 'SK_DPD_DEF': ['max', 'mean']\n", " }\n", " for cat in cat_cols:\n", " aggregations[cat] = ['mean']\n", " \n", " pos_agg = pos.groupby('SK_ID_CURR').agg(aggregations)\n", " pos_agg.columns = pd.Index(['POS_' + e[0] + \"_\" + e[1].upper() for e in pos_agg.columns.tolist()])\n", " # Count pos cash accounts\n", " pos_agg['POS_COUNT'] = pos.groupby('SK_ID_CURR').size()\n", " del pos\n", " gc.collect()\n", " return pos_agg\n", " \n", "# Preprocess installments_payments.csv\n", "def installments_payments(num_rows = None, nan_as_category = True):\n", " ins = pd.read_csv('./input/installments_payments.csv', nrows = num_rows)\n", " ins, cat_cols = one_hot_encoder(ins, nan_as_category= True)\n", " # Percentage and difference paid in each installment (amount paid and installment value)\n", " ins['PAYMENT_PERC'] = ins['AMT_PAYMENT'] / ins['AMT_INSTALMENT']\n", " ins['PAYMENT_DIFF'] = ins['AMT_INSTALMENT'] - ins['AMT_PAYMENT']\n", " # Days past due and days before due (no negative values)\n", " ins['DPD'] = ins['DAYS_ENTRY_PAYMENT'] - ins['DAYS_INSTALMENT']\n", " ins['DBD'] = ins['DAYS_INSTALMENT'] - ins['DAYS_ENTRY_PAYMENT']\n", " ins['DPD'] = ins['DPD'].apply(lambda x: x if x > 0 else 0)\n", " ins['DBD'] = ins['DBD'].apply(lambda x: x if x > 0 else 0)\n", " # Features: Perform aggregations\n", " aggregations = {\n", " 'NUM_INSTALMENT_VERSION': ['nunique'],\n", " 'DPD': ['max', 'mean', 'sum','min','std' ],\n", " 'DBD': ['max', 'mean', 'sum','min','std'],\n", " 'PAYMENT_PERC': [ 'max','mean', 'var','min','std'],\n", " 'PAYMENT_DIFF': [ 'max','mean', 'var','min','std'],\n", " 'AMT_INSTALMENT': ['max', 'mean', 'sum','min','std'],\n", " 'AMT_PAYMENT': ['min', 'max', 'mean', 'sum','std'],\n", " 'DAYS_ENTRY_PAYMENT': ['max', 'mean', 'sum','std']\n", " }\n", " for cat in cat_cols:\n", " aggregations[cat] = ['mean']\n", " ins_agg = ins.groupby('SK_ID_CURR').agg(aggregations)\n", " ins_agg.columns = pd.Index(['INSTAL_' + e[0] + \"_\" + e[1].upper() for e in ins_agg.columns.tolist()])\n", " # Count installments accounts\n", " ins_agg['INSTAL_COUNT'] = ins.groupby('SK_ID_CURR').size()\n", " del ins\n", " gc.collect()\n", " return ins_agg\n", "\n", "# Preprocess credit_card_balance.csv\n", "def credit_card_balance(num_rows = None, nan_as_category = True):\n", " cc = pd.read_csv('./input/credit_card_balance.csv', nrows = num_rows)\n", " cc, cat_cols = one_hot_encoder(cc, nan_as_category= True)\n", " # General aggregations\n", " cc.drop(['SK_ID_PREV'], axis= 1, inplace = True)\n", " cc_agg = cc.groupby('SK_ID_CURR').agg([ 'max', 'mean', 'sum', 'var'])\n", " cc_agg.columns = pd.Index(['CC_' + e[0] + \"_\" + e[1].upper() for e in cc_agg.columns.tolist()])\n", " # Count credit card lines\n", " cc_agg['CC_COUNT'] = cc.groupby('SK_ID_CURR').size()\n", " del cc\n", " gc.collect()\n", " return cc_agg\n", "\n", "# LightGBM GBDT with KFold or Stratified KFold\n", "# Parameters from Tilii kernel: https://www.kaggle.com/tilii7/olivier-lightgbm-parameters-by-bayesian-opt/code\n", "def kfold_lightgbm(df, num_folds, stratified = False, debug= False):\n", " # Divide in training/validation and test data\n", " train_df = df[df['TARGET'].notnull()]\n", " test_df = df[df['TARGET'].isnull()]\n", " print(\"Starting LightGBM. Train shape: {}, test shape: {}\".format(train_df.shape, test_df.shape))\n", " del df\n", " gc.collect()\n", " # Cross validation model\n", " if stratified:\n", " folds = StratifiedKFold(n_splits= num_folds, shuffle=True, random_state=47)\n", " else:\n", " folds = KFold(n_splits= num_folds, shuffle=True, random_state=47)\n", " # Create arrays and dataframes to store results\n", " oof_preds = np.zeros(train_df.shape[0])\n", " sub_preds = np.zeros(test_df.shape[0])\n", " feature_importance_df = pd.DataFrame()\n", " feats = [f for f in train_df.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]\n", " \n", " for n_fold, (train_idx, valid_idx) in enumerate(folds.split(train_df[feats], train_df['TARGET'])):\n", " train_x, train_y = train_df[feats].iloc[train_idx], train_df['TARGET'].iloc[train_idx]\n", " valid_x, valid_y = train_df[feats].iloc[valid_idx], train_df['TARGET'].iloc[valid_idx]\n", "\n", " # LightGBM parameters found by Bayesian optimization\n", " clf = LGBMClassifier(\n", " nthread=4,\n", " #is_unbalance=True,\n", " n_estimators=10000,\n", " learning_rate=0.01,\n", " num_leaves=32,\n", " colsample_bytree=0.9497036,\n", " subsample=0.8715623,\n", " max_depth=8,\n", " reg_alpha=0.04,\n", " reg_lambda=0.073,\n", " min_split_gain=0.0222415,\n", " min_child_weight=40,\n", " silent=-1,\n", " verbose=-1,\n", " #scale_pos_weight=11\n", " )\n", "\n", " clf.fit(train_x, train_y, eval_set=[(train_x, train_y), (valid_x, valid_y)], \n", " eval_metric= 'auc', verbose= 1000, early_stopping_rounds= 200)\n", "\n", " oof_preds[valid_idx] = clf.predict_proba(valid_x, num_iteration=clf.best_iteration_)[:, 1]\n", " sub_preds += clf.predict_proba(test_df[feats], num_iteration=clf.best_iteration_)[:, 1] / folds.n_splits\n", "\n", " fold_importance_df = pd.DataFrame()\n", " fold_importance_df[\"feature\"] = feats\n", " fold_importance_df[\"importance\"] = clf.feature_importances_\n", " fold_importance_df[\"fold\"] = n_fold + 1\n", " feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n", " print('Fold %2d AUC : %.6f' % (n_fold + 1, roc_auc_score(valid_y, oof_preds[valid_idx])))\n", " del clf, train_x, train_y, valid_x, valid_y\n", " gc.collect()\n", "\n", " print('Full AUC score %.6f' % roc_auc_score(train_df['TARGET'], oof_preds))\n", " # Write submission file and plot feature importance\n", " if not debug:\n", " test_df['TARGET'] = sub_preds\n", " test_df[['SK_ID_CURR', 'TARGET']].to_csv(submission_file_name, index= False)\n", " display_importances(feature_importance_df)\n", " return feature_importance_df\n", "\n", "# Display/plot feature importance\n", "def display_importances(feature_importance_df_):\n", " cols = feature_importance_df_[[\"feature\", \"importance\"]].groupby(\"feature\").mean().sort_values(by=\"importance\", ascending=False)[:40].index\n", " best_features = feature_importance_df_.loc[feature_importance_df_.feature.isin(cols)]\n", " plt.figure(figsize=(8, 10))\n", " sns.barplot(x=\"importance\", y=\"feature\", data=best_features.sort_values(by=\"importance\", ascending=False))\n", " plt.title('LightGBM Features (avg over folds)')\n", " plt.tight_layout()\n", " plt.savefig('lgbm_importances01.png')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-08-08T11:48:54.488722Z", "start_time": "2018-08-08T11:48:53.263160Z" } }, "outputs": [], "source": [ "from hyperopt import STATUS_OK\n", "import csv\n", "from hyperopt import hp\n", "from hyperopt.pyll.stochastic import sample\n", "import lightgbm as lgb\n", "from hyperopt import tpe\n", "from hyperopt import Trials\n", "from hyperopt import fmin\n", "\n", "# Record results\n", "trials = Trials()\n", "\n", "# Create the algorithm\n", "tpe_algorithm = tpe.suggest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1) A Trials object that stores the dictionary returned from the objective function\n", "\n", "2) The optimization algorithm is the method for constructing the surrogate function (probability model) and selecting the next set of hyperparameters to evaluate in the objective function. Hyperopt has two choices: random search and Tree Parzen Estimator." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2018-08-08T11:48:54.559886Z", "start_time": "2018-08-08T11:48:54.490201Z" } }, "outputs": [], "source": [ "\n", "def objective(hyperparameters ):\n", " \n", " global ITERATION\n", " global df\n", " \n", " train_df = df[df['TARGET'].notnull()]\n", " test_df = df[df['TARGET'].isnull()] \n", " #del df\n", " gc.collect()\n", " N_FOLDS = 5\n", " feats = [f for f in train_df.columns if f not in ['TARGET','SK_ID_CURR','SK_ID_BUREAU','SK_ID_PREV','index']]\n", " train_set = lgb.Dataset(train_df[feats], train_df['TARGET'])\n", " \n", " #global ITERATION\n", " ITERATION += 1\n", "\n", " # Using early stopping to find number of trees trained\n", " if 'n_estimators' in hyperparameters:\n", " del hyperparameters['n_estimators']\n", "\n", " # Retrieve the subsample\n", " subsample = hyperparameters['boosting_type'].get('subsample', 1.0)\n", " \n", " # Extract the boosting type and subsample to top level keys\n", " hyperparameters['boosting_type'] = hyperparameters['boosting_type']['boosting_type']\n", " hyperparameters['subsample'] = subsample\n", " \n", " # Make sure parameters that need to be integers are integers\n", " for parameter_name in ['num_leaves', 'subsample_for_bin', 'min_child_samples']:\n", " hyperparameters[parameter_name] = int(hyperparameters[parameter_name])\n", " print(\"Starting LightGBM tuning. Train shape: {}, test shape: {}\".format(train_df.shape, test_df.shape))\n", "\n", " cv_results = lgb.cv(hyperparameters, train_set, num_boost_round = 10000, nfold = N_FOLDS, \n", " early_stopping_rounds = 100, metrics = 'auc', seed = 50)\n", " \n", " \n", " # Extract the best score\n", " best_score = cv_results['auc-mean'][-1]\n", " \n", " # Loss must be minimized\n", " loss = 1 - best_score\n", " \n", " # Boosting rounds that returned the highest cv score\n", " n_estimators = len(cv_results['auc-mean'])\n", " \n", " # Add the number of estimators to the hyperparameters\n", " hyperparameters['n_estimators'] = n_estimators\n", " OUT_FILE = 'bayes_test2.csv'\n", " # Write to the csv file ('a' means append)\n", " of_connection = open(OUT_FILE, 'a')\n", " writer = csv.writer(of_connection)\n", " writer.writerow([loss, hyperparameters, ITERATION, best_score])\n", " of_connection.close()\n", "\n", " # Dictionary with information for evaluation\n", " return {'loss': loss, 'hyperparameters': hyperparameters, 'iteration': ITERATION, 'status': STATUS_OK} " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2018-08-08T11:48:54.601496Z", "start_time": "2018-08-08T11:48:54.561891Z" } }, "outputs": [], "source": [ "import ast\n", "\n", "def evaluate(results, name):\n", " \"\"\"Evaluate model on test data using hyperparameters in results\n", " Return dataframe of hyperparameters\"\"\"\n", " \n", " new_results = results.copy()\n", " # String to dictionary\n", " new_results['hyperparameters'] = new_results['hyperparameters'].map(ast.literal_eval)\n", " \n", " # Sort with best values on top\n", " new_results = new_results.sort_values('score', ascending = False).reset_index(drop = True)\n", " \n", " # Print out cross validation high score\n", " print('The highest cross validation score from {} was {:.5f} found on iteration {}.'.format(name, new_results.loc[0, 'score'], new_results.loc[0, 'iteration']))\n", " \n", " # Use best hyperparameters to create a model\n", " hyperparameters = new_results.loc[0, 'hyperparameters']\n", " model = lgb.LGBMClassifier(*hyperparameters)\n", " \n", " # Train and make predictions\n", " model.fit(train_features, train_labels)\n", " preds = model.predict_proba(test_features)[:, 1]\n", " \n", " print('ROC AUC from {} on test data = {:.5f}.'.format(name, roc_auc_score(test_labels, preds)))\n", " \n", " # Create dataframe of hyperparameters\n", " hyp_df = pd.DataFrame(columns = list(new_results.loc[0, 'hyperparameters'].keys()))\n", "\n", " # Iterate through each set of hyperparameters that were evaluated\n", " for i, hyp in enumerate(new_results['hyperparameters']):\n", " hyp_df = hyp_df.append(pd.DataFrame(hyp, index = [0]), \n", " ignore_index = True)\n", " \n", " # Put the iteration and score in the hyperparameter dataframe\n", " hyp_df['iteration'] = new_results['iteration']\n", " hyp_df['score'] = new_results['score']\n", " \n", " return hyp_df" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2018-08-10T07:07:42.651248Z", "start_time": "2018-08-08T11:48:54.603502Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train samples: 307511, test samples: 48744\n", "Bureau df shape: (305811, 95)\n", "Process bureau and bureau_balance - done in 22s\n", "Previous applications df shape: (338857, 219)\n", "Process previous_applications - done in 23s\n", "Pos-cash balance df shape: (337252, 18)\n", "Process POS-CASH balance - done in 15s\n", "Installments payments df shape: (339587, 36)\n", "Process installments payments - done in 33s\n", "Credit card balance df shape: (103558, 113)\n", "Process credit card balance - done in 18s\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n", "Starting LightGBM tuning. Train shape: (307507, 721), test shape: (48744, 721)\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-5-2b11848bccaf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 101\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtimer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Full model run\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 102\u001b[0m \u001b[1;32mglobal\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mITERATION\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 103\u001b[1;33m \u001b[0mmain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-5-2b11848bccaf>\u001b[0m in 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\hyperopt\\fmin.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, N, block_until_done)\u001b[0m\n\u001b[0;32m 171\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 172\u001b[0m \u001b[1;31m# -- loop over trials and do the jobs directly\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 173\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mserial_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 174\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 175\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mstopped\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\hyperopt\\fmin.py\u001b[0m in \u001b[0;36mserial_evaluate\u001b[1;34m(self, N)\u001b[0m\n\u001b[0;32m 90\u001b[0m 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94\u001b[0m \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'job exception: %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\hyperopt\\base.py\u001b[0m in \u001b[0;36mevaluate\u001b[1;34m(self, config, ctrl, attach_attachments)\u001b[0m\n\u001b[0;32m 838\u001b[0m \u001b[0mmemo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmemo\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 839\u001b[0m print_node_on_error=self.rec_eval_print_node_on_error)\n\u001b[1;32m--> 840\u001b[1;33m \u001b[0mrval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpyll_rval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 841\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 842\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrval\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m<ipython-input-3-d545f37ad4a9>\u001b[0m in \u001b[0;36mobjective\u001b[1;34m(hyperparameters)\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 34\u001b[0m cv_results = lgb.cv(hyperparameters, train_set, num_boost_round = 10000, nfold = N_FOLDS, \n\u001b[1;32m---> 35\u001b[1;33m early_stopping_rounds = 100, metrics = 'auc', seed = 50)\n\u001b[0m\u001b[0;32m 36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\lightgbm\\engine.py\u001b[0m in \u001b[0;36mcv\u001b[1;34m(params, train_set, num_boost_round, folds, nfold, stratified, shuffle, metrics, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, fpreproc, verbose_eval, show_stdv, seed, callbacks)\u001b[0m\n\u001b[0;32m 445\u001b[0m \u001b[0mend_iteration\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_boost_round\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 446\u001b[0m evaluation_result_list=None))\n\u001b[1;32m--> 447\u001b[1;33m \u001b[0mcvfolds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 448\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m 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train_set, fobj)\u001b[0m\n\u001b[0;32m 1519\u001b[0m _safe_call(_LIB.LGBM_BoosterUpdateOneIter(\n\u001b[0;32m 1520\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1521\u001b[1;33m ctypes.byref(is_finished)))\n\u001b[0m\u001b[0;32m 1522\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__is_predicted_cur_iter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;32mFalse\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__num_dataset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1523\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mis_finished\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "def main(debug = False):\n", " num_rows = 10000 if debug else None\n", " global df, ITERATION\n", " \n", " df = application_train_test(num_rows)\n", " with timer(\"Process bureau and bureau_balance\"):\n", " bureau = bureau_and_balance(num_rows)\n", " print(\"Bureau df shape:\", bureau.shape)\n", " df = df.join(bureau, how='left', on='SK_ID_CURR')\n", " del bureau\n", " gc.collect()\n", " with timer(\"Process previous_applications\"):\n", " prev = previous_applications(num_rows)\n", " print(\"Previous applications df shape:\", prev.shape)\n", " df = df.join(prev, how='left', on='SK_ID_CURR')\n", " del prev\n", " gc.collect()\n", " with timer(\"Process POS-CASH balance\"):\n", " pos = pos_cash(num_rows)\n", " print(\"Pos-cash balance df shape:\", pos.shape)\n", " \n", " df = df.join(pos, how='left', on='SK_ID_CURR')\n", " del pos\n", " gc.collect()\n", " with timer(\"Process installments payments\"):\n", " ins = installments_payments(num_rows)\n", " print(\"Installments payments df shape:\", ins.shape)\n", " \n", " df = df.join(ins, how='left', on='SK_ID_CURR')\n", " del ins\n", " gc.collect()\n", " with timer(\"Process credit card balance\"):\n", " cc = credit_card_balance(num_rows)\n", " print(\"Credit card balance df shape:\", cc.shape)\n", " \n", " df = df.join(cc, how='left', on='SK_ID_CURR')\n", " del cc\n", " gc.collect()\n", " with timer(\"Run LightGBM with kfold\"):\n", " #feat_importance = kfold_lightgbm(df, num_folds= 5, stratified= False, debug= debug)\n", " space = {\n", " 'boosting_type': hp.choice('boosting_type', \n", " [{'boosting_type': 'gbdt', 'subsample': hp.uniform('gdbt_subsample', 0.5, 1)}, \n", " {'boosting_type': 'dart', 'subsample': hp.uniform('dart_subsample', 0.5, 1)} ]),\n", " 'num_leaves': hp.quniform('num_leaves', 20, 150, 1),\n", " 'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.3)),\n", " 'subsample_for_bin': hp.quniform('subsample_for_bin', 20000, 300000, 20000),\n", " 'min_child_samples': hp.quniform('min_child_samples', 20, 500, 5),\n", " 'reg_alpha': hp.uniform('reg_alpha', 0.0, 0.6),\n", " 'reg_lambda': hp.uniform('reg_lambda', 0.0, 0.6),\n", " 'colsample_bytree': hp.uniform('colsample_by_tree', 0.6, 1.0),\n", " 'is_unbalance': hp.choice('is_unbalance', [True, False]),\n", " }\n", " x = sample(space)\n", "\n", " # Conditional logic to assign top-level keys\n", " subsample = x['boosting_type'].get('subsample', 1.0)\n", " x['boosting_type'] = x['boosting_type']['boosting_type']\n", " x['subsample'] = subsample\n", " \n", " MAX_EVALS = 501\n", " \n", " # Create a new file and open a connection\n", " OUT_FILE = 'bayesian_trials_1000.csv'\n", " of_connection = open(OUT_FILE, 'w')\n", " writer = csv.writer(of_connection)\n", " \n", " \n", " ITERATION = 0\n", "\n", " # Write column names\n", " headers = ['loss', 'hyperparameters', 'iteration', 'score']\n", " writer.writerow(headers)\n", " of_connection.close()\n", " \n", " trials = Trials()\n", " \n", " best = fmin(fn = objective, space = space, algo = tpe.suggest, trials = trials, max_evals = MAX_EVALS)\n", " trials_dict = sorted(trials.results, key = lambda x: x['loss'])\n", " # Test the objective function\n", " results = objective(df, sample(space), ITERATION)\n", " \n", " print('Finished, best results')\n", " print(trials_dict[:1])\n", " \n", " # Save the trial results\n", " with open('trials.json', 'w') as f:\n", " f.write(json.dumps(trials_dict))\n", " bayes_results = pd.read_csv('bayesian_trials_1000.csv').sort_values('score', ascending = False).reset_index()\n", " bayes_params = evaluate(bayes_results, name = 'Bayesian')\n", " \n", " # Plot of scores over the course of searching\n", " best_bayes_params = bayes_params.iloc[bayes_params['score'].idxmax(), :].copy()\n", " sns.lmplot('iteration', 'ROC AUC', hue = 'search', data = scores, size = 8);\n", " plt.scatter(best_bayes_params['iteration'], best_bayes_params['score'], marker = '', s = 400, c = 'orange', edgecolor = 'k')\n", " plt.xlabel('Iteration'); plt.ylabel('ROC AUC'); plt.title(\"Validation ROC AUC versus Iteration\")\n", "\n", "if __name__ == \"__main__\":\n", " global df,ITERATION\n", " submission_file_name = \"submission_kernel03.csv\"\n", " with timer(\"Full model run\"):\n", " global df,ITERATION\n", " main()" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "https://www.kaggle.com/alexandrnikitin/xgboost-hyperparameter-optimization" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2018-08-10T07:07:56.102272Z", "start_time": "2018-08-10T07:07:56.080241Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The highest cross validation score from bayes optimiz was 0.78831 found on iteration 47.\n" ] }, { "data": { "text/plain": [ "[\"{'boosting_type': 'gbdt', 'colsample_bytree': 0.6014642048462456, 'is_unbalance': False, 'learning_rate': 0.01222878983248616, 'min_child_samples': 500, 'num_leaves': 126, 'reg_alpha': 0.07936876591402212, 'reg_lambda': 0.28430717120489335, 'subsample_for_bin': 160000, 'subsample': 0.619083596793821, 'metric': 'auc', 'verbose': 1, 'n_estimators': 1115}\"]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "bayes_results = pd.read_csv('bayes_test2_temp.csv').sort_values('score', ascending = False).reset_index() \n", "new_results = bayes_results.copy()\n", "\n", "# String to dictionary\n", "new_results['hyperparameters'] = new_results['hyperparameters'].map(ast.literal_eval)\n", " \n", "# Sort with best values on top\n", "new_results = new_results.sort_values('score', ascending = False).reset_index(drop = True)\n", " \n", "# Print out cross validation high score\n", "print('The highest cross validation score from {} was {:.5f} found on iteration {}.'.format('bayes optimiz', new_results.loc[0, 'score'], new_results.loc[0, 'iteration']))\n", "list(bayes_results[bayes_results.iteration == 47].hyperparameters) " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" }, "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 }	apache-2.0
leonardodaniel/quant-project	analysis/stock_analysis.ipynb	1	191313	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Stock analysis: returns and volatility\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook aims to explore the Markowitz theory on modern portfolios with a little of code and a little of maths. The modern portfolio theory seeks to build a portfolio of different assets in such way that increases the returns and reduces the risk of holding the portfolio. In almost any treatment of risk I have read, the risk is repressented by the standar deviation of the returns and is called _volatility_. Let's assume that the random walk for stocks is of the form\n", "\n", "$$ S_{t+1} = S_t \\mu \\Delta t + S_t \\sigma \\epsilon \\sqrt{\\Delta t} $$\n", "\n", "where $S$ is the stock price at time $t$, $\\mu$ and $\\sigma$ are mean and standar deviation, $\\Delta t$ the time step and $\\epsilon$ a normal distributed random variable with mean zero and variance one. Under this assumption, $\\sigma$ indicates how scattered are the returns in the future and under certain conditions, there is a probability that it can lead to a loss due to this scattering. So, an investor seeks to maximize returns, but keeping volatility at bay, so to reduce the chance of lossing money.\n", "\n", "For this analysis we will use only basic numerical libraries, to show how some algorithms work without much black-box and we will work with real stock data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fec4544bb38>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fec4544bb38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import datetime as dt\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.figure(figsize=(12,12))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The volatility calculation is made using the return for each day, given the close price of any stock. As we are interested in the annual volatility, this needs to be scaled by a factor $\\sqrt{n}$, where $n$ is the number of working days in a year (I will assume 260)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# beware! it takes a numpy array\n", "def calculate_volatility(df, t=10): # default timespan for volatility\n", " rets = np.diff(np.log(df))\n", " vol = np.zeros(rets.size - t)\n", " for i in range(vol.size):\n", " vol[i] = np.std(rets[i:(t+i)])\n", " return np.sqrt(260)vol" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the scripts on `utils` it's possible to download the data from _Yahoo! Finance_ for the following stocks (by default):\n", "\n", " AAPL\n", "* AMZN\n", "* GOOG\n", "* MSFT\n", "\n", "We will use the function defined above to plot the running volatility of those stocks on a 63 days window." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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lKee0dstGObtNBVHOxwLNsCaN20TkYlW9LY3l1FZZK2NVXYoFChGTRWRL4GLg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\nyeSvv7RN/eKL0uvxmjWBVcI//qjHtddqW7RsWfito0Db8i1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Kudj99wJzSkljDDAx1c9a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"text/plain": [ "<matplotlib.figure.Figure at 0x7fec1f9539b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "symbols = np.loadtxt(\"../utils/symbols\", dtype=str)\n", "n_sym = symbols.size\n", "\n", "data = {}\n", "for s in map(lambda x: x[2:-1], symbols):\n", " data[str(s)] = pd.read_csv(\"../data/{}.csv\".format(str(s)))\n", " data[str(s)].sort_values(by=\"Date\", ascending=True, inplace=True)\n", "\n", " \n", "t = data[\"AAPL\"].Date[64:]\n", "t = [dt.datetime.strptime(d,'%Y-%m-%d').date() for d in t]\n", "plt.subplot(411)\n", "plt.plot(t, calculate_volatility(data[\"AAPL\"].Close.values, 63))\n", "plt.subplot(412)\n", "plt.plot(t, calculate_volatility(data[\"AMZN\"].Close.values, 63))\n", "plt.subplot(413)\n", "plt.plot(t, calculate_volatility(data[\"GOOG\"].Close.values, 63))\n", "plt.subplot(414)\n", "plt.plot(t, calculate_volatility(data[\"MSFT\"].Close.values, 63))\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I forgot to check if in the time in which those stocks are defined there was any splitting (that's why it's important explorative analysis).\n", "\n", "AAPL splitted on Febbruary 28 2005 by 2:1 and June 9 2014 by 7:1, while GOOG splitted on April 2 2014 (not a real split, it generated another kind of stock, splitting by 2:1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+VNWvMxPZcWrO779D9+5QIaJF2uy8MyxdWtli1aWLLbviOI7j1B/y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"text/plain": [ "<matplotlib.figure.Figure at 0x7fec1a6dbd68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def repair_split(df, info): # info is a list of tuples [(date, split-ratio)]\n", " temp = df.Close.values.copy()\n", " for i in info:\n", " date, ratio = i\n", " mask = np.array(df.Date >= date)\n", " temp[mask] = temp[mask]ratio\n", " return temp\n", "\n", "\n", "aapl_info = [(\"2005-02-28\", 2), (\"2014-06-09\", 7)]\n", "plt.figure()\n", "plt.subplot(411)\n", "plt.plot(t, calculate_volatility(repair_split(data[\"AAPL\"], aapl_info), 63))\n", "plt.subplot(412)\n", "plt.plot(t, calculate_volatility(data[\"AMZN\"].Close.values, 63))\n", "plt.subplot(413)\n", "plt.plot(t, calculate_volatility(repair_split(data[\"GOOG\"], [(\"2014-03-27\", 2)]), 63))\n", "plt.subplot(414)\n", "plt.plot(t, calculate_volatility(data[\"MSFT\"].Close.values, 63))\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's build a data structure with only daily return for every stock. The model for returns is given by a compounding of the daily return, like\n", "\n", "$$ S_{n+1} = S_{n}e^{r} $$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rets = {key:np.diff(np.log(df.Close.values)) for key, df in data.items()}" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(dict_keys(['GOOG', 'MSFT', 'AMZN', 'AAPL']),\n", " array([[ 1. , 0.40808215, 0.40073829, 0.19216722],\n", " [ 0.40808215, 1. , 0.44450401, 0.21672978],\n", " [ 0.40073829, 0.44450401, 1. , 0.20380946],\n", " [ 0.19216722, 0.21672978, 0.20380946, 1. ]]))" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corr = np.corrcoef(list(rets.values()))\n", "rets.keys(), corr" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fec1a512b00>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fec1a96cba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.xticks(range(4), rets.keys(), rotation=45)\n", "plt.yticks(range(4), rets.keys())\n", "plt.imshow(corr, interpolation='nearest')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Too bad there arent negative correlations, after all, they all play more or less on the same areas. So, given those stocks, what combination yields the best portfolio (bigger return, lower risk) with a given ammount of investment capital? The optimization problem isn't so hard numericaly, but its possible to derive an analytical expression. What follows is know as [modern portfolio theory](https://en.wikipedia.org/wiki/Modern_portfolio_theory).\n", "\n", "First, let's explore what I think is a pretty beautifull result (because I didn't expected it). We will allocate randomly those stocks in order to generate a lot of random portfolios with the same initial value. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fec1a483c50>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5ooi8dgZjuvCgLsCMEJER4+G/eJ43BlUY7/UA7ot73/XXX4/m5ubQczt27MCO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\nbRpnQc/ktMM4jyX9wOuVklZKeljS1ySNGUSfffTE77QMg/5uwji+xT6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"text/plain": [ "<matplotlib.figure.Figure at 0x7fec1a4f85f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def normalize(v):\n", " return v/v.sum()\n", "\n", "portfolios = np.random.uniform(0, 1, size=(1000, 4)) # 1000 random portfolios\n", "portfolios = np.apply_along_axis(normalize, 1, portfolios) # normalize so that they sum 1\n", "# total returns per dollar per portfolio\n", "total_returns = np.dot(portfolios, list(rets.values()))\n", "\n", "mean = 260total_returns.mean(axis=1)\n", "std = np.sqrt(260)total_returns.std(axis=1)\n", "plt.scatter(std, mean)\n", "plt.xlabel(\"Annual volatility\")\n", "plt.ylabel(\"Annual returns\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's easy to see that there is a hard superior limit on the points for a given volatility. That curve is called _efficient frontier_ and it represents the best portfolio allocation (less risk for an expected return or bigger returns for a choice of risk). It seems somewhat to a parabolla. As said before, it's possible to derive some analytical expressions for that curve.\n", "\n", "Let $w_i$ be a weight vector that represents the ammount of stock $i$ and let its sum be one (scale isn't important here, so it can be put as the initial investment, but the number one is easy and I don't carry more symbols).\n", "\n", "The expected return of the i-th stock is $r_i$, so the total return for a portfolio is \n", "\n", "$$ r = w_i r^i $$\n", "\n", "(summation of same indices is implied). In the same way, the total volatility (standar deviation) of the portfolio is\n", "\n", "$$ \\sigma^2 = K_i^j w_j w^i $$\n", "\n", "where $K$ is the covariance matrix, and the condition on the weights is expressed as\n", "\n", "$$ w_i 1^i = 1 $$\n", "\n", "where $1^i$ is a vector of ones. If we choice an expected return, we can build an optimal portfolio by minimizing the standar deviation. So the problem becomes\n", "\n", "$$ min\\left( K_i^j w_j w^i \\,\\,\\,\|\\,\\,\\, w_i 1^i = 1,\\,\\,\\, r = w_i r^i \\right) $$\n", "\n", "the right side I think may bring some confusion: the $w_i$ isn't bounded, only the $r$. In fact, if $r^i$ is a n-dimentional vector, for a given $r$ there is a full subspace of dimension $n-1$ of weights. The Lagrange multiplier problem can be solved by minimizing\n", "\n", "$$ \\Lambda(w, \\lambda) = K_j^i w_i w^j + \\lambda_1 \\left( w_i 1^i - 1 \\right) + \\lambda_2 \\left( w_i r^i - r \\right) $$\n", "\n", "$$ \\frac{\\partial\\Lambda}{\\partial w_i} = 2 K_j^i w^j + \\lambda_1 1^i + \\lambda_2 r^i = 0 $$\n", "\n", "and solving for $w^j$ yields\n", "\n", "$$ w^j = -\\frac{1}{2} (K_j^i)^{-1} \\left( \\lambda_1 1^i + \\lambda_2 r^i \\right) $$\n", "\n", "the term between parentesis can be put in a concise way as\n", "\n", "$$ (\\lambda \\cdot R)^T $$\n", "\n", "where $\\lambda$ is a 2-dimensional row vector and R a $2 \\times q$ matrix (with q the number of stocks)\n", "\n", "$$ \\lambda = (\\lambda_1 \\,\\,\\,\\,\\,\\lambda_2) $$\n", "$$ R = (1^i\\,\\,\\,\\,\\,r^i)^T $$\n", "\n", "this way, the bounding conditions can be put also as\n", "\n", "$$ R w^j = (1\\,\\,\\,\\,\\,r)^T $$\n", "\n", "In this last expression, the weight can be changed with the solution above, returning\n", "\n", "$$ -\\frac{1}{2}\\lambda \\cdot \\left[ R (K_j^i)^{-1} R^T \\right] = (1\\,\\,\\,\\,\\,r) $$\n", "\n", "calling $M$ that messy $2\\times 2$ matrix in brackets, it's possible to solve $\\lambda$ as\n", "\n", "$$ \\lambda = -(2\\,\\,\\,\\,\\,2r) \\cdot M^{-1} $$\n", "\n", "It's easy to check that the matrix $M$, and hence also it's inverse, are symmetric. And with this, the variance can be (finaly) solved:\n", "\n", "$$ \\sigma^2 = K_i^j w_j w^i = \\frac{1}{4}\\lambda R K^{-1} K K^{-1} R^T \\lambda^T $$\n", "\n", "$$ = \\frac{1}{4}\\lambda R K^{-1} R^T \\lambda^T = \\frac{1}{4}\\lambda M \\lambda^T $$\n", "\n", "$$ = (1\\,\\,\\,\\,\\,r) M^{-1} (1\\,\\,\\,\\,\\,r)^T $$\n", "\n", "That will be a very long calculation. I will just put the final result (remember, that formula is a scalar). The elements of M are\n", "\n", "$$ M_{00} = 1_i (K_j^i)^{-1} 1^i $$\n", "$$ M_{11} = r_i (K_j^i)^{-1} r^i $$\n", "$$ M_{10} = M_{01} = 1_i (K_j^i)^{-1} r^i $$\n", "\n", "and the minimal variance, in function of the desidered return is\n", "\n", "$$ \\sigma^2(r) = \\frac{M_{00} r^2 - 2M_{01} r + M_{11}}{M_{00}M_{11} - M_{01}^2} $$\n", "\n", "and the weights are\n", "\n", "$$ w^j = (K_j^i)^{-1} R^T M^{-1} (1\\,\\,\\,\\,\\,r)^T $$\n", "\n", "I was wrong, the plot of variance-mean is a parabola, but it seems that volatility-mean is a hyperbolla.\n", "\n", "So, returning to code:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "K = 260np.cov(list(rets.values())) # annual covariance\n", "R = np.array([np.ones(4), 260np.mean(list(rets.values()), axis=1)])\n", "x = np.array([1, 0.15]) # I will select a 15% of annual return\n", "M = np.dot(R, np.dot(np.linalg.inv(K), R.transpose()))\n", "variance = np.dot(x, np.dot(np.linalg.inv(M), x.transpose()))\n", "volatility = np.sqrt(variance)\n", "weigths = np.dot(np.linalg.inv(K), np.dot(R.transpose(), np.dot(np.linalg.inv(M), x.transpose())))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.27167570703300697,\n", " array([ 0.21001437, 0.42240463, 0.36320754, 0.00437345]))" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "volatility, weigths" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, at this day (24 Jannuary 2017) the market closed with the following prices for the stocks in this list:\n", "\n", " GOOG: 823.87\n", "* MSFT: 63.52\n", "* AMZN: 822.44\n", "* AAPL: 119.97\n", "\n", "with the assets allocation suggested by the optimum, if I have $10000 to invest, I will need to:\n", "\n", "* Buy 2 stocks of GOOG (2.5)\n", "* Buy 66 stocks of MSFT (66.5)\n", "* Buy 4 stocks of AMZN (4.4)\n", "* Don't buy AAPL (0.4)\n", "* Put the remaining $870.18 to take LIBOR rate? or to rebalance the portfolio? options?\n", "\n", "Another kind of optimization that it's possible is to maximize the Sharpe ratio, defined as the ration of the expected return and the volatility. One can think of it as the returns for unit of risk, so maximizing it yields an optimization indeed. We know that any optimal portfolio is in the efficient frontier, so having an expression of this curve we only need to maximize\n", "\n", "$$ S = \\frac{r}{\\sigma} $$\n", "\n", "The expression for the volatility in function of the desidered return can be put as\n", "\n", "$$ \\sigma^2 = ar^2 + br + c $$\n", "\n", "As we are interested only on the optimal curve, we will consider only the right side of this parabolla. This way, we have an additional advantage of having an invertible function on its domain. The return then have solution\n", "\n", "$$ r = \\frac{-b + \\sqrt{b^2 - 4a(c - \\sigma^2)}}{2a} $$\n", "(seems like cheating, I know...), so the Sharpe ratio becomes\n", "\n", "$$ S = \\frac{-b + \\sqrt{b^2 - 4a(c - \\sigma^2)}}{2a \\sigma} $$\n", "\n", "do note that the part beyond square root is always positive thanks to the [Bessel inequality](https://en.wikipedia.org/wiki/Bessel's_inequality), at least for this problem, so the Sharpe ratio will always defined positive.\n", "\n", "Doing the derivative of S with respect to $\\sigma$ and solving the problem for the maximum the solutions are\n", "\n", "$$ \\sigma = \\pm \\sqrt{\\frac{4ac^2 - b^2 c}{b^2}} $$\n", "\n", "and we take the positive value. With the real values back we obtain a very simple expression for the volatility:\n", "\n", "$$ \\sigma = \\sqrt{\\frac{M_{11}}{M_{01}^2}} $$\n", "\n", "and the return:\n", "\n", "$$ r = \\frac{M_{00}}{M_{10}} $$\n", "\n", "With the volatility the other quantities can be calculated as well using the formulas, so let's return again to code:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.25450263575661258,\n", " 0.40190208713762882,\n", " array([ 0.20758566, -0.05706873, 0.928021 , -0.07853793]))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "volatility = np.sqrt(M[1,1]/(M[0,1]M[0,1]))\n", "returns = M[1,1]/M[1,0]\n", "x = np.array([1, returns])\n", "weigths = np.dot(np.linalg.inv(K), np.dot(R.transpose(), np.dot(np.linalg.inv(M), x.transpose())))\n", "returns, volatility, weigths" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This time the algorithm ask to buy a lot of AMZN, some of GOOG and sell a little of the others. With the same $10000 the portfolio distribution would be:\n", "\n", " Buy 2 stocks of GOOG (2.5)\n", "* Sell 9 stocks of MSFT (8.9)\n", "* Buy 11 stocks of AMZN (11.3)\n", "* Sell 6 stocks of AAPL (6.5)\n", "* With remaining $596.92 to play\n", "\n", "It's possible to visualize the Sharpe factor for differents volatilities, rewriting the equation of the returns in function of the volatility as\n", "\n", "$$ r = \\frac{M_{10} + \\sqrt{det(M)}\\sqrt{M_{00}\\sigma^2 - 1}}{M_{00}} $$\n", "\n", "and hence the Sharpe ratio as\n", "\n", "$$ S = \\frac{M_{10} + \\sqrt{det(M)}\\sqrt{M_{00}\\sigma^2 - 1}}{\\sigma M_{00}} $$" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fec1a83ed30>]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fec1a4c9400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sig = np.linspace(0.28, 0.6, 100)\n", "sharpe = (M[1,0] + np.sqrt(np.linalg.det(M))np.sqrt(sigsigM[0,0] - 1))/(sigM[0,0])\n", "plt.plot(sig, sharpe)" ] }, { "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
cdt15/lingam	examples/CausalEffect.ipynb	1	22531	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Effect" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import and settings\n", "In this example, we need to import `numpy`, `pandas`, and `graphviz` in addition to `lingam`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['1.16.2', '0.24.2', '0.11.1', '1.2.0']\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import lingam\n", "\n", "print([np.__version__, pd.__version__, graphviz.__version__, lingam.__version__])\n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=f'{coef:.2f}')\n", " return d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test data\n", "We use 'Auto MPG Data Set' (http://archive.ics.uci.edu/ml/datasets/Auto+MPG) " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(392, 6)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>horsepower</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>307.0</td>\n", " <td>130.0</td>\n", " <td>3504.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>15.0</td>\n", " <td>8.0</td>\n", " <td>350.0</td>\n", " <td>165.0</td>\n", " <td>3693.0</td>\n", " <td>11.5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>318.0</td>\n", " <td>150.0</td>\n", " <td>3436.0</td>\n", " <td>11.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>16.0</td>\n", " <td>8.0</td>\n", " <td>304.0</td>\n", " <td>150.0</td>\n", " <td>3433.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>17.0</td>\n", " <td>8.0</td>\n", " <td>302.0</td>\n", " <td>140.0</td>\n", " <td>3449.0</td>\n", " <td>10.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration\n", "0 18.0 8.0 307.0 130.0 3504.0 12.0\n", "1 15.0 8.0 350.0 165.0 3693.0 11.5\n", "2 18.0 8.0 318.0 150.0 3436.0 11.0\n", "3 16.0 8.0 304.0 150.0 3433.0 12.0\n", "4 17.0 8.0 302.0 140.0 3449.0 10.5" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "X.dropna(inplace=True)\n", "X.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(X.shape)\n", "X.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Causal Discovery\n", "To run causal discovery, we create a `DirectLiNGAM` object and call the `fit` method." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\r\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n", "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\r\n", " -->\r\n", "<!-- Title: %3 Pages: 1 -->\r\n", "<svg width=\"298pt\" height=\"479pt\"\r\n", " viewBox=\"0.00 0.00 298.00 479.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 475)\">\r\n", "<title>%3</title>\r\n", "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-475 294,-475 294,4 -4,4\"/>\r\n", "<!-- 1. cylinders -->\r\n", "<g id=\"node1\" class=\"node\"><title>1. cylinders</title>\r\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"141\" cy=\"-453\" rx=\"52.7911\" ry=\"18\"/>\r\n", "<text text-anchor=\"middle\" x=\"141\" y=\"-449.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">1. cylinders</text>\r\n", "</g>\r\n", "<!-- 0. mpg -->\r\n", "<g id=\"node2\" class=\"node\"><title>0. mpg</title>\r\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"63\" cy=\"-366\" rx=\"36.2938\" ry=\"18\"/>\r\n", "<text text-anchor=\"middle\" x=\"63\" y=\"-362.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">0. mpg</text>\r\n", "</g>\r\n", "<!-- 1. cylinders->0. mpg -->\r\n", "<g id=\"edge1\" class=\"edge\"><title>1. cylinders->0. mpg</title>\r\n", "<path fill=\"none\" stroke=\"black\" d=\"M119.421,-436.354C112.231,-430.656 104.424,-423.907 98,-417 91.0048,-409.479 84.2888,-400.484 78.6435,-392.211\"/>\r\n", "<polygon fill=\"black\" stroke=\"black\" points=\"81.4041,-390.039 72.9755,-383.62 75.5612,-393.894 81.4041,-390.039\"/>\r\n", "<text text-anchor=\"middle\" x=\"112.5\" y=\"-405.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">-3.56</text>\r\n", "</g>\r\n", "<!-- 2. displacement -->\r\n", "<g id=\"node3\" class=\"node\"><title>2. displacement</title>\r\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"200\" cy=\"-192\" rx=\"67.6881\" ry=\"18\"/>\r\n", "<text text-anchor=\"middle\" x=\"200\" y=\"-188.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">2. displacement</text>\r\n", "</g>\r\n", "<!-- 1. cylinders->2. displacement -->\r\n", "<g id=\"edge2\" class=\"edge\"><title>1. cylinders->2. displacement</title>\r\n", "<path fill=\"none\" stroke=\"black\" d=\"M153.216,-435.324C162.227,-422.099 173.933,-402.778 180,-384 198.147,-327.837 200.76,-258.016 200.645,-220.282\"/>\r\n", "<polygon fill=\"black\" stroke=\"black\" points=\"204.143,-220.054 200.544,-210.089 197.144,-220.124 204.143,-220.054\"/>\r\n", "<text text-anchor=\"middle\" x=\"210.5\" y=\"-318.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">33.49</text>\r\n", "</g>\r\n", "<!-- 4. weight -->\r\n", "<g id=\"node4\" class=\"node\"><title>4. weight</title>\r\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"132\" cy=\"-279\" rx=\"43.5923\" ry=\"18\"/>\r\n", "<text text-anchor=\"middle\" x=\"132\" y=\"-275.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">4. weight</text>\r\n", "</g>\r\n", "<!-- 1. cylinders->4. weight -->\r\n", "<g id=\"edge9\" class=\"edge\"><title>1. cylinders->4. weight</title>\r\n", "<path fill=\"none\" stroke=\"black\" d=\"M140.104,-434.879C138.541,-405.001 135.302,-343.113 133.427,-307.274\"/>\r\n", "<polygon fill=\"black\" stroke=\"black\" points=\"136.91,-306.855 132.892,-297.052 129.92,-307.221 136.91,-306.855\"/>\r\n", "<text text-anchor=\"middle\" x=\"157\" y=\"-362.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">315.37</text>\r\n", "</g>\r\n", "<!-- 0. mpg->4. weight -->\r\n", "<g id=\"edge8\" class=\"edge\"><title>0. mpg->4. weight</title>\r\n", "<path fill=\"none\" stroke=\"black\" d=\"M70.4791,-348.316C75.4836,-338.127 82.6676,-325.151 91,-315 94.8041,-310.366 99.2717,-305.859 103.833,-301.707\"/>\r\n", "<polygon fill=\"black\" stroke=\"black\" points=\"106.213,-304.276 111.496,-295.092 101.639,-298.977 106.213,-304.276\"/>\r\n", "<text text-anchor=\"middle\" x=\"109\" y=\"-318.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">-36.98</text>\r\n", "</g>\r\n", "<!-- 3. horsepower -->\r\n", "<g id=\"node5\" class=\"node\"><title>3. horsepower</title>\r\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"128\" cy=\"-18\" rx=\"63.0888\" ry=\"18\"/>\r\n", "<text text-anchor=\"middle\" x=\"128\" y=\"-14.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">3. horsepower</text>\r\n", "</g>\r\n", "<!-- 0. mpg->3. horsepower -->\r\n", "<g id=\"edge4\" class=\"edge\"><title>0. mpg->3. horsepower</title>\r\n", "<path fill=\"none\" stroke=\"black\" d=\"M43.4117,-350.789C24.918,-335.568 0,-309.573 0,-280 0,-280 0,-280 0,-104 0,-67.3032 36.9619,-45.5007 71.1614,-33.1819\"/>\r\n", "<polygon fill=\"black\" stroke=\"black\" points=\"72.3503,-36.4747 80.7012,-29.9544 70.1069,-29.8439 72.3503,-36.4747\"/>\r\n", "<text text-anchor=\"middle\" x=\"14.5\" y=\"-188.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">-0.39</text>\r\n", "</g>\r\n", "<!-- 5. acceleration -->\r\n", "<g id=\"node6\" class=\"node\"><title>5. acceleration</title>\r\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"188\" cy=\"-105\" rx=\"63.0888\" ry=\"18\"/>\r\n", "<text text-anchor=\"middle\" x=\"188\" y=\"-101.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">5. acceleration</text>\r\n", "</g>\r\n", "<!-- 0. mpg->5. acceleration -->\r\n", "<g id=\"edge10\" class=\"edge\"><title>0. mpg->5. acceleration</title>\r\n", "<path fill=\"none\" stroke=\"black\" d=\"M61.5957,-347.794C59.2189,-306.853 58.9694,-202.475 111,-141 117.475,-133.35 125.97,-127.292 134.948,-122.519\"/>\r\n", "<polygon fill=\"black\" stroke=\"black\" points=\"136.526,-125.644 144.03,-118.165 133.5,-119.332 136.526,-125.644\"/>\r\n", "<text text-anchor=\"middle\" x=\"83.5\" y=\"-231.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">0.04</text>\r\n", "</g>\r\n", "<!-- 2. displacement->3. horsepower -->\r\n", "<g id=\"edge5\" class=\"edge\"><title>2. displacement->3. horsepower</title>\r\n", "<path fill=\"none\" stroke=\"black\" d=\"M221.169,-174.644C245.164,-154.036 278.665,-117.612 260,-87 243.613,-60.1251 212.829,-43.4172 185.109,-33.2917\"/>\r\n", "<polygon fill=\"black\" stroke=\"black\" points=\"186.055,-29.9161 175.46,-29.9788 183.782,-36.5367 186.055,-29.9161\"/>\r\n", "<text text-anchor=\"middle\" x=\"277.5\" y=\"-101.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">0.10</text>\r\n", "</g>\r\n", "<!-- 2. displacement->5. acceleration -->\r\n", "<g id=\"edge11\" class=\"edge\"><title>2. displacement->5. acceleration</title>\r\n", "<path fill=\"none\" stroke=\"black\" d=\"M197.572,-173.799C195.929,-162.163 193.724,-146.548 191.845,-133.237\"/>\r\n", "<polygon fill=\"black\" stroke=\"black\" points=\"195.288,-132.588 190.425,-123.175 188.357,-133.567 195.288,-132.588\"/>\r\n", "<text text-anchor=\"middle\" x=\"209.5\" y=\"-144.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">-0.03</text>\r\n", "</g>\r\n", "<!-- 4. weight->2. displacement -->\r\n", "<g id=\"edge3\" class=\"edge\"><title>4. weight->2. displacement</title>\r\n", "<path fill=\"none\" stroke=\"black\" d=\"M143.205,-261.429C150.085,-251.517 159.231,-238.792 168,-228 170.882,-224.454 174.018,-220.8 177.163,-217.252\"/>\r\n", "<polygon fill=\"black\" stroke=\"black\" points=\"179.849,-219.5 183.966,-209.737 174.66,-214.802 179.849,-219.5\"/>\r\n", "<text text-anchor=\"middle\" x=\"180.5\" y=\"-231.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">0.05</text>\r\n", "</g>\r\n", "<!-- 4. weight->3. horsepower -->\r\n", "<g id=\"edge6\" class=\"edge\"><title>4. weight->3. horsepower</title>\r\n", "<path fill=\"none\" stroke=\"black\" d=\"M128.6,-260.875C122.399,-227.443 110.384,-151.143 116,-87 117.181,-73.5146 119.655,-58.6952 122.051,-46.3426\"/>\r\n", "<polygon fill=\"black\" stroke=\"black\" points=\"125.547,-46.7138 124.103,-36.2179 118.687,-45.3235 125.547,-46.7138\"/>\r\n", "<text text-anchor=\"middle\" x=\"128.5\" y=\"-144.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">0.02</text>\r\n", "</g>\r\n", "<!-- 5. acceleration->3. horsepower -->\r\n", "<g id=\"edge7\" class=\"edge\"><title>5. acceleration->3. horsepower</title>\r\n", "<path fill=\"none\" stroke=\"black\" d=\"M176.146,-87.2067C167.435,-74.8663 155.455,-57.8941 145.624,-43.9675\"/>\r\n", "<polygon fill=\"black\" stroke=\"black\" points=\"148.456,-41.9097 139.829,-35.7584 142.737,-45.9465 148.456,-41.9097\"/>\r\n", "<text text-anchor=\"middle\" x=\"176.5\" y=\"-57.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">-4.66</text>\r\n", "</g>\r\n", "</g>\r\n", "</svg>\r\n" ], "text/plain": [ "<graphviz.dot.Digraph at 0x1b86fb90320>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = lingam.DirectLiNGAM()\n", "model.fit(X)\n", "labels = [f'{i}. {col}' for i, col in enumerate(X.columns)]\n", "make_graph(model.adjacency_matrix_, labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prediction Model\n", "We create the linear regression model." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n", " normalize=False)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "\n", "target = 0 # mpg\n", "features = [i for i in range(X.shape[1]) if i != target]\n", "reg = LinearRegression()\n", "reg.fit(X.iloc[:, features], X.iloc[:, target])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Identification of Feature with Greatest Causal Influence on Prediction\n", "To identify of the feature having the greatest intervention effect on the prediction, we create a `CausalEffect` object and call the `estimate_effects_on_prediction` method. " ] }, { "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>feature</th>\n", " <th>effect_plus</th>\n", " <th>effect_minus</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>mpg</td>\n", " <td>1.937266</td>\n", " <td>1.937266</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>cylinders</td>\n", " <td>5.998015</td>\n", " <td>5.998015</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>displacement</td>\n", " <td>1.056742</td>\n", " <td>1.056742</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>horsepower</td>\n", " <td>1.739774</td>\n", " <td>1.739774</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>weight</td>\n", " <td>5.110672</td>\n", " <td>5.110672</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>acceleration</td>\n", " <td>0.501212</td>\n", " <td>0.501212</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " feature effect_plus effect_minus\n", "0 mpg 1.937266 1.937266\n", "1 cylinders 5.998015 5.998015\n", "2 displacement 1.056742 1.056742\n", "3 horsepower 1.739774 1.739774\n", "4 weight 5.110672 5.110672\n", "5 acceleration 0.501212 0.501212" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ce = lingam.CausalEffect(model)\n", "effects = ce.estimate_effects_on_prediction(X, target, reg)\n", "\n", "df_effects = pd.DataFrame()\n", "df_effects['feature'] = X.columns\n", "df_effects['effect_plus'] = effects[:, 0]\n", "df_effects['effect_minus'] = effects[:, 1]\n", "df_effects" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cylinders\n" ] } ], "source": [ "max_index = np.unravel_index(np.argmax(effects), effects.shape)\n", "print(X.columns[max_index[0]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimation of Optimal Intervention\n", "To estimate of the intervention such that the expectation of the prediction of the post-intervention observations is equal or close to a specified value, we use `estimate_optimal_intervention` method of `CausalEffect`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimal intervention: 7.871\n" ] } ], "source": [ "# mpg = 15\n", "c = ce.estimate_optimal_intervention(X, target, reg, 1, 15)\n", "print(f'Optimal intervention: {c:.3f}')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimal intervention: 6.167\n" ] } ], "source": [ "# mpg = 21\n", "c = ce.estimate_optimal_intervention(X, target, reg, 1, 21)\n", "print(f'Optimal intervention: {c:.3f}')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimal intervention: 3.610\n" ] } ], "source": [ "# mpg = 30\n", "c = ce.estimate_optimal_intervention(X, target, reg, 1, 30)\n", "print(f'Optimal intervention: {c:.3f}')" ] }, { "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" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	mit
rsignell-usgs/notebook	UGRID/fvcom_plot_depth.ipynb	2	50861	{ "metadata": { "name": "", "signature": "sha256:4ca594118be650adfdcbb922e232568fa2e2ce1e0d82b51aa1b61caf99bbdbdb" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import netCDF4\n", "import matplotlib.pyplot as plt\n", "import matplotlib.tri as Tri\n", "import numpy as np\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "url='http://geoport.whoi.edu/thredds/dodsC/usgs/vault0/models/tides/fvcom/spectral_tides.nc'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "nc = netCDF4.Dataset(url)\n", "ncv = nc.variables" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "# read node locations\n", "lat = ncv['lat'][:]\n", "lon = ncv['lon'][:]\n", "# read connectivity array\n", "nv = ncv['nv'][:].T - 1" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "# create a triangulation object, specifying the triangle connectivity array\n", "tri = Tri.Triangulation(lon,lat, triangles=nv)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "ncv['h'].shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 40, "text": [ "(40992,)" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "# plot depth using tricontourf\n", "h = nc.variables['h'][:]\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111,aspect=1.0/np.cos(lat.mean() * np.pi / 180.0))\n", "plt.tricontourf(tri,-h,levels=range(-300,10,10))\n", "plt.colorbar();" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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HLKvX4TKOb+c3ByPdloH9hW4f347nIcp7o53OmFDU4nAVFIhNpNrpAKa/or49HgoynVNo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"text": [ "<matplotlib.figure.Figure at 0x7f23f279db10>" ] } ], "prompt_number": 41 } ], "metadata": {} } ] }	mit
RadoslawDryzner/LeRepoDuGuerrier	project/project.ipynb	1	42459	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#As always, we import everything\n", "import pandas as pd\n", "import os\n", "import re\n", "import hdf5_getters as getters\n", "import requests\n", "from bs4 import BeautifulSoup\n", "import numpy as np\n", "from collections import OrderedDict\n", "from tqdm import tqdm\n", "\n", "import nltk\n", "from nltk import word_tokenize\n", "from nltk.corpus import stopwords as stop_words\n", "from textblob import Word\n", "import pycountry\n", "from textblob import TextBlob\n", "from textblob.sentiments import NaiveBayesAnalyzer\n", "from gensim import corpora, models\n", "\n", "import json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our project consists of exploring the lyrics of many songs and finding themes and the usage of the words used in these songs over time. We use the Million Song dataset to find information about the song as well as various other datasets and sources to find lyrics data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Collection and Descriptive Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We begin by getting a list of all the files from our dataset. The Million Song dataset organises the dataset in multiple files and directories. The following code snippet gets all these files and prints the number of the files found." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# Explore the Million Song Dataset directory structure to grab the filenames of the songs\n", "all_files = []\n", "for (dirpath, dirnames, filenames) in os.walk(\"million-song/data\"):\n", " all_files.extend([dirpath + \"/\" + filename for filename in filenames if filename.endswith(\".h5\")])\n", "all_files_num = len(all_files)\n", "all_files_num" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Million Song dataset is not given in simple text but encoded using the [Hierarchical Data Format](https://en.wikipedia.org/wiki/Hierarchical_Data_Format). The following functions are used to get the relevant data from a file. Each file is a single record of the dataset, a single song described with multiple fields. These functions simply call the getter functions provided with the dataset to access the data. Since we only need a few fields, we simply take the track id, title, artist name and year. This fields will be relevant later on for our analysis and vizualisation.\n", "\n", "The track id will obviously identify the track in our analysis while the title and the artist will help us find the lyrics of the song. The year will be used for our vizualisation tool to see the evolution of the vocabulary and themes used in the songs over time." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_num_songs(filename):\n", " \"\"\"\n", " Wrapper around getter method provided with the dataset.\n", " \"\"\"\n", " h5 = getters.open_h5_file_read(filename)\n", " track_id = getters.get_num_songs(h5)\n", " h5.close()\n", " return track_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_track_id(filename):\n", " \"\"\"\n", " Wrapper around getter method provided with the dataset.\n", " \"\"\"\n", " h5 = getters.open_h5_file_read(filename)\n", " track_id = getters.get_track_id(h5)\n", " h5.close()\n", " return track_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_title(filename):\n", " \"\"\"\n", " Wrapper around getter method provided with the dataset.\n", " \"\"\"\n", " h5 = getters.open_h5_file_read(filename)\n", " title = getters.get_title(h5).decode()\n", " h5.close()\n", " return title" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_artist_name(filename):\n", " \"\"\"\n", " Wrapper around getter method provided with the dataset.\n", " \"\"\"\n", " h5 = getters.open_h5_file_read(filename)\n", " artist_name = getters.get_artist_name(h5).decode()\n", " h5.close()\n", " return artist_name" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_year(filename):\n", " \"\"\"\n", " Wrapper around getter method provided with the dataset.\n", " \"\"\"\n", " h5 = getters.open_h5_file_read(filename)\n", " year = getters.get_year(h5)\n", " h5.close()\n", " return year" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Million Song dataset does not contain information about the genre of the songs. However, there is an additional dataset from the same source that contains this information. Unfortunetely, it's not present for all the tracks of the Million Song dataset. We read this genre dataset here and will later link the genres with the data we obtain from the main dataset.\n", "\n", "Note that the file read here is not the one directly obtained from the source but the one where we only take the genre and the track id, since these are the only ones we need.\n", "\n", "The genre dataset only has around 60000 tracks which is substantially smaller than the Million Song in its entirety. However we believe this amount of tracks will be enough for our data analysis and visualisation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We save the dataframe created by linking the genres for convenience.\n", "#df = pd.read_csv('MillionSongSubset/msd_genre_dataset.txt')\n", "#genre_dataset = df[['genre', 'track_id']].set_index('track_id')\n", "#genre_dataset.to_csv('MillionSongSubset/genre_dataset.txt')\n", "genre_dataset = pd.read_csv('MillionSongSubset/genre_dataset.txt').set_index('track_id')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function links gets the genres for a single track given its id." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_song_genres(track_id):\n", " \"\"\"\n", " Get's the genres of a song given it's ID in a single string, separated by &.\n", " \"\"\"\n", " if track_id in genre_dataset.index:\n", " return \"&\".join(genre_dataset.loc[[track_id]].values[0][0].split(' and '))\n", " else:\n", " return None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before starting our data collection, we make sure that all files correspond to only one song as they should." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for filename in tqdm(all_files): # tqdm for a nice progress barr\n", " assert get_num_songs(filename) == 1 # check whether each file correctly corresponds to a single song." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following snippet reads the Million Song dataset in its entirety and uses the genre dataset to link the two. It gets all the fields we need as we discussed above and also gets the genres of each track. This information is then put into a dataframe. \n", "\n", "For convenience, we save this data in a new `.csv` file." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# The dataframe is saved into a file for convenience.\n", "\"\"\"\n", "data = pd.DataFrame([])\n", "\n", "for filename in tqdm(all_files):\n", " track_id = get_track_id(filename).decode()\n", " genres = get_song_genres(track_id)\n", " if genres:\n", " to_add = [('track_id', track_id), ('genres', genres), ('artist_name', get_artist_name(filename)), ('title', get_title(filename)), ('year', get_year(filename)), ('lyrics', \"\")]\n", " data = data.append(pd.DataFrame(OrderedDict(to_add), index=[0]))\n", "\n", "data.set_index('track_id', inplace=True)\n", "data.to_csv('data/data.csv')\n", "\"\"\"\n", "\n", "data = pd.read_csv('data/data.csv').set_index('track_id')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we need to obtain lyrics data for our tracks. For this, we have found two datasets. Both of these contain artist, track title and lyrics data which we read in the following code snippets. We try to get the lyrics from both datasets, but it's possible that neither of them contains the lyrics for some on our tracks. For this reason, we will also look at genius.com which is a website containing many lyrics." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lyrics_df1 = pd.read_csv('lyrics/songdata1.csv')\n", "lyrics_df1.set_index(['artist', 'song'], inplace=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lyrics_df2_raw = pd.read_csv('lyrics/songdata2.csv', na_filter=False)\n", "lyrics_df2 = lyrics_df2_raw[['song', 'artist', 'lyrics']].set_index(['artist', 'song'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_lyrics_csv1(artist_name, title):\n", " \"\"\"\n", " Gets lyrics for an artist and title pair from the first dataset.\n", " \"\"\"\n", " if (artist_name, title) in lyrics_df1.index:\n", " return lyrics_df1.loc[artist_name, title].values[0][1]\n", " else:\n", " return None" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_lyrics_csv2(artist_name, title):\n", " \"\"\"\n", " Gets lyrics for an artist and title pair from the second dataset.\n", " In this file, the artist and title fields have hyphens instead of spaces\n", " and are exclusively in lower case, so we change our data to match this\n", " format when looking for songs.\n", " \"\"\"\n", " index_artist_name = artist_name.lower().replace(' ', '-')\n", " index_title = title.lower().replace(' ', '-')\n", " if (index_artist_name, index_title) in lyrics_df2.index:\n", " lyrics = lyrics_df2.loc[index_artist_name, index_title].values[0][0]\n", " if len(lyrics) == 0:\n", " return None\n", " else:\n", " return lyrics\n", " else:\n", " return None" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_lyrics(artist_name, title):\n", " \"\"\"\n", " Gets the lyrics for an artist name and title from both datasets.\n", " If lyrics are not found in either of them, returns an empty string.\n", " \"\"\"\n", " lyrics = get_lyrics_csv1(artist_name, title)\n", " if lyrics:\n", " return lyrics\n", " \n", " lyrics = get_lyrics_csv2(artist_name, title)\n", " if lyrics:\n", " return lyrics\n", " \n", " return \"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We make a new dataframe that contains lyrics information for our previous data. If the lyrics are not found in either of the lyrics datasets, we generate the genius.com url to search for that song's lyrics." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Match lyrics\n", "data_lyrics = data.copy()\n", "urls = {}\n", "i = 1\n", "for index, row in data.iterrows():\n", " lyrics = get_lyrics(row['artist_name'], row['title'])\n", " \n", " # We generate an URL to lookup in Genius if we haven't found any lyrics\n", " # in the first two datasets.\n", " if lyrics == \"\":\n", " # To create the URL to find the song on genius, the title and artist names\n", " # need to be processed to match the general format of Genius' songs URL.\n", " # For instance, spaces are replaced by hyphens and additional information\n", " # between parenthesis is removed.\n", " url = (row['artist_name'].lower().replace(' ', '-') + '-' + re.sub(r'\$[^)]\$', '', row['title']).rstrip().lower().replace(' ', '-') + '-lyrics').capitalize().replace(\"'\", '')\n", " urls[index] = 'https://genius.com/' + url\n", " \n", " print(i, end='\\r')\n", " i += 1\n", " data_lyrics.loc[index, 'lyrics'] = re.sub(r'[\\[].?[\\]]', '', lyrics.replace('\\n', ' '))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The genius.com URLs are collected in a file so that they can be fed into a scrapper." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with open('data/urls', 'w') as urls_files:\n", " for index, url in urls.items():\n", " print(index, url, file=urls_files)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point, we run our scrapper which is using `scrapy`. This is not done in this notebook but instead you can find the scrapper code in the `scrapper` folder in this repository. We obtain a file that contains the track ids as well as their lyrics found on genius.com.\n", "\n", "The resulting file is then read and its data is added to our data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import json\n", "with open('data/missing_lyrics.json') as lyrics_file:\n", " lyrics_json = json.load(lyrics_file)\n", " for item in lyrics_json:\n", " for index, lyrics in item.items():\n", " # Returned lyrics are modified to remove newline cahracters as well as some\n", " # special lyrics structures as we have seen before.\n", " data_lyrics.loc[index, 'lyrics'] = re.sub(r'[\\[].?[\\]]', '', lyrics.replace('\\n', ' '))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then detect the language of the lyrics if any. It is possible that some of the lyrics do not contain any features that allow language detection, in this case we do not assign a language to that lyrics." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Find the corresponding language for the lyrics\n", "from langdetect import detect\n", "\n", "for index, row in data_lyrics.iterrows():\n", " lyrics = data_lyrics.loc[index, 'lyrics']\n", " language = None\n", " if lyrics.strip() != \"\":\n", " try:\n", " language = detect(lyrics)\n", " except:\n", " language = \"\"\n", " if language != \"\":\n", " data_lyrics.loc[index, 'lang'] = language" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's see how many songs are there with lyrics in the Million Song Subset." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_lyrics = data_lyrics[data_lyrics.lyrics != '']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_lyrics.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_lyrics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We save the resulting data in a file for convenience. This is the final state of our data and contains verything we need." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_lyrics.to_csv('data/data_lyrics.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To obtain our analysis, we will follow the following steps." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lyrics Processing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We would like to be able to extract different themes from our lyrics. Other than being able to see the evolution of some words over time and depending of the genres of the song, it's interesting to see the themes or sentiments that the song's lyrics portray. For this, we will use Natural Language Processing (NLP) libraries to extract this information about each track." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the lyrics that we have, we apply the bag-of-words model and only keep the interesting (meaningful) words. That is, we remove the stop words and lemmatize each word to avoid, for instance, having both 'sleep' and 'sleeping'." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start by downloading `nltk` packages that will be of use for us. [ntlk](http://www.nltk.org/) is a well known framework for natural language processing in Python." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nltk.download('stopwords')\n", "nltk.download('punkt')\n", "nltk.download('averaged_perceptron_tagger')\n", "nltk.download('wordnet')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we define a few functions to do all our natural language analysis steps." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we define a function that tokenizes lyrics. As we have seen in class, working on a list of tokens instead of a string of characters is much better for machine learning and natural language processing techniques that we will use. For this we use TextBlob which we will also use later on for sentiment analysis since it gives a nice way to obtain tagged tokens." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def tokenize(text):\n", " return TextBlob(text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we will define a function that removes stopwords from our tokens. This function uses standard stop words list from `nltk`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_language_full_name(isocode):\n", " return pycountry.languages.get(alpha_2=isocode).name.lower()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stop_words_languages = {}\n", "# Other than the stopwords for a given language which we obtain from\n", "# nltk, we also add a list of common first names which we remove from\n", "# the lyrics to ease topic detection later on. This list was found in\n", "# https://www.cs.cmu.edu/Groups/AI/areas/nlp/corpora/names/.\n", "names_pd = pd.read_csv('data/common_names.csv')\n", "\n", "names = []\n", "for name in names_pd.values:\n", " names.append(name[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even if we only have English lyrics, the code below can support the stopwords from multiple languages. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def remove_stopwords(blob, language):\n", " if language not in stop_words_languages:\n", " stop_words_languages[language] = set(stop_words.words(get_language_full_name(language)))\n", " if language == 'en':\n", " # Additional stopwords not caught by nltk's list.\n", " stop_words_languages[language] \|= set(['na', 'gon', 'la', 'nt', 'i', '', \"'\"])\n", " stop_words_languages[language] \|= set(names)\n", " \n", " tokens = []\n", " for word, tag in blob.tags:\n", " lower = word.lower().replace(\"'\", '')\n", " if lower not in stop_words_languages[language]:\n", " tokens.append((lower, tag))\n", " return tokens" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next function, we lemmatize the tokens that are left so that words that variants of words that are essentially the same (conjugated verbs for examples) are counted as the same token." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def lemmatize(tokens):\n", " lemmas = []\n", " lemma = None\n", " for token, tag in tokens:\n", " if tag[0] == \"V\": #if the word is a verb\n", " lemma = Word(token).lemmatize(\"v\")\n", " else:\n", " lemma = Word(token).lemmatize()\n", " lemmas.append(lemma)\n", " return lemmas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next function combines the functions defined above to get the final tokens that we will consider for our topic detection task." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_final_tokens(lyrics):\n", " \"\"\"\n", " Combines our functions to get the final list of tokens.\n", " \"\"\"\n", " texts = []\n", " for lyric in lyrics:\n", " texts.append(lemmatize(remove_stopwords(tokenize(lyric), 'en')))\n", " return texts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For our data visualisation, we also need to have raw frequencies of the words that we use. While later on we will use different ways to obtain such information for our ML techniques, here for the visualisation we want a quick and simple count of the appearence of our tokens over the whole corpus passed as parameters. This way we can easily obtain the frequency of words appearing in all the lyrics for a given genre in a given year for example. The function defined below does precisely that." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_word_freq(texts):\n", " \"\"\"\n", " Gets the raw word count of each word appearing in\n", " the lyrics given by texts.\n", " \"\"\"\n", " word_count = {}\n", " for text in texts:\n", " for token in text:\n", " if token not in word_count:\n", " word_count[token] = 1\n", " else:\n", " word_count[token] += 1\n", " return word_count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To find the general sentiment of a song's lyrics, we use [TextBlob](https://textblob.readthedocs.io/en/dev/). This library has already a built-in sentiment analyser, which gives us inforation whether the song is 'positive' or 'negative'. The following function is used to do just that and takes as parameter a single lyrics string." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_sentiment(lyrics):\n", " \"\"\"\n", " Gets the sentiment polarity of a lyrics given as string.\n", " \"\"\"\n", " blob = TextBlob(lyrics)\n", " return blob.sentiment.polarity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have everything needed defined, we begin by getting our data (from the file written to in the previous section) into a dataframe. We only keep the lyrics that we detected to be in english since all our NLP and ML techniques wouldn't work on lyrics from different languages.\n", "\n", "We construct a genres list that will be used later in our data visualisation as well as a dictionary that links the genres to a list of indices of particular songs." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lyrics_df = pd.read_csv('data/data_lyrics.csv')\n", "lyrics_df.set_index(['track_id'], inplace=True)\n", "\n", "lyrics_df = lyrics_df[lyrics_df.lang == 'en']\n", "genres_list = ['all'] # Added a special genre, 'all' which will capture all songs\n", "genres_indices = {}\n", "for index, row in lyrics_df.iterrows():\n", " genre = row['genres']\n", " genre = genre.replace('&', ',') # Replaced for later on when we use it in HTML\n", " genres_indices.setdefault(genre, []).append(index)\n", " genres_indices.setdefault('all', []).append(index)\n", " if genre not in genres_list:\n", " genres_list.append(genre)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We call our functions on the lyrics of each song and insert these tokens as a new column in the dataframe." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lyrics_df['tokens'] = get_final_tokens(lyrics_df.lyrics.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we add yet another column that is the sentiment of the lyrics of a particular song, obtained using the method described above. We scale the sentiment between 0 and 1 for our final visualisation. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all_lyrics = lyrics_df.lyrics.values\n", "sentiments = []\n", "for lyric in all_lyrics:\n", " sentiment = get_sentiment(lyric)\n", " sentiment = (sentiment + 1) / 2.0\n", " sentiments.append(sentiment)\n", "lyrics_df['sentiment'] = sentiments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we collect all our remaining data and output it into files that we will use for our visualisation. We first begin by aggregating the word frequencies by genre and year of release (songs without a year of release are dropped). Each word results in a file where each datapoint corresponds to a year and the frequency of the word in the particular genre and the particular year.\n", "\n", "The sentiment files are done in the same way, except that there is one file per genre.\n", "\n", "Moreover, a list of all words appearing for every genre is also created for our final data visualisation." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# We will collect here a list of words for each genre\n", "# for our visualisation's autocomplete feature.\n", "genres_words = {}\n", "to_save = {}\n", "for genre, indices in genres_indices.items():\n", " curr_df = lyrics_df.loc[indices]\n", " genres_words[genre] = []\n", " \n", " # create the directory for words in each genre\n", " directory_words = 'data/final_data/words/' + genre\n", " if not os.path.exists(directory_words):\n", " os.makedirs(directory_words)\n", " \n", " # create the directory for sentiments\n", " directory_sentiments = 'data/final_data/sentiments'\n", " if not os.path.exists(directory_sentiments):\n", " os.makedirs(directory_sentiments)\n", " \n", " for year in curr_df[curr_df.year != 0].year.sort_values().unique():\n", " curr_year_df = curr_df[curr_df.year == year]\n", " # get the frequencies of words in the current genre and year\n", " freqs = get_word_freq(curr_year_df.tokens.values)\n", " # get the word count over this data for proportion calculation\n", " word_count = sum(freqs.values())\n", " \n", " # get the sentiment values and calculate the proportion\n", " sentiments = curr_year_df.sentiment.values\n", " sentiment_avg = sum(sentiments) / float(len(sentiments))\n", " filepath = directory_sentiments + '/' + genre + '.csv'\n", " if not os.path.exists(filepath):\n", " with open(filepath, 'a') as output_file:\n", " print('year,value', file=output_file)\n", " with open(filepath, 'a') as output_file:\n", " print(str(year) + ',' + str(sentiment_avg), file=output_file)\n", " \n", " # save word data (proportions as well)\n", " for word, freq in freqs.items():\n", " # remove undesirable characters from filenames\n", " for ch in ['/', '', '\"', ':', '\\\\']:\n", " word = word.replace(ch, '-')\n", " if word != \"\":\n", " filepath = directory_words + '/' + word + '.csv'\n", " # saved in a directory to later output in a file\n", " # this is done so that we don't append to the file\n", " # before we know that there is more than one value.\n", " # having a single value in our visualisation doesn't\n", " # show anything but potentially makes other graphs \n", " # look bad because of scaling.\n", " to_save.setdefault((filepath, word, genre), []).append(str(year) + ',' + str(freq/float(word_count)))\n", "\n", "# save word data to files\n", "for filepath_word, lines in to_save.items():\n", " filepath, word, genre = filepath_word\n", " if len(lines) > 1: # save only if more than one datapoint\n", " if word not in genres_words[genre]:\n", " genres_words[genre].append(word)\n", " with open(filepath, 'w') as output_file:\n", " print('year,value', file=output_file)\n", " for line in lines:\n", " print(line, file=output_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finnaly, the list of genres that we created earlier will be of use for our data visualisation as an auto-complete feature when searching for genres. The same is the case for the lists of words per genre. This information is saved in files as defined below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for genre, words in genres_words.items():\n", " with open('data/final_data/words/' + genre + '/allWords.json', 'w') as output_file:\n", " json.dump(words, output_file)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with open('data/final_data/allGenres.json', 'w') as output_file:\n", " json.dump(genres_list, output_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Topics Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally, to find the topics of our songs we use [Mallet](http://mallet.cs.umass.edu/topics.php).\n", "\n", "This method doesn't create a list of themes with clear names, but rather assumes that a topic is a collection of words, thus we will need to manually assign a name for each topic.\n", "\n", "The LDA model will output, for each song, a list of topics with different weights.\n", "\n", "The following code just outputs the collection of tokens with corresponding track ids." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "path = r'mallet/lyrics' \n", "if not os.path.exists(path):\n", " os.makedirs(path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for index, row in lyrics_df.iterrows():\n", " with open(path + '/' + index + '.txt', \"w\") as text_file:\n", " tokens = ''\n", " for token in row.tokens:\n", " tokens += token + ' '\n", " text_file.write(tokens)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#if the number of topics is modified, you need to change it as well in the second command below\n", "num_topics = 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To process the output, just run the following command in Mallet's directory:\n", "\n", "`bin/mallet import-dir --input mallet/* --output lyrics.mallet --remove-stopwords --keep-sequence`\n", "\n", "We keep the `--remove-stopwords` just to be sure to remove all the stopwords." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then this command :\n", "\n", "\n", "`bin/mallet train-topics --input lyrics.mallet --num-topics 20 --num-iterations 1000 --optimize-interval 10 --output-topic-keys topics_composition.txt --output-doc-topics songs_composition.txt?`\n", "\n", "The output should be in the folder `data` for the next operations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then load the prediction of the LDA model on the lyrics and add it to the lyrics dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "songs_composition = pd.read_csv('data/songs_composition.txt', header=None, sep='\\t')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def df_get_id(filenames):\n", " \"\"\"\n", " Get the id of a song from the corresponding filename.\n", " We need this because Mallet outputs the filepath and not the original ID.\n", " \"\"\"\n", " ids = []\n", " for filename in filenames:\n", " id_ = filename[1].split('/')[-1].split('.')[0]\n", " ids.append(id_)\n", " return ids\n", "\n", "def df_get_topics(df_topics):\n", " \"\"\"\n", " Get a single list of topics from the topics columns from\n", " Mallets output file.\n", " \"\"\"\n", " list_topics = []\n", " for row in df_topics:\n", " topics = {}\n", " for i in range(num_topics):\n", " topics[i] = row[i + 2]\n", " list_topics.append(topics)\n", " return list_topics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "songs_composition['track_id'] = df_get_id(songs_composition.values)\n", "songs_composition['topics'] = df_get_topics(songs_composition.values)\n", "id_topics = songs_composition[['track_id', 'topics']]\n", "\n", "id_topics = id_topics.set_index('track_id')\n", "# We add the topics column to the lyrics dataset and name it result\n", "result = pd.concat([id_topics, lyrics_df], axis=1, join='inner')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have the topics coefficient for each song, we can retrieve interesting information.\n", "The goal is to have for a given genre, the coefficient of a given topic for a given year.\n", "For instance to kown what is the importance of the Gangsta topics in the pop genre in the year 2007." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "topics_genres = {}\n", "topics_all = {}\n", "\n", "# We iterate through the dataset\n", "for index, row in result.iterrows():\n", " genre = row['genres']\n", " genre = genre.replace('&', ',')\n", " topics = row['topics']\n", " year = row['year']\n", " \n", " # Add genre if not in topics_genres\n", " if genre not in topics_genres:\n", " topics_genres[genre] = {}\n", " \n", " for topic in topics:\n", " # Add topic if not in topics_genres[genres]\n", " # This is where we add the topic value for a given genre\n", " if not topic in topics_genres[genre]:\n", " topics_genres[genre][topic] = {}\n", " # Add year for this topic and initialize it\n", " if not year in topics_genres[genre][topic]:\n", " topics_genres[genre][topic][year] = []\n", " topics_genres[genre][topic][year].append(0)\n", " topics_genres[genre][topic][year].append(0)\n", " # Sum the topic of this song for this year\n", " topics_genres[genre][topic][year][0] += topics[topic]\n", " # Increase the number of songs for this genre/topic/year \n", " # in order to have a mean \n", " topics_genres[genre][topic][year][1] += 1\n", " \n", " # Same as before but for all genre (special case)\n", " if topic not in topics_all:\n", " topics_all[topic] = {}\n", " \n", " if not year in topics_all[topic]:\n", " topics_all[topic][year] = []\n", " topics_all[topic][year].append(0)\n", " topics_all[topic][year].append(0)\n", " topics_all[topic][year][0] += topics[topic]\n", " topics_all[topic][year][1] += 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Compute the mean for this genre/topic/year combination\n", "results = {}\n", "for genre in topics_genres:\n", " results[genre] = {}\n", " for topic in topics_genres[genre]:\n", " results[genre][topic] = {}\n", " for year in topics_genres[genre][topic]:\n", " if year:\n", " mean = topics_genres[genre][topic][year][0] / topics_genres[genre][topic][year][1] \n", " results[genre][topic][year] = mean\n", "results\n", "\n", "# Same as above\n", "results_all = {}\n", "for topic in topics_all:\n", " results_all[topic] = {}\n", " for year in topics_all[topic]:\n", " if year:\n", " mean = topics_all[topic][year][0] / topics_all[topic][year][1] \n", " results_all[topic][year] = mean" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Save the final result\n", "path = 'data/final_data/topics'\n", "for genre in results:\n", " for topic in results[genre]:\n", " directory = path + '/' + genre + '/'\n", " if not os.path.exists(directory):\n", " os.makedirs(directory)\n", " with open((directory + str(topic) + '.csv'), 'w') as csvfile:\n", " print('year,value', file=csvfile)\n", " for year in sorted(results[genre][topic].keys()):\n", " csvfile.write(str(year) + ',' + str(results[genre][topic][year]) + '\\n')\n", "\n", "path = 'data/final_data/topics'\n", "directory = path + '/' + 'all' + '/'\n", "if not os.path.exists(directory):\n", " os.makedirs(directory)\n", " \n", "for topic in results_all:\n", " with open((directory + str(topic) + '.csv'), 'w') as csvfile:\n", " print('year,value', file=csvfile)\n", " for year in sorted(results_all[topic].keys()):\n", " csvfile.write(str(year) + ',' + str(results_all[topic][year]) + '\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we have planned, we have a data visualisation website that displays our data as plots over the time of different distributions of topics, words or sentiments, separated by genre or not. The visualisation is available at the end of our data story [here](http://adelamare.eu/ADA/Project/). It is also available standalone [here](http://adelamare.eu/ADA/Project/fullViz.html)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
asazo/CC2	5_PDE/convection_difussion.ipynb	1	516750	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Resolviendo una PDE: Ecuación de Convección Difusión\n", "\n", "La Ecuación de Convección Difusión describe el comportamiento del calor mediante la difusión, en donde fluye de un lugar de menor concentración a uno de mayor, y la convección, donde existe un transporte de calor a través del material.\n", "\n", "$$u_t = Du_{xx} + cu_x$$\n", "\n", "Similar a la ecuación de calor, ahora agregamos un término de advección (conocido vulgarmente también como convección) $u_x$ ponderada por un coeficiente $c$." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from scipy.integrate import fixed_quad\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "from matplotlib import cm\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 213, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def convection_diffusion(xl, xr, yb, yt, M, N, D, c):\n", " \"\"\"\n", " Convection Diffusion equation solver. This kind of problem combines\n", " the diffusion equation, where the heat just goes from the warmer zone\n", " to the most cold zone, with a advection equation where the heat moves\n", " uniformly around the domain.\n", " Args:\n", " [xl, xr]: Space interval\n", " [yb, yt]: Time interval\n", " M, N: Space and time steps\n", " D: Diffusion coefficient\n", " c: Advection coefficient\n", " \"\"\"\n", " # Boundary conditions for all t\n", " l = lambda t: 0t\n", " r = lambda t: 0t\n", " # Initial condition\n", " f = lambda x: np.cos(2np.pix)\n", " # Step sizes and sigma constant\n", " h, k = (xr-xl)/M, (yt-yb)/N\n", " m, n = M-1, N\n", " sigma = Dk/(h2)\n", " tau = ck/(2h)\n", " print(\"h (dx)=%f\" % h)\n", " print(\"k (dt)=%f\" % k)\n", " print(\"Sigma=%f, Tau=%f\" % (sigma, tau))\n", " # Finite differences matrix\n", " A = np.diag((1+2sigma)np.ones(m)) + \\\n", " np.diag((-sigma+tau)np.ones(m-1),-1) + \\\n", " np.diag((-sigma-tau)np.ones(m-1),1)\n", " # Uncomment for periodic boundary conditions\n", " A[0][-1] = A[1][0]\n", " A[-1][0] = A[0][1]\n", " # Left boundary condition u(xl,t) from time yb\n", " lside = -(sigma-tau)l(yb+np.arange(0,n+1)k)\n", " # Right boundary condition u(xr,t) from time yt\n", " rside = -(sigma+tau)r(yb+np.arange(0,n+1)k)\n", " # Initial conditions\n", " W = np.zeros((m, n+1))\n", " W[:,0] = f(xl + np.arange(1,m+1)h)\n", " for j in range(0,n-1):\n", " s_j = np.concatenate(([lside[j]], np.zeros(m-2), [rside[j]]))\n", " W[:,j+1] = np.linalg.solve(A, W[:,j] + s_j)\n", " print(\"System solved.\")\n", " return np.vstack([lside, W, rside]).T" ] }, { "cell_type": "code", "execution_count": 216, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "h (dx)=0.050000\n", "k (dt)=0.050000\n", "Sigma=0.002000, Tau=0.500000\n", "System solved.\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " 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this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(xl, xr, yb, yt, M, N, D, c) = (-1, 1, 0, 2, 40, 40, 1e-4, 1)\n", "W = convection_diffusion(xl, xr, yb, yt, M, N, D, c)\n", "\n", "# Plot results\n", "fig = plt.figure(figsize=(10,6))\n", "ax = fig.add_subplot(111)\n", "matrix = ax.imshow(W, aspect='auto',cmap=cm.RdYlBu_r)\n", "ax.set_xlabel(\"$x$\", fontsize=20)\n", "ax.set_ylabel(\"$t$\", fontsize=20)\n", "ax.set_xticks(np.linspace(0,M,5))\n", "ax.set_xticklabels(np.linspace(xl,xr,5))\n", "ax.set_title(\"Temperature $T(x,t)$ in convection-diffusion regime\")\n", "fig.colorbar(matrix)\n", "plt.draw()" ] }, { "cell_type": "code", "execution_count": 217, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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GPBJNlQySNTll1/eksy0IoBsiKiZyQZBQ3mBkLRqkDPIKWQ7q6GmoVyiSjI3Sd8+FCPINeZ0TJps8qggkJDkcag2KFo2yAhTHIQeBYV1Q8PURxADc8P6Qs3793//d0PgIY34C/IHczXk0pIxVCA293YR690SQKJqmS9Mzi94wQsWwMFGD8G0yiP/R3lEtbONtQSZgVBjXqSBh202LQ5rHxKMEpY0gTLvYJiM8M6KAs6bAELCwJ5nnQ8T1mDaRJkmgJiXIf8Q/OSxSXeOP/qjP5p7tnl+UX1t44OQuQPztNk0CTzrA+WTd1GycW3WAGs++YGH5SC5PnAZ+PGPf2w+LJKN9QUx7kQAeYfw4YMybhtrhA+irHcd64EPF555VD/ej0lXEdY2fUESWWeuNRBwBNCthE4IOALYCaE+f9+KAFoFLukLl9zM+CK3L1Ze1PgAsonZZjeD9Iaa3pDtcZiv0j5r+A9yHXylUAwgeFmNMfJFjV+RJYCQFl70yWZVSY5lA4Lc8DJHQWSztapKXgQQ9ZMNP9lQWyxxTt8L9wHhRjnNaq0IIIoMhDa9eULukspLuk/IGUSuVQAQKgdKDgTghS984ZwSxzghdVwTBZZNGP80CBBqFOZhGwTC+fwOv1FMpXwoMK+oaqgrqDvgD/kDLxrzg/rXaq5RkCCkYEnD/NiqdUsAUfpQkCG0+OIlG0QZomOVQesDCAmxLa24WROi/T0KK8Qfs69VTZOpcSCyXGfYBJDxQZJ4tsAAwgUp4/nBz5GWJoBW6YPoJ9NKoXyx5qwy2Ood0EpJT88h7xXMv3zgJBsm+lYECkz5mHz9619v3EQg4dY9xPZhlcFuCGCa5HbzrkOJtGpx1jPOOutk/m65oEfwF0UTwC8dkL8CeOIdTgHMc6k6Apgnmom+WhFAuyGhitmNNjkEfFgwpdC6eSnac1sRwE4KIKodvn+8zFetWrUIDZQRginsRsyGhv9XsqG+QVr4qrcNcylmPFSZTgSQa7Op8Dd+fLYxdjbF9IbCBpokCRwP8WRjtGpX+kYIjmhlFsubAKISsmGz8acbBA51CLUVEy7mYktYUF1ZExA/SD9/oxx1IoA2ehOsUNMgf/SNCRtFFJ81SCJrBHxQUmwgjk0pwzghYBAySCnnQVRbtW4JoFUArctBsj+uB2G1RLkbAgh2+Hnaxr1h/huWAsgcoZ4SbISvmm0ozazdrFySkCWULcgfRBjCijrYSgFEjbdmUPq36xMfQEzaeRDA9Icl18Gsiy8eHx92fSTnC/MrptpWCiDvBUjqoAggfrM852CcZVbHYtHK3D6g13ypuy2aAH75oPwJ4NNvcwQwz0XnCGCeaHZBANlAUIjw2WMDaNfyIIBpH0Dre4UihhkYtRHlCOKAWteqtSOAmK4xI6KY2QbpwzyVJICQEjZBlKykiQl/Pgga53C8bZgD8f/phgCCJRsY5Afy3UvLmwCiomHOgxDjU2cbmyrqDoQAExYY4LfHz8EEkxyED1McWHEchLBbAgixeMxjHmM2cZQalD9MctZ0jKKHGRalKCuIxQaPoLTgg8aGi09YVlqZVnPZygeQuaQ/2+w6tKTGEtA0uW/lc5eeX+sakTQVcwzqNwSzVRCI7adbEzDH4wKAeR0iYpslP8nrpMcICQR3fH/t859MBI1iiP8qvpusZdusMmj9CQdFAK2CiJJu3QiyniPrNkLQhc2lyNqBiBOwMSgCiOsEQW0Qbz5AXGuPgCOAboV0QsARwE4I9fn7dlHARBdi6rORwJhMiXTEcRzCwKZAy4MAot6xqdgoYDYqfKR4eVvlCT8/FDWc53G4R+FAZcFEhN8Vm107AmhNP5AGzDCQGszGmK5QC6wCiCIFyURFxPcLYoF5zt4r18SkSQQmvmr0Q8RoNwQQAoWJEfWE+8E5nTGzIbHhoqi1qpqRNwEkgTA+TVwfczmKLvfAvGOSx+8OsycmafzTwIhzuHfIGvdN1YleCSA4okKhknE9S/QsscNMTAAL/oT4pfFxgKkR3MGH67PBMm7mEQUQIsJxpIXBfYHNFwLUai6zsLSEgVQh3DPXox/WIaQoGQXcLwHk3nmeUDcxBbOuMNPj5tAuCrgTAWQ86fREluDzMcOzhWKGaR28LAHERxRzKj5r+M/hy2g/CgjmgHBmRQETfMG4IX18JEDaSdvDvFm3h3YEELeA9HjTr7Cs94o9BjUfJR6rgA2mwheUMeH7y/uEYA3eUzxPuA2gdpNFgA8eTLt8DLJ+LalPVgKx99yPCZj+WEt8IBG0xMcJSiZ+n8wBH6L4CrrWQKBoAvjVg/NXAJ/6W6cA5rm+HQHME81EXyhakCkiELMaZA8TJy9rG13Ki5mADPvi5f+YNdjIbePlD6FAsUs61fNFjFmZzZuWNBthdkbx4zqY3HiRY85JNggXpA0FDWUKlRIfMvrF7EO/mIghb+mvb4gKGzpKCxsdhBElhmvx8ic6j8ZxBK6wOUJ4MF0S9EJjQ4CUgAvXwV8NMykqYpIAYqJkU85ST9mYwBRyAsnAzwmTEEpY2m8xee8W07RKhCLD75KNOYGgJiNas+aXTRBMOA7lgnsBT8iw9f/EaR+TGSqszQMIvlbBtASQDRZik/QBxIeTzR5/v2QCX+aW69IH5td0Q4FmPlgPEBaua+cac7SNvoXwgSVj5F74SGFeWWOQF4gjAT6ktknOZRaWjIHngHPTeQAtUeCYrLm16w4ls1M+R9YShJWgAchuMg9gem4hKzZinmszPwRl2bXKz7KigPk5zxFjYR1zTSKFWbuQb3sd1FiIKASXjzueYwidTVFDP1kEkA8AzMvMD+fxnEL+WL/WL7DVO6AXH8D0e8WuEz4WeHZ5H/DxxPpgLUG2WFfWTYRAIpsHEBIGuedvlGEwsWuSOU2+A9oRwG7fdXzQsZZ4PnlGwAjlnDnBN9a1BgKOALqV0AkBRwA7IbRMf99KJVimt7Pshp0u4wbpswokmxYRvpAxCFerKiYch4oK4aQlCSD/h/DapM/9AMQYuYYtN8f4UGX50yrND2PmDy2ZcNpWI+lnHO6c0UAAVZ+PhXQk/mjc3fK7i6IJ4NcOyV8BfMotHRXAcyWRV4oKBvgkkSqjkRttcSNf2DskUd+ReptX4Aor6ZblN9v9jdgRwP5wK/1ZjgAWN0UQJJQc/k4SPwgXpA0FCbMnakorszSj53zyQ6IAclyyzzwIYBKhNBmE3FkymIxIteck6xE7MljcWivqyijPfJgQzMJHCson2QBQDklu71rxCKxAAkjJJ/IiPV0SEWxvlPRiSSS7bJia5hvc5w5Jn5BESR9KaH1EEiWyji1+9oYzAkcAh4Pz0K/Sykw09IGsoAu2U/1Q2VD9OKad6peEa5gEME0GrSqIQtgtGbSkEDXQlqLj3HYkdwUtj5G6VczE+A3jo8s6JQAEv0H8WV0rBwJFE8BvHJq/AvikX7dVAH8liTqLjVI7UiDp9qaqB9FLNjKlb26qfzZ57ImSPi2pfcmhckxvLqNwBDAXGF0nKx2Bdqofih/KH/5UpC3plhAVRQDbkUHInVUGs+4jrQxyrE043a56zEpfP+7+HQJ5I1A0Afzm/e6vNT4cLJ+2Kwr1xF+1JICYfLcROynpB4kr4kAPwXtNxijei6tkQgEkuezepmqYz6BL3osjgCWfIDe8ciPQjerHHaD6QYZ6ad0QQEiYjaLtpe9+jmU8Vhnk3/2QwaQy2M8Y3DkOAYdAdwisMAKI6RbfvQcRA5lAiFxNlMfKqhFIxvt/k0TkEFzop03z8d3dIbz8j3IEcPnPobuDghCwARRZvn5EQtrUOSh/3ap+yVspGwFMjy1JBpPBI62UQRs8Qj8cDxnk7ywfw4Km1F3WITAyCBRNAP/rsPtrTZCjAhiGevyNuSmA928Gh1BfEt8/vs6pmYoPAwWup0dmIbS5EUcAV8Isu3vMFYFkQmc6TgZ6EKiBrx+kBtVvKWbPMhPAJKDJSGKw6ZYM2rJ2mIgtGeyHKOc6ua4zh8CIIDAKBPD7u3eLP7RaHOni7dv55/qmqpeeqSwfwNskvboZ7JE8noSRH5Y0lfjhWklc4FGS/mdElkHb23AEcCXMsrvHXBCwCZVtZY0k8YOs4etn89Zhll0qmeF65BCkEgiEMisKeJgm4E4gJhNOow5apc+qfO2UQVt6DMJsS9ctFb9O43W/dwiMMgJFE8DvPCB/BfBx/9s2CAQ/P6KACeaADFKrkShgyjGlo4APkXSdpD+XdIGkqqTXN8kivzNMc9SbI4CjPsPu/nJBIKn6JYkfnZOfD9UP8rJU1S852E4E0Cb9HpYPYC9Atssx2CoyGIILlpY8W3/BrFJ0vYzFHesQWIkIrEACyDS/SdKZklDzfpjIA3hvSddKepqk7zXXw5MlvVnSEZLiZrAIpPG7K2W9OAK4Umba3WdfCCSJjDVZWmUK1Q9fP4gLOf0gLHmqVsuZAKaJbDrhNGbftP8fOFosXcLpvparO8khMIdA0QTwuw/MXwF87C87JoJ2K6AHBBwB7AEsd+jKQsBGvUJe0gmdUaogf5AYVL9BBDKMCgFMk8FWOQbB2RJAe45LOL2ynjl3t/khUDQBvPKI/AngY653BDC/FdIIfe6rxdZpp6+z3UkOgfIiYFU/yAgt7euHuRcSA/FL1uHN+45GkQC2I4NWPW1VIcWSQZdwOu+V5vobRQQcARzFWc33nhwBzBdP19syR6BdQmd87gj0wHwJSRmE6pcmSO2CQMrsA9jrMgB3VFUbPNJPjkGXcLpX1N3xo4xA0QTwqiPzVwB/71qnAOa5Zh0BzBNN19eyRaBdQmdMk6h+kBTr6zesG92yZYvWr19vAkzSUcCjRADBk/uDABIEkk44DbkDg26qj1giyN+DJunDWgfuOg6BXhFwBLBXxFbe8Y4Arrw5d3ecQqCd6kcJN1Q/SEkr0+QgAV2JBJA0MLbZuYEQ9pJj0J7vEk4PcnW6vsuMQNEE8L+Pyl8BfOTPnQKY55pzBDBPNF1fywoBm9pl586dJvdcMj0JhAPVj2Pw9UNNKqKlCSBqpFW1RlUBTBJAi3k6x6Alg1bl65RjkN+7hNNFrGB3zaIQcASwKOSXz3UdAVw+c+VGmiMCVlmCUG3fvn0u2TLEAsUP5Y8SbpCRPFO79HoLSQJozaIrkQAmcUsm5AYT5sdWH2mVY9Ce46qP9LoC3fHLFYGiCeAPf+cBuZeCe/g1/8t0tKoEslynqrBxOwJYGPTuwkUgkPb1Ywzbtm0zBBBSiOpHQ/WDVBTdOhFA7geiOgrN+gBmKYCt7q9Vwul2/n9pMkgkN39a+RiOArbuHlYeAkUTwB899IG5E8BjfvJLRwBzXMqOAOYIpuuqvAi0KuNmU63g44dJFfIBoSpS9Uui6Ahg92sqmb7HmsqTymBWTy7hdPf4uiOXFwKOAC6v+SpitI4AFoG6u+ZQEWhXxg3VCR9A1J81a9aYv8vUSAOzdu1ao0ZmmYBXugLYThlslXA6KzLYJZwu06p3Y8kDgaIJ4I+POTx3BfBhP7rBKYB5LI5mH44A5gim66pcCLRS/Rglyg++fjaQYt26daUw+aYRbEcAyZvHfYyKCZj7QbnrxQTczYqzZn+bRqfbHIMu4XQ36LpjyoqAI4BlnZnyjMsRwPLMhRtJjgi0U/0gGvj6QQTw9SMIxBHAHMHvs6tBEcDkcGx5P9RB/t0tGeRYGkqsSzjd5wS704aKQOEE8OFHaG0lP4vKznqoh/3weqcA5riKHAHMEUzXVfEIJIMCbMSn9edjE6d+r603i+M/v0uqbMXfwcIROAVwcDNiySDrodscg1ZVZlTJtDIu4fTg5sn13B8CjgD2h9tKOssRwJU02yN+r+0SOqMuQf5QcFD9khu2I4DlWBjDUACz7rRVjkHWSrvqI1YVtMogHxSc48hgOdbTSh9F0QTwJ496UO4K4EN/cJ1TAHNc2I4A5gim66oYBNqVcbOpXTD52YTO6Qhf0sAUmey5HWorTQEs2qcxTRSzIxoAACAASURBVAYtubPEziWcLuYZd1ftHQFHAHvHbKWd4QjgSpvxEbvfdqofAR4EemCqo4xbK2WmzAQwObZ0FPAoBoEUTQCTj0erHIOODI7YS2REb6doAvjTRx+ZuwL4kO9f6xTAHNerI4A5gum6Gh4C7VQ/IkkJ8oBMQPwwzbVrjgAOb97aXanMhDZNBvmY6JRjMBmFzvFEN7uE0+VYaythFEUTwKsf++DcCeDvfvcXjgDmuHgdAcwRTNfVcBBop/pRwg3Vj8TOkL9uEjoTBWw35+HcQfdXcQpg91gN68jkx0c3CadZj/gSWgWavyGCKNOu+siwZm3lXccRwJU3573esSOAvSLmji8MgXaqH+ZRVD+O6dWfbzkTwEHkzStqgsusALbCJIsMQuxQB+3HBwTQpo9xCaeLWl0r77pFE8CfPe6o3BXAo7/zc6cA5riUHQHMEUzX1WAQaJfQmd9Z1Y+EyCh53ah+yZE6AjiYeeu11+VIAJP32CrHIPdlFb/k8ZYMuoTTva4Ud3w3CDgC2A1KK/sYRwBX9vyX/u6TCZ0ZLOTOEjyr+vFzVD9Uln7ajh07jMmYP2VrnUzATgEs24w1xpMmg9bs2y6tTDLHoEs4Xc55XU6jKpoA/vzxR+euAB71Xz9zCmCOi9ARwBzBdF3lh0An1Q+zGsofih/KX6+qX3KkZSaASXUSskfSYutLVlTevPxmeWFPRG2PUm1je3e4JjBnkEKXcHpQq8f1m0bAEUC3Jjoh4AhgJ4Tc74eOQLsybhAgNlQI35o1a4wT/VIbBBATXRlr6rYjgGCBCpp37dyl4tnv+aNKAElAbqN/k8ogOCUjidvlGLSYcrxLON3vCltZ5xVNAH/xpN/JXQF88DeucQpgjsvYEcAcwXRdLQ2BTmXcUP0gCUT3Yq5diuqXHOnOnTtNRKYjgEubv6WePaoEkA8W1mvSRcHlGFzqanHnd0KgaAJ47ZMforXVpX+g2/vcWQt15Nd/6ghgp4nv4feOAPYAljt0cAi0Su3CFTF1somi9uHrl4fq5wjg4Oay355HmQDycdFq3bYig3yUtEpebl0kbL3rZF3ivD6M+p1Hd145EHAEsBzzUOZROAJY5tlZAWOzmx/mTFoyyANSiPmM39mEzoPY3Hbt2mU25zKaUleaCZg1UMZgnKU8iny8tCOAyb6Tz0M3OQY5N00GMRG7hNNLmbHROLdoAnjdUx+auwL4oK/+xCmAOS5PRwBzBNN11RsC7RI6o/pB/jCbofq1UkJ6u2L20Y4A5oHi0vtAARxFAsj64uOiV+W614TTYMczxR+aSzi99DW5nHtwBHA5z95wxu4I4HBwdldJINAuoTObF4oJwQ02ofMgVL/khJSdAKIeoYqlo4BHMQjEEcDsVwXPhZ1//g2ZtAEkrYJHkmllLBlEGeTfg36m3AuveASKJoDXP/1huSuAR3z5x04BzHFpOQKYI5iuq84ItFP9UIAI9MCfCZPvIFW/5EhtVDHXLFtLpqhxBLBss9PdePjAyHM9t0o4naw+khyZSzjd3TyN2lGOAI7ajOZ/P44A5o+p6zEDgXaqH8QGEsbGZn39hgmiI4DDRLv1tUbZBJwnAUwiaMkganA/OQZdwulyrP1BjKJoAnjDicfkrgAe/qUfOQUwx8XiCGCOYLquWpuv2KDYrJJBHmxYbPr4+mHiZJMswjS1nAmgDZAZhbU3igSQNc76GhQBtPNuzb24TvCnHzKYjCQelvo+Cuu2rPdQNAH85TMhgP1VZ8rCdGetrgd+3hHAPNebI4B5oun6WoBAkuDZGr3pMm4cY339ioIPAmrHUdQYWl23kwnYEcCyzdjC8VgCyBof1sdNmgwyIpdwutzrZBCjcwRwEKiOVp+OAI7WfJbibpJl3FAj8IGampoyY+N3lHDD14/gBksMixz4ciCAbODWTG43c+sTWEbfxX7mk3UBSRqlNDBFEMAk9u0STrfLS2jL1jEfLsdgP6u5+HOKJoD/96yH564A3v9zPwTY9ZJ2FI/w8h+BI4DLfw5LdQfpMm5sJOSy27hxozFNQWJoKCLJyghF3kSZCSDYsVGj9Fknf3AEV8x04F2U6TzvOXMEMG9EF6uRfDRYMzHrJ6kMZl3dJZwe7JwMsndHAAeJ7mj07QjgaMxj4XfRqowbRGXbtm1G1cHHC8UP5W9Y5rBugEGNZGOktnCZmiXPljBbIsjGze/IlchmTrMbOceUCdte8BxFAmiTmQ/TBNwN5v3kGHQJp7tBtjzHFE0Ab3z2I3JXAA/7zP84BTDHJeYIYI5grtSubCQiJCoZ5AEekBSbZw+C1Wsy3GFgWjYCyEZrE2Fz/9ZUbkmfddCH/EGq+b1VdZJkcLnlextlAli2j4vkc5Umg51yDHKuTTjNurNVdDAVL+cPkGG8a4Z5DUcAh4n28ryWI4DLc95KMepWqh+D43eYVm1k5/r160tJ/hgrxAMT69q1awvHNZ0IG/zYWCF5WQQQomh9ALP8vTgXdXA5RHUyDzZhceETkdMArAJYZgKYvNVecwzaaj12fbmE0zktnBy6KZoA/up5j8xdAbzfxf/tFMAc1obtwhHAHMFcSV21SuhsVT82BjYDyAmRrBs2bCgtCSkDAUyqfslE2Dt37uyaALZTdZgLSwbLaiIeRQKIKo7CvFwIYDsymOVmgE8v7h38ziWcLtcOUDQBvOn5j9LasRzTwMzWdd9P/8ARwByXmSOAOYK5ErrqVMYN4oeahq8fGwNkY8uWLXIKYOvVkVb9KNdlW78EMIsM2lyM3Zj4iljLjgAWgXp310wGjyRzDDJnWXWOLRm0dYldwunucM7zqBVKAM+V9HJJ6ySRNPAsSb9og+tLJb1a0v0k7ZL0H5L+Is95KHNfjgCWeXZKNrZ2ZdyszxovehzekybHshNAzKz8WbeOd8bwWivVLzmCZJ3iTibgbkbea+WIbvrM65hRJYDcF8/EKLSshNN8ULSrMZwmgy7h9HBWQuEE8LTf07ocFcAdKICfuqqdAvhaSWdLerqkGyW9UdKLJT1Q0p4M1M+R9CeSXiSJjsclHS7pp8OZoeKv4ghg8XNQ+hF0Uv0wA+EMbsu4pU2MW7duNf51ZUn7kga8CALYTvUbJAG0ffeTLHjQC9URwEEjnG//NschBNAGgHWbcJpzeU9wPORxufip5ovgYHtbgQTwV5LOk3R+E9lA0u2SXiXpEym0cfi+TdIpki4b7EyUt3dHAMs7N6UYWTeqX9JnLWvQEEB8oDiujG3YBBC1FNKcpZam8clbAczCPx080k1+uEHM4ygSQBupPSoKYHLeLQG0eSiTZmKbQLodsXM5BgfxFM33WTQB/PXpj8ldATz0E1e2UgAx32yT9GhJxlGw2b4q6WeSXpNC+6lN4odq+Mqmyfgnkl4v6ZrBzkx5encEsDxzUaqRtFP9eNHbqhRW9Ws3ePIAFl3urd34IGQ46uOnOMhmI0Jt+TaUj04BGZ0IIOQ1T3KRlR9uWMEjo0oAk5Hag1xfw+67VZWTPHIMuuojS5/NFUYAD5Z0i6QHSbohgR4+fVQNOSOF6OmSLpR0RVMF3CoJ/0FMxpiBdy59BsrfgyOA5Z+joY7QfpVDUqyZxpIU/g/hINCDAI9uK1BQzQJH8WRww1BvqsPFhkEAe1H9ksMdNgFMXjsrJcggc71Bwq0/WZnWx1LGggK40ghgEq9+PihcwumlrLj5c4smgDe/6NglK4Df+O0WffO3W8xNzUSxPnwdVtvMUnC9KoDPkPSfkp4m6fIman6T+D1bEsrhyDdHAEd+iru/QV68ED8UPloyqXNS9etVzVvJBLAf1a8sBDBNBlkbEBobBQoZzDPZtCOA3T+rZTiy1yonveYY5B5twmn+bXMMDvIjpAy45jWGogngb1762CUTwCQWBIHc+9+/24oA8vMsH0AYI1G+aR9AqxgmCSA+g6iFjgB2WoQxu4BrI4FAJ9UP0xybs61I0clsmQaFPIAohvwpY4PYYNImV2GerV/VLzkGxgXeqK2tKoHkaQLudP/J4BFws478+HottcrLKBJAMLIm/07YLrffLyXJda/qcjqS2CWc7rxaViABxM+PKOATm2TwDQmTblYU8KWSNkl6vqTtzahhIoIxI5MSZuSbUwBHforb32BS9UuXcUPtgYDQIBn9RvGuNAK4VNWvWwKIKltkipG8g0dGlQDyHOECMWotryTXNtAsqS7bD4qsj02XcLq7lVQ0AfztHx6XuwJ48Edw2cs0AVtQ3iTpTElE+f4wkQfw3pKubZp8v9c8mOLv724qfpi9KDQMieS4FdEcAVwR07z4JpObd5avH5uxTfKK8ter6pe8YjKZcRnhRqHB125qamrJw8tD9VsuBDA5zrSvVz/JpllzNmHwkieiJB1Ys/moEsA8P0Cycgza9dDK1SCtDLqE0/MLf4USwJI8+ctjGI4ALo95ynWU7cq4WXMohG8pqt9yIoAoD5DUpRDAPFW/dgTQml05pmgFsNWiTJv3skqIZZ07igSQDwLmaRQJ4CBT3CTdUrhO0tWgWzK40hNOF00Ab33F43JXAO/1we90UgBz3StHvTNHAEd9hhP31y61C78jupcoX/zNbBm3POBJRrLm0V/efSyVAFrSzMYEaV6qL9xyVABbzUm6hJhNKZO1iTsCmPfKHmx/w4pwTrsaWDJog5Cy7jIdSbwSE047AjjY9T8KvTsCOAqz2MU9tFP9bBm3QRAYhpYMZOhiqEM/BJJCpPLGjRt7uvYgSbMdSLsgkLIqgO02ZGsSzdrER5UA8uzhRjFqrYgAF0sGbbaCbpKWr9SE00UTwNvOPD53BfCg93/bKYA5vkgcAcwRzDJ21U71S5otMVHlqfq1UrHKiFE/BHCQql8aO/6PspiOAl5OBDB5T602cRQlVJ2yVozpZ+3ycTXKBLDIABeXcLr9inQEsJ8ndmWd4wjgCM93uzJulsB0U45sqRBhWuZlPcx0Jb2MuRcCOAzVLzn2JHajQgDTZBASwR9bT9bWhl1K4FEv8z/IY3GpYM2MogJYJv9GSwZ5r/GcdBOENOoJpwsngH/8eK0br+T2eO2Yqeugf/0vpwDmhqjkCGCOYJalq06qH2ZFNlxbxm3QG23ZCSAbBuXqCAJph8WwVL9eCCBmU+osj0JjnYC/3Zi7DR4p872POgEso7rZa8LpVjkGl3vC6aIJ4O1nPSF3Anjg+77lCGCOLzxHAHMEswxdWad7XoLJvH685KyvHy82yB/+M8NokBTGVVai0okADlv1W8kE0JqAk8Ej4GHJYJ6VR4ax9iGAtLImQV8KBsvBvG3JoC1t2emjIk0GeYeyJlFwl9vacwRwKat7ZZzrCOCIzHM71c+WceNvzLDDrsm7nAlgEaqfI4DVOQiyIkCTkcRlf3xHmQAup3trlWOwU8Jpq+BaNZB3p/132dde4QTwz56UvwL43m84BTDHhecIYI5gFtFVMl9WVkJnXmCY1lAgCPQYluqXxIJksRCptWtJzl6+Bm5bt241peAsPkWqfo4AzhPAJBatgkfYkAftxtDvqmXtM7ZRVACXEwFMr6OkmbidwmznD+Jn37Wvfe1rdcYZZ+iRj3xkv8tiKOc5AjgUmJf1RRwBXMbTxwvJpkNIl3Gzqh8vOlS/IiMr2Sj4s27dulKinSaARat+aQLIHGI+TweBLKUWaxkngg+VbqOA+3H6L+KeIRC2bm0R1x/kNUeB3LbKMWiVwaz5e8ELXqC3ve1tOvroowcJ75L7LpwA/sWT81cA3/11pwAueWXMd+AIYI5gDqsr+9IiwbLdXKwCwu94aWF2xW8F1a9odWS5EEAIKn5NeZXAy2M9JM3nK4EA2gjgXrDr1c+rl76XeuyoE8BRIrdpMsi98TNbUcSuhWc84xn66Ec/qvvc5z5LXR4DPd8RwIHCOxKdOwK4zKbRbnZW4eNL1ZaZIrKXCF9aXmXc8oAHUgWRWb9+fR7dDaSPLVu2zDl5o7blWc1jKQN2BLB79NJ+XpxZdPDIKBNA1ibPybB9irtfEf0faRVma+aGDF5++eV6xCMeIRTAb33rW6V+n3HnRRPAO85BAcx25+hnZnbM1HTAPzkFsB/sWp3jCGCeaA6wr6T/U/PhNr59qHsQQF7GZVKuklCUmQCCq8UOPy2io4tWTJPYOQLY30OVpeYkyWB/vfZ+1iiTpFGs3JKeYeuWwIf3qaeeqm9/+9s65JBDhB/g8573PG3atKnjorjooov0vve9T1dffbWw2uBi0s4Xm5RUZ511li677DLzLjrxxBN1/vnn90w4CyeAr31K/gTwnV8Db5SEHR2Bdwd0RMARwI4QFX9Aq4TOqH32S5UXRZlUvyRq1qeOIIsytaRiiqKKCRiSUKbmCODSZyMZIc88s/naSOJBk/1RJkm9+GwufRaL6YF3LK401iJw6623ChMwxO/HP/6xnvKUp+i0004zqmArUve1r31NWBjA6+Uvf3lHAgjh450JcWTtnnLKKebd/rnPfa4nEBwB7AmuFXmwI4AlnvZ2qV343Y4dO0x+PRRAXlKD3sz6hapsBDCp+lns+OomSrnsBBAs7TyPYhBIPz6AvazLrCTBg0z4O+oEcNDz1cvcDuJYFDveEZYA8u7ADHz99dfrpptuMiTtRz/6kS6++OKO71/Uwyc+8YltCeAtt9yiQw89VNdcc42OOuooc0v8+yEPeYj43cEHH9z1bRZOAP/y9/NXAP/+cqcAdr0COh/oCGBnjAo5opXqx2BsQmf+zYuprOlVLHAobTt37jSVNopurfwkSQNTVgLImBmbTebtCGA+q8g+Y+ALtp2SBPdz1VFWyVDHcJso20dTP/OUdQ5rgntMJs3n3Xvcccfp2muv7fky3RDAz3/+80bx48Mh2fjAv+SSS3TSSSd1fV1HALuGasUe6Ahgyaa+nepnFR9UIL5K7bFlJ4C91Nod1HQko6OzFFMUwKLT5WTdezKHoiOAg1kd7YJHlhoMNOoEMGkeHczsFNerJYC8F+xH1+bNm40v4FVXXdXzwLohgB//+MeNf+Htt9++oP8DDjhA5513njE1d9uKJoB3/tVTtW4ixyCQ6Zr2f/tXnQLY7QLo4jhHALsAaViHdFL9+Brla5sXEv4mkAO+SMuaX8/iVjQB7CY6ejkTwOQGNay1OojrQJaKNinmHTxShnsaxFzRZ9o8OqjrFNWv/eBOPl+Yfc855xzh19dr64YAogBCMFk3y10BdASw1xUy/OMdARw+5ouu2En1g/hBYjBFsEHar9Gy59ezN9qp1u6gpqCXnIiOAA5qFrrvt2wmxXTwCGqgNRN362876gRwmDXFu19J+RyZ5WOLPx5K3KWXXtrzRbohgPj53fe+9zURw0kfwIc+9KG6+eabl5UP4J1veHr+CuBbv+wUwJ5XXusTHAHMEcxeu7KmJ1uoPFnNw5r7rAkp60Vb5vQqSSyKIIDdqH7JMW7fvt2Y1cuW06wbE/CoKIBlI4DpNcya4g/rGTLYTfBIme+p1/dV8vgs/7il9FfGc7Fc8PzxfNn23e9+V5/+9Kd1wQUXdD1ka9mBAD796U83/tA2f2LWhwRRxuwJn/jEJ4ybz+mnn24+/j/72c92fU0OLNoEfNffnpA7AdzvzZc5AtjTKmh/sCOAOYLZS1c82LaMW/NhnVP2bJJn/ubl04qUlC26ttX9c6/pWru9YNXLsb2ofo4A9oLs4I9dLmQpK3gEMohbRnpDT6cRGTyKw7lCln/ccK48vKtA9rGyJAkgufmuvPJKvec97+l6IFQNednLXja3NsCOdUIyadS+I488Ul/5yld07LHHmj6xRpx99tn64he/aI6DEJIHsFdXH0cAu56iFXugI4BDnvpOqh8vHFQ/outQpNolDC1TdG07GIdFACHM+CVxPap59BKdiAKIQzu4l6kl/TzTQSCjtgkvFwJo10fyWeZZZLNmzVkyyHGOAJbpaeptLMwpVhbUN9tI+4Ip9i1veUtvnRVwdOEE8NwT81cA3/glkHSJoHNaT44A5gRkN90kVb+kuZdzreqHutBtNOpyIYDcH4lQKQW31KjKLJz7Vf2SfZFTEfJXNgKY9PNcCQRwuUaVtgoegUAk88h1855YDsdkBUgsh3H3MkYsLPxJEsAPfehDxg2ASN2yN0cAyz5DxY/PEcAhzEFyc7DyvzUVLYW82Oha8ut165Q+hNvNvMSg8uz1Q5yzBugIYFErY/66o6KWpYNHhll5ZFizOGpJyLNwg/xB9mytdY4hAOTAAw/UGWecMSyo+75O4QTwrSflrwC+4YtOAex7RSw+0RHAHMHM6qpdapdeAxXS/RcRXNEvXHkTQDbZpLl8qTV8cczGdIcCVabmFMAyzUbvY8ElgXXFhwrP6yCSTfc+qqWfwf2QrBhXi1FtqLfMWfKdcO6555pKICRrLntzBLDsM1T8+BwBHNActEvtwu94eeLfxcuFL8x+FLxh+dblAVGeaVbyUv2S9+UIYB6zvLQ+RkUBTKIAAbQR/KxbG0nMs2vrEWcFjywNycGfnRUhO/irDvcKfHwxT0kC+JrXvEYnn3yyTjjhhOEOpo+rFU0A737bM3JXAPf96y84BbCPtdDqFEcAcwTTdtVO9bORuxA+fP16CVRID9USwEH51uUJTR4EMG/VzxHAPGd46X2NGgFslSol7S+YFTyydDQH20NWhOxgrzj83iGAtKRf8JlnnqmzzjprLmJ3+KPq/oqFE8B/eGb+BPD1n3cEsPsl0PFIRwA7QtT9AZ1UP6J7ealklSLr/ioLjxxkcEW/Y8o6b6l59gah+iXHiVJDgErS3yfP+++3L9YLSjEk3wWB9ItiMee1IoDJ0VgyaFNCoQZaM3G7DADF3NH8VbMiZIseU97X57ljDpJpuCjF9ra3vU1HH3103pfLvT9HAHOHdOQ6dAQwpyntRvXjZYLql2ckbN6+dTnBsaibfgngIFW/5UAAk8m+HQEc1OocTL+9pumxH5CQQZtsutfKI4O5k8W9ZkXIDuvaw7oObjo2YbO95kknnaQLL7xQhxxyyLCG0fd1CieA73iW1k3mWAt4b037vu5z4OHSwPS9Khae6AjgEoFsp/rZSDlelihLmBL68fVrN8Q8TKtLhKCr0/uJsh206jcqBHBUynGNWm3ZXglgcj3y7kBls1WCyhY8khUh29WLYBkdBAG0eR3tsI8//niTwBlFvuzNEcCyz1Dx43MEcAlzYJ26eVmn8/qh3ODTxAsE1W9Q5px+lbUl3HZfp/ZCAIel+iVvhLliDpM5v/q60ZxP6kYBdAQwZ9Bz6i6PXHk22bQNHmFoSRNx3h+U3d76SiCA6TrOzMXDH/5wXXfddblacbrFvNfjiiaA9/zTH+SuAO5zjimH5xTAXhdDi+MdAewDyGRCZ05Pkj9e+pAJXthszPiPDPIlXdYKFmlYu42yHabq5whgH4t/gKeMmgKYBwFMwp0VPJKMJB7g1CzqOitFyjCvP4xrpSvTWAJ4ww03DPSdnte9OQKYF5Kj248jgD3Mrf0at2aZJPGz/ll8NfJSHpYq04uy1sOt5n5opyCLovCzN8rLnpas+5k7CH10mFYACQqxanI3QQZ9XLKwU0aVAA4iV17RyaZXCgFMVqYB80c+8pFGARzkR31eD2DhBPBdz85fAXzVZ5wCmNcCQbzqt6+Yp2EFtaTql1XGDeKH6geBSEaNDRqibpW1QY+jU//tCGBSNR02fnbczB9zXFYCSCF4SCobL47p1gzIz4b1sdFpjpf6+2TOvKX2VYbzh1UtIyt4hI9Q1smgiEpWjrwyYJ7nGNLrkWfvuOOO07XXXpvnZQbWlyOAA4N2ZDp2BLDDVCbNLvw7rfolq1EQ6DEoX79Ww+ykrJVlpWb52BWt+iWxKSsBRG1mjll3NkVII0BgRlHEHXgmuAhCOKjNflhraNQIYBHVMoYVPJKVI29Y62QY18lS1zdv3qxTTz1VV1111TCGsORrFE4A3/uc/BXAP7vUKYBLXhnzHTgC2AZM+zLlRZ6l+kFqOAbViC/uIlpZgxfSWKTHWQbVLzlGIv6Y50GY6/pdF2xCkCJbkB5lmX97Xk2rK/8jL55uiPixp0iBomhModarpsMkjfV72cLOG0UCSC65IlTldsEjeaSh4r54JyaTJBe2cAZw4awI7ptuuknnnHOOvva1rw3givl3WTQB3Hz+c3MngJvOvsQRwByXiiOAGWAmk7MajcXz5tQVfsfLD8LAy2+pNWiXOpfLhQBahQ28MKUM21eyE85lI4C2TjTrjT9TU1PNRNA7tGbsp/KjPQtvCY+MhiQofDMirdasDlZd95bkd7r9UvzeEcDBTEM6eCSPZNNZSZIHM/pies0K4Lnmmmt03nnn6dJLjQpV+uYIYOmnqPABOgKYmoJ2CZ3tpswpSy3jltfMl9V0mb4/xgm21l+pKF+/VriXhQAmPzBwQMe0C3YbNmxQHO9SVVfLD3fIaz65xhU3DuTFtcW3BnlUoOn4Xqp5h8vzyk0ER40AlrFcWl7BI1lJkvN6p5Whnyzz/RVXXKGLL75YF1xwQRmG2HEMhRPAf31+/grgH3+6kwJ4rqSXS1on6UeSzpL0iw5grZX0c0kHS8KU1/iSXgHNEcDmJNsXo/W3YvO1PlX8jhceX738HF+/svhblYW4tHtWkqZMzJhlDFpgbjGvrl3Lu6CYlkyBgyka8mdrR2/YEKjqXSuvBvmrK459RfG46nVP1WCvPIXyjPYnQ7Ll+fLicO5GQo1pZ+1oKdhofAnLsn6TSDsCONx1Z11cIKq28ki3wSNZSZKHO/rBXo1nMW2+v+yyy3TllVfqPe95z2AvnlPvK5AAvlbS2ZKeLulGSW+U9GJJD5SUMpksAPnDkg6S9PuOAHa5+EYpCjip+vFio1m/HbsBs2GWRfVLTlEZiEu7JWN9/cAR36OyZtAvEkcIWzKYKOlWAG7TezdrhW2S7QAAIABJREFU4/pbFYdQvJp8ry5PMwrrk6poyxz8MRTQqIGzRvnzVV8wNdDD6XAf7Zg9QpXKmCGYww5aardWRpEA4u5QtuTiWXNg34GQQdajrYDR6mPBunAU5fvc5TbV92FZtY4vuugi3XzzzXrLW97Sd7/DPLFwAvgBFMD8fJF37J3VpjPaKoC/knSepPObOAeSbpf0KkmfaIH9MyS9QdJfScK50ymA3SzSUSGAdvO1Eb4QAb7+IHu85NiYUfySimA3+AzrGMbLJkOakLK1ZDUUNhReqkUqbO3wKYoAdgqGiaNb5elOVX1PfoLs1cNVqkTz5I97q0frFGjbvBIolD5MxE1lUA1f1khj2jZzhGZqaxaklClSFRy1nIZmPup182wuBwJon41k8Egj4MhbQAbtcekqGWV79yx1PFm1jj/0oQ+ZveE1r3nNUrsfyvkrjACyAW6T9GhJP0gA/FVJP5OUNWmbmmZiFMP9JH3TEcAul+aoEEBul5e0TWsIEbA5rvj6hQjmETXXJaw9H8ZY+VMmApisgWyroYBx2caZBLsIHC1Bzk4cPqPAv0WBf7fqdV8V7ZkjdmE4Jj+kdN28ibcerVegLYsSe6IIxqrI06xkTL+Nu0Yt3BM9WNO1TQvqzTKWIkzEo0gAs0hEzw94gSe0Cx7hPYk7Bx92o9iySt0RAHLggQfqjDPOWBa3XDQB3PLBU7RuVY4K4J5ZbXzFRWCfVQoO/71bJD1I0g2JCfoPSTskZU0anf1U0tslHe8IYA/LepQIIF/qfNlBXDBD2TJuRPkWqYp0Mx3JShHdHD/oY1rVQC6CYPVyr8McnyU7Nr1LOpWG792jSnCjSfcSR7Hqs76qASlfpDjyFdcjYwKONKl6XJEXV+QbMsjP+Ts0/n/8PtD25s8gfYFib0y+3zANowvO6P6ajQ41a59130716QXPXo91BLBXxIZ7fDp4hKvzsTDoUpfDvcv5q/EeY0/A+mPbueeeq0c84hE65ZRTihpWT9ctnAB+5NQlE8CvXn2rLr/6NnPfs/VI/3q54XZZBLBXBfBUSedIelQz6OPxkr7RzJ81/2XdE+LL72AXBNI01eD7R0oV1A9edkRdLoc2HyRQ7HizVL8keS4bUU3PLQQQVWPQPop2vlhnBHos9MGrqxL8SoF/19zwwtoqBdpq/h9pncK65Hsz8j3ysOE7OKlxH8vHfAvjVSZHYCw2r1i+dixQBkOtk+/PGgUREljzDtZMxIdzI4CEjY9x8jfjs/VmB/kxNKoEEFKdJBHL4Z3SaYz2WWc9WH9BlMBBVh7pNKa8f59V6QTT78knn6wTTjgh78sNpL9RIIBJYHagAP4hgl4mAeTnWT6AsMdXZ/gAEsr9XNyim9fA9w8SeU+TGF44kEkpWaeOAEravn27ifK1zvc27UbJ5ipzOGwwlIMjT1xRrZXqlxxP2QngoMeXjCRnnaXVZU/bVa1QZH52DrY4qihGlYsnNDbuKap7CqLN8+QwnGimhJmvyliP1yqIUf3mMxlEmlCkVfK1yySOxgeQRNGxRxAIASXTCr0pTUdHL6gOyZitKYxN35afG8RGn5V4t6j1nNd1s8yIefVddD+2bjPj4B1kg0fsx0IRbgR5YpJV6eTMM8/UWWedpWOPPTbPSw2sr8IJ4L+ftmQFcBEBfOmn2hFA/PyIAj6xSQYJ7iAK+PCMKGBUxNWJ/h8jCZPwfSTxkm1Eg454cwSw6QNo1Y4yEKpe1hzj3bFjhzZu3NjLabkc20n1S16kLEplqxsfJAFM5o9E9VvoUxopCH4jPyBYjcexqjiuSLFvKnz4/k55XqQ48qTabMOXDzUwqgg50NfM3C3V4/UK4s2L/ACjuKpIa+Rpt2JvtSr+TnMOtDH0NqlSgRhKtWiTQm+javV9m77Q82glo0T5aZIM5rGYRpEAZpkR88CqDH2kI7Zt8Iglva2CR8ow9m7GkFXp5AUveIHe/va366ijjuqmi8KPWYEEEMzfJOlMSeTz+mEiDyAZ8Sni/DRJ38uYHOcD2MuKHSUfQMgfmzSNf6MIFkGoesHfHmvHiwI4SBNdemzdqH7Jc8pOrG11kjxN/+mkzun8kZ63W0HlRvn+4hRVUTShwJv/eTQ7IT9uqH/4BUbhuPH1C6OqvCCQIqKEMfuS7JnycIYlmmCPwNu6QBGkXJzvN9LJ0OraqEp1d/PfUwoq06qHmwwRDKOF0eWDqCph7imOjQsGQVfDXMf9PHPdnsOagjiTQWCUWidzfZYbgf1gKFPaoXZzklXp5KSTTtKFF16oQw45ZFlMZ9EEcOuFL8hdAZx60SfBPssHcFnMSdkG6RRAs09GxtRF49/btm0zJtXlsBENe7xcD3O5TW/RrRN42Qlg3gplVlLn+Yc/luffrUrlpkaalowWY95tEsPabKBKjGoXN/wAQ5JCbzXKoCFw4aSCCLPvfF9hPCmFM0YxjDz8Q+sKvIbSZwiXqor8dap4DTUQEhgEu+T5nuqaUqXaIJ9RNK5auI8hg3G8kMikAwNQNu1G3+uz4whg2baG1uPpZa7sGuH5ssmm+10jw0QoK9H18ccfr29961sD9xPO6z4dAcwLydHtxxHAFAHkhbV161YTBLIcvlaHOd5kAEOv6XHKrqzmRQDbJXVuvEZmFFR+Lc/fpThaLc/fJj/1FEbhKgV+g6yh9tX2xqqMrZaCuhTV5dV2zZG/MKzIC2fle/Ol4OrRGvkRgR8LE0FjBsb/j+hieVVTLcREBXtED+9tRA0He42AiE+gJYFmHLGnWrifwnit6jX8TcmxOt+SUcTdJBJOv1J7IRXL5XU8qgpgVp3cbuYkXXlkkD6l3Yyn3THpRNesTyKAr7322lKnBUveU+EE8BMvzF8BPP3j3KJTAJe6wJvnOwLYggASDVrm/H92/i0BHOR4uYZNip0VwNDNWizKVN3N2IwClkMwTTKpM75+6SoJnn+P/OA3c+StQfBWyfN3yG8qgSYfZTxuonyjyFcYrlXgb5fvN9S+cDpQ4JHWinM91Wu+qk2lEHNvPVprAkWSaiDH2kCQQDsVaVx+4JloYtMnJuHKrORVFMarJT+QH+9W5E2qUt0rCCXKoB80jocM1usbVK9tVBhiIp5/jWT5giUDA1rNxygSwKxI0m7XY5mPswSQNd5Ps2vEBo/QR9JE3Kt63M8YOp2TTnRtCeD111+/LCxD3F/hBPBTL86fAJ72MUcAOy3eHn7vCGDT/4ivddtQAKlYsVySnG7ZssWYJQZBWJei+iXX4bBN1T08A+bQpRLA9kmdyd1ym3x/i3x/cYqpJAmMwtUK/J2qhVMmjrfqQQ4b6h6l34KQLAWNVquvU4z6F1Tk+aHJEViJ7jF+gJh4jZmXgBL8A+Pt8pMRxqoo8jeo4jfIpEkNU6nL82PV4w0KxmYURZOK4jFVxhr+gVmtXl9tyGBY36A4xi9xvvWSUmZUCSBopPM89ro2y3Y8H3OYSPslgFlrxJJByF83HwyDxgR/VHw37TvVEcCeEOercPtWRwB7Aq2Igx0BzCCA+ABi4uxY5zKclYL8Mp33uwAGQVjzUP2S9zOqBDAdCb1os0et82+VCLiIJ1UJtmdOcxxNyvN3i7/DeEzyZhTEFVV8G/hBFHBNimcUehtMdY9qnMgXGI03qoOYRNCNFuKzB0FsBodg1vXimQXBJXWigH2qikQKtVZ+JZLnRw0/wLFpU1sYs7EfzJuYkzcwW9swpwxG4YTC+nqFdd7/CytEdDL/9WtW7PeZGcZ5WalEhnHdQV8DAkiQhK2Xntf10j6lw8pBmTV+m+bGEkA+8I477jhjAl4urXAF8KKX5q8AnvLvwO9MwDktQkcAMwggUcBEbBLg0K55d/5Y8f4Py2kq+u+ma8La5SXyUv3SX/pd+Vbec7W8+k7F+zxMqqzqcsRLP6wfH8V2SZ0J10D18/xGEmfTSOvikVx5oW+e/XW9vl7yGj54HDvm4evXjE6fnZQ8gkfI2zereDY0ZmLTLdVBQqKA56OGMQX7JjBk8bVCbVCkijEDm/Opf+5FCrzdijEPV3APDBvRwWN7FUVjijEBp9TLem1twy8x1TBdowrG4Rpj4k6aiDnURt3byHubRNiSijKYAJe+omRIEvcyagog8wa5zZsApt8XtjLNsINHsqKcN2/erFNPPVVXXXVVHktjKH04AjgUmJf1RRwBbE6f/Vrnv+TV46Xd6cUd/OzDCh/8Epkds8DWLWHtNMS8Vb8sAtjSVD29WcHPPiLv1v+VxsckUmdMrFY8dajC+z1HGluYjqTTvfT6+14IIDhhAmODz/KJjAmo8O8whlhUvQWthQoYRZ4iInebhC+IA1X8LYpialWv0Xh1byN4A3P1zIQqJldpo9Vqk6pqm0n5Emm1wnhcXkQEcF1eTO5A/h0r1BrF3rhJFE3cb+jva0zAkBRUvjiYaPw7GpcCCsftUugTDDKtMJyUF9SIGzHN+CeaIJbFBBCzsGy5uRhyukZRfY2UEUWcJoN8dKG8jwIJHGUCaLMA9Pqc9XN8VoDRICuPZLkj3HTTTaISyOWXX97PLRRyTuEE8OKX5a8APo8CHk4BzGtBOQLYRJIXmnHAl0xlDTahTvm7gh+/V+G9nyDtSwWF4hoEkLF2IqztRjgI1S99vUxfRcqN/eoz8m/4Fo5486f4vrR+rbzpvYr3OVD1o14pjW8aGMjdBqm0S+ocK1bs3aPY22pEL49gjqaP3dzAjQoYm1JsyUYwRdxU9BrqXyNYg7JucW2PxscaUcERUb+1ZnLoWJqprVVg+ouMAhhGVAfZsiAIpJEIGiXOb1QJaZJM+sMsHEDsvHqjQkjAmGdNtK9XrSiKUAdDVUxuwDXyg70mIGS2NiW/WZ84PSm1+lp5qfvjmDokMMIsTQL+hVHE1q8Ms59VfHgGB1F1ZGCLKNVxVi65YV17kNfhGRgmAbT3MqzgkSx3hGuuuUbnnXeeLr300kFCm2vfjgDmCudIduYIYAYBxP+DjadTDU8IoMbXN1TAAlu3imXWEAep+qWvl+Wr6F/3cfm/vEKqViQSGgcTig03iOXt2SOtXS/t2S7vwHur9sCXSJMHDgTpTj6K4MSGjvIH2U4ndY4wy3r3SM30LWaQhuzNLM71l1IB0+qfTxUQVRQTpRt7qoqcf43bDmcq8uMdhrhBxLzaZpPGxZBDUzoOn7/56iCNusBUDGlUNkLXw4eQKF8bBUyEsAJSz+xZQAKNH+A4H0aBZsN1CrxZc74XhIriIDOHYRQFCuPK3HjtZDG2yMqHJkn1eIMIRtQrRnWMTKQ5gQXJqiPgvlzLi40qASxDiTsbYGSDR/hwyCvZdFaU8xVXXKGLL75YF1xgFKhl0QongJf8kdatzs9HfsfuWU0998Ng73wAc1qBjgBmEEAiwDBBYd5r1yCA3s7fqH7sm6UKG1kxrVvFMj06q/pxr4tLlOV/L4sI4M7fqPJfb8MprEFOMPuGFWnndsn35W3Ex6wijVfkzUxL66dUO/xF0upDcx9cOwLYLqkzql+onQq9rfJVlZ8kgFCbePHPyNfse758v+HDl1T/iLwN/Nk5cy9ud6SBMQSvPkFxD/kBfoSzqk9XVNG2BnYR/VQVxI2oXtMv6V0y8gGavjSmyJtqKJUed+GZ9C/4AZofBmONa+AHOE5E8LhCjzJ1vgj2CKrZpTKNyucvDhgJw1WKM8zF5Dw0jL8+rj27alqzhgpOjdZvSpncF0efHfKxwIdkJ1/iPrsv7LQyEMDkzecdPJIV5HLZZZfpyiuv1Hve857CcO/1woUTwM+8In8C+OwPAoMjgL0uhhbHOwLYBMZmque/KBG8VDo5OUMA/e03qXb4KdJBv5fTlPTeTbeKZXJjtT5sKFkoWsPwuVoQrBLHqnzvXOme2xRXq9LEKmnvNAVppVmUr2ZVi/32k793q7RhCtYkjVVVe9AfSquo2Z1fy0qo3Smpc6y6at7mOdOtF1cULPL5G1eQNgMbdjOhSrDDELoIM28cqh5vVIXo36CZ5y8OVI23GDWtHq2TL8q3NQI96rUxVcItcwDgd+dHmxV56yR/vEEUo2nFXtPUCkOMMbHjXxcaFRHPP5I/qzKmwKOMnBT6+xg/P+oRe96MCTQJ/U0mIhgTcGzyEWKmJZfgQjM2g6nVyRm4mADWUftSPzcpD03Ec2OuIwJZ4lUSVVBSUcTpTX4pVUfyWzXte8qqJjGsaw/yOmWucZyONmed9OpKkBXkctFFF+mWW27Rm9/85kFCm2vfjgDmCudIduYIYAYB5MXNV2CnPFeWAIbrD1P0sLMLWyDdKpaGONTrgjAOS/VLgpIkgN7NX1Xwk0ulqU2Kw5q0uaFkGW5UCaTJ1U0/Ot8EJHizu+Xtu59RBuPVG1Q//CzJr+aGeZoAdkrqHGq3+BP7CSUslgKvbgjW/M3AW1M/a/7SV6A4riqMfdWiSaMlTla2zSWKRv3zvJ2ark+pGpATsJED0NQCnsUYy4eKpxp5+4Tpd08jnQtBIDUI3vw4uI5JAB3vUeivb5SOa46TnqJgShV/5xwJrFR2K4onFAVrpWjGmOcrlVnVwvXygnojPUwcLIhoDokWTiSFthgQyEJfRBonW4zJ2tq2E7+IQ0ghrocT8qJxEd+cbJ1SyuS2KJbY0SgTwOVQ4zjtSoCJGDKIubjdB2+Wj+OHPvQhsycQCLJcWuEE8HNn5q8APuv9wO8UwJwWoSOAGQQQ3x0UQZJBt2uWAJoKDI/+G2mCElnDbxBAWjvFMhm5OkzVL4nGXLRyvFeV//pbeWtXKULxu6uZ6w7iN7FG2rptziysffdtRLFijp/dK2/DJqNkRYc8TOH+z8oN7CQBtIEebBa4ASRLAkLSatqqyNsrFD+lgh2I3vWalTns4LwYc2qj5u4CAhSPmyobYQzTk8a8SJWgSYQhV3FozK6KZ1WJKffWIJtEAVPtIwxQ62oGFxQ8mjHR1mPj42dbqNXyotqcH6A5TlWF3npjMiYAxLjl+fs2zM2er8jfqKCyxwSD+GP4/EFQq80k1RvkV6jt2kgfY+sZtwr+II1MlPGmod5xnJkYuyrZn8dcAn/BcYGjl1F1JF1Rwm7yuS2OPjsaVQK43CqcJINHeK9D/pJl6NLTm2XiJgDkwAMP1BlnnNHnahj+aY4ADh/z5XZFRwCbM8YmwhcejRccf9ata596xBJAsynf90TFhz65kPnvZLJORq5CEouqcGKjlVf98gL5225UPDOjeNaXdu+SptYr3jXd8PlD8EEsqtcV79kjf2q9NLtH3ob10vikvImKVAkU3u9kRauPygVzSwAhD+Bl07skOw81rRrpV6ySBWsiN1/ThMmxkBQqeSxkehk/M0EbqzUbN1XMWJqsNFQ5gkI8oYCi1EL4pMmq9fXzFNYn5FcafoAze6saV9IUPGnIYYPkeQrjKQWYkRNqoB0bkcGRt1qxRyWRpsldFQXmujVTKSSo7G0khR6fNRHGJJf2sN2i0AV14w8on0AXT7U6Ze0WVzpp7f83OZcuxo6pYfn306kDpYjkhJE8iGDEGBamXuql6kguC6aLTtL1ZLs4ZVkcspwTXHcTPJJl4j733HNNLeBTTjllWcyReRcNw68nG41GJZDPv1LrVi+sDrQU8HbsntHUM/+NLpwCuBQgE+c6AphBAHkB8PVOzrp2LUkAo8n9FT7q9Tx1OU1N9920MlmXQfVL3gXRypPhXZr8yfmGRMTBuOItO+RtWqtox7S0rUGcDK9at1bxdA3bobypNUYR9MJpefsdZIiHJiel1WtVu/dLpEp7pbYbJPnqJ5gGcozpf6HqF6mmHYqIrk2UUzMvWdKZNPPzNQbvq5I0CzdviATQyfQrEB1T7q1JHq36B8nC126sco9ZSph7vfq0qpUZY0YNwwlVKw0ySC4+Te8yyaVptXCNyf8nH5/OWHEcya83zcYaV+yNGd8+VEKigqkLbFvdqH+ogQSPbJAXEPDhmYjfwJ+WqRhi/ADXmpiNOApUjyaNzyImZOYsThDhJOZZ/n9m/IY8LiSM9Ks5MproBQKYDCKJKk0yiIl4YUoZ6y9o/Xr78QPrZs10OiZdT7bT8cvl96OS37CVXymmY36XTAOG6ffkk0/WCSecsFymyRHAZTNTxQ20b7YS26R5xY091yvbhLRmI63VhFl1w4YNXRNADpx9+Dny1h6c67i66SzLZF0W1S85fgjWulsvUeXWnyr2K4probzAM0Qjvn2LIc8xpHvvjLS9kffOtNWT8laPyVs9CeuRt2F1I8/d2jWKNxyk+gEv7Jt4J0kyl0L1TSqkpHeZ1VbFppQbRyxU/HwSKNv8fc3hQoe8NFFM5QSkQsYMARCmy1iTlZ0m0KMWeRoPZudVxKiiqu5RPd6kGBOxyePX8KWrT4+pEm829X7DYKMq4T1zlT/I/RfXpuVrPlCDur7GjBtvV6yxpi8gKWYa/ZH02TdqXmgIX7W6p9G3h4/hTkWVNcYPcDaEIIaKwnHNkhaGtDVRxaiF6dbw/xufC/SwvzdzjoqXegPFmJUzVEQZYphSFyMiqWebKWVYGxMLTMRcK+kHxv+TqUIGLZCMMgHkA2mUopvTfqW2govNQ3nmmWfq7LPP1mMe85huXsmlOKZwBfCLf5K/AnjSv4CtUwBzWmGOADaBTBJAyBNkZWqqvU9fUgE0G/K9Hqf4gX+Q09R03w0EENUS8tIpX133veZ/5M7t2zV1zdvlze5VTNTvTqJdPUXbpo1JV2s3KL7ltsaFSQSN39+qccVhJI+yWuO+vNVr5K0dlzc53qgWMlZVuN+jFa3rPQo7ndQZhRK/T0gCiVHq2qW6diwgKR7yV0LxI80LJtBk8zNTv+A/N09qZ+vz6h/GTEqzUYEXkjlZgZTFRv3z40i1cEzVsZo80rx4DfNuWK/Kr21TTRtUGQ8VYz1Xo+wc6mJYH2tW/GiMjDq/XrRnQY5AQ5A0rshfpyAi2jhW5K0hkkW+N6vQ20eV6m6TjNqrkBSaMnFUA4lUiyCBuE2sUh3ibnz1KgpSyaEhiVGGokegR9zi54vKy1nrdOpt5UXkJExgHyNfTpooYpJwJ1s3pr+8VzwfkSRnL8rlIu/7sf2Nanobe3+8T60K+KpXvcrMIYFzb3zjG3X00cUm/e9lTgsngJednT8BPOF8RwB7WQQdjnUEMIMAdlsWLE0Ao+pahce+yTjRD7NZn0X81pIBIWXbeGq3XqFV119CtEoj0KOZj86wh6iuaOssBVQVB7481L3ZaO4475AD5BEsEdbl7Tslspt4qyak1WsMEazdi5J83dUOTpLkZECMzVPoV+KG6qdmOpPEUwK5MwmabYv9ReXQIIVJsmcPNaXhSKYcTWqmSVCiaMJEDhuFUdK4FyoImnn/qOrhkROwrjjyVInxzWuae2dXKwhmjWJXnyUlzHxpOHLxBdHdiknX4q0TaiDJoE1d4LgRDIJPoIn+9ahAgooXKvaJ1IVt+fJUM/dgysVVIMIQzZrCaNKYokkaXRc+f5EI/oh8zGaQwEBBgpS1Mv/G4WRmXsBMBTBL/WOUEL50yhmSb/tgXGkkmuZPhr+gDRzhWe89pUysIL5L1fi38qOtpjJLw2l1PgbaRGqrqnpwgMLqAxQFg6tiM8x3Ddca1eCWJAFE4cQf+Pvf/74++clPmgogGzdu1Itf/GK98IUv1BFHHNEV7JBGIoj5uDzmmGP0vve9Tw9+8IMzz3384x9vrgfh5B2FCvmOd7xDr3zlK7u6VvogRwD7gm1FneQIYHO6ranIkJIoEilLUADbmYnSBJBza0efIe3zoKEuIgigDQTJqlIx1MG0uZj/0/Pkb79NHsrdli2KqpMNU+6uXYr8CcV3bpX2Wa946x5ThiweWyUvmlW8d68xC3v32lfeeMWQRZJEYz7W2il5k1VFGx+gcN3TOt5qu6TOEMBV6wNFKHVNfzaCDSw5o/NFBNBomCZnSYIUeiYqN+0OakvD1cINqkcEaKw3pCwImsqgUf8aaWBIDk2lDt/6E9YrzdrAgerhBo0Fd5vrQTTC6VgVk8CZNIqkbeEbJJSvXQ3Vrr7N5Pyb46yxb/z8GGAQc73G2Bv1gNeYZNAmSri6j4m45v5IC2P9AEkFE5OOxyhwmJg91fANNCSQXH4IuM26xSR6zkwA3WMASEYfvrnRhallMBUnS9SZoBJSyZCI2pSgy04pg9sHm27rVCHQuV+rGt0iL97dWLd0Xp9tZOGeBxfnxoU/M/6RFYWV/TRbfYhJubOc26gTwCyF87jjjtPrXvc6ffazn9UXvvAFowRSHaRdudB3vvOdOv/88/XlL39Zhx12mAgk+djHPqZf/vKXmUUGnvCEJ+hxj3ucOS6PVjgB/Mqf5q8APu2fgcaZgPNYII2U//21UfMBTBLArKTAWShlEsADj5OOeHZ/oPZxls3rx/jT/mt9dDe4U2q7VP3u3yie2k/ePXeYCN8omJS3e4fi6riiO3bIO2g/xbv2KL6tGcXqefI2UA0kUDxTM76AgXGbi6SptfJXjUur18ubDKSxMdUPeIbiSrYPZuekzqH21O+Ujz02Eczgx6QwmfejS5uAGQ3F2pRUBZuVbkmkvKDFFROwsbe+QbV4UpFiTZI4umlSJhCENCwEh8BbJio2759UiSnTRjUOav3uVaVpTp6dHtMYfoCYcoMNjdJwsilhAsX1yOT+s80kfiZJdNwIAGmcRzqYRsLpRk7AjQo8gl5WyR8LFHtVkwYm8LYrrEypUpnRbLTR+AGSgBpShRJZi8ZMAIcJMoEUEdGMiTzt55dKAD03trovb2Fwb+NX6QAQfmaUvsUJp73IX2gWbprEq00/zShcq6i+TvhgJgfWKlVIteppsvIrVf07pdndC4kdWQPCRDJsk9mazN4pX0UjquInierqKRy7t6bHHqPGYlt+bVR9G+1MpAkua4MI4GuvvdaoxYgD3/nOd/TfovtGAAAgAElEQVTMZz6z7eTd737306tf/WrjO0jj45NUMu9617t0+umnLzoXAgjRzCvZtCOAy+/ZGvaIHQFsIp4kgPxoy5YtJgqYB75VyyKA02MPlHfMy+Sjcg2wJc2YOGPjA4iJoqwt+M0X5d9+lWJUvztuVbx+g3TXPY3o0agqr+oprvuKbrmrURd4ik06aKSIISAEdjK1Qf6+q+XVa/I2rMFeKm/VGnlrVjX8Adftp9raP2hstonWOanzHpPbz6hJUXVBbjrMufGCKF/DPhakfoEkxs2ybvaymX6ABArVpzRNUmRTk4O8f83SbUQdBtOaDSdNzrwJf2YuEIQ0MBWicol+jig41yTIkadoekZRQODKtOq1qirhXXN3HtZXzaWE4YekcwnCrXOBIgsw8lYroh5wjB+hpzDYVxUIn7dOQTU0EcQmKXRYN/m3jelX683fs/V1pqYIRA2dDOKHiRkTbRwsTgvTKgF0WAvkV63DX3ICFweALPL/ax7uxdGiVDReSBLrRFCRWXMVQwQhhHFzPuwVG/6CezRZ+aXGAxRSKQ7rjXw8thHdUm8QbbMiMNk1XRkaOXIaORmN8mpkyIX3hSI4O/E7qlWPLOsj23Jco04A0/dnCeD111/fdcUkTL4EEWLSfdSjHjWH5VOf+lSjHv7jP/7jInwhgD//+c+NBWr//fc3UcdveMMbOlakajVRhRPAy/88fwXw900pPqcA5vTWcAQw8QKHRNm2qG5tBuBZBDDc53DtnP1drT7m0TlN0eJu+JLEKZkXk61WwgunzASw8qO3yatGindPy5veo7gyJm3eonj1Wmn7TsVhqGg3aUdio/RFN94xf+NjVWnTeskfM1Up/NUVE/zhT602JmGPfI3VwJDBcOqhCsePmTuXOcUvslVS57q2KWyWVzMnpQigyXmSNO8aMzCq2Lz6lJUQOtMPMB7TjjokvfHYTWLebaqEPoSEPlE9jSnYpoGh7FtV1fHm9WqRKs3ScrXa2kY6l2C2kRJmZuccuauH6xSE95hcfrFfNcJUpX6PIg8/P5JLY8Q0dehM7kVM0V60u6EiQl5MCTlfgbdFkb/JRPjiL+hXQ5O8OqhMN6qY+ARbeJqtYwImcGVMdaKCMRzHvgJ/ryGJydYqAXS9VlFQTZl0WwaAoPSlStFFXmZ5usDUT14YoYxPZTXATI+5fZXqtY2q14n69zQxfpMq4W1zuRNNwoPa3rnyhIbsGdNvAzscUuMwlhcuvAYR1H7EHJPX0DMR7GkiGFbWa3r8WMVBeT/e0m+gUQ1usfeZvr9+COBvf/tbHXLIIbruuut0+OGHz0F46qmnGkvNBz7wgUUv9quuusr4FkIcf/azn+klL3mJOfdTn/pUX3uJI4B9wbaiTnIEsAUBXFC3tsWSyCKA0b5HaMfmKa0+/vm5L6RWEb42aKWTz2LuA+q2w923qPp/H20cfcedijdtku64WzGb554ZCsgaf0BIiab3KNo2I+2eVrzPlLyxquLN26Vdjc3Vv99B8iqeiSTWQfvLn2wEjGjNKnnjE9LkGs2ue6apccuXPL5dWUmdI1ES7S5TizZKJkmmPFmKsDQSE87f7OJAkMUJoTkn8Kg6ME9oZuubNN1M/UKqmDF/eyNiN97QMOnOBYLUVAn4XUVhtFaVoBndGwWqRJvNcOrxPs06wY3gkfrMmAn8MP/WRuM7iQ+gqfdL8ubaPDnkGFMazltlghhQ+4Jo3heQesBULjFJqP0Dm/kLfVXGCPzYpMr4tPBjbLRYlO2Noopmo8mG8hWOq24DociLiD9kIoVLRKRuhk9fWA9EAM6C1jIAhMXQuPe5FnL+vCrXuE9K6EH0FvaLgrhYFWwGcVA1pT5fSSWu1xaYekOinU2AULNPclrWplOWbk9xOLvIxwYT+7xvaGNtoAZOTz5OYXBgt09UocdBkPB9a2cdKXSAS7x4+v74iMQ0iwm429aPApju+9vf/rae8pSnmIwUBIb02gongF9/Vf4K4JPfBQxOAex1MbQ43hHABDA2wz0/mitbNtbM1ZYBYCsCuOvWaU0+9c9zmqJGN8ngBap5oGjZ1m3QSq4D6qGz4JaLFWy7wagkmt6rGLP6bXcq3udA6be/VQzGOH9txx9wUtFtW+TdZ19FN29u5AScGGuYfCtVxTt2yT8QYhjIm1wlb10gb6Iqbw0m4fGGCrjmYG2eOcYkc85K6hx5mxV5lDxD8ZtUmAiQaChjJo3+3B2mzcDZgSAoQQsVKdIt2/JtiqvaUScS1Kp/KGihavEaEZIxbwqWVlWo7DGm6XCVJgLq+zZIjVcnHGOH6h4Erz5nCg7rFXm1LYq9VVJlteLZaRPc0SBAmNYpKzef9JkScCYlTDyvWEUe6VqoEbzZmLlCf2MjAIXUMBVKzjX8BSv+duMj6Ffw+dtofP4o9YYiGIaTqhnF1DNJnkOLYeypQjRzk7BlJoDGfY7o4/QbKdP/L24kv16UFmax/1+2+TdQNWhUWaGhnnqx14hexvw7HSloqsLGrY8qNBxYbaidmpknh5mBIIY5p9RJM4EEzoTNSiqe8W1tmIfNLzUz+UjVKvfv4ckq5lCsD0TPjyoB5P6SJSA3b96s0047zZhze2lZPoAHHXSQKCuX5QOY7hs/wyc/+ckmgrhdsEmrMTkC2MtsrcxjHQFMzHuSAPLQ8dXV7surFQGsbb1L9cNerrGDlv5F3yl4obHJx8JkjekgWcGiLEu6eu075dVnFO2elTcmxTsb5jR8/rR5s+LJVdLWHYrHJxVt3S1/w6SivZ7iW+9SvH6N/DWTjZQw0zOKd+6Rf5/95Vciae16eVNj8icrDRVwYkLx5Fr542PaPXmsKtWDF/jsxMKMiUkzYarDjGlMlolmIn/nlbt0IAjVPpJVPcz2jbLXxg9wtr5R083UL1DMMS/UTDM9SdIUbAJBvFlNQ6rkabzS9PcLCazA349kx7Pya3tNihja7F5ffmW8kbYlHFdQayiBNCp3WL/A0F/bCOyIpuVFpIJpEpvErYceCaeprbHdEEKTaBn2V11j7rnu76NAuyU+QHwMx2sMJyIVjElnI9/kBaSF9UlcFhvNkEBM3PXsBNBhIGH+T7csAkgC6LT518zBYv+/LPMvvolBsMcQP0zlYGwVQpNSp1lKj6HUaqtNmTxjyiY348xeE4U+1/ANDJPBKEYCnft1pEB+xO8xAacUS47ycSdo+goylxNHabb6u2V5dDPHkSZIpR5sj4PjXYoCmCSAN910k6gEcvnll/fUG35+RAF/6UtfEmTwrW99q4kCvuGGGxZFAd911136yU9+YpRGrv2LX/xCL33pS3XooYfq4osv7um69uCiCeC2b5yTuwK44Un/xO05BbCvFbH4JEcAE5gg9dsCJ8juqGztvrwyCeB+RygI79a22nFadczxS5qmdqpfsmNLADsFrSxpMP2ePH2bxn75EYNrhIIVz0p33KN43ynpZgjeWumOLQ3VbWK1CfowUcE33S3vPvspumun4s075G3cIG/dhOIdu6WZmvwHHChv7x75995P/oTfUAgnxhWvmpK/elLxqo2qVZ9iNl6SOsu7R/LvVhyvUtg0tTaIiUyN22TkL+lajD9esy0KBGmelzTvZiWExn+w4u/RAvXPmEQhTg2SEJB8BZ8+nsQYs7BUMxG0niY88vw1ghei+qT8CiQkUjTbqA5Cm52dVOBTOWTGpIQxleDUOIfKIsb5zw/ka48JtvFrd84JZyZPoFabesD0S4UQ8v9hpgyr+xryjBna1zbJJwm038gVyPyEYwpMpZDVConSjsc0a2r0YtIeb5i+vbrIc0hQSwPrRooYL1gcvRuHVSkjYMQwyJRJPjMAJMP/L8v8S/BHxafqynrjcVCJ66pWGnkXG/g11D8COEzamxhy2KyUEo7Ln23g3jxBqs0061c3Sux5NdTBeSJryulB/Mw84AuICshyS5BBcA4oaddIH1Mfu6+mx8tZcSKLIPX7aijjefb+sLLYFGBXX3213v3ud+uSSy7pechvetOb9P73v9+YcR/+8IfP5QH8zW9+oyOPPFJf+cpXdOyxx+qWW27R8573PEMOsegccMABes5znrOsg0C2ffO1Wremd9N1K5B37JrRhie+k187AtjzSsw+wRHABC5JAshXLiYOTB2tWjsCuH3HYZp87Gl9TVM3ql+6426ilvsazBJP8u/6uip3XGVUDs1Om7QkComM9BTfdpe0aUrxLXcq3riPdPsdxjwc7ajJ33eT4ukZhTfeLq2ZkKY2NhJBE2Dps3NX5W8YN5Zjfx8igcelNRvkr8YPEFNooHDtw1TXoVJwq+Q1VT829qaZcm4fD1cpSgR6pAlgdiAIKmEyBUgjF2DahBkoVC1cp73koosxkfqqBvMmxEmqiDA2iALRqM3fQaQmgkZqlumZSU2MN8ifaTMzCry9qsfrFdVnNRY0zLv12ioF9TsbqUaC/RTU706UhhuTN0v+ukRpOG+1YGd+RGk4X2Fl3wYJ9JpJoiukg9kpTMZEAeNPaEy9wSZVJmqq1dYrqOxWCNGshCLquEZAROyJBNCmNrDJjEL6mka9YFqV3If+rgX50qOwatLKLGi9BIBk+P8pDFRNRP+aj7uImsgk7ybvn6cJH9/HxoXqs+OqRPc0aiGPBYpmqc/S8L80v6faStOUTnS08BU0KmpzoBlpYRoEcDHhhZQ3WkL1DFBVMaH7qgf7a7p67P9n703ALMnO6sBz742It+WeWVlbr+pNSyO1VkBYy4DnAzSyhx0zGAPGRhgENgINizGMsDFtjxEMi5ixxjAY9DFikc18SB5kDchiERItCSR1t7rVW3XXmrXk9rZY7r3znf9G5It8mVmVXZVZ1Q159bWq6i3xIm7Eu+/E+f9zzjV++/b+7dsBpL3/lBu3RYIv9g7XASD9/sjC/eqv/uqN27Gr+OQbzgAeAMCrOGvX9y0HALA231V4PB9iGYB3gKTjdxo7lYDZjL++1EbyZd+/a9uA6jN2y/qN79NuVMvX99IKnxY9+R+g18+En7mcps4ZQCZpNYVqGLiVfijtFoqTHlTBwwzeatgT56CPzQkrY0+chz4yB9VO4JaWodtt6NsWYBINtdCBnoihpucon4VqTwIE7o1JFMnNsASMtWEdwUntR3msD5BsXh0QbvxObxKCbM0AVjQZrrOL5B9dA71iDjm3CQUCvspXkICqqQneNHLXQUKlaqkKTqQvcFVMn3NboN0IdjGuiBC7C8gp9DAOJqvUwgouzeB1Ah0nUpWsSr/BnSQWoUc1CrUAUzA7eDPoojBEzKAlCcXAlSCwMIvQmmYvDcTcL7MAE9MPcF4EEU5FAuDyfAq29ALMRRQi5oKwjqzqqNROJjCh0KQUh1BEMq4W3jb/V1TYdJrevN/b+f+RODSl+pcA07o2TDTq/TPOb2L/bKqh4mZQPFsDlREMl+Awb8EUS2KLo9inWthNljvsg9RFYBLD9VKBwq1lX0leqUAhAR+Zwer19AYUZbFCHt2GNBop2m/E93f8M7cDSM+F/dqrfeD6Sx/Ayl2B2/3ABz4g/X9kAZ9P44YDwA//0N4zgG/8NzwFBwzgHl2IBwCwNpF1AFgla/BOcKexPQN4j9hvZH2L9Ni3onHLbbs6VVfD+tU3vBvV8q52ZC9f5Bzih/6tWLSguy4sEw2gpf9vaQ2YacE/cQ44tgD/5Fn4iQ6w0ofPCgE6eq4Fl1oUnzsNTHagFugN6KEJ6BKaFDvodgQ93YSeakHPTwDtluQFo9ESlhCTC8jU0U1pHjQB3lwGTmBZD9z48S5DvS4jBJGS75jR83aG0FT+9uxEhQrQNEzZCOCgoXJolWPoWmBMXCMqARptYMyalHAJUxtYRRSVgDV1ArZMksMNFSJQMBLYP0n20LQHMkDKcm5gDNkHSPDCBApP2xbOoV2FV00BeexZUz4NqSsSBxdJMotGKuIRF82LMbSNDsv2Cx2YQjQ6YoFSaKZskL0iA0pfwMoSJkLOvscSBDKVxNZjEgkCy7ST3QtA2HpYbBWAjPX/VeVfeknmbhpOsdQ+2DCP5vNNvRZK6o6X5TTimMrncs6G0Qb7x5ItrXhUlAgjGuaXCusA7mj3wpsbzpdgP8Tl3AfbF/KImq+l5Q5Z7HGjaL4qaoRSMeeKoJC+j0ohi1+M3FzfZKHLLQF/EwAgs4Dr6/573/teKdHulUHzXi6xl9vWAQC8XjP9/P2cAwC4AwDkXSDvBut3guOn+XIAkK9dc69F874vveLVcbWsX33Du1EtX3FH9vgFuvcIosd/G35iEli+CE8BQ9oTQYf4zKVD+NU+0EjgT10AJifhz1yCm56CtEN1uyjOdGGOzKCwBsWjz0DPTEAfmoZfWoGebCG+fQEq1jA3zUNPxlCTMyEjmMwtQWCjBRstoNC1Ur43watuQ6UaGKpNqlIxgB6xTFclBPEJlrMjG8rfJjNqN0CjR1NZDAkmoNCmgIT9gmRNy/NAeMFnNxJBCLK8hY5yyQbWaRkbx947OOhSLZynoZQp4E8sYQpoBBsY6yehc5aGR6VHiYBDR0Cg12ZU5tQzQBTBg8bV7I1jf+aMMHX0CySQMY1MbGyIgZgKQtEDRSxkPCtLmJxHtAMIFH9CeERU5ZIWrI/dCkC26/+z7MljigubDtlG6JHEpek2WUQHJNEKCttG7jtI1DqMCWrrOvvHHkiW2iOQaS3ZwLS5YbnD11vJG07hDX0ZU7HEiYozY9+mmjJYaTgytT7fBAbp16iEDWSrgwmgUHkMk1ej0Lfs8bfz6jZXAcDLrYtXt+XnxruYrMRWoHrl593vfrf05VEI8nwaNxwAfuSH954BfP39PAUHDOAeXYgHALA2kfzyE4xx8C6QjODk5OSOU70dAPSL90Db8OPb7d6G5LV/f8f3XyvrNw4AKVi5Gr+oPbqWtmwmOvt7UCufpyEesLYqrIaAvpRMkYVd6kHNNOFOrQBT09IL6FoNoJfC94eieCXIc8Mc6UOnYI7Pw7L3r5EIY4ZGDN00iPjn0Wno2RlJA1ESEUeTaANETbGGydXMprIvy4H1jF9/hT7A3QhB2CvI3rJq9PPDSCkwkcGkj5L9Y1QaaPwc+vEI3lqGtjRkiKgI1Rv71lQpjFkXUAFP77VQZrSpQewvIOd+6xYaCMpfqoBVRuVwJMydylegfeg5dFQqF0NRE1fD0lLGrm48Jv2DZPoc+w8drJ4NilsKV9hX6LvQcQSrJ2AaZBfbiOMuMjsPFRXI8mmxf6FQQ5TUVP26Jooq443laNcQAK5ZYvYaTsChQ+SHSOKRXcyuBSBl/19IYmuiYP4xP7vyCRRxzWb2L1HdELmnczGsbor1ThhFyf4VajaktaXrG4prlpKRMU2FPK6BlNLdmvQChjlubPFcZE+hln7MsUE8SSuYDZPo8vmkKWVhjygoj73HIHkdnD60X1/VXW+X6+M4Q7brNz8PXsg1v/IOrXaXti2McPvO7/zO58ERjHbxAAA+r07XDdnZAwBYm/Y6AKQlDP+ja/tO40oAsHchhnnDD0JvEydXZ/14t8k4t2sZu7GtuZbtX81748d+kfQVcOl8AH8sA4uLMa1ANLDWF5Bmn7oAdfQQ3CMngcNzEgfnZ2eh8hxutY90qY/ktgWkK0Pkj5+Fiwwatx8Ben3BTMm9d8O0O9BHJqDnZqE6BIBT4h/IPkCdWAEv0tdXXvHedWA39QG2YWv9cLR1cZvUwmQMN6dUaLGL2er3RuDEMvNKPvrBbir2yQ0FxA1dhE7EbN+wPVH7mi7rgCKeiEpVapUI4pxBamfQjslCcdusPvfE7FolwTzblBY0xTAwbzRUZhndFCFRRfoALZkr9vaRgZoRQMikCiaAVPnB1Xl2ekLKxSz1UvRBT0Cvg88gS56RWRelsIkzAVKirFW0hPFIi2niWPk8Aj3KPwhgGRNHgCk8GsU40jO5dQkyKJCoPjSj7zhHm3oveXC1/k2KiSz7Kw1y0pBKw3iKVUZWP+PsnysS6T2s+g9jn4ktjAA42tFkA7h4GlE0EIPtqDTYFnCYcs4uwklPZC4G0PWsZVtwjkdg0mIWWnotR9cOAR1Vv9oxTaR83JQq4up1MUGgEqZcxQ1hfPvR6+DVzjekV/Mdfbbv+ZsAAPk7UBf/veMd75As4G/8xm98ttN1Q19/wwHgn/wIpijg26Ox1h1i5m/9NLd2wADu0ZweAMAdACDLACwD01rl2QHAu6Ft8G4rcovB3N9D4/ZRFBAXf26bPYbbxZNd7XndjW3N1W77qt5nB0ge+Xl45hOfegaYmQcuLMHHMVSvB8eeKvry9V0gQNYyILewK73wmzgs4LsDFK0OTLMFm+cYfOZpJC+6GcMnLyK5dUF2SzVp+zKDZCqCOTqFaI6ZwG2gPQnVZmRcBz6eAJIWrG6NAJunFrXe9zfeB0gGa6wkyT7B2mOSAVyWXas5IrPnkWE9OwZaNYfBcm+G1MVgSm6iio1yI59rSx6wQb+YQJuihdKnsKEsjO6hl00g0Q6NOFxXNo3ERkQlHjYzSHzF/rWkP41RcZxDR7scBCBU+FkpCCtFU+g1UAgTZac3Ti2NoL2agBO1L8umAap5RXuSHmjgqDWj4oL4g6XlSNG7cUqSLFhGpaUNs3+9byClAIRsomW/ZRCCSEycRMyFQT8+ejCy123ToMaDYKy0xjGcB86cZAyTeRM9MiwZV25H/AWrqSbbRy+cUfk+UWT/MliboKDYJqK4owReTqEZjZS+eTYBE9Nj0Uq/qR9mG6V1a5uiYlcRI/L6yIsOouLcxq5bNw1Tln4ZuZcxxUV0LyELWA6H/28dlB9CM5d5U+upkj7DjR5BMoHSExgBEa+1GF28EYqRiDdobFcivUG7si8fy7WZ5d66/RdLv1/1VV+Fr/zKr9yXz9yvjd5wAPhn/3zvAeBrf4rTdQAA9+iiOQCAtYnk3S0XOA6WAagEprnyTmN7BnAEAPm+Lr4QyUvpR8fSl5Nt8jPYZHytrF99v3ZjW7NH18yuNqNXH4BZfQBgn9PKihg0q2UygQYYUGWZQBXrsOeH0DMJ7OeZ/rEA+9ApqOPH4D7/NNzhOaiVPqxVGJ5ZRnLzAtJzXeRnVxDddQugaE6s4c6dROcLX4FooQNzKIGeJwvYKvsAG1DNjpQxh/oYlICFspdLVKolUKB1iYtHqu0ymaKOEcfLwNsKQcgc+hirzPyVmiRTK3xgu8rRocq0ZBsJBmmQ3LNtATeSAlICn5YZYmgJWoG2XoEmsOG1OYwRNwMLZnsZYr0ubB5LqxETTjYsYc4K4CjMUUTFqY2+P8v0kXRpUx+g1TNQNoV263B6Uhgq40PPXJEcY0ua9NQZfx4uPgIT9VGoRWEOdcMh93NSsWXOL5+zlmV3lq0VcjmGcqmxiVjFbAzBmnpDHFLh67omR17rPQxTScZWrNjnm6L7IumRHMXBkf2LTE8MnykEIQ8ZRaNUFPoAsv/QuhiZnUFTTKFLIUiN/aP62VnOb5mw4hRcNhJ+hPJ6Dh+xYmBFPGNzinRqkXI+EibU2HIbqgHL7GVeHSUY5PkKIDAISHzcDr2RJohQMjeNdfdqRFF0Q0zf/7oDQFZ9eJNeB4Bvectb8Na3vhWvfe1z05txpwX5AADu6qfqb/SLDgDgDgCQCx1ZNebrXgsA7A+Ow7zm2/aF9avv125sa67nlR6dei9U3BPxB1MjmP5BBgdra8ETcMgoNLoek6pScCsDwGi4i+tiWOxoCbNG30CLfHIOqrcGGzeRn12FuelmdP/kU1CHjyK++RZxzYjnZtGc1TA3z8AcmoamaXRnCpo9hUkLoHWHZjxZZ0Ns4RxNkEflxKJobhYiCMM3svHYKgTh12fM+4+Rb+k8UqeQliTTRExuLIxIWcSlNQnxYYMlUxGCQCxidOnpp50ZlaxtgYlGYP+Gwwk0m6XRc64R5+dpJQ0XzyBKzwathdNwTKwgAEsm4LNMIt44BKhkA2gfQJLkDZsZmPzsZg0M2bv4aHif8rDJUWEWC31YfAO53cBMNUNJOBkgdfMSDZcXU3KM7MVzVGx7jcyWbJag1jEQKHY5NK5mBZdl3K1XqqGR+JhIhGBvUx4we/0o5CgBHFlQWmAHIUgA4w3dG+USO42GuSS5xlQns3RMYYjMi7B/VGkPxBCa5dgoX9rYsSLvwNjA/jFhheym8Sz1ljcXdhqRDeX3MM9aTLGNHZWHq+esmQuG5TreEO9QjCNAkMbRLAPzhoGiEBOhZ2/FWnqr+JQSCPK/yrR4v7/j2/XI7fdnXs/tV2lQ9V5qxsDdf//9uPfee6/nrlzzZ5UAcIMbv+YN7n4DvAtaXfnzf7H3DOAX/UvuxQEDuPtzcdlXHgDA2vSQoeMCJ4u6tZIHTAC40+K6PQN4F3RtkR8sa/Re+k+E79lr1q9+ZgkAOS5nW7NH18yuNhOf+g9QTQ1/6iSwsAj/zDNQM9PA0pJEthEIOhtBFX3YVQPd8cifWoE+Mg37mVNQtx2BfegZuJsWUTx2BliYgWXLVB6h9+hJJHfdjeGJM1BT00iOH4ZfOomJl70QyS0ziI90oGcngIkpoBEBzSlJCyEwGComXPgARn1SGnmEQ7J5I8huy7GbRBBmyG6wiPSPzGaxnI36XiYitcn/rmOGobdQAAF70aoeQo8OwUe5X7ILZX9hbLtoNHpIM0az0buuZOZ6LogPmh24IWFgABd5TjaRZOtQwBnZvgBC6AdIu5E+fDQbgFKeQtu1wPqpUmXs2FsZDI6doQl0SBJxjUVoGkPHR6FsIf1/BC86ZtmXUXgOOX0EJRWkJSVeMnZUDTOBI3cENFuZQGEABfCSMaNtSlDuVkPAX6nmHZ0bMoIUp4xeF5F54zHbKIgxxLh61AtIgBfX2D8ePtlaJpMEMN6FLoUjjITTxQp8PBcYzT7j8SpwqOEzblfBJQvCcpusVgr2bTVZu8YAACAASURBVOicQpEA+2kGzcQU+i7Wh9XTAtJN2TLC5wqyq8IGWhH3sNws25EcYs5L8Ans4dVIiympJnDdqoAgQeF+gkGuj+M9crtaEJ4nL6LAhfNXB4BvfvOb8eu//uu45ZbnhhKbU8lzzthP/k4xqo7JImwr4r/Zt87M4XvuuYd9GCM6/PqdgwMAeP3m+po+6QAA1qavDgD5d3rrXSsAZDnhovlyTLz4VftastmNb+E1XSnP5s35RSSX/pP88GFtGWg04S9chCIbd+mS+PP5HkUPZAAz2G4MleSwp7tQky3YU2tAM4ZbGyA7vQxPW5fpQyhOnEaqJhEtzCO70EWxdA44fruUy5KJGNFkC62b22jddQjm8CzURAeq3QoRc3ECH7HHLULBPNwycaOQGLjwQ02Fp7CTGwDwykKQehnYuRZO90ctA/xydWrsH4PXGlEf3jYwcDE6UQZXQlCWgmMCQPbPFW002JcmbJ7HRHweWT6FXDXQiSohCMUfA/gGFa8e6K9I3xrzbalwjqt0kFSXwg92AC5Cu670AHLfCjeDKDu16cw6NSGsoNeR9LlRhMEePxcvQheX4JMQG6eSJizY47YKl8wJG0gwJeF2hjqfiaD8JfDzsQhBRGEsbCfnWEE8+kDT7fEsZnEnDAKQsh9xnBUcL/1yniKWiPlJJdu3qRdQ2L8+VAXwihChVwlBlFVoxLVewGFTAC4BoaUnZcrScJgqei56rRFpgoUChcxxCQ5FbJPAuFURvbh4Hr7w0I4pLDwepuDQy3ESyvYFdAvgE8fHMOoCHP6dqTbyfMJyMF/BvsAEXbxBPAe5VhGUVe0rBIPsL96PXHD2yBFkXC4h6dksFc+117LvmyC63p7zhje8AR/+8IcvKwi8XsfBG/0TJ05IhNzv/u7vgjF13Gf+zvDmn/8RpPP8nDp16s8BfALAfwPwJwDGvYn2a7cDAPz4T+w9A/iad3CfDxjAPTpzBwBwBwBY5euyB3CnhdR88hegV5/YdCr8oTuh3eiHhE/21SsRfcH+NhDvxrdwj66ZK27G9D4OM3wQfr1fmkB3A3PR5Y+kgl9ZkbKw6nZhaavhLHyf9ToPe7YHzE7DfvpJ4LYjyP7qKfhjt8M+9nm42++DWzoF25iFS1PYuIXuA59Cct8rka31MXVsCo2WxfTr7kR0ZE4MotGZgGpE8I1Z+ZM9aalqA559gWuol4EJWNkLtsGgiEBh7HB3EoJ4gzO9BXJQG2+YjFm4G/27pTPk7DVjJi6FITV2qsPIMq+kF7BthlA6MLrGplL+K0wbibJITOnvx/40WqYYh7yvkTjmHBtkfgYN5h6TCSwaUrZk/5qP56GH7AMMPY8WU9BDln1DyZKWI1bPwWR8rATEmgIaijvWICKRhGwgTasH8KaDKEmR45D8WzcVcj9fmtoEo73c0oCZKl2WfEtBDAHhhjhGKsYSzVcH3tUEKh+EE+G/SpATJCDiyUdmrXyGfZR14UfknWQnV4MsIplTgjOmrpA9jKIwx/yuN0qhCK8BRtwlcc0WZmAQ+fBvgl74QqL4OAoCyXzE/hV+RiILeTxUUDNr2aSjUrC8R83DZGdqmcxMXJkXdlQXLLlT/RsHA267JHNNECjoL6E3IAEgb1YS9PG3No6Rx0FwRgDAP7luEQjuZYl4O5HEFReE59ELuI5WALq6NqgAfuihhwQY3sjxyU9+Er/yK7+C973vfXjpS18KAtNXvvKVYlFDwEr2kr3gFy5cEOPq7/u+72N0yX2Svgg8xVATAL9Pi9p9Po4bCQCJEP8RAO4Dwe/3AHhwm+OlRQODhV8PgIpCLpr/N4D/hRHr+zw/z5nNHwDA2qmoFLrVQ1fK190OALqFF4SUhNoYDo9Av4bX5P6N3fgW7t+nb95yvPZ7UPkl+LV1qE4b/swZYH4OeOoEMDcrmb9oM/VjBVY1oYddFGv8TXPIn+lCH56FPXEBRaMDl8XIH3sceMEXIHvw08BNd8Jbj2KYI1vuQs0u4NIDn0Xrvvuw9vDjmDzUwNHX3obpL30xovnJUAZuN6EaoZyWa/7QeuQUQqg8+BHWTJELlkhrhJSkPtSUv0qsX0Z9gfw314uVdA7dYhR8zk204pB/SyAj1h/8YS9/9jssO5b9h+wQbJgh+jaYVbeZAVz2uzESLiv5sLZeEzsWqlkZy0ZlK4db70Mjg03moDN60oWqTz4g66ZhEohaOC571lgsRtoX7z/2/zkzCZWH9yhHdeooRURYu8Zx6W+j0bGKCU6oGI6BmCA6h2Mpk2C1kQsAZdlZSuy0RXZMC1FwtoWimtgxEMjpJXPpNvJxQ9ybaGfHFMKKwE7UyaNrTtP2RTKVy8fE96+mBCb4U0NY36qKsmgYpn4EUKmsRiO+BOuayF0bEYYiDJG5ZSTckDd0HjY+AtgCkR+VckM+8FqZv3y4zF8ObB6Picr2uk1MAH8j4F0dhUMTLpqGtj0xgK7WkCI+Gnot5Ux2xYqHJXen56Aij8zeJmKckX142CKvNzKCVboRwQvB4LWWiLcTSVyvdeV6fE7lzsC5qubxVa96FR555JF9La1f7ti4tv/mb/6mlKG/9Eu/FG9729suG1FabavsAWQT+6sBfAUAqlg+AuDfA3hsH+czAMAH3rH3DOCrfoK7vRMD+HYAbwVAtuVxAHzxPwBwN3mYseO9HQB9fd4L4EkALwDwnwD8fwDeto9z85za9AEArJ2OcQB4pXzd7QBgMX8bYoxUhvJD3PUoXv4DMK2dc4Wv9arYjW/htX7Grt7PH9+V34BnGO1gGJgLln077QD8JibgLxLgaLh+H8hcKB2mOZB7sS+xKynQmcXwk5+DfuHLkX3qAdjmLByVuusDWCKa9jTytR5jhKE6k1h/8jT04nGsPPh5ZMsreMn3fAmOfvm9SG4+AsV8YIoWWvS2C/1hhVIYFvNoxctiRVKVgcf7ANkneCUhSGGbODfc7BcpvX8eSJ3nIWI6pu/dqB+7Ew03IuH4U1MVAI0botkI4AOSTkJRgwJBTjs+D+dipMUk2o2SCRxqRPlF2MaCHELFRnGfaExtxBLGAMMVYf9YKs0xL2VjjR6UG8DlDRgyTxV+gpb0FA/2KBIkUZ0RQ/tV+KgDHbH375AALkO/QQJD+p2w1w8JCh3DSfwdGwAdcgJOMn2WQpwSXY+BQGnCsxRURCglD1s9AmkWzR7BsVWLcXJiOl2OuhJYPBMJ4nkc5Wv4fETGtRyxT0Ucw2uCPZ0NXSv3DiJRRvvGHDS9FgfMEg43ABX7J2bZcQyXO2HsqpHlHSS1f1saS+eBSZQiNwExe0GZvZxRlR22G0D3MUkbIf4lCKQAhznEkpuiDaK4D0tj7ihFoeYkyYW50dbxNZtL6lVrS71EXKmIn22/4HYiiV2tC8+TFxEAkk3j/Mi58F48AD/3uc/dMABI4Penf/qn+IVf+AUB8TsN7mu1z/zTGDrwbxrHAZAh450m/3x0n05LAICf/Mm9B4Cv+HHu8k4AkOW4dwL4xfK4SNmy7P39AN6zi2P9pwC+DcDLd/HavxYvOQCAtdM4DgCvlK+7fQn4BUElWd+ubmA492aYY/uX6flcAYDKnkK88kF4ywa2HILQuDAN+sHagh6ATPg4fwaecW2XaDQ8Bb12URIYlCmQ055ucRH5yfNiR+KjJgYf/zjUHfcie/pp+KhNYgW2PYNsWMAixpBsV5wgLzQWXn4bzr7nt9C+9w685me+Fmp6UqxgVEMLIB1gIQgcfBupbYWEjtJixRWx9HdV40pCECo7T3YJdkbviZmaUbq/yUKsgFYU2ECOli5E/CEaYqpHGRG38dyqsGo0kpayZ+kJmGCI2PQwKGaEzapizfI1C53EiBoFbJcdcCvCFbEPMCpFC3nWQlSclVJwrubQsCc3jq+gWjUd9QESmBDcCSDxOWy8AGXSwDBSEBJPS4lTR124+LAAvoildIohqFY1LHPPiDUL1bXB9JkK11CypThko5luDAQSnIaeSvYr8mWblycCty02MHzlJmPooAT27LPzbRQ+EuXvRnmY5V7GzlVKYaaIEIqbACCNtYhLI25hJTPNdjvpBSyGI1No7itZVbKgzEn2chNzcaQCZpk5oz+jFsNsEXukpzdAnkwLI/hoPM3jYoycXZcezWoQhCsqw5GiiI9Jv6UAb948IZhxI6bpdsSKsOBJ3iAUxTzyYkGOf9M6VJaIq35Blogr8chu+wX/ugNA9tjRAqYq97Lk/frXvx4PPrhdFfH6YAD2+n3t136tfBh/o3YL2ms2MKHpVm6xZHwNgK8D8BZgjK3Ym0O6EQCQn8keoy8G8LHaYfwBgM8A2E2O3/sBsF/jO/ZmGp77WzkAgGPnqFrg+PCV8nW3B4C3Q7vNLRZkTdLOK6FvZbvB/ozdGFfvzydv3qoZ/hlM75FgIFz04de7AKPZzpyT3j6ceAZu/gj0yafgWZ69eCkwgIMUDtNiIuxyhfxSD7rTwODjfwlzz8uQnnga2Znz0LfdjeHnHsWwiKAOH8dgaRk4dByFVVj8qq/ATd/wJlkgT/6zf4yn/vTzOPZ3XoG7vvvLoMpUEIoYqGrNPD0EhxgUbP/QpeiCDBmZO/6ahq8GAYkkiFSjxhaS0XpawN/mr1HbKGQ1D+nN7B8wUfrU9YsGGgIGA/+nvRVG0vs2+raJjghBQvm4Yy4hc7Ow/HuZCOIsAQoQNwu4QkMNSwNyfQhxEUyeWY7FYAXOTEHF/PvaRkkyt5OI09AXTuNiKnqVzWDyzR6BTrfhGxMhFSQ+DMR002tKNrBuNFFgRkqwqkml7zyM7iMXksEgdWF+OFc0dBZGsDa/FIkUjh6G9AIsY9HKsjl9/6Q8T3WwCCfGjbk9YrJym8rBfE2VMgIEtm/kxVdXAucF2wBGpty8T2mqkK/MvxcF00BoGl3ZwgQwxkGQa9ANJtT8d9ZAVLJ97CMs7IT0AcaqKwIOGmKbIhh2cwTxRgJTbO4XthHZvETSWSQGTsVyE8QexCI5Lj6EohTWPQijiBQ6UbBqBlFjFPHHz+BnEggSEPLz6qMqEVfpR7u1lGE5kmBxLz1Mr8e6tNvPYA8dBRQVALx48SJoA/PRj350t5vYl9dVqt/dbpznV2vpqdgcXxR8DrigvaLsdfvsbrf5LF4XAOCn/hWmJvcwCWR9iJmX/xh3YzsG8CYATwMgy/JIbV/Z18cf5Cvl+P0LAN9VlstHDvnP4qCfjy89AIBjZ41AqqLSLxevxtfFf/lLSHq85kbDL94md/KbHkumkekjUHd//b5dI7sxrt63D69tOO7+HlR6aQQABwMgiYCzS8DUJHD6DPzkLNSFc8JUMVkBF9dgW7cAKydg1c1Qw1NIlxT0/AyKlRTZpTXohUX0Pv4J+M4s3Owiug89hu7pZZi7X4LOS16IO3/8bTCt0WKTX1rCiX/4TVguJvGCt3wZDr/xRfDNCeiEJcQWhpgA2T3uQ992pNzaiC7AmFT61TZKimSveHybIhuCufEzvc4W8MfS77C25I6zf+zpi41Dz7LEpNBSPaiyUkPxB9dr9gI2dC7MGoe27FVU4qkn0Cu+EABKPoUkDurTom+kZGijRaDINvzkyP5x38XKpGhtsH3ON4EshYuDcbF3GUx/WaCTqH6jWQEfyoaeQOmBax1D5M+jaNwkpWWWIEl8UoxS0F5He5iGxdDPggbMiBxs0QkCEMbZSTpIKMkWvoqFoyCG1f+yJDp+kXrKU0JaTOgKDH/yb4nKy4wNE3z2oDb3AkrM3hj7p/vynoxxdcqiGRHwhQ9leEgSXRKz5sxPI1GrI1sYim4shTYK1ixA2R5MafJMIIc0eABStev8JCI7YlULP70pdYV9pc63YIrNnoCy7fgwtA0soI9iGL8ezLybt5ZKYaaQUHnN+SZzOC0MexQP4PQsTLzZ9UPaJvyMWPPYfAbWMkpua4m46hfk2ldXEY+zTX+dASCPnQwgozkrRvSJJ57A29/+dnzwgx+8Hsvntp+xE+vHxz/zmc+IIGS7cRkjaF7xY3dTe3p4NwIAXgsDSHNB9gp+2T73Ru7pJO/Fxg4A4GUA4HbxarwTY58IAdf8E/8RZo3iqto4dCtUrYwjC3ljXoxmi+PfDN0hI7L3YzfG1Xv/qWNb9CmStd+RUq9nokqRBYVv7qGyPmWpIQe4OwRiDZxZgp8+Bpx6CnbyBcDFJ+HMTXDZAD5XyJYHMC2D3meegD52syRkdf/sAbhb74ZrTqD75GmsPH4Gr/3Ef0VMdnFsXPqpt2HpEw+j35jBvT/xd9G+9TjQaKHQjC9j390ktEoxLKbFPmQ962AquogGm+xrucAgWKklhgzzCZzqN7eUYhK2dG3K1xj1/skdObW21AWQRSQ/5jI0kkpQ4kGV8IBgAgodev3R546VU+uho4AqW7orubVpNo/EEKBkwbS4z9L6DEySAz3aihTCZFrXQqRXQijJIBP2z6k2CACNPR+i1ajCFc3EiLkWFitehCnW4AkSDU2XCZhpcbIE1zwsZWxnZiUlRJlEmCoVsUzbRqGTYI5sBsjyaQGvVANnCEIZ5cjC1phWllSpuhbBw2iw95HAbjwzmExe8AYcLWHcuq9FwG1l/0IkW2AgeSRjSmAwq5qPNsXDsBGN+iJtnx2chfRA8vyYvCYEoWegvQAXHxP207NPsGQKpTScD6A4wQLvmwLUKLbZ0DgT3FoNY9c2nwMVwTYWJYuYpXnXOAStMlFrsx9T7JX0pJR/2dcqSTFxEAhVI7P0MRypoXmui3watpiFc1tLxJWlDNc3gr9xS5lxley+rynX8QMqAEgrlQr40mbl537u5/A7v/M713FPtv8ospNc5ycmJuS8UO379V//9fijP/qjTaVhnrvf/u3fxjd/8zezzPu7N2DHAwD8q399zQzgH3zkc/iDjwRCL8sKvOs3/pR/fTY9gGTzKOrYqQfwlwAwqovg75kbMFc39CMPAODY9NcZwPF4NT7HO0R++bhIkAEct4HBNgDQtRahphSGvVfCvIDM+94PLgxkLOeYvXuDhrGPwaz+ifiVoc++KPZ0Wdj1FJr9d0vngelp+CdPAIuH4Z86AT9zF3D+aXhawrSOQS2fRKaPI1p/CsPBrJRuXebR/dwTiG+5DflKF5c+9Qj0rXcKCER7Ci/+JQkI3zLSM09j6QffgrV+DHf0MF760/8TzNSExNExDi1TGmk+j4YZoO/a4gPYc21J5GBpMdLMgc1B3o3gZD1r4sIwEUASKyDR7GljmTJ8jVqGViij3YgU0DQEex6DwkkvYExRzIYSmOKFUF4Owgf+cNNw2KJZetJRTKF1KEMS77TjJWGvrE/QiYP9SD4w8lzUylEMIun3E9YI84h8ECUUzK1NT0FKjKqJaDhirm0xBVOWgqV/kJ5/+dpYP9qsqE5p+ZI3bhFAo3iA8ZQkWBCU0vOOmcEmWkfqF6WXkqwjn8sINhSzgVsoEECegEAKRKpBkOx1iJDjnHjC8K3gj0rgUB4dLV8sn7OXclQOZv5yf6P3j+dWaI8KIHqPlpR3KyUwmUiLQiv58Y/9AKbsv7R5IobZqqEEXAVT6NDnyx5OT7AUN8Q3kR6BJh/ZrTESTxdL4qNINS/BfJSNejDl3KhDiAYnS7/FQ8FYumYmXyRHYfzF4LXYmJGyL824xY4m6sFFi9BRLiIR9m2yH1SuCzsDHW0uC1dTzVxjXkOumIIjKyhq9vqp2N5ShuCCQoTLiRFu0PJzzR9b3dzXAeAf//EfC/ij/cqNHLT1ec973oNf/uVfxld/9VdLNjHNqr/1W79VPAq572XZV0DiX/zFX+BLvuRLqIhlOeo/Xud93zMAWN/vNZaAX/ajfGgnAMg+Px7z/wCAghDWi8ns3bONCpgLMeeF9CkB4Gavpus8YTfq4w4A4NjMV7YJfLiKV2NTcMX6sTzA/hf+SGzXA4hDt0j/Tn249jHoKYf0/HGol+yPH+Bukkv2+yKLso9Arz8OtGeB9QuhK91mcAMnFi9eysBT8KfPw0/fDKwuw6/14FuzUM88CDtzN3DmYRTJrfBpX5jD4aUMceLRX86Rrw2h5uYwOL+Olc89Axw6gtt/5Psx88Wv2vHQln/qe3DpoZNY62lMvPwOvOiffw1UHGOIOUFUhZ8QWxVCL4K8Xs54uEjUpquMqZNSo0NqRaS8ZfAL1DQBEI4320RKoW9pThzGNJXBpdUJmbdWycywcT/SZLoCGGybgfgAyg8zucmyFNxQjJ1LMaBwRaUbsWVpP0GjFUp/BNsG6yjMYajioog3KvbPxdPSO+aGXsyHOQo/B9M/Ddc8JHmzNC02vafh4ll404JyaUi1UE4se3xjUrbvWsfhNe1KluW9BJVkv1SrLbm7TCGhaTfNsdnjR1BrJRaOwIRzXKmBaQZdA4FSIiajxZIu1dljSxQtXyQTePPjFMmI0KQckvEb9QXUF64l+08gWo0YNN4OrRpiWM5UmqjUYlslmcDhORLZbckPVloFU+jsUgCTBNk001b8N5lUDT+g4XOw58ncJDQFGuhJ9B5FGUqEIqMrheDPDE5uSb+jKIS2MASD7CFkTy1j6WRyEsYZGvEEpJ8jxSFIyGq34COCTA3FGw9TbJm+6vizfEbEPdUxetcSMOjZDjFWIiawqBJAql40roHXaimz3+vRs91+BQDJsFXjAx/4gPT/kQW80eP973+/ZBKfO3dOokW5n0wn+emf/mnJKR6PLVVKHQHwjwE8AOD/vY77HwDgZ+6/Zgawvs8CAL/gh2UpvYyXIX38KG7h4snjrnwAbwbwUGmHQxqRDfl/BAhVX5VhqtL4ZkuH6zhx1/ujDgDg2IzXASBBX9UkXbF+dbXctgBw4SYoPyq5cPOucxP0ZIH0ZAz1im/dl3O8m+SSffng2kaT/u8B2Tq8bkINGWum4FkGzizgcqCfihiAwgrV7QGtDvzDD8Afeynw1F9JydDqKagLJ5F1XgBz4TEM1RHY3EK5DL2Ta8jjNnS7jcHyAKtn1vDq//rblz2s7M//C869+//A6opCr+dwy7f/97j5m14nZc8UjD/L0c8XQTKrgIMrFHq+/P479uoFUFFmV2BQ/rv+oR1DA+fN6LCl69kOdGJl796o5EkfQKgCaZGIXUwzrpiaEAnnfVN6AScIRFQhd/dtQ/DXlA64SUkEsUizDhKCE2KYPIIZnkUhvXlKzIY5ioKlPi0CAhEjDEOPs9UzInoxlhYxOSymobsjI+gKHNCSRDJptYay7DUjYO3BN6fgTfBT1M1IDI4JknTTiwk02TVntKSYUL3rHU2MCWKtqK83DBdLNXAok5OdpTNiiIXj/+pgj3Fvdos6uJA4t9HwSHyOwhMgkUFkzNvmXsCWzJmFsw3QMidJRsp94wrEhgy2RlbMIjHBjkXmchAj8hdDyoeZhSnOjZS/zAfOTwdgKAB8DRHKfj7G2dnmpr6/Qi3ADGjOvXlQec3zpfPz8ASBhsxoHxTjuGRRWkzIxnozDZMMkasjwsya2Ep/oo4LAb3GbO9n6xz7AtsbSuj6p+fFJOBYU24HG6KxveNNMddArjcc9Qi6/V5f9nv7vIlmj2M9TvO9732vmCr/5E/+5H5//K63//DDD0vZl8wk2T+OQ4cO4b777sOb3vQmvO51r8PLXvYynhteWjQ85s7/k3IZG79P3fXnPosXBgD42X+DqcnQ97sXY219gJl7f4ibOkgC2YsJ3fLtfhYb9ZVS4lm85/nw0goAcoFjDyAXhSrDd7whensG8CbxVqsPN3krdCdFcWYAe893QDX33g9wN8kl+zn/3l5Co/9BSlLh+8yRLQD28rEMbAv4XiFikPx8jmjKwD/5NHD0BfCffwh+8ih8mgLPfB5u/h7g6QeRd26DG+ZQaxfRz6dkezZ3WDvdhWOJOU6QvPKLcct3keG//Lj0Y9+CCyf6WD+foT8A/rsP/wi87mCACbEu8a6Jnp2kjZ0AicK2JaqNq2da2A3mj/8m09elcKUcVPyS5auPihWsM4ZTdPswVZnNYyLK0M0JCxUmIzI84SaUkXAsPfdtG7EqRn1o3DeCKHrvOYt2ch4s42VFA62kZPN6fIFH1HEoulbYORFHuPZGjq0dsteMitJjUMVQFL8c7CEDFZ42sGSSDJKwFLyyIUqwyaHQZ8b+SZaJCToaDpk5KhnFUYOsXUtKpYw6c+wtlL7IkRo4E4aJQI/sI+eYnZHSKoqCtig1M+hqTtkHKFYtYgC9ue+PxpEJ+yDZ4yhlTJbDiw1bF26DoNGU0X/yb0+rlzUUdlLEJw2dSq+iHLcDmprzxsSWSRhPMBjml0plNaTpcxNImnJPE7nQC1ixfyyHk42zhUWcj6pK1s/CpKH0GxTX81AFQX9lDc6jM6ShBeAZEd6E4XQTPpmSSD8RfSQGTs9I/Bz9H7XJBAzy/VE0hEumwnVdimfGvx0C8spIvPpzBIaW10GFSOkfSSDIc1aWiAkAWXok+8e18VosZa7wtb3uT/NY6AJRB4Dvfve7Bez+4A/uxkVkf3e5+smtfod+//d/H7/2a7+GH/3RH8W73vUufOQjH5FcYKZXEQB+6EMf+rcAGM9DpRgPgF+7AwC4v6fpebX1AwZw7HRxEeBdIBc6Dt7tMlx7u7E9A3gcyo8p8aZuh24PYIsCWfcVMHe/Zs8vkgoATk9PX/fIIpYjjP0rtNJHgNlj8EsnAsjtLUtTv8qGsPkCjDqH7LxHNKHhl9aBZBI+HcKfPQk/dxvw6F/Ax3MSx4alZ5DNvhB66SmkagrZEPDZAIWNsL5ciL/b3b/47xDPjbJ3d5rU4W/9DC58+GNYveSwdqnAfT/77ejce4+Ar8xPQekBevkhMUzOCqDVKLCWB/sSNoqs5/WsECpLgbXCYTvmj/swGSkMasss9a5NKqH5y8pUCvhSIhAqDpNxuqFwbeoCQ/7wUghCvzoybVQiM/2j7Odq7I9UEwAAIABJREFUgkKQIfrFPJrqEiKTCpNn+w6mRTNtAwwI7DwKdQRxEYAHvQHN4DSK5i0iUjC9oFQleHJ5Gzq7CNdchDcJ1IAilGA6LCCwvKmRsmSspQew6NwqHnYmypCTDYtcEIyoCHEzReoX4HkuTQZHS5JSDTws+wGV06UQJJSDlaWAZHMec3VON/oBa9FwnD327TFub6MfUAAheytHV0NDegFH6S0NNURO6xnF42AkX10J7GFUikyRuSDUY/k7lObpAwhXwIjYnN6W6xu+fnlORk2FMrMvYAeUFZVpImhApesBvEUNsYnywxTajUrSBIViaVg6CEivpmnC5BTq8PrTsM2jiNwSbHQImqruaDF8Ppllw4QQeluGJBuCcMsSdFnmrX83soLXfJ0xDc/aog1XE5BU7/G2KSIpZZsYdC2ajZFNSrh+9id1ZM8XyStskGs/1zK2+VTjne98p0Stfed3XslF5PrtbVWGZ08gDaIJ/tj/Td/aRx99VFjBD33oQ/jYxz5G36FPAbi/LHfut/q3moTAAD74v+49A/gShn0cMIB7dbUdAMCxmeQXqbIC4FNcEJ4dADy2ofirNu2m74BuBUA5XDoGfe+b9ur8bdrOlaLr9vpD64roxYlPQQ2Y9LEAf+EZqPY00L0g8VnscXJ2SqwzCv4mktEhqDl/CZiegf/sX8Affwn8hSXg1AnYxRcCTz2EIjmEgn1NJx9BOnEbsiF/dNeQ5jHyw3fg7vtp3XTlUSydwvo7fwDnzzp0Vy1mXvti3PNDfwe050hBpew0tLLo2Q6yLChRG9qhL4pcyV7Y6Afkv8mtEKLl3mNIa7s62GCf2NguzSa0PQk/lN3cYZr/Ll/T0mSnQqlOgwxahRzJEpJdchhk02gma2X+LX0ElzAgYAXQiUOZN09b0vNGK5a8FIKEMuQQxoV+NptG8MkEIiyjGMaISoaqUIcFtNDuhb1q1s7KOauGMHTxHFw0BV2sSY8bt8PP8kxXIViKqQJOEOmBlIYLm8Aw/cS0UeSTAoyYwRuygRtIN9TA7FIcRegRkJE2FE/AcuwkBhElcOkTWL12XAlsaj6AIRWErs5kMcNJ26wEJrvqYaX1UIG4r1FmL0tCTdaESdifqYIpdOn7R7U1/StpCM3BkntVfpdysFqEcfRfDIDQ2mmYmvm2gD/LHOCtEa1Od+BYElYFlKfKuRXsfhqHxSaIBtFk+4yhMTfZQPJ+pfVPMwfZPt4sVIOs8Xj/ZPVcYAa3AkOC90o8w9OjeDNCGyGXQI2ViOsq4itZylz5m3t9X1H1OdIHsBrveMc78JrXvAbf8A3fcH13Zhef9ulPfxpc89/4xjduefWJEydw2223fWGZAcy7wesF/rgvBwBwF+fvufCSAwA4dhZYAmBpgyWOK5krb88AHhElXn24mTuhm2HxT8+0oF72zfty7q8UXbeXH1r5DnKeOu0WmsV/CawI1WhD9lu1yxKwhsq6QN6Dz+m9t4p8WSGadHCfPw0cvRX+zFn4QonAAA89ADd1E2yhoJ56BOnhF6NYXoa7uIThxK3IhwXc8gpmvvcHMfvancUf48fa/6Xvx/Ij53DpXI5hbvBF73s7chWSKnKygPDoFy1h23plJm+l1yUYsM5JrBurv/U+QJaEGzR+Zj6x2LTQXiZ8rfgDmPD5yAjwI7RjydiIMjiMqZgG2C6U2EhblT/ADZUhjpZR2FkUhUOzEUqCsbeBcSFI9cMN3zqWP8WCxHm4HpNNOtAN1qtp6uyE/ZM0CqzCsRevd0aOOU9uRtR/aqOPzWIBpntiY/+8SiCl36IrTCBLjgL2dDOAEjJaPAbDpBX6J3YAo6GbSkqmGY2gowjDYhKJzoMCmEkpRRtFqfbdTg1MVTTtcoJr7Q5KYCkJj5YwqoOjeiYw2PtH3z9a4rRgfYxE19hAz4QWsn8UcATjZILoasQsxWqmiijkxQySUpkttju01MEADm1YNYHYjcq9BUU2jtFtBG+ziIej+bSYgO6P+gadagBMIylGJV+CRhfNiPAjJLIUsPG8iFSY31zEtODhjUoMo9fhE9rsUHDSh0pasJr9l0ZEICzN87h1qX6uiz/GvyMU5wjqHRucl40kldpzzkbQPoHm9bSNirgeQSfK6jiWnsHdpo7s5Xq1m21xvefaXweALP1ScfuVX7k/4r3d7NduXzNeIq75AF5P8CfLmjCAD//M3jOAL/oBbv+gB3C3F8UVXncAALcsdqGvheNK5srbAsD5I6Vh62jDbvZO6DLfNTs1hL/ve3Yd5/NszvOVouuezbZ2ei0XGYpjCJRZKpF+IH8S0fDjwMQ8/Mp5oDEP31sFog7U+hMSGWb6T8O6BZjiNLLlJqK2hbtA8zkWRD38Iw/CH38h/NNPwF9chj/0AtinHkM2KOAO3YHs5Am4QYF04hhs3MLtP/vvntXh5H/yu1j9/d/BykWF5fMFvuBffiM6L7tLDH9pv9IrFhEjFTZKuQjrNhbwsZYFcxYOgrt1Unk7jKYvQt6vYmFS2rkwFetNxtDCBpbvj5RDM0olvqtbRJhJhoHRAlm+NfHxIyvZVhelzMoRe4dcMmQ9OppCEIpYFtCOlkJubBYDWQ+mFcOmBiajKIH7MyPiBQ6bJtDZWdjmcbpJIxqGUrBV09Dr54QrcK0jwcw77QnIMtmFjRsbghqaPEtfZpPJIElIGWGOb0K17wxUg513wWcx1y3kOWPoWvQ8gYnDMbIfsFIDU7laEAxVw3GvCV2578GapT7YxzcuBiGvWC9hijWMsigkz5lais2pIATTccys5Aip7SAxA/FVLCcDTUPjZ/YvziFWPQHYMk9ZIr2TVODqJpW/TAgJDD/7R2mpk0eHEfHcZnojAUTIzcwI08q+PjFzzocl+ONNAK80B6sXoPMunCGTSLAYrkBmBvuE37eumHGrWMn+KV2Iyr6IjkEjh0ky2IjpH3yOc81rMpfXFp7+lVuvYV6DVq74zYPglznJ40oV6Xmsv9QZAYICBrdRERNYcT2tbq4r8chuY812/NLt4RMEgAStdH2oBku/3/u93ysq2+fTKC1hggfV9R8HAPD6z/lVfeIBABybtqqxWRbzohAhyLi8vnrL9gzgonjfbVob5+6CToLlhO0NkcV/G+amF17VCbvcm64UXXetH8j5oDciF21aEFRxSZH9GHR+FmpiFvbCKvTsFPzZJ+CnboO++CBs8zaY7uNwbhrILqLo80cslILdmQvAwiz8w4/CTy7Kj5b/7Cfh525FwXiwz30G+eQxFI1Z5CceRzHwaHzdt2Pxm+hxuvvhrcX6v/6HWFkqsLbiMXHfnbjj7V+HgZ8M5s1+AsOC3mhe2JVB3hTgQYbvEj1gyrETCEx8geHYDyh7BCs2kG9v6soHMGxs0uTCcvUthQgOjbLHjz/4bdNHl2VzOHSic6FiSVNhWowQkHmHdnQWg2xWttWKA7jLewam6YJf3Vof2q+jiI8LEOS2mFqh+uz1OyR9gGq97DGjaTSmpX9MF8siAlG9dTEs5mB8Ho2ImRJiUqpTWfY1ktNMgYKOcziyg8QaMUuQBqZVIHXz4lziI49hRqZKI0+pBvaIjBXT5aocyzg7yxg0uYWq4ku5Se4NS5uh12878Ff5AEqJsizS02DG1axhGrRRqUqc9AFk2dTHSAlceH7KVBUer2EknhoiE7KB524U42Z7ZB6NpJ64gmAw+DFyFGkExIy9Ww1l8CEZ2PI5NwtV9IBkAtpdgmVCSK3ULu/XhxD1R360ZFptsgBdsA+zDzKyrjUP4y6haN0cPoe9gEqLF6CPZ2TutbECFgtHNwwCaGYjE6SPeiHr3x76RNZNtKvnWK53m4NDwvVgmZm9lS1UTLhRCtqSFdy5RMy1hGCrriK+0WBwu5xjxsDdf//9uPfee3e/2DxHXnmZJJD93sMAAD/3zr1nAF9IT+cDBnCvTuABABybyapswYev5K1nPvmL0KuPb97CPH9YNwut3Pxd0PEoHm546VboF9J7cm8HASDvXsnK7eXg3SSFMUwBYHmEn1FfrBP7h+IJp0wBt5pCd5rwF5+G79DY+VH4eBGqf1ZKwkXGH8ce3EoC1V6He6oHzC/Arw/hT56CP3wL/JNPwF1cgTt8O/Jz55A/+STskTuFyclOPI2j737PrsQf43Mw/LUfR/ehx3HxvEPqm3jZr/6AxJ1lnj+aA6znh+Byjygh96SxRgNg6c3zWM1G55RArl+M/P22A3/8YrUjvck7cLYRegHDLyjFBiw/h6/gdJxtmEJTG5wJmFRo6QEiswLvtIBUXXkC+r7k2A7QQdOtII4GUnL1lrFvVEwb6P7pMq4tgskCy1dkHREAkM0q0jai4TPC9FGVyr+HXdPwOVnCURIGS7wUgCiqhF0G1zkCbdkPZ+Gas0DMJIohXGNOes5cg+JDI32BVjFBJEg2BpbiGo2UZUUqVXMyeey5CpF7RrHDkK/dbmnyiIjwtgzOJXlkAsgKJOaijq0Gb8oSKoHLzVJZbJhLXJpSNxSVwKUC2rE3sI8MbQHExjK+LyiBbRHTmg9RM2yb00G1NQdbCVgW3sgLHsaIbQCOVs8GAYkLpV5HoEvPQD/ax3HwV+27HFfjMLxOpP9QUkRMA8ZdgGsdliQWyXpm+ZY2SQkBaiSJLDqxyAqWk9meEe1oDM1c5G2VwbX+v/q07wQAGfm3US72Cpo3HATYcvOyedRVxHxmPHVkmxO9rw9xjeO6Vl8/3/zmN4sB8003MWr2+TVKAFh9ka4nExgA4CM/t/cA8J5/xpNwUALeo0vxAACOTWQdAF7JW29bADi3sKXE4hbugi5NZ/lx2fkp4CV731R8ueziq71euEiT9SMIrCKI6tvS/hw0TkGbdfiC2b7i4guR7Yq3Rwo1XIXXbejuCWTuEBJ3BsX6NFQ0gF/3cMMCqhXBffIv4Y/fBc+y66ceQLF4u5TKss8/gmxpGf7YXdC33InFH5ZA8Gc97Gf/EN3f+hWsr0FA4N1vexPar3o1hmSh2CPmNAaujbSI0Y7p0RchZUatlPbclt6/3FpoguOxkpesUDG3NdpF6QWM6QXj4VjeZJm1NEMm9JlMAtNGIYj0gJWVm8nokmiGh/k8WjGVuSzleXTUMgaYlqrhZBSEIFk+Kz2BHHmXSRtDmE4C2x3C+DXp/aNqV6uhlD3RZWZzLADOrNGPLjA61tEc+hk40wqsn3XSh6aZd1sygswQVo0GfMwUiRxod+CjyaBIFTBI2qwpJeDUHxLAQyqQDGQG+sxFIRtYDAwRysPlIDgjkJMIuNoIj3O2Nj+eSAbvZppKev9qpU4qf1GWd2kOrZWrAZXNqSC+iDZMlIk3m7gkDJrE4xWTiKMSxBWmNHb2sGoWnqCyBIMFWwiGFPF42MZNodS+KSFkc/pKQSsdKqSE6i2PUW4OdCgDl0BRrHkkoaSQfGAx9E4S6TVEZGDQh0vmgYjsW8gT5uUpSSwsCYsP49gN6rj9S23Od+7/43a3MoBSax8vMXvuAoFrQ5TEQVs/Gryet7OUYc/g9WQFt8s5fsMb3iCq2p2EgM96EbpObyhLwFf9+36Nu3kAAK9xAq/X26/6Avnr6gNYB4BX8tbbHgDObVm0xgFgfs7Bf8He2wpsl118tRcSj50lEfb78Y6Y/X6bFmPGVamnoeTHkOKCFdi+hmZZbC2VCDe/vAQw5ePiw3CtW2FWH0VOkUF2Hn7YkqQPmBTu6RWohSm4M2tw6wNgdgHu8SeQnV+GP3I77HoXw4ceQr5eYP5fvROtV3/R1R0WVbg/+13on1zF6nqExovvwM1v+xYpLZIF1BiiW0zTMhnLmcF0TCPowMQRXnTJVpX30coRorDdj2kQQFa7v2YmMEty9Vvu2Zjmu7SUCWKQ2Ua0wQZORGzWZ9FTo5dFaJb5wEzPYG/fMJ+S0mgnCZ5z2tIvRMHT7LlIpfzL2C8WTiPdFVBouz3oNv3blAg+CJqsXkRUjJhAJlqg2YbLY0T9EA1HxSqZKoIEnV2QfjSz/qQ855WGax6RHkc9XIKP2wEERg1JDTHNQsqVhm4pekKAiYhRdCa2KhRSGJUjk548JcbIGykghUJOYUk1xJuGitwAFljyDuX0zUsWFdlyXdZ6BGOmZ2xi/xwSw5sYDes60nMaR0GUxVFPBWEplMIPMpsclRKY4C9zC0jUxZEtDE2h3RJo6Ez7P6Sjcm8mz63CtxbFxBl95jOXABvTkr4i5d3mYrDSWT+x6choiK6H6wKubZNsaw/ahSqCgD3mLzMSrnNTEPf4FbjmQshj9hlMM0ahGQupoONMjpvKapqtKx5fbRoZzye5ymMjnAKW6Dc/FzwXeZGPv2Eb8CeRf0wmKUvP0voRByAo/YKbN3IjLWXGc465L69+9avx0EMPXXdrratb4Da/SynF/pC7SlEG3d8f3ovt7mIbAQA++vN7zwDe/X38+AMGcBcnYTcvOQCAWxY9L+rfalzOWmU7AKjmWfraPNyhO6HN6AenuLiO4tjfh56mSfvejfHs4qvdMkEwrXAqE+ytuZ+rMOZp6Mo817KsS2uRJqLGAPZiF3q6BX/6cfiZF0Cd/yw8vefWnpBSppgRowvXJWN0Ae5siLViUdV9+mHgpjvg0xzpA5+AO3SLqIPzc0vof/YRHH//f4NuXr27fPq++9H/xGexvuLQtw28+F3fg8Ickp6zQTEH5foo9ASyIsLAalBIy77AqIwxW03zoAgeY/0aWkk2cOrYv6cF3AkuKSELsW5V/p2IFFTN8HgmSQWoXcooBHEbpeA2VZ2KIHQCLawjistriFFfUfhBbTrGkDn0MYcJdVbYZ/ahSfQZEyF6FCGclexYla1IT6CYQw88Y3tDu8LaqoAGqktN/4ywfRwFUyXWRsCEP4gs7XqKe2wm54XKYBUbWOZdGyPiD/YH6iQK/YItAqdZmQynDPp2SoquwVlRobABmAiwKQhSx0AgTcRhUIi53RbEISrrjVg5bsQ7NGhFU3spxT3sIsxZhmQ+s/gClt9x6QVcJ0pBzig0KDRqvYCx6wUWk72M8GiUvYCi1xh0pbQbMpgNTGULw0SbtIBJjLy3yDuIyhK8gFCqbeNE+vgI/vyQKmB69YZBFa8erm2wrXJYPFME3ygkTUQEIY0mNAqx4mEJWPtBEN9Eh0OEYMI+wE5If4n6yItpeO3EhkeXMYTc9k72L/Rw3DS35f5JO8J2vxxklbfpMdQsC29KagkbEmAo5tetHS1lKmsWXnt1FfF+MIO82a3nHPMzX/WqV+GRRx65rkzk1a7b9ffRD/Cee+75vwDcB+C9AHj3SG+nvwIwCqveiw/buo0DALg/87rnWz0AgGNTyi99HQBezlplHACyV0fPh6b8+qCKVdeSCPjccO1O6Du3+jddyxmusovrRqbPdnsV68eFkNvZbNngoM0z0LoUJVQ/CGx2V2twBXsD+3BrOVQrhr90DmjOQHWX+DvH2qL0paX2EBr2NIohwXIXfqDhzq9Dz8awT61IwgKmppGfOIX06dNQx2+HzRXy88s4/L9fW665feQP0fvNX8PgUh8r/RaO/6OvQPJFrxVwkdlp5GToIv6waiyzoZ+8jSVYDCwIU4OHbmc7fYK77phSeDo2GLAsXo65BsFI+EesKOYosJyFlJCZRr7BHNIImipg7sOEWRJWiqklNK4WJaejEvgM+vawyB6aJogR8qyNOCYT6OHWGc3XgUoi6H7J/rlZSZVQqhCPvqh3ArZxVMyko95Tsg2rJ6F6F4UN9K0jcNEEkA+hinUYMocEJTqBmzwWDIvdGtzEgljAoDUjtiM+mQE04+DIuhlpD7N2AilL7pblPZooMhaO9iPBJJvsUM4SMQftVlgelPIhTaLZXzYq9TZEMbu5nJjQYrsCIRTWeh38CkvwSKuYBvsoy5WPzCLZwdROwyqNlupCV755lqB+GSnFG8YjtiMlcJEmoq9kLyDBoO/THzHYP+VuHpGnQjvkA6NPw2ie1wgF1cH5MyNhiJ3dYF9l3s0M9GBFMpjrg++lyIbiE8kBjiKJV5Q5pO1Ow6FIaEJPRTB7AZkNTOlzAxRXM9LOaDKBBGk0C082jjMvmC28tZzrbEsA4/jYuf9vBwDIOdgOGPLmoypHs9XC8rpo3zBLGQJAZhyzF1Gub+/FA5DRa/sBOJ/t2vxsXv/BD34QX/7lX14tOmy8JSj7tjIJ5N0AQuPq/owAAB/7xb1nAO98K/f4gAHco/N2AAC3mchKDcanLmetsgUAqgh6LqgG68Mt3l727YweTZfnoO75mj06jWEzVXpJPcpotx9QN3Wuou82vVf1EJknBHxsGfwd548f2QySR4UHVbfIcyDjD7KBuvQEXOtYWQaeFyWpy5pgbLJmLNdJBzXVhB86FA89Bn/8ZrjUYfipz8C1Z4DJeajFmzH7VvGBuvpBG5uf/270n1lBb81D33UHbvmhf4ChY4KFxWp2CIly9OUFfc7WCy19fuvsS6zMg+n157z8Vx+dSKE3Bv7o+yfWMOVoaoU4GoEWGkH3+GFQaBuHuGT2NqLP2APmM7STi+IVmNsJJHEAYLHN4AiQogaabg2RXkduJxGpYBpdpDHU8JJkJ9tUw+RnJIZMgLjvh4SR9T58MgsdDYHlS8FsWMUo6I1o4sBM8TAJcGzoU6SC2EUtmMGZYOPTIdAzwjaJLUrUgW/Sl86HCLM4xdAfCl6HJsOwmJVkiZwCA2kgcMgITDgLLJ078lq0yxkr+TIruPRBFNWrYR20toQRzKk02J0wUxgKTfZM1kBMAwOoWjpGgkLSSFjeJjhsRez3CydLU+hD5lcCtLitEJsXegFbiOMgGuE8m8pUWx8W2xdJ6OBzJftHho7zowYXxWxb5hFNqO5yiK5TEYrmMeiMSSIbSqFgrUnBiMsQMcO53DkpC2v2DBoUjUXpKaUfo/g0EnAlTeiY6l/mA0+LUCR1bSgmswgYI6cYrjJbCmHGv1R1A+j6c7sSgNTeIM0QW/oClbQ9bBlkBflaglK5+dnc21mViKuMdjoR7JWlTBVzVwFAEgGvf/3r8eCDD179enOD3kniYm5u7ocZZATg3wt1HAZZBzqOf3Ifd+0AAO7j5O7lpg8A4DazWQeAl7NW2QIAdSwWKOPDL94KRYPa2sgvKvgXfcdenkvp1xOBQIcL5+4HyywsH3Ph43vrrJ8klOoz0ObUtv5hZI1YemLWaaQuwQ40dFLArQyh2gn8hbPA5CL0mb+EnbwLevlzcDYBct7xX4AdHILR52EvNeBTC9XxKB69CMsYsolJ5KcvYPjwY1BHbkH77/49tN/4t3d/YDu8Mv/P96P3lw+jf6aPvm7hjl/4n5FGVAIrDNJ2YL8EPjiskAVUWuLbLtWUwFTwknWrmMHtMoH58RMxzZBHOzLXMKL8ZeJFbh0iQ7FDGLOcN0XpA59nyTQ808SqGBQPi0NomnXoUq2auCFS0jsemFDs8zPI7OyICewSFGjJ73W0dPEpbHwc0TCwfBQ0oChg4i5sNoFo/UkBIjY5IqwghzBPhYFOA+iUxygcacxK35/YoRTLUI1YFKm0hkGzBUUxSMTMXCZvNOBp/0LwSU85eAxoEC0ee1QD08AQSPm8fECwfdlJDSxiEM8rMyxf8v8U2RD01hJEOLMxS70bOkiypGT/yICxWtwMvYIlqGpgiKjsDRSRDGUoJXg0RcgEll5Au4BGNLKFKXo0aF6Di47I968SewjAHgzgoxnpwyNDXuUBc5dt1obOV2Hbh2H8KlwawQxHhtLiwRctSHwfh0tmgzfg8EzQzjQOSdQb+/5cc16Og+V4FZf2b80p6cWkNyDzg2kJQ5aTkW8C/8r86e3sXGT/3Nb+P9kPzs2YkKS8MHYAetvYxTizCYhvXFz0EywZWMHBFAoRDPLPHVJH9spShgCQDgeVvdXFixdBG5iPfvSjO6wkz+2Ht7GBub5ZwI+/a+8ZwDu+m5N+wADu0aV3AAC3mUje+VWu6pdT1m4BgKYBPUO2ffPwizdvyd4sltZgX/S9wrLs1WATM/v2qNbdzdjO1Lle6uBPsNVLaOhVaB2MbscHlaURVpHZw0j0Wdi0BZP0UFwcwExH8Keehp++Ber8o/BUlA5WoHvnYJPjML0nUWSHJSVEFRmKczn0lIZb80gfOwl1+DBc4TF89Gmkl/pY/N/eDTN/7X2T9rPvR+//eR+GS330egpz3/I/YuINr0ZqZ1jLQ9cfgc2JEHIBaOsErGUk3HINzfHLQ0uYwjuJhBtvox83gY4UkyeCEnhNIuGCyTEHWZLJRpA59LIYnYS9b6UptFkS02Takkwx+k3Kv3wX5ZUKOs/Qjs9jKKXgPmK9KgCHcW9xg9Y7VKSego1Y5rWI8tMh+YKiDM3WIA2/0hWWyU7eBLN8IrBSZBfVHEzvJFwyA9ecE0ZXtKmDUa+gnblb9kljXd7PH3AKEgyxqWpAtwmfE+R+EoVvSS+d9QlyJoFYFm2DbZFjmV+PLIxYniULWFf9ko2lQnqLQpiinDFGsEk2sOYDyE9i3ypVwLmbQEI2u+pN8x7tiGVxB2fpn9dAQtV1mZXc9ATAZCoXpO9uZAsTQQ3PwUZHEDdS2G4mfZYckg9sIhhFFtXDDy20C32cBebkNNIPUAy1/SzMehDihDOr4fScgL0t37l4Cs5MijpYyuzMYE5moGK2L7TFCoaZw7yBMQ2PIpqjTwxMI0Waz4rgo7ATYqStnBJLoFKQPvp8b+C2YQaF9H5WAhACva1MXxCGjHqtqw/mnGyXU2xKwO7Yp0lhSm1Ib6pz4tvKm1muYVdjKcOb4HrbyxNPPIG3v/3tYDn1+TSq+YiijVLD1jr+/h5QYAAPAOD+zvIebP0AAG4ziXUASGUtF5N6PFD1li0AkM3v06Mg8Y3FfPHYBpNQPWYLJo68DOqWq1S0brPftDHgAjg5SfPXy4+dTJ039k+twqnzYE45AAAgAElEQVQL8sNuoDcyUce36m2LjnnI3Dwa+hxc0RbBi10poJoKfmUNIgtN+2x8lOb1aPlRFMkRqO5pIE9g+wmMPon84pw08TPyKn18BYU30BMdFCt99B4+gaO/8Z+vdFi7ez5PMfg/34b+M130lnowt9+Coz/2T6UUmGax+NYVaGE1VZiIPFLp+QulKBHIlGVegjYaRReOLKlCaoNNCQcZwsToDdUwF+WZWKNn/QYjWPcFnIwcjCkE/Bnt0ShLwbGiU10XAz8LetU14qAEdkVro5E/KVYl+ivTLbQ9hSAOed4RIMhR9Nhfl8I0DVyXQpAUhTqEyAa2SbJ6107ATR0PHndrQfWbR8dCRrAbiCeg1Ydg1gJ7yL4/2zkKna1KX6CbvhWIm6G/rD0tPYeiEm52pH+OgEV6DnUTw3wGkaIJdNlj6VohIo4gycXl4+FUkiklBCzYlbkD+OPzsYhMRssZewYTpqdUj3kKONZRuAkUPhJw1KA1U/mW2BdI4hURTAxdS4QhVSoI2+wSdUHAH/sYCQal945z16dRoEGUpLB5DF0yeI5CG99BpMpzYDujxBVzCIrReqWqlyVr389F6Stz6w0sc33TZWGfq50kO+sj9shZUMFt6bWYMJOXJf11uPb/z96bQF2WlVWC+5xz733z+8eYIzIjR1SGcgQFLRvaJQU4IS2iUtALUKmF0LZY0q1UI7QWxVIEuqBWCYhDazmA4Ii6igU2qC1qMSeZSQ6RMcc/j2+6955zeu3vvPu/+97/IofIPzO77Thr5co/3njfHc7dZ3/f3puMYArPWD4qsSOygmSBjTCz1tWgTYYB2Vcq320juLYQHOrdMRBIhXYZPBcXFs26pwSGCMCc1kd4NQEIj+W+14twigBu/DJmS4B4OA4HrYRcPgOCQTYQlEdhKVOkjrCaUYhHHqyPj+8rMuCLCsjnP/95vOtd78IHP/jBhzevPM6v+sxnPoM//uM/xqte9SqcOnVq37c/4UbQ9//ng2cAb341f+d1BvCAzrXrAHDKjuTkwRs9B1eFnBCmCSv2A8C6qF/3jSNHpigYgXTnKHDLdx3QoYTYtvC/B/OseihTZ8seK7MET6VkMUQp2YFS+1fskhRAoOYzaLUbaLDcwmcOPictRu+TTaDahFq6A7Z5M8zaHfCmBTfwMIMl5NlxaEZrDaqwqzSStsi3FfoPrEBJSd1g0Ktg4ef+9wPbV/kf/wJ2PnMv+msp+gPgxH98E1ylim46g9jQYHlWGJr1AVChkMEBMU14lcJOGtppaOkyyfqxFEzFMKtw1I2QMWGvIP+O9Kjcy5QQXcoEZvm3kxmkXmGW7N+wxFZTffQc5boazWhTmFjm6BqdCVPC41l3K+jTroUxVvqyJD6QxSL4IBMo5d9aG84aieRzirYfDUT5krzfbTugzti2XWA9qE/z2kno7TOjjOD4KMzGyPSc5V8XM/e3GkAJ1cC1mqRceEZpJVX4KtWnXmxLokqKvqNHJhXAMXpMORFxQLjOgjE0I+5CibEwaB4iIhj2XEor3v4pi6VfljbLg+XcAqhQDMP3O/YpFn2cqi/l02LU1I5Eng18EgQ1wz5LPh/ZHnLU6UUTbGH2lMAKNosE/HEQDBq7IqX4ARZQ9aNyrqiu3ZYw32wvMKUEkDwPQhDJXW4eZ0benhin2L48OQLduyJKYOYGazpQs0xbOwaNLXiKPnQEVOrwCU2dDTSZwZjm4bmIS1SkpO9S4vGY7EKwTW9IYQI1oqgEsq5iAE2DcRVN8RS+mgJY8q0nSsBCXOf7bWRK5d/ysdROw0yI6Pi8t4n0PnL/WQGD0/sFi/mcpV2CQf5/EgwWAJAtMMVzn/rUp/ChD30IH/jABw5s3jmoD6I7xUte8hJ87GMfw/d///fLdsp1NBjgwx/+sBABL3/5y6/5/v4otzMwgGd+9eAB4E0/zk27DgAf5QEq3n7NJ8g/Vx9A7pgyAHwwYcU+AMjJtj2ewsFVuzqyOPVwZTtz8Le86IAO5UMDwMLUmV/Iia5odi42IPMper4njfCTofAsVMdm1Ae2t9FWoWuPoqHPwQrjsQHbM9BxH24zg27F8BfPA/MngItfgGvcCL+9BN1fg6sch9m+HzZbhO+l0FhDuj4DTx83pOid64mdhm7WoZ70TDS/5+D2lfvsh9D9+MfQvbCNzlaO2Zc8H5VnP0duor2h5Qf7EHcHTHggAwRspuFGRp8/grpBYQo4cQSbRmF34jlmAJeNoUMvYHgjAUekyTSG3q2yEpgso5Q7vUM7ocqXXoFzaFRCD5rOh+W1yIhKNdabUgquePaJOeQp1apMNxmIOTQtYVzlBHSP0XA5LPsA6UOnt2HTNszW/cjrJwS5mp2hIjg+BL1xFj6ZgasviHGz7q1LDJzusDeU/frHgVpLxCW2dVzKjS6ZB8gAkqXSkZh6W7AfMBdLkoyAx9IsmguFHJkasufUAAyzaSnMIPgOxXEvvZgCtob5wExNoYCjTBtR/hEJOKaCO4hJKioLCmVBDh5VYf/CZ5EtjJQPJWkAFIrsgSE3VCcPdTsEg+K16AmmZpGYkJbCXljfCcJKlyzCDnpIEKxdaPui+0twlcMweifYIOXDXGY0oDrbcPXDoswmCNXb9BMcVe3yylHoXSqHw/ZKykeNi6YNYWcDCNyGZQ+mprq3J3Fx7NXUZGWjWPpAfdxAzjK8sK3E2xb0ACzAtgCtIQjk4wU4L5/eBLwqmlJRnMYAUrQjNd2JC8RRwDOl/Ct9gUEgUx6Rz/eVhbmuJItc+DVSbZ3ns7DZLJzb3wNd+LsWOe+FcISLewK+QgRXBoAf/ehHpf/vne98575teqIfOHv2LN7+9rfj2c9+tiziCALlushzfOlLX8Lf/u3f4id+4id+FMD7n4BtvQ4An4Cdfi1feR0ATtlrZQBIYQUnh2l9dfsAIDNRW+M9fSyVqUOzU49N3m3C3fiDU9nBazmYLF2zD3BmZlyJ/FCmznyewC9jzixzZQ17gkoMoNx1+DhLjaPVPNklMn8b2a2Yj+9Cao8i0ZdCH2C0A7thoWcU3IVLQPsIsLUMnzspwSWbX4GrnQQ2LgIp7SkOw2TnkO/MItvJoCsD5NsG/Ss7gIlQf9n/hOTmW69lt0x/T2cD/d/8OXQudtFZ7cHNHcKR//Cz4lDXzxfhfYScmaYeWCuIIjeygyGUYE/fLl2gS6NmFPq26N4LT1RZDy4xVCwbV1mWExuYgEOK22HDMB81MIze8oYY9neMHmrxOnr5YRj09/rTVGahYmYZe9T9EqyvI/d1VBEsX7JBA3GyEwDK9qqUFpl1bHrnBE5YP4vIrQYHlq00ePwlGbC2LH2AjH9zaAQj4sEqLJmo9fv3wIiYD9MPcPcC3OxN8LU6NPsHW/PCBGoaKZoaVI0RdKmogdk3SgDQzWmZxFg43rDJrvGEKkp6CpnT6JMlnDIIjCkUIQNUxMgVf0nEXilJpKIIIEbHSbs+kiFrJzVQ8akrjIrZC8gSLy1caFbNc3l4LTiFCoISOPWHEPldmGFvLPstdbYi4E9TZdsNNjAcecpewBxG9WBdHWZox8NfYNWiqLPJCgobm9ZFIV+MPDkG3Rk3iS6e87oGWz8OM1gWcK4JLusnQ/+fGogwh1Y9ulYTv0IeT1Vhv+p8yFWmKMX0kbuq9ANyaIq6CCAp1plydyCgpq3O2Cj+ORXo7c8eZsm5rMQuPotgvwB0e5/vNOIp7B8c7VpG3qrF613OXOcUuZ1Dli2Qyx3f1KukjhAIsoWmPM//3u/9Hs6fP4+3vOUtU8/BJ/JBWtP85m/+Jt761rfuMZZc4BeAlr3rMzMzVAFTwvxnj/O2DgHge9Fu72+JutZt2d7uYvYmCVC4zgBe606ceN91ADhlRxYWA3yKgIr/ntZXtw8AVmagmxM9KXELamG6Kpf5q7l+GtTcLQdyOAlcyVjOzo4A50OZOqeOUWY7wjMVo6JTmGHmbHnDGLkVDTNR+Ti9xLwz6Nk5NM19SN0xVPTF0AeotmFJssQenukeQ2sLtXoGg+pRVNbvgIsX4VMnKQiZuhGKDErWw2C7Cdg+lO2ht+IlAWT+re85UC8usZP4yJuwe+dFdC/10O8CN/7GL6I3qCGKDbbzBbEFYVWpl0FEHgR8nVIaCPdBXQObFIwMmUESf5PE4D72LzFSzmSb/VrfYr5KHiuMQgmc5RESQ/+6AADral3UtF3bQNOshF4vMikEN1Jlc6ioJfRxBJHtIlZrchOnnxxv2PStC5YwNdieg8mXkUfHYAbBk06YwF4HupLCsidw6z7x/vOqISIQDhvPQm8ti1hEjIlrhwGKmGjdwoUODRyrFda6gfoMkS98dU7SQlQUCwNFoJequpgR84v7nibAjNyrDWPyTKkH0MMwkWaq6zDhU2ACy2N/OdihpllSH72qqneE3WI5c+AbErFXVJaZIpLEW+LfN8hnUIn4XDgGOqeqeE3AHz+viuC7yJHvZiK+iOIBsn6MKB/2V/o2jNuR/kfZh4MEJlsSOx6XHEbUuX/0GVhAtD1UX5sa8soRqGySEQsReSbbgsqYLuJha8eBpCal58iviy2Pi2elN1F6BJWGqdBkm3nBTN8JkYfwFSkBizTUCZwOPb/ShzvuWsCNdGRDp5TggyhkPysYgN5+AYiWsvDE4z6o1SeHlmzr8cUoz/vIDMZcCQjWwdQeOVY8J3MoR/Y4ETCY25BNXR6TljJ8jqlHZAfJCr7vfe+Txf9P//RP79uuJ/IBbje3jyXq97znPfju7/5uPO95z6Pty9hmKaWeCuClAAgEHy8FMLfhOgB8Ik+QR/Dd1wHglJ1VBoBcFZJZm9ZXtx8AzkI3JyaZZA5qfrwsvPeVs8eRrrWAY9/2CA7Z1V/K7aZoZW4umFE/mKkz8diuAzqOStbNsXmdpZUk2u8TSmuSqgnsCAcFIH07Q0c3ERywpyhSNA9mA1wqUW9i7ULh6+Y6VGsGeOAzGLRuRtJZArpb8NUj0Gt3w7o52EEFpn8/0u5RZF26k20j7yfoDZqYf8PBrcK5UiZQjj//Qdh/+kf0lgbYWeri5C+9AencMRF0dPJ54Zj6Ob3hRiwgCa2NYSm4OBLs+2NMHNmosuULn29EI6Uv/80LjuKP3TQXCxmCw8LcmDfzdiWTXr2tTGG+SiVwSLeo6w10/bzwWo04gAub0xMwKFVj2xPVJnvhqnYJmpFr7jASNQQiux4qjkTY4HdWRJ3rojlE/QA48n4LxmzJd/kNMisOrnkMeuUrss1O18BmSQo7yPhRta03zkupkYDC9Ng3GiFfuD18R30eqkbFRFssSJDMsF0Q1rPkWBVLEoJBssE0eM4pBqICWMq/wcevGJF3GJChHPm5TAV/ZAV57Mqq4SqBV4n9i1gANV3kto4UFSS+iygegayq2paSdE/Oa7eXCsKbbsVtI/Nt+EhB5zkSPSzjZlwIOUQxQbmH6/aYKwMLAugqIhuAovMVqM6S9L+iEjwvTRqOj2dKSYcJJirsd7cLtcXs5VGp1OmGrKN0ugFmMfsKTaNZglfC/Bm3JmBQYvyqNIY+CmNSyQamEEf8GQ174AbiFyl9gHk9lL7pbcgF3ZCp5j7fJwx5pAIQmjvvA3pkbWkAPcEiWvYsjoNdSf8QUchEDyHnmWGpmu0BjuIWZYXNLF5rsxoisyNsMyPz5By388jyRVhHAmn81sfFcxEA8Pu///v4p3/6Jxw5cgS33347fuzHDj6289FO+EtLS6JQ/u3f/m1h/RYWFvC0pz1NgCC9C7ndMzMz9Ew5BuB/e0IA4Nn3HzwDeOOruOuuM4CP9gQavv86AHwIAHi1sirftg8AVuegG+MlK5rm6tlxUCgTPu0xZudBP0B39PkHcjgJAEn9kwFk6ZqT2jRTZ/aebeUjZ9Aay1jskdobLIOtTS0BsZzGfjH5DXkVG/kJzJpLyHwdFcVSYgRNFoKMU9yDW8+gZzTchStQ80fgL5PtqyAyEfTynXD1G4DNi0B/C1bfBOxcFnFI2mvB9zsweQe9xW/CzEtfcSD7qADFdPyvd5eRfuTt2L3Qxe5ahvaLXoDqs5+JgZ1HonPs2Db6FHoE0gTd4X2o3A/ImxTNnelLJw3pDuhZrtDD5s4k9LoLalYSY4lS2M5H4pHDtXivF5BKYPZMbaQaDeORxIE1ohG0lOxUhJruIo42YPPG8KYXvB8T10Gma9CWRa+LyP2slDDJBLL8m/e12JTkArLPwdZPCQA3dh3Wt8WPjgpM9lHprbPwM8fg+156AsUbsHJc8n91SiV3A+j15e9i2NYpYV9oJO1mT4Wb7sxheMb21epQ0ouWwEXMJwZyHaNH8QOtXrSWfZyKEMSEkriLENKWw5BeSEc1cNB9TjJ/fE1VfADL199+9i9BKkIPSdQgsI7oCxjYK/YCMiWkT0GBMahiF6YAJZbtABYuolG1R8VvCMgOptAtxCb0+xHImsFlsbsxtQbQXRuZQqc1AXSaEW3Mw91h6XdYJnaHRDpOEMdSrbCwQ+aVn8tSvM/60HmwmCmGRPOZCky6LMfUuFXkrVuk4yDE8jWhIqaytBDpLvJkQXKBI9OT0jtJvdy2grjGa2QiOBoq2UW4tL3XvXD1/r+rJYBMEYBctf+PtjATDKAziEsRmjLnuEgAPNnt0KfIhWI6LmBxsZhtFwtVvk4rxiqGfU1WMLOLyPNDcGSeh33fnC/p9vDFL34Rv/Zrv4Y//MM/lH+/+tWvxste9jLcfPPND2sOetOb3oT3v//9Mhd/wzd8g7B0T37yk6e+l0EDr3nNa8B+QwL5F7zgBXj3u9+9r41n73hT8a01fuu3fku28/Wvfz2oBv6Lv/gLfPzjH8d9990n7yUA/Ju/+ZvfAfBfAHz0OgB8WIfu/3cvug4ApxxyMkRFszABIMFUuaxavGUfAKwtQE+0PLjaUej94SDCvphWFXa9i/zIDxzIicftpnF14YO1z9TZe2zRV29CLUe/tMmEj3Bj3N+ozfJNleBQbnZtbNqjWIzuxlZ+GrPmTmTuCBJckJIiGaV8AzDNHG5pSwQB6PdgN69AtY9BX/wskCzAscyzcT9ydQK2k0rP0yA9Dtvz0Nk28JxXoPq13/Co9tHVkk7sH/47dO5dRmdlAJy8ATM/8SqJTmP6Qs/Ss05juWelV48wXgvDBKQZTZsV+jnFG+NsBkvF7PujapjQIrU+gEKWcqnEHL6c/YLVeNQy0I4tNtMAYOYrdk8JzN44PxQwtGTfZ9jJDmEmCZYvnuwjG9/o0JH1EWMNfXUMFXdZSsTsQWNUH0FB3qFogp5wCtg6H6Lu9GFEg/OhB20H8LU2jOrALy+JQMS2b0W0GtIQ2FPmc8bKsd+tCdc4JrYl7AWVMistf8i4sRRca0GRPmU5mIbQ1RZMZNGXbGCygXX0PXOFqc7NhYVKEdoleK/uF2XJ4ZGnATRBW+YJhsenLkb0UeVbHgX7J7m97OdjLJwZhjRLoTBFXOojoxUMmUf5bGY6x6NUEEeLl3i4SLIEm1RQh17ARGLfClsYKmp3gUpDkmSidGji7GPwM0y0G0Qz7JPth9J6HrOPb2kPKFrMwmwGZpbDJvPwWQpl6cFIKlTM+OCZwkJAbRL5W9FGJq6LmMTOnBRmWVEVbBIR4kiZnsemEqOfz5AvRib7kQbiwzg+p5FJ/1/YvzwOie4EwUgeQdGQb3I8AgHIVP8/KWk6+a882I9ohqbnfJwlaO9qoQdRpWETnR7LbpbXEYSX3if7mDY8LP2XBoFwt//VsqQoMofLdl8/+ZM/iWPHjomoggDtGc94hjBuN95441Xnol/6pV8SAEdAdsstt+DNb36zgDVm805zkiDg43eTdeT194M/+IOyaP+jP5pueVWUf9mfyHmer+dgq9KVK1fkez75yU/i13/913H58uVfBPDGq27sY/eElIC3zn3gwBnAmRuECLjOAB7QsbsOAKfsyDIALFIyirJq+eXms++G3ixZY9QWoevjYMA1TkC3puRsJsdAIsVubiKbeR4UFZOPYhQ2BgSsnGjYy1K2OqBqdWnAmy/QYFpA6QZKVk9ipUqjxnLKxGPF01XVh0IH/fwYOq6Bxeg+bOU3Yi76Mvr2BKo4C+sIIjZht5UIGXyfjXR9AQb+7OfgF78Kev08sLsBXz8GLN8JPzDI4xNQG+dESZz5GdidDuqv/ffQlfFm7keyq4reSK6c2eRdTjpxn/xV9L/wReyc6yBVFcz94r+j5gQ76WFUTI6+qyDLHbYyMn20hQnHkiCPUK1IApncHr6W5eDyLW020WOvP1yl3Um4BNlf2Zcbe1C7FuVfZ9kbxaw95oM4tOIr6KYLwhTWkyAWyFONOAlsYZKtwqINpyJUPUEdEx2aiLApPYNudwu62YYdGJjeWeTJcejekqhJc9uWDaaSO89mYNbvgZ27VY6R6TBpRMNGhwKwoFK7vwU/8NC9NbjmUfGaM90lWBGDNIFKIpnOogSuJUDMhJAarKoiZzmU+zeboykK+vQt5E1dU5schFQEHz0Xyf95fqdyaRVqYPHADqkqKlj0jJtDOxAAWkdJAPlDhbq2IdptOOpksvdyg4f9Y8OnyRTGXARJK0UdSUIGKbxRQLbZEl9AblIhDHFOw3d3pOxtTIZ8N4fxgRnMcAixD6Vg9s1iJxhA59VTzBzc8wgM6SEs827CxTNwjUWorYvQ+ej6lGNaPQGd7cLTaHooGqEamPvaqRiG12g9Cp6AVUbd1aBr9LicETqbgHHACDwuctin5yKkPljCUBSTsSS956FI5rovboyThtF7PkgTd5IA9KYJQDgfTPT/CSs4kX3sKZBinx+NuYNxt5zfw5YH2Y/DEjFLv8WweW0f0Cuey/MmomFvM0vB/ZSMXlhwcd7kIpFJIMVg6fe1r30tnvnMZ2JlZQV/8Ad/IJ57nFuvNsgS/tRP/RQVuPIS3ksIIt/xjnfgR37kR8bedu7cOZw+fRpf+MIX8JSnPEWe499f+7VfCz538uTJfV9TAMDf+Z3fkXmMSSXTBlXCp0+ffg2Avwbw5atu8GPzRACA53/j4AHgKcYZXweAB3XYrgPAKXuysAwoLmCyapMNtnIjmASANGKtja9iXfMUdHN/I7Sr3gBTCz5u2e4R+PlvuuZjWjZ15oRDtrIMcpg6sZKOyo5NKlBLAJDZt0VZt9iIhAbEUTCxnRwGConaxkZ6CrHKUdVboiSs6UvI3AwSXIRn4kPeFU9A13dQiYdfWoVamIc982WgeRzKZtC0hpm5FWp3DVi7iCw5Dbe9C715FgN1SoQHjR/9X69p34i6udcTdd80UMwPdec+g/y//gZ2LwywszLAobf+NNA8JOrBTtYMDfcKwgJyxMpja9jox4uHLB73b3mQhyJhV+4HDMbQLBWHV/K2M1sNQIfnG58vypqNyCOJcvHJ6+YatWEpuKr7MGqAjp1BQ28ijnaEZaJy2VAtbFkKXsXALCKyfSRYRuYWROVLhorlX+In9rzl233x+/PVBZjtsIjJqWy1V4L6dX0XvnFUlLt6afh88xaYja+IEbHEwbkG9G5IqgjYVcHN3QSdrcPN3yTKWt9ehKrXoWp1IKnDEJSIjrkFZs2SWeq5Nryl4pfegETNwfomKHHJBE6Cu/Le9jSWEYAXeLHwXyK2LiNGkL2TFNWMypu5CDzkGmfMmB8XG9Qk/SZDlreFOazEAcix/Fv1K0gtWc0IJu1LmZQjG1TFaFnAX6alZC4CG3MYym6LRY7s56wpfoy2dgMM00h2VkUFLM+5+QCoG4uSKSwJIdsjoYgkhCSHYXbJ3oZhG6eg8m1ouyt/G78By9YKmlGyBE+hTqURgDuZQBVDxVauVUK0LG8JRA5s8PCWQC9GH8m5oBgH6JlJ3RGRyxj5yn7fUo9lsU1XNYDmCmNSMDLN/48CEkJOiaOTsHEBhIU5N7+HIJPm3cXgAocMcRFxN3ZRDv9BECgK4ZytcaPB1hBp5ygBQIKrt73tbVct305+ftF+Q+sYsoXFeO5zn4unPvWp+OVf/uWxt/zJn/yJMHico8qD20Bfv+/6rgf3iP2FX/gF6U1/3eteF86dPBcBCw2s+Rlf9VVfReNU5tj9xd7KadpOOfjHrgPAg9+nj8knXgeAU3ZrGQDyb/ZpkAGcNA/dDwCPQNfGV72udRq6sd/bytVPi1caR7ZTg5t/ziM+wJOmzlyZclvZA0KzUyZUrAwcOhMlylbMEtqoV4o+dAkFAKURGuCn+P7xNVx5c4mXH0JTb0rJqKp25CbGmxzZIY1d+DSS3Fq76WHaDvbCOtTMHNLVFUR5CtRmoc7+Y2AAyfRcvlPEAU7NA6v3i2IyO/0s1L/3Xz/iffNQnoeju4ZH/vtvQPf8DnYu99H+oe+B+vpvl+b5jfSwWI2wJGitw0YayrjW0UpjxPQ2IoXtEsCelgs8yf7RB5AgfWeQS3l4sRbtgUOWfwl+VnoGCxUqkQPAbJpd7LJPDhozESPhcvTTOdQroSRvshRWMYNXIUlXxA5jgMOouhAxFixhduHIQG2dl/5Ln6aIeudg48Mh3k3KZbOkvWQBkPcbMFtnYOdug147I6A+xJQtiKDBsdTIc2mLTO4clOuJDYmPE6CWQNWbUI0YvjEPRRaXatWoJeVi9o06lkZZgkMs/XP0AmS/ZGBPw/REcELgVbCl5ZOBzGnIDB4Nns+hVF9m+8bZv5piWTOT0icZxzpTU4aGxVTB0gg6y9oYoCrPFQyVyp30Ofo4hmO/pVsRQETVMDOui9LjYFcj8cuwZlF8+BjFFy4dBb9L4D0PY3YEDNIEmsOpOhzqMFnwAaRZN7auBFA87Btmtq/pjQymi18t3oDNk+Lv6FrsBVxD3roZOhrAVxehIm7zPEyUyaLK028wTtHPW8G42pJhY4k1EUZW9pzYEwWV9+h6sdKHqnU/kLFXNYAmMJxgAL0SUDwZOjoAACAASURBVD05yvFvZEcdQZ8c+NH7eQnE8aiEy4UBo/z4edbGyF07zD+Kc88Ahts3MWiQ3u/fKtWJyVHkv5fZPQIwMm3TmLh9HwDgwoULuOGGG0CLlic96Ul7L6FhM4Hae9/73rG3sZxMIcfly+Nxf0ePHsWv/Mqv4Id/+Ienfc3YY2QlyU6++MUvxqFDISbzDW94g3zX5ubm3wH4NyQWnxAAeOG3Dp4BPPky/sTrJeCHPDMe3guuA8Ap+6kMAAmyNjY29rFqfNs+ANg4Cl0dn+DczE3QtfEVnsytBIZDqwW7kyOf/96Hd8SGr7oawKFDPAFg1ylc6ltZ1zOZojyaVCPocrM8y5vsHRzfBPqhTQ1957xvm+i7Ktp6BdtuEfPmrBgoN3EGmZ9Dgkvizm/UOuxOBF1N4da68NogtzmipfuBhdNQK2eBLmOsjgNL99I/Hnn9FtjNLUQb5zD4l69F9ekPXyUtpcI0FZUvJ3Myfw8WAcVf7D7+HzH48l3YWcqAW29H42UvFQbT+gg9W0c3zdGqGKz0wv6kCGQrDX8Xg0xgj4pho7AzjIornmO5mMegzBMeqobP42PMDDZ7qSAeC1WL9b4BP+ZQbaQEjjX73yJRXbeSKyCbwRJnNQksiM4sHNld61Fz5zDAUWiXSemRNh8SaEcmsB9T9ouo7mHXN0LyR+UU4k5g+rIB+8NWJZkCywQUJ8WL0SzdKaDKNm+Fynahdy/JQoLngt4ONzExiqYXYOcy7ImnQTOfdu4IVKMJVa3CV5qIKh6Za4v4I83nWFhGCuYts6eS4JA9hV7aFcaUv3ys5OcyDfxxG6q0vRkTkIyzf2SJCGLECkVpRGL9MgIXNIJmXyPBH9nOery2d23ozMEZ+tUJgkVFrQhL2cvmUR/G9BGs2l26blMIEiHvqT0lMAUjLP3SPzCAwa6U3xmR6PQMot6o9y9Pa3tgz9ZPwFslAhFBXsOLVY4RH7G0WmK6BhtBk2D+bDJRaauEWcFzULGR1BYtopCq5AFr7QTkMTkmFSseLnAqcEPQR+U/ldrlBaOca57MP8vidAXfX+qV2WWC6Zvq/yekrBXQR/NvKsJp/TPWw8cFpyb7N5pbyf5Jfytzgdmr7BkXR7udwBo7+i2CQDX0MdOMuze4FZ5l7imDVQIuyCgOK8a3f/u346//+q8fNFmp/FHXwgASHLLHvDweLgPI9xTVH9qUcdHP8Xd/93dScv7Qhz70cwD+EzB0JJ/6yx+TBwMDeB0APiY79yA/9DoAnLI3CxDBpwoAWLBq5ZfvB4DHBOiUh5u9Bbo6iljae659w14vjOvsImt/D0CrjYcYD2XqvLaxgV7SxFap7bAicaKjQ80YMjWcLIqvq7Hfb+hVVjxWpwErS1RTRpbPIfNGGMBdN4tFcxc23E1YwBfQ9zeiijNit2D8GmyXfVyZmED77S24Vh3mzB3wszcAgxTqwufhZm8HurvApXtg9QKsagJXzkK9/JdhFqYnqUxu1tWEHg+1T92dH4P9v/8U2xf6SJMW5n/mJzHADCKVYjObF4sP+v+ROV0PjWhj/YD8N1kn9qGxGEnCleKQQqgwl2gxjK4wSm5Y/qX9TjEO11huC/+iETT72npWyd+1ZHggiQaJJFl2VttIol1sp0fRNMuIyPIMP4/3bZ2liPwm0ngByWAJBj1k/hDioYlxTqPtWizqYL15BnntBqhBFyZdQq5mYQbD/rR8Fuhtw9Qs3GYK3VtBPvckREtfku1gn6HDHPTmBbGGce2jwbzadYG4EoDNwlGoZgu+1YSuVyUSkCyg1c1QalYROvmseLaJTNoxiSX0exEEhvSU0bnLb6Sqmokg7O2bHNNi4cZ6/xz5S8bCjd5bU7tQRTKFsNsFIGV/Xx/xsO+MvWiavXXDt0bZLozqihCE4CuhBRL7yfoRVLqDqEm21sPvhv3JT3a+CuOLfOAmot1zovAFe3a3V4Vd5MjVAszm/bD1o1BJBT5XMKV+Y75G+gB3zknZ07VuhM7WxPdREkGqtKAxUOzDJPPK/xMUJhSMML6PopBEwJ6csxQIZU3kYgkzCQK5piA4G9/f1GIQbBGc8Vphu4CAPlrVTCkLFwIQsqXMfBb21xn4YY+r/HAq2tVgLD6O38N2BznnxPalCaMpXBsBQgpGouFrinOiMPLmdw1SCjemm4rz9SzDEkAVAJDz7Dd90zfhy1/+8h6weqh5hM9P6wE8fvy4MHrTegBvuukmKdmWewC/7uu+DuzhW1xcHCtJP5zvL14zNIK+5vv7I/muKa8NAPDi/3nwDOAJqQZdZwAf5QEq3n7NJ8g/5yi4MgDkjiIDyBXWZHTaPgDYPC4JFuXh5m6FrowLLKTbZvbYHiZzWYbc3wbfmG4VUHweAQ6zifl/KsWYa1ke3dzhUmeAwTDSqniuTlKodBMlS1WsFovXVHVP0gPKo8pVtxlXzhXP98Rln2Cki76vYs6cR9cdQgt3o++PixCEPV6abtDWwXUcdMPDXlyDm2tDLa9CWcop21AP/Df4yhHxTPPn74DfzeDaN8F2+4hf+R8e1qleCD34uybVzw/1AbSbsR9+E3qXe9hZyzD7pp8VhesgD7YkqUuw2s0Ef9HSzjEGjNYw8OL/l2iWgAObV76wqPiNVehNo1iEQhyOuQqZxfA3P7NVCUpgXozsMezRmwPAopR/2dvH7/TQQwDYNstIycD6KmaT80FVmjf2YrzidBM5veaoRk4fgAPZzJqog3nDdQOFqNJH3jFQg1WgOQu1TkWqgzVHpSQsgo8dD12nerYGc/lu2JnTwi6a9XvFlsZWb5A+LSn79rbhehp6ZxmOzK7fha+24dsLUI0Eam4WaM1JT5quVQRE9fy8KD3yvC0CBJvH4qXnbBUDBKaGyt+eROQNB02KXUhaYYm4nA3MbZkUgxS9fwTIuVNiwM0eyz0VsVjBbBbaEgEme9m13qMRbUDpXMq71iViEi3DKlT8EjI/D0vgkNMWJoC3QSdCVHEwkcOgZ5BkFNAoyQGO+qHcy+E6YTtof2jzOqLdEL3Hkiz/rZhbmy2LaTS2N8c8AW315FhvoLwvqsPWj0jOsGvdAOMZD3cUfLuvzARbGFoF1Qj6IulhTO2c9PcJCBv2AwrQE1/ASmk/kUWj4Xcsi2LD6LVSj2Xxm4zPRMxjNK1uSodteH4Xvo/hh7L1pDfmCcjyu+Fje8fbI9EUoDhY25TjTS/HIrIu7EjaxeyvYBAApulx5FkojT7YIADk/F7MqQUAvOuuux6yglD+XPb5UQX853/+5wIG2adHFfDdd989VQVME2fOXSw18zsJElm1+MhHPgJWc/7hH/4B3/md3znW0/1Qv6V4Xu0vfQQF1WM/rgPAx34fH8g3XAeAU3bjJABkX900wLUPALZOQCfjvSeOvVOVcRDldR16Iq4t77Vh21cvdT64qbPH8sBhlaka3okZcHk0S9YjfJwlzKRkP8LHqjRcHcZaFe+NlRXPuX3DG4nxYimSQCDBADGZQvblMJjeGxh0oOwufM5etw7sdgTTSpFf2oGNEzHS1cvn4BdOQS2fh9/ahGudANauABcfANXTbvFmmBewheXq4+EIPR7OlWL/4t8jP3MZa+cHmP0fXwx/+1MxsG0BeR1aTziH5b7dY/4KAQhvYvT1mzb4GtrBFGCPryGLSIavGItVJtfSbAZIc9q58Bs5vJR/OVa7CgsUFxEswKKh17HjDqGquqjFK7CWzfpMMOhIr1ySbyOnWfMgRcVdxECfQGyXpScwy2iHQeNvBbuxDc/MWOcRbd8nXn+quwxtu8j9ArTrQPtd5N0KVNaDqifA5XNSFs1mnoRoOVjDOFOD6yfQO0tBjNE8LEbgmGGaSh/+8DFgbh6K53y1ClVpij1JMISuSX9dR5hWg75vgYuRgaNgJZzH3PcsB7M3kIxguSxMe54gg6FJOW1ZApYr9jCZQpaDC4FNVTuU3WIqiswPz12FlH5+PI/3egEtaglBc4xuPotGtLZXhlSZg/Ypcsau0YrED0vgZLQkUzewU9nOkI01R4W+SuzQxJu2K4M+dIXHOIfftdD5tvy2PDmJqHtm7zewJ9PsBuBIG568ejLYIw1tcWjVI4upUHeFS+bESNrX5sT/ztUOQVU1HEFg0oCKvCiMOXRMi6IWvKsK6GfJdCB+jPt9AXmu04cvp2Z9nxw4nNGcf0RuNtFPEnOumLjbUGmNsiJ4Cvun5E0KXhoFxQ0ciaGVzgjHkCmOJuLi2PIw6J8WtvDhDJZhyf4Vi3zOK09/+tOln++hWkgmP//nf/7n8au/+qtiyv+N3/iNez6AjJX7mq/5GvzlX/4lnvWsZ8nbeG+hYvjP/uzP5HsICAkg2TPI8u7P/uzPCjvIfsRpQsQH+20lAFgGfmQNvhnA34s4/bEZAQBe/p2DZwCPiZL6OgN4QMftOgC8yo4smoL5NFXA9Icq94fIZDehAmavlEnGS6Zu4TboUn+R3DCjBZjWqNdEHusbZK1/tW9rHqqsObAOF3rB3kWmf07SE5PvJADkfaKejEfWJXTSn/DJIscS+gAnNstV0WX/kLAwQFOto48GmlhFjgoqnurFBhJ3HtbNwmANeacKTWPo7RQ5V9uNCtR9d8Av3gSkA+Dez8ItPAkYZPD3fQEedfhv/gHor/vOq57qD1vo8TAuFveZ/wL7mU9je2mA6Mn/AvH3vkR+29agBc39qTTWe2Rqx8u/3DUUgWxOpIPwK9nbN6kQLrN/fM0h+uTxRtDP0Yz1HnivG4d6YtEZGGEGmtVwgKuqh1R6pSK09Jrkoe70F9Gs0LPOQeVMvqBpoULUY1RYjiyeQzUN2b9kZekJSLbNd3Zhmgb5jpc8WSpIo/W7w+vUHAyzbVGDX12Bmp2Dyyowy3fCLtwOtXpeypzOVOGyupg8I6mw/gm/sgLfmINqtUQMohoV6GYCv7AA1ZoVqxIj8Xcx+qohViQs7aWK3nlk/6qB+aPgYli4I+fHvtarDfYD8m5WFn4Q/GnprRy9rxnZMdawrtk/yf5N9ubRIm9kbl1VHSk1EvyRSSxsdwQw50Pwx5J7miJW61LSzvwiEh3seUSh3bkEGx1DVEnhdrZFoMCRZrMSK0cgY20LZvvssL/yBujti2BeMUeu52A2zko5l/3Ekjiy/pWx3SBgLmdfX3OoBu6K4bZrHQ+9iuy9jOnL2ICOQj+giTPkZk58/ZxnKkokPaUEy4alebm6gzKYDLgCrVjofRT6M0UxPAECJct3KJoqbyBBYaHkLR7n8aUZdXluiTwXQH3BtSyVU7QR08+v1EsoCSXDFBB+FhcNlZJbARlu+vtlg5MidHm4o+gZLgAg+4iZqHHHHWGR80QO2tGcOnUK7BekyGTShobzQzGKv/n/KIrYFFhend4E4EUA6C/zBpLVj9HvCgDwyu8ePAA8KrY31wHgAR246wDwKjuSE0BxMbGfghfd5IW3DwC2T8HE4w29bvE26IneFFc5DjPpF9gfIGt8N5fke1tUeBByUrqaqfNO7rBdNJAN3yk9TuWbngmlr/KYtIKh2pVllMlR5+Q6lhJCIqOFvmWGKLXCFi29jl0/i3nch22cwqy/AwN/ElV3L6xfgKEasp+IkS23LL+yimixBX/hCqDZo1QH7v28MBeIW3CXHgCuXAF+6C1QJ0ZquvIk90iFHg91vbilL8D/199Ed3mAtLaIyquDj1cna0u6CUE1wfVSL7ByvAmyr68YBNllYcg08DfJ/vE99dhguZdJ3+ChUioIy7+sEq8NFBYSlrzCN7E/LRvGpM3GF5DnNKxuoV1dlud9rqHjPNiV9M8hpa+izZA4plPMwdC0mGCpG4sYQPqo1pZAkQFoI7FzP/LqcelN48izWUkI0XYrWMNU2vDVJsyFO8RLzlZOimWPytl7dhh+fR2qH1oeRBXcbgdm6uSN8IfmoduzUDSJrjZF+EAQkTkyUBkyNIJFDBkpCkHY8lBSWxPQ9YcWMeXjGcQgHOPTWcOQWxux4RXaHZXWPdQes9+sQwWs0pjsBWRpuJfPCNtVVbt7fWhktQlKJF5NGNc1ESSkOAzte4hUYPyzXiRlW1P1sHlFSrOyTzEHY4N6WP7doZ3Mqqh3BVxuBdsX6WEb6BD5hq7091E0IvZKw2EZpdhZgbIBMPqoBttgGf8iXHURqM/CEwQ2mtJjKawgewoJ2KMKnK5K2glzj8miprLvGeUYh4g4OdeBzNFLcLIHcAQErwb+iOYYLTkZ50xfwb1Se/ixhJiwbAUQkQvP9Rx6rBy8n/1TNghi2FdIkEnxh02PPGi/X/ncKf4mAKT4omiNWV1dFRUuLV2e6EGV71ve8hYxlyYIJBtIm5mHYiaHDCAzF5kJTJ+xpwOgfxCzgR/LcR0APpZ79wA/+zoAvMrOLANAUvnsDSl7RPFt+wHgDTDxuOBjKgCk/9ekWjgdII+/Fj65WYAnSxJkIa9m6sySb88xKxPYnVCd8kZZtsygLcmkFXUrYvP16PATFFSnAMCa6e0rDTMuLPVsFg/muQ21gy4amMc92PKnMYfPo+dOo+7vgfWz0PmaTPCOebSNDOmFXZhWDarTh1pbgp8/CixdhltdBmZugN/ehr/vS8Brf0NKheVBRpSTNcsj02LurvnacA72I2/E4MImup0Irf/l32LgCX4YzdZExu9LImz0cikzsh2PwpASPhEFcN86OSYs+04228wmkQCY4d0dcxWNNSoa2EupFSolVnaxYrHcDzfcwywFh3vi3mcy/qoZX8FWegwV7KBW2QmebXI/tEBmEeU7sLUWou4GNLaR6eNI8qElTL+GKN6BTatQu1ekPOvXNqGybdjGjYi27pFSPoU8zLLNMQ+1dgGYacJvW2Gp8kNPgXngs7KN7D3zq2vi+0dAzxuyWr4If+SkbI9qNaCOLAAL89CNuqSN6JhK5yYybdBN5wQA5JolVYOu532Lp5eVvOpisEDO2L2ijM6FC8/tMvMn+1NR2DCeDFJm/wi0BEwPVbQhFo6LnXB8jKdoQwfhAxnYmICNfW0qsKgFC5UDFX8l9AJGCSo501mCX2BORxyKVKlK7iiYfEmsXVzUluQVjtxWYbaXkDeOIY47sFsWJgv2S1l8Aibfgc5CG0aORZitkfG8rZ2C3mKpeH8Lgm2elGQQX5uHVj24xhEp4VOFjaQKTRAumpsWUh5j3UfKiDyfYEB19NASpujz427JCHzLCHp4UCShZdiT+XBKv2x6oHlzSGgJvpBkWP3Q76+4Pios65ZKvQX7F4QgzEUe+QDyuNjsMByFS9cw2FvNKk8BAO+//378zM/8DP7qr/7qGj7t4N7C3kSWi7/jO75DDKU/9rGPyTbR0P5bv/Vbceutt+LEiROSW8xtp5qZ9w32rb/xjW+k7wwTBqgu5H8MVP+/HgdLmAAAl37/4BnAI5J8cp0BPKBT7DoAvMqOLANATg68uMoxQXKTmCwBt2+EiccFH+7QbfuEFK5xGiaZcL4nAMnnMag8Q4QetCQgwJkUa+xmDssp+2zC4C1uMo2CsVhlheSkCpjva5ENGLOCIQgZ5aIWu6WiMpgJQ+hBNi+qQKkuKIeK6kuEVgtL6PsZNJhF6xqI/TrgctYTodUO8p0EutZBerkHoxU0FRVnviJ9gJ5GrHd+Bli8Vco6/twZqB+ng8FoPBgjehDXg/3k/wH3lfuxvZyi8dKXIztJ9pHl3QY0KPTIQRDHXkCOmiZDNw6t68OKIwUffaqehzRPK6aYxIsSmIIOlu55jAuQeKicCqLcng9ezXi0KlZASGoNkigc+bralM7Bjm+jpS8hioJpcWGMa/odOCo9NVDpnxNVKb8sdlckCxWOoh+PfJdNdIn0tumV+5A3TwuDZ/pXJJUl6gRhQt6JgVosJs36wj2wi7cB3RR65V5hnNiDyMQKlfWRz94Cde7O0I7A0uPxmwHa8SzUoI4eBuYWoKpN6EoFKehDp5HaCgZoh/KiZlm0hpRCEC4chj2Ao2NMCUPwCizEIOXjHxYzhBSj6Y1m5xHXLGQQHRkthQpR/PD4JOgjiobMGukq/jdUstImppYw55odCguoxBt7sWUmZTxbiixqSQ5zgpD2kaYxIk0Gy4ra2u+sBkfDxiLQ2YJ2odScD5rUGQVPQKZYbA4BOqsE/TXpx+Swug21cVHAnq0fgq/Mw6zeKRUD9gVCU+BBhW9DYgNp/0PLJVk4RDR+VvD1NjzLwBWKORoSzScq5TjBwAYzaKe9AFyx5RE7lQS2EHuIv0oUlMLDwWMsYpzh8oSnf9HFKjIdVYbmQSXPLkIRGQ1XNSEne+grOPxcLgZYDt4bojamAK4u9kw8NomiD+BARCx2cAJegOsjH0WKEhfbhYE+lbnvete78MEPfvCRf+ABvuPSpUviQ0hxCNXBZCaXl5fx/Oc/XxhB9ijSR5Cgj9vOexQ9a/lbPvGJT/zlMAXkzwB8YrhZRefOAW7lvo+6DgAfy717gJ99HQBeZWcSbPDGw0HGiXT7ZJbjPgA4cxpmstx76FboyUDzkgdg+euznsZq/gy5iMk2lil+EkfbJHaY5zvRb8as2TLbVKFVRqn0xfscAVd57PcCZG/bfiUwV+u84Y0mYoVuPo9emqCW0HSV5R2mMTBhmNFXhJ45jCMjFSNyF+EwD+NXkHdrEjlHGxJs7yBabMA9cEn80nylCn/f3YBL4BsLYparXhQSQA5K6PFQ1427+09h/+7j6K4OoL/+m6Ge/a/QyVpIjMNOXsP2gL/XQystViQcjNTdSG2Ihhv6A+7dHIdJIQxkoA/gTkbQEo4UY+A6JfpwgTFdEn8VymGD4QFdrLCPzaI3AKoR7XvCE3W/jC4W5dY7k1wEPHu0Gnvm3bpPA+BI4kiS/ALSyklEvWUR56R+UYAg9yuVvlHTI+8mMDvn4OePQV05IyVRZ+ZhuhdhVRuMpIvsMvJ0Fqq3LRm//uxZobhs/Rj05buljO1nbwZWLgBzx2SB4TspIH2ABrpioE6fAo4ehqoFWxhvKui7GXrXSMQdeTkREel0WAoODFyWs/QdzmHPsiJBEZXRhF/83jGbo/HSL9/T0Fb6N5kqQkjSZPm2tADai4XzGv28iapkYYcjSdsdCmy48OE3Vkt+f0m+hdTMCoiLUtrC7ApLmObNvd60fJBA9y/C1Y4LcNTDFA8KKqyv7kWX2W4FpnsBWZX7LkKyHcA3R57VoLIO0D4KPViG62bQ6ahfkUwei6a6uyTlcztzE0znrKSA5PO3hazmJs24mQU8G8r4CVXfMyEjGk1k3Kti7DxAZmnIbvZZwoi+w2sx7iZYD8dk/20khO9RvDH+XOIzuAmLGIpBChXy8ACjzP6RKdSeGc1cHIQLj9+dRFtwZKkHx+BdyJC+llEAQC64izn3U5/6lKRxfOADH7iWjzyw9xDgvehFLxI7msnBKgjvTexR53/8m/ctVqtoIXPbbbeVd/7jAfyKTQwAcPmDB88AHv4Bfsd1BvCAzrDrAPAqO7IMAFmO5STBCaI89gHA2ZtghlmTxevc4Zuh9XhfIGZOSgTW5HCDAXrJdyEqmZHyNQQb9PXj1MfV9SQAJKsxtKeTj5wEgHwsYYtMaTJmWZiK0/KoipfXxLZSjiFCkKJ0WUE3b2Gzn2C2mkpmgFMWdayjhxm0/WX0MYemvQcDdQw1ew8sDsP4K7CDCOgPADagX95FNF+HX+8A6+vw84eA5VW48+fgD50GGseg/uWPSJYmGVEOlj0mGdEDug7kY9z2WbiPvhv52gDZzDGYH/5x5AKsCNYSAR2XuxmqWmFzEEQbidHIrBMm8Gr+CkwB2SzlwhHkVSP2T4bBfkEeCz6+0k0xNwSDfO5I3cLmHqtphENVHh/m8BJwpshQQ+y20axuoDNYRC3eCYpWAZYWise4F44njX+T3hnmu4glDOPhbEaWh31xA9j1figPkhBc+wry1k0w6/eL8XAWnUDUO8O8GLjVHah2E442JRfuhDv1NcA9d0C5HPmhr4baXIbaWpLPcnkFimCQ33/TU6DaDahjszAnjwCz7AVswid1ZL6KzIYc2j5m4PIEqTdgRrWwgMP+y34emFAugspKaqIUKtslPIwYgT6Xw8b40KVG15ZRMgj/okVPwf6JqTEBn+dxboeki4J98g7NeBlZPoMBantgUH5UFoAnVbXeAhUbyr8DAdjr0EODZGYCc5ETVQfIuwYmDakgWXQScRZKwfTEU2tX4GpHoSs9uB0twhy5/rEgvyKx9AnMketDMBv37p36zADWndWx3kA+aXkNsc9fG7j2kXD904qHbCwrAAmtYVgvqELFGfr5LAPzkLtEzq+9bOCSJYzoObi49PQAGGdZR9eimMxM8Q2kEXk2hhdZPqbQpowhC/aPzKl1bCdQiCP6lI6+IVYUkORw6WF4Zlg/ilEI7coAkDYuf//3f493vvOdj+KTr/2tRebvZz/7WVECk+0rwN3D/dRhD2AhBHk87F+KTbsOAB/uQXqCX3cdAF7lAHB1RfDBwT4M/k0AUh4PBwD6w6ehSrFE7HdRs4emNvASAOaVZ8DHJ8JNkxYk1mGX0V3DMQ0AcmlXthqZLAHzrZNegAQxQSQ2GrzhkqGbHHV6AXKSFtVdA728iU5m0EjSsDLXbN/fQgczmPP3YsPfhEX3D+ip21C3dyFXi9DZCsDVfyeGqXWRXhogEv87D3/mAfiFI/BUAH/xc8DCjfC3/UsMbnm69EKSDSUr+lBNzwdxLeUffTPs2VWkBEeveaOUuNf6bbAUOyBAGGRScmf5d2VYCub+b8QsFe/vxaLQo0NT6NLGsdxbLtsfZmkVwJVOilbCVJBwXBLtMcdewK4GS5gzwwqXpq/iMDmkqSjqSLHjjmKuclYAiMsSmDio0aPuCmx1QXoCk+wcMnMcJmXkG0vGM4j1utiWsH9PLbThtjx0bwm2fQrR8p1wLBUOdsXIO3eL/uyOxgAAIABJREFUcgx1tgq/1IObZ9kN0Ge+BHf0duCBu6CchW0dhe9kQJ3CAxonGvjzZ4BbvkqAlXnybcDJ41C1FlS1BqcoZCH7QvDUEvVnN68L28bPY98k94hY5ewDf6MdS2DH1xWWL8UzND4vP9agqmEveYUMH/t2qTIOYpCGGSXgxKDJcYaebw57AZf3yr8E0CYeyk8yQqdl5OwF1BEqLti9OGvg+hZR3YXy886OxCXmMbOwezA29PtJKTgbQFd7oso1a8N4OJpE55lYxMjrVB16aznkMZsq8uYN0D3G2LHcG/oP5FyLIqisK/Y9rhpYPhV7sejx9TpQa0IlNVaOpQ/QMUKQzDY9I3Na9MSgBQu5VincMhYuj4SxHXkocn/TZmfCmkcA94TpsmfJdpoVzAC+zAjK61JhRmX2pWUVw/7MqGWG4N7oLjy9SJnJ/ChHAQDL8/vv/d7vgbYtFF88EaMAgEwioaXM7/7u70pFqpzxXt6ushK4eFyHFz+ewK/46gAAVz+EdvvamdnJ/b693cHM4v/Ah68zgAd0Ul4HgFfZkWUAyMZarr5oBl0e+wHgzTATQgp/+IbgkD8c3rSg29O9qVyew6kTsNVvFK1nH9sSkdShZ9joE7A7UQKeFIIwLaGsfuRbw01wNEiAVCe8AGP2SQ2D7cu/s2rYXxKUjXk+g81BA4mh9xYpEC1GuTX00EMNs/4BdHAUbXs3BuoIKpZsB5uvPJRdh+0ximoL+SpvahZq1sDfcwU+rsGzMf2+B+B6ObrPejnS2eMCuicNrw/o3J/6Mdk//Gfgjnsw2MxgXvoK2IWbMLAJerRNUdQ9e2EBOVjmYgbw3qwXk+kbMYGhLEwbjdEcbCjIjM2eeIRl48VqhMudTGbqo/V4j82djSx6uScEwWyUIUlCkojLPdTQFHomuoDddBFGezQr7O/TsDkTDfrwmYfJ+0C9AsW+Ur+FPDmKuHe/gCsvyRZd5H322rlgYXRlGbZ1AipNYbbuR9a6FdH6lwUA5DiCeMB+wsOc3qEqHbhl+s8Z+OYi1H1fhJs9Ad/pCQtIAYWfuxnq3i+FcvDR08ChI4iaA+hnPF2sZQgAc9WSHlIqbhOTo+8bAho7ln3r7EmzSIcy0gDyRmbZ5euCLGDxuuJxAvXxBAsPgvICxLCcTA/MvvTIs4g6QFJq46DHJcURRKcxUlTjkPbBnGAmTxR+dFHK68PDxk1Jt0n0UMiRNhDRt45CEALGzjlYMwfNdI4OgTjBlYLt12Di8Hl5r4Fo96wAM/b7xSXbl5y9eruXkM3cCGO3gd0+TBqiADlYOnbHvhpoNxHlQ2U4TZuzOQHyqNahagaozQEJe3Kb8MbARTVRu+euKmwpPRFlv9FPD8NsXmmJifYxezw5CiAopfkpHoExS9ATpV8jti/DudGRTYzFW9TT9mV4ZyLYo+9f8W9eI8I75lXAzh3IVMDFPef4coXnfe97n1R9Xv/61x/IdzzSDynA3oc//GH8+q//Ov70T//0QQHgtM+fYgT9SDfjWl8fAODahw8eAC58P7fpOgC81iMz8b7rAPAqO7IMANlgy/9ozlke+wDg3C0w4itWui2JCnIEvVx8CKY57sFXfr1NY3Qbz0KKoCZWvoKdfHyV28vyMfUpb3zl/NlJGxh+zqQXIA88LUjKrBrfV5miBI5VjphpCWJWPI/7N2u4oR1+kyhOCRyRSjN2g55/nl6AmzKBCodgl2DF+uIybFoH0m14a6E2B1CzEfxSB25jG5ifg1/ZhL3vPnRf8lY02rPX5ID/aK6N9J4/h/rHT8KudOC+4duQfct3Sdlqpd+CdhYmitBJM2H1iMEIxssibHoCErSxTEmhDW16yqPM/nF/NyKNjSGI5HJ9vkZ/vDDIw3QR2MFDlUxKuv2UwD0Y7Ro/EOHOrj+Epl6XlIpBNodKFDwBpU+sQoAORJ1LyA0jyyzi7IKUEaM8sFTZbgVRbRcur0GvnQcWFuCXqNx2wh5GG/cIcDRbgWHMum2YSh/ON+Dvvwc4dRL+3AqQMvrtZqj7vwBXmw0+dFkqMXOO5by1DVJiiG47DfPUUzBP+mqoRkuYrEFOS5IK+rYtIIj9Z1nOfNpYQBJNsstpNrR+6QiwDlNYVY3nBMu5OUUMUtOFNyB3hchQpU+uGA0ugIoFGxNI5PwO38E+QWM6sLYuQKkSB5DHVJDEriCNFmS/R+m6nDNkMtkHa4RhpJBGQeUbUHV6KhqYQgmMBZiczCLBoAHW1oOwo92C38338oDzaFE8M+ndp9MN5OYIorV79rbdVuehjh6VNgAbzcO40VyUV0+EqEW2s7RoD1MPdjxJDYgpHDHIPD0vufQM210YQntHDm7oqcdrmqKLiRQQsptsaiiOiBiC89hQ7OX3l34d7XMkLUQ0weLNqHlu0oi7dFcqs3+yjPIK2lagDoD5K3Yc53rO72UAyOg2Rrj96I/+6KOZTq75vQSlrAS8973vxatf/Wq89rWvxXOf+1x8y7d8i9yHiipBwfxNVkfYNtNqtdg7MDxJr3lTruWNTyQAfDOAV7GzBsB/A/AaAFczc6Rk/D0Anj9kSv8cAP2/9vuhXcte+P/Ae64DwKscJF6AnBg4qAhmGZh5wOVhPvse6M1RL46bCgAZ+TZigLLoKJLW1Xe7SzNsNL5l78amvMFOzut4NLhdhfEzH6UQYXvCCmbyZzUfhhcgb4ZVvT9SiVN5jcyHB7rZYXxlM8Htc330shh1UTPzRsporFB2TBhBhQRVewWpmpM+wEwdQZRfhMsiOLIW9QxulWUpkWTCnr0M127DDTLgnrPQr3jH41LyndxP3bVzSD7xn+DWBsjnTyJ/0SuGSRAUaUTYTXO0KxGuDFlAWr+sDL0Bi89iNBwf384YVj861gR4rUQ61ZiOJ72cs5UIgyFDOF8xDFGVjzEul1Ki/A2HhRr73ygkMUEVTNCDbQxcTfz4yARqxcziRbQql8KmpIwZs3CpQ9S/BNc8Bmyvw2ATeXQKcfpAYJ+yWBSw+Q4FABo6yqGZybxwO7B+MYCN6ilEm/fCqhlRaUfZFaQ7M1D1GmvOwN1fhrv5KfD33C0lS08W8fxX4EwMu3Az/MoVYJY+jzW4nAuGZVRe+a+hmrOiBu9ykUPNSh4U5mz4J9+5m5OZozmxQ9cSYIz2Z1ACByuk/qTRHBc9hm1646XIVhTEI5llSV9J/+UeQ+gdGoXxuePxNmJXImPYC0hg1MnnUDcEg+F8VxlBog4l1kyJ5yLHAEdR8eFYiLnxzgZ89TCieIB8pw/jtuAUS91VAYAcedqG2b0M3ya75aFWzgd9rarAmjZML/QPOt2A2lyFotoXGtnCbUjaOdSQZXNMG8JIRZtXTiDyy5L369lUzHaWegtImjBMo1E1sArMhB/NaDhjZUFQqIGZGZzzBfJjPDT7AodqYII/snKT+5rbz73PJQ0BHmfBAAxZ1nWSP1weVfTGxCAF+xfeR7lPGDql+Omab13jXypVjVzm+LLI781vfrMkgbz4xS/e9/rH44ECAL7jHe8QFpKgj360rIYQCL7gBS+Q/6gQLg/+DgYW/NEf/RFe+MIXvhIAVSxlB6nHY/MDAFz/yMEzgPMv5PZfjQH8t0MA9zwA9Et6E4CXAbidZhlTfjgBH09qestwH/0+NZ8Avu/x2En/b/iOa76K/jlnAfPAlAFgkTM7OzvuMbUfAN4Ko0eKWbmFHTk8FouUVU4iqe8XgAhXphdg0wzdyglYPSz7evr8McuydKhokFtiliYZQG4/b4plf+hpXoCTZtB8X1VT8bw/IUjyUqGw1j2E1b7CqfYA690YC42B9AGKEMRvoos2Zt1Z7OrjmM2+gK6+GQ17BzIKQXiTY8xYp4a4tgu7xo1MoZoe6d0r8CqCmanDX+pDfc9PPSHXh0RCffrd8PcvI88N/I/9DDpZHbG2WE9rGGS0sAjl387wMBKAF3YwtNzh8ztZ4PEIBhkHZ1hC1LzBjgyk5yq0hhn9zCO10FhPvzM+XACTmdiiEjmsdjVmE49oeB+ObQeZaUg/2GzlHPrpgvT21SobAuykVMZ+ty7LbIwwiWG2ziBTnD81EseetTYi2vXwnF/PodsKtptAb10CFg9Dnb8Ltn4cancdOt1CXrkR0c49sLoFu60RmWXku3PBcoQtBXd9Ef7mfwH/hX8S4YlrHAVWL8H3e7DHboM9c6+ATDXThjpcQfPHXwHdaCKzMfp9mhIDXXMELif4IkOk0XWhBEkQWFZOi/kwnUmGu1Bi4IZsHcFhZIIWtRhkDbkYY5yciCqUR1LK066gK0CYecC7tomW+AIOj6OUhrcEYPP4NuOlkeVdpqGTIcs/YPfcGgZ2HtpEiH1gWfM0EXFOVBnA5TFU50LwGaydgN49K0w52RyKP1BPhDXM+y1E22fkushbpxGtjZSglqbeW2dh2yeh4gh+kYwurWbCEOg10rnAJkdghmSQxTwcJ4d66MFEpQaXhCQUp2oY+AqcZX9gD7mtC0PHH01GloC+GBSk51K6Hc8aL1+4BHr5pIE0c0akzDs6NvRdnLSCkag4PYCVRA8FQ6uirA71KBS/0yYVzu8EgWWbL4KuF77whXje84glHv9RAEDGyl28eBHf933fh/e///349Kc/DRpD81xhMMFTn/pUAYTPec5zJLmkOP/f+ta3UjzyiwDeOHQL23/Teex+1hMFAOmg/isA3j38aWwJ5mrsfwbwOxM/9wYAlNk/DcCXhs/x788B4HNBvfbPfFwHgFc5wGUAyMmBZtD0VyqPfQBw/jYYNWLcWcZRh+nDORqueRomHjU0ix+WPgKrbwNUC8gomuiiH41Wdv1sHrno6sKgBcI2WYfiLFcBWJQHgUhhVcLHq5LvPn64p1nB1MShfzzPmO+vMZIJwFc25tCMgblqhs2BwVxtALhYbDxqvoOeamDO3octfQPms88IAKzZM3D0iWPPT76CrN8MpfIOkG/2YeY98vM96K0ucGhGlH3qmdLs+7gPAsDKV34X7ot3I1/vwb789XCtBfTyCD3Lcp7CpU4qDB+ZkEiHG2A/o1mzwsZV1MA8HjxEZePocjmYgKVdjREr9hjmON6gEjb8/MNVi0GusJFpHKmGUjBZF2GWmWChu6iadWykJzAbXxJmKksbqAwzqNn/5+s16QmMe2eQ109D7V5i1x0Gbh4VLAsocalCVOnCrqQCDhg7Zpa/DLv41TAXPgdvWL7vS+pEipOI0sti22IvbEIvJMjXmItGIFgH7vo83K1fD/e5T8PNH4Vlz1lnB67bgW8tID/7APzuJurf822o/Pf/HdSRE7j4u3+DK2/9NRx9xfei/m9+Al3VRJUlST/qOzPOYtcGG5juUBm/d10Mc67ZV8nyfPAIFOvCEOE3IQZpR1x0jYRQDb0hZduOa4IpIbVSLFxNbyG1DVgVSfZ1Uf6l0tmYrkSuSYpHui6f4SptmMGO2MJwZP0G4mSYENKPEQ0uwFZOCVQz3aHXom2JytcoWijRZHJL2FU3dwM8vUi7gU200SLUzhr8zJFg08N5p9aDyke9xnwdM5q1D3NNrmfFyoaDc07uFwUs+OaMGHcT1+V6Rnry+nZBWNi+q1ETLMkz8lqS9b4mrR6BvNPB+keENxN03pD5m9YPmFC9OzEXVdEd6xEUoYfqS18gP98oGltpmGy8GnIQEwRZM873ZQDI+LXXve51UnJ9IkYBAN/whjfg9ttvxytfSTIvsJWf+MQnJDv4T/7kT3CWVkxsETl0SEyhmT1M1vLtb387PvKRj5AR++UnDABu/PHBM4Bz38ufO40BJOgkS8ED9unSMaOT9xcB/PTEcfyeIeMXmn9Hgzc/3nzonfjPflwHgFc5xGzC5cpQJlxrxWdpMoz7IQGgqUMdGu8b9K0boaMwUVt1FLn5akCNDEw9e4TcHdhNyFqHYW0bvSELwn9PUwIPyshCABuVwaMfR0ZQT3gBUhiiJqxgrqYErmj6IgJ3bLRwY9OGtAPvUU04ecYwJkPFpxioBDP2PHpqDg17AVY1RfVo8lU4NQuTnUfO7NjuNnRikS5RIQmovoW7sA60GnCHng51+zOekIuPpX69/ElE//hJZCs92Kc/B9nXfRu2BlUB0R0bCQvIjF+yqkU0HA2ejSK7N110x16/Tom1ZSxc2ah4PoawUQSX/KxWNTAtLJgdqjpc6dEmxmNBUkEUbErRRrjpNtUKctTR9zXMJ2fkXTSFriZU+AK+N4BpxFJ610yHaM4h2roXTjUFtBi/hX63hgoBCst/S2tQh2dgV1Mom8JX2zCXv4R87kmIlr4IJ0XpWSkrp/mJECeXp7D3nYc6cRju/hVg7hAsy+QXH0B+4na4L38O7qanwvUHcP0+kNRht9eQnzsPc2QB2ambsPrFZfQ/f5cITipzdSy+8gfhXvYaxFqhm9lhxDGtXjx2H4TPqEpJePz0aVDPUPL9IzdYJ2M5BCJkoGLdR3fILjX0LnShPPVMdyEQCn1wzA8myKaPX+7qqMSh147l39guIY0OC8xKsstBle2qUK6/V561Ox14XaMzDCx7/PIVATlWLe71ZVJsZdbvg529CZqLsmWyhAG8MX7PdM8LWHRxC/oQ+yapMmb03GhxaeN5GBu2jcweIwL35pRkAY5JM7UGVLUu4hBvqMim2j5D3zXEdHkAJrNopPxs6eWjy2cEOwH4DE2/RRE8bGGQfsASBTn8YqavENuWR0TQOyy1h1AR9n1SXJTKMReQrFKYrC090Qc9CAA535eTnn7oh34Ib3vb2/DkJz/5oL/uYX1eAQBf8YpXCLv30pe+dMwGhtcA7093/T/svQmQJNlZJvi5P7/izvuos7v6lFpqhC4kEEIYAsQIGCSOAVoyYDmXHdhdA7Rry2KgFcMMDCskQ8JmYdDYzJiQBqTdMRgWhIZhdbWkltR3dVfX1VWVVZWVd2acfrz3fO177p7hGZVdXerOrmqJfLJUZ0V6eEQ89/D3+f9/x4kT+Nu//VvzQ8UwB8FgXiUkF+5P/5EAQFZMKJ1/CYCnSpP8UQCU0P/cyMS/A8C/BjA/8jhL9mw//dl1Haiv8Y32AeAzHMAyAOTvm5ubpgJYJtteBQAn74RAphLkUKIOMT2i+G0dMFUSKe4xit/R6CSjlo0+hU7A8zg/PLpqWlLF2A0AklS9s+WbVUjKY9QLkFWsUV892o2IXZTAzPPsxQLnuwFub0msDGzMVBRsm60rFqIUqOpjMyhIWcEgUI0hdBfKqsKLn4KyD8JJzkFLG0lXw6+FkKsOPTBgBxLq6cxEN331j8MaH/1e3phvmrH86a0jeOCPoS50oCYOQP7Qfw+l6f2XKYFZXVqkwbER6bAam7UJebTIKaMxdHmMe+Kqx2YqjhGSFGOuIkzlj4/QJLoAK7QsoSl0rC20RIIK0Q1BHWPfclPohr2IjpqFmw7QrCwaHh3J9BQsEJRYgpm/QLq5gtQfN4Q0NzwH6R2BCBkllkKqJhysI+77vOOAW42RXrgAPX0M2NiE3V005sLO0uOQjIpbO2ksQhI9Dzd8GrGcN95ySCT0qVOwjh6BfOg4cNe9iB99DJicQXRhEahUkfQVUs8HauOQK08jWuthYyHE+CtvQU1uoHOpi0FPo78pcegP3w185/eb9m+Z5+pbqTm/R+E22/GFiXb5GFAEVRaRNFhFLW5+WG3SEknOueQEN0h5KLi7ilEd+TE1sXBcI2iIPoOqWIcogCL5q+TJMWgllKayyuOUYBZezgVU0gP6q7DrY1nWONNTOP/eQcPvK7T6agsGeDtiHTJqQGyeyUBi6044a08OrzEzd8HJK5UKdQg55K+btq9cMduaeaIhd/6ZEncWUNIkfdiNJuwgAIRn0mOkrpubE7Z+mQ1sBB/SRmxlxRKT8GL+fvXyYYBg/vdy1GT2/WAu8DAdJ3tjWZKQkZBQhcxWL+2oLFZC4yza0NQgWf3bG9Xv6JWEAhCOctY783Y//OEPX8WxuzFXoazowGvzt3/7t+MXf/EX8cM//MPPqAImEFxbWzOt4i9/+csmLu7jH/84jwMrWR/PHZSu9qh64T5M1gLe/Ku9rwCOfR/f9V5VAAkOR+Nj9iuA13NefL1zAMsAkBc0ZiuSA1j2YXo2AKi9CdgTuXrOELldYOxOJM4rgPxiuttcW+Fn0fMOQ9vFBXenEpgLRmcEZIx6ATJbllnB5cGq4I54LBvwmI9VGgSX3i5KYC4hF9su+srBna0EJzYFXjIRI5QOAoeV0uz64qcEfC4qeg2J1UBVPo1QHEA1egyJNQ+RXIGt+wgHNQT+FvSGDd2LYbUIOCR0O0T61l+DtYuVxPWcl893GwJAkzN8/N9CnViE1D6in/hfDJ/uyqCCwCL53dquApLXtxXtzARuuTa2EmXaY6yy0vOvfCQMJ9CUNsjTS007mZXDYpv5Kq1ws8WVQGegs99N+9e2sDawMVXhQspWZwyCc7brataqSZ/YiI5gsnIuU9NK8urIawOszgqssUmozbapDNFA2O2dyYyA1cAs6UkngFOlNYwHrF+BM11D+vQFqLFbYPU2ITqXIOu3wlk9gcQ/CmvzPBiDIXtNuPoy4t6Yyf1VBHlXrkBPzUA+/BCsl7wK4ec/B+tlr0Z4//2wbn8Z4oWL6Cy34bR8NO+mP2GCsWoPlx9aQxJprF1OMP2W18B/378xaxhV2J0StjY5wOSm5RNnzIe3z8ThmUAxyM5WZIoGeXM0ceY+Y2VynguKhG+R75e1bhXnjxXO3Li9aP+GyaQxCW/mghvTkueNAIU8HP22iS4j95VCDSe/MZQD11j40Hw7iTw4/XPQYgyp24DosXoLKF1Hyna8k3E507UOLNWHGrsdGHQgenkruHYQzthwXVf2JESciUk4pH/AiHWKob0W7JwLT8Dp2JuI4ha048EKGrCDOixHGFseE6+WGz4PjD+iZZTL0s4tYUwKy9UiG4JDimwy0/qMK5sVWS0ayJTsdzJBCM9d2s5kG7HSlxglsoGfljQ3ko4dQcTjZg8vxKAFDG/sywCQfLpPfepTVzk/vBCvv9s+eQ1yHMe0f1/3utcZ/h/FHdcz+FxyA0+cOPHdAP7uZolANrf++nkDwE984gF84hNfMh87jhP80Qf/M399JhHIbhxAfllY0duNA8gv3DeMcAAZbn50nwP4LGfa1zsAJOhja4CjAIBUAZcrZqMAUI7fDrckAtGVGditrN/BO1xVfw105Z6rq34jc21Fx5E4EqHIqmC7KYH7sdwRAT/qBTjKAeR+aryRL9218wJd8UYvqrsrgYk2Tm76plpxa1PhzJaNuydDrHR9TNcj8x4Z5l7RbYR2HS15DlviMCbjr6AnbkclfgqSvm6phKdXECUNuCxzSAW9JWB5IdjXk6sK1nf90vVc516QbQrPx/qlv4J85DjkSh/qF/43RLpu5q6XOJBKI3Bt493HUW4FF2/KiG6IAK2dJt38+6Sb+dWxtclqIX0Ai9Yx28z1ILvQWzpro2V8KY25qoZUNrZiYLKaixNMYkO2/bh70bQkqQxu+QvGekVrB64TQUcUTGjYNMZbvwTtTCDVmSVMbM/DSxayaLioDsdrQ26Sy0fyv4S98ATUgdsgzp+Arh+A1VmHHW4gcechthaQeEdhdxlB5kBtsoKzivhCBHFkHuGFDdhTk4gurRrBQXhxCfbUDHpfeRTON74G3dMLgD3Ase+5C4MNicrWZSTtAc7dv5Rl1EqBo1/4/zAw7Vd6AupcxJHNNM9HNssp7GBltlwF59/57auQ61c675nXzO8xW/k9DTQovi7dCNVF27S1Cf4G2kfdpRgkez22f5naMUgbCKwegtwKRkvH+GEa6zxmAseL0KhDeQ24IQUj0rTbpazCdTM+rezEsOUG0voc0kFsDLrN4+ksHOOfya9HC2L1FFTzWEYdKVrBrMrNHYVN25R8KGcWIswV4LsAQBXMQOhMpKZ47ETbcA0ZDWj5ASzfQWI1wfjmvmJ2tDCfiS1s4wVocpl9yNyaqPD/Yx6QmetUm/kvC2+K90YlcAbCh1VDHhWPHYQc/DFgTuRA22aaualWajipDSGfX9rHtS4W/M7zxr4AWPwevOY1rzHxay9k8tC13lNhBP2rv/qr5PLhN3/zN40ghV0oAsPdhhERkbcrBN7ylrfgE5/4xKsAPPi1DADLn5NG0GOtt/KhZwKA5PnRxoUbEQxSAEMVMEPdd1MB/1WuAr4vnyOCRG5npMb/GMZ+C/gZjnIZAHITVgBpBF3+8o0CwLh1G/zcL4/P0cxIbabQziRU801InetrYVjyElR6EX331uzd7aIEfjYrGC6Go3S03axgaEo86iFVoekz/c9Kg/yiU1sOmi79u1IwBONgI8JSz8NsPTKmwpbhAQ4QWz6acgFdMY9W8iTClD5na3B0D6lowonPI1FViDCC7XYh15lz2oflJUiWG7De+BM37bu3DQD7j0Dd/0kkS13ot/80onFysTSWQt+0ZwjepB62f9l6XI2Gc0YgZxZaE2KfGULT0Jitet8V2Ei0qRDyOFVKsXCzFccslGx5dqIEgZ9xnupOioancKlnY4r2bbTPyQVBBIismIx757EZH0VNbJm82jgZh+esmeJK3I7gNRwTWSaoLB07CmytwVJbkM4c3Oi8AejMCjZmxCsSYkxDbRGtRrxTgLjwGKLpl8BfeBiqNgusXTEVtMQ7DGfjLBJx1OTUKnsM2vRn+4ieXoY7N4Xuwyfhv+Sl6HzpUTgveRmihUUMpAcxPobGyxqYaCnEqz3Yy0tgI/Dyw2tIQoWlcxFu+3e/g/j1351bgaSImAtcsn1h1ZpYgXCBRbi8YGrmh0KPcoYwDwtTcNqGM5td/sa9oRUMQUnd2YBSAbq6hrrdg3AKK5gUVbGJnpowN3ENsWK4a8ZKR9Xg5FVDJn946QoSb94oULwkExRK3TQWPPy+aab7bF1wGdx0AAAgAElEQVSEqhw2tjDpxpJp/3Lu7GTLJK+Y53QMOoXw+lBJczsCLpm+F56XtXeLIZ15OOFQvKjo3Zjby5h9BQfg6Ow5FJIIJwOPiT2fKbODujGEpvCCPoyxVYdMArhOjEgFxmSakyxVkOUE58PW2pznsZnPq5eUrAo4jOIrnkeBj7FapCqYuXoWc7Yz0xfSRzgy7t84G8A7Pute/oNVf17XC8P5AgCSX3cj0oee6bMUIJDv4wMf+IBp87I1TZHHM5njFwbStLD50pe+RCL50ChyLyft2vsyLeCNzb953hXA8ssQAI6PGVX2tYygfwvAz9NxC8CXSz6AVFxRRv8WAJ/L90tbDyqGvzdnSRAQEkAOQ7Zv3JzdlFfaB4DXmPaCG8JNyAGkUWj5i3d1C/guCAytGHT9ENKZe6BqrzRtsuseqgNLPoq2d/v2BYgGvzSY2B4jVjDX5QXIxXCENNVwGOW28zSoGDPWIZmcrxklLs53BeaMIIGJFSnqnkY/sdFgxqmkl1wCj9mxlo06PQBRh0uCu5KwnAB+dArSOQQnOmsSK8D2GlYhN32gF8Jq9iE374D1an4fb87YNv0OLKjPvg/yXBfyFd+G5BXfil5Mzhu5Sqz+xZjwbNPeJdDmFMZSYyA1KPCgGng30g3BxlbOGeQnnPYF+qUNmQRC9e3iQGI2cKByleqMr9CVbBVbOMDqH+1dMvtF80P7HteK0FETmHAvmfzpfjKPupcBAh1ZEAFbvBGsJIQzVoO1dhbanYWlItjJMqRNr8bLUMqB7ltwKz3Ii21YkxNIOwmszjJ0cwbOhUcQTt4Db+ERJNU5OCsL0Kz+OQfgbJ1BqA4YhXCopuA4KaJealrX0Xoftu+id+YynPlptBfasOcP4siv/wycL/576Esd6MVlCBkh7kssPLCM0Kqg8qqXY+L/fB/6eaXTWPAktIjJ2ozl+eNnpS2SCUrJE9+MStXwKKkE3lkF9+0UQckKpmKxFc74xcyGqeVsALm3HpXBbPuSB0pFe9O7bMA17XdoEVNk/9JwWVHkwRcbRHBT2nYISEzARe73F1J2OzACKBn7EF1mLQvIyhG4nWzNZiSc7gwgKplhdbrehSV7UGPHgPEGRLxSBL+Z7aUza4Q5xVDu1HbUnPl77gVo9k2fQE6U4SpPZVnEXgWW7yIRTdiOQpi0jBo40jXYKoG2XXMc+axYMTkkU8IXAJtAPPu9fD2hT+PVfEG2g22bYjI2mnMOraVzzt+w/StYwdajNK29vTZQ+c/rehkAEkA9+eSTNxUAmuM0Ev9GXiItYUZz6UdnhBzAH/qhH+JJvFvla28n8Oq93UwA+EJ/tq+r/e8DwGsczjIApAqYNgFFm8B8MR/8ANxOxtvhSKfugp1mADD1xiAP/VOkdd54fJXDBG9+ET3voDGANRfpESUw2y2F+IB/380LkItjuY43GgfH5zGtgjFd5cFoLMvOLCOKsTHwsRJamAlSAwCPNRlTZqHmpnAF20Q2bCGNmz8rGfSoSxVJ3BYCzZSEGVQHDyFxjkBES7BogqsnYckrSHvIMoKdLpLqt8K69RVf5YTt3ebl1Bf9pQ8gOXUZeuwI4u++D73EMy3FLSlMFZAgjx1VWvLQU5ELXTuWO1JZyu+M3MDRZJCyFyA5mk1X4MqAYhALs1Vv+/hNeRrLUYZqDtSyBbMXW6j5GaJviHX0VMvUUqb806Z1yN8DZ9Vk/bIFaYQgW12klQpSqeD2zkLWboFoUzwnoe0JCLlkcmnpZ0nDZ7XUhTtlQy1sIqUwp9M2HoGqcQT2xccQj98N9+JjiOpH4S2fhvInoBIfdm8JtC/ywsvobAWo+DG2rkh4kzUM2tQUBCaazHnz2zD9Iz8A58vvh356Gfqpc7DjrDK19OQGQh1g63IfY5/5NGydmvmgFQ8ra12901Zn9CzIouF2PspM7DINYpxq7NL5T++5MAearpWgWljBmMIUb5ayHdK2JPDWIGUd5Mg1vIxrZ1SsYQ9WtZbZwoSrYMIuq2zkXW5z8Lop7CA1IirZTU37V1aOAkm0XcVL5CQcbBgRhNRjECsnoeqHYFdd2HU6xJAXOVzfTVUvXNr+wJpALh1me0t/Hk5xfeJWfiNLjLECpE4TGhXY1ZpJZCF/V9ixUQMrRUPoACkTboxjAY2gU0NjKLfWuUuC8zIQpNfi1RYxGgFbvTb/ks2nTsmLza5WrAYan0BWz2WjZAG9d9/z8p6M96fnbXd3SP0hB/D48WcKkHhh3se19lpUA7+aV77ZUXDrm3+35xXAibHv4hTsR8F9NSfCNbbdB4DXmBxeCIqoHbqwkyTMHz7e6/UwfuY/wO1kHl7mIjZ1N0S6AjXxCqj57wTE87AsiB9D7DiI7dz36lmUwLSlG/UCpOq3bDR8vV6AnqUg8uzf4rOd3/IRaSYnAJtxirvGEpzecnDHmESiLXhUmuYUfEd1IC0fNbWMyJ1CI3wcA+8YqoPjUNYYUp3ATS5BYwLo0TexB91uAnEH6o4fg9XY6Z24R+f6de1mBwA88+dQDz4KiQbit/+PZKBhbcDILAuenVUBCQil1tsWLxR48EtVVvjyhXl82DYu5wJTHVwGKOMixYbMhDqsIvp5VjOrJWyxcYbZCm4GGlJmoN1xMjBYtbvo6zp8a4CGt4DN+Ba0nCWjBGZ+rEtTYy7d7T7spg/diYF4C6iPQ2ycQmzPwJHrBqwogg2aRPerGQfQjpCePw/7wAz0uUvQE0eB9TVYg01odxL28hnI+hGI5TMI67fDW3wCYe0YnK0riN0JWFGIOGEsR0RsBLu/hk46AeEKzP3e7xsFqj77X+Ce/DL0I2dh9XIBRqxw6akBOld6aLz/fbBe+U2IZMYzM589r/KNgjz+jcA8M3weDnJgi2oVHyX4aJADyxY6+Y+K/XHeNmWXxYbIrWCMCTJbvMOYsoZYNaC6ncygavfhuRm3juKNVDBujrYwMO1fhRaUV4cX5dVYZUMrG46TtY/Z/tVOC3bNBdZXYKWRYTamSsDm3RHPsa6bVWprlgGBjtiE0jWIZOg6YABfVDKit9wsqjEfpiJolVTCwTREXiDSwQSUrMHyfaRuDVo4iHXL0AGoBg6ZIMIWsBqqgekLpfizi2DLcDNT8vsyuyNTrM4td3wrgRCxucnZXoCMkwAdBSgMoSrYhtAuPP08rqHX9Y2HuZbTAqbg+62uruK+++7D/ffff517ePFtxnXLHvX9unFv01QA9wHgjZvw5/pK+wDwGjNXBoA0giZPhJU/ggTGBtWe/LewN5k4kw09ey/0wW9B2hp6+D3XA2MnpxFZESKHKSCkx3joyGESyXV5AY4sgrt5AVKsYI94AVJd6ZaVwNsCkIx0z3banWMSJzYc3DMZYrXvYqoWm8XBEhJO3Ib0moYH2HEOYGzwMEL3EPxkCZYcQFkNuDHVp02kYQKRrkK260j7MdJXj9o1PdcZfG7PK8f+pcufh37w75FcGSB657uM7cnqgGbNlokRk1JhM1bGYHh1wEiubBCaVe0UbTX8ejHmjduWx7YRNC/WWpnkiALMFEpg7oEE+iLqbLai4dgpLnZtHKxz1cyI8ln+szCVQKomN5I5TPsnM/CgWvDcdQNIaPHC4lu6vg4ETZPJLFgJrB6D0z0FzSxaxcpMAtmpwfE3oMKaOU6Wr2Gdfhz6yJ1Z9Nvc3bDOn0Tq14CtNtKEYhMBO+ygX70VwfJJdOu3o7L4BDq12+CunEG/ehg6jowS2fmut2Hyx3/UvMf+1gqaj/8x9AOngY0hSFm5LBEubkLd+1rY7/kDU11iykox15wf0h/KbeDdwB+3Y0e2bF3SIoIUAuSwUYxDCx+TaJJ9m9Ey/n6pMYA2la2cC5i1fxfRjWeN4r3pLMHO84NVEkC4OWdwkMDRq0j8OVisuKpM5JHolkkLMccm9mF3F5A255EqG2Iru57I4FY43SxmUqcB0o0NWE2mdcRIm5Owrdi0lJ1o2PJNLQ9WMhSF8LmpTeFWxifUogG7iLbjqeAf2AaE5Asy9YTCFSvIqoDSqpoUDk01uRaIdCMTsGl3RzawkuQuDikuBB+0b85uj4rByiATihJ4bmJ2o5Rtkg+NjVTe/uXvrh2Z1/MV4+ye8xJ13RcA5uayu1MAwLNnz+Jd73oXRRTXvY8X44Y3uwK4tvFf97wCODn+Zk71fgVwj0645/zt+npXAZsLdULz46yMwApg4c1E/gUvFmUOoJ56GdSdPwz4e6NWs8jF0svoucwSJtfGRldObR92LkjtEUBB1WlZ+EFwR45aMXiwPZaiSoOEeMfZyU8kyAtKALAQgAT0XtOZEfWhujYg5M7JEBc2PRwZixENgKCi4RoeoIOGXERfjKMeX4CyAsObov9c4hyAG50GNAF1BSK5DNVxoeUY0pczlvHmjR25z2Eb6Rf+ENG5DtTbfgZh9ZARbmwQ5BDo2da2HyDb61cGOyP0xlwbmwlFBTx2O8tRNYIPy4abKgNoKq7YYa47V/PMsSRAcwWVmtmcHKxp0/rdjIEDdXq7kA5IpXC2Afl/PE9Yc2n555HIMSNUEHYfOhQQQZy1Kdcvw56Ygdokr6wLVKZhd8jRnIMTXjCV77hHc+i2SfhgyzEdOEh7bePlh1OPQB99GewnHoQ8fC/EU1+BmrkD1pnjUGMHkV48B1mfQbrZhnRq0J0upD+O9NI5RDN3Ir34NCb+8ENwx7ObGrbhmuf/HOmnPo/08lDcEGoXmyeuoJvU4H38bwy8ZkV0a4TMSntEVvzY9t3VB1AUIoUhIGEFlhXFzFQ6NbnMhYLVt2IEbgeJrKCvq2g67W0rmMAaGNQSogoaSDf8HNjJBhzR3fbaE/11KLsFK7Bg9ToQaBvLHSqzHRNawMpeitTx4fghZNeCM1iA8mZMBdDpszXP6xDtp2IIZmwHByD8vCpoT5vtyyNVrGYODZ+124KtsgQSQjArNw83r51bwZjfedzdHmQ6CbgBlFNnIDUGFLxoDxCxUUUnFu8eaFXjm1SUbMd8QCK2hDlvhNaQJUCYb4RARCZSsQB9xsuS4hDGFubtX9JHTM1QefBfANPn0SsL3y8rgLyhLyy+HnnkEbz//e/HX/zFX9y8C9EevPI+ANyDSfw638U+ALzGASYAJOijSozqUFYAqQQuWhkEgFZnAeqOtyGde+2z2rt8VeeS7gLyFPrejFHl8aLbk8whLe6qU/RitcNfbtQLkHynUTNoVkfKMUysChatxvL7q9AMOs9BLQQgjlaIrSyPlnhG2CnmaxJLXQtzTQaqC/heAodKQQuoqRVIyzPqXyE7kGIMQe9BJN4xWINFOGoDqZgGOpcBmUDhbqR30Lrq5o0dAJBr28N/hOSpRST3fgeiO15v1LaX+65R9FZdgVDqYWVPyR0+dfwUDSdvLxq+VOaPJtMU5J7Rf66XS1bnKu52zi1NiwPPNUDnSi/BRDWzeSE8ma2mWOjZGHNT1IM8C1cXptDc7yI2kgOmElhxl7EZHcWYX/YE7MGERUQhnKYDvXQJujILyBAivJhl/fZOQes6rITtRI1oXSBoRJBLCaxWwwhC0n4XYOXv7GNQB18K8dSDSA68zPxXzb0U1ulHIGfvhnX6UQzm7oF98kH0p+5Eevk8ksiC98Y3Y/znKbjLBgFgtfsQxF9/GOnTQ++6xPIQd/pYe3IDwe9/AOFdrzTbE2RkSt7hCPL5JTYsV/r4vfByD8w0zWLkeN6ziltsN2oF0xBZHBvFIITmNXIB86tlgAFCZB6dhRWM1i5COY6ql4k8dAI4SRs6IA+fwO6yqZ7H4iDc5IL5ncBH9jScWm6evL5uwBurgdhYMvw+gnXSJhyVcYtV4/C2UbvcBQBq1GDLoYhRudMQqtQm9ljNzJOI3DkIkYFDZY9D+LSWqkDbLdALRtoNpJZAqKuIkwCeEyPRTAsRpvAc68D8vRiWUobicHVLOEXN7cO1yW3NWuzMEC7uRQv+X9H+pSF0RVXKDeIX7IJQAEDe1BfX9c985jP42Mc+hg996EMv2OveiB3fbAC4uvH3aDZHghCexwdvt7uYGv8O7mG/Avg85rH81H0AeI2JJPBj69csOCKzSymrr+yzfwM9/1qgsvf5lCYRJH4MfbcJZWcquFiOI06HSuA4kTs4ZKNegOT8hbvwoMokeJ4ANMEdHcyXhZUtFBSAbEaMF6NtAxNAUlzpWzhUVVm1wbLQrGgoJeA4MSxj0ZFZwth6AGX7qIRPI/QOo9p7FNKZhoz7qJAfZU8b01yh1hF73wIceu0endrPbTcE/awI0PSbIz31nyBPPIWkeQuib3m78QEkaIikjUvdCEfqnhFtcBCgdRK1XYVl2sR6uNMkmttR7EGAvF1tsi14pSoslcBUFZ/vxGBiiJWLFMbd1MT79VWmBLZFiii2ELipMYUm/4+cLS7YrAQaNauaNACQKlTeLbCSpDsJbJrh8dxYOwNr6lbotUXzd+b9isEFKHsOIrwATbWniWHrQ15qw53xEV/owp6aRbrCtAwbKVXNq5ehgklYl85A1eZgXWHVtwYM+ojaIXR9AsmFBagjd0M+/CD8938I7oFDRnnJGyt+1zzbQvCffx36iSGvVkrAm/Jx5TMXob7lO6D+p3ebhrdRxSptKtIUJDCGrzcSfZgVu8gpI/DOfBmZj83NKH4q8wGp6B4CF426PdhWAtfsvjFuzk4I/h8rr9mNGK1gbDtEL56DL/pwWSnkFgMFy7Xo4AIdpfDkZUhrEqnw4MZZ1S6JK6Yly+qXohJ46wxU/Vaj8GYMHIf0D8HpZfOhRR1Wjdeh7K0oinYGwxZw9tg4RDx0IpDePBw5FIYof9JUEs22YhzCzb1OCcyCqtl3gjlYrgPpZgbQsWoiUQ5i1Mw5Tk4gASznNlLk6GVzy/p3Rllg+zdTA9NOuu714Nja7KOgpdHPUmx3iLNc66L9C1mBX7rOPbdv8vU9ix0e3nyUAeBf//Vf4wtf+ALe9773Xd9OXqRb3WwAuLz+D3sOAGcmvp2zvQ8A9+ic2weA15jIdd6RW5bhh7ACyGpgvb53dzTPdgzt5An0rSokvSK4AKgG+nqYXc0qVLnFO6oE3k0FyerSqBUMFayjkXQBY5gIAhmwuOUjTmgwapv22qQHrETALbUQa5GL+XoGZRwa4ZJ0ThsO3YeyPdSSRfTdabR6jyP0j8IPFwAtESkP1eSsyQemcS4rJFHrB2BN5t6HzzY5L9DfrwKAS/dDP/z3SNIxRN/5MxhIspJSrEbCcADXI4km21fCNcDPz/mArIJulhU4pffb8rBd+ePDc4yFK4GXAzUXl7uxOU6H665RW5qrnpNiI8l+P1zPnnClB8zmpyRB+8BYZqSY9k+hmxw0+bYVdwlxMgHfzT3geoBbk1C9FGnUg2hWkS6fhK7eCqt3xXgDansmqwiqSTjqCpSsIo1S2E4P6swi7EPj0E9cRHrbHcCJM8D8ERMBl07OIT19GnrmEHDiScjDd0MdfwTqlpdBPvogkua8yZ4d/4M/NhQL+lkWNAsqMSv/9feQfoHetQXIceG1BLZObaDbdZD+6V9C5a1DzkTWws0qq7sN8gPZPi/XCpkMIkuVKypVM0P0TFXipAqyaG8aL8GtzArG+P1RrV7EAGo0vUvmxmyQ1tFyFo1xMoeMbLh+3ortDwzoksEcrLC37cWX6DE4FkVQbAXbsGQfdsOF6jkG9GlRg3bG4PTOZ9swgq/sM2rXYQ+GYM9sI2bgRFlL2vx7JA1EBXMQOqsI0mGA5s/bc+1PQ9ihAapwfKR2FYnNnGGFMK1CyQoSPkfbuVJag9Fvob7a44+PVGx2TSIwIZAj5twV/ph0DTAuMjvbv6wsBqp6Q6p/fE8FACxf1z/60Y/i4sWLePe73/0MZ9XXxsP7APBr4zjdzHe5DwCvMftlDmBhEMwW8I0aVnIWEVtwTt521hV01fD1mRRRthUZ9QLktXZ0YdzNDLrhMsVj56ngM4/T7ppFjwkgl9Z6ODpZNYuplyqEEEYJ/OSGwMsmJbYigfEgMpUmLoKujiCFi1p8BX13Es3BU0jEBGw1MMT1MJ1ARV4w5RhtNWB1zyM59HOGgH4zxygARLSF9MF/g+RSjO4//TWj8NgYWEYRTSuey30JVpo6kTImxORU1hxgNRwKFcqfZ9yz0CkdFM76OBMYcoTCFj2hHVXE/Bu5gNyclEHyDwkyXSvFbE2jH5ugVFS97MlOqiHJK7QitNzLWI1vwaR30Si6B8k8at5FY03CUo3wFeTGAFYlMF4q1tZpYOIYsPwkUncSCNuwVReKat1kCXJAW5BN6KSGlFxHV0GfOQ/ryCzw0HHou+8FvvRlpC99NdIHPo/07ldDf+l+yGPfAPXwA0hmboVeX0d4/gqqP/JO1N/JnPosZYeLMCuAHNWHPgrnk//vEJS4FXj1FNFGiKXPXYL89/8PrOkD2MrJrjzHOR+jil/uwPA0dzFEH63+jbsWtJ1VtaJEIXCGxtDb7V+mjSQ1VCgEyflqgcW2Zg8dSXWt3LaCSRIPrsN4scwWxhlcgRSzJiNY9K6YFqxCE1baGwo01ttAYxy2SKBXNw1vT7Vug7V5ETYtlfjumjOwrVxgUnD6op12TRmHM0sRMQBwpAJY9gI08+9T6ZuDVm4rupkfoT0HuFVoYSPU48bLMxUuItU0yl5QFJJX6ViBTYxFT3ENSVF1eqi7HdMmpgm6oQmmTm6CnXH/MgDG7kpmis7z1pJV+C9Q5Ntu15WC4lMGgH/yJ39izstf+ZVfuZmXouf92jcbAC6tf3rPK4CzE2/kvOxXAJ/32ZHtYB8AXmMiWZ0w1hCsNESR+Wk290bkcT3Hz9JXkMg2IreV5ZamLjpymCYyqgTezQswq2kMx25egBQpGGZ2abgW1aZtUAByoe3giaUObp2uGf9hs7DAwu0thZObNl4+FeHcpodbx5mjSz+t2FRRlG2hmqwiFjUEzChNlalq+J2H0BdHEag12ANaYMzBGqxC3vqz1zMtL+g2POZs+zNyqRjpo3+E+OQyku/57xD7M1ijBkBpJLYLtuGpICWoKKLh+Dxy/0LJluNw9jnNrthZgZ30aQWTfQ0p2qEdyWYObvg3CnT4V80FmP1ERsn5KapuivMd4HCNHuOlEpcF1AUFBha6qomZ4CSUrkJpD4G7DK1cOCIDW3KtC2eiCrkZG3U2KnXY609C1e+AWD/OVGj2KU0qhYzH4WAJySa94mLonmdi/Pjm9OVFpK1xpCdOQB+6DdYTjyM9cjfSE08gGZtH2m4jWlyFdeQ29D/3BdR/94PwXvpyQ7oviPeFGa99/PNw/+9h6027Ptw6AUSKS393HtYv/88Iv/NHIGm6XQLSBM69UU7gLhQIVsDLvnQEfS3fMfPOTGdz7IKiGgiY9q+IEMkKKEhpeUMuYM3ewEA1DSeuanWNFQwrWoOwiVols2PRMSCSDnSlbvr+TFwhmJPuIZOIw6ESz7TRnYBCCxdi7SxUMA8EPsRqpgSWwUE4/tDTb/vcjCkEH4JC6c7BGQwBIAUlQpZawqwIpkORjfJnIaxsv6Z66OXnhj1rqoDKrkFZFYRJlmGM1EOY1nIhCDNbsnOS80cDc2rhm94WAmYdKxfIK6kJLW3ya0xCC5xSUg7bw8qIyxSq5P6N3Iy+kF94Xt95c1+m9rz3ve/FgQMH8LM/e/OvR8/ns+8DwOcze/84nrsPAK9xnHlxICDgGBUH3JDTI+1Dy/MY2BNIyZgeUQKPAkBen0f958gLLLd8yT8bNW+tOfY2z6z4XFQZ+2LLJICcXFHoxQnGmlV4qUYI29hizFczU+i7JxJc6rg41BwgTHwEbgRB0YOtEMgN83pUS7rREiL/AKqdBxFa00bk4PZOQol5A3Lk/A/ekGm91ovsCgDPfAzqqRNIXvodaM9+I3SqsJE4cBifBWChm/HDqERdZUZePjj3bC928k7gVEUYVXB5zFZcQ5xnFWW5LzFWGVYDD9dot2GZTGCuiQVwOVjVaMfAVmLhaINea4CUVHNnYHPMWcKmnDaWGxPBGbSjI6g7jC3rQsomPGfdRMKpnoLTsKBW1oHqGNI4gt09B904BrF2HMo/DNE5i5QtPunARg9Juw432ECyaMGadKCXFayKB72R0QVUZ2BMptVGF1a1ivipM7BvvwuDz9wP8Y2vQ/+Br2Diz/6LqWoWHpvkAHIRNma85x6H/R/+j+0p0q4Ht56hhdWvLEG/6vXo/PPfMedfN9Y7jM6zdm9qPBNrBIS79IV5s1PmwFL8QWPp9fy4UJyTeQFmg+1fqV10VQVVO4RXWLykGh5zmJmRy+3EMmwRmVQQzpPvZfOR9hOknmu4gLQ5ohVMIuYMgHJUxt9LInoMtg3wkTRE7y0Y0209sOB0zpiMZTl1D9x4yI0s3p9ipKLM2shm/p1piHAootHOOGxd8v7zZiEw3N5UBK1MkcxKPC2CzH7QQOq2MjWwrdFLmA3swaIaWNWQWJkwLdGsUGfHRyBCzVmH4A0J/bCZ9JOnDJXbvwX/j/dGNHzOooAlVNcIj7d5oQVg3P5wL8Av/L7z2k4VcDF+67d+C9/0Td9kIte+lsfNBoBX1j675xXAuck38JDsVwD36MTcB4DXmMgyALyqNbhHB+BZd5McR9+egs4lc71kqAQetYLhwQyfpQrCduVV2b/C2iX0PEVgb2Fz4OJz53q4ddyH8OkzJqEcBy2XFhEw7c7pamZNMl6JEEoXgUsHMMPQgpNGcGUXiVNHtXcSg+AWVPqnIRMNy23A7TyOlOR4fw5qyhB8b+rggkDLn4mJoRm1Xvoc9MnPInKPoPeNbzeCg0t9Vus0XOEYcMx4Nx6iKNFXHQPGv0ml0RtJpaAVTMN1sNxLDAicDLiEDr+SB2qeeS2KQQ7X/aEVTFVjoWeBMWaz5F9Skcnqklukgmyio8ZMJbDirGIlOobZ4MlsYYjNgn0AACAASURBVFd1AwDVwILt5hW8lcuwJ2eg1jayyELLhWifgqzeDmfzSShnEqK/CJ16SCOaC29CLQk4kwMkT3UgjjaQHF+DODYP+dAZWHfeCvm5h5C+/F7En/oc0m94NeIHHgSO3A4ELYz/779j2r4EgPyO8YeLsInjunIWzp/++vY5kHo+nFo2J93zbfRUE933fiQTdyDFxgjPktDNs3TGqRypJLEqm+RghcfOePtZ1rbHIl9j0ne2VfKsbwUiRFtmtIQxt2vSKzhIdaWuxvyeKrR8RuhV0JETaLkXYeelch1aJvHDbNdZMy1fXZ8BuusQacdUA7XyIawMMOr1LnQwDicIoVc7sJMNyMYx2HXHGG/TJLo8FMYgoiHgU4L/Hqp+Uzsw3oHFUM44BKkd+ShbwRjSQcCWcHYTQzBJUVBit0yxl3zARFbhiRAhucgmEo9CMw++3UfLW8xi47KEuF3bv3y8sH+R2oIrlEkBIZ2iqnxzw110Xii8480Bf16oqiCv6/wpA0C2fn/wB38Q3/3dN9eR4PleCPcB4POdwa//5+8DwGscYy5SvDhw7FYZuhGnR5qcQog6JKt0VOTJMcR5VBVvwbvP4gVYsS0MSm1Iw5ka8QL02Zp0rlYC++jg3LrC4ysxXj7XAB1LIr6eIzDpAythisN18ncsVB0NX9CWhpFOFIOQID4wGbGVeBlRMI165wRil4kTm0D/CrQ3B69/1qhMdeuV0K2bFwG3vUAqBcb+FQCQICXavAjniY8YQ+Xwm9+JbuIY8NJNgI1QYrriYKGXnSds47KSVx7kXYZUUBtemmUWSGKTmit2VAznaqw0ZWCBsXEN3zFKY1Z2xypZIgLBDTtpfWlhOqB4IUU3tFEnD5DqUdOEo/7XwYR7GTINEKkKxoPTRqzg2p2M6B8GcPy+yQjW/RBOy4G+fBZp61ZgaxkWLUyYHNE5k1cCz0DqCdjJElJdQxrT9LsPdTmBmFBIHrsCcfsU5BfPwL73TiT3Hwde+lLEjz4FPXsIqt3D4NIqvO//Z6j/kx/YXtQJ/tj+5TAL/fIC/D/+te3pS10HTj07N2U/wdKXV4E//3uEeetRpBqbeYmbbUjyAenrxwsbz2uTFmLMl4EqY9+MEpjCBYDJIEPj5+zYlS2RfMQYGJTHo6aH7V/mWKesFGbHmbFwvruOTjwPYVH1mokwFKum/C5wcwm4g/NI3MPE17B757OqtzVjRDYcbM+nW1uwWx5S5cJeOQvtNJFO0bC5DxWJHdm+Zk5YSaewKh/arsCOhwDPnE20bcl9Io3wwx2WRpU7ayrD2+c/K4QiOx7SnoLjJpB2E3FaR5SOIU0DA/6EMTXi3aSCb7cROFuIDSjMjhUrflbR/tUCdq6aVrn4w8wP+ZFCG0upOgUipUQRXnt5zeX11xhL52pxVgX3EgwWQiQK/Yrxcz/3c/jlX/5lvP71r99+7Gvxl5sNABfX7t/zCuD85DfzUOxXAPfohNwHgNeYyDIA5EJVBgZ7NP/Pvht5DgOarjq+qT7Qn62vhu2KMJE7zJ9Hq4C7eQGORsIRD1by2LHyG6J/35cuRVjtS7z8QAOJZJXLMlWqlgNsJCnuaCksdGwcG5MIJYGIzHI9RQz6BiobqMRrhsdY7Z8DFygiGGfzccjKrXDiVdidi5Cz34+0evTZ5+MF3qJ8nHn8aQnDhah18aPQV/rofPsvIZLkpCksRzaihCAgNf/r5bgvgMZqlHm70Qx6ObeJKb/1hmej1C02aSIO1dj5OFhzDLjsytSohAugUrWpes2VwDXGPdEuBjjSYJwCjCpT5ZWvaf8s1qKjqIlN1LzLaEdH0fLOZCkMbM+JPmRHwTZ+KSmwchbWzBGkF09BVw/C6q/Ditag6SXXWzCtelrEJOE4bHsVOmoYA2Awvq6bmOKhWu0BgQe91jNqXYpOwour8A5OYeNvPofWn3wUssHrd2atxLll65dRXJyvdH0J9h/8wnCqhA3RzHwQOS59ZhH6/f8OauYW0/o29i6sGplKVCaS2W0Q7JWV1txmzKPB9vASOOmJ7VxgSyvDuy3oElU7gueS/GmhE9XQIB8vf2pdrEGqijGGrllb8NxNUwFLJBNY8gi+gTKiGjQa0AMNJ14wZs9STMNNsog42Sc4k+a7w9+drVNQE7cjDRpw0jXIpAonGSp8zXMY4TfIDKM5TBVPFuFr2WPaacDWQ7FI6gbbVT4tWrDd4Q2LdOfhOBkg5PuzvMAAWGkFCNN5RFbTKMITE1WnUPHWjOCDn3egKBTKXnMgAzh5+7ds/0IuIJNsyukfSGzUiIp3GYVIqABqBIC8USAg3AsgyMozv+c8/4rxYz/2Y/jd3/1d3HPPPbufTF8jj95sAHhp7Qt7DgAPTr6Os78PAPfoHNwHgNeYyDIA5O+bm5tGHLAXF57rPn5qBZHqI7LHYVOBOKIENiq2UtuX999lMnxFWDv+ztetMtKztPDxMVajrvpcKsEnTzOMHrhzroHF9R4OT9aMlxqBJcHJnWMUggjcOxVhoe3icDNEojzDlTI8QEsiSLagbBdO0oFQXSTeJIL1ByC9w7BTCWfzOOLDPw24N05g80zzXxxnqr0J/ghSjEfY0x+DOnsW/W/5CZNvvDGQSFLbOKJd6EagtQhteRyRKapjqc3PM1nBjAcC3RI5s1z943ub8CyscqIB4zVI02ITHsKKZA5yyP/bimzjwTZezYQg9GBMTSVQoumuYCOZx6S7YKp+3WQWY/4ZaM14sMhUaeVWCqehobraRLnZVR/W5cegp+8x/02DGVi9dViyA40G7HgdiZqFoxeQbLYgglXItTpELYFcAayaBXmpB3vMR3jiMtwjU2j/w0OovublaD96CdN/8pFt1W/Br+UcEwRyUUe/A/zOO4eHx7IgxoZ5sKsnexi89Z8h+c4fx3re/iUXlnzSAatyu4zd/DB5/prA3nzwmS0j/mC1XyFWKYKSP6Zp/1oS/ZgtTo0gF0tQ5lt3NtCVE6asSysYei1GSROOzczbPJqt20VaqcF2UugOrWBWId2DsJL+ti1LEtXh5oBRrUcgcVC0LJMmwnYxM52dPE+4eN/KnoQoiT74uE592Cqr4nEodxJCDTOCtT+2nTHMmQOpHflNgxJTEN6wzawoDCHwNYCQ7MaDiDCJWPqoeyvmM3KERryRzR9vACXPsXyfsWZ1L3svmqblBNaF+ldbqCpnR/Xvmb6bBINFVZDfU543PGcKj9Znet61Hqewj/stA8C3vvWt+LM/+zMcOnTouezyRfOcfQD4ojkUL9o3sg8Ar3FoTBxWnBulpik2NjaMQfCNICdvv610gEQuYmBNwnaiq5TAo1Ywo0pgtsFyHLG9S3LPRisldZLfR0Ld2/0IX1zYwlTVxfxYgIcXtvCSAy1zMTdzkwLHminOti3cOxXiqXUPd02GGMQeqn4IOyUYiuEzaowJB5aAPziPqHIE3uZxNuloSAdn/TEkt/zzvU1SeY5fuaICyKeTF+T72UKmFj8NnPw8ojvehP70q7AZKsMm6ygLvTAybceqhe1qnzE1ti2jEB4dU4G4KspssuqY40SQ59NQOu/SZVYwvmkfh3GCwCXUASp2ipkaq38W5iopPJpBK3qqZabQVbsDmdJDMMCM/xRCOQXbSlB1FxEm0wiczBxY9mw4lQRyvQ+7VkEaSWBzAdbEYViXHoEavxti6VFobwpW90pm26IqEOkWZG8SjriM+EoN3tg6wtM23IMxwkf68G6rovPFRQT3HEH3yVU4s1OQzaOo/+wvbbd8C94VqztF7jaFM8G/KgFAdjDHg+38m/ayxmDyCDZ+6V8b25ui/WuOl00zaNZih5e1Z7KCGa3+seUubBu9WJp9TPu2sYbhKNq/iQzQUQHG3J6xa+FwERl7EyqBKXRq+peMIXo7OYAxfyG3gmF5UsKuCVMRtTuLlFgB9RasNhNCyIMLYGkaeWukbDEvXzatXwpSLCuzrKb4QpSyfw2gspuwB1n6SDGYHCKSIQ9QenNw5HAbo/xNh0IQ7U/AtjLQp60AdjCsximrBccftox57xHZ0ybaMS/wXVX9i6UHK587Jn5YueciOX/sIHCwJe/aGg45wyPXnev56hY358VNRFEV/GqvzQSAHPyeF+ONb3wjPv3pT5vUp6/lcbMB4MXVB/a8AnhoygQF7FcA9+jE3AeA15jI3QBgq9XaRTCxR0fjGXajklPoY8ZwjrhAdOX09pZs+W0lQ5BBwNcuVZYI1kZ0IdjNDLpipRCswJTG6aU2nt4McdtExTj4X9kKMTtRg8sqlNkuxVwV2AyB28cTLHYdHGyG6EYeGkFoDGNh5UKQpI3Im0CtewpxMAu7uwgn2YIO5uC0n4Y8tHPRf2FndPe9c1FhMDwXFV78WV0oWlCqcxnO6b9AUjmG9h3fZ0LHlroS2sqyaS/1pVkQVaLRK+X+TvgCayHb4tlrGoDnCCP6KAargVziadGz0o0wZbiA2V8zKxhmx2oTHVdw1GaCFJGysJWwQpjx/7YGQCunMtXsDnq6YTiBU8FTWA3vwoS3AMfeQj85gJp7EZqm1qk0NjJytQNnsga5ykoXhSA27NUnoSfvhlh8GLJxB5y1J6DcCdjkb2ofaUwhTA+qXYfw1xEvBnBbfUQXLNgTAtFiDCvwoAYK0Xof7vffB3nXPdst33LFuRCEEAgG//IdYAu2GNZE1YgEOPpdG93lCOu//zFz/mmp0M/j9MxCbnz/0m3luw+FyMSPDcdo9c/Ms2djnV6OWScdk2y750CS7V+anG8mFINoTPgdllrNDstikAp6CLxVdAbTRlVf9zOQrWMB203M/RVtYZzBeajgsKnYikFu8mwzsSNvBYe+Icg51Sjj4eVgTWsPdji0cDHfQMuDFQ6j3/gY28pOSRgyagZt/l22ggnmTU5xMQwgzCt7/JSpOw4hGONGwFlFhAlYBfojmFMBdKmFG6nM+49jZ/t3aP+i2QPWNprP0/OvOG8K8QgBYMEXvJ5ODdXnfA4r0GY+0xSvec1r8MQTT9zw6/xeX/P2AeBez+jX3/72AeA1jmkZAHIzVgAJDLgg38ih5DmEaROSLSERop9MQueL2vV4AY6+190qgEwGs3d8rhT/7dSKIWrfM1vHqZUeZhsefC7oUQzLc1FzNGJtgckWLS/7fcxPkGgbPiPh2DSiR5llw483EAUTqHVOQzoNpEkIr3saqnoMItyEmv3eGzmlV70WgQdbvlw8+DsrvVmrSpsf/m4f/7+gdQ0b99wHpTUu9TQCKzXHpRvFxoKHQoKVHoUYw0GbHQMuZWqi3UaVq/M1FypNsbCVaYCnG942gDlc80xL+Xw7xOG6tx1fxqrflYHxz8j4f6mFbgzUc8WpsShJPdTsNnzRNvnAs8Hx3BPQReCsQCZ1OGIrs4TpJnCaAurSZVgTc9AbW7BkiNSrQaw+AdW6G2LlMcjaLRCbJzPxwmABMqG5dxsqYWScRJoAaaQN6GNlK1wO4dcV1h5fg/ee96HabG0vts90wNP3/DgQDjlr1mQNdg4II+mhc3IJnQ/+jWmLU8nLTOyrsn/tDL6N8v74muRlZlFlGWeS36EBW+f5G2o55GMOv+OMhWubyDMbAauofvbeUkXT81xxksfCURzSVtOo22vwmB9sxCAVOG72HN2NYekIdrMK1UngyEVjv6Jpf6zyqmzXg6hmRtIm7i3NbFrMa/ZpHr1TZJTGzIQeKn2vMoMeSQMZNYNW/kGI3ArGvF+PGcHD+ZcW28ISiT1hYh13And2GGhUn70/ijw0/SPz+Y0lr1nZHwv7F15TKEqh8KMAinvx5ef1upwuUyiIr9UipgF5QUEw85sDwBMnTtxYqs9eTMDIPm42AFxY/cqeVwAPT72Kn3K/ArhH58s+AHyWiSxaBNyMHEDywQxX6QYOLS8ZIUhsB3CdPhI1hkhnd6zX4wXIpay8ZLACUl4wuR9Wn6yS/xlJOp88lRnI3jNTwyOLHbxsrgFL2OgNYji+i5ZLLzrgaF2jkwCTQUaZ9xxWb1I4dpS17mwNL2ojduuoMKWA6mpRQ2X9IajqUdgUuUwYddcNH7zgU4XK48xjy0oAgX5h+M2/c8EzP2f/Aun6KjZe8ZPox8JU/zqJyWgw7cMFIjADBKwdptBmCizy+mzDLSuGxdliBiojeempA2Cm6iApmXIfqvtYG8ToJBpHGv42MKwJtjupvk4xVU2x0bdArYSgF6CJac2yWukJGOqaaVNOB0+iHVMUsgxh95AYVfAq1IDizRSWraEXFyHmZqAWLgKNWaCzAZOG4o1DbJwaAkDvIJz2WST6INz4LKLeHBxrAfHmJFx3Ff2lCoL6JraedlCf6GFleQKTv/3e66qqpL/308DW0LzYmm7CltncJtpF7+kVJP/rB7E2fcw8xuzZrNWeXc7SVIPqd1ZZyWwwWbWkLJikluxYsRVLLmWSWgYQhiXEPhOI7Vxg+jNS71p43Y05PdgOUa6Ffhyg6uccv1RjzLuMreigaQePuQtGASzZtuX3wM6pJO0NoNoyXMB0cx122oWqHIHVW4SN0FikqMSH4/ZzlS1ZpsNqqIosCDUEhPx0SgUQVNbnYxQAGkBXbgF7rPiV84IPmMrwjufngJWPyXQMW/Il8INxiOD8tqLYHI/R6p/0tykyipVZ0/61TAXcnO80wiGHWAv4z6H1e70XiMLCi4CQ392yiri8DwLAon2cnTspXvva1+LJJ5/cB4DXO9lXb0cy99Y+AHzuE3ijnrkPAJ9lpgtuEjejCph2AUW74EYdJK3WEKoYoUUT3z50WttWAhN8sDU4BBVXewGO8gC5YNrDJHbzVPLVmDpRDIK8+y9ki8qRlo+z6wO84mALMvdvSywbTdcy7WYqgU9uWrhnUmF9IDBbjxAlwhDlRUoFaww37huFpaN6cMJVDKpzqKw9DogG4N8C3XjJjZrO7dfhIsGWLwejoFgJKEQgPM5cGMqcInXpv0FcfhSb97wTMePSoHGxpzBI2GZ1sRpK4wHIL5WtNbZKHnX0oDPCkVLrl687W3OxVpIDH27521YwbNUzp/liLzHHZ7yagX4aTheAZTZI4bspzrUt3NrM+H+gSjg346UVzEYyh5rYQsNdwJXwbsz5j2eyCeWbhV8OfDjBwADBdBBC1B2k54/DOnw30nOnoRvzsNorQNIHfeXs3mVoxvr1FiGteTiDc4jiQ/DUGYTtA/Ctc+iuTSHwV7C5VINf00iOvAH1t//odR3j9H3/A7CStUM57LkxWFGWdqGEj3BhDVtveDui7/lJUwXk8JAaUQhtYOi5WDxefkFuyRudfskg2tgYm/M+2w85iM08CYQWMxTyWKXK+IRPoJSiHdVQc2IIgkHz+rRHpxClDltHGKtcNmCuG8+hGWTJHJp3YWFk5pe/250L0PYEUK1DtJ8220iMG8Wv+azWOASGwMw8llQgkqHvn3ls1AvQYY7zUC1sfBz1UARi/m0PW77GPNopiUbscYg8x1hiFt305YhjnXnl2QMIbzHjNpLaqBo7QoQiGRi6CEekaJae/R4rC27+O6kHzVIW83WdFM9xo6JFTCDI7/uotyBv/owBeX6Meb3/tm/7Njz++OPP8RVfPE+72RXACysPotncOx5lu93BkelXcoL3K4B7dJrtA8BnmcgyAKRBMMnCZcLwHh2Ha+5G6xixvIJITEPTWFnY6MqhYrYfyx2Zv1Ixl3M4mP6RF5jMg6xquEUWU74ZF76yB9r51S5OrvVRdy30Eo1EpXj5wRaubPYxP8ZYLEaOMW4sxW1NjVNbFl4xHeOJNQf3TA3QjXw0gj5s5oWSQ5WQExhBOy6C7gWEwQy8ziWIpA899s1Ig7kbMZXbr8GKH1u+VP8R7LFKUCwWrApwwShaQ0VShe6dQWPhHzA4+kZ0xr4RvSgxbV0WmS50IjC540oO5owAZJBA6swKZmmkLcw3wtYwhTRF8YnCkXqV8DwbU76NK/mBm69yHqk6ZnEvy/zlIP/PpIJEKQ7np0SybQqdoiE20FVjGHcXjZihJ8cxYUQhs/CtZZMDq5IqhNOF3ExgBwIpFUK0Y5meAM48DH3wG2Cdewi6dSustTNInQqs3ibRCNh+JLFNxS3YahVJd9IIDMJuFeDxjizI0IL/o78M5/D12fykf/SrwKVT28fKPjABa5AbJbs+kvUONurHsPEL/wqKXDnevAjbzDXb68/gBAOmfvRKfEGzkji0Kx+qh5kLTCoEAfwyPR4DZ7sayHzsmt9FFFMM4mMqYCxc9mp+2s8i0iwLfrqBWrCJKJmATgWqORdQhZZppfLQqX4KES1A144ijSWc6ILhHBrVrczAW5n/V0yGlHU48TDqzWxnTcEJh4BZORMQJa4g4xftNK9UEoiKukktKYa267DzPGk+xqY4gipi+04k1h1IcnPmwivPctYgXCakeFAUs+RDavYj2B3JlpVQOdugL5Y26HLENnCDNlEvYPXvmS4ko96C/F6TO8jredHVWV1dxX333Yf777//hl6PXogXu9kA8PzKw3sOAI9OG6/YfQC4RyfMPgB8loksA0BmxPJCUbYM2KPj8Ky7ieKLCK2W4dlVvdgkDgwXBWkA2fagSKP0b/LS+iNKEFYFy0kJpjqybXuR4jOnWXXUxuR4sRvDExbummviwfMbeMWRMfNSvKHn69AMmmrUb5iK8dSGi5dM9rE58DFe7QMpI8RCk5bgJm1Iv4mgcx6xqEPIPrzOBcj5HwLs4ULyrJPxPDYgyCPwI8ArWr6F0IMLBMe2fUUcGz5gwQGsuALe2f+IuH4rOofegnbEULEUa3GKdhibliPbv5xXcpsItLdCidVdfAD5OtNVFxt5/iz/fbDhoZ+jF4JrVgsLS59bGr5pWVL0ELjMD84mgQDwXAcYJw+zwmQNLq4G5cO1BgaA0BR62juNtjwAzxqg7l7AVnQMLfcU0tQ2lSoqhOWmNpYwBIKmYUctyOIpYO4YrKcfhJq7F/bCl6HG7oC48jhUdQ5i/TyUPQ70lqD1BKz+BsKoAUeuIwzHESSL6MhDaPzKv7juo5b+6W8AZx/d3t4+TAVy7k0nqLjV6G56WPjVD4EVvPViMngO2zA3JaMg0IhDCp+8fM+86REjlXCCbqkU1hOYpJFWZchnazl90/5cj6vwLIlmkHMBMw2OoQRw1O3LcITCZnwQTXcVrtMxjxetXfN7m4DMgmgG0BttYwUjnVnYsgvbJISwy1yBncutislQug5BGkVpjHoBarsGOx5W+DLD5ozKwGH+bWyfsp2YmrVfG5pFw8Wg+q3QzANmmzdJDFAqmyVb3mIWTVgCcuXoN1YHSTvIbqwyaxitBZqpvae8v+s+qUobjnoL8j0SDJL6wYrgu971LnziE594Lrt+UT1nHwC+qA7Hi/LN7APAZzksvPgVwIDtQlaFyhfCG3VU4+Qy+mnLgICa19+hBCanrltSnnLhKpPfaSJSrnLwPe/KAyy8ANMUnzxJjy/L+Nsx33a+4WOy5uH4Yht3HGjBo+GwnXGrJv2sCnX7mMJy38ahZoxeLFD32LYjUT821Ssv2oIMavDbCyA/iIa0wdqTkIfecUOmkYsYjyHbumz58r9F1a/M9TOLYm4BRJVgUR0woHH5ryBSG2vHfswspBuD2Ch2fSvFhW4MCmyW2vE2AKEKeIvq0pHW75gvTPWwPA62fAPUaSez3s94lhz8kh6o+6Y9eWYzxJEWraaBikjB6u5qBBysMs0lxZUuMFfPnuRC5vnBKWb8U1iO7sSEQyVwF91kHi33NBLdgmtl7UHZtTJLmKU2xEQNajNiUG1mBrx4HGrmLoiFB6Gm7oG4+CBU606IJQpDjkKsnkQsboG7eRI9fcTE/fVxDEHnKXRm3oTafT9/3cc4/Y//AjjxwPb21tF52OTOGVBkw5rw0X1yA+d/4+NGIJNxAId3PASFBHsybw9z/mi0PWqHNFr9q9iAL6xtf8ExkUJ4wxuTcbeLtZgyaxtjzgCOm/H6tHRhiazmzursmH8e/XgWUVrBRHDOKPcV495sei8qwz/E1grSxiwsilU2aOScQtePwu6cySxfLCq4h0KMYjKMx9+o7Ys9BTEYVgANwJOZdcz280QFdilGTvuNTKCVD+3TJmgAZTcRBm9Eag9bd7wJYvu0fN1jCrN2OrByOxzuJlY0mM7Vv6X0j4RV1xcJ+Bs9CXk9YAv4+PHjePOb34w3vOEN5t9/+Zd/+byoPh/72MfwG7/xG7hw4QJuueUW/PZv/zbe9ra3PeN34N3vfjfe8573mDZ7cS36vu/7Pnz4wx++7u/N6IY3GwA+vfzonlcAb525lx9zvwL4nM+KnU/cB4DPMpFlAMjKES9w5dzIPToOz7qbOFlHnNoYwIPvhIhUCypXAtMio52H2XNHo16ALvR2BmrxQrspgRtuZmIcRgk+c24DTZHRz7dihbuma1jvZQveeKuCuB/Dq3qGVB+pFOM+galZotHw+CwNYWu4dgKLVSZbwo17JkfYC1eBsAvZmEVl+QnIAz/8rJ//+Wxg4tyiyNzdcxFjBbfc8t0N/HFbLno81gU/yFQOLn0SYus8Vu/4cWirhoudGL5DcYKFtX5kKnOct0udoSqToMK0hEvVvvHAMcKOYjAZJGAObaJMu/jImI9O3sef8AQqHjODI9M2ruTAcMxLsW5coS3c0shayVd6KebztZsKV22l8K0BAtHFlpzGjHcCkRo3woKKuIxQziMQl0xUGZQyYhC53IMz5UNdWgeaY0jbXSAZAEyQWHkKunUbxOIjUGN3QlwZAsDQPgh/62lEBIJbJzFIj8DpXUD02p9G8M3fdt2HMP3z9wKPfGp7e+vYUdgbmUKW56dzSxP9B5ew8ZP/Eqvzd2WRaorZ1ENAzWYka59hau/a+mWVlFVtI+FhpjMtehixV2oRz5Tav7ZODKiWuW3JFK1gbLa/8P4KrQAAIABJREFUBaQSEE4ms/LRh2ttoZvOw7UiNP0MmPWjGdT8PCIuNpmKEA0XKgKc7tNgAgccF07/nNk+cQ7BVZevmjNW0zDomHZ+MYxR9CCbn2Jo7cLWQ4BnMoD1kE+o/Cnj5bi9D/8AUqeBMHidyYIuj93SMrafJ3pIBXnJNCoaPo9RcCyumkqgEmikjIN7zsvNdZ87X82GRTeA33HeDJ47dw4f/OAH8ZGPfMSAv3e84x34qZ/6Kdx7rwEd1z2++MUv4k1vepPZz/d+7/caMMl9ffazn8UrX/n/s/cmUJJdZ5ng97Z4L/bIiIzcK2uvUqlKlmVLsmRjG/tgfBg3GDBuTKs57vaAgZlpn+l2Y5hpzmC6WdoNhh7ODMsxmGGZxg0DzdKGYYxp8CJbRtZWKtW+ZeW+RWSsb79zvvviZb5MZSmzKrNUZZH3nFKpIu6778W9b/ne///f98katpc1AsDPf/7zUn9wt9oeANytmXztjnPbV6Tg1fMPoMXG5PypBAX82UwdvtrN95vohAF8xYQdUNDXhB1G0YmtpGAYOUpIA8ptNtMCzOsEaiqmam1cXurg+kwTA/0ZyeAjA/jZyRXcN5BDytJRa3RRyKeln2rTFxjPhag5CkZygdSnK5kuHF9FlixJHqfqwKCsCGNSoQu1OQu/OAJreRpB9V13bDpjOzeCuZjBvVnKN45ccL25znGkd6OwbFg7A2Py77B09J/Ign+myRsk4aiaXAd6AkuuY0CXjvUi0BSAZjq4wvlLRKz448fyKSx1XDR7nw8X6bkaXWJ0AvGCEDdaHg4WTPi9BynFoKNIr8CBAqOGCkwNyFpClqYRJDAIlteW0QkoNaJi0HwBS+5JFLXr0JUm7GAIaW1S2ntpSkOKSUt5kgIQXLsBdWwE4fUZiGI/UCc5IZQqvlIU2ihBWb4GX++D3pyG76WlJl3gZgDPRtBlhNiC9qGfhpplWHJ7TfzJrwB/v5aCU46cgLoUkSTY1ANF+FfrWHng2zHzTf9EQiGe45zvZCUE+2aVULJOCc/iFC0/Z12mR4a3iOR5iJ5Ng6Iy0S2R7iFZMyLdSJgYBPB74sZ0rymno+jcsp1FH50zCAZlCnoZHT8HoVKCpw4rtQTPzyMIDaRTkYaf3wY02uayFrDhSlKHKI0hbNnS6i2EBZEZhtqdkPve2EI7hBpGaWU2Kd5sb2AGI7+OGRzQ45dWLb3mm8PQRcQE5m92so/Dt05sKsa+mVtG8pgCxYGr+r00cwT6KEJOcGyFGox7DPjFxx4DQOn00zvGz372s/jqV7+K7/zO78SnP/1pMJL3q7/6q/jgBz/4snW42Qcf+tCHJGHwj/7oj1a7fPd3fzcqlQo+9alPbbrZaxMAnkF+F0kgzUYTBwekPd9eBHDbZ+Mrd9wDgFtMZBIAkhxAIMH04avdqDtnewvooiDTvZU0Nc4iILoRAGphgE7CFouLvPExQjbkejWxiJRAAduvXl1E4Iaod1yoDG8BOFrJ4KW5Fh4aK8raoa7rQ00ZsuZtxQ97RBAVD/Z7uN7QcKSvgxWbdYBthBSG1WzogQPVbyMwLOj1GwjSfTDtEGFJajvtemP0likeRvB4k49Tvkltv9W6qF6UkA87RggZAdhMSDZ0m9Au/id0h9+Ceu6UrIO83nTgOS6yVgqzHU+CEKkH2HJXBaDjH0cHCs4og1UUwyW5hn+zmjAWh+5jXjfBPD2QS+HSSlS0f7hkySgjo7wUPCZ2yUkpGODKioL9eda1CdgukE5FoCSnraAVFGEpbeSNaSy4RzFkPAdfFOQLTUpdktJCBhYRtAFVRnCBYK4GbSAHcf4l4OBxiMsXIPoPADOXgUwZWJpESEIIo3OpHJT5aQTpKpSFKXiZfdDmrsAZfiOMD/zrW1pb8Ze/BXzpT9a2OfIAtKUEKWS8iHCpg4Z1EO0n/i2WeqCZdX5LPdBNyzyC4aQoOuEdWcIGQZ66ng1MiR57nS8wCwTZU6Dt+MimtFXpJOoCWqaLVocWfQbKaeroRIebRgtdRPeHkjENVbWx4owjb8xB72nr+XYKutVLHy8vQRh56AUd4eIcVNFBYI1D020EHW9dlC6ekMA118m6yGvb4YtHQi6GaWF3LSoYmCPr2MO+NQo9nIfgC2X+mxGk9t10jTZzy9isMysvScHhFWAIbdX145YW/1XszPsAX/aSAPAzn/kMJicnQUDGRuIf7wO34grCKN/3fu/34sd+7MdWf83P/dzPSUD49NNPb/oLub9f+IVfkBkH/nnzm9+Mn/mZn5Hp49ttdz8CuAcAb3ftXq3t9gDgFjOdBICsByOouJWbwW4uZNtdgIMCmn6AkhmiG0YPmo1SMJSvSKay2Ifl6cnoCAHKRpMyfkaNtC9fWcKL12s4OVpAo5dWK1s65lou3ri/hFrTQTGTgqsooINIOwQO5CNLuIcGHDwzp+ORoTYWO2kM5NpR/ZNCmQwPutdCYGWh1yYRqikY6gBENtJz261GUEOwzvVK2rndrN6PDwL259+x/MsrHsuF34GXHkFt6J2gc9oS/0NdOT9ASgNme4ycvApMtyKZEDbOr+OF6CbqNfn5UI4SMmursb9krc47AQuV4BilYpRrqGBJrOE4HvQeaWc4Dalxt2ADh4sRAaJhk8DQY6gqNhxhoaAxAqSiHeQwkDqNhncYOe0GFNjwA9YCLkWSMGZbAkHhh1ANQExPQhkdAM5+HeLQ64GXvoZg7EHol56CN3A/9OvPIqjcB/XaCwjKR6FMnIHfdwzqxBm4j34A+lvee0tLKz7/GeBvfn9tm8OnoC1fWv23MlaC4rrozgtc/ZHf6J3bkb0h6wE5vw6lYDaGA3sjRGzgteE5nzkCvMRbUtWK2NgNCayATC/tzq0qqZYkNSy5GeRVG1ZPMoXuJUxRM7RH0lOfdQ2uV5bgu2JdliQLpmYVynkzPUoN6foU0DdMS2Go9asymof8PmiiBgJFzV8v+cL9b8YEZh2iGqxFBQN9EJq9lkLe6AZCQAjVh114F4T2yh7cvI4Igl5t9YNbOmluo7P0Ue92173QM0LH+8RHP/rRl43IdPBv//Zvr5aPbOzAtO/f/M3f4MiRI/jRH/1R/NAPrdW9/tqv/Rp+8Rd/ERcuXNj0SOk8wufKvn37MDMzI7dnJPKFF1647ZKjuw0Ar8yd3fUI4KFBKRe2FwG8jfN9s032AOAWExkLirIb34T5JxYJ3qU12PYwHXcZjpKRsiyqGkLV1iKRlCSRD58eaYB6dMnGiFGCLClTXHH/uJ+pKrJA/UsXFnBhroXXHyjJujX6BFPDjo0M4OcnanjD/j75wCXTlZp0Ixlgsg0pBXOxpksm8HLXRH+mgyCk2bsHVaPcRReBmYJen4EIfGi51wFG37bnYKuOXC/Wasapekb/XinlS0Afi8HGcjBb7UNM/iUUp4WF8e+RKVvWOhHAzTRsjORSUj4kmn0BzScbVCBPzcTuy8kgrENTGY1NeP8OFE0J6FinRlZrzHIdzujSoUInQYV2Cj0G6z56ArekeAf2F1kPF0napIwwysdJqRIVZWMCdW9YRgIL+hUseCdR1Z9HIPJSu05VOpHOnNaEv+xDy6sIWyEE/VIZCZ6+Agztg3r+a3D3vR6pi08hGHsj1ItPIhh8EOqlv0fQfwLKtecR5A9Amb4I/3t/GurI4a2mdN33gtE/RgHjdvB+aPUra/8eLUEzBJzTC3jxX/2BJG7Qbo+N7HamHBsbza57W1OPMWZWxwMWVAFfSzh/sM7S0FC3PQki+1MKRO97ptUrZhsLdkaSgPrNbuQGQmDmKtB7cioptJFNLaLu7ENKsZG3olpA2+2XdnFsQZe5Ul86sPitsGcRNw4YJnTU4HspaAlLt/h4/SAP3VkjfcixRAGau+b36+vD66Rh/BSt5tYigm7mJNzi4y+r99tsoTbapd3SYt7DnXmv4G9LlvR88pOfxOjoKH7wB3/wZUfOaCH736yRLEYQdzsRwI1jsu6StqN//ud/Lskpt9PuNgC8PHd+1wHg4cHjnIo9AHg7J8Qm2+wBwC0mMgkAeVESLPDCvBut6zVhCx0MFtUdH32yrqonuup6qzZhPDbPDxEmam8I+JJSMfSk1RJ+ntyGgKPVcfFHT0cyE8fGCpLgMWBqmGp7SOsa7h/J46mry3jjwTIU2qQpiqwvLKWAhitwrC/AYlfBaM6DHajI0gNVYfWZAkW1oVFWRaMHah1Kuwa1/1vWSUnsZF5jOzembxn5i4keW6V8Cfx4896OdyiPL1x8GtriGSwcfkKSAqjDuOiE0EWIG00XdJJgela6h4Bz6mCxu7k+3cboXzWjw9c0pEhI6HjIpw1ZY8l2uGjK+b5e6+JAyZKMcLZ+E5i3IYk4fZbAdAsYzUbaJArZ1tQoIXlHn0LNH0VJm4EGG+2wH336WXSCMaQFPWkFRE+2x19Roec8+PNdKPkURNOFcB25f3XxBlAehUptwKGTUC9/DUH1FNQrzyAoHQamzkmhaLRXIP6H37jl9RVf+3+BP/3VtVNh/Di0ZuSZK9tIH3RLwH1pERP//JOo58ckCztO/7ILgR7nLUm+5vnNPxuxYSm1JqvDbcs60PDXXpho3xc756QVB36owJGEhxBVqyMvwSDgSw7DeBGSz2AevjDhKkXk1EVYqWUEQRp+mIZpLMs+QcODlqedHBDU2lCCBtS+QekXrCltGRUUnZV1qV25XZiGZq+PDPpKBXpCHoaSMnoiAhgYVWjBkqz3c/Nvgpd/aNuX2ka7tG1veI93ZHYndgCKD/XjH/84HnvsMbz//bdPTGMNIFPHrB+M2/ve9z6Uy+Wb1gBunKrYjvJP//RP8a533V6N9B4AvMdPwHvg8PYA4BaLkASAjBYxukSf2LvRHN+WRBACq5rjo5JOQ6hR5EIw8pWI+oVBsI75u5kWIEHGRtDz1Pl5XJxvo5xNIV8wkTVUaGH0cN1XsmAZKq4tdXFstIhOy4aVNWUNInddMgUyBu23WH8lYGmB/DxtOBDCgKZ1ZERMSHZiCKU2D736zh1PZWznxptmrO3HQWPgt5HlG9f+8HMCRRI+bqWFnVnoV/8Ui8eegIs8phpdGBrtrhRcX7FBcO06vozUsnEO6YJQ30AKsXRFRmGTwdrxPkuSFupNW9rGZbPmav3mwZIlay+ZVj7QZ8ntmKgkoCFAGcuS2QpcWhE4WqLhKn1oVWg6AbiPDOVfwhL69UvoBhUZlc1q19D0DiOvngc15lTUI1ZtS4OeduHPtKANpOBNrkDkslDqlP7QANeD0m1AKAaUlXmEyEJpLlH7WUYfxcwMMHofxPv+za1MbXQukwFMJnDcRg9D6yS070Yr0M0A3vUVNN7+QVw9Ej0gSehIMqul1iVJs73sOp1rNkrvFAzOTC9y3vMFjlyMo8/okR2zrvlvEwG6vfrajOohazqytmLRTqM/UQtY0GfR8AYlqaKoXYNhuKh39yGfWpB+3mxBR4GejWRhxNK0FEPXuEOXagMRaPcpGJ1w8ZDzw/13a+tkXny1Kr2Z4xbo/dDstYhfqBVk6tnuexcC6+b1fpst1ka7tFte0Ht0AwJA3jOSqg5M/RKsvfvd777toyYL+B3veIdkAb/nPe+RUTyygL/4xS/elAX8h3/4h3jnO98piSJzc3MyBUzW8OnTp2+bdHi3AeCl2Yu7HgE8MnSU67IXAbzts3P9hnsAcIuJJFgg8JM3Y98HxaD7+nYvZXkr60jNvZbXgBOaWGg7yKUsmFbEBKZzQSNRW0bWIqvu4kbdv86Goj9yDSL33qgxnfs7X4pkKO4bzoNStWYYQmc6zAlwYjCLSwsd9GcNVEoZzCy2MFDJyto2PljHciGWusCBQoAVR8VI3saKneoRQWi31JHsWM1rIDDTUObnYVS/6Vam4GV9N7Nz25jyTTJ5uZax/VMsB3OrByDCAMrl30Vz8O1oWwfQ8WgHF0Z1ZUEgfYDzhorp+vp0EX1+lzr+au1lv6WinmDiMDo1UrQwW++iy/ksmmj3iAlkbQ/mTFxc7qKY0lCgM0gPoFCHkO1QAajbQsqhjOSlMnFkC0xArrThCkuKowwaL2Leuw992iR0pQ3b70dWvQwnHIKJyUgSJgyhaALeggO9rMC9NAXjwCDCyzPAwCAwOQlRrACzk0CuD7hxDaJvCOLiaYiRE8BLz0C86bsg3vyPb3V6Ic4+Bfzez65tNzi+Lr2J0X7opo9gsYPW6Jsw844fkecfgXfL9ddF+GR9nx550W5M/XIH1Gl0QgFK5qx49PPV1tnIVaQ+TDTXLFkgSSq+ZiopW/r9Nh0TqiKQNSNiB9PpuirgwoJCxrB1BZ6flYCwkr4iSVSBr0PT3Cj65yhQmtNR9C9IrQN8N6sDDLueJIzELVCr0BIAkIBPdRL2b8Yg7Mq3QpDefYvttQoANxO4/vCHP4yPfOQjePzxx29xltZ3J+HjJ37iJ3D9+nVJ5PjZn/1ZySyO26lTpyQo/PEf/3H50Xvf+15Z88cAA58vb3vb26Qu4KFDt18fvQcAd7SE/yA23gOAWyxzEgASbJDezwt0u+nC3TyLCGxqTh1OYMoUsB3qGC5m5C62YgJT5La7oSh+IxO43rDxX5+PtMoe2l+CIwRevFrD/uG8jDadHMzi+ekmTg7nkaX3bcNGNmf2mMACB/MhLpEIUnVxbtnA6wfamGulMZhvwfdTSBkdsFBe82yEVgrh7DLM6qO3NUVJbb/N7Nw20/Zj/U78xh+LO9/WzomPrv8JPLOKpb7Hpb7Z9YYjxYb5dL/WA35FXcFkj70b7ydjqKDlGxm/TJ0ToHHtWKNJ0DhvB6v1gOPl9Gr6d19Wx7IdyAjX/kIq8lXm7kg+6YH4QwWBSyvAUBrIWyFatoqcGUUCU3DhIgUNLkraFJbD/ahqZ+GGfVIg2FRm0A3HkMY1+GEWumiA0nc+BakzQDBVgz6SR/jiOSjHj0A8fxo4egri+acgDj8EfP2LEEcfAZ75EsIjjwLPfhnhe38UOLS57tkrzbugCwjdQOJWGV4fBesBQIKgtltF7Z9+AvO9GlWWOtCTOdnSmiKjqVEKmK4nESGK887ruO6GqwQpSvQkFXrohCMBX+BLoG/1pGE4ftVqIwx1zDsmBswu1FgM2nch9Ni3uYVsahorzgHoioecGUUyu3YOmXTk1uE3fHkcek6B76Rk/V/cgsCAukHjj98FrgHNj2oJ5b/p39td8/+VkVkvegHx0kfh9L0DktFzG22jX+5tDHFPbrKZwPX3fd/34ROf+AROnpRyI9/Q7W4DwAuzV3Y9AnhsSALivQjgLp2ZewBwi4lMAkD+f71evysAMNaz6woPYSqLjs9IU4BDxSwUjR6xQgo2x00XAdrhWgSQ6d4N0nMv0wL8wplZTCxHnqGn9pfg2h5qLRdGJnqYHSiZuLTUxcP7S7DdQAIfYWhIKxGrkrZkV5vAG6sunp438OaRJqaaGYzm2/BDHSlGPEgGcQMIU4E/00R6YPu1SPFvi1O4fIOnJA/B3CsRPQjc+RCLRbw3avvd1rU090V4bgdLQ++EF2iYabsQQSj9lFuOL0kJnH3hB2hsSP1SjoSs6o4XSlDBOku2SlpfTVGS2JBM/w6lNUy3I2BzpC+q//NdH2ZKl0AyowmUTAWTbQJxQNdCTDSA8V65amRVBmQU1pQJdEQOg/qzqPknUFCuSbcWTxRhYgp+WIEu5uE0BIy0B+GoEF0PSgYQN+agjlQgTj8HHD8FPP0liPsfB576O4T3PQY8/QWERx4Bnn0S4Q//OpC+9YiTmLwI/GpCOqZQluUDcVNG+6GZvlxzlrld+me/Il1r4ghfXA9IKRiC8vpGEczeQBsJIUVDXacVyO9Thg4Krc90fJCEE6eLDeGhaDmY75FBhqgLqLCGkhOtyugpW06dkxG/ZjCIvLYA06ghpIduaCJlRELMzmIbZl86igbaBKprv1WmhzttVu6tO019LwvdW2P5hkoGqh3VFsZNBBrcwsO3VO+32bXwWgaAvJckrT2ZsmXqlkSQb/S2BwC/0Vfwzh//HgDcYo5jSzB2kxG4Wk3WAO4KiNjm+ib17BRLQ1ek0HY9mZY1oKFSoGco7diSAJApybXlZZXbRtmXpBh0Mv1r6ioOj+TxwpVlnBwtotmrpLc0pthCvOlgGedmVnB0MC9FidVeFGooLTDVAV7f7+JC3cDr+iMmcDlNDbtQRl5UtQuNqimqB2dBIFe9b5uzEHWL7dxYt5fU9iPI2yzqF7N8KWHBP7sVuQ2b56EsnMb82HdCGqTQvs32sdR2MZK3cGUlir7QcWW+4aySERiFYkRqaYM/8GDWkJGouA3ndNi9+s6cJtDxI/ICV3S8ZMno30S9i4ECrcmAAZOexFKjGUdLQjKBac9XzpBJ0BtVAQrqPJphGSk4KOlXsBjchyqehYsqNNGEjjocrwRTXYLbtWCkGghWhLRqpgCxFBg0VGB+HmApxJULEEMHgbPPQ4wdg7hwBqIyDjE9DfGh/3hLa7sKXOZvAP/7/7S2rZWFlk6cvSNV6FZUluFM+XjmiV+RQI/nF2WM+HO9gNFSgY1s+HjQElnZG9ggdP5IRskH05okUU93Itg3kKGnTtRKuotuQC1BAyl4KGcinUbXM5DSI9cQtj7jGlbcUYSKgT7jKjTNQ8sZQTY1K9PHJHoE7RBGDggDBYrXSlp0R+d8h3W4a9E++dkGJrAUsHYYC46OUCgWusV3IbR2DmSYluS1Ezvi3Nai3oMbbaZvyNQr3TjultTXbk7T3QaA52euIl+49RfAm81Bs9HA8eGD/HovArhLJ8oeANxiIpMAkF2Xl5clC/hWiQO3s16b6dnReaITeuh4UaqrbQscreak40MrAQA30wLceAzJFHBtpYvPvhAxCw/0Z6RN1ouTDbzxUB+W7EC6V8z1rOAIAL9wYQFvOdYvc2ncN7FBwWAUUuB4X4CarWIk50mLNJJCLIM6e4YUw1UDUjHbaDUqKFWGtzU1Mtpj25KFvV07N/aNTex3mvLdeJCh14J6/c+wsP+74IaWBByTbR+e40t1FqZ42z2AUdIV3FhxJG6ill8tofkXj1vNptYB+Dj9m6XbR9dDaETpuz5LQ84y0Oy4sHQVmhGRgCopgQUn0oQ8UhKYaakom4CZChH4CjQ9jkjV0BZF5LAMXSETuA8V9Xk0whPIibMIhQYRkLTCtD3dQerw6xr0vAN/3oOa0yF64FZ4PtCl04sC0WpJFxGCENHoSp9b8W0JELetVY46iZVF4D/892tb0GWlmEhfDvdDT0fRUL5EzL7nY5jKjkt/64Xe3DL6SvC9skn0j+l2nrNJ8g2t+gx9PRmoamky8iefOEzdG2vf53UfdS+a+6LWQdoMEfpAyzNQSEfgVIOHjNZEMyhLEg5rAamJ2XCHUM5EziaBo0PTo1pA3zWhi/VRPPbxHFM6hCRbEGag2es/Cz0Naki7xX7Yfe+G0Nb8fG9h+l/WlQCQUbJX4563k+O81W036hvyHvPII4+Amnyvhd+6BwBv9Yz4h9d/DwBuY83jN0V2ZQSQb4d3+m14M3KDfGAwCunacEMNUy0HbV/BsKWjmLPgeP6q5RuBwEacsdESLqkF+LcvzmCyFkWtTo3m8dz1uoxaPXCgD003QFnWp/kSGD4wWsSTl5fwpqP96LRdWJkU/JD1VAI5A8gZEQuYwC+XIhtWRSXdhOenkTJa0huYOnqN1oiURtiqMU1DRw/+zZTvVtp+BH0Ef4zSEizesWjt1T/A0si3oIs+LHQcGaFj5u/ScheDGeoPRkCDUTmZCrb9l6WD+dsHsgZWEtE/soMzGVOm6K8ttrG/nFmNVu3LatA1FRdqDo6V03B7oaaUIiQIpxxPNSNwvqbgWElIAkjbVpG1osgQNQAdpNGnTKIrSlCFh7xyGUviBPrxLLrBICyQPRogDDTQfdpvpaCnO/CnbeiDGoLrTSjVHEJ6BRfzEJPzUIoFhNcnoQwOInjhDMQj7wVef3tMStFtAT/9xLrTQqnkoBJhsQ32Q89G/+82NTROfRsuHXq3jH3pLIVIeixvIgdTNCKJmGRLRv+YOiZBilIwcUumf6UOY89CTR6O2YGihqjZaeR0F4YRRStN0YCLLISiSQZ2JjWNljMq2bg5K3rZcp0szFQk4Ow7hozAbmyBr0G112zc+D3lepQurfnWGlncodYHp/h2QFnTNdzq+trqe157vI5eC6Ao+Vs36hvGAPDcuXO7linYam7v5Pd3GwCem7m+6xHA+4b3c8r2IoC7dOLsAcBtTGQSALIGMPaU3camt9zlZuSG5ECLjg07VFGzXSx3hQRhDx0oIwhCyQCNG8WCY29T+UBSsc4dgYBQVRXwgfe7PfYv+40XTVxa7EjJl+FqDgVTk64Qy26AkaIJXVEw33RwdKyEG7MNjA4WpLA0a7BGs6HUATxc8rHU1XCgaGO2ncJYYQWOl4alM8WlQHF8NJqVLesp4/Q3I3ixZVOy3k+O1dM7jKO1vLHvdsp3s4UMZ/8a3fQoVqz70HR9BIHAshei2XGkQHRGgXRPYSPxg9G7uV5EKTnexujfvqIFRYvAH0H4cF96tX7zcMnEZMNGyxM4UrborAwKxVBTmI1SMNyGotzH+wiJFNieAitFKgOBCfPIKsrqNdTDfciLWSiijW5YRp92Di1xFLnwHAKRgxIuS3gZdDRopgN/IYBeDuFfXoJOP97T16EeHUP49HkoJw4j+PLXoTz0IILP/R3E9/07YOjWBKDjOaGkEf6371435cpgBaob+e9ioCo1Ctm8IIVu5jDm3/kvJIGGKXbbD9bVu/K85+8gC34zKRhukzN0acmnK0KWVpRNHZ2kOHcv/ctaW/oyi9gXGCEG0hQ7NzBnpzCSoS1ctKEaUPMyqp/Nq7PQdQ81dx9KxiwMvS4j4mGgw9Cj3xV0A2jq5kLDfrMj3XRNtEIIAAAgAElEQVSSTXS7UER0fkk/3/Rj8C0plLurjQCQUil37EVqV492+4NtZDfz/vHoo4/i7NmzewBw+9O4WU/mfVfOTt/YdQB4YkRKGO0BwJ2tz+rWewBwGxNJtpgs7uZZvbIi34YpNrzbLSZ6MIIVkxs220fdY/2RirrtYMUJ0XVCHO3PwtBUNBNSMBu1ABnViDXROC4Xn2nJWsPGX7wwKx+eFL2daroyonewmpUkD831kc2mZI3b8YEsXppuYLycwVAlg+uzLQxWc9KxgrZx49kQ11oq3lB1cHophUeH2ri+YuFgaQVdz0LasKEqHhTPRGMle1MAmEx/x9p+sbDzZvV+sZ0bv+PD6k5HaDl/Ye3r8Hwb8/nHpNA1iSCMwpFxfXm5Kx0qGm1XMlDjRvIHgXmrF6WqZgw0EhEr9jtYtnB5sSNTlIwOhj1fYK7X/mIKF5dtWfM2VGT9n4DXdaD15IAO5QWuNqJ13V+ij62KtC6gaoK60BAUKhYCJXUGDTGIQnAJDvpgKB7SynUJAPPhS3AxBCOYAC3GFAoCUWtwJYSW9xHcaEMdMRGenoB63wjCLz4P5eETCD7/FNRHHoH/N1+B+B8/DSTcNW71WhE/+X7mRNduVKMjUDu99Gi5H3opCs8FqgnXzWP6vT+NhZ4PM+d/cQPxhnOX1yEZ1pR9iXX+OEYlxbpBsoF9CRw5t0ZCGzJO/zKtvNRxpFRM2PttRd2XOpcznbQEj1UCwAiRRW21FvAyWs4gPDWHinlJ1v913CFk9FkplB4GKlR/zcpt43z5XR1asOb0we99W4Ee1qV9nJ39ZoTG0K1O85b9eR0yBfxaBIAkt/DFMi4P4Ys+7dxefPHFLeflG6HD3Y4A7gHAe/8s2QOA21ijJACkwnscXdrGptvukiR6xOSGm23coH1ZqKDWdWV9HgWI3a6H1+0rrWMCU5PO2UILkMSOz78wi3rHheKFSOkK5mLbt/0lyXp87sICjh3sk0DmWDWD09NNPDhWlHZZja4nxaAtPigYgcoAE23gDVUXzywYeMtIC5NNC/sKHfiBKt0SdL0L1Stipa5tCgC3o+2XjPwRMPNmzhRV7ACy7YnfQceAzgu1M5grv11Gcy43bBR0VRIJFhq2BBp9poqJ5fWRGwJtAr/ZtocBWfvnI6Or0sKMRAbWdsaY8VBlLf1bNhn1jUDKaD56cJGW4Achgl5EatQKcKOrYTANlKwQV1YUHCr22COhAkULYAhHOoG4Sg5lcRo1nEARE9Kr2RN5pMMrsLEPZnAZgShJAkLYIbOVTGfW+/lQ0kA4vQKlP4vw4jSUkX6E564Do8MIL81BfOCndjCzgPiZ7wc6kUyKxFH790NtRMLGolCG0R9F2ULVQOCauPjffUKKbS/0gN9GEMh/kxDCNYlxWVSTGUn3JCPng2RoJySTmP4lYKx3XbAGtz9L7c3o1jlg2Wh7Opq+hrLhwUpFoNXzUlL8mY21gAVjHjVvFJpwZP0fnUOazjD60pHDSeBa0MT6lG5yAlkfqLnra/5cx4ISulgx3gItVZQvPbtFcor3HQPAOPq+o0W9xzbeyG5eXFzEE088gSeffPIeO9LbO5y7DQDPTE/vegTw5MgIJ2MvAnh7p8TLttoDgNuYSIIzRpjYKATNB29SOmAbQ9y0y2ZEj61u4nxY1dxIl+xGi1VgCi5ONfDtDwyhlYg2qYGPrnSIjVpaVdBNemNJZwOBv3xuGguLbdTaHu7fX5SEBbY3HCrD7nqYWmoj3xfpDY7mU7iy3MVjh8pYbDgoZVMQugYKT3uqigFLwWxX4MGKh0srOk7121hxNBRND7rKedRlHaAaDGJl2XsZo5pv4fGN+Vbs3LgejMpuNXc7WauN24aBA8z9f5itfCscPxIUXul6skaP58vVejSPeQ2YJiV3QxvOEgQEWEkAPjJ8Y007dh8rZ1aZrH2GItOcbKz/86Bgut7BeCktZWFYc8lawKavYl+G9ZoqJloKDpV6zsQiigSaQQOuGjHH+5TLWMYxVMRpOBiAHjZhiAU4YghmeBU+Pwum4dsZ6MYKghUFihlCdASE70Oh3VqLaU+ykGyIgJyQMsTbv39HUy1+4QeB2lrESzl4BGo98r8V6TyM4ei8FoqK0NHwzLf+PDIpQxJpVj2xRShldXIaUHM3t+LrS6nriCKM8hVNfVVMOmL/6mjarqwJLKdU6D1CDvdfNR2Z+mUbTjtQJLNXgx+qMMgGJhlFbcIXBhxhIaM1pC5g2x6ksjRyVvQbHTsNU1nP9E1OYEhJp87iOvcPVzkERz8pbR/5EsRzjiCQ9yema3fjWogll16LAHAjueXKlSv42Mc+hr/6q7/a0bl7r2y8BwBvuhLfA+DfsdoJAJ0PfgLAf3mFdfs5AO9hAobOngD+DsDHAKw35L5XFv4WjmMPAG5jspIAkPUwjDQxDbzTdjOix1bjslZpzmEKKUTN8eB4Ai3bRzWTQiEfOYOwaaGPjlgDgBtrANmnUe/gD790DV4gZPSpUsnA7qWRHzzQh+cuLuLYSB5dVZXfU36EoOWbjvbjKxcW8eiRinz4h6EA41zUTiPz8v4+auGpGMoyhhg9fAtmG45vIZ1qQvPHUK91VhnVr7ad21ZzvN3vw7m/xHL5m9D0MjLWeqXhoNVyMNKXxtXlriQmsP6v0/XQTaTn+9M6ZhrOaqaQ+5OsXl1djf5VMjqUHthII0SgKKvM4mOVjHzgX1jq4GAlK8fJ6QINL7qkjxZDUNIxCAUGCwpcN2IEE0FofgdCN5ESbaTQQQv9qOJrqOFBFIPzlKWWYxtiDh6qMIIZBH4BmrIEv65Dz9vwZ3yofQqCGQdqTkM424KStxBMLCE89g7g+M6cFMQvfwSYW/P/VQ7fB3U5cqkRmg5jP32woxaqaUy94YdxNXsQBT0S2aYuINej6wdY3iiA2duOzF9yQZL66P10Bum5r7AbxxMiQC3CchjNUvQlmuMUQkm6itLJIqr/g8B8N40BSh/1agGzagPtMJLDYP2frrWw2D2EcmoautaQL0UOGddeCyn9FdLALRcaWjJdbaunEBhHVucgrouN3S0IAGMwuBMg+FoGgBvJLc899xx++Zd/GbRley20uw0AX5ya3fUI4KlRWeawkwjgmwD8LYDvA/BfAXwHgN8DQEuqZ26y7j8DgMbOp6XNN0Cj8vvpl/CNfp7sAcBtrGASAPKtMRYU3samm3bZDtFjq7HnHdqP+VjsemjaAXRdxZmJFbzr1NDqA00LA3R6vqUcj+AtSX5kQftv/sU52L0atAPVDJZ6/1/KGBivZPD1S0t4+EgFS26AfkuT9YFsbzlSwd+dW8RbT1ThOD6MlA6blloSJIboT0cRKbKRy2mSVXSM5utou1nkzCb08BBqy3UJADkfvBnzocVIAwH2VnZucQE3gfhOHnBbzfNW34ulL2IlexQ1UYXjhZjrejCEQL3joZgxVuerlFJxo8ey3gz8cT8bo3+Hymk0AooJAzP1DqysFQEPTcFYMY0btTbyKQ25TAT6aSMb28Ld1ydwrgaMZ+llKzC7EmKoGDlfaCFJDAJZsQQbOblGJZzDEh5Af/AUuupBmN6EZAILQWuyZekTrIka/FYGeroBfyaEXg3gnW9A22/Cf2EJ+tES3KcmIL79R4FC/1ZT94rfi1//GDBxfrWPcuR+qEtXVv+tHqpA7SlbinI/5jMPY+LYe2QqN8kEJtFJ1mJuqLOUTxGDNbPr2cBVi+dxtBsCSFP4aPauIRkNTHgzEwDaPbCYUQOU0jY838CCbWAk24myxJJY4sNHJGNTsS7B8frR8ouoWhegKLyOh6DrTakJSHa8kRC9Tk6SbxtQ/Dra6hspKiOvEV4rXNMkQYOfMyIY37cIBPkn7nsrCxMDQNYkv5baZrWN1P/74z/+Y/zmb/7ma+Kn7gHATZfx0z0A+b7Et3/M8l4AP7jNhX+wBxYpYRGpuX+Dtj0AuI2F482U0To2pid58yBQuZ22XaLHVmMvuSG6XoCppi3TvikoePZ6DacGchgfjqINarhmXL/ZeM+8NIuvXVyrOzq5v4SJnsbb0cEcrs020XEDvOFYv6w9KyjAghtA1xQ8OFrEU1dqePy+KmYWWhjoz4LCE6x/G0iHaHoKDhYCzLZVHC93JBHkSHkZTTuLgtWFER6QmopM3ZK1e6t2bneKiLPVvG/8Puiched7mDdOok73D1VBzfExsdDBYM5Ah3VngZAROlrD0UFlY+SPY7J+zTJ1eIkU/b5yRuoGnptt4mAlg1YPmIzmDFi6hvNLHekKEmqa7MedMBWc1QUG0wquNBkJjOR5ZpshRkqRmLkqAiiqgmy4gLZaRUbUYKCJNqooB19HTX0QRe9p+OowdI9yJVFak96zgW1BM1rwF1XoJQfe5S6MfQrcZ5eQOlmA/bfXIT74yVudxpf1F7/1k8Cl51Y/V46egrp4afXf6pFRqXnHJgYH0fCHMf/oD2Gm48m5aHvBqiQS+5QMFXU3WH052gz8UXZH6dVSWkooSU8W2cG9vfalNBg93UUylZnqj2UGq6YLQ/Mx1U6jYATIm1H6n5JHohcJ1OGhZF2X0T+SbvqsyxBCQ9MdRzoVpYJFqED3GlCVHpkkMTN+0AdXHAfUKPobp31jAXQCvI1M3djJiH3leWYYEgxul9HL+x6vz9u93+34RLhDA2xW2/jZz34WTz31FH7pl37pDu311R32bgPA01Pzux4BfGB0gJO4kwggo3z/GcAnEqvxvwAgIHx4myvE9O8P0359m/3v2W57AHAbS5MEgIw88aZ4O2/EJJMwgsgb8FZEj60Oi64RK16AWsfFTNdD0PXlg2q56eD4QA7VShaKCGEn3EA4ZuwI4nZcfPpzF9cesACGBrNo9Yroj1TSODvdlEEMEkBIUnBtHzU/xFDBhCKioviT+8t48coSjh8sS9kTgpSRbIjptiodQZ5b0PGW0SYuLmdwvNJA1zORS4VQ/SFpq8foxa3YufGAWRt4r2iSBe4cgtYVzKYfRiBUtFw/kiPxA+kDzBqzG3Ub6ZSKjK5BV4HZVi+fmFjkfUVzVcRY3uEsDeWsiQuzTQkeDw/kVqNYR0oWrtQ7oO73iWpWgr7Q86HqEVgZzlCQGGh6wLFigI4bSb/k0hQrVqDrPTDg1eAZfcgFN+CrBSgiQDa8goZ6TALArnYMafclBEoZqj8X5Up9AcE6t7oCJechuOFCGwjhn2tBH8/APWtDfMdHtzp9t/xe/Kd/D5z5ytr5eex1UBcurAHA4wehepFnbjg4CM/N4PJjP4aWQ6IGrfHWRKHjjejAwvIJ1mpGfszrD2Mwrcv0e9v1ZWSw31TXpYNHswY8qKDI+kLXQym9pgQwkrbR9HSseDqG0650/JDM6cCAFtcCag0pxdMK+pDT6sgY07C9AQRIIaWv+f9GILAGVVmrG+04FbScg0ilLAni4qg3AR7/xMz4V4oKsg+jgvyb1w/H2Soq+FoFgJultj/zmc9gcnISP/VTOyMwbXlyv0od7jYAfH5ybtcB4INjgzcDgL8F4IM9/v1muIZp33cC4FvkzwP49cQyEMz9Kz7qtrE039KrF6RO1ee20f+e7rIHALexPEkAyLdh3kRvxSoorm8jwSEpabKNXd+0CxmNlLpYsT3pO9toOdIh4uvX6wjaLr7jTWPQU4Zk7iaTXPQEZina733uAhq0E+m10QrTjVHPkYIpa9YWWi6qBRO5UhqmT89MTRIUjlSzOD/TwoFKBvuqWVyebWJoIAczDNFVVAyngZkuJBHk2UUdbx1tYrJhYTTflcdCu/v2iikfXJwPsqpvpu3H4yFwJvAmyYORwruZ8t24ICLwETS+KgGgFxqYaNoSRKQ1FZfn2xLw+V4g6ybjRs9fRvpij2DWVjJd3E2kI/cVUri+3JUpe0YHy0VWAUZtLGfgWo+oc7SalanK+YYtxcDZRjOQlnwaQhzpE5hoKBjNAboewnFVpFMEJwIGHHiKhYJ/AQ3tELLeNHTVJWcV2eBFtLWTyLrPw1P3wfCuIFTyUP0aQkcFhAdBHEl/Yi1EWPOhpBR4zgjwCMtqdtbE//MfgWf/2+ogytHXQV1cA4DK8WPQvF7UrFqVMioXH/lJaKoio4Bsm8vBCBR1dV0phLxCBEALRIqdxwzfKB28dvUMZU3puU3iFeWSoEZe2yTTDFoOprrR/I9lIy1ACf4Y/VOjtS/q82h4FSkM3WdMQVcbWLKPIp9agrpR/y9QoPskhbDO8CjccETed3gt8LrhtcA/yRehGAjGYJD73CzaF0cPOV7cJyaObFw13vu4T750vZbaZqntT33qU/I+9NGP7vwF5l6Yq9cCAPzCX38OX/h8hLN4Hv7epyRu2ywCyBM0ugA3bzzZWWC7kwjgPwLwuz2g+Wf3whrv9Bj2AOA2ZpA31DiFQhDHP4VtehxyO0b92Bjp2q3IFSMZM3aIeseRAJAOIZ4TSIkWPpwuTq7g+995GIqqrhN/5kPx2YuL+NsXI0mNuMXs37GCiYnpBoKe7dX9+4qAoeGF8wt44Gg/VhwfR8ppnJvv4MF9RRiago7rI03w4foIDR1VS8GCLXCy7ONCTcEbBmzUHR05w4el2/C7Bgy1LEFd7OxxMy/fO2nnto2l31aXYOVJLFoPoeOnZPo3CEPp3EFpnqWOh5Kp4UZtfUqPoG8wm8JC28Vg3pTSL3lDlSLbJPSouiJ9l9n2lSzY9AojqNGAjhdKkMlxy3kLwgsgQUxPL7BshFj2VJSMEINZBWdrwP0VjiXQcYGcGchonyB0VBQUxQQa6hgK7kXYoOxLCxaZwPoYMu5LcLRDMN3z8NVB6N4UAocR2BUENRWKZSNkNNAIEdQCBGPvAMZYH72zJv7s14Gn/mJtkCMPQFtKRKzvO7EqixL2VyVB6flT/wZpM1oDh7iL8EmEq8QZArWUQr2/DaE/1uaZClYSzh95IncO2mtMGWdTOmbbrgSPUTQw+r6gBeiEjChSykdgMNORn8+20xiSYDAaJKs10A4KEnxXrYtwgyJWvCpK5qRM029swk8h9AcQorT6FQEKrxU+DAng4khekgFPcBPXAfJvmfYniav3Jx4sHov3KP7h93GKOH7Jeq0CwM0im5/85CcxNjaGH/iBH9jZyXuPbH23AeCzN3Y/AvjQvptGALc766wBZI0UmcBx+yMmz7aoAaQ10f8B4P0A/nq7O7vX++0BwG2sUBIAxtEokhdeqe0G0WOrQ5vq+LIO8ErdBlUirk6uSFmWpZaLiakVjFfS+LZH98uidi40HT86bQf/119fimQ7Em18OIesoeH5C4s4MV7CVDOqYXr4aAW+H+KZi0sYHy9GzhTZlJSKefxIGROLHYz0WTBYv+YG8DVNui00PIGjRR+LtoJ9OTKBo1TkYL4NLajC0PJSVJvRvxgUJ7X9OOest4wJN9utWdpqzu7E937z62hYh7Hk5CRm4NwZlOGAwKWFCAxQw+96jwSSPAa6rFB4eLHtr7KER/Ip1BK+zkcGspJZTRJPWhGo9QrPDpZMWZN2Yb6F4zIVHBUCEuiEULGvV6Y60QpxokK9QB5TCEMPoIYqQjWUQDCDZXTUCsreC6hrJ5DzLiGQnsACaTEJWx1H2r8ETx2F4V1HEPZBC+fhraRhpGvwplVoFRveZYHwbT8MGK/0Ir69FRB/9dvAF1ib3WuHT0FbXqsBxH2noLuRCkNYrkLvM3G1+F24nD2CobS+KgRN5judPVgXyGh4Uu8vHpqgmtgv6ZrDMehsEzdqAdLDmTWdBJZ0b4m/zSgCrV6pRTnlI5Ny4HgpKdZesiINSA2+1Otk9M+Ai5J5CcvOUWiKQDa1Xt9P/qYwjcClSkXCA3nD1MXON3Fal+AtjgomU8S8luIa5jhFvPFFlJ/HDGLJAO/VCsY1hK+1CCCBbZyRiaf14x//OB577DG8//18xn/jtz0AuOkakgXM1AJZwJ8F8O09FvBbX4EFTFNzysYwAvjlb/wzY+0X7AHAbaxmfBNkV94kGdErldbeyjcOERM9eNO9k7Zxs90AdaaAuy5Y5jW31EE2peHCQgdFTcGZiTqODWZx9sYK3KQ/alqXLhJuGGKmbqNatFApmHiuRwg5ebAPk/XowfXGY/148fKSBJZaNhWRFdRIkPitx/vxt2fn8db7qhBEhqoKW6Y/FThBiOFsCFXhAzNEOdXFbCeFY/0r0MMD1PIARbX5oCIIjJ07Xm07t20s/5ZdQuci2mEK88EwAqHgcsOB4vrIZVOYq3fR9SJ2tAhCqfkXN37GusDFRCqe342VTCz1xLh5gQ5XsjJlrHk+0qaOem8tT1Qzcl0vLnelQ4snFMladXo+sEcKAhNN0WMNB1hsqRjIBGQHQQSaFIWmFEwIDQF09AXnUDfuQ5/zLLr6fqTcaSB0EYQ6LDEDB4MwgykEKEPz5+B36Im9AG/BglFswrluIXzbh7ecr+10EP/tD4C//r/Xuh44AW3l6tq/jz8A3bsRAcBSBUY1ixnjIUwNv0uSPaTPNSgHw/8RUtw8ocKz7hCKBtBO1MoSxBdS2qocD4Nz2d5LDTesmBo0PZJXMiDkmserOpJ2oKoBJttpDFreqhZgDLi5TU5dga62UfdHkFGbMI2ew0nvqEK/D4E/3OMhb2e2WGsYRQX5hy9LBILJtG4cFWS/+P9vFhVMEkdiIPlqCqxv7xfvrNdmkU2mft/3vvfh3e++PQ/rnR3R7m99twHgMxOzyG0zU7adX99qNPCG8R3LwHBXJHz8dE/Xj9pS/yuAP0kcA61gKA3z7+NLko9+ynX2/t2j3OHbvtEB4R4A3MaZlwSAvHFQDLqvr2/TLWOiB2++d9o+iab3C20Py7Yn2bq+EJhd6mCu7aGc1vHCpYjhy4jS5VnqV768ZUwNhwayeOF6xGZnSrdE79kgioe88WgFT56Zx4MH+7ASClRNDfOdyKnizUcq+ML5Bbz95ABqKw6KBVPWqRELZoxQkhTGsg7mOjpOVjo4V8vgwYFlaMFBhEHEYiSgjlNZfGjFNU6vlp3bNpZ/yy6BX4fvTmJGHEfDCeEKAZsEnaaD/ryJy0tR+reY0jBVj+ogGR2k4PDcBkJIX3p93dlgLiXT6pSCuTjXwlh/dtXNggSQ8/MtaCowWo7CfTrLFXo2ZYfzAhcbkT9wwQrw0iJwqj+qRwtCDZoWwBRtKQqthzbS4RJa6hDK7pdRNx9FsfsMPI1M4DlAuAgDDYZYhhfkYIgaAjsDTV2GV8vCyNZhL4xCPJxUV9hy6m7aQTz558Bnf2Pt+/Hj0JpruoA49jroPqVqgDBfgjFUQFMZxsShf4bptgtTVbDsePJcZCPDl+d0T0d7dVzaw3UTun/8guc4X2TYMpoCzxerd35+Fqd/CQEbjod0ai1KN5btoOmmJBlkLGNH9X8cn1IyvVrAkj6Ntl+BBxMFfRGa1mMzkzTiD0MEldueuDiSx+uIYC+2Oku6hMS1gjGBhDu7mZwMa57j6OFui0zf9o/chQ3j+04ysvnhD38YH/nIR/D44zvTsNyFw9uVIfYA4K5M42t6kD0AuI3lTQJA3gyZuiQATJIRkkLGvKm8Gq4UJILMdn3MNrpYsQOkTA1fObeAas6Ujgheq4u5FVdGBenowdqyja0va0gyCIWg2Q4N5bHkRP0Gi5aMFkwtdfHI8X4s2D4ygUCj93B8aF8Bf3+tjm+6fwAvXl3GfQfKMASkIHS/GWLeVnCy2MWZFQtvHWnipaUMXldtAi6jG8xCK/IP98F0DB9abJw7RgXv5bTvunkMQ7j2i5jDUbR9XdYA3mh7qNW7GMinJMiLgUjZ1HBtuYP+tIHpnqZicqz9fRbmExFBEm6IxM9NN1HNGoAZgY2MrmAkb+LCUhdDWR2ZjAVTieq9XDJVITCQjsggR4qRyPd0CzhQIgtUg0ZPYEXAEB58VUM6qCFUyILVkPfPYSV1En2dL6JtvR7Z9nPw9SHo7kyUJvUdqMJBQLaz6sFf0aFaXTjdh4ATzKTsvImnPwf8F5bc9NroYWidqbV/H3kAetiLAGbzMEb64Ig0Xjr8P8PzfPDliA4gM71IqgQ5CoXKVSyzQLBH9cil1JelhQfo10yNPUbVuz5Gssa6dPBQL/3bsD3kDQ1hjwySVkNULBeTHUsC/NGeFiDTwSaZwL06v5I+i7o/FHkyy/q/6Hh8vwThj+188nojJGsF4+uK11byuorTwzeLCsYvZASS8Qsbr9lblZPZtR+1SwPFADkp6P+BD3wAP//zP4/77995DesuHeaOhrnbAPDp67sfAXx4/65EAHc0r6+ljfcA4DZWM05LsitvlJQvSQLAmOjBG2MsZLyNYXfchUSQay1P2o9NNF1kNeDiTBN5phWdAEVD4IWrUWRvX18aFyZfrlnJOsFrvTo19nvgUB8merVqh6oZnJuKnAnecLxfEk3slou2oqIvo0urscWWg0ePVfG18wt43dEqUkxFaRqKmoear+OBiofTSwbeMtzEjYaFwyUbij+wCp7jWkkCwLgekDdnzikjDnxg3QmP0x1P/oYBJAAUh9ANUtIfmXqJWQW4ON/GvnxKRp+IE2jb57Fuc5N6wLShQpVSJWuDHx3I4cJMQ0ZcZS1gjyk8kjMw24wICcf6M/AVFStNGwMFS8rCFHVawgn4QsGJssBSB9BVgXLGR72jo5T2JbBUSRKgKLQ/h47aDyusQxcNOFoF+e7fo2E9gmL7y7DN+2F1XoCvj0C3ryNUslCdJfiOCiVwIVwV3co/glYd3xWWtnjhi8B//oW1iRgch+auWcPh0EnoiAChMNPQx6sIhI6zh/+1LG+dbkcMV6ZolxP1lPwspwp0AgWllIb6hrxwVleQMzSZgucLFusu8+aaFmAxpcKkNqAfSHA5nk/J1Dtbf8pD29fQDVXk9QBF1v8JBYvdFPozUfZIhwtNCSRYpS5g3pyWnyw0F58AACAASURBVDPNHrhHoSSsG3frHI0JIa90XSWjgjFxhFHBmCkcW1/GY8XKCNuVk9mt37Jb48TANmnp+Z73vAe///u/j9HR0d3azV0d524DwK9dm9n1FPCjB2TwYCc6gHd1Te61ne8BwG2sSBIA8v9rtZqsASTgi71rk0LG2xhy17pMtCKmKXXJOh0HhqHh0nRTavJ1VQXTs03pUCFBYMnChanG6r7392dwdT5KP8Vt33AeDduXUcOyZeDyIp1PgMMH+pBhVERVseAE2F9O4/JCGyMlC0eG87g808TIUAF+uws9m5YAhPZZtIQ7V9Pw8EBbMoFHsyxBo4B6BKbJ8uXfG7X9+Flc08S+BIc3k6rYtcncwUCefQ41ZQxN38J004GqKLDDEBPzbRl9C/wQjUT+cTifkvWAZPTG7UCfhblE9I/uIbYv0OoBmMODa1qAFUNgoVeRcv9ATqaVz840cHioEIlOG/S/jZAkGcBnFikKDViGj5mmjuGCL6VPJPtUEcj4y+jqJSkF46sZqKEP07+BjnEA+c7X0LEeRKb9dbjGYaS65+Brg9C71+EHBejBEtxGGrXxfyzJRbFEyU4iuOL808DvsO6618pD0ER97d/j90HXKVLN7KoG/fAoA2p4fv+/hGZYaLkeWj3iDEWhaQ+XbLQs5E9f96kAUjoZ7GtrQm3AOB3M7RkNZLvedCWxpD+7Zr1YTtF2LqoNHLJc6DpLM0xJyrFSveg2XLiI9APTShtWalEet+vuhyryOzgDt7fpxusqrhXcKCcTi0wTAMZ1uluJTCdTxNs7mrvXi/dt3suTAPBtb3sb6AZyKxJfd+8XbL3nPQC49Rz9Q++xBwC3cQYkASC708GCN4kYvNxJosdWhzdn+5huOBJMNLsespaOr1xcQiYIUSqa0guWFnFs1DljJXy97Up9uqxpoNZeE5sd6kuD1Wr0pA27HlRTl2BwsGQh35fGtSvLOHW0H4tdH/tKJq4udXFyJI+UrsH2fBSKaXSbXZj5jPRQJVnhSMHHXFfB/rwnCSH9pgklzMnIwnbs3OKIA2/YcU3TRqbjVnP0anzvO1fRRA6LbkkSNqjPWPcFDD+Q7N8+S8ONZXsd4KBfLd1CGMljNC5v6VKomGnFtuPLyNNsL5JlGSry+Yhdm1XJ8o18gXkBH67m0O26knhT6GkBRrZwAhldYCwHaQt3qhzI/azYjOD6UjtP64lCW0ETjpZF3r2ElrEfGW9GComHSMFyL8A2jyPdfgZO6jjMzovw9X3QOxcjaRh7El4wDu/ge2XkdjciuOLqGeA3WJvda/kyND1iVLOJ0SMwTOrkRU05PC4jrBer34NJfRwD6RSme2Cac8HrJG6sB2TJRILkK7/K0NmjpyQb9x3KGutSxKz/Y7SdEHHQ0qH2yCBkBjPtG/kC97QAAUy0TIxlXKhaVHupChWhQpkW7m8ZltGE65eg7GLqdzvn+8aoIAFgDAYJ+Pg9r09ep8lyFoLAzaznNopMx/Zz95JmZ3JeeD9h44ulPJ+EwCOPPIKXXnpp16S6trMOd7LP3QaAX706vesRwMcOjnDK9iKAu3Ti7AHAbU5kfMNgdwLAuA7mThM9tjo82lUxanRjxZbOqJ7j49xMExXLwOlLi3jjiSqeubTGMhwpWrgy3cDhwSwuzKwnhjxwoCTr0qxQoMMUGIumANAiLm3p+PJzM3jgxICMWo3mU7het/HooTKuzjUxWraQzZgIAoFA16QIL2U0RjO036LzgkA14yKv5uHakJFT1t8kXQ22+q2bMR1fjVrLrY6L3/teHd2gjjl/GKFQcblmI2NENY4XewQceilfXY7Y1cmWMVQJBOnpPNd0JDDh1JN5bfdSlONlC11QJxBwOi6UXi0gRaWreQtnphs4Vs1A6LqMTMmXFgEMpUnKYa0o8Lp+Hw1HA8XArRSBmgbT9BiKlanJQE0h715DyxxHvnsBrj4Aw1uOLOCULFL2RbipQzA7L8HTx2F0LsDXRmQk0LEeQTi4Vjy/WaRpY/3ZK82rmL4C/J//cq2LlYGWXovMicH9MLKJkoZD+6FpArO5hzFZfgc6PoVwImtCNlq7LTihtONb7FID8eWtYtHJY20fTAfrPYDH3jynmXKnFAzbeC61qgWoiQCBEkX/GPGjF3DTMVHzNIznqAXIcCOPiOAd6PgZjGQobK0gcI/ckdTvds5b9olrnAnced4QvMXEj9ix6HZEpjlWXCu4W/qn2/1NW/UjuSVmS7NvDADPnTu3KyUMW+3/1fh+DwC+GrP8jb2PPQC4zfWLa0aoTUfwwtTBvaCN1bR9STiYadoy4jQ908BAyZKp3eW5FnIZA5m0gameEDHTkYcqGbw0UV8lfsRTcN/+EpyOh3MTK3jd4fKqL/Ajx/oxu9jG7HIHg6MlKQWTM1QsdDw8dqiEL15YxttPVOHTYsvQEJDcQcFaRBGoajrAQlfFyUobYSsDIZQd2bltZDpu5oqwzWXd1W62ewnT3j44viKBNP1iKaLdabuSrc25V0OBWoKYwAMoW5r8ntHauI2XLCnwHbdD/RaaoQrNC2BoKuJY2OFyWm53abGDB0bykRag70NopIEAB/ICVxuUQwFOlH1cquk4VAyhqiHaroac5UnPaMGDCwWywQw6qSEU28+hkT6JfOccfL0Ejf60ISVj0tCd6/C1IRjdKwjUQWj2JJzKtyPMHXzZfG6n/myzRRCL08Av/cjaV6oGjXntXhPlYRilhLj2oUPQNB9tYwwXx/4pJumMowp4fgjTIOlFheuHktSxWSukVHTWzFpkl5EstQCj3pSVIXyLazD52WguYr2zQpARvaAn1l3QfRRMDxMtCySGVLMR6O86rGcN0A3S0BQPo9kL8NwDUERuV8/D2x0svq4Y+WPbGBXkZ7ciMp2Uk9lMZPp2j3M3tuNvjH8fx+Nvf/TRR3H27Nk9ALjzCabQ8sqXGQHMR770u9FazQbeshcB3I2pXB1jDwBuczoJ/Cj/whtZXLNG4HEvtJeWu1jueGCtU73pwNRUnJlpoqwrOH21jiNDObQ6LlZankz/EjDkLB1DJQuarqLeceF4AcYqWZy+EnmSnjxSxlRPC/BN9/XjC8/O4MhoASKdAi2yVtyofu2R/QU8ebmOdz4whKmFFkaqORlRchUFGS1E01dwouTiTM3AmwdWoLrlXbVzi1OOSSmZW4kq7ub6Od4VzHtDqDsR+KLLR6vjoVJI4fxsVGtZNDVMU7g7licxVHTdYFUEOj6esT5rFShKLcD+DDKqgjNTTdw/ml91s6AW4NnZloxMnRwpyOghySDZXJTaoi3cjbZAvyWkLuPpJU1GAgnCGewyDR8KVcRVH1roQJP2cFkU7DNoZk6g2Ph7dDInkG6fR6D3QXOXID3g/ACqtwQhMlDdJXTHfgDQXtku7FaigqKxDHzin69bHqU/BzWIAJzIlWEMJCKCB45AN1zYWgmn9/0wPNfFsidkLSztC9k4j2RhLyVs+eId0FWllbDiY19p2SdTtcBUw0Ulm1qV4CGBJJ2Koq3XGzb2FyzZl431f01PRdPXUDV9pFMuRKhivmsgbfDc0JHTa8hpNrRQshrviRaL3PPlltdQ0npupyLTMYOY58C9ICcTl5/wd7Hxpf6bv/mb8eKLlIB7bbS7HQHcA4D3/nm0BwC3sUZ8O1xYWFj1oiUQZO1IXD+yjSHuaJerKw6mGrbUAmRt2MxSW9YFFkwVZy9FgG5sAwFk4wERVJy5ERFEUrqKQiUNv5fqenB/CV87t4CHj1dRIzOy68JO6bLvg2MFPH2tjne8bghPnpnDm+4fROB4CAxdEhFWPOBowcb5FRNvG27BhLTy2fUW12nGaSze2F9tKRnPu44Zrx9tPwXPD3C14cJu2agWLMw2bSkIzVZN67iy1JWpWE1hPdr6qFT/hrqzai6FfDaF81MNGWnaP5hbJTUc4roudpDRVYxVqc8XSjAPI1qfkkEWLHAwH0nBTLSiSGDb1ZExgqg2TYpCezCCJnw1DU14sLxZdFIjKDW/jEbhcRTqX4adOQmzeRpBahha6yqEnofaWYBQDDhjH9r2mm4nKiicLvBvP7BuTGWwH6oblS2IVBrG2BoBI9x/HEaqK72NX9j3L6CqCqZ6UdfAox3cGlik5R6j1bErCIWdk5E9jl+xNElooXg3ASQBYiz3Iq+nnCHdVmpdF34Yoj+75n4yaLmY6UbAYozsXzXAVNOEEyqoZrguKgrGArKiDLUXNdz25N2BjkkmPrMaMSiKd7VbItMcL64VjK3nYjD4atcK8oU+VhjgcS0uLuKJJ57Ak08+eQdm+O4MebcB4BevTO16BPCthyRDe68GcJdOqT0AuM2JjIEFuxMA8iaZZJBtc5g70m2iQQDoYJ7kjpSGr5yZk9E9FrprtouJhS6yFLclUWSD6wQPiAQDmoSReMB2ZDS/aqVVyhjodj2pFfjYqUG0/BArC/8/e+8BJUlWXgnfsOl9lsnytqu7uqvtdE+PYwwjEIJFYkFCEpJYWIQkQGKRhH4ZVgKhs2dX3iLEilmMBAgJkIARQgxmvG9vq015X5WV3kSG+c/3IqMyuqaqO6squ6u7p945dboz88WLyPciI25833fvTYP3u1mdWmPAiZH5HB4YaMQTJ6dw564YStkCBDeJ3AIpFejyFHAx48BDzRTxWL/IbTWTRzczusnQE/2NlpJR1XHMawEsKg4sphWkNAMEUc5MptBb58JYypQmoR+dgwMDibPLhKDp884waQFWQGFPnZsRSBghxydDcJrggmzOdAIyqgFKGTtdMqbjWSb5U2RVZTqLDBJVZEdQx0SWh8QbaParGE6I6AyalAdN4yGKKiStAFWUIZeSJqGBk+ApXEDG3YdA4gdIB+6Bd+EHULx7IMefh+rqhZQ8A83VCaWeHJXW3laLCjJA8D/fwrTyrMY1N4HPmfWs9J2EzsCSXa/e0g/JlWFGeMfa/gdUXoaiqlgsakzMmVLs9ro/AsMUxaPPmU7mMkZIs1tkeowWSGz2ykjb+rT5HCiWVCYFE3MRGcQE3LyhszkmKRhqja48ZnISijrVbxoIuUzQ6hHTCHKbn/q19EtXYuIvX82VRKZXs56rRmR6eVSQwKAlSL32M2ltW5Cbk92B6PLly/iN3/gNfPvb317bQDdx780GgI9fqj0AvL97CwDW8pTbAoBVzialQ+iiRi2TybALlV1EtMphrks3IoBMExEkVWTAYnwhB5kJ/5ZQ7xJwdNC8abZHXDhbZgTbD6Qv5sM5mzzM3m0RDMfNOqDuqBtnxsxi+3076iDo5BmqI2EAEY+I2ZSCkFvCrrYgzo4n0dMeRmoxC2/QDQ80ZCGg3atiMsvhvpgOTr+6h3ItJ2gtKcda7FfTUkhrOcwWI4wFrOnAfEHF9HzGlPogORLVgEPkWJqeWNkUqbX7MjsEDg6bDZnEA0GHhHHqB2B7o5cBS2p1Dh5TWRMo9jd4IQg8jowlsKclwAggpket+c36QzrOLPJo9xoIODWcXRDQX2e6e/CcBo7XIeoaNIGDsziDohSCrKYhammUpCDc6RPI+XbDG38COf8BuOefhuIdgBw/glLgENTg4Q1N4UpRQdcfvRtcqUKa4drbwadmlvbD9zSA1815URv74PCZ5+zpup9A3tnO2OmjGfNzn8Bhqsyoth9ohKR2NJ2l5Kk0gqaWalxJS9ECjATl6EHIyhCT1WGdS8Jw2S+7w+dYIoOQGLdFPOF0FRSTVw0THHpFAy7ZwfyY/WIRXm7jnskbmXTLb5vKWtZq9WYXmbYIcWsVmbaO3V4rSO/dCJFpAoD0AG+RU44dO4a/+qu/wpe//OWNTOlNte0WALypluOmPJgtAFjlsljCp9SdLh500bsZSCB0PFTcfnwuizi5fWQUyA6RkTyCToFJscxOpZFXTCRAbNJz5VQvvQ56JFanZjmB0Htd7QHEsyU0B5ys1uvyfI5FrbZtq0NiJo22Jj9mKRrlkTCeLKK3wQuvU0CuqKGxzot4PANP0AMPdGTBI+bSWIRlX0QEZ1y9TqzK5VhTt+XgYqV6pjUNeJXOaWUU04U6qBAwly5isWTABR0XZrKIukUMzduICwDI+s0pCZjJmHI8bQEZC0WdOX1InBmVtYgI9Pn2Jh9SJR2UxiSEuVhe14EmPxazRYwnC+hpNAuvZUNDgVEXDPQEgItJDn1BKhIgYMqh2a8gXRDhd5gyNDwjqupwKfMoyCG4ClPQBDd4rQiptICSFIUzfQwFz0644s+i6NsLx8KLKNa9Cbq7q1ZTyB60KOIu/el7wWcrTF+usxt8ouIGwm9rA6+aZQtqtBuOkBlhnfMdwlDwNYwsk1Y0Nl/0nYmAk7REoQ0DfiIyrUAKIekXa15pvKiLONKVS2W9U2DAngAhvdvic5bJIBSwJCtAsJpOqskNOQjumezgkAMQBfLTVhAgD2zeBIab0eiBltKgVinLelOw1aTzVxOZrkZOhn6r1yMqSA/xdgUH0v/76le/ik9/+tObsRzXZZ+bDQC/f3G85ingB3uYU85WCrhGZ8wWAKxyIu0AkC6cdOEjiYSbpR2byTAJESIUOGQRT56dhd/Q4Qm6mD3biTK5g9K9Rklf0v/rafAy9xCrhX0ydIeIqFfGpctxxBo8WMypaAg50dIcxLMvT+CufTGmBdjslTCcKGJ/RxBj8zm0Rl3wOEQUFY2lgB0wUACHgKTBK2vo83nAocLk3Iy5Wx65sBjE670BLv8O+dIkxgshqLqAs3NZVvQv8BwuTJpAhcAE6ScubxEX1ZwBAZfMLPsmkmbkiqzgpsrgkHyaGyJuBjoSiTyiAddSbduuJj+OjSXQ5HfA7yNbOJOxSalg8ruVBQMLRQ67IhrmsgJcko6Aq4RpEoX2KcyxglmScQYcpSQUyQtP7jLyrja4chMweAnQVYjKHFQxBDl1GoqnD474EeSb3wWItRcxNv70F4GFqaWpMjp7ISZM+zfWtnVBVM3oth5uhxQxY3Zpdy/ORN+CqUwRrV55yQ6O/IETebLlM+AROCwsNwamSKHEXwH2aLwmr8w0FalR3SYRP8hlhVpIFuAuS/KkCwr8DpGllUmTkRqxi7lynV+diwM4CQ4+jzDvYGLhN7rZ6/0og1FLIls1EXfqY4lM07FQo9TvSiLTVorY6lMrIXjaLz3E2wHgo48+iueffx5/9md/dqOX5LrtbwsAXrepvW0GXvcVyLB+vbfNVFz9i9gBIDHICEh4vZtfw2Md9enZLC4v5pgMiKGoGJxOswjH5FQanR1hHDtfEc1tCbtwYSzJBJ6n4/kraqN2dYaQUDQmIaPrGjiXyXSm9yml9typGQzsaoCi6YjIArPcuqsnjCfPz+PebWGUVAMetwxOMGugWApMJz/aEtrcQWaFdjO0leqZLCu6jRxfXp3CnBJAuiggUVRRLKrI0JoUSxhPFFh6kWr/SPNveWv2yxi26QQScFyuBagKAtwwMLaQQyDgMkGIk7QAZZyezqC/wcO0AJW8wuR/KBpV7zQwyzKpHGMAn5gTsD2swyFqmMkIaPSZqWCB/GopBadmoIoueHIjyHta4U6dR9HVAkcZCHKlPPhSktnBidkRFFp/fiNTtuq2xl9/CJi6vPS50b0dYnx46bXe2wtZm2OvDW89xJiZUs07G3C87p0sGjebLyFI0dJy3SBp9JEMT2IFJjD1oah2ohxVZXPBgwE8girEBibhdLn8mj4nLUCSf6Hav+lsEQIvQF2qWzTgl8UlWZF6NzGABbiFPEL8jU//WuLOdC27npaVK0UFCbzZ2flWjSBdR6k//VmRPjsYtGp66Zgt4oiVIl7vQ5sFAGkOrDG+9KUvYXx8HB/72Meuy7m8GYNuNgD87oXaRwBf27sVAazlubTuu/GrDQDShYouQNRIRJRSVH5/7TSONrqoZ2YzLFI0V1AxO5NGNOTG2YkkFqfS6G0LIlPUMDFfcVLojLiQzioYsfkA0zHs6Y1gcjqFsdkcdnUGMUEuFQDu7K/HyQumbVV9a5BFskhjjSKBd7T58dxQAg8PNDBLuO7mALSSBkMSWOE76avtCGpocd0882Wfb4s0UgspmZI6g2nFj0XyyDUMJpZN9Zhk0XemXGdJ8iLDy6KA9R4Jk8niFWC8PexkosVW62/ysc+PjyTQW+9BvgymO0NOjCUKLO24t9kPleNwfjyBriaz3jIsgzGBHbyBnqCBkws89kRVqDoHwkF+pwKlJMJBdmW6ymrUwAtw5ydRcDfCkziOnH8XPIkTUNytkFND0CUf+PwiixwqDT+20dN3xe2N//tbwPCZpc+43l3g5y8uvdZ6++DQzJpAQ/ZCbDctBlXJh7MtvwTC2GOZIiOBzGcqJBAif+RV0uO7Ug6aJHpsLnBsrEa3hJxB/sFga0kR1rxtsw4/xbmBSwkz2U51ndbHJNrtKbPlaaw6txn9DggFeG8wACTAZS9d2YhN31oWe7nItL38whqnWpFp66HNiiCuV2Sa9kdZHDsA/NSnPsXA4K/+6q+u5evd1H23AOBNvTw3xcFtAcAql8EOAIlhSiAwELhxhIZrHeZ8TsGp2Ryyiop0tgRKF748kkBUAk5cTOBgXxQzyQJjPFKleypLUR8dTllgkhlFVWdWcgG3iPNjZkp43/Yohsug8c7tdXjy+DS2tQZgeGR4NB0FgbxqdeyMeXB8PI3X7W3Cd45M4MF9TcimCpB8TngFDilVx66QgZjz5omYrjSfK0nJWCzHa82/9bmqpRBXDczmHYzwMbiQh1GkFLqLkXPSZaZ1WOYwkjDBtU8WmISP5fphjdURcTOxbav1NfkwOptBMq9ioMW/VKfWHpRxccEkSxABhKRLplMFhMoRQloDSmHWuwCnYGA4zWFfPYlTiwg6NMiSimxRgtdZBGWBSbYEugaHmkTJEYQndQY5/3Z4555GLrIf7pkXoAS2QZ47Dc3dAzV0d7XTs6Z+xmd/Hxh8ubJNzwCEhQuV1327IJbG2WuDonDdLYwVrPMyjrf8MnKGwCL1VK9HDiCT6cpcEjgLyDzmbJFYAmhUL2hvMa8MVdMwnjLXivy0LScQSgfXuR2YyRaXWMQuOwDkOXgks/6PUvABh4MJQIcFDnLZNWRNE7LOzgSYCPBYygXrjZytc/fm+pTZ+fTgvNqDliUyTWtm/Z+AqvVn7Z/G2ojINI1PWRx7BudP/uRP0NLSgve85z0b+Zo31babDQAfGxyDp4ZC0Nl0Cg9va6U53qoBrNGZtgUAq5xIOwC0BFNvJgBIF8XHhxOsxonSrzNzWYws5k0twME4A4QekcdUmd270tfuqnfj8mwlStje6keqXCRP4INEpQ/21yFtAHNjCfjKZIPWkIORG163J4ZHXxzHQ3e0ILmQhj/ig48HkpqBvWEOdY4bTwCpcnmv6Lb8ZkU1SpZm2DVvnoaBxdIsJvNBJElGpKSzSOmlmQy2xbwYnDEFocmZQ1cNRvKgOkES8ra3IEVYbelyv1NgQsTny7ZyvU0+Jv9CP2CyLKO6M6fAobPRz1xbKCXMlYWKaVySIOzwGpjKMcMP9EcVnJyTsCuqgud05FUBblkBpwsAr0BUs2bdHww4WSSwGd6F55CL3gHP+H8iH3sNXKPfhRJ5HXRP73qm+ZrbGF/6Q+Dk00v91M5+OBKVlDD6dkMsjVbG6e6GwJuRvsGGn0TK0crq7EbSVAkJSLQ2y1LvQZlnkVMCblkb9qNkLWkr0lgL5d+AR+LgdFbE3xucAtIl/QrQaAeAxPQmX21qPonklmRIXBF1gmwnfl9zHtbbwXqgoYfVWtf7rfeYLDBI11DLXcnyILaTPZZHBS3CyHJLOSvdbKk0VCMyTddymhN7DfdHP/pRHD58GD/+4z++ka92U2272QDwP8+P1hwAvq6vbQsA1vAs2wKAVU6m9cTJbqYlYttmEAqFqtz6xnR7ejSBYfKgdYh4/tQUYlEPZtIK5KKK4ekMGgJOTMykX2EBR0dH9ehhrwMLZWmLxrALRQqnUO0T3SQLGoolHXfvbYSm65iaSoMPuJk3rUH+pgZw3/Z6vDA4h307GjAzlUR9LAAPDAYYD0ZEBOWKcO+NmZGN76Wawvble0kos5gt+EGqIyTKTDqN8ws5No88ecJyBCwoOsQzh5axpBldsjfS/puyaQS2BR24WE7Xh8ner+z0EZI4FsUioNIScCDqc+Kl4UXsbwswAohIpQuCGYUiMehLKY7VBDb5VLw8K+JgQxFFVYTIa0wL0NBE8AKxfnPQZCdEJQPOKLGomjM7jKK3De7pJ5GP3Qv38H+g0PROGNL1iYQbX/0r4OXHlqbF6N4FMV5JAaN3AKJmI4X07IDAmQB7NnI/Lsn7wYkii4qTx6+T5xgotxl+sL6kp0gPR4pmoKTpLDVMQtGNXgnzNsDYGnAgVwaJBDxEnmPb2BPJBMItdOcsS/3QPogM4hBFOPkCIsL1/x3Y6/2I7EDA6GZr1oOW5ThiefPa/b2rjQrSd7Me0mk8S5pmJYKJ5R5kV3H4tV/7Nbz1rW/F61//+pttmtZ9PFsAcN1T96rZcAsAVrnUdgBIF5BUKoVw2Kw5ulnas6MJTGYUptU3kygwL9rRlIJmn4wXT82yw+xt9OLEJZM5aW99zX7mNGG1Pd0hjFpM1KgLZyZMB4aDu+uRTxUhiAJILpikTabTCmIBJ5rDLsyRDElrEPMLWfjCHjgo3cLzuLtOhkfcXAbwRtZppcJ2izSyPCqYKc1jPO+GoglYSBcwk9fgNjQMzubR5JNxYc4EKdQoshdySphIFpiLCzXC3WGfg0UPqV4wX1SZO8tsWceur9HLolUukYNSVBnTmhppASolHedmM9hPvs4GUMwV4XDbbeFMICjyBkbSwK66ImYzDtR7iowFrGkCRFGBqKnQRR5yfg6qHICgZCBoWWiiC47UJSj+TjinnkWh9Rc2Mq1X3Vb/mOrYvwAAIABJREFUxqfAPfdopU/3Lgh2ANg9ANGwAcDuAQi8KRuTDu/GcdfrkCsoCDhFTJRrKVdKBdNZuSQPU94bRfKo+tKeECY3HQKHjLRQ7mezbzYFvssRP/qYIogkRcPWWRbYZ35egU+4vhaSVo0bOwa3+xUM2+u2YBsY+HqITFvEPYoa2uVkCCDSnx0Avve978UHP/hBFgW8XdpmA8D/OFf7COAPb9+KANby/NwCgFXOppVOoe70pJlMJlkE8JopwSrHr0W3y/Eckx5JZxQ4HCJOXY7D4RSQVQ3MjKdQUDR2k2oKOHBxsiL9QveogFtGvCw3QseyZ1sEo/E82kNOaKqOy2X9ut276nHm9Cz276zHbEFDq0/GUKIAIigksiVEfTIiASdzHXF4HeA0HUWOw4MNLoi8eTO81Zs9Kkjrv1xKpqDGMV10IaPwuDyfhU5wgeNwcTLD5t8rcQw025tXphSvhKlkEU1BJ2ildFXDSDyPgEsEUTOsNtAaYClfTlHhcYlLtYBEADk+nmSCxv2tQUY+yRdK4MsyJeTMkiiRKLSG6RzHnFxa/UWcmpcxUEf+xAII9nC8BkHnYAga5Pw8Sq4wHNlpaJILgpIFr2ah8S5IqTEojW+9LstJN2jt25+D49mvV8bv6IeQtKWAO/oh8pNLnxtduyEKCfY672/HmdDbmSwSwbW0SlqUZlenoWOhoDM5F0r1pmysX2uw5dE/n8wz9i9zSLGF/OwAkCKC9Gc1b1kCiF4TU5vqAOsEQLyO9m9WvR9FvijtezNdn6o9UVaSalouAUN97LWC9D0J6K0kJ0PnkkXgo3mhvrStXcj/J3/yJ/FHf/RH6O/vr/Ywb/p+WwDwpl+iTT/ALQBY5RLYASD9f3FxEcFg8KZ6us6XNHzn0gLyRQ0uWcD3j08zEojqdiLAczh2YYF9WxJtzmdLTG+O2vYWP86NV6J/dA9rbPQy94MTZ+bR1xXEdEoBaQRu747g8RfGcfcdTZjLqWh0ChjPlnBHZwjPD87jnh31DDASMBGY8T2gcxzub7g16v+qPB1Yt9WiFuDymFYEzKQNFFWNMbDjigGe0sFxqss0RaIpzbi8UVSv3iszwG19SozfSRtg7I75QG4UR4cT6Kc6zTKA2dXgxbGJFLOLIxZ4Ka/A4xShcDxkzkBBN3/uAxEVx+Z59AQMBB0qLiQk9EXyyBZleOUC0wLkDVKG1iAVU1AdXjhTQ1B8TZCTY9BlH/hCCpzKQw3ft5Ypu2Zfu06d5+VvQ/juP1a2aeuDkB6pvG7tgyhVnEGMjj0QJdP7uuSuw4XG/84ieyPJAprcIrLkYsPm3ECpoLJSCHo4euUavDL6R+lfAuE0g/myxh9tZ19CcnGxAy6K+lmvoy4JIqeisezRfM2JWEcHqzaZHC7sadR1DHVTbLL897VSLe56RabpC1pWcLRGb3zjG/HFL34Rzc3Mauy2aJsNAP/97EjNawB/ZEc7rc0WCaRGZ+gWAKxyIlcCgEQCWV6UXOVw163bv56dwWJeg6hqGJ7NwkE6fKoBl9+J42dMzTRqHXVunB1OQBQ4eJ0ii95Zra3BA03gkZjLIpNTEYi6mK3ZjvYABJ7H0fPz2Lc3xiJIomFghqRgOoJ4ZnABr9sbw2Xyvm0NQlFUCA4RXonHgYipWXe7NitqQTdhCvCkOUr9esDzAnNSIbcPAtanyi4sMZ+Mi7ZUsDUvBDSWC0XHgk6kyrp1BA49Xgcm5zKMXFIfpSpLSgfzcEk8ZtMKtjd4IDkknB5dxN72EEsFk4xJqhwB2xHScSrOYSBMKo1Aqsgh5i9gjlLB3hzZgTB7OAKComLWAjpTo1ACMTjnzkAJdUOevwTd0QnNu71mS0o3c2JnWr60/Av/AXzzU5Xxm7sg5GwRv1g3JJf5UEPNaNkF0WU+yGiyB6div4iEykNTVfawIho6EuSPzVEUlmeR0swKAJC0ABdsOoGkx9jodzAwlyuqMGxRvtVSwHRhJVFoagLHIeySIEFBvVT7Mgi6NlnSVJTWpGjZ7daqqcWtVmSa5sqqLXz/+9+Puro6nDt3Dt/4xjfg89Ve0Hyz1mILAG7WzN86+90CgGtYK5J/sVo8Hmc6gDdbcfV/DM6zOsDUfBbRkAsnhhcxM5HC/p11mJnLYdImNNxd74Fa0q6I/tH329cbxmKygPMjKfS2+LBQjjAd3FGHl8/Mshr31p4ooiKHEs8xluTuZh9eGkrgDQea8djL43j4YCvm5qkO0I1Wt4i+wPUvfF/DUl6XrvboVVEoYizvhqoZuBwvQNQMiC4Jc/E8UuXIK8mQMC/gcqvzSJhJFa+IKsUCjitSlDtiPqiajjOTaXRGXSiVCR51LhFT5SjhvtYAcxV5aWgRezrDDCA6YSBncJB4AzG3gaE0h/11JUxnRfhkHQEn2cg50BrMQVUlyJJ5XLxahCFKcGSnUPJG4Zw9hWJDP5xjL6EUfi0MqTZ1sASgSaeOHqgsX1rjyPeAr/xFZa0a2iAoZi0rA3zRVkj+SimDHtsByWvWV5IszFDbOzFvRJl2JZUpkL0elTlYKVy6+JGeJZ2/Vq0fkTgoYm29dssC87q2InlpSqmX6/poXHv80F4DKHKcKblEkSaBZ2DQx5XgrzEZg845knixQPPN9kBa6x/aSrW4yxn6K4lM2+VkrOs4bffkk0/is5/9LL72ta/hwIEDTAbm7W9/+5qB4IkTJ/Cbv/mbIE/h6elpPPbYY3jooYeu+fV/7/d+D3//93/Paspp/3/zN3+DnTt3XnO7ajpsNgD85pnaRwDf1L8VAaxm7avtswUAq50pAHYAmEgkmIzAzfa0fWwqjUsLOeRyJcgCh+cuLCAqcTh9OYG79zTipUEzYhLxysyHlkgEFN0j9iNFN1L5ErY1efHSeTOVdkd/HYtgUTu4LYJnTs2is8kHMehCYTYDb4OX6dd1hFwYnMngRw404RvPjeENd7djZCSOWGsI/QEZzWUR3DVM9y3VdTnrMqNlMZmXEM+oUDQN6byKTEFDyCvjzKRJqKG6P/LvJSYppQ+pQjK9zKGit8GDybIGHW2zu9nP9B2pDbRWtADrnMISa5gIILOJPNMWjNV5WS2goRuMFRx1kGwJpTE5HGxQcGTGlIKRBB2LOQFRbx5FxQGXI09ecqzmjZpcnIfqDsIxfx5KXS9cw0+hGPuZJcbrRhZrtdSlceoZ4Iv/pzJ0uBGCYX53BvKC9ZDClVpKva4bUrASyV5oewMuGzuR0znkCiWWDg7JPPOvtjciaxDwixc11LkE5t9MrcErw+8UrxCMvhoApPUjsg6bL55j8j7USAvQLQKNEtVdrvuS+4opJtBM4I/AjQWaN7IOt9q2y6OC1YpM07zRnFEKmJ1HhoH9+/fjQx/6EPMCHhwcZILQv//7v1/1lFAE8emnn8bevXtx6NAhfOc737kmAKSaw7/+67/Gt771LXR3dzMXks997nNs/7Xwmd8CgFUv36u247qvRq82JxA6Q+hGRRcLakQCuZm0tawzeCZTxOOX49ANDnNzGQwt5BBxi6yWL+KX0Rh0YWQqgwVb5Ml+9gc9ItJ5jaXHqA30RTCxaIoMd9V7cH48hf3bo9CdIs6dnEGsJ2Le5EiLLqfidXsa8djRSTx4qA0XL82jsyuCQxEnAuVoyO34S7NuxBQlsliX2VIGY3keGYVDSVFxabGA2YUsuqIuXF6oRPmsVHCjV2ZWcfZGhAKfW2IAkRrZkhGoWCin63e2BpZADUUaGTEEwN72IF68HMf2Bi8cZMunksSLwFLBDS5gMmewSCDVAh6Zk3BHfZHpAFLcy+MoIlNwwO8iACiC4xVwahE8SjB4EXJ6AqVAE5zjR6E0vm1Dy2mlLi1G5vJounHhKPCZj1b24QtDECs6lYbbD6mxQiwygi0Q6yqvldh2vMC9HhLTM+QwlDTnl6Khlraf/Qs0uMjGDVB1HXUBN4JEjMoWWUTQapliCVyZzLQ8AkipZYv16xL4JTBIdnAyb6CF9GZq1GjOCPwRiKG/W5HsUaOpWJPINJFB6EGeAKBFLKG5I9B29uxZNo9HjhzBzMwM3vCGN6zrEGnsaiKAXV1dDGh+4AMfYPuh60gsFmN+xO94xzvWtW/7RpsNAL9+erjmNYBv3tlBX3GrBnDDZ4c5wBYAXMNELgeAVGxtPUWuYZjr2pWM7j/78gRz9zh6ahotDR5MLCrgcyWMz2XR3ejFmaFKFGX5wWxv9eNcuU6NohnBOjONWe+TMZsssrqze/Y1srjQ+EQKQsgNYkjGMyV2wzvYHcb5iSTu2NGA0Ykk6mJ+PNjgvoIdeV0n4AYPbkWvlt+IC2oOUwXS6ANSmSImMyX4BA5nJ9PoCjmQLekslc60zwCcna1Iw1hfoSPiYrVr1IiVSvZ6k1nzNXkKx+o97P/JRB4+n5ORSqIeGU1BB46MJnGgLcA0ACdm02iqM23kQjIwXzQQdRiIugycS/A41JjHWMqBmIccZErIKhJ8zgIMVQYvFiAUszBkCTx5AGtF6JITcnwapfAD657taqRKjJGzwKd+s7IPpxuCqyLMYggSpHZzDqgZngjEpsprNdqKQrgbL6f2QRZJrqiIom6waF8qVyoTQsxt6T06t6nusS8WgLts4ZbIKTBn3GwZRV0CW8sBIDmDWCxUuw1c2ClB4jTEavAQZC8zuF3r/dZ9UhEpR9eZxItdZNpy87GXGRD4s+oAqS+lawkA1qJVAwAp5UskwmeffRZ33nnn0m5Jh3BgYAB//Md/vOFD2WwA+K+nhmoOAH9sVyfNyxYA3PDZYQ6wBQDXMJGW2jxtkk6nl6yV1jDEde9Kx/jPp2bAaQbmEkVAUzG0qKDV78DzJ03GZE+DF6eHXwkC/aQ5V3auYP1a/IgrGqgInliUVMtG7Z47mjA7lYabrLN4Hs0eCUOLBTSHnEvep5QmTqSKCEfcuLf+9mQAE2GBIgoUCV5eCqBrGqaUPGZzAqZJ408HCprO6i1Z6jdFAswVMENRWp9LwlS6uFSj1lXnQVbRIOo6Ls9mQVqNFhu4PeKCJgoIkqTMYgGi0yz831bvxkyyiIVcCXd2hVkt29BMGrE6s7jdxYPZwpEWIOlMU3Z5T30eL8+4cKA+D83gWc2cQypCU2WIBABLJRgyIGTj0J0e8IUcBEWG5l2fZEa1UiXG9DDwVx+s/GZ4HkLgSg09vjsC3jDZLYbogthZt9Rf9TdAiPpxQT+Is4kI6j0yRsrp9KDEY6IcBXeQO6JmwOfk0FMnw+ArIHI+UzSFGSlCoxtsDa1m6DqrNWT7Ngzm+mFF4qwUMKszdMvwQAPtcyPNXmZA5Se3e73fRudquZwMzR+BQXpwp3WyagXJBeTf/u3fcOnSpVfs8l3veherEaT+VvbH3umBBx7A9773vSu2qwYAjo+Po62tjYHOvr6+pe1JjoZqy8mbeKNtCwBudAZv/+23AOAa1tgOAMkJhC7Adi2pNQxV865WOo1AyfG4hqnFIrOEO3NxDmRTT1ZwI0OLLJrnkHg4OA5zy2qh+tsCODNqCulSO7CjjqWQO4JOqKqGoTIAPHygCU8+M4p7DzZjtmhGNkYyCnY2+3FxMoWBjhDbhyTyqAu6sDfsrPn33cwBV0r5rnQ80/k0JrICVA1IZIuYyangFA2ji3kmzn1+yqwFtDe3xKPBLyNX0lm94IWJFEsB0/r5/M4lFxfyAs7pBoaIcd3gRbLMZu1v8OBEWePxQFcYpaJpR+d0O5junWYYzBauL6jjfIJDndNAu7+EF2Zk3BXLIFlwMUIIz6kolckgPI0tqRDTi9D8AYiLMzCkbhhyBWxVsx5rtSYz4jPAn7z3iqG5qA+8Vqnz43ubwGuVtDDX2wG+TOFQnSGITVHmYvLdxfsBTsJiUV8ieHh4MOtECo02h3jsbHRgMqmDFysPLLMZSheal0lF1djcWU0pmal1avTdrJo/ek0sY2LMUxo/6BARE3X2er2NwAqRZOxlBusd69W0nT1iaoG4z3zmM6zmj/7e9773McLHJz/5SQYOlzdKsxNreLVGD37LmcPVAMBXQwTwqydrHwH8rwNbEcBa/n63AOAaZtMOAO0X4zUMcV260s2BACn9SwbnQ0kFz5IINGkBvjSB7ogTkwUdYZHDqUsmuSMWcmBsOssIINQ8ToH9n8SirbZ7exSKquPMyRns2d2A4bkciwYe3tuI7z49igfubkW8oMGj6Zgt6djTGsCRy3E8uLsRUwQcYz60BJzo8V9f54PrMqmrDGrVXtkjCavtf6GYx0jGgKYBl2czKHEcApKAE2MmyI44BIws5FfcvNknYnSxQlboavBgruxmQRtQ/R/J7JweT2FvR5CtA7XWgMyAus8hoKspgEsTCfQ2+FgqWDZ05MtB/x6/gYspDl1+HU5Bx3BKwEB9BpcXPegJZZgoNIUCBaEE3qDcZhFCLgPd44Y0NwrNfy/AmeCnmmaxVQk8V2tNZmSTwP/6uSuG5xrrwBcrzF++rwN8qRLN5nq2gefMG7YuOMG3M/N4ZIR6fGVsDxpdAoqawc5jHhyKJQXtUQlNAVOyZTQByJL5wELHPJ9TlqJ6xL6mFLLViFhjlOsD7QCQ/h9wmILDZAdHJJM257ovtSzKTECEwIYVvapmzl/tfZYTs6jGlOaS2Lpf+MIXWPnOwYMHGfGjoaGhZtNVDQCkna1UA9jU1IQ//dM/vS1qALcAYM1Oqes20LqvSq9GEohlLUSrQRdkusDYDcWv2ypdZWCqXyEwSjcHOha66WSKKj5/bBIuzcD5sQR8Ig9RFljk4+iZ+aXR+pp8OFEGhDvbAjhti/6RPmB9o5eKnjA8kUZHTxjpgormOg+CXgkvn5nDXYdbGcNUUzRM5VUMNPtwbDiBH76jGU8cn8aDB5rQG3Si0XXz+ZCuda3Wo7WWVAoYz+nIFoFLcxn4JB4J1cB8PIesojMv4FRGYRE6e2vyyxiLXwkMe5t8mC57A1NUidLBxy7HWV3fthY/CqqxFOGjiGFX1I2o34mnL8zj7p4ICgYHo6BAL7uCtHgMjGc57AyriBfMyFSbP4tjsx7sb0wjpzjglkx7ONO+uASB9NNcMqSFGWi+6i2z7GxVipgvd2tYbS2MUhH46E9cCQCbm8DnKlaGXF8vhFJF3xI9/RC4SmTVaO8BJ5hc5lOFXTi2GGPuNKSrGHSWsL+piJCvcWkfY0kBUtmykB5+UkqlApA8sAs2GxB7DaBdAoZSxaT7R81HYtC6ji7P2n8Da42YrvWcvp37WzWm1jXafs6dOnUKP//zP4+HH36YET6eeeYZvPnNb2ZkjPvvv3/d00LkEtofPeAQs5fSwwQ6V0vVU50fsYAfffRRBgb/4A/+gLGAz58/f1uwgP/lxBDcNdRVzKXTeNvurQjguk/QFTbcAoBrmE07AKRUK93YKOK2Gc2KqNBFh4CfXdKAjuv/HZ1GNp6Dxy3h3EgC02MpbOuPYnoyjXi5DoqCFyRwO72QhyDwyJb16djTabMPQY+M545MoZH05so1Zru7QzgxuABKf/XvjcFd0iC4JOZT2xt14dxUBj9ysBlff3YMb76vA/sjLpYOu5Xb1W4mV/teRVXBcFbFfFZnzFLySZYlAbpm4HQ5TUvewIO2VDCJOVPBILmHWM1JKXu3vKQP2BZ2sfknVxGS8yFhaGp1Th5TZZLI3hY/FE3H8fEU7uyJMgCUyxYhuGQm7eKVgHSJw75oCcfmRHQHTEA0nJTRG8lgLu1GvS8DQyd7LQKC5GRCtYACxHgSundvVUu6Ubaq8T/fwubDalx7O/hUxf2D27EDQnGqciw2P2B6U23ugkhFfkwbUMRXZu4Cz7nhEhJ4qHsBk5lmuB1mfaROHto5YtSa5ys9SNlr/vKKegUjmICevebPShXTOCELAEoCXIaKmE1LsJqJWyl6Vc12W31MNu1K8jg0pwTMfvd3fxef//znme4etQsXLuCRRx5h3u4f/vCH1zWFIyMj6OzsfAUbm3T+aH/Udu3ahZ/5mZ9hEUirUf3h3/3d37Ga8jvuuOO20gHcAoDrOpVu6EZbAHAN020HgFQXQje3zVCOpwscpXypEQClJ0y6uFlFzfT+ty/GMTWfg0sW8dSpGQSp+D+noqPNjxdtUcCAW0TUI+HMaCWtRtsf2BbBi8enWc3g3v4oxsqgcW93CM+dmkVj1I1QSwBT5+fR3hdFRtHQ4BYxmSzi9ftiePTFCbzpvg68psENvobaZ2tYrpp0pTUmsL+e9BudLyP5EmZyOgzVwPnZDBbiOfS1BnGynAY2gZuIobLWYrNfZsDO3nobvZgus4Hp/Y6gjME5sw8JQ2cNcgLhIKg6EuUI1cHOEE6MJljN3462MCRdBzHEFZ6Hk6/Ywu2Jqjg6J2JPRGHSQdkSuYJkMZbwoC2YhlKS4ZSzzPOMgwYIPMSECt3de9X5XU/EdKUBjY//NFCoMKS5zh7wifGlrtz2XRCUymujcw9E0SxzoFaq64Tkq0TfMlwQF9Vu7GlIIFkQUUTPEhcuWdCR1yr1f+m8wqRzrGbXAKT0OKkkWo0i4ZZANO3NU3YBCcgi/MUkZErBy3JVFm3VMKRrcnLfhoOsli6nOf3EJz6Br371q/jKV75yW1m+rbaMm00C+fLx2kcAf2LPVgSwlj/bLQC4htkk4GWZilPkjf6IsXUjG+2TUr5UC2SZvdvBH0Uk6I+IACdmMsinCjg2lEBzQMaLJ+ZwcGcdTo8kEPbKiHhkJBIFjEymGcmAagYpQuUgE3uBw3A5MnVoXyMuzZmF9j31bpwaSWJgW4S5fDzz1Ah6djcySzmq9COXiwd2NeCJk9P4L/d14K66W5MBbAcwG9F7HMvmMZ4zkM0qGIrn4eaAXFGDUxYwWtZXpKhfPldiwtzDZSBoP6e6m3yYLad/m7wSi0zNlwHh7lYfEiWADOEkicd8uRbwYHsQz1yOoz3sQiTkwUI8i8awGwUD8IscUow0YmB7SMeZuICDDQVMZiSEHBr8ziImU060BDLIFR3wOjOAIbA0MBU0CjkfDMfqNVP2iCmlwzbCVjX+8N1AsmL3xnX3gY/b/ID7BiCWxpamS2/fDUmu1AQqwVbIZRISY+x66wGHyfIdS0bhcFSILDNpHQZfOV9nUgUIZWFn6m8HgJQWr8Roye7NrgHIQRbNhzKfJKDbzS9Jk9A1xC5YvFy/r1qG9I285twq+7IkmZb/Xun9X//1XweJ9xOjd7PLdm7UfG42APzSscs1TwH/5N4umr4tGZganURbAHANE2kHgNbFhvyAb0SjmwkBP4pI0QXMYqzR+3Rc9K8F/uh4phfz+LeLcVw4O4dwyMmkQpKzWaSyJezpCLIavtVac9SJifkK8217fx3mUkXU+x3I5UuYTxVx974YA4kXLsXhiflZ9G8qWWTvDbQFMTGfw8MHmrA7dOsxgGsJYKZyBSa8THNGbivZoooTwwnsbPIyUEfAWeJ5JqQ9MpdFMm9XnQN8ThGcLMJBXr8wEKdxyg4TtH47Wvxs7U8PLWJHRxCZkskY7oy4cHoqgz0tfjicMp49N4dDfVEmdOzlgIwB+EQDHsnAZI7H4cYcXp51YXeUGMAGsgqPsDuHZN6JkDsNXZMhiAVwhQI4rhfgVq5pqzWAMf78/cCcLeLXuxP8vE2uY9tuiOro0qls9wOmN0ueRkgNPpBmIPyNgGASkojwNJvvgUDvl9tE0oAgVjyrp1MFiHYAWFSXGME05xQTtRoxrKmMghrVdoo8z1LKfpFHjy0CafeMZhqQtqjgao4oN+L6civvw3pYW0lQfHFxET/3cz/HtPY+/vGPb+hh5Faboy0AeKut2I0/3i0AuIY5t0RG2Y2lVGKAjMQ8r3ejmyqlfOmGQSlf+nd5ytcO/qwamG8MpjA+nWH6cqdGk2jxOfDCqVm4HQLzpiUwsVLb1urHYFkM2uMS4YqYUZE6gcNoxrTeeuBwK8bGknA6BBScEpocAsYyChqCTpYuDHhkPLirHp3eW4sBbKV8qXjbirBuZH3niyWMpkvIqwYymSKGk0UoOYVp9YVkAROLlXQvkTtawi6Wqp0kVxCOAxF1CLRNzmaQzKnY0erHeNnz1+sQEA65IasaRuYyqKs3a9kavCLmMiXGdj3cFWa6fsdHEujrCLFIlWiYtnAxl47pnOl7e6ihgOdnXDjcmEWqKMEpanBLBSwWnIi401BVJyQpB65IAtbdr5gSOh+tuaPoNAGbWrhTGJ/4NWDi4tL+uN5d4Ocrr9GzG6JeAYB6Yx8kX2VOdSkAvaMLQqCOVJqXxpnOeMGLJkPYauNJDqJY8ay2A0Bak7xNt9Fe/0cTTLaLFtGAiB+s7EE30ODgESMfuGXNmi8CfZY1GV1fNhJt3sh5eqtua9VCr+SFfPHiRfzsz/4sc9sgEFiL8/FWmqfNBoBfOHqp5hHAn97Hrj1bEcAanYhbAHANE2kHgATKqHA3FAqtYYS1dbU0rKigmW4MlgTESilfGtl+EyZSyPcHE5jNq5ibzTI9vzqfjOOnzMhfR4MHl4cTS8QC68jCfgcSGYVFL6ht7w5irqijPehAKp7HVJmc8ODdrfjP7w0xLcB5zUBY0zGnG6B6NRI7pmjT/dsiqHeunf24tlmqTW+7Xlgtb8KpkorhVAlZVcfYXJbZtZH8y9HhBAJOEfFkgUntLG8Bl4g6n4Ol5Qm8lx0IGTlnrmwFRz7BToeEoxfmmcNLqaxJ1+gRMEaGvwAOd0cws5hjWoQkyu0gOgjHsdq2mMvARI5DQDbQ4Svh+IKMu5vSuLToRqefIn06FI2Dx5GDUnLBQbWAJRd4o8Katc47SxS7WomXalfN+PRHgMsnl7pzvQPg5y9UNu/aBRGVCKEe6YQULgtDg4PacD/y0RB8nivrKkdTLXDKJmC22kRKuiJCNJsuLNX10RqVrEWgqkF7CphEoJeisgbI+o3ABlFPtnnOb/n1AAAgAElEQVRF5vO8WrOizQQCqS2PClY7T6/GfpY24kpeyE8++SQ++MEPMoLFRpi9t/K8bgHAW3n1bsyxbwHANcyzHQDSBZv8gIk5dj2adXGj/VDK13KasIgey1O+dtag5UzxxOkZjCk6nnphAoGAjHRBg6xoS7V9/S1+xui1t52dQZy2WcUd2tvILLTmLy+ipyeES2Ux6AcONePbj4/gtfe1Ia0aELIKSJyjr9HLtOke2tOIB7tCcNlSaNdjnmoxJs0pAZiVoggbHb+oaRhKKUgoOoZmsyYRQzcwPJlm1m1tQQfOT1xJwLH2SfV7Q+XaS3ov6JGg2dK/5PkbT+ZxaSaLfV0hxBUTSNa5REyWI7WHuiN4fnAeO5u9kNxO5NIFeH1OFvWLyBzmigZaPBqLDFIq+FBjCs9M+HB3UwY5IoCIRUgCuZY44ZRz4NQwOL1S9nC9CQvG5/8AOPeiDQDuBj8/WFmW9h0QhQoLWPfHIDVIMHgnlLYfhe7rQUEbQtBbkY7JFEXkjAr5gwYrlgwsKpX0L6Xrk8VKOj6vaNBtV8uCokKWzIcbWlN3eV0oxugrE0AoLbzTX0kxLz+XlrNV6XPLxsxeK7jcI3mj5+TtsP1qZA+6Dv7DP/wD/vZv/xb/8i//gp4eWudXZ9tsAPgPR2ofAfyZ/VsRwFqezVsAcA2zaelysYu+rrOiYooA1jq1QDcBSvnShZ/A30opX7uuFV0MCcDQe3adNao7+9a5ebxwcgYdYSeGU0W0Bl149tg0+9ZUfxZ1SRgv+9A6ZZ6lroikYDUSgOaKKl4+Oo3d+xuZGDSlfSlN/OLJOdx/XxsEHcyndrqgorvOzRwu3nCwGQ+1B2o+N2tYrqq61rpmbflOKZI6nC1iIk3OGjoWUwUsFDVGBiFfYGYV5hQwukwQ2iXzIIeznE2Y257+pf3sbw8ycEdtX08E8YKKkIOEjgm8aAi7JWxr9OGpwQUc6gpCFwWMjCfR1mKWLbgFIKMCfQFKUwtwy0BvMI9nplx4TXMSk2kPmnwm25wYwpJYBFeKgTPMus6NMKSrWhyKav/TnwAnnqh07xmAsFCJABotvZDkSj2r4QyC7+mG0v42GA7z4SxXmkTYXwGJI4kwXM4rSSxzaQ2azQJuuQQM1W5alnA0ZlEhMocpL0Nr7C77/NoBoJvnsM1W/2f/zteSx1leK0gRfXoIrPW1ptp1uJn62Wsl7V7sNGcf+9jHcOLECfzTP/3Tdc3O3EzzsdqxbAHAW2GVNvcYtwDgGubfDgDp/1RgTDWA1QrbXmtXVhSPJGYolUYXN8u+aCWih10odrW6q08+dgmKAczNZVmUIl7SMHzZtISj1hByYnaWyCU6dnUGccoW/SOAOLCrHi8/Z6bYGrpDDBx2xLyYns0imSnh4OEWqIs5+EIuZnXWHnIyxvB/vasVd7fcWIb0tebX/nk1c7eW8a7Wl2zyxrIKjJKBc1Np+CQOvCjgeNmPmVw7UukiCjZB6OXRPxrfnv6lwGrYKbEaQvoRd7cFUSJnipwC3Smy1P62eg/zuD0/k8U9fVF2iJPzGYQjXvCUCjYAFRx2BFWcTYjo8KkIODRcSojY25DC8Rkv9jUkkC854ZRMeziu1E6CeowBT3+1TJevNIfGv34CePHblY+6d0GIV2oAjYYOSJ4K61druQPagXcukT1ow0IpgYD3EogETFncqVw3xDIZxBp4LEGAruIBnMwpqBjOAdlCiUngWI1SwmL5NZ1Llg2crukIkWZjuf6veVn9n73UgH7jyz2kl8+BVdZBc00PnRZpZCPM6lqd1zd6nKvNHdVjv+c972HyLn/xF39xzXm90ce+GfvbbAD4uSMX4fZeWWaxkXnIZdL4uf0sorvRGsC3Afg4ad8DGAbwEQBfq/LYqN+PAngYwJUm0FUOcDN12wKAa1iNlQAgsYBrcTEmgEcXMdoHRf0o+nc1ooeVtryWtdbf/+dFyAEnHn9pElyqiM7tESh5FScuVFJi/a1+nDy/gEjAgXmbP3AXOUxkFYxNpBFr8CBbjnL0twdw9NwC8/vt3dOIkePT2LmvEYsFDfUuAZPpEn72/g7sqa/cUNcwzde9a7VzV6sDmcmXcDmpIFco4dxMFryiIhxyMbu8uTKhozXgwGBZHLo56MTYsohg0C1BK88//Wg7Qg4MTpv6eLGgE7LXgaBDwOx8FhyF8igq2BpgtYZEIrmzL4oiszUDeKcMB7OFMwFNp1vBUE5Gf0hBtsSDcGhnMIOXp7041BTHXNaLek8KgAROaWYiu1ZpQi3O/avNs/HvjwBP/1ulS0c/hOTlpddGpAlSIMcYudrOH4Pe98OvGE7VVYjSKThlDYmCFwquJH/QBiOLBhxyJQUczxShlz2A6fPFbBGybKZ82e+SlVJWLp/OcqkD2cMFXRIDa/0+ItNUQKNFWFjv3FG0mqJfFD2keScw+GqJCtpLXOj6aD/vJicn8VM/9VNMZPlXfuVXtqKk5V/AZgPAz7xUewD43+7YMAC8E8APAPwUgG8CeDOAfwBwL4Aj17jmky/lO8rg74e2AGCt7pC30Dj0JG41igCSEPRGa3QsOze6oFNUwIr6WfV+doYv7dtKW9JF0Oq/2hSS/MfZ+RyeOj6DzpATFy7GsXtvI54+aqaBrba/I8CiejLZxvGUCgZL9b5YJo3s2VWPoaQpDbOjyYdjlxbR3uRFa1cYP/jPS9h3b5tZ7K7pDAi++zXN6AmZEY6bKW21lrmr1Wm5UNAwnFYwny6wKF8mV8LwVArbWgM4NmL6AlNr9IgYX8jDJQpI5OzxJ2B7ix8TGYWxTUnGhfQax4gpTA4DbQGkdUDPKvC5JSTKBJ6BmA9Hx1LwOQVsawvhwsii6SHM8xBLKhTRBDQxp4apgoA94SzOJ5xo9WoIu0oYSsrYHl3ESMKPjlAChupGLul+RalBreZppXGMx74AfP+fKh+1bYOQtsm++CIQm51QD/53GA07Vz0UhTsPvzPDnD9E8ZWR6dEEaWBW6vVmUwXwNvC2mFPYnLPfn6ZfEfWnd6VyXysFbGg69oUrjGKrppd+C/Sb3UjWwHoQpevGqyEqeLU606NHjzJbtz/8wz/EG9/4xpvqWnM9fxfVjL0FAFecpUfKEcS32j79KgAqhv/5q8xrC4CnykCRLkBbEcBqTsLbrQ9ddOkCTI1qAO0EjbV+VysiQGNWo+1nT4FUK7WRypXwZ48OsloytaRh8OQMdmyLYJZSjoqOjjo30vE8YwTbGwU36qIezJXFig/cEcOFmSwiPhkujmPEg/39dQj4HXjppQk09dcj5hIxkyuBvGh/7fVd8HIqmysrbbWRm95a53Z5/xuZ8l2+72xJx2CygJSiI59TMDiXw8JcBi0RN8aSRZCkCDWq2XMZOgZnTNFte+tu9jEZkkK6iOl4Hk2N3iW7uAM9YRble/7ULA7112G+bOnX4ndiOJ5ndZkNYTeeODOL+/vrmRh0iWzh3A6WCnaLBjIqj/3RPF6ac2IgaEbUCjqPFn8KF+MB9EYXUci4wOvBpdKEja5JNdsbT34N+I/PVLo2dUHITy69Nhp7YLzlfwCeiqDzSuMWjFF4HIuIK91LVm9Wv+UWcPT+ZDK/BPjodaqgLEWdyIZPLDOu6TM7AHTwHGMEC4aOgaAJAFcjLFTz/a/Wh85pq1bwdo0KWpmR5bJM9N2//vWvM//cL33pSxgYGNjodN522282AHzkhQs1TwG/+xBzINpICpiifPRE+X9sC/5bAAgQ3nGVk4DqUL4M4NNU9rsFAC0UdNv9bK7+hewAkFjA662BsuzcKCJgpTSulfKl1Bv1Wau7wm88cgStLX4cvRBHEAYGLy3iQH8dTpyeRcHm/2v/5r3tAVycMAkA1Pp212NqsYA2AnkFDQQsX3OoGQuzWTaGWO9BxDAwoxnwuyX8fz/cC5k3fTkpako3QbqIU10jRS5vZFTQnj6qtUxJNac/sX3PJYqIF1XMLuQwnlIQljgmCL2twYPxuSybz5JmsMhrU8gFlywwLb+soiHglhANOzE6kUImr6I54kLGphyzqyuEVKKAwYk0BvoiIMBJlsKKajDB431tAQg8h+cuxXF3fz0o7kdrZsgiXLyBfJneSnZwR+cl3Fmfw3haRNChIORSMJl2oj2chpaPQhJqV9NTzdwZz38L+PonK10b2iAos+y1sf1uGK//RUCqRNpWGzOvzQNiFiW8EiimCjpyNgs4GsOuAUjnT7akLZ2zdgYw9bVEoKmfnzQAeR5BAWj3iCxlSzW9671OVDNHbC6IXa4o7O92iQquRpSh7/fnf/7nzNeXrN0aGlZ3pal2/m7Hfq8yAPj/ALyT2RvBptBeWVhK+z4EgAqI/wjA39nW/BcB/CqpNq1yHryvXPf3+vLnWwDQ2AKASKVSDNDYmWjXupBYUTwCc9eyc7PG2qg48ee+exmLBZWRDjqjLjz//AQDBE0hJ4bHqbbrla2vK4zBUTM9SVp0rkYPYkEXpgbnkfeaN9zX3tuGH3x/CHsHGpCRBThTRWTcElojLnzwIWbZs9Toom3doAj8VeuLeq35vNbndqkNO0P6WtvV+vMLySKLjo7OZFAgX10DOH05zlLugqYjXpZtse+XorDNIReiPgdOjCSYbAy1XZ2hpfSvRxbQ0xpg0T+vS0R9zARoMbe05N98V28YQzMZpPIl7OiMEDJktnFF8PCJnCnjAwPdARXnEiLuiWXwwrQbe+tyIFJDQeVQH8ihkKxjdXLXu+7PPgfGsR8A//xnlbfCjeCRgvGanwYO/peql0nRcigICsC9Uph8IWughEr9H8m6LLB6SbPGj9jbik0DMF9U4SjXA2qavuT7S5HcsEtiYKzXzYNXFfbgcyMfOm6HqKA9Wr+cKEMPk6TvR7/rT3/60+wautVWnoHNBoB///xgzSOA77mTYbSVIoDkVnC1k4Fqakhva60RQLqRUeqXagctz8ktAPhqBYAExgjMUCMhaKpxq/YiZNWy0Bjk6GExAK2LNv1rr/ej1xQ9IOC0kQjCkYtxfOHJYcRzKiNuzA0tokCCxH4Hsqkisvkr683CAQeSeW1JDLqnK4TZkoaoTn6zAobLYOWH7m7Ftx67jAfva0NB4FGcyyLrkrC90Yv33Ne+4lXJYjVaDgjXi9Vo7YfkcSyQfiOjjsu/PGkBjpLos6ZjIVnENIk5F0oYX8gh5ndgaDq9JPZs35bkRQhYkA6d1Tqb/YiXawR7SAC6pOH8eBp9LX6UZIFp+gU4DrNlXcB7tkXw+Nk5lgqORr0Yn0qiMxZgYtA+Hqx+MCTrzAJursDjnlgWT066cU9jAgt5B4IunbmCKOk6di7eSAKCceZ54B//V2VKIk3g3vpLQPvuNd/3F42cnci7tP1QXIOr7A9Mb+YVlQl3W41SvvZfiB0A0md+l1k7SLWBEbfM/t0um1tstN5vzV/StsGtGBW8Gtljfn6eOXu89rWvxUc+8pEN1VFuZF5vlW1vBwB4/Knv48TTFLwD1JKC73yRlYNsJAVMNYBUBExMYKt9BWAytivVAFJUkaKFFCmxWF8RABQdoVQyRQ9v2bbFAl7j0tkBIGn10c2QwNm1mmXnRv1X0/Zbyc6N3qPxNxJ1oRvWu//iOXQ2+TCdLKLZLeHoyRl2yH1tAZw5b2rJWW13XxQnLy0uvT6wrxEaB7z0xCgOHG7GuTL79J49DXj82XE8/FAn09coZkuY1Q082BfFG3dfOy2znNVIQI3SxBsFapud8l3pXJjOqRiM51jxyKXJNPI6EHbwIHBOjeRzyEFleeuqc+PijMn2pRb1O1CyyZEMtAbwYlnM+46+COIlHVGHwORf5gomaDzUFcIzFxawvyMIl1vG4yencc/ORnYsJAldAI9mt4bxLMfS9gORPF6YdeGBlkWcXfCjL5xlfsW82nBFqpHmmR5iCMRv5Py82m/HuHQceOR3zS6xLuAdvwkudO1za6UxF7Q8JNGMotrbUBxwOSrBg+USMEVFhWpj/OYLKhxlsedCUUXQU/YX1nSE3TJKhSJ2+kxNzo2ey1ebm2o/u1Wiglcje5w7dw7vfOc78Tu/8zt4+9vfXtN5/a3f+i08+uijGBkZYQ/m5BxCpJKWFqr7X7nRg9CHPvQhfPnLX2YPRbTNJz7xiatuU+161arfZgPATz17Hq4aysDkM2m8964+mp6NAECK5H2/zAJ+FAClEYgFfN8qLGC6MCx3eyBdtLcD+A5RAWq1XpsxzhYAXOOsE2ixbJtItsVi9a02jBXFo0hUNXZuNI4ldEo3Vsv+bY2H+YruH/n8MTh4HuPpIhr8Dhx5qVJIT7IuJ8+ZIFAQOPh8TiRtKcn9d8QwcSGOiYk0dh1qZo4WFElsDDpxaSSJB1/bidx8Dm6/A5N5Fe++pw39TdXXitnTw3QMGyGNWCnfWrAtNzrn9u2p/u/CYoGRcV4eWkSjW0ZSMzAxlWJWcOQD7OSBOZsMD63TdLJwRWRwV0cAY0nTj5nqBRs88pJbyMH+OiwUVKRmM4jF/EwMOugSEXJLuDCbxb3bIux8pXTyto4wHJz5SEukkCa3zgBgVFYRdpRwPuXAA61xPD4Wxv2tixB0Hzi94nu9HFQQcKd1qwWAt8+bMTYIfPLDwJ77gbe8H1wV9X6rrVu8VIQoVyKpVr/JtASeNxm+1JYzgEm+x7CBboq4CmUSiB0A0nkccslwlIrYHvHUFKTU6ly8WaOCy11R7MD5e9/7Hj784Q/jkUcewV133VWrqVgah0Dl2972NkYkodKcX/qlX8KZM2dADOPV2vvf/348/fTT+OY3v8m0YOn1yZMnceTItZREan74qw642QDwk0+fqzkA/MV7tm8UANL2RPj4A3ruLusA/jaAf7VN5KkyKPzfq0wuXUS2ZGBu3Kl88+zJDgAtUgZF9FZqdEOgKCH9S0+WllzMWuzcavXNv/TEML53fBqNHgkTeQ1GsoDZeZNtSnVoxECdmMlge1cI50evjETdeSCGpx8fYX3rt0eRLahojrowOpZi4OTw/e248NIkdt3RhJlsCR/70e1LzghrOX66OdH8Up3PeqywLOB8M6R8l3/vgqbj1GwOC/kS0gUVyUQBDqcISiGeKdda1vtkjM1kTH9mw0BDwImpstSLNV5vWwCzaYUBxrDEYyKlLLGId/SG4ZV4vHRqBm0dEVYN3RFx4fIcWdCBMYDnF/MoqDrCBFAUFaJDRMkAorKB2SIRFxTkNB4FjcPeujQen/DjtW1z4NQGcAaV2byy1RLALx/dmJ8Azr8E7h7SXt1Yy2olaOKV5Q75ko6kcuX3mkrkIZU1F2mP8XSRrZXVSESdL2sEFosqAuUIoAgShBax0yfAYWMJb+yor8/WN1NUcDWyBx0jgb7PfvazzNato6Pj+kzGslGPHz+O/fv3Ix6Pg3Relze6PpEFKLmNvOlNb2IfLywsIBaL4fvf/z7uueeeG3Kc19rJFgC81gxtfb4VAVzjOWAHgBTVI6BC4G55s+zcKEVGAHG5th/1t8uiXO/I1emRBD72xZOISTwiTX5whoHnbVHAxrCLuYW0xny4bPOmpXrAUraExUQB9fVuZMsiwztbfDg+GIcs8Th4Txue+PcLuOf13Uw648Ov37j/pt0K61o1Z1aUlebc8kFe47Je9+4kNXIqnsd0pgRd1TA4ncHMdAY7e8I4cqHix0yWfWfHUuisczOpHXvzu0TwTgmSwMGpE2OYM2sJyf834IAv4oaaKoBKVPkyUacj5MTFMtC/t78eL5ybw66WAAS3jPhcBuE689x1QkXOEJgY9Jm4iGavhiaPglMLThxungentIFjgierNwvAExDfTNb3akeoGzrSXIGJYVttPqNB5a58gJtM5CHbAWCmuJType9IEjlWI2klb7kGkDeITAPc3bAyUL7uJ9k6d7CZUUECUyuxpOn8ocjc8PAw/vEf/xF+/41zFaL07yc/+UlcvlwRHLdPLVnN7du3DyRAbWcg9/X14Zd/+ZfxgQ98YJ0rUdvNNhsAfuKpszWPAL7v3h00SRtJAdd2km/x0bYA4BoXkIAJXZyo0YWLQAeJQVvNqj+jzyxtv2rt3K5n5IqYjO/682fRFnbh3IkZ7D7cgpdfnLgivbi/N4xcSiFzU0YSSaWKqK9z48SgWae2a6AeQ2mFRQy7g06cHE2ircmHbX0RfPvRC9jzYAcOtAfxtgNNa5zVq4MKiz1Mc7s8PXy9gXPNvgiAs/ECRtMK0wI8PZmGS9MhiRzyJQ2zNO/ltG7UKbJUMPnR2lt/a4CleHlFw/B0BgPdYYzE8+badATgcEl47vg0DmyLIlHmMDBx6ZQCFwH13ii+e2Ia9++ogyYIGJ5IoK05CA4mcCHLQLKFO70oYEfIPJ5kkceOaBJ8iVyTqm8rsb7pYWgztSDp6Bf0HMp6zuzLzGYAnbuSPDiVolrBCti1i0ATKxp8xd2D2NNWX4/Agdd07K+/tQCg/dp1o3QF7Q9ty1nSpK7wrne9Czt27GC1eBsV2q/+rAUee+wxvOUtb2HyMj/0Q5Tle2V76qmnWM0fZYDsChCHDx/Gm9/8Zvz2b1NGcfPbFgDc/DW42Y9gCwCucYXsANDyQ7WeTi1tPxqSooIUuaILnZXypfftRI8bbUn28S+dxCBpyY0kEGvyQXbLuDicQEejF1xRw7llZBA63ra2AMamTD3AA4dbcG46g756D6j26dJUBvt21UPiOQyen0fDQAN+6lAzA4G1blbKyq4pSPNLr2tZK1nr47aPR04qxAaeXcxjIaPAxXM4OriA3e0BVhtIP0ZKKaZzJZC8i8FxmE8XmRYgtYGuEKZmspiYz7G+MZsY9KHtUaTSBZwcSuDwrgZGAPESo8MwkChoLBVc53cyMshDA43svJxZyCIY8UImAk9ZQKvbr+FCksf+ugLG0hJCDg2t/iJ4tXFdU7Oc9W0njWwGQWJBLUKSKnWAsxkOOneljuBcpgjOZgOXKpD1mgn6iBAiSZV0ME0xgVqLAdwokqNLxVFkXZN2E2xE1yZ6uLV0T2tJ9qFzgsAT7cMixFlfmYgY73jHO/De974Xv/ALv3BD6yipno9YxpRyJiC3WtuKAF7zBKVwbfKvnzhT8wjgB17TTzvfigBecwmq67AFAKubp6VedgBo1ZxRnQgBEbqorcfO7Ubp033t2TF88fFhdPodOH5sGgd21WNhPodLlyuMX/t0NMe8mJw3I0zUdh5swuRiAepoEu4WPxIZBa+5qwWnjk8jHHZDaPTit3+kF+FyTdQap7bq7lbUj24gBCLoKZzmfTMARdUHTe4SGQUXFwtI5VVk8gpG4gXMz6ShqgYDgzNlqz37mPQDrQ84mAPLXKqI2YRpRUg1mGkbn+Fwfx2eOj7FIrr7dzYwAkiIN7Cgmr61e9sCTMh7fDGP+wcakU7mIJLtn9sJWdNQKEe1LDLI4YYcnp9xYU+0iIiTB6+F1vJVV+xrT+sTaKJ1u9FWgUm1BE6q1AGOJYQlOSY6aAJyCVvkVaNouFZhDucKKpxlBjCl9SmySucdPRCFXBIORhw3/Xm4loWsda2gZYlH67/cxvL555/H+973PvzlX/7lqtG3tRz7WvpSmplSt//8z/+Mhx8ml6/V20o1gCRR09zcDCKsbNUAMpmVLQC4lhNwk/puAcA1Trz1ZEybWXV+BD6up53bGg9x1e4XJ9P47c8dQ3vUjZMv/P/tfQd4VFX+9pvee68EAgkQSugdAbGCvWLv5VN3Levu2huuim3969oV11VXXRUXEF1siAqIoYUeSiAhvSeTZJJJ+Z73zNxwM6TMTO5kZpJznicPIXPvuee858y97/2V91cgjhuRGIR9Jhev+YljR0dh1wGj+5dxUxEjIpAc6oucrUUwhBulb06enYS1aw9j2tQEuIf54qHFIk3fbk2RiuCDicRZcTWSXNhLU1CrydQ2tWBbST0oz1dcUY+86iZEebsLq110sI9IAFHKwplfMy02EPsLqWNqbONSw3Ck0lgLmMaqjCEh2JpTIbKzk4aEC6FvQ00DWv2N1q1pw8LwS04FfL3cMTE9GgeOVCAjOUzUBYa+GW0+XsIVzLJwdQY3TInW4/dSX0yLrUeQewDc2rtOdLIFG0fGnBna2tDoYcSNgs8l9SRsx1265hIwlFBqU1kD6/UG+PkYLXytLW0IMCWHMFM4zNsT02Nd0/1ryTr21SrYXUk87gcmeTz//PNCVmXkSJHp2W/tlVdewUMPPSQyei0lbySLGzZswH//+1+RBcz/79q1C1u2bOm3cfd2IUe7gP9v3W7NLYB/mCfqfUsLYG+Lb+HnkgBaCJRymJoA8k2QUjCMUbF3OTcrh9nl4bRYXPfSRjQ2tcK7Ro+yikYE+HvBq7UdFVXHLX08mdYh/0Bv1JmSDOLiAqEP8BbWv9j4IOSZhIjnTYnH9z8dxbz5KYiPD8IV05O0GGqXffRUEcVemoJaTobk7vdiHXTNrcjOrUJTW7uQaNltItnDIv2x6+iJslLhAV6oqTd0VALhmFiqr8QUNxgX5oPiigZR+m1YXBDcAnyEFmBDQzNqTQkLE5KCsTm3GkmhPhiWFIbvtxfi1Mx4EffXqNPDK8AHfh7tqDeFHWZGNmF7uQ/mJNTCpy0KbqKAnLZNa+uSpaOrbG+Apzuga26HztBZw7OsVg83fmhqtOy1qghgc1MLPJUqIC2tCPA1ksFGvQFDAr0wyvRiZOlYXPE4W9ZN8ZZQ1kodN8f76dNPPw3G1ZEERkZG9jsktEbSEq2Mi/OjVZel5hRCyDjvN998E0uWLBHj43zuuece/Pvf/+7QAWTiCK2AztIcTQD//sMuzQngnQvGSAKo4QaTBNBKMHlzUGL/6PJlCwsLOyHLVx3rx2P6Ws7NymF2e/iyz3Yj62Al0iL9kWWyAqbEB+FIbpUQD1baqBER2HfkOBkZOz4GXj6e2PxDLibPTFfucakAACAASURBVMLeYmNc4HhmsWaXYOHpwzE1PQKzh1MkXdumxry3iij2lCTRYlZbi3Wixm9JrR5oaUN+XTNQ34ziar2w5IX6euCYyu3Oaw6LDsAhE978PytPuPlSdMRo/UuN8MMek3VQSQDRlemQHBeE0iajnzgpxBuHK/TITApBSJAPvt9ZjAWZ8fAQNWRb0OrlKcrC1RjawJ6HhRiQU+ONeYk18GrVLqmnOwzNrUt90YLsbZ3KWxrh7cVyb4ChvXMCiHkGcJWuCd4mly/7rWtoRoCfUfhZrQFIDGfF+MNfnWHS20AGwOe9WQXV313zsm5UUaDLl38nebKmpOYAgM7uU5AE0O4Qu/wFJAG0cgnpaqyqqhKZwLT6UeePBJCNnylvj0o8mpLtppRz6++YJ/Pp/bStCK+uPSjqyx7aXtzx8ZjUMOzYWdrx/7Th4Thw1FgLmG0KE0C2FqGqshETZicjx0Q4Qrw9UFmtx4IzR2DJjEQkhPZeFcUayNUuXz4oLK040VdNQWvGaM2xu8oacKSqUdjl8op1cPdyB52v20xSMLT2lVY2CnFoNtZrLqjobJ2lcDcze+mWj/b1EFmpjO1jYwIIKd+vWwoxLTMOlfoWUeeXsYFNre2YnR6JmvpmHCipw8T0GLQ0NMHPzwtNcEOwO1Dd2o5wHyNprGzywIJEHTxabau8YQ0uyrFdScloLTBdYWiCl3crynRuaDVLACmoYmbncWtnRV0TfFUagA1NLfA1WQDpHg5TqoA0t2BBouXi57Zg48zndGcVVJJIzL+7JSUlItnjnHPOESLPjs4Od2ZsbR2bowngC9/v1NwCePfJYwmHdAHbuinMzpME0EogSfJqamo6yjyRDKplYMyzfBWxaGvIi5VDsupwXYMByz7Kxr7yBkS2A4dzjQkgJBNDYwNx4FAVwkN9UdNgMAoSm9q0yXH4dZ1RDDplYhzKqvUYGhOAg7lGK+H8RSNwz2nDhTadVk2JGaKLvS9ltazRFNRq7N31c7Rajz3ljaB+3Naj1Wita0J0XBAO5FZ14D000h/UbWRLCPPFMTMCmDEsDPlVesQHemH34SqERfjBYLLeTsiIgb5Wj+xDlRg5Klq4jaP9PVBQa0x8mJ8RjY05ZYgJ9kVCXAiO5FVhRHKoqAvs394GHdyQGMCycO4iHnBGrAHureaVkOyNkrF/e0nJ1Le2oNWzGYV17nB3N1rzlFZWp4ebqupHdT3J4nFCaGhtg4cpYYZWv2CTLiaxmxKjXZxk/yBsn6so60ZPCRtfeukBUUSVGStHmZfHH38c559//oBKmrEPorb1KgmgbbgNprNsflq385VvkDblxsYbXXV1dUcWqvotVlG3d0aJkif+vhE1AV7w9XRH1kaWNTS24EBvtOtbkJwYjJ2muDQ/X08kRgegqLAW1dVN8Pf3hHtckLAojY8PErVsyfkuuCgD181hZZ2+N3OXr1ZWU3XyQVeagn0fee89lDcYsK2kDnU6A3JK6xFAYeF2wM3dXUj0sInM3yAvoLUdh83EoFkBJCzCH1H+nti6rxwjEoNRrjcG7jEBZOSISGzZXoToUF8ExRgtUnG+nsgzlfZbkBGNtdnFmDw0DEEhfvgu6xjmTUwQrmRq2DW5uSHBvw0FDe6I829FRpg73NpOFDrvfabaHaG1lAwFoWuhR3G99wmWp5I6fYfkC2egJoDMEO4k3m6KAWRs5wh/DyQFd5aT0Q4B1+qJL1yMjVYE3GkFnD17NqKiojBv3jwR68es20mTJrnWxFxstI4mgM9+lw2/AO2s4o31dbh34TiugrQAarQXJQG0AUgSQEXbTxEp5v+VuCX+zZmrUmzcUogX392K6IxoHN1VCopEK42lxnw93NFqaENVRQOOHqlGeloE9pviAUekRaCwpQ1xYX4Ibm8XsWdREX648uIxWDg6ygY0O5+iaCPyX3tZTbvSFGT8ER9Y9paSoUXul7walNc1o6mlFRW1Tdixpwwzx0Rju8maykofCaG+MDS1COsfNQKVNjQuUGSeZu0pE3+amB6BXJOFMDUuCKFBPti4swRjhoWgydsbgd4e8GptE7GA/LLPGBGB9fvKcNKoaFHtImt/GTLSouBDN7GpLnCML1CiB4aHNCPFPxBucB5dO3MpGeU7Z+26FRsaUWvoTNhI5Kr0nUvFVTc0w8sU10eXr4/KGshUEYqiNzW3YmG8v3RjmmKd6fVgsodamoku3yeeeAIrVqwQOF133XVC62/EiBF9vmfIDrpGQBJAuTN6Q0ASwN4QMvuc5I6aT4r4M29mCqFg9Q8+oNiUkmTWPpisHI5Nh7e2tuHiW1YjYVgYvAM8sWuXkUykJYegLK8GRSbhZ6XzjDHR2HvQKAczaXoC9pU2YHiQN1hSYf+xWoxKj8StF2dgeHTfXGCKy5dEzFwjzKaJWnBSV25Ge2sKrj9ajdrmNujqmrC7WIe2uiZU1zcjNToAJZWNKCxv6CQHEx7ojYhgH1F2j+Rv8/7jpeNGpISi3GTdmzA8HHsOV6GusQUzxsWgvLkNYe5uaPNwQ3ljC6ICvRHk5ynK0J0yLhZ19c1gjFtMbDBa6OoM8KbRESFeTAZpx9jwZsT4sFKIzbcJC1bAtkMUq6DyMmatBNAxfTMa2jpnNuubW6BTvQzxGvWG1o6XAl2jAf6mrF9KyPh5GjUAYWjBSQnaWTpsQ8SxZ/WU7MGXYcb5sV7u8uXLkZWVJTJqSQavv/56vPrqq44d/AC9uqMJ4DNrd2huAfzLqeO5WtICqNGetfnOPlhdwLt378YFF1wgSgHdcsstSEtLEwTwX//6F+bMmSMKgrPxpqeIFGvlwtRozUU3ryzfhhXfHMTM2ckoLqsH6g04kHOcWCjXogsY3h7CyiEI4JxkFNc0oXBzAYZMjENxZSOmT0vAfUvGwksVO2XNWBXXLAm0ueXAmn76cqy5m9FaQmHNtbcU1okavgXFdSILOD7AS1j0EsP9cPBYTafyfOp+A3w90NLajiaD0WIbRhKuSlgYlxyErAPG2MEZE4wJIAUHK5GYGg59SxvSYgKwv6hOkLxTMuOx+3AF4iP84Rvki9LCWkTGGUmMn4cbGlvbkRnRgihv3mudt9kiScLZVDa3obxzpT1U65rQopZ8MbRCbQ+srW9GoCnmr6WlDYGm5JAYD2BkhLbJT86L+Ikj4xowo1dJjFMnajFE5qqrrsKUKVOwdOnSTklcpaWlKCgoEHV1ZdMeAUkAtcd0oPUoCaANK0orIONYKCCq1O/dunWrEDGdOnWq6NEZ4s16mtq+g5W47YHv4eHhhsRIfxw2uR/Nzxk7Jhq7TdY/fpY2JR5Bbm7Ytv4ofIeFgdqCixYOxW3niCLdVjelLBQtp+Y1Qa3uTKMT7K0pmFPRgIMVjcgrrYeOEixtwIGDFYL4pUb7Y1c3azEqMRh7jxnjBNlYGu5YrTHQnrwlwt8LBSZx6LFjYhDo6Y5de0oRkWwszccSfruLjGLSCzPj8fXmfCwcH4t2L0/kHqlE0pAweLsBpJct7cDkyHaEeTk2/s+aJbVGSobHHmg06r0prbxOD6heYij54q6SddE1NMPfJAFT32hARJCPKA23MD5g0Lp/lSx9YsjvrzpG8tChQ6K02p133omrr77a7uEV1uyVwXCsowng099sh6+GMYD6+jr89fRMLp20AGq0gSUB7AOQLF3ELDZarRjgfMkllwhpA9YBVpp5vJmja6Gqp3vtXd8gr7BOZP021TWhlpp0Zi19VCQOKLFpnu4IT49Aze4yREYHoNxkLbn+wtG44KQUq5FUB4v3Vzk8awZpL03BwromZB2rQUsrUF7VKKyAgW1tyMmvhaeHmyBuRZWdpV+C/TyhN7R1yMNwHuPTIpBnOi4lzBc5xfVieuHB3oiID4G7rklkFhv8jDF8SSE+yK1sBKVmJqZGYE1WAc6YbBSuLSmrR3BkAHzRDr3J5Tsz2gMBHp118qzBz1HHdhXj2ZWUzCEdRZ6Piz4XVrOU43G3cGVdE3zUEjCNBviaXMB19U2ICvFDm96A+cmsfDX4mvL97SpLn8LOf/zjH4W+H70lsvU/Ao4mgH/7epvmBPD+M4S1WBJAjbaTJIA2AEli8OKLL4ryQY8++qhQhC8uLsZrr70makmefvrpIsA5JSWl01uvM8mRcNofrtiLdz/eJRAYMTQUOXvLhUVPaaGhPtA1t3X8bcjQUETFBWHT/w5hwvQEHDAlHzx262RMGR1tMZJql69iQXXGWEk1iadVkPFmXEOFxPPBZ0trbm3DN7Sq0vKXV41WT3cEeXkga7dRhzEmxAfHSnTC3au0kYnB2Key/tFwFRcbiEZDG4aE+aGxwdBhDcxICYVXkA9+/70A0zNjRT1gITLt44HSegPSYwMR7O+FX/eV44wpiWisbxZl0dz9veFlMMDgZSSM82O84enuPAkgtmDdHYnnOpY0NKPB67jr1pwAltfqhUai0ho6EcBmRIX4Is3PHXFBgy/7V1E5MP/+8rv9wQcfiHshvSTDhw+3ZdnkORogIAmgBiAO8C4kAbRhgUkG6NK44447MH369E49MBaG5YEY2BwfHy+U7imBoHaNOIt7mFafy+9Y0xFzNjY9Atu3HReHzhwfg2xVwkHm5Hjk7S1HabEOU+enYM+xWgT4eeJfTyyAryoWrSdI1fFCzuLytWYLaEXivz1YiXp9C37LKUektyfqPd1FHB7L9LENjwnAzsNGjcbQAC9xrKL1x78lRflB127MFt6+uwyZoyKRW2asTDN9bAza29qxPqsQc6YmoJQJIH6eIiawqaUNU1PDkVtch8IqPU6enIi8/GqkxAWh2cMdbfVNcA/wgbdbO+bGBjhlAog169Udieffvbx8kNfmBXeTJbuoprEj45efl9Xo4e9/nACqs4DpDg7w8sDpQwaX9U/98mZelYffDWr7bd++XYTDKAL5tqyXPKfvCDiaAC79aqvmFsAHF00kMNIC2PftIXqQBFAjIM27oeVh/fr1ePnll5Gbm4sbb7wRF198scgONn8oMaaQpFKpR2lptQsthn73Y+uEDAkbn4MkAgdMMX8pqWHIKzDGjAliMTkev/x4RPw+7qQhOFxYh7NPGoJbLhQFunttfEBQIoLWPvN4oV5PdrID+kriN+bVIL+iEZQZqa81uhqZnb3dpL8oqnwEeONIiQ7pCcHYb9IIVGCYkBaBuuZW5OZWoUHfimEpoag21WeeNSEOO/eWoaxKj6mT41HT1IrQ9jaUm9R+5qRH4vudJYgM8sb49Gis25yHU6YmiYQHikh7Bfki1MsdUyIHVmKDOt6U1lt+5wrbveHuZRSDLqtv6hwTqLIACrdyO78jxlumXm9AtI8HZgyi7F+lqhGtf+Yvb9T9o9cjNjYW//d//yfuZbI5FgFJAB2LvytcXRJAO68Sb5pHjhzBP/7xD6xcuVKUPuKNktZBtdtTK8uStdNZ80Munn8jq+O0kGAfEddEi15pjTHBgJnAw5KCUVupR65JDzA+Mwa19Qa88cBcJMX2niigFIN3BZevNRjaqim4s1iHfWUNaDO04GCRDjk55Zg5OQGbdx0vxxcW4IX2llaUVjd1koXh+Bj/dzS/BiWVesSE+8HgeTyWbXZmLL7feAzenm4YNjoa3u5uaKltRJOvkehMGxaGn/eVY1RCEIbGhWDVxqM4Y0Yy3NvbYWhuRau3J5IDPJE+gISNlZcPWuKVeFOuXWmjATXwhL65FToGZapabaMBHiZcmw2t8PT06Pi01dCKuXH+CFTFDFqzb1ztWIU888WWJTDVHo2ioiIsWbIEl112mYj7c+ZwDlfDvS/jdTQBfHwVLYC9PxssnaO+XoeHz5IWQEvxsuQ4SQAtQUmjY1g3mHIxb7zxhhBApXuYWcPqG6ZiWVKqjShyJPaqlVnfYMBFN6/qkHnhVFOHhAi9uUZ9C9xa27F/dymCg31QWWtMEgkN80VLpL8gIU/dMa1HdNQuX0UbUSM4na4bazQF82v02JBXi+aGZuzIq4FvSytaGKvn6Y6Saj0iAr0R6ueJhgYD2tvamFaONjc3YyJIaxuCfL1wIN+YEZzJZJBqvfidLvnkqABs31+BYQlB8Ar3R5SPB5r1LagwxXeOig0U15yRFgk/bw+syy7C3ClJaBNZrl7Qu7lhXKgPYvxsi3F0toWhxYqhGbRKMWFL/X3jmu2vNybX1DMdW9Xqmlo63MPM+vUzJYBwT8PQitNTnFsiR6t1UJNnc31OuntvuOEGPPPMM1i8eLEkf1qBrkE/kgBqAOIA70ISQAcsMB863333HV566SWUlZXh5ptvFtnEtI4pjQ8Z88QDpVqF1kN+4u+bsG5jfke3o1LDcDSnAlWqGrQTJsR1uIpHjY1Gvr4F918/AbMzjbqHXbWB5PK1BnNLNAWpy/fNgQqUltUjv1KPSH9P/JZdgrEpoThSWIfSKiOh66plDAnBXhP54+cTR0fhSLkx/m9YbCByjtYIi+GUjCjUurvDUNGA2LhAlDS2iLJ9TDihePTCcbEoLK9HVV0T0tOiUJhfjaGmusBzov1ERRhXbj3Fq6nntbemGfUt7WhWVbdkdRz1/xsbDfAxEUBaA1P8PDC2j8LnroCtIs5uTp6J7apVq0R1D8Y8jxsnSnTJ5kQIOJoAPrJyi+YWwMfOFuUDZQygRvtMEkCNgLSlG95Ec3JyhJ7g2rVrRYwglfGjoztn1JJI0SJISwbjA0kEGcOklatl09YiPPDMLwgL8UFkgDd2bT+eCKLMKzUtArlHa8R/J89JRpm+Be89Oq9T3VQ1BorL1xlrIduyVraeo9YU5Jqp5Ui+2l+O8mo9WGGiqFqP/NwqkfkbF+qLQ6rYS/W1PdzdEBLojWpT9Q9+gROTglFvSh5JiwnETpObfs7keFBMZsOGPMyakYTKxhaE+nqi1KQdeMakeHy3tRDp8UGIig3G+s35OGlqErzc3TA3xt/WKTvFedYkGxU3GHBY12IMgjU1rombyq1eVatHSJBREofxf4tTguGhkpBxiklrPAjlO2ye7MEXWL68rlmzBl988QViYmI0vrLsTgsEHE0AH/pSewL4xLmSAGqxN5Q+JAHUEs0+9FVTU4N3330X77zzjlDGv/XWWzF+/PgT3FVK7WFeikRQi7JlTD544G8/Y/vvBdB1oQUYEeGH6npDR7bw5PkpmDQ2BpefeWIdTyVQnOOku0gGgxs3RVdyJOvzdKjVt6KcwtDlDQincPOhSoT6e6GmtgkNJlKn3lajkkNE+T2lJccEQGdSi0kK90NLcyuOmLKBZ01NQHtzK376/RjGTIgH6xAnB3kLEWq2MybGY/Vv+ZiTEY3gYF+s31qAqRMSEO3rgfFhrqf/p2DSkzhxV19RHr+hTA93lcWzRtcMT5/jMX9VNXqEBBsx8WpqxrwhIQNW/LmnZA++iFLYmS+jvFepk9r6cPuTp9oBAUkA7QDqAOtSEkAnW1Ba+1avXi0y6UiiWG7urLPOEhY/pVniYrR2Whs3HcOf7/+uyzJkmRNikb2nvKPL0bOT8Lc7piE8pDNJsPbBa+0YB8Lxatf+tpIGFNW1YH9uNWpb2hEX7IOsnSVimqkxAdh12FjWTWk0UEWG+KLcZMHj3yeNjBTiziF+nijJq4F/uD8aTWX7xk+IQ/7BchHHFpJgjFeL9fFAnonkLxgTjW+3FeHUSfFAO3CwoAZDh0ZgRJAXUgKNCSOu1hSXZVfixD3NZXt5IxpUogjmVUCq65oQHOgDfaMBC6I90dbGpBBP8RJGq7xW1nhH461O9uALnFqRgLV8Wdlj/vz5QgPVXnHJjsZgoFzf0QTwgS+yNHcBP3n+ZC6PdAFrtEklAdQISK274Y14165dgghSVf+KK64Q2oPh4eGdLqV2D5u7GK0d0zvLt2H5+ztOOG14eiQOm9yKLB13+kWj8ZdrOtfvVIRhB7vL1xrMD1fWI+tIDfbn1yDI2x2F9S2oLa1HrUnOZWiUP/YcMbrd2UYmBSPHzDU8flQkCiobEcCYNUMbak3WwMgQH6SlR2LdL0cxYWQk9L5ewv3r0dKGogaDqAaSEOaH7blVWDQ9yVgPmgk+UYGYHO6LMJX1y5o5OfJYxWVpSz1pvrz8WtoID1Omr7kIdF0DM+M9MD7YGwnBPkIQnNdTan4ryVquTASJAeVcupJp2r9/v6jpe//99+PSSy8dMITXkfvV3teWBNDeCLt+/5IAusAa8s37zTffxPvvv49Zs2YJq+CoUaO6dQ/zBm7LA4lVQO697zv8trmgA5WwMF/U6Vs7qoEkDAnBfX+aiXEjIsQxapeveayQC0Dr0CE2GFrx5c4SNDW1orioGq1eXvBub8P2HKMANDN03VrbUFHbLGxTcZF+KDbV+uXnLBsXGROIWH8vbN5ejEljonHY5N4dMzwcPl7u2LijGHOnJKDM0IZwdzcY3IGKhhakxwXiaKkO9fpWnDkjGRt3FGF8ajg8A3wwP9YfHqoauQ4FyYKL9+SytOD0jkOqm1qwq8YgLKbmBJAyMQHt7Vhglvlrbo3va5UYa8ar5bE9WU7XrVsnqh3R5Ttz5kwtLyv7siMCjiaA9332u+YWwKcunELEpAVQo30jCaBGQPZHN7Q2rFixQohLk+AxTvC0007r5Kbpq3u4trYJ19+8CkXFOjElc/fv9JmJWPbwPPGZ4vLlNc3dRf2Bx0C4xsc7ioQUzL68WjTpmhEdH4Qdu43C3GxJkX44mFeL1PggHCoyronShicGIzDQG5uzCsWfJoyL6Yj/m5kZi83ZxWjQt2DejCSRAVyRW4nw5FBRUWRMQhB2mKy6JIArfz6CxdOTRK3b6VGuIwCt3oPm+nS27I89lXpUtwEVuqaO6jZ8MdI3tfSa+OEoLU9b5qk+pzvLKb/Xy5cvFz+ff/65KG0pm+sgIAmg66yVo0YqCaCjkO/DdXljzsrKEu7hbdu2CdcwY3OCg4+XpVIEihU3lTXxSvtzKnDrHWvQ3NyKESMjcSjXGIs2bkw0brlhAsaMjBKuL2qrWRtr1YdpD6hTFavVN4dqUFPThN8PVCKwHYiOCkBJRQOOmVyynHRqtB9qdC0oUMny8O9TRkVi++5S1De0iATW2OTQjvi/mWOjsW6LkRhOn54ID7hhz84ShA8JFX9Lp3u5sE64gqdnxGDlhjycM3sIIv29MCrENWrbknDRZck4NXN9Ols3C9dlQ0kDagyt8DK5g1taWjEq0BspFibGKPIz/O6RoCrW+P6s8GPJ/DlOJnXwxzxhixbBBx98EIcOHRIyL+p7iyV9W3LMJ598IgTyd+zYAWqk8p7SU1zhvHnzsHHjRhF3ybHT07Fs2TLhEZHtRAQcTQD/+tlm+PhrJwTd1KDD0xdO5USlBVCjDS8JoEZAOqobqvBTWJo36YULFwpNwdTU1D67h1evOYDX38qCrqkNkzJjceWSMchkjVnVQ0O6fG1bdXWyzK6qFuw6Uo3iqkZ4t7vhtx3FmDMpDpv3lsPXyx2J4X6o1zWjuloPL093eHm5w9vTQ8jv0C2ZW2gs1ZccF4TjRfuoBxiAfXk18PJ0Q9rYWAS3A9XVjWj2NyZ3JAZ542hlo5CAYWLJTztLcNacFIwI9ka8qv6tbTO0/1lKzKk9KsvomlqQXd6IYG93hPp4ItzPA/5e1otim7+E2UPCyVak1TI5tJyqyWldXR2uvfZapKWl4bnnnuuUgGbr9bo679tvv0VlZaUoD0kx6d4IIJNP5s6di8cee0zLYQzYvhxNAP/86W+aE8BlF4vCA5IAarRrJQHUCEhHd8O3eOWNOiIiQlQZ4Ruz+o1acQ/zWP6u1B7u7q175ZocjEgNx6j0SDE96fLt+yqbx1rlVemx/mAl6uubcbCwFnm51QgL9kFUoDf2Hq4SiR1dtaRoP+SXGuVc2NTxf9QRzC/TgYU/UuKD4BcdiOKccqSlhqO8tR1eHm7wd3dDZYMB09IiUVzZgNwSHU6dlYIZUX4IUOnf9X3G2vbQk9VK2ytp21tXMkC0DDoik7anbP28vDxR0o21y2lZ64+klp9++gkLFiywiADOmTMHjz/+uLaLM0B7kwRwgC6shtOSBFBDMJ2hKz4gf/31V+Eepsg0haWZtce3fKUplgkSQRISS9zDtsprOAMmzjCG7qpSsKrEZ7tKUVVejz0FdYj08UR2TgVGJgZjV05Ft0MfnhCIgwXHYwInjI0R1UBI7pICvUSdYbYpY6LhE+SD73/IxYI5yShuaBEl3kp1zUIXcP7YGHy/vQhRIb6YMjYWJ8X498tD35Y16UmixJb+HHGOIgNE97Dy3VOLg9t7TIrbvKvQjc2bN4u4Yt47TjnlFHsPpaN/awgglRFIYCk+zbrqdFOr7239NmgXuJCjCeA9H2/S3AL4/KXTiby0AGq0/yQB1AhIZ+uGD5r8/Hy8+uqrInGEdTr5Vp+UlHSCe5hEkA8kWiPoUqNlUHnzVxMXW+Q1nA0XR4xH7W5jrJVa05Hj+e/uUmTvK0NxXTMSw3yxebtRC3B4dAD2mRI11OOOCvVBRU2TsPCxMWE3LjkEjc1tSGH8nhuQU2qUdTlpagL0jS1Yv7kAc+cMQXmDAdFuQJFJK/CkjGh8v6MYY5JDMG5EJCaGO6cAdE/1aB2xplpcs6va0fzu2csq2J3bnPvzs88+E+7eTz/9VCgM9GezlABu2rQJI0eORGhoKHbu3Clin9PT00X4i2wnIiAJoNwVvSEgCWBvCA2Azxko/+GHH+L1118XmXx8y58xY8YJ7mGlyggfCIpVguSQD9+uiMsAgMbuU7CkHvK6/eXYmlslyrtVNxhQXaJDXUMLAnw94W5oRZVZdZaMoaHYbUrM4QSS4gKhgxviQ32wc2sRWKv5mKmW8LyZSfg9qxCVNU3InJogagR765qh8zZWuZg6PBwb9pVj1uhojEsOQWqQ8wlAD3SNye6kZLQSmO7OoN30pAAAIABJREFU+sz1Jwl95pln8PPPPwsSGBlpDPfoz2YpATQfE8+jpZIxi+o66v05dme+lqMJ4F0fbdTcAvjiZTMIubQAarTxJAE0AzI7Oxt//etfsX37dhQXF+O7774T8Sm9tUceeQRvv/02amtrMWnSJJHdlpGR0dtp/fo5b/bU9GIdz4KCAtx000248MILQcue0hQXlUL8aAlksocs6Wb9UlmaqLDtcCWySxtQUarDocpGxAd6I8skBTM0JhAHj1R1VGgJ8PVASxvQZLLgcVSM/yusbYKhogEVlY0ITQ5BMw+iBXBaAtauO4rocD9EDg1DjL8nmuuaUW4yHw6PCcC+gjqcNikeExKChAvaWVpPxMVZxqj1ONRSMl1Z5K29Xk/WZ71eL2KF+f1nIpmjSJStBHD9+vUi8Y33XPU9zFqMBurxkgAO1JXVbl6SAJphuW/fPhFDl5mZialTp4KZar0RwGeffRavvPIKvv76a5GByyw1ijYzBo+WM2drfCgcPnxY6AmyoPsFF1wg3MOxsbHCIvDPf/4Tp59+unC1sNEySGsErYJq97CzzctZxmNtooKu0YAvdpRgd04FwoO90dzejq3ZpR3TGZUYjJ2meMAxQ0OxS2X940ETx8WgTd+CLduLkZIcjEqTa9jP1xOjUkKwcVsJxowIQ1uwH4KaW+AX4C2qgQgjYLsbGppbcdaMZMyMD4QX9WScoPXmNneCIdp1COqELVulZHrSSCwpKRHVhc4++2zce++9dnM79wQSx8eXJBLAM844Q1jylPuMefJJaWmpkLxiEgjvqbt378Y111wjPBr/+c9/7LoWrtq5owngHz/coLkF8KXLhRC5tABqtCltvtu38w41wBvfwC2xAA4bNgx33303br/9doEI3+Lj4uLw4osv4vLLL3dqlPj2TML31ltvYcSIEUJXjVbQL7/8EmPGjBFjV+uaKe5hR2UwOjWYfciU/mhTPn7OLkGAoQ1ekX6oKWtAkUn3T1T88PNCUXk9ggO8O7mEGf83eVwsNm3MF9BMzIzFwQpjAsjwpGDkF9ahVmfArEmxqIY7jmwvQuaUeJQ3tCAqwAtFNU3i2IvmpGBa7PFEIUfiLGtKH0e/KykZS17EeoqZVMgTX1T58tcfmb5d7Sfedyg3o4435u8//vgjhg4ditGjR+Obb74R1Y+YnXzRRReBJem4P/iyyrHLJJDuv6mOJoB3/OtXzQngy1fOkgRQw5uzJIA9gGkJASSBoqWMAqXTpgmNItFYoWPs2LEisNoV2pYtW3DWWWcJVwrfqhlgfe6553Zy/ZpnMCoyMs4mcOsovJVMaVuEiT/+5Sjya/Roqm2Grq4JYVH+2KSyAsaE+iLUx6NT7B/nmRjtj9qqJlTV6MW0MyfF4bApASRjSCi27isXf2cGcCuA7/93CJNmJ0Hf0o7kYG8cNpWWu+bkYRgT7vgKIDLbvPvdq1jMaJHv6UVMwZDfT36f1QRr7dq1eOCBB8RL35QpoqyWbAMUAUkAB+jCajitQUMA+abJmx5vhl0ZL6mZ98MPP3SC1hICeOzYMSQnJ2Pv3r0iI01plF6hej5r+DpzIxas8XnnnXeK2Mf77rtPvGVTCoLxgkuWLBFv6ebB4eZlrxg/xOxWR1kTHI1xd+W0LB3XL3tKsbtYh71HqnF4dynmzErGhp1GN3CQnycSQ31RUlgLvb4FBkObiPEzGFqRkRaJXYeMtYPZEkaEo07fIn5PiwnETlMW8UlzhsBQ14QdO0uQmBFtPNbXA0d1dAW74aaFwzAkyMeh69dXDC3F2tWP60lKRqnQYy7SznNee+014S5lWbfExERXh0GOvxcEHE0Ab/vnL5pbAP9x9WzOuq8u4AsBPEH9fABHADwIYEUvcDL7ZCmVtejkA7AbgBiMK7dBQwCpNs+g5+4a35aDgoI6fWwJAXR1CyDLuTHej0ks5rGOVVVVIrGFtUBp3WT2MBNb1CRPcQ8zaYRNkZGxl5SFs33ZtIpVq6htwkcb8rDtUBXC3IAj+TXISI9EU30z9u4rh8GU1KGev4enOwIDvFFbbxB/josJQL0pu3d4VACqdM0orDSKRU+clojDO4oRFeEH9wh/BPp4gA7fgnoDEsL9cOnESAR4OGb9iCG/myQvMtvcuh2ulpJRXmzNy7oRV8b5lZeXi9jkwEDtynNZN1p5dH8iIAlgl2jTTbcOwBIAqwGcDeADE5nb2s36kPytAXAHAAac8oY7CcDv/bme9rjWoCGAtoBnCQFkv13FAMbHx+OFF15w+hhAjp8Pjp4sd3QpMSaQiS48jhUCGLSt1rMzzx4eDO5hrWPVnlmxR7hvae377bcCJEYH4FhBLVq7IH9ct1Gp4dh3tKZja2eOi8bhKj0CfDzQVFiHxgBvoRUYGeaL0RnR+GHtIcycGo8ad3fE+nqiqb0dZfUGjEsJxRWT4kTsqpL9zfVTpIBs+e5Yek5PiQqW9jHYj+N3j7G7xJL3LH5fn376aSxatEjE8TKcY/LkyXjyySc7lXwb7LgN9Pk7mgDeuvxnzS2Ar107h8vWFwvgu6bzL1Ct/xcAqLp/Yzd7Yj2A3wDcO9D2jCSAXayoUiqNb9LM7KV7mGSnu1g3xvmRHH311VeCDC5dulS8adOV6oxZwLZuYj5oKI9D9zCrBlx55ZW46qqrOrKFlX4VIkHLgzPVP7V13l2d112cVV+u8cqq/ahoMqCkUo/ig5XCzUvNv+w9ZV12OzQ5FEdMtYB5wKTJcThQUo+hgd6ordaj0FRGbmxaBEKCfLDu56M4eV4KivUt8KxshFuEHxoNbZiVHoHzxsV2XMPcvW9J0oEt8+6pKoUt/Q3Gc0j6SP5I/Hiv4Qsa///QQw/h448/FhZ5xiPz/sSQFNkGDwKSAHa51rTyfQLgGdWn9wEgIZzcxRkMjGaZ9ecBzAOQCiAXwFMASBxdukkCaLZ8R48eFRlo5hYxukgffvhhcTTfqimhwJg5pT366KNCS4tSBnzbdkYdQC13KmUZOF8KTJ900knCKsji8Wrcuqp0QDLhynGCaokX8zirvuL73fYiZB2qRE5xPeJ8PbFjd6mo8jE0OgAHzCqCJMQEoLCic0jD0NGR8Pf2QPav+Zg4JQH7TMkgsyfHI2d/OQpL6nHyyUNRoW/B0W1FiB5tjAU8c3wMFqSdKABsz+xvJd6PBIU/rrwn+rrutp7f00sIpazuueceYalnbDPlrXjPuu222zqy+229rjzPNRBwNAG8+e31mlsA37hhLsHvygK4HMDVdGixOFIXK0S3LwV9DwJ4FsAbqmNuAXA3w6a7OC8BACUWWJ5pEYDtAM4B8DEADoaWQZdtkgC67NI5x8D5IGdgOQkvYygpLHvyySefUGWE1kAeS6sPSSB/XC17mISIsaScA+uPaj3+gooGvLxqv3DLJkf44ffNBWKRQwK80aJvFnIuShuTFondh48nf4SG+MA9zBfe1U04dqwW46Yl4FCJsRzcAopB/8hYZ2DmvBT4ubsh/0AF3GKMsWDXzkpGRnzn+Ff17uoq6YCkzZZKFdZqJDrHLne+UXSXMEN8P/roI/F95PeS0k5sWVlZIgmECWt8mZVt4CPgcAL45jp4+2sXb9rcoMMbN9EI1yUBpOBuT3UsefOkJc9aCyDN5tUAngZwv2rXfANgGwBaD122SQLoskvnXAPng4e1OikuzYLtzBymBqJ5wDmtFnx4kRDSra7EmTm7BUitq0bLn72SXG56eRNSogKRW9UIXWEt6kwJHsMTg5FzsEJUBPH39UC7uwf0qmogY0ZFwsfXE7/9nCc2RuyoSNQ1GrOBZ4yJxs+bC+Dt5Y60SfFCDLqttR3VXh7wcHfD4+ekw8fTWBqut9YXq65CoNkH3ZVaE+jexj4QPu+JQHOPPv744yJMgzV9w8LCBsKU5RxsRGAgEMCj2RuRl71RINDaYsDO74Tod19jAEnqmAmstM8BVPYQA3jAlPwhCaCC2GAQgrbxezeoT+MDimXmFMkJuqBuvvlmDBkyxGXdw5aWdNNi4Z/7Yg9qqhrR5OkBPzeI6h5Ky0gJQfbecoyl9IvK+sfPp02KQ9amY2hqakVMTAB0fl4d58UF+yC/WIehScEISAjGse3FyJwYh6LmVqRGBeDWeSlWD12pVGGpVbcnYWKrLz5IT1BnnJtboBn3x+9ZdHS0eAmTpRsH6SZRTdvRBPDGN36Et5+GFsBGHd66eX5fCSCzgH80ZQF/BeAsUxYws0u6ywL+AwDGe50BINt0juICznLlnSYtgK68ek4+dkrM/Pvf/8arr76KhIQEISMze/bsXt3DdC/ay8JmDWSKPAlJjrm0hjX9WHPsV78X4LttRTBUNSI8KRRbs4xuYDZWBIkK8ha1gEtElZB2BPt7IyTAC76e7th7gC+xwHhWA6k2xgcOifLHIVOm8JTMGPiH++OH1TlYeMZwFNYbsGhcDOannxj/Z+mYzStVdGXVVQg0rb1qYWJLryGPg8jwZfgBG/ei+vtRVFSEyy67TGh2/vGPf5TxlHLDCAQkAex2IzDhg5p+Q0w6gLTsfak6epeJFNLtq7S/ALjNZH2kRfBRk4yMS+82SQBdevlcY/B8eLFwO7OHjxw5IuoOX3zxxaArVWldEQlb48y0QEUtT9Kf7srDxXX463vb4V/fjNT0SOTm1aC03Pjg9/X2QHp8EKrKG1BX14TKaj2aDW2IjQ5ASdXxhJBJMxKxr0gnIqEzovyw/WitOH/+nGS0t7bj++8OY87pw1HRYMA9p6YiLqSn0BnL0VS7h3kWCR8bs+q1TpixfFSuf2RP2dJ0995www145plnsHjxYkn+XH+5NZuBowng9a/9oLkF8J1bmcfRJxewZvgOhI4kARwIq+gicyDJIwFkgPqqVatwzjnnCDJIzcSesocVcen+ihN0ZDmytrZ2XPv3jUgK9sH2LYWYNj0R2/aUYURcEA7uL0dtXfMJqz0+IxrZOZSxMrYRE+OEAHRaTADam1qx1yQVs/DkoTi0uxSHcqsx9qQUoRf40OLj1Wu02kaKe5jizvydVkFa/mTMn/UIdxd+QFxXr14tYv6Y9DF+/HjrO5dnDGgEJAEc0MuryeQkAdQERtmJtQjodDpRmo+l8igfw+zhqVOnnlBlhA9ARZdRyR62l3tYkT0hcSFhcZRkzZOf7ERRZSMObitGfHSAqAZSWma0Apo3Dw83BAX7okZnJIZ+fp7wjA2El6c7WvJqEZ8egdxinfhs4cKh+N/KHMRE+yNiZBSmDg3FxZOpcqBtU7TpSNhJ3pXEHxJAe2kKajsDx/em3ovm4QfEl9Z0EsAvvvgCsbHHNRwdP3I5AmdBwNEE8Np/fK+5BXD5bScT3r4kgTjL8jjFOCQBdIplGLyD4MPs22+/FQ80lqpiIPt5550niIPSFPcwiSDJBC1KWruHtSrppsVKfv5rHj75+SjCWtpwNK8GGalhyDbVBTbvf9SIcOw7crwayIgR4UIAemSkPzb/dAQRIyPR2NQq9ARPnjsE//v6IMaNjUZLmB+unJGI8Ym8l2rXurOe2lNTULvRO0dPPSV78Dtw1113iZeid999t1MYhXOMXo7CWRCQBNBZVsJ5xyEJoPOuzaAaGR96OTk5IoORhJAxgtdff73IalQ3EkY+/JiYQUugFu5htcXKPMDeEYuwJ68aj360E2nRASKzly09OQR79pWfMJy04eE4oCoHN2lqAkoaDCjeXoTwSH/UeHuKc6Ij/BEb4Ydt24sxZ84QVLkDj50zEn5elsm/WIID16U366mWmoKWjMnVjumpNF5FRYWovsPKRBSlt5cl3NUwk+PtGgFHE8Cr/48WQFYc16Y1N9bjn3+QFkBt0DT2IgmglmjKvjRBoLq6Wlg33nnnHUycOFFkDzPGSR0DqJVFiS5mZitTNsNZMlRZAu6aFzcg0NcTx3aVinq+fj4e8Pdw70gIIdDhob6oaTCIz5U2bnoC2uuase23AmSyNFylMTlkTFo49u4uE0kjC09Lhbe/N25fMFST9VKypYklCbS6RnRPF+iLpqAmA3eyTnqSymFZSdb0ZfUhZvv2Vzysk0Ekh2MFAo4mgFf+/TvNCeC/7lxIBKQL2Ip90NOhkgBqBKTsRnsE+EBknBPdw7T4sdzcWWed1YlgmFuUSOQUcemeRmTPkm5aIPHc57ux+UAlYtzdcOCgUd4lKTYQx/Jq0EIdGMq9mCV/8G+sALLth1whGD15TjL2FlD8HpiQFoEsk6bggjNHIDMlFAtHRfV5qD1ZrCztXEkaoQWR/blqpRhL59vVcXSdU8uvq9J469atE2Xd+EI0c+bMvlxGnjuIEJAEcBAtto1TlQTQRuDkaf2HAAkCq4u89NJLYI1T1jSlNSQ8PLzTIEgYSRT501PCgb1LummBzLe/HMWnW4sQE+SNrI1GNzDbmOHh2JFdImL6oqIDUVZJPUBjowC0v5cHDphcxSOnJSDflDySkRCEbJNO4IxTU3Ht7GQkhh2X4bFlzD3Jk9jSnyWagrb06+znKK5zc6kc4rF8+XLx89lnn4ka5bJJBCxFwNEE8IoXvtXcAvjB3adw+tICaOkm6OU4SQA1AlJ20z8IMFHkrbfewvvvv49Zs2YJq+CoUaMsdg/3V0m3vqJRp2vGI69uRpW3B8pzKmAwWf3Y74iEIGEFPHTMaN0jGYyN9EdidAA2/mYUjmYWsP/QULS0tiMhwk/IweSX1otycBPmD8XDi9P65EZUatF2ZbHq69x5vrmmoBLrOZDi3npyndMi+NBDD+HgwYNC5iUkRNtkHS3WSPbh3AhIAujc6+MMo5ME0BlWwY5joOWAD5K8vDykpKRg6dKlIsu2u/bYY4/hiSeeELFcfEAx1ohu1w8//NCOo7S+a8abUQKDSSMkB4wTPO200zppzZm7h2kVJAG0F2mxfhY9n/Hgsl/QHu4Hva4Zu/eUdRwcFOCF9IRgNDS2oKGuCXl5NWioN2DkqCjk5FaJ44YOC0Op6Ywh3h7I1zULEpmSHILZc4fgsmmJNg23p1q0NnXYy0nKGtJKxrWji1/JALfH9fqrT8UKTaLLsm5qYltXV4frrrsOI0aMwHPPPWdxTGV/jV1exzUQcDQBvOy5/2luAfzoT6cRfGkB1GgLSgKoEZDO2M1vv/0mMgZZjo1VAlauXCncp7/88otIruiqkQB+//33onKHKzQ+SLOyskSc4LZt24RrmJmSwcGs921sJA4kECSNbCSCJBFMVnDmYPoffs3D829mYcr8odi0IV+MPSE6AGgw4JDJnavMMSTEB/WGNlBImm3itETsL6tHcpQ/6nKrUe5u/KpPnhSHcxcMxcTkUKuXV01a+rM6inodLXHxWz2xfj5ByTon6SOO6j3IF7XLL79cZMDzpcaZ92c/wyYvZyUCkgBaCdggPFwSwAG86LQi1NTU4PPPP++Y5fnnn4+IiAjhRh0IBFA9B9ZEff311/Hxxx9j4cKFwj3MuCoSQuoLXnDBBeKBqohL81xndi026ltw4U0rkZgSipqGZsSH+GF7VmEHyVPPPXN8DLL3H68GMnFOMvYX1CGOtWMDvJBjigWcNz8Fty1KQ4CPUR7G0tZThqqlfWh1nFYZ4FqNx5p+FJ3ErrLOf//9d7FnGet66qmnWtOtPFYicAICjiaAly77RnML4Md/Pp3zlBZAjfa7JIAaAemM3dDKd8kll+Avf2Eda2N76qmnBCGk1aw7Aki3Ey0T/GHW4ZNPPincx67SaO375JNP8PTTT6O4uFjMgRmUQUFBHVMwdy06a+bp0pc24ccN+cgcHYktWUXdLkHKsDDkmUq+8aBhk+Lg7+2B7WsPY+qCFOzJN9YDvuDsdFx/SqpVS9ldOTKrOrHDwa6mKajETVJuyFzonOEMy5YtE/t29OjRdkALom+WYdyxYwdYiYfr2lNMJeWYbrvtNqxZs0a8OC1atAivvPKKjEe0y+po36mjCeDFT38Nb18NdQD19fj0r2dIAqjhVpEEUEMw+6ura6+9VpRR402ZD0HzRrfvDz/8gOHDh+Pee+8V1i+l0UL2wgsvCNHlrtqePXsEUUpKSgItajx/06ZNyM7OFoTQFRox4YOOxPemm27CsWPHcODAAeFWu/TSS0XMlbqpXcTO5h7ekFWIh579FfTgpsQFdUjCqMefEBeEoqrj2cAB/l5wjwtEpKENB3aXYdSsJJEAwnbndRNw6gTaBXtvzi6Vo56BIhBOUsPvhULoncGF2lPcJMdN4vfTTz+JTN+oqL5L83S3shRYr6ysRENDA2644YZeCSAJH/EkceQc+DLJ786XX37Z++aRRzgcAUkAHb4ETj8ASQCdfolOHCBv4Ky40F2je4kkzhYLoHmftFowA3HVqlXCreoKjQ83Wi3+85//iExhPrzy8/Px6quvisQRJrXceOONguSqCYIzChMzceOim1airt6AkGAftOkNqK5u6rQMmZmxyFZVCUkfFQmfMD9s/d8heHq6I2hEeEcW8SsPzsWwmMBel7GncmS9nuzAA5xNU7CnuEl+h1kDm9ZA1sRWWwXtCSHJ5oIFC3okgErSGF/8xowZI4bD3zMzM0VCWWKibUlE9pyX7LszAo4mgBf9bQ28NLQAGvT1+M/9Z3KS0gWs0WaXBFAjIJ2xG8YA1tbWCsuC0hgHR/287mIAuyKAoaGh+O9//4tTThEaTE7f6OKm2zcu7kRLF8V2mdFMSyh11RhzNWPGjE6uMIVEkPzSOuho9/Czr/+Ob348InBPHRKCg/srOuIAKQETGRuIiqrjLwSTZiSiJLcaRw5UIiU1rCMBJDzYB+8vXQB3ntRDc7bSeLZsOGfQFFREsjl+8xKDpaWlIiGLVjZaqvtT3sYSAsiEMVr8WCVH3ei+5v2ESWWyOTcCkgA69/o4w+gkAXSGVbDTGJgFPH/+fJEFzAcNrXh86Pz888/dZgHTakbrABNFSkpKhAuYWcM7d+48wXVqp2H3S7d8OP/4448i4L6wsFC4ii+88EJRDk7dGLRPIkhXGLOGlSoj/ela3JJdgj8/eTwre1x6BLZtKxbDHJ4ahsOmah/KuGfOSsL6bw6J/06ckYic8gbx+9VnpeGSU4f3iG9PSQr9sjB2uIgjNAV7EslmmMU111yDRx55ROy5/txLhNcSAvjBBx+I7z7DQNQtNjZWhJBcdtlldlgp2aWWCDiaAF6w9CvNLYCfP7iIEEkLoEYbRRJAjYB01m5oDXvwwQdx9OhRkcjxt7/9Deeee27HcOneISlkjVG2c845R8T80VIWFhaGuXPnCl3AYcOGOesU+zQuWooOHTokgtvpNqaFlO5hPujUzZHu4da2dlxyyypU1Rhdv4wHHJYQhP05lRg3Pga7VNm/8TEBaGtqxTFT0oeSABLg54l/PjYf/n5e3eKlVKQwT1LoE8BOdHJ/aQoqSTPEkS8MCsHj9deuXYsHHnhAxPBOmTLFIehYQgBpAWS8LMNN1E1aAB2yZDZdVBJAm2AbVCdJAjiolltOticE6C5/7733hHucxJjxWYyjVFtouooxY+yWvV14ryzfhhXfHOwYfnCQN7zb24X2X0SYH0IDvFCUVwNvbw/kFeo6jhs1Own5JfW4+JRUXHN2epfT76kixUDdMZaWDbRm/j0le/CzN954QyRU0IXK+FNHNUsIIOP8GCLBjGF1DOCECRPEy6SMAXTU6ll+XUcTwPMfX625BfCLh0XogbQAWr4NejxSEkCNgJTdDBwESA6++eYbIS7NqgzMoqbVlMk1SusqxkypUGEPl97unAr84aEfOq5PK+CEkZE4uL8cpcXGDF+2CZPisGOXsWqIh4cbQtIiRKm49x6bj5AgnxMWSYlT43zMK1IMnBXtfiZaaQr2lDRDiyDj/BhS8a9//QuBgb0n4dgDe641x0ICeMYZZ4i9rdTM7mrPMlmKxzNmlvOjQDVjGVesWGGP4ck+NUbA0QTw3EdXaU4Av3z0LEkANdwnkgBqCKbsamAhwIfe3r17BRFct24dlixZAkrwREZGdpqouXtYEZfWmghefvtXKC5rQESYLwLd3JCjyvzlgIwJIUGoqDQG7g8ZFoYKDzecfdIQ3HJhxgmL01Oc2sBayd5n0xdNwZ6SPailx+o0tCQz/IKEy1GNbmfuX7VLmr8zFpbWPuoP8sWHmfNsHPvtt9+O1atXd5SEZKiEusqOei6MOWYYAcNGZHM8ApIAOn4NnH0EkgA6+wrJ8TkFAtRPo5j08uXLMW3aNFGmKyMjo1v3MAmFkj2slXv47Y92YseuUuRkF6O+3ljWTt2GDQvDEVVCCBNADlc14u2H5yE63K/TsWpRYnWcmlOA7eBBWKMp2BOJPnz4sKhCc8cdd3QiXg6ent0uz6x7pfSk3S4iO7YYAUcTwLMfWqm5BXDlE2dz/tIFbPEu6PlASQA1AlJ2MzgQYJYshXBpCaH1hDIydKcxQ1hpinuY1hAeT9cxSRatP32xCpaU1ePaG1aitrazDqBy3QkT47Bjt9H9y8YEkPj4INx9xfhOY6P+HAkg3Xlqt/bgWEHLZ9mbpmBPFVI2bNggrGevvfaayMQf6G3fvn3CgkhrOectm+MRkATQ8Wvg7COQBNDZV0iOzykRIDnYtm0bXn75ZWzevFlYeq666ipQM1HdFGsSCZcSb0XSZSsR3LT5GP583/cn1AN2d3dDeHQAKlV6gBlzkvHADRORaBJ+7kmU2ClBdpJBdRXvqdSUZq1pkns1+afsEl8QPv30U6SlpTnJLOw7jLffflvEyvI7MW7cOPteTPZuEQKOJoCLH/hScwvg6ieFgoW0AFq0A3o/SBLA3jGSR0gEekSAor7M8KR2Gl1gtArywW+ePUwSyJ++uoff/ed2vPve9k5jGj48HIdN0i/8gAkgp180Gn+5ZoI4jq5KSnrQHU3Ln60EdLBvBeJIcWT+q2BISy9LuPFvS5cuxZYtWwT5o+A1l5VvAAAZJElEQVT6YGl8+WGsIEMlZHMOBCQBdI51cOZRSALozKsjx+ZSCJDcUUibdYhZio8yMieffPIJVUYUcWm1e1jtQu5t0iSQ9973HTb9VtBxaOaEOGTvOe7+TR4aikfvn4PhSSEik5Pkj8kp/JHkrzeEu/7c3IJK6+5XX30lakwzS1yn0yE+Pl5Y/waDa52SNs8995wAa+vWrSI5Kjk5WewvfgccpXNo2+oOvLMcTQDPvG+F5hbANU+dJy2AGm5VSQA1BFN2JREgAiQKFNOme3jXrl0iAYASGubyH33RomMc4PU3r0JRsQ50/4ZG+qPaJBTNMZx6yjA8cPcMkZXJH3NXpVwp6xDoyYK6fv16PPXUU2LNJ02aJGLgWOFD7Rq27mqudfSxY8cE8Vu2bBn+9Kc/udbgB/BoHU0AT//rF/DyCdAMYUNTPb55+nxJADVDFJAEUEMwZVcSATUCJIIFBQUiEYCWQSaLME5qyJAhmriH9+dU4NY71iAlJRQHj9aIS48fG42Lzh2JmdMS0NxsTEKhvp8j5UdcfVf0VB6PQsm0AD799NOYM2cO3n//fWH9oqg43cCDQRKFc+ZLzsaNGzF16lRXX+4BM35JAAfMUtptIpIA2g1a2bFE4DgCjBtjcsCrr76KhIQEISMze/bsLt3DtNjR4kQ3oiIu3R2Wq7/KwbfrjiA2NhAXnjsSI1LDQdckS/nRFcd4P61kaAbjeqrlcrgWSiO5p/v3sccew0cffYTx449nWhP/7777DqyawdjAgd6uu+46Ud2EuoFyrznPajuaAJ765881twCuXXaBtABquMUkAdQQTNmVRKA3BEgO6DKkXMaRI0dE3eGLL75YuGjVzdw9TPLBOMGu4vfq6poQZKry0ZO1qrexyc+PI6Au60YLqjpGk2tI9/6qVavwxRdfnFA3erDhmJqaKpKevv7668E2daeerySATr08TjE4SQCdYhnkIAYbAiQYJIB0F65cuVIkEZAMMolATfKU8l20CrKRCHYl3KyUNKPGn4z369tu6kkuhxbBu+66S2QCUxTcnLj37cqud7YS/8cqJ3/9619dbwIDeMSOJoCn3POZ5hbAb5+/UFoANdyzkgBqCKbsSiJgCwLMHmWZrjfffFNYUpg9zFgqcxkZWvcU97BSZYSxfeo6tHT5WpNRbMt4B/I5PbnPKXFCvUfG9T3yyCPS3QmImEfG//3yyy+YMWOG2Bp///vfceeddw7kbeISc5ME0CWWyaGDlATQofDLi/eGAB+0FJllUD0zLGkxYwm2rhpjkG677TasWbNGkKdFixYJSY6QEOqGOn8j+Vi7dq1wL5aXl4uEkfPPP/+EbFK6h0kEKe+iEEDOjq5KGYNl+zor7nMSaFr21AQ8JydHCH3/5S9/wWWXXSaldEww/+EPf8Drr78uZIaIW25urghvePHFF21fCHmmJgg4mgAuvOszePr4azIXdtLS1IDvXpQWQM0AZf14Wztrp9lBNomAHRF49tlnBYFjbBHjjBhwT4sDH8a0dJk3Ej6SIuqTcXtecsklghSxdJsrNY6dcyQR/Pbbb0WMIDNNo6OjO01D0ffjH0lWunMPu9LcHTVWBUtfX98TXOw//fQT7r77bvEiMmvWLEcN0Smv++ijj4rEJoqh8yWNLy0khK7y0uWUoGo0KEkANQJyAHcjCeAAXlxXn9qwYcPEg1epLUrLFwvO07pAXT11y8vLQ0pKCrKzszFmzBjxEX/PzMwEP0tMTHRJOGjVfPfdd/HOO+9g4sSJwj3MUlvMPKW1kG5jZgsr4tLESO0edslJ9+Og1cke5rWR+Rld88SeWa5Dhw7tx5G5xqWqqqpw6aWXIiIiQuzDhx56CMOHD3eNwQ/wUTqaAC744380twD+8NJFXDVZCk6jvSsJoEZAym60RYDWBNbVpbbYtGnTOjo/7bTTMHbs2I4KBMoHTKSgxY/B+epGiw4f3osXL9Z2gP3cG4kdS2299NJLwj189OhRYZGi1VPdFCJIixZdciSD3WUP9/MUnO5y6thJc61E4vjwww8LSyzle6RFy+mWTw6oFwQcTQDn3/GJ5gTwx5cvkQRQw50vCaCGYMqutENAyS7cu3cv0tPTOzqmtSE4OFhYvtSNdXjvvfdeFBUVdfp7bGwsXnjhBRG35eqN1pYlS5YIUkLR4c2bN+OKK67A1VdffULdWcYTKrWH6R5WrIKyDJxxFxAfxq2xmWsl1tXVCZc7ww6ef/55mVTj6l+cQTp+SQAH6cJbMW1JAK0ASx7afwjYYgEkOVQe6spIB4oFkET47LPPxsiRI/Hhhx8KEkxL4FtvvSXiIhmbRnFpfm6ePUxrIJNGSHpIBBkrOJiTRWhNpVB2V8ke+fn5IryAma10t0vC3H/feXklbRFwNAGc9/8+1twCuO7VS6UFUMNtIgmghmDKrrRFoKsYQOrk0aLXVQwgY7RYmksdA8hqDHSXumoMoILonj17RDk5xliZkzcSPAoSM2mE5I5EkK5ydfk3ujuV7GG6N0l+lCojg4nkKMkenDt/1HPPysoSSQyUMSF+skkEXBkBSQBdefX6Z+ySAPYPzvIqNiDw3HPPiSxgltwiGVy6dKmwdu3fv7/LLOCzzjpLZAHTQkbCQ5JI996KFStsuLrrncI5k8RQhmPbtm245pprhIuY1kJ1M3cPkwgxgH8gE8GehLL5GQn0smXLRAb56NGjXW/x5YglAmYIOJoAnnTLvzW3AP70+hItLIDUknkCQDKAIwAeBNDTQ4LyC38HsACAN4C9AO4DsN7VN50kgK6+ggN8/JSZeOONN8C4rMmTJ3foANJVxwf1N9980yHNwYxZZgwzWYJkhoSQBNKcAA1wyMT0GAtJOY6PP/4YCxcuxC233CJIdHfuYZIgJU5woLmHOTdWSOHLgblQNskwXzR++OEHfP7554Oidu9g2P9yjkIayubnex/x4xtnjZMSQGYUrgNAJrkawNkAPgAwG8DWbub9OYBIAOcBqAJwF4BHTQSyuo9YOfR0mzeI1AF06LrJi0sELEKAsX8kgdRqi4yMFHFtJ510Uic3srl7mNZAkkG6kB33DLFoer0exLkx3o//mgtlkxTyhYHucMZS0hIqm0RgoCDgaAI496aP4OmtoRB0cwPWvymS+foiA/Ou6fwLVOv8BYAKADd2s/bbAbwD4GXT5wEA6gCQTP7uyvtFEkBXXj05domAhQiQAP3666/CPcwsYma5MmmGpEjdaBEjaWQGMQkgiaCruocZ88ikIFo0aflTk9mysjIRInDmmWeKGrYDzepp4baQhw1gBBxNAOfc8KHmBPDnt4X+a18IIK18nwB4RrX0dOeSEE7uZjvQWkhySPZJong3gOsBjAVgLNLuok0SQBddODlsiYAtCJAI0n3OknqMjaSb/MYbb0RSUtIJ7mFFRsYV3cNKWTeSV2aCq8kfE2oYH8kygxdeeKFdrZzWlDKcN2+e0L2kJZKYc8yMS6T7XjaJgLUIDDICuBzA1QBYoawrXkO3L2P4DgJ4FsAbKjz5BSOpS+sGY8YKvg7gdFakA1BpcgdvtHZNnO14SQCdbUXkeCQC/YQAXaNMmGGsIDOoSTRmzJhxgntYEZfmv4p7mG5TZ20krhQEZz1fWjCVRlLF0nr33XefqPAxdepUu07B2lKG8+fPx9y5c0XJQ9kkAn1FwOEE8Np/9dkCWJG/DZX59MAC7W0tKNj9DX/tygJIX7NvD5gZTG5bay2A5EgkjSSQJIl0/bKqwPsA5rLgVF/XyZHnSwLoSPTltSUCToAA3b4//vijqDJSWFiIm266SVjGaDlTN7pUFaugM7qHe0r24GcUD2dVD1aGSU7mS719mzWlDDkSEkAKfD/++OP2HZjsfVAgMBAIoHqhWpob8PPyK7sjgJauKWMAmaTCTGClMcmDVr2uYgDDAZQDmABgh+qcLQA+NlkTLb220x0nCaDTLYkckETAMQiQJB06dEhkTn/99de44IILcMMNN4DVVNRNkVRhrCCbM2QPc0yM9yOZZbyfWgOR2b+M82NmNCvGBAYG2h1ga4XMFQK4a9cuMYeYmBicc845ePDBB0+I07T74OUFBgQCjiaAs69+v88WQHMC+Ms/r+orAWTixo+mLOCvAJxlygKe00MW8G4AvwK4B4AOAOtv/gfAmaa+XHa/SALosksnBy4RsB8CJDDvvfeeyI6lsDazhydOnHhCnCDdwiSCtA7SPayIS9tvZCf2TMJEdzZj5pjUoo73q6mpEaXyMjMz8dRTT3UihvYco7WlDDmWTZs2iUourIG9c+dOMW6WQaTVUjaJgLUISALYLWJM+FgKYIhJB/B+AF+qjt5lIoVPm/6WCuA5ADMBUCog36QLyMxgl26SALr08g2MwVO/j3FPJBMHDx7ExRdfLGr3srYvLTusgfvAAw9g1KhRA2PCLjQLEjtaA1llRKfTiUoZtEyR7KmbUmWE1jZa30gEGSdobxmZnpI9cnNzhRA2pV6uu+46u49FjYctFkDzbfHTTz/hlFNOERqYUqLGhb40TjJURxPAWVf8U3ML4K8fMM+jT1nATrI6zjEMSQCdYx0G7ShIGGjpYHk3uhrz8vJEQgLr3rIkFyVLFi1aJBIUKGEim2MQIBFnPWKuwbp167BkyRJRL5fagupGa5xSe5h/J3Ghi9geRFBJ9mCsovk1NmzYgDvuuEPoHzK2zhHNmlKGXY1v/fr1QsSbZNI8HtMR85HXdC0EHE0AZ172nuYEcMNH10gCqOE2lARQQzBlV9YjQMsSXYuzZs0SJ5eWlgoieOeddwpSyEoff/rTn4QrkhmqsjkegcrKSrz99ttYvnw5pk+fLmoPZ2Rk9OgeVuIE1bF5ts6EZJRuZ/4w3k9tjeRnFL4mUWXt5LS07pQdbL265edZU8qQ+57l+5gEwjnt3r1bSNWkpKSIecgmEbAWAUkArUVs8B0vCeDgW3OnmjHjm2hNUtqaNWuwePFiQfxOPfVUpxqrHExnBOh+/fLLL0XSCC18tNKeccYZwvWrbmr3MD8jGbTVPUyCR4kXXpvxfmpCSesj60WzHvKnn36K8HAm8Dm2WVrKkJbviy66SNS55jz4EsQkHJkE4tj1c+WrO5wAXvqu9hbAj6/jkvRFCNqVl1TzsUsCqDmkssO+IEBrH603jPszr1LRl37lufZDgKSM1iuu2+bNm3HVVVeJHyYzqBuJjSIjQ8KoWAUtdQ/zfGb6stFKpq7ewb8zPpGkjyLXav0/+81c9iwRcF4EJAF03rVxlpFJAugsKyHHIRCYNGmSeHizIoJsrocAXZlvvPGGEJhmZQtaBUeMGHGCe5hxgiSDtA5a4h7mccz0peWQAs9q0lhcXCyShpg8xNABWdbN9faNHLH2CDiaAM64+B14emlYC9jQgI2fsgKbtABqtVskAdQKSdlPnxFgNjCTCpj9S8kOpb3zzjuidq1sroMAyR1j12iNCwoKEjIyJ5988gnkTKkyQkJIcqfIyKgJHj+jhY+f8Uf9WXZ2tsjw5X5h4pCl1kTXQVKOVCJgGwKSANqG22A6SxLAwbTaTjbX8vJynHnmmTjttNPwxBNPCL2zyy+/HJ9//jnOO+88MVoeQxFfJh0MlDaY6sPSPUx9Oyb7UOSYZI3WOnMx5q7cw0zuIPnT6/VdJnt89dVXQj6I1kbq/MkmEZAIHEfA0QRw+oVva24B3PTZDdICqOEmlwRQQzBlV9YhQKLHwHcGyv/5z38WySAM4P/b3/6GK6+8Urj8brzxRixbtgyJiYnWde6kRw/W+rAkggUFBXjttdeEZZDJIozZGzJkSJfuYWb4khSymdf05d9JKFetWiVeFuLi4px0teWwJAKOQ8DRBHDaeW9pTgB/WyGqtckkEI22lSSAGgEpu7EeAQoL33333SIGjL/fd999QvPs/vvvF8SAbkQSQ1aiGChN1oeFyOL96KOPBBlMSEgQMjKzZ8/ucA/T6suYPxI/ZvnSTUxJIOr5UXaGiUJ8OaAMDZNBZJMISAROREASQLkrekNAEsDeEJKfSwQ0QsCW6hAkPQO1PiwteRQ7ZvbwkSNHhLWXmpB0ETOLmESPMX10Az/88MN4//33het4woQJQutPkj+NNqbsZkAi4GgCOPWcNzW3AG7+703SAqjhbpUEUEMwZVcSgZ4QkPVhu0aH7mESQFp7V69eLSq/PPnkk8LtryR1HDhwQFQemTx5Mn799VchGE4XMq2HtCLKJhGQCHRGQBJAuSN6Q0ASwN4Qkp9LBDRCwBYLoPmlB2p9WErHMBzg+eefF+7fN998U1TxYPYw4wHvuusuUQ2GrmISxu+//15YDukK5u+ySQQkAk5GABe/AU8vP82WpcXQiM2rb5YWQM0QBSQB1BBM2ZVEoDcEZH3YExGite/FF1/EihUrRCk0NrqH165di6effhr79u0Da/sSO/PGOFEp+tzbrpOfD0YEHG4BlATQ6bedJIBOv0RygAMJAVkf9sTV3Llzp4jnS01NPeFDWvuoASirwgykb4GcS38g4GgCOOXM1zW3AP6+5hZpAdRw80gCqCGYsiuJgCUIyPqwlqAkj5EISAT6goCjCeDk017TnABm/e9WSQD7sinMzpUEUEMwZVcSAYmAREAiIBFwBgQkAXSGVXDuMUgC6NzrI0cnEZAISAQkAhIBqxFwNAGcdMqrmlsAt3z7/6QF0Oqd0P0JkgBqCKbsSiIgEZAISAQkAs6AgCSAzrAKzj0GSQCde33k6CQCEgGJgERAImA1Ag4ngCf/Ax6e2snAtLY0Ysv3t0kLoNU7QVoANYRMdiURkAhIBCQCEgHnRkASQOdeH2cYnbQAOsMqyDFIBCQCEgGJgERAQwQcTgDnv6y9BfDHO6QFUMs9Ymtf7RTokk0iIBGQCEgEJAISAadDQBJAp1sSpxuQtAA63ZLIAUkEJAISAYmARKBvCDiaAE6c+3+aWwC3rv+DtAD2bVt0OlsSQA3BlF1JBCQCEgGJgETAGRBwNAGcMPslzQngtl/+KAmghptLEkANwZRdSQQkAhIBiYBEwBkQkATQGVbBuccgCaBzr48cnURAIiARkAhIBKxGwNEEMHPG3zW3AG7feKe0AFq9E7o/QRJADcGUXUkEJAISAYmARMAZEJAE0BlWwbnHIAmgc6+PHJ1EQCLQCwKPPPII3n77bdTW1mLSpEn4xz/+gYyMjC7Pqq6uxm233YY1a9bAzc0NixYtwiuvvIKQkBCJs0RgQCHgcAI47QXtLYC/3S0tgBruUkkANQRTdiURkAj0LwLPPvusIHBff/01UlNT8dhjj+H9999HTk4O/P39TxgMCZ/BYMAnn3wCKlldcsklCAgIwJdfftm/A5dXkwjYGQFJAO0M8ADoXhLAAbCIcgoSgcGKwLBhw3D33Xfj9ttvFxC0trYiLi4OL774Ii6//PJOsOTl5SElJQXZ2dkYM2aM+Iy/Z2Zmgp8lJiYOVhjlvAcgAo4mgOMnP6+5BXBH1j3SAqjhXpUEUEMwZVcSgcGKAC1qdL3u2LEDOp1OWNnc3d27hUMLVyxdvqGhodi4cSOmTZvWca3TTjsNY8eOxXPPPdfp+itXrhQWv8bGxk5/9/X1xWeffYbFixcP1uWT8x6ACDiaAI6b9Bw8PDSsBdzaiOwtf5IEUMO9KgmghmDKriQCgxWBb7/9FpWVlWhoaMANN9zQKwHUwhV77NgxJCcnY+/evUhPT++A/tJLL0VwcDDefPPNTsvxwQcf4N5770VRUVGnv8fGxuKFF17AZZddNliXT857ACLgaAKYkbkUHh6+miHb2qrH7u0PSgKoGaKAzQRQwzHIriQCEoGBg8BJAH4A4AWgrZtpJQM4AmAcgF2mY/j7dgD87JiFcAQDqAYwA8BvqnP+B2AnAGEuULWzAXwMwDw4UA/gQgCrLbyuPEwiIBHoHgGyvlwAsXYAqRjAUAD8zsrWRwQkAewjgPJ0iYBEoBMClhBAErFPAJj7h2whYocBvADgFdMoPAAUAmC64Idma0NyyQfTeDPiuQ3AECuIp1xyiYBEoGcESAK97QBSsyR/2qEqCaB2WMqeJAISAcASAngFgGcBxJkBxrd7ErePrACSVj5mgCwCQDJIH9FVAOgTbuiin1Um6yQzRHj/I0nkcedZcU15qERAIiARcHkEJAF0+SWUE5AIOBUClhBArV2xjwK4GUAQgCwAtwHYDSAJwB4ApwP41YRSqMlayIyPdgAkhCSQtU6FohyMREAiIBGwMwKSANoZYNm9RGCQIWAJAZSu2EG2KeR0JQISAedDQBJA51sTOSKJgCsiQM0XJn6QAH5tssa1AmDMDi1t5k26Yl1xleWYJQISgQGDgCSAA2Yp5UQkAg5F4GoAy1Vkj/cWEr/5psQL6Yp16PLIi0sEJAISgc4ISAIod4REQCIgEZAISAQkAhKBQYaAJICDbMHldCUCEgGJgERAIiARkAhIAij3gERAIiARkAhIBCQCEoFBhoAkgINsweV0JQISAYmAREAiIBGQCEgCKPeAREAiIBGQCEgEJAISgUGGgCSAg2zB5XQlAhIBiYBEQCIgEZAISAIo94BEQCIgEZAISAQkAhKBQYbA/wco6vNzaTo7lgAAAABJRU5ErkJggg==\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "[X, T] = np.meshgrid(np.linspace(xl, xr, M+1), np.linspace(yb, yt, N+1))\n", "# Plot results\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111, projection='3d')\n", "ax.set_xlabel(\"$x$\", fontsize=20)\n", "ax.set_ylabel(\"$t$\", fontsize=20)\n", "ax.set_zlabel(\"$T(x,t)$\", fontsize=20)\n", "ax.set_title(\"Temperature in convection-diffusion regime\")\n", "surface = ax.plot_surface(X, T, W, cmap=cm.RdYlBu_r, linewidth=0, antialiased=True, rstride=1, cstride=1)\n", "fig.colorbar(surface)\n", "plt.tight_layout()\n", "plt.draw()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Disclaimer\n", "\n", "El presente notebook ha sido creado para el curso ILI286 - Computación Científica 2, del Departamento de Informática, Universidad Técnica Federico Santa María. El material ha sido creado por Alejandro Sazo ([email protected]). En caso de encontrar un error, por favor no dude en contactar al email especificado. Puede encontrar la última versión del código en https://github.com/asazo/CC2" ] }, { "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
turbomanage/training-data-analyst	courses/machine_learning/deepdive/10_recommend/labs/content_based_using_neural_networks.ipynb	1	16298	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Content-Based Filtering Using Neural Networks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This lab relies on files created in the [content_based_preproc.ipynb](./content_based_preproc.ipynb) notebook. Be sure to complete the TODOs in that notebook and run the code there before completing this lab. \n", "Also, we'll be using the python3 kernel from here on out so don't forget to change the kernel if it's still python2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This lab illustrates:\n", "1. how to build feature columns for a model using tf.feature_column\n", "2. how to create custom evaluation metrics and add them to Tensorboard\n", "3. how to train a model and make predictions with the saved model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tensorflow Hub should already be installed. You can check using pip freeze." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "pip freeze \| grep tensor" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If 'tensorflow-hub' isn't one of the outputs above, then you'll need to install it. Uncomment the cell below and execute the commands. After doing the pip install, click \"Reset Session\" on the notebook so that the Python environment picks up the new packages." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip3 install tensorflow-hub==0.4.0\n", "!pip3 install --upgrade tensorflow==1.13.1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import tensorflow as tf\n", "import numpy as np\n", "import tensorflow_hub as hub\n", "import shutil\n", "\n", "PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID\n", "BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME\n", "REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-central1\n", "\n", "# do not change these\n", "os.environ['PROJECT'] = PROJECT\n", "os.environ['BUCKET'] = BUCKET\n", "os.environ['REGION'] = REGION\n", "os.environ['TFVERSION'] = '1.13.1'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "gcloud config set project $PROJECT\n", "gcloud config set compute/region $REGION" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the feature columns for the model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To start, we'll load the list of categories, authors and article ids we created in the previous Create Datasets notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "categories_list = open(\"categories.txt\").read().splitlines()\n", "authors_list = open(\"authors.txt\").read().splitlines()\n", "content_ids_list = open(\"content_ids.txt\").read().splitlines()\n", "mean_months_since_epoch = 523" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the cell below we'll define the feature columns to use in our model. If necessary, remind yourself the [various feature columns](https://www.tensorflow.org/api_docs/python/tf/feature_column) to use. \n", "For the embedded_title_column feature column, use a Tensorflow Hub Module to create an embedding of the article title. Since the articles and titles are in German, you'll want to use a German language embedding module. \n", "Explore the text embedding Tensorflow Hub modules [available here](https://alpha.tfhub.dev/). Filter by setting the language to 'German'. The 50 dimensional embedding should be sufficient for our purposes. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "embedded_title_column = #TODO: use a Tensorflow Hub module to create a text embeddding column for the article \"title\". \n", " # Use the module available at https://alpha.tfhub.dev/ filtering by German language.\n", " \n", "embedded_content_column = #TODO: create an embedded categorical feature column for the article id; i.e. \"content_id\".\n", "\n", "embedded_author_column = #TODO: create an embedded categorical feature column for the article \"author\"\n", "\n", "category_column = #TODO: create a categorical feature column for the article \"category\"\n", "\n", "months_since_epoch_boundaries = list(range(400,700,20))\n", "months_since_epoch_bucketized = #TODO: create a bucketized numeric feature column of values for the \"months since epoch\"\n", "\n", "crossed_months_since_category_column = #TODO: create a crossed feature column using the \"category\" and \"months since epoch\" values\n", "\n", "feature_columns = [embedded_content_column,\n", " embedded_author_column,\n", " category_column,\n", " embedded_title_column,\n", " crossed_months_since_category_column] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create the input function.\n", "\n", "Next we'll create the input function for our model. This input function reads the data from the csv files we created in the previous labs. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "record_defaults = [[\"Unknown\"], [\"Unknown\"],[\"Unknown\"],[\"Unknown\"],[\"Unknown\"],[mean_months_since_epoch],[\"Unknown\"]]\n", "column_keys = [\"visitor_id\", \"content_id\", \"category\", \"title\", \"author\", \"months_since_epoch\", \"next_content_id\"]\n", "label_key = \"next_content_id\"\n", "def read_dataset(filename, mode, batch_size = 512):\n", " def _input_fn():\n", " def decode_csv(value_column):\n", " columns = tf.decode_csv(value_column,record_defaults=record_defaults)\n", " features = dict(zip(column_keys, columns)) \n", " label = features.pop(label_key) \n", " return features, label\n", "\n", " # Create list of files that match pattern\n", " file_list = tf.gfile.Glob(filename)\n", "\n", " # Create dataset from file list\n", " dataset = tf.data.TextLineDataset(file_list).map(decode_csv)\n", "\n", " if mode == tf.estimator.ModeKeys.TRAIN:\n", " num_epochs = None # indefinitely\n", " dataset = dataset.shuffle(buffer_size = 10 * batch_size)\n", " else:\n", " num_epochs = 1 # end-of-input after this\n", "\n", " dataset = dataset.repeat(num_epochs).batch(batch_size)\n", " return dataset.make_one_shot_iterator().get_next()\n", " return _input_fn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create the model and train/evaluate\n", "\n", "\n", "Next, we'll build our model which recommends an article for a visitor to the Kurier.at website. Look through the code below. We use the input_layer feature column to create the dense input layer to our network. This is just a sigle layer network where we can adjust the number of hidden units as a parameter.\n", "\n", "Currently, we compute the accuracy between our predicted 'next article' and the actual 'next article' read next by the visitor. Resolve the TODOs in the cell below by adding additional performance metrics to assess our model. You will need to \n", "* use the [tf.metrics library](https://www.tensorflow.org/api_docs/python/tf/metrics) to compute an additional performance metric\n", "* add this additional metric to the metrics dictionary, and \n", "* include it in the tf.summary that is sent to Tensorboard." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def model_fn(features, labels, mode, params):\n", " net = tf.feature_column.input_layer(features, params['feature_columns'])\n", " for units in params['hidden_units']:\n", " net = tf.layers.dense(net, units=units, activation=tf.nn.relu)\n", " # Compute logits (1 per class).\n", " logits = tf.layers.dense(net, params['n_classes'], activation=None) \n", "\n", " predicted_classes = tf.argmax(logits, 1)\n", " from tensorflow.python.lib.io import file_io\n", " \n", " with file_io.FileIO('content_ids.txt', mode='r') as ifp:\n", " content = tf.constant([x.rstrip() for x in ifp])\n", " predicted_class_names = tf.gather(content, predicted_classes)\n", " if mode == tf.estimator.ModeKeys.PREDICT:\n", " predictions = {\n", " 'class_ids': predicted_classes[:, tf.newaxis],\n", " 'class_names' : predicted_class_names[:, tf.newaxis],\n", " 'probabilities': tf.nn.softmax(logits),\n", " 'logits': logits,\n", " }\n", " return tf.estimator.EstimatorSpec(mode, predictions=predictions)\n", " table = tf.contrib.lookup.index_table_from_file(vocabulary_file=\"content_ids.txt\")\n", " labels = table.lookup(labels)\n", " # Compute loss.\n", " loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)\n", "\n", " # Compute evaluation metrics.\n", " accuracy = tf.metrics.accuracy(labels=labels,\n", " predictions=predicted_classes,\n", " name='acc_op')\n", " top_10_accuracy = #TODO: Compute the top_10 accuracy, using the tf.nn.in_top_k and tf.metrics.mean functions in Tensorflow\n", " \n", " metrics = {\n", " 'accuracy': accuracy,\n", " #TODO: Add top_10_accuracy to the metrics dictionary\n", " }\n", " \n", " tf.summary.scalar('accuracy', accuracy[1])\n", " #TODO: Add the top_10_accuracy metric to the Tensorboard summary\n", "\n", " if mode == tf.estimator.ModeKeys.EVAL:\n", " return tf.estimator.EstimatorSpec(\n", " mode, loss=loss, eval_metric_ops=metrics)\n", "\n", " # Create training op.\n", " assert mode == tf.estimator.ModeKeys.TRAIN\n", "\n", " optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)\n", " train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())\n", " return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train and Evaluate" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "outdir = 'content_based_model_trained'\n", "shutil.rmtree(outdir, ignore_errors = True) # start fresh each time\n", "tf.summary.FileWriterCache.clear() # ensure filewriter cache is clear for TensorBoard events file\n", "estimator = tf.estimator.Estimator(\n", " model_fn=model_fn,\n", " model_dir = outdir,\n", " params={\n", " 'feature_columns': feature_columns,\n", " 'hidden_units': [200, 100, 50],\n", " 'n_classes': len(content_ids_list)\n", " })\n", "\n", "train_spec = tf.estimator.TrainSpec(\n", " input_fn = read_dataset(\"training_set.csv\", tf.estimator.ModeKeys.TRAIN),\n", " max_steps = 200)\n", "\n", "eval_spec = tf.estimator.EvalSpec(\n", " input_fn = read_dataset(\"test_set.csv\", tf.estimator.ModeKeys.EVAL),\n", " steps = None,\n", " start_delay_secs = 30,\n", " throttle_secs = 60)\n", "\n", "tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make predictions with the trained model. \n", "\n", "With the model now trained, we can make predictions by calling the predict method on the estimator. Let's look at how our model predicts on the first five examples of the training set. \n", "To start, we'll create a new file 'first_5.csv' which contains the first five elements of our training set. We'll also save the target values to a file 'first_5_content_ids' so we can compare our results. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "head -5 training_set.csv > first_5.csv\n", "head first_5.csv\n", "awk -F \"\\\",\\\"\" '{print $2}' first_5.csv > first_5_content_ids" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall, to make predictions on the trained model we pass a list of examples through the input function. Complete the code below to make predicitons on the examples contained in the \"first_5.csv\" file we created above. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = #TODO: Use the predict method on our trained model to find the predictions for the examples contained in \"first_5.csv\"." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "recommended_content_ids = [np.asscalar(d[\"class_names\"]).decode('UTF-8') for d in output]\n", "content_ids = open(\"first_5_content_ids\").read().splitlines()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we'll map the content id back to the article title. We can then compare our model's recommendation for the first of our examples. This can all be done in BigQuery. Look through the query below and make sure it is clear what is being returned." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from google.cloud import bigquery\n", "recommended_title_sql=\"\"\"\n", "#standardSQL\n", "SELECT\n", "(SELECT MAX(IF(index=6, value, NULL)) FROM UNNEST(hits.customDimensions)) AS title\n", "FROM `cloud-training-demos.GA360_test.ga_sessions_sample`, \n", " UNNEST(hits) AS hits\n", "WHERE \n", " # only include hits on pages\n", " hits.type = \"PAGE\"\n", " AND (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(hits.customDimensions)) = \\\"{}\\\"\n", "LIMIT 1\"\"\".format(recommended_content_ids[0])\n", "\n", "current_title_sql=\"\"\"\n", "#standardSQL\n", "SELECT\n", "(SELECT MAX(IF(index=6, value, NULL)) FROM UNNEST(hits.customDimensions)) AS title\n", "FROM `cloud-training-demos.GA360_test.ga_sessions_sample`, \n", " UNNEST(hits) AS hits\n", "WHERE \n", " # only include hits on pages\n", " hits.type = \"PAGE\"\n", " AND (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(hits.customDimensions)) = \\\"{}\\\"\n", "LIMIT 1\"\"\".format(content_ids[0])\n", "recommended_title = bigquery.Client().query(recommended_title_sql).to_dataframe()['title'].tolist()[0].encode('utf-8').strip()\n", "current_title = bigquery.Client().query(current_title_sql).to_dataframe()['title'].tolist()[0].encode('utf-8').strip()\n", "print(\"Current title: {} \".format(current_title))\n", "print(\"Recommended title: {}\".format(recommended_title))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
ES-DOC/esdoc-jupyterhub	notebooks/mohc/cmip6/models/sandbox-2/land.ipynb	1	173498	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Land \n", "MIP Era: CMIP6 \n", "Institute: MOHC \n", "Source ID: SANDBOX-2 \n", "Topic: Land \n", "Sub-Topics: Soil, Snow, Vegetation, Energy Balance, Carbon Cycle, Nitrogen Cycle, River Routing, Lakes. \n", "Properties: 154 (96 required) \n", "Model descriptions: [Model description details](https://specializations.es-doc.org/cmip6/land?client=jupyter-notebook) \n", "Initialized From: -- \n", "\n", "Notebook Help: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "Notebook Initialised: 2018-02-15 16:54:15" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "IMPORTANT: to be executed each time you run the notebook " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'mohc', 'sandbox-2', 'land')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "Set document authors" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "Specify document contributors " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "Specify document publication status " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --> Conservation Properties](#2.-Key-Properties--->-Conservation-Properties) \n", "[3. Key Properties --> Timestepping Framework](#3.-Key-Properties--->-Timestepping-Framework) \n", "[4. Key Properties --> Software Properties](#4.-Key-Properties--->-Software-Properties) \n", "[5. Grid](#5.-Grid) \n", "[6. Grid --> Horizontal](#6.-Grid--->-Horizontal) \n", "[7. Grid --> Vertical](#7.-Grid--->-Vertical) \n", "[8. Soil](#8.-Soil) \n", "[9. Soil --> Soil Map](#9.-Soil--->-Soil-Map) \n", "[10. Soil --> Snow Free Albedo](#10.-Soil--->-Snow-Free-Albedo) \n", "[11. Soil --> Hydrology](#11.-Soil--->-Hydrology) \n", "[12. Soil --> Hydrology --> Freezing](#12.-Soil--->-Hydrology--->-Freezing) \n", "[13. Soil --> Hydrology --> Drainage](#13.-Soil--->-Hydrology--->-Drainage) \n", "[14. Soil --> Heat Treatment](#14.-Soil--->-Heat-Treatment) \n", "[15. Snow](#15.-Snow) \n", "[16. Snow --> Snow Albedo](#16.-Snow--->-Snow-Albedo) \n", "[17. Vegetation](#17.-Vegetation) \n", "[18. Energy Balance](#18.-Energy-Balance) \n", "[19. Carbon Cycle](#19.-Carbon-Cycle) \n", "[20. Carbon Cycle --> Vegetation](#20.-Carbon-Cycle--->-Vegetation) \n", "[21. Carbon Cycle --> Vegetation --> Photosynthesis](#21.-Carbon-Cycle--->-Vegetation--->-Photosynthesis) \n", "[22. Carbon Cycle --> Vegetation --> Autotrophic Respiration](#22.-Carbon-Cycle--->-Vegetation--->-Autotrophic-Respiration) \n", "[23. Carbon Cycle --> Vegetation --> Allocation](#23.-Carbon-Cycle--->-Vegetation--->-Allocation) \n", "[24. Carbon Cycle --> Vegetation --> Phenology](#24.-Carbon-Cycle--->-Vegetation--->-Phenology) \n", "[25. Carbon Cycle --> Vegetation --> Mortality](#25.-Carbon-Cycle--->-Vegetation--->-Mortality) \n", "[26. Carbon Cycle --> Litter](#26.-Carbon-Cycle--->-Litter) \n", "[27. Carbon Cycle --> Soil](#27.-Carbon-Cycle--->-Soil) \n", "[28. Carbon Cycle --> Permafrost Carbon](#28.-Carbon-Cycle--->-Permafrost-Carbon) \n", "[29. Nitrogen Cycle](#29.-Nitrogen-Cycle) \n", "[30. River Routing](#30.-River-Routing) \n", "[31. River Routing --> Oceanic Discharge](#31.-River-Routing--->-Oceanic-Discharge) \n", "[32. Lakes](#32.-Lakes) \n", "[33. Lakes --> Method](#33.-Lakes--->-Method) \n", "[34. Lakes --> Wetlands](#34.-Lakes--->-Wetlands) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "Land surface key properties" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of land surface model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Name of land surface model code (e.g. MOSES2.2)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General description of the processes modelled (e.g. dymanic vegation, prognostic albedo, etc.)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Land Atmosphere Flux Exchanges\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Fluxes exchanged with the atmopshere." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_atmosphere_flux_exchanges') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"water\" \n", "# \"energy\" \n", "# \"carbon\" \n", "# \"nitrogen\" \n", "# \"phospherous\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Atmospheric Coupling Treatment\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the treatment of land surface coupling with the Atmosphere model component, which may be different for different quantities (e.g. dust: semi-implicit, water vapour: explicit)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.atmospheric_coupling_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Land Cover\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Types of land cover defined in the land surface model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"bare soil\" \n", "# \"urban\" \n", "# \"lake\" \n", "# \"land ice\" \n", "# \"lake ice\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Land Cover Change\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe how land cover change is managed (e.g. the use of net or gross transitions)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover_change') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.8. Tiling\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the general tiling procedure used in the land surface (if any). Include treatment of physiography, land/sea, (dynamic) vegetation coverage and orography/roughness" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --> Conservation Properties \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Energy\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe if/how energy is conserved globally and to what level (e.g. within X [units]/year)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.energy') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Water\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe if/how water is conserved globally and to what level (e.g. within X [units]/year)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.water') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Carbon\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe if/how carbon is conserved globally and to what level (e.g. within X [units]/year)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.carbon') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --> Timestepping Framework \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Timestep Dependent On Atmosphere\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is a time step dependent on the frequency of atmosphere coupling?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestep_dependent_on_atmosphere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Overall timestep of land surface model (i.e. time between calls)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Timestepping Method\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General description of time stepping method and associated time step(s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestepping_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --> Software Properties \n", "Software properties of land surface code" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Repository\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Location of code for this component." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Code Version\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Code version identifier." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Code Languages\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### Code language(s)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Grid \n", "Land surface grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of the grid in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Grid --> Horizontal \n", "The horizontal grid in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the general structure of the horizontal grid (not including any tiling)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Matches Atmosphere Grid\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the horizontal grid match the atmosphere?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.matches_atmosphere_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Grid --> Vertical \n", "The vertical grid in the soil" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the general structure of the vertical grid in the soil (not including any tiling)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Total Depth\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### The total depth of the soil (in metres)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.total_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Soil \n", "Land surface soil" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of soil in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Heat Water Coupling\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the coupling between heat and water in the soil" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_water_coupling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Number Of Soil layers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### The number of soil layers" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.number_of_soil layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the soil scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Soil --> Soil Map \n", "Key properties of the land surface soil map" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General description of soil map" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Structure\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil structure map" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.structure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Texture\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil texture map" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.texture') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Organic Matter\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil organic matter map" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.organic_matter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Albedo\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil albedo map" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Water Table\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil water table map, if any" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.water_table') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.7. Continuously Varying Soil Depth\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the soil properties vary continuously with depth?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.continuously_varying_soil_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.8. Soil Depth\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil depth map" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.soil_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Soil --> Snow Free Albedo \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Prognostic\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is snow free albedo prognostic?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.prognostic') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Functions\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### If prognostic, describe the dependancies on snow free albedo calculations" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"soil humidity\" \n", "# \"vegetation state\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Direct Diffuse\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.1\n", "### If prognostic, describe the distinction between direct and diffuse albedo" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.direct_diffuse') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"distinction between direct and diffuse albedo\" \n", "# \"no distinction between direct and diffuse albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.4. Number Of Wavelength Bands\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If prognostic, enter the number of wavelength bands used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.number_of_wavelength_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Soil --> Hydrology \n", "Key properties of the land surface soil hydrology" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General description of the soil hydrological model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of river soil hydrology in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil hydrology tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Vertical Discretisation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the typical vertical discretisation" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Number Of Ground Water Layers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### The number of soil layers that may contain water" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.number_of_ground_water_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.6. Lateral Connectivity\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Describe the lateral connectivity between tiles" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.lateral_connectivity') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"perfect connectivity\" \n", "# \"Darcian flow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.7. Method\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### The hydrological dynamics scheme in the land surface model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bucket\" \n", "# \"Force-restore\" \n", "# \"Choisnel\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Soil --> Hydrology --> Freezing \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Number Of Ground Ice Layers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### How many soil layers may contain ground ice" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.number_of_ground_ice_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Ice Storage Method\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method of ice storage" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.ice_storage_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Permafrost\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the treatment of permafrost, if any, within the land surface scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.permafrost') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Soil --> Hydrology --> Drainage \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General describe how drainage is included in the land surface scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Types\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Different types of runoff represented by the land surface model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Gravity drainage\" \n", "# \"Horton mechanism\" \n", "# \"topmodel-based\" \n", "# \"Dunne mechanism\" \n", "# \"Lateral subsurface flow\" \n", "# \"Baseflow from groundwater\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Soil --> Heat Treatment \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General description of how heat treatment properties are defined" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of soil heat scheme in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil heat treatment tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Vertical Discretisation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the typical vertical discretisation" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Heat Storage\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Specify the method of heat storage" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.heat_storage') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Force-restore\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Processes\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Describe processes included in the treatment of soil heat" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"soil moisture freeze-thaw\" \n", "# \"coupling with snow temperature\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Snow \n", "Land surface snow" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of snow in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the snow tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Number Of Snow Layers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### The number of snow levels used in the land surface scheme/model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.number_of_snow_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Density\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Description of the treatment of snow density" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.density') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Water Equivalent\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Description of the treatment of the snow water equivalent" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.water_equivalent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Heat Content\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Description of the treatment of the heat content of snow" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.heat_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.7. Temperature\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Description of the treatment of snow temperature" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.temperature') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.8. Liquid Water Content\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Description of the treatment of snow liquid water" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.liquid_water_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.9. Snow Cover Fractions\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Specify cover fractions used in the surface snow scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_cover_fractions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ground snow fraction\" \n", "# \"vegetation snow fraction\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.10. Processes\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Snow related processes in the land surface scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"snow interception\" \n", "# \"snow melting\" \n", "# \"snow freezing\" \n", "# \"blowing snow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.11. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the snow scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Snow --> Snow Albedo \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Describe the treatment of snow-covered land albedo" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"prescribed\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Functions\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### If prognostic, " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"snow age\" \n", "# \"snow density\" \n", "# \"snow grain type\" \n", "# \"aerosol deposition\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Vegetation \n", "Land surface vegetation" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of vegetation in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of vegetation scheme in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Dynamic Vegetation\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there dynamic evolution of vegetation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.dynamic_vegetation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.4. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the vegetation tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.5. Vegetation Representation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Vegetation classification used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation types\" \n", "# \"biome types\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.6. Vegetation Types\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### List of vegetation types in the classification, if any" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"broadleaf tree\" \n", "# \"needleleaf tree\" \n", "# \"C3 grass\" \n", "# \"C4 grass\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.7. Biome Types\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### List of biome types in the classification, if any" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biome_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"evergreen needleleaf forest\" \n", "# \"evergreen broadleaf forest\" \n", "# \"deciduous needleleaf forest\" \n", "# \"deciduous broadleaf forest\" \n", "# \"mixed forest\" \n", "# \"woodland\" \n", "# \"wooded grassland\" \n", "# \"closed shrubland\" \n", "# \"opne shrubland\" \n", "# \"grassland\" \n", "# \"cropland\" \n", "# \"wetlands\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.8. Vegetation Time Variation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How the vegetation fractions in each tile are varying with time" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_time_variation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed (not varying)\" \n", "# \"prescribed (varying from files)\" \n", "# \"dynamical (varying from simulation)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.9. Vegetation Map\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If vegetation fractions are not dynamically updated , describe the vegetation map used (common name and reference, if possible)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.10. Interception\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is vegetation interception of rainwater represented?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.interception') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.11. Phenology\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Treatment of vegetation phenology" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic (vegetation map)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.12. Phenology Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### General description of the treatment of vegetation phenology" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.13. Leaf Area Index\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Treatment of vegetation leaf area index" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prescribed\" \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.14. Leaf Area Index Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### General description of the treatment of leaf area index" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.15. Biomass\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Treatment of vegetation biomass " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.16. Biomass Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### General description of the treatment of vegetation biomass" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.17. Biogeography\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Treatment of vegetation biogeography" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.18. Biogeography Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### General description of the treatment of vegetation biogeography" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.19. Stomatal Resistance\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Specify what the vegetation stomatal resistance depends on" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"light\" \n", "# \"temperature\" \n", "# \"water availability\" \n", "# \"CO2\" \n", "# \"O3\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.20. Stomatal Resistance Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### General description of the treatment of vegetation stomatal resistance" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.21. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the vegetation scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Energy Balance \n", "Land surface energy balance" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of energy balance in land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the energy balance tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Number Of Surface Temperatures\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### The maximum number of distinct surface temperatures in a grid cell (for example, each subgrid tile may have its own temperature)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.number_of_surface_temperatures') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.4. Evaporation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Specify the formulation method for land surface evaporation, from soil and vegetation" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"alpha\" \n", "# \"beta\" \n", "# \"combined\" \n", "# \"Monteith potential evaporation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.5. Processes\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Describe which processes are included in the energy balance scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"transpiration\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Carbon Cycle \n", "Land surface carbon cycle" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of carbon cycle in land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.2. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the carbon cycle tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.3. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of carbon cycle in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.4. Anthropogenic Carbon\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Describe the treament of the anthropogenic carbon pool" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.anthropogenic_carbon') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"grand slam protocol\" \n", "# \"residence time\" \n", "# \"decay time\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.5. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the carbon scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Carbon Cycle --> Vegetation \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Number Of Carbon Pools\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Enter the number of carbon pools used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Carbon Pools\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the carbon pools used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.3. Forest Stand Dynamics\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the treatment of forest stand dyanmics" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.forest_stand_dynamics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Carbon Cycle --> Vegetation --> Photosynthesis \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Method\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the general method used for photosynthesis (e.g. type of photosynthesis, distinction between C3 and C4 grasses, Nitrogen depencence, etc.)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.photosynthesis.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Carbon Cycle --> Vegetation --> Autotrophic Respiration \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Maintainance Respiration\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the general method used for maintainence respiration" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.maintainance_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Growth Respiration\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the general method used for growth respiration" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.growth_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Carbon Cycle --> Vegetation --> Allocation \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Method\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the general principle behind the allocation scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Allocation Bins\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Specify distinct carbon bins used in allocation" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_bins') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"leaves + stems + roots\" \n", "# \"leaves + stems + roots (leafy + woody)\" \n", "# \"leaves + fine roots + coarse roots + stems\" \n", "# \"whole plant (no distinction)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Allocation Fractions\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Describe how the fractions of allocation are calculated" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_fractions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed\" \n", "# \"function of vegetation type\" \n", "# \"function of plant allometry\" \n", "# \"explicitly calculated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Carbon Cycle --> Vegetation --> Phenology \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Method\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the general principle behind the phenology scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.phenology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Carbon Cycle --> Vegetation --> Mortality \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Method\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the general principle behind the mortality scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.mortality.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Carbon Cycle --> Litter \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Number Of Carbon Pools\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Enter the number of carbon pools used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Carbon Pools\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the carbon pools used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.3. Decomposition\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the decomposition methods used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.4. Method\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the general method used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Carbon Cycle --> Soil \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Number Of Carbon Pools\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Enter the number of carbon pools used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Carbon Pools\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the carbon pools used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Decomposition\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the decomposition methods used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.4. Method\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the general method used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Carbon Cycle --> Permafrost Carbon \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Is Permafrost Included\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is permafrost included?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.is_permafrost_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.2. Emitted Greenhouse Gases\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the GHGs emitted" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.emitted_greenhouse_gases') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.3. Decomposition\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the decomposition methods used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.4. Impact On Soil Properties\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the impact of permafrost on soil properties" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.impact_on_soil_properties') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Nitrogen Cycle \n", "Land surface nitrogen cycle" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of the nitrogen cycle in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.2. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the notrogen cycle tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.3. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of nitrogen cycle in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.4. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the nitrogen scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. River Routing \n", "Land surface river routing" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of river routing in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.2. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the river routing, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of river routing scheme in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Grid Inherited From Land Surface\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is the grid inherited from land surface?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_inherited_from_land_surface') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.5. Grid Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### General description of grid, if not inherited from land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.6. Number Of Reservoirs\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Enter the number of reservoirs" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.number_of_reservoirs') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.7. Water Re Evaporation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### TODO" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.water_re_evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"flood plains\" \n", "# \"irrigation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.8. Coupled To Atmosphere\n", "Is Required: FALSE    Type: BOOLEAN    Cardinality: 0.1\n", "### Is river routing coupled to the atmosphere model component?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_atmosphere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.9. Coupled To Land\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the coupling between land and rivers" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_land') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.10. Quantities Exchanged With Atmosphere\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### If couple to atmosphere, which quantities are exchanged between river routing and the atmosphere model components?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.quantities_exchanged_with_atmosphere') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.11. Basin Flow Direction Map\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What type of basin flow direction map is being used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.basin_flow_direction_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"present day\" \n", "# \"adapted for other periods\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.12. Flooding\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the representation of flooding, if any" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.flooding') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.13. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the river routing" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. River Routing --> Oceanic Discharge \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Discharge Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Specify how rivers are discharged to the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.discharge_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"direct (large rivers)\" \n", "# \"diffuse\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Quantities Transported\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Quantities that are exchanged from river-routing to the ocean model component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.quantities_transported') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Lakes \n", "Land surface lakes" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of lakes in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.2. Coupling With Rivers\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Are lakes coupled to the river routing model component?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.coupling_with_rivers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.3. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of lake scheme in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Quantities Exchanged With Rivers\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### If coupling with rivers, which quantities are exchanged between the lakes and rivers" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.quantities_exchanged_with_rivers') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.5. Vertical Grid\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the vertical grid of lakes" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.vertical_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.6. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the lake scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Lakes --> Method \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Ice Treatment\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is lake ice included?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.ice_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.2. Albedo\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Describe the treatment of lake albedo" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.3. Dynamics\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Which dynamics of lakes are treated? horizontal, vertical, etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"No lake dynamics\" \n", "# \"vertical\" \n", "# \"horizontal\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.4. Dynamic Lake Extent\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is a dynamic lake extent scheme included?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamic_lake_extent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.5. Endorheic Basins\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Basins not flowing to ocean included?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.endorheic_basins') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 34. Lakes --> Wetlands \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the treatment of wetlands, if any" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.wetlands.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	gpl-3.0
RyanSkraba/beam	examples/notebooks/get-started/try-apache-beam-go.ipynb	11	21411	{ "license": [ "Licensed to the Apache Software Foundation (ASF) under one", "or more contributor license agreements. See the NOTICE file", "distributed with this work for additional information", "regarding copyright ownership. The ASF licenses this file", "to you 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." ], "nbformat": 4, "cells": [ { "source": [ "<a href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/get-started/try-apache-beam-go.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ], "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" } }, { "source": [ "# Try Apache Beam - Go\n", "\n", "In this notebook, we set up your development environment and work through a simple example using the [DirectRunner](https://beam.apache.org/documentation/runners/direct/). You can explore other runners with the [Beam Capatibility Matrix](https://beam.apache.org/documentation/runners/capability-matrix/).\n", "\n", "To navigate through different sections, use the table of contents. From View drop-down list, select Table of contents.\n", "\n", "To run a code cell, you can click the Run cell button at the top left of the cell, or by select it and press `Shift+Enter`. Try modifying a code cell and re-running it to see what happens.\n", "\n", "To learn more about Colab, see [Welcome to Colaboratory!](https://colab.sandbox.google.com/notebooks/welcome.ipynb)." ], "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "lNKIMlEDZ_Vw" } }, { "source": [ "# Setup\n", "\n", "First, you need to set up your environment." ], "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Fz6KSQ13_3Rr" } }, { "source": [ "import os\n", "\n", "# Run and print a shell command.\n", "def run(cmd):\n", " print('>> {}'.format(cmd))\n", " !{cmd}\n", " print('')\n", "\n", "# Change directory to $HOME.\n", "print(f\"Changing directory to $HOME: {os.environ['HOME']}\\n\")\n", "os.chdir(os.environ['HOME'])\n", "\n", "# Copy the input file into the local filesystem.\n", "run('mkdir -p data')\n", "run('gsutil cp gs://dataflow-samples/shakespeare/kinglear.txt data/')" ], "cell_type": "code", "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Changing directory to $HOME: /root\n", "\n", ">> mkdir -p data\n", "\n", ">> gsutil cp gs://dataflow-samples/shakespeare/kinglear.txt data/\n", "Copying gs://dataflow-samples/shakespeare/kinglear.txt...\n", "/ [1 files][153.6 KiB/153.6 KiB] \n", "Operation completed over 1 objects/153.6 KiB. \n", "\n" ] } ], "metadata": { "outputId": "54640caa-f322-4b1e-c124-bfb274a70b56", "colab_type": "code", "id": "GOOk81Jj_yUy", "colab": { "base_uri": "https://localhost:8080/", "height": 170 } } }, { "source": [ "## Installing development tools\n", "\n", "Let's start by installing Go. This will take a while, so feel free to go for a walk or do some stretching." ], "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "bgSegMTHlSqb" } }, { "source": [ "# Update and upgrade the system before installing anything else.\n", "run('apt-get update > /dev/null')\n", "run('apt-get upgrade > /dev/null')\n", "\n", "# Install the Go package.\n", "run('apt-get install golang-go > /dev/null')\n", "\n", "# Check the Go version to see if everything is working well.\n", "run('go version')\n", "\n", "# Finally, let's install the Apache Beam SDK for Go.\n", "run('go get -u github.com/apache/beam/sdks/go/...')" ], "cell_type": "code", "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ ">> apt-get update > /dev/null\n", "\n", ">> apt-get upgrade > /dev/null\n", "\n", ">> apt-get install golang-go > /dev/null\n", "\n", ">> go version\n", "go version go1.10.4 linux/amd64\n", "\n", ">> go get -u github.com/apache/beam/sdks/go/...\n", "\n" ] } ], "metadata": { "outputId": "60d90f8a-a949-4584-c824-f671cd241d5d", "colab_type": "code", "id": "ibHfVnpolP3b", "colab": { "base_uri": "https://localhost:8080/", "height": 204 } } }, { "source": [ "## Creating the directory structure\n", "\n", "Go requires all packages to be contained within the `GOPATH`. By default it is located in `$HOME/go`, you can check yours using the `go env GOPATH` command.\n", "\n", "Inside the `GOPATH` there should be a `src` directory that holds up all the packages, and a `bin` directory will be created containing all the compiled binaries.\n", "\n", "To learn more about Go's directory structure, see [How to Write Go Code](https://golang.org/doc/code.html)." ], "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ungfz1aFqYnn" } }, { "source": [ "# Get the GOPATH.\n", "cmd_stdout = !go env GOPATH\n", "GOPATH = cmd_stdout[0]\n", "print(f\"GOPATH={GOPATH}\\n\")\n", "\n", "# Create our source code wordcount package.\n", "run(f\"mkdir -p {GOPATH}/src/wordcount\")" ], "cell_type": "code", "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GOPATH=/root/go\n", "\n", ">> mkdir -p /root/go/src/wordcount\n", "\n" ] } ], "metadata": { "outputId": "8171ef7c-9498-4cb7-9b55-6a61fca3ed14", "colab_type": "code", "id": "yU3F1Snrt6Nv", "colab": { "base_uri": "https://localhost:8080/", "height": 85 } } }, { "source": [ "# Minimal word count\n", "\n", "The following example is the \"Hello, World!\" of data processing, a basic implementation of word count. We're creating a simple data processing pipeline that reads a text file and counts the number of occurrences of every word.\n", "\n", "There are many scenarios where all the data does not fit in memory. Notice that the outputs of the pipeline go to the file system, which allows for large processing jobs in distributed environments." ], "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "cPvvFB19uXNw" } }, { "source": [ "## wordcount.go" ], "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "3MUaWD4Dm5NB" } }, { "source": [ "%%writefile go/src/wordcount/wordcount.go\n", "\n", "package main\n", "\n", "import (\n", "\t\"context\"\n", " \t\"flag\"\n", "\t\"fmt\"\n", "\t\"regexp\"\n", "\n", "\t\"github.com/apache/beam/sdks/go/pkg/beam\"\n", "\t\"github.com/apache/beam/sdks/go/pkg/beam/io/textio\"\n", "\t\"github.com/apache/beam/sdks/go/pkg/beam/runners/direct\"\n", "\t\"github.com/apache/beam/sdks/go/pkg/beam/transforms/stats\"\n", "\n", "\t_ \"github.com/apache/beam/sdks/go/pkg/beam/io/filesystem/local\"\n", ")\n", "\n", "var (\n", "\tinput = flag.String(\"input\", \"data/\", \"File(s) to read.\")\n", "\toutput = flag.String(\"output\", \"outputs/wordcounts.txt\", \"Output filename.\")\n", ")\n", "\n", "var wordRE = regexp.MustCompile(`[a-zA-Z]+('[a-z])?`)\n", "\n", "func main() {\n", " flag.Parse()\n", "\n", "\tbeam.Init()\n", "\n", "\tpipeline := beam.NewPipeline()\n", "\troot := pipeline.Root()\n", "\n", "\tlines := textio.Read(root, input)\n", "\twords := beam.ParDo(root, func(line string, emit func(string)) {\n", "\t\tfor _, word := range wordRE.FindAllString(line, -1) {\n", "\t\t\temit(word)\n", "\t\t}\n", "\t}, lines)\n", "\tcounted := stats.Count(root, words)\n", "\tformatted := beam.ParDo(root, func(word string, count int) string {\n", "\t\treturn fmt.Sprintf(\"%s: %v\", word, count)\n", "\t}, counted)\n", "\ttextio.Write(root, output, formatted)\n", "\n", "\tdirect.Execute(context.Background(), pipeline)\n", "}" ], "cell_type": "code", "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Writing go/src/wordcount/wordcount.go\n" ] } ], "metadata": { "outputId": "cdb8f492-e653-4daa-dd1d-e2c3c257dd0e", "colab_type": "code", "id": "oUqfqWyMuIfR", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } } }, { "source": [ "## Building and running\n", "\n", "Go allows us to run a program without having to explicitly compile it. Internally it will compile the source code into a binary and then run it." ], "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "to3rfuOhq0i3" } }, { "source": [ "# Build and run the program.\n", "run('rm -rf outputs/')\n", "run(f\"go run {GOPATH}/src/wordcount/.go\")\n", "\n", "# Sample the first 20 results, remember there are no ordering guarantees.\n", "run('head -n 20 outputs/')" ], "cell_type": "code", "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ ">> rm -rf outputs/\n", "\n", ">> go run /root/go/src/wordcount/.go\n", "2019/03/04 23:05:37 Executing pipeline with the direct runner.\n", "2019/03/04 23:05:37 Pipeline:\n", "2019/03/04 23:05:37 Nodes: {1: []uint8/bytes GLO}\n", "{2: string/string[string] GLO}\n", "{3: string/string[string] GLO}\n", "{4: string/string[string] GLO}\n", "{5: string/string[string] GLO}\n", "{6: KV<string,int>/KV<string[string],int[varintz]> GLO}\n", "{7: CoGBK<string,int>/CoGBK<string[string],int[varintz]> GLO}\n", "{8: KV<string,int>/KV<string[string],int[varintz]> GLO}\n", "{9: string/string[string] GLO}\n", "{10: KV<int,string>/KV<int[varintz],string[string]> GLO}\n", "{11: CoGBK<int,string>/CoGBK<int[varintz],string[string]> GLO}\n", "Edges: 1: Impulse [] -> [Out: []uint8 -> {1: []uint8/bytes GLO}]\n", "2: ParDo [In(Main): []uint8 <- {1: []uint8/bytes GLO}] -> [Out: T -> {2: string/string[string] GLO}]\n", "3: ParDo [In(Main): string <- {2: string/string[string] GLO}] -> [Out: string -> {3: string/string[string] GLO}]\n", "4: ParDo [In(Main): string <- {3: string/string[string] GLO}] -> [Out: string -> {4: string/string[string] GLO}]\n", "5: ParDo [In(Main): string <- {4: string/string[string] GLO}] -> [Out: string -> {5: string/string[string] GLO}]\n", "6: ParDo [In(Main): T <- {5: string/string[string] GLO}] -> [Out: KV<T,int> -> {6: KV<string,int>/KV<string[string],int[varintz]> GLO}]\n", "7: CoGBK [In(Main): KV<string,int> <- {6: KV<string,int>/KV<string[string],int[varintz]> GLO}] -> [Out: CoGBK<string,int> -> {7: CoGBK<string,int>/CoGBK<string[string],int[varintz]> GLO}]\n", "8: Combine [In(Main): int <- {7: CoGBK<string,int>/CoGBK<string[string],int[varintz]> GLO}] -> [Out: KV<string,int> -> {8: KV<string,int>/KV<string[string],int[varintz]> GLO}]\n", "9: ParDo [In(Main): KV<string,int> <- {8: KV<string,int>/KV<string[string],int[varintz]> GLO}] -> [Out: string -> {9: string/string[string] GLO}]\n", "10: ParDo [In(Main): T <- {9: string/string[string] GLO}] -> [Out: KV<int,T> -> {10: KV<int,string>/KV<int[varintz],string[string]> GLO}]\n", "11: CoGBK [In(Main): KV<int,string> <- {10: KV<int,string>/KV<int[varintz],string[string]> GLO}] -> [Out: CoGBK<int,string> -> {11: CoGBK<int,string>/CoGBK<int[varintz],string[string]> GLO}]\n", "12: ParDo [In(Main): CoGBK<int,string> <- {11: CoGBK<int,string>/CoGBK<int[varintz],string[string]> GLO}] -> []\n", "2019/03/04 23:05:37 Plan[plan]:\n", "14: Impulse[0]\n", "1: ParDo[textio.writeFileFn] Out:[]\n", "2: CoGBK. Out:1\n", "3: Inject[0]. Out:2\n", "4: ParDo[beam.addFixedKeyFn] Out:[3]\n", "5: ParDo[main.main.func2] Out:[4]\n", "6: Combine[stats.sumIntFn] Keyed:false Out:5\n", "7: CoGBK. Out:6\n", "8: Inject[0]. Out:7\n", "9: ParDo[stats.mapFn] Out:[8]\n", "10: ParDo[main.main.func1] Out:[9]\n", "11: ParDo[textio.readFn] Out:[10]\n", "12: ParDo[textio.expandFn] Out:[11]\n", "13: ParDo[beam.createFn] Out:[12]\n", "2019/03/04 23:05:37 Reading from data/kinglear.txt\n", "2019/03/04 23:05:37 Writing to outputs/wordcounts.txt\n", "\n", ">> head -n 20 outputs/\n", "breeding: 3\n", "alas: 1\n", "condition: 2\n", "whole: 1\n", "rarity: 1\n", "hoping: 1\n", "oath: 4\n", "pretence: 2\n", "beastly: 1\n", "chide: 1\n", "mile: 1\n", "Villain: 1\n", "preach: 1\n", "rescue: 1\n", "Alarum: 2\n", "loath: 1\n", "clotpoll: 1\n", "shortly: 2\n", "alack: 3\n", "What: 75\n", "\n" ] } ], "metadata": { "outputId": "bb7e1169-e080-4d80-deb7-11af178a5230", "colab_type": "code", "id": "0FbGD-rpocgx", "colab": { "base_uri": "https://localhost:8080/", "height": 1193 } } }, { "source": [ "# Word count with comments\n", "\n", "Below is mostly the same code as above, but with comments explaining every line in more detail." ], "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "k-HubCrk-h_G" } }, { "source": [ "%%writefile go/src/wordcount/wordcount.go\n", "\n", "package main\n", "\n", "import (\n", "\t\"context\"\n", " \"flag\"\n", "\t\"fmt\"\n", "\t\"regexp\"\n", "\n", "\t\"github.com/apache/beam/sdks/go/pkg/beam\"\n", "\t\"github.com/apache/beam/sdks/go/pkg/beam/io/textio\"\n", "\t\"github.com/apache/beam/sdks/go/pkg/beam/runners/direct\"\n", "\t\"github.com/apache/beam/sdks/go/pkg/beam/transforms/stats\"\n", "\n", "\t_ \"github.com/apache/beam/sdks/go/pkg/beam/io/filesystem/local\"\n", ")\n", "\n", "var (\n", "\tinput = flag.String(\"input\", \"data/\", \"File(s) to read.\")\n", "\toutput = flag.String(\"output\", \"outputs/wordcounts.txt\", \"Output filename.\")\n", ")\n", "\n", "var wordRE = regexp.MustCompile(`[a-zA-Z]+('[a-z])?`)\n", "\n", "func main() {\n", " flag.Parse()\n", "\n", "\tbeam.Init()\n", "\n", "\tpipeline := beam.NewPipeline()\n", "\troot := pipeline.Root()\n", "\n", " // Read lines from a text file.\n", "\tlines := textio.Read(root, input)\n", "\n", " // Use a regular expression to iterate over all words in the line.\n", "\twords := beam.ParDo(root, func(line string, emit func(string)) {\n", "\t\tfor _, word := range wordRE.FindAllString(line, -1) {\n", "\t\t\temit(word)\n", "\t\t}\n", "\t}, lines)\n", "\n", " // Count each unique word.\n", "\tcounted := stats.Count(root, words)\n", "\n", " // Format the results into a string so we can write them to a file.\n", "\tformatted := beam.ParDo(root, func(word string, count int) string {\n", "\t\treturn fmt.Sprintf(\"%s: %v\", word, count)\n", "\t}, counted)\n", "\n", " // Finally, write the results to a file.\n", "\ttextio.Write(root, output, formatted)\n", "\n", " // We have to explicitly run the pipeline, otherwise it's only a definition.\n", "\tdirect.Execute(context.Background(), pipeline)\n", "}" ], "cell_type": "code", "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Overwriting go/src/wordcount/wordcount.go\n" ] } ], "metadata": { "outputId": "6242a213-effd-4cf9-aaba-caf3ebf72ef2", "colab_type": "code", "id": "x_D7sxUHFzUp", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } } }, { "source": [ "# Build and run the program.\n", "run('rm -rf outputs/')\n", "run('go run go/src/wordcount/.go 2>/dev/null')\n", "\n", "# Sample the first 20 results, remember there are no ordering guarantees.\n", "run('head -n 20 outputs/')" ], "cell_type": "code", "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ ">> rm -rf outputs/\n", "\n", ">> go run go/src/wordcount/.go 2>/dev/null\n", "\n", ">> head -n 20 outputs/\n", "hawthorn: 2\n", "With: 31\n", "vain: 3\n", "football: 1\n", "showest: 1\n", "rarest: 1\n", "Acquaint: 1\n", "Bids: 1\n", "another: 9\n", "tadpole: 1\n", "Oppressed: 1\n", "Revoke: 1\n", "images: 1\n", "lameness: 1\n", "Instantly: 1\n", "rages: 1\n", "Neither: 1\n", "quest: 1\n", "mills: 1\n", "weapons: 1\n", "\n" ] } ], "metadata": { "outputId": "78f66139-06df-4991-f19c-1d13803980bc", "colab_type": "code", "id": "H620PSl46wDK", "colab": { "base_uri": "https://localhost:8080/", "height": 459 } } } ], "metadata": { "kernelspec": { "display_name": "Python 3", "name": "python3" }, "colab": { "include_colab_link": true, "name": "Try Apache Beam - Go", "toc_visible": true, "provenance": [], "collapsed_sections": [], "version": "0.3.2" } }, "nbformat_minor": 0 }	apache-2.0
astroumd/GradMap	notebooks/Lectures2017/Lecture2/Lecture_2_Rough.ipynb	1	35553	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture 2 - Arrays, Logic, And Loops\n", "\n", "This iPython notebook covers some of the most important aspects of the Python language that is used day to day by real Astronomers and Pysicists. Topics will include:\n", "\n", "A review of numpy arrays and a discussion of their usefulness in solving real problems\n", "Reading in data from text and numpy file formats, along with creating your own outputs to be used later\n", "The logic of Python, including while loops and if/else statements\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Numpy Arrays - Review of Basics and Some More Advanced Topics\n", "\n", "Recall in the first lecture that we introduced a python module known as numpy and type of variable known as a numpy array. For review, we will call numpy to be imported into this notebook so we can use it's contents." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we are calling in the contents of numpy and giving it the shorthand name 'np' for convenience.\n", "\n", "To create an array variable (let's call it 'x'), we simply assign 'x' to be equal to the output of the np.array() function, using a list as an input. You can then verify its contents by using the print() function." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = np.array([1,2,3,4,5])\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we learned in Lecture 1, numpy arrays are convenient because they allow us to do math across the whole array and not just individual numbers.\n", "\n", "For example, let's say we want to make a new variable 'y' such that y = $x^{2}$, then this is done simply as" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y = x*2\n", "print(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The documentation of possible functions that can be applied to integers and floats (i.e. single numbers), as well as numpy arrays, can be found here: https://docs.scipy.org/doc/numpy/reference/routines.math.html\n", "\n", "As discussed previously, there are numerous ways to create arrays beyond np.numpy(). These include:\n", " np.arange()\n", " np.linspace()\n", " \n", "These create arrays of numbers within a range with a specific step-size between each consecutive number in the array.\n", "\n", "It is sometimes convenient to have Python create other arrays for you, depending on the problem that you are going to solve. For example, sometimes it is handy to create an array of all zeros, which can then be replaced later with data. This can be done by using np.zeros().\n", "\n", "Say that we have some sort of experiment such that we want to collect 10 different measurments, the result being some number. To ready such an array, you simply do the following." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = np.zeros(10)\n", "print(data)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "After we do our experiment or calculation, we can then assign a new value to an element of the array. In order to do that, we use the syntax\n", " array_name[index_number] = value\n", "In this, the array (with the name \"array_name\" or whatever it is you have named it) will have \"value\" replace whatever is in the position corresponding to \"index_number.\" Arrays are numbered starting from 0, such that\n", "\n", "First position = 0\n", "Second position = 1\n", "Third position = 2\n", "etc.\n", "\n", "It is a bit confusing, but after a bit of time, this becomes quite natural. Here is an example.\n", "\n", "Let's say we have taken our first measurement of this 10 measurement experiment that was discussed above, and the value we got was 5. We then can store that value in the first position (0 index number) in the data array we made above." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data[0] = 5\n", "print(data[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you try it. Let's say that the 2nd measurement comes back with a value of 7. Now store that value in the second position in the array and print it to verify." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Your code goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python array indexing is fairly straight forward, but let's say that you had an array and you wanted the last element of the array. One of the easiest ways to access that, is to use negative indexing.\n", "\n", "Negative indexing is the same as normal indexing, but backward, in the sense that you start with the last element of the array and count forward. More explicitly, for any array:\n", "\n", "array[-1] = last element of array\n", "array[-2] = second to last element of the array\n", "array[-3] = third to last element of the array\n", "etc\n", "\n", "Now then, let's create an array using np.arange() with 10 elements, and see if you can access the last element and the second to last element using negative indexing. Print out these values." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Your code goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, sometimes its useful to access more than one element of an array. Let's say that we have an array with 100 elements starting with a range of 0-10. If you recall, this is done via the np.linspace() function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "x = np.linspace(0,10,100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now then, in order to get a range of elements rather than simply a single one, we use the notation:\n", "\n", "x[i_start,i_end+1]\n", "\n", "For example, let's say you want the 1st, 2nd, and 3rd element, then you'd have to do\n", "\n", "x[0:3]\n", "\n", "In this notation, \":\" represents you want everything between 0 and 3, and including 0. Let's test this." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x[0:3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want everything passed a certain point of the array (including that point), then you would just eliminate the right number, for example\n", "\n", "x[90:]\n", "\n", "would give you everything passed, and including, the 90 index element. Similarly, if you want everything before a certain index.\n", "\n", "x[:90]\n", "\n", "This would give you everything before the 90 index element.\n", "\n", "So, let's say that you would want everything up to the 10-th element of the array x we've defined above (remember, the 10-th element has an index of 9). How would you do that?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Your code goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, simply using the \":\" gives you all the elements in the array." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading And Saving Data Arrays\n", "\n", "So, we have learned all about data arrays and how we can manipulate them, either through mathematics or indexing. However, up until this point, all we've done is use arrays that we ourselves created. But what happens if we have data from elsewhere? Can Python use that?\n", "\n", "The answer is of course yes, and while there are ways to import data that are a bit complicated at times, we're going to teach you some of the most basic, and most useful, ways.\n", "\n", "For this section, we will be using plotting to visualize the data as you're working with it, and as such, we will be loading in the package \"matplotlib.pyplot\" which you used in Lecture 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now then, let's say we are doing a timing experiment, where we look at the brightness of an object as a function of time. This is actually a very common type of measurement that you may do in research, such as looking for dips in the brightness of stars as a way to detect planets.\n", "\n", "Now, then, this data is stored in a text file named \"timeseries_data.txt\" in the directory \"lecture2_data\". Let's load it in." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "timeseriesData = np.loadtxt(\"./lecture2_data/timeseries_data.txt\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have the data loaded into Python as a numpy array, and one handy thing you can do is to use Python to find the dimensions of the array. This is done by using \".shape\" as so." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "timeseriesData.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this format, we know that this is a 2x1000 array (two rows, 1000 columns). Another way you can think about this is that you have two 1000-element arrays contained within another array, where each of those arrays are elements (think of it as an array of arrays).\n", "\n", "The first row is the time stamps when each measurement was taken, while the second row is that of the value of the measurement itself.\n", "\n", "For ease of handling this data, one can in principle take each of these rows and create new arrays out of them. Let's do just that." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "t = timeseriesData[0,:]\n", "signal = timeseriesData[1,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, you have 2 dimensions with the array 'timeseriesData', and as such much specify the row first and then the column. So,\n", "\n", "array_name[n,:] is the n-th row, and all columns within that row.\n", "array_name[:,n] is the n-th column, and all rows within that particular column.\n", "\n", "Now then, let's see what the data looks like using the plot() function that you learned last time. Do you remember how to do it? Why don't you try! Plot t as your x-axis and signal as your y-axis." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#Your code here\n", "plt.plot(t,signal)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at our data, you see clear spikes that jump well above most of the signal. I've added this to the data to represent outliers that may sometimes appear when your messing with raw data, and those must be dealt with. In astronomy, you sometimes have relativistic charged particles, not from your source, that hit the detector known as cosmic rays, and we often times have to remove these.\n", "\n", "There are some very complex codes that handle cosmic rays, but for our purposes (keeping it easy), we're going to just set a hard cut off of, let's say 15.\n", "\n", "In order to do this, we can use the np.where() function in place of an normal indices." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cutOff = 15.\n", "tFix = t[np.where(signal<cutOff)]\n", "signalFix = signal[np.where(signal<cutOff)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "np.where() is an incredibly useful function that takes a logical statment (in this case \"signal < cutOff\") and searches the array for indices that correspond to places where this statement is true (we will be doing more with logic soon). So, in this case, it would keep the data and the corresponding time stamps that we have deemed \"good\" by this criteria.\n", "\n", "Now let's plot it. You try." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#Your code goes here\n", "plt.plot(tFix,signalFix)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that you have your data all cleaned up, it would be nice if we could save it for later and not have to go through the process of cleaning it up every time. Fear not! Python has you covered.\n", "\n", "There are two formats that we are going to cover, one that is Python-specific, and the other a simple text format.\n", "\n", "First, we must package our two cleaned up arrays into one again. This can be done simply with the np.array() function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataFix = np.array([tFix,signalFix])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we can use either the np.save() function or the np.savetxt function, the first saving the array into a '.npy' file and the other, into a '.txt' file. The syntax is pretty much the same for each." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.save('./lecture2_data/dataFix.npy',dataFix)\n", "np.savetxt('./lecture2_data/dataFix.txt',dataFix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that your data files are saved, you can load them up again, using np.loadtxt() and np.load() for .txt and .npy files respectively. We used np.loadtxt() above, and np.load works the same way. So, let's load in the .npy file and see if our data was saved correctly." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = np.load('./lecture2_data/dataFix.npy')\n", "t = data[0,:]\n", "signal = data[1,:]\n", "plt.plot(t,signal)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's see if you can do the same thing, but with the .txt file that we saved." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Your code goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, to summarize, not only can you manipulate arrays, but now you can save them and load them. In a way, those are some of the most important skills in scientific computing. Almost everything you'll be doing requires you know this, and now that you've mastered it, you're well on your way to being an expert in computational physics and astronomy!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Logic, If/Else, and Loops\n", "\n", "Briefly mentioned in the last section, you can make conditional (logical) in Python, which return either \"True\" or \"False\", also known as \"Booleans.\"\n", "\n", "A basic logic statement is something that we've used already: x < y. Here is this one again, and a few more.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Example conditional statements\n", "x = 1\n", "y = 2\n", "x<y #x is less than y" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#x is greater than y\n", "x>y" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#x is less-than or equal to y\n", "x<=y" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#x is greater-than or equal to y\n", "x>=y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you let a and b be conditional statements (like the above statements, e.g. a = x < y), then you can combine the two together using logical operators, which can be thought of as functions for conditional statements.\n", "\n", "There are three logical operators that are handy to know:\n", "\n", "And operator: a and b\n", "Or operator: a or\n", "Not operator: not(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Example of and operator\n", "(1<2)and(2<3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Example of or operator\n", "(1<2)or(2>3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Example of not operator\n", "not(1>2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, these might not seem especially useful at first, but we've already seen their importance in the np.where() function. Even more importantly, they are used when we are doing if/else statements or loops, which we will now cover.\n", "\n", "An if/else statement (or simply an if statement) are segments of code that have a conditional statement built into it, such that the code within that segment doesn't activate unless the conditional statement is true.\n", "\n", "Here's an example. Play around with the variables x and y to see what happens." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = 1\n", "y = 2\n", "if (x < y):\n", " print(\"Yup, totally true!\")\n", "else:\n", " print(\"Nope, completely wrong!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The idea here is that Python checks to see if the statement (in this case \"x < y\") is True. If it is, then it will do what is below the if statement. The else statement tells Python what to do if the condition is False.\n", "\n", "Note that Python requires you to indent these segments of code, and WILL NOT like it if you don't. Some languages don't require it, but Python is very particular when it comes to this point.\n", "\n", "You also do not need an \"else\" segment, which effectively means that if the condition isn't True, then that segment of code doesn't do anything, and Python will just continue on passed the if statement.\n", "\n", "Here is an example of such a case. Play around with it to see what happens when you change the values of x and y." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = 1\n", "y = 2\n", "if (x>y):\n", " print(\"The condition is True!\")\n", "x+y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While-loops are similar to if statements, in the sense that they also have a conditional statement that is built into it and it executes when the conditional is True. However, the only difference is, it will KEEP executing that segment of code until the conditional statement becomes False.\n", "\n", "This might seem a bit strange, but you can get the hang of it!\n", "\n", "For example, let's say we want Python to count from 1 to 10." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = 1\n", "while (x <= 10):\n", " print(x)\n", " x = x+1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note here that we tell Python to print the number x (x starts at 1) and then redefining x as itself +1 (so, x=1 gets redefined to x = x+1 = 1+1 = 2). Python then executes the loop again, but now x has been incremented by 1. We continue this process from x = 1 to x = 10, printing out x every time. Thus, with a fairly compact bit of code, you get 10 lines of output.\n", "\n", "It is sometimes handy to define what is known as a DUMMY VARIABLE, whose only job is to count the number of times the loop has been executed. Let's call this dummy variable i." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = 2\n", "i = 0\n", "while (i<10):\n", " x = 2x\n", " print(x)\n", " i = i+1 #another way to write this is i+=1, but it's understandably odd and thus isn't used here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Defining Your Own Functions\n", "\n", "So far, we have really focused on using built in functions (such as from numpy), but what about defining our own? This is easy to do, and can be a way to not only clean up your code, but also allows you to apply the same set of operations to multiple variables without having to explicitely write it out every time.\n", "\n", "For example, let's say we want to define a line function y = mx + b that allows us to calculate the value of y, given x, for any m and b." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Defining a linear function y = mx+b\n", "def line(x, m, b):\n", " return (mx)+b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this, you are defining a function (called \"line\") that has three variables (x,m,b). You then do something with (x,m,b) (in this case mx + b), and then returning that value to the user.\n", "\n", "Why don't you play around with our new function, giving it some numbers and seeing how it works." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Your code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also use arrays as variables. For example, let's use our line, but instead let's put an numpy array in for x." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = np.array([1,2,3])\n", "line(x,2,3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we know how to define a function, let's try something a bit harder:\n", "\n", "We're going to make a set of lines, and plot them. First, let's create a set of arrays with the values of the y-intercepts, and to make it easy, let's assume that our slope for all of these lines is 2 (i.e. m = 2). Then we're going to make an array representing the sample of x-values for the family of lines." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m = 2. #defining a constant slope (same for all lines to be plotted)\n", "bArray = np.array([0.,2.,4.,6.]) #Defining the y-intercept array\n", "x = np.linspace(0.,10.,100) #Defining x sample array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we use our line function, and then use a while-loop to plot a line with a different intercept for each iteration." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "i = 0\n", "endWhile = len(bArray) #Finding how long bArray is\n", "while(i < endWhile):\n", " b = bArray[i]\n", " plt.plot(x,line(x,m,b))\n", " i = i+1\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can play around with the y-intercept array (bArray) to get a different family of lines.\n", "\n", "Now then, can you do it yourself? Let's define a function $y = x^{a} + b$, and using the line example that we just did, can you plot a family of parabolas (a=2, same y-intercepts as before) within the range of -10 to 10 (say, 100 sample points). Good luck!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Your code goes here\n", "#Answer:\n", "def funct(x,a,b):\n", " return (x*2.)+b\n", "a=2.\n", "bArray = np.array([0.,2.,4.,6.])\n", "x = np.linspace(-10.,10.,100)\n", "\n", "i=0\n", "endWhile = len(bArray)\n", "while(i < endWhile):\n", " b = bArray[i]\n", " plt.plot(x,funct(x,a,b))\n", " i = i+1\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Challenge Problems\n", "\n", "Here are two challenge problems to help you learn the concepts in this lecture. One is a mathematical problem where you will calculate what is known as the Fibonacci Sequence, and the second one, you will be building what is known as a NUMERICAL INTEGRATOR in order to predict the projectiles trajectory through a gravitational field (i.e. what happens when you throw a ball through the air)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Challenge 1 - Fibonacci Sequence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Challenge 2 - Projectile Motion\n", "\n", "Let's say that you have a projectile (let's say a ball) in a world with 2 spatial dimensions (dimensions x and y). This world has a constant acceleration due to gravity (call it simply g) that points in the -y direction and has a surface at y = 0.\n", "\n", "Can we calculate the motion of the projectile in the x-y plane after the projectile is given some initial velocity vector v? In particular, can we predict where the ball will land? With loops, yes we can!\n", "\n", "Let's first define all of the relevant variables so far. Let g = -9.8 (units of m/s, so an Earth-like world), the initial velocity vector being an array v = [3.,3.], and an initial position vector (call it r) in the x-y plane of r = [0.,1.]. For ease, let's use numpy arrays for the vectors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Your code here\n", "#Answers\n", "g = -9.8\n", "v = np.array([3.,3.])\n", "r = np.array([0.,0.])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now then, remember that,\n", "\n", "$a = \\frac{dv}{dt}$ and thus, $dv = a\\ dt$\n", "\n", "So, the change of an objects velocity ($dv$) is equal to the acceleration ($a = g$ in this case) multiplied by the change in time ($dt$)\n", "\n", "Likewise:\n", "\n", "$v_{x} = \\frac{dx}{dt}$ and $v_{y} = \\frac{dy}{dt}$, or\n", "\n", "$v_{x}\\ dt = dx$ and $v_{y}\\ dt = dy$\n", "\n", "Now, in this case, since there is only downward acceleration, the change of $v_{x}$ is 0 until the projective hits the ground.\n", "\n", "Now, we're going to define two functions, one that will calculate the velocity vector components and the other, the position vector components, and returning a new vector with the new components. I'll give you the first one." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def intV(v,g,dt):\n", " deltaVy = gdt\n", " vXnew = v[0]\n", " vYnew = v[1]+deltaVy\n", " return np.array([vXnew,vYnew])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've defined intV (short for \"integrate v\"), let's use it real quick, just to test it out. Let dt = 0.1 (meaning, your taking a step forward in time by 0.1 seconds)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dt = 0.1\n", "intV(v,g,dt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, $V_{x}$ hasn't changed, but $V_{y}$ has decreased, representing the projectile slowing down as it's going upward. \n", "\n", "I'll let you define the function now for the position vector. Call it intR, and it should be a function of (r,v,dt), and remember that now both $r_{x}$ and $r_{y}$ are changing. Remember to return an array." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Your code here.\n", "#Answer\n", "def intR(r,v,dt):\n", " rXnew = r[0]+(v[0]dt)\n", " rYnew = r[1]+(v[1]dt)\n", " return np.array([rXnew,rYnew])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have the functions that calculate the changes in the position and velocity vectors. We're almost there!\n", "\n", "Now, we will need a while-loop in order to step the projectile through its trajectory. What would the condition be?\n", "\n", "Well, we know that the projectile stops when it hits the ground. So, one way we can do this is to have the condition being (r[1] >= 0.) since the ground is defined at y = 0.\n", "\n", "So, having your intV and intR functions, along with a while-loop and a dt = 0.1 (known as the \"step-size\"), can you use Python to predict where the projectile will land?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Your code here.\n", "#Answer\n", "dt = 0.01\n", "while (r[1] >= 0.):\n", " v = intV(v,g,dt)\n", " r = intR(r,v,dt)\n", "print(r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, note that we've defined the while-loop such that it doesn't stop exactly at 0. Firstly, this was strategic, since the initial y = 0, and thus the while-loop wouldn't initialize to begin with (you can try to change it). One way you can overcome this issue is to decrease dt, meaning that you're letting less time pass between each step. Ideally, you'd want dt to be infinitely small, but we don't have that convenience in reality. Re-run the cells, but with dt = 0.01 and we will get much closer to the correct answer.\n", "\n", "So, we know where it's going to land...can we plot the full trajectory? Yes, but this is a bit complicated, and requires one last function: np.append(). https://docs.scipy.org/doc/numpy/reference/generated/numpy.append.html\n", "\n", "The idea is to use np.append() to make an array that keeps track of where the ball has been.\n", "\n", "Let's define two empty arrays that will store our information (x and y). This is an odd idea, defining an array variable without any elements, so instead think of it as a basket without anything inside of it yet, and we will np.append() to fill it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = np.array([]) #defining an empty array that will store x position\n", "y = np.array([]) #defining an empty array that will store y position" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, all you have to do, is each time the while-loop executes, you use np.append() for the x and y arrays, adding the new values to the end of them.\n", "\n", "How do you do that? Well, looking at the np.append() documentation, for x, you do\n", "x = np.append(x,[r[0]])\n", "\n", "The same syntax is used for the y array.\n", "\n", "After that, you simply use plt.plot(x,y,'o') to plot the trajectory of the ball (the last 'o' is used to change the plotting from a line to points).\n", "\n", "Good luck! Also, don't forget to reset your v and r arrays (otherwise, this will not work)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Your code goes here\n", "\n", "#Answer\n", "v = np.array([3.,3.])\n", "r = np.array([0.,0.])\n", "\n", "dt = 0.01\n", "while (r[1] >= 0.):\n", " v = intV(v,g,dt)\n", " r = intR(r,v,dt)\n", " x = np.append(x,r[0])\n", " y = np.append(y,r[1])\n", "print(r)\n", "\n", "plt.plot(x,y,'o')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If everything turns out alright, you should get the characteristic parabola.\n", "\n", "Also, if you're going to experiment with changing the intial position and velocity, remember to re-run the cell where we define the x and y arrays in order to clear the plot.\n", "\n", "Now you've learned how to do numerical integration. This technique is used all throughout Physics and Astronomy, and while there are more advanced ways to do it in order to increase accuracy, the heart of the idea is the same.\n", "\n", "Here is a figure made by Corbin Taylor (head of the Python team) that used numerical integration to track the position of a ray of light as it falls into a spinning black hole." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"./raytrace_picture.jpg\">" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
magic-lantern/NLP_Project	parallel_tests.ipynb	1	1118	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Hello, World', 'Hello, World', 'Hello, World', 'Hello, World']" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import ipyparallel as ipp\n", "c = ipp.Client()\n", "c.ids\n", "c[:].apply_sync(lambda : \"Hello, World\")\n", "dview = c[:] # use all engines\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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
sinhrks/cesiumpy	examples/04_terrainproviders.ipynb	1	4848	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import cesiumpy" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "options = dict(animation=True, baseLayerPicker=False, fullscreenButton=False,\n", " geocoder=False, homeButton=False, infoBox=False, sceneModePicker=True,\n", " selectionIndicator=False, navigationHelpButton=False,\n", " timeline=False, navigationInstructionsInitiallyVisible=False)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "CesiumTerrainProvider(url=\"//assets.agi.com/stk-terrain/world\")" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "url = '//assets.agi.com/stk-terrain/world'\n", "terrainProvider = cesiumpy.CesiumTerrainProvider(url=url)\n", "terrainProvider" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script src=\"https://cesiumjs.org/Cesium/Build/Cesium/Cesium.js\"></script>\n", "<link rel=\"stylesheet\" href=\"http://cesiumjs.org/Cesium/Build/CesiumUnminified/Widgets/widgets.css\" type=\"text/css\">\n", "<div id=\"container-4345015696\" style=\"width:100%; height:100%;\"><div>\n", "<script type=\"text/javascript\">\n", " var widget = new Cesium.Viewer(\"container-4345015696\", {animation : true, baseLayerPicker : false, fullscreenButton : false, geocoder : false, homeButton : false, infoBox : false, sceneModePicker : true, selectionIndicator : false, timeline : false, navigationHelpButton : false, navigationInstructionsInitiallyVisible : false, terrainProvider : new Cesium.CesiumTerrainProvider({url : \"//assets.agi.com/stk-terrain/world\"})});\n", "</script>" ], "text/plain": [ "<cesiumpy.viewer.Viewer at 0x102fbad90>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v = cesiumpy.Viewer(terrainProvider=terrainProvider, options)\n", "v" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "CesiumTerrainProvider(url=\"//assets.agi.com/stk-terrain/world\")" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "terrainProvider = cesiumpy.CesiumTerrainProvider(url=url, requestWaterMask=True)\n", "terrainProvider" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script src=\"https://cesiumjs.org/Cesium/Build/Cesium/Cesium.js\"></script>\n", "<link rel=\"stylesheet\" href=\"http://cesiumjs.org/Cesium/Build/CesiumUnminified/Widgets/widgets.css\" type=\"text/css\">\n", "<div id=\"container-4343147408\" style=\"width:100%; height:100%;\"><div>\n", "<script type=\"text/javascript\">\n", " var widget = new Cesium.Viewer(\"container-4343147408\", {animation : true, baseLayerPicker : false, fullscreenButton : false, geocoder : false, homeButton : false, infoBox : false, sceneModePicker : true, selectionIndicator : false, timeline : false, navigationHelpButton : false, navigationInstructionsInitiallyVisible : false, terrainProvider : new Cesium.CesiumTerrainProvider({url : \"//assets.agi.com/stk-terrain/world\", requestWaterMask : true})});\n", "</script>" ], "text/plain": [ "<cesiumpy.viewer.Viewer at 0x102df2b90>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v = cesiumpy.Viewer(terrainProvider=terrainProvider, options)\n", "v" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
michaelaye/planetpy	notebooks/downloading_index_files.ipynb	1	36789	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Set database path" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from planetarypy import io" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved database path [data_archive]\n", "path = \"/Users/klay6683/Dropbox/data/planetarypy/\"\n", " into /Users/klay6683/.planetarypy.toml.\n" ] } ], "source": [ "io.set_database_path(\"/Users/klay6683/Dropbox/data/planetarypy/\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from planetarypy.pdstools import indices" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cassini:\n", " iss:\n", " index: https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_index.lbl\n", " inventory: https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_inventory.lbl\n", " moon_summary: https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_moon_summary.lbl\n", " ring_summary: https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_ring_summary.lbl\n", " saturn_summary: https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_saturn_summary.lbl\n", " uvis:\n", " index: https://pds-rings.seti.org/holdings/metadata/COUVIS_0xxx/COUVIS_0999/COUVIS_0999_index.lbl\n", " moon_summary: https://pds-rings.seti.org/holdings/metadata/COUVIS_0xxx/COUVIS_0999/COUVIS_0999_moon_summary.lbl\n", " ring_summary: https://pds-rings.seti.org/holdings/metadata/COUVIS_0xxx/COUVIS_0999/COUVIS_0999_ring_summary.lbl\n", " saturn_summary: https://pds-rings.seti.org/holdings/metadata/COUVIS_0xxx/COUVIS_0999/COUVIS_0999_saturn_summary.lbl\n", " supplemental_index: https://pds-rings.seti.org/holdings/metadata/COUVIS_0xxx/COUVIS_0999/COUVIS_0999_supplemental_index.lbl\n", "mro:\n", " hirise:\n", " dtm: https://hirise-pds.lpl.arizona.edu/PDS/INDEX/DTMCUMINDEX.LBL\n", " edr: https://hirise-pds.lpl.arizona.edu/PDS/INDEX/EDRCUMINDEX.LBL\n", " rdr: https://hirise-pds.lpl.arizona.edu/PDS/INDEX/RDRCUMINDEX.LBL\n", "\n", "Use indices.download('mission:instrument:index') to download in index file.\n", "For example: indices.download('cassini:uvis:moon_summary'\n" ] } ], "source": [ "indices.list_available_index_files()" ] }, { "cell_type": "markdown", "metadata": { "toc-hr-collapsed": false }, "source": [ "# UVIS metadata" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2018-07-06T21:29:21.862372Z", "start_time": "2018-07-06T21:29:21.101771Z" } }, "outputs": [], "source": [ "from planetarypy.pdstools import indices\n", "from planetarypy.utils import download\n", "from tqdm import tqdm\n", "\n", "class ProgressBar(tqdm):\n", " \"\"\"Provides `update_to(n)` which uses `tqdm.update(delta_n)`.\"\"\"\n", " def update_to(self, b=1, bsize=1, tsize=None):\n", " \"\"\"\n", " b : int, optional\n", " Number of blocks transferred so far [default: 1].\n", " bsize : int, optional\n", " Size of each block (in tqdm units) [default: 1].\n", " tsize : int, optional\n", " Total size (in tqdm units). If [default: None] remains unchanged.\n", " \"\"\"\n", " if tsize is not None:\n", " self.total = tsize\n", " self.update(b * bsize - self.n) # will also set self.n = b * bsize\n", "\n", "META_URL = 'http://pds-rings.seti.org/metadata'\n", "\n", "\n", "class META:\n", " INDICES = {'index': 'Cumulative product index of volume series',\n", " 'inventory': 'Cumulative list of observed bodies by product',\n", " 'moon_summary': 'Cumulative list of observed geometry on moons',\n", " 'ring_summary': 'Cumulative list of observed geometry on rings',\n", " 'saturn_summary': 'Cumulative list of observed geometry on Saturn'\n", " }\n", "\n", " def __init__(self, name=''):\n", " if name == '':\n", " print(\"Call me with one of the following index names:\")\n", " for k,v in self.INDICES.items():\n", " print(k,\": \", v)\n", " raise ValueError(\"Provide index name.\")\n", " else:\n", " self._name = name\n", " \n", " @property\n", " def name(self):\n", " return self._name\n", " \n", " @property\n", " def folder_url(self):\n", " return META_URL + f'/CO{self.id}xxx/CO{self.id}999/'\n", " \n", " @property\n", " def meta_filename(self):\n", " return f'CO{self.id}999_{self.name}'\n", " \n", " @property\n", " def label_url(self):\n", " return self.folder_url + self.meta_filename + '.lbl'\n", " \n", " @property\n", " def table_url(self):\n", " return self.folder_url + self.meta_filename + '.tab'\n", " \n", " def download_table(self, local_folder='.'):\n", " baseurl = self.folder_url + self.meta_filename\n", " for ext in ['.lbl', '.tab']:\n", " filename = self.meta_filename + ext\n", " url = self.folder_url + filename\n", " local_path = f\"{local_folder}/{filename}\"\n", " print(\"Downloading\", local_path)\n", " with ProgressBar(unit='B', unit_scale=True, miniters=1, desc=url) as t:\n", " download(url, local_path, reporthook=t.update_to, data=None)\n", " \n", " @property\n", " def label(self):\n", " return indices.IndexLabel(self.meta_filename + '.lbl')\n", "\n", " def read_table(self, kwargs):\n", " return self.label.read_index_data(kwargs)\n", "\n", " \n", "class UVIS_META(META):\n", " id = 'UVIS_0'\n", " INDICES = {'index': 'Cumulative product index of volume series',\n", " 'supplemental_index': 'Cumulative product index of volume series',\n", " 'moon_summary': 'Cumulative list of observed geometry on moons',\n", " 'ring_summary': 'Cumulative list of observed geometry on rings',\n", " 'saturn_summary': 'Cumulative list of observed geometry on Saturn'\n", " }\n", " \n", "class ISS_META(META):\n", " id = 'ISS_2'\n", "\n", "class VIMS_META(META):\n", " id = 'VIMS_0'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-06T21:29:31.296277Z", "start_time": "2018-07-06T21:29:31.263117Z" } }, "outputs": [], "source": [ "meta = UVIS_META('index')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-06T21:31:09.068886Z", "start_time": "2018-07-06T21:31:09.035647Z" } }, "outputs": [], "source": [ "meta.label_url" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-06T21:31:21.490287Z", "start_time": "2018-07-06T21:31:21.453812Z" } }, "outputs": [], "source": [ "meta.label_url" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-06T21:30:54.235063Z", "start_time": "2018-07-06T21:30:54.182177Z" }, "scrolled": false }, "outputs": [], "source": [ "lbl = meta.label" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-02-06T01:32:45.522034Z", "start_time": "2018-02-06T01:32:45.504717Z" } }, "outputs": [], "source": [ "lbl.index_path" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-02-06T03:01:27.218113Z", "start_time": "2018-02-06T03:01:27.201960Z" } }, "outputs": [], "source": [ "meta = ISS_META('ring_summary')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-02-06T03:20:39.194805Z", "start_time": "2018-02-06T03:01:32.961333Z" }, "scrolled": false }, "outputs": [], "source": [ "meta.download_table()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-06T21:30:33.140122Z", "start_time": "2018-07-06T21:30:33.030329Z" } }, "outputs": [], "source": [ "meta.read_table??" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-06T21:29:54.025601Z", "start_time": "2018-07-06T21:29:53.742235Z" } }, "outputs": [], "source": [ "index = meta.read_table(convert_times=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "index.to_hdf(\"coiss_index.hdf\", 'df')" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "from planetarypy.pdstools import indices" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cassini:\n", " iss:\n", " index: https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_index.lbl\n", " inventory: https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_inventory.lbl\n", " moon_summary: https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_moon_summary.lbl\n", " ring_summary: https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_ring_summary.lbl\n", " saturn_summary: https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_saturn_summary.lbl\n", " uvis:\n", " index: https://pds-rings.seti.org/holdings/metadata/COUVIS_0xxx/COUVIS_0999/COUVIS_0999_index.lbl\n", " moon_summary: https://pds-rings.seti.org/holdings/metadata/COUVIS_0xxx/COUVIS_0999/COUVIS_0999_moon_summary.lbl\n", "mro:\n", " hirise:\n", " dtm: https://hirise-pds.lpl.arizona.edu/PDS/INDEX/DTMCUMINDEX.LBL\n", " edr: https://hirise-pds.lpl.arizona.edu/PDS/INDEX/EDRCUMINDEX.LBL\n", " rdr: https://hirise-pds.lpl.arizona.edu/PDS/INDEX/RDRCUMINDEX.LBL\n", "\n", "Use indices.download('mission:instrument:index') to download in index file.\n", "For example: indices.download('cassini:uvis:moon_summary'\n" ] } ], "source": [ "indices.list_available_index_files()" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "from pyciss import index" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-12-10 01:14:31,587 - planetarypy.pdstools.indices - Downloading https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_ring_summary.lbl.\n", "2018-12-10 01:14:31,588 - planetarypy.utils - Downloading https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_ring_summary.lbl into /Users/klay6683/Dropbox/data/ciss/db\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "06dabab44d284ebd8d49baad883d3971", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='COISS_2999_ring_summary.lbl', max=1, style=…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "2018-12-10 01:14:32,355 - planetarypy.pdstools.indices - Downloading https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_ring_summary.tab.\n", "2018-12-10 01:14:32,356 - planetarypy.utils - Downloading https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_ring_summary.tab into /Users/klay6683/Dropbox/data/ciss/db\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1ce84c7ec1674aac839a71ffe70bf4c0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='COISS_2999_ring_summary.tab', max=1, style=…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Done.\n", "Downloaded and converted to pandas HDF: /Users/klay6683/Dropbox/data/ciss/db/COISS_2999_ring_summary.hdf\n" ] } ], "source": [ "index.download_ring_summary_index()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "from nbtools.logging import setup_live_logging" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<Logger planetarypy (DEBUG)>" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "setup_live_logging('planetarypy', 'DEBUG')" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "from planetarypy.utils import download" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_index.lbl'" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "url" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "p = Path('./downloads')\n", "p.mkdir()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-12-09 23:36:00,857 - planetarypy.utils - Downloading https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_index.lbl into downloads\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b673c67ebedc4327b9d35ba075a78abb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='COISS_2999_index.lbl', max=1, style=Progres…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "ret = download(url, p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "indices.download('cassini:iss:index')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('python.png', <http.client.HTTPMessage at 0x11180a0b8>)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "url = 'https://pds-rings.seti.org/holdings/metadata/COISS_2xxx/COISS_2999/COISS_2999_index.lbl'\n", " \n", "# downloading with urllib\n", " \n", "# imported the urllib library\n", "import urllib\n", " \n", "# Copy a network object to a local file\n", "urllib.request.urlretrieve(url, \"python.png\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "label = indices.IndexLabel(\"/Users/klay6683/Dropbox/data/planetarypy/EDRCUMINDEX.LBL\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Converting times for column OBSERVATION_START_TIME.\n", "Converting times for column START_TIME.\n", "Converting times for column STOP_TIME.\n", "Done.\n" ] } ], "source": [ "df = label.read_index_data()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/klay6683/miniconda3/envs/py37/lib/python3.7/site-packages/pandas/core/generic.py:1996: PerformanceWarning: \n", "your performance may suffer as PyTables will pickle object types that it cannot\n", "map directly to c-types [inferred_type->mixed,key->block3_values] [items->['VOLUME_ID', 'FILE_NAME_SPECIFICATION', 'INSTRUMENT_HOST_ID', 'INSTRUMENT_ID', 'OBSERVATION_ID', 'PRODUCT_ID', 'HICAL_VERSION', 'TARGET_NAME', 'MISSION_PHASE_NAME', 'RATIONALE_DESC', 'OBSERVATION_START_COUNT', 'SPACECRAFT_CLOCK_START_COUNT', 'SPACECRAFT_CLOCK_STOP_COUNT', 'CCD_NAME', 'FILTER_NAME', 'FELICS_COMPRESSION_FLAG', 'STIMULATION_LAMP_FLAG_RED', 'STIMULATION_LAMP_FLAG_BLUEGREEN', 'STIMULATION_LAMP_FLAG_NEARINFRARED', 'LOOKUP_TABLE_TYPE', 'STEREO_FLAG']]\n", "\n", " return pytables.to_hdf(path_or_buf, key, self, **kwargs)\n" ] } ], "source": [ "df.to_hdf(\"/Users/klay6683/local_data/EDRCUMINDEX.hdf\", 'df')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-10T15:28:19.193267Z", "start_time": "2018-07-10T15:28:18.993763Z" } }, "outputs": [], "source": [ "lab = indices.IndexLabel(\"/Volumes/USB128II/uvis/COUVIS_0999_index.lbl\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-07T03:51:18.024996Z", "start_time": "2018-07-07T03:50:59.708698Z" } }, "outputs": [], "source": [ "df = lab.read_index_data(convert_times=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-07T03:51:20.253547Z", "start_time": "2018-07-07T03:51:20.216484Z" } }, "outputs": [], "source": [ "time_cols = [col for col in df.columns if 'TIME' in col]\n", "time_cols" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-10T15:28:45.797557Z", "start_time": "2018-07-10T15:28:43.581315Z" } }, "outputs": [], "source": [ "df = pd.read_hdf(\"/Volumes/USB128II/uvis/COUVIS_0999_index.hdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-10T15:28:52.481818Z", "start_time": "2018-07-10T15:28:52.442976Z" } }, "outputs": [], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-10T15:27:58.880614Z", "start_time": "2018-07-10T15:27:48.331977Z" } }, "outputs": [], "source": [ "df.to_hdf(\"/Volumes/USB128II/uvis/COUVIS_0999_supplemental_index.hdf\", 'df')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-10T15:27:13.768223Z", "start_time": "2018-07-10T15:27:13.578618Z" } }, "outputs": [], "source": [ "df.START_TIME.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-06T21:34:44.019957Z", "start_time": "2018-07-06T21:34:43.973697Z" } }, "outputs": [], "source": [ "from planetarypy.utils import nasa_datetime_to_iso" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-06T21:35:48.811068Z", "start_time": "2018-07-06T21:35:48.765452Z" } }, "outputs": [], "source": [ "nasa_datetime_to_iso('1999-01-07T16:53:07.953')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-06T21:38:24.077964Z", "start_time": "2018-07-06T21:38:20.701015Z" } }, "outputs": [], "source": [ "df.to_hdf(\"/Volumes/USB128II/uvis/COUVIS_0999_index.hdf\", 'df')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-06T21:39:53.051580Z", "start_time": "2018-07-06T21:39:53.000824Z" } }, "outputs": [], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-07T03:38:26.421969Z", "start_time": "2018-07-07T03:38:26.378591Z" } }, "outputs": [], "source": [ "df.H_LEVEL.value_counts" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-06T21:40:05.080935Z", "start_time": "2018-07-06T21:40:04.996352Z" } }, "outputs": [], "source": [ "df.TARGET_NAME.value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-07T02:37:14.531850Z", "start_time": "2018-07-07T02:37:13.690895Z" } }, "outputs": [], "source": [ "df.OBSERVATION_TYPE.value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-07-07T02:40:16.802410Z", "start_time": "2018-07-07T02:40:16.646836Z" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = df.assign(fname=df.FILE_SPECIFICATION_NAME.map(lambda x: x.split('/')[-1][:-4]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.fname.is_unique" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "index = pd.read_hdf(\"/Volumes/Research/pyciss/coiss_index.hdf\")\n", "index= index.assign(fname=index.FILE_SPECIFICATION_NAME.map(lambda x: x.split('/')[-1][:-4]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rings = index[index.TARGET_DESC.str.contains('Saturn-Rings')]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset = df.merge(rings, on='fname')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset[dataset.COARSEST_RADIAL_RESOLUTION<0] = np.nan" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset[dataset.FINEST_RADIAL_RESOLUTION < 0] = np.nan" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset.COARSEST_RADIAL_RESOLUTION.hist(range=(0,1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset.FINEST_RADIAL_RESOLUTION.hist(range=(0,1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset.query(\"COARSEST_RADIAL_RESOLUTION<0.4\").IMAGE_TIME.str[:4].value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "dataset.query(\"'N1467345090' in fname\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "index[index.fname.str.contains('N1467345090')].TARGET_NAME" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset.dropna(how='all', inplace=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset[dataset.fname.str.contains('N1467345090')]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img_list = dataset.query(\"COARSEST_RADIAL_RESOLUTION<0.01\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img_list.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img_list.TARGET_DESC.value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ringdata = index[index.TARGET_DESC.str.contains('ring', case=False)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ringdata.TARGET_DESC.value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pyciss import downloader" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "downloader.download_file_id(img_list.fname.iloc[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tqdm import tqdm" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "index['fname'] = index.FILE_SPECIFICATION_NAME.map(lambda x: x.split('/')[-1][:-4])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "index['fname'].head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset = img_list.merge(index, on='fname')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset = pd.read_hdf('dataset_metadata.hdf', 'df')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset.filter(regex='TIME').columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pyciss.pipeline import Calibrator\n", "from pyciss import io" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pysis.exceptions import ProcessError" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def calib_image(fname):\n", " pm = io.PathManager(fname)\n", " if pm.cubepath.exists():\n", " return\n", " calib = Calibrator(fname, final_resolution=100)\n", " try:\n", " calib.standard_calib()\n", " except ProcessError as e:\n", " print(e.stderr)\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "dataset.fname.map(calib_image)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from planetarypy.utils import nasa_datetime_to_iso" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset['time'] = pd.to_datetime(dataset.IMAGE_TIME.map(nasa_datetime_to_iso))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset.time" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "newer = dataset[pd.DatetimeIndex(dataset.time).year >2014].fname" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pyciss.ringcube import RingCube" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset.TARGET_NAME.value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "imp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "calib_image(newer.iloc[6])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cube = RingCube(newer.iloc[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cube.imshow()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# CTX" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [], "source": [ "from string import Template\n", "from pathlib import Path\n", "\n", "class CTXIndex:\n", " volumes_url = \"https://pds-imaging.jpl.nasa.gov/volumes/mro.html\"\n", " release_url_template = \\\n", " Template(\"https://pds-imaging.jpl.nasa.gov/volumes/mro/release${release}.html\")\n", " volume_url_template = \\\n", " Template(\"https://pds-imaging.jpl.nasa.gov/data/mro/mars_reconnaissance_orbiter/ctx/mrox_${volume}/\")\n", " @property\n", " def web_tables_list(self):\n", " print(\"Scraping volumes page ...\")\n", " return pd.read_html(self.volumes_url)\n", " \n", " @property\n", " def release_number(self):\n", " l = self.web_tables_list\n", " # The last item of last table looks like \"Release XX\"\n", " return l[-1].iloc[-1, 0].split()[-1]\n", " \n", " @property\n", " def release_url(self):\n", " return self.release_url_template.substitute(release=self.release_number)\n", " \n", " @property\n", " def latest_volume_url(self):\n", " print(\"Scraping latest release page ...\")\n", " l = pd.read_html(self.release_url)\n", " # get last row of 4th table\n", " row = l[3].iloc[-1]\n", " number = None\n", " # first number that is NAN breaks the loop over last row of table\n", " for elem in row.values:\n", " try:\n", " number = int(elem.split()[-1])\n", " except AttributeError:\n", " break\n", " return self.volume_url_template.substitute(volume=number)\n", " \n", " @property\n", " def latest_index_label_url(self):\n", " return f\"{self.latest_volume_url}index/cumindex.lbl\"\n", " " ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [], "source": [ "index = CTXIndex()" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scraping latest release page ...\n", "Scraping volumes page ...\n" ] }, { "data": { "text/plain": [ "'https://pds-imaging.jpl.nasa.gov/data/mro/mars_reconnaissance_orbiter/ctx/mrox_3267/index/cumindex.lbl'" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index.latest_index_label_url" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [], "source": [ "from planetarypy.pdstools import indices" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scraping latest release page ...\n", "Scraping volumes page ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a7c8008db0f24702b3e4661a9c1bba02", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='cumindex.lbl', max=1, style=ProgressStyle(d…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1eb53db6a8a045f3a92051b0fdd90217", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=1, bar_style='info', description='cumindex.tab', max=1, style=ProgressStyle(d…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Converting times for column IMAGE_TIME.\n", "Done.\n", "Downloaded and converted to pandas HDF: /Users/klay6683/Dropbox/data/ctx/cumindex.hdf\n" ] } ], "source": [ "indices.download(label_url=index.latest_index_label_url, \n", " local_dir=Path(\"~/Dropbox/data/ctx\").expanduser())" ] }, { "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" }, "nbpresent": { "slides": {}, "themes": { "default": "1142fcc4-f546-4424-a178-5da319831e1c", "theme": {} } }, "toc": { "base_numbering": 1, "nav_menu": { "height": "12px", "width": "252px" }, "number_sections": false, "sideBar": false, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-3-clause
luoshao23/ML_algorithm	Deep_Learning/CNN/opencv_img_similar.ipynb	1	5575	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import cv2\n", "import numpy as np\n", "import os.path\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ROOT = '/Users/shouzeluo/Desktop/Belle/data/imgs/'\n", "\n", "file_list = np.asarray([f for f in os.listdir(ROOT) if f.endswith('png')])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'SJK08100DL2BH6.png'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "img0=cv2.imread(os.path.join(ROOT, 'SJK08100DM1BH6.png'))\n", "\n", "H1 = cv2.calcHist([img0], [0], None, [256], [0, 256])\n", "H1=cv2.normalize(H1,H1,0,1,cv2.NORM_MINMAX,-1)\n", "\n", "dist = []\n", "\n", "for f in file_list:\n", " img = cv2.imread(os.path.join(ROOT, f))\n", " tmp = cv2.calcHist([img], [0], None, [256], [0, 256])\n", " tmp = cv2.normalize(tmp,tmp,0,1,cv2.NORM_MINMAX,-1)\n", " similarity = cv2.compareHist(H1,tmp,0)\n", " dist.append(similarity)\n", "\n", "dist = np.asarray(dist)\n", "file_name = file_list[dist.argpartition(-2)[-2]]\n", "file_name" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "\n", "class CompareImage(object):\n", "\n", " def __init__(self, image_1_path, image_2_path):\n", " self.minimum_commutative_image_diff = 0.25\n", " self.image_1_path = image_1_path\n", " self.image_2_path = image_2_path\n", "\n", " def compare_image(self):\n", " image_1 = cv2.imread(self.image_1_path, 0)\n", " image_2 = cv2.imread(self.image_2_path, 0)\n", " img_hist_diff, img_template_diff, commutative_image_diff = self.get_image_difference(image_1, image_2)\n", "\n", "# if img_hist_diff<0.3 and img_template_diff<0.3:\n", "# if commutative_image_diff < self.minimum_commutative_image_diff:\n", "# print(\"Matched\")\n", "# return commutative_image_diff\n", " return commutative_image_diff # random failure value\n", "\n", " @staticmethod\n", " def get_image_difference(image_1, image_2):\n", " first_image_hist = cv2.calcHist([image_1], [0], None, [256], [0, 256])\n", " second_image_hist = cv2.calcHist([image_2], [0], None, [256], [0, 256])\n", "\n", " img_hist_diff = 1-cv2.compareHist(first_image_hist, second_image_hist,0)\n", " img_template_probability_match = cv2.matchTemplate(first_image_hist, second_image_hist, cv2.TM_CCOEFF_NORMED)[0][0]\n", " img_template_diff = 1 - img_template_probability_match\n", "\n", " # taking only 10% of histogram diff, since it's less accurate than template method\n", " commutative_image_diff = (img_hist_diff / 10) + img_template_diff\n", " return [img_hist_diff,img_template_diff,commutative_image_diff]\n", "\n", "\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p1 = os.path.join(ROOT, 'SJK01500DQ1AM6.png')\n", "dist = []\n", "for f in file_list:\n", " p2 = os.path.join(ROOT, f)\n", "\n", " compare_image = CompareImage(p1, p2)\n", " image_difference = compare_image.compare_image()\n", " dist.append(image_difference)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['SJK01500DQ1AM6.png', 'SJK11600DM1BK6.png', 'SJK01500DB1AM6.png',\n", " 'SJK08100DP2BH6.png', 'SJK11600DP1BK6.png', 'SJK08100DB1BH6.png',\n", " 'S79E5101DM1AM6.png', 'SJK08100DM2BH6.png', 'SC5C9101DL1AM6.png',\n", " 'S79E5101DL5AM6.png', 'S79E5101DM5AM6.png', 'SJK08100DL2BH6.png',\n", " 'SJK18400DK1BH6.png', 'SJK08100DM1BH6.png', 'SC5C9101DU1AM6.png',\n", " 'SJK18400DM1BH6.png', 'SJK08100DQ1BH6.png', 'SC5C9101DP2AM6.png'],\n", " dtype='\|S18')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dist = np.asarray(dist)\n", "file_list[dist.argsort()]" ] }, { "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
JJINDAHOUSE/deep-learning	language-translation/dlnd_language_translation.ipynb	1	74459	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Language Translation\n", "In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.\n", "## Get the Data\n", "Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import helper\n", "import problem_unittests as tests\n", "\n", "source_path = 'data/small_vocab_en'\n", "target_path = 'data/small_vocab_fr'\n", "source_text = helper.load_data(source_path)\n", "target_text = helper.load_data(target_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore the Data\n", "Play around with view_sentence_range to view different parts of the data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset Stats\n", "Roughly the number of unique words: 227\n", "Number of sentences: 137861\n", "Average number of words in a sentence: 13.225277634719028\n", "\n", "English sentences 0 to 10:\n", "new jersey is sometimes quiet during autumn , and it is snowy in april .\n", "the united states is usually chilly during july , and it is usually freezing in november .\n", "california is usually quiet during march , and it is usually hot in june .\n", "the united states is sometimes mild during june , and it is cold in september .\n", "your least liked fruit is the grape , but my least liked is the apple .\n", "his favorite fruit is the orange , but my favorite is the grape .\n", "paris is relaxing during december , but it is usually chilly in july .\n", "new jersey is busy during spring , and it is never hot in march .\n", "our least liked fruit is the lemon , but my least liked is the grape .\n", "the united states is sometimes busy during january , and it is sometimes warm in november .\n", "\n", "French sentences 0 to 10:\n", "new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\n", "les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .\n", "california est généralement calme en mars , et il est généralement chaud en juin .\n", "les états-unis est parfois légère en juin , et il fait froid en septembre .\n", "votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .\n", "son fruit préféré est l'orange , mais mon préféré est le raisin .\n", "paris est relaxant en décembre , mais il est généralement froid en juillet .\n", "new jersey est occupé au printemps , et il est jamais chaude en mars .\n", "notre fruit est moins aimé le citron , mais mon moins aimé est le raisin .\n", "les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre .\n" ] } ], "source": [ "view_sentence_range = (0, 10)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import numpy as np\n", "\n", "print('Dataset Stats')\n", "print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))\n", "\n", "sentences = source_text.split('\\n')\n", "word_counts = [len(sentence.split()) for sentence in sentences]\n", "print('Number of sentences: {}'.format(len(sentences)))\n", "print('Average number of words in a sentence: {}'.format(np.average(word_counts)))\n", "\n", "print()\n", "print('English sentences {} to {}:'.format(view_sentence_range))\n", "print('\\n'.join(source_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))\n", "print()\n", "print('French sentences {} to {}:'.format(view_sentence_range))\n", "print('\\n'.join(target_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implement Preprocessing Function\n", "### Text to Word Ids\n", "As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function `text_to_ids()`, you'll turn `source_text` and `target_text` from words to ids. However, you need to add the `<EOS>` word id at the end of `target_text`. This will help the neural network predict when the sentence should end.\n", "\n", "You can get the `<EOS>` word id by doing:\n", "```python\n", "target_vocab_to_int['<EOS>']\n", "```\n", "You can get other word ids using `source_vocab_to_int` and `target_vocab_to_int`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):\n", " \"\"\"\n", " Convert source and target text to proper word ids\n", " :param source_text: String that contains all the source text.\n", " :param target_text: String that contains all the target text.\n", " :param source_vocab_to_int: Dictionary to go from the source words to an id\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :return: A tuple of lists (source_id_text, target_id_text)\n", " \"\"\"\n", " # TODO: Implement Function\n", " EOS = '<EOS>'\n", " \n", " source_id_text = [[source_vocab_to_int[word] for word in sentence.split()]\n", " for sentence in source_text.split('\\n')]\n", " \n", " target_id_text = [[target_vocab_to_int[word] for word in sentence.split()] + [target_vocab_to_int[EOS]]\n", " for sentence in target_text.split('\\n')]\n", " \n", " return (source_id_text, target_id_text)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_text_to_ids(text_to_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Preprocess all the data and save it\n", "Running the code cell below will preprocess all the data and save it to file." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "helper.preprocess_and_save_data(source_path, target_path, text_to_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Check Point\n", "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import numpy as np\n", "import helper\n", "\n", "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check the Version of TensorFlow and Access to GPU\n", "This will check to make sure you have the correct version of TensorFlow and access to a GPU" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorFlow Version: 1.1.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/junhuiliao/anaconda/envs/tensorflow/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: No GPU found. Please use a GPU to train your neural network.\n", " from ipykernel import kernelapp as app\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "from distutils.version import LooseVersion\n", "import warnings\n", "import tensorflow as tf\n", "from tensorflow.python.layers.core import Dense\n", "\n", "# Check TensorFlow Version\n", "assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer'\n", "print('TensorFlow Version: {}'.format(tf.__version__))\n", "\n", "# Check for a GPU\n", "if not tf.test.gpu_device_name():\n", " warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n", "else:\n", " print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build the Neural Network\n", "You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below:\n", "- `model_inputs`\n", "- `process_decoder_input`\n", "- `encoding_layer`\n", "- `decoding_layer_train`\n", "- `decoding_layer_infer`\n", "- `decoding_layer`\n", "- `seq2seq_model`\n", "\n", "### Input\n", "Implement the `model_inputs()` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n", "\n", "- Input text placeholder named \"input\" using the TF Placeholder name parameter with rank 2.\n", "- Targets placeholder with rank 2.\n", "- Learning rate placeholder with rank 0.\n", "- Keep probability placeholder named \"keep_prob\" using the TF Placeholder name parameter with rank 0.\n", "- Target sequence length placeholder named \"target_sequence_length\" with rank 1\n", "- Max target sequence length tensor named \"max_target_len\" getting its value from applying tf.reduce_max on the target_sequence_length placeholder. Rank 0.\n", "- Source sequence length placeholder named \"source_sequence_length\" with rank 1\n", "\n", "Return the placeholders in the following the tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def model_inputs():\n", " \"\"\"\n", " Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences.\n", " :return: Tuple (input, targets, learning rate, keep probability, target sequence length,\n", " max target sequence length, source sequence length)\n", " \"\"\"\n", " # TODO: Implement Function\n", " inputs = tf.placeholder(tf.int32, shape=[None, None], name='input')\n", " targets = tf.placeholder(tf.int32, shape=[None, None], name='targets')\n", " learning_rate = tf.placeholder(tf.float32, name='learning_rate')\n", " keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n", " target_sequence_length = tf.placeholder(tf.int32, [None], name='target_sequence_length')\n", " max_target_sequence_length = tf.reduce_max(target_sequence_length)\n", " source_sequence_length = tf.placeholder(tf.int32, [None], name='source_sequence_length')\n", " \n", " return (inputs, targets, learning_rate, keep_prob, target_sequence_length, \n", " max_target_sequence_length, source_sequence_length)\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_model_inputs(model_inputs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Process Decoder Input\n", "Implement `process_decoder_input` by removing the last word id from each batch in `target_data` and concat the GO ID to the begining of each batch." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def process_decoder_input(target_data, target_vocab_to_int, batch_size):\n", " \"\"\"\n", " Preprocess target data for encoding\n", " :param target_data: Target Placehoder\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :param batch_size: Batch Size\n", " :return: Preprocessed target data\n", " \"\"\"\n", " # TODO: Implement Function\n", " x = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])\n", " y = tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['<GO>']), x], 1)\n", " return y\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_process_encoding_input(process_decoder_input)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Encoding\n", "Implement `encoding_layer()` to create a Encoder RNN layer:\n", " * Embed the encoder input using [`tf.contrib.layers.embed_sequence`](https://www.tensorflow.org/api_docs/python/tf/contrib/layers/embed_sequence)\n", " * Construct a [stacked](https://github.com/tensorflow/tensorflow/blob/6947f65a374ebf29e74bb71e36fd82760056d82c/tensorflow/docs_src/tutorials/recurrent.md#stacking-multiple-lstms) [`tf.contrib.rnn.LSTMCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/LSTMCell) wrapped in a [`tf.contrib.rnn.DropoutWrapper`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/DropoutWrapper)\n", " * Pass cell and embedded input to [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "from imp import reload\n", "reload(tests)\n", "\n", "def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, \n", " source_sequence_length, source_vocab_size, \n", " encoding_embedding_size):\n", " \"\"\"\n", " Create encoding layer\n", " :param rnn_inputs: Inputs for the RNN\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param keep_prob: Dropout keep probability\n", " :param source_sequence_length: a list of the lengths of each sequence in the batch\n", " :param source_vocab_size: vocabulary size of source data\n", " :param encoding_embedding_size: embedding size of source data\n", " :return: tuple (RNN output, RNN state)\n", " \"\"\"\n", " # TODO: Implement Function\n", " # embedding input\n", " encoding_input = tf.contrib.layers.embed_sequence(rnn_inputs, source_vocab_size, encoding_embedding_size)\n", " cell = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.LSTMCell(rnn_size) for _ in range(num_layers)])\n", " cell = tf.contrib.rnn.DropoutWrapper(cell, keep_prob)\n", " rnn_output, rnn_state = tf.nn.dynamic_rnn(cell, encoding_input, source_sequence_length, dtype=tf.float32)\n", " \n", " return rnn_output, rnn_state\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_encoding_layer(encoding_layer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Decoding - Training\n", "Create a training decoding layer:\n", "* Create a [`tf.contrib.seq2seq.TrainingHelper`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/TrainingHelper) \n", "* Create a [`tf.contrib.seq2seq.BasicDecoder`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/BasicDecoder)\n", "* Obtain the decoder outputs from [`tf.contrib.seq2seq.dynamic_decode`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_decode)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "\n", "def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, \n", " target_sequence_length, max_summary_length, \n", " output_layer, keep_prob):\n", " \"\"\"\n", " Create a decoding layer for training\n", " :param encoder_state: Encoder State\n", " :param dec_cell: Decoder RNN Cell\n", " :param dec_embed_input: Decoder embedded input\n", " :param target_sequence_length: The lengths of each sequence in the target batch\n", " :param max_summary_length: The length of the longest sequence in the batch\n", " :param output_layer: Function to apply the output layer\n", " :param keep_prob: Dropout keep probability\n", " :return: BasicDecoderOutput containing training logits and sample_id\n", " \"\"\"\n", " # TODO: Implement Function\n", " helper = tf.contrib.seq2seq.TrainingHelper(dec_embed_input, target_sequence_length)\n", " decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer)\n", " final_outputs, _ = tf.contrib.seq2seq.dynamic_decode(decoder, impute_finished=True, \n", " maximum_iterations=max_summary_length)\n", " \n", " \n", " return final_outputs\n", "\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer_train(decoding_layer_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Decoding - Inference\n", "Create inference decoder:\n", "* Create a [`tf.contrib.seq2seq.GreedyEmbeddingHelper`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/GreedyEmbeddingHelper)\n", "* Create a [`tf.contrib.seq2seq.BasicDecoder`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/BasicDecoder)\n", "* Obtain the decoder outputs from [`tf.contrib.seq2seq.dynamic_decode`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_decode)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id,\n", " end_of_sequence_id, max_target_sequence_length,\n", " vocab_size, output_layer, batch_size, keep_prob):\n", " \"\"\"\n", " Create a decoding layer for inference\n", " :param encoder_state: Encoder state\n", " :param dec_cell: Decoder RNN Cell\n", " :param dec_embeddings: Decoder embeddings\n", " :param start_of_sequence_id: GO ID\n", " :param end_of_sequence_id: EOS Id\n", " :param max_target_sequence_length: Maximum length of target sequences\n", " :param vocab_size: Size of decoder/target vocabulary\n", " :param decoding_scope: TenorFlow Variable Scope for decoding\n", " :param output_layer: Function to apply the output layer\n", " :param batch_size: Batch size\n", " :param keep_prob: Dropout keep probability\n", " :return: BasicDecoderOutput containing inference logits and sample_id\n", " \"\"\"\n", " # TODO: Implement Function\n", " start_tokens = tf.tile(tf.constant([start_of_sequence_id], dtype=tf.int32), [batch_size], name='start_tokens')\n", " helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings, start_tokens, end_of_sequence_id)\n", " decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer)\n", " final_output, _ = tf.contrib.seq2seq.dynamic_decode(decoder, impute_finished=True, \n", " maximum_iterations=max_target_sequence_length)\n", " return final_output\n", "\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer_infer(decoding_layer_infer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Decoding Layer\n", "Implement `decoding_layer()` to create a Decoder RNN layer.\n", "\n", "* Embed the target sequences\n", "* Construct the decoder LSTM cell (just like you constructed the encoder cell above)\n", "* Create an output layer to map the outputs of the decoder to the elements of our vocabulary\n", "* Use the your `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_target_sequence_length, output_layer, keep_prob)` function to get the training logits.\n", "* Use your `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob)` function to get the inference logits.\n", "\n", "Note: You'll need to use [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) to share variables between training and inference." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "from tensorflow.python.layers import core as layers_core\n", "def decoding_layer(dec_input, encoder_state,\n", " target_sequence_length, max_target_sequence_length,\n", " rnn_size,\n", " num_layers, target_vocab_to_int, target_vocab_size,\n", " batch_size, keep_prob, decoding_embedding_size):\n", " \"\"\"\n", " Create decoding layer\n", " :param dec_input: Decoder input\n", " :param encoder_state: Encoder state\n", " :param target_sequence_length: The lengths of each sequence in the target batch\n", " :param max_target_sequence_length: Maximum length of target sequences\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :param target_vocab_size: Size of target vocabulary\n", " :param batch_size: The size of the batch\n", " :param keep_prob: Dropout keep probability\n", " :param decoding_embedding_size: Decoding embedding size\n", " :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)\n", " \"\"\"\n", " # TODO: Implement Function\n", " \n", " # embedding target sequence\n", " dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, decoding_embedding_size]))\n", " dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)\n", " dec_cell = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.LSTMCell(rnn_size) for _ in range(num_layers)])\n", " # create output layer to map the outputs of the decoder to the elements of our vocabulary\n", " output_layer = layers_core.Dense(target_vocab_size, \n", " kernel_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1))\n", " \n", " # decoder train\n", " with tf.variable_scope(\"decoding\") as decoding_scope:\n", " dec_outputs_train = decoding_layer_train(encoder_state, dec_cell, dec_embed_input, \n", " target_sequence_length, max_target_sequence_length, \n", " output_layer, keep_prob)\n", " \n", " # decoder inference\n", " start_of_sequence_id = target_vocab_to_int[\"<GO>\"]\n", " end_of_sequence_id = target_vocab_to_int[\"<EOS>\"]\n", " \n", " with tf.variable_scope(\"decoding\", reuse=True) as decoding_scope:\n", " dec_outputs_infer = decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, \n", " end_of_sequence_id, max_target_sequence_length, \n", " target_vocab_size, output_layer, batch_size, keep_prob)\n", " \n", " return dec_outputs_train, dec_outputs_infer\n", "\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer(decoding_layer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Neural Network\n", "Apply the functions you implemented above to:\n", "\n", "- Encode the input using your `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size)`.\n", "- Process target data using your `process_decoder_input(target_data, target_vocab_to_int, batch_size)` function.\n", "- Decode the encoded input using your `decoding_layer(dec_input, enc_state, target_sequence_length, max_target_sentence_length, rnn_size, num_layers, target_vocab_to_int, target_vocab_size, batch_size, keep_prob, dec_embedding_size)` function." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def seq2seq_model(input_data, target_data, keep_prob, batch_size,\n", " source_sequence_length, target_sequence_length,\n", " max_target_sentence_length,\n", " source_vocab_size, target_vocab_size,\n", " enc_embedding_size, dec_embedding_size,\n", " rnn_size, num_layers, target_vocab_to_int):\n", " \"\"\"\n", " Build the Sequence-to-Sequence part of the neural network\n", " :param input_data: Input placeholder\n", " :param target_data: Target placeholder\n", " :param keep_prob: Dropout keep probability placeholder\n", " :param batch_size: Batch Size\n", " :param source_sequence_length: Sequence Lengths of source sequences in the batch\n", " :param target_sequence_length: Sequence Lengths of target sequences in the batch\n", " :param source_vocab_size: Source vocabulary size\n", " :param target_vocab_size: Target vocabulary size\n", " :param enc_embedding_size: Decoder embedding size\n", " :param dec_embedding_size: Encoder embedding size\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)\n", " \"\"\"\n", " # TODO: Implement Function\n", " enc_output, enc_state = encoding_layer(input_data, rnn_size, num_layers, keep_prob, source_sequence_length, \n", " source_vocab_size, enc_embedding_size)\n", " \n", " dec_input = process_decoder_input(target_data, target_vocab_to_int, batch_size)\n", " \n", " dec_outputs_train, dec_outputs_infer = decoding_layer(dec_input, enc_state, target_sequence_length, \n", " max_target_sentence_length, rnn_size, num_layers, \n", " target_vocab_to_int, target_vocab_size, batch_size, \n", " keep_prob, dec_embedding_size)\n", " return dec_outputs_train, dec_outputs_infer\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_seq2seq_model(seq2seq_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neural Network Training\n", "### Hyperparameters\n", "Tune the following parameters:\n", "\n", "- Set `epochs` to the number of epochs.\n", "- Set `batch_size` to the batch size.\n", "- Set `rnn_size` to the size of the RNNs.\n", "- Set `num_layers` to the number of layers.\n", "- Set `encoding_embedding_size` to the size of the embedding for the encoder.\n", "- Set `decoding_embedding_size` to the size of the embedding for the decoder.\n", "- Set `learning_rate` to the learning rate.\n", "- Set `keep_probability` to the Dropout keep probability\n", "- Set `display_step` to state how many steps between each debug output statement" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Number of Epochs\n", "epochs = 10\n", "# Batch Size\n", "batch_size = 512\n", "# RNN Size\n", "rnn_size = 512\n", "# Number of Layers\n", "num_layers = 4\n", "# Embedding Size\n", "encoding_embedding_size = 128\n", "decoding_embedding_size = 128\n", "# Learning Rate\n", "learning_rate = 0.001\n", "# Dropout Keep Probability\n", "keep_probability = 0.8\n", "display_step = 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Graph\n", "Build the graph using the neural network you implemented." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "save_path = 'checkpoints/dev'\n", "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()\n", "max_target_sentence_length = max([len(sentence) for sentence in source_int_text])\n", "\n", "train_graph = tf.Graph()\n", "with train_graph.as_default():\n", " input_data, targets, lr, keep_prob, target_sequence_length, max_target_sequence_length, source_sequence_length = model_inputs()\n", "\n", " #sequence_length = tf.placeholder_with_default(max_target_sentence_length, None, name='sequence_length')\n", " input_shape = tf.shape(input_data)\n", "\n", " train_logits, inference_logits = seq2seq_model(tf.reverse(input_data, [-1]),\n", " targets,\n", " keep_prob,\n", " batch_size,\n", " source_sequence_length,\n", " target_sequence_length,\n", " max_target_sequence_length,\n", " len(source_vocab_to_int),\n", " len(target_vocab_to_int),\n", " encoding_embedding_size,\n", " decoding_embedding_size,\n", " rnn_size,\n", " num_layers,\n", " target_vocab_to_int)\n", "\n", "\n", " training_logits = tf.identity(train_logits.rnn_output, name='logits')\n", " inference_logits = tf.identity(inference_logits.sample_id, name='predictions')\n", "\n", " masks = tf.sequence_mask(target_sequence_length, max_target_sequence_length, dtype=tf.float32, name='masks')\n", "\n", " with tf.name_scope(\"optimization\"):\n", " # Loss function\n", " cost = tf.contrib.seq2seq.sequence_loss(\n", " training_logits,\n", " targets,\n", " masks)\n", "\n", " # Optimizer\n", " optimizer = tf.train.AdamOptimizer(lr)\n", "\n", " # Gradient Clipping\n", " gradients = optimizer.compute_gradients(cost)\n", " capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n", " train_op = optimizer.apply_gradients(capped_gradients)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Batch and pad the source and target sequences" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "def pad_sentence_batch(sentence_batch, pad_int):\n", " \"\"\"Pad sentences with <PAD> so that each sentence of a batch has the same length\"\"\"\n", " max_sentence = max([len(sentence) for sentence in sentence_batch])\n", " return [sentence + [pad_int] * (max_sentence - len(sentence)) for sentence in sentence_batch]\n", "\n", "\n", "def get_batches(sources, targets, batch_size, source_pad_int, target_pad_int):\n", " \"\"\"Batch targets, sources, and the lengths of their sentences together\"\"\"\n", " for batch_i in range(0, len(sources)//batch_size):\n", " start_i = batch_i * batch_size\n", "\n", " # Slice the right amount for the batch\n", " sources_batch = sources[start_i:start_i + batch_size]\n", " targets_batch = targets[start_i:start_i + batch_size]\n", "\n", " # Pad\n", " pad_sources_batch = np.array(pad_sentence_batch(sources_batch, source_pad_int))\n", " pad_targets_batch = np.array(pad_sentence_batch(targets_batch, target_pad_int))\n", "\n", " # Need the lengths for the _lengths parameters\n", " pad_targets_lengths = []\n", " for target in pad_targets_batch:\n", " pad_targets_lengths.append(len(target))\n", "\n", " pad_source_lengths = []\n", " for source in pad_sources_batch:\n", " pad_source_lengths.append(len(source))\n", "\n", " yield pad_sources_batch, pad_targets_batch, pad_source_lengths, pad_targets_lengths\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train\n", "Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forms to see if anyone is having the same problem." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0 Batch 10/269 - Train Accuracy: 0.2901, Validation Accuracy: 0.3591, Loss: 3.5808\n", "Epoch 0 Batch 20/269 - Train Accuracy: 0.3606, Validation Accuracy: 0.4172, Loss: 2.8581\n", "Epoch 0 Batch 30/269 - Train Accuracy: 0.4054, Validation Accuracy: 0.4324, Loss: 2.4944\n", "Epoch 0 Batch 40/269 - Train Accuracy: 0.4198, Validation Accuracy: 0.4658, Loss: 2.3792\n", "Epoch 0 Batch 50/269 - Train Accuracy: 0.4337, Validation Accuracy: 0.4877, Loss: 2.2823\n", "Epoch 0 Batch 60/269 - Train Accuracy: 0.4793, Validation Accuracy: 0.4931, Loss: 2.0475\n", "Epoch 0 Batch 70/269 - Train Accuracy: 0.4629, Validation Accuracy: 0.4746, Loss: 2.4171\n", "Epoch 0 Batch 80/269 - Train Accuracy: 0.4839, Validation Accuracy: 0.4989, Loss: 2.0276\n", "Epoch 0 Batch 90/269 - Train Accuracy: 0.4675, Validation Accuracy: 0.5216, Loss: 1.9985\n", "Epoch 0 Batch 100/269 - Train Accuracy: 0.5057, Validation Accuracy: 0.5163, Loss: 1.6939\n", "Epoch 0 Batch 110/269 - Train Accuracy: 0.5000, Validation Accuracy: 0.5297, Loss: 1.6305\n", "Epoch 0 Batch 120/269 - Train Accuracy: 0.4847, Validation Accuracy: 0.5265, Loss: 1.5857\n", "Epoch 0 Batch 130/269 - Train Accuracy: 0.4800, Validation Accuracy: 0.5222, Loss: 1.5891\n", "Epoch 0 Batch 140/269 - Train Accuracy: 0.4796, Validation Accuracy: 0.5099, Loss: 1.4230\n", "Epoch 0 Batch 150/269 - Train Accuracy: 0.5282, Validation Accuracy: 0.5411, Loss: 1.3939\n", "Epoch 0 Batch 160/269 - Train Accuracy: 0.5288, Validation Accuracy: 0.5445, Loss: 1.3294\n", "Epoch 0 Batch 170/269 - Train Accuracy: 0.5510, Validation Accuracy: 0.5629, Loss: 1.2575\n", "Epoch 0 Batch 180/269 - Train Accuracy: 0.5507, Validation Accuracy: 0.5595, Loss: 1.2014\n", "Epoch 0 Batch 190/269 - Train Accuracy: 0.5399, Validation Accuracy: 0.5479, Loss: 1.1511\n", "Epoch 0 Batch 200/269 - Train Accuracy: 0.5392, Validation Accuracy: 0.5566, Loss: 1.1691\n", "Epoch 0 Batch 210/269 - Train Accuracy: 0.4637, Validation Accuracy: 0.4613, Loss: 1.4141\n", "Epoch 0 Batch 220/269 - Train Accuracy: 0.5027, Validation Accuracy: 0.5169, Loss: 1.3185\n", "Epoch 0 Batch 230/269 - Train Accuracy: 0.5230, Validation Accuracy: 0.5516, Loss: 1.2532\n", "Epoch 0 Batch 240/269 - Train Accuracy: 0.5871, Validation Accuracy: 0.5570, Loss: 1.0287\n", "Epoch 0 Batch 250/269 - Train Accuracy: 0.5532, Validation Accuracy: 0.5781, Loss: 1.0968\n", "Epoch 0 Batch 260/269 - Train Accuracy: 0.5408, Validation Accuracy: 0.5755, Loss: 1.1008\n", "Epoch 1 Batch 10/269 - Train Accuracy: 0.5563, Validation Accuracy: 0.5813, Loss: 1.0212\n", "Epoch 1 Batch 20/269 - Train Accuracy: 0.5656, Validation Accuracy: 0.5801, Loss: 1.0304\n", "Epoch 1 Batch 30/269 - Train Accuracy: 0.5787, Validation Accuracy: 0.5820, Loss: 0.9282\n", "Epoch 1 Batch 40/269 - Train Accuracy: 0.5635, Validation Accuracy: 0.5875, Loss: 0.9621\n", "Epoch 1 Batch 50/269 - Train Accuracy: 0.5602, Validation Accuracy: 0.5906, Loss: 1.0033\n", "Epoch 1 Batch 60/269 - Train Accuracy: 0.5959, Validation Accuracy: 0.5912, Loss: 0.8762\n", "Epoch 1 Batch 70/269 - Train Accuracy: 0.5980, Validation Accuracy: 0.5906, Loss: 0.8901\n", "Epoch 1 Batch 80/269 - Train Accuracy: 0.5999, Validation Accuracy: 0.5915, Loss: 0.8654\n", "Epoch 1 Batch 90/269 - Train Accuracy: 0.5570, Validation Accuracy: 0.5914, Loss: 0.9147\n", "Epoch 1 Batch 100/269 - Train Accuracy: 0.6019, Validation Accuracy: 0.5888, Loss: 0.8091\n", "Epoch 1 Batch 110/269 - Train Accuracy: 0.5858, Validation Accuracy: 0.6064, Loss: 0.7954\n", "Epoch 1 Batch 120/269 - Train Accuracy: 0.5870, Validation Accuracy: 0.6048, Loss: 0.8227\n", "Epoch 1 Batch 130/269 - Train Accuracy: 0.5728, Validation Accuracy: 0.5874, Loss: 0.8055\n", "Epoch 1 Batch 140/269 - Train Accuracy: 0.6100, Validation Accuracy: 0.6126, Loss: 0.7755\n", "Epoch 1 Batch 150/269 - Train Accuracy: 0.6086, Validation Accuracy: 0.6141, Loss: 0.7551\n", "Epoch 1 Batch 160/269 - 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Train Accuracy: 0.9781, Validation Accuracy: 0.9590, Loss: 0.0260\n", "Epoch 8 Batch 90/269 - Train Accuracy: 0.9632, Validation Accuracy: 0.9573, Loss: 0.0263\n", "Epoch 8 Batch 100/269 - Train Accuracy: 0.9653, Validation Accuracy: 0.9614, Loss: 0.0258\n", "Epoch 8 Batch 110/269 - Train Accuracy: 0.9673, Validation Accuracy: 0.9553, Loss: 0.0208\n", "Epoch 8 Batch 120/269 - Train Accuracy: 0.9719, Validation Accuracy: 0.9623, Loss: 0.0226\n", "Epoch 8 Batch 130/269 - Train Accuracy: 0.9733, Validation Accuracy: 0.9608, Loss: 0.0243\n", "Epoch 8 Batch 140/269 - Train Accuracy: 0.9648, Validation Accuracy: 0.9683, Loss: 0.0214\n", "Epoch 8 Batch 150/269 - Train Accuracy: 0.9712, Validation Accuracy: 0.9744, Loss: 0.0216\n", "Epoch 8 Batch 160/269 - Train Accuracy: 0.9692, Validation Accuracy: 0.9711, Loss: 0.0189\n", "Epoch 8 Batch 170/269 - Train Accuracy: 0.9685, Validation Accuracy: 0.9706, Loss: 0.0203\n", "Epoch 8 Batch 180/269 - Train Accuracy: 0.9820, Validation Accuracy: 0.9704, Loss: 0.0170\n", "Epoch 8 Batch 190/269 - Train Accuracy: 0.9736, Validation Accuracy: 0.9755, Loss: 0.0196\n", "Epoch 8 Batch 200/269 - Train Accuracy: 0.9811, Validation Accuracy: 0.9641, Loss: 0.0148\n", "Epoch 8 Batch 210/269 - Train Accuracy: 0.9708, Validation Accuracy: 0.9703, Loss: 0.0165\n", "Epoch 8 Batch 220/269 - Train Accuracy: 0.9709, Validation Accuracy: 0.9634, Loss: 0.0203\n", "Epoch 8 Batch 230/269 - Train Accuracy: 0.9784, Validation Accuracy: 0.9696, Loss: 0.0185\n", "Epoch 8 Batch 240/269 - Train Accuracy: 0.9753, Validation Accuracy: 0.9672, Loss: 0.0169\n", "Epoch 8 Batch 250/269 - Train Accuracy: 0.9689, Validation Accuracy: 0.9644, Loss: 0.0179\n", "Epoch 8 Batch 260/269 - Train Accuracy: 0.9772, Validation Accuracy: 0.9654, Loss: 0.0204\n", "Epoch 9 Batch 10/269 - Train Accuracy: 0.9720, Validation Accuracy: 0.9646, Loss: 0.0163\n", "Epoch 9 Batch 20/269 - Train Accuracy: 0.9737, Validation Accuracy: 0.9656, Loss: 0.0174\n", "Epoch 9 Batch 30/269 - Train Accuracy: 0.9778, Validation Accuracy: 0.9699, Loss: 0.0191\n", "Epoch 9 Batch 40/269 - Train Accuracy: 0.9658, Validation Accuracy: 0.9676, Loss: 0.0202\n", "Epoch 9 Batch 50/269 - Train Accuracy: 0.9636, Validation Accuracy: 0.9738, Loss: 0.0212\n", "Epoch 9 Batch 60/269 - Train Accuracy: 0.9748, Validation Accuracy: 0.9755, Loss: 0.0174\n", "Epoch 9 Batch 70/269 - Train Accuracy: 0.9765, Validation Accuracy: 0.9600, Loss: 0.0175\n", "Epoch 9 Batch 80/269 - Train Accuracy: 0.9777, Validation Accuracy: 0.9733, Loss: 0.0147\n", "Epoch 9 Batch 90/269 - Train Accuracy: 0.9635, Validation Accuracy: 0.9633, Loss: 0.0164\n", "Epoch 9 Batch 100/269 - Train Accuracy: 0.9748, Validation Accuracy: 0.9710, Loss: 0.0191\n", "Epoch 9 Batch 110/269 - Train Accuracy: 0.9779, Validation Accuracy: 0.9696, Loss: 0.0156\n", "Epoch 9 Batch 120/269 - Train Accuracy: 0.9745, Validation Accuracy: 0.9673, Loss: 0.0172\n", "Epoch 9 Batch 130/269 - Train Accuracy: 0.9786, Validation Accuracy: 0.9719, Loss: 0.0211\n", "Epoch 9 Batch 140/269 - Train Accuracy: 0.9736, Validation Accuracy: 0.9644, Loss: 0.0173\n", "Epoch 9 Batch 150/269 - Train Accuracy: 0.9860, Validation Accuracy: 0.9790, Loss: 0.0171\n", "Epoch 9 Batch 160/269 - Train Accuracy: 0.9852, Validation Accuracy: 0.9776, Loss: 0.0152\n", "Epoch 9 Batch 170/269 - Train Accuracy: 0.9720, Validation Accuracy: 0.9716, Loss: 0.0157\n", "Epoch 9 Batch 180/269 - Train Accuracy: 0.9827, Validation Accuracy: 0.9681, Loss: 0.0147\n", "Epoch 9 Batch 190/269 - Train Accuracy: 0.9783, Validation Accuracy: 0.9731, Loss: 0.0148\n", "Epoch 9 Batch 200/269 - Train Accuracy: 0.9896, Validation Accuracy: 0.9727, Loss: 0.0127\n", "Epoch 9 Batch 210/269 - Train Accuracy: 0.9754, Validation Accuracy: 0.9704, Loss: 0.0141\n", "Epoch 9 Batch 220/269 - Train Accuracy: 0.9722, Validation Accuracy: 0.9591, Loss: 0.0169\n", "Epoch 9 Batch 230/269 - Train Accuracy: 0.9777, Validation Accuracy: 0.9719, Loss: 0.0157\n", "Epoch 9 Batch 240/269 - Train Accuracy: 0.9806, Validation Accuracy: 0.9728, Loss: 0.0137\n", "Epoch 9 Batch 250/269 - Train Accuracy: 0.9718, Validation Accuracy: 0.9737, Loss: 0.0144\n", "Epoch 9 Batch 260/269 - Train Accuracy: 0.9743, Validation Accuracy: 0.9688, Loss: 0.0168\n", "Model Trained and Saved\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "def get_accuracy(target, logits):\n", " \"\"\"\n", " Calculate accuracy\n", " \"\"\"\n", " max_seq = max(target.shape[1], logits.shape[1])\n", " if max_seq - target.shape[1]:\n", " target = np.pad(\n", " target,\n", " [(0,0),(0,max_seq - target.shape[1])],\n", " 'constant')\n", " if max_seq - logits.shape[1]:\n", " logits = np.pad(\n", " logits,\n", " [(0,0),(0,max_seq - logits.shape[1])],\n", " 'constant')\n", "\n", " return np.mean(np.equal(target, logits))\n", "\n", "# Split data to training and validation sets\n", "train_source = source_int_text[batch_size:]\n", "train_target = target_int_text[batch_size:]\n", "valid_source = source_int_text[:batch_size]\n", "valid_target = target_int_text[:batch_size]\n", "(valid_sources_batch, valid_targets_batch, valid_sources_lengths, valid_targets_lengths ) = next(get_batches(valid_source,\n", " valid_target,\n", " batch_size,\n", " source_vocab_to_int['<PAD>'],\n", " target_vocab_to_int['<PAD>'])) \n", "with tf.Session(graph=train_graph) as sess:\n", " sess.run(tf.global_variables_initializer())\n", "\n", " for epoch_i in range(epochs):\n", " for batch_i, (source_batch, target_batch, sources_lengths, targets_lengths) in enumerate(\n", " get_batches(train_source, train_target, batch_size,\n", " source_vocab_to_int['<PAD>'],\n", " target_vocab_to_int['<PAD>'])):\n", "\n", " _, loss = sess.run(\n", " [train_op, cost],\n", " {input_data: source_batch,\n", " targets: target_batch,\n", " lr: learning_rate,\n", " target_sequence_length: targets_lengths,\n", " source_sequence_length: sources_lengths,\n", " keep_prob: keep_probability})\n", "\n", "\n", " if batch_i % display_step == 0 and batch_i > 0:\n", "\n", "\n", " batch_train_logits = sess.run(\n", " inference_logits,\n", " {input_data: source_batch,\n", " source_sequence_length: sources_lengths,\n", " target_sequence_length: targets_lengths,\n", " keep_prob: 1.0})\n", "\n", "\n", " batch_valid_logits = sess.run(\n", " inference_logits,\n", " {input_data: valid_sources_batch,\n", " source_sequence_length: valid_sources_lengths,\n", " target_sequence_length: valid_targets_lengths,\n", " keep_prob: 1.0})\n", "\n", " train_acc = get_accuracy(target_batch, batch_train_logits)\n", "\n", " valid_acc = get_accuracy(valid_targets_batch, batch_valid_logits)\n", "\n", " print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.4f}, Validation Accuracy: {:>6.4f}, Loss: {:>6.4f}'\n", " .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss))\n", "\n", " # Save Model\n", " saver = tf.train.Saver()\n", " saver.save(sess, save_path)\n", " print('Model Trained and Saved')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save Parameters\n", "Save the `batch_size` and `save_path` parameters for inference." ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Save parameters for checkpoint\n", "helper.save_params(save_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Checkpoint" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import tensorflow as tf\n", "import numpy as np\n", "import helper\n", "import problem_unittests as tests\n", "\n", "_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()\n", "load_path = helper.load_params()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sentence to Sequence\n", "To feed a sentence into the model for translation, you first need to preprocess it. Implement the function `sentence_to_seq()` to preprocess new sentences.\n", "\n", "- Convert the sentence to lowercase\n", "- Convert words into ids using `vocab_to_int`\n", " - Convert words not in the vocabulary, to the `<UNK>` word id." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def sentence_to_seq(sentence, vocab_to_int):\n", " \"\"\"\n", " Convert a sentence to a sequence of ids\n", " :param sentence: String\n", " :param vocab_to_int: Dictionary to go from the words to an id\n", " :return: List of word ids\n", " \"\"\"\n", " # TODO: Implement Function\n", " unk = vocab_to_int['<UNK>']\n", " s = sentence.lower()\n", " word_ids = [vocab_to_int[w] if w in vocab_to_int else unk for w in s.split()]\n", " return word_ids\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_sentence_to_seq(sentence_to_seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Translate\n", "This will translate `translate_sentence` from English to French." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from checkpoints/dev\n", "Input\n", " Word Ids: [56, 144, 161, 70, 213, 191, 152]\n", " English Words: ['he', 'saw', 'a', 'old', 'yellow', 'truck', '.']\n", "\n", "Prediction\n", " Word Ids: [106, 233, 242, 53, 302, 197, 91, 227, 1]\n", " French Words: il a vu est moins le jaune . <EOS>\n" ] } ], "source": [ "translate_sentence = 'he saw a old yellow truck .'\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)\n", "\n", "loaded_graph = tf.Graph()\n", "with tf.Session(graph=loaded_graph) as sess:\n", " # Load saved model\n", " loader = tf.train.import_meta_graph(load_path + '.meta')\n", " loader.restore(sess, load_path)\n", "\n", " input_data = loaded_graph.get_tensor_by_name('input:0')\n", " logits = loaded_graph.get_tensor_by_name('predictions:0')\n", " target_sequence_length = loaded_graph.get_tensor_by_name('target_sequence_length:0')\n", " source_sequence_length = loaded_graph.get_tensor_by_name('source_sequence_length:0')\n", " keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n", "\n", " translate_logits = sess.run(logits, {input_data: [translate_sentence]batch_size,\n", " target_sequence_length: [len(translate_sentence)2]batch_size,\n", " source_sequence_length: [len(translate_sentence)]batch_size,\n", " keep_prob: 1.0})[0]\n", "\n", "print('Input')\n", "print(' Word Ids: {}'.format([i for i in translate_sentence]))\n", "print(' English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))\n", "\n", "print('\\nPrediction')\n", "print(' Word Ids: {}'.format([i for i in translate_logits]))\n", "print(' French Words: {}'.format(\" \".join([target_int_to_vocab[i] for i in translate_logits])))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imperfect Translation\n", "You might notice that some sentences translate better than others. Since the dataset you're using only has a vocabulary of 227 English words of the thousands that you use, you're only going to see good results using these words. For this project, you don't need a perfect translation. However, if you want to create a better translation model, you'll need better data.\n", "\n", "You can train on the [WMT10 French-English corpus](http://www.statmt.org/wmt10/training-giga-fren.tar). This dataset has more vocabulary and richer in topics discussed. However, this will take you days to train, so make sure you've a GPU and the neural network is performing well on dataset we provided. Just make sure you play with the WMT10 corpus after you've submitted this project.\n", "## Submitting This Project\n", "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_language_translation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
dipanjank/ml	statistics/hypothesis_testing_intro.ipynb	1	87036	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "The general idea of hypothesis testing involves:\n", "\n", "* Making an initial assumption.\n", "* Collecting evidence (data).\n", "* Based on the available evidence (data), deciding whether to reject or not reject the initial assumption." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example\n", "\n", "Consider the population of many, many adults. A researcher hypothesizes that the average adult body temperature is lower than the often-advertised 98.6 degrees F. That is, the researcher wants an answer to the question: \"Is the average adult body temperature 98.6 degrees? Or is it lower?\" To answer his research question, the researcher starts by assuming that the average adult body temperature was 98.6 degrees F. This is called a 'null hypothesis'." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Null Hypthesis $H_0$: Average adult body temperature is 98.6 degrees F.\n", "\n", "Alt. Hypothesis $H_1$: Average adult body temperature is lower than 98.6 degrees F." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, the researcher goes out and tries to find evidence that refutes his initial assumption. In doing so, he selects a random sample of 130 adults. The average body temperature of the 130 sampled adults is 98.25 degrees.\n", "\n", "Then, the researcher uses the data he collected to make a decision about his initial assumption. It is either likely or unlikely that the researcher would collect the evidence he did given his initial assumption that the average adult body temperature is 98.6 degrees:\n", "\n", "* If it is likely, then the researcher does not reject his initial assumption that the average adult body temperature is 98.6 degrees. There is not enough evidence to do otherwise.\n", "\n", "* If it is unlikely, then:\n", " \n", " - either the researcher's initial assumption is correct and he experienced a very unusual event;\n", " - or the researcher's initial assumption is incorrect.\n", "\n", "In statistics, we generally don't make claims that require us to believe that a very unusual event happened. That is, in the practice of statistics, if the evidence (data) we collected is unlikely in light of the initial assumption, then we reject our initial assumption." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Making the Decision" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In statistics, there are two ways to determine whether the evidence is likely or unlikely given the initial assumption:\n", "\n", "* We could take the \"critical value approach\" (favored in many of the older textbooks).\n", "* Or, we could take the \"P-value approach\" (what is used most often in research, journal articles, and statistical software)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Errors in Hypothesis Testing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Type I error: The null hypothesis is rejected when it is true.\n", "\n", "Type II error: The null hypothesis is not rejected when it is false." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Critical Value Approach" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Specify the null and alternative hypotheses.\n", "\n", "2. Using the sample data and assuming the null hypothesis is true, calculate the value of the test statistic. To conduct the hypothesis test for the population mean $\\mu$, we use the t-statistic $t =\\dfrac {\\bar{x}− \\mu} {s / \\sqrt{n}}$ which follows a t-distribution with n - 1 degrees of freedom (s is the stddev of the sample).\n", "\n", "3. Determine the critical value by finding the value of the known distribution of the test statistic such that the probability of making a Type I error — which is denoted α (greek letter \"alpha\") and is called the \"significance level of the test\" — is small (typically 0.01, 0.05, or 0.10).\n", "\n", "4. Compare the test statistic to the critical value. If the test statistic is more extreme in the direction of the alternative than the critical value, reject the null hypothesis in favor of the alternative hypothesis. If the test statistic is less extreme than the critical value, do not reject the null hypothesis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# P-value Approach" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Specify the null and alternative hypotheses.\n", "\n", "2. Using the sample data and assuming the null hypothesis is true, calculate the value of the test statistic. Again, to conduct the hypothesis test for the population mean $\\mu$, we use the t-statistic $t = \\dfrac {\\bar{x} - \\mu} {s / \\sqrt{n}}$\n", "which follows a t-distribution with n - 1 degrees of freedom.\n", "\n", "3. Using the known distribution of the test statistic, calculate the P-value: \"If the null hypothesis is true, what is the probability that we'd observe a more extreme test statistic in the direction of the alternative hypothesis than we did?\" \n", "\n", "4. Set the significance level, α, the probability of making a Type I error to be small — 0.01, 0.05, or 0.10. Compare the P-value to α. If the P-value is less than (or equal to) α, reject the null hypothesis in favor of the alternative hypothesis. If the P-value is greater than α, do not reject the null hypothesis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example: 2-tailed Test\n", "\n", "A manufacturer claims that the thickness of the spearmint gum it produces is 7.5 one-hundredths of an inch. A quality control specialist regularly checks this claim. On one production run, he took a random sample of n = 10 pieces of gum and measured their thickness." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "%pylab inline\n", "pylab.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = np.array([7.65, 7.60, 7.65, 7.70, 7.55, 7.55, 7.40, 7.40, 7.50, 7.50])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$H_0$: $\\mu = 7.5$\n", "\n", "$H_1$: $\\mu \\neq 7.5$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "t-statistic = 1.54\n" ] } ], "source": [ "sample_mean = np.mean(data)\n", "sample_stddev = np.std(data, ddof=1)\n", "\n", "# Assuming null hypothesis is true:\n", "population_mean = 7.5\n", "n = len(data)\n", "t_stat = (sample_mean - population_mean) / (sample_stddev / np.sqrt(n))\n", "print('t-statistic = {:.2f}'.format(t_stat))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The assumptions here are:\n", "\n", "* The sample mean follows normal distribution assuming the null hypothesis is true.\n", "* The t-statistic follows Student's t-distribution assuming the null hypothesis is true.\n", "* Population variance = sample variance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us set the significance level of the test as" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sig_level = 0.05" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we're using the p-value approach, we'll need to find the probability of having a more extreme value than the test statistic." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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8PDx00xqNRq9A/NOuXbvQoUMH3bQQAtHR0YiKisLOnTtrNSszT1SQD0rcCRHy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\nAnsu5QBcOBhj1VAs7LAYD2NJm5Z4SUp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"text/plain": [ "<matplotlib.figure.Figure at 0x11de614a518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.stats import t\n", "\n", "x = np.linspace(-3, 3, num=50)\n", "y = t.pdf(x, df=n-1)\n", "\n", "figure, ax = pylab.subplots(1, 1)\n", "pylab.plot(x, y)\n", "ax.fill_between(x, 0, y, where=x > abs(t_stat))\n", "ax.fill_between(x, 0, y, where=x < -abs(t_stat))\n", "pylab.title(\"Area under PDF curve, df=n-1\")\n", "pylab.xlabel('x')\n", "pylab.ylabel('p(x)');" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p_val = 2 * t.sf(t_stat, df=n-1)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P-value=0.16 at significance level=0.05\n" ] } ], "source": [ "print('P-value={:.2f} at significance level={:.2f}'.format(p_val, sig_level))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since P-value of observing a test statistic more extreme than the test statistic is > the significance level, we fail to reject the null hypothesis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example: Right-tailed Test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example: Right-tailed test\n", "\n", "An engineer measured the Brinell hardness of 25 pieces of ductile iron that were subcritically annealed. The resulting data were:\n", "\n", "170 \t167 \t174 \t179 \t179\n", "156 \t163 \t156 \t187 \t156\n", "183 \t179 \t174 \t179 \t170\n", "156 \t187 \t179 \t183 \t174\n", "187 \t167 \t159 \t170 \t179\n", "\n", "Conduct a hypothesis test with $H_0$: the mean Brinell hardness of all such ductile iron pieces is greater than 170." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$H_0$: Mean Brinell hardness of all such ductile iron pieces > 170.\n", "\n", "$H_{alt}$: Mean Brinell hardness of all such ductile iron pieces is less than 170." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = np.array([170, 167, 174, 179, 179, 156, 163, 156, 187, 156, 183,\n", " 179, 174, 179, 170, 156, 187, 179, 183, 174, 187, 167, 159, \n", " 170, 179])\n", "\n", "sample_mean = np.mean(data)\n", "sample_std = np.std(data, ddof=1)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "t_stat = 1.22\n" ] } ], "source": [ "population_mean = 170\n", "n = len(data)\n", "t_stat = (sample_mean - population_mean) / (sample_std / np.sqrt(n))\n", "\n", "print('t_stat = {:.2f}'.format(t_stat))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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K0c5EiL5hEE3slI7C7iAcnSCC+oN2b1Y6CjMyoxUQrVYLd/d//jrRaDR6BeJO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\nYVbioHtn5N0qRunh/UpHYTD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"text/plain": [ "<matplotlib.figure.Figure at 0x11de614a3c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.stats import t\n", "\n", "x = np.linspace(-3, 3, num=50)\n", "y = t.pdf(x, df=n-1)\n", "\n", "figure, ax = pylab.subplots(1, 1)\n", "pylab.plot(x, y)\n", "ax.fill_between(x, 0, y, where=x > abs(t_stat))\n", "pylab.title(\"Area under PDF curve, df=n-1\")\n", "pylab.xlabel('x')\n", "pylab.ylabel('p(x)');" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P-value=0.12 at significance level=0.05\n" ] } ], "source": [ "p_val = t.sf(t_stat, df=n-1)\n", "sig_level = 0.05\n", "print('P-value={:.2f} at significance level={:.2f}'.format(p_val, sig_level))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the p_value is greater than the significance level, we fail to reject the null hypothesis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example: Left-tailed Test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A biologist was interested in determining whether sunflower seedlings treated with an extract from Vinca minor roots resulted in a lower average height of sunflower seedlings than the standard height of 15.7 cm. The biologist treated a random sample of n = 33 seedlings with the extract and subsequently obtained the following heights:\n", "\n", "\n", " 11.5 \t11.8 \t15.7 \t16.1 \t14.1 \t10.5\n", " 15.2 \t19.0 \t12.8 \t12.4 \t19.2 \t13.5\n", " 16.5 \t13.5 \t14.4 \t16.7 \t10.9 \t13.0\n", " 15.1 \t17.1 \t13.3 \t12.4 \t8.5 \t14.3\n", " 12.9 \t11.1 \t15.0 \t13.3 \t15.8 \t13.5\n", " 9.3 \t12.2 \t10.3\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, \n", "\n", "$H_0$: Average height of sunflower seedlings < 15.7 cm.\n", "\n", "$H_{alt}$: Average height of sunflower seedlings $\\geq$ 15.7 cm." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "t_stat = -4.60\n" ] } ], "source": [ "data = np.array([\n", " 11.5, 11.8, 15.7, 16.1, 14.1, 10.5,\n", "15.2, 19.0, 12.8, 12.4, 19.2, 13.5,\n", "16.5, 13.5, 14.4, 16.7, 10.9, 13.0,\n", "15.1, 17.1, 13.3, 12.4, 8.5, 14.3,\n", "12.9, 11.1, 15.0, 13.3, 15.8, 13.5,\n", "9.3, 12.2, 10.3])\n", "\n", "sample_mean = np.mean(data)\n", "sample_std = np.std(data, ddof=1)\n", "\n", "population_mean = 15.7\n", "n = len(data)\n", "t_stat = (sample_mean - population_mean) / (sample_std / np.sqrt(n))\n", "print('t_stat = {:.2f}'.format(t_stat))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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frW9mZiYyM/+6B01UVBRUKpVJcjeWvb292WcEOKexWVJOm2MpsB8QAXszzmuu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\nLiCMMcYahQsIY4yxRvn/GhLDtKr1HzwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11de7b45e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.stats import t\n", "\n", "x = np.linspace(-6, 2, num=50)\n", "y = t.pdf(x, df=n-1)\n", "\n", "figure, ax = pylab.subplots(1, 1)\n", "pylab.plot(x, y)\n", "ax.fill_between(x, 0, y, where=x < t_stat)\n", "pylab.title(\"Area under PDF curve, df=n-1\")\n", "pylab.xlabel('x')\n", "pylab.ylabel('p(x)');" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P-value=1.00 at significance level=0.05\n" ] } ], "source": [ "p_val = t.sf(t_stat, df=n-1)\n", "sig_level = 0.05\n", "print('P-value={:.2f} at significance level={:.2f}'.format(p_val, sig_level))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since p-value is > the significance level, we fail to reject the null hypothesis. Thus, the biologist concludes that the mean height of all such sunflower seedlings is less than 15.7 cm." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Power of a Test\n", "\n", "The probability of not committing a type-II error is called the power of a test.\n", "\n", "# Factors Affecting the Power of a Test\n", "\n", "* Sample Size: As sample size increases, the probability of not committing a type-II error increases, so the power also increases.\n", "\n", "* Significance level: The higher the significance level, the higher the power of the test.\n", "\n", "* The \"true\" value of the parameter being tested: The greater the difference between the \"true\" value of a parameter and the value specified in the null hypothesis, the greater the power of the test. That is, the greater the effect size, the greater the power of the test." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Why is Power Analysis Important?\n", "\n", "Consider a research experiment where the p-value computed from the data was 0.12. As a result, one would fail to reject the null hypothesis because this p-value is larger than α = 0.05. However, there still exist two possible cases for which we failed to reject the null hypothesis:\n", "\n", "* The null hypothesis is a reasonable conclusion,\n", "* The sample size is not large enough to either accept or reject the null hypothesis, i.e., additional samples might provide additional evidence.\n", "\n", "Power analysis is the procedure that researchers can use to determine if the test contains enough power to make a reasonable conclusion. From another perspective power analysis can also be used to calculate the number of samples required to achieve a specified level of power." ] } ], "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": 2 }	gpl-3.0
greenelab/GCB535	14_R-II/In_Class_R_2.ipynb	1	7313	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to R#" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember to set your kernel to R (SageMath).\n", "\n", "Load the same dataset from previous exercises (genes.table) into a variable called \"genes\":" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Linear Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are the expression values for geneA and geneC correlated across samples? Try to plot the data with \"plot\" function (put geneA on the X axis and geneC on the Y axis):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do a simple linear regression using geneC as the response variable and geneA as the explanatory variable." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the regression result on the scatter plot using \"abline\":" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the \"summary\" function on the linear regression result to see if there is a significant correlation between geneA and geneC :" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the p-value for the regression? What is the R-squared value? Is there a correlation between geneA and geneC?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Plotting With R" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use boxplot to plot the expression level of geneA" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do boxplots show the mean or the median of geneA?" ] }, { "cell_type": "raw", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let’s plot the distributions of gene expression values with histograms. Plot the histogram for each gene (geneA, geneB, geneC, geneD). You can plot them in four separate plots. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above histograms, which genes have approximately normally distributed expression values? " ] }, { "cell_type": "raw", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Homework (10 points) #" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the dataset \"single_cell_rnaseq_hw1.txt\" into a variable named \"scdata\":" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Attach the dataset so that you can refer to the columns by column names." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the expression level of \"Sub1\" and \"Scg2\" using a scatter plot (put \"Sub1\" on Y axis and \"Scg2\" on X axis)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do a simple linear regression on \"Sub1\" and \"Scg2\" (Sub1 as the response variable and Scg2 as the explanatory variable)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the regression line on the previous scatter plot." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check the regression results. What is the p-value for the regression? " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the same regression results, what is the R-squared value?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given these regression results, would you say there is or is not an interesting correlation between Sub1 and Scg2 and why or why not?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use a boxplot to plot the expression levels of Sub1 and Scg2." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which gene has larger median?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the distribution of gene Sub1 with a histogram." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the distribution of gene Scg2 with a histogram." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do they look normally distributed?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.5.1" }, "name": "In_Class_R_2.ipynb" }, "nbformat": 4, "nbformat_minor": 1 }	bsd-3-clause
rdhyee/dlab-finance	basic-taq/Convert BBO numpy pytables.ipynb	1	26098	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Remaining questions\n", "\n", " - Are there better approaches to allocating the table? (i.e., setting size correctly from start)\n", " - I see 24 threads for python 3.4 on my macbook retina, but pegged at 100% We should be saturating the disk (i.e., <100% CPU)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from zipfile import ZipFile\n", "from datetime import datetime\n", "\n", "from pytz import timezone\n", "import numpy as np\n", "from numpy.lib import recfunctions\n", "import tables as tb" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fname = '../local_data/EQY_US_ALL_BBO_20140206.zip'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "fields of dailyquotes file taqquote\n", "[0:8]HHMMSSXXX\n", "[9] text EXCHANGE N Nyse T/Q NASDAQ\n", "[10:25] text symbol 6+10\n", "[26:36] bid price 7+4\n", "[37:43] bid size (units)\n", "[44:54] ask price 7+4\n", "[55:61] ask size\n", "[62] text Condition of quote\n", "[63:66] market maker\n", "[67] bid exchange\n", "[68] ask aexchange\n", "[69:84] int seqno\n", "[85] int bbo indicator\n", "[86] int NASDAQ BBO indocator\n", "[87] text cancel/correction\n", "[88] text C=CTA N=UTP\n", "[90] text Retail interest indicator\n", "[...]\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read this in" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "```\n", "# Two characters are also used at the end of each line as a line indicator\n", "widths = [9, 1, 16, 11, 7, 11, 7, 1, 4, 1, 1, 16, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n", "\n", "w = np.array(widths)\n", "w.cumsum()\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we'll have 98 bytes total with the `\\r\\n` on the end." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "BYTES_PER_LINE = 98" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "```\n", "# Note - we're using object here (as pandas will do this anyway), \n", "# and we'll need to convert back to fixed width strings later\n", "# We can get the widths from the widths list above\n", "old_dtype = [('Time', np.datetime64),\n", " ('Exchange', object), # \|S1\n", " ('Symbol', object), # \|S16, etc.\n", " ('Bid_Price', np.float64),\n", " ('Bid_Size', np.int32),\n", " ('Ask_Price', np.float64),\n", " ('Ask_Size', np.int32),\n", " ('Quote_Condition', object),\n", " ('Market_Maker', np.int), # This is blank - want to skip?\n", " ('Bid_Exchange', object),\n", " ('Ask_Exchange', object),\n", " ('Sequence_Number', np.int64),\n", " ('National_BBO_Ind', np.int8), # These aren't really numbers\n", " ('National_BBO_Ind', np.int8), # Maybe should be string?\n", " ('Quote_Cancel_Correction', object),\n", " ('Source_of_Quote', object),\n", " ('Retail_Interest_Indicator_RPI', object),\n", " ('Short_Sale_Restriction_Indicator', object),\n", " ('LULD_BBO_Indicator_CQS', object),\n", " ('LULD_BBO_Indicator_UTP', object),\n", " ('FINRA_ADF_MPID_Indicator', object),\n", " ('SIP_generated_Message_Identifier', object),\n", " ('National_BBO_LULD_Indicator', object)\n", " ] # Then there's two characters for newline\n", "\n", "# This was for pandas' screwball approach to dtype\n", "# names = [a for a,b in dtype]\n", "# dtype = dict(dtype)\n", "```" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Note that the '\|' character means byte order doesn't matter, \n", "# which it never will for \"bytes\" (which is what 'S' stands for)\n", "initial_dtype = [('Time', 'S9'), # HHMMSSmmm, should be in Eastern Time (ET)\n", " # ('hour', '\|S2'),\n", " # ('minute', '\|S2'),\n", " # ('second', '\|S2'),\n", " # ('msec', '\|S3'),\n", " ('Exchange', 'S1'),\n", " ('Symbol', 'S16'), # Maybe should split into 6 root + 10 suffix\n", " ('Bid_Price', 'S11'), # 7.4 (fixed point)\n", " ('Bid_Size', 'S7'),\n", " ('Ask_Price', 'S11'), # 7.4\n", " ('Ask_Size', 'S7'),\n", " ('Quote_Condition', 'S1'),\n", " ('Market_Maker', 'S4'), # This ends up getting discarded, it should always be b' '\n", " ('Bid_Exchange', 'S1'),\n", " ('Ask_Exchange', 'S1'),\n", " ('Sequence_Number', 'S16'),\n", " ('National_BBO_Ind', 'S1'),\n", " ('NASDAQ_BBO_Ind', 'S1'),\n", " ('Quote_Cancel_Correction', 'S1'),\n", " ('Source_of_Quote', 'S1'),\n", " ('Retail_Interest_Indicator_RPI', 'S1'),\n", " ('Short_Sale_Restriction_Indicator', 'S1'),\n", " ('LULD_BBO_Indicator_CQS', 'S1'),\n", " ('LULD_BBO_Indicator_UTP', 'S1'),\n", " ('FINRA_ADF_MPID_Indicator', 'S1'),\n", " ('SIP_generated_Message_Identifier', 'S1'),\n", " ('National_BBO_LULD_Indicator', 'S1'),\n", " ('newline', 'S2')]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Justin and Pandas (I think) use time64, as does PyTables.\n", "# We could use msec from beginning of day for now in an int16\n", "# (maybe compare performance to datetime64? But dates should compress very well...)\n", "time_col = 'Time'\n", "\n", "convert_dtype = [\n", " ('Bid_Price', np.float64),\n", " ('Bid_Size', np.int32),\n", " ('Ask_Price', np.float64),\n", " ('Ask_Size', np.int32),\n", " # ('Market_Maker', np.int8), # This is not currently used, and should always be b' '\n", " ('Sequence_Number', np.int64),\n", " # ('National_BBO_Ind', np.int8), # The _Ind fields are actually categorical - leaving as strings\n", " # ('NASDAQ_BBO_Ind', np.int8),\n", " ]\n", "\n", "passthrough_strings = ['Exchange',\n", " 'Symbol',\n", " 'Quote_Condition',\n", " 'Bid_Exchange',\n", " 'Ask_Exchange',\n", " 'National_BBO_Ind', # The _Ind fields are actually categorical - leaving as strings\n", " 'NASDAQ_BBO_Ind',\n", " 'Quote_Cancel_Correction',\n", " 'Source_of_Quote',\n", " 'Retail_Interest_Indicator_RPI',\n", " 'Short_Sale_Restriction_Indicator',\n", " 'LULD_BBO_Indicator_CQS',\n", " 'LULD_BBO_Indicator_UTP',\n", " 'FINRA_ADF_MPID_Indicator',\n", " 'SIP_generated_Message_Identifier',\n", " 'National_BBO_LULD_Indicator']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Lifted from blaze.pytables\n", "def dtype_to_pytables(dtype):\n", " \"\"\" Convert NumPy dtype to PyTable descriptor\n", " Examples\n", " --------\n", " >>> from tables import Int32Col, StringCol, Time64Col\n", " >>> dt = np.dtype([('name', 'S7'), ('amount', 'i4'), ('time', 'M8[us]')])\n", " >>> dtype_to_pytables(dt) # doctest: +SKIP\n", " {'amount': Int32Col(shape=(), dflt=0, pos=1),\n", " 'name': StringCol(itemsize=7, shape=(), dflt='', pos=0),\n", " 'time': Time64Col(shape=(), dflt=0.0, pos=2)}\n", " \"\"\"\n", " d = {}\n", " for pos, name in enumerate(dtype.names):\n", " dt, _ = dtype.fields[name]\n", " if issubclass(dt.type, np.datetime64):\n", " tdtype = tb.Description({name: tb.Time64Col(pos=pos)}),\n", " else:\n", " tdtype = tb.descr_from_dtype(np.dtype([(name, dt)]))\n", " el = tdtype[0] # removed dependency on toolz -DJC\n", " getattr(el, name)._v_pos = pos\n", " d.update(el._v_colobjects)\n", " return d" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# The \"easy\" dtypes are the \"not datetime\" dtypes\n", "easy_dtype = []\n", "convert_dict = dict(convert_dtype)\n", "\n", "for name, dtype in initial_dtype:\n", " if name in convert_dict:\n", " easy_dtype.append( (name, convert_dict[name]) )\n", " elif name in passthrough_strings:\n", " easy_dtype.append( (name, dtype) )\n", "\n", "# PyTables will not accept np.datetime64, we hack below, but we use it to work with the blaze\n", "# function above.\n", "# We also shift Time to the end (while I'd rather maintain order), as it's more efficient for Dav\n", "# given the technical debt he's already built up.\n", "pytables_dtype = easy_dtype + [('Time', 'datetime64[ms]')]\n", "\n", "pytables_desc = dtype_to_pytables(np.dtype(pytables_dtype))\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class TAQ2HDF5:\n", " \n", " def __init__(self, taq_fname):\n", " self.taq_fname = taq_fname\n", " \n", " def convert_taq(self):\n", " # The below doesn't work for pandas (and neither does `unzip` from the command line). Probably want to use\n", " # something like `7z x -so my_file.zip 2> /dev/null` if we use pandas.\n", " with ZipFile(self.taq_fname) as zfile:\n", " for inside_f in zfile.filelist:\n", " # The original filename is available as inside_f.filename\n", " with zfile.open(inside_f.filename) as infile:\n", " first = infile.readline()\n", "\n", " # You need to use bytes to split bytes\n", " dateish, numlines = first.split(b\":\")\n", " numlines = int(numlines)\n", " \n", " # Get dates to combine with times later\n", " # This is a little over-trusting of the spec...\n", " self.month = int(dateish[2:4])\n", " self.day = int(dateish[4:6])\n", " self.year = int(dateish[6:10])\n", "\n", " # Should I use a context manager here?\n", " h5_table = self.setup_hdf5(inside_f.filename, numlines)\n", " try:\n", " self.raw_conversion(numlines, infile, h5_table)\n", " finally:\n", " self.finalize_hdf5()\n", " \n", " def setup_hdf5(self, h5_fname_root, numlines):\n", " # We're using aggressive compression and checksums, since this will likely stick around\n", " # Stopping one level short of max compression - don't be greedy.\n", " self.h5 = tb.open_file(h5_fname_root + '.h5', title=h5_fname_root, mode='w', \n", " filters=tb.Filters(complevel=8, complib='blosc:lz4hc', fletcher32=True) )\n", " \n", " return self.h5.create_table('/', 'daily_quotes', description=pytables_desc, expectedrows=numlines)\n", " \n", " \n", " def finalize_hdf5(self):\n", " self.h5.close()\n", "\n", " def process_chunk(self, all_strings):\n", " # This is unnecessary copying\n", " easy_converted = all_strings.astype(easy_dtype)\n", " \n", " # These don't have the decimal point in the TAQ file\n", " for dollar_col in ['Bid_Price', 'Ask_Price']:\n", " easy_converted[dollar_col] /= 10000\n", " \n", " # Currently, there doesn't seem to be any utility to converting to numpy.datetime64\n", " # PyTables wants float64's corresponding to the POSIX Standar (relative to 1970-01-01, UTC)\n", " converted_time = [datetime( self.year, self.month, self.day, \n", " int(raw[:2]), int(raw[2:4]), int(raw[4:6]),\n", " int(raw[6:9]) * 1000, # msec needs to be microsec \n", " tzinfo=timezone('US/Eastern') ).timestamp()\n", " for raw in all_strings['Time'] ]\n", "\n", " \n", " # More unnecessary copying\n", " records = recfunctions.append_fields(easy_converted, 'Time', converted_time, usemask=False)\n", " \n", " return records\n", " \n", " \n", " # at some point, we might optimize chunksize. For now, assume PyTables is smart\n", " def raw_conversion(self, numlines, infile, out, chunksize=None):\n", " '''Read raw bytes from TAQ, write to HDF5'''\n", " index = 0\n", " \n", " if chunksize is None:\n", " chunksize = out.chunkshape[0]\n", " \n", " while(True):\n", " raw_bytes = infile.read(BYTES_PER_LINE * chunksize)\n", " \n", " ## Break after 10 lines\n", " index = index + 1\n", " if index == 10:\n", " break\n", " \n", " if not raw_bytes:\n", " break\n", " # If we use asarray with this dtype, it crashes Python! (might not be true anymore)\n", " # ndarray gives 'S' arrays instead of chararrays (as recarray does)\n", " all_strings = np.ndarray(chunksize, buffer=raw_bytes, dtype=initial_dtype)\n", "\n", " # This approach doesn't work...\n", " # out[chunk_start:chunk_stop, 1:] = all_strings[:,1:-1]\n", " \n", " out.append( self.process_chunk(all_strings) )" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[b'040000901PA', b'000000000000000000000000000000000000R', b'PP000000000000001422', b'C']\n", "b'00' b'04' : b'00' b'901'\n", "[b'075300081PA', b'000000000000000000000007294000000027R', b'PP000000000007625512', b'C']\n", "b'53' b'07' : b'00' b'081'\n", "[b'075300085PA', b'000000000000000000000006076000000010R', b'PP000000000007625612', b'C']\n", "b'53' b'07' : b'00' b'085'\n", "[b'075300089PA', b'000004190000000027000006076000000010R', b'PP000000000007625712', b'C']\n", "b'53' b'07' : b'00' b'089'\n", "[b'075300094PA', b'000005407000000027000006076000000010R', b'PP000000000007625812', b'C']\n", "b'53' b'07' : b'00' b'094'\n" ] } ], "source": [ "i = 0\n", "with ZipFile(fname) as zfile:\n", " for inside_f in zfile.filelist:\n", " with zfile.open(inside_f.filename) as infile:\n", " first = infile.readline() # I dont want to print the first line\n", " while i < 5:\n", " first = infile.readline()\n", " i = i + 1\n", "\n", " dateish = first.split()\n", " print(dateish) # split \n", " #print(dateish[0]) # the first chunck node \n", " \n", " hour = dateish[0][0:2]\n", " month = dateish[0][2:4]\n", " second = dateish[0][4:6]\n", " mesc = dateish[0][6:9]\n", " print(month, hour, \":\", second, mesc)\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class read:\n", " def __init__(self, taq_fname):\n", " self.taq_fname = taq_fname\n", " \n", " def convert_taq(self):\n", " # The below doesn't work for pandas (and neither does `unzip` from the command line). Probably want to use\n", " # something like `7z x -so my_file.zip 2> /dev/null` if we use pandas\n", " i = 0\n", " with ZipFile(self.taq_fname) as zfile:\n", " for inside_f in zfile.filelist:\n", " with zfile.open(inside_f.filename) as infile:\n", " first = infile.readline() # I dont want to print the first line\n", " while i < 1:\n", " first = infile.readline()\n", " i = i + 1\n", "\n", " dateish = first.split()\n", " print(dateish) # split \n", " \n", " hour = dateish[0][0:2]\n", " month = dateish[0][2:4]\n", " second = dateish[0][4:6]\n", " mesc = dateish[0][6:9]\n", " print(hour, month, second, mesc)\n", "\n", " # Should I use a context manager here?\n", " h5_table = self.setup_hdf5(inside_f.filename, numlines)\n", " try:\n", " self.raw_conversion(numlines, infile, h5_table)\n", " finally:\n", " self.finalize_hdf5()\n", " \n", " def setup_hdf5(self, h5_fname_root, numlines):\n", " # We're using aggressive compression and checksums, since this will likely stick around\n", " # Stopping one level short of max compression - don't be greedy.\n", " self.h5 = tb.open_file(h5_fname_root + '.h5', title=h5_fname_root, mode='w', \n", " filters=tb.Filters(complevel=8, complib='blosc:lz4hc', fletcher32=True) )\n", " \n", " return self.h5.create_table('/', 'daily_quotes', description=pytables_desc, expectedrows=numlines)\n", " \n", " \n", " def finalize_hdf5(self):\n", " self.h5.close()\n", "\n", " def process_chunk(self, all_strings):\n", " # This is unnecessary copying\n", " easy_converted = all_strings.astype(easy_dtype)\n", " \n", " # These don't have the decimal point in the TAQ file\n", " for dollar_col in ['Bid_Price', 'Ask_Price']:\n", " easy_converted[dollar_col] /= 10000\n", " \n", " # Currently, there doesn't seem to be any utility to converting to numpy.datetime64\n", " # PyTables wants float64's corresponding to the POSIX Standar (relative to 1970-01-01, UTC)\n", " converted_time = [datetime( self.year, self.month, self.day, \n", " int(raw[:2]), int(raw[2:4]), int(raw[4:6]),\n", " int(raw[6:9]) * 1000, # msec needs to be microsec \n", " tzinfo=timezone('US/Eastern') ).timestamp()\n", " for raw in all_strings['Time'] ]\n", "\n", " \n", " # More unnecessary copying\n", " records = recfunctions.append_fields(easy_converted, 'Time', converted_time, usemask=False)\n", " \n", " return records\n", " \n", " \n", " # at some point, we might optimize chunksize. For now, assume PyTables is smart\n", " def raw_conversion(self, numlines, infile, out, chunksize=None):\n", " '''Read raw bytes from TAQ, write to HDF5'''\n", " index = 0\n", " \n", " if chunksize is None:\n", " chunksize = out.chunkshape[0]\n", " \n", " while(True):\n", " raw_bytes = infile.read(BYTES_PER_LINE * chunksize)\n", " \n", " if not raw_bytes:\n", " break\n", " # If we use asarray with this dtype, it crashes Python! (might not be true anymore)\n", " # ndarray gives 'S' arrays instead of chararrays (as recarray does)\n", " all_strings = np.ndarray(chunksize, buffer=raw_bytes, dtype=initial_dtype)\n", "\n", " # This approach doesn't work...\n", " # out[chunk_start:chunk_stop, 1:] = all_strings[:,1:-1]\n", " \n", " out.append( self.process_chunk(all_strings) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's process our file" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_run = TAQ2HDF5(fname)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 2.69 s\n" ] } ], "source": [ "%time test_run.convert_taq()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "h5 = tb.open_file('./taqquote20140206.h5')\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ (b'P', b'A ', 0.0, 0, 0.0, 0, b'R', b'P', b'P', 14, b'2', b'2', b' ', b'C', b' ', b' ', b' ', b' ', b' ', b' ', b' ', 1391676960.901),\n", " (b'P', b'A ', 0.0, 0, 72.94, 27, b'R', b'P', b'P', 76255, b'1', b'2', b' ', b'C', b' ', b' ', b' ', b' ', b' ', b' ', b' ', 1391690940.081),\n", " (b'P', b'A ', 0.0, 0, 60.76, 10, b'R', b'P', b'P', 76256, b'1', b'2', b' ', b'C', b' ', b' ', b' ', b' ', b' ', b' ', b' ', 1391690940.085),\n", " ...,\n", " (b'N', b'A ', 58.17, 4, 58.19, 7, b'R', b'N', b'N', 21017149, b'6', b'2', b'A', b'C', b'A', b' ', b' ', b' ', b' ', b' ', b'A', 1391717739.258),\n", " (b'N', b'A ', 58.17, 5, 58.19, 7, b'R', b'N', b'N', 21017154, b'0', b'2', b'A', b'C', b'A', b' ', b' ', b' ', b' ', b' ', b'A', 1391717739.258),\n", " (b'B', b'A ', 58.17, 1, 58.2, 1, b'R', b'B', b'B', 21017424, b'0', b'2', b' ', b'C', b' ', b' ', b' ', b' ', b' ', b' ', b' ', 1391717739.419)], \n", " dtype=[('Exchange', 'S1'), ('Symbol', 'S16'), ('Bid_Price', '<f8'), ('Bid_Size', '<i4'), ('Ask_Price', '<f8'), ('Ask_Size', '<i4'), ('Quote_Condition', 'S1'), ('Bid_Exchange', 'S1'), ('Ask_Exchange', 'S1'), ('Sequence_Number', '<i8'), ('National_BBO_Ind', 'S1'), ('NASDAQ_BBO_Ind', 'S1'), ('Quote_Cancel_Correction', 'S1'), ('Source_of_Quote', 'S1'), ('Retail_Interest_Indicator_RPI', 'S1'), ('Short_Sale_Restriction_Indicator', 'S1'), ('LULD_BBO_Indicator_CQS', 'S1'), ('LULD_BBO_Indicator_UTP', 'S1'), ('FINRA_ADF_MPID_Indicator', 'S1'), ('SIP_generated_Message_Identifier', 'S1'), ('National_BBO_LULD_Indicator', 'S1'), ('Time', '<f8')])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h5.root.daily_quotes[:]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h5.close()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<__main__.test at 0x82aa320>" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "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 }	isc
catalystcomputing/DSIoT-Python-sessions	Session3/code/03 Supervised Learning - 08 Decision Tree Classifier.ipynb	1	67385	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Decision Tree Classifier\n", "from sklearn import datasets\n", "from sklearn import metrics\n", "from sklearn.tree import DecisionTreeClassifier\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# load the iris datasets\n", "# for info on this dataset, refer to the logistic_regression script\n", "dataset = datasets.load_iris()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Let us now build a pandas dataframe hosting the data at hand\n", "\n", "# We first need the list of feature names for our columns\n", "# It is already stored in the dataset. Let's use it\n", "lfeat = dataset.feature_names " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We now build the Dataframe, with the data as argument\n", "# and the list of column names as keyword argument\n", "df_iris = pd.DataFrame(dataset.data, columns = lfeat)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Printing data up to the 5th sample\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sepal length (cm)</th>\n", " <th>sepal width (cm)</th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", "0 5.1 3.5 1.4 0.2\n", "1 4.9 3.0 1.4 0.2\n", "2 4.7 3.2 1.3 0.2\n", "3 4.6 3.1 1.5 0.2\n", "4 5.0 3.6 1.4 0.2" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print \"Printing data up to the 5th sample\"\n", "df_iris.iloc[:5,:] # Look at the first 5 samples for all features." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We also want to add the regression target\n", "# Let's create a new column :\n", "df_iris[\"Species\"] = dataset.target # Must have the correct size of course" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Printing data up to the 5th sample\n", "Also print the target\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sepal length (cm)</th>\n", " <th>sepal width (cm)</th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " <th>Species</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", "0 5.1 3.5 1.4 0.2 \n", "1 4.9 3.0 1.4 0.2 \n", "2 4.7 3.2 1.3 0.2 \n", "3 4.6 3.1 1.5 0.2 \n", "4 5.0 3.6 1.4 0.2 \n", "\n", " Species \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Let's review our complete dataframe:\n", "print\n", "print \"Printing data up to the 5th sample\"\n", "print \"Also print the target\"\n", "df_iris.iloc[:5,:] # Look at the first 5 samples for all features incuding target" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# we are now going to fit a Decision Tree model to the data\n", "\n", "# Let's use an example to understand what decision trees do\n", "# Picture a doctor and his sick patient\n", "# The doctor follows a protocol to find out what ails the patient\n", "# He may ask : how old are you, where does it hurt and so on\n", "# This will allow him to narrow down the options and eventually\n", "# find out the problem\n", "\n", "# Decision trees proceed in the same way :\n", "# They make a series of separation in the feature space\n", "# e.g. if feat1 > c => classify as class 1\n", "# The features on which to make the separation and the threshold value\n", "# are learnt on the training data by optimising a criterion like minimising the classification error at each split\n", "\n", "#As before, we create an instance of the model\n", "model = DecisionTreeClassifier()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", " max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=None, splitter='best')\n" ] } ], "source": [ "# Which we then fit to the training data X, Y\n", "# with pandas we have to split the df in two :\n", "# the feature part (X) and the target part (Y)\n", "# This is done below :\n", "\n", "data = df_iris[lfeat].values\n", "target = df_iris[\"Species\"].values\n", "model.fit(data, target)\n", "print(model)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 50\n", " 1 1.00 1.00 1.00 50\n", " 2 1.00 1.00 1.00 50\n", "\n", "avg / total 1.00 1.00 1.00 150\n", "\n", "[[50 0 0]\n", " [ 0 50 0]\n", " [ 0 0 50]]\n" ] } ], "source": [ "# make predictions\n", "# as before, we can use the model to make predictions on any data\n", "expected = target\n", "predicted = model.predict(data)\n", "# and evaluate the performance of the classification with standard metrics\n", "print(metrics.classification_report(expected, predicted))\n", "print(metrics.confusion_matrix(expected, predicted))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# This time we can see we got a perfect prediction - again" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Decision surface of a decision tree using paired features\n", "\n", "An example of plotting a \n", "\n", "http://scikit-learn.org/stable/auto_examples/tree/plot_iris.html" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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8/gfExHgGGenSBfbvdy9jlJTAtz9t5vZxlbtZea9/27UtrVrEMvHmc13v9J1/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"text/plain": [ "<matplotlib.figure.Figure at 0x19e09f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.datasets import load_iris\n", "from sklearn.tree import DecisionTreeClassifier\n", "\n", "# Parameters\n", "n_classes = 3\n", "plot_colors = \"bry\"\n", "plot_step = 0.02\n", "\n", "# Load data\n", "iris = load_iris()\n", "\n", "for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],\n", " [1, 2], [1, 3], [2, 3]]):\n", " # We only take the two corresponding features\n", " X = iris.data[:, pair]\n", " y = iris.target\n", "\n", " # Shuffle\n", " idx = np.arange(X.shape[0])\n", " np.random.seed(13)\n", " np.random.shuffle(idx)\n", " X = X[idx]\n", " y = y[idx]\n", "\n", " # Standardize\n", " mean = X.mean(axis=0)\n", " std = X.std(axis=0)\n", " X = (X - mean) / std\n", "\n", " # Train\n", " clf = DecisionTreeClassifier().fit(X, y)\n", "\n", " # Plot the decision boundary\n", " plt.subplot(2, 3, pairidx + 1)\n", "\n", " x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", " y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n", " np.arange(y_min, y_max, plot_step))\n", "\n", " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", " Z = Z.reshape(xx.shape)\n", " cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n", "\n", " plt.xlabel(iris.feature_names[pair[0]])\n", " plt.ylabel(iris.feature_names[pair[1]])\n", " plt.axis(\"tight\")\n", "\n", " # Plot the training points\n", " for i, color in zip(range(n_classes), plot_colors):\n", " idx = np.where(y == i)\n", " plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],\n", " cmap=plt.cm.Paired)\n", "\n", " plt.axis(\"tight\")\n", "\n", "plt.suptitle(\"Decision surface of a decision tree using paired features\")\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
JanetMatsen/Neo4j_meta4	jupyter/assess_networks_binary.ipynb	1	7029	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import Counter\n", "import glob\n", "import pandas as pd\n", "import re\n", "import subprocess\n", "import matplotlib as mpl\n", "mpl.use('Agg') \n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Analyze one csv:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tsv_files = []\n", "for filename in glob.iglob('../data_mining_Neo4j_v2_3_2/databases/*.tsv'):\n", " print(filename)\n", " tsv_files.append(filename)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cc = pd.read_csv(tsv_files[0], \n", " usecols=[1, 2, 3, 4], \n", " sep='\\t')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cc.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def assess_connected_components_tsv(tsv):\n", " results = dict()\n", " \n", " # general characteristics:\n", " results['# nodes in cc'] = tsv.shape[0] # number of nodes in connected components\n", " results['# organisms in cc'] = len(tsv['organism'].unique().tolist())\n", " \n", " num_components = len(tsv['ConnectedComponents'].unique().tolist())\n", " print(\"num unique connected components: {}\".format(num_components))\n", " results['# of components'] = num_components\n", " \n", " results['organism counts'] = dict(Counter(tsv['organism'])) #dict(Counter(tsv['organism']))\n", " \n", " return results\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def assess_connected_components_tsvs(cc_files):\n", " summary = pd.DataFrame()\n", " for cc in cc_files:\n", " tsv = pd.read_csv(cc, usecols=[1, 2, 3, 4], sep='\\t')\n", " print(tsv.shape)\n", " info_dict = assess_connected_components_tsv(tsv)\n", " \n", " # get file name\n", " m = re.search('/(db_binary_[.0-9]+.tsv)', cc) #.groups(1)\n", " info_dict['file'] = m.group(1)\n", " \n", " # get ready for Pandas\n", " for k, v in info_dict.items():\n", " info_dict[k] = [v]\n", " print(info_dict)\n", " info_df_row = pd.DataFrame(info_dict)\n", " summary = pd.concat([summary, info_df_row], axis=0)\n", " print('summary shape: {}'.format(summary.shape))\n", " print(summary)\n", " print(summary.shape[0])\n", " return summary" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assess_connected_components_tsvs(tsv_files)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def assess_sub_graphs(cc_files):\n", " summary = pd.DataFrame()\n", " for cc_file in cc_files: \n", " \n", " tsv = pd.read_csv(cc_file, usecols=[1, 2, 3, 4], sep='\\t')\n", " components = dict(Counter(tsv['ConnectedComponents'])) \n", " for c in components.keys():\n", " print(c)\n", " c_info = dict()\n", " c_info['ConnectedComponent'] = c\n", " c_info['Cutoff'] = None # TODO: parse from file name. \n", " \n", " nodes = tsv[tsv['ConnectedComponents'] == c]\n", " species_counts = dict(Counter(tsv['organism']))\n", " c_info['nodes'] = nodes.shape[0]\n", " c_info['species counts'] = species_counts\n", " \n", " c_info['cross-species'] = len(species_counts.keys()) > 1\n", " \n", " # get file name\n", " m = re.search('/(db_binary_[.0-9]+.tsv)', cc_file) #.groups(1)\n", " c_info['file'] = m.group(1)\n", " \n", " for k, v in c_info.items():\n", " c_info[k] = [v]\n", " print(c_info)\n", " \n", " c_info = pd.DataFrame(c_info)\n", " summary = pd.concat([summary, c_info], axis=0)\n", " print('summary shape: {}'.format(summary.shape))\n", " print(summary)\n", " print(summary.shape[0])\n", " return summary" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "connected_components = assess_sub_graphs(tsv_files)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "connected_components.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "connected_components.plot.scatter(x=)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assess_connected_components_tsv(cc)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pd.DataFrame.from_dict(assess_connected_components_tsv(cc))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def assess_component(cc):\n", " # Metrics for a signle connected component.\n", " # return counts of each organism,\n", " # entropy (?)\n", " pass" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
TheGU/deep_trading_notebook	CNN_Windows_MA_RSI.ipynb	1	932290	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "% matplotlib inline\n", "import os\n", "\n", "import numpy as np\n", "import pandas as pd \n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "from tensorflow.python.framework import ops\n", "from tensorflow.python.ops import clip_ops\n", "\n", "import pandas_ta as ta\n", "import utils \n", "\n", "tf.reset_default_graph()\n", "logs_path = os.path.join('log_tf')\n", "visualize_group_timeseries = True\n", "plot_row = 5 #How many rows do you want to plot in the visualization" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "\"\"\"Hyperparameters\"\"\"\n", "num_filt_1 = 16 #Number of filters in first conv layer\n", "num_filt_2 = 14 #Number of filters in second conv layer\n", "num_filt_3 = 8 #Number of filters in thirs conv layer\n", "num_fc_1 = 128 #Number of neurons in hully connected layer\n", "max_iterations = 2000\n", "model_num=7 #Number of model used for voting\n", "voting_times=3 #Threshold of voting\n", "batch_size = 64\n", "dropout = 0 # 1.0 #Dropout rate in the fully connected layer\n", "regularization = 1e-4\n", "learning_rate = 2e-6\n", "input_norm = False # Do you want z-score input normalization?\n", "# np.set_printoptions(threshold=np.inf)#print full array" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "group_by_percent = [0,5,10,25]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Time</th>\n", " <th>Open</th>\n", " <th>High</th>\n", " <th>Low</th>\n", " <th>Close</th>\n", " <th>Volume</th>\n", " <th>EMA_5</th>\n", " <th>EMA_30</th>\n", " <th>DIFF_NEXT_20</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1998-01-02</th>\n", " <td>0</td>\n", " <td>3.31397</td>\n", " <td>3.95098</td>\n", " <td>3.28236</td>\n", " <td>3.95098</td>\n", " <td>24947201.10</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1998-01-05</th>\n", " <td>0</td>\n", " <td>4.01177</td>\n", " <td>4.02635</td>\n", " <td>3.69325</td>\n", " <td>3.89020</td>\n", " <td>22344145.08</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>1998-01-06</th>\n", " <td>0</td>\n", " <td>3.87561</td>\n", " <td>4.98432</td>\n", " <td>3.58628</td>\n", " <td>4.60502</td>\n", " <td>63150252.55</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1998-01-07</th>\n", " <td>0</td>\n", " <td>4.57341</td>\n", " <td>4.68040</td>\n", " <td>4.20871</td>\n", " <td>4.24032</td>\n", " <td>36978255.52</td>\n", " <td>4.241065</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1998-01-08</th>\n", " <td>0</td>\n", " <td>4.24032</td>\n", " <td>4.52965</td>\n", " <td>4.11875</td>\n", " <td>4.39107</td>\n", " <td>27687622.95</td>\n", " <td>4.298650</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Time Open High Low Close Volume EMA_5 \\\n", "Date \n", "1998-01-02 0 3.31397 3.95098 3.28236 3.95098 24947201.10 NaN \n", "1998-01-05 0 4.01177 4.02635 3.69325 3.89020 22344145.08 NaN \n", "1998-01-06 0 3.87561 4.98432 3.58628 4.60502 63150252.55 NaN \n", "1998-01-07 0 4.57341 4.68040 4.20871 4.24032 36978255.52 4.241065 \n", "1998-01-08 0 4.24032 4.52965 4.11875 4.39107 27687622.95 4.298650 \n", "\n", " EMA_30 DIFF_NEXT_20 \n", "Date \n", "1998-01-02 NaN 2 \n", "1998-01-05 NaN 3 \n", "1998-01-06 NaN 0 \n", "1998-01-07 NaN 2 \n", "1998-01-08 NaN 1 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datadir = os.path.join('Data','daily_sp500_1998-2013')\n", "dataset = os.path.join(datadir, 'table_aapl.csv')\n", "data = pd.read_csv(dataset,header=None,names=['Date','Time','Open','High','Low','Close','Volume'],parse_dates=['Date']) \n", "#data = data.sort_values(by='Date') \n", "data.set_index('Date',inplace=True) \n", "\n", "# add indicator\n", "data = ta.EMA(data, 5, column='Close')\n", "data = ta.EMA(data, 30, column='Close')\n", "\n", "# label helper\n", "data = utils.diff_next_n_bar(data, 20, column='Close', cat_diff=True, cat_diff_group_by_percent=group_by_percent)\n", "\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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+jYOpE83KVzAiz8AC7QFyKnJwN7hjsHfAYO9wyTTs1ncNIS02hl3fLebnL+Zx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\n/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fB4M+YQHaYQHeYQLaYQHUERBl7NfZODm/I5q2wq+JQofG78yuPJp8JDhfkIx0JyiEtw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\nz8yXRMRxwOeBtw32Qt3d3XR2d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cC1dSiLJEmSJEkjYjBjjiVJkiRJGpMMjiVJkiRJLc/g\nWJIkSZLU8gyOJUmSJEktryHBcUR8OyIejog7quyPiPhKRCyPiNsiYt9GlEuSJEmSJGhcy/F3gTf0\nsf8wYJdiOwn4egPKJEmSJEkS0KDgODN/DTzWxyFHAhdkyY3AlIiY1oiySZIkSZI06HWO62QG8ECP\n151F2sreB0bESZRal9lxxx0bUrhamLVgMfcvmjfi+ZeP6+9xMOeX9T6v576+9vd3XjVDPa/V9X6f\nqtVDpfMG8jNS7TyNfgP9WZEaZaj/Z5pdtf9ng/2/V+2+B5p/Nf297yNtqJ9xanXdehvuZ7nhXhua\nr85HWq1+ZzXymmVCrqiQlpUOzMzzM3NuZs6dOnVqnYslSZIkSWoFzRIcdwI79Hg9E3hwhMoiSZIk\nSWoxzRIcXwa8s5i1en9gTWZu0qVakiRJkqR6aMiY44i4EDgQ2DYiOoFPAh0AmfkN4ArgcGA58DTw\nrkaUS5IkSZIkaFBwnJnH97M/gfc3oiySJEmSJPXWLN2qJUmSJEkaMQbHkiRJkqSWZ3AsSZIkSWp5\nBseSJEmSpJZncCxJkiRJankGx5IkSZKklmdwLEmSJElqeQbHkiRJkqSWZ3AsSZIkSWp5BseSJEmS\npJZncCxJkiRJankGx5IkSZKklmdwLEmSJElqeQbHkiRJkqSWZ3AsSZIkSWp5BseSJEmSpJbXsOA4\nIt4QEfdExPKIWFBh//yIWBURy4rtnxpVNkmSJElSa2tvxEUiog34KvB6oBP4Q0Rclpl39Tr0osz8\nQCPKJEmSJElSWaNajl8JLM/M+zLzWeBHwJENurYkSZIkSX1qVHA8A3igx+vOIq23t0TEbRHx44jY\noTFFkyRJkiS1ukYFx1EhLXu9vhyYlZn7AP8FfK9iRhEnRcSSiFiyatWqGhdTkiRJktSKGhUcdwI9\nW4JnAg/2PCAzH83MZ4qX/wm8vFJGmXl+Zs7NzLlTp06tS2ElSZIkSa2lUcHxH4BdImKniNgMOA64\nrOcBETGtx8sjgLsbVDZJkiRJUotryGzVmbkuIj4AXAW0Ad/OzDsj4tPAksy8DPhgRBwBrAMeA+Y3\nomySJEmSJDUkOAbIzCuAK3qlndHj+enA6Y0qjyRJkiRJZY3qVi1JkiRJUtMyOJYkSZIktTyDY0mS\nJElSyzM4liRJkiS1PINjSZIkSVLLMziWJEmSJLU8g2NJkiRJUsszOJYkSZIktTyDY0mSJElSyzM4\nliRJkiS1PINjSZIkSVLLMziWJEmSJLU8g2NJkiRJUsszOJYkSZIktTyDY0mSJElSyzM4liRJkiS1\nvIYFxxHxhoi4JyKWR8SCCvs3j4iLiv03RcSsRpVNkiRJktTaGhIcR0Qb8FXgMGAP4PiI2KPXYe8B\nHs/MlwBfBj7fiLJJkiRJktSoluNXAssz877MfBb4EXBkr2OOBL5XPP8xcHBERIPKJ0mSJElqYY0K\njmcAD/R43VmkVTwmM9cBa4BtGlI6SZIkSVJLi8ys/0Ui3gocmpn/VLw+AXhlZv5Lj2PuLI7pLF7f\nWxzzaK+8TgJOKl7uCtxT9xuonW2BR0a6EKor63jss45bg/XcOqzrscl6Hfus47GvXMcvysypjbhg\neyMuQqmleIcer2cCD1Y5pjMi2oHJwGO9M8rM84Hz61TOuoqIJZk5d6TLofqxjsc+67g1WM+tw7oe\nm6zXsc86HvtGoo4b1a36D8AuEbFTRGwGHAdc1uuYy4ATi+fHAL/KRjRrS5IkSZJaXkNajjNzXUR8\nALgKaAO+nZl3RsSngSWZeRnwLeD7EbGcUovxcY0omyRJkiRJjepWTWZeAVzRK+2MHs/XAm9tVHlG\nyKjsDq5BsY7HPuu4NVjPrcO6Hpus17HPOh77Gl7HDZmQS5IkSZKkZtaoMceSJEmSJDUtg+M+RMQO\nEXFtRNwdEXdGxMlF+tYRcXVE/Ll43KpI3y0ifhcRz0TER/vLp8o13xAR90TE8ohY0CM9IuJzEfGn\nIp8P1vPeW0WT1fHBEbE0IpZFxPUR8ZJ63nurGKE6/nZEPBwRd/RKr3hNDV+T1fPZEfHHiLgtIn4a\nEVPqdd+tqIZ1PT4ifh8Rtxb5fKqPa55Y5PvniDixSNsiIhYXdX1nRCyq972PZc1Sr732X9b791tD\n10x1HBGbRcT5Ufpc/ceIeEs9771VNFkdHx8Rtxf/i6+MiG0HdBOZ6VZlA6YB+xbPJwF/AvYAvgAs\nKNIXAJ8vnm8HvAL4HPDR/vKpcL024F5gZ2Az4NbyccC7gAuAceVrjfT7Mxa2JqvjPwG7F8//F/Dd\nkX5/xsLW6Dou9r8W2Be4o1d6xWu6jbl6PgRoL55/3npu2roOYMvieQdwE7B/hettDdxXPG5VPN8K\n2AJ4XXHMZsBvgMNG+v0ZrVuz1GuP/UcDP+z9++02NuoY+BTw2eL5OGDbkX5/xsLWLHVMaV6th8v1\nWlx/4UDuwZbjPmTmysxcWjz/O3A3MAM4Evhecdj3gKOKYx7OzD8A3QPMp7dXAssz877MfBb4UXEt\ngPcBn87MDeVr1exGW1iT1XECLyieT2bTtcA1BCNQx2Tmr6mwTnu1a2r4mqmeM/OXmbmueHkjMHN4\nd6eealjXmZlPFi87iq3SRCyHAldn5mOZ+ThwNfCGzHw6M68t8noWWIp1PWTNUq8AEbEl8GHgs7W7\nQzVTHQPvBs4q8tuQmY/U5i5bWxPVcRTbxIgISp+vB/S52uB4gCJiFjCH0jcXL8zMlVD6IaD0rcdQ\n8ultBvBAj9edPPeh7MXA2yJiSUT8IiJ2GdwdqD9NUMf/BFwREZ3ACYBd9GqsQXXclyFfUwPXBPXc\n07uBXwzjfPVhuHUdEW0RsYxSC8PVmTnYv9vlfKYAbwKuGfxdqLcmqNfPAF8Cnh7iLagfI1nH8dxQ\nl89EaTjb/42IFw75ZlTRSNZxZnZTali8nVJQvAelZYP7ZXA8AMU3iJcAp2TmE3XMJyqklb8l2RxY\nm5lzgf8Evj3UcmhTTVLHHwIOz8yZwHeAfx9qObSpBtaxRlAz1XNE/CuwDvjBUMuh6mpRR5m5PjNn\nU2rxfWVE7FXpUpVO7VGOduBC4CuZed9QyqHnjHS9RsRs4CWZ+dOhXFv9G+k6ptTldiZwQ2buC/wO\n+OJQyqHKRrqOI6KDUnA8B5gO3AacPpDrGhz3o3hzLwF+kJk/KZL/FhHTiv3TKH2jMeh8ikHry4rt\nvZS+7dihx2kzea4LQGdxPsBPgX2Gd2cqa4Y6joipwMt6fCt2EfDqGtyeaHgd92XQ19TANVE9U0wK\n8kbgHZnpmok1Vqu6LsvM1cB1wBsiYr8edX0Eff9vhtI6nH/OzHOGfEMCmqZeXwW8PCLuB64HXhoR\n1w3rxrRRk9Txo5R6BZS/APm/lOaPUA00SR3PLs69t/gffDED/FxtcNyHoo/6t4C7M7NnK95lQHk2\ntBOBnw0ln8x8IDNnF9s3gD8Au0TEThGxGXBccS2AS4GDiuf/g9IAdw1TE9Xx48DkiHhpcerrKY3T\n0DCNQB33ZVDX1MA1Uz1HxBuA04AjMtNumTVWw7qeWu5eGRETgH8E/piZN/Wo68uAq4BDImKrKM2w\nekiRRkR8ltIcEafU7g5bU7PUa2Z+PTOnZ+Ys4DXAnzLzwNrdaetqojpO4HLgwCLLg4G7anKTLa5Z\n6hhYAexRND7BYD5XZxPMbNasG6U/ikmpKX5ZsR0ObENpXNGfi8eti+O3p/QNxhPA6uL5C6rlU+Wa\nh1MKfO8F/rVH+hRgMaW+87+j1Mo44u/RaN+arI7fXNTvrZS+Idt5pN+fsbCNUB1fCKykNMFEJ/Ce\nIr3iNd3GXD0vpzQGqnz+N0b6/RlLWw3reh/gliKfO4Az+rjmu4t6XQ68q0ibWZTj7h7l+KeRfn9G\n69Ys9dpr/yycrXpM1jHwIuDXRR7XADuO9PszFrYmq+P3Fn+fb6P0Zcg2A7mHKE6WJEmSJKll2a1a\nkiRJktTyDI4lSZIkSS3P4FiSJEmS1PIMjiVJkiRJLc/gWJIkSZLU8gyOJUkaYRGxPiKWRcSdEXFr\nRHw4Ivr8Hx0RsyLi7Y0qoyRJY53BsSRJI68rM2dn5p7A6ymtC/nJfs6ZBRgcS5JUI65zLEnSCIuI\nJzNzyx6vdwb+AGwLvAj4PjCx2P2BzPxtRNwI7A78Bfge8BVgEXAgsDnw1cz8j4bdhCRJo5zBsSRJ\nI6x3cFykPQ7sBvwd2JCZayNiF+DCzJwbEQcCH83MNxbHnwRsl5mfjYjNgRuAt2bmXxp6M5IkjVLt\nI10ASZJUURSPHcB5ETEbWA+8tMrxhwD7RMQxxevJwC6UWpYlSVI/DI4lSWoyRbfq9cDDlMYe/w14\nGaW5QtZWOw34l8y8qiGFlCRpjHFCLkmSmkhETAW+AZyXpbFPk4GVmbkBOAFoKw79OzCpx6lXAe+L\niI4in5dGxEQkSdKA2HIsSdLImxARyyh1oV5HaQKufy/2fQ24JCLeClwLPFWk3wasi4hbge8C51Ka\nwXppRASwCjiqUTcgSdJoV7cJuSJiV+CiHkk7A2cAFxTps4D7gWMz8/HiH/m5lJaveBqYn5lL61I4\nSZIkSZJ6qFu36sy8p1izcTbwckoB70+BBcA1mbkLcE3xGuAwShOH7AKcBHy9XmWTJEmSJKmnRo05\nPhi4NzP/ChxJaT1Gisdyl68jgQuy5EZgSkRMa1D5JEmSJEktrFFjjo8DLiyevzAzVwJk5sqI2K5I\nnwE80OOcziJtZc+MinUcTwKYOHHiy3fbbbd6lluSJEmSNEJuvvnmRzJzaiOuVffgOCI2A44ATu/v\n0AppmwyIzszzgfMB5s6dm0uWLBl2GSVJkiRJzSci/tqoazWiW/VhwNLM/Fvx+m/l7tLF48NFeiew\nQ4/zZgIPNqB8kiRJkqQW14jg+Hie61INcBlwYvH8ROBnPdLfGSX7A2vK3a8lSZIkSaqnunarjogt\ngNcD/9wjeRFwcUS8B/hv4K1F+hWUlnFaTmlm63fVs2ySxoZLb1nB2Vfdw4rVXbRFsD6TGVMmcOqh\nu3LUnBkjXTxJkiSNEnVb57gRHHMsta5Lb1nBwsvuZHVXd8X9HeOCLce3s/rpbqYbLEuSpBbW3d1N\nZ2cna9euHemiVDV+/HhmzpxJR0fH89Ij4ubMnNuIMjRqtmpJqplLb1nB6T+5na7u9VWP6d6QPP50\nKXBesbqL039yO4ABsiRJajmdnZ1MmjSJWbNmEVFpHuSRlZk8+uijdHZ2stNOO41YORq1zrEk1czZ\nV93TZ2BcSVf3es6+6p46lUiSJKl5rV27lm222aYpA2OAiGCbbbYZ8ZZtg2NJo86Dq7saep4kSdJo\n16yBcVkzlM/gWFJTu/SWFRyw6FfstGAxByz6FZfesoLpUyYMKa+hnidJkqThufLKK9l11115yUte\nwqJFi0a6OBU55lhS0+o9trg8dvgtL5/BJTevGFTX6gkdbZx66K4bZ7d+cHWXE3VJkiRVUOvPS+vX\nr+f9738/V199NTNnzuQVr3gFRxxxBHvssUcNSz18thxLalqVxhZ3da/n2j+u4qyj92bGlAkEMGPK\nBM5522zOedtsJnS0bZLPVlt0cNbRewNw+k9uZ8XqLpLngu1Lb1nRgLuRJElqfuXGiVp+Xvr973/P\nS17yEnbeeWc222wzjjvuOH72s5/VrtA1YsuxpKZVbYzwg6u7OGrOjKrfYFb7pvOARb+qGGyffdU9\nth5LkiRRvXFiOJ+XVqxYwQ477LDx9cyZM7npppuGVc56MDiW1JQuvWUF4yJYX2Et9r7GDvcVNPcV\nbEuSJKk+n5eywue5ZpiAqzeDY0kNMZixK+XuPJUC4/LY4aGYPmUCKyr8YZ8+ZYJjkSVJkuj789JQ\nzZw5kwceeGDj687OTqZPnz7k/OrFMceS6m6wY1eqrWPcFsFZR+895KD11EN33WRM8oSONl6321TH\nIkuSJFH989JQGycAXvGKV/DnP/+Zv/zlLzz77LP86Ec/4ogjjhhuUWvO4FhSXV16ywo+cvGtVceu\nVFKt286GzGG15h41Z8YmE3mddfTeXPvHVYMqnyRJ0lhV7fPScD6Dtbe3c95553HooYey++67c+yx\nx7LnnnvWrtA1YrdqSc9T7l68YnUXbcWY3xmD6Gbcs3vy5AkdPPXsuordo6F6EFyP7jxllcYkf+ii\nZYMqnyRJ0ljW1xwuQ3X44Ydz+OGH1zTPWrPlWNJGPbs/A6zP5Ihx13PR0/+TIy7dk6c/vxvcdvGA\nzk9gdVc33esrB8ZQPditR3eeSmU9YNGv2GnBYsZVmRCiFsG4JEmSRgdbjiVt1Hus7xHjrmdRxzfZ\nIp4FYIuulXD5B0s79zm2z/OPGHc9H2u/mOnxCA/mtnxh3bFctuE1G4/tK9gtf1NZrwmyykF8uaxD\nmfjLCbwkSZLGFoNjqVXcdjFc82lY0wmTZ8LBZ2wS4PbuRvyx9os3BsYbdXeV8qkQHJfP7x1Uz4xH\nWNTxTeiGyza8ps+JtXoHnV9+2+yaB519Tfi1IXPAs2mX8yhP4AUYIEuSJI1Sde1WHRFTIuLHEfHH\niLg7Il4VEQsjYkVELCu2w3scf3pELI+IeyLi0HqWTWopt11cavFd8wCQpcfLP7hJF+ne3YinxyOV\n81vTWTG5fH6loHqLeJaPtV/MhI42vnTsy6oGxo2YNbraWOL1mbxj/x2B0jjkAxb9quK1KwXXXd3r\n+cjFtzrDtSRJ0ihV7zHH5wJXZuZuwMuAu4v0L2fm7GK7AiAi9gCOA/YE3gB8LSLaKmUqqX+L71vM\nIT8+hH2+tw+HLPk0izfrNa62uwt++l5YOAW+vBfcdvEmY30fzG0r5r2O4OSPn75J8Fg+v1pQPX3c\no5u0GPcc+zvYWa2Hqq+xxP/nxv/uNzjvK7h2CShJkqTRqW7BcUS8AHgt8C2AzHw2M1f3ccqRwI8y\n85nM/AuwHHhlvconjWWL71vMwt8uZOVTK0mSlW3Bwm23ZvHELVg8cQsOmTmdfWbtwCEzXsjiiRM2\ntiQf1XbDxqn7Ab64/m08nZttkn87Gzir45u8/ImrnxcMlqf+fzimVizXuMkzNwmMe7YUD3ZW66Gq\nNOFXNZWC876Ca5eAkiRJGp3q2XK8M7AK+E5E3BIR34yIicW+D0TEbRHx7YjYqkibATzQ4/zOIu15\nIuKkiFgSEUtWrVpVx+JLo9e5S89l7fq1z0tbO24cZ229FQu33ZqVHe1kBCs72jcGzeWxxEfNmcEN\nCw7i/kXzOOdzZ7LFW74KFTpxlLtJ9w4Gj5ozg+2PPhM6egWQHRNK45x7qDb2t7dazxpdDuIHqmdw\nfuktK3jqmXUDPl6SJEnw7ne/m+2224699tprpItSVT2D43ZgX+DrmTkHeApYAHwdeDEwG1gJfKk4\nvtJaKps0I2Xm+Zk5NzPnTp1auXVKanUPPfVQxfQ1beNYO+75v/Zrx43j3K2mFAdUGEu8z7GQGyrm\nNz0eBSoEg/scC2/6CkzeAYjS45u+0u8EYJXUegmnsqPmzNjYQt6f6VMmcOktK5j9qV9yykXLWN3V\n3e/xkiRJo9ZtF5eG3fUYfjdc8+fP58orr6xB4eqnnsFxJ9CZmTcVr38M7JuZf8vM9Zm5AfhPnus6\n3Qns0OP8mcCDdSyfNGZtP3H7QR3/UHvRMjx5ZuUDqqQ/mNsAVYLBfY6FD90BC1eXHivMbj2QILLa\nrNa1MJDu1RM62njdblM5/Se39xsUl4+vRzAvSZLUEAOcyHWwXvva17L11lvXpox1UrfgODMfAh6I\niPKnxIOBuyJiWo/D3gzcUTy/DDguIjaPiJ2AXYDf16t80lh28r4nM75t/KY7olIHDdh+3fqK3Z43\nOviMTbpJP52b8YV1xw4rGOwvOJ0xZUJdl0Yqd6+eMWUCUVzv/9t/x+e9Puvovbn2j6v67f7d83iX\nc5IkSaPWNZ8uDbfrqbyU5xhX73WO/wX4QURsBtwHvAv4SkTMptRl+n7gnwEy886IuBi4C1gHvD8z\n+x+MKGkT83aeB8DHr/84G6p0iS4bv2EDJz/TVrHb80bl9Gs+Ta7p5G9sy1ndb+XmF7yes/pYD7g/\n5fMWXnbnJq2yjWqBPWrOjH7L/6GLlvW5f0axLvLZV93Dhy5axtlX3dPnOsmSJElNq8qSnVXTx5C6\nBseZuQyY2yv5hD6O/xzwuXqWSWoV83aex+m/Ob3q/iDYfuL2nLzvyRuD6T7tcyzscywBbE9pnbZa\nKAenl96ygrOvuocHV3cxvQg2myW4nD5lAiuqjI/u2e263LpcXgIKGPY9NPP7IkmSxqDJM4su1RXS\nx7h6txxLqrPF9y3m3KXn8tBTD20S7G4/cXtWPrVyk3OmTZzGL4/5ZaOL2qeBtOCOlFMP3fV5wW/Z\nVlt08Mk37Vlx1u3yLN7DuafyUlf1CLolSZIqOviM0hjjnl2r+xp+N4bUc0IuSXW2yXrGT61k4W8X\nsvi+xUDlscfj28Zz8r4nj0RxR61KY5PPedtsbjnjEI6aM6PqrNvDXdKpWtD9kYtv3bh9zp2kAAAg\nAElEQVS2tCRJUk0NcNWRwTr++ON51atexT333MPMmTP51re+VZvy1pAtx9IoVnE94/VrOXfpuczb\ned7GFuRqLcsauL5atqt1ux7ukk7Vguv1mbYgS5Kk+imG09XShRdeWNP86sHgWBrFqq1n3DO9Z5Cs\n+qjU7brahGKDGUPc11jnWnTbliRJ0nMMjqVRrNqY4sGuc6zhKQeolYLensHw5AkdPPXsOrrXJ9D/\nGOJqY53LhtttW5IkSc8xOJZGqcX3Lebp7qc3SXdM8cio1O2694RavZergr5bgMtpH7n4VtZnbrJ/\nuN22JUmS9ByDY2kUKk/E1Xu88ZTNp7DglQvsRt0kKk2oVUmlFuC+WpyhNt22JUmS9ByDY2kUqjQR\nF8CE9gkGxk1koN2ee7cAV2px7hgXbLVFB48/3U1bxMYWZ3iuhdmlnyRJkobO4FhqcuV1jFc+tZJx\nMY4NuaHqsdUm6NLI6GtCrbKOccHTz65jpwWLN7b0Vmpx7t6QZJZajKsFv/Vab1mSJKkVuM6x1MR6\nrmMM9BkYgxNxNZtTD92VCR1tfR7TvSF5/OlukueC3WoB9equ7qrBL1RvqXbiLkmSNJIeeOABXve6\n17H77ruz5557cu655450kSoyOJaaWLXu05U4EVfzOWrODM46em+mTOgY8Dld3etpixjUdcrBb7UJ\nupy4S5IkDcbi+xZzyI8PYZ/v7cMhPz6ExfctHlZ+7e3tfOlLX+Luu+/mxhtv5Ktf/Sp33XVXjUpb\nOwbHUhMbTDfpha9e6HjjJnTUnBks++QhnPO22cyYMoGAfoPf9ZmbtDhP6Ghjqy0qB9nl4LdSS3W1\nibskSZIq6dlzMUlWPrWShb9dOKwAedq0aey7774ATJo0id13350VK1bUqsg1Y3AsNYlK39ANtJv0\ntInTDIyb3FFzZnDDgoP4y6J5bKiwLFNPM6ZM4Kyj994YTJdff/JNe/YZ/JZbqnuf53hjSZI0UJV6\nLq5dv5Zzl9amK/T999/PLbfcwn777VeT/GrJCbmkBilPrPXQUw+x/cTtOXnfk5m38zwW37eYs246\nizXPrtl4bPkbuiNfciQ/W/6zPrtW25169Olroq5ysFtp3eSyvpZq6us8SZKk/lTruViLiV+ffPJJ\n3vKWt3DOOefwghe8YNj51ZrBsdQAvdclXvnUShb8ZgELfrOg6jlr16/l152/ZuGrF24yW3X5cdrE\naRuDbI0epx666/OWXCrbaosOPvmmPfsMbg1+JUlSPW0/cfuNk8H2Th+O7u5u3vKWt/COd7yDo48+\nelh51Utdg+OImAJ8E9gLSODdwD3ARcAs4H7g2Mx8PCICOBc4HHgamJ+ZS+tZPqlRBjOxVk8PPfUQ\n83aeZ/A7xpSD275agGvl0ltWNOQ6kiRpbDh535Of16gDw++pmJm85z3vYffdd+fDH/5wLYpZF/Vu\nOT4XuDIzj4mIzYAtgI8D12TmoohYACwATgMOA3Yptv2ArxeP0qhSqfv0ULuhuDTT2FXvFuBLb1nB\nwsvuZHVX98a03usiS5Ik9VZulKk0HHCobrjhBr7//e+z9957M3v2bADOPPNMDj/88JqUuVYi+5kY\nZsgZR7wAuBXYOXtcJCLuAQ7MzJURMQ24LjN3jYj/KJ5f2Pu4ateYO3duLlmypC7l1wi57WK45tOw\nphMmz4SDz4B9jh35vAaod/dpKH3TNr59PKufWT2ovMa3jXcGag3JpbesqNhtu2zGlAncsOCgBpdK\nkiSNlLvvvpvdd999pIvRr0rljIibM3NuI65fz9mqdwZWAd+JiFsi4psRMRF4YTngLR63K46fATzQ\n4/zOIk2t4raL4fIPwpoHgCw9Xv7BUvpI5jUI1Wb3y0zGt40fcD5TNp9iYKw+XXrLCg5Y9Ct2WrCY\nAxb9iktveW45hLOvuqdqYAylFuSex0uSJKm+wXE7sC/w9cycAzxFqQt1NZUW/tykWTsiToqIJRGx\nZNWqVbUpqRqmrw/0XPNp6O41g293Vyl9sGqZVxVrLr+cPx90MHfvvgd/Puhg1lx+edXu0088+wQL\nX72QyZtN7jPPKZtPYdE/LOI3x/3GwFhVlVuGV6zuInmuu3T59+nBKjNh99TzeEmSJNU3OO4EOjPz\npuL1jykFy38rulNTPD7c4/gdepw/E3iwd6aZeX5mzs3MuVOnTq1b4VV7/X2gZ01n5RPXPNB/i+9t\nF8OX94KFk+FTWxctxpXyqnKNQVpz+eWs/LczWPfgg5DJugcfpPMT/8pr7txQ8fjtJ27PvJ3ncf3x\n17PoHxYxbeI0AMZF6Vdw2sRpBsUasEotw13d6zn7qnuA0lJR/el5vCRJkuo4IVdmPhQRD0TErpl5\nD3AwcFexnQgsKh5/VpxyGfCBiPgRpYm41vQ13lijT88P9EeMu56PtV/M9HiEh382FdrOLI0LrhbU\n/uR/wi9Ogz3fDH/+5fPHEUOpy3S5pTifHzQsnrgF5241hYfa29h+A5x83+JhB6APf/kccu3zu0+P\ne6ab/3U5bCC4Yc+2jem9Z/dz9mkNV7WW4XJ6taWieit3r3ZyLkmSpPrPVv0vwA+KmarvA95FqbX6\n4oh4D/DfwFuLY6+gtIzTckpLOb2rzmVTg5U/uB8x7noWdXyTLeJZALZnVSm4fdnb4dYfbtoduqzr\nMVjyredel8cRt0+oes7iiVuwcNutWTuu1EK7sg0W/nYhAK+5cwMPf/kc1q1cSfu0aWz3oVOY/KY3\nDehe1q2s/L1NW8I/X5HAem7Ys41xMc6xw6q56VMmsKJCgFxuMe69VNS4CNZXmXzR2aslSZJK6tmt\nmsxcVnSB3iczj8rMxzPz0cw8ODN3KR4fK47NzHx/Zr44M/fOTKehHmPKH9w/1n7xxsB4o+6uUovw\nm74yuEy7u0pBcxXnbjVlY2Bctnb9Wq7/9pmbdIu+7/SPcfonDmDxfYv7vWz7tGlV941fB2+/rhSI\nZKaBsWru1EN3ZUJH2/PSJnS0ceqhu258fdScGdyw4CD+smgeXzr2ZZscX2b3akmSpJJ6txyr1fVY\nTunqCdtzxmZvYXo8UvnYNZ2lpZau+XT17tWD9FB75YDgsF8+Rj6/VzTj15XSP7LbQoA+g9rtPnQK\nK//tjE26Vpdt80Tp0XWKVQ+9W4anT5nAqYfuWrX1t5x+ykXLKu6v1E370ltWcPZV97BidRdtRcvz\njH6uI0mSVMnatWt57WtfyzPPPMO6des45phj+NSnPjXSxdqEwbHqp7ycUtHleYuulSzq+CZPrJ/E\nFP6+6fGTZ5YeDz7j+WOI+zNha1jXVfH47detZ2XHpj/m2z5ROattnii1LJ+79Nw+g+Ny9+sHF5wO\n6zcd1/noCzYdayzV0lFzZgwqSD1qzoyNwW5vvSfw6r1OcrlLdnkSvXJ+kiRpbFpz+eVDHn5Yyeab\nb86vfvUrttxyS7q7u3nNa17DYYcdxv7771/DUg9fXbtVq8VVWE6pff1apkzogI5es+l2THhucq19\nji11r56wdf/X6JgAh32+dPzk8mTnz60KdvLjqxm/4fkzSI9vG8+67aZUzO7RF5Qeqy3JBM8t4fTg\nx04jJk0iOjqet39tO/zikK0da6ymM5Du2ND3OsmjtRt2tWXk+lxeTpKkFlRpVZaV/3YGay6/fMh5\nRgRbbrklAN3d3XR3dxNRaSXfkWXLserjtourd43uehyOPn9jd+uNs07vc+xzx+xzbGm77eLSTNXV\nvOkrz53X8/yiO/e8NZ2wRXDuVpN5qPsJtp+4PSfvezIvmrxhk27Ra9vhhweWfkmrdYcu/7Eon5er\nV0N7O21TprB+zRrap01j5w+dwlnD+GZNqpeBdsfub53kgayj3Ex6t4SXW8CX/PUxLrl5xSbpYMu4\nJKl1VVqVJdeu5eEvnzOs1uP169fz8pe/nOXLl/P+97+f/fbbb7hFrTmDY9VeuTt1NZNnPhf89qev\nMciTd6ieR4/85xXb8+xcevjr2WfS/vBqHnlBKTC+Yc+2PrtDV/pjwbp1xBZbsPuNv+v/fqQRNpDu\n2NVmw+65fzSpti70/7nxvzc5tqt7PR+5+Fag9F6Vx14PZGy3JEljQbVVWaqlD1RbWxvLli1j9erV\nvPnNb+aOO+5gr732GlaetWa3atVehe7UG/XsPj1QB5/RdzfsIZr8pjexz69/x1+u+CKf/dgO/HbP\ndqZNnNZnd+h6/bGQmkml7tdllbphN7vBtnSvz+T0n9zOJy69ndN/cjsrVneRPNeybNdrSdJYVm1V\nlr5WaxmMKVOmcOCBB3LllVfWJL9asuW4VWycNfoBiDbI9aWW197dmWthTWf1fT27QQ9U+fi+umEP\nw7yd5w14bHD7tGml8RcV0qWxomf36/5mqx4NLav9tYRX0tW9ngtvemCT9aHLY66b7R4lSaqVSquy\nxPjxbPehU4ac56pVq+jo6GDKlCl0dXXxX//1X/z/7d15nFx1me/xz9ML6U6AdEIC6XRAQFERCUlo\ndTCOA3gnLiEQUYPj6ItxGXRGrwluBB2xCWoiiCHccRlcZtRRISrEhEbFS2AURMdAkoYIAgbmkqQj\nWzrRpEN6ee4f55xOVfU5VdXdtXZ9369Xvar6nFNnqV910s/5/X7Pc9lllxXidAvKPOM//mrS3t7u\nmzapHHJOGVmjh2meGiS1Gk2wmVKqaShozTYM+tIHR36MMonL0gfE/mPRetWKMc3BEKlGmXN5I1Mm\nNvKZRadVTACZdJ6jZcDjq5RsT0REqsdDDz3Eqaeemvf2hc5W3dXVxcUXX8zAwACDg4MsWbKEK64Y\nPgo07jzN7D53bx/1wUdAwXEtWP3y3HWD6xphwlFBsqx8e2bjgu7GZjjjHbD1+8OXj6bXuEwyE2/B\n4SAYKOg/FiLVav6qjYk9ss2N9ay88PSKCpA/unbrsJ7gbKIe80xtLc3cs/zcQp6eiIhIUY00OC6X\ncgfHec05NrOJZvZpM/t6+PMpZnZecU+ttnVu72TBjxYw+9uzWfCjBXRu7xz9zrINc44M9kHvc4AH\ngfSGDwfBbzZxc4v7euHR21NKK1nwXEWBMeTO0nfKxjs49aHfc8rGOxQYS83KNpe30ko+LZ7bxrVL\nzkicS52pubGev3vV8bHbHzjUr3nHIiIi41C+c47/HbgPOCv8eQfwQ+DWYpzUeDCWeXid2zvp+HUH\nBweC4Kx7fzcdv+4AGF3d3MmzcvccZ+rrDYLfbAFtUtC9d0f+2agrlBJviWSImUIxs2Va1rm8lVby\nKfo3uGP9Nnp6+9LWNdYZRzY10HOgL+3f7PYXTB22/Z4DfSr5JCIiMg7lm636he5+NdAH4O69BNOu\nJEY0v220GU7X3L9mKDCOHBw4yJr71wz9PKKe5bhsz/nI1eM8edbIlleJvRs2QF38r4YSb0lNiqZQ\n7H2S1NEl173s0aw9seUo+bRu807mr9rIScs7mb9q47B/dxfPbWPLZxZw3UVzaGtpxgiGSV/ztjPY\nfMUCHl+1kHuWnzsU9C6e28akCcPvI1daz7iIiIiMXb49x4fMrBlwADN7IfB80c6qyiXV1Mw3w+nu\n/buzLs/WswxBcL17/25mTJrB0nlLWZiW7flJgvsaecy7iwtyU3uPmqdA/REwcOjw+gKUWCqXvRs2\n0P25z+M9PbHrx5qlT6RqJUyheMUf/w8rL/x5bE9sOUo+ZSbeim5MwvAe3nzqPUeSesArrWdcRERE\nxibf4PgzwM+A483se8B84B+KdVLVbjR/SHVu72TN/Wvo3p88bHfGpBl0bu/kk3d/kkEfTFt3cOAg\ny395GdjhDv204diZw5y71sLN/5h8EWGQG53X7v3dzOgfZOlzz7Fw/4Fgm97ngkRezVNHlsirAsUl\n4EpTX6+M1FK7skyhiILMUpV0ynacsd6YTJJUCqocPeMiIiJSPHkFx+7+CzO7H/grgm7Hpe7+TFHP\nrIqN9A+pzJ7gOE31Tbx21mvp+HXHsMB4iA0f6R4Nxx42V3n2kuSSS1YPi66n88hJ6T3UDXV0TJsK\ncDhAHuyDg3vhwhuqMiiOxCXgSjM4qMBYaldS3oKU0SUj6YkdrVw9w8Xq4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RQAAB3DSURB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"text/plain": [ "<matplotlib.figure.Figure at 0x1ecf7fe8ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Check Data\n", "sub_data = data.iloc[-300:]\n", "\n", "fig = plt.figure(figsize=(16, 10))\n", "ax = fig.add_subplot(3,1,1)\n", "ax.plot(sub_data.index, sub_data['Close'], label='Close')\n", "ax.plot(sub_data.index, sub_data['Open'], label='Open')\n", "ax.plot(sub_data.index, sub_data['High'], label='High')\n", "ax.plot(sub_data.index, sub_data['Low'], label='Low')\n", "ax.plot(sub_data.index, sub_data['EMA_5'], label='EMA 5')\n", "ax.plot(sub_data.index, sub_data['EMA_30'], label='EMA 30')\n", "ax.set_xlabel('Date')\n", "ax.set_ylabel('Price')\n", "ax.legend()\n", "\n", "ax = fig.add_subplot(3,1,2)\n", "ax.bar(sub_data.index, sub_data['DIFF_NEXT_20'], label='DIFF_NEXT_20')\n", "ax.set_xlabel('Date')\n", "ax.set_ylabel('Change')\n", "ax.legend()\n", "\n", "check_field = 'DIFF_NEXT_20'\n", "color = ['red','green']\n", "grouped = sub_data.groupby(check_field)\n", "ax = fig.add_subplot(3,1,3)\n", "for key, group in grouped:\n", " ax.plot(group.index, group['Close'], label=key, linestyle='none', marker='o')\n", "ax.set_xlabel('Date')\n", "ax.set_ylabel('Change')\n", "ax.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "We have 3426 observations with 8 dimensions\n" ] } ], "source": [ "# Splite train,test,val\n", "label_field = 'DIFF_NEXT_20'\n", "test_size = 400\n", "data_train = data.iloc[100:-test_size]\n", "data_test,data_val = np.split(data.iloc[-test_size:],2)\n", "\n", "X_train = data_train.iloc[:,:-1]\n", "X_test = data_test.iloc[:,:-1]\n", "X_val = data_val.iloc[:,:-1]\n", "\n", "N = X_train.shape[0]\n", "Ntest = X_test.shape[0]\n", "D = X_train.shape[1]\n", "y_train = data_train.loc[:, label_field]\n", "y_val = data_val.loc[:, label_field]\n", "y_test = data_test.loc[:, label_field]\n", "print('We have %s observations with %s dimensions'%(N,D))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train with approximately 37 epochs\n" ] } ], "source": [ "num_classes = len(np.unique(y_train))\n", "base = np.min(y_train) #Check if data is 0-based\n", "if base != 0:\n", " y_train -= base\n", " y_test -= base\n", " y_val -= base\n", "\n", "if input_norm:\n", " mean = np.mean(X_train,axis=0)\n", " variance = np.var(X_train,axis=0)\n", " X_train -= mean\n", " #The 1e-9 avoids dividing by zero\n", " X_train /= np.sqrt(variance)+1e-9\n", " X_val -= mean\n", " X_val /= np.sqrt(variance)+1e-9\n", " X_test -= mean\n", " X_test /= np.sqrt(variance)+1e-9\n", " \n", "epochs = np.floor(batch_sizemax_iterations / N)\n", "print('Train with approximately %d epochs' %(epochs)) " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Group : 0\n" ] }, { "data": { "image/png": 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vtcdOa4eDWoWNJINGg/J12Ba5OUbKzjIltU1UNxoBCZUkIdFcoYqESpJRIaOS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\nuwK/Apc7zj+neWCnO7q6fXu7PZuUv4xTA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n15/HykGLPNToS/G1eODOXKkaEM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zNjR7qwdusuroryJWaEX5+XmRBCCCHEuaitrSUmJqb1/apVq1iyZAkAc+bM4Z577sFisZCU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\nUJ1eJ8NEEpG+du+2kWRkyLzT6bmkB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"text/plain": [ "<matplotlib.figure.Figure at 0x1ecf799dbe0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Group : 1\n" ] }, { "data": { "image/png": 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m28/Vsr/Nj8Mi8/Tedn780hH+6cpZXDk3T/yiLQhTnK6PnuCom72tg+g65Lqt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Dz5bh23PbGdpDgrv7lxGcuLRm5xEY5EeWxzHXe9tIdWdzdzLE1cne/h3NQ2SqKHMbXsMobb0UM3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\nzdz5v10cyq9i2f0DiQk6jzCXJDjyE/z+T6jKFUmQb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DMAACAASURB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PjA+WQnHeJGrlKw+q/m86GNFdBaDcrYv9+dDunDzdbPtOFmEJo+HFLywXrqt/IZyqA90E5r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\nkuRzFOAmTO1Zbz9u+zsHpwEXK9M8d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"text/plain": [ "<matplotlib.figure.Figure at 0x1ecf7d1aa58>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Group : 2\n" ] }, { "data": { "image/png": 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gZ1oZno4qHhzfh9tHBF/2eqirDubwz5+Os+jqPvzr+ghyyrXc/OkezBb4cdEo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12fzszfX87dMd7Gzy8/srpuF2HNlmc4FwlLZAmDZ/mFZ/hDZ/uNvPYdoCkW7bxrojFOXSGfnc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\n+85DP9YBB4EG/7jvAVdPUsdk35UfG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"text/plain": [ "<matplotlib.figure.Figure at 0x1ecfa8a3390>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Group : 3\n" ] }, { "data": { "image/png": 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b1hzI49ejxdw8PowjBdX8bU0Sb/16nHunRXHDmFB0WhF2LxZf7zvBsaIa3r55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DrSuLubwqG5vVDFCiYWh+A468CPV/MwLbkRo8TxYUXwxLPwBFF0PBMnBO3nzUZU5iegUiAQ70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\nXH/99RijMo0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jWA+33cqighTet6KYRQUpLC5MZU5O0lnpDfnmpYVYlOKTT+7ijh9u43/u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"text/plain": [ "<matplotlib.figure.Figure at 0x1ecf7d61b38>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Group : 4\n" ] }, { "data": { "image/png": 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YnlxAmwtus8ce0zMu8B17HPFGKAmU5LbiQDEBK/CG6yaEEEKcqSRgPUUbnm/i\n5aZ+rlhaxtqFJYR8x/kR+sKw+sNw3oeg9UU3cH1lA2z7LngjULsG6i5yuxRXrwbv8WcYffK1Tv6/\nH7/M0PBwLk+N6Wo3su+1DMJek/BIQOs1CWcD2tDIvsck5DeJeC2CPouQzyTsNQn5LAojQUqKZPyt\nGNUxmOBLD+3iN7sOs6Qiwt9etQjLOHaQGUtl6BxK0jmYpHMoQcdggh2tA/REkzgT9A4tDHrcoHYk\nuM3LBrRH7Ae80gJ2Jhg7Lnd+/vxTutZ2bJJ2kngmnuuSnLATuZbdkf2knSTtpHNdl1NOirTtHqec\nlJuO3c+ej2fiDCQHcteOK5s9PpawJ5wLXke6OhcHit0uzyP7wVIKfAUYSr7EEUIIIWASA1al1H8D\n7wQ6tdYrsnlFwCagHjgE/LnWuu9Y95gNOgeT/HpHO5u2NuM1DS5oKOLyxWVcubSMecWhiS9SCmrO\nc7er/wVefxya/wSNz8GTdwIaDAsqz3ED2JKzINEPw10w3I0T7eRweytnDXfxRzWI138SLQgOEM9u\nb8CAWYRZdhbh6mVufUoWuWleDRwnUBFzi+NofvR8E//2yB5StsPfX7OYj13SgMd8Y78DGduhZziV\nC2THBrWdQ0k6h5Ls7+ymK5okbY9EthoTBwubfJ9BecSiJGgS9ioiHkXQA+FsGvJAyIKy4gIuXHUu\nhiXfyZ1pTMMkaEzuEkOnYqQL9GBqMNc6PLL1xHtyrcSv9b7GM/FnGE4PH3UPS1mUBkvHLUdUGaqk\nMjx6HPHK8BIhhBBnBqX18SfDOOkbKfU2IAr8z5iA9StAr9b6LqXUF4BCrfXnT3Sv1atX661bt05K\nvaZC2nZ44VAvT+7p5Ik9nezvcj9wNJSGuGJxGetWVLB6XuHJTQ4S74PmF6DpOWj6E7RuAzs7PtD0\nkQ6UcDAepCUVorC0kpVnLcQKFcK4b9/HPGfcM4+db2tNKuOQtDWptEPStklmNMmMQ0//AD1Nu6l1\nWllithPWQ6OXWgEoXgAFdZBfC/k12a0WCmohVCYB7RzxeucQtz+4gxcO9XHxgmLuvGEl9SXH+FLG\nTkPnq+7vb8s2GGoDJwOOfXSqR/ZHNmf8sXbQY46Vdt5Q/VNYJPLmE6lehio9a/SLl+JFsm6ymDFi\n6dhot+dsF+iuWBedsU7ah9tpH26nY7iDjB4/g3PQCubG8+YmpxozUVW+Lx+/OTors9f05rory8RV\n4kRuuvc5ADbddtE010QIMVcppbZprVefVNnJClizD64HHh4TsL4GXKa1bldKVQK/11ovPtF9ZnrA\neqSmnhhP7Ong8T2dbDnQS8rXoJekAAAgAElEQVR2OLsmn49d0sC1KyqwTqU1KpOEoXZ0oIif7ujn\nS798Fa9l8JX3nM3Vyyum7kUcIZrM8KMtjXz7qQPY0W6uKR/k/QuTLPMcRvXuh/5mGGiB5MD4Cw2P\nG7hWnQs157vdnivOBtNz2uou3pxkxuZbT+7nW79/nZDP4h+vW8p7z6sZ/ZCrNfQ3QetWNzht3Qbt\nr8DIJDmBIihqcHsNGJY7VnuiVJlHlDnieNx5y/0iZGRfmaPXZMtpZZDBJGnDvubD7H31RYrijSz1\nHKZKd2Boe/RFhsogUAiBAvAXHLFfAKFSKFvmBrjyuyumme3Y9CR6cssRtUfb6Yh1HBXkxjKxk7rf\n2KWJRgLZI5cmOl4Zr+kl5AlR6C+k2F9Mkb+IQn8hYU9YguE5QgJWIcRUm0kBa7/WumDM+T6tdeGJ\n7jPbAtaxhpMZHnq5je88fYAD3cNUFwT4yNr53LSmlvDxxruOMZhI84+bd/LLV9q4sKGIu29aRWX+\n9EzWkUjb/OzFFu75w36ae+OcVR7mo2vnU1sYJOA1CesYkeRhAvF2ArE2PEOtqL4DbhAz2OrexPJn\nA9g17hI/1ashUnHalvpJZRw6BhO09cdpH0jQHU1y+ZIyFpRKK9sIrTW72gb5+Uut/OKVNjqHkly/\nqoovvnMZJWGf2zLa9CfY8zDsfhgGmtwLLb/blb36vNGtsH5GLONkO5pf72jnG4/vo7Gzj7VFg3xi\nhcPqUDfGQJPb7T7eB/F+SPSj4/2o5OD4m5heKF0CFSvdydTKV7j707jGcudggm2NfRSFvFQVBKjI\n97/hLtpibomlY+6kVLEuBlODufG5E20jMzYfWSZlp8YtS5TMJEk6o+WPt0SRx/BQ5C+iyF9ESaCE\nurw6aiO11EXqqMuroypchceQL4BmAwlYhRBTbVYGrEqpjwMfB6irqzuvsbFx0uo1HRxH8/ieTr79\n1AGeP9RLnt/i/RfO40MX11Oe58+VG05maB9wA6n2/gRtA3F+9mILbf0J/vbti/iryxbOiKVBMrbD\nr3a0880nX2dvR/S4ZfMDHt59ThUfXemhPr4LWl6A5ufdVriRGTwND4TLIVzmBq/hsuxxudu6pZTb\nzdQ5svvoSPfSNDgZ7Eya4USSaCzBcCJJLJ4gnkgSTyZJpFIkkykymTQmNh5sTGwMNJ0Ukl+1mIvW\nrKG0bokbZHn8x31dc1FTT4yHXm7l5y+3sr9rGI+puGxxGbdeVM/a+RE4+BTs/iXs+RXEusH0wYIr\nYNHb3S8eypfP+BZIx9E8svMw//H4XvZ2RGkoDfGet9TQH0vRNpCgPftFRudQEu3YRIhRqXq5JO8w\n7yrvZanRhKfrVRjuHL1poAi8IbB8btBu+dyfzdjjXDpRvt8Nho9V1rAgFYPUMKSGGBjoZ09jGwda\nO+nr7yWAO2zAwUCjCPg8hHweQn4vIb+XvKCf2nkLyK9oGO22L92gxZuktSbjZEjaSaLpKH2JPnoT\nvRNuHcMdNA81j2v1NZVJVbiKukgdNZEaaiO11IRrqInUUB2uJuyV39GZQgJWIcRUm0kB6xnRJfhE\nXmrq4ztPH+SRne2YhmL1vCL6Yina+uMMJjJHlV9UFuau96zkvHkzb6Zex9HsOTzEYCJNPGUTS9nE\nUplsahNPZWjsjfHIjsOkbIe3nVXKh99az6WLSjHspBu0tr8Mg20Q7YToYTcdOuwGRG+QrRUZTDKY\n2Jhow0QrC2VYKNPdTMuLaXkwFGT6WwnYo2NzNQqVVw1F892t8IjUnz8ZP75p5TiawUSanuEUz77e\nzc9fauXFpn4ALphfxHtX5HNN+QCRof1w4Pew91FIDrozWp91NSx9Fyx8+6xdS9hxNL/ZdZhvPL6P\nPYeH8FkGVQUBKvP9VOZn0wI/VfkB+mIpfrSlia2Nffgsg3efU8WHzwmyzGhy11nuPeB2388k3NRO\njjnO5mWS48tkEnCc1qmTZSsL7QmgUWjHAcdBa8ftqo2D0g4WDoY64lmBwtHx5vm1Y36/G6Bwnhso\nCzGJtNb0JHpoHmqmabCJpqEmmgebaRpy94dSQ+PKF/gKqA5XUxOpoSpcRXmwnPJgOWXBMsqCZZQE\nSrAMmUjtdJCAVQgx1WZSwPpVoGfMpEtFWuu/P9F95lrAOqKpJ8b9zxzgpeZ+yiJ+qgrcD8pVBX4q\n8vxUFQQoz/PPiTUpu6NJfrSliR/+qZHOoSQNJSFuvbie95xXM65rtONoWvri7O+Ksv9wHx3tLfR3\nt9E5lKJzOEPCMclgYGs3GPV4PJTkBynNC1OaH6I8P0R5YYjKgmCue2Se/+Ra/Do62nnwd0+xd/d2\n5qsOLi+LssTXjaf/AMR6xhcOFLmtsCMf8vOrIVLptg5HKt1W4Tewru6bFU/ZE8622xtN0RtL0Tec\noi+Woi+WZiCWIKgT5KthalQXb83v4bKiPs4yWvH1vw5D7aM3DhbD4utg6buh4dI5FcxorRlMZMjz\nWyccb/dq2yA/3NLIz19qJZayOacmn/dfOI93n1OFaSiSGcedvCxjk0w7pGyHZDp7PPZcxiGZtkml\nU9jJOJlUAicdx04ncdJxSCdx0m6wqzNJhobjvNpjE9M+ykpLuHBxHW9bOZ+Gqgqwjr+eqe1oDnYO\n8NSLO9m+aydOXzPVRjdvyY+yLDhIue7GGmyGccGCcoPZomwAWzAPPMEjxhUfOSb5WMfZPNNz/Gs8\nQbeV+TR1H3ccTedQkpa+GK39cZRSFAY9FAa9FGTToNeUMZin0UBygNZoKy1DLbREW2gZaskdtw23\nkXHGf6FrKINifzHlwXKKA8X4Lf/R429Hxt5mJ5qacJyu5cNnjI7VHXudLCfkkoBVCDHVpiVgVUpt\nAC4DSoAO4EvAz4EfA3VAE/A+rXXvie41VwPWM1Eq4/DIzna+++whXm7uJ+KzeOc5lUSTNq93RjnQ\nFSWZGZ0FtiTspaE0TE3haMvXSGBfme8nP+CZ9A+UTT0x/uPxfWx+qYWAx+Sja+dzZUOAWtVJYaIF\n1XcI+g5C3yHoPQgDzXDkzLXKcLszjwSwgSLw57kts75sOvZYGW63ZjuTTd0uzthptJ0inkwyOJwg\nGosTjccZjiUYjseJJZIkEgkSySSpZBLHdrs7W9h4yGBh4zVsCs0UBWacAhUjoocJ6SH89vC4dXsB\n8Ibd2XNLF7tbSTYtrJ+WAHymGkyk2fxiKz/8UyP7Oo/fJf5UeC0Dn2ng8xj4LNM9tgzy/B4uXVzK\ntSsqaHiTY61fOzzEw9vbeHh7Owe7h7EMxcULinlrFZwd7GOR1UlxsgXVf8htOe6d4AubKaKVheMJ\nob0h8IYwfGGUL4zyht0uzL6Iu3kjo/u+yGj3ZjsNdgrsFE4mxdBwjP6hYQaiMYaGh4nG4gzH4sQT\ncZKJOKZO4yGDV9loDTYGNu6XYg4GWll4PBaWx4PH48Xn8eD1evF5Pfi8Xvw+H36vh4DfR9DvJeDz\nEfT5MK0JJhPz50Nepfv3YA594XO6ONqhL9FHZ6yTzlgnHbGO3H5nrJOeRM/oGNsxY2/Tzkks+3Yc\nXsMNbv2Wf3RSqUBRbmxusb+Y4kDxuH2vefwvkWYjCViFmN2Gkxn8HnNGDCs8lmlrYZ0sErDOTS81\n9fH9Px7ikZ2HKY34WFgWZmFp2E2zW0Fw+v7H/3rnEHc/to9f7RhtafR7DGoKg9QWBqgpDFJTGKA2\n30Ox6ieS6iaY7CKQ7MaX6MQb68AT68Ac7oB4HyoxgJpgjcXJ4mDgKAttWGB6UKYHw/SgLA/KE8rO\neps/ZhtznFflBqZ51TNigqTZQmvNloO9/HF/Dx5D5QJNn2Vkg82x+wY+zxHHY4JSr2lgnMb/kYxM\nrPXL7W387tUODnQPM/LnP+yzWFwRYUlFhKWVeSwuNMBJkUgmiSdSpFLulyjJVIpkMkkqlSKVdvPd\n/TSZTJp0OkUmu59JpzG0nf1CxcFU2TQ7ntxPkpBKECRJiARBlSBEgpBKElZJIkaCMHFCOoaX1Jt6\n7WnlwVEetOlFWV6M7LhrbWfQI+PktbvEkqEz2W7VRw/XeMOCxdkeGZXZILbK7aWRV53tol3tjokW\nb5qjnVwAOzagHdnGTSg1smWOnnAqlomNjsmNu+mxZmGOeCO54LUkUEKx301LAiUUB4pzSw0V+Ysw\nZ8kXgRKwCjF77WgZ4NMbX+KGc6v59JWLprs6xyQBq5jRtNYzuttdU0+MfZ1DNPfGaOmL09wXo7k3\nTktfbMIxx8dj4BAmRp6KkZdNI8RQ6NyYW4/XRyTgJxIKkhcOUBAKUhAOUhgJUhgJUZQXoiQ/RDjg\nR5nebJdKj6x3K96UeMrmtY4h9rQPsrt9kN2H3f0T/Y5bhiLoNQn5rFwa8Iw59loEvCYhn0nQaxHy\numnQ554Lek38HpO07TCcsoklM26ayjCcdNNoMsNgPMNAPEV/LM1wLEY6PoQTH8TrxAgTQ6MIBYMU\n5YUozgtTWhChrCBCeWGEyqI8KovyCAQC2VbPN/j3xnFA22jbndytfzjBUCzBwHCCwViCoVicwZg7\n6Vs0nnQngIu7k78lh7opU31Uql6WhqI0+IaoUL2E012YE43XDxRC3si61tVuz4fq89xZqaWFdkaI\nZ+K5ALYn0UNPvIeeRA/d8e7cfk/c3YbSQ0ddbyiDQl+hG8wG3fVzSwOllAZLKQuUjVtH1zPNk9lJ\nwCrE7OM4mvufOchXHt1DccjH19ev4sKG4umu1jGdSsAqsxeI024mB6sAdcVB6oqDE54biKdp7Ysz\nnMqQSNvZ8YqOu58dr5hIOxgKPKaBx1TZ1MAyFV7TwDINCoIeyiI+yiJ+At7Z8Y27mFsCXpNVtQWs\nqs1N5I7WmraBBPs7o1imIuS1CPlMAmMCz+kcY6+1JpF2GEykyfN7pv7fjmEABsr0EPYGCeed/KVD\niTQvNfWz9VAvP2js46WmfuJpdy3gefkWF5elWRoaoMHTT5XqocTpJpxoxxhohsY/jq5xbXrdoLX6\nPHdm7prV7jjjGf53dC4KWAGqw9VUh6tPWDaRSeSC2e5Yt5sm3PVye+I9dMW72Ne3j554D/bYNaKz\nCnwFBKwAPtOHx/TgM9wxtiPjcUf2vcbEeWOPfaYvV25s2QJfAcWBYnymfCEiziz7OoZ4eHs7Syoi\nrF1UQuQk5z6ZybqGkvzdT17hqb1dXL2snH97z9kUhubOcAVpYRVCCCGmWNp2eLVtkK2NfWw91Mu+\nzihNvTFSY8bwGwoq8wPUFQa4tDLN+yoOU9y/A1pfhLaXYGSIgb/A7dIfLIFQcTYtGXNc7Hb9t/xj\nllvyvbnWZjElbMemL9lHV6wrt4ZuZ7yTnnhPbp3clJPKdVke6c6cdtJH5aWc1FETVZ2MsCfsjskN\nFOe6Nj/67FIsw+Lz1/vI9+VT6C+kwFdAvi+fgDU968IL8Wa90tzPN598nd++2pHLswzFmvoiLl9S\nyhVLylhQGp7xDStH+sPeLv7uxy8zlMjwxXcs4QMNcVTrNihZBHUXTnf1jkm6BAshhBAz3MjMxU29\nMXfrGaapN8ahnhjbW/rRwBWLy/jAhfN428JCzJ690LIVWre6k8AN97jLgcV63HG4J6KM8QHs2P0T\nHo9dO9ibPed180yfOyu05csee91J6EoXS4B8mjnaGQ1gxwS7aTudG7ObttMk7AT9yX564j30JnrH\nd2lO9NC2988BCM6776hn+E1/LojN9+VT4CsYt42cC3vChDyhcZssSzTzaa3Z2xHlYHeUNfVFFIdn\nVgu87WheOzzE611RGkpCLK6I4DGP3fNHa81z+3v41u/388zr3eQHPNx6cT0fuLCOQ90xntjTye9f\n62TP4SFAM6/Ax+VnFXPJwmIKQx4MpTANhaEUlmVgKoUy3NQ0DExDYZoGhjKwDIVhZvPU2HNT07sw\nlXH45sPPseP5J3h7pIk/K2kn1P2KuyQhwPkfh+u+OunPnSwSsAohhBCzWGt/nA1bmtj4QjPd0SQ1\nhQFuPr+Om9bUUnLEB8hEKsP+5lYam5voaG+ht7MdnRwgYNgEjAxBI4NfZfAbGfyk8apsmt08pPHo\nNB6dwtJpLJ3CdEY3w0lh2EmUnUKdwiy8duECzBU3wPLroXyFBK+zyJ/f+0dsJ8NXbq6mP9lPf6Kf\n/mQ/fck+BpID9CWyafa4P9nPQHIAfYK1pn2mj5AnRNAK4jN9GIaBpSwMZWAaJqYasxnj9w2VLWsY\nxywzsu8GD9a49MhyPtNH0BMkZIUIeoLuvieUO56Lsz8fi+NoXmnp59FdHTy66zAHu93eHErBiqp8\n3nZWCZeeVca5dQXHDQ6nwlAizcvN/Ww91MeLTe7wimhy9As6r2WwtDKPs6vzWVmTz9k1+SwsDWMo\nxe92d/B/n9xHV+t+zgt2sH5+nNXBTjy9e6Fnv7tGurbBsdHaRh25AsQUcLT7dzCboBn5u6hy+yP/\nijLKQ8bwYxs+HCsAHj+GJ4DpC6IND7G23VQ6hwGwlUm8fBnDlWcTK19KrGQhBWUrqM6rnfLX9EZJ\nwCqEEELMAWnb4be7Ovjhnxp57kAPHlNxzYpKllRE2HN4iN3tgxzsHsZ23P+XBzwmZ1VEKIv4SI0b\nX++uBTx2rH0yY5O2T+0zgMLBSwYfabxksssEucsFFfo0ZUFFaUBhd7zGDf6tnGtvdz8EFi+EZddL\n8DpLvJFJl2zHZig15Aa4yX6i6SjD6eFxWywdy+WnnTS2Y+Noh4zO4GgH27GxdXYbu589drRDxsnk\nrjne9c6bDD4sZRHwBAhYAYJWkIDl7gc8RxyP2fyWP7fvNbyYholCYSgDpVQumFaocedM5a4BbSi3\npc7AGHfNyDlTjV4zdsuVG3M/FKhsAKQY/fc20tKXztj86VAXj+5q5fHd7XRGY5im5i3z8rjkrELq\niwO83NzPC4f6eLV9EMfRhLwWq+oKWFNfyPKqfAwDMrbG0ZqM45CxNbajsbUmYzu5fdvOnnc0zsh5\nx8G2R/Y1tu24ZR1NRmuGExn2dPTT2DOEIk2RMcCS/CSLIjFq/DEiRpRYMs1QPMlQIs1wMo3jOIDG\nNMBv2oScXvKNARzDJqmUu3lDJAMFJH0RbMPEBmylcYAMuKnWxDM2Dm4LLdlUQ3bTuP+NP0f2nIbs\nLPxj8kfuw/jrRvJy1+rRfbRGawetHcBBoTHQ2dBWEzNMMl4PSeUQd46eTf/Dyz/MZ1d/9g3+C5h6\nErAKIYQQc8zrnVF+tKWJn25rZjCRobogwNLKPJZWussRLamIMK84dErr7tmOJplxJ5BLZI49kdzY\nNJmdWK404qc8z508rjTiGzcJ1jP7uvnY/2xlSSTBdy/soODgr+DQ0+4a1sULYck73bFVNee7427F\njDIXZgnWWucC14yTOWo/aSeJpWPEMrFcMD2cHiaWieXy45k4sXQ2PeJ47Ja0k9P9csVJsJSFz/Lh\nM91tbIv7SKA/0iqvlMLAbU0eCfInCvxzXwqMOc6VyyVHlz1emaPujULjdgFO2zr3ZWNlXh7FwUiu\nV0DIEyJgBXJd8OflzWN+/vzJ/BFOKglYhRBCiDkqkbZJ2Q55M3xmy62Hevnwd18gL+Dhgb+8gHp/\nDPb8Enb9HA4943bFA3fW45rzoXaNm5YtA1PGOk6nuRCwnk6OdkhkEsQyMRKZBPFMnJSTQmuNo51x\nm2Y0kB4JqseVwxl3LneNY6M5+n5j7+toZ9z9YLQ1L5ay2Xqol5eb+7Edh0XlEZZVFLCwrICgx4tl\nWFipGGbfIay+Q5iDbah0DFJxSMcgHYcjhgSc6KsxdUSMcVR5ZbibYYFhoJSFMkxQFpgWVqAQK1SK\nGSzFCpdhhcqxIhVYkUoMf36utfrIAHCkpXlkRmy/6cdremUM9QwjAasQQgghpt3O1gE+eP8WLNPg\ngb+8gLPKI+6JVMyd+bjleWh+wU2Hu9xznhCUL3fXpvVFwJ8HvrwxaT54Q9lZj013+SFlgmFOnDfu\nnDE+L3dugjyd7QB4RJpIu2sGF+XnoeboetgSsM4dnUMJ7vvDAX64pZFUxuHPVlXz11csZEFxADpf\nheYt0LTFTfsb3YtMH5Qvg0DR+H9//nzw5bv73rA7wZphupOuGZ5sarnbyP64c+aYfVlP/kwn67AK\nIYQQYtqtqM5n020X8YHvbOGme5/jfz5yAStr8sEbhPq3uhu4wWB/42jw2rkbhtqhe68742Vi8KjW\nneniz262ViSMAGkzhOMNYfgieIN5+EP5GP7I6DJDoVJ3G1l+KFTiftiXcbxiCnUOJbj3Dwd4YEsj\nKpPg44uTfHD+EKXDf4CHd0L7dkgNuYVDZVB3gTurbO0FUHmOOwu4EDOEtLAKIYQQYkod6h7m/d/Z\nwmA8zXc/vIbV9UUnfW3HYIKn93bxp72tbN/fTDo2QJAEFjYmDgYOJhpDOZg4WNjZPCeXjt1Gyo0/\nb2dTnctDQcBrEfBYBHwWAa+HoM8k6PXgNRWx6ACx6CDp2CAqHSVMgpCKEyZBgZmgkCH8Oj7xi7IC\nkFcJedXull8NeVWQV5NNq7JLCXlGW4ZPY4A7FS2sWmv2HB7iiT2d/GFvF9FEBq9l4LUMfNnNaxl4\nTWNMvjkub1w5y8BrmuPuMVLWd+T12XyPqcYtL6K1JplxGE5miKVs4mmbWMomlsyQzDikbXfMYMZx\n03TGxsmkyKTd2bOVtjF0JpvabupkMLQ766zjOOBku+0esa8d251Qx9Fobbup42TzHDRuinZwHHfy\nHbLlwc6W1dk8d1Ie7bjHaId0tJfFHGJNoI3ydAtqpAu+J+S2nlasdIPT2gugsF6+QBGnnbSwCiGE\nEGLGqC8J8ZNPuC2tH7z/eb76vrOpLw4ds3zXUJKn93XzzOtd7O2IAlAS9rJ20ULWLirlvHmFeK3R\n7oRjP2qP/dw9fpIUJizPBOVNQ5Ef8Jz0BFbRZIYDXVFe7xzdDnQP09HTS549QJEaolgNUOuLsTAY\nZ55/mBqzj5JoD+GepzGjh0cDimMxxnaxzHZxzuVZo10xj+ySOdINc9y1HvCFwV/gdvMMFIzfz8Td\nH0x/s/uDU4Z7PBI4j4w7HFl71zAnrHI8Hmfr7td5ac/r7Dt4CIa7KVRDXJ+XotCrsRNu8OZo7aaO\nG5zZ2WBs5NjRTm7RD4VDBrDRxHN5Y1PtVjE7H+u4fMBUYBpgahvTSWVnus5kl3jKECBDPmk8ys7N\nhO3BPe9VJ3iPZhITMpEarKqzofwmt5t9xUoonC9dccWsIy2sQgghhDgtuoaSfPD+Lew5PHTCsl7L\n4Pz6Ii5ZVMIli0pZUhHBOIUZkGeCjO3Q0hfnQHeUA13D7O8a5kBXlP1dw3RHR2eWjXgVq0synJs/\nzNLQIHWeQUydQmcyOHYaHDfVdgbsDNpJZ9OMuzauk8ltyrFRTjrX2qdyLYBuy5+hM5g6Q4g4YSeK\nReaoet+U/CIAm3z/clKv08bAVh4yyoNjeNCGBzM9TFAPH/si04sbBKsTpNmwU42EnEZ2DcuRMJQx\nC32A1hPnObk8dy1MR1lo04M2vWB4wfKiLB/K8mJk05FjZXoxLC/K48OwfO6+mf3CwLTQyhrz5cDI\nvoFhWBhGdhka012eJjfREGP2x34RkDunjpF/ZHk1cb435H4BIcQMJZMuCSGEEGJGiiYzPH+wB/s4\ny2SGfCZvqSvE75m45W4u6BtOsa8zyt6OIV7Ppns7ouMC2ZNhKPCY2S6vltvtdeTYyu6PnnePTaUY\niKfpHkoQHY5iJgfIUzHyiZKnYrziLMBAc5n5MgYaU2kK/BYFAXfL9xvgZEinkmRSCex0EiedxLFT\nkEmi7DSGP0JxWSW11XXMq6vFE8mO4w0WuxNqyUzQQpzRpEuwEEIIIWaksM/iiiXl012NaVcY8nL+\n/CLOnz9+PG/fcIqDPcMosoGoZWAZKrfvMUeDUo9pnNK6u8eSSNt0DSXpjibpjqY4+OvdWIbi5vde\nR1V+gNKI75Sek7EdLFO6nQohJocErEIIIYQQM0RhyEth6PTO0Or3mNQWBaktCgLwnacPAPCWusI3\ndD8JVoUQk0n+ogghhBBCCCGEmJEkYBVCCCGEEEIIMSPNyEmXlFJDwGvTXQ/xhpUA3dNdCfGmyHs4\nu8n7N/vJezi7yfs3+8l7OPvJezizzdNal55MwZk6hvW1k501Ssw8Sqmt8v7NbvIezm7y/s1+8h7O\nbvL+zX7yHs5+8h7OHdIlWAghhBBCCCHEjCQBqxBCCCGEEEKIGWmmBqz3TXcFxJsi79/sJ+/h7Cbv\n3+wn7+HsJu/f7Cfv4ewn7+EcMSMnXRJCCCGEEEIIIWZqC6sQQgghhBBCiDPcSQWsSqlapdSTSqnd\nSqldSqnPZPOLlFKPKaX2ZdPCbL5SSn1DKfW6Umq7UuotY+51a7b8PqXUrcd4XnH2eVGl1P9j777D\nozruxf+/Z/uqrFZdIKFG72B6jG1sx2DHSWxIMTj3xiVxi1Oc3HwTk9xf4tw0J75xmp04LqlOgNgx\nN9W4O65gi2JAdIQkhHrblbR9d35/nEVIIAkJBJLQ5/U85zmzc+acM3tGC/vZmTPnoZO2zVNK7Yof\n+2dKKdXLMa5WSu2Pl7u3h+0/V0q19+f9j3QjtP1+rZSqV0rtPil/jlJqs1Jqh1KqRCm18Gyvz0gw\nzNrwu0qpo6f7/PTW1kqpj8XfQ0wpNSpm7xuh7ddjOaXUzUqphvhncIdS6tNnel1GkpHWhkqpBKXU\nP5VS++L1vb/LtgKl1Evxer2qlMo72+sz3A2X9uurXXo4Rm+fwTvj/7buUEq9oZSadrbXZyQYaW14\nms/gj7v8G3pAKdU6WNdpuBrk9tuklGpVSv3jNOfssZ37829oX+WUUnal1IZ43bYopQoHfkXEgGit\nT7sAY4CL4ulk4AAwDfghcG88/17gB/H0B4BnAQUsBrbE89OAsvg6NZ5O7eF8icBS4E7goZO2vQMs\niR/7WeCaHvY3A4eBYgRFvucAACAASURBVMAGvAdM67J9PvAHoL0/73+kLyOt/eLlLgUuAnaflP/8\n8X3i9Xx1qK/vKGzDxfH69Pn56a2tganAZOBVYP5QX1tpv17r3GM54OaTjzkalpHWhkACcHk8bQNe\n7/IZfAq4KZ6+AvjDUF/f0dJ+fbVLD8fo7TPo6pL+MLBpqK+vtGGP30X7W+5zwK+H+vqOlPaLb7sS\n+BDwjz7O12s79/bZ6uEYvX0GPwM8Ek+vBjYM9fW90Jd+9bBqrWu01tvi6TZgL5ALXAf8Ll7sd8D1\n8fR1wO+1YTPgVkqNAVYAL2itm7XWLcALwNU9nK9Da/0GEOiaHz+GS2v9tjb+Sn7f5ZxdLQQOaa3L\ntNYhYH28TiilzMADwFf6894vBCOw/dBavwY097QJcMXTKUB1f67BSDdc2jC+bbPWuqav+vbV1lrr\nvVrr/QO8BCPaSGu/gZQbLUZaG2qtfVrrV+LpELANON6TOg14KZ5+JV7XC9pwab/TtMvJx+ixnbXW\n3i4vEzH+X7zgjbQ2HEBbrwHW9f9KjEyD2H5orV8C2k5zyl7beRD+H+xa56eBK5XqecSgGBwDvoc1\n3u09F9gCZB9vyPg6K14sFzjaZbeqeF5v+f2VG9/ndPv3dZ7PAn8brV/ERkj79eUe4AGl1FHgf4G1\nA9x/xBviNuyvwWjrC9IIab/T+Uh8iNbTSqlxQ3D+ITXS2lAp5cbojTgepL4HfCSeXgkkK6XSz2Ud\nhpPh0n49tMtA9r1bKXUYo3fq82dy/pFspLVhb+WUUgVAEfDymZx/pDrL9uuvc/lvbeextdYRwAOM\nmn9Dh8KAAlalVBLwF+Cek37hO6VoD3m6j/x+V6Gf+/dYTik1FvgY8PMBnPOCMYLary93AV/UWo8D\nvgg8McD9R7Rh0Ib9db7OM6KMoPbry9+BQq31LOBFTvzKPCqMtDZUSlkwem9+prUui2d/GbhMKbUd\nuAw4BkTOVR2Gk+HSfr20S79prR/WWo8Hvgr890D3H8lGWhueptxq4GmtdXSg5x+pBqH9+n2qs9x/\nqI4tetDvgFUpZcX4A/uj1vqZeHbd8e75+Lo+nl8FdP3VPQ9j6GaP+UqplV1uPu9rEpYqug+nOL7/\nuC7739nH+ecCE4BDSqlyIEEpdaifl2BEG2Ht15ebgOP1fwpj+PeoMEzasLe6mbvs/z/00tYDPe6F\nZIS1X6+01k1a62D85WPAvIGeb6QaoW34KHBQa/2T4xla62qt9Sqt9Vzg6/E8z0DPOdIMs/br1i4D\n+QyeZD293FpzIRqhbXjKZ7CL1YyC4cDHDVL79XbsRV2u/4fPYP+BfAY7jx3/QSKFnm9jE4NF9+9G\naYVxD9pPTsp/gO43Sv8wnr6W7jdKv6NP3AB9BOPm59R4Oq2P897MqZNNvBs/5vGJXD7Qw34WjJur\nizgx6dL0HsqNlkmXRlT7dSlbyKmTLu0FlsXTVwJbh/r6jrY27LLtdJMV9NnWjK5Jl0Zc+/VWDhjT\nJb0S2DzU11fasNd9v4Px5dB0Un7G8Tzgu8D/DPX1HU3t11u79HGMkz+DE7ukPwSUDPX1lTbsdd9e\ny2FMPlgOqKG+tiOp/brst4zTT7rUZzuf7t/Q3soBd9N90qU/D/X1vdCX/v6RLcXo6t4J7IgvH8AY\nr/0ScDC+TuvyR/kwxky9u+jypRS4FTgUX27p45zlGL9WtGP8kjEtnj8f2B0/9kO9fdDj9TsQL/f1\n/vwBXqjLCG2/dUANEI7v/6ku72Urxo8QW4B5Q319R2Eb/jD+OhZf39fL/j22NUaQUwUEgTrguaG+\nvtJ+Pe7fYzng+0Bp/DP4CjBlqK+vtOGpbYjRm6AxfuQ7Xt9Px7d9NF7fA8DjgH2or+9oab++2qWH\n/Xv7DP40/hncEf8MnvKD/IW4jLQ2PF054D7g/qG+riO0/V4HGgB/vF1W9HLOHtu5t89WD/v39hl0\nYIzyO4TxRITiob6+F/py/AukEEIIIYQQQggxrAx4lmAhhBBCCCGEEOJ8kIBVCCGEEEIIIcSwJAGr\nEEIIIYQQQohhyTLUFehJRkaGLiwsHOpqCCGEEEKIPpQ1dABQnJk4xDURQowkW7dubdRaZ/an7LAM\nWAsLCykpKRnqagghhBBCiD7c8Ku3Adhwx5IhrokQYiRRSlX0t6wMCRZCCCGEEEIIMSxJwCqEEEII\nIYQQI8DuYx7+4/EtbNxexWh5POmwHBIshBBCCCGEEMNFLKb5+85qfvnqYaxmE/MKUplfmMr8gjRy\nUhznpQ4bt1dx7192EdOaNw418uKeer5z/QxSE23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8AX57Sz+C1abD8NK3YM9fIX0C3PR3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XpQ+zMgF5BAnhDize4CW0UZCMxGAxePH8rF44ey51ATn+RX0uQN0uQN4vEFae60VDR68fhCWM0G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7+PI1s/E4LDT0BfjtlsM09gWoKXBy/YI8PC5FTI8RS8YIJ8NEkhEiyYi5nIikHkeSEdRpNs3U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"text/plain": [ "<matplotlib.figure.Figure at 0x1ecf7c38eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if visualize_group_timeseries: #Set true if you want to visualize the actual time-series \n", " for c in np.unique(y_train): #Loops over classes, plot as columns\n", " ind = np.where(y_train == c)\n", " ind_plot = np.random.choice(ind[0][5:-5],size=plot_row)\n", " print(\"Group : \" + str(c))\n", " f, axarr = plt.subplots(plot_row, 1, figsize=(16,10))\n", " \n", " for n in range(plot_row): #Loops over rows\n", " c = int(c)\n", " \n", " data_part = X_train.iloc[ind_plot[n]-50:ind_plot[n]+20]\n", " axarr[n].plot(data_part.index, data_part.loc[:,'Close'],label=\"Close\")\n", " axarr[n].plot(data_part.index, data_part.loc[:,'EMA_5'],label=\"EMA_5\")\n", " axarr[n].plot(data_part.index, data_part.loc[:,'EMA_30'],label=\"EMA_30\")\n", " axarr[n].legend()\n", " axarr[n].axvline(x=data_part.index[50], label=\"Action\")\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Define functions for initializing variables and standard layers\n", "#For now, this seems superfluous, but in extending the code\n", "#to many more layers, this will keep our code\n", "#read-able\n", "\n", "def weight_variable(shape, name):\n", " initial = tf.truncated_normal(shape, stddev=0.1)\n", " return tf.Variable(initial, name = name)\n", "\n", "def bias_variable(shape, name):\n", " initial = tf.constant(0.1, shape=shape)\n", " return tf.Variable(initial, name = name)\n", "\n", "def conv2d(x, W):\n", " return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n", "\n", "def max_pool_2x2(x):\n", " return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],\n", " strides=[1, 2, 2, 1], padding='SAME')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training at model 0\n" ] } ], "source": [ "total_rst=np.zeros((model_num,len(y_test)), dtype=np.float)\n", "\n", "initializer = tf.contrib.layers.xavier_initializer()\n", "xx = 0\n", "# for xx in range(0,model_num):\n", "print(\"Training at model \"+str(xx))\n", "with tf.variable_scope('model_'+str(xx)):\n", "\n", " x = tf.placeholder(\"float\", shape=[None, D], name = 'Input_data')\n", " y_ = tf.placeholder(tf.int64, shape=[None], name = 'Ground_truth')\n", " keep_prob = tf.placeholder(\"float\")\n", " bn_train = tf.placeholder(tf.bool) #Boolean value to guide batchnorm\n", "\n", " with tf.name_scope(\"Reshaping_data\") as scope:\n", " x_image = tf.reshape(x, [-1,D,1,1])\n", "\n", " ## Build the graph\n", " # ewma is the decay for which we update the moving average of the \n", " # mean and variance in the batch-norm layers\n", " with tf.name_scope(\"Conv1\") as scope:\n", " # W_conv1 = weight_variable([4, 1, 1, num_filt_1], 'Conv_Layer_1')\n", " W_conv1 = tf.get_variable(\"Conv_Layer_1\", shape=[5, 1, 1, num_filt_1],initializer=initializer)\n", " b_conv1 = bias_variable([num_filt_1], 'bias_for_Conv_Layer_1')\n", " a_conv1 = conv2d(x_image, W_conv1) + b_conv1\n", "\n", " with tf.name_scope('Batch_norm_conv1') as scope:\n", " # ewma = tf.train.ExponentialMovingAverage(decay=0.99) \n", " # bn_conv1 = ConvolutionalBatchNormalizer(num_filt_1, 0.001, ewma, True) \n", " # update_assignments = bn_conv1.get_assigner() \n", " # a_conv1 = bn_conv1.normalize(a_conv1, train=bn_train) \n", " a_conv1 = tf.contrib.layers.batch_norm(a_conv1,is_training=bn_train,updates_collections=None)\n", " h_conv1 = tf.nn.relu(a_conv1)\n", "\n", " with tf.name_scope(\"Conv2\") as scope:\n", " # W_conv2 = weight_variable([4, 1, num_filt_1, num_filt_2], 'Conv_Layer_2')\n", " W_conv2 = tf.get_variable(\"Conv_Layer_2\", shape=[4, 1, num_filt_1, num_filt_2],initializer=initializer)\n", " b_conv2 = bias_variable([num_filt_2], 'bias_for_Conv_Layer_2')\n", " a_conv2 = conv2d(h_conv1, W_conv2) + b_conv2 \n", "\n", " with tf.name_scope('Batch_norm_conv2') as scope:\n", " # bn_conv2 = ConvolutionalBatchNormalizer(num_filt_2, 0.001, ewma, True) \n", " # update_assignments = bn_conv2.get_assigner() \n", " # a_conv2 = bn_conv2.normalize(a_conv2, train=bn_train) \n", " a_conv2 = tf.contrib.layers.batch_norm(a_conv2,is_training=bn_train,updates_collections=None)\n", " h_conv2 = tf.nn.relu(a_conv2) \n", "\n", " with tf.name_scope(\"Conv3\") as scope:\n", " # W_conv3 = weight_variable([4, 1, num_filt_2, num_filt_3], 'Conv_Layer_3')\n", " W_conv3 = tf.get_variable(\"Conv_Layer_3\", shape=[4, 1, num_filt_2, num_filt_3],initializer=initializer)\n", " b_conv3 = bias_variable([num_filt_3], 'bias_for_Conv_Layer_3')\n", " a_conv3 = conv2d(h_conv2, W_conv3) + b_conv3\n", "\n", " with tf.name_scope('Batch_norm_conv3') as scope:\n", " # bn_conv3 = ConvolutionalBatchNormalizer(num_filt_3, 0.001, ewma, True) \n", " # update_assignments = bn_conv3.get_assigner() \n", " # a_conv3 = bn_conv3.normalize(a_conv3, train=bn_train) \n", " a_conv3 = tf.contrib.layers.batch_norm(a_conv3,is_training=bn_train,updates_collections=None)\n", " h_conv3 = tf.nn.relu(a_conv3) \n", "\n", " with tf.name_scope(\"Fully_Connected1\") as scope:\n", " # W_fc1 = weight_variable([Dnum_filt_3, num_fc_1], 'Fully_Connected_layer_1')\n", " # W_fc1 = tf.get_variable(\"Fully_Connected_layer_1\", shape=[Dnum_filt_2, num_fc_1],initializer=initializer)\n", " W_fc1 = tf.get_variable(\"Fully_Connected_layer_1\", shape=[Dnum_filt_3, num_fc_1],initializer=initializer)\n", " b_fc1 = bias_variable([num_fc_1], 'bias_for_Fully_Connected_Layer_1')\n", " # h_conv3_flat = tf.reshape(h_conv3, [-1, Dnum_filt_3])\n", " # h_conv3_flat = tf.reshape(h_conv2, [-1, Dnum_filt_2])\n", " h_conv3_flat = tf.reshape(h_conv3, [-1, Dnum_filt_3])\n", " h_fc1 = tf.nn.relu(tf.matmul(h_conv3_flat, W_fc1) + b_fc1) \n", "\n", " with tf.name_scope(\"Fully_Connected2\") as scope:\n", " h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n", " # W_fc2 = tf.Variable(tf.truncated_normal([num_fc_1, num_classes], stddev=0.1),name = 'W_fc2')\n", " W_fc2 = tf.get_variable(\"W_fc2\", shape=[num_fc_1, num_classes],initializer=initializer)\n", " b_fc2 = tf.Variable(tf.constant(0.1, shape=[num_classes]),name = 'b_fc2')\n", " h_fc2 = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 \n", "\n", " with tf.name_scope(\"SoftMax\") as scope:\n", " # regularizers = (tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(b_conv1) +\n", " # tf.nn.l2_loss(W_conv2) + tf.nn.l2_loss(b_conv2) +\n", " # tf.nn.l2_loss(W_conv3) + tf.nn.l2_loss(b_conv3) +\n", " # tf.nn.l2_loss(W_fc1) + tf.nn.l2_loss(b_fc1) +\n", " # tf.nn.l2_loss(W_fc2) + tf.nn.l2_loss(b_fc2))\n", " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=h_fc2,labels=y_)\n", " cost = tf.reduce_sum(loss) / batch_size\n", " # cost += regularizationregularizers\n", " loss_summ = tf.summary.scalar(\"cross_entropy_loss\", cost)\n", "\n", " ## define train optimizer##\n", " with tf.name_scope(\"train\") as scope:\n", " tvars = tf.trainable_variables()\n", " #We clip the gradients to prevent explosion\n", " grads = tf.gradients(cost, tvars)\n", " optimizer = tf.train.AdamOptimizer(learning_rate)\n", " gradients = list(zip(grads, tvars))\n", " train_step = optimizer.apply_gradients(gradients)\n", " # The following block plots for every trainable variable\n", " # - Histogram of the entries of the Tensor\n", " # - Histogram of the gradient over the Tensor\n", " # - Histogram of the grradient-norm over the Tensor\n", " numel = tf.constant([[0]])\n", " for gradient, variable in gradients:\n", " if isinstance(gradient, ops.IndexedSlices):\n", " grad_values = gradient.values\n", " else:\n", " grad_values = gradient\n", "\n", " numel +=tf.reduce_sum(tf.size(variable))\n", "\n", " #h1 = tf.summary.histogram(variable.name.replace(':','_'), variable)\n", " #h2 = tf.summary.histogram(variable.name.replace(':','_') + \"/gradients\", grad_values)\n", " #h3 = tf.summary.histogram(variable.name.replace(':','_') + \"/gradient_norm\", clip_ops.global_norm([grad_values]))\n", "\n", " with tf.name_scope(\"Evaluating_accuracy\") as scope:\n", " correct_prediction = tf.equal(tf.argmax(h_fc2,1), y_)\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", " accuracy_summary = tf.summary.scalar(\"accuracy\", accuracy)\n", "\n", " # merge all summaries into a single \"operation\" which we can execute in a session \n", " merged = tf.summary.merge_all()\n", "\n", " ## run session and evaluate performance##\n", " perf_collect = np.zeros((3,int(np.floor(max_iterations /100))))\n", " cost_ma = 0.0\n", " acc_ma = 0.0\n", " with tf.Session() as sess:\n", " writer = tf.summary.FileWriter(logs_path, sess.graph)\n", " # sess.run(tf.global_variables_initializer())\n", " sess.run(tf.global_variables_initializer())\n", "\n", " step = 0 # Step is a counter for filling the numpy array perf_collect\n", " for i in range(max_iterations):#training process\n", " batch_ind = np.random.choice(N,batch_size,replace=False).tolist()\n", "\n", " if i==0:\n", " acc_test_before = sess.run(accuracy, feed_dict={ x: X_test, y_: y_test, keep_prob: 1.0, bn_train : False})\n", " if i%1000 == 0:\n", " #Check training performance\n", " result = sess.run([cost,accuracy],feed_dict = { x: X_train, y_: y_train, keep_prob: 1.0, bn_train : False})\n", " # print(\" Training accuracy at %s out of %s is %s\" % (i,max_iterations, result))\n", " # step +=1\n", " perf_collect[1,step] = acc_train = result[1]\n", " cost_train = result[0]\n", "\n", " #Check validation performance\n", " result = sess.run([cost, accuracy, merged], feed_dict={ x: X_val, y_: y_val, keep_prob: 1.0, bn_train : False})\n", " perf_collect[1,step] = acc_val = result[1]\n", " cost_val = result[0]\n", " if i == 0: cost_ma = cost_train\n", " if i == 0: acc_ma = acc_train\n", " cost_ma = 0.8cost_ma+0.2cost_train\n", " acc_ma = 0.8acc_ma + 0.2acc_train\n", "\n", " #Write information to TensorBoard\n", " writer.add_summary(result[2], i)\n", " writer.flush() #Don't forget this command! It makes sure Python writes the summaries to the log-file\n", " print(\"At %5.0f/%5.0f Cost: train%5.3f val%5.3f(%5.3f) Acc: train%5.3f val%5.3f(%5.3f) \" % \n", " (i,max_iterations, cost_train,cost_val,cost_ma,acc_train,acc_val,acc_ma))\n", " step +=1\n", " sess.run(train_step,feed_dict={x:X_train.iloc[batch_ind], y_: y_train.iloc[batch_ind], keep_prob: dropout, bn_train : True})\n", "\n", " #training process done!\n", " predict=sess.run(tf.argmax(h_fc2,1), feed_dict={ x: X_test, y_: y_test, keep_prob: 1.0, bn_train : False})\n", " total_rst[xx]=predict\n", "\n", " result = sess.run([accuracy,numel], feed_dict={ x: X_test, y_: y_test, keep_prob: 1.0, bn_train : False})\n", " acc_test = result[0]\n", " print('The network has %s trainable parameters'%(result[1])) \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## show the graph of voting result\n", "print(total_rst)\n", "\n", "print('The accuracy on the test data is %.3f, before training was %.3f' %(acc_test,acc_test_before))\n", "plt.figure()\n", "plt.plot(perf_collect[0],label='Valid accuracy')\n", "plt.plot(perf_collect[1],label = 'Train accuracy')\n", "plt.axis([0, step, 0, np.max(perf_collect)])\n", "plt.legend()\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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
wasit7/visionmarker	beta_python3/10_yolo_annotate_maker.ipynb	1	275681	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import cv2\n", "from codecs import open as copen\n", "from shutil import copyfile, rmtree\n", "\n", "from sklearn.model_selection import train_test_split\n", "from django.db.models import Q\n", "from django.contrib.auth.models import User\n", "\n", "from app.models import Label,Image,Batch, Comment, STATUS_CHOICES\n", "from app.models import TODO, TAGGING, REVIEWING, DONE" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "dataset_path = os.path.join('.', 'static', 'dataset')\n", "yolo_path = os.path.join('.', 'yolo')\n", "yolo_anno_dir = os.path.join(yolo_path, 'annotate')\n", "obj_path = os.path.join(yolo_anno_dir, 'obj')\n", "\n", "if os.path.exists(obj_path):\n", " rmtree(obj_path)\n", "os.makedirs(obj_path)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "classcnt = 0\n", "classidx = dict()\n", "stop = ['/.', '.', './', '\\.', '\\.']\n", "\n", "def get_class(label):\n", " global classidx, classcnt\n", " info = [label.brand, label.model]\n", "# print(info)\n", "# cls = \"{}.{}\".format(info)\n", " cls = label.brand\n", " print\n", " if not any(info) or cls in stop:\n", " cls = \"unknown\"\n", " if not cls in classidx:\n", " classidx[cls] = classcnt\n", " classcnt += 1\n", " return cls\n", "\n", "def get_coord(label):\n", " return label.x, label.y, label.width, label.height\n", "\n", "def get_yolo_anno(mat, label):\n", " imh, imw = mat.shape[:2]\n", " x, y, w, h = get_coord(label)\n", " print(x, y, w, h)\n", " cls = get_class(label)\n", " dw = 1.0/imw\n", " dh = 1.0/imh\n", " x = (x+(x+w))/2.0\n", " y = (y+(y+h))/2.0\n", " x = xdw\n", " w = wdw\n", " y = ydh\n", " h = hdh\n", " \n", "# x, y, w, h = get_coord(label)\n", "# rx = (x+(w/2.0))/imw\n", "# ry = (y+(h/2.0))/imh\n", "# rw = w/2.0/imw\n", "# rh = h/2.0/imh\n", "# cls = get_class(label)\n", " return classidx[cls], x, y, w, h\n", "\n", "def recheck_ratio(mat, labels, filename='test.png'):\n", " imh, imw = mat.shape[:2]\n", " print(imh)\n", " tmp = mat.copy()\n", " for label in labels:\n", " cls, rx, ry, rw, rh = label\n", " w = int(rwimw)\n", " h = int(rhimh)\n", " x1 = int(rximw-w/2.0)\n", " y1 = int(ryimh-h/2.0)\n", " x2 = int(x1+w)\n", " y2 = int(y1+h)\n", " tmp = cv2.rectangle(tmp, (x1, y1), (x2, y2), (0, 255, 0))\n", " w = int(rwimw)\n", " h = int(rhimh)\n", " x = int(rximw)\n", " y = int(ryimh)\n", " tmp = cv2.circle(tmp, (x, y), h, (0,0,255))\n", " cv2.imwrite(filename, tmp)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5, 318, 1300, 759)\n", "\n", "1080\n", "(939, 203, 586, 539)\n", "\n", "1080\n", "(1063, 227, 759, 651)\n", "\n", "(1146, 537, 152, 111)\n", "\n", "(0, 7, 1089, 1062)\n", "\n", "1080\n", "(1105, 261, 563, 464)\n", "\n", "(1218, 435, 112, 67)\n", "\n", "1080\n", "(1005, 14, 732, 760)\n", "\n", "(1251, 594, 98, 63)\n", "\n", "(1651, 213, 216, 265)\n", "\n", "1080\n", "(763, 207, 403, 662)\n", "\n", "(775, 607, 81, 82)\n", "\n", "1080\n", "(1303, 181, 429, 384)\n", "\n", "(1437, 434, 87, 47)\n", "\n", "1080\n", "(797, 219, 928, 682)\n", "\n", "(963, 533, 146, 105)\n", "\n", "1080\n", "(1257, 241, 472, 416)\n", "\n", "(1373, 402, 89, 55)\n", "\n", "1080\n", "(1558, 325, 239, 207)\n", "\n", "1080\n", "(1427, 277, 337, 307)\n", "\n", "(1522, 399, 64, 39)\n", "\n", "(1, 602, 771, 474)\n", "\n", "1080\n", "(649, 285, 456, 633)\n", "\n", "(662, 710, 80, 95)\n", "\n", "(1547, 325, 219, 211)\n", "\n", "(1361, 309, 192, 159)\n", "\n", "(1611, 418, 67, 40)\n", "\n", "(1398, 369, 47, 28)\n", "\n", "(899, 238, 489, 306)\n", "\n", "1080\n", "(1247, 322, 246, 443)\n", "\n", "(1286, 597, 68, 67)\n", "\n", "1080\n", "(0, 0, 1615, 1071)\n", "\n", "1080\n", "(1086, 294, 599, 464)\n", "\n", "(1209, 487, 128, 71)\n", "\n", "1080\n", "(1123, 278, 622, 494)\n", "\n", "(1285, 530, 115, 67)\n", "\n", "(826, 245, 490, 307)\n", "\n", "(919, 442, 57, 40)\n", "\n", "1080\n", "(369, 219, 325, 250)\n", "\n", "(501, 393, 76, 40)\n", "\n", "(243, 201, 175, 171)\n", "\n", "(313, 310, 64, 27)\n", "\n", "(610, 199, 72, 118)\n", "\n", "1080\n", "(1059, 2, 779, 720)\n", "\n", "1080\n", "(562, 1, 1148, 909)\n", "\n", "(743, 599, 197, 155)\n", "\n", "(1, 349, 424, 656)\n", "\n", "(1727, 310, 117, 127)\n", "\n", "(1763, 387, 29, 22)\n", "\n", "1080\n", "(739, 0, 1182, 1078)\n", "\n", "1080\n", "(1478, 133, 330, 403)\n", "\n", "1080\n", "(563, 329, 530, 636)\n", "\n", "(537, 790, 96, 99)\n", "\n", "(850, 266, 522, 258)\n", "\n", "(1347, 301, 218, 163)\n", "\n", "(1505, 313, 121, 124)\n", "\n", "(1598, 322, 99, 98)\n", "\n", "1080\n", "(1353, 105, 356, 447)\n", "\n", "1080\n", "(1262, 59, 498, 543)\n", "\n", "1080\n", "(1002, 2, 780, 698)\n", "\n", "1080\n", "(874, 1, 852, 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41)\n", "\n", "1080\n", "(879, 309, 385, 408)\n", "\n", "(953, 524, 83, 71)\n", "\n", "1080\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(99, 42, 375, 410)\n", "\n", "1080\n", "(285, 6, 1430, 916)\n", "\n", "1080\n", "(1440, 206, 351, 334)\n", "\n", "(1548, 312, 65, 44)\n", "\n", "1080\n", "(1369, 334, 364, 291)\n", "\n", "(1460, 465, 80, 53)\n", "\n", "(1689, 342, 141, 121)\n", "\n", "(592, 404, 535, 491)\n", "\n", "(604, 677, 105, 68)\n", "\n", "(816, 224, 564, 322)\n", "\n", "1080\n", "(13, 491, 649, 567)\n", "\n", "1080\n", "(0, 9, 1571, 1065)\n", "\n", "1080\n", "(5, 33, 1221, 1047)\n", "\n", "1080\n", "(1087, 213, 620, 434)\n", "\n", "(1266, 419, 104, 64)\n", "\n", "1080\n", "(267, 68, 1638, 1003)\n", "\n", "1080\n", "(14, 476, 1130, 594)\n", "\n", "(996, 345, 224, 383)\n", "\n", "(1023, 561, 74, 65)\n", "\n", "(1552, 394, 151, 268)\n", "\n", "(1580, 551, 48, 59)\n", "\n", "1080\n", "(214, 212, 345, 305)\n", "\n", "(343, 417, 90, 54)\n", "\n", "1080\n", "(1498, 321, 303, 214)\n", "\n", "(1603, 422, 57, 47)\n", "\n", "1080\n", "(983, 390, 336, 333)\n", "\n", "(1057, 571, 91, 59)\n", "\n", "1080\n", "(11, 289, 1601, 785)\n", "\n", "(1536, 334, 273, 192)\n", "\n", "(1624, 413, 51, 16)\n", "\n", "1080\n", "(919, 396, 438, 463)\n", "\n", "(975, 600, 101, 95)\n", "\n", "1080\n", "(354, 258, 1397, 806)\n", "\n", "(528, 685, 141, 150)\n", "\n", "1080\n", "(1052, 404, 397, 459)\n", "\n", "(1084, 611, 95, 109)\n", "\n", "1080\n", "(179, 207, 472, 419)\n", "\n", "(346, 477, 127, 75)\n", "\n", "1080\n", "(331, 270, 1281, 778)\n", "\n", "(428, 661, 123, 113)\n", "\n", "1080\n", "(456, 286, 163, 280)\n", "\n", "1080\n", "(1373, 291, 397, 345)\n", "\n", "(1477, 432, 91, 55)\n", "\n", "1080\n", "(353, 210, 209, 185)\n", "\n", "1080\n", "(54, 208, 371, 201)\n", "\n", "1080\n", "(1561, 177, 264, 338)\n", "\n", "(1402, 318, 140, 232)\n", "\n", "(1421, 447, 32, 32)\n", "\n", "(934, 414, 323, 351)\n", "\n", "(976, 604, 77, 72)\n", "\n", "(854, 242, 511, 268)\n", "\n", "(9, 622, 964, 445)\n", "\n", "1080\n" ] } ], "source": [ "images = Image.objects.filter(batch__status=REVIEWING)\\\n", " .exclude(label__isnull=True)\n", " \n", "for i, image in enumerate(images):\n", " labels = list()\n", " labelled = Label.objects.filter(image=image)\n", " image_path = os.path.join(dataset_path, image.src_path)\n", " fname, ext = os.path.splitext(image.src_path)\n", " fname = image.id\n", " obj_img_path = os.path.join(obj_path, \"{}.png\".format(fname))\n", " obj_txt_path = os.path.join(obj_path, \"{}.txt\".format(fname))\n", " mat = cv2.imread(image_path)\n", " for label in labelled:\n", " ret = get_yolo_anno(mat, label)\n", " labels.append(ret)\n", " \n", "# !!!DEBUG\n", " recheck_ratio(mat, labels, \"./yolo/{}.png\".format(i))\n", " copyfile(image_path, obj_img_path)\n", " with open(obj_txt_path, 'w+') as fp:\n", " for label in labels:\n", " fp.write(\" \".join([str(x) for x in label]))\n", " fp.write(os.linesep)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1616 180\n" ] } ], "source": [ "namespath = os.path.join(yolo_anno_dir, 'obj.names')\n", "with copen(namespath, 'w+', \"utf8\") as fp: \n", " for k, v in sorted( classidx.items(), key=lambda x:x[1] ):\n", " fp.write(k)\n", " fp.write(os.linesep)\n", "\n", "datapath = os.path.join(yolo_anno_dir, 'obj.data')\n", "context = \"\"\"classes= {}\n", "train = yolo/annotate/train.txt\n", "valid = yolo/annotate/test.txt\n", "names = yolo/annotate/obj.names\n", "backup = backup/\"\"\".format(len(classidx))\n", "with open(datapath, 'w+') as fp:\n", " fp.write(context)\n", "\n", "all_images = images.all().values_list('id')\n", "train, test = train_test_split(all_images, test_size=0.1)\n", "print len(train), len(test)\n", "with copen(os.path.join(yolo_anno_dir, 'train.txt'), 'w+', \"utf8\") as fp:\n", " for x in train:\n", " fname = \"{}.png\".format(x[0])\n", " fp.write(os.path.join(obj_path, fname)+os.linesep)\n", " \n", "with copen(os.path.join(yolo_anno_dir, 'test.txt'), 'w+', \"utf8\") as fp:\n", " for x in test:\n", " fname = \"{}.png\".format(x[0])\n", " fp.write(os.path.join(obj_path, fname)+os.linesep)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def create_cfg(args) :\n", " return \"\"\"[net]\n", "batch={}\n", "subdivisions={}\n", "width=608\n", "height=608\n", "channels=3\n", "momentum=0.9\n", "decay=0.0005\n", "angle=0\n", "saturation = 1.5\n", "exposure = 1.5\n", "hue=.1\n", "\n", "learning_rate=0.001\n", "burn_in=1000\n", "max_batches = 500200\n", "policy=steps\n", "steps=400000,450000\n", "scales=.1,.1\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=32\n", "size=3\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[maxpool]\n", "size=2\n", "stride=2\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=64\n", "size=3\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[maxpool]\n", "size=2\n", "stride=2\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=128\n", "size=3\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=64\n", "size=1\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=128\n", "size=3\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[maxpool]\n", "size=2\n", "stride=2\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=256\n", "size=3\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=128\n", "size=1\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=256\n", "size=3\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[maxpool]\n", "size=2\n", "stride=2\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=512\n", "size=3\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=256\n", "size=1\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=512\n", "size=3\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=256\n", "size=1\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=512\n", "size=3\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[maxpool]\n", "size=2\n", "stride=2\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=1024\n", "size=3\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=512\n", "size=1\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=1024\n", "size=3\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=512\n", "size=1\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "filters=1024\n", "size=3\n", "stride=1\n", "pad=1\n", "activation=leaky\n", "\n", "\n", "#######\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "size=3\n", "stride=1\n", "pad=1\n", "filters=1024\n", "activation=leaky\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "size=3\n", "stride=1\n", "pad=1\n", "filters=1024\n", "activation=leaky\n", "\n", "[route]\n", "layers=-9\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "size=1\n", "stride=1\n", "pad=1\n", "filters=64\n", "activation=leaky\n", "\n", "[reorg]\n", "stride=2\n", "\n", "[route]\n", "layers=-1,-4\n", "\n", "[convolutional]\n", "batch_normalize=1\n", "size=3\n", "stride=1\n", "pad=1\n", "filters=1024\n", "activation=leaky\n", "\n", "[convolutional]\n", "size=1\n", "stride=1\n", "pad=1\n", "filters={}\n", "activation=linear\n", "\n", "\n", "[region]\n", "anchors = 0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828\n", "bias_match=1\n", "classes={}\n", "coords=4\n", "num=5\n", "softmax=1\n", "jitter=.3\n", "rescore=1\n", "\n", "object_scale=5\n", "noobject_scale=1\n", "class_scale=1\n", "coord_scale=1\n", "\n", "absolute=1\n", "thresh = .6\n", "random=1\"\"\".format(args)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "nclass = len(classidx)\n", "train_cfg = (64, 8, (nclass+5)5, nclass)\n", "test_cfg = (1, 1, (nclass+5)5, nclass)\n", "\n", "path = os.path.join(yolo_path, 'yolo-obj-train.cfg')\n", "with open(path, 'w+') as fp:\n", " fp.write(create_cfg(train_cfg))\n", "\n", "path = os.path.join(yolo_path, 'yolo-obj-test.cfg')\n", "with open(path, 'w+') as fp:\n", " fp.write(create_cfg(test_cfg))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1796" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ./darknet detector train yolo/annotate/obj.data cfg/yolo-obj.cfg darknet19_448.conv.23\n", "len(images)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Django Shell-Plus", "language": "python", "name": "django_extensions" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15rc1" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
mbatchkarov/ExpLosion	notebooks/get_misc_and_baseline_results.ipynb	1	3182	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Purpose of this notebook\n", "Query the results database for baseline results that are best displayed in a table (as opposed to a graph)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/miroslavbatchkarov/NetBeansProjects/ExpLosion\n" ] } ], "source": [ "%cd ~/NetBeansProjects/ExpLosion/\n", "from notebooks.common_imports import *\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5 J+N+AN+NN AN+NN amazon_grouped-tagged 0.845399832355 0.853062092978 0.837671996905 0.00769504803662\n", "6 J+N+AN+NN AN+NN reuters21578/r8-tagged-grouped 0.927997311828 0.944559811828 0.91095094086 0.0168044354839\n", "7 J+N+AN+NN J+N+AN+NN amazon_grouped-tagged 0.897175814536 0.903261904762 0.891037593985 0.00611215538847\n", "247 J+N+V+SVO SVO amazon_grouped-tagged 0.731692939245 0.752873563218 0.706689244663 0.0230921592775\n" ] } ], "source": [ "# bag-of-NPs results\n", "for e in Experiment.objects.filter(expansions__decode_handler='BaseFeatureHandler'):\n", " mean, low, high, _ = get_ci(e.id)\n", " print(e.id, e.document_features_tr, e.document_features_ev, e.labelled, mean, high, low, (high-low)/2)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 amazon_grouped-tagged random_neigh 0.21804459735 0.226905262487 0.209670616718 0.00861732288481\n", "2 amazon_grouped-tagged random_vect 0.218249108053 0.227223814985 0.209600535168 0.00881163990826\n", "3 reuters21578/r8-tagged-grouped random_neigh 0.502913612565 0.533383507853 0.476763743455 0.028309882199\n", "4 reuters21578/r8-tagged-grouped random_vect 0.502280104712 0.537964659686 0.465307591623 0.0363285340314\n" ] } ], "source": [ "# random vectors/neighbours\n", "means = []\n", "for r in Experiment.objects.filter(expansions__vectors__algorithm__startswith='random_'):\n", " mean, low, high, _ = get_ci(r.id)\n", " print(r.id, r.labelled, r.expansions.vectors.algorithm, mean, high, low, (high-low)/2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.3.5" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
nmsutton/MemoryModule	analyses_descriptions/synapse_weights_data.ipynb	2	3512	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Datasets Created for Predicting Synapse Weights#\n", "\n", "\n", "Tool\n", "http://www.xuru.org/rt/MPR.asp\n", "\n", "##Layers EC3 -> EC5##\n", "\n", "<style type=\"text/css\">\n", "\ttable.tableizer-table {\n", "\t\tfont-size: 14px;\n", "\t\tborder: 1px solid #CCC; \n", "\t\tfont-family: Arial, Helvetica, sans-serif;\n", "\t} \n", "\t.tableizer-table td {\n", "\t\tpadding: 4px;\n", "\t\tmargin: 3px;\n", "\t\tborder: 1px solid #CCC;\n", "\t}\n", "\t.tableizer-table th {\n", "\t\tbackground-color: #104E8B; \n", "\t\tcolor: #FFF;\n", "\t\tfont-weight: bold;\n", "\t}\n", "</style>\n", "<table class=\"tableizer-table\">\n", "<thead><tr class=\"tableizer-firstrow\"><th>X_1</th><th>X_2</th><th>y</th></tr></thead><tbody>\n", " <tr><td>351</td><td>1.4917910927</td><td>1.7</td></tr>\n", " <tr><td>201</td><td>2.2081522942</td><td>3.8</td></tr>\n", " <tr><td>300</td><td>2.2081522942</td><td>3.8</td></tr>\n", " <tr><td>150</td><td>0.6152781477</td><td>0.12</td></tr>\n", " <tr><td>204</td><td>0.3024657593</td><td>0.04</td></tr>\n", " <tr><td>804</td><td>0.3024657593</td><td>0.02</td></tr>\n", "</tbody></table>\n", "\n", "Regression fit:\n", "\n", "$y = -3.243224433 * 10^{-6} * x1^{2} - 8.453549021 * 10^{-4} * x1 * x2 - 8.808208543 * 10^{-1} * x2^{2}$\n", "<br>$ + 3.491527807 * 10^{-3} * x1 - 6.541997654 * 10^{-2} * x2 - 5.459357633 * 10^{-1}$\n", "\n", "##Layers EC5 -> CA1##\n", "\n", "<style type=\"text/css\">\n", "\ttable.tableizer-table {\n", "\t\tfont-size: 14px;\n", "\t\tborder: 1px solid #CCC; \n", "\t\tfont-family: Arial, Helvetica, sans-serif;\n", "\t} \n", "\t.tableizer-table td {\n", "\t\tpadding: 4px;\n", "\t\tmargin: 3px;\n", "\t\tborder: 1px solid #CCC;\n", "\t}\n", "\t.tableizer-table th {\n", "\t\tbackground-color: #104E8B; \n", "\t\tcolor: #FFF;\n", "\t\tfont-weight: bold;\n", "\t}\n", "</style>\n", "<table class=\"tableizer-table\">\n", "<thead><tr class=\"tableizer-firstrow\"><th>X_1</th><th>X_2</th><th>y</th></tr></thead><tbody>\n", " <tr><td>468</td><td>6.8897750706</td><td>20</td></tr>\n", " <tr><td>402</td><td>4.6546178486</td><td>20</td></tr>\n", " <tr><td>600</td><td>1.6016256574</td><td>4.4</td></tr>\n", " <tr><td>100</td><td>5.7480236917</td><td>7.7</td></tr>\n", " <tr><td>34</td><td>5.7480236917</td><td>9.7</td></tr>\n", " <tr><td>268</td><td>7.6722021983</td><td>9.8</td></tr>\n", "</tbody></table>\n", "\n", "Regression fit:\n", "\n", "$y = 3.18151867 * 10^{-4} * x1^{2} - 1.525851879 * 10^{-1} * x1 * x2 - 11.90618389 * x2^{2}$\n", "<br>$ + 8.041278944 * 10^{-1} * x1 + 197.9136262 * x2 - 732.4225399$" ] }, { "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
diging/methods	2.0. Co-author networks/2.0. Co-Author Graphs.ipynb	1	164418	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import networkx as nx\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2.0. Co-author Graphs\n", "\n", "Social network analysis is a popular method in the field of historical network research. By far the most accessible source of information about social networks in science are bibliographic metadata: we can examine relationships among published authors by modeling coauthorship as a graph. Of course, using bibliographic metadata alone has some drawbacks; for one, it renders many participants in scientific production invisible by focusing only on those who \"merit\" authorship -- laboratory technicians and other support staff, for example, aren't usually included as authors. Nevertheless, the sheer volume of available data makes bibliographic metadata an attractive place to start.\n", "\n", "In this notebook, we'll use metadata collected from the Web of Science database to build a coauthor graph. For background, reading, and resources (including instructions on how to download WoS metadata in the correct format), please see [this page](https://diging.atlassian.net/wiki/display/DCH/Module+2%3A+Introduction+to+Networks)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading data\n", "\n", "I have provided some sample WoS metadata in the ``data`` directory. [Tethne](http://diging.github.io/tethne) (the Python package that we will use to parse these metadata) can parse multiple files at once; we just pass the path to the folder that contains our metadata files.\n", "\n", "This set of metadata is from several years of the journal Plant Physiology -- so this is an artificially myopic sample of texts, but it works just fine for demonstration purposes." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tethne.readers import wos" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "metadata = wos.read('../data/Baldwin/PlantPhysiology', \n", " streaming=True, index_fields=['date'], index_features=['authors'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see how many records were parsed by taking the ``len()`` of the metadata." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7849" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(metadata)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create the graph\n", "\n", "``Tethne``'s graph-building methods create ``NetworkX`` Graph objects." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tethne import coauthors" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "graph = coauthors(metadata, edge_attrs=['date']) # Just pass the metadata!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see how many authors (nodes) and edges are present by using the ``.order()`` and ``.size()`` methods, respectively." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(27139, 120180, 982)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "graph.order(), graph.size(), nx.number_connected_components(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "YIKES! That's really big. Of course, for analytic purposes this shouldn't scare us. But it would be nice to make a visualization, and that's an awfully large graph to lay out in the time available during the class. So we'll be a bit more choosy. \n", "\n", "The ``coauthors()`` function accepts a few optional arguments. ``min_weight`` sets the minimum number of papers that two authors must publish together for an edge to be created between them." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "graph = coauthors(metadata, min_weight=2., edge_attrs=['date'])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(5607, 9365, 1137)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "graph.order(), graph.size(), nx.number_connected_components(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That has a fairly significant impact on the overall size of the graph." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nx.write_graphml(graph, 'coauthors.graphml')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resulting graph looks something like this:\n", "\n", "![](images/coauthor_graph.png)\n", "\n", "There is a large connected component in the upper left, and then a whole host of smaller connected components." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Central authors\n", "\n", "Depending on our research question, we may be interested in identifying the most \"important\" or \"central\" nodes in the coauthor graph. Networkx has a whole bunch of [algorithms](https://networkx.readthedocs.io/en/stable/reference/algorithms.html) that you can use to analyze your graph. Please take a look through the list; there are quite a few useful functions in there!\n", "\n", "### Degree Centrality\n", "\n", "Let's evaluate the degree-centrality of the nodes in our network." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "degree_data = pd.DataFrame(columns=['Surname', 'Forename', 'Degree'])\n", "\n", "i = 0\n", "for (surname, forename), d in nx.degree(graph).items():\n", " degree_data.loc[i] = [surname, forename, d]\n", " i += 1" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10d2c1950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(degree_data.Degree, bins=np.arange(0, 70, 1))\n", "plt.ylabel('Number of nodes')\n", "plt.xlabel('Degree')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Surname FERNIE\n", "Forename ALISDAIR R\n", "Degree 68\n", "Name: 2581, dtype: object\n", "-------------------------\n", "Surname CANNON\n", "Forename STEVEN B\n", "Degree 38\n", "Name: 2524, dtype: object\n", "-------------------------\n", "Surname DESHPANDE\n", "Forename SHWETA\n", "Degree 37\n", "Name: 4254, dtype: object\n", "-------------------------\n", "Surname CANNON\n", "Forename ETHALINDA\n", "Degree 37\n", "Name: 1841, dtype: object\n", "-------------------------\n", "Surname ASHFIELD\n", "Forename TOM\n", "Degree 37\n", "Name: 4776, dtype: object\n", "-------------------------\n" ] } ], "source": [ "# np.argsort() gives us row indices in ascending order of\n", "# value. [::-1] reverses the array, so that the values are\n", "# descending.\n", "sort_indices = np.argsort(degree_data.Degree)[::-1]\n", "\n", "# Here are the nodes with the highest degree.\n", "for i in sort_indices[:5]: # (just the top 5)\n", " print degree_data.loc[i]\n", " print '-' * 25" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Closeness Centrality\n", "\n", "Now let's try closeness centrality. Recall that:\n", "\n", "$\n", "C(x) = \\frac{1}{\\sum_y d(y, x)}\n", "$\n", "\n", "Where $d(y, x)$ is the distance between the nodes $y$ and $x$. $d(y, x)$ can be any function that calculates a distance value for two nodes. $y$ is all of the other nodes in the network (i.e. that are not $x$) reachable from $x$.\n", "\n", "By default, NetworkX uses shortest path length as the distance parameter, and normalizes closeness based on the size of the connected component (i.e. the number of possible paths)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "closeness_data = pd.DataFrame(columns=['Surname', 'Forename', 'Closeness'])\n", "\n", "i = 0\n", "for (surname, forename), d in nx.closeness_centrality(graph).items():\n", " closeness_data.loc[i] = [surname, forename, d]\n", " i += 1" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10979e250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(closeness_data.Closeness)\n", "plt.ylabel('Number of nodes')\n", "plt.xlabel('Closeness Centrality')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Surname FERNIE\n", "Forename ALISDAIR R\n", "Closeness 0.01672587\n", "Name: 2581, dtype: object\n", "-------------------------\n", "Surname LUNN\n", "Forename JOHN E\n", "Closeness 0.01522558\n", "Name: 1207, dtype: object\n", "-------------------------\n", "Surname URBANCZYKWOCHNIAK\n", "Forename EWA\n", "Closeness 0.01443648\n", "Name: 4451, dtype: object\n", "-------------------------\n", "Surname VAN DONGEN\n", "Forename JOOST T\n", "Closeness 0.01431285\n", "Name: 1410, dtype: object\n", "-------------------------\n", "Surname BAUWE\n", "Forename HERMANN\n", "Closeness 0.01420988\n", "Name: 2, dtype: object\n", "-------------------------\n" ] } ], "source": [ "# And we can get the nodes with the highest closeness...\n", "sort_indices = np.argsort(closeness_data.Closeness)[::-1]\n", "\n", "# Here are the nodes with the highest degree.\n", "for i in sort_indices[:5]: # (just the top 5)\n", " print closeness_data.loc[i]\n", " print '-' * 25" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Betweenness Centrality\n", "\n", "Betweenness centrality is often interpreted as a measure of \"power\" in social networks -- the extent to which an individual controls information flow across the graph. Recall:\n", "\n", "$\n", "g(x) = \\sum_{s \\neq x \\neq t} \\frac{\\sigma_{st}(x)}{\\sigma_{st}}\n", "$\n", "\n", "Where $\\sigma_{st}$ is the total number of shortest paths from node $s$ to node $t$, and $\\sigma_{st}(x)$ is the number of those paths that pass through node $x$.\n", "\n", "Note that betweenness centrality is fairly computationally expensive, since we have to calculate all of the shortest paths for all of the nodes in the network. So this may take a minute or two." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "betweenness_data = pd.DataFrame(columns=['Surname', 'Forename', 'Betweenness'])\n", "\n", "i = 0\n", "for (surname, forename), d in nx.betweenness_centrality(graph).items():\n", " betweenness_data.loc[i] = [surname, forename, d]\n", " i += 1" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10b0b0550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(betweenness_data.Betweenness)\n", "# It's a pretty lopsided distribution, so a log scale makes it \n", "# easier to see.\n", "plt.yscale('log')\n", "plt.ylabel('Number of nodes')\n", "plt.xlabel('Betweenness Centrality')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Change over time\n", "\n", "We can use Tethne to look at how graphs evolve. To do this, we use the ``slice()`` method. ``slice()`` is an iterator, which means that you can iterate over it (e.g. use it in a ``for``-loop). It yields subsets of the metadata, and we can use those build a graph for each time period.\n", "\n", "By default, it returns a subset for each year." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2013\n" ] } ], "source": [ "graphs = []\n", "years = []\n", "for year, subset in metadata.slice(feature_name='authors'):\n", " graph = coauthors(subset)\n", " graphs.append(graph)\n", " years.append(year)\n", " print '\\r', year," ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1999 1641 3674 327\n", "2000 1757 4262 312\n", "2001 2155 5513 364\n", "2002 2201 5717 363\n", "2003 2627 8611 359\n", "2004 2687 9440 348\n", "2005 2639 9365 310\n", "2006 2350 7365 357\n", "2007 2633 8799 337\n", "2008 2749 10045 345\n", "2009 2918 11020 336\n", "2010 2912 11324 327\n", "2011 3154 12549 329\n", "2012 2892 11166 319\n", "2013 2408 9953 268\n" ] } ], "source": [ "for year, graph in zip(years, graphs):\n", " print year, graph.order(), graph.size(), nx.number_connected_components(graph)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nx.write_graphml(graphs[0], 'coauthors_1999.graphml')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resulting graph is pretty sparse.\n", "\n", "![](images/coauthor_graph_subset_0.png)\n", "\n", "Sometimes it's helpful to use a sliding time-window: select subsets of several years, that overlap. This \"smooths\" things out. We'll try 3 years, since that's a pretty typical funding cycle." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2011\n" ] } ], "source": [ "graphs = []\n", "years = []\n", "for year, subset in metadata.slice(window_size=3, feature_name='authors'):\n", " graph = coauthors(subset)\n", " graphs.append(graph)\n", " years.append(year)\n", " print '\\r', year," ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1999 4752 12848 558\n", "2000 5206 14856 544\n", "2001 5890 18999 507\n", "2002 6416 22940 507\n", "2003 6752 26548 455\n", "2004 6903 25629 619\n", "2005 7215 25132 783\n", "2006 7033 25595 671\n", "2007 7286 29036 553\n", "2008 7514 31498 540\n", "2009 7852 33935 506\n", "2010 7829 34103 474\n", "2011 7455 32665 464\n" ] } ], "source": [ "for year, graph in zip(years, graphs):\n", " print year, graph.order(), graph.size(), nx.number_connected_components(graph)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nx.write_graphml(graphs[0], 'coauthors_1999-2001.graphml')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This looks a bit better:\n", "\n", "![](images/coauthor_subset_threeyear.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Following a specific node." ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": false }, "outputs": [], "source": [ "focal_author = ('FERNIE', 'ALISDAIR R')\n", "fernie_data = pd.DataFrame(columns=['Year', 'Degree'])\n", "i = 0\n", "for year, graph in zip(years, graphs):\n", " degree = nx.degree(graph)\n", " # If the focal author is not in the graph for this time-period, then\n", " # we will assign a closeness of 0.0.\n", " focal_degree = degree.get(focal_author, 0.0)\n", " fernie_data.loc[i] = [year, focal_degree]\n", " i += 1" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x15f137110>" ] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x15f52f850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(fernie_data.Year, fernie_data.Degree)\n", "plt.ylabel('Degree Centrality')\n", "plt.xlabel('Year')" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2011\n" ] } ], "source": [ "fernie_closeness = pd.DataFrame(columns=['Year', 'Closeness'])\n", "i = 0\n", "for year, subset in metadata.slice(window_size=3):\n", " graph = coauthors(subset, min_weight=2.)\n", " \n", " if focal_author in graph.nodes():\n", " focal_closeness = nx.algorithms.closeness_centrality(graph, u=focal_author)\n", " else:\n", " focal_closeness = 0.0\n", "\n", " fernie_closeness.loc[i] = [year, focal_closeness] \n", " print '\\r', year,\n", " \n", " i += 1" ] }, { "cell_type": "code", "execution_count": 150, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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LSTAREZ3TtUF+Sa+SdAewutw+RNJn220YOJxi+f+1tp8AlgFHjagzH1gKYPt6YJqkPcrt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\n82AiOkzSyyiWMDm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"text/plain": [ "<matplotlib.figure.Figure at 0x15fa7ec10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(fernie_closeness.Year, fernie_closeness.Closeness)\n", "plt.ylabel('Closeness Centrality')\n", "plt.xlabel('Year')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## International collaboration\n", "\n", "We can use the information in the ``authorAddress`` attribute of each record to look at collaboration between scientists in different countries.\n", "\n", "Note that the address is a bit \"messy\" -- it was written for human use, not for computers." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'[Berr, Alexandre; McCallum, Emily J.; Alioua, Abdelmalek; Heintz, Dimitri; Heitz, Thierry; Shen, Wen-Hui] Univ Strasbourg, CNRS, Inst Biol Mol Plantes, F-67084 Strasbourg, France.'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "metadata[5].authorAddress" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[u'Arena Pharmaceut, San Diego, CA 92121 USA.',\n", " u'Univ Calif San Diego, Div Biol Sci, La Jolla, CA 92093 USA.']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "metadata[205].authorAddress" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7849" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(metadata)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So we define a procedure to extract the country from each address. Note that the country seems to come at the end, so...\n", "* We split on commas and take the last element;\n", "* We have to strip the '.' at the end;\n", "* Note that USA addresses have state zip and country in the last position;" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def extract_country(address):\n", " country = address.split(',')[-1].strip().replace('.', '')\n", " if country.endswith('USA'):\n", " return u'USA'\n", " return country" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Arena Pharmaceut, San Diego, CA 92121 USA.\n", "USA\n" ] } ], "source": [ "print metadata[205].authorAddress[0]\n", "print extract_country(metadata[205].authorAddress[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make a graph by hand." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from collections import Counter\n", "from itertools import combinations" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "node_counts = Counter()\n", "edge_counts = Counter()\n", "\n", "for paper in metadata:\n", " if not hasattr(paper, 'authorAddress'):\n", " continue\n", " \n", " addresses = getattr(paper, 'authorAddress', [])\n", " if not type(addresses) is list:\n", " addresses = [addresses]\n", " \n", " countries = [extract_country(address) for address in addresses]\n", " # Combinations is pretty cool. It will give us all of the\n", " # possible combinations of countries in this paper.\n", " for u, v in combinations(countries, 2):\n", " edge_key = tuple(sorted([u, v]))\n", " edge_counts[edge_key] += 1.\n", " \n", " for u in set(countries):\n", " node_counts[u] += 1." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "international = nx.Graph()\n", "for u, count in node_counts.items():\n", " international.add_node(u, weight=count)\n", " \n", "for (u, v), count in edge_counts.items():\n", " if count > 1. and u != v:\n", " international.add_edge(u, v, weight=count)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "matrix([[ 0., 6., 0., ..., 0., 2., 6.],\n", " [ 6., 0., 0., ..., 0., 0., 0.],\n", " [ 0., 0., 0., ..., 0., 2., 0.],\n", " ..., \n", " [ 0., 0., 0., ..., 0., 0., 0.],\n", " [ 2., 0., 2., ..., 0., 0., 0.],\n", " [ 6., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nx.adjacency_matrix(international).todense()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x10dd49a10>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10c956e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(nx.adjacency_matrix(international).todense(), \n", " interpolation='none')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "73 419 6\n" ] } ], "source": [ "print international.order(), \\\n", " international.size(), \\\n", " nx.number_connected_components(international)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nx.write_graphml(international, 'international.graphml')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](images/international.png)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def international_collaboration(subset):\n", " node_counts = Counter()\n", " edge_counts = Counter()\n", "\n", " for paper in subset:\n", " if not hasattr(paper, 'authorAddress'):\n", " continue\n", "\n", " addresses = getattr(paper, 'authorAddress', [])\n", " if not type(addresses) is list:\n", " addresses = [addresses]\n", "\n", " countries = [extract_country(address) for address in addresses]\n", " # Combinations is pretty cool. It will give us all of the\n", " # possible combinations of countries in this paper.\n", " for u, v in combinations(countries, 2):\n", " edge_key = tuple(sorted([u, v]))\n", " edge_counts[edge_key] += 1.\n", "\n", " for u in set(countries):\n", " node_counts[u] += 1.\n", " \n", " graph = nx.Graph()\n", " for u, count in node_counts.items():\n", " graph.add_node(u, weight=count)\n", "\n", " for (u, v), count in edge_counts.items():\n", " if count > 1.:\n", " graph.add_edge(u, v, weight=count)\n", " \n", " return graph" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2011 57 285\n" ] } ], "source": [ "years = []\n", "graphs = []\n", "for year, subset in metadata.slice(window_size=3):\n", " graph = international_collaboration(subset)\n", " graphs.append(graph)\n", " years.append(year)\n", " print year, graph.order(), graph.size()," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## With whom does the Netherlands collaborate?" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "netherlands_data = pd.DataFrame(columns=['Year', 'Neighbor', 'Collaboration'])" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2011\n" ] } ], "source": [ "i = 0\n", "for year, graph in zip(years, graphs):\n", " if 'Netherlands' not in graph.nodes():\n", " continue\n", " \n", " counts = Counter()\n", " for neighbor in graph.neighbors('Netherlands'):\n", " counts[neighbor] += graph['Netherlands'][neighbor]['weight']\n", " \n", " N_all = sum(counts.values())\n", " for neighbor, count in counts.items():\n", " netherlands_data.loc[i] = [year, neighbor, count/N_all]\n", " i += 1\n", " \n", " print '\\r', year," ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "grouped = netherlands_data.groupby('Neighbor')" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "collaboration_means = grouped.Collaboration.mean()\n", "collaboration_std = grouped.Collaboration.std()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Neighbor \n", "Argentina count 2.000000\n", " mean 0.011870\n", " std 0.000531\n", " min 0.011494\n", " 25% 0.011682\n", " 50% 0.011870\n", " 75% 0.012057\n", " max 0.012245\n", "Australia count 10.000000\n", " mean 0.034122\n", " std 0.033650\n", " min 0.009217\n", " 25% 0.015401\n", " 50% 0.016228\n", " 75% 0.043264\n", " max 0.095745\n", "Belgium count 13.000000\n", " mean 0.081166\n", " std 0.038387\n", " min 0.016260\n", " 25% 0.050000\n", " 50% 0.085714\n", " 75% 0.107280\n", " max 0.145833\n", "Brazil count 3.000000\n", " mean 0.010831\n", " std 0.005060\n", " min 0.007663\n", " 25% 0.007913\n", " 50% 0.008163\n", " ... \n", "Spain std 0.018407\n", " min 0.012245\n", " 25% 0.030303\n", " 50% 0.040650\n", " 75% 0.046875\n", " max 0.083333\n", "Sweden count 5.000000\n", " mean 0.031102\n", " std 0.010935\n", " min 0.016807\n", " 25% 0.023438\n", " 50% 0.032520\n", " 75% 0.040323\n", " max 0.042424\n", "Switzerland count 9.000000\n", " mean 0.021721\n", " std 0.012111\n", " min 0.007663\n", " 25% 0.013825\n", " 50% 0.020408\n", " 75% 0.025000\n", " max 0.040650\n", "USA count 13.000000\n", " mean 0.109436\n", " std 0.043192\n", " min 0.064516\n", " 25% 0.085106\n", " 50% 0.092166\n", " 75% 0.108333\n", " max 0.210084\n", "dtype: float64" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped.Collaboration.describe()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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F0FgQBK8qBhO0OVFmkDk2hRskTTGzazsuTRCMEmNhlDcWZAicwQRtThRyOoVN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\ntlGrsk4gT1OyKK6B6C4dKlaWW7vTMuQwrtVH6p8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"text/plain": [ "<matplotlib.figure.Figure at 0x10b894f10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "positions = np.arange(len(collaboration_means))\n", "plt.bar(positions, collaboration_means.values, yerr=collaboration_std, alpha=0.5)\n", "plt.xticks(positions + 0.4, collaboration_means.keys(), rotation=90)\n", "plt.ylabel('Proportion of collaborations (with standard error)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Has collaboration between Netherlands and specific neighbors changed over time?" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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NQSBJLWcQSFLLGQSS1HIGgSS1nEEgSS1nEEhSyxkEktRyBoEktZxBIEktNzAIkkwlOZDk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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x112536d90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "collaboration_england = netherlands_data[netherlands_data.Neighbor == 'England']\n", "plt.scatter(collaboration_england.Year, collaboration_england.Collaboration)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7gxXShX0AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10a0d3690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "collaboration_usa = netherlands_data[netherlands_data.Neighbor == 'USA']\n", "plt.scatter(collaboration_usa.Year, collaboration_usa.Collaboration)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How about with itself? I.e. is Netherlands becoming more or less externally collaborative over time?" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3zoXAUttKksZv0a6hqjqV5AagC2wA7qiqQ0l2NetvA34DeAHw0SQAJ6tq89naruLPIkk6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\nGQSS1HIGgSS1nEEgSS1nEEhSy/1/d+vaQCXNuCwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1094bdcd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "collaboration_self = netherlands_data[netherlands_data.Neighbor == 'Netherlands']\n", "plt.scatter(collaboration_self.Year, collaboration_self.Collaboration)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How central is the Netherlands in terms of international collaboration?" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "netherlands_centrality = pd.DataFrame(columns=['Year', 'Centrality'])\n", "i = 0\n", "for year, graph in zip(years, graphs):\n", " if 'Netherlands' not in graph.nodes():\n", " continue\n", " centrality = nx.closeness_centrality(graph, u='Netherlands')\n", " netherlands_centrality.loc[i] = [year, centrality]\n", " i += 1" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x108ecb410>" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10bc23550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(netherlands_centrality.Year, netherlands_centrality.Centrality)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "------- ---------- -----------" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Below: to be completed\n", "\n", "### Connectivity over time\n", "\n", "Ok, we have a graph for each time-period. Let's look at some whole-graph parameters. How about average clustering.\n", "\n", "$$\n", "C = \\frac{1}{n} \\sum_{v \\in G} c_v\n", "$$\n", "\n", "and\n", "\n", "$$\n", "c_u = \\frac{2 T}{deg(u)(deg(u) - 1)}\n", "$$\n", "\n", "where $T(u)$ is the number of triangles through node $u$, and $deg(u)$ is the degree of $u$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "clustering_data = pd.DataFrame(columns=['Year', 'Clustering'])\n", "\n", "i = 0\n", "# zip() zips two lists together into a list of 2-tuples.\n", "for year, graph in zip(years, graphs):\n", " clustering_data.loc[i] = [year, nx.algorithms.average_clustering(graph)]\n", " i += 1\n", " \n", "plt.scatter(clustering_data.Year, clustering_data.Clustering)\n", "plt.ylabel('Average Clustering Coefficient')\n", "plt.xlabel('Year')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I wonder if that upward trend has anything to do with the overall number of authors or number of papers in each time period." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "authorship_data = pd.DataFrame(columns=['Year', 'AverageNumAuthors', 'NumPapers'])\n", "i = 0\n", "for year, subset in metadata.slice(window_size=3):\n", " avg_no_authors = np.mean([len(paper.authors) for paper in subset])\n", " authorship_data.loc[i] = [year, avg_no_authors, len(subset)]\n", " i += 1" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'authorship_data' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-33-c280215726f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauthorship_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mYear\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mauthorship_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAverageNumAuthors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'green'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Average Number of Authors (green)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0max2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtwinx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'authorship_data' is not defined" ] } ], "source": [ "plt.scatter(authorship_data.Year, authorship_data.AverageNumAuthors, c='green')\n", "plt.legend(loc=4)\n", "plt.ylabel('Average Number of Authors (green)')\n", "ax = plt.gca()\n", "ax2 = plt.twinx()\n", "\n", "ax2.scatter(authorship_data.Year, authorship_data.NumPapers)\n", "plt.ylabel('Number of Papers (blue)')\n", "\n", "plt.xlabel('Year')\n", "plt.legend(loc='best')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's an interesting pattern -- the average number of authors in each period steadily increases, while the number of papers decreases. That certainly might explain the additional clustering! Let's combine these two values, so that we'll have \n", "\n", "$\n", "\\frac{N_{Authors}}{N_{Papers}} * \\frac{1}{N_{Papers}} = \\frac{N_{Authors}}{N_{Papers}^2}\n", "$\n", "\n", "And compare that to the average clustering coefficient.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.scatter(authorship_data.AverageNumAuthors/authorship_data.NumPapers,\n", " clustering_data.Clustering)\n", "xlim(0.002, 0.006)\n", "plt.xlabel('$\\\\frac{N_{Authors}}{N_{Papers}^2}$', size=24)\n", "plt.ylabel('Average Clustering Coefficient')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from scipy.stats import linregress\n", "authorship = authorship_data.AverageNumAuthors/authorship_data.NumPapers\n", "Beta, Beta0, r, p, stderr = linregress(authorship, clustering_data.Clustering)\n", "\n", "plt.scatter(authorship_data.AverageNumAuthors/authorship_data.NumPapers,\n", " clustering_data.Clustering)\n", "\n", "X = np.arange(authorship.min(), authorship.max(), 0.0001)\n", "plt.plot(X, Beta0 + BetaX)\n", "xlim(0.002, 0.006)\n", "plt.xlabel('$\\\\frac{N_{Authors}}{N_{Papers}^2}$', size=24)\n", "plt.ylabel('Average Clustering Coefficient')\n", "plt.show()\n", "\n", "Y_hat = Beta0 + Betaauthorship\n", "residuals = clustering_data.Clustering - Y_hat\n", "plt.scatter(clustering_data.Year, residuals)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
ky822/Data_Bootcamp	Code/IPython/SQL_Intro_Old.ipynb	1	143371	{ "metadata": { "name": "", "signature": "sha256:9a806c1fef1920dc6e2eb70ce223cad51e2a627ea9ec8cab85bd7f39c8ebabab" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import sqlite3" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "About SQL:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Stands for \"Structured Query Language\"\n", "\n", "- Can be pronounced \"S. Q. L.\" or \"sequel\" \n", "\n", "- Requests are often called \"queries\"\n", "\n", "- One of the most widely-used languages for managing relational databases\n", "\n", "- A relational database has multiple tables. It's sort of like an Excel file:\n", " - Database = a single Excel file\n", " - Table = a single sheet in the same Excel file\n", "\n", "- SQL is the language, but the software optimized for storing relational databases that you can access with SQL varies (MySQL, Microsoft SQL Server, Oracle, SQLite)\n", " - We're using SQLite in these examples\n", "\n", "- For a little more background, this is a quick but helpful read: http://sql.learncodethehardway.org/book/introduction.html\n", "\n", "Structure and formatting:\n", "\n", "- INDENTATIONS and RETURNS:\n", " - indentations and returns are arbitrary in MySQL\n", " - a query can be written entirely on a single line or on multiple lines\n", " - most people opt to use structure to organize their thoughts (but not necessary)\n", "\n", "- CAPITALIZATION:\n", " - Important:\n", " - Variables (anything that appears in quotation marks)\n", " - Nicknames\n", " - NOT Important:\n", " - Clauses like SELECT/select\n", " - Functions like SUM/sum\n", " - Column names like COLUMN_A/column_a\n", " - Table names like TABLE_A/table_a\n", "- DATA TYPES:\n", " - Varies depending on the database software that you're using (MySQL, Microsoft, etc) - easy to google it \n", " - SQLite (used in examples below):\n", " - To see the data types for each column in a table, run:\n", " - PRAGMA TABLE_INFO(sales_table)\n", " - Common data types:\n", " - TEXT: various characters. Dates will be saved as text in SQLite (major SQLite detractor)\n", " - INTEGER: integer\n", " - REAL: floating-point value\n", " - For more info: https://www.sqlite.org/datatype3.html\n", "\n", "- ORDER OF COMMANDS:\n", " - Very strict\n", " - Not all clauses need to be present in a query, but when they are present, then must be in the following order:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "SELECT\n", " column_x, \n", " column_y\n", "FROM\n", " table_a A\n", " JOIN table_b B\n", " ON A.column = B.column\n", "WHERE\n", " column_x = 'variable1' AND\n", " column_y = 'variable2'\n", "GROUP BY\n", " column_x\n", "ORDER BY\n", " column_y \n", "LIMIT [some number]\n", " " ] }, { "cell_type": "heading", "level": 5, "metadata": {}, "source": [ "DEBUGGING CHECKLIST:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Are all clauses in the right order?\n", "- If any variables are TEXT, are they:\n", " - enclosed in quotation marks?\n", " - properly capitalized?\n", "- IN SQL ONLY: If any variables are dates, are they:\n", " - enclosed in quotation marks?\n", " - in the standard date format? 'YYYY-MM-DD'\n" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Setting up the SQLite Database:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's not necessary to learn what's going on here (for now), just run the code to set up the database" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Set up a SQLite database using an excel file:\n", "## http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/master/cookbook/Chapter%209%20-%20Loading%20data%20from%20SQL%20databases.ipynb\n", "\n", "import xlrd as xl\n", "\n", "# Select a sample file. \n", "# Using an excel file purely because pandas can select different sheets from .xlsx files and this will be useful when creating multiple tables within a single SQLite database\n", "path = ('/Users/sarahbeckett-hile/Dropbox/Data_Bootcamp/Code/SQL/book_sales.xlsx')\n", "\n", "# if this .sqlite db doesn't already exists, this will create it\n", "# if the .sqlite db does already exist, this establishes the desired connection\n", "con = sqlite3.connect(\"sqlite_cars.sqlite\")\n", "\n", "# this pulls out the names of the sheets in the workbook. We'll use these to name the different tables in the SQLite database that we'll create\n", "table_names = xl.open_workbook(path).sheet_names()\n", "\n", "# this loop makes it possible to use any other .xls sheet, since the sheet names aren't called out specifically\n", "for table in table_names:\n", " df = pd.read_excel(path, sheetname='{}'.format(table))\n", " con.execute(\"DROP TABLE IF EXISTS {}\".format(table))\n", " pd.io.sql.to_sql(df, \"{}\".format(table), con, index=False)\n", "\n", "# now the spreadsheets are in tables in a mini database!\n", "\n", "# Finally, a little function to make it easy to run queries on this mini-database\n", "def run(query):\n", " results = pd.read_sql(\"{}\".format(query), con)\n", " return results" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# Set up a SQLite database using several .csv files:\n", "\n", "# if this .sqlite db doesn't already exists, this will create it\n", "# if the .sqlite db does already exist, this establishes the desired connection\n", "con = sqlite3.connect(\"sqlite_cars.sqlite\")\n", "\n", "# create dataframes from each .csv file:\n", "sales_table = pd.read_csv('https://raw.githubusercontent.com/DaveBackus/Data_Bootcamp/master/Code/SQL/sales_table.csv')\n", "car_table = pd.read_csv('https://raw.githubusercontent.com/DaveBackus/Data_Bootcamp/master/Code/SQL/car_table.csv')\n", "salesman_table = pd.read_csv('https://raw.githubusercontent.com/DaveBackus/Data_Bootcamp/master/Code/SQL/salesman_table.csv')\n", "cust_table = pd.read_csv('https://raw.githubusercontent.com/DaveBackus/Data_Bootcamp/master/Code/SQL/cust_table.csv')\n", "\n", "# make a list of the tables (dataframes) and table names:\n", "tables = [sales_table, car_table, salesman_table, cust_table]\n", "table_names = ['sales_table', 'car_table', 'salesman_table', 'cust_table']\n", "\n", "# drop each table name if it already exists to avoid error if you rerun this bit of code\n", "# then add it back (or for the first time, if the table didn't already exist)\n", "for i in range(len(tables)):\n", " table_name = table_names[i]\n", " table = tables[i]\n", " con.execute(\"DROP TABLE IF EXISTS {}\".format(table_name))\n", " pd.io.sql.to_sql(table, \"{}\".format(table_name), con, index=False)\n", "\n", "# Finally, a little function to make it easy to run queries on this mini-database\n", "def run(query):\n", " results = pd.read_sql(\"{}\".format(query), con)\n", " return results" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 61 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "SELECT\n", " milk\n", "FROM\n", " dairy_aisle\n", "\n", "# \"Show me milk, from the dairy aisle\"" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "EXAMPLES & EXERCISES:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've created a mini SQLite database containing 4 tables that you might expect to see in a company's system. Each of these tables has one or more columns that will correspond to similar columns in other tables. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Table names:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "sales_table\n", "car_table\n", "salesman_table\n", "cust_table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start by looking at the columns and their data types in the sales_table:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " PRAGMA TABLE_INFO(sales_table)\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>cid</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>notnull</th>\n", " <th>dflt_value</th>\n", " <th>pk</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 0</td>\n", " <td> id</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> None</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> model_id</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> None</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 2</td>\n", " <td> customer_id</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> None</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 3</td>\n", " <td> revenue</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> None</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 4</td>\n", " <td> payment_type</td>\n", " <td> TEXT</td>\n", " <td> 0</td>\n", " <td> None</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 5</td>\n", " <td> salesman_id</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> None</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 6</td>\n", " <td> date</td>\n", " <td> TEXT</td>\n", " <td> 0</td>\n", " <td> None</td>\n", " <td> 0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ " cid name type notnull dflt_value pk\n", "0 0 id INTEGER 0 None 0\n", "1 1 model_id INTEGER 0 None 0\n", "2 2 customer_id INTEGER 0 None 0\n", "3 3 revenue INTEGER 0 None 0\n", "4 4 payment_type TEXT 0 None 0\n", "5 5 salesman_id INTEGER 0 None 0\n", "6 6 date TEXT 0 None 0" ] } ], "prompt_number": 64 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Do the same for the other tables to get a sense of they contain:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " \n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>cid</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>notnull</th>\n", " <th>dflt_value</th>\n", " <th>pk</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 0</td>\n", " <td> model_id</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> None</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> make</td>\n", " <td> TEXT</td>\n", " <td> 0</td>\n", " <td> None</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 2</td>\n", " <td> model</td>\n", " <td> TEXT</td>\n", " <td> 0</td>\n", " <td> None</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 3</td>\n", " <td> sticker_price</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> None</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 4</td>\n", " <td> cogs</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> None</td>\n", " <td> 0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 65, "text": [ " cid name type notnull dflt_value pk\n", "0 0 model_id INTEGER 0 None 0\n", "1 1 make TEXT 0 None 0\n", "2 2 model TEXT 0 None 0\n", "3 3 sticker_price INTEGER 0 None 0\n", "4 4 cogs INTEGER 0 None 0" ] } ], "prompt_number": 65 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "SELECT * FROM table_name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we'll look at everything - all rows and columns - from the sales_table, basically like looking at a simple excel spreadsheet. You do this with an asterisk after \"SELECT\":" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " \n", " FROM\n", " sales_table\n", " ''')\n", "# \"Show me all columns from the sales table\"" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0 </th>\n", " <td> 54858</td>\n", " <td> 36</td>\n", " <td> 237906</td>\n", " <td> 21222</td>\n", " <td> finance</td>\n", " <td> 492</td>\n", " <td> 1/7/14</td>\n", " </tr>\n", " <tr>\n", " <th>1 </th>\n", " <td> 43161</td>\n", " <td> 20</td>\n", " <td> 967016</td>\n", " <td> 19140</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/26/14</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 862</td>\n", " <td> 1/17/14</td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> 92495</td>\n", " <td> 31</td>\n", " <td> 633030</td>\n", " <td> 19010</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/13/14</td>\n", " </tr>\n", " <tr>\n", " <th>4 </th>\n", " <td> 78000</td>\n", " <td> 51</td>\n", " <td> 341877</td>\n", " <td> 22022</td>\n", " <td> finance</td>\n", " <td> 862</td>\n", " <td> 1/12/14</td>\n", " </tr>\n", " <tr>\n", " <th>5 </th>\n", " <td> 13154</td>\n", " <td> 75</td>\n", " <td> 720210</td>\n", " <td> 21409</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/20/14</td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> 36535</td>\n", " <td> 31</td>\n", " <td> 908558</td>\n", " <td> 19894</td>\n", " <td> finance</td>\n", " <td> 862</td>\n", " <td> 1/30/14</td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> 22813</td>\n", " <td> 46</td>\n", " <td> 705508</td>\n", " <td> 12960</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/29/14</td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <td> 56245</td>\n", " <td> 36</td>\n", " <td> 248621</td>\n", " <td> 25938</td>\n", " <td> cash</td>\n", " <td> 276</td>\n", " <td> 1/19/14</td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> 88118</td>\n", " <td> 51</td>\n", " <td> 341344</td>\n", " <td> 19844</td>\n", " <td> finance</td>\n", " <td> 492</td>\n", " <td> 1/23/14</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td> 84469</td>\n", " <td> 31</td>\n", " <td> 733566</td>\n", " <td> 21441</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/20/14</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td> 37412</td>\n", " <td> 31</td>\n", " <td> 750195</td>\n", " <td> 17462</td>\n", " <td> cash</td>\n", " <td> 276</td>\n", " <td> 1/11/14</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td> 68513</td>\n", " <td> 31</td>\n", " <td> 461723</td>\n", " <td> 17020</td>\n", " <td> cash</td>\n", " <td> 803</td>\n", " <td> 1/18/14</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td> 74380</td>\n", " <td> 20</td>\n", " <td> 468665</td>\n", " <td> 18040</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/5/14</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td> 24047</td>\n", " <td> 75</td>\n", " <td> 556188</td>\n", " <td> 18671</td>\n", " <td> finance</td>\n", " <td> 862</td>\n", " <td> 1/12/14</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td> 40603</td>\n", " <td> 51</td>\n", " <td> 241759</td>\n", " <td> 22506</td>\n", " <td> finance</td>\n", " <td> 862</td>\n", " <td> 1/4/14</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td> 69883</td>\n", " <td> 20</td>\n", " <td> 161369</td>\n", " <td> 20460</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/25/14</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td> 43338</td>\n", " <td> 46</td>\n", " <td> 731692</td>\n", " <td> 13440</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/29/14</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td> 39727</td>\n", " <td> 51</td>\n", " <td> 656750</td>\n", " <td> 18634</td>\n", " <td> finance</td>\n", " <td> 492</td>\n", " <td> 1/1/14</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td> 21022</td>\n", " <td> 22</td>\n", " <td> 619020</td>\n", " <td> 17312</td>\n", " <td> cash</td>\n", " <td> 862</td>\n", " <td> 1/14/14</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td> 18303</td>\n", " <td> 36</td>\n", " <td> 413891</td>\n", " <td> 23318</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/17/14</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td> 28176</td>\n", " <td> 20</td>\n", " <td> 965672</td>\n", " <td> 21780</td>\n", " <td> cash</td>\n", " <td> 813</td>\n", " <td> 1/11/14</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td> 62604</td>\n", " <td> 75</td>\n", " <td> 217720</td>\n", " <td> 19418</td>\n", " <td> cash</td>\n", " <td> 862</td>\n", " <td> 1/30/14</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td> 83930</td>\n", " <td> 22</td>\n", " <td> 946265</td>\n", " <td> 17756</td>\n", " <td> cash</td>\n", " <td> 862</td>\n", " <td> 1/13/14</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td> 87626</td>\n", " <td> 31</td>\n", " <td> 140795</td>\n", " <td> 21441</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/13/14</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td> 10682</td>\n", " <td> 31</td>\n", " <td> 145479</td>\n", " <td> 17905</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/20/14</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td> 23444</td>\n", " <td> 20</td>\n", " <td> 961650</td>\n", " <td> 16720</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/1/14</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td> 87998</td>\n", " <td> 31</td>\n", " <td> 457742</td>\n", " <td> 20778</td>\n", " <td> cash</td>\n", " <td> 680</td>\n", " <td> 1/3/14</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td> 95912</td>\n", " <td> 75</td>\n", " <td> 978159</td>\n", " <td> 19667</td>\n", " <td> cash</td>\n", " <td> 813</td>\n", " <td> 1/25/14</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td> 74410</td>\n", " <td> 20</td>\n", " <td> 474218</td>\n", " <td> 19360</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/6/14</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>70</th>\n", " <td> 61014</td>\n", " <td> 36</td>\n", " <td> 253239</td>\n", " <td> 20174</td>\n", " <td> finance</td>\n", " <td> 680</td>\n", " <td> 1/21/14</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td> 18946</td>\n", " <td> 20</td>\n", " <td> 614301</td>\n", " <td> 18480</td>\n", " <td> cash</td>\n", " <td> 225</td>\n", " <td> 1/28/14</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td> 75834</td>\n", " <td> 51</td>\n", " <td> 168495</td>\n", " <td> 23474</td>\n", " <td> cash</td>\n", " <td> 215</td>\n", " <td> 1/10/14</td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td> 57366</td>\n", " <td> 31</td>\n", " <td> 699431</td>\n", " <td> 19231</td>\n", " <td> cash</td>\n", " <td> 225</td>\n", " <td> 1/12/14</td>\n", " </tr>\n", " <tr>\n", " <th>74</th>\n", " <td> 57711</td>\n", " <td> 22</td>\n", " <td> 528354</td>\n", " <td> 18865</td>\n", " <td> cash</td>\n", " <td> 813</td>\n", " <td> 1/4/14</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td> 65870</td>\n", " <td> 36</td>\n", " <td> 751098</td>\n", " <td> 24104</td>\n", " <td> cash</td>\n", " <td> 862</td>\n", " <td> 1/23/14</td>\n", " </tr>\n", " <tr>\n", " <th>76</th>\n", " <td> 22147</td>\n", " <td> 36</td>\n", " <td> 480153</td>\n", " <td> 20436</td>\n", " <td> finance</td>\n", " <td> 492</td>\n", " <td> 1/23/14</td>\n", " </tr>\n", " <tr>\n", " <th>77</th>\n", " <td> 99759</td>\n", " <td> 51</td>\n", " <td> 208677</td>\n", " <td> 22990</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/16/14</td>\n", " </tr>\n", " <tr>\n", " <th>78</th>\n", " <td> 64406</td>\n", " <td> 20</td>\n", " <td> 349494</td>\n", " <td> 19580</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/16/14</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td> 55585</td>\n", " <td> 22</td>\n", " <td> 206310</td>\n", " <td> 17756</td>\n", " <td> finance</td>\n", " <td> 949</td>\n", " <td> 1/1/14</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td> 24710</td>\n", " <td> 75</td>\n", " <td> 185223</td>\n", " <td> 18920</td>\n", " <td> cash</td>\n", " <td> 276</td>\n", " <td> 1/16/14</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td> 53831</td>\n", " <td> 75</td>\n", " <td> 656181</td>\n", " <td> 24646</td>\n", " <td> finance</td>\n", " <td> 949</td>\n", " <td> 1/18/14</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td> 97494</td>\n", " <td> 19</td>\n", " <td> 393613</td>\n", " <td> 14006</td>\n", " <td> cash</td>\n", " <td> 680</td>\n", " <td> 1/14/14</td>\n", " </tr>\n", " <tr>\n", " <th>83</th>\n", " <td> 13004</td>\n", " <td> 36</td>\n", " <td> 488910</td>\n", " <td> 24890</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/12/14</td>\n", " </tr>\n", " <tr>\n", " <th>84</th>\n", " <td> 30779</td>\n", " <td> 75</td>\n", " <td> 846630</td>\n", " <td> 23401</td>\n", " <td> finance</td>\n", " <td> 147</td>\n", " <td> 1/25/14</td>\n", " </tr>\n", " <tr>\n", " <th>85</th>\n", " <td> 29708</td>\n", " <td> 51</td>\n", " <td> 534633</td>\n", " <td> 20328</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/29/14</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td> 14082</td>\n", " <td> 46</td>\n", " <td> 909110</td>\n", " <td> 15200</td>\n", " <td> cash</td>\n", " <td> 813</td>\n", " <td> 1/28/14</td>\n", " </tr>\n", " <tr>\n", " <th>87</th>\n", " <td> 82119</td>\n", " <td> 19</td>\n", " <td> 628031</td>\n", " <td> 16189</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/12/14</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td> 31815</td>\n", " <td> 46</td>\n", " <td> 743097</td>\n", " <td> 15040</td>\n", " <td> cash</td>\n", " <td> 215</td>\n", " <td> 1/25/14</td>\n", " </tr>\n", " <tr>\n", " <th>89</th>\n", " <td> 32345</td>\n", " <td> 22</td>\n", " <td> 476047</td>\n", " <td> 20197</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/19/14</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td> 63526</td>\n", " <td> 51</td>\n", " <td> 862765</td>\n", " <td> 23232</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/23/14</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td> 20256</td>\n", " <td> 19</td>\n", " <td> 597655</td>\n", " <td> 16734</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/16/14</td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td> 15930</td>\n", " <td> 36</td>\n", " <td> 486534</td>\n", " <td> 20960</td>\n", " <td> cash</td>\n", " <td> 949</td>\n", " <td> 1/26/14</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td> 68446</td>\n", " <td> 75</td>\n", " <td> 339830</td>\n", " <td> 24148</td>\n", " <td> cash</td>\n", " <td> 215</td>\n", " <td> 1/22/14</td>\n", " </tr>\n", " <tr>\n", " <th>94</th>\n", " <td> 68330</td>\n", " <td> 20</td>\n", " <td> 283797</td>\n", " <td> 19140</td>\n", " <td> cash</td>\n", " <td> 215</td>\n", " <td> 1/9/14</td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td> 85407</td>\n", " <td> 22</td>\n", " <td> 635204</td>\n", " <td> 21751</td>\n", " <td> finance</td>\n", " <td> 949</td>\n", " <td> 1/18/14</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td> 42428</td>\n", " <td> 75</td>\n", " <td> 619016</td>\n", " <td> 20413</td>\n", " <td> cash</td>\n", " <td> 680</td>\n", " <td> 1/11/14</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td> 37620</td>\n", " <td> 51</td>\n", " <td> 183947</td>\n", " <td> 20328</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/3/14</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td> 96344</td>\n", " <td> 20</td>\n", " <td> 731677</td>\n", " <td> 17600</td>\n", " <td> finance</td>\n", " <td> 813</td>\n", " <td> 1/15/14</td>\n", " </tr>\n", " <tr>\n", " <th>99</th>\n", " <td> 53062</td>\n", " <td> 31</td>\n", " <td> 907549</td>\n", " <td> 19894</td>\n", " <td> finance</td>\n", " <td> 680</td>\n", " <td> 1/28/14</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>100 rows \u00d7 7 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 60, "text": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 54858 36 237906 21222 finance 492 1/7/14\n", "1 43161 20 967016 19140 finance 215 1/26/14\n", "2 40112 46 819010 14720 cash 862 1/17/14\n", "3 92495 31 633030 19010 finance 803 1/13/14\n", "4 78000 51 341877 22022 finance 862 1/12/14\n", "5 13154 75 720210 21409 cash 492 1/20/14\n", "6 36535 31 908558 19894 finance 862 1/30/14\n", "7 22813 46 705508 12960 finance 225 1/29/14\n", "8 56245 36 248621 25938 cash 276 1/19/14\n", "9 88118 51 341344 19844 finance 492 1/23/14\n", "10 84469 31 733566 21441 finance 215 1/20/14\n", "11 37412 31 750195 17462 cash 276 1/11/14\n", "12 68513 31 461723 17020 cash 803 1/18/14\n", "13 74380 20 468665 18040 finance 215 1/5/14\n", "14 24047 75 556188 18671 finance 862 1/12/14\n", "15 40603 51 241759 22506 finance 862 1/4/14\n", "16 69883 20 161369 20460 finance 803 1/25/14\n", "17 43338 46 731692 13440 finance 225 1/29/14\n", "18 39727 51 656750 18634 finance 492 1/1/14\n", "19 21022 22 619020 17312 cash 862 1/14/14\n", "20 18303 36 413891 23318 cash 492 1/17/14\n", "21 28176 20 965672 21780 cash 813 1/11/14\n", "22 62604 75 217720 19418 cash 862 1/30/14\n", "23 83930 22 946265 17756 cash 862 1/13/14\n", "24 87626 31 140795 21441 finance 215 1/13/14\n", "25 10682 31 145479 17905 cash 147 1/20/14\n", "26 23444 20 961650 16720 finance 276 1/1/14\n", "27 87998 31 457742 20778 cash 680 1/3/14\n", "28 95912 75 978159 19667 cash 813 1/25/14\n", "29 74410 20 474218 19360 finance 276 1/6/14\n", ".. ... ... ... ... ... ... ...\n", "70 61014 36 253239 20174 finance 680 1/21/14\n", "71 18946 20 614301 18480 cash 225 1/28/14\n", "72 75834 51 168495 23474 cash 215 1/10/14\n", "73 57366 31 699431 19231 cash 225 1/12/14\n", "74 57711 22 528354 18865 cash 813 1/4/14\n", "75 65870 36 751098 24104 cash 862 1/23/14\n", "76 22147 36 480153 20436 finance 492 1/23/14\n", "77 99759 51 208677 22990 cash 492 1/16/14\n", "78 64406 20 349494 19580 cash 147 1/16/14\n", "79 55585 22 206310 17756 finance 949 1/1/14\n", "80 24710 75 185223 18920 cash 276 1/16/14\n", "81 53831 75 656181 24646 finance 949 1/18/14\n", "82 97494 19 393613 14006 cash 680 1/14/14\n", "83 13004 36 488910 24890 finance 225 1/12/14\n", "84 30779 75 846630 23401 finance 147 1/25/14\n", "85 29708 51 534633 20328 finance 276 1/29/14\n", "86 14082 46 909110 15200 cash 813 1/28/14\n", "87 82119 19 628031 16189 cash 492 1/12/14\n", "88 31815 46 743097 15040 cash 215 1/25/14\n", "89 32345 22 476047 20197 finance 803 1/19/14\n", "90 63526 51 862765 23232 finance 225 1/23/14\n", "91 20256 19 597655 16734 cash 492 1/16/14\n", "92 15930 36 486534 20960 cash 949 1/26/14\n", "93 68446 75 339830 24148 cash 215 1/22/14\n", "94 68330 20 283797 19140 cash 215 1/9/14\n", "95 85407 22 635204 21751 finance 949 1/18/14\n", "96 42428 75 619016 20413 cash 680 1/11/14\n", "97 37620 51 183947 20328 finance 276 1/3/14\n", "98 96344 20 731677 17600 finance 813 1/15/14\n", "99 53062 31 907549 19894 finance 680 1/28/14\n", "\n", "[100 rows x 7 columns]" ] } ], "prompt_number": 60 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Write a query to select everything from the car_table:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " \n", " ''')\n", "# Show me all columns from the car_table" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Risks of using an asterisk:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Works well when a table doesn't have a significant number of rows \n", "- However, most table will have thousands or even millions of rows\n", "- With a large table, you'll run into some problems, running \"SELECT FROM table_name\" might:\n", " - Take several minutes (or even hours) to return the information \n", " - Crash your computer\n", " - Muck up the server's processes, and you'll face the wrath of your company's system administrators once they figure out that you are the reason why the whole system has slowed down" ] }, { "cell_type": "heading", "level": 5, "metadata": {}, "source": [ "LIMIT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " - LIMIT N" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, you can use \"LIMIT\" and specify a few rows. This way, you'll just see the first few rows (and avoid overloading your system):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " \n", " FROM\n", " sales_table\n", " LIMIT\n", " 5\n", " ''')\n", "# \"Show me all columns from the sales_table, but limit it to the first 5 rows\"" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 54858</td>\n", " <td> 36</td>\n", " <td> 237906</td>\n", " <td> 21222</td>\n", " <td> finance</td>\n", " <td> 492</td>\n", " <td> 1/7/14</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 43161</td>\n", " <td> 20</td>\n", " <td> 967016</td>\n", " <td> 19140</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/26/14</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 862</td>\n", " <td> 1/17/14</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 92495</td>\n", " <td> 31</td>\n", " <td> 633030</td>\n", " <td> 19010</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/13/14</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 78000</td>\n", " <td> 51</td>\n", " <td> 341877</td>\n", " <td> 22022</td>\n", " <td> finance</td>\n", " <td> 862</td>\n", " <td> 1/12/14</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 54858 36 237906 21222 finance 492 1/7/14\n", "1 43161 20 967016 19140 finance 215 1/26/14\n", "2 40112 46 819010 14720 cash 862 1/17/14\n", "3 92495 31 633030 19010 finance 803 1/13/14\n", "4 78000 51 341877 22022 finance 862 1/12/14" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Write a query to select all columns and the first 10 rows from the car_table:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " \n", " ''')\n", "# Show me all columns from the car_table, but but limit it to the first 10 rows" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 5, "metadata": {}, "source": [ "SELECT SPECIFIC COLUMNS:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of using an asterisk for \"all columns\", you can specify a particular column or columns:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " model_id, customer_id\n", " FROM\n", " sales_table\n", " LIMIT\n", " 5\n", " ''')\n", "# \"Show me all columns from the sales_table, but limit it to the first 5 rows\"" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 36</td>\n", " <td> 237906</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 20</td>\n", " <td> 967016</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 31</td>\n", " <td> 633030</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 51</td>\n", " <td> 341877</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 77, "text": [ " model_id customer_id\n", "0 36 237906\n", "1 20 967016\n", "2 46 819010\n", "3 31 633030\n", "4 51 341877" ] } ], "prompt_number": 77 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Write a query to select model_id and model from the car_table and limit it to 10 results:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " \n", " ''')\n", "# Show me x and y columns from the car_table, but but limit it to the first 10 rows" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 5, "metadata": {}, "source": [ "WHERE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " - WHERE column_name = x / != x / in (x, y) / not in (x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "WHERE allows you to select rows matching specific values:\n", "\n", "= x : returns all rows where the values in column_name are equal to x\n", "\n", "!= x: returns all rows that DO NOT match x\n", "\n", "IN (x, y) : returns all rows where the values in column_name match either x or y\n", "\n", "NOT IN (x, y) : returns all rows where the values in column_name DOES NOT match x or y\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " \n", " FROM\n", " sales_table\n", " WHERE\n", " payment_type = 'cash'\n", " LIMIT 5\n", " ''')\n", "# \"Show me all columns and rows from the sales table, but only where the payment type is cash, and limit it to the first 5 rows\"" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 862</td>\n", " <td> 1/17/14</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 13154</td>\n", " <td> 75</td>\n", " <td> 720210</td>\n", " <td> 21409</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/20/14</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 56245</td>\n", " <td> 36</td>\n", " <td> 248621</td>\n", " <td> 25938</td>\n", " <td> cash</td>\n", " <td> 276</td>\n", " <td> 1/19/14</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 37412</td>\n", " <td> 31</td>\n", " <td> 750195</td>\n", " <td> 17462</td>\n", " <td> cash</td>\n", " <td> 276</td>\n", " <td> 1/11/14</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 68513</td>\n", " <td> 31</td>\n", " <td> 461723</td>\n", " <td> 17020</td>\n", " <td> cash</td>\n", " <td> 803</td>\n", " <td> 1/18/14</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 67, "text": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 40112 46 819010 14720 cash 862 1/17/14\n", "1 13154 75 720210 21409 cash 492 1/20/14\n", "2 56245 36 248621 25938 cash 276 1/19/14\n", "3 37412 31 750195 17462 cash 276 1/11/14\n", "4 68513 31 461723 17020 cash 803 1/18/14" ] } ], "prompt_number": 67 }, { "cell_type": "markdown", "metadata": {}, "source": [ " Rewrite the query above but ask for all rows that DO NOT involve cash transactions: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " \n", " ''')\n", "# Show me all columns and rows from the sales table, but only where the payment type is NOT cash, and limit it to the first 5 rows" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Try to rewrite the query to return rows where the model_id is either 31 or 36" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " \n", " ''')\n", "# Show me all columns and rows from the sales table, but only where the model_id is either 31 or 36" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 54858</td>\n", " <td> 36</td>\n", " <td> 237906</td>\n", " <td> 21222</td>\n", " <td> finance</td>\n", " <td> 492</td>\n", " <td> 1/7/14</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 92495</td>\n", " <td> 31</td>\n", " <td> 633030</td>\n", " <td> 19010</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/13/14</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 36535</td>\n", " <td> 31</td>\n", " <td> 908558</td>\n", " <td> 19894</td>\n", " <td> finance</td>\n", " <td> 862</td>\n", " <td> 1/30/14</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 56245</td>\n", " <td> 36</td>\n", " <td> 248621</td>\n", " <td> 25938</td>\n", " <td> cash</td>\n", " <td> 276</td>\n", " <td> 1/19/14</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 84469</td>\n", " <td> 31</td>\n", " <td> 733566</td>\n", " <td> 21441</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/20/14</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 68, "text": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 54858 36 237906 21222 finance 492 1/7/14\n", "1 92495 31 633030 19010 finance 803 1/13/14\n", "2 36535 31 908558 19894 finance 862 1/30/14\n", "3 56245 36 248621 25938 cash 276 1/19/14\n", "4 84469 31 733566 21441 finance 215 1/20/14" ] } ], "prompt_number": 68 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can add additional conditions to WHERE with the following format:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " WHERE column_a = x AND column_b = y" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " \n", " FROM\n", " sales_table\n", " WHERE\n", " payment_type = 'cash' \n", " AND model_id in (31, 36)\n", " LIMIT 5\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 56245</td>\n", " <td> 36</td>\n", " <td> 248621</td>\n", " <td> 25938</td>\n", " <td> cash</td>\n", " <td> 276</td>\n", " <td> 1/19/14</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 37412</td>\n", " <td> 31</td>\n", " <td> 750195</td>\n", " <td> 17462</td>\n", " <td> cash</td>\n", " <td> 276</td>\n", " <td> 1/11/14</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 68513</td>\n", " <td> 31</td>\n", " <td> 461723</td>\n", " <td> 17020</td>\n", " <td> cash</td>\n", " <td> 803</td>\n", " <td> 1/18/14</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 18303</td>\n", " <td> 36</td>\n", " <td> 413891</td>\n", " <td> 23318</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/17/14</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 10682</td>\n", " <td> 31</td>\n", " <td> 145479</td>\n", " <td> 17905</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/20/14</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 79, "text": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 56245 36 248621 25938 cash 276 1/19/14\n", "1 37412 31 750195 17462 cash 276 1/11/14\n", "2 68513 31 461723 17020 cash 803 1/18/14\n", "3 18303 36 413891 23318 cash 492 1/17/14\n", "4 10682 31 145479 17905 cash 147 1/20/14" ] } ], "prompt_number": 79 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "ORDER BY:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " ORDER BY column_name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ORDER BY sorts the data by a particular column. \n", "\n", "By default, ORDER BY sorts numbers or alphabetically in ascending order.\n", "\n", "To sort in descending order, add DESC after the column name " ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " \n", " FROM\n", " sales_table\n", " WHERE\n", " payment_type = 'cash'\n", " ORDER BY\n", " revenue\n", " LIMIT 5\n", " ''')\n", "# Show me all columns and rows for cash transactions, starting with the lowest sale price, limit it to 5 results" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 30046</td>\n", " <td> 46</td>\n", " <td> 982483</td>\n", " <td> 12640</td>\n", " <td> cash</td>\n", " <td> 803</td>\n", " <td> 1/29/14</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 70434</td>\n", " <td> 46</td>\n", " <td> 944843</td>\n", " <td> 13600</td>\n", " <td> cash</td>\n", " <td> 949</td>\n", " <td> 1/25/14</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 98201</td>\n", " <td> 19</td>\n", " <td> 697575</td>\n", " <td> 13642</td>\n", " <td> cash</td>\n", " <td> 680</td>\n", " <td> 1/4/14</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 97494</td>\n", " <td> 19</td>\n", " <td> 393613</td>\n", " <td> 14006</td>\n", " <td> cash</td>\n", " <td> 680</td>\n", " <td> 1/14/14</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 862</td>\n", " <td> 1/17/14</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 70, "text": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 30046 46 982483 12640 cash 803 1/29/14\n", "1 70434 46 944843 13600 cash 949 1/25/14\n", "2 98201 19 697575 13642 cash 680 1/4/14\n", "3 97494 19 393613 14006 cash 680 1/14/14\n", "4 40112 46 819010 14720 cash 862 1/17/14" ] } ], "prompt_number": 70 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Rewrite the query above to order by a different column and in descending order." ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " \n", " ''')\n", "# Translation: Show me all columns and rows for cash transactions, starting with the lowest sale price" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "GROUP BY & FUNCTIONS:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- GROUP BY column_name : lets you specify one or more columns to create groups. Each unique value in the GROUP BY column will get its own group\n", "- FUNCTIONS: \n", " - in the SELECT clause, you can apply functions to columns\n", " - http://www.w3schools.com/sql/sql_functions.asp" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " model_id, sum(revenue)\n", " FROM\n", " sales_table\n", " GROUP BY\n", " model_id\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model_id</th>\n", " <th>sum(revenue)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 19</td>\n", " <td> 157886</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 20</td>\n", " <td> 212080</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 22</td>\n", " <td> 201083</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 31</td>\n", " <td> 234748</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 36</td>\n", " <td> 365751</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 46</td>\n", " <td> 154880</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 51</td>\n", " <td> 297418</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 75</td>\n", " <td> 317406</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 74, "text": [ " model_id sum(revenue)\n", "0 19 157886\n", "1 20 212080\n", "2 22 201083\n", "3 31 234748\n", "4 36 365751\n", "5 46 154880\n", "6 51 297418\n", "7 75 317406" ] } ], "prompt_number": 74 }, { "cell_type": "code", "collapsed": false, "input": [ "Add " ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model_id isn't very helpful for analysis - the name would be better. However, each car's Model and Make information isn't located on the sales_table. Write out a query to return all columns and rows from the car_table to find out what information might be helpful in there. \n", " " ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " ???\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model_id</th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>sticker_price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 20</td>\n", " <td> Toyota</td>\n", " <td> Camry</td>\n", " <td> 22000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 46</td>\n", " <td> Toyota</td>\n", " <td> Corolla</td>\n", " <td> 16000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 51</td>\n", " <td> Toyota</td>\n", " <td> Prius</td>\n", " <td> 24200</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 19</td>\n", " <td> Honda</td>\n", " <td> Civic</td>\n", " <td> 18190</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 31</td>\n", " <td> Honda</td>\n", " <td> Accord</td>\n", " <td> 22105</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 75</td>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 24895</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 22</td>\n", " <td> Subaru</td>\n", " <td> Forester</td>\n", " <td> 22195</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 36</td>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 26200</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ " model_id make model sticker_price\n", "0 20 Toyota Camry 22000\n", "1 46 Toyota Corolla 16000\n", "2 51 Toyota Prius 24200\n", "3 19 Honda Civic 18190\n", "4 31 Honda Accord 22105\n", "5 75 Subaru Outback 24895\n", "6 22 Subaru Forester 22195\n", "7 36 Toyota Tundra 26200" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can add data from two different tables using a \"JOIN\" in the FROM clause:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " make,\n", " model,\n", " revenue\n", " FROM\n", " sales_table\n", " JOIN car_table \n", " ON sales_table.model_id = car_table.model_id\n", " WHERE\n", " payment_type = 'cash'\n", " ORDER BY\n", " revenue DESC\n", " LIMIT \n", " 5\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 25938</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 25937</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 24628</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 24366</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 24148</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ " make model revenue\n", "0 Toyota Tundra 25938\n", "1 Toyota Tundra 25937\n", "2 Toyota Tundra 24628\n", "3 Toyota Tundra 24366\n", "4 Subaru Outback 24148" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What's going on here:\n", "- To JOIN two tables, find columns on each table that contain corresponding data\n", "- In this case, the model_id column in the sales_table matched up to the model_id column in the car_table. Both have the ID numbers that the dealership has assigned to each model of the cars they are selling.\n", "- Follow the format below, where a column is referred to by table_name.column_name: " ] }, { "cell_type": "raw", "metadata": {}, "source": [ "FROM tableA JOIN tableB ON tableA.column_name = tableB.column_name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Take care: corresponding columns from different tables don't necessarily always have identical names. So, if the model_id column in the car_table were named car_model_id instead, this would be the FROM clause:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ " FROM\n", " sales_table\n", " JOIN car_table \n", " ON sales_table.model_id = car_table.car_model_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's say you wanted to see the model_id in the table as well. If you just try adding \"model_id\" in the SELECT clause, suddenly you'll hit an error:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " model_id,\n", " make,\n", " model,\n", " revenue\n", " FROM\n", " sales_table\n", " JOIN car_table \n", " ON sales_table.model_id = car_table.model_id\n", " WHERE\n", " payment_type = 'cash'\n", " ORDER BY\n", " revenue DESC\n", " LIMIT \n", " 10\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "ERROR: An unexpected error occurred while tokenizing input\n", "The following traceback may be corrupted or invalid\n", "The error message is: ('EOF in multi-line string', (1, 4))\n", "\n" ] }, { "ename": "DatabaseError", "evalue": "Execution failed on sql: \n SELECT\n model_id,\n make,\n model,\n revenue\n FROM\n sales_table\n JOIN car_table \n ON sales_table.model_id = car_table.model_id\n WHERE\n payment_type = 'cash'\n ORDER BY\n revenue DESC\n LIMIT \n 10\n ", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mDatabaseError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-17-5ca1644591a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mLIMIT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m ''')\n\u001b[0m", "\u001b[0;32m<ipython-input-7-6fb62674a9bd>\u001b[0m in \u001b[0;36mrun\u001b[0;34m(query)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# a pandas dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m 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def read_sql(self, sql, index_col=None, coerce_float=True, params=None,\n", "\u001b[0;32m//anaconda/envs/py3k/lib/python3.3/site-packages/pandas/compat/__init__.py\u001b[0m in \u001b[0;36mraise_with_traceback\u001b[0;34m(exc, traceback)\u001b[0m\n\u001b[1;32m 703\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtraceback\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mEllipsis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 705\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 706\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 707\u001b[0m \u001b[0;31m# this version of raise is a syntax error in Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/envs/py3k/lib/python3.3/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, args, *kwargs)\u001b[0m\n\u001b[1;32m 1004\u001b[0m \u001b[0mcur\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\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[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1005\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1006\u001b[0;31m \u001b[0mcur\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\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 1007\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcur\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1008\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\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;31mDatabaseError\u001b[0m: Execution failed on sql: \n SELECT\n model_id,\n make,\n model,\n revenue\n FROM\n sales_table\n JOIN car_table \n ON sales_table.model_id = car_table.model_id\n WHERE\n payment_type = 'cash'\n ORDER BY\n revenue DESC\n LIMIT \n 10\n " ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is because you've introduced another table with a column named \"model_id\" - the query doesn't know if it should pick the data from the model_id column in the sales_table or the car_table. So, you have to specify which table you want the query to use:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " sales_table.model_id,\n", " make,\n", " model,\n", " revenue\n", " FROM\n", " sales_table\n", " JOIN car_table \n", " ON sales_table.model_id = car_table.model_id\n", " WHERE\n", " payment_type = 'cash'\n", " ORDER BY\n", " revenue DESC\n", " LIMIT \n", " 5\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model_id</th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 36</td>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 25938</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 36</td>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 25937</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 36</td>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 24628</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 36</td>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 24366</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 75</td>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 24148</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ " model_id make model revenue\n", "0 36 Toyota Tundra 25938\n", "1 36 Toyota Tundra 25937\n", "2 36 Toyota Tundra 24628\n", "3 36 Toyota Tundra 24366\n", "4 75 Subaru Outback 24148" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: because the data in the sales_table.model_id column corresponds to the data in car_table.model_id, you can specify either one in the SELECT clause in this case. There are instances where this won't be the case, but that can be addressed later.\n", "\n", "* Although you technically only need to specify the column's table name in the SELECT clause when there are one or more columns of the same name (so if the joined tables both have a column named \"model_id\", for instance), it's a good habit to get into labeling all column names with the table they are coming from. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also give tables nicknames so that you don't have to write out the entire name of the table each time. You can use the nickname in the SELECT clause, even though you don't state the nicknae until the FROM clause. Just put the nickname immediately after in the FROM clause. One or two letters for a nickname is pretty common.\n", "\n", "In this case, I'm going to give sales_table the nickname \"S\", and the car_table the nickname \"C\". Now it's easy to label the columns in the SELECT clause:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " S.model_id,\n", " C.make,\n", " C.model,\n", " S.revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C\n", " ON S.model_id = C.model_id\n", " WHERE\n", " payment_type = 'cash'\n", " ORDER BY\n", " revenue DESC\n", " LIMIT \n", " 5\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model_id</th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 36</td>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 25938</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 36</td>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 25937</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 36</td>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 24628</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 36</td>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 24366</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 75</td>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 24148</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ " model_id make model revenue\n", "0 36 Toyota Tundra 25938\n", "1 36 Toyota Tundra 25937\n", "2 36 Toyota Tundra 24628\n", "3 36 Toyota Tundra 24366\n", "4 75 Subaru Outback 24148" ] } ], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we can see the Make and Model information for each car, let's throw in the sticker_price as well - also located on the car_table:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " C.make,\n", " C.model,\n", " S.revenue, \n", " C.sticker_price\n", " FROM\n", " sales_table S\n", " JOIN car_table C\n", " ON S.model_id = C.model_id\n", " WHERE\n", " payment_type = 'cash'\n", " ORDER BY\n", " revenue DESC\n", " LIMIT \n", " 5\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>revenue</th>\n", " <th>sticker_price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 25938</td>\n", " <td> 26200</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 25937</td>\n", " <td> 26200</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 24628</td>\n", " <td> 26200</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 24366</td>\n", " <td> 26200</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 24148</td>\n", " <td> 24895</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ " make model revenue sticker_price\n", "0 Toyota Tundra 25938 26200\n", "1 Toyota Tundra 25937 26200\n", "2 Toyota Tundra 24628 26200\n", "3 Toyota Tundra 24366 26200\n", "4 Subaru Outback 24148 24895" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can see that each car sold for some amount less than the listing price, presumably because the customer negotiated a better price with the salesman. You can add a function in the SELECT clause to see the percent under the listing price. You can perform any mathematical function by using the column names:\n", "- columnA + columnB = sumAB\n", "- columnA columnB = productAB" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## hacky fix, need to figure out the deal with integers and pandas.to_sql on SQLite\n", "run('''\n", " SELECT\n", " C.make,\n", " C.model,\n", " S.revenue, \n", " C.sticker_price,\n", " S.revenue100/C.sticker_price\n", " FROM\n", " sales_table S\n", " JOIN car_table C\n", " ON S.model_id = C.model_id\n", " WHERE\n", " payment_type = 'cash'\n", " ORDER BY\n", " revenue DESC\n", " LIMIT \n", " 5\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>revenue</th>\n", " <th>sticker_price</th>\n", " <th>S.revenue100/C.sticker_price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 25938</td>\n", " <td> 26200</td>\n", " <td> 99</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 25937</td>\n", " <td> 26200</td>\n", " <td> 98</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 24628</td>\n", " <td> 26200</td>\n", " <td> 94</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 24366</td>\n", " <td> 26200</td>\n", " <td> 93</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 24148</td>\n", " <td> 24895</td>\n", " <td> 96</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ " make model revenue sticker_price S.revenue100/C.sticker_price\n", "0 Toyota Tundra 25938 26200 99\n", "1 Toyota Tundra 25937 26200 98\n", "2 Toyota Tundra 24628 26200 94\n", "3 Toyota Tundra 24366 26200 93\n", "4 Subaru Outback 24148 24895 96" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "But that column name looks like a mess, so you can rename it by adding \"as [desired_column_name]\" " ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " C.make,\n", " C.model,\n", " S.revenue, \n", " C.sticker_price,\n", " S.revenue100/C.sticker_price AS percent_of_sticker_price\n", " FROM\n", " sales_table S\n", " JOIN car_table C\n", " ON S.model_id = C.model_id\n", " WHERE\n", " payment_type = 'cash'\n", " ORDER BY\n", " revenue DESC\n", " LIMIT \n", " 5\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>revenue</th>\n", " <th>sticker_price</th>\n", " <th>percent_of_sticker_price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 25938</td>\n", " <td> 26200</td>\n", " <td> 99</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 25937</td>\n", " <td> 26200</td>\n", " <td> 98</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 24628</td>\n", " <td> 26200</td>\n", " <td> 94</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 24366</td>\n", " <td> 26200</td>\n", " <td> 93</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 24148</td>\n", " <td> 24895</td>\n", " <td> 96</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ " make model revenue sticker_price percent_of_sticker_price\n", "0 Toyota Tundra 25938 26200 99\n", "1 Toyota Tundra 25937 26200 98\n", "2 Toyota Tundra 24628 26200 94\n", "3 Toyota Tundra 24366 26200 93\n", "4 Subaru Outback 24148 24895 96" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's say you want to see the average price that each model of car sold for compared to the list price for the entire month. This is where the GROUP BY clause helps. We'll also need to use the AVG( ) function in the SELECT clause:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# I've elimated the constraints from the WHERE, ORDER BY and LIMIT clause so all rows from the sale_table are included in the average\n", "run('''\n", " SELECT\n", " C.make,\n", " C.model,\n", " AVG(S.revenue) AS average_revenue, \n", " C.sticker_price,\n", " S.revenue100/C.sticker_price AS percent_of_sticker_price\n", " FROM\n", " sales_table S\n", " JOIN car_table C\n", " ON S.model_id = C.model_id\n", " GROUP BY\n", " model\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>average_revenue</th>\n", " <th>sticker_price</th>\n", " <th>percent_of_sticker_price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Honda</td>\n", " <td> Accord</td>\n", " <td> 19562.333333</td>\n", " <td> 22105</td>\n", " <td> 89</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Toyota</td>\n", " <td> Camry</td>\n", " <td> 19280.000000</td>\n", " <td> 22000</td>\n", " <td> 80</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Honda</td>\n", " <td> Civic</td>\n", " <td> 15788.600000</td>\n", " <td> 18190</td>\n", " <td> 91</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Toyota</td>\n", " <td> Corolla</td>\n", " <td> 14080.000000</td>\n", " <td> 16000</td>\n", " <td> 94</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Subaru</td>\n", " <td> Forester</td>\n", " <td> 18280.272727</td>\n", " <td> 22195</td>\n", " <td> 97</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 21160.400000</td>\n", " <td> 24895</td>\n", " <td> 81</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Toyota</td>\n", " <td> Prius</td>\n", " <td> 21244.142857</td>\n", " <td> 24200</td>\n", " <td> 84</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 22859.437500</td>\n", " <td> 26200</td>\n", " <td> 80</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ " make model average_revenue sticker_price percent_of_sticker_price\n", "0 Honda Accord 19562.333333 22105 89\n", "1 Toyota Camry 19280.000000 22000 80\n", "2 Honda Civic 15788.600000 18190 91\n", "3 Toyota Corolla 14080.000000 16000 94\n", "4 Subaru Forester 18280.272727 22195 97\n", "5 Subaru Outback 21160.400000 24895 81\n", "6 Toyota Prius 21244.142857 24200 84\n", "7 Toyota Tundra 22859.437500 26200 80" ] } ], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Average_Revenue numbers are messy, you can round them with ROUND(). To round to the nearest cent: ROUND( ,2)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " make,\n", " model,\n", " ROUND(AVG(revenue), 2) AS average_revenue, \n", " sticker_price,\n", " revenue100/sticker_price AS percent_of_sticker_price\n", " FROM\n", " sales_table S\n", " JOIN car_table C\n", " ON S.model_id = C.model_id\n", " WHERE\n", " C.model in ('Accord', 'Camry')\n", " GROUP BY\n", " model\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>average_revenue</th>\n", " <th>sticker_price</th>\n", " <th>percent_of_sticker_price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Honda</td>\n", " <td> Accord</td>\n", " <td> 19562.33</td>\n", " <td> 22105</td>\n", " <td> 96</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Toyota</td>\n", " <td> Camry</td>\n", " <td> 19280.00</td>\n", " <td> 22000</td>\n", " <td> 99</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 51, "text": [ " make model average_revenue sticker_price percent_of_sticker_price\n", "0 Honda Accord 19562.33 22105 96\n", "1 Toyota Camry 19280.00 22000 99" ] } ], "prompt_number": 51 }, { "cell_type": "markdown", "metadata": {}, "source": [ "ORDER BY the percent_of_sticker_price to highlight which cars are selling for the furthest below their list price:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " make,\n", " model,\n", " ROUND(AVG(revenue), 2) AS average_revenue, \n", " sticker_price,\n", " revenue100/sticker_price AS percent_of_sticker_price\n", " FROM\n", " sales_table S\n", " JOIN car_table C\n", " ON S.model_id = C.model_id\n", " GROUP BY\n", " model\n", " ORDER BY \n", " percent_of_sticker_price\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>average_revenue</th>\n", " <th>sticker_price</th>\n", " <th>percent_of_sticker_price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Toyota</td>\n", " <td> Camry</td>\n", " <td> 19280.00</td>\n", " <td> 22000</td>\n", " <td> 80</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 22859.44</td>\n", " <td> 26200</td>\n", " <td> 80</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 21160.40</td>\n", " <td> 24895</td>\n", " <td> 81</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Toyota</td>\n", " <td> Prius</td>\n", " <td> 21244.14</td>\n", " <td> 24200</td>\n", " <td> 84</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Honda</td>\n", " <td> Accord</td>\n", " <td> 19562.33</td>\n", " <td> 22105</td>\n", " <td> 89</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Honda</td>\n", " <td> Civic</td>\n", " <td> 15788.60</td>\n", " <td> 18190</td>\n", " <td> 91</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Toyota</td>\n", " <td> Corolla</td>\n", " <td> 14080.00</td>\n", " <td> 16000</td>\n", " <td> 94</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Subaru</td>\n", " <td> Forester</td>\n", " <td> 18280.27</td>\n", " <td> 22195</td>\n", " <td> 97</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ " make model average_revenue sticker_price percent_of_sticker_price\n", "0 Toyota Camry 19280.00 22000 80\n", "1 Toyota Tundra 22859.44 26200 80\n", "2 Subaru Outback 21160.40 24895 81\n", "3 Toyota Prius 21244.14 24200 84\n", "4 Honda Accord 19562.33 22105 89\n", "5 Honda Civic 15788.60 18190 91\n", "6 Toyota Corolla 14080.00 16000 94\n", "7 Subaru Forester 18280.27 22195 97" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "One last thing - we'll add a count of how many sales were made of each car model. If we use \"id\" column in car_sales, we can count" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " make,\n", " model,\n", " count() as units_sold,\n", " ROUND(AVG(revenue), 2) AS average_revenue, \n", " sticker_price,\n", " revenue100/sticker_price AS percent_of_sticker_price\n", " FROM\n", " sales_table S\n", " JOIN car_table C\n", " ON S.model_id = C.model_id \n", " GROUP BY\n", " model\n", " ORDER BY \n", " percent_of_sticker_price\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>units_sold</th>\n", " <th>average_revenue</th>\n", " <th>sticker_price</th>\n", " <th>percent_of_sticker_price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Toyota</td>\n", " <td> Camry</td>\n", " <td> 11</td>\n", " <td> 19280.00</td>\n", " <td> 22000</td>\n", " <td> 80</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 16</td>\n", " <td> 22859.44</td>\n", " <td> 26200</td>\n", " <td> 80</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 15</td>\n", " <td> 21160.40</td>\n", " <td> 24895</td>\n", " <td> 81</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Toyota</td>\n", " <td> Prius</td>\n", " <td> 14</td>\n", " <td> 21244.14</td>\n", " <td> 24200</td>\n", " <td> 84</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Honda</td>\n", " <td> Accord</td>\n", " <td> 12</td>\n", " <td> 19562.33</td>\n", " <td> 22105</td>\n", " <td> 89</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Honda</td>\n", " <td> Civic</td>\n", " <td> 10</td>\n", " <td> 15788.60</td>\n", " <td> 18190</td>\n", " <td> 91</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Toyota</td>\n", " <td> Corolla</td>\n", " <td> 11</td>\n", " <td> 14080.00</td>\n", " <td> 16000</td>\n", " <td> 94</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Subaru</td>\n", " <td> Forester</td>\n", " <td> 11</td>\n", " <td> 18280.27</td>\n", " <td> 22195</td>\n", " <td> 97</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 46, "text": [ " make model units_sold average_revenue sticker_price \\\n", "0 Toyota Camry 11 19280.00 22000 \n", "1 Toyota Tundra 16 22859.44 26200 \n", "2 Subaru Outback 15 21160.40 24895 \n", "3 Toyota Prius 14 21244.14 24200 \n", "4 Honda Accord 12 19562.33 22105 \n", "5 Honda Civic 10 15788.60 18190 \n", "6 Toyota Corolla 11 14080.00 16000 \n", "7 Subaru Forester 11 18280.27 22195 \n", "\n", " percent_of_sticker_price \n", "0 80 \n", "1 80 \n", "2 81 \n", "3 84 \n", "4 89 \n", "5 91 \n", "6 94 \n", "7 97 " ] } ], "prompt_number": 46 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So for some reason, customers are negotiating harder (or more successfully) on the Toyota Camry and Tundra - and it would probably be causing a problem for the dealership. This kind of issue comes up a lot in B2B as well, since companies have bargaining power with their partners. \n", "\n", "Being able to pull this information quickly makes it easy to pinpoint problems, although more queries are then needed to dig into the reasons. The data in this SQLite db could help answer questions like:\n", "- What age group is buying Tundras and Camrys? Maybe if younger people are drawn to them, they bargain harder because they have less expenadable income\n", "- What salesperson has been selling these cars? Maybe he's not a very good negotiator" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Salesperson performance:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Count the number of women and men who bought each model: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "#run('''\n", "# SELECT\n", "# model,\n", "# sum(if(gender='female', 1, 0)) as female,\n", "# sum(if(gender='male', 1, 0)) as male\n", "# FROM\n", "# sales_table\n", "# JOIN car_table \n", "# ON sales_table.model_id = car_table.model_id\n", "# JOIN cust_table\n", "# ON sales_table.customer_id = cust_table.customer_id\n", "# GROUP BY\n", "# model\n", "# ''')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " first_name,\n", " last_name,\n", " count() as units_sold,\n", " ROUND(AVG(revenue), 2) AS avg_revenue, \n", " ROUND(AVG(sticker_price), 2) avg_sticker_price,\n", " ROUND(AVG(revenue)/AVG(sticker_price), 2) AS percent_of_sticker_price\n", " FROM\n", " sales_table S\n", " JOIN car_table C\n", " ON S.model_id = C.model_id \n", " JOIN salesman_table SM \n", " ON S.salesman_id = SM.id\n", " GROUP BY\n", " first_name\n", " ORDER BY \n", " percent_of_sticker_price\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>first_name</th>\n", " <th>last_name</th>\n", " <th>units_sold</th>\n", " <th>avg_revenue</th>\n", " <th>avg_sticker_price</th>\n", " <th>percent_of_sticker_price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Claudine</td>\n", " <td> Hatch</td>\n", " <td> 12</td>\n", " <td> 19463.92</td>\n", " <td> 23465.00</td>\n", " <td> 0.83</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Kathleen</td>\n", " <td> March</td>\n", " <td> 8</td>\n", " <td> 19053.00</td>\n", " <td> 22549.38</td>\n", " <td> 0.84</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Rosemarie</td>\n", " <td> Self</td>\n", " <td> 5</td>\n", " <td> 19803.20</td>\n", " <td> 23479.00</td>\n", " <td> 0.84</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Justin</td>\n", " <td> Avellaneda</td>\n", " <td> 10</td>\n", " <td> 19393.70</td>\n", " <td> 22908.00</td>\n", " <td> 0.85</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Matthew</td>\n", " <td> Luna</td>\n", " <td> 11</td>\n", " <td> 19856.64</td>\n", " <td> 23343.18</td>\n", " <td> 0.85</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Michael</td>\n", " <td> Hill</td>\n", " <td> 10</td>\n", " <td> 17110.30</td>\n", " <td> 20167.00</td>\n", " <td> 0.85</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Joseph</td>\n", " <td> Seney</td>\n", " <td> 11</td>\n", " <td> 19521.09</td>\n", " <td> 22198.18</td>\n", " <td> 0.88</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Elton</td>\n", " <td> Elzy</td>\n", " <td> 12</td>\n", " <td> 20012.67</td>\n", " <td> 22525.00</td>\n", " <td> 0.89</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> Samantha</td>\n", " <td> Douglas</td>\n", " <td> 8</td>\n", " <td> 19600.75</td>\n", " <td> 22095.63</td>\n", " <td> 0.89</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> Jared</td>\n", " <td> Case</td>\n", " <td> 13</td>\n", " <td> 20084.00</td>\n", " <td> 21737.69</td>\n", " <td> 0.92</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 72, "text": [ " first_name last_name units_sold avg_revenue avg_sticker_price \\\n", "0 Claudine Hatch 12 19463.92 23465.00 \n", "1 Kathleen March 8 19053.00 22549.38 \n", "2 Rosemarie Self 5 19803.20 23479.00 \n", "3 Justin Avellaneda 10 19393.70 22908.00 \n", "4 Matthew Luna 11 19856.64 23343.18 \n", "5 Michael Hill 10 17110.30 20167.00 \n", "6 Joseph Seney 11 19521.09 22198.18 \n", "7 Elton Elzy 12 20012.67 22525.00 \n", "8 Samantha Douglas 8 19600.75 22095.63 \n", "9 Jared Case 13 20084.00 21737.69 \n", "\n", " percent_of_sticker_price \n", "0 0.83 \n", "1 0.84 \n", "2 0.84 \n", "3 0.85 \n", "4 0.85 \n", "5 0.85 \n", "6 0.88 \n", "7 0.89 \n", "8 0.89 \n", "9 0.92 " ] } ], "prompt_number": 72 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although Claudine Hatch has good volume, she is giving up too much in negotations - she might need extra training. Meanwhile, Jared Case might have earned himself a bonus for performance this month." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What impact is Claudine's weak negotiating having on the overall margins for the dealership? How much could they gain if she got up to Jared's standards? We'll include cogs to get some Net info:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "run('''\n", " SELECT\n", " first_name,\n", " last_name,\n", " count() as units_sold,\n", " sum(revenue) AS total_revenue, \n", " sum(cogs) total_cogs, \n", " sum(revenue) - sum(cogs) AS Net,\n", " (sum(revenue) - sum(cogs))100 / sum(revenue) Margin,\n", " (sum(revenue)100 / (select sum(revenue) from sales_table)) as percent_of_gross,\n", " (sum(revenue) - sum(cogs))100 / (select sum(revenue)-sum(cogs) from sales_table S join car_table C on S.model_id = C.model_id) as percent_of_net\n", " FROM\n", " sales_table S\n", " JOIN car_table C\n", " ON S.model_id = C.model_id \n", " JOIN salesman_table SM \n", " ON S.salesman_id = SM.id\n", " GROUP BY\n", " first_name\n", " ORDER BY \n", " total_revenue desc\n", " ''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>first_name</th>\n", " <th>last_name</th>\n", " <th>units_sold</th>\n", " <th>total_revenue</th>\n", " <th>total_cogs</th>\n", " <th>Net</th>\n", " <th>Margin</th>\n", " <th>percent_of_gross</th>\n", " <th>percent_of_net</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Jared</td>\n", " <td> Case</td>\n", " <td> 13</td>\n", " <td> 261092</td>\n", " <td> 169554</td>\n", " <td> 91538</td>\n", " <td> 35</td>\n", " <td> 13</td>\n", " <td> 15</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Elton</td>\n", " <td> Elzy</td>\n", " <td> 12</td>\n", " <td> 240152</td>\n", " <td> 162180</td>\n", " <td> 77972</td>\n", " <td> 32</td>\n", " <td> 12</td>\n", " <td> 13</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Claudine</td>\n", " <td> Hatch</td>\n", " <td> 12</td>\n", " <td> 233567</td>\n", " <td> 168948</td>\n", " <td> 64619</td>\n", " <td> 27</td>\n", " <td> 12</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Matthew</td>\n", " <td> Luna</td>\n", " <td> 11</td>\n", " <td> 218423</td>\n", " <td> 154065</td>\n", " <td> 64358</td>\n", " <td> 29</td>\n", " <td> 11</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Joseph</td>\n", " <td> Seney</td>\n", " <td> 11</td>\n", " <td> 214732</td>\n", " <td> 146508</td>\n", " <td> 68224</td>\n", " <td> 31</td>\n", " <td> 11</td>\n", " <td> 11</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Justin</td>\n", " <td> Avellaneda</td>\n", " <td> 10</td>\n", " <td> 193937</td>\n", " <td> 137448</td>\n", " <td> 56489</td>\n", " <td> 29</td>\n", " <td> 9</td>\n", " <td> 9</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Michael</td>\n", " <td> Hill</td>\n", " <td> 10</td>\n", " <td> 171103</td>\n", " <td> 121002</td>\n", " <td> 50101</td>\n", " <td> 29</td>\n", " <td> 8</td>\n", " <td> 8</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Samantha</td>\n", " <td> Douglas</td>\n", " <td> 8</td>\n", " <td> 156806</td>\n", " <td> 106059</td>\n", " <td> 50747</td>\n", " <td> 32</td>\n", " <td> 8</td>\n", " <td> 8</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> Kathleen</td>\n", " <td> March</td>\n", " <td> 8</td>\n", " <td> 152424</td>\n", " <td> 108237</td>\n", " <td> 44187</td>\n", " <td> 28</td>\n", " <td> 7</td>\n", " <td> 7</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> Rosemarie</td>\n", " <td> Self</td>\n", " <td> 5</td>\n", " <td> 99016</td>\n", " <td> 70437</td>\n", " <td> 28579</td>\n", " <td> 28</td>\n", " <td> 5</td>\n", " <td> 4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 89, "text": [ " first_name last_name units_sold total_revenue total_cogs Net \\\n", "0 Jared Case 13 261092 169554 91538 \n", "1 Elton Elzy 12 240152 162180 77972 \n", "2 Claudine Hatch 12 233567 168948 64619 \n", "3 Matthew Luna 11 218423 154065 64358 \n", "4 Joseph Seney 11 214732 146508 68224 \n", "5 Justin Avellaneda 10 193937 137448 56489 \n", "6 Michael Hill 10 171103 121002 50101 \n", "7 Samantha Douglas 8 156806 106059 50747 \n", "8 Kathleen March 8 152424 108237 44187 \n", "9 Rosemarie Self 5 99016 70437 28579 \n", "\n", " Margin percent_of_gross percent_of_net \n", "0 35 13 15 \n", "1 32 12 13 \n", "2 27 12 10 \n", "3 29 11 10 \n", "4 31 11 11 \n", "5 29 9 9 \n", "6 29 8 8 \n", "7 32 8 8 \n", "8 28 7 7 \n", "9 28 5 4 " ] } ], "prompt_number": 89 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Claudine's sales make up 12% of gross revenues, but only contribute 10% to net - she's hurting margins and could benefit from training, but doesn't appear to be a lost cause. However, Rosemarie Self has weak margins and* is barely moving inventory. This might not be the right role for her. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You could even use this data to figure out which salesperson would be best to approach a new customer who just walked in the door." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "WORK IN PROGRESS STUFF" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Debugging Tips:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's crazy easy to make mistakes in SQL, " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remeber this structure:\n", "Although capitalization and indentation do not matter, *the order of the clauses cannot change. You don't need* every clause in a query to make it work, but if you do use it, it has to appear in the right order.\n", "\n", "Some of these we haven't covered yet, but will below. For now, just use " ] }, { "cell_type": "code", "collapsed": false, "input": [ "SELECT\n", "FROM \n", "WHERE\n", "GROUP BY \n", "ORDER BY\n", "LIMIT" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "debugging = pd.DataFrame()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "tip_1 = [\"If you are joining more than one table, check to make sure that all of your columns are labeled\"]\n", "debugging.append(tip_1)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> If you are joining more than one table, check ...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [ " 0\n", "0 If you are joining more than one table, check ..." ] } ], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> If you are joining more than one table, check ...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ " 0\n", "0 If you are joining more than one table, check ..." ] } ], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "How to read basic queries in English:\n", "\n", "Queries are much more like normal English syntax than most programming languages out there. Reading them out loud can help with understanding their structure. \n", "\n", "The two most basic components of a query are nearly always \"SELECT\" and \"FROM\". Think about going to the grocery store to look for milk. This could be the structure of your search:" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
hsgui/interest-only	deeplearning/jupyter-notebook/gradient_descent_toy_example.ipynb	1	11896	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'\\nhttp://iamtrask.github.io/2015/07/27/python-network-part2/\\n'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''\n", "http://iamtrask.github.io/2015/07/27/python-network-part2/\n", "'''" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])\n", "y = np.array([[0,1,1,0]]).T\n", "alpha, hidden_dim = (0.5, 4)\n", "synapse_0 = 2np.random.random((3,hidden_dim)) - 1\n", "synapse_1 = 2np.random.random((hidden_dim,1)) - 1\n", "for j in range(60000):\n", " layer_1 = 1 / (1 + np.exp(-(np.dot(X,synapse_0))))\n", " layer_2 = 1 / (1 + np.exp(-(np.dot(layer_1,synapse_1))))\n", " layer_2_delta = (layer_2 - y) * (layer_2(1-layer_2))\n", " layer_1_delta = layer_2_delta.dot(synapse_1.T) (layer_1 * (1-layer_1))\n", " synapse_1 -= (alpha * layer_1.T.dot(layer_2_delta))\n", " synapse_0 -= (alpha * X.T.dot(layer_1_delta))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sigmoid(x):\n", " output = 1 / (1 + np.exp(-x))\n", " return output" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sigmoid_output_to_derivative(output):\n", " return output * (1 - output)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X1 = np.array( [ [0, 1], [0, 1], [1, 0], [1, 0] ])\n", "y1 = np.array( [[0, 0, 1, 1]]).T" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# initialize weights randomly with mean 0\n", "syn_0 = 2 * np.random.random( (2, 1) ) - 1" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loss: 1.68616414077e-05\n", "Loss: 1.58019191401e-05\n", "Loss: 1.48673683188e-05\n", "Loss: 1.40370603972e-05\n", "Loss: 1.32944846773e-05\n", "output after traininig:\n", "[[ 0.00251266]\n", " [ 0.00251266]\n", " [ 0.99748737]\n", " [ 0.99748737]]\n" ] } ], "source": [ "# one layer that contains 2 neural\n", "for iter in range(10000):\n", " # forward propagation:\n", " layer_0 = X1\n", " layer_1 = sigmoid(np.dot(layer_0, syn_0))\n", " \n", " loss = np.sum(np.square(layer_1 - y1)) / 2\n", " if iter % 2000 == 0:\n", " print(\"Loss: \", loss)\n", " layer_1_error = layer_1 - y1\n", " \n", " layer_1_delta = layer_1_error * sigmoid_output_to_derivative(layer_1)\n", " syn_0_derivative = np.dot(layer_0.T, layer_1_delta)\n", " \n", " # update weights\n", " syn_0 -= syn_0_derivative\n", "print(\"output after traininig:\")\n", "print(layer_1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Training with alpha: 0.001\n", "Loss: 0.495186535402\n", "Loss: 6.85224212314e-05\n", "Loss: 3.20160508308e-05\n", "Sync 0: [[ 4.6013571 4.17197193 -6.30956245 -4.19745118]\n", " [-2.58413484 -5.81447929 -6.60793435 -3.68396123]\n", " [ 0.97538679 -2.02685775 2.52949751 5.84371739]]\n", "sync 0 update direction change: [[ 1. 1. 0. 0.]\n", " [ 2. 0. 0. 2.]\n", " [ 4. 2. 1. 1.]]\n", "Sync 1: [[ -6.96765763]\n", " [ 7.14101949]\n", " [-10.31917382]\n", " [ 7.86128405]]\n", "Sync 1 update direction change: [[ 2.]\n", " [ 1.]\n", " [ 0.]\n", " [ 1.]]\n", "\n", "Training with alpha: 0.01\n", "Loss: 0.495186535402\n", "Loss: 6.85224212314e-05\n", "Loss: 3.20160508308e-05\n", "Sync 0: [[ 4.6013571 4.17197193 -6.30956245 -4.19745118]\n", " [-2.58413484 -5.81447929 -6.60793435 -3.68396123]\n", " [ 0.97538679 -2.02685775 2.52949751 5.84371739]]\n", "sync 0 update direction change: [[ 1. 1. 0. 0.]\n", " [ 2. 0. 0. 2.]\n", " [ 4. 2. 1. 1.]]\n", "Sync 1: [[ -6.96765763]\n", " [ 7.14101949]\n", " [-10.31917382]\n", " [ 7.86128405]]\n", "Sync 1 update direction change: [[ 2.]\n", " [ 1.]\n", " [ 0.]\n", " [ 1.]]\n", "\n", "Training with alpha: 0.1\n", "Loss: 0.495186535402\n", "Loss: 6.85224212314e-05\n", "Loss: 3.20160508308e-05\n", "Sync 0: [[ 4.6013571 4.17197193 -6.30956245 -4.19745118]\n", " [-2.58413484 -5.81447929 -6.60793435 -3.68396123]\n", " [ 0.97538679 -2.02685775 2.52949751 5.84371739]]\n", "sync 0 update direction change: [[ 1. 1. 0. 0.]\n", " [ 2. 0. 0. 2.]\n", " [ 4. 2. 1. 1.]]\n", "Sync 1: [[ -6.96765763]\n", " [ 7.14101949]\n", " [-10.31917382]\n", " [ 7.86128405]]\n", "Sync 1 update direction change: [[ 2.]\n", " [ 1.]\n", " [ 0.]\n", " [ 1.]]\n", "\n", "Training with alpha: 1\n", "Loss: 0.495186535402\n", "Loss: 6.85224212314e-05\n", "Loss: 3.20160508308e-05\n", "Sync 0: [[ 4.6013571 4.17197193 -6.30956245 -4.19745118]\n", " [-2.58413484 -5.81447929 -6.60793435 -3.68396123]\n", " [ 0.97538679 -2.02685775 2.52949751 5.84371739]]\n", "sync 0 update direction change: [[ 1. 1. 0. 0.]\n", " [ 2. 0. 0. 2.]\n", " [ 4. 2. 1. 1.]]\n", "Sync 1: [[ -6.96765763]\n", " [ 7.14101949]\n", " [-10.31917382]\n", " [ 7.86128405]]\n", "Sync 1 update direction change: [[ 2.]\n", " [ 1.]\n", " [ 0.]\n", " [ 1.]]\n", "\n", "Training with alpha: 10\n", "Loss: 0.495186535402\n", "Loss: 6.85224212314e-05\n", "Loss: 3.20160508308e-05\n", "Sync 0: [[ 4.6013571 4.17197193 -6.30956245 -4.19745118]\n", " [-2.58413484 -5.81447929 -6.60793435 -3.68396123]\n", " [ 0.97538679 -2.02685775 2.52949751 5.84371739]]\n", "sync 0 update direction change: [[ 1. 1. 0. 0.]\n", " [ 2. 0. 0. 2.]\n", " [ 4. 2. 1. 1.]]\n", "Sync 1: [[ -6.96765763]\n", " [ 7.14101949]\n", " [-10.31917382]\n", " [ 7.86128405]]\n", "Sync 1 update direction change: [[ 2.]\n", " [ 1.]\n", " [ 0.]\n", " [ 1.]]\n", "\n", "Training with alpha: 100\n", "Loss: 0.495186535402\n", "Loss: 6.85224212314e-05\n", "Loss: 3.20160508308e-05\n", "Sync 0: [[ 4.6013571 4.17197193 -6.30956245 -4.19745118]\n", " [-2.58413484 -5.81447929 -6.60793435 -3.68396123]\n", " [ 0.97538679 -2.02685775 2.52949751 5.84371739]]\n", "sync 0 update direction change: [[ 1. 1. 0. 0.]\n", " [ 2. 0. 0. 2.]\n", " [ 4. 2. 1. 1.]]\n", "Sync 1: [[ -6.96765763]\n", " [ 7.14101949]\n", " [-10.31917382]\n", " [ 7.86128405]]\n", "Sync 1 update direction change: [[ 2.]\n", " [ 1.]\n", " [ 0.]\n", " [ 1.]]\n", "\n", "Training with alpha: 1000\n", "Loss: 0.495186535402\n", "Loss: 6.85224212314e-05\n", "Loss: 3.20160508308e-05\n", "Sync 0: [[ 4.6013571 4.17197193 -6.30956245 -4.19745118]\n", " [-2.58413484 -5.81447929 -6.60793435 -3.68396123]\n", " [ 0.97538679 -2.02685775 2.52949751 5.84371739]]\n", "sync 0 update direction change: [[ 1. 1. 0. 0.]\n", " [ 2. 0. 0. 2.]\n", " [ 4. 2. 1. 1.]]\n", "Sync 1: [[ -6.96765763]\n", " [ 7.14101949]\n", " [-10.31917382]\n", " [ 7.86128405]]\n", "Sync 1 update direction change: [[ 2.]\n", " [ 1.]\n", " [ 0.]\n", " [ 1.]]\n" ] } ], "source": [ "alphas = [0.001,0.01,0.1,1,10,100,1000]\n", "X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])\n", "y = np.array([[0,1,1,0]]).T\n", "for alpha in alphas:\n", " print(\"\\nTraining with alpha: \" + str(alpha))\n", " np.random.seed(1)\n", " \n", " syn_0 = 2 * np.random.random( (3, 4) ) - 1\n", " syn_1 = 2 * np.random.random( (4, 1) ) - 1\n", " \n", " prev_sync_0_weight_update = np.zeros_like(syn_0)\n", " prev_sync_1_weight_update = np.zeros_like(syn_1)\n", " \n", " syn_0_direction_count = np.zeros_like(syn_0)\n", " syn_1_direction_count = np.zeros_like(syn_1)\n", " \n", " for j in range(60000):\n", " layer_0 = X\n", " layer_1 = sigmoid(np.dot(layer_0, syn_0))\n", " layer_2 = sigmoid(np.dot(layer_1, syn_1))\n", " \n", " loss = np.sum(np.square(layer_2 - y)) / 2\n", " if j % 20000 == 0:\n", " print(\"Loss: \", loss)\n", " \n", " layer_2_error = layer_2 - y\n", " \n", " layer_2_delta = layer_2_error * sigmoid_output_to_derivative(layer_2)\n", " \n", " layer_1_error = layer_2_delta.dot(syn_1.T)\n", " \n", " layer_1_delta = layer_1_error * sigmoid_output_to_derivative(layer_1)\n", " \n", " syn_1_weight_update = (layer_1.T.dot(layer_2_delta))\n", " syn_0_weight_update = (layer_0.T.dot(layer_1_delta))\n", " \n", " if (j > 0):\n", " syn_0_direction_count += np.abs(((syn_0_weight_update > 0) + 0) - ((prev_sync_0_weight_update > 0) + 0))\n", " syn_1_direction_count += np.abs(((syn_1_weight_update > 0) + 0) - ((prev_sync_1_weight_update > 0) + 0))\n", " \n", " syn_1 -= syn_1_weight_update\n", " syn_0 -= syn_0_weight_update\n", " \n", " prev_sync_0_weight_update = syn_0_weight_update\n", " prev_sync_1_weight_update = syn_1_weight_update\n", " \n", " print(\"Sync 0: \", syn_0)\n", " print(\"sync 0 update direction change: \", syn_0_direction_count)\n", " print(\"Sync 1: \", syn_1)\n", " print(\"Sync 1 update direction change: \", syn_1_direction_count)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-2.0
mbeyeler/opencv-machine-learning	notebooks/05.02-Using-Decision-Trees-to-Diagnose-Breast-Cancer.ipynb	1	69109	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<!--BOOK_INFORMATION-->\n", "<a href=\"https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv\" target=\"_blank\"><img align=\"left\" src=\"data/cover.jpg\" style=\"width: 76px; height: 100px; background: white; padding: 1px; border: 1px solid black; margin-right:10px;\"></a>\n", "This notebook contains an excerpt from the book [Machine Learning for OpenCV](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv) by Michael Beyeler.\n", "The code is released under the [MIT license](https://opensource.org/licenses/MIT),\n", "and is available on [GitHub](https://github.com/mbeyeler/opencv-machine-learning).\n", "\n", "Note that this excerpt contains only the raw code - the book is rich with additional explanations and illustrations.\n", "If you find this content useful, please consider supporting the work by\n", "[buying the book](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv)!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!--NAVIGATION-->\n", "< [Building Our First Decision Tree](05.01-Building-Our-First-Decision-Tree.ipynb) \| [Contents](../README.md) \| [Using Decision Trees for Regression](05.03-Using-Decision-Trees-for-Regression.ipynb) >" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Decision Trees to Diagnose Breast Cancer\n", "\n", "Now that we have built our first decision trees, it's time to turn our attention to a real dataset: The Breast Cancer Wisconsin dataset <https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)>.\n", "\n", "In order to make the take feasible, the researchers performed feature extraction on the images, like we did in Chapter 4, Representing Data and Engineering Features. They went through a total of 569 images, and extracted 30 different features that describe the characteristics of the cell nuclei present in the images, including:\n", "\n", "- cell nucleus texture (represented by the standard deviation of the gray-scale values)\n", "\n", "- cell nucleus size (calculated as the mean of distances from center to points on the perimeter)\n", "\n", "- tissue smoothness (local variation in radius lengths)\n", "\n", "- tissue compactness\n", "\n", "The goal of the research was then to classify tissue samples into benign and malignant (a binary classification task)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the dataset\n", "\n", "The full dataset is part of Scikit-Learn's example datasets:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import datasets\n", "data = datasets.load_breast_cancer()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As in previous examples, all data is contained in a 2-D feature matrix data.data, where the rows represent data samples, and the columns are the feature values:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(569, 30)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.data.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With a look at the provided feature names, we recognize some that we mentioned above:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n", " 'mean smoothness', 'mean compactness', 'mean concavity',\n", " 'mean concave points', 'mean symmetry', 'mean fractal dimension',\n", " 'radius error', 'texture error', 'perimeter error', 'area error',\n", " 'smoothness error', 'compactness error', 'concavity error',\n", " 'concave points error', 'symmetry error', 'fractal dimension error',\n", " 'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n", " 'worst smoothness', 'worst compactness', 'worst concavity',\n", " 'worst concave points', 'worst symmetry', 'worst fractal dimension'], \n", " dtype='<U23')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.feature_names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since this is a binary classification task, we expect to find exactly two target names:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['malignant', 'benign'], \n", " dtype='<U9')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.target_names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's split the dataset into training and test sets using a healthy 80-20 split:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import sklearn.model_selection as ms\n", "X_train, X_test, y_train, y_test = ms.train_test_split(data.data, data.target, test_size=0.2, random_state=42)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((455, 30), (114, 30))" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape, X_test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building the decision tree" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import tree\n", "dtc = tree.DecisionTreeClassifier(random_state=42)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", " max_features=None, max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=42, splitter='best')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dtc.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we did not specify any pre-pruning parameters, we would expect this decision tree to grow quite large and result in a perfect score on the training set:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dtc.score(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, to our surprise, the test error is not too shabby, either:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.94736842105263153" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dtc.score(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "with open(\"tree.dot\", 'w') as f:\n", " f = tree.export_graphviz(dtc, out_file=f,\n", " feature_names=data.feature_names,\n", " class_names=data.target_names)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we want to do some model exploration. For example, we mentioned above that the depth of a tree influences its performance. If we wanted to study this dependency more systematically, we could repeat building the tree for different values of `max_depth`:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "max_depths = np.array([1, 2, 3, 5, 7, 9, 11])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each of these values, we want to run the full model cascade from start to finish. We also want to record the train and test scores. We do this in a for loop:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "train_score = []\n", "test_score = []\n", "for d in max_depths:\n", " dtc = tree.DecisionTreeClassifier(max_depth=d, random_state=42)\n", " dtc.fit(X_train, y_train)\n", " train_score.append(dtc.score(X_train, y_train))\n", " test_score.append(dtc.score(X_test, y_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot the scores as a function of the tree depth using Matplotlib:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f13a6747358>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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qPLk73tupo1X1zdqTYiL0xg0DA/regdQRfRJKnbFf6JPwRL+EH/qkfXr1avuj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\nT8IT/RJ+6JPACUpwc7vdKio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"text/plain": [ "<matplotlib.figure.Figure at 0x7f13a87c9e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 6))\n", "plt.plot(max_depths, train_score, 'o-', linewidth=3, label='train')\n", "plt.plot(max_depths, test_score, 's-', linewidth=3, label='test')\n", "plt.xlabel('max_depth')\n", "plt.ylabel('score')\n", "plt.ylim(0.85, 1.1)\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's do one more. What about the minimum numbers of samples required to make a node a leaf node?\n", "\n", "We repeat the procedure from above:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "train_score = []\n", "test_score = []\n", "min_samples = np.array([2, 4, 8, 16, 32])\n", "for s in min_samples:\n", " dtc = tree.DecisionTreeClassifier(min_samples_leaf=s, random_state=42)\n", " dtc.fit(X_train, y_train)\n", " train_score.append(dtc.score(X_train, y_train))\n", " test_score.append(dtc.score(X_test, y_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This leads to a plot that looks quite different from the one before:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f13a668a5f8>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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2OXMlyafzSU6dS/LpfJJT55J8ts8lhVtMTAzFxcXNHxcXFxMVFdVqTHR0NM888wwADoeD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\nfLjVmIEDB5Kdnc2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h96W/bFyhO+fz0hZeFzs/tNfC63JWBYOibE135sxirjMtvLw1p9ow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\niIiIn/j/3XPkjTlJzpEAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f13a679add8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 6))\n", "plt.plot(min_samples, train_score, 'o-', linewidth=3, label='train')\n", "plt.plot(min_samples, test_score, 's-', linewidth=3, label='test')\n", "plt.xlabel('min_samples_leaf')\n", "plt.ylabel('score')\n", "plt.ylim(0.9, 1)\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What does this all mean? Refer to Chapter 5 for the answers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!--NAVIGATION-->\n", "< [Building Our First Decision Tree](05.01-Building-Our-First-Decision-Tree.ipynb) \| [Contents](../README.md) \| [Using Decision Trees for Regression](05.03-Using-Decision-Trees-for-Regression.ipynb) >" ] } ], "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": 2 }	mit
rbaghdadi/ISIR	utils/speedup_model/speedup_adam_batch_norm_True_MSE_nlayers_5_log_False_ELU.ipynb	2	155001	{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from os import environ\n", "from sklearn.metrics import r2_score\n", "from sklearn.metrics import mean_squared_error \n", "\n", "environ['optimizer'] = 'Adam'\n", "environ['num_workers']= '2'\n", "environ['batch_size']= '8192'\n", "environ['n_epochs']= '1500'\n", "environ['batch_norm']= 'True'\n", "environ['loss_func']='MSE'\n", "environ['layers'] = '600 350 200 180'\n", "environ['dropouts'] = '0.3 '*4\n", "environ['log'] = 'False'\n", "environ['weight_decay'] = '0.01'\n", "environ['cuda_device'] ='cuda:0'\n", "environ['dataset'] = '/data/scratch/mmerouani/data/speedup_dataset_research_batch1001-2500.pkl'\n", "\n", "%run utils.ipynb" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 458876/458876 [13:48<00:00, 553.55it/s] \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "data loaded\n", "(458876, 3592)\n" ] } ], "source": [ "print(\"loading data\")\n", "\n", "# train_dl, val_dl, test_dl = train_dev_split(dataset,val_size=10000, test_size=10000, batch_size=batch_size, num_workers=num_workers, log=log)\n", "train_dl, val_dl, test_dl = train_dev_split_transform(dataset,val_size=10000, test_size=10000, batch_size=batch_size, num_workers=num_workers, log=log,\n", " transform_func=lambda Y : 1/Y,\n", " filter_func=None)\n", "db = fai.basic_data.DataBunch(train_dl, val_dl, test_dl, device=device)\n", "\n", "print(\"data loaded\")\n", "print(val_dl.dataset.X.shape)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<function mse_criterion at 0x7fb3cb050158>\n" ] } ], "source": [ "input_size = train_dl.dataset.X.shape[1]\n", "output_size = train_dl.dataset.Y.shape[1]\n", "\n", "model = None \n", "\n", "if batch_norm:\n", " model = Model_BN_ELU(input_size, output_size, hidden_sizes=layers_sizes, drops=drops)\n", "else:\n", " model = Model(input_size, output_size)\n", "\n", "# model = nn.DataParallel(model)\n", "# model.to(device)\n", "\n", "if loss_func == 'MSE':\n", " criterion = mse_criterion\n", "else:\n", " criterion = mape_criterion\n", "\n", "l = fai.basic_train.Learner(db, model, loss_func=criterion, metrics=[mse_criterion],\n", " callback_fns=[partial(EarlyStoppingCallback, mode='min', \n", " monitor='mse_criterion', min_delta=0, patience=150)],silent=True)\n", "\n", "if optimizer == 'SGD':\n", " l.opt_func = optim.SGD \n", " \n", "print(criterion) " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n" ] }, { "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": [ "l.lr_find()\n", "l.recorder.plot()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "lr = 1e-03" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "l.fit_one_cycle(1500, lr)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "l.recorder.plot_losses()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": true }, "outputs": [], "source": [ "l.save(f\"speedup_{optimizer}_batch_norm_{batch_norm}_{loss_func}_nlayers_{len(layers_sizes)}_log_{log}_batch1001-2500_ELU_MSE_inverse\")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "val_df = get_results_df(val_dl, l.model)\n", "train_df = get_results_df(train_dl, l.model)\n", "test_df=get_results_df(test_dl, l.model)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>index</th>\n", " <th>prediction</th>\n", " <th>target</th>\n", " <th>speedup</th>\n", " <th>abs_diff</th>\n", " <th>APE</th>\n", " <th>SMAPE</th>\n", " <th>interchange</th>\n", " <th>tile</th>\n", " <th>unroll</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>10000.00000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>4999.50000</td>\n", " <td>11.098552</td>\n", " <td>10.903996</td>\n", " <td>0.438427</td>\n", " <td>3.075651</td>\n", " <td>66.447647</td>\n", " <td>85.732559</td>\n", " <td>0.757900</td>\n", " <td>0.940400</td>\n", " <td>0.750000</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>2886.89568</td>\n", " <td>23.467976</td>\n", " <td>23.508726</td>\n", " <td>0.384616</td>\n", " <td>5.840970</td>\n", " <td>85.896927</td>\n", " <td>79.414223</td>\n", " <td>0.428376</td>\n", " <td>0.236756</td>\n", " <td>0.433034</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.378068</td>\n", " <td>0.004559</td>\n", " <td>0.000561</td>\n", " <td>0.040809</td>\n", " <td>0.040818</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>2499.75000</td>\n", " <td>0.000000</td>\n", " <td>1.288857</td>\n", " <td>0.086719</td>\n", " <td>0.954347</td>\n", " <td>17.093031</td>\n", " <td>17.113403</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>0.750000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>4999.50000</td>\n", " <td>3.474495</td>\n", " <td>3.233476</td>\n", " <td>0.309265</td>\n", " <td>1.355621</td>\n", " <td>47.712870</td>\n", " <td>50.623348</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>7499.25000</td>\n", " <td>12.104778</td>\n", " <td>11.531492</td>\n", " <td>0.775881</td>\n", " <td>2.601631</td>\n", " <td>100.000000</td>\n", " <td>200.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>9999.00000</td>\n", " <td>191.724838</td>\n", " <td>219.370346</td>\n", " <td>2.645030</td>\n", " <td>64.777817</td>\n", " <td>1059.229370</td>\n", " <td>200.000015</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " index prediction target speedup abs_diff \\\n", "count 10000.00000 10000.000000 10000.000000 10000.000000 10000.000000 \n", "mean 4999.50000 11.098552 10.903996 0.438427 3.075651 \n", "std 2886.89568 23.467976 23.508726 0.384616 5.840970 \n", "min 0.00000 0.000000 0.378068 0.004559 0.000561 \n", "25% 2499.75000 0.000000 1.288857 0.086719 0.954347 \n", "50% 4999.50000 3.474495 3.233476 0.309265 1.355621 \n", "75% 7499.25000 12.104778 11.531492 0.775881 2.601631 \n", "max 9999.00000 191.724838 219.370346 2.645030 64.777817 \n", "\n", " APE SMAPE interchange tile unroll \n", "count 10000.000000 10000.000000 10000.000000 10000.000000 10000.000000 \n", "mean 66.447647 85.732559 0.757900 0.940400 0.750000 \n", "std 85.896927 79.414223 0.428376 0.236756 0.433034 \n", "min 0.040809 0.040818 0.000000 0.000000 0.000000 \n", "25% 17.093031 17.113403 1.000000 1.000000 0.750000 \n", "50% 47.712870 50.623348 1.000000 1.000000 1.000000 \n", "75% 100.000000 200.000000 1.000000 1.000000 1.000000 \n", "max 1059.229370 200.000015 1.000000 1.000000 1.000000 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.describe()" ] }, { "cell_type": "code", "execution_count": 23, "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>index</th>\n", " <th>prediction</th>\n", " <th>target</th>\n", " <th>speedup</th>\n", " <th>abs_diff</th>\n", " <th>APE</th>\n", " <th>SMAPE</th>\n", " <th>interchange</th>\n", " <th>tile</th>\n", " <th>unroll</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>10000.00000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>14999.50000</td>\n", " <td>11.488132</td>\n", " <td>11.780406</td>\n", " <td>0.434748</td>\n", " <td>2.922600</td>\n", " <td>62.541176</td>\n", " <td>86.289261</td>\n", " <td>0.782900</td>\n", " <td>0.947300</td>\n", " <td>0.750000</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>2886.89568</td>\n", " <td>25.617477</td>\n", " <td>25.610207</td>\n", " <td>0.526266</td>\n", " <td>5.782752</td>\n", " <td>75.401016</td>\n", " <td>81.565407</td>\n", " <td>0.412292</td>\n", " <td>0.223445</td>\n", " <td>0.433034</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>10000.00000</td>\n", " <td>0.000000</td>\n", " <td>0.145649</td>\n", " <td>0.004063</td>\n", " <td>0.000078</td>\n", " <td>0.000571</td>\n", " <td>0.000571</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>12499.75000</td>\n", " <td>0.000000</td>\n", " <td>1.463150</td>\n", " <td>0.088260</td>\n", " <td>0.948128</td>\n", " <td>15.817019</td>\n", " <td>15.958954</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>0.750000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>14999.50000</td>\n", " <td>2.718983</td>\n", " <td>3.093708</td>\n", " <td>0.323237</td>\n", " <td>1.397799</td>\n", " <td>42.369080</td>\n", " <td>43.798750</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>17499.25000</td>\n", " <td>11.731441</td>\n", " <td>11.330190</td>\n", " <td>0.683457</td>\n", " <td>2.539973</td>\n", " <td>100.000000</td>\n", " <td>200.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>19999.00000</td>\n", " <td>220.992310</td>\n", " <td>246.104568</td>\n", " <td>6.865816</td>\n", " <td>141.481262</td>\n", " <td>840.645874</td>\n", " <td>200.000015</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " index prediction target speedup abs_diff \\\n", "count 10000.00000 10000.000000 10000.000000 10000.000000 10000.000000 \n", "mean 14999.50000 11.488132 11.780406 0.434748 2.922600 \n", "std 2886.89568 25.617477 25.610207 0.526266 5.782752 \n", "min 10000.00000 0.000000 0.145649 0.004063 0.000078 \n", "25% 12499.75000 0.000000 1.463150 0.088260 0.948128 \n", "50% 14999.50000 2.718983 3.093708 0.323237 1.397799 \n", "75% 17499.25000 11.731441 11.330190 0.683457 2.539973 \n", "max 19999.00000 220.992310 246.104568 6.865816 141.481262 \n", "\n", " APE SMAPE interchange tile unroll \n", "count 10000.000000 10000.000000 10000.000000 10000.000000 10000.000000 \n", "mean 62.541176 86.289261 0.782900 0.947300 0.750000 \n", "std 75.401016 81.565407 0.412292 0.223445 0.433034 \n", "min 0.000571 0.000571 0.000000 0.000000 0.000000 \n", "25% 15.817019 15.958954 1.000000 1.000000 0.750000 \n", "50% 42.369080 43.798750 1.000000 1.000000 1.000000 \n", "75% 100.000000 200.000000 1.000000 1.000000 1.000000 \n", "max 840.645874 200.000015 1.000000 1.000000 1.000000 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val_df.describe()" ] }, { "cell_type": "code", "execution_count": 24, "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>index</th>\n", " <th>prediction</th>\n", " <th>target</th>\n", " <th>speedup</th>\n", " <th>abs_diff</th>\n", " <th>APE</th>\n", " <th>SMAPE</th>\n", " <th>interchange</th>\n", " <th>tile</th>\n", " <th>unroll</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>438876.000000</td>\n", " <td>438876.000000</td>\n", " <td>438876.000000</td>\n", " <td>438876.000000</td>\n", " <td>438876.000000</td>\n", " <td>438876.000000</td>\n", " <td>438876.000000</td>\n", " <td>438876.000000</td>\n", " <td>438876.000000</td>\n", " <td>438876.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>239437.500000</td>\n", " <td>10.832894</td>\n", " <td>11.355379</td>\n", " <td>0.417869</td>\n", " <td>1.869952</td>\n", " <td>50.621685</td>\n", " <td>81.134720</td>\n", " <td>0.770386</td>\n", " <td>0.943353</td>\n", " <td>0.750000</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>126692.732707</td>\n", " <td>24.059313</td>\n", " <td>24.043819</td>\n", " <td>0.450577</td>\n", " <td>2.339988</td>\n", " <td>47.508152</td>\n", " <td>83.214241</td>\n", " <td>0.420585</td>\n", " <td>0.231167</td>\n", " <td>0.433013</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>20000.000000</td>\n", " <td>0.000000</td>\n", " <td>0.037623</td>\n", " <td>0.002826</td>\n", " <td>0.000009</td>\n", " <td>0.000106</td>\n", " <td>0.000106</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>129718.750000</td>\n", " <td>0.000000</td>\n", " <td>1.414381</td>\n", " <td>0.089580</td>\n", " <td>0.794494</td>\n", " <td>11.088913</td>\n", " <td>11.078519</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>0.750000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>239437.500000</td>\n", " <td>2.884928</td>\n", " <td>3.154334</td>\n", " <td>0.317024</td>\n", " <td>1.226655</td>\n", " <td>34.062506</td>\n", " <td>35.285439</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>349156.250000</td>\n", " <td>10.820855</td>\n", " <td>11.163268</td>\n", " <td>0.707023</td>\n", " <td>2.011203</td>\n", " <td>100.000000</td>\n", " <td>200.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>458875.000000</td>\n", " <td>347.571716</td>\n", " <td>353.865967</td>\n", " <td>26.579260</td>\n", " <td>98.714508</td>\n", " <td>1013.419067</td>\n", " <td>200.000015</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " index prediction target speedup \\\n", "count 438876.000000 438876.000000 438876.000000 438876.000000 \n", "mean 239437.500000 10.832894 11.355379 0.417869 \n", "std 126692.732707 24.059313 24.043819 0.450577 \n", "min 20000.000000 0.000000 0.037623 0.002826 \n", "25% 129718.750000 0.000000 1.414381 0.089580 \n", "50% 239437.500000 2.884928 3.154334 0.317024 \n", "75% 349156.250000 10.820855 11.163268 0.707023 \n", "max 458875.000000 347.571716 353.865967 26.579260 \n", "\n", " abs_diff APE SMAPE interchange \\\n", "count 438876.000000 438876.000000 438876.000000 438876.000000 \n", "mean 1.869952 50.621685 81.134720 0.770386 \n", "std 2.339988 47.508152 83.214241 0.420585 \n", "min 0.000009 0.000106 0.000106 0.000000 \n", "25% 0.794494 11.088913 11.078519 1.000000 \n", "50% 1.226655 34.062506 35.285439 1.000000 \n", "75% 2.011203 100.000000 200.000000 1.000000 \n", "max 98.714508 1013.419067 200.000015 1.000000 \n", "\n", " tile unroll \n", "count 438876.000000 438876.000000 \n", "mean 0.943353 0.750000 \n", "std 0.231167 0.433013 \n", "min 0.000000 0.000000 \n", "25% 1.000000 0.750000 \n", "50% 1.000000 1.000000 \n", "75% 1.000000 1.000000 \n", "max 1.000000 1.000000 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.describe()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test set:\n", "MSE: 43.57326889038086\n", "R-square score: 0.9211494653942405\n" ] } ], "source": [ "print(\"Test set:\")\n", "print(f\"MSE: {mean_squared_error(np.array(test_df['target']), np.array(test_df['prediction']))}\")\n", "print(f\"R-square score: {r2_score(np.array(test_df['target']), np.array(test_df['prediction']))}\")" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Validation set:\n", "MSE: 41.97845458984375\n", "R-square score: 0.9359906839134117\n" ] } ], "source": [ "print(\"Validation set:\")\n", "print(f\"MSE: {mean_squared_error(np.array(val_df['target']), np.array(val_df['prediction']))}\")\n", "print(f\"R-square score: {r2_score(np.array(val_df['target']), np.array(val_df['prediction']))}\")" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train set:\n", "MSE: 8.973251342773438\n", "R-square score: 0.984477366046974\n" ] } ], "source": [ "print(\"Train set:\")\n", "print(f\"MSE: {mean_squared_error(np.array(train_df['target']), np.array(train_df['prediction']))}\")\n", "print(f\"R-square score: {r2_score(np.array(train_df['target']), np.array(train_df['prediction']))}\")" ] }, { "cell_type": "code", "execution_count": 43, "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>prediction</th>\n", " <th>target</th>\n", " <th>abs_diff</th>\n", " <th>APE</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>32.000000</td>\n", " <td>32.0</td>\n", " <td>32.000000</td>\n", " <td>32.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>1.595547</td>\n", " <td>1.0</td>\n", " <td>1.752714</td>\n", " <td>175.271362</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>2.100466</td>\n", " <td>0.0</td>\n", " <td>1.267664</td>\n", " <td>126.766365</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.000000</td>\n", " <td>1.0</td>\n", " <td>0.350441</td>\n", " <td>35.044098</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>0.000000</td>\n", " <td>1.0</td>\n", " <td>1.000000</td>\n", " <td>100.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>0.000000</td>\n", " <td>1.0</td>\n", " <td>1.000000</td>\n", " <td>100.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>3.766825</td>\n", " <td>1.0</td>\n", " <td>2.766825</td>\n", " <td>276.682465</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>5.354717</td>\n", " <td>1.0</td>\n", " <td>4.354717</td>\n", " <td>435.471680</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " prediction target abs_diff APE\n", "count 32.000000 32.0 32.000000 32.000000\n", "mean 1.595547 1.0 1.752714 175.271362\n", "std 2.100466 0.0 1.267664 126.766365\n", "min 0.000000 1.0 0.350441 35.044098\n", "25% 0.000000 1.0 1.000000 100.000000\n", "50% 0.000000 1.0 1.000000 100.000000\n", "75% 3.766825 1.0 2.766825 276.682465\n", "max 5.354717 1.0 4.354717 435.471680" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = val_df\n", "df[(df.interchange==0) & (df.unroll == 0) & (df.tile == 0)][['prediction','target', 'abs_diff','APE']].describe()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# df1 = val_df[(df.interchange==0) & (df.unroll == 0) & (df.tile == 0)]\n", "df1 = test_df\n", "\n", "joint_plot(df1, f\"Test dataset, {loss_func} loss\")\n", "df2 = val_df\n", "joint_plot(df2, f\"Validation dataset, {loss_func} loss\")" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "df_ = val_df.sort_values(by=[\"abs_diff\"])\n", "\n", "df_['x'] = range(len(df_))" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7fb3c6ea0f98>" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 2880x2160 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot('x', 'abs_diff', 'bo', data=df_)\n", "\n", "\n", "plt.xlabel('scheduled program')\n", "plt.ylabel('abs_diff')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "l=l.load(\"speedup_Adam_batch_norm_True_MSE_nlayers_4_log_False_batch1001-2500_ELU_MSE_inverse\")" ] }, { "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 }	mit
hyunyoung2/hyunyoung2.github.io	img/Image/Languages/Python/2017-05-16-How_To_Plot_Vector_And_Plane_With_Python/an_example_of_plotting_vector.ipynb	2	191032	{ "cells": [ { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===== x axis =====\n", "[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", "===== y axis =====\n", "[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.\n", " 15.]\n", "===== the location of X coordinate =====\n", "[[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n", " [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]]\n", "===== the location of Y coordinate =====\n", "[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", " [ 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]\n", " [ 3. 3. 3. 3. 3. 3. 3. 3. 3. 3. 3.]\n", " [ 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.]\n", " [ 5. 5. 5. 5. 5. 5. 5. 5. 5. 5. 5.]\n", " [ 6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 6.]\n", " [ 7. 7. 7. 7. 7. 7. 7. 7. 7. 7. 7.]\n", " [ 8. 8. 8. 8. 8. 8. 8. 8. 8. 8. 8.]\n", " [ 9. 9. 9. 9. 9. 9. 9. 9. 9. 9. 9.]\n", " [ 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10.]\n", " [ 11. 11. 11. 11. 11. 11. 11. 11. 11. 11. 11.]\n", " [ 12. 12. 12. 12. 12. 12. 12. 12. 12. 12. 12.]\n", " [ 13. 13. 13. 13. 13. 13. 13. 13. 13. 13. 13.]\n", " [ 14. 14. 14. 14. 14. 14. 14. 14. 14. 14. 14.]\n", " [ 15. 15. 15. 15. 15. 15. 15. 15. 15. 15. 15.]]\n", "===== u matrix =====\n", "[[ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n", " [ 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]]\n", "===== v matrix =====\n", "[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 5. 5. 5. 5. 5. 5. 5. 5. 5. 5. 5.]\n", " [ 10. 10. 10. 10. 10. 10. 10. 10. 10. 10. 10.]\n", " [ 15. 15. 15. 15. 15. 15. 15. 15. 15. 15. 15.]\n", " [ 20. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20.]\n", " [ 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25.]\n", " [ 30. 30. 30. 30. 30. 30. 30. 30. 30. 30. 30.]\n", " [ 35. 35. 35. 35. 35. 35. 35. 35. 35. 35. 35.]\n", " [ 40. 40. 40. 40. 40. 40. 40. 40. 40. 40. 40.]\n", " [ 45. 45. 45. 45. 45. 45. 45. 45. 45. 45. 45.]\n", " [ 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50.]\n", " [ 55. 55. 55. 55. 55. 55. 55. 55. 55. 55. 55.]\n", " [ 60. 60. 60. 60. 60. 60. 60. 60. 60. 60. 60.]\n", " [ 65. 65. 65. 65. 65. 65. 65. 65. 65. 65. 65.]\n", " [ 70. 70. 70. 70. 70. 70. 70. 70. 70. 70. 70.]\n", " [ 75. 75. 75. 75. 75. 75. 75. 75. 75. 75. 75.]]\n", "q : <matplotlib.quiver.Quiver object at 0x000001E6C84C4828>\n" ] }, { "data": { "image/png": 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PAdW9DUGW//UE8L0vv+OPIUhIYKMBXDhVCV3nInX//mYB886d1XAbqqveImiH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\nKRR+swvR0UyosislKToaWL+ewoN2IC6O4pbHj9vDn5YGfPs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"text/plain": [ "<matplotlib.figure.Figure at 0x1e6c81cd2b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# http://kestrel.nmt.edu/~raymond/software/python_notes/paper004.html\n", "# 4.3.4 Vector plots\n", "# linapce funtion in this documentaion,https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.linspace.html\n", "# meshgrid from https://docs.scipy.org/doc/numpy/reference/generated/numpy.meshgrid.html\n", "# plt.quiver from http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.quiver\n", "% matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "x = np.linspace(0,10,11) # the number of row, after meshgrid\n", "y = np.linspace(0,15,16) # the number of column, after meshgrid\n", "print ('===== x axis =====')\n", "print (x)\n", "print ('===== y axis =====')\n", "print (y)\n", "(X,Y) = np.meshgrid(x,y)\n", "print ('===== the location of X coordinate =====')\n", "print (X)\n", "print ('===== the location of Y coordinate =====')\n", "print (Y)\n", "u = 5X\n", "print ('===== u matrix =====')\n", "print (u)\n", "v = 5Y\n", "print ('===== v matrix =====')\n", "print (v)\n", "q = plt.quiver(X,Y,u,v,angles =\"xy\", scale=1000, color='b')\n", "print (\"q : {0}\".format(q))\n", "#p = plt.quiver(q,1,16.5,5,50, \"50 m/s\", coordinates='data', color='r')\n", "xl = plt.xlabel(\"x (km)\")\n", "yl = plt.ylabel('y (km)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x : [0 1 2]\n", "y : [0 1 2]\n", "====X matrix====\n", "[[0 1 2]\n", " [0 1 2]\n", " [0 1 2]]\n", "====Y matrix====\n", "[[0 0 0]\n", " [1 1 1]\n", " [2 2 2]]\n", "====u matrix====\n", "[[ 0 5 10]\n", " [ 0 5 10]\n", " [ 0 5 10]]\n", "====x matrix====\n", "[[ 0 0 0]\n", " [ 5 5 5]\n", " [10 10 10]]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1e6c7e84828>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "===== xl =====\n", "xl : Text(0.5,17.2,'x (km)')\n", "===== yl =====\n", "yl : Text(17.2,0.5,'y (km)')\n" ] } ], "source": [ "# form stackoverflow http://stackoverflow.com/questions/12265234/how-to-plot-2d-math-vectors-with-matplotlib\n", "\n", "% matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "x = np.arange(3) # the number of row, after meshgrid\n", "y = np.arange(3) # the number of column, after meshgrid\n", "(X,Y) = np.meshgrid(x,y) # python format from https://pyformat.info/\n", "print (\"x : {0}\".format(x))\n", "print (\"y : {0}\".format(y))\n", "print (\"====X matrix====\")\n", "print (X)\n", "print (\"====Y matrix====\")\n", "print (Y)\n", "u = 5X\n", "print (\"====u matrix====\")\n", "print (u)\n", "v = 5Y\n", "print (\"====x matrix====\")\n", "print (v)\n", "q = plt.quiver(X,Y,u,v,color=\"b\")\n", "# X : 1D or 2D array, sequence, optional\n", "# The x coordinates of the arrow locations\n", "# Y : 1D or 2D array, sequence, optional\n", "# The y coordinates of the arrow locations\n", "# U : 1D or 2D array or masked array, sequence\n", "# The x components of the arrow vectors\n", "# V : 1D or 2D array or masked array, sequence\n", "# The y components of the arrow vectors\n", "xl=plt.xlabel(\"x (km)\")\n", "yl=plt.ylabel(\"y (km)\")\n", "plt.show()\n", "\n", "print (\"===== xl =====\")\n", "print (\"xl : {0}\".format(xl))\n", "print (\"===== yl =====\")\n", "print (\"yl : {0}\".format(yl))" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X : (0, 0, 0)\n", "Y : (0, 0, 0)\n", "U : (3, 1, 9)\n", "V : (2, 1, 9)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1e6c7d10cc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# stackoverflow of http://stackoverflow.com/questions/12265234/how-to-plot-2d-math-vectors-with-matplotlib\n", "# thai is about how to draw vector in python with matplotlib\n", "\n", "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "vec = np.array([[0,0,3,2],[0,0,1,1],[0,0,9,9]])\n", "X, Y, U, V = zip(vec)\n", "print (\"X : {0}\".format(X))\n", "print (\"Y : {0}\".format(Y))\n", "print (\"U : {0}\".format(U))\n", "print (\"V : {0}\".format(V))\n", "\n", "plt.figure()\n", "ax = plt.gca()\n", "plt.grid()\n", "ax.quiver(X,Y,U,V, angles=\"xy\", scale_units=\"xy\", scale=1)\n", "ax.quiver(0,0,1,1,angles=\"xy\", scale_units=\"xy\",scale=1, color=\"r\")\n", "ax.quiver(0,1,angles=\"xy\", scale_units=\"xy\", scale=1, color=\"r\")\n", "ax.set_xlim([-2,10])\n", "ax.set_ylim([-2,10])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ingrese coordenada inicial\n" ] }, { "data": { "image/png": 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2nulqdElngXG2kbbTCfziFyZs2nQ2tFoat9/ux49+FIDZXI5AoFg8JmNjY2GG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\ngFhM6VUZ/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"text/plain": [ "<matplotlib.figure.Figure at 0x1e6c7f2e278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from itertools import product, combinations\n", "from numpy import sin, cos\n", "from matplotlib.patches import Rectangle, Circle, PathPatch\n", "import mpl_toolkits.mplot3d.art3d as art3d\n", "\n", "\n", "fig = plt.figure()\n", "ax = fig.gca(projection='3d')\n", "ax.set_aspect(\"auto\")\n", "ax.set_autoscale_on(True)\n", "\n", "\n", "#dibujar cubo\n", "r = [-1, 1]\n", "for s, e in combinations(np.array(list(product(r,r,r))), 2):\n", " if np.sum(np.abs(s-e)) == r[1]-r[0]:\n", " ax.plot3D(zip(s,e), color=\"b\")\n", "\n", "#dibujar punto\n", "ax.scatter([0],[0],[0],color=\"g\",s=100)\n", "\n", "#dibujar vector\n", "from matplotlib.patches import FancyArrowPatch\n", "from mpl_toolkits.mplot3d import proj3d\n", "\n", "class Arrow3D(FancyArrowPatch):\n", " def __init__(self, xs, ys, zs, args, kwargs):\n", " FancyArrowPatch.__init__(self, (0,0), (0,0), args, *kwargs)\n", " self._verts3d = xs, ys, zs\n", "\n", " def draw(self, renderer):\n", " xs3d, ys3d, zs3d = self._verts3d\n", " xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)\n", " self.set_positions((xs[0],ys[0]),(xs[1],ys[1]))\n", " FancyArrowPatch.draw(self, renderer)\n", "print (\"ingrese coordenada inicial\")\n", "#m=float(raw_input())\n", "a = Arrow3D([0,0],[0,1],[0,0], mutation_scale=20, lw=1, arrowstyle=\"-\|>\", color=\"k\")\n", "b = Arrow3D([0,-1],[0,0],[0,0], mutation_scale=20, lw=1, arrowstyle=\"-\|>\", color=\"r\")\n", "c = Arrow3D([0,0],[0,0],[0,1], mutation_scale=20, lw=1, arrowstyle=\"-\|>\", color=\"b\")\n", "d = Arrow3D([0,0],[0,0],[0,-1], mutation_scale=20, lw=1, arrowstyle=\"-\|>\", color=\"g\")\n", "e = Arrow3D([0,1],[0,0],[0,0], mutation_scale=20, lw=1, arrowstyle=\"-\|>\", color=\"c\")\n", "f = Arrow3D([0,0],[0,-0.5],[0,0], mutation_scale=20, lw=1, arrowstyle=\"-\|>\", color=\"m\")\n", "\n", "ax.add_artist(a)\n", "ax.add_artist(b)\n", "ax.add_artist(c)\n", "ax.add_artist(d)\n", "ax.add_artist(e)\n", "ax.add_artist(f)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "image/png": 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ILPuCWPblLgrHZhzYZe1FOpPBwuJK3u+rlQq0NhkxNWqBXCaFTqNmCdtk0LEE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"text/plain": [ "<matplotlib.figure.Figure at 0x1e6c7cd8550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "\n", "point = np.array([1, 2, 3])\n", "normal = np.array([1, 1, 2])\n", "\n", "# a plane is ax+by+cz+d=0\n", "# [a,b,c] is the normal. Thus, we have to calculate\n", "# d and we're set\n", "d = -point.dot(normal)\n", "\n", "# create x,y\n", "xx, yy = np.meshgrid(range(10), range(10))\n", "\n", "# calculate corresponding z\n", "z = (-normal[0] * xx - normal[1] * yy - d) * 1. /normal[2]\n", "\n", "# plot the surface\n", "plt3d = plt.figure().gca(projection='3d')\n", "plt3d.plot_surface(xx, yy, z)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
Shinichi-Nakagawa/pitchpx-example-ichiro-2016	pitches.ipynb	1	30470	{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "import time\n", "import random\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from bokeh.io import push_notebook, show, output_notebook\n", "from bokeh.plotting import figure\n", "\n", "from IPython.display import display\n", "import ipywidgets as widgets" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# サンプルデータ(ダミー)\n", "pitches_colors = {\n", " 'Four-seam Fastball': 'Black',\n", " 'Two-seam Fastball': 'DimGray',\n", " 'Cutter': 'CadetBlue',\n", " 'Splitter': 'Coral',\n", " 'Forkball': 'Crimson',\n", " 'Curveball': 'Gold',\n", " 'Slider': 'Indigo',\n", " 'Slurve': 'LawnGreen',\n", " 'Screwball': 'Orange',\n", " 'Changeup': 'Orchid',\n", " 'Palmball': 'SaddleBrown',\n", " 'Circle Changeup': 'PaleVioletRed'\n", "}\n", "\n", "df = pd.DataFrame(np.random.randint(0, 100, (100, 2)))\n", "df.columns = ['x', 'y']\n", "df['speed'] = np.random.randint(80, 160, 100)\n", "df['pitches'] = [random.choice(list(pitches_colors.keys())) for x in df.index]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-root\">\n", " <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span id=\"bf36f8d9-f12d-4c16-9939-22a6783fe47f\">Loading BokehJS ...</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = \"1\";\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() + 5000;\n", " window._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. 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i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }if (force === \"1\") {\n", " display_loaded();\n", " }} else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!window._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " window._bokeh_failed_load = true;\n", " } else if (!force) {\n", " var cell = $(\"#bf36f8d9-f12d-4c16-9939-22a6783fe47f\").parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(this));" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# %matplotlib inline みたいな処理\n", "output_notebook()\n", "\n", "# グラフの初期化\n", "opts = dict(plot_width=250, plot_height=250, min_border=0)\n", "xy_range = df['x'].min(), df['x'].max()\n", "p = figure(**opts, x_range=(xy_range), y_range=(xy_range))\n", "p.line([20, 20, 80, 80, 20],\n", " [20, 80, 80, 20, 20],\n", " line_width=4, color=\"firebrick\")\n", "\n", "# 直近\n", "size0 = df.loc[0, 'speed'] / 5\n", "r = p.circle([df.loc[0, 'x']], [df.loc[0, 'y']],\n", " size=size0, line_color=None)\n", "# 一投前\n", "r1 = p.circle_x([df.loc[0, 'x']], [df.loc[0, 'y']],\n", " size=size0, line_color='Black')\n", "# 二投前\n", "r2 = p.circle_cross([df.loc[0, 'x']], [df.loc[0, 'y']],\n", " size=size0, line_color='Black')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "def draw(n):\n", " def plot_pitches(i, renderer):\n", " x, y, speed, pitches = df.loc[i]\n", " renderer.data_source.data['x'] = [x]\n", " renderer.data_source.data['y'] = [y]\n", " renderer.glyph.size = speed / 5\n", " renderer.glyph.fill_color = pitches_colors[pitches]\n", "\n", " x, y, speed, pitches = df.loc[n]\n", " plot_pitches(n, r)\n", " p.title.text = '{}: {}km/h'.format(pitches, speed)\n", "\n", " if n > 1:\n", " plot_pitches(n - 2, r2)\n", "\n", " if n:\n", " plot_pitches(n - 1, r1)\n", "\n", " # handle=t がポイント、tで描画したセルをハンドリング\n", " push_notebook(handle=t)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# ipywidgetsのUI\n", "slider = widgets.SelectionSlider(\n", " options=list(df.index.map(str)),\n", " value='0',\n", " description='num',\n", " disabled=False,\n", " continuous_update=False,\n", " orientation='horizontal',\n", " readout=True, )\n", "\n", "\n", "def on_value_change(change):\n", " n = int(change['new'])\n", " draw(n)\n", "\n", "\n", "slider.observe(on_value_change, names='value')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " <div class=\"bk-root\">\n", " <div class=\"plotdiv\" id=\"0e9f4f4f-1b0f-4d14-b0c1-f839cdb6ceb5\"></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. 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Application\",\"version\":\"0.12.3\"}};\n", " var render_items = [{\"docid\":\"0bcb84fc-b076-4540-a5c5-c24fea7e693f\",\"elementid\":\"0e9f4f4f-1b0f-4d14-b0c1-f839cdb6ceb5\",\"modelid\":\"723998cc-e01d-40ea-a579-a54a7fb09fcd\",\"notebook_comms_target\":\"9d6aa30f-a7d9-43e5-bf88-0b653c1f0e17\"}];\n", " \n", " Bokeh.embed.embed_items(docs_json, render_items);\n", " });\n", " },\n", " function(Bokeh) {\n", " }\n", " ];\n", " \n", " function run_inline_js() {\n", " \n", " if ((window.Bokeh !== undefined) \|\| (force === \"1\")) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }if (force === \"1\") {\n", " display_loaded();\n", " }} else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!window._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " window._bokeh_failed_load = true;\n", " } else if (!force) {\n", " var cell = $(\"#0e9f4f4f-1b0f-4d14-b0c1-f839cdb6ceb5\").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": [ "# notebook_handle=True をつけることで、あとで書き換えられる\n", "t = show(p, notebook_handle=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "display(slider)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# 1秒毎にアニメーション\n", "for x in df.index[:30]:\n", " draw(x)\n", " time.sleep(1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": 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": "3.5.2" }, "widgets": { "state": { "765eb0012f344a958e4a027565629a80": { "views": [ { "cell_index": 7 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
Wedg/Alice.jl	demo/stl10/Demo_STL10_A_Sparse_Autoencoder.ipynb	1	279939	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## STL-10 Demo\n", "This is the first part of demo on the reduced STL-10 dataset taken from Stanford's [UFLDL tutorial](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial). The original [STL-10 dataset](https://cs.stanford.edu/~acoates/stl10/) has 10 classes (airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck). This reduced dataset contains the images from 4 of the classes (airplane, car, cat, dog).\n", "\n", "In this first part of the demo we are going to train a sparse autoencoder on randomly selected small patches of the original images. There are `2 000` training images of size `64x64x3` (width `x` height `x` 3 RGB channels) and from that set `100 000` \"patches\" of size `8x8x3` have been randomly selected. The aim is to use the learned weights as pre-trained weights for a convolution layer. See the Stanford tutorial for a thorough description of a sparse autoencoder.\n", "\n", "In the [second part of the demo]() these weights will be used to initialise a convolutional neural network" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Load Alice\n", "And best to start Julia with multiple threads for processing speed." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Alice" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Base.Threads.nthreads()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load and display the image patches\n", "Load the \"patches\" using the `load_patches` function." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(192,100000)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "patches = load_patches()\n", "num_feats, num_patches = size(patches)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "402×807 Array{RGB{Any},2}:\n", " RGB{Float64}(0.764706,0.654902,0.647059) … RGB{Float64}(0.741176,0.513725,0.329412)\n", " RGB{Float64}(0.764706,0.654902,0.647059) RGB{Float64}(0.741176,0.513725,0.329412)\n", " RGB{Float64}(0.764706,0.654902,0.647059) RGB{Float64}(0.741176,0.513725,0.329412)\n", " RGB{Float64}(0.756863,0.647059,0.619608) RGB{Float64}(0.796078,0.54902,0.329412) \n", " RGB{Float64}(0.756863,0.647059,0.619608) RGB{Float64}(0.796078,0.54902,0.329412) \n", " RGB{Float64}(0.756863,0.647059,0.619608) … RGB{Float64}(0.796078,0.54902,0.329412) \n", " RGB{Float64}(0.780392,0.67451,0.643137) RGB{Float64}(0.882353,0.678431,0.466667)\n", " RGB{Float64}(0.780392,0.67451,0.643137) RGB{Float64}(0.882353,0.678431,0.466667)\n", " RGB{Float64}(0.780392,0.67451,0.643137) RGB{Float64}(0.882353,0.678431,0.466667)\n", " RGB{Float64}(0.756863,0.65098,0.623529) RGB{Float64}(0.862745,0.709804,0.521569)\n", " RGB{Float64}(0.756863,0.65098,0.623529) … RGB{Float64}(0.862745,0.709804,0.521569)\n", " RGB{Float64}(0.756863,0.65098,0.623529) RGB{Float64}(0.862745,0.709804,0.521569)\n", " RGB{Float64}(0.811765,0.690196,0.67451) RGB{Float64}(0.768627,0.635294,0.454902)\n", " ⋮ ⋱ \n", " RGB{Float64}(0.619608,0.34902,0.356863) … RGB{Float64}(0.866667,0.721569,0.647059)\n", " RGB{Float64}(0.619608,0.34902,0.356863) RGB{Float64}(0.866667,0.721569,0.647059)\n", " RGB{Float64}(0.619608,0.34902,0.356863) RGB{Float64}(0.866667,0.721569,0.647059)\n", " RGB{Float64}(0.556863,0.286275,0.270588) RGB{Float64}(0.913725,0.796078,0.709804)\n", " RGB{Float64}(0.556863,0.286275,0.270588) RGB{Float64}(0.913725,0.796078,0.709804)\n", " RGB{Float64}(0.556863,0.286275,0.270588) … RGB{Float64}(0.913725,0.796078,0.709804)\n", " RGB{Float64}(0.603922,0.364706,0.352941) RGB{Float64}(0.886275,0.745098,0.654902)\n", " RGB{Float64}(0.603922,0.364706,0.352941) RGB{Float64}(0.886275,0.745098,0.654902)\n", " RGB{Float64}(0.603922,0.364706,0.352941) RGB{Float64}(0.886275,0.745098,0.654902)\n", " RGB{Float64}(0.690196,0.501961,0.529412) RGB{Float64}(0.988235,0.898039,0.780392)\n", " RGB{Float64}(0.690196,0.501961,0.529412) … RGB{Float64}(0.988235,0.898039,0.780392)\n", " RGB{Float64}(0.690196,0.501961,0.529412) RGB{Float64}(0.988235,0.898039,0.780392)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display_rgb_cols(patches[:, 1:450], scale = 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pre-process the images with ZCA Whitening\n", "Whitening is a pre-processing step with similar steps to PCA (Principle Components Analysis). But instead of using it do reduce the number of dimensions it is used to make the inputs less redundant and more suitable for learning. From the Stanford tutorial:\n", ">If we are training on images, the raw input is redundant, since adjacent pixel values are highly correlated. The goal of whitening is to make the input less redundant; more formally, our desiderata are that our learning algorithms sees a training input where (i) the features are less correlated with each other, and (ii) the features all have the same variance.\n", "\n", "See [this page](http://deeplearning.stanford.edu/wiki/index.php/Whitening) for a thorough description which explains the steps below." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Subtract mean patch\n", "mean_patch = mean(patches, 2)\n", "patches .-= mean_patch\n", "\n", "# Apply ZCA whitening\n", "ϵ = 0.1\n", "sigma = patches * patches' ./ num_patches\n", "U, S, V = svd(sigma)\n", "ZCAWhite = U * diagm(1 ./ sqrt(S .+ ϵ)) * U'\n", "patches = ZCAWhite * patches;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build and train sparse autoencoder" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Neural Network\n", "Training Data Dimensions - (192,100000)\n", "Layers:\n", "Layer 1 - InputLayer{Float64}, Dimensions - (192,100000)\n", "Layer 2 - SparseEncoderLayer{Float64}, Activation - logistic, Dimensions - (400,100000)\n", "Layer 3 - MultiLinearOutputLayer{Float64}, Dimensions - (192,100000)\n" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Set seed to be able to replicate\n", "srand(123)\n", "\n", "# Data Box and Input Layer - the patches are both the input and the target\n", "# Note that for a psarse autoencoder we must use full batch training (i.e. batch_size = num_patches)\n", "databox = Data(patches, patches)\n", "batch_size = num_patches\n", "input = InputLayer(databox, batch_size)\n", "\n", "# Sparse Encoder\n", "num_hidden = 400 # number of hidden layer neurons\n", "ρ = 0.035 # desired average activation of the hidden units\n", "β = 5.0 # weight of sparsity penalty term\n", "encoder = SparseEncoderLayer(size(input), num_hidden, ρ, β, activation = :logistic)\n", "\n", "# Linear Output Layer\n", "dim_in = 30\n", "num_classes = 10\n", "output = MultiLinearOutputLayer(databox, size(encoder))\n", "\n", "# Model\n", "λ = 3e-3 # weight decay parameter\n", "sparse_auto_encoder = NeuralNet(databox, [input, encoder, output], λ, regularisation = :L2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Good idea to check the gradients" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Check gradients of a tiny network with similar architecture:\n", "\n", "\u001b[1m\u001b[34mLayer 1, InputLayer{Float64}, Dimensions - (9,40) :\n", "\n", "\u001b[0m\u001b[1m\u001b[34mLayer 2, SparseEncoderLayer{Float64}, Activation - logistic, Dimensions - (9,40) :\n", "\n", "\u001b[0m Bias parameters relative difference :\n", " 2.3452069658844116e-11\n", "\n", " Weight parameters relative difference :\n", " 6.710240169789753e-10\n", "\n", "\u001b[1m\u001b[34mLayer 3, MultiLinearOutputLayer{Float64}, Dimensions - (9,40) :\n", "\n", "\u001b[0m Bias parameters relative difference :\n", " 1.040198493302405e-10\n", "\n", " Weight parameters relative difference :\n", " 2.6426712204813006e-10\n", "\n" ] } ], "source": [ "check_gradients(sparse_auto_encoder)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train the model\n", "Because we are using full batch training we can use the `train_nlopt` function that calls the [NLopt package](https://github.com/JuliaOpt/NLopt.jl) that is a wrapper to the [NLopt library](http://ab-initio.mit.edu/wiki/index.php/NLopt) of nonlinear optimisation routines. The default algorithm is `:LD_LBFGS` (low storage BFGS) but you can choose any of the supported algorithms via the named `algorithm` keyword into the function. E.g. we could call:\n", "`train_nlopt(sparse_autoencoder, maxiter = 400, algorithm = :LD_TNEWTON)` to run the optimisation using the truncated newton algorithm. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training Loss:\n", " -> 1097.976 -> 163.804 -> 44.312 -> 64.574 -> 28.256 -> 15.171 -> 13.191 -> 12.803 -> 12.519 -> 11.899 -> 10.881 -> 9.814 -> 8.290 -> 7.824 -> 6.768 -> 6.356 -> 6.280 -> 6.115 -> 5.923 -> 5.733 -> 5.588 -> 5.394 -> 5.334 -> 5.218 -> 5.088 -> 4.986 -> 4.811 -> 4.880 -> 4.750 -> 4.650 -> 4.610 -> 4.566 -> 4.512 -> 4.450 -> 4.406 -> 4.384 -> 4.371 -> 4.345 -> 4.332 -> 4.316 -> 4.296 -> 4.287 -> 4.280 -> 4.263 -> 4.256 -> 4.250 -> 4.241 -> 4.230 -> 4.222 -> 4.214 -> 4.210 -> 4.206 -> 4.203 -> 4.200 -> 4.195 -> 4.189 -> 4.184 -> 4.180 -> 4.178 -> 4.173 -> 4.164 -> 4.167 -> 4.159 -> 4.150 -> 4.147 -> 4.144 -> 4.139 -> 4.134 -> 4.128 -> 4.121 -> 4.119 -> 4.115 -> 4.113 -> 4.109 -> 4.103 -> 4.103 -> 4.099 -> 4.093 -> 4.090 -> 4.087 -> 4.084 -> 4.081 -> 4.075 -> 4.071 -> 4.068 -> 4.064 -> 4.061 -> 4.058 -> 4.055 -> 4.051 -> 4.049 -> 4.044 -> 4.042 -> 4.039 -> 4.036 -> 4.035 -> 4.030 -> 4.029 -> 4.026 -> 4.022 -> 4.025 -> 4.020 -> 4.015 -> 4.012 -> 4.010 -> 4.007 -> 4.004 -> 4.001 -> 3.997 -> 3.994 -> 3.991 -> 3.988 -> 3.987 -> 3.984 -> 3.980 -> 3.977 -> 3.974 -> 3.973 -> 3.972 -> 3.969 -> 3.968 -> 3.965 -> 3.962 -> 3.961 -> 3.958 -> 3.956 -> 3.954 -> 3.953 -> 3.951 -> 3.949 -> 3.947 -> 3.946 -> 3.944 -> 3.941 -> 3.938 -> 3.936 -> 3.935 -> 3.934 -> 3.932 -> 3.930 -> 3.929 -> 3.927 -> 3.927 -> 3.926 -> 3.925 -> 3.924 -> 3.922 -> 3.925 -> 3.921 -> 3.919 -> 3.917 -> 3.916 -> 3.915 -> 3.914 -> 3.911 -> 3.911 -> 3.910 -> 3.909 -> 3.908 -> 3.908 -> 3.906 -> 3.905 -> 3.904 -> 3.903 -> 3.906 -> 3.903 -> 3.902 -> 3.901 -> 3.899 -> 3.901 -> 3.899 -> 3.898 -> 3.897 -> 3.897 -> 3.896 -> 3.895 -> 3.893 -> 3.892 -> 3.892 -> 3.891 -> 3.890 -> 3.889 -> 3.888 -> 3.889 -> 3.887 -> 3.887 -> 3.886 -> 3.886 -> 3.885 -> 3.884 -> 3.884 -> 3.883 -> 3.882 -> 3.882 -> 3.881 -> 3.880 -> 3.880 -> 3.880 -> 3.879 -> 3.878 -> 3.878 -> 3.878 -> 3.877 -> 3.877 -> 3.876 -> 3.876 -> 3.875 -> 3.875 -> 3.875 -> 3.874 -> 3.874 -> 3.873 -> 3.874 -> 3.873 -> 3.873 -> 3.872 -> 3.872 -> 3.872 -> 3.871 -> 3.871 -> 3.871 -> 3.870 -> 3.870 -> 3.869 -> 3.869 -> 3.869 -> 3.868 -> 3.868 -> 3.868 -> 3.868 -> 3.867 -> 3.867 -> 3.866 -> 3.866 -> 3.866 -> 3.866 -> 3.865 -> 3.865 -> 3.865 -> 3.864 -> 3.864 -> 3.864 -> 3.864 -> 3.863 -> 3.863 -> 3.863 -> 3.863 -> 3.863 -> 3.863 -> 3.862 -> 3.862 -> 3.862 -> 3.861 -> 3.861 -> 3.861 -> 3.861 -> 3.860 -> 3.860 -> 3.860 -> 3.860 -> 3.860 -> 3.859 -> 3.859 -> 3.859 -> 3.859 -> 3.858 -> 3.858 -> 3.858 -> 3.858 -> 3.858 -> 3.858 -> 3.858 -> 3.857 -> 3.857 -> 3.857 -> 3.857 -> 3.857 -> 3.857 -> 3.857 -> 3.856 -> 3.857 -> 3.856 -> 3.856 -> 3.856 -> 3.856 -> 3.856 -> 3.856 -> 3.856 -> 3.856 -> 3.855 -> 3.855 -> 3.855 -> 3.855 -> 3.855 -> 3.855 -> 3.855 -> 3.855 -> 3.854 -> 3.854 -> 3.854 -> 3.854 -> 3.854 -> 3.854 -> 3.854 -> 3.854 -> 3.854 -> 3.853 -> 3.853 -> 3.853 -> 3.853 -> 3.853 -> 3.853 -> 3.853 -> 3.853 -> 3.853 -> 3.853 -> 3.853 -> 3.853 -> 3.852 -> 3.852 -> 3.852 -> 3.852 -> 3.852 -> 3.852 -> 3.852 -> 3.852 -> 3.852 -> 3.852 -> 3.852 -> 3.852 -> 3.852 -> 3.852 -> 3.852 -> 3.852 -> 3.851 -> 3.851 -> 3.851 -> 3.851 -> 3.851 -> 3.851 -> 3.851 -> 3.851 -> 3.851 -> 3.851 -> 3.851 -> 3.851 -> 3.851 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.850 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.849 -> 3.848 -> 3.848 -> 3.848 -> 3.848 -> 3.848 -> 3.848 -> 3.848 -> 3.848 -> 3.848 -> 3.848 -> 3.848 -> 3.848 -> 3.848" ] } ], "source": [ "train_nlopt(sparse_auto_encoder, maxiter=400);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Display the learned network weights\n", "These are the images that maximally \"fire\" each neuron - given the learned weights. I.e. these are the images that the weights and neurons are - in a sense - looking for. There are many that look like edge detections." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "402×753 Array{RGB{Any},2}:\n", " RGB{Float64}(0.484032,0.482503,0.462706) … RGB{Float64}(0.198237,0.487422,0.717314)\n", " RGB{Float64}(0.484032,0.482503,0.462706) RGB{Float64}(0.198237,0.487422,0.717314)\n", " RGB{Float64}(0.484032,0.482503,0.462706) RGB{Float64}(0.198237,0.487422,0.717314)\n", " RGB{Float64}(0.517125,0.514539,0.503668) RGB{Float64}(0.338156,0.588652,0.778936)\n", " RGB{Float64}(0.517125,0.514539,0.503668) RGB{Float64}(0.338156,0.588652,0.778936)\n", " RGB{Float64}(0.517125,0.514539,0.503668) … RGB{Float64}(0.338156,0.588652,0.778936)\n", " RGB{Float64}(0.52461,0.52352,0.515428) RGB{Float64}(0.51387,0.695821,0.83025) \n", " RGB{Float64}(0.52461,0.52352,0.515428) RGB{Float64}(0.51387,0.695821,0.83025) \n", " RGB{Float64}(0.52461,0.52352,0.515428) RGB{Float64}(0.51387,0.695821,0.83025) \n", " RGB{Float64}(0.518818,0.520733,0.5167) RGB{Float64}(0.616516,0.738977,0.840527)\n", " RGB{Float64}(0.518818,0.520733,0.5167) … RGB{Float64}(0.616516,0.738977,0.840527)\n", " RGB{Float64}(0.518818,0.520733,0.5167) RGB{Float64}(0.616516,0.738977,0.840527)\n", " RGB{Float64}(0.491006,0.494337,0.490406) RGB{Float64}(0.645501,0.734185,0.829274)\n", " ⋮ ⋱ \n", " RGB{Float64}(0.395407,0.386231,0.392337) … RGB{N0f8}(1.0,1.0,1.0) \n", " RGB{Float64}(0.395407,0.386231,0.392337) RGB{N0f8}(1.0,1.0,1.0) \n", " RGB{Float64}(0.395407,0.386231,0.392337) RGB{N0f8}(1.0,1.0,1.0) \n", " RGB{Float64}(0.330913,0.331023,0.329337) RGB{N0f8}(1.0,1.0,1.0) \n", " RGB{Float64}(0.330913,0.331023,0.329337) RGB{N0f8}(1.0,1.0,1.0) \n", " RGB{Float64}(0.330913,0.331023,0.329337) … RGB{N0f8}(1.0,1.0,1.0) \n", " RGB{Float64}(0.494235,0.498101,0.493272) RGB{N0f8}(1.0,1.0,1.0) \n", " RGB{Float64}(0.494235,0.498101,0.493272) RGB{N0f8}(1.0,1.0,1.0) \n", " RGB{Float64}(0.494235,0.498101,0.493272) RGB{N0f8}(1.0,1.0,1.0) \n", " RGB{Float64}(0.498185,0.503847,0.505031) RGB{N0f8}(1.0,1.0,1.0) \n", " RGB{Float64}(0.498185,0.503847,0.505031) … RGB{N0f8}(1.0,1.0,1.0) \n", " RGB{Float64}(0.498185,0.503847,0.505031) RGB{N0f8}(1.0,1.0,1.0) " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display_rgb_weights(encoder.W', scale=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Modify the learned weights and bias for the whitening pre-processing\n", "We pre-processed the 8x8 image patches by doing the following:\n", "- subtracted the mean of each patch (to zero the mean of each patch)\n", "- whitened by multiplying by the matrix `ZCAWhite`\n", "\n", "When we apply these learned weights in our final neural network we will need to pre-process the images in the same way i.e. we need the following for each of the patch inputs into the activation function ($T$ is the whitening matrix and $\\bar{x}$ is the mean patch):\n", "\n", "$$Z = W(T(x - \\bar{x}) + b)$$\n", "\n", "But, expanding, we see we can also modify the weights and bias\n", "\n", "$$Z = WTx - WT\\,\\bar{x} + b$$\n", "\n", "I.e. we can set\n", "$$\\tilde{W} = WT \\hspace{0.8cm} \\text{and} \\hspace{0.8cm} \\tilde{b} = b - WT\\,\\bar{x}$$\n", "\n", "and use these adjusted weights and bias on the images without any further pre-processing." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "W = encoder.W * ZCAWhite\n", "b = encoder.b .- squeeze(W * mean_patch, 2);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save the learned weights and bias\n", "You can save the weights and bias variables for re-use in your own folders as shown below. For the next part of the tutorial / demo a version of these are already saved in the demo folder." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "using JLD\n", "save(\"C:/...mypath.../stl_features.jld\", \"W\", W, \"b\", b);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Julia 0.5.0", "language": "julia", "name": "julia-0.5" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.5.0" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
ucsd-ccbb/mali-dual-crispr-pipeline	dual_crispr/distributed_files/notebooks/Dual CRISPR 5-Count Plots.ipynb	1	7906	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dual CRISPR Screen Analysis\n", "# Step 5: Count Plots\n", "Amanda Birmingham, CCBB, UCSD ([email protected])\n", "\n", "## Instructions\n", "\n", "To run this notebook reproducibly, follow these steps:\n", "1. Click Kernel > Restart & Clear Output\n", "2. When prompted, click the red Restart & clear all outputs button\n", "3. Fill in the values for your analysis for each of the variables in the [Input Parameters](#Input-Parameters) section\n", "4. Click Cell > Run All\n", "\n", "## Input Parameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "g_dataset_name = \"Notebook5Test\"\n", "g_fastq_counts_run_prefix = \"TestSet5\"\n", "g_fastq_counts_dir = '~/dual_crispr/test_data/test_set_5'\n", "g_collapsed_counts_run_prefix = \"\"\n", "g_collapsed_counts_dir = \"\"\n", "g_combined_counts_run_prefix = \"\"\n", "g_combined_counts_dir = \"\"\n", "g_plots_run_prefix = \"\"\n", "g_plots_dir = '~/dual_crispr/test_outputs/test_set_5'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Automated Set-Up" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import inspect\n", "\n", "import ccbb_pyutils.analysis_run_prefixes as ns_runs\n", "import ccbb_pyutils.files_and_paths as ns_files\n", "import ccbb_pyutils.notebook_logging as ns_logs\n", "\n", "\n", "def describe_var_list(input_var_name_list):\n", " description_list = [\"{0}: {1}\\n\".format(name, eval(name)) for name in input_var_name_list]\n", " return \"\".join(description_list)\n", "\n", "\n", "ns_logs.set_stdout_info_logger()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "g_fastq_counts_dir = ns_files.expand_path(g_fastq_counts_dir)\n", "g_collapsed_counts_run_prefix = ns_runs.check_or_set(g_collapsed_counts_run_prefix, g_fastq_counts_run_prefix)\n", "g_collapsed_counts_dir = ns_files.expand_path(ns_runs.check_or_set(g_collapsed_counts_dir, g_fastq_counts_dir))\n", "g_combined_counts_run_prefix = ns_runs.check_or_set(g_combined_counts_run_prefix, g_collapsed_counts_run_prefix)\n", "g_combined_counts_dir = ns_files.expand_path(ns_runs.check_or_set(g_combined_counts_dir, g_collapsed_counts_dir))\n", "g_plots_run_prefix = ns_runs.check_or_set(g_plots_run_prefix, ns_runs.generate_run_prefix(g_dataset_name))\n", "g_plots_dir = ns_files.expand_path(ns_runs.check_or_set(g_plots_dir, g_combined_counts_dir))\n", "\n", "print(describe_var_list(['g_fastq_counts_dir', 'g_collapsed_counts_run_prefix','g_collapsed_counts_dir', \n", " 'g_combined_counts_run_prefix', 'g_combined_counts_dir', \n", " 'g_plots_run_prefix', 'g_plots_dir']))\n", "\n", "ns_files.verify_or_make_dir(g_collapsed_counts_dir)\n", "ns_files.verify_or_make_dir(g_combined_counts_dir)\n", "ns_files.verify_or_make_dir(g_plots_dir)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Count File Suffixes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import dual_crispr.construct_counter as ns_counter\n", "print(inspect.getsource(ns_counter.get_counts_file_suffix))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import dual_crispr.count_combination as ns_combine\n", "print(inspect.getsource(ns_combine.get_collapsed_counts_file_suffix))\n", "print(inspect.getsource(ns_combine.get_combined_counts_file_suffix))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Count Plots Functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import dual_crispr.count_plots as ns_plot\n", "print(inspect.getsource(ns_plot))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Individual fastq Plots" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(ns_files.check_file_presence(g_fastq_counts_dir, g_fastq_counts_run_prefix, \n", " ns_counter.get_counts_file_suffix(),\n", " check_failure_msg=\"Count plots could not detect any individual fastq count files.\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "ns_plot.plot_raw_counts(g_fastq_counts_dir, g_fastq_counts_run_prefix, ns_counter.get_counts_file_suffix(), \n", " g_plots_dir, g_plots_run_prefix, ns_plot.get_boxplot_suffix())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Individual Sample Plots" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(ns_files.check_file_presence(g_collapsed_counts_dir, g_collapsed_counts_run_prefix, \n", " ns_combine.get_collapsed_counts_file_suffix(),\n", " check_failure_msg=\"Count plots could not detect any individual sample count files.\")\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "ns_plot.plot_raw_counts(g_collapsed_counts_dir, g_collapsed_counts_run_prefix, \n", " ns_combine.get_collapsed_counts_file_suffix(), g_plots_dir, g_plots_run_prefix, ns_plot.get_boxplot_suffix())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Combined Samples Plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(ns_files.check_file_presence(g_combined_counts_dir, g_combined_counts_run_prefix, \n", " ns_combine.get_combined_counts_file_suffix(),\n", " check_failure_msg=\"Count plots could not detect a combined count file.\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ns_plot.plot_combined_raw_counts(g_combined_counts_dir, g_combined_counts_run_prefix, \n", " ns_combine.get_combined_counts_file_suffix(), g_plots_dir, g_plots_run_prefix, ns_plot.get_boxplot_suffix())" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
TheOregonian/long-term-care-db	notebooks/analysis/facilities-analysis.ipynb	1	19182	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This is a dataset of Assisted Living, Nursing and Residential Care facilities in Oregon, open as of September, 2016. For each, we have:\n", "\n", "Data were munged [here](https://github.com/TheOregonian/long-term-care-db/blob/master/notebooks/transformation/mung-3-29-scrape.ipynb).\n", "\n", "1. <i>facility_id:</i> Unique ID used to join to complaints\n", "2. <i>fac_ccmunumber:</i> Unique ID used to join to ownership history\n", "3. <i>facility_type:</i> NF - Nursing Facility; RCF - Residential Care Facility; ALF - Assisted Living Facility\n", "4. <i>fac_capacity:</i> Number of beds facility is licensed to have. Not necessarily the number of beds facility does have.\n", "5. <i>facility_name:</i> Facility name at time of September extract.\n", "6. <i>offline:</i> created in munging notebook, a count of complaints that DO NOT appear when facility is searched on state's [complaint search website](https://apps.state.or.us/cf2/spd/facility_complaints/).\n", "7. <i>online:</i> created in munging notebook, a count of complaints that DO appear when facility is searched on state's [complaint search website](https://apps.state.or.us/cf2/spd/facility_complaints/)." ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style>.container { width:100% !important; }</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "from IPython.core.display import display, HTML\n", "display(HTML(\"<style>.container { width:100% !important; }</style>\"))" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv('../../data/processed/facilities-3-29-scrape.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>How many facilities are there?</h3>" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "642" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.count()[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>How many facilities have accurate records online?</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Those that have no offline records." ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "59" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df['offline'].isnull())].count()[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>How many facilities have inaccurate records online?<h/3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Those that have offline records." ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "583" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df['offline'].notnull())].count()[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>How many facilities had more than double the number of complaints shown online?</h3>" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "358" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df['offline']>df['online']) & (df['online'].notnull())].count()[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>How many facilities show zero complaints online but have complaints offline?</h3>" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "59" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df['online'].isnull()) & (df['offline'].notnull())].count()[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>How many facilities have complaints and are accurate online?</h3>" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "16" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df['online'].notnull()) & (df['offline'].isnull())].count()[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>How many facilities have complaints?</h3>" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "599" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df['online'].notnull()) \| df['offline'].notnull()].count()[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>What percent of facilities have accurate records online?</h3>" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "9.1900311526479754" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df['offline'].isnull())].count()[0]/df.count()[0]100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>What is the total capacity of all facilities with inaccurate records?</h3>" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "35129.0" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['offline'].notnull()].sum()['fac_capacity']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>How many facilities appear to have no complaints, whether or not they do?</h3>" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "102" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['online'].isnull()].count()[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>What are the ten facilities with >50 complaints that have the highest disparities?</h3>\n", "<i>For graphics</i>" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [], "source": [ "over_50 = df[((df['offline']+df['online'])>50)]" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/fzarkhin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " if __name__ == '__main__':\n" ] } ], "source": [ "over_50['total'] = over_50['online']+over_50['offline']" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/fzarkhin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " if __name__ == '__main__':\n" ] } ], "source": [ "over_50['pct_offline'] = over_50['offline']/over_50['total']100" ] }, { "cell_type": "code", "execution_count": 117, "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>facility_id</th>\n", " <th>fac_ccmunumber</th>\n", " <th>facility_type</th>\n", " <th>fac_capacity</th>\n", " <th>facility_name</th>\n", " <th>offline</th>\n", " <th>online</th>\n", " <th>total</th>\n", " <th>pct_offline</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>4</th>\n", " <td>385024</td>\n", " <td>385024</td>\n", " <td>NF</td>\n", " <td>91.0</td>\n", " <td>Avamere Health Services of Rogue Valley</td>\n", " <td>67.0</td>\n", " <td>27.0</td>\n", " <td>94.0</td>\n", " <td>71.276596</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " facility_id fac_ccmunumber facility_type fac_capacity \\\n", "4 385024 385024 NF 91.0 \n", "\n", " facility_name offline online total \\\n", "4 Avamere Health Services of Rogue Valley 67.0 27.0 94.0 \n", "\n", " pct_offline \n", "4 71.276596 " ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "over_50[over_50['facility_name']=='Avamere Health Services of Rogue Valley']" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>facility_id</th>\n", " <th>fac_ccmunumber</th>\n", " <th>facility_type</th>\n", " <th>fac_capacity</th>\n", " <th>facility_name</th>\n", " <th>offline</th>\n", " <th>online</th>\n", " <th>total</th>\n", " <th>pct_offline</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>50</th>\n", " <td>385166</td>\n", " <td>385166</td>\n", " <td>NF</td>\n", " <td>165.0</td>\n", " <td>Maryville Nursing Home</td>\n", " <td>53.0</td>\n", " <td>12.0</td>\n", " <td>65.0</td>\n", " <td>81.538462</td>\n", " </tr>\n", " <tr>\n", " <th>78</th>\n", " <td>385219</td>\n", " <td>385219</td>\n", " <td>NF</td>\n", " <td>93.0</td>\n", " <td>Care Center East Health & Specialty Care Center</td>\n", " <td>63.0</td>\n", " <td>16.0</td>\n", " <td>79.0</td>\n", " <td>79.746835</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>385157</td>\n", " <td>385157</td>\n", " <td>NF</td>\n", " <td>114.0</td>\n", " <td>Life Care Center Of Coos Bay</td>\n", " <td>74.0</td>\n", " <td>21.0</td>\n", " <td>95.0</td>\n", " <td>77.894737</td>\n", " </tr>\n", " <tr>\n", " <th>63</th>\n", " <td>385190</td>\n", " <td>385190</td>\n", " <td>NF</td>\n", " <td>78.0</td>\n", " <td>Prestige Post-Acute and Rehabilitation Center-...</td>\n", " <td>50.0</td>\n", " <td>15.0</td>\n", " <td>65.0</td>\n", " <td>76.923077</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>385143</td>\n", " <td>385143</td>\n", " <td>NF</td>\n", " <td>118.0</td>\n", " <td>Umpqua Valley Nursing & Rehabilitation Center</td>\n", " <td>55.0</td>\n", " <td>17.0</td>\n", " <td>72.0</td>\n", " <td>76.388889</td>\n", " </tr>\n", " <tr>\n", " <th>144</th>\n", " <td>50A263</td>\n", " <td>50A263</td>\n", " <td>RCF</td>\n", " <td>59.0</td>\n", " <td>Brookdale Bend</td>\n", " <td>40.0</td>\n", " <td>13.0</td>\n", " <td>53.0</td>\n", " <td>75.471698</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>385120</td>\n", " <td>385120</td>\n", " <td>NF</td>\n", " <td>121.0</td>\n", " <td>Valley West Health Care Center</td>\n", " <td>55.0</td>\n", " <td>20.0</td>\n", " <td>75.0</td>\n", " <td>73.333333</td>\n", " </tr>\n", " <tr>\n", " <th>113</th>\n", " <td>385270</td>\n", " <td>385270</td>\n", " <td>NF</td>\n", " <td>96.0</td>\n", " <td>Prestige Post-Acute and Rehabilitation Center ...</td>\n", " <td>50.0</td>\n", " <td>19.0</td>\n", " <td>69.0</td>\n", " <td>72.463768</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>385024</td>\n", " <td>385024</td>\n", " <td>NF</td>\n", " <td>91.0</td>\n", " <td>Avamere Health Services of Rogue Valley</td>\n", " <td>67.0</td>\n", " <td>27.0</td>\n", " <td>94.0</td>\n", " <td>71.276596</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>385132</td>\n", " <td>385132</td>\n", " <td>NF</td>\n", " <td>148.0</td>\n", " <td>Avamere Rehabilitation of King City</td>\n", " <td>36.0</td>\n", " <td>15.0</td>\n", " <td>51.0</td>\n", " <td>70.588235</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " facility_id fac_ccmunumber facility_type fac_capacity \\\n", "50 385166 385166 NF 165.0 \n", "78 385219 385219 NF 93.0 \n", "45 385157 385157 NF 114.0 \n", "63 385190 385190 NF 78.0 \n", "34 385143 385143 NF 118.0 \n", "144 50A263 50A263 RCF 59.0 \n", "23 385120 385120 NF 121.0 \n", "113 385270 385270 NF 96.0 \n", "4 385024 385024 NF 91.0 \n", "27 385132 385132 NF 148.0 \n", "\n", " facility_name offline online \\\n", "50 Maryville Nursing Home 53.0 12.0 \n", "78 Care Center East Health & Specialty Care Center 63.0 16.0 \n", "45 Life Care Center Of Coos Bay 74.0 21.0 \n", "63 Prestige Post-Acute and Rehabilitation Center-... 50.0 15.0 \n", "34 Umpqua Valley Nursing & Rehabilitation Center 55.0 17.0 \n", "144 Brookdale Bend 40.0 13.0 \n", "23 Valley West Health Care Center 55.0 20.0 \n", "113 Prestige Post-Acute and Rehabilitation Center ... 50.0 19.0 \n", "4 Avamere Health Services of Rogue Valley 67.0 27.0 \n", "27 Avamere Rehabilitation of King City 36.0 15.0 \n", "\n", " total pct_offline \n", "50 65.0 81.538462 \n", "78 79.0 79.746835 \n", "45 95.0 77.894737 \n", "63 65.0 76.923077 \n", "34 72.0 76.388889 \n", "144 53.0 75.471698 \n", "23 75.0 73.333333 \n", "113 69.0 72.463768 \n", "4 94.0 71.276596 \n", "27 51.0 70.588235 " ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "over_50.sort_values('pct_offline',ascending = False).head(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
wikistat/Apprentissage	Pic-ozone/Apprent-Python-Ozone.ipynb	1	89842	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<center>\n", "<a href=\"http://www.insa-toulouse.fr/\" ><img src=\"http://www.math.univ-toulouse.fr/~besse/Wikistat/Images/logo-insa.jpg\" style=\"float:left; max-width: 120px; display: inline\" alt=\"INSA\"/></a> \n", "\n", "<a href=\"http://wikistat.fr/\" ><img src=\"http://www.math.univ-toulouse.fr/~besse/Wikistat/Images/wikistat.jpg\" style=\"float:right; max-width: 250px; display: inline\" alt=\"Wikistat\"/></a>\n", "\n", "</center>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# [Scénarios d'Apprentissage Statistique](https://github.com/wikistat/Apprentissage)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Adaptation Statistique d'un Modèle de Prévision du Pic d'Ozone en <a href=\"https://www.python.org/\"><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/f/f8/Python_logo_and_wordmark.svg/390px-Python_logo_and_wordmark.svg.png\" style=\"max-width: 120px; display: inline\" alt=\"Python\"/></a> avec <a href=\"http://scikit-learn.org/stable/#\"><img src=\"http://scikit-learn.org/stable/_static/scikit-learn-logo-small.png\" style=\"max-width: 100px; display: inline\" alt=\"Scikit-learn\"/></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Résumé: Exploration puis modélisation de données climatiques en utilisant Python et la librairie [Scikit-learn](http://scikit-learn.org/stable/#). L'objectif est de prévoir pour le lendemain un possible dépassement d'un seuil de concentration en ozone à partir d'une prévision déterministe sur un maillage grossier et de variables climatiques locales. Estimation par différentes méthodes: régression [logistique](http://wikistat.fr/pdf/st-m-app-rlogit.pdf), [k plus proches voisins](http://wikistat.fr/pdf/st-m-app-add.pdf), [arbre de décision](http://wikistat.fr/pdf/st-m-app-cart.pdf), [agrégation de modèle](http://wikistat.fr/pdf/st-m-app-agreg.pdf), [SVM](http://wikistat.fr/pdf/st-m-app-svm.pdf). Comparaison des [erreurs de prévision](http://wikistat.fr/pdf/st-m-app-risque-estim.pdf) sur un échantillon test puis des courbes ROC. Itération sur plusieurs échantillons tests pour analyser la distribution de l'erreur de prévision. Ce calepin vient compléter l'[étude faite avec R](http://www.math.univ-toulouse.fr/~besse/Wikistat/Notebooks/Notebook-R-Ozone.html) pour en comparer les deux approches." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Avertissement \n", "\n", "* Ce calepin complète [celui en R](https://github.com/wikistat/Apprentissage/blob/master/Pic-ozone/Apprent-R-Ozone.ipynb) afin de comparer les performances respectives des deux environnements: complétude des résultats et efficacité du code. Les explications sont plus sommaires dans ce tutoriel qui est en principe exécuté après ou parallèlement à celui réalisé en R. \n", "* Comme pour R il est découpé en 5 séances de travaux dirigés syncronisées avec le cours d'apprentissage automatique. \n", "* Réfléchir aux réponses aux questions marquées Q issues du sujet d'examen.\n", "* Toutes les options n'ont pas été testées et certaines sont posées en exercice." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'objectif, sur ces données, est d'améliorer la prévision déterministe (MOCAGE), calculée par les services de MétéoFrance, de la concentration d'ozone dans certaines stations de prélèvement. Il s'agit d'un problème dit d'adaptation statistique ou post-traitement d'une prévision locale de modèles à trop grande échelle en s'aidant d'autre variables également gérées par MétéoFrance, mais à plus petite échelle (température, force du vent...). \n", "\n", "La question posée reste: quelle est la meilleure stratégie pour prévoir l'occurrence d'un pic de pollution. \n", "\n", "Comme avec R différentes méthodes sont testées : régression logistique, k plus proches voisins, arbre de décision, random forest, SVM. De façon générale on suppose que l'utilisateur dispose d'une installation python à jour. Le calepin a été testé avec la version 3.8.\n", "\n", "Question subsidiaire quand préférer R ou Python ? Python conduit a des résultats (conclusions) identiques à ceux de R, moins complets pour leur interprétation, mais plus rapidement. Il s'agit des principales différences entre R pour \"statisticien\" et python pour \"informaticien\", on perd en interprétabilité mais on gagne en vitesse d'exécution. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# <FONT COLOR=\"Red\">Épisode 1</font>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prise en compte des données" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données ont été extraites et mises en forme par le service concerné de Météo France. Elles sont décrites par les variables suivantes:\n", "\n", "\n", "* JOUR Le type de jour ; férié (1) ou pas (0) ;\n", "* O3obs La concentration d'ozone effectivement observée le lendemain à 17h locales correspondant souvent au maximum de pollution observée ;\n", "* MOCAGE Prévision de cette pollution obtenue par un modèle déterministe de mécanique des fluides (équation de Navier et Stockes);\n", "* TEMPE Température prévue par MétéoFrance pour le lendemain 17h ;\n", "* RMH2O Rapport d'humidité ;\n", "* NO2 Concentration en dioxyde d'azote ;\n", "* NO Concentration en monoxyde d'azote ;\n", "* STATION Lieu de l'observation : Aix-en-Provence, Rambouillet, Munchhausen, Cadarache et Plan de Cuques ;\n", "* VentMOD Force du vent ;\n", "* VentANG Orientation du vent. \n", "\n", "Ce sont des données \"propres\", sans trous, bien codées et de petites tailles. Elles présentent avant tout un caractère pédagogique.\n", "\n", "Il est choisi ici de lire les données avec la librairie `pandas` pour bénéficier de la classe DataFrame. Ce n'est pas nécessaire pour l'objectif de prévision car les variables qualitatives ainsi construites ne peuvent être utilisées pour l'interprétation des modèles obtenus dans `scikit-learn` qui ne reconnaît pas la classe DataFrame." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:10.756181Z", "start_time": "2019-11-18T09:19:10.033317Z" } }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "# Lecture des données\n", "## Charger les données ou les lire directement en précisant le chemin\n", "path=\"\"\n", "ozone=pd.read_csv(path+\"depSeuil.dat\",sep=\",\",header=0)\n", "# Vérification du contenu\n", "ozone.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ce qui suit permet d'affecter le bon type aux variables." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:10.784429Z", "start_time": "2019-11-18T09:19:10.762200Z" } }, "outputs": [], "source": [ "ozone[\"STATION\"]=pd.Categorical(ozone[\"STATION\"],ordered=False)\n", "ozone[\"JOUR\"]=pd.Categorical(ozone[\"JOUR\"],ordered=False)\n", "ozone[\"O3obs\"]=pd.DataFrame(ozone[\"O3obs\"], dtype=float)\n", "ozone.dtypes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:10.823473Z", "start_time": "2019-11-18T09:19:10.787257Z" } }, "outputs": [], "source": [ "ozone.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Même si les données ne présentent pas de défauts particuliers, une étude exploratoire préliminaire est indispensable afin de s'assurer le leur bonne cohérence, proposer d'éventuelles transformations et analyser les structures de corrélations ou plus généralement de liaisons entre les variables, de groupes des individus ou observations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Unidimensionnelle" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:10.936092Z", "start_time": "2019-11-18T09:19:10.826112Z" } }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:11.125737Z", "start_time": "2019-11-18T09:19:10.937377Z" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "ozone[\"O3obs\"].hist()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:11.262897Z", "start_time": "2019-11-18T09:19:11.128038Z" } }, "outputs": [], "source": [ "ozone[\"MOCAGE\"].hist()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercice Traiter ainsi toutes les variables. Ceci suggère des transformations pour une meilleure utilisation des modèles linéaires. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:11.275823Z", "start_time": "2019-11-18T09:19:11.264575Z" } }, "outputs": [], "source": [ "from math import sqrt, log\n", "ozone[\"SRMH2O\"]=ozone[\"RMH2O\"].map(lambda x: sqrt(x))\n", "ozone[\"LNO2\"]=ozone[\"NO2\"].map(lambda x: log(x))\n", "ozone[\"LNO\"]=ozone[\"NO\"].map(lambda x: log(x))\n", "del ozone[\"RMH2O\"]\n", "del ozone[\"NO2\"]\n", "del ozone[\"NO\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercice Vérifier l'opportunité de ces transformations (histogrammes des nouvelles variables)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Retirer les variables initiales et construire ci-dessous la variable \"dépassement de seuil\" pour obtenir le fichier qui sera effectivement utilisé." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:11.297416Z", "start_time": "2019-11-18T09:19:11.279311Z" } }, "outputs": [], "source": [ "ozone[\"DepSeuil\"]=ozone[\"O3obs\"].map(lambda x: x > 150)\n", "ozone.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exploration multidimensionnelle" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:16.123259Z", "start_time": "2019-11-18T09:19:11.299583Z" } }, "outputs": [], "source": [ "# scatter plot matrix des variables quantitatives\n", "from pandas.plotting import scatter_matrix\n", "scatter_matrix(ozone[[\"O3obs\",\"MOCAGE\",\"TEMPE\",\"VentMOD\",\"VentANG\",\"SRMH2O\",\"LNO2\",\"LNO\"]], alpha=0.2, figsize=(15, 15), diagonal='kde')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Commenter les relations entre les variables prises 2 à 2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Analyse en composantes principales](http://wikistat.fr/pdf/st-m-explo-acp.pdf)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:16.608748Z", "start_time": "2019-11-18T09:19:16.124724Z" } }, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "from sklearn.preprocessing import scale\n", "# réduction des variables\n", "#X=scale(ozone[[\"O3obs\",\"MOCAGE\",\"TEMPE\",\"VentMOD\",\"VentANG\",\"SRMH2O\",\"LNO2\",\"LNO\"]])\n", "X=scale(ozone[[\"MOCAGE\",\"TEMPE\",\"VentMOD\",\"VentANG\",\"SRMH2O\",\"LNO2\",\"LNO\"]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tous les résultats numétriques classiques sont fournis par l'[implémentation](http://scikit-learn.org/stable/modules/decomposition.html) de scikit-learn mais des efforts sont à produire pour construire les graphiques usuels généralement automatiquement produits par des librairies dédiées comme [FactoMineR](http://factominer.free.fr/) de R.\n", "\n", "Les commandes suivantes permettent de réaliser une analyse en composantes principales sur les seules variables quantitatives. Par ailleurs la variable à modéliser (O3obs, concentration observée) n'est pas utilisée." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:16.792776Z", "start_time": "2019-11-18T09:19:16.610421Z" } }, "outputs": [], "source": [ "pca = PCA()\n", "## Estimation, calcul des composantes principales\n", "C = pca.fit(X).transform(X)\n", "## Décroissance de la variance expliquée\n", "plt.plot(pca.explained_variance_ratio_)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:17.039276Z", "start_time": "2019-11-18T09:19:16.798186Z" } }, "outputs": [], "source": [ "## distribution des composantes principales\n", "plt.boxplot(C[:,0:20])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Commenter ces résultats: quel choix de la dimension? \n", "\n", "Q Présence de valeurs atypiques." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:19.969368Z", "start_time": "2019-11-18T09:19:17.040897Z" } }, "outputs": [], "source": [ "## Repésentation des individus\n", "plt.figure(figsize=(5,5))\n", "for i, j, nom in zip(C[:,0], C[:,1], ozone[\"DepSeuil\"]):\n", " color = \"red\" if nom else \"blue\"\n", " plt.plot(i, j, \"o\",color=color)\n", "plt.axis((-4,6,-4,6)) \n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:20.137406Z", "start_time": "2019-11-18T09:19:19.973057Z" } }, "outputs": [], "source": [ "## coordonnées et représentation des variables\n", "coord1=pca.components_[0]np.sqrt(pca.explained_variance_[0])\n", "coord2=pca.components_[1]np.sqrt(pca.explained_variance_[1])\n", "fig = plt.figure(figsize=(5,5))\n", "ax = fig.add_subplot(1, 1, 1)\n", "for i, j, nom in zip(coord1,coord2, ozone[[\"MOCAGE\",\"TEMPE\",\"VentMOD\",\n", " \"VentANG\",\"SRMH2O\",\"LNO2\",\"LNO\"]].columns):\n", " plt.text(i, j, nom)\n", " plt.arrow(0,0,i,j,color='black')\n", "plt.axis((-1.2,1.2,-1.2,1.2))\n", "# cercle\n", "c=plt.Circle((0,0), radius=1, color='gray', fill=False)\n", "ax.add_patch(c)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Commenter la structure de corrélation des variables.\n", "\n", "Q L'objectif est de définir une surface séparant les deux classes. Une discriminaiton linéaire (hyperplan) semble-t-elle possible? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ce n'est pas utile ici mais une classification non supervisée est facile à obtenir à titre illustratif, par exemple en 4 classes, par l'algorithme k-means:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:20.327504Z", "start_time": "2019-11-18T09:19:20.138931Z" } }, "outputs": [], "source": [ "from sklearn.cluster import KMeans\n", "from sklearn.metrics import confusion_matrix\n", "clust=KMeans(n_clusters=4)\n", "clust.fit(X)\n", "classe=clust.labels_\n", "print(classe)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:20.591749Z", "start_time": "2019-11-18T09:19:20.329120Z" } }, "outputs": [], "source": [ "## Repésentation des individus dans les coordonnées de l'acp.\n", "plt.figure(figsize=(10,8))\n", "plt.scatter(C[:,0], C[:,1], c=classe) \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modélisations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La recherche d'une meilleure méthode de prévision suit généralement le protocole suivant dont la première étape est déja réalisée.\n", "\n", "\n", "1. Etape descriptive préliminaire uni et multidimensionnelle visant à repérer les incohérences, les variables non significatives ou de distribution exotique, les individus non concernés ou atypiques... et à étudier les structures des données. Ce peut être aussi la longue étape de construction de variables, attributs ou features spécifiques des données. \n", "2. Procéder à un tirage aléatoire d'un échantillon test qui ne sera utilisé que lors de la dernière étape de comparaison des méthodes.\n", "3. La partie restante est l'échantillon d'apprentissage pour l'estimation des paramètres des modèles.\n", "4. Pour chacune des méthodes, optimiser la complexité des modèles en minimisant une estimation \"sans biais\" de l'erreur de prévision, par exemple par [validation croisée](http://wikistat.fr/pdf/st-m-app-risque-estim.pdf).\n", " - Variables et interactions à prendre en compte dans la régression linéaire ou logistique;\n", " - variables et méthode pour l'analyse discriminante;\n", " - nombre de feuilles dans l'arbre de régression ou de classification;\n", " - architecture (nombre de neurones, pénalisation) du perceptron;\n", " - algorithme d'agrégation, \n", " - noyau et pénalisation des SVMs.\n", "5. Comparaison des qualités de prévision sur la base du taux de mal classés pour le seul échantillon test qui est resté à l'écart de tout effort ou \"acharnement\" pour l'optimisation des modèles.\n", "\n", "Remarques\n", "* En cas d'échantillon relativement \"petit\" il est recommandé d'itérer la procédure de découpage apprentissage / test ([validation croisée Monte Carlo](http://wikistat.fr/pdf/st-m-app-risque-estim.pdf)), afin de réduire la variance (moyenne) des estimations des erreurs de prévision.\n", "* Attention: ne pas \"tricher\" en modifiant le modèle obtenu lors de l'étape précédente afin d'améliorer le résultat sur l'échantillon test !\n", "* Le critère utilisé dépend du problème : erreur quadratique, taux de mauvais classement, AUC (aire sous la courbe ROC), indice de Pierce, log loss function...\n", "* L'étape \"choix\" de la meilleure méthode peut être remplacée par une combinaisons de prévision comme c'est souvent le cas dans les soutions \"gagnantes\" mais lourdes du site [kaggle](https://www.kaggle.com/competitions)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extraction des échantillons apprentissage et test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Transformation des données pour l'apprentissage. \n", "\n", "Q Pourquoi les variables qualitatives sont-elles transformées en paquets d'indicatrices ou dummy variables?\n", "\n", "Q Pourquoi le type data frame est transformé en une matrice. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:20.610161Z", "start_time": "2019-11-18T09:19:20.594438Z" } }, "outputs": [], "source": [ "ozone.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:20.645052Z", "start_time": "2019-11-18T09:19:20.611990Z" } }, "outputs": [], "source": [ "# Variables explicatives\n", "ozoneDum=pd.get_dummies(ozone[[\"JOUR\",\"STATION\"]])\n", "del ozoneDum[\"JOUR_0\"]\n", "ozoneQuant=ozone[[\"MOCAGE\",\"TEMPE\",\"VentMOD\",\"VentANG\",\"SRMH2O\",\"LNO2\",\"LNO\"]]\n", "dfC=pd.concat([ozoneDum,ozoneQuant],axis=1)\n", "dfC.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:20.652330Z", "start_time": "2019-11-18T09:19:20.647878Z" } }, "outputs": [], "source": [ "# variable à expliquer binaire\n", "Yb=ozone[\"DepSeuil\"].map(lambda x: int(x))\n", "# variable à expliquer réelle\n", "Yr=ozone[\"O3obs\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:20.824319Z", "start_time": "2019-11-18T09:19:20.653924Z" } }, "outputs": [], "source": [ "Yr.hist()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extractions des échantillons d'apprentissage et test pour les deux types de modèles. Comme le générateur est initalisé de façon identique, ce sont les mêmes échantillons dans les deux cas." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:20.838969Z", "start_time": "2019-11-18T09:19:20.825953Z" } }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split \n", "X_train,X_test,Yb_train,Yb_test=train_test_split(dfC,Yb,test_size=200,random_state=11)\n", "X_train,X_test,Yr_train,Yr_test=train_test_split(dfC,Yr,test_size=200,random_state=11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'étape suivante est une étape de standardisation des données ou normalisation. Les variables sont divisées par leur écart-type. Ce n'est pas utile dans le cas d'un modèle linéaire élémentaire car la solution est identique mais indispensbale pour beaucoup d'autres méthodes non linéaires (SVM, réseaux de neurones, modèles avec pénalisation). Cette étape est donc concrètement systématiquement exécutée pour éviter des soucis. Attention, les mêmes paramètres (moyennes, écarts-types) estimés sur l'échantillon d'apprentissage sont utilisés pour normaliser l'échantillon test. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:20.849578Z", "start_time": "2019-11-18T09:19:20.840871Z" } }, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler \n", "# L'algorithme ds réseaux de neurones nécessite éventuellement une normalisation \n", "# des variables explicatives avec les commandes ci-dessous\n", "scaler = StandardScaler() \n", "scaler.fit(X_train) \n", "Xr_train = scaler.transform(X_train) \n", "# Meme transformation sur le test\n", "Xr_test = scaler.transform(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Modèles linéaires" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les fonctions de modéles linéaires et linéaires généralisées sont limitées dans [Scikit-learn](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning) et sans sorties numériques (tests) détaillées qui sont à rechercher dans une autre librairie ([StatsModels](http://statsmodels.sourceforge.net/stable/examples/notebooks/generated/glm.html)). Dans les deux cas, les stratégies classiques (forward, backward, stepwise, Furnival et Wilson) de sélection de variables par optimisation d'un critère (Cp, AIC, BIC) ne semblent pas disponibles, même si AIC et BIC sont présents dans scikit-learn, et le type DataFrame (package pandas) n'est pas reconnu.\n", "\n", "La façon efficace de procéder est donc d'introduire une [pénalisation Lasso](http://wikistat.fr/pdf/st-m-app-select.pdf) pour opérer une sélection de variables ou plutôt la sélection de variables quantitatives et d'indicatrices des modalités de celles qualitatives mais sans analyse fine des interactions comme cela est possible avec R.\n", "\n", "Q Quel autre type de pénalisation est aussi utilisée en régression?\n", "\n", "Q Quelle la méthode qui combine les deux?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A titre de comparaison, on trace la prévision de la concentration de l'échantillon test par la seule valeur du modèle Mocage ainsi que les résidus à ce modèle fonction de la valeur prédite (Mocage)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:21.112432Z", "start_time": "2019-11-18T09:19:20.851395Z" } }, "outputs": [], "source": [ "plt.plot(X_train[\"MOCAGE\"],Yr_train,\"o\")\n", "plt.xlabel(\"Mocage\")\n", "plt.ylabel(\"O3 observee\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:21.118987Z", "start_time": "2019-11-18T09:19:21.114624Z" } }, "outputs": [], "source": [ "from sklearn.metrics import r2_score\n", "print(\"R2=\",r2_score(Yr_train,X_train[\"MOCAGE\"]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:21.259504Z", "start_time": "2019-11-18T09:19:21.121039Z" } }, "outputs": [], "source": [ "plt.plot(X_test[\"MOCAGE\"],Yr_test,\"o\")\n", "plt.xlabel(\"Mocage\")\n", "plt.ylabel(\"O3 observee\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:21.436658Z", "start_time": "2019-11-18T09:19:21.261005Z" } }, "outputs": [], "source": [ "plt.plot(X_test[\"MOCAGE\"],X_test[\"MOCAGE\"]-Yr_test,\"o\")\n", "plt.xlabel(\"Mocage\")\n", "plt.ylabel(\"Residus\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Commenter la qualité de ces résidus." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:21.445464Z", "start_time": "2019-11-18T09:19:21.439951Z" } }, "outputs": [], "source": [ "# Erreur quadratique moyenne\n", "from sklearn.metrics import mean_squared_error\n", "print(\"MSE=\",mean_squared_error(X_test[\"MOCAGE\"],Yr_test))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:21.454376Z", "start_time": "2019-11-18T09:19:21.447037Z" } }, "outputs": [], "source": [ "# Le coefficient de détermination \n", "# peut être négatif en prévision avec un mauvais modèle, \n", "# est nul si la prévision est constante égale à la moyennne\n", "from sklearn.metrics import r2_score\n", "print(\"R2=\",r2_score(Yr_test,X_test[\"MOCAGE\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### [Régression linéaire](http://wikistat.fr/pdf/st-m-app-select.pdf) ou modèle gaussien" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparer cette prévision déterministe (équation de Navier et Stockes) par l'adaptation statistique la plus élémentaire. Il s'agit d'une régression avec choix de modèle par régularisation avec une pénalisation lasso. \n", "\n", "Q Quelles est la valeur par défaut du paramètre de pénalisation Lasso?." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:21.465143Z", "start_time": "2019-11-18T09:19:21.457333Z" } }, "outputs": [], "source": [ "from sklearn import linear_model\n", "regLasso = linear_model.Lasso()\n", "regLasso.fit(Xr_train,Yr_train)\n", "prev=regLasso.predict(Xr_test)\n", "print(\"MSE=\",mean_squared_error(Yr_test,prev))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:21.471807Z", "start_time": "2019-11-18T09:19:21.466586Z" } }, "outputs": [], "source": [ "from sklearn.metrics import r2_score\n", "print(\"R2=\",r2_score(Yr_test,prev))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le paramètre de pénalisation lasso est optimisé par validation croisée." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:23.608300Z", "start_time": "2019-11-18T09:19:21.473424Z" } }, "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "# grille de valeurs du paramètre alpha à optimiser\n", "param=[{\"alpha\":[0.05,0.1,0.2,0.3,0.4,0.5,1]}]\n", "regLasso = GridSearchCV(linear_model.Lasso(), param,cv=5,n_jobs=-1)\n", "regLassOpt=regLasso.fit(Xr_train, Yr_train)\n", "# paramètre optimal\n", "regLassOpt.best_params_[\"alpha\"]\n", "print(\"Meilleur R2 = %f, Meilleur paramètre = %s\" % (regLassOpt.best_score_,regLassOpt.best_params_))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Quelle validation croisée est exécutée?\n", "\n", "Prévision avec la valeur optimale de `alpha` puis calcul et tracé des résidus." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:23.616625Z", "start_time": "2019-11-18T09:19:23.610359Z" } }, "outputs": [], "source": [ "prev=regLassOpt.predict(Xr_test)\n", "print(\"MSE=\",mean_squared_error(prev,Yr_test))\n", "print(\"R2=\",r2_score(Yr_test,prev))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:23.773914Z", "start_time": "2019-11-18T09:19:23.618387Z" } }, "outputs": [], "source": [ "plt.plot(prev,Yr_test,\"o\")\n", "plt.xlabel(u\"O3 Prédite\")\n", "plt.ylabel(\"O3 observee\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:23.955256Z", "start_time": "2019-11-18T09:19:23.778464Z" } }, "outputs": [], "source": [ "plt.plot(prev,Yr_test-prev,\"o\")\n", "plt.xlabel(u\"Prédites\")\n", "plt.ylabel(u\"Résidus\")\n", "plt.hlines(0,40,220)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Comparer ces résidus avec ceux précédents (mocage) et noter l'amélioration. \n", "\n", "Q Commenter la forme du nuage et donc la validité du modèle. \n", "\n", "L'interprétation nécessite de connaître les valeurs des coefficients du modèle alors que l'objet `regLassOpt` issu de `GridSearchCV` ne retient pas les paramètres estimés. Il faut donc le ré-estimer avec la valeur optimale du paramètre de pénalisation si l'on souhaite afficher ces coefficients." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:23.966794Z", "start_time": "2019-11-18T09:19:23.959069Z" } }, "outputs": [], "source": [ "# Coefficients\n", "regLasso=linear_model.Lasso(alpha=regLassOpt.best_params_['alpha'])\n", "model_lasso=regLasso.fit(Xr_train,Yr_train)\n", "model_lasso.coef_" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:23.973385Z", "start_time": "2019-11-18T09:19:23.968424Z" } }, "outputs": [], "source": [ "coef = pd.Series(model_lasso.coef_, index = X_train.columns)\n", "print(\"Lasso conserve \" + str(sum(coef != 0)) + \n", " \" variables et en supprime \" + str(sum(coef == 0)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:24.238690Z", "start_time": "2019-11-18T09:19:23.974918Z" } }, "outputs": [], "source": [ "imp_coef = coef.sort_values()\n", "plt.rcParams['figure.figsize'] = (8.0, 10.0)\n", "imp_coef.plot(kind = \"barh\")\n", "plt.title(u\"Coefficients du modèle lasso\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Noter les conséquences de la pénalisation; interpréter l'effet de chaque variable sur la concentration en ozone.\n", "\n", "C'est ici qu'apparaît une insuffisance de la librairie python. Il faudrait construire \"à la main\" ou utiliser la librairie Statsmodels pour afficher les statistiques des tests et p-valeurs. Même avec ces compléments, la prise en compte des interactions et de leur sélection ne sont pas prévues. De plus l'interprétation est compliquée par l'éclatement de chaque variable qualitative en paquets d'indicatrices. C'est encore compréhensible avec peu de variables mais devient rapidement inexploitable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le graphe quivant permet d'identifier les bonnes et mauvaises prévisions de dépassement du seuil légal, ici fixé à $ 150 \\mu g $." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:24.418626Z", "start_time": "2019-11-18T09:19:24.240884Z" } }, "outputs": [], "source": [ "plt.plot(prev,Yr_test,\"o\")\n", "plt.xlabel(u\"Valeurs prédites\")\n", "plt.ylabel(u\"O3 observée\")\n", "plt.hlines(150,50,300)\n", "plt.vlines(150,0,300)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:24.443895Z", "start_time": "2019-11-18T09:19:24.420117Z" } }, "outputs": [], "source": [ "# Dénombrement des erreurs par\n", "# matrice de confusion\n", "table=pd.crosstab(prev>150,Yr_test>150)\n", "print(table)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Observer l'asymétrie de cette matrice. A quoi est-elle due au moins en partie ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scikit-learn propose d'autres procédures d'optimisation du paramètre de régularisation lasso par validation croisée en régression; `lassoCV` utilise un algorithme de coordinate descent, sans calcul de dérivée puisque la norme l1 n'est pas dérivable, tandis que `lassoLarsCV` est basée sur l'algorithme de least angle regression. Ces fonctions permettent de tracer également les chemins de régularisation. Voici l'exemple de `lassoCV` qui offre plus d'options." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:24.818339Z", "start_time": "2019-11-18T09:19:24.446157Z" } }, "outputs": [], "source": [ "from sklearn.linear_model import LassoCV, LassoLarsCV\n", "model = LassoCV(cv=5, alphas=np.array(range(1,50,1))/20.,n_jobs=-1,random_state=13).fit(Xr_train,Yr_train)\n", "m_log_alphas = -np.log10(model.alphas_)\n", "\n", "plt.figure()\n", "# ymin, ymax = 2300, 3800\n", "plt.plot(m_log_alphas, model.mse_path_, ':')\n", "plt.plot(m_log_alphas, model.mse_path_.mean(axis=-1), 'k',\n", " label='MSE moyen', linewidth=2)\n", "plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',\n", " label='alpha: optimal par VC')\n", "\n", "plt.legend()\n", "\n", "plt.xlabel('-log(alpha)')\n", "plt.ylabel('MSE')\n", "plt.title('MSE de chaque validation: coordinate descent ')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Vérifier que c'est bien la même valeur optimale que celle précédemment trouvée.\n", "\n", "Tracés des chemins de régularisation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:25.069976Z", "start_time": "2019-11-18T09:19:24.819818Z" } }, "outputs": [], "source": [ "from itertools import cycle\n", "\n", "from sklearn.linear_model import lasso_path\n", "alphas_lasso, coefs_lasso, _ = lasso_path(Xr_train,Yr_train, alphas=np.array(range(1,50,1))/20.,)\n", "\n", "\n", "plt.figure()\n", "ax = plt.gca()\n", "\n", "styles = cycle(['-', '--', '-.', ':'])\n", "\n", "neg_log_alphas_lasso = -np.log10(alphas_lasso)\n", "for coef_l, s in zip(coefs_lasso, styles):\n", " l1 = plt.plot(neg_log_alphas_lasso, coef_l, linestyle=s,c='b')\n", "plt.xlabel('-Log(alpha)')\n", "plt.ylabel('Coefficients')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### [Régression logistique](http://wikistat.fr/pdf/st-m-app-rlogit.pdf) ou modèle binomial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La même démarche est déroulée mais en modélisant directement la variable binaire Yb de dépassement ou non du seuil. Il s'agit d'une régression logistique avec toujours une pénalisation Lasso pour opérer une sélection de variables." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:25.074229Z", "start_time": "2019-11-18T09:19:25.071680Z" } }, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:25.292166Z", "start_time": "2019-11-18T09:19:25.080838Z" } }, "outputs": [], "source": [ "# Optimisation du paramètre de pénalisation\n", "# grille de valeurs\n", "param=[{\"C\":[1,1.2,1.5,1.7,2,3,4]}]\n", "logit = GridSearchCV(LogisticRegression(penalty=\"l1\",solver=\"liblinear\"), param,cv=5,n_jobs=-1)\n", "logitOpt=logit.fit(Xr_train, Yb_train) # GridSearchCV est lui même un estimateur\n", "# paramètre optimal\n", "logitOpt.best_params_[\"C\"]\n", "print(\"Meilleur score = %f, Meilleur paramètre = %s\" % (1.-logitOpt.best_score_,logitOpt.best_params_))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:25.301641Z", "start_time": "2019-11-18T09:19:25.295698Z" } }, "outputs": [], "source": [ "# erreur sur l'échantillon test\n", "1-logitOpt.score(Xr_test, Yb_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le modèle \"optimal\" obtenu est utilisé pour prédire l'échantillon test et estimer ainsi, sans biais, une erreur de prévision. \n", "\n", "La matrice de confusion croise les dépassements de seuils prédits avec ceux effectivement observés. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:25.324130Z", "start_time": "2019-11-18T09:19:25.303337Z" } }, "outputs": [], "source": [ "# Prévision\n", "y_chap = logitOpt.predict(Xr_test)\n", "# matrice de confusion\n", "table=pd.crosstab(y_chap,Yb_test)\n", "print(table)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'interprétation du modèle est basée sur les valeurs des coefficients avec les mêmes difficultés ou restrictions que pour la régression. Attention, `GridSearch` ne retient pas les coefficients, il faut les ré-estimer." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:25.342364Z", "start_time": "2019-11-18T09:19:25.326204Z" } }, "outputs": [], "source": [ "# Coefficients\n", "logitLasso=LogisticRegression(penalty=\"l1\",C=logitOpt.best_params_['C'],\n", " solver=\"liblinear\")\n", "logitCoef=logitLasso.fit(Xr_train,Yb_train).coef_\n", "print(logitCoef[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:25.352838Z", "start_time": "2019-11-18T09:19:25.345009Z" } }, "outputs": [], "source": [ "coef = pd.Series(logitCoef[0], index = X_train.columns)\n", "print(\"Lasso conserve \" + str(sum(coef != 0)) + \n", " \" variables et en supprime \" + str(sum(coef == 0)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:25.597658Z", "start_time": "2019-11-18T09:19:25.354622Z" } }, "outputs": [], "source": [ "imp_coef = coef.sort_values()\n", "plt.rcParams['figure.figsize'] = (6.0, 6.0)\n", "imp_coef.plot(kind = \"barh\")\n", "plt.title(u\"Coefficients du modèle lasso\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Interpréter l'effet des variables retenues." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:25.803353Z", "start_time": "2019-11-18T09:19:25.599427Z" } }, "outputs": [], "source": [ "from sklearn.metrics import roc_curve\n", "probas_ = LogisticRegression(penalty=\"l1\", solver=\"liblinear\",\n", " C=logitOpt.best_params_['C']).fit(X_train, Yb_train).predict_proba(X_test)\n", "fpr, tpr, thresholds = roc_curve(Yb_test, probas_[:,1])\n", "plt.plot(fpr, tpr, lw=1)\n", "plt.xlabel('Taux de faux positifs')\n", "plt.ylabel('Taux de vrais positifs')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Commenter la courbe ROC à propos du choix de la valeur seuil." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# <FONT COLOR=\"Red\">Épisode 2</font>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [K plus proches voisins](http://wikistat.fr/pdf/st-m-app-add.pdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici un cas d'application d'analyses discriminantes [non paramétriques](http://scikit-learn.org/stable/modules/neighbors.html), celles [paramétriques](http://scikit-learn.org/stable/modules/lda_qda.html) (gaussienes) linéaires et quadratiques sont également présentes dans scikit-learn mais laissées en exercice.\n", "\n", "Le paramètre de compléxité ($k$) est optimisé sur une grille prédéfinie en minimisant l'erreur estimée par validation croisée; scikit-learn propose de nombreuses options de validation croisée. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:26.282117Z", "start_time": "2019-11-18T09:19:25.805488Z" } }, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "# Optimisation de k\n", "# grille de valeurs\n", "param_grid=[{\"n_neighbors\":list(range(1,15))}]\n", "knn=GridSearchCV(KNeighborsClassifier(),param_grid,cv=5,n_jobs=-1)\n", "knnOpt=knn.fit(Xr_train, Yb_train) # GridSearchCV est lui même un estimateur\n", "# paramètre optimal\n", "knnOpt.best_params_[\"n_neighbors\"]\n", "print(\"Meilleur score = %f, Meilleur paramètre = %s\" % (1.-knnOpt.best_score_,knnOpt.best_params_))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:26.301358Z", "start_time": "2019-11-18T09:19:26.284307Z" } }, "outputs": [], "source": [ "# Estimation de l'erreur de prévision sur l'échantillon test\n", "1-knnOpt.score(Xr_test,Yb_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:26.352100Z", "start_time": "2019-11-18T09:19:26.304330Z" } }, "outputs": [], "source": [ "# Prévision de l'échantillon test\n", "y_chap = knnOpt.predict(Xr_test)\n", "# matrice de confusion\n", "table=pd.crosstab(y_chap,Yb_test)\n", "print(table)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercice Compléter les résultats en utilisant la fonction [KNeighborsRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html) pour modéliser la concentration; optimiser $k$, calculer la prévision de l'échantillon test, tracer le graphe des résidus, calculer le MSE sur l'échantillon test." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Arbre binaire de décision](http://wikistat.fr/pdf/st-m-app-cart.pdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les [arbres binaires de décision](http://scikit-learn.org/stable/modules/tree.html) : discrimination ou régression, sont bien implémentés dans scikit-learn mais avec une insuffisance pour leur élagage. Ce n'est pas une pénalisation de la complexité, et donc précisément le nombre de feuilles qui est optimisé, mais la profondeur globale de l'arbre au risque d'élaguer, à une profondeur donnée, des feuilles importantes ou de conserver des feuilles ambigües.\n", "\n", "Comme précédemment, la validation croisée permet d'optimiser le paramètre sur une grille." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:26.625493Z", "start_time": "2019-11-18T09:19:26.354381Z" } }, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeClassifier\n", "# Optimisation de la profondeur de l'arbre\n", "param=[{\"max_depth\":list(range(2,10))}]\n", "tree= GridSearchCV(DecisionTreeClassifier(),param,cv=10,n_jobs=-1)\n", "treeOpt=tree.fit(Xr_train, Yb_train)\n", "# paramètre optimal\n", "print(\"Meilleur score = %f, Meilleur paramètre = %s\" % (1. - treeOpt.best_score_,treeOpt.best_params_))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:26.632813Z", "start_time": "2019-11-18T09:19:26.627275Z" } }, "outputs": [], "source": [ "# Estimation de l'erreur de prévision\n", "1-treeOpt.score(Xr_test,Yb_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:26.664280Z", "start_time": "2019-11-18T09:19:26.634973Z" } }, "outputs": [], "source": [ "# prévision de l'échantillon test\n", "y_chap = treeOpt.predict(Xr_test)\n", "# matrice de confusion\n", "table=pd.crosstab(y_chap,Yb_test)\n", "print(table)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Autre difficulté dans la représentation d'un arbre de décision binaire. Le logiciel conseillé (Graphviz) semble délicat d'installation et d'utilisation pour un néophyte. Il est possible de lister la construction des noeuds avec quelques [lignes de commande.](http://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html#sphx-glr-auto-examples-tree-plot-unveil-tree-structure-py)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:26.806240Z", "start_time": "2019-11-18T09:19:26.666103Z" } }, "outputs": [], "source": [ "from sklearn.tree import export_graphviz\n", "from sklearn.externals.six import StringIO \n", "import pydotplus\n", "treeG=DecisionTreeClassifier(max_depth=treeOpt.best_params_['max_depth'])\n", "treeG.fit(Xr_train,Yb_train)\n", "dot_data = StringIO() \n", "export_graphviz(treeG, out_file=dot_data) \n", "graph=pydotplus.graph_from_dot_data(dot_data.getvalue()) \n", "graph.write_png(\"treeOpt.png\") " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:26.819123Z", "start_time": "2019-11-18T09:19:26.808625Z" } }, "outputs": [], "source": [ "from IPython.display import Image\n", "Image(filename='treeOpt.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Que dire de l'interprétation de l'arbre? Comparer les rôles des variables avec le modèle logit.\n", "\n", "Exercice Compléter les résultats en utilisant la fonction [DecisionTreeRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html) pour modéliser concentration; optimiser la profondeur, calculer la prévision de l'échantillon test, tracer les résidus, calculer le MSE sur l'échantillon test." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# <FONT COLOR=\"Red\">Épisode 3</font>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Réseau de neurones](http://wikistat.fr/pdf/st-m-app-rn.pdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les réseaux neuronaux (perceptron multicouche) ne sont présents dans le package `Scikit-learn` qu'à partir de la version 0.18. Les méthodes profondes (deep learning) nécessitent l'installation des librairies [theano](http://deeplearning.net/software/theano/) et [Lasagne](http://lasagne.readthedocs.io/en/latest/index.html) ou [theano](http://deeplearning.net/software/theano/), [TensorFlow](https://www.tensorflow.org/versions/r0.11/get_started/os_setup.html) et [Keras](https://keras.io/). Ces dernières sont nettement plus complexes à installer, surtout sous Windows. Elles feront l'objet d'un autre tutoriel." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:26.835311Z", "start_time": "2019-11-18T09:19:26.821031Z" } }, "outputs": [], "source": [ "from sklearn.neural_network import MLPClassifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Définition des paramètres dont le nombre de neurones et `alpha` qui règle la régularisation par défaut 10-5. Le nombre de neurones est optimisé mais ce peut être `alpha` avec un nombre grand de neurones. Le nombre max d'itérations par défaut (200) semble insuffisant. Il est fixé à 500." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:38.412805Z", "start_time": "2019-11-18T09:19:26.836996Z" } }, "outputs": [], "source": [ "param_grid=[{\"hidden_layer_sizes\":list([(5,),(6,),(7,),(8,)])}]\n", "nnet= GridSearchCV(MLPClassifier(max_iter=500),param_grid,cv=10,n_jobs=-1)\n", "nnetOpt=nnet.fit(Xr_train, Yb_train)\n", "# paramètre optimal\n", "print(\"Meilleur score = %f, Meilleur paramètre = %s\" % (1. - nnetOpt.best_score_,nnetOpt.best_params_))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:38.422719Z", "start_time": "2019-11-18T09:19:38.414606Z" } }, "outputs": [], "source": [ "# Estimation de l'erreur de prévision sur le test\n", "1-nnetOpt.score(Xr_test,Yb_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:38.505699Z", "start_time": "2019-11-18T09:19:38.424800Z" } }, "outputs": [], "source": [ "# prévision de l'échantillon test\n", "y_chap = nnetOpt.predict(Xr_test)\n", "# matrice de confusion\n", "table=pd.crosstab(y_chap,Yb_test)\n", "print(table)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercice Remplacer ensuite la fonction MLPClassifier par celle [MLPRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html) de régression. Optimiser le paramètre, calculer la prévision, les résidus, le MSE." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Forêts aléatoires](http://wikistat.fr/pdf/st-m-app-agreg.pdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La librairie randomForest de R utilise le programme historique développé par [Breiman et Cutler](https://www.stat.berkeley.edu/~breiman/RandomForests/cc_software.htm)(2001) et interfacé par [Liaw et Wiener](https://cran.r-project.org/web/packages/randomForest/randomForest.pdf). Cette interface est toujours mise à jour mais il n'est pas sûr que le programme original continue d'évoluer depuis 2004. Pour des tailles importantes d'échantillons, quelques milliers, cette implémentation atteint des temps d'exécution rédhibitoires (cf. cet [exemple](https://github.com/wikistat/Ateliers-Big-Data/blob/master/2-MNIST/Atelier-MNIST-R.ipynb)) au contraire de celle en Python dont gestion mémoire et capacité de parallélisation ont été finement optimisées par [Louppe et al.](http://fr.slideshare.net/glouppe/accelerating-random-forests-in-scikitlearn)(2014). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "De même que le boosting, deux fonctions de forêt sont proposés dans [scikit-learn](http://scikit-learn.org/stable/modules/ensemble.html) ; une pour la régression et une pour la classification ainsi qu'une version \"plus aléatoire\". Par rapport à la version originale de R, moins d'options sont proposées mais l'utilisation de base est très similaire avec le même jeu de paramètres.\n", "\n", "Q20 Identifier les paramètres, les valeurs par défaut." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:39.568151Z", "start_time": "2019-11-18T09:19:38.507445Z" } }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier \n", "# définition des paramètres\n", "forest = RandomForestClassifier(n_estimators=500, \n", " criterion='gini', max_depth=None,\n", " min_samples_split=2, min_samples_leaf=1, \n", " max_features='auto', max_leaf_nodes=None,\n", " bootstrap=True, oob_score=True)\n", "# apprentissage\n", "rfFit = forest.fit(Xr_train,Yb_train)\n", "print(1-rfFit.oob_score_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparer l'erreur out-of-bag ci-dessus avec celle sur l'échantillon test." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:39.621139Z", "start_time": "2019-11-18T09:19:39.570086Z" } }, "outputs": [], "source": [ "# erreur de prévision sur le test\n", "1-rfFit.score(Xr_test,Yb_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optimisation par validation croisée du nombre de variables tirés aléatoirement lors de la construction de chaque noeud. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:43.165576Z", "start_time": "2019-11-18T09:19:39.622794Z" } }, "outputs": [], "source": [ "param=[{\"max_features\":list(range(2,10,1))}]\n", "rf= GridSearchCV(RandomForestClassifier(n_estimators=100),\n", " param,cv=5,n_jobs=-1)\n", "rfOpt=rf.fit(Xr_train, Yb_train)\n", "# paramètre optimal\n", "print(\"Meilleur score = %f, Meilleur paramètre = %s\" % (1. - rfOpt.best_score_,rfOpt.best_params_))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plusieurs exécutions, rendues aléatoires par la validation croisée, peuvent conduire à des valeurs \"optimales\" différentes de ce paramètre sans pour autant nuire à la qualité de prévision sur l'échantillon test." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:43.182784Z", "start_time": "2019-11-18T09:19:43.167657Z" } }, "outputs": [], "source": [ "# erreur de prévision sur le test\n", "1-rfOpt.score(Xr_test,Yb_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercice Tester différentes valeurs de min_samples_split de celle trouvée optimale. Conclusion sur la sensibilité de l'optimisation de ce paramètre ?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:43.291927Z", "start_time": "2019-11-18T09:19:43.184872Z" } }, "outputs": [], "source": [ "# prévision\n", "y_chap = rfFit.predict(Xr_test)\n", "# matrice de confusion\n", "table=pd.crosstab(y_chap,Yb_test)\n", "print(table)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comme avec R, il est possible de calculer un indicateur d'importance des variables pour aider à une forme d'interprétation. Celui-ci dépend de la position de la variable dans l'arbre et correspond donc au mean decrease in Gini index de R plutôt qu'au mean descrease in accuracy. La forêt doit être réestimée car GridSearch ne connaît pas le paramètre d'importance." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:43.502454Z", "start_time": "2019-11-18T09:19:43.293577Z" } }, "outputs": [], "source": [ "rf= RandomForestClassifier(n_estimators=100,max_features=2)\n", "rfFit=rf.fit(Xr_train, Yb_train)\n", "# Importance décroissante des variables\n", "importances = rfFit.feature_importances_\n", "indices = np.argsort(importances)[::-1]\n", "for f in range(Xr_train.shape[1]):\n", " print(dfC.columns[indices[f]], importances[indices[f]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:43.693998Z", "start_time": "2019-11-18T09:19:43.504115Z" } }, "outputs": [], "source": [ "# Graphe des importances\n", "plt.figure()\n", "plt.title(\"Importances des variables\")\n", "plt.bar(range(Xr_train.shape[1]), importances[indices])\n", "plt.xticks(range(Xr_train.shape[1]), indices)\n", "plt.xlim([-1, Xr_train.shape[1]])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Comparer les importances des variables et les sélections opérées précédemment. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercice Remplacer ensuite la fonction RandomForestClassifier par celle [RandomForestRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html) de régression. Optimiser le paramètre, calculer la prévision, les résidus, le MSE.\n", "\n", "Exercice Expérimenter également le boosting sur ces données en exécutant la fonction [GradientBoostingClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier) opérant l'agorithme de gradient tree boosting. \n", "\n", "Remarque: Une version \"améliorée\" de boosting mieux paralélisée et incluant d'autres paramètres (pénalisation), est proposé dans le package: [`XGBoost`](https://xgboost.readthedocs.io/en/latest/build.html#python-package-installation) qui peut être utilisé à partir de Python mais aussi R, Julia ou Java. Nénamoins le choix est fait d'arrêter l'acharnement sur ces données; `XGBoost` est testé en python sur un autre jeu de données. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# <FONT COLOR=\"Red\">Épisode 4</font>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Support Vector Machine](http://wikistat.fr/pdf/st-m-app-svm.pdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "De nombreux paramètres sont associés à cette méthode. La liste est à consulter dans la [documentation](http://scikit-learn.org/stable/modules/svm.html) en ligne.\n", "\n", "L'optimisation de la pénalisation (paramètre C) est recherchée sur une grille par validation croisée. Remarque: il serait nécessaire d'optimiser également la valeur du coefficient gamma lié au noyau gaussien (\"écart-type\").\n", "\n", "Il est souvent nécessaire de normaliser des données avant d'opérer les SVM." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:44.153231Z", "start_time": "2019-11-18T09:19:43.696057Z" } }, "outputs": [], "source": [ "from sklearn.svm import SVC\n", "param=[{\"C\":[0.4,0.5,0.6,0.8,1,1.4]}]\n", "svm= GridSearchCV(SVC(),param,cv=10,n_jobs=-1)\n", "svmOpt=svm.fit(Xr_train, Yb_train)\n", "# paramètre optimal\n", "print(\"Meilleur score = %f, Meilleur paramètre = %s\" % (1. - svmOpt.best_score_,svmOpt.best_params_))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:44.166775Z", "start_time": "2019-11-18T09:19:44.155679Z" } }, "outputs": [], "source": [ "# erreur de prévision sur le test\n", "1-svmOpt.score(Xr_test,Yb_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:44.191961Z", "start_time": "2019-11-18T09:19:44.170189Z" } }, "outputs": [], "source": [ "# prévision de l'échantillon test\n", "y_chap = svmOpt.predict(Xr_test)\n", "# matrice de confusion\n", "table=pd.crosstab(y_chap,Yb_test)\n", "print(table)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercice Comme précédemment, remplacer ensuite la fonction SVC par celle [SVR](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR) de régression. Optimiser le paramètre, calculer la prévision, les résidus; le MSE." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Synthèse: comparaison des méthodes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Courbes ROC" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dans toute méthode, la prévision de dépassement ou non est associée au choix d'un seuil qui est par défaut 0.5. L'optimisaiton de ce seuil dépend des coûts respectifs associés aux faux positifs et aux faux négatifs qui ne sont pas nécessairement égaux. La courbe ROC permet de représenter l'influence de ce seuil sur les taux de faux positifs et vrais positifs. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:19:44.198684Z", "start_time": "2019-11-18T09:19:44.193534Z" } }, "outputs": [], "source": [ "from sklearn.metrics import roc_curve\n", "listMethod=[[\"RF\",rfOpt],[\"NN\",nnetOpt],[\"Tree\",treeOpt],[\"K-nn\",knnOpt],[\"Logit\",logitOpt]]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-18T09:20:02.322746Z", "start_time": "2019-11-18T09:19:44.200268Z" } }, "outputs": [], "source": [ "for method in enumerate(listMethod):\n", " probas_ = method[1][1].fit(Xr_train, Yb_train).predict_proba(Xr_test)\n", " fpr, tpr, thresholds = roc_curve(Yb_test, probas_[:,1])\n", " plt.plot(fpr, tpr, lw=1,label=\"%s\"%method[1][0])\n", "plt.xlabel('Taux de faux positifs')\n", "plt.ylabel('Taux de vrais positifs')\n", "plt.legend(loc=\"best\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q22 Le critère d'AUC (aire sous la courbe) permet-il d'ordonner les courbes et donc les méthodes? \n", "\n", "C'est à un taux de faux positif admissible et donc à valeur de seuil fixé qu'il faut choisir la méthode d'apprentissage à privilégier. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Itération sur plusieurs échantillons de test ([validation croisée Monte Carlo](http://wikistat.fr/pdf/st-m-app-risque-estim.pdf))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'échantillon test est de taille modeste et donc l'estimation de l'erreur de prévision peut présenter une variance importante. Celle-ci est réduite en opérant une forme de validation croisée (Monte Carlo) en tirant plusieurs couples d'échantillon apprentissage et test pour itérer les traitements précédents. Les données sont normalisées pour toutes les méthodes car les autres que SVM et NN ne sont pas affectées.\n", "\n", "Les fonctionnalités de scikit-learn se prètent bien à l'automatisation de ces traitements enchaînant extraction d'échantillons, estimation de plusieurs modèles, optimisation de leurs paramètres et estimation de l'erreur de prévision sur le test.\n", "\n", "Le code est compact et d'exécution efficace car bien parallélisé par les fonctions utilisées." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T14:51:22.438772Z", "start_time": "2019-07-03T14:50:38.969063Z" } }, "outputs": [], "source": [ "from sklearn.utils import check_random_state\n", "import time\n", "check_random_state(13)\n", "tps0=time.perf_counter()\n", "# définition des estimateurs\n", "logit= LogisticRegression(penalty=\"l1\",solver=\"liblinear\")\n", "knn = KNeighborsClassifier()\n", "tree = DecisionTreeClassifier()\n", "nnet = MLPClassifier(max_iter=600)\n", "rf = RandomForestClassifier(n_estimators=100)\n", "svm = SVC()\n", "# Nombre d'itérations\n", "B=3 # pour exécuter après le test, mettre plutôt B=30\n", "# définition des grilles de paramètres\n", "listMethGrid=[[svm,{\"C\":[0.4,0.5,0.6,0.8,1,1.4]}],\n", " [rf,{\"max_features\":list(range(2,10,2))}],\n", " [nnet,{\"hidden_layer_sizes\":list([(5,),(6,),(7,),(8,)])}],\n", " [tree,{\"max_depth\":list(range(2,10))}],\n", " [knn,{\"n_neighbors\":list(range(1,15))}],\n", " [logit,{\"C\":[0.5,1,5,10,12,15,30]}]]\n", "# Initialisation à 0 des erreurs pour chaque méthode (colonne) et chaque itération (ligne)\n", "arrayErreur=np.empty((B,6))\n", "for i in range(B): # itérations sur B échantillons test\n", " # extraction apprentissage et test\n", " X_train,X_test,Yb_train,Yb_test=train_test_split(dfC,Yb,test_size=200)\n", " scaler = StandardScaler() \n", " scaler.fit(X_train) \n", " X_train = scaler.transform(X_train) \n", " # Meme transformation sur le test\n", " X_test = scaler.transform(X_test)\n", " # optimisation de chaque méthode et calcul de l'erreur sur le test\n", " for j,(method, grid_list) in enumerate(listMethGrid):\n", " methodGrid=GridSearchCV(method,grid_list,cv=10,n_jobs=-1,\n", " iid=\"TRUE\").fit(X_train, Yb_train)\n", " methodOpt = methodGrid.best_estimator_\n", " methFit=methodOpt.fit(X_train, Yb_train)\n", " arrayErreur[i,j]=1-methFit.score(X_test,Yb_test)\n", "tps1=time.perf_counter()\n", "print(\"Temps execution en mn :\",(tps1 - tps0))\n", "dataframeErreur=pd.DataFrame(arrayErreur,columns=[\"SVM\",\"RF\",\"NN\",\"Tree\",\"Knn\",\"Logit\"]) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T14:51:28.031583Z", "start_time": "2019-07-03T14:51:27.827667Z" } }, "outputs": [], "source": [ "# Distribution des erreurs de prévisions\n", "# Les SVM présentant des erreurs atypiques sont laissés de côté.\n", "dataframeErreur[[\"SVM\",\"RF\",\"NN\",\"Tree\",\"Knn\",\"Logit\"]].boxplot(return_type='dict')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T14:51:46.326851Z", "start_time": "2019-07-03T14:51:46.320143Z" } }, "outputs": [], "source": [ "# Moyennes\n", "dataframeErreur.mean()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Conclusion sur l'apprentissage\n", "Q Quel méthode retenir? Est-ce cohérent avec les résultats e R?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cet exemple, traité en R puis en Python, résume bien l'intérêt et le contexte des méthodes d'apprentissage.\n", "* Par rapport à la base line : prévision MOCAGE présentant un taux moyen d'erreur de 30%, un modèle statistique élémentaire améliore très sensiblement le résultat.\n", "* Une méthode plus sophistiquée, ici SVM ou random forest apporte une amélioration statistiquement significative mais assez faible au prix de l'interprétation fine des résultats fournie par une régression logistique.\n", "* Python, outil d'apprentissage machine, est plus efficace que R pour les simulations.\n", "* En revanche, R, outil d'apprentissage statistique, permet la sélection et l'interprétation des variables et de leurs interactions pour un modèle de régression linéaire ou logistique classique. La prise en compte d'interactions (modèle quadratique) améliore sensiblement la qualité des prévisions.\n", "* Les forêts aléatoires et les SVM font mieux sur cet exemple, c'est souvent le cas comme avec le boosting, mais d'autres exemples mettent en avant d'autres méthodes: neurones pour une modélisation physique, SVM pour du criblage virtuelle de molécules, régression PLS pour la spectrométrie en proche infra-rouge (NIR)... pas de règle générale.\n", "* Jupyter est un support pédagogique efficace pour des analyses sans développement volumineux de code.\n", "* Avant d'éventuellement passer à [Julia](http://julialang.org/), R et Python sont à l'usage très complémentaires.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# <FONT COLOR=\"Red\">Épisode 5</font>\n", "Remarque Il est possible d'exécuter directement l'épisode 5 sans passer par toutes les étapes de classification supervisée. Il suffit d'exécuter jusqu'à la section 4.1 de l'épidode 1, phase exploratoire et préparation des échantillons, afin de construire les données utilisées dans les sections 12 et 13 d'imputation des données manquantes et de détection d'atypiques." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [Gestion des données manquantes](http://wikistat.fr/pdf/st-m-app-idm.pdf)\n", "Les vraies données sont le plus souvent mitées par l'absence de données, conséquences d'erreurs de saisie, de pannes de capteurs... Les librairies de Python (`pandas`) offrent des choix rudimentaires pour faire des imputations de données manquantes quand celles-ci le sont de façon complètement aléatoire. \n", "\n", "Le [calepin R](https://github.com/wikistat/Apprentissage/blob/master/Pic-ozone/Apprent-R-Ozone.ipynb) d'analyse de ces mêmes données propose une comparaison assez détaillée de deux stratégiées afin d'évaluer leurs performances respectives. \n", "\n", "La première stratégie commence par imputer les données manquantes en les prévoyant par l'algorithme `missForest`. Une fois les données manquantes imputées, différentes méthodes de prévision sont utilisables comme précédemment. Deux sont exécutées: forêts aléatoires et extrem gradient boosting.\n", "\n", "La deuxième stratégie évite l'étape d'imputation en exécutant directement un algorithme de prévision tolérant des données manquantes. Peu le fond, c'est le cas de `XGBoost`.\n", "\n", "Sur ces données, mais sans gros effort d'optimisation de `XGBoost`, la première stratégie enchaînant `missForest` puis `randomForest` conduit à de meilleurs résultats. Seule celle-ci est employée dans ce calepin mais, bien évidemment, l'exécution de `xgboost` sans imputation préalable est une option également possible en Python.\n", "\n", "Bien moins de méthodes sont proposées en Python, `SCikit-learn` ne proposant que des imputations basiques par la moyenne ou la médiane comme dans `pandas`. Néanmoins une imputation par prévision utilisant k-nn, ou des forêts aléatoires (Missforest) est disponible dans la librairie `missingpy`.\n", "\n", "Les commandes ci-dessous font appel aux fichiers suivants:\n", "- `X` données complètes initiales \n", "- `Xna` les données avec des trous, \n", "- `XnaImp` les données avec imputations \n", "\n", "\n", "### Préparation des trous dans `ozone`\n", "Les données initiales de la base `ozone` sont reprises. Seule la variable à expliquer de dépassement de seuil est conservée. La première opération consiste à générer aléatoirement un certain taux de données manquantes par la fonction définie ci-dessous." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T14:54:34.117257Z", "start_time": "2019-07-03T14:54:34.110315Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import numpy.ma as ma\n", "import random\n", "\n", "def input_nan(x, tx):\n", " \"\"\"\n", " x : a 2D matrix of float dtype\n", " tx: the rate of nan value to put in the matrix\n", " \"\"\"\n", " n_total = x.shape[0] * x.shape[1]\n", " mask = np.array([random.random() for _ in range(n_total)]).reshape(x.shape)<tx\n", " mx = ma.masked_array(x, mask=mask, fill_value=np.nan)\n", " return mx.filled()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T14:54:35.714762Z", "start_time": "2019-07-03T14:54:35.707142Z" } }, "outputs": [], "source": [ "# données initiales avec \n", "X=dfC \n", "# Génération de 10% de valeurs manquantes\n", "Xna=input_nan(X, .1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imputation par `missForest`\n", "Le même algorithme que celui présent dans la librairie de R `MissForest` est implémenté dans la librairie `Scikit-learn`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from missingpy import MissForest\n", "imputer = MissForest()\n", "XnaImp = imputer.fit_transform(Xna)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Séparation des échantillons\n", "Des cas sont consiédérés: les données sans données manquantes et les données après imputation des données manquantes. Les mêmes échantillons sont considérés en utilisant la même initialisation du générateur." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T15:08:59.148966Z", "start_time": "2019-07-03T15:08:59.139158Z" } }, "outputs": [], "source": [ "# Données sans trous\n", "X_train,X_test,Yb_train,Yb_test=train_test_split(X,Yb,test_size=200,random_state=11)\n", "XnaImp_train,XnaImp_test,Yb_train,Yr_test=train_test_split(XnaImp,Yb,test_size=200,random_state=11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prévision par forêt aléatoire\n", "Prévision du dépassement d'ozone sans données manquantes et avec données manquantes imputées. Comparaison des erreurs de prévision sur l'échantillon test. Les valeurs par défaut des paramètres sont conservées. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T15:09:03.941566Z", "start_time": "2019-07-03T15:09:03.362895Z" } }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier \n", "# prévision sans trous\n", "forest = RandomForestClassifier(n_estimators=500)\n", "# apprentissage\n", "rfFit = forest.fit(X_train,Yb_train)\n", "# erreur de prévision\n", "# erreur de prévision sur le test\n", "1-rfFit.score(X_test,Yb_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T15:09:05.304552Z", "start_time": "2019-07-03T15:09:04.733321Z" } }, "outputs": [], "source": [ "# prévision avec trous imputés\n", "forest = RandomForestClassifier(n_estimators=500)\n", "# apprentissage\n", "rfFit = forest.fit(XnaImp_train,Yb_train)\n", "# erreur de prévision\n", "# erreur de prévision sur le test\n", "1-rfFit.score(XnaImp_test,Yb_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Que dire de la qualité de prévision avec 10% de trous\n", "\n", "Exercice Faire varier le taux de trous et étudier la dégradation de la prévision.\n", "\n", "Exercice Comparer avec une approche directe de la prévision avec `XGBoost` sans imputation préalable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# <FONT COLOR=\"Red\">Épisode 5 bis</font>\n", "Remarque Il est possible d'exécuter directement l'épisode 5 sans passer par toutes les étapes de classification supervisée. Il suffit d'exécuter jusqu'à la section 4.1 de l'épidode 1, phase exploratoire et préparation des échantillons, afin de construire les données utilisées dans les sections 6 et 7 d'imputation des données manquantes et de détection d'atypiques.\n", "## Détection d'observations atypiques\n", "\n", "Le [calepin R](https://github.com/wikistat/Apprentissage/blob/master/Pic-ozone/Apprent-R-Ozone.ipynb) d'analyse de ces mêmes données propose une comparaison assez détaillée des scores de détection des anomalies. Comme dans R, `Scikit-learn` propose des fonctions en Pyhton de détection d'atypiques multidimensionnels. Les principales sont LOF et Isolation Forest dont les résultats sont comparés ci-dessous.\n", "\n", "\n", "### Local Outlier Factor\n", "Les données sont restreintes aux seules variables quantitatives explicatives.\n", "\n", "Q Quel est le rôle du paramètre k ci-dessous?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T15:09:09.064105Z", "start_time": "2019-07-03T15:09:09.039201Z" } }, "outputs": [], "source": [ "ozone.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T15:09:10.225913Z", "start_time": "2019-07-03T15:09:10.210390Z" } }, "outputs": [], "source": [ "ozoneR=ozone[[\"MOCAGE\",\"TEMPE\",\"VentMOD\",\"VentANG\",\"SRMH2O\",\"LNO2\",\"LNO\"]]\n", "ozoneR.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T15:09:11.500912Z", "start_time": "2019-07-03T15:09:11.349314Z" } }, "outputs": [], "source": [ "from sklearn.neighbors import LocalOutlierFactor\n", "clf = LocalOutlierFactor(n_neighbors=20, contamination=0.05 ) # choix de n_n par défaut\n", "scoreLOF=clf.fit_predict(ozoneR)\n", "scoreAtyp=-clf._decision_function(ozoneR)# opposé du LOF\n", "plt.boxplot(scoreAtyp)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Comment se comporte le LOF en fonction de k?\n", "\n", "Q Quel taux d'observations par défaut sont considérées comme atypiques?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T15:09:13.463557Z", "start_time": "2019-07-03T15:09:13.446299Z" } }, "outputs": [], "source": [ "atypLofInd = clf.fit_predict(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'analyse en composante principale est utilisée pour représenter les observations atypiques." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T15:09:17.797466Z", "start_time": "2019-07-03T15:09:15.737146Z" } }, "outputs": [], "source": [ "## Repésentation des atypiques\n", "plt.figure(figsize=(5,5))\n", "for i, j, nom in zip(C[:,0], C[:,1], atypLofInd):\n", " color = \"red\" if nom!=1 else \"blue\"\n", " plt.plot(i, j, \"o\",color=color)\n", "plt.axis((-4,6,-4,6)) \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### OCC SVM\n", "Q Quels sont les paramètres de cette fonction." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T15:09:20.975581Z", "start_time": "2019-07-03T15:09:20.760546Z" } }, "outputs": [], "source": [ "from sklearn.svm import OneClassSVM\n", "clf=OneClassSVM(nu=0.1, gamma=0.01)\n", "scoreSVM=clf.fit(ozoneR)\n", "scoreAtypSVM=clf._decision_function(ozoneR)\n", "plt.boxplot(scoreAtypSVM)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Quel taux d'atypiques par défaut?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T15:09:22.970638Z", "start_time": "2019-07-03T15:09:22.960893Z" } }, "outputs": [], "source": [ "atypSVMInd = clf.predict(ozoneR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T15:09:26.678417Z", "start_time": "2019-07-03T15:09:24.607060Z" } }, "outputs": [], "source": [ "## Repésentation des atypiques\n", "plt.figure(figsize=(5,5))\n", "for i, j, nom in zip(C[:,0], C[:,1], atypSVMInd):\n", " color = \"red\" if nom!=1 else \"blue\"\n", " plt.plot(i, j, \"o\",color=color)\n", "plt.axis((-4,6,-4,6)) \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Isolation forest\n", "Q Comment se mesure l\"atypicité\" d'une observation dans le cas d'isolation forest?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T15:10:04.393891Z", "start_time": "2019-07-03T15:10:04.070051Z" } }, "outputs": [], "source": [ "from sklearn.ensemble import IsolationForest\n", "clf = IsolationForest(max_samples=1000, contamination=0.05,behaviour=\"new\")\n", "scoreIF=clf.fit(ozoneR)\n", "scoreAtypIF=clf.decision_function(ozoneR)\n", "plt.boxplot(scoreAtypIF)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-07-03T15:10:09.667586Z", "start_time": "2019-07-03T15:10:07.527164Z" } }, "outputs": [], "source": [ "atypIFInd = clf.predict(ozoneR)\n", "## Repésentation des atypiques\n", "plt.figure(figsize=(5,5))\n", "for i, j, nom in zip(C[:,0], C[:,1], atypIFInd):\n", " color = \"red\" if nom!=1 else \"blue\"\n", " plt.plot(i, j, \"o\",color=color)\n", "plt.axis((-4,6,-4,6)) \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Q Les observations définies comme des anomalies se retrouve-t-elles généralement d'une approche à l'autre?\n", "\n", "Remarques\n", "\n", "- la littérature sur la détection d'anomalies ou de nouveautés multidimensionnelles est vaste et fort peu consensuelle. Ceci est encore renforcé par le fait qu'il est difficile de définir un critère efficace de mesure de la qualité d'une détection. Voir à ce sujet l'[article](http://www.dbs.ifi.lmu.de/research/outlier-evaluation/) de Campos et al. (2016). Il importe donc, en fonctin du cas et des données traitées, de pouvoir disposer d'une \"vérité terrain\": quelle méthode est le pllus susceptible de retrouver des anomalies identifiées en tant que telle?\n", "- Conrairement à la librairie originale `randomForest` de R, il ne semble pas exister de librairie proposant la détection d'anomalies relativemement à la construction d'un modèle de prévision y=f(X) par forêt aléatoire. Il importe de suivre l'évolution des librairies en cours de développement." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": true, "toc_cell": false, "toc_position": { "height": "333.133px", "left": "528px", "top": "179.283px", "width": "231.05px" }, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
spatialaudio/nbsphinx	doc/gallery/thumbnail-from-conf-py.ipynb	1	3375	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbsphinx": "hidden" }, "source": [ "This notebook is part of the `nbsphinx` documentation: https://nbsphinx.readthedocs.io/." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Specifying Thumbnails in `conf.py`\n", "\n", "This notebook doesn't contain any thumbnail metadata.\n", "\n", "But in the file [conf.py](../conf.py),\n", "a thumbnail is specified (via the\n", "[nbsphinx_thumbnails](../usage.ipynb#nbsphinx_thumbnails)\n", "option),\n", "which will be used in the [gallery](../subdir/gallery.ipynb).\n", "\n", "The keys in the `nbsphinx_thumbnails` dictionary can contain wildcards,\n", "which behave very similarly to the\n", "[html_sidebars](https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-html_sidebars)\n", "option.\n", "\n", "The thumbnail files can be local image files somewhere in the source directory,\n", "but you'll need to create at least one\n", "[link](../markdown-cells.ipynb#Links-to-Local-Files)\n", "to them in order to copy them to the HTML output directory.\n", "\n", "You can also use files from the `_static` directory\n", "(which contains all files in your [html_static_path](https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-html_static_path)).\n", "\n", "If you want, you can also use files from the `_images` directory,\n", "which contains all notebook outputs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To demonstrate this feature,\n", "we are creating an image file here:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib agg" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.plot([4, 8, 15, 16, 23, 42])\n", "fig.savefig('a-local-file.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please note that the previous cell doesn't have any outputs,\n", "but it has generated a file named `a-local-file.png` in the notebook's directory.\n", "\n", "We have to create a link to this file (which is a good idea anyway):\n", "[a-local-file.png](a-local-file.png).\n", "\n", "Now we can use this file in our [conf.py](../conf.py) like this:\n", "\n", "```python\n", "nbsphinx_thumbnails = {\n", " 'gallery/thumbnail-from-conf-py': 'gallery/a-local-file.png',\n", "}\n", "```\n", "\n", "Please note that the notebook name does not contain the `.ipynb` suffix." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
sdpython/ensae_teaching_cs	_doc/notebooks/exams/td_note_2022_rattrapage.ipynb	1	17778	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1A - Enonc\u00e9 15 novembre 2021 - rattrapage\n", "\n", "Correction de l'examen du 15 novembre 2021." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n", "<script>\n", "function repeat_indent_string(n){\n", " var a = \"\" ;\n", " for ( ; n > 0 ; --n)\n", " a += \" \";\n", " return a;\n", "}\n", "// look up into all sections and builds an automated menu //\n", "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item, begin_format, end_format) {\n", " var anchors = document.getElementsByClassName(\"section\");\n", " if (anchors.length == 0) {\n", " anchors = document.getElementsByClassName(\"text_cell_render rendered_html\");\n", " }\n", " var i,t;\n", " var text_menu = begin;\n", " var text_memo = \"<pre>\\nlength:\" + anchors.length + \"\\n\";\n", " var ind = \"\";\n", " var memo_level = 1;\n", " var href;\n", " var tags = [];\n", " var main_item = 0;\n", " var format_open = 0;\n", " for (i = 0; i <= llast; i++)\n", " tags.push(\"h\" + i);\n", "\n", " for (i = 0; i < anchors.length; i++) {\n", " text_memo += \"*\" + anchors[i].id + \"--\\n\";\n", "\n", " var child = null;\n", " for(t = 0; t < tags.length; t++) {\n", " var r = anchors[i].getElementsByTagName(tags[t]);\n", " if (r.length > 0) {\n", "child = r[0];\n", "break;\n", " }\n", " }\n", " if (child == null) {\n", " text_memo += \"null\\n\";\n", " continue;\n", " }\n", " if (anchors[i].hasAttribute(\"id\")) {\n", " // when converted in RST\n", " href = anchors[i].id;\n", " text_memo += \"#1-\" + href;\n", " // passer \u00e0 child suivant (le chercher)\n", " }\n", " else if (child.hasAttribute(\"id\")) {\n", " // in a notebook\n", " href = child.id;\n", " text_memo += \"#2-\" + href;\n", " }\n", " else {\n", " text_memo += \"#3-\" + \"\" + \"\\n\";\n", " continue;\n", " }\n", " var title = child.textContent;\n", " var level = parseInt(child.tagName.substring(1,2));\n", "\n", " text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n", "\n", " if ((level < lfirst) \|\| (level > llast)) {\n", " continue ;\n", " }\n", " if (title.endsWith('\u00b6')) {\n", " title = title.substring(0,title.length-1).replace(\"<\", \"<\")\n", " .replace(\">\", \">\").replace(\"&\", \"&\");\n", " }\n", " if (title.length == 0) {\n", " continue;\n", " }\n", "\n", " while (level < memo_level) {\n", " text_menu += end_format + \"</ul>\\n\";\n", " format_open -= 1;\n", " memo_level -= 1;\n", " }\n", " if (level == lfirst) {\n", " main_item += 1;\n", " }\n", " if (keep_item != -1 && main_item != keep_item + 1) {\n", " // alert(main_item + \" - \" + level + \" - \" + keep_item);\n", " continue;\n", " }\n", " while (level > memo_level) {\n", " text_menu += \"<ul>\\n\";\n", " memo_level += 1;\n", " }\n", " text_menu += repeat_indent_string(level-2);\n", " text_menu += begin_format + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n", " format_open += 1;\n", " }\n", " while (1 < memo_level) {\n", " text_menu += end_format + \"</ul>\\n\";\n", " memo_level -= 1;\n", " format_open -= 1;\n", " }\n", " text_menu += send;\n", " //text_menu += \"\\n\" + text_memo;\n", "\n", " while (format_open > 0) {\n", " text_menu += end_format;\n", " format_open -= 1;\n", " }\n", " return text_menu;\n", "};\n", "var update_menu = function() {\n", " var sbegin = \"\";\n", " var sformat = '<a href=\"#__HREF__\">__TITLE__</a>';\n", " var send = \"\";\n", " var begin_format = '<li>';\n", " var end_format = '</li>';\n", " var keep_item = -1;\n", " var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item,\n", " begin_format, end_format);\n", " var menu = document.getElementById(\"my_id_menu_nb\");\n", " menu.innerHTML=text_menu;\n", "};\n", "window.setTimeout(update_menu,2000);\n", " </script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercice 1 : optimisation de volume\n", "\n", "On cherche \u00e0 expliquer la forme des briques de lait. On rappelle quelques formules :\n", "\n", "* aire d'une surface : S = longueur x largeur\n", "* volume d'une brique : V = longueur x largeur x hauteur" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Q1 : \u00e9crire une fonction qui calcule l'aire d'une surface" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "12" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def surface(longueur, largeur):\n", " return longueur * largeur\n", "\n", "surface(3, 4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Q2 : \u00e9crire une fonction qui calcule le volume d'une brique" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3, 4, 5)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def volume(longueur, largeur, hauteur):\n", " return longueur, largeur, hauteur\n", "\n", "volume(3, 4, 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On veut conna\u00eetre les dimensions de la brique id\u00e9ale d'un litre : son volume est 1 et sa surface (la somme des surfaces de toutes les faces) est minimale. Ceci afin de minimiser l'utilisation de mati\u00e8res premi\u00e8res." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Q3 : \u00e9crire une fonction qui retourne la somme des surfaces des faces d'une brique" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "94" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def surface_brique(longueur, largeur, hauteur):\n", " return (surface(longueur, largeur) + \n", " surface(largeur, hauteur) + \n", " surface(hauteur, longueur)) * 2\n", "\n", "surface_brique(3, 4, 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Q4 : la brique optimale\n", "\n", "On consid\u00e8re l'ensemble `[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1., 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.]`. On fait varier plusieurs dimensions dans cet ensemble, on ne garde que celle dont le volume est 1 et la surface minimale. Quelles sont les dimensions optimales ?" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "(6.0, (1.0, 1.0, 1.0))" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy\n", "\n", "def brique_optimale(ensemble):\n", " meilleur = None\n", " \n", " for longueur in ensemble:\n", " for largeur in ensemble:\n", " hauteur = 1 / (longueur * largeur)\n", " surf = surface_brique(longueur, largeur, hauteur)\n", " if meilleur is None or surf < meilleur:\n", " meilleur = surf\n", " solution = longueur, largeur, hauteur\n", " return meilleur, solution\n", "\n", "\n", "ensemble = (numpy.arange(20) + 1) / 10\n", "brique_optimale(ensemble)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Q5 : on inclut la surface n\u00e9cessaire pour coller les extremit\u00e9s\n", "\n", "Pour fermer une brique, il faut pouvoir coller les faces entre elles. La surface additionnelle est \u00e9gale \u00e0 une fois la plus petite des faces + la surface d'un carr\u00e9 de c\u00f4t\u00e9 la plus petite dimension. Modifier la fonction pr\u00e9c\u00e9dente pour en tenir compte." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(7.4471428571428575, (1.0, 0.7, 1.4285714285714286))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def brique_optimale_surplus(ensemble):\n", " meilleur = None\n", " \n", " for longueur in ensemble:\n", " for largeur in ensemble:\n", " hauteur = 1 / (longueur * largeur)\n", " surf = (surface_brique(longueur, largeur, hauteur) + \n", " surface(longueur, largeur) + largeur ** 2)\n", " if meilleur is None or surf < meilleur:\n", " meilleur = surf\n", " solution = longueur, largeur, hauteur\n", " return meilleur, solution\n", "\n", "\n", "ensemble = (numpy.arange(20) + 1) / 10\n", "brique_optimale_surplus(ensemble)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dans cette \u00e9criture, le programme suppose implicitement que `largeur` est le plus petit c\u00f4t\u00e9. Ce n'est pas toujours le cas. Quand ce n'est pas le cas, on peut v\u00e9rifier en permutant `longueur` et `largeur`, la surface est plus grande. L'optimisation ne choisira pas cette solution. Si ce n'\u00e9tait pas le cas, il suffirait d'exclure tous les cas o\u00f9 `largeur > longueur`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Q6 : une bouteille de deux litres deux fois plus large\n", "\n", "Le producteur souhaite \u00e9couler la moiti\u00e9 de sa marchandise avec des bouteilles de lait de deux litres, aussi hautes et longues mais deux fois plus large pour pouvoir les stocker facilement. La surface de cette bouteille est celle-ci :\n", "\n", "`surface_brique2(longueur, largeur, hauteur) = surface_brique(longueur, largeur, hauteur) - 2 * surface(largeur, hauteur)`.\n", "\n", "Quelles sont les dimensions optimales ?" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(6.6938095238095245, (0.8, 0.7, 1.7857142857142858))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def brique_optimale_surplus_deux_litres(ensemble):\n", " meilleur = None\n", " \n", " for longueur in ensemble:\n", " for largeur in ensemble:\n", " if largeur > longueur:\n", " continue\n", " hauteur = 1 / (longueur * largeur)\n", " surf1 = (surface_brique(longueur, largeur, hauteur) + \n", " surface(longueur, largeur) + largeur ** 2)\n", " surf2 = surf1 - surface(largeur, hauteur) * 2\n", " surf = surf1 * 2 / 3 + surf2 / 3 # donc autant de litres de lait dans chacun des contenants\n", " if meilleur is None or surf < meilleur:\n", " meilleur = surf\n", " solution = longueur, largeur, hauteur\n", " return meilleur, solution\n", "\n", "\n", "ensemble = (numpy.arange(20) + 1) / 10\n", "brique_optimale_surplus_deux_litres(ensemble)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Q7 : petite brique en bleue, grosse brique en rouge\n", "\n", "Le producteur se sert d'une rang\u00e9e de longueur de 10 briques de 1 litre dans laquelle il ins\u00e8re des briques de deux litres pour envoyer des messages cod\u00e9s (en binaire).\n", "\n", "Example de message : `A A B B A B B A B B` A = une bouteille de 1 litre, B = moiti\u00e9 d'une bouteille de deux litres.\n", "\n", "Combien y a-t-il de possibilit\u00e9s dans une rang\u00e9e d'une longueur de 10 briques de 1 litre." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def messages(n):\n", " possibilites = [1 for i in range(n + 1)]\n", " for i in range(2, n + 1):\n", " possibilites[i] = possibilites[i-1] + possibilites[i-2]\n", " return possibilites\n", "\n", "messages(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "C'est un probl\u00e8me assez classique, le nombre de possibilit\u00e9s pour n=10 est \u00e9gale au nombre de possibilit\u00e9s pour n=8 et une bouteille de 2 litres + le nombre de possibilit\u00e9s pour n=9 et une bouteille de 1 litre." ] }, { "cell_type": "code", "execution_count": 9, "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": "3.9.5" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
hanezu/cs231n-assignment	assignment2/Dropout.ipynb	1	49708	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dropout\n", "Dropout [1] is a technique for regularizing neural networks by randomly setting some features to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to optionally use dropout.\n", "\n", "[1] Geoffrey E. Hinton et al, \"Improving neural networks by preventing co-adaptation of feature detectors\", arXiv 2012" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# As usual, a bit of setup\n", "\n", "import time\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from cs231n.classifiers.fc_net import \n", "from cs231n.data_utils import get_CIFAR10_data\n", "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n", "from cs231n.solver import Solver\n", "\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", "# for auto-reloading external modules\n", "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "def rel_error(x, y):\n", " \"\"\" returns relative error \"\"\"\n", " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_val: (1000, 3, 32, 32)\n", "X_train: (49000, 3, 32, 32)\n", "X_test: (1000, 3, 32, 32)\n", "y_val: (1000,)\n", "y_train: (49000,)\n", "y_test: (1000,)\n" ] } ], "source": [ "# Load the (preprocessed) CIFAR10 data.\n", "\n", "data = get_CIFAR10_data()\n", "for k, v in data.iteritems():\n", " print '%s: ' % k, v.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Dropout forward pass\n", "In the file `cs231n/layers.py`, implement the forward pass for dropout. Since dropout behaves differently during training and testing, make sure to implement the operation for both modes.\n", "\n", "Once you have done so, run the cell below to test your implementation." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running tests with p = 0.3\n", "Mean of input: 9.99997969635\n", "Mean of train-time output: 9.95562408585\n", "Mean of test-time output: 9.99997969635\n", "Fraction of train-time output set to zero: 0.701364\n", "Fraction of test-time output set to zero: 0.0\n", "\n", "Running tests with p = 0.6\n", "Mean of input: 9.99997969635\n", "Mean of train-time output: 10.0036978302\n", "Mean of test-time output: 9.99997969635\n", "Fraction of train-time output set to zero: 0.399676\n", "Fraction of test-time output set to zero: 0.0\n", "\n", "Running tests with p = 0.75\n", "Mean of input: 9.99997969635\n", "Mean of train-time output: 9.98866141316\n", "Mean of test-time output: 9.99997969635\n", "Fraction of train-time output set to zero: 0.250872\n", "Fraction of test-time output set to zero: 0.0\n", "\n" ] } ], "source": [ "x = np.random.randn(500, 500) + 10\n", "\n", "for p in [0.3, 0.6, 0.75]:\n", " out, _ = dropout_forward(x, {'mode': 'train', 'p': p})\n", " out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})\n", "\n", " print 'Running tests with p = ', p\n", " print 'Mean of input: ', x.mean()\n", " print 'Mean of train-time output: ', out.mean()\n", " print 'Mean of test-time output: ', out_test.mean()\n", " print 'Fraction of train-time output set to zero: ', (out == 0).mean()\n", " print 'Fraction of test-time output set to zero: ', (out_test == 0).mean()\n", " print" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Dropout backward pass\n", "In the file `cs231n/layers.py`, implement the backward pass for dropout. After doing so, run the following cell to numerically gradient-check your implementation." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dx relative error: 5.44560635204e-11\n" ] } ], "source": [ "x = np.random.randn(10, 10) + 10\n", "dout = np.random.randn(x.shape)\n", "\n", "dropout_param = {'mode': 'train', 'p': 0.8, 'seed': 123}\n", "out, cache = dropout_forward(x, dropout_param)\n", "dx = dropout_backward(dout, cache)\n", "dx_num = eval_numerical_gradient_array(lambda xx: dropout_forward(xx, dropout_param)[0], x, dout)\n", "\n", "print 'dx relative error: ', rel_error(dx, dx_num)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fully-connected nets with Dropout\n", "In the file `cs231n/classifiers/fc_net.py`, modify your implementation to use dropout. Specificially, if the constructor the the net receives a nonzero value for the `dropout` parameter, then the net should add dropout immediately after every ReLU nonlinearity. After doing so, run the following to numerically gradient-check your implementation." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running check with dropout = 0\n", "Initial loss: 2.3031564446\n", "W1 relative error: 4.41e-07\n", "W2 relative error: 2.06e-06\n", "W3 relative error: 9.60e-08\n", "b1 relative error: 2.94e-08\n", "b2 relative error: 3.73e-08\n", "b3 relative error: 1.12e-10\n", "\n", "Running check with dropout = 0.25\n", "Initial loss: 2.30437455085\n", "W1 relative error: 3.01e-08\n", "W2 relative error: 6.22e-09\n", "W3 relative error: 1.55e-07\n", "b1 relative error: 1.02e-09\n", "b2 relative error: 1.41e-10\n", "b3 relative error: 1.19e-10\n", "\n", "Running check with dropout = 0.5\n", "Initial loss: 2.29642723358\n", "W1 relative error: 3.00e-07\n", "W2 relative error: 4.41e-07\n", "W3 relative error: 1.81e-07\n", "b1 relative error: 2.71e-09\n", "b2 relative error: 6.79e-10\n", "b3 relative error: 1.12e-10\n", "\n" ] } ], "source": [ "N, D, H1, H2, C = 2, 15, 20, 30, 10\n", "X = np.random.randn(N, D)\n", "y = np.random.randint(C, size=(N,))\n", "\n", "for dropout in [0, 0.25, 0.5]:\n", " print 'Running check with dropout = ', dropout\n", " model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n", " weight_scale=5e-2, dtype=np.float64,\n", " dropout=dropout, seed=123)\n", "\n", " loss, grads = model.loss(X, y)\n", " print 'Initial loss: ', loss\n", "\n", " for name in sorted(grads):\n", " f = lambda _: model.loss(X, y)[0]\n", " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n", " print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))\n", " print" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Regularization experiment\n", "As an experiment, we will train a pair of two-layer networks on 500 training examples: one will use no dropout, and one will use a dropout probability of 0.75. We will then visualize the training and validation accuracies of the two networks over time." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "(Iteration 1 / 125) loss: 8.596245\n", "(Epoch 0 / 25) train acc: 0.224000; val_acc: 0.183000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "cs231n/layers.py:627: RuntimeWarning: divide by zero encountered in log\n", " dx = probs.copy()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(Epoch 1 / 25) train acc: 0.382000; val_acc: 0.219000\n", "(Epoch 2 / 25) train acc: 0.484000; val_acc: 0.248000\n", "(Epoch 3 / 25) train acc: 0.620000; val_acc: 0.274000\n", "(Epoch 4 / 25) train acc: 0.654000; val_acc: 0.246000\n", "(Epoch 5 / 25) train acc: 0.726000; val_acc: 0.280000\n", "(Epoch 6 / 25) train acc: 0.786000; val_acc: 0.304000\n", "(Epoch 7 / 25) train acc: 0.814000; val_acc: 0.265000\n", "(Epoch 8 / 25) train acc: 0.844000; val_acc: 0.271000\n", "(Epoch 9 / 25) train acc: 0.898000; val_acc: 0.289000\n", "(Epoch 10 / 25) train acc: 0.932000; val_acc: 0.291000\n", "(Epoch 11 / 25) train acc: 0.960000; val_acc: 0.274000\n", "(Epoch 12 / 25) train acc: 0.948000; val_acc: 0.275000\n", "(Epoch 13 / 25) train acc: 0.970000; val_acc: 0.293000\n", "(Epoch 14 / 25) train acc: 0.944000; val_acc: 0.279000\n", "(Epoch 15 / 25) train acc: 0.948000; val_acc: 0.277000\n", "(Epoch 16 / 25) train acc: 0.976000; val_acc: 0.289000\n", "(Epoch 17 / 25) train acc: 0.972000; val_acc: 0.282000\n", "(Epoch 18 / 25) train acc: 0.966000; val_acc: 0.288000\n", "(Epoch 19 / 25) train acc: 0.976000; val_acc: 0.275000\n", "(Epoch 20 / 25) train acc: 0.980000; val_acc: 0.302000\n", "(Iteration 101 / 125) loss: 0.129639\n", "(Epoch 21 / 25) train acc: 0.984000; val_acc: 0.299000\n", "(Epoch 22 / 25) train acc: 0.984000; val_acc: 0.301000\n", "(Epoch 23 / 25) train acc: 0.984000; val_acc: 0.299000\n", "(Epoch 24 / 25) train acc: 0.988000; val_acc: 0.289000\n", "(Epoch 25 / 25) train acc: 0.982000; val_acc: 0.283000\n", "0.75\n", "(Iteration 1 / 125) loss: 10.053351\n", "(Epoch 0 / 25) train acc: 0.274000; val_acc: 0.230000\n", "(Epoch 1 / 25) train acc: 0.352000; val_acc: 0.211000\n", "(Epoch 2 / 25) train acc: 0.444000; val_acc: 0.269000\n", "(Epoch 3 / 25) train acc: 0.566000; val_acc: 0.263000\n", "(Epoch 4 / 25) train acc: 0.650000; val_acc: 0.257000\n", "(Epoch 5 / 25) train acc: 0.678000; val_acc: 0.281000\n", "(Epoch 6 / 25) train acc: 0.766000; val_acc: 0.310000\n", "(Epoch 7 / 25) train acc: 0.764000; val_acc: 0.269000\n", "(Epoch 8 / 25) train acc: 0.808000; val_acc: 0.273000\n", "(Epoch 9 / 25) train acc: 0.884000; val_acc: 0.285000\n", "(Epoch 10 / 25) train acc: 0.860000; val_acc: 0.273000\n", "(Epoch 11 / 25) train acc: 0.930000; val_acc: 0.309000\n", "(Epoch 12 / 25) train acc: 0.926000; val_acc: 0.292000\n", "(Epoch 13 / 25) train acc: 0.904000; val_acc: 0.273000\n", "(Epoch 14 / 25) train acc: 0.910000; val_acc: 0.286000\n", "(Epoch 15 / 25) train acc: 0.928000; val_acc: 0.304000\n", "(Epoch 16 / 25) train acc: 0.944000; val_acc: 0.308000\n", "(Epoch 17 / 25) train acc: 0.978000; val_acc: 0.308000\n", "(Epoch 18 / 25) train acc: 0.952000; val_acc: 0.305000\n", "(Epoch 19 / 25) train acc: 0.986000; val_acc: 0.301000\n", "(Epoch 20 / 25) train acc: 0.974000; val_acc: 0.301000\n", "(Iteration 101 / 125) loss: 0.208757\n", "(Epoch 21 / 25) train acc: 0.978000; val_acc: 0.322000\n", "(Epoch 22 / 25) train acc: 0.978000; val_acc: 0.306000\n", "(Epoch 23 / 25) train acc: 0.978000; val_acc: 0.288000\n", "(Epoch 24 / 25) train acc: 0.986000; val_acc: 0.294000\n", "(Epoch 25 / 25) train acc: 0.992000; val_acc: 0.308000\n" ] } ], "source": [ "# Train two identical nets, one with dropout and one without\n", "\n", "num_train = 500\n", "small_data = {\n", " 'X_train': data['X_train'][:num_train],\n", " 'y_train': data['y_train'][:num_train],\n", " 'X_val': data['X_val'],\n", " 'y_val': data['y_val'],\n", "}\n", "\n", "solvers = {}\n", "dropout_choices = [0, 0.75]\n", "for dropout in dropout_choices:\n", " model = FullyConnectedNet([500], dropout=dropout)\n", " print dropout\n", "\n", " solver = Solver(model, small_data,\n", " num_epochs=25, batch_size=100,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': 5e-4,\n", " },\n", " verbose=True, print_every=100)\n", " solver.train()\n", " solvers[dropout] = solver" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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bIuLF1UlNkiRJkupf1YeGDtEdwLGZ+VhEvAFYBZwwUMOIOB84H+DYY4+tXoaS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\n6Ovf3ze+8Q3OPffcIT1OVd4/E0FJkiSNOVeu3EDv9p27lfV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"text/plain": [ "<matplotlib.figure.Figure at 0x117d2f1d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot train and validation accuracies of the two models\n", "\n", "train_accs = []\n", "val_accs = []\n", "for dropout in dropout_choices:\n", " solver = solvers[dropout]\n", " train_accs.append(solver.train_acc_history[-1])\n", " val_accs.append(solver.val_acc_history[-1])\n", "\n", "plt.subplot(3, 1, 1)\n", "for dropout in dropout_choices:\n", " plt.plot(solvers[dropout].train_acc_history, 'o', label='%.2f dropout' % dropout)\n", "plt.title('Train accuracy')\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Accuracy')\n", "plt.legend(ncol=2, loc='lower right')\n", " \n", "plt.subplot(3, 1, 2)\n", "for dropout in dropout_choices:\n", " plt.plot(solvers[dropout].val_acc_history, 'o', label='%.2f dropout' % dropout)\n", "plt.title('Val accuracy')\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Accuracy')\n", "plt.legend(ncol=2, loc='lower right')\n", "\n", "plt.gcf().set_size_inches(15, 15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Question\n", "Explain what you see in this experiment. What does it suggest about dropout?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Answer\n", "\n", "drop out has a little bit slower convergent speed, but the final test convergent result are about the same.\n", "the val acc is a little bit higher.\n", "\n", "it suggests that dropout is harder to train but would not effect result, and suggests dropout to be less prone to overfitting." ] } ], "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": 0 }	mit
a-mt/dev-roadmap	docs/!ml/notebooks/One Hot encoding.ipynb	1	6848	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "corpus = [\n", " \"another five fish find another faraway fish\",\n", " \"i love fantastic flying fish\"\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bag of words" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from sklearn.feature_extraction.text import CountVectorizer" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[2, 0, 1, 1, 2, 1, 0, 0, 0],\n", " [0, 1, 0, 0, 1, 0, 1, 1, 1]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vectorizer = CountVectorizer(\n", " tokenizer=None,\n", " token_pattern=r\"(?u)\\b\\w+\\b\"\n", ")\n", "vectorizer.fit_transform(corpus).toarray()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'another': 0,\n", " 'five': 5,\n", " 'fish': 4,\n", " 'find': 3,\n", " 'faraway': 2,\n", " 'i': 7,\n", " 'love': 8,\n", " 'fantastic': 1,\n", " 'flying': 6}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vectorizer.vocabulary_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TF-IDF\n", "\n", "https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_extraction/text.py#L609" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[2.81093022, 0. , 1.40546511, 1.40546511, 2. ,\n", " 1.40546511, 0. , 0. , 0. ],\n", " [0. , 1.40546511, 0. , 0. , 1. ,\n", " 0. , 1.40546511, 1.40546511, 1.40546511]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vectorizer = TfidfVectorizer(\n", " norm=None,\n", " token_pattern=r\"(?u)\\b\\w+\\b\"\n", ")\n", "vectorizer.fit_transform(corpus).toarray()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Integer encoding" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['another', 'five', 'fish', 'find', 'another', 'faraway', 'fish'],\n", " ['i', 'love', 'fantastic', 'flying', 'fish']]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokens = [txt.split(' ') for txt in corpus]\n", "tokens" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['another', 'fantastic', 'faraway', 'find', 'fish', 'five',\n", " 'flying', 'i', 'love'], dtype='<U9')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vocab = np.unique(np.concatenate(tokens))\n", "vocab" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([0, 5, 4, 3, 0, 2, 4]), array([7, 8, 1, 6, 4])]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vectorizer = LabelEncoder()\n", "vectorizer.fit(vocab)\n", "[vectorizer.transform(x) for x in tokens]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## One-hot encoding" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import OneHotEncoder" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([['another'],\n", " ['fantastic'],\n", " ['faraway'],\n", " ['find'],\n", " ['fish'],\n", " ['five'],\n", " ['flying'],\n", " ['i'],\n", " ['love']], dtype='<U9')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vocab.reshape(-1,1)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([[1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 1., 0., 0., 0.],\n", " [0., 0., 0., 0., 1., 0., 0., 0., 0.],\n", " [0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", " [1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 1., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 1., 0., 0., 0., 0.]]),\n", " array([[0., 0., 0., 0., 0., 0., 0., 1., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0., 1.],\n", " [0., 1., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 1., 0., 0.],\n", " [0., 0., 0., 0., 1., 0., 0., 0., 0.]])]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vectorizer = OneHotEncoder(handle_unknown='ignore', sparse=False)\n", "vectorizer.fit(vocab.reshape(-1,1))\n", "[vectorizer.transform(np.array(x).reshape(-1,1)) for x in tokens]" ] } ], "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": 2 }	mit
JJINDAHOUSE/deep-learning	autoencoder/Convolutional_Autoencoder.ipynb	1	1899551	null	mit
sraejones/Phys202-project	project/Presentation.ipynb	1	145973	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Neural Networks\n", "Sara Jones" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Abstract \n", "\n", "This project will take hand written digits 0 to 9 and recognize them through a computer-learning program. The neural network will require a training sets to 'teach' the network how to recognize the indiviualites between the diffrent digits and return the proper identification. The Network will be required to know the diffrences between the diffrent styles of handwriting (such as bars or no bars in sevens) and account for other factors such as messy handwriting. these factors will be determined by giving weights to the characteristics of each digits (accounting for various stylization diffrences) to detrmine what factors are important for identification of a digit and what can be given less weight or even ignored in identifcation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Base question\n", "\n", "The base question fir this project is taking hand written numbers and recognizing the through a neural network. This will require a computerized learning system that must be trained to recognize the digits. This network should have over 90% accuracy when recognizing hand written digits. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.display import display\n", "from IPython.display import Image" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import math\n", "import random\n", "from scipy import optimize\n", "from scipy.interpolate import griddata" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from IPython.html.widgets import interact" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1797, 64)\n" ] } ], "source": [ "from sklearn.datasets import load_digits\n", "digits = load_digits()\n", "print(digits.data.shape)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = digits.target" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def show_digit(i):\n", " plt.matshow(digits.images[i], cmap='gray');" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb8f2c21390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_digit(0)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb8f2719f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_digit(2)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb8f254b0f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_digit(6)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb8f25b4588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_digit(9)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb8eede9588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "interact(show_digit, i=(0,100));" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, ..., 8, 9, 8])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "digits.target" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"deffintion of layers weights and biases for the network\"\"\"\n", "\n", "\n", "def initialize_weights(layers):\n", " nlayers = len(layers)\n", " weights = []\n", " for i in range(nlayers-1):\n", " w = np.random.randn(layers[i+1],layers[i])\n", " weights.append(w)\n", " return weights\n", "\n", "def initialize_biases(layers):\n", " nlayers = len(layers)\n", " biases = []\n", " for i in range(nlayers-1):\n", " b = np.random.randn(layers[i+1])\n", " biases.append(b)\n", " return biases\n", "\n", " return weights\n", "\n", "def initialize(layers):\n", " nlayers = len(layers)\n", " biases = []\n", " for i in range(nlayers-1):\n", " t = np.random.randn(layers[i+1])\n", " biases.append(b)\n", " y = np.random.randn(layers[i+1],layers[i])\n", " weights.append(w)\n", " return biases, weights" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[array([[-2.46300409, 1.06667564, -0.06314531, 0.0196614 ],\n", " [ 0.60701328, -0.65577449, 0.20831386, -0.75966499],\n", " [ 0.40467794, -0.12545396, 1.52203111, -0.1519065 ]]),\n", " array([[-2.10665881, 0.86922705, 0.23413697],\n", " [-0.23998871, -0.06256537, 0.09722866],\n", " [ 0.92264316, 0.90868384, 0.15553074],\n", " [-0.37892518, -0.18286887, 1.93648099],\n", " [ 0.42631645, 0.55161602, 0.59971509]])]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initialize_weights([4,3,5])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[array([-0.91125265, 1.05912391, 0.62763642]),\n", " array([-3.20344516, 0.04504446, 0.57331792, -0.91206437, 0.87429496])]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initialize_biases([4,3,5])" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "w = initialize_weights([4,3,5])\n", "assert w[0].shape == (3,4)\n", "assert w[1].shape == (5,3)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "b = initialize_biases([4,3,5])\n", "assert b[0].shape == (3,)\n", "assert b[1].shape == (5,)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sigmoid(z):\n", " return 1.0/(1.0+np.exp(-z))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert sigmoid(0) == 0.5 \n", "assert sigmoid(1000) == 1.0 " ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def sigmoid_prime(z):\n", " return sigmoid(z)(1-sigmoid(z))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "assert sigmoid_prime(0) == 0.25\n", "assert sigmoid_prime(1000) == 0.0" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"Return the output if k is the input\"\"\" \n", "def feedforward_1(weights, biases, k):\n", " for w, b in zip(weights, biases):\n", " k = sigmoid(np.dot(w, k)+b)\n", " return k" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def backprop_1(x, y):\n", " nabla_b = [np.zeros(b.shape) for b in initialize_biases(layers)]\n", " nabla_w = [np.zeros(w.shape) for w in initialize_weights(layers)]\n", " activation = x\n", " activations = [x] \n", " zs = []\n", " for b, w in zip(initialize_biases(layers), initialize_weights(layers)):\n", " z = np.dot(w, activation)+b\n", " zs.append(z)\n", " activation = sigmoid_vec(z)\n", " activations.append(activation)\n", " # backward pass\n", " delta = self.cost_derivative(activations[-1], y) sigmoid_prime_vec(zs[-1])\n", " nabla_b[-1] = delta\n", " nabla_w[-1] = np.dot(delta, activations[-2].transpose())\n", " for l in range(2, layers):\n", " z = zs[-l]\n", " spv = sigmoid_prime_vec(z)\n", " delta = np.dot(initialize_weights(layers)[-l+1].transpose(), delta) * spv\n", " nabla_b[-l] = delta\n", " nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())\n", " return (nabla_b, nabla_w)\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/jpeg": 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Bc5HZpUpWxtFmT4nTE157aBd7bUhXswW4WzV5o5onDbXxuDmkfQhZURFGZq1b\nxuOltY7FuyFsuYPgRvawuBIGyT7Ad1JNJLQSNHXcfJeotU1a9T7RarU4hO9pc4xsDXSkeAT7/mtb\nj+Qu5TCVruRxsmNtSgl9WR3U6PuQNn6jR/NSaIm1gmkfLVn+xPidO1rgzqO2h+uwdr6+VpYyvet4\nOvHyKClLdI3OyFpdF1b2NB3y7fmt6zOynUmsPBLIWF5DRs6A32XJeHM5fyyjyTlwtGq/K1n18TUk\nlPREB2D/AKHY86+ZVz9OeI/wO4hWx8zWG88mW3Iw9XXIT89DYA0FbUI2NHwqDyT0mwedv/pKnNZw\n2Q6Ogz493ww78Wjsf7lDwcW9WcVKIKPMMdcpxjUZvQ7eR/yj0k7/AOkV47075zn3RDk/OZBWaep0\nGNj+Fvv46gB+8gq38V4BgOIMLsfV67bzuS5YPXM89/2j48+2laEREQgEaPhYK1KrSY5lWtDA1x6n\nCJgaCfmde6jqlm3iMDLa5Jcql8HxJJZ4mlrBGCS3t8w3W1J1rMNyrFZryCSGVgex7fDmkbBWVEXH\n+f8AFLPN/VvB42Vn/wCFUaYs2nEkbaZCC3z5PSB2+q85Bwkcz9X6sUkDG4PB1ImTMG29RO3NjHzG\ntb17dl0TlmWh43xDI5AtaGV65EbA3t1EdLRr5bIXNM5k6XF7vCuOsibZsUqrrkULWaM9ks6Y27H6\nvU4vJ/JdYws2TsYqKXL1Ial12y+GGX4jWDfYdWhs61tSCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiI\nqzzviVfmXFrONlaBYA+LVl3oxyj9U7/uP0KrPpnziazG7ivJnurckoH4bmz6BsNHgtPudfv8rpiI\nuaepPNJmsHEeME2uRZEGMCHThAzenFx39063+HlWng/FK3DeK1cRBovaPiTyf/ElIHU7+7X4AKas\nUatqavNPXillrv64XvaCY3a1sH2Oiqhl+RcYz+cu8EzteSOR7B0i03ojsbAIMb9+Rv8Af4XzxD04\nj4TmrE+KzFs4uePpdjptPaH9tODv3+3v9FYcfyGHIZ/I4hlO7FJQDeuaWEtik6v5jvdS4cDvRB0d\nHuvURc8yvOcpxn1HhxecqxtwGS6Y6FyMfqSe4efxP5diuhoirHO4OSyYD43FbbIMhXkExjewOE7G\ng7Z+awen/MpOYYR0tujNRyNVwitwSMLQH63tu/Y/L2VuWOeeGtC6axLHFE39Z8jg1o/ElVXnNXlm\nUxtelxO1VqiwSLNuR56o2a7Fmh5PfutrhfDafC8Q+lWsWLEkz/i2Jpn7MkmgCde3jwrIoaLJZR/K\np8bJh3MxjazZY8h8UEPfvRZ0+R/4LO2leZm2zx32Mxba/wAMUG12j+U3vr6/Pjt06UkiIiIiIiIi\n8c1r2lrmhzSNEEbBUFyyvyKbAuj4rarVciHtLXzsBb0DyNEEb8eyrnp7y/kmWuXMLyrCTUslUHX9\nobERDK3evPjf4HuugovOkb3ob8bQNAJIA2fJVf5hxj+FmJgoG4azYrcVlxEYeHhh30kE6IKjeR+n\nGJ5HauXpZJoMhOyFsVqPXVXMRJaWfme/zVspwyVqUMEth9iSNga6aQAOkIH6x122VnRERERERERE\nREREREREREREREREREREREVP5r6d4rmUbbEhfUy0DdVr8BLXxnyN68gH/HsqZXterfDCWW6UHKMd\nH2+JG/U2v/q/eCt13rHkGVml/p/nxYc7pbH0HpP/AEun5fRR8131c5o9gpUYeNYuV3aWR4+M1h9z\ns9W/wA7q98M4BiOGV3PrNdYyMzf9ZvTd5JXeSe/gE99fv2rWir/J+GYbl0VduVge6Ss/rgmieWSR\nnYPYj8ApKXK0amTqYqWwG3LTHvgiIJLwwDqO/HbYW7ra5Vn/AE+5Thr8+T4Fn7ETrMnXYoW5etjn\nE93NLt/399Dz7Lo9/LU8NjhdytmKrCC1r5HH7oc4gAb/ABK3mkOaCDsHuCvVG5nA4vkFeKDK047U\ncMrZow8fqvHghKWdx2Qyt/GVpy+3QLBYZ0EdBcNjuRo/kpJEWpkftbMbadjmROu/DcYWydmufr7u\n/pvSgslzCDifEq2U5WY61tzGiSCuesulI7tZ8/8A13XsbMb6kcIj+30LUNG+0PMEx+HIAHdj2Pvo\nH8CrDUqxUacFSBvTDBG2ONu96aBoD9yzLm/qVzPI4+9juK8ZLv4Q5F7XNf0Atii2duO+3sfwAKkP\nUvPZLAcJDaG3ZW9JHShkZ26ZH+XfTsDr8lO8Qw1jj/FMdi7dp1qzXi6ZZnOLupxJJ7nvrvofRTaI\niIiIiIiIiaRaGLyjMrBNKyrariKZ8JbZiMZcW/tAHy0+xW/tERERERERERERERERERERERERERER\nERERERERERERF8OijdKyV0bTIwENcR3bvzo+ypGA9Sq2S5fe4zlaMuKyMMrm1mzu7WWAnRadeSBv\nX7levKw2qda9AYLdeKeIkOLJWBw2DsHR+RVf5qOWMw7ZeIPpfa4iXyRWWbMrQP1W+wO/n+9YOD8u\nvcmq2I8ngruKv1Ols7Z4yI3uO/1CfPj8tjyraonP5anxnCX85Zgc6OvH8SX4LAXvA7D5b8+6zYTN\nUeQ4etlMdMJa1hgc0g9x8wfkR4IUgiisxm2YeSgx9O5Z+2WBAHV4usRE/tP+TfqqT6q8FyHIDR5B\nhXiTLYr70VWYB0czQerXSe3Vv9/hWfhHJJuTcdhuW8fYoXGfydiCaF0engdy3flp9lZFF8hz1TjW\nGlyl1k74Ii0OEEZe7uQB2H4r6hx+Nt36+dbTjN11cMjsOZqRsbu/T38eVjxGVizgtuOPtV/sdp8A\n+1xdPWW/ts+bTvsVLIiIiIiIiIiIiLDbri3UmrufIxsrCwvjd0ubsa2D7FQT3t4LwpnUMjlmUIg0\nlo+LPL38+2/P7gsXE+f4DmNcPx1sR2QS19SchkzCPP3ff8RtWhERERERERERERERERERERERERER\nERERERERERERERV/kHCsDye5TuZWl8WxTd1QyNkcxze+9baRsbC3WZ6g7kUmBbI79IR1m2SzoOvh\nkloO/HkKTRFF1cbHg6WQfRZNPJNNLbMckpcXSO79LSf1RsaA8BUrCeq2NzOU/g/yHD28JdsN6Gw3\nm/cm326QSB5+o7qW4BwZ/Bxl60d341G1a+NVh0R8BuvHc+fH7grmua805HyrI8h/gjxClNWsjofa\nys0f8lCwjf3Sdg/L+4LoGNgsVcZVguWTasxxNbLOWgGRwHd2h42VtaUZn25h2GnGBkqR5I6+E62C\nYx377138bWyyd1TGNmyMsLHxRB1iQHpjBA+8RvwPPlQPCuYs5nXyNuvUdFTr23QV5iSRYaP2x2WL\nm3OqvDWUIjUkvXr84igqwkdbhvu78v8AFWxp20EjW/b5L1EREREREREREREVC5b6V4XkUz8lR6sV\nnAeuO/WJaev5uAPf8fK3eBVOYY2veo8suQXvhPb9ktxnvIwg7DuwPbt5+atNW5WuxukqzxTMa90b\nnRvDgHA6I7e4KzoiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi+ehoeZAxvWRrq13I+W1QeJ\n+pP6U5Ba47yKh+hs5HIfg13uJbMz2LXHsT/j5C6AiLSvYjHZKatNdpQWJar/AIkD5GBxjd8x8lT/\nAFHxL8occL3KGYXANk/1xgkEb53bHQGv9v8A0V7meYYvj/McXj7PKalSrHAWWKUrep73OA+G50nf\np+ffXz91eo3slYJI3Ne1w2HNOwR+K+kRcs9XOOW+Z4WX+D2XdLdxoLbGNgmB+KHaPS5oPZ3bsD5V\nv4CwRcGxMX6LkxZZAGOqSDTmEdiTv5kb/NYbfDIrvqNS5XLO14qUzXjruj3p5JPWDvt2cR4VqRER\nEREREREREREReEbGlHw46vhcXPFh6MMWg+VkDB0NfIe/f8T7rWw+VujjUOQ5NBWxVkN6rDDMPhx9\n9Dbj2Ht7qWr2IbcDJ68sc0Mg6mSRuDmuHzBHlZERFC8m5Xh+JY37dmLjYIyS2Nvl8jtb00e65ueX\n+pfMzHHxzjv6DpynqF+73JjPYEBw/PsCsrfTj1FdGHP9SLAkI2WtY4gH6Hf/AGLAM96pcGd8PM4p\nnJqDW7FmmPvsaPJdpu96+Y/Pyug8S5vg+Z0PtGKtAyN/4WtJoSxH6t+X18KxoiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiIiIoq7xvDZHL08rbx8Et+m7qgsFv3mH8ffz7rLXy8FjNXMWyGy2aoxj3yPhIjc\nHjY6XeCfmpBFjE8Lp3QCVhma0OdGHDqAPgkeddiub1fTbIZvmM2e5nkRfgrzuOPx7P8AgmM390uH\nYb1rtr8SVEeq/prazGfo8kxOOjyMgLYblB2mCRg7dXVsd9dv3fJdB49x6PhvE3Y7FNnsuia+WOOx\nLsuee4Z1a0BvspDD3btnB1reXpNx1t0YM8BlDhGf+cOykGva9ocxwc0+CDsKIynKcPh8xjsTetiO\n7kX9FaINJLzvXsOw381s0cJjMbduXadKGCzdcH2JGN0ZCPBP7yo7mFW3lsJNhcXk4aOQtgAPc/Tx\nF1D4haB3307G/qpXFY+LE4urj4pZZWV4xG180he9wHuSfJW4iIiL5L2g6LgD+K9Dmu8OB/Ar1ERE\nRERERERFo5jE0s7irGMyMAmqWGdMjD7j/sIPdcodwvmvpxKy1w3IS5rFt22TFW3d2gne299fu0e/\ngq68F57V5lXsQvrS0ctTIbcpSjToz8x8xvat7nBrS5xAAGyT7LRxGZx+dpfbcZaZZr9bo+th7dTT\noj96+c7mqfHcLayuQlEdaszqefn8gPqToLmPBuNWee3f4c8yhE3xdtx2OkbuKGPY0/R8nsfPnz8t\ndeAAAAGgPAXqaXJ/UHhcuBtN5zxCIVslR+/ZqQsAjsx724uA130e/wA9fNX7inJaXLeO1cvRcCyZ\nv32b2Y3/ALTT9QVNIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIvmTq+G74euvR6d+N+yoHDef3L+e\nucY5RSjx2drlz4wzYjsR/NpP07+e4/ArM31Lq5Lm1bj3H6UmVYHEXrcJ1FXGux6vB7/+GyrazD49\nmZky7KsYyEsIgfOB95zAdgfvW8ijKjcwM5kH25ahxZEYpxxtPxQdffLz48+AFlzOIp5/D2cXkIvi\nVbLOiRu9H5gg/MEA/kqVw/05vcJz7pKHIrFjByxkSUbQ6nB/sWkdh+5RXGMD9t9ZuRZHN2bFq/ji\n37A0tPwY4ZBsa2PIHbt9T3XV/Zc04hx7KZH1Jz3L87DJEYZHUcbG4aHwgSC8A/MAd/fZWx6icU5B\nbyFPk/FL0zcxRAYKjpAIpo9kkaPbff38q74exbt4enYyFX7Jclha6ev1dXw3kd27+hW6iKNzufxn\nG8VLkstaZWqx+XO7kn2AA7k/QLl8HJeeepPx/wCDMMeCwLpfhtyM4/l3NA7lo3/h4+flbH8RNKwT\nNf5TnbFp53JL8YDqP5g/4rx3oxbw4+1cW5flaeQb4NiTrjePPS4DXbYHz/BfWP8AUjO8SycWJ9R6\nTK7ZtNrZSq3cUh9+rXjyPGtfJdWiljmiZLE9r43gOa5p2CD4IK+0REREREREREWCOlVhsy2Yq8LJ\n5tfFkawBz9eOo+T+a4d6n8vynGMHmMFZztbJXcnZeIo4m9L6VYjZa7R8kEAfmudel3qZLwG/PFZg\ndZxdrvLGw/eY4Ds5u+30I/yVryXqHa9W48ZxNlJlWS5ktyhjiemBoBB6vG/199vYfNfo6CFlavHB\nGNMjaGNH0A0siIvCA4EEbB8hco9K4X8Y5fyvhnZ1etM25XeXbd0PA7Ht37dK6wiIiIiIixWJvs9a\nWbp6vhsLtb86G1QeKesvFeShkMtoY2849P2e0dAn/kv8H+4/RdCBDgCCCD4IXqIiIiIiIiIiIiIi\nIiIiIiIiIigK7IM5ksgL+AdAahNaC3OxvVMxzfvFh8hvkLFxKpjsXHcw+Lwk+Nq0JBC2SRgAsdtl\nzTvbvPk/NWREUJy7F3czxa/Rx16aldkj3DPE7pcHg7A2PY60fxVd9KuWX+SccmgzLZW5fHSmvbMk\nfR1H2P468qyZHC08/bxd83LGqE3x4hWm1HI7x9/z1BTGhveu65ZyrCeoWC5BNmuH5GTJ1rcgdPjb\nbwRGfkzegG67diD+K6RUsyjFV7GSZHVndEx07OsdMbyBsb+h7LbBBGx3BUZl6OSuTY92Pyf2JkFg\nSWG/CD/jx6O2d/H4qRbNE6V0TZGGRo25gd3H4hfaFcVhr/xv+o1ixZa48WwLzCyBxLftE3f73b22\nN/hr5ldoYxsbAxjQ1rRoADQC+kUZnsDj+SYexi8lAJa07S09h1NPs5p9iPmubenOVucV5jkPTzL2\nXvgg+/h3ytG3xdzrq9+2u30K64iIiIiIiIiIm18MlZICY3tdolp6TvRHsvxl6n2W2/UXMytyBvfy\n7mmT4Xw+gtOujXv061v30qgumekzLOP9ReL27lT4Fa0Joq0oYG/F+64bPz7nW/wX6zRERcFzsfKc\nn685R/EHV4bdKiyKaawQY+kgHR7Hvsj9yn/0f62/0tgv6o/7q8/R/rb/AErgv6o/7qfo/wBbf6Ww\nX9Uf91P0f62/0rgv6o/7qfo/1t/pXBf1R/3U/R/rb/SuC/qj/ur39H+tv9LYL+qP+6vP0f62/wBL\nYL+qP+6n6P8AW3+lcF/VH/dXv6P9bf6WwX9Uf91efo/1t/pXBf1R/wB1YrVD1pFOczZXBmP4buvT\nRvWu/wCyvzG7fUfntdq9J63qk2aJ2Oe+LD9tjKdRhLf+QP1vH83QX6SG9d/K9VX5rzihwigy3ep3\nrDX7/wDZoeoN1r9Zx0G7J9yvvKc0p4zgjOWuqWpqj68VgQxtBkDZOnW++u3V37+ymqeQguYuvkGn\n4cM0LZgZDrpaRvuttEREREREREREREREREREUVyTMSYHAWslDj7OQkhbtteuNvef8vmqXwHH80yW\nXPKeT5F9aCzE5sOHaCGxtJ+6XD2P9/fv8l0laWXivzYe3Hi546990ThBLI3bWP12JCpXp/ze/kL1\nji3JoDX5FRaS9xGm2mg662dh9PouhIi596vXc/V4c6rx+jNYlvSfZ55IWFzoYz2JAHz8b+qsvEOO\nVuJ8Xo4et3bAz77z+2893O/MqSyWRqYnHT3707IK0DC+SR50AAqZ6Z8hz3KoctmMi34eKntEYyN8\nXS8Rj32PI8fPuCrZnMFjuR4qTGZWsLFSQgujLi3ZB2O4IPkKG5i7kmK45AeHVK01is9gdWl79UIG\nulvfz491rcH59/C59qrYwt7F36rWuljsRkNIP80/j7FRPNOCZP8AT8fLuHTivnGEfaYHyEMuMGvu\nn2HYfh+a6LA6R1eJ0zAyUsBexp2Gu13APuq/z7LnBcDzOQY8MkjrOEZ3rT3fdb/eQo/0qwEfH/T3\nGR/CLLFqMWrBd+sXvAPf8BofkroiIuXeq8bsbnuHclYyN5p5EQPa7sXCTQHf6aP711EIiIiIiwWL\ntWm0us2YYWgE7keG9h791DRc64rPdbTi5FjH2Xv+G2JtlpcXb1od/K2cpyTHYfJ4zH25JBYyUpir\ntZGXbIGzsjwP815fzklLkWMxTcZbnZdbIXWo27jg6Rv7x+qiea5LmdL7LHxPDVLpk7SzWJdCPuNf\nd2N+/dVmrhPV+86SW7yjF43uAyKCs2UH6929v3q4cRw+exFWyOQZ92XsTSdbXfBEbYh8gApDC4Cn\ngW2203WCLU7rEnxpnSffd51vwFnx+JoYlkzKFSKu2eV00ojGut7vLj9V+ev9InDYehmMZfpsjjyN\nwSG0xjh94Dp6XlvsTsjfvpcp4xx63yrkVPC0S1s9lxAc/fS0AEknXsACv0h6m8RtV/TfEvxDC+/x\n0xSQujGz0saA4ge/gHX0V+4pyGpynjdLLU5WvZNGOsA92P195p+RBU0iKOzmbpcdw1nK5GURVa7O\npx9z8gPmT4AXP/RbE2TisnynI1/h3s3adOHEaJi3sdvYElx/cuooiIiIiIsViL49aWHeviMLd/LY\n0qPxT0h4rxZ8dllQ3rze/wBotado/NrfA/xV91pEVf51Vbd4HnoHNc7qoTENb5JDCR/eAuf4PMWO\nQemHGuL46w8ZHI0xFLO2MFtevGel7nfLbR0D5kqZ9QcHRzFTjXDGNeWzWmO02TTo68TD1OP5abv5\nldHREREREREREREREREREXjnBrS5xAA7kn2Ubi+QYnOOtMxeQhtmq/4cxhd1BjvlvwmBoZHG4tlb\nKZV2TtB7ibLohGSCdgaHbsOyk0RYjWgNoWTDGbAZ0CXpHUG+db86+i0cNcyVyO0cnjhRdHYfHCBK\nH/FjH6r+3jfyUmvl+y0hp0SOx14Whha+Tq4xkWXvR3bgc7qnjiEYcNnp+6PppVO3z6zhfUhnHM1Q\nbBj7zW/o66w7D3e4f8u/b6dvmrPlIcHnxPx7Iuq2nPjEstJ0g6+jY04tB2BvXdb1CjWxlCCjThbD\nWgYGRxt8NaPAWwiaG96UVRx9jEsyUzr1vIfHmdYjilIJiGh/Js+mx2/Fc5w/qJS5NyzFsy0uR4zk\nKsj2soTnUV0P+6ASQO4I7DX4KY9S7uO5F6ecnxuPvwz26UfVPDE8OewscHEEfkp/gOUGY4Fg7vxG\nve+nG2Qhwd99rQHb177HhWRERcs9b5X2cPg8FAXGfJZONoYxvUS1vcnXnsSPC6k0dLQPkvU2sTLV\neSxJXZPG6aMAvjDwXNB8bHkKn8o9VOLcTyDsdftSyXmtBdBXiL3DY2AfYEqqXPXKXUb8ZwrNWo3A\nnrkYWDXtrTXb2F0fCZW/mONQ5GXFSY+7LGXCpZd3a722QPB7e2/oskLMxb40GWZIKOXlrlrpIR8R\nkMhHYgH9YA99FfVbHXf4PNx97JyzXDB8KS7EwRvLta6wO4BXPYvQjBz2TYzeXy2WlJ7mafp2N+O3\nfx28qx4/0q4Ti5689XAwNnruD45HPe5wcDsHue5VwLGuc1xaC5vgkeF9IiL5kkbFG6R50xoLnH5A\nKs57MVsjwWXK47kLMTWmY18eSMfUGDqA/Vd8/C+qXEcDZyEPIZ68OQyc1SON1542JW9OuoN30jqH\nyHuovh3pfieHciyuXquEj7bz9nYWa+zRk7LG9+43768AK8uaHNLXAEEaIK47kcdmPSHKy5Xj9SfJ\ncWtOJsYxhJdWkOvvt7E67f36PsVfeN8/43ymkyxj8nCHud0GvM8Mla75FpO1ZdqEznMMBx2lZs5H\nJ1oxXH34hIHSb9gGA7JXMK7M76z5WKa9WmxfDqkoLqsmw+6R3B8DtojwdD22V2eCCOtBHBCxscUb\nQ1jGjQaB4AWRERERERERERVvOclxgxturTsVchfeXVWUYbDet8p7dB0dt1vufYArS9OeDw8I42yq\nel96fUlqQHf3v5gP81uyB8/PuoqFklz13sS2Y74jp4xsdRxhPwSXHbz1a17j8x9F0ZERERERERER\nERYLdyvQqyWrU8cMETS58kjtNaB7krnOT9Z8cLclXjeHyHIpYj/KvpsIjaPn1aO/3a+qwfxn8vLh\nMPTTKfZfh9X/AA339/h0f+K+8d600hdiq8lwWR4+ZnahltMJjd8ySQNe3sfK6TSvVclTit0rEViv\nK0OZLE4Oa4fiFsIi1796tjKE965KIq0DDJLIfDWgbJVc5RhRz/ikFfH5manTtGOYzQD/AIaE9+n2\nIBB/zClOOcZxPFMWMfh6ja8HV1O13c92gOpxPk9gpdERFE8lx2QyvHrdLFZF2Ouyt1FZaNlh3v8A\nv8fmqzwHkPJJ61zEcpxNqPJY0aNtsf8AJ2m+xafBd+HlRnCLPI+X8zu8oyDb2Mw1dprUsdI9zQ93\nhz3N7A+/keT9F09VfnNTjP6DdleTVI5q2OPx2OO+trvYN0Qdk67LLxnknGuVyzZHDSQTWmxMbO/4\nXTK1rh1Na46/u35VjRaNrM4uj1fa8jUg6SA74szW6J8eSvcnl8fhqLr2RuQ1arSAZZXabs+O60+S\nclo8ZwEmZuCWSrH0/wDAM63HqOhr96puJ5bg/UTL16ljh1+SGNxkiu3ag+Gwt7j73tv5Kv8ALeJ3\nMl6o17PCY61a0xj/ANLWvjB0WyB9yWIfNv8AW39CVl9NshPwHlVv0/zrmg2JTZx07e0bw7y0A+N6\n7D57C7MiL5kkZFG6SR7WMaCXOcdAD5lcJyF/k/qLzh2c4pBVmocasfDqNndptp7uz3B3btoA6341\n81YZsR6xZUNkkzuIxWgP5KvH1bPvskH/ABVh4hxzmGNyLrnJeV/pJhiLBUjgDWB2+zt+/b6DyrGz\nB12cjkzYmsmw+uK5iMp+EGg72G+N/VK3H8VTzdvM16bGZC41rJ5gTt4Hj6ewWZ+Gxcl511+OqOtu\nABnMDS8gePva2twAAaA0B7L1EREREVO5v6i4rhcMcb2uvZKZwZDQruHxHE+CfJA/JfXAr3LMtQs5\nHk9aCmyy8OqU2sIfCz3D9/Pse/dbmGyrs3dzGKt8emqVKMohjfYYDFZb37tGta7fXyFYgA0ANAAH\nYAey9ReEAjRHZUnN+kvDc5aktTYoV7Tz1GWq8xHetb0O2/fx5UP/ABL1O8LeV8jbSLuo1vtn3SpT\nD+kPDsRYZZOPdetNeZPj3ZDK4n6g9j+5XprWtaGtAAA0APZeoiIiIiIiIiIoXGcSwOHytzJ0MZDD\nduOL5pgNucSSTrfjZPsppNIiIiIiIiIiIiLXvXa2Ooz3bcrYq8DDJJI7w1oGyVxnGVst6zcgdk8k\n6epw2nLqtUGwLhB8u79/qfbwPcrr+JwuNwVJtPF0oaldpJEcTdDf/at9aOUw+OzdJ9LJ04bdZ/mO\nVuwuOZvH5T0Xy0eawks9riViwftePJ2IC7sNE/U9j9ACuz47IVsrjq9+nI2SvYjEkbmnewQtpVTm\n/OqXDasDHQS3MlcJZTpwg9Uruw8+w2R+9bvGJM5kOPNk5RTqQXJy4urQguaxh8NdsnZ159lOMYyN\njWRtDWNGmtaNAD5BfSIiItO3lcfQG7l6tXG9bmlazv8ALuVAcj5XY49lKxtwVq+CmiLXZOWbYZMd\n9DOgdzv5/wCSxT8pl4bxCLI8ztQy2DJ0GTHwuLX7J6dD2OvwC+OK+otXl2TfVpYXLwwBrnC3Yr9E\nR0ded+foov1F4vmuZ8hweHDXM421xsX5WvA63A9ma8+PHt3+i1PUm7yvhmK+3cRpYyDE14m/aXiL\nco190dvBaB09/Kn/AExu5fK8Or5LL5ivk5bf8ox0MYb8IeCw68kH6BT+Jxt2i6+bmUlvCxYdLCHs\nDfgMPhg15A+aqDPRThv6TkyFqtauTySuld9psucCSd9/n+f5q82sdSvVBUt1ILFYaIiljDm9vHY9\nuy+rk1apRmntuYytEwvkc/8AVa0DZJVAxPqtWznKTRxdAPwccjYH5aWYRMMjh91rWuH3iT2A8q0Y\nridPEcnzOdrzTmfK/D+NE5w6GlgIBHb6rQ53wGhzfHxtkkdUyNc9VW7GPvxH5fUfRUzF895JwKOP\nG8/xdmSoxxZHmYP5Rrx26eoD8+57/RXCt6rcGtSCOPkdMOI39/qYP3uAC073rLwWlA+QZtlh7dgR\nQRPc5x+Q7aVUvTcx9XWx1KtOxx3jD+p0lmU/ytpu9AdPbt9PH1K6rgMDQ41ha2LxsLYq8DA3sNF5\n93O+ZPklSaIiIiIiIi8J0NnwoHIW5+ScVtv4plqzLUrTHBb/AF2McD33rwfI+nyUHw70yx3HZv0p\nkpHZfPyHrlv2fvEO/wCRvx+PlfPIefTwc4xXFMBBDdvSzA5DqDiK0PYknXg6O/fXy7q+jwiIiIiI\niIiIiIiIiIiIio/qLT5S3Dz5XjuedTfSi+ManwWFsvTsu249/Ht9FC4HJ8o9TcVRydW+cDjI3RuL\n4G9UtqVp1IO50I9hwA9/f5K28o5lT4nE19qlkLYEZlldVg6xFGCAXPJIAGz+P0UzicpUzeKq5OhL\n8SrZjEkb9EbB+hW4iIiIiIiKp8355R4KzGz5GCWSrbnML5I+5i0N9WvcKT47yvCcqputYbIRWo2n\nTw3Ycw/Vp7hUD1nuHIP49xGGeVkmWvM+O2NwG4QQDv8AM7/6K6fj8fVxePgoUoWQ1oGBkcbRoNAW\nyiLXvUq2RozU7kLJq8zCySN42HArl/pBZkwNvkPCLryJsXZdPAXv3uB2tEewHg/9NdEuWbl/G1rH\nH7VKT4ksbjLKS+N8XV9/pLffXhb74IZZI5JImPkiJLHOaCWk/I+yxWMnQpvayzdrQvd+q2SVrSf3\nlY8vmKGCxkmRyVltepGWh8rgSB1ODR4+pC18/wAioccwb8vedKajOnvCwvJ6joaA/FVzivqZV5dm\nW0aeDy8ELoTKLdmANj7HxvZ/erGbuX/hO2mMW39EfZy83jMN/E32YGefzVd5VF6jWcx8DjU+Hq40\nxj+XsBzpA737aI/Dspzi1HO4/DmDkOUiyV74rnfGij6AGnw3X07rYweHfhq1iF+QtXTNZknD7Luo\nsDjvob/yQuGep3AuN46/FjMPWyF7lOYf1QRyTlzY29Wy8k/gQNn5ruFbCwWON0sZl4IbgihjbI2V\ngc0uaB3148haHOeHQcz42/GPnlryMcJa8kbtBsjQenY9wtD06PLa2Hkx/L67RYqv6ILfxg82G9+5\n772NeT5Cm+O4OfBxXWT5e3kjZtPna6yQTEHfsN17KXmhjnhfDLG2SJ7S1zHDYcD5BCqvEOC0eE2s\nrJQtz/ZL0olbVeR8Ov5/V/f/AHBW1EUNjTezOLtQ8hxMFcPlki+zmQStli8An8R7KjV/T29kueif\nJwV6nGcNK2TEUaoa1kj+x6nAfIjvv3+i6kixzQRWInRTxMljcNOY9oII+oKrx9POHEknjOL2f/7Z\nv+SzVeDcVo2GWKvHsbFMw7a9tZux/cp/QRERERERERedQ3rY2fAUbjrWVsZHJw38dHXqQyNbUmbK\nHmdpHckfs6K2qOOpYyua9GrDWhLy8siYGjqJ2Toe5Vf57zGvwrjj70jXSWpj8GpC1uzJKR2H4e6g\n/SrhtnC0LGfzgkdyHLEy2jIBuMEkho1432J/Iey6Km0RERERERNoiIiIiIiIiLlcvOpfUdljjPG6\nV+jJKXRXrl2v0ivBrTtAH/hDvQade6h/S3OZrB9XARhXzW8decJrDvuRR1nO255Pu7ueka77CsHr\nTLm63E/j03ROwwc1uThH3ZXxlw7Nd4APg9t91fMFFThwGPZjoBXp/Z2GGID9RpaCApBFA8q/hOcf\nF/BU48XPij4n27q6OjR8a996VR16yfPi375P8k16yfPi375P8k16yfPi375P8k16yfPi375P8k16\nyfPi375P8k16yfPi375P8k16yfPi375P8k16yfPi375P8k16yfPi375P8lzL1nHORhsb/Cs4j7P9\nod8H7AXdXV0997HjS5Hj7l6laZLj554bAcC0wuIdsHY8fVdw4pb5Xc9XeJy81hZHYNGb7N8RjWvc\nOl/dwHh2/wAF+hUREX5j9XsLfyfqxdr8cq25rTseya2yEnbtA78HuOno7fNdZ9GX5D+LSpXyUMla\nStJLCwPjLHBgPYkH6k9/orXgcQMfhDTflbeTZI57vtFiQOeQ4k6DmgdhvsqfW9DuGxXHWrEN248v\nDwLFlxA+nbW/zXRH14ZIBC+Jj4gAOhzdjt47L70NaTQC9RFrZG2aGOs2xXmsGGN0nwYG9T36G9NH\nuSsWOnZk6VTJPpSV5ZYg8R2IwJYtjZafkfmt5FxrmsGa9QfUuvxfHTTYyjhQLU90NId8Qj7pb3G/\nYD8XH2XT8e8YTE46jl8tHPcLBF8edwY6w8DuQCe5UstDM4eln8XNjcjEZas2utgcW70QfI7+QsVf\nN4kZp/H4bcQyFeFsjqu/vNj9j9fb+5YeVWs5T49Ym47RhuZMaEUUr+kdzon66862FWOAcMzuLvWO\nQ8nzNi1mLjCySs2TcETSQQAPGxr27DatuXxtrJGi6plZ6Ir2WzSfBAPx2De43b9ipRERERERERER\nERc55v6nx4i4OP8AHIBleSTOEbIIx1MhP/L0fP0/fpfXBuBZLH5qXlPKMk+5n7DXD4bHn4UDXa+6\nB79hr5f4roiKKxlyXLss/bsRLTNa06OMWA13xA3xI36HalVU/ULm1fgvGpMjIxstqQ/CqwE/rvI9\n/oPJVYx3DOUcuxVfK8j5dkqE9homjp4wiKOFpGwD22To+/hYzyvJenvNsdxzkGVdlMTkGf6vdnYG\nzQO3oB7h2cN67+e66r1t309Q6vltOtvV09Q6vltVTO82gxXNMFxyIwSWMg9/xw533oWBpIP0JPjf\n1Vr629vvDv47+V8snike5jJWOc06c0OBIPyK+utvV09Q6vlvuhe0O6S4b+W1TeT85hw3MOP8frTV\nn2r9kNsxv7ujiIOj57EnWtq5ggjYOwuT+ueOkrcZ/hNQyF2nfpuZDuCZ7WvY52iCB2Hc732+XyVh\n9K8W+rwnH5GxkLt23kYGTyvszuf07Gw1oJ0AN/mrwtMZbHOvGi2/VNsDqMAlb16+fTvazTWq9d7G\nTTxRukOmB7wC4/TflfbZo3x/FbI0x631g7GvxXyy1BJB8dk0bodE/Ea8Fv71rUM1i8q57cfkqltz\nP1xBM15b+Ois01+pXmjhmtQxyyb6GPkAc7Q2dA+ey8pZCnkYTNStQWY2uLC+GQPAcPI2PdbKIiIi\n8axrSS1oBPc6HledI24gAOPkgd1R5uFZ3Lzuqci5IzIYVths7azabI5JOl3U1r3DtoHXgd9K8gBr\nQ0AAAaAC9RERERERFRvUrgEnP6uLqC62rDWsmSZ3T1OLS3X3fr+K3uJ+nXHOGxOGMpB07v1rE/35\nD29ifA+gVV9Za/6NPHeWRRzF+Jvs+NJEAemFx+9v8wB+a6Zj71bJ4+C9TlbLWsMEkb2+HNI2Fsoi\nxWbENStLYsSNihiaXve86DQPJJXKPSIP5HyLk/OJu4uWTUrb7ERN0e4Hbx0d/oV1a1WjuVJq0zdx\nTMLHgHWwRoqIiwcmB4c7D8ZMcEteBzaZskva152R1e57lVDjfJPUCjyGvh+W4BtiG04hmRoDbGdt\n/e12A/HR/FdGdarx2Y6z54mzyAlkReA54HkgeTpZkRFjnnirQSTzyNjijaXPe86DQPJJVH4Rzq5z\nXkGa+zUo24Cm4RVrf3uqZ++57jWtd/p2+aviLVyNqSljrNqKtJakijc9sEX60hA30j6lMfPJbo17\nU1V1WeWJrnwvILoyRvpJHuFAc94TS5xx99CxqO1Ht9Sx33FJ8/wPuFi9PKvKcfx40eVOgksVpPhQ\nTRv6jJEAAC76/wB/zVpsWYKsD57E0cMLBt8kjg1rR8yT4XIMvUd6h5iPlfAbbIMpip3UpZp29Mdh\nhH6zT33oE+f8l1HAYhuCwtbHNs2LPwQdy2JOt7iTs7P5qA9SZ+SfwfiocXrPfdvzCu+do2K8ZB6n\nk+3y3/4Ka4rgW8Z4zQwzbD7H2WLoMr/Lzskn6dyeymERERERERERETah6edNvkV/Efo29EKjGu+1\nyR6hl6vZjvc/5LX43wvB8UNp+LqdEtqQySyyPL3uJ9uo99fRWBFEZj9OfbcYcR9kNYWP9eE+9mLR\n/U177Uui4B6/n/8AjDijbJlFLuX9Pj/hG9Wvbev+xd8iLTEzp7t6Rr8FxX/SQFf+DeGc7/2oXHfD\n876en739/StS3Qr471W9PJ60ZimvVRJacHH+Vf0Hue/lSPq7Tjqc74VfpsMN2e82OSWMd3gOZrY2\nAfJUdz3ieFteunHoJqkkkWVYZLbWyuHW4bAO97A0BvWvCw+ofEIuOcz4VW47fu0XW7L4o/iTumZX\n05mixrif557e6w+pHAv4vK1Hl/G8hbbarWG/aXTydZe937ff5nYI+qzcgtC56t+neVjibXmyVeCe\nwIzoOc4nz8+3b8FNestCKDk/DcpDuOzJkWQyOZ2L29TSNn6d/wB6jvUTiuIf618UL6rXMyrybcey\nBIWnye/4fuXbKFCrjKMVKlC2GtEOmONvhoVE9cT/APyly/8Az4P/ALzFO+nH/u345/8Ap8X/ANIW\n5y6tbucSydahkGY+zJXcGWZD0tj+ZJ9u3v7eV+eeYnCU/TupRxOJfPdpTRMtZ2GL+TdL94ua2XsX\n7Pv4XXvUjhjOYcAaYmtGTpwCerKd72Ggubsd+4H79Ku4XmdOx6DtjELWXDH+h21oiOozOHQ06899\n9R/NQ/Nas/FYuFcBxkTbMU72yW4HSGNlx/UBpzvIaXbOvw+S3eQ8C5fezuLzOCweFwFih5dWu9pA\nCNdQDANDuPwKi+XcDxM/rTg8fN9pEOXjfYttbOT9/wC8T0k9wOy7RxXiWK4di3Y7ERSRwPkMrviS\nF5LjoeT+AU4iIiIiIiIiIiIiIiItPKY2tl8Vax1tnVBZidFIPfRGu31XHsPmsj6O5r+DufEs3FZn\nk4/IBm/g7JOnkD94/Mdl2HG5bH5mqLWNuwW4CdfEheHDfy7LcWtev1MZUfavWoa1dn60krw1o/Mr\njnKeRZL1Xv8A8FOItkZhC8tv5ZzD8M9PfpB9x2H47Hsut4LDVOP4SpiqUYZXrRhjdDW9eSfqT3Ui\niaUdZweOt5mll5qzXX6TXtgm2QWB404fXt81WeZ0/UA3G3eJ5Si2vHG0GhYhG5HbOz1n6a9x4U3x\nTI5rJ4NlnPYkYy91Oa6ASB4IHh3038lJUcjTycBno2obMQeWF8Tw4dQ8jY+S2lD258PyCXJ8bneJ\n3thaLdf7w0x+9d/ro+CtrD4ahgMVBjMZXbBUgb0sYDv95Pcn6reQ9gqLx/nM3JPUXLYnHMjnwuOr\ntD7TATucnx1eNeR/0Sr0ir3N5KsfD8g+7bv1KwYPiTY8EzNGx+roH8/oq5ymtxTkWB49ic3m7VSC\nyY5K8U0piktAADUmx5Ox513KuuHw+PwONix+Lqx1qsQ+7Gwf3n5n6lasVPMQ5XLWZMrG+pPG0Uq7\noRqu4N0STv7wJ76Wxg4cpBiIIszbgt32giWaCPoa7v20PwUiiIiIiIiIiIiruf5vgeN2q9S9c3cs\nSNjjqwNMkpJ8HpHfSkZ8dNPmad9uQsxQ12Pa+qzXw5i7Wi7tvY12UjpEVM9RucR8OwZFYNnzFs/C\np1gQXFx7B3T5IB/f4UpwyjmMfxepDnr7ruSIMk0jh+qXHfT9db0p9FT/AFF4JW57x77C+QQXIXfE\nrTkEhjvcEDyCFW6POM5wzG1MLyLieSnlrRiCK5j2/FinDQA0/MEjW9rSfxbO+p3LqGa5HjDisDjz\nuDHzncs+xsl2vHcN8+w0tn1XwWdhz3H+X8fq/bZcS7odTYwlzgT5GvbyPp5Vd5gOfcxzHGsvU4o6\npFUtGSvXmfuQEFp6pT2DGnWgPoVPeoFHkFTmnFOX1MLJkPsbDDZp1XF7mOdvejruO5769vbai/VX\nI5M5/wBOch+iXNyP2mWQY8ygu6uqLTC7xv8AwUlyp+e9T4K3GYePZDDVPjNlv27zAGsDf2Wa/XO/\ncL49TeH5TH2OM8j4vSdblwIZCKgBcTG0/dOh3PyOvn9FC8zg9ROcT4C9DxgUI6trriqSShzw8AO+\nJI4gBre2gPoVO+oeG5N/CviXLauMN91BzY58fWd1Fjydkh2hsHxsjtoLqWKnu2cbDNkabadp42+u\n2QSdHfsOodida2uf+s9fNZji5wOHwFnIPtObI6xGR0wdLgffyTrX4FavEuTcqwHFMbiLfAMtLNTg\nbCZInxhrg3sD3PyWpkcTy/nuI5RNexc+Mlkrsq4ylNKA0s6w95Ov2j0gb8eyg8thefck9Kq3H3cW\nip/o34ey+wPi2ejsOhmtDse+z39l2Ti8+VscfqvzOOZj7fQAa7ZvidIAGtnQ7/T2XM8NwfHP9dcr\nYrxy/o2i1lwxNJ+E228eD7bAcXAe21P+qvAb/La1HI4Sz9nzGMcXwd+kyeDoO9jsbHsoDF1/VHl8\nUeF5LVixmKa4NvWW6bNYaO5a3pOu+tEjQ7rLn8Ryyb1VxGepcVkkxuIjNePVyIOmb94dQ27t58H5\nLr0bnOja5zCxxAJaTvR+S+kREREREREREREREREWrkcbSy9GWjkK0ditK3pfHI3YIXNL3o19hvPu\ncM5Dd4+6UBskMZL4yPp3B9h5JXreAeojahrj1JlLT+0au3/1t7WOt6NWcnbjm5lyrIZyKFwMdcuL\nGfXeyfp414XTMZiqGGox0sbUhq1o/wBWKJgaPx/H6rcRERE8qPr4epj8XNRxcMePjeHlv2dgb0Pd\nvbgPG9na4/k+Qcz9Lc1jamSzEPJaeQk6GQyDpsN+93I9/f32PwXXbmZxeLuUYbs7K9rIv+DXa5p6\npHAb6dge2/f5qTRal69RqCGG9YiiFp/wImyO18RxH6o+ZPdaPHOLYfilKSph6ba8Ushkf3JLnH5k\n9+3splUj1A5te4z9jxuGxEuSzGQDvszGjbGdOtl3v7/T8VM8Qr5yHjVZvJLTbOUeC+YtaAGbOwzt\n2OvG1r814TjebYR9G6wMnYCa1kD70D/mPmPGx7rWxYyXBvT+d+eyBy02Oie8SMb0ufG39Vvc9zr3\n/wAVS+N8U5BznDZ3L8ou5ClJl4/hU6bJOhsMQIew9P4gDR1sb35Vi9KMryCfCW8RyaraZkMXMIfj\nzN7Ss193737RHz+RC6CiIiIiIiIiIuZc25tyN3IZeI8PxMkuU+G10t6Vuo4GuHkb7dtjue2+2it3\ngvpjV41O/L5acZXkMr3SPuyAksJ8huz+Pfz3XQEReHsCdb+i5RxHjGY5B6h5DmfKqT6r6rzBjakg\n/VaN6f5+R/eSV1hEREREXOObcP5Hyfl2FyVKTG1q2Fl+NAZ3vLp3OLS4EAfdA6APzXRY+sxM+IGi\nTpHUGnYB99FfSIiIiKrc147nOQ1arMHySfCywvcZHRN2JQQOx0Qe2v71s8Q4u3i2IdXkuS3r08hm\nt3Jj96aQ+Sfp7AKwIiIiIiIiIiIiIiIiIiIiIiIiIiIi1b+RpYuuLF+1DWhLwwPleGjqJ0Bs+5Vf\n/gNQl587l9ieWxZEAiggkAMcGgB1N99+f3lWSWpWnmgmmgiklgJdE9zQTGSNEg+3bsqxzbi+a5Ey\nm/C8ksYaes5zv5MEtlJ1rq0R419fKz8Mi5dXx88PLZqM88cmoJquwZGfNw0AD+CzYnKYHmtMXa8U\nduOnbcxpmi7xSsOtjfg+4I+al7+QqYujNevWI69WFpfJLIdBoWhZsW8xjsdc4/frCvNLHM+V7S4S\nQeSG/IkfNS/SN7IGx7r1FW8vgpeUjI4jO1K7sI4xOruhmcJXuB6j1dgBogeFjyfNeP8AGM5j+P5C\nd9WSxEPgPew/C0Puhpf8+yk+QWMxVxL5MDSguX+pobFPJ0M0T3JP0Ck4y4sb1gB+h1Bp2N/RfSIi\nIiIiIii85yHF8bpx28tbZWgklbC17993O8Dt+f7kmqZV3Iq1qLIsZi2QOZNTMQLpJCezur20PZSe\nhveu69REUZcdmhnMeKbKZxRD/tjpS74oOvudGu3nztSaIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi\nIiIiIiIiIhOhs+FXaeQ47z7G2o4xFkaVez8J/W37vxGaOx8/buFYQABoeF6ir/NqGYyfD8jSwMzY\ncjNH0Rvc7pGiR1d9du21o8Yw+H4bgMdxT7XFFcsQvOuvT536/lHN3+P7tLnGV9CMvPkI4qvKJZsM\n2VjhWuPe5zW7+94+6T512C63kco3AHE0q+Ls2I7MzarRVjHTA0N/Wd8mgBSklqCGWKKWaNkkp1Gx\nzgC8/Qe6zLkHIcnyPm/qPDxzBSW8diMTM1+QusBZ1uGjoH3+QHv58BdeaNDW9/ioDmHD8XzTCPx2\nRj7/AK0M7f14X/zmn/s91jxIr8I4vjaGazfxnMc2u21ZPSZHuJ6W/wDYN/JUrkPJc3wX1WgsZKxJ\na43mgyCKPq0Krx0gkD577n5h30XWQdhEREREREXOOY+qTMXkW4HjFMZvPSOcwwQnqbCR/P17+e30\n7kK6UoZMphKTs5Rr/azGySaAtD2Ry6763vwdqTRERaOTgZkaFrGNuOrzTwOaHxO1JGCNdY/Anyvc\nRQdi8PUoOtS2nV4mxmeY7fJoeT9VuoiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIir3N8F\nkOScUt4rGZA0LM/SPjgkabsdQ7d+42Fn4rxmjxLA18VQjaGRtHxJA3Rlfru8/UqaRFyfkfqHLV5w\nMfxSzJnclLGK7sU0AQQua4lzzJ/O1sa8fPxpdKdjal2xSyFyjCbtZpMT3AOdCXD7wB/uW8iqXNuA\nY3m8Nc2Z7FS7V2a1qu8h0ZOvbwfAUdgMJynhratFt+bkla1e/l57T+h9SHp8jZJd376/zVsxd+xd\nnvtnxk1NsFgxRvkI/wBYaB+uNe34rNRyuPyZmFG7XsmB5jlEMgcWOHYg68HstxYLNOtcaxtqvFO1\njw9olYHBrh4I37j5qjZ/m2Mx3NYcByfDshoSBklDIWGiSOSbevlphG/O/wDFWjFcgjyuYy2OZTtQ\nnHSMYZpWaZN1N3th9x/4KYRCdKAyHOOLYqZ0N7P4+GVuupjp29Q3vyB+C+8VzLjebldFjc3RsyN1\ntjJh1d/p5KnERFgtXatJrHWrMMDZHiNhleGhzj4aN+SfkqvzvEcoz1WrjcBkIcfVmfq9Z6j8Zsfb\nszQ/H3HstnhvBsRwvGtr0YmyWnD/AFi49v8AKTO9yT30Pp4VmRERQ3KeSUeKcftZa9I1rIWnoYTo\nyP8A2Wj6kqo+mWIyN2e7zfPMMeTy7QIoNHVeAa00bPvoH9y6OiIiIiIiIiIiIiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiLUs5ShTtV61m5BFPZd0QxPeA6Q/IDyVqYHAQYCK1HBauWPtM7p3utTmQgu9m78\nD6KWReEge4XKPVjP8p+3VOI4Og1gzTRHFeExa4EHbwNfq6Gu/wAiVeOL8Pw/FaMcVGjBHZ+G0TWA\n378rtDZLj37kbVgRFRfUnnE3FaMFDF1pbOdyW46MbY+oA7ALj+G/CsXF62VqcaoQZuybOSbEPtEp\nA7u867djrxv30pjS5xlPRzDWcrJk8PkMjhbUsrZZPscumOIdsnp+fn30Pkrpk7V/H1Kv2Cg/ISOm\njikBkDC1hOnSEnzrzpfWUzuLwn2c5S9BUFmT4URmd0hztb1v8AtTkXHcNzPCSY7IsZYrvO2vjcOq\nN385p9iqxzTltX0u4nSoUorFq6YjFRbKHSdXRrZe7e/BVi4XyypzPjVbLVdNc8dM0O9mKQeWn/s+\ni+uXcuxnDMJJk8k89IPTHEzu+Vx8AD/1pc1o4HnHqfYiyXIr0+BwJHVBRpyFkkg9ifyPk/LwFasV\n6McIxkPQ/FfbpD+tLceXuP8AgB+QWLLeifDMkWvr0pcbM1wcJKUpYe30Ox/cqvYbzn0lmEsdiXkP\nFfiAymUF09dpP3vfY9+/cfguscfz9Dk2Fr5bGyl9adu29Q05vzBHsVKLBZmeypPJWjFiaNji2IOA\n63Adm79tqKfi4OVYGiOSYeJkocyw6o9/xPgyN7j7w8/+JCnAABoDQREREVB5XwnI8v5nipMjYg/g\n1QHxxXaPvyzb8OB7Futflv5q+hoaAAAABoAL1ERERERERERERERERERERERERERERERERERERERE\nREPhcw43wrO3vUi9y/lra4krkwY6CIhzWsG9P/cTrffZK6ei+XEhpLRsgdh81yvGcZ5jy3lNbO8u\nm/RuOoymSpi6suiXA/dLyP8APv8AIBdCwuQkzFR1mzirGPlimfGI7Qb1djrqBBPYqURFq5GzLSxl\nq1BWfZlhic9kDP1pCBsNH1K1cTL+mMZj8pdxn2S46IPEUzQZIC4d2712UoiIofkfGMRyzGHH5io2\nxBvqb305jvm0jwVHcQ4BhuEfaf0Q+302AA9k83W0a33A128qTxpytm5f/S1KpFBFPqi6Nxe58eu7\nnb8Hfy/8Tx3hXqFxbinOuTYaWF1GnayDjHZLy6Nrm7BDhr7oJ3rQ99ey3eK1IvVb1DuctyEcb8Pi\nn/ZaNVx2HuGyHuHj33+75LtYGkRfMkbZY3RvaHMcC1zSNgg+y4ri4P4pfVM4/s3jXISTA97wBBI3\nZ13PYDevwI+Skcr6mZblGcfxzgFUPna90c+UsNJhiA3st1v8if3K7cH4oeIYJ1KS/NftTTOsWLEp\n7vkdrevp2VlREREUdkoquYqXMOL74ZpIi15rTBs0QP7Q9wtmjVFGhXqCWWYQxtjEkrup79DW3H3K\n2EREREREREREREREREREREREREREREREREREREREREQnXlRF+pkruUxNrH5ZsFCF7nWoWsDvtLS3\n7oDvbR+Sl14XBoJcQAO5JVUoeoWEyvNX8Yx0j7ViOF0sliLToWka+71b89/bspijg6+PzGSycU1l\n818sMjJJS5jegEDpb7ee6qnLeeWcbzTBcWwcUNq/bnDrjXAn4MPv4PY62e/sPqr+ERaWWydbC4m1\nkrj+ivWidK879gPA37nwqL6acj5FzDJZbP2wYOPTOEeOrSAdYIPd2wO4/Pz+C6OiIi1m5Cm+8+i2\n1C62xge6APHW1vzLfOlSeQ+qUGD5HYwVfj2ZyVuCMPeasG26I2Nd9kd/OlYsbfyOf4i242rJhsja\ngf8ADist6nQP7hpI9/Y6+q5NzPisDeK5m5y3BV25DHVGuhzFF/Q25M8kfeYAO/Vrex7lXz0fxkWM\n9McMIgN2IzZeQPLnnf8Ahofkr0iIuQ/6QuLitcKp5F7AX07jR1b0Qx/YgfmG/uXROK4XF4Pj1Sri\nKba1V0bZOkD7ziQO7j7n6qaREREWN08LJmQulY2WQEsYXfecB50PfWx+9aVfBYurmrWYgpRx5C0w\nRzTt8vaPAP8Ad+5SKIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIuf+qt3kLsRVwXHcfYl\nsZeQwSXI99NZvbZcR42Ce/yBVn4ngGcY4tjsMyT4hqwhj5O/33eXHv42d9lMk6G1W4reP9QeHXGV\nZLUFW4yWq57mfDkYRtp7Fe8R4RhOF4/7NiawbI8ATWH95JSPcn/sHZT808VaF808jIomDqe97g1r\nR8yT4UFjuJYerym7yquDLeyEbGmQuDmtaAP1Px0FYURQ7bdTP2MthbWMsGvXDY5XWYNRWA8E/cJ/\nWA9/xCk61aCnWirVomRQRNDI42DTWtHYABZVXszzrjGAkMWTzdKCYHRiMgc8fi0bIWxmc+Mbx79L\n06NnKMcGOjipt6nva4jTh9NHaxcoHILPGZRxl0MGVkDfhuteGAkdW+x7gb/NV/hfF+a4nJi3yPlv\n6SgdEWuqtj+6HnwQ4gePwVph43iYORT5+Om1uUniEMljqO3MGu2t69h7ey3L80lWhZswVnWJ44nO\nZC06dIQNhoP18Ll3D+P8y5Fy6PmHKbNjHQQPd9jxQOulpGiHD2HYee5+ivfNsOc9wrL4xu+ues8M\n03qPUBsdvxAVe9Gs1HlvTmjAG/Dnx26c0Z8tLfG/xBC6AiIuT+uTpMlisJxirDJJcyt4CItOmtDN\nbJHv+t/dtdRpwfZaUFfq6vhRtj6ta3oa2s6IiItbIX6uLx1i/dmbDWrxmSWR3hrQNlcl9P8AIZP1\nB9RrnNHj4GGoxPpUondy7q1v8/c/iB7LsaIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIm151D\n5hAQV6iLDadOynO6rGySwI3GJj3dLXO12BPsN+61MHJlJcNWkzUMEORc3c0cDtsad+Afw0tPl/J6\n/EON2MxZglnbFprIoh3e9x00b9u/uojgV7mGXbeynJqzKFaw5v2GgGjqiZruXHz32PPy8BXNrWtG\nmgAedAL1QNrK8fzWVu8RsyR2LRr9dio5p0Yzr38e491M1a0NOpDVrRtighYI42N8NaBoAfksqbXP\n876jPr8+xvEsFTr5G5M7dxzpukV29ifA7kN2dfgtznfqHW4P9khfjbl+3b38KGu3t20O7vxI7DZW\n7wrOcgz+Mlt57A/odxePgRGTqc9uvJBG2/mpHBUctRjuDLZRuQdLZfJARCI/hRH9Vnbzr5qtwekP\nC4srPkpcV9qszSOlcbMrntBcdn7pOv3hXeKKOCFkUTGsjY0Na1o0GgeAAvteEgDZ7BV/Gc449meR\nWMFjsg2zdrxmSQRAlgAIB+94J7jwtrC4WfEzZCSbK3LwtTmVjLDtiBp/Yb9FLoVxKGw70d9RbMVt\n838E824zNnLC4V5iT22N7+X4EfIrtUcrJo2yRvD2PAc1zTsEfNfaLVyORq4rHWL92URVq8ZkkeRv\npaPK5LxNlv1L9RncysGxDgsS8x4uJwIEztEF/wBPYn8h7LsiIiIiqvLuPUPULjs+HZlTFGywPiyV\nnB5a5vljh+fg/RTWEw1PAYari6MTY69eMMaGtA6tDu4/UnuVIIqJ6r5/K8Y4fJlsVk4acsbwzpkr\nfFMrneACTpvudkFWTi0+Rs8Vxc+X/wD6hJXY+fsB94jZ7Dt+5S6IiIiIiIiIiIiIiIiIiIiIiIiI\niIiIiIiIiovOvU3G8PIx8Eb7+cmb/IUoQSdnsC4jwPp5Kq0XEfUPnjvtHKc3JgqDmgtoY9xDiP8A\nlaPy+ZP4LZ/iIof7057/AK8f5LVl9NOZcQn+3cL5NPc24fEo5F22yfmTr5/I/VTvC/VOLM5Y8c5B\nVOL5HHI6IwaJjlLR3LXe3g9vf2JXR1itWIqdWWzM7piiYXvdremgbK5nwPkOe5xzC/yL4stXi9dj\nq9Suewndv9dw+Y87/AexXQJ7br2PvMw1yq+7EHRtcT1sjl12DwD+HZfePgtPw9SLL/AmuiJn2gsb\n9x0gA2QD7b8LeRfMhcI3/D0X6PTvxtcl4rWpcEyGW5JzvL06+eyUjvuOmD/hwg7AYBs9+3b5ABdD\nbyKve4u/O4VjsnCYXSwRw/ddNrfYdQ7HY+SwtyGYzPCvt2OpjHZexX6oa90E/CefZ3b/ALFz7H4H\nkON5nhL/ADHmdmS7ZsOFWhTjeYHHp+812tNaPyXQKHCON47Nz5mtia7cjNK6Z1gjbg5w0enfjffx\n8yp4sa4guaCR42PC+kRF449IJPgd1V7QxnqTw+zXp3rkFSeQxGeEOikDmPGwOoeNjX1CycQ4NhOF\n45tbGVmmYg/EtSNBlk383a8dvHhWVEUbncFjuR4ifGZSu2erMNFp8tPsQfYj5rlTMN6g+mUrouPt\nPI+PDuytM7+Vi2fA9/3du/hSGO9ecC5vwc3j8ji7jNiWJ0JkDXD27d/3hfOR9dsVIW1uN4nIZa/I\n7pjj+EYxvt38E+59vZaUPEea+pGRisc4ccVhIh1x42rJp0pPs7ROvHv379tLreNxtPEY6ChQrsr1\nYGBkcbBoAf8Ar3W2iIiKMy+eoYR9Ft6R7DdstrQdMbn7kdvQOh2HbyVsUsZRxzrD6dSGu6zIZpjG\nwN+I8+XHXkrbRFxH1ztXMhyDivHcY1ti7JP9oFV421ztgMLvbX635bX1yHlvOPTzOYWznL9HI0Mi\n4Ry061fo+DojYYfJOndifPyXUrvJ6FDO4vDStnNzJNc6ANiJb0tG3EnwNdv3qaRERERERERERERE\nRERERERV3lvLBxGgL02IyF2qAXTS1GtcIQPdwJB1+HyUPb9UcTJFV/g/Ut5+xMxsroKDNuijJ11P\nJ7NP0PdXeN/XG15a5hcAel3kfQr7RERERERERFW+dcsq8N4taydiQNl6THWZ09XXKQeka+XufoFU\n/SbiE0FSTl+dM0ufywMkhnDf5NhO26HkEjX5aGuy6giLn3qh6fx8sxP6Qx4+Bn6I+JVsMPS52u/Q\nT/gfYrb9LeXycw4bFat6GQqvNa0PG3tA+9rfuCPz2p+bNCLOSY6xRniqNrCY35dCAuLg34e9/rd1\nW/UHjmfz+Px2DwE8VDGTSluQkYQxzYu3ZoHz79vft7Kd4nxLF8Nw4x2LjcGl3XLLIdvlf7ucVK3M\nhTx0TZbtmKvG97Y2uleGguJ0B39yojmHLK/DsJ+k7FK1bYZBGI6zQ52zvRPyHZQHCeW8v5JmZHZT\nixxeFdCXQzSuIk6wQNEHzvZ9h4UvfxnLDzCtfx2ZqtwruhtmjPCS4AE9RYR7nfutbI+l3FMvySTP\nZGg61ckILmyyuMZ0AB93x7ePCtdarBTrx160McMEbelkcbQ1rR8gB4UTyzlOP4fgJ8rkZAGsGo49\n6dK/2a36lZeNZWXP8bx+Vs0jUlsxCX4Dj1Fm/Hf6jR/NS6IiKuc15njuEYJ2SyHU9zj0QQM/Wlf8\nh8vxUb6c5fledoXchyajFSimla6hC1unCIjf3u/4ee/lXRrQ0aaAB9F6iIiLG6CJx6nRMcT7loXr\nYYmO6mxsafmGgL7REREUfnM3R47hrOVyUvwqtdvU92tnzoAD5kkBRPB+Vt5txuPMig+ox8r2sZI4\nO2GnQcD/AOu+1ZkRFxXEZbDj1z5Nk+Q5KvXkoRtr0WWiGdLdAuLd/gf6xW7NG/1R9RMRkqTJRxrB\nP+KLT2lgsz7BHRvyAWjv9D8wpWnXZnfXG7kGGR0OCoMrb6vuiaTZIA9/uk7+q6QiIiIiIiIiIiIi\nIiIiIiIiqvPc5jsdxO/BYkEs9uJ9WCtEOuSaRzSA1rfcqh+iPIMJiuBvqXXQUL1a6a9j4umvle9x\n6AR5J7luvoVc7cuP5D6iQY8TZWG7gGC074RDK8nxAAA4+T2H091c0RfMkscQ3I9rB4246WP7ZW/4\nxD/XCfbK3/GIf64T7ZW/4xD/AFwn2yt/xiH+uE+2Vv8AjEP9cJ9srf8AGIf64T7ZW/4xD/XCfbK3\n/GIf64T7ZW/4xD/XC5l63cjuYLimPv4bImC3HkGadE8HY6H9iPcfQqrcR/0iGPd9n5VTEYDfu2qj\nSdn/AJTSff5j9ykvUXP4/l2b4Jj8bbrW8dcyAne5v3jtpaNEe3ZztgrtQ8IiIfC/OfL+RW/R/wBT\nMs7CV680OYrMtGGYO6Y3lzgSNEe4d+9dN4pl8b6ucDEmYxsZZ8bonrh56fiM0QQfIHcKz4a5kb2H\nlfPjP0ZYY+SKGGV4eNNJDHHXsex0ueV/TrneayP2jlPNpGV2ydQr4xzmgj5b00D9xXTMjhsdl6kV\nbJVIrcUT2yMbM3q05vg/it8KD5dyStxLjNzM2mucyBumta3Zc89mj9+lDemEXJP4Ji5yey+a5dmd\nYYx5+9FG7Wmke3z17bV1WC7crY6lNcuTMhrQsL5JHnQa0eSVW8lxrj3PZcJnJZPttWoXTVwxwMUo\nd/OBHcbH9ytTWtY0Na0NaBoADQAXqIiicxYyVjj9mXjUlOa/0kQGZ24i4HRBI/P81kq05L+JpjPV\nKct1rWvlY1vXGyTXfp6v8VJeEREREREREREXDOSXLXq36hM4rR+PHx3FSk5CZrtCVwOuxGx57D8y\nu10aNfG0YKVSJsVeBgjjY3w1o8BbCIi0beFxd+b41zG1LEmtdcsLXHXy2QtuOKOGJsUTGsjYOlrW\njQA+QCqfB+IXeLTZya9khdkyd02QQzXQDvz9f7uwVvRERERERERERERERERERERU/DemnHcHymzy\nGrDM67MXEfFlL2xlx24tB8FIPTnjFHl9jlX2b/XpCZf5R/8AJxv13eB7Hyd/UqJ9M6zcjl+T8uEs\nr48pdMNYvOwYYiWgj8Tv8gujIihOT8TxHL6EVLMQySwRyfFaGSuYerRHkH6lVT+I3gv9H2v7ZL/m\nn8RvBf6Ptf2yX/NP4jeC/wBH2v7ZL/mn8RvBf6Ptf2yX/NP4jeC/0fa/tkv+afxG8F/o+1/bJf8A\nNP4jeC/0fa/tkv8Amn8RvBf6Ptf2yX/NP4jeC/0fa/tkv+a556xem3HOKcUq3MLUnZalush+9O+T\nYLXHQBJ77AVH4l6Q8p5Wz47KwoVA4AzXA5nUN9+lutnX7vqrvmvT+l6a824VcoS3ZopbjYrEz9a6\n9gDWta2C7t8gv0SF6iIuK3sXiec/6QFmtdrNyFHGY1rJWk6Y2QO2AdHv3f4/Ht2XYqVCpjaza1Gt\nDWgb4jhYGNH5BbCIigG5ihmeQ5DjVjGWJBViZK+SxX3BJvRAaT2JGx/f8lPgaGh4Xy97Y2l73BrW\njZJOgAqxg+YYfmOSzGKpQvs1qOopbDmh0ExdvbWnuD4Vlr14aldlevEyGGMdLI42hrWj5ADwsiIs\nc1iGuwPnljiaSAHPcGjfy7rIsNarXpwiGrBHDECSGRtDWgk7PYfUrMiIiIiIiIiIiicnyCti8rjc\ndPXtvkyD3MjkigL2MIG/vkfq7X1h+OYjAG0cVRiqfa5TNP8AD399/wA+/j8B2UoiIiIiIiIiIiIi\nIiIiIiIiIiIiIiis1x6hyBkEeQbM5kLnODY5nR9W2lpB6SNggnstnF4ulhcbBjsfXbXqQN6Y42+A\nFuIiIiIiIiLDPVr2TH8eCOX4TxIz4jQ7pcPBG/B+qzKnepfEhy/iE9WNz2Xax+01HM8/EaDoa379\nx9N7Uf6Xc7bybDjGZNzoeQ0B8O3XlHS92u3Xo/3/ACK6CiKrc75xj+DYJ1+3/K2Hnor1muAdK76f\nQeSVB+kXGLeIwNrM5ZgGWzU5tzH3aw92tI9jsuP5/RdFREVU59zipwTAjIzwmzNJIIoK4f0mR346\nOgArPXkdLXikfGY3vYHOYTstJHj8lkUNkP0ZyWllsAzINMvwjDabXkHxIQ8HW/l235XvGeN4/ieC\nr4nGx9MEI7uP60jvdzj8yphEVP5r6i4jhsTYZOq7lZC0Q4+A7lfvwT50FvNx9bmnGsfJyLC/Bc4s\nsmnM/qMTx3GyNb/A/mFYgNDSIm0RERERERERYbUskNSaWGF08rGOcyJpALyB2aCew34WDE2bV3FV\nbN6kaVqSMOlrF4eYnfLqHlbqIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIue819M253Kw5/\nAXv0Nn4SXG1G06m7aAdo/wB/ft7FQsHqHzXi1dlflvDrdoROLJMhRIc149ndIGv7x+Cko/XXhDoX\nukt3IpG6/kn1H9R+g12/eVpS+q+czvxmcM4bevMH3WXLP8nHv569x59wtnjvphZtZiLkfOb/AOmM\noGAxVnN/kart70B4dr27a/FdO8IiIqnmuEYflXJcVnrk8lhuOa5sdcPDoXO6t7I+YPn8BvwrYqly\nP1BxXHc7jsG5k1vJ3ZmRitXG3RtcddbvorBTw+OoXblypShgs3HB9iVjAHSkDQJK3URRuQvX61/H\nwVMY63BYlLbE4lawV2gb6iD3d+AXjuO4d+dGcfjoHZMR/DFkt28N+n+ak0RFxzBTZfJ+u2VrUuQ5\nKxhMaDJYhfOXRiRw18Pp7DQJOux10rsaIiIiIiIi+Jpo68Ek0rwyONpe9x8AAbJXNeD8ozvNuaZL\nL13yQ8SgYYKzHxgfHkHlwPn5n9wXTURERERERERERERERERERERERERERERERERERERERERar8bR\nkeXvpVnPPcudE0k/3LZa0NGgAB8gvUREUXd5DicfmKOIt3GRXr/V9mhIO5Nee+tD81sYzF0sPTFT\nH12V64e54Yzxtx2T+8r6sZKlVt1qli1FHYtOLYInOAdIQNnQ9+yrXHvT+jg+T5PkMluxfyF156JL\nRDjAz+a0/wB34ABW9EXKM1xvm/POTTVcnYfguM1ZfuMrTAyWwCdO2D22Pn4+RK6pDGIYI4mlxaxo\naC47J18yvtERauSvw4vF28hYd0wVoXzSHW9NaCT/AILh/plwm7yk5Lll/J3sdSyduWZlSlMYvi93\nacXA70CTofRTvpDlct8HllSxfmydPG23Mp2bDnEyEdWx1HZ191p17bVx9OuUX+YcSjzGRox1JJJX\ntY2PfS5gPZw3+Y/JWxERETaIiLnfqRR5NyS5juNYivPXxll4kyORZIGhsW9GP57I769+31V1w+Ho\n4HFwY3G12QVYW6Yxg1+JPzJPclb6IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi51\ni+G5e76q3+W8gdH8Cp1QYmGN/UAwgjrI9jon8yfkFceQchxvGMRLlMrY+DVi0CdbLifAA9ysdA4v\nktTF54UtuDPjVX2IumSLqGvy2FMIqfmvUnA4bk9Ljrnz2slZlbGYqrA/4XUdAvO+3z150pmbAV5+\nTV866xb+PBAYGQtmIhIJJ6izwT3/APWlLoiIip/qdiMvnuB38ZhYmyW7HQ3pdJ0bZ1Au7/gFAUsl\nyiPgtPAcf4xcrZSvVjrST32NigjIAa5zT1bf7kaH+S0s/jB6X+i1qhSe+a/cc2KSYO0XTSkBzgfY\nAA6/AL30uwbMw5meuGzUlxb/ANGwYxkxEUPwmgFzgP1nElx34XW0RfEsjIonySPDGMBLnOOgB81y\nLKeruRzNqSrwjHRzRwuDZchf2yLZPSA0b9yRon9yi7mI5rlMbfnzXNbEDdPD4ajA2MFo+8NjR00g\n9Wvy2sFSjzLCRufjOemyYmwuEFxhe17pNdLSXA6Du2j+O9KxYT1Yv462yjzrGDHmWYwwX6wJrvcH\ndLg4k9tH37rq7HtkY17HBzXAEOB2CPmvpaOZkyMeHtvxEUM2RbGTXjncWsc/2BK+sU+9Jiaj8nHF\nHfdE02GRHbWv13A+m1uIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKOzlbJ28RND\nh77KF52vh2HxCUN79/unsdjstoSCpSa+3YZ/JsHxZn6Y0kDu4+wVS5ZwGrzjMYy1kshK/E1GF32C\nPs2V5PZ5eDvWu2v8yrmGhoAA0B2AC9URXzte5yK/gm1rQlqQskkmMeonB/gNd8//AF7LR4zwPAcT\nmtWMbWcbNl/XJPO8ySfgHHwFZURERERRWb47jeQtptyULpWVLLbMTQ4gdbfG9eR38LVx/DsTi+UX\n+Q1WTMu3m6mHxT8Mnts9PjfYd1Poi4h6gZezzflj+K07kUOBoOjdfsN2S6TeujbflsDXz3vwpzGw\nVcZQuUqnTDXic6KOOMNGgOnud+T/ADj4d+yvmR8f2a4DLX2ZK5HTEeo9vY+P+Z/N7dSkZpWNryNE\n7j/JubohuvI3+X84eXfsqLydepk8XkaNw15IZp4Y3sMJ6iCD4d4B/mH9nw5a3p5yCfi3KHcFyt42\nasw68PO7uQwdW43H2Oh2Hz8eQuuWLUFSB01maOGJvl8jw1o/ElacNCw3OzZE5KZ9WSBsTaZa34bH\nA76wdb2fH/oKRREREREREREREREREREREREREREREREREREREREREREREREWvfuRY7HWbs/V8KvE\n6V/SNnpaCTofkue+lGQyvJJM9yq/JM2nkbIZRrvkJEUbOodh4Hkd/fRVidkOM8/gzXHRKLsVZzYb\njG9TQDvY04edFp8fJWGnUho0oKldnRDBG2ONu96aBoBYsrkI8TibeQljlkjrROlcyJvU5wA3oD5r\nn3BcxzTmOd/hFc6cXxotc2tQcwOfN7dROt+e+/yA13XTdDe0REREREREReEgDZ7LiHp/V+y0ZpI4\n3ySTZGeQhpDHEguBa0nxoDZJ7EHQ7qaldC1mRcTU6dVnAvhc/t1Dp1/ySf1B5Ye5WxaJGNyDnRAt\nJmH3yHNB+91/d331+3/P9l8zyRFlh5+zA9dTf+qkk+Onv9f2T+x7r6vvLcDcAjAj+LNtzu4ABPVt\nvyH7Y/2nkKmeoAdRjr8npljL2JnryBzIuh3S4NGnH9oEdx/NHY91IZXMP9Y+Y1uOY2SUcXqMZYyM\nsfb4j+5DQ78SB+RPsu2wQx1q8cETemONoY0fIAaCyIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiI\niIiIiIiIiIiKHyHIIKPIcZhZKtmSTItkLJWR7jYGDZ6j7b2sXJ8dkLHEr+P48+GpdkhMcDv1Ws35\n1odu2+60uBcKp8I49HTh+/clDZLk/UT8SXXcjft50p2vl8dbyE9Cvery24Gh0sLJA5zAfmB4WHB1\n8rXx7o81cht2jK8/Ehj6G9Bceka+g0pINDQAAAB4AXqIiIiIiIiIoXl8M1jhuahgOpn0Zgw7136C\nuUcFfDLw3Hu6oj/IRNcLDS/uJDvevLN/qtHdru57KyB0XRlGiV4BkJHQ9rT4G/xd/OPh47N7rHK+\nN1KyPiQBwEH6kJB1s+CfH/I/mftaUhZewVLAE8hHSCPvt0e/nW/H88ft/s7UdLKwVLjfjQiQWIRr\n4JDj58P8b/mH/Z/tKvep9uOPhl9jpnOMkYYwADZJkZ41+qP5wP6x7jsuh+m/DqPD+J1a9aMG1Yjb\nNamI7yPI/wAB4AVwREREREREREREREREREREREREREREREREREREREREREREREWjmMrVwmHt5O48\nMr1ozI8k67D2H1PhQnGObUs7xzF5W58LGyZN7mVq80zeqQhxaOnxsnXj6qVp42LEWcnekyFmSO0/\n4722JdxwAN0ej+aNDZ/Ba+bZb5DxSwzjuWhrz24x9nus++0AkbI19Njfsorgnp1i+DVZHV3PsZGw\n0CzclO3PPkgfIb7/AOJVxREXjnBoJcQAPJKxfa63/GIv64WUEEbHhYW3az7klNtiJ1mNoe+EPHW1\np3okedHRWdERF4HA+DvXZeoi8c0OaWkAgjRBXC+OMk4lyy7w66QJBaN2lIyQR/Ejf2DWE9mn2I9w\nCFYJGtfHkXPjqbDIWjdZzv1SDrt4A9m+Y/JW1fljOLtgPh2XOPSXdQ2/egW++/f/AOJ7LyTpEc7y\nK36lc/8Asrv2SN9/p7H/AGfuvm5aikxN1rSzbpXfcJ6v1ydfc99+4/2vkKrW6c3L/VHF4evHHLjq\nBjs5J0ILQ1zW/da93udjs323r2K7uiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiEgeV\nR+RerHFePSPrm6b11ruj7LSb8R/V8ifA/eqwPWjLPaZI+B5Ix6LgXSaJaPJ10+3v8lK4L1s4zkSI\ncsJ8La1ssuM+4Rrew8f9ul0WtZguVorNaVk0ErQ+ORjttc0+CD8llVZ5zxFvNcHFipL0tWEWY5pe\ngb+Kxu9sP47/AHgLnYwFPjHIZ+Xc7mqVatE/BwmNjk6xG1g+70Dtt2hvXzOyr7xHkUvOsBasXsDP\nQoTgxxNsOB+0xOBBOuxA1/j2VixuNp4fHQY+hA2CpAzoijbvTR+fdbaIiLVyWOqZbHz0L0Imqzt6\nJYySA4fLt3XB/TDg3FczluWQZWhHO2lf+DXbJM9vQzbvk4b8DuVKemWYtUPUvM8WxVybKcZhDnxT\nySdYrkAHQd7jZLfy2r1hstwvMc9uWsTbjtZwVBHPJE5xYI2u1r+bvel5b9VeK05pA+zZfWjnFd9y\nOs91dr/kZAOnspvL8nx+IFZjvjWrNsE1q1RnxJJgBslo+QHuSAo6j6i8buYf9KS3TRgFh1Z7bjDG\n9koGywj5gL6/jI4b/vFS/rn/ACW8+3R5Vx2d+KysnwJAWi1Sk6XNI+R9lRfQWaefiOUfYnknlOUl\n6pJXFznHpZ3JXVURFSvUHgEPMacNirKypmajg+rbLd+Dvpd9N/uK51BzK5jpJ8Ny2mcZln/fEz3f\nDjn7hpeHeNkAkkfreOytbni5i5p6slCeN7GObLCAN9GyCHb7dvH/AMPydrJksiKVGw+xPSijEbHb\nfbHh3d3b5a7kf7T6KoT8rv8AIZbWF4dQbkLUj42vvMh/kqoBIbJ1e/nbR+xrQ2un8G4RU4ZjHsa8\n2claPXduOJ6p37J9z2A2Va0RERERERERERERERERERERERERERERERERYLlkUqU1l0UsoiYXmOFn\nU92hvTR7lU2n6tcWyAlNJ2SsiI6k+Djpn9B+R03spvjXMsHy6Kd+HuCYwO6ZY3MLHsP1ae+v8lmj\n5LSk5XLxwR2ftsVcWHOMJ+H0k67O+amEREUbnM9jeOYyTI5a2ytVZ2L3+5+QHkn6BfHHuRY3lGJZ\nk8TOZqj3OaHlhadg6I0e6lUXy97Y2Oe9waxo25xOgB81wzPcny3qbyafAYK+aXHqry2a5Xceqx27\n9/l5AHYH5lS+O4tgsLQZXpUeotjL/jSwkSP8/ec4jsd603yzXUfCkmNidkoS+OQuNRmhJL8buddR\nJH08nxJ4HhaEuExWSsM+24mKxEK8rW9Z6NN79P3T318h5i9/KrdHKZj0qlq2oHWr3E5v+HrSDbqj\nif2HfIEgA+H+3zXbTyDEtwkWZkyFePHSsa9tiSQNZp3jufxVVznHchjs1Y5hx63eu2XRd8T9o3BY\ncQGg9zpoA79vOvZWK9x/F8lr4+fOYmKWauRMyKb73wnkdx27H/DspljGsaGtAa0DQAGgAvURERFw\nr0x4xgeRcg5pNlaENt8eTc2NzyezSXk60Vj4xVg4h67nj3Gbr58RYhc+1W+L1Nhd07Pf3I0Pr30p\n+EUsV66chn+DHFWjwQmmbGwDYGi46HvoKicgnlyXo3byOMno4PjzrPTBiIx8SW074o2XyOO+rt1a\nHs1WrI4KfkOM4lZ49yAUuU0sNHLXicTqePTQe/gHewex379lI+n9zPckxOYqj7Dg8tTyRF6WOqJ2\n2JC3TiWkgNOwO47K0/wb5b/vbU//ANNH/wB9TmLp5Cji5YslkI7023ESsrCABuvHSCfr3XPPQH/8\nn5T/APVZf/pausIiIo/KYLFZuJseUx1a4xu+kTxB3T+G/Co170Q4panMlV+Qx7HA7irWT0d/PZ29\nL6x/ojxCpYbPaZdyD2uBAtWCW9h2BA0CPxV7x2Jx2IgMGOo16kROyyCMMBPz7LcRERERERERERER\nEREREREREREREREREREREQ9lwT0o5XBg5OWNmxmVsdeSfLurUdKGjv2drweyz+k92HkHqzyrPQkU\nmSM6RQkPTK7ZH3i36dPf5Fyu3GeZZjK+oma49lMRBRbSgEkRbJ1ve3q00kjtogg69lXMn6jcxiw+\ncz9fH4yrjcdadBFDc6xNO1rg0ub3APc/L5/JSua9UjVr8TZBHXpy59jZXT2yXRVWEDe9a6js68hW\nvi1zP24rzc9DUa6Kx01p6hJjni6QQ8bJ13JGvosXKcpyXHPh/QOMx91jmOMn2q38FwI8Bo1o/vVc\nbyn1GdGHHi+FaSQOk5Ruxv3/ACVntRS5fhE/6dx9QTurSOkga4TRtcAdEEjv81VPQT/3XVv/AJmb\n/wCpdNRc+9YM9cw3D21KDA6xlZxRDiN9DXg9RA9zoa/NQ2Cw1XBsxWPpVZjHFXL3ySNMTnFw8uH7\nR3+we7OzlnAMUMbXwv71GtP+tGXbg4k9/ckf7T9v9X3WWRrH5aB0daQNNcuDQDB2LQB936H9n/Z/\nre6VS1sbWmqXPNYN24fFOwTv7/8A+7/bePZa8cDZ8bUbPUdNC90zHMdL1NeHa0Cz5fT/AGP5Lkrs\nZyrK2XemWPBdSjtutsdPtpZH48k92A7PYdz3C/SXD+Ms4jxqth2XJ7Yh2TLMdnZ8gD2b8gp5ERER\nFr3qUWRozU5/ifCmYWP+HI5jtH5OaQR+Splb0f4bTc51Wjagc4acYr0zSfx05WLB8UwXG2vGIxkF\nVz/15Gt2934uPf2+ai4PTbjMGbdmG1bDrzyeuSS3K/rB8tcC7Rb9D2Wmz0i4WwXWjDscy2O7HPJb\nD9Ywf1D+C2bXplxizjqVNlKSqaMfw61irM6KaMb2fvjud7O9/MqcwHHsdxrGNoY2EsiBLnucep8j\nj5c5x7uJ+ZUotbIUIMpQmpWfifBmb0P+HI5jtfRzSCFA8c4Bx7idp8+GqzV3PBDmmzI5p379JcRv\nt5VnREREREREREREREREREREREREREREREREREREREREWpk8lUw+MsZC9KIqtdhfI8jegPoFwX0t\n9QMFxcZ5+Zdbqx5G8+1We6s8tezv7gefH71u8ax9nmXrUOYYrG28ZhoWCR80zDGbLukt7Dwdnz9B\n9Vv4bklJvr7yExMnJsQCjE5zXdDp2AEt3+yD0kD27Kh3sxXy3HOTRZzEZC9zN8j2ueYS9lONrmkd\nPswDR8f4Kdi5DiM3xfjOB5PgbtfBMpfDfk313B0U7NAOY4b03Wwdj3HyVt9G6FjG5DP1cdknZDiz\nJWilO8EEya24N37AdiR2JHb3V/z/AA/A8oMDszjo7ToN/DcXFpaD5GwR27KGf6S8Hke55wMQJO9N\nkeB+QDuyksuMTxHglxjGipjqtV7GtG3dOxoD3J2SP3qk/wCj/lKk/AzjGS/65Vne+WItILQ47afr\ntdZRc39XWtfDxhjvBy7B4J/Yf8v8fZfcZkgy1aNrnkfZ/gEfEdL+q0O/W/P9f9v9X2WB0s08dfpk\neeqsJnA13RAlxLfH/wDz/Z/W9kPVWyleD4sh3WMey902+kB3d35/rft/q+y9oNdJHHP8SbRiLyep\n0Z3ISP1P/wBv+y899rBDM51SjX65Puvkb2iI6vh6Our3/H/a/TajOPQ/B9card76sK9/eUyeZCff\nx/zfbwuyoiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIvCARojYXhjYQAWt\nIHjsvoDXheBrQdgDfnwvOho2dDZ89vK9LQRoga+SBoaNNAA+QXqLwtDhogEfIrxrGs/VaB+AX0i5\nl61faa/HsRk4GyfCoZOOaeSMbMbNFu9fLuB+a+KVmtPcp2K9508T6LXMe2MRhzteAB5IHcs8M/W8\nlfcnS19YNsdYMPUGtnMu29++z7b7CTy4/dPlIRH9rgcZ5us1BoOZ8E9Q8gt+nkt/YH3h5XxVDXFg\nZZmMZbIOtsYePfpPWfYnw7/aeCvmAgYyiGWHfFLxpvXvZBHUOn217j/Ze3hRHp1HYz3qplc5E9js\nfjYHUWPDAPiOc7q0COztd/veTsfNdpRERERERERERERERERERERERERERERERERERERERERERERE\nRERERERERERERERFp5XHQZfE28dZb1QWonRPH0I0uGYOS3wTkDeMcnsywU4g9mPvfD1HYYe4YT7a\nJ32777E6VuEu/sZkmuvaao0+WJrCXAkuOh26+nyzwG7cO4Xsc9c5SuYLEnwDATuHcrdEAM+8e/R1\nb6XeXHbT2C+sc+ENiEsshlI1t/3XlzS74n3B26ta62+GDuFUs/yOZ9XH4bjNie5m55HgQxRgiJuw\nWuJHfpaO4d+0O7trrHB+KQ8O41FjWP8AizueZ7Mv/wASV2uo/QdgB+CsaIiIiIiIiIiIiIiIiIiI\niIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiieQ8bxXKMW/HZaq2eB3cE9nMP85p9iuXS\nenPNOKTyy8XzEORoDtHRvH7wZvZaCe29+/ba1I8r6hMnd9p4LJJNDXbG0xyNazZ/WcNdjsaGh40S\nFmr8Z9TM/HPE+SpxymSGsHUJJujZ7Bze/YE99gn3XQeF+n+G4TVcKTHTXZWgT3Ju8kn/AHR9B/er\nWiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLQyeaxmFjikyd+vTZK/oY6eQMDna3rZXxj+Q\nYfLTOhx2Up25Gt6nMgna8gfPQKkkRERNrHPPFWgknnkZFDG0ue9501oHkk+wUMOa8XJ0ORYsn/5t\nn+anGkOaHNIII2CF6iIiIiIiIiIi+JZGwwvldvpY0uPSNnQ+Q91z3+O3hv2v7J8W/wDaOrp+F9jf\n1b+WtbVh5LzjDcUoVbuUNpkFkbY5ldzunx+t2+75HlYOLeomA5jPNDh32pDC3qe99dzWe3bq8b7j\nstri3NcLzFlt2HmlkFR4ZKJIiwgnx5/AqVyuTq4bF2cldeWVq0ZkkcGlxAH0HlQvD+dYfnEd2XD/\nAGgx1HtY900fR1EjY1337e+lK5zOUOOYifKZOUxVIQOtwaXHudAAD6rS4ly/F80xUuRxJmNeOd0B\nMrOklwAOwN+NOCnkRFXeVc1xHDooZcubLY5d6fFA6Rrda8kdh5C1uMeoeD5fYMWIbelaN7mdVe2M\nEexd4B7+Fa0REREREREREREREREREREREREREREREREREREUBzajVvcLzLLdeKdrKUz2iRod0uDC\nQR8iqX6OzYvC+kVXL3DXqsaZXT2XNDTr4hA2fJ9gpGx60cQjxDshXsWLWpHxiCGEmQ9OvvEeze47\nnSsVLmeCvcUHJWX448Z0dT5JCAWH+a4ezt9tKszet3C4cTFkPtliQSyOY2COLco6T3cW77D5E+VN\ny+oeCbxAcnrus3cb1dLjVhL3xn36m/s699r5f6iYQ8RHJ6rbtzG9ZY51auXOZryXNOtAfNaOV9U8\nHV4JJyig6W3CXOhgYInAulHs75AeSVGejvO/4UYP7Jfs2J8ux8s0rnxO6Okv7AO1rt1AaXTHsZLG\n5kjWvY4ac1w2CPkVwr0zw+Nn9ZOaNlo13srSPELHRgtj3Id6HgeF3cDQ0FWuTc6wvFZ4Ktx8896f\nvFTqRGWZw79+ke3Y9/onHOdYTlONtXMdLNup1fHrSx9M0evmzue/stPCep/GeQ5huKxs9uW4d7Ya\nkg6Pq4kfdH1K3rPNsPT5bFxmd1luSma10YFdxY4HfhwGu2u58KKz/qpgMFdkqNZdyMsDg2yaEHxW\n1++vvu8A/TypP+H/ABf+Dbc+cxXGOc4sEhOj1630dPnq+nlRVb1d4hayOLoxX3OmyTWmP7mxGXdg\n1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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 30, "metadata": { "image/jpeg": { "height": 600, "width": 700 } }, "output_type": "execute_result" } ], "source": [ "Image(url ='https://www.soils.org/images/publications/sssaj/70/6/1851fig1.jpeg', embed = True, width = 700, height = 600)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def SGD(training_data, epochs, batch_size, test_data=None):\n", " if test_data: n_test = len(test_data)\n", " n = len(training_data)\n", " for j in range(epochs):\n", " random.shuffle(training_data)\n", " for k in range(0, n, batch_size):\n", " mini_batches = [training_data[k:k+batch_size]]\n", " for mini_batch in mini_batches:\n", " update_mini_batch (mini_batch, 1)\n", " if test_data:\n", " print (\"Epoch {0}: {1} / {2}\".format(j, test_data, n_test))\n", " else:\n", " print (\"Epoch {0} complete\".format(j))\n", " return mini_batches " ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"Up date biases and weights by using backproagation to\n", "each mini_batch from above\"\"\"\n", "def batch(mini_batch, rat):\n", " nabla_b = [np.zeros(b.shape) for b in biases]\n", " nabla_w = [np.zeros(w.shape) for w in weights]\n", " for x, y in mini_batch:\n", " delta_nabla_b, delta_nabla_w = self.backprop(x, y)\n", " nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]\n", " nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]\n", " new_weights = [w-(rat/len(mini_batch))nw for w, nw in zip(weights, nabla_w)]\n", " new_biases = [b-(rat/len(mini_batch))nb for b, nb in zip(biases, nabla_b)]\n", " return new_weights, new_biases\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def cost_derivative(output_activations, y):\n", " return (output_activations-y) " ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def test(test_data):\n", " test_results = [(np.argmax(feedforward(x)), y) \n", " for (x, y) in test_data]\n", " return sum(int(x == y) for (x, y) in test_results)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"Trainnign the network on a portion of the data\"\"\"\n", "\n", "def train_net(net, nput, target, cycles = 30, dig_cycle = 1400, batch_size = 15, learn_rate = 1):\n", " train_index = np.linspace(0, dig_cycle, dig_cycle + 1)\n", " targ_list = [target(n) for n in target[0:dig_cycle]]\n", " np.random.seed(1)\n", " np.random.shuffle(train_index)\n", " for j in range(cycles):\n", " for n in train_index:\n", " if n+batch_size <= dig_cycle:\n", " train_dat = nput[int(n):int(n+cycles)]\n", " target_data = target_list[int(n):int(n+cycles)]\n", " else: \n", " train_dat = nput[int(n-cycles):dig_cycle]\n", " assert len(train_dat)!=0\n", " target_data = target_list[int(n-cycles):dig_cycle]\n", " stoc_dec(net, train_dat, target_data, learn_rate)\n", " " ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def learn(network, X, y, learning_rate=0.2, epochs=10000):\n", " X = np.atleast_2d(X)\n", " temp = np.ones([X.shape[0], X.shape[1]+1])\n", " temp[:, 0:-1] = X \n", " X = temp\n", " y = np.array(y)\n", "\n", " for i in range(epochs):\n", " k = np.random.randint(X.shape[0])\n", " a = [X[i]]\n", "\n", " for j in range(len(weights)):\n", " a.append(activation(np.dot(a[j], weights[j])))\n", " error = y[i] - a[-1]\n", " deltas = [error * activation_deriv(a[-1])]\n", "\n", " for i in range(len(a) - 2, 0, -1):\n", " deltas.append(deltas[-1].dot(weights[i].T)activation_deriv(a[i]))\n", " deltas.reverse()\n", " \n", " for i in range(len(weights)):\n", " layer = np.atleast_2d(a[i])\n", " delta = np.atleast_2d(deltas[i])\n", " weights[i] += learning_rate layer.T.dot(delta)\n", " \n", " return weights[i]" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"This is the network, it is called Markus!\"\"\"\n", "\"\"\"This takes all the functions from above and puts them together to train and analyze the data.\"\"\"\n", "\n", "\n", "\n", "class Network():\n", "\n", " def __init__(self, sizes):\n", " self.num_layers = len(sizes)\n", " self.sizes = sizes\n", " self.biases = [np.random.randn(y, 1) for y in sizes[1:]]\n", " self.weights = [np.random.randn(y, x) \n", " for x, y in zip(sizes[:-1], sizes[1:])]\n", "\n", " def feedforward(self, a):\n", " for b, w in zip(self.biases, self.weights):\n", " a = sigmoid_vec(np.dot(w, a)+b)\n", " return a\n", "\n", " def SGD(self, train_data, epochs, mini_batch_size, eta,test_data=None):\n", " n = len(train_data)\n", " for j in range(epochs):\n", " random.shuffle(train_data)\n", " mini_batches = [\n", " train_data[k:k+mini_batch_size]\n", " for k in range(0, n, mini_batch_size)]\n", " for mini_batch in mini_batches:\n", " self.update_mini_batch(mini_batch, eta)\n", "\n", "\n", " def update_mini_batch(self, mini_batch, eta):\n", " new_b = [np.zeros(b.shape) for b in self.biases]\n", " new_w = [np.zeros(w.shape) for w in self.weights]\n", " self.weights = [w-(eta/len(mini_batch))nw for w, nw in zip(self.weights, new_w)]\n", " self.biases = [b-(eta/len(mini_batch))nb for b, nb in zip(self.biases, new_b)]\n", "\n", " def backprop(self, t, q):\n", " new_b = [np.zeros(b.shape) for b in self.biases]\n", " new_w = [np.zeros(w.shape) for w in self.weights]\n", " act = t\n", " acts = [t]\n", " p = [] \n", " for b, w in zip(self.biases, self.weights):\n", " z = np.dot(w, act)+b\n", " p.append(z)\n", " act = sigmoid_vec(z)\n", " acts.append(act)\n", " delta = self.cost_derivative(acts[-1], q) * sigmoid_prime_vec(p[-1])\n", " new_b[-1] = delta\n", " new_w[-1] = np.dot(delta, acts[-2].transpose())\n", "\n", " for j in range(2, self.num_layers):\n", " z = p[-j]\n", " spv = sigmoid_prime_vec(z)\n", " delta = np.dot(self.weights[-j+1].transpose(), delta) * spv\n", " new_b[-j] = delta\n", " new_w[-j] = np.dot(delta, acts[-j-1].transpose())\n", " return (new_b, new_w)\n", "\n", " def evaluate(self, test_data):\n", " test_results = [(np.argmax(self.feedforward(x)), y) \n", " for (x, y) in test_data]\n", " return sum(int(x == y) for (x, y) in test_results)\n", " \n", " def cost_derivative(self, output_acts, y):\n", " return (output_acts-y) \n", "\n", "#### Miscellaneous functions\n", "def sigmoid(z):\n", " return 1.0/(1.0+np.exp(-z))\n", "\n", "sigmoid_vec = np.vectorize(sigmoid)\n", "\n", "def sigmoid_prime(z):\n", " return sigmoid(z)*(1-sigmoid(z))\n", "\n", "sigmoid_prime_vec = np.vectorize(sigmoid_prime)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "__main__.Network" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Network([5,37,12])\n", "Network" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Sigmoid Functions\n", "\n", "These functions introduce a non-linear factor in to the neural network allowing it to combine and use non-linear terms. Without the sigmoid the network would only be able to process and learn from leaner systems or combinations of linear systems." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 49, "metadata": { "image/png": { "height": 400, "width": 500 } }, "output_type": "execute_result" } ], "source": [ "Image(url ='http://www.saedsayad.com/images/ANN_Sigmoid.png', embed = True, width = 500, height = 400)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 50, "metadata": { "image/png": { "height": 400, "width": 500 } }, "output_type": "execute_result" } ], "source": [ "Image(url ='http://www.dplot.com/functions/tanh.png', embed = True, width = 500, height = 400)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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sAgkg6EBwdDuEAII+BDODaCWAoLVWhrzOBJoRtP8nR1gYLRFA0C1Vu7y5NiPo863FOx7o\nVF4lyTiIAIIOwkangwi0I+hEzww9qC4McxABBH0QaIYJItCSoM+AfD8oEgSTTuURQNDl1ayljFsT\ndEu1Za4eBBC0BySaZCOAoLOhZ2ANBBC0hiqQwxoBBM3aaJoAgm66/Oonj6DVl4gEJQkgaEm6xI4l\ngKBjCdK/aAIIuujyVZ88gq6+xEzQRQBBsz40E0DQmqtDbuIEELQ4YgaIIICgI+DRtXwCCLr8GtY8\nAwRdc3WZ2yYBBL2JiAYZCSDojPAZOj8BBJ2/BmSwTgBBszqaJoCgmy6/+skjaPUlIkFJAghaki6x\nYwkg6FiC9C+aAIIuunzVJ4+gqy8xE3QRQNCsD80EELTm6pCbOAEELY6YASIIIOgIeHQtnwCCLr+G\nNc8AQddcXea2SQBBbyKiQUYCCDojfIbOTwBB568BGawTQNCsjqYJIOimy69+8ghafYlIUJIAgpak\nS+xYAgg6liD9iyaAoIsuX/XJI+jqS8wEXQQQNOtDMwEErbk65CZOAEGLI2aACAIIOgIeXcsngKDL\nr2HNM0DQNVeXuW0SQNCbiGiQkQCCzgifofMTQND5a0AG6wQQNKujaQIIuunyq588glZfIhKUJICg\nJekSO5YAgo4lSP+iCSDoostXffIIuvoSM0EXAQTN+tBMAEFrrg65iRNA0OKIGSCCAIKOgEfX8gkg\n6PJrWPMMEHTN1WVumwQQ9CYiGmQkgKAzwmfo/AQQdP4akME6AQTN6miaAIJuuvzqJ4+g1ZeIBCUJ\nIGhJusSOJYCgYwnSv2gCCLro8lWfPIKuvsRM0EUAQbM+NBNA0JqrQ27iBBC0OGIGiCCAoCPg0bV8\nAgi6/BrWPAMEXXN1mdsmAQS9iYgGGQkg6IzwGTo/AQSdvwZksE4AQbM6miaAoJsuv/rJI2j1JSJB\nSQIIWpIusWMJIOhYgvQvmgCCLrp81SePoKsvMRN0EUDQrA/NBBC05uqQmzgBBC2OmAEiCCDoCHh0\nLZ8Agi6/hjXPAEHXXF3mtkkAQW8iokFGAgg6I3yGzk8AQeevARmsE0DQrI6mCSDopsuvfvIIWn2J\nSFCSAIKWpEvsWAIIOpYg/YsmgKCLLl/1ySPo6kvMBF0EEDTrQzMBBK25OuQmTgBBiyOucYC/f/8e\nMy0EfQznj14EvHQSOOzn7aDVxjDCBE6n0/39/ZcvX15eXkSHQtCieG/BdYqJrHoCX7vX09PT7L/m\nr58/fx6+6P6D+aE1Dfr/9qHMy/wMmwjm9fDw0P+hfw0Nhj/0Y81efTTruOOW/Sjmv9ZZmCB946HZ\ncqbDV4aEx11MBGuXYWrGVubPptlaDuPZjYewth8Sns3RWiZr44HwLO0BwhC5T8bKbYZiXAsz3/HP\njokg5BEELQSWsMkImO3t29tbsnAEgkA0AfN22wu6f2f68eNHdEh7AAQtBJawEIBAtQSMkc2G2pxv\nvL+/i04SQYviJTgEIACBcAIIOpwdPSEAAQiIEkDQongJDgEIQCCcAIIOZ0dPCEAAAqIEELQoXoJD\nAAIQCCeAoMPZ0RMCEICAKAEELYqX4BCAAATCCSDocHb0hAAEICBKAEGL4iU4BCAAgXACCDqcHT0h\nAAEIiBJA0KJ4CQ4BCEAgnACCDmdHTwhAAAKiBBC0KF6CQwACEAgngKDD2dETAhCAgCgBBC2Kl+AQ\ngAAEwgkg6HB29IQABCAgSgBBi+IlOAQgAIFwAgg6nB09IQABCIgSQNCieAkOAQhAIJwAgg5nR08I\nQAACogQQtChegkMAAhAIJ4Cgw9nREwIQgIAoAQQtipfgEIAABMIJIOhwdvSEAAQgIEoAQYviJTgE\nIACBcAIIOpwdPSEAAQiIEkDQongJDgEIQCCcAIIOZ0dPCEAAAqIEELQoXoJDAAIQCCfwf0C0zXwG\n0u7zAAAAAElFTkSuQmCC\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 51, "metadata": { "image/png": { "height": 400, "width": 500 } }, "output_type": "execute_result" } ], "source": [ "Image(url ='http://www.saedsayad.com/images/ANN_Unit_step.png', embed = True, width = 500, height = 400)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#Neural Network libraries" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sknn.mlp import Classifier, Layer\n", "\n", "def example_1(w):\n", " nn = Classifier(\n", " layers=[\n", " Layer(\"Maxout\", units=100, pieces=2),\n", " Layer(\"Softmax\")],\n", " learning_rate=0.001,\n", " n_iter=25)\n", " nn.fit(train, train)\n", " nn.fit(X_train, y_train)\n", "\n", " y_example = nn.predict(train)\n", " y_valid = nn.predict(test)\n", "\n", " score = nn.score(train, test)\n" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sknn.mlp import Classifier, Convolution, Layer\n", "\n", "def example_2(x):\n", " nn = Classifier(\n", " layers=[\n", " Convolution(\"Rectifier\", channels=8, kernel_shape=(3,3)),\n", " Layer(\"Softmax\")],\n", " learning_rate=0.02,\n", " n_iter=5)\n", " nn.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sknn.mlp import Classifier, Layer\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import MinMaxScaler\n", "\n", "def example_3(x):\n", " pipeline = Pipeline([\n", " ('min/max scaler', MinMaxScaler(feature_range=(0.0, 1.0))),\n", " ('neural network', Classifier(layers=[Layer(\"Softmax\")], n_iter=25))])\n", " pipeline.fit(digits.target, digits.target)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "I have no ownership nor crated the images, all are courtesy of google." ] } ], "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
mprat/gh-repos-explore	add_collab.ipynb	2	1586	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import requests\n", "\n", "user_repo = {\"ameena12-meet\": \"MEET-YL2\"}\n", "for u in user_repo:\n", " username = u\n", " repo = user_repo[u]\n", " passwd=\"\"\n", " if repo == \"MEET-YL1\":\n", " passwd = \"meetyear13\"\n", " else: \n", " passwd = \"meetyear2\"\n", " r = requests.put('https://api.github.com/repos/' + username + '/' + repo + '/collaborators/mprat', auth=(username, passwd))\n", " print r.headers" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CaseInsensitiveDict({'status': '204 No Content', 'x-ratelimit-remaining': '4999', 'x-github-media-type': 'github.beta', 'x-content-type-options': 'nosniff', 'access-control-expose-headers': 'ETag, Link, X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset, X-OAuth-Scopes, X-Accepted-OAuth-Scopes, X-Poll-Interval', 'x-github-request-id': '50B2914C:7396:1193597:529F02C3', 'vary': 'Accept-Encoding', 'server': 'GitHub.com', 'x-ratelimit-limit': '5000', 'access-control-allow-credentials': 'true', 'date': 'Wed, 04 Dec 2013 10:24:04 GMT', 'access-control-allow-origin': '*', 'x-ratelimit-reset': '1386156244'})\n" ] } ], "prompt_number": 16 } ], "metadata": {} } ] }	gpl-3.0
m2dsupsdlclass/lectures-labs	labs/03_neural_recsys/Explicit_Feedback_Neural_Recommender_System.ipynb	1	37077	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Explicit Feedback Neural Recommender Systems\n", "\n", "Goals:\n", "- Understand recommender data\n", "- Build different models architectures using Keras\n", "- Retrieve Embeddings and visualize them\n", "- Add metadata information as input to the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import os.path as op\n", "\n", "from zipfile import ZipFile\n", "try:\n", " from urllib.request import urlretrieve\n", "except ImportError: # Python 2 compat\n", " from urllib import urlretrieve\n", "\n", "\n", "ML_100K_URL = \"http://files.grouplens.org/datasets/movielens/ml-100k.zip\"\n", "ML_100K_FILENAME = ML_100K_URL.rsplit('/', 1)[1]\n", "ML_100K_FOLDER = 'ml-100k'\n", "\n", "if not op.exists(ML_100K_FILENAME):\n", " print('Downloading %s to %s...' % (ML_100K_URL, ML_100K_FILENAME))\n", " urlretrieve(ML_100K_URL, ML_100K_FILENAME)\n", "\n", "if not op.exists(ML_100K_FOLDER):\n", " print('Extracting %s to %s...' % (ML_100K_FILENAME, ML_100K_FOLDER))\n", " ZipFile(ML_100K_FILENAME).extractall('.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ratings file\n", "\n", "Each line contains a rated movie: \n", "- a user\n", "- an item\n", "- a rating from 1 to 5 stars" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "raw_ratings = pd.read_csv(op.join(ML_100K_FOLDER, 'u.data'), sep='\\t',\n", " names=[\"user_id\", \"item_id\", \"rating\", \"timestamp\"])\n", "raw_ratings.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Item metadata file\n", "\n", "The item metadata file contains metadata like the name of the movie or the date it was released. The movies file contains columns indicating the movie's genres. Let's only load the first five columns of the file with `usecols`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m_cols = ['item_id', 'title', 'release_date', 'video_release_date', 'imdb_url']\n", "items = pd.read_csv(op.join(ML_100K_FOLDER, 'u.item'), sep='\|',\n", " names=m_cols, usecols=range(5), encoding='latin-1')\n", "items" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's write a bit of Python preprocessing code to extract the release year as an integer value:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def extract_year(release_date):\n", " if hasattr(release_date, 'split'):\n", " components = release_date.split('-')\n", " if len(components) == 3:\n", " return int(components[2])\n", " # Missing value marker\n", " return 1920\n", "\n", "\n", "items['release_year'] = items['release_date'].map(extract_year)\n", "items.hist('release_year', bins=50);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enrich the raw ratings data with the collected items metadata:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all_ratings = pd.merge(items, raw_ratings)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all_ratings.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data preprocessing\n", "\n", "To understand well the distribution of the data, the following statistics are computed:\n", "- the number of users\n", "- the number of items\n", "- the rating distribution\n", "- the popularity of each movie" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "min_user_id = all_ratings['user_id'].min()\n", "min_user_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "max_user_id = all_ratings['user_id'].max()\n", "max_user_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "min_item_id = all_ratings['item_id'].min()\n", "min_item_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "max_item_id = all_ratings['item_id'].max()\n", "max_item_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all_ratings['rating'].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's do a bit more pandas magic compute the popularity of each movie (number of ratings):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "popularity = all_ratings.groupby('item_id').size().reset_index(name='popularity')\n", "items = pd.merge(popularity, items)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "items['popularity'].plot.hist(bins=30);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(items['popularity'] == 1).sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "items.nlargest(10, 'popularity')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "items[\"title\"][181]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "items[\"title\"].loc[181]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "items[\"title\"].iloc[181]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "indexed_items = items.set_index('item_id')\n", "indexed_items.nlargest(10, 'popularity')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "indexed_items[\"title\"][181]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "indexed_items[\"title\"].loc[181]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "indexed_items[\"title\"].iloc[181]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all_ratings = pd.merge(popularity, all_ratings)\n", "all_ratings.describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all_ratings.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Later in the analysis we will assume that this popularity does not come from the ratings themselves but from an external metadata, e.g. box office numbers in the month after the release in movie theaters.\n", "\n", "Let's split the enriched data in a train / test split to make it possible to do predictive modeling:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "ratings_train, ratings_test = train_test_split(\n", " all_ratings, test_size=0.2, random_state=0)\n", "\n", "user_id_train = np.array(ratings_train['user_id'])\n", "item_id_train = np.array(ratings_train['item_id'])\n", "rating_train = np.array(ratings_train['rating'])\n", "\n", "user_id_test = np.array(ratings_test['user_id'])\n", "item_id_test = np.array(ratings_test['item_id'])\n", "rating_test = np.array(ratings_test['rating'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Explicit feedback: supervised ratings prediction\n", "\n", "For each pair of (user, item) try to predict the rating the user would give to the item.\n", "\n", "This is the classical setup for building recommender systems from offline data with explicit supervision signal. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predictive ratings as a regression problem\n", "\n", "The following code implements the following architecture:\n", "\n", "<img src=\"images/rec_archi_1.svg\" style=\"width: 600px;\" />" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.layers import Embedding, Flatten, Dense, Dropout\n", "from tensorflow.keras.layers import Dot\n", "from tensorflow.keras.models import Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# For each sample we input the integer identifiers\n", "# of a single user and a single item\n", "class RegressionModel(Model):\n", " def __init__(self, embedding_size, max_user_id, max_item_id):\n", " super().__init__()\n", " \n", " self.user_embedding = Embedding(output_dim=embedding_size,\n", " input_dim=max_user_id + 1,\n", " input_length=1,\n", " name='user_embedding')\n", " self.item_embedding = Embedding(output_dim=embedding_size,\n", " input_dim=max_item_id + 1,\n", " input_length=1,\n", " name='item_embedding')\n", " \n", " # The following two layers don't have parameters.\n", " self.flatten = Flatten()\n", " self.dot = Dot(axes=1)\n", " \n", " def call(self, inputs):\n", " user_inputs = inputs[0]\n", " item_inputs = inputs[1]\n", " \n", " user_vecs = self.flatten(self.user_embedding(user_inputs))\n", " item_vecs = self.flatten(self.item_embedding(item_inputs))\n", " \n", " y = self.dot([user_vecs, item_vecs])\n", " return y\n", "\n", "\n", "model = RegressionModel(64, max_user_id, max_item_id)\n", "model.compile(optimizer=\"adam\", loss='mae')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Useful for debugging the output shape of model\n", "initial_train_preds = model.predict([user_id_train, item_id_train])\n", "initial_train_preds.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model error\n", "\n", "Using `initial_train_preds`, compute the model errors:\n", "- mean absolute error\n", "- mean squared error\n", "\n", "Converting a pandas Series to numpy array is usually implicit, but you may use `rating_train.values` to do so explicitly. Be sure to monitor the shapes of each object you deal with by using `object.shape`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load solutions/compute_errors.py\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Monitoring runs\n", "\n", "Keras enables to monitor various variables during training. \n", "\n", "`history.history` returned by the `model.fit` function is a dictionary\n", "containing the `'loss'` and validation loss `'val_loss'` after each epoch" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "# Training the model\n", "history = model.fit([user_id_train, item_id_train], rating_train,\n", " batch_size=64, epochs=10, validation_split=0.1,\n", " shuffle=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(history.history['loss'], label='train')\n", "plt.plot(history.history['val_loss'], label='validation')\n", "plt.ylim(0, 2)\n", "plt.legend(loc='best')\n", "plt.title('Loss');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Questions:\n", "\n", "- Why is the train loss higher than the first loss in the first few epochs?\n", "- Why is Keras not computing the train loss on the full training set at the end of each epoch as it does on the validation set?\n", "\n", "\n", "Now that the model is trained, the model MSE and MAE look nicer:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def plot_predictions(y_true, y_pred):\n", " plt.figure(figsize=(4, 4))\n", " plt.xlim(-1, 6)\n", " plt.xlabel(\"True rating\")\n", " plt.ylim(-1, 6)\n", " plt.ylabel(\"Predicted rating\")\n", " plt.scatter(y_true, y_pred, s=60, alpha=0.01)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import mean_squared_error\n", "from sklearn.metrics import mean_absolute_error\n", "\n", "test_preds = model.predict([user_id_test, item_id_test])\n", "print(\"Final test MSE: %0.3f\" % mean_squared_error(test_preds, rating_test))\n", "print(\"Final test MAE: %0.3f\" % mean_absolute_error(test_preds, rating_test))\n", "plot_predictions(rating_test, test_preds)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train_preds = model.predict([user_id_train, item_id_train])\n", "print(\"Final train MSE: %0.3f\" % mean_squared_error(train_preds, rating_train))\n", "print(\"Final train MAE: %0.3f\" % mean_absolute_error(train_preds, rating_train))\n", "plot_predictions(rating_train, train_preds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Embeddings\n", "\n", "- It is possible to retrieve the embeddings by simply using the Keras function `model.get_weights` which returns all the model learnable parameters.\n", "- The weights are returned the same order as they were build in the model\n", "- What is the total number of parameters?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# weights and shape\n", "weights = model.get_weights()\n", "[w.shape for w in weights]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Solution: \n", "# model.summary()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "user_embeddings = weights[0]\n", "item_embeddings = weights[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "item_id = 181\n", "print(f\"Title for item_id={item_id}: {indexed_items['title'][item_id]}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(f\"Embedding vector for item_id={item_id}\")\n", "print(item_embeddings[item_id])\n", "print(\"shape:\", item_embeddings[item_id].shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Finding most similar items\n", "\n", "Finding k most similar items to a point in embedding space\n", "\n", "- Write in numpy a function to compute the cosine similarity between two points in embedding space.\n", "- Test it on the following cells to check the similarities between popular movies.\n", "- Bonus: try to generalize the function to compute the similarities between one movie and all the others and return the most related movies.\n", "\n", "Notes:\n", "- you may use `np.linalg.norm` to compute the norm of vector, and you may specify the `axis=`\n", "- the numpy function `np.argsort(...)` enables to compute the sorted indices of a vector\n", "- `items[\"name\"][idxs]` returns the names of the items indexed by array idxs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "EPSILON = 1e-07 # to avoid division by 0.\n", "\n", "\n", "def cosine(x, y):\n", " # TODO: implement me!\n", " return 0." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load solutions/similarity.py\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def print_similarity(item_a, item_b, item_embeddings, titles):\n", " print(titles[item_a])\n", " print(titles[item_b])\n", " similarity = cosine(item_embeddings[item_a],\n", " item_embeddings[item_b])\n", " print(f\"Cosine similarity: {similarity:.3}\")\n", " \n", "print_similarity(50, 181, item_embeddings, indexed_items[\"title\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print_similarity(181, 288, item_embeddings, indexed_items[\"title\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print_similarity(181, 1, item_embeddings, indexed_items[\"title\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print_similarity(181, 181, item_embeddings, indexed_items[\"title\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def cosine_similarities(item_id, item_embeddings):\n", " \"\"\"Compute similarities between item_id and all items embeddings\"\"\"\n", " query_vector = item_embeddings[item_id]\n", " dot_products = item_embeddings @ query_vector\n", "\n", " query_vector_norm = np.linalg.norm(query_vector)\n", " all_item_norms = np.linalg.norm(item_embeddings, axis=1)\n", " norm_products = query_vector_norm * all_item_norms\n", " return dot_products / (norm_products + EPSILON)\n", "\n", "\n", "similarities = cosine_similarities(181, item_embeddings)\n", "similarities" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(similarities, bins=30);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def most_similar(item_id, item_embeddings, titles,\n", " top_n=30):\n", " sims = cosine_similarities(item_id, item_embeddings)\n", " # [::-1] makes it possible to reverse the order of a numpy\n", " # array, this is required because most similar items have\n", " # a larger cosine similarity value\n", " sorted_indexes = np.argsort(sims)[::-1]\n", " idxs = sorted_indexes[0:top_n]\n", " return list(zip(idxs, titles[idxs], sims[idxs]))\n", "\n", "\n", "most_similar(50, item_embeddings, indexed_items[\"title\"], top_n=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "items[items['title'].str.contains(\"Star Trek\")]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "most_similar(227, item_embeddings, indexed_items[\"title\"], top_n=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The similarities do not always make sense: the number of ratings is low and the embedding does not automatically capture semantic relationships in that context. Better representations arise with higher number of ratings, and less overfitting in models or maybe better loss function, such as those based on implicit feedback." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualizing embeddings using TSNE\n", "\n", "- we use scikit learn to visualize items embeddings\n", "- Try different perplexities, and visualize user embeddings as well\n", "- What can you conclude ?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.manifold import TSNE\n", "\n", "item_tsne = TSNE(learning_rate=\"auto\", init=\"pca\", perplexity=30).fit_transform(item_embeddings)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.figure(figsize=(10, 10))\n", "plt.scatter(item_tsne[:, 0], item_tsne[:, 1]);\n", "plt.xticks(()); plt.yticks(());\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %pip install -q plotly" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "import plotly.express as px\n", "\n", "tsne_df = pd.DataFrame(item_tsne, columns=[\"tsne_1\", \"tsne_2\"])\n", "tsne_df[\"item_id\"] = np.arange(item_tsne.shape[0])\n", "tsne_df = tsne_df.merge(items.reset_index())\n", "\n", "px.scatter(tsne_df, x=\"tsne_1\", y=\"tsne_2\",\n", " color=\"popularity\",\n", " hover_data=[\"item_id\", \"title\", \"popularity\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively with [Uniform Manifold Approximation and Projection](https://github.com/lmcinnes/umap):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %pip install umap-learn" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# import umap\n", "\n", "# item_umap = umap.UMAP().fit_transform(item_embeddings)\n", "# plt.figure(figsize=(10, 10))\n", "# plt.scatter(item_umap[:, 0], item_umap[:, 1]);\n", "# plt.xticks(()); plt.yticks(());\n", "# plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A Deep recommender model\n", "\n", "Using a similar framework as previously, the following deep model described in the course was built (with only two fully connected)\n", "\n", "<img src=\"images/rec_archi_2.svg\" style=\"width: 600px;\" />\n", "\n", "To build this model we will need a new kind of layer:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.layers import Concatenate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Exercise\n", "\n", "- The following code has 4 errors that prevent it from working correctly. Correct them and explain why they are critical." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class DeepRegressionModel(Model):\n", "\n", " def __init__(self, embedding_size, max_user_id, max_item_id):\n", " super().__init__()\n", " \n", " self.user_embedding = Embedding(output_dim=embedding_size,\n", " input_dim=max_user_id + 1,\n", " input_length=1,\n", " name='user_embedding')\n", " self.item_embedding = Embedding(output_dim=embedding_size,\n", " input_dim=max_item_id + 1,\n", " input_length=1,\n", " name='item_embedding')\n", " \n", " # The following two layers don't have parameters.\n", " self.flatten = Flatten()\n", " self.concat = Concatenate()\n", " \n", " self.dropout = Dropout(0.99)\n", " self.dense1 = Dense(64, activation=\"relu\")\n", " self.dense2 = Dense(2, activation=\"tanh\")\n", " \n", " def call(self, inputs, training=False):\n", " user_inputs = inputs[0]\n", " item_inputs = inputs[1]\n", " \n", " user_vecs = self.flatten(self.user_embedding(user_inputs))\n", " item_vecs = self.flatten(self.item_embedding(item_inputs))\n", " \n", " input_vecs = self.concat([user_vecs, item_vecs])\n", " \n", " y = self.dropout(input_vecs, training=training)\n", " y = self.dense1(y)\n", " y = self.dense2(y)\n", " \n", " return y\n", " \n", "model = DeepRegressionModel(64, max_user_id, max_item_id)\n", "model.compile(optimizer='adam', loss='binary_crossentropy')\n", "\n", "initial_train_preds = model.predict([user_id_train, item_id_train])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load solutions/deep_explicit_feedback_recsys.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "history = model.fit([user_id_train, item_id_train], rating_train,\n", " batch_size=64, epochs=10, validation_split=0.1,\n", " shuffle=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(history.history['loss'], label='train')\n", "plt.plot(history.history['val_loss'], label='validation')\n", "plt.ylim(0, 2)\n", "plt.legend(loc='best')\n", "plt.title('Loss');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train_preds = model.predict([user_id_train, item_id_train])\n", "print(\"Final train MSE: %0.3f\" % mean_squared_error(train_preds, rating_train))\n", "print(\"Final train MAE: %0.3f\" % mean_absolute_error(train_preds, rating_train))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_preds = model.predict([user_id_test, item_id_test])\n", "print(\"Final test MSE: %0.3f\" % mean_squared_error(test_preds, rating_test))\n", "print(\"Final test MAE: %0.3f\" % mean_absolute_error(test_preds, rating_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The performance of this model is not necessarily significantly better than the previous model but you can notice that the gap between train and test is lower, probably thanks to the use of dropout.\n", "\n", "Furthermore this model is more flexible in the sense that we can extend it to include metadata for hybrid recsys as we will see in the following." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Home assignment: \n", " - Add another layer, compare train/test error.\n", " - Can you improve the test MAE? \n", " - Try adding more dropout and change layer sizes.\n", " \n", " \n", "Manual tuning of so many hyperparameters is tedious. In practice it's better to automate the design of the model using an hyperparameter search tool such as:\n", "\n", "- https://keras-team.github.io/keras-tuner/ (Keras specific)\n", "- https://optuna.org/ (any machine learning framework, Keras included)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using item metadata in the model\n", "\n", "Using a similar framework as previously, we will build another deep model that can also leverage additional metadata. The resulting system is therefore an Hybrid Recommender System that does both Collaborative Filtering and Content-based recommendations.\n", "\n", "<img src=\"images/rec_archi_3.svg\" style=\"width: 600px;\" />\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import QuantileTransformer\n", "\n", "meta_columns = ['popularity', 'release_year']\n", "\n", "scaler = QuantileTransformer()\n", "item_meta_train = scaler.fit_transform(ratings_train[meta_columns])\n", "item_meta_test = scaler.transform(ratings_test[meta_columns])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class HybridModel(Model):\n", "\n", " def __init__(self, embedding_size, max_user_id, max_item_id):\n", " super().__init__()\n", " \n", " self.user_embedding = Embedding(output_dim=embedding_size,\n", " input_dim=max_user_id + 1,\n", " input_length=1,\n", " name='user_embedding')\n", " self.item_embedding = Embedding(output_dim=embedding_size,\n", " input_dim=max_item_id + 1,\n", " input_length=1,\n", " name='item_embedding')\n", " \n", " # The following two layers don't have parameters.\n", " self.flatten = Flatten()\n", " self.concat = Concatenate()\n", " \n", " self.dense1 = Dense(64, activation=\"relu\")\n", " self.dropout = Dropout(0.3)\n", " self.dense2 = Dense(64, activation='relu')\n", " self.dense3 = Dense(1)\n", " \n", " def call(self, inputs, training=False):\n", " user_inputs = inputs[0]\n", " item_inputs = inputs[1]\n", " meta_inputs = inputs[2]\n", "\n", " user_vecs = self.flatten(self.user_embedding(user_inputs))\n", " user_vecs = self.dropout(user_vecs, training=training)\n", "\n", " item_vecs = self.flatten(self.item_embedding(item_inputs))\n", " item_vecs = self.dropout(item_vecs, training=training)\n", "\n", " input_vecs = self.concat([user_vecs, item_vecs, meta_inputs])\n", "\n", " y = self.dense1(input_vecs)\n", " y = self.dropout(y, training=training)\n", " y = self.dense2(y)\n", " y = self.dropout(y, training=training)\n", " y = self.dense3(y)\n", " return y\n", " \n", "model = HybridModel(64, max_user_id, max_item_id)\n", "model.compile(optimizer='adam', loss='mae')\n", "\n", "initial_train_preds = model.predict([user_id_train,\n", " item_id_train,\n", " item_meta_train])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "history = model.fit([user_id_train, item_id_train, item_meta_train],\n", " rating_train,\n", " batch_size=64, epochs=10, validation_split=0.1,\n", " shuffle=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_preds = model.predict([user_id_test, item_id_test, item_meta_test])\n", "print(\"Final test MSE: %0.3f\" % mean_squared_error(test_preds, rating_test))\n", "print(\"Final test MAE: %0.3f\" % mean_absolute_error(test_preds, rating_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The additional metadata seems to improve the predictive power of the model a bit but this should be re-run several times to see the impact of the random initialization of the model.\n", "\n", "\n", "### A recommendation function for a given user\n", "\n", "Once the model is trained, the system can be used to recommend a few items for a user, that he/she hasn't already seen:\n", "- we use the `model.predict` to compute the ratings a user would have given to all items\n", "- we build a reco function that sorts these items and exclude those the user has already seen" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "def recommend(user_id, top_n=10):\n", " item_ids = range(1, max_item_id)\n", " seen_mask = all_ratings[\"user_id\"] == user_id\n", " seen_movies = set(all_ratings[seen_mask][\"item_id\"])\n", " item_ids = list(filter(lambda x: x not in seen_movies, item_ids))\n", "\n", " print(\"User %d has seen %d movies, including:\" % (user_id, len(seen_movies)))\n", " for title in all_ratings[seen_mask].nlargest(20, 'popularity')['title']:\n", " print(\" \", title)\n", " print(\"Computing ratings for %d other movies:\" % len(item_ids))\n", " \n", " item_ids = np.array(item_ids)\n", " user_ids = np.zeros_like(item_ids)\n", " user_ids[:] = user_id\n", " items_meta = scaler.transform(indexed_items[meta_columns].loc[item_ids])\n", " \n", " rating_preds = model.predict([user_ids, item_ids, items_meta])\n", " \n", " item_ids = np.argsort(rating_preds[:, 0])[::-1].tolist()\n", " rec_items = item_ids[:top_n]\n", " return [(items[\"title\"][movie], rating_preds[movie][0])\n", " for movie in rec_items]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for title, pred_rating in recommend(5):\n", " print(\" %0.1f: %s\" % (pred_rating, title))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Home assignment: Predicting ratings as a classification problem\n", "\n", "In this dataset, the ratings all belong to a finite set of possible values:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "np.unique(rating_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maybe we can help the model by forcing it to predict those values by treating the problem as a multiclassification problem. The only required changes are:\n", "\n", "- setting the final layer to output class membership probabities using a softmax activation with 5 outputs;\n", "- optimize the categorical cross-entropy classification loss instead of a regression loss such as MSE or MAE." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load solutions/classification.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.9" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
littlewizardLI/Udacity-ML-nanodegrees	CapstonePoject/xgb[3].ipynb	1	134279	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Rossmann Store Sales\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Administrator\\Anaconda2\\envs\\gl-env\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import xgboost as xgb\n", "\n", "import pylab\n", "import csv\n", "import datetime\n", "import math\n", "import re\n", "import time\n", "import random\n", "import os\n", "\n", "from pandas.tseries.offsets import \n", "from operator import \n", "\n", "from sklearn.cross_validation import train_test_split\n", "\n", "%matplotlib inline\n", "\n", "# plt.style.use('ggplot') # Good looking plots\n", "\n", "np.set_printoptions(precision=4, threshold=10000, linewidth=100, edgeitems=999, suppress=True)\n", "\n", "pd.set_option('display.max_columns', None)\n", "pd.set_option('display.max_rows', None)\n", "pd.set_option('display.width', 100)\n", "pd.set_option('expand_frame_repr', False)\n", "pd.set_option('precision', 6)\n", "\n", "start_time = time.time()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "# Thanks to Chenglong Chen for providing this in the forum\n", "def ToWeight(y):\n", " w = np.zeros(y.shape, dtype=float)\n", " ind = y != 0\n", " w[ind] = 1./(y[ind]*2)\n", " return w\n", "\n", "def rmspe(yhat, y):\n", " w = ToWeight(y)\n", " rmspe = np.sqrt(np.mean( w (y - yhat)*2 ))\n", " return rmspe\n", "\n", "def rmspe_xg(yhat, y):\n", " # y = y.values\n", " y = y.get_label()\n", " y = np.exp(y) - 1\n", " yhat = np.exp(yhat) - 1\n", " w = ToWeight(y)\n", " rmspe = np.sqrt(np.mean(w (y - yhat)2))\n", " return \"rmspe\", rmspe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting seed" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "seed = 42" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading sales data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Administrator\\Anaconda2\\envs\\gl-env\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2723: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] } ], "source": [ "nrows = None\n", "\n", "df_train = pd.read_csv('train.csv', \n", " nrows=nrows,\n", " parse_dates=['Date'],\n", " date_parser=(lambda dt: pd.to_datetime(dt, format='%Y-%m-%d')))\n", "\n", "nrows = nrows\n", "\n", "df_submit = pd.read_csv('test.csv', \n", " nrows=nrows,\n", " parse_dates=['Date'],\n", " date_parser=(lambda dt: pd.to_datetime(dt, format='%Y-%m-%d')))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Setting a variable to easily distinguish train (1) from submit (0) set\n", "df_train['Set'] = 1\n", "df_submit['Set'] = 0" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Combine train and test set\n", "frames = [df_train, df_submit]\n", "df = pd.concat(frames)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "features_x = ['Store', 'Date', 'DayOfWeek', 'Open', 'Promo', 'SchoolHoliday', 'StateHoliday']\n", "features_y = ['SalesLog']" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Remove rows where store is open, but no sales.\n", "df = df.loc[~((df['Open'] == 1) & (df['Sales'] == 0))]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.loc[df['Set'] == 1, 'SalesLog'] = np.log1p(df.loc[df['Set'] == 1]['Sales']) # = np.log(df['Sales'] + 1)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df['StateHoliday'] = df['StateHoliday'].astype('category').cat.codes" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "var_name = 'Date'\n", "\n", "df[var_name + 'Day'] = pd.Index(df[var_name]).day\n", "df[var_name + 'Week'] = pd.Index(df[var_name]).week\n", "df[var_name + 'Month'] = pd.Index(df[var_name]).month\n", "df[var_name + 'Year'] = pd.Index(df[var_name]).year\n", "df[var_name + 'DayOfYear'] = pd.Index(df[var_name]).dayofyear\n", "\n", "df[var_name + 'Day'] = df[var_name + 'Day'].fillna(0)\n", "df[var_name + 'Week'] = df[var_name + 'Week'].fillna(0)\n", "df[var_name + 'Month'] = df[var_name + 'Month'].fillna(0)\n", "df[var_name + 'Year'] = df[var_name + 'Year'].fillna(0)\n", "df[var_name + 'DayOfYear'] = df[var_name + 'DayOfYear'].fillna(0)\n", "\n", "features_x.remove(var_name)\n", "features_x.append(var_name + 'Day')\n", "features_x.append(var_name + 'Week')\n", "features_x.append(var_name + 'Month')\n", "features_x.append(var_name + 'Year')\n", "features_x.append(var_name + 'DayOfYear')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df['DateInt'] = df['Date'].astype(np.int64)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_store = pd.read_csv('store.csv', \n", " nrows=nrows)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Convert Storetype and Assortment to numerical categories\n", "df_store['StoreType'] = df_store['StoreType'].astype('category').cat.codes\n", "df_store['Assortment'] = df_store['Assortment'].astype('category').cat.codes" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "### Convert competition open year and month to float\n", "def convertCompetitionOpen(df):\n", " try:\n", " date = '{}-{}'.format(int(df['CompetitionOpenSinceYear']), int(df['CompetitionOpenSinceMonth']))\n", " return pd.to_datetime(date)\n", " except:\n", " return np.nan\n", "\n", "df_store['CompetitionOpenInt'] = df_store.apply(lambda df: convertCompetitionOpen(df), axis=1).astype(np.int64)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Convert competition open year and month to float\n", "\n", "def convertPromo2(df):\n", " try:\n", " date = '{}{}1'.format(int(df['Promo2SinceYear']), int(df['Promo2SinceWeek']))\n", " return pd.to_datetime(date, format='%Y%W%w')\n", " except:\n", " return np.nan\n", "\n", "df_store['Promo2SinceFloat'] = df_store.apply(lambda df: convertPromo2(df), axis=1).astype(np.int64)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s = df_store['PromoInterval'].str.split(',').apply(pd.Series, 1)\n", "s.columns = ['PromoInterval0', 'PromoInterval1', 'PromoInterval2', 'PromoInterval3']\n", "df_store = df_store.join(s)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def monthToNum(date):\n", " return{\n", " 'Jan' : 1,\n", " 'Feb' : 2,\n", " 'Mar' : 3,\n", " 'Apr' : 4,\n", " 'May' : 5,\n", " 'Jun' : 6,\n", " 'Jul' : 7,\n", " 'Aug' : 8,\n", " 'Sept' : 9, \n", " 'Oct' : 10,\n", " 'Nov' : 11,\n", " 'Dec' : 12\n", " }[date]\n", "\n", "df_store['PromoInterval0'] = df_store['PromoInterval0'].map(lambda x: monthToNum(x) if str(x) != 'nan' else np.nan)\n", "df_store['PromoInterval1'] = df_store['PromoInterval1'].map(lambda x: monthToNum(x) if str(x) != 'nan' else np.nan)\n", "df_store['PromoInterval2'] = df_store['PromoInterval2'].map(lambda x: monthToNum(x) if str(x) != 'nan' else np.nan)\n", "df_store['PromoInterval3'] = df_store['PromoInterval3'].map(lambda x: monthToNum(x) if str(x) != 'nan' else np.nan)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "del df_store['PromoInterval']" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "store_features = ['Store', 'StoreType', 'Assortment', \n", " 'CompetitionDistance', 'CompetitionOpenInt',\n", " 'PromoInterval0']\n", "\n", "### Features not helping\n", "# PromoInterval1, PromoInterval2, PromoInterval3\n", "\n", "features_x = list(set(features_x + store_features))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.merge(df, df_store[store_features], how='left', on=['Store'])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Convert every NAN to -1\n", "for feature in features_x:\n", " df[feature] = df[feature].fillna(-1)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "store_dates_to_remove = { 105:1.368e18, 163:1.368e18,\n", " 172:1.366e18, 364:1.37e18,\n", " 378:1.39e18, 523:1.39e18,\n", " 589:1.37e18, 663:1.39e18,\n", " 676:1.366e18, 681:1.37e18,\n", " 700:1.373e18, 708:1.368e18,\n", " 709:1.423e18, 730:1.39e18,\n", " 764:1.368e18, 837:1.396e18,\n", " 845:1.368e18, 861:1.368e18,\n", " 882:1.368e18, 969:1.366e18,\n", " 986:1.368e18, 192:1.421e18,\n", " 263:1.421e18, 500:1.421e18,\n", " 797:1.421e18, 815:1.421e18,\n", " 825:1.421e18}\n", "\n", "for key,value in store_dates_to_remove.iteritems():\n", " df.loc[(df['Store'] == key) & (df['DateInt'] < value), 'Delete'] = True" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Delete the data where sales in the first period is much different from the rest\n", "df = df.loc[df['Delete'] != True]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def mad_based_outlier(points, thresh=3.5):\n", " if len(points.shape) == 1:\n", " points = points[:,None]\n", " median = np.median(points, axis=0)\n", " diff = np.sum((points - median)2, axis=-1)\n", " diff = np.sqrt(diff)\n", " med_abs_deviation = np.median(diff)\n", "\n", " modified_z_score = 0.6745 * diff / med_abs_deviation\n", "\n", " return modified_z_score > thresh\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "for i in df['Store'].unique():\n", " df.loc[(df['Set'] == 1) & (df['Store'] == i) & (df['Open'] == 1), 'Outlier'] = \\\n", " mad_based_outlier(df.loc[(df['Set'] == 1) & (df['Store'] == i) & (df['Open'] == 1)]['Sales'], 3)\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(df.loc[(df['Set'] == 1) & (df['Open'] == 1) & (df['Outlier'] == False)][features_x],\n", " df.loc[(df['Set'] == 1) & (df['Open'] == 1) & (df['Outlier'] == False)][features_y],\n", " test_size=0.1, random_state=seed)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "dtrain = xgb.DMatrix(X_train, y_train)\n", "dtest = xgb.DMatrix(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "num_round = 20000\n", "evallist = [(dtrain, 'train'), (dtest, 'test')]" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Will train until test error hasn't decreased in 250 rounds.\n", "[0]\ttrain-rmspe:0.999863\ttest-rmspe:0.999863\n", "[250]\ttrain-rmspe:0.491216\ttest-rmspe:0.487971\n", "[500]\ttrain-rmspe:0.198972\ttest-rmspe:0.188309\n", "[750]\ttrain-rmspe:0.166821\ttest-rmspe:0.156818\n", "[1000]\ttrain-rmspe:0.137129\ttest-rmspe:0.132996\n", "[1250]\ttrain-rmspe:0.122311\ttest-rmspe:0.121035\n", "[1500]\ttrain-rmspe:0.109952\ttest-rmspe:0.112465\n", "[1750]\ttrain-rmspe:0.100481\ttest-rmspe:0.106788\n", "[2000]\ttrain-rmspe:0.093883\ttest-rmspe:0.102796\n", "[2250]\ttrain-rmspe:0.088570\ttest-rmspe:0.100460\n", "[2500]\ttrain-rmspe:0.083871\ttest-rmspe:0.098587\n", "[2750]\ttrain-rmspe:0.080263\ttest-rmspe:0.097114\n", "[3000]\ttrain-rmspe:0.077273\ttest-rmspe:0.095896\n", "[3250]\ttrain-rmspe:0.074326\ttest-rmspe:0.094886\n", "[3500]\ttrain-rmspe:0.071833\ttest-rmspe:0.094089\n", "[3750]\ttrain-rmspe:0.069701\ttest-rmspe:0.093385\n", "[4000]\ttrain-rmspe:0.067834\ttest-rmspe:0.092811\n", "[4250]\ttrain-rmspe:0.066103\ttest-rmspe:0.092347\n", "[4500]\ttrain-rmspe:0.064509\ttest-rmspe:0.091941\n", "[4750]\ttrain-rmspe:0.062960\ttest-rmspe:0.091569\n", "[5000]\ttrain-rmspe:0.061581\ttest-rmspe:0.091271\n", "[5250]\ttrain-rmspe:0.060224\ttest-rmspe:0.091016\n", "[5500]\ttrain-rmspe:0.058971\ttest-rmspe:0.090792\n", "[5750]\ttrain-rmspe:0.057782\ttest-rmspe:0.090605\n", "[6000]\ttrain-rmspe:0.056656\ttest-rmspe:0.090459\n", "[6250]\ttrain-rmspe:0.055568\ttest-rmspe:0.090350\n", "[6500]\ttrain-rmspe:0.054547\ttest-rmspe:0.090252\n", "[6750]\ttrain-rmspe:0.053527\ttest-rmspe:0.090143\n", "[7000]\ttrain-rmspe:0.052577\ttest-rmspe:0.090067\n", "[7250]\ttrain-rmspe:0.051698\ttest-rmspe:0.090008\n", "[7500]\ttrain-rmspe:0.050825\ttest-rmspe:0.089956\n", "[7750]\ttrain-rmspe:0.050012\ttest-rmspe:0.089897\n", "[8000]\ttrain-rmspe:0.049207\ttest-rmspe:0.089844\n", "[8250]\ttrain-rmspe:0.048413\ttest-rmspe:0.089800\n", "[8500]\ttrain-rmspe:0.047679\ttest-rmspe:0.089768\n", "[8750]\ttrain-rmspe:0.046973\ttest-rmspe:0.089742\n", "[9000]\ttrain-rmspe:0.046274\ttest-rmspe:0.089741\n", "Stopping. Best iteration:\n", "[8751]\ttrain-rmspe:0.046970\ttest-rmspe:0.089741\n", "\n" ] } ], "source": [ "param = {'bst:max_depth':12,\n", " 'bst:eta':0.01,\n", " 'subsample':0.8,\n", " 'colsample_bytree':0.7,\n", " 'silent':1,\n", " 'objective':'reg:linear',\n", " 'nthread':6,\n", " 'seed':seed}\n", "\n", "plst = param.items()\n", "\n", "bst = xgb.train(plst, dtrain, num_round, evallist, feval=rmspe_xg, verbose_eval=250, early_stopping_rounds=250)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dpred = xgb.DMatrix(df.loc[(df['Set'] == 1) & (df['Open'] == 1) & (df['Outlier'] == True)][features_x])" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ypred_bst = bst.predict(dpred)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.loc[(df['Set'] == 1) & (df['Open'] == 1) & (df['Outlier'] == True), 'SalesLog'] = ypred_bst\n", "df.loc[(df['Set'] == 1) & (df['Open'] == 1) & (df['Outlier'] == True), 'Sales'] = np.exp(ypred_bst) - 1" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "### Get total sales, customers and open days per store\n", "store_data_sales = df.groupby([df['Store']])['Sales'].sum()\n", "store_data_customers = df.groupby([df['Store']])['Customers'].sum()\n", "store_data_open = df.groupby([df['Store']])['Open'].count()" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "### Calculate sales per day, customers per day and sales per customers per day\n", "store_data_sales_per_day = store_data_sales / store_data_open\n", "store_data_customers_per_day = store_data_customers / store_data_open\n", "store_data_sales_per_customer_per_day = store_data_sales_per_day / store_data_customers_per_day" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_store = pd.merge(df_store, store_data_sales_per_day.reset_index(name='SalesPerDay'), how='left', on=['Store'])\n", "df_store = pd.merge(df_store, store_data_customers_per_day.reset_index(name='CustomersPerDay'), how='left', on=['Store'])\n", "df_store = pd.merge(df_store, store_data_sales_per_customer_per_day.reset_index(name='SalesPerCustomersPerDay'), how='left', on=['Store'])" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [ "store_features = ['Store', 'SalesPerDay', 'CustomersPerDay', 'SalesPerCustomersPerDay']\n", "\n", "features_x = list(set(features_x + store_features))\n" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "df = pd.merge(df, df_store[store_features], how='left', on=['Store'])" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(df.loc[(df['Set'] == 1) & (df['Open'] == 1)][features_x],\n", " df.loc[(df['Set'] == 1) & (df['Open'] == 1)][features_y],\n", " test_size=0.1, random_state=seed)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dtrain = xgb.DMatrix(X_train, y_train)\n", "dtest = xgb.DMatrix(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": true }, "outputs": [], "source": [ "num_round = 20000\n", "evallist = [(dtrain, 'train'), (dtest, 'test')]" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Will train until test error hasn't decreased in 250 rounds.\n", "[0]\ttrain-rmspe:0.999866\ttest-rmspe:0.999865\n", "[250]\ttrain-rmspe:0.531137\ttest-rmspe:0.529227\n", "[500]\ttrain-rmspe:0.137918\ttest-rmspe:0.131062\n", "[750]\ttrain-rmspe:0.110675\ttest-rmspe:0.111607\n", "[1000]\ttrain-rmspe:0.100810\ttest-rmspe:0.106202\n", "[1250]\ttrain-rmspe:0.092724\ttest-rmspe:0.102566\n", "[1500]\ttrain-rmspe:0.087041\ttest-rmspe:0.099832\n", "[1750]\ttrain-rmspe:0.082683\ttest-rmspe:0.097939\n", "[2000]\ttrain-rmspe:0.078698\ttest-rmspe:0.096432\n", "[2250]\ttrain-rmspe:0.075622\ttest-rmspe:0.095216\n", "[2500]\ttrain-rmspe:0.072704\ttest-rmspe:0.094271\n", "[2750]\ttrain-rmspe:0.070362\ttest-rmspe:0.093525\n", "[3000]\ttrain-rmspe:0.068244\ttest-rmspe:0.092888\n", "[3250]\ttrain-rmspe:0.066329\ttest-rmspe:0.092319\n", "[3500]\ttrain-rmspe:0.064502\ttest-rmspe:0.091857\n", "[3750]\ttrain-rmspe:0.062939\ttest-rmspe:0.091523\n", "[4000]\ttrain-rmspe:0.061489\ttest-rmspe:0.091201\n", "[4250]\ttrain-rmspe:0.060084\ttest-rmspe:0.090915\n", "[4500]\ttrain-rmspe:0.058776\ttest-rmspe:0.090721\n", "[4750]\ttrain-rmspe:0.057532\ttest-rmspe:0.090546\n", "[5000]\ttrain-rmspe:0.056319\ttest-rmspe:0.090379\n", "[5250]\ttrain-rmspe:0.055231\ttest-rmspe:0.090255\n", "[5500]\ttrain-rmspe:0.054148\ttest-rmspe:0.090183\n", "[5750]\ttrain-rmspe:0.053105\ttest-rmspe:0.090085\n", "[6000]\ttrain-rmspe:0.052128\ttest-rmspe:0.090017\n", "[6250]\ttrain-rmspe:0.051182\ttest-rmspe:0.089965\n", "[6500]\ttrain-rmspe:0.050276\ttest-rmspe:0.089904\n", "[6750]\ttrain-rmspe:0.049418\ttest-rmspe:0.089879\n", "[7000]\ttrain-rmspe:0.048590\ttest-rmspe:0.089835\n", "[7250]\ttrain-rmspe:0.047803\ttest-rmspe:0.089802\n", "[7500]\ttrain-rmspe:0.047017\ttest-rmspe:0.089793\n", "[7750]\ttrain-rmspe:0.046294\ttest-rmspe:0.089776\n", "[8000]\ttrain-rmspe:0.045572\ttest-rmspe:0.089759\n", "[8250]\ttrain-rmspe:0.044861\ttest-rmspe:0.089749\n", "[8500]\ttrain-rmspe:0.044187\ttest-rmspe:0.089727\n", "[8750]\ttrain-rmspe:0.043539\ttest-rmspe:0.089736\n", "Stopping. Best iteration:\n", "[8575]\ttrain-rmspe:0.043983\ttest-rmspe:0.089723\n", "\n" ] } ], "source": [ "param = {'bst:max_depth':12,\n", " 'bst:eta':0.0095,\n", " 'subsample':0.8,\n", " 'colsample_bytree':0.7,\n", " 'silent':1, \n", " 'objective':'reg:linear',\n", " 'nthread':6,\n", " 'seed':seed}\n", "\n", "plst = param.items()\n", "\n", "bst1 = xgb.train(plst, dtrain, num_round, evallist, feval=rmspe_xg, verbose_eval=250, early_stopping_rounds=250)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10b46080>" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gQlkCAgyt1/UAMAe9rgeAOeh1PQDMQa/rAWAOel0PABut1+t1PcK8YQkIAAAAAGNOJyCd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HRK0DL5pWXyS8vkl5bJLy2T3wFDQAAAAAAYczoB6UwpRfgA2GQsWLAwq1ff3PUyAABgTtbXCWgI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\ntYur6phhu8/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"text/plain": [ "<matplotlib.figure.Figure at 0x275c15c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xgb.plot_importance(bst1)" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "X_submit = df.loc[df['Set'] == 0]" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "dsubmit = xgb.DMatrix(X_submit[features_x])" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ypred_bst = bst1.predict(dsubmit)\n", "\n", "df_ypred = X_submit['Id'].reset_index()\n", "del df_ypred['index']\n", "df_ypred['Id'] = df_ypred['Id'].astype('int')\n", "\n", "# Scale back the sales a bit\n", "df_ypred['Sales'] = (np.exp(ypred_bst) - 1) * 0.985\n", "\n", "df_ypred.sort_values('Id', inplace=True)\n", "df_ypred[['Id', 'Sales']].to_csv('rossmann_best_no_ext_data_scaled.csv', index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
mihaic/brainiak	examples/funcalign/srm_image_prediction_example.ipynb	2	12013	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Import some libraries that we will need" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import scipy.io\n", "from scipy.stats import stats\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.svm import NuSVC\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run SRM with the movie data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import brainiak.funcalign.srm\n", "help(brainiak.funcalign.srm.SRM)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the input data that contains the movie stimuli for unsupervised training with SRM" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "movie_file = scipy.io.loadmat('data/movie_data.mat')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Convert data to a list of arrays matching SRM input.\n", "Each element is a matrix of voxels by TRs.\n", "Also, concatenate data from both hemispheres in the brain." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "movie_data_left = movie_file['movie_data_lh']\n", "movie_data_right = movie_file['movie_data_rh']\n", "subjects = movie_data_left.shape[2]\n", "movie_data = []\n", "for s in range(subjects):\n", " movie_data.append(np.concatenate([movie_data_left[:, :, s], movie_data_right[:, :, s]], axis=0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Z-score the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for subject in range(subjects):\n", " movie_data[subject] = stats.zscore(movie_data[subject],axis=1,ddof=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run SRM " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "srm = brainiak.funcalign.srm.SRM(n_iter=10, features=50)\n", "srm.fit(movie_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the input data that contains the image stimuli and its labels for training a classifier" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "image_file = scipy.io.loadmat('data/image_data.mat')\n", "image_data_left = image_file['image_data_lh']\n", "image_data_right = image_file['image_data_rh']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Convert data to a list of arrays matching SRM input. Each element is a matrix of voxels by TRs. Also, concatenate data from both hemispheres in the brain." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "image_data = []\n", "for s in range(subjects):\n", " image_data.append(np.concatenate([image_data_left[:, :, s], image_data_right[:, :, s]], axis=0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Z-score the image data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for subject in range(subjects):\n", " image_data[subject] = stats.zscore(image_data[subject],axis=1,ddof=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Z-score the Shared Response data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "image_data_shared = srm.transform(image_data)\n", "for subject in range(subjects):\n", " image_data_shared[subject] = stats.zscore(image_data_shared[subject], axis=1, ddof=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read the labels of the image data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "labels = scipy.io.loadmat('data/label.mat')\n", "labels = np.squeeze(labels['label'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run a leave-one-out cross validation with the subjects. We use a $\\nu$-SVM classifier." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_labels = np.tile(labels, subjects-1)\n", "test_labels = labels\n", "accuracy = np.zeros((subjects))\n", "cm = [None] * subjects\n", "for subject in range(subjects):\n", " # Concatenate the subjects' data for training into one matrix\n", " train_subjects = list(range(subjects))\n", " train_subjects.remove(subject)\n", " TRs = image_data_shared[0].shape[1]\n", " train_data = np.zeros((image_data_shared[0].shape[0], len(train_labels)))\n", " for train_subject in range(len(train_subjects)):\n", " start_index = train_subjectTRs\n", " end_index = start_index+TRs\n", " train_data[:, start_index:end_index] = image_data_shared[train_subjects[train_subject]]\n", "\n", " # Train a Nu-SVM classifier using scikit learn\n", " classifier = NuSVC(nu=0.5, kernel='linear', gamma='auto')\n", " classifier = classifier.fit(train_data.T, train_labels)\n", "\n", " # Predict on the test data\n", " predicted_labels = classifier.predict(image_data_shared[subject].T)\n", " accuracy[subject] = sum(predicted_labels == test_labels)/float(len(predicted_labels))\n", "\n", " # Create a confusion matrix to see the accuracy of each class\n", " cm[subject] = confusion_matrix(test_labels, predicted_labels)\n", "\n", " # Normalize the confusion matrix\n", " cm[subject] = cm[subject].astype('float') / cm[subject].sum(axis=1)[:, np.newaxis]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a function that presents the output of the experiment in a plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_confusion_matrix(cm, title=\"Confusion Matrix\"):\n", " \"\"\"Plots a confusion matrix for each subject\"\"\"\n", " import matplotlib.pyplot as plt\n", " import math\n", " plt.figure()\n", " subjects = len(cm)\n", " root_subjects = math.sqrt(subjects)\n", " cols = math.ceil(root_subjects)\n", " rows = math.ceil(subjects/cols)\n", " classes = cm[0].shape[0]\n", " for subject in range(subjects):\n", " plt.subplot(rows, cols, subject+1)\n", " plt.imshow(cm[subject], interpolation='nearest', cmap=plt.cm.bone)\n", " plt.xticks(np.arange(classes), range(1,classes+1))\n", " plt.yticks(np.arange(classes), range(1,classes+1))\n", " cbar = plt.colorbar(ticks=[0.0,1.0], shrink=0.6)\n", " cbar.set_clim(0.0, 1.0)\n", " plt.xlabel(\"Predicted\")\n", " plt.ylabel(\"True label\")\n", " plt.title(\"{0:d}\".format(subject + 1))\n", " plt.suptitle(title)\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Plot the confusion matrices and print the accuracy results" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_confusion_matrix(cm, title=\"Confusion matrices for different test subjects\")\n", "print(\"The average accuracy among all subjects is {0:f} +/- {1:f}\".format(np.mean(accuracy), np.std(accuracy)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we repeat the experiment with the Deterministic SRM" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "srm = brainiak.funcalign.srm.DetSRM(n_iter=10, features=50)\n", "srm.fit(movie_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Transform the image stimuli data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "image_data_shared = srm.transform(image_data)\n", "for subject in range(subjects):\n", " image_data_shared[subject] = stats.zscore(image_data_shared[subject], axis=1, ddof=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run a leave-one-out cross validation with the subjects" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "accuracy = np.zeros((subjects,))\n", "cm = [None] subjects\n", "for subject in range(subjects):\n", " # Concatenate the subjects' data for training into one matrix\n", " train_subjects = list(range(subjects))\n", " train_subjects.remove(subject)\n", " TRs = image_data_shared[0].shape[1]\n", " train_data = np.zeros((image_data_shared[0].shape[0], len(train_labels)))\n", " for train_subject in range(len(train_subjects)):\n", " start_index = train_subject*TRs\n", " end_index = start_index+TRs\n", " train_data[:, start_index:end_index] = image_data_shared[train_subjects[train_subject]]\n", "\n", " # Train a Nu-SVM classifier using scikit learn\n", " classifier = NuSVC(nu=0.5, kernel='linear', gamma='auto')\n", " classifier = classifier.fit(train_data.T, train_labels)\n", "\n", " # Predict on the test data\n", " predicted_labels = classifier.predict(image_data_shared[subject].T)\n", " accuracy[subject] = sum(predicted_labels == test_labels)/float(len(predicted_labels))\n", "\n", " # Create a confusion matrix to see the accuracy of each class\n", " cm[subject] = confusion_matrix(test_labels, predicted_labels)\n", "\n", " # Normalize the confusion matrix\n", " cm[subject] = cm[subject].astype('float') / cm[subject].sum(axis=1)[:, np.newaxis]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot and print the results" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_confusion_matrix(cm, title=\"Confusion matrices for different test subjects with Deterministic SRM\")\n", "print(\"Det. SRM: The average accuracy among all subjects is {0:f} +/- {1:f}\".format(np.mean(accuracy), np.std(accuracy)))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
DTUWindEnergy/Python4WindEnergy	lesson 7/cython/Cython2.ipynb	1	8337	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Cython 2\n", "Since the Cython notebook was created, the module, `cython_import` has been updated, extended and renamed to `cython_compile`\n", "\n", "It can be used similar to `cython_import` but in addition it can be invoked via a pythonic decorator syntax.\n", "\n", "Futhermore an experimental autodeclaration wrapper has been added" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Python implementation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "p = 32416190071 # large prime" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "def pycheck(p):\n", " for y in xrange(2, int(math.sqrt(p)) + 1):\n", " if p % y == 0:\n", " return False\n", " return True\n", "%timeit pycheck(p)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10 loops, best of 3: 44 ms per loop\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cython implementation in external module" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check the module cycheck.py\n", "\n", "It contains three functions\n", "- cycheck: similar to pycheck above\n", "- cycheck_pure: similar to cycheck, but with `pure` variable declarations\n", "- cycheck_cdef: similar to cycheck, but with `cdef` variable delcarations\n", "\n", "Run the script below and notice the difference" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from cython_compile import cython_import\n", "cython_import('cycheck')\n", "import cycheck\n", "\n", "%timeit pycheck(p)\n", "%timeit cycheck.cycheck(p)\n", "%timeit cycheck.cycheck_pure(p)\n", "%timeit cycheck.cycheck_cdef(p)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10 loops, best of 3: 43.8 ms per loop\n", "10 loops, best of 3: 37.8 ms per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "1000 loops, best of 3: 1.31 ms per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "1000 loops, best of 3: 1.31 ms per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So far the module is similar to the previous `cython_import` module, but in the next section the difference will become clear" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Python function with decorator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###No declarations" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from cython_compile import cython_compile\n", "\n", "@cython_compile\n", "def pycheck(p):\n", " import math\n", " for y in xrange(2, int(math.sqrt(p)) + 1):\n", " if p % y == 0:\n", " return False\n", " return True\n", "%timeit pycheck(p)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "compiling ipythoninput8b8c820fa671b_pycheck: python.exe setup.py build_ext --inplace --compiler=mingw32\n", "Compiling succeeded" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "1 loops, best of 3: 38.2 ms per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Pure declarations" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import cython\n", "from cython_compile import cython_compile\n", "\n", "@cython_compile\n", "@cython.ccall\n", "@cython.locals(y=cython.int, p=cython.ulonglong)\n", "def pycheck_pure(p):\n", " import math\n", " for y in xrange(2, int(math.sqrt(p)) + 1):\n", " if p % y == 0:\n", " return False\n", " return True\n", "%timeit pycheck_pure(p)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "compiling ipythoninput9572b104157e1_pycheck_pure: python.exe setup.py build_ext --inplace --compiler=mingw32\n", "Compiling succeeded" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "1 loops, best of 3: 1.36 ms per loop\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###cdef declarations" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from cython_compile import cython_compile\n", "\n", "@cython_compile\n", "def pycheck_cdef(p): #cpdef pycheck_cdef(unsigned long long p):\n", " import math\n", " #cdef int y\n", " for y in xrange(2, int(math.sqrt(p)) + 1):\n", " if p % y == 0:\n", " return False\n", " return True\n", "%timeit pycheck_cdef(p)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "compiling ipythoninput10a0fe4c6a4ab1_pycheck_cdef: python.exe setup.py build_ext --inplace --compiler=mingw32\n", "Compiling succeeded" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "1 loops, best of 3: 1.35 ms per loop\n" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Auto declarations\n", "A bit slower because p is declared as `long long` instead of `unsigned long long`" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from cython_compile import cython_compile_autodeclare\n", "@cython_compile_autodeclare\n", "def pycheck(p):\n", " import math\n", " for y in xrange(2, int(math.sqrt(p)) + 1):\n", " if p % y == 0:\n", " return False\n", " return True\n", "%timeit pycheck(17)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "compiling ipythoninput11e49677a00e7c_pycheck: python.exe setup.py build_ext --inplace --compiler=mingw32\n", "Compiling succeeded" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "1 loops, best of 3: 108 \u00b5s per loop\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	apache-2.0
lfairchild/PmagPy	data_files/notebooks/Intro to MagIC Contributions.ipynb	2	140072	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook demonstrates how to make and use a Python Contribution object. A Contribution is built by reading in MagIC tables from a single directory. Those tables are then stored in pandas DataFrames. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting started" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# do basic imports and unpack McMurdo data\n", "\n", "from pmagpy import ipmag\n", "reload(ipmag)\n", "from pmagpy import pmag\n", "from pmagpy import contribution_builder as cb\n", "from pmagpy import data_model3\n", "import os\n", "import pandas as pd\n", "import numpy as np\n", "from pandas import DataFrame\n", "from pmagpy.contribution_builder import Contribution\n", "\n", "wdir = os.path.join(\"..\", \"3_0\", \"McMurdo\")\n", "#infile = os.path.join(wdir, \"lawrence09.v30.txt\")\n", "#infile = os.path.join(wdir, \"mcmurdo3-with-upgrade.txt\") \n", "#ipmag.download_magic(infile, overwrite=True, dir_path=wdir)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Several ways of creating a contribution" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-I- Getting method codes from earthref.org\n", "-I- Importing controlled vocabularies from https://earthref.org\n", "-I- Importing suggested vocabularies from https://earthref.org\n", "tables created: ['measurements', 'ages', 'sites', 'locations', 'samples', 'criteria', 'images', 'contribution', 'specimens']\n", "-\n", "tables created: ['measurements', 'ages', 'sites', 'locations', 'samples', 'criteria', 'images', 'contribution', 'specimens']\n", "-\n", "tables created: ['specimens']\n", "-\n", "tables created: ['sites']\n", "-\n" ] } ], "source": [ "reload(nb)\n", "\n", "# test out various ways of creating a contribution\n", "\n", "#class Contribution(object):\n", "# \"\"\" \n", "# A Contribution is a collection of MagicDataFrames, \n", "# each of which corresponds to one MagIC table. \n", "# The Contribution object also has methods for \n", "# manipulating one or more tables in the contribution -- \n", "# for example, renaming a site. \n", "# \"\"\"\n", "# def __init__(self, directory, read_tables='all',\n", "# custom_filenames=None, single_file=None):\n", "\n", "\n", "\n", "# make contribution reading in all default filenames from working directory\n", "wdir = os.path.join(\"..\", \"3_0\", \"McMurdo\")\n", "con = cb.Contribution(wdir)\n", "print 'tables created:', con.tables.keys()\n", "print '-'\n", "\n", "# make contribution with some custom filenames\n", "con = cb.Contribution(wdir, custom_filenames={'specimens': 'custom_specimens.txt'})\n", "print 'tables created:', con.tables.keys()\n", "print '-'\n", "\n", "# make contribution with custom filenames, and only read in the specimen table to start\n", "con = Contribution(wdir, read_tables=['specimens'], custom_filenames={'sites': 'custom_sites.txt',\n", " 'specimens': 'custom_specimens.txt'})\n", "print 'tables created:', con.tables.keys()\n", "print '-'\n", "\n", "# make contribution with a single, mystery file (can be any datatype)\n", "con = cb.Contribution(wdir, single_file='sites.txt')\n", "print 'tables created:', con.tables.keys()\n", "print '-'\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-I- Getting method codes from earthref.org\n", "-I- Importing controlled vocabularies from https://earthref.org\n", "-I- Importing suggested vocabularies from https://earthref.org\n", "{'measurements': 'measurements.txt', 'ages': 'ages.txt', 'sites': 'custom_sites.txt', 'locations': 'locations.txt', 'samples': 'custom_samples.txt', 'criteria': 'criteria.txt', 'images': 'images.txt', 'contribution': 'contribution.txt', 'specimens': 'custom_specimens.txt'}\n", "['specimens']\n" ] } ], "source": [ "# make McMurdo contribution, starting with specimens table\n", "\n", "reload(nb)\n", "\n", "con = cb.Contribution(wdir, read_tables=['specimens'], custom_filenames={'specimens': 'custom_specimens.txt', 'samples': 'custom_samples.txt',\n", " 'sites': 'custom_sites.txt'})\n", "\n", "print con.filenames\n", "print con.tables.keys()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['specimens', 'samples']\n" ] } ], "source": [ "# then, add another table to the contribution\n", "# here, we are providing data type but no filename\n", "# this works because we already gave the custom sample filename when we created the contribution\n", "# so the contribution already knows where to look (con.filenames)\n", "con.add_magic_table('samples')\n", "print con.tables.keys()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['specimens', 'samples', 'criteria']\n" ] } ], "source": [ "# add another table to the same contribution\n", "# this time, provide a filename but no data type\n", "\n", "con.add_magic_table(dtype=\"unknown\", fname=\"criteria.txt\")\n", "# criteria table now included\n", "print con.tables.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Functionality with a contribution" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-I- Getting method codes from earthref.org\n", "-I- Importing controlled vocabularies from https://earthref.org\n", "-I- Importing suggested vocabularies from https://earthref.org\n" ] } ], "source": [ "# create full McMurdo contribution\n", "\n", "reload(nb)\n", "\n", "con = cb.Contribution(wdir, custom_filenames={'specimens': 'specimens.txt', 'samples': 'samples.txt',\n", " 'sites': 'sites.txt'})\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sites</th>\n", " </tr>\n", " <tr>\n", " <th>location</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>McMurdo</th>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>McMurdo</th>\n", " <td>mc03 : mc04 : mc06 : mc07 : mc08 : mc09 : mc10...</td>\n", " </tr>\n", " <tr>\n", " <th>McMurdo</th>\n", " <td>mc102 : mc103 : mc105 : mc109 : mc110 : mc112 ...</td>\n", " </tr>\n", " <tr>\n", " <th>McMurdo</th>\n", " <td>mc02 : mc03 : mc04 : mc06 : mc07 : mc08 : mc09...</td>\n", " </tr>\n", " <tr>\n", " <th>McMurdo</th>\n", " <td>mc09 : mc105 : mc109 : mc111 : mc113 : mc115 :...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sites\n", "location \n", "McMurdo None\n", "McMurdo mc03 : mc04 : mc06 : mc07 : mc08 : mc09 : mc10...\n", "McMurdo mc102 : mc103 : mc105 : mc109 : mc110 : mc112 ...\n", "McMurdo mc02 : mc03 : mc04 : mc06 : mc07 : mc08 : mc09...\n", "McMurdo mc09 : mc105 : mc109 : mc111 : mc113 : mc115 :..." ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.tables['locations'].df[['sites']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rename an item" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " <th>age_sigma</th>\n", " <th>age_unit</th>\n", " <th>analysts</th>\n", " <th>citations</th>\n", " <th>criteria</th>\n", " <th>description</th>\n", " <th>dir_alpha95</th>\n", " <th>dir_comp_name</th>\n", " <th>dir_dec</th>\n", " <th>...</th>\n", " <th>vadm_n_samples</th>\n", " <th>vadm_sigma</th>\n", " <th>vdm</th>\n", " <th>vdm_n_samples</th>\n", " <th>vdm_sigma</th>\n", " <th>vgp_dm</th>\n", " <th>vgp_dp</th>\n", " <th>vgp_lat</th>\n", " <th>vgp_lon</th>\n", " <th>vgp_n_samples</th>\n", " </tr>\n", " <tr>\n", " <th>site</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>extra_special_site</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>This study</td>\n", " <td>None</td>\n", " <td>10-m thick basalt flow, NE of Scott Base, Hut ...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>extra_special_site</th>\n", " <td>0.348</td>\n", " <td>0.004</td>\n", " <td>Ma</td>\n", " <td>Lisa Tauxe</td>\n", " <td>This study</td>\n", " <td>DE-SPEC</td>\n", " <td>Direction included in Pmag_Results.</td>\n", " <td>2.3</td>\n", " <td>A</td>\n", " <td>352</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>extra_special_site</th>\n", " <td>0.348</td>\n", " <td>0.004</td>\n", " <td>Ma</td>\n", " <td>Kristin Lawrence</td>\n", " <td>This study</td>\n", " <td>DE-SITE</td>\n", " <td>Site VGPA comp: (geog. coord).</td>\n", " <td>2.3</td>\n", " <td>None</td>\n", " <td>352</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>3.8</td>\n", " <td>4.4</td>\n", " <td>87.1</td>\n", " <td>123.1</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>extra_special_site</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>4 rows × 47 columns</p>\n", "</div>" ], "text/plain": [ " age age_sigma age_unit analysts citations \\\n", "site \n", "extra_special_site None None None None This study \n", "extra_special_site 0.348 0.004 Ma Lisa Tauxe This study \n", "extra_special_site 0.348 0.004 Ma Kristin Lawrence This study \n", "extra_special_site None None None None None \n", "\n", " criteria \\\n", "site \n", "extra_special_site None \n", "extra_special_site DE-SPEC \n", "extra_special_site DE-SITE \n", "extra_special_site None \n", "\n", " description \\\n", "site \n", "extra_special_site 10-m thick basalt flow, NE of Scott Base, Hut ... \n", "extra_special_site Direction included in Pmag_Results. \n", "extra_special_site Site VGPA comp: (geog. coord). \n", "extra_special_site None \n", "\n", " dir_alpha95 dir_comp_name dir_dec ... \\\n", "site ... \n", "extra_special_site None None None ... \n", "extra_special_site 2.3 A 352 ... \n", "extra_special_site 2.3 None 352 ... \n", "extra_special_site None None None ... \n", "\n", " vadm_n_samples vadm_sigma vdm vdm_n_samples vdm_sigma \\\n", "site \n", "extra_special_site None None None None None \n", "extra_special_site None None None None None \n", "extra_special_site None None None None None \n", "extra_special_site None None None None None \n", "\n", " vgp_dm vgp_dp vgp_lat vgp_lon vgp_n_samples \n", "site \n", "extra_special_site None None None None None \n", "extra_special_site None None None None None \n", "extra_special_site 3.8 4.4 87.1 123.1 6 \n", "extra_special_site None None None None None \n", "\n", "[4 rows x 47 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reload(nb)\n", "# rename one of the Contribution's sites\n", "con.rename_item('sites', 'mc03', 'extra_special_site')\n", "con.tables['sites'].df.ix[['extra_special_site']]\n", "# all rows previously named 'mc01' are now named 'extra_special_site'" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sites</th>\n", " </tr>\n", " <tr>\n", " <th>location</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>McMurdo</th>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>McMurdo</th>\n", " <td>extra_special_site:mc04:mc06:mc07:mc08:mc09:mc...</td>\n", " </tr>\n", " <tr>\n", " <th>McMurdo</th>\n", " <td>mc102 : mc103 : mc105 : mc109 : mc110 : mc112 ...</td>\n", " </tr>\n", " <tr>\n", " <th>McMurdo</th>\n", " <td>mc02:extra_special_site:mc04:mc06:mc07:mc08:mc...</td>\n", " </tr>\n", " <tr>\n", " <th>McMurdo</th>\n", " <td>mc09 : mc105 : mc109 : mc111 : mc113 : mc115 :...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sites\n", "location \n", "McMurdo None\n", "McMurdo extra_special_site:mc04:mc06:mc07:mc08:mc09:mc...\n", "McMurdo mc102 : mc103 : mc105 : mc109 : mc110 : mc112 ...\n", "McMurdo mc02:extra_special_site:mc04:mc06:mc07:mc08:mc...\n", "McMurdo mc09 : mc105 : mc109 : mc111 : mc113 : mc115 :..." ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# additionally, 'mc03' has been replaced in the location table under site_names\n", "#con.tables['locations'].df.ix[[\"Osler Volcanics, Nipigon Strait, Lower Reversed\"]][['site_names']]\n", "con.tables['locations'].df[['sites']]#, 'sites_list']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Propagate data from one table into another" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-I- Getting method codes from earthref.org\n", "-I- Importing controlled vocabularies from https://earthref.org\n", "-I- Importing suggested vocabularies from https://earthref.org\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>specimen</th>\n", " <th>sample</th>\n", " <th>site</th>\n", " <th>location</th>\n", " </tr>\n", " <tr>\n", " <th>specimen</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mc01a</th>\n", " <td>mc01a</td>\n", " <td>mc01a</td>\n", " <td>mc01</td>\n", " <td>McMurdo</td>\n", " </tr>\n", " <tr>\n", " <th>mc01a</th>\n", " <td>mc01a</td>\n", " <td>mc01a</td>\n", " <td>mc01</td>\n", " <td>McMurdo</td>\n", " </tr>\n", " <tr>\n", " <th>mc01b</th>\n", " <td>mc01b</td>\n", " <td>mc01b</td>\n", " <td>mc01</td>\n", " <td>McMurdo</td>\n", " </tr>\n", " <tr>\n", " <th>mc01b</th>\n", " <td>mc01b</td>\n", " <td>mc01b</td>\n", " <td>mc01</td>\n", " <td>McMurdo</td>\n", " </tr>\n", " <tr>\n", " <th>mc01c</th>\n", " <td>mc01c</td>\n", " <td>mc01c</td>\n", " <td>mc01</td>\n", " <td>McMurdo</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " specimen sample site location\n", "specimen \n", "mc01a mc01a mc01a mc01 McMurdo\n", "mc01a mc01a mc01a mc01 McMurdo\n", "mc01b mc01b mc01b mc01 McMurdo\n", "mc01b mc01b mc01b mc01 McMurdo\n", "mc01c mc01c mc01c mc01 McMurdo" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "# normally, each table only has one relationship up (i.e., a measurement table will have specimen name, but not sample name)\n", "# sometimes, you need to access location_name at the site level (for example)\n", "# this function propagates names down through any available tables\n", "# the code snippet below won't work if the Contribution can't access the sample and site files!\n", "\n", "\n", "reload(nb)\n", "\n", "con = cb.Contribution(wdir, custom_filenames={'specimens': 'custom_specimens.txt', 'samples': 'custom_samples.txt',\n", " 'sites': 'custom_sites.txt'})\n", "\n", "con.propagate_name_down('location', 'specimens')\n", "\n", "# specimens table now has sample, site, and location_names\n", "con.tables['specimens'].df[['specimen', 'sample', 'site', 'location']].head()\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-I- Getting method codes from earthref.org\n", "-I- Importing controlled vocabularies from https://earthref.org\n", "-I- Importing suggested vocabularies from https://earthref.org\n", "-W- Column 'fake_col' isn't in samples table, skipping it\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>azimuth</th>\n", " <th>dip</th>\n", " </tr>\n", " <tr>\n", " <th>measurement</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mc01f-LP-DIR-AF1</th>\n", " <td>94</td>\n", " <td>-55</td>\n", " </tr>\n", " <tr>\n", " <th>mc01f-LP-DIR-AF2</th>\n", " <td>94</td>\n", " <td>-55</td>\n", " </tr>\n", " <tr>\n", " <th>mc01f-LP-DIR-AF3</th>\n", " <td>94</td>\n", " <td>-55</td>\n", " </tr>\n", " <tr>\n", " <th>mc01f-LP-DIR-AF4</th>\n", " <td>94</td>\n", " <td>-55</td>\n", " </tr>\n", " <tr>\n", " <th>mc01f-LP-DIR-AF5</th>\n", " <td>94</td>\n", " <td>-55</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " azimuth dip\n", "measurement \n", "mc01f-LP-DIR-AF1 94 -55\n", "mc01f-LP-DIR-AF2 94 -55\n", "mc01f-LP-DIR-AF3 94 -55\n", "mc01f-LP-DIR-AF4 94 -55\n", "mc01f-LP-DIR-AF5 94 -55" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# this function propagates values from arbitrary columns down\n", "# i.e., get sample-level azimuth into the measurements table\n", "# note: this will NOT work with names (specimen, sample, etc.). \n", "# for those relationships, use the above function: propagate_name_down\n", "\n", "reload(nb)\n", "con = cb.Contribution(wdir, custom_filenames={'specimens': 'custom_specimens.txt', 'samples': 'custom_samples.txt',\n", " 'sites': 'custom_sites.txt'})\n", "\n", "meas_container = con.tables['measurements']\n", "meas_df = meas_container.df\n", "\n", "meas_df = con.propagate_cols_down(['azimuth', 'dip', 'fake_col'], 'measurements', 'samples')\n", "meas_df.head()[['azimuth', 'dip']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writing out a MagIC file" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-I- writing samples data to /Users/nebula/Python/PmagPy/data_files/3_0/McMurdo/_samples.txt\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>azimuth</th>\n", " <th>azimuth_dec_correction</th>\n", " <th>citations</th>\n", " <th>description</th>\n", " <th>dip</th>\n", " <th>geologic_classes</th>\n", " <th>geologic_types</th>\n", " <th>lat</th>\n", " <th>lithologies</th>\n", " <th>lon</th>\n", " <th>method_codes</th>\n", " <th>orientation_flag</th>\n", " <th>sample</th>\n", " <th>site</th>\n", " </tr>\n", " <tr>\n", " <th>sample</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mc01a</th>\n", " <td>260</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-57</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Trachyte</td>\n", " <td>166.64</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc01a</td>\n", " <td>mc01</td>\n", " </tr>\n", " <tr>\n", " <th>mc01b</th>\n", " <td>189</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-63</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Trachyte</td>\n", " <td>166.64</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc01b</td>\n", " <td>mc01</td>\n", " </tr>\n", " <tr>\n", " <th>mc01c</th>\n", " <td>183</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-30</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Trachyte</td>\n", " <td>166.64</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc01c</td>\n", " <td>mc01</td>\n", " </tr>\n", " <tr>\n", " <th>mc01d</th>\n", " <td>133</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-57</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Trachyte</td>\n", " <td>166.64</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc01d</td>\n", " <td>mc01</td>\n", " </tr>\n", " <tr>\n", " <th>mc01e</th>\n", " <td>91</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-51</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Trachyte</td>\n", " <td>166.64</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc01e</td>\n", " <td>mc01</td>\n", " </tr>\n", " <tr>\n", " <th>mc01f</th>\n", " <td>94</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-55</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Trachyte</td>\n", " <td>166.64</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc01f</td>\n", " <td>mc01</td>\n", " </tr>\n", " <tr>\n", " <th>mc01g</th>\n", " <td>69</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-56</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Trachyte</td>\n", " <td>166.64</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc01g</td>\n", " <td>mc01</td>\n", " </tr>\n", " <tr>\n", " <th>mc01h</th>\n", " <td>157</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-41</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Trachyte</td>\n", " <td>166.64</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc01h</td>\n", " <td>mc01</td>\n", " </tr>\n", " <tr>\n", " <th>mc02a</th>\n", " <td>190</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-50</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Basalt</td>\n", " <td>166.69</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc02a</td>\n", " <td>mc02</td>\n", " </tr>\n", " <tr>\n", " <th>mc02b</th>\n", " <td>231</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-59</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Basalt</td>\n", " <td>166.69</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc02b</td>\n", " <td>mc02</td>\n", " </tr>\n", " <tr>\n", " <th>mc02c</th>\n", " <td>220</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-58</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Basalt</td>\n", " <td>166.69</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc02c</td>\n", " <td>mc02</td>\n", " </tr>\n", " <tr>\n", " <th>mc02d</th>\n", " <td>246</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-68</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Basalt</td>\n", " <td>166.69</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc02d</td>\n", " <td>mc02</td>\n", " </tr>\n", " <tr>\n", " <th>mc02e</th>\n", " <td>235</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions ## ...</td>\n", " <td>-43</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Basalt</td>\n", " <td>166.69</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>b</td>\n", " <td>mc02e</td>\n", " <td>mc02</td>\n", " </tr>\n", " <tr>\n", " <th>mc02f</th>\n", " <td>182</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-50</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Basalt</td>\n", " <td>166.69</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc02f</td>\n", " <td>mc02</td>\n", " </tr>\n", " <tr>\n", " <th>mc02g</th>\n", " <td>197</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-32</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Basalt</td>\n", " <td>166.69</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc02g</td>\n", " <td>mc02</td>\n", " </tr>\n", " <tr>\n", " <th>mc02h</th>\n", " <td>298</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-48</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>Basalt</td>\n", " <td>166.69</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc02h</td>\n", " <td>mc02</td>\n", " </tr>\n", " <tr>\n", " <th>mc03a</th>\n", " <td>306</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-68</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.76</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc03a</td>\n", " <td>mc03</td>\n", " </tr>\n", " <tr>\n", " <th>mc03b</th>\n", " <td>287</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-66</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.76</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc03b</td>\n", " <td>mc03</td>\n", " </tr>\n", " <tr>\n", " <th>mc03c</th>\n", " <td>291</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-76</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.76</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc03c</td>\n", " <td>mc03</td>\n", " </tr>\n", " <tr>\n", " <th>mc03d</th>\n", " <td>250</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-58</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.76</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc03d</td>\n", " <td>mc03</td>\n", " </tr>\n", " <tr>\n", " <th>mc03e</th>\n", " <td>309</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-68</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.76</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc03e</td>\n", " <td>mc03</td>\n", " </tr>\n", " <tr>\n", " <th>mc03f</th>\n", " <td>305</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-86</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.76</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc03f</td>\n", " <td>mc03</td>\n", " </tr>\n", " <tr>\n", " <th>mc03g</th>\n", " <td>288</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-41</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.76</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc03g</td>\n", " <td>mc03</td>\n", " </tr>\n", " <tr>\n", " <th>mc03h</th>\n", " <td>274</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-58</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.76</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc03h</td>\n", " <td>mc03</td>\n", " </tr>\n", " <tr>\n", " <th>mc04a</th>\n", " <td>91</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-43</td>\n", " <td>Intrusive:Igneous</td>\n", " <td>Volcanic Dike</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.7</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc04a</td>\n", " <td>mc04</td>\n", " </tr>\n", " <tr>\n", " <th>mc04b</th>\n", " <td>86</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-50</td>\n", " <td>Intrusive:Igneous</td>\n", " <td>Volcanic Dike</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.7</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc04b</td>\n", " <td>mc04</td>\n", " </tr>\n", " <tr>\n", " <th>mc04c</th>\n", " <td>121</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-33</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Volcanic Dike</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.7</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc04c</td>\n", " <td>mc04</td>\n", " </tr>\n", " <tr>\n", " <th>mc04d</th>\n", " <td>92</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-55</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Volcanic Dike</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.7</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc04d</td>\n", " <td>mc04</td>\n", " </tr>\n", " <tr>\n", " <th>mc04e</th>\n", " <td>124</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-68</td>\n", " <td>Intrusive:Igneous</td>\n", " <td>Volcanic Dike</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.7</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc04e</td>\n", " <td>mc04</td>\n", " </tr>\n", " <tr>\n", " <th>mc04f</th>\n", " <td>151</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-39</td>\n", " <td>Intrusive:Igneous</td>\n", " <td>Volcanic Dike</td>\n", " <td>-77.84</td>\n", " <td>Basalt</td>\n", " <td>166.7</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc04f</td>\n", " <td>mc04</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>mc44c</th>\n", " <td>102</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-25</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.36</td>\n", " <td>Basalt</td>\n", " <td>164.26</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc44c</td>\n", " <td>mc44</td>\n", " </tr>\n", " <tr>\n", " <th>mc44d</th>\n", " <td>145</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-26</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.36</td>\n", " <td>Basalt</td>\n", " <td>164.26</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc44d</td>\n", " <td>mc44</td>\n", " </tr>\n", " <tr>\n", " <th>mc44e</th>\n", " <td>102</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-14</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.36</td>\n", " <td>Basalt</td>\n", " <td>164.26</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc44e</td>\n", " <td>mc44</td>\n", " </tr>\n", " <tr>\n", " <th>mc44f</th>\n", " <td>67</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-17</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.36</td>\n", " <td>Basalt</td>\n", " <td>164.26</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc44f</td>\n", " <td>mc44</td>\n", " </tr>\n", " <tr>\n", " <th>mc44g</th>\n", " <td>179</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-17</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.36</td>\n", " <td>Basalt</td>\n", " <td>164.26</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc44g</td>\n", " <td>mc44</td>\n", " </tr>\n", " <tr>\n", " <th>mc44h</th>\n", " <td>232</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-20</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.36</td>\n", " <td>Basalt</td>\n", " <td>164.26</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc44h</td>\n", " <td>mc44</td>\n", " </tr>\n", " <tr>\n", " <th>mc48a</th>\n", " <td>138</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-53</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc48a</td>\n", " <td>mc48</td>\n", " </tr>\n", " <tr>\n", " <th>mc48b</th>\n", " <td>131</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-40</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc48b</td>\n", " <td>mc48</td>\n", " </tr>\n", " <tr>\n", " <th>mc48c</th>\n", " <td>156</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-46</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc48c</td>\n", " <td>mc48</td>\n", " </tr>\n", " <tr>\n", " <th>mc48d</th>\n", " <td>228</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-49</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc48d</td>\n", " <td>mc48</td>\n", " </tr>\n", " <tr>\n", " <th>mc48e</th>\n", " <td>151</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-54</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc48e</td>\n", " <td>mc48</td>\n", " </tr>\n", " <tr>\n", " <th>mc48f</th>\n", " <td>133</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-55</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc48f</td>\n", " <td>mc48</td>\n", " </tr>\n", " <tr>\n", " <th>mc48g</th>\n", " <td>240</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-53</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc48g</td>\n", " <td>mc48</td>\n", " </tr>\n", " <tr>\n", " <th>mc48h</th>\n", " <td>175</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-53</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc48h</td>\n", " <td>mc48</td>\n", " </tr>\n", " <tr>\n", " <th>mc49a</th>\n", " <td>248</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-60</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc49a</td>\n", " <td>mc49</td>\n", " </tr>\n", " <tr>\n", " <th>mc49b</th>\n", " <td>208</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-73</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc49b</td>\n", " <td>mc49</td>\n", " </tr>\n", " <tr>\n", " <th>mc49c</th>\n", " <td>206</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-31</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc49c</td>\n", " <td>mc49</td>\n", " </tr>\n", " <tr>\n", " <th>mc49d</th>\n", " <td>271</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-53</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc49d</td>\n", " <td>mc49</td>\n", " </tr>\n", " <tr>\n", " <th>mc49e</th>\n", " <td>311</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-19</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc49e</td>\n", " <td>mc49</td>\n", " </tr>\n", " <tr>\n", " <th>mc49f</th>\n", " <td>206</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-34</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc49f</td>\n", " <td>mc49</td>\n", " </tr>\n", " <tr>\n", " <th>mc49g</th>\n", " <td>257</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-51</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc49g</td>\n", " <td>mc49</td>\n", " </tr>\n", " <tr>\n", " <th>mc49h</th>\n", " <td>250</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-37</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.24</td>\n", " <td>Basalt</td>\n", " <td>163.36</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc49h</td>\n", " <td>mc49</td>\n", " </tr>\n", " <tr>\n", " <th>mc50a</th>\n", " <td>19</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-28</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.25</td>\n", " <td>Basalt</td>\n", " <td>163.22</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc50a</td>\n", " <td>mc50</td>\n", " </tr>\n", " <tr>\n", " <th>mc50b</th>\n", " <td>48</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-16</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.25</td>\n", " <td>Basalt</td>\n", " <td>163.22</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc50b</td>\n", " <td>mc50</td>\n", " </tr>\n", " <tr>\n", " <th>mc50c</th>\n", " <td>140</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-43</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.25</td>\n", " <td>Basalt</td>\n", " <td>163.22</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc50c</td>\n", " <td>mc50</td>\n", " </tr>\n", " <tr>\n", " <th>mc50d</th>\n", " <td>90</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-32</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.25</td>\n", " <td>Basalt</td>\n", " <td>163.22</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc50d</td>\n", " <td>mc50</td>\n", " </tr>\n", " <tr>\n", " <th>mc50e</th>\n", " <td>139</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-41</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.25</td>\n", " <td>Basalt</td>\n", " <td>163.22</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc50e</td>\n", " <td>mc50</td>\n", " </tr>\n", " <tr>\n", " <th>mc50f</th>\n", " <td>148</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-46</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.25</td>\n", " <td>Basalt</td>\n", " <td>163.22</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc50f</td>\n", " <td>mc50</td>\n", " </tr>\n", " <tr>\n", " <th>mc50g</th>\n", " <td>155</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-18</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.25</td>\n", " <td>Basalt</td>\n", " <td>163.22</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc50g</td>\n", " <td>mc50</td>\n", " </tr>\n", " <tr>\n", " <th>mc50h</th>\n", " <td>148</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-23</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.25</td>\n", " <td>Basalt</td>\n", " <td>163.22</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc50h</td>\n", " <td>mc50</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1418 rows × 14 columns</p>\n", "</div>" ], "text/plain": [ " azimuth azimuth_dec_correction citations \\\n", "sample \n", "mc01a 260 0 This study \n", "mc01b 189 0 This study \n", "mc01c 183 0 This study \n", "mc01d 133 0 This study \n", "mc01e 91 0 This study \n", "mc01f 94 0 This study \n", "mc01g 69 0 This study \n", "mc01h 157 0 This study \n", "mc02a 190 0 This study \n", "mc02b 231 0 This study \n", "mc02c 220 0 This study \n", "mc02d 246 0 This study \n", "mc02e 235 0 This study \n", "mc02f 182 0 This study \n", "mc02g 197 0 This study \n", "mc02h 298 0 This study \n", "mc03a 306 0 This study \n", "mc03b 287 0 This study \n", "mc03c 291 0 This study \n", "mc03d 250 0 This study \n", "mc03e 309 0 This study \n", "mc03f 305 0 This study \n", "mc03g 288 0 This study \n", "mc03h 274 0 This study \n", "mc04a 91 0 This study \n", "mc04b 86 0 This study \n", "mc04c 121 0 This study \n", "mc04d 92 0 This study \n", "mc04e 124 0 This study \n", "mc04f 151 0 This study \n", "... ... ... ... \n", "mc44c 102 0 This study \n", "mc44d 145 0 This study \n", "mc44e 102 0 This study \n", "mc44f 67 0 This study \n", "mc44g 179 0 This study \n", "mc44h 232 0 This study \n", "mc48a 138 0 This study \n", "mc48b 131 0 This study \n", "mc48c 156 0 This study \n", "mc48d 228 0 This study \n", "mc48e 151 0 This study \n", "mc48f 133 0 This study \n", "mc48g 240 0 This study \n", "mc48h 175 0 This study \n", "mc49a 248 0 This study \n", "mc49b 208 0 This study \n", "mc49c 206 0 This study \n", "mc49d 271 0 This study \n", "mc49e 311 0 This study \n", "mc49f 206 0 This study \n", "mc49g 257 0 This study \n", "mc49h 250 0 This study \n", "mc50a 19 0 This study \n", "mc50b 48 0 This study \n", "mc50c 140 0 This study \n", "mc50d 90 0 This study \n", "mc50e 139 0 This study \n", "mc50f 148 0 This study \n", "mc50g 155 0 This study \n", "mc50h 148 0 This study \n", "\n", " description dip \\\n", "sample \n", "mc01a Archived samples from 1965, 66 expeditions. -57 \n", "mc01b Archived samples from 1965, 66 expeditions. -63 \n", "mc01c Archived samples from 1965, 66 expeditions. -30 \n", "mc01d Archived samples from 1965, 66 expeditions. -57 \n", "mc01e Archived samples from 1965, 66 expeditions. -51 \n", "mc01f Archived samples from 1965, 66 expeditions. -55 \n", "mc01g Archived samples from 1965, 66 expeditions. -56 \n", "mc01h Archived samples from 1965, 66 expeditions. -41 \n", "mc02a Archived samples from 1965, 66 expeditions. -50 \n", "mc02b Archived samples from 1965, 66 expeditions. -59 \n", "mc02c Archived samples from 1965, 66 expeditions. -58 \n", "mc02d Archived samples from 1965, 66 expeditions. -68 \n", "mc02e Archived samples from 1965, 66 expeditions ## ... -43 \n", "mc02f Archived samples from 1965, 66 expeditions. -50 \n", "mc02g Archived samples from 1965, 66 expeditions. -32 \n", "mc02h Archived samples from 1965, 66 expeditions. -48 \n", "mc03a Archived samples from 1965, 66 expeditions. -68 \n", "mc03b Archived samples from 1965, 66 expeditions. -66 \n", "mc03c Archived samples from 1965, 66 expeditions. -76 \n", "mc03d Archived samples from 1965, 66 expeditions. -58 \n", "mc03e Archived samples from 1965, 66 expeditions. -68 \n", "mc03f Archived samples from 1965, 66 expeditions. -86 \n", "mc03g Archived samples from 1965, 66 expeditions. -41 \n", "mc03h Archived samples from 1965, 66 expeditions. -58 \n", "mc04a Archived samples from 1965, 66 expeditions. -43 \n", "mc04b Archived samples from 1965, 66 expeditions. -50 \n", "mc04c Archived samples from 1965, 66 expeditions. -33 \n", "mc04d Archived samples from 1965, 66 expeditions. -55 \n", "mc04e Archived samples from 1965, 66 expeditions. -68 \n", "mc04f Archived samples from 1965, 66 expeditions. -39 \n", "... ... .. \n", "mc44c Archived samples from 1965, 66 expeditions. -25 \n", "mc44d Archived samples from 1965, 66 expeditions. -26 \n", "mc44e Archived samples from 1965, 66 expeditions. -14 \n", "mc44f Archived samples from 1965, 66 expeditions. -17 \n", "mc44g Archived samples from 1965, 66 expeditions. -17 \n", "mc44h Archived samples from 1965, 66 expeditions. -20 \n", "mc48a Archived samples from 1965, 66 expeditions. -53 \n", "mc48b Archived samples from 1965, 66 expeditions. -40 \n", "mc48c Archived samples from 1965, 66 expeditions. -46 \n", "mc48d Archived samples from 1965, 66 expeditions. -49 \n", "mc48e Archived samples from 1965, 66 expeditions. -54 \n", "mc48f Archived samples from 1965, 66 expeditions. -55 \n", "mc48g Archived samples from 1965, 66 expeditions. -53 \n", "mc48h Archived samples from 1965, 66 expeditions. -53 \n", "mc49a Archived samples from 1965, 66 expeditions. -60 \n", "mc49b Archived samples from 1965, 66 expeditions. -73 \n", "mc49c Archived samples from 1965, 66 expeditions. -31 \n", "mc49d Archived samples from 1965, 66 expeditions. -53 \n", "mc49e Archived samples from 1965, 66 expeditions. -19 \n", "mc49f Archived samples from 1965, 66 expeditions. -34 \n", "mc49g Archived samples from 1965, 66 expeditions. -51 \n", "mc49h Archived samples from 1965, 66 expeditions. -37 \n", "mc50a Archived samples from 1965, 66 expeditions. -28 \n", "mc50b Archived samples from 1965, 66 expeditions. -16 \n", "mc50c Archived samples from 1965, 66 expeditions. -43 \n", "mc50d Archived samples from 1965, 66 expeditions. -32 \n", "mc50e Archived samples from 1965, 66 expeditions. -41 \n", "mc50f Archived samples from 1965, 66 expeditions. -46 \n", "mc50g Archived samples from 1965, 66 expeditions. -18 \n", "mc50h Archived samples from 1965, 66 expeditions. -23 \n", "\n", " geologic_classes geologic_types lat lithologies lon \\\n", "sample \n", "mc01a Extrusive:Igneous Lava Flow -77.85 Trachyte 166.64 \n", "mc01b Extrusive:Igneous Lava Flow -77.85 Trachyte 166.64 \n", "mc01c Extrusive:Igneous Lava Flow -77.85 Trachyte 166.64 \n", "mc01d Extrusive:Igneous Lava Flow -77.85 Trachyte 166.64 \n", "mc01e Extrusive:Igneous Lava Flow -77.85 Trachyte 166.64 \n", "mc01f Extrusive:Igneous Lava Flow -77.85 Trachyte 166.64 \n", "mc01g Extrusive:Igneous Lava Flow -77.85 Trachyte 166.64 \n", "mc01h Extrusive:Igneous Lava Flow -77.85 Trachyte 166.64 \n", "mc02a Extrusive:Igneous Lava Flow -77.85 Basalt 166.69 \n", "mc02b Extrusive:Igneous Lava Flow -77.85 Basalt 166.69 \n", "mc02c Extrusive:Igneous Lava Flow -77.85 Basalt 166.69 \n", "mc02d Extrusive:Igneous Lava Flow -77.85 Basalt 166.69 \n", "mc02e Extrusive:Igneous Lava Flow -77.85 Basalt 166.69 \n", "mc02f Extrusive:Igneous Lava Flow -77.85 Basalt 166.69 \n", "mc02g Extrusive:Igneous Lava Flow -77.85 Basalt 166.69 \n", "mc02h Extrusive:Igneous Lava Flow -77.85 Basalt 166.69 \n", "mc03a Extrusive:Igneous Lava Flow -77.84 Basalt 166.76 \n", "mc03b Extrusive:Igneous Lava Flow -77.84 Basalt 166.76 \n", "mc03c Extrusive:Igneous Lava Flow -77.84 Basalt 166.76 \n", "mc03d Extrusive:Igneous Lava Flow -77.84 Basalt 166.76 \n", "mc03e Extrusive:Igneous Lava Flow -77.84 Basalt 166.76 \n", "mc03f Extrusive:Igneous Lava Flow -77.84 Basalt 166.76 \n", "mc03g Extrusive:Igneous Lava Flow -77.84 Basalt 166.76 \n", "mc03h Extrusive:Igneous Lava Flow -77.84 Basalt 166.76 \n", "mc04a Intrusive:Igneous Volcanic Dike -77.84 Basalt 166.7 \n", "mc04b Intrusive:Igneous Volcanic Dike -77.84 Basalt 166.7 \n", "mc04c Extrusive:Igneous Volcanic Dike -77.84 Basalt 166.7 \n", "mc04d Extrusive:Igneous Volcanic Dike -77.84 Basalt 166.7 \n", "mc04e Intrusive:Igneous Volcanic Dike -77.84 Basalt 166.7 \n", "mc04f Intrusive:Igneous Volcanic Dike -77.84 Basalt 166.7 \n", "... ... ... ... ... ... \n", "mc44c Extrusive:Igneous Lava Flow -78.36 Basalt 164.26 \n", "mc44d Extrusive:Igneous Lava Flow -78.36 Basalt 164.26 \n", "mc44e Extrusive:Igneous Lava Flow -78.36 Basalt 164.26 \n", "mc44f Extrusive:Igneous Lava Flow -78.36 Basalt 164.26 \n", "mc44g Extrusive:Igneous Lava Flow -78.36 Basalt 164.26 \n", "mc44h Extrusive:Igneous Lava Flow -78.36 Basalt 164.26 \n", "mc48a Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc48b Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc48c Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc48d Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc48e Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc48f Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc48g Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc48h Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc49a Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc49b Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc49c Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc49d Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc49e Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc49f Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc49g Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc49h Extrusive:Igneous Lava Flow -78.24 Basalt 163.36 \n", "mc50a Extrusive:Igneous Lava Flow -78.25 Basalt 163.22 \n", "mc50b Extrusive:Igneous Lava Flow -78.25 Basalt 163.22 \n", "mc50c Extrusive:Igneous Lava Flow -78.25 Basalt 163.22 \n", "mc50d Extrusive:Igneous Lava Flow -78.25 Basalt 163.22 \n", "mc50e Extrusive:Igneous Lava Flow -78.25 Basalt 163.22 \n", "mc50f Extrusive:Igneous Lava Flow -78.25 Basalt 163.22 \n", "mc50g Extrusive:Igneous Lava Flow -78.25 Basalt 163.22 \n", "mc50h Extrusive:Igneous Lava Flow -78.25 Basalt 163.22 \n", "\n", " method_codes orientation_flag sample site \n", "sample \n", "mc01a SO-SIGHT:FS-FD g mc01a mc01 \n", "mc01b SO-SIGHT:FS-FD g mc01b mc01 \n", "mc01c SO-SIGHT:FS-FD g mc01c mc01 \n", "mc01d SO-SIGHT:FS-FD g mc01d mc01 \n", "mc01e SO-SIGHT:FS-FD g mc01e mc01 \n", "mc01f SO-SIGHT:FS-FD g mc01f mc01 \n", "mc01g SO-SIGHT:FS-FD g mc01g mc01 \n", "mc01h SO-SIGHT:FS-FD g mc01h mc01 \n", "mc02a SO-SIGHT:FS-FD g mc02a mc02 \n", "mc02b SO-SIGHT:FS-FD g mc02b mc02 \n", "mc02c SO-SIGHT:FS-FD g mc02c mc02 \n", "mc02d SO-SIGHT:FS-FD g mc02d mc02 \n", "mc02e SO-SIGHT:FS-FD b mc02e mc02 \n", "mc02f SO-SIGHT:FS-FD g mc02f mc02 \n", "mc02g SO-SIGHT:FS-FD g mc02g mc02 \n", "mc02h SO-SIGHT:FS-FD g mc02h mc02 \n", "mc03a SO-SIGHT:FS-FD g mc03a mc03 \n", "mc03b SO-SIGHT:FS-FD g mc03b mc03 \n", "mc03c SO-SIGHT:FS-FD g mc03c mc03 \n", "mc03d SO-SIGHT:FS-FD g mc03d mc03 \n", "mc03e SO-SIGHT:FS-FD g mc03e mc03 \n", "mc03f SO-SIGHT:FS-FD g mc03f mc03 \n", "mc03g SO-SIGHT:FS-FD g mc03g mc03 \n", "mc03h SO-SIGHT:FS-FD g mc03h mc03 \n", "mc04a SO-SIGHT:FS-FD g mc04a mc04 \n", "mc04b SO-SIGHT:FS-FD g mc04b mc04 \n", "mc04c SO-SIGHT:FS-FD g mc04c mc04 \n", "mc04d SO-SIGHT:FS-FD g mc04d mc04 \n", "mc04e SO-SIGHT:FS-FD g mc04e mc04 \n", "mc04f SO-SIGHT:FS-FD g mc04f mc04 \n", "... ... ... ... ... \n", "mc44c SO-SIGHT:FS-FD g mc44c mc44 \n", "mc44d SO-SIGHT:FS-FD g mc44d mc44 \n", "mc44e SO-SIGHT:FS-FD g mc44e mc44 \n", "mc44f SO-SIGHT:FS-FD g mc44f mc44 \n", "mc44g SO-SIGHT:FS-FD g mc44g mc44 \n", "mc44h SO-SIGHT:FS-FD g mc44h mc44 \n", "mc48a SO-SIGHT:FS-FD g mc48a mc48 \n", "mc48b SO-SIGHT:FS-FD g mc48b mc48 \n", "mc48c SO-SIGHT:FS-FD g mc48c mc48 \n", "mc48d SO-SIGHT:FS-FD g mc48d mc48 \n", "mc48e SO-SIGHT:FS-FD g mc48e mc48 \n", "mc48f SO-SIGHT:FS-FD g mc48f mc48 \n", "mc48g SO-SIGHT:FS-FD g mc48g mc48 \n", "mc48h SO-SIGHT:FS-FD g mc48h mc48 \n", "mc49a SO-SIGHT:FS-FD g mc49a mc49 \n", "mc49b SO-SIGHT:FS-FD g mc49b mc49 \n", "mc49c SO-SIGHT:FS-FD g mc49c mc49 \n", "mc49d SO-SIGHT:FS-FD g mc49d mc49 \n", "mc49e SO-SIGHT:FS-FD g mc49e mc49 \n", "mc49f SO-SIGHT:FS-FD g mc49f mc49 \n", "mc49g SO-SIGHT:FS-FD g mc49g mc49 \n", "mc49h SO-SIGHT:FS-FD g mc49h mc49 \n", "mc50a SO-SIGHT:FS-FD g mc50a mc50 \n", "mc50b SO-SIGHT:FS-FD g mc50b mc50 \n", "mc50c SO-SIGHT:FS-FD g mc50c mc50 \n", "mc50d SO-SIGHT:FS-FD g mc50d mc50 \n", "mc50e SO-SIGHT:FS-FD g mc50e mc50 \n", "mc50f SO-SIGHT:FS-FD g mc50f mc50 \n", "mc50g SO-SIGHT:FS-FD g mc50g mc50 \n", "mc50h SO-SIGHT:FS-FD g mc50h mc50 \n", "\n", "[1418 rows x 14 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con = cb.Contribution(wdir, custom_filenames={'specimens': 'custom_specimens.txt', 'samples': 'custom_samples.txt',\n", " 'sites': 'custom_sites.txt'})\n", "samp_container = con.tables['samples']\n", "samp_container.write_magic_file(custom_name='_samples.txt', dir_path='../3_0/McMurdo')\n", "\n", "samp_container.df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding an empty dataframe to a contribution" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-W- fake is not a valid MagIC table name\n", "-I- Valid table names are: measurements, specimens, samples, sites, locations, contribution, criteria, ages, images\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>col1</th>\n", " <th>col2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [col1, col2]\n", "Index: []" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# columns is a list whichever columns you want\n", "dtype = \"specimens\"\n", "cols = [\"col_name1\", \"col_name2\"]\n", "data_container = cb.MagicDataFrame(dtype=dtype, columns=cols)\n", "\n", "# or:\n", "dtype = \"specimens\"\n", "groups = [\"Age\", \"Metadata\"]\n", "data_container = cb.MagicDataFrame(dtype=dtype, groups=groups)\n", "# and then:\n", "con.tables[dtype] = data_container\n", "\n", "con.tables[dtype].df\n", "\n", "con.add_empty_magic_table('fake', col_names=['col1', 'col2'])\n", "con.add_empty_magic_table('images', col_names=['col1', 'col2'])\n", "con.tables['images'].df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding a new item (i.e., a sample)\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-I- Getting method codes from earthref.org\n", "-I- Importing controlled vocabularies from https://earthref.org\n", "-I- Importing suggested vocabularies from https://earthref.org\n" ] }, { "data": { "text/plain": [ "['measurements',\n", " 'ages',\n", " 'sites',\n", " 'locations',\n", " 'samples',\n", " 'criteria',\n", " 'images',\n", " 'contribution',\n", " 'specimens']" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reload(nb)\n", "wdir = os.path.join(\"..\", \"3_0\", \"McMurdo\")\n", "con = cb.Contribution(wdir)\n", "con.tables.keys()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>azimuth</th>\n", " <th>azimuth_dec_correction</th>\n", " <th>citations</th>\n", " <th>description</th>\n", " <th>dip</th>\n", " <th>geologic_classes</th>\n", " <th>geologic_types</th>\n", " <th>lat</th>\n", " <th>lithologies</th>\n", " <th>lon</th>\n", " <th>method_codes</th>\n", " <th>orientation_flag</th>\n", " <th>sample</th>\n", " <th>site</th>\n", " <th>cooling_rate</th>\n", " </tr>\n", " <tr>\n", " <th>sample</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mc50e</th>\n", " <td>139</td>\n", " <td>0.0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-41</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.25</td>\n", " <td>Basalt</td>\n", " <td>163.22</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc50e</td>\n", " <td>mc50</td>\n", " <td>1e+10</td>\n", " </tr>\n", " <tr>\n", " <th>mc50f</th>\n", " <td>148</td>\n", " <td>0.0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-46</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.25</td>\n", " <td>Basalt</td>\n", " <td>163.22</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc50f</td>\n", " <td>mc50</td>\n", " <td>1e+10</td>\n", " </tr>\n", " <tr>\n", " <th>mc50g</th>\n", " <td>155</td>\n", " <td>0.0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-18</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.25</td>\n", " <td>Basalt</td>\n", " <td>163.22</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc50g</td>\n", " <td>mc50</td>\n", " <td>1e+10</td>\n", " </tr>\n", " <tr>\n", " <th>mc50h</th>\n", " <td>148</td>\n", " <td>0.0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-23</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-78.25</td>\n", " <td>Basalt</td>\n", " <td>163.22</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc50h</td>\n", " <td>mc50</td>\n", " <td>1e+10</td>\n", " </tr>\n", " <tr>\n", " <th>new_sample</th>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>new_sample</td>\n", " <td>mc01</td>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " azimuth azimuth_dec_correction citations \\\n", "sample \n", "mc50e 139 0.0 This study \n", "mc50f 148 0.0 This study \n", "mc50g 155 0.0 This study \n", "mc50h 148 0.0 This study \n", "new_sample None NaN None \n", "\n", " description dip \\\n", "sample \n", "mc50e Archived samples from 1965, 66 expeditions. -41 \n", "mc50f Archived samples from 1965, 66 expeditions. -46 \n", "mc50g Archived samples from 1965, 66 expeditions. -18 \n", "mc50h Archived samples from 1965, 66 expeditions. -23 \n", "new_sample None None \n", "\n", " geologic_classes geologic_types lat lithologies lon \\\n", "sample \n", "mc50e Extrusive:Igneous Lava Flow -78.25 Basalt 163.22 \n", "mc50f Extrusive:Igneous Lava Flow -78.25 Basalt 163.22 \n", "mc50g Extrusive:Igneous Lava Flow -78.25 Basalt 163.22 \n", "mc50h Extrusive:Igneous Lava Flow -78.25 Basalt 163.22 \n", "new_sample None None None None None \n", "\n", " method_codes orientation_flag sample site cooling_rate \n", "sample \n", "mc50e SO-SIGHT:FS-FD g mc50e mc50 1e+10 \n", "mc50f SO-SIGHT:FS-FD g mc50f mc50 1e+10 \n", "mc50g SO-SIGHT:FS-FD g mc50g mc50 1e+10 \n", "mc50h SO-SIGHT:FS-FD g mc50h mc50 1e+10 \n", "new_sample None None new_sample mc01 None " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add a new sample\n", "site_name = con.tables['sites'].df.index[0]\n", "samp_name = 'new_sample'\n", "data = {'sample': samp_name, 'site': site_name}\n", "\n", "con.add_item('samples', data, samp_name)\n", "con.tables['samples'].df.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mucking around with measurements" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/site-packages/pandas/core/indexing.py:140: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self._setitem_with_indexer(indexer, value)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>treatment</th>\n", " <th>treat_ac_field</th>\n", " <th>treat_temp</th>\n", " </tr>\n", " <tr>\n", " <th>measurement</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mc01f-LP-DIR-AF1</th>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>273</td>\n", " </tr>\n", " <tr>\n", " <th>mc01f-LP-DIR-AF2</th>\n", " <td>0.005</td>\n", " <td>0.0050</td>\n", " <td>273</td>\n", " </tr>\n", " <tr>\n", " <th>mc01f-LP-DIR-AF3</th>\n", " <td>0.0075</td>\n", " <td>0.0075</td>\n", " <td>273</td>\n", " </tr>\n", " <tr>\n", " <th>mc01f-LP-DIR-AF4</th>\n", " <td>0.01</td>\n", " <td>0.0100</td>\n", " <td>273</td>\n", " </tr>\n", " <tr>\n", " <th>mc01f-LP-DIR-AF5</th>\n", " <td>0.0125</td>\n", " <td>0.0125</td>\n", " <td>273</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " treatment treat_ac_field treat_temp\n", "measurement \n", "mc01f-LP-DIR-AF1 0 NaN 273\n", "mc01f-LP-DIR-AF2 0.005 0.0050 273\n", "mc01f-LP-DIR-AF3 0.0075 0.0075 273\n", "mc01f-LP-DIR-AF4 0.01 0.0100 273\n", "mc01f-LP-DIR-AF5 0.0125 0.0125 273" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con = cb.Contribution(wdir, custom_filenames={'specimens': 'custom_specimens.txt', 'samples': 'custom_samples.txt',\n", " 'sites': 'custom_sites.txt'})\n", "\n", "meas_container = con.tables['measurements']\n", "meas_data = meas_container.df\n", "\n", "meas_data['treatment'] = meas_data['treat_ac_field'].where(cond=meas_data['treat_ac_field'] != \"0\", other=meas_data['treat_temp'])\n", "meas_data[['treatment', 'treat_ac_field', 'treat_temp']]\n", "\n", "meas_data['treat_ac_field'].ix[0] = None\n", "meas_data['treat_ac_field'] = meas_data['treat_ac_field'].astype(float)\n", "\n", "meas_data[['treatment', 'treat_ac_field', 'treat_temp']].head()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dealing with criteria" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# grab a copy of the criteria and sites table to play with\n", "criteria = con.tables['criteria'].df.copy()\n", "sites = con.tables['sites'].df.copy()\n", "locations = con.tables['locations'].df.copy()\n", "specimens = con.tables['specimens'].df.copy()\n", "samples = con.tables['samples'].df.copy()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index([u'sites.int_n_specimens', u'sites.int_sigma', u'sites.int_sigma_perc',\n", " u'specimens.int_b_beta', u'specimens.int_frac', u'specimens.int_dang',\n", " u'specimens.int_mad', u'specimens.int_n_ptrm', u'specimens.int_scat',\n", " u'specimens.dir_mad_free', u'specimens.dir_alpha95',\n", " u'specimens.dir_n_measurements', u'samples.dir_alpha95',\n", " u'sites.dir_alpha95', u'sites.dir_n_samples',\n", " u'sites.dir_n_specimens_lines', u'sites.dir_k', u'sites.dir_polarity',\n", " u'sites.dir_polarity'],\n", " dtype='object', name=u'table_column')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "criteria.index" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "cond = criteria.index.str.contains('sample')\n", "samp_crit = criteria[cond].copy()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>citations</th>\n", " <th>criterion</th>\n", " <th>criterion_operation</th>\n", " <th>criterion_value</th>\n", " <th>definition</th>\n", " <th>table_column</th>\n", " </tr>\n", " <tr>\n", " <th>column_name</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>dir_alpha95</th>\n", " <td>This study</td>\n", " <td>DE-SAMP</td>\n", " <td><=</td>\n", " <td>180</td>\n", " <td>acceptance criteria for study</td>\n", " <td>samples.dir_alpha95</td>\n", " </tr>\n", " <tr>\n", " <th>sites.dir_n_samples</th>\n", " <td>This study</td>\n", " <td>DE-SITE</td>\n", " <td>>=</td>\n", " <td>5</td>\n", " <td>acceptance criteria for study</td>\n", " <td>sites.dir_n_samples</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " citations criterion criterion_operation criterion_value \\\n", "column_name \n", "dir_alpha95 This study DE-SAMP <= 180 \n", "sites.dir_n_samples This study DE-SITE >= 5 \n", "\n", " definition table_column \n", "column_name \n", "dir_alpha95 acceptance criteria for study samples.dir_alpha95 \n", "sites.dir_n_samples acceptance criteria for study sites.dir_n_samples " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get all criteria for samples\n", "\n", "# only criteria with 'samples' in table_column_name\n", "cond = criteria.index.str.contains('sample')\n", "samp_crit = criteria[cond].copy()\n", "# remove table name from index\n", "if len(samp_crit):\n", " samp_crit.index = samp_crit.index.str.replace('samples.', '')\n", " samp_crit.index.name = 'column_name'\n", "\n", "\n", "cols = samp_crit.index\n", "list(cols)\n", "cols = list(cols)\n", "\n", "samp_crit" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "#sites.head().ix[cols]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# create string --> operator conversion \n", "import operator\n", "ops = {\"<\": operator.lt, \">\": operator.gt, \"==\": operator.eq, \"<=\": operator.le, \">=\": operator.gt}\n", "\n", "# function for applying criteria\n", "\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-I- Getting method codes from earthref.org\n", "-I- Importing controlled vocabularies from https://earthref.org\n", "-I- Importing suggested vocabularies from https://earthref.org\n", "['IE-SPEC_int_b_beta_pass', 'IE-SPEC_int_frac_pass', 'IE-SPEC_int_dang_pass', 'IE-SPEC_int_mad_pass', 'IE-SPEC_int_n_ptrm_pass', 'IE-SPEC_int_scat_pass']\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>IE-SPEC_int_b_beta_pass</th>\n", " <th>IE-SPEC_int_frac_pass</th>\n", " <th>IE-SPEC_int_dang_pass</th>\n", " <th>IE-SPEC_int_mad_pass</th>\n", " <th>IE-SPEC_int_n_ptrm_pass</th>\n", " <th>IE-SPEC_int_scat_pass</th>\n", " </tr>\n", " <tr>\n", " <th>specimen</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mc01a</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>mc01a</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>mc01b</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>mc01b</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>mc01c</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " IE-SPEC_int_b_beta_pass IE-SPEC_int_frac_pass IE-SPEC_int_dang_pass \\\n", "specimen \n", "mc01a True True True \n", "mc01a True True True \n", "mc01b True True True \n", "mc01b True True True \n", "mc01c True True True \n", "\n", " IE-SPEC_int_mad_pass IE-SPEC_int_n_ptrm_pass IE-SPEC_int_scat_pass \n", "specimen \n", "mc01a True True True \n", "mc01a True True True \n", "mc01b True True True \n", "mc01b True True True \n", "mc01c True True True " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create full McMurdo contribution\n", "\n", "reload(nb)\n", "\n", "con = cb.Contribution(wdir, custom_filenames={'specimens': 'specimens.txt', 'samples': 'samples.txt',\n", " 'sites': 'sites.txt'})\n", "\n", "\n", "criteria = con.tables['criteria'].df.copy()\n", "\n", "\n", "\n", "\n", "def apply_crit(series, crit_series):#, criteria_type):\n", " \"\"\"\n", " Apply 1 criterion (i.e., 1 row of the criteria table) to another table.\n", " Return series with boolean values for whether the row passes.\n", " \"\"\"\n", " col_name = crit_series.name\n", " # if there's no value, pass == True\n", " if col_name not in series:\n", " return True\n", " if not series[col_name]:\n", " return True\n", " #return \"{} not in row\".format(col_name)\n", " # if there is a value, test that it is within correct limits\n", " crit_name = crit_series['criterion']\n", " crit_value = float(crit_series['criterion_value'])\n", " op_str = crit_series['criterion_operation']\n", " op = ops[op_str]\n", " value = float(series[col_name])\n", " result = op(value, crit_value)\n", " return result\n", "\n", "\n", "\n", "def add_criteria_named(category_name, dtype):\n", " df = con.tables[dtype].df\n", " criteria_subset = criteria[criteria['criterion'] == category_name]\n", " criteria_subset.index = criteria_subset.index.str.replace(dtype + '.', '')\n", " pass_col_names = []\n", " for crit_name, crit_row in criteria_subset.iterrows():\n", " #print 'crit_name', crit_name\n", " col_name = category_name + \"_\" + crit_name + \"_pass\"\n", " #print 'col_name', col_name\n", " pass_col_names.append(col_name)\n", " df[col_name] = df.apply(apply_crit, args=(crit_row,), axis=1)\n", " return pass_col_names\n", " \n", "\n", "#DE_SPEC = criteria[criteria['criterion'] == 'DE-SPEC']\n", "#DE_SPEC.index = DE_SPEC.index.str.replace('specimens.', '')\n", "#pass_col_names = []\n", "#for crit_name, crit_row in DE_SPEC.iterrows():\n", "# #print 'crit_name', crit_name\n", "# col_name = 'DE-SPEC_' + crit_name + \"_pass\"\n", "# #print 'col_name', col_name\n", "# pass_col_names.append(col_name)\n", "# specimens[col_name] = specimens.apply(apply_crit, args=(crit_row,), axis=1)\n", " \n", " \n", "dtype = 'specimens'\n", "criteria_name = 'IE-SPEC'\n", "pass_col = criteria_name + \"_pass\"\n", "pass_col_names = add_criteria_named(criteria_name, dtype)\n", "print pass_col_names\n", "df = con.tables[dtype].df\n", "\n", "\n", "col_names = df.columns[df.columns.str.contains(criteria_name)]\n", "df[df[col_names].all(1)]#[col_names]\n", "\n", "\n", "## all specimens that pass all DE-SPEC criteria\n", "#df[df[pass_col]].index\n", "#df.head()\n", "df.head()[col_names]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-I- Getting method codes from earthref.org\n", "-I- Importing controlled vocabularies from https://earthref.org\n", "-I- Importing suggested vocabularies from https://earthref.org\n" ] }, { "data": { "text/plain": [ "{'ages': <pmagpy.contribution_builder.MagicDataFrame at 0x10c5ea910>,\n", " 'contribution': <pmagpy.contribution_builder.MagicDataFrame at 0x111394510>,\n", " 'criteria': <pmagpy.contribution_builder.MagicDataFrame at 0x1108f7450>,\n", " 'images': <pmagpy.contribution_builder.MagicDataFrame at 0x10c7e5050>,\n", " 'locations': <pmagpy.contribution_builder.MagicDataFrame at 0x111429550>,\n", " 'measurements': <pmagpy.contribution_builder.MagicDataFrame at 0x110331210>,\n", " 'samples': <pmagpy.contribution_builder.MagicDataFrame at 0x1101a4dd0>,\n", " 'sites': <pmagpy.contribution_builder.MagicDataFrame at 0x110f23c90>,\n", " 'specimens': <pmagpy.contribution_builder.MagicDataFrame at 0x111e9a0d0>}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reload(nb)\n", "import pmagpy.pmag\n", "reload(pmagpy.pmag)\n", "con = cb.Contribution(wdir)#, custom_filenames={'specimens': 'custom_specimens.txt', 'samples': 'custom_samples.txt',\n", " #'sites': 'custom_sites.txt'})\n", "\n", "\n", "\n", "con.tables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ways of dropping rows in dataframes -- non-unique index is a problem" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:20: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " <th>age_sigma</th>\n", " <th>age_unit</th>\n", " <th>analysts</th>\n", " <th>citations</th>\n", " <th>criteria</th>\n", " <th>description</th>\n", " <th>dir_alpha95</th>\n", " <th>dir_comp_name</th>\n", " <th>dir_dec</th>\n", " <th>...</th>\n", " <th>vadm_n_samples</th>\n", " <th>vadm_sigma</th>\n", " <th>vdm</th>\n", " <th>vdm_n_samples</th>\n", " <th>vdm_sigma</th>\n", " <th>vgp_dm</th>\n", " <th>vgp_dp</th>\n", " <th>vgp_lat</th>\n", " <th>vgp_lon</th>\n", " <th>vgp_n_samples</th>\n", " </tr>\n", " <tr>\n", " <th>site</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mc01</th>\n", " <td>1.18</td>\n", " <td>0.005</td>\n", " <td>Ma</td>\n", " <td>Lisa Tauxe</td>\n", " <td>This study</td>\n", " <td>IE-SPEC</td>\n", " <td>Trachyte flow, N flank Observation hill, Hut P...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>mc01</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>mc02</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>This study</td>\n", " <td>None</td>\n", " <td>Upper basalt flow, S. flank Crater Hill, Hut P...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>mc02</th>\n", " <td>0.33</td>\n", " <td>0.01</td>\n", " <td>Ma</td>\n", " <td>Lisa Tauxe</td>\n", " <td>This study</td>\n", " <td>DE-SPEC</td>\n", " <td>Direction included in Pmag_Results.</td>\n", " <td>2.1</td>\n", " <td>A</td>\n", " <td>328.6</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>mc02</th>\n", " <td>0.33</td>\n", " <td>0.01</td>\n", " <td>Ma</td>\n", " <td>Kristin Lawrence</td>\n", " <td>This study</td>\n", " <td>DE-SITE</td>\n", " <td>Site VGPA comp: (geog. coord).</td>\n", " <td>2.1</td>\n", " <td>None</td>\n", " <td>328.6</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>2.5</td>\n", " <td>4.1</td>\n", " <td>79</td>\n", " <td>101.2</td>\n", " <td>6</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 47 columns</p>\n", "</div>" ], "text/plain": [ " age age_sigma age_unit analysts citations criteria \\\n", "site \n", "mc01 1.18 0.005 Ma Lisa Tauxe This study IE-SPEC \n", "mc01 None None None None None None \n", "mc02 None None None None This study None \n", "mc02 0.33 0.01 Ma Lisa Tauxe This study DE-SPEC \n", "mc02 0.33 0.01 Ma Kristin Lawrence This study DE-SITE \n", "\n", " description dir_alpha95 \\\n", "site \n", "mc01 Trachyte flow, N flank Observation hill, Hut P... None \n", "mc01 None None \n", "mc02 Upper basalt flow, S. flank Crater Hill, Hut P... None \n", "mc02 Direction included in Pmag_Results. 2.1 \n", "mc02 Site VGPA comp: (geog. coord). 2.1 \n", "\n", " dir_comp_name dir_dec ... vadm_n_samples vadm_sigma vdm \\\n", "site ... \n", "mc01 None None ... None None None \n", "mc01 None None ... None None None \n", "mc02 None None ... None None None \n", "mc02 A 328.6 ... None None None \n", "mc02 None 328.6 ... None None None \n", "\n", " vdm_n_samples vdm_sigma vgp_dm vgp_dp vgp_lat vgp_lon vgp_n_samples \n", "site \n", "mc01 None None None None None None None \n", "mc01 None None None None None None None \n", "mc02 None None None None None None None \n", "mc02 None None None None None None None \n", "mc02 None None 2.5 4.1 79 101.2 6 \n", "\n", "[5 rows x 47 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "site_container = con.tables['sites']\n", "site_df = con.tables['sites'].df\n", "#thingee = set([0, 4])\n", "#site_df.index[list(thingee)]\n", "\n", "to_drop = [0, 4]\n", "#site_df.drop(site_df.iloc[list(thingee)], inplace=True)\n", "# this doesn't work, because it drops extra values with the same index value (mc01)\n", "#site_df.drop(to_drop, inplace=True)\n", "\n", "# this works\n", "df = site_df.iloc[sorted(set(range(len(site_df))) - set([0, 4]))]\n", "\n", "# this works\n", "df = site_df.iloc[[i for i in range(len(site_df)) if i not in to_drop]]\n", "\n", "# this works\n", "site_df = site_df.reset_index(drop=True).drop(to_drop).set_index('site')\n", "site_df.columns[site_df.columns.str.contains('age')]\n", "site_df.columns[site_df.columns.str.contains(\"age($\|_).\")]\n", "\n", "\n", "# Solution in contribution_builder:\n", "site_container.delete_row(1)\n", "site_container.delete_row(1).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convert 2.5 files --> 3.0 files" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# See data_model_conversion.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extract minimum/maximum lat/lon by location from sites table" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "about to group\n", "max longitude:\n", " lon\n", "location \n", "McMurdo 2.0\n", "\n", "min latitude:\n", " lat\n", "location \n", "McMurdo -78.4\n", "McMurdo2 14.2\n" ] } ], "source": [ "# get minimum/maximum latitude/longitude grouped by location\n", "\n", "# set up\n", "wdir = os.path.join(\"..\", '3_0', 'McMurdo')\n", "con = cb.Contribution(wdir, single_file='sites.txt')\n", "site_container = con.tables['sites']\n", "site_df = site_container.df\n", "# Fill in some values\n", "site_container.df['lon'] = ''\n", "site_container.df.iloc[1] = pd.Series({'lon': '2', 'location': 'McMurdo'})\n", "site_container.df.iloc[2] = pd.Series({'location': 'McMurdo2', 'lat': '14.2'})\n", "# fill in string values with None or np.nan\n", "site_container.df['lon'] = np.where(site_container.df['lon'].str.len(), site_container.df['lon'], None)\n", "site_container.df['lat'] = np.where(site_container.df['lat'].str.len(), site_container.df['lat'], None)\n", "# group lat/lon by location name\n", "print 'about to group'\n", "\n", "site_container.df['lon'] = site_container.df['lon'].astype(float)\n", "\n", "grouped_lon = site_container.df[['lon', 'location']].dropna().groupby('location')\n", "grouped_lat = site_container.df[['lat', 'location']].dropna().groupby('location')\n", "\n", "#grouped_lon = site_container.df['lon'].astype(float).dropna().groupby(site_container.df['location'])\n", "#grouped_lat = site_container.df['lat'].astype(float).dropna().groupby(site_container.df['location'])\n", "# get output\n", "print \"max longitude:\"\n", "print grouped_lon.max()\n", "print ''\n", "print 'min latitude:'\n", "print grouped_lat.min()\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<pandas.core.groupby.DataFrameGroupBy object at 0x10c4294d0>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "site_container.df[['lon', 'location']].dropna(subset=['lon']).groupby('location')\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Propagating values from one df to another" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-I- Getting method codes from earthref.org\n", "-I- Importing controlled vocabularies from https://earthref.org\n", "-I- Importing suggested vocabularies from https://earthref.org\n" ] } ], "source": [ "reload(nb)\n", "\n", "\n", "wdir = os.path.join(\"..\", '3_0', 'McMurdo')\n", "con = cb.Contribution(wdir)#, custom_filenames={'specimens': 'custom_specimens.txt', 'samples': 'custom_samples.txt',\n", "\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lithologies in sample table? True\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>lithologies</th>\n", " </tr>\n", " <tr>\n", " <th>site</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mc01</th>\n", " <td>litho1</td>\n", " </tr>\n", " <tr>\n", " <th>mc01</th>\n", " <td>litho1</td>\n", " </tr>\n", " <tr>\n", " <th>mc01</th>\n", " <td>litho1</td>\n", " </tr>\n", " <tr>\n", " <th>mc01</th>\n", " <td>litho2</td>\n", " </tr>\n", " <tr>\n", " <th>mc02</th>\n", " <td>litho1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " lithologies\n", "site \n", "mc01 litho1\n", "mc01 litho1\n", "mc01 litho1\n", "mc01 litho2\n", "mc02 litho1" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print 'lithologies in sample table?', 'lithologies' in con.tables['samples'].df.columns\n", "con.tables['sites'].df.lithologies = 'litho1'\n", "con.tables['sites'].df.iloc[3] = pd.Series({'site': 'mc04', 'lithologies': 'litho2'})\n", "con.tables['sites'].df.head()[['lithologies']]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>lithologies</th>\n", " </tr>\n", " <tr>\n", " <th>sample</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mc01a</th>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>mc01b</th>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>mc01c</th>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>mc01d</th>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>mc01e</th>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " lithologies\n", "sample \n", "mc01a None\n", "mc01b None\n", "mc01c None\n", "mc01d None\n", "mc01e None" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.tables['samples'].df['lithologies'] = None\n", "\n", "con.tables['samples'].df.loc['mc01f', 'lithologies'] = 'litho3'\n", "con.tables['samples'].df.head()[['lithologies']]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "site already in samples\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>azimuth</th>\n", " <th>azimuth_dec_correction</th>\n", " <th>citations</th>\n", " <th>description</th>\n", " <th>dip</th>\n", " <th>geologic_classes</th>\n", " <th>geologic_types</th>\n", " <th>lat</th>\n", " <th>lithologies</th>\n", " <th>lon</th>\n", " <th>method_codes</th>\n", " <th>orientation_flag</th>\n", " <th>sample</th>\n", " <th>site</th>\n", " <th>cooling_rate</th>\n", " </tr>\n", " <tr>\n", " <th>sample</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mc01a</th>\n", " <td>260</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-57</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>litho1</td>\n", " <td>166.64</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc01a</td>\n", " <td>mc01</td>\n", " <td>1e+10</td>\n", " </tr>\n", " <tr>\n", " <th>mc01a</th>\n", " <td>260</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-57</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>litho1</td>\n", " <td>166.64</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc01a</td>\n", " <td>mc01</td>\n", " <td>1e+10</td>\n", " </tr>\n", " <tr>\n", " <th>mc01a</th>\n", " <td>260</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-57</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>litho1</td>\n", " <td>166.64</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc01a</td>\n", " <td>mc01</td>\n", " <td>1e+10</td>\n", " </tr>\n", " <tr>\n", " <th>mc01a</th>\n", " <td>260</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-57</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>litho2</td>\n", " <td>166.64</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc01a</td>\n", " <td>mc01</td>\n", " <td>1e+10</td>\n", " </tr>\n", " <tr>\n", " <th>mc01b</th>\n", " <td>189</td>\n", " <td>0</td>\n", " <td>This study</td>\n", " <td>Archived samples from 1965, 66 expeditions.</td>\n", " <td>-63</td>\n", " <td>Extrusive:Igneous</td>\n", " <td>Lava Flow</td>\n", " <td>-77.85</td>\n", " <td>litho1</td>\n", " <td>166.64</td>\n", " <td>SO-SIGHT:FS-FD</td>\n", " <td>g</td>\n", " <td>mc01b</td>\n", " <td>mc01</td>\n", " <td>1e+10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " azimuth azimuth_dec_correction citations \\\n", "sample \n", "mc01a 260 0 This study \n", "mc01a 260 0 This study \n", "mc01a 260 0 This study \n", "mc01a 260 0 This study \n", "mc01b 189 0 This study \n", "\n", " description dip geologic_classes \\\n", "sample \n", "mc01a Archived samples from 1965, 66 expeditions. -57 Extrusive:Igneous \n", "mc01a Archived samples from 1965, 66 expeditions. -57 Extrusive:Igneous \n", "mc01a Archived samples from 1965, 66 expeditions. -57 Extrusive:Igneous \n", "mc01a Archived samples from 1965, 66 expeditions. -57 Extrusive:Igneous \n", "mc01b Archived samples from 1965, 66 expeditions. -63 Extrusive:Igneous \n", "\n", " geologic_types lat lithologies lon method_codes \\\n", "sample \n", "mc01a Lava Flow -77.85 litho1 166.64 SO-SIGHT:FS-FD \n", "mc01a Lava Flow -77.85 litho1 166.64 SO-SIGHT:FS-FD \n", "mc01a Lava Flow -77.85 litho1 166.64 SO-SIGHT:FS-FD \n", "mc01a Lava Flow -77.85 litho2 166.64 SO-SIGHT:FS-FD \n", "mc01b Lava Flow -77.85 litho1 166.64 SO-SIGHT:FS-FD \n", "\n", " orientation_flag sample site cooling_rate \n", "sample \n", "mc01a g mc01a mc01 1e+10 \n", "mc01a g mc01a mc01 1e+10 \n", "mc01a g mc01a mc01 1e+10 \n", "mc01a g mc01a mc01 1e+10 \n", "mc01b g mc01b mc01 1e+10 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# you should be able to run this cell over and over again without generate extra lithologies columns\n", "# this is a fix!\n", "\n", "for_propagation = ['lithologies']\n", "con.propagate_cols_down(for_propagation, 'samples', 'sites')\n", "\n", "con.tables['samples'].df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scratch" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "site\n", "mc01 [McMurdo]\n", "mc01 [McMurdo]\n", "mc01 [McMurdo2]\n", "mc01 None\n", "mc02 [McMurdo]\n", "Name: location, dtype: object" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "site_df.iloc[0, 1] = np.nan\n", "site_df.iloc[1, 1] = None\n", "site_df.location.str.split()[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Timing" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0241508483887\n", "0.143810987473\n", "0.000504016876221\n", "2.55077600479\n" ] } ], "source": [ "import timeit\n", "t = timeit.Timer('char in text', setup='text = \"sample string\"; char = \"g\"')\n", "t.timeit()\n", "\n", "t = timeit.Timer('if None: pass')\n", "print t.timeit()\n", "# approximately 0.04\n", "t = timeit.Timer('if isinstance(x, type(None)): pass', setup='x=None')\n", "print t.timeit()\n", "# approximately 0.4\n", "\n", "\n", "t = timeit.Timer('x = None')\n", "print t.timeit(10000)\n", "\n", "t = timeit.Timer('x = pd.DataFrame()', setup='import pandas as pd')\n", "print t.timeit(10000)\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cleanup" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rm: sites.txt: No such file or directory\n", "rm: samples.txt: No such file or directory\n", "rm: specimens.txt: No such file or directory\n", "rm: measurements.txt: No such file or directory\n", "rm: ages.txt: No such file or directory\n", "rm: contribution.txt: No such file or directory\n", "rm: images.txt: No such file or directory\n", "rm: criteria.txt: No such file or directory\n", "rm: locations.txt: No such file or directory\n", "rm: .png: No such file or directory\n", "rm: .jpg: No such file or directory\n" ] } ], "source": [ "!rm sites.txt samples.txt specimens.txt measurements.txt ages.txt contribution.txt images.txt criteria.txt locations.txt\n", "!rm .png\n", "!rm *.jpg" ] }, { "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": 1 }	bsd-3-clause
mne-tools/mne-tools.github.io	0.18/_downloads/e52b6a53120d8703a6509530cf6251dc/plot_roi_erpimage_by_rt.ipynb	1	4684	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n===========================================================\nPlot single trial activity, grouped by ROI and sorted by RT\n===========================================================\n\nThis will produce what is sometimes called an event related\npotential / field (ERP/ERF) image.\n\nThe EEGLAB example file - containing an experiment with button press responses\nto simple visual stimuli - is read in and response times are calculated.\nRegions of Interest are determined by the channel types (in 10/20 channel\nnotation, even channels are right, odd are left, and 'z' are central). The\nmedian and the Global Field Power within each channel group is calculated,\nand the trials are plotted, sorting by response time.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: Jona Sassenhagen <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne.event import define_target_events\nfrom mne.channels import make_1020_channel_selections\n\nprint(__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load EEGLAB example data (a small EEG dataset)\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_path = mne.datasets.testing.data_path()\nfname = data_path + \"/EEGLAB/test_raw.set\"\nmontage = data_path + \"/EEGLAB/test_chans.locs\"\n\nevent_id = {\"rt\": 1, \"square\": 2} # must be specified for str events\neog = {\"FPz\", \"EOG1\", \"EOG2\"}\nraw = mne.io.read_raw_eeglab(fname, eog=eog, montage=montage,\n stim_channel=False)\nevents = mne.events_from_annotations(raw, event_id)[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create Epochs\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# define target events:\n# 1. find response times: distance between \"square\" and \"rt\" events\n# 2. extract A. \"square\" events B. followed by a button press within 700 msec\ntmax = .7\nsfreq = raw.info[\"sfreq\"]\nreference_id, target_id = 2, 1\nnew_events, rts = define_target_events(events, reference_id, target_id, sfreq,\n tmin=0., tmax=tmax, new_id=2)\n\nepochs = mne.Epochs(raw, events=new_events, tmax=tmax + .1,\n event_id={\"square\": 2})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot using GFP\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Parameters for plotting\norder = rts.argsort() # sorting from fast to slow trials\n\nselections = make_1020_channel_selections(epochs.info, midline=\"12z\")\n\n# The actual plots (GFP)\nepochs.plot_image(group_by=selections, order=order, sigma=1.5,\n overlay_times=rts / 1000., combine='gfp',\n ts_args=dict(vlines=[0, rts.mean() / 1000.]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot using median\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "epochs.plot_image(group_by=selections, order=order, sigma=1.5,\n overlay_times=rts / 1000., combine='median',\n ts_args=dict(vlines=[0, rts.mean() / 1000.]))" ] } ], "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
CorySimon/pyIAST	test/Isotherm tests.ipynb	2	116497	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Test isotherm fitting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our strategy here is to generate data points that follow a given isotherm model, then fit an isotherm model to the data using pyIAST, and check that pyIAST identifies the parameters correctly." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import pyiast\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We test all analytical models implemented in pyIAST." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Langmuir', 'Quadratic', 'BET', 'Henry', 'TemkinApprox', 'DSLangmuir']" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "models = pyiast._MODELS\n", "models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This dictionary gives the model parameters for which we generate synthetic data to test pyIAST fitting. Note that, because the DSLF model has so many parameters, it is highly likely that such a model will overfit the data. Thus, we expect pyIAST to reach a local minimum for DSLF yet still obtain a reasonable fit with the default starting guess." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "model_params = {\n", " \"Langmuir\": {\"M\": 10.0, \"K\": 10.0},\n", " \"Quadratic\": {\"M\": 10.0, \"Ka\": 10.0, \"Kb\": 10.0 ** 2 * 3},\n", " \"BET\": {\"M\": 10.0, \"Ka\": 10.0, \"Kb\": .2},\n", " \"DSLangmuir\": {\"M1\": 10.0, \"K1\": 1.0,\n", " \"M2\": 30.0, \"K2\": 30.0}, # warning: 1/2 is arbitrary\n", " \"Henry\": {\"KH\": 10.0},\n", " \"TemkinApprox\": {\"M\": 10.0, \"K\": 10.0, \"theta\": -0.1}\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The loading function generates synthetic data for a given model. We pass it an array of pressures and it returns loading using the given model. Note that the parameters for each model are taken from the above dictionary." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def loading(P, model):\n", " \"\"\"\n", " Return loading at pressure P using a given model.\n", " \n", " :param P: np.array array of pressures\n", " :param model: string specify model\n", " \"\"\"\n", " if model not in models:\n", " raise Exception(\"This model is not implemented in the test suite.\")\n", " \n", " if model == \"Langmuir\":\n", " M = model_params[model][\"M\"] \n", " K = model_params[model][\"K\"] \n", " return M * K * P / (1.0 + K * P)\n", " \n", " if model == \"Quadratic\":\n", " M = model_params[model][\"M\"] \n", " Ka = model_params[model][\"Ka\"] \n", " Kb = model_params[model][\"Kb\"] \n", " return M * P * (Ka + 2.0 * Kb * P) / (1.0 + Ka * P + Kb * P ** 2)\n", " \n", " if model == \"BET\":\n", " M = model_params[model][\"M\"] \n", " Ka = model_params[model][\"Ka\"] \n", " Kb = model_params[model][\"Kb\"]\n", " return M * Ka * P / (1.0 - Kb * P) / (1.0 - Kb * P + Ka * P)\n", " \n", " if model == \"DSLangmuir\":\n", " M1 = model_params[model][\"M1\"] \n", " K1 = model_params[model][\"K1\"]\n", " \n", " M2 = model_params[model][\"M2\"] \n", " K2 = model_params[model][\"K2\"]\n", " \n", " return M1 * K1 * P / (1.0 + K1 * P) +\\\n", " M2 * K2 * P / (1.0 + K2 * P)\n", " \n", " if model == \"TemkinApprox\":\n", " M = model_params[model][\"M\"]\n", " K = model_params[model][\"K\"]\n", " theta = model_params[model][\"theta\"]\n", " \n", " fractional_langmuir_loading = K * P / (1.0 + K * P)\n", " return M * (fractional_langmuir_loading + theta \n", " fractional_langmuir_loading * 2 * \n", " (fractional_langmuir_loading - 1.0))\n", " \n", " if model == \"Henry\":\n", " return model_params[model][\"KH\"] * P" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test model fits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loop through all models, generate synthetic data using parameters in `model_params` and the `loading` function here, then fit model using pyIAST. Plot data and fits, check that pyIAST identified parameters match the model." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing model: Langmuir\n", "Langmuir identified model parameters:\n", "\tM = 10.000008\n", "\tK = 9.999948\n", "RMSE = 4.185844720128586e-06\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Testing model: Quadratic\n", "Quadratic identified model parameters:\n", "\tM = 10.000000\n", "\tKa = 10.000002\n", "\tKb = 299.999978\n", "RMSE = 1.2329463414954408e-07\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Testing model: BET\n", "BET identified model parameters:\n", "\tM = 9.999978\n", "\tKa = 10.000081\n", "\tKb = 0.200001\n", "RMSE = 4.221734254017726e-06\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Testing model: Henry\n", "Henry identified model parameters:\n", "\tKH = 9.999995\n", "RMSE = 3.148658101891919e-06\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Testing model: TemkinApprox\n", "TemkinApprox identified model parameters:\n", "\tM = 10.000008\n", "\tK = 10.000030\n", "\ttheta = -0.099990\n", "RMSE = 1.6903461075627526e-06\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Testing model: DSLangmuir\n", "DSLangmuir identified model parameters:\n", "\tM1 = 9.999958\n", "\tK1 = 1.000018\n", "\tM2 = 29.999976\n", "\tK2 = 30.000034\n", "RMSE = 3.0559622089099224e-06\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for model in models:\n", " print(\"Testing model:\", model)\n", " \n", " # Generate synthetic data\n", " df = pd.DataFrame()\n", " df['P'] = np.linspace(0, 1, 40)\n", " df['L'] = loading(df['P'], model)\n", " \n", " # use pyIAST to fit model to data\n", " isotherm = pyiast.ModelIsotherm(df, pressure_key='P', loading_key='L', \n", " model=model)\n", " isotherm.print_params()\n", " \n", " # plot fit\n", " P_plot = np.linspace(0, 1, 100)\n", "\n", " fig = plt.figure()\n", " plt.scatter(df['P'], df['L'], label='Synthetic data', clip_on=False)\n", " plt.plot(P_plot, isotherm.loading(P_plot), label='pyIAST fit')\n", " plt.xlim([0, 1])\n", " plt.ylim(ymin=0)\n", " plt.xlabel('Pressure')\n", " plt.ylabel('Uptake')\n", " plt.title(model)\n", " plt.legend(loc='lower right')\n", " plt.show()\n", " \n", " # assert parameters are equal\n", " for param in isotherm.params.keys():\n", " np.testing.assert_almost_equal(isotherm.params[param], \n", " model_params[model][param],\n", " decimal=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quick visual test on the Interpolator isotherm" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "isotherm = pyiast.InterpolatorIsotherm(df, pressure_key='P', loading_key='L')\n", "pyiast.plot_isotherm(isotherm)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
schoolie/bokeh	examples/howto/charts/deep_dive-attributes.ipynb	6	11138	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bokeh Charts Attributes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of Bokeh Charts main contributions is that it provides a flexible interface for applying unique attributes based on the unique values in column(s) of a DataFrame.\n", "\n", "Internally, the bokeh chart uses the AttrSpec to define the mapping, but allows the user to pass in their own spec, or utilize a function to produce a customized one." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from bokeh.charts.attributes import AttrSpec, ColorAttr, MarkerAttr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simple Examples\n", "\n", "The AttrSpec assigns values in the iterable to values in items." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "attr = AttrSpec(items=[1, 2, 3], iterable=['a', 'b', 'c'])\n", "attr.attr_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will see that the key in the mapping will be a tuple, and it will always be a tuple. The mapping works like this because the AttrSpec(s) are often used with Pandas DataFrames groupby method. The groupby method can return a single value or a tuple of values when used with multiple columns, so this is just making sure that is consistent. \n", "\n", "However, you can still access the values in the following way:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "attr[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `ColorAttr` is just a custom `AttrSpec` that has a default palette as the iterable, but can be customized, and will likely provide some other color generation functionality. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "color = ColorAttr(items=[1, 2, 3])\n", "color.attr_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's assume that you don't know how many unique items you are working with, but you have defined the things that you want to assign the items to. The `AttrSpec` will automatically cycle the iterable for you. This is important for exploratory analysis." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "color = ColorAttr(items=list(range(0, 10)))\n", "color.attr_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because there are only 6 unique colors in the default palette, the palette repeats starting on the 7th item." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using with Pandas" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from bokeh.sampledata.autompg import autompg as df" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "color_attr = ColorAttr(df=df, columns=['cyl', 'origin'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "color_attr.attr_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will notice that this is similar to a pandas series with a MultiIndex, which is seen below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "color_attr.series" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can think of this as a SQL table with 3 columns, two of which are an index. You can imagine how you might join this view data into the original data source to assign these colors to the associated rows." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combining with ChartDataSource" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from bokeh.charts.data_source import ChartDataSource" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fill_color = ColorAttr(columns=['cyl', 'origin'])\n", "\n", "ds = ChartDataSource.from_data(df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds.join_attrs(fill_color=fill_color).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multiple Attributes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# add new column\n", "df['large_displ'] = df['displ'] >= 350\n", "\n", "fill_color = ColorAttr(columns=['cyl', 'origin'])\n", "line_color = ColorAttr(columns=['large_displ'])\n", "\n", "ds = ChartDataSource.from_data(df)\n", "\n", "ds.join_attrs(fill_color=fill_color, line_color=line_color).head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Custom Iterable\n", "\n", "You will see that the output contains the combined chart_index and the columns for both attributes. The values of each are joined in based on the original assignment. For example, line_color only has two colors because the large_displ column only has two values.\n", "\n", "If we wanted to change the true/false, we can modify the ColorAttr." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "line_color = ColorAttr(df=df, columns=['large_displ'], palette=['Green', 'Red'])\n", "ds.join_attrs(fill_color=fill_color, line_color=line_color).head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Altering Attribute Assignment Order\n", "\n", "You may not have wanted to assign the values in the order that occured. So, you would have five options.\n", "\n", "\n", "1. Pre order the data and tell the attribute not to sort.\n", "2. Make the column a categorical and set the order.\n", "3. Specify the sort options to the `AttrSpec`\n", "4. Manually specify the items in the order you want them to be assigned.\n", "5. Specify the iterable in the order you want." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Pre order the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_sorted = df.sort(columns=['large_displ'], ascending=False)\n", "\n", "line_color = ColorAttr(df=df_sorted, columns=['large_displ'], palette=['Green', 'Red'], sort=False)\n", "\n", "ds.join_attrs(fill_color=fill_color, line_color=line_color).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Make the column a categorical and set the order\n", "\n", "We'll show the default sort order of a boolean column, which is ascending." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df.sort(columns='large_displ').head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "df_cat = df.copy()\n", "\n", "# create the categorical and set the default (ascending)\n", "df_cat['large_displ'] = pd.Categorical.from_array(df.large_displ).reorder_categories([True, False])\n", "\n", "# we don't have to sort here, but doing it so you can see the order that the attr spec will see\n", "df_cat.sort(columns='large_displ').head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "line_color = ColorAttr(df=df_cat, columns=['large_displ'], palette=['Green', 'Red'])\n", "\n", "ds.join_attrs(fill_color=fill_color, line_color=line_color).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Specify the sort options to the `AttrSpec`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# the items will be sorted descending (uses same sorting options as pandas)\n", "line_color = ColorAttr(df=df, columns=['large_displ'], palette=['Green', 'Red'], sort=True, ascending=False)\n", "\n", "ds.join_attrs(fill_color=fill_color, line_color=line_color).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Manually specify the items in the order you want them" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# remove df so the items aren't auto-calculated\n", "# still need column name for when palette is joined into the dataset\n", "line_color = ColorAttr(columns=['large_displ'], items=[True, False], palette=['Green', 'Red'])\n", "\n", "ds.join_attrs(fill_color=fill_color, line_color=line_color).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Change the order of the iterable" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "line_color = ColorAttr(df=df, columns=['large_displ'], palette=['Red', 'Green'])\n", "\n", "ds.join_attrs(fill_color=fill_color, line_color=line_color).head()" ] } ], "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": "3.4.4" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
ES-DOC/esdoc-jupyterhub	notebooks/noaa-gfdl/cmip6/models/gfdl-cm4/landice.ipynb	1	37254	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Landice \n", "MIP Era: CMIP6 \n", "Institute: NOAA-GFDL \n", "Source ID: GFDL-CM4 \n", "Topic: Landice \n", "Sub-Topics: Glaciers, Ice. \n", "Properties: 30 (21 required) \n", "Model descriptions: [Model description details](https://specializations.es-doc.org/cmip6/landice?client=jupyter-notebook) \n", "Initialized From: -- \n", "\n", "Notebook Help: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "Notebook Initialised: 2018-02-20 15:02:34" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "IMPORTANT: to be executed each time you run the notebook " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'gfdl-cm4', 'landice')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "Set document authors" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "Specify document contributors " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "Specify document publication status " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --> Software Properties](#2.-Key-Properties--->-Software-Properties) \n", "[3. Grid](#3.-Grid) \n", "[4. Glaciers](#4.-Glaciers) \n", "[5. Ice](#5.-Ice) \n", "[6. Ice --> Mass Balance](#6.-Ice--->-Mass-Balance) \n", "[7. Ice --> Mass Balance --> Basal](#7.-Ice--->-Mass-Balance--->-Basal) \n", "[8. Ice --> Mass Balance --> Frontal](#8.-Ice--->-Mass-Balance--->-Frontal) \n", "[9. Ice --> Dynamics](#9.-Ice--->-Dynamics) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "Land ice key properties" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of land surface model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Name of land surface model code" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Ice Albedo\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Specify how ice albedo is modelled" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.ice_albedo') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prescribed\" \n", "# \"function of ice age\" \n", "# \"function of ice density\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Atmospheric Coupling Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Which variables are passed between the atmosphere and ice (e.g. orography, ice mass)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.atmospheric_coupling_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Oceanic Coupling Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Which variables are passed between the ocean and ice" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.oceanic_coupling_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Prognostic Variables\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Which variables are prognostically calculated in the ice model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ice velocity\" \n", "# \"ice thickness\" \n", "# \"ice temperature\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --> Software Properties \n", "Software properties of land ice code" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Repository\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Location of code for this component." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Code Version\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Code version identifier." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Code Languages\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### Code language(s)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Grid \n", "Land ice grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of the grid in the land ice scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Adaptive Grid\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is an adative grid being used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Base Resolution\n", "Is Required: TRUE    Type: FLOAT    Cardinality: 1.1\n", "### The base resolution (in metres), before any adaption" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.base_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Resolution Limit\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If an adaptive grid is being used, what is the limit of the resolution (in metres)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.resolution_limit') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.5. Projection\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### The projection of the land ice grid (e.g. albers_equal_area)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.projection') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Glaciers \n", "Land ice glaciers" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of glaciers in the land ice scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.glaciers.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the treatment of glaciers, if any" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.glaciers.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Dynamic Areal Extent\n", "Is Required: FALSE    Type: BOOLEAN    Cardinality: 0.1\n", "### Does the model include a dynamic glacial extent?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.glaciers.dynamic_areal_extent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Ice \n", "Ice sheet and ice shelf" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of the ice sheet and ice shelf in the land ice scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Grounding Line Method\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Specify the technique used for modelling the grounding line in the ice sheet-ice shelf coupling" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.grounding_line_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"grounding line prescribed\" \n", "# \"flux prescribed (Schoof)\" \n", "# \"fixed grid size\" \n", "# \"moving grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Ice Sheet\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Are ice sheets simulated?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.ice_sheet') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Ice Shelf\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Are ice shelves simulated?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.ice_shelf') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Ice --> Mass Balance \n", "Description of the surface mass balance treatment" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Surface Mass Balance\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe how and where the surface mass balance (SMB) is calulated. Include the temporal coupling frequeny from the atmosphere, whether or not a seperate SMB model is used, and if so details of this model, such as its resolution" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.surface_mass_balance') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Ice --> Mass Balance --> Basal \n", "Description of basal melting" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Bedrock\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the implementation of basal melting over bedrock" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.basal.bedrock') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Ocean\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the implementation of basal melting over the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.basal.ocean') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Ice --> Mass Balance --> Frontal \n", "Description of claving/melting from the ice shelf front" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Calving\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the implementation of calving from the front of the ice shelf" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.frontal.calving') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Melting\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the implementation of melting from the front of the ice shelf" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.frontal.melting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Ice --> Dynamics \n", "" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality:** 1.1\n", "### General description if ice sheet and ice shelf dynamics" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Approximation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Approximation type used in modelling ice dynamics" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.approximation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"SIA\" \n", "# \"SAA\" \n", "# \"full stokes\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Adaptive Timestep\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there an adaptive time scheme for the ice scheme?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.adaptive_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Timestep\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Timestep (in seconds) of the ice scheme. If the timestep is adaptive, then state a representative timestep." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	gpl-3.0
sintefmath/equelle	examples/iPython_demos/heateq.ipynb	3	12040	{ "metadata": { "name": "heateq" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%%file heateq.equelle\n", "\n", "# Heat conduction with no boundary conditions or source terms.\n", "\n", "# Physics that requires specification\n", "k : Scalar = InputScalarWithDefault(\"k\", 0.3) # Heat diffusion constant.\n", "\n", "# @afr: time step strategy assumed given outside.\n", "dt : Scalar = InputScalarWithDefault(\"dt\", 0.5) # Time step length.\n", "\n", "# u0 should be given initial values (in user input)\n", "u0 : Collection Of Scalar On AllCells()\n", "u0 = InputCollectionOfScalar(\"u0\", AllCells())\n", "\n", "# Trying to add support for Dirichlet boundaries\n", "dirichlet_boundary : Collection Of Face Subset Of (BoundaryFaces())\n", "dirichlet_boundary = InputDomainSubsetOf(\"dirichlet_boundary\", BoundaryFaces())\n", "dirichlet_val : Collection Of Scalar On dirichlet_boundary\n", "dirichlet_val = InputCollectionOfScalar(\"dirichlet_val\", dirichlet_boundary)\n", "\n", "# Compute interior transmissibilities.\n", "vol = \|AllCells()\| # Deduced type: Collection Of Scalar On AllCells()\n", "interior_faces = InteriorFaces() # Deduced type: Collection Of Face\n", "first = FirstCell(interior_faces) # Deduced type: Collection Of Cell On interior_faces\n", "\t\t\t\t\t\t\t # Equivalent to: Collection Of Cell On InteriorFaces()\n", "second = SecondCell(interior_faces) # Deduced type: Same as for 'first'.\n", "itrans : Collection Of Scalar On interior_faces = k * \|interior_faces\| / \|Centroid(first) - Centroid(second)\| \n", "\n", "# Compute boundary transmissibilities.\n", "bf = BoundaryFaces()\n", "bf_cells = IsEmpty(FirstCell(bf)) ? SecondCell(bf) : FirstCell(bf)\n", "bf_sign = IsEmpty(FirstCell(bf)) ? (-1 Extend bf) : (1 Extend bf)\n", "btrans = k * \|bf\| / \|Centroid(bf) - Centroid(bf_cells)\|\n", "\n", "# Compute quantities needed for boundary conditions.\n", "dir_sign = bf_sign On dirichlet_boundary\n", "\n", "# Compute flux for interior faces.\n", "computeInteriorFlux : Function(u : Collection Of Scalar On AllCells()) -> Collection Of Scalar On InteriorFaces()\n", "computeInteriorFlux(u) = {\n", " -> -itrans * Gradient(u)\n", "}\n", "\n", "# Compute flux for boundary faces.\n", "computeBoundaryFlux : Function(u : Collection Of Scalar On AllCells()) -> Collection Of Scalar On BoundaryFaces()\n", "computeBoundaryFlux(u) = {\n", " # Compute flux at Dirichlet boundaries.\n", " u_dirbdycells = u On (bf_cells On dirichlet_boundary)\n", " dir_fluxes = (btrans On dirichlet_boundary) * dir_sign * (u_dirbdycells - dirichlet_val)\n", " # Extending with zero away from Dirichlet boundaries (i.e. assuming no-flow elsewhere).\n", " -> dir_fluxes Extend BoundaryFaces()\n", "}\n", "\n", "# Compute the residual for the heat equation.\n", "computeResidual : Function(u : Collection Of Scalar On AllCells()) -> Collection Of Scalar On AllCells()\n", "computeResidual(u) = {\n", " ifluxes = computeInteriorFlux(u)\n", " bfluxes = computeBoundaryFlux(u)\n", " # Extend both ifluxes and bfluxes to AllFaces() and add to get all fluxes.\n", " fluxes = (ifluxes Extend AllFaces()) + (bfluxes Extend AllFaces())\n", " # Deduced type: Collection Of Scalar On AllCells()\n", " residual = u - u0 + (dt / vol) * Divergence(fluxes)\n", " -> residual\n", "}\n", "\n", "# NewtonSolve takes a function (that should accept the primary variable as input) and the initial guess for the primary variable.\n", "explicitu = u0 - computeResidual(u0)\n", "u = NewtonSolve(computeResidual, u0)\n", "\n", "Output(\"explicitu\", explicitu)\n", "Output(\"u\", u)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Overwriting heateq.equelle" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "!/home/jse/projects/equelle/compiler/ec < heateq.equelle > heateq.cpp" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "!cat heateq.cpp" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "// This program was created by the Equelle compiler from SINTEF.\r\n", "\r\n", "#include <opm/core/utility/parameters/ParameterGroup.hpp>\r\n", "#include <opm/core/linalg/LinearSolverFactory.hpp>\r\n", "#include <opm/core/utility/ErrorMacros.hpp>\r\n", "#include <opm/autodiff/AutoDiffBlock.hpp>\r\n", "#include <opm/autodiff/AutoDiffHelpers.hpp>\r\n", "#include <opm/core/grid.h>\r\n", "#include <opm/core/grid/GridManager.hpp>\r\n", "#include <algorithm>\r\n", "#include <iterator>\r\n", "#include <iostream>\r\n", "#include <cmath>\r\n", "#include <array>\r\n", "\r\n", "#include \"EquelleRuntimeCPU.hpp\"\r\n", "\r\n", "void ensureRequirements(const EquelleRuntimeCPU& er);\r\n", "\r\n", "int main(int argc, char** argv)\r\n", "{\r\n", " // Get user parameters.\r\n", " Opm::parameter::ParameterGroup param(argc, argv, false);\r\n", "\r\n", " // Create the Equelle runtime.\r\n", " EquelleRuntimeCPU er(param);\r\n", "\r\n", " ensureRequirements(er);\r\n", "\r\n", " // ============= Generated code starts here ================\r\n", "\r\n", " const Scalar k = er.inputScalarWithDefault(\"k\", double(0.3));\r\n", " const Scalar dt = er.inputScalarWithDefault(\"dt\", double(0.5));\r\n", " const CollOfScalar u0 = er.inputCollectionOfScalar(\"u0\", er.allCells());\r\n", " const CollOfFace dirichlet_boundary = er.inputDomainSubsetOf(\"dirichlet_boundary\", er.boundaryFaces());\r\n", " const CollOfScalar dirichlet_val = er.inputCollectionOfScalar(\"dirichlet_val\", dirichlet_boundary);\r\n", " const CollOfScalar vol = er.norm(er.allCells());\r\n", " const CollOfFace interior_faces = er.interiorFaces();\r\n", " const CollOfCell first = er.firstCell(interior_faces);\r\n", " const CollOfCell second = er.secondCell(interior_faces);\r\n", " const CollOfScalar itrans = (k * (er.norm(interior_faces) / er.norm((er.centroid(first) - er.centroid(second)))));\r\n", " const CollOfFace bf = er.boundaryFaces();\r\n", " const CollOfCell bf_cells = er.trinaryIf(er.isEmpty(er.firstCell(bf)), er.secondCell(bf), er.firstCell(bf));\r\n", " const CollOfScalar bf_sign = er.trinaryIf(er.isEmpty(er.firstCell(bf)), er.operatorExtend(-double(1), bf), er.operatorExtend(double(1), bf));\r\n", " const CollOfScalar btrans = (k * (er.norm(bf) / er.norm((er.centroid(bf) - er.centroid(bf_cells)))));\r\n", " const CollOfScalar dir_sign = er.operatorOn(bf_sign, er.boundaryFaces(), dirichlet_boundary);\r\n", " std::function<CollOfScalar(const CollOfScalar&)> computeInteriorFlux = [&](const CollOfScalar& u) -> CollOfScalar {\r\n", " return (-itrans * er.gradient(u));\r\n", " };\r\n", " std::function<CollOfScalar(const CollOfScalar&)> computeBoundaryFlux = [&](const CollOfScalar& u) -> CollOfScalar {\r\n", " const CollOfScalar u_dirbdycells = er.operatorOn(u, er.allCells(), er.operatorOn(bf_cells, er.boundaryFaces(), dirichlet_boundary));\r\n", " const CollOfScalar dir_fluxes = ((er.operatorOn(btrans, er.boundaryFaces(), dirichlet_boundary) * dir_sign) * (u_dirbdycells - dirichlet_val));\r\n", " return er.operatorExtend(dir_fluxes, dirichlet_boundary, er.boundaryFaces());\r\n", " };\r\n", " std::function<CollOfScalar(const CollOfScalar&)> computeResidual = [&](const CollOfScalar& u) -> CollOfScalar {\r\n", " const CollOfScalar ifluxes = computeInteriorFlux(u);\r\n", " const CollOfScalar bfluxes = computeBoundaryFlux(u);\r\n", " const CollOfScalar fluxes = (er.operatorExtend(ifluxes, er.interiorFaces(), er.allFaces()) + er.operatorExtend(bfluxes, er.boundaryFaces(), er.allFaces()));\r\n", " const CollOfScalar residual = ((u - u0) + ((dt / vol) * er.divergence(fluxes)));\r\n", " return residual;\r\n", " };\r\n", " const CollOfScalar explicitu = (u0 - computeResidual(u0));\r\n", " const CollOfScalar u = er.newtonSolve(computeResidual, u0);\r\n", " er.output(\"explicitu\", explicitu);\r\n", " er.output(\"u\", u);\r\n", "\r\n", " // ============= Generated code ends here ================\r\n", "\r\n", " return 0;\r\n", "}\r\n", "\r\n", "void ensureRequirements(const EquelleRuntimeCPU& er)\r\n", "{\r\n", " (void)er;\r\n", "}\r\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "!g++ -Wall -I/usr/include/eigen3 -I/home/jse/projects/equelle/examples/include -std=c++11 heateq.cpp -o heateq -lopmautodiff -lopmcore -ldunecommon /home/jse/projects/equelle/examples/build/libequelle_rt.a" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "!./heateq" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "grid_dim not found. Using default value '2'.\r\n", "ny not found. Using default value '1'.\r\n", "dy not found. Using default value '1'.\r\n", "nx not found. Using default value '6'.\r\n", "dx not found. Using default value '1'.\r\n", "linsolver not found. Using default value 'umfpack'.\r\n", "output_to_file not found. Using default value 'false'.\r\n", "verbose not found. Using default value '0'.\r\n", "max_iter not found. Using default value '10'.\r\n", "abs_res_tol not found. Using default value '1e-06'.\r\n", "k not found. Using default value '0.3'.\r\n", "dt not found. Using default value '0.5'.\r\n", "u0_from_file not found. Using default value 'false'.\r\n", "ERROR: The group '' does not contain an element named 'u0'.\r\n", "terminate called after throwing an instance of 'Opm::parameter::ParameterGroup::NotFoundException'\r\n", " what(): std::exception\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Aborted (core dumped)\r\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	agpl-3.0
siberianisaev/NeutronBarrel	Python/times.ipynb	2	70799	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "sigma: 1.1157663930959538\n", "w_bin: 0.6310194549586894\n", "sigma: 1.1157663930959538\n", "Lower bound: 1.13974946429351\n", "Upper bound: 1.42025053570649\n", "sigma test failed!\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[ 4. 2. 10. 20. 42. 48. 47. 38. 23. 1.]\n", "[10.5 11.13101945 11.76203891 12.39305836 13.02407782 13.65509727\n", " 14.28611673 14.91713618 15.54815564 16.17917509 16.81019455]\n", "[2. 1.41421356 3.16227766 4.47213595 6.4807407 6.92820323\n", " 6.8556546 6.164414 4.79583152 1. 0. ]\n", "\n", "SF count: 235.0\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Lambda: 6.487600673306731e-07\n", "n0: 146.60988664990046\n", "bin_size: 0.6310194549586894\n", "n0 / bin_size: 232.33814028681334\n", "Cov: [[2.85734594e-15 2.64241254e-10]\n", " [2.64241254e-10 7.29165663e+01]]\n", "Max: 14.24820288454329\n", "T1/2, mks: 1541401.8993408543\n", "Err(T1/2)+, mks: 138406.69573591975\n", "Err(T1/2)-, mks: -117335.02889720001\n" ] } ], "source": [ "import io\n", "import numpy as np\n", "from scipy import optimize\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from random import randint\n", "\n", "# Schmidt fit\n", "# Eur.Phys.J.A (2000) 8: 141-145 https://link.springer.com/content/pdf/10.1007/s100500070129.pdf\n", "# Z.Phys.A 316 (1984): 19-26 https://link.springer.com/content/pdf/10.1007/BF01415656.pdf\n", "\n", "times_mks = np.array([36524, 43330, 52955, 54271, 113700, 121933, 134890, 144682, 148865, 155592, 164368, 181860, 188046, 196222, 202754, 226749, 242346, 249782, 258321, 271440, 281185, 296368, 299862, 300615, 312508, 315593, 336648, 370767, 383361, 389309, 389374, 397739, 398456, 423094, 439250, 452550, 457111, 472086, 519749, 520160, 542842, 543900, 547212, 560199, 568640, 569050, 569147, 572258, 595751, 605581, 608221, 611537, 635777, 641818, 651118, 657705, 659498, 680663, 686695, 687737, 691133, 693151, 695360, 704195, 714467, 718245, 729140, 734282, 739135, 765680, 770597, 776670, 791079, 791281, 830004, 831423, 835344, 845631, 855774, 857804, 857904, 863115, 875960, 923219, 940752, 942173, 969721, 1.01844E6, 1.02154E6, 1.04026E6, 1.06026E6, 1.07073E6, 1.07647E6, 1.09406E6, 1.09585E6, 1.10289E6, 1.1368E6, 1.14767E6, 1.16581E6, 1.17554E6, 1.2055E6, 1.20764E6, 1.22948E6, 1.25052E6, 1.25893E6, 1.26667E6, 1.29691E6, 1.30436E6, 1.35465E6, 1.36076E6, 1.39357E6, 1.42259E6, 1.43151E6, 1.47927E6, 1.47953E6, 1.48246E6, 1.5013E6, 1.51279E6, 1.534E6, 1.54131E6, 1.55265E6, 1.5636E6, 1.56714E6, 1.57289E6, 1.57694E6, 1.57833E6, 1.64007E6, 1.66297E6, 1.66752E6, 1.67005E6, 1.71086E6, 1.71917E6, 1.72574E6, 1.74577E6, 1.74832E6, 1.78726E6, 1.84991E6, 1.85314E6, 1.86772E6, 1.91093E6, 1.91987E6, 1.94286E6, 1.95048E6, 1.96764E6, 1.99648E6, 2.0157E6, 2.0836E6, 2.08462E6, 2.08593E6, 2.1052E6, 2.11502E6, 2.13059E6, 2.14588E6, 2.14801E6, 2.29846E6, 2.34736E6, 2.36947E6, 2.41603E6, 2.42512E6, 2.45456E6, 2.52137E6, 2.55297E6, 2.56087E6, 2.56741E6, 2.59796E6, 2.61277E6, 2.62263E6, 2.63611E6, 2.64752E6, 2.74567E6, 2.82044E6, 2.82444E6, 2.99642E6, 3.0767E6, 3.11527E6, 3.13229E6, 3.13974E6, 3.16848E6, 3.18782E6, 3.22119E6, 3.31206E6, 3.32692E6, 3.33157E6, 3.38756E6, 3.39045E6, 3.45728E6, 3.47505E6, 3.58018E6, 3.59584E6, 3.65087E6, 3.65386E6, 3.71367E6, 3.8099E6, 3.92573E6, 3.93779E6, 4.21417E6, 4.33484E6, 4.42146E6, 4.4983E6, 4.51007E6, 4.51445E6, 4.5346E6, 4.56178E6, 4.58506E6, 4.71364E6, 4.94311E6, 4.95928E6, 5.22799E6, 5.2896E6, 5.30529E6, 5.43087E6, 5.6916E6, 5.71718E6, 6.06616E6, 6.09879E6, 6.10864E6, 6.18989E6, 6.32579E6, 6.83541E6, 7.19457E6, 7.21487E6, 7.25257E6, 7.33612E6, 7.6793E6, 7.75131E6, 7.97512E6, 7.99551E6, 8.4153E6, 8.46129E6, 8.75199E6, 8.90905E6, 9.07541E6, 9.08037E6, 9.71414E6, 1.06572E7])\n", "times_ln = np.log(times_mks)\n", "\n", "# Otimal bin size calculation \n", "# Biometrika, 66, #3 (1979): 605–610 https://doi.org/10.1093/biomet/66.3.605\n", "sigma = np.std(times_ln)\n", "size = times_mks.size\n", "print(\"\\nsigma: \", sigma)\n", "w_bin = (3.49 * sigma) / pow(size, 1/3.0) # 0.733\n", "print(\"w_bin: \", w_bin)\n", "\n", "# sigma test\n", "# Eur.Phys.J.A (2000) 8: 141-145 https://link.springer.com/content/pdf/10.1007/s100500070129.pdf\n", "print(\"sigma: \", sigma)\n", "s2Lower = 1.28 - 2.15/pow(size, 0.5)\n", "print(\"Lower bound: \", s2Lower)\n", "s2Upper = 1.28 + 2.15/pow(size, 0.5)\n", "print(\"Upper bound: \", s2Upper)\n", "if sigma >= s2Lower and sigma <= s2Upper:\n", " print(\"sigma test passed!\")\n", "else:\n", " print(\"sigma test failed!\")\n", "\n", "# for x in range(1000, 20000):\n", "# print((x/1000.0))\n", "\n", "# minErrPos = 1000000000000.0\n", "# minErrNeg = 1000000000000.0\n", "# for x in range(300, 800):\n", "bin_size = w_bin #float(x)/1000.0\n", "counts, bins, bars = plt.hist(times_ln.astype('float'), bins=np.arange(10.5, 17.0, step=bin_size), color='blue')\n", "plt.show()\n", "print(counts)\n", "print(bins)\n", "\n", "x = bins\n", "y = np.append(counts, 0)\n", "dy = np.array(list(map(lambda c : np.sqrt(c), y)))\n", "print(dy)\n", "\n", "sf = np.sum(y)\n", "print(\"\\nSF count: \", sf)\n", "n0_approx = sf * bin_size\n", "lam_approx = 6.4935e-07\n", "\n", "def func(x, lam, n0):\n", " return n0np.exp(x + np.log(lam))np.exp(-np.exp(x + np.log(lam)))\n", "\n", "# https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html\n", "def fit_param_errors(pcov_matrix):\n", " return np.sqrt(np.diag(pcov_matrix))\n", "\n", "popt, pcov = optimize.curve_fit(func, x, y, [lam_approx, n0_approx])\n", "\n", "x_cont = np.arange(x.min(), x.max(), 0.01)\n", "\n", "plt.plot(x, y, \"o\", label='Experimental data')\n", "plt.errorbar(x, y, yerr=dy, fmt='.k')\n", "plt.plot(x_cont, func(x_cont, popt), label='Schmidt fit')\n", "# plt.plot(x_cont, func(x_cont, lam_approx, n0_approx), label='Schmidt distr. for known T1/2')\n", "plt.legend()\n", "plt.title('246Fm')\n", "plt.xlabel('ln(dT, mks)')\n", "plt.ylabel('Count')\n", "plt.show()\n", "\n", "print(\"Lambda: \", popt[0])\n", "print(\"n0: \", popt[1])\n", "print(\"bin_size: \", bin_size)\n", "print(\"n0 / bin_size: \", popt[1]/bin_size)\n", "print(\"Cov: \", pcov)\n", "hl = 1/popt[0]\n", "print(\"Max: \", np.log(hl))\n", "print(\"T1/2, mks: \", hl)\n", "err = fit_param_errors(pcov)[0]\n", "errPos = (1/(popt[0]-err))-hl\n", "errNeg = (1/(popt[0]+err))-hl\n", "print(\"Err(T1/2)+, mks: \", errPos)\n", "print(\"Err(T1/2)-, mks: \", errNeg)\n", "# if errPos < minErrPos:\n", "# minErrPos = errPos\n", "# if abs(errNeg) < abs(minErrNeg):\n", "# minErrNeg = errNeg\n", "\n", "# print(\"minErrPos: \", minErrPos)\n", "# print(\"minErrNeg: \", minErrNeg)\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Integral: (238.56946296158327, 1.3721186629539472e-06)\n" ] } ], "source": [ "# https://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html\n", "from scipy.integrate import quad\n", "integral = quad(func, 0, 20, args=(popt[0], popt[1]/bin_size))\n", "print(\"Integral: \", integral)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "xx1 = []\n", "yy1 = []\n", "zz1 = []\n", "for i in range(100, 2000, 1):\n", " b = i / 1000\n", " counts1, bins1, bars1 = plt.hist(times_ln.astype('float'), bins=np.arange(10.5, 17.0, step=b), color='black')\n", " x1 = bins1\n", " y1 = np.append(counts1, 0)\n", " popt1, pcov1 = optimize.curve_fit(func, x1, y1, [lam_approx, sf b])\n", " hl1 = 1/popt1[0]\n", " err1 = fit_param_errors(pcov1)[0]\n", " xx1 = np.append(xx1, b)\n", " yy1 = np.append(yy1, hl1/1000000)\n", " zz1 = np.append(zz1, err1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(xx1, yy1, \"o\", label='test bins')\n", "plt.xlabel('bin size')\n", "plt.ylabel('half life, s')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(xx1, zz1, \"o\", label='test bins')\n", "plt.xlabel('bin size')\n", "plt.ylabel('lambda error')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 5 }	mit
azogue/enerpi	notebooks/LDR - seaborn plots.ipynb	1	3074563	null	mit

Subsets and Splits